diff --git a/.gitignore b/.gitignore index b5306b8b79c37166e5496cf17a3e39b86b9a6314..5afe375f46f07b3b557ae23f75740b337517d3bd 100644 --- a/.gitignore +++ b/.gitignore @@ -29,6 +29,7 @@ Podfile.lock /tensorflow/contrib/lite/examples/ios/simple/data/*.tflite xcuserdata/** /api_init_files_list.txt +/estimator_api_init_files_list.txt # Android .gradle diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md index 2f3df7cda9cec29ed0c2266629022f0a22b37df9..52faed9297cfcaf8c93bb9c79686c9258a53c560 100644 --- a/ISSUE_TEMPLATE.md +++ b/ISSUE_TEMPLATE.md @@ -15,9 +15,10 @@ If you open a GitHub issue, here is our policy: ### System information - **Have I written custom code (as opposed to using a stock example script provided in TensorFlow)**: - **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: +- **Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device**: - **TensorFlow installed from (source or binary)**: - **TensorFlow version (use command below)**: -- **Python version**: +- **Python version**: - **Bazel version (if compiling from source)**: - **GCC/Compiler version (if compiling from source)**: - **CUDA/cuDNN version**: diff --git a/README.md b/README.md index 05fcb23f7edd657f2ea495d848fadc226e56b524..a35ba14dc8cba490ae8970fba7881702fc3154fe 100644 --- a/README.md +++ b/README.md @@ -82,12 +82,12 @@ The TensorFlow project strives to abide by generally accepted best practices in | Build Type | Status | Artifacts | | --- | --- | --- | | **Linux CPU** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-cc.png) | [pypi](https://pypi.org/project/tf-nightly/) | -| **Linux GPU** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-gpu-cc.png) | [pypi](https://pypi.org/project/tf-nightly-gpu/) | -| **Linux XLA** | TBA | TBA | +| **Linux GPU** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-gpu-py3.png) | [pypi](https://pypi.org/project/tf-nightly-gpu/) | +| **Linux XLA** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.png | TBA | | **MacOS** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/macos-py2-cc.png) | [pypi](https://pypi.org/project/tf-nightly/) | -| **Windows CPU** | [![Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-win-cmake-py)](https://ci.tensorflow.org/job/tensorflow-master-win-cmake-py) | [pypi](https://pypi.org/project/tf-nightly/) | -| **Windows GPU** | [![Status](http://ci.tensorflow.org/job/tf-master-win-gpu-cmake/badge/icon)](http://ci.tensorflow.org/job/tf-master-win-gpu-cmake/) | [pypi](https://pypi.org/project/tf-nightly-gpu/) | -| **Android** | [![Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-android)](https://ci.tensorflow.org/job/tensorflow-master-android) | [![Download](https://api.bintray.com/packages/google/tensorflow/tensorflow/images/download.svg)](https://bintray.com/google/tensorflow/tensorflow/_latestVersion) [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/) [build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/) | +| **Windows CPU** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/windows-cpu.png) | [pypi](https://pypi.org/project/tf-nightly/) | +| **Windows GPU** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/windows-gpu.png) | [pypi](https://pypi.org/project/tf-nightly-gpu/) | +| **Android** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/android.png) | [![Download](https://api.bintray.com/packages/google/tensorflow/tensorflow/images/download.svg)](https://bintray.com/google/tensorflow/tensorflow/_latestVersion) | ### Community Supported Builds diff --git a/RELEASE.md b/RELEASE.md index 4b0339442768afbd97ac21323bb0351eea13a6ca..6b67072f8ecafa08c747f8296c7c2a59eb2350fa 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -21,7 +21,7 @@ * The [distributions.Bijector](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/distributions/bijectors/Bijector) API supports broadcasting for Bijectors with new API changes. -## Breaking Chances +## Breaking Changes * If you're opening empty variable scopes; replace `variable_scope('', ...)` by `variable_scope(tf.get_variable_scope(), ...)`. * Headers used for building custom ops have been moved from site-packages/external into site-packages/tensorflow/include/external. @@ -34,18 +34,22 @@ * Using `tf.layers` in a subclassed `tf.keras.Model` class. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/layers) for more details * `tf.data`: - * The `DatasetBase::DebugString()` method is now `const`. - * Added the `tf.contrib.data.sample_from_datasets()` API for randomly sampling from multiple datasets. + * `Dataset.from_generator()` now accepts an `args` list, in order to create nested generators. + * `Dataset.list_files()` now produces determinstic results when `shuffle=False` or a `seed` is passed. + * `tf.contrib.data.sample_from_datasets()` and `tf.contrib.data.choose_from_datasets()` make it easier to sample or deterministically choose elements from multiple datasets. + * `tf.contrib.data.make_csv_dataset()` now supports line breaks in quoted strings, and two infrequently used arguments removed. + * (C++) `DatasetBase::DebugString()` is now `const`. + * (C++) `DatasetBase::MakeIterator()` has been renamed to `DatasetBase::MakeIteratorInternal()`. + * (C++) `IteratorBase::Initialize()` method was added to support raising errors during iterator construction. * Eager Execution: + * Added the ability to pause recording operations for gradient computation via `tf.GradientTape.stop_recording`. + * Updated documentation, introductory notebooks. * `tf.keras`: * Move Keras code out of _impl folder and remove API files. * `tf.keras.Model.save_weights` now saves in TensorFlow format by default. * Enable dataset iterators to be passed to `tf.keras.Model` training/eval methods. -* Accelerated Linear Algebra (XLA): -* TensorFlow Debugger (tfdbg): fix an issue in which the TensorBoard Debugger Plugin could not handle total source file size exceeding gRPC message size limit (4 MB). +* TensorFlow Debugger (tfdbg) CLI: fix an issue in which the TensorBoard Debugger Plugin could not handle total source file size exceeding gRPC message size limit (4 MB). * `tf.contrib`: - * Add `tf.contrib.data.choose_from_datasets()`. - * `tf.contrib.data.make_csv_dataset()` now supports line breaks in quoted strings. Two arguments were removed from `make_csv_dataset`. * `tf.contrib.framework.zero_initializer` supports ResourceVariable. * Adding "constrained_optimization" to tensorflow/contrib. * Other: @@ -55,7 +59,6 @@ * More consistent GcsFileSystem behavior for certain reads past EOF. * Update benchmark for tf.scan to match ranges across eager and graph modes. * Fixed bug in `tf.reduce_prod gradient` for complex dtypes. - * Add optional `args` argument to `Dataset.from_generator()`. * Allow the use of '.' in variables (e.g. "hparams.parse('a.b=1.0')"), which would previously raise an error. This will correspond to an attribute name with an embedded '.' symbol (e.g. 'a.b'), which can only be accessed indirectly (e.g. through getattr and setattr). To set this up the user will first need to explicitly add the variable to the hparam object (e.g. "hparams.add_hparam(name='a.b', value=0.0)"). * Benchmark for tf.scan in graph and eager modes. * Added complex128 support to FFT, FFT2D, FFT3D, IFFT, IFFT2D, and IFFT3D. @@ -65,7 +68,6 @@ * LinearOperator[1D,2D,3D]Circulant added to `tensorflow.linalg`. * Conv3D, Conv3DBackpropInput, Conv3DBackpropFilter now supports arbitrary. * Added `tf.train.Checkpoint` for reading/writing object-based checkpoints. - * `Dataset.list_files()` now produces determinstic results when `shuffle=False` or a `seed` is passed. * Added LinearOperatorKronecker, a dense-free implementation of the Kronecker Product. * Allow LinearOperator to broadcast. * SavedModelBuilder will now deduplicate asset names that point to files with the same basename and the same contents. Note that this may result in new asset files included in SavedModels in cases where assets with the same name but different contents were previously overwriting each other. diff --git a/WORKSPACE b/WORKSPACE index fd7570a80ae2ee0087f7d2fd771fcce5b9690028..17961829a605c2d1f2d2ba86a7c30c47618c139b 100644 --- a/WORKSPACE +++ b/WORKSPACE @@ -18,7 +18,7 @@ closure_repositories() # files, in case the parsing of those build files depends on the bazel # version we require here. load("//tensorflow:version_check.bzl", "check_bazel_version_at_least") -check_bazel_version_at_least("0.10.0") +check_bazel_version_at_least("0.15.0") load("//tensorflow:workspace.bzl", "tf_workspace") diff --git a/configure.py b/configure.py index 31a83b4a1589b7f038bcdde5cec9007cd16b261c..f97bf8a66836a6647ba6aca625cb1526e11b39af 100644 --- a/configure.py +++ b/configure.py @@ -35,8 +35,8 @@ except ImportError: _DEFAULT_CUDA_VERSION = '9.0' _DEFAULT_CUDNN_VERSION = '7' -_DEFAULT_NCCL_VERSION = '1.3' -_DEFAULT_CUDA_COMPUTE_CAPABILITIES = '3.5,5.2' +_DEFAULT_NCCL_VERSION = '2.2' +_DEFAULT_CUDA_COMPUTE_CAPABILITIES = '3.5,7.0' _DEFAULT_CUDA_PATH = '/usr/local/cuda' _DEFAULT_CUDA_PATH_LINUX = '/opt/cuda' _DEFAULT_CUDA_PATH_WIN = ('C:/Program Files/NVIDIA GPU Computing ' @@ -680,7 +680,7 @@ def create_android_sdk_rule(environ_cp): if is_windows() or is_cygwin(): default_sdk_path = cygpath('%s/Android/Sdk' % environ_cp['APPDATA']) elif is_macos(): - default_sdk_path = '%s/library/Android/Sdk/ndk-bundle' % environ_cp['HOME'] + default_sdk_path = '%s/library/Android/Sdk' % environ_cp['HOME'] else: default_sdk_path = '%s/Android/Sdk' % environ_cp['HOME'] @@ -835,6 +835,8 @@ def set_tf_cuda_version(environ_cp): '[Default is %s]: ') % (tf_cuda_version, default_cuda_path) cuda_toolkit_path = get_from_env_or_user_or_default( environ_cp, 'CUDA_TOOLKIT_PATH', ask_cuda_path, default_cuda_path) + if is_windows() or is_cygwin(): + cuda_toolkit_path = cygpath(cuda_toolkit_path) if is_windows(): cuda_rt_lib_path = 'lib/x64/cudart.lib' @@ -880,7 +882,7 @@ def set_tf_cudnn_version(environ_cp): default_cudnn_path = environ_cp.get('CUDA_TOOLKIT_PATH') ask_cudnn_path = (r'Please specify the location where cuDNN %s library is ' 'installed. Refer to README.md for more details. [Default' - ' is %s]:') % (tf_cudnn_version, default_cudnn_path) + ' is %s]: ') % (tf_cudnn_version, default_cudnn_path) cudnn_install_path = get_from_env_or_user_or_default( environ_cp, 'CUDNN_INSTALL_PATH', ask_cudnn_path, default_cudnn_path) @@ -1095,8 +1097,10 @@ def set_tf_nccl_install_path(environ_cp): raise ValueError('Currently NCCL is only supported on Linux platforms.') ask_nccl_version = ( - 'Please specify the NCCL version you want to use. ' - '[Leave empty to default to NCCL %s]: ') % _DEFAULT_NCCL_VERSION + 'Please specify the NCCL version you want to use. If NCCL %s is not ' + 'installed, then you can use version 1.3 that can be fetched ' + 'automatically but it may have worse performance with multiple GPUs. ' + '[Default is %s]: ') % (_DEFAULT_NCCL_VERSION, _DEFAULT_NCCL_VERSION) for _ in range(_DEFAULT_PROMPT_ASK_ATTEMPTS): tf_nccl_version = get_from_env_or_user_or_default( @@ -1134,9 +1138,7 @@ def set_tf_nccl_install_path(environ_cp): nccl_lib_path = os.path.join(nccl_install_path, nccl_lib_path) nccl_hdr_path = os.path.join(nccl_install_path, 'include/nccl.h') - nccl_license_path = os.path.join(nccl_install_path, 'NCCL-SLA.txt') - if os.path.exists(nccl_lib_path) and os.path.exists( - nccl_hdr_path) and os.path.exists(nccl_license_path): + if os.path.exists(nccl_lib_path) and os.path.exists(nccl_hdr_path): # Set NCCL_INSTALL_PATH environ_cp['NCCL_INSTALL_PATH'] = nccl_install_path write_action_env_to_bazelrc('NCCL_INSTALL_PATH', nccl_install_path) @@ -1199,7 +1201,7 @@ def set_tf_cuda_compute_capabilities(environ_cp): 'https://developer.nvidia.com/cuda-gpus.\nPlease' ' note that each additional compute ' 'capability significantly increases your ' - 'build time and binary size. [Default is: %s]' % + 'build time and binary size. [Default is: %s]: ' % default_cuda_compute_capabilities) tf_cuda_compute_capabilities = get_from_env_or_user_or_default( environ_cp, 'TF_CUDA_COMPUTE_CAPABILITIES', @@ -1234,28 +1236,13 @@ def set_tf_cuda_compute_capabilities(environ_cp): def set_other_cuda_vars(environ_cp): """Set other CUDA related variables.""" - if is_windows(): - # The following three variables are needed for MSVC toolchain configuration - # in Bazel - environ_cp['CUDA_PATH'] = environ_cp.get('CUDA_TOOLKIT_PATH') - environ_cp['CUDA_COMPUTE_CAPABILITIES'] = environ_cp.get( - 'TF_CUDA_COMPUTE_CAPABILITIES') - environ_cp['NO_WHOLE_ARCHIVE_OPTION'] = 1 - write_action_env_to_bazelrc('CUDA_PATH', environ_cp.get('CUDA_PATH')) - write_action_env_to_bazelrc('CUDA_COMPUTE_CAPABILITIE', - environ_cp.get('CUDA_COMPUTE_CAPABILITIE')) - write_action_env_to_bazelrc('NO_WHOLE_ARCHIVE_OPTION', - environ_cp.get('NO_WHOLE_ARCHIVE_OPTION')) - write_to_bazelrc('build --config=win-cuda') - write_to_bazelrc('test --config=win-cuda') + # If CUDA is enabled, always use GPU during build and test. + if environ_cp.get('TF_CUDA_CLANG') == '1': + write_to_bazelrc('build --config=cuda_clang') + write_to_bazelrc('test --config=cuda_clang') else: - # If CUDA is enabled, always use GPU during build and test. - if environ_cp.get('TF_CUDA_CLANG') == '1': - write_to_bazelrc('build --config=cuda_clang') - write_to_bazelrc('test --config=cuda_clang') - else: - write_to_bazelrc('build --config=cuda') - write_to_bazelrc('test --config=cuda') + write_to_bazelrc('build --config=cuda') + write_to_bazelrc('test --config=cuda') def set_host_cxx_compiler(environ_cp): @@ -1415,14 +1402,36 @@ def set_build_strip_flag(): write_to_bazelrc('build --strip=always') -def set_windows_build_flags(): - if is_windows(): - # The non-monolithic build is not supported yet - write_to_bazelrc('build --config monolithic') - # Suppress warning messages - write_to_bazelrc('build --copt=-w --host_copt=-w') - # Output more verbose information when something goes wrong - write_to_bazelrc('build --verbose_failures') +def set_windows_build_flags(environ_cp): + """Set Windows specific build options.""" + # The non-monolithic build is not supported yet + write_to_bazelrc('build --config monolithic') + # Suppress warning messages + write_to_bazelrc('build --copt=-w --host_copt=-w') + # Output more verbose information when something goes wrong + write_to_bazelrc('build --verbose_failures') + # The host and target platforms are the same in Windows build. So we don't + # have to distinct them. This avoids building the same targets twice. + write_to_bazelrc('build --distinct_host_configuration=false') + # Enable short object file path to avoid long path issue on Windows. + # TODO(pcloudy): Remove this flag when upgrading Bazel to 0.16.0 + # Short object file path will be enabled by default. + write_to_bazelrc('build --experimental_shortened_obj_file_path=true') + + if get_var( + environ_cp, 'TF_OVERRIDE_EIGEN_STRONG_INLINE', 'Eigen strong inline', + True, + ('Would you like to override eigen strong inline for some C++ ' + 'compilation to reduce the compilation time?'), + 'Eigen strong inline overridden.', + 'Not overriding eigen strong inline, ' + 'some compilations could take more than 20 mins.'): + # Due to a known MSVC compiler issue + # https://github.com/tensorflow/tensorflow/issues/10521 + # Overriding eigen strong inline speeds up the compiling of + # conv_grad_ops_3d.cc and conv_ops_3d.cc by 20 minutes, + # but this also hurts the performance. Let users decide what they want. + write_to_bazelrc('build --define=override_eigen_strong_inline=true') def config_info_line(name, help_text): @@ -1442,7 +1451,7 @@ def main(): # environment variables. environ_cp = dict(os.environ) - check_bazel_version('0.10.0') + check_bazel_version('0.15.0') reset_tf_configure_bazelrc(args.workspace) cleanup_makefile() @@ -1462,11 +1471,23 @@ def main(): # TODO(ibiryukov): Investigate using clang as a cpu or cuda compiler on # Windows. environ_cp['TF_DOWNLOAD_CLANG'] = '0' + environ_cp['TF_ENABLE_XLA'] = '0' + environ_cp['TF_NEED_GDR'] = '0' + environ_cp['TF_NEED_VERBS'] = '0' + environ_cp['TF_NEED_MPI'] = '0' + environ_cp['TF_SET_ANDROID_WORKSPACE'] = '0' if is_macos(): environ_cp['TF_NEED_JEMALLOC'] = '0' environ_cp['TF_NEED_TENSORRT'] = '0' + # The numpy package on ppc64le uses OpenBLAS which has multi-threading + # issues that lead to incorrect answers. Set OMP_NUM_THREADS=1 at + # runtime to allow the Tensorflow testcases which compare numpy + # results to Tensorflow results to succeed. + if is_ppc64le(): + write_action_env_to_bazelrc("OMP_NUM_THREADS", 1) + set_build_var(environ_cp, 'TF_NEED_JEMALLOC', 'jemalloc as malloc', 'with_jemalloc', True) set_build_var(environ_cp, 'TF_NEED_GCP', 'Google Cloud Platform', @@ -1538,7 +1559,8 @@ def main(): set_grpc_build_flags() set_cc_opt_flags(environ_cp) set_build_strip_flag() - set_windows_build_flags() + if is_windows(): + set_windows_build_flags(environ_cp) if get_var( environ_cp, 'TF_SET_ANDROID_WORKSPACE', 'android workspace', @@ -1550,11 +1572,15 @@ def main(): create_android_ndk_rule(environ_cp) create_android_sdk_rule(environ_cp) - print('Preconfigured Bazel build configs. You can use any of the below by ' - 'adding "--config=<>" to your build command. See tools/bazel.rc for ' - 'more details.') - config_info_line('mkl', 'Build with MKL support.') - config_info_line('monolithic', 'Config for mostly static monolithic build.') + # On Windows, we don't have MKL support and the build is always monolithic. + # So no need to print the following message. + # TODO(pcloudy): remove the following if check when they make sense on Windows + if not is_windows(): + print('Preconfigured Bazel build configs. You can use any of the below by ' + 'adding "--config=<>" to your build command. See tools/bazel.rc for ' + 'more details.') + config_info_line('mkl', 'Build with MKL support.') + config_info_line('monolithic', 'Config for mostly static monolithic build.') if __name__ == '__main__': main() diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 51eea94847e47ac3ffee89ed6bbae269b7b92c77..60db234c9c56fcca32418fcc3b10385f8d82bd45 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -20,10 +20,18 @@ load( "tf_additional_binary_deps", ) load( - "//tensorflow/tools/api/generator:api_gen.bzl", + "//tensorflow/python/tools/api/generator:api_gen.bzl", "gen_api_init_files", # @unused ) +# Config setting used when building for products +# which requires restricted licenses to be avoided. +config_setting( + name = "no_lgpl_deps", + values = {"define": "__TENSORFLOW_NO_LGPL_DEPS__=1"}, + visibility = ["//visibility:public"], +) + # Config setting for determining if we are building for Android. config_setting( name = "android", @@ -373,6 +381,14 @@ config_setting( }, ) +# Setting to use when loading kernels dynamically +config_setting( + name = "dynamic_loaded_kernels", + define_values = { + "dynamic_loaded_kernels": "true", + }, +) + config_setting( name = "using_cuda_nvcc", define_values = { @@ -400,14 +416,6 @@ config_setting( visibility = ["//visibility:public"], ) -# TODO(laigd): consider removing this option and make TensorRT enabled -# automatically when CUDA is enabled. -config_setting( - name = "with_tensorrt_support", - values = {"define": "with_tensorrt_support=true"}, - visibility = ["//visibility:public"], -) - package_group( name = "internal", packages = [ diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc index 5c218d3f25e01f0e78916d4a5a8b1d2751f9dc25..10bc8cdbee5a9df6d2084c10adab4ed6e5e6f0d3 100644 --- a/tensorflow/c/c_api.cc +++ b/tensorflow/c/c_api.cc @@ -33,6 +33,7 @@ limitations under the License. #include "tensorflow/core/common_runtime/eval_const_tensor.h" #include "tensorflow/core/common_runtime/shape_refiner.h" #include "tensorflow/core/framework/allocation_description.pb.h" +#include "tensorflow/core/framework/kernel_def.pb.h" #include "tensorflow/core/framework/log_memory.h" #include "tensorflow/core/framework/node_def_util.h" #include "tensorflow/core/framework/op_kernel.h" @@ -327,6 +328,7 @@ TF_Buffer* TF_NewBufferFromString(const void* proto, size_t proto_len) { } void TF_DeleteBuffer(TF_Buffer* buffer) { + if (buffer == nullptr) return; if (buffer->data_deallocator != nullptr) { (*buffer->data_deallocator)(const_cast(buffer->data), buffer->length); @@ -356,6 +358,7 @@ void TF_CloseDeprecatedSession(TF_DeprecatedSession* s, TF_Status* status) { void TF_DeleteDeprecatedSession(TF_DeprecatedSession* s, TF_Status* status) { status->status = Status::OK(); + if (s == nullptr) return; delete s->session; delete s; } @@ -906,6 +909,7 @@ TF_Library* TF_LoadLibrary(const char* library_filename, TF_Status* status) { TF_Buffer TF_GetOpList(TF_Library* lib_handle) { return lib_handle->op_list; } void TF_DeleteLibraryHandle(TF_Library* lib_handle) { + if (lib_handle == nullptr) return; tensorflow::port::Free(const_cast(lib_handle->op_list.data)); delete lib_handle; } @@ -963,6 +967,7 @@ TF_DEVICELIST_METHOD(const char*, TF_DeviceListName, name().c_str(), nullptr); TF_DEVICELIST_METHOD(const char*, TF_DeviceListType, device_type().c_str(), nullptr); TF_DEVICELIST_METHOD(int64_t, TF_DeviceListMemoryBytes, memory_limit(), -1); +TF_DEVICELIST_METHOD(uint64_t, TF_DeviceListIncarnation, incarnation(), 0); #undef TF_DEVICELIST_METHOD @@ -1852,6 +1857,7 @@ TF_Graph::TF_Graph() TF_Graph* TF_NewGraph() { return new TF_Graph; } void TF_DeleteGraph(TF_Graph* g) { + if (g == nullptr) return; g->mu.lock(); g->delete_requested = true; const bool del = g->sessions.empty(); @@ -2527,6 +2533,7 @@ void TF_CloseSession(TF_Session* s, TF_Status* status) { void TF_DeleteSession(TF_Session* s, TF_Status* status) { status->status = Status::OK(); + if (s == nullptr) return; TF_Graph* const graph = s->graph; if (graph != nullptr) { graph->mu.lock(); @@ -2725,7 +2732,34 @@ TF_Buffer* TF_ApiDefMapGet(TF_ApiDefMap* api_def_map, const char* name, TF_Buffer* ret = TF_NewBuffer(); status->status = MessageToBuffer(*api_def, ret); + if (!status->status.ok()) { + TF_DeleteBuffer(ret); + return nullptr; + } return ret; #endif // __ANDROID__ } + +TF_Buffer* TF_GetAllRegisteredKernels(TF_Status* status) { + tensorflow::KernelList kernel_list = tensorflow::GetAllRegisteredKernels(); + TF_Buffer* ret = TF_NewBuffer(); + status->status = MessageToBuffer(kernel_list, ret); + if (!status->status.ok()) { + TF_DeleteBuffer(ret); + return nullptr; + } + return ret; +} + +TF_Buffer* TF_GetRegisteredKernelsForOp(const char* name, TF_Status* status) { + tensorflow::KernelList kernel_list = + tensorflow::GetRegisteredKernelsForOp(name); + TF_Buffer* ret = TF_NewBuffer(); + status->status = MessageToBuffer(kernel_list, ret); + if (!status->status.ok()) { + TF_DeleteBuffer(ret); + return nullptr; + } + return ret; +} } // end extern "C" diff --git a/tensorflow/c/c_api.h b/tensorflow/c/c_api.h index 1eb75ef11ff337dfcb2e016e09804fc04662fcda..7e97351c8a1cff7ee7b0abe03efd88b34b8c5906 100644 --- a/tensorflow/c/c_api.h +++ b/tensorflow/c/c_api.h @@ -44,6 +44,7 @@ limitations under the License. // * size_t is used to represent byte sizes of objects that are // materialized in the address space of the calling process. // * int is used as an index into arrays. +// * Deletion functions are safe to call on nullptr. // // Questions left to address: // * Might at some point need a way for callers to provide their own Env. @@ -1235,6 +1236,11 @@ TF_CAPI_EXPORT extern TF_Function* TF_GraphToFunction( int noutputs, const TF_Output* outputs, const char* const* output_names, const TF_FunctionOptions* opts, const char* description, TF_Status* status); +// Returns the name of the graph function. +// The return value points to memory that is only usable until the next +// mutation to *func. +TF_CAPI_EXPORT extern const char* TF_FunctionName(TF_Function* func); + // Write out a serialized representation of `func` (as a FunctionDef protocol // message) to `output_func_def` (allocated by TF_NewBuffer()). // `output_func_def`'s underlying buffer will be freed when TF_DeleteBuffer() @@ -1521,6 +1527,13 @@ TF_CAPI_EXPORT extern const char* TF_DeviceListType(const TF_DeviceList* list, TF_CAPI_EXPORT extern int64_t TF_DeviceListMemoryBytes( const TF_DeviceList* list, int index, TF_Status* status); +// Retrieve the incarnation number of a given device. +// +// If index is out of bounds, an error code will be set in the status object, +// and 0 will be returned. +TF_CAPI_EXPORT extern uint64_t TF_DeviceListIncarnation( + const TF_DeviceList* list, int index, TF_Status* status); + // -------------------------------------------------------------------------- // Load plugins containing custom ops and kernels @@ -1603,6 +1616,18 @@ TF_CAPI_EXPORT extern TF_Buffer* TF_ApiDefMapGet(TF_ApiDefMap* api_def_map, size_t name_len, TF_Status* status); +// -------------------------------------------------------------------------- +// Kernel definition information. + +// Returns a serialized KernelList protocol buffer containing KernelDefs for all +// registered kernels. +TF_CAPI_EXPORT extern TF_Buffer* TF_GetAllRegisteredKernels(TF_Status* status); + +// Returns a serialized KernelList protocol buffer containing KernelDefs for all +// kernels registered for the operation named `name`. +TF_CAPI_EXPORT extern TF_Buffer* TF_GetRegisteredKernelsForOp( + const char* name, TF_Status* status); + #ifdef __cplusplus } /* end extern "C" */ #endif diff --git a/tensorflow/c/c_api_experimental.cc b/tensorflow/c/c_api_experimental.cc index 170046c8024dc85c899108b254cd3a95a3be4096..69b3ffe2a1f620e346405607ecf742fb863aa644 100644 --- a/tensorflow/c/c_api_experimental.cc +++ b/tensorflow/c/c_api_experimental.cc @@ -84,6 +84,18 @@ TF_Buffer* TF_CreateConfig(unsigned char enable_xla_compilation, return ret; } +TF_Buffer* TF_CreateRunOptions(unsigned char enable_full_trace) { + tensorflow::RunOptions options; + if (enable_full_trace) { + options.set_trace_level(tensorflow::RunOptions::FULL_TRACE); + } else { + options.set_trace_level(tensorflow::RunOptions::NO_TRACE); + } + TF_Buffer* ret = TF_NewBuffer(); + TF_CHECK_OK(MessageToBuffer(options, ret)); + return ret; +} + const char* TF_GraphDebugString(TF_Graph* graph, size_t* len) { tensorflow::mutex_lock c(graph->mu); const auto& debug_str = graph->graph.ToGraphDefDebug().DebugString(); diff --git a/tensorflow/c/c_api_experimental.h b/tensorflow/c/c_api_experimental.h index 2d81c01e0dd056e9beb3b45f24809381554a7924..6617c5a572e90e78369f73d714f39942f213040f 100644 --- a/tensorflow/c/c_api_experimental.h +++ b/tensorflow/c/c_api_experimental.h @@ -70,6 +70,12 @@ TF_CAPI_EXPORT extern TF_Buffer* TF_CreateConfig( unsigned char enable_xla_compilation, unsigned char gpu_memory_allow_growth); +// Create a serialized tensorflow.RunOptions proto, where RunOptions.trace_level +// is set to FULL_TRACE if `enable_full_trace` is non-zero, and NO_TRACE +// otherwise. +TF_CAPI_EXPORT extern TF_Buffer* TF_CreateRunOptions( + unsigned char enable_full_trace); + // Returns the graph content in a human-readable format, with length set in // `len`. The format is subject to change in the future. // The returned string is heap-allocated, and caller should call free() on it. diff --git a/tensorflow/c/c_api_function.cc b/tensorflow/c/c_api_function.cc index 384e6c8cb97022264c5327da5ca5861057608fbe..a2c5a42c11361779de61b515e0f08dcc45e609b9 100644 --- a/tensorflow/c/c_api_function.cc +++ b/tensorflow/c/c_api_function.cc @@ -536,6 +536,10 @@ TF_Function* TF_GraphToFunction(const TF_Graph* fn_body, const char* fn_name, return tf_function; } +const char* TF_FunctionName(TF_Function* func) { + return func->fdef.signature().name().c_str(); +} + void TF_GraphCopyFunction(TF_Graph* g, const TF_Function* func, const TF_Function* grad, TF_Status* status) { if (func == nullptr) { diff --git a/tensorflow/c/c_api_function_test.cc b/tensorflow/c/c_api_function_test.cc index 610274696f5940c063e68f2310cfd9cc1e0bd964..bb9433ce25e0e3b9cfb54698c940cc1b38c88d31 100644 --- a/tensorflow/c/c_api_function_test.cc +++ b/tensorflow/c/c_api_function_test.cc @@ -193,6 +193,7 @@ class CApiFunctionTest : public ::testing::Test { ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); ASSERT_NE(func_, nullptr); + ASSERT_EQ(std::string(func_name_), std::string(TF_FunctionName(func_))); TF_GraphCopyFunction(host_graph_, func_, nullptr, s_); ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); } @@ -1516,7 +1517,8 @@ void DefineStatefulFunction(const char* name, TF_Function** func) { TF_Output inputs[] = {}; TF_Output outputs[] = {{random, 0}}; - *func = TF_GraphToFunction(func_graph.get(), name, /*append_hash=*/false, -1, + *func = TF_GraphToFunction(func_graph.get(), name, + /*append_hash_to_fn_name=*/false, -1, /*opers=*/nullptr, 0, inputs, 1, outputs, /*output_names=*/nullptr, /*opts=*/nullptr, "", s.get()); diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc index bc04b53fbb7fa9ba46228ae5a4ec8ee96df5f3dc..e674b1623cf540eb8024d9be5ed8d77aa2fe17ba 100644 --- a/tensorflow/c/c_api_test.cc +++ b/tensorflow/c/c_api_test.cc @@ -29,9 +29,11 @@ limitations under the License. #include "tensorflow/core/framework/api_def.pb.h" #include "tensorflow/core/framework/common_shape_fns.h" #include "tensorflow/core/framework/graph.pb_text.h" +#include "tensorflow/core/framework/kernel_def.pb.h" #include "tensorflow/core/framework/node_def.pb_text.h" #include "tensorflow/core/framework/node_def_util.h" #include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/partial_tensor_shape.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.pb.h" @@ -1424,6 +1426,29 @@ TEST(CAPI, SavedModelNullArgsAreValid) { TF_DeleteStatus(s); } +TEST(CAPI, DeletingNullPointerIsSafe) { + TF_Status* status = TF_NewStatus(); + + TF_DeleteStatus(nullptr); + TF_DeleteBuffer(nullptr); + TF_DeleteTensor(nullptr); + TF_DeleteSessionOptions(nullptr); + TF_DeleteGraph(nullptr); + TF_DeleteImportGraphDefOptions(nullptr); + TF_DeleteImportGraphDefResults(nullptr); + TF_DeleteFunction(nullptr); + TF_DeleteSession(nullptr, status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TF_DeletePRunHandle(nullptr); + TF_DeleteDeprecatedSession(nullptr, status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TF_DeleteDeviceList(nullptr); + TF_DeleteLibraryHandle(nullptr); + TF_DeleteApiDefMap(nullptr); + + TF_DeleteStatus(status); +} + REGISTER_OP("TestOpWithNoGradient") .Input("x: T") .Output("y: T") @@ -2312,6 +2337,57 @@ TEST(TestApiDef, TestCreateApiDefWithOverwrites) { TF_DeleteLibraryHandle(lib); } +class DummyKernel : public tensorflow::OpKernel { + public: + explicit DummyKernel(tensorflow::OpKernelConstruction* context) + : OpKernel(context) {} + void Compute(tensorflow::OpKernelContext* context) override {} +}; + +// Test we can query kernels +REGISTER_OP("TestOpWithSingleKernel") + .Input("a: float") + .Input("b: float") + .Output("o: float"); +REGISTER_KERNEL_BUILDER( + Name("TestOpWithSingleKernel").Device(tensorflow::DEVICE_CPU), DummyKernel); + +TEST(TestKernel, TestGetAllRegisteredKernels) { + TF_Status* status = TF_NewStatus(); + TF_Buffer* kernel_list_buf = TF_GetAllRegisteredKernels(status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + KernelList kernel_list; + kernel_list.ParseFromArray(kernel_list_buf->data, kernel_list_buf->length); + ASSERT_GT(kernel_list.kernel_size(), 0); + TF_DeleteBuffer(kernel_list_buf); + TF_DeleteStatus(status); +} + +TEST(TestKernel, TestGetRegisteredKernelsForOp) { + TF_Status* status = TF_NewStatus(); + TF_Buffer* kernel_list_buf = + TF_GetRegisteredKernelsForOp("TestOpWithSingleKernel", status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + KernelList kernel_list; + kernel_list.ParseFromArray(kernel_list_buf->data, kernel_list_buf->length); + ASSERT_EQ(kernel_list.kernel_size(), 1); + EXPECT_EQ(kernel_list.kernel(0).op(), "TestOpWithSingleKernel"); + EXPECT_EQ(kernel_list.kernel(0).device_type(), "CPU"); + TF_DeleteBuffer(kernel_list_buf); + TF_DeleteStatus(status); +} + +TEST(TestKernel, TestGetRegisteredKernelsForOpNoKernels) { + TF_Status* status = TF_NewStatus(); + TF_Buffer* kernel_list_buf = TF_GetRegisteredKernelsForOp("Unknown", status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + KernelList kernel_list; + kernel_list.ParseFromArray(kernel_list_buf->data, kernel_list_buf->length); + ASSERT_EQ(kernel_list.kernel_size(), 0); + TF_DeleteBuffer(kernel_list_buf); + TF_DeleteStatus(status); +} + #undef EXPECT_TF_META } // namespace diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc index 82ca2be2cff885967dd798a1cb84b164a9df399e..7321b4b791ffa722e9d3c7722c43297b0eae1eab 100644 --- a/tensorflow/c/eager/c_api.cc +++ b/tensorflow/c/eager/c_api.cc @@ -288,7 +288,7 @@ TFE_Context* TFE_NewContext(const TFE_ContextOptions* opts, TF_Status* status) { opts->async, std::move(device_mgr), r); } -void TFE_DeleteContext(TFE_Context* ctx, TF_Status* status) { delete ctx; } +void TFE_DeleteContext(TFE_Context* ctx) { delete ctx; } TF_DeviceList* TFE_ContextListDevices(TFE_Context* ctx, TF_Status* status) { TF_DeviceList* list = new TF_DeviceList; @@ -336,7 +336,7 @@ TFE_TensorHandle* TFE_NewTensorHandle(TF_Tensor* t, TF_Status* status) { } void TFE_DeleteTensorHandle(TFE_TensorHandle* h) { - DCHECK(h); + if (h == nullptr) return; if (h->handle) { h->handle->Unref(); } @@ -664,17 +664,17 @@ TFE_TensorHandle* TFE_NewTensorHandle(const tensorflow::Tensor& t) { const tensorflow::Tensor* TFE_TensorHandleUnderlyingTensorInHostMemory( TFE_TensorHandle* h, TF_Status* status) { - tensorflow::Device* d = nullptr; - tensorflow::Device* op_device = nullptr; - const tensorflow::Tensor* t = nullptr; - status->status = h->handle->TensorAndDevice(&t, &d, &op_device); - if (!status->status.ok()) return nullptr; - if (d != nullptr) { + if (!h->handle->OnHostCPU()) { status->status = tensorflow::errors::FailedPrecondition( "TFE_TensorHandle is placed in device (not host) memory. Cannot return " "a tensorflow::Tensor"); return nullptr; } + tensorflow::Device* d = nullptr; + tensorflow::Device* op_device = nullptr; + const tensorflow::Tensor* t = nullptr; + status->status = h->handle->TensorAndDevice(&t, &d, &op_device); + if (!status->status.ok()) return nullptr; return t; } diff --git a/tensorflow/c/eager/c_api.h b/tensorflow/c/eager/c_api.h index fdbd5374b2afe815c3a81b453930eb8f1fa351d3..ea019a5711c1bbd4547819e976acf98fc06ecbde 100644 --- a/tensorflow/c/eager/c_api.h +++ b/tensorflow/c/eager/c_api.h @@ -102,8 +102,7 @@ typedef struct TFE_Context TFE_Context; TF_CAPI_EXPORT extern TFE_Context* TFE_NewContext( const TFE_ContextOptions* opts, TF_Status* status); -TF_CAPI_EXPORT extern void TFE_DeleteContext(TFE_Context* ctx, - TF_Status* status); +TF_CAPI_EXPORT extern void TFE_DeleteContext(TFE_Context* ctx); TF_CAPI_EXPORT extern TF_DeviceList* TFE_ContextListDevices(TFE_Context* ctx, TF_Status* status); diff --git a/tensorflow/c/eager/c_api_test.cc b/tensorflow/c/eager/c_api_test.cc index 3504a8b5e78480732d3454097c1b2197ac2b2e17..0bdea70fe6b53ec374d856984741b211258b1d13 100644 --- a/tensorflow/c/eager/c_api_test.cc +++ b/tensorflow/c/eager/c_api_test.cc @@ -49,7 +49,7 @@ void BM_InitOp(int iters) { } tensorflow::testing::StopTiming(); TFE_DeleteTensorHandle(m); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } @@ -80,7 +80,7 @@ void BM_Execute(int iters, int async) { tensorflow::testing::StopTiming(); TFE_DeleteOp(matmul); TFE_DeleteTensorHandle(m); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } @@ -95,7 +95,7 @@ TEST(CAPI, Context) { TF_DeviceList* devices = TFE_ContextListDevices(ctx, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); const int num_devices = TF_DeviceListCount(devices); @@ -195,7 +195,7 @@ void TestRemoteExecute(bool async) { TFE_DeleteOp(matmul); TFE_ContextAsyncWait(ctx, status); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); @@ -281,7 +281,7 @@ void TestRemoteExecuteSilentCopies(bool async) { TFE_DeleteOp(matmul); TFE_ContextAsyncWait(ctx, status); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); @@ -380,8 +380,7 @@ void TensorHandleCopyBetweenDevices(bool async) { TF_DeleteDeviceList(devices); TF_DeleteTensor(t); TFE_DeleteTensorHandle(hcpu); - TFE_DeleteContext(ctx, status.get()); - EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); + TFE_DeleteContext(ctx); } TEST(CAPI, TensorHandleCopyBetweenDevices) { @@ -418,7 +417,7 @@ void TensorHandleCopyBetweenDevicesError(bool async) { TFE_DeleteTensorHandle(hcopy); TFE_DeleteTensorHandle(hcpu); if (hdevice != nullptr) TFE_DeleteTensorHandle(hdevice); - TFE_DeleteContext(ctx, status.get()); + TFE_DeleteContext(ctx); } TEST(CAPI, TensorHandleCopyBetweenDevicesError) { @@ -451,7 +450,7 @@ void TensorHandleCopyBetweenTwoGPUDevices(bool async) { TF_DeleteDeviceList(devices); TF_DeleteTensor(t); TFE_DeleteTensorHandle(hcpu); - TFE_DeleteContext(ctx, status.get()); + TFE_DeleteContext(ctx); return; } const string gpu_1_name(TF_DeviceListName(devices, 1, status.get())); @@ -484,8 +483,7 @@ void TensorHandleCopyBetweenTwoGPUDevices(bool async) { TF_DeleteDeviceList(devices); TF_DeleteTensor(t); TFE_DeleteTensorHandle(hcpu); - TFE_DeleteContext(ctx, status.get()); - EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); + TFE_DeleteContext(ctx); } TEST(CAPI, TensorHandleCopyBetweenTwoGPUDevices) { @@ -533,8 +531,7 @@ void TensorHandleSilentCopy(bool async) { TFE_DeleteTensorHandle(hcpu); TFE_ContextAsyncWait(ctx, status.get()); EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); - TFE_DeleteContext(ctx, status.get()); - EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); + TFE_DeleteContext(ctx); } TEST(CAPI, TensorHandleSilentCopy) { TensorHandleSilentCopy(false); } @@ -580,8 +577,7 @@ void TensorHandleSilentCopyLocal(bool async) { TFE_DeleteTensorHandle(hcpu); TFE_ContextAsyncWait(ctx, status.get()); EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); - TFE_DeleteContext(ctx, status.get()); - EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); + TFE_DeleteContext(ctx); } TEST(CAPI, TensorHandleSilentCopyLocal) { TensorHandleSilentCopyLocal(false); } TEST(CAPI, TensorHandleSilentCopyLocalAsync) { @@ -614,7 +610,7 @@ void SetAndGetOpDevices(bool async) { TFE_DeleteOp(matmul); TFE_DeleteTensorHandle(m); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } @@ -640,7 +636,7 @@ void Execute_MatMul_CPU(bool async) { TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteTensorHandle(retvals[0]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); float product[4] = {0}; EXPECT_EQ(sizeof(product), TF_TensorByteSize(t)); @@ -712,7 +708,7 @@ void Execute_MatMul_CPU_Runtime_Error(bool async) { TFE_DeleteTensorHandle(m1); TFE_DeleteTensorHandle(m2); TFE_DeleteTensorHandle(retvals[0]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); TF_DeleteStatus(status); } TEST(CAPI, Execute_MatMul_CPU_Runtime_Error) { @@ -743,7 +739,7 @@ void Execute_MatMul_CPU_Type_Error(bool async) { if (retvals[0] != nullptr) { TFE_DeleteTensorHandle(retvals[0]); } - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); TF_DeleteStatus(status); } @@ -781,7 +777,7 @@ TEST(CAPI, Execute_Min_CPU) { TF_DeleteTensor(t); EXPECT_EQ(1, output[0]); EXPECT_EQ(3, output[1]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } @@ -823,7 +819,7 @@ void Execute_MatMul_XLA_CPU(bool async) { EXPECT_EQ(10, product[1]); EXPECT_EQ(15, product[2]); EXPECT_EQ(22, product[3]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); TF_DeleteStatus(status); } TEST(CAPI, Execute_MatMul_XLA_CPU) { Execute_MatMul_XLA_CPU(false); } @@ -862,7 +858,7 @@ void Execute_Min_XLA_CPU(bool async) { TF_DeleteTensor(t); EXPECT_EQ(1, output[0]); EXPECT_EQ(3, output[1]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); TF_DeleteStatus(status); } TEST(CAPI, Execute_Min_XLA_CPU) { Execute_Min_XLA_CPU(false); } @@ -898,7 +894,7 @@ void ExecuteWithTracing(bool async) { TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status); TFE_DeleteTensorHandle(retvals[0]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); float product[4] = {0}; EXPECT_EQ(sizeof(product), TF_TensorByteSize(t)); @@ -974,7 +970,7 @@ TEST(CAPI, Function_ident_CPU) { TF_DeleteTensor(r); TFE_DeleteTensorHandle(result[0]); } - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); TF_DeleteStatus(status); } @@ -1044,7 +1040,7 @@ TEST(CAPI, Function_ident_XLA_CPU) { TF_DeleteTensor(r); TFE_DeleteTensorHandle(result[0]); } - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); TF_DeleteStatus(status); } @@ -1120,7 +1116,7 @@ void FunctionDefAndExecute(bool async) { EXPECT_EQ(10, product[1]); EXPECT_EQ(15, product[2]); EXPECT_EQ(22, product[3]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } @@ -1161,7 +1157,7 @@ void BM_ExecuteFunction(int iters, int async) { tensorflow::testing::StopTiming(); TFE_DeleteTensorHandle(m); TFE_DeleteTensorHandle(retval[0]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } @@ -1249,7 +1245,7 @@ TEST(CAPI, Variables) { TFE_DeleteTensorHandle(var_handle); TFE_DeleteTensorHandle(value_handle); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } @@ -1288,7 +1284,7 @@ void BM_ReadVariable(int iters) { TFE_DeleteOp(op); TFE_DeleteTensorHandle(var_handle); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } diff --git a/tensorflow/c/eager/tape.h b/tensorflow/c/eager/tape.h index 734e712daa39c03f0177eb199b1acb1b19e5d845..1adb0458c35193117b5fa5cfe9ceffbaaf699af7 100644 --- a/tensorflow/c/eager/tape.h +++ b/tensorflow/c/eager/tape.h @@ -520,7 +520,12 @@ Status GradientTape::ComputeGradient( } } else { any_gradient_nonzero = true; - auto new_gradients = vspace.AggregateGradients(grad_it->second); + Gradient* new_gradients = nullptr; + if (grad_it->second.size() == 1) { + new_gradients = grad_it->second.at(0); + } else { + new_gradients = vspace.AggregateGradients(grad_it->second); + } if (sources_set.find(grad_it->first) == sources_set.end()) { gradients.erase(grad_it); } else { diff --git a/tensorflow/c/python_api.cc b/tensorflow/c/python_api.cc index e18fdf6c57bd3f432d8cb73536fb816df90b3963..8486b585c8587e18e8eea18a893fac0a40ff4a27 100644 --- a/tensorflow/c/python_api.cc +++ b/tensorflow/c/python_api.cc @@ -155,7 +155,7 @@ void SetResourceHandleShapeAndType(TF_Graph* graph, TF_Output output, tensorflow::shape_inference::ShapeHandle shape; status->status = ic->MakeShapeFromShapeProto(shape_and_type_proto.shape(), &shape); - if (status->status.ok()) return; + if (!status->status.ok()) return; shapes_and_types.emplace_back(shape, shape_and_type_proto.dtype()); } ic->set_output_handle_shapes_and_types(output.index, shapes_and_types); diff --git a/tensorflow/cc/gradients/math_grad_test.cc b/tensorflow/cc/gradients/math_grad_test.cc index fd7b6fe6625f27bda92e2f56f60908658cdecd7e..1c9bdff5e1295135abe60c282d565c39071fd78a 100644 --- a/tensorflow/cc/gradients/math_grad_test.cc +++ b/tensorflow/cc/gradients/math_grad_test.cc @@ -475,11 +475,7 @@ TEST_F(CWiseUnaryGradTest, Tan_Complex) { auto x_fn = [this](const int i) { return CRV({{1, 0}, {0, 1}, {2, -1}, {1, 2}, {3, 4}}); }; - // TODO(kbsriram) - // Enable when tan kernel supports complex inputs - if (false) { - TestCWiseGrad(TAN, x_fn); - } + TestCWiseGrad(TAN, x_fn); } TEST_F(CWiseUnaryGradTest, Atan) { diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc index c73482d5f4d13ade0dc0412941251d1651371b6e..588e96cb196189780037f66266484962ba0385e4 100644 --- a/tensorflow/cc/gradients/nn_grad.cc +++ b/tensorflow/cc/gradients/nn_grad.cc @@ -47,6 +47,72 @@ Status SoftmaxGrad(const Scope& scope, const Operation& op, } REGISTER_GRADIENT_OP("Softmax", SoftmaxGrad); +bool IsZero(const Scope& scope, const Output& grad) { + string op_type_name = grad.op().node()->type_string(); + if (op_type_name == "ZerosLike" || op_type_name == "Zeros") { + return true; + } + // The Operation we were provided is not named something obvious so + // we need to actually look at its contents. + // The original python code did this by calling a utility function called + // tensor_util.constant_value. + // There is no C++ equivalent to tensor_util.constant_value so we do nothing + // for the moment. + return false; +} + +// Multiply after broadcasting vec to match dimensions of mat. +// Args: +// vec: A 1-D tensor of dimension [D0] +// mat: A 2-D tensor of dimesnion [D0, D1] +// +// Returns: +// A tensor of dimension [D0, D1], the result fo vec * mat. +Output BroadcastMul(const Scope& scope, const Output& vec, const Output& mat) { + auto reshaped = ExpandDims(scope, vec, -1); + return Multiply(scope, reshaped, mat); +} + +Status SoftmaxCrossEntropyWithLogitsGrad(const Scope& scope, + const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + // Softmax gradient with cross entropy logits function. + // We multiply the backprop for cost with the gradients - op.output[1]. + // There is no gradient for labels. + + // The outputs of the network are at input index 0. + auto logits = op.input(0); + // The "truth" labels are at index 1. + auto softmax_grad = op.output(1); + + // The loss is the output at index 0, and backprop is the output at index 1. + auto grad_loss = grad_inputs[0]; + auto grad_grad = grad_inputs[1]; + + auto grad = BroadcastMul(scope, grad_loss, softmax_grad); + if (!IsZero(scope, grad_grad)) { + std::vector axis; + auto logits_softmax = Softmax(scope, logits); + + auto grad_grad_expand = ExpandDims(scope, grad_grad, 1); + auto logits_softmax_expand = ExpandDims(scope, logits_softmax, 2); + auto matmul_result = + BatchMatMul(scope, grad_grad_expand, logits_softmax_expand); + axis.push_back(1); + auto squeeze_result = Squeeze(scope, matmul_result, Squeeze::Axis(axis)); + auto subtraction_result = Subtract(scope, grad_grad, squeeze_result); + auto multiply_result = Multiply(scope, subtraction_result, logits_softmax); + grad = Add(scope, grad, multiply_result); + } + auto minus_log_softmax = Multiply(scope, LogSoftmax(scope, logits), -1.0f); + grad_outputs->push_back(grad); + grad_outputs->push_back(BroadcastMul(scope, grad_loss, minus_log_softmax)); + return scope.status(); +} +REGISTER_GRADIENT_OP("SoftmaxCrossEntropyWithLogits", + SoftmaxCrossEntropyWithLogitsGrad); + Status LogSoftmaxGrad(const Scope& scope, const Operation& op, const std::vector& grad_inputs, std::vector* grad_outputs) { @@ -195,9 +261,9 @@ Status MaxPool3DGradHelper(const Scope& scope, const Operation& op, TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format)); MaxPool3DGrad::Attrs grad_attrs; - auto dx = MaxPool3DGrad(scope, op.input(0), op.output(0), grad_inputs[0], - ksize, strides, padding, - grad_attrs.DataFormat(data_format)); + auto dx = + MaxPool3DGrad(scope, op.input(0), op.output(0), grad_inputs[0], ksize, + strides, padding, grad_attrs.DataFormat(data_format)); grad_outputs->push_back(dx); return scope.status(); } @@ -216,10 +282,9 @@ Status AvgPoolGradHelper(const Scope& scope, const Operation& op, TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format)); internal::AvgPoolGrad::Attrs grad_attrs; - auto dx = - internal::AvgPoolGrad(scope, Shape(scope, op.input(0)), grad_inputs[0], - ksize, strides, padding, - grad_attrs.DataFormat(data_format)); + auto dx = internal::AvgPoolGrad(scope, Shape(scope, op.input(0)), + grad_inputs[0], ksize, strides, padding, + grad_attrs.DataFormat(data_format)); grad_outputs->push_back(dx); return scope.status(); } @@ -238,9 +303,9 @@ Status AvgPool3DGradHelper(const Scope& scope, const Operation& op, TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format)); AvgPool3DGrad::Attrs grad_attrs; - auto dx = AvgPool3DGrad(scope, Shape(scope, op.input(0)), grad_inputs[0], - ksize, strides, padding, - grad_attrs.DataFormat(data_format)); + auto dx = + AvgPool3DGrad(scope, Shape(scope, op.input(0)), grad_inputs[0], ksize, + strides, padding, grad_attrs.DataFormat(data_format)); grad_outputs->push_back(dx); return scope.status(); } diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc index b4d457a9d14eb79232cda9412fa0050f6a9968cc..aa72cf7ba2a958f54d50b59f0edaefb27edf0e86 100644 --- a/tensorflow/cc/gradients/nn_grad_test.cc +++ b/tensorflow/cc/gradients/nn_grad_test.cc @@ -25,6 +25,8 @@ limitations under the License. namespace tensorflow { namespace { +using ops::AvgPool; +using ops::AvgPool3D; using ops::BiasAdd; using ops::Conv2D; using ops::Elu; @@ -33,11 +35,9 @@ using ops::FractionalMaxPool; using ops::L2Loss; using ops::LogSoftmax; using ops::LRN; -using ops::AvgPool; -using ops::AvgPool3D; using ops::MaxPool; -using ops::MaxPoolV2; using ops::MaxPool3D; +using ops::MaxPoolV2; using ops::Placeholder; using ops::Relu; using ops::Relu6; @@ -111,6 +111,20 @@ TEST_F(NNGradTest, SoftmaxGrad) { RunTest(x, shape, y, shape); } +TEST_F(NNGradTest, SoftmaxCrossEntropyWithLogitsGrad) { + TensorShape logits_shape({5, 3}); + TensorShape loss_shape({5}); + + auto logits = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(logits_shape)); + auto labels = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(logits_shape)); + auto y = + tensorflow::ops::SoftmaxCrossEntropyWithLogits(scope_, logits, labels); + // Note the reversal of the backprop and loss orders. Issue #18734 has been + // opened for this. + RunTest({logits, labels}, {logits_shape, logits_shape}, {y.backprop, y.loss}, + {logits_shape, loss_shape}); +} + TEST_F(NNGradTest, LogSoftmaxGrad) { TensorShape shape({5, 3}); auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)); @@ -253,7 +267,7 @@ TEST_F(NNGradTest, AvgPool3DGradHelper) { RunTest(x, x_shape, y, y_shape); } -TEST_F(NNGradTest, LRN){ +TEST_F(NNGradTest, LRN) { TensorShape x_shape({1, 1, 2, 1}); auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); auto y = LRN(scope_, x); diff --git a/tensorflow/cc/saved_model/BUILD b/tensorflow/cc/saved_model/BUILD index 06a3be18e08f611d3ecf9804908d791d15fdab13..3d3895c8fa82c3c0e2974228e9cad767d0e00df4 100644 --- a/tensorflow/cc/saved_model/BUILD +++ b/tensorflow/cc/saved_model/BUILD @@ -33,6 +33,46 @@ cc_library( hdrs = ["tag_constants.h"], ) +cc_library( + name = "reader", + srcs = ["reader.cc"], + hdrs = ["reader.h"], + deps = [ + ":constants", + ] + if_not_mobile([ + # TODO(b/111634734): :lib and :protos_all contain dependencies that + # cannot be built on mobile platforms. Instead, include the appropriate + # tf_lib depending on the build platform. + "//tensorflow/core:lib", + "//tensorflow/core:protos_all_cc", + ]) + if_mobile([ + # Mobile-friendly SavedModel proto. See go/portable-proto for more info. + "//tensorflow/core:saved_model_portable_proto", + ]) + if_android([ + "//tensorflow/core:android_tensorflow_lib", + ]) + if_ios([ + "//tensorflow/core:ios_tensorflow_lib", + ]), +) + +tf_cc_test( + name = "reader_test", + srcs = ["reader_test.cc"], + data = [ + ":saved_model_half_plus_two", + ], + linkstatic = 1, + deps = [ + ":constants", + ":reader", + ":tag_constants", + "//tensorflow/core:lib", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + "//tensorflow/core:testlib", + ], +) + cc_library( name = "loader", hdrs = ["loader.h"], @@ -54,6 +94,7 @@ cc_library( hdrs = ["loader.h"], deps = [ ":constants", + ":reader", ] + if_not_mobile([ "//tensorflow/core:core_cpu", "//tensorflow/core:framework", diff --git a/tensorflow/cc/saved_model/loader.cc b/tensorflow/cc/saved_model/loader.cc index faa1e378d07ea94ad08ee084d18bf6a113f054af..98be66a6add67a8053e286521e564286cdb8ef8d 100644 --- a/tensorflow/cc/saved_model/loader.cc +++ b/tensorflow/cc/saved_model/loader.cc @@ -18,8 +18,10 @@ limitations under the License. #include #include "tensorflow/cc/saved_model/constants.h" +#include "tensorflow/cc/saved_model/reader.h" #include "tensorflow/core/lib/io/path.h" #include "tensorflow/core/lib/monitoring/counter.h" +#include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/protobuf_internal.h" @@ -43,56 +45,6 @@ auto* load_latency = monitoring::Counter<1>::New( constexpr char kLoadAttemptFail[] = "fail"; constexpr char kLoadAttemptSuccess[] = "success"; -Status ReadSavedModel(const string& export_dir, SavedModel* saved_model_proto) { - const string saved_model_pb_path = - io::JoinPath(export_dir, kSavedModelFilenamePb); - if (Env::Default()->FileExists(saved_model_pb_path).ok()) { - return ReadBinaryProto(Env::Default(), saved_model_pb_path, - saved_model_proto); - } - const string saved_model_pbtxt_path = - io::JoinPath(export_dir, kSavedModelFilenamePbTxt); - if (Env::Default()->FileExists(saved_model_pbtxt_path).ok()) { - return ReadTextProto(Env::Default(), saved_model_pbtxt_path, - saved_model_proto); - } - return Status(error::Code::NOT_FOUND, - "Could not find SavedModel .pb or .pbtxt at supplied export " - "directory path: " + - export_dir); -} - -string GetTagsAsString(const std::unordered_set& tags) { - string tags_as_string = "{ "; - for (const string& tag : tags) { - tags_as_string = strings::StrCat(tags_as_string, tag, " "); - } - tags_as_string = strings::StrCat(tags_as_string, "}"); - return tags_as_string; -} - -Status FindMetaGraphDefToLoad(const SavedModel& saved_model_proto, - const std::unordered_set& tags, - MetaGraphDef* meta_graph_def_to_load) { - for (const MetaGraphDef& meta_graph_def : saved_model_proto.meta_graphs()) { - // Get tags from the meta_graph_def. - std::unordered_set graph_tags; - for (const string& tag : meta_graph_def.meta_info_def().tags()) { - graph_tags.insert(tag); - } - // Match with the set of tags provided. - if (graph_tags == tags) { - *meta_graph_def_to_load = meta_graph_def; - return Status::OK(); - } - } - return Status(error::Code::NOT_FOUND, - "Could not find meta graph def matching supplied tags: " + - GetTagsAsString(tags) + - ". To inspect available tag-sets in the SavedModel, please " - "use the SavedModel CLI: `saved_model_cli`"); -} - Status LoadMetaGraphIntoSession(const MetaGraphDef& meta_graph_def, const SessionOptions& session_options, std::unique_ptr* session) { @@ -122,6 +74,54 @@ void AddAssetsTensorsToInputs(const StringPiece export_dir, } } +// Like Session::Run(), but uses the Make/Run/ReleaseCallable() API to avoid +// leaving behind non-GC'ed state. +// +// Detailed motivation behind this approach, from ashankar@: +// +// Each call to Session::Run() that identifies a new subgraph (based on feeds +// and fetches) creates some datastructures that live as long as the session +// (the partitioned graph, associated executors etc.). +// +// A pathological case of this would be if say the initialization op +// (main_op/legacy_init_op) involves the use of a large constant. Then we +// allocate memory for that large constant that will just stick around till the +// session dies. With this Callable mechanism, that memory will be released +// right after ReleaseCallable returns. +// +// However, the resource manager state remains. +Status RunOnce(const RunOptions& run_options, + const std::vector>& inputs, + const std::vector& output_tensor_names, + const std::vector& target_node_names, + std::vector* outputs, RunMetadata* run_metadata, + Session* session) { + CallableOptions callable_options; + std::vector feed_tensors; + *callable_options.mutable_run_options() = run_options; + for (const auto& input : inputs) { + const string& name = input.first; + const Tensor& tensor = input.second; + callable_options.add_feed(name); + feed_tensors.push_back(tensor); + } + for (const string& output_tensor_name : output_tensor_names) { + callable_options.add_fetch(output_tensor_name); + } + for (const string& target_node_name : target_node_names) { + callable_options.add_target(target_node_name); + } + + Session::CallableHandle callable_handle; + TF_RETURN_IF_ERROR(session->MakeCallable(callable_options, &callable_handle)); + const Status run_status = session->RunCallable(callable_handle, feed_tensors, + outputs, run_metadata); + // Be sure to call ReleaseCallable() regardless of the outcome of + // RunCallable(). + session->ReleaseCallable(callable_handle).IgnoreError(); + return run_status; +} + bool HasMainOp(const MetaGraphDef& meta_graph_def) { const auto& collection_def_map = meta_graph_def.collection_def(); if (collection_def_map.find(kSavedModelMainOpKey) != @@ -134,10 +134,11 @@ bool HasMainOp(const MetaGraphDef& meta_graph_def) { Status RunMainOp(const RunOptions& run_options, const string& export_dir, const MetaGraphDef& meta_graph_def, const std::vector& asset_file_defs, - Session* session) { - LOG(INFO) << "Running MainOp on SavedModel bundle."; + Session* session, const string& main_op_key) { + LOG(INFO) << "Running MainOp with key " << main_op_key + << " on SavedModel bundle."; const auto& collection_def_map = meta_graph_def.collection_def(); - const auto main_op_it = collection_def_map.find(kSavedModelMainOpKey); + const auto main_op_it = collection_def_map.find(main_op_key); if (main_op_it != collection_def_map.end()) { if (main_op_it->second.node_list().value_size() != 1) { return errors::FailedPrecondition( @@ -147,8 +148,8 @@ Status RunMainOp(const RunOptions& run_options, const string& export_dir, AddAssetsTensorsToInputs(export_dir, asset_file_defs, &inputs); RunMetadata run_metadata; const StringPiece main_op_name = main_op_it->second.node_list().value(0); - return session->Run(run_options, inputs, {}, {main_op_name.ToString()}, - nullptr /* outputs */, &run_metadata); + return RunOnce(run_options, inputs, {}, {main_op_name.ToString()}, + nullptr /* outputs */, &run_metadata, session); } return Status::OK(); } @@ -185,32 +186,8 @@ Status RunRestore(const RunOptions& run_options, const string& export_dir, AddAssetsTensorsToInputs(export_dir, asset_file_defs, &inputs); RunMetadata run_metadata; - return session->Run(run_options, inputs, {}, {restore_op_name.ToString()}, - nullptr /* outputs */, &run_metadata); -} - -Status RunLegacyInitOp(const RunOptions& run_options, const string& export_dir, - const MetaGraphDef& meta_graph_def, - const std::vector& asset_file_defs, - Session* session) { - LOG(INFO) << "Running LegacyInitOp on SavedModel bundle."; - const auto& collection_def_map = meta_graph_def.collection_def(); - const auto init_op_it = collection_def_map.find(kSavedModelLegacyInitOpKey); - if (init_op_it != collection_def_map.end()) { - if (init_op_it->second.node_list().value_size() != 1) { - return errors::FailedPrecondition(strings::StrCat( - "Expected exactly one serving init op in : ", export_dir)); - } - std::vector> inputs; - AddAssetsTensorsToInputs(export_dir, asset_file_defs, &inputs); - RunMetadata run_metadata; - const StringPiece legacy_init_op_name = - init_op_it->second.node_list().value(0); - return session->Run(run_options, inputs, {}, - {legacy_init_op_name.ToString()}, nullptr /* outputs */, - &run_metadata); - } - return Status::OK(); + return RunOnce(run_options, inputs, {}, {restore_op_name.ToString()}, + nullptr /* outputs */, &run_metadata, session); } Status GetAssetFileDefs(const MetaGraphDef& meta_graph_def, @@ -235,18 +212,8 @@ Status LoadSavedModelInternal(const SessionOptions& session_options, const string& export_dir, const std::unordered_set& tags, SavedModelBundle* const bundle) { - if (!MaybeSavedModelDirectory(export_dir)) { - return Status(error::Code::NOT_FOUND, - "SavedModel not found in export directory: " + export_dir); - } - LOG(INFO) << "Loading SavedModel with tags: " << GetTagsAsString(tags) - << "; from: " << export_dir; - - SavedModel saved_model_proto; - TF_RETURN_IF_ERROR(ReadSavedModel(export_dir, &saved_model_proto)); - - TF_RETURN_IF_ERROR( - FindMetaGraphDefToLoad(saved_model_proto, tags, &bundle->meta_graph_def)); + TF_RETURN_IF_ERROR(ReadMetaGraphDefFromSavedModel(export_dir, tags, + &bundle->meta_graph_def)); TF_RETURN_IF_ERROR(LoadMetaGraphIntoSession( bundle->meta_graph_def, session_options, &bundle->session)); @@ -262,11 +229,11 @@ Status LoadSavedModelInternal(const SessionOptions& session_options, if (HasMainOp(bundle->meta_graph_def)) { TF_RETURN_IF_ERROR(RunMainOp(run_options, export_dir, bundle->meta_graph_def, asset_file_defs, - bundle->session.get())); + bundle->session.get(), kSavedModelMainOpKey)); } else { - TF_RETURN_IF_ERROR(RunLegacyInitOp(run_options, export_dir, - bundle->meta_graph_def, asset_file_defs, - bundle->session.get())); + TF_RETURN_IF_ERROR(RunMainOp( + run_options, export_dir, bundle->meta_graph_def, asset_file_defs, + bundle->session.get(), kSavedModelLegacyInitOpKey)); } return Status::OK(); } @@ -288,8 +255,8 @@ Status LoadSavedModel(const SessionOptions& session_options, return end_microseconds - start_microseconds; }(); auto log_and_count = [&](const string& status_str) { - LOG(INFO) << "SavedModel load for tags " << GetTagsAsString(tags) - << "; Status: " << status_str << ". Took " + LOG(INFO) << "SavedModel load for tags { " << str_util::Join(tags, " ") + << " }; Status: " << status_str << ". Took " << load_latency_microsecs << " microseconds."; load_attempt_count->GetCell(export_dir, status_str)->IncrementBy(1); }; diff --git a/tensorflow/cc/saved_model/reader.cc b/tensorflow/cc/saved_model/reader.cc new file mode 100644 index 0000000000000000000000000000000000000000..2146c8a19745fa9ea2484c4bb4a2104a38d85144 --- /dev/null +++ b/tensorflow/cc/saved_model/reader.cc @@ -0,0 +1,88 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/cc/saved_model/reader.h" + +#include + +#include "tensorflow/cc/saved_model/constants.h" +#include "tensorflow/core/lib/io/path.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/platform/env.h" +#include "tensorflow/core/protobuf/saved_model.pb.h" + +namespace tensorflow { +namespace { + +Status ReadSavedModel(const string& export_dir, SavedModel* saved_model_proto) { + LOG(INFO) << "Reading SavedModel from: " << export_dir; + + const string saved_model_pb_path = + io::JoinPath(export_dir, kSavedModelFilenamePb); + if (Env::Default()->FileExists(saved_model_pb_path).ok()) { + return ReadBinaryProto(Env::Default(), saved_model_pb_path, + saved_model_proto); + } + const string saved_model_pbtxt_path = + io::JoinPath(export_dir, kSavedModelFilenamePbTxt); + if (Env::Default()->FileExists(saved_model_pbtxt_path).ok()) { + return ReadTextProto(Env::Default(), saved_model_pbtxt_path, + saved_model_proto); + } + return Status(error::Code::NOT_FOUND, + "Could not find SavedModel .pb or .pbtxt at supplied export " + "directory path: " + + export_dir); +} + +Status FindMetaGraphDef(const SavedModel& saved_model_proto, + const std::unordered_set& tags, + MetaGraphDef* meta_graph_def) { + LOG(INFO) << "Reading meta graph with tags { " << str_util::Join(tags, " ") + << " }"; + for (const MetaGraphDef& graph_def : saved_model_proto.meta_graphs()) { + // Get tags from the graph_def. + std::unordered_set graph_tags; + for (const string& tag : graph_def.meta_info_def().tags()) { + graph_tags.insert(tag); + } + // Match with the set of tags provided. + if (graph_tags == tags) { + *meta_graph_def = graph_def; + return Status::OK(); + } + } + return Status( + error::Code::NOT_FOUND, + strings::StrCat( + "Could not find meta graph def matching supplied tags: { ", + str_util::Join(tags, " "), + " }. To inspect available tag-sets in the SavedModel, please " + "use the SavedModel CLI: `saved_model_cli`")); +} + +} // namespace + +Status ReadMetaGraphDefFromSavedModel(const string& export_dir, + const std::unordered_set& tags, + MetaGraphDef* const meta_graph_def) { + SavedModel saved_model_proto; + TF_RETURN_IF_ERROR(ReadSavedModel(export_dir, &saved_model_proto)); + TF_RETURN_IF_ERROR(FindMetaGraphDef(saved_model_proto, tags, meta_graph_def)); + return Status::OK(); +} + +} // namespace tensorflow diff --git a/tensorflow/cc/saved_model/reader.h b/tensorflow/cc/saved_model/reader.h new file mode 100644 index 0000000000000000000000000000000000000000..5815108df2a1883b6618e801f30c1915cde8c895 --- /dev/null +++ b/tensorflow/cc/saved_model/reader.h @@ -0,0 +1,39 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +/// Functions to read the SavedModel proto, or parts of it. + +#ifndef TENSORFLOW_CC_SAVED_MODEL_READER_H_ +#define TENSORFLOW_CC_SAVED_MODEL_READER_H_ + +#include +#include + +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/protobuf/meta_graph.pb.h" + +namespace tensorflow { + +// Reads the SavedModel proto from saved_model.pb(txt) in the given directory, +// finds the MetaGraphDef that matches the given set of tags and writes it to +// the `meta_graph_def` parameter. Returns a failure status when the SavedModel +// file does not exist or no MetaGraphDef matches the tags. +Status ReadMetaGraphDefFromSavedModel(const string& export_dir, + const std::unordered_set& tags, + MetaGraphDef* const meta_graph_def); + +} // namespace tensorflow + +#endif // TENSORFLOW_CC_SAVED_MODEL_READER_H_ diff --git a/tensorflow/cc/saved_model/reader_test.cc b/tensorflow/cc/saved_model/reader_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..620e9c2eece886c9600a8c93cede3b132ccbccaa --- /dev/null +++ b/tensorflow/cc/saved_model/reader_test.cc @@ -0,0 +1,108 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/cc/saved_model/reader.h" + +#include "tensorflow/cc/saved_model/constants.h" +#include "tensorflow/cc/saved_model/tag_constants.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/lib/io/path.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { +namespace { + +constexpr char kTestDataPbTxt[] = + "cc/saved_model/testdata/half_plus_two_pbtxt/00000123"; +constexpr char kTestDataSharded[] = + "cc/saved_model/testdata/half_plus_two/00000123"; + +class ReaderTest : public ::testing::Test { + protected: + ReaderTest() {} + + void CheckMetaGraphDef(const MetaGraphDef& meta_graph_def) { + const auto& tags = meta_graph_def.meta_info_def().tags(); + EXPECT_TRUE(std::find(tags.begin(), tags.end(), kSavedModelTagServe) != + tags.end()); + EXPECT_NE(meta_graph_def.meta_info_def().tensorflow_version(), ""); + EXPECT_EQ( + meta_graph_def.signature_def().at("serving_default").method_name(), + "tensorflow/serving/predict"); + } +}; + +TEST_F(ReaderTest, TagMatch) { + MetaGraphDef meta_graph_def; + + const string export_dir = + io::JoinPath(testing::TensorFlowSrcRoot(), kTestDataSharded); + TF_ASSERT_OK(ReadMetaGraphDefFromSavedModel(export_dir, {kSavedModelTagServe}, + &meta_graph_def)); + CheckMetaGraphDef(meta_graph_def); +} + +TEST_F(ReaderTest, NoTagMatch) { + MetaGraphDef meta_graph_def; + + const string export_dir = + io::JoinPath(testing::TensorFlowSrcRoot(), kTestDataSharded); + Status st = ReadMetaGraphDefFromSavedModel(export_dir, {"missing-tag"}, + &meta_graph_def); + EXPECT_FALSE(st.ok()); + EXPECT_TRUE(str_util::StrContains( + st.error_message(), + "Could not find meta graph def matching supplied tags: { missing-tag }")) + << st.error_message(); +} + +TEST_F(ReaderTest, NoTagMatchMultiple) { + MetaGraphDef meta_graph_def; + + const string export_dir = + io::JoinPath(testing::TensorFlowSrcRoot(), kTestDataSharded); + Status st = ReadMetaGraphDefFromSavedModel( + export_dir, {kSavedModelTagServe, "missing-tag"}, &meta_graph_def); + EXPECT_FALSE(st.ok()); + EXPECT_TRUE(str_util::StrContains( + st.error_message(), + "Could not find meta graph def matching supplied tags: ")) + << st.error_message(); +} + +TEST_F(ReaderTest, PbtxtFormat) { + MetaGraphDef meta_graph_def; + + const string export_dir = + io::JoinPath(testing::TensorFlowSrcRoot(), kTestDataPbTxt); + TF_ASSERT_OK(ReadMetaGraphDefFromSavedModel(export_dir, {kSavedModelTagServe}, + &meta_graph_def)); + CheckMetaGraphDef(meta_graph_def); +} + +TEST_F(ReaderTest, InvalidExportPath) { + MetaGraphDef meta_graph_def; + + const string export_dir = + io::JoinPath(testing::TensorFlowSrcRoot(), "missing-path"); + Status st = ReadMetaGraphDefFromSavedModel(export_dir, {kSavedModelTagServe}, + &meta_graph_def); + EXPECT_FALSE(st.ok()); +} + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD index 2119c8ec47f941a76e81346ae5d20da78eae11a3..fef8b8d4d4cdcc97a913ae2ba6d1a8b0b0084f89 100644 --- a/tensorflow/compiler/aot/BUILD +++ b/tensorflow/compiler/aot/BUILD @@ -68,6 +68,7 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:compile_only_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:compiler", "//tensorflow/compiler/xla/service/cpu:cpu_compiler", "//tensorflow/core:core_cpu_internal", diff --git a/tensorflow/compiler/aot/compile.cc b/tensorflow/compiler/aot/compile.cc index bbc35da2ef6d14ff0d3570ef2d5cf6743456c674..2b5f97b34cd928d32eb220536342c715d91d45bb 100644 --- a/tensorflow/compiler/aot/compile.cc +++ b/tensorflow/compiler/aot/compile.cc @@ -25,6 +25,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/tf2xla_util.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/compile_only_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/cpu/cpu_compiler.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/util.h" diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD index c2245b8eae8fd27d96feaf58e26418b92e646910..e34347b9d4e31be1b37b7ef1cb30911dd290ea7b 100644 --- a/tensorflow/compiler/jit/BUILD +++ b/tensorflow/compiler/jit/BUILD @@ -166,6 +166,7 @@ cc_library( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/service:stream_pool", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", @@ -304,11 +305,13 @@ cc_library( name = "compilation_passes", srcs = [ "build_xla_launch_ops_pass.cc", + "deadness_analysis.cc", "encapsulate_subgraphs_pass.cc", "mark_for_compilation_pass.cc", ], hdrs = [ "build_xla_launch_ops_pass.h", + "deadness_analysis.h", "encapsulate_subgraphs_pass.h", "mark_for_compilation_pass.h", ], @@ -325,6 +328,7 @@ cc_library( "//tensorflow/compiler/tf2xla:dump_graph", "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:util", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", @@ -377,6 +381,7 @@ tf_cc_test( name = "compilation_passes_test", size = "small", srcs = [ + "deadness_analysis_test.cc", "encapsulate_subgraphs_pass_test.cc", "mark_for_compilation_pass_test.cc", ], @@ -387,6 +392,7 @@ tf_cc_test( "//tensorflow/cc:cc_ops_internal", "//tensorflow/cc:function_ops", "//tensorflow/cc:ops", + "//tensorflow/cc:sendrecv_ops", "//tensorflow/compiler/jit/kernels:xla_launch_op", "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/kernels:xla_ops", @@ -458,6 +464,7 @@ cc_library( visibility = ["//visibility:public"], deps = [ ":common", + ":compilation_passes", ":union_find", ":xla_cluster_util", "//tensorflow/compiler/jit/graphcycles", diff --git a/tensorflow/compiler/jit/deadness_analysis.cc b/tensorflow/compiler/jit/deadness_analysis.cc new file mode 100644 index 0000000000000000000000000000000000000000..d81e5fe9008975c126bcd8e0ea7cef19f1eb1bf3 --- /dev/null +++ b/tensorflow/compiler/jit/deadness_analysis.cc @@ -0,0 +1,566 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/jit/deadness_analysis.h" +#include "tensorflow/core/graph/algorithm.h" +#include "tensorflow/core/graph/tensor_id.h" +#include "tensorflow/core/lib/gtl/flatset.h" +#include "tensorflow/core/lib/hash/hash.h" + +// ALGORITHM OVERVIEW +// +// We map every output produced by each node in the TensorFlow graph (including +// control dependence) into an instance of the Predicate class. Instances of +// Predicate denote logical formulas and mapping a node `n` to a predicate +// `pred` implies that `n` is executed whenver `pred` is true. Then we can +// deduce mismatching liveness in the inputs to node by comparing the predicate +// those inputs are mapped to. +// +// Loops are handled pessimistically -- we map Merge nodes with backedges to +// uninterpreted symbols (the same kind we use to represent Switch and _Recv). +// Predicate equality has to hold over all possible assignments to these +// uninterpreted symbols. + +namespace tensorflow { + +namespace { + +// Represents a logical predicate, used as described in the algorithm overview +// above. +class Predicate { + public: + enum class Kind { kAnd, kOr, kNot, kSymbol }; + + virtual string ToString() const = 0; + int64 hash() const { return hash_; } + + virtual Kind kind() const = 0; + virtual ~Predicate() {} + + protected: + explicit Predicate(int64 hash) : hash_(hash) {} + + private: + const int64 hash_; + + TF_DISALLOW_COPY_AND_ASSIGN(Predicate); +}; + +int64 HashPredicateSequence(Predicate::Kind kind, + gtl::ArraySlice preds) { + int64 hash = ::tensorflow::hash()(kind); + for (Predicate* pred : preds) { + hash = Hash64Combine(hash, pred->hash()); + } + return hash; +} + +// Represents a logical conjunction of a set of predicates. +class AndPredicate : public Predicate { + public: + explicit AndPredicate(std::vector operands) + : Predicate(HashPredicateSequence(Kind::kAnd, operands)), + operands_(std::move(operands)) {} + + string ToString() const override { + if (operands().empty()) { + return "#true"; + } + + std::vector operands_str; + std::transform(operands().begin(), operands().end(), + std::back_inserter(operands_str), + [](Predicate* pred) { return pred->ToString(); }); + + return strings::StrCat("(", str_util::Join(operands_str, " & "), ")"); + } + + Kind kind() const override { return Kind::kAnd; } + + const gtl::ArraySlice operands() const { return operands_; } + + private: + std::vector operands_; +}; + +// Represents a logical disjunction of a set of predicates. +class OrPredicate : public Predicate { + public: + explicit OrPredicate(std::vector operands) + : Predicate(HashPredicateSequence(Kind::kOr, operands)), + operands_(std::move(operands)) {} + + string ToString() const override { + if (operands().empty()) { + return "#false"; + } + + std::vector operands_str; + std::transform(operands().begin(), operands().end(), + std::back_inserter(operands_str), + [](Predicate* pred) { return pred->ToString(); }); + + return strings::StrCat("(", str_util::Join(operands_str, " | "), ")"); + } + + Kind kind() const override { return Kind::kOr; } + const gtl::ArraySlice operands() const { return operands_; } + + private: + std::vector operands_; +}; + +// Represents a logical negation of a set of predicates. +class NotPredicate : public Predicate { + public: + explicit NotPredicate(Predicate* operand) + : Predicate(HashPredicateSequence(Kind::kNot, {operand})), + operand_(operand) {} + + string ToString() const override { + return strings::StrCat("~", operand()->ToString()); + } + + Kind kind() const override { return Kind::kNot; } + Predicate* operand() const { return operand_; } + + private: + Predicate* operand_; +}; + +// Represents an uninterpreted symbol in a logical predicate. +// +// Two predicates are equivalent iff they are equivalent for all assignments to +// the symbols contained in them. +class SymbolPredicate : public Predicate { + public: + explicit SymbolPredicate(TensorId tensor_id, bool must_be_true) + : Predicate(Hash(tensor_id, must_be_true)), + tensor_id_(std::move(tensor_id)), + must_be_true_(must_be_true) {} + + string ToString() const override { return tensor_id_.ToString(); } + Kind kind() const override { return Kind::kSymbol; } + + // If `must_be_true()` is true this SymbolPredicate represents the proposition + // "tensor_id() is live and evaluates to true". + // + // If `must_be_true()` is false then this SymbolPredicate represents the + // proposition "tensor_id() is live (and may evalutate to any value)" + TensorId tensor_id() const { return tensor_id_; } + bool must_be_true() const { return must_be_true_; } + + private: + TensorId tensor_id_; + bool must_be_true_; + + static int64 Hash(const TensorId tensor_id, bool must_be_true) { + return Hash64Combine( + ::tensorflow::hash()(must_be_true), + Hash64Combine(::tensorflow::hash()(Kind::kSymbol), + TensorId::Hasher{}(tensor_id))); + } +}; + +// Creates and owns Predicate instances. Simplifies predicates as it creates +// them. +class PredicateFactory { + public: + Predicate* MakeAndPredicate(gtl::ArraySlice operands) { + return MakeAndOrImpl(operands, /*is_and=*/true); + } + + Predicate* MakeOrPredicate(gtl::ArraySlice operands) { + return MakeAndOrImpl(operands, /*is_and=*/false); + } + + Predicate* MakeNotPredicate(Predicate* pred) { + SignatureForNot signature = pred; + auto it = interned_not_instances_.find(signature); + if (it == interned_not_instances_.end()) { + std::unique_ptr new_pred = Make(pred); + Predicate* new_pred_ptr = new_pred.get(); + interned_not_instances_.emplace(signature, std::move(new_pred)); + return new_pred_ptr; + } else { + return it->second.get(); + } + } + + Predicate* MakeSymbolPredicate(TensorId tensor_id, bool must_be_true) { + SignatureForSymbol signature = {tensor_id, must_be_true}; + auto it = interned_symbol_instances_.find(signature); + if (it == interned_symbol_instances_.end()) { + std::unique_ptr new_pred = + Make(tensor_id, must_be_true); + Predicate* new_pred_ptr = new_pred.get(); + interned_symbol_instances_.emplace(std::move(signature), + std::move(new_pred)); + return new_pred_ptr; + } else { + return it->second.get(); + } + } + + Predicate* MakeTrue() { return MakeAndPredicate({}); } + Predicate* MakeFalse() { return MakeOrPredicate({}); } + + private: + template + std::unique_ptr Make(Args&&... args) { + return std::unique_ptr( + new PredicateT(std::forward(args)...)); + } + + Predicate* MakeAndOrImpl(gtl::ArraySlice operands, bool is_and); + + // Predicate instances are interned, meaning that there is only a single + // instance of a Predicate object with a given content. This makes checking + // for structural equality super-cheap -- we can just compare pointers. + // + // We intern predicates by maintaining a map from the content of a Predicate + // to the only instance of said predicate we allow to exist in the + // interned_and_or_instances_, interned_not_instances_ and + // interned_symbol_instances_ fields. These maps also double up as storage + // for the owning pointers to predicate instances. + + using SignatureForAndOr = + std::pair>; + using SignatureForNot = Predicate*; + using SignatureForSymbol = std::pair; + + struct HashSignatureForAndOr { + size_t operator()(const SignatureForAndOr& signature) const { + size_t hash = ::tensorflow::hash()(signature.first); + for (Predicate* p : signature.second) { + hash = Hash64Combine(hash, ::tensorflow::hash()(p)); + } + return hash; + } + }; + + struct HashSignatureForSymbol { + size_t operator()(const SignatureForSymbol& signature) const { + return Hash64Combine(SafeTensorId::Hasher()(signature.first), + ::tensorflow::hash()(signature.second)); + } + }; + + gtl::FlatMap, + HashSignatureForAndOr> + interned_and_or_instances_; + gtl::FlatMap> + interned_not_instances_; + gtl::FlatMap, + HashSignatureForSymbol> + interned_symbol_instances_; +}; + +// Common code to create AndPredicate or OrPredicate instances. +Predicate* PredicateFactory::MakeAndOrImpl(gtl::ArraySlice operands, + bool is_and) { + Predicate::Kind pred_kind = + is_and ? Predicate::Kind::kAnd : Predicate::Kind::kOr; + gtl::FlatSet simplified_ops_set; + std::vector simplified_ops; + for (Predicate* op : operands) { + // Simplify A&A => A and A|A => A. + if (!simplified_ops_set.insert(op).second) { + continue; + } + + if (op->kind() == pred_kind) { + // "Inline" the operands of an inner And/Or into the parent And/Or. + gtl::ArraySlice operands = + is_and ? dynamic_cast(op)->operands() + : dynamic_cast(op)->operands(); + for (Predicate* subop : operands) { + if (simplified_ops_set.insert(subop).second) { + simplified_ops.push_back(subop); + } + } + } else { + simplified_ops.push_back(op); + } + } + + if (simplified_ops.size() == 1) { + return simplified_ops[0]; + } + + // Simplify "A&~A=>False" and "A|~A=>True". + gtl::FlatSet negated_ops; + for (Predicate* op : simplified_ops) { + if (op->kind() == Predicate::Kind::kNot) { + negated_ops.insert(dynamic_cast(*op).operand()); + } + } + + for (Predicate* op : simplified_ops) { + if (negated_ops.count(op)) { + return is_and ? MakeFalse() : MakeTrue(); + } + } + + std::stable_sort( + simplified_ops.begin(), simplified_ops.end(), + [](Predicate* a, Predicate* b) { return a->hash() < b->hash(); }); + + auto it = interned_and_or_instances_.find({pred_kind, simplified_ops}); + if (it == interned_and_or_instances_.end()) { + simplified_ops.shrink_to_fit(); + // NB! Because we'll use a non-owning reference to simplified_ops in the + // key for interned_and_or_instances_ we need to be careful to std::move() + // it all the way through. + gtl::ArraySlice operands_slice = simplified_ops; + std::unique_ptr new_pred = + is_and ? Make(std::move(simplified_ops)) + : Make(std::move(simplified_ops)); + + Predicate* new_pred_ptr = new_pred.get(); + CHECK(interned_and_or_instances_ + .emplace(SignatureForAndOr(pred_kind, operands_slice), + std::move(new_pred)) + .second); + return new_pred_ptr; + } else { + return it->second.get(); + } +} + +class DeadnessAnalysisImpl : public DeadnessAnalysis { + public: + explicit DeadnessAnalysisImpl(const Graph* graph) + : graph_(*graph), vlog_(VLOG_IS_ON(2)) {} + + Status Populate(); + bool HasInputsWithMismatchingDeadness(const Node& node) override; + void Print() const override; + + private: + enum class EdgeKind { kDataAndControl, kDataOnly, kControlOnly }; + + std::vector GetIncomingPreds(Node* n, EdgeKind edge_kind); + void SetPred(Node* n, int output_idx, Predicate* pred) { + CHECK( + predicate_map_.insert({TensorId(n->name(), output_idx), pred}).second); + } + void SetPred(Node* n, gtl::ArraySlice output_idxs, Predicate* pred) { + for (int output_idx : output_idxs) { + SetPred(n, output_idx, pred); + } + } + + Status HandleSwitch(Node* n); + Status HandleMerge(Node* n); + Status HandleRecv(Node* n); + Status HandleGeneric(Node* n); + + const Graph& graph_; + gtl::FlatMap predicate_map_; + PredicateFactory predicate_factory_; + bool vlog_; +}; + +TensorId InputEdgeToTensorId(const Edge* e) { + return TensorId(e->src()->name(), e->src_output()); +} + +std::vector DeadnessAnalysisImpl::GetIncomingPreds( + Node* n, DeadnessAnalysisImpl::EdgeKind edge_kind) { + std::vector incoming_preds; + for (const Edge* in_edge : n->in_edges()) { + bool should_process = + edge_kind == EdgeKind::kDataAndControl || + (in_edge->IsControlEdge() && edge_kind == EdgeKind::kControlOnly) || + (!in_edge->IsControlEdge() && edge_kind == EdgeKind::kDataOnly); + + if (should_process) { + auto it = predicate_map_.find(InputEdgeToTensorId(in_edge)); + CHECK(it != predicate_map_.end()); + incoming_preds.push_back(it->second); + } + } + return incoming_preds; +} + +Status DeadnessAnalysisImpl::HandleSwitch(Node* n) { + std::vector input_preds = + GetIncomingPreds(n, EdgeKind::kDataAndControl); + const Edge* pred_edge; + TF_RETURN_IF_ERROR(n->input_edge(1, &pred_edge)); + Predicate* true_switch = predicate_factory_.MakeSymbolPredicate( + TensorId(pred_edge->src()->name(), pred_edge->src_output()), + /*must_be_true=*/true); + Predicate* false_switch = predicate_factory_.MakeNotPredicate(true_switch); + + // Output 0 is alive iff all inputs are alive and the condition is false. + input_preds.push_back(false_switch); + SetPred(n, 0, predicate_factory_.MakeAndPredicate(input_preds)); + input_preds.pop_back(); + + // Output 1 is alive iff all inputs are alive and the condition is true. + input_preds.push_back(true_switch); + SetPred(n, 1, predicate_factory_.MakeAndPredicate(input_preds)); + input_preds.pop_back(); + + // Control is alive iff any inputs are alive. + SetPred(n, Graph::kControlSlot, + predicate_factory_.MakeAndPredicate(input_preds)); + + return Status::OK(); +} + +Status DeadnessAnalysisImpl::HandleMerge(Node* n) { + // Merge ignores deadness of its control inputs. A merge that isn't the + // target of a backedge has is alive iff any of its data inputs are. We treat + // the liveness of a merge that is the target of a backedge symbolically. + + bool has_backedge = std::any_of( + n->in_edges().begin(), n->in_edges().end(), [](const Edge* e) { + return !e->IsControlEdge() && e->src()->IsNextIteration(); + }); + + Predicate* input_data_pred = + has_backedge ? predicate_factory_.MakeSymbolPredicate( + TensorId(n->name(), 0), /*must_be_true=*/false) + : predicate_factory_.MakeOrPredicate( + GetIncomingPreds(n, EdgeKind::kDataOnly)); + + SetPred(n, {0, 1, Graph::kControlSlot}, input_data_pred); + return Status::OK(); +} + +Status DeadnessAnalysisImpl::HandleRecv(Node* n) { + // In addition to being alive or dead based on the inputs, a _Recv can also + // acquire a dead signal from a _Send. + std::vector input_preds = + GetIncomingPreds(n, EdgeKind::kDataAndControl); + input_preds.push_back(predicate_factory_.MakeSymbolPredicate( + TensorId(n->name(), 0), /*must_be_true=*/false)); + SetPred(n, {0, Graph::kControlSlot}, + predicate_factory_.MakeAndPredicate(input_preds)); + return Status::OK(); +} + +Status DeadnessAnalysisImpl::HandleGeneric(Node* n) { + // Generally nodes are alive iff all their inputs are alive. + Predicate* pred = predicate_factory_.MakeAndPredicate( + GetIncomingPreds(n, EdgeKind::kDataAndControl)); + for (int output_idx = 0; output_idx < n->num_outputs(); output_idx++) { + SetPred(n, output_idx, pred); + } + SetPred(n, Graph::kControlSlot, pred); + return Status::OK(); +} + +Status DeadnessAnalysisImpl::Populate() { + std::vector rpo; + GetReversePostOrder(graph_, &rpo, /*stable_comparator=*/{}, + /*edge_filter=*/[](const Edge& edge) { + return !edge.src()->IsNextIteration(); + }); + + // This an abstract interpretation over the deadness propagation semantics of + // the graph executor. + for (Node* n : rpo) { + if (n->IsSwitch()) { + TF_RETURN_IF_ERROR(HandleSwitch(n)); + } else if (n->IsMerge()) { + TF_RETURN_IF_ERROR(HandleMerge(n)); + } else if (n->IsControlTrigger()) { + SetPred(n, Graph::kControlSlot, predicate_factory_.MakeTrue()); + } else if (n->IsRecv() || n->IsHostRecv()) { + TF_RETURN_IF_ERROR(HandleRecv(n)); + } else { + TF_RETURN_IF_ERROR(HandleGeneric(n)); + } + } + + return Status::OK(); +} + +bool DeadnessAnalysisImpl::HasInputsWithMismatchingDeadness(const Node& node) { + CHECK(!node.IsMerge()); + + if (vlog_) { + VLOG(2) << "HasInputsWithMismatchingDeadness(" << node.name() << ")"; + } + + Predicate* pred = nullptr; + for (const Edge* edge : node.in_edges()) { + auto it = predicate_map_.find(InputEdgeToTensorId(edge)); + CHECK(it != predicate_map_.end()); + if (vlog_) { + VLOG(2) << " " << InputEdgeToTensorId(edge).ToString() << ": " + << it->second->ToString(); + } + + // Today we just compare the predicates for equality (with some + // canonicalization/simplification happening before) but we could be more + // sophisticated here if need be. Comparing pointers is sufficient because + // we intern Predicate instances by their content. + if (pred != nullptr && pred != it->second) { + if (vlog_) { + VLOG(2) << "HasInputsWithMismatchingDeadness(" << node.name() + << ") -> true"; + } + return true; + } + pred = it->second; + } + + if (vlog_) { + VLOG(2) << "HasInputsWithMismatchingDeadness(" << node.name() + << ") -> false"; + } + + return false; +} + +void DeadnessAnalysisImpl::Print() const { + std::vector tensor_ids; + for (const auto& kv_pair : predicate_map_) { + tensor_ids.push_back(kv_pair.first); + } + + std::sort(tensor_ids.begin(), tensor_ids.end()); + + for (TensorId tensor_id : tensor_ids) { + auto it = predicate_map_.find(tensor_id); + CHECK(it != predicate_map_.end()) << tensor_id.ToString(); + VLOG(2) << tensor_id.ToString() << " -> " << it->second->ToString(); + } +} + +} // namespace + +DeadnessAnalysis::~DeadnessAnalysis() {} + +/*static*/ Status DeadnessAnalysis::Run( + const Graph& graph, std::unique_ptr* result) { + std::unique_ptr analysis( + new DeadnessAnalysisImpl(&graph)); + TF_RETURN_IF_ERROR(analysis->Populate()); + + if (VLOG_IS_ON(2)) { + analysis->Print(); + } + + *result = std::move(analysis); + return Status::OK(); +} + +} // namespace tensorflow diff --git a/tensorflow/compiler/jit/deadness_analysis.h b/tensorflow/compiler/jit/deadness_analysis.h new file mode 100644 index 0000000000000000000000000000000000000000..6e7ab411619ba08060aa4925e91dce06299d1d23 --- /dev/null +++ b/tensorflow/compiler/jit/deadness_analysis.h @@ -0,0 +1,68 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_JIT_DEADNESS_ANALYSIS_H_ +#define TENSORFLOW_COMPILER_JIT_DEADNESS_ANALYSIS_H_ + +#include "tensorflow/core/graph/graph.h" + +namespace tensorflow { + +// This analyzes a TensorFlow graph to identify nodes which may have partially +// dead inputs (i.e. these nodes may have some dead inputs and some alive +// inputs). +// +// For example, the ADD node in the following graph +// +// V0 PRED0 V1 PRED1 +// | | | | +// v v v v +// SWITCH SWITCH +// | | +// +---+ + ---+ +// | | +// v v +// ADD +// +// can have its inputs independently dead or alive based on the runtime values +// of PRED0 and PRED1. +// +// It is tempting to call this a liveness analysis but I avoided that because +// "liveness" already has other connotations. +class DeadnessAnalysis { + public: + // Returns true if `node` may have some live inputs and some dead inputs. + // + // This is a conservatively correct routine -- if it returns false then `node` + // is guaranteed to not have inputs with mismatching liveness, but not the + // converse. + // + // REQUIRES: node is not a Merge operation. + virtual bool HasInputsWithMismatchingDeadness(const Node& node) = 0; + + // Prints out the internal state of this instance. For debugging purposes + // only. + virtual void Print() const = 0; + virtual ~DeadnessAnalysis(); + + // Run the deadness analysis over `graph` and returns an error or a populated + // instance of DeadnessAnalysis in `result`. + static Status Run(const Graph& graph, + std::unique_ptr* result); +}; + +} // namespace tensorflow + +#endif // TENSORFLOW_COMPILER_JIT_DEADNESS_ANALYSIS_H_ diff --git a/tensorflow/compiler/jit/deadness_analysis_test.cc b/tensorflow/compiler/jit/deadness_analysis_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..584385cab7665dce9c7c92eab6293436ca22c9b7 --- /dev/null +++ b/tensorflow/compiler/jit/deadness_analysis_test.cc @@ -0,0 +1,443 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/jit/deadness_analysis.h" + +#include "tensorflow/cc/framework/ops.h" +#include "tensorflow/cc/ops/array_ops.h" +#include "tensorflow/cc/ops/control_flow_ops_internal.h" +#include "tensorflow/cc/ops/function_ops.h" +#include "tensorflow/cc/ops/sendrecv_ops.h" +#include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/compiler/jit/defs.h" +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/core/framework/node_def_util.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/graph/algorithm.h" +#include "tensorflow/core/graph/graph_constructor.h" +#include "tensorflow/core/graph/graph_def_builder.h" +#include "tensorflow/core/graph/graph_def_builder_util.h" +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { +namespace { + +Status AnalyzeDeadness(Graph* graph, + std::unique_ptr* result) { + FixupSourceAndSinkEdges(graph); + return DeadnessAnalysis::Run(*graph, result); +} + +ops::Switch CreateSwitch(const Scope& root, const string& prefix) { + Output value = ops::Placeholder(root.WithOpName(prefix + "/value"), DT_FLOAT); + Output predicate = + ops::Placeholder(root.WithOpName(prefix + "/pred"), DT_BOOL); + return ops::Switch(root.WithOpName(prefix + "/switch"), value, predicate); +} + +Output CreateInductionVariable(const Scope& root, const string& prefix, + const string& frame_name, int32 init) { + Output initial_value = ops::Const(root.WithOpName(prefix + "/init"), init); + Output enter_initial_value = ops::internal::Enter( + root.WithOpName(prefix + "/enter"), initial_value, frame_name); + + ops::Merge iv(root.WithOpName(prefix + "/iv"), {enter_initial_value}); + Output increment_by = ops::Const(root.WithOpName(prefix + "/incr"), 1); + Output final_value = ops::Const(root.WithOpName(prefix + "/final"), 10); + Output loop_cond_expr = + ops::Less(root.WithOpName(prefix + "/less"), iv.output, final_value); + Output loop_cond = + ops::LoopCond(root.WithOpName(prefix + "/cond"), loop_cond_expr); + ops::Switch latch(root.WithOpName(prefix + "/latch"), iv.output, loop_cond); + ops::internal::Exit exit(root.WithOpName(prefix + "/exit"), iv.output); + Output iv_next = + ops::Add(root.WithOpName(prefix + "/ivnext"), iv.output, increment_by); + Output next_iteration = + ops::NextIteration(root.WithOpName(prefix + "next_iteration"), iv_next); + + root.graph()->AddEdge(next_iteration.node(), 0, iv.output.node(), 1); + root.graph()->AddControlEdge(iv.output.node(), increment_by.node()); + root.graph()->AddControlEdge(iv.output.node(), final_value.node()); + + return iv.output; +} + +TEST(DeadnessAnalysisTest, BasicPositive) { + Scope root = Scope::NewRootScope().ExitOnError(); + + ops::Switch sw = CreateSwitch(root, "0"); + Output add = + ops::Add(root.WithOpName("add"), sw.output_true, sw.output_false); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_TRUE(result->HasInputsWithMismatchingDeadness(*add.node())); +} + +TEST(DeadnessAnalysisTest, BasicNegative) { + Scope root = Scope::NewRootScope().ExitOnError(); + + Output a = ops::Placeholder(root.WithOpName("a"), DT_FLOAT); + Output b = ops::Placeholder(root.WithOpName("b"), DT_FLOAT); + Output add = ops::Add(root.WithOpName("add"), a, b); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_FALSE(result->HasInputsWithMismatchingDeadness(*add.node())); +} + +TEST(DeadnessAnalysisTest, AndIsCommutative) { + Scope root = Scope::NewRootScope().ExitOnError(); + + ops::Switch sw_0 = CreateSwitch(root, "0"); + ops::Switch sw_1 = CreateSwitch(root, "1"); + + Output a0 = + ops::Add(root.WithOpName("a0"), sw_0.output_false, sw_1.output_false); + Output a1 = + ops::Add(root.WithOpName("a1"), sw_1.output_false, sw_0.output_false); + + Output b0 = + ops::Add(root.WithOpName("b0"), sw_0.output_false, sw_1.output_true); + Output b1 = + ops::Add(root.WithOpName("b1"), sw_1.output_true, sw_0.output_false); + + Output live0 = ops::Add(root.WithOpName("live0"), a0, a1); + Output live1 = ops::Add(root.WithOpName("live1"), b0, b1); + + Output halfdead0 = ops::Add(root.WithOpName("halfdead0"), a0, b0); + Output halfdead1 = ops::Add(root.WithOpName("halfdead1"), a1, b1); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_FALSE(result->HasInputsWithMismatchingDeadness(*live0.node())); + EXPECT_FALSE(result->HasInputsWithMismatchingDeadness(*live1.node())); + + EXPECT_TRUE(result->HasInputsWithMismatchingDeadness(*halfdead0.node())); + EXPECT_TRUE(result->HasInputsWithMismatchingDeadness(*halfdead1.node())); +} + +TEST(DeadnessAnalysisTest, AndIsAssociative) { + Scope root = Scope::NewRootScope().ExitOnError(); + + ops::Switch sw_0 = CreateSwitch(root, "0"); + ops::Switch sw_1 = CreateSwitch(root, "1"); + ops::Switch sw_2 = CreateSwitch(root, "2"); + + Output a0 = + ops::Add(root.WithOpName("a0"), sw_0.output_false, sw_1.output_false); + Output a1 = ops::Add(root.WithOpName("a1"), a0, sw_2.output_false); + + Output b0 = + ops::Add(root.WithOpName("b0"), sw_1.output_false, sw_2.output_false); + Output b1 = ops::Add(root.WithOpName("b1"), sw_0.output_false, b0); + + Output add = ops::Add(root.WithOpName("add"), a1, b1); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_FALSE(result->HasInputsWithMismatchingDeadness(*add.node())); +} + +TEST(DeadnessAnalysisTest, OrIsCommutative) { + Scope root = Scope::NewRootScope().ExitOnError(); + + ops::Switch sw_0 = CreateSwitch(root, "0"); + ops::Switch sw_1 = CreateSwitch(root, "1"); + + ops::Merge m0(root.WithOpName("m0"), {sw_0.output_false, sw_1.output_false}); + ops::Merge m1(root.WithOpName("m1"), {sw_1.output_false, sw_0.output_false}); + ops::Merge m2(root.WithOpName("m2"), {sw_0.output_false, sw_1.output_true}); + ops::Merge m3(root.WithOpName("m3"), {sw_1.output_true, sw_0.output_false}); + + Output live0 = ops::Add(root.WithOpName("live0"), m0.output, m1.output); + Output live1 = ops::Add(root.WithOpName("live1"), m2.output, m3.output); + + Output halfdead0 = + ops::Add(root.WithOpName("halfdead0"), m0.output, m2.output); + Output halfdead1 = + ops::Add(root.WithOpName("halfdead1"), m1.output, m3.output); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_FALSE(result->HasInputsWithMismatchingDeadness(*live0.node())); + EXPECT_FALSE(result->HasInputsWithMismatchingDeadness(*live1.node())); + + EXPECT_TRUE(result->HasInputsWithMismatchingDeadness(*halfdead0.node())); + EXPECT_TRUE(result->HasInputsWithMismatchingDeadness(*halfdead1.node())); +} + +TEST(DeadnessAnalysisTest, OrIsAssociative) { + Scope root = Scope::NewRootScope().ExitOnError(); + + ops::Switch sw_0 = CreateSwitch(root, "0"); + ops::Switch sw_1 = CreateSwitch(root, "1"); + ops::Switch sw_2 = CreateSwitch(root, "2"); + + ops::Merge m0(root.WithOpName("m0"), {sw_0.output_false, sw_1.output_false}); + ops::Merge m1(root.WithOpName("m1"), {m0.output, sw_2.output_false}); + ops::Merge m2(root.WithOpName("m2"), {sw_1.output_false, sw_2.output_false}); + ops::Merge m3(root.WithOpName("m3"), {sw_0.output_false, m2.output}); + + Output add = ops::Add(root.WithOpName("add"), m1.output, m3.output); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_FALSE(result->HasInputsWithMismatchingDeadness(*add.node())); +} + +TEST(DeadnessAnalysisTest, AndOfOr) { + Scope root = Scope::NewRootScope().ExitOnError(); + + ops::Switch sw_0 = CreateSwitch(root, "0"); + ops::Switch sw_1 = CreateSwitch(root, "1"); + ops::Switch sw_2 = CreateSwitch(root, "2"); + ops::Switch sw_3 = CreateSwitch(root, "3"); + + ops::Merge m0(root.WithOpName("m0"), {sw_0.output_false, sw_1.output_false}); + ops::Merge m1(root.WithOpName("m1"), {sw_2.output_false, sw_3.output_false}); + + Output add0 = ops::Add(root.WithOpName("add0"), m0.output, m1.output); + Output add1 = ops::Add(root.WithOpName("add1"), m0.output, m1.output); + + Output add2 = ops::Add(root.WithOpName("add2"), add0, add1); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_FALSE(result->HasInputsWithMismatchingDeadness(*add2.node())); +} + +TEST(DeadnessAnalysisTest, OrOfAnd) { + Scope root = Scope::NewRootScope().ExitOnError(); + + ops::Switch sw_0 = CreateSwitch(root, "0"); + ops::Switch sw_1 = CreateSwitch(root, "1"); + ops::Switch sw_2 = CreateSwitch(root, "2"); + ops::Switch sw_3 = CreateSwitch(root, "3"); + + Output add0 = + ops::Add(root.WithOpName("add0"), sw_0.output_false, sw_1.output_false); + Output add1 = + ops::Add(root.WithOpName("add1"), sw_2.output_false, sw_3.output_false); + + ops::Merge m0(root.WithOpName("m0"), {add0, add1}); + ops::Merge m1(root.WithOpName("m1"), {add0, add1}); + + Output add2 = ops::Add(root.WithOpName("add2"), m0.output, m1.output); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_FALSE(result->HasInputsWithMismatchingDeadness(*add2.node())); +} + +TEST(DeadnessAnalysisTest, NEGATIVE_AndOrDistributive) { + // This demonstrates one of the weaknesses in the current approach -- since we + // only do some basic simplifications we can't see that "(A|B)&C" == + // "(A&C)|(B&C)". + Scope root = Scope::NewRootScope().ExitOnError(); + + ops::Switch sw_0 = CreateSwitch(root, "0"); + ops::Switch sw_1 = CreateSwitch(root, "1"); + ops::Switch sw_2 = CreateSwitch(root, "2"); + + ops::Merge m0(root.WithOpName("m0"), {sw_0.output_false, sw_1.output_false}); + Output add0 = ops::Add(root.WithOpName("add0"), m0.output, sw_2.output_false); + + Output add1 = + ops::Add(root.WithOpName("add1"), sw_0.output_false, sw_2.output_false); + Output add2 = + ops::Add(root.WithOpName("add2"), sw_1.output_false, sw_2.output_false); + ops::Merge m1(root.WithOpName("m1"), {add1, add2}); + + Output add3 = ops::Add(root.WithOpName("add3"), add0, m1.output); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_TRUE(result->HasInputsWithMismatchingDeadness(*add2.node())); +} + +TEST(DeadnessAnalysisTest, Ternary) { + Scope root = Scope::NewRootScope().ExitOnError(); + + Output predicate = ops::Placeholder(root.WithOpName("predicate"), DT_BOOL); + Output true_value = ops::Placeholder(root.WithOpName("true_value"), DT_FLOAT); + Output false_value = + ops::Placeholder(root.WithOpName("false_value"), DT_FLOAT); + + ops::Switch predicated_true(root.WithOpName("predicated_true"), true_value, + predicate); + + ops::Switch predicated_false(root.WithOpName("predicated_false"), true_value, + predicate); + ops::Merge merge(root.WithOpName("ternary"), {predicated_true.output_true, + predicated_false.output_false}); + Output addend = ops::Placeholder(root.WithOpName("addend"), DT_FLOAT); + Output add = ops::Add(root.WithOpName("add"), merge.output, addend); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_FALSE(result->HasInputsWithMismatchingDeadness(*add.node())); +} + +TEST(DeadnessAnalysisTest, Recv) { + Scope root = Scope::NewRootScope().ExitOnError(); + + Output recv_a = ops::_Recv(root.WithOpName("recv_a"), DT_FLOAT, "tensor_a", + "sender", 0, "receiver"); + Output recv_b = ops::_Recv(root.WithOpName("recv_b"), DT_FLOAT, "tensor_b", + "sender", 0, "receiver"); + Output add = ops::Add(root.WithOpName("add"), recv_a, recv_b); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_TRUE(result->HasInputsWithMismatchingDeadness(*add.node())); +} + +TEST(DeadnessAnalysisTest, HostRecv) { + Scope root = Scope::NewRootScope().ExitOnError(); + + Output recv_a = ops::_HostRecv(root.WithOpName("recv_a"), DT_FLOAT, + "tensor_a", "sender", 0, "receiver"); + Output recv_b = ops::_HostRecv(root.WithOpName("recv_b"), DT_FLOAT, + "tensor_b", "sender", 0, "receiver"); + Output add = ops::Add(root.WithOpName("add"), recv_a, recv_b); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_TRUE(result->HasInputsWithMismatchingDeadness(*add.node())); +} + +TEST(DeadnessAnalysisTest, Loop) { + Scope root = Scope::NewRootScope().ExitOnError(); + Output iv0 = CreateInductionVariable(root, "iv0", "fr0", 0); + Output iv1 = CreateInductionVariable(root, "iv1", "fr0", 0); + Output iv2 = CreateInductionVariable(root, "iv2", "fr0", 1); + Output add0 = ops::Add(root.WithOpName("add0"), iv0, iv1); + Output add1 = ops::Add(root.WithOpName("add1"), iv1, iv2); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + // NB! iv0 and iv1 are equivalent and a smarter deadness analysis would have + // noticed that. Today we are pessimistic here because we assign an + // uninterpreted symbol to merges with backedges. + + EXPECT_TRUE(result->HasInputsWithMismatchingDeadness(*add0.node())); + EXPECT_TRUE(result->HasInputsWithMismatchingDeadness(*add1.node())); +} + +TEST(DeadnessAnalysisTest, ControlInputs) { + Scope root = Scope::NewRootScope().ExitOnError(); + ops::Switch sw = CreateSwitch(root, "0"); + + Output id0 = ops::Identity(root.WithOpName("id0"), sw.output_false); + Output id1 = ops::Identity(root.WithOpName("id1"), sw.output_true); + + Output const0 = ops::Const(root.WithOpName("const0"), 1); + Output const1 = ops::Const(root.WithOpName("const1"), 2); + + Output add = ops::Add(root.WithOpName("add"), const0, const1); + + root.graph()->AddControlEdge(id0.node(), const0.node()); + root.graph()->AddControlEdge(id1.node(), const1.node()); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_TRUE(result->HasInputsWithMismatchingDeadness(*add.node())); +} + +TEST(DeadnessAnalysisTest, ControlTrigger) { + Scope root = Scope::NewRootScope().ExitOnError(); + ops::Switch sw = CreateSwitch(root, "0"); + + Output id0 = ops::Identity(root.WithOpName("id0"), sw.output_false); + Output id1 = ops::Identity(root.WithOpName("id1"), sw.output_true); + + ops::ControlTrigger ctrl_trigger0(root.WithOpName("ctrl_trigger0")); + ops::ControlTrigger ctrl_trigger1(root.WithOpName("ctrl_trigger1")); + + Output const0 = ops::Const(root.WithOpName("const0"), 1); + Output const1 = ops::Const(root.WithOpName("const1"), 2); + + Output add = ops::Add(root.WithOpName("add"), const0, const1); + + root.graph()->AddControlEdge(id0.node(), ctrl_trigger0.operation.node()); + root.graph()->AddControlEdge(ctrl_trigger0.operation.node(), const0.node()); + + root.graph()->AddControlEdge(id1.node(), ctrl_trigger1.operation.node()); + root.graph()->AddControlEdge(ctrl_trigger1.operation.node(), const1.node()); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_FALSE(result->HasInputsWithMismatchingDeadness(*add.node())); +} + +TEST(DeadnessAnalysisTest, ControlInputsToMerge) { + Scope root = Scope::NewRootScope().ExitOnError(); + ops::Switch sw = CreateSwitch(root, "0"); + + Output id0 = ops::Identity(root.WithOpName("id0"), sw.output_false); + Output id1 = ops::Identity(root.WithOpName("id1"), sw.output_true); + + Output constant = ops::Const(root.WithOpName("constant"), 5); + ops::Merge m0(root.WithOpName("m0"), {constant}); + ops::Merge m1(root.WithOpName("m0"), {constant}); + Output add = ops::Add(root.WithOpName("add"), m0.output, m1.output); + + root.graph()->AddControlEdge(id0.node(), m0.output.node()); + root.graph()->AddControlEdge(id1.node(), m1.output.node()); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_FALSE(result->HasInputsWithMismatchingDeadness(*add.node())); +} + +TEST(DeadnessAnalysisTest, RecvVsSwitch) { + // Demonstrates why we need the must_be_true bit on SymbolP. + Scope root = Scope::NewRootScope().ExitOnError(); + + Output recv = ops::_Recv(root.WithOpName("recv"), DT_BOOL, "tensor", "sender", + 0, "receiver"); + Output value = ops::Placeholder(root.WithOpName("value"), DT_BOOL); + ops::Switch sw(root.WithOpName("switch"), value, recv); + Output logical_and = + ops::LogicalAnd(root.WithOpName("and"), recv, sw.output_true); + + std::unique_ptr result; + TF_ASSERT_OK(AnalyzeDeadness(root.graph(), &result)); + + EXPECT_TRUE(result->HasInputsWithMismatchingDeadness(*logical_and.node())); +} + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc index b3a1c19c9e555161ec64aae46bfd4deb6b05e9ff..fdd71c6a588ad96301f543651c8531e6f9c3ca05 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc @@ -60,9 +60,9 @@ const char* const kXlaHostTransferSequencerAttr = namespace { -bool AreAllParentsConst(const Node& n, - const gtl::FlatSet& runtime_const_nodes) { - if (n.type_string() == "GuaranteeConst" || n.type_string() == "Const") { +bool AreAllParentsGuaranteedConst( + const Node& n, const gtl::FlatSet& runtime_const_nodes) { + if (n.type_string() == "GuaranteeConst") { // If the current node is itself a cast-to-const, no need // to look at the incoming edges. return true; @@ -93,7 +93,8 @@ void MarkGuaranteedConstants( ReverseDFSFrom(graph, srcs, /*enter=*/nullptr, /*leave=*/[&guaranteed_const_nodes](const Node* n) { // TODO(vinuraja): Doesn't work in the presence of loops. - if (AreAllParentsConst(*n, guaranteed_const_nodes)) { + if (AreAllParentsGuaranteedConst(*n, + guaranteed_const_nodes)) { guaranteed_const_nodes.insert(n); } }); @@ -137,7 +138,7 @@ class Encapsulator { // Find subgraphs marked with 'group_attribute', and build a new // subgraph, one for each value of 'group_attribute'. - Status SplitIntoSubgraphs(); + Status SplitIntoSubgraphs(FunctionLibraryDefinition* library); // Build a FunctionDef for each subgraph, and add it 'library'. The values of // the 'group_attribute' annotations become the function names. @@ -1477,7 +1478,7 @@ Status Encapsulator::CopySubgraphEdges( return Status::OK(); } -Status Encapsulator::SplitIntoSubgraphs() { +Status Encapsulator::SplitIntoSubgraphs(FunctionLibraryDefinition* library) { Status s; // Map from input graph nodes to subgraph nodes. @@ -1512,6 +1513,15 @@ Status Encapsulator::SplitIntoSubgraphs() { TF_RETURN_IF_ERROR(BuildControlFlowInfo(subgraph.GetGraph(), &dummy)); } + if (VLOG_IS_ON(1)) { + // Dump subgraphs. + for (auto& entry : subgraphs_) { + dump_graph::DumpGraphToFile( + strings::StrCat("encapsulate_subgraphs_subgraph_", entry.first), + *entry.second.GetGraph(), library); + } + } + return s; } @@ -1935,6 +1945,8 @@ Status Encapsulator::DoStaticShapeInferenceForOutsideCompilationSend( // continue. TensorShapeProto proto; context->ShapeHandleToProto(shape, &proto); + VLOG(2) << "Node " << src_node->name() + << " has known shape: " << proto.DebugString(); if (dummy_node_images.find(src_node) == dummy_node_images.end()) { dummy_node_images[src_node] = AddDummyShapedNode(src_node, src_port, control_flow_info, @@ -1952,6 +1964,8 @@ Status Encapsulator::DoStaticShapeInferenceForOutsideCompilationSend( if (VLOG_IS_ON(2)) { TensorShapeProto proto; context->ShapeHandleToProto(shape, &proto); + VLOG(2) << "Node " << src_node->name() + << " has unknown shape: " << proto.DebugString(); } stack.push_back({src_node, false}); } @@ -2194,6 +2208,23 @@ Status Encapsulator::FindClusterDependencies() { } } } + if (VLOG_IS_ON(2)) { + // Print debug information. + VLOG(2) << "node_ancestors_map:"; + for (const auto& node_iter : node_ancestors_map) { + VLOG(2) << "\t" << node_iter.first->name() << ": subgraph = '" + << node_iter.second.subgraph + << "', outside_compilation_cluster = '" + << node_iter.second.outside_compilation_cluster + << "', ancestor_clusters: " + << (node_iter.second.ancestor_clusters.empty() ? "(empty)" : ""); + for (const auto& cluster_iter : node_iter.second.ancestor_clusters) { + VLOG(2) << "\t\tsubgraph = '" << cluster_iter.subgraph + << "', outside_compilation_cluster = '" + << cluster_iter.outside_compilation_cluster << "'"; + } + } + } return Status::OK(); } @@ -2401,7 +2432,7 @@ Status EncapsulateSubgraphsInFunctions( std::move(outside_compilation_attribute), &graph_in); TF_RETURN_IF_ERROR(encapsulator.FindClusterDependencies()); - TF_RETURN_IF_ERROR(encapsulator.SplitIntoSubgraphs()); + TF_RETURN_IF_ERROR(encapsulator.SplitIntoSubgraphs(library)); TF_RETURN_IF_ERROR(encapsulator.BuildFunctionDefs( rewrite_subgraph_fn, reuse_existing_functions, library)); @@ -2450,7 +2481,7 @@ Status EncapsulateSubgraphsPass::Run( const GraphOptimizationPassOptions& options) { VLOG(1) << "EncapsulateSubgraphsPass::Run"; if (VLOG_IS_ON(1)) { - dump_graph::DumpGraphToFile("before_encapsulate_subgraphs", **options.graph, + dump_graph::DumpGraphToFile("encapsulate_subgraphs_before", **options.graph, options.flib_def); } @@ -2533,7 +2564,7 @@ Status EncapsulateSubgraphsPass::Run( "EncapsulateSubgraphsPass failed"); if (VLOG_IS_ON(1)) { - dump_graph::DumpGraphToFile("after_encapsulate_subgraphs", *graph_out, + dump_graph::DumpGraphToFile("encapsulate_subgraphs_after", *graph_out, options.flib_def); } diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc index 4eb389e0c653f2d32c17f448687f865a44a11b96..c0543a00792235c5dd090e81930d8c219dc7f1a3 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc @@ -742,10 +742,13 @@ TEST(EncapsulateSubgraphsWithGuaranteeConstOpTest, Simple) { Scope root = Scope::NewRootScope().ExitOnError().WithDevice( "/job:localhost/replica:0/task:0/cpu:0"); auto x1 = ops::Placeholder(root.WithOpName("x1"), DT_FLOAT); - auto const_x2 = ops::Const(root.WithOpName("const_x2"), 10.0f); + auto x2 = ops::Placeholder(root.WithOpName("x2"), DT_FLOAT); + auto const_guarantee_x2 = + ops::GuaranteeConst(root.WithOpName("const_guarantee_x2"), x2); auto const_guarantee_x1 = ops::GuaranteeConst(root.WithOpName("const_guarantee_x1"), x1); - auto add1 = ops::Add(root.WithOpName("add1"), const_guarantee_x1, const_x2); + auto add1 = + ops::Add(root.WithOpName("add1"), const_guarantee_x1, const_guarantee_x2); add1.node()->AddAttr("_encapsulate", "encapsulate1"); Graph graph_before(OpRegistry::Global()); diff --git a/tensorflow/compiler/jit/kernels/xla_launch_op.cc b/tensorflow/compiler/jit/kernels/xla_launch_op.cc index 251a07304eaeb21f1313d7a6ef6af668f99d8551..b313d48011b561eaab618692df49d1558c34a77c 100644 --- a/tensorflow/compiler/jit/kernels/xla_launch_op.cc +++ b/tensorflow/compiler/jit/kernels/xla_launch_op.cc @@ -51,7 +51,11 @@ XlaLocalLaunchBase::XlaLocalLaunchBase(OpKernelConstruction* ctx, if (device_type_ == DeviceType(DEVICE_CPU)) { platform_id_ = se::host::kHostPlatformId; } else if (device_type_ == DeviceType(DEVICE_GPU)) { - platform_id_ = se::cuda::kCudaPlatformId; + platform_id_ = ctx->device() + ->tensorflow_gpu_device_info() + ->stream->parent() + ->platform() + ->id(); } else { platform_id_ = nullptr; } @@ -115,6 +119,7 @@ void XlaLocalLaunchBase::Compute(OpKernelContext* ctx) { const XlaDevice::Metadata* metadata = nullptr; Status s = XlaDevice::GetMetadata(ctx, &metadata); bool allocate_xla_tensors = s.ok(); + bool use_multiple_streams = s.ok() && metadata->UseMultipleStreams(); // Get the platform_id_ for XLA_* devices. if (platform_id_ == nullptr) { @@ -148,6 +153,10 @@ void XlaLocalLaunchBase::Compute(OpKernelContext* ctx) { XlaCompiler::Options options; options.client = client; + if (ctx->op_device_context() != nullptr) { + options.device_ordinal = + ctx->op_device_context()->stream()->parent()->device_ordinal(); + } options.device_type = cache->device_type(); options.flib_def = ctx->function_library()->GetFunctionLibraryDefinition(); options.graph_def_version = ctx->function_library()->graph_def_version(); @@ -180,8 +189,8 @@ void XlaLocalLaunchBase::Compute(OpKernelContext* ctx) { VLOG(1) << "Executing XLA Computation..."; - XlaComputationLaunchContext launch_context(client, xla_allocator, - allocate_xla_tensors); + XlaComputationLaunchContext launch_context( + client, xla_allocator, allocate_xla_tensors, use_multiple_streams); launch_context.PopulateInputs(ctx, kernel, variables); // Execute the computation. diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass.cc b/tensorflow/compiler/jit/mark_for_compilation_pass.cc index 8c3882116dd4f048ea3e32c037bf4139c67a3eb9..38eb6d830f4d4e889810acd0f928e93d0b22bde8 100644 --- a/tensorflow/compiler/jit/mark_for_compilation_pass.cc +++ b/tensorflow/compiler/jit/mark_for_compilation_pass.cc @@ -21,6 +21,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/jit/deadness_analysis.h" #include "tensorflow/compiler/jit/defs.h" #include "tensorflow/compiler/jit/graphcycles/graphcycles.h" #include "tensorflow/compiler/jit/legacy_flags/mark_for_compilation_pass_flags.h" @@ -28,6 +29,7 @@ limitations under the License. #include "tensorflow/compiler/jit/xla_cluster_util.h" #include "tensorflow/compiler/tf2xla/dump_graph.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/framework/graph_def_util.h" #include "tensorflow/core/framework/memory_types.h" @@ -462,18 +464,19 @@ Status MarkForCompilationPass::Run( VLOG(1) << "flags->tf_xla_fusion_only = " << flags->tf_xla_fusion_only; const FunctionLibraryDefinition* fld = options.flib_def; - auto is_compilable = [global_jit_level, cpu_global_jit, fusion_only, fld]( - const Node* node, const DeviceType& device_type) { + std::unique_ptr deadness; + { + XLA_SCOPED_LOGGING_TIMER_LEVEL("DeadnessAnalysis", 1); + TF_RETURN_IF_ERROR(DeadnessAnalysis::Run(**options.graph, &deadness)); + } + + auto is_compilable = [&](const Node* node, const DeviceType& device_type) { const XlaOpRegistry::DeviceRegistration* registration; if (!XlaOpRegistry::GetCompilationDevice(device_type.type(), ®istration)) { return false; } - // Don't compile control trigger nodes. We won't preserve their deadness - // semantics correctly, so it's safest not to compile them. - if (node->IsControlTrigger()) return false; - // If this device requires a JIT, we must say yes. if (registration->requires_compilation) return true; @@ -485,6 +488,14 @@ Status MarkForCompilationPass::Run( status = fld->GetAttr(*node, kXlaCompileAttr, &compile); if (status.ok()) return compile; + // If inputs to `node` can have conflicting deadness (i.e. some are alive + // and some are dead) then don't compile it. XLA cannot represent the + // deadness semantics of these nodes correctly and auto-clustering these + // nodes can cause deadness to propagate to nodes that should be live. + if (node->IsMerge() || deadness->HasInputsWithMismatchingDeadness(*node)) { + return false; + } + // Check for fusable ops only if requested. if (global_jit_level > 0 && fusion_only && !IsXlaFusable(node->def())) { return false; diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc b/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc index 772c92d369e67f431b5d030d1d5cdc5ae2700d39..2c5f4fb774fcab082c0d0d316cdc6757cacc1e96 100644 --- a/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc +++ b/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/cc/ops/array_ops.h" #include "tensorflow/cc/ops/control_flow_ops_internal.h" #include "tensorflow/cc/ops/function_ops.h" +#include "tensorflow/cc/ops/sendrecv_ops.h" #include "tensorflow/cc/ops/standard_ops.h" #include "tensorflow/compiler/jit/defs.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" @@ -680,5 +681,37 @@ TEST(XlaCompilationTest, ClusterIdentityWithNonRefInput) { EXPECT_EQ(clusters, expected_clusters); } +TEST(XlaCompilationTest, ClusterControlTrigger) { + Scope root = Scope::NewRootScope().ExitOnError(); + + Output recv_a = ops::_Recv(root.WithOpName("recv_a"), DT_BOOL, "tensor_a", + "sender", 0, "receiver"); + Output recv_b = ops::_Recv(root.WithOpName("recv_b"), DT_BOOL, "tensor_b", + "sender", 0, "receiver"); + Output const_a = ops::Const(root.WithOpName("const_a"), 42); + + ops::ControlTrigger ctrl_trigger_a(root.WithOpName("ctrl_trigger_a")); + ops::ControlTrigger ctrl_trigger_b(root.WithOpName("ctrl_trigger_b")); + root.graph()->AddControlEdge(recv_a.node(), ctrl_trigger_a.operation.node()); + root.graph()->AddControlEdge(recv_b.node(), ctrl_trigger_a.operation.node()); + root.graph()->AddControlEdge(ctrl_trigger_b.operation.node(), const_a.node()); + + std::unique_ptr graph(new Graph(OpRegistry::Global())); + + TF_ASSERT_OK(root.ToGraph(graph.get())); + TF_ASSERT_OK(MarkForCompilation(&graph)); + + std::unordered_map clusters = GetClusters(*graph); + + ASSERT_FALSE(clusters.empty()); + string cluster_name = clusters.begin()->second; + + // ctrl_trigger_a has inputs with mismatching deadness so it won't be + // clustered. ctrl_trigger_b is okay to cluster. + std::unordered_map expected_clusters( + {{"const_a", cluster_name}, {"ctrl_trigger_b", cluster_name}}); + EXPECT_EQ(clusters, expected_clusters); +} + } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/jit/xla_compilation_cache.cc b/tensorflow/compiler/jit/xla_compilation_cache.cc index 54a41a4daa790401c797277e7eaab531dd34ac80..08c357c87919760fffa43f0d014e5ce82035d138 100644 --- a/tensorflow/compiler/jit/xla_compilation_cache.cc +++ b/tensorflow/compiler/jit/xla_compilation_cache.cc @@ -209,7 +209,9 @@ Status XlaCompilationCache::BuildExecutable( argument_layouts[i] = &result.xla_input_shapes[i]; } xla::ExecutableBuildOptions build_options; - build_options.set_device_ordinal(client_->default_device_ordinal()); + build_options.set_device_ordinal(options.device_ordinal != -1 + ? options.device_ordinal + : client_->default_device_ordinal()); build_options.set_result_layout(result.xla_output_shape); build_options.set_device_allocator(options.device_allocator); diff --git a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc index baccea2d6a793df8c5cf8c8941706d41d2c044ca..d288d37bc75380168a31937024dd41bdbe7dce9d 100644 --- a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc +++ b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc @@ -53,7 +53,9 @@ Status XlaCompileOnDemandOp::Run(OpKernelContext* ctx, // Builds an XLA allocator for the device. XlaComputationLaunchContext launch_context( - client, client->backend().memory_allocator(), true); + client, client->backend().memory_allocator(), + /*allocate_xla_tensors=*/true, + /*use_multiple_streams=*/metadata.UseMultipleStreams()); launch_context.PopulateInputs(ctx, result, variables); diff --git a/tensorflow/compiler/jit/xla_cpu_device.cc b/tensorflow/compiler/jit/xla_cpu_device.cc index 43648402f65c656b6b4eb2e83e61ce45f1c73669..7e159e3171113b0d53f03bb676ac9c21db7fe77a 100644 --- a/tensorflow/compiler/jit/xla_cpu_device.cc +++ b/tensorflow/compiler/jit/xla_cpu_device.cc @@ -54,6 +54,7 @@ Status XlaCpuDeviceFactory::CreateDevices(const SessionOptions& options, DEVICE_CPU_XLA_JIT, options, name_prefix, registration, /*transfer_as_literal=*/false, + /*use_multiple_streams=*/false, /*shape_representation_fn=*/{}, /*padded_shape_fn=*/{}, &device)); devices->push_back(device.release()); diff --git a/tensorflow/compiler/jit/xla_device.cc b/tensorflow/compiler/jit/xla_device.cc index ed007d603ea1b3d27dd25f00726261cdd029c20c..c55eba2f79ddcf10931ea659a64df559cef06ec5 100644 --- a/tensorflow/compiler/jit/xla_device.cc +++ b/tensorflow/compiler/jit/xla_device.cc @@ -130,7 +130,7 @@ Status DefaultPaddedShapeFn(const Tensor& tensor, xla::Shape* shape) { const string& jit_device_name, const SessionOptions& options, const string& name_prefix, const XlaOpRegistry::DeviceRegistration& registration, - bool transfer_as_literal, + bool transfer_as_literal, bool use_multiple_streams, const XlaCompiler::ShapeRepresentationFn& shape_representation_fn, const PaddedShapeFn& padded_shape_fn, std::unique_ptr* device) { VLOG(1) << "XlaDevice::Create " << platform_name << " " << device_name << ":" @@ -151,22 +151,24 @@ Status DefaultPaddedShapeFn(const Tensor& tensor, xla::Shape* shape) { DeviceType(device_name), Bytes(16ULL << 30), DeviceLocality(), strings::StrCat("device: ", device_name, " device")); - device->reset(new XlaDevice( - options, attrs, device_ordinal, DeviceType(jit_device_name), - platform.ValueOrDie(), transfer_as_literal, shape_representation_fn, - padded_shape_fn ? padded_shape_fn : DefaultPaddedShapeFn)); + device->reset( + new XlaDevice(options, attrs, device_ordinal, DeviceType(jit_device_name), + platform.ValueOrDie(), transfer_as_literal, + use_multiple_streams, shape_representation_fn, + padded_shape_fn ? padded_shape_fn : DefaultPaddedShapeFn)); return Status::OK(); } XlaDevice::Metadata::Metadata( int device_ordinal, se::Platform* platform, const DeviceType& device_type, XlaCompiler::ShapeRepresentationFn shape_representation_fn, - PaddedShapeFn padded_shape_fn) + PaddedShapeFn padded_shape_fn, bool use_multiple_streams) : device_ordinal_(device_ordinal), device_type_(device_type), platform_(platform), shape_representation_fn_(std::move(shape_representation_fn)), - padded_shape_fn_(std::move(padded_shape_fn)) {} + padded_shape_fn_(std::move(padded_shape_fn)), + use_multiple_streams_(use_multiple_streams) {} int XlaDevice::Metadata::device_ordinal() const { return device_ordinal_; } @@ -200,16 +202,18 @@ const DeviceType& XlaDevice::Metadata::jit_device_type() const { XlaDevice::XlaDevice( const SessionOptions& options, const DeviceAttributes& attrs, int device_ordinal, const DeviceType& jit_device_name, - se::Platform* platform, bool transfer_as_literal, + se::Platform* platform, bool transfer_as_literal, bool use_multiple_streams, const XlaCompiler::ShapeRepresentationFn& shape_representation_fn, const PaddedShapeFn& padded_shape_fn) : LocalDevice(options, attrs), xla_metadata_(device_ordinal, platform, jit_device_name, - shape_representation_fn, padded_shape_fn), + shape_representation_fn, padded_shape_fn, + use_multiple_streams), device_ordinal_(device_ordinal), jit_device_name_(jit_device_name), xla_allocator_(nullptr), platform_(platform), + use_multiple_streams_(use_multiple_streams), transfer_as_literal_(transfer_as_literal), shape_representation_fn_(shape_representation_fn) { VLOG(1) << "Created XLA device " << jit_device_name; @@ -253,6 +257,30 @@ xla::StatusOr XlaDevice::GetStream() { return stream_.get(); } +xla::StatusOr XlaDevice::GetDeviceToHostStream() { + if (!use_multiple_streams_) { + return GetStream(); + } + if (!device_to_host_stream_) { + xla::Backend* backend = client()->mutable_backend(); + TF_ASSIGN_OR_RETURN(device_to_host_stream_, + backend->BorrowStream(device_ordinal_)); + } + return device_to_host_stream_.get(); +} + +xla::StatusOr XlaDevice::GetHostToDeviceStream() { + if (!use_multiple_streams_) { + return GetStream(); + } + if (!host_to_device_stream_) { + xla::Backend* backend = client()->mutable_backend(); + TF_ASSIGN_OR_RETURN(host_to_device_stream_, + backend->BorrowStream(device_ordinal_)); + } + return host_to_device_stream_.get(); +} + Status XlaDevice::CreateAndSetGpuDeviceInfo() { if (gpu_device_info_ == nullptr) { TF_ASSIGN_OR_RETURN(se::Stream * stream, GetStream()); @@ -263,8 +291,9 @@ Status XlaDevice::CreateAndSetGpuDeviceInfo() { // gpu_device_info_->default_context. gpu_device_info_ = MakeUnique(); gpu_device_info_->stream = stream; - gpu_device_info_->default_context = new XlaDeviceContext( - stream, client(), transfer_as_literal_, shape_representation_fn_); + gpu_device_info_->default_context = + new XlaDeviceContext(stream, stream, stream, client(), + transfer_as_literal_, shape_representation_fn_); set_tensorflow_gpu_device_info(gpu_device_info_.get()); } @@ -276,10 +305,16 @@ Status XlaDevice::FillContextMap(const Graph* graph, VLOG(1) << "XlaDevice::FillContextMap"; device_context_map->resize(graph->num_node_ids()); TF_ASSIGN_OR_RETURN(se::Stream * stream, GetStream()); + TF_ASSIGN_OR_RETURN(se::Stream * device_to_host_stream, + GetDeviceToHostStream()); + TF_ASSIGN_OR_RETURN(se::Stream * host_to_device_stream, + GetHostToDeviceStream()); + // Call GetAllocator for the side-effect of ensuring the allocator is created. GetAllocator({}); - auto ctx = new XlaDeviceContext(stream, client(), transfer_as_literal_, - shape_representation_fn_); + auto ctx = new XlaDeviceContext( + stream, host_to_device_stream, device_to_host_stream, client(), + transfer_as_literal_, shape_representation_fn_); for (Node* n : graph->nodes()) { VLOG(2) << n->id() << " : " << n->type_string() << " : " << n->name(); ctx->Ref(); @@ -326,8 +361,13 @@ Status XlaDevice::MakeTensorFromProto(const TensorProto& tensor_proto, Tensor copy(GetAllocator(alloc_attrs), parsed.dtype(), parsed.shape()); Notification n; TF_ASSIGN_OR_RETURN(se::Stream * stream, GetStream()); - XlaTransferManager manager(stream, client(), transfer_as_literal_, - shape_representation_fn_); + TF_ASSIGN_OR_RETURN(se::Stream * device_to_host_stream, + GetDeviceToHostStream()); + TF_ASSIGN_OR_RETURN(se::Stream * host_to_device_stream, + GetHostToDeviceStream()); + XlaTransferManager manager(stream, host_to_device_stream, + device_to_host_stream, client(), + transfer_as_literal_, shape_representation_fn_); manager.CopyCPUTensorToDevice(&parsed, this, ©, [&n, &status](const Status& s) { status = s; diff --git a/tensorflow/compiler/jit/xla_device.h b/tensorflow/compiler/jit/xla_device.h index 02e88ee6793e984a7b782790f8011cbcbc5a5026..4a5942fbd7f5bfd28e1ec96c6b0dc9e28dd418c5 100644 --- a/tensorflow/compiler/jit/xla_device.h +++ b/tensorflow/compiler/jit/xla_device.h @@ -29,6 +29,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/core/common_runtime/device_factory.h" #include "tensorflow/core/common_runtime/local_device.h" #include "tensorflow/core/framework/allocator.h" @@ -57,7 +58,7 @@ class XlaDevice : public LocalDevice { Metadata(int device_ordinal, se::Platform* platform, const DeviceType& device_type, XlaCompiler::ShapeRepresentationFn shape_representation_fn, - PaddedShapeFn padded_shape_fn); + PaddedShapeFn padded_shape_fn, bool use_multiple_streams); // The index of the device on this host. int device_ordinal() const; @@ -70,12 +71,15 @@ class XlaDevice : public LocalDevice { } const PaddedShapeFn& padded_shape_fn() const { return padded_shape_fn_; } + bool UseMultipleStreams() const { return use_multiple_streams_; } + private: const int device_ordinal_; const DeviceType device_type_; se::Platform* platform_; // Not owned. XlaCompiler::ShapeRepresentationFn shape_representation_fn_; PaddedShapeFn padded_shape_fn_; + const bool use_multiple_streams_; TF_DISALLOW_COPY_AND_ASSIGN(Metadata); }; @@ -89,6 +93,8 @@ class XlaDevice : public LocalDevice { // 'transfer_as_literal' is true if device<->host transfers must be done using // XLA's TransferLiteral{To,From}Device interface. If false, we can use // ThenMemcpy instead. + // If 'use_multiple_streams' is true, we create separate streams for + // host-to-device and device-to-host communication. // If padded_shape_fn is empty, a default implementation that returns // the on-host shape is used. static Status Create( @@ -96,7 +102,7 @@ class XlaDevice : public LocalDevice { int device_ordinal, const string& jit_device_name, const SessionOptions& options, const string& name_prefix, const XlaOpRegistry::DeviceRegistration& registration, - bool transfer_as_literal, + bool transfer_as_literal, bool use_multiple_streams, const XlaCompiler::ShapeRepresentationFn& shape_representation_fn, const PaddedShapeFn& padded_shape_fn, std::unique_ptr* device); @@ -106,6 +112,7 @@ class XlaDevice : public LocalDevice { XlaDevice(const SessionOptions& options, const DeviceAttributes& attrs, int device_ordinal, const DeviceType& jit_device_name, se::Platform* platform, bool transfer_as_literal, + bool use_multiple_streams, const XlaCompiler::ShapeRepresentationFn& shape_representation_fn, const PaddedShapeFn& padded_shape_fn); ~XlaDevice() override; @@ -126,6 +133,8 @@ class XlaDevice : public LocalDevice { xla::LocalClient* client() const; const Metadata& metadata() { return xla_metadata_; } xla::StatusOr GetStream(); + xla::StatusOr GetHostToDeviceStream(); + xla::StatusOr GetDeviceToHostStream(); // If not already set, create and set GpuDeviceInfo. // Not thread-safe @@ -145,7 +154,17 @@ class XlaDevice : public LocalDevice { // stream are executed on the device. Operations include data // copying back and forth between CPU and the device, and // computations enqueued by XLA. - xla::Backend::StreamPtr stream_; + xla::StreamPool::Ptr stream_; + // If true, only stream_ is valid and all computation and transfers use + // stream_. If false, computation is performed by stream_ and transfers are + // performed by host_to_device/device_to_host_stream. + bool use_multiple_streams_; + // If use_multiple_streams_, host to device transfers are performed using this + // stream. + xla::StreamPool::Ptr host_to_device_stream_; + // If use_multiple_streams_, device to host transfers are performed using this + // stream. + xla::StreamPool::Ptr device_to_host_stream_; // Must we use XLA's transfer manager for correct host<->device transfers? if // false, we can use ThenMemcpy() instead. bool transfer_as_literal_; diff --git a/tensorflow/compiler/jit/xla_device_context.cc b/tensorflow/compiler/jit/xla_device_context.cc index 3bbf97afadd2c8a70add16b748a35832a2ef8538..8cf198239c84c3720585f53ebc95876ce4396793 100644 --- a/tensorflow/compiler/jit/xla_device_context.cc +++ b/tensorflow/compiler/jit/xla_device_context.cc @@ -48,13 +48,20 @@ void XlaDeviceAllocator::DeallocateRaw(void* ptr) { void XlaDeviceAllocator::GetStats(AllocatorStats* stats) { stats->Clear(); } XlaTransferManager::XlaTransferManager( - se::Stream* stream, xla::LocalClient* client, bool transfer_as_literal, + se::Stream* compute_stream, se::Stream* host_to_device_stream, + se::Stream* device_to_host_stream, xla::LocalClient* client, + bool transfer_as_literal, XlaCompiler::ShapeRepresentationFn shape_representation_fn) - : stream_(stream), + : stream_(compute_stream), + host_to_device_stream_(host_to_device_stream), + device_to_host_stream_(device_to_host_stream), client_(client), transfer_manager_(client->backend().transfer_manager()), transfer_as_literal_(transfer_as_literal), shape_representation_fn_(std::move(shape_representation_fn)) { + CHECK(host_to_device_stream_ != nullptr); + CHECK(device_to_host_stream_ != nullptr); + CHECK(stream_ != nullptr); if (!shape_representation_fn_) { shape_representation_fn_ = [](const TensorShape& shape, @@ -74,15 +81,26 @@ Status XlaTransferManager::TransferLiteralToDevice( auto literal = std::make_shared( static_cast(DMAHelper::base(&host_tensor)), xla_shape); - const xla::ShapedBuffer& shaped_buffer = - XlaTensor::FromTensor(device_tensor)->shaped_buffer(); + XlaTensor* xla_tensor = XlaTensor::FromTensor(device_tensor); + const xla::ShapedBuffer& shaped_buffer = xla_tensor->shaped_buffer(); VLOG(1) << "Transfer to device as literal: " << literal->ToString() << " " << shaped_buffer.ToString(); + if (UseMultipleStreams()) { + // Initially wait for the compute stream so that memory allocations are + // synchronized. + host_to_device_stream_->ThenWaitFor(stream_); + } TF_RETURN_IF_ERROR(transfer_manager_->TransferLiteralToDeviceAsync( - stream_, *literal, shaped_buffer)); + host_to_device_stream_, *literal, shaped_buffer)); + if (UseMultipleStreams()) { + se::Event event(stream_->parent()); + TF_RET_CHECK(event.Init()) << "Event failed to initialize!"; + host_to_device_stream_->ThenRecordEvent(&event); + xla_tensor->SetDefinedOn(host_to_device_stream_, std::move(event)); + } // Unref the host tensor, and capture the literal shared_ptr too so it goes // out of scope when the lambda completes. - stream_->ThenDoHostCallback([ref, literal]() { ref.Unref(); }); + host_to_device_stream_->ThenDoHostCallback([ref, literal]() { ref.Unref(); }); return Status::OK(); } @@ -94,7 +112,7 @@ void XlaTransferManager::TransferLiteralFromDevice( TensorReference ref(device_tensor); transfer_manager_->TransferLiteralFromDevice( - stream_, shaped_buffer, + device_to_host_stream_, shaped_buffer, [=, &shaped_buffer]( xla::StatusOr > literal_or) { ref.Unref(); @@ -120,67 +138,73 @@ void XlaTransferManager::CopyCPUTensorToDevice(const Tensor* cpu_tensor, Device* device, Tensor* device_tensor, StatusCallback done) const { - if (cpu_tensor->NumElements() > 0) { - VLOG(2) << "CopyCPUTensorToDevice " - << reinterpret_cast(cpu_tensor->tensor_data().data()) - << " " - << reinterpret_cast( - device_tensor->tensor_data().data()) - << " " << cpu_tensor->NumElements() << " " - << cpu_tensor->shape().DebugString() << " " - << device_tensor->shape().DebugString(); - - void* src_ptr = const_cast(DMAHelper::base(cpu_tensor)); - const int64 total_bytes = cpu_tensor->TotalBytes(); - - XlaTensor* xla_tensor = XlaTensor::FromTensor(device_tensor); - CHECK(xla_tensor); - - Status status; - xla::StatusOr shape_or_status = shape_representation_fn_( - device_tensor->shape(), device_tensor->dtype()); - if (!shape_or_status.ok()) { - done(shape_or_status.status()); + if (cpu_tensor->NumElements() == 0) { + VLOG(2) << "CopyCPUTensorToDevice empty tensor"; + done(Status::OK()); + return; + } + + VLOG(2) << "CopyCPUTensorToDevice " + << reinterpret_cast(cpu_tensor->tensor_data().data()) + << " " + << reinterpret_cast(device_tensor->tensor_data().data()) + << " " << cpu_tensor->NumElements() << " " + << cpu_tensor->shape().DebugString() << " " + << device_tensor->shape().DebugString(); + + void* src_ptr = const_cast(DMAHelper::base(cpu_tensor)); + const int64 total_bytes = cpu_tensor->TotalBytes(); + + XlaTensor* xla_tensor = XlaTensor::FromTensor(device_tensor); + CHECK(xla_tensor); + + xla::StatusOr shape_or_status = + shape_representation_fn_(device_tensor->shape(), device_tensor->dtype()); + if (!shape_or_status.ok()) { + done(shape_or_status.status()); + return; + } + TensorShape shape = shape_or_status.ValueOrDie(); + if (!xla_tensor->has_shaped_buffer()) { + Status s = + xla_tensor->AllocateShapedBuffer(device_tensor->dtype(), shape, client_, + stream_->parent()->device_ordinal()); + if (!s.ok()) { + done(s); return; } - TensorShape shape = shape_or_status.ValueOrDie(); - if (!xla_tensor->has_shaped_buffer()) { - status = xla_tensor->AllocateShapedBuffer( - device_tensor->dtype(), shape, client_, - stream_->parent()->device_ordinal()); - if (!status.ok()) { - return done(status); - } - } + } - if (transfer_as_literal_) { - Tensor reshaped_cpu_tensor; - if (!reshaped_cpu_tensor.CopyFrom(*cpu_tensor, shape)) { - done(errors::Internal( - "Tensor::CopyFrom failed when copying from CPU to XLA device")); - return; - } - status = TransferLiteralToDevice(reshaped_cpu_tensor, device_tensor); - } else { - se::DeviceMemoryBase dev_dst_ptr = - XlaTensor::DeviceMemoryFromTensor(*device_tensor); - stream_->ThenMemcpy(&dev_dst_ptr, src_ptr, total_bytes); - // TODO(hpucha): Make this asynchronous. - Status block_status = stream_->BlockHostUntilDone(); - if (!block_status.ok()) { - status = xla::InternalError( - "Failed to complete data transfer on stream %p: %s", stream_, - block_status.error_message().c_str()); - } + Status status; + if (transfer_as_literal_) { + Tensor reshaped_cpu_tensor; + if (!reshaped_cpu_tensor.CopyFrom(*cpu_tensor, shape)) { + done(errors::Internal( + "Tensor::CopyFrom failed when copying from CPU to XLA device")); + return; + } + status = TransferLiteralToDevice(reshaped_cpu_tensor, device_tensor); + if (status.ok()) { + xla_tensor->set_host_tensor(*cpu_tensor); + host_to_device_stream_->ThenDoHostCallback( + [done]() { done(Status::OK()); }); + return; + } + } else { + se::DeviceMemoryBase dev_dst_ptr = + XlaTensor::DeviceMemoryFromTensor(*device_tensor); + host_to_device_stream_->ThenMemcpy(&dev_dst_ptr, src_ptr, total_bytes); + // TODO(hpucha): Make this asynchronous. + Status block_status = host_to_device_stream_->BlockHostUntilDone(); + if (!block_status.ok()) { + status = xla::InternalError( + "Failed to complete data transfer on stream %p: %s", + host_to_device_stream_, block_status.error_message().c_str()); } - xla_tensor->set_host_tensor(*cpu_tensor); - - done(status); - return; } + xla_tensor->set_host_tensor(*cpu_tensor); - VLOG(2) << "CopyCPUTensorToDevice empty tensor"; - done(Status::OK()); + done(status); } void XlaTransferManager::CopyDeviceTensorToCPU(const Tensor* device_tensor, @@ -188,51 +212,65 @@ void XlaTransferManager::CopyDeviceTensorToCPU(const Tensor* device_tensor, Device* device, Tensor* cpu_tensor, StatusCallback done) { - if (device_tensor->NumElements() > 0) { - VLOG(2) << "CopyDeviceTensorToCPU " - << reinterpret_cast( - device_tensor->tensor_data().data()) - << " " - << reinterpret_cast(cpu_tensor->tensor_data().data()) - << " " << device_tensor->NumElements() << " " - << cpu_tensor->shape().DebugString() << " " - << device_tensor->shape().DebugString(); - - const int64 total_bytes = cpu_tensor->TotalBytes(); - se::DeviceMemoryBase dev_src_ptr = - XlaTensor::DeviceMemoryFromTensor(*device_tensor); - void* dst_ptr = DMAHelper::base(cpu_tensor); + if (device_tensor->NumElements() == 0) { + VLOG(2) << "CopyDeviceTensorToCPU empty tensor"; + done(Status::OK()); + return; + } + VLOG(2) << "CopyDeviceTensorToCPU " + << reinterpret_cast(device_tensor->tensor_data().data()) + << " " + << reinterpret_cast(cpu_tensor->tensor_data().data()) + << " " << device_tensor->NumElements() << " " + << cpu_tensor->shape().DebugString() << " " + << device_tensor->shape().DebugString(); + + const int64 total_bytes = cpu_tensor->TotalBytes(); + se::DeviceMemoryBase dev_src_ptr = + XlaTensor::DeviceMemoryFromTensor(*device_tensor); + void* dst_ptr = DMAHelper::base(cpu_tensor); + XlaTensor* xla_tensor = XlaTensor::FromTensor(device_tensor); + + if (se::Event* event = + xla_tensor->GetDefinitionEvent(device_to_host_stream_)) { + device_to_host_stream_->ThenWaitFor(event); + xla_tensor->SetDefinedOn(device_to_host_stream_); + } - Status status; - if (transfer_as_literal_) { - TransferLiteralFromDevice(cpu_tensor, *device_tensor, done); - return; - } else { - stream_->ThenMemcpy(dst_ptr, dev_src_ptr, total_bytes); - // TODO(hpucha): Make this asynchronous. - Status block_status = stream_->BlockHostUntilDone(); - if (!block_status.ok()) { - status = xla::InternalError( - "Failed to complete data transfer on stream %p: %s", stream_, - block_status.error_message().c_str()); - } - done(status); - } + Status status; + if (transfer_as_literal_) { + TransferLiteralFromDevice(cpu_tensor, *device_tensor, done); return; + } else { + device_to_host_stream_->ThenMemcpy(dst_ptr, dev_src_ptr, total_bytes); + // TODO(hpucha): Make this asynchronous. + Status block_status = device_to_host_stream_->BlockHostUntilDone(); + if (!block_status.ok()) { + status = xla::InternalError( + "Failed to complete data transfer on stream %p: %s", stream_, + block_status.error_message().c_str()); + } } - VLOG(2) << "CopyDeviceTensorToCPU empty tensor"; - done(Status::OK()); + done(status); } void XlaTransferManager::CopyDeviceTensorToDevice(const Tensor& src_tensor, Tensor* dst_tensor, const StatusCallback& done) { + VLOG(2) << "CopyDeviceTensorToDevice " + << reinterpret_cast(src_tensor.tensor_data().data()) + << " " + << reinterpret_cast(dst_tensor->tensor_data().data()); // Perform memory allocation now, and enqueue the device-to-device transfer. Status status = [&]() -> Status { if (src_tensor.NumElements() == 0) { return Status::OK(); } + // TODO(jmolloy): We co-opt the device_to_host stream for device to device + // transfers; perhaps we should have a dedicated device to device stream? or + // one per device? + auto device_to_device_stream = stream_; XlaTensor* xla_src = XlaTensor::FromTensor(&src_tensor); XlaTensor* xla_dst = XlaTensor::FromTensor(dst_tensor); CHECK(xla_src && xla_dst) @@ -244,13 +282,32 @@ void XlaTransferManager::CopyDeviceTensorToDevice(const Tensor& src_tensor, TF_RETURN_IF_ERROR( xla_dst->AllocateShapedBuffer(src_tensor.dtype(), shape, client_, stream_->parent()->device_ordinal())); + if (stream_ != device_to_device_stream) { + // Initially wait for the compute stream so that memory allocations are + // synchronized. + device_to_device_stream->ThenWaitFor(stream_); + } } + + if (se::Event* event = + xla_src->GetDefinitionEvent(device_to_device_stream)) { + device_to_device_stream->ThenWaitFor(event); + xla_src->SetDefinedOn(device_to_device_stream); + } + auto from_iter = xla_src->shaped_buffer().buffers().begin(); auto to_iter = xla_dst->shaped_buffer().buffers().begin(); for (auto end_iter = xla_src->shaped_buffer().buffers().end(); from_iter != end_iter; ++from_iter, ++to_iter) { - stream_->ThenMemcpyD2D(&to_iter->second, from_iter->second, - to_iter->second.size()); + device_to_device_stream->ThenMemcpyD2D( + &to_iter->second, from_iter->second, to_iter->second.size()); + } + + if (UseMultipleStreams()) { + se::Event event(stream_->parent()); + CHECK(event.Init()); + device_to_device_stream->ThenRecordEvent(&event); + xla_dst->SetDefinedOn(device_to_device_stream, std::move(event)); } return Status::OK(); }(); @@ -262,9 +319,12 @@ void XlaTransferManager::CopyDeviceTensorToDevice(const Tensor& src_tensor, } XlaDeviceContext::XlaDeviceContext( - se::Stream* stream, xla::LocalClient* client, bool transfer_as_literal, + se::Stream* compute_stream, se::Stream* host_to_device_stream, + se::Stream* device_to_host_stream, xla::LocalClient* client, + bool transfer_as_literal, XlaCompiler::ShapeRepresentationFn shape_representation_fn) - : manager_(stream, client, transfer_as_literal, + : manager_(compute_stream, host_to_device_stream, device_to_host_stream, + client, transfer_as_literal, std::move(shape_representation_fn)) {} void XlaDeviceContext::CopyCPUTensorToDevice(const Tensor* cpu_tensor, diff --git a/tensorflow/compiler/jit/xla_device_context.h b/tensorflow/compiler/jit/xla_device_context.h index c5c81d65fe0f4a2774aab9f742454467e052071e..912f8d779e72f44821bc4fb25efa30bd35d01412 100644 --- a/tensorflow/compiler/jit/xla_device_context.h +++ b/tensorflow/compiler/jit/xla_device_context.h @@ -47,7 +47,9 @@ class XlaDeviceAllocator : public Allocator { class XlaTransferManager { public: explicit XlaTransferManager( - se::Stream* stream, xla::LocalClient* client, bool transfer_as_literal, + se::Stream* compute_stream, se::Stream* host_to_device_stream, + se::Stream* device_to_host_stream, xla::LocalClient* client, + bool transfer_as_literal, XlaCompiler::ShapeRepresentationFn shape_representation_fn); void CopyCPUTensorToDevice(const Tensor* cpu_tensor, Device* device, @@ -67,10 +69,17 @@ class XlaTransferManager { void TransferLiteralFromDevice(Tensor* host_tensor, const Tensor& device_tensor, const StatusCallback& done) const; + bool UseMultipleStreams() const { return stream_ != host_to_device_stream_; } - // Stream obtained from a Device, used to transfer tensors between - // CPU and device. + // The main compute stream of the device, used to synchronize the transfer + // streams if they are set. se::Stream* stream_; + // The stream to use for transferring data from host to device. Can be + // idential to stream_, but must not be nullptr. + se::Stream* host_to_device_stream_; + // The stream to use for transferring data from device to host. Can be + // idential to stream_, but must not be nullptr. + se::Stream* device_to_host_stream_; // For the underlying memory allocator and XLA's TransferManager. xla::LocalClient* client_; // Transfer manager, for marshalling data to and from the device. @@ -86,7 +95,9 @@ class XlaTransferManager { class XlaDeviceContext : public DeviceContext { public: explicit XlaDeviceContext( - se::Stream* stream, xla::LocalClient* client, bool transfer_as_literal, + se::Stream* compute_stream, se::Stream* host_to_device_stream, + se::Stream* device_to_host_stream, xla::LocalClient* client, + bool transfer_as_literal, XlaCompiler::ShapeRepresentationFn shape_representation_fn); void CopyCPUTensorToDevice(const Tensor* cpu_tensor, Device* device, diff --git a/tensorflow/compiler/jit/xla_device_ops.h b/tensorflow/compiler/jit/xla_device_ops.h index a605335a94f8687e0af4566f912b38dca9b5ac26..6adda327f186a607b4e7371bf4c5071dd86582da 100644 --- a/tensorflow/compiler/jit/xla_device_ops.h +++ b/tensorflow/compiler/jit/xla_device_ops.h @@ -77,9 +77,7 @@ class XlaAssignVariableOp : public AsyncOpKernel { ConstantOp); \ REGISTER_KERNEL_BUILDER( \ Name("Identity").Device(DEVICE).TypeConstraint("T", TYPES), IdentityOp); \ - REGISTER_KERNEL_BUILDER( \ - Name("IdentityN").Device(DEVICE).TypeConstraint("T", TYPES), \ - IdentityNOp); \ + REGISTER_KERNEL_BUILDER(Name("IdentityN").Device(DEVICE), IdentityNOp); \ REGISTER_KERNEL_BUILDER(Name("Placeholder").Device(DEVICE), PlaceholderOp); \ REGISTER_KERNEL_BUILDER(Name("PlaceholderV2").Device(DEVICE), \ PlaceholderOp); \ @@ -90,6 +88,9 @@ class XlaAssignVariableOp : public AsyncOpKernel { REGISTER_KERNEL_BUILDER( \ Name("ReadVariableOp").Device(DEVICE).HostMemory("resource"), \ ReadVariableOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("DestroyResourceOp").Device(DEVICE).HostMemory("resource"), \ + DestroyResourceOp); \ REGISTER_KERNEL_BUILDER(Name("Shape") \ .Device(DEVICE) \ .HostMemory("output") \ diff --git a/tensorflow/compiler/jit/xla_fusion_optimizer.cc b/tensorflow/compiler/jit/xla_fusion_optimizer.cc index 74257b09a808a39454eace3b1a9bf57a2e071360..4b499b161371ecece14447b29fbf809b6e8857db 100644 --- a/tensorflow/compiler/jit/xla_fusion_optimizer.cc +++ b/tensorflow/compiler/jit/xla_fusion_optimizer.cc @@ -20,6 +20,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/jit/deadness_analysis.h" #include "tensorflow/compiler/jit/defs.h" #include "tensorflow/compiler/jit/graphcycles/graphcycles.h" #include "tensorflow/compiler/jit/union_find.h" @@ -146,6 +147,9 @@ Status XlaFusionOptimizer::Optimize(grappler::Cluster* cluster, TF_RETURN_IF_ERROR( ImportGraphDef(options, item.graph, &graph, &shape_refiner)); + std::unique_ptr deadness; + TF_RETURN_IF_ERROR(DeadnessAnalysis::Run(graph, &deadness)); + // Collect nodes that can be fused via XLA, while ignoring those that // explicitly ask for XLA: (*) nodes that are marked to be compiled // explicitly. (*) nodes assigned to XLA device. @@ -185,6 +189,14 @@ Status XlaFusionOptimizer::Optimize(grappler::Cluster* cluster, continue; } + // If inputs to `node` can have conflicting deadness (i.e. some are alive + // and some are dead) then don't compile it. XLA cannot represent the + // deadness semantics of these nodes correctly and auto-clustering these + // nodes can cause deadness to propagate to nodes that should be live. + if (node->IsMerge() || deadness->HasInputsWithMismatchingDeadness(*node)) { + continue; + } + compilation_candidates.insert(node); } diff --git a/tensorflow/compiler/jit/xla_gpu_device.cc b/tensorflow/compiler/jit/xla_gpu_device.cc index c0d86a28c7698c302e28bab972bb2f847cc00ca4..851b118b0c18cfd752302b8f8dec27dae3e12acd 100644 --- a/tensorflow/compiler/jit/xla_gpu_device.cc +++ b/tensorflow/compiler/jit/xla_gpu_device.cc @@ -49,6 +49,7 @@ Status XlaGpuDeviceFactory::CreateDevices(const SessionOptions& options, XlaDevice::Create("CUDA", DEVICE_XLA_GPU, 0, DEVICE_GPU_XLA_JIT, options, name_prefix, registration, /*transfer_as_literal=*/false, + /*use_multiple_streams=*/false, /*shape_representation_fn=*/{}, /*padded_shape_fn=*/{}, &device); if (!status.ok()) { diff --git a/tensorflow/compiler/jit/xla_interpreter_device.cc b/tensorflow/compiler/jit/xla_interpreter_device.cc index 661187f4a873b03b8d013aa74cb6b6315bb4e2eb..45745596749207189c60ee1e3dcf19b6ecb7eb5b 100644 --- a/tensorflow/compiler/jit/xla_interpreter_device.cc +++ b/tensorflow/compiler/jit/xla_interpreter_device.cc @@ -52,6 +52,7 @@ Status XlaInterpreterDeviceFactory::CreateDevices( DEVICE_INTERPRETER_XLA_JIT, options, name_prefix, registration, /*transfer_as_literal=*/false, + /*use_multiple_streams=*/false, /*shape_representation_fn=*/{}, /*padded_shape_fn=*/{}, &device)); devices->push_back(device.release()); diff --git a/tensorflow/compiler/jit/xla_launch_util.cc b/tensorflow/compiler/jit/xla_launch_util.cc index 5ceccc769fa2e95d4cf4d2b4ebd8dbf312ebdfd0..6134b8c6946429918a5ca37188cbff13a6cd1c79 100644 --- a/tensorflow/compiler/jit/xla_launch_util.cc +++ b/tensorflow/compiler/jit/xla_launch_util.cc @@ -64,11 +64,13 @@ xla::StatusOr XlaAllocator::Allocate( int device_ordinal, uint64 size, bool retry_on_failure) { AllocationAttributes attrs; attrs.no_retry_on_failure = !retry_on_failure; - void* data = - wrapped_->AllocateRaw(Allocator::kAllocatorAlignment, size, attrs); - if (data == nullptr) { - return errors::ResourceExhausted("Out of memory while trying to allocate ", - size, " bytes."); + void* data = nullptr; + if (size != 0) { + data = wrapped_->AllocateRaw(Allocator::kAllocatorAlignment, size, attrs); + if (data == nullptr) { + return errors::ResourceExhausted( + "Out of memory while trying to allocate ", size, " bytes."); + } } return xla::OwningDeviceMemory(se::DeviceMemoryBase(data, size), device_ordinal, this); @@ -115,14 +117,22 @@ using internal::ExtractSubShapedBuffer; XlaComputationLaunchContext::XlaComputationLaunchContext( xla::LocalClient* client, xla::DeviceMemoryAllocator* xla_allocator, - bool allocate_xla_tensors) + bool allocate_xla_tensors, bool use_multiple_streams) : client_(client), xla_allocator_(xla_allocator), - allocate_xla_tensors_(allocate_xla_tensors) {} + allocate_xla_tensors_(allocate_xla_tensors), + use_multiple_streams_(use_multiple_streams) { + if (use_multiple_streams_) { + CHECK(allocate_xla_tensors_) << "To use multiple streams correctly we must " + "be allocating XLA tensors!"; + } +} void XlaComputationLaunchContext::PopulateInputs( OpKernelContext* ctx, const XlaCompiler::CompilationResult* kernel, const std::map& variables) { + se::Stream* stream = + ctx->op_device_context() ? ctx->op_device_context()->stream() : nullptr; // Build ShapedBuffers that point directly to the Tensor buffers. arg_buffers_.reserve(kernel->xla_input_shapes.size() + 1); arg_buffers_.resize(kernel->xla_input_shapes.size()); @@ -140,6 +150,16 @@ void XlaComputationLaunchContext::PopulateInputs( t = &(ctx->input(arg_num)); } + if (use_multiple_streams_) { + CHECK(stream) << "Must have a stream available when using XLA tensors!"; + XlaTensor* xla_tensor = XlaTensor::FromTensor(t); + CHECK(xla_tensor); + if (se::Event* event = xla_tensor->GetDefinitionEvent(stream)) { + stream->ThenWaitFor(event); + xla_tensor->SetDefinedOn(stream); + } + } + const xla::Shape on_device_shape = client_->backend().transfer_manager()->HostShapeToDeviceShape(shape); if (xla::ShapeUtil::IsTuple(on_device_shape)) { @@ -248,6 +268,12 @@ void XlaComputationLaunchContext::PopulateOutputs( if (xla_tensor) { xla_tensor->set_shaped_buffer(ScopedShapedBuffer( ExtractSubShapedBuffer(&output, output_num, xla_allocator_))); + if (use_multiple_streams_) { + se::Event event(stream->parent()); + CHECK(event.Init()); + stream->ThenRecordEvent(&event); + xla_tensor->SetDefinedOn(stream, std::move(event)); + } } else { // xla_tensor wasn't valid, which must mean this is a zero-element // tensor. @@ -302,6 +328,12 @@ void XlaComputationLaunchContext::PopulateOutputs( CHECK(xla_tensor); xla_tensor->set_shaped_buffer( ExtractSubShapedBuffer(&output, output_num, xla_allocator_)); + if (use_multiple_streams_) { + se::Event event(stream->parent()); + CHECK(event.Init()); + stream->ThenRecordEvent(&event); + xla_tensor->SetDefinedOn(stream, std::move(event)); + } *variable->tensor() = output_tensor; } else { Tensor output_tensor = XlaTensorBuffer::MakeTensor( diff --git a/tensorflow/compiler/jit/xla_launch_util.h b/tensorflow/compiler/jit/xla_launch_util.h index 4390701ccbd0bc3971413ddcd917c11019990087..1ea3fa4cf29266e8c452385226e56bd0b82622d9 100644 --- a/tensorflow/compiler/jit/xla_launch_util.h +++ b/tensorflow/compiler/jit/xla_launch_util.h @@ -76,9 +76,15 @@ class XlaComputationLaunchContext { // Create a new launch context. 'allocate_xla_tensors' is true if allocated // output tensors and variables are always XlaTensors. If false they are // assumed to be "normal" device pointers. + // If 'use_multiple_streams' is true, tensors may be defined and used on + // multiple streams and so se::Events must be defined and waited for. If + // 'use_multiple_streams' is true, 'allocate_xla_tensors' must also be true + // because we track inter-stream dependencies through events inside XlaTensor + // objects. XlaComputationLaunchContext(xla::LocalClient* client, xla::DeviceMemoryAllocator* xla_allocator, - bool allocate_xla_tensors); + bool allocate_xla_tensors, + bool use_multiple_streams); // Add all inputs within `ctx` as XLA arguments (returned by arguments()). // `variables` is a map from TensorFlow argument number to resource variable. @@ -99,6 +105,7 @@ class XlaComputationLaunchContext { xla::LocalClient* client_; xla::DeviceMemoryAllocator* xla_allocator_; bool allocate_xla_tensors_; + bool use_multiple_streams_; std::vector> arg_buffers_; std::vector arg_ptrs_; }; @@ -115,7 +122,11 @@ class XlaTensorBuffer : public TensorBuffer { data_ = const_cast(ptr); } - ~XlaTensorBuffer() override { allocator_->DeallocateRaw(data_); } + ~XlaTensorBuffer() override { + if (data_) { + allocator_->DeallocateRaw(data_); + } + } void* data() const override { return data_; } size_t size() const override { return expected_size_; } diff --git a/tensorflow/compiler/jit/xla_tensor.cc b/tensorflow/compiler/jit/xla_tensor.cc index 3c44c4ae6df7f3e2d60d8933561c0c71888e8c3f..d777dfa5a34fb9615ddcf393ed53be1491cb70af 100644 --- a/tensorflow/compiler/jit/xla_tensor.cc +++ b/tensorflow/compiler/jit/xla_tensor.cc @@ -73,6 +73,34 @@ Status XlaTensor::AllocateShapedBuffer(DataType dtype, const TensorShape& shape, return Status::OK(); } +se::Event* XlaTensor::GetDefinitionEvent(se::Stream* stream) { + mutex_lock lock(mu_); + if (!definition_event_.has_value()) { + return nullptr; + } + + // The set of defined streams is expected to be very small indeed (usually + // 1-2), so a simple linear scan should be fast enough. + if (std::find(streams_defined_on_.begin(), streams_defined_on_.end(), + stream) != streams_defined_on_.end()) { + // stream is in streams_defined_on_; it doesn't need to be waited on. + return nullptr; + } + + return &*definition_event_; +} + +void XlaTensor::SetDefinedOn(se::Stream* stream, se::Event event) { + mutex_lock lock(mu_); + definition_event_ = std::move(event); + streams_defined_on_ = {stream}; +} + +void XlaTensor::SetDefinedOn(se::Stream* stream) { + mutex_lock lock(mu_); + streams_defined_on_.push_back(stream); +} + // The pointer tag, OR-ed into the XlaTensor's address to distinguish it from // device-side tensors, which are either CPU or GPU memory pointers. This works // because we're guaranteed that CPU and GPU pointers are aligned to > 1 bits. diff --git a/tensorflow/compiler/jit/xla_tensor.h b/tensorflow/compiler/jit/xla_tensor.h index c54001a999998f45c0cdacd752ca4036f0792857..f7e401c731163200c518074f2caa6907efb1f684 100644 --- a/tensorflow/compiler/jit/xla_tensor.h +++ b/tensorflow/compiler/jit/xla_tensor.h @@ -85,6 +85,24 @@ class XlaTensor { host_tensor_.reset(new Tensor(tensor)); } + // If the tensor's content is not yet defined on 'stream', and there exists an + // se::Event declaring when the tensor's content is defined, return it. + // Otherwise, return nullptr. If this function returns nullptr then the + // tensor's content can be read on 'stream' without additional + // synchronization. + se::Event* GetDefinitionEvent(se::Stream* stream); + + // Assert that the tensor's content is defined on 'stream' by the time 'event' + // triggers. + void SetDefinedOn(se::Stream* stream, se::Event event); + + // Assert that the tensor's content is defined on 'stream'. This version does + // not provide an event, and must be called *after* SetDefinedOn(Stream, + // Event). This call can be read as an assertion that the definition event has + // been waited on by 'stream', so further calls to GetDefinitionEvent(stream) + // do not need to also wait on the event. + void SetDefinedOn(se::Stream* stream); + // Convert from a raw pointer to an XlaTensor, removing the pointer tag. static XlaTensor* FromOpaquePointer(void* ptr); // Convert to a raw pointer from an XlaTensor, adding the pointer tag. @@ -95,6 +113,14 @@ class XlaTensor { std::unique_ptr shaped_buffer_; // An optional host tensor value. std::unique_ptr host_tensor_; + // An optional event that is triggered when the tensor's content has been + // defined. If this event is nullptr, it is assumed that the tensor's content + // is always defined. + gtl::optional definition_event_; + // A list of all streams for which the tensor's content is defined for any + // newly enqueued command. + gtl::InlinedVector streams_defined_on_ GUARDED_BY(mu_); + mutex mu_; }; } // namespace tensorflow diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index 273641f1978f2aa13265d491f15f0994c08bb0e7..080bed50e68ba353a5029f5eb959003b51327f4a 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -97,6 +97,19 @@ tf_xla_py_test( ], ) +tf_xla_py_test( + name = "adagrad_da_test", + size = "small", + srcs = ["adagrad_da_test.py"], + deps = [ + ":xla_test", + "//tensorflow/python:array_ops", + "//tensorflow/python:framework", + "//tensorflow/python:platform_test", + "//tensorflow/python:training", + ], +) + tf_xla_py_test( name = "adam_test", size = "small", @@ -111,6 +124,48 @@ tf_xla_py_test( ], ) +tf_xla_py_test( + name = "adamax_test", + size = "small", + srcs = ["adamax_test.py"], + deps = [ + ":xla_test", + "//tensorflow/contrib/opt:opt_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:training", + ], +) + +tf_xla_py_test( + name = "addsign_test", + size = "small", + srcs = ["addsign_test.py"], + deps = [ + ":xla_test", + "//tensorflow/contrib/opt:opt_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:training", + ], +) + +tf_xla_py_test( + name = "powersign_test", + size = "small", + srcs = ["powersign_test.py"], + deps = [ + ":xla_test", + "//tensorflow/contrib/opt:opt_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:training", + ], +) + tf_xla_py_test( name = "argminmax_test", size = "small", @@ -180,7 +235,7 @@ tf_xla_py_test( tf_xla_py_test( name = "cholesky_op_test", - size = "small", + size = "medium", srcs = ["cholesky_op_test.py"], tags = ["optonly"], deps = [ @@ -363,7 +418,7 @@ tf_xla_py_test( tf_xla_py_test( name = "eager_test", - size = "small", + size = "large", srcs = ["eager_test.py"], disabled_backends = [ # TODO(b/78199195) Support XLA CPU devices in eager runtime @@ -609,6 +664,27 @@ tf_xla_py_test( ], ) +tf_xla_py_test( + name = "qr_op_test", + size = "medium", + srcs = ["qr_op_test.py"], + disabled_backends = [ + # Test is very slow on CPU. + "cpu", + "cpu_ondemand", + ], + tags = ["optonly"], + deps = [ + ":xla_test", + "//tensorflow/python:array_ops", + "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform_test", + "//tensorflow/python:training", + "@absl_py//absl/testing:parameterized", + ], +) + tf_xla_py_test( name = "random_ops_test", size = "small", @@ -924,7 +1000,7 @@ tf_xla_py_test( tf_xla_py_test( name = "sort_ops_test", - size = "small", + size = "medium", srcs = ["sort_ops_test.py"], # Times out in fastbuild mode. tags = ["optonly"], diff --git a/tensorflow/compiler/tests/adagrad_da_test.py b/tensorflow/compiler/tests/adagrad_da_test.py new file mode 100644 index 0000000000000000000000000000000000000000..dc1625793aa44b96d3b96e175237caf96e7d7e74 --- /dev/null +++ b/tensorflow/compiler/tests/adagrad_da_test.py @@ -0,0 +1,165 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for AdagradDA optimizer.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.tests import xla_test +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.training import adagrad_da + + +class AdagradDAOptimizerTest(xla_test.XLATestCase): + + def testAdagradDAWithoutRegularizationBasic1(self): + for dtype in self.float_types: + with self.test_session(), self.test_scope(): + global_step = resource_variable_ops.ResourceVariable( + 0, dtype=dtypes.int64) + var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype) + grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) + grads1 = constant_op.constant([0.01, 0.02], dtype=dtype) + opt = adagrad_da.AdagradDAOptimizer( + 3.0, + global_step, + initial_gradient_squared_accumulator_value=0.1, + l1_regularization_strength=0.0, + l2_regularization_strength=0.0) + update = opt.apply_gradients( + zip([grads0, grads1], [var0, var1]), global_step=global_step) + variables.global_variables_initializer().run() + + self.assertAllClose([0.0, 0.0], var0.eval()) + self.assertAllClose([0.0, 0.0], var1.eval()) + + # Run a step of AdagradDA + update.run() + + # Let g to be gradient accumulator, gg to be gradient squared + # accumulator, T be the global step, lr is the learning rate, and k the + # initial gradient squared accumulator value. + # w = \dfrac{sign(-g)*lr*|g - l1*T|_{+}}{l2*T*lr + \sqrt{k+gg})} + # For -0.1*3.0*(0.1 - 0)/(0 + sqrt(0.1 + 0.1*0.1)) = -0.904534 + # similarly for others. + self.assertAllCloseAccordingToType( + np.array([-0.904534, -1.603567]), var0.eval()) + self.assertAllCloseAccordingToType( + np.array([-0.094821, -0.189358]), var1.eval()) + + def testAdagradDAwithoutRegularizationBasic2(self): + for dtype in self.float_types: + with self.test_session(), self.test_scope(): + global_step = resource_variable_ops.ResourceVariable( + 0, dtype=dtypes.int64) + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype) + grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) + grads1 = constant_op.constant([0.01, 0.02], dtype=dtype) + + opt = adagrad_da.AdagradDAOptimizer( + 3.0, + global_step, + initial_gradient_squared_accumulator_value=0.1, + l1_regularization_strength=0.0, + l2_regularization_strength=0.0) + update = opt.apply_gradients( + zip([grads0, grads1], [var0, var1]), global_step=global_step) + variables.global_variables_initializer().run() + + self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval()) + self.assertAllCloseAccordingToType([4.0, 3.0], var1.eval()) + + # Run a step of AdagradDA + update.run() + + self.assertAllCloseAccordingToType( + np.array([-0.904534, -1.603567]), var0.eval()) + self.assertAllCloseAccordingToType( + np.array([-0.094821, -0.189358]), var1.eval()) + + def testAdagradDAWithL1(self): + for dtype in self.float_types: + with self.test_session(), self.test_scope(): + global_step = resource_variable_ops.ResourceVariable( + 0, dtype=dtypes.int64) + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype) + grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) + grads1 = constant_op.constant([0.01, 0.02], dtype=dtype) + + opt = adagrad_da.AdagradDAOptimizer( + 3.0, + global_step, + initial_gradient_squared_accumulator_value=0.1, + l1_regularization_strength=0.001, + l2_regularization_strength=0.0) + update = opt.apply_gradients( + zip([grads0, grads1], [var0, var1]), global_step=global_step) + variables.global_variables_initializer().run() + + self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval()) + self.assertAllCloseAccordingToType([4.0, 3.0], var1.eval()) + + # Run a step of AdagradDA + update.run() + + self.assertAllCloseAccordingToType( + np.array([-0.895489, -1.59555]), var0.eval()) + self.assertAllCloseAccordingToType( + np.array([-0.085339, -0.17989]), var1.eval()) + + def testAdagradDAWithL1_L2(self): + for dtype in self.float_types: + with self.test_session(), self.test_scope(): + global_step = resource_variable_ops.ResourceVariable( + 0, dtype=dtypes.int64) + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype) + grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) + grads1 = constant_op.constant([0.01, 0.02], dtype=dtype) + + opt = adagrad_da.AdagradDAOptimizer( + 3.0, + global_step, + initial_gradient_squared_accumulator_value=0.1, + l1_regularization_strength=0.001, + l2_regularization_strength=2.0) + update = opt.apply_gradients( + zip([grads0, grads1], [var0, var1]), global_step=global_step) + variables.global_variables_initializer().run() + + self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval()) + self.assertAllCloseAccordingToType([4.0, 3.0], var1.eval()) + + # Run a step of AdagradDA + update.run() + + self.assertAllCloseAccordingToType( + np.array([-0.046907, -0.093659]), var0.eval()) + self.assertAllCloseAccordingToType( + np.array([-0.004275, -0.009023]), var1.eval()) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/compiler/tests/adamax_test.py b/tensorflow/compiler/tests/adamax_test.py new file mode 100644 index 0000000000000000000000000000000000000000..c4fdbc5974319db9243eb2c323746cbaaea795f6 --- /dev/null +++ b/tensorflow/compiler/tests/adamax_test.py @@ -0,0 +1,139 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for AdaMax optimizer.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.tests import xla_test +from tensorflow.contrib.opt.python.training import adamax +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import ops +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.ops import variables +from tensorflow.python.platform import test + + +def adamax_update_numpy(param, + g_t, + t, + m, + v, + alpha=0.001, + beta1=0.9, + beta2=0.999, + epsilon=1e-8): + m_t = beta1 * m + (1 - beta1) * g_t + v_t = np.maximum(beta2 * v, np.abs(g_t)) + param_t = param - (alpha / (1 - beta1**t)) * (m_t / (v_t + epsilon)) + return param_t, m_t, v_t + + +class AdaMaxOptimizerTest(xla_test.XLATestCase): + + def testBasic(self): + for i, dtype in enumerate(self.float_types): + with self.test_session(), self.test_scope(): + variable_scope.get_variable_scope().set_use_resource(True) + # Initialize variables for numpy implementation. + m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0 + var0_np = np.array([1.0, 2.0], dtype=dtype) + grads0_np = np.array([0.1, 0.1], dtype=dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype) + grads1_np = np.array([0.01, 0.01], dtype=dtype) + + var0 = resource_variable_ops.ResourceVariable( + var0_np, name="var0_%d" % i) + var1 = resource_variable_ops.ResourceVariable( + var1_np, name="var1_%d" % i) + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + + opt = adamax.AdaMaxOptimizer() + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + opt_variables = opt.variables() + beta1_power = opt._get_beta_accumulators() + self.assertTrue(beta1_power is not None) + self.assertIn(beta1_power, opt_variables) + + with ops.Graph().as_default(): + # Shouldn't return non-slot variables from other graphs. + self.assertEqual(0, len(opt.variables())) + + variables.global_variables_initializer().run() + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + beta1_power = opt._get_beta_accumulators() + + # Run 3 steps of AdaMax + for t in range(1, 4): + update.run() + + self.assertAllCloseAccordingToType(0.9**(t + 1), beta1_power.eval()) + + var0_np, m0, v0 = adamax_update_numpy(var0_np, grads0_np, t, m0, v0) + var1_np, m1, v1 = adamax_update_numpy(var1_np, grads1_np, t, m1, v1) + + # Validate updated params + self.assertAllCloseAccordingToType(var0_np, var0.eval(), rtol=1e-2) + self.assertAllCloseAccordingToType(var1_np, var1.eval(), rtol=1e-2) + self.assertEqual("var0_%d/AdaMax:0" % (i,), + opt.get_slot(var=var0, name="m").name) + + def testTensorLearningRate(self): + for dtype in self.float_types: + with self.test_session(), self.test_scope(): + variable_scope.get_variable_scope().set_use_resource(True) + # Initialize variables for numpy implementation. + m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0 + var0_np = np.array([1.0, 2.0], dtype=dtype) + grads0_np = np.array([0.1, 0.1], dtype=dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype) + grads1_np = np.array([0.01, 0.01], dtype=dtype) + + var0 = resource_variable_ops.ResourceVariable(var0_np) + var1 = resource_variable_ops.ResourceVariable(var1_np) + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + opt = adamax.AdaMaxOptimizer(constant_op.constant(0.001)) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + variables.global_variables_initializer().run() + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + beta1_power = opt._get_beta_accumulators() + + # Run 3 steps of AdaMax + for t in range(1, 4): + self.assertAllCloseAccordingToType(0.9**t, beta1_power.eval()) + update.run() + + var0_np, m0, v0 = adamax_update_numpy(var0_np, grads0_np, t, m0, v0) + var1_np, m1, v1 = adamax_update_numpy(var1_np, grads1_np, t, m1, v1) + + # Validate updated params + self.assertAllCloseAccordingToType(var0_np, var0.eval()) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/compiler/tests/addsign_test.py b/tensorflow/compiler/tests/addsign_test.py new file mode 100644 index 0000000000000000000000000000000000000000..9ec5a964cbb4dd98d2ef2d0b684872292118800f --- /dev/null +++ b/tensorflow/compiler/tests/addsign_test.py @@ -0,0 +1,142 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for AddSign.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.tests import xla_test +from tensorflow.contrib.opt.python.training import addsign +from tensorflow.contrib.opt.python.training import sign_decay +from tensorflow.python.framework import constant_op +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test + + +def py_linear_decay_fn(decay_steps): + def linear_decay(step): + step = min(step, decay_steps) + return float(decay_steps - step) / decay_steps + return linear_decay + + +def addsign_update_numpy(params, + g_t, + m, + lr, + alpha=1.0, + beta=0.9, + py_sign_decay_fn=None, + t=None): + m_t = beta * m + (1 - beta) * g_t + if py_sign_decay_fn is None: + sign_decayed = 1.0 + else: + sign_decayed = py_sign_decay_fn(t-1) + multiplier = alpha + sign_decayed * np.sign(g_t) * np.sign(m_t) + params_t = params - lr * multiplier * g_t + return params_t, m_t + + +class AddSignTest(xla_test.XLATestCase): + + def _testDense(self, + learning_rate=0.1, + sign_decay_fn=None, + py_sign_decay_fn=None, + alpha=1.0, + beta=0.9): + for dtype in self.float_types: + with self.test_session(), self.test_scope(): + # Initialize variables for numpy implementation. + m0, m1 = 0.0, 0.0 + var0_np = np.array([1.0, 2.0], dtype=dtype) + grads0_np = np.array([0.1, 0.1], dtype=dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype) + grads1_np = np.array([0.01, 0.01], dtype=dtype) + + var0 = resource_variable_ops.ResourceVariable(var0_np) + var1 = resource_variable_ops.ResourceVariable(var1_np) + global_step = resource_variable_ops.ResourceVariable(0, trainable=False) + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + + opt = addsign.AddSignOptimizer( + learning_rate=learning_rate, + alpha=alpha, + beta=beta, + sign_decay_fn=sign_decay_fn, + ) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + neg_update = opt.apply_gradients(zip([-grads0, -grads1], [var0, var1]), + global_step=global_step) + variables.global_variables_initializer().run() + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + # Run 7 steps of AddSign + # first 4 steps with positive gradient + # last 3 steps with negative gradient (sign(gm) should be -1) + for t in range(1, 8): + if t < 5: + update.run() + else: + neg_update.run() + + var0_np, m0 = addsign_update_numpy( + var0_np, + grads0_np if t < 5 else -grads0_np, + m0, + learning_rate, + alpha=alpha, + beta=beta, + py_sign_decay_fn=py_sign_decay_fn, + t=t, + ) + var1_np, m1 = addsign_update_numpy( + var1_np, + grads1_np if t < 5 else -grads1_np, + m1, + learning_rate, + alpha=alpha, + beta=beta, + py_sign_decay_fn=py_sign_decay_fn, + t=t, + ) + + # Validate updated params + self.assertAllCloseAccordingToType( + var0_np, var0.eval(), half_rtol=1e-2) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) + + def testDense(self): + decay_steps = 10 + sign_decay_fn = sign_decay.get_linear_decay_fn(decay_steps) + py_sign_decay_fn = py_linear_decay_fn(decay_steps) + self._testDense() + self._testDense(learning_rate=0.01, alpha=0.1, beta=0.8) + self._testDense( + sign_decay_fn=sign_decay_fn, py_sign_decay_fn=py_sign_decay_fn) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py index 9cb3d0454608c37e669d5b4360bc39bf1bf7e68c..0aafda7fb4d710f154157ee352d6616e5aa8935f 100644 --- a/tensorflow/compiler/tests/binary_ops_test.py +++ b/tensorflow/compiler/tests/binary_ops_test.py @@ -691,11 +691,13 @@ class BinaryOpsTest(xla_test.XLATestCase): np.array([[10], [7], [2]], dtype=np.float32), np.float32(7), expected=np.array([[False], [False], [True]], dtype=np.bool)) - self._testBinary( - less_op, - np.array([[10], [7], [2], [-1]], dtype=np.int64), - np.int64(7), - expected=np.array([[False], [False], [True], [True]], dtype=np.bool)) + if np.int64 in self.numeric_types: + self._testBinary( + less_op, + np.array([[10], [7], [2], [-1]], dtype=np.int64), + np.int64(7), + expected=np.array( + [[False], [False], [True], [True]], dtype=np.bool)) for less_equal_op in [math_ops.less_equal, (lambda x, y: x <= y)]: self._testBinary( diff --git a/tensorflow/compiler/tests/cholesky_op_test.py b/tensorflow/compiler/tests/cholesky_op_test.py index d2867278af93812eae804b66a7a6b706f98fa600..ed532db0ee5553a275192e6cc3ebf394075fa0e1 100644 --- a/tensorflow/compiler/tests/cholesky_op_test.py +++ b/tensorflow/compiler/tests/cholesky_op_test.py @@ -18,8 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import unittest - import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin @@ -103,9 +101,8 @@ class CholeskyOpTest(xla_test.XLATestCase): with self.assertRaises(ValueError): linalg_ops.cholesky(tensor3) - @unittest.skip("Test is slow") - def testLarge(self): - n = 200 + def testLarge2000x2000(self): + n = 2000 shape = (n, n) data = np.ones(shape).astype(np.float32) / (2.0 * n) + np.diag( np.ones(n).astype(np.float32)) @@ -128,6 +125,5 @@ class CholeskyOpTest(xla_test.XLATestCase): matrix = np.dot(np.dot(w, np.diag(v)), w.T).astype(dtype) self._verifyCholesky(matrix, atol=1e-4) - if __name__ == "__main__": test.main() diff --git a/tensorflow/compiler/tests/conv2d_test.py b/tensorflow/compiler/tests/conv2d_test.py index 98d41ba7edd52eedbf035097a48a1ce2ac7d5e9e..f9db103f6d0f9ea0e393a0971593552ec5c14079 100644 --- a/tensorflow/compiler/tests/conv2d_test.py +++ b/tensorflow/compiler/tests/conv2d_test.py @@ -33,12 +33,9 @@ from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import nn_ops from tensorflow.python.platform import googletest - DATA_FORMATS = ( ("_data_format_NHWC", "NHWC"), ("_data_format_NCHW", "NCHW"), - ("_data_format_HWNC", "HWNC"), - ("_data_format_HWCN", "HWCN"), ) diff --git a/tensorflow/compiler/tests/eager_test.py b/tensorflow/compiler/tests/eager_test.py index 3524666499cbb2ef3eae2bb3b314dda0a9be64c8..6ead15da13b86b9d2b4cf2c19e5cf2a90b061b91 100644 --- a/tensorflow/compiler/tests/eager_test.py +++ b/tensorflow/compiler/tests/eager_test.py @@ -403,7 +403,7 @@ class EagerFunctionTest(xla_test.XLATestCase): def testSliceInDefun(self): with self.test_scope(): - @function.defun(compiled=True) + @function.defun def f(x, y): return x[0::2, y:, ...] @@ -418,6 +418,22 @@ class EagerFunctionTest(xla_test.XLATestCase): self.assertAllEqual(np.ones([1, 2, 4]), z.numpy()) self.assertAllEqual((2, 3, 4), dz.shape.as_list()) + def testNestedDefun(self): + self.skipTest('Nested defuns do not work on TPU at the moment') + with self.test_scope(): + + @function.defun + def times_two(x): + return 2 * x + + @function.defun + def two_x_plus_1(x): + return times_two(x) + 1 + + x = constant_op.constant([2, 3, 4]) + y = two_x_plus_1(x) + self.assertAllEqual([5, 7, 9], y.numpy()) + class ExcessivePaddingTest(xla_test.XLATestCase): """Test that eager execution works with TPU flattened tensors. @@ -470,6 +486,36 @@ class ExcessivePaddingTest(xla_test.XLATestCase): self.assertAllEqual(100 * [[36.0]], reduced) +def multiple_tpus(): + devices = context.context().devices() + return len([d for d in devices if 'device:TPU:' in d]) > 1 + + +class MultiDeviceTest(xla_test.XLATestCase): + """Test running TPU computation on more than one core.""" + + def testBasic(self): + if not multiple_tpus(): + self.skipTest('MultiDeviceTest requires multiple TPU devices.') + + # Compute 10 on TPU core 0 + with ops.device('device:TPU:0'): + two = constant_op.constant(2) + five = constant_op.constant(5) + ten = two * five + self.assertAllEqual(10, ten) + + # Compute 6 on TPU core 1 + with ops.device('device:TPU:1'): + two = constant_op.constant(2) + three = constant_op.constant(3) + six = two * three + self.assertAllEqual(6, six) + + # Copy 10 and 6 to CPU and sum them + self.assertAllEqual(16, ten + six) + + if __name__ == '__main__': ops.enable_eager_execution( config=config_pb2.ConfigProto(log_device_placement=True)) diff --git a/tensorflow/compiler/tests/image_ops_test.py b/tensorflow/compiler/tests/image_ops_test.py index 8b01ef96db3e8ab58850df234c2e05b764be52ba..bf986ade06b11358552ee92df3169f965ce3f534 100644 --- a/tensorflow/compiler/tests/image_ops_test.py +++ b/tensorflow/compiler/tests/image_ops_test.py @@ -26,6 +26,7 @@ import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.compiler.tests import xla_test +from tensorflow.python.compat import compat from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops @@ -579,5 +580,140 @@ class ResizeBilinearTest(xla_test.XLATestCase): large_tolerance=True) +class NonMaxSuppressionTest(xla_test.XLATestCase): + + def testNMS128From1024(self): + # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU. + if self.device in ["XLA_CPU", "XLA_GPU"]: + return + + with compat.forward_compatibility_horizon(2018, 8, 8): + num_boxes = 1024 + boxes_np = np.random.normal(50, 10, (num_boxes, 4)).astype("f4") + scores_np = np.random.normal(0.5, 0.1, (num_boxes,)).astype("f4") + + max_output_size = 128 + iou_threshold_np = np.array(0.5, dtype=np.float32) + score_threshold_np = np.array(0.0, dtype=np.float32) + + with self.test_session() as sess: + boxes = array_ops.placeholder(boxes_np.dtype, shape=boxes_np.shape) + scores = array_ops.placeholder(scores_np.dtype, shape=scores_np.shape) + iou_threshold = array_ops.placeholder(iou_threshold_np.dtype, + iou_threshold_np.shape) + score_threshold = array_ops.placeholder(score_threshold_np.dtype, + score_threshold_np.shape) + with self.test_scope(): + selected_indices = image_ops.non_max_suppression_padded( + boxes=boxes, + scores=scores, + max_output_size=max_output_size, + iou_threshold=iou_threshold, + score_threshold=score_threshold, + pad_to_max_output_size=True) + inputs_feed = { + boxes: boxes_np, + scores: scores_np, + score_threshold: score_threshold_np, + iou_threshold: iou_threshold_np + } + (indices_tf, _) = sess.run(selected_indices, feed_dict=inputs_feed) + + self.assertEqual(indices_tf.size, max_output_size) + + def testNMS3From6Boxes(self): + # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU. + if self.device in ["XLA_CPU", "XLA_GPU"]: + return + + with compat.forward_compatibility_horizon(2018, 8, 8): + # Three boxes are selected based on IOU. + boxes_data = [[0, 0, 1, 1], [0, 0.1, 1, 1.1], [0, -0.1, 1, 0.9], + [0, 10, 1, 11], [0, 10.1, 1, 11.1], [0, 100, 1, 101]] + boxes_np = np.array(boxes_data, dtype=np.float32) + + scores_data = [0.9, 0.75, 0.6, 0.95, 0.5, 0.3] + scores_np = np.array(scores_data, dtype=np.float32) + + max_output_size = 3 + iou_threshold_np = np.array(0.5, dtype=np.float32) + score_threshold_np = np.array(0.0, dtype=np.float32) + + with self.test_session() as sess: + boxes = array_ops.placeholder(boxes_np.dtype, shape=boxes_np.shape) + scores = array_ops.placeholder(scores_np.dtype, shape=scores_np.shape) + iou_threshold = array_ops.placeholder(iou_threshold_np.dtype, + iou_threshold_np.shape) + score_threshold = array_ops.placeholder(score_threshold_np.dtype, + score_threshold_np.shape) + with self.test_scope(): + selected_indices = image_ops.non_max_suppression_padded( + boxes=boxes, + scores=scores, + max_output_size=max_output_size, + iou_threshold=iou_threshold, + score_threshold=score_threshold, + pad_to_max_output_size=True) + inputs_feed = { + boxes: boxes_np, + scores: scores_np, + score_threshold: score_threshold_np, + iou_threshold: iou_threshold_np + } + (indices_tf, num_valid) = sess.run( + selected_indices, feed_dict=inputs_feed) + + self.assertEqual(indices_tf.size, max_output_size) + self.assertEqual(num_valid, 3) + self.assertAllClose(indices_tf[:num_valid], [3, 0, 5]) + + def testNMS3Then2WithScoreThresh(self): + # Three boxes are selected based on IOU. + # One is filtered out by score threshold. + + # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU. + if self.device in ["XLA_CPU", "XLA_GPU"]: + return + + with compat.forward_compatibility_horizon(2018, 8, 8): + boxes_data = [[0, 0, 1, 1], [0, 0.1, 1, 1.1], [0, -0.1, 1, 0.9], + [0, 10, 1, 11], [0, 10.1, 1, 11.1], [0, 100, 1, 101]] + boxes_np = np.array(boxes_data, dtype=np.float32) + + scores_data = [0.9, 0.75, 0.6, 0.95, 0.5, 0.3] + scores_np = np.array(scores_data, dtype=np.float32) + max_output_size = 3 + iou_threshold_np = np.array(0.5, dtype=np.float32) + score_threshold_np = np.array(0.4, dtype=np.float32) + + with self.test_session() as sess: + boxes = array_ops.placeholder(boxes_np.dtype, shape=boxes_np.shape) + scores = array_ops.placeholder(scores_np.dtype, shape=scores_np.shape) + iou_threshold = array_ops.placeholder(iou_threshold_np.dtype, + iou_threshold_np.shape) + score_threshold = array_ops.placeholder(score_threshold_np.dtype, + score_threshold_np.shape) + with self.test_scope(): + selected_indices = image_ops.non_max_suppression_padded( + boxes=boxes, + scores=scores, + max_output_size=max_output_size, + iou_threshold=iou_threshold, + score_threshold=score_threshold, + pad_to_max_output_size=True) + inputs_feed = { + boxes: boxes_np, + scores: scores_np, + iou_threshold: iou_threshold_np, + score_threshold: score_threshold_np + } + (indices_tf, num_valid) = sess.run( + selected_indices, feed_dict=inputs_feed) + + self.assertEqual(indices_tf.size, max_output_size) + self.assertEqual(num_valid, 2) + self.assertAllClose(indices_tf[:num_valid], [3, 0]) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/compiler/tests/powersign_test.py b/tensorflow/compiler/tests/powersign_test.py new file mode 100644 index 0000000000000000000000000000000000000000..5fa7706d7294f2cffb7d24a56851be02d759335a --- /dev/null +++ b/tensorflow/compiler/tests/powersign_test.py @@ -0,0 +1,142 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for PowerSign.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math +import numpy as np + +from tensorflow.compiler.tests import xla_test +from tensorflow.contrib.opt.python.training import powersign +from tensorflow.contrib.opt.python.training import sign_decay +from tensorflow.python.framework import constant_op +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test + + +def py_linear_decay_fn(decay_steps): + def linear_decay(step): + step = min(step, decay_steps) + return float(decay_steps - step) / decay_steps + return linear_decay + + +def powersign_update_numpy(params, + g_t, + m, + lr, + base=math.e, + beta=0.9, + py_sign_decay_fn=None, + t=None): + m_t = beta * m + (1 - beta) * g_t + if py_sign_decay_fn is None: + sign_decayed = 1.0 + else: + sign_decayed = py_sign_decay_fn(t-1) + multiplier = base ** (sign_decayed * np.sign(g_t) * np.sign(m_t)) + params_t = params - lr * multiplier * g_t + return params_t, m_t + + +class PowerSignTest(xla_test.XLATestCase): + + def _testDense(self, + learning_rate=0.1, + sign_decay_fn=None, + py_sign_decay_fn=None, + base=math.e, + beta=0.9): + for dtype in self.float_types: + with self.test_session(), self.test_scope(): + # Initialize variables for numpy implementation. + m0, m1 = 0.0, 0.0 + var0_np = np.array([1.0, 2.0], dtype=dtype) + grads0_np = np.array([0.1, 0.1], dtype=dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype) + grads1_np = np.array([0.01, 0.01], dtype=dtype) + + var0 = resource_variable_ops.ResourceVariable(var0_np) + var1 = resource_variable_ops.ResourceVariable(var1_np) + global_step = resource_variable_ops.ResourceVariable(0, trainable=False) + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + + opt = powersign.PowerSignOptimizer( + learning_rate=learning_rate, + base=base, + beta=beta, + sign_decay_fn=sign_decay_fn, + ) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + neg_update = opt.apply_gradients(zip([-grads0, -grads1], [var0, var1]), + global_step=global_step) + + variables.global_variables_initializer().run() + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + # Run 7 steps of powersign + # first 4 steps with positive gradient + # last 3 steps with negative gradient (sign(gm) should be -1) + for t in range(1, 8): + if t < 5: + update.run() + else: + neg_update.run() + + var0_np, m0 = powersign_update_numpy( + var0_np, + grads0_np if t < 5 else -grads0_np, + m0, + learning_rate, + base=base, + beta=beta, + py_sign_decay_fn=py_sign_decay_fn, + t=t, + ) + var1_np, m1 = powersign_update_numpy( + var1_np, + grads1_np if t < 5 else -grads1_np, + m1, + learning_rate, + base=base, + beta=beta, + py_sign_decay_fn=py_sign_decay_fn, + t=t, + ) + + # Validate updated params + self.assertAllCloseAccordingToType(var0_np, var0.eval()) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) + + def testDense(self): + decay_steps = 10 + sign_decay_fn = sign_decay.get_linear_decay_fn(decay_steps) + py_sign_decay_fn = py_linear_decay_fn(decay_steps) + self._testDense() + self._testDense(learning_rate=0.1, base=10.0, beta=0.8) + self._testDense( + sign_decay_fn=sign_decay_fn, py_sign_decay_fn=py_sign_decay_fn) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/compiler/tests/qr_op_test.py b/tensorflow/compiler/tests/qr_op_test.py new file mode 100644 index 0000000000000000000000000000000000000000..1b969ee2b3886fca6ec9951d1621ca5af6a673d8 --- /dev/null +++ b/tensorflow/compiler/tests/qr_op_test.py @@ -0,0 +1,115 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for tensorflow.ops.math_ops.matrix_inverse.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import itertools + +from absl.testing import parameterized +import numpy as np + +from tensorflow.compiler.tests import xla_test +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import linalg_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import test + + +class QrOpTest(xla_test.XLATestCase, parameterized.TestCase): + + def AdjustedNorm(self, x): + """Computes the norm of matrices in 'x', adjusted for dimension and type.""" + norm = np.linalg.norm(x, axis=(-2, -1)) + return norm / (max(x.shape[-2:]) * np.finfo(x.dtype).eps) + + def CompareOrthogonal(self, x, y, rank): + # We only compare the first 'rank' orthogonal vectors since the + # remainder form an arbitrary orthonormal basis for the + # (row- or column-) null space, whose exact value depends on + # implementation details. Notice that since we check that the + # matrices of singular vectors are unitary elsewhere, we do + # implicitly test that the trailing vectors of x and y span the + # same space. + x = x[..., 0:rank] + y = y[..., 0:rank] + # Q is only unique up to sign (complex phase factor for complex matrices), + # so we normalize the sign first. + sum_of_ratios = np.sum(np.divide(y, x), -2, keepdims=True) + phases = np.divide(sum_of_ratios, np.abs(sum_of_ratios)) + x *= phases + self.assertTrue(np.all(self.AdjustedNorm(x - y) < 30.0)) + + def CheckApproximation(self, a, q, r): + # Tests that a ~= q*r. + precision = self.AdjustedNorm(a - np.matmul(q, r)) + self.assertTrue(np.all(precision < 10.0)) + + def CheckUnitary(self, x): + # Tests that x[...,:,:]^H * x[...,:,:] is close to the identity. + xx = math_ops.matmul(x, x, adjoint_a=True) + identity = array_ops.matrix_band_part(array_ops.ones_like(xx), 0, 0) + precision = self.AdjustedNorm(xx.eval() - identity.eval()) + self.assertTrue(np.all(precision < 5.0)) + + def _test(self, dtype, shape, full_matrices): + np.random.seed(1) + x_np = np.random.uniform( + low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype) + + with self.test_session() as sess: + x_tf = array_ops.placeholder(dtype) + with self.test_scope(): + q_tf, r_tf = linalg_ops.qr(x_tf, full_matrices=full_matrices) + q_tf_val, r_tf_val = sess.run([q_tf, r_tf], feed_dict={x_tf: x_np}) + + q_dims = q_tf_val.shape + np_q = np.ndarray(q_dims, dtype) + np_q_reshape = np.reshape(np_q, (-1, q_dims[-2], q_dims[-1])) + new_first_dim = np_q_reshape.shape[0] + + x_reshape = np.reshape(x_np, (-1, x_np.shape[-2], x_np.shape[-1])) + for i in range(new_first_dim): + if full_matrices: + np_q_reshape[i, :, :], _ = np.linalg.qr( + x_reshape[i, :, :], mode="complete") + else: + np_q_reshape[i, :, :], _ = np.linalg.qr( + x_reshape[i, :, :], mode="reduced") + np_q = np.reshape(np_q_reshape, q_dims) + self.CompareOrthogonal(np_q, q_tf_val, min(shape[-2:])) + self.CheckApproximation(x_np, q_tf_val, r_tf_val) + self.CheckUnitary(q_tf_val) + + SIZES = [1, 2, 5, 10, 32, 100, 300] + DTYPES = [np.float32] + PARAMS = itertools.product(SIZES, SIZES, DTYPES) + + @parameterized.parameters(*PARAMS) + def testQR(self, rows, cols, dtype): + # TODO(b/111317468): implement full_matrices=False, test other types. + for full_matrices in [True]: + # Only tests the (3, 2) case for small numbers of rows/columns. + for batch_dims in [(), (3,)] + [(3, 2)] * (max(rows, cols) < 10): + self._test(dtype, batch_dims + (rows, cols), full_matrices) + + def testLarge2000x2000(self): + self._test(np.float32, (2000, 2000), full_matrices=True) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/compiler/tests/random_ops_test.py b/tensorflow/compiler/tests/random_ops_test.py index b880b2a3fea3ee72af96396bc2d61b2887e6e9b8..14c5e7a975e478ca6ceed37c28339b40612801c8 100644 --- a/tensorflow/compiler/tests/random_ops_test.py +++ b/tensorflow/compiler/tests/random_ops_test.py @@ -140,10 +140,10 @@ class RandomOpsTest(xla_test.XLATestCase): def testShuffle1d(self): with self.test_session() as sess: with self.test_scope(): - x = math_ops.range(20) + x = math_ops.range(1 << 16) shuffle = random_ops.random_shuffle(x) result = sess.run(shuffle) - expected = range(20) + expected = range(1 << 16) # Compare sets to avoid randomness behavior changes but make sure still # have all the values. self.assertAllEqual(set(result), set(expected)) diff --git a/tensorflow/compiler/tests/rmsprop_test.py b/tensorflow/compiler/tests/rmsprop_test.py index 9489fded32a7b6aada0543721a8bfe5f2d74575e..ff8bbac911abe73f946464663984ff1626302882 100644 --- a/tensorflow/compiler/tests/rmsprop_test.py +++ b/tensorflow/compiler/tests/rmsprop_test.py @@ -30,31 +30,102 @@ from tensorflow.python.training import rmsprop class RmspropTest(xla_test.XLATestCase): + def _rmsprop_update_numpy(self, + var, + g, + mg, + rms, + mom, + lr, + decay=0.9, + momentum=0.0, + epsilon=1e-10, + centered=False): + rms_t = rms * decay + (1 - decay) * g * g + denom_t = rms_t + epsilon + if centered: + mg_t = mg * decay + (1 - decay) * g + denom_t -= mg_t * mg_t + else: + mg_t = mg + mom_t = momentum * mom + lr * g / np.sqrt(denom_t, dtype=denom_t.dtype) + var_t = var - mom_t + return var_t, mg_t, rms_t, mom_t + def testBasic(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): - var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) - var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - rms_opt = rmsprop.RMSPropOptimizer(3.0) - rms_update = rms_opt.apply_gradients( - zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - - # Fetch params to validate initial values - self.assertAllClose([1.0, 2.0], var0.eval()) - self.assertAllClose([3.0, 4.0], var1.eval()) - - # Run 3 steps of RMSProp - for _ in range(3): - rms_update.run() - - # Validate updated params - self.assertAllCloseAccordingToType( - np.array([2.91705132e-04, 1.00029182e+00]), var0.eval()) - self.assertAllCloseAccordingToType( - np.array([2.89990854, 3.89990854]), var1.eval()) + for centered in [False, True]: + with self.test_session(), self.test_scope(): + # Initialize variables for numpy implementation. + var0_np = np.array([1.0, 2.0], dtype=dtype) + grads0_np = np.array([0.1, 0.1], dtype=dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype) + grads1_np = np.array([0.01, 0.01], dtype=dtype) + mg0_np = np.array([0.0, 0.0], dtype=dtype) + mg1_np = np.array([0.0, 0.0], dtype=dtype) + rms0_np = np.array([1.0, 1.0], dtype=dtype) + rms1_np = np.array([1.0, 1.0], dtype=dtype) + mom0_np = np.array([0.0, 0.0], dtype=dtype) + mom1_np = np.array([0.0, 0.0], dtype=dtype) + + var0 = resource_variable_ops.ResourceVariable(var0_np) + var1 = resource_variable_ops.ResourceVariable(var1_np) + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + learning_rate = 3.0 + rms_opt = rmsprop.RMSPropOptimizer(learning_rate, centered=centered) + rms_update = rms_opt.apply_gradients( + zip([grads0, grads1], [var0, var1])) + variables.global_variables_initializer().run() + + mg0 = rms_opt.get_slot(var0, "mg") + self.assertEqual(mg0 is not None, centered) + mg1 = rms_opt.get_slot(var1, "mg") + self.assertEqual(mg1 is not None, centered) + rms0 = rms_opt.get_slot(var0, "rms") + self.assertTrue(rms0 is not None) + rms1 = rms_opt.get_slot(var1, "rms") + self.assertTrue(rms1 is not None) + mom0 = rms_opt.get_slot(var0, "momentum") + self.assertTrue(mom0 is not None) + mom1 = rms_opt.get_slot(var1, "momentum") + self.assertTrue(mom1 is not None) + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + # Run 3 steps of RMSProp + for _ in range(3): + rms_update.run() + + var0_np, mg0_np, rms0_np, mom0_np = self._rmsprop_update_numpy( + var0_np, + grads0_np, + mg0_np, + rms0_np, + mom0_np, + learning_rate, + centered=centered) + var1_np, mg1_np, rms1_np, mom1_np = self._rmsprop_update_numpy( + var1_np, + grads1_np, + mg1_np, + rms1_np, + mom1_np, + learning_rate, + centered=centered) + + # Validate updated params + if centered: + self.assertAllCloseAccordingToType(mg0_np, mg0.eval()) + self.assertAllCloseAccordingToType(mg1_np, mg1.eval()) + self.assertAllCloseAccordingToType(rms0_np, rms0.eval()) + self.assertAllCloseAccordingToType(rms1_np, rms1.eval()) + self.assertAllCloseAccordingToType(mom0_np, mom0.eval()) + self.assertAllCloseAccordingToType(mom1_np, mom1.eval()) + self.assertAllCloseAccordingToType(var0_np, var0.eval()) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) if __name__ == "__main__": diff --git a/tensorflow/compiler/tests/sort_ops_test.py b/tensorflow/compiler/tests/sort_ops_test.py index 9e2ef964a1ff00a861a874135b7dfa1358a7020e..7ff01be3cb4848d6bb85b8ab96b3ee1db6889791 100644 --- a/tensorflow/compiler/tests/sort_ops_test.py +++ b/tensorflow/compiler/tests/sort_ops_test.py @@ -88,6 +88,38 @@ class XlaSortOpTest(xla_test.XLATestCase): topk, [x.astype(dtype)], expected=[x[indices].astype(dtype), indices]) + def testTopK2D(self): + # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU. + if self.device in ["XLA_CPU", "XLA_GPU"]: + return + + supported_types = set( + [dtypes.bfloat16.as_numpy_dtype, np.float32, np.int32, np.uint32]) + for dtype in supported_types.intersection(self.numeric_types): + # Use small input size for bfloat16. Otherwise, we'll get duplicate values + # after conversion to bfloat16, so the possible resulting index array is + # no longer unique. + if dtype == dtypes.bfloat16.as_numpy_dtype: + array_size = 10 + k_options = [0, 1, 2, 10] + else: + array_size = 200 * 1000 + k_options = [0, 1, 2, 10, 20, 100, 1000, 200 * 1000] + batch = 16 + for x in [np.arange(batch * array_size)]: + np.random.shuffle(x) + x = np.reshape(x, [batch, array_size]) + for k in k_options: + indices = x.argsort(axis=1)[::, -1:-k - 1:-1] + expected = np.sort(x, axis=1)[::, -1:-k - 1:-1] + + def topk(v, k=k): + return nn_ops.top_k(v, k=k, sorted=True) + + self._assertOpOutputMatchesExpected( + topk, [x.astype(dtype)], + expected=[expected.astype(dtype), indices]) + def testTopKZeros(self): """Tests that positive and negative zeros sort correctly.""" # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU. diff --git a/tensorflow/compiler/tests/unary_ops_test.py b/tensorflow/compiler/tests/unary_ops_test.py index 6a7011aea6cc3f942fecf27a640b998bfc10c0de..5f25ff9002964e94db384d7b01f07cfc4f8938b1 100644 --- a/tensorflow/compiler/tests/unary_ops_test.py +++ b/tensorflow/compiler/tests/unary_ops_test.py @@ -382,6 +382,62 @@ class UnaryOpsTest(xla_test.XLATestCase): expected=np.array( [[True, False, True], [False, True, True]], dtype=np.bool)) + self._assertOpOutputMatchesExpected( + math_ops.lgamma, + np.array( + [[1, 2, 3], [4, 5, 6], [1 / 2, 3 / 2, 5 / 2], + [-3 / 2, -7 / 2, -11 / 2]], + dtype=dtype), + expected=np.array( + [ + [0, 0, np.log(2.0)], + [np.log(6.0), np.log(24.0), + np.log(120)], + [ + np.log(np.pi) / 2, + np.log(np.pi) / 2 - np.log(2), + np.log(np.pi) / 2 - np.log(4) + np.log(3) + ], + [ + np.log(np.pi) / 2 - np.log(3) + np.log(4), + np.log(np.pi) / 2 - np.log(105) + np.log(16), + np.log(np.pi) / 2 - np.log(10395) + np.log(64), + ], + ], + dtype=dtype)) + + self._assertOpOutputMatchesExpected( + math_ops.digamma, + np.array( + [[1.0, 0.5, 1 / 3.0], [0.25, 1 / 6.0, 0.125], [2.0, 3.0, 4.0], + [6.0, 8.0, 9.0]], + dtype=dtype), + expected=np.array( + [ + [ + -np.euler_gamma, -2 * np.log(2) - np.euler_gamma, + -np.pi / 2 / np.sqrt(3) - 3 * np.log(3) / 2 - + np.euler_gamma + ], + [ + -np.pi / 2 - 3 * np.log(2) - np.euler_gamma, + -np.pi * np.sqrt(3) / 2 - 2 * np.log(2) - + 3 * np.log(3) / 2 - np.euler_gamma, + -np.pi / 2 - 4 * np.log(2) - + (np.pi + np.log(2 + np.sqrt(2)) - np.log(2 - np.sqrt(2))) + / np.sqrt(2) - np.euler_gamma + ], + [ + 1 - np.euler_gamma, 1.5 - np.euler_gamma, + 11 / 6.0 - np.euler_gamma + ], + [ + 137 / 60.0 - np.euler_gamma, 363 / 140.0 - np.euler_gamma, + 761 / 280.0 - np.euler_gamma + ], + ], + dtype=dtype)) + def quantize_and_dequantize_v2(x): return array_ops.quantize_and_dequantize_v2( x, -127, 127, signed_input=True, num_bits=8) diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD index 40e32f2e757c96de86414b5699b67935f4d92776..338943201bb11a66370d82f301736a0d8d0fc7ed 100644 --- a/tensorflow/compiler/tf2xla/BUILD +++ b/tensorflow/compiler/tf2xla/BUILD @@ -81,7 +81,7 @@ cc_library( "//tensorflow/compiler/tf2xla/kernels:xla_cpu_only_ops", "//tensorflow/compiler/tf2xla/kernels:xla_ops", "//tensorflow/compiler/xla/client", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", @@ -119,6 +119,7 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:cpu_plugin", "//tensorflow/compiler/xla/service/cpu:cpu_executable", "//tensorflow/core:lib", @@ -162,7 +163,7 @@ cc_library( ":sharding_util", ":tf2xla_util", "//tensorflow/compiler/tf2xla/lib:util", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -170,11 +171,11 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/lib:numeric", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", @@ -202,7 +203,7 @@ cc_library( ], visibility = [":friends"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/core:core_cpu_internal", @@ -285,10 +286,12 @@ tf_cc_test( deps = [ ":tf2xla", ":tf2xla_proto", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:cpu_plugin", "//tensorflow/core:framework", "//tensorflow/core:lib", @@ -327,7 +330,7 @@ tf_cc_test( "//tensorflow/cc:ops", "//tensorflow/cc:resource_variable_ops", "//tensorflow/compiler/tf2xla/kernels:xla_ops", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla/client:client_library", @@ -364,6 +367,7 @@ tf_cc_test( ], deps = [ ":common", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/core:framework", "//tensorflow/core:test", diff --git a/tensorflow/compiler/tf2xla/dump_graph.cc b/tensorflow/compiler/tf2xla/dump_graph.cc index 03603ee9baefd1d20d220faf63c9c1c427ebdf31..24616c01c7e54b2e8662457ca6af23a0bc563e08 100644 --- a/tensorflow/compiler/tf2xla/dump_graph.cc +++ b/tensorflow/compiler/tf2xla/dump_graph.cc @@ -33,7 +33,7 @@ struct NameCounts { std::unordered_map counts; }; -string MakeUniquePath(string name) { +string MakeUniqueFilename(string name) { static NameCounts& instance = *new NameCounts; // Remove illegal characters from `name`. @@ -50,26 +50,41 @@ string MakeUniquePath(string name) { count = instance.counts[name]++; } - legacy_flags::DumpGraphFlags* flags = legacy_flags::GetDumpGraphFlags(); - string path = strings::StrCat(flags->tf_dump_graph_prefix, "/", name); + string filename = name; if (count > 0) { - strings::StrAppend(&path, "_", count); + strings::StrAppend(&filename, "_", count); } - strings::StrAppend(&path, ".pbtxt"); - return path; + strings::StrAppend(&filename, ".pbtxt"); + return filename; +} + +string WriteTextProtoToUniqueFile( + Env* env, const string& name, const char* proto_type, + const ::tensorflow::protobuf::Message& proto) { + const string& dirname = + legacy_flags::GetDumpGraphFlags()->tf_dump_graph_prefix; + Status status = env->RecursivelyCreateDir(dirname); + if (!status.ok()) { + LOG(WARNING) << "Failed to create " << dirname << " for dumping " + << proto_type << ": " << status; + return "(unavailable)"; + } + string filepath = strings::StrCat(dirname, "/", MakeUniqueFilename(name)); + status = WriteTextProto(Env::Default(), filepath, proto); + if (!status.ok()) { + LOG(WARNING) << "Failed to dump " << proto_type << " to file: " << filepath + << " : " << status; + return "(unavailable)"; + } + LOG(INFO) << "Dumped " << proto_type << " to " << filepath; + return filepath; } } // anonymous namespace string DumpGraphDefToFile(const string& name, GraphDef const& graph_def) { - string path = MakeUniquePath(name); - Status status = WriteTextProto(Env::Default(), path, graph_def); - if (!status.ok()) { - VLOG(1) << "Failed to dump GraphDef to file: " << path << " : " << status; - path.clear(); - path = "(unavailable)"; - } - return path; + return WriteTextProtoToUniqueFile(Env::Default(), name, "GraphDef", + graph_def); } string DumpGraphToFile(const string& name, Graph const& graph, @@ -83,15 +98,7 @@ string DumpGraphToFile(const string& name, Graph const& graph, } string DumpFunctionDefToFile(const string& name, FunctionDef const& fdef) { - string path = MakeUniquePath(name); - Status status = WriteTextProto(Env::Default(), path, fdef); - if (!status.ok()) { - VLOG(1) << "Failed to dump FunctionDef to file: " << path << " : " - << status; - path.clear(); - path = "(unavailable)"; - } - return path; + return WriteTextProtoToUniqueFile(Env::Default(), name, "FunctionDef", fdef); } } // namespace dump_graph diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc index 6cc95149a16a59fce8486c5d103ad09e3e262765..0904778f97c95628c81054cd4bc2ff32ff440a33 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc @@ -177,8 +177,8 @@ Status CheckNoCycleContains(const Node* node, const int num_nodes) { visited[current_node->id()] = true; for (const Edge* out : current_node->out_edges()) { if (out->dst() == node) { - return errors::Internal("Detect a cycle: Node \"", node->name(), "\"(", - node->def().op(), ") feeds into itself."); + return errors::Internal("Detected a cycle: ", FormatNodeForError(*node), + "(", node->def().op(), ") feeds into itself."); } else if (!visited[out->dst()->id()]) { ready.push_back(out->dst()); } @@ -324,7 +324,7 @@ Status AddMissingFunctionDef(const FunctionDef& fdef, if (library->Find(node.op())) { continue; } - // The function refered by 'SymbolicGradient' node is specified in its + // The function referred by 'SymbolicGradient' node is specified in its // attribute 'f'. if (node.op() == FunctionLibraryDefinition::kGradientOp) { const AttrValue* attr = @@ -437,22 +437,24 @@ Status FunctionalizeLoop(const FunctionLibraryDefinition* lookup_library, continue; } if (enter_merge != nullptr) { - return errors::Internal( - "Enter node for loop-varying argument ", arg.enter->name(), - " has multiple successors: ", enter_merge->dst()->name(), " and ", - e->dst()->name()); + return errors::Internal("Enter node for loop-varying argument ", + FormatNodeForError(*arg.enter), + " has multiple successors: ", + FormatNodeForError(*enter_merge->dst()), + " and ", FormatNodeForError(*e->dst())); } enter_merge = e; } if (enter_merge == nullptr) { return errors::Internal("Enter node for loop-varying argument ", - arg.enter->name(), " has zero successors"); + FormatNodeForError(*arg.enter), + " has zero successors"); } arg.merge = enter_merge->dst(); if (!IsMerge(arg.merge)) { return errors::InvalidArgument( "Successor of Enter node for loop-varying argument ", - arg.merge->name(), + FormatNodeForError(*arg.merge), " is not a Merge node; got: ", arg.merge->type_string()); } @@ -462,7 +464,7 @@ Status FunctionalizeLoop(const FunctionLibraryDefinition* lookup_library, return errors::InvalidArgument( "Unexpected number of inputs to Merge node for loop-varying " "argument ", - arg.merge->name(), "; expected 2, got ", + FormatNodeForError(*arg.merge), "; expected 2, got ", arg.merge->input_types().size()); } TF_RETURN_IF_ERROR(arg.merge->input_node(1 - enter_merge->dst_input(), @@ -470,7 +472,7 @@ Status FunctionalizeLoop(const FunctionLibraryDefinition* lookup_library, if (!IsNextIteration(arg.next_iteration)) { return errors::InvalidArgument( "Expected NextIteration node as input to Merge node; got node ", - arg.next_iteration->name(), " with kind ", + FormatNodeForError(*arg.next_iteration), " with kind ", arg.next_iteration->type_string()); } @@ -481,14 +483,14 @@ Status FunctionalizeLoop(const FunctionLibraryDefinition* lookup_library, switches.find(edge->dst()) != switches.end()) { if (arg.switch_node != nullptr) { return errors::InvalidArgument("Duplicate Switch successors to ", - arg.merge->name()); + FormatNodeForError(*arg.merge)); } arg.switch_node = edge->dst(); } } if (arg.switch_node == nullptr) { return errors::InvalidArgument("Missing Switch successor to ", - arg.merge->name()); + FormatNodeForError(*arg.merge)); } // Update the device on the Identity outputs of the switch to match their @@ -516,14 +518,15 @@ Status FunctionalizeLoop(const FunctionLibraryDefinition* lookup_library, possible_exit.pop_front(); if (IsExit(edge->dst())) { if (arg.exit != nullptr) { - return errors::InvalidArgument("Duplicate Exit successors to ", - arg.switch_node->name()); + return errors::InvalidArgument( + "Duplicate Exit successors to ", + FormatNodeForError(*arg.switch_node)); } arg.exit = edge->dst(); } else { if (!IsIdentity(edge->dst())) { return errors::Unimplemented("General graph between switch (", - arg.switch_node->name(), + FormatNodeForError(*arg.switch_node), ") and exit node of frame ", frame->name, " not supported yet."); } @@ -1470,7 +1473,7 @@ Status FunctionalizeControlFlow(const FunctionLibraryDefinition* lookup_library, if (!unreachable_nodes.empty()) { return errors::InvalidArgument( "The following nodes are unreachable from the source in the graph: ", - tensorflow::str_util::Join(unreachable_nodes, ", ")); + errors::FormatNodeNamesForError(unreachable_nodes)); } // Builds Frames, indexed by name. diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc index aae2f8ee5acd6249f8b6002d94c877f18064f936..ccf249b35d66861888ad5e5e904b5f63b8ac50a1 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc @@ -1064,7 +1064,10 @@ TEST(FunctionalizeControlFlow, Cycle) { // less -> XlaIf <--> identity. Status status = FunctionalizeControlFlow(graph.get(), &library); EXPECT_FALSE(status.ok()); - EXPECT_TRUE(str_util::StrContains(status.error_message(), "Detect a cycle")) + EXPECT_TRUE(str_util::StrContains(status.error_message(), "Detected a cycle")) + << status.error_message(); + EXPECT_TRUE( + str_util::StrContains(status.error_message(), "{{node cond/Less_5_If}}")) << status.error_message(); } diff --git a/tensorflow/compiler/tf2xla/graph_compiler.cc b/tensorflow/compiler/tf2xla/graph_compiler.cc index 4900af6df17f360630abb1e64b7f144ccd4a0289..e4fdf0a6186eb69a2e3413838c91616b992ef2d6 100644 --- a/tensorflow/compiler/tf2xla/graph_compiler.cc +++ b/tensorflow/compiler/tf2xla/graph_compiler.cc @@ -29,7 +29,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/executor.h" #include "tensorflow/core/common_runtime/function.h" @@ -161,9 +161,8 @@ Status GraphCompiler::Compile() { outputs.resize(n->num_outputs()); for (int o = 0; o < n->num_outputs(); ++o) { outputs[o] = op_context.release_output(o); - if (*op_context.is_output_dead() || outputs[o].tensor == nullptr) { + if (outputs[o].tensor == nullptr) { return errors::Internal("Missing xla_context ", o, "-th output from ", - (*op_context.is_output_dead() ? "(dead)" : ""), SummarizeNode(*n)); } } diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index a8eb7d942dfbabff3c53e2b5225c1018b01eb315..0609e223381550645d1a41ba75e4cd57f893ee95 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -58,6 +58,7 @@ tf_kernel_library( "pack_op.cc", "pad_op.cc", "pooling_ops.cc", + "qr_op.cc", "quantize_and_dequantize_op.cc", "random_ops.cc", "reduce_window_op.cc", @@ -107,6 +108,7 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/lib:batch_dot", "//tensorflow/compiler/tf2xla/lib:cholesky", + "//tensorflow/compiler/tf2xla/lib:qr", "//tensorflow/compiler/tf2xla/lib:random", "//tensorflow/compiler/tf2xla/lib:scatter", "//tensorflow/compiler/tf2xla/lib:triangular_solve", @@ -114,17 +116,21 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla/lib:while_loop", "//tensorflow/compiler/tf2xla/ops:xla_ops", "//tensorflow/compiler/xla:array4d", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/lib:math", "//tensorflow/compiler/xla/client/lib:numeric", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client/lib:prng", + "//tensorflow/compiler/xla/client/lib:sorting", "//tensorflow/core:framework", "//tensorflow/core:image_ops_op_lib", "//tensorflow/core:lib", @@ -159,8 +165,9 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla:common", "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/ops:xla_ops", - "//tensorflow/compiler/xla:literal_util", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", @@ -175,8 +182,8 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla:common", "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/ops:xla_ops", - "//tensorflow/compiler/xla:literal_util", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", @@ -210,10 +217,11 @@ tf_kernel_library( ":index_ops_kernel_argmax_float_2d", "//tensorflow/compiler/tf2xla:common", "//tensorflow/compiler/tf2xla:xla_compiler", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla/client:client_library", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core/kernels:argmax_op", diff --git a/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc b/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc index e33532828040123243f839ab1aa655b4bbc72520..41a453da80dec6b6f57a4d222e2c33ef6b786a10 100644 --- a/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { namespace { diff --git a/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc b/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc index c4af79281d2162b1dbfb0a7881720892f4bc49d2..b3ad0aea84eef601de08909f760699b8700d28f4 100644 --- a/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/util/tensor_format.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc index 26130fd9e7fce75c6d2a5a53cfc85842cf762b35..48f2a005ab16651fe29d0f6f9d881f95693da461 100644 --- a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { namespace { diff --git a/tensorflow/compiler/tf2xla/kernels/bcast_ops.cc b/tensorflow/compiler/tf2xla/kernels/bcast_ops.cc index ee2c920453c3bbaef2c145df743fddf999167c39..ba3b1c9dab79a387c48e8e25e4804917f328f8a0 100644 --- a/tensorflow/compiler/tf2xla/kernels/bcast_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/bcast_ops.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/platform/types.h" #include "tensorflow/core/util/bcast.h" diff --git a/tensorflow/compiler/tf2xla/kernels/bias_ops.cc b/tensorflow/compiler/tf2xla/kernels/bias_ops.cc index e9b2c0b16d39cb3b747c0316621fb01de709b12e..41f540506ba41fbe7f91393e7b8e26a89e72ef0a 100644 --- a/tensorflow/compiler/tf2xla/kernels/bias_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/bias_ops.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/util/tensor_format.h" diff --git a/tensorflow/compiler/tf2xla/kernels/binary_ops.cc b/tensorflow/compiler/tf2xla/kernels/binary_ops.cc index d6d4ae89376b67c14af8ef4f3a608fcc83b6fb59..2c328102e0bd84709707f102272691b6aec9a577 100644 --- a/tensorflow/compiler/tf2xla/kernels/binary_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/binary_ops.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/bucketize_op.cc b/tensorflow/compiler/tf2xla/kernels/bucketize_op.cc index efbdb76eaaf78904fe783a018940b1b096ec39bd..5078f8662bd397eaa51274ec816c130b8ced92cc 100644 --- a/tensorflow/compiler/tf2xla/kernels/bucketize_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/bucketize_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/cast_op.cc b/tensorflow/compiler/tf2xla/kernels/cast_op.cc index 62eebf762b3e063da8ec456cc4726d3cc9b77d1d..8cc2479dd555380da7500abe6b2aca380110333b 100644 --- a/tensorflow/compiler/tf2xla/kernels/cast_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/cast_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/core/framework/kernel_def_builder.h" diff --git a/tensorflow/compiler/tf2xla/kernels/categorical_op.cc b/tensorflow/compiler/tf2xla/kernels/categorical_op.cc index 1784e712b56145bbdff5f1daa2e031b65d0774b6..e7fef77edcba0ea5a521956a704225ac4f7fcb22 100644 --- a/tensorflow/compiler/tf2xla/kernels/categorical_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/categorical_op.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" diff --git a/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc b/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc index 4e6d33304c4ae08a0fd1e0a8373267a527087528..547fe48046e8c934e3bc14d02c8448e107c1a406 100644 --- a/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/tensor_shape.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/concat_op.cc b/tensorflow/compiler/tf2xla/kernels/concat_op.cc index e3a32a5c0e2f93237c8c7ebeea3668b5d1ab6c23..f4106051043859a6786705009d76b02a64cd3ff1 100644 --- a/tensorflow/compiler/tf2xla/kernels/concat_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/concat_op.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/const_op.cc b/tensorflow/compiler/tf2xla/kernels/const_op.cc index f4360d8c3f6fc4007c31fdcfd7f7634de15c76d4..da8cf3fc6fa694f592280f8c249d317827d9cd09 100644 --- a/tensorflow/compiler/tf2xla/kernels/const_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/const_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/tensor.pb.h" diff --git a/tensorflow/compiler/tf2xla/kernels/conv_ops.cc b/tensorflow/compiler/tf2xla/kernels/conv_ops.cc index 48ac4867edcef97be001a24f42f6a35225d466c9..5da7972397b32fb4a2f216913e065c04131a3773 100644 --- a/tensorflow/compiler/tf2xla/kernels/conv_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/conv_ops.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/cross_op.cc b/tensorflow/compiler/tf2xla/kernels/cross_op.cc index 500a564f3f0489a42dbc9d5b70ae7708a7a43973..db579a5b35d69deb3dca578e31c1b54fada76342 100644 --- a/tensorflow/compiler/tf2xla/kernels/cross_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/cross_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { namespace { diff --git a/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc b/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc index 9ff3e0222831cb4339943966810eeae451e47a2c..ef1015552d181a183d412f9c269dd5ec608b388f 100644 --- a/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/util/bcast.h" diff --git a/tensorflow/compiler/tf2xla/kernels/cwise_ops.h b/tensorflow/compiler/tf2xla/kernels/cwise_ops.h index 4f92dbc8740b697322424058530b8477c35d809a..a5b870f8dbf70bcee331992345d63fd5d986bdca 100644 --- a/tensorflow/compiler/tf2xla/kernels/cwise_ops.h +++ b/tensorflow/compiler/tf2xla/kernels/cwise_ops.h @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/util/bcast.h" diff --git a/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc index f3149200250935629a6e4bf67bff0c048135ce3e..12b0e38288e8f222ed506a75ec2575f27141c859 100644 --- a/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/util/tensor_format.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/diag_op.cc b/tensorflow/compiler/tf2xla/kernels/diag_op.cc index 6dec414c53bee6b0102e229c86cfafb4072a35f0..ed44ad218b6dc073583ec339da082b6881ad672d 100644 --- a/tensorflow/compiler/tf2xla/kernels/diag_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/diag_op.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/framework/op_kernel.h" @@ -123,8 +123,6 @@ class DiagPartOp : public XlaOpKernel { explicit DiagPartOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::XlaBuilder* builder = ctx->builder(); - const TensorShape input_shape = ctx->InputShape(0); auto dims = input_shape.dim_sizes(); @@ -150,37 +148,13 @@ class DiagPartOp : public XlaOpKernel { new_dims.push_back(dims[i]); } - xla::XlaOp diag = ctx->Input(0); - - // TODO(b/30878775): use Slice with strides when supported, in place of - // the Pad -> Reshape -> Slice. - - // Picture: - // [[1, 0, 0, 0] pad and reshape to [[1, 0, 0, 0, 0], - // [0, 2, 0, 0] =================> [2, 0, 0, 0, 0], - // [0, 0, 3, 0] [3, 0, 0, 0, 0], - // [0, 0, 0, 4]] [4, 0, 0, 0, 0]] - // and then slice out the first column. - - // Flattens the input to 1D. - int64 size = input_shape.num_elements(); - diag = xla::Reshape(diag, {size}); - - // Adds padding after the last element of 'new_size'. - xla::PaddingConfig config; - auto* dim = config.add_dimensions(); - dim->set_edge_padding_high(new_size); - auto zero = XlaHelpers::Zero(builder, input_type(0)); - diag = xla::Pad(diag, zero, config); - - // Reshapes so the diagonal is now in the first column. - diag = xla::Reshape(diag, {new_size, new_size + 1}); + xla::XlaOp input = ctx->Input(0); - // Slices out the first column and reshapes to the final shape. - diag = xla::Slice(diag, {0, 0}, {new_size, 1}, {1, 1}); - diag = xla::Reshape(diag, new_dims); + xla::XlaOp output = xla::Reshape( + xla::GetMatrixDiagonal(xla::Reshape(input, {new_size, new_size})), + new_dims); - ctx->SetOutput(0, diag); + ctx->SetOutput(0, output); } }; @@ -220,8 +194,6 @@ class MatrixDiagPartOp : public XlaOpKernel { explicit MatrixDiagPartOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::XlaBuilder* builder = ctx->builder(); - const TensorShape input_shape = ctx->InputShape(0); auto dims = input_shape.dim_sizes(); @@ -229,71 +201,8 @@ class MatrixDiagPartOp : public XlaOpKernel { errors::InvalidArgument("Expected 2 <= dims, got shape ", input_shape.DebugString())); - xla::XlaOp diag = ctx->Input(0); - - int last_dim = dims.size() - 1; - int64 last_dim_size = dims[last_dim]; - - // The smaller of the last two dimension sizes. - int64 smaller_dim_size = std::min(dims[last_dim - 1], dims[last_dim]); - - // TODO(b/30878775): use Slice with strides when supported, in place of - // the Pad -> Reshape -> Slice. - - // Picture: for each 2D matrix in the tensor's last two dimensions: - // [[1, 0, 0, 0] pad and reshape to [[1, 0, 0, 0, 0], - // [0, 2, 0, 0] =================> [2, 0, 0, 0, 0], - // [0, 0, 3, 0]] [3, 0, 0, 0, 0], - // and then slice out the first column. - // - // Another example, with tall and narrow input. - // [[1, 0] pad and reshape to [[1, 0, 0], - // [0, 2] =================> [2, 0, 0]] - // [0, 0] - // [0, 0]] - - // Collapses the last two dimensions. - std::vector flattened_dims(dims.begin(), dims.end() - 1); - flattened_dims.back() *= dims.back(); - diag = xla::Reshape(diag, flattened_dims); - - // Slices or pads the last dimension to 'target_size'. - int64 actual_size = flattened_dims.back(); - int64 target_size = smaller_dim_size * (last_dim_size + 1); - if (actual_size < target_size) { - xla::PaddingConfig config = - xla::MakeNoPaddingConfig(flattened_dims.size()); - auto* dim = config.mutable_dimensions(flattened_dims.size() - 1); - dim->set_edge_padding_high(target_size - actual_size); - auto zero = XlaHelpers::Zero(builder, input_type(0)); - diag = xla::Pad(diag, zero, config); - } else if (actual_size > target_size) { - std::vector start(flattened_dims.size(), 0); - std::vector limits(flattened_dims.begin(), flattened_dims.end()); - std::vector strides(flattened_dims.size(), 1); - limits[flattened_dims.size() - 1] = target_size; - diag = xla::Slice(diag, start, limits, strides); - } - - // Reshape so the target values are in the first position of the last - // dimension. - std::vector unflattened_dims(dims.begin(), dims.end()); - dims[last_dim - 1] = smaller_dim_size; - dims[last_dim] = last_dim_size + 1; - diag = xla::Reshape(diag, dims); - - // Slices out the first column and reshapes to the final shape. - std::vector start(dims.size(), 0); - std::vector limits(dims.begin(), dims.end()); - std::vector strides(dims.size(), 1); - limits[last_dim] = 1; - diag = xla::Slice(diag, start, limits, strides); - - // Collapses away the last dimension. - dims.pop_back(); - diag = xla::Reshape(diag, dims); - - ctx->SetOutput(0, diag); + xla::XlaOp input = ctx->Input(0); + ctx->SetOutput(0, xla::GetMatrixDiagonal(input)); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc b/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc index 3b86ea34c9e7d943eb9c7de222e0a2be049ebc68..a3389d5b905bf3ee15744ab4fcee193d312e2ae0 100644 --- a/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/compiler/tf2xla/type_util.h" diff --git a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc index 958231505b50431b9bb267b0a3cc5ed56e3aeb21..cb73053666d4c32bc0a2ef19b174aee1a29f101e 100644 --- a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/elu_op.cc b/tensorflow/compiler/tf2xla/kernels/elu_op.cc index 2c76bcee2593b820eafe09af3a52736ed8a92f86..5fdb1d972c55efb876972d3f472b53a1f7cde1c2 100644 --- a/tensorflow/compiler/tf2xla/kernels/elu_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/elu_op.cc @@ -18,8 +18,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/kernels/no_op.h" diff --git a/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc b/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc index 65d42a302fca48c7b5f88813f80e975823f63ddf..c68b0bfd7961892294c2931e5c4c44de534a7740 100644 --- a/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/util/tensor_format.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc b/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc index 2fd1a34741e1c7235397f9a69dd8444b4679fa22..cdba6680dee3fade5bdf0c453ed672b653072b0d 100644 --- a/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/platform/macros.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/fft_ops.cc b/tensorflow/compiler/tf2xla/kernels/fft_ops.cc index b2b00e51e3b00fa93c258af489cf0f4a3e6e764b..80bcef966360ec9a1ca63a02741108ce41b31846 100644 --- a/tensorflow/compiler/tf2xla/kernels/fft_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/fft_ops.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/fill_op.cc b/tensorflow/compiler/tf2xla/kernels/fill_op.cc index 95faa1d058f4c0d3fa802b157c6daba1e1adaf41..54b21a278229024e3e54e9135548be6b69b077e1 100644 --- a/tensorflow/compiler/tf2xla/kernels/fill_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/fill_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/gather_op.cc b/tensorflow/compiler/tf2xla/kernels/gather_op.cc index 5f041be5df226ed996b21844c0cf92b6dfac005c..35de96e0aab847fa39ef26d5f3052c392062fd7d 100644 --- a/tensorflow/compiler/tf2xla/kernels/gather_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/gather_op.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h b/tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h index d898e43b858bac706d524c7c271f48b1b5fa258f..92346283c31dfe1d638526ac4b26ef762cd7fd14 100644 --- a/tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h +++ b/tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/util/bcast.h" diff --git a/tensorflow/compiler/tf2xla/kernels/if_op.cc b/tensorflow/compiler/tf2xla/kernels/if_op.cc index f5fcf3cacdbff8297bc42fcb0cf79c2bc83a4e11..ceb2af756c2d2020c7449086b957c9fbc1cc2979 100644 --- a/tensorflow/compiler/tf2xla/kernels/if_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/if_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { @@ -246,6 +246,7 @@ void XlaIfOp::Compile(XlaOpKernelContext* ctx) { VLOG(1) << "Done building If"; } +REGISTER_XLA_OP(Name("If").AllowResourceTypes(), XlaIfOp); REGISTER_XLA_OP(Name("XlaIf").AllowResourceTypes(), XlaIfOp); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/image_ops.cc b/tensorflow/compiler/tf2xla/kernels/image_ops.cc index cb4caf7bcb4caaa1bf7e0e79e52bb966a8838db3..33a73fe5fdf403e513be085dd7bcea3255277b4a 100644 --- a/tensorflow/compiler/tf2xla/kernels/image_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/image_ops.cc @@ -17,7 +17,12 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/lib/sorting.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/core/framework/tensor_shape.h" namespace tensorflow { namespace { @@ -311,5 +316,150 @@ class AdjustHueOp : public XlaOpKernel { }; REGISTER_XLA_OP(Name("AdjustHue"), AdjustHueOp); +class NonMaxSuppressionOp : public XlaOpKernel { + public: + explicit NonMaxSuppressionOp(OpKernelConstruction* context) + : XlaOpKernel(context) { + OP_REQUIRES_OK(context, context->GetAttr("pad_to_max_output_size", + &pad_to_max_output_size_)); + } + + void Compile(XlaOpKernelContext* context) override { + // TODO(b/111646731): Improve scalability of this op, using blocking. + int num_boxes_dim = 0; + int coords_dim = 1; + const TensorShape& boxes_shape = context->InputShape("boxes"); + OP_REQUIRES(context, TensorShapeUtils::IsMatrix(boxes_shape), + errors::InvalidArgument("boxes must be 2-D, currently: ", + boxes_shape.DebugString())); + const int64 num_boxes = boxes_shape.dim_size(num_boxes_dim); + OP_REQUIRES(context, boxes_shape.dim_size(coords_dim) == 4, + errors::InvalidArgument("boxes must have 4 columns", + boxes_shape.DebugString())); + const TensorShape& scores_shape = context->InputShape("scores"); + OP_REQUIRES(context, TensorShapeUtils::IsVector(scores_shape), + errors::InvalidArgument("scores must be 1-D, currently: ", + scores_shape.DebugString())); + OP_REQUIRES( + context, scores_shape.dim_size(0) == num_boxes, + errors::InvalidArgument("scores size must equal number of boxes", + scores_shape.DebugString())); + OP_REQUIRES(context, pad_to_max_output_size_, + errors::InvalidArgument( + "XLA compilation requires pad_to_max_output_size == True")); + + xla::XlaOp boxes = context->Input("boxes"); + xla::XlaOp scores = context->Input("scores"); + int64 output_size; + OP_REQUIRES_OK(context, context->ConstantInputAsIntScalar(2, &output_size)); + OP_REQUIRES( + context, output_size >= 0, + errors::InvalidArgument("Need output_size >= 0, got ", output_size)); + xla::XlaOp score_thresh = context->Input("score_threshold"); + xla::XlaOp iou_thresh = context->Input("iou_threshold"); + + xla::XlaBuilder* const builder = context->builder(); + + // Choose a more convenient layout. + xla::XlaOp boxes_t = xla::Transpose(boxes, {1, 0}); + coords_dim = 0; + num_boxes_dim = 1; + + // Shapes are henceforth [1, num_boxes]. + xla::XlaOp coord_y0 = xla::SliceInDim(boxes_t, + /*start_index=*/0, + /*limit_index=*/1, + /*stride=*/1, + /*dimno=*/coords_dim); + xla::XlaOp coord_x0 = xla::SliceInDim(boxes_t, + /*start_index=*/1, + /*limit_index=*/2, + /*stride=*/1, + /*dimno=*/coords_dim); + xla::XlaOp coord_y1 = xla::SliceInDim(boxes_t, + /*start_index=*/2, + /*limit_index=*/3, + /*stride=*/1, + /*dimno=*/coords_dim); + xla::XlaOp coord_x1 = xla::SliceInDim(boxes_t, + /*start_index=*/3, + /*limit_index=*/4, + /*stride=*/1, + /*dimno=*/coords_dim); + xla::XlaOp y1 = + xla::Select(xla::Le(coord_y0, coord_y1), coord_y0, coord_y1); + xla::XlaOp y2 = + xla::Select(xla::Le(coord_y0, coord_y1), coord_y1, coord_y0); + xla::XlaOp x1 = + xla::Select(xla::Le(coord_x0, coord_x1), coord_x0, coord_x1); + xla::XlaOp x2 = + xla::Select(xla::Le(coord_x0, coord_x1), coord_x1, coord_x0); + xla::XlaOp area = (y2 - y1) * (x2 - x1); + + // Transpose the 1xN tensors, instead of the NxN tensors. + xla::XlaOp y1_t = xla::Transpose(y1, {1, 0}); + xla::XlaOp y2_t = xla::Transpose(y2, {1, 0}); + xla::XlaOp x1_t = xla::Transpose(x1, {1, 0}); + xla::XlaOp x2_t = xla::Transpose(x2, {1, 0}); + xla::XlaOp area_t = xla::Transpose(area, {1, 0}); + + // Shapes are henceforth [num_boxes, num_boxes]. + xla::XlaOp i_xmin = xla::Max(x1, x1_t); + xla::XlaOp i_ymin = xla::Max(y1, y1_t); + xla::XlaOp i_xmax = xla::Min(x2, x2_t); + xla::XlaOp i_ymax = xla::Min(y2, y2_t); + auto square_zero = xla::ZerosLike(i_xmin); + + xla::XlaOp i_area = xla::Max(i_xmax - i_xmin, square_zero) * + xla::Max(i_ymax - i_ymin, square_zero); + xla::XlaOp u_area = area + area_t - i_area; + xla::XlaOp iou = i_area / u_area; + + xla::XlaOp iou_thresh_mask = xla::Gt(iou, iou_thresh + square_zero); + xla::XlaOp scores_2d = xla::Reshape(scores, {num_boxes, 1}); + xla::XlaOp score_cmp_mask = + xla::Gt(scores_2d, xla::Transpose(scores_2d, {1, 0})); + xla::XlaOp suppress = xla::And(iou_thresh_mask, score_cmp_mask); + + // Shapes are [num_boxes] after the reduce. + xla::XlaOp included_iou = xla::Not(xla::Reduce( + suppress, + /*init_value=*/xla::ConstantR0(builder, false), + /*computation=*/CreateScalarOrComputation(xla::PRED, builder), + /*dimensions_to_reduce=*/{0})); + xla::XlaOp included_score = + xla::Gt(scores, xla::Broadcast(score_thresh, {num_boxes})); + xla::XlaOp included = xla::And(included_iou, included_score); + xla::XlaOp neg_inf = + xla::Broadcast(xla::MinValue(builder, xla::F32), {num_boxes}); + xla::XlaOp scores_included = xla::Select(included, scores, neg_inf); + + xla::XlaOp ones_included = xla::Select( + included, + xla::Broadcast(xla::ConstantR0(builder, 1), {num_boxes}), + xla::Broadcast(xla::ConstantR0(builder, 0), {num_boxes})); + + // num_valid is scalar. + xla::XlaOp num_valid = xla::Reduce( + ones_included, + /*init_value=*/xla::ConstantR0(builder, 0), + /*computation=*/CreateScalarAddComputation(xla::S32, builder), + /*dimensions_to_reduce=*/{0}); + + xla::XlaOp output_tuple = TopK(scores_included, output_size); + xla::XlaOp selected_indices = xla::GetTupleElement(output_tuple, 1); + + context->SetOutput(0, selected_indices); + context->SetOutput(1, num_valid); + } + + private: + bool pad_to_max_output_size_; +}; + +REGISTER_XLA_OP( + Name("NonMaxSuppressionV4").CompileTimeConstInput("max_output_size"), + NonMaxSuppressionOp); + } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc b/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc index d6bf92fb3df8d38909df99e11c85ede4fac2bf81..8d75624e74028ea083c3facc4f9578ec14c50e6d 100644 --- a/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/lib/math/math_util.h" diff --git a/tensorflow/compiler/tf2xla/kernels/index_ops_cpu.cc b/tensorflow/compiler/tf2xla/kernels/index_ops_cpu.cc index a020ebc729e4c07d1b182cc0585ba0f2bca46403..22a45b2a11e8ecb688f8e773ef4b286eafe68f4f 100644 --- a/tensorflow/compiler/tf2xla/kernels/index_ops_cpu.cc +++ b/tensorflow/compiler/tf2xla/kernels/index_ops_cpu.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" @@ -78,14 +78,14 @@ class ArgMaxCustomCallOp : public XlaOpKernel { std::vector args; args.push_back(ctx->Input(0)); args.push_back(xla::ConstantLiteral( - &b, *xla::Literal::CreateR1(input_shape.dim_sizes()))); + &b, *xla::LiteralUtil::CreateR1(input_shape.dim_sizes()))); if (input_shape.dims() > 1) { // Don't bother passing the output shape and dim for the 1d case, since // the shape is always a scalar and the dim is always 0. args.push_back(xla::ConstantLiteral( - &b, *xla::Literal::CreateR1(output_shape.dim_sizes()))); + &b, *xla::LiteralUtil::CreateR1(output_shape.dim_sizes()))); args.push_back( - xla::ConstantLiteral(&b, *xla::Literal::CreateR0(dim))); + xla::ConstantLiteral(&b, *xla::LiteralUtil::CreateR0(dim))); } xla::Shape xla_shape = diff --git a/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc b/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc index 9e64711051d31107db1bf6f1966f9ed6f5630c34..f028e361bccd51de0bd69a1d2227c7afaed53455 100644 --- a/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/kernels/no_op.h" diff --git a/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc b/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc index 2fb072f827906d40dcf410f0312394c4f568a28d..a11bbe918f7f8eb050aaa40d4344f9cc9e9a10a4 100644 --- a/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/lib/core/errors.h" diff --git a/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc b/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc index dc934543cb2f94fbe1e8f1f865156eb082d6a127..87ee2d3aede50eb24e65570f106d49030e1d4236 100644 --- a/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/matmul_op.cc b/tensorflow/compiler/tf2xla/kernels/matmul_op.cc index 844080b8cf5462da201ce7671e4f9d02fa52c861..6440770c29894c951f010f6c1deb929f4fe79bbf 100644 --- a/tensorflow/compiler/tf2xla/kernels/matmul_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matmul_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { @@ -54,10 +54,14 @@ class MatMulOp : public XlaOpKernel { const TensorShape b_shape = ctx->InputShape(1); // Check that the dimensions of the two matrices are valid. - OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(a_shape), - errors::InvalidArgument("In[0] is not a matrix")); - OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(b_shape), - errors::InvalidArgument("In[1] is not a matrix")); + OP_REQUIRES( + ctx, TensorShapeUtils::IsMatrix(a_shape), + errors::InvalidArgument("In[0] is not a matrix. Instead it has shape ", + a_shape.DebugString())); + OP_REQUIRES( + ctx, TensorShapeUtils::IsMatrix(b_shape), + errors::InvalidArgument("In[1] is not a matrix. Instead it has shape ", + b_shape.DebugString())); int first_index = transpose_a_ ? 0 : 1; int second_index = transpose_b_ ? 1 : 0; diff --git a/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc b/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc index e06c87db7adb1840606208fe15cd68a3ca4d137a..8dfd7de591c4a3c4768dd60b41e03d294ad49397 100644 --- a/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/tensor_shape.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc b/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc index e2ab4b83cfb45b2f9a7f3aba2d2a927d10ad8b85..c0ca881ff82cee04e0c5e35f9a2d5732fabdd8a6 100644 --- a/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc b/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc index 529959dbd90b05f8860360f70e087ef225150600..eedfc3c9140d7b1ccc1944611de98c1d49fbdaf2 100644 --- a/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/util/mirror_pad_mode.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/pack_op.cc b/tensorflow/compiler/tf2xla/kernels/pack_op.cc index 3aed47de2603f3e187ad515d4db3f884da4c6cc8..a9b519d8928cc2807831fd6b4f12e60b7d58ea55 100644 --- a/tensorflow/compiler/tf2xla/kernels/pack_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/pack_op.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/pad_op.cc b/tensorflow/compiler/tf2xla/kernels/pad_op.cc index 89fd610bc63349d008836c3c4e6ec8927c232a54..e5937b56c17d01892928b073da09f38941ea1bbb 100644 --- a/tensorflow/compiler/tf2xla/kernels/pad_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/pad_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc index a81f5fddf69523619d03ea2041c40222de46174e..3d506e71e03d6b804d1ea0e63c760cfb82629f12 100644 --- a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc @@ -21,8 +21,9 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/qr_op.cc b/tensorflow/compiler/tf2xla/kernels/qr_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..de9068a640dc03b141b6954eaa1629dd6c8c1f3a --- /dev/null +++ b/tensorflow/compiler/tf2xla/kernels/qr_op.cc @@ -0,0 +1,47 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/lib/qr.h" +#include "tensorflow/compiler/tf2xla/xla_op_kernel.h" +#include "tensorflow/compiler/tf2xla/xla_op_registry.h" + +namespace tensorflow { +namespace { + +class QROp : public XlaOpKernel { + public: + explicit QROp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { + bool full_matrices; + OP_REQUIRES_OK(ctx, ctx->GetAttr("full_matrices", &full_matrices)); + OP_REQUIRES( + ctx, full_matrices, + errors::Unimplemented("full_matrices=False case of QR decomposition is " + "not implemented in TF/XLA")); + } + void Compile(XlaOpKernelContext* ctx) override { + auto result = QRDecomposition(ctx->Input(0)); + if (!result.ok()) { + ctx->SetStatus(result.status()); + return; + } + ctx->SetOutput(0, result.ValueOrDie().q); + ctx->SetOutput(1, result.ValueOrDie().r); + } +}; + +REGISTER_XLA_OP(Name("Qr").TypeConstraint("T", kFloatTypes), QROp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc b/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc index e88221e4f400abeec59d85c1539d4f70bf515d3c..6f4ed496a1774dde68dd9d5fbd37995d615b678c 100644 --- a/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc @@ -19,7 +19,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/platform/macros.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/random_ops.cc b/tensorflow/compiler/tf2xla/kernels/random_ops.cc index 9a0a7f9b9004f210adac44ed8b6e32cff131d23b..2da9340625db08b14b78340c471f096baf15689d 100644 --- a/tensorflow/compiler/tf2xla/kernels/random_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/random_ops.cc @@ -27,7 +27,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" @@ -74,56 +74,121 @@ class RandomShuffleOp : public XlaOpKernel { for (tensorflow::TensorShapeDim dimension : input_shape) { num_elements *= dimension.size; } + if (num_elements <= 1 || n <= 1) { // No shuffling is required, so copy input directly to output ctx->SetOutput(0, input); - } else { - // Generate the random swaps for the indices. - auto swaps_shape = xla::ShapeUtil::MakeShape(xla::S32, {n}); - auto swaps = - xla::RngUniform(xla::ConstantR0(builder, 0), - xla::ConstantR0(builder, n), swaps_shape); - - // Generate range(n) as the initial value for the indices to be swapped. - xla::XlaOp indices = xla::Iota(builder, xla::S32, n); - - // Swap the indices at i and swaps[i]. - auto swap_body_fn = [&](xla::XlaOp i, - gtl::ArraySlice loop_vars, - xla::XlaBuilder* builder) - -> xla::StatusOr> { - auto swaps = loop_vars[0]; - auto indices = loop_vars[1]; - i = xla::Reshape(i, {1}); - // temp = indices[i] - auto temp = xla::DynamicSlice(indices, i, {1}); - // swap_index = swaps[i] - auto swap_index = xla::DynamicSlice(swaps, i, {1}); - // swap_value = indices[swaps[i]] - auto swap_value = xla::DynamicSlice(indices, swap_index, {1}); - // indices[i] = indices[swaps[i]] - indices = xla::DynamicUpdateSlice(indices, swap_value, i); - // indices[swaps[i]] = temp - indices = xla::DynamicUpdateSlice(indices, temp, swap_index); - return std::vector{swaps, indices}; - }; - // for i in range(n): - auto swap_loop_result = - XlaForEachIndex(n, xla::S32, swap_body_fn, {swaps, indices}, - "indices_swap_loop", builder) - .ValueOrDie(); - auto swapped_indices = swap_loop_result[1]; - - // Gather the data using the swapped indices as the shuffled order. - auto indices_tensor_shape = TensorShape({n}); - DataType type = ctx->expected_output_dtype(0); - xla::XlaOp gather; - OP_REQUIRES_OK(ctx, XlaGather(input, input_shape, swapped_indices, - indices_tensor_shape, - /*axis=*/0, /*indices_are_nd=*/false, type, - DT_INT32, builder, &gather)); - ctx->SetOutput(0, gather); + return; + } + + if (input_shape.dims() == 1) { + // For R1s, shuffle values by sorting instead of the obvious Fisher-Yates + // algorithm. Fisher-Yates is simple to implement and correct, but not + // easily parallelizable. For a sufficiently parallel architecture, it is + // faster to sort many times, than Fisher-Yates shuffle once. + + // Shuffle values by assigning each value a random key and sorting the + // keys. Keys can collide causing detectable patterns in the shuffled + // output. Collisions translates into more ascending sub-sequences in the + // shuffled output than would be expected by chance. To avoid collisions, + // the number of possible key values must be sufficiently large. + + // How are more than 2^32 keys created? In each loop iteration, the + // algorithm sorts by random keys. Conceptually, the earlier iterations + // are sorting on the lower-order bits of larger keys that are never + // actually assembled. + + // The expected number of collisions is n - d + d(1 - 1/d)^n, where d is + // the number of possible keys and n is the number of values. If d = n^2, + // then the limit as n goes to infinity is 1/2. If d = n^3, then the limit + // as n goes to infinity is zero. + + // This implementation ensures that the key-space is greater than or equal + // to the cube of the number of values. The risk of collisions can be + // further reduced by increasing Exponent at the expense of + // performance. + + // For Exponent = 2, the expected number of collisions per shuffle is + // maximized at n = floor((2^32-1)^(1/2)) = 65535 where the expectation is + // about 1/2. + + // For Exponent = 3, the expected number of collisions per shuffle is + // maximized at n = floor((2^32-1)^(1/3)) = 1625 where the expectation is + // about 1/3255. + + // For Exponent = 4, the expected number of collisions per shuffle is + // maximized at n = floor((2^32-1)^(1/4)) = 255 where the expectation is + // about 1/132622. + constexpr int Exponent = 3; + const int rounds = static_cast( + std::ceil(Exponent * std::log(num_elements) / std::log(kuint32max))); + + const xla::Shape key_shape = + xla::ShapeUtil::MakeShape(xla::U32, {num_elements}); + xla::XlaOp zero = xla::ConstantR0(builder, 0U); + + // Unfortunately, xla::RngUniform gives values in the half open interval + // rather than the closed interval, so instead of 2^32 possible keys there + // are only 2^32 - 1 (kuint32max). + xla::XlaOp max_value = xla::ConstantR0(builder, kuint32max); + + xla::XlaOp curr = input; + for (int i = 0; i < rounds; ++i) { + xla::XlaOp keys = xla::RngUniform(zero, max_value, key_shape); + xla::XlaOp sorted = xla::Sort(keys, curr); + curr = xla::GetTupleElement(sorted, 1); + } + + ctx->SetOutput(0, curr); + return; } + + // The Fisher-Yates algorithm. + + // Generate the random swaps for the indices. + auto swaps_shape = xla::ShapeUtil::MakeShape(xla::S32, {n}); + auto swaps = + xla::RngUniform(xla::ConstantR0(builder, 0), + xla::ConstantR0(builder, n), swaps_shape); + + // Generate range(n) as the initial value for the indices to be swapped. + xla::XlaOp indices = xla::Iota(builder, xla::S32, n); + + // Swap the indices at i and swaps[i]. + auto swap_body_fn = [&](xla::XlaOp i, gtl::ArraySlice loop_vars, + xla::XlaBuilder* builder) + -> xla::StatusOr> { + auto swaps = loop_vars[0]; + auto indices = loop_vars[1]; + i = xla::Reshape(i, {1}); + // temp = indices[i] + auto temp = xla::DynamicSlice(indices, i, {1}); + // swap_index = swaps[i] + auto swap_index = xla::DynamicSlice(swaps, i, {1}); + // swap_value = indices[swaps[i]] + auto swap_value = xla::DynamicSlice(indices, swap_index, {1}); + // indices[i] = indices[swaps[i]] + indices = xla::DynamicUpdateSlice(indices, swap_value, i); + // indices[swaps[i]] = temp + indices = xla::DynamicUpdateSlice(indices, temp, swap_index); + return std::vector{swaps, indices}; + }; + // for i in range(n): + auto swap_loop_result = + XlaForEachIndex(n, xla::S32, swap_body_fn, {swaps, indices}, + "indices_swap_loop", builder) + .ValueOrDie(); + auto swapped_indices = swap_loop_result[1]; + + // Gather the data using the swapped indices as the shuffled order. + auto indices_tensor_shape = TensorShape({n}); + DataType type = ctx->expected_output_dtype(0); + xla::XlaOp gather; + OP_REQUIRES_OK(ctx, XlaGather(input, input_shape, swapped_indices, + indices_tensor_shape, + /*axis=*/0, /*indices_are_nd=*/false, type, + DT_INT32, builder, &gather)); + ctx->SetOutput(0, gather); } private: @@ -220,5 +285,5 @@ REGISTER_XLA_OP(Name("TruncatedNormal") .TypeConstraint("dtype", DT_FLOAT), TruncatedNormalOp); -} // anonymous namespace +} // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc b/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc index 76bd1e62aa1efd85d6ed489b9a6d22a2bacf2a8b..b11a4ce36da9907ce8fe377c075023a4540797fa 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc @@ -19,7 +19,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc index 46fae59ad4fa30b57946671518251a7e53ac4c8c..0d260fa8fcaa513d7854c1e9215952404d555c70 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc @@ -20,8 +20,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops.h b/tensorflow/compiler/tf2xla/kernels/reduction_ops.h index 8333f9b288e27efe9497306f031980c9eec7c99c..466e79828d111ee7cadcf713703e8f252c63e62c 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops.h +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops.h @@ -19,7 +19,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_TF2XLA_KERNELS_REDUCTION_OPS_H_ #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc index 909783ecb3c2a866136e1a09767144c91c46525c..b52f0a0ab6290f2019bb58120be5c2364ec15bb6 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc @@ -19,8 +19,9 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/relu_op.cc b/tensorflow/compiler/tf2xla/kernels/relu_op.cc index a4ba6c748a73f161ea252e2adf4050eb5dda7df5..d35777ccb1271ec6a7c9972c714d06b2415d9c34 100644 --- a/tensorflow/compiler/tf2xla/kernels/relu_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/relu_op.cc @@ -18,8 +18,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/kernels/no_op.h" diff --git a/tensorflow/compiler/tf2xla/kernels/reshape_op.cc b/tensorflow/compiler/tf2xla/kernels/reshape_op.cc index e0ca8dd8e27914ad60d0b97e8ac5f0b91a4fd9a6..121750a82a8c5cbe940068555ad273b7e0d22dfc 100644 --- a/tensorflow/compiler/tf2xla/kernels/reshape_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reshape_op.cc @@ -19,8 +19,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" diff --git a/tensorflow/compiler/tf2xla/kernels/retval_op.cc b/tensorflow/compiler/tf2xla/kernels/retval_op.cc index 5be70a4ded31a988cb77cdabe3fc8a041bc3ad16..1911e6ea362f999c787cbf95dcc9137a6a630273 100644 --- a/tensorflow/compiler/tf2xla/kernels/retval_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/retval_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/reverse_op.cc b/tensorflow/compiler/tf2xla/kernels/reverse_op.cc index 037c422258555289711b8754f2277d077d0cd6a7..d962ef4a5f53470838643541f8a1e693d2f4011c 100644 --- a/tensorflow/compiler/tf2xla/kernels/reverse_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reverse_op.cc @@ -19,8 +19,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" diff --git a/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc b/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc index c810456f94322acfccae18d78efa861eede4648c..03a50ef8a059e5a005c4cc2e5e98acedfea8619a 100644 --- a/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/tensor_shape.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/scan_ops.cc b/tensorflow/compiler/tf2xla/kernels/scan_ops.cc index 76924c6a01a44e7a723b8c8895e8decbdd466c79..ab094d7dd1ce9856a3c2854fd2776827d6c4b76f 100644 --- a/tensorflow/compiler/tf2xla/kernels/scan_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/scan_ops.cc @@ -20,8 +20,9 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/partial_tensor_shape.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc b/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc index 14709bb6cbce4b3ae0f7ff859b0fa622c6eda293..f1f32699fee5f03f603f830722fe65622dee5d3e 100644 --- a/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc b/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc index e2ac7da2c2630725efe3dbcc51c3f3d30e7aca2c..b22ecb7c6dbb42a33a4f4d90b18b20816df16a50 100644 --- a/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { namespace { diff --git a/tensorflow/compiler/tf2xla/kernels/select_op.cc b/tensorflow/compiler/tf2xla/kernels/select_op.cc index 5c010c9df23ba6c7732d87fa014879d93ff586ce..6ce50efb4aa6e3434a7c6009cf9f52f6cff9cc9f 100644 --- a/tensorflow/compiler/tf2xla/kernels/select_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/select_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/kernels/bounds_check.h" diff --git a/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc b/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc index 6281d6c6533f7f49a269f5c7e52226ba0f1d29f6..a7f5a8f1698b9d02560de427d356e9e6be5caa7c 100644 --- a/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc b/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc index bc3d0bf5dfe9e5af8e50a25e27db7148e05e0cfd..25a5bcbe1dd27d741ce3b74125ba9ce425ee78f3 100644 --- a/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/sequence_ops.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" diff --git a/tensorflow/compiler/tf2xla/kernels/shape_op.cc b/tensorflow/compiler/tf2xla/kernels/shape_op.cc index 5798823cd54c66dd179e3611c0041f7c5a1ff2b5..4e0cf99d8e7ff45ed9145981b5e2e637ce4d4e4b 100644 --- a/tensorflow/compiler/tf2xla/kernels/shape_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/shape_op.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/kernels/bounds_check.h" diff --git a/tensorflow/compiler/tf2xla/kernels/slice_op.cc b/tensorflow/compiler/tf2xla/kernels/slice_op.cc index 1864584adee357ce35a3e8a38a4e3c58c356bfca..6adc3c58de63ee70c26bed47eebef955893df4a5 100644 --- a/tensorflow/compiler/tf2xla/kernels/slice_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/slice_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" diff --git a/tensorflow/compiler/tf2xla/kernels/softmax_op.cc b/tensorflow/compiler/tf2xla/kernels/softmax_op.cc index a71fbcd901e8919949db5873675a7e3e785bdf4e..1d7a63dc311c60927f460e281601963e21232ec7 100644 --- a/tensorflow/compiler/tf2xla/kernels/softmax_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/softmax_op.cc @@ -20,7 +20,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" diff --git a/tensorflow/compiler/tf2xla/kernels/sort_ops.cc b/tensorflow/compiler/tf2xla/kernels/sort_ops.cc index faaf8964ff7c40d75a493b03e6b400632117cb45..aaeeae01ccb303091a6d37d1aeb4b2a3377dc638 100644 --- a/tensorflow/compiler/tf2xla/kernels/sort_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/sort_ops.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { namespace { diff --git a/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc b/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc index 8a8525efa186ed4aa02c494f7505f6245677e96e..7327258c31f21f45ff7ffffbc9db7a2a70b4a14c 100644 --- a/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { namespace { diff --git a/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc b/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc index 47d282fe9ec664bbc424793e93f778ebb13c6877..4493539fe34f0ce635fdc58660d4ff90af9c9379 100644 --- a/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/util/tensor_format.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/split_op.cc b/tensorflow/compiler/tf2xla/kernels/split_op.cc index ca74cf24507e1666070751a17fb940a3ad594695..93fc14e9efca868e84444dd0e07d7f0dfa84c042 100644 --- a/tensorflow/compiler/tf2xla/kernels/split_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/split_op.cc @@ -19,8 +19,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" diff --git a/tensorflow/compiler/tf2xla/kernels/stack_ops.cc b/tensorflow/compiler/tf2xla/kernels/stack_ops.cc index 591e61b4c82836bc1995cd11c4c0314c9d854e50..df91900570107609c0f1c2281faaab8a5e65b98b 100644 --- a/tensorflow/compiler/tf2xla/kernels/stack_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/stack_ops.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/partial_tensor_shape.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc b/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc index a6f5769e7b7b1e550b7908caa35289cf3030120f..5412e135478361d08965e4621ec52cfb4a792f1d 100644 --- a/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc @@ -23,7 +23,8 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/math.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/lib/prng.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" @@ -33,134 +34,6 @@ limitations under the License. namespace tensorflow { namespace { -// Rotates a 32-bit integer 'v' left by 'distance' bits. -xla::XlaOp RotateLeftS32(xla::XlaBuilder* builder, const xla::XlaOp& v, - int distance) { - return xla::Or( - xla::ShiftLeft(v, xla::ConstantR0(builder, distance)), - xla::ShiftRightLogical(v, xla::ConstantR0(builder, 32 - distance))); -} - -using ThreeFry2x32State = std::array; - -// Implements the ThreeFry counter-based PRNG algorithm. -// Salmon et al. SC 2011. Parallel random numbers: as easy as 1, 2, 3. -// http://www.thesalmons.org/john/random123/papers/random123sc11.pdf -ThreeFry2x32State ThreeFry2x32(xla::XlaBuilder* builder, - ThreeFry2x32State input, ThreeFry2x32State key) { - // Rotation distances specified by the Threefry2x32 algorithm. - constexpr std::array rotations = {13, 15, 26, 6, 17, 29, 16, 24}; - ThreeFry2x32State x; - - std::array ks; - // 0x1BD11BDA is a parity constant specified by the ThreeFry2x32 algorithm. - ks[2] = xla::ConstantR0(builder, 0x1BD11BDA); - for (int i = 0; i < 2; ++i) { - ks[i] = key[i]; - x[i] = input[i]; - ks[2] = xla::Xor(ks[2], key[i]); - } - - x[0] = xla::Add(x[0], ks[0]); - x[1] = xla::Add(x[1], ks[1]); - - // Performs a single round of the Threefry2x32 algorithm, with a rotation - // amount 'rotation'. - auto round = [builder](ThreeFry2x32State v, int rotation) { - v[0] = xla::Add(v[0], v[1]); - v[1] = RotateLeftS32(builder, v[1], rotation); - v[1] = xla::Xor(v[0], v[1]); - return v; - }; - - // There are no known statistical flaws with 13 rounds of Threefry2x32. - // We are conservative and use 20 rounds. - x = round(x, rotations[0]); - x = round(x, rotations[1]); - x = round(x, rotations[2]); - x = round(x, rotations[3]); - x[0] = xla::Add(x[0], ks[1]); - x[1] = xla::Add(xla::Add(x[1], ks[2]), xla::ConstantR0(builder, 1)); - - x = round(x, rotations[4]); - x = round(x, rotations[5]); - x = round(x, rotations[6]); - x = round(x, rotations[7]); - x[0] = xla::Add(x[0], ks[2]); - x[1] = xla::Add(xla::Add(x[1], ks[0]), xla::ConstantR0(builder, 2)); - - x = round(x, rotations[0]); - x = round(x, rotations[1]); - x = round(x, rotations[2]); - x = round(x, rotations[3]); - x[0] = xla::Add(x[0], ks[0]); - x[1] = xla::Add(xla::Add(x[1], ks[1]), xla::ConstantR0(builder, 3)); - - x = round(x, rotations[4]); - x = round(x, rotations[5]); - x = round(x, rotations[6]); - x = round(x, rotations[7]); - x[0] = xla::Add(x[0], ks[1]); - x[1] = xla::Add(xla::Add(x[1], ks[2]), xla::ConstantR0(builder, 4)); - - x = round(x, rotations[0]); - x = round(x, rotations[1]); - x = round(x, rotations[2]); - x = round(x, rotations[3]); - x[0] = xla::Add(x[0], ks[2]); - x[1] = xla::Add(xla::Add(x[1], ks[0]), xla::ConstantR0(builder, 5)); - - return x; -} - -// Returns a tensor of 'shape' random values uniformly distributed in the range -// [minval, maxval) -xla::XlaOp RandomUniform(xla::XlaBuilder* builder, const xla::XlaOp& seed, - const TensorShape& shape, double minval, - double maxval) { - // Split the seed into two 32-bit scalars to form a key. - auto seed0 = xla::Reshape(xla::Slice(seed, {0}, {1}, {1}), {}); - auto seed1 = xla::Reshape(xla::Slice(seed, {1}, {2}, {1}), {}); - ThreeFry2x32State key = {seed0, seed1}; - const int64 size = shape.num_elements(); - - const int64 half_size = MathUtil::CeilOfRatio(size, 2); - const bool size_is_odd = (half_size * 2 != size); - - // Fill the generator inputs with unique counter values. - ThreeFry2x32State inputs; - inputs[0] = xla::Iota(builder, xla::S32, half_size); - inputs[1] = xla::Add(inputs[0], xla::ConstantR0(builder, half_size)); - ThreeFry2x32State outputs = ThreeFry2x32(builder, inputs, key); - - if (size_is_odd) { - outputs[1] = xla::Slice(outputs[1], {0}, {half_size - 1}, {1}); - } - - auto bits = - xla::Reshape(xla::ConcatInDim(builder, outputs, 0), shape.dim_sizes()); - - // Form 22 random mantissa bits, with a leading 1 bit. The leading 1 bit - // forces the random bits into the mantissa. - constexpr int kFloatBits = 32; - constexpr int kMantissaBits = 23; - bits = xla::Or( - xla::ShiftRightLogical( - bits, xla::ConstantR0(builder, kFloatBits - kMantissaBits)), - xla::ConstantR0(builder, bit_cast(1.0f))); - auto floats = xla::BitcastConvertType(bits, xla::F32); - - // We have a floating point number in the range [1.0, 2.0). - // Subtract 1.0f to shift to the range [0.0, 1.0) - floats = xla::Sub(floats, xla::ConstantR0(builder, 1.0f)); - // Multiply and add to shift to the range [minval, maxval). - floats = xla::Mul(floats, xla::ConstantR0(builder, maxval - minval)); - floats = xla::Add(floats, xla::ConstantR0(builder, minval)); - return floats; -} - -} // namespace - class StatelessRandomUniformOp : public XlaOpKernel { public: explicit StatelessRandomUniformOp(OpKernelConstruction* ctx) @@ -177,7 +50,17 @@ class StatelessRandomUniformOp : public XlaOpKernel { errors::InvalidArgument("seed must have shape [2], not ", seed_shape.DebugString())); xla::XlaOp seed = ctx->Input(1); - ctx->SetOutput(0, RandomUniform(builder, seed, shape, 0.0, 1.0)); + + xla::Shape xla_shape; + OP_REQUIRES_OK(ctx, TensorShapeToXLAShape(DT_FLOAT, shape, &xla_shape)); + + auto seed0 = xla::Reshape(xla::Slice(seed, {0}, {1}, {1}), {}); + auto seed1 = xla::Reshape(xla::Slice(seed, {1}, {2}, {1}), {}); + + auto uniform = xla::StatelessRngUniform( + {seed0, seed1}, xla_shape, xla::ConstantR0(builder, 0.0), + xla::ConstantR0(builder, 1.0)); + ctx->SetOutput(0, uniform); } private: @@ -206,8 +89,16 @@ class StatelessRandomNormalOp : public XlaOpKernel { seed_shape.DebugString())); xla::XlaOp seed = ctx->Input(1); xla::XlaBuilder* builder = ctx->builder(); - auto uniform = - RandomUniform(builder, seed, shape, std::nextafter(-1.0f, 0.0f), 1.0); + xla::Shape xla_shape; + OP_REQUIRES_OK(ctx, TensorShapeToXLAShape(DT_FLOAT, shape, &xla_shape)); + + auto seed0 = xla::Reshape(xla::Slice(seed, {0}, {1}, {1}), {}); + auto seed1 = xla::Reshape(xla::Slice(seed, {1}, {2}, {1}), {}); + + auto uniform = xla::StatelessRngUniform( + {seed0, seed1}, xla_shape, + xla::ConstantR0(builder, std::nextafter(-1.0f, 0.0f)), + xla::ConstantR0(builder, 1.0)); // Convert uniform distribution to normal distribution by computing // sqrt(2) * erfinv(x) auto normal = @@ -240,10 +131,18 @@ class StatelessTruncatedNormalOp : public XlaOpKernel { errors::InvalidArgument("seed must have shape [2], not ", seed_shape.DebugString())); xla::XlaOp seed = ctx->Input(1); - xla::XlaBuilder* b = ctx->builder(); + xla::XlaBuilder* builder = ctx->builder(); + + auto seed0 = xla::Reshape(xla::Slice(seed, {0}, {1}, {1}), {}); + auto seed1 = xla::Reshape(xla::Slice(seed, {1}, {2}, {1}), {}); + + xla::Shape xla_shape; + OP_REQUIRES_OK(ctx, TensorShapeToXLAShape(DT_FLOAT, shape, &xla_shape)); + auto uniform = xla::StatelessRngUniform( + {seed0, seed1}, xla_shape, + xla::ConstantR0(builder, std::numeric_limits::min()), + xla::ConstantR0(builder, 1.0)); - auto uniform = - RandomUniform(b, seed, shape, std::numeric_limits::min(), 1.0); ctx->SetOutput(0, TruncatedNormal(uniform)); } @@ -257,4 +156,5 @@ REGISTER_XLA_OP(Name("StatelessTruncatedNormal") .TypeConstraint("Tseed", DT_INT32), StatelessTruncatedNormalOp); +} // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc index c2165ccd86dfa1c119790beb20af0844fb1bbda8..1062399d91bd9a9bf8c3820c5ecac534c110746d 100644 --- a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" diff --git a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc index 2f650ce3052ee4502912891cd3f60cfaec8b1d7c..be1814d8e3ae2c0ddad0134b9288e0ea084aa81b 100644 --- a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc @@ -25,8 +25,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/tf2xla/xla_resource.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/partial_tensor_shape.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/tile_ops.cc b/tensorflow/compiler/tf2xla/kernels/tile_ops.cc index c9e56942625a009fb3660f413a845547192460d5..1233a37565d3a40c6dd2882b3139dedbf690a7b6 100644 --- a/tensorflow/compiler/tf2xla/kernels/tile_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/tile_ops.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" diff --git a/tensorflow/compiler/tf2xla/kernels/topk_op.cc b/tensorflow/compiler/tf2xla/kernels/topk_op.cc index 9962f1207d65edea5eba0083436fa380921bb4fd..183879c7602ccbbd74fca6cb9fa3fc94c066c37d 100644 --- a/tensorflow/compiler/tf2xla/kernels/topk_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/topk_op.cc @@ -13,12 +13,12 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/lib/sorting.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/kernels/no_op.h" @@ -41,35 +41,18 @@ class TopKOp : public XlaOpKernel { OP_REQUIRES(context, input_shape.dims() >= 1, errors::InvalidArgument("input must be >= 1-D, got shape ", input_shape.DebugString())); + int last_dim = input_shape.dims() - 1; + int last_dim_size = input_shape.dim_size(last_dim); OP_REQUIRES( - context, input_shape.dim_size(input_shape.dims() - 1) >= k, + context, last_dim_size >= k, errors::InvalidArgument("input must have at least k columns. Had ", - input_shape.dim_size(input_shape.dims() - 1), - ", needed ", k)); - - OP_REQUIRES( - context, input_shape.dims() == 1, - errors::Unimplemented("TopK is implemented for 1-D inputs, got shape ", - input_shape.DebugString())); - - xla::XlaBuilder* const b = context->builder(); - if (input_shape.dim_size(0) < k) { - k = input_shape.dim_size(0); + last_dim_size, ", needed ", k)); + if (last_dim_size < k) { + k = last_dim_size; } - const xla::XlaOp input = context->Input(0); - xla::XlaOp iota_s32 = xla::Iota(b, xla::S32, input_shape.dim_size(0)); - xla::XlaOp sort_result = xla::Sort(xla::Neg(input), iota_s32); - xla::XlaOp values = - xla::Neg(xla::Slice(xla::GetTupleElement(sort_result, 0), - /*start_indices=*/{0}, - /*limit_indices=*/{k}, - /*strides=*/{1})); - xla::XlaOp indices = xla::Slice(xla::GetTupleElement(sort_result, 1), - /*start_indices=*/{0}, - /*limit_indices=*/{k}, - /*strides=*/{1}); - context->SetOutput(0, values); - context->SetOutput(1, indices); + xla::XlaOp output_tuple = TopK(context->Input(0), k); + context->SetOutput(0, xla::GetTupleElement(output_tuple, 0)); + context->SetOutput(1, xla::GetTupleElement(output_tuple, 1)); } private: diff --git a/tensorflow/compiler/tf2xla/kernels/training_ops.cc b/tensorflow/compiler/tf2xla/kernels/training_ops.cc index bef6161e8547dcc84d20b29aa74d6ef50045970b..be5e91138656716daddcc3c7a68dbb78ecb69103 100644 --- a/tensorflow/compiler/tf2xla/kernels/training_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/training_ops.cc @@ -18,8 +18,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/math.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/kernels/no_op.h" @@ -268,6 +268,83 @@ REGISTER_XLA_OP( Name("ResourceApplyProximalAdagrad").TypeConstraint("T", kFloatTypes), ResourceApplyProximalAdagrad); +class ResourceApplyAdagradDA : public XlaOpKernel { + public: + explicit ResourceApplyAdagradDA(OpKernelConstruction* ctx) + : XlaOpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &dtype_)); + } + + void Compile(XlaOpKernelContext* ctx) override { + TensorShape var_shape, accum_shape, squared_accum_shape; + xla::XlaOp var, accum, squared_accum; + OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, dtype_, &var_shape, &var)); + OP_REQUIRES_OK(ctx, + ctx->ReadVariableInput(1, dtype_, &accum_shape, &accum)); + OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(2, dtype_, &squared_accum_shape, + &squared_accum)); + OP_REQUIRES(ctx, var_shape.IsSameSize(accum_shape), + errors::InvalidArgument( + "var and accum do not have the same shape", + var_shape.DebugString(), " ", accum_shape.DebugString())); + OP_REQUIRES( + ctx, var_shape.IsSameSize(squared_accum_shape), + errors::InvalidArgument( + "var and squared accum do not have the same shape", + var_shape.DebugString(), " ", squared_accum_shape.DebugString())); + + TensorShape grad_shape = ctx->InputShape(3); + TensorShape lr_shape = ctx->InputShape(4); + TensorShape l1_shape = ctx->InputShape(5); + TensorShape l2_shape = ctx->InputShape(6); + TensorShape global_step_shape = ctx->InputShape(7); + + OP_REQUIRES(ctx, var_shape.IsSameSize(grad_shape), + errors::InvalidArgument( + "var and grad do not have the same shape", + var_shape.DebugString(), " ", grad_shape.DebugString())); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(lr_shape), + errors::InvalidArgument("lr is not a scalar: ", + lr_shape.DebugString())); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(l1_shape), + errors::InvalidArgument("l1 is not a scalar: ", + l1_shape.DebugString())); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(l2_shape), + errors::InvalidArgument("l2 is not a scalar: ", + l2_shape.DebugString())); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(global_step_shape), + errors::InvalidArgument("global step is not a scalar: ", + global_step_shape.DebugString())); + + xla::XlaOp grad = ctx->Input(3); + xla::XlaOp lr = ctx->Input(4); + xla::XlaOp l1 = ctx->Input(5); + xla::XlaOp l2 = ctx->Input(6); + xla::XlaBuilder* const b = ctx->builder(); + xla::XlaOp global_step = + XlaHelpers::ConvertElementType(b, ctx->Input(7), dtype_); + + accum = accum + grad; + squared_accum = squared_accum + xla::Square(grad); + xla::XlaOp zero = xla::ScalarLike(lr, 0.0); + xla::XlaOp denominator = global_step * lr * l2 + xla::Sqrt(squared_accum); + xla::XlaOp l1_le_zero = -lr * accum / denominator; + xla::XlaOp l1_gt_zero = -lr * xla::Sign(accum) * + xla::Max(xla::Abs(accum) - global_step * l1, zero) / + denominator; + + var = xla::Select(xla::Gt(l1, zero), l1_gt_zero, l1_le_zero); + OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, dtype_, var)); + OP_REQUIRES_OK(ctx, ctx->AssignVariable(1, dtype_, accum)); + OP_REQUIRES_OK(ctx, ctx->AssignVariable(2, dtype_, squared_accum)); + } + + private: + DataType dtype_; +}; +REGISTER_XLA_OP(Name("ResourceApplyAdagradDA").TypeConstraint("T", kFloatTypes), + ResourceApplyAdagradDA); + class ResourceApplyAdam : public XlaOpKernel { public: explicit ResourceApplyAdam(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { @@ -353,36 +430,112 @@ class ResourceApplyAdam : public XlaOpKernel { REGISTER_XLA_OP(Name("ResourceApplyAdam").TypeConstraint("T", kFloatTypes), ResourceApplyAdam); -class ResourceApplyRMSProp : public XlaOpKernel { +class ResourceApplyAdaMax : public XlaOpKernel { public: - explicit ResourceApplyRMSProp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} + explicit ResourceApplyAdaMax(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &dtype_)); + } void Compile(XlaOpKernelContext* ctx) override { - DataType type = ctx->input_type(3); + TensorShape var_shape, m_shape, v_shape; + xla::XlaOp var, m, v; + OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, dtype_, &var_shape, &var)); + OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(1, dtype_, &m_shape, &m)); + OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(2, dtype_, &v_shape, &v)); - TensorShape var_shape, ms_shape, mom_shape; - xla::XlaOp var, ms, mom; - OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, type, &var_shape, &var)); - OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(1, type, &ms_shape, &ms)); - OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(2, type, &mom_shape, &mom)); + TensorShape beta1_power_shape = ctx->InputShape(3); + TensorShape lr_shape = ctx->InputShape(4); + TensorShape beta1_shape = ctx->InputShape(5); + TensorShape beta2_shape = ctx->InputShape(6); + TensorShape epsilon_shape = ctx->InputShape(7); + TensorShape grad_shape = ctx->InputShape(8); - TensorShape lr_shape = ctx->InputShape(3); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(beta1_power_shape), + errors::InvalidArgument("beta1_power is not a scalar: ", + beta1_power_shape.DebugString())); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(lr_shape), + errors::InvalidArgument("lr is not a scalar : ", + lr_shape.DebugString())); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(beta1_shape), + errors::InvalidArgument("beta1 is not a scalar: ", + beta1_shape.DebugString())); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(beta2_shape), + errors::InvalidArgument("beta2 is not a scalar: ", + beta2_shape.DebugString())); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(epsilon_shape), + errors::InvalidArgument("epsilon is not a scalar: ", + epsilon_shape.DebugString())); + OP_REQUIRES(ctx, var_shape.IsSameSize(m_shape), + errors::InvalidArgument("var and m do not have the same shape", + var_shape.DebugString(), " ", + m_shape.DebugString())); + OP_REQUIRES(ctx, var_shape.IsSameSize(v_shape), + errors::InvalidArgument("var and v do not have the same shape", + var_shape.DebugString(), " ", + v_shape.DebugString())); + OP_REQUIRES(ctx, var_shape.IsSameSize(grad_shape), + errors::InvalidArgument( + "var and grad do not have the same shape", + var_shape.DebugString(), " ", grad_shape.DebugString())); + + xla::XlaOp beta1_power = ctx->Input(3); + xla::XlaOp lr = ctx->Input(4); + xla::XlaOp beta1 = ctx->Input(5); + xla::XlaOp beta2 = ctx->Input(6); + xla::XlaOp epsilon = ctx->Input(7); + xla::XlaOp grad = ctx->Input(8); + + xla::XlaOp one = xla::ScalarLike(lr, 1.0); + m = beta1 * m + (one - beta1) * grad; + v = xla::Max(beta2 * v, xla::Abs(grad)); + var = var - lr / (one - beta1_power) * (m / (v + epsilon)); + + OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, dtype_, var)); + OP_REQUIRES_OK(ctx, ctx->AssignVariable(1, dtype_, m)); + OP_REQUIRES_OK(ctx, ctx->AssignVariable(2, dtype_, v)); + } + + private: + DataType dtype_; +}; +REGISTER_XLA_OP(Name("ResourceApplyAdaMax").TypeConstraint("T", kFloatTypes), + ResourceApplyAdaMax); + +class ResourceApplyRMSProp : public XlaOpKernel { + public: + explicit ResourceApplyRMSProp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &dtype_)); + } + + void Compile(XlaOpKernelContext* ctx) override { + TensorShape var_shape, ms_shape, mom_shape, mg_shape; + xla::XlaOp var, ms, mom, mg; + OP_REQUIRES_OK(ctx, + ctx->ReadVariableInput("var", dtype_, &var_shape, &var)); + if (centered_) { + OP_REQUIRES_OK(ctx, ctx->ReadVariableInput("mg", dtype_, &mg_shape, &mg)); + } + OP_REQUIRES_OK(ctx, ctx->ReadVariableInput("ms", dtype_, &ms_shape, &ms)); + OP_REQUIRES_OK(ctx, + ctx->ReadVariableInput("mom", dtype_, &mom_shape, &mom)); + + TensorShape lr_shape = ctx->InputShape("lr"); OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(lr_shape), errors::InvalidArgument("lr is not a scalar: ", lr_shape.DebugString())); - TensorShape rho_shape = ctx->InputShape(4); + TensorShape rho_shape = ctx->InputShape("rho"); OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(rho_shape), errors::InvalidArgument("rho is not a scalar: ", rho_shape.DebugString())); - TensorShape momentum_shape = ctx->InputShape(5); + TensorShape momentum_shape = ctx->InputShape("momentum"); OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(momentum_shape), errors::InvalidArgument("momentum is not a scalar: ", momentum_shape.DebugString())); - TensorShape epsilon_shape = ctx->InputShape(6); + TensorShape epsilon_shape = ctx->InputShape("epsilon"); OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(epsilon_shape), errors::InvalidArgument("epsilon is not a scalar: ", epsilon_shape.DebugString())); - TensorShape grad_shape = ctx->InputShape(7); + TensorShape grad_shape = ctx->InputShape("grad"); // var should be the same shape as mom and ms. OP_REQUIRES(ctx, var_shape.IsSameSize(ms_shape), @@ -398,11 +551,11 @@ class ResourceApplyRMSProp : public XlaOpKernel { "var and grad do not have the same shape", var_shape.DebugString(), " ", grad_shape.DebugString())); - xla::XlaOp lr = ctx->Input(3); - xla::XlaOp rho = ctx->Input(4); - xla::XlaOp momentum = ctx->Input(5); - xla::XlaOp epsilon = ctx->Input(6); - xla::XlaOp grad = ctx->Input(7); + xla::XlaOp lr = ctx->Input("lr"); + xla::XlaOp rho = ctx->Input("rho"); + xla::XlaOp momentum = ctx->Input("momentum"); + xla::XlaOp epsilon = ctx->Input("epsilon"); + xla::XlaOp grad = ctx->Input("grad"); // ms <- rho * ms_{t-1} + (1-rho) * grad * grad // mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) @@ -421,20 +574,46 @@ class ResourceApplyRMSProp : public XlaOpKernel { // ms <- grad**2 (1 - rho) + ms * rho // // Which is the equation listed above. - xla::XlaOp new_ms = - ms + (xla::Square(grad) - ms) * (xla::ScalarLike(ms, 1.0) - rho); - xla::XlaOp new_mom = - mom * momentum + grad * lr * xla::Rsqrt(new_ms + epsilon); + xla::XlaOp one = xla::ScalarLike(ms, 1.0); + xla::XlaOp new_ms = xla::Square(grad) * (one - rho) + ms * rho; + xla::XlaOp denominator; + if (centered_) { + mg = grad * (one - rho) + mg * rho; + denominator = new_ms - xla::Square(mg) + epsilon; + } else { + denominator = new_ms + epsilon; + } + xla::XlaOp new_mom = mom * momentum + grad * lr * xla::Rsqrt(denominator); xla::XlaOp new_var = var - new_mom; - OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, type, new_var)); - OP_REQUIRES_OK(ctx, ctx->AssignVariable(1, type, new_ms)); - OP_REQUIRES_OK(ctx, ctx->AssignVariable(2, type, new_mom)); + OP_REQUIRES_OK(ctx, ctx->AssignVariable("var", dtype_, new_var)); + if (centered_) { + OP_REQUIRES_OK(ctx, ctx->AssignVariable("mg", dtype_, mg)); + } + OP_REQUIRES_OK(ctx, ctx->AssignVariable("ms", dtype_, new_ms)); + OP_REQUIRES_OK(ctx, ctx->AssignVariable("mom", dtype_, new_mom)); } + + protected: + bool centered_ = false; + + private: + DataType dtype_; }; REGISTER_XLA_OP(Name("ResourceApplyRMSProp").TypeConstraint("T", kFloatTypes), ResourceApplyRMSProp); +class ResourceApplyCenteredRMSProp : public ResourceApplyRMSProp { + public: + explicit ResourceApplyCenteredRMSProp(OpKernelConstruction* ctx) + : ResourceApplyRMSProp(ctx) { + centered_ = true; + } +}; +REGISTER_XLA_OP( + Name("ResourceApplyCenteredRMSProp").TypeConstraint("T", kFloatTypes), + ResourceApplyCenteredRMSProp); + void CompileFtrl(XlaOpKernelContext* ctx, DataType dtype, bool has_l2_shrinkage) { xla::XlaBuilder* b = ctx->builder(); @@ -640,5 +819,107 @@ class ResourceApplyAdadelta : public XlaOpKernel { REGISTER_XLA_OP(Name("ResourceApplyAdadelta").TypeConstraint("T", kFloatTypes), ResourceApplyAdadelta); +class ResourceApplySignBase : public XlaOpKernel { + public: + explicit ResourceApplySignBase(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &dtype_)); + } + + void Compile(XlaOpKernelContext* ctx) override { + TensorShape var_shape, m_shape; + xla::XlaOp var, m; + OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(0, dtype_, &var_shape, &var)); + OP_REQUIRES_OK(ctx, ctx->ReadVariableInput(1, dtype_, &m_shape, &m)); + OP_REQUIRES(ctx, var_shape.IsSameSize(m_shape), + errors::InvalidArgument("var and m do not have the same shape", + var_shape.DebugString(), " ", + m_shape.DebugString())); + TensorShape grad_shape = ctx->InputShape(6); + OP_REQUIRES(ctx, var_shape.IsSameSize(grad_shape), + errors::InvalidArgument( + "var and grad do not have the same shape", + var_shape.DebugString(), " ", grad_shape.DebugString())); + CheckScalarParams(ctx); + + xla::XlaOp lr = ctx->Input(2); + xla::XlaOp alpha = ctx->Input(3); + xla::XlaOp sign_decay = ctx->Input(4); + xla::XlaOp beta = ctx->Input(5); + xla::XlaOp grad = ctx->Input(6); + + m = m * beta + grad * (xla::ScalarLike(beta, 1.0) - beta); + xla::XlaOp decay = xla::Sign(grad) * xla::Sign(m) * sign_decay; + + xla::XlaOp grad_scale = ComputeGradientScale(alpha, decay); + var = var - lr * grad_scale * grad; + OP_REQUIRES_OK(ctx, ctx->AssignVariable(0, dtype_, var)); + OP_REQUIRES_OK(ctx, ctx->AssignVariable(1, dtype_, m)); + } + + virtual void CheckScalarParams(XlaOpKernelContext* ctx) { + TensorShape lr_shape = ctx->InputShape(2); + TensorShape sign_decay_shape = ctx->InputShape(4); + TensorShape beta_shape = ctx->InputShape(5); + + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(lr_shape), + errors::InvalidArgument("lr is not a scalar: ", + lr_shape.DebugString())); + + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(sign_decay_shape), + errors::InvalidArgument("sign_decay is not a scalar: ", + sign_decay_shape.DebugString())); + + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(beta_shape), + errors::InvalidArgument("beta is not a scalar: ", + beta_shape.DebugString())); + } + + virtual xla::XlaOp ComputeGradientScale(xla::XlaOp alpha, + xla::XlaOp decay) = 0; + + private: + DataType dtype_; +}; + +class ResourceApplyAddSign : public ResourceApplySignBase { + public: + explicit ResourceApplyAddSign(OpKernelConstruction* ctx) + : ResourceApplySignBase(ctx) {} + + void CheckScalarParams(XlaOpKernelContext* ctx) override { + ResourceApplySignBase::CheckScalarParams(ctx); + TensorShape alpha_shape = ctx->InputShape(3); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(alpha_shape), + errors::InvalidArgument("alpha is not a scalar: ", + alpha_shape.DebugString())); + } + + xla::XlaOp ComputeGradientScale(xla::XlaOp alpha, xla::XlaOp decay) override { + return alpha + decay; + } +}; +REGISTER_XLA_OP(Name("ResourceApplyAddSign").TypeConstraint("T", kFloatTypes), + ResourceApplyAddSign); + +class ResourceApplyPowerSign : public ResourceApplySignBase { + public: + explicit ResourceApplyPowerSign(OpKernelConstruction* ctx) + : ResourceApplySignBase(ctx) {} + + void CheckScalarParams(XlaOpKernelContext* ctx) override { + ResourceApplySignBase::CheckScalarParams(ctx); + TensorShape logbase_shape = ctx->InputShape(3); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(logbase_shape), + errors::InvalidArgument("logbase is not a scalar: ", + logbase_shape.DebugString())); + } + + xla::XlaOp ComputeGradientScale(xla::XlaOp alpha, xla::XlaOp decay) override { + return xla::Exp(alpha * decay); + } +}; +REGISTER_XLA_OP(Name("ResourceApplyPowerSign").TypeConstraint("T", kFloatTypes), + ResourceApplyPowerSign); + } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/transpose_op.cc b/tensorflow/compiler/tf2xla/kernels/transpose_op.cc index 6c721c48fe3af45aff5cd0bd5e74e2693faf9f97..f9148b394212777271f9eba51313ee17b19819af 100644 --- a/tensorflow/compiler/tf2xla/kernels/transpose_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/transpose_op.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/kernels/bounds_check.h" diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc index 116a020437e263f1d3d82fee5c0ea0ca4f97e634..0bdfc05726105e2d18362a691cbe2aab00bf77f3 100644 --- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/math.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" namespace tensorflow { @@ -51,43 +51,18 @@ XLAJIT_MAKE_UNARY(Conj, xla::Conj(x)); // Return x if x>0, otherwise -x. XLAJIT_MAKE_UNARY(Abs, xla::Abs(x)); - -// acos(x) = 2 * atan(sqrt(1 - x^2) / (1 + x)) -XLAJIT_MAKE_UNARY(Acos, - xla::ScalarLike(x, 2.0) * - xla::Atan2(xla::Sqrt(xla::ScalarLike(x, 1.0) - x * x), - xla::ScalarLike(x, 1.0) + x)); - -// acosh(x) = log(x + sqrt(x^2 - 1)) -// = log(x + sqrt((x+1)*(x-1))) -XLAJIT_MAKE_UNARY(Acosh, - xla::Log(x + xla::Sqrt((x + xla::ScalarLike(x, 1.0)) * - (x - xla::ScalarLike(x, 1.0))))); - -// asin(x) = 2 * atan(x / (1 + sqrt(1 - x^2))) -XLAJIT_MAKE_UNARY( - Asin, xla::ScalarLike(x, 2.0) * - xla::Atan2(x, xla::ScalarLike(x, 1.0) + - xla::Sqrt(xla::ScalarLike(x, 1.0) - x * x))); - -// asinh(x) = log(x + sqrt(x^2 + 1)) -XLAJIT_MAKE_UNARY(Asinh, - xla::Log(x + xla::Sqrt(x * x + xla::ScalarLike(x, 1.0)))); - -XLAJIT_MAKE_UNARY(Atan, xla::Atan2(x, xla::ScalarLike(x, 1.0))); - -// atanh(x) = 0.5 * log((1 + x) / (1 - x)) -XLAJIT_MAKE_UNARY(Atanh, xla::Log((xla::ScalarLike(x, 1.0) + x) / - (xla::ScalarLike(x, 1.0) - x)) * - xla::ScalarLike(x, 0.5)); +XLAJIT_MAKE_UNARY(Acos, xla::Acos(x)); +XLAJIT_MAKE_UNARY(Acosh, xla::Acosh(x)); +XLAJIT_MAKE_UNARY(Asin, xla::Asin(x)) +XLAJIT_MAKE_UNARY(Asinh, xla::Asinh(x)); +XLAJIT_MAKE_UNARY(Atan, xla::Atan(x)); +XLAJIT_MAKE_UNARY(Atanh, xla::Atanh(x)); XLAJIT_MAKE_UNARY(Ceil, xla::Ceil(x)); XLAJIT_MAKE_UNARY(Cos, xla::Cos(x)); -XLAJIT_MAKE_UNARY(Cosh, (xla::Exp(x) + xla::Exp(-x)) * xla::ScalarLike(x, 0.5)); +XLAJIT_MAKE_UNARY(Cosh, xla::Cosh(x)); XLAJIT_MAKE_UNARY(Sin, xla::Sin(x)); XLAJIT_MAKE_UNARY(Exp, xla::Exp(x)); - XLAJIT_MAKE_UNARY(Expm1, xla::Expm1(x)); - XLAJIT_MAKE_UNARY(Floor, xla::Floor(x)); XLAJIT_MAKE_UNARY(IsFinite, xla::IsFinite(x)); XLAJIT_MAKE_UNARY( @@ -99,7 +74,6 @@ XLAJIT_MAKE_UNARY(IsNan, xla::Ne(x, x)); XLAJIT_MAKE_UNARY(Inv, xla::ScalarLike(x, 1.0) / x); XLAJIT_MAKE_UNARY(Reciprocal, xla::ScalarLike(x, 1.0) / x); XLAJIT_MAKE_UNARY(Log, xla::Log(x)); - XLAJIT_MAKE_UNARY(Log1p, xla::Log1p(x)); XLAJIT_MAKE_UNARY(Invert, xla::Not(x)); @@ -136,7 +110,7 @@ XLAJIT_MAKE_UNARY(Sigmoid, Sigmoid(x)); // Returns 0 if x is 0, -1 if x < 0 and 1 if x > 0. XLAJIT_MAKE_UNARY(Sign, xla::Sign(x)); -XLAJIT_MAKE_UNARY(Sinh, (xla::Exp(x) - xla::Exp(-x)) * xla::ScalarLike(x, 0.5)); +XLAJIT_MAKE_UNARY(Sinh, xla::Sinh(x)); // softplus(x) = log(1 + exp(x)) // @@ -153,7 +127,7 @@ XLAJIT_MAKE_UNARY(Softplus, xla::Max(x, xla::ScalarLike(x, 0.0)) + XLAJIT_MAKE_UNARY(Softsign, x / (xla::Abs(x) + xla::ScalarLike(x, 1.0))); XLAJIT_MAKE_UNARY(Sqrt, xla::Sqrt(x)); XLAJIT_MAKE_UNARY(Square, x* x); -XLAJIT_MAKE_UNARY(Tan, xla::Sin(x) / xla::Cos(x)); +XLAJIT_MAKE_UNARY(Tan, xla::Tan(x)); XLAJIT_MAKE_UNARY(Tanh, xla::Tanh(x)); XLAJIT_MAKE_UNARY(Real, xla::Real(x)); @@ -189,5 +163,51 @@ class ErfcOp : public XlaOpKernel { }; REGISTER_XLA_OP(Name("Erfc"), ErfcOp); +class LgammaOp : public XlaOpKernel { + public: + explicit LgammaOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} + // Calculate lgamma using the Lanczos approximation + // (https://en.wikipedia.org/wiki/Lanczos_approximation). + void Compile(XlaOpKernelContext* ctx) override { + xla::XlaOp input = ctx->Input(0); + xla::PrimitiveType input_type = ctx->input_xla_type(0); + + if (input_type == xla::F16 || input_type == xla::BF16) { + // The approximation works better with at least 32-bits of accuracy. + xla::XlaOp input_f32 = xla::ConvertElementType(input, xla::F32); + xla::XlaOp result_f32 = xla::Lgamma(input_f32); + xla::XlaOp result_x16 = xla::ConvertElementType(result_f32, input_type); + ctx->SetOutput(0, result_x16); + } else { + xla::XlaOp result = xla::Lgamma(input); + ctx->SetOutput(0, result); + } + } +}; // namespace +REGISTER_XLA_OP(Name("Lgamma"), LgammaOp); + +class DigammaOp : public XlaOpKernel { + public: + explicit DigammaOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} + // Calculate lgamma using the Lanczos approximation + // (https://en.wikipedia.org/wiki/Lanczos_approximation). + void Compile(XlaOpKernelContext* ctx) override { + xla::XlaOp input = ctx->Input(0); + xla::PrimitiveType input_type = ctx->input_xla_type(0); + + if (input_type == xla::F16 || input_type == xla::BF16) { + // The approximation works better with at least 32-bits of accuracy. + xla::XlaOp input_f32 = xla::ConvertElementType(input, xla::F32); + xla::XlaOp result_f32 = xla::Digamma(input_f32); + xla::XlaOp result_x16 = xla::ConvertElementType(result_f32, input_type); + ctx->SetOutput(0, result_x16); + } else { + xla::XlaOp result = xla::Digamma(input); + ctx->SetOutput(0, result); + } + } +}; // namespace +REGISTER_XLA_OP(Name("Digamma"), DigammaOp); + } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/unpack_op.cc b/tensorflow/compiler/tf2xla/kernels/unpack_op.cc index 0e5d58ecbaeb13571f82a1311e29dc0ba91c11ac..8671632976023fded04c26a9780c1a67638b0916 100644 --- a/tensorflow/compiler/tf2xla/kernels/unpack_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/unpack_op.cc @@ -22,8 +22,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" diff --git a/tensorflow/compiler/tf2xla/kernels/variable_ops.cc b/tensorflow/compiler/tf2xla/kernels/variable_ops.cc index febac8287350e32fccfd4cb5613f21b9a5fbcb95..2c92a585f5679242d672d0402e617ff199b94f17 100644 --- a/tensorflow/compiler/tf2xla/kernels/variable_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/variable_ops.cc @@ -19,8 +19,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.cc b/tensorflow/compiler/tf2xla/kernels/while_op.cc index 340165bac6a2a214d8f84d5a116a4197b1df2c7b..1e8a376765d36ffa677ece06fbd131744299e04b 100644 --- a/tensorflow/compiler/tf2xla/kernels/while_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/while_op.cc @@ -21,8 +21,9 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/op_kernel.h" @@ -299,6 +300,7 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) { VLOG(1) << "Done building while loop"; } +REGISTER_XLA_OP(Name("While").AllowResourceTypes(), XlaWhileOp); REGISTER_XLA_OP(Name("XlaWhile").AllowResourceTypes(), XlaWhileOp); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/BUILD b/tensorflow/compiler/tf2xla/lib/BUILD index dfa3c0595acbfeb35f944209b4354b357b11bf3c..cb7a40e23d539f758d963791f1c2b4d37374ade5 100644 --- a/tensorflow/compiler/tf2xla/lib/BUILD +++ b/tensorflow/compiler/tf2xla/lib/BUILD @@ -25,8 +25,8 @@ cc_library( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/core:lib", ], ) @@ -40,13 +40,13 @@ cc_library( ":triangular_solve", ":util", ":while_loop", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:constants", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) @@ -59,13 +59,35 @@ cc_library( "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/lib:math", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:protos_all_cc", ], ) +cc_library( + name = "qr", + srcs = ["qr.cc"], + hdrs = ["qr.h"], + deps = [ + ":batch_dot", + ":util", + ":while_loop", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", + "//tensorflow/compiler/xla/client/lib:arithmetic", + "//tensorflow/compiler/xla/client/lib:constants", + "//tensorflow/compiler/xla/client/lib:math", + "//tensorflow/compiler/xla/client/lib:numeric", + "//tensorflow/core:lib", + ], +) + cc_library( name = "scatter", srcs = ["scatter.cc"], @@ -73,14 +95,14 @@ cc_library( deps = [ ":util", ":while_loop", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) @@ -92,14 +114,15 @@ cc_library( deps = [ ":batch_dot", ":util", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:constants", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client/lib:numeric", "//tensorflow/core:lib", ], ) @@ -111,7 +134,7 @@ xla_test( deps = [ ":triangular_solve", "//tensorflow/compiler/xla:array2d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", @@ -119,7 +142,7 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -133,13 +156,14 @@ cc_library( srcs = ["util.cc"], hdrs = ["util.h"], deps = [ + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/core:lib", ], ) @@ -151,7 +175,7 @@ xla_test( ":batch_dot", ":util", "//tensorflow/compiler/xla:array2d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", @@ -176,8 +200,8 @@ cc_library( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/core:lib", ], ) diff --git a/tensorflow/compiler/tf2xla/lib/batch_dot.cc b/tensorflow/compiler/tf2xla/lib/batch_dot.cc index f9f3a8c8cfcbcd0a2ac853360c629d90c94db8b0..f666d22ea44216beef74608bb4d9f33fb2fe82c6 100644 --- a/tensorflow/compiler/tf2xla/lib/batch_dot.cc +++ b/tensorflow/compiler/tf2xla/lib/batch_dot.cc @@ -18,7 +18,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" @@ -84,7 +84,7 @@ xla::XlaOp BatchDot(xla::XlaOp x, xla::XlaOp y, bool transpose_x, dimensions.push_back(y_shape.dimensions(y_outer_dim)); return xla::Broadcast( xla::ConstantLiteral(builder, - xla::Literal::Zero(x_shape.element_type())), + xla::LiteralUtil::Zero(x_shape.element_type())), dimensions); } diff --git a/tensorflow/compiler/tf2xla/lib/batch_dot.h b/tensorflow/compiler/tf2xla/lib/batch_dot.h index d07a9486f18c0b8f26782123a8fba4ba228f71ee..8757b16a1ca6a8cec5e3c801c885e7bbbb2f2c76 100644 --- a/tensorflow/compiler/tf2xla/lib/batch_dot.h +++ b/tensorflow/compiler/tf2xla/lib/batch_dot.h @@ -16,8 +16,8 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_BATCH_DOT_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_BATCH_DOT_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.cc b/tensorflow/compiler/tf2xla/lib/cholesky.cc index cc840de393ebc2983ddf7659c6c18d8136de5dd6..87d73eb3f07ebd7dfa4fef50ebe76cad0c4ed117 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.cc +++ b/tensorflow/compiler/tf2xla/lib/cholesky.cc @@ -23,8 +23,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/util.h" #include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.h b/tensorflow/compiler/tf2xla/lib/cholesky.h index 0f6e0e9d152ec5daedeb9c0e355bfb9731759094..1bef9bb166c576ec665bb48265b4da200ddca2a0 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.h +++ b/tensorflow/compiler/tf2xla/lib/cholesky.h @@ -16,8 +16,8 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_CHOLESKY_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_CHOLESKY_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/lib/qr.cc b/tensorflow/compiler/tf2xla/lib/qr.cc new file mode 100644 index 0000000000000000000000000000000000000000..fc0c1ee838190b1f1a7ca5b901c97e0a35232a97 --- /dev/null +++ b/tensorflow/compiler/tf2xla/lib/qr.cc @@ -0,0 +1,387 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/tf2xla/lib/qr.h" + +#include +#include + +#include "tensorflow/compiler/tf2xla/lib/batch_dot.h" +#include "tensorflow/compiler/tf2xla/lib/util.h" +#include "tensorflow/compiler/tf2xla/lib/while_loop.h" +#include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/lib/math.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/core/lib/core/errors.h" + +namespace tensorflow { + +namespace { + +// Computes a Householder reflection of the form: +// H = I - tau v v.T. +// such that +// H . ( x1 ) = ( x1 ) +// ( x2 ) = ( x2 ) +// ( ... ) = ( ... ) +// ( xk ) = ( beta ) +// ( ... ) ( 0 ) +// ( ... ) ( 0 ) +// Unlike the usual formulation, we allow the caller to supply 'k' rather than +// only providing the relevant part of 'x' to maintain XLA's static shape +// invariant. In addition, the implementation supports batching. +// Pseudo-code, without batching: +// alpha = x[k] +// x_copy = np.copy(x) +// x_copy[:k+1] = 0 +// xnorm = norm2(x_copy) +// if xnorm == 0: +// beta = alpha +// tau = 0 +// v = np.zeros_like(x) +// else: +// beta = - np.sign(alpha) * dlapy2(alpha, xnorm) +// tau = (beta - alpha) / beta +// v = x / (alpha - beta) +// v[k] = 1 +// return (v, tau, beta) +// TODO(phawkins): LAPACK's xLARFG implementation has code for handling +// overflows in the norm/beta calculations. Perhaps do the same here. +xla::Status House(xla::XlaOp x, xla::XlaOp k, gtl::ArraySlice batch_dims, + const int64 m, xla::XlaOp* v, xla::XlaOp* tau, + xla::XlaOp* beta) { + xla::XlaBuilder* const builder = x.builder(); + TF_ASSIGN_OR_RETURN(xla::Shape x_shape, builder->GetShape(x)); + const xla::PrimitiveType type = x_shape.element_type(); + + std::vector batch_dim_ids(batch_dims.size()); + std::iota(batch_dim_ids.begin(), batch_dim_ids.end(), 0); + const int64 minor_dim = batch_dims.size(); + + xla::XlaOp zero = xla::ScalarLike(x, 0.0); + xla::XlaOp one = xla::ScalarLike(x, 1.0); + + // alpha = x[k] + xla::XlaOp alpha = + xla::Reshape(DynamicSliceInMinorDims(x, {k}, {1}), batch_dims); + + // Compute x[k+1:] (padded with zeros in elements 0..k) + xla::XlaOp iota = xla::Iota(builder, xla::S32, m); + xla::XlaOp x_after_k = + xla::Mul(x, xla::ConvertElementType(xla::Gt(iota, k), type), + /*broadcast_dimensions=*/{minor_dim}); + + // sigma = np.dot(x[k+1:], x[k+1:]) + auto sigma = + xla::Reduce(x_after_k * x_after_k, zero, + xla::CreateScalarAddComputation(type, builder), {minor_dim}); + // mu = np.sqrt(x[k]*x[k] + sigma) + auto mu = xla::Sqrt(xla::Square(alpha) + sigma); + + auto sigma_is_zero = xla::Eq(sigma, zero); + + *beta = xla::Select(sigma_is_zero, alpha, -xla::Sign(alpha) * mu); + *tau = xla::Select(sigma_is_zero, xla::Broadcast(zero, batch_dims), + (*beta - alpha) / *beta); + auto divisor = xla::Select(sigma_is_zero, xla::Broadcast(one, batch_dims), + alpha - *beta); + + auto e_k = xla::Broadcast(xla::ConvertElementType(xla::Eq(iota, k), type), + std::vector(batch_dims.size(), 1)); + + // Form v as [0, 0, ..., 1] ++ x[k+1:] / divisor + // If sigma is zero, x[k+1:] is zero, so use any non-zero divisor. + *v = e_k + + xla::Div(x_after_k, divisor, /*broadcast_dimensions=*/batch_dim_ids); + return Status::OK(); +} + +// Householder QR decomposition. Algorithm 5.2.1 from Golub and Van +// Loan "Matrix Computations", 4th Edition. This is an unblocked implementation +// used as an inner routine of the blocked implementation. +// Algorithm is adapted slightly so the shapes inside the loop are static, at +// the cost of some redundant computation. Since this is used as an inner block +// kernel, accumulates the Householder transformations (vs, taus) rather than +// the matrix q. +// Equivalent Python code, without batching: +// def qr(a): +// m = a.shape[0] +// n = a.shape[1] +// vs = np.zeros([m, n]) +// taus = np.zeros([n]) +// for j in xrange(min(m, n)): +// v, tau, beta = house(a[:, j], j) +// # Unusually, we apply the Householder transformation to the entirety of +// # a, wasting FLOPs to maintain the static shape invariant that XLA +// # requires. For columns that precede j this has no effect. +// a[:, :] -= tau * np.dot(v[:, np.newaxis], +// np.dot(v[np.newaxis, :], a[:, :])) +// # Form column j explicitly rather than relying on the precision of the +// # Householder update. +// a[j, j] = beta +// a[j+1:, j] = np.zeros([m - j - 1], dtype=a.dtype) +// vs[:, j] = v +// taus[j] = tau +// return (q, vs, taus) +struct QRBlockResult { + // The factored R value + xla::XlaOp r; + + // Representation of the Householder matrices I - beta v v.T + xla::XlaOp taus; // Shape: [..., n] + xla::XlaOp vs; // Shape: [..., m, n] +}; +xla::StatusOr QRBlock(xla::XlaOp a) { + xla::XlaBuilder* builder = a.builder(); + TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); + const int num_dims = xla::ShapeUtil::Rank(a_shape); + if (num_dims < 2) { + return errors::InvalidArgument("Arguments to QR must have rank >= 2: ", + num_dims); + } + xla::PrimitiveType type = a_shape.element_type(); + + const int64 m = xla::ShapeUtil::GetDimension(a_shape, -2); + const int64 n = xla::ShapeUtil::GetDimension(a_shape, -1); + + const int64 num_batch_dims = num_dims - 2; + std::vector batch_dims(num_batch_dims); + for (int i = 0; i < num_batch_dims; ++i) { + batch_dims[i] = xla::ShapeUtil::GetDimension(a_shape, i); + } + + std::vector batch_dim_indices(num_batch_dims); + std::iota(batch_dim_indices.begin(), batch_dim_indices.end(), 0); + + auto qr_body_fn = + [&](xla::XlaOp j, gtl::ArraySlice values, + xla::XlaBuilder* builder) -> xla::StatusOr> { + auto a = values[0]; + auto vs = values[1]; + auto taus = values[2]; + + // v, beta = house(a[:, j], j) + auto x = DynamicSliceInMinorDims(a, {j}, {1}); + xla::XlaOp v, tau, beta; + TF_RETURN_IF_ERROR(House(xla::Collapse(x, {num_dims - 2, num_dims - 1}), j, + batch_dims, m, &v, &tau, &beta)); + + std::vector shape = batch_dims; + shape.push_back(1); + shape.push_back(m); + auto v_broadcast = xla::Reshape(v, shape); + // a[:, :] -= tau * np.dot(v[:, np.newaxis], + // np.dot(v[np.newaxis, :], a[:, :])) + auto vva = BatchDot(v_broadcast, a); + vva = BatchDot(v_broadcast, vva, /*transpose_x=*/true); + a = a - xla::Mul(tau, vva, + /*broadcast_dimensions=*/batch_dim_indices); + + // It is more precise to populate column 'k' explicitly, rather than + // computing it implicitly by applying the Householder transformation. + // a[k,k] = beta + // a[k+1:,k] = np.zeros([m-k-1], dtype=a.dtype) + auto iota = xla::Reshape(xla::Iota(a.builder(), xla::S32, m), {m, 1}); + auto predecessor_mask = xla::ConvertElementType(xla::Lt(iota, j), type); + auto mask = xla::Broadcast(xla::ConvertElementType(xla::Eq(iota, j), type), + std::vector(batch_dims.size(), 1)); + auto new_x = + xla::Mul(x, predecessor_mask, + /*broadcast_dimensions=*/{num_dims - 2, num_dims - 1}) + + xla::Mul(beta, mask, /*broadcast_dimensions=*/batch_dim_indices); + a = DynamicUpdateSliceInMinorDims(a, new_x, {j}); + + // vs[:, j] = v + vs = DynamicUpdateSliceInMinorDims( + vs, xla::Reshape(v, ConcatVectors(batch_dims, {m, 1})), {j}); + // taus[j] = tau + taus = DynamicUpdateSliceInMinorDims( + taus, xla::Reshape(tau, ConcatVectors(batch_dims, {1})), {j}); + return std::vector{a, vs, taus}; + }; + + auto vs = xla::Zeros(builder, xla::ShapeUtil::MakeShape( + type, ConcatVectors(batch_dims, {m, n}))); + auto taus = xla::Zeros( + builder, xla::ShapeUtil::MakeShape(type, ConcatVectors(batch_dims, {n}))); + + TF_ASSIGN_OR_RETURN(auto values, + XlaForEachIndex(std::min(m, n), xla::S32, qr_body_fn, + {a, vs, taus}, "qr", builder)); + + QRBlockResult result; + result.r = values[0]; + result.vs = values[1]; + result.taus = values[2]; + return result; +} + +// Computes W and Y such that I-WY is equivalent to the sequence of Householder +// transformations given by vs and taus. +// Golub and van Loan, "Matrix Computations", algorithm 5.1.2. +// Y = np.zeros([m, n]) +// W = np.zeros([m, n]) +// Y[:, 0] = vs[:, 0] +// W[:, 0] = -taus[0] * vs[:, 0] +// for j in xrange(1, n): +// v = vs[:, j] +// z = -taus[j] * v - taus[j] * np.dot(W, np.dot(Y.T, v)) +// W[:, j] = z +// Y[:, j] = v +// return W +// There is no need to return Y since at termination of the loop it is equal to +// vs. +xla::StatusOr ComputeWYRepresentation( + xla::PrimitiveType type, gtl::ArraySlice batch_dims, xla::XlaOp vs, + xla::XlaOp taus, int64 m, int64 n) { + std::vector batch_dim_indices(batch_dims.size()); + std::iota(batch_dim_indices.begin(), batch_dim_indices.end(), 0); + int64 n_index = batch_dims.size() + 1; + + auto body_fn = + [&](xla::XlaOp j, gtl::ArraySlice values, + xla::XlaBuilder* builder) -> xla::StatusOr> { + auto w = values[0]; + auto y = values[1]; + const auto vs = values[2]; + const auto taus = values[3]; + + // Want j values in range [1, ... n). + j = j + xla::ConstantR0(builder, 1); + // vs has shape [..., m, 1] + auto v = DynamicSliceInMinorDims(vs, {j}, {1}); + // beta has shape [..., 1] + auto beta = DynamicSliceInMinorDims(taus, {j}, {1}); + + // yv has shape [..., n, 1] + auto yv = BatchDot(y, v, /*transpose_x=*/true); + // wyv has shape [..., m, 1] + auto wyv = BatchDot(w, yv); + + auto z = xla::Mul( + -beta, v + wyv, + /*broadcast_dimensions=*/ConcatVectors(batch_dim_indices, {n_index})); + + w = DynamicUpdateSliceInMinorDims(w, z, {j}); + y = DynamicUpdateSliceInMinorDims(y, v, {j}); + + return std::vector{w, y, vs, taus}; + }; + + xla::XlaBuilder* builder = vs.builder(); + auto w = xla::Zeros(builder, xla::ShapeUtil::MakeShape( + type, ConcatVectors(batch_dims, {m, n}))); + auto y = w; + auto v = SliceInMinorDims(vs, {0}, {1}); + auto beta = SliceInMinorDims(taus, {0}, {1}); + y = UpdateSliceInMinorDims(y, v, {0}); + auto bv = xla::Mul( + -beta, v, + /*broadcast_dimensions=*/ConcatVectors(batch_dim_indices, {n_index})); + w = UpdateSliceInMinorDims(w, bv, {0}); + + TF_ASSIGN_OR_RETURN( + auto values, XlaForEachIndex(n - 1, xla::S32, body_fn, {w, y, vs, taus}, + "wy", builder)); + return values[0]; +} + +} // namespace + +// Block Householder QR Factorization. Algorithm 5.2.2 of Golub and van Loan. +// def qr_blocked(a, block_size): +// m = a.shape[0] +// n = a.shape[1] +// q = np.eye(m) +// for i in xrange(0, min(m, n), block_size): +// k = min(block_size, min(m, n) - s) +// (a, vs, taus) = qr(a[i:, i:i+k]) +// y = vs +// w = ComputeWYRepresentation(vs, taus, m-i, k) +// a[i:, i+r:] += np.dot(y, np.dot(w.T, a[i:, i+k:])) +// q[:, i:] += np.dot(q[:, i:], np.dot(w, y.T)) +// return (q, a) +// TODO(phawkins): consider using UT transformations (in the form I - V U V') +// rather than WY transformations. +xla::StatusOr QRDecomposition(xla::XlaOp a, + int64 block_size) { + xla::XlaBuilder* builder = a.builder(); + TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); + const int num_dims = xla::ShapeUtil::Rank(a_shape); + if (num_dims < 2) { + return errors::InvalidArgument("Arguments to QR must have rank >= 2: ", + num_dims); + } + xla::PrimitiveType type = a_shape.element_type(); + + const int64 m = xla::ShapeUtil::GetDimension(a_shape, -2); + const int64 n = xla::ShapeUtil::GetDimension(a_shape, -1); + const int64 p = std::min(m, n); + + if (block_size < 1) { + return errors::InvalidArgument( + "block_size argument to QR must be >= 1; got ", block_size); + } + + const int64 num_batch_dims = num_dims - 2; + std::vector batch_dims(num_batch_dims); + for (int i = 0; i < num_batch_dims; ++i) { + batch_dims[i] = xla::ShapeUtil::GetDimension(a_shape, i); + } + + auto q = xla::Broadcast(xla::IdentityMatrix(builder, type, m, m), batch_dims); + for (int64 i = 0; i < p; i += block_size) { + int64 k = std::min(block_size, p - i); + + auto a_block = SliceInMinorDims(a, {i, i}, {m, i + k}); + TF_ASSIGN_OR_RETURN(auto qr_block, QRBlock(a_block)); + + a = UpdateSliceInMinorDims(a, qr_block.r, {i, i}); + + // Compute the I-WY block representation of a product of Householder + // matrices. + TF_ASSIGN_OR_RETURN(auto w, + ComputeWYRepresentation(type, batch_dims, qr_block.vs, + qr_block.taus, m - i, k)); + auto y = qr_block.vs; + + // a[i:, i+k:] += np.dot(Y, np.dot(W.T, a[i:, i+k:])) + auto a_panel = SliceInMinorDims(a, {i, i + k}, {m, n}); + auto a_update = BatchDot(w, a_panel, /*transpose_x=*/true); + a_update = BatchDot(y, a_update); + a_panel = a_panel + a_update; + a = UpdateSliceInMinorDims(a, a_panel, {i, i + k}); + + // q[:, i:] += np.dot(np.dot(q[:, i:], W), Y.T)) + auto q_panel = SliceInMinorDims(q, {0, i}, {m, m}); + auto q_update = BatchDot(q_panel, w); + q_update = + BatchDot(q_update, y, /*transpose_x=*/false, /*transpose_y=*/true); + q_panel = q_panel + q_update; + q = UpdateSliceInMinorDims(q, q_panel, {0, i}); + } + QRDecompositionResult result; + result.q = q; + result.r = a; + return result; +} + +} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/qr.h b/tensorflow/compiler/tf2xla/lib/qr.h new file mode 100644 index 0000000000000000000000000000000000000000..abd2316ac961f583dd29f90f43cf6209de30bd6a --- /dev/null +++ b/tensorflow/compiler/tf2xla/lib/qr.h @@ -0,0 +1,40 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_QR_H_ +#define TENSORFLOW_COMPILER_TF2XLA_LIB_QR_H_ + +#include "tensorflow/compiler/xla/client/xla_builder.h" + +namespace tensorflow { + +// Computes the QR decompositions of a batch of matrices. That is, +// given a (batched) matrix a, computes an orthonormal matrix Q and an +// upper-triangular matrix R such that a = QR. +// `a` must be a (batched) matrix of size [..., m, n]. +// The algorithm implements a blocked QR decomposition; `block_size` is +// the block size to use. +// TODO(phawkins): handle the complex case. +struct QRDecompositionResult { + xla::XlaOp q; + xla::XlaOp r; +}; + +xla::StatusOr QRDecomposition(xla::XlaOp a, + int64 block_size = 128); + +} // namespace tensorflow + +#endif // TENSORFLOW_COMPILER_TF2XLA_LIB_QR_H_ diff --git a/tensorflow/compiler/tf2xla/lib/random.cc b/tensorflow/compiler/tf2xla/lib/random.cc index 8ff10fbd3fbf9308140af84c752a5a50bec8fd32..5e7cf00ee5e063aef36a9531ff87d8fe6928ca1f 100644 --- a/tensorflow/compiler/tf2xla/lib/random.cc +++ b/tensorflow/compiler/tf2xla/lib/random.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/math.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/status_macros.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/lib/random.h b/tensorflow/compiler/tf2xla/lib/random.h index 2c573fd85b2783fdac13457cdb277cf988ac40c4..59fc5d0433a51328bc78006ab1c3495d908b44ac 100644 --- a/tensorflow/compiler/tf2xla/lib/random.h +++ b/tensorflow/compiler/tf2xla/lib/random.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_RANDOM_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_RANDOM_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/framework/types.pb.h" diff --git a/tensorflow/compiler/tf2xla/lib/scatter.cc b/tensorflow/compiler/tf2xla/lib/scatter.cc index 85e3d3ab85a89615cc5a01bdb4ec8f7fec30d58e..ba22eff73abab11abeb57283c63318b2e50a9ca1 100644 --- a/tensorflow/compiler/tf2xla/lib/scatter.cc +++ b/tensorflow/compiler/tf2xla/lib/scatter.cc @@ -21,8 +21,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/util.h" #include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/util.h" @@ -114,7 +114,7 @@ xla::StatusOr XlaScatter( auto buffer = loop_vars[2]; auto zero_index = xla::ConstantLiteral( - body_builder, xla::Literal::Zero(indices_shape.element_type())); + body_builder, xla::LiteralUtil::Zero(indices_shape.element_type())); // Slice the i-th index from the indices array. xla::XlaOp index; @@ -132,7 +132,7 @@ xla::StatusOr XlaScatter( // Discard updates with negative indices, since some users expect this. auto index_in_range = xla::ReduceAll( xla::Le(zero_index, index), xla::ConstantR0(body_builder, true), - xla::CreateScalarAndComputation(body_builder)); + xla::CreateScalarAndComputation(xla::PRED, body_builder)); // Make the index in bounds to prevent implementation defined behavior. index = xla::Max(index, zero_index); diff --git a/tensorflow/compiler/tf2xla/lib/scatter.h b/tensorflow/compiler/tf2xla/lib/scatter.h index 87309e10ede320a81d173cd0a64492f88a2c7376..13a5f1b850a612bddeeac39bef431c19925351ca 100644 --- a/tensorflow/compiler/tf2xla/lib/scatter.h +++ b/tensorflow/compiler/tf2xla/lib/scatter.h @@ -18,8 +18,8 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc index 588afaac65122fbdc6fe9a399a7a50a3a49749cb..04fa10108cef66f429392951eea70e59643a2d29 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc @@ -21,16 +21,316 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/batch_dot.h" #include "tensorflow/compiler/tf2xla/lib/util.h" #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/math/math_util.h" namespace tensorflow { +// Get the diagonal blocks of the coefficient matrix +xla::XlaOp DiagonalBlocks(xla::XlaOp a, int64 block_size) { + xla::XlaBuilder* builder = a.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(a)); + int ndims = xla::ShapeUtil::Rank(shape); + int64 n = xla::ShapeUtil::GetDimension(shape, -1); + int64 num_blocks = n / block_size; + + xla::XlaOp diag_blocks; + + // If the coefficient matrix is exactly the block size, we just add a + // singleton dimension i.e. [..., n, n] -> [..., 1, n, n] + if (n == block_size) { + std::vector permutation(ndims); + std::iota(permutation.begin(), permutation.end(), 1); + permutation.insert(permutation.end() - 2, 0); + return Transpose(Broadcast(a, /*broadcast_sizes=*/{1}), permutation); + } + + // We can grab entire blocks using gather + if (n > block_size) { + // Construct the starting indices of the diagonal blocks + auto gather_indices = + Transpose(Broadcast(Mul(Iota(builder, xla::S32, num_blocks), + xla::ConstantR0(builder, block_size)), + /*broadcast_sizes=*/{2}), + /*permutation=*/{1, 0}); + + // Gather the diagonal blocks + xla::GatherDimensionNumbers dim_numbers; + dim_numbers.add_output_window_dims(ndims - 1); + dim_numbers.add_output_window_dims(ndims); + dim_numbers.add_gather_dims_to_operand_dims(ndims - 2); + dim_numbers.add_gather_dims_to_operand_dims(ndims - 1); + dim_numbers.set_index_vector_dim(1); + diag_blocks = Gather(a, gather_indices, dim_numbers, + /*window_bounds=*/{block_size, block_size}); + } + + // The last block might be smaller than the block size, + // so we will need to pad it + if (n % block_size != 0) { + // Pad with zeros + auto last_blocks = + SliceInMinorDims(a, {n - n % block_size, n - n % block_size}, {n, n}); + xla::PaddingConfig config = xla::MakeNoPaddingConfig(ndims); + int64 padding = block_size - n % block_size; + config.mutable_dimensions(ndims - 1)->set_edge_padding_high(padding); + config.mutable_dimensions(ndims - 2)->set_edge_padding_high(padding); + last_blocks = + Pad(last_blocks, Zero(builder, shape.element_type()), config); + + // Add a singleton dimension + // i.e. [..., block_size, block_size] -> [..., 1, block_size, block_size] + TF_ASSIGN_OR_RETURN(xla::Shape blocks_shape, + builder->GetShape(last_blocks)); + auto shape_dims = xla::AsInt64Slice(blocks_shape.dimensions()); + auto last_blocks_dims = std::vector(ndims); + std::copy(shape_dims.begin(), shape_dims.end(), last_blocks_dims.begin()); + last_blocks_dims.insert(last_blocks_dims.end() - 2, 1); + last_blocks = Reshape(last_blocks, last_blocks_dims); + + // Concatenate with the other blocks if necessary + if (n > block_size) { + diag_blocks = + xla::ConcatInDim(builder, {diag_blocks, last_blocks}, ndims - 2); + } else { + diag_blocks = last_blocks; + } + } + + return diag_blocks; + }); +} + +xla::XlaOp InvertDiagonalBlocks(xla::XlaOp diag_blocks, bool lower, + bool transpose_a, bool conjugate_a) { + xla::XlaBuilder* builder = diag_blocks.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + // Input is a batch of square lower triangular square matrices. Its shape is + // (..., size, size). We resize this to (num_blocks, size, size). + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(diag_blocks)); + int64 block_size = xla::ShapeUtil::GetDimension(shape, -1); + int64 num_blocks = xla::ShapeUtil::ElementsIn(shape) / + tensorflow::MathUtil::IPow(block_size, 2); + diag_blocks = Reshape(diag_blocks, {num_blocks, block_size, block_size}); + + // The input must be triangular because we rely on that when doing + // multiplications later on + diag_blocks = Triangle(diag_blocks, /*lower=*/lower); + + // Rescale blocks to be unit triangular, but avoid dividing by + // zero (which can happen if the last block was padded) otherwise it will + // introduce nans which will propagate + auto diags = GetMatrixDiagonal(diag_blocks); + TF_ASSIGN_OR_RETURN(xla::Shape diags_shape, builder->GetShape(diags)); + auto one = ScalarLike(diags, 1); + auto ones = Broadcast(one, xla::AsInt64Slice(diags_shape.dimensions())); + diags = Select(Eq(diags, Zero(builder, shape.element_type())), ones, diags); + auto scaled_diag_blocks = Div(diag_blocks, diags, {0, 2}); + + // We can now use the fact that for an upper triangular matrix + // [[L11, 0], [L21, L22]], given the inverses L11' and L22', we have + // L22' = -L22' * L21 * L11'. In our case, L21 is a vector and our blocks + // have been rescaled to be unit triangular, so L22 = L22' = 1. + + // Initialize the output matrix with -1s on the diagonal. We use -1 instead + // of 1 because we cannot do matrix-vector multiplies with variable shapes + // inside of a loop, or do irregularly shaped in-place updates. Hence, + // L21 <- -L22 * L21 * L11 cannot be done naively. Instead, we update the + // entire row i.e. we calculate + // [L21 L22 0] <- -[L21 L22 0] @ diag_blocks([L11', -I, -I]) + // which means [L21 L22 0] <- [-L21 * L11', L22, 0]. + auto identity = + IdentityMatrix(builder, shape.element_type(), block_size, block_size); + auto neg_identity = -identity; + + // The first or last diagonal element should be set to 1 instead of -1 + // though, since we never update it + auto pos_one = Reshape(One(builder, shape.element_type()), {1, 1}); + auto start_index = (lower) ? 0 : block_size - 1; + auto output_block = DynamicUpdateSlice( + neg_identity, pos_one, + /*start_indices=*/xla::ConstantR1(builder, 2, start_index)); + + // Broadcast diag([1, -1, -1, ...]) to every block + xla::XlaOp output = Broadcast(output_block, + /*broadcast_sizes=*/{num_blocks}); + + // Now we construct a loop that performs matrix-vector multiplications + // inverting the blocks one row at a time + std::vector tuple_shapes = { + // The loop iteration counter is a scalar, incremented each iteration. + xla::ShapeUtil::MakeShape(xla::S32, {}), + // The output has the shape of A, with one row updated each iteration. + xla::ShapeUtil::MakeShape(shape.element_type(), + {num_blocks, block_size, block_size}), + // The input is a loop invariant. + xla::ShapeUtil::MakeShape(shape.element_type(), + {num_blocks, block_size, block_size})}; + xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); + + auto init_i = One(builder, xla::S32); + auto init = xla::Tuple(builder, {init_i, output, scaled_diag_blocks}); + + // Construct the loop condition function. + std::unique_ptr condb = + builder->CreateSubBuilder("InvertDiagCond"); + { + auto i = GetTupleElement( + Parameter(condb.get(), 0, tuple_shape, "InvertDiagCondTuple"), 0); + Lt(i, xla::ConstantR0(condb.get(), block_size)); + } + TF_ASSIGN_OR_RETURN(auto cond, condb->Build()); + + // Construct the loop body function. + std::unique_ptr bodyb = + builder->CreateSubBuilder("InvertDiagBody"); + { + auto input_tuple = + Parameter(bodyb.get(), 0, tuple_shape, "InvertDiagBodyTuple"); + + auto i = GetTupleElement(input_tuple, 0); + auto body_out = GetTupleElement(input_tuple, 1); + auto body_input = GetTupleElement(input_tuple, 2); + + auto zero = xla::ConstantR1(bodyb.get(), 1, 0); + auto j = (lower) ? i : ScalarLike(i, block_size - 1) - i; + auto start_indices = + xla::ConcatInDim(bodyb.get(), {zero, Reshape(j, {1}), zero}, 0); + auto input_row = + DynamicSlice(body_input, start_indices, + /*slice_sizes=*/{num_blocks, 1, block_size}); + + // We want -L21 L11^{-1} + xla::DotDimensionNumbers dnums; + dnums.add_lhs_batch_dimensions(0); + dnums.add_rhs_batch_dimensions(0); + dnums.add_lhs_contracting_dimensions(2); + dnums.add_rhs_contracting_dimensions(1); + auto update = -DotGeneral(input_row, body_out, dnums); + + body_out = DynamicUpdateSlice(body_out, update, start_indices); + + auto next_i = i + ScalarLike(i, 1); + xla::Tuple(bodyb.get(), {next_i, body_out, body_input}); + } + TF_ASSIGN_OR_RETURN(auto body, bodyb->Build()); + + // Construct the While loop and return the result, + // return while_loop(cond_fun, body_fun, init)[1] + auto invert_while = While(cond, body, init); + auto inv_diag_blocks = GetTupleElement(invert_while, 1); + + // Undo the scaling + inv_diag_blocks = Div(inv_diag_blocks, diags, + /*broadcast_dimensions=*/{0, 1}); + + // Reshape back to original batch major dimensions + return Reshape(inv_diag_blocks, xla::AsInt64Slice(shape.dimensions())); + }); +} + +xla::XlaOp SolveWithInvertedDiagonalBlocks(xla::XlaOp a, xla::XlaOp b, + xla::XlaOp inv_diag_blocks, + bool left_side, bool lower, + bool transpose_a, bool conjugate_a) { + xla::XlaBuilder* builder = a.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape blocks_shape, + builder->GetShape(inv_diag_blocks)); + TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b)); + int64 block_size = xla::ShapeUtil::GetDimension(blocks_shape, -1); + + TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); + int64 ndims = xla::ShapeUtil::Rank(a_shape); + int64 n = xla::ShapeUtil::GetDimension(a_shape, -1); + int64 num_blocks = n / block_size + (n % block_size != 0); + int64 m_dim = (left_side) ? -1 : -2; + int64 m = xla::ShapeUtil::GetDimension(b_shape, m_dim); + + // Initialize the solution + auto x = ZerosLike(b); + + // This loop is unrolled for performance reasons, but it could be expressed + // rolled as well since the matrices are of the same size each iteration + for (int i = 0; i < num_blocks; i++) { + // High-level intuition: We have B[i] = L[i] @ X. Since L is upper + // triangular this means B[i] = L[i, :i + 1] @ X[:i + 1]. We can split + // this into two parts: B[i] = L[i, :i] @ X[:i] + L[i, i] @ X[i] which + // can be solved for X[i] as X[i] = inv(L[i, i]) @ B[i] - L[i, :i] @ X[:i] + + // Decide whether we go from first block to last or vice versa + auto j = (left_side ^ lower ^ transpose_a) ? num_blocks - 1 - i : i; + + // Get the size of the inverse blocks (the last one might be smaller) + int64 block = (n % block_size != 0 && j + 1 == num_blocks) + ? n % block_size + : block_size; + auto inv_block = + MaybeConjugate(Collapse(SliceInMinorDims(inv_diag_blocks, {j, 0, 0}, + {j + 1, block, block}), + /*dimensions=*/{ndims - 2, ndims - 1}), + conjugate_a); + + // Get the corresponding row of B + int64 k = std::min((j + 1) * block_size, n); + std::vector start = {j * block_size, 0}; + std::vector end = {k, m}; + if (!left_side) { + std::swap(start[0], start[1]); + std::swap(end[0], end[1]); + } + auto b_row = SliceInMinorDims(b, start, end); + + xla::XlaOp remainder; + if (i == 0) { + remainder = b_row; + } else { + // This matrix multiply involves a lot of multiplying with zero (namely, + // X[i * block_size:] = 0), but this is faster than slicing... + end = {k, n}; + if (!left_side) { + std::swap(end[0], end[1]); + } + if (transpose_a) { + std::swap(start[0], start[1]); + std::swap(end[0], end[1]); + } + auto a_row = + MaybeConjugate(SliceInMinorDims(a, start, end), conjugate_a); + if (left_side) { + remainder = b_row - BatchDot(a_row, x, transpose_a, false); + } else { + remainder = b_row - BatchDot(x, a_row, false, transpose_a); + } + } + + xla::XlaOp x_update; + auto zero = Zero(builder, xla::S32); + auto start_index = + xla::ConstantR0WithType(builder, xla::S32, j * block_size); + std::vector update_starts = {start_index, zero}; + if (left_side) { + x_update = BatchDot(inv_block, remainder, transpose_a, false); + } else { + x_update = BatchDot(remainder, inv_block, false, transpose_a); + std::swap(update_starts[0], update_starts[1]); + } + x = DynamicUpdateSliceInMinorDims(x, x_update, /*starts=*/update_starts); + } + + return x; + }); +} + xla::XlaOp TriangularSolve(xla::XlaOp a, xla::XlaOp b, bool left_side, bool lower, bool transpose_a, bool conjugate_a, int64 block_size) { @@ -44,7 +344,7 @@ xla::XlaOp TriangularSolve(xla::XlaOp a, xla::XlaOp b, bool left_side, xla::ShapeUtil::HumanString(a_shape), " vs. ", xla::ShapeUtil::HumanString(b_shape)); } - const int ndims = xla::ShapeUtil::Rank(a_shape); + const int64 ndims = xla::ShapeUtil::Rank(a_shape); if (ndims < 2) { return errors::InvalidArgument( "Arguments to TriangularSolve must have rank >= 2: ", ndims); @@ -84,510 +384,18 @@ xla::XlaOp TriangularSolve(xla::XlaOp a, xla::XlaOp b, bool left_side, block_size); } - std::map base_computations; - auto get_base_triangular_solve = - [&](int k) -> xla::StatusOr { - xla::XlaComputation& computation = base_computations[k]; - if (computation.IsNull()) { - std::unique_ptr sub = builder->CreateSubBuilder( - tensorflow::strings::StrCat("trsm_base_", k)); - - auto a_param = xla::Parameter( - sub.get(), 0, - xla::ShapeUtil::MakeShape(b_shape.element_type(), - ConcatVectors(batch_dimensions, {k, k})), - "a"); - - std::array b_lastd; - if (left_side) { - b_lastd = {k, n}; - } else { - b_lastd = {m, k}; - } - auto b_param = xla::Parameter( - sub.get(), 1, - xla::ShapeUtil::MakeShape(b_shape.element_type(), - ConcatVectors(batch_dimensions, b_lastd)), - "b"); - - // We use a left-looking or right-looking subroutine on the block - // diagonal in the lower=true cases, while falling back to a recursive - // call in others. The left-looking and right-looking subroutines are - // written with a While loop and so yields much faster compile times. - // Moreover, they can give higher performance on smaller (sub)problems. - if (left_side && lower) { - TriangularSolveLeftLooking(a_param, b_param, transpose_a, - conjugate_a); - } else if (!left_side && lower) { - TriangularSolveRightLooking(a_param, b_param, transpose_a, - conjugate_a); - } else { - TriangularSolve(a_param, b_param, left_side, lower, transpose_a, - conjugate_a, - /*block_size=*/1); - } - - TF_ASSIGN_OR_RETURN(computation, sub->Build()); - } - return &computation; - }; - - xla::XlaOp output = xla::ZerosLike(b); - - // Right-looking blocked triangular solve. - // For an explanation of the algorithm, see the TRSM discussion in: - // Goto, Kazushige, and Robert Van De Geijn. "High-performance - // implementation of the level-3 BLAS." ACM Transactions on Mathematical - // Software (TOMS) 35.1 (2008): 4. - - // In the code comments below, T = lambda x: np.swapaxes(x, -1, -2) if - // conjugate_a is False, or T = lambda x: np.conj(np.swapaxes(x, -1, -2)) if - // conjugate_a is True. - - if (!left_side && lower == transpose_a) { - // for i in range(0, a.shape[-1], block_size): - for (int64 i = 0; i < n; i += block_size) { - int64 k = std::min(block_size, n - i); - - // output[..., :, i:i+k] = triangular_solve( - // a[..., i:i+k, i:i+k], b[..., :, i:i+k], ..., block_size=1) - auto a_slice = SliceInMinorDims(a, {i, i}, {i + k, i + k}); - auto b_slice = SliceInMinorDims(b, {0, i}, {m, i + k}); - xla::XlaOp update; - if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, - get_base_triangular_solve(k)); - update = xla::Call(builder, *solve, {a_slice, b_slice}); - } else { - auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); - update = b_slice / a_slice_conj; - } - output = UpdateSliceInMinorDims(output, update, {0, i}); - - // if i + k < a.shape[-1]: - // a_slice_2 = a[..., i+k:, i:i+k] if lower else a[..., i:i+k, i+k:] - // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 - // b[..., :, i+k:] -= np.matmul(output[..., :, i:i+k], a_slice_2) - if (i + k < n) { - xla::XlaOp a_slice_2; - if (lower) { - a_slice_2 = SliceInMinorDims(a, {i + k, i}, {n, i + k}); - } else { - a_slice_2 = SliceInMinorDims(a, {i, i + k}, {i + k, n}); - } - - auto b_update = BatchDot(update, a_slice_2, - /*transpose_x=*/false, - /*transpose_y=*/transpose_a, - /*conjugate_x=*/false, - /*conjugate_y=*/conjugate_a); - auto b_slice_2 = SliceInMinorDims(b, {0, i + k}, {m, n}); - b = UpdateSliceInMinorDims(b, b_slice_2 - b_update, {0, i + k}); - } - } - - } else if (left_side && lower != transpose_a) { - // for i in range(0, a.shape[-1], block_size): - for (int64 i = 0; i < m; i += block_size) { - int64 k = std::min(block_size, m - i); - - // output[..., i:i+k, :] = triangular_solve( - // a[..., i:i+k, i:i+k], b[..., i:i+k, :], ..., block_size=1) - auto a_slice = SliceInMinorDims(a, {i, i}, {i + k, i + k}); - auto b_slice = SliceInMinorDims(b, {i, 0}, {i + k, n}); - xla::XlaOp update; - if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, - get_base_triangular_solve(k)); - update = xla::Call(builder, *solve, {a_slice, b_slice}); - } else { - auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); - update = b_slice / a_slice_conj; - } - output = UpdateSliceInMinorDims(output, update, {i, 0}); - - // if i + k < a.shape[-1]: - // a_slice_2 = a[..., i+k:, i:i+k] if lower else a[..., i:i+k, i+k:] - // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 - // b[..., i+k:, :] -= np.matmul(a_slice_2, output[..., i:i+k, :]) - if (i + k < m) { - xla::XlaOp a_slice_2; - if (lower) { - a_slice_2 = SliceInMinorDims(a, {i + k, i}, {m, i + k}); - } else { - a_slice_2 = SliceInMinorDims(a, {i, i + k}, {i + k, m}); - } - - auto b_update = BatchDot(a_slice_2, update, - /*transpose_x=*/transpose_a, - /*transpose_y=*/false, - /*conjugate_x=*/conjugate_a, - /*conjugate_y=*/false); - auto b_slice_2 = SliceInMinorDims(b, {i + k, 0}, {m, n}); - b = UpdateSliceInMinorDims(b, b_slice_2 - b_update, {i + k, 0}); - } - } - } else if (!left_side && lower != transpose_a) { - // for i in reversed(range(0, a.shape[-1], block_size)): - const int64 last_blk_ix = - xla::RoundUpToNearest(n, block_size) - block_size; - for (int64 i = last_blk_ix; i >= 0; i -= block_size) { - int64 k = std::min(block_size, n - i); - - // output[..., :, i:i+k] triangular_solve( - // a[..., i:i+k, i:i+k], b[..., :, i:i+k], ..., block_size=1) - auto a_slice = SliceInMinorDims(a, {i, i}, {i + k, i + k}); - auto b_slice = SliceInMinorDims(b, {0, i}, {m, i + k}); - xla::XlaOp update; - if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, - get_base_triangular_solve(k)); - update = xla::Call(builder, *solve, {a_slice, b_slice}); - } else { - auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); - update = b_slice / a_slice_conj; - } - output = UpdateSliceInMinorDims(output, update, {0, i}); - - // if i - k >= 0: - // a_slice_2 = a[..., i:i+k, :i] if lower else a[..., :i, i:i+k] - // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 - // b[..., :, :i] -= np.matmul(out[..., :, i:i+k], a_slice_2) - if (i - k >= 0) { - xla::XlaOp a_slice_2; - if (lower) { - a_slice_2 = SliceInMinorDims(a, {i, 0}, {i + k, i}); - } else { - a_slice_2 = SliceInMinorDims(a, {0, i}, {i, i + k}); - } - - auto b_update = BatchDot(update, a_slice_2, - /*transpose_x=*/false, - /*transpose_y=*/transpose_a, - /*conjugate_x=*/false, - /*conjugate_y=*/conjugate_a); - auto b_slice_2 = SliceInMinorDims(b, {0, 0}, {m, i}); - b = UpdateSliceInMinorDims(b, b_slice_2 - b_update, {0, 0}); - } - } - } else { // left_side && lower == transpose_a - // for i in reversed(range(0, a.shape[-1], block_size)): - const int64 last_blk_ix = - xla::RoundUpToNearest(m, block_size) - block_size; - for (int64 i = last_blk_ix; i >= 0; i -= block_size) { - int64 k = std::min(block_size, m - i); - - // output[..., i:i+k, :] triangular_solve( - // a[..., i:i+k, i:i+k], b[..., i:i+k, :], ..., block_size=1) - auto a_slice = SliceInMinorDims(a, {i, i}, {i + k, i + k}); - auto b_slice = SliceInMinorDims(b, {i, 0}, {i + k, n}); - xla::XlaOp update; - if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, - get_base_triangular_solve(k)); - update = xla::Call(builder, *solve, {a_slice, b_slice}); - } else { - auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); - update = b_slice / a_slice_conj; - } - output = UpdateSliceInMinorDims(output, update, {i, 0}); - - // if i - k >= 0: - // a_slice_2 = a[..., i:i+k, :i] if lower else a[..., :i, i:i+k] - // a_slice_2 = T(a_slice_2) if transpose_a else a_slice_2 - // b[..., :i, :] -= np.matmul(a_slice_2, out[..., i:i+k, :]) - if (i - k >= 0) { - xla::XlaOp a_slice_2; - if (lower) { - a_slice_2 = SliceInMinorDims(a, {i, 0}, {i + k, i}); - } else { - a_slice_2 = SliceInMinorDims(a, {0, i}, {i, i + k}); - } - - auto b_update = BatchDot(a_slice_2, update, - /*transpose_x=*/transpose_a, - /*transpose_y=*/false, - /*conjugate_x=*/conjugate_a, - /*conjugate_y=*/false); - auto b_slice_2 = SliceInMinorDims(b, {0, 0}, {i, n}); - b = UpdateSliceInMinorDims(b, b_slice_2 - b_update, {0, 0}); - } - } - } - - return output; - }); -} - -xla::XlaOp TriangularSolveLeftLooking(xla::XlaOp a, xla::XlaOp b, - bool transpose_a, bool conjugate_a) { - xla::XlaBuilder* builder = a.builder(); - return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { - TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); - TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b)); - const int64 m = xla::ShapeUtil::GetDimension(b_shape, -2); - const int64 n = xla::ShapeUtil::GetDimension(b_shape, -1); - const int64 ndims = xla::ShapeUtil::Rank(a_shape); - - std::vector batch_dimensions; - for (int i = 0; i < ndims - 2; ++i) { - int64 a_size = a_shape.dimensions(i); - batch_dimensions.push_back(a_size); - } - - // The main computation is performed in a While loop. - - // Allocate the output and set its first or last row, - // output = np.zeros_like(b) - // if transpose_a: - // output[..., m-1:, :] = b[..., m-1:, :] / a[..., m-1:, m-1:] - // else: - // output[..., :1, :] = b[..., :1, :] / a[..., :1, :1] - xla::XlaOp output = xla::ZerosLike(b); - { - auto i = transpose_a ? m - 1 : 0; - auto a_slice = SliceInMinorDims(a, {i, i}, {i + 1, i + 1}); - auto b_slice = SliceInMinorDims(b, {i, 0}, {i + 1, n}); - auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); - auto update = b_slice / a_slice_conj; - output = UpdateSliceInMinorDims(output, update, {i, 0}); - } - - // Construct the initial loop carry tuple, - // if transpose_a: - // init = (m-2, output, a, b) - // else: - // init = (1, output, a, b) - std::vector tuple_shapes = { - // The loop iteration counter is a scalar, incremented each iteration. - xla::ShapeUtil::MakeShape(xla::S32, {}), - // The output has the shape of b, with one row updated each iteration. - b_shape, - // The coefficient matrix a is a loop invariant. - a_shape, - // The right-hand-side matrix b is a loop invariant. - b_shape}; - xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); - auto init_i = xla::ConstantR0(builder, transpose_a ? m - 2 : 1); - auto init = xla::Tuple(builder, {init_i, output, a, b}); - - // Construct the loop condition function, - // def cond_fun(loop_carry): - // i, output, a, b = loop_carry - // return i >= 0 if transpose_a else i < m - std::unique_ptr condb = - builder->CreateSubBuilder("TriangularSolveLeftLookingWhileCond"); - { - auto i = xla::GetTupleElement( - xla::Parameter(condb.get(), 0, tuple_shape, - "TriangularSolveLeftLookingWhileTuple"), - 0); - if (transpose_a) { - xla::Ge(i, xla::ConstantR0(condb.get(), 0)); - } else { - xla::Lt(i, xla::ConstantR0(condb.get(), m)); - } - } - TF_ASSIGN_OR_RETURN(auto cond, condb->Build()); - - // Construct the loop body function, - // def body_fun(loop_carry): - // i, output, a, b = loop_carry - // if transpose_a: - // a_row = np.swapaxes(a[..., i+1:, i:i+1], -1 -2) - // else: - // a_row = a[..., i:i+1, :i] - // result_row = b[..., i:i+1, :] - np.matmul(a_row, output[..., :, :]) - // output[..., i:i+1, :] = result_row / a[..., i:i+1, i:i+1] - // if transpose_a: - // return (i - 1, output, a, b) - // else: - // return (i + 1, output, a, b) - // We have to do some extra FLOPs propagating zeros in the matrix multiply - // because we can't have the size of its arguments depend on the loop - // counter. - std::unique_ptr bodyb = - builder->CreateSubBuilder("TriangularSolveLeftLookingWhileBody"); - { - auto input_tuple = xla::Parameter(bodyb.get(), 0, tuple_shape, - "TriangularSolveLeftLookingWhileTuple"); - - // i, output, a, b = loop_carry - auto i = xla::GetTupleElement(input_tuple, 0); - auto body_out = xla::GetTupleElement(input_tuple, 1); - auto body_a = xla::GetTupleElement(input_tuple, 2); - auto body_b = xla::GetTupleElement(input_tuple, 3); - auto zero = xla::ConstantR0(bodyb.get(), 0); - - // We'd like to implement this: - // if transpose_a: - // a_row = T(a[..., i+1:, i:i+1]) - // result_row = (b[..., i:i+1, :] - // - np.matmul(a_row, body_out[..., i+1:, :])) - // else: - // result_row = (b[..., i:i+1, :] - // - np.matmul(a[..., i:i+1, :i], body_out[..., :i, :])) - // But since we can't have intermediate array sizes depend on the loop - // counter, we instead exploit the fact that we initialized the output to - // all zeros and use that as zero-padding (doing unnecessary FLOPs). - xla::XlaOp a_row; - if (transpose_a) { - a_row = DynamicSliceInMinorDims(body_a, {zero, i}, {m, 1}); - } else { - a_row = DynamicSliceInMinorDims(body_a, {i, zero}, {1, m}); - } - auto b_update = BatchDot(a_row, body_out, - /*transpose_x=*/transpose_a, - /*transpose_y=*/false, - /*conjugate_x=*/conjugate_a, - /*conjugate_y=*/false); - auto result_row_slice = - DynamicSliceInMinorDims(body_b, {i, zero}, {1, n}); - auto result_row = result_row_slice - b_update; - - // body_out[..., i:i+1, :] = result_row / a[..., i:i+1, i:i+1] - auto a_elt = DynamicSliceInMinorDims(body_a, {i, i}, {1, 1}); - auto a_elt_conj = MaybeConjugate(a_elt, conjugate_a); - auto div_result = xla::Div(result_row, a_elt_conj); - body_out = DynamicUpdateSliceInMinorDims(body_out, div_result, {i, zero}); - - // if transpose_a: - // return (i - 1, body_out, a, b) - // else: - // return (i + 1, body_out, a, b) - auto next_i = xla::Add( - i, xla::ConstantR0(bodyb.get(), transpose_a ? -1 : 1)); - xla::Tuple(bodyb.get(), {next_i, body_out, body_a, body_b}); - } - TF_ASSIGN_OR_RETURN(auto body, bodyb->Build()); - - // Construct the While loop and return the result, - // return while_loop(cond_fun, body_fun, init)[1] - auto triangular_solve_left_looking_while = xla::While(cond, body, init); - return xla::GetTupleElement(triangular_solve_left_looking_while, 1); - }); -} + // We find the diagonal blocks of the coefficient matrix + auto diag_blocks = DiagonalBlocks(a, block_size); -xla::XlaOp TriangularSolveRightLooking(xla::XlaOp a, xla::XlaOp b, - bool transpose_a, bool conjugate_a) { - xla::XlaBuilder* builder = a.builder(); - return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { - TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); - TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b)); - const int64 m = xla::ShapeUtil::GetDimension(b_shape, -2); - const int64 n = xla::ShapeUtil::GetDimension(b_shape, -1); - const int64 ndims = xla::ShapeUtil::Rank(a_shape); - - std::vector batch_dimensions; - for (int i = 0; i < ndims - 2; ++i) { - int64 a_size = a_shape.dimensions(i); - batch_dimensions.push_back(a_size); - } + // We invert these blocks in parallel using batched matrix-vector products + auto inv_diag_blocks = + InvertDiagonalBlocks(diag_blocks, lower, transpose_a, conjugate_a); - // The main computation is performed in a While loop. - xla::XlaOp output = xla::ZerosLike(b); + // We now find the solution using GEMMs + auto x = SolveWithInvertedDiagonalBlocks(a, b, inv_diag_blocks, left_side, + lower, transpose_a, conjugate_a); - // Construct the initial loop carry tuple, - // if transpose_a: - // init = (0, output, a, b) - // else: - // init = (n-1, output, a, b) - std::vector tuple_shapes = { - // The loop iteration counter is a scalar, incremented each iteration. - xla::ShapeUtil::MakeShape(xla::S32, {}), - // The output has the shape of b, with one row updated each iteration. - b_shape, - // The coefficient matrix a is a loop invariant. - a_shape, - // The right-hand-side matrix b is a loop invariant. - b_shape}; - xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); - auto init_i = xla::ConstantR0(builder, transpose_a ? 0 : n - 1); - auto init = xla::Tuple(builder, {init_i, output, a, b}); - - // Construct the loop condition function, - // def cond_fun(loop_carry): - // i, output, a, b = loop_carry - // return i < n if transpose_a else i >= 0 - std::unique_ptr condb = - builder->CreateSubBuilder("TriangularSolveRightLookingWhileCond"); - { - auto i = xla::GetTupleElement( - xla::Parameter(condb.get(), 0, tuple_shape, - "TriangularSolveRightLookingWhileTuple"), - 0); - if (transpose_a) { - xla::Lt(i, xla::ConstantR0(condb.get(), n)); - } else { - xla::Ge(i, xla::ConstantR0(condb.get(), 0)); - } - } - TF_ASSIGN_OR_RETURN(auto cond, condb->Build()); - - // Construct the loop body function, - // def body_fun(loop_carry): - // i, output, a, b = loop_carry - // if transpose_a: - // a_row = np.swapaxes(a[..., :, i:i+1], -1, -2) - // else: - // a_row = a[..., :, i:i+1] - // result_row = b[..., :, i:i+1] - np.matmul(output, a_row) - // output[..., :, i:i+1] = result_row / a[..., i:i+1, i:i+1] - // if transpose_a: - // return (i - 1, output, a, b) - // else: - // return (i + 1, output, a, b) - // We have to do some extra FLOPs propagating zeros in the matrix multiply - // because we can't have the size of its arguments depend on the loop - // counter. - std::unique_ptr bodyb = - builder->CreateSubBuilder("TriangularSolveRightLookingWhileBody"); - { - auto input_tuple = xla::Parameter( - bodyb.get(), 0, tuple_shape, "TriangularSolveRightLookingWhileTuple"); - - // i, output, a, b = loop_carry - auto i = xla::GetTupleElement(input_tuple, 0); - auto body_out = xla::GetTupleElement(input_tuple, 1); - auto body_a = xla::GetTupleElement(input_tuple, 2); - auto body_b = xla::GetTupleElement(input_tuple, 3); - auto zero = xla::ConstantR0(bodyb.get(), 0); - - // result = b - np.matmul(output, a) - // result_row = result[..., :, i:i+1] - auto body_b_slice = DynamicSliceInMinorDims(body_b, {zero, i}, {m, 1}); - xla::XlaOp a_slice; - if (transpose_a) { - a_slice = DynamicSliceInMinorDims(body_a, {i, zero}, {1, n}); - } else { - a_slice = DynamicSliceInMinorDims(body_a, {zero, i}, {n, 1}); - } - auto b_update = body_b_slice - BatchDot(body_out, a_slice, - /*transpose_x=*/false, - /*transpose_y=*/transpose_a, - /*conjugate_x=*/false, - /*conjugate_y=*/conjugate_a); - - // body_out[..., :, i:i+1] = b_update / a[..., i:i+1, i:i+1] - auto a_ii = DynamicSliceInMinorDims(body_a, {i, i}, {1, 1}); - auto a_ii_conj = MaybeConjugate(a_ii, conjugate_a); - body_out = DynamicUpdateSliceInMinorDims(body_out, b_update / a_ii_conj, - {zero, i}); - - // if transpose_a: - // return (i + 1, body_out, a, b) - // else: - // return (i - 1, body_out, a, b) - auto next_i = xla::Add( - i, xla::ConstantR0(bodyb.get(), transpose_a ? 1 : -1)); - xla::Tuple(bodyb.get(), {next_i, body_out, body_a, body_b}); - } - TF_ASSIGN_OR_RETURN(auto body, bodyb->Build()); - - // Construct the While loop and return the result, - // return while_loop(cond_fun, body_fun, init)[1] - auto triangular_solve_left_looking_while = xla::While(cond, body, init); - return xla::GetTupleElement(triangular_solve_left_looking_while, 1); + return x; }); } diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.h b/tensorflow/compiler/tf2xla/lib/triangular_solve.h index 80c2bc4c9c38ec101db419d48db26e67e25d169b..555760b7efabddfb25c9135b109a1c48b487415e 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.h +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.h @@ -16,8 +16,8 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_TRIANGULAR_SOLVE_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_TRIANGULAR_SOLVE_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" namespace tensorflow { @@ -59,13 +59,7 @@ namespace tensorflow { // blocking is used. xla::XlaOp TriangularSolve(xla::XlaOp a, xla::XlaOp b, bool left_side, bool lower, bool transpose_a, bool conjugate_a, - int64 block_size = 256); - -xla::XlaOp TriangularSolveLeftLooking(xla::XlaOp a, xla::XlaOp b, - bool transpose_a, bool conjugate_a); - -xla::XlaOp TriangularSolveRightLooking(xla::XlaOp a, xla::XlaOp b, - bool transpose_a, bool conjugate_a); + int64 block_size = 128); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc index d5ffc1498e4b6dcfbc9f24f9b5dce58fddca8ab1..aeebf16028d40189203cdfd815f06a339ee72902 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc @@ -20,8 +20,8 @@ limitations under the License. #include #include "tensorflow/compiler/xla/array2d.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" @@ -207,6 +207,28 @@ XLA_TEST_F(TriangularSolveTest, SimpleLeftLowerNotranspose) { xla::ErrorSpec(1e-2, 1e-2)); } +XLA_TEST_F(TriangularSolveTest, SimpleLeftLowerNotransposeIrregularblock) { + xla::XlaBuilder builder(TestName()); + + xla::XlaOp a, b; + auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); + auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); + TriangularSolve(a, b, + /*left_side=*/true, /*lower=*/true, + /*transpose_a=*/false, /*conjugate_a=*/false, + /*block_size=*/3); + + xla::Array2D expected({ + {0.5, 1.0, 1.5}, + {0.41666667, 0.33333333, 0.25}, + {0.23148148, 0.18518519, 0.13888889}, + {0.16835017, 0.13468013, 0.1010101}, + }); + + ComputeAndCompareR2(&builder, expected, {a_data.get(), b_data.get()}, + xla::ErrorSpec(1e-2, 1e-2)); +} + XLA_TEST_F(TriangularSolveTest, SimpleLeftUpperTranspose) { xla::XlaBuilder builder(TestName()); @@ -307,47 +329,5 @@ XLA_TEST_F(TriangularSolveTest, SimpleLeftUpperTransposeNoconjugate) { xla::ErrorSpec(1e-2, 1e-2)); } -XLA_TEST_F(TriangularSolveLeftLookingTest, Simple) { - xla::XlaBuilder builder(TestName()); - - xla::XlaOp a, b; - auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); - auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); - TriangularSolveLeftLooking(a, b, - /*transpose_a=*/false, - /*conjugate_a=*/false); - - xla::Array2D expected({ - {0.5, 1.0, 1.5}, - {0.41666667, 0.33333333, 0.25}, - {0.23148148, 0.18518519, 0.13888889}, - {0.16835017, 0.13468013, 0.1010101}, - }); - - ComputeAndCompareR2(&builder, expected, {a_data.get(), b_data.get()}, - xla::ErrorSpec(1e-2, 1e-2)); -} - -XLA_TEST_F(TriangularSolveLeftLookingTest, NonzeroUpperTriangle) { - xla::XlaBuilder builder(TestName()); - - xla::XlaOp a, b; - auto a_data = CreateR2Parameter(AValsFull(), 0, "a", &builder, &a); - auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); - TriangularSolveLeftLooking(a, b, - /*transpose_a=*/false, - /*conjugate_a=*/false); - - xla::Array2D expected({ - {0.5, 1.0, 1.5}, - {0.41666667, 0.33333333, 0.25}, - {0.23148148, 0.18518519, 0.13888889}, - {0.16835017, 0.13468013, 0.1010101}, - }); - - ComputeAndCompareR2(&builder, expected, {a_data.get(), b_data.get()}, - xla::ErrorSpec(1e-2, 1e-2)); -} - } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/lib/util.cc b/tensorflow/compiler/tf2xla/lib/util.cc index fdc8bfca4932fe62a4d2a8db49f4104c3eb0cd3b..8b5beba383cda45d36e2ee27ca5e3b3c5988b6b7 100644 --- a/tensorflow/compiler/tf2xla/lib/util.cc +++ b/tensorflow/compiler/tf2xla/lib/util.cc @@ -18,7 +18,8 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -28,6 +29,13 @@ limitations under the License. namespace tensorflow { +xla::XlaOp Zeros(xla::XlaBuilder* builder, const xla::Shape& shape) { + return xla::Broadcast( + xla::ConstantLiteral(builder, + xla::LiteralUtil::Zero(shape.element_type())), + xla::AsInt64Slice(shape.dimensions())); +} + xla::XlaOp FloatLiteral(xla::XlaBuilder* builder, xla::PrimitiveType type, double value) { switch (type) { @@ -56,31 +64,31 @@ xla::XlaOp IntegerLiteral(xla::XlaBuilder* builder, xla::PrimitiveType type, xla::Literal literal; switch (type) { case xla::U8: - literal = std::move(*xla::Literal::CreateR0(value)); + literal = std::move(*xla::LiteralUtil::CreateR0(value)); break; case xla::U32: - literal = std::move(*xla::Literal::CreateR0(value)); + literal = std::move(*xla::LiteralUtil::CreateR0(value)); break; case xla::U64: - literal = std::move(*xla::Literal::CreateR0(value)); + literal = std::move(*xla::LiteralUtil::CreateR0(value)); break; case xla::S8: - literal = std::move(*xla::Literal::CreateR0(value)); + literal = std::move(*xla::LiteralUtil::CreateR0(value)); break; case xla::S32: - literal = std::move(*xla::Literal::CreateR0(value)); + literal = std::move(*xla::LiteralUtil::CreateR0(value)); break; case xla::S64: - literal = std::move(*xla::Literal::CreateR0(value)); + literal = std::move(*xla::LiteralUtil::CreateR0(value)); break; case xla::F32: - literal = std::move(*xla::Literal::CreateR0(value)); + literal = std::move(*xla::LiteralUtil::CreateR0(value)); break; case xla::F64: - literal = std::move(*xla::Literal::CreateR0(value)); + literal = std::move(*xla::LiteralUtil::CreateR0(value)); break; case xla::C64: - literal = std::move(*xla::Literal::CreateR0(value)); + literal = std::move(*xla::LiteralUtil::CreateR0(value)); break; case xla::PRED: LOG(FATAL) << "pred element type is not integral"; @@ -89,11 +97,11 @@ xla::XlaOp IntegerLiteral(xla::XlaBuilder* builder, xla::PrimitiveType type, LOG(FATAL) << "u16/s16 literals not yet implemented"; case xla::BF16: literal = std::move( - *xla::Literal::CreateR0(static_cast(value))); + *xla::LiteralUtil::CreateR0(static_cast(value))); break; case xla::F16: - literal = std::move( - *xla::Literal::CreateR0(static_cast(value))); + literal = std::move(*xla::LiteralUtil::CreateR0( + static_cast(value))); break; case xla::TUPLE: LOG(FATAL) << "tuple element type is not integral"; diff --git a/tensorflow/compiler/tf2xla/lib/util.h b/tensorflow/compiler/tf2xla/lib/util.h index 6cb6c088e9d20af05193f0a3da6c2595966eb495..b4905c952820a45371e090aa98466654e2db9661 100644 --- a/tensorflow/compiler/tf2xla/lib/util.h +++ b/tensorflow/compiler/tf2xla/lib/util.h @@ -16,8 +16,8 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_UTIL_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_UTIL_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/lib/gtl/array_slice.h" diff --git a/tensorflow/compiler/tf2xla/lib/util_test.cc b/tensorflow/compiler/tf2xla/lib/util_test.cc index 7d0f2222a9aa3ef09cb8be20c5f9b26431c6498c..442fe92c34ca26cb1a854cc90da8dc034bca79bb 100644 --- a/tensorflow/compiler/tf2xla/lib/util_test.cc +++ b/tensorflow/compiler/tf2xla/lib/util_test.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/batch_dot.h" #include "tensorflow/compiler/xla/array2d.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" diff --git a/tensorflow/compiler/tf2xla/lib/while_loop.cc b/tensorflow/compiler/tf2xla/lib/while_loop.cc index 7cc88f34d291f25814fba9f802c93117973120e7..d64394f1401d7ceea004a59c991ef6f4a1c58b41 100644 --- a/tensorflow/compiler/tf2xla/lib/while_loop.cc +++ b/tensorflow/compiler/tf2xla/lib/while_loop.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/tf2xla/lib/util.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -100,8 +100,9 @@ xla::StatusOr> XlaForEachIndex( std::vector updated_values; updated_values.reserve(values.size()); updated_values.push_back(xla::Add( - iteration, xla::ConstantLiteral( - body_builder, xla::Literal::One(num_iterations_type)))); + iteration, + xla::ConstantLiteral(body_builder, + xla::LiteralUtil::One(num_iterations_type)))); values.remove_prefix(1); TF_ASSIGN_OR_RETURN(std::vector body_outputs, @@ -113,8 +114,8 @@ xla::StatusOr> XlaForEachIndex( std::vector values; values.reserve(initial_values.size() + 1); - values.push_back( - xla::ConstantLiteral(builder, xla::Literal::Zero(num_iterations_type))); + values.push_back(xla::ConstantLiteral( + builder, xla::LiteralUtil::Zero(num_iterations_type))); values.insert(values.end(), initial_values.begin(), initial_values.end()); TF_ASSIGN_OR_RETURN(values, XlaWhileLoop(while_cond_fn, while_body_fn, values, diff --git a/tensorflow/compiler/tf2xla/lib/while_loop.h b/tensorflow/compiler/tf2xla/lib/while_loop.h index 5b6684c995889efbb1378c7ac4903548891d090a..9493b1f109be0725f7f733b9f9da664264275a69 100644 --- a/tensorflow/compiler/tf2xla/lib/while_loop.h +++ b/tensorflow/compiler/tf2xla/lib/while_loop.h @@ -19,8 +19,8 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/gtl/array_slice.h" diff --git a/tensorflow/compiler/tf2xla/literal_util.cc b/tensorflow/compiler/tf2xla/literal_util.cc index b43405a1a407b5fa98dd740c62af91e048cc9490..2fb66913ada375d53512b9a1115326b3cc2afea4 100644 --- a/tensorflow/compiler/tf2xla/literal_util.cc +++ b/tensorflow/compiler/tf2xla/literal_util.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/type_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/common_runtime/dma_helper.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/literal_util.h b/tensorflow/compiler/tf2xla/literal_util.h index ab7e861f3336097d2ea52487092f16edb5c14531..0610a57029e72dff79a84742346f78a42b7f4ff1 100644 --- a/tensorflow/compiler/tf2xla/literal_util.h +++ b/tensorflow/compiler/tf2xla/literal_util.h @@ -18,7 +18,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LITERAL_UTIL_H_ #define TENSORFLOW_COMPILER_TF2XLA_LITERAL_UTIL_H_ -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/status.h" diff --git a/tensorflow/compiler/tf2xla/literal_util_test.cc b/tensorflow/compiler/tf2xla/literal_util_test.cc index f3d6787daaa1165b28ce63dfd501533fa0963edd..a3404c2b3df7bf25011359d1f5f5b88c29a3f83b 100644 --- a/tensorflow/compiler/tf2xla/literal_util_test.cc +++ b/tensorflow/compiler/tf2xla/literal_util_test.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/numeric_types.h" #include "tensorflow/core/framework/tensor_testutil.h" @@ -27,7 +28,7 @@ TEST(LiteralUtil, LiteralToHostTensor) { { std::vector int64_values = {1, 2, 3}; std::unique_ptr int64_values_literal = - xla::Literal::CreateR1(gtl::ArraySlice(int64_values)); + xla::LiteralUtil::CreateR1(gtl::ArraySlice(int64_values)); Tensor host_tensor; EXPECT_EQ("Cannot convert literal of type S64 to tensor of type int32", LiteralToHostTensor(*int64_values_literal, DT_INT32, &host_tensor) @@ -48,7 +49,7 @@ TEST(LiteralUtil, LiteralToHostTensor) { Tensor host_tensor; std::vector int32_values = {10, 11}; std::unique_ptr int32_values_literal = - xla::Literal::CreateR1(gtl::ArraySlice(int32_values)); + xla::LiteralUtil::CreateR1(gtl::ArraySlice(int32_values)); EXPECT_TRUE( LiteralToHostTensor(*int32_values_literal, DT_INT32, &host_tensor) .ok()); diff --git a/tensorflow/compiler/tf2xla/tf2xla.cc b/tensorflow/compiler/tf2xla/tf2xla.cc index ac768b206e2a8d163a4253432a1911152f89ce86..48568c825b7a0f13011d3d6e8e62ec5db026760f 100644 --- a/tensorflow/compiler/tf2xla/tf2xla.cc +++ b/tensorflow/compiler/tf2xla/tf2xla.cc @@ -27,6 +27,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/tf2xla_util.h" #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/graph.pb.h" diff --git a/tensorflow/compiler/tf2xla/tf2xla.h b/tensorflow/compiler/tf2xla/tf2xla.h index d02fc56c5b8f58f0e4cfe1779ad34fe3b79324c7..432a12a51622b56ae74a677420da321c58960ee6 100644 --- a/tensorflow/compiler/tf2xla/tf2xla.h +++ b/tensorflow/compiler/tf2xla/tf2xla.h @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/tf2xla.pb.h" #include "tensorflow/compiler/xla/client/client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/framework/graph.pb.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/tf2xla_test.cc b/tensorflow/compiler/tf2xla/tf2xla_test.cc index 84c133ffabe20dbdaa4d5a64e035efb5e4c4c44b..56f7045a98201ed398244f9e3f5ff23788135b75 100644 --- a/tensorflow/compiler/tf2xla/tf2xla_test.cc +++ b/tensorflow/compiler/tf2xla/tf2xla_test.cc @@ -18,6 +18,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/tf2xla.pb.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/framework/attr_value.pb.h" @@ -73,8 +75,8 @@ TEST(ConvertGraphDefToXla, Sum) { TF_EXPECT_OK(ConvertGraphDefToXla(graph_def, config, client, &computation)); // Set up arguments. - auto x_literal = xla::Literal::CreateR0(10); - auto y_literal = xla::Literal::CreateR0(32); + auto x_literal = xla::LiteralUtil::CreateR0(10); + auto y_literal = xla::LiteralUtil::CreateR0(32); auto x_global_or = client->TransferToServer(*x_literal); auto y_global_or = client->TransferToServer(*y_literal); TF_EXPECT_OK(x_global_or.status()); diff --git a/tensorflow/compiler/tf2xla/xla_compilation_device.cc b/tensorflow/compiler/tf2xla/xla_compilation_device.cc index fe7ec633eca2504faf6cbb2f5fd7f59780ab7976..e89f4733281194f0263ae8cc4907caa0ad781165 100644 --- a/tensorflow/compiler/tf2xla/xla_compilation_device.cc +++ b/tensorflow/compiler/tf2xla/xla_compilation_device.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/sharding_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/common_runtime/local_device.h" #include "tensorflow/core/framework/device_base.h" #include "tensorflow/core/platform/mem.h" diff --git a/tensorflow/compiler/tf2xla/xla_compilation_device.h b/tensorflow/compiler/tf2xla/xla_compilation_device.h index d0b9e34e162f3412cd6662a2e2bbfe3df213c4c2..a6e78825334fec748be5fee80669649df699d2fb 100644 --- a/tensorflow/compiler/tf2xla/xla_compilation_device.h +++ b/tensorflow/compiler/tf2xla/xla_compilation_device.h @@ -19,7 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/tf2xla/xla_resource.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/common_runtime/local_device.h" #include "tensorflow/core/framework/device_base.h" diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc index 319cbc74e96262881d32bdc9de2251b53f2b05d6..226c89bcf1e66b5afb43cddb03db39b931ca55a8 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.cc +++ b/tensorflow/compiler/tf2xla/xla_compiler.cc @@ -28,12 +28,14 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compilation_device.h" #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/executor.h" #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/common_runtime/graph_optimizer.h" #include "tensorflow/core/framework/attr_value_util.h" +#include "tensorflow/core/framework/node_def_util.h" #include "tensorflow/core/graph/algorithm.h" #include "tensorflow/core/graph/graph_constructor.h" #include "tensorflow/core/graph/node_builder.h" @@ -422,16 +424,18 @@ Status BuildComputation( // assignment will be placed on this value, which will cause the resource // update to be returned from the same device that provided the resource. handle = xla::GetTupleElement(xla::Tuple(builder, {handle}), 0); - elems.push_back(handle); } } *num_computation_outputs = elems.size(); - // Builds the XLA computation. - if (always_return_tuple || elems.size() != 1) { - xla::Tuple(builder, elems); + // Builds the XLA computation. We *always* form a tuple here to ensure that + // the output value is the last thing added into the XLA computation, even + // if there is only one output value. + auto tuple = xla::Tuple(builder, elems); + if (!always_return_tuple && elems.size() == 1) { + xla::GetTupleElement(tuple, 0); } builder->ClearOpMetadata(); @@ -686,12 +690,12 @@ Status ValidateFunctionDef(const FunctionDef* fdef, Status ValidateGraph(const Graph* graph, const FunctionLibraryDefinition& flib_def, const DeviceType& device_type, const string& name) { - auto maybe_error = [&](const string& op, const Status& s) -> Status { + auto maybe_error = [&](const Node* node, const Status& s) -> Status { if (!s.ok()) { return errors::InvalidArgument(strings::StrCat( "Detected unsupported operations when trying to compile graph ", name, - " on ", device_type.type_string(), ": ", op, " (", s.error_message(), - ")")); + " on ", device_type.type_string(), ": ", node->def().op(), " (", + s.error_message(), ")", FormatNodeForError(*node))); } return Status::OK(); }; @@ -704,15 +708,15 @@ Status ValidateGraph(const Graph* graph, Status s; if (fdef) { s = ValidateFunctionDef(fdef, flib_def); - TF_RETURN_IF_ERROR(maybe_error(node->def().op(), s)); + TF_RETURN_IF_ERROR(maybe_error(node, s)); continue; } const OpDef* op_def; s = OpRegistry::Global()->LookUpOpDef(node->def().op(), &op_def); - TF_RETURN_IF_ERROR(maybe_error(node->def().op(), s)); + TF_RETURN_IF_ERROR(maybe_error(node, s)); TF_RETURN_IF_ERROR(ValidateNodeDef(node->def(), *op_def)); s = FindKernelDef(device_type, node->def(), nullptr, nullptr); - TF_RETURN_IF_ERROR(maybe_error(node->def().op(), s)); + TF_RETURN_IF_ERROR(maybe_error(node, s)); } return Status::OK(); } diff --git a/tensorflow/compiler/tf2xla/xla_compiler.h b/tensorflow/compiler/tf2xla/xla_compiler.h index 079c99797e1f1ec26205e33b3c7c16d3764f15ca..25332c8d8e3210a0217a1ba3f5767115fe6b1d93 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.h +++ b/tensorflow/compiler/tf2xla/xla_compiler.h @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compilation_device.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/device_mgr.h" @@ -251,6 +252,12 @@ class XlaCompiler { // The default empty value is invalid. DeviceType device_type = DeviceType(""); + // The device to use during compilation to execute instructions on, for + // example for auto-tuning. + // Valid values are defined by `xla::Backend::devices_ordinal_supported()`. + // -1 indicates the default device should be used. + int device_ordinal = -1; + xla::Client* client = nullptr; // Function library in which to find function definitions. Must be non-null. diff --git a/tensorflow/compiler/tf2xla/xla_compiler_test.cc b/tensorflow/compiler/tf2xla/xla_compiler_test.cc index 07af8ef54b79b215e9e99faa161c8279488ebbf7..be00ed8813fdf2778d6af81556001ef51538dd34 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler_test.cc +++ b/tensorflow/compiler/tf2xla/xla_compiler_test.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" @@ -206,9 +206,9 @@ TEST_F(XlaCompilerTest, Simple) { // Tests that the generated computation works. std::unique_ptr param0_literal = - xla::Literal::CreateR1({7, 42}); + xla::LiteralUtil::CreateR1({7, 42}); std::unique_ptr param1_literal = - xla::Literal::CreateR1({-3, 101}); + xla::LiteralUtil::CreateR1({-3, 101}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); std::unique_ptr param1_data = @@ -222,12 +222,64 @@ TEST_F(XlaCompilerTest, Simple) { client_->Transfer(*actual).ConsumeValueOrDie(); std::unique_ptr expected0 = - xla::Literal::CreateR1({4, 143}); + xla::LiteralUtil::CreateR1({4, 143}); std::unique_ptr expected_literal = - xla::Literal::MakeTuple({expected0.get()}); + xla::LiteralUtil::MakeTuple({expected0.get()}); EXPECT_TRUE(xla::LiteralTestUtil::Equal(*expected_literal, *actual_literal)); } +// Tests compilation of a graph where the _Retval node is not necessarily last +// amongst the graph nodes in construction order, and always_return_tuple is +// false. Regression test for bug where the wrong value was returned. +TEST_F(XlaCompilerTest, OutOfOrderGraph) { + Scope scope = Scope::NewRootScope().ExitOnError(); + auto a = ops::_Arg(scope.WithOpName("A"), DT_INT32, 0); + auto b = ops::_Arg(scope.WithOpName("B"), DT_INT32, 1); + // The _Retval node is not last in construction order. + auto d = ops::_Retval(scope.WithOpName("D"), a, 0); + auto c = ops::Add(scope.WithOpName("C"), a, b); + + std::unique_ptr graph(new Graph(OpRegistry::Global())); + TF_ASSERT_OK(scope.ToGraph(graph.get())); + + // Builds a description of the arguments. + std::vector args(2); + args[0].kind = XlaCompiler::Argument::kParameter; + args[0].type = DT_INT32; + args[0].shape = TensorShape({2}); + args[1].kind = XlaCompiler::Argument::kParameter; + args[1].type = DT_INT32; + args[1].shape = TensorShape({2}); + + // Compiles the graph. + XlaCompiler compiler(DefaultOptions()); + + XlaCompiler::CompileOptions compile_options; + compile_options.always_return_tuple = false; + XlaCompiler::CompilationResult result; + TF_ASSERT_OK(compiler.CompileGraph(compile_options, "add", std::move(graph), + args, &result)); + + // Tests that the generated computation works. + std::unique_ptr param0_literal = + xla::LiteralUtil::CreateR1({7, 42}); + std::unique_ptr param1_literal = + xla::LiteralUtil::CreateR1({-3, 101}); + std::unique_ptr param0_data = + client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); + std::unique_ptr param1_data = + client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); + + std::unique_ptr actual = + client_ + ->Execute(*result.computation, {param0_data.get(), param1_data.get()}) + .ConsumeValueOrDie(); + std::unique_ptr actual_literal = + client_->Transfer(*actual).ConsumeValueOrDie(); + + EXPECT_TRUE(xla::LiteralTestUtil::Equal(*param0_literal, *actual_literal)); +} + TEST_F(XlaCompilerTest, HasSaneErrorOnNonCompileTimeConstantInputToReshape) { // Builds a graph that adds reshapes a tensor, but with the shape not // statically known. @@ -260,7 +312,7 @@ TEST_F(XlaCompilerTest, HasSaneErrorOnNonCompileTimeConstantInputToReshape) { str_util::StrContains(status.error_message(), "depends on a parameter")) << status.error_message(); EXPECT_TRUE( - str_util::StrContains(status.error_message(), "[[Node: C = Reshape")) + str_util::StrContains(status.error_message(), "[[{{node C}} = Reshape")) << status.error_message(); } @@ -306,7 +358,7 @@ TEST_F(XlaCompilerTest, ConstantOutputs) { // Tests that the generated computation works. std::unique_ptr param0_literal = - xla::Literal::CreateR1({7, 42}); + xla::LiteralUtil::CreateR1({7, 42}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -317,9 +369,9 @@ TEST_F(XlaCompilerTest, ConstantOutputs) { client_->Transfer(*actual).ConsumeValueOrDie(); std::unique_ptr expected0 = - xla::Literal::CreateR1({-7, -42}); + xla::LiteralUtil::CreateR1({-7, -42}); std::unique_ptr expected_literal = - xla::Literal::MakeTuple({expected0.get()}); + xla::LiteralUtil::MakeTuple({expected0.get()}); EXPECT_TRUE( xla::LiteralTestUtil::Equal(*expected_literal, *actual_literal)); } @@ -341,7 +393,7 @@ TEST_F(XlaCompilerTest, ConstantOutputs) { // Tests that the generated computation works. std::unique_ptr param0_literal = - xla::Literal::CreateR1({7, 42}); + xla::LiteralUtil::CreateR1({7, 42}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -351,11 +403,12 @@ TEST_F(XlaCompilerTest, ConstantOutputs) { std::unique_ptr actual_literal = client_->Transfer(*actual).ConsumeValueOrDie(); - std::unique_ptr expected0 = xla::Literal::CreateR0(7); + std::unique_ptr expected0 = + xla::LiteralUtil::CreateR0(7); std::unique_ptr expected1 = - xla::Literal::CreateR1({-7, -42}); + xla::LiteralUtil::CreateR1({-7, -42}); std::unique_ptr expected = - xla::Literal::MakeTuple({expected0.get(), expected1.get()}); + xla::LiteralUtil::MakeTuple({expected0.get(), expected1.get()}); EXPECT_TRUE(xla::LiteralTestUtil::Equal(*expected, *actual_literal)); } } @@ -569,11 +622,11 @@ TEST_F(XlaCompilerTest, CanPassTensorArraysToAndFromComputation) { // Tests that the generated computation works. std::unique_ptr input_base = - xla::Literal::CreateR1({7, 42}); + xla::LiteralUtil::CreateR1({7, 42}); std::unique_ptr input_grad2 = - xla::Literal::CreateR1({-3, 101}); + xla::LiteralUtil::CreateR1({-3, 101}); std::unique_ptr input = - xla::Literal::MakeTuple({input_base.get(), input_grad2.get()}); + xla::LiteralUtil::MakeTuple({input_base.get(), input_grad2.get()}); std::unique_ptr param0_data = client_->TransferToServer(*input).ConsumeValueOrDie(); @@ -583,17 +636,18 @@ TEST_F(XlaCompilerTest, CanPassTensorArraysToAndFromComputation) { std::unique_ptr actual_literal = client_->Transfer(*actual).ConsumeValueOrDie(); - std::unique_ptr output_read = xla::Literal::CreateR0(42); + std::unique_ptr output_read = + xla::LiteralUtil::CreateR0(42); std::unique_ptr output_base = - xla::Literal::CreateR1({7, 42}); + xla::LiteralUtil::CreateR1({7, 42}); std::unique_ptr output_grad1 = - xla::Literal::CreateR1({0, 1}); + xla::LiteralUtil::CreateR1({0, 1}); std::unique_ptr output_grad2 = - xla::Literal::CreateR1({-3, 101}); - std::unique_ptr output_resource = xla::Literal::MakeTuple( + xla::LiteralUtil::CreateR1({-3, 101}); + std::unique_ptr output_resource = xla::LiteralUtil::MakeTuple( {output_base.get(), output_grad1.get(), output_grad2.get()}); std::unique_ptr expected_literal = - xla::Literal::MakeTuple({output_read.get(), output_resource.get()}); + xla::LiteralUtil::MakeTuple({output_read.get(), output_resource.get()}); EXPECT_TRUE(xla::LiteralTestUtil::Equal(*expected_literal, *actual_literal)); } @@ -796,9 +850,9 @@ TEST_F(XlaCompilerTest, Variables) { // Tests that the generated computation works. std::unique_ptr param0_literal = - xla::Literal::CreateR1({7, 42}); + xla::LiteralUtil::CreateR1({7, 42}); std::unique_ptr param1_literal = - xla::Literal::CreateR1({-3, 101}); + xla::LiteralUtil::CreateR1({-3, 101}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); std::unique_ptr param1_data = @@ -812,11 +866,11 @@ TEST_F(XlaCompilerTest, Variables) { client_->Transfer(*actual).ConsumeValueOrDie(); std::unique_ptr expected0 = - xla::Literal::CreateR1({5, 144}); + xla::LiteralUtil::CreateR1({5, 144}); std::unique_ptr expected1 = - xla::Literal::CreateR1({4, 143}); + xla::LiteralUtil::CreateR1({4, 143}); std::unique_ptr expected_literal = - xla::Literal::MakeTuple({expected0.get(), expected1.get()}); + xla::LiteralUtil::MakeTuple({expected0.get(), expected1.get()}); EXPECT_TRUE(xla::LiteralTestUtil::Equal(*expected_literal, *actual_literal)); } @@ -884,9 +938,9 @@ TEST_F(XlaCompilerTest, VariableRepresentationShapeFunction) { // Tests that the generated computation works. std::unique_ptr param0_literal = - xla::Literal::CreateR2({{4, 55}, {1, -3}}); + xla::LiteralUtil::CreateR2({{4, 55}, {1, -3}}); std::unique_ptr param1_literal = - xla::Literal::CreateR1({22, 11, 33, 404}); + xla::LiteralUtil::CreateR1({22, 11, 33, 404}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); std::unique_ptr param1_data = @@ -900,11 +954,11 @@ TEST_F(XlaCompilerTest, VariableRepresentationShapeFunction) { client_->Transfer(*actual).ConsumeValueOrDie(); std::unique_ptr expected0 = - xla::Literal::CreateR2({{27, 67}, {35, 402}}); + xla::LiteralUtil::CreateR2({{27, 67}, {35, 402}}); std::unique_ptr expected1 = - xla::Literal::CreateR1({26, 66, 34, 401}); + xla::LiteralUtil::CreateR1({26, 66, 34, 401}); std::unique_ptr expected_literal = - xla::Literal::MakeTuple({expected0.get(), expected1.get()}); + xla::LiteralUtil::MakeTuple({expected0.get(), expected1.get()}); EXPECT_TRUE(xla::LiteralTestUtil::Equal(*expected_literal, *actual_literal)); } @@ -953,9 +1007,9 @@ TEST_F(XlaCompilerTest, ArgRetvalShapeRepresentationFunction) { // Tests that the generated computation works. std::unique_ptr param0_literal = - xla::Literal::CreateR1({4, 55, 1, -3}); + xla::LiteralUtil::CreateR1({4, 55, 1, -3}); std::unique_ptr param1_literal = - xla::Literal::CreateR1({22, 11, 33, 404}); + xla::LiteralUtil::CreateR1({22, 11, 33, 404}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); std::unique_ptr param1_data = @@ -969,11 +1023,11 @@ TEST_F(XlaCompilerTest, ArgRetvalShapeRepresentationFunction) { client_->Transfer(*actual).ConsumeValueOrDie(); std::unique_ptr expected0 = - xla::Literal::CreateR1({27, 67, 35, 402}); + xla::LiteralUtil::CreateR1({27, 67, 35, 402}); std::unique_ptr expected1 = - xla::Literal::CreateR1({26, 66, 34, 401}); + xla::LiteralUtil::CreateR1({26, 66, 34, 401}); std::unique_ptr expected_literal = - xla::Literal::MakeTuple({expected0.get(), expected1.get()}); + xla::LiteralUtil::MakeTuple({expected0.get(), expected1.get()}); EXPECT_TRUE(xla::LiteralTestUtil::Equal(*expected_literal, *actual_literal)); } @@ -1023,6 +1077,8 @@ TEST_F(XlaCompilerTest, FunctionWithInvalidOp) { ASSERT_FALSE(status.ok()); EXPECT_TRUE(str_util::StrContains(status.error_message(), "InvalidOp")) << status.error_message(); + EXPECT_TRUE(str_util::StrContains(status.error_message(), "{{node fill_fn}}")) + << status.error_message(); } // Tests a graph which has a node with invalid data type. @@ -1047,6 +1103,8 @@ TEST_F(XlaCompilerTest, NodeWithInvalidDataType) { EXPECT_TRUE(str_util::StrContains(status.error_message(), "is not in the list of allowed values")) << status.error_message(); + EXPECT_TRUE(str_util::StrContains(status.error_message(), "{{node Shape}}")) + << status.error_message(); } TEST_F(XlaCompilerTest, SingleOpWithoutInputs) { @@ -1068,9 +1126,10 @@ TEST_F(XlaCompilerTest, SingleOpWithoutInputs) { status = compiler.CompileGraph(XlaCompiler::CompileOptions(), "NoOp", std::move(graph_copy), args, &result); ASSERT_FALSE(status.ok()); - EXPECT_TRUE(str_util::StrContains(status.error_message(), - "The following nodes are unreachable " - "from the source in the graph: NoOp")) + EXPECT_TRUE( + str_util::StrContains(status.error_message(), + "The following nodes are unreachable " + "from the source in the graph: {{node NoOp}}")) << status.error_message(); } diff --git a/tensorflow/compiler/tf2xla/xla_context.cc b/tensorflow/compiler/tf2xla/xla_context.cc index fd39a58ce64acad12768a031c3c9d03c26c01b71..b24e3aabbe6ba858a8bfb4dd435726984cc7b0f5 100644 --- a/tensorflow/compiler/tf2xla/xla_context.cc +++ b/tensorflow/compiler/tf2xla/xla_context.cc @@ -25,9 +25,10 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/common_runtime/dma_helper.h" #include "tensorflow/core/lib/gtl/array_slice.h" diff --git a/tensorflow/compiler/tf2xla/xla_context.h b/tensorflow/compiler/tf2xla/xla_context.h index 38d8cd653cbbe5b01325d6b478589d88909bac56..3db37afdba71342cfb20af8841a40cb54709ca73 100644 --- a/tensorflow/compiler/tf2xla/xla_context.h +++ b/tensorflow/compiler/tf2xla/xla_context.h @@ -22,8 +22,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compilation_device.h" #include "tensorflow/compiler/tf2xla/xla_compiler.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/xla_cpu_backend.cc b/tensorflow/compiler/tf2xla/xla_cpu_backend.cc index ead229aaccc292d4944db0c1eaf98c82583533cd..23d04d43b358e858ad1ab2463322ce0ab93b23c2 100644 --- a/tensorflow/compiler/tf2xla/xla_cpu_backend.cc +++ b/tensorflow/compiler/tf2xla/xla_cpu_backend.cc @@ -31,6 +31,10 @@ bool CpuOpFilter(KernelDef* kdef) { DT_FLOAT); return true; } + // TODO(b/26783907): The CPU backend currently does not implement sort. + if (kdef->op() == "XlaSort" || kdef->op() == "TopKV2") { + return false; + } if (kdef->op() == "Const") { AddDtypeToKernalDefConstraint("dtype", DT_STRING, kdef); } diff --git a/tensorflow/compiler/tf2xla/xla_gpu_backend.cc b/tensorflow/compiler/tf2xla/xla_gpu_backend.cc index 62168b648331844bfe2db1a4d5dcad895c8726f3..1398e9ee536a9675e5b703ec3fabf4a8b9d89cbf 100644 --- a/tensorflow/compiler/tf2xla/xla_gpu_backend.cc +++ b/tensorflow/compiler/tf2xla/xla_gpu_backend.cc @@ -20,12 +20,6 @@ limitations under the License. namespace tensorflow { bool GpuOpFilter(KernelDef* kdef) { - // TODO(b/31361304): The GPU backend does not parallelize PRNG ops, leading to - // slow code. - if (kdef->op() == "RandomStandardNormal" || kdef->op() == "RandomUniform" || - kdef->op() == "RandomUniformInt" || kdef->op() == "TruncatedNormal") { - return false; - } if (kdef->op() == "Const") { AddDtypeToKernalDefConstraint("dtype", DT_STRING, kdef); } diff --git a/tensorflow/compiler/tf2xla/xla_helpers.cc b/tensorflow/compiler/tf2xla/xla_helpers.cc index edbc5e95a8c22dd35dd7c384afdfaf80553eceaf..8efb3d55c88757b9366bdf9622287bdd0a72e295 100644 --- a/tensorflow/compiler/tf2xla/xla_helpers.cc +++ b/tensorflow/compiler/tf2xla/xla_helpers.cc @@ -26,7 +26,8 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/status.h" @@ -94,13 +95,13 @@ xla::XlaOp ArgMinMax(xla::XlaOp input, xla::PrimitiveType output_type, int axis, xla::XlaOp XlaHelpers::Zero(xla::XlaBuilder* b, DataType data_type) { xla::PrimitiveType type; TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type)); - return xla::ConstantLiteral(b, xla::Literal::Zero(type)); + return xla::ConstantLiteral(b, xla::LiteralUtil::Zero(type)); } xla::XlaOp XlaHelpers::One(xla::XlaBuilder* b, DataType data_type) { xla::PrimitiveType type; TF_CHECK_OK(DataTypeToPrimitiveType(data_type, &type)); - return xla::ConstantLiteral(b, xla::Literal::One(type)); + return xla::ConstantLiteral(b, xla::LiteralUtil::One(type)); } xla::XlaOp XlaHelpers::IntegerLiteral(xla::XlaBuilder* b, DataType data_type, diff --git a/tensorflow/compiler/tf2xla/xla_helpers.h b/tensorflow/compiler/tf2xla/xla_helpers.h index d6ca4ab9346593892917e8375b07a8790dc26e79..e6522157a535fc3e4ec96cb0496b6be2e525c336 100644 --- a/tensorflow/compiler/tf2xla/xla_helpers.h +++ b/tensorflow/compiler/tf2xla/xla_helpers.h @@ -19,7 +19,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_TF2XLA_XLA_HELPERS_H_ #include "tensorflow/compiler/tf2xla/xla_context.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/gtl/array_slice.h" diff --git a/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc b/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc index 9e17756b27733e2453ea1688d13e1d718c25cfc8..00ccfb1c7873c85564b1bf4cf582cd31baa17ad5 100644 --- a/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc +++ b/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compiled_cpu_function.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/cpu/cpu_executable.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc index 359cb4c4670227e592ed4b8339825e7f95b16899..82028c8b9ca9f65a73f8b50edc0a47c7068aba9a 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc @@ -21,7 +21,8 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/common_runtime/dma_helper.h" @@ -66,10 +67,18 @@ const xla::XlaOp& XlaOpKernelContext::Input(int index) { return GetComputationFromTensor(context_->input(index)); } +const xla::XlaOp& XlaOpKernelContext::Input(StringPiece name) { + return GetComputationFromTensor(GetInputTensorByName(name)); +} + TensorShape XlaOpKernelContext::InputShape(int index) { return context_->input(index).shape(); } +TensorShape XlaOpKernelContext::InputShape(StringPiece name) { + return GetInputTensorByName(name).shape(); +} + DataType XlaOpKernelContext::input_type(int index) const { return context_->input(index).dtype(); } @@ -332,10 +341,11 @@ Status XlaOpKernelContext::ConstantInputList( return Status::OK(); } -Status XlaOpKernelContext::ReadVariableInput(int index, DataType type, - TensorShape* shape, - xla::XlaOp* value) { - const Tensor& tensor = context_->input(index); +namespace { + +Status ReadVariableInputTensor(const Tensor& tensor, DataType type, + const OpKernelContext* ctx, TensorShape* shape, + xla::XlaOp* value) { const XlaExpression* expression = CastExpressionFromTensor(tensor); XlaResource* variable = expression->resource(); TF_RET_CHECK(variable != nullptr); @@ -353,7 +363,7 @@ Status XlaOpKernelContext::ReadVariableInput(int index, DataType type, *shape = variable->shape(); } - XlaContext& xla_context = XlaContext::Get(context_); + XlaContext& xla_context = XlaContext::Get(ctx); TF_ASSIGN_OR_RETURN( TensorShape representation_shape, xla_context.RepresentationShape(variable->shape(), variable->type())); @@ -365,6 +375,22 @@ Status XlaOpKernelContext::ReadVariableInput(int index, DataType type, return Status::OK(); } +} // namespace + +Status XlaOpKernelContext::ReadVariableInput(int index, DataType type, + TensorShape* shape, + xla::XlaOp* value) { + return ReadVariableInputTensor(context_->input(index), type, context_, shape, + value); +} + +Status XlaOpKernelContext::ReadVariableInput(StringPiece name, DataType type, + TensorShape* shape, + xla::XlaOp* value) { + return ReadVariableInputTensor(GetInputTensorByName(name), type, context_, + shape, value); +} + Status XlaOpKernelContext::GetVariableTypeAndShape(int index, DataType* type, TensorShape* shape) const { const Tensor& tensor = context_->input(index); @@ -455,17 +481,17 @@ Status XlaOpKernelContext::GetResourceInput(int index, XlaResource** resource) { return Status::OK(); } -Status XlaOpKernelContext::AssignVariable(int input_index, DataType type, - xla::XlaOp handle) { - TF_RET_CHECK(handle.valid()); +namespace { - const XlaExpression* expression = - CastExpressionFromTensor(context_->input(input_index)); +Status AssignVariableTensor(const Tensor& tensor, DataType type, + const OpKernelContext* ctx, xla::XlaOp handle, + xla::XlaBuilder* builder) { + const XlaExpression* expression = CastExpressionFromTensor(tensor); XlaResource* variable = expression->resource(); TF_RET_CHECK(variable != nullptr); TF_RET_CHECK(variable->kind() == XlaResource::kVariable); - auto shape_or_status = builder()->GetShape(handle); + auto shape_or_status = builder->GetShape(handle); if (!shape_or_status.ok()) { return shape_or_status.status(); } @@ -475,7 +501,7 @@ Status XlaOpKernelContext::AssignVariable(int input_index, DataType type, TF_RETURN_IF_ERROR(variable->SetTypeAndShape(type, shape)); - XlaContext& xla_context = XlaContext::Get(context_); + XlaContext& xla_context = XlaContext::Get(ctx); TF_ASSIGN_OR_RETURN(TensorShape representation_shape, xla_context.RepresentationShape(shape, type)); if (shape != representation_shape) { @@ -484,6 +510,22 @@ Status XlaOpKernelContext::AssignVariable(int input_index, DataType type, return variable->SetValue(handle); } +} // namespace + +Status XlaOpKernelContext::AssignVariable(int input_index, DataType type, + xla::XlaOp handle) { + TF_RET_CHECK(handle.valid()); + return AssignVariableTensor(context_->input(input_index), type, context_, + handle, builder()); +} + +Status XlaOpKernelContext::AssignVariable(StringPiece name, DataType type, + xla::XlaOp handle) { + TF_RET_CHECK(handle.valid()); + return AssignVariableTensor(GetInputTensorByName(name), type, context_, + handle, builder()); +} + XlaCompiler* XlaOpKernelContext::compiler() const { return XlaContext::Get(context_).compiler(); } @@ -523,6 +565,12 @@ const xla::XlaComputation* XlaOpKernelContext::GetOrCreateMul( return XlaContext::Get(context_).GetOrCreateMul(type); } +const Tensor& XlaOpKernelContext::GetInputTensorByName(StringPiece name) { + const Tensor* tensor; + CHECK(context_->input(name, &tensor).ok()); + return *tensor; +} + XlaOpKernel::XlaOpKernel(OpKernelConstruction* context) : OpKernel(context) {} void XlaOpKernel::Compute(OpKernelContext* context) { diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.h b/tensorflow/compiler/tf2xla/xla_op_kernel.h index 2bde2c983d0cca05558e86a36698d6f0e097705a..ac9dfe3369078df7392a4ef04679f7d7beacf8bb 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.h +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.h @@ -17,7 +17,8 @@ limitations under the License. #define TENSORFLOW_COMPILER_TF2XLA_XLA_OP_KERNEL_H_ #include "tensorflow/compiler/tf2xla/xla_compiler.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/platform/macros.h" @@ -67,21 +68,26 @@ class XlaOpKernelContext { // Returns the number of inputs to the operator. int num_inputs() const { return context_->num_inputs(); } - // Returns the type of input 'index'. + // Returns the type of input `index`. DataType input_type(int index) const; - // Returns the type of input 'index' as an xla::PrimitiveType. If the type + // Returns the type of input `index` as an xla::PrimitiveType. If the type // is not representable as an XLA type, sets an error status and returns // xla::PRIMITIVE_TYPE_INVALID. xla::PrimitiveType input_xla_type(int index); - // Returns the shape of input 'index'. + // Returns the shape of input `index`. TensorShape InputShape(int index); - // Returns input 'index' as a XlaOp. Unlike + // Returns the shape of input `name`. + TensorShape InputShape(StringPiece name); + + // Returns input `index` as a XlaOp. Unlike // OpKernelContext::Input returns a symbolic value rather than a concrete // Tensor. const xla::XlaOp& Input(int index); + // Returns input `name` as a XlaOp. + const xla::XlaOp& Input(StringPiece name); // Returns true if all inputs are the same shape, otherwise sets the // status to a non-OK value and returns false. @@ -96,13 +102,13 @@ class XlaOpKernelContext { // Helper methods for constant inputs. - // Evaluates input 'index' and stores it in '*constant_literal'. If the + // Evaluates input `index` and stores it in `*constant_literal`. If the // expression cannot be evaluated, e.g., because it depends on unbound // parameters, returns a non-OK status. Status ConstantInput(int index, xla::Literal* constant_literal); - // Evaluates input 'index', reshapes it to 'new_shape' if new_shape != - // InputShape(index), and stores it in '*constant_literal'. If the input + // Evaluates input `index`, reshapes it to `new_shape` if new_shape != + // InputShape(index), and stores it in `*constant_literal`. If the input // cannot be evaluated, e.g., because it depends on unbound parameters, // returns a non-Ok status. If InputShape(index).num_elements() != // new_shape.num_elements(), returns an error status. @@ -137,17 +143,17 @@ class XlaOpKernelContext { return context_->expected_output_dtype(index); } - // Sets output 'index' to the XlaOp 'handle'. + // Sets output `index` to the XlaOp `handle`. // All outputs should be set using SetOutput and SetConstantOutput, not // via the underlying OpKernelContext. void SetOutput(int index, const xla::XlaOp& handle); - // Sets output 'index' to compile-time constant 'host_tensor', where - // 'host_tensor' is a tensor in host memory. It is preferable to use + // Sets output `index` to compile-time constant `host_tensor`, where + // `host_tensor` is a tensor in host memory. It is preferable to use // SetConstantOutput where possible. void SetConstantOutput(int index, const Tensor& host_tensor); - // Sets output 'index' to an invalid value. + // Sets output `index` to an invalid value. // Any subsequent attempt to consume this output will cause an error. void SetInvalidOutput(int index); @@ -157,10 +163,10 @@ class XlaOpKernelContext { // Variables - // Sets '*resource' to the resource associated with input `index`. + // Sets `*resource` to the resource associated with input `index`. Status GetResourceInput(int index, XlaResource** resource); - // Sets output 'index' to be a reference to resource 'resource'. + // Sets output `index` to be a reference to resource `resource`. void SetResourceOutput(int index, XlaResource* resource); // Sets `*type` and `*shape` to the current type and shape of a variable's @@ -169,17 +175,23 @@ class XlaOpKernelContext { TensorShape* shape) const; // Reads the current value of the resouce variable referred to by input - // 'index'. If `shape` is not nullptr, sets `*shape` to the shape of the + // `index`. If `shape` is not nullptr, sets `*shape` to the shape of the // variable. Returns an error if the variable has not been initialized, or if // its type does not match `type`. Status ReadVariableInput(int index, DataType type, TensorShape* shape, xla::XlaOp* value); + // Reads the current value of the resouce variable referred to by input + // `name`. + Status ReadVariableInput(StringPiece name, DataType type, TensorShape* shape, + xla::XlaOp* value); // Assigns the value `handle` to the variable referenced by input // `input_index`. The variable must be of `type`. Returns an error if the // variable has been initialized with a different type or with a // different shape. Status AssignVariable(int input_index, DataType type, xla::XlaOp handle); + // Assigns the value `handle` to the variable referenced by input `name`. + Status AssignVariable(StringPiece name, DataType type, xla::XlaOp handle); // Helper routines for the OP_REQUIRES macros void CtxFailure(const Status& s); @@ -227,6 +239,9 @@ class XlaOpKernelContext { const xla::XlaComputation* GetOrCreateMul(const DataType type); private: + // Returns the tensor of input `name`. + const Tensor& GetInputTensorByName(StringPiece name); + OpKernelContext* const context_; }; diff --git a/tensorflow/compiler/tf2xla/xla_op_registry.h b/tensorflow/compiler/tf2xla/xla_op_registry.h index 2d4593ea4999ad6d8cd0f0e2eec9c6d69c3020b8..fc14834ca6441ea785eacc57e1f502086f36657e 100644 --- a/tensorflow/compiler/tf2xla/xla_op_registry.h +++ b/tensorflow/compiler/tf2xla/xla_op_registry.h @@ -279,7 +279,7 @@ class XlaOpRegistrar { #define REGISTER_XLA_OP_UNIQ(CTR, BUILDER, OP) \ static ::tensorflow::XlaOpRegistrar xla_op_registrar__body__##CTR##__object( \ - XlaOpRegistrationBuilder::BUILDER.Build( \ + ::tensorflow::XlaOpRegistrationBuilder::BUILDER.Build( \ [](::tensorflow::OpKernelConstruction* context) \ -> ::tensorflow::OpKernel* { return new OP(context); })); diff --git a/tensorflow/compiler/tf2xla/xla_resource.cc b/tensorflow/compiler/tf2xla/xla_resource.cc index baea8149658ec0849ebb570931ca68518ec5284e..7928fa034725206a752cbfe086d01f15cd235df9 100644 --- a/tensorflow/compiler/tf2xla/xla_resource.cc +++ b/tensorflow/compiler/tf2xla/xla_resource.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/sharding_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/xla_resource.h b/tensorflow/compiler/tf2xla/xla_resource.h index 4de18a77887496d30e3b1407ecd9042e619653af..2438490be13809b9f3571a362900b44cb838e76b 100644 --- a/tensorflow/compiler/tf2xla/xla_resource.h +++ b/tensorflow/compiler/tf2xla/xla_resource.h @@ -18,7 +18,7 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/framework/types.pb.h" diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD index 03e542855ba0e3ae81e0b754eb319cadbd5079ba..fdf13bb18c2567d2994612d15119ae87cbfa9137 100644 --- a/tensorflow/compiler/xla/BUILD +++ b/tensorflow/compiler/xla/BUILD @@ -254,6 +254,7 @@ tf_cc_test( ":types", ":util", ":xla_data_proto", + "//tensorflow/core:lib", "//tensorflow/core:test_main", ], ) @@ -281,9 +282,9 @@ tf_cc_test( ) cc_library( - name = "literal_util", - srcs = ["literal_util.cc"], - hdrs = ["literal_util.h"], + name = "literal", + srcs = ["literal.cc"], + hdrs = ["literal.h"], visibility = ["//visibility:public"], deps = [ ":array2d", @@ -300,11 +301,12 @@ cc_library( ) tf_cc_test( - name = "literal_util_test", - srcs = ["literal_util_test.cc"], + name = "literal_test", + srcs = ["literal_test.cc"], deps = [ ":array3d", ":array4d", + ":literal", ":literal_util", ":shape_util", ":test", @@ -316,6 +318,26 @@ tf_cc_test( ], ) +cc_library( + name = "literal_util", + srcs = ["literal_util.cc"], + hdrs = ["literal_util.h"], + visibility = ["//visibility:public"], + deps = [ + ":array2d", + ":array3d", + ":array4d", + ":literal", + ":shape_util", + ":sparse_index_array", + ":status_macros", + ":types", + ":util", + ":xla_data_proto", + "//tensorflow/core:lib", + ], +) + cc_library( name = "error_spec", hdrs = ["error_spec.h"], @@ -327,6 +349,7 @@ cc_library( hdrs = ["literal_comparison.h"], deps = [ ":error_spec", + ":literal", ":literal_util", ":util", "//tensorflow/core:lib", @@ -458,7 +481,7 @@ cc_library( hdrs = ["packed_literal_reader.h"], visibility = [":internal"], deps = [ - ":literal_util", + ":literal", ":shape_util", ":status_macros", ":statusor", @@ -489,7 +512,7 @@ cc_library( hdrs = ["text_literal_reader.h"], visibility = [":internal"], deps = [ - ":literal_util", + ":literal", ":shape_util", ":status_macros", ":statusor", @@ -505,7 +528,7 @@ tf_cc_test( name = "text_literal_reader_test", srcs = ["text_literal_reader_test.cc"], deps = [ - ":literal_util", + ":literal", ":shape_util", ":test", ":text_literal_reader", @@ -522,7 +545,7 @@ cc_library( hdrs = ["text_literal_writer.h"], visibility = [":internal"], deps = [ - ":literal_util", + ":literal", ":shape_util", ":status_macros", ":types", @@ -535,6 +558,7 @@ tf_cc_test( name = "text_literal_writer_test", srcs = ["text_literal_writer_test.cc"], deps = [ + ":literal", ":literal_util", ":test", ":test_helpers", @@ -607,11 +631,12 @@ cc_library( ":array2d", ":array3d", ":array4d", + ":literal_util", ":util", ":window_util", ":xla_data_proto", "//tensorflow/compiler/xla/client:padding", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_evaluator", "//tensorflow/compiler/xla/service:shape_inference", @@ -627,7 +652,7 @@ tf_cc_test( ":array2d", ":array3d", ":array4d", - ":literal_util", + ":literal", ":reference_util", ":test", ":util", diff --git a/tensorflow/compiler/xla/client/BUILD b/tensorflow/compiler/xla/client/BUILD index 8f08d3b2e04670ad6590aca1db0fd9d25faed83f..ad3fcee05b80181369bfdf3cdcdb5452ec9e7e89 100644 --- a/tensorflow/compiler/xla/client/BUILD +++ b/tensorflow/compiler/xla/client/BUILD @@ -64,8 +64,9 @@ cc_library( hdrs = ["client.h"], deps = [ ":global_data", + ":xla_computation", "//tensorflow/compiler/xla:execution_options_util", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:service_interface", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -73,7 +74,6 @@ cc_library( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla:xla_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", "//tensorflow/compiler/xla/service:hlo_proto", "//tensorflow/core:lib", @@ -100,12 +100,12 @@ cc_library( deps = [ ":client", ":executable_build_options", + ":xla_computation", "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:backend", "//tensorflow/compiler/xla/service:compiler", "//tensorflow/compiler/xla/service:device_memory_allocator", @@ -114,6 +114,7 @@ cc_library( "//tensorflow/compiler/xla/service:local_service", "//tensorflow/compiler/xla/service:shaped_buffer", "//tensorflow/compiler/xla/service:source_map_util", + "//tensorflow/compiler/xla/service:stream_pool", "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", "@llvm//:support", @@ -126,11 +127,11 @@ cc_library( hdrs = ["compile_only_client.h"], deps = [ ":client", + ":xla_computation", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:compile_only_service", "//tensorflow/compiler/xla/service:compiler", "//tensorflow/core:stream_executor_no_cuda", @@ -174,3 +175,60 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", ], ) + +cc_library( + name = "xla_computation", + srcs = ["xla_computation.cc"], + hdrs = ["xla_computation.h"], + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo_proto", + ], +) + +cc_library( + name = "xla_builder", + srcs = ["xla_builder.cc"], + hdrs = ["xla_builder.h"], + visibility = ["//visibility:public"], + deps = [ + ":padding", + ":sharding_builder", + ":xla_computation", + "//tensorflow/compiler/xla:execution_options_util", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_proto", + "//tensorflow/compiler/xla/service:shape_inference", + "//tensorflow/core:lib", + ], +) + +tf_cc_test( + name = "xla_builder_test", + srcs = ["xla_builder_test.cc"], + deps = [ + ":xla_builder", + ":xla_computation", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:test_helpers", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_matchers", + "//tensorflow/core:test", + ], +) diff --git a/tensorflow/compiler/xla/client/client.cc b/tensorflow/compiler/xla/client/client.cc index 3d596a6e65430b6e9692aabd65fc8aa84b7b873d..d0ce5e8a6afa262d4cffdfe8431aab570ffd28df 100644 --- a/tensorflow/compiler/xla/client/client.cc +++ b/tensorflow/compiler/xla/client/client.cc @@ -18,9 +18,10 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" @@ -409,8 +410,10 @@ StatusOr Client::ExecutionStatsAsString( return string("[Execution Statistics] not available."); } -StatusOr Client::CreateChannelHandle() { +StatusOr Client::CreateChannelHandleByType( + ChannelHandle::ChannelType type) { CreateChannelHandleRequest request; + request.set_channel_type(type); CreateChannelHandleResponse response; VLOG(1) << "making create channel handle request"; @@ -424,4 +427,16 @@ StatusOr Client::CreateChannelHandle() { return response.channel(); } +StatusOr Client::CreateChannelHandle() { + return CreateChannelHandleByType(ChannelHandle::DEVICE_TO_DEVICE); +} + +StatusOr Client::CreateHostToDeviceChannelHandle() { + return CreateChannelHandleByType(ChannelHandle::HOST_TO_DEVICE); +} + +StatusOr Client::CreateDeviceToHostChannelHandle() { + return CreateChannelHandleByType(ChannelHandle::DEVICE_TO_HOST); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/client/client.h b/tensorflow/compiler/xla/client/client.h index 68f0d0ac78c859fde7a6a007cd250b047a7bfcda..be50cebfcc0e3c19002635dbd280b14048aa0c93 100644 --- a/tensorflow/compiler/xla/client/client.h +++ b/tensorflow/compiler/xla/client/client.h @@ -20,8 +20,8 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/global_data.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/service_interface.h" #include "tensorflow/compiler/xla/statusor.h" @@ -178,10 +178,15 @@ class Client { StatusOr> GetComputationShape( const XlaComputation& computation); - // Creates a channel handle that can be used to transfer data between - // two computations via a pair of Send and Recv instructions. + // Creates a channel handle that can be used to transfer data between two + // computations on different devices via a pair of Send and Recv instructions. StatusOr CreateChannelHandle(); + // Create a channel for communicating with the host via a SendtoHost or + // RecvFromHost operation. + StatusOr CreateHostToDeviceChannelHandle(); + StatusOr CreateDeviceToHostChannelHandle(); + StatusOr LoadSnapshot(const HloSnapshot& module); ServiceInterface* stub() { return stub_; } @@ -192,6 +197,9 @@ class Client { StatusOr ExecutionStatsAsString(const XlaComputation& computation, const ExecutionProfile& profile); + StatusOr CreateChannelHandleByType( + ChannelHandle::ChannelType type); + ServiceInterface* stub_; // Stub that this client is connected on. TF_DISALLOW_COPY_AND_ASSIGN(Client); diff --git a/tensorflow/compiler/xla/client/compile_only_client.h b/tensorflow/compiler/xla/client/compile_only_client.h index 332c96503637344d56e363e19db4880c37ca9684..a551edeab0943ec5213c5cb035644c02c3cf54d7 100644 --- a/tensorflow/compiler/xla/client/compile_only_client.h +++ b/tensorflow/compiler/xla/client/compile_only_client.h @@ -17,7 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_XLA_CLIENT_COMPILE_ONLY_CLIENT_H_ #include "tensorflow/compiler/xla/client/client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/compile_only_service.h" #include "tensorflow/compiler/xla/service/compiler.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/client/lib/BUILD b/tensorflow/compiler/xla/client/lib/BUILD index a6b9b4725324adf26a136d490cf28a89c92571c0..39d5582d19dbb9942ae87e1962fc9fa713bcdd50 100644 --- a/tensorflow/compiler/xla/client/lib/BUILD +++ b/tensorflow/compiler/xla/client/lib/BUILD @@ -29,8 +29,8 @@ cc_library( "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/core:lib", ], ) @@ -45,7 +45,7 @@ cc_library( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", ], ) @@ -58,7 +58,7 @@ xla_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", ], @@ -72,7 +72,7 @@ cc_library( ":constants", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", ], ) @@ -82,10 +82,11 @@ xla_test( tags = ["enable_for_xla_interpreter"], deps = [ ":math", + "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", ], @@ -96,9 +97,12 @@ cc_library( srcs = ["numeric.cc"], hdrs = ["numeric.h"], deps = [ + ":arithmetic", + ":constants", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/core:lib", ], ) @@ -111,6 +115,52 @@ xla_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/tests:client_library_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + ], +) + +cc_library( + name = "prng", + srcs = ["prng.cc"], + hdrs = ["prng.h"], + deps = [ + ":constants", + ":math", + ":numeric", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/core:lib", + ], +) + +cc_library( + name = "sorting", + srcs = ["sorting.cc"], + hdrs = ["sorting.h"], + deps = [ + ":numeric", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + ], +) + +xla_test( + name = "sorting_test", + srcs = ["sorting_test.cc"], + blacklisted_backends = [ + "cpu", + "gpu", + ], + tags = ["enable_for_xla_interpreter"], + deps = [ + ":sorting", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -123,7 +173,7 @@ cc_library( hdrs = ["testing.h"], deps = [ "//tensorflow/compiler/xla:execution_options_util", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:types", @@ -131,8 +181,8 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client", "//tensorflow/compiler/xla/client:global_data", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:test_utils", "//tensorflow/core:lib", ], diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.cc b/tensorflow/compiler/xla/client/lib/arithmetic.cc index 978fc40f3492cd7d9d7831c370b287bf45e6d3e0..9225b1acd69c214d6f08a45372a8082ed789c18c 100644 --- a/tensorflow/compiler/xla/client/lib/arithmetic.cc +++ b/tensorflow/compiler/xla/client/lib/arithmetic.cc @@ -18,8 +18,8 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" @@ -94,16 +94,18 @@ XlaComputation CreateScalarMinComputation(PrimitiveType type, }); } -XlaComputation CreateScalarAndComputation(XlaBuilder* builder) { +XlaComputation CreateScalarAndComputation(PrimitiveType type, + XlaBuilder* builder) { return CreateScalarComputation( - "and", PRED, builder, + "and", type, builder, [](XlaBuilder* b, const XlaOp& lhs, const XlaOp& rhs) { return And(lhs, rhs); }); } -XlaComputation CreateScalarOrComputation(XlaBuilder* builder) { - return CreateScalarComputation("or", PRED, builder, +XlaComputation CreateScalarOrComputation(PrimitiveType type, + XlaBuilder* builder) { + return CreateScalarComputation("or", type, builder, [](XlaBuilder* b, const XlaOp& lhs, const XlaOp& rhs) { return Or(lhs, rhs); }); } @@ -112,7 +114,7 @@ XlaOp Any(XlaOp predicates) { XlaBuilder* builder = predicates.builder(); return builder->ReportErrorOrReturn([&]() -> StatusOr { auto f = ConstantR0(builder, false); - XlaComputation logical_or = CreateScalarOrComputation(builder); + XlaComputation logical_or = CreateScalarOrComputation(PRED, builder); TF_ASSIGN_OR_RETURN(const Shape& predicates_shape, builder->GetShape(predicates)); std::vector all_dimensions(ShapeUtil::Rank(predicates_shape)); diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.h b/tensorflow/compiler/xla/client/lib/arithmetic.h index d0b916e8c8f742406caad0571d6e99224ed81404..632e8cc8bc64fad236a0226c6e93079aadde7050 100644 --- a/tensorflow/compiler/xla/client/lib/arithmetic.h +++ b/tensorflow/compiler/xla/client/lib/arithmetic.h @@ -18,8 +18,8 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/xla_data.pb.h" namespace xla { @@ -45,10 +45,12 @@ XlaComputation CreateScalarMinComputation(PrimitiveType type, XlaBuilder* builder); // Creates a scalar logical AND computation and returns it. -XlaComputation CreateScalarAndComputation(XlaBuilder* builder); +XlaComputation CreateScalarAndComputation(PrimitiveType type, + XlaBuilder* builder); // Creates a scalar logical OR computation and returns it. -XlaComputation CreateScalarOrComputation(XlaBuilder* builder); +XlaComputation CreateScalarOrComputation(PrimitiveType type, + XlaBuilder* builder); // Returns whether any predicate in "predicates" is set. // diff --git a/tensorflow/compiler/xla/client/lib/constants.cc b/tensorflow/compiler/xla/client/lib/constants.cc index 1686389a234659a433f1508bd3e0458793541e47..031d62e4ffef188082303a28866bbc72a154e9b1 100644 --- a/tensorflow/compiler/xla/client/lib/constants.cc +++ b/tensorflow/compiler/xla/client/lib/constants.cc @@ -21,7 +21,7 @@ limitations under the License. namespace xla { XlaOp Zero(XlaBuilder* builder, PrimitiveType type) { - return ConstantLiteral(builder, Literal::Zero(type)); + return ConstantLiteral(builder, LiteralUtil::Zero(type)); } XlaOp Zeros(XlaBuilder* builder, const Shape& shape) { @@ -38,7 +38,7 @@ XlaOp ZerosLike(XlaOp prototype) { } XlaOp One(XlaBuilder* builder, PrimitiveType type) { - return ConstantLiteral(builder, Literal::One(type)); + return ConstantLiteral(builder, LiteralUtil::One(type)); } XlaOp Epsilon(XlaBuilder* builder, PrimitiveType type) { @@ -61,7 +61,7 @@ XlaOp Epsilon(XlaBuilder* builder, PrimitiveType type) { } XlaOp MinValue(XlaBuilder* builder, PrimitiveType type) { - return ConstantLiteral(builder, Literal::MinValue(type)); + return ConstantLiteral(builder, LiteralUtil::MinValue(type)); } XlaOp MinFiniteValue(XlaBuilder* builder, PrimitiveType type) { @@ -81,7 +81,7 @@ XlaOp MinFiniteValue(XlaBuilder* builder, PrimitiveType type) { } XlaOp MaxValue(XlaBuilder* builder, PrimitiveType type) { - return ConstantLiteral(builder, Literal::MaxValue(type)); + return ConstantLiteral(builder, LiteralUtil::MaxValue(type)); } XlaOp MaxFiniteValue(XlaBuilder* builder, PrimitiveType type) { diff --git a/tensorflow/compiler/xla/client/lib/constants.h b/tensorflow/compiler/xla/client/lib/constants.h index b47f5243f008ecb2045456e4505d1a571fbed745..0c8a9b8cc02ba0c1ebdf6a060d4b99262dceb178 100644 --- a/tensorflow/compiler/xla/client/lib/constants.h +++ b/tensorflow/compiler/xla/client/lib/constants.h @@ -18,7 +18,7 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" diff --git a/tensorflow/compiler/xla/client/lib/constants_test.cc b/tensorflow/compiler/xla/client/lib/constants_test.cc index f1e3439862344c01af15ec0571155ca46a579e54..f4320f65c1f76d4d4c384110b39d6606773aaf01 100644 --- a/tensorflow/compiler/xla/client/lib/constants_test.cc +++ b/tensorflow/compiler/xla/client/lib/constants_test.cc @@ -14,7 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/test_macros.h" diff --git a/tensorflow/compiler/xla/client/lib/math.cc b/tensorflow/compiler/xla/client/lib/math.cc index 558755904007431cc0902d95a49627ea07f59127..0221de7672c7b7c02b1f8b9c7ff4f92151e567c6 100644 --- a/tensorflow/compiler/xla/client/lib/math.cc +++ b/tensorflow/compiler/xla/client/lib/math.cc @@ -25,11 +25,9 @@ XlaOp Sqrt(XlaOp operand) { return Pow(operand, ScalarLike(operand, 0.5)); } XlaOp Rsqrt(XlaOp operand) { return Pow(operand, ScalarLike(operand, -0.5)); } -XlaOp Square(XlaOp operand) { return Pow(operand, ScalarLike(operand, 2.0)); } +XlaOp Square(XlaOp operand) { return operand * operand; } -XlaOp Reciprocal(XlaOp operand) { - return Pow(operand, ScalarLike(operand, -1.0)); -} +XlaOp Reciprocal(XlaOp operand) { return ScalarLike(operand, 1.0) / operand; } namespace { @@ -149,4 +147,158 @@ XlaOp ErfInv(XlaOp x) { }); } +namespace { +// Coefficients for the Lanczos approximation of the gamma function. The +// coefficients are uniquely determined by the choice of g and n (kLanczosGamma +// and kLanczosCoefficients.size() + 1). The coefficients below correspond to +// [7, 9]. [5, 7], [7, 9], [9, 10], and [607/128.0, 15] were evaluated and [7, +// 9] seemed to be the least sensitive to the quality of the log function. In +// particular, [5, 7] is the only choice where -1.5e-5 <= lgamma(2) <= 1.5e-5 +// for a particularly inaccurate log function. +static constexpr double kLanczosGamma = 7; // aka g +static constexpr double kBaseLanczosCoeff = 0.99999999999980993227684700473478; +static constexpr std::array kLanczosCoefficients = { + 676.520368121885098567009190444019, -1259.13921672240287047156078755283, + 771.3234287776530788486528258894, -176.61502916214059906584551354, + 12.507343278686904814458936853, -0.13857109526572011689554707, + 9.984369578019570859563e-6, 1.50563273514931155834e-7}; +} // namespace + +// Compute the Lgamma function using Lanczos' approximation from "A Precision +// Approximation of the Gamma Function". SIAM Journal on Numerical Analysis +// series B. Vol. 1: +// lgamma(z + 1) = (log(2) + log(pi)) / 2 + (z + 1/2) * log(t(z)) - t(z) + A(z) +// t(z) = z + kLanczosGamma + 1/2 +// A(z) = kBaseLanczosCoeff + sigma(k = 1, n, kLanczosCoefficients[i] / (z + k)) +XlaOp Lgamma(XlaOp input) { + XlaOp one_half = ScalarLike(input, 0.5); + XlaOp one = ScalarLike(input, 1); + + XlaOp pi = ScalarLike(input, M_PI); + XlaOp log_pi = ScalarLike(input, std::log(M_PI)); + XlaOp log_sqrt_two_pi = ScalarLike(input, (std::log(2) + std::log(M_PI)) / 2); + + XlaOp lanczos_gamma_plus_one_half = ScalarLike(input, kLanczosGamma + 0.5); + XlaOp log_lanczos_gamma_plus_one_half = + ScalarLike(input, std::log(kLanczosGamma + 0.5)); + + XlaOp base_lanczos_coeff = ScalarLike(input, kBaseLanczosCoeff); + + // If the input is less than 0.5 use Gauss's reflection formula: + // gamma(x) = pi / sin(pi * x) * gamma(1 - x) + XlaOp need_to_reflect = Lt(Real(input), one_half); + XlaOp z = Select(need_to_reflect, -input, input - one); + + XlaOp x = base_lanczos_coeff; + for (int i = 0; i < kLanczosCoefficients.size(); ++i) { + XlaOp lanczos_coefficient = ScalarLike(input, kLanczosCoefficients[i]); + XlaOp index = ScalarLike(input, i); + x = x + lanczos_coefficient / (z + index + one); + } + + // To improve accuracy on platforms with less-precise log implementations, + // compute log(lanczos_gamma_plus_one_half) at compile time and use log1p on + // the device. + // log(t) = log(kLanczosGamma + 0.5 + z) + // = log(kLanczosGamma + 0.5) + log1p(z / (kLanczosGamma + 0.5)) + XlaOp t = lanczos_gamma_plus_one_half + z; + XlaOp log_t = + log_lanczos_gamma_plus_one_half + Log1p(z / lanczos_gamma_plus_one_half); + + XlaOp log_y = log_sqrt_two_pi + (z + one_half) * log_t - t + Log(x); + + XlaOp reflection = log_pi - Log(Sin(pi * input)) - log_y; + XlaOp result = Select(need_to_reflect, reflection, log_y); + return result; +} + +// Compute the Digamma function using Lanczos' approximation from "A Precision +// Approximation of the Gamma Function". SIAM Journal on Numerical Analysis +// series B. Vol. 1: +// digamma(z + 1) = log(t(z)) + A'(z) / A(z) - kLanczosGamma / t(z) +// t(z) = z + kLanczosGamma + 1/2 +// A(z) = kBaseLanczosCoeff + sigma(k = 1, n, kLanczosCoefficients[i] / (z + k)) +// A'(z) = sigma(k = 1, n, kLanczosCoefficients[i] / (z + k) / (z + k)) +XlaOp Digamma(XlaOp input) { + XlaOp zero = ScalarLike(input, 0); + XlaOp one_half = ScalarLike(input, 0.5); + XlaOp one = ScalarLike(input, 1); + + XlaOp pi = ScalarLike(input, M_PI); + + XlaOp lanczos_gamma = ScalarLike(input, kLanczosGamma); + XlaOp lanczos_gamma_plus_one_half = ScalarLike(input, kLanczosGamma + 0.5); + XlaOp log_lanczos_gamma_plus_one_half = + ScalarLike(input, std::log(kLanczosGamma + 0.5)); + + XlaOp base_lanczos_coeff = ScalarLike(input, kBaseLanczosCoeff); + + // If the input is less than 0.5 use Gauss's reflection formula: + // digamma(x) = digamma(1 - x) - pi * cot(pi * x) + XlaOp need_to_reflect = Lt(Real(input), one_half); + XlaOp z = Select(need_to_reflect, -input, input - one); + + XlaOp num = zero; + XlaOp denom = base_lanczos_coeff; + for (int i = 0; i < kLanczosCoefficients.size(); ++i) { + XlaOp lanczos_coefficient = ScalarLike(input, kLanczosCoefficients[i]); + XlaOp index = ScalarLike(input, i); + num = num - lanczos_coefficient / ((z + index + one) * (z + index + one)); + denom = denom + lanczos_coefficient / (z + index + one); + } + + // To improve accuracy on platforms with less-precise log implementations, + // compute log(lanczos_gamma_plus_one_half) at compile time and use log1p on + // the device. + // log(t) = log(kLanczosGamma + 0.5 + z) + // = log(kLanczosGamma + 0.5) + log1p(z / (kLanczosGamma + 0.5)) + XlaOp t = lanczos_gamma_plus_one_half + z; + XlaOp log_t = + log_lanczos_gamma_plus_one_half + Log1p(z / lanczos_gamma_plus_one_half); + + XlaOp y = log_t + num / denom - lanczos_gamma / t; + XlaOp reflection = y - pi * Cos(pi * input) / Sin(pi * input); + XlaOp result = Select(need_to_reflect, reflection, y); + return result; +} + +// Trigonometric functions. + +// acos(x) = 2 * atan(sqrt(1 - x^2) / (1 + x)) +XlaOp Acos(XlaOp x) { + return ScalarLike(x, 2.0) * + Atan2(Sqrt(ScalarLike(x, 1.0) - x * x), ScalarLike(x, 1.0) + x); +} + +// asin(x) = 2 * atan(x / (1 + sqrt(1 - x^2))) +XlaOp Asin(XlaOp x) { + return ScalarLike(x, 2.0) * + Atan2(x, ScalarLike(x, 1.0) + Sqrt(ScalarLike(x, 1.0) - x * x)); +} + +XlaOp Atan(XlaOp x) { return Atan2(x, ScalarLike(x, 1.0)); } + +XlaOp Tan(XlaOp x) { return Sin(x) / Cos(x); } + +// Hyperbolic trigonometric functions. + +// acosh(x) = log(x + sqrt(x^2 - 1)) +// = log(x + sqrt((x+1)*(x-1))) +XlaOp Acosh(XlaOp x) { + return Log(x + Sqrt((x + ScalarLike(x, 1.0)) * (x - ScalarLike(x, 1.0)))); +} + +// asinh(x) = log(x + sqrt(x^2 + 1)) +XlaOp Asinh(XlaOp x) { return Log(x + Sqrt(x * x + ScalarLike(x, 1.0))); } + +// atanh(x) = 0.5 * log((1 + x) / (1 - x)) +XlaOp Atanh(XlaOp x) { + return Log((ScalarLike(x, 1.0) + x) / (ScalarLike(x, 1.0) - x)) * + ScalarLike(x, 0.5); +} + +XlaOp Cosh(XlaOp x) { return (Exp(x) + Exp(-x)) * ScalarLike(x, 0.5); } + +XlaOp Sinh(XlaOp x) { return (Exp(x) - Exp(-x)) * ScalarLike(x, 0.5); } + } // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/math.h b/tensorflow/compiler/xla/client/lib/math.h index e7c8b50273067a979158f79aa80abc6058901040..13db2325569cf2e25e3ff1200adf4b2544dc2f73 100644 --- a/tensorflow/compiler/xla/client/lib/math.h +++ b/tensorflow/compiler/xla/client/lib/math.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_MATH_H_ #define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_MATH_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace xla { @@ -46,6 +46,43 @@ XlaOp Erf(XlaOp x); // Computes an approximation of the inverse of the error function. XlaOp ErfInv(XlaOp x); +// Computes an approximation of the lgamma function. +XlaOp Lgamma(XlaOp input); + +// Computes an approximation of the digamma function. +XlaOp Digamma(XlaOp input); + +// Trigonometric functions + +// Computes the arc cosine of 'x'. +XlaOp Acos(XlaOp x); + +// Computes the arc sine of 'x'. +XlaOp Asin(XlaOp x); + +// Computes the arc tangent of 'x'. +XlaOp Atan(XlaOp x); + +// Computes the tangent of 'x'. +XlaOp Tan(XlaOp x); + +// Hyperbolic trigonometric functions + +// Computes the inverse hyperbolic cosine of 'x'. +XlaOp Acosh(XlaOp x); + +// Computes the inverse hyperbolic sine of 'x'. +XlaOp Asinh(XlaOp x); + +// Computes the inverse hyperbolic tangent of 'x'. +XlaOp Atanh(XlaOp x); + +// Computes the hyperbolic cosine of 'x'. +XlaOp Cosh(XlaOp x); + +// Computes the hyperbolic sine of 'x'. +XlaOp Sinh(XlaOp x); + } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_MATH_H_ diff --git a/tensorflow/compiler/xla/client/lib/math_test.cc b/tensorflow/compiler/xla/client/lib/math_test.cc index 1df4e6ea42a2211c285075a3ed9159a9d603ccf5..14c259a7fa2a47642663b65d2785e5bbdc040cfd 100644 --- a/tensorflow/compiler/xla/client/lib/math_test.cc +++ b/tensorflow/compiler/xla/client/lib/math_test.cc @@ -14,7 +14,8 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/client/lib/math.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/test_macros.h" @@ -31,7 +32,7 @@ class MathTest : public ClientLibraryTestBase { XLA_TEST_F(MathTest, SqrtF32) { XlaBuilder builder(TestName()); - Literal zero_literal = Literal::Zero(PrimitiveType::F32); + Literal zero_literal = LiteralUtil::Zero(PrimitiveType::F32); std::unique_ptr zero_data = client_->TransferToServer(zero_literal).ConsumeValueOrDie(); @@ -81,5 +82,59 @@ XLA_TEST_F(MathTest, SqrtSixValues) { std::vector expected = {4, 1, 32, 0.4, 0.4472, 111.1080}; ComputeAndCompareR1(&builder, expected, {}, error_spec_); } + +XLA_TEST_F(MathTest, Lgamma) { + XlaBuilder builder(TestName()); + auto x = ConstantR1(&builder, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 0.5, 1.5, + 2.5, -1.5, -3.5, -5.5}); + Lgamma(x); + + std::vector expected = { + 0, + 0, + static_cast(std::log(2)), + static_cast(std::log(6)), + static_cast(std::log(24)), + static_cast(std::log(120)), + static_cast(std::log(M_PI) / 2), + static_cast(std::log(M_PI) / 2 - std::log(2)), + static_cast(std::log(M_PI) / 2 - std::log(4) + std::log(3)), + static_cast(std::log(M_PI) / 2 - std::log(3) + std::log(4)), + static_cast(std::log(M_PI) / 2 - std::log(105) + std::log(16)), + static_cast(std::log(M_PI) / 2 - std::log(10395) + std::log(64))}; + error_spec_ = ErrorSpec{0.001}; + ComputeAndCompareR1(&builder, expected, {}, error_spec_); +} + +XLA_TEST_F(MathTest, Digamma) { + XlaBuilder builder(TestName()); + auto x = ConstantR1(&builder, {1.0, 0.5, 1 / 3.0, 0.25, 1 / 6.0, 0.125, + 2.0, 3.0, 4.0, 6.0, 8.0, 9.0}); + Digamma(x); + + constexpr double euler_mascheroni = + 0.57721566490153286060651209008240243104215933593992; + std::vector expected = { + static_cast(-euler_mascheroni), + static_cast(-2 * std::log(2) - euler_mascheroni), + static_cast(-M_PI / 2 / std::sqrt(3) - 3 * std::log(3) / 2 - + euler_mascheroni), + static_cast(-M_PI / 2 - 3 * std::log(2) - euler_mascheroni), + static_cast(-M_PI * std::sqrt(3) / 2 - 2 * std::log(2) - + 3 * std::log(3) / 2 - euler_mascheroni), + static_cast( + -M_PI / 2 - 4 * std::log(2) - + (M_PI + std::log(2 + std::sqrt(2)) - std::log(2 - std::sqrt(2))) / + std::sqrt(2) - + euler_mascheroni), + static_cast(1 - euler_mascheroni), + static_cast(1.5 - euler_mascheroni), + static_cast(11 / 6.0 - euler_mascheroni), + static_cast(137 / 60.0 - euler_mascheroni), + static_cast(363 / 140.0 - euler_mascheroni), + static_cast(761 / 280.0 - euler_mascheroni)}; + ComputeAndCompareR1(&builder, expected, {}, error_spec_); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/numeric.cc b/tensorflow/compiler/xla/client/lib/numeric.cc index cbe9e7fdd1330164f1f9c4520c2bb81e38f4ceb9..1c91237ae1574f92cda78c9bddc6f4ac1d68f47c 100644 --- a/tensorflow/compiler/xla/client/lib/numeric.cc +++ b/tensorflow/compiler/xla/client/lib/numeric.cc @@ -13,11 +13,14 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/lib/numeric.h" - #include #include +#include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/core/lib/gtl/array_slice.h" + namespace xla { namespace { @@ -28,7 +31,7 @@ XlaOp MakeIota(XlaBuilder* builder, int64 size) { for (int64 i = 0; i < size; ++i) { values[i] = static_cast(i); } - return xla::ConstantR1(builder, values); + return ConstantR1(builder, values); } } // namespace @@ -68,4 +71,67 @@ XlaOp Iota(XlaBuilder* builder, PrimitiveType type, int64 size) { } } +XlaOp IdentityMatrix(XlaBuilder* builder, PrimitiveType type, int64 m, + int64 n) { + auto a = Iota(builder, type, m); + auto b = Iota(builder, type, n); + auto indicator = Eq(a, Broadcast(b, {m}), /*broadcast_dimensions=*/{0}); + return ConvertElementType(indicator, type); +} + +XlaOp GetMatrixDiagonal(XlaOp x) { + XlaBuilder* builder = x.builder(); + return builder->ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(Shape shape, builder->GetShape(x)); + const int64 n_dims = ShapeUtil::Rank(shape); + TF_RET_CHECK(n_dims >= 2); + const int64 m = shape.dimensions(n_dims - 2); + const int64 n = shape.dimensions(n_dims - 1); + tensorflow::gtl::ArraySlice major_dims( + AsInt64Slice(shape.dimensions()), /*pos=*/0, /*len=*/n_dims - 2); + auto a = Iota(builder, U32, n); + auto b = Iota(builder, U32, m); + auto indicator = Eq(b, Broadcast(a, {m}), /*broadcast_dimensions=*/{0}); + auto mask = Broadcast(indicator, major_dims); + + // TPUs don't support S64 add reduction at the moment. But fortunately + // OR-reductions work just as well for integers. + XlaComputation reducer = + primitive_util::IsIntegralType(shape.element_type()) + ? CreateScalarOrComputation(shape.element_type(), builder) + : CreateScalarAddComputation(shape.element_type(), builder); + + return Reduce(Select(mask, x, Zeros(builder, shape)), ScalarLike(x, 0), + reducer, {m >= n ? n_dims - 2 : n_dims - 1}); + }); +} + +XlaOp Triangle(XlaOp x, bool lower) { + XlaBuilder* builder = x.builder(); + return builder->ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(Shape shape, builder->GetShape(x)); + const int64 n_dims = ShapeUtil::Rank(shape); + TF_RET_CHECK(n_dims >= 2); + const int64 m = shape.dimensions(n_dims - 2); + const int64 n = shape.dimensions(n_dims - 1); + tensorflow::gtl::ArraySlice major_dims( + AsInt64Slice(shape.dimensions()), /*pos=*/0, /*len=*/n_dims - 2); + auto a = Iota(builder, U32, n); + auto b = Iota(builder, U32, m); + xla::XlaOp indicator; + if (lower) { + indicator = Ge(b, Broadcast(a, {m}), /*broadcast_dimensions=*/{0}); + } else { + indicator = Le(b, Broadcast(a, {m}), /*broadcast_dimensions=*/{0}); + } + auto mask = Broadcast(indicator, major_dims); + + return Select(mask, x, Zeros(builder, shape)); + }); +} + +XlaOp UpperTriangle(XlaOp x) { return Triangle(x, false); } + +XlaOp LowerTriangle(XlaOp x) { return Triangle(x, true); } + } // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/numeric.h b/tensorflow/compiler/xla/client/lib/numeric.h index 2a409ae31147a4a88367422ce31c9fbcb22fdbca..efd8cdc25724198633e0bf1c48c4e7d9e4b4c9e1 100644 --- a/tensorflow/compiler/xla/client/lib/numeric.h +++ b/tensorflow/compiler/xla/client/lib/numeric.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_NUMERIC_H_ #define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_NUMERIC_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" @@ -25,6 +25,24 @@ namespace xla { // Returns a rank 1 tensor of `type` containing values [0, 1, 2, ...]. XlaOp Iota(XlaBuilder* builder, PrimitiveType type, int64 size); +// Returns an m x n matrix with 1s on the diagonal elements, zeros everywhere +// else. +XlaOp IdentityMatrix(XlaBuilder* builder, PrimitiveType type, int64 m, int64 n); + +// Get the diagonals of the last two dimensions. If 'x' has shape +// [..., M, N], then the output has shape [..., min(M, N)], containing the +// diagonal elements (i.e., with indices [..., i, i]). +XlaOp GetMatrixDiagonal(XlaOp x); + +// Get the upper or lower triangle part of the last two dimensions +XlaOp Triangle(XlaOp x, bool lower); + +// Get the upper triangle part of the last two dimensions +XlaOp UpperTriangle(XlaOp x); + +// Get the lower triangle part of the last two dimensions +XlaOp LowerTriangle(XlaOp x); + } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_NUMERIC_H_ diff --git a/tensorflow/compiler/xla/client/lib/numeric_test.cc b/tensorflow/compiler/xla/client/lib/numeric_test.cc index bc8a73e9d793ef8f65c321759e03b0de75edd500..8a96ec68d2dca8485215258b1f6731b934e6f2a8 100644 --- a/tensorflow/compiler/xla/client/lib/numeric_test.cc +++ b/tensorflow/compiler/xla/client/lib/numeric_test.cc @@ -14,7 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/test_macros.h" @@ -24,8 +24,15 @@ limitations under the License. namespace xla { namespace { -using NumericTest = ClientLibraryTestBase; +class NumericTest : public ClientLibraryTestBase { + protected: + template + void TestMatrixDiagonal(); +}; +// TODO(b/64798317): Delete this test case once xla::IotaGen is converted to +// xla::Iota. This test is already implemented for xla::IotaGen in +// xla/tests/iota_test.cc. XLA_TEST_F(NumericTest, Iota) { XlaBuilder builder(TestName()); Iota(&builder, S32, 10); @@ -33,5 +40,39 @@ XLA_TEST_F(NumericTest, Iota) { ComputeAndCompareR1(&builder, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {}); } +XLA_TEST_F(NumericTest, Triangle) { + XlaBuilder builder(TestName()); + Array3D input(2, 3, 4); + input.FillIota(0); + + XlaOp a; + auto a_data = CreateR3Parameter(input, 0, "a", &builder, &a); + LowerTriangle(a); + Array3D expected({{{0, 0, 0, 0}, {4, 5, 0, 0}, {8, 9, 10, 0}}, + {{12, 0, 0, 0}, {16, 17, 0, 0}, {20, 21, 22, 0}}}); + + ComputeAndCompareR3(&builder, expected, {a_data.get()}); +} + +template +void NumericTest::TestMatrixDiagonal() { + XlaBuilder builder("GetMatrixDiagonal"); + Array3D input(2, 3, 4); + input.FillIota(0); + + XlaOp a; + auto a_data = CreateR3Parameter(input, 0, "a", &builder, &a); + GetMatrixDiagonal(a); + Array2D expected({{0, 5, 10}, {12, 17, 22}}); + + ComputeAndCompareR2(&builder, expected, {a_data.get()}); +} + +XLA_TEST_F(NumericTest, GetMatrixDiagonal_S32) { TestMatrixDiagonal(); } + +XLA_TEST_F(NumericTest, GetMatrixDiagonal_S64) { TestMatrixDiagonal(); } + +XLA_TEST_F(NumericTest, GetMatrixDiagonal_F32) { TestMatrixDiagonal(); } + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/prng.cc b/tensorflow/compiler/xla/client/lib/prng.cc new file mode 100644 index 0000000000000000000000000000000000000000..3a744148fba9957c10c825c00d500960f134396c --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/prng.cc @@ -0,0 +1,150 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include + +#include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/lib/math.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/core/casts.h" + +namespace xla { +namespace { + +// Rotates a 32-bit integer 'v' left by 'distance' bits. +XlaOp RotateLeftS32(XlaOp v, int distance) { + return (v << ConstantR0(v.builder(), distance)) | + ShiftRightLogical(v, ConstantR0(v.builder(), 32 - distance)); +} + +using ThreeFry2x32State = std::array; + +// Implements the ThreeFry counter-based PRNG algorithm. +// Salmon et al. SC 2011. Parallel random numbers: as easy as 1, 2, 3. +// http://www.thesalmons.org/john/random123/papers/random123sc11.pdf +ThreeFry2x32State ThreeFry2x32(ThreeFry2x32State input, ThreeFry2x32State key) { + XlaBuilder* builder = input[0].builder(); + // Rotation distances specified by the Threefry2x32 algorithm. + constexpr std::array rotations = {13, 15, 26, 6, 17, 29, 16, 24}; + ThreeFry2x32State x; + + std::array ks; + // 0x1BD11BDA is a parity constant specified by the ThreeFry2x32 algorithm. + ks[2] = ConstantR0(builder, 0x1BD11BDA); + for (int i = 0; i < 2; ++i) { + ks[i] = key[i]; + x[i] = input[i]; + ks[2] = ks[2] ^ key[i]; + } + + x[0] = x[0] + ks[0]; + x[1] = x[1] + ks[1]; + + // Performs a single round of the Threefry2x32 algorithm, with a rotation + // amount 'rotation'. + auto round = [builder](ThreeFry2x32State v, int rotation) { + v[0] = v[0] + v[1]; + v[1] = RotateLeftS32(v[1], rotation); + v[1] = v[0] ^ v[1]; + return v; + }; + + // There are no known statistical flaws with 13 rounds of Threefry2x32. + // We are conservative and use 20 rounds. + x = round(x, rotations[0]); + x = round(x, rotations[1]); + x = round(x, rotations[2]); + x = round(x, rotations[3]); + x[0] = x[0] + ks[1]; + x[1] = x[1] + ks[2] + ConstantR0(builder, 1); + + x = round(x, rotations[4]); + x = round(x, rotations[5]); + x = round(x, rotations[6]); + x = round(x, rotations[7]); + x[0] = x[0] + ks[2]; + x[1] = x[1] + ks[0] + ConstantR0(builder, 2); + + x = round(x, rotations[0]); + x = round(x, rotations[1]); + x = round(x, rotations[2]); + x = round(x, rotations[3]); + x[0] = x[0] + ks[0]; + x[1] = x[1] + ks[1] + ConstantR0(builder, 3); + + x = round(x, rotations[4]); + x = round(x, rotations[5]); + x = round(x, rotations[6]); + x = round(x, rotations[7]); + x[0] = x[0] + ks[1]; + x[1] = x[1] + ks[2] + ConstantR0(builder, 4); + + x = round(x, rotations[0]); + x = round(x, rotations[1]); + x = round(x, rotations[2]); + x = round(x, rotations[3]); + x[0] = x[0] + ks[2]; + x[1] = x[1] + ks[0] + ConstantR0(builder, 5); + + return x; +} + +} // namespace + +XlaOp StatelessRngUniform(std::array seeds, const Shape& shape, + XlaOp minval, XlaOp maxval) { + XlaBuilder* builder = seeds[0].builder(); + if (shape.element_type() != F32) { + return builder->ReportError(Unimplemented( + "Types other than F32 are not implemented by StatelessRngUniform.")); + } + ThreeFry2x32State key = seeds; + const int64 size = ShapeUtil::ElementsIn(shape); + + const int64 half_size = CeilOfRatio(size, 2); + const bool size_is_odd = (half_size * 2 != size); + + // Fill the generator inputs with unique counter values. + ThreeFry2x32State inputs; + inputs[0] = Iota(builder, S32, half_size); + inputs[1] = inputs[0] + ConstantR0(builder, half_size); + ThreeFry2x32State outputs = ThreeFry2x32(inputs, key); + + if (size_is_odd) { + outputs[1] = Slice(outputs[1], {0}, {half_size - 1}, {1}); + } + + auto bits = Reshape(ConcatInDim(builder, outputs, 0), + AsInt64Slice(shape.dimensions())); + + // Form 23 random mantissa bits, with a leading 1 bit. The leading 1 bit + // forces the random bits into the mantissa. + constexpr int kFloatBits = 32; + constexpr int kMantissaBits = 23; + bits = ShiftRightLogical( + bits, ConstantR0(builder, kFloatBits - kMantissaBits)) | + ConstantR0(builder, tensorflow::bit_cast(1.0f)); + auto floats = BitcastConvertType(bits, F32); + + // We have a floating point number in the range [1.0, 2.0). + // Subtract 1.0f to shift to the range [0.0, 1.0) + floats = floats - ConstantR0(builder, 1.0f); + // Multiply and add to shift to the range [minval, maxval). + return floats * (maxval - minval) + minval; +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/prng.h b/tensorflow/compiler/xla/client/lib/prng.h new file mode 100644 index 0000000000000000000000000000000000000000..ad000b1fa1d0655c8fccc0bb33379f2499b77f26 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/prng.h @@ -0,0 +1,34 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_PRNG_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_PRNG_H_ + +#include + +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" + +namespace xla { + +// Returns a tensor containing 'shape' random values uniformly distributed in +// the range [minval, maxval). Requires 2 32-bit integer seeds. +// Currently only 'shape's of type F32 are implemented. +XlaOp StatelessRngUniform(std::array seeds, const Shape& shape, + XlaOp minval, XlaOp maxval); + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_PRNG_H_ diff --git a/tensorflow/compiler/xla/client/lib/sorting.cc b/tensorflow/compiler/xla/client/lib/sorting.cc new file mode 100644 index 0000000000000000000000000000000000000000..a904be259a3870a679b2c4699ec01e2a11b1ce46 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/sorting.cc @@ -0,0 +1,46 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/client/lib/sorting.h" +#include "tensorflow/compiler/xla/client/lib/numeric.h" + +namespace xla { + +XlaOp TopK(XlaOp input, int64 k) { + XlaBuilder* const builder = input.builder(); + return builder->ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(Shape input_shape, builder->GetShape(input)); + int last_dim = input_shape.dimensions_size() - 1; + int last_dim_size = input_shape.dimensions(last_dim); + + XlaOp iota_s32 = Iota(builder, S32, last_dim_size); + auto input_dims = input_shape.dimensions(); + std::vector broadcast_dims(input_dims.begin(), input_dims.end() - 1); + XlaOp broadcast_s32 = Broadcast(iota_s32, broadcast_dims); + XlaOp sort_result = Sort(Neg(input), broadcast_s32); + std::vector start_indices(input_shape.dimensions_size(), 0); + std::vector limit_indices(input_dims.begin(), input_dims.end()); + limit_indices[last_dim] = k; + std::vector strides(input_shape.dimensions_size(), 1); + + XlaOp values = Neg(Slice(GetTupleElement(sort_result, 0), start_indices, + limit_indices, strides)); + XlaOp indices = Slice(GetTupleElement(sort_result, 1), start_indices, + limit_indices, strides); + return Tuple(builder, {values, indices}); + }); +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/sorting.h b/tensorflow/compiler/xla/client/lib/sorting.h new file mode 100644 index 0000000000000000000000000000000000000000..404b4783c3878ca0fab811fa8c3d02686af44316 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/sorting.h @@ -0,0 +1,31 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SORTING_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SORTING_H_ + +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" + +namespace xla { + +// Returns a tuple composed of the top `k` values and corresponding indices in +// `input`. Output values are in descending order, from largest to smallest. +XlaOp TopK(XlaOp input, int64 k); + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SORTING_H_ diff --git a/tensorflow/compiler/xla/client/lib/sorting_test.cc b/tensorflow/compiler/xla/client/lib/sorting_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..b6eee762a5f002e00fd6118d91f25343e22f13d3 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/sorting_test.cc @@ -0,0 +1,60 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/client/lib/sorting.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/client_library_test_base.h" +#include "tensorflow/compiler/xla/tests/test_macros.h" +#include "tensorflow/compiler/xla/types.h" + +namespace xla { +namespace { + +using SortingTest = ClientLibraryTestBase; + +XLA_TEST_F(SortingTest, TopK3From8Values) { + XlaBuilder builder(TestName()); + auto x = + ConstantR1(&builder, {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0}); + xla::GetTupleElement(xla::TopK(x, 3), 0); + ComputeAndCompareR1(&builder, {7.0, 6.0, 5.0}, {}); +} + +XLA_TEST_F(SortingTest, TopK3From8Indices) { + XlaBuilder builder(TestName()); + auto x_rev = + ConstantR1(&builder, {7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0}); + xla::GetTupleElement(xla::TopK(x_rev, 3), 1); + ComputeAndCompareR1(&builder, {0, 1, 2}, {}); +} + +XLA_TEST_F(SortingTest, TopKFullSort) { + XlaBuilder builder(TestName()); + const int kSize = 16; + std::mt19937 eng; + std::uniform_real_distribution u_dist(0.0, 100.0); + auto gen = std::bind(u_dist, eng); + std::vector inputs(kSize); + std::generate(inputs.begin(), inputs.end(), gen); + auto x = ConstantR1(&builder, inputs); + xla::GetTupleElement(xla::TopK(x, kSize), 0); + + std::sort(inputs.begin(), inputs.end(), std::greater()); + ComputeAndCompareR1(&builder, inputs, {}); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/testing.cc b/tensorflow/compiler/xla/client/lib/testing.cc index 731ad13b8d0e5d65acc316e72be9fe7d35e826a4..b1a776b8b84eb0954e0d874d1b707e46c92f6389 100644 --- a/tensorflow/compiler/xla/client/lib/testing.cc +++ b/tensorflow/compiler/xla/client/lib/testing.cc @@ -15,9 +15,9 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/testing.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/execution_options_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/tests/test_utils.h" @@ -49,7 +49,7 @@ int64 DataSizeOfShape(const Shape& shape) { XlaOp BuildFakeDataOpOnDevice(const Shape& shape, XlaBuilder* builder) { if (ShapeUtil::IsArray(shape)) { return Broadcast( - ConstantLiteral(builder, Literal::One(shape.element_type())), + ConstantLiteral(builder, LiteralUtil::One(shape.element_type())), AsInt64Slice(shape.dimensions())); } std::vector parts; diff --git a/tensorflow/compiler/xla/client/lib/testing.h b/tensorflow/compiler/xla/client/lib/testing.h index dc613099e2b42a60d0c11a654ab5cd41f8bd4f6f..03695ce2a339735e3e49522f4fe1bbf2d83a3834 100644 --- a/tensorflow/compiler/xla/client/lib/testing.h +++ b/tensorflow/compiler/xla/client/lib/testing.h @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/global_data.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/xla_data.pb.h" namespace xla { diff --git a/tensorflow/compiler/xla/client/local_client.cc b/tensorflow/compiler/xla/client/local_client.cc index 5f9710914bd0ceff55f5b0a2db05e553ce8bd637..e7250e11d5e59bb01026d5cf304901d17fd2ba42 100644 --- a/tensorflow/compiler/xla/client/local_client.cc +++ b/tensorflow/compiler/xla/client/local_client.cc @@ -18,10 +18,12 @@ limitations under the License. #include #include "llvm/ADT/Triple.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/backend.h" #include "tensorflow/compiler/xla/service/service_executable_run_options.h" #include "tensorflow/compiler/xla/service/source_map_util.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/compiler/xla/status_macros.h" using xla::source_map_util::InvalidParameterArgument; @@ -29,8 +31,8 @@ using xla::source_map_util::InvalidParameterArgument; namespace xla { namespace { -StatusOr BorrowStreamForDevice(int device_ordinal, - Backend* backend) { +StatusOr BorrowStreamForDevice(int device_ordinal, + Backend* backend) { if (device_ordinal < 0) { device_ordinal = backend->default_device_ordinal(); } @@ -141,7 +143,7 @@ StatusOr LocalExecutable::Run( TF_RETURN_IF_ERROR( ValidateExecutionOptions(arguments, run_options, *backend_)); - Backend::StreamPtr stream; + StreamPool::Ptr stream; if (run_options.stream() == nullptr) { // NB! The lifetime of `stream` needs to match the lifetime of // `actual_options` (otherwise we will end up using a returned stream in diff --git a/tensorflow/compiler/xla/client/local_client.h b/tensorflow/compiler/xla/client/local_client.h index 4d9e0d7cd9d6ddebead1e12b23e94b529038039b..ae23809261757c637ab4aec036750c371ac60cdc 100644 --- a/tensorflow/compiler/xla/client/local_client.h +++ b/tensorflow/compiler/xla/client/local_client.h @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/executable_build_options.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/executable_run_options.h" #include "tensorflow/compiler/xla/service/compiler.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc b/tensorflow/compiler/xla/client/xla_builder.cc similarity index 91% rename from tensorflow/compiler/xla/client/xla_client/xla_builder.cc rename to tensorflow/compiler/xla/client/xla_builder.cc index 12efcb4b4f787da9a2fd694b4ee09dd490a68a52..53be5a79c23438e103e353b8c5fc0e2446ad78c0 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc +++ b/tensorflow/compiler/xla/client/xla_builder.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include #include @@ -22,6 +22,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/sharding_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/shape_inference.h" @@ -736,7 +737,7 @@ void XlaBuilder::Trace(const string& tag, const XlaOp& operand) { ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; *instr.mutable_shape() = ShapeUtil::MakeNil(); - *instr.mutable_literal() = Literal::CreateR1U8(tag)->ToProto(); + *instr.mutable_literal() = LiteralUtil::CreateR1U8(tag)->ToProto(); return AddInstruction(std::move(instr), HloOpcode::kTrace, {operand}); }); } @@ -1117,6 +1118,35 @@ XlaOp XlaBuilder::Infeed(const Shape& shape, const string& config) { }); } +XlaOp XlaBuilder::InfeedWithToken(const XlaOp& token, const Shape& shape, + const string& config) { + return ReportErrorOrReturn([&]() -> StatusOr { + HloInstructionProto instr; + if (!LayoutUtil::HasLayout(shape)) { + return InvalidArgument("Given shape to Infeed must have a layout"); + } + const Shape infeed_instruction_shape = + ShapeUtil::MakeTupleShape({shape, ShapeUtil::MakeTokenShape()}); + *instr.mutable_shape() = infeed_instruction_shape; + instr.set_infeed_config(config); + + if (ShapeUtil::IsArray(shape) && sharding() && + sharding()->type() == OpSharding::Type::OpSharding_Type_OTHER) { + // TODO(b/110793772): Support tiled array-shaped infeeds. + return InvalidArgument( + "Tiled sharding is not yet supported for array-shaped infeeds"); + } + + if (sharding() && + sharding()->type() == OpSharding::Type::OpSharding_Type_REPLICATED) { + return InvalidArgument( + "Replicated sharding is not yet supported for infeeds"); + } + + return AddInstruction(std::move(instr), HloOpcode::kInfeed, {token}); + }); +} + void XlaBuilder::Outfeed(const XlaOp& operand, const Shape& shape_with_layout, const string& outfeed_config) { ReportErrorOrReturn([&]() -> StatusOr { @@ -1162,6 +1192,53 @@ void XlaBuilder::Outfeed(const XlaOp& operand, const Shape& shape_with_layout, }); } +XlaOp XlaBuilder::OutfeedWithToken(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, + const string& outfeed_config) { + return ReportErrorOrReturn([&]() -> StatusOr { + HloInstructionProto instr; + + *instr.mutable_shape() = ShapeUtil::MakeTokenShape(); + + // Check and set outfeed shape. + if (!LayoutUtil::HasLayout(shape_with_layout)) { + return InvalidArgument("Given shape to Outfeed must have a layout"); + } + TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); + if (!ShapeUtil::Compatible(operand_shape, shape_with_layout)) { + return InvalidArgument( + "Outfeed shape %s must be compatible with operand shape %s", + ShapeUtil::HumanStringWithLayout(shape_with_layout).c_str(), + ShapeUtil::HumanStringWithLayout(operand_shape).c_str()); + } + *instr.mutable_outfeed_shape() = shape_with_layout; + + instr.set_outfeed_config(outfeed_config); + + return AddInstruction(std::move(instr), HloOpcode::kOutfeed, + {operand, token}); + }); +} + +XlaOp XlaBuilder::CreateToken() { + return ReportErrorOrReturn([&]() -> StatusOr { + HloInstructionProto instr; + *instr.mutable_shape() = ShapeUtil::MakeTokenShape(); + return AddInstruction(std::move(instr), HloOpcode::kAfterAll); + }); +} + +XlaOp XlaBuilder::AfterAll(tensorflow::gtl::ArraySlice tokens) { + return ReportErrorOrReturn([&]() -> StatusOr { + if (tokens.empty()) { + return InvalidArgument("AfterAll requires at least one operand"); + } + HloInstructionProto instr; + *instr.mutable_shape() = ShapeUtil::MakeTokenShape(); + return AddInstruction(std::move(instr), HloOpcode::kAfterAll, tokens); + }); +} + XlaOp XlaBuilder::CustomCall(const string& call_target_name, tensorflow::gtl::ArraySlice operands, const Shape& shape) { @@ -1365,7 +1442,8 @@ XlaOp XlaBuilder::Rev(const XlaOp& operand, }); } -XlaOp XlaBuilder::Sort(XlaOp keys, tensorflow::gtl::optional values) { +XlaOp XlaBuilder::Sort(XlaOp keys, tensorflow::gtl::optional values, + int64 dimension) { return ReportErrorOrReturn([&]() -> StatusOr { HloInstructionProto instr; std::vector operand_shape_ptrs; @@ -1379,6 +1457,11 @@ XlaOp XlaBuilder::Sort(XlaOp keys, tensorflow::gtl::optional values) { TF_ASSIGN_OR_RETURN(*instr.mutable_shape(), ShapeInference::InferVariadicOpShape( HloOpcode::kSort, operand_shape_ptrs)); + if (dimension == -1) { + TF_ASSIGN_OR_RETURN(const Shape& keys_shape, GetShape(keys)); + dimension = ShapeUtil::Rank(keys_shape) - 1; + } + instr.add_dimensions(dimension); return values.has_value() ? AddInstruction(std::move(instr), HloOpcode::kSort, {keys, *values}) @@ -1763,10 +1846,6 @@ XlaOp XlaBuilder::CrossReplicaSum( tensorflow::gtl::ArraySlice replica_group_ids, const tensorflow::gtl::optional& channel_id) { return ReportErrorOrReturn([&]() -> StatusOr { - if (channel_id.has_value()) { - return Unimplemented("channel_id is not supported in AllReduce"); - } - HloInstructionProto instr; TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); TF_ASSIGN_OR_RETURN( @@ -1776,6 +1855,10 @@ XlaOp XlaBuilder::CrossReplicaSum( instr.add_replica_group_ids(replica_group_id); } + if (channel_id.has_value()) { + instr.set_all_reduce_id(channel_id->handle()); + } + AddCalledComputation(computation, &instr); return AddInstruction(std::move(instr), HloOpcode::kCrossReplicaSum, @@ -1858,6 +1941,17 @@ void XlaBuilder::Send(const XlaOp& operand, const ChannelHandle& handle) { TF_ASSIGN_OR_RETURN(XlaOp token, AddInstruction(std::move(token_instr), HloOpcode::kAfterAll, {})); + return SendWithToken(operand, token, handle); + }); +} + +XlaOp XlaBuilder::SendWithToken(const XlaOp& operand, const XlaOp& token, + const ChannelHandle& handle) { + return ReportErrorOrReturn([&]() -> StatusOr { + if (handle.type() != ChannelHandle::DEVICE_TO_DEVICE) { + return InvalidArgument("Send must use a device-to-device channel"); + } + // Send instruction produces a tuple of {aliased operand, U32 context, // token}. HloInstructionProto send_instr; @@ -1888,6 +1982,27 @@ XlaOp XlaBuilder::Recv(const Shape& shape, const ChannelHandle& handle) { TF_ASSIGN_OR_RETURN(XlaOp token, AddInstruction(std::move(token_instr), HloOpcode::kAfterAll, {})); + XlaOp recv = RecvWithToken(token, shape, handle); + + // The RecvDone instruction produces a tuple of the data and a token + // type. Return XLA op containing the data. + // TODO(b/80000000): Remove this when clients have been updated to handle + // tokens. + HloInstructionProto recv_data; + *recv_data.mutable_shape() = shape; + recv_data.set_tuple_index(0); + return AddInstruction(std::move(recv_data), HloOpcode::kGetTupleElement, + {recv}); + }); +} + +XlaOp XlaBuilder::RecvWithToken(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle) { + return ReportErrorOrReturn([&]() -> StatusOr { + if (handle.type() != ChannelHandle::DEVICE_TO_DEVICE) { + return InvalidArgument("Recv must use a device-to-device channel"); + } + // Recv instruction produces a tuple of {receive buffer, U32 context, // token}. HloInstructionProto recv_instr; @@ -1901,19 +2016,91 @@ XlaOp XlaBuilder::Recv(const Shape& shape, const ChannelHandle& handle) { *recv_done_instr.mutable_shape() = ShapeUtil::MakeTupleShape({shape, ShapeUtil::MakeTokenShape()}); recv_done_instr.set_channel_id(handle.handle()); - TF_ASSIGN_OR_RETURN(XlaOp recv_done, - AddInstruction(std::move(recv_done_instr), - HloOpcode::kRecvDone, {recv})); + return AddInstruction(std::move(recv_done_instr), HloOpcode::kRecvDone, + {recv}); + }); +} - // The RecvDone instruction produces a tuple of the data and a token - // type. Return XLA op containing the data. - // TODO(b/80000000): Remove this when clients have been updated to handle - // tokens. - HloInstructionProto recv_data; - *recv_data.mutable_shape() = shape; - recv_data.set_tuple_index(0); - return AddInstruction(std::move(recv_data), HloOpcode::kGetTupleElement, - {recv_done}); +XlaOp XlaBuilder::SendToHost(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, + const ChannelHandle& handle) { + return ReportErrorOrReturn([&]() -> StatusOr { + if (!LayoutUtil::HasLayout(shape_with_layout)) { + return InvalidArgument("Shape passed to SendToHost must have a layout"); + } + TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); + if (!ShapeUtil::Compatible(operand_shape, shape_with_layout)) { + return InvalidArgument( + "SendToHost shape %s must be compatible with operand shape %s", + ShapeUtil::HumanStringWithLayout(shape_with_layout).c_str(), + ShapeUtil::HumanStringWithLayout(operand_shape).c_str()); + } + // TODO(b/111544877): Support tuple shapes. + if (!ShapeUtil::IsArray(operand_shape)) { + return InvalidArgument("SendToHost only supports array shapes, shape: %s", + ShapeUtil::HumanString(operand_shape).c_str()); + } + + if (handle.type() != ChannelHandle::DEVICE_TO_HOST) { + return InvalidArgument("SendToHost must use a device-to-host channel"); + } + + // Send instruction produces a tuple of {aliased operand, U32 context, + // token}. + HloInstructionProto send_instr; + *send_instr.mutable_shape() = ShapeUtil::MakeTupleShape( + {shape_with_layout, ShapeUtil::MakeShape(U32, {}), + ShapeUtil::MakeTokenShape()}); + send_instr.set_channel_id(handle.handle()); + send_instr.set_is_host_transfer(true); + TF_ASSIGN_OR_RETURN(XlaOp send, + AddInstruction(std::move(send_instr), HloOpcode::kSend, + {operand, token})); + + HloInstructionProto send_done_instr; + *send_done_instr.mutable_shape() = ShapeUtil::MakeTokenShape(); + send_done_instr.set_channel_id(handle.handle()); + send_done_instr.set_is_host_transfer(true); + return AddInstruction(std::move(send_done_instr), HloOpcode::kSendDone, + {send}); + }); +} + +XlaOp XlaBuilder::RecvFromHost(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle) { + return ReportErrorOrReturn([&]() -> StatusOr { + if (!LayoutUtil::HasLayout(shape)) { + return InvalidArgument("Shape passed to RecvFromHost must have a layout"); + } + + // TODO(b/111544877): Support tuple shapes. + if (!ShapeUtil::IsArray(shape)) { + return InvalidArgument( + "RecvFromHost only supports array shapes, shape: %s", + ShapeUtil::HumanString(shape).c_str()); + } + + if (handle.type() != ChannelHandle::HOST_TO_DEVICE) { + return InvalidArgument("RecvFromHost must use a host-to-device channel"); + } + + // Recv instruction produces a tuple of {receive buffer, U32 context, + // token}. + HloInstructionProto recv_instr; + *recv_instr.mutable_shape() = ShapeUtil::MakeTupleShape( + {shape, ShapeUtil::MakeShape(U32, {}), ShapeUtil::MakeTokenShape()}); + recv_instr.set_channel_id(handle.handle()); + recv_instr.set_is_host_transfer(true); + TF_ASSIGN_OR_RETURN(XlaOp recv, AddInstruction(std::move(recv_instr), + HloOpcode::kRecv, {token})); + + HloInstructionProto recv_done_instr; + *recv_done_instr.mutable_shape() = + ShapeUtil::MakeTupleShape({shape, ShapeUtil::MakeTokenShape()}); + recv_done_instr.set_channel_id(handle.handle()); + recv_done_instr.set_is_host_transfer(true); + return AddInstruction(std::move(recv_done_instr), HloOpcode::kRecvDone, + {recv}); }); } @@ -2565,8 +2752,9 @@ XlaOp Rev(const XlaOp& operand, tensorflow::gtl::ArraySlice dimensions) { return operand.builder()->Rev(operand, dimensions); } -XlaOp Sort(XlaOp keys, tensorflow::gtl::optional values) { - return keys.builder()->Sort(keys, std::move(values)); +XlaOp Sort(XlaOp keys, tensorflow::gtl::optional values, + int64 dimension) { + return keys.builder()->Sort(keys, std::move(values), dimension); } XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max) { @@ -2624,6 +2812,45 @@ XlaOp Recv(XlaBuilder* builder, const Shape& shape, return builder->Recv(shape, handle); } +XlaOp SendWithToken(const XlaOp& operand, const XlaOp& token, + const ChannelHandle& handle) { + return operand.builder()->SendWithToken(operand, token, handle); +} + +XlaOp RecvWithToken(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle) { + return token.builder()->RecvWithToken(token, shape, handle); +} + +XlaOp SendToHost(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, const ChannelHandle& handle) { + return operand.builder()->SendToHost(operand, token, shape_with_layout, + handle); +} + +XlaOp RecvFromHost(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle) { + return token.builder()->RecvFromHost(token, shape, handle); +} + +XlaOp InfeedWithToken(const XlaOp& token, const Shape& shape, + const string& config) { + return token.builder()->InfeedWithToken(token, shape, config); +} + +XlaOp OutfeedWithToken(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, + const string& outfeed_config) { + return operand.builder()->OutfeedWithToken(operand, token, shape_with_layout, + outfeed_config); +} + +XlaOp CreateToken(XlaBuilder* builder) { return builder->CreateToken(); } + +XlaOp AfterAll(XlaBuilder* builder, tensorflow::gtl::ArraySlice tokens) { + return builder->AfterAll(tokens); +} + XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, const XlaOp& offset, float epsilon, int64 feature_index) { @@ -2647,4 +2874,11 @@ XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, grad_output, epsilon, feature_index); } +XlaOp IotaGen(XlaBuilder* builder, PrimitiveType type, int64 size) { + HloInstructionProto instr; + *instr.mutable_shape() = ShapeUtil::MakeShape(type, {size}); + return builder->ReportErrorOrReturn( + builder->AddInstruction(std::move(instr), HloOpcode::kIota)); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/client/xla_builder.h b/tensorflow/compiler/xla/client/xla_builder.h new file mode 100644 index 0000000000000000000000000000000000000000..ae331407d6cbb08f8bfc25baabbedd1ba897231f --- /dev/null +++ b/tensorflow/compiler/xla/client/xla_builder.h @@ -0,0 +1,2241 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_XLA_BUILDER_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_XLA_BUILDER_H_ + +#include +#include +#include +#include + +#include "tensorflow/compiler/xla/client/padding.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/service/hlo.pb.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/stringpiece.h" +#include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/lib/gtl/flatset.h" +#include "tensorflow/core/platform/macros.h" +#include "tensorflow/core/platform/stacktrace.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +class XlaBuilder; + +// This represents an instruction that has been enqueued using the XlaBuilder. +// This is used to pass to subsequent computations that depends upon the +// instruction as an operand. +class XlaOp { + public: + XlaOp() : handle_(-1), builder_(nullptr) { + static_assert(std::is_trivially_destructible::value, + "XlaOp should be trivially destructible"); + } + ~XlaOp() = default; + + // Precondition: !IsUninitialized(). + // + // It's very common to do foo.builder()->bar(). Without this precondition, if + // foo.builder() is null, the call to bar will segfault at some point possibly + // deep in the callstack when we finally dereference `this`. The precondition + // lets us avoid this tricky-to-debug problem. + XlaBuilder* builder() const { + CHECK(builder_ != nullptr); + return builder_; + } + + // Returns true if the XlaOp represents valid, non-erroneous value. + bool valid() const { return handle_ >= 0; } + + // Returns true if the XlaOp was created by the XlaOp() constructor and + // not returned by a builder. + bool IsUninitialized() const { return builder_ == nullptr; } + + bool IsIdenticalTo(const XlaOp& rhs) const { + return handle_ == rhs.handle_ && builder_ == rhs.builder_; + } + + friend std::ostream& operator<<(std::ostream& out, const XlaOp& op) { + out << op.handle(); + return out; + } + + private: + explicit XlaOp(XlaBuilder* builder) : handle_(-1), builder_(builder) {} + XlaOp(int64 handle, XlaBuilder* builder) + : handle_(handle), builder_(builder) {} + + int64 handle() const { return handle_; } + + friend class XlaBuilder; + + // < 0 means "invalid handle". + int64 handle_; + + // Not owned. Non-null for any handle returned by XlaBuilder, even if the + // handle is invalid. + XlaBuilder* builder_; +}; + +// Arithmetic operator overloads for the XlaOp type. +XlaOp operator-(const XlaOp& x); +XlaOp operator+(const XlaOp& x, const XlaOp& y); +XlaOp operator-(const XlaOp& x, const XlaOp& y); +XlaOp operator*(const XlaOp& x, const XlaOp& y); +XlaOp operator/(const XlaOp& x, const XlaOp& y); +XlaOp operator%(const XlaOp& x, const XlaOp& y); + +// Bitwise operator overloads for the XlaOp type. +XlaOp operator~(const XlaOp& x); +XlaOp operator&(const XlaOp& x, const XlaOp& y); +XlaOp operator|(const XlaOp& x, const XlaOp& y); +XlaOp operator^(const XlaOp& x, const XlaOp& y); +XlaOp operator<<(const XlaOp& x, const XlaOp& y); +// Performs a right arithmetic shift if 'x' is a signed type, otherwise performs +// a right logical shift. +XlaOp operator>>(const XlaOp& x, const XlaOp& y); + +// We don't overload the relational operators (==, !=, <, <=, >, >=) because the +// semantics might be surprising since their result types are usually 'bool'. +// Further programmers may expect == to be a structural equality. +// We also choose not to overload any of the mutating operators (e.g., +=, -=) +// because the semantics might be misleading — XLA computations are immutable. + +// A convenient interface for building up computations. +// +// Thread-compatible. +class XlaBuilder { + public: + // computation_name: name to use for the built computation. + XlaBuilder(const string& computation_name); + + XlaBuilder(const XlaBuilder&) = delete; + XlaBuilder& operator=(const XlaBuilder&) = delete; + + ~XlaBuilder(); + + // Returns the computation name. + const string& name() const { return name_; } + + // Sets OpMetadata that will be added to all instructions until cleared. + // + // OpMetadata is often applied to a series of XLA HLO instructions. As a + // result, OpMetadata is set on the Computation Builder. All subsequent + // instructions generated via this Computation Builder will have the same + // OpMetadata attached until a call to ClearOpMetadata. + void SetOpMetadata(const OpMetadata& metadata) { metadata_ = metadata; } + + // Clears the HloMetadata state. + void ClearOpMetadata() { metadata_.Clear(); } + + // Sets an OpSharding that will be attached to all instructions until cleared. + void SetSharding(const OpSharding& sharding) { sharding_ = sharding; } + + // Clears the sharding. Ops will be sharded according to the default placement + // policy. + void ClearSharding() { sharding_ = tensorflow::gtl::nullopt; } + + // Returns the OpSharding that will be attached to all instructions. + const tensorflow::gtl::optional& sharding() const { + return sharding_; + } + + // Sets the builder to a mode where it will die immediately when an error is + // encountered, rather than producing it in a deferred fashion when Build() is + // called (which is the default). + void set_die_immediately_on_error(bool enabled) { + die_immediately_on_error_ = enabled; + } + + // Default dimension numbers used for a 2D convolution. + static constexpr int64 kConvBatchDimension = 0; + static constexpr int64 kConvFeatureDimension = 1; + static constexpr int64 kConvFirstSpatialDimension = 2; + static constexpr int64 kConvSecondSpatialDimension = 3; + static constexpr int64 kConvKernelOutputDimension = 0; + static constexpr int64 kConvKernelInputDimension = 1; + static constexpr int64 kConvKernelFirstSpatialDimension = 2; + static constexpr int64 kConvKernelSecondSpatialDimension = 3; + + // Creates a default ConvolutionDimensionNumbers. For a 2D convolution, for + // the input operand {batch, feature, height, width} = {0, 1, 2, 3} and for + // the kernel operand + // {output_feature, input_feature, height, width} = {0, 1, 2, 3}. + static ConvolutionDimensionNumbers CreateDefaultConvDimensionNumbers( + int num_spatial_dims = 2); + + // Returns an error if the convolution dimension numbers have conflicts. + static Status Validate(const ConvolutionDimensionNumbers& dnum); + + // Returns a new XlaBuilder whose resultant Computation is used only by this + // XlaBuilder. The sub-XlaBuilder has the same die_immediately_on_error + // behavior as the parent. + std::unique_ptr CreateSubBuilder(const string& computation_name); + + // Builds the computation with the requested operations, or returns a non-ok + // status. Note that all ops that have been enqueued will be moved to the + // computation being returned. + StatusOr Build(); + + // Builds the computation with the requested operations, or notes an error in + // the parent XlaBuilder and returns an empty computation if building failed. + // This function is intended to be used where the returned XlaComputation is + // only used by the parent XlaBuilder and hence further operation on the + // returned XlaComputation will simply be error'ed out if an error occurred + // while building this computation. If the built computation is to be used by + // a XlaBuilder other than the parent XlaBuilder then Build() should be used + // instead. + XlaComputation BuildAndNoteError(); + + // Returns a subgraph that roots on the given root. If the root is not a + // compile-time constant (see `IsConstant`), returns an error. + // + // This will copy the needed ops/computations to the subgraph. + StatusOr BuildConstantSubGraph(const XlaOp& root_op) const; + + // Returns the first error that was encountered while building the + // computation. When an error is encountered, by default we return a vacuous + // XlaOp and inform the user of the error that occurred while + // building the computation when they make a final call to Build(). + // + // See also set_die_immediately_on_error(). + Status first_error() const { return first_error_; } + + // Returns the shape of the given op. + StatusOr GetShape(const XlaOp& op) const; + + // Returns the (inferred) result for the current computation's shape. + StatusOr GetProgramShape() const; + + // Reports an error to the builder, by + // * storing it internally and capturing a backtrace if it's the first error + // (this deferred value will be produced on the call to + // Build()/GetShape()/...) + // * dying if die_immediately_on_error_ is true. + // Returns an XlaOp with an invalid handle but a valid builder. This value can + // be returned in place of a value in APIs that return an XlaOp. + XlaOp ReportError(const Status& error); + + // A helper function that converts a StatusOr into an XlaOp. + // If the Status was an error, reports the error to builder and returns an + // invalid XlaOp handle. + XlaOp ReportErrorOrReturn(const StatusOr& op); + + // A helper function that runs a function that returns a StatusOr and + // returns an XlaOp. + XlaOp ReportErrorOrReturn(const std::function()>& op_creator); + + // Returns true if 'operand' is a compile-time constant. A compile-time + // constant does not depend on any parameters, or on stateful operators such + // as `RngNormal` or `Infeed`. + // + // This tests whether a computation is a compile-time constant without + // evaluating the computation. + StatusOr IsConstant(const XlaOp& operand) const; + + private: + // Enqueues a "retrieve parameter value" instruction for a parameter that was + // passed to the computation. + XlaOp Parameter(int64 parameter_number, const Shape& shape, + const string& name); + + // Enqueues a constant with the value of the given literal onto the + // computation. + XlaOp ConstantLiteral(const LiteralSlice& literal); + + // Enqueues a constant onto the computation. Methods are templated on the + // native host type (NativeT) which corresponds to a specific XLA + // PrimitiveType as given in the following table: + // + // Native Type PrimitiveType + // ----------------------------- + // bool PRED + // int32 S32 + // int64 S64 + // uint32 U32 + // uint64 U64 + // float F32 + // double F64 + // + // Note: not all primitive types defined in xla_data.proto have a + // corresponding native type yet. + template + XlaOp ConstantR0(NativeT value); + template + XlaOp ConstantR1(tensorflow::gtl::ArraySlice values); + XlaOp ConstantR1(const tensorflow::core::Bitmap& values); + template + XlaOp ConstantR2( + std::initializer_list> values); + template + XlaOp ConstantFromArrayWithLayout(const Array& values, + const Layout& layout); + template + XlaOp ConstantFromArray(const Array& values); + template + XlaOp ConstantR2FromArray2DWithLayout(const Array2D& values, + const Layout& layout); + template + XlaOp ConstantR2FromArray2D(const Array2D& values); + template + XlaOp ConstantR3FromArray3DWithLayout(const Array3D& values, + const Layout& layout); + template + XlaOp ConstantR3FromArray3D(const Array3D& values); + template + XlaOp ConstantR4FromArray4DWithLayout(const Array4D& values, + const Layout& layout); + template + XlaOp ConstantR4FromArray4D(const Array4D& values); + + // Enqueues a rank one constant (vector) onto the computation. The vector has + // size 'length' and every element has the value 'value'. + template + XlaOp ConstantR1(int64 length, NativeT value); + + // Adds dimensions to an array by duplicating the data in the array. + // + // The new dimensions are inserted on the left, i.e. if + // broadcast_sizes has values {a0, ..., aN} and the operand shape + // has dimensions {b0, ..., bM} then the shape of the output has + // dimensions {a0, ..., aN, b0, ..., bM}. + // + // The new dimensions index into copies of the operand, i.e. + // + // output[i0, ..., iN, j0, ..., jM] = operand[j0, ..., jM] + XlaOp Broadcast(const XlaOp& operand, + tensorflow::gtl::ArraySlice broadcast_sizes); + + // Performs in-dimension-style broadcast. + // + // Operand specifies the input to be broadcast. "shape" is expected output + // shape. "broadcast_dimensions" are the dimensions to be broadcasting into. + // Dimension numbers in broadcast_dimensions map to individual dimensions + // of the operand, and specify what dimension of the output shape they + // should be broadcast. + // e.g. + // Say operand = [1, 2], i.e., a 1D tensor with 2 elements. + // and dimension of shape is [2,2]. + // Specifying {1} as brodcast_dimension will generate output + // [1 , 2] + // [1 , 2] + // On the other hand, specifying {0} as broadcast_dimension + // will generate output + // [1 , 1] + // [2 , 2] + XlaOp BroadcastInDim( + const XlaOp& operand, const Shape& shape, + const tensorflow::gtl::ArraySlice broadcast_dimensions); + + // Enqueues a pad operation onto the computation that pads the given value on + // the edges as well as between the elements of the input. padding_config + // specifies the padding amount for each dimension. + XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, + const PaddingConfig& padding_config); + + // Enqueues an operation onto the computation that flattens the operand based + // on the dimension order (major/slowest-varying to minor/fastest-varying) + // given, followed by reshaping it into the shape with the given dimension + // sizes (also major to minor). Conceptually, this is a limited form of + // "shape casting". + XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice new_sizes); + + // Enqueues an operation onto the computation that collapses the operand, from + // first to last dimension (C order), then reshapes it to the given dimension + // sizes. Conceptually, this is a limited form of "shape casting". + XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice new_sizes); + + // Wrapper for Reshape. + // Enqueues an operation to collapse the provided dimensions; e.g. an + // operand with dimensions {x=256, y=2, z=2, p=32} can be collapsed to + // {x=1024, y=32} by collapsing dims {0, 1, 2}. Collapsing dimensions must + // be a consecutive, in-order subsequence of the operand dimensions. + // + // Note that collapsing a single dimension does nothing: + // + // {256} collapsing {0} => {256} + // {1} collapsing {0} => {1} + // + // Collapsing multiple dimensions produces a single result dimension: + // + // {256, 2} collapsing {0,1} => {512} + // {256, 2, 3} collapsing {0,1} => {512, 3} + // + // This could potentially cause data to be moved -- it provides a more + // structured form of reshaping than an arbitrary Reshape operation. + XlaOp Collapse(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions); + + // Enqueues a slice operation onto the computation that slices the operand + // from the start indices to the limit indices; e.g. + // + // x + // [ 0 1 2 3 ] + // y [ 4 5 6 7 ] => slice(start={1, 1}, limit={2, 3}) => [ 5 6 ] + // [ 8 9 a b ] + // + // Note that "limit" means up-to-but-not-including; i.e. [start, limit) in 1D + // range notation. + // The strides parameter determines the stride over the slice + XlaOp Slice(const XlaOp& operand, + tensorflow::gtl::ArraySlice start_indices, + tensorflow::gtl::ArraySlice limit_indices, + tensorflow::gtl::ArraySlice strides); + + // Enqueues a slice operation in a given dimension, taking all other + // dimensions as they are; e.g. if dimno is 1 from start_index 2 to + // limit_index 4 by 1, and the shape is f32[7,8,9], this call is short-hand + // for: + // + // array[:, 2:4:1, :] + XlaOp SliceInDim(const XlaOp& operand, int64 start_index, int64 limit_index, + int64 stride, int64 dimno); + + // Enqueues a slice operation onto the computation that slices the 'operand' + // from dynamic start indices which are passed in 'start_indices'. + // The size of the slice in each dimension is passed in 'slice_sizes', + // which specify the end point of exclusive slice intervals in each + // dimension [start, start + size). + // The shape of 'start_indices' must be rank == 1, with dimension size + // equal to the rank of the 'operand'. + // Slice index calculations are computed modulo input dimension sizes to + // prevent dynamic start indices from generating out-of-bound array accesses. + XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, + tensorflow::gtl::ArraySlice slice_sizes); + + // Enqueues a dynamic update slice operation onto the computation, which + // updates a slice of 'operand' with 'update' at dynamic 'start_indices'. + // The shape of 'update' determines the shape of the slice of 'operand' + // which is updated. + // The indices specified in 'start_indices' specify the offset of the slice + // of 'operand' which is updated. + // + // update = {10, 11} // calculated at runtime. + // [1 2 3] start = {1, 1} // calculated at runtime. [1 2 3 ] + // [4 5 6] => DynamicUpdateslice(data, update, start) => [4 10 11] + // [7 8 9] [7 8 9 ] + // + // The shape of 'start_indices' must be rank == 1, with dimension size + // equal to the rank of the 'operand'. + // Slice index calculations are computed modulo update dimension sizes to + // prevent dynamic start indices from generating out-of-bound array accesses. + XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, + const XlaOp& start_indices); + + // Enqueues a concatenate instruction onto the computation. 'operands' must + // have >= 1 entry. + XlaOp ConcatInDim(tensorflow::gtl::ArraySlice operands, + int64 dimension); + + // Enqueue a tracing operation onto the computation; the computation will emit + // a logging message with the operand. + void Trace(const string& tag, const XlaOp& operand); + + // Enqueues a conditional-move-like select operation onto the computation; + // predicated on pred, selects between on_true and on_false. + XlaOp Select(const XlaOp& pred, const XlaOp& on_true, const XlaOp& on_false); + + // Enqueues a tuple-creation instruction onto the computation. + XlaOp Tuple(tensorflow::gtl::ArraySlice elements); + + // Enqueues a tuple-element-get instruction onto the computation. + XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index); + + // Enqueues an equal-to comparison instruction onto the computation. + XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a not-equal comparison instruction onto the computation. + XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a greater-or-equal comparison instruction onto the computation. + XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a greater-than comparison instruction onto the computation. + XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a less-than comparison instruction onto the computation. + XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a less-or-equal comparison instruction onto the computation. + XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a dot instruction onto the computation. + XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs); + + // Enqueues a general dot instruction onto the computation. + XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, + const DotDimensionNumbers& dimension_numbers); + + // Enqueues a convolution instruction onto the computation, which uses the + // default convolution dimension numbers. + XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + Padding padding); + + // Enqueues a convolution instruction onto the computation, with the caller + // provided padding configuration in the format returned by MakePadding(). + XlaOp ConvWithGeneralPadding( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + + // Enqueues a convolution instruction onto the computation, with the caller + // provided dimension numbers configuration. + XlaOp ConvWithGeneralDimensions( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, Padding padding, + const ConvolutionDimensionNumbers& dimension_numbers); + + // Enqueues a convolution instruction onto the computation, with the caller + // provided padding configuration as well as the dimension numbers. + XlaOp ConvGeneral( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const ConvolutionDimensionNumbers& dimension_numbers); + + // Enqueues a convolution instruction onto the computation, with the caller + // provided padding configuration, dilation factors and dimension numbers. + XlaOp ConvGeneralDilated( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + tensorflow::gtl::ArraySlice lhs_dilation, + tensorflow::gtl::ArraySlice rhs_dilation, + const ConvolutionDimensionNumbers& dimension_numbers); + + // Enqueues an FFT instruction onto the computation, of the given type and + // with the given FFT length. + XlaOp Fft(const XlaOp& operand, FftType fft_type, + tensorflow::gtl::ArraySlice fft_length); + + // Enqueues an infeed instruction onto the computation, which writes data of + // the given shape to the infeed buffer of the device. + XlaOp Infeed(const Shape& shape, const string& config = ""); + XlaOp InfeedWithToken(const XlaOp& token, const Shape& shape, + const string& config = ""); + + // Enqueues an outfeed instruction onto the computation. This instruction + // generates outgoing data transfers for the given data. + // + // shape_with_layout communicates the laid out shape that we want to outfeed + // -- if !ShapeUtil::Compatible(GetShape(operand), shape_with_layout) an error + // will occur. + void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, + const string& outfeed_config); + XlaOp OutfeedWithToken(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, + const string& outfeed_config); + + // Enqueues a call instruction onto the computation. + XlaOp Call(const XlaComputation& computation, + tensorflow::gtl::ArraySlice operands); + + // Enqueues a custom call instruction onto the computation. + // During code generation, a call instruction is emitted which targets a + // symbol with the name |call_target_name|. The |operands| are passed to the + // call instruction. |shape| is the resultant shape. + XlaOp CustomCall(const string& call_target_name, + tensorflow::gtl::ArraySlice operands, + const Shape& shape); + + // Enqueues a pseudo-op to represent host-side computation data-dependencies. + // During code generation, host send and receive operations will be generated + // to transfer |operands| to the host and a single result of |shape| back to + // the device. Host send/recv operations are emitted using |channel_name|. + // Dataflow dependencies and the |cost_estimate_ns| field may be used in HLO + // instruction scheduling. + XlaOp HostCompute(tensorflow::gtl::ArraySlice operands, + const string& channel_name, int64 cost_estimate_ns, + const Shape& shape); + + // The following methods enqueue element-wise binary arithmetic operations + // onto the computation. The shapes of the operands have to match unless one + // of the operands is a scalar, or an explicit broadcast dimension is given + // (see g3doc for more details). + + // Enqueues a complex compose instruction onto the computation. + XlaOp Complex(const XlaOp& real, const XlaOp& imag, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a complex conjugate instruction onto the computation. + XlaOp Conj(const XlaOp& operand); + + // Enqueues an add instruction onto the computation. + XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a subtract instruction onto the computation. + XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a multiply instruction onto the computation. + XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a divide instruction onto the computation. + XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a remainder instruction onto the computation. + XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a max instruction onto the computation. + XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a min instruction onto the computation. + XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Element-wise logical operators + XlaOp And(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + XlaOp Not(const XlaOp& operand); + + XlaOp ShiftLeft(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + XlaOp ShiftRightArithmetic( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + XlaOp ShiftRightLogical( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Reduces an array among the provided dimensions, given "computation" as a + // reduction operator. + XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions_to_reduce); + + // Convenience wrapper around the above that reduces all the dimensions in the + // operand shape. + XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation); + + // Enqueues a windowed reduce instruction onto the computation. + XlaOp ReduceWindow(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + Padding padding); + + // As ReduceWindow(), but the padding is given in the format + // returned by MakePadding(). + XlaOp ReduceWindowWithGeneralPadding( + const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + + // Returns the sum of the operand value within each subgroup of replicas. All + // replicas supply one input to the sum and all replicas receive the resulting + // sum for each subgroup. + XlaOp CrossReplicaSum( + const XlaOp& operand, + tensorflow::gtl::ArraySlice replica_group_ids = {}); + + // Enqueues an operation that do an AllReduce of the operand cross cores. Here + // AllReduce means doing a reduction on the input operand cross cores and then + // broadcasting the reduction result to those cores. The reduction function is + // defined by `computation`, which should be a commutative computation on + // scalars, e.g., add, min, or max. The way that AllReduce is applied is + // configured by: + // + // - `replica_group_ids`: maps replica ids to subgroup ids. If empty, all + // replicas belong to one group. Allreduce will be applied within subgroups. + // For example, we have 4 replicas, then replica_group_ids={0,1,0,1} means, + // replica 0 and 2 are in subgroup 0, replica 1 and 3 are in subgroup 1. + // + // - `channel_id`: for Allreduce nodes from different models, if they have the + // same channel_id, they will be 'Allreduce'd. If empty, Allreduce will not be + // applied cross models. + // + // TODO(b/79737069): Rename this to AllReduce when it's ready to use. + XlaOp CrossReplicaSum( + const XlaOp& operand, const XlaComputation& computation, + tensorflow::gtl::ArraySlice replica_group_ids = {}, + const tensorflow::gtl::optional& channel_id = + tensorflow::gtl::nullopt); + + // Enqueues an operation that scatters the `source` array to the selected + // indices of each window. + XlaOp SelectAndScatter(const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + Padding padding, const XlaOp& source, + const XlaOp& init_value, + const XlaComputation& scatter); + + // As SelectAndScatter(), but the padding is given in the format + // returned by MakePadding(). + XlaOp SelectAndScatterWithGeneralPadding( + const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const XlaOp& source, const XlaOp& init_value, + const XlaComputation& scatter); + + // Enqueues an abs instruction onto the computation. + XlaOp Abs(const XlaOp& operand); + + // Enqueues a atan2 instruction onto the computation. + XlaOp Atan2(const XlaOp& y, const XlaOp& x, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues an exp instruction onto the computation. + XlaOp Exp(const XlaOp& operand); + + // Enqueues an expm1 instruction onto the computation. + XlaOp Expm1(const XlaOp& operand); + + // Enqueues a floor instruction onto the computation. + XlaOp Floor(const XlaOp& operand); + + // Enqueues a ceil instruction onto the computation. + XlaOp Ceil(const XlaOp& operand); + + // Enqueues a round instruction onto the computation, rounding to nearest even + // with half-way cases rounding away from zero. + XlaOp Round(const XlaOp& operand); + + // Enqueues an log instruction (natural logarithm) onto the computation. + XlaOp Log(const XlaOp& operand); + + // Enqueues an log1p instruction (log(x+1)) onto the computation. + XlaOp Log1p(const XlaOp& operand); + + // Enqueues a sign instruction onto the computation. + XlaOp Sign(const XlaOp& operand); + + // Enqueues a count leading zeros instruction onto the computation. + XlaOp Clz(const XlaOp& operand); + + // Enqueues a cosine instruction onto the computation. + XlaOp Cos(const XlaOp& operand); + + // Enqueues a sine instruction onto the computation. + XlaOp Sin(const XlaOp& operand); + + // Enqueues a tanh instruction onto the computation. + XlaOp Tanh(const XlaOp& operand); + + // Enqueues a real-part instruction onto the computation. + XlaOp Real(const XlaOp& operand); + + // Enqueues an imaginary-part instruction onto the computation. + XlaOp Imag(const XlaOp& operand); + + // Enqueues a lhs^rhs computation onto the computation. + XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues an operator that tests if the operand's values are finite, i.e., + // not Inf or NaN. Defined only for floating-point types. Returns an array of + // booleans with the same shape where entries are true iff the corresponding + // entry was NaN. + XlaOp IsFinite(const XlaOp& operand); + + // Enqueues a convert instruction onto the computation that changes the + // element type of the operand array to primitive_type. + XlaOp ConvertElementType(const XlaOp& operand, + PrimitiveType new_element_type); + + // Enqueues a no-op instruction onto the computation that changes + // the element type of the operand array to primitive_type. The + // bit-widths of the source and destination element types must be + // identical. + XlaOp BitcastConvertType(const XlaOp& operand, + PrimitiveType new_element_type); + + // Enqueues a negate instruction onto the computation. + XlaOp Neg(const XlaOp& operand); + + // Enqueues a transpose instruction onto the computation. + XlaOp Transpose(const XlaOp& operand, + tensorflow::gtl::ArraySlice permutation); + + // Enqueues a reverse instruction onto the computation. The order of the + // elements in the given dimensions is reversed (i.e., the element at index i + // is moved to index dimension_size - 1 - i). + XlaOp Rev(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions); + + // Enqueues a sort (as increasing order) instruction onto the computation. + // If only keys are provided: + // * If the keys are an rank-1 tensor (an array), the result is a sorted array + // of keys, in ascending order. + // * If the keys have higher rank, the keys are sorted along the provided + // dimension. For example, for a rank-2 tensor (a matrix) of keys, a dimension + // value of 0 will indepenently sort every column, and a dimension value of 1 + // will independently sort each row. If no dimension number is provided, then + // the last dimension is chosen by default. + // + // If both keys and values are provided: + // * The keys and the values must tensors with the same dimensions. The + // element types of the tensors may be different. + // * The result is a tuple that consists of a sorted tensor of keys (along the + // provided dimension, as above) as the first element, and a tensor with their + // corresponding values as the second element. + XlaOp Sort(XlaOp keys, + tensorflow::gtl::optional values = tensorflow::gtl::nullopt, + int64 dimension = -1); + + // Enqueues a clamp instruction onto the computation. + XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); + + // Enqueues a map instruction onto the computation. + XlaOp Map(tensorflow::gtl::ArraySlice operands, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice static_operands = {}); + + // Enqueues a N(mu, sigma) random number generation instruction onto the + // computation. + XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, const Shape& shape); + + // Enqueues a U(a, b) random number generation instruction onto the + // computation. Returns values in the semi-open interval [a, b). + XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape); + + // Enqueues a while node onto the computation. + XlaOp While(const XlaComputation& condition, const XlaComputation& body, + const XlaOp& init); + + // Enqueues a conditional node onto the computation. + XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, + const XlaComputation& true_computation, + const XlaOp& false_operand, + const XlaComputation& false_computation); + + // Enqueues a ReducePrecision node onto the computation. + XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, + const int mantissa_bits); + + // Enqueues a Gather node onto the computation. + XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, + const GatherDimensionNumbers& dimension_numbers, + tensorflow::gtl::ArraySlice window_bounds); + + // Enqueues a Send node onto the computation for device-to-device + // communication, to send the given operand to a Recv instruction that shares + // the same channel handle. + void Send(const XlaOp& operand, const ChannelHandle& handle); + XlaOp SendWithToken(const XlaOp& operand, const XlaOp& token, + const ChannelHandle& handle); + + // Enqueues a Send node which sends data to the host. + XlaOp SendToHost(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, const ChannelHandle& handle); + + // Enqueues a Recv node which receives data from the host. + XlaOp RecvFromHost(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle); + + // Enqueues an AfterAll operation with no operands producing a token-shaped + // value. + XlaOp CreateToken(); + + // Enqueues an AfterAll operation with no operands producing a token-shaped + // value. + XlaOp AfterAll(tensorflow::gtl::ArraySlice tokens); + + // Enqueues a Recv node onto the computation. The data comes from a Send + // instruction that shares the same channel handle and its shape must + // be the same as the given shape. + XlaOp Recv(const Shape& shape, const ChannelHandle& handle); + XlaOp RecvWithToken(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle); + + // Normalizes operand across spatial and batch dimensions for each feature. + // + // Returns a tuple (normalized, batch_mean, batch_var) where `normalized` + // is the normalized result and batch_mean and batch_var are the mean and + // variance, respectively, across batch for the operand. + XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, float epsilon, + int64 feature_index); + + // Normalizes operand across spatial and batch dimensions for each feature. + // + // `BatchNormInference` is equivalent to calling `BatchNormTraining` without + // computing `mean` and `variance` for each batch inside the operation. It + // uses the input `mean` and `variance` instead as estimated values. The + // purpose of this op is to reduce latency in inference, hence the name + // `BatchNormInference`. + // + // The output has the same shape as `operand`, and contains the normalized + // values for each batch. + XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, const XlaOp& mean, + const XlaOp& variance, float epsilon, + int64 feature_index); + + // Calculates the gradients of a batch norm op. + // + // The inputs `batch_mean` and `batch_var` represent the mean and variance + // across the batch. + // + // Returns a tuple of three elements: + // - grad_operand: Gradient with respect to input `operand` + // - grad_offset: Gradient with respect to input `offset` + // - grad_scale: Gradient with respect to input `scale` + XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, + const XlaOp& batch_mean, const XlaOp& batch_var, + const XlaOp& grad_output, float epsilon, + int64 feature_index); + + StatusOr AddInstruction( + HloInstructionProto&& instr, HloOpcode opcode, + tensorflow::gtl::ArraySlice operands = {}); + + void AddCalledComputation(const XlaComputation& computation, + HloInstructionProto* instr); + + StatusOr LookUpInstruction(const XlaOp& op) const; + + // Internal helper method that does the building for an arbitrary unary op. + XlaOp UnaryOp(HloOpcode unop, const XlaOp& operand); + + // Internal helper method that does the building for an arbitrary binary op. + // broadcast_dimensions specifies which dimensions to use for broadcasting + // when the operation is between tensors of different ranks. + XlaOp BinaryOp(HloOpcode binop, const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + + // Internal helper method that does the building for an arbitrary ternary op. + XlaOp TernaryOp(HloOpcode triop, const XlaOp& lhs, const XlaOp& rhs, + const XlaOp& ehs); + + XlaOp RngOp(RandomDistribution distribution, + tensorflow::gtl::ArraySlice parameters, + const Shape& shape); + + StatusOr InDimBroadcast( + const Shape& shape, const XlaOp& operand, + tensorflow::gtl::ArraySlice broadcast_dimensions); + + // Internal helper method that creates a sequence of instructions that + // performs an explicit broadcast of the operand to the target shape. + StatusOr AddBroadcastSequence(const Shape& output_shape, + const XlaOp& operand); + + // Internal helper method for creating a Reshape op with the already inferred + // shape. + StatusOr Reshape(const Shape& shape, const XlaOp& operand); + + // Returns the (inferred) result for the program shape for the current + // computation and fills the root_id in the pointer. + StatusOr GetProgramShape(int64* root_id) const; + + // Returns shapes for the operands. + StatusOr> GetOperandShapes( + tensorflow::gtl::ArraySlice operands) const; + + // A visitor which checks whether an operation is a compile-time constant, + // meaning that it doesn't depend on any parameters, or on any stateful + // operation such as `RngNormal` or `Infeed`. The visitor walks the + // computation starting at a given operation and sets is_constant to false iff + // a parameter or stateful operation is encountered. + void IsConstantVisitor(const int64 op_handle, std::set* visited, + bool* is_constant) const; + + // Checks bounds for convolution parameters. + Status VerifyConvolution( + const Shape& lhs_shape, const Shape& rhs_shape, + const ConvolutionDimensionNumbers& dimension_numbers) const; + + // Helper function for creating a Window proto from user-supplied data. + // Returns error if the user-supplied data was invalid. + StatusOr MakeWindow( + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + tensorflow::gtl::ArraySlice lhs_dilation, + tensorflow::gtl::ArraySlice rhs_dilation) const; + + string name_; // Name to use for the built computation. + + // The first error encountered while building the computation. + // This is OK until the first error is encountered. + Status first_error_; + + // The saved stack trace from the point at which the first error occurred. + tensorflow::SavedStackTrace first_error_backtrace_; + + // The instructions of this computation. + std::vector instructions_; + + // The embedded computations used by this computation. Each computation was + // the entry computation of some XlaComputation, the key is the unique id of + // that XlaComputation. + std::map embedded_; + + // The unique parameter numbers. + tensorflow::gtl::FlatSet parameter_numbers_; + + // The metadata to attach to each op. This is structured as a "modal"-like + // operation, in order to simplify client code (and not sprinkle this metadata + // throughout the TensorFlow op kernel implementations). + OpMetadata metadata_; + + // Sharding for this operator. This is structured as a "model"-like operation, + // in order to simplify client code, similar to metadata_. + tensorflow::gtl::optional sharding_; + + // Mode bit that indicates whether to die when a first error is encountered. + bool die_immediately_on_error_ = false; + + XlaBuilder* parent_builder_{nullptr}; + + friend XlaOp Parameter(XlaBuilder* builder, int64 parameter_number, + const Shape& shape, const string& name); + friend XlaOp ConstantLiteral(XlaBuilder* builder, + const LiteralSlice& literal); + template + friend XlaOp ConstantR0(XlaBuilder* builder, NativeT value); + template + friend XlaOp ConstantR1(XlaBuilder* builder, + tensorflow::gtl::ArraySlice values); + friend XlaOp ConstantR1(XlaBuilder* builder, + const tensorflow::core::Bitmap& values); + template + friend XlaOp ConstantR2( + XlaBuilder* builder, + std::initializer_list> values); + template + friend XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, + const Array& values, + const Layout& layout); + template + friend XlaOp ConstantFromArray(XlaBuilder* builder, + const Array& values); + template + friend XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, + const Array2D& values, + const Layout& layout); + template + friend XlaOp ConstantR2FromArray2D(XlaBuilder* builder, + const Array2D& values); + template + friend XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, + const Array3D& values, + const Layout& layout); + template + friend XlaOp ConstantR3FromArray3D(XlaBuilder* builder, + const Array3D& values); + template + friend XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, + const Array4D& values, + const Layout& layout); + template + friend XlaOp ConstantR4FromArray4D(XlaBuilder* builder, + const Array4D& values); + + template + friend XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value); + + friend XlaOp Broadcast(const XlaOp& operand, + tensorflow::gtl::ArraySlice broadcast_sizes); + + friend XlaOp BroadcastInDim( + const XlaOp& operand, const Shape& shape, + const tensorflow::gtl::ArraySlice broadcast_dimensions); + + friend XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, + const PaddingConfig& padding_config); + + friend XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice new_sizes); + + friend XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice new_sizes); + + friend XlaOp Collapse(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions); + + friend XlaOp Slice(const XlaOp& operand, + tensorflow::gtl::ArraySlice start_indices, + tensorflow::gtl::ArraySlice limit_indices, + tensorflow::gtl::ArraySlice strides); + + friend XlaOp SliceInDim(const XlaOp& operand, int64 start_index, + int64 limit_index, int64 stride, int64 dimno); + + friend XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, + tensorflow::gtl::ArraySlice slice_sizes); + + friend XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, + const XlaOp& start_indices); + + friend XlaOp ConcatInDim(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, + int64 dimension); + + friend void Trace(const string& tag, const XlaOp& operand); + + friend XlaOp Select(const XlaOp& pred, const XlaOp& on_true, + const XlaOp& on_false); + friend XlaOp Tuple(XlaBuilder* builder, + tensorflow::gtl::ArraySlice elements); + friend XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index); + friend XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs); + friend XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, + const DotDimensionNumbers& dimension_numbers); + friend XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + Padding padding); + friend XlaOp ConvWithGeneralPadding( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + friend XlaOp ConvWithGeneralDimensions( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, Padding padding, + const ConvolutionDimensionNumbers& dimension_numbers); + friend XlaOp ConvGeneral( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const ConvolutionDimensionNumbers& dimension_numbers); + friend XlaOp ConvGeneralDilated( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + tensorflow::gtl::ArraySlice lhs_dilation, + tensorflow::gtl::ArraySlice rhs_dilation, + const ConvolutionDimensionNumbers& dimension_numbers); + friend XlaOp Fft(const XlaOp& operand, FftType fft_type, + tensorflow::gtl::ArraySlice fft_length); + friend XlaOp Infeed(XlaBuilder* builder, const Shape& shape, + const string& config); + friend void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, + const string& outfeed_config); + friend XlaOp Call(XlaBuilder* builder, const XlaComputation& computation, + tensorflow::gtl::ArraySlice operands); + friend XlaOp CustomCall(XlaBuilder* builder, const string& call_target_name, + tensorflow::gtl::ArraySlice operands, + const Shape& shape); + friend XlaOp HostCompute(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, + const string& channel_name, int64 cost_estimate_ns, + const Shape& shape); + friend XlaOp Complex(const XlaOp& real, const XlaOp& imag, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Conj(const XlaOp& operand); + friend XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp And(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Not(const XlaOp& operand); + friend XlaOp ShiftLeft( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp ShiftRightArithmetic( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp ShiftRightLogical( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions_to_reduce); + friend XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation); + friend XlaOp ReduceWindow( + const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, Padding padding); + friend XlaOp ReduceWindowWithGeneralPadding( + const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + friend XlaOp CrossReplicaSum( + const XlaOp& operand, + tensorflow::gtl::ArraySlice replica_group_ids); + friend XlaOp CrossReplicaSum( + const XlaOp& operand, const XlaComputation& computation, + tensorflow::gtl::ArraySlice replica_group_ids, + const tensorflow::gtl::optional& channel_id); + friend XlaOp SelectAndScatter( + const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, Padding padding, + const XlaOp& source, const XlaOp& init_value, + const XlaComputation& scatter); + friend XlaOp SelectAndScatterWithGeneralPadding( + const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const XlaOp& source, const XlaOp& init_value, + const XlaComputation& scatter); + friend XlaOp Abs(const XlaOp& operand); + friend XlaOp Atan2(const XlaOp& y, const XlaOp& x, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Exp(const XlaOp& operand); + friend XlaOp Expm1(const XlaOp& operand); + friend XlaOp Floor(const XlaOp& operand); + friend XlaOp Ceil(const XlaOp& operand); + friend XlaOp Round(const XlaOp& operand); + friend XlaOp Log(const XlaOp& operand); + friend XlaOp Log1p(const XlaOp& operand); + friend XlaOp Sign(const XlaOp& operand); + friend XlaOp Clz(const XlaOp& operand); + friend XlaOp Cos(const XlaOp& operand); + friend XlaOp Sin(const XlaOp& operand); + friend XlaOp Tanh(const XlaOp& operand); + friend XlaOp Real(const XlaOp& operand); + friend XlaOp Imag(const XlaOp& operand); + friend XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp IsFinite(const XlaOp& operand); + // TODO(b/64798317): Finish CPU & GPU implementation, then replace xla::Iota + // in xla/client/lib/numeric.h with this (renamed to xla::Iota). + friend XlaOp IotaGen(XlaBuilder* builder, PrimitiveType type, int64 size); + friend XlaOp ConvertElementType(const XlaOp& operand, + PrimitiveType new_element_type); + friend XlaOp BitcastConvertType(const XlaOp& operand, + PrimitiveType new_element_type); + friend XlaOp Neg(const XlaOp& operand); + friend XlaOp Transpose(const XlaOp& operand, + tensorflow::gtl::ArraySlice permutation); + friend XlaOp Rev(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions); + friend XlaOp Sort(XlaOp keys, tensorflow::gtl::optional values, + int64 dimension); + friend XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); + friend XlaOp Map(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice static_operands); + friend XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, + const Shape& shape); + friend XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape); + friend XlaOp While(const XlaComputation& condition, + const XlaComputation& body, const XlaOp& init); + friend XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, + const XlaComputation& true_computation, + const XlaOp& false_operand, + const XlaComputation& false_computation); + friend XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, + const int mantissa_bits); + friend XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, + const GatherDimensionNumbers& dimension_numbers, + tensorflow::gtl::ArraySlice window_bounds); + friend void Send(const XlaOp& operand, const ChannelHandle& handle); + friend XlaOp Recv(XlaBuilder* builder, const Shape& shape, + const ChannelHandle& handle); + friend XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, float epsilon, + int64 feature_index); + friend XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, const XlaOp& mean, + const XlaOp& variance, float epsilon, + int64 feature_index); + friend XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, + const XlaOp& batch_mean, const XlaOp& batch_var, + const XlaOp& grad_output, float epsilon, + int64 feature_index); + friend XlaOp SendWithToken(const XlaOp& operand, const XlaOp& token, + const ChannelHandle& handle); + friend XlaOp RecvWithToken(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle); + friend XlaOp SendToHost(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, + const ChannelHandle& handle); + friend XlaOp RecvFromHost(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle); + friend XlaOp InfeedWithToken(const XlaOp& token, const Shape& shape, + const string& config); + friend XlaOp OutfeedWithToken(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, + const string& outfeed_config); + friend XlaOp CreateToken(XlaBuilder* builder); + friend XlaOp AfterAll(XlaBuilder* builder, + tensorflow::gtl::ArraySlice tokens); +}; + +// RAII-style object: sets the current sharding assignment in builder on +// construction, and sets back to the previous assignment on destruction. +class XlaScopedShardingAssignment { + public: + XlaScopedShardingAssignment(xla::XlaBuilder* builder, + tensorflow::gtl::optional sharding) + : builder_(builder), prev_sharding_(builder->sharding()) { + SetSharding(sharding); + } + + XlaScopedShardingAssignment(const XlaScopedShardingAssignment&) = delete; + XlaScopedShardingAssignment& operator=(const XlaScopedShardingAssignment&) = + delete; + + ~XlaScopedShardingAssignment() { SetSharding(prev_sharding_); } + + private: + void SetSharding(const tensorflow::gtl::optional& sharding) { + if (sharding.has_value()) { + builder_->SetSharding(sharding.value()); + } else { + builder_->ClearSharding(); + } + } + + xla::XlaBuilder* const builder_; + tensorflow::gtl::optional prev_sharding_; +}; + +// Free functions for building XlaOps. The intention is that these will +// become the public API for building XlaOps rather than calling methods on +// XlaBuilder directly. + +// Enqueues a "retrieve parameter value" instruction for a parameter that was +// passed to the computation. +XlaOp Parameter(XlaBuilder* builder, int64 parameter_number, const Shape& shape, + const string& name); + +// Enqueues a constant with the value of the given literal onto the +// computation. +XlaOp ConstantLiteral(XlaBuilder* builder, const LiteralSlice& literal); + +// Enqueues a constant onto the computation. Methods are templated on the +// native host type (NativeT) which corresponds to a specific XLA +// PrimitiveType as given in the following table: +// +// Native Type PrimitiveType +// ----------------------------- +// bool PRED +// int32 S32 +// int64 S64 +// uint32 U32 +// uint64 U64 +// float F32 +// double F64 +// +// Note: not all primitive types defined in xla_data.proto have a +// corresponding native type yet. +template +XlaOp ConstantR0(XlaBuilder* builder, NativeT value); +template +XlaOp ConstantR1(XlaBuilder* builder, + tensorflow::gtl::ArraySlice values); +XlaOp ConstantR1(XlaBuilder* builder, const tensorflow::core::Bitmap& values); +template +XlaOp ConstantR2(XlaBuilder* builder, + std::initializer_list> values); +template +XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, + const Array& values, + const Layout& layout); +template +XlaOp ConstantFromArray(XlaBuilder* builder, const Array& values); +template +XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, + const Array2D& values, + const Layout& layout); +template +XlaOp ConstantR2FromArray2D(XlaBuilder* builder, + const Array2D& values); +template +XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, + const Array3D& values, + const Layout& layout); +template +XlaOp ConstantR3FromArray3D(XlaBuilder* builder, + const Array3D& values); +template +XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, + const Array4D& values, + const Layout& layout); +template +XlaOp ConstantR4FromArray4D(XlaBuilder* builder, + const Array4D& values); + +// Enqueues a rank one constant (XlaBuilder* builder, vector) onto the +// computation. The vector has size 'length' and every element has the value +// 'value'. +template +XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value); + +// Adds dimensions to an array by duplicating the data in the array. +// +// The new dimensions are inserted on the left, i.e. if +// broadcast_sizes has values {a0, ..., aN} and the operand shape +// has dimensions {b0, ..., bM} then the shape of the output has +// dimensions {a0, ..., aN, b0, ..., bM}. +// +// The new dimensions index into copies of the operand, i.e. +// +// output[i0, ..., iN, j0, ..., jM] = operand[j0, ..., jM] +XlaOp Broadcast(const XlaOp& operand, + tensorflow::gtl::ArraySlice broadcast_sizes); + +// Performs in-dimension-style broadcast. +// +// Operand specifies the input to be broadcast. "shape" is expected output +// shape. "broadcast_dimensions" are the dimensions to be broadcasting into. +// Dimension numbers in broadcast_dimensions map to individual dimensions +// of the operand, and specify what dimension of the output shape they +// should be broadcast. +// e.g. +// Say operand = [1, 2], i.e., a 1D tensor with 2 elements. +// and dimension of shape is [2,2]. +// Specifying {1} as brodcast_dimension will generate output +// [1 , 2] +// [1 , 2] +// On the other hand, specifying {0} as broadcast_dimension +// will generate output +// [1 , 1] +// [2 , 2] +XlaOp BroadcastInDim( + const XlaOp& operand, const Shape& shape, + const tensorflow::gtl::ArraySlice broadcast_dimensions); + +// Enqueues a pad operation onto the computation that pads the given value on +// the edges as well as between the elements of the input. padding_config +// specifies the padding amount for each dimension. +XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, + const PaddingConfig& padding_config); + +// Enqueues an operation onto the computation that flattens the operand based +// on the dimension order (major/slowest-varying to minor/fastest-varying) +// given, followed by reshaping it into the shape with the given dimension +// sizes (also major to minor). Conceptually, this is a limited form of +// "shape casting". +XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice new_sizes); + +// Enqueues an operation onto the computation that collapses the operand, from +// first to last dimension (C order), then reshapes it to the given dimension +// sizes. Conceptually, this is a limited form of "shape casting". +XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice new_sizes); + +// Wrapper for Reshape. +// Enqueues an operation to collapse the provided dimensions; e.g. an +// operand with dimensions {x=256, y=2, z=2, p=32} can be collapsed to +// {x=1024, y=32} by collapsing dims {0, 1, 2}. Collapsing dimensions must +// be a consecutive, in-order subsequence of the operand dimensions. +// +// Note that collapsing a single dimension does nothing: +// +// {256} collapsing {0} => {256} +// {1} collapsing {0} => {1} +// +// Collapsing multiple dimensions produces a single result dimension: +// +// {256, 2} collapsing {0,1} => {512} +// {256, 2, 3} collapsing {0,1} => {512, 3} +// +// This could potentially cause data to be moved -- it provides a more +// structured form of reshaping than an arbitrary Reshape operation. +XlaOp Collapse(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions); + +// Enqueues a slice operation onto the computation that slices the operand +// from the start indices to the limit indices; e.g. +// +// x +// [ 0 1 2 3 ] +// y [ 4 5 6 7 ] => slice(start={1, 1}, limit={2, 3}) => [ 5 6 ] +// [ 8 9 a b ] +// +// Note that "limit" means up-to-but-not-including; i.e. [start, limit) in 1D +// range notation. +// The strides parameter determines the stride over the slice +XlaOp Slice(const XlaOp& operand, + tensorflow::gtl::ArraySlice start_indices, + tensorflow::gtl::ArraySlice limit_indices, + tensorflow::gtl::ArraySlice strides); + +// Enqueues a slice operation in a given dimension, taking all other +// dimensions as they are; e.g. if dimno is 1 from start_index 2 to +// limit_index 4 by 1, and the shape is f32[7,8,9], this call is short-hand +// for: +// +// array[:, 2:4:1, :] +XlaOp SliceInDim(const XlaOp& operand, int64 start_index, int64 limit_index, + int64 stride, int64 dimno); + +// Enqueues a slice operation onto the computation that slices the 'operand' +// from dynamic start indices which are passed in 'start_indices'. +// The size of the slice in each dimension is passed in 'slice_sizes', +// which specify the end point of exclusive slice intervals in each +// dimension [start, start + size). +// The shape of 'start_indices' must be rank == 1, with dimension size +// equal to the rank of the 'operand'. +// Slice index calculations are computed modulo input dimension sizes to +// prevent dynamic start indices from generating out-of-bound array accesses. +XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, + tensorflow::gtl::ArraySlice slice_sizes); + +// Enqueues a dynamic update slice operation onto the computation, which +// updates a slice of 'operand' with 'update' at dynamic 'start_indices'. +// The shape of 'update' determines the shape of the slice of 'operand' +// which is updated. +// The indices specified in 'start_indices' specify the offset of the slice +// of 'operand' which is updated. +// +// update = {10, 11} // calculated at runtime. +// [1 2 3] start = {1, 1} // calculated at runtime. [1 2 3 ] +// [4 5 6] => DynamicUpdateslice(data, update, start) => [4 10 11] +// [7 8 9] [7 8 9 ] +// +// The shape of 'start_indices' must be rank == 1, with dimension size +// equal to the rank of the 'operand'. +// Slice index calculations are computed modulo update dimension sizes to +// prevent dynamic start indices from generating out-of-bound array accesses. +XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, + const XlaOp& start_indices); + +// Enqueues a concatenate instruction onto the computation. 'operands' must +// have >= 1 entry. +XlaOp ConcatInDim(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, int64 dimension); + +// Enqueue a tracing operation onto the computation; the computation will emit +// a logging message with the operand. +void Trace(const string& tag, const XlaOp& operand); + +// Enqueues a conditional-move-like select operation onto the computation; +// predicated on pred, selects between on_true and on_false. +XlaOp Select(const XlaOp& pred, const XlaOp& on_true, const XlaOp& on_false); + +// Enqueues a tuple-creation instruction onto the computation. +XlaOp Tuple(XlaBuilder* builder, tensorflow::gtl::ArraySlice elements); + +// Enqueues a tuple-element-get instruction onto the computation. +XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index); + +// Enqueues an equal-to comparison instruction onto the computation. +XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a not-equal comparison instruction onto the computation. +XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a greater-or-equal comparison instruction onto the computation. +XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a greater-than comparison instruction onto the computation. +XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a less-than comparison instruction onto the computation. +XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a less-or-equal comparison instruction onto the computation. +XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a dot instruction onto the computation. +XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs); + +// Enqueues a general dot instruction onto the computation. +XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, + const DotDimensionNumbers& dimension_numbers); + +// Enqueues a convolution instruction onto the computation, which uses the +// default convolution dimension numbers. +XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, Padding padding); + +// Enqueues a convolution instruction onto the computation, with the caller +// provided padding configuration in the format returned by MakePadding(). +XlaOp ConvWithGeneralPadding( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + +// Enqueues a convolution instruction onto the computation, with the caller +// provided dimension numbers configuration. +XlaOp ConvWithGeneralDimensions( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, Padding padding, + const ConvolutionDimensionNumbers& dimension_numbers); + +// Enqueues a convolution instruction onto the computation, with the caller +// provided padding configuration as well as the dimension numbers. +XlaOp ConvGeneral(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const ConvolutionDimensionNumbers& dimension_numbers); + +// Enqueues a convolution instruction onto the computation, with the caller +// provided padding configuration, dilation factors and dimension numbers. +XlaOp ConvGeneralDilated( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + tensorflow::gtl::ArraySlice lhs_dilation, + tensorflow::gtl::ArraySlice rhs_dilation, + const ConvolutionDimensionNumbers& dimension_numbers); + +// Enqueues an FFT instruction onto the computation, of the given type and +// with the given FFT length. +XlaOp Fft(const XlaOp& operand, FftType fft_type, + tensorflow::gtl::ArraySlice fft_length); + +// Enqueues an infeed instruction onto the computation, which writes data of +// the given shape to the infeed buffer of the device. +XlaOp Infeed(XlaBuilder* builder, const Shape& shape, + const string& config = ""); + +// Variant of Infeed which takes a token-shaped operand and produces a +// two-element tuple containing the data value and a token-shaped value. +// Tokens are used for ordering side-effecting operations. +// TODO(b/110532604): Replace all uses of the non-token form with this variant. +XlaOp InfeedWithToken(const XlaOp& token, const Shape& shape, + const string& config = ""); + +// Enqueues an outfeed instruction onto the computation. This instruction +// generates outgoing data transfers for the given data. +// +// shape_with_layout communicates the laid out shape that we want to outfeed +// -- if !ShapeUtil::Compatible(GetShape(operand), shape_with_layout) an error +// will occur. +void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, + const string& outfeed_config); + +// Variant of Outfeed which takes a token-shaped operand and produces a +// token-shaped value. Tokens are used for ordering side-effecting operations. +// TODO(b/110532604): Replace all uses of the non-token form with this variant. +XlaOp OutfeedWithToken(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, + const string& outfeed_config); + +// Enqueues a call instruction onto the computation. +XlaOp Call(XlaBuilder* builder, const XlaComputation& computation, + tensorflow::gtl::ArraySlice operands); + +// Enqueues a custom call instruction onto the computation. +// During code generation, a call instruction is emitted which targets a +// symbol with the name |call_target_name|. The |operands| are passed to the +// call instruction. |shape| is the resultant shape. +XlaOp CustomCall(XlaBuilder* builder, const string& call_target_name, + tensorflow::gtl::ArraySlice operands, + const Shape& shape); + +// Enqueues a pseudo-op to represent host-side computation data-dependencies. +// During code generation, host send and receive operations will be generated +// to transfer |operands| to the host and a single result of |shape| back to +// the device. Host send/recv operations are emitted using |channel_name|. +// Dataflow dependencies and the |cost_estimate_ns| field may be used in HLO +// instruction scheduling. +XlaOp HostCompute(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, + const string& channel_name, int64 cost_estimate_ns, + const Shape& shape); + +// The following methods enqueue element-wise binary arithmetic operations +// onto the computation. The shapes of the operands have to match unless one +// of the operands is a scalar, or an explicit broadcast dimension is given +// (see g3doc for more details). + +// Enqueues a complex compose instruction onto the computation. +XlaOp Complex(const XlaOp& real, const XlaOp& imag, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a complex conjugate instruction onto the computation. +XlaOp Conj(const XlaOp& operand); + +// Enqueues an add instruction onto the computation. +XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a subtract instruction onto the computation. +XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a multiply instruction onto the computation. +XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a divide instruction onto the computation. +XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a remainder instruction onto the computation. +XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a max instruction onto the computation. +XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a min instruction onto the computation. +XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Element-wise logical operators +XlaOp And(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +XlaOp Not(const XlaOp& operand); + +XlaOp ShiftLeft(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); +XlaOp ShiftRightArithmetic( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); +XlaOp ShiftRightLogical( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Reduces an array among the provided dimensions, given "computation" as a +// reduction operator. +XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions_to_reduce); + +// Convenience wrapper around the above that reduces all the dimensions in the +// operand shape. +XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation); + +// Enqueues a windowed reduce instruction onto the computation. +XlaOp ReduceWindow(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + Padding padding); + +// As ReduceWindow(), but the padding is given in the format +// returned by MakePadding(). +XlaOp ReduceWindowWithGeneralPadding( + const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + +// Returns the sum of the operand value within each subgroup of replicas. All +// replicas supply one input to the sum and all replicas receive the resulting +// sum for each subgroup. +XlaOp CrossReplicaSum( + const XlaOp& operand, + tensorflow::gtl::ArraySlice replica_group_ids = {}); + +// Enqueues an operation that do an AllReduce of the operand cross cores. Here +// AllReduce means doing a reduction on the input operand cross cores and then +// broadcasting the reduction result to those cores. The reduction function is +// defined by `computation`, which should be a commutative computation on +// scalars, e.g., add, min, or max. The way that AllReduce is applied is +// configured by: +// +// - `replica_group_ids`: maps replica ids to subgroup ids. If empty, all +// replicas belong to one group. Allreduce will be applied within subgroups. +// For example, we have 4 replicas, then replica_group_ids={0,1,0,1} means, +// replica 0 and 2 are in subgroup 0, replica 1 and 3 are in subgroup 1. +// +// - `channel_id`: for Allreduce nodes from different models, if they have the +// same channel_id, they will be 'Allreduce'd. If empty, Allreduce will not be +// applied cross models. +// +// TODO(b/79737069): Rename this to AllReduce when it's ready to use. +XlaOp CrossReplicaSum(const XlaOp& operand, const XlaComputation& computation, + tensorflow::gtl::ArraySlice replica_group_ids = {}, + const tensorflow::gtl::optional& + channel_id = tensorflow::gtl::nullopt); + +// Enqueues an operation that scatters the `source` array to the selected +// indices of each window. +XlaOp SelectAndScatter(const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + Padding padding, const XlaOp& source, + const XlaOp& init_value, const XlaComputation& scatter); + +// As SelectAndScatter(), but the padding is given in the format +// returned by MakePadding(). +XlaOp SelectAndScatterWithGeneralPadding( + const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const XlaOp& source, const XlaOp& init_value, + const XlaComputation& scatter); + +// Enqueues an abs instruction onto the computation. +XlaOp Abs(const XlaOp& operand); + +// Enqueues a atan2 instruction onto the computation. +XlaOp Atan2(const XlaOp& y, const XlaOp& x, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues an exp instruction onto the computation. +XlaOp Exp(const XlaOp& operand); + +// Enqueues an expm1 instruction onto the computation. +XlaOp Expm1(const XlaOp& operand); + +// Enqueues a floor instruction onto the computation. +XlaOp Floor(const XlaOp& operand); + +// Enqueues a ceil instruction onto the computation. +XlaOp Ceil(const XlaOp& operand); + +// Enqueues a round instruction onto the computation, rounding to nearest even +// with half-way cases rounding away from zero. +XlaOp Round(const XlaOp& operand); + +// Enqueues an log instruction (natural logarithm) onto the computation. +XlaOp Log(const XlaOp& operand); + +// Enqueues an log1p instruction (log(x+1)) onto the computation. +XlaOp Log1p(const XlaOp& operand); + +// Enqueues a sign instruction onto the computation. +XlaOp Sign(const XlaOp& operand); + +// Enqueues a count leading zeros instruction onto the computation. +XlaOp Clz(const XlaOp& operand); + +// Enqueues a cosine instruction onto the computation. +XlaOp Cos(const XlaOp& operand); + +// Enqueues a sine instruction onto the computation. +XlaOp Sin(const XlaOp& operand); + +// Enqueues a tanh instruction onto the computation. +XlaOp Tanh(const XlaOp& operand); + +// Enqueues a real-part instruction onto the computation. +XlaOp Real(const XlaOp& operand); + +// Enqueues an imaginary-part instruction onto the computation. +XlaOp Imag(const XlaOp& operand); + +// Enqueues a lhs^rhs computation onto the computation. +XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues an operator that tests if the operand's values are finite, i.e., +// not Inf or NaN. Defined only for floating-point types. Returns an array of +// booleans with the same shape where entries are true iff the corresponding +// entry was NaN. +XlaOp IsFinite(const XlaOp& operand); + +// Enqueues a convert instruction onto the computation that changes the +// element type of the operand array to primitive_type. +XlaOp ConvertElementType(const XlaOp& operand, PrimitiveType new_element_type); + +// Enqueues a no-op instruction onto the computation that changes +// the element type of the operand array to primitive_type. The +// bit-widths of the source and destination element types must be +// identical. +XlaOp BitcastConvertType(const XlaOp& operand, PrimitiveType new_element_type); + +// Enqueues a negate instruction onto the computation. +XlaOp Neg(const XlaOp& operand); + +// Enqueues a transpose instruction onto the computation. +XlaOp Transpose(const XlaOp& operand, + tensorflow::gtl::ArraySlice permutation); + +// Enqueues a reverse instruction onto the computation. The order of the +// elements in the given dimensions is reversed (i.e., the element at index i +// is moved to index dimension_size - 1 - i). +XlaOp Rev(const XlaOp& operand, tensorflow::gtl::ArraySlice dimensions); + +// Enqueues a sort (as increasing order) instruction onto the computation. +// If only keys are provided: +// * If the keys are an rank-1 tensor (an array), the result is a sorted array +// of keys, in ascending order. +// * If the keys have higher rank, the keys are sorted along the provided +// dimension. For example, for a rank-2 tensor (a matrix) of keys, a dimension +// value of 0 will indepenently sort every column, and a dimension value of 1 +// will independently sort each row. If no dimension number is provided, then +// the last dimension is chosen by default. +// +// If both keys and values are provided: +// * The keys and the values must tensors with the same dimensions. The +// element types of the tensors may be different. +// * The result is a tuple that consists of a sorted tensor of keys (along the +// provided dimension, as above) as the first element, and a tensor with their +// corresponding values as the second element. +XlaOp Sort(XlaOp keys, + tensorflow::gtl::optional values = tensorflow::gtl::nullopt, + int64 dimension = -1); + +// Enqueues a clamp instruction onto the computation. +XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); + +// Enqueues a map instruction onto the computation. +XlaOp Map(XlaBuilder* builder, tensorflow::gtl::ArraySlice operands, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice static_operands = {}); + +// Enqueues a N(mu, sigma) random number generation instruction onto the +// computation. +XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, const Shape& shape); + +// Enqueues a U(a, b) random number generation instruction onto the +// computation. Returns values in the semi-open interval [a, b). +XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape); + +// Enqueues a while node onto the computation. +XlaOp While(const XlaComputation& condition, const XlaComputation& body, + const XlaOp& init); + +// Enqueues a conditional node onto the computation. +XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, + const XlaComputation& true_computation, + const XlaOp& false_operand, + const XlaComputation& false_computation); + +// Enqueues a ReducePrecision node onto the computation. +XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, + const int mantissa_bits); + +// Enqueues a Gather node onto the computation. +XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, + const GatherDimensionNumbers& dimension_numbers, + tensorflow::gtl::ArraySlice window_bounds); + +// Enqueues a Send node onto the computation for device-to-device +// communication. This operation sends the given operand to +// a Recv instruction in a different computation that shares the same channel +// handle. +void Send(const XlaOp& operand, const ChannelHandle& handle); + +// Variant of Send which takes a token-shaped operand and produces a +// token-shaped value. Tokens are used for ordering side-effecting operations. +// TODO(b/110532604): Replace all uses of the non-token form with this variant. +XlaOp SendWithToken(const XlaOp& operand, const XlaOp& token, + const ChannelHandle& handle); + +// Enqueues a Recv node onto the computation for device-to-device +// communication. The data comes from a Send instruction in a different +// computation that shares the same channel handle and its shape must be the +// same as the given shape. +XlaOp Recv(XlaBuilder* builder, const Shape& shape, + const ChannelHandle& handle); + +// Variant of Recv which takes a token-shaped operand and produces a two-element +// tuple containing the data value and a token-shaped value. Tokens are used +// for ordering side-effecting operations. +// TODO(b/110532604): Replace all uses of the non-token form with this variant. +XlaOp RecvWithToken(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle); + +// Enqueues a Send node which transfers data from the device to the host. The +// 'shape_with_layout' argument defines the layout of the data transferred; its +// shape must be compatible with the shape of the operand. The operand must be +// array-shaped. +// TODO(b/111544877): Support tuple shapes. +XlaOp SendToHost(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, const ChannelHandle& handle); + +// Enqueues a Recv node which transfers data from the host to the device. The +// given shape must contain a layout and must be an array. +// TODO(b/111544877): Support tuple shapes. +XlaOp RecvFromHost(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle); + +// Enqueues an operation (AfterAll) with no operands that produces a +// token-shaped value. Tokens are used for ordering side-effecting operations. +// This is a separate method from AfterAll to facility the removal of +// operand-less AfterAll instructions. +// TODO(b/110532604): Remove this function when all tokens are derived from a +// single token generated or passed into the entry computation. +XlaOp CreateToken(XlaBuilder* builder); + +// Enqueues an AfterAll instruction which produces a token-shaped value and +// takes a variadic number of token-shaped operands. The number of operands must +// be greater than zero. Used for joining tokens. +XlaOp AfterAll(XlaBuilder* builder, tensorflow::gtl::ArraySlice tokens); + +// Normalizes operand across spatial and batch dimensions for each feature. +// +// Returns a tuple (normalized, batch_mean, batch_var) where `normalized` +// is the normalized result and batch_mean and batch_var are the mean and +// variance, respectively, across batch for the operand. +XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, float epsilon, + int64 feature_index); + +// Normalizes operand across spatial and batch dimensions for each feature. +// +// `BatchNormInference` is equivalent to calling `BatchNormTraining` without +// computing `mean` and `variance` for each batch inside the operation. It +// uses the input `mean` and `variance` instead as estimated values. The +// purpose of this op is to reduce latency in inference, hence the name +// `BatchNormInference`. +// +// The output has the same shape as `operand`, and contains the normalized +// values for each batch. +XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, const XlaOp& mean, + const XlaOp& variance, float epsilon, + int64 feature_index); + +// Calculates the gradients of a batch norm op. +// +// The inputs `batch_mean` and `batch_var` represent the mean and variance +// across the batch. +// +// Returns a tuple of three elements: +// - grad_operand: Gradient with respect to input `operand` +// - grad_offset: Gradient with respect to input `offset` +// - grad_scale: Gradient with respect to input `scale` +XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, + const XlaOp& batch_mean, const XlaOp& batch_var, + const XlaOp& grad_output, float epsilon, + int64 feature_index); + +// Implementation details below this point. + +template +XlaOp XlaBuilder::ConstantR0(NativeT value) { + return ConstantLiteral(*LiteralUtil::CreateR0(value)); +} + +template +XlaOp XlaBuilder::ConstantR1(tensorflow::gtl::ArraySlice values) { + return ConstantLiteral(*LiteralUtil::CreateR1(values)); +} + +template +XlaOp XlaBuilder::ConstantR1(int64 length, NativeT value) { + Literal literal(ShapeUtil::MakeShape( + primitive_util::NativeToPrimitiveType(), {length})); + literal.PopulateWithValue(value); + return ConstantLiteral(literal); +} + +inline XlaOp XlaBuilder::ConstantR1(const tensorflow::core::Bitmap& values) { + return ConstantLiteral(*LiteralUtil::CreateR1(values)); +} + +template +XlaOp XlaBuilder::ConstantR2( + std::initializer_list> values) { + return ConstantLiteral(*LiteralUtil::CreateR2(values)); +} + +template +XlaOp XlaBuilder::ConstantFromArrayWithLayout(const Array& values, + const Layout& layout) { + return ConstantLiteral( + *LiteralUtil::CreateFromArrayWithLayout(values, layout)); +} + +template +XlaOp XlaBuilder::ConstantFromArray(const Array& values) { + return ConstantLiteral(*LiteralUtil::CreateFromArray(values)); +} + +template +XlaOp XlaBuilder::ConstantR2FromArray2DWithLayout( + const Array2D& values, const Layout& layout) { + return ConstantLiteral( + *LiteralUtil::CreateFromArrayWithLayout(values, layout)); +} + +template +XlaOp XlaBuilder::ConstantR2FromArray2D(const Array2D& values) { + return ConstantLiteral(*LiteralUtil::CreateR2FromArray2D(values)); +} + +template +XlaOp XlaBuilder::ConstantR3FromArray3DWithLayout( + const Array3D& values, const Layout& layout) { + return ConstantLiteral( + *LiteralUtil::CreateR3FromArray3DWithLayout(values, layout)); +} + +template +XlaOp XlaBuilder::ConstantR3FromArray3D(const Array3D& values) { + return ConstantFromArray(values); +} + +template +XlaOp XlaBuilder::ConstantR4FromArray4DWithLayout( + const Array4D& values, const Layout& layout) { + return ConstantFromArrayWithLayout(values, layout); +} + +template +XlaOp XlaBuilder::ConstantR4FromArray4D(const Array4D& values) { + return ConstantFromArray(values); +} + +// Free function template implementations. + +template +XlaOp ConstantR0(XlaBuilder* builder, NativeT value) { + return ConstantLiteral(builder, *LiteralUtil::CreateR0(value)); +} + +template +XlaOp ConstantR1(XlaBuilder* builder, + tensorflow::gtl::ArraySlice values) { + return ConstantLiteral(builder, *LiteralUtil::CreateR1(values)); +} + +template +XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value) { + Literal literal(ShapeUtil::MakeShape( + primitive_util::NativeToPrimitiveType(), {length})); + literal.PopulateWithValue(value); + return ConstantLiteral(builder, literal); +} + +inline XlaOp ConstantR1(XlaBuilder* builder, + const tensorflow::core::Bitmap& values) { + return ConstantLiteral(builder, *LiteralUtil::CreateR1(values)); +} + +template +XlaOp ConstantR2(XlaBuilder* builder, + std::initializer_list> values) { + return ConstantLiteral(builder, *LiteralUtil::CreateR2(values)); +} + +template +XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, + const Array& values, + const Layout& layout) { + return ConstantLiteral( + builder, + *LiteralUtil::CreateFromArrayWithLayout(values, layout)); +} + +template +XlaOp ConstantFromArray(XlaBuilder* builder, const Array& values) { + return ConstantLiteral(builder, + *LiteralUtil::CreateFromArray(values)); +} + +template +XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, + const Array2D& values, + const Layout& layout) { + return ConstantLiteral( + builder, + *LiteralUtil::CreateFromArrayWithLayout(values, layout)); +} + +template +XlaOp ConstantR2FromArray2D(XlaBuilder* builder, + const Array2D& values) { + return ConstantLiteral(builder, + *LiteralUtil::CreateR2FromArray2D(values)); +} + +template +XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, + const Array3D& values, + const Layout& layout) { + return ConstantLiteral( + builder, + *LiteralUtil::CreateR3FromArray3DWithLayout(values, layout)); +} + +template +XlaOp ConstantR3FromArray3D(XlaBuilder* builder, + const Array3D& values) { + return ConstantFromArray(builder, values); +} + +template +XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, + const Array4D& values, + const Layout& layout) { + return ConstantFromArrayWithLayout(builder, values, layout); +} + +template +XlaOp ConstantR4FromArray4D(XlaBuilder* builder, + const Array4D& values) { + return ConstantFromArray(builder, values); +} + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_XLA_BUILDER_H_ diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc b/tensorflow/compiler/xla/client/xla_builder_test.cc similarity index 99% rename from tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc rename to tensorflow/compiler/xla/client/xla_builder_test.cc index 3b8beb2c7840e23752b5f47bbc5f55d89751884d..28a207b137d901213ec43d506a638ef08a6bded9 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc +++ b/tensorflow/compiler/xla/client/xla_builder_test.cc @@ -13,10 +13,11 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h" #include "tensorflow/compiler/xla/service/hlo_matchers.h" #include "tensorflow/compiler/xla/service/hlo_module.h" diff --git a/tensorflow/compiler/xla/client/xla_client/BUILD b/tensorflow/compiler/xla/client/xla_client/BUILD index ee00a9eada8dd906c26e07a4affccdaf544f1693..2e131dbad26970d4cb9860c17c3de3d52de36223 100644 --- a/tensorflow/compiler/xla/client/xla_client/BUILD +++ b/tensorflow/compiler/xla/client/xla_client/BUILD @@ -23,56 +23,11 @@ filegroup( load("//tensorflow:tensorflow.bzl", "tf_cc_test") -cc_library( - name = "xla_computation", - srcs = ["xla_computation.cc"], - hdrs = ["xla_computation.h"], - deps = [ - "//tensorflow/compiler/xla:status_macros", - "//tensorflow/compiler/xla:util", - "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/service:hlo_proto", - ], -) - cc_library( name = "xla_builder", - srcs = ["xla_builder.cc"], hdrs = ["xla_builder.h"], visibility = ["//visibility:public"], deps = [ - ":xla_computation", - "//tensorflow/compiler/xla:execution_options_util", - "//tensorflow/compiler/xla:literal_util", - "//tensorflow/compiler/xla:shape_util", - "//tensorflow/compiler/xla:status_macros", - "//tensorflow/compiler/xla:statusor", - "//tensorflow/compiler/xla:types", - "//tensorflow/compiler/xla:util", - "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client:padding", - "//tensorflow/compiler/xla/client:sharding_builder", - "//tensorflow/compiler/xla/service:hlo", - "//tensorflow/compiler/xla/service:hlo_proto", - "//tensorflow/compiler/xla/service:shape_inference", - "//tensorflow/core:lib", - ], -) - -tf_cc_test( - name = "xla_builder_test", - srcs = ["xla_builder_test.cc"], - deps = [ - ":xla_builder", - "//tensorflow/compiler/xla:literal_util", - "//tensorflow/compiler/xla:shape_util", - "//tensorflow/compiler/xla:status_macros", - "//tensorflow/compiler/xla:test", - "//tensorflow/compiler/xla:test_helpers", - "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", - "//tensorflow/compiler/xla/service:hlo", - "//tensorflow/compiler/xla/service:hlo_matchers", - "//tensorflow/core:test", + "//tensorflow/compiler/xla/client:xla_builder", ], ) diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.h b/tensorflow/compiler/xla/client/xla_client/xla_builder.h index 274aba8a31072db1e821b1834178a85288d64521..ce2a8afd4cb1e7037e68a02670af707f3ff9252c 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder.h +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.h @@ -16,2094 +16,6 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_BUILDER_H_ #define TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_BUILDER_H_ -#include -#include -#include -#include - -#include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" -#include "tensorflow/compiler/xla/literal_util.h" -#include "tensorflow/compiler/xla/service/hlo.pb.h" -#include "tensorflow/compiler/xla/service/hlo_opcode.h" -#include "tensorflow/compiler/xla/shape_util.h" -#include "tensorflow/compiler/xla/status_macros.h" -#include "tensorflow/compiler/xla/statusor.h" -#include "tensorflow/compiler/xla/types.h" -#include "tensorflow/compiler/xla/xla_data.pb.h" -#include "tensorflow/core/lib/core/stringpiece.h" -#include "tensorflow/core/lib/gtl/array_slice.h" -#include "tensorflow/core/lib/gtl/flatset.h" -#include "tensorflow/core/platform/macros.h" -#include "tensorflow/core/platform/stacktrace.h" -#include "tensorflow/core/platform/types.h" - -namespace xla { - -class XlaBuilder; - -// This represents an instruction that has been enqueued using the XlaBuilder. -// This is used to pass to subsequent computations that depends upon the -// instruction as an operand. -class XlaOp { - public: - XlaOp() : handle_(-1), builder_(nullptr) { - static_assert(std::is_trivially_destructible::value, - "XlaOp should be trivially destructible"); - } - ~XlaOp() = default; - - XlaBuilder* builder() const { return builder_; } - - // Returns true if the XlaOp represents valid, non-erroneous value. - bool valid() const { return handle_ >= 0; } - - // Returns true if the XlaOp was created by the XlaOp() constructor and - // not returned by a builder. - bool IsUninitialized() const { return builder_ == nullptr; } - - bool IsIdenticalTo(const XlaOp& rhs) const { - return handle_ == rhs.handle_ && builder_ == rhs.builder_; - } - - friend std::ostream& operator<<(std::ostream& out, const XlaOp& op) { - out << op.handle(); - return out; - } - - private: - explicit XlaOp(XlaBuilder* builder) : handle_(-1), builder_(builder) {} - XlaOp(int64 handle, XlaBuilder* builder) - : handle_(handle), builder_(builder) {} - - int64 handle() const { return handle_; } - - friend class XlaBuilder; - - // < 0 means "invalid handle". - int64 handle_; - - // Not owned. Non-null for any handle returned by XlaBuilder, even if the - // handle is invalid. - XlaBuilder* builder_; -}; - -// Arithmetic operator overloads for the XlaOp type. -XlaOp operator-(const XlaOp& x); -XlaOp operator+(const XlaOp& x, const XlaOp& y); -XlaOp operator-(const XlaOp& x, const XlaOp& y); -XlaOp operator*(const XlaOp& x, const XlaOp& y); -XlaOp operator/(const XlaOp& x, const XlaOp& y); -XlaOp operator%(const XlaOp& x, const XlaOp& y); - -// Bitwise operator overloads for the XlaOp type. -XlaOp operator~(const XlaOp& x); -XlaOp operator&(const XlaOp& x, const XlaOp& y); -XlaOp operator|(const XlaOp& x, const XlaOp& y); -XlaOp operator^(const XlaOp& x, const XlaOp& y); -XlaOp operator<<(const XlaOp& x, const XlaOp& y); -// Performs a right arithmetic shift if 'x' is a signed type, otherwise performs -// a right logical shift. -XlaOp operator>>(const XlaOp& x, const XlaOp& y); - -// We don't overload the relational operators (==, !=, <, <=, >, >=) because the -// semantics might be surprising since their result types are usually 'bool'. -// Further programmers may expect == to be a structural equality. -// We also choose not to overload any of the mutating operators (e.g., +=, -=) -// because the semantics might be misleading — XLA computations are immutable. - -// A convenient interface for building up computations. -// -// Thread-compatible. -class XlaBuilder { - public: - // computation_name: name to use for the built computation. - XlaBuilder(const string& computation_name); - - XlaBuilder(const XlaBuilder&) = delete; - XlaBuilder& operator=(const XlaBuilder&) = delete; - - ~XlaBuilder(); - - // Returns the computation name. - const string& name() const { return name_; } - - // Sets OpMetadata that will be added to all instructions until cleared. - // - // OpMetadata is often applied to a series of XLA HLO instructions. As a - // result, OpMetadata is set on the Computation Builder. All subsequent - // instructions generated via this Computation Builder will have the same - // OpMetadata attached until a call to ClearOpMetadata. - void SetOpMetadata(const OpMetadata& metadata) { metadata_ = metadata; } - - // Clears the HloMetadata state. - void ClearOpMetadata() { metadata_.Clear(); } - - // Sets an OpSharding that will be attached to all instructions until cleared. - void SetSharding(const OpSharding& sharding) { sharding_ = sharding; } - - // Clears the sharding. Ops will be sharded according to the default placement - // policy. - void ClearSharding() { sharding_ = tensorflow::gtl::nullopt; } - - // Returns the OpSharding that will be attached to all instructions. - const tensorflow::gtl::optional& sharding() const { - return sharding_; - } - - // Sets the builder to a mode where it will die immediately when an error is - // encountered, rather than producing it in a deferred fashion when Build() is - // called (which is the default). - void set_die_immediately_on_error(bool enabled) { - die_immediately_on_error_ = enabled; - } - - // Default dimension numbers used for a 2D convolution. - static constexpr int64 kConvBatchDimension = 0; - static constexpr int64 kConvFeatureDimension = 1; - static constexpr int64 kConvFirstSpatialDimension = 2; - static constexpr int64 kConvSecondSpatialDimension = 3; - static constexpr int64 kConvKernelOutputDimension = 0; - static constexpr int64 kConvKernelInputDimension = 1; - static constexpr int64 kConvKernelFirstSpatialDimension = 2; - static constexpr int64 kConvKernelSecondSpatialDimension = 3; - - // Creates a default ConvolutionDimensionNumbers. For a 2D convolution, for - // the input operand {batch, feature, height, width} = {0, 1, 2, 3} and for - // the kernel operand - // {output_feature, input_feature, height, width} = {0, 1, 2, 3}. - static ConvolutionDimensionNumbers CreateDefaultConvDimensionNumbers( - int num_spatial_dims = 2); - - // Returns an error if the convolution dimension numbers have conflicts. - static Status Validate(const ConvolutionDimensionNumbers& dnum); - - // Returns a new XlaBuilder whose resultant Computation is used only by this - // XlaBuilder. The sub-XlaBuilder has the same die_immediately_on_error - // behavior as the parent. - std::unique_ptr CreateSubBuilder(const string& computation_name); - - // Builds the computation with the requested operations, or returns a non-ok - // status. Note that all ops that have been enqueued will be moved to the - // computation being returned. - StatusOr Build(); - - // Builds the computation with the requested operations, or notes an error in - // the parent XlaBuilder and returns an empty computation if building failed. - // This function is intended to be used where the returned XlaComputation is - // only used by the parent XlaBuilder and hence further operation on the - // returned XlaComputation will simply be error'ed out if an error occurred - // while building this computation. If the built computation is to be used by - // a XlaBuilder other than the parent XlaBuilder then Build() should be used - // instead. - XlaComputation BuildAndNoteError(); - - // Returns a subgraph that roots on the given root. If the root is not a - // compile-time constant (see `IsConstant`), returns an error. - // - // This will copy the needed ops/computations to the subgraph. - StatusOr BuildConstantSubGraph(const XlaOp& root_op) const; - - // Returns the first error that was encountered while building the - // computation. When an error is encountered, by default we return a vacuous - // XlaOp and inform the user of the error that occurred while - // building the computation when they make a final call to Build(). - // - // See also set_die_immediately_on_error(). - Status first_error() const { return first_error_; } - - // Returns the shape of the given op. - StatusOr GetShape(const XlaOp& op) const; - - // Returns the (inferred) result for the current computation's shape. - StatusOr GetProgramShape() const; - - // Reports an error to the builder, by - // * storing it internally and capturing a backtrace if it's the first error - // (this deferred value will be produced on the call to - // Build()/GetShape()/...) - // * dying if die_immediately_on_error_ is true. - // Returns an XlaOp with an invalid handle but a valid builder. This value can - // be returned in place of a value in APIs that return an XlaOp. - XlaOp ReportError(const Status& error); - - // A helper function that converts a StatusOr into an XlaOp. - // If the Status was an error, reports the error to builder and returns an - // invalid XlaOp handle. - XlaOp ReportErrorOrReturn(const StatusOr& op); - - // A helper function that runs a function that returns a StatusOr and - // returns an XlaOp. - XlaOp ReportErrorOrReturn(const std::function()>& op_creator); - - // Returns true if 'operand' is a compile-time constant. A compile-time - // constant does not depend on any parameters, or on stateful operators such - // as `RngNormal` or `Infeed`. - // - // This tests whether a computation is a compile-time constant without - // evaluating the computation. - StatusOr IsConstant(const XlaOp& operand) const; - - private: - // Enqueues a "retrieve parameter value" instruction for a parameter that was - // passed to the computation. - XlaOp Parameter(int64 parameter_number, const Shape& shape, - const string& name); - - // Enqueues a constant with the value of the given literal onto the - // computation. - XlaOp ConstantLiteral(const LiteralSlice& literal); - - // Enqueues a constant onto the computation. Methods are templated on the - // native host type (NativeT) which corresponds to a specific XLA - // PrimitiveType as given in the following table: - // - // Native Type PrimitiveType - // ----------------------------- - // bool PRED - // int32 S32 - // int64 S64 - // uint32 U32 - // uint64 U64 - // float F32 - // double F64 - // - // Note: not all primitive types defined in xla_data.proto have a - // corresponding native type yet. - template - XlaOp ConstantR0(NativeT value); - template - XlaOp ConstantR1(tensorflow::gtl::ArraySlice values); - XlaOp ConstantR1(const tensorflow::core::Bitmap& values); - template - XlaOp ConstantR2( - std::initializer_list> values); - template - XlaOp ConstantFromArrayWithLayout(const Array& values, - const Layout& layout); - template - XlaOp ConstantFromArray(const Array& values); - template - XlaOp ConstantR2FromArray2DWithLayout(const Array2D& values, - const Layout& layout); - template - XlaOp ConstantR2FromArray2D(const Array2D& values); - template - XlaOp ConstantR3FromArray3DWithLayout(const Array3D& values, - const Layout& layout); - template - XlaOp ConstantR3FromArray3D(const Array3D& values); - template - XlaOp ConstantR4FromArray4DWithLayout(const Array4D& values, - const Layout& layout); - template - XlaOp ConstantR4FromArray4D(const Array4D& values); - - // Enqueues a rank one constant (vector) onto the computation. The vector has - // size 'length' and every element has the value 'value'. - template - XlaOp ConstantR1(int64 length, NativeT value); - - // Adds dimensions to an array by duplicating the data in the array. - // - // The new dimensions are inserted on the left, i.e. if - // broadcast_sizes has values {a0, ..., aN} and the operand shape - // has dimensions {b0, ..., bM} then the shape of the output has - // dimensions {a0, ..., aN, b0, ..., bM}. - // - // The new dimensions index into copies of the operand, i.e. - // - // output[i0, ..., iN, j0, ..., jM] = operand[j0, ..., jM] - XlaOp Broadcast(const XlaOp& operand, - tensorflow::gtl::ArraySlice broadcast_sizes); - - // Performs in-dimension-style broadcast. - // - // Operand specifies the input to be broadcast. "shape" is expected output - // shape. "broadcast_dimensions" are the dimensions to be broadcasting into. - // Dimension numbers in broadcast_dimensions map to individual dimensions - // of the operand, and specify what dimension of the output shape they - // should be broadcast. - // e.g. - // Say operand = [1, 2], i.e., a 1D tensor with 2 elements. - // and dimension of shape is [2,2]. - // Specifying {1} as brodcast_dimension will generate output - // [1 , 2] - // [1 , 2] - // On the other hand, specifying {0} as broadcast_dimension - // will generate output - // [1 , 1] - // [2 , 2] - XlaOp BroadcastInDim( - const XlaOp& operand, const Shape& shape, - const tensorflow::gtl::ArraySlice broadcast_dimensions); - - // Enqueues a pad operation onto the computation that pads the given value on - // the edges as well as between the elements of the input. padding_config - // specifies the padding amount for each dimension. - XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, - const PaddingConfig& padding_config); - - // Enqueues an operation onto the computation that flattens the operand based - // on the dimension order (major/slowest-varying to minor/fastest-varying) - // given, followed by reshaping it into the shape with the given dimension - // sizes (also major to minor). Conceptually, this is a limited form of - // "shape casting". - XlaOp Reshape(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions, - tensorflow::gtl::ArraySlice new_sizes); - - // Enqueues an operation onto the computation that collapses the operand, from - // first to last dimension (C order), then reshapes it to the given dimension - // sizes. Conceptually, this is a limited form of "shape casting". - XlaOp Reshape(const XlaOp& operand, - tensorflow::gtl::ArraySlice new_sizes); - - // Wrapper for Reshape. - // Enqueues an operation to collapse the provided dimensions; e.g. an - // operand with dimensions {x=256, y=2, z=2, p=32} can be collapsed to - // {x=1024, y=32} by collapsing dims {0, 1, 2}. Collapsing dimensions must - // be a consecutive, in-order subsequence of the operand dimensions. - // - // Note that collapsing a single dimension does nothing: - // - // {256} collapsing {0} => {256} - // {1} collapsing {0} => {1} - // - // Collapsing multiple dimensions produces a single result dimension: - // - // {256, 2} collapsing {0,1} => {512} - // {256, 2, 3} collapsing {0,1} => {512, 3} - // - // This could potentially cause data to be moved -- it provides a more - // structured form of reshaping than an arbitrary Reshape operation. - XlaOp Collapse(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions); - - // Enqueues a slice operation onto the computation that slices the operand - // from the start indices to the limit indices; e.g. - // - // x - // [ 0 1 2 3 ] - // y [ 4 5 6 7 ] => slice(start={1, 1}, limit={2, 3}) => [ 5 6 ] - // [ 8 9 a b ] - // - // Note that "limit" means up-to-but-not-including; i.e. [start, limit) in 1D - // range notation. - // The strides parameter determines the stride over the slice - XlaOp Slice(const XlaOp& operand, - tensorflow::gtl::ArraySlice start_indices, - tensorflow::gtl::ArraySlice limit_indices, - tensorflow::gtl::ArraySlice strides); - - // Enqueues a slice operation in a given dimension, taking all other - // dimensions as they are; e.g. if dimno is 1 from start_index 2 to - // limit_index 4 by 1, and the shape is f32[7,8,9], this call is short-hand - // for: - // - // array[:, 2:4:1, :] - XlaOp SliceInDim(const XlaOp& operand, int64 start_index, int64 limit_index, - int64 stride, int64 dimno); - - // Enqueues a slice operation onto the computation that slices the 'operand' - // from dynamic start indices which are passed in 'start_indices'. - // The size of the slice in each dimension is passed in 'slice_sizes', - // which specify the end point of exclusive slice intervals in each - // dimension [start, start + size). - // The shape of 'start_indices' must be rank == 1, with dimension size - // equal to the rank of the 'operand'. - // Slice index calculations are computed modulo input dimension sizes to - // prevent dynamic start indices from generating out-of-bound array accesses. - XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, - tensorflow::gtl::ArraySlice slice_sizes); - - // Enqueues a dynamic update slice operation onto the computation, which - // updates a slice of 'operand' with 'update' at dynamic 'start_indices'. - // The shape of 'update' determines the shape of the slice of 'operand' - // which is updated. - // The indices specified in 'start_indices' specify the offset of the slice - // of 'operand' which is updated. - // - // update = {10, 11} // calculated at runtime. - // [1 2 3] start = {1, 1} // calculated at runtime. [1 2 3 ] - // [4 5 6] => DynamicUpdateslice(data, update, start) => [4 10 11] - // [7 8 9] [7 8 9 ] - // - // The shape of 'start_indices' must be rank == 1, with dimension size - // equal to the rank of the 'operand'. - // Slice index calculations are computed modulo update dimension sizes to - // prevent dynamic start indices from generating out-of-bound array accesses. - XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, - const XlaOp& start_indices); - - // Enqueues a concatenate instruction onto the computation. 'operands' must - // have >= 1 entry. - XlaOp ConcatInDim(tensorflow::gtl::ArraySlice operands, - int64 dimension); - - // Enqueue a tracing operation onto the computation; the computation will emit - // a logging message with the operand. - void Trace(const string& tag, const XlaOp& operand); - - // Enqueues a conditional-move-like select operation onto the computation; - // predicated on pred, selects between on_true and on_false. - XlaOp Select(const XlaOp& pred, const XlaOp& on_true, const XlaOp& on_false); - - // Enqueues a tuple-creation instruction onto the computation. - XlaOp Tuple(tensorflow::gtl::ArraySlice elements); - - // Enqueues a tuple-element-get instruction onto the computation. - XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index); - - // Enqueues an equal-to comparison instruction onto the computation. - XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a not-equal comparison instruction onto the computation. - XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a greater-or-equal comparison instruction onto the computation. - XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a greater-than comparison instruction onto the computation. - XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a less-than comparison instruction onto the computation. - XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a less-or-equal comparison instruction onto the computation. - XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a dot instruction onto the computation. - XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs); - - // Enqueues a general dot instruction onto the computation. - XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, - const DotDimensionNumbers& dimension_numbers); - - // Enqueues a convolution instruction onto the computation, which uses the - // default convolution dimension numbers. - XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - Padding padding); - - // Enqueues a convolution instruction onto the computation, with the caller - // provided padding configuration in the format returned by MakePadding(). - XlaOp ConvWithGeneralPadding( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding); - - // Enqueues a convolution instruction onto the computation, with the caller - // provided dimension numbers configuration. - XlaOp ConvWithGeneralDimensions( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, Padding padding, - const ConvolutionDimensionNumbers& dimension_numbers); - - // Enqueues a convolution instruction onto the computation, with the caller - // provided padding configuration as well as the dimension numbers. - XlaOp ConvGeneral( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - const ConvolutionDimensionNumbers& dimension_numbers); - - // Enqueues a convolution instruction onto the computation, with the caller - // provided padding configuration, dilation factors and dimension numbers. - XlaOp ConvGeneralDilated( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - tensorflow::gtl::ArraySlice lhs_dilation, - tensorflow::gtl::ArraySlice rhs_dilation, - const ConvolutionDimensionNumbers& dimension_numbers); - - // Enqueues an FFT instruction onto the computation, of the given type and - // with the given FFT length. - XlaOp Fft(const XlaOp& operand, FftType fft_type, - tensorflow::gtl::ArraySlice fft_length); - - // Enqueues an infeed instruction onto the computation, which writes data of - // the given shape to the infeed buffer of the device. - XlaOp Infeed(const Shape& shape, const string& config = ""); - - // Enqueues an outfeed instruction onto the computation. This instruction - // generates outgoing data transfers for the given data. - // - // shape_with_layout communicates the laid out shape that we want to outfeed - // -- if !ShapeUtil::Compatible(GetShape(operand), shape_with_layout) an error - // will occur. - void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, - const string& outfeed_config); - - // Enqueues a call instruction onto the computation. - XlaOp Call(const XlaComputation& computation, - tensorflow::gtl::ArraySlice operands); - - // Enqueues a custom call instruction onto the computation. - // During code generation, a call instruction is emitted which targets a - // symbol with the name |call_target_name|. The |operands| are passed to the - // call instruction. |shape| is the resultant shape. - XlaOp CustomCall(const string& call_target_name, - tensorflow::gtl::ArraySlice operands, - const Shape& shape); - - // Enqueues a pseudo-op to represent host-side computation data-dependencies. - // During code generation, host send and receive operations will be generated - // to transfer |operands| to the host and a single result of |shape| back to - // the device. Host send/recv operations are emitted using |channel_name|. - // Dataflow dependencies and the |cost_estimate_ns| field may be used in HLO - // instruction scheduling. - XlaOp HostCompute(tensorflow::gtl::ArraySlice operands, - const string& channel_name, int64 cost_estimate_ns, - const Shape& shape); - - // The following methods enqueue element-wise binary arithmetic operations - // onto the computation. The shapes of the operands have to match unless one - // of the operands is a scalar, or an explicit broadcast dimension is given - // (see g3doc for more details). - - // Enqueues a complex compose instruction onto the computation. - XlaOp Complex(const XlaOp& real, const XlaOp& imag, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a complex conjugate instruction onto the computation. - XlaOp Conj(const XlaOp& operand); - - // Enqueues an add instruction onto the computation. - XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a subtract instruction onto the computation. - XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a multiply instruction onto the computation. - XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a divide instruction onto the computation. - XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a remainder instruction onto the computation. - XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a max instruction onto the computation. - XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a min instruction onto the computation. - XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Element-wise logical operators - XlaOp And(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - XlaOp Not(const XlaOp& operand); - - XlaOp ShiftLeft(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - XlaOp ShiftRightArithmetic( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - XlaOp ShiftRightLogical( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Reduces an array among the provided dimensions, given "computation" as a - // reduction operator. - XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice dimensions_to_reduce); - - // Convenience wrapper around the above that reduces all the dimensions in the - // operand shape. - XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation); - - // Enqueues a windowed reduce instruction onto the computation. - XlaOp ReduceWindow(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - Padding padding); - - // As ReduceWindow(), but the padding is given in the format - // returned by MakePadding(). - XlaOp ReduceWindowWithGeneralPadding( - const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding); - - // Returns the sum of the operand value within each subgroup of replicas. All - // replicas supply one input to the sum and all replicas receive the resulting - // sum for each subgroup. - XlaOp CrossReplicaSum( - const XlaOp& operand, - tensorflow::gtl::ArraySlice replica_group_ids = {}); - - // Enqueues an operation that do an AllReduce of the operand cross cores. Here - // AllReduce means doing a reduction on the input operand cross cores and then - // broadcasting the reduction result to those cores. The reduction function is - // defined by `computation`, which should be a commutative computation on - // scalars, e.g., add, min, or max. The way that AllReduce is applied is - // configured by: - // - // - `replica_group_ids`: maps replica ids to subgroup ids. If empty, all - // replicas belong to one group. Allreduce will be applied within subgroups. - // For example, we have 4 replicas, then replica_group_ids={0,1,0,1} means, - // replica 0 and 2 are in subgroup 0, replica 1 and 3 are in subgroup 1. - // - // - `channel_id`: for Allreduce nodes from different models, if they have the - // same channel_id, they will be 'Allreduce'd. If empty, Allreduce will not be - // applied cross models. - // - // TODO(b/79737069): Rename this to AllReduce when it's ready to use. - XlaOp CrossReplicaSum( - const XlaOp& operand, const XlaComputation& computation, - tensorflow::gtl::ArraySlice replica_group_ids = {}, - const tensorflow::gtl::optional& channel_id = - tensorflow::gtl::nullopt); - - // Enqueues an operation that scatters the `source` array to the selected - // indices of each window. - XlaOp SelectAndScatter(const XlaOp& operand, const XlaComputation& select, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - Padding padding, const XlaOp& source, - const XlaOp& init_value, - const XlaComputation& scatter); - - // As SelectAndScatter(), but the padding is given in the format - // returned by MakePadding(). - XlaOp SelectAndScatterWithGeneralPadding( - const XlaOp& operand, const XlaComputation& select, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - const XlaOp& source, const XlaOp& init_value, - const XlaComputation& scatter); - - // Enqueues an abs instruction onto the computation. - XlaOp Abs(const XlaOp& operand); - - // Enqueues a atan2 instruction onto the computation. - XlaOp Atan2(const XlaOp& y, const XlaOp& x, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues an exp instruction onto the computation. - XlaOp Exp(const XlaOp& operand); - - // Enqueues an expm1 instruction onto the computation. - XlaOp Expm1(const XlaOp& operand); - - // Enqueues a floor instruction onto the computation. - XlaOp Floor(const XlaOp& operand); - - // Enqueues a ceil instruction onto the computation. - XlaOp Ceil(const XlaOp& operand); - - // Enqueues a round instruction onto the computation, rounding to nearest even - // with half-way cases rounding away from zero. - XlaOp Round(const XlaOp& operand); - - // Enqueues an log instruction (natural logarithm) onto the computation. - XlaOp Log(const XlaOp& operand); - - // Enqueues an log1p instruction (log(x+1)) onto the computation. - XlaOp Log1p(const XlaOp& operand); - - // Enqueues a sign instruction onto the computation. - XlaOp Sign(const XlaOp& operand); - - // Enqueues a count leading zeros instruction onto the computation. - XlaOp Clz(const XlaOp& operand); - - // Enqueues a cosine instruction onto the computation. - XlaOp Cos(const XlaOp& operand); - - // Enqueues a sine instruction onto the computation. - XlaOp Sin(const XlaOp& operand); - - // Enqueues a tanh instruction onto the computation. - XlaOp Tanh(const XlaOp& operand); - - // Enqueues a real-part instruction onto the computation. - XlaOp Real(const XlaOp& operand); - - // Enqueues an imaginary-part instruction onto the computation. - XlaOp Imag(const XlaOp& operand); - - // Enqueues a lhs^rhs computation onto the computation. - XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues an operator that tests if the operand's values are finite, i.e., - // not Inf or NaN. Defined only for floating-point types. Returns an array of - // booleans with the same shape where entries are true iff the corresponding - // entry was NaN. - XlaOp IsFinite(const XlaOp& operand); - - // Enqueues a convert instruction onto the computation that changes the - // element type of the operand array to primitive_type. - XlaOp ConvertElementType(const XlaOp& operand, - PrimitiveType new_element_type); - - // Enqueues a no-op instruction onto the computation that changes - // the element type of the operand array to primitive_type. The - // bit-widths of the source and destination element types must be - // identical. - XlaOp BitcastConvertType(const XlaOp& operand, - PrimitiveType new_element_type); - - // Enqueues a negate instruction onto the computation. - XlaOp Neg(const XlaOp& operand); - - // Enqueues a transpose instruction onto the computation. - XlaOp Transpose(const XlaOp& operand, - tensorflow::gtl::ArraySlice permutation); - - // Enqueues a reverse instruction onto the computation. The order of the - // elements in the given dimensions is reversed (i.e., the element at index i - // is moved to index dimension_size - 1 - i). - XlaOp Rev(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions); - - // Enqueues a sort (as increasing order) instruction onto the computation. - // If only keys are provided: - // * The keys must be a rank-1 tensor (i.e. an array). - // * The result is a sorted array of keys. - // - // If both keys and values are provided: - // * The keys and the values must be rank-1 tensors with the same dimensions. - // The element types of the tensors may be different. - // * The result is a tuple that consists of a sorted array of keys as the - // first element, and an array with their corresponding values as the second - // element. - XlaOp Sort(XlaOp keys, tensorflow::gtl::optional values = - tensorflow::gtl::nullopt); - - // Enqueues a clamp instruction onto the computation. - XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); - - // Enqueues a map instruction onto the computation. - XlaOp Map(tensorflow::gtl::ArraySlice operands, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice dimensions, - tensorflow::gtl::ArraySlice static_operands = {}); - - // Enqueues a N(mu, sigma) random number generation instruction onto the - // computation. - XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, const Shape& shape); - - // Enqueues a U(a, b) random number generation instruction onto the - // computation. Returns values in the semi-open interval [a, b). - XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape); - - // Enqueues a while node onto the computation. - XlaOp While(const XlaComputation& condition, const XlaComputation& body, - const XlaOp& init); - - // Enqueues a conditional node onto the computation. - XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, - const XlaComputation& true_computation, - const XlaOp& false_operand, - const XlaComputation& false_computation); - - // Enqueues a ReducePrecision node onto the computation. - XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, - const int mantissa_bits); - - // Enqueues a Gather node onto the computation. - XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, - const GatherDimensionNumbers& dimension_numbers, - tensorflow::gtl::ArraySlice window_bounds); - - // Enqueues a Send node onto the computation, to send the given operand to - // a Recv instruction that shares the same channel handle. - void Send(const XlaOp& operand, const ChannelHandle& handle); - - // Enqueues a Recv node onto the computation. The data comes from a Send - // instruction that shares the same channel handle and its shape must - // be the same as the given shape. - XlaOp Recv(const Shape& shape, const ChannelHandle& handle); - - // Normalizes operand across spatial and batch dimensions for each feature. - // - // Returns a tuple (normalized, batch_mean, batch_var) where `normalized` - // is the normalized result and batch_mean and batch_var are the mean and - // variance, respectively, across batch for the operand. - XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, - const XlaOp& offset, float epsilon, - int64 feature_index); - - // Normalizes operand across spatial and batch dimensions for each feature. - // - // `BatchNormInference` is equivalent to calling `BatchNormTraining` without - // computing `mean` and `variance` for each batch inside the operation. It - // uses the input `mean` and `variance` instead as estimated values. The - // purpose of this op is to reduce latency in inference, hence the name - // `BatchNormInference`. - // - // The output has the same shape as `operand`, and contains the normalized - // values for each batch. - XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, - const XlaOp& offset, const XlaOp& mean, - const XlaOp& variance, float epsilon, - int64 feature_index); - - // Calculates the gradients of a batch norm op. - // - // The inputs `batch_mean` and `batch_var` represent the mean and variance - // across the batch. - // - // Returns a tuple of three elements: - // - grad_operand: Gradient with respect to input `operand` - // - grad_offset: Gradient with respect to input `offset` - // - grad_scale: Gradient with respect to input `scale` - XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, - const XlaOp& batch_mean, const XlaOp& batch_var, - const XlaOp& grad_output, float epsilon, - int64 feature_index); - - StatusOr AddInstruction( - HloInstructionProto&& instr, HloOpcode opcode, - tensorflow::gtl::ArraySlice operands = {}); - - void AddCalledComputation(const XlaComputation& computation, - HloInstructionProto* instr); - - StatusOr LookUpInstruction(const XlaOp& op) const; - - // Internal helper method that does the building for an arbitrary unary op. - XlaOp UnaryOp(HloOpcode unop, const XlaOp& operand); - - // Internal helper method that does the building for an arbitrary binary op. - // broadcast_dimensions specifies which dimensions to use for broadcasting - // when the operation is between tensors of different ranks. - XlaOp BinaryOp(HloOpcode binop, const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - - // Internal helper method that does the building for an arbitrary ternary op. - XlaOp TernaryOp(HloOpcode triop, const XlaOp& lhs, const XlaOp& rhs, - const XlaOp& ehs); - - XlaOp RngOp(RandomDistribution distribution, - tensorflow::gtl::ArraySlice parameters, - const Shape& shape); - - StatusOr InDimBroadcast( - const Shape& shape, const XlaOp& operand, - tensorflow::gtl::ArraySlice broadcast_dimensions); - - // Internal helper method that creates a sequence of instructions that - // performs an explicit broadcast of the operand to the target shape. - StatusOr AddBroadcastSequence(const Shape& output_shape, - const XlaOp& operand); - - // Internal helper method for creating a Reshape op with the already inferred - // shape. - StatusOr Reshape(const Shape& shape, const XlaOp& operand); - - // Returns the (inferred) result for the program shape for the current - // computation and fills the root_id in the pointer. - StatusOr GetProgramShape(int64* root_id) const; - - // Returns shapes for the operands. - StatusOr> GetOperandShapes( - tensorflow::gtl::ArraySlice operands) const; - - // A visitor which checks whether an operation is a compile-time constant, - // meaning that it doesn't depend on any parameters, or on any stateful - // operation such as `RngNormal` or `Infeed`. The visitor walks the - // computation starting at a given operation and sets is_constant to false iff - // a parameter or stateful operation is encountered. - void IsConstantVisitor(const int64 op_handle, std::set* visited, - bool* is_constant) const; - - // Checks bounds for convolution parameters. - Status VerifyConvolution( - const Shape& lhs_shape, const Shape& rhs_shape, - const ConvolutionDimensionNumbers& dimension_numbers) const; - - // Helper function for creating a Window proto from user-supplied data. - // Returns error if the user-supplied data was invalid. - StatusOr MakeWindow( - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - tensorflow::gtl::ArraySlice lhs_dilation, - tensorflow::gtl::ArraySlice rhs_dilation) const; - - string name_; // Name to use for the built computation. - - // The first error encountered while building the computation. - // This is OK until the first error is encountered. - Status first_error_; - - // The saved stack trace from the point at which the first error occurred. - tensorflow::SavedStackTrace first_error_backtrace_; - - // The instructions of this computation. - std::vector instructions_; - - // The embedded computations used by this computation. Each computation was - // the entry computation of some XlaComputation, the key is the unique id of - // that XlaComputation. - std::map embedded_; - - // The unique parameter numbers. - tensorflow::gtl::FlatSet parameter_numbers_; - - // The metadata to attach to each op. This is structured as a "modal"-like - // operation, in order to simplify client code (and not sprinkle this metadata - // throughout the TensorFlow op kernel implementations). - OpMetadata metadata_; - - // Sharding for this operator. This is structured as a "model"-like operation, - // in order to simplify client code, similar to metadata_. - tensorflow::gtl::optional sharding_; - - // Mode bit that indicates whether to die when a first error is encountered. - bool die_immediately_on_error_ = false; - - XlaBuilder* parent_builder_{nullptr}; - - friend XlaOp Parameter(XlaBuilder* builder, int64 parameter_number, - const Shape& shape, const string& name); - friend XlaOp ConstantLiteral(XlaBuilder* builder, - const LiteralSlice& literal); - template - friend XlaOp ConstantR0(XlaBuilder* builder, NativeT value); - template - friend XlaOp ConstantR1(XlaBuilder* builder, - tensorflow::gtl::ArraySlice values); - friend XlaOp ConstantR1(XlaBuilder* builder, - const tensorflow::core::Bitmap& values); - template - friend XlaOp ConstantR2( - XlaBuilder* builder, - std::initializer_list> values); - template - friend XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, - const Array& values, - const Layout& layout); - template - friend XlaOp ConstantFromArray(XlaBuilder* builder, - const Array& values); - template - friend XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, - const Array2D& values, - const Layout& layout); - template - friend XlaOp ConstantR2FromArray2D(XlaBuilder* builder, - const Array2D& values); - template - friend XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, - const Array3D& values, - const Layout& layout); - template - friend XlaOp ConstantR3FromArray3D(XlaBuilder* builder, - const Array3D& values); - template - friend XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, - const Array4D& values, - const Layout& layout); - template - friend XlaOp ConstantR4FromArray4D(XlaBuilder* builder, - const Array4D& values); - - template - friend XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value); - - friend XlaOp Broadcast(const XlaOp& operand, - tensorflow::gtl::ArraySlice broadcast_sizes); - - friend XlaOp BroadcastInDim( - const XlaOp& operand, const Shape& shape, - const tensorflow::gtl::ArraySlice broadcast_dimensions); - - friend XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, - const PaddingConfig& padding_config); - - friend XlaOp Reshape(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions, - tensorflow::gtl::ArraySlice new_sizes); - - friend XlaOp Reshape(const XlaOp& operand, - tensorflow::gtl::ArraySlice new_sizes); - - friend XlaOp Collapse(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions); - - friend XlaOp Slice(const XlaOp& operand, - tensorflow::gtl::ArraySlice start_indices, - tensorflow::gtl::ArraySlice limit_indices, - tensorflow::gtl::ArraySlice strides); - - friend XlaOp SliceInDim(const XlaOp& operand, int64 start_index, - int64 limit_index, int64 stride, int64 dimno); - - friend XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, - tensorflow::gtl::ArraySlice slice_sizes); - - friend XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, - const XlaOp& start_indices); - - friend XlaOp ConcatInDim(XlaBuilder* builder, - tensorflow::gtl::ArraySlice operands, - int64 dimension); - - friend void Trace(const string& tag, const XlaOp& operand); - - friend XlaOp Select(const XlaOp& pred, const XlaOp& on_true, - const XlaOp& on_false); - friend XlaOp Tuple(XlaBuilder* builder, - tensorflow::gtl::ArraySlice elements); - friend XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index); - friend XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs); - friend XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, - const DotDimensionNumbers& dimension_numbers); - friend XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - Padding padding); - friend XlaOp ConvWithGeneralPadding( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding); - friend XlaOp ConvWithGeneralDimensions( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, Padding padding, - const ConvolutionDimensionNumbers& dimension_numbers); - friend XlaOp ConvGeneral( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - const ConvolutionDimensionNumbers& dimension_numbers); - friend XlaOp ConvGeneralDilated( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - tensorflow::gtl::ArraySlice lhs_dilation, - tensorflow::gtl::ArraySlice rhs_dilation, - const ConvolutionDimensionNumbers& dimension_numbers); - friend XlaOp Fft(const XlaOp& operand, FftType fft_type, - tensorflow::gtl::ArraySlice fft_length); - friend XlaOp Infeed(XlaBuilder* builder, const Shape& shape, - const string& config); - friend void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, - const string& outfeed_config); - friend XlaOp Call(XlaBuilder* builder, const XlaComputation& computation, - tensorflow::gtl::ArraySlice operands); - friend XlaOp CustomCall(XlaBuilder* builder, const string& call_target_name, - tensorflow::gtl::ArraySlice operands, - const Shape& shape); - friend XlaOp HostCompute(XlaBuilder* builder, - tensorflow::gtl::ArraySlice operands, - const string& channel_name, int64 cost_estimate_ns, - const Shape& shape); - friend XlaOp Complex(const XlaOp& real, const XlaOp& imag, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Conj(const XlaOp& operand); - friend XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp And(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Not(const XlaOp& operand); - friend XlaOp ShiftLeft( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp ShiftRightArithmetic( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp ShiftRightLogical( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice dimensions_to_reduce); - friend XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation); - friend XlaOp ReduceWindow( - const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, Padding padding); - friend XlaOp ReduceWindowWithGeneralPadding( - const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding); - friend XlaOp CrossReplicaSum( - const XlaOp& operand, - tensorflow::gtl::ArraySlice replica_group_ids); - friend XlaOp CrossReplicaSum( - const XlaOp& operand, const XlaComputation& computation, - tensorflow::gtl::ArraySlice replica_group_ids, - const tensorflow::gtl::optional& channel_id); - friend XlaOp SelectAndScatter( - const XlaOp& operand, const XlaComputation& select, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, Padding padding, - const XlaOp& source, const XlaOp& init_value, - const XlaComputation& scatter); - friend XlaOp SelectAndScatterWithGeneralPadding( - const XlaOp& operand, const XlaComputation& select, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - const XlaOp& source, const XlaOp& init_value, - const XlaComputation& scatter); - friend XlaOp Abs(const XlaOp& operand); - friend XlaOp Atan2(const XlaOp& y, const XlaOp& x, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Exp(const XlaOp& operand); - friend XlaOp Expm1(const XlaOp& operand); - friend XlaOp Floor(const XlaOp& operand); - friend XlaOp Ceil(const XlaOp& operand); - friend XlaOp Round(const XlaOp& operand); - friend XlaOp Log(const XlaOp& operand); - friend XlaOp Log1p(const XlaOp& operand); - friend XlaOp Sign(const XlaOp& operand); - friend XlaOp Clz(const XlaOp& operand); - friend XlaOp Cos(const XlaOp& operand); - friend XlaOp Sin(const XlaOp& operand); - friend XlaOp Tanh(const XlaOp& operand); - friend XlaOp Real(const XlaOp& operand); - friend XlaOp Imag(const XlaOp& operand); - friend XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp IsFinite(const XlaOp& operand); - friend XlaOp ConvertElementType(const XlaOp& operand, - PrimitiveType new_element_type); - friend XlaOp BitcastConvertType(const XlaOp& operand, - PrimitiveType new_element_type); - friend XlaOp Neg(const XlaOp& operand); - friend XlaOp Transpose(const XlaOp& operand, - tensorflow::gtl::ArraySlice permutation); - friend XlaOp Rev(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions); - friend XlaOp Sort(XlaOp keys, tensorflow::gtl::optional values); - friend XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); - friend XlaOp Map(XlaBuilder* builder, - tensorflow::gtl::ArraySlice operands, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice dimensions, - tensorflow::gtl::ArraySlice static_operands); - friend XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, - const Shape& shape); - friend XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape); - friend XlaOp While(const XlaComputation& condition, - const XlaComputation& body, const XlaOp& init); - friend XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, - const XlaComputation& true_computation, - const XlaOp& false_operand, - const XlaComputation& false_computation); - friend XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, - const int mantissa_bits); - friend XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, - const GatherDimensionNumbers& dimension_numbers, - tensorflow::gtl::ArraySlice window_bounds); - friend void Send(const XlaOp& operand, const ChannelHandle& handle); - friend XlaOp Recv(XlaBuilder* builder, const Shape& shape, - const ChannelHandle& handle); - friend XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, - const XlaOp& offset, float epsilon, - int64 feature_index); - friend XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, - const XlaOp& offset, const XlaOp& mean, - const XlaOp& variance, float epsilon, - int64 feature_index); - friend XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, - const XlaOp& batch_mean, const XlaOp& batch_var, - const XlaOp& grad_output, float epsilon, - int64 feature_index); -}; - -// RAII-style object: sets the current sharding assignment in builder on -// construction, and sets back to the previous assignment on destruction. -class XlaScopedShardingAssignment { - public: - XlaScopedShardingAssignment(xla::XlaBuilder* builder, - tensorflow::gtl::optional sharding) - : builder_(builder), prev_sharding_(builder->sharding()) { - SetSharding(sharding); - } - - XlaScopedShardingAssignment(const XlaScopedShardingAssignment&) = delete; - XlaScopedShardingAssignment& operator=(const XlaScopedShardingAssignment&) = - delete; - - ~XlaScopedShardingAssignment() { SetSharding(prev_sharding_); } - - private: - void SetSharding(const tensorflow::gtl::optional& sharding) { - if (sharding.has_value()) { - builder_->SetSharding(sharding.value()); - } else { - builder_->ClearSharding(); - } - } - - xla::XlaBuilder* const builder_; - tensorflow::gtl::optional prev_sharding_; -}; - -// Free functions for building XlaOps. The intention is that these will -// become the public API for building XlaOps rather than calling methods on -// XlaBuilder directly. - -// Enqueues a "retrieve parameter value" instruction for a parameter that was -// passed to the computation. -XlaOp Parameter(XlaBuilder* builder, int64 parameter_number, const Shape& shape, - const string& name); - -// Enqueues a constant with the value of the given literal onto the -// computation. -XlaOp ConstantLiteral(XlaBuilder* builder, const LiteralSlice& literal); - -// Enqueues a constant onto the computation. Methods are templated on the -// native host type (NativeT) which corresponds to a specific XLA -// PrimitiveType as given in the following table: -// -// Native Type PrimitiveType -// ----------------------------- -// bool PRED -// int32 S32 -// int64 S64 -// uint32 U32 -// uint64 U64 -// float F32 -// double F64 -// -// Note: not all primitive types defined in xla_data.proto have a -// corresponding native type yet. -template -XlaOp ConstantR0(XlaBuilder* builder, NativeT value); -template -XlaOp ConstantR1(XlaBuilder* builder, - tensorflow::gtl::ArraySlice values); -XlaOp ConstantR1(XlaBuilder* builder, const tensorflow::core::Bitmap& values); -template -XlaOp ConstantR2(XlaBuilder* builder, - std::initializer_list> values); -template -XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, - const Array& values, - const Layout& layout); -template -XlaOp ConstantFromArray(XlaBuilder* builder, const Array& values); -template -XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, - const Array2D& values, - const Layout& layout); -template -XlaOp ConstantR2FromArray2D(XlaBuilder* builder, - const Array2D& values); -template -XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, - const Array3D& values, - const Layout& layout); -template -XlaOp ConstantR3FromArray3D(XlaBuilder* builder, - const Array3D& values); -template -XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, - const Array4D& values, - const Layout& layout); -template -XlaOp ConstantR4FromArray4D(XlaBuilder* builder, - const Array4D& values); - -// Enqueues a rank one constant (XlaBuilder* builder, vector) onto the -// computation. The vector has size 'length' and every element has the value -// 'value'. -template -XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value); - -// Adds dimensions to an array by duplicating the data in the array. -// -// The new dimensions are inserted on the left, i.e. if -// broadcast_sizes has values {a0, ..., aN} and the operand shape -// has dimensions {b0, ..., bM} then the shape of the output has -// dimensions {a0, ..., aN, b0, ..., bM}. -// -// The new dimensions index into copies of the operand, i.e. -// -// output[i0, ..., iN, j0, ..., jM] = operand[j0, ..., jM] -XlaOp Broadcast(const XlaOp& operand, - tensorflow::gtl::ArraySlice broadcast_sizes); - -// Performs in-dimension-style broadcast. -// -// Operand specifies the input to be broadcast. "shape" is expected output -// shape. "broadcast_dimensions" are the dimensions to be broadcasting into. -// Dimension numbers in broadcast_dimensions map to individual dimensions -// of the operand, and specify what dimension of the output shape they -// should be broadcast. -// e.g. -// Say operand = [1, 2], i.e., a 1D tensor with 2 elements. -// and dimension of shape is [2,2]. -// Specifying {1} as brodcast_dimension will generate output -// [1 , 2] -// [1 , 2] -// On the other hand, specifying {0} as broadcast_dimension -// will generate output -// [1 , 1] -// [2 , 2] -XlaOp BroadcastInDim( - const XlaOp& operand, const Shape& shape, - const tensorflow::gtl::ArraySlice broadcast_dimensions); - -// Enqueues a pad operation onto the computation that pads the given value on -// the edges as well as between the elements of the input. padding_config -// specifies the padding amount for each dimension. -XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, - const PaddingConfig& padding_config); - -// Enqueues an operation onto the computation that flattens the operand based -// on the dimension order (major/slowest-varying to minor/fastest-varying) -// given, followed by reshaping it into the shape with the given dimension -// sizes (also major to minor). Conceptually, this is a limited form of -// "shape casting". -XlaOp Reshape(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions, - tensorflow::gtl::ArraySlice new_sizes); - -// Enqueues an operation onto the computation that collapses the operand, from -// first to last dimension (C order), then reshapes it to the given dimension -// sizes. Conceptually, this is a limited form of "shape casting". -XlaOp Reshape(const XlaOp& operand, - tensorflow::gtl::ArraySlice new_sizes); - -// Wrapper for Reshape. -// Enqueues an operation to collapse the provided dimensions; e.g. an -// operand with dimensions {x=256, y=2, z=2, p=32} can be collapsed to -// {x=1024, y=32} by collapsing dims {0, 1, 2}. Collapsing dimensions must -// be a consecutive, in-order subsequence of the operand dimensions. -// -// Note that collapsing a single dimension does nothing: -// -// {256} collapsing {0} => {256} -// {1} collapsing {0} => {1} -// -// Collapsing multiple dimensions produces a single result dimension: -// -// {256, 2} collapsing {0,1} => {512} -// {256, 2, 3} collapsing {0,1} => {512, 3} -// -// This could potentially cause data to be moved -- it provides a more -// structured form of reshaping than an arbitrary Reshape operation. -XlaOp Collapse(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions); - -// Enqueues a slice operation onto the computation that slices the operand -// from the start indices to the limit indices; e.g. -// -// x -// [ 0 1 2 3 ] -// y [ 4 5 6 7 ] => slice(start={1, 1}, limit={2, 3}) => [ 5 6 ] -// [ 8 9 a b ] -// -// Note that "limit" means up-to-but-not-including; i.e. [start, limit) in 1D -// range notation. -// The strides parameter determines the stride over the slice -XlaOp Slice(const XlaOp& operand, - tensorflow::gtl::ArraySlice start_indices, - tensorflow::gtl::ArraySlice limit_indices, - tensorflow::gtl::ArraySlice strides); - -// Enqueues a slice operation in a given dimension, taking all other -// dimensions as they are; e.g. if dimno is 1 from start_index 2 to -// limit_index 4 by 1, and the shape is f32[7,8,9], this call is short-hand -// for: -// -// array[:, 2:4:1, :] -XlaOp SliceInDim(const XlaOp& operand, int64 start_index, int64 limit_index, - int64 stride, int64 dimno); - -// Enqueues a slice operation onto the computation that slices the 'operand' -// from dynamic start indices which are passed in 'start_indices'. -// The size of the slice in each dimension is passed in 'slice_sizes', -// which specify the end point of exclusive slice intervals in each -// dimension [start, start + size). -// The shape of 'start_indices' must be rank == 1, with dimension size -// equal to the rank of the 'operand'. -// Slice index calculations are computed modulo input dimension sizes to -// prevent dynamic start indices from generating out-of-bound array accesses. -XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, - tensorflow::gtl::ArraySlice slice_sizes); - -// Enqueues a dynamic update slice operation onto the computation, which -// updates a slice of 'operand' with 'update' at dynamic 'start_indices'. -// The shape of 'update' determines the shape of the slice of 'operand' -// which is updated. -// The indices specified in 'start_indices' specify the offset of the slice -// of 'operand' which is updated. -// -// update = {10, 11} // calculated at runtime. -// [1 2 3] start = {1, 1} // calculated at runtime. [1 2 3 ] -// [4 5 6] => DynamicUpdateslice(data, update, start) => [4 10 11] -// [7 8 9] [7 8 9 ] -// -// The shape of 'start_indices' must be rank == 1, with dimension size -// equal to the rank of the 'operand'. -// Slice index calculations are computed modulo update dimension sizes to -// prevent dynamic start indices from generating out-of-bound array accesses. -XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, - const XlaOp& start_indices); - -// Enqueues a concatenate instruction onto the computation. 'operands' must -// have >= 1 entry. -XlaOp ConcatInDim(XlaBuilder* builder, - tensorflow::gtl::ArraySlice operands, int64 dimension); - -// Enqueue a tracing operation onto the computation; the computation will emit -// a logging message with the operand. -void Trace(const string& tag, const XlaOp& operand); - -// Enqueues a conditional-move-like select operation onto the computation; -// predicated on pred, selects between on_true and on_false. -XlaOp Select(const XlaOp& pred, const XlaOp& on_true, const XlaOp& on_false); - -// Enqueues a tuple-creation instruction onto the computation. -XlaOp Tuple(XlaBuilder* builder, tensorflow::gtl::ArraySlice elements); - -// Enqueues a tuple-element-get instruction onto the computation. -XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index); - -// Enqueues an equal-to comparison instruction onto the computation. -XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a not-equal comparison instruction onto the computation. -XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a greater-or-equal comparison instruction onto the computation. -XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a greater-than comparison instruction onto the computation. -XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a less-than comparison instruction onto the computation. -XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a less-or-equal comparison instruction onto the computation. -XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a dot instruction onto the computation. -XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs); - -// Enqueues a general dot instruction onto the computation. -XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, - const DotDimensionNumbers& dimension_numbers); - -// Enqueues a convolution instruction onto the computation, which uses the -// default convolution dimension numbers. -XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, Padding padding); - -// Enqueues a convolution instruction onto the computation, with the caller -// provided padding configuration in the format returned by MakePadding(). -XlaOp ConvWithGeneralPadding( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding); - -// Enqueues a convolution instruction onto the computation, with the caller -// provided dimension numbers configuration. -XlaOp ConvWithGeneralDimensions( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, Padding padding, - const ConvolutionDimensionNumbers& dimension_numbers); - -// Enqueues a convolution instruction onto the computation, with the caller -// provided padding configuration as well as the dimension numbers. -XlaOp ConvGeneral(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - const ConvolutionDimensionNumbers& dimension_numbers); - -// Enqueues a convolution instruction onto the computation, with the caller -// provided padding configuration, dilation factors and dimension numbers. -XlaOp ConvGeneralDilated( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - tensorflow::gtl::ArraySlice lhs_dilation, - tensorflow::gtl::ArraySlice rhs_dilation, - const ConvolutionDimensionNumbers& dimension_numbers); - -// Enqueues an FFT instruction onto the computation, of the given type and -// with the given FFT length. -XlaOp Fft(const XlaOp& operand, FftType fft_type, - tensorflow::gtl::ArraySlice fft_length); - -// Enqueues an infeed instruction onto the computation, which writes data of -// the given shape to the infeed buffer of the device. -XlaOp Infeed(XlaBuilder* builder, const Shape& shape, - const string& config = ""); - -// Enqueues an outfeed instruction onto the computation. This instruction -// generates outgoing data transfers for the given data. -// -// shape_with_layout communicates the laid out shape that we want to outfeed -// -- if !ShapeUtil::Compatible(GetShape(operand), shape_with_layout) an error -// will occur. -void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, - const string& outfeed_config); - -// Enqueues a call instruction onto the computation. -XlaOp Call(XlaBuilder* builder, const XlaComputation& computation, - tensorflow::gtl::ArraySlice operands); - -// Enqueues a custom call instruction onto the computation. -// During code generation, a call instruction is emitted which targets a -// symbol with the name |call_target_name|. The |operands| are passed to the -// call instruction. |shape| is the resultant shape. -XlaOp CustomCall(XlaBuilder* builder, const string& call_target_name, - tensorflow::gtl::ArraySlice operands, - const Shape& shape); - -// Enqueues a pseudo-op to represent host-side computation data-dependencies. -// During code generation, host send and receive operations will be generated -// to transfer |operands| to the host and a single result of |shape| back to -// the device. Host send/recv operations are emitted using |channel_name|. -// Dataflow dependencies and the |cost_estimate_ns| field may be used in HLO -// instruction scheduling. -XlaOp HostCompute(XlaBuilder* builder, - tensorflow::gtl::ArraySlice operands, - const string& channel_name, int64 cost_estimate_ns, - const Shape& shape); - -// The following methods enqueue element-wise binary arithmetic operations -// onto the computation. The shapes of the operands have to match unless one -// of the operands is a scalar, or an explicit broadcast dimension is given -// (see g3doc for more details). - -// Enqueues a complex compose instruction onto the computation. -XlaOp Complex(const XlaOp& real, const XlaOp& imag, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a complex conjugate instruction onto the computation. -XlaOp Conj(const XlaOp& operand); - -// Enqueues an add instruction onto the computation. -XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a subtract instruction onto the computation. -XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a multiply instruction onto the computation. -XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a divide instruction onto the computation. -XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a remainder instruction onto the computation. -XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a max instruction onto the computation. -XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a min instruction onto the computation. -XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Element-wise logical operators -XlaOp And(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -XlaOp Not(const XlaOp& operand); - -XlaOp ShiftLeft(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); -XlaOp ShiftRightArithmetic( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); -XlaOp ShiftRightLogical( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Reduces an array among the provided dimensions, given "computation" as a -// reduction operator. -XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice dimensions_to_reduce); - -// Convenience wrapper around the above that reduces all the dimensions in the -// operand shape. -XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation); - -// Enqueues a windowed reduce instruction onto the computation. -XlaOp ReduceWindow(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - Padding padding); - -// As ReduceWindow(), but the padding is given in the format -// returned by MakePadding(). -XlaOp ReduceWindowWithGeneralPadding( - const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding); - -// Returns the sum of the operand value within each subgroup of replicas. All -// replicas supply one input to the sum and all replicas receive the resulting -// sum for each subgroup. -XlaOp CrossReplicaSum( - const XlaOp& operand, - tensorflow::gtl::ArraySlice replica_group_ids = {}); - -// Enqueues an operation that do an AllReduce of the operand cross cores. Here -// AllReduce means doing a reduction on the input operand cross cores and then -// broadcasting the reduction result to those cores. The reduction function is -// defined by `computation`, which should be a commutative computation on -// scalars, e.g., add, min, or max. The way that AllReduce is applied is -// configured by: -// -// - `replica_group_ids`: maps replica ids to subgroup ids. If empty, all -// replicas belong to one group. Allreduce will be applied within subgroups. -// For example, we have 4 replicas, then replica_group_ids={0,1,0,1} means, -// replica 0 and 2 are in subgroup 0, replica 1 and 3 are in subgroup 1. -// -// - `channel_id`: for Allreduce nodes from different models, if they have the -// same channel_id, they will be 'Allreduce'd. If empty, Allreduce will not be -// applied cross models. -// -// TODO(b/79737069): Rename this to AllReduce when it's ready to use. -XlaOp CrossReplicaSum(const XlaOp& operand, const XlaComputation& computation, - tensorflow::gtl::ArraySlice replica_group_ids = {}, - const tensorflow::gtl::optional& - channel_id = tensorflow::gtl::nullopt); - -// Enqueues an operation that scatters the `source` array to the selected -// indices of each window. -XlaOp SelectAndScatter(const XlaOp& operand, const XlaComputation& select, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - Padding padding, const XlaOp& source, - const XlaOp& init_value, const XlaComputation& scatter); - -// As SelectAndScatter(), but the padding is given in the format -// returned by MakePadding(). -XlaOp SelectAndScatterWithGeneralPadding( - const XlaOp& operand, const XlaComputation& select, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - const XlaOp& source, const XlaOp& init_value, - const XlaComputation& scatter); - -// Enqueues an abs instruction onto the computation. -XlaOp Abs(const XlaOp& operand); - -// Enqueues a atan2 instruction onto the computation. -XlaOp Atan2(const XlaOp& y, const XlaOp& x, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues an exp instruction onto the computation. -XlaOp Exp(const XlaOp& operand); - -// Enqueues an expm1 instruction onto the computation. -XlaOp Expm1(const XlaOp& operand); - -// Enqueues a floor instruction onto the computation. -XlaOp Floor(const XlaOp& operand); - -// Enqueues a ceil instruction onto the computation. -XlaOp Ceil(const XlaOp& operand); - -// Enqueues a round instruction onto the computation, rounding to nearest even -// with half-way cases rounding away from zero. -XlaOp Round(const XlaOp& operand); - -// Enqueues an log instruction (natural logarithm) onto the computation. -XlaOp Log(const XlaOp& operand); - -// Enqueues an log1p instruction (log(x+1)) onto the computation. -XlaOp Log1p(const XlaOp& operand); - -// Enqueues a sign instruction onto the computation. -XlaOp Sign(const XlaOp& operand); - -// Enqueues a count leading zeros instruction onto the computation. -XlaOp Clz(const XlaOp& operand); - -// Enqueues a cosine instruction onto the computation. -XlaOp Cos(const XlaOp& operand); - -// Enqueues a sine instruction onto the computation. -XlaOp Sin(const XlaOp& operand); - -// Enqueues a tanh instruction onto the computation. -XlaOp Tanh(const XlaOp& operand); - -// Enqueues a real-part instruction onto the computation. -XlaOp Real(const XlaOp& operand); - -// Enqueues an imaginary-part instruction onto the computation. -XlaOp Imag(const XlaOp& operand); - -// Enqueues a lhs^rhs computation onto the computation. -XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues an operator that tests if the operand's values are finite, i.e., -// not Inf or NaN. Defined only for floating-point types. Returns an array of -// booleans with the same shape where entries are true iff the corresponding -// entry was NaN. -XlaOp IsFinite(const XlaOp& operand); - -// Enqueues a convert instruction onto the computation that changes the -// element type of the operand array to primitive_type. -XlaOp ConvertElementType(const XlaOp& operand, PrimitiveType new_element_type); - -// Enqueues a no-op instruction onto the computation that changes -// the element type of the operand array to primitive_type. The -// bit-widths of the source and destination element types must be -// identical. -XlaOp BitcastConvertType(const XlaOp& operand, PrimitiveType new_element_type); - -// Enqueues a negate instruction onto the computation. -XlaOp Neg(const XlaOp& operand); - -// Enqueues a transpose instruction onto the computation. -XlaOp Transpose(const XlaOp& operand, - tensorflow::gtl::ArraySlice permutation); - -// Enqueues a reverse instruction onto the computation. The order of the -// elements in the given dimensions is reversed (i.e., the element at index i -// is moved to index dimension_size - 1 - i). -XlaOp Rev(const XlaOp& operand, tensorflow::gtl::ArraySlice dimensions); - -// * The result is a sorted array of keys. -// -// If both keys and values are provided: -// * The keys and the values must be rank-1 tensors with the same dimensions. -// The element types of the tensors may be different. -// * The result is a tuple that consists of a sorted array of keys as the -// first element, and an array with their corresponding values as the second -// element. -XlaOp Sort(XlaOp keys, - tensorflow::gtl::optional values = tensorflow::gtl::nullopt); - -// Enqueues a clamp instruction onto the computation. -XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); - -// Enqueues a map instruction onto the computation. -XlaOp Map(XlaBuilder* builder, tensorflow::gtl::ArraySlice operands, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice dimensions, - tensorflow::gtl::ArraySlice static_operands = {}); - -// Enqueues a N(mu, sigma) random number generation instruction onto the -// computation. -XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, const Shape& shape); - -// Enqueues a U(a, b) random number generation instruction onto the -// computation. Returns values in the semi-open interval [a, b). -XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape); - -// Enqueues a while node onto the computation. -XlaOp While(const XlaComputation& condition, const XlaComputation& body, - const XlaOp& init); - -// Enqueues a conditional node onto the computation. -XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, - const XlaComputation& true_computation, - const XlaOp& false_operand, - const XlaComputation& false_computation); - -// Enqueues a ReducePrecision node onto the computation. -XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, - const int mantissa_bits); - -// Enqueues a Gather node onto the computation. -XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, - const GatherDimensionNumbers& dimension_numbers, - tensorflow::gtl::ArraySlice window_bounds); - -// Enqueues a Send node onto the computation, to send the given operand to -// a Recv instruction that shares the same channel handle. -void Send(const XlaOp& operand, const ChannelHandle& handle); - -// Enqueues a Recv node onto the computation. The data comes from a Send -// instruction that shares the same channel handle and its shape must -// be the same as the given shape. -XlaOp Recv(XlaBuilder* builder, const Shape& shape, - const ChannelHandle& handle); - -// Normalizes operand across spatial and batch dimensions for each feature. -// -// Returns a tuple (normalized, batch_mean, batch_var) where `normalized` -// is the normalized result and batch_mean and batch_var are the mean and -// variance, respectively, across batch for the operand. -XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, - const XlaOp& offset, float epsilon, - int64 feature_index); - -// Normalizes operand across spatial and batch dimensions for each feature. -// -// `BatchNormInference` is equivalent to calling `BatchNormTraining` without -// computing `mean` and `variance` for each batch inside the operation. It -// uses the input `mean` and `variance` instead as estimated values. The -// purpose of this op is to reduce latency in inference, hence the name -// `BatchNormInference`. -// -// The output has the same shape as `operand`, and contains the normalized -// values for each batch. -XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, - const XlaOp& offset, const XlaOp& mean, - const XlaOp& variance, float epsilon, - int64 feature_index); - -// Calculates the gradients of a batch norm op. -// -// The inputs `batch_mean` and `batch_var` represent the mean and variance -// across the batch. -// -// Returns a tuple of three elements: -// - grad_operand: Gradient with respect to input `operand` -// - grad_offset: Gradient with respect to input `offset` -// - grad_scale: Gradient with respect to input `scale` -XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, - const XlaOp& batch_mean, const XlaOp& batch_var, - const XlaOp& grad_output, float epsilon, - int64 feature_index); - -// Implementation details below this point. - -template -XlaOp XlaBuilder::ConstantR0(NativeT value) { - return ConstantLiteral(*Literal::CreateR0(value)); -} - -template -XlaOp XlaBuilder::ConstantR1(tensorflow::gtl::ArraySlice values) { - return ConstantLiteral(*Literal::CreateR1(values)); -} - -template -XlaOp XlaBuilder::ConstantR1(int64 length, NativeT value) { - Literal literal(ShapeUtil::MakeShape( - primitive_util::NativeToPrimitiveType(), {length})); - literal.PopulateWithValue(value); - return ConstantLiteral(literal); -} - -inline XlaOp XlaBuilder::ConstantR1(const tensorflow::core::Bitmap& values) { - return ConstantLiteral(*Literal::CreateR1(values)); -} - -template -XlaOp XlaBuilder::ConstantR2( - std::initializer_list> values) { - return ConstantLiteral(*Literal::CreateR2(values)); -} - -template -XlaOp XlaBuilder::ConstantFromArrayWithLayout(const Array& values, - const Layout& layout) { - return ConstantLiteral( - *Literal::CreateFromArrayWithLayout(values, layout)); -} - -template -XlaOp XlaBuilder::ConstantFromArray(const Array& values) { - return ConstantLiteral(*Literal::CreateFromArray(values)); -} - -template -XlaOp XlaBuilder::ConstantR2FromArray2DWithLayout( - const Array2D& values, const Layout& layout) { - return ConstantLiteral( - *Literal::CreateFromArrayWithLayout(values, layout)); -} - -template -XlaOp XlaBuilder::ConstantR2FromArray2D(const Array2D& values) { - return ConstantLiteral(*Literal::CreateR2FromArray2D(values)); -} - -template -XlaOp XlaBuilder::ConstantR3FromArray3DWithLayout( - const Array3D& values, const Layout& layout) { - return ConstantLiteral( - *Literal::CreateR3FromArray3DWithLayout(values, layout)); -} - -template -XlaOp XlaBuilder::ConstantR3FromArray3D(const Array3D& values) { - return ConstantFromArray(values); -} - -template -XlaOp XlaBuilder::ConstantR4FromArray4DWithLayout( - const Array4D& values, const Layout& layout) { - return ConstantFromArrayWithLayout(values, layout); -} - -template -XlaOp XlaBuilder::ConstantR4FromArray4D(const Array4D& values) { - return ConstantFromArray(values); -} - -// Free function template implementations. - -template -XlaOp ConstantR0(XlaBuilder* builder, NativeT value) { - return ConstantLiteral(builder, *Literal::CreateR0(value)); -} - -template -XlaOp ConstantR1(XlaBuilder* builder, - tensorflow::gtl::ArraySlice values) { - return ConstantLiteral(builder, *Literal::CreateR1(values)); -} - -template -XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value) { - Literal literal(ShapeUtil::MakeShape( - primitive_util::NativeToPrimitiveType(), {length})); - literal.PopulateWithValue(value); - return ConstantLiteral(builder, literal); -} - -inline XlaOp ConstantR1(XlaBuilder* builder, - const tensorflow::core::Bitmap& values) { - return ConstantLiteral(builder, *Literal::CreateR1(values)); -} - -template -XlaOp ConstantR2(XlaBuilder* builder, - std::initializer_list> values) { - return ConstantLiteral(builder, *Literal::CreateR2(values)); -} - -template -XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, - const Array& values, - const Layout& layout) { - return ConstantLiteral( - builder, *Literal::CreateFromArrayWithLayout(values, layout)); -} - -template -XlaOp ConstantFromArray(XlaBuilder* builder, const Array& values) { - return ConstantLiteral(builder, *Literal::CreateFromArray(values)); -} - -template -XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, - const Array2D& values, - const Layout& layout) { - return ConstantLiteral( - builder, *Literal::CreateFromArrayWithLayout(values, layout)); -} - -template -XlaOp ConstantR2FromArray2D(XlaBuilder* builder, - const Array2D& values) { - return ConstantLiteral(builder, - *Literal::CreateR2FromArray2D(values)); -} - -template -XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, - const Array3D& values, - const Layout& layout) { - return ConstantLiteral( - builder, - *Literal::CreateR3FromArray3DWithLayout(values, layout)); -} - -template -XlaOp ConstantR3FromArray3D(XlaBuilder* builder, - const Array3D& values) { - return ConstantFromArray(builder, values); -} - -template -XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, - const Array4D& values, - const Layout& layout) { - return ConstantFromArrayWithLayout(builder, values, layout); -} - -template -XlaOp ConstantR4FromArray4D(XlaBuilder* builder, - const Array4D& values) { - return ConstantFromArray(builder, values); -} - -} // namespace xla +#include "tensorflow/compiler/xla/client/xla_builder.h" #endif // TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_BUILDER_H_ diff --git a/tensorflow/compiler/xla/client/xla_client/xla_computation.cc b/tensorflow/compiler/xla/client/xla_computation.cc similarity index 94% rename from tensorflow/compiler/xla/client/xla_client/xla_computation.cc rename to tensorflow/compiler/xla/client/xla_computation.cc index 72e3935696e0c44ae3893fc8f1ceb261fa5e2646..3543d41fc2656ec028646edebc0bf5b6af7f67a5 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_computation.cc +++ b/tensorflow/compiler/xla/client/xla_computation.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include diff --git a/tensorflow/compiler/xla/client/xla_client/xla_computation.h b/tensorflow/compiler/xla/client/xla_computation.h similarity index 90% rename from tensorflow/compiler/xla/client/xla_client/xla_computation.h rename to tensorflow/compiler/xla/client/xla_computation.h index 0ffba208b1f8683fe1d26107cbfd096b856267f1..71598ef8b296a760b0ee818fce0a59aed5cfc6b4 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_computation.h +++ b/tensorflow/compiler/xla/client/xla_computation.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_COMPUTATION_H_ -#define TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_COMPUTATION_H_ +#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_XLA_COMPUTATION_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_XLA_COMPUTATION_H_ #include @@ -64,4 +64,4 @@ class XlaComputation { } // namespace xla -#endif // TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_COMPUTATION_H_ +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_XLA_COMPUTATION_H_ diff --git a/tensorflow/compiler/xla/literal.cc b/tensorflow/compiler/xla/literal.cc new file mode 100644 index 0000000000000000000000000000000000000000..0545deb096e9eace5a9713f200e10559aa718441 --- /dev/null +++ b/tensorflow/compiler/xla/literal.cc @@ -0,0 +1,1969 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/literal.h" + +#include +#include +#include +#include +#include +#include + +#include "tensorflow/compiler/xla/index_util.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/lib/core/casts.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/hash/hash.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/lib/strings/strcat.h" +#include "tensorflow/core/lib/strings/stringprintf.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/types.h" + +using tensorflow::strings::Printf; +using tensorflow::strings::StrCat; + +namespace xla { + +namespace { + +constexpr bool kLittleEndian = __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__; + +// Converts between little and big endian. +// +// Precondition: size % 2 == 0 (elements in the array are 16 bits long) +void ConvertEndianShort(string* bytes) { + CHECK_EQ(bytes->size() / 2, 0); + for (int64 i = 0; i < bytes->size(); i += 2) { + std::swap((*bytes)[i], (*bytes)[i + 1]); + } +} + +void ConvertEndianShort(char* bytes, int64 size) { + CHECK_EQ(size / 2, 0); + for (int64 i = 0; i < size; i += 2) { + std::swap(bytes[i], bytes[i + 1]); + } +} + +} // namespace + +LiteralBase::~LiteralBase() {} + +std::ostream& operator<<(std::ostream& out, const Literal& literal) { + out << literal.ToString(); + return out; +} + +Literal::StrideConfig::StrideConfig( + const Shape& source_shape, const Shape& dest_shape, + tensorflow::gtl::ArraySlice dimensions) + : dimensions(dimensions), + base(dimensions.size(), 0), + step(dimensions.size(), 1) { + if (!dimensions.empty()) { + // Selects the shape with the largest minor dimension as the one upon + // which to run the tight stride loop. + if (dimensions[LayoutUtil::Minor(source_shape.layout(), 0)] >= + dimensions[LayoutUtil::Minor(dest_shape.layout(), 0)]) { + minor_dimension = LayoutUtil::Minor(source_shape.layout(), 0); + dest_stride = IndexUtil::GetDimensionStride(dest_shape, minor_dimension); + } else { + minor_dimension = LayoutUtil::Minor(dest_shape.layout(), 0); + source_stride = + IndexUtil::GetDimensionStride(source_shape, minor_dimension); + } + minor_loop_size = dimensions[minor_dimension]; + step[minor_dimension] = minor_loop_size; + } +} + +Literal::Literal(const Shape& shape) + : Literal(shape, /*allocate_arrays=*/true) {} + +void Literal::SetPiece(const Shape& shape, Piece* piece, bool allocate_arrays) { + if (ShapeUtil::IsTuple(shape)) { + for (int i = 0; i < ShapeUtil::TupleElementCount(shape); ++i) { + const Shape& subshape = shape.tuple_shapes(i); + + auto child_piece = Piece(); + child_piece.set_subshape(&subshape); + + SetPiece(subshape, &child_piece, allocate_arrays); + + piece->emplace_back(std::move(child_piece)); + } + } else if (ShapeUtil::IsArray(shape)) { + if (allocate_arrays) { + if (LayoutUtil::IsSparseArray(shape)) { + // For sparse arrays, the buffer must be of the size of the maximum + // number of sparse elements possible. + const int64 max_sparse_elements = + LayoutUtil::MaxSparseElements(shape.layout()); + piece->set_buffer( + new char[max_sparse_elements * + ShapeUtil::ByteSizeOfPrimitiveType(shape.element_type())]); + piece->set_sparse_indices( + new SparseIndexArray(max_sparse_elements, ShapeUtil::Rank(shape))); + } else { + piece->set_buffer(new char[piece->size_bytes()]); + } + } + } else { + // If the shape is neither an array nor tuple, then it must be + // zero-sized. Otherwise, some memory needs to be allocated for it. + CHECK_EQ(piece->size_bytes(), 0); + } +} + +Literal::Literal(const Shape& shape, bool allocate_arrays) + : LiteralBase(), shape_(MakeUnique(shape)) { + CHECK(LayoutUtil::HasLayout(*shape_)); + root_piece_ = new Piece(); + root_piece_->set_subshape(shape_.get()); + CHECK(&root_piece_->subshape() == shape_.get()); + + SetPiece(*shape_, root_piece_, allocate_arrays); +} + +Literal::~Literal() { + if (root_piece_ != nullptr) { + DeallocateBuffers(); + delete root_piece_; + } +} + +void Literal::DeallocateBuffers() { + root_piece_->ForEachMutableSubpiece( + [&](const ShapeIndex& index, Piece* piece) { + if (piece->buffer() != nullptr) { + delete[] piece->buffer(); + delete piece->sparse_indices(); + } + }); +} + +Literal::Literal(Literal&& other) : LiteralBase() { *this = std::move(other); } + +Literal& Literal::operator=(Literal&& other) { + DCHECK(&other.root_piece_->subshape() == other.shape_.get()); + using std::swap; + swap(shape_, other.shape_); + swap(root_piece_, other.root_piece_); + DCHECK(&root_piece_->subshape() == shape_.get()); + + return *this; +} + +std::unique_ptr LiteralBase::CreateFromShape(const Shape& shape) { + auto literal = MakeUnique(shape); + literal->root_piece_->ForEachMutableSubpiece( + [&](const ShapeIndex& index, Piece* piece) { + if (ShapeUtil::IsArray(piece->subshape())) { + memset(piece->untyped_data(), 0, piece->size_bytes()); + } + }); + return literal; +} + +const SparseIndexArray* LiteralBase::sparse_indices( + const ShapeIndex& shape_index) const { + return piece(shape_index).sparse_indices(); +} + +SparseIndexArray* Literal::sparse_indices(const ShapeIndex& shape_index) { + return piece(shape_index).sparse_indices(); +} + +template +Status Literal::CopySliceFromInternal( + const LiteralBase& src_literal, tensorflow::gtl::ArraySlice src_base, + tensorflow::gtl::ArraySlice dest_base, + tensorflow::gtl::ArraySlice copy_size) { + TF_RET_CHECK(ShapeUtil::Rank(src_literal.shape()) == src_base.size()); + TF_RET_CHECK(ShapeUtil::Rank(shape()) == dest_base.size()); + + auto linear_index = [](const Shape& shape, + tensorflow::gtl::ArraySlice multi_index) { + return IndexUtil::MultidimensionalIndexToLinearIndex(shape, multi_index); + }; + + if (ShapeUtil::Rank(src_literal.shape()) == 0 || + ShapeUtil::Rank(shape()) == 0) { + // If any of the two shapes are scalars, we can just call the StridedCopy() + // directly, and we know we will be copying only one value. + TF_RET_CHECK(copy_size.empty()); + StridedCopy(data(), linear_index(shape(), dest_base), 0, + src_literal.data(), + linear_index(src_literal.shape(), src_base), 0, 1); + } else if (!ShapeUtil::IsZeroElementArray(shape()) && + !ShapeUtil::IsZeroElementArray(src_literal.shape())) { + // Perform copy if neither src nor dest has dimensions with zero element, + // otherwise it's a no-op. + TF_RET_CHECK(src_base.size() == dest_base.size()); + TF_RET_CHECK(src_base.size() == copy_size.size()); + + // Scan the source from minor, stepping in copy size blocks, then within + // the index enumaration functor, do a strided copy advancing source index + // by one (walking through the minor dimension), and destination index by + // proper stride size at the matching dimension. + DimensionVector src_indexes(src_base.size(), 0); + DimensionVector dest_indexes(dest_base.size(), 0); + Literal::StrideConfig stride_config(src_literal.shape(), shape(), + copy_size); + + auto copy_proc = [&](tensorflow::gtl::ArraySlice indexes) { + // Map from multi-dimensional index, to source index. + std::transform(indexes.begin(), indexes.end(), src_base.begin(), + src_indexes.begin(), std::plus()); + // Map from multi-dimensional index, to destination index. + std::transform(indexes.begin(), indexes.end(), dest_base.begin(), + dest_indexes.begin(), std::plus()); + + int64 src_index = linear_index(src_literal.shape(), src_indexes); + int64 dest_index = linear_index(shape(), dest_indexes); + + // `this->` is needed to workaround MSVC bug: #16882 + StridedCopy(this->data(), dest_index, stride_config.dest_stride, + src_literal.data(), src_index, + stride_config.source_stride, stride_config.minor_loop_size); + return true; + }; + + ShapeUtil::ForEachIndex(src_literal.shape(), stride_config.base, + stride_config.dimensions, stride_config.step, + copy_proc); + } + return Status::OK(); +} + +Status Literal::CopyElementFrom(const LiteralSlice& src_literal, + tensorflow::gtl::ArraySlice src_index, + tensorflow::gtl::ArraySlice dest_index) { + DCHECK_EQ(shape().element_type(), src_literal.shape().element_type()); + const int64 src_linear_index = IndexUtil::MultidimensionalIndexToLinearIndex( + src_literal.shape(), src_index); + const int64 dest_linear_index = + IndexUtil::MultidimensionalIndexToLinearIndex(shape(), dest_index); + const int64 primitive_size = + ShapeUtil::ByteSizeOfPrimitiveType(shape().element_type()); + + char* dest_address = + static_cast(untyped_data()) + dest_linear_index * primitive_size; + const char* source_address = + static_cast(src_literal.untyped_data()) + + src_linear_index * primitive_size; + if (dest_address != source_address) { + memcpy(dest_address, source_address, primitive_size); + } + return Status::OK(); +} + +/* static */ StatusOr> Literal::CreateFromProto( + const LiteralProto& proto) { + if (!proto.has_shape()) { + return InvalidArgument("LiteralProto has no shape"); + } + if (!LayoutUtil::HasLayout(proto.shape())) { + return InvalidArgument("LiteralProto has no layout"); + } + + auto literal = MakeUnique(proto.shape()); + + TF_RETURN_IF_ERROR(literal->root_piece_->ForEachMutableSubpieceWithStatus( + [&](const ShapeIndex& index, Piece* piece) { + const LiteralProto* proto_element = &proto; + for (int64 i : index) { + CHECK(i < proto_element->tuple_literals_size()); + proto_element = &proto_element->tuple_literals(i); + } + + if (ShapeUtil::IsTuple(piece->subshape())) { + if (proto_element->tuple_literals_size() != + ShapeUtil::TupleElementCount(piece->subshape())) { + return InvalidArgument( + "Expected %lld tuple elements in LiteralProto, has %d", + ShapeUtil::TupleElementCount(piece->subshape()), + proto_element->tuple_literals_size()); + } + return Status::OK(); + } + if (piece->subshape().element_type() == TOKEN) { + return Status::OK(); + } + + CHECK(ShapeUtil::IsArray(piece->subshape())); + TF_RETURN_IF_ERROR(piece->CopyFromProto(*proto_element)); + + return Status::OK(); + })); + + return std::move(literal); +} + +std::vector Literal::DecomposeTuple() { + CHECK(ShapeUtil::IsTuple(shape())); + std::vector elements; + for (int i = 0; i < ShapeUtil::TupleElementCount(shape()); ++i) { + elements.push_back(Literal(ShapeUtil::GetSubshape(shape(), {i}), + /*allocate_arrays=*/false)); + Literal& element = elements.back(); + element.root_piece_->ForEachMutableSubpiece( + [&](const ShapeIndex& index, Piece* dest_piece) { + ShapeIndex src_index = {i}; + for (int64 j : index) { + src_index.push_back(j); + } + Piece& src_piece = piece(src_index); + + // Move the respective buffer and sparse indices over to the element + // Literal. + dest_piece->set_buffer(src_piece.buffer()); + src_piece.set_buffer(nullptr); + dest_piece->set_sparse_indices(src_piece.sparse_indices()); + src_piece.set_sparse_indices(nullptr); + }); + } + // Set this literal to be nil-shaped. + *this = Literal(); + return elements; +} + +namespace { + +// Copies the elements in 'src' to 'dest'. The shape and layout of the data in +// the array slices are indicated by dest_shape and src_shape respectively. +template +void CopyElementsBetween(tensorflow::gtl::MutableArraySlice dest, + tensorflow::gtl::ArraySlice src, + const Shape& dest_shape, const Shape& src_shape) { + CHECK(ShapeUtil::Compatible(dest_shape, src_shape)); + if (ShapeUtil::IsZeroElementArray(dest_shape)) { + return; + } + std::vector index(ShapeUtil::Rank(dest_shape)); + do { + dest[IndexUtil::MultidimensionalIndexToLinearIndex(dest_shape, index)] = + src[IndexUtil::MultidimensionalIndexToLinearIndex(src_shape, index)]; + } while (IndexUtil::BumpIndices(dest_shape, &index)); +} + +} // namespace + +Status LiteralBase::Piece::CopyFrom(const LiteralBase::Piece& src) { + CHECK(subshape_ != nullptr); + CHECK(src.subshape_ != nullptr); + if (ShapeUtil::Equal(subshape(), src.subshape())) { + // If the layouts are equal it's faster just to memcpy. + memcpy(buffer(), src.buffer(), src.size_bytes()); + } else { + TF_RET_CHECK(ShapeUtil::Compatible(src.subshape(), subshape())); + std::vector origin(ShapeUtil::Rank(subshape()), 0); + switch (subshape().element_type()) { +#define COPY_ELEMENTS(XLA_T, NATIVE_T) \ + case (XLA_T): \ + CopyElementsBetween(data(), src.data(), \ + subshape(), src.subshape()); \ + break; + COPY_ELEMENTS(U8, uint8); + COPY_ELEMENTS(U16, uint16); + COPY_ELEMENTS(U32, uint32); + COPY_ELEMENTS(U64, uint64); + COPY_ELEMENTS(S8, int8); + COPY_ELEMENTS(S16, int16); + COPY_ELEMENTS(S32, int32); + COPY_ELEMENTS(S64, int64); + COPY_ELEMENTS(F16, half); + COPY_ELEMENTS(BF16, bfloat16); + COPY_ELEMENTS(F32, float); + COPY_ELEMENTS(F64, double); + COPY_ELEMENTS(C64, complex64); + COPY_ELEMENTS(PRED, bool); +#undef COPY_ELEMENTS + default: + return Unimplemented( + "Copying a Literal object with element type %s is not implemented.", + PrimitiveType_Name(subshape().element_type()).c_str()); + } + } + return Status::OK(); +} + +Status Literal::CopyFrom(const LiteralSlice& src_literal, + const ShapeIndex& dest_shape_index, + const ShapeIndex& src_shape_index) { + const Shape& dest_subshape = + ShapeUtil::GetSubshape(shape(), dest_shape_index); + const Shape& src_subshape = + ShapeUtil::GetSubshape(src_literal.shape(), src_shape_index); + if (!ShapeUtil::Compatible(dest_subshape, src_subshape)) { + return InvalidArgument( + "Destination subshape incompatible with source subshape: %s vs %s", + ShapeUtil::HumanString(dest_subshape).c_str(), + ShapeUtil::HumanString(src_subshape).c_str()); + } + return root_piece_->ForEachMutableSubpieceWithStatus( + [&](const ShapeIndex& index, Piece* piece) { + if (!ShapeUtil::IsArray(piece->subshape())) { + return Status::OK(); + } + + // Determine if this index is in the part of this literal that we want + // to copy over from src_literal. + bool in_subtree_to_copy = true; + for (int i = 0; i < dest_shape_index.size(); ++i) { + if (index[i] != dest_shape_index[i]) { + in_subtree_to_copy = false; + break; + } + } + if (!in_subtree_to_copy) { + return Status::OK(); + } + // Construct the index of the corresponding piece in the source literal. + ShapeIndex src_piece_index = src_shape_index; + for (int64 i = dest_shape_index.size(); i < index.size(); ++i) { + src_piece_index.push_back(index[i]); + } + TF_RETURN_IF_ERROR(piece->CopyFrom(src_literal.piece(src_piece_index))); + return Status::OK(); + }); +} + +Status Literal::MoveFrom(Literal&& src_literal, + const ShapeIndex& dest_shape_index) { + const Shape& dest_subshape = + ShapeUtil::GetSubshape(shape(), dest_shape_index); + if (!ShapeUtil::Equal(dest_subshape, src_literal.shape())) { + return InvalidArgument( + "Destination subshape not equal to source shape: %s vs %s", + ShapeUtil::HumanString(dest_subshape).c_str(), + ShapeUtil::HumanString(src_literal.shape()).c_str()); + } + + src_literal.root_piece_->ForEachSubpiece( + [&](const ShapeIndex& src_index, const Piece& src_piece) { + if (!ShapeUtil::IsArray(src_piece.subshape())) { + return; + } + + ShapeIndex dest_index = dest_shape_index; + for (int64 i : src_index) { + dest_index.push_back(i); + } + Piece& dest_piece = piece(dest_index); + delete[] dest_piece.buffer(); + dest_piece.set_buffer(src_piece.buffer()); + delete dest_piece.sparse_indices(); + dest_piece.set_sparse_indices(src_piece.sparse_indices()); + }); + + src_literal.shape_ = MakeUnique(ShapeUtil::MakeNil()); + delete src_literal.root_piece_; + src_literal.root_piece_ = new LiteralBase::Piece(); + src_literal.root_piece_->set_subshape(src_literal.shape_.get()); + + return Status::OK(); +} + +Status Literal::CopySliceFrom(const LiteralSlice& src_literal, + tensorflow::gtl::ArraySlice src_base, + tensorflow::gtl::ArraySlice dest_base, + tensorflow::gtl::ArraySlice copy_size) { + TF_RET_CHECK(ShapeUtil::IsArray(shape())) << ShapeUtil::HumanString(shape()); + TF_RET_CHECK(ShapeUtil::IsArray(src_literal.shape())) + << ShapeUtil::HumanString(src_literal.shape()); + TF_RET_CHECK(ShapeUtil::SameElementType(src_literal.shape(), shape())); + + switch (shape().element_type()) { + case U8: + return CopySliceFromInternal(src_literal, src_base, dest_base, + copy_size); + case U16: + return CopySliceFromInternal(src_literal, src_base, dest_base, + copy_size); + case U32: + return CopySliceFromInternal(src_literal, src_base, dest_base, + copy_size); + case U64: + return CopySliceFromInternal(src_literal, src_base, dest_base, + copy_size); + case S8: + return CopySliceFromInternal(src_literal, src_base, dest_base, + copy_size); + case S16: + return CopySliceFromInternal(src_literal, src_base, dest_base, + copy_size); + case S32: + return CopySliceFromInternal(src_literal, src_base, dest_base, + copy_size); + case S64: + return CopySliceFromInternal(src_literal, src_base, dest_base, + copy_size); + case F16: + return CopySliceFromInternal(src_literal, src_base, dest_base, + copy_size); + case BF16: + return CopySliceFromInternal(src_literal, src_base, dest_base, + copy_size); + case F32: + return CopySliceFromInternal(src_literal, src_base, dest_base, + copy_size); + case F64: + return CopySliceFromInternal(src_literal, src_base, dest_base, + copy_size); + case C64: + return CopySliceFromInternal(src_literal, src_base, dest_base, + copy_size); + case PRED: + return CopySliceFromInternal(src_literal, src_base, dest_base, + copy_size); + default: + break; + } + return Unimplemented( + "Copying a slice from a Literal object with element type %d is not " + "implemented.", + shape().element_type()); +} + +void Literal::PopulateR1(const tensorflow::core::Bitmap& values) { + CHECK(ShapeUtil::IsArray(shape())); + CHECK_EQ(ShapeUtil::Rank(shape()), 1); + CHECK_EQ(element_count(), values.bits()); + CHECK_EQ(shape().element_type(), PRED); + for (int64 i = 0; i < static_cast(values.bits()); ++i) { + Set({i}, values.get(i)); + } +} + +std::unique_ptr LiteralBase::Relayout( + const Layout& new_layout, const ShapeIndex& shape_index) const { + // Create new shape with 'new_layout' set at the given shape index. + Shape new_shape = shape(); + Shape* subshape = ShapeUtil::GetMutableSubshape(&new_shape, shape_index); + TF_CHECK_OK(LayoutUtil::ValidateLayoutForShape(new_layout, *subshape)); + *subshape->mutable_layout() = new_layout; + auto result = MakeUnique(new_shape); + TF_CHECK_OK(result->CopyFrom(*this)); + return result; +} + +std::unique_ptr LiteralBase::Relayout( + const Shape& shape_with_layout) const { + CHECK(ShapeUtil::Compatible(shape_with_layout, shape())) + << "Given shape_with_layout " << ShapeUtil::HumanString(shape_with_layout) + << " not compatible with literal shape " + << ShapeUtil::HumanString(shape()); + std::unique_ptr result = CreateFromShape(shape_with_layout); + ShapeUtil::ForEachSubshape( + result->shape(), + [this, &result](const Shape& subshape, const ShapeIndex& index) { + if (ShapeUtil::IsArray(subshape)) { + TF_CHECK_OK(result->CopyFrom(*this, + /*dest_shape_index=*/index, + /*src_shape_index=*/index)); + } + }); + return result; +} + +StatusOr> LiteralBase::Broadcast( + const Shape& result_shape, + tensorflow::gtl::ArraySlice dimensions) const { + if (!ShapeUtil::IsArray(shape())) { + return InvalidArgument("Broadcast only supports arrays."); + } + + for (int64 i = 0; i < dimensions.size(); i++) { + TF_RET_CHECK(shape().dimensions(i) == + result_shape.dimensions(dimensions[i])); + } + + std::unique_ptr result = MakeUnique(result_shape); + + // scratch_source_index is temporary storage space for the computed index into + // the input literal. We put it here to avoid allocating an std::vector in + // every iteration of ShapeUtil::ForEachIndex. + std::vector scratch_source_index(shape().dimensions_size()); + + char* dest_data = static_cast(result->untyped_data()); + const char* source_data = static_cast(untyped_data()); + const int64 primitive_size = + ShapeUtil::ByteSizeOfPrimitiveType(shape().element_type()); + + ShapeUtil::ForEachIndex( + result_shape, [&](tensorflow::gtl::ArraySlice output_index) { + for (int64 i = 0; i < dimensions.size(); ++i) { + scratch_source_index[i] = output_index[dimensions[i]]; + } + int64 dest_index = IndexUtil::MultidimensionalIndexToLinearIndex( + result_shape, output_index); + int64 source_index = IndexUtil::MultidimensionalIndexToLinearIndex( + shape(), scratch_source_index); + memcpy(dest_data + primitive_size * dest_index, + source_data + primitive_size * source_index, primitive_size); + return true; + }); + + return std::move(result); +} + +StatusOr> LiteralBase::Reshape( + tensorflow::gtl::ArraySlice dimensions) const { + if (!ShapeUtil::IsArray(shape())) { + return InvalidArgument("Reshape does not support tuples."); + } + std::unique_ptr output; + if (!LayoutUtil::IsMonotonicWithDim0Major(shape().layout())) { + output = + Relayout(LayoutUtil::GetDefaultLayoutForRank(ShapeUtil::Rank(shape()))); + } else { + output = CloneToUnique(); + } + // Because the layout is monotonic, we can simply reuse the same sequence of + // values without changing their order. + *output->mutable_shape_do_not_use() = + ShapeUtil::MakeShape(shape().element_type(), dimensions); + + int64 elements_before = ShapeUtil::ElementsIn(shape()); + int64 elements_after = ShapeUtil::ElementsIn(output->shape()); + if (elements_before != elements_after) { + return InvalidArgument( + "Shapes before and after Literal::Reshape have different numbers " + "of elements: %s vs %s.", + ShapeUtil::HumanString(shape()).c_str(), + ShapeUtil::HumanString(output->shape()).c_str()); + } + return std::move(output); +} + +std::unique_ptr LiteralBase::Transpose( + tensorflow::gtl::ArraySlice permutation) const { + CHECK(ShapeUtil::IsArray(shape())) << "Tuple is not supported for transpose"; + CHECK(IsPermutation(permutation, ShapeUtil::Rank(shape()))) + << "Given permutation is not a permutation of dimension numbers"; + // To transpose the array, we just permute the dimensions and layout, and + // do a straight memory copy of the raw data set. + // This is considerably faster than iterating over every array element using + // the EachCell<>() and Set<>() APIs. + std::vector inverse_permutation = InversePermutation(permutation); + Shape permuted_shape = + ShapeUtil::PermuteDimensions(inverse_permutation, shape()); + // Replace the layout with one affine to this shape, such that a + // transpose operation can be performed by leaving the flat values + // representation intact. + // For example, consider the shape F32[11,8]{1,0} under a {1,0} permutation. + // The shape with affine layout resulting from that operation will be + // F32[8,11]{0,1}, since it leaves the original most minor (the 8 sized), the + // most minor. + // + // Essentially, given MinMaj(Di) the position of the Di dimension within the + // minor to major vector, and given T(Di) the index that the original Di + // dimension has within the transposed array, a layout is affine if + // MinMaj(Di) == TMinMaj(T(Di)), with TMinMaj() being the minor to major + // vector of the affine layout. + CHECK(LayoutUtil::IsDenseArray(permuted_shape)); + Layout* layout = permuted_shape.mutable_layout(); + layout->clear_minor_to_major(); + for (auto index : LayoutUtil::MinorToMajor(shape())) { + layout->add_minor_to_major(inverse_permutation[index]); + } + auto new_literal = MakeUnique(permuted_shape); + DCHECK_EQ(ShapeUtil::ByteSizeOf(new_literal->shape()), + ShapeUtil::ByteSizeOf(shape())); + std::memcpy(new_literal->untyped_data(), untyped_data(), size_bytes()); + return new_literal; +} + +template +std::unique_ptr LiteralBase::SliceInternal( + const Shape& result_shape, + tensorflow::gtl::ArraySlice start_indices) const { + auto result_literal = MakeUnique(result_shape); + DimensionVector new_indices(ShapeUtil::Rank(result_shape)); + result_literal->EachCell( + [&](tensorflow::gtl::ArraySlice indices, NativeT /*value*/) { + for (int64 i = 0; i < ShapeUtil::Rank(result_shape); ++i) { + new_indices[i] = indices[i] + start_indices[i]; + } + NativeT value = Get(new_indices); + result_literal->Set(indices, value); + }); + return result_literal; +} + +std::unique_ptr LiteralBase::Slice( + tensorflow::gtl::ArraySlice start_indices, + tensorflow::gtl::ArraySlice limit_indices) const { + CHECK(ShapeUtil::IsArray(shape())) << "tuple is not supported for slice"; + + DimensionVector result_dimensions; + for (int64 dnum = 0; dnum < ShapeUtil::Rank(shape()); ++dnum) { + CHECK_GE(start_indices[dnum], 0); + CHECK_LE(limit_indices[dnum], shape().dimensions(dnum)) + << "dnum = " << dnum; + int64 dimension = limit_indices[dnum] - start_indices[dnum]; + CHECK_GE(dimension, 0) << "dnum = " << dnum; + result_dimensions.push_back(dimension); + } + const auto result_shape = + ShapeUtil::MakeShapeWithLayout(shape().element_type(), result_dimensions, + LayoutUtil::MinorToMajor(shape())); + switch (result_shape.element_type()) { + case F32: + return SliceInternal(result_shape, start_indices); + case BF16: + return SliceInternal(result_shape, start_indices); + case C64: + return SliceInternal(result_shape, start_indices); + case S32: + return SliceInternal(result_shape, start_indices); + case U32: + return SliceInternal(result_shape, start_indices); + default: + LOG(FATAL) << "not yet implemented: " + << PrimitiveType_Name(result_shape.element_type()); + } +} + +Literal LiteralBase::Clone() const { + Literal result(shape()); + TF_CHECK_OK(result.CopyFrom(*this)); + return result; +} + +std::unique_ptr LiteralBase::CloneToUnique() const { + auto result = MakeUnique(shape()); + TF_CHECK_OK(result->CopyFrom(*this)); + return result; +} + +string LiteralBase::GetAsString(tensorflow::gtl::ArraySlice multi_index, + const ShapeIndex& shape_index) const { + const Shape& subshape = ShapeUtil::GetSubshape(shape(), shape_index); + CHECK(LayoutUtil::IsDenseArray(subshape)); + switch (subshape.element_type()) { + case PRED: + return Get(multi_index, shape_index) ? "true" : "false"; + case S8: + return StrCat(Get(multi_index, shape_index)); + case S16: + return StrCat(Get(multi_index, shape_index)); + case S32: + return StrCat(Get(multi_index, shape_index)); + case S64: + return StrCat(Get(multi_index, shape_index)); + case U8: + return StrCat(Get(multi_index, shape_index)); + case U16: + return StrCat(Get(multi_index, shape_index)); + case U32: + return StrCat(Get(multi_index, shape_index)); + case U64: + return StrCat(Get(multi_index, shape_index)); + case F16: + return StrCat(static_cast(Get(multi_index, shape_index))); + case F32: + return StrCat(Get(multi_index, shape_index)); + case BF16: + return StrCat( + static_cast(Get(multi_index, shape_index))); + case F64: + return StrCat(Get(multi_index, shape_index)); + case C64: { + complex64 c = Get(multi_index, shape_index); + return StrCat("(", c.real(), ", ", c.imag(), ")"); + } + default: + LOG(FATAL) << PrimitiveType_Name(subshape.element_type()); + } +} + +string LiteralBase::GetSparseElementAsString( + int64 sparse_element_number, const ShapeIndex& shape_index) const { + const Shape& subshape = ShapeUtil::GetSubshape(shape(), shape_index); + CHECK(LayoutUtil::IsSparseArray(subshape)); + switch (subshape.element_type()) { + case PRED: + return GetSparseElement(sparse_element_number, shape_index) + ? "true" + : "false"; + case S8: + return StrCat(GetSparseElement(sparse_element_number, shape_index)); + case S16: + return StrCat( + GetSparseElement(sparse_element_number, shape_index)); + case S32: + return StrCat( + GetSparseElement(sparse_element_number, shape_index)); + case S64: + return StrCat( + GetSparseElement(sparse_element_number, shape_index)); + case U8: + return StrCat( + GetSparseElement(sparse_element_number, shape_index)); + case U16: + return StrCat( + GetSparseElement(sparse_element_number, shape_index)); + case U32: + return StrCat( + GetSparseElement(sparse_element_number, shape_index)); + case U64: + return StrCat( + GetSparseElement(sparse_element_number, shape_index)); + case F16: + return StrCat(static_cast( + GetSparseElement(sparse_element_number, shape_index))); + case F32: + return StrCat( + GetSparseElement(sparse_element_number, shape_index)); + case BF16: + return StrCat(static_cast( + GetSparseElement(sparse_element_number, shape_index))); + case F64: + return StrCat( + GetSparseElement(sparse_element_number, shape_index)); + case C64: { + complex64 c = + GetSparseElement(sparse_element_number, shape_index); + return StrCat("(", c.real(), ", ", c.imag(), ")"); + } + default: + LOG(FATAL) << "Invalid element type for sparse arrays: " + << PrimitiveType_Name(subshape.element_type()); + } +} + +StatusOr LiteralBase::GetIntegralAsS64( + tensorflow::gtl::ArraySlice multi_index) const { + CHECK(LayoutUtil::IsDenseArray(shape())); + switch (shape().element_type()) { + case PRED: + return Get(multi_index); + case U8: + return Get(multi_index); + case S32: + return Get(multi_index); + case S64: + return Get(multi_index); + case U32: + return Get(multi_index); + case U64: + return Get(multi_index); + default: + return FailedPrecondition( + "Array element type is not integral: %s", + PrimitiveType_Name(shape().element_type()).c_str()); + } +} + +size_t LiteralBase::Hash() const { + using tensorflow::Hash64; + using tensorflow::Hash64Combine; + + size_t hash_value = ShapeUtil::Hash(shape()); + + ShapeUtil::ForEachSubshape( + shape(), [&](const Shape& subshape, const ShapeIndex& index) { + if (!ShapeUtil::IsArray(subshape)) { + return; + } + + CHECK(LayoutUtil::IsDense(subshape.layout())); + hash_value = Hash64Combine( + hash_value, Hash64(static_cast(untyped_data(index)), + size_bytes(index))); + }); + + return hash_value; +} + +Status Literal::SetIntegralAsS64(tensorflow::gtl::ArraySlice multi_index, + int64 value) { + CHECK(LayoutUtil::IsDenseArray(shape())); + switch (shape().element_type()) { + case PRED: + Set(multi_index, value); + break; + case U8: + Set(multi_index, value); + break; + case S32: + Set(multi_index, value); + break; + case S64: + Set(multi_index, value); + break; + case U32: + Set(multi_index, value); + break; + case U64: + Set(multi_index, value); + break; + default: + return FailedPrecondition( + "Array element type is not integral: %s", + PrimitiveType_Name(shape().element_type()).c_str()); + } + return Status::OK(); +} + +tensorflow::gtl::ArraySlice LiteralBase::GetSparseIndex( + int64 sparse_element_number, const ShapeIndex& shape_index) const { + const Piece& p = piece(shape_index); + CHECK_GE(sparse_element_number, 0); + CHECK_LT(sparse_element_number, p.sparse_indices()->index_count()); + return p.sparse_indices()->At(sparse_element_number); +} + +void Literal::SortSparseElements(const ShapeIndex& shape_index) { + piece(shape_index).SortSparseElements(); +} + +void LiteralBase::Piece::SortSparseElements() { + switch (subshape().element_type()) { + case PRED: + SortSparseElementsInternal(); + break; + case S8: + SortSparseElementsInternal(); + break; + case U8: + SortSparseElementsInternal(); + break; + case S16: + SortSparseElementsInternal(); + break; + case U16: + SortSparseElementsInternal(); + break; + case S32: + SortSparseElementsInternal(); + break; + case U32: + SortSparseElementsInternal(); + break; + case S64: + SortSparseElementsInternal(); + break; + case U64: + SortSparseElementsInternal(); + break; + case F32: + SortSparseElementsInternal(); + break; + case F64: + SortSparseElementsInternal(); + break; + case C64: + SortSparseElementsInternal(); + break; + case F16: + SortSparseElementsInternal(); + break; + case BF16: + SortSparseElementsInternal(); + break; + default: + LOG(FATAL) << "Element type not valid for sparse array: " + << PrimitiveType_Name(subshape().element_type()); + } +} + +template +void LiteralBase::Piece::SortSparseElementsInternal() { + CHECK(LayoutUtil::IsSparseArray(subshape())); + int64 num_elements = sparse_indices()->index_count(); + auto values = data(); + CHECK_LE(num_elements, values.size()); + sparse_indices()->SortWithValues( + tensorflow::gtl::MutableArraySlice(values.data(), num_elements)); +} + +namespace { + +void ToStringHelper(const LiteralBase& literal, const ShapeIndex& shape_index, + bool print_layout, std::vector* pieces) { + const Shape& subshape = ShapeUtil::GetSubshape(literal.shape(), shape_index); + CHECK(LayoutUtil::HasLayout(literal.shape())); + CHECK(LayoutUtil::HasLayout(subshape)); + + auto shape_to_string = [print_layout](const Shape& shape) { + if (print_layout) { + return ShapeUtil::HumanStringWithLayout(shape); + } else { + return ShapeUtil::HumanString(shape); + } + }; + + // TODO(b/32894291): refactor this code to reduce code duplication. + if (ShapeUtil::IsTuple(subshape)) { + pieces->push_back(shape_to_string(subshape)); + pieces->push_back(" (\n"); + std::vector tuple_pieces; + for (int i = 0; i < ShapeUtil::TupleElementCount(subshape); ++i) { + ShapeIndex element_index = shape_index; + element_index.push_back(i); + std::vector element_pieces; + ToStringHelper(literal, element_index, print_layout, &element_pieces); + tuple_pieces.push_back(tensorflow::str_util::Join(element_pieces, "")); + } + pieces->push_back(tensorflow::str_util::Join(tuple_pieces, ",\n")); + pieces->push_back("\n)"); + return; + } + + if (ShapeUtil::IsToken(subshape)) { + pieces->push_back("token"); + return; + } + + if (LayoutUtil::IsSparseArray(subshape)) { + pieces->push_back(shape_to_string(subshape)); + pieces->push_back("{"); + int64 rank = ShapeUtil::Rank(subshape); + int64 num_elements = literal.sparse_element_count(); + for (int64 i = 0; i < num_elements; ++i) { + if (i > 0) { + pieces->push_back(", "); + } + if (rank == 1) { + pieces->push_back(StrCat(literal.GetSparseIndex(i)[0])); + pieces->push_back(": "); + } else { + pieces->push_back("["); + pieces->push_back( + tensorflow::str_util::Join(literal.GetSparseIndex(i), ", ")); + pieces->push_back("]: "); + } + pieces->push_back(literal.GetSparseElementAsString(i)); + } + pieces->push_back("}"); + return; + } + + CHECK(LayoutUtil::IsDenseArray(subshape)); + + auto element_to_string = + [&](tensorflow::gtl::ArraySlice indices) -> string { + PrimitiveType element_type = subshape.element_type(); + if (element_type == PRED) { + // We display predicates in a densely packed form. + return literal.Get(indices, shape_index) ? "1" : "0"; + } + return ((!indices.empty() && indices.back() > 0) ? ", " : "") + + literal.GetAsString(indices, shape_index); + }; + + if (ShapeUtil::Rank(subshape) == 0) { + pieces->push_back(literal.GetAsString({}, shape_index)); + } else if (ShapeUtil::Rank(subshape) == 1) { + pieces->push_back("{"); + for (int64 i0 = 0; i0 < subshape.dimensions(0); ++i0) { + pieces->push_back(element_to_string({i0})); + } + pieces->push_back("}"); + } else if (ShapeUtil::Rank(subshape) == 2) { + pieces->push_back(shape_to_string(subshape)); + pieces->push_back(" {\n"); + for (int64 i0 = 0; i0 < subshape.dimensions(0); ++i0) { + pieces->push_back(" { "); + for (int64 i1 = 0; i1 < subshape.dimensions(1); ++i1) { + pieces->push_back(element_to_string({i0, i1})); + } + pieces->push_back(" "); + pieces->push_back(i0 == subshape.dimensions(0) - 1 ? "}\n" : "},\n"); + } + pieces->push_back("}"); + } else if (ShapeUtil::Rank(subshape) == 3) { + pieces->push_back(shape_to_string(subshape)); + pieces->push_back(" {\n"); + for (int64 i0 = 0; i0 < subshape.dimensions(0); ++i0) { + pieces->push_back(i0 > 0 ? ",\n{" : "{"); + for (int64 i1 = 0; i1 < subshape.dimensions(1); ++i1) { + pieces->push_back(i1 > 0 ? ",\n { " : " { "); + for (int64 i2 = 0; i2 < subshape.dimensions(2); ++i2) { + pieces->push_back(element_to_string({i0, i1, i2})); + } + pieces->push_back(" }"); + } + pieces->push_back(" }"); + } + pieces->push_back("\n}"); + } else if (ShapeUtil::Rank(subshape) == 4) { + pieces->push_back(shape_to_string(subshape)); + pieces->push_back(" {\n"); + for (int64 i0 = 0; i0 < subshape.dimensions(0); ++i0) { + pieces->push_back(Printf(" { /*i0=%lld*/\n", i0)); + for (int64 i1 = 0; i1 < subshape.dimensions(1); ++i1) { + pieces->push_back(Printf(" { /*i1=%lld*/\n", i1)); + for (int64 i2 = 0; i2 < subshape.dimensions(2); ++i2) { + pieces->push_back(" {"); + for (int64 i3 = 0; i3 < subshape.dimensions(3); ++i3) { + pieces->push_back(element_to_string({i0, i1, i2, i3})); + } + pieces->push_back(i2 == subshape.dimensions(2) - 1 ? "}\n" : "},\n"); + } + pieces->push_back(i1 == subshape.dimensions(1) - 1 ? " }\n" + : " },\n"); + } + pieces->push_back(i0 == subshape.dimensions(0) - 1 ? " }\n" : " },\n"); + } + pieces->push_back("}"); + } else if (ShapeUtil::Rank(subshape) == 5) { + pieces->push_back(shape_to_string(subshape)); + pieces->push_back(" {\n"); + for (int64 i0 = 0; i0 < subshape.dimensions(0); ++i0) { + pieces->push_back(Printf(" { /*i0=%lld*/\n", i0)); + for (int64 i1 = 0; i1 < subshape.dimensions(1); ++i1) { + pieces->push_back(Printf(" { /*i1=%lld*/\n", i1)); + for (int64 i2 = 0; i2 < subshape.dimensions(2); ++i2) { + pieces->push_back(Printf(" { /*i2=%lld*/\n", i2)); + for (int64 i3 = 0; i3 < subshape.dimensions(3); ++i3) { + pieces->push_back(" {"); + for (int64 i4 = 0; i4 < subshape.dimensions(4); ++i4) { + pieces->push_back(element_to_string({i0, i1, i2, i3, i4})); + } + pieces->push_back(i3 == subshape.dimensions(3) - 1 ? "}\n" + : "},\n"); + } + pieces->push_back(i2 == subshape.dimensions(2) - 1 ? " }\n" + : " },\n"); + } + pieces->push_back(i1 == subshape.dimensions(1) - 1 ? " }\n" + : " },\n"); + } + pieces->push_back(i0 == subshape.dimensions(0) - 1 ? " }\n" : " },\n"); + } + pieces->push_back("}"); + } else { + pieces->push_back(shape_to_string(subshape)); + pieces->push_back(" {"); + literal.EachCellAsString( + [&](tensorflow::gtl::ArraySlice indices, const string& value) { + pieces->push_back(" "); + pieces->push_back(value); + }); + pieces->push_back("}"); + } +} + +} // namespace + +int64 LiteralBase::sparse_element_count() const { + CHECK(LayoutUtil::IsSparseArray(shape())); + return sparse_indices()->index_count(); +} + +string LiteralBase::ToString(bool print_layout) const { + std::vector pieces; + CHECK(LayoutUtil::HasLayout(this->shape())); + ToStringHelper(*this, {}, print_layout, &pieces); + return tensorflow::str_util::Join(pieces, ""); +} + +void LiteralBase::EachCellAsString( + const std::function indices, + const string& value)>& per_cell) const { + if (ShapeUtil::IsZeroElementArray(shape())) { + return; + } + std::vector indices = IndexUtil::LinearIndexToMultidimensionalIndex( + shape(), /*linear_index=*/0); + do { + per_cell(indices, GetAsString(indices)); + } while (IndexUtil::BumpIndices(shape(), &indices)); +} + +namespace { +template +std::unique_ptr ConvertBetweenNativeTypesWithConverter( + const LiteralBase& src_literal, const ConverterType& converter) { + CHECK(ShapeUtil::IsArray(src_literal.shape())); + auto result_literal = MakeUnique(ShapeUtil::ChangeElementType( + src_literal.shape(), + primitive_util::NativeToPrimitiveType())); + auto src_data = src_literal.data(); + auto dest_data = result_literal->template data(); + int64 num_elements = src_literal.element_count(); + + for (int64 i = 0; i < num_elements; ++i) { + dest_data[i] = converter(src_data[i]); + } + return result_literal; +} + +template +std::unique_ptr ConvertBetweenNativeTypes( + const LiteralBase& src_literal) { + auto converter = [](NativeSrcT src) { return static_cast(src); }; + return ConvertBetweenNativeTypesWithConverter( + src_literal, converter); +} + +template +typename std::enable_if<(sizeof(NativeSrcT) == sizeof(NativeDestT)), + std::unique_ptr>::type +BitcastBetweenNativeTypes(const LiteralBase& src_literal) { + auto converter = [](NativeSrcT src) { + return tensorflow::bit_cast(src); + }; + return ConvertBetweenNativeTypesWithConverter( + src_literal, converter); +} + +// This template specialization is here to make the compiler happy. bit_cast has +// a static check that the types are the same size. This specialization should +// never be used because the source and destination types are checked for +// identical sizes higher up. +template +typename std::enable_if<(sizeof(NativeSrcT) != sizeof(NativeDestT)), + std::unique_ptr>::type +BitcastBetweenNativeTypes(const LiteralBase& src_literal) { + LOG(FATAL) << "Invalid bitcast between types of different sizes."; +} + +template +std::unique_ptr ConvertToC64(const LiteralBase& src_literal) { + CHECK(ShapeUtil::IsArray(src_literal.shape())); + auto result_literal = MakeUnique( + ShapeUtil::ChangeElementType(src_literal.shape(), C64)); + using NativeSrcT = + typename primitive_util::PrimitiveTypeToNative::type; + tensorflow::gtl::ArraySlice src_data = + src_literal.data(); + tensorflow::gtl::MutableArraySlice dest_data = + result_literal->data(); + int64 num_elements = src_literal.element_count(); + for (int64 i = 0; i < num_elements; ++i) { + dest_data[i] = complex64(static_cast(src_data[i]), 0); + } + return result_literal; +} + +template +std::unique_ptr ConvertIfTypesMatch(const LiteralBase& src_literal, + bool bitcast) { + CHECK_EQ(primitive_src_type, src_literal.shape().element_type()); + if (bitcast) { + return BitcastBetweenNativeTypes< + typename primitive_util::PrimitiveTypeToNative< + primitive_src_type>::type, + typename primitive_util::PrimitiveTypeToNative< + primitive_dest_type>::type>(src_literal); + } else { + return ConvertBetweenNativeTypes< + typename primitive_util::PrimitiveTypeToNative< + primitive_src_type>::type, + typename primitive_util::PrimitiveTypeToNative< + primitive_dest_type>::type>(src_literal); + } +} + +template +StatusOr> ConvertIfDestTypeMatches( + const LiteralBase& src_literal, PrimitiveType primitive_dest_type, + bool bitcast) { + switch (primitive_dest_type) { +#define CONVERT_IF_TYPES_MATCH(type) \ + case (type): \ + return ConvertIfTypesMatch(src_literal, \ + bitcast); + CONVERT_IF_TYPES_MATCH(PRED) + CONVERT_IF_TYPES_MATCH(S8) + CONVERT_IF_TYPES_MATCH(S32) + CONVERT_IF_TYPES_MATCH(S64) + CONVERT_IF_TYPES_MATCH(U8) + CONVERT_IF_TYPES_MATCH(U32) + CONVERT_IF_TYPES_MATCH(U64) + CONVERT_IF_TYPES_MATCH(F16) + CONVERT_IF_TYPES_MATCH(F32) + CONVERT_IF_TYPES_MATCH(F64) + CONVERT_IF_TYPES_MATCH(BF16) +#undef CONVERT_IF_TYPES_MATCH + case C64: + if (!bitcast) { + return ConvertToC64(src_literal); + } + break; + // Other types are not yet supported. + default: + break; + } + return Unimplemented( + "Converting from type %s to type %s is not implemented.", + PrimitiveType_Name(src_literal.shape().element_type()).c_str(), + PrimitiveType_Name(primitive_dest_type).c_str()); +} + +StatusOr> ConvertSwitch( + const LiteralBase& literal, PrimitiveType primitive_dest_type, + bool bitcast) { + TF_RET_CHECK(ShapeUtil::IsArray(literal.shape())); + if (literal.shape().element_type() == primitive_dest_type) { + return literal.CloneToUnique(); + } + switch (literal.shape().element_type()) { +#define CONVERT_IF_DEST_TYPE_MATCHES(type) \ + case (type): \ + return ConvertIfDestTypeMatches<(type)>(literal, primitive_dest_type, \ + bitcast); + CONVERT_IF_DEST_TYPE_MATCHES(PRED) + CONVERT_IF_DEST_TYPE_MATCHES(S8) + CONVERT_IF_DEST_TYPE_MATCHES(S32) + CONVERT_IF_DEST_TYPE_MATCHES(S64) + CONVERT_IF_DEST_TYPE_MATCHES(U8) + CONVERT_IF_DEST_TYPE_MATCHES(U32) + CONVERT_IF_DEST_TYPE_MATCHES(U64) + CONVERT_IF_DEST_TYPE_MATCHES(F16) + CONVERT_IF_DEST_TYPE_MATCHES(F32) + CONVERT_IF_DEST_TYPE_MATCHES(F64) + CONVERT_IF_DEST_TYPE_MATCHES(BF16) +#undef CONVERT_IF_DEST_TYPE_MATCHES + // Other types are not yet supported. + default: + return Unimplemented( + "%s from type %s to type %s is not implemented.", + (bitcast ? "Bitcast converting" : "Converting"), + PrimitiveType_Name(literal.shape().element_type()).c_str(), + PrimitiveType_Name(primitive_dest_type).c_str()); + } +} + +} // namespace + +StatusOr> LiteralBase::Convert( + PrimitiveType primitive_dest_type) const { + return ConvertSwitch(*this, primitive_dest_type, /*bitcast=*/false); +} + +StatusOr> LiteralBase::BitcastConvert( + PrimitiveType primitive_dest_type) const { + if (primitive_util::BitWidth(shape().element_type()) != + primitive_util::BitWidth(primitive_dest_type)) { + return InvalidArgument( + "Cannot bitcast convert from %s to %s, bit widths are different: %d != " + "%d", + PrimitiveType_Name(shape().element_type()).c_str(), + PrimitiveType_Name(primitive_dest_type).c_str(), + primitive_util::BitWidth(shape().element_type()), + primitive_util::BitWidth(primitive_dest_type)); + } + return ConvertSwitch(*this, primitive_dest_type, /*bitcast=*/true); +} + +StatusOr> LiteralBase::ConvertToShape( + const Shape& dest_shape, bool round_f32_to_bf16) const { + if (!ShapeUtil::IsTuple(dest_shape)) { + if (round_f32_to_bf16 && shape().element_type() == F32 && + dest_shape.element_type() == BF16) { + auto converter = [](float src) { + return tensorflow::bfloat16::round_to_bfloat16(src); + }; + return ConvertBetweenNativeTypesWithConverter(*this, + converter); + } + return Convert(dest_shape.element_type()); + } + std::vector elements; + for (int i = 0; i < ShapeUtil::TupleElementCount(shape()); ++i) { + auto element = LiteralSlice(*this, {i}); + TF_ASSIGN_OR_RETURN( + auto new_element, + element.ConvertToShape(ShapeUtil::GetSubshape(dest_shape, {i}))); + elements.push_back(std::move(*new_element)); + } + auto converted = MakeUnique(); + *converted = Literal::MoveIntoTuple(&elements); + return std::move(converted); +} + +/* static */ Literal Literal::MoveIntoTuple( + tensorflow::gtl::MutableArraySlice elements) { + std::vector element_shapes; + for (const Literal& element : elements) { + element_shapes.push_back(element.shape()); + } + Literal literal(ShapeUtil::MakeTupleShape(element_shapes), + /*allocate_arrays=*/false); + for (int i = 0; i < elements.size(); ++i) { + TF_CHECK_OK( + literal.MoveFrom(std::move(elements[i]), /*dest_shape_index=*/{i})); + } + return literal; +} + +template +bool LiteralBase::Piece::EqualElementsInternal( + const LiteralBase::Piece& other, std::vector* multi_index) const { + if (multi_index->size() == ShapeUtil::Rank(subshape())) { + return (Get(*multi_index) == other.Get(*multi_index)); + } + for (int64 i = 0; i < subshape().dimensions(multi_index->size()); ++i) { + multi_index->push_back(i); + if (!EqualElementsInternal(other, multi_index)) { + return false; + } + multi_index->pop_back(); + } + return true; +} + +bool LiteralBase::Piece::EqualElements(const LiteralBase::Piece& other) const { + DCHECK(ShapeUtil::Compatible(subshape(), other.subshape())); + + std::vector multi_index; + switch (subshape().element_type()) { + case PRED: + return EqualElementsInternal(other, &multi_index); + case U8: + return EqualElementsInternal(other, &multi_index); + case S32: + return EqualElementsInternal(other, &multi_index); + case S64: + return EqualElementsInternal(other, &multi_index); + case U32: + return EqualElementsInternal(other, &multi_index); + case U64: + return EqualElementsInternal(other, &multi_index); + case F32: + return EqualElementsInternal(other, &multi_index); + case F64: + return EqualElementsInternal(other, &multi_index); + case F16: + return EqualElementsInternal(other, &multi_index); + case BF16: + return EqualElementsInternal(other, &multi_index); + case C64: + return EqualElementsInternal(other, &multi_index); + default: + LOG(FATAL) << "Unimplemented: LiteralBase::Piece::EqualElements for type " + << PrimitiveType_Name(subshape().element_type()); + } +} + +bool LiteralBase::operator==(const LiteralBase& other) const { + if (!ShapeUtil::Compatible(shape(), other.shape())) { + return false; + } + + return root_piece().ForEachSubpieceWithBool( + [&](const ShapeIndex& index, const Piece& piece) { + if (!ShapeUtil::IsArray(piece.subshape())) { + return true; + } + + const Piece& other_piece = other.piece(index); + if (!piece.EqualElements(other_piece)) { + return false; + } + return true; + }); +} + +namespace { + +template +static bool AllElementsEqualValue(tensorflow::gtl::ArraySlice data, + NativeT value) { + for (int64 i = 0; i < data.size(); ++i) { + if (data[i] != value) { + return false; + } + } + return true; +} + +} // namespace + +bool LiteralBase::IsAll(int8 value) const { + return root_piece().ForEachSubpieceWithBool([&](const ShapeIndex& index, + const Piece& piece) { + if (!ShapeUtil::IsArray(piece.subshape())) { + return true; + } + + auto piece_is_all = [&]() { + switch (shape().element_type()) { + case U8: + if (value >= 0) { + return AllElementsEqualValue(piece.data(), value); + } + return false; + case U32: + if (value >= 0) { + return AllElementsEqualValue(piece.data(), value); + } + return false; + case U64: + if (value >= 0) { + return AllElementsEqualValue(piece.data(), value); + } + return false; + case S8: + return AllElementsEqualValue(piece.data(), value); + case S32: + return AllElementsEqualValue(piece.data(), value); + case S64: + return AllElementsEqualValue(piece.data(), value); + case F32: + return AllElementsEqualValue(piece.data(), value); + case F64: + return AllElementsEqualValue(piece.data(), value); + case F16: + return AllElementsEqualValue(piece.data(), + static_cast(value)); + case BF16: + return AllElementsEqualValue(piece.data(), + static_cast(value)); + case PRED: + if (value == 0) { + return AllElementsEqualValue(piece.data(), false); + } + if (value == 1) { + return AllElementsEqualValue(piece.data(), true); + } + return false; + default: + return false; + } + return false; + }; + + if (!piece_is_all()) { + return false; + } + return true; + }); +} + +bool LiteralBase::IsAllFloat(float value) const { + return root_piece().ForEachSubpieceWithBool( + [&](const ShapeIndex& index, const Piece& piece) { + if (!ShapeUtil::IsArray(piece.subshape())) { + return true; + } + + auto piece_is_all = [&]() { + switch (shape().element_type()) { + case F32: + return AllElementsEqualValue(piece.data(), value); + case F64: + return AllElementsEqualValue(piece.data(), value); + case F16: + return AllElementsEqualValue(piece.data(), + static_cast(value)); + case BF16: + return AllElementsEqualValue( + piece.data(), static_cast(value)); + default: + return false; + } + }; + if (!piece_is_all()) { + return false; + } + return true; + }); +} + +bool LiteralBase::IsAllComplex(complex64 value) const { + switch (shape().element_type()) { + case C64: + return AllElementsEqualValue(root_piece().data(), + value); + default: + return false; + } +} + +bool LiteralBase::IsAllFirst() const { + return root_piece().ForEachSubpieceWithBool( + [&](const ShapeIndex& index, const Piece& piece) { + if (!ShapeUtil::IsArray(piece.subshape())) { + return true; + } + + // Empty shapes are not all the first element since there is no first + // element. + if (ShapeUtil::IsZeroElementArray(piece.subshape())) { + return false; + } + auto piece_is_all = [&]() { + switch (piece.subshape().element_type()) { + case PRED: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + // 8 bit types + case S8: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case U8: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + // 16 bit types + case BF16: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case F16: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case S16: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case U16: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + // 32 bit types + case F32: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case U32: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case S32: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + // 64 bit types + case C64: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case F64: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case S64: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + case U64: { + auto data = piece.data(); + return AllElementsEqualValue(data, data[0]); + } + default: + return false; + } + }; + + if (!piece_is_all()) { + return false; + } + return true; + }); +} + +bool LiteralBase::IsZero(tensorflow::gtl::ArraySlice indices) const { + CHECK(ShapeUtil::IsArray(shape())); + switch (shape().element_type()) { + case U8: + return Get(indices) == 0; + case U32: + return Get(indices) == 0; + case U64: + return Get(indices) == 0; + case S8: + return Get(indices) == 0; + case S32: + return Get(indices) == 0; + case S64: + return Get(indices) == 0; + case F32: + return Get(indices) == 0.0f; + case F64: + return Get(indices) == 0.0; + case C64: + return Get(indices) == complex64(0.0f, 0.0f); + case F16: + return Get(indices) == static_cast(0.0f); + case BF16: + return Get(indices) == static_cast(0.0f); + case PRED: + return Get(indices) == false; + default: + LOG(FATAL) << "Input literal must be an array."; + } +} + +namespace { + +template +void CopyToRepeatedField(RepeatedFieldT* dest, + const tensorflow::gtl::ArraySlice src) { + *dest = RepeatedFieldT(src.begin(), src.end()); +} + +} // namespace + +void LiteralBase::Piece::WriteToProto(LiteralProto* proto) const { + *proto->mutable_shape() = subshape(); + switch (subshape().element_type()) { + case PRED: + CopyToRepeatedField(proto->mutable_preds(), data()); + break; + case U8: + proto->set_u8s(static_cast(data().data()), + element_count()); + break; + case U32: + CopyToRepeatedField(proto->mutable_u32s(), data()); + break; + case U64: + CopyToRepeatedField(proto->mutable_u64s(), data()); + break; + case S32: + CopyToRepeatedField(proto->mutable_s32s(), data()); + break; + case S64: + CopyToRepeatedField(proto->mutable_s64s(), data()); + break; + case F16: + *proto->mutable_f16s() = string( + reinterpret_cast(data().data()), size_bytes()); + if (!kLittleEndian) { + ConvertEndianShort(proto->mutable_f16s()); + } + break; + case BF16: + *proto->mutable_bf16s() = string( + reinterpret_cast(data().data()), size_bytes()); + if (!kLittleEndian) { + ConvertEndianShort(proto->mutable_bf16s()); + } + break; + case F32: + CopyToRepeatedField(proto->mutable_f32s(), data()); + break; + case F64: + CopyToRepeatedField(proto->mutable_f64s(), data()); + break; + case C64: + for (complex64 value : data()) { + proto->add_c64s(value.real()); + proto->add_c64s(value.imag()); + } + break; + case TUPLE: + case TOKEN: + // Nothing to do but assign the shape which is done above. + return; + default: + // TODO(b/111551621): Support serializing more PrimitiveTypes. + LOG(FATAL) << "Unhandled primitive type " + << PrimitiveType_Name(subshape().element_type()); + } +} + +const void* LiteralBase::Piece::untyped_data() const { + CHECK(ShapeUtil::IsArray(subshape())) << ShapeUtil::HumanString(subshape()); + return buffer(); +} + +void* LiteralBase::Piece::untyped_data() { + CHECK(ShapeUtil::IsArray(subshape())) << ShapeUtil::HumanString(subshape()); + return buffer(); +} + +namespace { + +template +Status CopyFromRepeatedField(tensorflow::gtl::MutableArraySlice dest, + const RepeatedFieldT& src) { + if (dest.size() != src.size()) { + return InvalidArgument( + "Expected %lu elements in LiteralProto repeated field, has %d", + dest.size(), src.size()); + } + std::copy(src.begin(), src.end(), dest.begin()); + return Status::OK(); +} + +} // namespace + +Status LiteralBase::Piece::CopyFromProto(const LiteralProto& proto) { + // These conditions should have been checked in Literal::CreateFromProto. + TF_RET_CHECK(proto.has_shape()); + TF_RET_CHECK(LayoutUtil::HasLayout(proto.shape())); + TF_RET_CHECK(ShapeUtil::Equal(proto.shape(), subshape())); + + switch (subshape().element_type()) { + case PRED: + TF_RETURN_IF_ERROR(CopyFromRepeatedField(data(), proto.preds())); + break; + case U8: { + auto u8_data = data(); + TF_RET_CHECK(proto.u8s().size() == u8_data.size()); + std::copy(proto.u8s().begin(), proto.u8s().end(), u8_data.begin()); + } break; + case S32: + TF_RETURN_IF_ERROR(CopyFromRepeatedField(data(), proto.s32s())); + break; + case S64: + TF_RETURN_IF_ERROR(CopyFromRepeatedField(data(), proto.s64s())); + break; + case U32: + TF_RETURN_IF_ERROR(CopyFromRepeatedField(data(), proto.u32s())); + break; + case U64: + TF_RETURN_IF_ERROR(CopyFromRepeatedField(data(), proto.u64s())); + break; + case F16: { + const string& s(proto.f16s()); + TF_RET_CHECK(data().size() * sizeof(half) == s.size()); + memcpy(untyped_data(), s.data(), s.size()); + if (!kLittleEndian) { + ConvertEndianShort(reinterpret_cast(untyped_data()), s.size()); + } + } break; + + case BF16: { + const string& s(proto.bf16s()); + TF_RET_CHECK(data().size() * sizeof(bfloat16) == s.size()); + memcpy(untyped_data(), s.data(), s.size()); + if (!kLittleEndian) { + ConvertEndianShort(reinterpret_cast(untyped_data()), s.size()); + } + } break; + case F32: + TF_RETURN_IF_ERROR(CopyFromRepeatedField(data(), proto.f32s())); + break; + case F64: + TF_RETURN_IF_ERROR(CopyFromRepeatedField(data(), proto.f64s())); + break; + case C64: { + auto complex_data = data(); + TF_RET_CHECK(proto.c64s_size() == complex_data.size() * 2); + for (int64 i = 0; i < complex_data.size(); ++i) { + complex_data[i] = complex64{proto.c64s(i * 2), proto.c64s(i * 2 + 1)}; + } + } break; + case TUPLE: + LOG(FATAL) << "Should not be called on tuple shapes: " + << ShapeUtil::HumanString(subshape()); + break; + default: + LOG(FATAL) << "Unhandled primitive type " << subshape().element_type(); + } + return Status::OK(); +} + +LiteralProto LiteralBase::ToProto() const { + LiteralProto proto; + root_piece().ForEachSubpiece( + [&](const ShapeIndex& index, const Piece& piece) { + LiteralProto* proto_piece = &proto; + for (int64 i : index) { + while (proto_piece->tuple_literals_size() <= i) { + proto_piece->add_tuple_literals(); + } + proto_piece = proto_piece->mutable_tuple_literals(i); + } + piece.WriteToProto(proto_piece); + }); + + if (LayoutUtil::IsSparseArray(shape())) { + CopyToRepeatedField(proto.mutable_sparse_indices(), + sparse_indices()->data()); + } + + return proto; +} + +const void* LiteralBase::untyped_data(const ShapeIndex& shape_index) const { + return piece(shape_index).untyped_data(); +} + +void* Literal::untyped_data(const ShapeIndex& shape_index) { + return piece(shape_index).untyped_data(); +} + +int64 LiteralBase::size_bytes(const ShapeIndex& shape_index) const { + return piece(shape_index).size_bytes(); +} + +string LiteralBase::GetR1U8AsString() const { + CHECK(ShapeUtil::IsArray(shape())); + CHECK_EQ(ShapeUtil::Rank(shape()), 1); + CHECK_EQ(shape().element_type(), U8); + return string(tensorflow::bit_cast(data().data()), + ShapeUtil::ElementsIn(shape())); +} + +void BorrowingLiteral::BuildPieceSubtree(const Shape& shape, Piece* piece) { + CHECK(ShapeUtil::IsTuple(shape)); + for (int i = 0; i < ShapeUtil::TupleElementCount(shape); ++i) { + const Shape& subshape = shape.tuple_shapes(i); + + auto child_piece = Piece(); + child_piece.set_subshape(&subshape); + + if (ShapeUtil::IsTuple(subshape)) { + BuildPieceSubtree(subshape, &child_piece); + } + + piece->emplace_back(std::move(child_piece)); + } +} + +LiteralSlice::LiteralSlice(const LiteralBase& literal) + : LiteralBase(), root_piece_(&literal.root_piece()) {} + +LiteralSlice::LiteralSlice(const LiteralBase& literal, + const ShapeIndex& view_root) + : LiteralBase(), root_piece_(&literal.piece(view_root)) {} + +BorrowingLiteral::BorrowingLiteral(const char* src_buf_ptr, const Shape& shape) + : LiteralBase(), shape_(MakeUnique(shape)) { + CHECK(ShapeUtil::IsArray(*shape_)); + CHECK(LayoutUtil::HasLayout(*shape_)); + + root_piece_ = Piece(); + root_piece_.set_buffer(const_cast(src_buf_ptr)); + root_piece_.set_subshape(shape_.get()); +} + +BorrowingLiteral::BorrowingLiteral( + tensorflow::gtl::ArraySlice src_buf_ptrs, const Shape& shape) + : LiteralBase(), shape_(MakeUnique(shape)) { + CHECK(ShapeUtil::IsTuple(*shape_)); + CHECK(!ShapeUtil::IsNestedTuple(*shape_)); + CHECK_EQ(src_buf_ptrs.size(), ShapeUtil::TupleElementCount(*shape_)); + root_piece_ = Piece(); + root_piece_.set_subshape(shape_.get()); + BuildPieceSubtree(*shape_, &root_piece_); + + for (int i = 0; i < src_buf_ptrs.size(); ++i) { + const auto& src_shape = shape_->tuple_shapes(i); + CHECK(ShapeUtil::IsArray(src_shape)); + root_piece_.child(i).set_buffer(const_cast(src_buf_ptrs[i])); + } +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/literal.h b/tensorflow/compiler/xla/literal.h new file mode 100644 index 0000000000000000000000000000000000000000..dd67dfa8d4a556aea179bc47abfdc9a9c8872c45 --- /dev/null +++ b/tensorflow/compiler/xla/literal.h @@ -0,0 +1,1152 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_LITERAL_H_ +#define TENSORFLOW_COMPILER_XLA_LITERAL_H_ + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "tensorflow/compiler/xla/array2d.h" +#include "tensorflow/compiler/xla/array3d.h" +#include "tensorflow/compiler/xla/array4d.h" +#include "tensorflow/compiler/xla/index_util.h" +#include "tensorflow/compiler/xla/layout_util.h" +#include "tensorflow/compiler/xla/primitive_util.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/sparse_index_array.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/bitmap.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/core/stringpiece.h" +#include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/macros.h" +#include "tensorflow/core/platform/protobuf.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +// Forward declare Literal and LiteralSlice class to be used by the creation +// methods in the base class. +class Literal; +class LiteralSlice; + +// Abstract base class for literals. +class LiteralBase { + public: + virtual ~LiteralBase() = 0; + + // Literals are equal if they have compatible shapes and the same data + // values. Layout is not compared. + bool operator==(const LiteralBase& other) const; + bool operator!=(const LiteralBase& other) const { return !(*this == other); } + + // Returns the shape of the literal. + const Shape& shape() const { return root_piece().subshape(); } + + // Serialize to proto. + LiteralProto ToProto() const; + + // Returns an ArraySlice of the array for this literal for the given NativeT + // (e.g., float). CHECKs if the subshape of the literal at the given + // ShapeIndex is not array. See primitive_util.h for the mapping from XLA type + // to native type. + template + tensorflow::gtl::ArraySlice data( + const ShapeIndex& shape_index = {}) const; + + // Returns a const pointer to the sparse index array. Returns nullptr if the + // literal is not a sparse array. + const SparseIndexArray* sparse_indices( + const ShapeIndex& shape_index = {}) const; + + // Returns a const pointer to (or size of) the underlying buffer holding the + // array at the given shape index. CHECKs if the subshape of the literal at + // the given ShapeIndex is not array. + const void* untyped_data(const ShapeIndex& shape_index = {}) const; + int64 size_bytes(const ShapeIndex& shape_index = {}) const; + + // Returns this literal's data as a string. This literal must be a rank-1 U8 + // array. + string GetR1U8AsString() const; + + // Returns a string representation of the literal value. + // Warning: this function can take minutes for multi-million element Literals. + string ToString(bool print_layout = false) const; + + // Gets an element in the literal at the given index. The multi_index is + // CHECKed against the dimension sizes. + template + NativeT Get(tensorflow::gtl::ArraySlice multi_index, + const ShapeIndex& shape_index) const; + // Overloads of Get for array literals. CHECKs if the literal is not + // array-shaped and dense. + template + NativeT Get(tensorflow::gtl::ArraySlice multi_index) const; + + // Returns the element value at index (0, ..., 0), however many zeroes are + // required for that index. + template + NativeT GetFirstElement() const; + + // As Get(), but determines the correct type and converts the value + // into text. + string GetAsString(tensorflow::gtl::ArraySlice multi_index, + const ShapeIndex& shape_index = {}) const; + // As GetSparseElement(), but determines the correct type and converts the + // value into text. + string GetSparseElementAsString(int64 sparse_element_number, + const ShapeIndex& shape_index = {}) const; + // As Get(), but determines the correct type and converts the value into + // int64. This literal must be an array. + StatusOr GetIntegralAsS64( + tensorflow::gtl::ArraySlice multi_index) const; + + // Returns the multi-index of the element in a sparse literal at the given + // sparse element number. The sparse element number is the position with in + // the sparse array's list of (index, value) pairs, and is checked against the + // total number of (index, value) pairs in the sparse array. + tensorflow::gtl::ArraySlice GetSparseIndex( + int64 sparse_element_number, const ShapeIndex& shape_index = {}) const; + + // Returns the value of the element in a sparse literal at the given sparse + // element number. The sparse element number is the position with in the + // sparse array's list of (index, value) pairs, and is checked against the + // total number of (index, value) pairs in the sparse array. + template + NativeT GetSparseElement(int64 sparse_element_number, + const ShapeIndex& shape_index = {}) const; + + // Invokes the "per cell" callback for each element in the provided + // literal with the element's indices and a string representation of + // the element's value. + // + // This function is useful if you want a polymorphic representation + // of the tensor's elements (turning it to a string for something + // like representation in a protobuf). + // + // This literal must have a dense layout. + void EachCellAsString( + const std::function indices, + const string& value)>& per_cell) const; + template + void EachCell(std::function indices, + NativeT value)> + per_cell) const; + + // Returns whether every element in this literal is equal to value. + // + // value is an int8 because we expect this to be called with small + // compile-time constants (0, -1, etc.) and so that whatever value you pass + // can be represented exactly by floating-point types as small as 16 bits. + // + // If value doesn't fit in this literal's type, returns false. Values of 1/0 + // are considered equal to true/false; other values are not considered equal + // to true. Also if this literal is not array-shaped false is returned. + bool IsAll(int8 value) const; + + // Like IsAll(const Literal&, int8), except we check whether the literal is + // equal to a particular floating-point number. + // + // If the literal is not a floating-point value, this always returns false. + // + // This casts value to the type of literal, then compares using ==. The usual + // admonishments about floating-point equality checks apply. We expect you to + // use this to check for values that can be expressed precisely as a float, + // e.g. -0.5. Also if this literal is not array-shaped false is returned. + bool IsAllFloat(float value) const; + + // Like IsAll(const Literal&, int8), except we check whether the literal is + // equal to a particular complex number. + // + // If the literal is not a complex value, this always returns false. + // + // This casts value to the type of literal, then compares using ==. The usual + // admonishments about floating-point equality checks apply. We expect you to + // use this to check for complex values that can be expressed precisely as + // float pairs e.g. (-0.5, 1.0). + // + // This literal must have a dense layout. + bool IsAllComplex(complex64 value) const; + + // Literal consists entirely of the first element of the literal. + bool IsAllFirst() const; + + // Returns whether this literal is zero at the specified index. This literal + // must be an array with a dense layout. + bool IsZero(tensorflow::gtl::ArraySlice indices) const; + + // Returns the count of the elements in the array at the given shape index in + // this literal. + int64 element_count(const ShapeIndex& index = {}) const { + return ShapeUtil::ElementsIn(ShapeUtil::GetSubshape(shape(), index)); + } + + // Returns the count of the elements in the sparse array at the given shape + // index in this literal, which will be no larger than + // LayoutUtil::MaxSparseElements(SetSubshape(shape(), index).layout()). + int64 sparse_element_count() const; + + // Compute a hash for this literal. This literal must not be a sparse tensor + // or a tuple containing a sparse tensor. + size_t Hash() const; + + // Converts this literal to the given shape. Returns an error is the + // conversion is not possible. + // + // round_f32_to_bf16: if true, converting F32 elements to BF16 uses rounding + // instead of truncation; otherwise, truncation is used. + // + // TODO(b/69266521): remove the round_to_bfloat16 flag when rounding becomes + // the default behavior. + StatusOr> ConvertToShape( + const Shape& dest_shape, bool round_f32_to_bf16 = false) const; + + // Converts this literal to another primitive type using a bitcast + // conversion. The to and from primitive types must have the same bit + // width. Returns an error if the conversion is not possible. This literal + // must be array-shaped. + StatusOr> BitcastConvert( + PrimitiveType primitive_dest_type) const; + + // Converts this literal to another primitive type. Returns an error if the + // conversion is not possible. This literal must be array-shaped. + StatusOr> Convert( + PrimitiveType primitive_dest_type) const; + + // Clones the underlying buffers into a new Literal, or new + // std::unique_ptr. + Literal Clone() const; + std::unique_ptr CloneToUnique() const; + + // TODO(b/67651157): The methods below which perform computation on Literals + // (Reshape, Slice, etc) should be moved elsewhere, and perhaps combined with + // evaluator code which operates on Literals. + // + // Creates a new value that has the equivalent value as this + // literal, but conforms to new_layout; e.g. a literal matrix that was in {0, + // 1} minor-to-major dimension layout can be re-layed-out as {1, 0} + // minor-to-major dimension layout and the value in the cell at any given + // logical index (i0, i1) will be the same. + // + // For tuple shaped literals, shape_index should be used to select the inner + // array that the new layout applies to. + // + // Note: this is useful when the client wants to ensure that a value placed in + // the XLA allocation tracker has a particular layout; for efficiency + // purposes or avoiding unimplemented operation/layout combinations. + std::unique_ptr Relayout(const Layout& new_layout, + const ShapeIndex& shape_index = {}) const; + + // An overload of Relayout which changes the layout of the entire shape rather + // than being limited to a single array within the shape. + std::unique_ptr Relayout(const Shape& shape_with_layout) const; + + // Creates a new literal by reshaping this literal to have the given + // dimensions. The total number of elements must not change; The + // implementation currently only supports monotonic dim0-major layouts. + // This literal must be an array. + StatusOr> Reshape( + tensorflow::gtl::ArraySlice dimensions) const; + + // Creates a new literal by broadcasting this literal with `dimensions` to + // yield a literal of shape `result_shape`. + StatusOr> Broadcast( + const Shape& result_shape, + tensorflow::gtl::ArraySlice dimensions) const; + + // Creates a new literal by reordering the dimensions of this literal. + // The given `permutation` must be a permutation of the dimension numbers + // in the original literal, and it specifies the order of the new dimensions + // in the result literal (i.e., new_order[i] = old_order[permutation[i]]). + // For example, a transpose call on a literal of shape [3 x 8 x 4] and + // `permutation` = {2, 0, 1} returns a new literal of shape [4 x 3 x 8]. + // This literal must be an array. + std::unique_ptr Transpose( + tensorflow::gtl::ArraySlice permutation) const; + + // Creates a sub-array from this literal by extracting the indices + // [start_index, limit_index) of each dimension. The result literal has the + // same rank and layout as for the given literal. The number of indices in + // start_indices and limit_indices must be the rank of the literal, and the + // indices follow the order of the dimensions. + // This literal must be an array. + std::unique_ptr Slice( + tensorflow::gtl::ArraySlice start_indices, + tensorflow::gtl::ArraySlice limit_indices) const; + + // Creates a literal with a prepended dimension with bound "times"; e.g. a + // f32[3x2] with times=4 will produce a f32[4x3x2] with the 3x2 from this + // literal replicated four times. + // This literal must be an array. + template + std::unique_ptr Replicate(int64 times) const; + + // Creates a new Literal object with the shape specified as parameter. + // The content of the literal values is the default value of the primitive + // type of literal itself (0 for numeric types, and false for predicates). + // + // Note: It's an antipattern to use this method then immediately call + // Literal::Populate on the result (since that results in zero initialization, + // then reinitialization. Conside if a call to MakeUnique(shape), + // followed by the call to Literal::Populate can be used instead. + static std::unique_ptr CreateFromShape(const Shape& shape); + + protected: + // A data structure representing a subshape at a particular ShapeIndex within + // the literal. For array-shaped ShapeIndexes, this data structure holds the + // pointer to the memory allocated for the array data. + class Piece { + public: + // Returns the buffer holding the array data for this piece as an array + // slice. This piece must be array-shaped. + template + tensorflow::gtl::ArraySlice data() const; + template + tensorflow::gtl::MutableArraySlice data(); + + // Returns the buffer holding the array data for this piece as a void*. This + // piece must be array-shaped. + void* untyped_data(); + const void* untyped_data() const; + + // Gets or sets an element in the array at the given index. The multi_index + // is CHECKed against the dimension sizes of the array. This piece must be + // array-shaped. + template + NativeT Get(tensorflow::gtl::ArraySlice index) const; + template + void Set(tensorflow::gtl::ArraySlice index, NativeT value); + + // Gets/sets the buffer holding the array data. + char* buffer() const { return buffer_; } + void set_buffer(char* buffer) { buffer_ = buffer; } + + // The array of multi-indices that provide the locations of non-zero + // elements in a sparse array. Only used if + // LayoutUtil::IsSparseArray(shape()) is true. + SparseIndexArray* sparse_indices() const { return sparse_indices_; } + void set_sparse_indices(SparseIndexArray* sparse_indices) { + sparse_indices_ = sparse_indices; + } + + // Gets or sets the subshape of this piece. This reference points to a + // subshape within the shape in the containing Literal (Literal::shape_). + const Shape& subshape() const { return *subshape_; } + void set_subshape(const Shape* subshape) { subshape_ = subshape; } + + // Returns the size in bytes of the buffer holding the array data. + int64 size_bytes() const { return ShapeUtil::ByteSizeOf(subshape()); } + + // Returns the number of elements in this piece's array. + int64 element_count() const { + // If this is a sparse array, use the number of elements represented by + // the indices in the associated SparseIndexArray. + return LayoutUtil::IsSparseArray(subshape()) + ? sparse_indices()->index_count() + : ShapeUtil::ElementsIn(subshape()); + } + + // Returns the child piece at 'index' of this piece. + Piece& child(int64 index) { return children_[index]; } + + // Adds a child piece to this piece's children. + void emplace_back(Piece child_piece) { + children_.emplace_back(std::move(child_piece)); + } + + // Returns the size of children pieces of this piece. + int64 children_size() { return children_.size(); } + + // Visitor functions that recursively traverses the piece and calls the + // given function at each child piece. The function has the type: + // void (const ShapeIndex& index, const Piece& piece) + template + void ForEachSubpiece(const Fn& func) const { + ShapeIndex index; + return ForEachHelper( + [&func](const ShapeIndex& index, const Piece& piece) { + func(index, piece); + return Status::OK(); + }, + *this, &index) + .IgnoreError(); + } + // Same as above, but the function has the type: + // Status (const ShapeIndex& index, const Piece& piece) + // The first non-OK return value is returned by the function. + template + Status ForEachSubpieceWithStatus(const Fn& func) const { + ShapeIndex index; + return ForEachHelper(func, *this, &index); + } + // Same as above, but the function has the type: + // Bool (const ShapeIndex& index, const Piece& piece) + // The first non-true return value is returned by the function. + template + bool ForEachSubpieceWithBool(const Fn& func) const { + ShapeIndex index; + return ForEachHelperBool(func, *this, &index); + } + // Same as above, but the function has the type: + // Void (const ShapeIndex& index, Piece& piece) + template + void ForEachMutableSubpiece(const Fn& func) { + ShapeIndex index; + return ForEachMutableHelper( + [&func](const ShapeIndex& index, Piece* piece) { + func(index, piece); + return Status::OK(); + }, + const_cast(this), &index) + .IgnoreError(); + } + // Same as above, but the function has the type: + // Status (const ShapeIndex& index, Piece& piece) + // The first non-OK return value is returned by the function. + template + Status ForEachMutableSubpieceWithStatus(const Fn& func) { + ShapeIndex index; + return ForEachMutableHelper( + func, const_cast(this), &index); + } + + // Returns true if this piece and 'other' contain the same data. This piece + // and 'other' must be array-shaped and compatible. + bool EqualElements(const Piece& other) const; + + // Writes the shape and data (if array-shaped) into the given proto. + void WriteToProto(LiteralProto* proto) const; + + // Copy the data from 'src' into this piece's buffer. Shapes of this piece + // and src must be compatible. + Status CopyFrom(const Piece& src); + + // Copies the data from the given proto into this piece. The shape of this + // piece must be equal (not just compatible) to the shape of the proto. + Status CopyFromProto(const LiteralProto& proto); + + // Sorts the elements in a sparse array. + void SortSparseElements(); + + private: + // Helpers for traversing the piece via ForEachSubpiece rooted at 'index'. + // The first non-OK (or non-true) value is returned by the function. + // The callable 'func' has the same signature as described above in + // ForEachSubpiece*. + template + Status ForEachHelper(const Fn& func, const Piece& piece, + ShapeIndex* index) const { + TF_RETURN_IF_ERROR(func(*index, piece)); + for (int64 i = 0; i < piece.children_.size(); ++i) { + index->push_back(i); + TF_RETURN_IF_ERROR(ForEachHelper(func, piece.children_[i], index)); + index->pop_back(); + } + return Status::OK(); + } + template + bool ForEachHelperBool(const Fn& func, const Piece& piece, + ShapeIndex* index) const { + if (!func(*index, piece)) { + return false; + } + for (int64 i = 0; i < piece.children_.size(); ++i) { + index->push_back(i); + if (!ForEachHelperBool(func, piece.children_[i], index)) { + return false; + } + index->pop_back(); + } + return true; + } + template + Status ForEachMutableHelper(const Fn& func, Piece* piece, + ShapeIndex* index) { + TF_RETURN_IF_ERROR(func(*index, piece)); + for (int64 i = 0; i < piece->children_.size(); ++i) { + index->push_back(i); + TF_RETURN_IF_ERROR( + ForEachMutableHelper(func, &piece->children_[i], index)); + index->pop_back(); + } + return Status::OK(); + } + + // Recursive helper for EqualElements. + template + bool EqualElementsInternal(const Piece& other, + std::vector* multi_index) const; + + // Helper for SortSparseElements that has the element type as a template + // parameter. + template + void SortSparseElementsInternal(); + + // For array-shaped pieces, this is the buffer holding the literal data. + char* buffer_ = nullptr; + + // For sparse arrays, this is the array of indices. + SparseIndexArray* sparse_indices_ = nullptr; + + // The shape of piece. This points into the shape of the containing Literal + // (Literal::shape_). + const Shape* subshape_ = nullptr; + + // Children pieces for tuple shaped pieces. + std::vector children_ = {}; + }; // class Piece + + const Piece& piece(const ShapeIndex& shape_index) const { + Piece* piece = &const_cast(root_piece()); + for (const auto i : shape_index) { + DCHECK_GE(i, 0); + DCHECK_LT(i, piece->children_size()); + piece = &piece->child(i); + } + return *piece; + } + + // Returns the piece at the root of the shape. + virtual const Piece& root_piece() const = 0; + + // LiteralSlice and Literal must access Pieces of other Literals. + friend class Literal; + friend class LiteralSlice; + friend class BorrowingLiteral; + + private: + template + std::unique_ptr SliceInternal( + const Shape& result_shape, + tensorflow::gtl::ArraySlice start_indices) const; +}; + +// Class representing literal values in XLA. +// +// The underlying buffer and shape is always owned by this class. +class Literal : public LiteralBase { + public: + Literal() : Literal(ShapeUtil::MakeNil()) {} + + // Create a literal of the given shape. The literal is allocated sufficient + // memory to hold the shape. Memory is uninitialized. + explicit Literal(const Shape& shape); + virtual ~Literal(); + + // Literals are moveable, but not copyable. To copy a literal use + // Literal::Clone or Literal::CloneToUnique. This prevents inadvertent copies + // of literals which can be expensive. + Literal(const Literal& other) = delete; + Literal& operator=(const Literal& other) = delete; + Literal(Literal&& other); + // 'allocate_arrays' indicates whether to allocate memory for the arrays in + // the shape. If false, buffer pointers inside of the Literal::Pieces are set + // to nullptr. + Literal(const Shape& shape, bool allocate_arrays); + Literal& operator=(Literal&& other); + + // TODO(b/67651157): Remove this accessor. Literal users should not be able to + // mutate the shape as this can produce malformed Literals. + Shape* mutable_shape_do_not_use() { return shape_.get(); } + + // Returns a MutableArraySlice view of the array for this literal for the + // given NativeT (e.g., float). CHECKs if the subshape of the literal at the + // given ShapeIndex is not array. See primitive_util.h for the mapping from + // XLA type to native type. + template + tensorflow::gtl::MutableArraySlice data( + const ShapeIndex& shape_index = {}); + // Unhide const method from parent class. + using LiteralBase::data; + + // Returns a pointer to the sparse index array. Returns nullptr if the literal + // is not a sparse array. + SparseIndexArray* sparse_indices(const ShapeIndex& shape_index = {}); + + // Returns a pointer to the underlying buffer holding the array at the given + // shape index. CHECKs if the subshape of the literal at the given ShapeIndex + // is not array. + void* untyped_data(const ShapeIndex& shape_index = {}); + // Unhide const method from parent class. + using LiteralBase::untyped_data; + + // Populates a literal with a sparse layout with the given indices and values. + // Each index in the indices array is CHECKed against the dimensions in the + // literal's shape. If sort is true, then the indices and values will be + // sorted. If sort is false, then the indices and values are assumed to + // already be in sorted order. See CreateSparse for an example of how data + // are populated. + template + void PopulateSparse(SparseIndexArray indices, + tensorflow::gtl::ArraySlice values, + bool sort = true); + + // Copy values from 'src_literal' rooted at 'src_shape_index' into this + // literal rooted at 'dest_shape_index'. The subshape of this literal rooted + // at 'dest_shape_index' must be compatible with the subshape of 'src_literal' + // rooted at 'src_shape_index', but need not be arrays. + Status CopyFrom(const LiteralSlice& src_literal, + const ShapeIndex& dest_shape_index = {}, + const ShapeIndex& src_shape_index = {}); + + // Returns a vector containing the tuple elements of this Literal as separate + // Literals. This Literal must be tuple-shaped and can be a nested tuple. The + // elements are moved into the new Literals; no data is copied. Upon return + // this Literal is set to a nil shape (empty tuple) + std::vector DecomposeTuple(); + + // Similar to CopyFrom, but with move semantincs. The subshape of this literal + // rooted at 'dest_shape_index' must be *equal* to the shape 'src_literal' + // (layouts and shapes must match), but need not be arrays. The memory + // allocated in this literal for the subshape at dest_shape_index is + // deallocated, and the respective buffers are replaced with those in + // src_literal. Upon return, src_literal is set to a nil shape (empty tuple). + Status MoveFrom(Literal&& src_literal, + const ShapeIndex& dest_shape_index = {}); + + // Copies the values from src_literal, starting at src_base shape indexes, + // to this literal, starting at dest_base, where the copy size in each + // dimension is specified by copy_size. + // The src_literal and this literal must have the same primitive type, + // src_base+copy_size must fit the source literal dimensions, as well as + // dest_base+copy_size must fit the destination literal dimensions. + // Note: if either src_literal or this literal contains dimensions with zero + // element, then copy_size must be 0 in these dimensions while the + // corresponding base indices being 0. + // This literal and 'src_literal' must be arrays. + Status CopySliceFrom(const LiteralSlice& src_literal, + tensorflow::gtl::ArraySlice src_base, + tensorflow::gtl::ArraySlice dest_base, + tensorflow::gtl::ArraySlice copy_size); + + // Copies one element from src_literal[src_index] to (*this)[dest_index]. + Status CopyElementFrom(const LiteralSlice& src_literal, + tensorflow::gtl::ArraySlice src_index, + tensorflow::gtl::ArraySlice dest_index); + + // Sets an element in the literal at the given index. The multi_index is + // CHECKed against the dimension sizes. + template + void Set(tensorflow::gtl::ArraySlice multi_index, + const ShapeIndex& shape_index, NativeT value); + // Overloads of Set for array literals. CHECKs if the literal is not + // array-shaped and dense. + template + void Set(tensorflow::gtl::ArraySlice multi_index, NativeT value); + + // Appends the given element to the literal. If the elements are not appended + // in sorted order, then SortSparseElements should be called before calling + // other methods. This literal must have a sparse layout. + template + void AppendSparseElement(tensorflow::gtl::ArraySlice multi_index, + NativeT value, const ShapeIndex& shape_index = {}); + + // Sorts the elements in a sparse array. + void SortSparseElements(const ShapeIndex& shape_index = {}); + + // As Set(), but truncates `value` to the literal element type before storing. + // This literal must be an array. + Status SetIntegralAsS64(tensorflow::gtl::ArraySlice multi_index, + int64 value); + + // Populate this literal with the given values. Examples: + // + // // Populate with floats. + // Array2D float_values = ... + // literal.PopulateR2FromArray2D(values); + // + // // Populate with int32s. + // literal.PopulateR2({{1, 2}, {3, 4}}); + // + // The shape and element type of this literal must match given values. For + // example, in the call above to literal.PopulateR2(), 'literal' must be a 2x2 + // array of S32. + template + void PopulateR1(tensorflow::gtl::ArraySlice values); + void PopulateR1(const tensorflow::core::Bitmap& values); + template + void PopulateR2(std::initializer_list> values); + template + void PopulateFromArray(const Array& values); + template + void PopulateR2FromArray2D(const Array2D& values); + template + void PopulateR3FromArray3D(const Array3D& values); + template + void PopulateR4FromArray4D(const Array4D& values); + + // Populates literal values by calling the generator function for every cell + // in this literal object. + // + // generator must be a callable of the type + // NativeT(tensorflow::gtl::ArraySlice indexes) or compatible. + // + // This literal must have a dense layout. + template + Status Populate(const FnType& generator); + + // A parallel version of Populate(). This can be used if the generator is + // thread-safe and the values for the shape's different elements are + // independent. + template + Status PopulateParallel(const FnType& generator); + + // Fills this literal with the given value. + template + void PopulateWithValue(NativeT value); + + // This operation is the inverse of DecomposeTuple. The given elements are + // moved into the tuple elements of a new tuple-shaped Literal which is + // returned. Upon return, each of the Literals in 'elements' is set to a nil + // shape (empty tuple). + static Literal MoveIntoTuple( + tensorflow::gtl::MutableArraySlice elements); + + // Serialize from a proto. + static StatusOr> CreateFromProto( + const LiteralProto& proto); + + private: + // Recursively sets the subshapes and buffers of all subpieces rooted at + // 'piece'. If 'allocate_array' is true, memory is allocated for the arrays in + // the shape. + void SetPiece(const Shape& shape, Piece* piece, bool allocate_arrays); + + // Returns the piece at the given ShapeIndex. + Piece& piece(const ShapeIndex& shape_index) { + return const_cast(LiteralBase::piece(shape_index)); + } + + Piece& root_piece() const override { return *root_piece_; }; + + // Internal template helper for the Literal::CopySliceFrom(), matching its + // arguments one by one. + template + Status CopySliceFromInternal(const LiteralBase& src_literal, + tensorflow::gtl::ArraySlice src_base, + tensorflow::gtl::ArraySlice dest_base, + tensorflow::gtl::ArraySlice copy_size); + + // Utility structure which is used to create the optimal configuration for + // a ShapeUtil::ForEachIndex() scan across two literals. + struct StrideConfig { + StrideConfig(const Shape& source_shape, const Shape& dest_shape, + tensorflow::gtl::ArraySlice dimensions); + + // The dimensions of the stride operation. Essentially every dimension + // will be iterated from base[i] to base[i]+dimensions[i], in step[i] + // steps. + tensorflow::gtl::ArraySlice dimensions; + DimensionVector base; + DimensionVector step; + int64 minor_dimension = 0; + // The size of the strides for source and destination. One of the two + // (the one looping through its most minor dimension) will be 1, while + // the other will be the stride size at the dimension matching the other + // shape most minor dimension being scanned. + int64 dest_stride = 1; + int64 source_stride = 1; + // The size of the inner loop on the most minor dimension. + int64 minor_loop_size = 1; + }; + + // Literal class always owns the shape. The parent class borrows this shape. + std::unique_ptr shape_; + + Piece* root_piece_ = nullptr; + + // Implementation details shared between Populate() and PopulateParallel() + template + Status PopulateInternal(const FnType& generator, bool parallel); + + // Deallocate the buffers held by this literal. + void DeallocateBuffers(); + + friend class LiteralBase; +}; +std::ostream& operator<<(std::ostream& out, const Literal& literal); + +// A read-only view of a Literal. A LiteralSlice contains pointers to shape and +// literal buffers always owned by others. +class LiteralSlice : public LiteralBase { + public: + LiteralSlice() : LiteralBase() {} + + // Implicit conversion constructors. + LiteralSlice(const LiteralBase& literal); + LiteralSlice(const LiteralBase& literal, const ShapeIndex& view_root); + + private: + const Piece& root_piece() const override { return *root_piece_; }; + + const Piece* root_piece_; // Not owned. +}; + +// A read-only Literal where the underlying buffers are never owned by this +// class. +class BorrowingLiteral : public LiteralBase { + public: + BorrowingLiteral() : LiteralBase() {} + + // 'src_buf_ptr' is not owned by this class and must outlive the + // lifetime of this class. It points to an appropirately sized buffer with + // data interpretered as indicated by 'shape'. + // This constructor is only used for array shapes. + BorrowingLiteral(const char* src_buf_ptr, const Shape& shape); + // Similar as above, except to be used for constructing non-nested tuples. + BorrowingLiteral(tensorflow::gtl::ArraySlice src_buf_ptrs, + const Shape& shape); + // TODO(b/79707221): adding constructors for nested tuples as well. + + private: + // Recursively builds the subtree for the given piece and sets the subshapes + // of the given piece with the given shape. + void BuildPieceSubtree(const Shape& shape, Piece* piece); + + // Accessor for the root piece of this literal. + const Piece& root_piece() const override { return root_piece_; }; + Piece root_piece_; + + // Shape of this literal. Stored as unique_ptr so such that the (default) + // move construction of this class would be trivially correct: the pointer to + // Shape root_piece_ stores will still point to the correct address. + std::unique_ptr shape_; +}; + +template +tensorflow::gtl::ArraySlice LiteralBase::Piece::data() const { + CHECK(ShapeUtil::IsArray(subshape())) << ShapeUtil::HumanString(subshape()); + CHECK_EQ(subshape().element_type(), + primitive_util::NativeToPrimitiveType()) + << "Attempting to access " + << PrimitiveType_Name(primitive_util::NativeToPrimitiveType()) + << " type, but literal element type is " + << PrimitiveType_Name(subshape().element_type()); + return tensorflow::gtl::ArraySlice( + reinterpret_cast(buffer()), element_count()); +} + +template +tensorflow::gtl::MutableArraySlice LiteralBase::Piece::data() { + CHECK(ShapeUtil::IsArray(subshape())) << ShapeUtil::HumanString(subshape()); + CHECK_EQ(subshape().element_type(), + primitive_util::NativeToPrimitiveType()) + << "Attempting to access " + << PrimitiveType_Name(primitive_util::NativeToPrimitiveType()) + << " type, but literal element type is " + << PrimitiveType_Name(subshape().element_type()); + return tensorflow::gtl::MutableArraySlice( + reinterpret_cast(buffer()), element_count()); +} + +template +NativeT LiteralBase::Piece::Get( + tensorflow::gtl::ArraySlice multi_index) const { + CHECK(LayoutUtil::IsDenseArray(subshape())); + return data()[IndexUtil::MultidimensionalIndexToLinearIndex( + subshape(), multi_index)]; +} + +template +void LiteralBase::Piece::Set(tensorflow::gtl::ArraySlice multi_index, + NativeT value) { + CHECK(LayoutUtil::IsDenseArray(subshape())); + data()[IndexUtil::MultidimensionalIndexToLinearIndex( + subshape(), multi_index)] = value; +} + +template +tensorflow::gtl::ArraySlice LiteralBase::data( + const ShapeIndex& shape_index) const { + return piece(shape_index).data(); +} + +template +tensorflow::gtl::MutableArraySlice Literal::data( + const ShapeIndex& shape_index) { + return piece(shape_index).data(); +} + +template +inline NativeT LiteralBase::Get(tensorflow::gtl::ArraySlice multi_index, + const ShapeIndex& shape_index) const { + return piece(shape_index).Get(multi_index); +} + +template +inline NativeT LiteralBase::Get( + tensorflow::gtl::ArraySlice multi_index) const { + return root_piece().Get(multi_index); +} + +template +inline void Literal::Set(tensorflow::gtl::ArraySlice multi_index, + const ShapeIndex& shape_index, NativeT value) { + return piece(shape_index).Set(multi_index, value); +} + +template +inline void Literal::Set(tensorflow::gtl::ArraySlice multi_index, + NativeT value) { + return root_piece().Set(multi_index, value); +} + +template +NativeT LiteralBase::GetFirstElement() const { + return data().at(0); +} + +template +NativeT LiteralBase::GetSparseElement(int64 sparse_element_number, + const ShapeIndex& shape_index) const { + CHECK( + LayoutUtil::IsSparseArray(ShapeUtil::GetSubshape(shape(), shape_index))); + return data(shape_index)[sparse_element_number]; +} + +template +void Literal::AppendSparseElement( + tensorflow::gtl::ArraySlice multi_index, NativeT value, + const ShapeIndex& shape_index) { + Piece& p = piece(shape_index); + const Shape& subshape = p.subshape(); + CHECK(LayoutUtil::IsSparseArray(subshape)); + int64 rank = ShapeUtil::Rank(subshape); + CHECK_EQ(multi_index.size(), rank); + int64 last_element = p.sparse_indices()->index_count(); + CHECK_LT(last_element, LayoutUtil::MaxSparseElements(subshape.layout())); + p.sparse_indices()->Append(multi_index); + CHECK_LT(last_element, p.data().size()); + p.data()[last_element] = value; +} + +template +void LiteralBase::EachCell( + std::function indices, + NativeT value)> + per_cell) const { + if (ShapeUtil::IsZeroElementArray(shape())) { + return; + } + std::vector indices(ShapeUtil::Rank(shape()), 0); + do { + per_cell(indices, Get(indices)); + } while (IndexUtil::BumpIndices(shape(), &indices)); +} + +template +inline void Literal::PopulateR1(tensorflow::gtl::ArraySlice values) { + CHECK(ShapeUtil::IsArray(shape())); + CHECK_EQ(ShapeUtil::Rank(shape()), 1); + CHECK_EQ(ShapeUtil::ElementsIn(shape()), values.size()); + CHECK_EQ(shape().element_type(), + primitive_util::NativeToPrimitiveType()); + for (int64 i = 0; i < values.size(); ++i) { + Set({i}, values[i]); + } +} + +template +void Literal::PopulateR2( + std::initializer_list> values) { + CHECK(ShapeUtil::IsArray(shape())); + CHECK_EQ(ShapeUtil::Rank(shape()), 2); + CHECK_EQ(shape().element_type(), + primitive_util::NativeToPrimitiveType()); + + const int64 dim0_size = values.size(); + const int64 dim1_size = values.begin()->size(); + CHECK_EQ(dim0_size, shape().dimensions(0)); + CHECK_EQ(dim1_size, shape().dimensions(1)); + + int64 dim0 = 0; + for (auto inner_list : values) { + int64 dim1 = 0; + for (auto value : inner_list) { + Set({dim0, dim1}, value); + ++dim1; + } + CHECK_EQ(dim1_size, dim1); + ++dim0; + } +} + +template +void Literal::PopulateFromArray(const Array& values) { + CHECK(ShapeUtil::IsArray(shape())); + CHECK_EQ(shape().element_type(), + primitive_util::NativeToPrimitiveType()); + CHECK_EQ(ShapeUtil::Rank(shape()), values.num_dimensions()); + for (int dim = 0; dim < values.num_dimensions(); ++dim) { + CHECK_EQ(values.dim(dim), shape().dimensions(dim)); + } + values.Each([this](tensorflow::gtl::ArraySlice indices, + NativeT value) { this->Set(indices, value); }); +} + +template +void Literal::PopulateR2FromArray2D(const Array2D& values) { + PopulateFromArray(values); +} + +template +void Literal::PopulateR3FromArray3D(const Array3D& values) { + PopulateFromArray(values); +} + +template +void Literal::PopulateR4FromArray4D(const Array4D& values) { + PopulateFromArray(values); +} + +template +void Literal::PopulateSparse(SparseIndexArray indices, + tensorflow::gtl::ArraySlice values, + bool sort) { + CHECK(LayoutUtil::IsSparseArray(shape())); + int rank = ShapeUtil::Rank(shape()); + CHECK_EQ(indices.rank(), rank); + int64 max_elements = LayoutUtil::MaxSparseElements(shape().layout()); + CHECK_LE(indices.max_indices(), max_elements); + int64 num_elements = values.size(); + CHECK_LE(num_elements, max_elements); + CHECK_EQ(num_elements, indices.index_count()); + auto root_data = root_piece().data(); + // Piece::data() returns an ArraySlice of size equal to the number of indices + // in the SparseIndexArray. So there is no need to adjust the size of the data + // here. It is enough to just copy the incoming values into the data buffer. + std::copy(values.begin(), values.end(), root_data.begin()); + *this->root_piece().sparse_indices() = std::move(indices); + if (sort) { + auto root_data = this->root_piece().data(); + this->root_piece().sparse_indices()->SortWithValues(root_data); + } + DCHECK(this->root_piece().sparse_indices()->Validate(shape())); +} + +template +Status Literal::PopulateInternal(const FnType& generator, bool parallel) { + const Shape& this_shape = shape(); + const int64 rank = ShapeUtil::Rank(this_shape); + TF_RET_CHECK(LayoutUtil::IsDenseArray(this_shape)); + TF_RET_CHECK(this_shape.element_type() == + primitive_util::NativeToPrimitiveType()); + tensorflow::gtl::MutableArraySlice literal_data = data(); + if (rank > 0) { + StrideConfig stride_config(this_shape, this_shape, + AsInt64Slice(this_shape.dimensions())); + int64 minor_dimension_size = + ShapeUtil::GetDimension(this_shape, stride_config.minor_dimension); + + auto init_function = [&](tensorflow::gtl::ArraySlice indexes) { + DimensionVector minor_scan_indexes(rank, 0); + const int64 index = + IndexUtil::MultidimensionalIndexToLinearIndex(shape(), indexes); + std::copy(indexes.begin(), indexes.end(), minor_scan_indexes.begin()); + for (int64 i = 0; i < minor_dimension_size; ++i) { + minor_scan_indexes[stride_config.minor_dimension] = i; + literal_data.at(index + i) = generator(minor_scan_indexes); + } + }; + if (parallel) { + ShapeUtil::ForEachIndexParallel(this_shape, stride_config.base, + stride_config.dimensions, + stride_config.step, init_function); + } else { + ShapeUtil::ForEachIndex( + this_shape, stride_config.base, stride_config.dimensions, + stride_config.step, + [&init_function](tensorflow::gtl::ArraySlice indexes) { + init_function(indexes); + return true; + }); + } + } else { + // For scalars. + literal_data.at(0) = generator({}); + } + return Status::OK(); +} +template +Status Literal::Populate(const FnType& generator) { + return PopulateInternal(generator, /*parallel=*/false); +} + +template +Status Literal::PopulateParallel(const FnType& generator) { + return PopulateInternal(generator, /*parallel=*/true); +} + +template +void Literal::PopulateWithValue(NativeT value) { + CHECK(ShapeUtil::IsArray(shape())); + CHECK_EQ(shape().element_type(), + primitive_util::NativeToPrimitiveType()); + for (NativeT& element : data()) { + element = value; + } +} + +template +std::unique_ptr LiteralBase::Replicate(int64 times) const { + DimensionVector bounds = {times}; + bounds.reserve(shape().dimensions_size() + 1); + for (int64 bound : shape().dimensions()) { + bounds.push_back(bound); + } + auto literal = + MakeUnique(ShapeUtil::MakeShape(shape().element_type(), bounds)); + int64 elements = ShapeUtil::ElementsIn(literal->shape()); + if (elements == 0) { + return literal; + } + + DimensionVector output_indices(bounds.size(), 0); + tensorflow::gtl::ArraySlice input_indices = output_indices; + input_indices.remove_prefix(1); + + bool done = false; + while (!done) { + const auto element = Get(input_indices); + literal->Set(output_indices, element); + + done = true; + for (int n = 0; n < output_indices.size(); ++n) { + ++output_indices[n]; + if (output_indices[n] < bounds[n]) { + done = false; + break; + } + output_indices[n] = 0; + } + } + return literal; +} + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_LITERAL_H_ diff --git a/tensorflow/compiler/xla/literal_comparison.cc b/tensorflow/compiler/xla/literal_comparison.cc index 2125ab7c61ab5e30fe51e16994e0da4883d509c4..94993cc87443ba8c22fd7c2eacfc8756d3f48edc 100644 --- a/tensorflow/compiler/xla/literal_comparison.cc +++ b/tensorflow/compiler/xla/literal_comparison.cc @@ -19,6 +19,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/casts.h" #include "tensorflow/core/lib/strings/strcat.h" @@ -217,7 +218,7 @@ class NearComparator { return Printf( "actual %s, expected %s, index %s, rel error %8.3g, abs error %8.3g", FpValueToString(actual).c_str(), FpValueToString(expected).c_str(), - Literal::MultiIndexAsString( + LiteralUtil::MultiIndexAsString( IndexUtil::LinearIndexToMultidimensionalIndex(shape, linear_index)) .c_str(), @@ -722,7 +723,7 @@ Status Equal(const LiteralSlice& expected, const LiteralSlice& actual) { return AppendStatus(result, tensorflow::strings::Printf( "\nat index: %s\nexpected: %s\nactual: %s", - Literal::MultiIndexAsString(multi_index).c_str(), + LiteralUtil::MultiIndexAsString(multi_index).c_str(), ToStringTruncated(expected).c_str(), ToStringTruncated(actual).c_str())); } diff --git a/tensorflow/compiler/xla/literal_comparison.h b/tensorflow/compiler/xla/literal_comparison.h index 00a13e361932e74a9a1e614d5c851d3851208852..9e5bf7c1d062ef0f25d07a80d6ded8106df5dacc 100644 --- a/tensorflow/compiler/xla/literal_comparison.h +++ b/tensorflow/compiler/xla/literal_comparison.h @@ -20,7 +20,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_XLA_LITERAL_COMPARISON_H_ #include "tensorflow/compiler/xla/error_spec.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/lib/core/status.h" namespace xla { diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_test.cc similarity index 76% rename from tensorflow/compiler/xla/literal_util_test.cc rename to tensorflow/compiler/xla/literal_test.cc index 493d807591dd3c425293e4ee796bca3036a3088c..e8f919950f0efc8b508f7ad4aee5233176bc0abd 100644 --- a/tensorflow/compiler/xla/literal_util_test.cc +++ b/tensorflow/compiler/xla/literal_test.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array3d.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/layout_util.h" +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/types.h" @@ -76,11 +77,11 @@ class LiteralUtilTest : public ::testing::Test { layout_r4_dim0minor_ = LayoutUtil::MakeLayout({0, 1, 2, 3}); literal_r4_2x2x3x3_dim0major_ = - Literal::CreateR4FromArray4DWithLayout(arr4d, - layout_r4_dim0major_); + LiteralUtil::CreateR4FromArray4DWithLayout(arr4d, + layout_r4_dim0major_); literal_r4_2x2x3x3_dim0minor_ = - Literal::CreateR4FromArray4DWithLayout(arr4d, - layout_r4_dim0minor_); + LiteralUtil::CreateR4FromArray4DWithLayout(arr4d, + layout_r4_dim0minor_); } Layout layout_r2_dim0major_; @@ -94,47 +95,47 @@ class LiteralUtilTest : public ::testing::Test { }; TEST_F(LiteralUtilTest, LiteralScalarToString) { - auto true_lit = Literal::CreateR0(true); + auto true_lit = LiteralUtil::CreateR0(true); ASSERT_EQ("true", true_lit->ToString()); - auto false_lit = Literal::CreateR0(false); + auto false_lit = LiteralUtil::CreateR0(false); ASSERT_EQ("false", false_lit->ToString()); - auto u32_lit = Literal::CreateR0(42); + auto u32_lit = LiteralUtil::CreateR0(42); ASSERT_EQ("42", u32_lit->ToString()); - auto s32_lit = Literal::CreateR0(-999); + auto s32_lit = LiteralUtil::CreateR0(-999); ASSERT_EQ("-999", s32_lit->ToString()); - auto f32_lit = Literal::CreateR0(3.14f); + auto f32_lit = LiteralUtil::CreateR0(3.14f); ASSERT_EQ("3.14", f32_lit->ToString()); - auto f16_lit = Literal::CreateR0(static_cast(0.5f)); + auto f16_lit = LiteralUtil::CreateR0(static_cast(0.5f)); ASSERT_EQ("0.5", f16_lit->ToString()); - auto c64_lit = Literal::CreateR0({3.14f, 2.78f}); + auto c64_lit = LiteralUtil::CreateR0({3.14f, 2.78f}); ASSERT_EQ("(3.14, 2.78)", c64_lit->ToString()); - auto bf16_lit = Literal::CreateR0(static_cast(0.5f)); + auto bf16_lit = LiteralUtil::CreateR0(static_cast(0.5f)); ASSERT_EQ("0.5", bf16_lit->ToString()); // 3.14 will be truncated to 3.125 in bfloat16 format. auto bf16_lit_truncated = - Literal::CreateR0(static_cast(3.14f)); + LiteralUtil::CreateR0(static_cast(3.14f)); ASSERT_EQ("3.125", bf16_lit_truncated->ToString()); auto bf16_lit_truncated2 = - Literal::CreateR0(static_cast(9.001f)); + LiteralUtil::CreateR0(static_cast(9.001f)); ASSERT_EQ("9", bf16_lit_truncated2->ToString()); } TEST_F(LiteralUtilTest, LiteralVectorToString) { - auto pred_vec = Literal::CreateR1({true, false, true}); + auto pred_vec = LiteralUtil::CreateR1({true, false, true}); ASSERT_EQ("{101}", pred_vec->ToString()); } TEST_F(LiteralUtilTest, R2ToString) { - const auto literal = Literal::CreateR2({{1, 2}, {3, 4}, {5, 6}}); + const auto literal = LiteralUtil::CreateR2({{1, 2}, {3, 4}, {5, 6}}); const string expected = R"(s32[3,2] { { 1, 2 }, { 3, 4 }, @@ -144,7 +145,8 @@ TEST_F(LiteralUtilTest, R2ToString) { } TEST_F(LiteralUtilTest, R3ToString) { - const auto literal = Literal::CreateR3({{{1}, {2}}, {{3}, {4}}, {{5}, {6}}}); + const auto literal = + LiteralUtil::CreateR3({{{1}, {2}}, {{3}, {4}}, {{5}, {6}}}); const string expected = R"(s32[3,2,1] { { { 1 }, { 2 } }, @@ -157,9 +159,9 @@ TEST_F(LiteralUtilTest, R3ToString) { } TEST_F(LiteralUtilTest, TupleToString) { - auto scalar = Literal::CreateR0(1.0); - auto matrix = Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); - auto tuple = Literal::MakeTuple({scalar.get(), matrix.get()}); + auto scalar = LiteralUtil::CreateR0(1.0); + auto matrix = LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + auto tuple = LiteralUtil::MakeTuple({scalar.get(), matrix.get()}); const string expected = R"((f32[], f32[2,2]) ( 1, f32[2,2] { @@ -182,7 +184,7 @@ TEST_F(LiteralUtilTest, CreateR3FromArray3d) { }); // clang-format on - auto literal = Literal::CreateR3FromArray3D(array_3d); + auto literal = LiteralUtil::CreateR3FromArray3D(array_3d); EXPECT_THAT(literal->shape().dimensions(), ElementsAre(2, 3, 2)); string result = literal->ToString(); const string expected = R"(f32[2,3,2] { @@ -205,7 +207,7 @@ TEST_F(LiteralUtilTest, CreateSparse) { {3, 5, 6}, }; std::vector values = {7, 8, 9, 10}; - auto literal = Literal::CreateSparse( + auto literal = LiteralUtil::CreateSparse( dimensions, SparseIndexArray(indices.n1() + 3, indices), values); Array2D expected_indices = { @@ -224,7 +226,7 @@ TEST_F(LiteralUtilTest, CreateSparse) { TEST_F(LiteralUtilTest, LiteralR4F32ProjectedStringifies) { // clang-format off - auto literal = Literal::CreateR4Projected({ + auto literal = LiteralUtil::CreateR4Projected({ {1, 2}, {1001, 1002}, {2001, 2002}, @@ -284,7 +286,7 @@ TEST_F(LiteralUtilTest, LiteralR4F32Stringifies) { TEST_F(LiteralUtilTest, EachCellR2F32) { // clang-format off - auto literal = Literal::CreateR2({ + auto literal = LiteralUtil::CreateR2({ {3.1f, 4.2f}, {9.3f, 12.4f}, }); @@ -303,26 +305,27 @@ TEST_F(LiteralUtilTest, EachCellR2F32) { TEST_F(LiteralUtilTest, ScalarEquality) { // Test equality with scalars. - auto f32_42 = Literal::CreateR0(42.0); - auto f32_42_clone = Literal::CreateR0(42.0); + auto f32_42 = LiteralUtil::CreateR0(42.0); + auto f32_42_clone = LiteralUtil::CreateR0(42.0); EXPECT_EQ(*f32_42, *f32_42); EXPECT_EQ(*f32_42, *f32_42_clone); - auto f32_123 = Literal::CreateR0(123.0); + auto f32_123 = LiteralUtil::CreateR0(123.0); EXPECT_NE(*f32_42, *f32_123); - auto f64_42 = Literal::CreateR0(42.0); + auto f64_42 = LiteralUtil::CreateR0(42.0); EXPECT_NE(*f32_42, *f64_42); } TEST_F(LiteralUtilTest, NonScalarEquality) { // Test equality with nonscalars. - auto matrix = Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); - auto matrix_clone = Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); - auto matrix_different = Literal::CreateR2({{4.0, 3.0}, {1.0, 2.0}}); - auto vector_literal = Literal::CreateR1({1.0, 2.0, 3.0, 4.0}); - auto scalar = Literal::CreateR0(1.0); + auto matrix = LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + auto matrix_clone = LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + auto matrix_different = + LiteralUtil::CreateR2({{4.0, 3.0}, {1.0, 2.0}}); + auto vector_literal = LiteralUtil::CreateR1({1.0, 2.0, 3.0, 4.0}); + auto scalar = LiteralUtil::CreateR0(1.0); Literal nil(ShapeUtil::MakeNil()); EXPECT_EQ(*matrix, *matrix); @@ -335,19 +338,19 @@ TEST_F(LiteralUtilTest, NonScalarEquality) { } TEST_F(LiteralUtilTest, TokenEquality) { - auto token0 = Literal::CreateToken(); - auto token1 = Literal::CreateToken(); - auto scalar = Literal::CreateR0(1.0); + auto token0 = LiteralUtil::CreateToken(); + auto token1 = LiteralUtil::CreateToken(); + auto scalar = LiteralUtil::CreateR0(1.0); EXPECT_EQ(*token0, *token1); EXPECT_NE(*token0, *scalar); - EXPECT_EQ(*Literal::MakeTuple({token0.get()}), - *Literal::MakeTuple({token0.get()})); - EXPECT_EQ(*Literal::MakeTuple({token0.get(), scalar.get()}), - *Literal::MakeTuple({token1.get(), scalar.get()})); - EXPECT_NE(*Literal::MakeTuple({token0.get(), scalar.get()}), - *Literal::MakeTuple({scalar.get(), token1.get()})); + EXPECT_EQ(*LiteralUtil::MakeTuple({token0.get()}), + *LiteralUtil::MakeTuple({token0.get()})); + EXPECT_EQ(*LiteralUtil::MakeTuple({token0.get(), scalar.get()}), + *LiteralUtil::MakeTuple({token1.get(), scalar.get()})); + EXPECT_NE(*LiteralUtil::MakeTuple({token0.get(), scalar.get()}), + *LiteralUtil::MakeTuple({scalar.get(), token1.get()})); } TEST_F(LiteralUtilTest, DifferentLayoutEquality) { @@ -371,43 +374,46 @@ TEST_F(LiteralUtilTest, DifferentLayoutEquality) { TEST_F(LiteralUtilTest, TupleEquality) { // Test equality with tuples. - auto scalar = Literal::CreateR0(1.0); - auto matrix = Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); - auto tuple1 = Literal::MakeTuple({scalar.get(), matrix.get()}); + auto scalar = LiteralUtil::CreateR0(1.0); + auto matrix = LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + auto tuple1 = LiteralUtil::MakeTuple({scalar.get(), matrix.get()}); // Tuple with the same elements. One element is shared with the original // tuple, the other is a clone of the element in the original tuple. - auto scalar_clone = Literal::CreateR0(1.0); - auto tuple2 = Literal::MakeTuple({scalar_clone.get(), matrix.get()}); + auto scalar_clone = LiteralUtil::CreateR0(1.0); + auto tuple2 = LiteralUtil::MakeTuple({scalar_clone.get(), matrix.get()}); EXPECT_EQ(*tuple1, *tuple2); // Tuple with elements reversed. - auto reversed_tuple = Literal::MakeTuple({matrix.get(), scalar.get()}); + auto reversed_tuple = LiteralUtil::MakeTuple({matrix.get(), scalar.get()}); EXPECT_NE(*tuple1, *reversed_tuple); // Tuple with different value. - auto scalar_42 = Literal::CreateR0(42.0); - auto different_tuple = Literal::MakeTuple({scalar_42.get(), matrix.get()}); + auto scalar_42 = LiteralUtil::CreateR0(42.0); + auto different_tuple = + LiteralUtil::MakeTuple({scalar_42.get(), matrix.get()}); EXPECT_NE(*tuple1, *different_tuple); } TEST_F(LiteralUtilTest, C64Equality) { // Test equality with tuples. - auto vector = Literal::CreateR1({{1.0, 2.0}, {3.0, 4.0}}); + auto vector = LiteralUtil::CreateR1({{1.0, 2.0}, {3.0, 4.0}}); // Tuple with the same elements. One element is shared with the original // tuple, the other is a clone of the element in the original tuple. - auto vector_clone = Literal::CreateR1({{1.0, 2.0}, {3.0, 4.0}}); + auto vector_clone = + LiteralUtil::CreateR1({{1.0, 2.0}, {3.0, 4.0}}); EXPECT_EQ(*vector, *vector_clone); - auto vector_reversed = Literal::CreateR1({{3.0, 4.0}, {1.0, 2.0}}); + auto vector_reversed = + LiteralUtil::CreateR1({{3.0, 4.0}, {1.0, 2.0}}); EXPECT_NE(*vector, *vector_reversed); } TEST_F(LiteralUtilTest, IsAllTuple) { - auto element1 = Literal::CreateR0(0.0); - auto element2 = Literal::CreateR2({{0.0, 0.0}, {0.0, 0.0}}); - auto tuple = Literal::MakeTuple({element1.get(), element1.get()}); + auto element1 = LiteralUtil::CreateR0(0.0); + auto element2 = LiteralUtil::CreateR2({{0.0, 0.0}, {0.0, 0.0}}); + auto tuple = LiteralUtil::MakeTuple({element1.get(), element1.get()}); // Tuples should always return false for IsAll. EXPECT_FALSE(tuple->IsAll(0)); @@ -416,140 +422,141 @@ TEST_F(LiteralUtilTest, IsAllTuple) { // Verifies that CreateFromShape works for tuples. TEST_F(LiteralUtilTest, CreateFromShapeTuple) { - auto scalar = Literal::CreateR0(0.0); - auto matrix = Literal::CreateR2({{0, 0}, {0, 0}}); - auto tuple = Literal::MakeTuple({scalar.get(), matrix.get()}); + auto scalar = LiteralUtil::CreateR0(0.0); + auto matrix = LiteralUtil::CreateR2({{0, 0}, {0, 0}}); + auto tuple = LiteralUtil::MakeTuple({scalar.get(), matrix.get()}); auto x = Literal::CreateFromShape(tuple->shape()); EXPECT_EQ(*tuple, *x); } TEST_F(LiteralUtilTest, IsAll) { - EXPECT_TRUE(Literal::CreateR0(false)->IsAll(0)); - EXPECT_TRUE(Literal::CreateR0(true)->IsAll(1)); - EXPECT_FALSE(Literal::CreateR0(false)->IsAll(1)); - EXPECT_FALSE(Literal::CreateR0(false)->IsAll(2)); - EXPECT_FALSE(Literal::CreateR0(true)->IsAll(0)); - EXPECT_FALSE(Literal::CreateR0(true)->IsAll(2)); - EXPECT_FALSE(Literal::CreateR0(true)->IsAll(-1)); + EXPECT_TRUE(LiteralUtil::CreateR0(false)->IsAll(0)); + EXPECT_TRUE(LiteralUtil::CreateR0(true)->IsAll(1)); + EXPECT_FALSE(LiteralUtil::CreateR0(false)->IsAll(1)); + EXPECT_FALSE(LiteralUtil::CreateR0(false)->IsAll(2)); + EXPECT_FALSE(LiteralUtil::CreateR0(true)->IsAll(0)); + EXPECT_FALSE(LiteralUtil::CreateR0(true)->IsAll(2)); + EXPECT_FALSE(LiteralUtil::CreateR0(true)->IsAll(-1)); // We shouldn't reinterpret int8_min as an unsigned type and then decide that // it is equal to 255. auto int8_min = std::numeric_limits::min(); - EXPECT_FALSE(Literal::CreateR0(255)->IsAll(int8_min)); + EXPECT_FALSE(LiteralUtil::CreateR0(255)->IsAll(int8_min)); - EXPECT_TRUE(Literal::CreateR0(42.0)->IsAll(42)); - EXPECT_FALSE(Literal::CreateR0(42.0001)->IsAll(42)); + EXPECT_TRUE(LiteralUtil::CreateR0(42.0)->IsAll(42)); + EXPECT_FALSE(LiteralUtil::CreateR0(42.0001)->IsAll(42)); - EXPECT_TRUE(Literal::CreateR1({100, 100, 100})->IsAll(100)); - EXPECT_FALSE(Literal::CreateR1({100, 100, 100.001})->IsAll(100)); + EXPECT_TRUE(LiteralUtil::CreateR1({100, 100, 100})->IsAll(100)); + EXPECT_FALSE(LiteralUtil::CreateR1({100, 100, 100.001})->IsAll(100)); - EXPECT_TRUE(Literal::CreateR2({{8, 8}, {8, 8}})->IsAll(8)); - EXPECT_FALSE(Literal::CreateR2({{8, 8}, {8, 9}})->IsAll(8)); - EXPECT_FALSE(Literal::CreateR2({{9, 8}, {8, 8}})->IsAll(8)); + EXPECT_TRUE(LiteralUtil::CreateR2({{8, 8}, {8, 8}})->IsAll(8)); + EXPECT_FALSE(LiteralUtil::CreateR2({{8, 8}, {8, 9}})->IsAll(8)); + EXPECT_FALSE(LiteralUtil::CreateR2({{9, 8}, {8, 8}})->IsAll(8)); half h8(8.0f); half h9(9.0f); - EXPECT_TRUE(Literal::CreateR2({{h8}, {h8}})->IsAll(8)); - EXPECT_FALSE(Literal::CreateR2({{h8}, {h9}})->IsAll(8)); - EXPECT_FALSE(Literal::CreateR2({{h9}, {h8}})->IsAll(8)); + EXPECT_TRUE(LiteralUtil::CreateR2({{h8}, {h8}})->IsAll(8)); + EXPECT_FALSE(LiteralUtil::CreateR2({{h8}, {h9}})->IsAll(8)); + EXPECT_FALSE(LiteralUtil::CreateR2({{h9}, {h8}})->IsAll(8)); bfloat16 b8(8.0f); bfloat16 b9(9.0f); - EXPECT_TRUE(Literal::CreateR2({{b8}, {b8}})->IsAll(8)); - EXPECT_FALSE(Literal::CreateR2({{b8}, {b9}})->IsAll(8)); - EXPECT_FALSE(Literal::CreateR2({{b9}, {b8}})->IsAll(8)); + EXPECT_TRUE(LiteralUtil::CreateR2({{b8}, {b8}})->IsAll(8)); + EXPECT_FALSE(LiteralUtil::CreateR2({{b8}, {b9}})->IsAll(8)); + EXPECT_FALSE(LiteralUtil::CreateR2({{b9}, {b8}})->IsAll(8)); // 9.001 will be truncated to 9.0 bfloat16 b91(9.001f); bfloat16 b90(9.00f); - EXPECT_TRUE(Literal::CreateR2({{b91}, {b90}})->IsAll(9.0)); + EXPECT_TRUE(LiteralUtil::CreateR2({{b91}, {b90}})->IsAll(9.0)); complex64 c8_9 = {8, 9}; - EXPECT_FALSE(Literal::CreateR2({{c8_9}, {c8_9}})->IsAll(8)); + EXPECT_FALSE(LiteralUtil::CreateR2({{c8_9}, {c8_9}})->IsAll(8)); auto uint64_max = std::numeric_limits::max(); - EXPECT_FALSE(Literal::CreateR2( + EXPECT_FALSE(LiteralUtil::CreateR2( {{uint64_max, uint64_max}, {uint64_max, uint64_max}}) ->IsAll(-1)); } TEST_F(LiteralUtilTest, IsAllFloat) { // IsAllFloat always returns false when the literal is not floating-point. - EXPECT_FALSE(Literal::CreateR0(false)->IsAllFloat(0)); - EXPECT_FALSE(Literal::CreateR0(0)->IsAllFloat(0)); - EXPECT_FALSE(Literal::CreateR0(0)->IsAllFloat(0)); - EXPECT_FALSE(Literal::CreateR0(0)->IsAllFloat(0)); - - EXPECT_TRUE(Literal::CreateR0(0)->IsAllFloat(0)); - EXPECT_TRUE(Literal::CreateR0(.5)->IsAllFloat(.5)); - EXPECT_TRUE(Literal::CreateR0(-.5)->IsAllFloat(-.5)); - EXPECT_FALSE(Literal::CreateR0(-.5)->IsAllFloat(-.49)); + EXPECT_FALSE(LiteralUtil::CreateR0(false)->IsAllFloat(0)); + EXPECT_FALSE(LiteralUtil::CreateR0(0)->IsAllFloat(0)); + EXPECT_FALSE(LiteralUtil::CreateR0(0)->IsAllFloat(0)); + EXPECT_FALSE(LiteralUtil::CreateR0(0)->IsAllFloat(0)); + + EXPECT_TRUE(LiteralUtil::CreateR0(0)->IsAllFloat(0)); + EXPECT_TRUE(LiteralUtil::CreateR0(.5)->IsAllFloat(.5)); + EXPECT_TRUE(LiteralUtil::CreateR0(-.5)->IsAllFloat(-.5)); + EXPECT_FALSE(LiteralUtil::CreateR0(-.5)->IsAllFloat(-.49)); EXPECT_FALSE( - Literal::CreateR2({{0, 0, 0}, {0, .1, 0}})->IsAllFloat(0)); - EXPECT_TRUE( - Literal::CreateR2({{.5, .5, .5}, {.5, .5, .5}})->IsAllFloat(.5)); - - EXPECT_TRUE(Literal::CreateR0(0)->IsAllFloat(0)); - EXPECT_TRUE(Literal::CreateR0(.5)->IsAllFloat(.5)); - EXPECT_TRUE(Literal::CreateR0(-.5)->IsAllFloat(-.5)); - EXPECT_FALSE(Literal::CreateR0(-.5)->IsAllFloat(-.49)); + LiteralUtil::CreateR2({{0, 0, 0}, {0, .1, 0}})->IsAllFloat(0)); + EXPECT_TRUE(LiteralUtil::CreateR2({{.5, .5, .5}, {.5, .5, .5}}) + ->IsAllFloat(.5)); + + EXPECT_TRUE(LiteralUtil::CreateR0(0)->IsAllFloat(0)); + EXPECT_TRUE(LiteralUtil::CreateR0(.5)->IsAllFloat(.5)); + EXPECT_TRUE(LiteralUtil::CreateR0(-.5)->IsAllFloat(-.5)); + EXPECT_FALSE(LiteralUtil::CreateR0(-.5)->IsAllFloat(-.49)); EXPECT_FALSE( - Literal::CreateR2({{0, 0, 0}, {0, .1, 0}})->IsAllFloat(0)); + LiteralUtil::CreateR2({{0, 0, 0}, {0, .1, 0}})->IsAllFloat(0)); } TEST_F(LiteralUtilTest, IsAllComplex) { // IsAllComplex always returns false when the literal is not complex. - EXPECT_FALSE(Literal::CreateR0(false)->IsAllComplex(0)); - EXPECT_FALSE(Literal::CreateR0(0)->IsAllComplex(0)); - EXPECT_FALSE(Literal::CreateR0(0)->IsAllComplex(0)); - EXPECT_FALSE(Literal::CreateR0(0)->IsAllComplex(0)); - EXPECT_FALSE(Literal::CreateR0(0)->IsAllComplex(0)); - EXPECT_FALSE(Literal::CreateR0(0)->IsAllComplex(0)); + EXPECT_FALSE(LiteralUtil::CreateR0(false)->IsAllComplex(0)); + EXPECT_FALSE(LiteralUtil::CreateR0(0)->IsAllComplex(0)); + EXPECT_FALSE(LiteralUtil::CreateR0(0)->IsAllComplex(0)); + EXPECT_FALSE(LiteralUtil::CreateR0(0)->IsAllComplex(0)); + EXPECT_FALSE(LiteralUtil::CreateR0(0)->IsAllComplex(0)); + EXPECT_FALSE(LiteralUtil::CreateR0(0)->IsAllComplex(0)); complex64 c8_9 = {8, 9}; complex64 c7_9 = {7, 9}; - EXPECT_TRUE(Literal::CreateR2({{c8_9}, {c8_9}}) + EXPECT_TRUE(LiteralUtil::CreateR2({{c8_9}, {c8_9}}) ->IsAllComplex({8.0f, 9.0f})); - EXPECT_FALSE(Literal::CreateR2({{c7_9}, {c8_9}}) + EXPECT_FALSE(LiteralUtil::CreateR2({{c7_9}, {c8_9}}) ->IsAllComplex({8.0f, 9.0f})); - EXPECT_FALSE(Literal::CreateR2({{c8_9}, {c7_9}}) + EXPECT_FALSE(LiteralUtil::CreateR2({{c8_9}, {c7_9}}) ->IsAllComplex({8.0f, 9.0f})); } TEST_F(LiteralUtilTest, IsAllFirst) { // IsAllComplex always returns false when the literal is not complex. - EXPECT_FALSE(Literal::CreateR1({false, true})->IsAllFirst()); - EXPECT_TRUE(Literal::CreateR1({false, false})->IsAllFirst()); - EXPECT_FALSE(Literal::CreateR1({1, 1, 2})->IsAllFirst()); - EXPECT_TRUE(Literal::CreateR1({5, 5, 5, 5})->IsAllFirst()); - EXPECT_FALSE(Literal::CreateR1({1, 1, 2})->IsAllFirst()); - EXPECT_TRUE(Literal::CreateR1({5, 5, 5, 5})->IsAllFirst()); - EXPECT_FALSE(Literal::CreateR1({1, 1, 2})->IsAllFirst()); - EXPECT_TRUE(Literal::CreateR1({5, 5, 5, 5})->IsAllFirst()); - EXPECT_FALSE(Literal::CreateR1({1, 1, 2})->IsAllFirst()); + EXPECT_FALSE(LiteralUtil::CreateR1({false, true})->IsAllFirst()); + EXPECT_TRUE(LiteralUtil::CreateR1({false, false})->IsAllFirst()); + EXPECT_FALSE(LiteralUtil::CreateR1({1, 1, 2})->IsAllFirst()); + EXPECT_TRUE(LiteralUtil::CreateR1({5, 5, 5, 5})->IsAllFirst()); + EXPECT_FALSE(LiteralUtil::CreateR1({1, 1, 2})->IsAllFirst()); + EXPECT_TRUE(LiteralUtil::CreateR1({5, 5, 5, 5})->IsAllFirst()); + EXPECT_FALSE(LiteralUtil::CreateR1({1, 1, 2})->IsAllFirst()); + EXPECT_TRUE(LiteralUtil::CreateR1({5, 5, 5, 5})->IsAllFirst()); + EXPECT_FALSE(LiteralUtil::CreateR1({1, 1, 2})->IsAllFirst()); complex64 c8_9 = {8, 9}; complex64 c7_9 = {7, 9}; - EXPECT_TRUE(Literal::CreateR2({{c8_9}, {c8_9}})->IsAllFirst()); - EXPECT_FALSE(Literal::CreateR2({{c7_9}, {c8_9}})->IsAllFirst()); + EXPECT_TRUE(LiteralUtil::CreateR2({{c8_9}, {c8_9}})->IsAllFirst()); + EXPECT_FALSE( + LiteralUtil::CreateR2({{c7_9}, {c8_9}})->IsAllFirst()); } TEST_F(LiteralUtilTest, IsZero) { - auto scalar_zero = Literal::CreateR0(0.0f); - auto scalar_one = Literal::CreateR0(1.0f); + auto scalar_zero = LiteralUtil::CreateR0(0.0f); + auto scalar_one = LiteralUtil::CreateR0(1.0f); EXPECT_TRUE(scalar_zero->IsZero({})); EXPECT_FALSE(scalar_one->IsZero({})); - auto array = Literal::CreateR2({{1, 2, 0, 3}, {1, 0, 1, 2}}); + auto array = LiteralUtil::CreateR2({{1, 2, 0, 3}, {1, 0, 1, 2}}); EXPECT_FALSE(array->IsZero({0, 1})); EXPECT_TRUE(array->IsZero({0, 2})); EXPECT_TRUE(array->IsZero({1, 1})); EXPECT_FALSE(array->IsZero({1, 2})); - auto complex_zero = Literal::CreateR0(0.0f); - auto complex_nonzero = Literal::CreateR0(0.5f); + auto complex_zero = LiteralUtil::CreateR0(0.0f); + auto complex_nonzero = LiteralUtil::CreateR0(0.5f); EXPECT_TRUE(complex_zero->IsZero({})); EXPECT_FALSE(complex_nonzero->IsZero({})); } @@ -563,7 +570,7 @@ TYPED_TEST_CASE(LiteralUtilTestTemplated, TestedTypes); TYPED_TEST(LiteralUtilTestTemplated, Relayout2x2) { // Make a non-integer for floating point types. TypeParam half = TypeParam(1) / TypeParam(2); - auto data = Literal::CreateR2({{half, 2}, {3, 4}}); + auto data = LiteralUtil::CreateR2({{half, 2}, {3, 4}}); const Layout layout01 = LayoutUtil::MakeLayout({0, 1}); const Layout layout10 = LayoutUtil::MakeLayout({1, 0}); @@ -577,7 +584,7 @@ TYPED_TEST(LiteralUtilTestTemplated, Relayout2x2) { } TEST_F(LiteralUtilTest, ReshapeR0) { - auto original = Literal::CreateR0(1.7f); + auto original = LiteralUtil::CreateR0(1.7f); auto reshape = original->Reshape(/*dimensions=*/{}).ConsumeValueOrDie(); EXPECT_EQ(*original, *reshape); } @@ -585,13 +592,13 @@ TEST_F(LiteralUtilTest, ReshapeR0) { TEST_F(LiteralUtilTest, ReshapeR4) { // clang-format off // F32[1x3x2x4] - auto original = Literal::CreateR4WithLayout({{ + auto original = LiteralUtil::CreateR4WithLayout({{ {{10, 11, 12, 13}, {14, 15, 16, 17}}, {{18, 19, 20, 21}, {22, 23, 24, 25}}, {{26, 27, 28, 29}, {30, 31, 32, 33}}, }}, layout_r4_dim0major_); // F32[1x3x4x2] - auto expected = Literal::CreateR3WithLayout({ + auto expected = LiteralUtil::CreateR3WithLayout({ {{10, 11}, {12, 13}, {14, 15}, {16, 17}}, {{18, 19}, {20, 21}, {22, 23}, {24, 25}}, {{26, 27}, {28, 29}, {30, 31}, {32, 33}}, @@ -605,13 +612,13 @@ TEST_F(LiteralUtilTest, ReshapeR4) { TEST_F(LiteralUtilTest, ReshapeR4Dim0Minor) { // clang-format off // F32[1x3x2x4] - auto original = Literal::CreateR4WithLayout({{ + auto original = LiteralUtil::CreateR4WithLayout({{ {{10, 11, 12, 13}, {14, 15, 16, 17}}, {{18, 19, 20, 21}, {22, 23, 24, 25}}, {{26, 27, 28, 29}, {30, 31, 32, 33}}, }}, layout_r4_dim0minor_); // F32[1x3x4x2] - auto expected = Literal::CreateR3WithLayout({ + auto expected = LiteralUtil::CreateR3WithLayout({ {{10, 11}, {12, 13}, {14, 15}, {16, 17}}, {{18, 19}, {20, 21}, {22, 23}, {24, 25}}, {{26, 27}, {28, 29}, {30, 31}, {32, 33}}, @@ -623,7 +630,7 @@ TEST_F(LiteralUtilTest, ReshapeR4Dim0Minor) { } TEST_F(LiteralUtilTest, TransposeR0) { - auto original = Literal::CreateR0(1.7f); + auto original = LiteralUtil::CreateR0(1.7f); auto reshape = original->Transpose(/*permutation=*/{}); EXPECT_EQ(*original, *reshape); } @@ -631,7 +638,7 @@ TEST_F(LiteralUtilTest, TransposeR0) { TEST_F(LiteralUtilTest, TransposeR4) { // clang-format off // F32[1x3x2x4] - auto original = Literal::CreateR4({{ + auto original = LiteralUtil::CreateR4({{ {{10, 11, 12, 13}, {14, 15, 16, 17}}, {{18, 19, 20, 21}, {22, 23, 24, 25}}, {{26, 27, 28, 29}, {30, 31, 32, 33}}, @@ -659,7 +666,7 @@ TEST_F(LiteralUtilTest, TestR4RelayoutEquivalence) { TEST_F(LiteralUtilTest, TestR2LinearLayout) { // Test expected memory layout of R2 dim0-minor (column-major) literal. - auto mat_dim0minor = Literal::CreateR2WithLayout( + auto mat_dim0minor = LiteralUtil::CreateR2WithLayout( {{1, 2, 3}, {4, 5, 6}}, layout_r2_dim0minor_); EXPECT_EQ(mat_dim0minor->element_count(), 6); EXPECT_THAT(mat_dim0minor->data(), ElementsAre(1, 4, 2, 5, 3, 6)); @@ -670,7 +677,7 @@ TEST_F(LiteralUtilTest, TestR2LinearLayout) { ElementsAre(1, 2, 3, 4, 5, 6)); // Test expected memory layout of R2 created with dim0-major (row-major). - auto mat_dim0major = Literal::CreateR2WithLayout( + auto mat_dim0major = LiteralUtil::CreateR2WithLayout( {{1, 2, 3}, {4, 5, 6}}, layout_r2_dim0major_); EXPECT_EQ(mat_dim0major->element_count(), 6); EXPECT_THAT(mat_dim0major->data(), ElementsAre(1, 2, 3, 4, 5, 6)); @@ -695,8 +702,8 @@ TEST_F(LiteralUtilTest, TestR3LinearLayout) { {10, 11, 12}, }, }); // clang-format on - auto lit_dim0minor = - Literal::CreateR3FromArray3DWithLayout(arr3d, layout_r3_dim0minor_); + auto lit_dim0minor = LiteralUtil::CreateR3FromArray3DWithLayout( + arr3d, layout_r3_dim0minor_); EXPECT_EQ(lit_dim0minor->element_count(), 12); std::vector expected_dim0minor{1, 7, 4, 10, 2, 8, 5, 11, 3, 9, 6, 12}; @@ -710,8 +717,8 @@ TEST_F(LiteralUtilTest, TestR3LinearLayout) { testing::ElementsAreArray(expected_dim0major)); // Test expected memory layout of R3 created with dim0-major (row-major). - auto lit_dim0major = - Literal::CreateR3FromArray3DWithLayout(arr3d, layout_r3_dim0major_); + auto lit_dim0major = LiteralUtil::CreateR3FromArray3DWithLayout( + arr3d, layout_r3_dim0major_); EXPECT_EQ(lit_dim0major->element_count(), 12); EXPECT_THAT(lit_dim0major->data(), testing::ElementsAreArray(expected_dim0major)); @@ -723,28 +730,28 @@ TEST_F(LiteralUtilTest, TestR3LinearLayout) { } TEST_F(LiteralUtilTest, SliceR0S32) { - auto input = Literal::CreateR0(1); + auto input = LiteralUtil::CreateR0(1); auto result = input->Slice({}, {}); EXPECT_EQ(*input, *result); } TEST_F(LiteralUtilTest, SliceR1F32) { - auto input = Literal::CreateR1({1.0, 2.0, 3.0, 4.0, 5.0}); + auto input = LiteralUtil::CreateR1({1.0, 2.0, 3.0, 4.0, 5.0}); auto result = input->Slice({3}, {4}); - auto expected = Literal::CreateR1({4.0}); + auto expected = LiteralUtil::CreateR1({4.0}); EXPECT_EQ(*expected, *result); } TEST_F(LiteralUtilTest, SliceR2U32) { - auto input_3x4 = - Literal::CreateR2({{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}); + auto input_3x4 = LiteralUtil::CreateR2( + {{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}); auto result = input_3x4->Slice({0, 2}, {2, 4}); - auto expected = Literal::CreateR2({{3, 4}, {7, 8}}); + auto expected = LiteralUtil::CreateR2({{3, 4}, {7, 8}}); EXPECT_EQ(*expected, *result); } TEST_F(LiteralUtilTest, SliceR3U32Full) { - auto input_2x3x2 = Literal::CreateR3( + auto input_2x3x2 = LiteralUtil::CreateR3( {{{1, 2}, {3, 4}, {5, 6}}, {{7, 8}, {9, 10}, {11, 12}}}); auto result = input_2x3x2->Slice({0, 0, 0}, {2, 3, 2}); EXPECT_EQ(*input_2x3x2, *result); @@ -753,21 +760,21 @@ TEST_F(LiteralUtilTest, SliceR3U32Full) { TEST_F(LiteralUtilTest, PopulateR1S64) { Literal output(ShapeUtil::MakeShape(S64, {1})); output.PopulateR1({77}); - auto expected = Literal::CreateR1({77}); + auto expected = LiteralUtil::CreateR1({77}); EXPECT_EQ(output, *expected); } TEST_F(LiteralUtilTest, PopulateR1U64) { Literal output(ShapeUtil::MakeShape(U64, {2})); output.PopulateR1({{77, 88}}); - auto expected = Literal::CreateR1({{77, 88}}); + auto expected = LiteralUtil::CreateR1({{77, 88}}); EXPECT_EQ(output, *expected); } TEST_F(LiteralUtilTest, PopulateR1C64) { Literal output(ShapeUtil::MakeShape(C64, {1})); output.PopulateR1({{77, 88}}); - auto expected = Literal::CreateR1({{77, 88}}); + auto expected = LiteralUtil::CreateR1({{77, 88}}); EXPECT_EQ(output, *expected); } @@ -775,7 +782,7 @@ TEST_F(LiteralUtilTest, PopulateR2C64) { Literal output(ShapeUtil::MakeShape(C64, {2, 2})); output.PopulateR2({{{7, 8}, {9, 10}}, {{1, 2}, {3, 4}}}); auto expected = - Literal::CreateR2({{{7, 8}, {9, 10}}, {{1, 2}, {3, 4}}}); + LiteralUtil::CreateR2({{{7, 8}, {9, 10}}, {{1, 2}, {3, 4}}}); EXPECT_EQ(output, *expected); } @@ -783,7 +790,7 @@ TEST_F(LiteralUtilTest, PopulateWithValueR0BF16) { Literal output(ShapeUtil::MakeShape(BF16, {})); bfloat16 h(0.25f); output.PopulateWithValue(h); - auto expected = Literal::CreateR0(h); + auto expected = LiteralUtil::CreateR0(h); EXPECT_EQ(output, *expected); } @@ -791,7 +798,7 @@ TEST_F(LiteralUtilTest, PopulateWithValueR1BF16) { Literal output(ShapeUtil::MakeShape(BF16, {3})); bfloat16 h(0.5f); output.PopulateWithValue(h); - auto expected = Literal::CreateR1({h, h, h}); + auto expected = LiteralUtil::CreateR1({h, h, h}); EXPECT_EQ(output, *expected); } @@ -799,28 +806,28 @@ TEST_F(LiteralUtilTest, PopulateWithValueR2BF16) { Literal output(ShapeUtil::MakeShape(BF16, {2, 2})); bfloat16 h(2.0f); output.PopulateWithValue(h); - auto expected = Literal::CreateR2({{h, h}, {h, h}}); + auto expected = LiteralUtil::CreateR2({{h, h}, {h, h}}); EXPECT_EQ(output, *expected); } TEST_F(LiteralUtilTest, PopulateWithValueR0F32) { Literal output(ShapeUtil::MakeShape(F32, {})); output.PopulateWithValue(2.5f); - auto expected = Literal::CreateR0(2.5f); + auto expected = LiteralUtil::CreateR0(2.5f); EXPECT_EQ(output, *expected); } TEST_F(LiteralUtilTest, PopulateWithValueR1S64) { Literal output(ShapeUtil::MakeShape(S64, {3})); output.PopulateWithValue(-7); - auto expected = Literal::CreateR1({-7, -7, -7}); + auto expected = LiteralUtil::CreateR1({-7, -7, -7}); EXPECT_EQ(output, *expected); } TEST_F(LiteralUtilTest, PopulateWithValueR2U64) { Literal output(ShapeUtil::MakeShape(U64, {2, 2})); output.PopulateWithValue(42); - auto expected = Literal::CreateR2({{42, 42}, {42, 42}}); + auto expected = LiteralUtil::CreateR2({{42, 42}, {42, 42}}); EXPECT_EQ(output, *expected); } @@ -828,7 +835,7 @@ TEST_F(LiteralUtilTest, PopulateWithValueR2C64) { Literal output(ShapeUtil::MakeShape(C64, {2, 2})); output.PopulateWithValue({4, 2}); auto expected = - Literal::CreateR2({{{4, 2}, {4, 2}}, {{4, 2}, {4, 2}}}); + LiteralUtil::CreateR2({{{4, 2}, {4, 2}}, {{4, 2}, {4, 2}}}); EXPECT_EQ(output, *expected); } @@ -836,7 +843,7 @@ TEST_F(LiteralUtilTest, PopulateWithValueR0F16) { Literal output(ShapeUtil::MakeShape(F16, {})); half h(0.25f); output.PopulateWithValue(h); - auto expected = Literal::CreateR0(h); + auto expected = LiteralUtil::CreateR0(h); EXPECT_EQ(output, *expected); } @@ -844,7 +851,7 @@ TEST_F(LiteralUtilTest, PopulateWithValueR1F16) { Literal output(ShapeUtil::MakeShape(F16, {3})); half h(0.5f); output.PopulateWithValue(h); - auto expected = Literal::CreateR1({h, h, h}); + auto expected = LiteralUtil::CreateR1({h, h, h}); EXPECT_EQ(output, *expected); } @@ -852,15 +859,15 @@ TEST_F(LiteralUtilTest, PopulateWithValueR2F16) { Literal output(ShapeUtil::MakeShape(F16, {2, 2})); half h(2.0f); output.PopulateWithValue(h); - auto expected = Literal::CreateR2({{h, h}, {h, h}}); + auto expected = LiteralUtil::CreateR2({{h, h}, {h, h}}); EXPECT_EQ(output, *expected); } TEST_F(LiteralUtilTest, ReplicateR2U32) { - auto input = - Literal::CreateR2({{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}); + auto input = LiteralUtil::CreateR2( + {{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}); auto output = input->Replicate(3); - auto expected = Literal::CreateR3( + auto expected = LiteralUtil::CreateR3( {{{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}, {{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}, {{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}}); @@ -914,12 +921,12 @@ TEST_F(LiteralUtilTest, CopySliceFrom) { } TEST_F(LiteralUtilTest, CopyFromScalars) { - auto zero = Literal::CreateR0(0); - auto nine = Literal::CreateR0(9); + auto zero = LiteralUtil::CreateR0(0); + auto nine = LiteralUtil::CreateR0(9); TF_EXPECT_OK(zero->CopyFrom(*nine)); EXPECT_EQ(*zero, *nine); - auto vect = Literal::CreateR1({3, 4, 9, 12, 5, 17, 21}); + auto vect = LiteralUtil::CreateR1({3, 4, 9, 12, 5, 17, 21}); TF_EXPECT_OK(zero->CopySliceFrom(*vect, {5}, {}, {})); EXPECT_EQ(zero->Get({}), 17); TF_EXPECT_OK(vect->CopySliceFrom(*zero, {}, {4}, {})); @@ -928,13 +935,13 @@ TEST_F(LiteralUtilTest, CopyFromScalars) { TEST_F(LiteralUtilTest, CopyFromAndToZeroElement) { const Shape empty_r1_shape = ShapeUtil::MakeShape(F32, {0}); - const auto const_nine = Literal::CreateR1({9}); + const auto const_nine = LiteralUtil::CreateR1({9}); const auto const_empty = Literal::CreateFromShape(empty_r1_shape); { // Source contains dimension with zero elements. const auto empty = Literal::CreateFromShape(empty_r1_shape); - auto nine = Literal::CreateR1({9}); + auto nine = LiteralUtil::CreateR1({9}); TF_EXPECT_OK(nine->CopySliceFrom(*empty, {0}, {0}, {0})); EXPECT_EQ(*nine, *const_nine); @@ -943,7 +950,7 @@ TEST_F(LiteralUtilTest, CopyFromAndToZeroElement) { { // Copy 0 element to destination with zero elements. const auto empty = Literal::CreateFromShape(empty_r1_shape); - auto nine = Literal::CreateR1({9}); + auto nine = LiteralUtil::CreateR1({9}); TF_EXPECT_OK(empty->CopySliceFrom(*nine, {0}, {0}, {0})); EXPECT_EQ(*empty, *const_empty); @@ -958,16 +965,16 @@ TEST_F(LiteralUtilTest, CopyFromNilShape) { } TEST_F(LiteralUtilTest, CopyFromArrays) { - auto scalar_42 = Literal::CreateR0(42.0); - auto scalar_123 = Literal::CreateR0(123.0); + auto scalar_42 = LiteralUtil::CreateR0(42.0); + auto scalar_123 = LiteralUtil::CreateR0(123.0); EXPECT_NE(*scalar_42, *scalar_123); TF_ASSERT_OK(scalar_42->CopyFrom(*scalar_123, /*dest_shape_index=*/{}, /*src_shape_index=*/{})); EXPECT_EQ(*scalar_42, *scalar_123); EXPECT_EQ(scalar_42->Get({}), 123.0f); - auto matrix_1234 = Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); - auto matrix_5678 = Literal::CreateR2({{5.0, 6.0}, {7.0, 8.0}}); + auto matrix_1234 = LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + auto matrix_5678 = LiteralUtil::CreateR2({{5.0, 6.0}, {7.0, 8.0}}); EXPECT_NE(*matrix_1234, *matrix_5678); EXPECT_EQ(matrix_1234->Get({0, 0}), 1.0f); TF_ASSERT_OK(matrix_1234->CopyFrom(*matrix_5678, /*dest_shape_index=*/{}, @@ -977,19 +984,19 @@ TEST_F(LiteralUtilTest, CopyFromArrays) { } TEST_F(LiteralUtilTest, CopyFromTuples) { - auto matrix = Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + auto matrix = LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); Literal nil_literal(ShapeUtil::MakeNil()); - auto nested_tuple = Literal::MakeTuple( + auto nested_tuple = LiteralUtil::MakeTuple( {matrix.get(), - Literal::MakeTuple({Literal::CreateR0(42).get(), - Literal::CreateR1({23.0, 44.0}).get(), - &nil_literal}) + LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(42).get(), + LiteralUtil::CreateR1({23.0, 44.0}).get(), &nil_literal}) .get()}); // Create a tuple the same shape as the inner tuple of nested_tuple but with // different values.. - auto tuple = Literal::MakeTuple({Literal::CreateR0(-5).get(), - Literal::CreateR1({2.0, 4.0}).get(), - &nil_literal}); + auto tuple = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(-5).get(), + LiteralUtil::CreateR1({2.0, 4.0}).get(), &nil_literal}); EXPECT_EQ(*matrix, LiteralSlice(*nested_tuple, {0})); EXPECT_EQ(nested_tuple->Get({}, {1, 0}), 42); @@ -1010,8 +1017,8 @@ TEST_F(LiteralUtilTest, CopyFromTuples) { EXPECT_EQ(nested_tuple->Get({1}, {1, 1}), 4.0); } TEST_F(LiteralUtilTest, CopyBetweenSameTuple) { - auto tuple = Literal::MakeTuple( - {Literal::CreateR0(-2).get(), Literal::CreateR0(4).get()}); + auto tuple = LiteralUtil::MakeTuple({LiteralUtil::CreateR0(-2).get(), + LiteralUtil::CreateR0(4).get()}); EXPECT_EQ(tuple->Get({}, {0}), -2); EXPECT_EQ(tuple->Get({}, {1}), 4); @@ -1025,8 +1032,8 @@ TEST_F(LiteralUtilTest, CopyBetweenSameTuple) { } TEST_F(LiteralUtilTest, CopyFromDifferentShapes) { - auto matrix = Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); - auto vector = Literal::CreateR1({5.0, 7.0}); + auto matrix = LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + auto vector = LiteralUtil::CreateR1({5.0, 7.0}); Status status = matrix->CopyFrom(*vector); ASSERT_FALSE(status.ok()); ASSERT_THAT(status.error_message(), @@ -1051,7 +1058,7 @@ TEST_F(LiteralUtilTest, F16) { half h1(1.0f); half h2(2.0f); - auto m2 = Literal::CreateR2({{h1, h2}, {h2, h1}}); + auto m2 = LiteralUtil::CreateR2({{h1, h2}, {h2, h1}}); Literal* l2 = m2.get(); const char* d2 = reinterpret_cast(l2->data().data()); EXPECT_EQ(d2[0], 0); @@ -1150,12 +1157,12 @@ TEST_F(LiteralUtilTest, PopulateParallel) { TEST_F(LiteralUtilTest, ConvertR4) { // clang-format off - auto original = Literal::CreateR4WithLayout({{ + auto original = LiteralUtil::CreateR4WithLayout({{ {{10, 11, 12, 13}, {14, 15, 16, 17}}, {{18, 19, 20, 21}, {22, 23, 24, 25}}, {{26, 27, 28, 29}, {30, 31, 32, 33}}, }}, layout_r4_dim0major_); - auto expected = Literal::CreateR4WithLayout({{ + auto expected = LiteralUtil::CreateR4WithLayout({{ {{10, 11, 12, 13}, {14, 15, 16, 17}}, {{18, 19, 20, 21}, {22, 23, 24, 25}}, {{26, 27, 28, 29}, {30, 31, 32, 33}}, @@ -1169,42 +1176,42 @@ TEST_F(LiteralUtilTest, ConvertR4) { TEST_F(LiteralUtilTest, ConvertIfTypesMatch) { // clang-format off - auto s8 = Literal::CreateR4WithLayout({{ + auto s8 = LiteralUtil::CreateR4WithLayout({{ {{10, 0, 12, 0}, {0, 15, 0, 17}}, {{0, 19, 0, 21}, {22, 0, 24, 0}}, {{26, 0, 28, 0}, {0, 31, 0, 33}}, }}, layout_r4_dim0major_); - auto s32 = Literal::CreateR4WithLayout({{ + auto s32 = LiteralUtil::CreateR4WithLayout({{ {{10, 0, 12, 0}, {0, 15, 0, 17}}, {{0, 19, 0, 21}, {22, 0, 24, 0}}, {{26, 0, 28, 0}, {0, 31, 0, 33}}, }}, layout_r4_dim0major_); - auto u32 = Literal::CreateR4WithLayout({{ + auto u32 = LiteralUtil::CreateR4WithLayout({{ {{10, 0, 12, 0}, {0, 15, 0, 17}}, {{0, 19, 0, 21}, {22, 0, 24, 0}}, {{26, 0, 28, 0}, {0, 31, 0, 33}}, }}, layout_r4_dim0major_); - auto s64 = Literal::CreateR4WithLayout({{ + auto s64 = LiteralUtil::CreateR4WithLayout({{ {{10, 0, 12, 0}, {0, 15, 0, 17}}, {{0, 19, 0, 21}, {22, 0, 24, 0}}, {{26, 0, 28, 0}, {0, 31, 0, 33}}, }}, layout_r4_dim0major_); - auto u64 = Literal::CreateR4WithLayout({{ + auto u64 = LiteralUtil::CreateR4WithLayout({{ {{10, 0, 12, 0}, {0, 15, 0, 17}}, {{0, 19, 0, 21}, {22, 0, 24, 0}}, {{26, 0, 28, 0}, {0, 31, 0, 33}}, }}, layout_r4_dim0major_); - auto pred = Literal::CreateR4WithLayout({{ + auto pred = LiteralUtil::CreateR4WithLayout({{ {{true, false, true, false}, {false, true, false, true}}, {{false, true, false, true}, {true, false, true, false}}, {{true, false, true, false}, {false, true, false, true}}, }}, layout_r4_dim0major_); - auto int32_pred = Literal::CreateR4WithLayout({{ + auto int32_pred = LiteralUtil::CreateR4WithLayout({{ {{1, 0, 1, 0}, {0, 1, 0, 1}}, {{0, 1, 0, 1}, {1, 0, 1, 0}}, {{1, 0, 1, 0}, {0, 1, 0, 1}}, }}, layout_r4_dim0major_); - auto f16 = Literal::CreateR4WithLayout({{ + auto f16 = LiteralUtil::CreateR4WithLayout({{ {{half(10.0), half(0.0), half(12.0), half(0.0)}, {half(0.0), half(15.0), half(0.0), half(17.0)}}, {{half(0.0), half(19.0), half(0.0), half(21.0)}, @@ -1212,7 +1219,7 @@ TEST_F(LiteralUtilTest, ConvertIfTypesMatch) { {{half(26.0), half(0.0), half(28.0), half(0.0)}, {half(0.0), half(31.0), half(0.0), half(33.0)}}, }}, layout_r4_dim0major_); - auto bf16 = Literal::CreateR4WithLayout({{ + auto bf16 = LiteralUtil::CreateR4WithLayout({{ {{bfloat16(10.0), bfloat16(0.0), bfloat16(12.0), bfloat16(0.0)}, {bfloat16(0.0), bfloat16(15.0), bfloat16(0.0), bfloat16(17.0)}}, {{bfloat16(0.0), bfloat16(19.0), bfloat16(0.0), bfloat16(21.0)}, @@ -1220,17 +1227,17 @@ TEST_F(LiteralUtilTest, ConvertIfTypesMatch) { {{bfloat16(26.0), bfloat16(0.0), bfloat16(28.0), bfloat16(0.0)}, {bfloat16(0.0), bfloat16(31.0), bfloat16(0.0), bfloat16(33.0)}}, }}, layout_r4_dim0major_); - auto f32 = Literal::CreateR4WithLayout({{ + auto f32 = LiteralUtil::CreateR4WithLayout({{ {{10.0f, 0.0f, 12.0f, 0.0f}, {0.0f, 15.0f, 0.0f, 17.0f}}, {{0.0f, 19.0f, 0.0f, 21.0f}, {22.0f, 0.0f, 24.0f, 0.0f}}, {{26.0f, 0.0f, 28.0f, 0.0f}, {0.0f, 31.0f, 0.0f, 33.0f}}, }}, layout_r4_dim0major_); - auto f64 = Literal::CreateR4WithLayout({{ + auto f64 = LiteralUtil::CreateR4WithLayout({{ {{10.0, 0.0, 12.0, 0.0}, {0.0, 15.0, 0.0, 17.0}}, {{0.0, 19.0, 0.0, 21.0}, {22.0, 0.0, 24.0, 0.0}}, {{26.0, 0.0, 28.0, 0.0}, {0.0, 31.0, 0.0, 33.0}}, }}, layout_r4_dim0major_); - auto c64 = Literal::CreateR4WithLayout({{ + auto c64 = LiteralUtil::CreateR4WithLayout({{ {{10.0f, 0.0f, 12.0f, 0.0f}, {0.0f, 15.0f, 0.0f, 17.0f}}, {{0.0f, 19.0f, 0.0f, 21.0f}, {22.0f, 0.0f, 24.0f, 0.0f}}, {{26.0f, 0.0f, 28.0f, 0.0f}, {0.0f, 31.0f, 0.0f, 33.0f}}, @@ -1302,18 +1309,18 @@ TEST_F(LiteralUtilTest, ConvertIfTypesMatch) { } TEST_F(LiteralUtilTest, BitcastConvert) { - auto original = - Literal::CreateR1({tensorflow::bit_cast(2.5f), - tensorflow::bit_cast(-42.25f), - tensorflow::bit_cast(100.f), 0xbeef}); - auto expected = Literal::CreateR1( + auto original = LiteralUtil::CreateR1( + {tensorflow::bit_cast(2.5f), + tensorflow::bit_cast(-42.25f), + tensorflow::bit_cast(100.f), 0xbeef}); + auto expected = LiteralUtil::CreateR1( {2.5f, -42.25f, 100.0f, tensorflow::bit_cast(0xbeef)}); TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr converted, original->BitcastConvert(F32)); } TEST_F(LiteralUtilTest, BitcastConvertBetweenInvalidTypes) { - auto literal = Literal::CreateR0(1234); + auto literal = LiteralUtil::CreateR0(1234); Status status = literal->BitcastConvert(F64).status(); EXPECT_NE(Status::OK(), status); EXPECT_TRUE(tensorflow::str_util::StrContains(status.error_message(), @@ -1348,7 +1355,7 @@ TEST_F(LiteralUtilTest, ToProto_f16) { half h1(1.0f); half h2(2.0f); - auto m = Literal::CreateR2({{h1, h2}, {h2, h1}}); + auto m = LiteralUtil::CreateR2({{h1, h2}, {h2, h1}}); Literal* l = m.get(); EXPECT_EQ(4, ShapeUtil::ElementsIn(l->shape())); EXPECT_EQ(4, l->data().size()); @@ -1391,10 +1398,10 @@ TEST_F(LiteralUtilTest, CopyFromProto_f16) { } TEST_F(LiteralUtilTest, LiteralSliceTest) { - auto scalar = Literal::CreateR0(1.0); - auto matrix = Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); - auto tuple = Literal::MakeTuple({scalar.get(), matrix.get()}); - auto nested_tuple = Literal::MakeTuple({tuple.get(), scalar.get()}); + auto scalar = LiteralUtil::CreateR0(1.0); + auto matrix = LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + auto tuple = LiteralUtil::MakeTuple({scalar.get(), matrix.get()}); + auto nested_tuple = LiteralUtil::MakeTuple({tuple.get(), scalar.get()}); Literal nil(ShapeUtil::MakeNil()); EXPECT_EQ(LiteralSlice(*scalar, {}), *scalar); @@ -1413,10 +1420,10 @@ TEST_F(LiteralUtilTest, LiteralSliceTest) { } TEST_F(LiteralUtilTest, MutatingLiteralSlice) { - auto scalar = Literal::CreateR0(1.0); - auto matrix = Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); - auto tuple = Literal::MakeTuple({scalar.get(), matrix.get()}); - auto nested_tuple = Literal::MakeTuple({tuple.get(), scalar.get()}); + auto scalar = LiteralUtil::CreateR0(1.0); + auto matrix = LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + auto tuple = LiteralUtil::MakeTuple({scalar.get(), matrix.get()}); + auto nested_tuple = LiteralUtil::MakeTuple({tuple.get(), scalar.get()}); // Verify that changing the underlying data beneath the view changes the // data of the view itself. const auto nested_tuple_view = LiteralSlice(*nested_tuple); @@ -1436,15 +1443,16 @@ TEST_F(LiteralUtilTest, MutatingLiteralSlice) { } TEST_F(LiteralUtilTest, LiteralSliceOfALiteralSlice) { - auto scalar = Literal::CreateR0(1.0); - auto matrix = Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); - auto tuple = Literal::MakeTuple({scalar.get(), matrix.get()}); - auto nested_tuple = Literal::MakeTuple({tuple.get(), scalar.get()}); + auto scalar = LiteralUtil::CreateR0(1.0); + auto matrix = LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + auto tuple = LiteralUtil::MakeTuple({scalar.get(), matrix.get()}); + auto nested_tuple = LiteralUtil::MakeTuple({tuple.get(), scalar.get()}); const auto nested_tuple_view = LiteralSlice(*nested_tuple); const auto tuple_view = LiteralSlice(nested_tuple_view, /*view_root=*/{0}); const auto matrix_view = LiteralSlice(tuple_view, /*view_root=*/{1}); - EXPECT_EQ(matrix_view, *Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}})); + EXPECT_EQ(matrix_view, + *LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}})); } TEST_F(LiteralUtilTest, BorrowingLiteralFromOneBufferPtr) { @@ -1488,7 +1496,7 @@ TEST_F(LiteralUtilTest, BorrowingLiteralFromMultipleBufferPtrs) { TEST_F(LiteralUtilTest, LiteralMove) { std::unique_ptr matrix = - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); Literal literal(std::move(*matrix)); EXPECT_TRUE( @@ -1501,11 +1509,11 @@ TEST_F(LiteralUtilTest, LiteralMove) { TEST_F(LiteralUtilTest, DecomposeTuple) { Literal nil_literal(ShapeUtil::MakeNil()); - auto nested_tuple = Literal::MakeTuple( - {Literal::CreateR2({{1, 2}, {3, 4}}).get(), - Literal::MakeTuple({Literal::CreateR0(42).get(), - Literal::CreateR1({23.0, 44.0}).get(), - &nil_literal}) + auto nested_tuple = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR2({{1, 2}, {3, 4}}).get(), + LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(42).get(), + LiteralUtil::CreateR1({23.0, 44.0}).get(), &nil_literal}) .get(), &nil_literal}); @@ -1542,13 +1550,13 @@ TEST_F(LiteralUtilTest, DecomposeEmptyTuple) { TEST_F(LiteralUtilTest, MoveIntoTuple) { std::vector elements; - elements.push_back(std::move(*Literal::CreateR0(1.0))); - elements.push_back(std::move(*Literal::CreateR1({4, 8}))); - elements.push_back(std::move( - *Literal::MakeTuple({Literal::CreateR0(42).get(), - Literal::CreateR1({23.0, 44.0}).get()}) + elements.push_back(std::move(*LiteralUtil::CreateR0(1.0))); + elements.push_back(std::move(*LiteralUtil::CreateR1({4, 8}))); + elements.push_back(std::move(*LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(42).get(), + LiteralUtil::CreateR1({23.0, 44.0}).get()}) - )); + )); Literal literal = Literal::MoveIntoTuple(&elements); ASSERT_TRUE(ShapeUtil::IsTuple(literal.shape())); @@ -1577,7 +1585,7 @@ TEST_F(LiteralUtilTest, LiteralMoveAssignment) { EXPECT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeNil(), literal.shape())); std::unique_ptr matrix = - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); literal = std::move(*matrix); EXPECT_TRUE( @@ -1590,7 +1598,7 @@ TEST_F(LiteralUtilTest, LiteralMoveAssignment) { TEST_F(LiteralUtilTest, LiteralSliceCopy) { std::unique_ptr matrix = - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); const auto matrix_view = LiteralSlice(*matrix); LiteralSlice matrix_view_copy(matrix_view); @@ -1601,9 +1609,9 @@ TEST_F(LiteralUtilTest, LiteralSliceCopy) { } TEST_F(LiteralUtilTest, GetSetTuple) { - auto tuple = Literal::MakeTuple( - {Literal::CreateR0(42.0).get(), - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}).get()}); + auto tuple = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(42.0).get(), + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}).get()}); EXPECT_EQ(tuple->Get(/*multi_index=*/{}, /*shape_index=*/{0}), 42.0); tuple->Set(/*multi_index=*/{}, /*shape_index=*/{0}, -5.0); EXPECT_EQ(tuple->Get(/*multi_index=*/{}, /*shape_index=*/{0}), -5.0); @@ -1644,20 +1652,20 @@ TEST_F(LiteralUtilTest, CreateFromShapeZeroInitialized) { TEST_F(LiteralUtilTest, ProtoRoundTrip) { // Test serializing then deserializing a Literal through a proto. - auto one_f32 = Literal::CreateR0(1.0); - auto two_f32 = Literal::CreateR0(2.0); - auto vector_int8 = Literal::CreateR1({-128, 0, 2, 4, 7, 56, 127}); - auto vector_c64 = Literal::CreateR1({{1.0, 2.0}, {3.0, 4.0}}); - auto vector_bfloat16 = Literal::CreateR1( + auto one_f32 = LiteralUtil::CreateR0(1.0); + auto two_f32 = LiteralUtil::CreateR0(2.0); + auto vector_int8 = LiteralUtil::CreateR1({-128, 0, 2, 4, 7, 56, 127}); + auto vector_c64 = LiteralUtil::CreateR1({{1.0, 2.0}, {3.0, 4.0}}); + auto vector_bfloat16 = LiteralUtil::CreateR1( {bfloat16{-1.0}, bfloat16{2.0}, bfloat16{-3.0}}); auto vector_half = - Literal::CreateR1({half{10.0}, half{20.0}, half{-30.0}}); + LiteralUtil::CreateR1({half{10.0}, half{20.0}, half{-30.0}}); auto matrix_pred = - Literal::CreateR2({{true, false, true}, {false, false, true}}); - auto tuple = Literal::MakeTuple( + LiteralUtil::CreateR2({{true, false, true}, {false, false, true}}); + auto tuple = LiteralUtil::MakeTuple( {one_f32.get(), vector_half.get(), matrix_pred.get(), matrix_pred.get()}); Literal nil_literal(ShapeUtil::MakeNil()); - auto nested_tuple = Literal::MakeTuple( + auto nested_tuple = LiteralUtil::MakeTuple( {tuple.get(), vector_bfloat16.get(), tuple.get(), &nil_literal}); auto to_from_proto = [](const Literal& literal) -> Literal { @@ -1790,8 +1798,8 @@ TEST_F(LiteralUtilTest, InvalidProtoTooManyTupleElements) { } TEST_F(LiteralUtilTest, SortSparseElements) { - auto literal = - Literal::CreateSparse({10, 10, 10}, SparseIndexArray(10, 3), {}); + auto literal = LiteralUtil::CreateSparse({10, 10, 10}, + SparseIndexArray(10, 3), {}); literal->AppendSparseElement({2, 3, 4}, 2.0); literal->AppendSparseElement({3, 4, 5}, 3.0); literal->AppendSparseElement({1, 2, 3}, 1.0); @@ -1805,21 +1813,22 @@ TEST_F(LiteralUtilTest, GetSparseElementAsString) { SparseIndexArray indices(10, {{1, 2, 3}, {2, 3, 4}, {3, 4, 5}}); ASSERT_EQ( - Literal::CreateSparse(dimensions, indices, {true, false, true}) + LiteralUtil::CreateSparse(dimensions, indices, {true, false, true}) ->GetSparseElementAsString(1), "false"); - ASSERT_EQ(Literal::CreateSparse(dimensions, indices, {1, 2, 3}) + ASSERT_EQ(LiteralUtil::CreateSparse(dimensions, indices, {1, 2, 3}) ->GetSparseElementAsString(1), tensorflow::strings::StrCat(int64{2})); - ASSERT_EQ(Literal::CreateSparse(dimensions, indices, {1.0, 2.0, 3.0}) - ->GetSparseElementAsString(1), - tensorflow::strings::StrCat(double{2.0})); - ASSERT_EQ(Literal::CreateSparse(dimensions, indices, - {half{1.0}, half{2.0}, half{3.0}}) + ASSERT_EQ( + LiteralUtil::CreateSparse(dimensions, indices, {1.0, 2.0, 3.0}) + ->GetSparseElementAsString(1), + tensorflow::strings::StrCat(double{2.0})); + ASSERT_EQ(LiteralUtil::CreateSparse(dimensions, indices, + {half{1.0}, half{2.0}, half{3.0}}) ->GetSparseElementAsString(1), tensorflow::strings::StrCat(static_cast(half{2.0}))); ASSERT_EQ( - Literal::CreateSparse( + LiteralUtil::CreateSparse( dimensions, indices, std::vector{{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}}) ->GetSparseElementAsString(1), @@ -1827,33 +1836,36 @@ TEST_F(LiteralUtilTest, GetSparseElementAsString) { } TEST_F(LiteralUtilTest, BroadcastVectorToMatrix0) { - std::unique_ptr literal = Literal::CreateR1({1, 2}); + std::unique_ptr literal = LiteralUtil::CreateR1({1, 2}); TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr broadcasted_literal, literal->Broadcast( /*result_shape=*/ShapeUtil::MakeShape(S64, {2, 2}), /*dimensions=*/{0})); - EXPECT_EQ(*broadcasted_literal, *Literal::CreateR2({{1, 1}, {2, 2}})); + EXPECT_EQ(*broadcasted_literal, + *LiteralUtil::CreateR2({{1, 1}, {2, 2}})); } TEST_F(LiteralUtilTest, BroadcastVectorToMatrix1) { - std::unique_ptr literal = Literal::CreateR1({1, 2}); + std::unique_ptr literal = LiteralUtil::CreateR1({1, 2}); TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr broadcasted_literal, literal->Broadcast( /*result_shape=*/ShapeUtil::MakeShape(S64, {2, 2}), /*dimensions=*/{1})); - EXPECT_EQ(*broadcasted_literal, *Literal::CreateR2({{1, 2}, {1, 2}})); + EXPECT_EQ(*broadcasted_literal, + *LiteralUtil::CreateR2({{1, 2}, {1, 2}})); } TEST_F(LiteralUtilTest, BroadcastScalarToMatrix) { - std::unique_ptr literal = Literal::CreateR0(9); + std::unique_ptr literal = LiteralUtil::CreateR0(9); TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr broadcasted_literal, literal->Broadcast( /*result_shape=*/ShapeUtil::MakeShape(S32, {2, 2}), /*dimensions=*/{})); - EXPECT_EQ(*broadcasted_literal, *Literal::CreateR2({{9, 9}, {9, 9}})); + EXPECT_EQ(*broadcasted_literal, + *LiteralUtil::CreateR2({{9, 9}, {9, 9}})); } } // namespace diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc index eeabf835ac348a5ba55699631188b0e329c98c43..548fbe8a83a3797aa8ac32dc1f6c085fc0100197 100644 --- a/tensorflow/compiler/xla/literal_util.cc +++ b/tensorflow/compiler/xla/literal_util.cc @@ -43,25 +43,6 @@ namespace xla { namespace { -constexpr bool kLittleEndian = __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__; - -// Converts between little and big endian. -// -// Precondition: size % 2 == 0 (elements in the array are 16 bits long) -void ConvertEndianShort(string* bytes) { - CHECK_EQ(bytes->size() / 2, 0); - for (int64 i = 0; i < bytes->size(); i += 2) { - std::swap((*bytes)[i], (*bytes)[i + 1]); - } -} - -void ConvertEndianShort(char* bytes, int64 size) { - CHECK_EQ(size / 2, 0); - for (int64 i = 0; i < size; i += 2) { - std::swap(bytes[i], bytes[i + 1]); - } -} - // Return a literal with all arrays of type FromNativeT converted to type // ToNativeT in the given literal. template @@ -103,505 +84,54 @@ std::unique_ptr ConvertType(LiteralSlice literal) { } // namespace -LiteralBase::~LiteralBase() {} - -std::ostream& operator<<(std::ostream& out, const Literal& literal) { - out << literal.ToString(); - return out; -} - -Literal::StrideConfig::StrideConfig( - const Shape& source_shape, const Shape& dest_shape, - tensorflow::gtl::ArraySlice dimensions) - : dimensions(dimensions), - base(dimensions.size(), 0), - step(dimensions.size(), 1) { - if (!dimensions.empty()) { - // Selects the shape with the largest minor dimension as the one upon - // which to run the tight stride loop. - if (dimensions[LayoutUtil::Minor(source_shape.layout(), 0)] >= - dimensions[LayoutUtil::Minor(dest_shape.layout(), 0)]) { - minor_dimension = LayoutUtil::Minor(source_shape.layout(), 0); - dest_stride = IndexUtil::GetDimensionStride(dest_shape, minor_dimension); - } else { - minor_dimension = LayoutUtil::Minor(dest_shape.layout(), 0); - source_stride = - IndexUtil::GetDimensionStride(source_shape, minor_dimension); - } - minor_loop_size = dimensions[minor_dimension]; - step[minor_dimension] = minor_loop_size; - } -} - -Literal::Literal(const Shape& shape) - : Literal(shape, /*allocate_arrays=*/true) {} - -void Literal::SetPiece(const Shape& shape, Piece* piece, bool allocate_arrays) { - if (ShapeUtil::IsTuple(shape)) { - for (int i = 0; i < ShapeUtil::TupleElementCount(shape); ++i) { - const Shape& subshape = shape.tuple_shapes(i); - - auto child_piece = Piece(); - child_piece.set_subshape(&subshape); - - SetPiece(subshape, &child_piece, allocate_arrays); - - piece->emplace_back(std::move(child_piece)); - } - } else if (ShapeUtil::IsArray(shape)) { - if (allocate_arrays) { - if (LayoutUtil::IsSparseArray(shape)) { - // For sparse arrays, the buffer must be of the size of the maximum - // number of sparse elements possible. - const int64 max_sparse_elements = - LayoutUtil::MaxSparseElements(shape.layout()); - piece->set_buffer( - new char[max_sparse_elements * - ShapeUtil::ByteSizeOfPrimitiveType(shape.element_type())]); - piece->set_sparse_indices( - new SparseIndexArray(max_sparse_elements, ShapeUtil::Rank(shape))); - } else { - piece->set_buffer(new char[piece->size_bytes()]); - } - } - } else { - // If the shape is neither an array nor tuple, then it must be - // zero-sized. Otherwise, some memory needs to be allocated for it. - CHECK_EQ(piece->size_bytes(), 0); - } -} - -Literal::Literal(const Shape& shape, bool allocate_arrays) - : LiteralBase(), shape_(MakeUnique(shape)) { - CHECK(LayoutUtil::HasLayout(*shape_)); - root_piece_ = new Piece(); - root_piece_->set_subshape(shape_.get()); - CHECK(&root_piece_->subshape() == shape_.get()); - - SetPiece(*shape_, root_piece_, allocate_arrays); -} - -Literal::~Literal() { - if (root_piece_ != nullptr) { - DeallocateBuffers(); - delete root_piece_; - } -} - -void Literal::DeallocateBuffers() { - root_piece_->ForEachMutableSubpiece( - [&](const ShapeIndex& index, Piece* piece) { - if (piece->buffer() != nullptr) { - delete[] piece->buffer(); - delete piece->sparse_indices(); - } - }); -} - -Literal::Literal(Literal&& other) : LiteralBase() { *this = std::move(other); } - -Literal& Literal::operator=(Literal&& other) { - DCHECK(&other.root_piece_->subshape() == other.shape_.get()); - using std::swap; - swap(shape_, other.shape_); - swap(root_piece_, other.root_piece_); - DCHECK(&root_piece_->subshape() == shape_.get()); - - return *this; -} - -std::unique_ptr LiteralBase::CreateFromShape(const Shape& shape) { - auto literal = MakeUnique(shape); - literal->root_piece_->ForEachMutableSubpiece( - [&](const ShapeIndex& index, Piece* piece) { - if (ShapeUtil::IsArray(piece->subshape())) { - memset(piece->untyped_data(), 0, piece->size_bytes()); - } - }); - return literal; -} - -const SparseIndexArray* LiteralBase::sparse_indices( - const ShapeIndex& shape_index) const { - return piece(shape_index).sparse_indices(); -} - -SparseIndexArray* Literal::sparse_indices(const ShapeIndex& shape_index) { - return piece(shape_index).sparse_indices(); -} - -/* static */ std::unique_ptr Literal::CreateFromDimensions( +/* static */ std::unique_ptr LiteralUtil::CreateFromDimensions( PrimitiveType primitive_type, tensorflow::gtl::ArraySlice dimensions) { - return CreateFromShape(ShapeUtil::MakeShape(primitive_type, dimensions)); + return Literal::CreateFromShape( + ShapeUtil::MakeShape(primitive_type, dimensions)); } -/* static */ std::unique_ptr Literal::ConvertBF16ToF32( +/* static */ std::unique_ptr LiteralUtil::ConvertBF16ToF32( const LiteralSlice& bf16_literal) { return ConvertType(bf16_literal); } -/* static */ std::unique_ptr Literal::ConvertF32ToBF16( +/* static */ std::unique_ptr LiteralUtil::ConvertF32ToBF16( const LiteralSlice& f32_literal) { return ConvertType(f32_literal); } -template -Status Literal::CopySliceFromInternal( - const LiteralBase& src_literal, tensorflow::gtl::ArraySlice src_base, - tensorflow::gtl::ArraySlice dest_base, - tensorflow::gtl::ArraySlice copy_size) { - TF_RET_CHECK(ShapeUtil::Rank(src_literal.shape()) == src_base.size()); - TF_RET_CHECK(ShapeUtil::Rank(shape()) == dest_base.size()); - - auto linear_index = [](const Shape& shape, - tensorflow::gtl::ArraySlice multi_index) { - return IndexUtil::MultidimensionalIndexToLinearIndex(shape, multi_index); - }; - - if (ShapeUtil::Rank(src_literal.shape()) == 0 || - ShapeUtil::Rank(shape()) == 0) { - // If any of the two shapes are scalars, we can just call the StridedCopy() - // directly, and we know we will be copying only one value. - TF_RET_CHECK(copy_size.empty()); - StridedCopy(data(), linear_index(shape(), dest_base), 0, - src_literal.data(), - linear_index(src_literal.shape(), src_base), 0, 1); - } else if (!ShapeUtil::IsZeroElementArray(shape()) && - !ShapeUtil::IsZeroElementArray(src_literal.shape())) { - // Perform copy if neither src nor dest has dimensions with zero element, - // otherwise it's a no-op. - TF_RET_CHECK(src_base.size() == dest_base.size()); - TF_RET_CHECK(src_base.size() == copy_size.size()); - - // Scan the source from minor, stepping in copy size blocks, then within - // the index enumaration functor, do a strided copy advancing source index - // by one (walking through the minor dimension), and destination index by - // proper stride size at the matching dimension. - DimensionVector src_indexes(src_base.size(), 0); - DimensionVector dest_indexes(dest_base.size(), 0); - Literal::StrideConfig stride_config(src_literal.shape(), shape(), - copy_size); - - auto copy_proc = [&](tensorflow::gtl::ArraySlice indexes) { - // Map from multi-dimensional index, to source index. - std::transform(indexes.begin(), indexes.end(), src_base.begin(), - src_indexes.begin(), std::plus()); - // Map from multi-dimensional index, to destination index. - std::transform(indexes.begin(), indexes.end(), dest_base.begin(), - dest_indexes.begin(), std::plus()); - - int64 src_index = linear_index(src_literal.shape(), src_indexes); - int64 dest_index = linear_index(shape(), dest_indexes); - - // `this->` is needed to workaround MSVC bug: #16882 - StridedCopy(this->data(), dest_index, stride_config.dest_stride, - src_literal.data(), src_index, - stride_config.source_stride, stride_config.minor_loop_size); - return true; - }; - - ShapeUtil::ForEachIndex(src_literal.shape(), stride_config.base, - stride_config.dimensions, stride_config.step, - copy_proc); - } - return Status::OK(); -} - -Status Literal::CopyElementFrom(const LiteralSlice& src_literal, - tensorflow::gtl::ArraySlice src_index, - tensorflow::gtl::ArraySlice dest_index) { - DCHECK_EQ(shape().element_type(), src_literal.shape().element_type()); - const int64 src_linear_index = IndexUtil::MultidimensionalIndexToLinearIndex( - src_literal.shape(), src_index); - const int64 dest_linear_index = - IndexUtil::MultidimensionalIndexToLinearIndex(shape(), dest_index); - const int64 primitive_size = - ShapeUtil::ByteSizeOfPrimitiveType(shape().element_type()); - - char* dest_address = - static_cast(untyped_data()) + dest_linear_index * primitive_size; - const char* source_address = - static_cast(src_literal.untyped_data()) + - src_linear_index * primitive_size; - if (dest_address != source_address) { - memcpy(dest_address, source_address, primitive_size); - } - return Status::OK(); -} - -/* static */ std::unique_ptr Literal::CreateToken() { +/* static */ std::unique_ptr LiteralUtil::CreateToken() { return MakeUnique(ShapeUtil::MakeTokenShape()); } -std::vector Literal::DecomposeTuple() { - CHECK(ShapeUtil::IsTuple(shape())); - std::vector elements; - for (int i = 0; i < ShapeUtil::TupleElementCount(shape()); ++i) { - elements.push_back(Literal(ShapeUtil::GetSubshape(shape(), {i}), - /*allocate_arrays=*/false)); - Literal& element = elements.back(); - element.root_piece_->ForEachMutableSubpiece( - [&](const ShapeIndex& index, Piece* dest_piece) { - ShapeIndex src_index = {i}; - for (int64 j : index) { - src_index.push_back(j); - } - Piece& src_piece = piece(src_index); - - // Move the respective buffer and sparse indices over to the element - // Literal. - dest_piece->set_buffer(src_piece.buffer()); - src_piece.set_buffer(nullptr); - dest_piece->set_sparse_indices(src_piece.sparse_indices()); - src_piece.set_sparse_indices(nullptr); - }); - } - // Set this literal to be nil-shaped. - *this = Literal(); - return elements; -} - -/* static */ Literal Literal::MoveIntoTuple( - tensorflow::gtl::MutableArraySlice elements) { - std::vector element_shapes; - for (const Literal& element : elements) { - element_shapes.push_back(element.shape()); - } - Literal literal(ShapeUtil::MakeTupleShape(element_shapes), - /*allocate_arrays=*/false); - for (int i = 0; i < elements.size(); ++i) { - TF_CHECK_OK( - literal.MoveFrom(std::move(elements[i]), /*dest_shape_index=*/{i})); - } - return literal; -} - -namespace { - -// Copies the elements in 'src' to 'dest'. The shape and layout of the data in -// the array slices are indicated by dest_shape and src_shape respectively. -template -void CopyElementsBetween(tensorflow::gtl::MutableArraySlice dest, - tensorflow::gtl::ArraySlice src, - const Shape& dest_shape, const Shape& src_shape) { - CHECK(ShapeUtil::Compatible(dest_shape, src_shape)); - if (ShapeUtil::IsZeroElementArray(dest_shape)) { - return; - } - std::vector index(ShapeUtil::Rank(dest_shape)); - do { - dest[IndexUtil::MultidimensionalIndexToLinearIndex(dest_shape, index)] = - src[IndexUtil::MultidimensionalIndexToLinearIndex(src_shape, index)]; - } while (IndexUtil::BumpIndices(dest_shape, &index)); -} - -} // namespace - -Status LiteralBase::Piece::CopyFrom(const LiteralBase::Piece& src) { - CHECK(subshape_ != nullptr); - CHECK(src.subshape_ != nullptr); - if (ShapeUtil::Equal(subshape(), src.subshape())) { - // If the layouts are equal it's faster just to memcpy. - memcpy(buffer(), src.buffer(), src.size_bytes()); - } else { - TF_RET_CHECK(ShapeUtil::Compatible(src.subshape(), subshape())); - std::vector origin(ShapeUtil::Rank(subshape()), 0); - switch (subshape().element_type()) { -#define COPY_ELEMENTS(XLA_T, NATIVE_T) \ - case (XLA_T): \ - CopyElementsBetween(data(), src.data(), \ - subshape(), src.subshape()); \ - break; - COPY_ELEMENTS(U8, uint8); - COPY_ELEMENTS(U16, uint16); - COPY_ELEMENTS(U32, uint32); - COPY_ELEMENTS(U64, uint64); - COPY_ELEMENTS(S8, int8); - COPY_ELEMENTS(S16, int16); - COPY_ELEMENTS(S32, int32); - COPY_ELEMENTS(S64, int64); - COPY_ELEMENTS(F16, half); - COPY_ELEMENTS(BF16, bfloat16); - COPY_ELEMENTS(F32, float); - COPY_ELEMENTS(F64, double); - COPY_ELEMENTS(C64, complex64); - COPY_ELEMENTS(PRED, bool); -#undef COPY_ELEMENTS - default: - return Unimplemented( - "Copying a Literal object with element type %s is not implemented.", - PrimitiveType_Name(subshape().element_type()).c_str()); - } - } - return Status::OK(); -} - -Status Literal::CopyFrom(const LiteralSlice& src_literal, - const ShapeIndex& dest_shape_index, - const ShapeIndex& src_shape_index) { - const Shape& dest_subshape = - ShapeUtil::GetSubshape(shape(), dest_shape_index); - const Shape& src_subshape = - ShapeUtil::GetSubshape(src_literal.shape(), src_shape_index); - if (!ShapeUtil::Compatible(dest_subshape, src_subshape)) { - return InvalidArgument( - "Destination subshape incompatible with source subshape: %s vs %s", - ShapeUtil::HumanString(dest_subshape).c_str(), - ShapeUtil::HumanString(src_subshape).c_str()); - } - return root_piece_->ForEachMutableSubpieceWithStatus( - [&](const ShapeIndex& index, Piece* piece) { - if (!ShapeUtil::IsArray(piece->subshape())) { - return Status::OK(); - } - - // Determine if this index is in the part of this literal that we want - // to copy over from src_literal. - bool in_subtree_to_copy = true; - for (int i = 0; i < dest_shape_index.size(); ++i) { - if (index[i] != dest_shape_index[i]) { - in_subtree_to_copy = false; - break; - } - } - if (!in_subtree_to_copy) { - return Status::OK(); - } - // Construct the index of the corresponding piece in the source literal. - ShapeIndex src_piece_index = src_shape_index; - for (int64 i = dest_shape_index.size(); i < index.size(); ++i) { - src_piece_index.push_back(index[i]); - } - TF_RETURN_IF_ERROR(piece->CopyFrom(src_literal.piece(src_piece_index))); - return Status::OK(); - }); -} - -Status Literal::MoveFrom(Literal&& src_literal, - const ShapeIndex& dest_shape_index) { - const Shape& dest_subshape = - ShapeUtil::GetSubshape(shape(), dest_shape_index); - if (!ShapeUtil::Equal(dest_subshape, src_literal.shape())) { - return InvalidArgument( - "Destination subshape not equal to source shape: %s vs %s", - ShapeUtil::HumanString(dest_subshape).c_str(), - ShapeUtil::HumanString(src_literal.shape()).c_str()); - } - - src_literal.root_piece_->ForEachSubpiece( - [&](const ShapeIndex& src_index, const Piece& src_piece) { - if (!ShapeUtil::IsArray(src_piece.subshape())) { - return; - } - - ShapeIndex dest_index = dest_shape_index; - for (int64 i : src_index) { - dest_index.push_back(i); - } - Piece& dest_piece = piece(dest_index); - delete[] dest_piece.buffer(); - dest_piece.set_buffer(src_piece.buffer()); - delete dest_piece.sparse_indices(); - dest_piece.set_sparse_indices(src_piece.sparse_indices()); - }); - - src_literal.shape_ = MakeUnique(ShapeUtil::MakeNil()); - delete src_literal.root_piece_; - src_literal.root_piece_ = new LiteralBase::Piece(); - src_literal.root_piece_->set_subshape(src_literal.shape_.get()); - - return Status::OK(); -} - -Status Literal::CopySliceFrom(const LiteralSlice& src_literal, - tensorflow::gtl::ArraySlice src_base, - tensorflow::gtl::ArraySlice dest_base, - tensorflow::gtl::ArraySlice copy_size) { - TF_RET_CHECK(ShapeUtil::IsArray(shape())) << ShapeUtil::HumanString(shape()); - TF_RET_CHECK(ShapeUtil::IsArray(src_literal.shape())) - << ShapeUtil::HumanString(src_literal.shape()); - TF_RET_CHECK(ShapeUtil::SameElementType(src_literal.shape(), shape())); - - switch (shape().element_type()) { - case U8: - return CopySliceFromInternal(src_literal, src_base, dest_base, - copy_size); - case U16: - return CopySliceFromInternal(src_literal, src_base, dest_base, - copy_size); - case U32: - return CopySliceFromInternal(src_literal, src_base, dest_base, - copy_size); - case U64: - return CopySliceFromInternal(src_literal, src_base, dest_base, - copy_size); - case S8: - return CopySliceFromInternal(src_literal, src_base, dest_base, - copy_size); - case S16: - return CopySliceFromInternal(src_literal, src_base, dest_base, - copy_size); - case S32: - return CopySliceFromInternal(src_literal, src_base, dest_base, - copy_size); - case S64: - return CopySliceFromInternal(src_literal, src_base, dest_base, - copy_size); - case F16: - return CopySliceFromInternal(src_literal, src_base, dest_base, - copy_size); - case BF16: - return CopySliceFromInternal(src_literal, src_base, dest_base, - copy_size); - case F32: - return CopySliceFromInternal(src_literal, src_base, dest_base, - copy_size); - case F64: - return CopySliceFromInternal(src_literal, src_base, dest_base, - copy_size); - case C64: - return CopySliceFromInternal(src_literal, src_base, dest_base, - copy_size); - case PRED: - return CopySliceFromInternal(src_literal, src_base, dest_base, - copy_size); - default: - break; - } - return Unimplemented( - "Copying a slice from a Literal object with element type %d is not " - "implemented.", - shape().element_type()); -} - -/* static */ Literal Literal::Zero(PrimitiveType primitive_type) { +/* static */ Literal LiteralUtil::Zero(PrimitiveType primitive_type) { switch (primitive_type) { case U8: - return std::move(*Literal::CreateR0(0)); + return std::move(*LiteralUtil::CreateR0(0)); case U32: - return std::move(*Literal::CreateR0(0)); + return std::move(*LiteralUtil::CreateR0(0)); case U64: - return std::move(*Literal::CreateR0(0)); + return std::move(*LiteralUtil::CreateR0(0)); case S8: - return std::move(*Literal::CreateR0(0)); + return std::move(*LiteralUtil::CreateR0(0)); case S32: - return std::move(*Literal::CreateR0(0)); + return std::move(*LiteralUtil::CreateR0(0)); case S64: - return std::move(*Literal::CreateR0(0)); + return std::move(*LiteralUtil::CreateR0(0)); case F16: - return std::move(*Literal::CreateR0(static_cast(0.0f))); + return std::move(*LiteralUtil::CreateR0(static_cast(0.0f))); case BF16: return std::move( - *Literal::CreateR0(static_cast(0.0f))); + *LiteralUtil::CreateR0(static_cast(0.0f))); case F32: - return std::move(*Literal::CreateR0(0)); + return std::move(*LiteralUtil::CreateR0(0)); case F64: - return std::move(*Literal::CreateR0(0)); + return std::move(*LiteralUtil::CreateR0(0)); case C64: - return std::move(*Literal::CreateR0(0)); + return std::move(*LiteralUtil::CreateR0(0)); case PRED: - return std::move(*Literal::CreateR0(false)); + return std::move(*LiteralUtil::CreateR0(false)); case S16: case U16: LOG(FATAL) << "u16/s16 literals not yet implemented"; @@ -614,33 +144,33 @@ Status Literal::CopySliceFrom(const LiteralSlice& src_literal, } } -/* static */ Literal Literal::One(PrimitiveType primitive_type) { +/* static */ Literal LiteralUtil::One(PrimitiveType primitive_type) { switch (primitive_type) { case U8: - return std::move(*Literal::CreateR0(1)); + return std::move(*LiteralUtil::CreateR0(1)); case U32: - return std::move(*Literal::CreateR0(1)); + return std::move(*LiteralUtil::CreateR0(1)); case U64: - return std::move(*Literal::CreateR0(1)); + return std::move(*LiteralUtil::CreateR0(1)); case S8: - return std::move(*Literal::CreateR0(1)); + return std::move(*LiteralUtil::CreateR0(1)); case S32: - return std::move(*Literal::CreateR0(1)); + return std::move(*LiteralUtil::CreateR0(1)); case S64: - return std::move(*Literal::CreateR0(1)); + return std::move(*LiteralUtil::CreateR0(1)); case F16: - return std::move(*Literal::CreateR0(static_cast(1.0f))); + return std::move(*LiteralUtil::CreateR0(static_cast(1.0f))); case BF16: return std::move( - *Literal::CreateR0(static_cast(1.0f))); + *LiteralUtil::CreateR0(static_cast(1.0f))); case F32: - return std::move(*Literal::CreateR0(1)); + return std::move(*LiteralUtil::CreateR0(1)); case F64: - return std::move(*Literal::CreateR0(1)); + return std::move(*LiteralUtil::CreateR0(1)); case C64: - return std::move(*Literal::CreateR0(1)); + return std::move(*LiteralUtil::CreateR0(1)); case PRED: - return std::move(*Literal::CreateR0(true)); + return std::move(*LiteralUtil::CreateR0(true)); case S16: case U16: LOG(FATAL) << "u16/s16 literals not yet implemented"; @@ -653,44 +183,44 @@ Status Literal::CopySliceFrom(const LiteralSlice& src_literal, } } -/* static */ Literal Literal::MinValue(PrimitiveType primitive_type) { +/* static */ Literal LiteralUtil::MinValue(PrimitiveType primitive_type) { switch (primitive_type) { case U8: return std::move( - *Literal::CreateR0(std::numeric_limits::min())); + *LiteralUtil::CreateR0(std::numeric_limits::min())); case U32: return std::move( - *Literal::CreateR0(std::numeric_limits::min())); + *LiteralUtil::CreateR0(std::numeric_limits::min())); case U64: return std::move( - *Literal::CreateR0(std::numeric_limits::min())); + *LiteralUtil::CreateR0(std::numeric_limits::min())); case S8: return std::move( - *Literal::CreateR0(std::numeric_limits::min())); + *LiteralUtil::CreateR0(std::numeric_limits::min())); case S32: return std::move( - *Literal::CreateR0(std::numeric_limits::min())); + *LiteralUtil::CreateR0(std::numeric_limits::min())); case S64: return std::move( - *Literal::CreateR0(std::numeric_limits::min())); + *LiteralUtil::CreateR0(std::numeric_limits::min())); case F32: - return std::move( - *Literal::CreateR0(-std::numeric_limits::infinity())); + return std::move(*LiteralUtil::CreateR0( + -std::numeric_limits::infinity())); case F64: - return std::move( - *Literal::CreateR0(-std::numeric_limits::infinity())); + return std::move(*LiteralUtil::CreateR0( + -std::numeric_limits::infinity())); case C64: LOG(FATAL) << "C64 element type has no minimum value"; case PRED: - return std::move(*Literal::CreateR0(false)); + return std::move(*LiteralUtil::CreateR0(false)); case S16: case U16: LOG(FATAL) << "u16/s16 literals not yet implemented"; case F16: - return std::move(*Literal::CreateR0( + return std::move(*LiteralUtil::CreateR0( static_cast(-std::numeric_limits::infinity()))); case BF16: - return std::move(*Literal::CreateR0( + return std::move(*LiteralUtil::CreateR0( static_cast(-std::numeric_limits::infinity()))); case TUPLE: LOG(FATAL) << "tuple element type has no minimum value"; @@ -701,42 +231,42 @@ Status Literal::CopySliceFrom(const LiteralSlice& src_literal, } } -/* static */ Literal Literal::MaxValue(PrimitiveType primitive_type) { +/* static */ Literal LiteralUtil::MaxValue(PrimitiveType primitive_type) { switch (primitive_type) { case U8: return std::move( - *Literal::CreateR0(std::numeric_limits::max())); + *LiteralUtil::CreateR0(std::numeric_limits::max())); case U32: return std::move( - *Literal::CreateR0(std::numeric_limits::max())); + *LiteralUtil::CreateR0(std::numeric_limits::max())); case U64: return std::move( - *Literal::CreateR0(std::numeric_limits::max())); + *LiteralUtil::CreateR0(std::numeric_limits::max())); case S8: return std::move( - *Literal::CreateR0(std::numeric_limits::max())); + *LiteralUtil::CreateR0(std::numeric_limits::max())); case S32: return std::move( - *Literal::CreateR0(std::numeric_limits::max())); + *LiteralUtil::CreateR0(std::numeric_limits::max())); case S64: return std::move( - *Literal::CreateR0(std::numeric_limits::max())); + *LiteralUtil::CreateR0(std::numeric_limits::max())); case F32: - return std::move( - *Literal::CreateR0(std::numeric_limits::infinity())); + return std::move(*LiteralUtil::CreateR0( + std::numeric_limits::infinity())); case F64: - return std::move( - *Literal::CreateR0(std::numeric_limits::infinity())); + return std::move(*LiteralUtil::CreateR0( + std::numeric_limits::infinity())); case PRED: - return std::move(*Literal::CreateR0(true)); + return std::move(*LiteralUtil::CreateR0(true)); case S16: case U16: LOG(FATAL) << "u16/s16 literals not yet implemented"; case F16: - return std::move(*Literal::CreateR0( + return std::move(*LiteralUtil::CreateR0( static_cast(std::numeric_limits::infinity()))); case BF16: - return std::move(*Literal::CreateR0( + return std::move(*LiteralUtil::CreateR0( static_cast(std::numeric_limits::infinity()))); case TUPLE: LOG(FATAL) << "tuple element type has no maximum value"; @@ -747,7 +277,7 @@ Status Literal::CopySliceFrom(const LiteralSlice& src_literal, } } -/* static */ std::unique_ptr Literal::CreateR1( +/* static */ std::unique_ptr LiteralUtil::CreateR1( const tensorflow::core::Bitmap& values) { auto literal = MakeUnique( ShapeUtil::MakeShape(PRED, {static_cast(values.bits())})); @@ -755,17 +285,7 @@ Status Literal::CopySliceFrom(const LiteralSlice& src_literal, return literal; } -void Literal::PopulateR1(const tensorflow::core::Bitmap& values) { - CHECK(ShapeUtil::IsArray(shape())); - CHECK_EQ(ShapeUtil::Rank(shape()), 1); - CHECK_EQ(element_count(), values.bits()); - CHECK_EQ(shape().element_type(), PRED); - for (int64 i = 0; i < static_cast(values.bits()); ++i) { - Set({i}, values.get(i)); - } -} - -/* static */ std::unique_ptr Literal::CreateR1U8( +/* static */ std::unique_ptr LiteralUtil::CreateR1U8( tensorflow::StringPiece value) { auto literal = MakeUnique( ShapeUtil::MakeShape(U8, {static_cast(value.size())})); @@ -775,116 +295,13 @@ void Literal::PopulateR1(const tensorflow::core::Bitmap& values) { return literal; } -/* static */ std::unique_ptr Literal::CreateR2F32Linspace(float from, - float to, - int64 rows, - int64 cols) { +/* static */ std::unique_ptr LiteralUtil::CreateR2F32Linspace( + float from, float to, int64 rows, int64 cols) { auto value = MakeLinspaceArray2D(from, to, rows, cols); return CreateR2FromArray2D(*value); } -std::unique_ptr LiteralBase::Relayout( - const Layout& new_layout, const ShapeIndex& shape_index) const { - // Create new shape with 'new_layout' set at the given shape index. - Shape new_shape = shape(); - Shape* subshape = ShapeUtil::GetMutableSubshape(&new_shape, shape_index); - TF_CHECK_OK(LayoutUtil::ValidateLayoutForShape(new_layout, *subshape)); - *subshape->mutable_layout() = new_layout; - auto result = MakeUnique(new_shape); - TF_CHECK_OK(result->CopyFrom(*this)); - return result; -} - -std::unique_ptr LiteralBase::Relayout( - const Shape& shape_with_layout) const { - CHECK(ShapeUtil::Compatible(shape_with_layout, shape())) - << "Given shape_with_layout " << ShapeUtil::HumanString(shape_with_layout) - << " not compatible with literal shape " - << ShapeUtil::HumanString(shape()); - std::unique_ptr result = CreateFromShape(shape_with_layout); - ShapeUtil::ForEachSubshape( - result->shape(), - [this, &result](const Shape& subshape, const ShapeIndex& index) { - if (ShapeUtil::IsArray(subshape)) { - TF_CHECK_OK(result->CopyFrom(*this, - /*dest_shape_index=*/index, - /*src_shape_index=*/index)); - } - }); - return result; -} - -StatusOr> LiteralBase::Broadcast( - const Shape& result_shape, - tensorflow::gtl::ArraySlice dimensions) const { - if (!ShapeUtil::IsArray(shape())) { - return InvalidArgument("Broadcast only supports arrays."); - } - - for (int64 i = 0; i < dimensions.size(); i++) { - TF_RET_CHECK(shape().dimensions(i) == - result_shape.dimensions(dimensions[i])); - } - - std::unique_ptr result = MakeUnique(result_shape); - - // scratch_source_index is temporary storage space for the computed index into - // the input literal. We put it here to avoid allocating an std::vector in - // every iteration of ShapeUtil::ForEachIndex. - std::vector scratch_source_index(shape().dimensions_size()); - - char* dest_data = static_cast(result->untyped_data()); - const char* source_data = static_cast(untyped_data()); - const int64 primitive_size = - ShapeUtil::ByteSizeOfPrimitiveType(shape().element_type()); - - ShapeUtil::ForEachIndex( - result_shape, [&](tensorflow::gtl::ArraySlice output_index) { - for (int64 i = 0; i < dimensions.size(); ++i) { - scratch_source_index[i] = output_index[dimensions[i]]; - } - int64 dest_index = IndexUtil::MultidimensionalIndexToLinearIndex( - result_shape, output_index); - int64 source_index = IndexUtil::MultidimensionalIndexToLinearIndex( - shape(), scratch_source_index); - memcpy(dest_data + primitive_size * dest_index, - source_data + primitive_size * source_index, primitive_size); - return true; - }); - - return std::move(result); -} - -StatusOr> LiteralBase::Reshape( - tensorflow::gtl::ArraySlice dimensions) const { - if (!ShapeUtil::IsArray(shape())) { - return InvalidArgument("Reshape does not support tuples."); - } - std::unique_ptr output; - if (!LayoutUtil::IsMonotonicWithDim0Major(shape().layout())) { - output = - Relayout(LayoutUtil::GetDefaultLayoutForRank(ShapeUtil::Rank(shape()))); - } else { - output = CloneToUnique(); - } - // Because the layout is monotonic, we can simply reuse the same sequence of - // values without changing their order. - *output->mutable_shape_do_not_use() = - ShapeUtil::MakeShape(shape().element_type(), dimensions); - - int64 elements_before = ShapeUtil::ElementsIn(shape()); - int64 elements_after = ShapeUtil::ElementsIn(output->shape()); - if (elements_before != elements_after) { - return InvalidArgument( - "Shapes before and after Literal::Reshape have different numbers " - "of elements: %s vs %s.", - ShapeUtil::HumanString(shape()).c_str(), - ShapeUtil::HumanString(output->shape()).c_str()); - } - return std::move(output); -} - -/* static */ std::unique_ptr Literal::ReshapeSlice( +/* static */ std::unique_ptr LiteralUtil::ReshapeSlice( tensorflow::gtl::ArraySlice new_dimensions, tensorflow::gtl::ArraySlice minor_to_major, const LiteralSlice& literal) { @@ -956,588 +373,77 @@ StatusOr> LiteralBase::Reshape( return new_literal; } -std::unique_ptr LiteralBase::Transpose( - tensorflow::gtl::ArraySlice permutation) const { - CHECK(ShapeUtil::IsArray(shape())) << "Tuple is not supported for transpose"; - CHECK(IsPermutation(permutation, ShapeUtil::Rank(shape()))) - << "Given permutation is not a permutation of dimension numbers"; - // To transpose the array, we just permute the dimensions and layout, and - // do a straight memory copy of the raw data set. - // This is considerably faster than iterating over every array element using - // the EachCell<>() and Set<>() APIs. - std::vector inverse_permutation = InversePermutation(permutation); - Shape permuted_shape = - ShapeUtil::PermuteDimensions(inverse_permutation, shape()); - // Replace the layout with one affine to this shape, such that a - // transpose operation can be performed by leaving the flat values - // representation intact. - // For example, consider the shape F32[11,8]{1,0} under a {1,0} permutation. - // The shape with affine layout resulting from that operation will be - // F32[8,11]{0,1}, since it leaves the original most minor (the 8 sized), the - // most minor. - // - // Essentially, given MinMaj(Di) the position of the Di dimension within the - // minor to major vector, and given T(Di) the index that the original Di - // dimension has within the transposed array, a layout is affine if - // MinMaj(Di) == TMinMaj(T(Di)), with TMinMaj() being the minor to major - // vector of the affine layout. - CHECK(LayoutUtil::IsDenseArray(permuted_shape)); - Layout* layout = permuted_shape.mutable_layout(); - layout->clear_minor_to_major(); - for (auto index : LayoutUtil::MinorToMajor(shape())) { - layout->add_minor_to_major(inverse_permutation[index]); - } - auto new_literal = MakeUnique(permuted_shape); - DCHECK_EQ(ShapeUtil::ByteSizeOf(new_literal->shape()), - ShapeUtil::ByteSizeOf(shape())); - std::memcpy(new_literal->untyped_data(), untyped_data(), size_bytes()); - return new_literal; -} - -template -std::unique_ptr LiteralBase::SliceInternal( - const Shape& result_shape, - tensorflow::gtl::ArraySlice start_indices) const { - auto result_literal = MakeUnique(result_shape); - DimensionVector new_indices(ShapeUtil::Rank(result_shape)); - result_literal->EachCell( - [&](tensorflow::gtl::ArraySlice indices, NativeT /*value*/) { - for (int64 i = 0; i < ShapeUtil::Rank(result_shape); ++i) { - new_indices[i] = indices[i] + start_indices[i]; - } - NativeT value = Get(new_indices); - result_literal->Set(indices, value); - }); - return result_literal; -} - -std::unique_ptr LiteralBase::Slice( - tensorflow::gtl::ArraySlice start_indices, - tensorflow::gtl::ArraySlice limit_indices) const { - CHECK(ShapeUtil::IsArray(shape())) << "tuple is not supported for slice"; - - DimensionVector result_dimensions; - for (int64 dnum = 0; dnum < ShapeUtil::Rank(shape()); ++dnum) { - CHECK_GE(start_indices[dnum], 0); - CHECK_LE(limit_indices[dnum], shape().dimensions(dnum)) - << "dnum = " << dnum; - int64 dimension = limit_indices[dnum] - start_indices[dnum]; - CHECK_GE(dimension, 0) << "dnum = " << dnum; - result_dimensions.push_back(dimension); - } - const auto result_shape = - ShapeUtil::MakeShapeWithLayout(shape().element_type(), result_dimensions, - LayoutUtil::MinorToMajor(shape())); - switch (result_shape.element_type()) { - case F32: - return SliceInternal(result_shape, start_indices); - case BF16: - return SliceInternal(result_shape, start_indices); - case C64: - return SliceInternal(result_shape, start_indices); - case S32: - return SliceInternal(result_shape, start_indices); - case U32: - return SliceInternal(result_shape, start_indices); - default: - LOG(FATAL) << "not yet implemented: " - << PrimitiveType_Name(result_shape.element_type()); - } -} - -Literal LiteralBase::Clone() const { - Literal result(shape()); - TF_CHECK_OK(result.CopyFrom(*this)); - return result; -} - -std::unique_ptr LiteralBase::CloneToUnique() const { - auto result = MakeUnique(shape()); - TF_CHECK_OK(result->CopyFrom(*this)); - return result; -} - -string LiteralBase::GetAsString(tensorflow::gtl::ArraySlice multi_index, - const ShapeIndex& shape_index) const { - const Shape& subshape = ShapeUtil::GetSubshape(shape(), shape_index); - CHECK(LayoutUtil::IsDenseArray(subshape)); - switch (subshape.element_type()) { +/* static */ Literal LiteralUtil::GetFirstScalarLiteral( + const LiteralSlice& literal) { + CHECK(ShapeUtil::IsArray(literal.shape())); + CHECK_GT(ShapeUtil::ElementsIn(literal.shape()), 0); + switch (literal.shape().element_type()) { case PRED: - return Get(multi_index, shape_index) ? "true" : "false"; + return std::move( + *LiteralUtil::CreateR0(literal.GetFirstElement())); + // 8 bit types. case S8: - return StrCat(Get(multi_index, shape_index)); - case S16: - return StrCat(Get(multi_index, shape_index)); - case S32: - return StrCat(Get(multi_index, shape_index)); - case S64: - return StrCat(Get(multi_index, shape_index)); + return std::move( + *LiteralUtil::CreateR0(literal.GetFirstElement())); case U8: - return StrCat(Get(multi_index, shape_index)); - case U16: - return StrCat(Get(multi_index, shape_index)); - case U32: - return StrCat(Get(multi_index, shape_index)); - case U64: - return StrCat(Get(multi_index, shape_index)); - case F16: - return StrCat(static_cast(Get(multi_index, shape_index))); - case F32: - return StrCat(Get(multi_index, shape_index)); + return std::move( + *LiteralUtil::CreateR0(literal.GetFirstElement())); + // 16 bit types. case BF16: - return StrCat( - static_cast(Get(multi_index, shape_index))); - case F64: - return StrCat(Get(multi_index, shape_index)); - case C64: { - complex64 c = Get(multi_index, shape_index); - return StrCat("(", c.real(), ", ", c.imag(), ")"); - } - default: - LOG(FATAL) << PrimitiveType_Name(subshape.element_type()); - } -} - -string LiteralBase::GetSparseElementAsString( - int64 sparse_element_number, const ShapeIndex& shape_index) const { - const Shape& subshape = ShapeUtil::GetSubshape(shape(), shape_index); - CHECK(LayoutUtil::IsSparseArray(subshape)); - switch (subshape.element_type()) { - case PRED: - return GetSparseElement(sparse_element_number, shape_index) - ? "true" - : "false"; - case S8: - return StrCat(GetSparseElement(sparse_element_number, shape_index)); + return std::move(*LiteralUtil::CreateR0( + literal.GetFirstElement())); + case F16: + return std::move( + *LiteralUtil::CreateR0(literal.GetFirstElement())); case S16: - return StrCat( - GetSparseElement(sparse_element_number, shape_index)); - case S32: - return StrCat( - GetSparseElement(sparse_element_number, shape_index)); - case S64: - return StrCat( - GetSparseElement(sparse_element_number, shape_index)); - case U8: - return StrCat( - GetSparseElement(sparse_element_number, shape_index)); + return std::move( + *LiteralUtil::CreateR0(literal.GetFirstElement())); case U16: - return StrCat( - GetSparseElement(sparse_element_number, shape_index)); - case U32: - return StrCat( - GetSparseElement(sparse_element_number, shape_index)); - case U64: - return StrCat( - GetSparseElement(sparse_element_number, shape_index)); - case F16: - return StrCat(static_cast( - GetSparseElement(sparse_element_number, shape_index))); + return std::move( + *LiteralUtil::CreateR0(literal.GetFirstElement())); + // 32 bit types. case F32: - return StrCat( - GetSparseElement(sparse_element_number, shape_index)); - case BF16: - return StrCat(static_cast( - GetSparseElement(sparse_element_number, shape_index))); - case F64: - return StrCat( - GetSparseElement(sparse_element_number, shape_index)); - case C64: { - complex64 c = - GetSparseElement(sparse_element_number, shape_index); - return StrCat("(", c.real(), ", ", c.imag(), ")"); - } - default: - LOG(FATAL) << "Invalid element type for sparse arrays: " - << PrimitiveType_Name(subshape.element_type()); - } -} - -StatusOr LiteralBase::GetIntegralAsS64( - tensorflow::gtl::ArraySlice multi_index) const { - CHECK(LayoutUtil::IsDenseArray(shape())); - switch (shape().element_type()) { - case PRED: - return Get(multi_index); - case U8: - return Get(multi_index); + return std::move( + *LiteralUtil::CreateR0(literal.GetFirstElement())); case S32: - return Get(multi_index); - case S64: - return Get(multi_index); + return std::move( + *LiteralUtil::CreateR0(literal.GetFirstElement())); case U32: - return Get(multi_index); + return std::move( + *LiteralUtil::CreateR0(literal.GetFirstElement())); + // 64 bit types. + case C64: + return std::move(*LiteralUtil::CreateR0( + literal.GetFirstElement())); + case F64: + return std::move( + *LiteralUtil::CreateR0(literal.GetFirstElement())); + case S64: + return std::move( + *LiteralUtil::CreateR0(literal.GetFirstElement())); case U64: - return Get(multi_index); + return std::move( + *LiteralUtil::CreateR0(literal.GetFirstElement())); default: - return FailedPrecondition( - "Array element type is not integral: %s", - PrimitiveType_Name(shape().element_type()).c_str()); + LOG(FATAL) << "Unhandled primitive type " + << literal.shape().element_type(); } } -size_t LiteralBase::Hash() const { - using tensorflow::Hash64; - using tensorflow::Hash64Combine; - - size_t hash_value = ShapeUtil::Hash(shape()); - - ShapeUtil::ForEachSubshape( - shape(), [&](const Shape& subshape, const ShapeIndex& index) { - if (!ShapeUtil::IsArray(subshape)) { - return; - } - - CHECK(LayoutUtil::IsDense(subshape.layout())); - hash_value = Hash64Combine( - hash_value, Hash64(static_cast(untyped_data(index)), - size_bytes(index))); - }); - - return hash_value; +/* static */ std::unique_ptr LiteralUtil::MakeTuple( + tensorflow::gtl::ArraySlice elements) { + std::vector element_shapes; + for (const auto* element : elements) { + element_shapes.push_back(element->shape()); + } + auto literal = MakeUnique(ShapeUtil::MakeTupleShape(element_shapes)); + for (int i = 0; i < elements.size(); ++i) { + TF_CHECK_OK(literal->CopyFrom(*elements[i], /*dest_shape_index=*/{i})); + } + return literal; } -Status Literal::SetIntegralAsS64(tensorflow::gtl::ArraySlice multi_index, - int64 value) { - CHECK(LayoutUtil::IsDenseArray(shape())); - switch (shape().element_type()) { - case PRED: - Set(multi_index, value); - break; - case U8: - Set(multi_index, value); - break; - case S32: - Set(multi_index, value); - break; - case S64: - Set(multi_index, value); - break; - case U32: - Set(multi_index, value); - break; - case U64: - Set(multi_index, value); - break; - default: - return FailedPrecondition( - "Array element type is not integral: %s", - PrimitiveType_Name(shape().element_type()).c_str()); - } - return Status::OK(); -} - -tensorflow::gtl::ArraySlice LiteralBase::GetSparseIndex( - int64 sparse_element_number, const ShapeIndex& shape_index) const { - const Piece& p = piece(shape_index); - CHECK_GE(sparse_element_number, 0); - CHECK_LT(sparse_element_number, p.sparse_indices()->index_count()); - return p.sparse_indices()->At(sparse_element_number); -} - -void Literal::SortSparseElements(const ShapeIndex& shape_index) { - piece(shape_index).SortSparseElements(); -} - -Literal LiteralBase::GetFirstScalarLiteral() const { - CHECK(ShapeUtil::IsArray(shape())); - CHECK_GT(ShapeUtil::ElementsIn(shape()), 0); - switch (shape().element_type()) { - case PRED: - return std::move(*Literal::CreateR0(GetFirstElement())); - // 8 bit types. - case S8: - return std::move(*Literal::CreateR0(GetFirstElement())); - case U8: - return std::move(*Literal::CreateR0(GetFirstElement())); - // 16 bit types. - case BF16: - return std::move( - *Literal::CreateR0(GetFirstElement())); - case F16: - return std::move(*Literal::CreateR0(GetFirstElement())); - case S16: - return std::move(*Literal::CreateR0(GetFirstElement())); - case U16: - return std::move(*Literal::CreateR0(GetFirstElement())); - // 32 bit types. - case F32: - return std::move(*Literal::CreateR0(GetFirstElement())); - case S32: - return std::move(*Literal::CreateR0(GetFirstElement())); - case U32: - return std::move(*Literal::CreateR0(GetFirstElement())); - // 64 bit types. - case C64: - return std::move( - *Literal::CreateR0(GetFirstElement())); - case F64: - return std::move(*Literal::CreateR0(GetFirstElement())); - case S64: - return std::move(*Literal::CreateR0(GetFirstElement())); - case U64: - return std::move(*Literal::CreateR0(GetFirstElement())); - default: - LOG(FATAL) << "Unhandled primitive type " << shape().element_type(); - } -} - -void LiteralBase::Piece::SortSparseElements() { - switch (subshape().element_type()) { - case PRED: - SortSparseElementsInternal(); - break; - case S8: - SortSparseElementsInternal(); - break; - case U8: - SortSparseElementsInternal(); - break; - case S16: - SortSparseElementsInternal(); - break; - case U16: - SortSparseElementsInternal(); - break; - case S32: - SortSparseElementsInternal(); - break; - case U32: - SortSparseElementsInternal(); - break; - case S64: - SortSparseElementsInternal(); - break; - case U64: - SortSparseElementsInternal(); - break; - case F32: - SortSparseElementsInternal(); - break; - case F64: - SortSparseElementsInternal(); - break; - case C64: - SortSparseElementsInternal(); - break; - case F16: - SortSparseElementsInternal(); - break; - case BF16: - SortSparseElementsInternal(); - break; - default: - LOG(FATAL) << "Element type not valid for sparse array: " - << PrimitiveType_Name(subshape().element_type()); - } -} - -template -void LiteralBase::Piece::SortSparseElementsInternal() { - CHECK(LayoutUtil::IsSparseArray(subshape())); - int64 num_elements = sparse_indices()->index_count(); - auto values = data(); - CHECK_LE(num_elements, values.size()); - sparse_indices()->SortWithValues( - tensorflow::gtl::MutableArraySlice(values.data(), num_elements)); -} - -namespace { - -void ToStringHelper(const LiteralBase& literal, const ShapeIndex& shape_index, - bool print_layout, std::vector* pieces) { - const Shape& subshape = ShapeUtil::GetSubshape(literal.shape(), shape_index); - CHECK(LayoutUtil::HasLayout(literal.shape())); - CHECK(LayoutUtil::HasLayout(subshape)); - - auto shape_to_string = [print_layout](const Shape& shape) { - if (print_layout) { - return ShapeUtil::HumanStringWithLayout(shape); - } else { - return ShapeUtil::HumanString(shape); - } - }; - - // TODO(b/32894291): refactor this code to reduce code duplication. - if (ShapeUtil::IsTuple(subshape)) { - pieces->push_back(shape_to_string(subshape)); - pieces->push_back(" (\n"); - std::vector tuple_pieces; - for (int i = 0; i < ShapeUtil::TupleElementCount(subshape); ++i) { - ShapeIndex element_index = shape_index; - element_index.push_back(i); - std::vector element_pieces; - ToStringHelper(literal, element_index, print_layout, &element_pieces); - tuple_pieces.push_back(tensorflow::str_util::Join(element_pieces, "")); - } - pieces->push_back(tensorflow::str_util::Join(tuple_pieces, ",\n")); - pieces->push_back("\n)"); - return; - } - - if (ShapeUtil::IsToken(subshape)) { - pieces->push_back("token"); - return; - } - - if (LayoutUtil::IsSparseArray(subshape)) { - pieces->push_back(shape_to_string(subshape)); - pieces->push_back("{"); - int64 rank = ShapeUtil::Rank(subshape); - int64 num_elements = literal.sparse_element_count(); - for (int64 i = 0; i < num_elements; ++i) { - if (i > 0) { - pieces->push_back(", "); - } - if (rank == 1) { - pieces->push_back(StrCat(literal.GetSparseIndex(i)[0])); - pieces->push_back(": "); - } else { - pieces->push_back("["); - pieces->push_back( - tensorflow::str_util::Join(literal.GetSparseIndex(i), ", ")); - pieces->push_back("]: "); - } - pieces->push_back(literal.GetSparseElementAsString(i)); - } - pieces->push_back("}"); - return; - } - - CHECK(LayoutUtil::IsDenseArray(subshape)); - - auto element_to_string = - [&](tensorflow::gtl::ArraySlice indices) -> string { - PrimitiveType element_type = subshape.element_type(); - if (element_type == PRED) { - // We display predicates in a densely packed form. - return literal.Get(indices, shape_index) ? "1" : "0"; - } - return ((!indices.empty() && indices.back() > 0) ? ", " : "") + - literal.GetAsString(indices, shape_index); - }; - - if (ShapeUtil::Rank(subshape) == 0) { - pieces->push_back(literal.GetAsString({}, shape_index)); - } else if (ShapeUtil::Rank(subshape) == 1) { - pieces->push_back("{"); - for (int64 i0 = 0; i0 < subshape.dimensions(0); ++i0) { - pieces->push_back(element_to_string({i0})); - } - pieces->push_back("}"); - } else if (ShapeUtil::Rank(subshape) == 2) { - pieces->push_back(shape_to_string(subshape)); - pieces->push_back(" {\n"); - for (int64 i0 = 0; i0 < subshape.dimensions(0); ++i0) { - pieces->push_back(" { "); - for (int64 i1 = 0; i1 < subshape.dimensions(1); ++i1) { - pieces->push_back(element_to_string({i0, i1})); - } - pieces->push_back(" "); - pieces->push_back(i0 == subshape.dimensions(0) - 1 ? "}\n" : "},\n"); - } - pieces->push_back("}"); - } else if (ShapeUtil::Rank(subshape) == 3) { - pieces->push_back(shape_to_string(subshape)); - pieces->push_back(" {\n"); - for (int64 i0 = 0; i0 < subshape.dimensions(0); ++i0) { - pieces->push_back(i0 > 0 ? ",\n{" : "{"); - for (int64 i1 = 0; i1 < subshape.dimensions(1); ++i1) { - pieces->push_back(i1 > 0 ? ",\n { " : " { "); - for (int64 i2 = 0; i2 < subshape.dimensions(2); ++i2) { - pieces->push_back(element_to_string({i0, i1, i2})); - } - pieces->push_back(" }"); - } - pieces->push_back(" }"); - } - pieces->push_back("\n}"); - } else if (ShapeUtil::Rank(subshape) == 4) { - pieces->push_back(shape_to_string(subshape)); - pieces->push_back(" {\n"); - for (int64 i0 = 0; i0 < subshape.dimensions(0); ++i0) { - pieces->push_back(Printf(" { /*i0=%lld*/\n", i0)); - for (int64 i1 = 0; i1 < subshape.dimensions(1); ++i1) { - pieces->push_back(Printf(" { /*i1=%lld*/\n", i1)); - for (int64 i2 = 0; i2 < subshape.dimensions(2); ++i2) { - pieces->push_back(" {"); - for (int64 i3 = 0; i3 < subshape.dimensions(3); ++i3) { - pieces->push_back(element_to_string({i0, i1, i2, i3})); - } - pieces->push_back(i2 == subshape.dimensions(2) - 1 ? "}\n" : "},\n"); - } - pieces->push_back(i1 == subshape.dimensions(1) - 1 ? " }\n" - : " },\n"); - } - pieces->push_back(i0 == subshape.dimensions(0) - 1 ? " }\n" : " },\n"); - } - pieces->push_back("}"); - } else if (ShapeUtil::Rank(subshape) == 5) { - pieces->push_back(shape_to_string(subshape)); - pieces->push_back(" {\n"); - for (int64 i0 = 0; i0 < subshape.dimensions(0); ++i0) { - pieces->push_back(Printf(" { /*i0=%lld*/\n", i0)); - for (int64 i1 = 0; i1 < subshape.dimensions(1); ++i1) { - pieces->push_back(Printf(" { /*i1=%lld*/\n", i1)); - for (int64 i2 = 0; i2 < subshape.dimensions(2); ++i2) { - pieces->push_back(Printf(" { /*i2=%lld*/\n", i2)); - for (int64 i3 = 0; i3 < subshape.dimensions(3); ++i3) { - pieces->push_back(" {"); - for (int64 i4 = 0; i4 < subshape.dimensions(4); ++i4) { - pieces->push_back(element_to_string({i0, i1, i2, i3, i4})); - } - pieces->push_back(i3 == subshape.dimensions(3) - 1 ? "}\n" - : "},\n"); - } - pieces->push_back(i2 == subshape.dimensions(2) - 1 ? " }\n" - : " },\n"); - } - pieces->push_back(i1 == subshape.dimensions(1) - 1 ? " }\n" - : " },\n"); - } - pieces->push_back(i0 == subshape.dimensions(0) - 1 ? " }\n" : " },\n"); - } - pieces->push_back("}"); - } else { - pieces->push_back(shape_to_string(subshape)); - pieces->push_back(" {"); - literal.EachCellAsString( - [&](tensorflow::gtl::ArraySlice indices, const string& value) { - pieces->push_back(" "); - pieces->push_back(value); - }); - pieces->push_back("}"); - } -} - -} // namespace - -int64 LiteralBase::sparse_element_count() const { - CHECK(LayoutUtil::IsSparseArray(shape())); - return sparse_indices()->index_count(); -} - -string LiteralBase::ToString(bool print_layout) const { - std::vector pieces; - CHECK(LayoutUtil::HasLayout(this->shape())); - ToStringHelper(*this, {}, print_layout, &pieces); - return tensorflow::str_util::Join(pieces, ""); -} - -/* static */ std::unique_ptr Literal::MakeTuple( - tensorflow::gtl::ArraySlice elements) { - std::vector element_shapes; - for (const auto* element : elements) { - element_shapes.push_back(element->shape()); - } - auto literal = MakeUnique(ShapeUtil::MakeTupleShape(element_shapes)); - for (int i = 0; i < elements.size(); ++i) { - TF_CHECK_OK(literal->CopyFrom(*elements[i], /*dest_shape_index=*/{i})); - } - return literal; -} - -/* static */ std::unique_ptr Literal::MakeTupleFromSlices( +/* static */ std::unique_ptr LiteralUtil::MakeTupleFromSlices( tensorflow::gtl::ArraySlice elements) { std::vector element_shapes; for (const auto& element : elements) { @@ -1550,7 +456,7 @@ string LiteralBase::ToString(bool print_layout) const { return literal; } -/* static */ std::unique_ptr Literal::MakeTupleOwned( +/* static */ std::unique_ptr LiteralUtil::MakeTupleOwned( std::vector> elements) { std::vector element_shapes; element_shapes.reserve(elements.size()); @@ -1565,822 +471,9 @@ string LiteralBase::ToString(bool print_layout) const { return literal; } -void LiteralBase::EachCellAsString( - const std::function indices, - const string& value)>& per_cell) const { - if (ShapeUtil::IsZeroElementArray(shape())) { - return; - } - std::vector indices = IndexUtil::LinearIndexToMultidimensionalIndex( - shape(), /*linear_index=*/0); - do { - per_cell(indices, GetAsString(indices)); - } while (IndexUtil::BumpIndices(shape(), &indices)); -} - -namespace { -template -std::unique_ptr ConvertBetweenNativeTypesWithConverter( - const LiteralBase& src_literal, const ConverterType& converter) { - CHECK(ShapeUtil::IsArray(src_literal.shape())); - auto result_literal = MakeUnique(ShapeUtil::ChangeElementType( - src_literal.shape(), - primitive_util::NativeToPrimitiveType())); - auto src_data = src_literal.data(); - auto dest_data = result_literal->template data(); - int64 num_elements = src_literal.element_count(); - - for (int64 i = 0; i < num_elements; ++i) { - dest_data[i] = converter(src_data[i]); - } - return result_literal; -} - -template -std::unique_ptr ConvertBetweenNativeTypes( - const LiteralBase& src_literal) { - auto converter = [](NativeSrcT src) { return static_cast(src); }; - return ConvertBetweenNativeTypesWithConverter( - src_literal, converter); -} - -template -typename std::enable_if<(sizeof(NativeSrcT) == sizeof(NativeDestT)), - std::unique_ptr>::type -BitcastBetweenNativeTypes(const LiteralBase& src_literal) { - auto converter = [](NativeSrcT src) { - return tensorflow::bit_cast(src); - }; - return ConvertBetweenNativeTypesWithConverter( - src_literal, converter); -} - -// This template specialization is here to make the compiler happy. bit_cast has -// a static check that the types are the same size. This specialization should -// never be used because the source and destination types are checked for -// identical sizes higher up. -template -typename std::enable_if<(sizeof(NativeSrcT) != sizeof(NativeDestT)), - std::unique_ptr>::type -BitcastBetweenNativeTypes(const LiteralBase& src_literal) { - LOG(FATAL) << "Invalid bitcast between types of different sizes."; -} - -template -std::unique_ptr ConvertToC64(const LiteralBase& src_literal) { - CHECK(ShapeUtil::IsArray(src_literal.shape())); - auto result_literal = MakeUnique( - ShapeUtil::ChangeElementType(src_literal.shape(), C64)); - using NativeSrcT = - typename primitive_util::PrimitiveTypeToNative::type; - tensorflow::gtl::ArraySlice src_data = - src_literal.data(); - tensorflow::gtl::MutableArraySlice dest_data = - result_literal->data(); - int64 num_elements = src_literal.element_count(); - for (int64 i = 0; i < num_elements; ++i) { - dest_data[i] = complex64(static_cast(src_data[i]), 0); - } - return result_literal; -} - -template -std::unique_ptr ConvertIfTypesMatch(const LiteralBase& src_literal, - bool bitcast) { - CHECK_EQ(primitive_src_type, src_literal.shape().element_type()); - if (bitcast) { - return BitcastBetweenNativeTypes< - typename primitive_util::PrimitiveTypeToNative< - primitive_src_type>::type, - typename primitive_util::PrimitiveTypeToNative< - primitive_dest_type>::type>(src_literal); - } else { - return ConvertBetweenNativeTypes< - typename primitive_util::PrimitiveTypeToNative< - primitive_src_type>::type, - typename primitive_util::PrimitiveTypeToNative< - primitive_dest_type>::type>(src_literal); - } -} - -template -StatusOr> ConvertIfDestTypeMatches( - const LiteralBase& src_literal, PrimitiveType primitive_dest_type, - bool bitcast) { - switch (primitive_dest_type) { -#define CONVERT_IF_TYPES_MATCH(type) \ - case (type): \ - return ConvertIfTypesMatch(src_literal, \ - bitcast); - CONVERT_IF_TYPES_MATCH(PRED) - CONVERT_IF_TYPES_MATCH(S8) - CONVERT_IF_TYPES_MATCH(S32) - CONVERT_IF_TYPES_MATCH(S64) - CONVERT_IF_TYPES_MATCH(U8) - CONVERT_IF_TYPES_MATCH(U32) - CONVERT_IF_TYPES_MATCH(U64) - CONVERT_IF_TYPES_MATCH(F16) - CONVERT_IF_TYPES_MATCH(F32) - CONVERT_IF_TYPES_MATCH(F64) - CONVERT_IF_TYPES_MATCH(BF16) -#undef CONVERT_IF_TYPES_MATCH - case C64: - if (!bitcast) { - return ConvertToC64(src_literal); - } - break; - // Other types are not yet supported. - default: - break; - } - return Unimplemented( - "Converting from type %s to type %s is not implemented.", - PrimitiveType_Name(src_literal.shape().element_type()).c_str(), - PrimitiveType_Name(primitive_dest_type).c_str()); -} - -StatusOr> ConvertSwitch( - const LiteralBase& literal, PrimitiveType primitive_dest_type, - bool bitcast) { - TF_RET_CHECK(ShapeUtil::IsArray(literal.shape())); - if (literal.shape().element_type() == primitive_dest_type) { - return literal.CloneToUnique(); - } - switch (literal.shape().element_type()) { -#define CONVERT_IF_DEST_TYPE_MATCHES(type) \ - case (type): \ - return ConvertIfDestTypeMatches<(type)>(literal, primitive_dest_type, \ - bitcast); - CONVERT_IF_DEST_TYPE_MATCHES(PRED) - CONVERT_IF_DEST_TYPE_MATCHES(S8) - CONVERT_IF_DEST_TYPE_MATCHES(S32) - CONVERT_IF_DEST_TYPE_MATCHES(S64) - CONVERT_IF_DEST_TYPE_MATCHES(U8) - CONVERT_IF_DEST_TYPE_MATCHES(U32) - CONVERT_IF_DEST_TYPE_MATCHES(U64) - CONVERT_IF_DEST_TYPE_MATCHES(F16) - CONVERT_IF_DEST_TYPE_MATCHES(F32) - CONVERT_IF_DEST_TYPE_MATCHES(F64) - CONVERT_IF_DEST_TYPE_MATCHES(BF16) -#undef CONVERT_IF_DEST_TYPE_MATCHES - // Other types are not yet supported. - default: - return Unimplemented( - "%s from type %s to type %s is not implemented.", - (bitcast ? "Bitcast converting" : "Converting"), - PrimitiveType_Name(literal.shape().element_type()).c_str(), - PrimitiveType_Name(primitive_dest_type).c_str()); - } -} - -} // namespace - -StatusOr> LiteralBase::Convert( - PrimitiveType primitive_dest_type) const { - return ConvertSwitch(*this, primitive_dest_type, /*bitcast=*/false); -} - -StatusOr> LiteralBase::BitcastConvert( - PrimitiveType primitive_dest_type) const { - if (primitive_util::BitWidth(shape().element_type()) != - primitive_util::BitWidth(primitive_dest_type)) { - return InvalidArgument( - "Cannot bitcast convert from %s to %s, bit widths are different: %d != " - "%d", - PrimitiveType_Name(shape().element_type()).c_str(), - PrimitiveType_Name(primitive_dest_type).c_str(), - primitive_util::BitWidth(shape().element_type()), - primitive_util::BitWidth(primitive_dest_type)); - } - return ConvertSwitch(*this, primitive_dest_type, /*bitcast=*/true); -} - -StatusOr> LiteralBase::ConvertToShape( - const Shape& dest_shape, bool round_f32_to_bf16) const { - if (!ShapeUtil::IsTuple(dest_shape)) { - if (round_f32_to_bf16 && shape().element_type() == F32 && - dest_shape.element_type() == BF16) { - auto converter = [](float src) { - return tensorflow::bfloat16::round_to_bfloat16(src); - }; - return ConvertBetweenNativeTypesWithConverter(*this, - converter); - } - return Convert(dest_shape.element_type()); - } - std::vector elements; - for (int i = 0; i < ShapeUtil::TupleElementCount(shape()); ++i) { - auto element = LiteralSlice(*this, {i}); - TF_ASSIGN_OR_RETURN( - auto new_element, - element.ConvertToShape(ShapeUtil::GetSubshape(dest_shape, {i}))); - elements.push_back(std::move(*new_element)); - } - auto converted = MakeUnique(); - *converted = Literal::MoveIntoTuple(&elements); - return std::move(converted); -} - -template -bool LiteralBase::Piece::EqualElementsInternal( - const LiteralBase::Piece& other, std::vector* multi_index) const { - if (multi_index->size() == ShapeUtil::Rank(subshape())) { - return (Get(*multi_index) == other.Get(*multi_index)); - } - for (int64 i = 0; i < subshape().dimensions(multi_index->size()); ++i) { - multi_index->push_back(i); - if (!EqualElementsInternal(other, multi_index)) { - return false; - } - multi_index->pop_back(); - } - return true; -} - -bool LiteralBase::Piece::EqualElements(const LiteralBase::Piece& other) const { - DCHECK(ShapeUtil::Compatible(subshape(), other.subshape())); - - std::vector multi_index; - switch (subshape().element_type()) { - case PRED: - return EqualElementsInternal(other, &multi_index); - case U8: - return EqualElementsInternal(other, &multi_index); - case S32: - return EqualElementsInternal(other, &multi_index); - case S64: - return EqualElementsInternal(other, &multi_index); - case U32: - return EqualElementsInternal(other, &multi_index); - case U64: - return EqualElementsInternal(other, &multi_index); - case F32: - return EqualElementsInternal(other, &multi_index); - case F64: - return EqualElementsInternal(other, &multi_index); - case F16: - return EqualElementsInternal(other, &multi_index); - case BF16: - return EqualElementsInternal(other, &multi_index); - case C64: - return EqualElementsInternal(other, &multi_index); - default: - LOG(FATAL) << "Unimplemented: LiteralBase::Piece::EqualElements for type " - << PrimitiveType_Name(subshape().element_type()); - } -} - -bool LiteralBase::operator==(const LiteralBase& other) const { - if (!ShapeUtil::Compatible(shape(), other.shape())) { - return false; - } - - return root_piece().ForEachSubpieceWithBool( - [&](const ShapeIndex& index, const Piece& piece) { - if (!ShapeUtil::IsArray(piece.subshape())) { - return true; - } - - const Piece& other_piece = other.piece(index); - if (!piece.EqualElements(other_piece)) { - return false; - } - return true; - }); -} - -namespace { - -template -static bool AllElementsEqualValue(tensorflow::gtl::ArraySlice data, - NativeT value) { - for (int64 i = 0; i < data.size(); ++i) { - if (data[i] != value) { - return false; - } - } - return true; -} - -} // namespace - -bool LiteralBase::IsAll(int8 value) const { - return root_piece().ForEachSubpieceWithBool([&](const ShapeIndex& index, - const Piece& piece) { - if (!ShapeUtil::IsArray(piece.subshape())) { - return true; - } - - auto piece_is_all = [&]() { - switch (shape().element_type()) { - case U8: - if (value >= 0) { - return AllElementsEqualValue(piece.data(), value); - } - return false; - case U32: - if (value >= 0) { - return AllElementsEqualValue(piece.data(), value); - } - return false; - case U64: - if (value >= 0) { - return AllElementsEqualValue(piece.data(), value); - } - return false; - case S8: - return AllElementsEqualValue(piece.data(), value); - case S32: - return AllElementsEqualValue(piece.data(), value); - case S64: - return AllElementsEqualValue(piece.data(), value); - case F32: - return AllElementsEqualValue(piece.data(), value); - case F64: - return AllElementsEqualValue(piece.data(), value); - case F16: - return AllElementsEqualValue(piece.data(), - static_cast(value)); - case BF16: - return AllElementsEqualValue(piece.data(), - static_cast(value)); - case PRED: - if (value == 0) { - return AllElementsEqualValue(piece.data(), false); - } - if (value == 1) { - return AllElementsEqualValue(piece.data(), true); - } - return false; - default: - return false; - } - return false; - }; - - if (!piece_is_all()) { - return false; - } - return true; - }); -} - -bool LiteralBase::IsAllFloat(float value) const { - return root_piece().ForEachSubpieceWithBool( - [&](const ShapeIndex& index, const Piece& piece) { - if (!ShapeUtil::IsArray(piece.subshape())) { - return true; - } - - auto piece_is_all = [&]() { - switch (shape().element_type()) { - case F32: - return AllElementsEqualValue(piece.data(), value); - case F64: - return AllElementsEqualValue(piece.data(), value); - case F16: - return AllElementsEqualValue(piece.data(), - static_cast(value)); - case BF16: - return AllElementsEqualValue( - piece.data(), static_cast(value)); - default: - return false; - } - }; - if (!piece_is_all()) { - return false; - } - return true; - }); -} - -bool LiteralBase::IsAllComplex(complex64 value) const { - switch (shape().element_type()) { - case C64: - return AllElementsEqualValue(root_piece().data(), - value); - default: - return false; - } -} - -bool LiteralBase::IsAllFirst() const { - return root_piece().ForEachSubpieceWithBool( - [&](const ShapeIndex& index, const Piece& piece) { - if (!ShapeUtil::IsArray(piece.subshape())) { - return true; - } - - // Empty shapes are not all the first element since there is no first - // element. - if (ShapeUtil::IsZeroElementArray(piece.subshape())) { - return false; - } - auto piece_is_all = [&]() { - switch (piece.subshape().element_type()) { - case PRED: { - auto data = piece.data(); - return AllElementsEqualValue(data, data[0]); - } - // 8 bit types - case S8: { - auto data = piece.data(); - return AllElementsEqualValue(data, data[0]); - } - case U8: { - auto data = piece.data(); - return AllElementsEqualValue(data, data[0]); - } - // 16 bit types - case BF16: { - auto data = piece.data(); - return AllElementsEqualValue(data, data[0]); - } - case F16: { - auto data = piece.data(); - return AllElementsEqualValue(data, data[0]); - } - case S16: { - auto data = piece.data(); - return AllElementsEqualValue(data, data[0]); - } - case U16: { - auto data = piece.data(); - return AllElementsEqualValue(data, data[0]); - } - // 32 bit types - case F32: { - auto data = piece.data(); - return AllElementsEqualValue(data, data[0]); - } - case U32: { - auto data = piece.data(); - return AllElementsEqualValue(data, data[0]); - } - case S32: { - auto data = piece.data(); - return AllElementsEqualValue(data, data[0]); - } - // 64 bit types - case C64: { - auto data = piece.data(); - return AllElementsEqualValue(data, data[0]); - } - case F64: { - auto data = piece.data(); - return AllElementsEqualValue(data, data[0]); - } - case S64: { - auto data = piece.data(); - return AllElementsEqualValue(data, data[0]); - } - case U64: { - auto data = piece.data(); - return AllElementsEqualValue(data, data[0]); - } - default: - return false; - } - }; - - if (!piece_is_all()) { - return false; - } - return true; - }); -} - -bool LiteralBase::IsZero(tensorflow::gtl::ArraySlice indices) const { - CHECK(ShapeUtil::IsArray(shape())); - switch (shape().element_type()) { - case U8: - return Get(indices) == 0; - case U32: - return Get(indices) == 0; - case U64: - return Get(indices) == 0; - case S8: - return Get(indices) == 0; - case S32: - return Get(indices) == 0; - case S64: - return Get(indices) == 0; - case F32: - return Get(indices) == 0.0f; - case F64: - return Get(indices) == 0.0; - case C64: - return Get(indices) == complex64(0.0f, 0.0f); - case F16: - return Get(indices) == static_cast(0.0f); - case BF16: - return Get(indices) == static_cast(0.0f); - case PRED: - return Get(indices) == false; - default: - LOG(FATAL) << "Input literal must be an array."; - } -} - -namespace { - -template -void CopyToRepeatedField(RepeatedFieldT* dest, - const tensorflow::gtl::ArraySlice src) { - *dest = RepeatedFieldT(src.begin(), src.end()); -} - -} // namespace - -void LiteralBase::Piece::WriteToProto(LiteralProto* proto) const { - *proto->mutable_shape() = subshape(); - switch (subshape().element_type()) { - case PRED: - CopyToRepeatedField(proto->mutable_preds(), data()); - break; - case U8: - proto->set_u8s(static_cast(data().data()), - element_count()); - break; - case U32: - CopyToRepeatedField(proto->mutable_u32s(), data()); - break; - case U64: - CopyToRepeatedField(proto->mutable_u64s(), data()); - break; - case S32: - CopyToRepeatedField(proto->mutable_s32s(), data()); - break; - case S64: - CopyToRepeatedField(proto->mutable_s64s(), data()); - break; - case F16: - *proto->mutable_f16s() = string( - reinterpret_cast(data().data()), size_bytes()); - if (!kLittleEndian) { - ConvertEndianShort(proto->mutable_f16s()); - } - break; - case BF16: - *proto->mutable_bf16s() = string( - reinterpret_cast(data().data()), size_bytes()); - if (!kLittleEndian) { - ConvertEndianShort(proto->mutable_bf16s()); - } - break; - case F32: - CopyToRepeatedField(proto->mutable_f32s(), data()); - break; - case F64: - CopyToRepeatedField(proto->mutable_f64s(), data()); - break; - case C64: - for (complex64 value : data()) { - proto->add_c64s(value.real()); - proto->add_c64s(value.imag()); - } - break; - case TUPLE: - case TOKEN: - // Nothing to do but assign the shape which is done above. - return; - default: - LOG(FATAL) << "Unhandled primitive type " << subshape().element_type(); - } -} - -const void* LiteralBase::Piece::untyped_data() const { - CHECK(ShapeUtil::IsArray(subshape())) << ShapeUtil::HumanString(subshape()); - return buffer(); -} - -void* LiteralBase::Piece::untyped_data() { - CHECK(ShapeUtil::IsArray(subshape())) << ShapeUtil::HumanString(subshape()); - return buffer(); -} - -namespace { - -template -Status CopyFromRepeatedField(tensorflow::gtl::MutableArraySlice dest, - const RepeatedFieldT& src) { - if (dest.size() != src.size()) { - return InvalidArgument( - "Expected %lu elements in LiteralProto repeated field, has %d", - dest.size(), src.size()); - } - std::copy(src.begin(), src.end(), dest.begin()); - return Status::OK(); -} - -} // namespace - -Status LiteralBase::Piece::CopyFromProto(const LiteralProto& proto) { - // These conditions should have been checked in Literal::CreateFromProto. - TF_RET_CHECK(proto.has_shape()); - TF_RET_CHECK(LayoutUtil::HasLayout(proto.shape())); - TF_RET_CHECK(ShapeUtil::Equal(proto.shape(), subshape())); - - switch (subshape().element_type()) { - case PRED: - TF_RETURN_IF_ERROR(CopyFromRepeatedField(data(), proto.preds())); - break; - case U8: { - auto u8_data = data(); - TF_RET_CHECK(proto.u8s().size() == u8_data.size()); - std::copy(proto.u8s().begin(), proto.u8s().end(), u8_data.begin()); - } break; - case S32: - TF_RETURN_IF_ERROR(CopyFromRepeatedField(data(), proto.s32s())); - break; - case S64: - TF_RETURN_IF_ERROR(CopyFromRepeatedField(data(), proto.s64s())); - break; - case U32: - TF_RETURN_IF_ERROR(CopyFromRepeatedField(data(), proto.u32s())); - break; - case U64: - TF_RETURN_IF_ERROR(CopyFromRepeatedField(data(), proto.u64s())); - break; - case F16: { - const string& s(proto.f16s()); - TF_RET_CHECK(data().size() * sizeof(half) == s.size()); - memcpy(untyped_data(), s.data(), s.size()); - if (!kLittleEndian) { - ConvertEndianShort(reinterpret_cast(untyped_data()), s.size()); - } - } break; - - case BF16: { - const string& s(proto.bf16s()); - TF_RET_CHECK(data().size() * sizeof(bfloat16) == s.size()); - memcpy(untyped_data(), s.data(), s.size()); - if (!kLittleEndian) { - ConvertEndianShort(reinterpret_cast(untyped_data()), s.size()); - } - } break; - case F32: - TF_RETURN_IF_ERROR(CopyFromRepeatedField(data(), proto.f32s())); - break; - case F64: - TF_RETURN_IF_ERROR(CopyFromRepeatedField(data(), proto.f64s())); - break; - case C64: { - auto complex_data = data(); - TF_RET_CHECK(proto.c64s_size() == complex_data.size() * 2); - for (int64 i = 0; i < complex_data.size(); ++i) { - complex_data[i] = complex64{proto.c64s(i * 2), proto.c64s(i * 2 + 1)}; - } - } break; - case TUPLE: - LOG(FATAL) << "Should not be called on tuple shapes: " - << ShapeUtil::HumanString(subshape()); - break; - default: - LOG(FATAL) << "Unhandled primitive type " << subshape().element_type(); - } - return Status::OK(); -} - -LiteralProto LiteralBase::ToProto() const { - LiteralProto proto; - root_piece().ForEachSubpiece( - [&](const ShapeIndex& index, const Piece& piece) { - LiteralProto* proto_piece = &proto; - for (int64 i : index) { - while (proto_piece->tuple_literals_size() <= i) { - proto_piece->add_tuple_literals(); - } - proto_piece = proto_piece->mutable_tuple_literals(i); - } - piece.WriteToProto(proto_piece); - }); - - if (LayoutUtil::IsSparseArray(shape())) { - CopyToRepeatedField(proto.mutable_sparse_indices(), - sparse_indices()->data()); - } - - return proto; -} - -/* static */ -StatusOr> Literal::CreateFromProto( - const LiteralProto& proto) { - if (!proto.has_shape()) { - return InvalidArgument("LiteralProto has no shape"); - } - if (!LayoutUtil::HasLayout(proto.shape())) { - return InvalidArgument("LiteralProto has no layout"); - } - - auto literal = MakeUnique(proto.shape()); - - TF_RETURN_IF_ERROR(literal->root_piece_->ForEachMutableSubpieceWithStatus( - [&](const ShapeIndex& index, Piece* piece) { - const LiteralProto* proto_element = &proto; - for (int64 i : index) { - CHECK(i < proto_element->tuple_literals_size()); - proto_element = &proto_element->tuple_literals(i); - } - - if (ShapeUtil::IsTuple(piece->subshape())) { - if (proto_element->tuple_literals_size() != - ShapeUtil::TupleElementCount(piece->subshape())) { - return InvalidArgument( - "Expected %lld tuple elements in LiteralProto, has %d", - ShapeUtil::TupleElementCount(piece->subshape()), - proto_element->tuple_literals_size()); - } - return Status::OK(); - } - if (piece->subshape().element_type() == TOKEN) { - return Status::OK(); - } - - CHECK(ShapeUtil::IsArray(piece->subshape())); - TF_RETURN_IF_ERROR(piece->CopyFromProto(*proto_element)); - - return Status::OK(); - })); - - return std::move(literal); -} - -/* static */ string Literal::MultiIndexAsString( +/* static */ string LiteralUtil::MultiIndexAsString( tensorflow::gtl::ArraySlice multi_index) { return StrCat("{", tensorflow::str_util::Join(multi_index, ","), "}"); } -const void* LiteralBase::untyped_data(const ShapeIndex& shape_index) const { - return piece(shape_index).untyped_data(); -} - -void* Literal::untyped_data(const ShapeIndex& shape_index) { - return piece(shape_index).untyped_data(); -} - -int64 LiteralBase::size_bytes(const ShapeIndex& shape_index) const { - return piece(shape_index).size_bytes(); -} - -string LiteralBase::GetR1U8AsString() const { - CHECK(ShapeUtil::IsArray(shape())); - CHECK_EQ(ShapeUtil::Rank(shape()), 1); - CHECK_EQ(shape().element_type(), U8); - return string(tensorflow::bit_cast(data().data()), - ShapeUtil::ElementsIn(shape())); -} - -void BorrowingLiteral::BuildPieceSubtree(const Shape& shape, Piece* piece) { - CHECK(ShapeUtil::IsTuple(shape)); - for (int i = 0; i < ShapeUtil::TupleElementCount(shape); ++i) { - const Shape& subshape = shape.tuple_shapes(i); - - auto child_piece = Piece(); - child_piece.set_subshape(&subshape); - - if (ShapeUtil::IsTuple(subshape)) { - BuildPieceSubtree(subshape, &child_piece); - } - - piece->emplace_back(std::move(child_piece)); - } -} - -LiteralSlice::LiteralSlice(const LiteralBase& literal) - : LiteralBase(), root_piece_(&literal.root_piece()) {} - -LiteralSlice::LiteralSlice(const LiteralBase& literal, - const ShapeIndex& view_root) - : LiteralBase(), root_piece_(&literal.piece(view_root)) {} - -BorrowingLiteral::BorrowingLiteral(const char* src_buf_ptr, const Shape& shape) - : LiteralBase(), shape_(MakeUnique(shape)) { - CHECK(ShapeUtil::IsArray(*shape_)); - CHECK(LayoutUtil::HasLayout(*shape_)); - - root_piece_ = Piece(); - root_piece_.set_buffer(const_cast(src_buf_ptr)); - root_piece_.set_subshape(shape_.get()); -} - -BorrowingLiteral::BorrowingLiteral( - tensorflow::gtl::ArraySlice src_buf_ptrs, const Shape& shape) - : LiteralBase(), shape_(MakeUnique(shape)) { - CHECK(ShapeUtil::IsTuple(*shape_)); - CHECK(!ShapeUtil::IsNestedTuple(*shape_)); - CHECK_EQ(src_buf_ptrs.size(), ShapeUtil::TupleElementCount(*shape_)); - root_piece_ = Piece(); - root_piece_.set_subshape(shape_.get()); - BuildPieceSubtree(*shape_, &root_piece_); - - for (int i = 0; i < src_buf_ptrs.size(); ++i) { - const auto& src_shape = shape_->tuple_shapes(i); - CHECK(ShapeUtil::IsArray(src_shape)); - root_piece_.child(i).set_buffer(const_cast(src_buf_ptrs[i])); - } -} - } // namespace xla diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h index 37ca8ea9f1d158b6bce8d5688288351f55c3b3c8..e3737a9d0051b32dc0becc19e1849c856a50e52e 100644 --- a/tensorflow/compiler/xla/literal_util.h +++ b/tensorflow/compiler/xla/literal_util.h @@ -32,6 +32,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/index_util.h" #include "tensorflow/compiler/xla/layout_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -51,679 +52,12 @@ limitations under the License. namespace xla { -// Forward declare Literal and LiteralSlice class to be used by the creation -// methods in the base class. -class Literal; -class LiteralSlice; - -// Abstract base class for literals. -class LiteralBase { +class LiteralUtil { public: - virtual ~LiteralBase() = 0; - - // Literals are equal if they have compatible shapes and the same data - // values. Layout is not compared. - bool operator==(const LiteralBase& other) const; - bool operator!=(const LiteralBase& other) const { return !(*this == other); } - - // Returns the shape of the literal. - const Shape& shape() const { return root_piece().subshape(); } - - // Serialize to proto. - LiteralProto ToProto() const; - - // Returns an ArraySlice of the array for this literal for the given NativeT - // (e.g., float). CHECKs if the subshape of the literal at the given - // ShapeIndex is not array. See primitive_util.h for the mapping from XLA type - // to native type. - template - tensorflow::gtl::ArraySlice data( - const ShapeIndex& shape_index = {}) const; - - // Returns a const pointer to the sparse index array. Returns nullptr if the - // literal is not a sparse array. - const SparseIndexArray* sparse_indices( - const ShapeIndex& shape_index = {}) const; - - // Returns a const pointer to (or size of) the underlying buffer holding the - // array at the given shape index. CHECKs if the subshape of the literal at - // the given ShapeIndex is not array. - const void* untyped_data(const ShapeIndex& shape_index = {}) const; - int64 size_bytes(const ShapeIndex& shape_index = {}) const; - - // Returns this literal's data as a string. This literal must be a rank-1 U8 - // array. - string GetR1U8AsString() const; - - // Returns a string representation of the literal value. - // Warning: this function can take minutes for multi-million element Literals. - string ToString(bool print_layout = false) const; - - // Gets an element in the literal at the given index. The multi_index is - // CHECKed against the dimension sizes. - template - NativeT Get(tensorflow::gtl::ArraySlice multi_index, - const ShapeIndex& shape_index) const; - // Overloads of Get for array literals. CHECKs if the literal is not - // array-shaped and dense. - template - NativeT Get(tensorflow::gtl::ArraySlice multi_index) const; - - // Returns the element value at index (0, ..., 0), however many zeroes are - // required for that index. - template - NativeT GetFirstElement() const; - - // As Get(), but determines the correct type and converts the value - // into text. - string GetAsString(tensorflow::gtl::ArraySlice multi_index, - const ShapeIndex& shape_index = {}) const; - // As GetSparseElement(), but determines the correct type and converts the - // value into text. - string GetSparseElementAsString(int64 sparse_element_number, - const ShapeIndex& shape_index = {}) const; - // As Get(), but determines the correct type and converts the value into - // int64. This literal must be an array. - StatusOr GetIntegralAsS64( - tensorflow::gtl::ArraySlice multi_index) const; - - // Returns the multi-index of the element in a sparse literal at the given - // sparse element number. The sparse element number is the position with in - // the sparse array's list of (index, value) pairs, and is checked against the - // total number of (index, value) pairs in the sparse array. - tensorflow::gtl::ArraySlice GetSparseIndex( - int64 sparse_element_number, const ShapeIndex& shape_index = {}) const; - - // Returns the value of the element in a sparse literal at the given sparse - // element number. The sparse element number is the position with in the - // sparse array's list of (index, value) pairs, and is checked against the - // total number of (index, value) pairs in the sparse array. - template - NativeT GetSparseElement(int64 sparse_element_number, - const ShapeIndex& shape_index = {}) const; - - // Invokes the "per cell" callback for each element in the provided - // literal with the element's indices and a string representation of - // the element's value. - // - // This function is useful if you want a polymorphic representation - // of the tensor's elements (turning it to a string for something - // like representation in a protobuf). - // - // This literal must have a dense layout. - void EachCellAsString( - const std::function indices, - const string& value)>& per_cell) const; - template - void EachCell(std::function indices, - NativeT value)> - per_cell) const; - - // Returns whether every element in this literal is equal to value. - // - // value is an int8 because we expect this to be called with small - // compile-time constants (0, -1, etc.) and so that whatever value you pass - // can be represented exactly by floating-point types as small as 16 bits. - // - // If value doesn't fit in this literal's type, returns false. Values of 1/0 - // are considered equal to true/false; other values are not considered equal - // to true. Also if this literal is not array-shaped false is returned. - bool IsAll(int8 value) const; - - // Like IsAll(const Literal&, int8), except we check whether the literal is - // equal to a particular floating-point number. - // - // If the literal is not a floating-point value, this always returns false. - // - // This casts value to the type of literal, then compares using ==. The usual - // admonishments about floating-point equality checks apply. We expect you to - // use this to check for values that can be expressed precisely as a float, - // e.g. -0.5. Also if this literal is not array-shaped false is returned. - bool IsAllFloat(float value) const; - - // Like IsAll(const Literal&, int8), except we check whether the literal is - // equal to a particular complex number. - // - // If the literal is not a complex value, this always returns false. - // - // This casts value to the type of literal, then compares using ==. The usual - // admonishments about floating-point equality checks apply. We expect you to - // use this to check for complex values that can be expressed precisely as - // float pairs e.g. (-0.5, 1.0). - // - // This literal must have a dense layout. - bool IsAllComplex(complex64 value) const; - - // Literal consists entirely of the first element of the literal. - bool IsAllFirst() const; - - // Returns whether this literal is zero at the specified index. This literal - // must be an array with a dense layout. - bool IsZero(tensorflow::gtl::ArraySlice indices) const; - - // Returns the count of the elements in the array at the given shape index in - // this literal. - int64 element_count(const ShapeIndex& index = {}) const { - return ShapeUtil::ElementsIn(ShapeUtil::GetSubshape(shape(), index)); - } - - // Returns the count of the elements in the sparse array at the given shape - // index in this literal, which will be no larger than - // LayoutUtil::MaxSparseElements(SetSubshape(shape(), index).layout()). - int64 sparse_element_count() const; - - // Compute a hash for this literal. This literal must not be a sparse tensor - // or a tuple containing a sparse tensor. - size_t Hash() const; - - // Converts this literal to the given shape. Returns an error is the - // conversion is not possible. - // - // round_f32_to_bf16: if true, converting F32 elements to BF16 uses rounding - // instead of truncation; otherwise, truncation is used. - // - // TODO(b/69266521): remove the round_to_bfloat16 flag when rounding becomes - // the default behavior. - StatusOr> ConvertToShape( - const Shape& dest_shape, bool round_f32_to_bf16 = false) const; - - // Converts this literal to another primitive type using a bitcast - // conversion. The to and from primitive types must have the same bit - // width. Returns an error if the conversion is not possible. This literal - // must be array-shaped. - StatusOr> BitcastConvert( - PrimitiveType primitive_dest_type) const; - - // Converts this literal to another primitive type. Returns an error if the - // conversion is not possible. This literal must be array-shaped. - StatusOr> Convert( - PrimitiveType primitive_dest_type) const; + LiteralUtil() = delete; // Returns a literal scalar representing the first element. - Literal GetFirstScalarLiteral() const; - - // Clones the underlying buffers into a new Literal, or new - // std::unique_ptr. - Literal Clone() const; - std::unique_ptr CloneToUnique() const; - - // TODO(b/67651157): The methods below which perform computation on Literals - // (Reshape, Slice, etc) should be moved elsewhere, and perhaps combined with - // evaluator code which operates on Literals. - // - // Creates a new value that has the equivalent value as this - // literal, but conforms to new_layout; e.g. a literal matrix that was in {0, - // 1} minor-to-major dimension layout can be re-layed-out as {1, 0} - // minor-to-major dimension layout and the value in the cell at any given - // logical index (i0, i1) will be the same. - // - // For tuple shaped literals, shape_index should be used to select the inner - // array that the new layout applies to. - // - // Note: this is useful when the client wants to ensure that a value placed in - // the XLA allocation tracker has a particular layout; for efficiency - // purposes or avoiding unimplemented operation/layout combinations. - std::unique_ptr Relayout(const Layout& new_layout, - const ShapeIndex& shape_index = {}) const; - - // An overload of Relayout which changes the layout of the entire shape rather - // than being limited to a single array within the shape. - std::unique_ptr Relayout(const Shape& shape_with_layout) const; - - // Creates a new literal by reshaping this literal to have the given - // dimensions. The total number of elements must not change; The - // implementation currently only supports monotonic dim0-major layouts. - // This literal must be an array. - StatusOr> Reshape( - tensorflow::gtl::ArraySlice dimensions) const; - - // Creates a new literal by broadcasting this literal with `dimensions` to - // yield a literal of shape `result_shape`. - StatusOr> Broadcast( - const Shape& result_shape, - tensorflow::gtl::ArraySlice dimensions) const; - - // Creates a new literal by reordering the dimensions of this literal. - // The given `permutation` must be a permutation of the dimension numbers - // in the original literal, and it specifies the order of the new dimensions - // in the result literal (i.e., new_order[i] = old_order[permutation[i]]). - // For example, a transpose call on a literal of shape [3 x 8 x 4] and - // `permutation` = {2, 0, 1} returns a new literal of shape [4 x 3 x 8]. - // This literal must be an array. - std::unique_ptr Transpose( - tensorflow::gtl::ArraySlice permutation) const; - - // Creates a sub-array from this literal by extracting the indices - // [start_index, limit_index) of each dimension. The result literal has the - // same rank and layout as for the given literal. The number of indices in - // start_indices and limit_indices must be the rank of the literal, and the - // indices follow the order of the dimensions. - // This literal must be an array. - std::unique_ptr Slice( - tensorflow::gtl::ArraySlice start_indices, - tensorflow::gtl::ArraySlice limit_indices) const; - - // Creates a literal with a prepended dimension with bound "times"; e.g. a - // f32[3x2] with times=4 will produce a f32[4x3x2] with the 3x2 from this - // literal replicated four times. - // This literal must be an array. - template - std::unique_ptr Replicate(int64 times) const; - - // Creates a new Literal object with the shape specified as parameter. - // The content of the literal values is the default value of the primitive - // type of literal itself (0 for numeric types, and false for predicates). - // - // Note: It's an antipattern to use this method then immediately call - // Literal::Populate on the result (since that results in zero initialization, - // then reinitialization. Conside if a call to MakeUnique(shape), - // followed by the call to Literal::Populate can be used instead. - static std::unique_ptr CreateFromShape(const Shape& shape); - - protected: - // A data structure representing a subshape at a particular ShapeIndex within - // the literal. For array-shaped ShapeIndexes, this data structure holds the - // pointer to the memory allocated for the array data. - class Piece { - public: - // Returns the buffer holding the array data for this piece as an array - // slice. This piece must be array-shaped. - template - tensorflow::gtl::ArraySlice data() const; - template - tensorflow::gtl::MutableArraySlice data(); - - // Returns the buffer holding the array data for this piece as a void*. This - // piece must be array-shaped. - void* untyped_data(); - const void* untyped_data() const; - - // Gets or sets an element in the array at the given index. The multi_index - // is CHECKed against the dimension sizes of the array. This piece must be - // array-shaped. - template - NativeT Get(tensorflow::gtl::ArraySlice index) const; - template - void Set(tensorflow::gtl::ArraySlice index, NativeT value); - - // Gets/sets the buffer holding the array data. - char* buffer() const { return buffer_; } - void set_buffer(char* buffer) { buffer_ = buffer; } - - // The array of multi-indices that provide the locations of non-zero - // elements in a sparse array. Only used if - // LayoutUtil::IsSparseArray(shape()) is true. - SparseIndexArray* sparse_indices() const { return sparse_indices_; } - void set_sparse_indices(SparseIndexArray* sparse_indices) { - sparse_indices_ = sparse_indices; - } - - // Gets or sets the subshape of this piece. This reference points to a - // subshape within the shape in the containing Literal (Literal::shape_). - const Shape& subshape() const { return *subshape_; } - void set_subshape(const Shape* subshape) { subshape_ = subshape; } - - // Returns the size in bytes of the buffer holding the array data. - int64 size_bytes() const { return ShapeUtil::ByteSizeOf(subshape()); } - - // Returns the number of elements in this piece's array. - int64 element_count() const { - // If this is a sparse array, use the number of elements represented by - // the indices in the associated SparseIndexArray. - return LayoutUtil::IsSparseArray(subshape()) - ? sparse_indices()->index_count() - : ShapeUtil::ElementsIn(subshape()); - } - - // Returns the child piece at 'index' of this piece. - Piece& child(int64 index) { return children_[index]; } - - // Adds a child piece to this piece's children. - void emplace_back(Piece child_piece) { - children_.emplace_back(std::move(child_piece)); - } - - // Returns the size of children pieces of this piece. - int64 children_size() { return children_.size(); } - - // Visitor functions that recursively traverses the piece and calls the - // given function at each child piece. The function has the type: - // void (const ShapeIndex& index, const Piece& piece) - template - void ForEachSubpiece(const Fn& func) const { - ShapeIndex index; - return ForEachHelper( - [&func](const ShapeIndex& index, const Piece& piece) { - func(index, piece); - return Status::OK(); - }, - *this, &index) - .IgnoreError(); - } - // Same as above, but the function has the type: - // Status (const ShapeIndex& index, const Piece& piece) - // The first non-OK return value is returned by the function. - template - Status ForEachSubpieceWithStatus(const Fn& func) const { - ShapeIndex index; - return ForEachHelper(func, *this, &index); - } - // Same as above, but the function has the type: - // Bool (const ShapeIndex& index, const Piece& piece) - // The first non-true return value is returned by the function. - template - bool ForEachSubpieceWithBool(const Fn& func) const { - ShapeIndex index; - return ForEachHelperBool(func, *this, &index); - } - // Same as above, but the function has the type: - // Void (const ShapeIndex& index, Piece& piece) - template - void ForEachMutableSubpiece(const Fn& func) { - ShapeIndex index; - return ForEachMutableHelper( - [&func](const ShapeIndex& index, Piece* piece) { - func(index, piece); - return Status::OK(); - }, - const_cast(this), &index) - .IgnoreError(); - } - // Same as above, but the function has the type: - // Status (const ShapeIndex& index, Piece& piece) - // The first non-OK return value is returned by the function. - template - Status ForEachMutableSubpieceWithStatus(const Fn& func) { - ShapeIndex index; - return ForEachMutableHelper( - func, const_cast(this), &index); - } - - // Returns true if this piece and 'other' contain the same data. This piece - // and 'other' must be array-shaped and compatible. - bool EqualElements(const Piece& other) const; - - // Writes the shape and data (if array-shaped) into the given proto. - void WriteToProto(LiteralProto* proto) const; - - // Copy the data from 'src' into this piece's buffer. Shapes of this piece - // and src must be compatible. - Status CopyFrom(const Piece& src); - - // Copies the data from the given proto into this piece. The shape of this - // piece must be equal (not just compatible) to the shape of the proto. - Status CopyFromProto(const LiteralProto& proto); - - // Sorts the elements in a sparse array. - void SortSparseElements(); - - private: - // Helpers for traversing the piece via ForEachSubpiece rooted at 'index'. - // The first non-OK (or non-true) value is returned by the function. - // The callable 'func' has the same signature as described above in - // ForEachSubpiece*. - template - Status ForEachHelper(const Fn& func, const Piece& piece, - ShapeIndex* index) const { - TF_RETURN_IF_ERROR(func(*index, piece)); - for (int64 i = 0; i < piece.children_.size(); ++i) { - index->push_back(i); - TF_RETURN_IF_ERROR(ForEachHelper(func, piece.children_[i], index)); - index->pop_back(); - } - return Status::OK(); - } - template - bool ForEachHelperBool(const Fn& func, const Piece& piece, - ShapeIndex* index) const { - if (!func(*index, piece)) { - return false; - } - for (int64 i = 0; i < piece.children_.size(); ++i) { - index->push_back(i); - if (!ForEachHelperBool(func, piece.children_[i], index)) { - return false; - } - index->pop_back(); - } - return true; - } - template - Status ForEachMutableHelper(const Fn& func, Piece* piece, - ShapeIndex* index) { - TF_RETURN_IF_ERROR(func(*index, piece)); - for (int64 i = 0; i < piece->children_.size(); ++i) { - index->push_back(i); - TF_RETURN_IF_ERROR( - ForEachMutableHelper(func, &piece->children_[i], index)); - index->pop_back(); - } - return Status::OK(); - } - - // Recursive helper for EqualElements. - template - bool EqualElementsInternal(const Piece& other, - std::vector* multi_index) const; - - // Helper for SortSparseElements that has the element type as a template - // parameter. - template - void SortSparseElementsInternal(); - - // For array-shaped pieces, this is the buffer holding the literal data. - char* buffer_ = nullptr; - - // For sparse arrays, this is the array of indices. - SparseIndexArray* sparse_indices_ = nullptr; - - // The shape of piece. This points into the shape of the containing Literal - // (Literal::shape_). - const Shape* subshape_ = nullptr; - - // Children pieces for tuple shaped pieces. - std::vector children_ = {}; - }; // class Piece - - const Piece& piece(const ShapeIndex& shape_index) const { - Piece* piece = &const_cast(root_piece()); - for (const auto i : shape_index) { - DCHECK_GE(i, 0); - DCHECK_LT(i, piece->children_size()); - piece = &piece->child(i); - } - return *piece; - } - - // Returns the piece at the root of the shape. - virtual const Piece& root_piece() const = 0; - - // LiteralSlice and Literal must access Pieces of other Literals. - friend class Literal; - friend class LiteralSlice; - friend class BorrowingLiteral; - - private: - template - std::unique_ptr SliceInternal( - const Shape& result_shape, - tensorflow::gtl::ArraySlice start_indices) const; -}; - -// Class representing literal values in XLA. -// -// The underlying buffer and shape is always owned by this class. -class Literal : public LiteralBase { - public: - Literal() : Literal(ShapeUtil::MakeNil()) {} - - // Create a literal of the given shape. The literal is allocated sufficient - // memory to hold the shape. Memory is uninitialized. - explicit Literal(const Shape& shape); - virtual ~Literal(); - - // Literals are moveable, but not copyable. To copy a literal use - // Literal::Clone or Literal::CloneToUnique. This prevents inadvertent copies - // of literals which can be expensive. - Literal(const Literal& other) = delete; - Literal& operator=(const Literal& other) = delete; - Literal(Literal&& other); - // 'allocate_arrays' indicates whether to allocate memory for the arrays in - // the shape. If false, buffer pointers inside of the Literal::Pieces are set - // to nullptr. - Literal(const Shape& shape, bool allocate_arrays); - Literal& operator=(Literal&& other); - - // TODO(b/67651157): Remove this accessor. Literal users should not be able to - // mutate the shape as this can produce malformed Literals. - Shape* mutable_shape_do_not_use() { return shape_.get(); } - - // Returns a MutableArraySlice view of the array for this literal for the - // given NativeT (e.g., float). CHECKs if the subshape of the literal at the - // given ShapeIndex is not array. See primitive_util.h for the mapping from - // XLA type to native type. - template - tensorflow::gtl::MutableArraySlice data( - const ShapeIndex& shape_index = {}); - // Unhide const method from parent class. - using LiteralBase::data; - - // Returns a pointer to the sparse index array. Returns nullptr if the literal - // is not a sparse array. - SparseIndexArray* sparse_indices(const ShapeIndex& shape_index = {}); - - // Returns a pointer to the underlying buffer holding the array at the given - // shape index. CHECKs if the subshape of the literal at the given ShapeIndex - // is not array. - void* untyped_data(const ShapeIndex& shape_index = {}); - // Unhide const method from parent class. - using LiteralBase::untyped_data; - - // Populates a literal with a sparse layout with the given indices and values. - // Each index in the indices array is CHECKed against the dimensions in the - // literal's shape. If sort is true, then the indices and values will be - // sorted. If sort is false, then the indices and values are assumed to - // already be in sorted order. See CreateSparse for an example of how data - // are populated. - template - void PopulateSparse(SparseIndexArray indices, - tensorflow::gtl::ArraySlice values, - bool sort = true); - - // Copy values from 'src_literal' rooted at 'src_shape_index' into this - // literal rooted at 'dest_shape_index'. The subshape of this literal rooted - // at 'dest_shape_index' must be compatible with the subshape of 'src_literal' - // rooted at 'src_shape_index', but need not be arrays. - Status CopyFrom(const LiteralSlice& src_literal, - const ShapeIndex& dest_shape_index = {}, - const ShapeIndex& src_shape_index = {}); - - // Similar to CopyFrom, but with move semantincs. The subshape of this literal - // rooted at 'dest_shape_index' must be *equal* to the shape 'src_literal' - // (layouts and shapes must match), but need not be arrays. The memory - // allocated in this literal for the subshape at dest_shape_index is - // deallocated, and the respective buffers are replaced with those in - // src_literal. Upon return, src_literal is set to a nil shape (empty tuple). - Status MoveFrom(Literal&& src_literal, - const ShapeIndex& dest_shape_index = {}); - - // Copies the values from src_literal, starting at src_base shape indexes, - // to this literal, starting at dest_base, where the copy size in each - // dimension is specified by copy_size. - // The src_literal and this literal must have the same primitive type, - // src_base+copy_size must fit the source literal dimensions, as well as - // dest_base+copy_size must fit the destination literal dimensions. - // Note: if either src_literal or this literal contains dimensions with zero - // element, then copy_size must be 0 in these dimensions while the - // corresponding base indices being 0. - // This literal and 'src_literal' must be arrays. - Status CopySliceFrom(const LiteralSlice& src_literal, - tensorflow::gtl::ArraySlice src_base, - tensorflow::gtl::ArraySlice dest_base, - tensorflow::gtl::ArraySlice copy_size); - - // Copies one element from src_literal[src_index] to (*this)[dest_index]. - Status CopyElementFrom(const LiteralSlice& src_literal, - tensorflow::gtl::ArraySlice src_index, - tensorflow::gtl::ArraySlice dest_index); - - // Sets an element in the literal at the given index. The multi_index is - // CHECKed against the dimension sizes. - template - void Set(tensorflow::gtl::ArraySlice multi_index, - const ShapeIndex& shape_index, NativeT value); - // Overloads of Set for array literals. CHECKs if the literal is not - // array-shaped and dense. - template - void Set(tensorflow::gtl::ArraySlice multi_index, NativeT value); - - // Appends the given element to the literal. If the elements are not appended - // in sorted order, then SortSparseElements should be called before calling - // other methods. This literal must have a sparse layout. - template - void AppendSparseElement(tensorflow::gtl::ArraySlice multi_index, - NativeT value, const ShapeIndex& shape_index = {}); - - // Sorts the elements in a sparse array. - void SortSparseElements(const ShapeIndex& shape_index = {}); - - // As Set(), but truncates `value` to the literal element type before storing. - // This literal must be an array. - Status SetIntegralAsS64(tensorflow::gtl::ArraySlice multi_index, - int64 value); - - // Populate this literal with the given values. Examples: - // - // // Populate with floats. - // Array2D float_values = ... - // literal.PopulateR2FromArray2D(values); - // - // // Populate with int32s. - // literal.PopulateR2({{1, 2}, {3, 4}}); - // - // The shape and element type of this literal must match given values. For - // example, in the call above to literal.PopulateR2(), 'literal' must be a 2x2 - // array of S32. - template - void PopulateR1(tensorflow::gtl::ArraySlice values); - void PopulateR1(const tensorflow::core::Bitmap& values); - template - void PopulateR2(std::initializer_list> values); - template - void PopulateFromArray(const Array& values); - template - void PopulateR2FromArray2D(const Array2D& values); - template - void PopulateR3FromArray3D(const Array3D& values); - template - void PopulateR4FromArray4D(const Array4D& values); - - // Populates literal values by calling the generator function for every cell - // in this literal object. - // - // generator must be a callable of the type - // NativeT(tensorflow::gtl::ArraySlice indexes) or compatible. - // - // This literal must have a dense layout. - template - Status Populate(const FnType& generator); - - // A parallel version of Populate(). This can be used if the generator is - // thread-safe and the values for the shape's different elements are - // independent. - template - Status PopulateParallel(const FnType& generator); - - // Fills this literal with the given value. - template - void PopulateWithValue(NativeT value); - - // Factory methods below. - // - - // Serialize from a proto. - static StatusOr> CreateFromProto( - const LiteralProto& proto); + static Literal GetFirstScalarLiteral(const LiteralSlice& literal); // Creates a new literal of a given rank. To minimize ambiguity (for users // and the compiler) these CreateR[0-2] methods should explicitly specify the @@ -889,7 +223,7 @@ class Literal : public LiteralBase { // As above, but intended to be invoked with move semantics; i.e. // // std::vector> elements = ...; - // auto result = Literal::MakeTupleOwned(std::move(elements)); + // auto result = LiteralUtil::MakeTupleOwned(std::move(elements)); // // This would have been declared as an overload, but there is ambiguity // in invocation between the above signature and this one. @@ -899,7 +233,7 @@ class Literal : public LiteralBase { // This overload lets you pass a braced list of unique_ptrs to // MakeTupleOwned: // - // Literal::MakeTupleOwned(Literal::CreateR1(...), ...). + // LiteralUtil::MakeTupleOwned(LiteralUtil::CreateR1(...), ...). // // Simply relying on the MakeTupleOwned(std::vector>) // overload doesn't work because std::initializer_list's elements are always @@ -920,19 +254,6 @@ class Literal : public LiteralBase { // Create a constant token literal. Token types have no value. static std::unique_ptr CreateToken(); - // Returns a vector containing the tuple elements of this Literal as separate - // Literals. This Literal must be tuple-shaped and can be a nested tuple. The - // elements are moved into the new Literals; no data is copied. Upon return - // this Literal is set to a nil shape (empty tuple) - std::vector DecomposeTuple(); - - // This operation is the inverse of DecomposeTuple. The given elements are - // moved into the tuple elements of a new tuple-shaped Literal which is - // returned. Upon return, each of the Literals in 'elements' is set to a nil - // shape (empty tuple). - static Literal MoveIntoTuple( - tensorflow::gtl::MutableArraySlice elements); - // Creates a new Literal object with its values havings the primitive_type // type, and with dimensions defined by the dimensions parameter. // The content of the literal values is the default value of the primitive @@ -1000,194 +321,12 @@ class Literal : public LiteralBase { // dimension 1 equal to 8. static string MultiIndexAsString( tensorflow::gtl::ArraySlice multi_index); - - private: - // Recursively sets the subshapes and buffers of all subpieces rooted at - // 'piece'. If 'allocate_array' is true, memory is allocated for the arrays in - // the shape. - void SetPiece(const Shape& shape, Piece* piece, bool allocate_arrays); - - // Returns the piece at the given ShapeIndex. - Piece& piece(const ShapeIndex& shape_index) { - return const_cast(LiteralBase::piece(shape_index)); - } - - Piece& root_piece() const override { return *root_piece_; }; - - // Internal template helper for the Literal::CopySliceFrom(), matching its - // arguments one by one. - template - Status CopySliceFromInternal(const LiteralBase& src_literal, - tensorflow::gtl::ArraySlice src_base, - tensorflow::gtl::ArraySlice dest_base, - tensorflow::gtl::ArraySlice copy_size); - - // Utility structure which is used to create the optimal configuration for - // a ShapeUtil::ForEachIndex() scan across two literals. - struct StrideConfig { - StrideConfig(const Shape& source_shape, const Shape& dest_shape, - tensorflow::gtl::ArraySlice dimensions); - - // The dimensions of the stride operation. Essentially every dimension - // will be iterated from base[i] to base[i]+dimensions[i], in step[i] - // steps. - tensorflow::gtl::ArraySlice dimensions; - DimensionVector base; - DimensionVector step; - int64 minor_dimension = 0; - // The size of the strides for source and destination. One of the two - // (the one looping through its most minor dimension) will be 1, while - // the other will be the stride size at the dimension matching the other - // shape most minor dimension being scanned. - int64 dest_stride = 1; - int64 source_stride = 1; - // The size of the inner loop on the most minor dimension. - int64 minor_loop_size = 1; - }; - - // Literal class always owns the shape. The parent class borrows this shape. - std::unique_ptr shape_; - - Piece* root_piece_ = nullptr; - - // Implementation details shared between Populate() and PopulateParallel() - template - Status PopulateInternal(const FnType& generator, bool parallel); - - // Deallocate the buffers held by this literal. - void DeallocateBuffers(); - - friend class LiteralBase; -}; -std::ostream& operator<<(std::ostream& out, const Literal& literal); - -// A read-only view of a Literal. A LiteralSlice contains pointers to shape and -// literal buffers always owned by others. -class LiteralSlice : public LiteralBase { - public: - LiteralSlice() : LiteralBase() {} - - // Implicit conversion constructors. - LiteralSlice(const LiteralBase& literal); - LiteralSlice(const LiteralBase& literal, const ShapeIndex& view_root); - - private: - const Piece& root_piece() const override { return *root_piece_; }; - - const Piece* root_piece_; // Not owned. -}; - -// A read-only Literal where the underlying buffers are never owned by this -// class. -class BorrowingLiteral : public LiteralBase { - public: - BorrowingLiteral() : LiteralBase() {} - - // 'src_buf_ptr' is not owned by this class and must outlive the - // lifetime of this class. It points to an appropirately sized buffer with - // data interpretered as indicated by 'shape'. - // This constructor is only used for array shapes. - BorrowingLiteral(const char* src_buf_ptr, const Shape& shape); - // Similar as above, except to be used for constructing non-nested tuples. - BorrowingLiteral(tensorflow::gtl::ArraySlice src_buf_ptrs, - const Shape& shape); - // TODO(b/79707221): adding constructors for nested tuples as well. - - private: - // Recursively builds the subtree for the given piece and sets the subshapes - // of the given piece with the given shape. - void BuildPieceSubtree(const Shape& shape, Piece* piece); - - // Accessor for the root piece of this literal. - const Piece& root_piece() const override { return root_piece_; }; - Piece root_piece_; - - // Shape of this literal. Stored as unique_ptr so such that the (default) - // move construction of this class would be trivially correct: the pointer to - // Shape root_piece_ stores will still point to the correct address. - std::unique_ptr shape_; }; -template -tensorflow::gtl::ArraySlice LiteralBase::Piece::data() const { - CHECK(ShapeUtil::IsArray(subshape())) << ShapeUtil::HumanString(subshape()); - CHECK_EQ(subshape().element_type(), - primitive_util::NativeToPrimitiveType()) - << "Attempting to access " - << PrimitiveType_Name(primitive_util::NativeToPrimitiveType()) - << " type, but literal element type is " - << PrimitiveType_Name(subshape().element_type()); - return tensorflow::gtl::ArraySlice( - reinterpret_cast(buffer()), element_count()); -} - -template -tensorflow::gtl::MutableArraySlice LiteralBase::Piece::data() { - CHECK(ShapeUtil::IsArray(subshape())) << ShapeUtil::HumanString(subshape()); - CHECK_EQ(subshape().element_type(), - primitive_util::NativeToPrimitiveType()) - << "Attempting to access " - << PrimitiveType_Name(primitive_util::NativeToPrimitiveType()) - << " type, but literal element type is " - << PrimitiveType_Name(subshape().element_type()); - return tensorflow::gtl::MutableArraySlice( - reinterpret_cast(buffer()), element_count()); -} - -template -NativeT LiteralBase::Piece::Get( - tensorflow::gtl::ArraySlice multi_index) const { - CHECK(LayoutUtil::IsDenseArray(subshape())); - return data()[IndexUtil::MultidimensionalIndexToLinearIndex( - subshape(), multi_index)]; -} - -template -void LiteralBase::Piece::Set(tensorflow::gtl::ArraySlice multi_index, - NativeT value) { - CHECK(LayoutUtil::IsDenseArray(subshape())); - data()[IndexUtil::MultidimensionalIndexToLinearIndex( - subshape(), multi_index)] = value; -} - -template -tensorflow::gtl::ArraySlice LiteralBase::data( - const ShapeIndex& shape_index) const { - return piece(shape_index).data(); -} - -template -tensorflow::gtl::MutableArraySlice Literal::data( - const ShapeIndex& shape_index) { - return piece(shape_index).data(); -} - -template -inline NativeT LiteralBase::Get(tensorflow::gtl::ArraySlice multi_index, - const ShapeIndex& shape_index) const { - return piece(shape_index).Get(multi_index); -} - -template -inline NativeT LiteralBase::Get( - tensorflow::gtl::ArraySlice multi_index) const { - return root_piece().Get(multi_index); -} - -template -inline void Literal::Set(tensorflow::gtl::ArraySlice multi_index, - const ShapeIndex& shape_index, NativeT value) { - return piece(shape_index).Set(multi_index, value); -} - -template -inline void Literal::Set(tensorflow::gtl::ArraySlice multi_index, - NativeT value) { - return root_piece().Set(multi_index, value); -} +std::ostream& operator<<(std::ostream& out, const Literal& literal); template -/* static */ std::unique_ptr Literal::CreateR0(NativeT value) { +/* static */ std::unique_ptr LiteralUtil::CreateR0(NativeT value) { auto literal = MakeUnique(ShapeUtil::MakeShape( primitive_util::NativeToPrimitiveType(), {})); literal->Set({}, value); @@ -1195,7 +334,7 @@ template } template -/* static */ std::unique_ptr Literal::CreateR1( +/* static */ std::unique_ptr LiteralUtil::CreateR1( tensorflow::gtl::ArraySlice values) { auto literal = MakeUnique( ShapeUtil::MakeShape(primitive_util::NativeToPrimitiveType(), @@ -1205,7 +344,7 @@ template } template -/* static */ std::unique_ptr Literal::CreateR2WithLayout( +/* static */ std::unique_ptr LiteralUtil::CreateR2WithLayout( std::initializer_list> values, const Layout& layout) { auto literal = MakeUnique(ShapeUtil::MakeShapeWithLayout( @@ -1218,13 +357,13 @@ template } template -/* static */ std::unique_ptr Literal::CreateR2( +/* static */ std::unique_ptr LiteralUtil::CreateR2( std::initializer_list> values) { return CreateR2WithLayout(values, LayoutUtil::GetDefaultLayoutForR2()); } template -/* static */ std::unique_ptr Literal::CreateR3WithLayout( +/* static */ std::unique_ptr LiteralUtil::CreateR3WithLayout( std::initializer_list>> values, const Layout& layout) { @@ -1249,14 +388,14 @@ template } template -/* static */ std::unique_ptr Literal::CreateR3( +/* static */ std::unique_ptr LiteralUtil::CreateR3( std::initializer_list>> values) { return CreateR3WithLayout(values, LayoutUtil::GetDefaultLayoutForR3()); } template -/* static */ std::unique_ptr Literal::CreateR4WithLayout( +/* static */ std::unique_ptr LiteralUtil::CreateR4WithLayout( std::initializer_list>>> values, @@ -1287,7 +426,7 @@ template } template -/* static */ std::unique_ptr Literal::CreateSparse( +/* static */ std::unique_ptr LiteralUtil::CreateSparse( tensorflow::gtl::ArraySlice dimensions, SparseIndexArray indices, tensorflow::gtl::ArraySlice values, bool sort) { int64 num_elements = values.size(); @@ -1302,7 +441,7 @@ template } template -/* static */ std::unique_ptr Literal::CreateR4( +/* static */ std::unique_ptr LiteralUtil::CreateR4( std::initializer_list>>> values) { @@ -1310,7 +449,7 @@ template } template -/* static */ std::unique_ptr Literal::CreateFromArrayWithLayout( +/* static */ std::unique_ptr LiteralUtil::CreateFromArrayWithLayout( const Array& values, const Layout& layout) { auto literal = MakeUnique(ShapeUtil::MakeShapeWithLayout( primitive_util::NativeToPrimitiveType(), values.dimensions(), @@ -1320,38 +459,40 @@ template } template -/* static */ std::unique_ptr Literal::CreateFromArray( +/* static */ std::unique_ptr LiteralUtil::CreateFromArray( const Array& values) { return CreateFromArrayWithLayout( values, LayoutUtil::GetDefaultLayoutForRank(values.num_dimensions())); } template -/* static */ std::unique_ptr Literal::CreateR2FromArray2DWithLayout( - const Array2D& values, const Layout& layout) { +/* static */ std::unique_ptr +LiteralUtil::CreateR2FromArray2DWithLayout(const Array2D& values, + const Layout& layout) { return CreateFromArrayWithLayout(values, layout); } template -/* static */ std::unique_ptr Literal::CreateR2FromArray2D( +/* static */ std::unique_ptr LiteralUtil::CreateR2FromArray2D( const Array2D& values) { return CreateFromArray(values); } template -/* static */ std::unique_ptr Literal::CreateR3FromArray3DWithLayout( - const Array3D& values, const Layout& layout) { +/* static */ std::unique_ptr +LiteralUtil::CreateR3FromArray3DWithLayout(const Array3D& values, + const Layout& layout) { return CreateFromArrayWithLayout(values, layout); } template -/* static */ std::unique_ptr Literal::CreateR3FromArray3D( +/* static */ std::unique_ptr LiteralUtil::CreateR3FromArray3D( const Array3D& values) { return CreateFromArray(values); } template -/* static */ std::unique_ptr Literal::CreateR3Projected( +/* static */ std::unique_ptr LiteralUtil::CreateR3Projected( std::initializer_list> values, int64 projection) { int64 dim0_size = projection; @@ -1376,7 +517,7 @@ template } template -/* static */ std::unique_ptr Literal::CreateR4Projected( +/* static */ std::unique_ptr LiteralUtil::CreateR4Projected( std::initializer_list> values, int64 projection_p, int64 projection_z) { int64 dim0_size = projection_p; @@ -1404,49 +545,21 @@ template } template -/* static */ std::unique_ptr Literal::CreateR4FromArray4D( +/* static */ std::unique_ptr LiteralUtil::CreateR4FromArray4D( const Array4D& values) { return CreateFromArray(values); } template -/* static */ std::unique_ptr Literal::CreateR4FromArray4DWithLayout( - const Array4D& values, const Layout& layout) { +/* static */ std::unique_ptr +LiteralUtil::CreateR4FromArray4DWithLayout(const Array4D& values, + const Layout& layout) { return CreateFromArrayWithLayout(values, layout); } -template -NativeT LiteralBase::GetFirstElement() const { - return data().at(0); -} - -template -NativeT LiteralBase::GetSparseElement(int64 sparse_element_number, - const ShapeIndex& shape_index) const { - CHECK( - LayoutUtil::IsSparseArray(ShapeUtil::GetSubshape(shape(), shape_index))); - return data(shape_index)[sparse_element_number]; -} - -template -void Literal::AppendSparseElement( - tensorflow::gtl::ArraySlice multi_index, NativeT value, - const ShapeIndex& shape_index) { - Piece& p = piece(shape_index); - const Shape& subshape = p.subshape(); - CHECK(LayoutUtil::IsSparseArray(subshape)); - int64 rank = ShapeUtil::Rank(subshape); - CHECK_EQ(multi_index.size(), rank); - int64 last_element = p.sparse_indices()->index_count(); - CHECK_LT(last_element, LayoutUtil::MaxSparseElements(subshape.layout())); - p.sparse_indices()->Append(multi_index); - CHECK_LT(last_element, p.data().size()); - p.data()[last_element] = value; -} - // Returns an identity matrix (rank 2) with the given row and column count. template -/* static */ std::unique_ptr Literal::MakeIdentityR2(int64 size) { +/* static */ std::unique_ptr LiteralUtil::MakeIdentityR2(int64 size) { Array2D array(size, size, 0); for (int64 i = 0; i < size; ++i) { array(i, i) = 1; @@ -1455,174 +568,8 @@ template } template -void LiteralBase::EachCell( - std::function indices, - NativeT value)> - per_cell) const { - if (ShapeUtil::IsZeroElementArray(shape())) { - return; - } - std::vector indices(ShapeUtil::Rank(shape()), 0); - do { - per_cell(indices, Get(indices)); - } while (IndexUtil::BumpIndices(shape(), &indices)); -} - -template -inline void Literal::PopulateR1(tensorflow::gtl::ArraySlice values) { - CHECK(ShapeUtil::IsArray(shape())); - CHECK_EQ(ShapeUtil::Rank(shape()), 1); - CHECK_EQ(ShapeUtil::ElementsIn(shape()), values.size()); - CHECK_EQ(shape().element_type(), - primitive_util::NativeToPrimitiveType()); - for (int64 i = 0; i < values.size(); ++i) { - Set({i}, values[i]); - } -} - -template -void Literal::PopulateR2( - std::initializer_list> values) { - CHECK(ShapeUtil::IsArray(shape())); - CHECK_EQ(ShapeUtil::Rank(shape()), 2); - CHECK_EQ(shape().element_type(), - primitive_util::NativeToPrimitiveType()); - - const int64 dim0_size = values.size(); - const int64 dim1_size = values.begin()->size(); - CHECK_EQ(dim0_size, shape().dimensions(0)); - CHECK_EQ(dim1_size, shape().dimensions(1)); - - int64 dim0 = 0; - for (auto inner_list : values) { - int64 dim1 = 0; - for (auto value : inner_list) { - Set({dim0, dim1}, value); - ++dim1; - } - CHECK_EQ(dim1_size, dim1); - ++dim0; - } -} - -template -void Literal::PopulateFromArray(const Array& values) { - CHECK(ShapeUtil::IsArray(shape())); - CHECK_EQ(shape().element_type(), - primitive_util::NativeToPrimitiveType()); - CHECK_EQ(ShapeUtil::Rank(shape()), values.num_dimensions()); - for (int dim = 0; dim < values.num_dimensions(); ++dim) { - CHECK_EQ(values.dim(dim), shape().dimensions(dim)); - } - values.Each([this](tensorflow::gtl::ArraySlice indices, - NativeT value) { this->Set(indices, value); }); -} - -template -void Literal::PopulateR2FromArray2D(const Array2D& values) { - PopulateFromArray(values); -} - -template -void Literal::PopulateR3FromArray3D(const Array3D& values) { - PopulateFromArray(values); -} - -template -void Literal::PopulateR4FromArray4D(const Array4D& values) { - PopulateFromArray(values); -} - -template -void Literal::PopulateSparse(SparseIndexArray indices, - tensorflow::gtl::ArraySlice values, - bool sort) { - CHECK(LayoutUtil::IsSparseArray(shape())); - int rank = ShapeUtil::Rank(shape()); - CHECK_EQ(indices.rank(), rank); - int64 max_elements = LayoutUtil::MaxSparseElements(shape().layout()); - CHECK_LE(indices.max_indices(), max_elements); - int64 num_elements = values.size(); - CHECK_LE(num_elements, max_elements); - CHECK_EQ(num_elements, indices.index_count()); - auto root_data = root_piece().data(); - // Piece::data() returns an ArraySlice of size equal to the number of indices - // in the SparseIndexArray. So there is no need to adjust the size of the data - // here. It is enough to just copy the incoming values into the data buffer. - std::copy(values.begin(), values.end(), root_data.begin()); - *this->root_piece().sparse_indices() = std::move(indices); - if (sort) { - auto root_data = this->root_piece().data(); - this->root_piece().sparse_indices()->SortWithValues(root_data); - } - DCHECK(this->root_piece().sparse_indices()->Validate(shape())); -} - -template -Status Literal::PopulateInternal(const FnType& generator, bool parallel) { - const Shape& this_shape = shape(); - const int64 rank = ShapeUtil::Rank(this_shape); - TF_RET_CHECK(LayoutUtil::IsDenseArray(this_shape)); - TF_RET_CHECK(this_shape.element_type() == - primitive_util::NativeToPrimitiveType()); - tensorflow::gtl::MutableArraySlice literal_data = data(); - if (rank > 0) { - StrideConfig stride_config(this_shape, this_shape, - AsInt64Slice(this_shape.dimensions())); - int64 minor_dimension_size = - ShapeUtil::GetDimension(this_shape, stride_config.minor_dimension); - - auto init_function = [&](tensorflow::gtl::ArraySlice indexes) { - DimensionVector minor_scan_indexes(rank, 0); - const int64 index = - IndexUtil::MultidimensionalIndexToLinearIndex(shape(), indexes); - std::copy(indexes.begin(), indexes.end(), minor_scan_indexes.begin()); - for (int64 i = 0; i < minor_dimension_size; ++i) { - minor_scan_indexes[stride_config.minor_dimension] = i; - literal_data.at(index + i) = generator(minor_scan_indexes); - } - }; - if (parallel) { - ShapeUtil::ForEachIndexParallel(this_shape, stride_config.base, - stride_config.dimensions, - stride_config.step, init_function); - } else { - ShapeUtil::ForEachIndex( - this_shape, stride_config.base, stride_config.dimensions, - stride_config.step, - [&init_function](tensorflow::gtl::ArraySlice indexes) { - init_function(indexes); - return true; - }); - } - } else { - // For scalars. - literal_data.at(0) = generator({}); - } - return Status::OK(); -} -template -Status Literal::Populate(const FnType& generator) { - return PopulateInternal(generator, /*parallel=*/false); -} - -template -Status Literal::PopulateParallel(const FnType& generator) { - return PopulateInternal(generator, /*parallel=*/true); -} - -template -void Literal::PopulateWithValue(NativeT value) { - CHECK(ShapeUtil::IsArray(shape())); - CHECK_EQ(shape().element_type(), - primitive_util::NativeToPrimitiveType()); - for (NativeT& element : data()) { - element = value; - } -} - -template -/* static */ std::unique_ptr Literal::CreateFullWithDescendingLayout( +/* static */ std::unique_ptr +LiteralUtil::CreateFullWithDescendingLayout( tensorflow::gtl::ArraySlice dimensions, NativeT value) { auto literal = MakeUnique(ShapeUtil::MakeShapeWithDescendingLayout( primitive_util::NativeToPrimitiveType(), dimensions)); @@ -1630,44 +577,9 @@ template return literal; } -template -std::unique_ptr LiteralBase::Replicate(int64 times) const { - DimensionVector bounds = {times}; - bounds.reserve(shape().dimensions_size() + 1); - for (int64 bound : shape().dimensions()) { - bounds.push_back(bound); - } - auto literal = - MakeUnique(ShapeUtil::MakeShape(shape().element_type(), bounds)); - int64 elements = ShapeUtil::ElementsIn(literal->shape()); - if (elements == 0) { - return literal; - } - - DimensionVector output_indices(bounds.size(), 0); - tensorflow::gtl::ArraySlice input_indices = output_indices; - input_indices.remove_prefix(1); - - bool done = false; - while (!done) { - const auto element = Get(input_indices); - literal->Set(output_indices, element); - - done = true; - for (int n = 0; n < output_indices.size(); ++n) { - ++output_indices[n]; - if (output_indices[n] < bounds[n]) { - done = false; - break; - } - output_indices[n] = 0; - } - } - return literal; -} - template -/* static */ StatusOr> Literal::CreateRandomLiteral( +/* static */ StatusOr> +LiteralUtil::CreateRandomLiteral( const Shape& shape, const std::function)>& generator) { using NativeT = typename primitive_util::PrimitiveTypeToNative::type; @@ -1681,8 +593,9 @@ template } template -/* static */ StatusOr> Literal::CreateRandomLiteral( - const Shape& shape, E* engine, T mean, T stddev) { +/* static */ StatusOr> +LiteralUtil::CreateRandomLiteral(const Shape& shape, E* engine, T mean, + T stddev) { using NativeT = typename primitive_util::PrimitiveTypeToNative::type; std::normal_distribution generator(mean, stddev); return CreateRandomLiteral( @@ -1692,8 +605,8 @@ template } template -/* static */ StatusOr> Literal::CreateRandomLiteral( - const Shape& shape, T mean, T stddev) { +/* static */ StatusOr> +LiteralUtil::CreateRandomLiteral(const Shape& shape, T mean, T stddev) { std::minstd_rand0 engine; return CreateRandomLiteral(shape, &engine, mean, stddev); } diff --git a/tensorflow/compiler/xla/packed_literal_reader.cc b/tensorflow/compiler/xla/packed_literal_reader.cc index 857aae0a7982a57bb3057a6f267f5f033a0fdde4..6b7fd10d63f8f97b0e0bf7570488c06323368d75 100644 --- a/tensorflow/compiler/xla/packed_literal_reader.cc +++ b/tensorflow/compiler/xla/packed_literal_reader.cc @@ -20,7 +20,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" diff --git a/tensorflow/compiler/xla/packed_literal_reader.h b/tensorflow/compiler/xla/packed_literal_reader.h index 45a9fe012784d3e4168e7549240dec962aa1a17a..98dccaa9a246520bf60217b96d67a13a24c34b4a 100644 --- a/tensorflow/compiler/xla/packed_literal_reader.h +++ b/tensorflow/compiler/xla/packed_literal_reader.h @@ -18,7 +18,7 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" diff --git a/tensorflow/compiler/xla/python/BUILD b/tensorflow/compiler/xla/python/BUILD index 22cc4e2436e5d3a7ed77a2b9f5515878661ef294..c8f2d65c223ccfe20862954c224d016cca421812 100644 --- a/tensorflow/compiler/xla/python/BUILD +++ b/tensorflow/compiler/xla/python/BUILD @@ -33,6 +33,7 @@ cc_library( srcs = ["numpy_bridge.cc"], hdrs = ["numpy_bridge.h"], deps = [ + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:xla_data_proto", @@ -52,9 +53,9 @@ cc_library( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:executable_build_options", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:math", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:shaped_buffer", "//tensorflow/core:framework_lite", "//tensorflow/core:lib", @@ -70,7 +71,7 @@ tf_py_wrap_cc( deps = [ ":local_computation_builder", ":numpy_bridge", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/service:cpu_plugin", diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index be55d50b234442ec569c85e4f5224ad1c179bca8..434d78d78dd58f8bfcb992eb4f3d81beaadb56c3 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/python/local_computation_builder.h" #include "tensorflow/compiler/xla/client/lib/math.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/executable_run_options.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/util.h" @@ -486,6 +486,11 @@ LocalOp LocalComputationBuilder::ConvertElementType( return xla::ConvertElementType(operand.op(), new_element_type); } +LocalOp LocalComputationBuilder::BitcastConvertType( + const LocalOp& operand, PrimitiveType new_element_type) { + return xla::BitcastConvertType(operand.op(), new_element_type); +} + LocalOp LocalComputationBuilder::Call( const LocalComputation& local_computation, tensorflow::gtl::ArraySlice operands) { @@ -614,6 +619,11 @@ _FORWARD_BINOP(Min) _FORWARD_BINOP(And) _FORWARD_BINOP(Or) _FORWARD_BINOP(Xor) +_FORWARD_BINOP(ShiftLeft) +_FORWARD_BINOP(ShiftRightArithmetic) +_FORWARD_BINOP(ShiftRightLogical) +_FORWARD_BINOP(Atan2) +_FORWARD_BINOP(Pow) _FORWARD_UNOP(Not) _FORWARD_UNOP(Abs) _FORWARD_UNOP(Exp) @@ -627,13 +637,27 @@ _FORWARD_UNOP(Sign) _FORWARD_UNOP(Cos) _FORWARD_UNOP(Sin) _FORWARD_UNOP(Tanh) -_FORWARD_UNOP(Sqrt) -_FORWARD_UNOP(Square) -_FORWARD_BINOP(Pow) _FORWARD_UNOP(IsFinite) -_FORWARD_UNOP(Reciprocal) _FORWARD_UNOP(Neg) _FORWARD_UNOP(Sort) +_FORWARD_UNOP(Sqrt) +_FORWARD_UNOP(Rsqrt) +_FORWARD_UNOP(Square) +_FORWARD_UNOP(Reciprocal) +_FORWARD_UNOP(Erfc) +_FORWARD_UNOP(Erf) +_FORWARD_UNOP(ErfInv) +_FORWARD_UNOP(Lgamma) +_FORWARD_UNOP(Digamma) +_FORWARD_UNOP(Acos) +_FORWARD_UNOP(Asin) +_FORWARD_UNOP(Atan) +_FORWARD_UNOP(Tan) +_FORWARD_UNOP(Acosh) +_FORWARD_UNOP(Asinh) +_FORWARD_UNOP(Atanh) +_FORWARD_UNOP(Cosh) +_FORWARD_UNOP(Sinh) #undef _FORWARD #undef _FORWARD_UNOP diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index 690ff277e884c6f1540b12e7002248571d07fe71..545aa63f9d6e2e2e26c26f49941a5160279154b3 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -19,8 +19,8 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/executable_build_options.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/shaped_buffer.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/gtl/array_slice.h" @@ -259,6 +259,9 @@ class LocalComputationBuilder { LocalOp ConvertElementType(const LocalOp& operand, PrimitiveType new_element_type); + LocalOp BitcastConvertType(const LocalOp& operand, + PrimitiveType new_element_type); + LocalOp Call(const LocalComputation& local_computation, tensorflow::gtl::ArraySlice operands); @@ -333,6 +336,11 @@ class LocalComputationBuilder { _FORWARD_BINOP(And) _FORWARD_BINOP(Or) _FORWARD_BINOP(Xor) + _FORWARD_BINOP(ShiftLeft) + _FORWARD_BINOP(ShiftRightArithmetic) + _FORWARD_BINOP(ShiftRightLogical) + _FORWARD_BINOP(Atan2) + _FORWARD_BINOP(Pow) _FORWARD_UNOP(Not) _FORWARD_UNOP(Abs) _FORWARD_UNOP(Exp) @@ -346,13 +354,27 @@ class LocalComputationBuilder { _FORWARD_UNOP(Cos) _FORWARD_UNOP(Sin) _FORWARD_UNOP(Tanh) - _FORWARD_UNOP(Sqrt) - _FORWARD_UNOP(Square) - _FORWARD_BINOP(Pow) _FORWARD_UNOP(IsFinite) - _FORWARD_UNOP(Reciprocal) _FORWARD_UNOP(Neg) _FORWARD_UNOP(Sort) + _FORWARD_UNOP(Sqrt) + _FORWARD_UNOP(Rsqrt) + _FORWARD_UNOP(Square) + _FORWARD_UNOP(Reciprocal) + _FORWARD_UNOP(Erfc) + _FORWARD_UNOP(Erf) + _FORWARD_UNOP(ErfInv) + _FORWARD_UNOP(Lgamma) + _FORWARD_UNOP(Digamma) + _FORWARD_UNOP(Acos) + _FORWARD_UNOP(Asin) + _FORWARD_UNOP(Atan) + _FORWARD_UNOP(Tan) + _FORWARD_UNOP(Acosh) + _FORWARD_UNOP(Asinh) + _FORWARD_UNOP(Atanh) + _FORWARD_UNOP(Cosh) + _FORWARD_UNOP(Sinh) #undef _FORWARD #undef _FORWARD_UNOP diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i index c44e69e6153239b39f9f8a40539a75ddffdef25d..9b8b0aa7f28e64f434bb24f88a3a9cbe177f8a78 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.i +++ b/tensorflow/compiler/xla/python/local_computation_builder.i @@ -109,7 +109,7 @@ limitations under the License. // Must be included first #include "tensorflow/python/lib/core/numpy.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/gtl/array_slice.h" @@ -957,6 +957,7 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalComputationBuilder::Tuple; %unignore xla::swig::LocalComputationBuilder::GetTupleElement; %unignore xla::swig::LocalComputationBuilder::ConvertElementType; +%unignore xla::swig::LocalComputationBuilder::BitcastConvertType; %unignore xla::swig::LocalComputationBuilder::Call; %unignore xla::swig::LocalComputationBuilder::Transpose; %unignore xla::swig::LocalComputationBuilder::Rev; @@ -989,6 +990,9 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalComputationBuilder::And; %unignore xla::swig::LocalComputationBuilder::Or; %unignore xla::swig::LocalComputationBuilder::Xor; +%unignore xla::swig::LocalComputationBuilder::ShiftLeft; +%unignore xla::swig::LocalComputationBuilder::ShiftRightArithmetic; +%unignore xla::swig::LocalComputationBuilder::ShiftRightLogical; %unignore xla::swig::LocalComputationBuilder::Not; %unignore xla::swig::LocalComputationBuilder::Abs; %unignore xla::swig::LocalComputationBuilder::Exp; @@ -1002,13 +1006,29 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalComputationBuilder::Cos; %unignore xla::swig::LocalComputationBuilder::Sin; %unignore xla::swig::LocalComputationBuilder::Tanh; -%unignore xla::swig::LocalComputationBuilder::Sqrt; -%unignore xla::swig::LocalComputationBuilder::Square; -%unignore xla::swig::LocalComputationBuilder::Pow; +%unignore xla::swig::LocalComputationBuilder::Atan2; %unignore xla::swig::LocalComputationBuilder::IsFinite; -%unignore xla::swig::LocalComputationBuilder::Reciprocal; +%unignore xla::swig::LocalComputationBuilder::Pow; %unignore xla::swig::LocalComputationBuilder::Neg; %unignore xla::swig::LocalComputationBuilder::Sort; +%unignore xla::swig::LocalComputationBuilder::Sqrt; +%unignore xla::swig::LocalComputationBuilder::Rsqrt; +%unignore xla::swig::LocalComputationBuilder::Square; +%unignore xla::swig::LocalComputationBuilder::Reciprocal; +%unignore xla::swig::LocalComputationBuilder::Erfc; +%unignore xla::swig::LocalComputationBuilder::Erf; +%unignore xla::swig::LocalComputationBuilder::ErfInv; +%unignore xla::swig::LocalComputationBuilder::Lgamma; +%unignore xla::swig::LocalComputationBuilder::Digamma; +%unignore xla::swig::LocalComputationBuilder::Acos; +%unignore xla::swig::LocalComputationBuilder::Asin; +%unignore xla::swig::LocalComputationBuilder::Atan; +%unignore xla::swig::LocalComputationBuilder::Tan; +%unignore xla::swig::LocalComputationBuilder::Acosh; +%unignore xla::swig::LocalComputationBuilder::Asinh; +%unignore xla::swig::LocalComputationBuilder::Atanh; +%unignore xla::swig::LocalComputationBuilder::Cosh; +%unignore xla::swig::LocalComputationBuilder::Sinh; %unignore xla::swig::DestructureLocalShapedBufferTuple; %unignore xla::swig::DeleteLocalShapedBuffer; %unignore xla::swig::DeleteLocalComputation; diff --git a/tensorflow/compiler/xla/python/numpy_bridge.cc b/tensorflow/compiler/xla/python/numpy_bridge.cc index 68648a3a176363de69a56ecb8070f82862874e94..71351abd593d45fb5080112438a91df368eee173 100644 --- a/tensorflow/compiler/xla/python/numpy_bridge.cc +++ b/tensorflow/compiler/xla/python/numpy_bridge.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/python/numpy_bridge.h" +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/core/platform/logging.h" @@ -374,7 +375,7 @@ StatusOr> XlaLiteralFromPyObject(PyObject* o) { TF_ASSIGN_OR_RETURN(auto literal, XlaLiteralFromPyObject(element)); elements.push_back(std::move(literal)); } - return Literal::MakeTupleOwned(std::move(elements)); + return LiteralUtil::MakeTupleOwned(std::move(elements)); } else if (PyArray_Check(o)) { PyArrayObject* py_array = reinterpret_cast(o); int rank = PyArray_NDIM(py_array); @@ -383,7 +384,7 @@ StatusOr> XlaLiteralFromPyObject(PyObject* o) { dimensions[i] = PyArray_DIM(py_array, i); } int np_type = PyArray_TYPE(py_array); - auto literal = Literal::CreateFromDimensions( + auto literal = LiteralUtil::CreateFromDimensions( NumpyTypeToPrimitiveType(np_type), dimensions); TF_RETURN_IF_ERROR( CopyNumpyArrayToLiteral(np_type, py_array, literal.get())); diff --git a/tensorflow/compiler/xla/python/numpy_bridge.h b/tensorflow/compiler/xla/python/numpy_bridge.h index 64f0aae0f9790f0199ac6cb931a5c9f6dc356f4c..a67c93a4fb7413f9bbcb9afd92c36fd118836e1f 100644 --- a/tensorflow/compiler/xla/python/numpy_bridge.h +++ b/tensorflow/compiler/xla/python/numpy_bridge.h @@ -25,7 +25,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/python/lib/core/numpy.h" diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py index 27aee634bac613a87c919a357e085ec71c7deeb1..c0105b385b02e13b360ad1fb5af734d2209a92c2 100644 --- a/tensorflow/compiler/xla/python/xla_client.py +++ b/tensorflow/compiler/xla/python/xla_client.py @@ -99,12 +99,27 @@ _UNARY_OPS = [ 'Cos', 'Sin', 'Tanh', + 'IsFinite', 'Sqrt', + 'Rsqrt', 'Square', - 'IsFinite', 'Reciprocal', 'Neg', 'Sort', + 'Erf', + 'Erfc', + 'ErfInv', + 'Lgamma', + 'Digamma', + 'Acos', + 'Asin', + 'Atan', + 'Tan', + 'Acosh', + 'Asinh', + 'Atanh', + 'Cosh', + 'Sinh', ] _BINARY_OPS = [ @@ -125,6 +140,10 @@ _BINARY_OPS = [ 'Or', 'Xor', 'Pow', + 'ShiftLeft', + 'ShiftRightArithmetic', + 'ShiftRightLogical', + 'Atan2', ] @@ -461,14 +480,16 @@ class LocalComputation(object): if self.is_compiled: raise ValueError('Attempt to compile a compiled local XLA computation.') + result_shape = _wrap_shape(self.c_local_computation.GetReturnValueShape()) + if layout_fn: argument_shapes = [ shape.map_leaves(layout_fn) for shape in argument_shapes ] - result_shape = _wrap_shape(self.c_local_computation.GetReturnValueShape()) result_shape = result_shape.map_leaves(layout_fn) - compile_options = compile_options or CompileOptions() - compile_options.result_shape = result_shape + + compile_options = compile_options or CompileOptions() + compile_options.result_shape = result_shape return LocalComputation( self.c_local_computation.Compile(argument_shapes, compile_options), is_compiled=True) @@ -700,6 +721,18 @@ class ComputationBuilder(object): """ return self._client.ConvertElementType(operand, new_element_type) + def BitcastConvertType(self, operand, new_element_type): + """Enqueues a bitcast type conversion operation onto the computation. + + Args: + operand: the operand to convert. + new_element_type: the target primitive type. + + Returns: + A LocalOp representing the added conversion op. + """ + return self._client.BitcastConvertType(operand, new_element_type) + def GetShape(self, operand): return _wrap_shape(self._client.GetShape(operand)) diff --git a/tensorflow/compiler/xla/python/xla_client_test.py b/tensorflow/compiler/xla/python/xla_client_test.py index 0564ddcb85ee3952f82649687e79a864999baf2c..fd98e19457f61aade947aa354d2e415148d127f6 100644 --- a/tensorflow/compiler/xla/python/xla_client_test.py +++ b/tensorflow/compiler/xla/python/xla_client_test.py @@ -171,6 +171,24 @@ class ComputationsWithConstantsTest(LocalComputationTest): c.Constant(NumpyArrayF32([[1, -1, 1], [-1, 1, -1]]))) self._ExecuteAndCompareClose(c, expected=[[2, 1, 4], [3, 6, 5]]) + def testShiftLeft(self): + c = self._NewComputation() + c.ShiftLeft(c.Constant(NumpyArrayS32([3])), + c.Constant(NumpyArrayS32([2]))) + self._ExecuteAndCompareClose(c, expected=[12]) + + def testShiftRightArithmetic(self): + c = self._NewComputation() + c.ShiftRightArithmetic(c.Constant(NumpyArrayS32([-2])), + c.Constant(NumpyArrayS32([1]))) + self._ExecuteAndCompareClose(c, expected=[-1]) + + def testShiftRightLogical(self): + c = self._NewComputation() + c.ShiftRightLogical(c.Constant(NumpyArrayS32([-1])), + c.Constant(NumpyArrayS32([1]))) + self._ExecuteAndCompareClose(c, expected=[2**31 - 1]) + def testGetProto(self): c = self._NewComputation() c.Add( @@ -471,6 +489,34 @@ class SingleOpTest(LocalComputationTest): for src_dtype, dst_dtype in itertools.product(xla_types, xla_types): _ConvertAndTest(x, src_dtype, dst_dtype) + def testBitcastConvertType(self): + xla_x32_types = { + np.int32: xla_client.xla_data_pb2.S32, + np.float32: xla_client.xla_data_pb2.F32, + } + + xla_x64_types = { + np.int64: xla_client.xla_data_pb2.S64, + np.float64: xla_client.xla_data_pb2.F64, + } + + def _ConvertAndTest(template, src_dtype, dst_dtype, dst_etype): + c = self._NewComputation() + x = c.Constant(np.array(template, dtype=src_dtype)) + c.BitcastConvertType(x, dst_etype) + + result = c.Build().Compile().Execute() + expected = np.array(template, src_dtype).view(dst_dtype) + + self.assertEqual(result.shape, expected.shape) + self.assertEqual(result.dtype, expected.dtype) + np.testing.assert_equal(result, expected) + + x = [0, 1, 0, 0, 1] + for xla_types in [xla_x32_types, xla_x64_types]: + for src_dtype, dst_dtype in itertools.product(xla_types, xla_types): + _ConvertAndTest(x, src_dtype, dst_dtype, xla_types[dst_dtype]) + def testCrossReplicaSumOneReplica(self): samples = [ NumpyArrayF32(42.0), diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc index c289c84cff743871a7126cb932d6cda823ceb696..a803520876952a0ab67ecb827b1f256c915335f9 100644 --- a/tensorflow/compiler/xla/reference_util.cc +++ b/tensorflow/compiler/xla/reference_util.cc @@ -18,7 +18,8 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h" #include "tensorflow/compiler/xla/service/hlo_evaluator.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -510,8 +511,8 @@ ReferenceUtil::ConvArray4DGeneralDimensionsDilated( std::pair lhs_dilation, std::pair rhs_dilation, ConvolutionDimensionNumbers dnums) { HloComputation::Builder b("ConvArray4DGeneralDimensionDilated"); - auto lhs_literal = Literal::CreateR4FromArray4D(lhs); - auto rhs_literal = Literal::CreateR4FromArray4D(rhs); + auto lhs_literal = LiteralUtil::CreateR4FromArray4D(lhs); + auto rhs_literal = LiteralUtil::CreateR4FromArray4D(rhs); std::array ordered_kernel_strides; std::array ordered_input_dimensions; diff --git a/tensorflow/compiler/xla/reference_util_test.cc b/tensorflow/compiler/xla/reference_util_test.cc index 9da9bc60a2025e63b57a3be9ed360d150f88d73c..8091bed4996a753649a5ecedda69a1ae48fb5897 100644 --- a/tensorflow/compiler/xla/reference_util_test.cc +++ b/tensorflow/compiler/xla/reference_util_test.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array3d.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" @@ -53,7 +53,7 @@ class ReferenceUtilTest : public ::testing::Test { TEST_F(ReferenceUtilTest, TransposeArray2D) { auto result = ReferenceUtil::TransposeArray2D(*matrix_); - auto actual_literal = Literal::CreateR2FromArray2D(*result); + auto actual_literal = LiteralUtil::CreateR2FromArray2D(*result); LiteralTestUtil::ExpectR2Near({{1.f, 4.f}, {2.f, 5.f}, {3.f, 6.f}}, *actual_literal, ErrorSpec(0.0001)); } @@ -65,7 +65,7 @@ TEST_F(ReferenceUtilTest, MatmulArray2D) { {11.f, 12.f}, }); auto result = ReferenceUtil::MatmulArray2D(*matrix_, rhs); - auto actual_literal = Literal::CreateR2FromArray2D(*result); + auto actual_literal = LiteralUtil::CreateR2FromArray2D(*result); LiteralTestUtil::ExpectR2Near({{58.f, 64.f}, {139.f, 154.f}}, *actual_literal, ErrorSpec(0.0001)); } @@ -73,7 +73,7 @@ TEST_F(ReferenceUtilTest, MatmulArray2D) { TEST_F(ReferenceUtilTest, ReduceToColArray2D) { auto add = [](float lhs, float rhs) { return lhs + rhs; }; auto result = ReferenceUtil::ReduceToColArray2D(*matrix_, 0.0f, add); - auto actual_literal = Literal::CreateR1(*result); + auto actual_literal = LiteralUtil::CreateR1(*result); LiteralTestUtil::ExpectR1Near({6.f, 15.f}, *actual_literal, ErrorSpec(0.0001)); } @@ -81,13 +81,13 @@ TEST_F(ReferenceUtilTest, ReduceToColArray2D) { TEST_F(ReferenceUtilTest, ReduceToRowArray2D) { auto add = [](float lhs, float rhs) { return lhs + rhs; }; auto result = ReferenceUtil::ReduceToRowArray2D(*matrix_, 0.0f, add); - auto actual_literal = Literal::CreateR1(*result); + auto actual_literal = LiteralUtil::CreateR1(*result); LiteralTestUtil::ExpectR1Near({5.f, 7.f, 9.f}, *actual_literal, ErrorSpec(0.0001)); } TEST_F(ReferenceUtilTest, Reduce4Dto1DZeroSizedArray) { - auto result = Literal::CreateR1(ReferenceUtil::Reduce4DTo1D( + auto result = LiteralUtil::CreateR1(ReferenceUtil::Reduce4DTo1D( Array4D(1, 0, 1, 1), /*init=*/0, /*dims=*/{0, 1, 2}, [](float a, float b) { return a + b; })); LiteralTestUtil::ExpectR1Equal({0}, *result); @@ -96,7 +96,7 @@ TEST_F(ReferenceUtilTest, Reduce4Dto1DZeroSizedArray) { TEST_F(ReferenceUtilTest, MapArray2D) { auto identity = [](float value) { return log(exp(value)); }; auto result = ReferenceUtil::MapArray2D(*matrix_, identity); - auto actual_literal = Literal::CreateR2FromArray2D(*result); + auto actual_literal = LiteralUtil::CreateR2FromArray2D(*result); LiteralTestUtil::ExpectR2NearArray2D(*matrix_, *actual_literal, ErrorSpec(0.0001)); } @@ -106,7 +106,7 @@ TEST_F(ReferenceUtilTest, MapWithIndexArray2D) { return value + row + col; }; auto result = ReferenceUtil::MapWithIndexArray2D(*matrix_, add_index); - auto actual_literal = Literal::CreateR2FromArray2D(*result); + auto actual_literal = LiteralUtil::CreateR2FromArray2D(*result); LiteralTestUtil::ExpectR2Near({{1.f, 3.f, 5.f}, {5.f, 7.f, 9.f}}, *actual_literal, ErrorSpec(0.0001)); } @@ -117,7 +117,7 @@ TEST_F(ReferenceUtilTest, MapArray4D) { input->FillWithMultiples(1.0f); auto multiply_by_two = [](float value) { return 2 * value; }; auto result = ReferenceUtil::MapArray4D(*input, multiply_by_two); - auto actual_literal = Literal::CreateR4FromArray4D(*result); + auto actual_literal = LiteralUtil::CreateR4FromArray4D(*result); Array4D expected(/*planes=*/2, /*depth=*/3, /*height=*/4, /*width=*/5); expected.FillWithMultiples(2.0f); @@ -134,7 +134,7 @@ TEST_F(ReferenceUtilTest, MapWithIndexArray4D) { return value - (3 * 4 * 5 * plane + 4 * 5 * depth + 5 * height + width); }; auto result = ReferenceUtil::MapWithIndexArray4D(*input, subtract_index); - auto actual_literal = Literal::CreateR4FromArray4D(*result); + auto actual_literal = LiteralUtil::CreateR4FromArray4D(*result); Array4D expected(/*planes=*/2, /*depth=*/3, /*height=*/4, /*width=*/5); expected.Fill(0.0f); @@ -144,7 +144,7 @@ TEST_F(ReferenceUtilTest, MapWithIndexArray4D) { TEST_F(ReferenceUtilTest, SliceArray2D) { auto result = ReferenceUtil::Slice2D(*matrix_, {{0, 0}}, {{2, 2}}, {{1, 1}}); - auto actual_literal = Literal::CreateR2FromArray2D(*result); + auto actual_literal = LiteralUtil::CreateR2FromArray2D(*result); LiteralTestUtil::ExpectR2Near({{1.f, 2.f}, {4.f, 5.f}}, *actual_literal, ErrorSpec(0.0001)); @@ -152,7 +152,7 @@ TEST_F(ReferenceUtilTest, SliceArray2D) { TEST_F(ReferenceUtilTest, SliceStridedArray2D) { auto result = ReferenceUtil::Slice2D(*matrix_, {{0, 0}}, {{2, 3}}, {{1, 2}}); - auto actual_literal = Literal::CreateR2FromArray2D(*result); + auto actual_literal = LiteralUtil::CreateR2FromArray2D(*result); LiteralTestUtil::ExpectR2Near({{1.f, 3.f}, {4.f, 6.f}}, *actual_literal, ErrorSpec(0.0001)); @@ -164,7 +164,7 @@ TEST_F(ReferenceUtilTest, SliceArray3D) { auto result = ReferenceUtil::Slice3D(input, {{0, 0, 0}}, {{2, 2, 2}}, {{1, 1, 1}}); - auto actual_literal = Literal::CreateR3FromArray3D(*result); + auto actual_literal = LiteralUtil::CreateR3FromArray3D(*result); LiteralTestUtil::ExpectR3Near( {{{0.f, 1.f}, {4.f, 5.f}}, {{12.f, 13.f}, {16.f, 17.f}}}, *actual_literal, @@ -177,7 +177,7 @@ TEST_F(ReferenceUtilTest, SliceStridedArray3D) { auto result = ReferenceUtil::Slice3D(input, {{0, 0, 0}}, {{2, 3, 4}}, {{1, 2, 2}}); - auto actual_literal = Literal::CreateR3FromArray3D(*result); + auto actual_literal = LiteralUtil::CreateR3FromArray3D(*result); LiteralTestUtil::ExpectR3Near( {{{0.f, 2.f}, {8.f, 10.f}}, {{12.f, 14.f}, {20.f, 22.f}}}, @@ -190,7 +190,7 @@ TEST_F(ReferenceUtilTest, SliceArray4D) { auto result = ReferenceUtil::Slice4D(input, {{1, 0, 0, 0}}, {{2, 2, 2, 2}}, {{1, 1, 1, 1}}); - auto actual_literal = Literal::CreateR4FromArray4D(*result); + auto actual_literal = LiteralUtil::CreateR4FromArray4D(*result); LiteralTestUtil::ExpectR4Near( {{{{60.f, 61.f}, {65.f, 66.f}}, {{80.f, 81.f}, {85.f, 86.f}}}}, @@ -203,7 +203,7 @@ TEST_F(ReferenceUtilTest, SliceStridedArray4D) { auto result = ReferenceUtil::Slice4D(input, {{1, 0, 0, 0}}, {{2, 3, 4, 5}}, {{1, 2, 2, 2}}); - auto actual_literal = Literal::CreateR4FromArray4D(*result); + auto actual_literal = LiteralUtil::CreateR4FromArray4D(*result); LiteralTestUtil::ExpectR4Near( {{{{60.f, 62.f, 64.f}, {70.f, 72.f, 74.f}}, @@ -218,7 +218,7 @@ TEST_F(ReferenceUtilTest, ConvArray3DWithSamePadding) { ReferenceUtil::ConvArray3D(input, weights, 1, Padding::kSame); Array3D expected = {{{17, 28, 39, 20}}}; - auto actual_literal = Literal::CreateR3FromArray3D(*actual); + auto actual_literal = LiteralUtil::CreateR3FromArray3D(*actual); LiteralTestUtil::ExpectR3NearArray3D(expected, *actual_literal, ErrorSpec(0.0001)); @@ -231,7 +231,7 @@ TEST_F(ReferenceUtilTest, ConvArray3DWithValidPadding) { ReferenceUtil::ConvArray3D(input, weights, 1, Padding::kValid); Array3D expected = {{{17, 28, 39}}}; - auto actual_literal = Literal::CreateR3FromArray3D(*actual); + auto actual_literal = LiteralUtil::CreateR3FromArray3D(*actual); LiteralTestUtil::ExpectR3NearArray3D(expected, *actual_literal, ErrorSpec(0.0001)); @@ -266,7 +266,7 @@ TEST_F(ReferenceUtilTest, ConvWithSamePadding) { })); // clang-format on - auto actual_literal = Literal::CreateR4FromArray4D(*actual); + auto actual_literal = LiteralUtil::CreateR4FromArray4D(*actual); LiteralTestUtil::ExpectR4NearArray4D(expected, *actual_literal, ErrorSpec(0.0001)); @@ -300,7 +300,7 @@ TEST_F(ReferenceUtilTest, ConvWithValidPadding) { })); // clang-format on - auto actual_literal = Literal::CreateR4FromArray4D(*actual); + auto actual_literal = LiteralUtil::CreateR4FromArray4D(*actual); LiteralTestUtil::ExpectR4NearArray4D(expected, *actual_literal, ErrorSpec(0.0001)); @@ -356,7 +356,7 @@ TEST_F(ReferenceUtilTest, ConvGeneralDimensionsWithSamePadding) { }}); // clang-format on - auto actual_literal = Literal::CreateR4FromArray4D(*actual); + auto actual_literal = LiteralUtil::CreateR4FromArray4D(*actual); LiteralTestUtil::ExpectR4NearArray4D(expected, *actual_literal, ErrorSpec(0.0001)); @@ -409,7 +409,7 @@ TEST_F(ReferenceUtilTest, ConvGeneralDimensionsWithValidPadding) { Array4D expected({{{{2514, 2685}}}}); // clang-format on - auto actual_literal = Literal::CreateR4FromArray4D(*actual); + auto actual_literal = LiteralUtil::CreateR4FromArray4D(*actual); LiteralTestUtil::ExpectR4NearArray4D(expected, *actual_literal, ErrorSpec(0.0001)); @@ -422,7 +422,7 @@ TEST_F(ReferenceUtilTest, ApplyElementwise2D) { auto actual = ReferenceUtil::ApplyElementwise2D( [](float x, float y, float z) { return 100 * x + 10 * y + z; }, a, b, c); - auto actual_literal = Literal::CreateR2FromArray2D(*actual); + auto actual_literal = LiteralUtil::CreateR2FromArray2D(*actual); LiteralTestUtil::ExpectR2Near({{300.f, 600.f}, {900.f, 1200.f}}, *actual_literal, ErrorSpec(0.0001)); } diff --git a/tensorflow/compiler/xla/rpc/BUILD b/tensorflow/compiler/xla/rpc/BUILD index 0b1cec1925d4424db086f8a3f62c91ede090189c..44b22a5586dee3f7dd8ea0edbf9deb2090986ac8 100644 --- a/tensorflow/compiler/xla/rpc/BUILD +++ b/tensorflow/compiler/xla/rpc/BUILD @@ -56,7 +56,7 @@ tf_cc_test( ":grpc_stub", "//tensorflow:grpc++", "//tensorflow/compiler/xla/client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/core:framework_internal", "//tensorflow/core:lib", diff --git a/tensorflow/compiler/xla/rpc/grpc_client_test.cc b/tensorflow/compiler/xla/rpc/grpc_client_test.cc index f8414468bd9e0a9faf0072c47d94d12ab11b908d..67886761813f0bb45a600661b017be91ffeade73 100644 --- a/tensorflow/compiler/xla/rpc/grpc_client_test.cc +++ b/tensorflow/compiler/xla/rpc/grpc_client_test.cc @@ -24,7 +24,7 @@ limitations under the License. #include "grpcpp/security/credentials.h" #include "tensorflow/compiler/xla/client/client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/rpc/grpc_stub.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/core/lib/io/path.h" @@ -97,7 +97,7 @@ TEST_F(GRPCClientTestBase, AxpyTenValues) { 1.85840735, -1.85840735, 2.28318531, -2.28318531, -6.42477796, 6.42477796, 10.56637061, -10.56637061, -14.70796327, 14.70796327}; std::unique_ptr expected_literal = - Literal::CreateR1(expected); + LiteralUtil::CreateR1(expected); TF_ASSERT_OK_AND_ASSIGN(auto computation, builder.Build()); TF_ASSERT_OK_AND_ASSIGN(auto result_literal, client_->ExecuteAndTransfer( computation, {}, nullptr)); diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index fe99f700d23dbab799ba011b705c59d6ef7a2e52..528b7fdfd3c39cc3a56afc92474dbae976a08ba8 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -136,7 +136,7 @@ cc_library( ":hlo_dce", ":hlo_pass", ":tuple_simplifier", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_tree", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:util", @@ -182,6 +182,7 @@ tf_cc_test( name = "shape_inference_test", srcs = ["shape_inference_test.cc"], deps = [ + ":hlo", ":shape_inference", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", @@ -227,6 +228,7 @@ cc_library( ":hlo", ":hlo_query", ":shape_inference", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", @@ -244,7 +246,7 @@ tf_cc_test( deps = [ ":hlo", ":hlo_evaluator", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status", @@ -254,7 +256,7 @@ tf_cc_test( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:hlo_element_type_converter", "//tensorflow/compiler/xla/tests:hlo_verified_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -294,6 +296,7 @@ cc_library( ":hlo_reachability", ":name_uniquer", "//tensorflow/compiler/xla:array", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:protobuf_util", "//tensorflow/compiler/xla:shape_tree", @@ -396,6 +399,7 @@ tf_cc_test( deps = [ ":hlo_matchers", ":hlo_parser", + "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", ], @@ -407,7 +411,7 @@ tf_cc_test( deps = [ ":hlo", ":hlo_parser", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:protobuf_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", @@ -424,7 +428,7 @@ tf_cc_test( srcs = ["hlo_sharding_test.cc"], deps = [ ":hlo", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:protobuf_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", @@ -453,7 +457,7 @@ tf_cc_test( srcs = ["call_graph_test.cc"], deps = [ ":call_graph", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:test", @@ -487,6 +491,7 @@ cc_library( hdrs = ["call_inliner.h"], deps = [ ":call_graph", + ":hlo_dce", ":hlo_pass", "//tensorflow/compiler/xla:statusor", "//tensorflow/core:lib", @@ -502,7 +507,7 @@ tf_cc_test( ":hlo", ":hlo_matchers", ":hlo_pass", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:types", @@ -521,7 +526,7 @@ tf_cc_test( deps = [ ":call_graph", ":flatten_call_graph", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:test", @@ -559,7 +564,7 @@ cc_library( ":computation_placer", ":device_memory_allocator", ":platform_util", - ":pool", + ":stream_pool", ":transfer_manager", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -593,6 +598,7 @@ cc_library( ":hlo_proto_util", ":platform_util", ":source_map_util", + ":stream_pool", ":transfer_manager", "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:execution_options_util", @@ -637,7 +643,7 @@ cc_library( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:executable_build_options", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", ], @@ -746,8 +752,8 @@ cc_library( ":hlo_execution_profile", ":hlo_graph_dumper", ":hlo_proto", - ":pool", ":shaped_buffer", + ":stream_pool", "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:status", "//tensorflow/compiler/xla:status_macros", @@ -797,7 +803,7 @@ cc_library( hdrs = ["transfer_manager.h"], deps = [ ":shaped_buffer", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -833,7 +839,7 @@ cc_library( hdrs = ["execution_tracker.h"], deps = [ ":backend", - ":pool", + ":stream_pool", "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", @@ -941,7 +947,6 @@ cc_library( "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", - "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", ], @@ -960,7 +965,7 @@ tf_cc_test( ":hlo", ":hlo_ordering", ":hlo_scheduling", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", @@ -1038,7 +1043,7 @@ tf_cc_test( ":hlo_ordering", ":hlo_value", ":tuple_points_to_analysis", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1052,6 +1057,7 @@ cc_library( hdrs = ["hlo_module_group_metadata.h"], deps = [ ":hlo", + ":hlo_casting_utils", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status", "//tensorflow/compiler/xla:status_macros", @@ -1121,7 +1127,7 @@ cc_library( hdrs = ["hlo_query.h"], deps = [ ":hlo", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", ], ) @@ -1170,6 +1176,7 @@ cc_library( deps = [ ":hlo", ":shape_inference", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", @@ -1200,6 +1207,7 @@ cc_library( deps = [ ":hlo", ":hlo_pass", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", @@ -1219,6 +1227,7 @@ cc_library( ":hlo_creation_utils", ":hlo_pass", ":while_util", + "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", ], @@ -1232,8 +1241,9 @@ tf_cc_test( ":batchnorm_expander", ":hlo", ":hlo_matchers", + ":hlo_parser", ":hlo_pass", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:types", @@ -1255,6 +1265,7 @@ cc_library( ":hlo_pass", ":hlo_query", ":pattern_matcher", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", @@ -1274,7 +1285,7 @@ tf_cc_test( ":hlo", ":hlo_matchers", ":hlo_pass", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:types", @@ -1310,7 +1321,7 @@ tf_cc_test( ":hlo", ":hlo_matchers", ":hlo_pass", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:types", @@ -1345,7 +1356,7 @@ cc_library( ":call_inliner", ":hlo", ":hlo_pass", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:types", @@ -1361,6 +1372,7 @@ tf_cc_test( ":conditional_simplifier", ":hlo", ":hlo_matchers", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", @@ -1420,7 +1432,7 @@ tf_cc_test( deps = [ ":defuser", ":hlo_matchers", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla/tests:hlo_verified_test_base", ], @@ -1448,7 +1460,7 @@ tf_cc_test( deps = [ ":hlo_matchers", ":implicit_broadcast_remover", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla/tests:hlo_verified_test_base", ], @@ -1490,7 +1502,7 @@ tf_cc_test( ":hlo", ":hlo_matchers", ":tuple_simplifier", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:types", @@ -1505,7 +1517,7 @@ cc_library( hdrs = ["reshape_mover.h"], deps = [ ":hlo_pass", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:util", @@ -1520,7 +1532,7 @@ tf_cc_test( ":hlo", ":hlo_matchers", ":reshape_mover", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", @@ -1555,7 +1567,7 @@ tf_cc_test( ":hlo", ":hlo_matchers", ":inliner", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:util", @@ -1572,7 +1584,7 @@ cc_library( hdrs = ["computation_placer.h"], deps = [ "//tensorflow/compiler/xla:array2d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status", "//tensorflow/compiler/xla:status_macros", @@ -1604,7 +1616,7 @@ cc_library( hdrs = ["generic_transfer_manager.h"], deps = [ ":transfer_manager", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -1651,8 +1663,8 @@ tf_cc_test( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -1695,7 +1707,7 @@ tf_cc_test( deps = [ ":hlo", ":hlo_matchers", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", @@ -1710,6 +1722,7 @@ tf_cc_binary( deps = [ ":hlo", ":hlo_graph_dumper", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:types", @@ -1724,7 +1737,7 @@ tf_cc_test( srcs = ["hlo_module_test.cc"], deps = [ ":hlo", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:util", @@ -1822,7 +1835,7 @@ tf_cc_test( ":hlo_matchers", ":hlo_ordering", ":instruction_fusion", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:test", @@ -1859,7 +1872,7 @@ tf_cc_test( deps = [ ":hlo", ":hlo_liveness_analysis", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:test", @@ -1920,7 +1933,7 @@ tf_cc_test( ":hlo_matchers", ":hlo_ordering", ":instruction_fusion", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", @@ -1955,6 +1968,7 @@ cc_library( ":hlo_dataflow_analysis", ":logical_buffer", ":logical_buffer_analysis", + "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_tree", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", @@ -1973,6 +1987,7 @@ tf_cc_test( ":hlo_matchers", ":instruction_fusion", ":tuple_points_to_analysis", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", @@ -1996,6 +2011,7 @@ cc_library( deps = [ ":computation_layout", ":hlo", + ":hlo_casting_utils", ":hlo_dce", ":hlo_graph_dumper", ":hlo_pass", @@ -2044,7 +2060,7 @@ tf_cc_test( ":hlo_graph_dumper", ":hlo_matchers", ":hlo_runner", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", @@ -2108,6 +2124,7 @@ tf_cc_test( srcs = ["hlo_verifier_test.cc"], deps = [ ":hlo", + ":hlo_parser", ":hlo_verifier", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", @@ -2169,6 +2186,7 @@ tf_cc_test( deps = [ ":hlo", ":hlo_dce", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:types", @@ -2189,7 +2207,7 @@ tf_cc_test( deps = [ ":hlo", ":hlo_module_dce", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", @@ -2213,7 +2231,7 @@ tf_cc_test( ":hlo", ":hlo_matchers", ":layout_assignment", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_layout", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", @@ -2272,7 +2290,7 @@ cc_library( ":hlo", ":hlo_domain_map", ":hlo_pass", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", @@ -2288,7 +2306,7 @@ tf_cc_test( ":hlo", ":hlo_cse", ":hlo_matchers", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", @@ -2310,7 +2328,7 @@ cc_library( ":hlo_evaluator", ":hlo_pass", ":hlo_query", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:types", "//tensorflow/core:lib", @@ -2325,7 +2343,7 @@ tf_cc_test( ":hlo_constant_folding", ":hlo_matchers", ":hlo_pass", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:types", @@ -2362,6 +2380,20 @@ cc_library( ], ) +cc_library( + name = "hlo_domain_verifier", + srcs = ["hlo_domain_verifier.cc"], + hdrs = ["hlo_domain_verifier.h"], + deps = [ + ":hlo", + ":hlo_domain_map", + ":hlo_graph_dumper", + ":hlo_pass", + "//tensorflow/compiler/xla:types", + "//tensorflow/core:lib", + ], +) + cc_library( name = "hlo_domain_isolator", srcs = ["hlo_domain_isolator.cc"], @@ -2381,8 +2413,8 @@ cc_library( hdrs = ["hlo_domain_remover.h"], deps = [ ":hlo", - ":hlo_domain_isolator", ":hlo_domain_map", + ":hlo_domain_verifier", ":hlo_graph_dumper", ":hlo_pass", "//tensorflow/compiler/xla:types", @@ -2417,7 +2449,7 @@ cc_library( ":hlo_evaluator", ":hlo_pass", ":hlo_query", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:types", "//tensorflow/core:lib", @@ -2552,7 +2584,7 @@ cc_library( hdrs = ["hlo_tfgraph_builder.h"], deps = [ ":hlo", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:xla_proto", "//tensorflow/core:framework", @@ -2583,7 +2615,7 @@ cc_library( ":hlo_casting_utils", ":hlo_execution_profile", ":hlo_tfgraph_builder", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:window_util", @@ -2601,6 +2633,7 @@ tf_cc_test( deps = [ ":hlo", ":hlo_graph_dumper", + "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:xla_proto", "//tensorflow/compiler/xla/tests:test_utils", @@ -2632,12 +2665,12 @@ tf_cc_test( ":hlo_matchers", ":shape_inference", ":transpose_folding", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/service/gpu:ir_emission_utils", "//tensorflow/compiler/xla/tests:hlo_test_base", @@ -2653,7 +2686,7 @@ cc_library( deps = [ ":hlo", ":hlo_pass", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:util", @@ -2668,13 +2701,13 @@ tf_cc_test( ":hlo", ":shape_inference", ":zero_sized_hlo_elimination", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -2682,21 +2715,25 @@ tf_cc_test( ) cc_library( - name = "pool", - hdrs = ["pool.h"], + name = "stream_pool", + srcs = ["stream_pool.cc"], + hdrs = ["stream_pool.h"], deps = [ + "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", "//tensorflow/core:lib", + "//tensorflow/core:stream_executor_no_cuda", ], ) tf_cc_test( - name = "pool_test", - srcs = ["pool_test.cc"], + name = "stream_pool_test", + srcs = ["stream_pool_test.cc"], deps = [ - ":pool", + ":stream_pool", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:stream_executor_no_cuda", ], ) @@ -2828,6 +2865,7 @@ cc_library( ":hlo", ":hlo_creation_utils", ":tuple_util", + "//tensorflow/compiler/xla:literal_util", "//tensorflow/core:lib", ], ) @@ -2963,6 +3001,7 @@ cc_library( ":hlo", ":hlo_lexer", ":hlo_sharding_metadata", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc index 1ddeb27e4041df22bd3d0ec200bcddbd09937e01..505c0e8dff44ace09bd67f54ecb3f2716a2fb167 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc @@ -23,6 +23,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" @@ -195,7 +196,7 @@ class AlgebraicSimplifierVisitor : public DfsHloVisitorWithDefault { HloInstruction* AddReduce(HloInstruction* hlo, int64 dim) { HloInstruction* zero = computation_->AddInstruction(HloInstruction::CreateConstant( - Literal::Zero(hlo->shape().element_type()).CloneToUnique())); + LiteralUtil::Zero(hlo->shape().element_type()).CloneToUnique())); HloComputation* AddReduce_computation = GetOrCreateScalarAddComputation(); Shape shape = ShapeUtil::DeleteDimension(dim, hlo->shape()); return computation_->AddInstruction(HloInstruction::CreateReduce( @@ -537,8 +538,8 @@ Status AlgebraicSimplifierVisitor::HandleConstant(HloInstruction* constant) { // If a literal is all the same element replace it with a scalar broadcast. if (ShapeUtil::ElementsIn(constant->shape()) > 1 && constant->literal().IsAllFirst()) { - std::unique_ptr unique_scalar = - MakeUnique(constant->literal().GetFirstScalarLiteral()); + std::unique_ptr unique_scalar = MakeUnique( + LiteralUtil::GetFirstScalarLiteral(constant->literal())); HloInstruction* scalar = computation_->AddInstruction( HloInstruction::CreateConstant(std::move(unique_scalar))); return ReplaceWithNewInstruction( @@ -1093,7 +1094,7 @@ Status AlgebraicSimplifierVisitor::HandleDot(HloInstruction* dot) { ShapeUtil::IsZeroElementArray(lhs->shape()) || ShapeUtil::IsZeroElementArray(rhs->shape())) { auto zero = computation_->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); return ReplaceWithNewInstruction( dot, HloInstruction::CreateBroadcast(dot->shape(), zero, {})); } @@ -1155,6 +1156,19 @@ Status AlgebraicSimplifierVisitor::HandleMultiply(HloInstruction* multiply) { return Status::OK(); } + // 0*A => 0. Only applies for integral types for correct NaN-handling. + if (IsAll(lhs, 0) && + primitive_util::IsIntegralType(multiply->shape().element_type()) && + ReplaceInstructionIfSameShape(multiply, lhs)) { + return Status::OK(); + } + // A*0 => 0 + if (IsAll(rhs, 0) && + primitive_util::IsIntegralType(multiply->shape().element_type()) && + ReplaceInstructionIfSameShape(multiply, rhs)) { + return Status::OK(); + } + // exp(A) * exp(B) => exp(A+B) if (Match(multiply, m::Multiply(m::Exp(m::Op(&lhs)), m::Exp(m::Op(&rhs))))) { auto add = computation_->AddInstruction(HloInstruction::CreateBinary( @@ -1519,7 +1533,7 @@ Status AlgebraicSimplifierVisitor::HandlePower(HloInstruction* power) { CHECK(Match(power, m::Power(m::Op(&lhs), m::Op(&rhs)))); if (IsAll(rhs, 0)) { auto one = HloInstruction::CreateConstant( - Literal::One(power->shape().element_type()).CloneToUnique()); + LiteralUtil::One(power->shape().element_type()).CloneToUnique()); std::unique_ptr ones; if (ShapeUtil::IsScalar(power->shape())) { ones = std::move(one); @@ -1554,7 +1568,7 @@ Status AlgebraicSimplifierVisitor::HandlePower(HloInstruction* power) { VLOG(10) << "trying transform [pow(A, -1) => 1/A]: " << power->ToString(); if (IsAll(rhs, -1)) { auto* one = computation_->AddInstruction(HloInstruction::CreateConstant( - Literal::One(rhs->shape().element_type()).CloneToUnique())); + LiteralUtil::One(rhs->shape().element_type()).CloneToUnique())); // Explicitly broadcast scalar 1 to the output shape, to avoid implicit // broadcast in divide HLO as we are trying to eliminate implicit @@ -1730,19 +1744,37 @@ Status AlgebraicSimplifierVisitor::HandleSlice(HloInstruction* slice) { if (ReplaceInstructionIfSameShape(slice, slice->mutable_operand(0))) { return Status::OK(); } + + auto is_unstrided_slice = [](const HloInstruction* hlo) { + return c_all_of(hlo->slice_strides(), + [](int64 stride) { return stride == 1; }); + }; + if (slice->operand(0)->opcode() == HloOpcode::kSlice && + is_unstrided_slice(slice) && is_unstrided_slice(slice->operand(0))) { + HloInstruction* operand_slice = slice->mutable_operand(0); + std::vector new_slice_starts = slice->slice_starts(); + std::vector new_slice_limits = slice->slice_limits(); + for (int64 i = 0; i < new_slice_starts.size(); ++i) { + new_slice_starts[i] += operand_slice->slice_starts(i); + new_slice_limits[i] += operand_slice->slice_starts(i); + } + return ReplaceWithNewInstruction( + slice, HloInstruction::CreateSlice( + slice->shape(), operand_slice->mutable_operand(0), + new_slice_starts, new_slice_limits, slice->slice_strides())); + } return Status::OK(); } Status AlgebraicSimplifierVisitor::HandleDynamicSlice( HloInstruction* dynamic_slice) { auto operand = dynamic_slice->mutable_operand(0); - auto start_indices = dynamic_slice->operand(1); if (ShapeUtil::IsScalar(dynamic_slice->shape())) { return ReplaceInstruction(dynamic_slice, operand); } - // DynamicSlice where operand has the same size as the output and - // start_indices are all zero is simply equal to operand. - if (IsAll(start_indices, 0) && SameShape(operand, dynamic_slice)) { + // DynamicSlice where operand has the same size as the output is simply equal + // to operand. + if (SameShape(operand, dynamic_slice)) { return ReplaceInstruction(dynamic_slice, operand); } return Status::OK(); @@ -1751,20 +1783,10 @@ Status AlgebraicSimplifierVisitor::HandleDynamicSlice( Status AlgebraicSimplifierVisitor::HandleDynamicUpdateSlice( HloInstruction* dynamic_update_slice) { auto update = dynamic_update_slice->mutable_operand(1); - auto start_indices = dynamic_update_slice->operand(2); - // DynamicUpdateSlice on a scalar just passes through the update argument. - if (ShapeUtil::IsScalar(dynamic_update_slice->shape())) { - return ReplaceInstruction(dynamic_update_slice, update); - } - // DynamicUpdateSlice where operand and update have the same size and - // start_indices are all zero is simply equal to update. - // - // (We require start_indices to be all zero because we want this optimization - // not to affect the visible behavior of this op even when the indices are out - // of range. Currently dynamic-update-slice wraps out-of-range indices, so - // we can only remove the op if its indices never wrap.) - if (IsAll(start_indices, 0) && SameShape(dynamic_update_slice, update)) { + // DynamicUpdateSlice where operand and update have the same size is simply + // equal to update. + if (SameShape(dynamic_update_slice, update)) { return ReplaceInstruction(dynamic_update_slice, update); } @@ -1890,6 +1912,26 @@ Status AlgebraicSimplifierVisitor::HandleReduce(HloInstruction* reduce) { new_reduce_dimensions, function)); } } + // Convert Reduce(concat({a,b,...})) to + // map(reduce(a),map(reduce(b),...,)) + // + // This should make fusion easier or use less memory bandwidth in the unfused + // case. + if (arg->opcode() == HloOpcode::kConcatenate && + c_linear_search(reduce->dimensions(), arg->concatenate_dimension())) { + HloInstruction* old_reduce = nullptr; + for (HloInstruction* operand : arg->operands()) { + HloInstruction* new_reduce = computation_->AddInstruction( + HloInstruction::CreateReduce(reduce->shape(), operand, init_value, + reduce->dimensions(), function)); + if (old_reduce != nullptr) { + new_reduce = computation_->AddInstruction(HloInstruction::CreateMap( + reduce->shape(), {old_reduce, new_reduce}, function)); + } + old_reduce = new_reduce; + } + return ReplaceInstruction(reduce, old_reduce); + } return Status::OK(); } @@ -2098,7 +2140,7 @@ Status AlgebraicSimplifierVisitor::HandleConvolution( HloInstruction::CreateBroadcast( convolution->shape(), computation_->AddInstruction(HloInstruction::CreateConstant( - Literal::Zero(convolution->shape().element_type()) + LiteralUtil::Zero(convolution->shape().element_type()) .CloneToUnique())), {})); } diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc index b733f6f59eb028b2dff921722c462441251772fe..8b81b4c97ef373bcfb89bf0761ebb16b6e14e3fc 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -60,7 +60,7 @@ TEST_F(AlgebraicSimplifierTest, AddZero) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r0f32, "param0")); HloInstruction* zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, param0, zero)); @@ -74,12 +74,32 @@ TEST_F(AlgebraicSimplifierTest, AddZero) { EXPECT_EQ(root, param0); } +// Test that A * 0 is simplified to 0 +TEST_F(AlgebraicSimplifierTest, MulZero) { + Shape r0s32 = ShapeUtil::MakeShape(S32, {}); + HloComputation::Builder builder(TestName()); + HloInstruction* param0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, r0s32, "param0")); + HloInstruction* zero = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))); + builder.AddInstruction( + HloInstruction::CreateBinary(r0s32, HloOpcode::kMultiply, param0, zero)); + + auto computation = module().AddEntryComputation(builder.Build()); + HloInstruction* root = computation->root_instruction(); + EXPECT_EQ(root->opcode(), HloOpcode::kMultiply); + AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, + non_bitcasting_callback()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); + EXPECT_EQ(computation->root_instruction(), zero); +} + // Test that Reduce(Reduce(A)) -> Reduce(A) TEST_F(AlgebraicSimplifierTest, TwoReducesToOne) { HloComputation::Builder builder(TestName()); // Create add computation. HloInstruction* zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); HloComputation* add_computation = nullptr; { HloComputation::Builder builder(TestName() + ".add"); @@ -119,7 +139,7 @@ TEST_F(AlgebraicSimplifierTest, AddConstOnLHS) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r0f32, "param0")); HloInstruction* constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, constant, param0)); @@ -140,9 +160,9 @@ TEST_F(AlgebraicSimplifierTest, AddReassociateMergeConstants) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r0f32, "param0")); HloInstruction* constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); HloInstruction* constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(3.14159f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(3.14159f))); HloInstruction* add1 = builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, param0, constant1)); @@ -165,7 +185,7 @@ TEST_F(AlgebraicSimplifierTest, AddBroadcastZeroR0Operand) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r2f32, "param0")); HloInstruction* zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); HloInstruction* bcast = builder.AddInstruction( HloInstruction::CreateBroadcast(r2f32, zero, {0, 1})); builder.AddInstruction( @@ -200,7 +220,7 @@ TEST_F(AlgebraicSimplifierTest, InlineTrivialMap) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r2f32, "param0")); HloInstruction* zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); builder.AddInstruction(HloInstruction::CreateMap( r2f32, {param0, builder.AddInstruction( @@ -223,7 +243,7 @@ TEST_F(AlgebraicSimplifierTest, AddBroadcastZeroR1Operand) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r2f32, "param0")); HloInstruction* zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({0, 0, 0}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({0, 0, 0}))); HloInstruction* bcast = builder.AddInstruction(HloInstruction::CreateBroadcast(r2f32, zero, {1})); builder.AddInstruction( @@ -242,7 +262,7 @@ TEST_F(AlgebraicSimplifierTest, AddBroadcastZeroR1Operand) { TEST_F(AlgebraicSimplifierTest, ConstantToBroadcast) { HloComputation::Builder builder(TestName()); builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({3.14f, 3.14f, 3.14f}))); + LiteralUtil::CreateR1({3.14f, 3.14f, 3.14f}))); auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); @@ -258,7 +278,7 @@ TEST_F(AlgebraicSimplifierTest, ConstantToBroadcast) { TEST_F(AlgebraicSimplifierTest, ConstantNotToBroadcast) { HloComputation::Builder builder(TestName()); builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({3.14, 3.14, 4}))); + LiteralUtil::CreateR1({3.14, 3.14, 4}))); auto computation = module().AddEntryComputation(builder.Build()); HloInstruction* root = computation->root_instruction(); @@ -277,7 +297,7 @@ TEST_F(AlgebraicSimplifierTest, SubZero) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r0f32, "param0")); HloInstruction* zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kSubtract, param0, zero)); @@ -298,7 +318,7 @@ TEST_F(AlgebraicSimplifierTest, SubConstCanonicalization) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r0f32, "param0")); HloInstruction* constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); builder.AddInstruction(HloInstruction::CreateBinary( r0f32, HloOpcode::kSubtract, param0, constant)); @@ -493,7 +513,7 @@ TEST_F(AlgebraicSimplifierTest, DivideByConstant) { HloInstruction::CreateParameter(0, r1f32, "param0")); HloInstruction* constant = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({0.f, 1.f, 2.f}))); + LiteralUtil::CreateR1({0.f, 1.f, 2.f}))); builder.AddInstruction(HloInstruction::CreateBinary(r1f32, HloOpcode::kDivide, param0, constant)); @@ -559,7 +579,7 @@ TEST_F(AlgebraicSimplifierTest, DivOneScalar) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r0f32, "param0")); HloInstruction* one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0f))); HloInstruction* div = builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kDivide, param0, one)); @@ -580,7 +600,7 @@ TEST_F(AlgebraicSimplifierTest, DivOneArray) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r2f32, "param0")); HloInstruction* one = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 1.0}, {1.0, 1.0}}))); + LiteralUtil::CreateR2({{1.0, 1.0}, {1.0, 1.0}}))); HloInstruction* div = builder.AddInstruction( HloInstruction::CreateBinary(r2f32, HloOpcode::kDivide, param0, one)); @@ -860,7 +880,7 @@ TEST_F(AlgebraicSimplifierTest, Pow0Scalar) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r0f32, "param0")); HloInstruction* zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))); builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, param0, zero)); @@ -884,7 +904,7 @@ TEST_F(AlgebraicSimplifierTest, Pow0Vector) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r1f32, "param0")); HloInstruction* zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))); builder.AddInstruction( HloInstruction::CreateBinary(r1f32, HloOpcode::kPower, param0, zero)); @@ -912,7 +932,7 @@ TEST_F(AlgebraicSimplifierTest, Pow1) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r0f32, "param0")); HloInstruction* one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, param0, one)); @@ -934,7 +954,7 @@ TEST_F(AlgebraicSimplifierTest, Pow2) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r0f32, "param0")); HloInstruction* two = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2))); builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, param0, two)); @@ -956,7 +976,7 @@ TEST_F(AlgebraicSimplifierTest, PowNegative1) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r0f32, "param0")); HloInstruction* negative_one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(-1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(-1))); builder.AddInstruction(HloInstruction::CreateBinary(r0f32, HloOpcode::kPower, param0, negative_one)); @@ -1047,7 +1067,7 @@ TEST_F(AlgebraicSimplifierTest, ZeroSizedReduceWindow) { builder.AddInstruction(HloInstruction::CreateReduceWindow( ShapeUtil::MakeShape(F32, {5, 2}), param, builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))), + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))), window, add_computation)); module().AddEntryComputation(builder.Build()); HloPassFix simplifier(/*is_layout_sensitive=*/false, @@ -1074,7 +1094,7 @@ TEST_F(AlgebraicSimplifierTest, ZeroSizedPad) { builder.AddInstruction(HloInstruction::CreatePad( ShapeUtil::MakeShape(F32, {5, 2}), param, builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))), + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))), padding)); module().AddEntryComputation(builder.Build()); EXPECT_THAT(module().entry_computation()->root_instruction(), @@ -1116,7 +1136,7 @@ TEST_F(AlgebraicSimplifierTest, ReshapeBroadcast) { TEST_F(AlgebraicSimplifierTest, ConvertBetweenSameType) { HloComputation::Builder builder(TestName()); HloInstruction* input = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); builder.AddInstruction( HloInstruction::CreateConvert(ShapeUtil::MakeShape(F32, {}), input)); @@ -1208,7 +1228,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveEmptyConcatenateOperands) { HloInstruction* param1 = builder.AddInstruction( HloInstruction::CreateParameter(1, r1f32, "param1")); HloInstruction* empty_literal = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({}))); HloInstruction* empty_slice = builder.AddInstruction(HloInstruction::CreateSlice( ShapeUtil::MakeShape(F32, {0}), param1, {42}, {42}, {1})); @@ -1230,6 +1250,55 @@ TEST_F(AlgebraicSimplifierTest, RemoveEmptyConcatenateOperands) { op::Concatenate(param0, param0, param1)); } +// Test that reduce of concat is simplified. +TEST_F(AlgebraicSimplifierTest, SimplifyReduceOfConcat) { + const int kParamLength = 100; + Shape r3f32 = + ShapeUtil::MakeShape(F32, {kParamLength, kParamLength, kParamLength}); + HloComputation::Builder builder(TestName()); + HloInstruction* param0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, r3f32, "param0")); + HloInstruction* param1 = builder.AddInstruction( + HloInstruction::CreateParameter(1, r3f32, "param1")); + HloInstruction* param2 = builder.AddInstruction( + HloInstruction::CreateParameter(2, r3f32, "param2")); + Shape concat_shape = + ShapeUtil::MakeShape(F32, {kParamLength, 3 * kParamLength, kParamLength}); + HloInstruction* Concatenate = + builder.AddInstruction(HloInstruction::CreateConcatenate( + concat_shape, {param0, param1, param2}, 1)); + HloComputation* add_computation = nullptr; + { + HloComputation::Builder builder(TestName() + ".add"); + const Shape scalar_shape = ShapeUtil::MakeShape(F32, {}); + HloInstruction* p0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, scalar_shape, "p0")); + HloInstruction* p1 = builder.AddInstruction( + HloInstruction::CreateParameter(1, scalar_shape, "p1")); + builder.AddInstruction( + HloInstruction::CreateBinary(scalar_shape, HloOpcode::kAdd, p0, p1)); + add_computation = module().AddEmbeddedComputation(builder.Build()); + } + Shape r4f32 = ShapeUtil::MakeShape(F32, {4, 5, 6, 7}); + Shape reduce_shape = ShapeUtil::MakeShape(F32, {kParamLength}); + + HloInstruction* zero = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))); + builder.AddInstruction(HloInstruction::CreateReduce( + reduce_shape, Concatenate, zero, {1, 2}, add_computation)); + + auto computation = module().AddEntryComputation(builder.Build()); + + AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, + non_bitcasting_callback()); + ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie()); + + EXPECT_THAT( + computation->root_instruction(), + op::Map(op::Map(op::Reduce(param0, zero), op::Reduce(param1, zero)), + op::Reduce(param2, zero))); +} + // Test a concatenate with only empty operands is removed. TEST_F(AlgebraicSimplifierTest, OnlyEmptyConcatenateOperands) { const int kParamLength = 100; @@ -1238,7 +1307,7 @@ TEST_F(AlgebraicSimplifierTest, OnlyEmptyConcatenateOperands) { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, r1f32, "param0")); HloInstruction* empty_literal = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({}))); HloInstruction* empty_slice = builder.AddInstruction(HloInstruction::CreateSlice( ShapeUtil::MakeShape(F32, {0}), param0, {42}, {42}, {1})); @@ -1420,7 +1489,7 @@ TEST_F(AlgebraicSimplifierTest, FailureToSinkReshapeDoesntAffectChangedBit) { builder.AddInstruction( HloInstruction::CreateParameter(0, shape, "param0")), builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{0, 0}, {0, 0}}))))); + LiteralUtil::CreateR2({{0, 0}, {0, 0}}))))); builder.AddInstruction( HloInstruction::CreateReshape(ShapeUtil::MakeShape(F32, {4}), add)); @@ -1443,7 +1512,7 @@ TEST_F(AlgebraicSimplifierTest, FailureToSinkBroadcastDoesntAffectChangedBit) { builder.AddInstruction( HloInstruction::CreateParameter(0, shape, "param0")), builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{0, 0}, {0, 0}}))))); + LiteralUtil::CreateR2({{0, 0}, {0, 0}}))))); builder.AddInstruction( HloInstruction::CreateBroadcast(ShapeUtil::MakeShape(F32, {2, 2, 2}), add, @@ -1726,7 +1795,7 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopPad) { builder.AddInstruction(HloInstruction::CreateParameter( 0, ShapeUtil::MakeShape(F32, {2, 2}), "param")); HloInstruction* zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); PaddingConfig no_padding; for (int i = 0; i < 2; ++i) { auto dimension = no_padding.add_dimensions(); @@ -1757,7 +1826,7 @@ TEST_F(AlgebraicSimplifierTest, NegativePadding) { builder.AddInstruction(HloInstruction::CreateParameter( 0, ShapeUtil::MakeShape(F32, {10, 10}), "param")); HloInstruction* zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); PaddingConfig padding; int64 low_padding[2] = {-1, -2}; int64 high_padding[2] = {2, -3}; @@ -1839,6 +1908,39 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopSlice) { EXPECT_THAT(computation->root_instruction(), param); } +TEST_F(AlgebraicSimplifierTest, SliceOfSliceToSlice) { + HloComputation::Builder builder(TestName()); + const int64 dim0 = 11; + const int64 dim1 = 12; + HloInstruction* param = + builder.AddInstruction(HloInstruction::CreateParameter( + 0, ShapeUtil::MakeShape(F32, {dim0, dim1}), "param")); + HloInstruction* original_slice = + builder.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {dim0 - 2, dim1 - 4}), param, + /*start_indices=*/{1, 2}, + /*limit_indices=*/{dim0 - 1, dim1 - 2}, /*strides=*/{1, 1})); + + builder.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {dim0 - 5, dim1 - 9}), original_slice, + /*start_indices=*/{2, 3}, + /*limit_indices=*/{dim0 - 3, dim1 - 6}, /*strides=*/{1, 1})); + auto module = CreateNewModule(); + HloComputation* computation = module->AddEntryComputation(builder.Build()); + + EXPECT_THAT(computation->root_instruction(), op::Slice(op::Slice(param))); + + AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, + non_bitcasting_callback()); + ASSERT_TRUE(simplifier.Run(module).ValueOrDie()); + + EXPECT_THAT(computation->root_instruction(), op::Slice(param)); + EXPECT_EQ(computation->root_instruction()->slice_starts(0), 3); + EXPECT_EQ(computation->root_instruction()->slice_starts(1), 5); + EXPECT_EQ(computation->root_instruction()->slice_limits(0), dim0 - 2); + EXPECT_EQ(computation->root_instruction()->slice_limits(1), dim1 - 4); +} + TEST_F(AlgebraicSimplifierTest, ConvertConvToMatmul) { struct ConvTestOptions { int in_batch = 10; @@ -2109,7 +2211,7 @@ TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToSlice) { TEST_F(AlgebraicSimplifierTest, ScalarBroadcastToTransposeReshape) { HloComputation::Builder builder(TestName()); HloInstruction* forty_two = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); Shape broadcast_shape = ShapeUtil::MakeShape(F32, {4, 5, 6}); HloInstruction* broadcast = builder.AddInstruction( @@ -2156,7 +2258,7 @@ TEST_F(AlgebraicSimplifierTest, FoldPadIntoReduceWindow) { padding.mutable_dimensions(3)->set_edge_padding_high(2); HloInstruction* pad_value = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(5.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(5.0f))); HloInstruction* pad = builder.AddInstruction(HloInstruction::CreatePad( ShapeUtil::MakeShape(F32, {1, 3, 3, 5}), operand, pad_value, padding)); @@ -2187,7 +2289,7 @@ TEST_F(AlgebraicSimplifierTest, FoldPadIntoReduceWindow) { const Shape reduce_window_shape = ShapeUtil::MakeShape(F32, {111, 113, 113, 115}); HloInstruction* reduce_init_value = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(5.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(5.0f))); HloInstruction* reduce_window = builder.AddInstruction(HloInstruction::CreateReduceWindow( reduce_window_shape, pad, reduce_init_value, window, @@ -2238,7 +2340,7 @@ TEST_F(AlgebraicSimplifierTest, FoldConvertedPadIntoReduceWindow) { padding.mutable_dimensions(3)->set_edge_padding_high(2); HloInstruction* pad_value = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(5.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(5.0f))); HloInstruction* pad = builder.AddInstruction(HloInstruction::CreatePad( ShapeUtil::MakeShape(BF16, {1, 3, 3, 5}), parameter, pad_value, padding)); @@ -2273,7 +2375,7 @@ TEST_F(AlgebraicSimplifierTest, FoldConvertedPadIntoReduceWindow) { const Shape reduce_window_shape = ShapeUtil::MakeShape(F32, {111, 113, 113, 115}); HloInstruction* reduce_init_value = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(5.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(5.0f))); HloInstruction* reduce_window = builder.AddInstruction(HloInstruction::CreateReduceWindow( reduce_window_shape, convert, reduce_init_value, window, @@ -2344,9 +2446,9 @@ TEST_F(AlgebraicSimplifierTest, IteratorInvalidation) { HloComputation::Builder call_builder(TestName() + ".Call"); HloInstruction* zero = call_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({0.0f}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({0.0f}))); HloInstruction* one = call_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1.0f}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({1.0f}))); call_builder.AddInstruction( HloInstruction::CreateCall(r1f32, {zero, one}, dot_computation.get())); @@ -2362,9 +2464,9 @@ TEST_F(AlgebraicSimplifierTest, ConstantTupleBecomesTupleOfConstants) { HloComputation::Builder builder(TestName()); const float constant_scalar = 7.3f; std::initializer_list constant_vector = {1.1f, 2.0f, 3.3f}; - std::unique_ptr value = - Literal::MakeTuple({Literal::CreateR0(constant_scalar).get(), - Literal::CreateR1(constant_vector).get()}); + std::unique_ptr value = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(constant_scalar).get(), + LiteralUtil::CreateR1(constant_vector).get()}); builder.AddInstruction(HloInstruction::CreateConstant(std::move(value))); auto computation = module().AddEntryComputation(builder.Build()); @@ -2387,8 +2489,8 @@ TEST_F(AlgebraicSimplifierTest, TrivialDynamicSlice) { shape, builder.AddInstruction( HloInstruction::CreateParameter(0, shape, "slice_from")), - builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({0, 0, 0}))), + builder.AddInstruction(HloInstruction::CreateParameter( + 1, ShapeUtil::MakeShape(U32, {3}), "slice_indices")), /*slice_sizes=*/{10, 100, 1000})); auto computation = module().AddEntryComputation(builder.Build()); @@ -2421,8 +2523,8 @@ TEST_F(AlgebraicSimplifierTest, TrivialDynamicUpdateSlice) { builder.AddInstruction( HloInstruction::CreateParameter(2, slice_shape, "to_update")), slice, - builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({0, 0, 0}))))); + builder.AddInstruction(HloInstruction::CreateParameter( + 3, ShapeUtil::MakeShape(U32, {3}), "update_indices")))); auto computation = module().AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, @@ -2437,7 +2539,7 @@ TEST_F(AlgebraicSimplifierTest, MergeBroadcasts) { HloComputation::Builder builder(TestName()); Shape r2f32 = ShapeUtil::MakeShape(F32, {2, 2}); HloInstruction* input_array = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({3, 4}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({3, 4}))); HloInstruction* inner_bcast = builder.AddInstruction( HloInstruction::CreateBroadcast(r2f32, input_array, {1})); Shape r3f32 = ShapeUtil::MakeShape(F32, {2, 2, 2}); @@ -2546,7 +2648,7 @@ TEST_P(PadReduceWindowEffectiveBroadcastTest, DoIt) { HloInstruction* pad = builder.AddInstruction(HloInstruction::CreatePad( pad_shape, input, builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))), + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))), padding)); HloComputation* add_computation = nullptr; @@ -2565,7 +2667,7 @@ TEST_P(PadReduceWindowEffectiveBroadcastTest, DoIt) { Window window = window_util::MakeWindow( decorate_spatials(param.reduce_window_spatials, 1, 1)); auto zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); TF_ASSERT_OK_AND_ASSIGN(const Shape output_shape, ShapeInference::InferReduceWindowShape( pad->shape(), zero->shape(), window, @@ -2704,7 +2806,7 @@ TEST_P(DotOfConcatSimplificationTest, ConstantLHS) { Shape lhs_shape = ShapeUtil::MakeShape(F32, {spec.m, spec.k}); auto* lhs = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2F32Linspace( + HloInstruction::CreateConstant(LiteralUtil::CreateR2F32Linspace( /*from=*/10.0, /*to=*/10000.0, /*rows=*/spec.m, /*cols=*/spec.k))); Shape rhs0_shape = ShapeUtil::MakeShape(F32, {k0, spec.n}); @@ -2783,7 +2885,7 @@ TEST_P(DotOfConcatSimplificationTest, ConstantRHS) { Shape rhs_shape = ShapeUtil::MakeShape(F32, {spec.k, spec.n}); auto* rhs = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2F32Linspace( + HloInstruction::CreateConstant(LiteralUtil::CreateR2F32Linspace( /*from=*/10.0, /*to=*/10000.0, /*rows=*/spec.k, /*cols=*/spec.n))); DotDimensionNumbers dot_dnums; @@ -2830,7 +2932,7 @@ TEST_F(AlgebraicSimplifierTest, DynamicUpdateSliceZeroUpdate) { HloInstruction* const update = builder.AddInstruction( HloInstruction::CreateParameter(1, update_shape, "update")); HloInstruction* const start_indices = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({0}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({0}))); builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice( dslice_shape, operand, update, start_indices)); const HloComputation* const computation = @@ -2879,7 +2981,7 @@ TEST_P(DotOfGatherSimplificationTest, ConstantRHS) { int64 lhs_cols = (spec.lcd == 0) ? spec.m : (spec.k + k_increase); Shape lhs_shape = ShapeUtil::MakeShape(F32, {lhs_rows, lhs_cols}); auto* lhs = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2F32Linspace( + HloInstruction::CreateConstant(LiteralUtil::CreateR2F32Linspace( /*from=*/10.0, /*to=*/10000.0, /*rows=*/lhs_rows, /*cols=*/lhs_cols))); @@ -2887,7 +2989,7 @@ TEST_P(DotOfGatherSimplificationTest, ConstantRHS) { int32 start_col = (spec.lcd == 0) ? spec.s : 0; const auto start_indices = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({start_row, start_col}))); + LiteralUtil::CreateR1({start_row, start_col}))); int64 slice_row_size = (spec.lcd == 0) ? spec.k : 1; int64 slice_col_size = (spec.lcd == 0) ? 1 : spec.k; Shape ds_shape = ShapeUtil::MakeShape(F32, {slice_row_size, slice_col_size}); @@ -2898,7 +3000,7 @@ TEST_P(DotOfGatherSimplificationTest, ConstantRHS) { int64 rhs_cols = (spec.rcd == 0) ? spec.n : spec.k; Shape rhs_shape = ShapeUtil::MakeShape(F32, {rhs_rows, rhs_cols}); auto* rhs = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2F32Linspace( + HloInstruction::CreateConstant(LiteralUtil::CreateR2F32Linspace( /*from=*/10.0, /*to=*/10000.0, /*rows=*/rhs_rows, /*cols=*/rhs_cols))); @@ -2946,7 +3048,7 @@ TEST_P(DotOfGatherSimplificationTest, ConstantLHS) { int64 lhs_cols = (spec.lcd == 0) ? spec.m : spec.k; Shape lhs_shape = ShapeUtil::MakeShape(F32, {lhs_rows, lhs_cols}); auto* lhs = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2F32Linspace( + HloInstruction::CreateConstant(LiteralUtil::CreateR2F32Linspace( /*from=*/10.0, /*to=*/10000.0, /*rows=*/lhs_rows, /*cols=*/lhs_cols))); @@ -2957,7 +3059,7 @@ TEST_P(DotOfGatherSimplificationTest, ConstantLHS) { int64 rhs_cols = (spec.rcd == 0) ? spec.n : (spec.k + k_increase); Shape rhs_shape = ShapeUtil::MakeShape(F32, {rhs_rows, rhs_cols}); auto* rhs = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2F32Linspace( + HloInstruction::CreateConstant(LiteralUtil::CreateR2F32Linspace( /*from=*/10.0, /*to=*/10000.0, /*rows=*/rhs_rows, /*cols=*/rhs_cols))); @@ -2965,7 +3067,7 @@ TEST_P(DotOfGatherSimplificationTest, ConstantLHS) { int32 start_col = (spec.rcd == 0) ? spec.s : 0; const auto start_indices = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({start_row, start_col}))); + LiteralUtil::CreateR1({start_row, start_col}))); int64 slice_row_size = (spec.rcd == 0) ? spec.k : 1; int64 slice_col_size = (spec.rcd == 0) ? 1 : spec.k; Shape ds_shape = ShapeUtil::MakeShape(F32, {slice_row_size, slice_col_size}); diff --git a/tensorflow/compiler/xla/service/backend.cc b/tensorflow/compiler/xla/service/backend.cc index 349b32451a697dbd6804b44cd1a36419c753bb14..d12be3e007fe0b16ac850d64521f0025d481b5d2 100644 --- a/tensorflow/compiler/xla/service/backend.cc +++ b/tensorflow/compiler/xla/service/backend.cc @@ -96,24 +96,19 @@ Backend::CreateDefaultBackend() { return CreateBackend(backend_options); } -StatusOr Backend::BorrowStream(int device_ordinal) { - TF_ASSIGN_OR_RETURN(auto exec, stream_executor(device_ordinal)); - return BorrowStream(exec); +StatusOr Backend::BorrowStream(int device_ordinal) { + TF_ASSIGN_OR_RETURN(auto executor, stream_executor(device_ordinal)); + return BorrowStream(executor); } -StatusOr Backend::BorrowStream( - se::StreamExecutor* executor) { +StatusOr Backend::BorrowStream(se::StreamExecutor* executor) { tensorflow::mutex_lock l(mu_); if (0 == stream_pools_.count(executor)) { stream_pools_.emplace(std::piecewise_construct, std::forward_as_tuple(executor), - std::forward_as_tuple([executor]() { - auto stream = MakeUnique(executor); - stream->Init(); - return stream; - })); + std::forward_as_tuple()); } - return stream_pools_.at(executor).Allocate(); + return stream_pools_.at(executor).BorrowStream(executor); } Backend::Backend( diff --git a/tensorflow/compiler/xla/service/backend.h b/tensorflow/compiler/xla/service/backend.h index 6546602473e3381cf13879ddebd05d34d1f7a055..1bc3796fa48c1627538474d04ef5358ba64dfce9 100644 --- a/tensorflow/compiler/xla/service/backend.h +++ b/tensorflow/compiler/xla/service/backend.h @@ -24,7 +24,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/compiler.h" #include "tensorflow/compiler/xla/service/computation_placer.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" -#include "tensorflow/compiler/xla/service/pool.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/compiler/xla/service/transfer_manager.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" @@ -63,11 +63,9 @@ class BackendOptions { // // It also offers a pooling API for creation/use of initialized streams: // -// StreamPtr stream = backend->BorrowStream().ConsumeValueOrDie(); +// StreamPool::Ptr stream = backend->BorrowStream().ConsumeValueOrDie(); class Backend { public: - using StreamPtr = Pool::SmartPtr; - // Creates a new backend. static StatusOr> CreateBackend( const BackendOptions& options); @@ -114,13 +112,13 @@ class Backend { // Borrows a stream for use by the caller, either by grabbing it from an // internal pool, or by constructing/initializating it, and returns the result // to the caller. - StatusOr BorrowStream(int device_ordinal); - StatusOr BorrowStream(se::StreamExecutor* executor); + StatusOr BorrowStream(int device_ordinal); + StatusOr BorrowStream(se::StreamExecutor* executor); // Returns a function to borrow a stream, as `BorrowStream` above does. // Purely for convenience, the caller could rather make this anonymous // function itself. - std::function(int)> StreamBorrower() { + std::function(int)> StreamBorrower() { return [this](int device_ordinal) { return BorrowStream(device_ordinal); }; } @@ -169,7 +167,7 @@ class Backend { tensorflow::mutex mu_; // Mapping from stream executor to stream pools, used by `BorrowStream` above. - std::map> stream_pools_ GUARDED_BY(mu_); + std::map stream_pools_ GUARDED_BY(mu_); // The default memory allocator to use. std::unique_ptr memory_allocator_; diff --git a/tensorflow/compiler/xla/service/batchnorm_expander.cc b/tensorflow/compiler/xla/service/batchnorm_expander.cc index ec13fadbc75e2315d1d6ef72e24a0faca0c7de40..c4cd60c1201f7ddbf0aba4b6d587952531b74bfa 100644 --- a/tensorflow/compiler/xla/service/batchnorm_expander.cc +++ b/tensorflow/compiler/xla/service/batchnorm_expander.cc @@ -20,6 +20,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" @@ -34,6 +35,7 @@ limitations under the License. #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/core/lib/gtl/flatmap.h" +#include "tensorflow/core/lib/gtl/optional.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" @@ -41,6 +43,8 @@ namespace xla { namespace { +using tensorflow::gtl::optional; + // BatchNormExpanderVisitor traverses the HLO computation and rewrites BatchNorm // operations into smaller operations. class BatchNormExpanderVisitor : public DfsHloVisitorWithDefault { @@ -97,7 +101,7 @@ class BatchNormExpanderVisitor : public DfsHloVisitorWithDefault { add_instruction(HloInstruction::CreateConvert( ShapeUtil::MakeShape(operand->shape().element_type(), {}), add_instruction(HloInstruction::CreateConstant( - Literal::CreateR0(-0.5f))))), + LiteralUtil::CreateR0(-0.5f))))), {})); return HloInstruction::CreateBinary(operand->shape(), HloOpcode::kPower, operand, exponent); @@ -113,7 +117,7 @@ class BatchNormExpanderVisitor : public DfsHloVisitorWithDefault { add_instruction(HloInstruction::CreateConvert( ShapeUtil::MakeShape(operand->shape().element_type(), {}), add_instruction(HloInstruction::CreateConstant( - Literal::CreateR0(1.0 / element_count))))), + LiteralUtil::CreateR0(1.0 / element_count))))), {})); return HloInstruction::CreateBinary(operand->shape(), HloOpcode::kMultiply, operand, elem_count_recip); @@ -200,11 +204,11 @@ Status BatchNormExpanderVisitor::HandleBatchNormTraining( HloInstruction* offset = batch_norm->mutable_operand(2); const Shape feature_shape = scale->shape(); - auto zero_literal = Literal::CreateR0(0.0f); + auto zero_literal = LiteralUtil::CreateR0(0.0f); TF_ASSIGN_OR_RETURN(zero_literal, zero_literal->Convert(ptype)); auto zero = add(HloInstruction::CreateConstant(std::move(zero_literal))); - auto epsilon_literal = Literal::CreateR0(batch_norm->epsilon()); + auto epsilon_literal = LiteralUtil::CreateR0(batch_norm->epsilon()); TF_ASSIGN_OR_RETURN(epsilon_literal, epsilon_literal->Convert(ptype)); auto epsilon = add(HloInstruction::CreateBroadcast( operand_shape, @@ -288,16 +292,22 @@ Status BatchNormExpanderVisitor::HandleBatchNormTraining( int64 instruction_count_after = computation_->instruction_count(); CHECK_EQ(instruction_count_after, instruction_count_before + added_instructions.size()); + const HloSharding& sharding = batch_norm->sharding(); HloSharding operand_sharding = - batch_norm->sharding().GetAsShapeTree(batch_norm->shape()).element({0}); + sharding.GetAsShapeTree(batch_norm->shape()).element({0}); + optional unique_device = batch_norm->sharding_unique_device(); + HloSharding default_sharding = + unique_device.has_value() + ? HloSharding::AssignDevice(unique_device.value()) + : HloSharding::Replicate(); for (HloInstruction* inst : added_instructions) { if (ShapeUtil::Equal(inst->shape(), operand_shape)) { inst->set_sharding(operand_sharding); } else { - inst->set_sharding(HloSharding::Replicate()); + inst->set_sharding(default_sharding); } } - tuple->set_sharding(batch_norm->sharding()); + tuple->set_sharding(sharding); } TF_CHECK_OK(ReplaceWithNewInstruction(batch_norm, std::move(tuple))); return Status::OK(); @@ -320,7 +330,7 @@ Status BatchNormExpanderVisitor::HandleBatchNormInference( HloInstruction* var = batch_norm->mutable_operand(4); const Shape feature_shape = scale->shape(); - auto epsilon_literal = Literal::CreateR0(batch_norm->epsilon()); + auto epsilon_literal = LiteralUtil::CreateR0(batch_norm->epsilon()); TF_ASSIGN_OR_RETURN(epsilon_literal, epsilon_literal->Convert(ptype)); auto epsilon = computation_->AddInstruction(HloInstruction::CreateBroadcast( operand_shape, @@ -388,14 +398,20 @@ Status BatchNormExpanderVisitor::HandleBatchNormInference( CHECK_EQ(instruction_count_after, instruction_count_before + added_instructions.size()); if (batch_norm->has_sharding()) { + const HloSharding& sharding = batch_norm->sharding(); + optional unique_device = batch_norm->sharding_unique_device(); + HloSharding default_sharding = + unique_device.has_value() + ? HloSharding::AssignDevice(unique_device.value()) + : HloSharding::Replicate(); for (HloInstruction* inst : added_instructions) { if (ShapeUtil::Equal(inst->shape(), operand_shape)) { - inst->set_sharding(batch_norm->sharding()); + inst->set_sharding(sharding); } else { - inst->set_sharding(HloSharding::Replicate()); + inst->set_sharding(default_sharding); } } - shifted_normalized->set_sharding(batch_norm->sharding()); + shifted_normalized->set_sharding(sharding); } TF_CHECK_OK( ReplaceWithNewInstruction(batch_norm, std::move(shifted_normalized))); @@ -447,11 +463,11 @@ Status BatchNormExpanderVisitor::HandleBatchNormGrad( const int64 feature_count = activation_shape.dimensions(feature_index); const int64 elements_per_feature_int64 = size_in_elements / feature_count; - auto zero_literal = Literal::CreateR0(0.0f); + auto zero_literal = LiteralUtil::CreateR0(0.0f); TF_ASSIGN_OR_RETURN(zero_literal, zero_literal->Convert(ptype)); auto zero = add(HloInstruction::CreateConstant(std::move(zero_literal))); - auto epsilon_literal = Literal::CreateR0(batch_norm->epsilon()); + auto epsilon_literal = LiteralUtil::CreateR0(batch_norm->epsilon()); TF_ASSIGN_OR_RETURN(epsilon_literal, epsilon_literal->Convert(ptype)); auto epsilon_scalar = add(HloInstruction::CreateConstant(std::move(epsilon_literal))); @@ -542,7 +558,7 @@ Status BatchNormExpanderVisitor::HandleBatchNormGrad( Mean(elements_per_feature_int64, scale_times_rsqrt_var_add_epsilon, add)); auto elements_per_feature_literal = - Literal::CreateR0(elements_per_feature_int64); + LiteralUtil::CreateR0(elements_per_feature_int64); TF_ASSIGN_OR_RETURN(elements_per_feature_literal, elements_per_feature_literal->Convert(ptype)); auto elements_per_feature = add( @@ -562,19 +578,25 @@ Status BatchNormExpanderVisitor::HandleBatchNormGrad( auto tuple = HloInstruction::CreateTuple({grad_activation, grad_scale, grad_beta}); if (batch_norm->has_sharding()) { + const HloSharding& sharding = batch_norm->sharding(); int64 instruction_count_after = computation_->instruction_count(); CHECK_EQ(instruction_count_after, instruction_count_before + added_instructions.size()); HloSharding activation_sharding = - batch_norm->sharding().GetAsShapeTree(batch_norm->shape()).element({0}); + sharding.GetAsShapeTree(batch_norm->shape()).element({0}); + auto unique_device = batch_norm->sharding_unique_device(); + HloSharding default_sharding = + unique_device.has_value() + ? HloSharding::AssignDevice(unique_device.value()) + : HloSharding::Replicate(); for (HloInstruction* inst : added_instructions) { if (ShapeUtil::Equal(inst->shape(), activation_shape)) { inst->set_sharding(activation_sharding); } else { - inst->set_sharding(HloSharding::Replicate()); + inst->set_sharding(default_sharding); } } - tuple->set_sharding(batch_norm->sharding()); + tuple->set_sharding(sharding); } TF_CHECK_OK(ReplaceWithNewInstruction(batch_norm, std::move(tuple))); diff --git a/tensorflow/compiler/xla/service/batchnorm_expander_test.cc b/tensorflow/compiler/xla/service/batchnorm_expander_test.cc index aa36e64b07099a372dab67babc7a18a2d39596bc..32f785a70adf0e7ea3ce281f7ff73224be8d424e 100644 --- a/tensorflow/compiler/xla/service/batchnorm_expander_test.cc +++ b/tensorflow/compiler/xla/service/batchnorm_expander_test.cc @@ -19,12 +19,13 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_matchers.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/service/hlo_pass_fix.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test.h" @@ -114,5 +115,33 @@ TEST_F(BatchNormExpanderTest, BatchNormGrad) { EXPECT_EQ(root->opcode(), HloOpcode::kTuple); } +TEST_F(BatchNormExpanderTest, BatchNormTrainingSharding) { + const char* module_str = R"( +HloModule module +ENTRY entry { + %param.0 = f32[8,4] parameter(0) + %param.1 = f32[4] parameter(1) + %param.2 = f32[4] parameter(2) + ROOT %batch-norm-training = (f32[8,4], f32[4], f32[4]) + batch-norm-training(f32[8,4] %param.0, f32[4] %param.1, f32[4] %param.2), + epsilon=0.001, feature_index=1, sharding={maximal device=1} +})"; + + TF_ASSERT_OK_AND_ASSIGN(auto module, ParseHloString(module_str)); + BatchNormExpander rewriter(/*rewrite_training_op=*/true, + /*rewrite_inference_op=*/true, + /*rewrite_grad_op=*/true); + ASSERT_TRUE(rewriter.Run(module.get()).ValueOrDie()); + + for (auto* instruction : module->entry_computation()->instructions()) { + if (instruction->opcode() == HloOpcode::kParameter) { + continue; + } + ASSERT_TRUE(instruction->has_sharding()); + TF_ASSERT_OK_AND_ASSIGN(int device, instruction->sharding().UniqueDevice()); + EXPECT_EQ(device, 1); + } +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/bfloat16_conversion_folding_test.cc b/tensorflow/compiler/xla/service/bfloat16_conversion_folding_test.cc index f7b4c1405dbc8719d8fba5476e6e41d2921ea877..7cf05ca443c00c3b40eeb7d756cf216b45c45c39 100644 --- a/tensorflow/compiler/xla/service/bfloat16_conversion_folding_test.cc +++ b/tensorflow/compiler/xla/service/bfloat16_conversion_folding_test.cc @@ -235,7 +235,8 @@ TEST_F(BFloat16ConversionFoldingTest, FoldCrossReplicaSumTupleOutput) { HloInstruction* crs = builder.AddInstruction(HloInstruction::CreateCrossReplicaSum( ShapeUtil::MakeTupleShape({f32_shape, f32_shape}), {convert_a, b}, - sum, /*replica_group_ids=*/{}, /*barrier=*/"")); + sum, /*replica_group_ids=*/{}, /*barrier=*/"", + /*all_reduce_id=*/tensorflow::gtl::nullopt)); HloInstruction* gte_a = builder.AddInstruction( HloInstruction::CreateGetTupleElement(f32_shape, crs, 0)); HloInstruction* gte_b = builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/bfloat16_normalization.cc b/tensorflow/compiler/xla/service/bfloat16_normalization.cc index 14c54ddd135af024327f63418b410da1ed3c4fd4..16e99b57220cc185fbfaa75d30a0de709cf61ee7 100644 --- a/tensorflow/compiler/xla/service/bfloat16_normalization.cc +++ b/tensorflow/compiler/xla/service/bfloat16_normalization.cc @@ -34,8 +34,10 @@ class BFloat16NormalizationVisitor : public DfsHloVisitorWithDefault { Status DefaultAction(HloInstruction* hlo) override; - // Special handling for cross-replica-sum which can have a tuple output. + // Special handling for cross-replica-sum and sort which can have a tuple + // output. Status HandleCrossReplicaSum(HloInstruction* crs) override; + Status HandleSort(HloInstruction* sort) override; static bool Run(HloComputation* computation, const BFloat16Support* bfloat16_support) { @@ -49,6 +51,10 @@ class BFloat16NormalizationVisitor : public DfsHloVisitorWithDefault { // conversions between F32 and BF16 to make it supported. Status HandleInstruction(HloInstruction* hlo); + // Handle instructions with tuple outputs by examining each output + // independently. + Status HandleMultipleOutputs(HloInstruction* hlo); + // Inserts a conversion HLO that changes the given HLO's output type. Status InsertConvertAfterOutput(HloInstruction* hlo, PrimitiveType to, HloComputation* computation); @@ -148,22 +154,35 @@ Status BFloat16NormalizationVisitor::HandleCrossReplicaSum( HloInstruction* crs) { if (!ShapeUtil::IsTuple(crs->shape())) { return HandleInstruction(crs); + } else { + return HandleMultipleOutputs(crs); } +} + +Status BFloat16NormalizationVisitor::HandleSort(HloInstruction* sort) { + if (!ShapeUtil::IsTuple(sort->shape())) { + return HandleInstruction(sort); + } else { + return HandleMultipleOutputs(sort); + } +} - std::vector operand_types(crs->operand_count()); - std::vector output_types(crs->operand_count()); +Status BFloat16NormalizationVisitor::HandleMultipleOutputs( + HloInstruction* hlo) { + std::vector operand_types(hlo->operand_count()); + std::vector output_types(hlo->operand_count()); int64 f32_count = 0; int64 bf16_count = 0; bool has_unsupported_bf16_operand = false; bool has_unsupported_bf16_output = false; - for (int64 i = 0; i < crs->operand_count(); ++i) { - operand_types[i] = crs->operand(i)->shape().element_type(); - output_types[i] = ShapeUtil::GetSubshape(crs->shape(), {i}).element_type(); + for (int64 i = 0; i < hlo->operand_count(); ++i) { + operand_types[i] = hlo->operand(i)->shape().element_type(); + output_types[i] = ShapeUtil::GetSubshape(hlo->shape(), {i}).element_type(); if (operand_types[i] == F32) { f32_count += 1; } else if (operand_types[i] == BF16) { bf16_count += 1; - if (!bfloat16_support_->SupportsBF16Operand(*crs, i)) { + if (!bfloat16_support_->SupportsBF16Operand(*hlo, i)) { has_unsupported_bf16_operand = true; } } @@ -171,7 +190,7 @@ Status BFloat16NormalizationVisitor::HandleCrossReplicaSum( f32_count += 1; } else if (output_types[i] == BF16) { bf16_count += 1; - if (!bfloat16_support_->SupportsBF16Output(*crs)) { + if (!bfloat16_support_->SupportsBF16Output(*hlo)) { has_unsupported_bf16_output = true; } } @@ -185,43 +204,43 @@ Status BFloat16NormalizationVisitor::HandleCrossReplicaSum( if (operand_types[i] != BF16) { return false; } - if (!bfloat16_support_->SupportsBF16Operand(*crs, i)) { + if (!bfloat16_support_->SupportsBF16Operand(*hlo, i)) { return true; } - if (bfloat16_support_->SupportsMixedPrecisions(*crs)) { + if (bfloat16_support_->SupportsMixedPrecisions(*hlo)) { return false; } return has_unsupported_bf16_operand || has_unsupported_bf16_output || f32_count > 0; }; - for (int64 i = 0; i < crs->operand_count(); ++i) { + for (int64 i = 0; i < hlo->operand_count(); ++i) { if (should_convert_operand(i)) { - TF_RETURN_IF_ERROR(InsertConvertBeforeOperand(crs, i, F32, computation_)); + TF_RETURN_IF_ERROR(InsertConvertBeforeOperand(hlo, i, F32, computation_)); f32_count += 1; bf16_count -= 1; } } if (!has_unsupported_bf16_output && - (bfloat16_support_->SupportsMixedPrecisions(*crs) || f32_count == 0 || + (bfloat16_support_->SupportsMixedPrecisions(*hlo) || f32_count == 0 || bf16_count == 0)) { return Status::OK(); } - std::vector materialized_users = crs->users(); - std::vector output_elements(crs->operand_count()); - auto original_shape = crs->shape(); - for (int64 i = 0; i < crs->operand_count(); ++i) { - auto subshape = ShapeUtil::GetMutableSubshape(crs->mutable_shape(), {i}); + std::vector materialized_users = hlo->users(); + std::vector output_elements(hlo->operand_count()); + auto original_shape = hlo->shape(); + for (int64 i = 0; i < hlo->operand_count(); ++i) { + auto subshape = ShapeUtil::GetMutableSubshape(hlo->mutable_shape(), {i}); if (output_types[i] != BF16) { output_elements[i] = computation_->AddInstruction( - HloInstruction::CreateGetTupleElement(*subshape, crs, i)); + HloInstruction::CreateGetTupleElement(*subshape, hlo, i)); continue; } subshape->set_element_type(F32); auto gte = computation_->AddInstruction( - HloInstruction::CreateGetTupleElement(*subshape, crs, i)); + HloInstruction::CreateGetTupleElement(*subshape, hlo, i)); output_elements[i] = computation_->AddInstruction(HloInstruction::CreateConvert( ShapeUtil::ChangeElementType(*subshape, BF16), gte)); @@ -229,11 +248,11 @@ Status BFloat16NormalizationVisitor::HandleCrossReplicaSum( auto tuple = computation_->AddInstruction( HloInstruction::CreateTuple(output_elements)); - // Use the crs' shape temporarily, in order to pass checks in + // Use the hlo' shape temporarily, in order to pass checks in // ReplaceUseWith. - *tuple->mutable_shape() = crs->shape(); + *tuple->mutable_shape() = hlo->shape(); for (auto* user : materialized_users) { - TF_RETURN_IF_ERROR(crs->ReplaceUseWith(user, tuple)); + TF_RETURN_IF_ERROR(hlo->ReplaceUseWith(user, tuple)); } *tuple->mutable_shape() = original_shape; return Status::OK(); diff --git a/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc b/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc index 830f26422bdc2b3bd789e7d5926bcebac815d34a..f9f1f64998f5b925102dc238941897ff6d441b3f 100644 --- a/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc +++ b/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc @@ -251,7 +251,8 @@ TEST_F(BFloat16NormalizationTest, ResolveMixedPrecisionTupleCrossReplicaSum) { HloInstruction* crs = builder.AddInstruction(HloInstruction::CreateCrossReplicaSum( ShapeUtil::MakeTupleShape({f32_shape, bf16_shape}), {a, b}, reduction, - /*replica_group_ids=*/{}, /*barrier=*/"")); + /*replica_group_ids=*/{}, /*barrier=*/"", + /*all_reduce_id=*/tensorflow::gtl::nullopt)); HloInstruction* gte = builder.AddInstruction( HloInstruction::CreateGetTupleElement(bf16_shape, crs, 1)); @@ -265,6 +266,33 @@ TEST_F(BFloat16NormalizationTest, ResolveMixedPrecisionTupleCrossReplicaSum) { EXPECT_EQ(ShapeUtil::GetSubshape(crs->shape(), {1}).element_type(), F32); } +TEST_F(BFloat16NormalizationTest, ResolveMixedPrecisionTupleSort) { + auto module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + Shape f32_shape = ShapeUtil::MakeShape(F32, {1024}); + Shape bf16_shape = ShapeUtil::MakeShape(BF16, {1024}); + Shape s32_shape = ShapeUtil::MakeShape(BF16, {1024}); + + HloInstruction* key = builder.AddInstruction( + HloInstruction::CreateParameter(0, f32_shape, "key")); + HloInstruction* value = builder.AddInstruction( + HloInstruction::CreateParameter(1, s32_shape, "value")); + + HloInstruction* sort = builder.AddInstruction(HloInstruction::CreateSort( + ShapeUtil::MakeTupleShape({bf16_shape, s32_shape}), 0, key, value)); + HloInstruction* gte = builder.AddInstruction( + HloInstruction::CreateGetTupleElement(bf16_shape, sort, 0)); + + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_TRUE(Normalize(module.get())); + + EXPECT_EQ(computation->root_instruction(), gte); + EXPECT_EQ(gte->shape().element_type(), BF16); + EXPECT_EQ(sort->operand(0)->shape().element_type(), F32); + EXPECT_EQ(ShapeUtil::GetSubshape(sort->shape(), {0}).element_type(), F32); +} + // Tests that the normalization should not cause unsupported mixed precision due // to resolving unsupported BF16 operand. TEST_F(BFloat16NormalizationTest, DoNotAddUnsupportedMixedPrecision) { diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.cc b/tensorflow/compiler/xla/service/bfloat16_propagation.cc index ff6d5027efba813042af65a0e50e172cc0a99ff8..2fb401c4289728f3f59538464c5b8ad49957985b 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation.cc +++ b/tensorflow/compiler/xla/service/bfloat16_propagation.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/bfloat16_propagation.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_dce.h" @@ -215,7 +215,12 @@ bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo, if (ContainsKey(values_that_must_be_kept_as_f32_, value)) { return false; } - if (ValueTypeAfterChange(value) == BF16) { + // We use the original type for the value because we are going to examine + // the uses of it, instead of the value itself. If ValueTypeAfterChange() + // were used, it would cause problems when there are aliasing buffers, i.e., + // ResolveInconsistencyOfAliasingBuffers() would fail to revert the + // tentative change to BF16 even if the uses require F32. + if (value->shape().element_type() == BF16) { continue; } for (const HloUse& use : value->uses()) { @@ -566,6 +571,9 @@ bool BFloat16Propagation::ResolveInconsistencyOfAliasingBuffersHelper( } visited_computations->insert(visited_in_while.begin(), visited_in_while.end()); + } else if (hlo->opcode() == HloOpcode::kFusion) { + ResolveInconsistencyOfAliasingBuffersHelper( + hlo->fused_instructions_computation(), visited_computations); } } // Now adjust parameters of called computations. @@ -615,7 +623,6 @@ Status BFloat16Propagation::ResolveInconsistentFusions(HloModule* module) { // (1) a is F32 but tuple is BF16 // (2) after adding conversion // (3) after tuple simplifier and DCE. - bool needs_tuple_simplifier = false; for (auto computation : module->MakeComputationPostOrder()) { auto insts = computation->MakeInstructionPostOrder(); for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) { @@ -629,67 +636,25 @@ Status BFloat16Propagation::ResolveInconsistentFusions(HloModule* module) { continue; } ShapeTree converted_outputs(hlo->shape()); - // Iterate through nodes in the shape tree in pre-order and initialize - // each non-root node with a corresponding get-tuple-element. For a leaf - // node, if its shape does not match the fusion output, create a - // conversion node to overwrite the node value. - for (auto it = converted_outputs.begin(); it != converted_outputs.end(); - ++it) { - ShapeIndex output_index = it->first; - HloInstruction*& output = it->second; - const Shape subshape = - ShapeUtil::GetSubshape(hlo->shape(), output_index); - if (output_index.empty()) { - output = fusion_root; - } else { - ShapeIndex parent_index = output_index; - parent_index.pop_back(); - output = fusion_computation->AddInstruction( - HloInstruction::CreateGetTupleElement( - subshape, converted_outputs.element(parent_index), - output_index.back())); - } - if (!ShapeUtil::IsArray(subshape)) { - continue; - } - if (!ShapeUtil::Compatible( - subshape, - ShapeUtil::GetSubshape(fusion_root->shape(), output_index))) { - output = fusion_computation->AddInstruction( - HloInstruction::CreateConvert(subshape, output)); - } - } - // Iterate through nodes in the shape tree in reverse pre-order and create - // a tuple instruction for each non-leaf node where the elements are the - // values of its child nodes. - for (auto it = converted_outputs.rbegin(); it != converted_outputs.rend(); - ++it) { - ShapeIndex output_index = it->first; - HloInstruction*& output = it->second; - const Shape& subshape = - ShapeUtil::GetSubshape(hlo->shape(), output_index); - if (!ShapeUtil::IsTuple(subshape)) { - continue; - } - std::vector elements( - ShapeUtil::TupleElementCount(subshape)); - ShapeIndex child_index = output_index; - for (int64 i = 0; i < elements.size(); ++i) { - child_index.push_back(i); - elements[i] = converted_outputs.element(child_index); - child_index.pop_back(); - } - output = fusion_computation->AddInstruction( - HloInstruction::CreateTuple(elements)); - } - fusion_computation->set_root_instruction(converted_outputs.element({})); - needs_tuple_simplifier |= ShapeUtil::IsTuple(hlo->shape()); + // Deep copy the fusion root, and convert a leaf node only if its shape + // does not match the fusion output. + TF_ASSIGN_OR_RETURN( + HloInstruction * copy, + fusion_computation->DeepCopyInstructionWithCustomCopier( + fusion_root, + [hlo](HloInstruction* leaf, const ShapeIndex& leaf_index, + HloComputation* comp) { + const Shape& hlo_subshape = + ShapeUtil::GetSubshape(hlo->shape(), leaf_index); + if (ShapeUtil::Compatible(leaf->shape(), hlo_subshape)) { + return leaf; + } + return comp->AddInstruction( + HloInstruction::CreateConvert(hlo_subshape, leaf)); + })); + fusion_computation->set_root_instruction(copy); } } - if (needs_tuple_simplifier) { - TupleSimplifier tuple_simplifier; - TF_RETURN_IF_ERROR(tuple_simplifier.Run(module).status()); - } return Status::OK(); } @@ -758,10 +723,38 @@ StatusOr BFloat16Propagation::Run(HloModule* module) { changes_to_bf16_.clear(); changed_ = false; + auto computations_topological_order = module->MakeComputationPostOrder(); + + // Before running the propagation pass, we insert copies (kConvert to the same + // type) of F32 inputs to while loops. This prevents other uses of the same + // input from aliasing the while loop input/output, so that there's greater + // chance to use BF16 inside the loop. If some of these added copies do not + // help, they will remain F32 after BF16 propagation and will be removed since + // they are no-ops. + for (auto computation : computations_topological_order) { + for (auto inst : computation->MakeInstructionPostOrder()) { + if (inst->opcode() != HloOpcode::kWhile) { + continue; + } + + auto operand = inst->mutable_operand(0); + TF_ASSIGN_OR_RETURN( + HloInstruction * copy, + computation->DeepCopyInstructionWithCustomCopier( + operand, [](HloInstruction* leaf, const ShapeIndex& leaf_index, + HloComputation* comp) { + if (leaf->shape().element_type() != F32) { + return leaf; + } + return comp->AddInstruction( + HloInstruction::CreateConvert(leaf->shape(), leaf)); + })); + TF_RETURN_IF_ERROR(operand->ReplaceUseWith(inst, copy)); + } + } + TF_ASSIGN_OR_RETURN(dataflow_, HloDataflowAnalysis::Run(*module)); - const auto& computations_topological_order = - module->MakeComputationPostOrder(); // The first step is a forward pass (parameters to root), where we determine // the potential candidate instructions to use bfloat16 in the outputs that // are not likely to cause overhead from extra explicit conversions. This is @@ -784,8 +777,7 @@ StatusOr BFloat16Propagation::Run(HloModule* module) { // propagation in reverse topological order. for (auto comp_it = computations_topological_order.rbegin(); comp_it != computations_topological_order.rend(); ++comp_it) { - if ((*comp_it)->IsFusionComputation()) { - // Fusion computations are handled when visiting the fusion instruction. + if (ContainsKey(computations_visited_in_backward_pass_, *comp_it)) { continue; } auto insts = (*comp_it)->MakeInstructionPostOrder(); @@ -793,6 +785,7 @@ StatusOr BFloat16Propagation::Run(HloModule* module) { DetermineInstructionPrecision(*inst_it, /*skip_parameters=*/true); } + computations_visited_in_backward_pass_.insert(*comp_it); } // It's possible that an instruction does not define a buffer, but the @@ -810,23 +803,27 @@ StatusOr BFloat16Propagation::Run(HloModule* module) { } } + // Removes redundant HLOs added by this pass, either when inserting + // de-aliasing copies to while loop inputs, or later when converting output + // types. + auto clean_up = [this, module]() { + TF_RETURN_IF_ERROR(SkipNoopConversions(module)); + TupleSimplifier tuple_simplifier; + TF_RETURN_IF_ERROR(tuple_simplifier.Run(module).status()); + HloDCE dce; + TF_RETURN_IF_ERROR(dce.Run(module).status()); + return Status::OK(); + }; + if (!changed_) { + TF_RETURN_IF_ERROR(clean_up()); return false; } TF_RETURN_IF_ERROR(ResolveInconsistentFusions(module)); TF_RETURN_IF_ERROR(ResolveConvertedConstants(module)); - // This pass could have turned an F32 -> BF16 conversion to a no-op (BF16 -> - // BF16), so we skip them now. - TF_RETURN_IF_ERROR(SkipNoopConversions(module)); - - { - // We may have dead HLOs after ResolveInconsistentFusions, - // ResolveConvertedConstants and SkipNoopConversions. - HloDCE dce; - TF_RETURN_IF_ERROR(dce.Run(module).status()); - } + TF_RETURN_IF_ERROR(clean_up()); return true; } diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc index 2124b302cccaca7f87dc4f3274233509d6a6161f..69b654d30e42b1ed69304206f09120e86831d468 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc +++ b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc @@ -133,9 +133,9 @@ TEST_F(BFloat16PropagationTest, ConvertConstantLiteral) { array_b.FillUnique(10.0f); HloInstruction* a = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateFromArray(array_a))); + HloInstruction::CreateConstant(LiteralUtil::CreateFromArray(array_a))); HloInstruction* b = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateFromArray(array_b))); + HloInstruction::CreateConstant(LiteralUtil::CreateFromArray(array_b))); HloInstruction* dot = builder.AddInstruction( HloInstruction::CreateBinary(shape, HloOpcode::kDot, a, b)); @@ -150,10 +150,10 @@ TEST_F(BFloat16PropagationTest, ConvertConstantLiteral) { EXPECT_EQ(dot->operand(0)->opcode(), HloOpcode::kConstant); EXPECT_EQ(dot->operand(1)->opcode(), HloOpcode::kConstant); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::ConvertF32ToBF16(*Literal::CreateFromArray(array_a)), + *LiteralUtil::ConvertF32ToBF16(*LiteralUtil::CreateFromArray(array_a)), dot->operand(0)->literal())); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::ConvertF32ToBF16(*Literal::CreateFromArray(array_b)), + *LiteralUtil::ConvertF32ToBF16(*LiteralUtil::CreateFromArray(array_b)), dot->operand(1)->literal())); } @@ -240,12 +240,10 @@ TEST_F(BFloat16PropagationTest, SameValueReferencedTwice) { EXPECT_TRUE(PropagatePrecision(module.get())); EXPECT_EQ(computation->root_instruction(), dot); - EXPECT_TRUE(OutputsBF16(add0)); EXPECT_TRUE(OutputsBF16(add1)); EXPECT_TRUE(OutputsBF16(lhs)); - // rhs is a get-tuple-element, which does not define a buffer, but its shape - // should also be adjusted accordingly. - EXPECT_TRUE(OutputsBF16(rhs)); + + // add0 and rhs have been eliminated by simplification and DCE. } // Tests that a non-fusion computation's root should not be changed. @@ -510,6 +508,63 @@ TEST_F(BFloat16PropagationTest, PropagateThroughSimpleWhile) { EXPECT_FALSE(OutputsBF16(dot)); } +// Tests that if the while condition prevents using BF16, no changes should be +// made to the while body and thus the fusion node inside it. +TEST_F(BFloat16PropagationTest, + ConditionPreventsPropagationForFusionInsideWhile) { + auto module = CreateNewModule(); + auto builder = HloComputation::Builder(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {4, 4}); + + HloInstruction* param0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, shape, "param0")); + HloInstruction* param1 = builder.AddInstruction( + HloInstruction::CreateParameter(1, shape, "param1")); + HloInstruction* add = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + + auto builder_cond = HloComputation::Builder("cond"); + auto cond_param = builder_cond.AddInstruction( + HloInstruction::CreateParameter(0, shape, "cond_param")); + builder_cond.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt, + builder_cond.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond_param, {0, 0}, {1, 1}, {1, 1})), + builder_cond.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond_param, {1, 1}, {2, 2}, {1, 1})))); + auto cond = module->AddEmbeddedComputation(builder_cond.Build()); + + auto builder_body = HloComputation::Builder("body"); + auto body_param = builder_body.AddInstruction( + HloInstruction::CreateParameter(0, shape, "body_param")); + auto body_transpose = builder_body.AddInstruction( + HloInstruction::CreateTranspose(shape, body_param, {0, 1})); + + auto builder_f = HloComputation::Builder("fusion"); + HloInstruction* a_f = + builder_f.AddInstruction(HloInstruction::CreateParameter(0, shape, "a")); + builder_f.AddInstruction(HloInstruction::CreateTranspose(shape, a_f, {0, 1})); + auto comp_f = module->AddEmbeddedComputation(builder_f.Build()); + auto body_fusion = builder_body.AddInstruction(HloInstruction::CreateFusion( + shape, HloInstruction::FusionKind::kCustom, {body_transpose}, comp_f)); + auto body = module->AddEmbeddedComputation(builder_body.Build()); + + auto while_hlo = builder.AddInstruction( + HloInstruction::CreateWhile(shape, cond, body, add)); + + auto dot = builder.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kDot, while_hlo, while_hlo)); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_FALSE(PropagatePrecision(module.get())); + EXPECT_EQ(computation->root_instruction(), dot); + EXPECT_FALSE(OutputsBF16(add)); + EXPECT_FALSE(OutputsBF16(body_fusion)); + EXPECT_FALSE(OutputsBF16(body_param)); + EXPECT_FALSE(OutputsBF16(body_transpose)); + EXPECT_FALSE(OutputsBF16(a_f)); +} + // Tests that BF16 is propagated properly through while computations with // tuple-shaped input/output. TEST_F(BFloat16PropagationTest, PropagateThroughTupleWhile) { @@ -555,10 +610,14 @@ TEST_F(BFloat16PropagationTest, PropagateThroughTupleWhile) { HloInstruction::CreateGetTupleElement(shape, body_param, 0)); auto body_rhs = builder_body.AddInstruction( HloInstruction::CreateGetTupleElement(shape, body_param, 1)); - auto body_dot = builder_body.AddInstruction( + auto body_dot1 = builder_body.AddInstruction( HloInstruction::CreateBinary(shape, HloOpcode::kDot, body_lhs, body_rhs)); + auto body_dot2 = builder_body.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, body_rhs, body_lhs)); + auto body_transpose = builder_body.AddInstruction( + HloInstruction::CreateTranspose(shape, body_dot2, {0, 1})); builder_body.AddInstruction( - HloInstruction::CreateTuple({body_dot, body_rhs})); + HloInstruction::CreateTuple({body_dot1, body_transpose})); auto body = module->AddEmbeddedComputation(builder_body.Build()); auto while_hlo = builder.AddInstruction( @@ -577,9 +636,11 @@ TEST_F(BFloat16PropagationTest, PropagateThroughTupleWhile) { EXPECT_EQ(computation->root_instruction(), dot); EXPECT_TRUE(OutputsBF16(lhs)); EXPECT_FALSE(OutputsBF16(rhs)); - EXPECT_TRUE(OutputsBF16(body_dot)); + EXPECT_TRUE(OutputsBF16(body_dot1)); EXPECT_TRUE(OutputsBF16(body_lhs)); EXPECT_FALSE(OutputsBF16(body_rhs)); + EXPECT_FALSE(OutputsBF16(body_dot2)); + EXPECT_FALSE(OutputsBF16(body_transpose)); EXPECT_TRUE(OutputsBF16(cond_lhs)); EXPECT_FALSE(OutputsBF16(cond_rhs)); EXPECT_TRUE(OutputsBF16(add0)); @@ -734,10 +795,8 @@ TEST_F(BFloat16PropagationTest, NoopConversionRemoved) { EXPECT_TRUE(PropagatePrecision(module.get())); EXPECT_EQ(computation->root_instruction(), add2); - EXPECT_EQ(add2->operand(0), gte0); - EXPECT_EQ(add2->operand(1), gte1); - EXPECT_EQ(gte0->shape().element_type(), BF16); - EXPECT_EQ(gte1->shape().element_type(), BF16); + EXPECT_EQ(add2->operand(0), add0); + EXPECT_EQ(add2->operand(1), add1); EXPECT_EQ(add0->shape().element_type(), BF16); EXPECT_EQ(add1->shape().element_type(), BF16); } diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc index afe4b2e1425f9e84320ffd5f08beceaac8168c22..e4d2e73b994819f748bceb6a9b2f9c1ca7c16308 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment.cc @@ -270,7 +270,7 @@ BufferAllocationProto BufferAllocation::ToProto() const { proto.set_index(index_); proto.set_size(size_); proto.set_is_thread_local(is_thread_local_); - proto.set_is_reusable(is_reusable_); + proto.set_is_tuple(is_tuple_); proto.set_color(color_.value()); if (is_entry_computation_parameter_) { proto.set_is_entry_computation_parameter(true); @@ -279,6 +279,7 @@ BufferAllocationProto BufferAllocation::ToProto() const { } proto.set_parameter_number(parameter_number_); } + proto.set_is_constant(is_constant_); proto.set_maybe_live_out(maybe_live_out_); for (const auto& buffer_offset_size : assigned_buffers_) { BufferAllocationProto::Assigned* proto_assigned = proto.add_assigned(); @@ -304,6 +305,9 @@ string BufferAllocation::ToString() const { StrAppend(&output, ", parameter ", parameter_number(), " at ShapeIndex ", param_shape_index().ToString()); } + if (is_constant()) { + StrAppend(&output, ", constant"); + } if (is_thread_local()) { StrAppend(&output, ", thread-local"); } @@ -491,20 +495,16 @@ BufferAssignment::GetUniqueTopLevelOutputSlice() const { } BufferAllocation* BufferAssignment::NewEmptyAllocation( - int64 size, bool is_thread_local, bool is_reusable, - LogicalBuffer::Color color) { + int64 size, LogicalBuffer::Color color) { BufferAllocation::Index index = allocations_.size(); - allocations_.emplace_back(index, size, is_thread_local, is_reusable, color); + allocations_.emplace_back(index, size, color); BufferAllocation* allocation = &allocations_.back(); return allocation; } BufferAllocation* BufferAssignment::NewAllocation(const LogicalBuffer& buffer, - int64 size, - bool is_thread_local, - bool is_reusable) { - BufferAllocation* allocation = - NewEmptyAllocation(size, is_thread_local, is_reusable, buffer.color()); + int64 size) { + BufferAllocation* allocation = NewEmptyAllocation(size, buffer.color()); AddAssignment(allocation, buffer, /*offset=*/0, size); allocation->peak_buffers_.push_back(&buffer); return allocation; @@ -517,7 +517,8 @@ void BufferAssignment::AddAssignment(BufferAllocation* allocation, CHECK_EQ(0, allocation_index_for_buffer_.count(&buffer)) << "LogicalBuffer " << buffer << " already has an allocation."; CHECK(allocation->is_reusable() || allocation->assigned_buffers().empty()) - << "Non-reusable allocation already assigned a buffer"; + << "Non-reusable allocation already assigned a buffer: " + << allocation->ToString(); TF_CHECK_OK(points_to_analysis().VerifyBuffer(buffer)); @@ -609,6 +610,10 @@ Status BufferAssignment::ComputeSummaryStats() { stats_.parameter_allocation_count++; stats_.parameter_allocation_bytes += allocation.size(); } + if (allocation.is_constant()) { + stats_.constant_allocation_count++; + stats_.constant_allocation_bytes += allocation.size(); + } if (allocation.maybe_live_out()) { stats_.maybe_live_out_allocation_count++; stats_.maybe_live_out_allocation_bytes += allocation.size(); @@ -645,6 +650,8 @@ string BufferAssignment::Stats::ToString() const { Appendf(&s, "BufferAssignment stats:\n"); Appendf(&s, " parameter allocation: %10s\n", HumanReadableNumBytes(parameter_allocation_bytes).c_str()); + Appendf(&s, " constant allocation: %10s\n", + HumanReadableNumBytes(constant_allocation_bytes).c_str()); Appendf(&s, " maybe_live_out allocation: %10s\n", HumanReadableNumBytes(maybe_live_out_allocation_bytes).c_str()); Appendf(&s, " preallocated temp allocation: %10s\n", @@ -722,8 +729,10 @@ StatusOr> BufferAssigner::Run( const HloModule* module, std::unique_ptr hlo_ordering, LogicalBuffer::SizeFunction buffer_size, LogicalBuffer::AlignmentFunction color_alignment, - bool allow_input_output_aliasing, BufferLiveness::Colorer colorer) { - BufferAssigner assigner(allow_input_output_aliasing, std::move(colorer)); + bool allow_input_output_aliasing, bool allocate_buffers_for_constants, + BufferLiveness::Colorer colorer) { + BufferAssigner assigner(allow_input_output_aliasing, + allocate_buffers_for_constants, std::move(colorer)); return assigner.CreateAssignment(module, std::move(hlo_ordering), std::move(buffer_size), std::move(color_alignment)); @@ -751,8 +760,8 @@ bool BufferAssigner::MaybeAssignBuffer(BufferAllocation* allocation, return false; } - if (allocation->is_entry_computation_parameter()) { - VLOG(4) << "Can't assign: allocation holds parameter"; + if (allocation->is_readonly()) { + VLOG(4) << "Can't assign: allocation is readonly"; return false; } @@ -808,8 +817,7 @@ bool BufferAssigner::MaybeAssignBuffer(BufferAllocation* allocation, } Status BufferAssigner::AssignBuffersForComputation( - const HloComputation* computation, const DebugOptions& debug_options, - bool is_thread_local, + const HloComputation* computation, bool is_thread_local, const FlatSet& colocated_buffers, const FlatSet& colocated_allocations, FlatMap>* @@ -905,15 +913,19 @@ Status BufferAssigner::AssignBuffersForComputation( TF_RET_CHECK(!assignment->HasAllocation(*buffer)); const HloInstruction* instruction = buffer->instruction(); + const int64 buffer_size = assignment->buffer_size_(*buffer); + if (instruction->opcode() == HloOpcode::kConstant) { - // No BufferAllocations for constants. - // TODO(b/32248867): For consistency, constants should get allocations. - VLOG(3) << "Skipping constant: " << *buffer; + if (allocate_buffers_for_constants_) { + BufferAllocation* allocation = + assignment->NewAllocation(*buffer, buffer_size); + allocation->set_constant(true); + VLOG(3) << "New allocation #" << allocation->index() << " for constant " + << *buffer; + } continue; } - const int64 buffer_size = assignment->buffer_size_(*buffer); - const bool is_entry_parameter = instruction->opcode() == HloOpcode::kParameter && computation == computation->parent()->entry_computation(); @@ -923,9 +935,7 @@ Status BufferAssigner::AssignBuffersForComputation( // computations do not need special allocations because they live inside // callers. BufferAllocation* allocation = - assignment->NewAllocation(*buffer, buffer_size, - /*is_thread_local=*/false, - /*is_reusable=*/false); + assignment->NewAllocation(*buffer, buffer_size); allocation->set_entry_computation_parameter( instruction->parameter_number(), buffer->index()); VLOG(3) << "New allocation #" << allocation->index() @@ -934,20 +944,18 @@ Status BufferAssigner::AssignBuffersForComputation( } if (is_thread_local) { - // We do not reuse thread-local buffers for now, because they are - // dynamically allocated and their lifetimes are hard to compute. - BufferAllocation* allocation = assignment->NewAllocation( - *buffer, buffer_size, is_thread_local, /*is_reusable=*/false); + BufferAllocation* allocation = + assignment->NewAllocation(*buffer, buffer_size); + allocation->set_is_thread_local(true); VLOG(3) << "New allocation #" << allocation->index() << " for thread-local: " << *buffer; continue; } if (ShapeUtil::IsTuple(buffer->shape())) { - // TODO(b/34669761): Don't reuse tuple buffers because the GPU backend - // assumes longer buffer liveness than indicated by the analysis. - BufferAllocation* allocation = assignment->NewAllocation( - *buffer, buffer_size, is_thread_local, /*is_reusable=*/false); + BufferAllocation* allocation = + assignment->NewAllocation(*buffer, buffer_size); + allocation->set_is_tuple(true); VLOG(3) << "New allocation #" << allocation->index() << " for tuple-shaped buffer: " << *buffer; continue; @@ -1030,8 +1038,8 @@ Status BufferAssigner::AssignBuffersForComputation( } if (!assignment->HasAllocation(*buffer)) { - BufferAllocation* allocation = assignment->NewAllocation( - *buffer, buffer_size, is_thread_local, /*is_reusable=*/true); + BufferAllocation* allocation = + assignment->NewAllocation(*buffer, buffer_size); allocation_indices.push_back(allocation->index()); VLOG(3) << "New allocation #" << allocation->index() << " for: " << *buffer; @@ -1085,6 +1093,7 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering( VLOG(2) << "Simulating heap for color " << color; int64 alignment = assignment->color_alignment_(color); HeapSimulator::Options options; + options.alloc_constants = allocate_buffers_for_constants_; BufferValueFlatSet buffer_value_set = ToBufferValueFlatSet(single_colored_set.second); options.buffers_to_assign = &buffer_value_set; @@ -1227,8 +1236,8 @@ void BufferAssigner::AssignBuffersFromHeapSimulator( result.fragmentation_size; } - BufferAllocation* allocation = assignment->NewEmptyAllocation( - result.heap_size, /*is_thread_local=*/false, /*is_reusable=*/true, color); + BufferAllocation* allocation = + assignment->NewEmptyAllocation(result.heap_size, color); for (const auto& buffer_chunk : result.chunk_map) { // TODO(lauj) Remove this down_cast after downstream users of // BufferAllocation::assigned_buffers() are updated to use BufferValue. @@ -1332,11 +1341,25 @@ BufferAssigner::MergeColocatedBufferSets( auto cannot_merge_buffer_sets = [&colocated_buffer_sets, &buffer_liveness, &buffer_size, &is_entry_parameter](int64 i, int64 j) { - // Do not merge if one of the sets includes live outs or entry parameters. + // Do not merge if one of the sets includes live outs, entry parameters or + // constants. + // + // Buffer liveness does not report the correct live range for entry + // parameter and live out buffers so we have to special case them here. On + // backends that support constant buffer allocations, constant buffers are + // assigned globals in readonly storage so we can't merge colocated buffer + // sets containing constants with colocated buffer sets containing writing + // instructions or other constants. + // + // Moreover (on the CPU/GPU backends) the entry parameter buffers belong to + // the caller of the executable so we can't write to entry parameters + // either, and the argument for not merging constants also applies to entry + // parameters. for (int64 key : {i, j}) { for (auto& buffer : colocated_buffer_sets[key]) { if (buffer_liveness.MaybeLiveOut(*buffer) || - is_entry_parameter(*buffer)) { + is_entry_parameter(*buffer) || + buffer->instruction()->opcode() == HloOpcode::kConstant) { return true; } } @@ -1444,8 +1467,23 @@ void BufferAssigner::BuildColocatedBufferSets( }); } else if (opcode == HloOpcode::kCall) { const HloInstruction* call_hlo = instruction; - const HloInstruction* root_hlo = - call_hlo->to_apply()->root_instruction(); + const HloComputation* callee = call_hlo->to_apply(); + const HloInstruction* root_hlo = callee->root_instruction(); + for (int64 i = 0; i < call_hlo->operand_count(); i++) { + const HloInstruction* call_param = callee->parameter_instruction(i); + const HloInstruction* call_operand = call_hlo->operand(i); + ShapeUtil::ForEachSubshape( + call_operand->shape(), + [&](const Shape& /*subshape*/, const ShapeIndex& index) { + std::vector colocated_set; + AddBufferToColocatedSet(call_param, index, points_to_analysis, + &colocated_set); + AddBufferToColocatedSet(call_operand, index, points_to_analysis, + &colocated_set); + AddSetToColocatedBufferSets(colocated_set, + colocated_buffer_sets); + }); + } ShapeUtil::ForEachSubshape( call_hlo->shape(), [this, call_hlo, root_hlo, &points_to_analysis, @@ -1551,6 +1589,7 @@ void BufferAssigner::AssignColocatedBufferSets( // param in 'colocated_buffer_set'. int64 entry_parameter_number = -1; const ShapeIndex* entry_parameter_shape_idx = nullptr; + bool is_constant = false; for (const LogicalBuffer* buffer : colocated_buffer_set) { const HloInstruction* instruction = buffer->instruction(); const HloComputation* computation = instruction->parent(); @@ -1558,10 +1597,14 @@ void BufferAssigner::AssignColocatedBufferSets( computation == computation->parent()->entry_computation()) { entry_parameter_number = instruction->parameter_number(); entry_parameter_shape_idx = &buffer->index(); - break; + } else if (instruction->opcode() == HloOpcode::kConstant) { + is_constant = true; } } + CHECK(!is_constant || entry_parameter_number == -1) + << "Copy insertion should have inserted copies to prevent this."; + for (const LogicalBuffer* buffer : colocated_buffer_set) { const int64 buffer_size = assignment->buffer_size_(*buffer); if (allocation == nullptr) { @@ -1569,18 +1612,14 @@ void BufferAssigner::AssignColocatedBufferSets( // allocations for each colocated buffer set. When liveness has // module-level scope, we can allow buffers to be shared across // computations (in some cases). - allocation = assignment->NewAllocation(*buffer, buffer_size, - /*is_thread_local=*/false, - /*is_reusable=*/true); + allocation = assignment->NewAllocation(*buffer, buffer_size); if (entry_parameter_number >= 0) { - // This colocated buffer set contains an entry parameter and other - // logical buffers which use the parameter as read-only in a while - // body computation (which updates in place). - // Set 'entry_computation_parameter' to indicate that it contains - // an entry parameter, and to prevent reuse in MaybeAssignBuffer. allocation->set_entry_computation_parameter( entry_parameter_number, *entry_parameter_shape_idx); } + if (is_constant) { + allocation->set_constant(true); + } colocated_allocations->insert(allocation->index()); } else { CHECK_EQ(buffer_size, allocation->size()) @@ -1638,7 +1677,7 @@ StatusOr> BufferAssigner::CreateAssignment( buffers_to_assign_sequentially; for (auto* computation : global_computations) { TF_RETURN_IF_ERROR(AssignBuffersForComputation( - computation, module->config().debug_options(), + computation, /*is_thread_local=*/false, colocated_buffers, colocated_allocations, &buffers_to_assign_sequentially, assignment.get())); } @@ -1659,7 +1698,7 @@ StatusOr> BufferAssigner::CreateAssignment( continue; } TF_RETURN_IF_ERROR(AssignBuffersForComputation( - computation, module->config().debug_options(), + computation, /*is_thread_local=*/true, colocated_buffers, colocated_allocations, /*buffers_to_assign_sequentially=*/nullptr, assignment.get())); } diff --git a/tensorflow/compiler/xla/service/buffer_assignment.h b/tensorflow/compiler/xla/service/buffer_assignment.h index ad0b0bf7c25d7194a06801e4ef1c9ee961f6b915..94495290c131e22392079dc2d0237d990b646d3e 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.h +++ b/tensorflow/compiler/xla/service/buffer_assignment.h @@ -32,7 +32,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" -#include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/lib/gtl/flatset.h" @@ -58,13 +57,8 @@ class BufferAllocation { // contiguously and can be used as array indexes. using Index = int64; - BufferAllocation(Index index, int64 size, bool is_thread_local, - bool is_reusable, LogicalBuffer::Color color) - : index_(index), - size_(size), - is_thread_local_(is_thread_local), - is_reusable_(is_reusable), - color_(color) {} + BufferAllocation(Index index, int64 size, LogicalBuffer::Color color) + : index_(index), size_(size), color_(color) {} ~BufferAllocation() {} // Returns the index of this allocation. @@ -74,9 +68,28 @@ class BufferAllocation { // inside of a map or reduce computation. Such allocations need to be thread // local. bool is_thread_local() const { return is_thread_local_; } + void set_is_thread_local(bool is_thread_local) { + is_thread_local_ = is_thread_local; + } // Whether this allocation can be used by more than one logical buffer. - bool is_reusable() const { return is_reusable_; } + bool is_reusable() const { + // We do not reuse thread-local buffers for now, because they are + // dynamically allocated and their lifetimes are hard to compute. + // + // TODO(b/34669761): Don't reuse tuple buffers because the GPU backend + // assumes longer buffer liveness than indicated by the analysis. + return !is_thread_local() && !is_tuple(); + } + + // Whether this allocation is readonly i.e. backed by memory we cannot write + // to. + bool is_readonly() const { + return is_entry_computation_parameter() || is_constant(); + } + + bool is_tuple() const { return is_tuple_; } + void set_is_tuple(bool is_tuple) { is_tuple_ = is_tuple; } // Whether this allocation holds a LogicalBuffer from a parameter of the entry // computation. These buffers have lifetimes which may be longer than the @@ -84,6 +97,13 @@ class BufferAllocation { bool is_entry_computation_parameter() const { return is_entry_computation_parameter_; } + + // Whether this allocation holds a constant. On the CPU and GPU backends + // constant allocations are not allocated dynamically, instead we resolve + // references to these buffer allocations to a global in the readonly section + // of the binary. + bool is_constant() const { return is_constant_; } + // If this allocation holds a Buffer from a parameter of the entry // computation, this methods returns the parameter number. CHECKs otherwise. int64 parameter_number() const { @@ -189,7 +209,9 @@ class BufferAllocation { // of the computation. !maybe_live_out() && // Thread-local buffers are allocated using `alloca`s. - !is_thread_local(); + !is_thread_local() && + // Constant buffers are allocated as global values. + !is_constant(); } // Add a heap trace which was used to assign slices to logical buffers in this @@ -245,6 +267,8 @@ class BufferAllocation { parameter_number_ = parameter_number; param_shape_index_ = std::move(param_shape_index); } + + void set_constant(bool is_constant) { is_constant_ = is_constant; } void set_maybe_live_out(bool value) { maybe_live_out_ = value; } void set_index(Index index) { index_ = index; } void set_size(int64 size) { size_ = size; } @@ -256,10 +280,10 @@ class BufferAllocation { int64 size_; // Whether this buffer needs to be thread-local. - bool is_thread_local_; + bool is_thread_local_ = false; - // Whether this buffer is usable by more than one logical buffer. - bool is_reusable_; + // Whether this buffer holds a tuple. + bool is_tuple_ = false; // Color of the allocation. LogicalBuffer::Color color_; @@ -283,6 +307,9 @@ class BufferAllocation { // might not actually escape. bool maybe_live_out_ = false; + // See comment on the is_constant() accessor. + bool is_constant_ = false; + // Mapping from the set of buffers assigned to this allocation to their // logical offsets and sizes. tensorflow::gtl::FlatMap assigned_buffers_; @@ -398,6 +425,8 @@ class BufferAssignment { struct Stats { int64 parameter_allocation_count = 0; int64 parameter_allocation_bytes = 0; + int64 constant_allocation_count = 0; + int64 constant_allocation_bytes = 0; int64 maybe_live_out_allocation_count = 0; int64 maybe_live_out_allocation_bytes = 0; int64 preallocated_temp_allocation_count = 0; @@ -426,14 +455,11 @@ class BufferAssignment { // Creates and returns a new BufferAllocation, with no assigned // LogicalBuffers. Ownership is maintained internally. - BufferAllocation* NewEmptyAllocation(int64 size, bool is_thread_local, - bool is_reusable, - LogicalBuffer::Color color); + BufferAllocation* NewEmptyAllocation(int64 size, LogicalBuffer::Color color); // Helper that calls NewEmptyAllocation and AddAssignment in one call, // creating an allocation containing a single LogicalBuffer. - BufferAllocation* NewAllocation(const LogicalBuffer& buffer, int64 size, - bool is_thread_local, bool is_reusable); + BufferAllocation* NewAllocation(const LogicalBuffer& buffer, int64 size); // Adds a LogicalBuffer to the set assigned to the given allocation. void AddAssignment(BufferAllocation* allocation, const LogicalBuffer& buffer, @@ -493,12 +519,15 @@ class BufferAssigner { LogicalBuffer::SizeFunction buffer_size, LogicalBuffer::AlignmentFunction color_alignment, bool allow_input_output_aliasing = false, + bool allocate_buffers_for_constants = false, BufferLiveness::Colorer colorer = BufferLiveness::DefaultColorer()); private: BufferAssigner(bool allow_input_output_aliasing, + bool allocate_buffers_for_constants, BufferLiveness::Colorer colorer) : allow_input_output_aliasing_(allow_input_output_aliasing), + allocate_buffers_for_constants_(allocate_buffers_for_constants), colorer_(colorer) {} virtual ~BufferAssigner() = default; @@ -513,8 +542,7 @@ class BufferAssigner { // true, then all assigned buffers have the is_thread_local flag set to // true. Status AssignBuffersForComputation( - const HloComputation* computation, const DebugOptions& debug_options, - bool is_thread_local, + const HloComputation* computation, bool is_thread_local, const tensorflow::gtl::FlatSet& colocated_buffers, const tensorflow::gtl::FlatSet& colocated_allocations, @@ -595,6 +623,9 @@ class BufferAssigner { // buffers can be shared if their sizes match. bool allow_input_output_aliasing_; + // If true, allocate buffers for constant instructions. + bool allocate_buffers_for_constants_; + // Functor used to assign colors to newly allocated logical buffers. BufferLiveness::Colorer colorer_; diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc index 6958ee722a8189b8089ba2d8f53aca8174f6a593..eccb146a0d7d628870be179a540d9750df3fe41c 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc @@ -21,7 +21,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/buffer_value.h" #include "tensorflow/compiler/xla/service/call_graph.h" @@ -89,7 +89,20 @@ class BufferAssignmentTest : public HloTestBase { return BufferAssigner::Run( module, xla::MakeUnique(module), backend().compiler()->BufferSizeBytesFunction(), - [alignment](LogicalBuffer::Color) { return alignment; }) + [alignment](LogicalBuffer::Color) { return alignment; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true) + .ConsumeValueOrDie(); + } + + std::unique_ptr RunBufferAssignmentNoBuffersForConstants( + HloModule* module, int64 alignment = 1) { + return BufferAssigner::Run( + module, xla::MakeUnique(module), + backend().compiler()->BufferSizeBytesFunction(), + [alignment](LogicalBuffer::Color) { return alignment; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/false) .ConsumeValueOrDie(); } @@ -98,8 +111,9 @@ class BufferAssignmentTest : public HloTestBase { return BufferAssigner::Run( module, xla::MakeUnique(module), backend().compiler()->BufferSizeBytesFunction(), - [alignment](LogicalBuffer::Color) { return alignment; }, false, - std::move(colorer)) + [alignment](LogicalBuffer::Color) { return alignment; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true, std::move(colorer)) .ConsumeValueOrDie(); } @@ -115,7 +129,9 @@ class BufferAssignmentTest : public HloTestBase { module, xla::MakeUnique(module, module_sequence), backend().compiler()->BufferSizeBytesFunction(), - [alignment](LogicalBuffer::Color) { return alignment; }) + [alignment](LogicalBuffer::Color) { return alignment; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true) .ConsumeValueOrDie(); } @@ -125,7 +141,7 @@ class BufferAssignmentTest : public HloTestBase { auto param = builder.AddInstruction(HloInstruction::CreateParameter(0, r0f32_, "x")); auto value = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); builder.AddInstruction( HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, param, value)); return builder.Build(); @@ -142,7 +158,7 @@ class BufferAssignmentTest : public HloTestBase { const string& name) { auto builder = HloComputation::Builder(name); auto const4 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(4))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(4))); auto param = builder.AddInstruction( HloInstruction::CreateParameter(0, t_s32_f32v4_, "x")); auto index = builder.AddInstruction( @@ -167,9 +183,9 @@ class BufferAssignmentTest : public HloTestBase { const string& name) { auto builder = HloComputation::Builder(name); auto const1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); auto constv = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.1f, 2.2f, 3.3f, 4.4f}))); + LiteralUtil::CreateR1({1.1f, 2.2f, 3.3f, 4.4f}))); auto param = builder.AddInstruction( HloInstruction::CreateParameter(0, t_s32_f32v4_, "x")); auto indexc = builder.AddInstruction( @@ -290,13 +306,19 @@ static bool BuffersDistinct(const std::vector& a, TEST_F(BufferAssignmentTest, ScalarConstant) { auto builder = HloComputation::Builder(TestName()); auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto module = CreateNewModule(); module->AddEntryComputation(builder.Build()); - auto buffers = RunBufferAssignment(module.get()); - // Check that the constant does not have a buffer assigned. - EXPECT_FALSE(buffers->HasTopLevelAllocation(const0)); + { + auto buffers = RunBufferAssignment(module.get()); + EXPECT_TRUE(buffers->HasTopLevelAllocation(const0)); + } + + { + auto buffers = RunBufferAssignmentNoBuffersForConstants(module.get()); + EXPECT_FALSE(buffers->HasTopLevelAllocation(const0)); + } } TEST_F(BufferAssignmentTest, BufferForConst) { @@ -304,20 +326,26 @@ TEST_F(BufferAssignmentTest, BufferForConst) { // no buffers assigned, and their consumer has a buffer. auto builder = HloComputation::Builder(TestName()); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.1f, 2.2f, 3.3f, 4.4f}))); + LiteralUtil::CreateR1({1.1f, 2.2f, 3.3f, 4.4f}))); auto const1 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({4.1f, 4.2f, 4.3f, 4.4f}))); + LiteralUtil::CreateR1({4.1f, 4.2f, 4.3f, 4.4f}))); auto add = builder.AddInstruction( HloInstruction::CreateBinary(f32vec4_, HloOpcode::kAdd, const0, const1)); auto module = CreateNewModule(); module->AddEntryComputation(builder.Build()); - auto buffers = RunBufferAssignment(module.get()); - // The two constant nodes have no buffers assigned. - EXPECT_FALSE(buffers->HasTopLevelAllocation(const0)); - EXPECT_FALSE(buffers->HasTopLevelAllocation(const1)); - // The add node has an output buffer. - GetAssignedOutputAllocation(*buffers, add); + { + auto buffers = RunBufferAssignment(module.get()); + EXPECT_TRUE(buffers->HasTopLevelAllocation(const0)); + EXPECT_TRUE(buffers->HasTopLevelAllocation(const1)); + GetAssignedOutputAllocation(*buffers, add); + } + { + auto buffers = RunBufferAssignmentNoBuffersForConstants(module.get()); + EXPECT_FALSE(buffers->HasTopLevelAllocation(const0)); + EXPECT_FALSE(buffers->HasTopLevelAllocation(const1)); + GetAssignedOutputAllocation(*buffers, add); + } } TEST_F(BufferAssignmentTest, HasAllocationAt) { @@ -327,7 +355,7 @@ TEST_F(BufferAssignmentTest, HasAllocationAt) { auto param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, f32vec100_, "param0")); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); auto negate = builder.AddInstruction( HloInstruction::CreateUnary(f32vec100_, HloOpcode::kNegate, param0)); auto tuple = builder.AddInstruction( @@ -352,7 +380,7 @@ TEST_F(BufferAssignmentTest, BufferForOutputConst) { // This computation copies a constant to output. auto builder = HloComputation::Builder(TestName()); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.1f, 2.2f, 3.3f, 4.4f}))); + LiteralUtil::CreateR1({1.1f, 2.2f, 3.3f, 4.4f}))); auto copy = builder.AddInstruction( HloInstruction::CreateUnary(const0->shape(), HloOpcode::kCopy, const0)); auto module = CreateNewModule(); @@ -660,7 +688,7 @@ TEST_F(BufferAssignmentTest, CannotReuseInputBufferOfReduce) { auto exp2 = builder.AddInstruction( HloInstruction::CreateUnary(f32a100x10_, HloOpcode::kExp, exp1)); auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); auto reduce = builder.AddInstruction(HloInstruction::CreateReduce( /*shape=*/f32vec10_, /*operand=*/exp2, @@ -708,9 +736,9 @@ TEST_F(BufferAssignmentTest, ExampleWhile) { // Creates the main kernel and verifies instruction counts. auto builder = HloComputation::Builder(TestName()); auto const3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))); auto const4 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.1f, 2.2f, 3.3f, 4.4f}))); + LiteralUtil::CreateR1({1.1f, 2.2f, 3.3f, 4.4f}))); auto tuple = builder.AddInstruction(HloInstruction::CreateTuple({const3, const4})); auto while_op = builder.AddInstruction(HloInstruction::CreateWhile( @@ -773,11 +801,11 @@ TEST_F(BufferAssignmentTest, ExampleConditional) { auto builder = HloComputation::Builder(TestName()); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); auto const1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(56.4f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(56.4f))); auto const2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(12.4f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(12.4f))); auto conditional = builder.AddInstruction(HloInstruction::CreateConditional( r0f32_, pred, const1, true_computation, const2, false_computation)); module->AddEntryComputation(builder.Build()); @@ -1094,7 +1122,7 @@ TEST_F(BufferAssignmentTest, EmbeddedComputationBuffers) { // Allocations for the call computation should not be thread-local. auto& call_param_alloc = GetTopLevelAllocation(*assignment, call_param); - EXPECT_FALSE(call_param_alloc.is_entry_computation_parameter()); + EXPECT_TRUE(call_param_alloc.is_entry_computation_parameter()); EXPECT_FALSE(call_param_alloc.maybe_live_out()); EXPECT_FALSE(call_param_alloc.is_thread_local()); @@ -1196,12 +1224,13 @@ TEST_F(BufferAssignmentTest, ElementOfNestedTupleParameterAsOutput) { // TODO(b/32248867): Enable when buffer assignment gives allocations to // constants. -TEST_F(BufferAssignmentTest, DISABLED_TupleConstantAsOutput) { +TEST_F(BufferAssignmentTest, TupleConstantAsOutput) { // Test that a tuple constant which is forwarded to the computation output // is properly handled. auto builder = HloComputation::Builder(TestName()); - builder.AddInstruction(HloInstruction::CreateConstant(Literal::MakeTuple( - {Literal::CreateR0(0).get(), Literal::CreateR0(1).get()}))); + builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::MakeTuple({LiteralUtil::CreateR0(0).get(), + LiteralUtil::CreateR0(1).get()}))); auto module = CreateNewModule(); module->AddEntryComputation(builder.Build()); @@ -1252,16 +1281,18 @@ TEST_F(BufferAssignmentTest, TupleCallAsOutput) { auto assignment = RunBufferAssignment(module.get()); - EXPECT_EQ(3, assignment->Allocations().size()); + EXPECT_EQ(2, assignment->Allocations().size()); // Buffers for call are colocated with the sub-computation. EXPECT_EQ(GetAllocation(*assignment, call, /*index=*/{}), GetAllocation(*assignment, sub_tuple, /*index=*/{})); EXPECT_EQ(GetAllocation(*assignment, call, /*index=*/{0}), GetAllocation(*assignment, sub_param, /*index=*/{})); - // The parameter isn't aliased with anything. + + // The parameter isn't aliased with the result tuple, but it is aliased with + // the call operand. EXPECT_NE(GetTopLevelAllocation(*assignment, param), GetTopLevelAllocation(*assignment, sub_tuple)); - EXPECT_NE(GetTopLevelAllocation(*assignment, param), + EXPECT_EQ(GetTopLevelAllocation(*assignment, param), GetTopLevelAllocation(*assignment, sub_param)); } @@ -1325,13 +1356,15 @@ TEST_F(BufferAssignmentTest, TupleChainedCallAsOutput) { GetAllocation(*assignment, c_call, /*index=*/{0})); EXPECT_EQ(GetAllocation(*assignment, c_call, /*index=*/{0}), GetAllocation(*assignment, d_param, /*index=*/{0})); - // The parameters aren't aliased with anything. + EXPECT_TRUE(BuffersDistinct({a_param}, {b_param}, *assignment)); EXPECT_TRUE(BuffersDistinct({a_param}, {c_param}, *assignment)); EXPECT_TRUE(BuffersDistinct({a_param}, {d_param}, *assignment)); - EXPECT_TRUE(BuffersDistinct({b_param}, {c_param}, *assignment)); - EXPECT_TRUE(BuffersDistinct({b_param}, {d_param}, *assignment)); - EXPECT_TRUE(BuffersDistinct({c_param}, {d_param}, *assignment)); + + EXPECT_EQ(GetAllocation(*assignment, b_param, /*index=*/{0}), + GetAllocation(*assignment, c_param, /*index=*/{0})); + EXPECT_EQ(GetAllocation(*assignment, c_param, /*index=*/{0}), + GetAllocation(*assignment, d_param, /*index=*/{0})); } TEST_F(BufferAssignmentTest, BitcastAsOutput) { @@ -1584,7 +1617,7 @@ TEST_F(BufferAssignmentTest, PeakBuffersWhile) { auto b = HloComputation::Builder(TestName() + ".cond"); b.AddInstruction(HloInstruction::CreateParameter(0, shape, "x")); b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); condition = module->AddEmbeddedComputation(b.Build()); } HloComputation* body; @@ -1639,6 +1672,66 @@ TEST_F(BufferAssignmentTest, PeakBuffersWhile) { nonbcast_buffer->instruction() == condition->parameter_instruction(0)); } +TEST_F(BufferAssignmentTest, ConstantBuffersAreNotReused) { + const char* hlo_text = R"( +HloModule Module + +True { + ROOT x.0.1 = f32[] parameter(0) +} + +False { + x.0.0 = f32[] parameter(0) + ROOT copy.1 = f32[] copy(x.0.0) +} + +ENTRY main { + pred.1.0 = pred[] parameter(0) + constant.1.1 = f32[] constant(56) + copy.2 = f32[] copy(constant.1.1) + constant.1.2 = f32[] constant(12) + ROOT conditional.1.3 = f32[] conditional(pred.1.0, copy.2, constant.1.2), + true_computation=True, false_computation=False +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseHloString(hlo_text)); + + HloInstruction* constant_1 = + module->entry_computation()->GetInstructionWithName("constant.1.1"); + HloInstruction* constant_2 = + module->entry_computation()->GetInstructionWithName("constant.1.2"); + + auto buffers = RunBufferAssignment(module.get()); + + { + const BufferAllocation& allocation_for_const_1 = + GetTopLevelAllocation(*buffers, constant_1); + EXPECT_TRUE(allocation_for_const_1.is_constant()); + for (const auto& buffer_offset_pair : + allocation_for_const_1.assigned_buffers()) { + EXPECT_NE(buffer_offset_pair.first->instruction()->opcode(), + HloOpcode::kCopy); + EXPECT_NE(buffer_offset_pair.first->instruction()->opcode(), + HloOpcode::kConditional); + } + } + + { + const BufferAllocation& allocation_for_const_2 = + GetTopLevelAllocation(*buffers, constant_2); + EXPECT_TRUE(allocation_for_const_2.is_constant()); + for (const auto& buffer_offset_pair : + allocation_for_const_2.assigned_buffers()) { + EXPECT_NE(buffer_offset_pair.first->instruction()->opcode(), + HloOpcode::kCopy); + EXPECT_NE(buffer_offset_pair.first->instruction()->opcode(), + HloOpcode::kConditional); + } + } +} + class WhileBufferAssignmentTest : public HloTestBase { protected: std::unique_ptr BuildWhileConditionComputation( @@ -1647,9 +1740,9 @@ class WhileBufferAssignmentTest : public HloTestBase { builder.AddInstruction( HloInstruction::CreateParameter(0, loop_state_shape_, "loop_state")); auto zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))); auto ten = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(10))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(10))); builder.AddInstruction(HloInstruction::CreateBinary( ShapeUtil::MakeShape(PRED, {}), HloOpcode::kLt, zero, ten)); return builder.Build(); @@ -1678,7 +1771,9 @@ class WhileBufferAssignmentTest : public HloTestBase { return BufferAssigner::Run( module, xla::MakeUnique(module, sequence), ByteSizeOf, - [alignment](LogicalBuffer::Color) { return alignment; }) + [alignment](LogicalBuffer::Color) { return alignment; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true) .ConsumeValueOrDie(); } @@ -1708,7 +1803,7 @@ TEST_F(WhileBufferAssignmentTest, TwoForwardWhileLoops) { HloInstruction::CreateParameter(2, data_shape_, "weights1")); auto zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0))); auto output0 = builder.AddInstruction( HloInstruction::CreateBroadcast(data_shape_, zero, {1})); auto output1 = builder.AddInstruction( @@ -1828,6 +1923,74 @@ ENTRY %test_module { EXPECT_NE(slice_param, slice_while1); } +TEST_F(WhileBufferAssignmentTest, ColocatedBufferWithConstant) { + const Shape r0s32 = ShapeUtil::MakeShape(S32, {}); + + const char* module_str = R"( +HloModule test_module + +%cond.v0 { + %param = s32[] parameter(0) + ROOT %constant = pred[] constant(true) +} + +%cond.v1 { + %param.0 = s32[] parameter(0) + ROOT %constant.0 = pred[] constant(true) +} + +%body.v0 { + ROOT %param.1 = s32[] parameter(0) +} + +%body.v1 { + %param.2 = s32[] parameter(0) + ROOT add = s32[] add(%param.2, %param.2) +} + +ENTRY %test_module { + %constant.42 = s32[] constant(42) + %while.0 = s32[] while(%constant.42), condition=%cond.v0, body=%body.v0 + %mul = s32[] multiply(%while.0, %while.0) + %while.1 = s32[] while(%mul), condition=%cond.v1, body=%body.v1 + ROOT %bcast = s32[1024,1024]{1,0} broadcast(s32[] %while.1), dimensions={} +})"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseHloString(module_str)); + + // Run CopyInsertion and check if the graph constructed above doesn't need + // any copies inserted for BufferAssignment to run. + int64 instruction_count = module->instruction_count(); + CopyInsertion copy_insertion; + ASSERT_IS_OK(copy_insertion.Run(module.get()).status()); + ASSERT_EQ(instruction_count, module->instruction_count()); + + // Get the instructions in the module. + const HloInstruction* bcast = module->entry_computation()->root_instruction(); + const HloInstruction* constant = + module->entry_computation()->GetInstructionWithName("constant.42"); + ASSERT_EQ(bcast->opcode(), HloOpcode::kBroadcast); + const HloInstruction* while1 = bcast->operand(0); + ASSERT_EQ(while1->opcode(), HloOpcode::kWhile); + const HloInstruction* while0 = while1->operand(0)->operand(0); + ASSERT_EQ(while0->opcode(), HloOpcode::kWhile); + + // Run buffer assignment. + auto assignment = RunBufferAssignment(module.get()); + TF_ASSERT_OK_AND_ASSIGN(auto slice_constant, + assignment->GetUniqueSlice(constant, {})); + TF_ASSERT_OK_AND_ASSIGN(auto slice_while0, + assignment->GetUniqueSlice(while0, {})); + TF_ASSERT_OK_AND_ASSIGN(auto slice_while1, + assignment->GetUniqueSlice(while1, {})); + + // The constant slice is part of the while0's colocation set (init value), but + // not merged into the while1's colocation set. + EXPECT_EQ(slice_constant, slice_while0); + EXPECT_NE(slice_constant, slice_while1); +} + // Tests that the colocated buffers for while instructions are properly assigned // during buffer assignment such that the result tuple elements are not assigned // to the same buffer. @@ -1851,7 +2014,7 @@ TEST_F(WhileBufferAssignmentTest, ColocatedBuffers) { auto build_cond = [&]() { auto builder = HloComputation::Builder("cond"); auto const4 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(4))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(4))); auto param = builder.AddInstruction(HloInstruction::CreateParameter(0, r0s32, "x")); builder.AddInstruction(HloInstruction::CreateBinary( @@ -1863,7 +2026,7 @@ TEST_F(WhileBufferAssignmentTest, ColocatedBuffers) { auto build_body = [&]() { auto builder = HloComputation::Builder("body"); auto const9 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(9))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(9))); auto param = builder.AddInstruction(HloInstruction::CreateParameter(0, r0s32, "x")); builder.AddInstruction( @@ -1875,7 +2038,7 @@ TEST_F(WhileBufferAssignmentTest, ColocatedBuffers) { auto module = CreateNewModule(); auto builder = HloComputation::Builder("entry"); - auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = builder.AddInstruction(HloInstruction::CreateToken()); auto infeed = builder.AddInstruction(HloInstruction::CreateInfeed(r0s32, token, "")); auto infeed_data = builder.AddInstruction( @@ -1891,7 +2054,7 @@ TEST_F(WhileBufferAssignmentTest, ColocatedBuffers) { HloInstruction::CreateWhile(r0s32, cond1, body1, while0)); auto zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))); auto add = builder.AddInstruction( HloInstruction::CreateBinary(r0s32, HloOpcode::kAdd, zero, zero)); auto cond2 = module->AddEmbeddedComputation(build_cond()); @@ -1922,7 +2085,9 @@ TEST_F(WhileBufferAssignmentTest, ColocatedBuffers) { module.get(), xla::MakeUnique(module.get(), sequence), backend().compiler()->BufferSizeBytesFunction(), - [](LogicalBuffer::Color) { return 1; })); + [](LogicalBuffer::Color) { return 1; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true)); // The result tuple elements must be assigned with different buffers. TF_ASSERT_OK_AND_ASSIGN(auto slice0, assignment->GetUniqueSlice(tuple, {0})); @@ -1953,7 +2118,7 @@ TEST_F(WhileBufferAssignmentTest, OneForwardBackwardWhileLoopSet) { HloInstruction::CreateParameter(1, data_shape_, "weights0")); auto zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0))); auto output0 = builder.AddInstruction( HloInstruction::CreateBroadcast(data_shape_, zero, {1})); @@ -1997,16 +2162,16 @@ TEST_F(BufferAssignmentTest, TwoCalls) { auto param = builder.AddInstruction( HloInstruction::CreateParameter(0, r0f32, "param")); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto add = builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, param, constant1)); sub_computation = module->AddEmbeddedComputation(builder.Build(add)); } auto builder = HloComputation::Builder(TestName()); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto constant3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(3.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(3.0))); auto call1 = builder.AddInstruction( HloInstruction::CreateCall(r0f32, {constant2}, sub_computation)); auto call2 = builder.AddInstruction( @@ -2030,6 +2195,56 @@ TEST_F(BufferAssignmentTest, TwoCalls) { EXPECT_TRUE(BuffersDistinct({call1}, {call2}, *assignment)); } +TEST_F(BufferAssignmentTest, CallParamCoAllocation) { + const char* hlo_text = R"( +HloModule CallParamCoAllocation + +Callee { + param0 = (f32[100],(f32[200],f32[300])) parameter(0) + param1 = s32[20] parameter(1) + ROOT constant = f32[] constant(1) +} + +ENTRY Main { + entry_param0 = f32[100] parameter(0) + entry_param1 = s32[20] parameter(1) + custom_call = (f32[200],f32[300]) custom-call(), custom_call_target="call-target" + call_op0 = (f32[100],(f32[200],f32[300])) tuple(entry_param0, custom_call) + ROOT call_result = f32[] call(call_op0, entry_param1), to_apply=Callee +} +)"; + + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr module, + HloRunner::CreateModuleFromString( + hlo_text, legacy_flags::GetDebugOptionsFromFlags())); + + auto buffers = RunBufferAssignment(module.get()); + + HloComputation* main = module->entry_computation(); + HloComputation* callee = module->GetComputationWithName("Callee"); + EXPECT_NE(callee, nullptr); + + HloInstruction* param0 = callee->parameter_instruction(0); + HloInstruction* param1 = callee->parameter_instruction(1); + + HloInstruction* entry_param0 = main->parameter_instruction(0); + HloInstruction* entry_param1 = main->parameter_instruction(1); + HloInstruction* custom_call = main->GetInstructionWithName("custom_call"); + + EXPECT_EQ(GetAllocation(*buffers, entry_param0, {}), + GetAllocation(*buffers, param0, {0})); + EXPECT_EQ(GetAllocation(*buffers, entry_param1, {}), + GetAllocation(*buffers, param1, {})); + + EXPECT_EQ(GetAllocation(*buffers, custom_call, {}), + GetAllocation(*buffers, param0, {1})); + EXPECT_EQ(GetAllocation(*buffers, custom_call, {0}), + GetAllocation(*buffers, param0, {1, 0})); + EXPECT_EQ(GetAllocation(*buffers, custom_call, {1}), + GetAllocation(*buffers, param0, {1, 1})); +} + static bool IsPostOrderTraversal( const std::vector& sequence) { tensorflow::gtl::FlatSet seen_so_far; @@ -2058,9 +2273,9 @@ TEST_F(WhileBufferAssignmentTest, WhileLoopsInterferingResultRange) { auto builder = HloComputation::Builder(TestName()); auto zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0))); auto one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto input0 = builder.AddInstruction( HloInstruction::CreateParameter(0, data_shape_, "input0")); @@ -2126,7 +2341,9 @@ TEST_F(WhileBufferAssignmentTest, WhileLoopsInterferingResultRange) { BufferAssigner::Run( module.get(), xla::MakeUnique(module.get(), sequence), - ByteSizeOf, [](LogicalBuffer::Color) { return 1; }) + ByteSizeOf, [](LogicalBuffer::Color) { return 1; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true) .ConsumeValueOrDie(); EXPECT_TRUE(BuffersDistinct({while0}, {while1}, *assignment)); @@ -2142,7 +2359,7 @@ TEST_F(WhileBufferAssignmentTest, WhilesDontShareEntryParamIfLiveOut) { HloInstruction::CreateParameter(1, data_shape_, "weights0")); auto zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0))); auto output0 = builder.AddInstruction( HloInstruction::CreateBroadcast(data_shape_, zero, {1})); auto output1 = builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/buffer_liveness_test.cc b/tensorflow/compiler/xla/service/buffer_liveness_test.cc index 7833ebe73ba5d2412101eede1b584ce86df084e8..4a927b57674345f8b3493c098778182a299c5902 100644 --- a/tensorflow/compiler/xla/service/buffer_liveness_test.cc +++ b/tensorflow/compiler/xla/service/buffer_liveness_test.cc @@ -327,7 +327,7 @@ TEST_F(BufferLivenessTest, RootInstructionIsNotLastInSequentialOrder) { builder.AddInstruction(HloInstruction::CreateParameter(0, vec_, "param")); auto add = builder.AddInstruction( HloInstruction::CreateBinary(vec_, HloOpcode::kAdd, param, param)); - auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = builder.AddInstruction(HloInstruction::CreateToken()); auto recv = builder.AddInstruction( HloInstruction::CreateRecv(vec_, token, /*channel_id=*/0)); auto recv_done = builder.AddInstruction(HloInstruction::CreateRecvDone(recv)); @@ -439,11 +439,13 @@ TEST_F(BufferLivenessTest, TupleConstantLiveOut) { // computation. The buffer containing {0, 1} is copied by GetTupleElement, and // the buffers containing {3} and 3 are dead. auto builder = HloComputation::Builder(TestName()); - auto inner_tuple0 = Literal::MakeTuple( - {Literal::CreateR0(0).get(), Literal::CreateR0(1).get()}); - auto inner_tuple1 = Literal::MakeTuple({Literal::CreateR0(3).get()}); + auto inner_tuple0 = + LiteralUtil::MakeTuple({LiteralUtil::CreateR0(0).get(), + LiteralUtil::CreateR0(1).get()}); + auto inner_tuple1 = + LiteralUtil::MakeTuple({LiteralUtil::CreateR0(3).get()}); auto tuple_constant = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::MakeTuple({inner_tuple0.get(), inner_tuple1.get()}))); + LiteralUtil::MakeTuple({inner_tuple0.get(), inner_tuple1.get()}))); builder.AddInstruction(HloInstruction::CreateGetTupleElement( inner_tuple0->shape(), tuple_constant, 0)); @@ -491,7 +493,7 @@ TEST_F(BufferLivenessTest, IndependentTupleElements) { builder.AddInstruction(HloInstruction::CreateGetTupleElement( tuple_element0_shape, tuple_param0, 0)); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}))); + LiteralUtil::CreateR1({1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}))); auto add0 = builder.AddInstruction(HloInstruction::CreateBinary( tuple_element0_shape, HloOpcode::kAdd, tuple_element0, const0)); @@ -503,7 +505,7 @@ TEST_F(BufferLivenessTest, IndependentTupleElements) { builder.AddInstruction(HloInstruction::CreateGetTupleElement( tuple_element1_shape, tuple_param0, 1)); auto const1 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({2.f, 2.f, 2.f, 2.f, 2.f, 2.f, 2.f, 2.f}))); + LiteralUtil::CreateR1({2.f, 2.f, 2.f, 2.f, 2.f, 2.f, 2.f, 2.f}))); auto add1 = builder.AddInstruction(HloInstruction::CreateBinary( tuple_element1_shape, HloOpcode::kAdd, tuple_element1, const1)); @@ -555,7 +557,7 @@ TEST_F(BufferLivenessTest, DependentTupleElements) { builder.AddInstruction(HloInstruction::CreateGetTupleElement( tuple_element0_shape, tuple_param0, 0)); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}))); + LiteralUtil::CreateR1({1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}))); auto add0 = builder.AddInstruction(HloInstruction::CreateBinary( tuple_element0_shape, HloOpcode::kAdd, tuple_element0, const0)); @@ -627,7 +629,7 @@ class FusedDynamicUpdateSliceLivenessTest : public BufferLivenessTest { HloInstruction::CreateGetTupleElement(data_shape, tuple_param0, 1)); auto update = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({2.f, 2.f, 2.f}))); + LiteralUtil::CreateR1({2.f, 2.f, 2.f}))); HloInstruction* slice = nullptr; if (update_uses_tuple_element1) { // Create a slice instruction as an additional user of 'gte1'. @@ -638,7 +640,7 @@ class FusedDynamicUpdateSliceLivenessTest : public BufferLivenessTest { } // Create a DynamicUpdateSlice instruction of tuple element 1 with 'update'. auto starts = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({2}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({2}))); auto dynamic_update_slice = builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice( data_shape, gte1, update, starts)); @@ -757,7 +759,7 @@ class DynamicUpdateSliceLivenessTest : public BufferLivenessTest { HloInstruction::CreateGetTupleElement(data_shape, tuple_param0, 1)); auto update = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({2.f, 2.f, 2.f}))); + LiteralUtil::CreateR1({2.f, 2.f, 2.f}))); if (tuple_element1_has_two_uses) { // Add 'gte0' and 'gte1' to create another user of 'gte1'. @@ -766,7 +768,7 @@ class DynamicUpdateSliceLivenessTest : public BufferLivenessTest { } // Create a DynamicUpdateSlice instruction of tuple element 1 with 'update'. auto starts = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({2}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({2}))); auto dynamic_update_slice = builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice( data_shape, gte1, update, starts)); diff --git a/tensorflow/compiler/xla/service/call_graph_test.cc b/tensorflow/compiler/xla/service/call_graph_test.cc index 1ea7d538cd515c3098b6a1f03c6146d288330406..cc80b7484313329104eec1ce71a150b47d8330c9 100644 --- a/tensorflow/compiler/xla/service/call_graph_test.cc +++ b/tensorflow/compiler/xla/service/call_graph_test.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/call_graph.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -82,7 +82,7 @@ class CallGraphTest : public HloTestBase { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, kScalarShape, "param0")); HloInstruction* zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); builder.AddInstruction(HloInstruction::CreateBinary( ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt, param0, zero)); return builder.Build(); @@ -247,11 +247,11 @@ TEST_F(CallGraphTest, ComputationWithConditional) { HloComputation::Builder builder(TestName()); HloInstruction* pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloInstruction* const1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(56.4f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(56.4f))); HloInstruction* const2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(12.6f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(12.6f))); HloInstruction* conditional = builder.AddInstruction(HloInstruction::CreateConditional( kScalarShape, pred, const1, true_computation, const2, diff --git a/tensorflow/compiler/xla/service/call_inliner.cc b/tensorflow/compiler/xla/service/call_inliner.cc index 482ccc5b67109258f544e5657ecfa0e8f62192c0..256d05a73e0bf61d959d21795c106286b52d0b19 100644 --- a/tensorflow/compiler/xla/service/call_inliner.cc +++ b/tensorflow/compiler/xla/service/call_inliner.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/service/call_graph.h" +#include "tensorflow/compiler/xla/service/hlo_dce.h" #include "tensorflow/core/lib/core/errors.h" namespace xla { @@ -151,6 +152,14 @@ StatusOr CallInliner::Run(HloModule* module) { } return Status::OK(); })); + if (did_mutate) { + // Run DCE to remove called computations which are now becoming unused. + // This can result then in problems if within the called computation, there + // were send/recv instructions, which the module group verifier will flag as + // error findingthe same channel ID used for multiple send/recv + // instructions. + TF_RETURN_IF_ERROR(HloDCE().Run(module).status()); + } return did_mutate; } diff --git a/tensorflow/compiler/xla/service/call_inliner_test.cc b/tensorflow/compiler/xla/service/call_inliner_test.cc index 924348c870b9ca3d86af560a0c8359af7220427e..ff968bca297077c7cf869ff8d2becb8bf739dce3 100644 --- a/tensorflow/compiler/xla/service/call_inliner_test.cc +++ b/tensorflow/compiler/xla/service/call_inliner_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -48,9 +48,9 @@ TEST_F(CallInlinerTest, ControlDependenciesAreCarriedToCaller) { // the "one" value. HloComputation::Builder inner(TestName() + ".inner"); HloInstruction* zero = inner.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(24.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(24.0f))); HloInstruction* one = inner.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); TF_ASSERT_OK(zero->AddControlDependencyTo(one)); auto module = CreateNewModule(); HloComputation* inner_computation = @@ -87,7 +87,7 @@ TEST_F(CallInlinerTest, CallsWithinWhileBodiesAreInlined) { // little trickier. HloComputation::Builder just_false(TestName() + ".false"); just_false.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloComputation* false_computation = module->AddEmbeddedComputation(just_false.Build()); @@ -99,7 +99,7 @@ TEST_F(CallInlinerTest, CallsWithinWhileBodiesAreInlined) { HloComputation::Builder outer(TestName() + ".outer"); HloInstruction* init_value = outer.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); outer.AddInstruction( HloInstruction::CreateWhile(pred, call_false, call_false, init_value)); @@ -123,9 +123,9 @@ TEST_F(CallInlinerTest, InlineWithoutRunningPass) { HloComputation::Builder just_false(TestName() + ".false"); auto* true_constant = just_false.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({true}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({true}))); auto* false_constant = just_false.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); TF_ASSERT_OK(false_constant->AddControlDependencyTo(true_constant)); HloComputation* false_computation = module->AddEmbeddedComputation(just_false.Build()); @@ -147,8 +147,8 @@ TEST_F(CallInlinerTest, CallToOutfeedComputationIsInlined) { HloComputation::Builder outfeeder(TestName() + ".outfeeder"); auto value = outfeeder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0))); - auto token = outfeeder.AddInstruction(HloInstruction::CreateAfterAll({})); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0))); + auto token = outfeeder.AddInstruction(HloInstruction::CreateToken()); outfeeder.AddInstruction( HloInstruction::CreateOutfeed(f32, value, token, /*outfeed_config=*/"")); diff --git a/tensorflow/compiler/xla/service/channel_tracker.cc b/tensorflow/compiler/xla/service/channel_tracker.cc index a5b392cbc33c12c3255f3c06e9842fc116e672e5..13008efed1494402eaff47904c2e4797334381a1 100644 --- a/tensorflow/compiler/xla/service/channel_tracker.cc +++ b/tensorflow/compiler/xla/service/channel_tracker.cc @@ -31,16 +31,23 @@ namespace xla { ChannelTracker::ChannelTracker() : next_channel_(1) {} -ChannelHandle ChannelTracker::NewChannel() { +StatusOr ChannelTracker::NewChannel( + ChannelHandle::ChannelType type) { + if (type != ChannelHandle::DEVICE_TO_DEVICE && + type != ChannelHandle::HOST_TO_DEVICE && + type != ChannelHandle::DEVICE_TO_HOST) { + return InvalidArgument("Invalid channel type: %d", type); + } tensorflow::mutex_lock lock(channel_mutex_); // Create a new channel handle with a unique value. - const ChannelHandle new_handle = AllocateHandle(); + ChannelHandle new_handle = AllocateHandle(type); // Register a channel object associated with the handle. Channel channel; channel.has_sender = false; channel.receiver_count = 0; + channel.type = type; opaque_to_channel_[new_handle.handle()] = channel; return new_handle; @@ -56,10 +63,11 @@ Status ChannelTracker::RegisterRecv(const ChannelHandle& handle) { return RegisterRecvInternal(handle); } -ChannelHandle ChannelTracker::AllocateHandle() { +ChannelHandle ChannelTracker::AllocateHandle(ChannelHandle::ChannelType type) { int64 handle_value = next_channel_++; ChannelHandle result; result.set_handle(handle_value); + result.set_type(type); return result; } @@ -68,6 +76,13 @@ Status ChannelTracker::RegisterSendInternal(const ChannelHandle& handle) { return NotFound("channel handle not found: %lld", handle.handle()); } Channel& channel = opaque_to_channel_[handle.handle()]; + if (channel.type == ChannelHandle::HOST_TO_DEVICE) { + return FailedPrecondition( + "host-to-device channels cannot be used with a Send operation; " + "channel handle: %lld", + handle.handle()); + } + if (channel.has_sender) { return FailedPrecondition( "when registering send, passed a channel handle that is already used " @@ -83,6 +98,13 @@ Status ChannelTracker::RegisterRecvInternal(const ChannelHandle& handle) { return NotFound("channel handle not found: %lld", handle.handle()); } Channel& channel = opaque_to_channel_[handle.handle()]; + if (channel.type == ChannelHandle::DEVICE_TO_HOST) { + return FailedPrecondition( + "device-to-host channels cannot be used with a Recv operation; " + "channel handle: %lld", + handle.handle()); + } + // TODO(b/33942691): Allow more than 1 receivers for broadcast. if (channel.receiver_count >= 1) { return FailedPrecondition( diff --git a/tensorflow/compiler/xla/service/channel_tracker.h b/tensorflow/compiler/xla/service/channel_tracker.h index fac0afd672ff3ed083aacf778dd9c4f90a2ee870..d773558c284a7d645f2766bb88c50f7da3777e5d 100644 --- a/tensorflow/compiler/xla/service/channel_tracker.h +++ b/tensorflow/compiler/xla/service/channel_tracker.h @@ -48,11 +48,12 @@ class ChannelTracker { struct Channel { bool has_sender; int64 receiver_count; + ChannelHandle::ChannelType type; }; // Creates a new Channel object and returns the corresponding // ChannelHandle for it. - ChannelHandle NewChannel(); + StatusOr NewChannel(ChannelHandle::ChannelType type); // Informs that the given channel handle is used for a Send operation. // Returns an error status if the handle is already used by another Send. @@ -65,7 +66,8 @@ class ChannelTracker { private: // Bumps the next_channel_ number and returns the allocated number // wrapped in a ChannelHandle. - ChannelHandle AllocateHandle() EXCLUSIVE_LOCKS_REQUIRED(channel_mutex_); + ChannelHandle AllocateHandle(ChannelHandle::ChannelType type) + EXCLUSIVE_LOCKS_REQUIRED(channel_mutex_); Status RegisterSendInternal(const ChannelHandle& handle) EXCLUSIVE_LOCKS_REQUIRED(channel_mutex_); diff --git a/tensorflow/compiler/xla/service/computation_placer.cc b/tensorflow/compiler/xla/service/computation_placer.cc index 7c1bacff92b231661477b9931a3066fd91110445..187ce568cbb6c6666e978b8c8114262313c70ba5 100644 --- a/tensorflow/compiler/xla/service/computation_placer.cc +++ b/tensorflow/compiler/xla/service/computation_placer.cc @@ -19,7 +19,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status.h" @@ -29,9 +29,13 @@ limitations under the License. #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" +using tensorflow::strings::StrAppend; +using tensorflow::strings::StrCat; + namespace xla { Status DeviceAssignment::Serialize(DeviceAssignmentProto* proto) const { @@ -71,6 +75,19 @@ DeviceAssignment::Deserialize(const DeviceAssignmentProto& proto) { return std::move(assignment); } +string DeviceAssignment::ToString() const { + string output = StrCat("Computations: ", computation_count(), + " Replicas: ", replica_count(), "\n"); + for (int computation = 0; computation < computation_count(); ++computation) { + StrAppend(&output, "Computation ", computation, ": "); + for (int replica = 0; replica < replica_count(); ++replica) { + StrAppend(&output, operator()(replica, computation), " "); + } + StrAppend(&output, "\n"); + } + return output; +} + StatusOr ComputationPlacer::DeviceId(int replica, int computation, int replica_count, int computation_count) { diff --git a/tensorflow/compiler/xla/service/computation_placer.h b/tensorflow/compiler/xla/service/computation_placer.h index 737d00e93ecb51a9bd544bbcbe99d93374d108fb..c899ffb9dc562426ef14c0d414469c04debeec70 100644 --- a/tensorflow/compiler/xla/service/computation_placer.h +++ b/tensorflow/compiler/xla/service/computation_placer.h @@ -55,6 +55,8 @@ class DeviceAssignment : public Array2D { // due to a StatusOr of an incomplete type (DeviceAssignment). static StatusOr> Deserialize( const DeviceAssignmentProto& proto); + + string ToString() const; }; // A generic implementation of the XLA computation placer, which assigns device diff --git a/tensorflow/compiler/xla/service/conditional_simplifier.cc b/tensorflow/compiler/xla/service/conditional_simplifier.cc index e9ec796121fff223474c3e81a5e973cc37f8caec..b7be3ba605a89a736b032eaab5a5085ac64fc549 100644 --- a/tensorflow/compiler/xla/service/conditional_simplifier.cc +++ b/tensorflow/compiler/xla/service/conditional_simplifier.cc @@ -19,7 +19,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/call_inliner.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" diff --git a/tensorflow/compiler/xla/service/conditional_simplifier_test.cc b/tensorflow/compiler/xla/service/conditional_simplifier_test.cc index 68f6ffc6b7012b7674b8a046df71c7aed7a386fa..c43a31b167d47af3c92ed35fa52594fa5da1e4af 100644 --- a/tensorflow/compiler/xla/service/conditional_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/conditional_simplifier_test.cc @@ -55,7 +55,7 @@ HloComputation* ConditionalSimplifierTest::MakeConditional(HloModule* module) { true_computation_builder.AddInstruction(HloInstruction::CreateParameter( 0, ShapeUtil::MakeShape(S32, {}), "param")); auto one = true_computation_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); true_computation_builder.AddInstruction(HloInstruction::CreateBinary( ShapeUtil::MakeShape(S32, {}), HloOpcode::kAdd, param, one)); @@ -73,7 +73,7 @@ HloComputation* ConditionalSimplifierTest::MakeConditional(HloModule* module) { HloInstruction::CreateParameter(0, ShapeUtil::MakeShape(S32, {}), "param")); auto forty_two = false_computation_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42))); false_computation_builder.AddInstruction(HloInstruction::CreateBinary( ShapeUtil::MakeShape(S32, {}), HloOpcode::kAdd, param, forty_two)); @@ -82,11 +82,11 @@ HloComputation* ConditionalSimplifierTest::MakeConditional(HloModule* module) { } auto false_instrn = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); auto false_param = builder.AddInstruction(HloInstruction::CreateParameter( 0, ShapeUtil::MakeShape(S32, {}), "false_param")); auto one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); builder.AddInstruction(HloInstruction::CreateConditional( ShapeUtil::MakeShape(S32, {}), false_instrn, one, true_computation, @@ -106,7 +106,7 @@ TEST_F(ConditionalSimplifierTest, ConditionalWithControlDependency) { HloComputation* computation = MakeConditional(&module()); auto* true_op = computation->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); TF_ASSERT_OK( true_op->AddControlDependencyTo(computation->root_instruction())); @@ -119,11 +119,10 @@ TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsSend) { ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional); auto* true_computation = conditional->true_computation(); - auto* token = - true_computation->AddInstruction(HloInstruction::CreateAfterAll({})); + auto* token = true_computation->AddInstruction(HloInstruction::CreateToken()); auto* send = true_computation->AddInstruction(HloInstruction::CreateSend( true_computation->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))), + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))), token, /*channel_id=*/0)); true_computation->AddInstruction(HloInstruction::CreateSendDone(send)); EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie()); @@ -135,8 +134,7 @@ TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsRecv) { ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional); auto* true_computation = conditional->true_computation(); - auto* token = - true_computation->AddInstruction(HloInstruction::CreateAfterAll({})); + auto* token = true_computation->AddInstruction(HloInstruction::CreateToken()); auto* recv = true_computation->AddInstruction(HloInstruction::CreateRecv( ShapeUtil::MakeShape(F32, {1}), token, /*channel_id=*/0)); true_computation->AddInstruction(HloInstruction::CreateRecvDone(recv)); @@ -148,8 +146,7 @@ TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsNonRemovableInstruction) { auto* conditional = computation->root_instruction(); ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional); auto* false_computation = conditional->false_computation(); - auto token = - false_computation->AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = false_computation->AddInstruction(HloInstruction::CreateToken()); false_computation->AddInstruction(HloInstruction::CreateInfeed( ShapeUtil::MakeShape(F32, {1}), token, "config")); EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie()); diff --git a/tensorflow/compiler/xla/service/copy_insertion.cc b/tensorflow/compiler/xla/service/copy_insertion.cc index 52e66b3e77097dfdb462ed4a953581b9d316064b..36fb9b43aa20bad788a0638b4fed6c88fc9023f0 100644 --- a/tensorflow/compiler/xla/service/copy_insertion.cc +++ b/tensorflow/compiler/xla/service/copy_insertion.cc @@ -76,15 +76,6 @@ SpecialCaseCopyPolicy GetSpecialCaseCopyPolicy(const CallGraphNode& node, policy.copy_parameters_and_constants = true; policy.copy_root_replicated_buffers = true; } - for (const CallSite& site : node.caller_callsites()) { - // The AddCopiesForConditional() already adds copies, but the copy remover - // removes them, so we re-add them by returning the policy here. But really - // the copy remover should not be removing them. - if (site.instruction()->opcode() == HloOpcode::kConditional) { - policy.copy_parameters_and_constants = true; - policy.copy_root_replicated_buffers = true; - } - } return policy; } @@ -360,26 +351,6 @@ Status StripControlDependenciesFrom(HloInstruction* instruction) { return Status::OK(); } -// Add kCopy instructions to the given module to guarantee there is no -// live-range interference. Generally interference can only occur around kWhile -// instructions which have update-in-place semantics. -Status AddCopiesToResolveInterference(HloModule* module) { - TF_ASSIGN_OR_RETURN(std::unique_ptr alias_analysis, - HloAliasAnalysis::Run(module)); - - for (HloComputation* computation : module->computations()) { - for (HloInstruction* instruction : computation->instructions()) { - if (instruction->opcode() == HloOpcode::kWhile) { - TF_RETURN_IF_ERROR(AddCopiesForWhile(*alias_analysis, instruction)); - } else if (instruction->opcode() == HloOpcode::kConditional) { - TF_RETURN_IF_ERROR( - AddCopiesForConditional(*alias_analysis, instruction)); - } - } - } - return Status::OK(); -} - // Class for removing unnecessary copies from the module. // // kCopy instructions are added conservatively to guarantee no live range @@ -954,6 +925,36 @@ class CopyRemover { BufferValueTracker buffer_value_tracker_; }; +void MaybeDumpModule(const string& message, const HloModule& module) { + if (VLOG_IS_ON(3)) { + VLOG(3) << message; + XLA_VLOG_LINES(3, module.ToString()); + hlo_graph_dumper::MaybeDumpHloModule(module, message); + } +} + +} // namespace + +// Add kCopy instructions to the given module to guarantee there is no +// live-range interference. Generally interference can only occur around kWhile +// instructions which have update-in-place semantics. +Status CopyInsertion::AddCopiesToResolveInterference(HloModule* module) { + TF_ASSIGN_OR_RETURN(std::unique_ptr alias_analysis, + HloAliasAnalysis::Run(module, fusion_can_share_buffer_)); + + for (HloComputation* computation : module->computations()) { + for (HloInstruction* instruction : computation->instructions()) { + if (instruction->opcode() == HloOpcode::kWhile) { + TF_RETURN_IF_ERROR(AddCopiesForWhile(*alias_analysis, instruction)); + } else if (instruction->opcode() == HloOpcode::kConditional) { + TF_RETURN_IF_ERROR( + AddCopiesForConditional(*alias_analysis, instruction)); + } + } + } + return Status::OK(); +} + // Add copies to address special constraints on the roots of computations not // related to live range interference: // @@ -964,9 +965,10 @@ class CopyRemover { // // (3) Constants and parameters cannot be live out of the entry computation // -Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) { +Status CopyInsertion::AddSpecialCaseCopies(const CallGraph& call_graph, + HloModule* module) { TF_ASSIGN_OR_RETURN(std::unique_ptr alias_analysis, - HloAliasAnalysis::Run(module)); + HloAliasAnalysis::Run(module, fusion_can_share_buffer_)); // Identify which shape indices of which instructions need to be copied. Store // these results in 'instructions_to_copy'. @@ -1074,24 +1076,14 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) { return Status::OK(); } -Status VerifyNoLiveRangeInterference(HloModule* module) { +Status CopyInsertion::VerifyNoLiveRangeInterference(HloModule* module) { TF_ASSIGN_OR_RETURN(std::unique_ptr alias_analysis, - HloAliasAnalysis::Run(module)); + HloAliasAnalysis::Run(module, fusion_can_share_buffer_)); DependencyHloOrdering ordering(module); TF_RET_CHECK(!alias_analysis->HasLiveRangeInterference(ordering)); return Status::OK(); } -void MaybeDumpModule(const string& message, const HloModule& module) { - if (VLOG_IS_ON(3)) { - VLOG(3) << message; - XLA_VLOG_LINES(3, module.ToString()); - hlo_graph_dumper::MaybeDumpHloModule(module, message); - } -} - -} // namespace - Status CopyInsertion::RemoveUnnecessaryCopies(const HloOrdering& ordering, HloModule* module) { MaybeDumpModule("after adding copies to resolve interference", *module); diff --git a/tensorflow/compiler/xla/service/copy_insertion.h b/tensorflow/compiler/xla/service/copy_insertion.h index c5573f76f31681ae9988039e9000636876478113..5ba64b78a3c9aff5f323691df2ece9b5e6bf3232 100644 --- a/tensorflow/compiler/xla/service/copy_insertion.h +++ b/tensorflow/compiler/xla/service/copy_insertion.h @@ -60,12 +60,6 @@ class CopyInsertion : public HloPassInterface { // (copies were inserted). StatusOr Run(HloModule* module) override; - // Try to remove as many copies from the module as possible without - // introducing live range interference. Only copy instructions that are - // eligible for copy elision are considered for removal. - Status RemoveUnnecessaryCopies(const HloOrdering& ordering, - HloModule* module); - // The CPU and GPU backend need additional copies added due to deficiencies in // buffer assignment. Specifically, copies are needed for constants live-out // of computations, and for values which are live-in and live-out of the same @@ -77,13 +71,26 @@ class CopyInsertion : public HloPassInterface { // TODO(b/62548313): Remove this when buffer assignment is module-scoped. static StatusOr AddCopiesForBufferAssignment(HloModule* module); + // Try to remove as many copies from the module as possible without + // introducing live range interference. Only copy instructions that are + // eligible for copy elision are considered for removal. + Status RemoveUnnecessaryCopies(const HloOrdering& ordering, + HloModule* module); + private: + // Verifies that no HLO values have interfering live ranged assuming the + // ordering used by copy insertion. + Status VerifyNoLiveRangeInterference(HloModule* module); + + Status AddCopiesToResolveInterference(HloModule* module); + + Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module); + // Backend specific function that decides whether a fusion can share buffer // with its operand. HloDataflowAnalysis::FusionCanShareBufferFunction fusion_can_share_buffer_; }; - } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_COPY_INSERTION_H_ diff --git a/tensorflow/compiler/xla/service/copy_insertion_test.cc b/tensorflow/compiler/xla/service/copy_insertion_test.cc index 105d117caccc21e6673261d44a59be30c28b9039..cd735256b83f5f1d69a89e693de6064d460a36e5 100644 --- a/tensorflow/compiler/xla/service/copy_insertion_test.cc +++ b/tensorflow/compiler/xla/service/copy_insertion_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_matchers.h" @@ -108,7 +108,7 @@ TEST_F(CopyInsertionTest, SingleConstant) { // be copied before entering the tuple. auto builder = HloComputation::Builder(TestName()); HloInstruction* constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); HloInstruction* tuple = builder.AddInstruction(HloInstruction::CreateTuple({constant})); @@ -132,7 +132,7 @@ TEST_F(CopyInsertionTest, ExistingCopiesNotRemoved) { auto builder = HloComputation::Builder(TestName()); HloInstruction* constant = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{0.f, 2.f}, {2.f, 4.f}}))); + LiteralUtil::CreateR2({{0.f, 2.f}, {2.f, 4.f}}))); auto minor_to_major = LayoutUtil::MinorToMajor(constant->shape()); Layout reversed_layout = LayoutUtil::MakeLayoutFromMajorToMinor(minor_to_major); @@ -167,9 +167,9 @@ TEST_F(CopyInsertionTest, MultipleConstantsAndParameters) { auto builder = HloComputation::Builder(TestName()); HloInstruction* constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); HloInstruction* constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); HloInstruction* x = builder.AddInstruction( HloInstruction::CreateParameter(0, ShapeUtil::MakeShape(F32, {}), "x")); @@ -197,11 +197,11 @@ TEST_F(CopyInsertionTest, AmbiguousPointsToSet) { // the computation result. Verify that copies are added properly. auto builder = HloComputation::Builder(TestName()); HloInstruction* constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); HloInstruction* constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); HloInstruction* constant3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(3.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(3.0))); HloInstruction* tuple1 = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); @@ -209,7 +209,7 @@ TEST_F(CopyInsertionTest, AmbiguousPointsToSet) { HloInstruction::CreateTuple({constant3, constant2})); HloInstruction* pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); builder.AddInstruction(HloInstruction::CreateTernary( tuple1->shape(), HloOpcode::kTupleSelect, pred, tuple1, tuple2)); @@ -255,8 +255,9 @@ TEST_F(CopyInsertionTest, BitcastConstant) { // The output of a bitcast is its operand (same buffer), so a bitcast // constant feeding the result must have a copy added. auto builder = HloComputation::Builder(TestName()); - HloInstruction* constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1.0, 42.0}))); + HloInstruction* constant = + builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR1({1.0, 42.0}))); HloInstruction* bitcast = builder.AddInstruction(HloInstruction::CreateUnary( ShapeUtil::MakeShape(F32, {2, 2}), HloOpcode::kBitcast, constant)); @@ -370,9 +371,9 @@ TEST_F(CopyInsertionTest, AmbiguousTopLevelRoot) { // copy is added. auto builder = HloComputation::Builder(TestName()); HloInstruction* constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); HloInstruction* constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); HloInstruction* tuple1 = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); @@ -380,7 +381,7 @@ TEST_F(CopyInsertionTest, AmbiguousTopLevelRoot) { HloInstruction::CreateTuple({constant2, constant1})); HloInstruction* pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloInstruction* select = builder.AddInstruction(HloInstruction::CreateTernary( tuple1->shape(), HloOpcode::kTupleSelect, pred, tuple1, tuple2)); HloInstruction* gte = @@ -413,7 +414,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest { const Shape& loop_state_shape) { auto builder = HloComputation::Builder(TestName() + ".Condition"); auto limit_const = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(10))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(10))); auto loop_state = builder.AddInstruction( HloInstruction::CreateParameter(0, loop_state_shape, "loop_state")); auto induction_variable = @@ -442,7 +443,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest { builder.AddInstruction(HloInstruction::CreateGetTupleElement( induction_variable_shape_, loop_state, 0)); auto inc = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); auto add0 = builder.AddInstruction(HloInstruction::CreateBinary( induction_variable->shape(), HloOpcode::kAdd, induction_variable, inc)); // Update data GTE(1). @@ -480,7 +481,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest { builder.AddInstruction(HloInstruction::CreateGetTupleElement( induction_variable_shape_, loop_state, 0)); auto inc = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); // add0 = Add(in0, 1) auto add0 = builder.AddInstruction(HloInstruction::CreateBinary( @@ -549,7 +550,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest { builder.AddInstruction(HloInstruction::CreateGetTupleElement( induction_variable_shape_, loop_state, 0)); auto inc = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); // add0 = Add(in0, 1) auto add0 = builder.AddInstruction(HloInstruction::CreateBinary( induction_variable->shape(), HloOpcode::kAdd, induction_variable, inc)); @@ -564,8 +565,9 @@ class WhileCopyInsertionTest : public CopyInsertionTest { data = builder.AddInstruction( HloInstruction::CreateGetTupleElement(data_shape_, loop_state, 1)); } - auto update = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}))); + auto update = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR1( + {1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}))); // add1 = Add(in1, {1, 1, 1, 1, 1, 1, 1, 1}) auto add1 = builder.AddInstruction(HloInstruction::CreateBinary( data_shape_, HloOpcode::kAdd, data, update)); @@ -598,7 +600,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest { auto gte0 = builder.AddInstruction(HloInstruction::CreateGetTupleElement( induction_variable_shape_, loop_state, 0)); auto inc = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); auto add0 = builder.AddInstruction(HloInstruction::CreateBinary( gte0->shape(), HloOpcode::kAdd, gte0, inc)); @@ -608,8 +610,9 @@ class WhileCopyInsertionTest : public CopyInsertionTest { // GTE(GTE(loop_state, 1), 0) -> Add auto gte10 = builder.AddInstruction( HloInstruction::CreateGetTupleElement(data_shape_, gte1, 0)); - auto update10 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}))); + auto update10 = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR1( + {1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}))); auto add10 = builder.AddInstruction(HloInstruction::CreateBinary( data_shape_, HloOpcode::kAdd, gte10, update10)); @@ -633,10 +636,11 @@ class WhileCopyInsertionTest : public CopyInsertionTest { bool nested = false) { auto builder = HloComputation::Builder(TestName() + ".While"); auto induction_var_init = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))); - auto data_init = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}))); + auto data_init = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR1( + {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}))); if (nested) { auto inner_init = builder.AddInstruction( @@ -659,8 +663,9 @@ class WhileCopyInsertionTest : public CopyInsertionTest { HloInstruction* BuildWhileInstruction_InitPointsToConstant() { auto builder = HloComputation::Builder(TestName() + ".While"); - auto data_init = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}))); + auto data_init = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR1( + {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}))); return BuildWhileInstructionWithCustomInit(loop_state_shape_, data_init, &builder); } @@ -677,11 +682,11 @@ class WhileCopyInsertionTest : public CopyInsertionTest { auto builder = HloComputation::Builder(TestName() + ".While"); auto one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto v1 = builder.AddInstruction( HloInstruction::CreateBroadcast(data_shape_, one, {1})); auto zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto v2 = builder.AddInstruction( HloInstruction::CreateBroadcast(data_shape_, zero, {1})); @@ -689,7 +694,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest { auto tuple2 = builder.AddInstruction(HloInstruction::CreateTuple({v2, v1})); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); auto data_init = builder.AddInstruction(HloInstruction::CreateTernary( nested_tuple_shape_, HloOpcode::kTupleSelect, pred, tuple1, tuple2)); @@ -701,7 +706,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest { auto builder = HloComputation::Builder(TestName() + ".While"); auto one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto one_vec = builder.AddInstruction( HloInstruction::CreateBroadcast(data_shape_, one, {1})); auto data_init = @@ -714,11 +719,12 @@ class WhileCopyInsertionTest : public CopyInsertionTest { HloInstruction* BuildWhileInstruction_InitPointsToInterfering() { auto builder = HloComputation::Builder(TestName() + ".While"); auto one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto data_init = builder.AddInstruction( HloInstruction::CreateBroadcast(data_shape_, one, {1})); - auto one_vec = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}))); + auto one_vec = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR1( + {1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f}))); // Take a reference to 'data_init' to make it interfere with while result. auto add = builder.AddInstruction(HloInstruction::CreateBinary( data_shape_, HloOpcode::kAdd, data_init, one_vec)); @@ -750,7 +756,7 @@ class WhileCopyInsertionTest : public CopyInsertionTest { const bool nested = ShapeUtil::Equal(loop_state_shape, nested_loop_state_shape_); auto induction_var_init = builder->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))); auto condition = module_->AddEmbeddedComputation( BuildConditionComputation(loop_state_shape)); auto body = module_->AddEmbeddedComputation( @@ -1252,7 +1258,6 @@ TEST_F(WhileCopyInsertionTest, InitPointsToNonDistinctUsedByTwoWhileLoops) { auto loop_init = builder.AddInstruction( HloInstruction::CreateTuple({iter_param, data_param, data_param})); - // Two while loops shares the same loop init tuple. auto while_hlo1 = builder.AddInstruction(HloInstruction::CreateWhile( loop_state_shape, condition1, body1, loop_init)); @@ -1310,7 +1315,7 @@ TEST_F(CopyInsertionTest, SwizzlingWhile) { cond_builder.AddInstruction( HloInstruction::CreateParameter(0, loop_state_shape, "param")); auto cond_constant = cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); cond_builder.AddInstruction(HloInstruction::CreateUnary( cond_constant->shape(), HloOpcode::kNot, cond_constant)); HloComputation* condition = @@ -1318,9 +1323,9 @@ TEST_F(CopyInsertionTest, SwizzlingWhile) { auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto xla_while = builder.AddInstruction( @@ -1375,7 +1380,7 @@ TEST_F(CopyInsertionTest, SwizzlingWhileWithOneOp) { cond_builder.AddInstruction( HloInstruction::CreateParameter(0, loop_state_shape, "param")); auto cond_constant = cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); cond_builder.AddInstruction(HloInstruction::CreateUnary( cond_constant->shape(), HloOpcode::kNot, cond_constant)); HloComputation* condition = @@ -1383,9 +1388,9 @@ TEST_F(CopyInsertionTest, SwizzlingWhileWithOneOp) { auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto xla_while = builder.AddInstruction( @@ -1435,7 +1440,7 @@ TEST_F(CopyInsertionTest, SwizzlingWhileSharedInput) { cond_builder.AddInstruction( HloInstruction::CreateParameter(0, loop_state_shape, "param")); auto cond_constant = cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); cond_builder.AddInstruction(HloInstruction::CreateUnary( cond_constant->shape(), HloOpcode::kNot, cond_constant)); HloComputation* condition = @@ -1443,7 +1448,7 @@ TEST_F(CopyInsertionTest, SwizzlingWhileSharedInput) { auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto tuple = builder.AddInstruction(HloInstruction::CreateTuple({constant, constant})); builder.AddInstruction( @@ -1520,7 +1525,7 @@ TEST_F(CopyInsertionTest, SequentialWhiles) { cond_builder.AddInstruction( HloInstruction::CreateParameter(0, loop_state_shape, "param")); auto cond_constant = cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); cond_builder.AddInstruction(HloInstruction::CreateUnary( cond_constant->shape(), HloOpcode::kNot, cond_constant)); HloComputation* condition = @@ -1575,14 +1580,14 @@ TEST_F(CopyInsertionTest, WhileBodyWithConstantRoot) { body_builder.AddInstruction( HloInstruction::CreateParameter(0, scalar_shape_, "param")); body_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(123.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(123.0))); HloComputation* body = module->AddEmbeddedComputation(body_builder.Build()); auto cond_builder = HloComputation::Builder("condition"); cond_builder.AddInstruction( HloInstruction::CreateParameter(0, scalar_shape_, "param")); cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloComputation* condition = module->AddEmbeddedComputation(cond_builder.Build()); @@ -1644,7 +1649,7 @@ std::unique_ptr MakeTrivialCondition(const Shape& shape) { builder.AddInstruction( HloInstruction::CreateParameter(0, shape, "loop_state")); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); builder.AddInstruction(HloInstruction::CreateUnary( constant->shape(), HloOpcode::kNot, constant)); return builder.Build(); diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index 3479240610a197aeed0c0a07099239e1161b1352..504b61d134a0099d055d0266408e1dfb94af5b2a 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -37,6 +37,7 @@ cc_library( srcs = ["cpu_transfer_manager.cc"], hdrs = ["cpu_transfer_manager.h"], deps = [ + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", @@ -72,7 +73,7 @@ cc_library( ":ir_emitter", ":parallel_task_assignment", ":simple_orc_jit", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:protobuf_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -89,7 +90,6 @@ cc_library( "//tensorflow/compiler/xla/service:dot_decomposer", "//tensorflow/compiler/xla/service:executable", "//tensorflow/compiler/xla/service:flatten_call_graph", - "//tensorflow/compiler/xla/service:gather_expander", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_constant_folding", "//tensorflow/compiler/xla/service:hlo_cse", @@ -252,12 +252,13 @@ cc_library( "//tensorflow/compiler/xla/service:hlo_module_config", "//tensorflow/compiler/xla/service:name_uniquer", "//tensorflow/compiler/xla/service/llvm_ir:alias_analysis", + "//tensorflow/compiler/xla/service/llvm_ir:buffer_assignment_util", + "//tensorflow/compiler/xla/service/llvm_ir:dynamic_update_slice_util", "//tensorflow/compiler/xla/service/llvm_ir:fused_ir_emitter", "//tensorflow/compiler/xla/service/llvm_ir:ir_array", "//tensorflow/compiler/xla/service/llvm_ir:llvm_loop", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/compiler/xla/service/llvm_ir:loop_emitter", - "//tensorflow/compiler/xla/service/llvm_ir:ops", "//tensorflow/compiler/xla/service/llvm_ir:tuple_ops", "//tensorflow/core:lib", "@llvm//:code_gen", @@ -355,7 +356,7 @@ tf_cc_binary( srcs = ["sample_harness.cc"], deps = [ "//tensorflow/compiler/xla:array4d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", @@ -363,8 +364,8 @@ tf_cc_binary( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/core:lib", ], ) @@ -444,6 +445,7 @@ cc_library( deps = [ ":vector_support_library", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", + "//tensorflow/compiler/xla/service/llvm_ir:math_ops", "//tensorflow/core:lib", "@llvm//:core", "@llvm//:transform_utils", @@ -717,7 +719,7 @@ tf_cc_test( deps = [ ":cpu_layout_assignment", ":target_machine_features_fake", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_layout", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", @@ -809,7 +811,7 @@ tf_cc_test( ":cpu_executable", ":parallel_task_assignment", ":target_machine_features_fake", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_layout", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", @@ -892,7 +894,7 @@ tf_cc_test( srcs = ["cpu_copy_insertion_test.cc"], deps = [ ":cpu_copy_insertion", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", diff --git a/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc b/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc index 375b017b09263c20c1b1ef8329f7e2f6a573dda4..547d4c696da5cfdde3dece03250ae5fa51c92f25 100644 --- a/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc +++ b/tensorflow/compiler/xla/service/cpu/conv_canonicalization_test.cc @@ -60,11 +60,11 @@ TEST_F(ConvCanonicalizationTest, NonCanonicalToCanonical) { auto builder = HloComputation::Builder(TestName()); // The input dimensions are in CNHW order. auto input = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR4FromArray4D(Array4D( + LiteralUtil::CreateR4FromArray4D(Array4D( kInputFeatureCount, kBatchSize, kInputSize, kInputSize)))); // The kernel dimensions are in OIHW order. auto kernel = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR4FromArray4D(Array4D( + LiteralUtil::CreateR4FromArray4D(Array4D( kOutputFeatureCount, kInputFeatureCount, kWindowSize, kWindowSize)))); ConvolutionDimensionNumbers dnums; @@ -122,11 +122,11 @@ TEST_F(ConvCanonicalizationTest, CanonicalStaysTheSame) { auto builder = HloComputation::Builder(TestName()); // The input dimensions are in NHWC order. auto input = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR4FromArray4D(Array4D( + LiteralUtil::CreateR4FromArray4D(Array4D( kBatchSize, kInputSize, kInputSize, kInputFeatureCount)))); // The kernel dimensions are in HWIO order. auto kernel = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR4FromArray4D(Array4D( + LiteralUtil::CreateR4FromArray4D(Array4D( kWindowSize, kWindowSize, kInputFeatureCount, kOutputFeatureCount)))); ConvolutionDimensionNumbers dnums; diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index 55962ba70d213939ccb49cad3bdd75395cc4eaa5..b49ea898962e437ec80dca0deec3aba70556b0dd 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -30,6 +30,7 @@ limitations under the License. #include "llvm/ADT/Triple.h" #include "llvm/IR/Function.h" #include "llvm/IR/LLVMContext.h" +#include "llvm/IR/Mangler.h" #include "llvm/IR/Module.h" #include "llvm/IR/Verifier.h" #include "llvm/Object/ObjectFile.h" @@ -38,7 +39,7 @@ limitations under the License. #include "llvm/Support/TargetSelect.h" #include "llvm/Target/TargetMachine.h" #include "llvm/Target/TargetOptions.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/protobuf_util.h" #include "tensorflow/compiler/xla/ptr_util.h" @@ -66,7 +67,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" #include "tensorflow/compiler/xla/service/dot_decomposer.h" #include "tensorflow/compiler/xla/service/flatten_call_graph.h" -#include "tensorflow/compiler/xla/service/gather_expander.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_constant_folding.h" @@ -297,8 +297,6 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile, pipeline.AddPass(/*is_layout_sensitive=*/false); pipeline.AddPass(); - pipeline.AddPass(); - ReducePrecisionInsertion::AddPasses( &pipeline, module->config().debug_options(), ReducePrecisionInsertion::PassTiming::AFTER_FUSION); @@ -564,7 +562,9 @@ StatusOr> CpuCompiler::RunBackend( BufferAssigner::Run( module.get(), xla::MakeUnique(module.get(), module_sequence), - BufferSizeBytesFunction(), memory_alignment)); + BufferSizeBytesFunction(), memory_alignment, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true)); // BufferAssignment::ToString() includes a header, so no need for us to // print one ourselves. XLA_VLOG_LINES(2, assignment->ToString()); @@ -586,6 +586,8 @@ StatusOr> CpuCompiler::RunBackend( std::move(computation_to_profile_idx), &target_machine_features); + TF_RETURN_IF_ERROR(ir_emitter.EmitConstantGlobals()); + for (auto embedded_computation : entry_computation->MakeEmbeddedComputationsList()) { if (embedded_computation->IsFusionComputation()) { @@ -607,7 +609,13 @@ StatusOr> CpuCompiler::RunBackend( /*is_top_level_computation=*/true, &module_sequence.at(entry_computation))); - string function_name = llvm_ir::AsString(entry_function->getName()); + string function_name = [&]() { + llvm::SmallVector function_name_vector; + llvm::Mangler::getNameWithPrefix( + function_name_vector, entry_function->getName(), jit->data_layout()); + return string(function_name_vector.begin(), function_name_vector.end()); + }(); + string ir_module_string; if (embed_ir_in_executable) { ir_module_string = llvm_ir::DumpModuleToString(*llvm_module); @@ -743,7 +751,9 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules, BufferAssigner::Run( module, xla::MakeUnique(module, module_sequence), - BufferSizeBytesFunction(), memory_alignment)); + BufferSizeBytesFunction(), memory_alignment, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true)); // BufferAssignment::ToString() includes a header, so no need for us to // print one ourselves. XLA_VLOG_LINES(2, assignment->ToString()); @@ -772,6 +782,9 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules, std::move(instruction_to_profile_idx), std::move(computation_to_profile_idx), &target_machine_features); + + TF_RETURN_IF_ERROR(ir_emitter.EmitConstantGlobals()); + HloComputation* computation = module->entry_computation(); for (auto embedded_computation : computation->MakeEmbeddedComputationsList()) { @@ -828,7 +841,8 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules, BufferSizes buffer_sizes; for (const BufferAllocation& allocation : assignment->Allocations()) { // Callers don't need to allocate temporary buffers for parameters. - if (allocation.is_entry_computation_parameter()) { + if (allocation.is_entry_computation_parameter() || + allocation.is_constant()) { buffer_sizes.push_back(-1); continue; } diff --git a/tensorflow/compiler/xla/service/cpu/cpu_copy_insertion_test.cc b/tensorflow/compiler/xla/service/cpu/cpu_copy_insertion_test.cc index a05a26941786cbf404c4685abb098c9ac8caaa09..4db7fa446ea9188940f930bcadf753bd3e6b79e3 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_copy_insertion_test.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_copy_insertion_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/cpu/cpu_copy_insertion.h" #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_matchers.h" @@ -74,14 +74,14 @@ TEST_F(CpuCopyInsertionTest, WhileBodyWithConstantRoot) { body_builder.AddInstruction( HloInstruction::CreateParameter(0, scalar_shape_, "param")); body_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(123.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(123.0))); HloComputation* body = module->AddEmbeddedComputation(body_builder.Build()); auto cond_builder = HloComputation::Builder("condition"); cond_builder.AddInstruction( HloInstruction::CreateParameter(0, scalar_shape_, "param")); cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloComputation* condition = module->AddEmbeddedComputation(cond_builder.Build()); @@ -114,7 +114,7 @@ TEST_F(CpuCopyInsertionTest, TupleCall) { auto sub_param = sub_builder.AddInstruction( HloInstruction::CreateParameter(0, scalar_shape_, "param")); auto constant = sub_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(123.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(123.0))); auto add = sub_builder.AddInstruction(HloInstruction::CreateBinary( scalar_shape_, HloOpcode::kAdd, sub_param, constant)); sub_builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc index 1093559892ddb9c238fd9c1f7e3d419ec7022776..81e17a5cd4de7151217ba0f2710c49546bce1f10 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc @@ -88,6 +88,11 @@ Status CpuExecutable::AllocateBuffers( continue; } + if (allocation.is_constant()) { + VLOG(3) << "allocation #" << i << " is a constant"; + continue; + } + if (allocation.is_thread_local()) { VLOG(3) << "buffer #" << i << " is thread-local"; continue; diff --git a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc index 750310c633286aa8f964c9ae5dcf847f2dc0557c..991b14f17dbc8cd061af98e032824d3f7075e78b 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc @@ -282,7 +282,7 @@ class OpcodeFusionTest : public InstructionFusionTest { builder.AddInstruction(HloInstruction::CreateParameter( 0, ShapeUtil::MakeShape(F32, {}), "arg0")); HloInstruction* one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); builder.AddInstruction(HloInstruction::CreateBinary( ShapeUtil::MakeShape(F32, {}), HloOpcode::kAdd, arg0, one)); return module->AddEmbeddedComputation(builder.Build()); @@ -595,7 +595,7 @@ TEST_F(OpcodeFusionTest, MessOfFusileNodes) { auto pad = builder.AddInstruction(HloInstruction::CreatePad( ShapeUtil::MakeShape(S32, {5}), idx_choice, builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))), + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))), padding_config)); auto slice = builder.AddInstruction(HloInstruction::CreateDynamicSlice( diff --git a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment_test.cc b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment_test.cc index 429fc7b78608da0e9cd794ac294851b326f5be24..3681d12d8da818d06d2f690024008c9ccb896286 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment_test.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_layout_assignment_test.cc @@ -21,7 +21,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/algebraic_simplifier.h" #include "tensorflow/compiler/xla/service/computation_layout.h" #include "tensorflow/compiler/xla/service/cpu/target_machine_features_fake.h" diff --git a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc index b877b295814a7e13569a1837ed3e1787f2fc3f56..156166bf2b1ea6d3821da8f67ea2b2eca6825ca6 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc @@ -19,6 +19,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/cpu/cpu_runtime.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -180,7 +181,7 @@ Status CpuTransferManager::TransferLiteralFromOutfeed( tensorflow::gtl::ArraySlice dimensions( tensorflow::bit_cast(literal_shape.dimensions().data()), literal_shape.dimensions().size()); - *literal = std::move(*Literal::CreateFromDimensions( + *literal = std::move(*LiteralUtil::CreateFromDimensions( literal_shape.element_type(), dimensions)); TF_ASSIGN_OR_RETURN(Shape received_shape, TransferArrayBufferFromOutfeed( @@ -211,7 +212,7 @@ Status CpuTransferManager::TransferLiteralFromOutfeed( tensorflow::bit_cast( tuple_element_shape.dimensions().data()), tuple_element_shape.dimensions().size()); - auto empty = Literal::CreateFromDimensions( + auto empty = LiteralUtil::CreateFromDimensions( tuple_element_shape.element_type(), dimensions); int64 size = GetByteSizeRequirement(tuple_element_shape); buffer_data.push_back({empty->untyped_data(), size}); @@ -232,7 +233,7 @@ Status CpuTransferManager::TransferLiteralFromOutfeed( for (int64 i = 0; i < literal_shape.tuple_shapes_size(); ++i) { *elements[i]->mutable_shape_do_not_use() = received_shape.tuple_shapes(i); } - *literal = std::move(*Literal::MakeTupleOwned(std::move(elements))); + *literal = std::move(*LiteralUtil::MakeTupleOwned(std::move(elements))); TF_RET_CHECK(ShapeUtil::Equal(literal->shape(), literal_shape)); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.h b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.h index 6dfc666f09dfa6df740cd54bea0957e3144181bc..593575c0fdaddc71cd6bd844fd179096a9fb0fdc 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.h +++ b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.h @@ -39,13 +39,14 @@ class CpuTransferManager : public GenericTransferManager { Status TransferLiteralToInfeed(se::StreamExecutor* executor, const LiteralSlice& literal) override; - Status TransferBufferToInfeed(se::StreamExecutor* executor, int64 size, - const void* source) override; Status TransferLiteralFromOutfeed(se::StreamExecutor* executor, const Shape& literal_shape, Literal* literal) override; private: + Status TransferBufferToInfeed(se::StreamExecutor* executor, int64 size, + const void* source); + // Transfers infeed data to device. InfeedBuffer->Done() must be // called to clean up the memory allocated for InfeedBuffer. StatusOr TransferBufferToInfeedInternal( diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc index 58228180ca55ede50c8579bbd73cfdfffc07e208..645888de783e4025cffd6fa4835e60b84bbd7d99 100644 --- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc @@ -49,15 +49,15 @@ class MemoryTile { // `tile_size_along_major_dim` vectors from the matrix `matrix`, starting at // `major_dim_offset` in the major dimension. The tile size along the minor // dimension is the vector size, and that is implicitly determined by `vsl`. - MemoryTile(VectorSupportLibrary* vsl, llvm::IRBuilder<>* ir_builder, + MemoryTile(VectorSupportLibrary* vsl, llvm::IRBuilder<>* b, llvm::Value* matrix, int64 matrix_size_along_minor_dim, llvm::Value* major_dim_offset, int64 tile_size_along_major_dim) - : vsl_(vsl), ir_builder_(ir_builder) { + : vsl_(vsl), b_(b) { pointers_.reserve(tile_size_along_major_dim); for (int64 i = 0; i < tile_size_along_major_dim; i++) { - llvm::Value* total_offset = ir_builder->CreateMul( - ir_builder->getInt64(matrix_size_along_minor_dim), - ir_builder->CreateAdd(ir_builder->getInt64(i), major_dim_offset)); + llvm::Value* total_offset = + b->CreateMul(b->getInt64(matrix_size_along_minor_dim), + b->CreateAdd(b->getInt64(i), major_dim_offset)); pointers_.push_back(vsl_->ComputeOffsetPointer(matrix, total_offset)); } } @@ -101,8 +101,7 @@ class MemoryTile { for (int64 i = 0; i < pointers_.size(); i++) { for (int64 j = 0; j < tile_size_along_middle_dim; j++) { result[i].push_back(vsl_->LoadBroadcast( - pointers_[i], ir_builder_->CreateAdd(minor_dim_offset, - ir_builder_->getInt64(j)))); + pointers_[i], b_->CreateAdd(minor_dim_offset, b_->getInt64(j)))); } } return result; @@ -110,7 +109,7 @@ class MemoryTile { private: VectorSupportLibrary* vsl_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; std::vector pointers_; }; @@ -249,16 +248,15 @@ class ColumnMajorMatrixVectorProductEmitter ColumnMajorMatrixVectorProductEmitter(const Config& config, llvm::Value* lhs, llvm::Value* rhs, llvm::Value* addend, llvm::Value* result, - llvm::IRBuilder<>* ir_builder) + llvm::IRBuilder<>* b) : config_(config), lhs_(lhs), rhs_(rhs), addend_(addend), result_(result), - ir_builder_(ir_builder), - ksl_(ir_builder_), - vsl_(config.scalar_type(), /*vector_size=*/config.tile_rows(), - ir_builder_, "") { + b_(b), + ksl_(b_), + vsl_(config.scalar_type(), /*vector_size=*/config.tile_rows(), b_, "") { CHECK(tile_rows() > 0 && IsPowerOfTwo(static_cast(tile_rows()))); CHECK(!has_addend() || addend != nullptr); } @@ -272,7 +270,7 @@ class ColumnMajorMatrixVectorProductEmitter bool is_first_column); MemoryTile GetLhsMemoryTile(llvm::Value* column_start, int64 column_count) { - return MemoryTile(&vsl_, ir_builder_, /*matrix=*/lhs_, + return MemoryTile(&vsl_, b_, /*matrix=*/lhs_, /*matrix_size_along_minor_dim=*/m(), /*major_dim_offset=*/column_start, /*tile_size_along_major_dim=*/column_count); @@ -302,7 +300,7 @@ class ColumnMajorMatrixVectorProductEmitter llvm::Value* rhs_; llvm::Value* addend_; llvm::Value* result_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; KernelSupportLibrary ksl_; VectorSupportLibrary vsl_; }; @@ -331,7 +329,7 @@ void ColumnMajorMatrixVectorProductEmitter::Emit() { }); if (column_remainder != 0) { - EmitOuterLoopBody(ir_builder_->getInt64(column_limit), column_remainder, + EmitOuterLoopBody(b_->getInt64(column_limit), column_remainder, column_limit == 0); } } @@ -364,7 +362,7 @@ void ColumnMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( return; } - llvm::Value* columns_llvm = ir_builder_->getInt64(columns); + llvm::Value* columns_llvm = b_->getInt64(columns); // for (col = current_tile_col; col < (columns + current_tile_col); col++) // for (row = row_start, row < m_; row++) { @@ -375,12 +373,11 @@ void ColumnMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( ksl_.ForReturnVoid( "dot.inner.epilg.outer", /*start=*/current_tile_col, - /*end=*/ir_builder_->CreateAdd(columns_llvm, current_tile_col), + /*end=*/b_->CreateAdd(columns_llvm, current_tile_col), /*step=*/1, /*peel_first_iteration=*/false, [&](llvm::Value* col, llvm::Value* is_first_scalar_col) { llvm::Value* rhs_element = vsl_.LoadScalar(rhs_, col); - llvm::Value* total_offset = - ir_builder_->CreateMul(col, ir_builder_->getInt64(m())); + llvm::Value* total_offset = b_->CreateMul(col, b_->getInt64(m())); llvm::Value* lhs_base_pointer = vsl_.ComputeOffsetPointer(lhs_, total_offset); ksl_.ForReturnVoid( @@ -388,9 +385,8 @@ void ColumnMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( /*step=*/1, [&](llvm::Value* scalar_row) { llvm::Value* product = vsl_.Mul( vsl_.LoadScalar(lhs_base_pointer, scalar_row), rhs_element); - llvm::Value* setting_result_first_time = ir_builder_->CreateAnd( - is_first_scalar_col, - ir_builder_->getInt1(is_first_tiled_column)); + llvm::Value* setting_result_first_time = b_->CreateAnd( + is_first_scalar_col, b_->getInt1(is_first_tiled_column)); ksl_.IfReturnVoid( setting_result_first_time, /*true_block_generator=*/ @@ -478,16 +474,15 @@ class RowMajorMatrixVectorProductEmitter RowMajorMatrixVectorProductEmitter(const Config& config, llvm::Value* lhs, llvm::Value* rhs, llvm::Value* addend, - llvm::Value* result, - llvm::IRBuilder<>* ir_builder) + llvm::Value* result, llvm::IRBuilder<>* b) : config_(config), lhs_(lhs), rhs_(rhs), addend_(addend), result_(result), - ir_builder_(ir_builder), - ksl_(ir_builder_), - vsl_(scalar_type(), /*vector_size=*/tile_cols(), ir_builder_, "") { + b_(b), + ksl_(b_), + vsl_(scalar_type(), /*vector_size=*/tile_cols(), b_, "") { CHECK(tile_cols() > 0 && IsPowerOfTwo(static_cast(tile_cols()))); CHECK(!has_addend() || addend != nullptr); } @@ -498,7 +493,7 @@ class RowMajorMatrixVectorProductEmitter private: MemoryTile GetLhsMemoryTile(llvm::Value* row_start, int64 row_count) { - return MemoryTile(&vsl_, ir_builder_, /*matrix=*/lhs_, + return MemoryTile(&vsl_, b_, /*matrix=*/lhs_, /*matrix_size_along_minor_dim=*/k(), /*major_dim_offset=*/row_start, /*tile_size_along_major_dim=*/row_count); @@ -517,7 +512,7 @@ class RowMajorMatrixVectorProductEmitter llvm::Value* rhs_; llvm::Value* addend_; llvm::Value* result_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; KernelSupportLibrary ksl_; VectorSupportLibrary vsl_; }; @@ -559,7 +554,7 @@ void RowMajorMatrixVectorProductEmitter::EmitOuterLoopBody(llvm::Value* row, for (int i = 0; i < row_count; i++) { llvm::Value* result_value = vsl_.Add(horizontal_sums[i], scalar_accumulators[i].Get()); - llvm::Value* offset = ir_builder_->CreateAdd(ir_builder_->getInt64(i), row); + llvm::Value* offset = b_->CreateAdd(b_->getInt64(i), row); if (addend_ && row_count != vsl_.vector_size()) { result_value = vsl_.Add(vsl_.LoadScalar(addend_, offset), result_value); } @@ -578,7 +573,7 @@ void RowMajorMatrixVectorProductEmitter::Emit() { [&](llvm::Value* row) { EmitOuterLoopBody(row, tile_rows()); }); if (row_remainder != 0) { - EmitOuterLoopBody(ir_builder_->getInt64(row_limit), row_remainder); + EmitOuterLoopBody(b_->getInt64(row_limit), row_remainder); } } @@ -609,9 +604,8 @@ void RowMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( } for (int r = 0; r < rows; r++) { - llvm::Value* total_offset = ir_builder_->CreateMul( - ir_builder_->CreateAdd(ir_builder_->getInt64(r), current_tile_row), - ir_builder_->getInt64(k())); + llvm::Value* total_offset = b_->CreateMul( + b_->CreateAdd(b_->getInt64(r), current_tile_row), b_->getInt64(k())); llvm::Value* lhs_base_pointer = vsl_.ComputeOffsetPointer(lhs_, total_offset); ksl_.ForReturnVoid( @@ -722,13 +716,13 @@ class MatrixMatrixBlockPanelEmitter { // `lhs` with `rhs` and stores the result in `result`. explicit MatrixMatrixBlockPanelEmitter(Config config, llvm::Value* lhs, llvm::Value* rhs, llvm::Value* result, - llvm::IRBuilder<>* ir_builder) + llvm::IRBuilder<>* b) : lhs_(lhs), rhs_(rhs), result_(result), config_(config), - ir_builder_(ir_builder), - ksl_(ir_builder_) { + b_(b), + ksl_(b_) { CHECK(max_vectorization_width() > 0 && IsPowerOfTwo(static_cast(max_vectorization_width()))); CHECK_GT(max_vector_count(), 0); @@ -761,7 +755,7 @@ class MatrixMatrixBlockPanelEmitter { int64 tile_size_m, llvm::Value* m_start, llvm::Value* m_end); - llvm::Value* GetInt64(int64 value) { return ir_builder_->getInt64(value); } + llvm::Value* GetInt64(int64 value) { return b_->getInt64(value); } Config config() const { return config_; } Dimensions dims() const { return config().dims(); } @@ -782,7 +776,7 @@ class MatrixMatrixBlockPanelEmitter { llvm::Value* result_; Config config_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; KernelSupportLibrary ksl_; }; @@ -804,8 +798,8 @@ void MatrixMatrixBlockPanelEmitter::HandleResiduesOnN() { current_vectorization_width >= min_vectorization_width()) { int64 n_end = dims().n() - (dims().n() % current_vectorization_width); if (n_start != n_end) { - VectorSupportLibrary vsl(scalar_type(), current_vectorization_width, - ir_builder_, "gebp"); + VectorSupportLibrary vsl(scalar_type(), current_vectorization_width, b_, + "gebp"); HandleResiduesOnK(&vsl, GetInt64(n_start), GetInt64(n_end)); n_start = n_end; } @@ -819,10 +813,9 @@ void MatrixMatrixBlockPanelEmitter::HandleResiduesOnN() { } if (n_start != dims().n()) { - VectorSupportLibrary vsl(scalar_type(), 1, ir_builder_, "gebp"); + VectorSupportLibrary vsl(scalar_type(), 1, b_, "gebp"); ksl_.ForReturnVoid("epi.n", n_start, dims().n(), 1, [&](llvm::Value* n_i) { - llvm::Value* n_i_next = - ir_builder_->CreateAdd(n_i, ir_builder_->getInt64(1)); + llvm::Value* n_i_next = b_->CreateAdd(n_i, b_->getInt64(1)); HandleResiduesOnK(&vsl, n_i, n_i_next); }); } @@ -935,11 +928,11 @@ void MatrixMatrixBlockPanelEmitter::EmitTiledGemm( ksl_.ForReturnVoid( "dot.m", m_start, m_end, tile_size_m, [&](llvm::Value* m_i) { MemoryTile result_memory_tile( - vsl, ir_builder_, /*matrix=*/result_, + vsl, b_, /*matrix=*/result_, /*matrix_size_along_minor_dim=*/dims().n(), /*major_dim_offset=*/m_i, /*tile_size_along_major_dim=*/tile_size_m); - MemoryTile lhs_memory_tile(vsl, ir_builder_, /*matrix=*/lhs_, + MemoryTile lhs_memory_tile(vsl, b_, /*matrix=*/lhs_, /*matrix_size_along_minor_dim=*/dims().k(), /*major_dim_offset=*/m_i, /*tile_size_along_major_dim=*/tile_size_m); @@ -949,8 +942,8 @@ void MatrixMatrixBlockPanelEmitter::EmitTiledGemm( result_memory_tile.LoadTile(n_i)); ksl_.ForReturnVoid( "dot.k", k_start, k_end, tile_size_k, [&](llvm::Value* k_i) { - MemoryTile rhs_memory_tile(vsl, ir_builder_, rhs_, - dims().n(), k_i, tile_size_k); + MemoryTile rhs_memory_tile(vsl, b_, rhs_, dims().n(), k_i, + tile_size_k); std::vector> lhs_tile = lhs_memory_tile.LoadBroadcastTile(k_i, tile_size_k); std::vector rhs_tile = @@ -980,7 +973,7 @@ DotOpEmitter::DotOpEmitter(const HloInstruction& dot, const llvm_ir::IrArray& rhs_array, const llvm_ir::IrArray* addend_array, llvm::Value* executable_run_options_value, - llvm::IRBuilder<>* ir_builder, + llvm::IRBuilder<>* b, const HloModuleConfig& hlo_module_config, const TargetMachineFeatures& target_machine_features) : dot_(dot), @@ -989,7 +982,7 @@ DotOpEmitter::DotOpEmitter(const HloInstruction& dot, rhs_array_(rhs_array), addend_array_(addend_array), executable_run_options_value_(executable_run_options_value), - ir_builder_(ir_builder), + b_(b), hlo_module_config_(hlo_module_config), target_machine_features_(target_machine_features) {} @@ -997,15 +990,14 @@ DotOpEmitter::DotOpEmitter(const HloInstruction& dot, const HloInstruction& dot, const llvm_ir::IrArray& target_array, const llvm_ir::IrArray& lhs_array, const llvm_ir::IrArray& rhs_array, const llvm_ir::IrArray* addend_array, - llvm::Value* executable_run_options_value, llvm::IRBuilder<>* ir_builder, + llvm::Value* executable_run_options_value, llvm::IRBuilder<>* b, const HloModuleConfig& hlo_module_config, const TargetMachineFeatures& target_machine_features) { PrimitiveType type = target_array.GetShape().element_type(); TF_RET_CHECK(F16 == type || F32 == type || F64 == type || C64 == type); DotOpEmitter dot_emitter(dot, target_array, lhs_array, rhs_array, - addend_array, executable_run_options_value, - ir_builder, hlo_module_config, - target_machine_features); + addend_array, executable_run_options_value, b, + hlo_module_config, target_machine_features); return dot_emitter.Emit(); } @@ -1050,13 +1042,13 @@ bool DotOpEmitter::EmitExperimentalGebpDotIfEnabled( } int64 size_bytes = m * n * ShapeUtil::ByteSizeOfPrimitiveType(primitive_type); - ir_builder_->CreateMemSet( - target, ir_builder_->getInt8(0), size_bytes, + b_->CreateMemSet( + target, b_->getInt8(0), size_bytes, target_machine_features_.minimum_alignment_for_allocation(size_bytes)); int64 max_target_vector_width = target_machine_features_.vector_register_num_elements( - *ir_builder_->GetInsertBlock()->getParent(), primitive_type); + *b_->GetInsertBlock()->getParent(), primitive_type); int64 tile_size_m, tile_size_k, tile_size_n_in_vector_width; std::tie(tile_size_m, tile_size_k, tile_size_n_in_vector_width) = @@ -1080,12 +1072,12 @@ bool DotOpEmitter::EmitExperimentalGebpDotIfEnabled( KernelSupportLibrary::EmitAndCallOutlinedKernel( /*enable_fast_math=*/enable_fast_math, - /*optimize_for_size=*/optimize_for_size, ir_builder_, - config.GetCacheKey(), lhs, rhs, target, + /*optimize_for_size=*/optimize_for_size, b_, config.GetCacheKey(), lhs, + rhs, target, [this, config](llvm::Value* lhs, llvm::Value* rhs, llvm::Value* target) { - MatrixMatrixBlockPanelEmitter gebp_emitter( - config, /*lhs=*/lhs, /*rhs=*/rhs, - /*result=*/target, ir_builder_); + MatrixMatrixBlockPanelEmitter gebp_emitter(config, /*lhs=*/lhs, + /*rhs=*/rhs, + /*result=*/target, b_); gebp_emitter.Emit(); }); @@ -1163,7 +1155,7 @@ bool DotOpEmitter::EmitLlvmIrDotIfProfitable() { const int target_vector_register_element_size = target_machine_features_.vector_register_num_elements( - *ir_builder_->GetInsertBlock()->getParent(), primitive_type); + *b_->GetInsertBlock()->getParent(), primitive_type); // We may not always know the vector register size for the target we're // compiling against, in which case target_vector_register_element_size is 0. @@ -1184,13 +1176,13 @@ bool DotOpEmitter::EmitLlvmIrDotIfProfitable() { KernelSupportLibrary::EmitAndCallOutlinedKernel( /*enable_fast_math=*/enable_fast_math, - /*optimize_for_size=*/optimize_for_size, ir_builder_, - config.GetCacheKey(), lhs_op, rhs_op, + /*optimize_for_size=*/optimize_for_size, b_, config.GetCacheKey(), + lhs_op, rhs_op, addend_array_ ? addend_array_->GetBasePointer() : nullptr, result_op, [this, config](llvm::Value* lhs_op, llvm::Value* rhs_op, llvm::Value* addend_op, llvm::Value* result_op) { ColumnMajorMatrixVectorProductEmitter emitter( - config, lhs_op, rhs_op, addend_op, result_op, ir_builder_); + config, lhs_op, rhs_op, addend_op, result_op, b_); emitter.Emit(); }); } else { @@ -1203,13 +1195,13 @@ bool DotOpEmitter::EmitLlvmIrDotIfProfitable() { KernelSupportLibrary::EmitAndCallOutlinedKernel( /*enable_fast_math=*/enable_fast_math, - /*optimize_for_size=*/optimize_for_size, ir_builder_, - config.GetCacheKey(), lhs_op, rhs_op, + /*optimize_for_size=*/optimize_for_size, b_, config.GetCacheKey(), + lhs_op, rhs_op, addend_array_ ? addend_array_->GetBasePointer() : nullptr, result_op, [this, config](llvm::Value* lhs_op, llvm::Value* rhs_op, llvm::Value* addend_op, llvm::Value* result_op) { - RowMajorMatrixVectorProductEmitter emitter( - config, lhs_op, rhs_op, addend_op, result_op, ir_builder_); + RowMajorMatrixVectorProductEmitter emitter(config, lhs_op, rhs_op, + addend_op, result_op, b_); emitter.Emit(); }); } @@ -1285,11 +1277,11 @@ Status DotOpEmitter::Emit() { // Create loop nests which loop through the LHS operand dimensions and the RHS // operand dimensions. The reduction dimension of the LHS and RHS are handled // in a separate innermost loop which performs the sum of products. - llvm_ir::ForLoopNest loop_nest(llvm_ir::IrName(&dot_), ir_builder_); - llvm_ir::IrArray::Index lhs_index = EmitOperandArrayLoopNest( - &loop_nest, lhs_array_, lhs_reduction_dimension, "lhs"); - llvm_ir::IrArray::Index rhs_index = EmitOperandArrayLoopNest( - &loop_nest, rhs_array_, rhs_reduction_dimension, "rhs"); + llvm_ir::ForLoopNest loop_nest(llvm_ir::IrName(&dot_), b_); + llvm_ir::IrArray::Index lhs_index = loop_nest.EmitOperandArrayLoopNest( + lhs_array_, /*dimension_to_skip=*/lhs_reduction_dimension, "lhs"); + llvm_ir::IrArray::Index rhs_index = loop_nest.EmitOperandArrayLoopNest( + rhs_array_, /*dimension_to_skip=*/rhs_reduction_dimension, "rhs"); // Create the loop which does the sum of products reduction. // @@ -1319,62 +1311,55 @@ Status DotOpEmitter::Emit() { // Function entry basic block. // - Emit alloca for accumulator llvm::Function* func = reduction_loop->GetPreheaderBasicBlock()->getParent(); - SetToFirstInsertPoint(&func->getEntryBlock(), ir_builder_); + SetToFirstInsertPoint(&func->getEntryBlock(), b_); llvm::Type* accum_type = target_array_.GetElementLlvmType(); - llvm::Value* accum_address = ir_builder_->CreateAlloca( - accum_type, /*ArraySize=*/nullptr, "accum_address"); + llvm::Value* accum_address = + b_->CreateAlloca(accum_type, /*ArraySize=*/nullptr, "accum_address"); // Preheader basic block of reduction loop: // - Initialize accumulator to zero. llvm::BasicBlock* preheader_bb = reduction_loop->GetPreheaderBasicBlock(); - ir_builder_->SetInsertPoint(preheader_bb->getTerminator()); + b_->SetInsertPoint(preheader_bb->getTerminator()); - ir_builder_->CreateStore(llvm::Constant::getNullValue(accum_type), - accum_address); + b_->CreateStore(llvm::Constant::getNullValue(accum_type), accum_address); // Body basic block of reduction loop: // - Load elements from lhs and rhs array. // - Multiply lhs-element and rhs-element. // - Load accumulator and add to product. // - Store sum back into accumulator. - SetToFirstInsertPoint(reduction_loop->GetBodyBasicBlock(), ir_builder_); + SetToFirstInsertPoint(reduction_loop->GetBodyBasicBlock(), b_); - llvm::Value* lhs_element = - lhs_array_.EmitReadArrayElement(lhs_index, ir_builder_); - llvm::Value* rhs_element = - rhs_array_.EmitReadArrayElement(rhs_index, ir_builder_); + llvm::Value* lhs_element = lhs_array_.EmitReadArrayElement(lhs_index, b_); + llvm::Value* rhs_element = rhs_array_.EmitReadArrayElement(rhs_index, b_); - llvm::Value* accum = ir_builder_->CreateLoad(accum_address); + llvm::Value* accum = b_->CreateLoad(accum_address); llvm::Value* updated_accum; if (ShapeUtil::ElementIsComplex(lhs_shape)) { - auto real = [&](llvm::Value* x) { - return ir_builder_->CreateExtractValue(x, {0}); - }; - auto imag = [&](llvm::Value* x) { - return ir_builder_->CreateExtractValue(x, {1}); - }; - llvm::Value* product_real = ir_builder_->CreateFSub( - ir_builder_->CreateFMul(real(lhs_element), real(rhs_element)), - ir_builder_->CreateFMul(imag(lhs_element), imag(rhs_element))); - llvm::Value* product_imag = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(real(lhs_element), imag(rhs_element)), - ir_builder_->CreateFMul(imag(lhs_element), real(rhs_element))); - updated_accum = ir_builder_->CreateInsertValue( - accum, ir_builder_->CreateFAdd(real(accum), product_real), {0}); - updated_accum = ir_builder_->CreateInsertValue( - updated_accum, ir_builder_->CreateFAdd(imag(accum), product_imag), {1}); + auto real = [&](llvm::Value* x) { return b_->CreateExtractValue(x, {0}); }; + auto imag = [&](llvm::Value* x) { return b_->CreateExtractValue(x, {1}); }; + llvm::Value* product_real = + b_->CreateFSub(b_->CreateFMul(real(lhs_element), real(rhs_element)), + b_->CreateFMul(imag(lhs_element), imag(rhs_element))); + llvm::Value* product_imag = + b_->CreateFAdd(b_->CreateFMul(real(lhs_element), imag(rhs_element)), + b_->CreateFMul(imag(lhs_element), real(rhs_element))); + updated_accum = b_->CreateInsertValue( + accum, b_->CreateFAdd(real(accum), product_real), {0}); + updated_accum = b_->CreateInsertValue( + updated_accum, b_->CreateFAdd(imag(accum), product_imag), {1}); } else { - llvm::Value* product = ir_builder_->CreateFMul(lhs_element, rhs_element); - updated_accum = ir_builder_->CreateFAdd(accum, product); + llvm::Value* product = b_->CreateFMul(lhs_element, rhs_element); + updated_accum = b_->CreateFAdd(accum, product); } - ir_builder_->CreateStore(updated_accum, accum_address); + b_->CreateStore(updated_accum, accum_address); // Exit basic block of reduction loop. // - Load accumulator value (the result). // - Store into output array. - SetToFirstInsertPoint(reduction_loop->GetExitBasicBlock(), ir_builder_); + SetToFirstInsertPoint(reduction_loop->GetExitBasicBlock(), b_); - llvm::Value* result = ir_builder_->CreateLoad(accum_address); + llvm::Value* result = b_->CreateLoad(accum_address); // Create index into target address. The target index is the concatenation of // the rhs and lhs indexes with the reduction dimensions removed. The terms @@ -1392,11 +1377,11 @@ Status DotOpEmitter::Emit() { } } - target_array_.EmitWriteArrayElement(target_index, result, ir_builder_); + target_array_.EmitWriteArrayElement(target_index, result, b_); // Set the IR builder insert point to the exit basic block of the outer most // loop. - ir_builder_->SetInsertPoint(loop_nest.GetOuterLoopExitBasicBlock()); + b_->SetInsertPoint(loop_nest.GetOuterLoopExitBasicBlock()); return Status::OK(); } @@ -1405,31 +1390,30 @@ Status DotOpEmitter::EmitScalarDot() { // A scalar dot is just a scalar multiply. llvm::Value* result; // Use the same index_type for all tensor accesses in the same kernel. - llvm::Type* index_type = ir_builder_->getInt64Ty(); + llvm::Type* index_type = b_->getInt64Ty(); llvm_ir::IrArray::Index element_index(index_type); llvm::Value* lhs_value = - lhs_array_.EmitReadArrayElement(/*index=*/element_index, ir_builder_); + lhs_array_.EmitReadArrayElement(/*index=*/element_index, b_); llvm::Value* rhs_value = - rhs_array_.EmitReadArrayElement(/*index=*/element_index, ir_builder_); + rhs_array_.EmitReadArrayElement(/*index=*/element_index, b_); if (ShapeUtil::ElementIsComplex(lhs_array_.GetShape())) { -#define REAL(x) ir_builder_->CreateExtractValue(x, {0}) -#define IMAG(x) ir_builder_->CreateExtractValue(x, {1}) - llvm::Value* real = ir_builder_->CreateFSub( - ir_builder_->CreateFMul(REAL(lhs_value), REAL(rhs_value)), - ir_builder_->CreateFMul(IMAG(lhs_value), IMAG(rhs_value))); - llvm::Value* imag = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(REAL(lhs_value), IMAG(rhs_value)), - ir_builder_->CreateFMul(IMAG(lhs_value), REAL(rhs_value))); +#define REAL(x) b_->CreateExtractValue(x, {0}) +#define IMAG(x) b_->CreateExtractValue(x, {1}) + llvm::Value* real = + b_->CreateFSub(b_->CreateFMul(REAL(lhs_value), REAL(rhs_value)), + b_->CreateFMul(IMAG(lhs_value), IMAG(rhs_value))); + llvm::Value* imag = + b_->CreateFAdd(b_->CreateFMul(REAL(lhs_value), IMAG(rhs_value)), + b_->CreateFMul(IMAG(lhs_value), REAL(rhs_value))); #undef IMAG #undef REAL result = llvm::ConstantAggregateZero::get(lhs_array_.GetElementLlvmType()); - result = ir_builder_->CreateInsertValue(result, real, {0}); - result = ir_builder_->CreateInsertValue(result, imag, {1}); + result = b_->CreateInsertValue(result, real, {0}); + result = b_->CreateInsertValue(result, imag, {1}); } else { - result = ir_builder_->CreateFMul(lhs_value, rhs_value); + result = b_->CreateFMul(lhs_value, rhs_value); } - target_array_.EmitWriteArrayElement(/*index=*/element_index, result, - ir_builder_); + target_array_.EmitWriteArrayElement(/*index=*/element_index, result, b_); return Status::OK(); } @@ -1452,7 +1436,7 @@ Status DotOpEmitter::EmitCallToRuntime() { fn_name = multi_threaded ? runtime::kEigenMatMulF16SymbolName : runtime::kEigenSingleThreadedMatMulF16SymbolName; - float_type = ir_builder_->getHalfTy(); + float_type = b_->getHalfTy(); break; case F32: fn_name = multi_threaded @@ -1461,7 +1445,7 @@ Status DotOpEmitter::EmitCallToRuntime() { : (use_mkl_dnn ? runtime::kMKLSingleThreadedMatMulF32SymbolName : runtime::kEigenSingleThreadedMatMulF32SymbolName); - float_type = ir_builder_->getFloatTy(); + float_type = b_->getFloatTy(); break; case F64: fn_name = multi_threaded @@ -1470,7 +1454,7 @@ Status DotOpEmitter::EmitCallToRuntime() { : (use_mkl_dnn ? runtime::kMKLSingleThreadedMatMulF64SymbolName : runtime::kEigenSingleThreadedMatMulF64SymbolName); - float_type = ir_builder_->getDoubleTy(); + float_type = b_->getDoubleTy(); break; default: return Unimplemented("Invalid type %s for dot operation", @@ -1478,16 +1462,16 @@ Status DotOpEmitter::EmitCallToRuntime() { } llvm::Type* float_ptr_type = float_type->getPointerTo(); - llvm::Type* int64_type = ir_builder_->getInt64Ty(); - llvm::Type* int32_type = ir_builder_->getInt32Ty(); - llvm::Type* int8_ptr_type = ir_builder_->getInt8Ty()->getPointerTo(); + llvm::Type* int64_type = b_->getInt64Ty(); + llvm::Type* int32_type = b_->getInt32Ty(); + llvm::Type* int8_ptr_type = b_->getInt8Ty()->getPointerTo(); llvm::FunctionType* matmul_type = llvm::FunctionType::get( - ir_builder_->getVoidTy(), + b_->getVoidTy(), {int8_ptr_type, float_ptr_type, float_ptr_type, float_ptr_type, int64_type, int64_type, int64_type, int32_type, int32_type}, /*isVarArg=*/false); - llvm::Function* function = ir_builder_->GetInsertBlock()->getParent(); + llvm::Function* function = b_->GetInsertBlock()->getParent(); llvm::Module* module = function->getParent(); llvm::Function* matmul_func = llvm::cast( @@ -1522,18 +1506,15 @@ Status DotOpEmitter::EmitCallToRuntime() { std::swap(transpose_lhs, transpose_rhs); } - ir_builder_->CreateCall( + b_->CreateCall( matmul_func, - {ir_builder_->CreateBitCast(executable_run_options_value_, int8_ptr_type), - ir_builder_->CreateBitCast(target_array_.GetBasePointer(), - float_ptr_type), - ir_builder_->CreateBitCast(lhs->GetBasePointer(), float_ptr_type), - ir_builder_->CreateBitCast(rhs->GetBasePointer(), float_ptr_type), - ir_builder_->getInt64(mat_mult_dims.m), - ir_builder_->getInt64(mat_mult_dims.n), - ir_builder_->getInt64(mat_mult_dims.k), - ir_builder_->getInt32(transpose_lhs), - ir_builder_->getInt32(transpose_rhs)}); + {b_->CreateBitCast(executable_run_options_value_, int8_ptr_type), + b_->CreateBitCast(target_array_.GetBasePointer(), float_ptr_type), + b_->CreateBitCast(lhs->GetBasePointer(), float_ptr_type), + b_->CreateBitCast(rhs->GetBasePointer(), float_ptr_type), + b_->getInt64(mat_mult_dims.m), b_->getInt64(mat_mult_dims.n), + b_->getInt64(mat_mult_dims.k), b_->getInt32(transpose_lhs), + b_->getInt32(transpose_rhs)}); return Status::OK(); } @@ -1556,36 +1537,6 @@ DotOpEmitter::MatMultDims DotOpEmitter::GetMatMultDims() const { LayoutUtil::Minor(target_array_.GetShape().layout(), 0) == 0}; } -llvm_ir::IrArray::Index DotOpEmitter::EmitOperandArrayLoopNest( - llvm_ir::ForLoopNest* loop_nest, const llvm_ir::IrArray& operand_array, - int64 reduction_dimension, tensorflow::StringPiece name_suffix) { - // Prepares the dimension list we will use to emit the loop nest. Outermost - // loops are added first. Add loops in major-to-minor order, and skip the - // reduction dimension. - std::vector dimensions; - const Shape& shape = operand_array.GetShape(); - for (int i = LayoutUtil::MinorToMajor(shape).size() - 1; i >= 0; --i) { - int64 dimension = LayoutUtil::Minor(shape.layout(), i); - if (dimension != reduction_dimension) { - dimensions.push_back(dimension); - } - } - - // Create loop nest with one for-loop for each dimension of the - // output. - llvm_ir::IrArray::Index index = - loop_nest->AddLoopsForShapeOnDimensions(shape, dimensions, name_suffix); - // Verify every dimension except the reduction dimension was set in the index. - for (int dimension = 0; dimension < index.size(); ++dimension) { - if (dimension == reduction_dimension) { - DCHECK_EQ(nullptr, index[dimension]); - } else { - DCHECK_NE(nullptr, index[dimension]); - } - } - return index; -} - // Return whether the given shape is a matrix with no padding. static bool IsRank2WithNoPadding(const Shape& shape) { return ShapeUtil::Rank(shape) == 2 && !LayoutUtil::IsPadded(shape); diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h index ed2a18976a0f1a88e7bb4632d3a63167d5c146ad..590032fbe907d7ca90bf69b7ccc3170b8efec72e 100644 --- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h @@ -61,7 +61,7 @@ class DotOpEmitter { const HloInstruction& dot, const llvm_ir::IrArray& target_array, const llvm_ir::IrArray& lhs_array, const llvm_ir::IrArray& rhs_array, const llvm_ir::IrArray* addend_array, - llvm::Value* executable_run_options_value, llvm::IRBuilder<>* ir_builder, + llvm::Value* executable_run_options_value, llvm::IRBuilder<>* b, const HloModuleConfig& hlo_module_config, const TargetMachineFeatures& target_machine_features); @@ -70,8 +70,7 @@ class DotOpEmitter { const llvm_ir::IrArray& lhs_array, const llvm_ir::IrArray& rhs_array, const llvm_ir::IrArray* addend_array, - llvm::Value* executable_run_options_value, - llvm::IRBuilder<>* ir_builder, + llvm::Value* executable_run_options_value, llvm::IRBuilder<>* b, const HloModuleConfig& hlo_module_config, const TargetMachineFeatures& target_machine_features); @@ -89,17 +88,6 @@ class DotOpEmitter { // Emits a call to the CPU runtime to perform the matrix multiply. Status EmitCallToRuntime(); - // Emits a series of nested loops for iterating over an operand array in the - // dot operation. Loops are constructed in major to minor dimension layout - // order. No loop is emitted for the given reduction_dimension. The function - // returns an IrArray index for the given operand_array containing the indvars - // of the loops. All dimensions of the index are filled except for the - // reduction dimension. name_suffix is the string to append to the names of - // LLVM constructs (eg, basic blocks) constructed by this method. - llvm_ir::IrArray::Index EmitOperandArrayLoopNest( - llvm_ir::ForLoopNest* loop_nest, const llvm_ir::IrArray& operand_array, - int64 reduction_dimension, tensorflow::StringPiece name_suffix); - // Represents the dimensions of a matrix-matrix multiply operation. struct MatMultDims { // The number of rows in the LHS. @@ -171,7 +159,7 @@ class DotOpEmitter { const llvm_ir::IrArray& rhs_array_; const llvm_ir::IrArray* addend_array_; llvm::Value* executable_run_options_value_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; const HloModuleConfig& hlo_module_config_; const TargetMachineFeatures& target_machine_features_; }; diff --git a/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.cc index e97113dfa0f59e791d614c0093d0781e49c48ee4..cf955a8add394c204673be0746a451d4edcadc96 100644 --- a/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.cc @@ -38,8 +38,7 @@ StatusOr CpuElementalIrEmitter::EmitFloatUnaryOp( switch (element_type) { case F16: cast_result_to_fp16 = true; - operand_value = ir_builder_->CreateFPCast(operand_value, - ir_builder_->getFloatTy()); + operand_value = b_->CreateFPCast(operand_value, b_->getFloatTy()); TF_FALLTHROUGH_INTENDED; case F32: function_name = "tanhf"; @@ -59,9 +58,9 @@ StatusOr CpuElementalIrEmitter::EmitFloatUnaryOp( function->setDoesNotThrow(); function->setDoesNotAccessMemory(); // Create an instruction to call the function. - llvm::Value* result = ir_builder_->CreateCall(function, operand_value); + llvm::Value* result = b_->CreateCall(function, operand_value); if (cast_result_to_fp16) { - result = ir_builder_->CreateFPCast(result, ir_builder_->getHalfTy()); + result = b_->CreateFPCast(result, b_->getHalfTy()); } return result; } @@ -77,8 +76,8 @@ StatusOr CpuElementalIrEmitter::EmitAtan2( switch (prim_type) { case F16: cast_result_to_fp16 = true; - lhs = ir_builder_->CreateFPCast(lhs, ir_builder_->getFloatTy()); - rhs = ir_builder_->CreateFPCast(rhs, ir_builder_->getFloatTy()); + lhs = b_->CreateFPCast(lhs, b_->getFloatTy()); + rhs = b_->CreateFPCast(rhs, b_->getFloatTy()); TF_FALLTHROUGH_INTENDED; case F32: function_name = "atan2f"; @@ -98,9 +97,9 @@ StatusOr CpuElementalIrEmitter::EmitAtan2( function->setDoesNotThrow(); function->setDoesNotAccessMemory(); // Create an instruction to call the function. - llvm::Value* result = ir_builder_->CreateCall(function, {lhs, rhs}); + llvm::Value* result = b_->CreateCall(function, {lhs, rhs}); if (cast_result_to_fp16) { - result = ir_builder_->CreateFPCast(result, ir_builder_->getHalfTy()); + result = b_->CreateFPCast(result, b_->getHalfTy()); } return result; } diff --git a/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.h index 4446dfd2821fb4b6e75f33694367392ecbcdd8bf..9598a886ab49fcecf5df7bd65f425fe485de3574 100644 --- a/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.h @@ -31,7 +31,7 @@ class CpuElementalIrEmitter : public ElementalIrEmitter { public: CpuElementalIrEmitter(const HloModuleConfig& module_config, IrEmitter* ir_emitter, llvm::Module* module) - : ElementalIrEmitter(module_config, module, ir_emitter->ir_builder()), + : ElementalIrEmitter(module_config, module, ir_emitter->b()), ir_emitter_(ir_emitter) {} llvm_ir::ElementGenerator MakeElementGenerator( diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 6b9a1d8c01aee46e271bc5a950e1a4bb45b7b822..a6d8551841dcba8b81e257f3deb2aacf9b8aff4a 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -51,10 +51,11 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_casting_utils.h" #include "tensorflow/compiler/xla/service/hlo_instructions.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h" +#include "tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" -#include "tensorflow/compiler/xla/service/llvm_ir/ops.h" #include "tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -89,14 +90,14 @@ IrEmitter::IrEmitter( : assignment_(assignment), module_(llvm_module), arch_type_(llvm::Triple(llvm_module->getTargetTriple()).getArch()), - ir_builder_(llvm_module->getContext()), + b_(llvm_module->getContext()), instruction_to_profile_idx_(std::move(instruction_to_profile_idx)), computation_to_profile_idx_(std::move(computation_to_profile_idx)), alias_analysis_(hlo_module, assignment, &llvm_module->getContext()), hlo_module_config_(hlo_module.config()), is_top_level_computation_(false), target_machine_features_(*target_machine_features) { - ir_builder_.setFastMathFlags(llvm_ir::GetFastMathFlags( + b_.setFastMathFlags(llvm_ir::GetFastMathFlags( /*fast_math_enabled=*/hlo_module_config_.debug_options() .xla_enable_fast_math())); } @@ -146,7 +147,7 @@ void IrEmitter::InitializeIrFunction(const string& function_name) { new IrFunction(function_name, linkage, options::OptimizeForSizeRequested(hlo_module_config_), hlo_module_config_.debug_options().xla_enable_fast_math(), - module_, &ir_builder_, num_dynamic_loop_bounds_)); + module_, &b_, num_dynamic_loop_bounds_)); } IrEmitter::~IrEmitter() {} @@ -154,9 +155,9 @@ IrEmitter::~IrEmitter() {} Status IrEmitter::HandleBitcast(HloInstruction* bitcast) { VLOG(2) << "HandleBitcast: " << bitcast->ToString(); emitted_value_[bitcast] = - ir_builder_.CreateBitCast(GetEmittedValueFor(bitcast->operand(0)), - IrShapeType(bitcast->shape())->getPointerTo(), - AsStringRef(IrName(bitcast))); + b_.CreateBitCast(GetEmittedValueFor(bitcast->operand(0)), + IrShapeType(bitcast->shape())->getPointerTo(), + AsStringRef(IrName(bitcast))); return Status::OK(); } @@ -175,25 +176,36 @@ llvm::Constant* IrEmitter::EmitGlobalForLiteral(const Literal& literal) { result_global, IrShapeType(literal.shape())->getPointerTo()); } -Status IrEmitter::HandleConstant(HloInstruction* constant) { - VLOG(2) << "HandleConstant: " << constant->ToString(); - const Literal& literal = constant->literal(); - llvm::Constant* global_for_const; +Status IrEmitter::EmitConstantGlobals() { + for (const BufferAllocation& allocation : assignment_.Allocations()) { + if (!allocation.is_constant()) { + continue; + } - auto it = emitted_literals_.find(&literal); - if (it != emitted_literals_.end()) { - global_for_const = it->second; - } else { - global_for_const = EmitGlobalForLiteral(literal); - emitted_literals_[&literal] = global_for_const; + const Literal& literal = llvm_ir::LiteralForConstantAllocation(allocation); + llvm::Constant* global_for_const; + auto it = emitted_literals_.find(&literal); + if (it != emitted_literals_.end()) { + global_for_const = it->second; + } else { + global_for_const = EmitGlobalForLiteral(literal); + InsertOrDie(&emitted_literals_, &literal, global_for_const); + } + + InsertOrDie(&constant_buffer_to_global_, allocation.index(), + global_for_const); } - emitted_value_[constant] = global_for_const; - VLOG(2) << " emitted value: " << llvm_ir::DumpToString(*global_for_const); - VLOG(2) << " its type: " - << llvm_ir::DumpToString(*global_for_const->getType()); + return Status::OK(); } +Status IrEmitter::HandleConstant(HloInstruction* constant) { + VLOG(2) << "HandleConstant: " << constant->ToString(); + // IrEmitter::EmitConstantGlobals has already taken care of emitting the body + // of the constant. + return EmitTargetAddressForOp(constant); +} + Status IrEmitter::HandleCopy(HloInstruction* copy) { if (ShapeUtil::IsTuple(copy->shape())) { // kCopy shallow copies a tuple so just memcpy the top-level buffer. @@ -273,7 +285,7 @@ Status IrEmitter::HandleGetTupleElement(HloInstruction* get_tuple_element) { const Shape& shape = get_tuple_element->shape(); emitted_value_[get_tuple_element] = llvm_ir::EmitGetTupleElement( shape, get_tuple_element->tuple_index(), MinimumAlignmentForShape(shape), - GetEmittedValueFor(operand), &ir_builder_, module_); + GetEmittedValueFor(operand), &b_, module_); return Status::OK(); } @@ -293,7 +305,7 @@ Status IrEmitter::HandleTupleSelect(HloInstruction* tuple_select) { TF_RETURN_IF_ERROR(EmitTargetAddressForOp(tuple_select)); llvm_ir::EmitTupleSelect(GetIrArrayFor(tuple_select), GetIrArrayFor(pred), GetEmittedValueFor(on_true), - GetEmittedValueFor(on_false), &ir_builder_, module_); + GetEmittedValueFor(on_false), &b_, module_); return Status::OK(); } @@ -316,8 +328,8 @@ Status IrEmitter::HandleInfeed(HloInstruction* instruction) { assignment_.GetUniqueSlice(infeed, {1})); llvm::Value* token_address = EmitTempBufferPointer( token_slice, ShapeUtil::GetTupleElementShape(infeed->shape(), 1)); - llvm_ir::EmitTuple(GetIrArrayFor(infeed), {data_address, token_address}, - &ir_builder_, module_); + llvm_ir::EmitTuple(GetIrArrayFor(infeed), {data_address, token_address}, &b_, + module_); if (ShapeUtil::IsTuple(data_shape)) { TF_RET_CHECK(!ShapeUtil::IsNestedTuple(data_shape)); @@ -348,7 +360,7 @@ Status IrEmitter::HandleInfeed(HloInstruction* instruction) { } llvm_ir::EmitTuple(llvm_ir::IrArray(data_address, data_shape), - tuple_element_addresses, &ir_builder_, module_); + tuple_element_addresses, &b_, module_); } else { TF_RETURN_IF_ERROR( EmitXfeedTransfer(XfeedKind::kInfeed, data_shape, data_address)); @@ -369,14 +381,14 @@ Status IrEmitter::EmitXfeedTransfer(XfeedKind kind, const Shape& shape, int32 length_32 = static_cast(length); int32 shape_length; - TF_ASSIGN_OR_RETURN(llvm::Value * shape_ptr, - llvm_ir::EncodeSelfDescribingShapeConstant( - shape, &shape_length, &ir_builder_)); + TF_ASSIGN_OR_RETURN( + llvm::Value * shape_ptr, + llvm_ir::EncodeSelfDescribingShapeConstant(shape, &shape_length, &b_)); // The signature of the acquire infeed buffer function is: // // (void*)(int32 length); - llvm::Type* int32_type = ir_builder_.getInt32Ty(); + llvm::Type* int32_type = b_.getInt32Ty(); llvm::Type* i8_ptr_type = llvm::Type::getInt8PtrTy(module_->getContext()); llvm::FunctionType* acquire_type = llvm::FunctionType::get( i8_ptr_type, {int32_type, i8_ptr_type, int32_type}, @@ -396,8 +408,7 @@ Status IrEmitter::EmitXfeedTransfer(XfeedKind kind, const Shape& shape, // // (void)(int32 length, void* buffer); llvm::FunctionType* release_type = llvm::FunctionType::get( - ir_builder_.getVoidTy(), - {int32_type, i8_ptr_type, i8_ptr_type, int32_type}, + b_.getVoidTy(), {int32_type, i8_ptr_type, i8_ptr_type, int32_type}, /*isVarArg=*/false); llvm::Function* release_func; @@ -414,25 +425,22 @@ Status IrEmitter::EmitXfeedTransfer(XfeedKind kind, const Shape& shape, // of size exactly 'length_32', and the runtime is responsible for // check-failing the process if there is a mismatch, versus passing us back a // buffer that we might overrun. - llvm::Value* acquired_pointer = ir_builder_.CreateCall( - acquire_func, {ir_builder_.getInt32(length_32), shape_ptr, - ir_builder_.getInt32(shape_length)}); + llvm::Value* acquired_pointer = b_.CreateCall( + acquire_func, + {b_.getInt32(length_32), shape_ptr, b_.getInt32(shape_length)}); if (kind == XfeedKind::kInfeed) { // Copy to the program buffer address from the acquired buffer. - ir_builder_.CreateMemCpy(program_buffer_address, /*DstAlign=*/1, - acquired_pointer, - /*SrcAlign=*/1, length_32); + b_.CreateMemCpy(program_buffer_address, /*DstAlign=*/1, acquired_pointer, + /*SrcAlign=*/1, length_32); } else { // Outfeed -- copy from the in-program address to the acquired buffer. - ir_builder_.CreateMemCpy(acquired_pointer, /*DstAlign=*/1, - program_buffer_address, - /*SrcAlign=*/1, length_32); + b_.CreateMemCpy(acquired_pointer, /*DstAlign=*/1, program_buffer_address, + /*SrcAlign=*/1, length_32); } - ir_builder_.CreateCall(release_func, - {ir_builder_.getInt32(length_32), acquired_pointer, - shape_ptr, ir_builder_.getInt32(shape_length)}); + b_.CreateCall(release_func, {b_.getInt32(length_32), acquired_pointer, + shape_ptr, b_.getInt32(shape_length)}); return Status::OK(); } @@ -453,7 +461,7 @@ Status IrEmitter::HandleOutfeed(HloInstruction* outfeed) { ShapeUtil::GetTupleElementShape(operand_shape, i); llvm::Value* tuple_element = llvm_ir::EmitGetTupleElement( tuple_element_shape, i, MinimumAlignmentForShape(tuple_element_shape), - value, &ir_builder_, module_); + value, &b_, module_); TF_RETURN_IF_ERROR(EmitXfeedTransfer(XfeedKind::kOutfeed, tuple_element_shape, tuple_element)); } @@ -472,46 +480,112 @@ Status IrEmitter::HandleTuple(HloInstruction* tuple) { for (auto operand : tuple->operands()) { base_ptrs.push_back(GetEmittedValueFor(operand)); } - llvm_ir::EmitTuple(GetIrArrayFor(tuple), base_ptrs, &ir_builder_, module_); + llvm_ir::EmitTuple(GetIrArrayFor(tuple), base_ptrs, &b_, module_); return Status::OK(); } +StatusOr IrEmitter::EmitTargetElementLoopBodyForMap( + HloMapInstruction* map, const llvm_ir::IrArray::Index& index) { + llvm::Function* mapped_ir_function = + FindOrDie(emitted_functions_, map->to_apply()); + std::vector parameter_addresses; + for (const HloInstruction* operand : map->operands()) { + const llvm_ir::IrArray& array = GetIrArrayFor(operand); + parameter_addresses.push_back(array.EmitArrayElementAddress(index, &b_)); + } + return EmitElementFunctionCall(mapped_ir_function, map->shape(), + parameter_addresses, "map_function"); +} + Status IrEmitter::HandleMap(HloInstruction* map) { - gtl::ArraySlice operands(map->operands()); - HloComputation* function = map->to_apply(); - // The called computation should have been emitted previously. - llvm::Function* mapped_ir_function = FindOrDie(emitted_functions_, function); - - return EmitTargetElementLoop(map, [this, map, operands, mapped_ir_function]( - const llvm_ir::IrArray::Index& index) { - std::vector parameter_addresses; - for (const HloInstruction* operand : operands) { - const llvm_ir::IrArray& array = GetIrArrayFor(operand); - parameter_addresses.push_back( - array.EmitArrayElementAddress(index, &ir_builder_)); - } - return EmitElementFunctionCall(mapped_ir_function, map->shape(), - parameter_addresses, "map_function"); + return EmitTargetElementLoop(map, [&](const llvm_ir::IrArray::Index& index) { + return EmitTargetElementLoopBodyForMap(Cast(map), index); }); } -Status IrEmitter::HandleReduceWindow(HloInstruction* reduce_window) { - auto operand = reduce_window->operand(0); +StatusOr IrEmitter::EmitTargetElementLoopBodyForReduceWindow( + HloReduceWindowInstruction* reduce_window, + const llvm_ir::IrArray::Index& index) { + const HloInstruction* operand = reduce_window->operand(0); const Window& window = reduce_window->window(); HloComputation* function = reduce_window->to_apply(); + // The called computation should have been emitted previously. + llvm::Function* reducer_function = FindOrDie(emitted_functions_, function); + + // We fold inputs into the accumulator and initialize it to + // the initial value on the reduce_window. + PrimitiveType operand_element_type = operand->shape().element_type(); + llvm::Value* accumulator_address = llvm_ir::EmitAllocaAtFunctionEntry( + llvm_ir::PrimitiveTypeToIrType(operand_element_type, module_), + "reduce_window_accumulator_address", &b_, + MinimumAlignmentForPrimitiveType(operand_element_type)); + b_.CreateStore(b_.CreateLoad(GetEmittedValueFor(reduce_window->operand(1))), + accumulator_address); + + llvm_ir::ForLoopNest loops(IrName(reduce_window, "inner"), &b_); + std::vector window_size; + for (const auto& dim : window.dimensions()) { + window_size.push_back(dim.size()); + } + const llvm_ir::IrArray::Index window_index = loops.AddLoopsForShape( + ShapeUtil::MakeShape(operand_element_type, window_size), "window"); + CHECK_EQ(window_index.size(), index.size()); + + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &b_); + + llvm_ir::IrArray::Index input_index(b_.getInt64Ty(), index.size()); + llvm::Value* in_bounds_condition = nullptr; + for (size_t i = 0; i < index.size(); ++i) { + llvm::Value* strided_index = + b_.CreateNSWMul(index[i], b_.getInt64(window.dimensions(i).stride())); + input_index[i] = + b_.CreateNSWSub(b_.CreateNSWAdd(strided_index, window_index[i]), + b_.getInt64(window.dimensions(i).padding_low())); + + // We need to check if 0 <= input_index[i] < bound, as otherwise we are in + // the padding so that we can skip the computation. That is equivalent to + // input_index[i] < bound as an *unsigned* comparison, since a negative + // value will wrap to a large positive value. + llvm::Value* index_condition = b_.CreateICmpULT( + input_index[i], + b_.getInt64(ShapeUtil::GetDimension(operand->shape(), i))); + if (in_bounds_condition == nullptr) { + in_bounds_condition = index_condition; + } else { + in_bounds_condition = b_.CreateAnd(in_bounds_condition, index_condition); + } + } + CHECK(in_bounds_condition != nullptr); + + llvm_ir::LlvmIfData if_data = + llvm_ir::EmitIfThenElse(in_bounds_condition, "in-bounds", &b_); + SetToFirstInsertPoint(if_data.true_block, &b_); + + // We are not in the padding, so carry out the computation. + llvm_ir::IrArray input_array(GetIrArrayFor(operand)); + llvm::Value* input_value_address = + input_array.EmitArrayElementAddress(input_index, &b_); + llvm::Value* result = EmitElementFunctionCall( + reducer_function, reduce_window->shape(), + {accumulator_address, input_value_address}, "reducer_function"); + b_.CreateStore(result, accumulator_address); + + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &b_); + return b_.CreateLoad(accumulator_address); +} + +Status IrEmitter::HandleReduceWindow(HloInstruction* reduce_window) { TF_RETURN_IF_ERROR(ElementTypesSameAndSupported( - /*instruction=*/*reduce_window, /*operands=*/{operand}, + /*instruction=*/*reduce_window, + /*operands=*/{reduce_window->operand(0)}, /*supported_types=*/{F32, BF16, S32})); // TODO(b/31410564): Implement dilation for reduce-window. - if (window_util::HasDilation(window)) { + if (window_util::HasDilation(reduce_window->window())) { return Unimplemented( "Dilation for ReduceWindow is not implemented on CPU."); } - // The called computation should have been emitted previously. - llvm::Function* reducer_function = FindOrDie(emitted_functions_, function); - // Pseudo code for reduce window: // // for (coordinates O in the output) @@ -526,73 +600,9 @@ Status IrEmitter::HandleReduceWindow(HloInstruction* reduce_window) { // This is completely un-optimized and just here to have something // that works. return EmitTargetElementLoop( - reduce_window, [this, reduce_window, operand, window, - reducer_function](const llvm_ir::IrArray::Index& index) { - // We fold inputs into the accumulator and initialize it to - // the initial value on the reduce_window. - PrimitiveType operand_element_type = operand->shape().element_type(); - llvm::Value* accumulator_address = llvm_ir::EmitAllocaAtFunctionEntry( - llvm_ir::PrimitiveTypeToIrType(operand_element_type, module_), - "reduce_window_accumulator_address", &ir_builder_, - MinimumAlignmentForPrimitiveType(operand_element_type)); - ir_builder_.CreateStore(ir_builder_.CreateLoad(GetEmittedValueFor( - reduce_window->operand(1))), - accumulator_address); - - llvm_ir::ForLoopNest loops(IrName(reduce_window, "inner"), - &ir_builder_); - std::vector window_size; - for (const auto& dim : window.dimensions()) { - window_size.push_back(dim.size()); - } - const llvm_ir::IrArray::Index window_index = loops.AddLoopsForShape( - ShapeUtil::MakeShape(operand_element_type, window_size), "window"); - CHECK_EQ(window_index.size(), index.size()); - - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); - - llvm_ir::IrArray::Index input_index(ir_builder_.getInt64Ty(), - index.size()); - llvm::Value* in_bounds_condition = nullptr; - for (size_t i = 0; i < index.size(); ++i) { - llvm::Value* strided_index = ir_builder_.CreateNSWMul( - index[i], ir_builder_.getInt64(window.dimensions(i).stride())); - input_index[i] = ir_builder_.CreateNSWSub( - ir_builder_.CreateNSWAdd(strided_index, window_index[i]), - ir_builder_.getInt64(window.dimensions(i).padding_low())); - - // We need to check if 0 <= input_index[i] < bound, as - // otherwise we are in the padding so that we can skip the - // computation. That is equivalent to input_index[i] < bound - // as an *unsigned* comparison, since a negative value will - // wrap to a large positive value. - llvm::Value* index_condition = ir_builder_.CreateICmpULT( - input_index[i], ir_builder_.getInt64(ShapeUtil::GetDimension( - operand->shape(), i))); - if (in_bounds_condition == nullptr) { - in_bounds_condition = index_condition; - } else { - in_bounds_condition = - ir_builder_.CreateAnd(in_bounds_condition, index_condition); - } - } - CHECK(in_bounds_condition != nullptr); - - llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - in_bounds_condition, "in-bounds", &ir_builder_); - SetToFirstInsertPoint(if_data.true_block, &ir_builder_); - - // We are not in the padding, so carry out the computation. - llvm_ir::IrArray input_array(GetIrArrayFor(operand)); - llvm::Value* input_value_address = - input_array.EmitArrayElementAddress(input_index, &ir_builder_); - llvm::Value* result = EmitElementFunctionCall( - reducer_function, reduce_window->shape(), - {accumulator_address, input_value_address}, "reducer_function"); - ir_builder_.CreateStore(result, accumulator_address); - - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_); - return ir_builder_.CreateLoad(accumulator_address); + reduce_window, [&](const llvm_ir::IrArray::Index& index) { + return EmitTargetElementLoopBodyForReduceWindow( + Cast(reduce_window), index); }); } @@ -644,141 +654,127 @@ Status IrEmitter::HandleSelectAndScatter(HloInstruction* select_and_scatter) { select_and_scatter, /*desc=*/IrName(select_and_scatter, "init"), [this, init_value](const llvm_ir::IrArray::Index& target_index) { llvm::Value* init_value_addr = GetEmittedValueFor(init_value); - return ir_builder_.CreateLoad(init_value_addr); + return b_.CreateLoad(init_value_addr); })); // Create a loop to iterate over the source array to scatter to the output. - llvm_ir::ForLoopNest source_loops(IrName(select_and_scatter), &ir_builder_); + llvm_ir::ForLoopNest source_loops(IrName(select_and_scatter), &b_); const llvm_ir::IrArray::Index source_index = source_loops.AddLoopsForShape(source->shape(), "source"); - SetToFirstInsertPoint(source_loops.GetInnerLoopBodyBasicBlock(), - &ir_builder_); + SetToFirstInsertPoint(source_loops.GetInnerLoopBodyBasicBlock(), &b_); // Allocate space to keep the currently selected value, its index, and // the boolean initialized_flag, which is initially set to false. llvm::Value* selected_value_address = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType(operand_element_type, module_), - "selected_value_address", &ir_builder_, + "selected_value_address", &b_, MinimumAlignmentForPrimitiveType(operand_element_type)); llvm::Value* selected_index_address = llvm_ir::EmitAllocaAtFunctionEntryWithCount( - ir_builder_.getInt64Ty(), ir_builder_.getInt32(rank), - "selected_index_address", &ir_builder_); + b_.getInt64Ty(), b_.getInt32(rank), "selected_index_address", &b_); llvm::Value* initialized_flag_address = llvm_ir::EmitAllocaAtFunctionEntry( - ir_builder_.getInt1Ty(), "initialized_flag_address", &ir_builder_); - ir_builder_.CreateStore(ir_builder_.getInt1(false), initialized_flag_address); + b_.getInt1Ty(), "initialized_flag_address", &b_); + b_.CreateStore(b_.getInt1(false), initialized_flag_address); // Create the inner loop to iterate over the window. - llvm_ir::ForLoopNest window_loops(IrName(select_and_scatter, "window"), - &ir_builder_); + llvm_ir::ForLoopNest window_loops(IrName(select_and_scatter, "window"), &b_); std::vector window_size; for (const auto& dim : window.dimensions()) { window_size.push_back(dim.size()); } const llvm_ir::IrArray::Index window_index = window_loops.AddLoopsForShape( ShapeUtil::MakeShape(operand_element_type, window_size), "window"); - SetToFirstInsertPoint(window_loops.GetInnerLoopBodyBasicBlock(), - &ir_builder_); + SetToFirstInsertPoint(window_loops.GetInnerLoopBodyBasicBlock(), &b_); // Compute the operand index to visit and evaluate the condition whether the // operand index is within the bounds. The unsigned comparison includes // checking whether the operand index >= 0. - llvm_ir::IrArray::Index operand_index(ir_builder_.getInt64Ty(), - source_index.size()); - llvm::Value* in_bounds_condition = ir_builder_.getTrue(); + llvm_ir::IrArray::Index operand_index(b_.getInt64Ty(), source_index.size()); + llvm::Value* in_bounds_condition = b_.getTrue(); for (int64 i = 0; i < rank; ++i) { - llvm::Value* strided_index = ir_builder_.CreateNSWMul( - source_index[i], ir_builder_.getInt64(window.dimensions(i).stride())); - operand_index[i] = ir_builder_.CreateNSWSub( - ir_builder_.CreateNSWAdd(strided_index, window_index[i]), - ir_builder_.getInt64(window.dimensions(i).padding_low())); - llvm::Value* index_condition = ir_builder_.CreateICmpULT( + llvm::Value* strided_index = b_.CreateNSWMul( + source_index[i], b_.getInt64(window.dimensions(i).stride())); + operand_index[i] = + b_.CreateNSWSub(b_.CreateNSWAdd(strided_index, window_index[i]), + b_.getInt64(window.dimensions(i).padding_low())); + llvm::Value* index_condition = b_.CreateICmpULT( operand_index[i], - ir_builder_.getInt64(ShapeUtil::GetDimension(operand->shape(), i))); - in_bounds_condition = - ir_builder_.CreateAnd(in_bounds_condition, index_condition); + b_.getInt64(ShapeUtil::GetDimension(operand->shape(), i))); + in_bounds_condition = b_.CreateAnd(in_bounds_condition, index_condition); } CHECK(in_bounds_condition != nullptr); // Only need to do something if the operand index is within the bounds. First // check if the initialized_flag is set. llvm_ir::LlvmIfData if_in_bounds = - llvm_ir::EmitIfThenElse(in_bounds_condition, "in-bounds", &ir_builder_); - SetToFirstInsertPoint(if_in_bounds.true_block, &ir_builder_); - llvm_ir::LlvmIfData if_initialized = - llvm_ir::EmitIfThenElse(ir_builder_.CreateLoad(initialized_flag_address), - "initialized", &ir_builder_); + llvm_ir::EmitIfThenElse(in_bounds_condition, "in-bounds", &b_); + SetToFirstInsertPoint(if_in_bounds.true_block, &b_); + llvm_ir::LlvmIfData if_initialized = llvm_ir::EmitIfThenElse( + b_.CreateLoad(initialized_flag_address), "initialized", &b_); // If the initialized_flag is false, initialize the selected value and index // with the currently visiting operand. - SetToFirstInsertPoint(if_initialized.false_block, &ir_builder_); + SetToFirstInsertPoint(if_initialized.false_block, &b_); const auto save_operand_index = [&](const llvm_ir::IrArray::Index& operand_index) { for (int64 i = 0; i < rank; ++i) { llvm::Value* selected_index_address_slot = - ir_builder_.CreateInBoundsGEP(selected_index_address, - {ir_builder_.getInt32(i)}); - ir_builder_.CreateStore(operand_index[i], - selected_index_address_slot); + b_.CreateInBoundsGEP(selected_index_address, {b_.getInt32(i)}); + b_.CreateStore(operand_index[i], selected_index_address_slot); } }; llvm_ir::IrArray operand_array(GetIrArrayFor(operand)); llvm::Value* operand_data = - operand_array.EmitReadArrayElement(operand_index, &ir_builder_); - ir_builder_.CreateStore(operand_data, selected_value_address); + operand_array.EmitReadArrayElement(operand_index, &b_); + b_.CreateStore(operand_data, selected_value_address); save_operand_index(operand_index); - ir_builder_.CreateStore(ir_builder_.getInt1(true), initialized_flag_address); + b_.CreateStore(b_.getInt1(true), initialized_flag_address); // If the initialized_flag is true, call the `select` function to potentially // update the selected value and index with the currently visiting operand. - SetToFirstInsertPoint(if_initialized.true_block, &ir_builder_); + SetToFirstInsertPoint(if_initialized.true_block, &b_); const Shape output_shape = ShapeUtil::MakeShape(PRED, {}); llvm::Value* operand_address = - operand_array.EmitArrayElementAddress(operand_index, &ir_builder_); + operand_array.EmitArrayElementAddress(operand_index, &b_); llvm::Value* result = EmitElementFunctionCall( select_function, output_shape, {selected_value_address, operand_address}, "select_function"); // If the 'select' function returns false, update the selected value and the // index to the currently visiting operand. - llvm::Value* cond = ir_builder_.CreateICmpNE( + llvm::Value* cond = b_.CreateICmpNE( result, llvm::ConstantInt::get(llvm_ir::PrimitiveTypeToIrType(PRED, module_), 0), "boolean_predicate"); llvm_ir::LlvmIfData if_select_lhs = - llvm_ir::EmitIfThenElse(cond, "if-select-lhs", &ir_builder_); - SetToFirstInsertPoint(if_select_lhs.false_block, &ir_builder_); - ir_builder_.CreateStore(ir_builder_.CreateLoad(operand_address), - selected_value_address); + llvm_ir::EmitIfThenElse(cond, "if-select-lhs", &b_); + SetToFirstInsertPoint(if_select_lhs.false_block, &b_); + b_.CreateStore(b_.CreateLoad(operand_address), selected_value_address); save_operand_index(operand_index); // After iterating over the window elements, scatter the source element to // the selected index of the output. The value we store at the output // location is computed by calling the `scatter` function with the source // value and the current output value. - SetToFirstInsertPoint(window_loops.GetOuterLoopExitBasicBlock(), - &ir_builder_); + SetToFirstInsertPoint(window_loops.GetOuterLoopExitBasicBlock(), &b_); llvm_ir::IrArray::Index selected_index(source_index.GetType()); for (int64 i = 0; i < rank; ++i) { - llvm::Value* selected_index_address_slot = ir_builder_.CreateInBoundsGEP( - selected_index_address, {ir_builder_.getInt32(i)}); - selected_index.push_back( - ir_builder_.CreateLoad(selected_index_address_slot)); + llvm::Value* selected_index_address_slot = + b_.CreateInBoundsGEP(selected_index_address, {b_.getInt32(i)}); + selected_index.push_back(b_.CreateLoad(selected_index_address_slot)); } llvm_ir::IrArray source_array(GetIrArrayFor(source)); llvm::Value* source_value_address = - source_array.EmitArrayElementAddress(source_index, &ir_builder_); + source_array.EmitArrayElementAddress(source_index, &b_); llvm_ir::IrArray output_array(GetIrArrayFor(select_and_scatter)); llvm::Value* output_value_address = - output_array.EmitArrayElementAddress(selected_index, &ir_builder_); + output_array.EmitArrayElementAddress(selected_index, &b_); llvm::Value* scatter_value = EmitElementFunctionCall( scatter_function, source->shape(), {output_value_address, source_value_address}, "scatter_function"); - output_array.EmitWriteArrayElement(selected_index, scatter_value, - &ir_builder_); + output_array.EmitWriteArrayElement(selected_index, scatter_value, &b_); - SetToFirstInsertPoint(source_loops.GetOuterLoopExitBasicBlock(), - &ir_builder_); + SetToFirstInsertPoint(source_loops.GetOuterLoopExitBasicBlock(), &b_); return Status::OK(); } @@ -817,21 +813,155 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { // Dot operation is complicated so we delegate to a helper class. return DotOpEmitter::EmitDotOperation( *dot, target_array, lhs_array, rhs_array, /*addend_array=*/nullptr, - GetExecutableRunOptionsArgument(), &ir_builder_, hlo_module_config_, + GetExecutableRunOptionsArgument(), &b_, hlo_module_config_, target_machine_features_); } +StatusOr IrEmitter::EmitTargetElementLoopBodyForConvolution( + HloConvolutionInstruction* convolution, + const llvm_ir::IrArray::Index& index) { + const HloInstruction* lhs = convolution->operand(0); + const HloInstruction* rhs = convolution->operand(1); + const Window& window = convolution->window(); + + const ConvolutionDimensionNumbers& dnums = + convolution->convolution_dimension_numbers(); + int num_spatial_dims = dnums.output_spatial_dimensions_size(); + std::vector output_spatial(num_spatial_dims); + for (int i = 0; i < num_spatial_dims; ++i) { + output_spatial[i] = index[dnums.output_spatial_dimensions(i)]; + } + llvm::Value* output_feature = index[dnums.output_feature_dimension()]; + llvm::Value* batch = index[dnums.output_batch_dimension()]; + + // We will accumulate the products into this sum to calculate the output entry + // at the given index. + PrimitiveType lhs_element_type = lhs->shape().element_type(); + llvm::Type* lhs_llvm_type = + llvm_ir::PrimitiveTypeToIrType(lhs_element_type, module_); + llvm::Value* sum_address = llvm_ir::EmitAllocaAtFunctionEntry( + lhs_llvm_type, "convolution_sum_address", &b_, + MinimumAlignmentForPrimitiveType(lhs_element_type)); + llvm::Value* constant_zero = llvm::Constant::getNullValue(lhs_llvm_type); + b_.CreateStore(constant_zero, sum_address); + + llvm_ir::ForLoopNest loops(IrName(convolution, "inner"), &b_); + std::vector kernel_spatial(num_spatial_dims); + for (int i = 0; i < num_spatial_dims; ++i) { + kernel_spatial[i] = + loops + .AddLoop( + 0, rhs->shape().dimensions(dnums.kernel_spatial_dimensions(i)), + tensorflow::strings::StrCat("k", i)) + ->GetIndVarValue(); + } + llvm::Value* input_feature = + loops + .AddLoop(0, lhs->shape().dimensions(dnums.input_feature_dimension()), + "iz") + ->GetIndVarValue(); + + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &b_); + + // Calculate the spatial index in the input array, taking striding, dilation + // and padding into account. An index in the padding will be out of the bounds + // of the array. + const auto calculate_input_index = [this](llvm::Value* output_index, + llvm::Value* kernel_index, + const WindowDimension& window_dim) { + llvm::Value* strided_index = + b_.CreateNSWMul(output_index, b_.getInt64(window_dim.stride())); + llvm::Value* dilated_kernel_index = b_.CreateNSWMul( + kernel_index, b_.getInt64(window_dim.window_dilation())); + return b_.CreateNSWSub(b_.CreateNSWAdd(strided_index, dilated_kernel_index), + b_.getInt64(window_dim.padding_low())); + }; + std::vector input_spatial(num_spatial_dims); + for (int i = 0; i < num_spatial_dims; ++i) { + input_spatial[i] = calculate_input_index( + output_spatial[i], kernel_spatial[i], window.dimensions(i)); + } + + // We need to check if 0 <= input dim < bound, as otherwise we are in the + // padding so that we can skip the computation. That is equivalent to input + // dim < bound as an *unsigned* comparison, since a negative value will wrap + // to a large positive value. The input dim is dilated, so we need to dilate + // the bound as well to match. + + // Also need to check that the input coordinates are not in one of the + // holes created by base dilation. + const auto not_in_hole = [&](llvm::Value* input_index, int64 base_dilation) { + llvm::Value* remainder = + b_.CreateSRem(input_index, b_.getInt64(base_dilation)); + return b_.CreateICmpEQ(remainder, b_.getInt64(0)); + }; + + llvm::Value* in_bounds_condition = b_.getInt1(true); + for (int i = 0; i < num_spatial_dims; ++i) { + llvm::ConstantInt* input_bound = b_.getInt64(window_util::DilatedBound( + lhs->shape().dimensions(dnums.input_spatial_dimensions(i)), + window.dimensions(i).base_dilation())); + llvm::Value* dim_in_bound = b_.CreateICmpULT(input_spatial[i], input_bound); + llvm::Value* dim_not_in_hole = + not_in_hole(input_spatial[i], window.dimensions(i).base_dilation()); + llvm::Value* dim_ok = b_.CreateAnd(dim_in_bound, dim_not_in_hole); + in_bounds_condition = b_.CreateAnd(in_bounds_condition, dim_ok); + } + + // Now we need to map the dilated base coordinates back to the actual + // data indices on the lhs. + const auto undilate = [&](llvm::Value* input_index, int64 base_dilation) { + return b_.CreateSDiv(input_index, b_.getInt64(base_dilation)); + }; + for (int i = 0; i < num_spatial_dims; ++i) { + input_spatial[i] = + undilate(input_spatial[i], window.dimensions(i).base_dilation()); + } + + llvm_ir::LlvmIfData if_data = + llvm_ir::EmitIfThenElse(in_bounds_condition, "in-bounds", &b_); + SetToFirstInsertPoint(if_data.true_block, &b_); + + // We are not in the padding, so carry out the computation. + int num_dims = num_spatial_dims + 2; + llvm_ir::IrArray::Index input_index(b_.getInt64Ty(), num_dims); + for (int i = 0; i < num_spatial_dims; ++i) { + input_index[dnums.input_spatial_dimensions(i)] = input_spatial[i]; + } + input_index[dnums.input_feature_dimension()] = input_feature; + input_index[dnums.input_batch_dimension()] = batch; + + llvm_ir::IrArray kernel_array(GetIrArrayFor(rhs)); + llvm_ir::IrArray::Index kernel_index(b_.getInt64Ty(), num_dims); + for (int i = 0; i < num_spatial_dims; ++i) { + kernel_index[dnums.kernel_spatial_dimensions(i)] = + window.dimensions(i).window_reversal() + ? b_.CreateNSWSub(b_.getInt64(window.dimensions(i).size() - 1), + kernel_spatial[i]) + : kernel_spatial[i]; + } + + kernel_index[dnums.kernel_input_feature_dimension()] = input_feature; + kernel_index[dnums.kernel_output_feature_dimension()] = output_feature; + + llvm_ir::IrArray input_array(GetIrArrayFor(lhs)); + llvm::Value* product = + b_.CreateFMul(input_array.EmitReadArrayElement(input_index, &b_), + kernel_array.EmitReadArrayElement(kernel_index, &b_)); + llvm::Value* sum = b_.CreateFAdd(b_.CreateLoad(sum_address), product); + b_.CreateStore(sum, sum_address); + + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &b_); + return b_.CreateLoad(sum_address); +} + Status IrEmitter::HandleConvolution(HloInstruction* convolution) { auto lhs = convolution->operand(0); auto rhs = convolution->operand(1); - const auto& window = convolution->window(); TF_RETURN_IF_ERROR(ElementTypesSameAndSupported( /*instruction=*/*convolution, /*operands=*/{lhs, rhs}, /*supported_types=*/{F16, F32, C64})); - const ConvolutionDimensionNumbers& dnums = - convolution->convolution_dimension_numbers(); - // TODO(tonywy): Add PotentiallyImplementedAsMKLCovolution to support // different data layouts. if (PotentiallyImplementedAsEigenConvolution(*convolution, @@ -911,12 +1041,12 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { PrimitiveType primitive_type = lhs->shape().element_type(); llvm::Type* ir_ptr_type = primitive_type == F16 - ? ir_builder_.getHalfTy()->getPointerTo() - : ir_builder_.getFloatTy()->getPointerTo(); - llvm::Type* int64_type = ir_builder_.getInt64Ty(); - llvm::Type* int8_ptr_type = ir_builder_.getInt8Ty()->getPointerTo(); + ? b_.getHalfTy()->getPointerTo() + : b_.getFloatTy()->getPointerTo(); + llvm::Type* int64_type = b_.getInt64Ty(); + llvm::Type* int8_ptr_type = b_.getInt8Ty()->getPointerTo(); llvm::FunctionType* conv_type = llvm::FunctionType::get( - ir_builder_.getVoidTy(), + b_.getVoidTy(), {int8_ptr_type, ir_ptr_type, ir_ptr_type, ir_ptr_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, @@ -948,34 +1078,34 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { conv_func->setCallingConv(llvm::CallingConv::C); conv_func->setDoesNotThrow(); conv_func->setOnlyAccessesArgMemory(); - ir_builder_.CreateCall( - conv_func, { - GetExecutableRunOptionsArgument(), - ir_builder_.CreateBitCast( - GetEmittedValueFor(convolution), ir_ptr_type), - ir_builder_.CreateBitCast(lhs_address, ir_ptr_type), - ir_builder_.CreateBitCast(rhs_address, ir_ptr_type), - ir_builder_.getInt64(input_batch), - ir_builder_.getInt64(input_rows), - ir_builder_.getInt64(input_cols), - ir_builder_.getInt64(input_channels), - ir_builder_.getInt64(kernel_rows), - ir_builder_.getInt64(kernel_cols), - ir_builder_.getInt64(kernel_channels), - ir_builder_.getInt64(kernel_filters), - ir_builder_.getInt64(output_rows), - ir_builder_.getInt64(output_cols), - ir_builder_.getInt64(row_stride), - ir_builder_.getInt64(col_stride), - ir_builder_.getInt64(padding_top), - ir_builder_.getInt64(padding_bottom), - ir_builder_.getInt64(padding_left), - ir_builder_.getInt64(padding_right), - ir_builder_.getInt64(lhs_row_dilation), - ir_builder_.getInt64(lhs_col_dilation), - ir_builder_.getInt64(rhs_row_dilation), - ir_builder_.getInt64(rhs_col_dilation), - }); + b_.CreateCall( + conv_func, + { + GetExecutableRunOptionsArgument(), + b_.CreateBitCast(GetEmittedValueFor(convolution), ir_ptr_type), + b_.CreateBitCast(lhs_address, ir_ptr_type), + b_.CreateBitCast(rhs_address, ir_ptr_type), + b_.getInt64(input_batch), + b_.getInt64(input_rows), + b_.getInt64(input_cols), + b_.getInt64(input_channels), + b_.getInt64(kernel_rows), + b_.getInt64(kernel_cols), + b_.getInt64(kernel_channels), + b_.getInt64(kernel_filters), + b_.getInt64(output_rows), + b_.getInt64(output_cols), + b_.getInt64(row_stride), + b_.getInt64(col_stride), + b_.getInt64(padding_top), + b_.getInt64(padding_bottom), + b_.getInt64(padding_left), + b_.getInt64(padding_right), + b_.getInt64(lhs_row_dilation), + b_.getInt64(lhs_col_dilation), + b_.getInt64(rhs_row_dilation), + b_.getInt64(rhs_col_dilation), + }); return Status::OK(); } @@ -988,150 +1118,9 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { // See the description of convolution in the XLA documentation for the pseudo // code for convolution. return EmitTargetElementLoop( - convolution, [this, convolution, lhs, rhs, window, - dnums](const llvm_ir::IrArray::Index& index) { - int num_spatial_dims = dnums.output_spatial_dimensions_size(); - std::vector output_spatial(num_spatial_dims); - for (int i = 0; i < num_spatial_dims; ++i) { - output_spatial[i] = index[dnums.output_spatial_dimensions(i)]; - } - llvm::Value* output_feature = index[dnums.output_feature_dimension()]; - llvm::Value* batch = index[dnums.output_batch_dimension()]; - - // We will accumulate the products into this sum to calculate - // the output entry at the given index. - PrimitiveType lhs_element_type = lhs->shape().element_type(); - llvm::Type* lhs_llvm_type = - llvm_ir::PrimitiveTypeToIrType(lhs_element_type, module_); - llvm::Value* sum_address = llvm_ir::EmitAllocaAtFunctionEntry( - lhs_llvm_type, "convolution_sum_address", &ir_builder_, - MinimumAlignmentForPrimitiveType(lhs_element_type)); - llvm::Value* constant_zero = - llvm::Constant::getNullValue(lhs_llvm_type); - ir_builder_.CreateStore(constant_zero, sum_address); - - llvm_ir::ForLoopNest loops(IrName(convolution, "inner"), &ir_builder_); - std::vector kernel_spatial(num_spatial_dims); - for (int i = 0; i < num_spatial_dims; ++i) { - kernel_spatial[i] = - loops - .AddLoop(0, - rhs->shape().dimensions( - dnums.kernel_spatial_dimensions(i)), - tensorflow::strings::StrCat("k", i)) - ->GetIndVarValue(); - } - llvm::Value* input_feature = - loops - .AddLoop( - 0, lhs->shape().dimensions(dnums.input_feature_dimension()), - "iz") - ->GetIndVarValue(); - - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); - - // Calculate the spatial index in the input array, taking striding, - // dilation and padding into account. An index in the padding will be - // out of the bounds of the array. - const auto calculate_input_index = - [this](llvm::Value* output_index, llvm::Value* kernel_index, - const WindowDimension& window_dim) { - llvm::Value* strided_index = ir_builder_.CreateNSWMul( - output_index, ir_builder_.getInt64(window_dim.stride())); - llvm::Value* dilated_kernel_index = ir_builder_.CreateNSWMul( - kernel_index, - ir_builder_.getInt64(window_dim.window_dilation())); - return ir_builder_.CreateNSWSub( - ir_builder_.CreateNSWAdd(strided_index, dilated_kernel_index), - ir_builder_.getInt64(window_dim.padding_low())); - }; - std::vector input_spatial(num_spatial_dims); - for (int i = 0; i < num_spatial_dims; ++i) { - input_spatial[i] = calculate_input_index( - output_spatial[i], kernel_spatial[i], window.dimensions(i)); - } - - // We need to check if 0 <= input dim < bound, as otherwise we are in - // the padding so that we can skip the computation. That is equivalent - // to input dim < bound as an *unsigned* comparison, since a negative - // value will wrap to a large positive value. The input dim is dilated, - // so we need to dilate the bound as well to match. - - // Also need to check that the input coordinates are not in one of the - // holes created by base dilation. - const auto not_in_hole = [&](llvm::Value* input_index, - int64 base_dilation) { - llvm::Value* remainder = ir_builder_.CreateSRem( - input_index, ir_builder_.getInt64(base_dilation)); - return ir_builder_.CreateICmpEQ(remainder, ir_builder_.getInt64(0)); - }; - - llvm::Value* in_bounds_condition = ir_builder_.getInt1(true); - for (int i = 0; i < num_spatial_dims; ++i) { - llvm::ConstantInt* input_bound = - ir_builder_.getInt64(window_util::DilatedBound( - lhs->shape().dimensions(dnums.input_spatial_dimensions(i)), - window.dimensions(i).base_dilation())); - llvm::Value* dim_in_bound = - ir_builder_.CreateICmpULT(input_spatial[i], input_bound); - llvm::Value* dim_not_in_hole = not_in_hole( - input_spatial[i], window.dimensions(i).base_dilation()); - llvm::Value* dim_ok = - ir_builder_.CreateAnd(dim_in_bound, dim_not_in_hole); - in_bounds_condition = - ir_builder_.CreateAnd(in_bounds_condition, dim_ok); - } - - // Now we need to map the dilated base coordinates back to the actual - // data indices on the lhs. - const auto undilate = [&](llvm::Value* input_index, - int64 base_dilation) { - return ir_builder_.CreateSDiv(input_index, - ir_builder_.getInt64(base_dilation)); - }; - for (int i = 0; i < num_spatial_dims; ++i) { - input_spatial[i] = - undilate(input_spatial[i], window.dimensions(i).base_dilation()); - } - - llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - in_bounds_condition, "in-bounds", &ir_builder_); - SetToFirstInsertPoint(if_data.true_block, &ir_builder_); - - // We are not in the padding, so carry out the computation. - int num_dims = num_spatial_dims + 2; - llvm_ir::IrArray::Index input_index(ir_builder_.getInt64Ty(), num_dims); - for (int i = 0; i < num_spatial_dims; ++i) { - input_index[dnums.input_spatial_dimensions(i)] = input_spatial[i]; - } - input_index[dnums.input_feature_dimension()] = input_feature; - input_index[dnums.input_batch_dimension()] = batch; - - llvm_ir::IrArray kernel_array(GetIrArrayFor(rhs)); - llvm_ir::IrArray::Index kernel_index(ir_builder_.getInt64Ty(), - num_dims); - for (int i = 0; i < num_spatial_dims; ++i) { - kernel_index[dnums.kernel_spatial_dimensions(i)] = - window.dimensions(i).window_reversal() - ? ir_builder_.CreateNSWSub( - ir_builder_.getInt64(window.dimensions(i).size() - 1), - kernel_spatial[i]) - : kernel_spatial[i]; - } - - kernel_index[dnums.kernel_input_feature_dimension()] = input_feature; - kernel_index[dnums.kernel_output_feature_dimension()] = output_feature; - - llvm_ir::IrArray input_array(GetIrArrayFor(lhs)); - llvm::Value* product = ir_builder_.CreateFMul( - input_array.EmitReadArrayElement(input_index, &ir_builder_), - kernel_array.EmitReadArrayElement(kernel_index, &ir_builder_)); - llvm::Value* sum = ir_builder_.CreateFAdd( - ir_builder_.CreateLoad(sum_address), product); - ir_builder_.CreateStore(sum, sum_address); - - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_); - return ir_builder_.CreateLoad(sum_address); + convolution, [&](const llvm_ir::IrArray::Index& index) { + return EmitTargetElementLoopBodyForConvolution( + Cast(convolution), index); }); } @@ -1155,11 +1144,11 @@ Status IrEmitter::HandleFft(HloInstruction* fft) { } // Args have been computed, make the call. - llvm::Type* int8_ptr_type = ir_builder_.getInt8Ty()->getPointerTo(); - llvm::Type* int32_type = ir_builder_.getInt32Ty(); - llvm::Type* int64_type = ir_builder_.getInt64Ty(); + llvm::Type* int8_ptr_type = b_.getInt8Ty()->getPointerTo(); + llvm::Type* int32_type = b_.getInt32Ty(); + llvm::Type* int64_type = b_.getInt64Ty(); llvm::FunctionType* fft_type = llvm::FunctionType::get( - ir_builder_.getVoidTy(), + b_.getVoidTy(), {int8_ptr_type, int8_ptr_type, int8_ptr_type, int32_type, int32_type, int64_type, int64_type, int64_type, int64_type}, /*isVarArg=*/false); @@ -1176,16 +1165,15 @@ Status IrEmitter::HandleFft(HloInstruction* fft) { fft_func->setDoesNotThrow(); fft_func->setOnlyAccessesInaccessibleMemOrArgMem(); const int fft_rank = fft_length.size(); - ir_builder_.CreateCall( + b_.CreateCall( fft_func, {GetExecutableRunOptionsArgument(), - ir_builder_.CreateBitCast(GetEmittedValueFor(fft), int8_ptr_type), - ir_builder_.CreateBitCast(operand_address, int8_ptr_type), - ir_builder_.getInt32(fft->fft_type()), ir_builder_.getInt32(fft_rank), - ir_builder_.getInt64(input_batch), - ir_builder_.getInt64(fft_rank > 0 ? fft_length[0] : 0), - ir_builder_.getInt64(fft_rank > 1 ? fft_length[1] : 0), - ir_builder_.getInt64(fft_rank > 2 ? fft_length[2] : 0)}); + b_.CreateBitCast(GetEmittedValueFor(fft), int8_ptr_type), + b_.CreateBitCast(operand_address, int8_ptr_type), + b_.getInt32(fft->fft_type()), b_.getInt32(fft_rank), + b_.getInt64(input_batch), b_.getInt64(fft_rank > 0 ? fft_length[0] : 0), + b_.getInt64(fft_rank > 1 ? fft_length[1] : 0), + b_.getInt64(fft_rank > 2 ? fft_length[2] : 0)}); return Status::OK(); } @@ -1224,11 +1212,10 @@ Status IrEmitter::HandleCrossReplicaSum(HloInstruction* crs) { operand_ptrs.push_back(EmitTempBufferPointer(out_slice, operand_shape)); // TODO(b/63762267): Be more aggressive about specifying alignment. - ir_builder_.CreateMemCpy(operand_ptrs.back(), /*DstAlign=*/1, in_ptr, - /*SrcAlign=*/1, - ShapeUtil::ByteSizeOf(operand_shape)); + b_.CreateMemCpy(operand_ptrs.back(), /*DstAlign=*/1, in_ptr, + /*SrcAlign=*/1, ShapeUtil::ByteSizeOf(operand_shape)); } - llvm_ir::EmitTuple(GetIrArrayFor(crs), operand_ptrs, &ir_builder_, module_); + llvm_ir::EmitTuple(GetIrArrayFor(crs), operand_ptrs, &b_, module_); return Status::OK(); } @@ -1274,9 +1261,8 @@ Status IrEmitter::HandleParameter(HloInstruction* parameter) { // example, float for an XLA F32 element type). llvm::Value* params = compute_function_->parameters_arg(); llvm::Value* param_address_offset = - llvm_ir::EmitBufferIndexingGEP(params, param_number, &ir_builder_); - llvm::LoadInst* param_address_untyped = - ir_builder_.CreateLoad(param_address_offset); + llvm_ir::EmitBufferIndexingGEP(params, param_number, &b_); + llvm::LoadInst* param_address_untyped = b_.CreateLoad(param_address_offset); param_address_untyped->setName(AsStringRef(IrName(parameter, "untyped"))); if (is_top_level_computation_ && hlo_module_config_.debug_options() @@ -1291,7 +1277,7 @@ Status IrEmitter::HandleParameter(HloInstruction* parameter) { llvm::MDNode::get(param_address_untyped->getContext(), /*MDs=*/{})); } - llvm::Value* param_address_typed = ir_builder_.CreateBitCast( + llvm::Value* param_address_typed = b_.CreateBitCast( param_address_untyped, IrShapeType(param_shape)->getPointerTo()); emitted_value_[parameter] = param_address_typed; @@ -1399,62 +1385,61 @@ IrEmitter::ReductionGenerator IrEmitter::MatchReductionGenerator( return nullptr; case HloOpcode::kAdd: - return [root_is_integral](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, + return [root_is_integral](llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs) { - return root_is_integral ? ir_builder->CreateAdd(lhs, rhs) - : ir_builder->CreateFAdd(lhs, rhs); + return root_is_integral ? b->CreateAdd(lhs, rhs) + : b->CreateFAdd(lhs, rhs); }; case HloOpcode::kMultiply: - return [root_is_integral](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, + return [root_is_integral](llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs) { - return root_is_integral ? ir_builder->CreateMul(lhs, rhs) - : ir_builder->CreateFMul(lhs, rhs); + return root_is_integral ? b->CreateMul(lhs, rhs) + : b->CreateFMul(lhs, rhs); }; case HloOpcode::kAnd: - return [](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, - llvm::Value* rhs) { return ir_builder->CreateAnd(lhs, rhs); }; + return [](llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs) { + return b->CreateAnd(lhs, rhs); + }; case HloOpcode::kOr: - return [](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, - llvm::Value* rhs) { return ir_builder->CreateOr(lhs, rhs); }; + return [](llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs) { + return b->CreateOr(lhs, rhs); + }; case HloOpcode::kXor: - return [](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, - llvm::Value* rhs) { return ir_builder->CreateXor(lhs, rhs); }; + return [](llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs) { + return b->CreateXor(lhs, rhs); + }; case HloOpcode::kMaximum: return [root_is_floating_point, root_is_signed]( - llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, - llvm::Value* rhs) { + llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs) { if (root_is_floating_point) { return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::maxnum, - {lhs, rhs}, {lhs->getType()}, - ir_builder); + {lhs, rhs}, {lhs->getType()}, b); } - return ir_builder->CreateSelect( - ir_builder->CreateICmp(root_is_signed ? llvm::ICmpInst::ICMP_SGE - : llvm::ICmpInst::ICMP_UGE, - lhs, rhs), + return b->CreateSelect( + b->CreateICmp(root_is_signed ? llvm::ICmpInst::ICMP_SGE + : llvm::ICmpInst::ICMP_UGE, + lhs, rhs), lhs, rhs); }; case HloOpcode::kMinimum: return [root_is_floating_point, root_is_signed]( - llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, - llvm::Value* rhs) { + llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs) { if (root_is_floating_point) { return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::minnum, - {lhs, rhs}, {lhs->getType()}, - ir_builder); + {lhs, rhs}, {lhs->getType()}, b); } - return ir_builder->CreateSelect( - ir_builder->CreateICmp(root_is_signed ? llvm::ICmpInst::ICMP_SLE - : llvm::ICmpInst::ICMP_ULE, - lhs, rhs), + return b->CreateSelect( + b->CreateICmp(root_is_signed ? llvm::ICmpInst::ICMP_SLE + : llvm::ICmpInst::ICMP_ULE, + lhs, rhs), lhs, rhs); }; } @@ -1523,34 +1508,31 @@ IrEmitter::EmitInnerLoopForVectorizedReduction( accumulator.reserve(accumulator_type.size()); for (auto accumulator_shard_type : accumulator_type) { accumulator.push_back(llvm_ir::EmitAllocaAtFunctionEntry( - accumulator_shard_type, "accumulator", &ir_builder_, 0)); + accumulator_shard_type, "accumulator", &b_, 0)); } - llvm::Value* init_value_ssa = - ir_builder_.CreateLoad(GetEmittedValueFor(init_value)); + llvm::Value* init_value_ssa = b_.CreateLoad(GetEmittedValueFor(init_value)); for (llvm::Value* accumulator_shard : accumulator) { llvm::Value* initial_value; auto shard_type = accumulator_shard->getType()->getPointerElementType(); if (auto vector_type = llvm::dyn_cast(shard_type)) { - initial_value = ir_builder_.CreateVectorSplat( - vector_type->getNumElements(), init_value_ssa); + initial_value = + b_.CreateVectorSplat(vector_type->getNumElements(), init_value_ssa); } else { initial_value = init_value_ssa; } - ir_builder_.CreateAlignedStore(initial_value, accumulator_shard, - element_alignment); + b_.CreateAlignedStore(initial_value, accumulator_shard, element_alignment); } llvm_ir::ForLoopNest reduction_loop_nest(IrName(arg, "vectorized_inner"), - &ir_builder_); + &b_); llvm_ir::IrArray::Index reduced_dims_index = reduction_loop_nest.AddLoopsForShapeOnDimensions(arg->shape(), dimensions, "reduction_dim"); - SetToFirstInsertPoint(reduction_loop_nest.GetInnerLoopBodyBasicBlock(), - &ir_builder_); + SetToFirstInsertPoint(reduction_loop_nest.GetInnerLoopBodyBasicBlock(), &b_); llvm_ir::IrArray arg_array(GetIrArrayFor(arg)); llvm_ir::IrArray::Index input_index = reduced_dims_index; @@ -1563,38 +1545,34 @@ IrEmitter::EmitInnerLoopForVectorizedReduction( } CHECK(output_index.end() == it); - llvm::Value* input_address = ir_builder_.CreateBitCast( - arg_array.EmitArrayElementAddress(input_index, &ir_builder_), - ir_builder_.getInt8PtrTy()); + llvm::Value* input_address = b_.CreateBitCast( + arg_array.EmitArrayElementAddress(input_index, &b_), b_.getInt8PtrTy()); for (int i = 0; i < accumulator.size(); i++) { auto input_address_typed = - ir_builder_.CreateBitCast(input_address, accumulator[i]->getType()); + b_.CreateBitCast(input_address, accumulator[i]->getType()); auto current_accumulator_value = - ir_builder_.CreateAlignedLoad(accumulator[i], element_alignment); - auto addend = - ir_builder_.CreateAlignedLoad(input_address_typed, element_alignment); + b_.CreateAlignedLoad(accumulator[i], element_alignment); + auto addend = b_.CreateAlignedLoad(input_address_typed, element_alignment); arg_array.AnnotateLoadStoreInstructionWithMetadata(addend); auto reduced_result = - reduction_generator(&ir_builder_, current_accumulator_value, addend); - ir_builder_.CreateAlignedStore(reduced_result, accumulator[i], - element_alignment); + reduction_generator(&b_, current_accumulator_value, addend); + b_.CreateAlignedStore(reduced_result, accumulator[i], element_alignment); if (i != (accumulator.size() - 1)) { - input_address = ir_builder_.CreateConstInBoundsGEP1_32( - reduced_result->getType(), input_address_typed, 1); + input_address = b_.CreateConstInBoundsGEP1_32(reduced_result->getType(), + input_address_typed, 1); } } - SetToFirstInsertPoint(reduction_loop_nest.GetOuterLoopExitBasicBlock(), - &ir_builder_); + SetToFirstInsertPoint(reduction_loop_nest.GetOuterLoopExitBasicBlock(), &b_); ShardedVector result_ssa; result_ssa.reserve(accumulator.size()); for (auto accumulator_shard : accumulator) { result_ssa.push_back( - ir_builder_.CreateAlignedLoad(accumulator_shard, element_alignment)); + b_.CreateAlignedLoad(accumulator_shard, element_alignment)); } return result_ssa; } @@ -1603,17 +1581,17 @@ void IrEmitter::EmitShardedVectorStore( llvm::Value* store_address, const std::vector& value_to_store, const int alignment, const llvm_ir::IrArray& containing_array) { for (int i = 0; i < value_to_store.size(); i++) { - auto store_address_typed = ir_builder_.CreateBitCast( + auto store_address_typed = b_.CreateBitCast( store_address, llvm::PointerType::getUnqual(value_to_store[i]->getType())); - auto store_instruction = ir_builder_.CreateAlignedStore( + auto store_instruction = b_.CreateAlignedStore( value_to_store[i], store_address_typed, alignment); containing_array.AnnotateLoadStoreInstructionWithMetadata( store_instruction); if (i != (value_to_store.size() - 1)) { - store_address = ir_builder_.CreateConstInBoundsGEP1_32( + store_address = b_.CreateConstInBoundsGEP1_32( value_to_store[i]->getType(), store_address_typed, 1); } } @@ -1679,8 +1657,8 @@ StatusOr IrEmitter::EmitVectorizedReduce( // } // } - llvm_ir::ForLoopNest loop_nest(IrName(reduce), &ir_builder_); - llvm_ir::IrArray::Index array_index(ir_builder_.getInt64Ty(), + llvm_ir::ForLoopNest loop_nest(IrName(reduce), &b_); + llvm_ir::IrArray::Index array_index(b_.getInt64Ty(), reduce->shape().dimensions_size()); for (int i = LayoutUtil::MinorToMajor(reduce->shape()).size() - 1; i > 0; --i) { @@ -1699,7 +1677,7 @@ StatusOr IrEmitter::EmitVectorizedReduce( if (llvm::BasicBlock* innermost_body_bb = loop_nest.GetInnerLoopBodyBasicBlock()) { - SetToFirstInsertPoint(innermost_body_bb, &ir_builder_); + SetToFirstInsertPoint(innermost_body_bb, &b_); } auto outermost_loop_exit_block = loop_nest.GetOuterLoopExitBasicBlock(); @@ -1713,7 +1691,7 @@ StatusOr IrEmitter::EmitVectorizedReduce( tensorflow::strings::Printf("dim.%lld", innermost_dimension)); array_index[innermost_dimension] = loop->GetIndVarValue(); - SetToFirstInsertPoint(loop->GetBodyBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loop->GetBodyBasicBlock(), &b_); ShardedVectorType vector_type = CreateShardedVectorType( reduce->shape().element_type(), vectorization_factor); @@ -1724,16 +1702,16 @@ StatusOr IrEmitter::EmitVectorizedReduce( llvm_ir::IrArray target_array = GetIrArrayFor(reduce); llvm::Value* output_address = - target_array.EmitArrayElementAddress(array_index, &ir_builder_); + target_array.EmitArrayElementAddress(array_index, &b_); EmitShardedVectorStore(output_address, accumulator, element_alignment, target_array); if (auto exit_terminator = loop->GetExitBasicBlock()->getTerminator()) { CHECK_GT(LayoutUtil::MinorToMajor(reduce->shape()).size(), 1); - ir_builder_.SetInsertPoint(exit_terminator); + b_.SetInsertPoint(exit_terminator); } else { CHECK_EQ(LayoutUtil::MinorToMajor(reduce->shape()).size(), 1); - ir_builder_.SetInsertPoint(loop->GetExitBasicBlock()); + b_.SetInsertPoint(loop->GetExitBasicBlock()); } } @@ -1743,8 +1721,8 @@ StatusOr IrEmitter::EmitVectorizedReduce( if (innermost_dimension_size % vectorization_factor) { // TODO(b/63775531): Consider using a scalar loop here to save on code size. array_index[innermost_dimension] = - ir_builder_.getInt64(innermost_dimension_size - - (innermost_dimension_size % vectorization_factor)); + b_.getInt64(innermost_dimension_size - + (innermost_dimension_size % vectorization_factor)); ShardedVectorType vector_type = CreateShardedVectorType( reduce->shape().element_type(), @@ -1756,18 +1734,76 @@ StatusOr IrEmitter::EmitVectorizedReduce( llvm_ir::IrArray target_array = GetIrArrayFor(reduce); llvm::Value* output_address = - target_array.EmitArrayElementAddress(array_index, &ir_builder_); + target_array.EmitArrayElementAddress(array_index, &b_); EmitShardedVectorStore(output_address, accumulator, element_alignment, target_array); } if (outermost_loop_exit_block) { - ir_builder_.SetInsertPoint(outermost_loop_exit_block); + b_.SetInsertPoint(outermost_loop_exit_block); } return true; } +StatusOr IrEmitter::EmitTargetElementLoopBodyForReduce( + HloReduceInstruction* reduce, const llvm_ir::IrArray::Index& index) { + const HloInstruction* arg = reduce->mutable_operand(0); + const HloInstruction* init_value = reduce->mutable_operand(1); + gtl::ArraySlice dimensions(reduce->dimensions()); + HloComputation* function = reduce->to_apply(); + // The called computation should have been emitted previously. + llvm::Function* reducer_function = FindOrDie(emitted_functions_, function); + + // Initialize an accumulator with init_value. + PrimitiveType accumulator_type = reduce->shape().element_type(); + llvm::AllocaInst* accumulator_addr = llvm_ir::EmitAllocaAtFunctionEntry( + llvm_ir::PrimitiveTypeToIrType(accumulator_type, module_), "accumulator", + &b_, MinimumAlignmentForPrimitiveType(accumulator_type)); + llvm::Value* init_value_addr = GetEmittedValueFor(init_value); + llvm::Value* load_init_value = b_.CreateLoad(init_value_addr); + b_.CreateStore(load_init_value, accumulator_addr); + + // The enclosing loops go over all the target elements. Now we have to compute + // the actual target element. For this, we build a new loop nest to iterate + // over all the reduction dimensions in the argument. + // AddLoopsForShapeOnDimensions will return an Index where induction Value*s + // are placed for each dimension in dimensions, and all the rest are nullptrs. + llvm_ir::ForLoopNest loops(IrName(reduce, "inner"), &b_); + const llvm_ir::IrArray::Index reduced_dims_index = + loops.AddLoopsForShapeOnDimensions(arg->shape(), dimensions, + "reduction_dim"); + + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &b_); + + // Build a full index for the input argument, using reduced_dims_index as the + // base. In reduced_dims_index only the reduction dimensions are filled in. We + // fill in the rest of the dimensions with induction Value*s taken from + // 'index' which iterates over the target array. See the high-level + // description in the XLA documentation for details. + llvm_ir::IrArray arg_array(GetIrArrayFor(arg)); + llvm_ir::IrArray::Index input_index = reduced_dims_index; + llvm_ir::IrArray::Index::const_iterator it = index.begin(); + + for (size_t i = 0; i < input_index.size(); ++i) { + if (input_index[i] == nullptr) { + input_index[i] = *it++; + } + } + CHECK(index.end() == it); + + // Apply the reduction function to the loaded value. + llvm::Value* input_address = + arg_array.EmitArrayElementAddress(input_index, &b_); + llvm::Value* result = EmitElementFunctionCall( + reducer_function, reduce->shape(), {accumulator_addr, input_address}, + "reduce_function"); + b_.CreateStore(result, accumulator_addr); + + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &b_); + return b_.CreateLoad(accumulator_addr); +} + Status IrEmitter::HandleReduce(HloInstruction* reduce) { auto arg = reduce->mutable_operand(0); auto init_value = reduce->mutable_operand(1); @@ -1789,61 +1825,11 @@ Status IrEmitter::HandleReduce(HloInstruction* reduce) { } } - // The called computation should have been emitted previously. - llvm::Function* reducer_function = FindOrDie(emitted_functions_, function); - return EmitTargetElementLoop( - reduce, [this, reduce, arg, init_value, dimensions, - reducer_function](const llvm_ir::IrArray::Index& index) { - // Initialize an accumulator with init_value. - PrimitiveType accumulator_type = reduce->shape().element_type(); - llvm::AllocaInst* accumulator_addr = llvm_ir::EmitAllocaAtFunctionEntry( - llvm_ir::PrimitiveTypeToIrType(accumulator_type, module_), - "accumulator", &ir_builder_, - MinimumAlignmentForPrimitiveType(accumulator_type)); - llvm::Value* init_value_addr = GetEmittedValueFor(init_value); - llvm::Value* load_init_value = ir_builder_.CreateLoad(init_value_addr); - ir_builder_.CreateStore(load_init_value, accumulator_addr); - - // The enclosing loops go over all the target elements. Now we have to - // compute the actual target element. For this, we build a new loop nest - // to iterate over all the reduction dimensions in the argument. - // AddLoopsForShapeOnDimensions will return an Index where induction - // Value*s are placed for each dimension in dimensions, and all the rest - // are nullptrs. - llvm_ir::ForLoopNest loops(IrName(reduce, "inner"), &ir_builder_); - const llvm_ir::IrArray::Index reduced_dims_index = - loops.AddLoopsForShapeOnDimensions(arg->shape(), dimensions, - "reduction_dim"); - - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); - - // Build a full index for the input argument, using reduced_dims_index - // as the base. In reduced_dims_index only the reduction dimensions are - // filled in. We fill in the rest of the dimensions with induction - // Value*s taken from 'index' which iterates over the target array. - // See the high-level description in the XLA documentation for details. - llvm_ir::IrArray arg_array(GetIrArrayFor(arg)); - llvm_ir::IrArray::Index input_index = reduced_dims_index; - llvm_ir::IrArray::Index::const_iterator it = index.begin(); - - for (size_t i = 0; i < input_index.size(); ++i) { - if (input_index[i] == nullptr) { - input_index[i] = *it++; - } - } - CHECK(index.end() == it); - - // Apply the reduction function to the loaded value. - llvm::Value* input_address = - arg_array.EmitArrayElementAddress(input_index, &ir_builder_); - llvm::Value* result = EmitElementFunctionCall( - reducer_function, reduce->shape(), - {accumulator_addr, input_address}, "reduce_function"); - ir_builder_.CreateStore(result, accumulator_addr); - - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_); - return ir_builder_.CreateLoad(accumulator_addr); - }); + return EmitTargetElementLoop(reduce, + [&](const llvm_ir::IrArray::Index& index) { + return EmitTargetElementLoopBodyForReduce( + Cast(reduce), index); + }); } Status IrEmitter::HandleSend(HloInstruction* send) { @@ -1945,7 +1931,7 @@ Status IrEmitter::HandleSlice(HloInstruction* slice) { llvm_ir::IrArray target_array = GetIrArrayFor(slice); const int64 num_outer_loops = outer_dims.size(); - llvm_ir::ForLoopNest loops(IrName(slice), &ir_builder_); + llvm_ir::ForLoopNest loops(IrName(slice), &b_); llvm_ir::IrArray::Index target_index = loops.AddLoopsForShapeOnDimensions(slice->shape(), outer_dims, "slice"); @@ -1954,21 +1940,21 @@ Status IrEmitter::HandleSlice(HloInstruction* slice) { // for the rest of the dimensions the copy writes to the full dimension. std::replace(target_index.begin(), target_index.end(), static_cast(nullptr), - static_cast(ir_builder_.getInt64(0))); + static_cast(b_.getInt64(0))); if (num_outer_loops > 0) { - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &b_); } llvm_ir::IrArray source_array = GetIrArrayFor(operand); const llvm_ir::IrArray::Index source_index = target_index.SourceIndexOfSlice( /*shape=*/slice->shape(), /*starts=*/slice->slice_starts(), - /*strides=*/slice->slice_strides(), /*builder=*/&ir_builder_); + /*strides=*/slice->slice_strides(), /*builder=*/&b_); - llvm::Value* memcpy_dest = target_array.EmitArrayElementAddress( - target_index, &ir_builder_, "slice.dest"); - llvm::Value* memcpy_source = source_array.EmitArrayElementAddress( - source_index, &ir_builder_, "slice.source"); + llvm::Value* memcpy_dest = + target_array.EmitArrayElementAddress(target_index, &b_, "slice.dest"); + llvm::Value* memcpy_source = + source_array.EmitArrayElementAddress(source_index, &b_, "slice.source"); const int64 memcpy_elements = primitive_elements_per_logical_element * memcpy_logical_elements; @@ -1985,7 +1971,7 @@ Status IrEmitter::HandleSlice(HloInstruction* slice) { } if (num_outer_loops > 0) { - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &b_); } return Status::OK(); @@ -2011,7 +1997,7 @@ Status IrEmitter::HandleDynamicUpdateSlice( auto operands = GetIrArraysForOperandsOf(dynamic_update_slice); return llvm_ir::EmitDynamicUpdateSliceInPlace( operands, GetIrArrayFor(dynamic_update_slice), - IrName(dynamic_update_slice, "in_place"), &ir_builder_); + IrName(dynamic_update_slice, "in_place"), &b_); } return DefaultAction(dynamic_update_slice); } @@ -2045,43 +2031,41 @@ Status IrEmitter::HandlePad(HloInstruction* pad) { [this, pad](const llvm_ir::IrArray::Index& target_index) { const HloInstruction* padding_value = pad->operand(1); llvm::Value* padding_value_addr = GetEmittedValueFor(padding_value); - return ir_builder_.CreateLoad(padding_value_addr); + return b_.CreateLoad(padding_value_addr); })); // Create a loop to iterate over the operand elements and update the output // locations where the operand elements should be stored. - llvm_ir::ForLoopNest loops(IrName(pad, "assign"), &ir_builder_); + llvm_ir::ForLoopNest loops(IrName(pad, "assign"), &b_); const HloInstruction* operand = pad->operand(0); const llvm_ir::IrArray::Index operand_index = loops.AddLoopsForShape(operand->shape(), "operand"); - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &b_); // Load an element from the operand. llvm_ir::IrArray operand_array(GetIrArrayFor(operand)); llvm::Value* operand_data = - operand_array.EmitReadArrayElement(operand_index, &ir_builder_); + operand_array.EmitReadArrayElement(operand_index, &b_); // Compute the output index the operand element should be assigned to. // output_index := edge_padding_low + operand_index * (interior_padding + 1) const PaddingConfig& padding_config = pad->padding_config(); llvm_ir::IrArray::Index output_index(operand_index.GetType()); for (size_t i = 0; i < operand_index.size(); ++i) { - llvm::Value* offset = ir_builder_.CreateMul( + llvm::Value* offset = b_.CreateMul( operand_index[i], - ir_builder_.getInt64(padding_config.dimensions(i).interior_padding() + - 1)); - llvm::Value* index = ir_builder_.CreateAdd( - offset, - ir_builder_.getInt64(padding_config.dimensions(i).edge_padding_low())); + b_.getInt64(padding_config.dimensions(i).interior_padding() + 1)); + llvm::Value* index = b_.CreateAdd( + offset, b_.getInt64(padding_config.dimensions(i).edge_padding_low())); output_index.push_back(index); } // Store the operand element to the computed output location. llvm_ir::IrArray output_array(GetIrArrayFor(pad)); - output_array.EmitWriteArrayElement(output_index, operand_data, &ir_builder_); + output_array.EmitWriteArrayElement(output_index, operand_data, &b_); - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &b_); return Status::OK(); } @@ -2103,8 +2087,7 @@ Status IrEmitter::HandleFusion(HloInstruction* fusion) { // Delegate to common implementation of fused in-place dynamic-update-slice. auto operands = GetIrArraysForOperandsOf(fusion); return llvm_ir::EmitFusedDynamicUpdateSliceInPlace( - fusion, operands, GetIrArrayFor(fusion), &elemental_emitter, - &ir_builder_); + fusion, operands, GetIrArrayFor(fusion), &elemental_emitter, &b_); } else if (fusion->fusion_kind() == HloInstruction::FusionKind::kLoop) { VLOG(3) << "HandleFusion kLoop"; CpuElementalIrEmitter elemental_emitter(hlo_module_config_, this, module_); @@ -2139,7 +2122,7 @@ Status IrEmitter::HandleFusion(HloInstruction* fusion) { TF_RETURN_IF_ERROR(DotOpEmitter::EmitDotOperation( *dot, target_array, lhs_array, rhs_array, &addend_array, - GetExecutableRunOptionsArgument(), &ir_builder_, hlo_module_config_, + GetExecutableRunOptionsArgument(), &b_, hlo_module_config_, target_machine_features_)); return Status::OK(); } else { @@ -2162,7 +2145,7 @@ Status IrEmitter::HandleCall(HloInstruction* call) { // ParallelTaskAssignment assigned partitions, emit call to // ParallelForkJoin. std::vector call_args = GetArrayFunctionCallArguments( - parameter_addresses, &ir_builder_, computation->name(), + parameter_addresses, &b_, computation->name(), /*return_value_buffer=*/emitted_value_[call], /*exec_run_options_arg=*/GetExecutableRunOptionsArgument(), /*temp_buffers_arg=*/GetTempBuffersArgument(), @@ -2170,8 +2153,8 @@ Status IrEmitter::HandleCall(HloInstruction* call) { HloInstruction* root = computation->root_instruction(); TF_RETURN_IF_ERROR(EmitCallToParallelForkJoin( - call_args, root->shape(), root->outer_dimension_partitions(), - &ir_builder_, call_ir_function, computation->name())); + call_args, root->shape(), root->outer_dimension_partitions(), &b_, + call_ir_function, computation->name())); } else { EmitArrayFunctionCallInto(call_ir_function, parameter_addresses, emitted_value_[call], computation->name()); @@ -2183,33 +2166,31 @@ Status IrEmitter::HandleCall(HloInstruction* call) { Status IrEmitter::HandleCustomCall(HloInstruction* custom_call) { gtl::ArraySlice operands(custom_call->operands()); tensorflow::StringPiece custom_call_target(custom_call->custom_call_target()); - llvm::Type* i8_ptr_type = ir_builder_.getInt8PtrTy(); + llvm::Type* i8_ptr_type = b_.getInt8PtrTy(); llvm::AllocaInst* operands_alloca = llvm_ir::EmitAllocaAtFunctionEntryWithCount( - i8_ptr_type, ir_builder_.getInt32(operands.size()), - "cc_operands_alloca", &ir_builder_); + i8_ptr_type, b_.getInt32(operands.size()), "cc_operands_alloca", &b_); for (size_t i = 0; i < operands.size(); ++i) { const HloInstruction* operand = operands[i]; llvm::Value* operand_as_i8ptr = - ir_builder_.CreatePointerCast(GetEmittedValueFor(operand), i8_ptr_type); - llvm::Value* slot_in_operands_alloca = ir_builder_.CreateInBoundsGEP( - operands_alloca, {ir_builder_.getInt64(i)}); - ir_builder_.CreateStore(operand_as_i8ptr, slot_in_operands_alloca); + b_.CreatePointerCast(GetEmittedValueFor(operand), i8_ptr_type); + llvm::Value* slot_in_operands_alloca = + b_.CreateInBoundsGEP(operands_alloca, {b_.getInt64(i)}); + b_.CreateStore(operand_as_i8ptr, slot_in_operands_alloca); } auto* custom_call_ir_function = llvm::cast(module_->getOrInsertFunction( AsStringRef(custom_call_target), llvm::FunctionType::get( - /*Result=*/ir_builder_.getVoidTy(), + /*Result=*/b_.getVoidTy(), /*Params=*/{i8_ptr_type, operands_alloca->getType()}, /*isVarArg=*/false))); TF_RETURN_IF_ERROR(EmitTargetAddressForOp(custom_call)); - auto* output_address_arg = ir_builder_.CreatePointerCast( - GetEmittedValueFor(custom_call), i8_ptr_type); + auto* output_address_arg = + b_.CreatePointerCast(GetEmittedValueFor(custom_call), i8_ptr_type); - ir_builder_.CreateCall(custom_call_ir_function, - {output_address_arg, operands_alloca}); + b_.CreateCall(custom_call_ir_function, {output_address_arg, operands_alloca}); return Status::OK(); } @@ -2274,8 +2255,8 @@ Status IrEmitter::HandleWhile(HloInstruction* xla_while) { llvm::BasicBlock* header_bb = llvm::BasicBlock::Create( module_->getContext(), AsStringRef(IrName(xla_while, "header")), compute_function_->function()); - ir_builder_.CreateBr(header_bb); - ir_builder_.SetInsertPoint(header_bb); + b_.CreateBr(header_bb); + b_.SetInsertPoint(header_bb); // Calls the condition function to determine whether to proceed with the // body. It must return a bool, so use the scalar call form. @@ -2283,7 +2264,7 @@ Status IrEmitter::HandleWhile(HloInstruction* xla_while) { llvm::Value* while_condition = EmitElementFunctionCall( condition_ir_function, condition->root_instruction()->shape(), {while_result}, IrName(xla_while, "cond")); - llvm::Value* while_predicate = ir_builder_.CreateICmpNE( + llvm::Value* while_predicate = b_.CreateICmpNE( while_condition, llvm::ConstantInt::get(llvm_ir::PrimitiveTypeToIrType(PRED, module_), 0)); @@ -2293,20 +2274,20 @@ Status IrEmitter::HandleWhile(HloInstruction* xla_while) { compute_function_->function()); llvm::BasicBlock* exit_bb = llvm::BasicBlock::Create( module_->getContext(), AsStringRef(IrName(xla_while, "exit"))); - ir_builder_.CreateCondBr(while_predicate, body_bb, exit_bb); + b_.CreateCondBr(while_predicate, body_bb, exit_bb); // Calls the body function from the body block. - ir_builder_.SetInsertPoint(body_bb); + b_.SetInsertPoint(body_bb); // Calls the body function. EmitArrayFunctionCallInto(body_ir_function, {while_result}, while_result, IrName(xla_while, "body")); // Finishes with a branch back to the header. - ir_builder_.CreateBr(header_bb); + b_.CreateBr(header_bb); // Adds the exit block to the function and sets the insert point there. compute_function_->function()->getBasicBlockList().push_back(exit_bb); - ir_builder_.SetInsertPoint(exit_bb); + b_.SetInsertPoint(exit_bb); return Status::OK(); } @@ -2348,21 +2329,21 @@ StatusOr IrEmitter::EmitFastConcatenate( std::vector outer_dims(std::next(concat_dim_layout_itr), output_min2maj.end()); - llvm::Type* i8_ptr_type = ir_builder_.getInt8PtrTy(); - llvm::Type* i8_type = ir_builder_.getInt8Ty(); + llvm::Type* i8_ptr_type = b_.getInt8PtrTy(); + llvm::Type* i8_type = b_.getInt8Ty(); TF_RETURN_IF_ERROR(EmitTargetAddressForOp(concatenate)); llvm_ir::IrArray target_array = GetIrArrayFor(concatenate); - llvm_ir::ForLoopNest loops(IrName(concatenate), &ir_builder_); + llvm_ir::ForLoopNest loops(IrName(concatenate), &b_); llvm_ir::IrArray::Index outer_dims_index = loops.AddLoopsForShapeOnDimensions(output_shape, outer_dims, "concat"); std::replace(outer_dims_index.begin(), outer_dims_index.end(), static_cast(nullptr), - static_cast(ir_builder_.getInt64(0))); + static_cast(b_.getInt64(0))); if (!outer_dims.empty()) { - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &b_); } PrimitiveType primitive_type = output_shape.element_type(); @@ -2371,10 +2352,10 @@ StatusOr IrEmitter::EmitFastConcatenate( // Contiguous subregions from each operand to the concatenate contribute to a // contiguous subregion in the target buffer starting at target_region_begin. - llvm::Value* target_region_begin = ir_builder_.CreateBitCast( - target_array.EmitArrayElementAddress(outer_dims_index, &ir_builder_, - "target_region"), - i8_ptr_type); + llvm::Value* target_region_begin = + b_.CreateBitCast(target_array.EmitArrayElementAddress( + outer_dims_index, &b_, "target_region"), + i8_ptr_type); int64 byte_offset_into_target_region = 0; int64 inner_dims_product = @@ -2388,14 +2369,13 @@ StatusOr IrEmitter::EmitFastConcatenate( for (HloInstruction* operand : operands) { const Shape& input_shape = operand->shape(); llvm_ir::IrArray source_array = GetIrArrayFor(operand); - llvm::Value* copy_source_address = ir_builder_.CreateBitCast( - source_array.EmitArrayElementAddress(outer_dims_index, &ir_builder_, - "src_addr"), + llvm::Value* copy_source_address = b_.CreateBitCast( + source_array.EmitArrayElementAddress(outer_dims_index, &b_, "src_addr"), i8_ptr_type); - llvm::Value* copy_target_address = ir_builder_.CreateGEP( - i8_type, target_region_begin, - ir_builder_.getInt64(byte_offset_into_target_region)); + llvm::Value* copy_target_address = + b_.CreateGEP(i8_type, target_region_begin, + b_.getInt64(byte_offset_into_target_region)); EmitTransferElements( copy_target_address, copy_source_address, @@ -2408,7 +2388,7 @@ StatusOr IrEmitter::EmitFastConcatenate( } if (!outer_dims.empty()) { - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &b_); } return true; @@ -2427,16 +2407,15 @@ void IrEmitter::EmitTransferElements(llvm::Value* target, llvm::Value* source, llvm_ir::PrimitiveTypeToIrType(primitive_type, module_)); if (element_count == 1) { - auto* load_instruction = ir_builder_.CreateAlignedLoad( - ir_builder_.CreateBitCast(source, primitive_ptr_type), - element_alignment); + auto* load_instruction = b_.CreateAlignedLoad( + b_.CreateBitCast(source, primitive_ptr_type), element_alignment); source_array.AnnotateLoadStoreInstructionWithMetadata(load_instruction); - auto* store_instruction = ir_builder_.CreateAlignedStore( - load_instruction, ir_builder_.CreateBitCast(target, primitive_ptr_type), + auto* store_instruction = b_.CreateAlignedStore( + load_instruction, b_.CreateBitCast(target, primitive_ptr_type), element_alignment); target_array.AnnotateLoadStoreInstructionWithMetadata(store_instruction); } else { - auto* memcpy_instruction = ir_builder_.CreateMemCpy( + auto* memcpy_instruction = b_.CreateMemCpy( target, /*DstAlign=*/element_alignment, source, /*SrcAlign=*/element_alignment, element_count * primitive_type_size); @@ -2506,24 +2485,24 @@ Status IrEmitter::HandleConditional(HloInstruction* conditional) { // cond_result = true_computation(true_operand) // else // cond_result = false_computation(false_operand) - llvm::LoadInst* pred_value = ir_builder_.CreateLoad( + llvm::LoadInst* pred_value = b_.CreateLoad( GetIrArrayFor(pred).GetBasePointer(), "load_predicate_value"); - llvm::Value* pred_cond = ir_builder_.CreateICmpNE( + llvm::Value* pred_cond = b_.CreateICmpNE( pred_value, llvm::ConstantInt::get(llvm_ir::PrimitiveTypeToIrType(PRED, module_), 0), "boolean_predicate"); llvm_ir::LlvmIfData if_data = - llvm_ir::EmitIfThenElse(pred_cond, "conditional", &ir_builder_); + llvm_ir::EmitIfThenElse(pred_cond, "conditional", &b_); - SetToFirstInsertPoint(if_data.true_block, &ir_builder_); + SetToFirstInsertPoint(if_data.true_block, &b_); EmitArrayFunctionCallInto(true_function, {GetEmittedValueFor(true_arg)}, conditional_result, IrName(conditional, "_true")); - SetToFirstInsertPoint(if_data.false_block, &ir_builder_); + SetToFirstInsertPoint(if_data.false_block, &b_); EmitArrayFunctionCallInto(false_function, {GetEmittedValueFor(false_arg)}, conditional_result, IrName(conditional, "_false")); - SetToFirstInsertPoint(if_data.after_block, &ir_builder_); + SetToFirstInsertPoint(if_data.after_block, &b_); return Status::OK(); } @@ -2534,6 +2513,28 @@ Status IrEmitter::HandleAfterAll(HloInstruction* gen_token) { return Status::OK(); } +Status IrEmitter::HandleIota(HloInstruction* iota) { + // TODO(b/64798317): implement iota on CPU. + return Unimplemented("Iota is not implemented on CPU."); +} + +Status IrEmitter::HandleRng(HloInstruction* rng) { + ElementalIrEmitter::HloToElementGeneratorMap operand_to_generator; + for (const HloInstruction* operand : rng->operands()) { + operand_to_generator[operand] = [=](const llvm_ir::IrArray::Index& index) { + return GetIrArrayFor(operand).EmitReadArrayElement(index, &b_); + }; + } + + CpuElementalIrEmitter elemental_emitter(hlo_module_config_, this, module_); + TF_RETURN_IF_ERROR(EmitTargetElementLoop( + rng, elemental_emitter.MakeElementGenerator(rng, operand_to_generator))); + + llvm_ir::IncrementVariableForPhiloxRngState(1, module_, &b_); + + return Status::OK(); +} + Status IrEmitter::FinishVisit(HloInstruction* root) { // When this method is called, we should have already emitted an IR value for // the root (return) op. The IR value holds the address of the buffer holding @@ -2551,7 +2552,7 @@ Status IrEmitter::FinishVisit(HloInstruction* root) { auto record_complete_computation = [&](llvm::Value* prof_counter) { if (prof_counter) { - profiling_state_.RecordCompleteComputation(&ir_builder_, prof_counter); + profiling_state_.RecordCompleteComputation(&b_, prof_counter); } }; @@ -2573,54 +2574,51 @@ llvm::Value* IrEmitter::GetProfileCounterCommon( int64 prof_counter_idx = it->second; string counter_name = IrName("prof_counter", hlo.name()); - return ir_builder_.CreateGEP(GetProfileCountersArgument(), - ir_builder_.getInt64(prof_counter_idx), - AsStringRef(counter_name)); + return b_.CreateGEP(GetProfileCountersArgument(), + b_.getInt64(prof_counter_idx), AsStringRef(counter_name)); } -void IrEmitter::ProfilingState::UpdateProfileCounter( - llvm::IRBuilder<>* ir_builder, llvm::Value* prof_counter, - llvm::Value* cycle_end, llvm::Value* cycle_start) { - auto* cycle_diff = ir_builder->CreateSub(cycle_end, cycle_start); +void IrEmitter::ProfilingState::UpdateProfileCounter(llvm::IRBuilder<>* b, + llvm::Value* prof_counter, + llvm::Value* cycle_end, + llvm::Value* cycle_start) { + auto* cycle_diff = b->CreateSub(cycle_end, cycle_start); llvm::LoadInst* old_cycle_count = - ir_builder->CreateLoad(prof_counter, "old_cycle_count"); + b->CreateLoad(prof_counter, "old_cycle_count"); auto* new_cycle_count = - ir_builder->CreateAdd(cycle_diff, old_cycle_count, "new_cycle_count"); - ir_builder->CreateStore(new_cycle_count, prof_counter); + b->CreateAdd(cycle_diff, old_cycle_count, "new_cycle_count"); + b->CreateStore(new_cycle_count, prof_counter); } -llvm::Value* IrEmitter::ProfilingState::ReadCycleCounter( - llvm::IRBuilder<>* ir_builder) { - llvm::Module* module = ir_builder->GetInsertBlock()->getModule(); +llvm::Value* IrEmitter::ProfilingState::ReadCycleCounter(llvm::IRBuilder<>* b) { + llvm::Module* module = b->GetInsertBlock()->getModule(); if (use_rdtscp_) { llvm::Function* func_llvm_readcyclecounter = llvm::Intrinsic::getDeclaration(module, llvm::Intrinsic::readcyclecounter); - return ir_builder->CreateCall(func_llvm_readcyclecounter); + return b->CreateCall(func_llvm_readcyclecounter); } llvm::Function* func_llvm_x86_rdtscp = llvm::Intrinsic::getDeclaration(module, llvm::Intrinsic::x86_rdtscp); if (!aux_i8ptr_) { - llvm::AllocaInst* rdtscp_aux = llvm_ir::EmitAllocaAtFunctionEntry( - ir_builder->getInt32Ty(), "rdtscp_aux", ir_builder); - aux_i8ptr_ = - ir_builder->CreateBitCast(rdtscp_aux, ir_builder->getInt8PtrTy()); + llvm::AllocaInst* rdtscp_aux = + llvm_ir::EmitAllocaAtFunctionEntry(b->getInt32Ty(), "rdtscp_aux", b); + aux_i8ptr_ = b->CreateBitCast(rdtscp_aux, b->getInt8PtrTy()); } - llvm::ConstantInt* alloca_size = ir_builder->getInt64(4); + llvm::ConstantInt* alloca_size = b->getInt64(4); llvm::Function* func_llvm_lifetime_start = llvm::Intrinsic::getDeclaration(module, llvm::Intrinsic::lifetime_start); - ir_builder->CreateCall(func_llvm_lifetime_start, {alloca_size, aux_i8ptr_}); - llvm::Value* rdtscp_call = - ir_builder->CreateCall(func_llvm_x86_rdtscp, aux_i8ptr_); + b->CreateCall(func_llvm_lifetime_start, {alloca_size, aux_i8ptr_}); + llvm::Value* rdtscp_call = b->CreateCall(func_llvm_x86_rdtscp, aux_i8ptr_); llvm::Function* func_llvm_lifetime_end = llvm::Intrinsic::getDeclaration(module, llvm::Intrinsic::lifetime_end); - ir_builder->CreateCall(func_llvm_lifetime_end, {alloca_size, aux_i8ptr_}); + b->CreateCall(func_llvm_lifetime_end, {alloca_size, aux_i8ptr_}); return rdtscp_call; } -void IrEmitter::ProfilingState::RecordCycleStart(llvm::IRBuilder<>* ir_builder, +void IrEmitter::ProfilingState::RecordCycleStart(llvm::IRBuilder<>* b, HloInstruction* hlo) { - auto* cycle_start = ReadCycleCounter(ir_builder); + auto* cycle_start = ReadCycleCounter(b); cycle_start->setName(AsStringRef(IrName(hlo, "cycle_start"))); cycle_starts_[hlo] = cycle_start; if (first_read_cycle_start_ == nullptr) { @@ -2628,20 +2626,20 @@ void IrEmitter::ProfilingState::RecordCycleStart(llvm::IRBuilder<>* ir_builder, } } -void IrEmitter::ProfilingState::RecordCycleDelta(llvm::IRBuilder<>* ir_builder, +void IrEmitter::ProfilingState::RecordCycleDelta(llvm::IRBuilder<>* b, HloInstruction* hlo, llvm::Value* prof_counter) { - auto* cycle_end = ReadCycleCounter(ir_builder); + auto* cycle_end = ReadCycleCounter(b); cycle_end->setName(AsStringRef(IrName(hlo, "cycle_end"))); auto* cycle_start = cycle_starts_[hlo]; - UpdateProfileCounter(ir_builder, prof_counter, cycle_end, cycle_start); + UpdateProfileCounter(b, prof_counter, cycle_end, cycle_start); last_read_cycle_end_ = cycle_end; } void IrEmitter::ProfilingState::RecordCompleteComputation( - llvm::IRBuilder<>* ir_builder, llvm::Value* prof_counter) { + llvm::IRBuilder<>* b, llvm::Value* prof_counter) { if (last_read_cycle_end_ && first_read_cycle_start_) { - UpdateProfileCounter(ir_builder, prof_counter, last_read_cycle_end_, + UpdateProfileCounter(b, prof_counter, last_read_cycle_end_, first_read_cycle_start_); } } @@ -2649,14 +2647,14 @@ void IrEmitter::ProfilingState::RecordCompleteComputation( Status IrEmitter::Preprocess(HloInstruction* hlo) { VLOG(3) << "Visiting: " << hlo->ToString(); if (instruction_to_profile_idx_.count(hlo)) { - profiling_state_.RecordCycleStart(&ir_builder_, hlo); + profiling_state_.RecordCycleStart(&b_, hlo); } return Status::OK(); } Status IrEmitter::Postprocess(HloInstruction* hlo) { if (auto* prof_counter = GetProfileCounterFor(*hlo)) { - profiling_state_.RecordCycleDelta(&ir_builder_, hlo, prof_counter); + profiling_state_.RecordCycleDelta(&b_, hlo, prof_counter); } return Status::OK(); } @@ -2715,22 +2713,24 @@ llvm::Value* IrEmitter::EmitTempBufferPointer( CHECK_EQ(1, assigned_buffers.size()); const Shape& shape = assigned_buffers.begin()->first->shape(); - llvm::AllocaInst*& tempbuf_address = thread_local_buffers_[{ - ir_builder_.GetInsertBlock()->getParent(), slice}]; + llvm::AllocaInst*& tempbuf_address = + thread_local_buffers_[{b_.GetInsertBlock()->getParent(), slice}]; if (tempbuf_address == nullptr) { tempbuf_address = llvm_ir::EmitAllocaAtFunctionEntry( IrShapeType(shape), - tensorflow::strings::StrCat("thread_local", slice.ToString()), - &ir_builder_, MinimumAlignmentForShape(target_shape)); + tensorflow::strings::StrCat("thread_local", slice.ToString()), &b_, + MinimumAlignmentForShape(target_shape)); } - return ir_builder_.CreateBitCast(tempbuf_address, - element_type->getPointerTo()); + return b_.CreateBitCast(tempbuf_address, element_type->getPointerTo()); + } + + if (allocation.is_constant()) { + return FindOrDie(constant_buffer_to_global_, allocation.index()); } llvm::Value* tempbuf_address_ptr = llvm_ir::EmitBufferIndexingGEP( - GetTempBuffersArgument(), slice.index(), &ir_builder_); - llvm::LoadInst* tempbuf_address_base = - ir_builder_.CreateLoad(tempbuf_address_ptr); + GetTempBuffersArgument(), slice.index(), &b_); + llvm::LoadInst* tempbuf_address_base = b_.CreateLoad(tempbuf_address_ptr); if (is_top_level_computation_ && hlo_module_config_.debug_options() .xla_llvm_enable_invariant_load_metadata()) { @@ -2749,11 +2749,11 @@ llvm::Value* IrEmitter::EmitTempBufferPointer( llvm::Value* tempbuf_address_untyped = tempbuf_address_base; if (slice.offset() > 0) { // Adjust the address to account for the slice offset. - tempbuf_address_untyped = ir_builder_.CreateInBoundsGEP( - tempbuf_address_base, ir_builder_.getInt64(slice.offset())); + tempbuf_address_untyped = + b_.CreateInBoundsGEP(tempbuf_address_base, b_.getInt64(slice.offset())); } - return ir_builder_.CreateBitCast(tempbuf_address_untyped, - element_type->getPointerTo()); + return b_.CreateBitCast(tempbuf_address_untyped, + element_type->getPointerTo()); } // Emits a function call returning a single array element. Allocates space @@ -2764,7 +2764,7 @@ llvm::Value* IrEmitter::EmitElementFunctionCall( tensorflow::StringPiece name) { llvm::Value* return_value_buffer = EmitArrayFunctionCall( function, return_shape, 1, parameter_addresses, name); - return ir_builder_.CreateLoad( + return b_.CreateLoad( return_value_buffer, AsStringRef(tensorflow::strings::StrCat(name, "_return_value"))); } @@ -2782,9 +2782,9 @@ llvm::Value* IrEmitter::EmitElementFunctionCall( void IrEmitter::EmitArrayFunctionCallInto( llvm::Function* function, gtl::ArraySlice parameter_addresses, llvm::Value* return_value_buffer, tensorflow::StringPiece name) { - ir_builder_.CreateCall( - function, GetArrayFunctionCallArguments( - parameter_addresses, &ir_builder_, name, + b_.CreateCall(function, + GetArrayFunctionCallArguments( + parameter_addresses, &b_, name, /*return_value_buffer=*/return_value_buffer, /*exec_run_options_arg=*/GetExecutableRunOptionsArgument(), /*temp_buffers_arg=*/GetTempBuffersArgument(), @@ -2796,13 +2796,13 @@ llvm::Value* IrEmitter::EmitArrayFunctionCall( gtl::ArraySlice parameter_addresses, tensorflow::StringPiece name) { llvm::Value* elements = - llvm::ConstantInt::get(ir_builder_.getInt64Ty(), element_count); + llvm::ConstantInt::get(b_.getInt64Ty(), element_count); PrimitiveType return_type = return_shape.element_type(); llvm::Value* return_value_buffer = llvm_ir::EmitAllocaAtFunctionEntryWithCount( llvm_ir::PrimitiveTypeToIrType(return_type, module_), elements, - tensorflow::strings::StrCat(name, "_return_value_address"), - &ir_builder_, MinimumAlignmentForPrimitiveType(return_type)); + tensorflow::strings::StrCat(name, "_return_value_address"), &b_, + MinimumAlignmentForPrimitiveType(return_type)); EmitArrayFunctionCallInto(function, parameter_addresses, return_value_buffer, name); return return_value_buffer; @@ -2824,8 +2824,7 @@ Status IrEmitter::EmitTargetAddressForOp(const HloInstruction* op) { attr_builder.addDereferenceableAttr(ByteSizeOf(target_shape)); retval->addAttrs(attr_builder); } - addr = ir_builder_.CreateBitCast(retval, - IrShapeType(target_shape)->getPointerTo()); + addr = b_.CreateBitCast(retval, IrShapeType(target_shape)->getPointerTo()); } else { // For other nodes, we need the temporary buffer allocated for this node to // write the result into. @@ -2867,14 +2866,14 @@ Status IrEmitter::EmitTargetElementLoop( llvm_ir::IrArray(op_target_address, element_shape)); } TF_RETURN_IF_ERROR( - llvm_ir::LoopEmitter(element_generator, output_arrays, &ir_builder_) + llvm_ir::LoopEmitter(element_generator, output_arrays, &b_) .EmitLoop(IrName(target_op))); std::vector tuple_operand_ptrs; for (int64 i = 0; i < output_arrays.size(); ++i) { tuple_operand_ptrs.push_back(output_arrays[i].GetBasePointer()); } - llvm_ir::EmitTuple(target_array, tuple_operand_ptrs, &ir_builder_, module_); + llvm_ir::EmitTuple(target_array, tuple_operand_ptrs, &b_, module_); } else { if (ShouldEmitParallelLoopFor(*target_op)) { @@ -2883,11 +2882,11 @@ Status IrEmitter::EmitTargetElementLoop( compute_function_->GetDynamicLoopBounds(); // Emit parallel loop with dynamic loop bounds for most-major dimensions. TF_RETURN_IF_ERROR(ParallelLoopEmitter(element_generator, target_array, - &dynamic_loop_bounds, &ir_builder_) + &dynamic_loop_bounds, &b_) .EmitLoop(IrName(target_op))); } else { TF_RETURN_IF_ERROR( - llvm_ir::LoopEmitter(element_generator, target_array, &ir_builder_) + llvm_ir::LoopEmitter(element_generator, target_array, &b_) .EmitLoop(IrName(target_op))); } } @@ -2900,8 +2899,8 @@ Status IrEmitter::EmitMemcpy(const HloInstruction& source, llvm::Value* destination_value = GetEmittedValueFor(&destination); int64 source_size = ByteSizeOf(source.shape()); // TODO(b/63762267): Be more aggressive about specifying alignment. - ir_builder_.CreateMemCpy(destination_value, /*DstAlign=*/1, source_value, - /*SrcAlign=*/1, source_size); + b_.CreateMemCpy(destination_value, /*DstAlign=*/1, source_value, + /*SrcAlign=*/1, source_size); return Status::OK(); } @@ -2929,7 +2928,7 @@ Status IrEmitter::DefaultAction(HloInstruction* hlo) { ElementalIrEmitter::HloToElementGeneratorMap operand_to_generator; for (const HloInstruction* operand : hlo->operands()) { operand_to_generator[operand] = [=](const llvm_ir::IrArray::Index& index) { - return GetIrArrayFor(operand).EmitReadArrayElement(index, &ir_builder_); + return GetIrArrayFor(operand).EmitReadArrayElement(index, &b_); }; } CpuElementalIrEmitter elemental_emitter(hlo_module_config_, this, module_); @@ -2944,8 +2943,8 @@ StatusOr IrEmitter::EmitScalarCall( std::vector argument_addrs; for (auto argument : arguments) { llvm::Value* argument_addr = llvm_ir::EmitAllocaAtFunctionEntry( - argument->getType(), "arg_addr", &ir_builder_); - ir_builder_.CreateStore(argument, argument_addr); + argument->getType(), "arg_addr", &b_); + b_.CreateStore(argument, argument_addr); argument_addrs.push_back(argument_addr); } return EmitElementFunctionCall(llvm_function, diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h index 3089f6451e7dc4b2752c6ae65b3f5f8ecc3d7405..03bbb2afb587e2f95bcd2743d396d3d996041a21 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h @@ -35,6 +35,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_instructions.h" #include "tensorflow/compiler/xla/service/hlo_module_config.h" #include "tensorflow/compiler/xla/service/llvm_ir/alias_analysis.h" #include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h" @@ -97,13 +98,16 @@ class IrEmitter : public DfsHloVisitorWithDefault { bool is_top_level_computation, std::vector* instruction_order); - llvm::IRBuilder<>* ir_builder() { return &ir_builder_; } + llvm::IRBuilder<>* b() { return &b_; } // Emits a call to `computation` with scalar arguments `arguments`. StatusOr EmitScalarCall( PrimitiveType return_type, HloComputation* computation, const std::vector& arguments, tensorflow::StringPiece name); + // Emit an LLVM global variable for every constant buffer allocation. + Status EmitConstantGlobals(); + protected: // // The following methods implement the DfsHloVisitor interface. @@ -147,6 +151,8 @@ class IrEmitter : public DfsHloVisitorWithDefault { Status HandleConcatenate(HloInstruction* concatenate) override; Status HandleConditional(HloInstruction* conditional) override; Status HandleAfterAll(HloInstruction* gen_token) override; + Status HandleIota(HloInstruction* iota) override; + Status HandleRng(HloInstruction* rng) override; Status FinishVisit(HloInstruction* root) override; Status Preprocess(HloInstruction* hlo) override; @@ -414,7 +420,7 @@ class IrEmitter : public DfsHloVisitorWithDefault { // creates the encapsulated llvm::Function s.t. it is added to the llvm // module's function list). std::unique_ptr compute_function_; - llvm::IRBuilder<> ir_builder_; + llvm::IRBuilder<> b_; // Maps HLO instructions to their index into the profile counter array. const std::unordered_map @@ -450,23 +456,22 @@ class IrEmitter : public DfsHloVisitorWithDefault { : use_rdtscp_(use_rdtscp), prof_counters_(prof_counters) {} // Record the cycle counter before an HLO executes. - void RecordCycleStart(llvm::IRBuilder<>* ir_builder, HloInstruction* hlo); + void RecordCycleStart(llvm::IRBuilder<>* b, HloInstruction* hlo); // Record the number of cycles it took for an HLO to execute. - void RecordCycleDelta(llvm::IRBuilder<>* ir_builder, HloInstruction* hlo, + void RecordCycleDelta(llvm::IRBuilder<>* b, HloInstruction* hlo, llvm::Value* prof_counter); // Record the number of cycles it took for the entire computation to // execute. - void RecordCompleteComputation(llvm::IRBuilder<>* ir_builder, + void RecordCompleteComputation(llvm::IRBuilder<>* b, llvm::Value* prof_counter); // Convenience function to generate a call to an intrinsic which reads the // CPU cycle counter. - llvm::Value* ReadCycleCounter(llvm::IRBuilder<>* ir_builder); + llvm::Value* ReadCycleCounter(llvm::IRBuilder<>* b); // Store the cycle counter delta to the per-HLO profile counter. - void UpdateProfileCounter(llvm::IRBuilder<>* ir_builder, - llvm::Value* prof_counter, llvm::Value* cycle_end, - llvm::Value* cycle_start); + void UpdateProfileCounter(llvm::IRBuilder<>* b, llvm::Value* prof_counter, + llvm::Value* cycle_end, llvm::Value* cycle_start); private: // Should we use the x86-specific rdtscp or the generic readcyclecounter @@ -514,6 +519,17 @@ class IrEmitter : public DfsHloVisitorWithDefault { // Returns the number of bytes within the shape. int64 ByteSizeOf(const Shape& shape) const; + StatusOr EmitTargetElementLoopBodyForMap( + HloMapInstruction* map, const llvm_ir::IrArray::Index& index); + StatusOr EmitTargetElementLoopBodyForReduceWindow( + HloReduceWindowInstruction* reduce_window, + const llvm_ir::IrArray::Index& index); + StatusOr EmitTargetElementLoopBodyForConvolution( + HloConvolutionInstruction* convolution, + const llvm_ir::IrArray::Index& index); + StatusOr EmitTargetElementLoopBodyForReduce( + HloReduceInstruction* reduce, const llvm_ir::IrArray::Index& index); + enum class XfeedKind { kInfeed, kOutfeed, @@ -547,6 +563,9 @@ class IrEmitter : public DfsHloVisitorWithDefault { LiteralPtrHashFunctor, LiteralPtrEqualityFunctor> emitted_literals_; + tensorflow::gtl::FlatMap + constant_buffer_to_global_; + TF_DISALLOW_COPY_AND_ASSIGN(IrEmitter); }; diff --git a/tensorflow/compiler/xla/service/cpu/ir_function.cc b/tensorflow/compiler/xla/service/cpu/ir_function.cc index 2d6f2f3818a7bd4424aaa7d918ca86abef15c0e9..6aff838462ac6bfe8a31971108a721b66dbe45bd 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_function.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_function.cc @@ -49,11 +49,10 @@ IrFunction::IrFunction(const string& function_name, llvm::Function::LinkageTypes linkage, const bool optimize_for_size_requested, const bool enable_fast_math, llvm::Module* llvm_module, - llvm::IRBuilder<>* ir_builder, - int64 num_dynamic_loop_bounds) - : ir_builder_(ir_builder), + llvm::IRBuilder<>* b, int64 num_dynamic_loop_bounds) + : b_(b), llvm_module_(llvm_module), - caller_insert_point_guard_(*ir_builder), + caller_insert_point_guard_(*b), num_dynamic_loop_bounds_(num_dynamic_loop_bounds) { Initialize(function_name, linkage, optimize_for_size_requested, enable_fast_math); @@ -61,7 +60,7 @@ IrFunction::IrFunction(const string& function_name, IrFunction::~IrFunction() { // Emit function return value. - ir_builder_->CreateRetVoid(); + b_->CreateRetVoid(); } DynamicLoopBounds IrFunction::GetDynamicLoopBounds() { @@ -174,7 +173,7 @@ void IrFunction::Initialize(const string& function_name, function_->addAttribute(argument.getArgNo() + 1, llvm::Attribute::NoAlias); } - ir_builder_->SetInsertPoint(llvm::BasicBlock::Create( + b_->SetInsertPoint(llvm::BasicBlock::Create( /*Context=*/llvm_module_->getContext(), /*Name=*/"entry", /*Parent=*/function_)); @@ -184,9 +183,8 @@ llvm::Value* IrFunction::GetDynamicLoopBound(const int64 offset) { CHECK_GT(num_dynamic_loop_bounds_, 0); CHECK_LT(offset, num_dynamic_loop_bounds_ * 2); string name = tensorflow::strings::StrCat("dynamic_loop_bound_", offset); - return ir_builder_->CreateLoad( - ir_builder_->CreateGEP(CHECK_NOTNULL(dynamic_loop_bounds_arg_), - ir_builder_->getInt64(offset), AsStringRef(name))); + return b_->CreateLoad(b_->CreateGEP(CHECK_NOTNULL(dynamic_loop_bounds_arg_), + b_->getInt64(offset), AsStringRef(name))); } // Emits code to allocate an array of parameter address pointers, and store @@ -195,27 +193,25 @@ llvm::Value* IrFunction::GetDynamicLoopBound(const int64 offset) { // address buffer). std::vector GetArrayFunctionCallArguments( tensorflow::gtl::ArraySlice parameter_addresses, - llvm::IRBuilder<>* ir_builder, tensorflow::StringPiece name, + llvm::IRBuilder<>* b, tensorflow::StringPiece name, llvm::Value* return_value_buffer, llvm::Value* exec_run_options_arg, llvm::Value* temp_buffers_arg, llvm::Value* profile_counters_arg) { llvm::Value* parameter_addresses_buffer = llvm_ir::EmitAllocaAtFunctionEntryWithCount( - ir_builder->getInt8PtrTy(), - ir_builder->getInt32(parameter_addresses.size()), - tensorflow::strings::StrCat(name, "_parameter_addresses"), - ir_builder); + b->getInt8PtrTy(), b->getInt32(parameter_addresses.size()), + tensorflow::strings::StrCat(name, "_parameter_addresses"), b); for (size_t i = 0; i < parameter_addresses.size(); ++i) { - llvm::Value* parameter_as_i8ptr = ir_builder->CreateBitCast( - parameter_addresses[i], ir_builder->getInt8PtrTy(), - AsStringRef(tensorflow::strings::StrCat(name, "_parameter_", i, - "_address_as_i8ptr"))); - llvm::Value* slot_in_param_addresses = ir_builder->CreateInBoundsGEP( - parameter_addresses_buffer, {ir_builder->getInt64(i)}); - ir_builder->CreateStore(parameter_as_i8ptr, slot_in_param_addresses); + llvm::Value* parameter_as_i8ptr = + b->CreateBitCast(parameter_addresses[i], b->getInt8PtrTy(), + AsStringRef(tensorflow::strings::StrCat( + name, "_parameter_", i, "_address_as_i8ptr"))); + llvm::Value* slot_in_param_addresses = + b->CreateInBoundsGEP(parameter_addresses_buffer, {b->getInt64(i)}); + b->CreateStore(parameter_as_i8ptr, slot_in_param_addresses); } const auto to_int8_ptr = [=](llvm::Value* ptr) { - return ir_builder->CreatePointerCast(ptr, ir_builder->getInt8PtrTy()); + return b->CreatePointerCast(ptr, b->getInt8PtrTy()); }; std::vector arguments{ to_int8_ptr(return_value_buffer), to_int8_ptr(exec_run_options_arg), @@ -230,22 +226,21 @@ std::vector GetArrayFunctionCallArguments( // calls to 'parallel_function' (and joins threads before returning). Status EmitCallToParallelForkJoin( const std::vector& arguments, const Shape& shape, - const std::vector& dimension_partition_counts, - llvm::IRBuilder<>* ir_builder, llvm::Function* parallel_function, - const string& name) { - llvm::Module* module = ir_builder->GetInsertBlock()->getModule(); + const std::vector& dimension_partition_counts, llvm::IRBuilder<>* b, + llvm::Function* parallel_function, const string& name) { + llvm::Module* module = b->GetInsertBlock()->getModule(); // Build ParallelForkJoin function type. std::vector compute_function_params = GetComputeFunctionParams(module, /*num_dynamic_loop_bounds=*/0); // Number of parallel compute functions. - compute_function_params.push_back(ir_builder->getInt32Ty()); + compute_function_params.push_back(b->getInt32Ty()); // Array of partitions. There is an array element for each // partition x partition_dim x 2 (for dimension start and limit). compute_function_params.push_back( llvm::Type::getInt64PtrTy(module->getContext())); // Number of partitioned most-major dimensions in 'shape'. - compute_function_params.push_back(ir_builder->getInt32Ty()); + compute_function_params.push_back(b->getInt32Ty()); // Function pointer for compute function to be dispatched in parallel. compute_function_params.push_back( llvm::Type::getInt8PtrTy(module->getContext())); @@ -268,7 +263,7 @@ Status EmitCallToParallelForkJoin( ShapePartitionIterator partition_iterator(shape, dimension_partition_counts); const int64 num_partitions = partition_iterator.GetTotalPartitionCount(); // Add argument specifying the number of parallel partitions. - fork_join_arguments.push_back(ir_builder->getInt32(num_partitions)); + fork_join_arguments.push_back(b->getInt32(num_partitions)); // The number of partitioned most-major dimensions in 'shape'. const int32 num_partitioned_dims = dimension_partition_counts.size(); @@ -293,15 +288,15 @@ Status EmitCallToParallelForkJoin( const std::pair& dim_partition = dim_partitions[j]; const int32 index = partition_index + j * dim_partition_size; // Store partition [dim_start, dim_limit) intervals for each dimension. - partitions[index] = ir_builder->getInt64(dim_partition.first); + partitions[index] = b->getInt64(dim_partition.first); partitions[index + 1] = - ir_builder->getInt64(dim_partition.first + dim_partition.second); + b->getInt64(dim_partition.first + dim_partition.second); } } // Create global variable out of dimension partitions in 'partitions'. llvm::ArrayType* partitions_array_type = - llvm::ArrayType::get(ir_builder->getInt64Ty(), partition_array_size); + llvm::ArrayType::get(b->getInt64Ty(), partition_array_size); llvm::Constant* partitions_array = llvm::ConstantArray::get(partitions_array_type, partitions); llvm::GlobalVariable* global_partitions_array = new llvm::GlobalVariable( @@ -315,16 +310,16 @@ Status EmitCallToParallelForkJoin( tensorflow::strings::StrCat(name, "_parallel_dimension_partitions"))); // Add argument specifying parallel dimension partitions. - fork_join_arguments.push_back(ir_builder->CreateBitCast( - global_partitions_array, - llvm::Type::getInt64PtrTy(module->getContext()))); + fork_join_arguments.push_back( + b->CreateBitCast(global_partitions_array, + llvm::Type::getInt64PtrTy(module->getContext()))); // Add argument specifying the number of partitioned most-major dimensions. - fork_join_arguments.push_back(ir_builder->getInt32(num_partitioned_dims)); + fork_join_arguments.push_back(b->getInt32(num_partitioned_dims)); // Add argument for parallel compute function pointer. fork_join_arguments.push_back( - ir_builder->CreateBitCast(parallel_function, ir_builder->getInt8PtrTy())); + b->CreateBitCast(parallel_function, b->getInt8PtrTy())); // Emit call to parallel fork/join. - ir_builder->CreateCall(fork_join_func, fork_join_arguments); + b->CreateCall(fork_join_func, fork_join_arguments); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/cpu/ir_function.h b/tensorflow/compiler/xla/service/cpu/ir_function.h index 2e55181eed867aca762f2b9b8310624ea12c7487..a41cbb64cdd9f5b6de5d1eadfbf7e63e1e984801 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_function.h +++ b/tensorflow/compiler/xla/service/cpu/ir_function.h @@ -54,7 +54,7 @@ class IrFunction { IrFunction(const string& function_name, llvm::Function::LinkageTypes linkage, const bool optimize_for_size_requested, const bool enable_fast_math, llvm::Module* llvm_module, - llvm::IRBuilder<>* ir_builder, int64 num_dynamic_loop_bounds); + llvm::IRBuilder<>* b, int64 num_dynamic_loop_bounds); ~IrFunction(); // Emit ir to read and return the set of ir values representing the dynamic @@ -97,7 +97,7 @@ class IrFunction { // 'offset' from the "dynamic_loop_bounds" argument of this function. llvm::Value* GetDynamicLoopBound(int64 offset); - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; llvm::Module* llvm_module_; llvm::IRBuilder<>::InsertPointGuard caller_insert_point_guard_; @@ -116,7 +116,7 @@ class IrFunction { // Returns an array of compute function call argument ir values. std::vector GetArrayFunctionCallArguments( tensorflow::gtl::ArraySlice parameter_addresses, - llvm::IRBuilder<>* ir_builder, tensorflow::StringPiece name, + llvm::IRBuilder<>* b, tensorflow::StringPiece name, llvm::Value* return_value_buffer, llvm::Value* exec_run_options_arg, llvm::Value* temp_buffers_arg, llvm::Value* profile_counters_arg); @@ -124,9 +124,8 @@ std::vector GetArrayFunctionCallArguments( // calls to 'parallel_function' (and joins threads before returning). Status EmitCallToParallelForkJoin( const std::vector& arguments, const Shape& shape, - const std::vector& dimension_partition_counts, - llvm::IRBuilder<>* ir_builder, llvm::Function* parallel_function, - const string& name); + const std::vector& dimension_partition_counts, llvm::IRBuilder<>* b, + llvm::Function* parallel_function, const string& name); } // namespace cpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc b/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc index 2e5cc96098241415b82f225afc81981f3e1069e0..cef5e57b0b12b7ae93af0d2508b2b9d6a592d390 100644 --- a/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc +++ b/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc @@ -21,6 +21,7 @@ limitations under the License. #include "llvm/IR/Verifier.h" #include "llvm/Transforms/Utils/Cloning.h" #include "tensorflow/compiler/xla/service/cpu/vector_support_library.h" +#include "tensorflow/compiler/xla/service/llvm_ir/math_ops.h" #include "tensorflow/core/lib/core/casts.h" #include "tensorflow/core/platform/logging.h" @@ -52,46 +53,14 @@ llvm::Function* EmitVectorF32TanhIfNeeded(llvm::Module* module, llvm::BasicBlock* vector_tanh_body = llvm::BasicBlock::Create(*context, "body", vector_tanh_function); - llvm::IRBuilder<> ir_builder(vector_tanh_body); + llvm::IRBuilder<> b(vector_tanh_body); llvm::FastMathFlags fast_math_flags; - fast_math_flags.setFast(); - ir_builder.setFastMathFlags(fast_math_flags); - - VectorSupportLibrary vsl(F32, vector_width, &ir_builder, "tanh_f32"); + fast_math_flags.setFast(enable_fast_math); + b.setFastMathFlags(fast_math_flags); llvm::Value* input = &*vector_tanh_function->arg_begin(); - CHECK_EQ(input->getType(), vsl.vector_type()); - - // This implements the same rational interpolant as implemented in Eigen3. - llvm::Value* input_clamped = - vsl.Clamp(input, /*low=*/GetIeeeF32(-9.0), /*high=*/GetIeeeF32(9.0)); - - std::array numerator_coeffs{ - -2.76076847742355e-16f, 2.00018790482477e-13f, -8.60467152213735e-11f, - 5.12229709037114e-08f, 1.48572235717979e-05f, 6.37261928875436e-04f, - 4.89352455891786e-03f}; - - std::array denominator_coeffs{ - 1.19825839466702e-06f, 1.18534705686654e-04f, 2.26843463243900e-03f, - 4.89352518554385e-03f}; - - llvm::Value* input_squared = vsl.Mul(input_clamped, input_clamped); - llvm::Value* numerator = vsl.SplatFloat(GetIeeeF32(numerator_coeffs[0])); - for (int i = 1; i < numerator_coeffs.size(); i++) { - numerator = - vsl.MulAdd(input_squared, numerator, GetIeeeF32(numerator_coeffs[i])); - } - - numerator = vsl.Mul(input_clamped, numerator); - - llvm::Value* denominator = vsl.SplatFloat(GetIeeeF32(denominator_coeffs[0])); - for (int i = 1; i < denominator_coeffs.size(); i++) { - denominator = vsl.MulAdd(input_squared, denominator, - GetIeeeF32(denominator_coeffs[i])); - } - - llvm::Value* result = vsl.Div(numerator, denominator); - ir_builder.CreateRet(result); + CHECK_EQ(vector_width, input->getType()->getVectorNumElements()); + b.CreateRet(llvm_ir::EmitFastTanh(&b, input)); DCHECK(!llvm::verifyFunction(*vector_tanh_function)); return vector_tanh_function; @@ -113,12 +82,12 @@ llvm::Function* EmitVectorF32ExpIfNeeded(llvm::Module* module, llvm::BasicBlock* vector_exp_body = llvm::BasicBlock::Create(*context, "body", vector_exp_function); - llvm::IRBuilder<> ir_builder(vector_exp_body); + llvm::IRBuilder<> b(vector_exp_body); llvm::FastMathFlags fast_math_flags; fast_math_flags.setFast(); - ir_builder.setFastMathFlags(fast_math_flags); + b.setFastMathFlags(fast_math_flags); - VectorSupportLibrary vsl(F32, vector_width, &ir_builder, "exp_f32"); + VectorSupportLibrary vsl(F32, vector_width, &b, "exp_f32"); // This implements the same polynomial approximation as implemented in Eigen3. @@ -160,21 +129,21 @@ llvm::Function* EmitVectorF32ExpIfNeeded(llvm::Module* module, // VectorSupportLibrary (intentionally) can't juggle more than one type at a // time so drop down to IRBuilder for this bit. llvm::Value* vector_constant_0x7f = - ir_builder.CreateVectorSplat(vector_width, ir_builder.getInt32(0x7f)); + b.CreateVectorSplat(vector_width, b.getInt32(0x7f)); llvm::Value* vector_constant_23 = - ir_builder.CreateVectorSplat(vector_width, ir_builder.getInt32(23)); + b.CreateVectorSplat(vector_width, b.getInt32(23)); llvm::Type* i32_vector_type = - llvm::VectorType::get(ir_builder.getInt32Ty(), vector_width); + llvm::VectorType::get(b.getInt32Ty(), vector_width); // fx is clamped so we don't have to worry about it being out of range for // i32. - llvm::Value* emm0 = ir_builder.CreateFPToSI(fx, i32_vector_type); - emm0 = ir_builder.CreateAdd(emm0, vector_constant_0x7f); - emm0 = ir_builder.CreateShl(emm0, vector_constant_23); - llvm::Value* emm0_f32 = ir_builder.CreateBitCast(emm0, vsl.vector_type()); + llvm::Value* emm0 = b.CreateFPToSI(fx, i32_vector_type); + emm0 = b.CreateAdd(emm0, vector_constant_0x7f); + emm0 = b.CreateShl(emm0, vector_constant_23); + llvm::Value* emm0_f32 = b.CreateBitCast(emm0, vsl.vector_type()); llvm::Value* result = vsl.Max(vsl.Mul(y, emm0_f32), input); - ir_builder.CreateRet(result); + b.CreateRet(result); DCHECK(!llvm::verifyFunction(*vector_exp_function)); return vector_exp_function; @@ -196,13 +165,13 @@ llvm::Function* EmitVectorF32LogIfNeeded(llvm::Module* module, llvm::BasicBlock* vector_log_body = llvm::BasicBlock::Create(*context, "body", vector_log_function); - llvm::IRBuilder<> ir_builder(vector_log_body); + llvm::IRBuilder<> b(vector_log_body); llvm::FastMathFlags fast_math_flags; fast_math_flags.setFast(); - ir_builder.setFastMathFlags(fast_math_flags); + b.setFastMathFlags(fast_math_flags); llvm::Value* input = &*vector_log_function->arg_begin(); - VectorSupportLibrary vsl(F32, vector_width, &ir_builder, "log_f32"); + VectorSupportLibrary vsl(F32, vector_width, &b, "log_f32"); const llvm::APFloat half = GetIeeeF32(0.5); const llvm::APFloat one = GetIeeeF32(1.0); @@ -238,22 +207,21 @@ llvm::Function* EmitVectorF32LogIfNeeded(llvm::Module* module, // VectorSupportLibrary (intentionally) can't juggle more than one type at a // time so drop down to IRBuilder for this bit. llvm::Value* vector_constant_0x7f = - ir_builder.CreateVectorSplat(vector_width, ir_builder.getInt32(0x7f)); + b.CreateVectorSplat(vector_width, b.getInt32(0x7f)); llvm::Value* vector_constant_23 = - ir_builder.CreateVectorSplat(vector_width, ir_builder.getInt32(23)); + b.CreateVectorSplat(vector_width, b.getInt32(23)); llvm::Type* i32_vector_type = - llvm::VectorType::get(ir_builder.getInt32Ty(), vector_width); + llvm::VectorType::get(b.getInt32Ty(), vector_width); - llvm::Value* emm0 = ir_builder.CreateLShr( - ir_builder.CreateBitCast(input, i32_vector_type), vector_constant_23); + llvm::Value* emm0 = + b.CreateLShr(b.CreateBitCast(input, i32_vector_type), vector_constant_23); // Keep only the fractional part. input = vsl.FloatAnd(input, inv_mant_mask); input = vsl.FloatOr(input, half); - emm0 = ir_builder.CreateSub(emm0, vector_constant_0x7f); - llvm::Value* e = - vsl.Add(one, ir_builder.CreateSIToFP(emm0, vsl.vector_type())); + emm0 = b.CreateSub(emm0, vector_constant_0x7f); + llvm::Value* e = vsl.Add(one, b.CreateSIToFP(emm0, vsl.vector_type())); // part2: // if( x < SQRTHF ) { @@ -294,7 +262,7 @@ llvm::Function* EmitVectorF32LogIfNeeded(llvm::Module* module, llvm::Value* or_rhs = vsl.FloatAnd(iszero_mask, minus_inf); llvm::Value* result = vsl.FloatOr(or_lhs, or_rhs); - ir_builder.CreateRet(result); + b.CreateRet(result); DCHECK(!llvm::verifyFunction(*vector_log_function)); return vector_log_function; diff --git a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc index 59ae5acd8b7cea049f09eaf4cc98b41339973c77..8560e4296aa95fe791446abb1b4363b9145f343e 100644 --- a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc @@ -25,8 +25,8 @@ namespace cpu { ParallelLoopEmitter::ParallelLoopEmitter( const llvm_ir::ElementGenerator& target_element_generator, const llvm_ir::IrArray& target_array, - const DynamicLoopBounds* dynamic_loop_bounds, llvm::IRBuilder<>* ir_builder) - : LoopEmitter(target_element_generator, target_array, ir_builder), + const DynamicLoopBounds* dynamic_loop_bounds, llvm::IRBuilder<>* b) + : LoopEmitter(target_element_generator, target_array, b), dynamic_loop_bounds_(dynamic_loop_bounds) {} std::vector @@ -37,7 +37,7 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( CHECK(!ShapeUtil::IsTuple(shape_)); CHECK(!ShapeUtil::IsScalar(shape_)); - llvm_ir::ForLoopNest loop_nest(loop_name, ir_builder_); + llvm_ir::ForLoopNest loop_nest(loop_name, b_); const int64 num_dims = shape_.dimensions_size(); llvm_ir::IrArray::Index array_index(index_type, num_dims); @@ -65,8 +65,7 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( } } // Point IR builder at inner loop BB. - llvm_ir::SetToFirstInsertPoint(loop_nest.GetInnerLoopBodyBasicBlock(), - ir_builder_); + llvm_ir::SetToFirstInsertPoint(loop_nest.GetInnerLoopBodyBasicBlock(), b_); // Set exit_bb_ to the exit block of the loop nest. exit_bb_ = loop_nest.GetOuterLoopExitBasicBlock(); diff --git a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h index 25e182a26d6f21c7eba550020cf17403aa92abf7..076c683ca566f2c53992c358903d2aadead290f9 100644 --- a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h @@ -54,7 +54,7 @@ class ParallelLoopEmitter : public llvm_ir::LoopEmitter { ParallelLoopEmitter(const llvm_ir::ElementGenerator& target_element_generator, const llvm_ir::IrArray& target_array, const DynamicLoopBounds* dynamic_loop_bounds, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); ParallelLoopEmitter(const ParallelLoopEmitter&) = delete; ParallelLoopEmitter& operator=(const ParallelLoopEmitter&) = delete; diff --git a/tensorflow/compiler/xla/service/cpu/sample_harness.cc b/tensorflow/compiler/xla/service/cpu/sample_harness.cc index 7e792a82b8bf28121c054332bc619d736858c729..f227e4ae139b92e56786e38ef8eef72c9e2cd424 100644 --- a/tensorflow/compiler/xla/service/cpu/sample_harness.cc +++ b/tensorflow/compiler/xla/service/cpu/sample_harness.cc @@ -21,9 +21,9 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" @@ -38,12 +38,13 @@ int main(int argc, char** argv) { // Transfer parameters. std::unique_ptr param0_literal = - xla::Literal::CreateR1({1.1f, 2.2f, 3.3f, 5.5f}); + xla::LiteralUtil::CreateR1({1.1f, 2.2f, 3.3f, 5.5f}); std::unique_ptr param0_data = client->TransferToServer(*param0_literal).ConsumeValueOrDie(); - std::unique_ptr param1_literal = xla::Literal::CreateR2( - {{3.1f, 4.2f, 7.3f, 9.5f}, {1.1f, 2.2f, 3.3f, 4.4f}}); + std::unique_ptr param1_literal = + xla::LiteralUtil::CreateR2( + {{3.1f, 4.2f, 7.3f, 9.5f}, {1.1f, 2.2f, 3.3f, 4.4f}}); std::unique_ptr param1_data = client->TransferToServer(*param1_literal).ConsumeValueOrDie(); diff --git a/tensorflow/compiler/xla/service/cpu/tests/BUILD b/tensorflow/compiler/xla/service/cpu/tests/BUILD index 66ae5ef0f66e90982102d73e474f5d0582f5415c..181cec3cdddeb40daf5276d9d1d6a139417a6072 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/BUILD +++ b/tensorflow/compiler/xla/service/cpu/tests/BUILD @@ -40,7 +40,7 @@ tf_cc_test( name = "cpu_fusion_test", srcs = ["cpu_fusion_test.cc"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", @@ -82,7 +82,7 @@ tf_cc_test( name = "cpu_noalias_test", srcs = ["cpu_noalias_test.cc"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", @@ -128,16 +128,16 @@ tf_cc_test( name = "cpu_infeed_test", srcs = ["cpu_infeed_test.cc"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:cpu_plugin", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h b/tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h index 7c8d07a10baf55dba8cbd347ebe1459b78e268e0..77b3a0301f2f90b577b7eaad86064dc30e2d9456 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h @@ -22,7 +22,7 @@ namespace xla { namespace cpu { // Tests that verify IR emitted by the CPU backend is as expected. -class CpuCodegenTest : public LLVMIRGenTestBase {}; +class CpuCodegenTest : public LlvmIrGenTestBase {}; } // namespace cpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_external_constants_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_external_constants_test.cc index 1d4bf483aedef5a15ef51cf216030b76255d4ec8..00a7aa2ad2f6bac4877302296ccb76222557535c 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/cpu_external_constants_test.cc +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_external_constants_test.cc @@ -40,7 +40,7 @@ class CpuExternalConstantsTest : public CpuCodegenTest { HloInstruction* constant = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2FromArray2D(backing_array))); + LiteralUtil::CreateR2FromArray2D(backing_array))); HloInstruction* param = builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "x")); builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_fusion_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_fusion_test.cc index 783b2820e922612973632c555fc8ae01418f1754..d98856fdbf4165a5909f193ebe8512e21af83dfc 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/cpu_fusion_test.cc +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_fusion_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" @@ -43,8 +43,8 @@ class CpuFusionTest : public HloTestBase { TEST_F(CpuFusionTest, FuseTwoElementwiseOps) { auto builder = HloComputation::Builder(TestName()); - auto input_literal1 = Literal::CreateR1({1.0, 2.0, 3.0}); - auto input_literal2 = Literal::CreateR1({-2.0, -42.0, 2.0}); + auto input_literal1 = LiteralUtil::CreateR1({1.0, 2.0, 3.0}); + auto input_literal2 = LiteralUtil::CreateR1({-2.0, -42.0, 2.0}); Shape vshape = input_literal1->shape(); auto input1 = builder.AddInstruction( @@ -83,7 +83,7 @@ TEST_F(CpuFusionTest, FuseTwoElementwiseOps) { TEST_F(CpuFusionTest, FuseElementwiseOpChain) { auto builder = HloComputation::Builder(TestName()); - auto input_literal = Literal::CreateR1({-1.5, -2.5, -3.0}); + auto input_literal = LiteralUtil::CreateR1({-1.5, -2.5, -3.0}); Shape vshape = input_literal->shape(); auto input = builder.AddInstruction( @@ -99,7 +99,7 @@ TEST_F(CpuFusionTest, FuseElementwiseOpChain) { auto two = builder.AddInstruction(HloInstruction::CreateBroadcast( vshape, builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))), + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))), {})); builder.AddInstruction( HloInstruction::CreateBinary(vshape, HloOpcode::kMultiply, two, floor)); @@ -134,7 +134,7 @@ TEST_F(CpuFusionTest, ElementwiseOpChainWithNonfusableInstruction) { // middle. auto module = CreateNewModule(); auto builder = HloComputation::Builder(TestName()); - auto input_literal = Literal::CreateR1({-1.5, -2.5, -3.0}); + auto input_literal = LiteralUtil::CreateR1({-1.5, -2.5, -3.0}); Shape vshape = input_literal->shape(); auto input = builder.AddInstruction( @@ -166,7 +166,7 @@ TEST_F(CpuFusionTest, ElementwiseOpChainWithNonfusableInstruction) { ShapeUtil::MakeShape(F32, {6, 1}), concatenate)), /*init_value=*/ builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))), + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))), /*dimensions_to_reduce=*/{1}, add_f32)); auto exp = builder.AddInstruction( @@ -176,7 +176,7 @@ TEST_F(CpuFusionTest, ElementwiseOpChainWithNonfusableInstruction) { auto two = builder.AddInstruction(HloInstruction::CreateBroadcast( cshape, builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))), + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))), {})); builder.AddInstruction( HloInstruction::CreateBinary(cshape, HloOpcode::kMultiply, two, floor)); @@ -231,7 +231,7 @@ TEST_F(CpuFusionTest, TestOperandOrderToAvoidDuplication) { // operand vectors. Test for this problem by counting the number of nodes in // each fusion instruction to ensure that negate is not duplicated. auto builder = HloComputation::Builder(TestName()); - auto input_literal = Literal::CreateR1({1.0, 2.0, 3.0}); + auto input_literal = LiteralUtil::CreateR1({1.0, 2.0, 3.0}); Shape vshape = input_literal->shape(); auto constant = builder.AddInstruction( @@ -292,10 +292,10 @@ TEST_F(CpuFusionTest, DoNotDuplicateExpensiveOps) { // computation. The duplication is caused by the other use of exp2 in the // tuple. auto builder = HloComputation::Builder(TestName()); - auto input_literal1 = Literal::CreateR1({1.0, 2.0, 3.0}); - auto input_literal2 = Literal::CreateR1({-2.0, -42.0, 2.0}); + auto input_literal1 = LiteralUtil::CreateR1({1.0, 2.0, 3.0}); + auto input_literal2 = LiteralUtil::CreateR1({-2.0, -42.0, 2.0}); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0))); Shape shape = constant->shape(); auto exp1 = builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc index ea7e479d66fbda1bfd388fd77b25db2db56f0d65..c433bddc8432949905041b5e9e31fc6af9e8bd44 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc @@ -19,9 +19,9 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test_helpers.h" @@ -58,52 +58,52 @@ class InfeedTest : public ClientLibraryTestBase { }; TEST_F(InfeedTest, SingleInfeedR0Bool) { - TestInfeedRoundTrip(*Literal::CreateR0(true)); + TestInfeedRoundTrip(*LiteralUtil::CreateR0(true)); } TEST_F(InfeedTest, SingleInfeedR1U32) { - TestInfeedRoundTrip(*Literal::CreateR1({1, 2, 3})); + TestInfeedRoundTrip(*LiteralUtil::CreateR1({1, 2, 3})); } TEST_F(InfeedTest, SingleInfeedR2F32) { - TestInfeedRoundTrip(*Literal::CreateR2F32Linspace(0.0, 1.0, 128, 64)); + TestInfeedRoundTrip(*LiteralUtil::CreateR2F32Linspace(0.0, 1.0, 128, 64)); } TEST_F(InfeedTest, SingleInfeedR3F32) { TestInfeedRoundTrip( - *Literal::CreateR3({{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, - {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}})); + *LiteralUtil::CreateR3({{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, + {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}})); } TEST_F(InfeedTest, SingleInfeedR3F32DifferentLayout) { const Layout r3_dim0minor = LayoutUtil::MakeLayout({0, 1, 2}); const Layout r3_dim0major = LayoutUtil::MakeLayout({2, 1, 0}); - TestInfeedRoundTrip( - *Literal::CreateR3WithLayout({{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, - {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}}, - r3_dim0minor)); + TestInfeedRoundTrip(*LiteralUtil::CreateR3WithLayout( + {{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, + {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}}, + r3_dim0minor)); - TestInfeedRoundTrip( - *Literal::CreateR3WithLayout({{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, - {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}}, - r3_dim0major)); + TestInfeedRoundTrip(*LiteralUtil::CreateR3WithLayout( + {{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, + {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}}, + r3_dim0major)); } TEST_F(InfeedTest, SingleInfeedR4S32) { - TestInfeedRoundTrip(*Literal::CreateR4( + TestInfeedRoundTrip(*LiteralUtil::CreateR4( {{{{1, -2}, {-4, 5}, {6, 7}}, {{8, 9}, {10, 11}, {12, 13}}}, {{{10, 3}, {7, -2}, {3, 6}}, {{2, 5}, {-11, 5}, {-2, -5}}}})); } TEST_F(InfeedTest, SingleInfeedTuple) { TestInfeedRoundTrip( - *Literal::MakeTuple({Literal::CreateR1({1, 2, 3}).get(), - Literal::CreateR0(false).get()})); + *LiteralUtil::MakeTuple({LiteralUtil::CreateR1({1, 2, 3}).get(), + LiteralUtil::CreateR0(false).get()})); } TEST_F(InfeedTest, SingleInfeedEmptyTuple) { - TestInfeedRoundTrip(*Literal::MakeTuple({})); + TestInfeedRoundTrip(*LiteralUtil::MakeTuple({})); } // Tests Infeed operation used in a while loop, as in the code below. The @@ -156,13 +156,16 @@ TEST_F(InfeedTest, DISABLED_SingleInfeedInWhile) { }); // Send 5 Infeed data of shape F32[3]. - ASSERT_IS_OK(client_->TransferToInfeed(*Literal::CreateR1({1, 2, 3}))); - ASSERT_IS_OK(client_->TransferToInfeed(*Literal::CreateR1({4, 5, 6}))); - ASSERT_IS_OK(client_->TransferToInfeed(*Literal::CreateR1({7, 8, 9}))); ASSERT_IS_OK( - client_->TransferToInfeed(*Literal::CreateR1({10, 11, 12}))); + client_->TransferToInfeed(*LiteralUtil::CreateR1({1, 2, 3}))); + ASSERT_IS_OK( + client_->TransferToInfeed(*LiteralUtil::CreateR1({4, 5, 6}))); + ASSERT_IS_OK( + client_->TransferToInfeed(*LiteralUtil::CreateR1({7, 8, 9}))); + ASSERT_IS_OK( + client_->TransferToInfeed(*LiteralUtil::CreateR1({10, 11, 12}))); ASSERT_IS_OK( - client_->TransferToInfeed(*Literal::CreateR1({13, 14, 15}))); + client_->TransferToInfeed(*LiteralUtil::CreateR1({13, 14, 15}))); delete computation_thread; // Joins the thread. auto result_literal = client_->Transfer(*result).ConsumeValueOrDie(); @@ -247,17 +250,17 @@ TEST_F(InfeedTest, DISABLED_TwoInfeedsInTotalOrder) { // Send the first 4 Infeed data of shape Tuple(F32[2], PRED). ASSERT_IS_OK(client_->TransferToInfeed( - *Literal::MakeTuple({Literal::CreateR1({1, 2}).get(), - Literal::CreateR0(true).get()}))); + *LiteralUtil::MakeTuple({LiteralUtil::CreateR1({1, 2}).get(), + LiteralUtil::CreateR0(true).get()}))); ASSERT_IS_OK(client_->TransferToInfeed( - *Literal::MakeTuple({Literal::CreateR1({3, 4}).get(), - Literal::CreateR0(true).get()}))); + *LiteralUtil::MakeTuple({LiteralUtil::CreateR1({3, 4}).get(), + LiteralUtil::CreateR0(true).get()}))); ASSERT_IS_OK(client_->TransferToInfeed( - *Literal::MakeTuple({Literal::CreateR1({5, 6}).get(), - Literal::CreateR0(true).get()}))); + *LiteralUtil::MakeTuple({LiteralUtil::CreateR1({5, 6}).get(), + LiteralUtil::CreateR0(true).get()}))); ASSERT_IS_OK(client_->TransferToInfeed( - *Literal::MakeTuple({Literal::CreateR1({7, 8}).get(), - Literal::CreateR0(false).get()}))); + *LiteralUtil::MakeTuple({LiteralUtil::CreateR1({7, 8}).get(), + LiteralUtil::CreateR0(false).get()}))); // Asynchronously launch the execution on the device. std::unique_ptr result; @@ -272,14 +275,14 @@ TEST_F(InfeedTest, DISABLED_TwoInfeedsInTotalOrder) { // Infeed data, and send the rest Infeed data of shape Tuple(F32[3], PRED). sleep(1); ASSERT_IS_OK(client_->TransferToInfeed( - *Literal::MakeTuple({Literal::CreateR1({1, 2, 3}).get(), - Literal::CreateR0(true).get()}))); + *LiteralUtil::MakeTuple({LiteralUtil::CreateR1({1, 2, 3}).get(), + LiteralUtil::CreateR0(true).get()}))); ASSERT_IS_OK(client_->TransferToInfeed( - *Literal::MakeTuple({Literal::CreateR1({7, 8, 9}).get(), - Literal::CreateR0(false).get()}))); + *LiteralUtil::MakeTuple({LiteralUtil::CreateR1({7, 8, 9}).get(), + LiteralUtil::CreateR0(false).get()}))); ASSERT_IS_OK(client_->TransferToInfeed( - *Literal::MakeTuple({Literal::CreateR1({4, 5, 6}).get(), - Literal::CreateR0(true).get()}))); + *LiteralUtil::MakeTuple({LiteralUtil::CreateR1({4, 5, 6}).get(), + LiteralUtil::CreateR0(true).get()}))); // Wait for the execution to be done, and transfer the result. delete computation_thread; // Joins the thread. diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_noalias_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_noalias_test.cc index 3b6b0ed74065615fb9e47a0ec3c6c4ab078e45c4..01daed4bcd38323bfe33e798a78c2b00b150a1bc 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/cpu_noalias_test.cc +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_noalias_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include #include "llvm/IR/Module.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h" @@ -42,7 +42,7 @@ TEST_F(CpuNoAliasTest, Concat) { HloComputation::Builder builder(TestName()); std::unique_ptr literal = - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); auto param_shape = ShapeUtil::MakeShape(F32, {2, 2}); HloInstruction* param_x = builder.AddInstruction( HloInstruction::CreateParameter(0, param_shape, "x")); @@ -78,7 +78,7 @@ TEST_F(CpuNoAliasTest, Concat) { llvm::Function* func = llvm::cast( ir_module.getOrInsertFunction("test_fn", llvm::Type::getVoidTy(context))); llvm::BasicBlock* bb = llvm::BasicBlock::Create(context, "body", func); - llvm::IRBuilder<> ir_builder(bb); + llvm::IRBuilder<> b(bb); auto* zero = llvm::ConstantInt::get(llvm::Type::getInt32Ty(context), 0); llvm_ir::IrArray::Index zero2D({zero, zero}); @@ -90,7 +90,7 @@ TEST_F(CpuNoAliasTest, Concat) { ir_module.getOrInsertGlobal("param_x", array2d_type); llvm_ir::IrArray param_x_array(param_x_val, param_shape); aa.AddAliasingInformationToIrArray(*param_x, ¶m_x_array); - param_x_array.EmitReadArrayElement(zero2D, &ir_builder) + param_x_array.EmitReadArrayElement(zero2D, &b) ->setName("read_param_x_array"); } @@ -100,7 +100,7 @@ TEST_F(CpuNoAliasTest, Concat) { auto shape = ShapeUtil::MakeShape(F32, {2, 4}); llvm_ir::IrArray concat1_array(concat1_val, shape); aa.AddAliasingInformationToIrArray(*concat1, &concat1_array); - concat1_array.EmitReadArrayElement(zero2D, &ir_builder) + concat1_array.EmitReadArrayElement(zero2D, &b) ->setName("read_concat1_array"); } @@ -110,7 +110,7 @@ TEST_F(CpuNoAliasTest, Concat) { auto shape = ShapeUtil::MakeShape(F32, {2, 6}); llvm_ir::IrArray concat2_array(concat2_val, shape); aa.AddAliasingInformationToIrArray(*concat2, &concat2_array); - concat2_array.EmitReadArrayElement(zero2D, &ir_builder) + concat2_array.EmitReadArrayElement(zero2D, &b) ->setName("read_concat2_array"); } diff --git a/tensorflow/compiler/xla/service/cpu/vector_support_library.cc b/tensorflow/compiler/xla/service/cpu/vector_support_library.cc index c444d151858d3a152a01b99657ffae89ebc6b487..3274be8d9dbfaa55e250748a389ad34fdeb81922 100644 --- a/tensorflow/compiler/xla/service/cpu/vector_support_library.cc +++ b/tensorflow/compiler/xla/service/cpu/vector_support_library.cc @@ -23,14 +23,14 @@ namespace xla { namespace cpu { VectorSupportLibrary::VectorSupportLibrary(PrimitiveType primitive_type, int64 vector_size, - llvm::IRBuilder<>* ir_builder, + llvm::IRBuilder<>* b, std::string name) : vector_size_(vector_size), primitive_type_(primitive_type), - ir_builder_(ir_builder), + b_(b), name_(std::move(name)) { scalar_type_ = llvm_ir::PrimitiveTypeToIrType( - primitive_type, ir_builder_->GetInsertBlock()->getModule()); + primitive_type, b_->GetInsertBlock()->getModule()); scalar_pointer_type_ = llvm::PointerType::getUnqual(scalar_type_); vector_type_ = llvm::VectorType::get(scalar_type_, vector_size); vector_pointer_type_ = llvm::PointerType::getUnqual(vector_type_); @@ -63,9 +63,9 @@ llvm::Value* VectorSupportLibrary::Mul(llvm::Value* lhs, llvm::Value* rhs) { llvm::Value* VectorSupportLibrary::MulInternal(llvm::Value* lhs, llvm::Value* rhs) { if (scalar_type_->isFloatingPointTy()) { - return ir_builder()->CreateFMul(lhs, rhs, name()); + return b()->CreateFMul(lhs, rhs, name()); } else { - return ir_builder()->CreateMul(lhs, rhs, name()); + return b()->CreateMul(lhs, rhs, name()); } } @@ -76,13 +76,13 @@ llvm::Value* VectorSupportLibrary::Add(llvm::Value* lhs, llvm::Value* rhs) { llvm::Value* VectorSupportLibrary::Sub(llvm::Value* lhs, llvm::Value* rhs) { AssertCorrectTypes({lhs, rhs}); - return ir_builder()->CreateFSub(lhs, rhs); + return b()->CreateFSub(lhs, rhs); } llvm::Value* VectorSupportLibrary::Max(llvm::Value* lhs, llvm::Value* rhs) { AssertCorrectTypes({lhs, rhs}); if (scalar_type_->isFloatingPointTy()) { - return llvm_ir::EmitFloatMax(lhs, rhs, ir_builder_); + return llvm_ir::EmitFloatMax(lhs, rhs, b_); } else { LOG(FATAL) << "Max for integers is unimplemented"; } @@ -91,13 +91,13 @@ llvm::Value* VectorSupportLibrary::Max(llvm::Value* lhs, llvm::Value* rhs) { llvm::Value* VectorSupportLibrary::Floor(llvm::Value* a) { AssertCorrectTypes({a}); return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::floor, {a}, - {a->getType()}, ir_builder()); + {a->getType()}, b()); } llvm::Value* VectorSupportLibrary::Div(llvm::Value* lhs, llvm::Value* rhs) { AssertCorrectTypes({lhs, rhs}); if (scalar_type_->isFloatingPointTy()) { - return ir_builder()->CreateFDiv(lhs, rhs, name()); + return b()->CreateFDiv(lhs, rhs, name()); } else { LOG(FATAL) << "Division for integers is unimplemented"; } @@ -111,42 +111,41 @@ llvm::Value* VectorSupportLibrary::Clamp(llvm::Value* a, CHECK(low.compare(high) == llvm::APFloat::cmpLessThan); CHECK(scalar_type_->isFloatingPointTy()); return llvm_ir::EmitFloatMin( - llvm_ir::EmitFloatMax(a, GetConstantFloat(type, low), ir_builder_), - GetConstantFloat(type, high), ir_builder_); + llvm_ir::EmitFloatMax(a, GetConstantFloat(type, low), b_), + GetConstantFloat(type, high), b_); } llvm::Value* VectorSupportLibrary::FCmpEQMask(llvm::Value* lhs, llvm::Value* rhs) { AssertCorrectTypes({lhs, rhs}); - return I1ToFloat(ir_builder()->CreateFCmpOEQ(lhs, rhs, name())); + return I1ToFloat(b()->CreateFCmpOEQ(lhs, rhs, name())); } llvm::Value* VectorSupportLibrary::FCmpOLTMask(llvm::Value* lhs, llvm::Value* rhs) { AssertCorrectTypes({lhs, rhs}); - return I1ToFloat(ir_builder()->CreateFCmpOLT(lhs, rhs, name())); + return I1ToFloat(b()->CreateFCmpOLT(lhs, rhs, name())); } llvm::Value* VectorSupportLibrary::FCmpULEMask(llvm::Value* lhs, llvm::Value* rhs) { AssertCorrectTypes({lhs, rhs}); - return I1ToFloat(ir_builder()->CreateFCmpULE(lhs, rhs, name())); + return I1ToFloat(b()->CreateFCmpULE(lhs, rhs, name())); } llvm::Value* VectorSupportLibrary::I1ToFloat(llvm::Value* i1) { bool is_vector = llvm::isa(i1->getType()); llvm::Type* integer_type = IntegerTypeForFloatSize(is_vector); - return ir_builder()->CreateBitCast( - ir_builder()->CreateSExt(i1, integer_type, name()), - is_vector ? vector_type() : scalar_type(), name()); + return b()->CreateBitCast(b()->CreateSExt(i1, integer_type, name()), + is_vector ? vector_type() : scalar_type(), name()); } llvm::Type* VectorSupportLibrary::IntegerTypeForFloatSize(bool vector) { CHECK(scalar_type()->isFloatingPointTy()); const llvm::DataLayout& data_layout = - ir_builder()->GetInsertBlock()->getModule()->getDataLayout(); + b()->GetInsertBlock()->getModule()->getDataLayout(); int64 float_size_bits = data_layout.getTypeSizeInBits(scalar_type()); - llvm::Type* scalar_int_type = ir_builder()->getIntNTy(float_size_bits); + llvm::Type* scalar_int_type = b()->getIntNTy(float_size_bits); if (vector) { return llvm::VectorType::get(scalar_int_type, vector_size()); } else { @@ -156,7 +155,7 @@ llvm::Type* VectorSupportLibrary::IntegerTypeForFloatSize(bool vector) { llvm::Value* VectorSupportLibrary::BroadcastScalar(llvm::Value* x) { CHECK_EQ(x->getType(), scalar_type()); - return ir_builder()->CreateVectorSplat(vector_size(), x, name()); + return b()->CreateVectorSplat(vector_size(), x, name()); } llvm::Value* VectorSupportLibrary::FloatAnd(llvm::Value* lhs, @@ -164,10 +163,9 @@ llvm::Value* VectorSupportLibrary::FloatAnd(llvm::Value* lhs, AssertCorrectTypes({lhs, rhs}); llvm::Type* int_type = IntegerTypeForFloatSize(lhs->getType() == vector_type()); - return ir_builder()->CreateBitCast( - ir_builder()->CreateAnd( - ir_builder()->CreateBitCast(lhs, int_type, name()), - ir_builder()->CreateBitCast(rhs, int_type, name()), name()), + return b()->CreateBitCast( + b()->CreateAnd(b()->CreateBitCast(lhs, int_type, name()), + b()->CreateBitCast(rhs, int_type, name()), name()), vector_type()); } @@ -175,9 +173,8 @@ llvm::Value* VectorSupportLibrary::FloatNot(llvm::Value* lhs) { AssertCorrectTypes({lhs}); llvm::Type* int_type = IntegerTypeForFloatSize(lhs->getType() == vector_type()); - return ir_builder()->CreateBitCast( - ir_builder()->CreateNot( - ir_builder()->CreateBitCast(lhs, int_type, name()), name()), + return b()->CreateBitCast( + b()->CreateNot(b()->CreateBitCast(lhs, int_type, name()), name()), vector_type()); } @@ -185,47 +182,43 @@ llvm::Value* VectorSupportLibrary::FloatOr(llvm::Value* lhs, llvm::Value* rhs) { AssertCorrectTypes({lhs, rhs}); llvm::Type* int_type = IntegerTypeForFloatSize(lhs->getType() == vector_type()); - return ir_builder()->CreateBitCast( - ir_builder()->CreateOr(ir_builder()->CreateBitCast(lhs, int_type, name()), - ir_builder()->CreateBitCast(rhs, int_type, name()), - name()), + return b()->CreateBitCast( + b()->CreateOr(b()->CreateBitCast(lhs, int_type, name()), + b()->CreateBitCast(rhs, int_type, name()), name()), vector_type(), name()); } llvm::Value* VectorSupportLibrary::AddInternal(llvm::Value* lhs, llvm::Value* rhs) { if (scalar_type_->isFloatingPointTy()) { - return ir_builder()->CreateFAdd(lhs, rhs, name()); + return b()->CreateFAdd(lhs, rhs, name()); } else { - return ir_builder()->CreateAdd(lhs, rhs, name()); + return b()->CreateAdd(lhs, rhs, name()); } } llvm::Value* VectorSupportLibrary::ComputeOffsetPointer( llvm::Value* base_pointer, llvm::Value* offset_elements) { if (base_pointer->getType() != scalar_pointer_type()) { - base_pointer = ir_builder()->CreateBitCast(base_pointer, - scalar_pointer_type(), name()); + base_pointer = + b()->CreateBitCast(base_pointer, scalar_pointer_type(), name()); } - return ir_builder()->CreateInBoundsGEP(base_pointer, {offset_elements}, - name()); + return b()->CreateInBoundsGEP(base_pointer, {offset_elements}, name()); } llvm::Value* VectorSupportLibrary::LoadVector(llvm::Value* pointer) { if (pointer->getType() != vector_pointer_type()) { - pointer = - ir_builder()->CreateBitCast(pointer, vector_pointer_type(), name()); + pointer = b()->CreateBitCast(pointer, vector_pointer_type(), name()); } - return ir_builder()->CreateAlignedLoad( + return b()->CreateAlignedLoad( pointer, ShapeUtil::ByteSizeOfPrimitiveType(primitive_type_), name()); } llvm::Value* VectorSupportLibrary::LoadScalar(llvm::Value* pointer) { if (pointer->getType() != scalar_pointer_type()) { - pointer = - ir_builder()->CreateBitCast(pointer, scalar_pointer_type(), name()); + pointer = b()->CreateBitCast(pointer, scalar_pointer_type(), name()); } - return ir_builder()->CreateAlignedLoad( + return b()->CreateAlignedLoad( pointer, ShapeUtil::ByteSizeOfPrimitiveType(primitive_type_), name()); } @@ -233,30 +226,28 @@ void VectorSupportLibrary::StoreVector(llvm::Value* value, llvm::Value* pointer) { AssertCorrectTypes({value}); if (pointer->getType() != vector_pointer_type()) { - pointer = ir_builder()->CreateBitCast(pointer, vector_pointer_type()); + pointer = b()->CreateBitCast(pointer, vector_pointer_type()); } - ir_builder()->CreateAlignedStore( - value, pointer, ShapeUtil::ByteSizeOfPrimitiveType(primitive_type_)); + b()->CreateAlignedStore(value, pointer, + ShapeUtil::ByteSizeOfPrimitiveType(primitive_type_)); } void VectorSupportLibrary::StoreScalar(llvm::Value* value, llvm::Value* pointer) { AssertCorrectTypes({value}); if (pointer->getType() != scalar_pointer_type()) { - pointer = - ir_builder()->CreateBitCast(pointer, scalar_pointer_type(), name()); + pointer = b()->CreateBitCast(pointer, scalar_pointer_type(), name()); } - ir_builder()->CreateAlignedStore( - value, pointer, ShapeUtil::ByteSizeOfPrimitiveType(primitive_type_)); + b()->CreateAlignedStore(value, pointer, + ShapeUtil::ByteSizeOfPrimitiveType(primitive_type_)); } llvm::Value* VectorSupportLibrary::LoadBroadcast(llvm::Value* pointer) { if (pointer->getType() != scalar_pointer_type()) { - pointer = - ir_builder()->CreateBitCast(pointer, scalar_pointer_type(), name()); + pointer = b()->CreateBitCast(pointer, scalar_pointer_type(), name()); } - return ir_builder()->CreateVectorSplat( - vector_size(), ir_builder()->CreateLoad(pointer), name()); + return b()->CreateVectorSplat(vector_size(), b()->CreateLoad(pointer), + name()); } llvm::Value* VectorSupportLibrary::AddReduce(llvm::Value* vector) { @@ -267,20 +258,19 @@ llvm::Value* VectorSupportLibrary::AddReduce(llvm::Value* vector) { for (unsigned j = 0; j < vector_size(); ++j) { if (j < (i / 2)) { - mask[j] = ir_builder()->getInt32(i / 2 + j); + mask[j] = b()->getInt32(i / 2 + j); } else { - mask[j] = llvm::UndefValue::get(ir_builder()->getInt32Ty()); + mask[j] = llvm::UndefValue::get(b()->getInt32Ty()); } } - llvm::Value* half_remaining_lanes = ir_builder()->CreateShuffleVector( - vector, llvm::UndefValue::get(vector_type()), - llvm::ConstantVector::get(mask), ""); + llvm::Value* half_remaining_lanes = + b()->CreateShuffleVector(vector, llvm::UndefValue::get(vector_type()), + llvm::ConstantVector::get(mask), ""); vector = Add(vector, half_remaining_lanes); } - return ir_builder()->CreateExtractElement(vector, ir_builder()->getInt32(0), - name()); + return b()->CreateExtractElement(vector, b()->getInt32(0), name()); } llvm::Value* VectorSupportLibrary::AvxStyleHorizontalAdd(llvm::Value* lhs, @@ -307,19 +297,19 @@ llvm::Value* VectorSupportLibrary::AvxStyleHorizontalAdd(llvm::Value* lhs, // vector, which are the lanes 2 and 3 in the rhs vector. for (int i = 0; i < vector_size(); i += 2) { int increment = i < vector_size() / 2 ? 0 : (vector_size() / 2); - mask_a.push_back(ir_builder()->getInt32(increment + i)); - mask_b.push_back(ir_builder()->getInt32(increment + i + 1)); + mask_a.push_back(b()->getInt32(increment + i)); + mask_b.push_back(b()->getInt32(increment + i + 1)); } for (int i = 0; i < vector_size(); i += 2) { int increment = i < vector_size() / 2 ? (vector_size() / 2) : vector_size(); - mask_a.push_back(ir_builder()->getInt32(increment + i)); - mask_b.push_back(ir_builder()->getInt32(increment + i + 1)); + mask_a.push_back(b()->getInt32(increment + i)); + mask_b.push_back(b()->getInt32(increment + i + 1)); } - llvm::Value* shuffle_0 = ir_builder()->CreateShuffleVector( - lhs, rhs, llvm::ConstantVector::get(mask_a)); - llvm::Value* shuffle_1 = ir_builder()->CreateShuffleVector( - lhs, rhs, llvm::ConstantVector::get(mask_b)); + llvm::Value* shuffle_0 = + b()->CreateShuffleVector(lhs, rhs, llvm::ConstantVector::get(mask_a)); + llvm::Value* shuffle_1 = + b()->CreateShuffleVector(lhs, rhs, llvm::ConstantVector::get(mask_b)); return Add(shuffle_0, shuffle_1); } @@ -327,23 +317,21 @@ llvm::Value* VectorSupportLibrary::AvxStyleHorizontalAdd(llvm::Value* lhs, llvm::Value* VectorSupportLibrary::ExtractLowHalf(llvm::Value* vector) { llvm::SmallVector mask; for (int i = 0; i < vector_size() / 2; i++) { - mask.push_back(ir_builder()->getInt32(i)); + mask.push_back(b()->getInt32(i)); } - return ir_builder()->CreateShuffleVector(vector, - llvm::UndefValue::get(vector_type()), - llvm::ConstantVector::get(mask)); + return b()->CreateShuffleVector(vector, llvm::UndefValue::get(vector_type()), + llvm::ConstantVector::get(mask)); } llvm::Value* VectorSupportLibrary::ExtractHighHalf(llvm::Value* vector) { llvm::SmallVector mask; for (int i = 0; i < vector_size() / 2; i++) { - mask.push_back(ir_builder()->getInt32(i + vector_size() / 2)); + mask.push_back(b()->getInt32(i + vector_size() / 2)); } - return ir_builder()->CreateShuffleVector(vector, - llvm::UndefValue::get(vector_type()), - llvm::ConstantVector::get(mask)); + return b()->CreateShuffleVector(vector, llvm::UndefValue::get(vector_type()), + llvm::ConstantVector::get(mask)); } std::vector VectorSupportLibrary::ComputeHorizontalSums( @@ -360,8 +348,8 @@ std::vector VectorSupportLibrary::ComputeHorizontalSums( [this](llvm::Value* vector) { return AddReduce(vector); }); if (init_values) { for (int64 i = 0, e = result.size(); i < e; i++) { - result[i] = Add(result[i], ir_builder()->CreateExtractElement( - init_values, ir_builder()->getInt32(i))); + result[i] = Add(result[i], + b()->CreateExtractElement(init_values, b()->getInt32(i))); } } return result; @@ -398,9 +386,9 @@ VectorSupportLibrary::ComputeAvxOptimizedHorizontalSums( std::vector results; for (int i = 0; i < lane_width; i++) { - llvm::Value* scalar_result = ir_builder()->CreateExtractElement( - i < (lane_width / 2) ? low : high, - ir_builder()->getInt32(i % (lane_width / 2)), name()); + llvm::Value* scalar_result = + b()->CreateExtractElement(i < (lane_width / 2) ? low : high, + b()->getInt32(i % (lane_width / 2)), name()); results.push_back(scalar_result); } @@ -415,17 +403,14 @@ llvm::Value* VectorSupportLibrary::GetZeroScalar() { return llvm::Constant::getNullValue(scalar_type()); } -LlvmVariable::LlvmVariable(llvm::Type* type, llvm::IRBuilder<>* ir_builder) - : ir_builder_(ir_builder) { - alloca_ = llvm_ir::EmitAllocaAtFunctionEntry(type, "", ir_builder_); +LlvmVariable::LlvmVariable(llvm::Type* type, llvm::IRBuilder<>* b) : b_(b) { + alloca_ = llvm_ir::EmitAllocaAtFunctionEntry(type, "", b_); } -llvm::Value* LlvmVariable::Get() const { - return ir_builder_->CreateLoad(alloca_); -} +llvm::Value* LlvmVariable::Get() const { return b_->CreateLoad(alloca_); } void LlvmVariable::Set(llvm::Value* new_value) { - ir_builder_->CreateStore(new_value, alloca_); + b_->CreateStore(new_value, alloca_); } TileVariable::TileVariable(VectorSupportLibrary* vector_support, diff --git a/tensorflow/compiler/xla/service/cpu/vector_support_library.h b/tensorflow/compiler/xla/service/cpu/vector_support_library.h index 49c2a4e2f4bae9e1672b7d2fe891301bce08bd4b..c728f6df0aef83e6ddc6c932a347f14da06d9d0d 100644 --- a/tensorflow/compiler/xla/service/cpu/vector_support_library.h +++ b/tensorflow/compiler/xla/service/cpu/vector_support_library.h @@ -46,11 +46,11 @@ class VectorSupportLibrary { // instance (i.e. LoadVector will load a vector of type <`vector_size` x // `primitive_type`>). VectorSupportLibrary(PrimitiveType primitive_type, int64 vector_size, - llvm::IRBuilder<>* ir_builder, std::string name); + llvm::IRBuilder<>* b, std::string name); llvm::Value* Mul(llvm::Value* lhs, llvm::Value* rhs); llvm::Value* Mul(int64 lhs, llvm::Value* rhs) { - return Mul(ir_builder()->getInt64(lhs), rhs); + return Mul(b()->getInt64(lhs), rhs); } llvm::Value* Mul(const llvm::APFloat& lhs, llvm::Value* rhs) { return Mul(GetConstantFloat(rhs->getType(), lhs), rhs); @@ -63,7 +63,7 @@ class VectorSupportLibrary { llvm::Value* Add(llvm::Value* lhs, llvm::Value* rhs); llvm::Value* Add(int64 lhs, llvm::Value* rhs) { - return Add(ir_builder()->getInt64(lhs), rhs); + return Add(b()->getInt64(lhs), rhs); } llvm::Value* Add(const llvm::APFloat& lhs, llvm::Value* rhs) { return Add(GetConstantFloat(rhs->getType(), lhs), rhs); @@ -147,13 +147,11 @@ class VectorSupportLibrary { llvm::Value* ComputeOffsetPointer(llvm::Value* base_pointer, llvm::Value* offset_elements, int64 scale) { return ComputeOffsetPointer( - base_pointer, - ir_builder_->CreateMul(ir_builder_->getInt64(scale), offset_elements)); + base_pointer, b_->CreateMul(b_->getInt64(scale), offset_elements)); } llvm::Value* ComputeOffsetPointer(llvm::Value* base_pointer, int64 offset_elements) { - return ComputeOffsetPointer(base_pointer, - ir_builder()->getInt64(offset_elements)); + return ComputeOffsetPointer(base_pointer, b()->getInt64(offset_elements)); } llvm::Value* LoadVector(llvm::Value* pointer); @@ -164,7 +162,7 @@ class VectorSupportLibrary { } llvm::Value* LoadVector(llvm::Value* base_pointer, int64 offset_elements) { - return LoadVector(base_pointer, ir_builder()->getInt64(offset_elements)); + return LoadVector(base_pointer, b()->getInt64(offset_elements)); } llvm::Value* LoadScalar(llvm::Value* pointer); @@ -175,7 +173,7 @@ class VectorSupportLibrary { } llvm::Value* LoadScalar(llvm::Value* base_pointer, int64 offset_elements) { - return LoadScalar(base_pointer, ir_builder()->getInt64(offset_elements)); + return LoadScalar(base_pointer, b()->getInt64(offset_elements)); } void StoreVector(llvm::Value* value, llvm::Value* pointer); @@ -187,7 +185,7 @@ class VectorSupportLibrary { void StoreVector(llvm::Value* value, llvm::Value* base_pointer, int64 offset_elements) { - StoreVector(value, base_pointer, ir_builder()->getInt64(offset_elements)); + StoreVector(value, base_pointer, b()->getInt64(offset_elements)); } void StoreScalar(llvm::Value* value, llvm::Value* pointer); @@ -198,7 +196,7 @@ class VectorSupportLibrary { void StoreScalar(llvm::Value* value, llvm::Value* base_pointer, int64 offset_elements) { - StoreScalar(base_pointer, ir_builder()->getInt64(offset_elements)); + StoreScalar(base_pointer, b()->getInt64(offset_elements)); } llvm::Value* LoadBroadcast(llvm::Value* pointer); @@ -207,7 +205,7 @@ class VectorSupportLibrary { return LoadBroadcast(ComputeOffsetPointer(base_pointer, offset_elements)); } llvm::Value* LoadBroadcast(llvm::Value* base_pointer, int64 offset_elements) { - return LoadBroadcast(base_pointer, ir_builder()->getInt64(offset_elements)); + return LoadBroadcast(base_pointer, b()->getInt64(offset_elements)); } // Compute the horizontal sum of each vector in `vectors`. The i'th element @@ -220,7 +218,7 @@ class VectorSupportLibrary { llvm::Value* GetZeroVector(); llvm::Value* GetZeroScalar(); - llvm::IRBuilder<>* ir_builder() const { return ir_builder_; } + llvm::IRBuilder<>* b() const { return b_; } int64 vector_size() const { return vector_size_; } llvm::Type* vector_type() const { return vector_type_; } llvm::Type* vector_pointer_type() const { return vector_pointer_type_; } @@ -277,7 +275,7 @@ class VectorSupportLibrary { int64 vector_size_; PrimitiveType primitive_type_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; llvm::Type* vector_type_; llvm::Type* vector_pointer_type_; llvm::Type* scalar_type_; @@ -289,22 +287,21 @@ class VectorSupportLibrary { // can later convert to a SSA value. class LlvmVariable { public: - LlvmVariable(llvm::Type*, llvm::IRBuilder<>* ir_builder); + LlvmVariable(llvm::Type*, llvm::IRBuilder<>* b); llvm::Value* Get() const; void Set(llvm::Value* new_value); private: llvm::AllocaInst* alloca_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; }; class VectorVariable : public LlvmVariable { public: VectorVariable(VectorSupportLibrary* vector_support, llvm::Value* initial_value) - : LlvmVariable(vector_support->vector_type(), - vector_support->ir_builder()) { + : LlvmVariable(vector_support->vector_type(), vector_support->b()) { Set(initial_value); } }; @@ -313,8 +310,7 @@ class ScalarVariable : public LlvmVariable { public: ScalarVariable(VectorSupportLibrary* vector_support, llvm::Value* initial_value) - : LlvmVariable(vector_support->scalar_type(), - vector_support->ir_builder()) { + : LlvmVariable(vector_support->scalar_type(), vector_support->b()) { Set(initial_value); } }; diff --git a/tensorflow/compiler/xla/service/defuser_test.cc b/tensorflow/compiler/xla/service/defuser_test.cc index 32b5c5d35fae61ae6cb17fafcada1abd6c3c088c..e727ba49cb6321e499b5d50d5f45e7f7f6bb6fef 100644 --- a/tensorflow/compiler/xla/service/defuser_test.cc +++ b/tensorflow/compiler/xla/service/defuser_test.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/defuser.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_matchers.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h" @@ -124,7 +124,7 @@ TEST_F(DefuserTest, NonTrivialFusionInstruction) { auto div = builder.AddInstruction( HloInstruction::CreateBinary(shape_, HloOpcode::kDivide, mul, param3)); auto constant = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); auto add2 = builder.AddInstruction( HloInstruction::CreateBinary(shape_, HloOpcode::kAdd, constant, div)); @@ -162,7 +162,7 @@ TEST_F(DefuserTest, MultipleFusionInstructions) { auto div = builder.AddInstruction( HloInstruction::CreateBinary(shape_, HloOpcode::kDivide, mul, param3)); auto constant = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); auto add2 = builder.AddInstruction( HloInstruction::CreateBinary(shape_, HloOpcode::kAdd, constant, div)); diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h index 52aa53dcee59379107e7da4e3afccec226ac5a6e..097fa23027bf55ad0b92c347c5a1209bb5836695 100644 --- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h +++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h @@ -19,7 +19,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/status.h" #include "tensorflow/compiler/xla/types.h" @@ -212,6 +212,7 @@ class DfsHloVisitorBase { virtual Status HandleReverse(HloInstructionPtr hlo) = 0; virtual Status HandleSort(HloInstructionPtr hlo) = 0; virtual Status HandleConstant(HloInstructionPtr hlo) = 0; + virtual Status HandleIota(HloInstructionPtr hlo) = 0; virtual Status HandleGetTupleElement(HloInstructionPtr hlo) = 0; virtual Status HandleReduce(HloInstructionPtr hlo) = 0; virtual Status HandleBitcast(HloInstructionPtr hlo) = 0; diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h index ecd97a87968edaa447ed2df801e95468e3dba0e4..f4316e0fb77855aad1c4710908df09c604da896e 100644 --- a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h +++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_DFS_HLO_VISITOR_WITH_DEFAULT_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_DFS_HLO_VISITOR_WITH_DEFAULT_H_ -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/types.h" @@ -115,6 +115,9 @@ class DfsHloVisitorWithDefaultBase Status HandleConstant(HloInstructionPtr constant) override { return DefaultAction(constant); } + Status HandleIota(HloInstructionPtr iota) override { + return DefaultAction(iota); + } Status HandleGetTupleElement(HloInstructionPtr get_tuple_element) override { return DefaultAction(get_tuple_element); } diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index 21c6f7d358bef171a54ebd97e7f4d2638ee179a8..f883eb828c7f6365dfd4d5e0b514dc6894adc12b 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -61,13 +61,13 @@ int64 GlobalRandomValue() { llvm::Value* EmitReducePrecisionFloat(llvm::Value* x, int64 exponent_bits, int64 mantissa_bits, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { // Integer and float types for casting and constant generation. llvm::Type* float_type = x->getType(); - llvm::IntegerType* int_type = ir_builder->getInt32Ty(); + llvm::IntegerType* int_type = b->getInt32Ty(); // Cast the input value to an integer for bitwise manipulation. - llvm::Value* x_as_int = ir_builder->CreateBitCast(x, int_type); + llvm::Value* x_as_int = b->CreateBitCast(x, int_type); if (mantissa_bits < 23) { // Last remaining mantissa bit. @@ -77,22 +77,22 @@ llvm::Value* EmitReducePrecisionFloat(llvm::Value* x, int64 exponent_bits, // equal to a base value of 0111... plus one bit if the last remaining // mantissa bit is 1. const uint32_t base_rounding_bias = (last_mantissa_bit_mask >> 1) - 1; - llvm::Value* x_last_mantissa_bit = ir_builder->CreateLShr( - ir_builder->CreateAnd( - x_as_int, llvm::ConstantInt::get(int_type, last_mantissa_bit_mask)), + llvm::Value* x_last_mantissa_bit = b->CreateLShr( + b->CreateAnd(x_as_int, + llvm::ConstantInt::get(int_type, last_mantissa_bit_mask)), (23 - mantissa_bits)); - llvm::Value* x_rounding_bias = ir_builder->CreateAdd( - x_last_mantissa_bit, - llvm::ConstantInt::get(int_type, base_rounding_bias)); + llvm::Value* x_rounding_bias = + b->CreateAdd(x_last_mantissa_bit, + llvm::ConstantInt::get(int_type, base_rounding_bias)); // Add rounding bias, and mask out truncated bits. Note that the case // where adding the rounding bias overflows into the exponent bits is // correct; the non-masked mantissa bits will all be zero, and the // exponent will be incremented by one. const uint32_t truncation_mask = ~(last_mantissa_bit_mask - 1); - x_as_int = ir_builder->CreateAdd(x_as_int, x_rounding_bias); - x_as_int = ir_builder->CreateAnd( - x_as_int, llvm::ConstantInt::get(int_type, truncation_mask)); + x_as_int = b->CreateAdd(x_as_int, x_rounding_bias); + x_as_int = b->CreateAnd(x_as_int, + llvm::ConstantInt::get(int_type, truncation_mask)); } if (exponent_bits < 8) { @@ -120,29 +120,29 @@ llvm::Value* EmitReducePrecisionFloat(llvm::Value* x, int64 exponent_bits, f32_exponent_bias - reduced_exponent_bias; // Do we overflow or underflow? - llvm::Value* x_exponent = ir_builder->CreateAnd( + llvm::Value* x_exponent = b->CreateAnd( x_as_int, llvm::ConstantInt::get(int_type, f32_exp_bits_mask)); - llvm::Value* x_overflows = ir_builder->CreateICmpUGT( + llvm::Value* x_overflows = b->CreateICmpUGT( x_exponent, llvm::ConstantInt::get(int_type, reduced_max_exponent << 23)); - llvm::Value* x_underflows = ir_builder->CreateICmpULE( + llvm::Value* x_underflows = b->CreateICmpULE( x_exponent, llvm::ConstantInt::get(int_type, reduced_min_exponent << 23)); // Compute appropriately-signed values of zero and infinity. - llvm::Value* x_signed_zero = ir_builder->CreateAnd( + llvm::Value* x_signed_zero = b->CreateAnd( x_as_int, llvm::ConstantInt::get(int_type, f32_sign_bit_mask)); - llvm::Value* x_signed_inf = ir_builder->CreateOr( + llvm::Value* x_signed_inf = b->CreateOr( x_signed_zero, llvm::ConstantInt::get(int_type, f32_exp_bits_mask)); // Force to zero or infinity if overflow or underflow. (Note that this // truncates all denormal values to zero, rather than rounding them.) - x_as_int = ir_builder->CreateSelect(x_overflows, x_signed_inf, x_as_int); - x_as_int = ir_builder->CreateSelect(x_underflows, x_signed_zero, x_as_int); + x_as_int = b->CreateSelect(x_overflows, x_signed_inf, x_as_int); + x_as_int = b->CreateSelect(x_underflows, x_signed_zero, x_as_int); } // Cast the result back to a floating-point type. - llvm::Value* result = ir_builder->CreateBitCast(x_as_int, float_type); + llvm::Value* result = b->CreateBitCast(x_as_int, float_type); // Correct result for NaN inputs. // @@ -154,53 +154,49 @@ llvm::Value* EmitReducePrecisionFloat(llvm::Value* x, int64 exponent_bits, // // If the fast-math flags are set to assume no NaNs, the comparison is likely // to be optimized away, so there's no point in even emitting it. - if (!ir_builder->getFastMathFlags().noNaNs()) { - llvm::Value* x_is_nan = ir_builder->CreateFCmpUNO(x, x); + if (!b->getFastMathFlags().noNaNs()) { + llvm::Value* x_is_nan = b->CreateFCmpUNO(x, x); if (mantissa_bits > 0) { - result = ir_builder->CreateSelect(x_is_nan, x, result); + result = b->CreateSelect(x_is_nan, x, result); } else { - result = ir_builder->CreateSelect( + result = b->CreateSelect( x_is_nan, llvm::ConstantFP::getInfinity(float_type), result); } } return result; } -llvm::Value* EmitF32ToBF16(llvm::Value* f32_value, - llvm::IRBuilder<>* ir_builder) { +llvm::Value* EmitF32ToBF16(llvm::Value* f32_value, llvm::IRBuilder<>* b) { auto reduced_precision = EmitReducePrecisionFloat( f32_value, /*exponent_bits=*/primitive_util::kBFloat16ExponentBits, - /*mantissa_bits=*/primitive_util::kBFloat16MantissaBits, ir_builder); - auto as_int32 = - ir_builder->CreateBitCast(reduced_precision, ir_builder->getInt32Ty()); - auto shifted = ir_builder->CreateLShr(as_int32, 16); - auto truncated = ir_builder->CreateTrunc(shifted, ir_builder->getInt16Ty()); - return ir_builder->CreateBitCast(truncated, ir_builder->getInt16Ty()); + /*mantissa_bits=*/primitive_util::kBFloat16MantissaBits, b); + auto as_int32 = b->CreateBitCast(reduced_precision, b->getInt32Ty()); + auto shifted = b->CreateLShr(as_int32, 16); + auto truncated = b->CreateTrunc(shifted, b->getInt16Ty()); + return b->CreateBitCast(truncated, b->getInt16Ty()); } -llvm::Value* EmitBF16ToF32(llvm::Value* bf16_value, - llvm::IRBuilder<>* ir_builder) { - auto as_int16 = - ir_builder->CreateBitCast(bf16_value, ir_builder->getInt16Ty()); - auto as_int32 = ir_builder->CreateZExt(as_int16, ir_builder->getInt32Ty()); - auto shifted = ir_builder->CreateShl(as_int32, 16); - return ir_builder->CreateBitCast(shifted, ir_builder->getFloatTy()); +llvm::Value* EmitBF16ToF32(llvm::Value* bf16_value, llvm::IRBuilder<>* b) { + auto as_int16 = b->CreateBitCast(bf16_value, b->getInt16Ty()); + auto as_int32 = b->CreateZExt(as_int16, b->getInt32Ty()); + auto shifted = b->CreateShl(as_int32, 16); + return b->CreateBitCast(shifted, b->getFloatTy()); } llvm::Value* EmitIntegralToFloating(llvm::Value* integer_value, PrimitiveType from_type, PrimitiveType to_type, llvm::Module* module, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { if (primitive_util::IsSignedIntegralType(from_type)) { - return ir_builder->CreateSIToFP( - integer_value, llvm_ir::PrimitiveTypeToIrType(to_type, module)); + return b->CreateSIToFP(integer_value, + llvm_ir::PrimitiveTypeToIrType(to_type, module)); } else { CHECK(primitive_util::IsUnsignedIntegralType(from_type) || from_type == PRED); - return ir_builder->CreateUIToFP( - integer_value, llvm_ir::PrimitiveTypeToIrType(to_type, module)); + return b->CreateUIToFP(integer_value, + llvm_ir::PrimitiveTypeToIrType(to_type, module)); } } @@ -226,39 +222,43 @@ StatusOr ElementalIrEmitter::EmitIntegerUnaryOp( case HloOpcode::kConvert: { PrimitiveType from_type = op->operand(0)->shape().element_type(); PrimitiveType to_type = op->shape().element_type(); - CHECK(primitive_util::IsIntegralType(from_type) || from_type == PRED); + CHECK(primitive_util::IsIntegralType(from_type) || from_type == PRED) + << from_type; if (from_type == to_type) { return operand_value; } + if (to_type == PRED) { + return b_->CreateZExt( + b_->CreateICmpNE(operand_value, llvm::ConstantInt::get( + operand_value->getType(), 0)), + llvm_ir::PrimitiveTypeToIrType(PRED, module_)); + } if (primitive_util::IsIntegralType(to_type)) { - return ir_builder_->CreateIntCast( + return b_->CreateIntCast( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_), primitive_util::IsSignedIntegralType(from_type)); } if (primitive_util::IsFloatingPointType(to_type)) { if (to_type == BF16) { - return EmitF32ToBF16( - EmitIntegralToFloating(operand_value, from_type, F32, module_, - ir_builder_), - ir_builder_); + return EmitF32ToBF16(EmitIntegralToFloating(operand_value, from_type, + F32, module_, b_), + b_); } return EmitIntegralToFloating(operand_value, from_type, to_type, - module_, ir_builder_); + module_, b_); } if (primitive_util::IsComplexType(to_type)) { auto to_ir_component_type = llvm_ir::PrimitiveTypeToIrType( primitive_util::ComplexComponentType(to_type), module_); if (primitive_util::IsSignedIntegralType(from_type)) { return EmitComposeComplex( - op, - ir_builder_->CreateSIToFP(operand_value, to_ir_component_type), + op, b_->CreateSIToFP(operand_value, to_ir_component_type), nullptr); } if (primitive_util::IsUnsignedIntegralType(from_type) || from_type == PRED) { return EmitComposeComplex( - op, - ir_builder_->CreateUIToFP(operand_value, to_ir_component_type), + op, b_->CreateUIToFP(operand_value, to_ir_component_type), nullptr); } } @@ -275,7 +275,7 @@ StatusOr ElementalIrEmitter::EmitIntegerUnaryOp( } if (primitive_util::BitWidth(from_type) == primitive_util::BitWidth(to_type)) { - return ir_builder_->CreateBitCast( + return b_->CreateBitCast( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_)); } return InvalidArgument( @@ -293,18 +293,18 @@ StatusOr ElementalIrEmitter::EmitIntegerUnaryOp( auto type = llvm_ir::PrimitiveTypeToIrType(op->shape().element_type(), module_); auto zero = llvm::ConstantInt::get(type, 0); - auto cmp = ir_builder_->CreateICmpSGE(operand_value, zero); - return ir_builder_->CreateSelect(cmp, operand_value, - ir_builder_->CreateNeg(operand_value)); + auto cmp = b_->CreateICmpSGE(operand_value, zero); + return b_->CreateSelect(cmp, operand_value, + b_->CreateNeg(operand_value)); } else { return operand_value; } } case HloOpcode::kClz: { - auto is_zero_undef = ir_builder_->getFalse(); - return llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::ctlz, {operand_value, is_zero_undef}, - {operand_value->getType()}, ir_builder_); + auto is_zero_undef = b_->getFalse(); + return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::ctlz, + {operand_value, is_zero_undef}, + {operand_value->getType()}, b_); } case HloOpcode::kSign: { bool is_signed = @@ -312,31 +312,28 @@ StatusOr ElementalIrEmitter::EmitIntegerUnaryOp( auto type = llvm_ir::PrimitiveTypeToIrType(op->shape().element_type(), module_); auto zero = llvm::ConstantInt::get(type, 0); - auto cmp = ir_builder_->CreateICmpEQ(operand_value, zero); + auto cmp = b_->CreateICmpEQ(operand_value, zero); if (is_signed) { - auto ashr = ir_builder_->CreateAShr(operand_value, - type->getIntegerBitWidth() - 1); - return ir_builder_->CreateSelect(cmp, zero, - ir_builder_->CreateOr(ashr, 1)); + auto ashr = + b_->CreateAShr(operand_value, type->getIntegerBitWidth() - 1); + return b_->CreateSelect(cmp, zero, b_->CreateOr(ashr, 1)); } else { - return ir_builder_->CreateSelect(cmp, zero, - llvm::ConstantInt::get(type, 1)); + return b_->CreateSelect(cmp, zero, llvm::ConstantInt::get(type, 1)); } } case HloOpcode::kNegate: - return ir_builder_->CreateNeg(operand_value); + return b_->CreateNeg(operand_value); case HloOpcode::kNot: { auto type = op->shape().element_type(); if (type == PRED) { // It is not sufficient to just call CreateNot() here because a PRED // is represented as an i8 and the truth value is stored only in the // bottom bit. - return ir_builder_->CreateZExt( - ir_builder_->CreateNot(ir_builder_->CreateTrunc( - operand_value, ir_builder_->getInt1Ty())), + return b_->CreateZExt( + b_->CreateNot(b_->CreateTrunc(operand_value, b_->getInt1Ty())), llvm_ir::PrimitiveTypeToIrType(PRED, module_)); } else if (primitive_util::IsIntegralType(type)) { - return ir_builder_->CreateNot(operand_value); + return b_->CreateNot(operand_value); } return Unimplemented("unary op Not is not defined for type '%d'", type); } @@ -352,7 +349,7 @@ StatusOr ElementalIrEmitter::EmitFloatUnaryOp( case HloOpcode::kConvert: { PrimitiveType from_type = op->operand(0)->shape().element_type(); PrimitiveType to_type = op->shape().element_type(); - CHECK(primitive_util::IsFloatingPointType(from_type)); + CHECK(primitive_util::IsFloatingPointType(from_type)) << from_type; if (from_type == to_type) { return operand_value; } @@ -364,32 +361,38 @@ StatusOr ElementalIrEmitter::EmitFloatUnaryOp( } return EmitComposeComplex( op, - ir_builder_->CreateFPCast( - operand_value, - llvm_ir::PrimitiveTypeToIrType(to_component_type, module_)), + b_->CreateFPCast(operand_value, llvm_ir::PrimitiveTypeToIrType( + to_component_type, module_)), nullptr); } if (from_type == BF16) { TF_RET_CHECK(to_type != BF16); - operand_value = EmitBF16ToF32(operand_value, ir_builder_); + operand_value = EmitBF16ToF32(operand_value, b_); from_type = F32; if (from_type == to_type) { return operand_value; } } if (from_type == F32 && to_type == BF16) { - return EmitF32ToBF16(operand_value, ir_builder_); + return EmitF32ToBF16(operand_value, b_); + } + if (to_type == PRED) { + return b_->CreateZExt( + b_->CreateFCmpUNE( + operand_value, + llvm::ConstantFP::get(operand_value->getType(), 0.0)), + llvm_ir::PrimitiveTypeToIrType(PRED, module_)); } if (primitive_util::IsFloatingPointType(to_type)) { - return ir_builder_->CreateFPCast( + return b_->CreateFPCast( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_)); } if (primitive_util::IsSignedIntegralType(to_type)) { - return ir_builder_->CreateFPToSI( + return b_->CreateFPToSI( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_)); } if (primitive_util::IsUnsignedIntegralType(to_type)) { - return ir_builder_->CreateFPToUI( + return b_->CreateFPToUI( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_)); } return Unimplemented("unhandled conversion operation: %s => %s", @@ -405,7 +408,7 @@ StatusOr ElementalIrEmitter::EmitFloatUnaryOp( } if (primitive_util::BitWidth(from_type) == primitive_util::BitWidth(to_type)) { - return ir_builder_->CreateBitCast( + return b_->CreateBitCast( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_)); } return InvalidArgument( @@ -429,45 +432,49 @@ StatusOr ElementalIrEmitter::EmitFloatUnaryOp( case HloOpcode::kSin: return EmitSin(op->shape().element_type(), operand_value); case HloOpcode::kFloor: - return llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::floor, {operand_value}, {operand_value->getType()}, - ir_builder_); + return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::floor, + {operand_value}, + {operand_value->getType()}, b_); case HloOpcode::kCeil: - return llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::ceil, {operand_value}, {operand_value->getType()}, - ir_builder_); + return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::ceil, + {operand_value}, + {operand_value->getType()}, b_); case HloOpcode::kAbs: - return llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::fabs, {operand_value}, {operand_value->getType()}, - ir_builder_); + return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::fabs, + {operand_value}, + {operand_value->getType()}, b_); case HloOpcode::kRoundNearestAfz: - return llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::round, {operand_value}, {operand_value->getType()}, - ir_builder_); + return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::round, + {operand_value}, + {operand_value->getType()}, b_); case HloOpcode::kSign: { // TODO(b/32151903): Ensure consistent sign behavior for -0.0. auto type = operand_value->getType(); auto zero = llvm::ConstantFP::get(type, 0.0); - auto oeq = ir_builder_->CreateFCmpOEQ(operand_value, zero); - auto olt = ir_builder_->CreateFCmpOLT(operand_value, zero); - return ir_builder_->CreateSelect( + auto oeq = b_->CreateFCmpOEQ(operand_value, zero); + auto olt = b_->CreateFCmpOLT(operand_value, zero); + return b_->CreateSelect( oeq, zero, - ir_builder_->CreateSelect(olt, llvm::ConstantFP::get(type, -1.0), - llvm::ConstantFP::get(type, 1.0))); + b_->CreateSelect(olt, llvm::ConstantFP::get(type, -1.0), + llvm::ConstantFP::get(type, 1.0))); } case HloOpcode::kIsFinite: { // abs(x) o!= inf, this works because the comparison returns false if // either operand is NaN. auto type = operand_value->getType(); auto abs_value = llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::fabs, {operand_value}, {type}, ir_builder_); + llvm::Intrinsic::fabs, {operand_value}, {type}, b_); auto infinity = llvm::ConstantFP::getInfinity(type); - auto not_infinite = ir_builder_->CreateFCmpONE(abs_value, infinity); - return ir_builder_->CreateZExt( - not_infinite, llvm_ir::PrimitiveTypeToIrType(PRED, module_)); + auto not_infinite = b_->CreateFCmpONE(abs_value, infinity); + return b_->CreateZExt(not_infinite, + llvm_ir::PrimitiveTypeToIrType(PRED, module_)); } case HloOpcode::kNegate: - return ir_builder_->CreateFNeg(operand_value); + return b_->CreateFNeg(operand_value); + case HloOpcode::kReal: + return operand_value; + case HloOpcode::kImag: + return llvm::ConstantFP::get(operand_value->getType(), 0.0); default: return Unimplemented("unary floating-point op '%s'", HloOpcodeString(op->opcode()).c_str()); @@ -487,13 +494,12 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( auto a = EmitExtractReal(operand_value); auto b = EmitExtractImag(operand_value); llvm::Type* llvm_ty = a->getType(); - auto sum_sq = ir_builder_->CreateFAdd(ir_builder_->CreateFMul(a, a), - ir_builder_->CreateFMul(b, b)); + auto sum_sq = b_->CreateFAdd(b_->CreateFMul(a, a), b_->CreateFMul(b, b)); TF_ASSIGN_OR_RETURN(auto log_sum_sq, EmitLog(component_type, sum_sq)); TF_ASSIGN_OR_RETURN(auto angle, EmitAtan2(component_type, b, a)); auto one_half = llvm::ConstantFP::get(llvm_ty, 0.5); - return EmitComposeComplex( - op, ir_builder_->CreateFMul(one_half, log_sum_sq), angle); + return EmitComposeComplex(op, b_->CreateFMul(one_half, log_sum_sq), + angle); } case HloOpcode::kLog1p: { // log1p(a+bi) = .5*log((a+1)^2+b^2) + i*atan2(b, a + 1) @@ -501,15 +507,14 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( auto b = EmitExtractImag(operand_value); llvm::Type* llvm_ty = a->getType(); auto one = llvm::ConstantFP::get(llvm_ty, 1.0); - auto a_plus_one = ir_builder_->CreateFAdd(a, one); - auto sum_sq = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(a_plus_one, a_plus_one), - ir_builder_->CreateFMul(b, b)); + auto a_plus_one = b_->CreateFAdd(a, one); + auto sum_sq = b_->CreateFAdd(b_->CreateFMul(a_plus_one, a_plus_one), + b_->CreateFMul(b, b)); TF_ASSIGN_OR_RETURN(auto log_sum_sq, EmitLog(component_type, sum_sq)); TF_ASSIGN_OR_RETURN(auto angle, EmitAtan2(component_type, b, a_plus_one)); auto one_half = llvm::ConstantFP::get(llvm_ty, 0.5); - return EmitComposeComplex( - op, ir_builder_->CreateFMul(one_half, log_sum_sq), angle); + return EmitComposeComplex(op, b_->CreateFMul(one_half, log_sum_sq), + angle); } case HloOpcode::kConvert: { PrimitiveType from_type = op->operand(0)->shape().element_type(); @@ -523,12 +528,11 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( primitive_util::ComplexComponentType(to_type); auto to_ir_component_type = llvm_ir::PrimitiveTypeToIrType(to_component_type, module_); - return EmitComposeComplex( - op, - ir_builder_->CreateFPCast(EmitExtractReal(operand_value), - to_ir_component_type), - ir_builder_->CreateFPCast(EmitExtractImag(operand_value), - to_ir_component_type)); + return EmitComposeComplex(op, + b_->CreateFPCast(EmitExtractReal(operand_value), + to_ir_component_type), + b_->CreateFPCast(EmitExtractImag(operand_value), + to_ir_component_type)); } case HloOpcode::kExp: { // e^(a+bi) = e^a*(cos(b)+sin(b)i) @@ -538,8 +542,8 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( auto cos_b, EmitCos(component_type, EmitExtractImag(operand_value))); TF_ASSIGN_OR_RETURN( auto sin_b, EmitSin(component_type, EmitExtractImag(operand_value))); - return EmitComposeComplex(op, ir_builder_->CreateFMul(exp_a, cos_b), - ir_builder_->CreateFMul(exp_a, sin_b)); + return EmitComposeComplex(op, b_->CreateFMul(exp_a, cos_b), + b_->CreateFMul(exp_a, sin_b)); } case HloOpcode::kExpm1: { // e^(a+bi)-1 = (e^a*cos(b)-1)+e^a*sin(b)i @@ -550,9 +554,8 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( TF_ASSIGN_OR_RETURN( auto sin_b, EmitSin(component_type, EmitExtractImag(operand_value))); auto one = llvm::ConstantFP::get(exp_a->getType(), 1.0); - auto real_result = - ir_builder_->CreateFSub(ir_builder_->CreateFMul(exp_a, cos_b), one); - auto imag_result = ir_builder_->CreateFMul(exp_a, sin_b); + auto real_result = b_->CreateFSub(b_->CreateFMul(exp_a, cos_b), one); + auto imag_result = b_->CreateFMul(exp_a, sin_b); return EmitComposeComplex(op, real_result, imag_result); } case HloOpcode::kCos: { @@ -567,18 +570,14 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( auto b = EmitExtractImag(operand_value); auto type = a->getType(); TF_ASSIGN_OR_RETURN(auto exp_b, EmitExp(component_type, b)); - auto half_exp_b = - ir_builder_->CreateFMul(llvm::ConstantFP::get(type, 0.5), exp_b); + auto half_exp_b = b_->CreateFMul(llvm::ConstantFP::get(type, 0.5), exp_b); auto half_exp_neg_b = - ir_builder_->CreateFDiv(llvm::ConstantFP::get(type, 0.5), exp_b); + b_->CreateFDiv(llvm::ConstantFP::get(type, 0.5), exp_b); TF_ASSIGN_OR_RETURN(auto cos_a, EmitCos(component_type, a)); TF_ASSIGN_OR_RETURN(auto sin_a, EmitSin(component_type, a)); return EmitComposeComplex( - op, - ir_builder_->CreateFMul( - cos_a, ir_builder_->CreateFAdd(half_exp_neg_b, half_exp_b)), - ir_builder_->CreateFMul( - sin_a, ir_builder_->CreateFSub(half_exp_neg_b, half_exp_b))); + op, b_->CreateFMul(cos_a, b_->CreateFAdd(half_exp_neg_b, half_exp_b)), + b_->CreateFMul(sin_a, b_->CreateFSub(half_exp_neg_b, half_exp_b))); } case HloOpcode::kSin: { // sin(z) = .5i(e^(-iz) - e^(iz)) @@ -594,18 +593,14 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( auto b = EmitExtractImag(operand_value); auto type = a->getType(); TF_ASSIGN_OR_RETURN(auto exp_b, EmitExp(component_type, b)); - auto half_exp_b = - ir_builder_->CreateFMul(llvm::ConstantFP::get(type, 0.5), exp_b); + auto half_exp_b = b_->CreateFMul(llvm::ConstantFP::get(type, 0.5), exp_b); auto half_exp_neg_b = - ir_builder_->CreateFDiv(llvm::ConstantFP::get(type, 0.5), exp_b); + b_->CreateFDiv(llvm::ConstantFP::get(type, 0.5), exp_b); TF_ASSIGN_OR_RETURN(auto cos_a, EmitCos(component_type, a)); TF_ASSIGN_OR_RETURN(auto sin_a, EmitSin(component_type, a)); return EmitComposeComplex( - op, - ir_builder_->CreateFMul( - sin_a, ir_builder_->CreateFAdd(half_exp_b, half_exp_neg_b)), - ir_builder_->CreateFMul( - cos_a, ir_builder_->CreateFSub(half_exp_b, half_exp_neg_b))); + op, b_->CreateFMul(sin_a, b_->CreateFAdd(half_exp_b, half_exp_neg_b)), + b_->CreateFMul(cos_a, b_->CreateFSub(half_exp_b, half_exp_neg_b))); } case HloOpcode::kTanh: { /* @@ -633,64 +628,61 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( TF_ASSIGN_OR_RETURN(auto exp_a, EmitExp(component_type, a)); TF_ASSIGN_OR_RETURN(auto cos_b, EmitCos(component_type, b)); TF_ASSIGN_OR_RETURN(auto sin_b, EmitSin(component_type, b)); - auto exp_neg_a = ir_builder_->CreateFDiv( - llvm::ConstantFP::get(exp_a->getType(), 1), exp_a); - auto exp_2a_minus_exp_neg_2a = ir_builder_->CreateFSub( - ir_builder_->CreateFMul(exp_a, exp_a), - ir_builder_->CreateFMul(exp_neg_a, exp_neg_a)); - auto cos_b_sq = ir_builder_->CreateFMul(cos_b, cos_b); - auto sin_b_sq = ir_builder_->CreateFMul(sin_b, sin_b); - auto real_num = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(cos_b_sq, exp_2a_minus_exp_neg_2a), - ir_builder_->CreateFMul(sin_b_sq, exp_2a_minus_exp_neg_2a)); - auto cos_b_sin_b = ir_builder_->CreateFMul(cos_b, sin_b); - auto exp_a_plus_exp_neg_a = ir_builder_->CreateFAdd(exp_a, exp_neg_a); + auto exp_neg_a = + b_->CreateFDiv(llvm::ConstantFP::get(exp_a->getType(), 1), exp_a); + auto exp_2a_minus_exp_neg_2a = b_->CreateFSub( + b_->CreateFMul(exp_a, exp_a), b_->CreateFMul(exp_neg_a, exp_neg_a)); + auto cos_b_sq = b_->CreateFMul(cos_b, cos_b); + auto sin_b_sq = b_->CreateFMul(sin_b, sin_b); + auto real_num = + b_->CreateFAdd(b_->CreateFMul(cos_b_sq, exp_2a_minus_exp_neg_2a), + b_->CreateFMul(sin_b_sq, exp_2a_minus_exp_neg_2a)); + auto cos_b_sin_b = b_->CreateFMul(cos_b, sin_b); + auto exp_a_plus_exp_neg_a = b_->CreateFAdd(exp_a, exp_neg_a); auto exp_a_plus_exp_neg_a_sq = - ir_builder_->CreateFMul(exp_a_plus_exp_neg_a, exp_a_plus_exp_neg_a); - auto exp_a_minus_exp_neg_a = ir_builder_->CreateFSub(exp_a, exp_neg_a); + b_->CreateFMul(exp_a_plus_exp_neg_a, exp_a_plus_exp_neg_a); + auto exp_a_minus_exp_neg_a = b_->CreateFSub(exp_a, exp_neg_a); auto exp_a_minus_exp_neg_a_sq = - ir_builder_->CreateFMul(exp_a_minus_exp_neg_a, exp_a_minus_exp_neg_a); - auto imag_num = ir_builder_->CreateFMul( - cos_b_sin_b, ir_builder_->CreateFSub(exp_a_plus_exp_neg_a_sq, - exp_a_minus_exp_neg_a_sq)); - auto denom = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(cos_b_sq, exp_a_plus_exp_neg_a_sq), - ir_builder_->CreateFMul(sin_b_sq, exp_a_minus_exp_neg_a_sq)); - return EmitComposeComplex(op, ir_builder_->CreateFDiv(real_num, denom), - ir_builder_->CreateFDiv(imag_num, denom)); + b_->CreateFMul(exp_a_minus_exp_neg_a, exp_a_minus_exp_neg_a); + auto imag_num = b_->CreateFMul( + cos_b_sin_b, + b_->CreateFSub(exp_a_plus_exp_neg_a_sq, exp_a_minus_exp_neg_a_sq)); + auto denom = + b_->CreateFAdd(b_->CreateFMul(cos_b_sq, exp_a_plus_exp_neg_a_sq), + b_->CreateFMul(sin_b_sq, exp_a_minus_exp_neg_a_sq)); + return EmitComposeComplex(op, b_->CreateFDiv(real_num, denom), + b_->CreateFDiv(imag_num, denom)); } case HloOpcode::kAbs: { - auto sum_sq = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(EmitExtractReal(operand_value), - EmitExtractReal(operand_value)), - ir_builder_->CreateFMul(EmitExtractImag(operand_value), - EmitExtractImag(operand_value))); + auto sum_sq = + b_->CreateFAdd(b_->CreateFMul(EmitExtractReal(operand_value), + EmitExtractReal(operand_value)), + b_->CreateFMul(EmitExtractImag(operand_value), + EmitExtractImag(operand_value))); return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::sqrt, {sum_sq}, - {sum_sq->getType()}, ir_builder_); + {sum_sq->getType()}, b_); } case HloOpcode::kSign: { // Sign(c) = c / |c| - auto sum_sq = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(EmitExtractReal(operand_value), - EmitExtractReal(operand_value)), - ir_builder_->CreateFMul(EmitExtractImag(operand_value), - EmitExtractImag(operand_value))); + auto sum_sq = + b_->CreateFAdd(b_->CreateFMul(EmitExtractReal(operand_value), + EmitExtractReal(operand_value)), + b_->CreateFMul(EmitExtractImag(operand_value), + EmitExtractImag(operand_value))); auto cplx_abs = llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::sqrt, {sum_sq}, {sum_sq->getType()}, ir_builder_); + llvm::Intrinsic::sqrt, {sum_sq}, {sum_sq->getType()}, b_); auto type = cplx_abs->getType(); auto zero = llvm::ConstantFP::get(type, 0.0); - auto oeq = ir_builder_->CreateFCmpOEQ(cplx_abs, zero); - return ir_builder_->CreateSelect( + auto oeq = b_->CreateFCmpOEQ(cplx_abs, zero); + return b_->CreateSelect( oeq, EmitComposeComplex(op, zero, zero), EmitComposeComplex( - op, - ir_builder_->CreateFDiv(EmitExtractReal(operand_value), cplx_abs), - ir_builder_->CreateFDiv(EmitExtractImag(operand_value), - cplx_abs))); + op, b_->CreateFDiv(EmitExtractReal(operand_value), cplx_abs), + b_->CreateFDiv(EmitExtractImag(operand_value), cplx_abs))); } case HloOpcode::kNegate: - return EmitComposeComplex( - op, ir_builder_->CreateFNeg(EmitExtractReal(operand_value)), - ir_builder_->CreateFNeg(EmitExtractImag(operand_value))); + return EmitComposeComplex(op, + b_->CreateFNeg(EmitExtractReal(operand_value)), + b_->CreateFNeg(EmitExtractImag(operand_value))); case HloOpcode::kReal: return EmitExtractReal(operand_value); case HloOpcode::kImag: @@ -724,15 +716,15 @@ StatusOr ElementalIrEmitter::EmitFloatBinaryOp( case HloOpcode::kComplex: return EmitComposeComplex(op, lhs_value, rhs_value); case HloOpcode::kAdd: - return ir_builder_->CreateFAdd(lhs_value, rhs_value); + return b_->CreateFAdd(lhs_value, rhs_value); case HloOpcode::kSubtract: - return ir_builder_->CreateFSub(lhs_value, rhs_value); + return b_->CreateFSub(lhs_value, rhs_value); case HloOpcode::kMultiply: - return ir_builder_->CreateFMul(lhs_value, rhs_value); + return b_->CreateFMul(lhs_value, rhs_value); case HloOpcode::kDivide: - return ir_builder_->CreateFDiv(lhs_value, rhs_value); + return b_->CreateFDiv(lhs_value, rhs_value); case HloOpcode::kRemainder: - return ir_builder_->CreateFRem(lhs_value, rhs_value); + return b_->CreateFRem(lhs_value, rhs_value); // LLVM comparisons can be "unordered" (U) or "ordered" (O) -- ordered // comparisons always return false when one of the operands is NaN, whereas // unordered comparisons return true. @@ -742,22 +734,22 @@ StatusOr ElementalIrEmitter::EmitFloatBinaryOp( // matches C++'s semantics. case HloOpcode::kEq: return llvm_ir::EmitComparison(llvm::CmpInst::FCMP_OEQ, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kNe: return llvm_ir::EmitComparison(llvm::CmpInst::FCMP_UNE, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kLt: return llvm_ir::EmitComparison(llvm::CmpInst::FCMP_OLT, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kGt: return llvm_ir::EmitComparison(llvm::CmpInst::FCMP_OGT, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kLe: return llvm_ir::EmitComparison(llvm::CmpInst::FCMP_OLE, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kGe: return llvm_ir::EmitComparison(llvm::CmpInst::FCMP_OGE, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kMaximum: return EmitFloatMax(lhs_value, rhs_value); @@ -778,64 +770,56 @@ StatusOr ElementalIrEmitter::EmitComplexBinaryOp( llvm::Value* rhs_value) const { switch (op->opcode()) { case HloOpcode::kAdd: - return EmitComposeComplex( - op, - ir_builder_->CreateFAdd(EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFAdd(EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value))); + return EmitComposeComplex(op, + b_->CreateFAdd(EmitExtractReal(lhs_value), + EmitExtractReal(rhs_value)), + b_->CreateFAdd(EmitExtractImag(lhs_value), + EmitExtractImag(rhs_value))); case HloOpcode::kSubtract: - return EmitComposeComplex( - op, - ir_builder_->CreateFSub(EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFSub(EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value))); + return EmitComposeComplex(op, + b_->CreateFSub(EmitExtractReal(lhs_value), + EmitExtractReal(rhs_value)), + b_->CreateFSub(EmitExtractImag(lhs_value), + EmitExtractImag(rhs_value))); case HloOpcode::kMultiply: return EmitComposeComplex( op, - ir_builder_->CreateFSub( - ir_builder_->CreateFMul(EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFMul(EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value))), - ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(EmitExtractReal(lhs_value), - EmitExtractImag(rhs_value)), - ir_builder_->CreateFMul(EmitExtractImag(lhs_value), - EmitExtractReal(rhs_value)))); + b_->CreateFSub(b_->CreateFMul(EmitExtractReal(lhs_value), + EmitExtractReal(rhs_value)), + b_->CreateFMul(EmitExtractImag(lhs_value), + EmitExtractImag(rhs_value))), + b_->CreateFAdd(b_->CreateFMul(EmitExtractReal(lhs_value), + EmitExtractImag(rhs_value)), + b_->CreateFMul(EmitExtractImag(lhs_value), + EmitExtractReal(rhs_value)))); case HloOpcode::kDivide: { // (a+bi) / (c+di) = ((a+bi)(c-di)) / ((c+di)(c-di)) // = ((ac + bd) + (bc - ad)i) / (c^2 + d^2) - auto rhs_sum_sq = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(EmitExtractReal(rhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFMul(EmitExtractImag(rhs_value), - EmitExtractImag(rhs_value))); + auto rhs_sum_sq = + b_->CreateFAdd(b_->CreateFMul(EmitExtractReal(rhs_value), + EmitExtractReal(rhs_value)), + b_->CreateFMul(EmitExtractImag(rhs_value), + EmitExtractImag(rhs_value))); auto type = rhs_sum_sq->getType(); auto zero = llvm::ConstantFP::get(type, 0.0); - auto oeq = ir_builder_->CreateFCmpOEQ(rhs_sum_sq, zero); - auto real_inf_or_nan = - ir_builder_->CreateFDiv(EmitExtractReal(lhs_value), zero); - auto imag_inf_or_nan = - ir_builder_->CreateFDiv(EmitExtractImag(lhs_value), zero); - return ir_builder_->CreateSelect( + auto oeq = b_->CreateFCmpOEQ(rhs_sum_sq, zero); + auto real_inf_or_nan = b_->CreateFDiv(EmitExtractReal(lhs_value), zero); + auto imag_inf_or_nan = b_->CreateFDiv(EmitExtractImag(lhs_value), zero); + return b_->CreateSelect( oeq, EmitComposeComplex(op, real_inf_or_nan, imag_inf_or_nan), EmitComposeComplex( op, - ir_builder_->CreateFDiv( - ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFMul(EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value))), + b_->CreateFDiv( + b_->CreateFAdd(b_->CreateFMul(EmitExtractReal(lhs_value), + EmitExtractReal(rhs_value)), + b_->CreateFMul(EmitExtractImag(lhs_value), + EmitExtractImag(rhs_value))), rhs_sum_sq), - ir_builder_->CreateFDiv( - ir_builder_->CreateFSub( - ir_builder_->CreateFMul(EmitExtractImag(lhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFMul(EmitExtractReal(lhs_value), - EmitExtractImag(rhs_value))), + b_->CreateFDiv( + b_->CreateFSub(b_->CreateFMul(EmitExtractImag(lhs_value), + EmitExtractReal(rhs_value)), + b_->CreateFMul(EmitExtractReal(lhs_value), + EmitExtractImag(rhs_value))), rhs_sum_sq))); } // LLVM comparisons can be "unordered" (U) or "ordered" (O) -- ordered @@ -846,21 +830,21 @@ StatusOr ElementalIrEmitter::EmitComplexBinaryOp( // unordered comparison. This makes x != y equivalent to !(x == y), and // matches C++'s semantics. case HloOpcode::kEq: - return ir_builder_->CreateAnd( + return b_->CreateAnd( llvm_ir::EmitComparison(llvm::CmpInst::FCMP_OEQ, EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value), ir_builder_), + EmitExtractReal(rhs_value), b_), llvm_ir::EmitComparison(llvm::CmpInst::FCMP_OEQ, EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value), ir_builder_)); + EmitExtractImag(rhs_value), b_)); case HloOpcode::kNe: - return ir_builder_->CreateOr( + return b_->CreateOr( llvm_ir::EmitComparison(llvm::CmpInst::FCMP_UNE, EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value), ir_builder_), + EmitExtractReal(rhs_value), b_), llvm_ir::EmitComparison(llvm::CmpInst::FCMP_UNE, EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value), ir_builder_)); + EmitExtractImag(rhs_value), b_)); case HloOpcode::kPower: { // (a+bi)^(c+di) = @@ -872,29 +856,26 @@ StatusOr ElementalIrEmitter::EmitComplexBinaryOp( auto b = EmitExtractImag(lhs_value); auto c = EmitExtractReal(rhs_value); auto d = EmitExtractImag(rhs_value); - auto aa_p_bb = ir_builder_->CreateFAdd(ir_builder_->CreateFMul(a, a), - ir_builder_->CreateFMul(b, b)); + auto aa_p_bb = b_->CreateFAdd(b_->CreateFMul(a, a), b_->CreateFMul(b, b)); auto one_half = llvm::ConstantFP::get(a->getType(), 0.5); - auto half_c = ir_builder_->CreateFMul(one_half, c); + auto half_c = b_->CreateFMul(one_half, c); TF_ASSIGN_OR_RETURN(auto aa_p_bb_to_half_c, EmitPow(component_type, aa_p_bb, half_c)); - auto neg_d = ir_builder_->CreateFNeg(d); + auto neg_d = b_->CreateFNeg(d); TF_ASSIGN_OR_RETURN(auto arg_lhs, EmitAtan2(component_type, b, a)); - auto neg_d_arg_lhs = ir_builder_->CreateFMul(neg_d, arg_lhs); + auto neg_d_arg_lhs = b_->CreateFMul(neg_d, arg_lhs); TF_ASSIGN_OR_RETURN(auto e_to_neg_d_arg_lhs, EmitExp(component_type, neg_d_arg_lhs)); - auto coeff = - ir_builder_->CreateFMul(aa_p_bb_to_half_c, e_to_neg_d_arg_lhs); + auto coeff = b_->CreateFMul(aa_p_bb_to_half_c, e_to_neg_d_arg_lhs); TF_ASSIGN_OR_RETURN(auto ln_aa_p_bb, EmitLog(component_type, aa_p_bb)); - auto half_d = ir_builder_->CreateFMul(one_half, d); - auto q = - ir_builder_->CreateFAdd(ir_builder_->CreateFMul(c, arg_lhs), - ir_builder_->CreateFMul(half_d, ln_aa_p_bb)); + auto half_d = b_->CreateFMul(one_half, d); + auto q = b_->CreateFAdd(b_->CreateFMul(c, arg_lhs), + b_->CreateFMul(half_d, ln_aa_p_bb)); TF_ASSIGN_OR_RETURN(auto cos_q, EmitCos(component_type, q)); TF_ASSIGN_OR_RETURN(auto sin_q, EmitSin(component_type, q)); - return EmitComposeComplex(op, ir_builder_->CreateFMul(coeff, cos_q), - ir_builder_->CreateFMul(coeff, sin_q)); + return EmitComposeComplex(op, b_->CreateFMul(coeff, cos_q), + b_->CreateFMul(coeff, sin_q)); } default: return Unimplemented("binary complex op '%s'", @@ -904,12 +885,12 @@ StatusOr ElementalIrEmitter::EmitComplexBinaryOp( llvm::Value* ElementalIrEmitter::EmitFloatMax(llvm::Value* lhs_value, llvm::Value* rhs_value) const { - return llvm_ir::EmitFloatMax(lhs_value, rhs_value, ir_builder_); + return llvm_ir::EmitFloatMax(lhs_value, rhs_value, b_); } llvm::Value* ElementalIrEmitter::EmitFloatMin(llvm::Value* lhs_value, llvm::Value* rhs_value) const { - return llvm_ir::EmitFloatMin(lhs_value, rhs_value, ir_builder_); + return llvm_ir::EmitFloatMin(lhs_value, rhs_value, b_); } StatusOr ElementalIrEmitter::EmitErfInv(PrimitiveType prim_type, @@ -921,15 +902,14 @@ StatusOr ElementalIrEmitter::EmitErfInv(PrimitiveType prim_type, "type F32."); } auto getFloat = [&](const float f) { - return llvm::ConstantFP::get(ir_builder_->getFloatTy(), f); + return llvm::ConstantFP::get(b_->getFloatTy(), f); }; auto multiply_add = [&](tensorflow::gtl::ArraySlice coefficients, llvm::Value* w) { llvm::Value* p = getFloat(coefficients.front()); coefficients.pop_front(); for (float coefficient : coefficients) { - p = ir_builder_->CreateFAdd(ir_builder_->CreateFMul(p, w), - getFloat(coefficient)); + p = b_->CreateFAdd(b_->CreateFMul(p, w), getFloat(coefficient)); } return p; }; @@ -947,50 +927,48 @@ StatusOr ElementalIrEmitter::EmitErfInv(PrimitiveType prim_type, // } // return p*x llvm::Function* logf_fn = llvm::Intrinsic::getDeclaration( - module_, llvm::Intrinsic::log, {ir_builder_->getFloatTy()}); + module_, llvm::Intrinsic::log, {b_->getFloatTy()}); - llvm::Value* w = ir_builder_->CreateFNeg(ir_builder_->CreateCall( - logf_fn, - {ir_builder_->CreateFMul(ir_builder_->CreateFSub(getFloat(1.0f), x), - ir_builder_->CreateFAdd(getFloat(1.0f), x))})); + llvm::Value* w = b_->CreateFNeg(b_->CreateCall( + logf_fn, {b_->CreateFMul(b_->CreateFSub(getFloat(1.0f), x), + b_->CreateFAdd(getFloat(1.0f), x))})); - llvm::Value* p_addr = llvm_ir::EmitAllocaAtFunctionEntry( - ir_builder_->getFloatTy(), "p.addr", ir_builder_); + llvm::Value* p_addr = + llvm_ir::EmitAllocaAtFunctionEntry(b_->getFloatTy(), "p.addr", b_); - llvm_ir::LlvmIfData if_data = - llvm_ir::EmitIfThenElse(ir_builder_->CreateFCmpOLT(w, getFloat(5.0f)), - "w_less_than_five", ir_builder_); + llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( + b_->CreateFCmpOLT(w, getFloat(5.0f)), "w_less_than_five", b_); // Handle true BB. - SetToFirstInsertPoint(if_data.true_block, ir_builder_); + SetToFirstInsertPoint(if_data.true_block, b_); { - llvm::Value* lw = ir_builder_->CreateFSub(w, getFloat(2.5f)); + llvm::Value* lw = b_->CreateFSub(w, getFloat(2.5f)); tensorflow::gtl::ArraySlice lq{ 2.81022636e-08f, 3.43273939e-07f, -3.5233877e-06f, -4.39150654e-06f, 0.00021858087f, -0.00125372503f, -0.00417768164f, 0.246640727f, 1.50140941f}; llvm::Value* p = multiply_add(lq, lw); - ir_builder_->CreateStore(p, p_addr); + b_->CreateStore(p, p_addr); } // Handle false BB. - SetToFirstInsertPoint(if_data.false_block, ir_builder_); + SetToFirstInsertPoint(if_data.false_block, b_); { llvm::Function* sqrtf_fn = llvm::Intrinsic::getDeclaration( - module_, llvm::Intrinsic::sqrt, {ir_builder_->getFloatTy()}); + module_, llvm::Intrinsic::sqrt, {b_->getFloatTy()}); - llvm::Value* gw = ir_builder_->CreateFSub( - ir_builder_->CreateCall(sqrtf_fn, {w}), getFloat(3.0f)); + llvm::Value* gw = + b_->CreateFSub(b_->CreateCall(sqrtf_fn, {w}), getFloat(3.0f)); tensorflow::gtl::ArraySlice gq{ -0.000200214257f, 0.000100950558f, 0.00134934322f, -0.00367342844f, 0.00573950773f, -0.0076224613f, 0.00943887047f, 1.00167406f, 2.83297682f}; llvm::Value* p = multiply_add(gq, gw); - ir_builder_->CreateStore(p, p_addr); + b_->CreateStore(p, p_addr); } - SetToFirstInsertPoint(if_data.after_block, ir_builder_); - llvm::Value* p = ir_builder_->CreateLoad(p_addr); - return ir_builder_->CreateFMul(p, x); + SetToFirstInsertPoint(if_data.after_block, b_); + llvm::Value* p = b_->CreateLoad(p_addr); + return b_->CreateFMul(p, x); } StatusOr ElementalIrEmitter::EmitErfcInv( @@ -998,13 +976,13 @@ StatusOr ElementalIrEmitter::EmitErfcInv( // Compute erfcinv(value) by calculating erfinv(1.0 - value). auto type = llvm_ir::PrimitiveTypeToIrType(prim_type, module_); auto one = llvm::ConstantFP::get(type, 1.0); - return EmitErfInv(prim_type, ir_builder_->CreateFSub(one, value)); + return EmitErfInv(prim_type, b_->CreateFSub(one, value)); } StatusOr ElementalIrEmitter::EmitLog(PrimitiveType prim_type, llvm::Value* value) const { return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::log, {value}, - {value->getType()}, ir_builder_); + {value->getType()}, b_); } StatusOr ElementalIrEmitter::EmitLog1p(PrimitiveType prim_type, @@ -1016,35 +994,34 @@ StatusOr ElementalIrEmitter::EmitLog1p(PrimitiveType prim_type, // When x is large, the naive evaluation of ln(x + 1) is more // accurate than the Taylor series. TF_ASSIGN_OR_RETURN(auto for_large_x, - EmitLog(prim_type, ir_builder_->CreateFAdd(x, one))); + EmitLog(prim_type, b_->CreateFAdd(x, one))); // The Taylor series for ln(x+1) is x - x^2/2 - x^3/3 + …. - auto for_small_x = ir_builder_->CreateFMul( - ir_builder_->CreateFAdd(ir_builder_->CreateFMul(negative_half, x), one), - x); + auto for_small_x = + b_->CreateFMul(b_->CreateFAdd(b_->CreateFMul(negative_half, x), one), x); const auto kAntilogarithmIsSmallThreshold = 1e-4; - auto abs_x = llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::fabs, {value}, - {type}, ir_builder_); - auto x_is_small = ir_builder_->CreateFCmpOLT( + auto abs_x = + llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::fabs, {value}, {type}, b_); + auto x_is_small = b_->CreateFCmpOLT( abs_x, llvm::ConstantFP::get(type, kAntilogarithmIsSmallThreshold)); - return ir_builder_->CreateSelect(x_is_small, for_small_x, for_large_x); + return b_->CreateSelect(x_is_small, for_small_x, for_large_x); } StatusOr ElementalIrEmitter::EmitSin(PrimitiveType prim_type, llvm::Value* value) const { return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::sin, {value}, - {value->getType()}, ir_builder_); + {value->getType()}, b_); } StatusOr ElementalIrEmitter::EmitCos(PrimitiveType prim_type, llvm::Value* value) const { return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::cos, {value}, - {value->getType()}, ir_builder_); + {value->getType()}, b_); } StatusOr ElementalIrEmitter::EmitExp(PrimitiveType prim_type, llvm::Value* value) const { return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::exp, {value}, - {value->getType()}, ir_builder_); + {value->getType()}, b_); } StatusOr ElementalIrEmitter::EmitExpm1(PrimitiveType prim_type, @@ -1056,25 +1033,25 @@ StatusOr ElementalIrEmitter::EmitExpm1(PrimitiveType prim_type, // When the exponent is large, the naive evaluation of e^(x) - 1 is more // accurate than the Taylor series. TF_ASSIGN_OR_RETURN(auto exp_x, EmitExp(prim_type, value)); - auto for_large_x = ir_builder_->CreateFSub(exp_x, one); + auto for_large_x = b_->CreateFSub(exp_x, one); // The Taylor series for exp(x) is 1 + x + x^2/2 + x^3/6 + …. // We want exp(x)-1 which is x + x^2/2 + x^3/6 + …. - auto x_squared = ir_builder_->CreateFAdd(x, x); - auto x_squared_over_two = ir_builder_->CreateFMul(x_squared, half); - auto for_small_x = ir_builder_->CreateFAdd(x, x_squared_over_two); + auto x_squared = b_->CreateFAdd(x, x); + auto x_squared_over_two = b_->CreateFMul(x_squared, half); + auto for_small_x = b_->CreateFAdd(x, x_squared_over_two); const auto kExponentIsSmallThreshold = 1e-5; - auto abs_x = llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::fabs, {value}, - {type}, ir_builder_); - auto x_is_small = ir_builder_->CreateFCmpOLT( + auto abs_x = + llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::fabs, {value}, {type}, b_); + auto x_is_small = b_->CreateFCmpOLT( abs_x, llvm::ConstantFP::get(type, kExponentIsSmallThreshold)); - return ir_builder_->CreateSelect(x_is_small, for_small_x, for_large_x); + return b_->CreateSelect(x_is_small, for_small_x, for_large_x); } StatusOr ElementalIrEmitter::EmitPow(PrimitiveType prim_type, llvm::Value* lhs, llvm::Value* rhs) const { return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::pow, {lhs, rhs}, - {lhs->getType()}, ir_builder_); + {lhs->getType()}, b_); } StatusOr ElementalIrEmitter::EmitAtan2(PrimitiveType prim_type, @@ -1089,11 +1066,10 @@ StatusOr ElementalIrEmitter::EmitReducePrecision( return Unimplemented("reduce-precision only implemented for F32"); } return EmitReducePrecisionFloat(x, /*exponent_bits=*/hlo->exponent_bits(), - /*mantissa_bits=*/hlo->mantissa_bits(), - ir_builder_); + /*mantissa_bits=*/hlo->mantissa_bits(), b_); } -static llvm::Value* SaturateShiftIfNecessary(llvm::IRBuilder<>* ir_builder, +static llvm::Value* SaturateShiftIfNecessary(llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs, llvm::Value* shift_result, bool saturate_to_sign_bit) { @@ -1106,15 +1082,14 @@ static llvm::Value* SaturateShiftIfNecessary(llvm::IRBuilder<>* ir_builder, llvm::ConstantInt* minus_one = llvm::ConstantInt::get(integer_type, -1); llvm::Value* saturated_value; if (saturate_to_sign_bit) { - saturated_value = ir_builder->CreateSelect( - ir_builder->CreateICmpSLT(lhs, zero), minus_one, zero); + saturated_value = + b->CreateSelect(b->CreateICmpSLT(lhs, zero), minus_one, zero); } else { saturated_value = zero; } llvm::Value* shift_amt_in_range = - ir_builder->CreateICmpULT(rhs, integer_bitsize_constant, "shft.chk"); - return ir_builder->CreateSelect(shift_amt_in_range, shift_result, - saturated_value); + b->CreateICmpULT(rhs, integer_bitsize_constant, "shft.chk"); + return b->CreateSelect(shift_amt_in_range, shift_result, saturated_value); } StatusOr ElementalIrEmitter::EmitIntegerBinaryOp( @@ -1123,49 +1098,49 @@ StatusOr ElementalIrEmitter::EmitIntegerBinaryOp( switch (op->opcode()) { // TODO(jingyue): add the "nsw" attribute for signed types. case HloOpcode::kAdd: - return ir_builder_->CreateAdd(lhs_value, rhs_value); + return b_->CreateAdd(lhs_value, rhs_value); case HloOpcode::kSubtract: - return ir_builder_->CreateSub(lhs_value, rhs_value); + return b_->CreateSub(lhs_value, rhs_value); case HloOpcode::kMultiply: - return ir_builder_->CreateMul(lhs_value, rhs_value); + return b_->CreateMul(lhs_value, rhs_value); case HloOpcode::kDivide: - return is_signed ? ir_builder_->CreateSDiv(lhs_value, rhs_value) - : ir_builder_->CreateUDiv(lhs_value, rhs_value); + return is_signed ? b_->CreateSDiv(lhs_value, rhs_value) + : b_->CreateUDiv(lhs_value, rhs_value); case HloOpcode::kRemainder: - return is_signed ? ir_builder_->CreateSRem(lhs_value, rhs_value) - : ir_builder_->CreateURem(lhs_value, rhs_value); + return is_signed ? b_->CreateSRem(lhs_value, rhs_value) + : b_->CreateURem(lhs_value, rhs_value); case HloOpcode::kEq: return llvm_ir::EmitComparison(llvm::CmpInst::ICMP_EQ, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kNe: return llvm_ir::EmitComparison(llvm::CmpInst::ICMP_NE, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kLt: return llvm_ir::EmitComparison( is_signed ? llvm::CmpInst::ICMP_SLT : llvm::CmpInst::ICMP_ULT, - lhs_value, rhs_value, ir_builder_); + lhs_value, rhs_value, b_); case HloOpcode::kGt: return llvm_ir::EmitComparison( is_signed ? llvm::CmpInst::ICMP_SGT : llvm::CmpInst::ICMP_UGT, - lhs_value, rhs_value, ir_builder_); + lhs_value, rhs_value, b_); case HloOpcode::kLe: return llvm_ir::EmitComparison( is_signed ? llvm::CmpInst::ICMP_SLE : llvm::CmpInst::ICMP_ULE, - lhs_value, rhs_value, ir_builder_); + lhs_value, rhs_value, b_); case HloOpcode::kGe: return llvm_ir::EmitComparison( is_signed ? llvm::CmpInst::ICMP_SGE : llvm::CmpInst::ICMP_UGE, - lhs_value, rhs_value, ir_builder_); + lhs_value, rhs_value, b_); case HloOpcode::kMinimum: return EmitIntegralMin(lhs_value, rhs_value, is_signed); case HloOpcode::kMaximum: return EmitIntegralMax(lhs_value, rhs_value, is_signed); case HloOpcode::kAnd: - return ir_builder_->CreateAnd(lhs_value, rhs_value); + return b_->CreateAnd(lhs_value, rhs_value); case HloOpcode::kOr: - return ir_builder_->CreateOr(lhs_value, rhs_value); + return b_->CreateOr(lhs_value, rhs_value); case HloOpcode::kXor: - return ir_builder_->CreateXor(lhs_value, rhs_value); + return b_->CreateXor(lhs_value, rhs_value); // Shifting out bits >= the number of bits in the type being shifted // produces a poison value in LLVM which is basically "deferred undefined @@ -1173,20 +1148,17 @@ StatusOr ElementalIrEmitter::EmitIntegerBinaryOp( // UB. We replace the poison value with a constant to avoid this deferred // UB. case HloOpcode::kShiftRightArithmetic: - return SaturateShiftIfNecessary( - ir_builder_, lhs_value, rhs_value, - ir_builder_->CreateAShr(lhs_value, rhs_value), - /*saturate_to_sign_bit=*/true); + return SaturateShiftIfNecessary(b_, lhs_value, rhs_value, + b_->CreateAShr(lhs_value, rhs_value), + /*saturate_to_sign_bit=*/true); case HloOpcode::kShiftLeft: - return SaturateShiftIfNecessary( - ir_builder_, lhs_value, rhs_value, - ir_builder_->CreateShl(lhs_value, rhs_value), - /*saturate_to_sign_bit=*/false); + return SaturateShiftIfNecessary(b_, lhs_value, rhs_value, + b_->CreateShl(lhs_value, rhs_value), + /*saturate_to_sign_bit=*/false); case HloOpcode::kShiftRightLogical: - return SaturateShiftIfNecessary( - ir_builder_, lhs_value, rhs_value, - ir_builder_->CreateLShr(lhs_value, rhs_value), - /*saturate_to_sign_bit=*/false); + return SaturateShiftIfNecessary(b_, lhs_value, rhs_value, + b_->CreateLShr(lhs_value, rhs_value), + /*saturate_to_sign_bit=*/false); default: return Unimplemented("binary integer op '%s'", HloOpcodeString(op->opcode()).c_str()); @@ -1196,21 +1168,19 @@ StatusOr ElementalIrEmitter::EmitIntegerBinaryOp( llvm::Value* ElementalIrEmitter::EmitIntegralMax(llvm::Value* lhs_value, llvm::Value* rhs_value, bool is_signed) const { - return ir_builder_->CreateSelect( - ir_builder_->CreateICmp( - is_signed ? llvm::ICmpInst::ICMP_SGE : llvm::ICmpInst::ICMP_UGE, - lhs_value, rhs_value), - lhs_value, rhs_value); + return b_->CreateSelect(b_->CreateICmp(is_signed ? llvm::ICmpInst::ICMP_SGE + : llvm::ICmpInst::ICMP_UGE, + lhs_value, rhs_value), + lhs_value, rhs_value); } llvm::Value* ElementalIrEmitter::EmitIntegralMin(llvm::Value* lhs_value, llvm::Value* rhs_value, bool is_signed) const { - return ir_builder_->CreateSelect( - ir_builder_->CreateICmp( - is_signed ? llvm::ICmpInst::ICMP_SLE : llvm::ICmpInst::ICMP_ULE, - lhs_value, rhs_value), - lhs_value, rhs_value); + return b_->CreateSelect(b_->CreateICmp(is_signed ? llvm::ICmpInst::ICMP_SLE + : llvm::ICmpInst::ICMP_ULE, + lhs_value, rhs_value), + lhs_value, rhs_value); } llvm_ir::IrArray::Index ElementalIrEmitter::ElementwiseSourceIndex( @@ -1253,180 +1223,254 @@ llvm_ir::IrArray::Index ElementalIrEmitter::ElementwiseSourceIndex( return source_index; } -llvm_ir::ElementGenerator ElementalIrEmitter::MakeRngElementGenerator( +StatusOr ElementalIrEmitter::ConvertValueForDistribution( const HloInstruction* hlo, - const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator) - const { - PrimitiveType param_prim_type = hlo->operand(0)->shape().element_type(); - llvm::Type* param_ir_type = - llvm_ir::PrimitiveTypeToIrType(param_prim_type, module_); - - // Same values as PCG library - // https://github.com/imneme/pcg-c/blob/master/include/pcg_variants.h - llvm::Value* multiplier = ir_builder_->getInt( - llvm::APInt(128, {0x4385DF649FCCF645, 0x2360ED051FC65DA4})); - llvm::Value* increment = ir_builder_->getInt( - llvm::APInt(128, {0x14057B7EF767814F, 0x5851F42D4C957F2D})); - - auto random_value_from_hlo = [hlo]() { - const HloModule* module = - hlo->IsFused() ? hlo->parent()->FusionInstruction()->parent()->parent() - : hlo->parent()->parent(); - return module->RandomNew64(); - }; + const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& index, llvm::Value* raw_value) const { + TF_ASSIGN_OR_RETURN(llvm::Value * a_or_mean, + operand_to_generator.at(hlo->operand(0))(index)); + TF_ASSIGN_OR_RETURN(llvm::Value * b_or_sigma, + operand_to_generator.at(hlo->operand(1))(index)); + PrimitiveType elem_prim_ty = hlo->shape().element_type(); + llvm::Type* elem_ir_ty = + llvm_ir::PrimitiveTypeToIrType(elem_prim_ty, module_); + llvm::Type* raw_value_ty = raw_value->getType(); + + // Convert raw integer to float in range [0, 1) if the element is a float. + llvm::Value* elem_value = raw_value; + if (elem_ir_ty->isFloatingPointTy()) { + elem_value = b_->CreateUIToFP(elem_value, elem_ir_ty); + unsigned raw_value_size_in_bits = raw_value_ty->getPrimitiveSizeInBits(); + CHECK(raw_value_size_in_bits == 32 || raw_value_size_in_bits == 64); + elem_value = b_->CreateFDiv( + elem_value, + llvm::ConstantFP::get(elem_ir_ty, + raw_value_size_in_bits == 64 ? 0x1p64 : 0x1p32)); + } - // Seed each RNG emitter with a new 64-bit seed from the HloModule. If the - // compilation order is deterministic (i.e., RandomNew64 invocation order is - // deterministic), then the order of RNG is deterministic for a given seed and - // hence tests will be deterministic. - // If the user provides a global seed instruction then we only use 64-bits of - // the host's random number generator to seed the 128 bit value with the other - // 64-bits is due to a user specified global seed instruction. - // Create a GlobalVariable to maintain state between invocations. There is a - // bug in NVPTX with GlobalVariable and 128 bit values, so using 2 64-bit + // Convert the value for the requested distribution. + switch (hlo->random_distribution()) { + case RNG_UNIFORM: { + if (elem_ir_ty->isFloatingPointTy()) { + return b_->CreateFAdd( + b_->CreateFMul(b_->CreateFSub(b_or_sigma, a_or_mean), elem_value), + a_or_mean); + } else { + // To generate a uniform random value in [a, b) from a raw random sample + // in range [0, 2^N), we let range = b - a and return + // (a + raw_value % range). If range is not a power of 2, raw values + // larger than (2^N - 2^N % range) are biased toward results in + // [a, a + (limit % range)). An unbiased algorithm would need to drop + // raw values and re-sample, but we don't do this because re-sampling in + // an efficient way is complex, and it's not clear that users need it. + // In particular, if one thread in a GPU warp needs to re-sample, we pay + // the same cost as if the whole warp were to re-sample. So an + // efficient re-sampling implementation on GPU would need to do + // nontrivial work to share entropy between threads in the warp. + auto range = b_->CreateSub(b_or_sigma, a_or_mean); + return b_->CreateAdd(a_or_mean, b_->CreateURem(elem_value, range)); + } + } + case RNG_NORMAL: { + TF_ASSIGN_OR_RETURN( + llvm::Value * r, + EmitErfcInv(elem_prim_ty, + b_->CreateFMul(llvm::ConstantFP::get(elem_ir_ty, 2.0), + elem_value))); + return b_->CreateFAdd(b_->CreateFMul(r, b_or_sigma), a_or_mean); + } + default: + return InvalidArgument( + "unhandled distribution %s", + RandomDistribution_Name(hlo->random_distribution()).c_str()); + } +} + +namespace { + +// Checks that the primitive type is supported by the elemental IR emitter for +// Philox RNG and returns the number of elements in each 128 bit sample of the +// Philox RNG algorithm. +int32 GetNumberOfElementsPerPhiloxRngSample(PrimitiveType elem_prim_ty) { + // Calculate the number of elements, that is the number of random numbers, in + // a 128 bit sample. + switch (elem_prim_ty) { + case U32: + case S32: + case F32: + // The algorithm uses 32 bits to generate values for F16. + case F16: + return 4; + case U64: + case F64: + return 2; + default: + // BF16 is converted to F16 by the hlo pass HloElementTypeConverter. + // Other data types are not supported by XLA random operation. + LOG(FATAL) << "Unrecognized primitive type for RNG " << elem_prim_ty; + } + return 0; +} + +// Calculates the four uint32 values for the 128-bit Philox sample. +std::array CalculateSampleValues( + llvm::Value* sample_idx, llvm::Value* hlo_random_value, + llvm::Value* global_random_number, llvm::Value* rng_state, + llvm::IRBuilder<>* b) { + llvm::Type* index_ty = sample_idx->getType(); + + std::array counter_values; + + // Use the sample index to initialize counter[0] and counter[1]. + unsigned index_ty_size_in_bits = index_ty->getPrimitiveSizeInBits(); + CHECK(index_ty_size_in_bits == 32 || index_ty_size_in_bits == 64); + if (index_ty_size_in_bits == 32) { + counter_values[0] = sample_idx; + counter_values[1] = b->getInt32(0); + } else { + std::tie(counter_values[0], counter_values[1]) = + llvm_ir::SplitInt64ToInt32s(b, sample_idx); + } + + // Xor the global state variable with the global random number seed and use + // the result to initialize counter[2] and counter[3]. + std::tie(counter_values[2], counter_values[3]) = llvm_ir::SplitInt64ToInt32s( + b, b->CreateXor(rng_state, global_random_number)); + + // The algorithm uses a 64 bit key, which is also interpreted as two uint32 // values. - llvm::GlobalVariable* state_ptr0 = new llvm::GlobalVariable( - /*M=*/*module_, - /*Ty=*/ir_builder_->getInt64Ty(), - /*isConstant=*/false, - /*Linkage=*/llvm::GlobalValue::PrivateLinkage, - /*Initializer=*/ir_builder_->getInt64(random_value_from_hlo()), - /*Name=*/"state_ptr0"); - - // When the module config seed is 0, the expected result of a prng is a random - // value. Instead of using the random_value_from_hlo, we need a global random - // value as the graph seed. This is because if we use random_value_from_hlo - // here, then for a newly built hlo graph, it always gives the same number. - uint64 graph_seed = hlo_module_config_.seed() != 0 ? hlo_module_config_.seed() - : GlobalRandomValue(); - llvm::GlobalVariable* state_ptr1 = new llvm::GlobalVariable( - /*M=*/*module_, - /*Ty=*/ir_builder_->getInt64Ty(), - /*isConstant=*/false, - /*Linkage=*/llvm::GlobalValue::PrivateLinkage, - /*Initializer=*/ir_builder_->getInt64(graph_seed), - /*Name=*/"state_ptr1"); - - // We want each thread to use its own stream, so we modify the increment per - // thread. We want the increment to remain odd, so we shift the thread id left - // 1 and add it to the increment. - increment = ir_builder_->CreateAdd(increment, - ir_builder_->CreateShl(EmitThreadId(), 1)); - - // PCG-XSL-RR algorithm - // http://www.pcg-random.org/pdf/toms-oneill-pcg-family-v1.02.pdf - // state = multiplier * state + increment - // return uint64_t(state ^ (state >> 64))) >>> (state >> 122) - // where ">>>" is bitwise rotation - auto get_next_i64 = [=]() { - llvm::Value* state0 = ir_builder_->CreateZExtOrTrunc( - ir_builder_->CreateLoad(state_ptr0, "state0"), - ir_builder_->getInt128Ty()); - llvm::Value* state1 = ir_builder_->CreateShl( - ir_builder_->CreateZExtOrTrunc( - ir_builder_->CreateLoad(state_ptr1, "state1"), - ir_builder_->getInt128Ty()), - 64); - llvm::Value* state = ir_builder_->CreateOr(state0, state1); - llvm::Value* updated = ir_builder_->CreateAdd( - ir_builder_->CreateMul(state, multiplier), increment); - ir_builder_->CreateStore( - ir_builder_->CreateTrunc(updated, ir_builder_->getInt64Ty()), - state_ptr0); - ir_builder_->CreateStore( - ir_builder_->CreateTrunc(ir_builder_->CreateLShr(updated, 64), - ir_builder_->getInt64Ty()), - state_ptr1); - - return llvm_ir::CreateRor( - ir_builder_->CreateTrunc( - ir_builder_->CreateXor(state, ir_builder_->CreateLShr(state, 64)), - ir_builder_->getInt64Ty()), - ir_builder_->CreateTrunc(ir_builder_->CreateLShr(state, 122), - ir_builder_->getInt64Ty()), - ir_builder_); - }; + llvm::Value* key_values[2]; + + // Use a module random number to initialize the key. + std::tie(key_values[0], key_values[1]) = + llvm_ir::SplitInt64ToInt32s(b, hlo_random_value); + + // Prepare the constants used in the Philox RNG Algorithm. + llvm::Value* philoxW32A = b->getInt32(0x9E3779B9); + llvm::Value* philoxW32B = b->getInt32(0xBB67AE85); + llvm::Value* philoxM4xW32A = b->getInt32(0xD2511F53); + llvm::Value* philoxM4xW32B = b->getInt32(0xCD9E8D57); + + // Compute the 128 bit value for the current sample by repeating the + // single round computation and key raising computation for ten times. + for (int round = 0; round < 10; ++round) { + // A single round of computation of the counter values is as follows: + // MultiplyHighLow(kPhiloxM4x32A, counter[0], &lo0, &hi0); + // MultiplyHighLow(kPhiloxM4x32B, counter[2], &lo1, &hi1); + // counter[0] = hi1 ^ counter[1] ^ key[0]; + // counter[1] = lo1; + // counter[2] = hi0 ^ counter[3] ^ key[1]; + // counter[3] = lo0; + llvm::Value* lo0; + llvm::Value* hi0; + std::tie(lo0, hi0) = + llvm_ir::UMulLowHigh32(b, philoxM4xW32A, counter_values[0]); + llvm::Value* lo1; + llvm::Value* hi1; + std::tie(lo1, hi1) = + llvm_ir::UMulLowHigh32(b, philoxM4xW32B, counter_values[2]); + counter_values[0] = + b->CreateXor(hi1, b->CreateXor(counter_values[1], key_values[0])); + counter_values[1] = lo1; + counter_values[2] = + b->CreateXor(hi0, b->CreateXor(counter_values[3], key_values[1])); + counter_values[3] = lo0; + key_values[0] = b->CreateAdd(key_values[0], philoxW32A); + key_values[1] = b->CreateAdd(key_values[1], philoxW32B); + } - auto get_next_uniform_float = [=]() { - return ir_builder_->CreateFDiv( - ir_builder_->CreateUIToFP(get_next_i64(), param_ir_type), - llvm::ConstantFP::get(param_ir_type, 0x1p64)); - }; + return counter_values; +} + +} // namespace +// Implements the Philox algorithm to generate random numbers in parallel. +// Salmon et al. SC 2011. Parallel random numbers: as easy as 1, 2, 3. +// http://www.thesalmons.org/john/random123/papers/random123sc11.pdf +// +// The paper presents a few variants of the Philox algorithm, we picked the +// 4x32_10 version of the algorithm for the following reasons: +// . 4x32 uses 32-bit multiplication which is fast on GPUs. +// . The authors recommend the 10-round variant, and TensorFlow also uses it. +// +// Precondition: the RNG instruction is not fused. +llvm_ir::ElementGenerator ElementalIrEmitter::MakePhiloxRngElementGenerator( + const HloInstruction* hlo, + const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator) + const { + VLOG(3) << "Using philox RNG algorithm"; + CHECK(!hlo->IsFused()); + // A random number generated by the per module random number generator. + // This ensures that each RNG HLO generates a different random sequence. + llvm::Value* hlo_random_value = b_->getInt64(hlo->GetModule()->RandomNew64()); + // A value specified by the configuration or generated by a global random + // number generator. + llvm::Value* global_random_number = + b_->getInt64(hlo_module_config_.seed() != 0 ? hlo_module_config_.seed() + : GlobalRandomValue()); + + int elems_per_sample = + GetNumberOfElementsPerPhiloxRngSample(hlo->shape().element_type()); + + // Allocate stack storage for the 128 bit sample as four int32. + llvm::Type* int32_ty = b_->getInt32Ty(); + llvm::Value* sample_address = llvm_ir::EmitAllocaAtFunctionEntryWithCount( + int32_ty, /*element_count=*/b_->getInt32(4), "sample", b_); + + // Load the global state variable for the Philox RNG algorithm. + llvm::GlobalVariable* rng_state_ptr = + llvm_ir::GetOrCreateVariableForPhiloxRngState(module_, b_); + llvm::Value* rng_state = b_->CreateLoad(rng_state_ptr, "rng_state_value"); + + // Build and return the elemental IR generator to generate a random value for + // the element corresponding to the current thread. + // + // This elemental IR generator computes one sample with multiple random + // numbers but only returns one random number. As a result, neighboring + // threads may calculate the same sample unnecessarily. However, if the + // kernel containing the RNG hlo is unrolled, LLVM is able to optimize away + // the duplicated computation of the same sample. In particular, if the unroll + // factor is a multiplier of elems_per_sample, LLVM is able to completely + // remove such duplicated computation. If the unroll factor is a non-trivial + // factor of elems_per_sample, LLVM can only partially remove such duplicated + // computation. return [=](const llvm_ir::IrArray::Index& index) -> StatusOr { - switch (hlo->random_distribution()) { - case RNG_UNIFORM: { - TF_ASSIGN_OR_RETURN(llvm::Value * p, - operand_to_generator.at(hlo->operand(0))(index)); - TF_ASSIGN_OR_RETURN(llvm::Value * q, - operand_to_generator.at(hlo->operand(1))(index)); - if (primitive_util::IsFloatingPointType(param_prim_type)) { - return ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(ir_builder_->CreateFSub(q, p), - get_next_uniform_float()), - p); - } else { - auto r = ir_builder_->CreateSub(q, p); - auto leading_zeros = llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::ctlz, {r, ir_builder_->getInt1(true)}, - {param_ir_type}, ir_builder_); - auto in_block = ir_builder_->GetInsertBlock(); - - // A terminator should be present iff we're emitting code - // into the middle (as opposed to the end) of a basic block. - CHECK_EQ(ir_builder_->GetInsertPoint() == in_block->end(), - in_block->getTerminator() == nullptr); - - llvm::BasicBlock* body_block; - llvm::BasicBlock* out_block; - - if (ir_builder_->GetInsertPoint() == in_block->end()) { - body_block = llvm_ir::CreateBasicBlock( - nullptr, IrName(hlo, "rng_body"), ir_builder_); - out_block = llvm_ir::CreateBasicBlock( - nullptr, IrName(hlo, "rng_out"), ir_builder_); - llvm::BranchInst::Create(body_block, in_block); - } else { - body_block = in_block->splitBasicBlock( - ir_builder_->GetInsertPoint(), "rng_body"); - out_block = body_block->splitBasicBlock( - ir_builder_->GetInsertPoint(), "rng_out"); - body_block->getTerminator()->eraseFromParent(); - } - - SetToFirstInsertPoint(body_block, ir_builder_); - auto random = ir_builder_->CreateAnd( - ir_builder_->CreateZExtOrTrunc(get_next_i64(), param_ir_type), - ir_builder_->CreateLShr(llvm::ConstantInt::get(param_ir_type, ~0), - leading_zeros)); - llvm::BranchInst::Create(out_block, body_block, - ir_builder_->CreateICmpULT(random, r), - body_block); - SetToFirstInsertPoint(out_block, ir_builder_); - return ir_builder_->CreateAdd( - p, ir_builder_->CreateSelect( - ir_builder_->CreateICmpEQ(p, q), - llvm::ConstantInt::get(param_ir_type, 0), random)); - } - } - case RNG_NORMAL: { - TF_ASSIGN_OR_RETURN(llvm::Value * m, - operand_to_generator.at(hlo->operand(0))(index)); - TF_ASSIGN_OR_RETURN(llvm::Value * s, - operand_to_generator.at(hlo->operand(1))(index)); - TF_ASSIGN_OR_RETURN( - llvm::Value * r, - EmitErfcInv(param_prim_type, - ir_builder_->CreateFMul( - llvm::ConstantFP::get(param_ir_type, 2.0), - get_next_uniform_float()))); - return ir_builder_->CreateFAdd(ir_builder_->CreateFMul(r, s), m); - } - default: - return InvalidArgument( - "unhandled distribution %s", - RandomDistribution_Name(hlo->random_distribution()).c_str()); + llvm::Type* index_ty = index.GetType(); + // Calculate the linear element index. + llvm::Value* elem_idx = index.linear(); + if (elem_idx == nullptr) { + elem_idx = index.Linearize(AsInt64Slice(hlo->shape().dimensions()), b_); + } + + // Calculate the index for the 128 bit sample and the offset of the current + // element within the sample. + llvm::Value* elems_per_sample_value = + llvm::ConstantInt::get(index_ty, elems_per_sample); + llvm::Value* sample_idx = b_->CreateUDiv(elem_idx, elems_per_sample_value); + llvm::Value* elem_offset = b_->CreateURem(elem_idx, elems_per_sample_value); + + std::array counter_values = CalculateSampleValues( + sample_idx, hlo_random_value, global_random_number, rng_state, b_); + + // Store the four counter_values into the sample_address alloca so we can + // load the elem_offset'th one below. + for (int idx = 0; idx < 4; ++idx) { + b_->CreateStore(counter_values[idx], + b_->CreateInBoundsGEP(sample_address, b_->getInt32(idx))); } + + llvm::Type* int64_ty = b_->getInt64Ty(); + CHECK(elems_per_sample == 2 || elems_per_sample == 4); + llvm::Type* raw_value_ty = elems_per_sample == 2 ? int64_ty : int32_ty; + // Retrieve the raw value for the current element from the current sample. + llvm::Value* raw_elem_value = b_->CreateLoad( + b_->CreateInBoundsGEP( + b_->CreatePointerCast(sample_address, raw_value_ty->getPointerTo()), + elem_offset), + "raw_elem_value"); + + return ConvertValueForDistribution(hlo, operand_to_generator, index, + raw_elem_value); }; } @@ -1443,9 +1487,8 @@ StatusOr ElementalIrEmitter::EmitElementalSelect( TF_ASSIGN_OR_RETURN(llvm::Value * on_false_value, operand_to_generator.at(hlo->operand(2))( ElementwiseSourceIndex(index, *hlo, 2))); - return ir_builder_->CreateSelect( - ir_builder_->CreateTrunc(pred_value, ir_builder_->getInt1Ty()), - on_true_value, on_false_value); + return b_->CreateSelect(b_->CreateTrunc(pred_value, b_->getInt1Ty()), + on_true_value, on_false_value); } StatusOr ElementalIrEmitter::EmitElementalClamp( @@ -1481,64 +1524,62 @@ StatusOr ElementalIrEmitter::EmitElementalConcatenate( const int64 concat_dim = hlo->dimensions(0); auto source_index = target_index; - llvm::BasicBlock* init_block = ir_builder_->GetInsertBlock(); + llvm::BasicBlock* init_block = b_->GetInsertBlock(); // A terminator should be present iff we're emitting code // into the middle (as opposed to the end) of a basic block. - CHECK_EQ(ir_builder_->GetInsertPoint() == init_block->end(), + CHECK_EQ(b_->GetInsertPoint() == init_block->end(), init_block->getTerminator() == nullptr); llvm::BasicBlock* exit_block; - if (ir_builder_->GetInsertPoint() == init_block->end()) { + if (b_->GetInsertPoint() == init_block->end()) { exit_block = llvm_ir::CreateBasicBlock( - /*insert_before=*/nullptr, IrName(hlo, "merge"), ir_builder_); + /*insert_before=*/nullptr, IrName(hlo, "merge"), b_); } else { - exit_block = init_block->splitBasicBlock(ir_builder_->GetInsertPoint(), + exit_block = init_block->splitBasicBlock(b_->GetInsertPoint(), AsStringRef(IrName(hlo, "merge"))); init_block->getTerminator()->eraseFromParent(); } - llvm_ir::SetToFirstInsertPoint(exit_block, ir_builder_); - llvm::PHINode* output = ir_builder_->CreatePHI( + llvm_ir::SetToFirstInsertPoint(exit_block, b_); + llvm::PHINode* output = b_->CreatePHI( llvm_ir::PrimitiveTypeToIrType(hlo->shape().element_type(), module_), hlo->operands().size()); - auto prior_insert_point = ir_builder_->GetInsertPoint(); + auto prior_insert_point = b_->GetInsertPoint(); - ir_builder_->SetInsertPoint(init_block); + b_->SetInsertPoint(init_block); for (int64 operand_idx = 0; operand_idx < hlo->operand_count(); ++operand_idx) { const HloInstruction* operand = hlo->operand(operand_idx); auto true_block = llvm_ir::CreateBasicBlock( - exit_block, StrCat("concat_index_from_operand", operand_idx), - ir_builder_); + exit_block, StrCat("concat_index_from_operand", operand_idx), b_); auto false_block = llvm_ir::CreateBasicBlock( - exit_block, StrCat("concat_index_not_from_operand", operand_idx), - ir_builder_); + exit_block, StrCat("concat_index_not_from_operand", operand_idx), b_); auto concat_dim_size = llvm::ConstantInt::get(source_index[concat_dim]->getType(), operand->shape().dimensions(concat_dim)); - ir_builder_->CreateCondBr( - ir_builder_->CreateICmpULT(source_index[concat_dim], concat_dim_size), + b_->CreateCondBr( + b_->CreateICmpULT(source_index[concat_dim], concat_dim_size), true_block, false_block); // Create the terminator of the true block before calling operand // generators, because they require non-degenerate basic blocks. - ir_builder_->SetInsertPoint( + b_->SetInsertPoint( llvm::BranchInst::Create(exit_block, /*InsertAtEnd=*/true_block)); TF_ASSIGN_OR_RETURN(llvm::Value * value, operand_to_generator.at(operand)(source_index)); - output->addIncoming(value, ir_builder_->GetInsertBlock()); + output->addIncoming(value, b_->GetInsertBlock()); // Subtract the size of the concat dimension of the current operand // from the source index. - ir_builder_->SetInsertPoint(false_block); + b_->SetInsertPoint(false_block); source_index[concat_dim] = - ir_builder_->CreateSub(source_index[concat_dim], concat_dim_size); + b_->CreateSub(source_index[concat_dim], concat_dim_size); } - ir_builder_->CreateUnreachable(); - ir_builder_->SetInsertPoint(exit_block, prior_insert_point); + b_->CreateUnreachable(); + b_->SetInsertPoint(exit_block, prior_insert_point); return output; } @@ -1562,22 +1603,16 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicSlice( // Clamp the start index so that the sliced portion fits in the operand: // start_index = clamp(start_index, 0, operand_dim_size - output_dim_size) + start_index_value = b_->CreateSExtOrTrunc(start_index_value, index_type); + int64 largest_valid_start_index = + input_hlo->shape().dimensions(i) - hlo->shape().dimensions(i); + CHECK_GE(largest_valid_start_index, 0); - // TODO(b/74360564): This is implementation defined behavior, but is - // currently respected by all implementations. Change this if we ever decide - // to oficially document different behavior. - start_index_value = - ir_builder_->CreateSExtOrTrunc(start_index_value, index_type); - llvm::Value* operand_dim_size = - index_typed_const(input_hlo->shape().dimensions(i)); - llvm::Value* output_dim_size = - index_typed_const(hlo->shape().dimensions(i)); - + bool is_signed = ShapeUtil::ElementIsSigned(hlo->operand(1)->shape()); start_index_value = EmitIntegralMin( - ir_builder_->CreateSub(operand_dim_size, output_dim_size), - EmitIntegralMax(index_typed_const(0), start_index_value, - /*is_signed=*/true), - /*is_signed=*/true); + index_typed_const(largest_valid_start_index), + EmitIntegralMax(index_typed_const(0), start_index_value, is_signed), + is_signed); start_index_value->setName( AsStringRef(IrName(hlo, StrCat("start_idx", i)))); @@ -1588,7 +1623,7 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicSlice( for (int64 i = 0; i < rank; ++i) { // Emit IR which computes: // input_index = start_index + offset_index - input_index[i] = ir_builder_->CreateAdd(slice_start_index[i], index[i]); + input_index[i] = b_->CreateAdd(slice_start_index[i], index[i]); } return operand_to_generator.at(input_hlo)(input_index); } @@ -1610,19 +1645,22 @@ StatusOr ElementalIrEmitter::EmitElementalGather( llvm::Type* index_type = index.GetType(); // This is the index into `operand` that holds the element we want to - // generate. This index "unsafe" as in the components in here may be - // out of bounds. - IrArray::Index unsafe_operand_index(index_type); - - // First copy in the window indices to unsafe_operand_index. - for (int64 i = 0, e = operand_shape.dimensions_size(), - unsafe_operand_index_dim = 0; + // generate. + IrArray::Index operand_index(index_type); + + // First copy in the window indices to operand_index. Also collect a mapping + // from operand dimension to output window dimension. Elided window dimensions + // map to -1. + std::vector operand_to_output_dim(operand_shape.dimensions_size(), -1); + for (int64 i = 0, e = operand_shape.dimensions_size(), operand_index_dim = 0; i < e; i++) { if (c_binary_search(dim_numbers.elided_window_dims(), i)) { - unsafe_operand_index.push_back(index.GetConstantWithIndexType(0)); + operand_index.push_back(index.GetConstantWithIndexType(0)); } else { - unsafe_operand_index.push_back( - index[dim_numbers.output_window_dims(unsafe_operand_index_dim++)]); + int64 output_window_dim = + dim_numbers.output_window_dims(operand_index_dim++); + operand_to_output_dim[i] = output_window_dim; + operand_index.push_back(index[output_window_dim]); } } @@ -1641,20 +1679,40 @@ StatusOr ElementalIrEmitter::EmitElementalGather( } } - auto add_to_unsafe_operand_index = [&](llvm::Value* index_component, - int64 dim) { + auto add_to_operand_index = [&](llvm::Value* index_component, int64 dim) { llvm::Value* gather_dim_component_extended = - ir_builder_->CreateSExtOrTrunc(index_component, index_type); - unsafe_operand_index[dim_numbers.gather_dims_to_operand_dims(dim)] = - ir_builder_->CreateAdd( - unsafe_operand_index[dim_numbers.gather_dims_to_operand_dims(dim)], - gather_dim_component_extended); + b_->CreateSExtOrTrunc(index_component, index_type); + int64 operand_dim = dim_numbers.gather_dims_to_operand_dims(dim); + int64 output_dim = operand_to_output_dim[operand_dim]; + // If 'output_dim' is -1, it means 'operand_dim' is an elided window dim. + // This means we set the iteration index to 0, so for the purpose of the + // following calculations we can consider the output dimension size to be 1. + int64 output_dim_size = + output_dim == -1 ? 1 : output_shape.dimensions(output_dim); + int64 largest_valid_start_index = + operand_shape.dimensions(operand_dim) - output_dim_size; + CHECK_GE(largest_valid_start_index, 0); + + // Clamp the gather index so that the gather region fits in the operand. + // gather_dim_component_extended_inbound = + // clamp(gather_dim_component_extended, 0, largest_valid_start_index); + + // TODO(b/111078873): This is implementation defined behavior. + bool is_signed = ShapeUtil::ElementIsSigned(indices_shape); + auto gather_dim_component_extended_inbound = EmitIntegralMin( + index.GetConstantWithIndexType(largest_valid_start_index), + EmitIntegralMax(index.GetConstantWithIndexType(0), + gather_dim_component_extended, is_signed), + is_signed); + + operand_index[operand_dim] = b_->CreateAdd( + operand_index[operand_dim], gather_dim_component_extended_inbound); }; if (indices_shape.dimensions_size() == dim_numbers.index_vector_dim()) { TF_ASSIGN_OR_RETURN(llvm::Value * gather_dim_component, indices_generator(gather_index_index)); - add_to_unsafe_operand_index(gather_dim_component, 0); + add_to_operand_index(gather_dim_component, 0); } else { int64 index_vector_size = indices_shape.dimensions(dim_numbers.index_vector_dim()); @@ -1663,18 +1721,10 @@ StatusOr ElementalIrEmitter::EmitElementalGather( index.GetConstantWithIndexType(i); TF_ASSIGN_OR_RETURN(llvm::Value * gather_dim_component, indices_generator(gather_index_index)); - add_to_unsafe_operand_index(gather_dim_component, i); + add_to_operand_index(gather_dim_component, i); } } - - IrArray::Index safe_operand_index(index_type); - for (int64 i = 0, e = unsafe_operand_index.size(); i < e; i++) { - safe_operand_index.push_back(ir_builder_->CreateURem( - unsafe_operand_index[i], - index.GetConstantWithIndexType(operand_shape.dimensions(i)))); - } - - return operand_generator(safe_operand_index); + return operand_generator(operand_index); } StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( @@ -1690,7 +1740,7 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( llvm_ir::IrArray::Index slice_limit_index(index.GetType(), rank); // Slice intersection gathers (ANDs) conditions on all ranks for which // 'input' is set to 'update' - llvm::Value* slice_intersection = ir_builder_->getTrue(); + llvm::Value* slice_intersection = b_->getTrue(); for (int64 i = 0; i < rank; ++i) { llvm::Type* index_type = index[0]->getType(); @@ -1703,36 +1753,29 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( // Clamp the start index so that the update region fits in the operand. // start_index = clamp(start_index, 0, input_dim_size - update_dim_size) - - // TODO(b/74360564): This is implementation defined behavior, but is - // currently respected by all implementations. Change this if we ever decide - // to oficially document different behavior. - start_index_value = - ir_builder_->CreateSExtOrTrunc(start_index_value, index_type); - llvm::Value* input_dim_size = - index_typed_const(input_hlo->shape().dimensions(i)); + start_index_value = b_->CreateSExtOrTrunc(start_index_value, index_type); llvm::Value* update_dim_size = index_typed_const(update_hlo->shape().dimensions(i)); + int64 largest_valid_start_index = + input_hlo->shape().dimensions(i) - update_hlo->shape().dimensions(i); + CHECK_GE(largest_valid_start_index, 0); - start_index_value = - EmitIntegralMin(ir_builder_->CreateSub(input_dim_size, update_dim_size), - EmitIntegralMax(index_typed_const(0), start_index_value, - /*is_signed=*/true), - /*is_signed=*/true); + bool is_signed = ShapeUtil::ElementIsSigned(start_hlo->shape()); + start_index_value = EmitIntegralMin( + index_typed_const(largest_valid_start_index), + EmitIntegralMax(index_typed_const(0), start_index_value, is_signed), + is_signed); start_index_value->setName( AsStringRef(IrName(hlo, StrCat("start_idx", i)))); slice_start_index[i] = start_index_value; - slice_limit_index[i] = - ir_builder_->CreateAdd(slice_start_index[i], update_dim_size); + slice_limit_index[i] = b_->CreateAdd(slice_start_index[i], update_dim_size); - slice_intersection = ir_builder_->CreateAnd( - slice_intersection, - ir_builder_->CreateICmpSGE(index[i], slice_start_index[i]), + slice_intersection = b_->CreateAnd( + slice_intersection, b_->CreateICmpSGE(index[i], slice_start_index[i]), "slice_intersection"); - slice_intersection = ir_builder_->CreateAnd( - slice_intersection, - ir_builder_->CreateICmpSLT(index[i], slice_limit_index[i]), + slice_intersection = b_->CreateAnd( + slice_intersection, b_->CreateICmpSLT(index[i], slice_limit_index[i]), "slice_intersection"); } @@ -1741,29 +1784,29 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( // else -> return data from 'input'. llvm::Value* ret_value_addr = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType(hlo->shape().element_type(), module_), - "ret_value_addr", ir_builder_); - llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - slice_intersection, "slice_intersection", ir_builder_); + "ret_value_addr", b_); + llvm_ir::LlvmIfData if_data = + llvm_ir::EmitIfThenElse(slice_intersection, "slice_intersection", b_); // Handle true BB (return data from 'update') - SetToFirstInsertPoint(if_data.true_block, ir_builder_); + SetToFirstInsertPoint(if_data.true_block, b_); // Compute update index for intersection case. llvm_ir::IrArray::Index update_index(index.GetType(), rank); for (int64 i = 0; i < rank; ++i) { - update_index[i] = ir_builder_->CreateSub(index[i], slice_start_index[i]); + update_index[i] = b_->CreateSub(index[i], slice_start_index[i]); } TF_ASSIGN_OR_RETURN(llvm::Value * true_value, operand_to_generator.at(update_hlo)(update_index)); - ir_builder_->CreateStore(true_value, ret_value_addr); + b_->CreateStore(true_value, ret_value_addr); // Handle false BB (return data from 'input') - SetToFirstInsertPoint(if_data.false_block, ir_builder_); + SetToFirstInsertPoint(if_data.false_block, b_); TF_ASSIGN_OR_RETURN(llvm::Value * false_value, operand_to_generator.at(input_hlo)(index)); - ir_builder_->CreateStore(false_value, ret_value_addr); + b_->CreateStore(false_value, ret_value_addr); - SetToFirstInsertPoint(if_data.after_block, ir_builder_); - return ir_builder_->CreateLoad(ret_value_addr); + SetToFirstInsertPoint(if_data.after_block, b_); + return b_->CreateLoad(ret_value_addr); } StatusOr ElementalIrEmitter::EmitElementalPad( @@ -1771,29 +1814,29 @@ StatusOr ElementalIrEmitter::EmitElementalPad( const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator, const llvm_ir::IrArray::Index& padded_index) const { auto index = padded_index; - llvm::Value* in_bounds = ir_builder_->getTrue(); + llvm::Value* in_bounds = b_->getTrue(); for (size_t i = 0; i < index.size(); ++i) { auto index_typed_const = [=](int64 n) { return llvm::ConstantInt::get(index[i]->getType(), n); }; const auto& pad_dim = hlo->padding_config().dimensions(i); - index[i] = ir_builder_->CreateSub( - index[i], index_typed_const(pad_dim.edge_padding_low())); - in_bounds = ir_builder_->CreateAnd( - in_bounds, ir_builder_->CreateICmpSGE(index[i], index_typed_const(0)), - "in_bounds"); - in_bounds = ir_builder_->CreateAnd( + index[i] = + b_->CreateSub(index[i], index_typed_const(pad_dim.edge_padding_low())); + in_bounds = b_->CreateAnd(in_bounds, + b_->CreateICmpSGE(index[i], index_typed_const(0)), + "in_bounds"); + in_bounds = b_->CreateAnd( in_bounds, - ir_builder_->CreateICmpEQ( + b_->CreateICmpEQ( index_typed_const(0), - ir_builder_->CreateURem( - index[i], index_typed_const(pad_dim.interior_padding() + 1))), + b_->CreateURem(index[i], + index_typed_const(pad_dim.interior_padding() + 1))), "in_bounds"); - index[i] = ir_builder_->CreateSDiv( + index[i] = b_->CreateSDiv( index[i], index_typed_const(pad_dim.interior_padding() + 1)); - in_bounds = ir_builder_->CreateAnd( + in_bounds = b_->CreateAnd( in_bounds, - ir_builder_->CreateICmpSLT( + b_->CreateICmpSLT( index[i], index_typed_const(hlo->operand(0)->shape().dimensions(i))), "in_bounds"); @@ -1806,26 +1849,26 @@ StatusOr ElementalIrEmitter::EmitElementalPad( // } llvm::Value* ret_value_addr = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType(hlo->shape().element_type(), module_), - "pad_result_addr", ir_builder_); + "pad_result_addr", b_); llvm_ir::LlvmIfData if_data = - llvm_ir::EmitIfThenElse(in_bounds, "in_bounds", ir_builder_); - SetToFirstInsertPoint(if_data.true_block, ir_builder_); + llvm_ir::EmitIfThenElse(in_bounds, "in_bounds", b_); + SetToFirstInsertPoint(if_data.true_block, b_); TF_ASSIGN_OR_RETURN(llvm::Value * operand_value, operand_to_generator.at(hlo->operand(0))(index)); - ir_builder_->CreateStore(operand_value, ret_value_addr); + b_->CreateStore(operand_value, ret_value_addr); - SetToFirstInsertPoint(if_data.false_block, ir_builder_); + SetToFirstInsertPoint(if_data.false_block, b_); TF_ASSIGN_OR_RETURN(llvm::Value * padding_value, operand_to_generator.at(hlo->operand(1))( IrArray::Index(index.GetType()))); - ir_builder_->CreateStore(padding_value, ret_value_addr); + b_->CreateStore(padding_value, ret_value_addr); - SetToFirstInsertPoint(if_data.after_block, ir_builder_); + SetToFirstInsertPoint(if_data.after_block, b_); // Don't create phi(operand_value, padding_value) here, because invoking // operand_to_generator may create new basic blocks, making the parent // of operand_value or padding_value no longer a predecessor of // if_data.after_block. - return ir_builder_->CreateLoad(ret_value_addr); + return b_->CreateLoad(ret_value_addr); } StatusOr ElementalIrEmitter::EmitElementalDot( @@ -1849,21 +1892,20 @@ StatusOr ElementalIrEmitter::EmitElementalDot( return llvm::ConstantInt::get(index_type, c); }; - std::unique_ptr inner_loop = - llvm_ir::ForLoop::EmitForLoop(IrName(hlo, "inner"), index_typed_const(0), - index_typed_const(contracted_dim_size), - index_typed_const(1), ir_builder_); + std::unique_ptr inner_loop = llvm_ir::ForLoop::EmitForLoop( + IrName(hlo, "inner"), index_typed_const(0), + index_typed_const(contracted_dim_size), index_typed_const(1), b_); - SetToFirstInsertPoint(inner_loop->GetPreheaderBasicBlock(), ir_builder_); + SetToFirstInsertPoint(inner_loop->GetPreheaderBasicBlock(), b_); PrimitiveType primitive_type = hlo->shape().element_type(); llvm::Type* primitive_type_llvm = llvm_ir::PrimitiveTypeToIrType(primitive_type, module_); - llvm::Value* accumulator_alloca = llvm_ir::EmitAllocaAtFunctionEntry( - primitive_type_llvm, "dot_acc", ir_builder_); - ir_builder_->CreateStore(llvm::Constant::getNullValue(primitive_type_llvm), - accumulator_alloca); + llvm::Value* accumulator_alloca = + llvm_ir::EmitAllocaAtFunctionEntry(primitive_type_llvm, "dot_acc", b_); + b_->CreateStore(llvm::Constant::getNullValue(primitive_type_llvm), + accumulator_alloca); - SetToFirstInsertPoint(inner_loop->GetBodyBasicBlock(), ir_builder_); + SetToFirstInsertPoint(inner_loop->GetBodyBasicBlock(), b_); // This is the inner reduction loop for a dot operation that produces // one element in the output. If the operands to the dot operation have @@ -1883,43 +1925,36 @@ StatusOr ElementalIrEmitter::EmitElementalDot( } rhs_index.InsertAt(rhs_contracting_dim, inner_loop->GetIndVarValue()); - llvm::Value* current_accumulator = - ir_builder_->CreateLoad(accumulator_alloca); + llvm::Value* current_accumulator = b_->CreateLoad(accumulator_alloca); TF_ASSIGN_OR_RETURN(llvm::Value * lhs_value, lhs_generator(lhs_index)); TF_ASSIGN_OR_RETURN(llvm::Value * rhs_value, rhs_generator(rhs_index)); llvm::Value* next_accumulator; if (primitive_util::IsComplexType(primitive_type)) { - llvm::Value* product_real = ir_builder_->CreateFSub( - ir_builder_->CreateFMul(EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFMul(EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value))); - llvm::Value* product_imag = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(EmitExtractReal(lhs_value), - EmitExtractImag(rhs_value)), - ir_builder_->CreateFMul(EmitExtractImag(lhs_value), - EmitExtractReal(rhs_value))); - next_accumulator = ir_builder_->CreateInsertValue( + llvm::Value* product_real = b_->CreateFSub( + b_->CreateFMul(EmitExtractReal(lhs_value), EmitExtractReal(rhs_value)), + b_->CreateFMul(EmitExtractImag(lhs_value), EmitExtractImag(rhs_value))); + llvm::Value* product_imag = b_->CreateFAdd( + b_->CreateFMul(EmitExtractReal(lhs_value), EmitExtractImag(rhs_value)), + b_->CreateFMul(EmitExtractImag(lhs_value), EmitExtractReal(rhs_value))); + next_accumulator = b_->CreateInsertValue( current_accumulator, - ir_builder_->CreateFAdd(EmitExtractReal(current_accumulator), - product_real), + b_->CreateFAdd(EmitExtractReal(current_accumulator), product_real), {0}); - next_accumulator = ir_builder_->CreateInsertValue( + next_accumulator = b_->CreateInsertValue( next_accumulator, - ir_builder_->CreateFAdd(EmitExtractImag(current_accumulator), - product_imag), + b_->CreateFAdd(EmitExtractImag(current_accumulator), product_imag), {1}); } else if (primitive_util::IsFloatingPointType(primitive_type)) { - next_accumulator = ir_builder_->CreateFAdd( - current_accumulator, ir_builder_->CreateFMul(lhs_value, rhs_value)); + next_accumulator = b_->CreateFAdd(current_accumulator, + b_->CreateFMul(lhs_value, rhs_value)); } else { - next_accumulator = ir_builder_->CreateAdd( - current_accumulator, ir_builder_->CreateMul(lhs_value, rhs_value)); + next_accumulator = + b_->CreateAdd(current_accumulator, b_->CreateMul(lhs_value, rhs_value)); } - ir_builder_->CreateStore(next_accumulator, accumulator_alloca); + b_->CreateStore(next_accumulator, accumulator_alloca); - SetToFirstInsertPoint(inner_loop->GetExitBasicBlock(), ir_builder_); - return ir_builder_->CreateLoad(accumulator_alloca); + SetToFirstInsertPoint(inner_loop->GetExitBasicBlock(), b_); + return b_->CreateLoad(accumulator_alloca); } llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( @@ -2019,7 +2054,7 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( const HloInstruction* operand = hlo->operand(0); auto source_index = target_index; for (int64 dim : hlo->dimensions()) { - source_index[dim] = ir_builder_->CreateSub( + source_index[dim] = b_->CreateSub( llvm::ConstantInt::get(target_index[dim]->getType(), hlo->shape().dimensions(dim) - 1), target_index[dim]); @@ -2032,16 +2067,16 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( const HloInstruction* operand = hlo->operand(0); // The `dimensions` member of the broadcast instruction maps from // input dimensions to output dimensions. - return operand_to_generator.at( - operand)(target_index.SourceIndexOfBroadcast( - hlo->shape(), operand->shape(), hlo->dimensions(), ir_builder_)); + return operand_to_generator.at(operand)( + target_index.SourceIndexOfBroadcast(hlo->shape(), operand->shape(), + hlo->dimensions(), b_)); }; case HloOpcode::kSlice: return [this, hlo, &operand_to_generator]( const IrArray::Index& index) -> StatusOr { IrArray::Index sliced_index = index.SourceIndexOfSlice( /*shape=*/hlo->shape(), /*starts=*/hlo->slice_starts(), - /*strides=*/hlo->slice_strides(), /*builder=*/ir_builder_); + /*strides=*/hlo->slice_strides(), /*builder=*/b_); return operand_to_generator.at(hlo->operand(0))(sliced_index); }; case HloOpcode::kDynamicSlice: @@ -2066,27 +2101,26 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( ShapeUtil::ElementsIn(hlo->operand(0)->shape())); return [this, hlo, &operand_to_generator](const IrArray::Index& index) { const HloInstruction* operand = hlo->operand(0); - return operand_to_generator.at(operand)(index.SourceIndexOfBitcast( - hlo->shape(), operand->shape(), ir_builder_)); + return operand_to_generator.at(operand)( + index.SourceIndexOfBitcast(hlo->shape(), operand->shape(), b_)); }; case HloOpcode::kReshape: CHECK_EQ(ShapeUtil::ElementsIn(hlo->shape()), ShapeUtil::ElementsIn(hlo->operand(0)->shape())); return [this, hlo, &operand_to_generator](const IrArray::Index& index) { const HloInstruction* operand = hlo->operand(0); - return operand_to_generator.at(operand)(index.SourceIndexOfReshape( - hlo->shape(), operand->shape(), ir_builder_)); + return operand_to_generator.at(operand)( + index.SourceIndexOfReshape(hlo->shape(), operand->shape(), b_)); }; case HloOpcode::kTranspose: return [this, hlo, &operand_to_generator](const IrArray::Index& target_index) { return operand_to_generator.at(hlo->operand(0))( target_index.SourceIndexOfTranspose( - hlo->shape(), hlo->operand(0)->shape(), hlo->dimensions(), - ir_builder_)); + hlo->shape(), hlo->operand(0)->shape(), hlo->dimensions(), b_)); }; case HloOpcode::kRng: - return MakeRngElementGenerator(hlo, operand_to_generator); + return MakePhiloxRngElementGenerator(hlo, operand_to_generator); case HloOpcode::kPad: return [this, hlo, &operand_to_generator]( const IrArray::Index& padded_index) -> StatusOr { @@ -2108,11 +2142,11 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( } llvm::Value* ElementalIrEmitter::EmitExtractReal(llvm::Value* value) const { - return ir_builder_->CreateExtractValue(value, {0}); + return b_->CreateExtractValue(value, {0}); } llvm::Value* ElementalIrEmitter::EmitExtractImag(llvm::Value* value) const { - return ir_builder_->CreateExtractValue(value, {1}); + return b_->CreateExtractValue(value, {1}); } llvm::Value* ElementalIrEmitter::EmitComposeComplex(const HloInstruction* op, @@ -2120,10 +2154,10 @@ llvm::Value* ElementalIrEmitter::EmitComposeComplex(const HloInstruction* op, llvm::Value* imag) const { auto cplx_type = llvm_ir::PrimitiveTypeToIrType(op->shape().element_type(), module_); - auto complex = ir_builder_->CreateInsertValue( + auto complex = b_->CreateInsertValue( llvm::ConstantAggregateZero::get(cplx_type), real, {0}); if (imag != nullptr) { - complex = ir_builder_->CreateInsertValue(complex, imag, {1}); + complex = b_->CreateInsertValue(complex, imag, {1}); } return complex; } diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/elemental_ir_emitter.h index d199473374ad394913413a7d3fe805f8782936f7..fcb34557a52d35ef30a5dee643171e17407d05c2 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.h +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.h @@ -34,10 +34,8 @@ class ElementalIrEmitter { std::unordered_map; ElementalIrEmitter(const HloModuleConfig& hlo_module_config, - llvm::Module* module, llvm::IRBuilder<>* ir_builder) - : ir_builder_(ir_builder), - module_(module), - hlo_module_config_(hlo_module_config) {} + llvm::Module* module, llvm::IRBuilder<>* b) + : b_(b), module_(module), hlo_module_config_(hlo_module_config) {} virtual ~ElementalIrEmitter() = default; @@ -54,7 +52,7 @@ class ElementalIrEmitter { const HloInstruction* hlo, const HloToElementGeneratorMap& operand_to_generator) const; - llvm::IRBuilder<>* ir_builder() const { return ir_builder_; } + llvm::IRBuilder<>* b() const { return b_; } llvm::Module* module() const { return module_; } protected: @@ -144,9 +142,7 @@ class ElementalIrEmitter { int64 operand_no) const; // Identifier of the thread unique among all threads on the device - virtual llvm::Value* EmitThreadId() const { - return ir_builder_->getIntN(128, 0); - } + virtual llvm::Value* EmitThreadId() const { return b_->getIntN(128, 0); } StatusOr EmitElementalSelect( const HloInstruction* hlo, @@ -188,7 +184,7 @@ class ElementalIrEmitter { const HloToElementGeneratorMap& operand_to_generator, const llvm_ir::IrArray::Index& dot_result_index) const; - llvm::IRBuilder<>* const ir_builder_; + llvm::IRBuilder<>* const b_; llvm::Module* module_; @@ -197,10 +193,17 @@ class ElementalIrEmitter { const HloModuleConfig& hlo_module_config_; private: - // Returns a ElementGenerator for a RNG HloInstruction. - llvm_ir::ElementGenerator MakeRngElementGenerator( + // Returns a ElementGenerator for an RNG HloInstruction using the Philox + // random number generation algorithm. + llvm_ir::ElementGenerator MakePhiloxRngElementGenerator( const HloInstruction* hlo, const HloToElementGeneratorMap& operand_to_generator) const; + // Converts the raw value generated by a random number generation algorithm + // to the distribution requested by the RNG HloInstruction. + StatusOr ConvertValueForDistribution( + const HloInstruction* hlo, + const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& index, llvm::Value* raw_value) const; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter_test.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter_test.cc index 8980d4303353a132ada2b3c685b4f2856c33c6a1..addb016b0481b744ff42ba827104099b6cdc3bb9 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter_test.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter_test.cc @@ -57,8 +57,8 @@ ENTRY main { } )"; - std::unique_ptr lhs = Literal::CreateR3({{{1}, {2}}}); - std::unique_ptr rhs = Literal::CreateR3({{{3}, {4}}}); + std::unique_ptr lhs = LiteralUtil::CreateR3({{{1}, {2}}}); + std::unique_ptr rhs = LiteralUtil::CreateR3({{{3}, {4}}}); RunTest(hlo_text, {lhs.get(), rhs.get()}); } } // namespace diff --git a/tensorflow/compiler/xla/service/execution_tracker.cc b/tensorflow/compiler/xla/service/execution_tracker.cc index 6794cfe297b0fb9a15eb9b7e6906d225f9597d07..228c3fac95c3114484637bd93ec51c60b44403cc 100644 --- a/tensorflow/compiler/xla/service/execution_tracker.cc +++ b/tensorflow/compiler/xla/service/execution_tracker.cc @@ -25,7 +25,7 @@ limitations under the License. namespace xla { AsyncExecution::AsyncExecution(Backend* backend, - std::vector streams, + std::vector streams, const ExecutionProfile& profile, GlobalDataHandle result) : backend_(CHECK_NOTNULL(backend)), @@ -46,9 +46,10 @@ Status AsyncExecution::BlockUntilDone() const { ExecutionTracker::ExecutionTracker() : next_handle_(1) {} -ExecutionHandle ExecutionTracker::Register( - Backend* backend, std::vector streams, - const ExecutionProfile& profile, GlobalDataHandle result) { +ExecutionHandle ExecutionTracker::Register(Backend* backend, + std::vector streams, + const ExecutionProfile& profile, + GlobalDataHandle result) { tensorflow::mutex_lock lock(execution_mutex_); int64 handle = next_handle_++; auto inserted = handle_to_execution_.emplace( diff --git a/tensorflow/compiler/xla/service/execution_tracker.h b/tensorflow/compiler/xla/service/execution_tracker.h index 4458152dd9a98890fc3a3e7f324245ec68821467..4e9b9f883e26f5564a9c63a40d2b4b9348908214 100644 --- a/tensorflow/compiler/xla/service/execution_tracker.h +++ b/tensorflow/compiler/xla/service/execution_tracker.h @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/executable_run_options.h" #include "tensorflow/compiler/xla/service/backend.h" -#include "tensorflow/compiler/xla/service/pool.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" @@ -40,7 +40,7 @@ namespace xla { // the stream when destructed. class AsyncExecution { public: - AsyncExecution(Backend* backend, std::vector streams, + AsyncExecution(Backend* backend, std::vector streams, const ExecutionProfile& profile, GlobalDataHandle result); Status BlockUntilDone() const; @@ -54,7 +54,7 @@ class AsyncExecution { Backend* backend_; // Stream on which the execution is launched. - std::vector streams_; + std::vector streams_; // Profile object of the execution to be returned to the user. ExecutionProfile profile_; @@ -72,7 +72,7 @@ class ExecutionTracker { // Registers an execution with its backend, streams, and data handle to the // execution result. Returns a handle for the registered execution. ExecutionHandle Register(Backend* backend, - std::vector stream, + std::vector stream, const ExecutionProfile& profile, GlobalDataHandle data); diff --git a/tensorflow/compiler/xla/service/flatten_call_graph_test.cc b/tensorflow/compiler/xla/service/flatten_call_graph_test.cc index d3854b40de3572a60df1ad99d8a4589f59ad7194..8f6608241ed02bbb7e9fde9b6d767c002435e777 100644 --- a/tensorflow/compiler/xla/service/flatten_call_graph_test.cc +++ b/tensorflow/compiler/xla/service/flatten_call_graph_test.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/flatten_call_graph.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/call_graph.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -80,7 +80,7 @@ class FlattenCallGraphTest : public HloTestBase { HloInstruction* param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, kScalarShape, "param0")); HloInstruction* zero = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); builder.AddInstruction(HloInstruction::CreateBinary( ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt, param0, zero)); return builder.Build(); @@ -157,7 +157,7 @@ TEST_F(FlattenCallGraphTest, SharedWhileConditionAndBody) { builder.AddInstruction(HloInstruction::CreateParameter( 0, ShapeUtil::MakeShape(PRED, {}), "param0")); HloInstruction* false_constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); builder.AddInstruction( HloInstruction::CreateBinary(ShapeUtil::MakeShape(PRED, {}), HloOpcode::kEq, param0, false_constant)); @@ -168,7 +168,7 @@ TEST_F(FlattenCallGraphTest, SharedWhileConditionAndBody) { { HloComputation::Builder builder(TestName() + ".entry"); HloInstruction* false_constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); builder.AddInstruction(HloInstruction::CreateWhile( ShapeUtil::MakeShape(PRED, {}), cond_computation, cond_computation, false_constant)); @@ -232,11 +232,11 @@ TEST_F(FlattenCallGraphTest, FlattenCallsInConditional) { // computation in the true and false branch. HloComputation::Builder builder(TestName()); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(56.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(56.0f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(12.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(12.0f))); builder.AddInstruction(HloInstruction::CreateConditional( kScalarShape, pred, constant1, sub_computation, constant2, sub_computation)); diff --git a/tensorflow/compiler/xla/service/gather_expander.cc b/tensorflow/compiler/xla/service/gather_expander.cc index 7cd2c9c136acac46e8e6c548c9e58b9bc8e6e0d2..e3a42d0d06be9e4c9ef96ed2e6ff5daa8eebaf3e 100644 --- a/tensorflow/compiler/xla/service/gather_expander.cc +++ b/tensorflow/compiler/xla/service/gather_expander.cc @@ -15,6 +15,7 @@ limitations under the License. #include +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/gather_expander.h" #include "tensorflow/compiler/xla/service/hlo_creation_utils.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -113,7 +114,7 @@ static StatusOr ExpandIndexVectorIntoOperandSpace( const Shape& index_shape = index_vector->shape(); HloInstruction* zero = computation->AddInstruction(HloInstruction::CreateConstant( - Literal::CreateFromDimensions(index_shape.element_type(), {1}))); + LiteralUtil::CreateFromDimensions(index_shape.element_type(), {1}))); // We extract out individual components from the smaller index and concatenate // them (interspersing zeros as needed) into the larger index. diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.cc b/tensorflow/compiler/xla/service/generic_transfer_manager.cc index 85e28a0dfe38415974e435106a2d0b75863f2df5..e314a469f00abdb9f60ae812c0b78d273dc95dbe 100644 --- a/tensorflow/compiler/xla/service/generic_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/generic_transfer_manager.cc @@ -20,7 +20,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/interpreter/platform_id.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -158,16 +158,10 @@ Status GenericTransferManager::TransferLiteralToInfeed( return Unimplemented("Generic transfer to Infeed"); } -Status GenericTransferManager::TransferBufferToInfeed( - se::StreamExecutor* executor, int64 size, const void* source) { - return Unimplemented("Generic transfer to Infeed"); -} - Status GenericTransferManager::TransferLiteralFromOutfeed( se::StreamExecutor* executor, const Shape& literal_shape, Literal* literal) { - return Unimplemented( - "Outfeed is not supported on this platform (b/30467474)"); + return Unimplemented("Generic transfer from Outfeed"); } Status GenericTransferManager::ResetDevices( diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.h b/tensorflow/compiler/xla/service/generic_transfer_manager.h index d216fe7d29e8f2e84ab4f558ee5caec32d07a70a..3cd002c1bf3555cc2d2891c88b3ad648f8d9fd8c 100644 --- a/tensorflow/compiler/xla/service/generic_transfer_manager.h +++ b/tensorflow/compiler/xla/service/generic_transfer_manager.h @@ -61,9 +61,6 @@ class GenericTransferManager : public TransferManager { int64 GetByteSizeRequirement(const Shape& shape) const override; protected: - Status TransferBufferToInfeed(se::StreamExecutor* executor, int64 size, - const void* source) override; - Status WriteSingleTupleIndexTable( se::Stream* stream, tensorflow::gtl::ArraySlice elements, diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index d90b0fb57d7acd24576e9e8e41316b19b6c44979..e0aae3866b3e5b25c611c49f4f3a8a4149e9f71e 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -36,6 +36,7 @@ cc_library( hdrs = ["gpu_constants.h"], deps = [ "//tensorflow/compiler/xla:types", + "//tensorflow/core:framework", ], ) @@ -113,11 +114,13 @@ cc_library( srcs = ["hlo_to_ir_bindings.cc"], hdrs = ["hlo_to_ir_bindings.h"], deps = [ + ":buffer_allocations", ":ir_emission_utils", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla/service:buffer_assignment", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service/llvm_ir:alias_analysis", + "//tensorflow/compiler/xla/service/llvm_ir:buffer_assignment_util", "//tensorflow/compiler/xla/service/llvm_ir:ir_array", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/compiler/xla/service/llvm_ir:tuple_ops", @@ -141,6 +144,7 @@ cc_library( ], deps = [ ":backend_configs", + ":buffer_allocations", ":cudnn_convolution_runner", ":elemental_ir_emitter", ":gpu_constants", @@ -150,7 +154,7 @@ cc_library( ":parallel_loop_emitter", ":partition_assignment", ":while_transformer", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -162,13 +166,16 @@ cc_library( "//tensorflow/compiler/xla/service:elemental_ir_emitter", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:name_uniquer", + "//tensorflow/compiler/xla/service/llvm_ir:buffer_assignment_util", + "//tensorflow/compiler/xla/service/llvm_ir:dynamic_update_slice_util", "//tensorflow/compiler/xla/service/llvm_ir:fused_ir_emitter", "//tensorflow/compiler/xla/service/llvm_ir:ir_array", "//tensorflow/compiler/xla/service/llvm_ir:kernel_support_library", + "//tensorflow/compiler/xla/service/llvm_ir:kernel_tiling", "//tensorflow/compiler/xla/service/llvm_ir:llvm_loop", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/compiler/xla/service/llvm_ir:loop_emitter", - "//tensorflow/compiler/xla/service/llvm_ir:ops", + "//tensorflow/compiler/xla/service/llvm_ir:sort_util", "//tensorflow/compiler/xla/service/llvm_ir:tuple_ops", "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", @@ -199,7 +206,7 @@ cc_library( srcs = ["elemental_ir_emitter.cc"], hdrs = ["elemental_ir_emitter.h"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -214,6 +221,7 @@ cc_library( "//tensorflow/compiler/xla/service/llvm_ir:llvm_loop", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/compiler/xla/service/llvm_ir:loop_emitter", + "//tensorflow/compiler/xla/service/llvm_ir:math_ops", "//tensorflow/core:lib", "@llvm//:core", "@llvm//:support", @@ -244,7 +252,7 @@ cc_library( deps = [ "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_execution_profile", - "//tensorflow/compiler/xla/service:pool", + "//tensorflow/compiler/xla/service:stream_pool", "//tensorflow/core:lib", "//tensorflow/core:ptr_util", "//tensorflow/core:stream_executor_no_cuda", @@ -265,7 +273,9 @@ cc_library( "infeed_thunk.cc", "kernel_thunk.cc", "memset_thunk.cc", + "outfeed_thunk.cc", "sequential_thunk.cc", + "thunk.cc", "thunk_schedule.cc", "tuple_thunk.cc", "while_thunk.cc", @@ -282,6 +292,7 @@ cc_library( "infeed_thunk.h", "kernel_thunk.h", "memset_thunk.h", + "outfeed_thunk.h", "sequential_thunk.h", "thunk.h", "thunk_schedule.h", @@ -289,15 +300,16 @@ cc_library( "while_thunk.h", ], deps = [ - ":backend_configs", ":buffer_allocations", ":cudnn_convolution_runner", ":hlo_execution_profiler", ":infeed_manager", ":ir_emission_utils", + ":outfeed_manager", ":partition_assignment", ":stream_assignment", "//tensorflow/compiler/xla:array2d", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_tree", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status", @@ -315,6 +327,7 @@ cc_library( "//tensorflow/compiler/xla/service:shaped_buffer", "//tensorflow/compiler/xla/service:transfer_manager", "//tensorflow/compiler/xla/service:tuple_points_to_analysis", + "//tensorflow/compiler/xla/service/llvm_ir:buffer_assignment_util", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", "//tensorflow/core:stream_executor_no_cuda", @@ -351,6 +364,7 @@ cc_library( ":cudnn_convolution_runner", ":gpu_executable", ":ir_emission_utils", + "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla/service:device_memory_allocator", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_pass", @@ -382,7 +396,7 @@ cc_library( hdrs = ["cudnn_convolution_rewriter.h"], deps = [ ":ir_emission_utils", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:window_util", "//tensorflow/compiler/xla:xla_data_proto", @@ -443,6 +457,7 @@ cc_library( srcs = ["multi_output_fusion.cc"], hdrs = ["multi_output_fusion.h"], deps = [ + ":instruction_fusion", ":ir_emission_utils", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla/service:hlo", @@ -455,6 +470,7 @@ tf_cc_test( name = "multi_output_fusion_test", srcs = ["multi_output_fusion_test.cc"], deps = [ + ":instruction_fusion", ":multi_output_fusion", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:util", @@ -517,6 +533,7 @@ cc_library( hdrs = ["pad_insertion.h"], deps = [ ":ir_emission_utils", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:window_util", @@ -527,13 +544,48 @@ cc_library( ], ) +cc_library( + name = "pad_for_tensor_cores", + srcs = ["pad_for_tensor_cores.cc"], + hdrs = ["pad_for_tensor_cores.h"], + deps = [ + ":ir_emission_utils", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:window_util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo_creation_utils", + "//tensorflow/compiler/xla/service:hlo_pass", + "//tensorflow/compiler/xla/service:shape_inference", + ], +) + +tf_cc_test( + name = "pad_for_tensor_cores_test", + srcs = ["pad_for_tensor_cores_test.cc"], + deps = [ + ":ir_emission_utils", + ":pad_for_tensor_cores", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/service:hlo_matchers", + "//tensorflow/compiler/xla/service:hlo_parser", + "//tensorflow/compiler/xla/tests:hlo_verified_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", # build_cleaner: keep + ], +) + cc_library( name = "gpu_transfer_manager", srcs = ["gpu_transfer_manager.cc"], hdrs = ["gpu_transfer_manager.h"], deps = [ ":gpu_compiler", + ":outfeed_manager", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_tree", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -552,8 +604,8 @@ cc_library( cc_library( name = "gpu_compiler", - srcs = ["gpu_compiler.cc"], - hdrs = ["gpu_compiler.h"], + srcs = ["nvptx_compiler.cc"], + hdrs = ["nvptx_compiler.h"], deps = [ ":cudnn_convolution_algorithm_picker", ":cudnn_convolution_rewriter", @@ -568,9 +620,11 @@ cc_library( ":ir_emission_utils", ":ir_emitter", ":multi_output_fusion", + ":pad_for_tensor_cores", ":pad_insertion", ":partition_assignment", ":stream_assignment", + ":stream_executor_util", "//tensorflow/compiler/xla:protobuf_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -624,24 +678,46 @@ cc_library( hdrs = ["cudnn_batchnorm_rewriter.h"], deps = [ ":ir_emission_utils", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_pass", ], ) +cc_library( + name = "xfeed_queue", + hdrs = ["xfeed_queue.h"], + deps = ["//tensorflow/core:lib"], +) + cc_library( name = "infeed_manager", srcs = ["infeed_manager.cc"], hdrs = ["infeed_manager.h"], deps = [ + ":xfeed_queue", + "//tensorflow/compiler/xla:shape_tree", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", - "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", ], ) +cc_library( + name = "outfeed_manager", + srcs = ["outfeed_manager.cc"], + hdrs = ["outfeed_manager.h"], + deps = [ + ":xfeed_queue", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:shape_tree", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:util", + "//tensorflow/core:lib", + ], +) + cc_library( name = "gpu_layout_assignment", srcs = ["gpu_layout_assignment.cc"], @@ -716,7 +792,7 @@ cc_library( srcs = ["while_transformer.cc"], hdrs = ["while_transformer.h"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -772,6 +848,7 @@ cc_library( deps = [ "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/core:stream_executor_no_cuda", ], diff --git a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc index ab5149dcdb09290cd0c0b2233029d0988a95f036..537295292b6ced72c4b2c456557b3c06e0aa5254 100644 --- a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc +++ b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc @@ -44,17 +44,27 @@ StatusOr> BufferAllocations::Builder::Build( num_buffers, device_ordinal, memory_allocator, buffer_assignment)); for (BufferAllocation::Index i = 0; i < num_buffers; ++i) { + const BufferAllocation& allocation = buffer_assignment->GetAllocation(i); + const int64 expected_alignment = [&] { + if (allocation.is_entry_computation_parameter()) { + return kEntryParameterAlignBytes; + } else if (allocation.is_constant()) { + return kConstantBufferAlignBytes; + } else { + return kXlaAllocatedBufferAlignBytes; + } + }(); + // If buffer #i's address is already registered (e.g. external arguments or // result buffers), use that registered buffer. if (registered_buffers_.count(i)) { se::DeviceMemoryBase address = FindOrDie(registered_buffers_, i); - if (reinterpret_cast(address.opaque()) % - kCudaMallocAlignBytes != + if (reinterpret_cast(address.opaque()) % expected_alignment != 0) { return InternalError( "Address of registered buffer %lld must be a multiple of %llx, but " "was %p", - i, kCudaMallocAlignBytes, address.opaque()); + i, kEntryParameterAlignBytes, address.opaque()); } buffer_allocations->SetBuffer(i, FindOrDie(registered_buffers_, i)); continue; @@ -62,7 +72,6 @@ StatusOr> BufferAllocations::Builder::Build( // Allocate each allocation that might escape, or is the temp buffer. bool seen_temp_buffer = false; - const BufferAllocation& allocation = buffer_assignment->GetAllocation(i); if (allocation.maybe_live_out() || allocation.IsPreallocatedTempBuffer()) { const int64 buffer_size = allocation.size(); se::DeviceMemoryBase buffer_address; @@ -70,13 +79,12 @@ StatusOr> BufferAllocations::Builder::Build( OwningDeviceMemory buffer; TF_ASSIGN_OR_RETURN( buffer, memory_allocator->Allocate(device_ordinal, buffer_size)); - if (reinterpret_cast(buffer.opaque()) % - kCudaMallocAlignBytes != + if (reinterpret_cast(buffer.opaque()) % expected_alignment != 0) { return InternalError( "Address returned by memory_allocator->Allocate must be a " "multiple of %llx, but was %p", - kCudaMallocAlignBytes, buffer.opaque()); + kXlaAllocatedBufferAlignBytes, buffer.opaque()); } // We do manual memory management within BufferAllocations. Be sure not // to do a TF_RETURN_IF_ERROR between this line and the @@ -165,5 +173,10 @@ void BufferAllocations::SetBuffer(BufferAllocation::Index buffer_index, buffers_[buffer_index] = buffer; } +bool ShouldEmitLiteralInLlvmIr(const Literal& literal) { + // LLVM can sometimes do interesting optimizations using scalar constants. + return ShapeUtil::IsScalar(literal.shape()); +} + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/buffer_allocations.h b/tensorflow/compiler/xla/service/gpu/buffer_allocations.h index 636623502597b3a66523938ba430e9d5a82f796c..f13eab0dd787a2bfa687c991f9d808568360fd24 100644 --- a/tensorflow/compiler/xla/service/gpu/buffer_allocations.h +++ b/tensorflow/compiler/xla/service/gpu/buffer_allocations.h @@ -107,6 +107,12 @@ class BufferAllocations { bool torn_down_ = false; }; +// LLVM and PTXAS don't deal well with large constants, so we only emit very +// small constants directly in LLVM IR. Larger constants are emitted with zero +// initializers in LLVM IR and are later overwritten when the PTX/CUBIN is +// loaded. +bool ShouldEmitLiteralInLlvmIr(const Literal& literal); + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc b/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc index 5e4fe1dd398dedd999e18d7ef6dfb5a4fd3bf4cb..5780e0af40699bb6ac2c190c09cd02023fb44db7 100644 --- a/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/conditional_thunk.cc @@ -33,8 +33,11 @@ ConditionalThunk::ConditionalThunk( predicate_buffer_index_(predicate_buffer_index), true_operand_buffer_index_(true_operand_buffer_index), false_operand_buffer_index_(false_operand_buffer_index), - true_thunk_(std::move(true_thunk_sequence), hlo), - false_thunk_(std::move(false_thunk_sequence), hlo) {} + // Pass nullptr as the HloInstruction* to the true_thunk_ and false_thunk_ + // constructors because these SequentialThunks are logically "part of" + // this ConditionalThunk, and shouldn't be profiled separately from it. + true_thunk_(std::move(true_thunk_sequence), nullptr), + false_thunk_(std::move(false_thunk_sequence), nullptr) {} Status ConditionalThunk::Initialize(const GpuExecutable& executable, se::StreamExecutor* executor) { diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_rewriter.cc b/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_rewriter.cc index c77e3c81c9d38af7857ad1389d20221514bf38f1..60289506524759580dbb9b82147c78c4ce1cb25e 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_rewriter.cc +++ b/tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_rewriter.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_rewriter.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" @@ -66,11 +67,12 @@ Status Visitor::HandleBatchNormInference(HloInstruction* batch_norm) { return Status::OK(); } - HloInstruction* epsilon = computation_->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(batch_norm->epsilon()))); + HloInstruction* epsilon = + computation_->AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR0(batch_norm->epsilon()))); HloInstruction* feature_index = computation_->AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR0(batch_norm->feature_index()))); + LiteralUtil::CreateR0(batch_norm->feature_index()))); std::vector operands(batch_norm->operands().begin(), batch_norm->operands().end()); @@ -101,11 +103,12 @@ Status Visitor::HandleBatchNormTraining(HloInstruction* batch_norm) { return Status::OK(); } - HloInstruction* epsilon = computation_->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(batch_norm->epsilon()))); + HloInstruction* epsilon = + computation_->AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR0(batch_norm->epsilon()))); HloInstruction* feature_index = computation_->AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR0(batch_norm->feature_index()))); + LiteralUtil::CreateR0(batch_norm->feature_index()))); std::vector operands(batch_norm->operands().begin(), batch_norm->operands().end()); @@ -128,8 +131,8 @@ Status Visitor::HandleBatchNormTraining(HloInstruction* batch_norm) { inverse_stddev->shape(), HloOpcode::kPower, inverse_stddev, computation_->AddInstruction(HloInstruction::CreateBroadcast( inverse_stddev->shape(), - computation_->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(-2))), + computation_->AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR0(-2))), {})))); HloInstruction* variance = computation_->AddInstruction(HloInstruction::CreateBinary( @@ -169,11 +172,12 @@ Status Visitor::HandleBatchNormGrad(HloInstruction* batch_norm) { return Status::OK(); } - HloInstruction* epsilon = computation_->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(batch_norm->epsilon()))); + HloInstruction* epsilon = + computation_->AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR0(batch_norm->epsilon()))); HloInstruction* feature_index = computation_->AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR0(batch_norm->feature_index()))); + LiteralUtil::CreateR0(batch_norm->feature_index()))); // The cudnn libcall expects its input to be rsqrt(variance + epsilon), but // the batchnorm HLO takes plain variance as input. Fix it up. @@ -189,7 +193,7 @@ Status Visitor::HandleBatchNormGrad(HloInstruction* batch_norm) { computation_->AddInstruction(HloInstruction::CreateBroadcast( var_plus_epsilon->shape(), computation_->AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR0(-.5))), + LiteralUtil::CreateR0(-.5))), {})))); std::vector operands(batch_norm->operands().begin(), diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc index 3dc98c4c93ea2b9b68dd3ee27794a39847f8756c..5a63e65208ac3e8e23944bc31634f4d29d91c10c 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc +++ b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/service/gpu/cudnn_convolution_algorithm_picker.h" +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/gpu/backend_configs.pb.h" #include "tensorflow/compiler/xla/service/gpu/convolution_thunk.h" #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" @@ -80,8 +81,7 @@ bool ShouldIncludeWinogradNonfusedAlgo(const Shape& input_shape, const ConvolutionDimensionNumbers& dnums, se::StreamExecutor* stream_exec) { // Skip this check for cudnn7 and newer. - auto version = - stream_exec->AsDnn()->GetVersion(); + auto version = stream_exec->AsDnn()->GetVersion(); if (version.ok() && version.ValueOrDie().major_version() >= 7) { return true; } @@ -338,8 +338,8 @@ StatusOr CudnnConvolutionAlgorithmPicker::RunOnInstruction( computation->AddInstruction(HloInstruction::CreateTuple( {computation->AddInstruction(HloInstruction::CreateGetTupleElement( new_call_shape.tuple_shapes(0), new_call, 0)), - computation->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({})))})); + computation->AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR1({})))})); TF_RETURN_IF_ERROR(instr->parent()->ReplaceInstruction(instr, new_tuple)); return true; diff --git a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.cc b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.cc index f9dccd287d955502858f6c24ccd4de80256fc148..905b5ee8767d0fa0514c7f1abf83bc089cd08045 100644 --- a/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.cc +++ b/tensorflow/compiler/xla/service/gpu/cudnn_convolution_rewriter.cc @@ -18,7 +18,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" diff --git a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc index 27d2c3e491bfc2108cbd168d1a5e1575c2eed11f..cc38db27e2680e950f74e104cef8829585c7b81c 100644 --- a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc @@ -29,12 +29,13 @@ limitations under the License. #include "llvm/IR/Intrinsics.h" #include "llvm/IR/Module.h" #include "llvm/IR/Type.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" +#include "tensorflow/compiler/xla/service/llvm_ir/math_ops.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" @@ -67,8 +68,8 @@ bool IsFPLiteralWithValue(const HloInstruction* operand, float value) { GpuElementalIrEmitter::GpuElementalIrEmitter( const HloModuleConfig& hlo_module_config, llvm::Module* module, - llvm::IRBuilder<>* ir_builder, NestedComputer compute_nested) - : ElementalIrEmitter(hlo_module_config, module, ir_builder), + llvm::IRBuilder<>* b, NestedComputer compute_nested) + : ElementalIrEmitter(hlo_module_config, module, b), hlo_module_config_(hlo_module_config), compute_nested_(std::move(compute_nested)) {} @@ -92,8 +93,8 @@ StatusOr GpuElementalIrEmitter::EmitLibdeviceMathCall( cast_result_to_fp16 = true; for (int64 i = 0; i < operands.size(); ++i) { if (input_types[i] == F16) { - converted_operands[i] = ir_builder_->CreateFPCast( - converted_operands[i], ir_builder_->getFloatTy()); + converted_operands[i] = + b_->CreateFPCast(converted_operands[i], b_->getFloatTy()); converted_input_types[i] = F32; } } @@ -112,7 +113,7 @@ StatusOr GpuElementalIrEmitter::EmitLibdeviceMathCall( converted_input_types, output_type) .ValueOrDie(); if (cast_result_to_fp16) { - result = ir_builder_->CreateFPCast(result, ir_builder_->getHalfTy()); + result = b_->CreateFPCast(result, b_->getHalfTy()); } return result; } @@ -215,7 +216,7 @@ StatusOr GpuElementalIrEmitter::EmitPowerOp( // LLVM's NVPTX backend knows how to transform 1/sqrt(A) into the NVPTX // rsqrt.approx instruction. TF_ASSIGN_OR_RETURN(auto* sqrt, make_sqrt()); - return ir_builder_->CreateFDiv(llvm::ConstantFP::get(llvm_ty, 1), sqrt); + return b_->CreateFDiv(llvm::ConstantFP::get(llvm_ty, 1), sqrt); } VLOG(10) << "emitting pow as regular call to pow(): " << op->ToString(); @@ -277,6 +278,16 @@ StatusOr GpuElementalIrEmitter::EmitFloatUnaryOp( PrimitiveType output_type = op->shape().element_type(); switch (op->opcode()) { case HloOpcode::kTanh: + // If we don't care much about precision, emit a fast approximation of + // tanh. + if (hlo_module_config_.debug_options().xla_enable_fast_math()) { + // Upcast F16 to F32 if necessary. + llvm::Type* type = + input_type == F16 ? b_->getFloatTy() : operand_value->getType(); + llvm::Value* input = b_->CreateFPCast(operand_value, type); + llvm::Value* fast_tanh = llvm_ir::EmitFastTanh(b_, input); + return b_->CreateFPCast(fast_tanh, operand_value->getType()); + } return EmitLibdeviceMathCall("__nv_tanh", {operand_value}, {input_type}, output_type); default: @@ -302,32 +313,31 @@ llvm::Value* GpuElementalIrEmitter::EmitDeviceFunctionCall( // Declares the callee if it is not declared already. llvm::Function* callee = llvm::cast( - ir_builder_->GetInsertBlock()->getModule()->getOrInsertFunction( + b_->GetInsertBlock()->getModule()->getOrInsertFunction( llvm_ir::AsStringRef(callee_name), callee_type)); for (auto attribute : attributes) { callee->addFnAttr(attribute); } - return ir_builder_->CreateCall(callee, llvm_ir::AsArrayRef(operands)); + return b_->CreateCall(callee, llvm_ir::AsArrayRef(operands)); } llvm::Value* GpuElementalIrEmitter::EmitThreadId() const { - llvm::Value* block_id = ir_builder_->CreateIntCast( + llvm::Value* block_id = b_->CreateIntCast( llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::nvvm_read_ptx_sreg_ctaid_x, - {}, {}, ir_builder_), - ir_builder_->getIntNTy(128), /*isSigned=*/true, "block.id"); - llvm::Value* thread_id_in_block = ir_builder_->CreateIntCast( + {}, {}, b_), + b_->getIntNTy(128), /*isSigned=*/true, "block.id"); + llvm::Value* thread_id_in_block = b_->CreateIntCast( llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::nvvm_read_ptx_sreg_tid_x, - {}, {}, ir_builder_), - ir_builder_->getIntNTy(128), /*isSigned=*/true, "thread.id"); - llvm::Value* threads_per_block = ir_builder_->CreateIntCast( + {}, {}, b_), + b_->getIntNTy(128), /*isSigned=*/true, "thread.id"); + llvm::Value* threads_per_block = b_->CreateIntCast( llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::nvvm_read_ptx_sreg_ntid_x, - {}, {}, ir_builder_), - ir_builder_->getIntNTy(128), /*isSigned=*/true, "threads_per_block"); - return ir_builder_->CreateNSWAdd( - ir_builder_->CreateNSWMul(block_id, threads_per_block), - thread_id_in_block); + {}, {}, b_), + b_->getIntNTy(128), /*isSigned=*/true, "threads_per_block"); + return b_->CreateNSWAdd(b_->CreateNSWMul(block_id, threads_per_block), + thread_id_in_block); } llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( @@ -373,12 +383,12 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( PrimitiveType operand_element_type = operand->shape().element_type(); llvm::Value* accum_ptr = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType(operand_element_type, module_), - "reduce_window_accum_ptr", ir_builder_); + "reduce_window_accum_ptr", b_); { TF_ASSIGN_OR_RETURN(llvm::Value * init_value, operand_to_generator.at(hlo->operand(1))( IrArray::Index(index.GetType()))); - ir_builder_->CreateStore(init_value, accum_ptr); + b_->CreateStore(init_value, accum_ptr); } llvm::Type* index_type = index.GetType(); @@ -386,7 +396,7 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( return index.GetConstantWithIndexType(c); }; - llvm_ir::ForLoopNest loops(IrName(hlo), ir_builder_, index_type); + llvm_ir::ForLoopNest loops(IrName(hlo), b_, index_type); std::vector window_size; for (const auto& dim : window.dimensions()) { window_size.push_back(dim.size()); @@ -395,15 +405,15 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( ShapeUtil::MakeShape(operand_element_type, window_size), "window"); CHECK_EQ(window_index.size(), index.size()); - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), ir_builder_); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), b_); IrArray::Index input_index(index_type, index.size()); - llvm::Value* in_bounds = ir_builder_->getInt1(true); + llvm::Value* in_bounds = b_->getInt1(true); for (size_t i = 0; i < index.size(); ++i) { - llvm::Value* stridden_index = ir_builder_->CreateNSWMul( + llvm::Value* stridden_index = b_->CreateNSWMul( index[i], index_typed_const(window.dimensions(i).stride())); - input_index[i] = ir_builder_->CreateNSWSub( - ir_builder_->CreateNSWAdd(stridden_index, window_index[i]), + input_index[i] = b_->CreateNSWSub( + b_->CreateNSWAdd(stridden_index, window_index[i]), index_typed_const(window.dimensions(i).padding_low())); // We must check whether 0 ≤ input_index[i] < bound, as otherwise @@ -411,16 +421,16 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( // comparison is equivalent to the unsigned comparison // input_index[i] < bound, as a negative value wraps to a large // positive value. - in_bounds = ir_builder_->CreateAnd( + in_bounds = b_->CreateAnd( in_bounds, - ir_builder_->CreateICmpULT( + b_->CreateICmpULT( input_index[i], index_typed_const(operand->shape().dimensions(i)))); } llvm_ir::LlvmIfData if_data = - llvm_ir::EmitIfThenElse(in_bounds, "in_bounds", ir_builder_); - SetToFirstInsertPoint(if_data.true_block, ir_builder_); + llvm_ir::EmitIfThenElse(in_bounds, "in_bounds", b_); + SetToFirstInsertPoint(if_data.true_block, b_); // We are not in pad, so do the computation. TF_ASSIGN_OR_RETURN(llvm::Value * input_value, @@ -428,26 +438,26 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( TF_ASSIGN_OR_RETURN( llvm::Value * accum_value, compute_nested_(*hlo->to_apply(), - {ir_builder_->CreateLoad(accum_ptr), input_value})); - ir_builder_->CreateStore(accum_value, accum_ptr); + {b_->CreateLoad(accum_ptr), input_value})); + b_->CreateStore(accum_value, accum_ptr); - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), ir_builder_); - return ir_builder_->CreateLoad(accum_ptr); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), b_); + return b_->CreateLoad(accum_ptr); }; case HloOpcode::kReduce: return [=, &operand_to_generator]( const IrArray::Index& output_index) -> StatusOr { const HloInstruction* operand = hlo->operand(0); llvm::Value* accum_ptr = - ir_builder()->CreateAlloca(llvm_ir::PrimitiveTypeToIrType( + b()->CreateAlloca(llvm_ir::PrimitiveTypeToIrType( hlo->shape().element_type(), module_)); llvm::Type* index_type = output_index.GetType(); TF_ASSIGN_OR_RETURN(llvm::Value * init_value, operand_to_generator.at(hlo->operand(1))( IrArray::Index(index_type))); - ir_builder()->CreateStore(init_value, accum_ptr); + b()->CreateStore(init_value, accum_ptr); - llvm_ir::ForLoopNest loops(IrName(hlo), ir_builder_, index_type); + llvm_ir::ForLoopNest loops(IrName(hlo), b_, index_type); IrArray::Index input_index = loops.AddLoopsForShapeOnDimensions( operand->shape(), hlo->dimensions(), "reduction_dim"); if (!ShapeUtil::IsScalar(hlo->shape())) { @@ -462,18 +472,17 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( CHECK_EQ(output_index.size(), j); } - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), ir_builder()); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), b()); TF_ASSIGN_OR_RETURN( llvm::Value * input_value, operand_to_generator.at(hlo->operand(0))(input_index)); TF_ASSIGN_OR_RETURN( llvm::Value * accum_value, - compute_nested_( - *hlo->to_apply(), - {ir_builder()->CreateLoad(accum_ptr), input_value})); - ir_builder()->CreateStore(accum_value, accum_ptr); - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), ir_builder()); - return ir_builder()->CreateLoad(accum_ptr); + compute_nested_(*hlo->to_apply(), + {b()->CreateLoad(accum_ptr), input_value})); + b()->CreateStore(accum_value, accum_ptr); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), b()); + return b()->CreateLoad(accum_ptr); }; default: return ElementalIrEmitter::MakeElementGenerator(hlo, diff --git a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h index 91f4d960aa62fff3e0699ece37a8c74d7dcf2f59..e3eacef133cb8b615a645ca2f11dd6dedf9f0176 100644 --- a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h +++ b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h @@ -43,7 +43,7 @@ class GpuElementalIrEmitter : public ElementalIrEmitter { const HloComputation&, tensorflow::gtl::ArraySlice)>; GpuElementalIrEmitter(const HloModuleConfig& hlo_module_config, - llvm::Module* module, llvm::IRBuilder<>* ir_builder, + llvm::Module* module, llvm::IRBuilder<>* b, NestedComputer compute_nested); llvm_ir::ElementGenerator MakeElementGenerator( diff --git a/tensorflow/compiler/xla/service/gpu/for_thunk.cc b/tensorflow/compiler/xla/service/gpu/for_thunk.cc index 4fdc55909a1afbac96aaa9bc931ed8ac6c0ae1df..b3a3c5dcb4d77889b65a119f09ddef9ba95d6b52 100644 --- a/tensorflow/compiler/xla/service/gpu/for_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/for_thunk.cc @@ -28,8 +28,11 @@ ForThunk::ForThunk(const int64 loop_limit, const HloInstruction* hlo) : Thunk(Kind::kWhile, hlo), loop_limit_(loop_limit), - body_thunk_sequence_( - MakeUnique(std::move(*body_thunk_sequence), hlo)) {} + body_thunk_sequence_(MakeUnique( + // Pass nullptr as the HloInstruction* to the body_thunk_sequence_ + // constructor because this SequentialThunk is logically "part of" + // this ForThunk, and shouldn't be profiled separately from it. + std::move(*body_thunk_sequence), nullptr)) {} Status ForThunk::Initialize(const GpuExecutable& executable, se::StreamExecutor* executor) { diff --git a/tensorflow/compiler/xla/service/gpu/gpu_constants.cc b/tensorflow/compiler/xla/service/gpu/gpu_constants.cc index aa360c7f73de2f0f9cf59c22b552b8e60ddb3a87..7f0b030fece8f25578bd90a538279d455350278a 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_constants.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_constants.cc @@ -14,12 +14,23 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/service/gpu/gpu_constants.h" +#include "tensorflow/core/framework/allocator.h" namespace xla { namespace gpu { -// http://docs.nvidia.com/cuda/cuda-c-programming-guide/#device-memory-accesses -const int64 kCudaMallocAlignBytes = 256; +// kEntryParameterAlignBytes is equal to EIGEN_MAX_ALIGN_BYTES, though including +// Eigen headers here to get that symbol may not be a good idea. +// EIGEN_MAX_ALIGN_BYTES may differ between CUDA-enabled builds vs CUDA-disabled +// builds and we don't want the IR generated by XLA:GPU to depend on that. +// +// TODO(b/111767313): Consider raising EIGEN_MAX_ALIGN_BYTES if it helps. +const int64 kEntryParameterAlignBytes = 16; + +const int64 kXlaAllocatedBufferAlignBytes = + tensorflow::Allocator::kAllocatorAlignment; + +const int64 kConstantBufferAlignBytes = kXlaAllocatedBufferAlignBytes; } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/gpu_constants.h b/tensorflow/compiler/xla/service/gpu/gpu_constants.h index eb1ca4c6c95a23d2a08f5f9c3cbc85e7d47d4f89..6f5f1fa09c57dfd246d702c0adc92c7e2e76805a 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_constants.h +++ b/tensorflow/compiler/xla/service/gpu/gpu_constants.h @@ -21,9 +21,15 @@ limitations under the License. namespace xla { namespace gpu { -// Minimum alignment of cudaMalloc. We require that buffers created by our -// DeviceMemoryAllocator, and all input/output buffers, have this alignment. -extern const int64 kCudaMallocAlignBytes; +// Minimum alignment for buffers passed as incoming arguments by TensorFlow. +extern const int64 kEntryParameterAlignBytes; + +// Minimum alignment for buffers allocated by XLA: the temp buffers and the live +// out (result) buffers. +extern const int64 kXlaAllocatedBufferAlignBytes; + +// Minimum alignment for constant buffers. +extern const int64 kConstantBufferAlignBytes; } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc b/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc index fbc1303085b579e898d2f503a341754109768567..75f414e47fe3edcc1b10b392ed5cc5038be6c190 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc @@ -48,80 +48,17 @@ StatusOr GpuCopyInsertion::Run(HloModule* module) { TF_ASSIGN_OR_RETURN(bool changed, generic_copy_insertion.Run(module)); - TF_ASSIGN_OR_RETURN(std::unique_ptr dataflow, - HloDataflowAnalysis::Run(*module)); - - // Make sure all operands of a library call are in memory instead of constants - // in IR. Also, init values of while and conditional nodes cannot be - // constants. Insert copies for any constants found at the operands of these - // nodes. - tensorflow::gtl::FlatSet inserted_copies; + // Check the assumption that the epsilon and feature_index constants of the + // CUDNN batchnorm op are not shared with other ops where we would replace + // them with a copy. These custom op calls are generated with the + // CudnnBatchNormRewriter, so this would only happen if HloCSE merges them. for (HloComputation* computation : module->computations()) { for (HloInstruction* hlo : computation->instructions()) { - // Inserts a copy of hlo->operand(n) if it's a constant. - auto copy_operand_if_constant = [&](int64 n) -> Status { - HloInstruction* operand = hlo->mutable_operand(n); - // Skip the operands that have already been replaced with a copy in a - // previous iteration (which is possible when a constant is used as an - // operand in multiple places). - if (ContainsKey(inserted_copies, operand)) { - return Status::OK(); - } - for (auto& pair : dataflow->GetInstructionValueSet(operand)) { - const HloValueSet& value_set = pair.second; - for (const HloValue* value : value_set.values()) { - if (value->defining_instruction()->IsConstant() && - !ContainsKey(hlo_to_copy_map_, value->defining_instruction())) { - HloInstruction* constant = value->defining_instruction(); - TF_ASSIGN_OR_RETURN(HloInstruction * copy, - FindOrInsertCopy(constant)); - TF_RETURN_IF_ERROR(constant->ReplaceAllUsesWith(copy)); - inserted_copies.insert(copy); - changed = true; - } - } - } - return Status::OK(); - }; - - if (IsCustomCallToDnnBatchNorm(*hlo)) { - // The epsilon and feature_index operands to a CUDNN batchnorm op don't - // need to be materialized in memory -- in fact, they must be constants. - // These are the last two operands of all three batchnorm ops. - for (int64 i = 0; i < hlo->operand_count() - 2; ++i) { - TF_RETURN_IF_ERROR(copy_operand_if_constant(i)); - } - } else if (ImplementedAsLibraryCall(*hlo) || - hlo->opcode() == HloOpcode::kCrossReplicaSum || - hlo->opcode() == HloOpcode::kWhile || - hlo->opcode() == HloOpcode::kConditional) { - // For all other library calls, cross-replica-sum, while and conditional - // ops materialize all the operands into memory. (Cross-replica-sum - // gets its constant args materialized even if it's not implemented as a - // libcall to simplify the implementation. It's slower, but we can - // constant fold away constant args *anyway*, so we just need to make it - // work.) - for (int64 i = 0; i < hlo->operand_count(); ++i) { - TF_RETURN_IF_ERROR(copy_operand_if_constant(i)); - } + if (!IsCustomCallToDnnBatchNorm(*hlo)) { + continue; } - } - } - - if (changed) { - // Check the assumption that the epsilon and feature_index constants of the - // CUDNN batchnorm op are not shared with other ops where we would replace - // them with a copy. These custom op calls are generated with the - // CudnnBatchNormRewriter, so this would only happen if HloCSE merges them. - for (HloComputation* computation : module->computations()) { - for (HloInstruction* hlo : computation->instructions()) { - if (!IsCustomCallToDnnBatchNorm(*hlo)) { - continue; - } - for (int64 i = hlo->operand_count() - 2; i < hlo->operand_count(); - ++i) { - CHECK_EQ(hlo->operand(i)->opcode(), HloOpcode::kConstant); - } + for (int64 i = hlo->operand_count() - 2; i < hlo->operand_count(); ++i) { + CHECK_EQ(hlo->operand(i)->opcode(), HloOpcode::kConstant); } } } diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc index 0cad2958c72797b4d70f00676928b2b21d7a3e8d..bb71c79fd7646c9d3bad282d8041a9a05aec0485 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" #include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h" #include "tensorflow/compiler/xla/service/logical_buffer.h" #include "tensorflow/compiler/xla/service/shaped_buffer.h" #include "tensorflow/compiler/xla/service/transfer_manager.h" @@ -84,7 +85,7 @@ Status GpuExecutable::ExecuteThunks( } // Stream 0 indicates `main_stream` and substreams start from stream 1. - std::vector::SmartPtr> sub_streams; + std::vector sub_streams; sub_streams.reserve(thunk_schedule_->StreamCount() - 1); while (sub_streams.size() + 1 < thunk_schedule_->StreamCount()) { sub_streams.emplace_back(); @@ -181,6 +182,55 @@ Status GpuExecutable::ExecuteThunks( return Status::OK(); } +StatusOr +GpuExecutable::ResolveConstantGlobals(se::StreamExecutor* executor) { + tensorflow::mutex_lock lock(module_handle_mutex_); + auto it = module_globals_.find(executor); + if (it != module_globals_.end()) { + return &it->second; + } + + se::MultiModuleLoaderSpec module_spec; + if (!cubin().empty()) { + module_spec.AddCudaCubinInMemory(cubin()); + } + module_spec.AddCudaPtxInMemory(ptx().c_str()); + + tensorflow::gtl::FlatMap globals; + se::ModuleHandle module_handle; + executor->LoadModule(module_spec, &module_handle); + + for (BufferAllocation::Index i = 0; i < assignment_->Allocations().size(); + ++i) { + const BufferAllocation& allocation = assignment_->GetAllocation(i); + if (allocation.is_constant()) { + TF_ASSIGN_OR_RETURN( + se::DeviceMemoryBase global, + executor->GetUntypedSymbol( + llvm_ir::ConstantBufferAllocationToGlobalName(allocation), + module_handle)); + VLOG(3) << "Resolved global " + << llvm_ir::ConstantBufferAllocationToGlobalName(allocation) + << " to " << global.opaque(); + InsertOrDie(&globals, i, global); + + const Literal& literal = + llvm_ir::LiteralForConstantAllocation(allocation); + CHECK(ShapeUtil::IsArray(literal.shape())); + if (!ShouldEmitLiteralInLlvmIr(literal)) { + VLOG(3) << "H2D memcpy for constant with shape " + << ShapeUtil::HumanString(literal.shape()); + TF_RETURN_IF_ERROR(executor->SynchronousMemcpyH2D( + literal.untyped_data(), allocation.size(), &global)); + } + } + } + + module_handles_.emplace(executor, + se::ScopedModuleHandle(executor, module_handle)); + return &module_globals_.emplace(executor, std::move(globals)).first->second; +} + StatusOr GpuExecutable::ExecuteOnStream( const ServiceExecutableRunOptions* run_options, tensorflow::gtl::ArraySlice arguments, @@ -192,6 +242,10 @@ StatusOr GpuExecutable::ExecuteOnStream( } BufferAllocations::Builder buffer_allocations_builder; + se::StreamExecutor* executor = run_options->stream()->parent(); + + TF_ASSIGN_OR_RETURN(auto* const globals, ResolveConstantGlobals(executor)); + for (BufferAllocation::Index i = 0; i < assignment_->Allocations().size(); ++i) { const BufferAllocation& allocation = assignment_->GetAllocation(i); @@ -213,8 +267,12 @@ StatusOr GpuExecutable::ExecuteOnStream( buffer_allocations_builder.RegisterBuffer(i, buffer); } + + if (allocation.is_constant()) { + buffer_allocations_builder.RegisterBuffer(i, FindOrDie(*globals, i)); + } } - se::StreamExecutor* executor = run_options->stream()->parent(); + TF_ASSIGN_OR_RETURN( auto buffer_allocations, buffer_allocations_builder.Build( diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.h b/tensorflow/compiler/xla/service/gpu/gpu_executable.h index 80ec38c3ac114fe4ad9d56784330c1144d913db1..c7ce6d0acbbbe594040271c0d45c71c016e36514 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.h +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.h @@ -34,6 +34,8 @@ limitations under the License. #include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/lib/gtl/flatmap.h" +#include "tensorflow/core/lib/gtl/optional.h" #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" @@ -66,7 +68,7 @@ class GpuExecutable : public Executable { } // Returns the compiled PTX for the computation. - tensorflow::StringPiece ptx() const { return ptx_; } + const string& ptx() const { return ptx_; } // Returns the cubin (compiled PTX) stored in this GpuExecutable. May be // empty, in which case compilation is left up to the GPU driver. @@ -98,6 +100,15 @@ class GpuExecutable : public Executable { // computation. Uses points-to analysis from buffer assignment. const PointsToSet& GetRootPointsToSet() const; + using BufferAllocToDeviceMemoryMap = + tensorflow::gtl::FlatMap; + + // Loads the PTX or CUBIN for this executable into `executor` and resolves the + // globals corresponding to constant buffers. Returns a map mapping buffer + // allocation indices to GPU pointers. + StatusOr ResolveConstantGlobals( + stream_executor::StreamExecutor* executor); + // The LLVM IR, in string format, of the unoptimized module generated for this // GpuExecutable. We save a string instead of an llvm::Module* because leaving // llvm::Module* in a singleton can cause the heap checker to emit false @@ -126,6 +137,14 @@ class GpuExecutable : public Executable { // memory for every output/temp buffers. const std::unique_ptr assignment_; + // Cache of module handles and constant buffer allocation maps used by + // `ResolveConstantGlobals`. + tensorflow::mutex module_handle_mutex_; + std::map + module_handles_ GUARDED_BY(module_handle_mutex_); + std::map + module_globals_ GUARDED_BY(module_handle_mutex_); + TF_DISALLOW_COPY_AND_ASSIGN(GpuExecutable); }; diff --git a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc index 09ef62c87f8875a5803497e8eb628769f883202a..6ac5dfbcd5e3bfcca179ff82819120ce14e3c9da 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc @@ -31,20 +31,13 @@ limitations under the License. namespace xla { namespace gpu { -using stream_executor::dnn::DataLayout; -using stream_executor::dnn::FilterLayout; - -static bool IsVoltaOrLater(const se::StreamExecutor& stream_executor) { - int major, minor; - CHECK(stream_executor.GetDeviceDescription().cuda_compute_capability(&major, - &minor)); - return major >= 7; -} +using se::dnn::DataLayout; +using se::dnn::FilterLayout; // Returns (input, filter, output) layouts. static std::tuple HeuristicLayoutAssignment(const HloInstruction* instr, - stream_executor::StreamExecutor* stream_executor) { + se::StreamExecutor* stream_executor) { // DataLayout and FilterLayout uses weird enum names. Translations: // N <=> Batch or Output // C <=> Depth or Input @@ -52,31 +45,44 @@ HeuristicLayoutAssignment(const HloInstruction* instr, // W <=> X // // Therefore kOutputInputYX and kBatchDepthYX mean NCHW. + // + // If you have trouble keeping these straight, consider that all that matters + // is the location of the channel dim: Is it major (NCHW), or minor (NHWC)? + + constexpr auto kAllNCHW = + std::make_tuple(DataLayout::kBatchDepthYX, FilterLayout::kOutputInputYX, + DataLayout::kBatchDepthYX); + constexpr auto kAllNHWC = + std::make_tuple(DataLayout::kBatchYXDepth, FilterLayout::kOutputYXInput, + DataLayout::kBatchYXDepth); - // As of today, our empirical evidence is that cudnn 7.0 is faster on V100 x - // fp16 with the mostly-NHWC layout. The heuristic may change as cudnn version - // changes, as well as the hardware updates. + // If we're not Volta or not fp16, the decision is easy: Use NCHW. if (!(instr->operand(0)->shape().element_type() == xla::PrimitiveType::F16 && IsVoltaOrLater(*stream_executor))) { - return std::make_tuple(DataLayout::kBatchDepthYX, - FilterLayout::kOutputInputYX, - DataLayout::kBatchDepthYX); + return kAllNCHW; } + VLOG(2) << "Using heuristic to figure out layouts for " << instr->ToString(); - // For BackwardInput that has stride, full NHWC layouts run significantly - // slower than (NHWC, NCHW, NCHW) or (NHWC, NCHW, NHWC). + + // Empirically we've found with Volta and cudnn 7 that backward-input convs + // with stride are significantly faster with NCHW layouts. // - // TODO(timshen): more closely compare (NHWC, NCHW, NCHW) and (NHWC, NCHW, - // NHWC). + // We could have used a mixed layout combination, e.g. (NHWC, NCHW, NCHW), + // which on paper gives good performance. However, there are two observations: + // * a mixed layout combination is more cuDNN-bug prone, based on empirical + // envidence. + // * we've also observed that for mixed layouts, cuDNN transposes data back + // and forth from a different layout combination. If we end up with + // transposes anyway, we prefer to have them in XLA, as they can be fused. + // TODO(timshen): Figure out the exact condition. This may be achieved by + // auto-tuning layouts offline. if (instr->custom_call_target() == kCudnnConvBackwardInputCallTarget && window_util::HasStride(instr->window())) { - return std::make_tuple(DataLayout::kBatchYXDepth, - FilterLayout::kOutputInputYX, - DataLayout::kBatchDepthYX); + return kAllNCHW; } - return std::make_tuple(DataLayout::kBatchYXDepth, - FilterLayout::kOutputYXInput, - DataLayout::kBatchYXDepth); + + // For other Volta f16 convolutions, use NHWC. + return kAllNHWC; } // Adds layout constraints on the cudnn custom-call instruction. The layout diff --git a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment_test.cc b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment_test.cc index e48165c1426ea04839c245bc20b851a0f1710246..95f78ae29326caad2f0785e2ba285a996e685899 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment_test.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment_test.cc @@ -132,10 +132,10 @@ TEST_F(LayoutAssignmentTest, BatchNormInference) { HloInstruction::CreateParameter(4, aux_shape, "variance")); auto* epsilon = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); auto* feature_index = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR0(kFeatureIndex))); + LiteralUtil::CreateR0(kFeatureIndex))); auto* batchnorm = builder.AddInstruction(HloInstruction::CreateCustomCall( shape, @@ -201,10 +201,10 @@ TEST_F(LayoutAssignmentTest, BatchNormTraining) { HloInstruction::CreateParameter(2, offset_scale_shape, "offset")); auto* epsilon = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); auto* feature_index = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR0(kFeatureIndex))); + LiteralUtil::CreateR0(kFeatureIndex))); auto* batchnorm = builder.AddInstruction(HloInstruction::CreateCustomCall( batchnorm_shape, {operand, scale, offset, epsilon, feature_index}, @@ -278,10 +278,10 @@ TEST_F(LayoutAssignmentTest, BatchNormGrad) { HloInstruction::CreateParameter(4, shape, "var")); auto* epsilon = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); auto* feature_index = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR0(kFeatureIndex))); + LiteralUtil::CreateR0(kFeatureIndex))); auto* batchnorm = builder.AddInstruction(HloInstruction::CreateCustomCall( diff --git a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc index 5343497c03c13a2589363da0fa33e18520220826..79b3f1efecdf06bfa93b17a1799f3009d517f3b5 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc @@ -20,8 +20,10 @@ limitations under the License. #include #include "llvm/IR/DataLayout.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" -#include "tensorflow/compiler/xla/service/gpu/gpu_compiler.h" +#include "tensorflow/compiler/xla/service/gpu/nvptx_compiler.h" +#include "tensorflow/compiler/xla/service/gpu/outfeed_manager.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" @@ -34,15 +36,14 @@ limitations under the License. #include "tensorflow/core/platform/stream_executor_no_cuda.h" namespace xla { +namespace gpu { // TODO(b/30467474) Once GPU infeed implementation settles, consider // folding back the cpu and gpu infeed implementations into a generic // one if possible. -GpuTransferManager::GpuTransferManager() - : GenericTransferManager( - se::cuda::kCudaPlatformId, - /*pointer_size=*/llvm::DataLayout(gpu::GpuCompiler::kDataLayout) - .getPointerSize(0 /* default address space */)) {} +GpuTransferManager::GpuTransferManager(se::Platform::Id id, + unsigned pointer_size) + : GenericTransferManager(id, pointer_size) {} Status GpuTransferManager::TransferLiteralToInfeed( se::StreamExecutor* executor, const LiteralSlice& literal) { @@ -50,48 +51,28 @@ Status GpuTransferManager::TransferLiteralToInfeed( VLOG(2) << "Transferring literal to infeed with shape: " << ShapeUtil::HumanString(shape); - if (!ShapeUtil::IsTuple(shape)) { - int64 size = GetByteSizeRequirement(shape); - return TransferBufferToInfeed(executor, size, literal.untyped_data()); - } - // For a tuple, we transfer each of its elements to the device and // enqueue the resulting destination device addresses with the // infeed manager. - std::vector buffers; - auto cleanup = tensorflow::gtl::MakeCleanup([buffers]() { - for (gpu::InfeedBuffer* b : buffers) { - b->Done(); - } - }); + ShapeTree buffer_tree(shape); TF_RETURN_IF_ERROR(ShapeUtil::ForEachSubshapeWithStatus( shape, [&](const Shape& literal_subshape, const ShapeIndex& index) { if (ShapeUtil::IsArray(literal_subshape)) { int64 tuple_element_size = GetByteSizeRequirement(literal_subshape); TF_ASSIGN_OR_RETURN( - gpu::InfeedBuffer * buffer, + *buffer_tree.mutable_element(index), TransferBufferToInfeedInternal(executor, tuple_element_size, literal.untyped_data(index))); - buffers.push_back(buffer); } return Status::OK(); })); - cleanup.release(); - return EnqueueBuffersToInfeed(executor, buffers); -} - -Status GpuTransferManager::TransferBufferToInfeed(se::StreamExecutor* executor, - int64 size, - const void* source) { - TF_ASSIGN_OR_RETURN(gpu::InfeedBuffer * buffer, - TransferBufferToInfeedInternal(executor, size, source)); - return EnqueueBuffersToInfeed(executor, {buffer}); + return EnqueueBuffersToInfeed(executor, std::move(buffer_tree)); } Status GpuTransferManager::EnqueueBuffersToInfeed( - se::StreamExecutor* executor, std::vector buffers) { + se::StreamExecutor* executor, ShapeTree buffers) { gpu::InfeedManager* infeed_manager = gpu::GetOrCreateInfeedManager(); se::Stream* stream = infeed_manager->GetStream(executor); @@ -101,21 +82,18 @@ Status GpuTransferManager::EnqueueBuffersToInfeed( // possible. Status block_status = stream->BlockHostUntilDone(); if (!block_status.ok()) { - for (gpu::InfeedBuffer* b : buffers) { - b->Done(); - } return InternalError("Failed to complete data transfer on stream %p: %s", stream, block_status.error_message().c_str()); } - infeed_manager->EnqueueBuffers(buffers); + infeed_manager->EnqueueDestination(std::move(buffers)); VLOG(2) << "Infeed data transferred"; return Status::OK(); } -StatusOr GpuTransferManager::TransferBufferToInfeedInternal( +StatusOr GpuTransferManager::TransferBufferToInfeedInternal( se::StreamExecutor* executor, int64 size, const void* source) { if (size > std::numeric_limits::max()) { return InvalidArgument("Infeed shape is too large: needs %lld bytes", size); @@ -131,23 +109,84 @@ StatusOr GpuTransferManager::TransferBufferToInfeedInternal( return InternalError("Failed to obtain a stream"); } - gpu::InfeedBuffer* buffer = new gpu::InfeedBuffer(executor, size); - stream->ThenMemcpy(buffer->device_memory(), source, size); + InfeedBuffer buffer(executor, size); + stream->ThenMemcpy(buffer.device_memory(), source, size); VLOG(2) << "Queued infeed data on stream " << stream; - return buffer; + return std::move(buffer); +} + +static std::unique_ptr ShapeTreeToLiteral( + ShapeTree>* shape_tree) { + // This is a struct instead of a lambda for std::function-free recursion. + struct Helper { + static std::unique_ptr helper( + ShapeTree>* shape_tree, + ShapeIndex* index) { + const Shape& shape = ShapeUtil::GetSubshape(shape_tree->shape(), *index); + if (ShapeUtil::IsArray(shape)) { + return (*shape_tree->mutable_element(*index))->WaitUntilAvailable(); + } + + CHECK(ShapeUtil::IsTuple(shape)) + << ShapeUtil::HumanStringWithLayout(shape); + const int64 tuple_element_count = ShapeUtil::TupleElementCount(shape); + index->push_back(0); + std::vector> tuple_operands; + for (int64 i = 0; i < tuple_element_count; ++i) { + index->back() = i; + tuple_operands.push_back(helper(shape_tree, index)); + } + index->pop_back(); + return LiteralUtil::MakeTupleOwned(std::move(tuple_operands)); + } + }; + ShapeIndex index; + return Helper::helper(shape_tree, &index); +} + +Status GpuTransferManager::TransferLiteralFromOutfeed( + se::StreamExecutor* /*executor*/, const Shape& literal_shape, + Literal* literal) { + ShapeTree> outfeed_buffers( + &literal_shape); + + // First create a tree of literal buffers that the device can write to. + outfeed_buffers.ForEachMutableElement( + [&](const ShapeIndex& index, + std::unique_ptr* buffer) { + const Shape& shape = ShapeUtil::GetSubshape(literal_shape, index); + // Do not transfer tuple index buffers. + if (ShapeUtil::IsTuple(shape)) { + return; + } + *buffer = MakeUnique(GetByteSizeRequirement(shape)); + }); + + // Give the tree of buffers to the outfeed mananger. The device will fill it + // while we're waiting for it below. + gpu::OutfeedManager* outfeed_manager = gpu::GetOrCreateOutfeedManager(); + outfeed_manager->EnqueueDestination(&outfeed_buffers); + + // Now turn the tree of buffers back into a literal. + *literal = std::move(*ShapeTreeToLiteral(&outfeed_buffers)); + return Status::OK(); } +} // namespace gpu } // namespace xla -static std::unique_ptr CreateGpuTransferManager() { - return xla::MakeUnique(); +static std::unique_ptr CreateNVPTXTransferManager() { + return xla::MakeUnique( + /*id=*/stream_executor::cuda::kCudaPlatformId, + /*pointer_size=*/llvm::DataLayout(xla::gpu::NVPTXCompiler::kDataLayout) + .getPointerSize(0 /* default address space */)); } static bool InitModule() { xla::TransferManager::RegisterTransferManager( - stream_executor::cuda::kCudaPlatformId, &CreateGpuTransferManager); + stream_executor::cuda::kCudaPlatformId, &CreateNVPTXTransferManager); return true; } static bool module_initialized = InitModule(); diff --git a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.h b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.h index 09f8227f508a3159f3def285898e15bfad544552..dceeb9e2eb01a7dd5e978d819ed1db56d828f353 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.h +++ b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.h @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/generic_transfer_manager.h" #include "tensorflow/compiler/xla/service/gpu/infeed_manager.h" #include "tensorflow/compiler/xla/service/transfer_manager.h" +#include "tensorflow/compiler/xla/shape_tree.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/platform/macros.h" @@ -28,33 +29,36 @@ limitations under the License. #include "tensorflow/core/platform/types.h" namespace xla { +namespace gpu { // An implementation of the XLA GenericTransferManager that // handles GPU-specific infeed. class GpuTransferManager : public GenericTransferManager { public: - GpuTransferManager(); + GpuTransferManager(se::Platform::Id id, unsigned pointer_size); ~GpuTransferManager() override {} Status TransferLiteralToInfeed(se::StreamExecutor* executor, const LiteralSlice& literal) override; - Status TransferBufferToInfeed(se::StreamExecutor* executor, int64 size, - const void* source) override; + Status TransferLiteralFromOutfeed(se::StreamExecutor* executor, + const Shape& literal_shape, + Literal* literal) override; private: // Initiates the infeed data transfers. InfeedBuffer->Done() must be // called to clean up the memory allocated for InfeedBuffer. - StatusOr TransferBufferToInfeedInternal( + StatusOr TransferBufferToInfeedInternal( se::StreamExecutor* executor, int64 size, const void* source); // Enqueues infeed data buffers with the infeed manager after their // transfer completes. Status EnqueueBuffersToInfeed(se::StreamExecutor* executor, - std::vector buffers); + ShapeTree buffers); TF_DISALLOW_COPY_AND_ASSIGN(GpuTransferManager); }; +} // namespace gpu } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_TRANSFER_MANAGER_H_ diff --git a/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc index 3e96beb575300614a04c856adbb6d742b34d11df..17226769302eef0dd01550b0bc5404e889ad78f8 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc +++ b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc @@ -17,12 +17,13 @@ limitations under the License. #include #include +#include #include #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_execution_profile.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" -#include "tensorflow/compiler/xla/service/pool.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" #include "tensorflow/core/util/ptr_util.h" @@ -36,10 +37,9 @@ void InitAndStartTimer(std::stack>* timers, stream->InitTimer(timers->top().get()).ThenStartTimer(timers->top().get()); } -uint64 GetCyclesTaken( - std::stack>* timers, - const std::vector::SmartPtr>& sub_streams, - se::Stream* stream, double clock_rate_ghz) { +uint64 GetCyclesTaken(std::stack>* timers, + const std::vector& sub_streams, + se::Stream* stream, double clock_rate_ghz) { CHECK_GT(timers->size(), 0); stream->ThenWaitFor(&sub_streams); stream->ThenStopTimer(timers->top().get()); @@ -52,7 +52,7 @@ uint64 GetCyclesTaken( HloExecutionProfiler::HloExecutionProfiler( bool do_profile, HloExecutionProfile* profile, se::Stream* stream, - const std::vector::SmartPtr>& sub_streams, + const std::vector& sub_streams, const HloComputation* computation) : do_profile_(do_profile), profile_(profile), @@ -99,6 +99,7 @@ void HloExecutionProfiler::StartHloInstruction() { void HloExecutionProfiler::FinishHloInstruction( const HloInstruction* hlo_instruction) { if (do_profile_) { + hlo_instructions_.erase(hlo_instruction); profile_->SetCyclesTakenBy( hlo_instruction, GetCyclesTaken(&timers_, sub_streams_, stream_, clock_rate_ghz_)); @@ -108,6 +109,12 @@ void HloExecutionProfiler::FinishHloInstruction( std::unique_ptr HloExecutionProfiler::MakeScopedInstructionProfiler( const HloInstruction* hlo_instruction) { + if (do_profile_ && hlo_instruction != nullptr) { + // Make sure that we are not already measuring the time for the same + // 'hlo_instruction'. + CHECK(hlo_instructions_.insert(hlo_instruction).second) + << hlo_instruction->name(); + } return MakeUnique(this, hlo_instruction); } diff --git a/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h index e5c655edc65a0c58bfde6c7701c8874d39c0b5d7..80cde75f2bbb555f514fffea58ad92edf92fd0d1 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h +++ b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h @@ -18,12 +18,13 @@ limitations under the License. #include #include +#include #include #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_execution_profile.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" -#include "tensorflow/compiler/xla/service/pool.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" namespace xla { @@ -37,10 +38,10 @@ class ScopedInstructionProfiler; class HloExecutionProfiler { public: // If profiling is enabled, start an execution timer running. - explicit HloExecutionProfiler( - bool do_profile, HloExecutionProfile* profile, se::Stream* stream, - const std::vector::SmartPtr>& sub_streams, - const HloComputation* computation); + explicit HloExecutionProfiler(bool do_profile, HloExecutionProfile* profile, + se::Stream* stream, + const std::vector& sub_streams, + const HloComputation* computation); // If profiling is enabled, sets the total cycle count on the profile from the // execution timer. @@ -71,9 +72,12 @@ class HloExecutionProfiler { double clock_rate_ghz_; HloExecutionProfile* profile_; se::Stream* stream_; - const std::vector::SmartPtr>& sub_streams_; + const std::vector& sub_streams_; const HloComputation* computation_; std::stack> timers_; + // Contains the HLO instructions for which we are currently measuring the + // time. + std::unordered_set hlo_instructions_; bool finished_execution_ = false; }; diff --git a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc index 375709150e08996ea6a40f5e9e66a8f8d9287008..19de37b0fbed15455e8c6a9bfe427ba3d9f0a9dc 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc +++ b/tensorflow/compiler/xla/service/gpu/hlo_schedule.cc @@ -100,7 +100,7 @@ GpuHloOrdering::GpuHloOrdering( if (last_instruction_per_stream[stream_no] != nullptr) { immediate_preds.push_back(last_instruction_per_stream[stream_no]); } - predecessor_map->SetReachabilityToUnion(immediate_preds, hlo); + predecessor_map->FastSetReachabilityToUnion(immediate_preds, hlo); last_instruction_per_stream[stream_no] = hlo; } else { // Only parameters and constants don't have an assigned stream, since they diff --git a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc index d420863b8569771b16a03591b6a0ddd0591f7e2e..8c11cd05419289d82b033c936bb60884f45cb636 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc +++ b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc @@ -18,8 +18,10 @@ limitations under the License. #include "llvm/IR/BasicBlock.h" #include "llvm/IR/Function.h" #include "llvm/IR/Instructions.h" +#include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h" #include "tensorflow/core/lib/strings/str_util.h" @@ -39,7 +41,7 @@ void HloToIrBindings::EmitBasePointersForHlos( // I/O HLOs are bound to the arguments of the current IR function. I.e., // // void IrFunction(io_0, io_1, ..., io_{m-1}, temp_buffer_base) { - llvm::Function* function = ir_builder_->GetInsertBlock()->getParent(); + llvm::Function* function = b_->GetInsertBlock()->getParent(); CHECK_EQ(io_hlos.size() + 1, function->arg_size()); // An HLO can have duplicated operands. This data structure remembers which @@ -79,8 +81,8 @@ void HloToIrBindings::EmitBasePointersForHlos( const int64 offset = slice.offset(); CHECK_NE(nullptr, temp_buffer_base_); // Emit IR for GetTupleElement instruction and bind to emitted value. - llvm::Value* base_ptr = ir_builder_->CreateInBoundsGEP( - temp_buffer_base_, ir_builder_->getInt64(offset)); + llvm::Value* base_ptr = + b_->CreateInBoundsGEP(temp_buffer_base_, b_->getInt64(offset)); BindHloToIrValue(*non_io_hlo, EmitGetTupleElement(non_io_hlo, base_ptr)); } @@ -108,15 +110,20 @@ void HloToIrBindings::EmitBasePointersForHlos( if (slice.allocation()->is_thread_local()) { llvm::Type* pointee_type = llvm_ir::ShapeToIrType(non_io_hlo->shape(), module_); - BindHloToIrValue(*non_io_hlo, - ir_builder_->CreateAlloca(pointee_type), index); + BindHloToIrValue(*non_io_hlo, b_->CreateAlloca(pointee_type), + index); + } else if (slice.allocation()->is_constant()) { + llvm::Value* global_for_constant = + module_->getGlobalVariable(llvm_ir::AsStringRef( + llvm_ir::ConstantBufferAllocationToGlobalName( + *slice.allocation()))); + BindHloToIrValue(*non_io_hlo, global_for_constant); } else { const int64 offset = slice.offset(); CHECK_NE(nullptr, temp_buffer_base_); BindHloToIrValue( *non_io_hlo, - ir_builder_->CreateInBoundsGEP(temp_buffer_base_, - ir_builder_->getInt64(offset)), + b_->CreateInBoundsGEP(temp_buffer_base_, b_->getInt64(offset)), index); } }); @@ -129,11 +136,19 @@ llvm::Value* HloToIrBindings::EmitGetTupleElement(const HloInstruction* gte, if (gte->operand(0)->opcode() != HloOpcode::kGetTupleElement) { return llvm_ir::EmitGetTupleElement( gte->shape(), gte->tuple_index(), /*alignment=*/1, - GetTypedIrValue(*gte->operand(0), {}, base_ptr), ir_builder_, module_); + GetTypedIrValue(*gte->operand(0), {}, base_ptr), b_, module_); } return llvm_ir::EmitGetTupleElement( gte->shape(), gte->tuple_index(), /*alignment=*/1, - EmitGetTupleElement(gte->operand(0), base_ptr), ir_builder_, module_); + EmitGetTupleElement(gte->operand(0), base_ptr), b_, module_); +} + +// Returns true if `value` has a name that should not be changed. +static bool HasMeaningfulName(llvm::Value* value) { + if (auto* global = llvm::dyn_cast(value)) { + return global->getLinkage() != llvm::GlobalValue::PrivateLinkage; + } + return false; } llvm::Value* HloToIrBindings::GetTypedIrValue(const HloInstruction& hlo, @@ -145,14 +160,18 @@ llvm::Value* HloToIrBindings::GetTypedIrValue(const HloInstruction& hlo, llvm::Value* typed_ir_value; if (llvm::isa(ir_value)) { - typed_ir_value = llvm::ConstantExpr::getBitCast( + typed_ir_value = llvm::ConstantExpr::getPointerBitCastOrAddrSpaceCast( llvm::cast(ir_value), dest_type); } else { - typed_ir_value = - ir_builder_->CreateBitCast(ir_value, pointee_type->getPointerTo()); + typed_ir_value = b_->CreateBitCast(ir_value, pointee_type->getPointerTo()); + } + if (!HasMeaningfulName(ir_value)) { + ir_value->setName(llvm_ir::AsStringRef(llvm_ir::IrName(&hlo, "raw"))); + } + if (!HasMeaningfulName(typed_ir_value)) { + typed_ir_value->setName( + llvm_ir::AsStringRef(llvm_ir::IrName(&hlo, "typed"))); } - ir_value->setName(llvm_ir::AsStringRef(llvm_ir::IrName(&hlo, "raw"))); - typed_ir_value->setName(llvm_ir::AsStringRef(llvm_ir::IrName(&hlo, "typed"))); return typed_ir_value; } diff --git a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h index a86e6e78c693ac53bb2c70d88b999a4e1273ecad..eee40b0e91fc03013a6978ae3cfe42b87633eed7 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h +++ b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h @@ -36,14 +36,13 @@ class HloToIrBindings { public: HloToIrBindings(const HloModule& module, const BufferAssignment* buffer_assignment, - llvm::IRBuilder<>* ir_builder, llvm::Module* llvm_module, + llvm::IRBuilder<>* b, llvm::Module* llvm_module, bool is_nested) : buffer_assignment_(buffer_assignment), is_nested_(is_nested), - ir_builder_(ir_builder), + b_(b), module_(llvm_module), - alias_analysis_(module, *buffer_assignment_, - &ir_builder_->getContext()) {} + alias_analysis_(module, *buffer_assignment_, &b_->getContext()) {} void EmitBasePointersForHlos( tensorflow::gtl::ArraySlice io_hlos, @@ -104,7 +103,7 @@ class HloToIrBindings { const bool is_nested_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; llvm::Module* module_; // Stores the underlying llvm::IrArray for each HloInstruction. diff --git a/tensorflow/compiler/xla/service/gpu/infeed_manager.cc b/tensorflow/compiler/xla/service/gpu/infeed_manager.cc index ae310beefad0c81c17fd4140b441b3a19a002e2c..c5f0cdf6cd5d3e076bffa875fbba991bf0681ee8 100644 --- a/tensorflow/compiler/xla/service/gpu/infeed_manager.cc +++ b/tensorflow/compiler/xla/service/gpu/infeed_manager.cc @@ -15,76 +15,13 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/infeed_manager.h" -#include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/ptr_util.h" -#include "tensorflow/core/platform/logging.h" namespace xla { namespace gpu { -InfeedManager::InfeedManager() : host_to_device_executor_(nullptr) {} - -void InfeedManager::Reset() { - tensorflow::mutex_lock l(mu_); - CHECK(dequeued_buffer_.empty()); - for (auto buffer : enqueued_buffer_) { - buffer->Done(); - } - enqueued_buffer_.clear(); -} - -void InfeedManager::EnqueueBuffers(const std::vector& buffers) { - tensorflow::mutex_lock l(mu_); - bool was_empty = enqueued_buffer_.empty(); - for (gpu::InfeedBuffer* b : buffers) { - enqueued_buffer_.push_back(b); - } - if (was_empty) { - // This has the potential to suffer from the notified thread - // immediately trying and failing to acquire mu_, but seems - // preferable to the alternative of notifying outside the lock - // on every enqueue. - cv_.notify_one(); - } -} - -InfeedBuffer* InfeedManager::BlockingDequeueBuffer() { - bool became_empty = false; - InfeedBuffer* current_buffer; - { - tensorflow::mutex_lock l(mu_); - while (enqueued_buffer_.empty()) { - cv_.wait(l); - } - current_buffer = enqueued_buffer_.front(); - enqueued_buffer_.pop_front(); - dequeued_buffer_.insert(current_buffer); - if (enqueued_buffer_.empty()) { - became_empty = true; - } - } - if (became_empty) { - for (const auto& callback : on_empty_callbacks_) { - callback(); - } - } - return current_buffer; -} - -void InfeedManager::ReleaseBuffers(const std::vector& buffers) { - { - tensorflow::mutex_lock l(mu_); - for (gpu::InfeedBuffer* b : buffers) { - CHECK(ContainsKey(dequeued_buffer_, b)); - dequeued_buffer_.erase(b); - } - } - for (gpu::InfeedBuffer* b : buffers) { - b->Done(); - } -} - se::Stream* InfeedManager::GetStream(se::StreamExecutor* executor) { + tensorflow::mutex_lock l(host_to_device_stream_mu_); if (host_to_device_executor_ == nullptr) { host_to_device_executor_ = executor; host_to_device_stream_ = MakeUnique(executor); @@ -100,10 +37,6 @@ se::Stream* InfeedManager::GetStream(se::StreamExecutor* executor) { return host_to_device_stream_.get(); } -void InfeedManager::RegisterOnEmptyCallback(std::function callback) { - on_empty_callbacks_.push_back(std::move(callback)); -} - InfeedManager* GetOrCreateInfeedManager() { static InfeedManager* manager = new InfeedManager; return manager; diff --git a/tensorflow/compiler/xla/service/gpu/infeed_manager.h b/tensorflow/compiler/xla/service/gpu/infeed_manager.h index a3fc15cfe36a490f38daabca9ff36fbb1012aead..7e418882e051a77e10bd12000bbc9769980f5f14 100644 --- a/tensorflow/compiler/xla/service/gpu/infeed_manager.h +++ b/tensorflow/compiler/xla/service/gpu/infeed_manager.h @@ -20,12 +20,9 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_INFEED_MANAGER_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_INFEED_MANAGER_H_ -#include -#include - +#include "tensorflow/compiler/xla/service/gpu/xfeed_queue.h" +#include "tensorflow/compiler/xla/shape_tree.h" #include "tensorflow/compiler/xla/types.h" -#include "tensorflow/core/lib/gtl/flatset.h" -#include "tensorflow/core/platform/mutex.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" namespace xla { @@ -47,90 +44,41 @@ namespace gpu { // the client. The client manages the memory of the buffer. class InfeedBuffer { public: + InfeedBuffer() = default; InfeedBuffer(se::StreamExecutor* executor, int64 length) - : executor_(executor), length_(length) { - device_memory_ = executor_->AllocateArray(length); - CHECK(!device_memory_.is_null()); + : device_memory_(executor, executor->AllocateArray(length)), + length_(length) { + CHECK(!device_memory_->is_null()); } - ~InfeedBuffer() { executor_->Deallocate(&device_memory_); } - int64 length() const { return length_; } - // Callback to signal that this buffer is consumed. This helps the - // client to manage memory for the infeed buffers. - void Done() { delete this; } - - se::DeviceMemoryBase* device_memory() { return &device_memory_; } + se::DeviceMemoryBase* device_memory() { return device_memory_.ptr(); } private: - se::StreamExecutor* executor_; // Not owned. - const int64 length_; - se::DeviceMemoryBase device_memory_; + se::ScopedDeviceMemory device_memory_; + int64 length_; }; // Client-side class used to enqueue infeed buffers. -class InfeedManager { +class InfeedManager : public XfeedQueue> { public: - InfeedManager(); - - // Calls the completion callback for any enqueued buffers that have - // not been dequeued by the runtime, and empties the infeed - // queue. Reset may not be called while a runtime computation is - // processing a dequeued buffer. The only safe way to ensure this - // condition is to call Reset when no computation is taking place. - void Reset(); - - // Adds a set of buffers to the infeed queue atomically. buffer->Done - // will be called when the buffer will no longer be accessed by the - // InfeedManager, either as a result of a call to Reset or because the - // runtime has dequeued and used the buffer. - void EnqueueBuffers(const std::vector& buffers); - - // Blocks until the infeed queue is non-empty, then returns the - // buffer at the head of the queue. Adds the current buffer to the - // to-be released set. - InfeedBuffer* BlockingDequeueBuffer(); - - // Releases a set of buffers from the to-be released set. - void ReleaseBuffers(const std::vector& buffers); - // Returns a cached stream associated with an executor. Allocates a // new stream on the first invocation. On subsequent invocations, if // the cached executor is not the same as the requested executor, // returns null. se::Stream* GetStream(se::StreamExecutor* executor); - // Registers a callback that will be called when 'enqueued_buffer_' becomes - // empty. - void RegisterOnEmptyCallback(std::function callback); - private: - // TODO(b/30467474): Revisit if this mutex becomes a point of - // contention. - tensorflow::mutex mu_; - - // Condition variable that is signaled every time a buffer is - // enqueued to an empty queue. - tensorflow::condition_variable cv_; - - // InfeedBuffer* queue contents are not owned, but buffer->Done must - // be called when the buffer is no longer needed by the runtime. - std::deque enqueued_buffer_; - - // Buffers that are dequeued and currently being processed by the - // runtime. Not owned. - tensorflow::gtl::FlatSet dequeued_buffer_; + // Mutex for serializing the creation of host_to_device_stream_. + tensorflow::mutex host_to_device_stream_mu_; // Cached host to device stream for queuing infeed data. - std::unique_ptr host_to_device_stream_; + std::unique_ptr host_to_device_stream_ + GUARDED_BY(host_to_device_stream_mu_); // Executor that the host_to_device_stream belongs to. Not owned. - se::StreamExecutor* host_to_device_executor_; - - // List of callbacks which will be called when 'enqueued_buffer_' becomes - // empty. - std::vector> on_empty_callbacks_; + se::StreamExecutor* host_to_device_executor_ = nullptr; }; // Singleton creator-or-accessor: Returns the GPU infeed manager. diff --git a/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc b/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc index 62915febb11d5defa0e44b688eacabb16a7621da..fee6d2af3bfd4976f5845edf592e8310b55a3feb 100644 --- a/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/infeed_thunk.cc @@ -30,51 +30,68 @@ InfeedThunk::InfeedThunk( Status InfeedThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, se::Stream* stream, HloExecutionProfiler* profiler) { - VLOG(2) << "Infeeding to GPU "; + VLOG(2) << "Infeeding to GPU: " << hlo_instruction()->ToString(); auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); - // First copy the infeed data which is element 0 of the infeed instruction's - // two-tuple output (the other element is a token). - se::DeviceMemoryBase data_address = - buffer_allocations.GetDeviceAddress(infeed_slices_.element({0})); - InfeedManager* infeed_manager = GetOrCreateInfeedManager(); - std::vector infeed_buffers; - const Shape& data_shape = - ShapeUtil::GetTupleElementShape(hlo_instruction()->shape(), 0); - if (ShapeUtil::IsTuple(data_shape)) { - CHECK(!ShapeUtil::IsNestedTuple(data_shape)); - // Transfer the tuple elements first. + ShapeTree infeed_buffers = + GetOrCreateInfeedManager()->BlockingGetNextDestination(); + + { + // The infeed buffer has an extra outer tuple with a token. Adjust the index + // accordingly. + ShapeIndex index = {0}; + std::function*)> copy_tuple_contents = + [&](std::vector* tuple_element_addresses) { + const Shape& shape = ShapeUtil::GetSubshape(infeed_buffers.shape(), + ShapeIndexView(index, 1)); + // For the leaf buffers of the tuple copy the elements directly. + if (ShapeUtil::IsArray(shape)) { + const BufferAllocation::Slice& tuple_element_buffer = + infeed_slices_.element(index); + se::DeviceMemoryBase tuple_element_address = + buffer_allocations.GetDeviceAddress(tuple_element_buffer); + + InfeedBuffer* buffer = + infeed_buffers.mutable_element(ShapeIndexView(index, 1)); + stream->ThenMemcpy(&tuple_element_address, + *(buffer->device_memory()), buffer->length()); + tuple_element_addresses->push_back(tuple_element_address.opaque()); + return; + } + + const int64 tuple_element_count = ShapeUtil::TupleElementCount(shape); + index.push_back(0); + std::vector inner_tuple_element_addresses; + for (int64 i = 0; i < tuple_element_count; ++i) { + index.back() = i; + copy_tuple_contents(&inner_tuple_element_addresses); + } + index.pop_back(); + + // Create a buffer of pointers for non-leaf buffers. + CHECK_EQ(tuple_element_count, inner_tuple_element_addresses.size()); + auto host_size = inner_tuple_element_addresses.size() * sizeof(void*); + se::DeviceMemoryBase tuple_address = + buffer_allocations.GetDeviceAddress( + infeed_slices_.element(index)); + stream->ThenMemcpy(&tuple_address, + inner_tuple_element_addresses.data(), host_size); + tuple_element_addresses->push_back(tuple_address.opaque()); + }; + std::vector tuple_element_addresses; - for (int i = 0; i < ShapeUtil::TupleElementCount(data_shape); ++i) { - const BufferAllocation::Slice& tuple_element_buffer = - infeed_slices_.element({0, i}); - se::DeviceMemoryBase tuple_element_address = - buffer_allocations.GetDeviceAddress(tuple_element_buffer); - - InfeedBuffer* buffer = infeed_manager->BlockingDequeueBuffer(); - infeed_buffers.push_back(buffer); - stream->ThenMemcpy(&tuple_element_address, *(buffer->device_memory()), - buffer->length()); - tuple_element_addresses.push_back(tuple_element_address.opaque()); - } - // Transfer the tuple outer buffer. - auto host_size = tuple_element_addresses.size() * sizeof(void*); - stream->ThenMemcpy(&data_address, tuple_element_addresses.data(), - host_size); - } else { - InfeedBuffer* buffer = infeed_manager->BlockingDequeueBuffer(); - infeed_buffers.push_back(buffer); - stream->ThenMemcpy(&data_address, *(buffer->device_memory()), - buffer->length()); + copy_tuple_contents(&tuple_element_addresses); + CHECK_EQ(1, tuple_element_addresses.size()); } // Construct top-level tuple of infeed containing the data and the token. Use // a nullptr for the token, it should never be dereferenced. - std::vector infeed_addresses = {data_address.opaque(), nullptr}; + se::DeviceMemoryBase data_address = + buffer_allocations.GetDeviceAddress(infeed_slices_.element({0})); + void* infeed_addresses[] = {data_address.opaque(), nullptr}; se::DeviceMemoryBase top_level_address = buffer_allocations.GetDeviceAddress(infeed_slices_.element({})); - stream->ThenMemcpy(&top_level_address, infeed_addresses.data(), - 2 * sizeof(void*)); + stream->ThenMemcpy(&top_level_address, infeed_addresses, 2 * sizeof(void*)); Status block_status = stream->BlockHostUntilDone(); if (!block_status.ok()) { @@ -82,8 +99,6 @@ Status InfeedThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, stream, block_status.error_message().c_str()); } - infeed_manager->ReleaseBuffers(infeed_buffers); - VLOG(2) << "Infeeding to GPU complete"; return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc index 64ed3d748febd8281a8e602194b31c937a4a682a..0f2c83aeb2633a007559d8caac78ea2d233539ed 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc @@ -73,6 +73,67 @@ bool IsIEEEFloatingPointScalarConstant(const HloInstruction* constant) { } } +// This function limits the maximum number of operands to a fusion. +// +// There's a cap on how many parameters we can pass to a CUDA kernel, but +// exactly what that limit is is hazy, as it depends on (among other things) how +// much GPU constant memory is in use for other purposes. +// +// Moreover, we don't even know at the point that we're running fusion how many +// arguments the CUDA kernel for a fusion node will have: It depends on buffer +// assignment, where we will decide which of the fusion's operands live in XLA's +// big temp buffer versus in other allocations. +// +// As a heuristic, we simply cap the number of fusion operands plus outputs at +// kMaxOperandsAndOutputsPerFusion. This puts an upper bound on the number of +// parameters to the kernel, working around the correctness problem. +// +// This limit is also often good for performance. In a fusion with many +// operands, each GPU thread likely has to do a lot of work, and so possibly +// uses a lot of registers, thus limiting occupancy. +/*static*/ bool GpuInstructionFusion::FusionWouldBeTooLarge( + const HloInstruction* a, const HloInstruction* b) { + // Compute the number of outputs of the (possibly multi-output) fusion node + // we're considering creating. + // + // This isn't precise; we may be off by one if + // - We're creating a multi-output fusion out of two non-MOFs. Creating a + // MOF adds a new buffer, namely, the tuple buffer. + // - We're merging two MOFs. In this case, we should count the tuple buffer + // only once. + // - WLOG there's an edge from `a` to `b` and `b` is the only consumer of + // `a`. In this case the result of `a` is not part of the output of the + // fusion. + // + // But because this is a heuristic and our limit + // kMaxOperandsAndOutputsPerFusion is a large value (so +/- 1 doesn't make a + // big difference), we ignore this small inaccuracy in favor of simplicity. + int64 num_output_buffers = ShapeUtil::SubshapeCount(a->shape()) + + ShapeUtil::SubshapeCount(b->shape()); + + // The new fusion will have no more operands and outputs than + // producer_operands + consumer_operands - 1 + num_output_buffers + // (minus one because we may be fusing a producer->consumer edge between `a` + // and `b`). + // + // This fact may be enough to let us avoid having to compute the true total + // number of operands, which can be expensive. + if (a->operand_count() + b->operand_count() - 1 + num_output_buffers <= + kMaxOperandsAndOutputsPerFusion) { + return false; + } + + // Compute the precise number of operands to the new fusion. + tensorflow::gtl::FlatSet operands( + a->operands().begin(), a->operands().end()); + operands.insert(b->operands().begin(), b->operands().end()); + // If there's an edge between `a` and `b`, don't count it: We're fusing that + // producer -> consumer relationship. + operands.erase(a); + operands.erase(b); + return operands.size() + num_output_buffers > kMaxOperandsAndOutputsPerFusion; +} + bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer, int64 operand_index) { HloInstruction* producer = consumer->mutable_operand(operand_index); @@ -141,6 +202,7 @@ bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer, IsIEEEFloatingPointScalarConstant(producer->operand(0)) && fused_parameter_users[0]->opcode() == HloOpcode::kMultiply; } + return false; } // Other output fusions are not currently supported on GPUs. @@ -183,8 +245,13 @@ bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer, return true; } - return IsFusile(*producer) && IsFusile(*consumer) && - InstructionFusion::ShouldFuse(consumer, operand_index); + if (!IsFusile(*producer) || !IsFusile(*consumer) || + !InstructionFusion::ShouldFuse(consumer, operand_index)) { + return false; + } + + // We put this check last because it's potentially expensive. + return !FusionWouldBeTooLarge(consumer, producer); } bool GpuInstructionFusion::ShouldFuseIntoMultiOutput(HloInstruction* consumer, diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion.h b/tensorflow/compiler/xla/service/gpu/instruction_fusion.h index f629d9ff2c7165b652369612c30979150f93bd24..c91f6343a69268ca687004dbe0ffbb863271a95c 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion.h +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion.h @@ -27,6 +27,19 @@ class GpuInstructionFusion : public InstructionFusion { explicit GpuInstructionFusion(bool may_duplicate) : InstructionFusion(GpuInstructionFusion::IsExpensive, may_duplicate) {} + // Maximum number of operands plus outputs allowed on a single fusion node. + // Exposed publicly mainly for tests. + static constexpr int64 kMaxOperandsAndOutputsPerFusion = 64; + + // Determines whether the combination of `a` and `b` into a (possibly + // multi-output) fusion would be "too large" -- i.e., have more operands and + // outputs than is allowed. + // + // `ShouldFuse` and `ShouldFuseIntoMultiOutput` call this; it's public so that + // other fusion passes (e.g. GPU multi-output fusion) can also call this. + static bool FusionWouldBeTooLarge(const HloInstruction* a, + const HloInstruction* b); + static bool IsExpensive(const HloInstruction& instruction); bool ShouldFuse(HloInstruction* consumer, int64 operand_index) override; diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc index 1963d9eef72d41fa0a275bea98f959671fa7e737..8d0522bd8fd6659e64d18c52807df8dc7fc2f3b8 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc @@ -33,7 +33,7 @@ TEST_F(InstructionFusionTest, CostlyProducerAndOperandElementReusingConsumerNotFused) { HloComputation::Builder builder(TestName()); HloInstruction* const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(5))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(5))); HloInstruction* exp1 = builder.AddInstruction(HloInstruction::CreateUnary( ShapeUtil::MakeShape(S32, {}), HloOpcode::kExp, const0)); HloInstruction* broadcast2 = @@ -53,7 +53,7 @@ TEST_F(InstructionFusionTest, NonCostlyProducerAndOperandElementReusingConsumerFused) { HloComputation::Builder builder(TestName()); HloInstruction* const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(5))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(5))); HloInstruction* negate1 = builder.AddInstruction(HloInstruction::CreateUnary( ShapeUtil::MakeShape(S32, {}), HloOpcode::kNegate, const0)); HloInstruction* broadcast2 = @@ -73,7 +73,7 @@ TEST_F(InstructionFusionTest, CostlyProducerAndNonOperandElementReusingConsumerFused_Reshape) { HloComputation::Builder builder(TestName()); HloInstruction* const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(5))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(5))); HloInstruction* exp1 = builder.AddInstruction(HloInstruction::CreateUnary( ShapeUtil::MakeShape(S32, {}), HloOpcode::kExp, const0)); HloInstruction* reshape2 = builder.AddInstruction( @@ -92,7 +92,7 @@ TEST_F(InstructionFusionTest, CostlyProducerAndNonOperandElementReusingConsumerFused_Transpose) { HloComputation::Builder builder(TestName()); HloInstruction* const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(5))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(5))); HloInstruction* exp1 = builder.AddInstruction(HloInstruction::CreateUnary( ShapeUtil::MakeShape(S32, {}), HloOpcode::kExp, const0)); HloInstruction* transpose2 = builder.AddInstruction( @@ -606,5 +606,35 @@ TEST_F(InstructionFusionTest, FuseScalarConstant) { op::Parameter())); } +// Check that we limit the number of operands to fusions we create. +TEST_F(InstructionFusionTest, AvoidsLargeFusion) { + constexpr int64 kNumParams = 200; + ASSERT_GT(kNumParams, GpuInstructionFusion::kMaxOperandsAndOutputsPerFusion); + + // Compute p0 + p1 + ... + pN. + HloComputation::Builder b(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {10, 100}); + auto param0 = + b.AddInstruction(HloInstruction::CreateParameter(0, shape, "p")); + auto sum = param0; + for (int64 i = 1; i < kNumParams; ++i) { + auto param = + b.AddInstruction(HloInstruction::CreateParameter(i, shape, "p")); + sum = b.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, sum, param)); + } + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(b.Build()); + EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()); + SCOPED_TRACE(module->ToString()); + for (const HloInstruction* instr : computation->instructions()) { + EXPECT_LE(instr->operand_count(), + GpuInstructionFusion::kMaxOperandsAndOutputsPerFusion) + << instr->ToString(); + } +} + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc index 388aa35d7dceeef92dbdb6c8a3bb7fb3796a0b61..6352b330d17d77da65ed4ffb5a225535ff6caf82 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc @@ -81,11 +81,6 @@ bool DotImplementedAsGemm(const HloInstruction& dot) { } // namespace bool ImplementedAsGemm(const HloInstruction& hlo) { - // We can only do this if the HLO is unnested. - if (hlo.parent() != hlo.GetModule()->entry_computation()) { - return false; - } - // For certain types of Dot, we can call pre-canned BLAS gemm. if (hlo.opcode() == HloOpcode::kDot) { return DotImplementedAsGemm(hlo); @@ -242,15 +237,17 @@ llvm::Value* EmitPrintf(tensorflow::StringPiece fmt, arguments_ptr}); } -llvm::Value* EmitShuffleDown(llvm::Value* value, llvm::Value* offset, - llvm::IRBuilder<>* builder) { +llvm::Value* EmitFullWarpShuffleDown(llvm::Value* value, llvm::Value* offset, + llvm::IRBuilder<>* builder) { int bit_width = value->getType()->getPrimitiveSizeInBits(); + llvm::Value* all_warps_mask = builder->getInt32(-1); // Special case for efficiency if (value->getType()->isFloatTy() && bit_width == 32) { return llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::nvvm_shfl_down_f32, - {value, offset, builder->getInt32(kWarpSize - 1)}, {}, builder); + llvm::Intrinsic::nvvm_shfl_sync_down_f32, + {all_warps_mask, value, offset, builder->getInt32(kWarpSize - 1)}, {}, + builder); } // We must split values wider than 32 bits as the "shfl" instruction operates @@ -264,10 +261,11 @@ llvm::Value* EmitShuffleDown(llvm::Value* value, llvm::Value* offset, for (int i = 0; i < num_segments; ++i) { x = builder->CreateInsertElement( x, - llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::nvvm_shfl_down_i32, - {builder->CreateExtractElement(x, i), - offset, builder->getInt32(kWarpSize - 1)}, - {}, builder), + llvm_ir::EmitCallToIntrinsic( + llvm::Intrinsic::nvvm_shfl_sync_down_i32, + {all_warps_mask, builder->CreateExtractElement(x, i), offset, + builder->getInt32(kWarpSize - 1)}, + {}, builder), i); } return builder->CreateBitCast( diff --git a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.h b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.h index 59455f389e733fee2d6cace7486f919a0c5e834e..5d23a3d01842c7b4ff405171cd49c96a19f7e5b0 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.h @@ -31,6 +31,12 @@ namespace gpu { constexpr int64 kWarpSize = 32; // Returns true if `hlo` will be implemented as a call to BLAS gemm. +// +// Precondition: `hlo` is in an "unnested context", meaning, it lives within the +// entry computation, within the either of a while loop's subcomputations, +// within any of a conditional's subcomputations, etc., but *does not* live +// within a reduce subcomputation, a map subcomputation, a fusion +// subcomputation, etc. It's OK if `hlo` *is* a fusion. bool ImplementedAsGemm(const HloInstruction& hlo); // A call to cuDNN for batch normalization is represented as CustomCall HLO with @@ -125,13 +131,17 @@ llvm::Value* EmitPrintf(tensorflow::StringPiece fmt, llvm::IRBuilder<>* builder); // Emits code to shuffle data between threads of a warp. This has the same -// semantics as the PTX "shfl.down" instruction [0] but works for values of any -// size. The last operand of the emitted "shfl" is `kWarpSize - 1`. +// semantics as the PTX "shfl.sync.down" instruction but works for values that +// aren't 32 bits in size. The last operand of the emitted "shfl" is +// `kWarpSize - 1`. +// +// This function emits a "full-warp" shuffle, which all threads of a warp +// participate in. *Do not use this function from a divergent context:* You +// can't correctly do so on both Volta and earlier GPUs. // -// [0] -// http://docs.nvidia.com/cuda/parallel-thread-execution/#data-movement-and-conversion-instructions-shfl -llvm::Value* EmitShuffleDown(llvm::Value* value, llvm::Value* offset, - llvm::IRBuilder<>* builder); +// https://docs.nvidia.com/cuda/parallel-thread-execution/#data-movement-and-conversion-instructions-shfl-sync +llvm::Value* EmitFullWarpShuffleDown(llvm::Value* value, llvm::Value* offset, + llvm::IRBuilder<>* builder); } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index fe83d017f4cde36cac37400ed16faab225878ea7..1295e83c0c4c16a1a18eaaadbafb5fd226be6eff 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -57,12 +57,12 @@ IrEmitter::IrEmitter(const HloModuleConfig& hlo_module_config, IrEmitterContext* ir_emitter_context, bool is_nested) : ir_emitter_context_(ir_emitter_context), module_(ir_emitter_context->llvm_module()), - ir_builder_(module_->getContext()), + b_(module_->getContext()), bindings_(ir_emitter_context->hlo_module(), - &ir_emitter_context->buffer_assignment(), &ir_builder_, module_, + &ir_emitter_context->buffer_assignment(), &b_, module_, is_nested), hlo_module_config_(hlo_module_config) { - ir_builder_.setFastMathFlags(llvm_ir::GetFastMathFlags( + b_.setFastMathFlags(llvm_ir::GetFastMathFlags( /*fast_math_enabled=*/hlo_module_config.debug_options() .xla_enable_fast_math())); } @@ -71,30 +71,16 @@ Status IrEmitter::DefaultAction(HloInstruction* hlo) { ElementalIrEmitter::HloToElementGeneratorMap operand_to_generator; for (const HloInstruction* operand : hlo->operands()) { operand_to_generator[operand] = [=](const llvm_ir::IrArray::Index& index) { - return GetIrArray(*operand, *hlo) - .EmitReadArrayElement(index, &ir_builder_); + return GetIrArray(*operand, *hlo).EmitReadArrayElement(index, &b_); }; } return EmitTargetElementLoop( - *hlo, GpuElementalIrEmitter(hlo_module_config_, module_, &ir_builder_, + *hlo, GpuElementalIrEmitter(hlo_module_config_, module_, &b_, GetNestedComputer()) .MakeElementGenerator(hlo, operand_to_generator)); } Status IrEmitter::HandleConstant(HloInstruction* constant) { - const Literal& literal = constant->literal(); - llvm::Constant* initializer = - llvm_ir::ConvertLiteralToIrConstant(literal, module_); - llvm::GlobalVariable* global_for_const = new llvm::GlobalVariable( - *module_, initializer->getType(), - /*isConstant=*/true, llvm::GlobalValue::PrivateLinkage, initializer, - /*Name=*/""); - VLOG(2) << "HandleConstant: " << constant->ToString() << std::endl - << " emitted_value: " << llvm_ir::DumpToString(*global_for_const) - << std::endl - << " its type: " - << llvm_ir::DumpToString(*global_for_const->getType()); - bindings_.BindHloToIrValue(*constant, global_for_const); return Status::OK(); } @@ -119,15 +105,10 @@ Status IrEmitter::HandleGetTupleElement(HloInstruction* get_tuple_element) { get_tuple_element->shape(), get_tuple_element->tuple_index(), // TODO(b/26344050): tighten the alignment here // based on the real element type. - /*alignment=*/1, GetBasePointer(*operand), &ir_builder_, module_)); + /*alignment=*/1, GetBasePointer(*operand), &b_, module_)); return Status::OK(); } -Status IrEmitter::HandleSort(HloInstruction*) { - // TODO(b/26783907): Implement sort on GPU. - return Unimplemented("sort"); -} - Status IrEmitter::HandleSend(HloInstruction*) { return Unimplemented("Send is not implemented on GPU"); } @@ -149,8 +130,7 @@ Status IrEmitter::HandleTuple(HloInstruction* tuple) { for (const HloInstruction* operand : tuple->operands()) { base_ptrs.push_back(GetBasePointer(*operand)); } - llvm_ir::EmitTuple(GetIrArray(*tuple, *tuple), base_ptrs, &ir_builder_, - module_); + llvm_ir::EmitTuple(GetIrArray(*tuple, *tuple), base_ptrs, &b_, module_); return Status::OK(); } @@ -171,7 +151,7 @@ Status IrEmitter::EmitCallToNestedComputation( std::vector arguments(operands.begin(), operands.end()); arguments.push_back(output); arguments.push_back(bindings_.GetTempBufferBase()); - ir_builder_.CreateCall(emitted_function, arguments); + b_.CreateCall(emitted_function, arguments); return Status::OK(); } @@ -193,21 +173,20 @@ bool IrEmitter::MaybeEmitDirectAtomicOperation( computation.root_instruction()->shape().element_type(); bool is_atomic_integral = element_type == S32 || element_type == U32 || element_type == S64 || element_type == U64; - llvm::Value* source = ir_builder_.CreateLoad(source_address, "source"); + llvm::Value* source = b_.CreateLoad(source_address, "source"); if (root_opcode == HloOpcode::kAdd) { // NVPTX supports atomicAdd on F32 and integer types. if (element_type == F32) { // F32 + F32 llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::nvvm_atomic_load_add_f32, {output_address, source}, - {output_address->getType()}, &ir_builder_); + {output_address->getType()}, &b_); return true; } if (is_atomic_integral) { // integral + integral - ir_builder_.CreateAtomicRMW(llvm::AtomicRMWInst::Add, output_address, - source, - llvm::AtomicOrdering::SequentiallyConsistent); + b_.CreateAtomicRMW(llvm::AtomicRMWInst::Add, output_address, source, + llvm::AtomicOrdering::SequentiallyConsistent); return true; } } @@ -218,8 +197,8 @@ bool IrEmitter::MaybeEmitDirectAtomicOperation( auto opcode = primitive_util::IsSignedIntegralType(element_type) ? llvm::AtomicRMWInst::Max : llvm::AtomicRMWInst::UMax; - ir_builder_.CreateAtomicRMW(opcode, output_address, source, - llvm::AtomicOrdering::SequentiallyConsistent); + b_.CreateAtomicRMW(opcode, output_address, source, + llvm::AtomicOrdering::SequentiallyConsistent); return true; } @@ -228,8 +207,8 @@ bool IrEmitter::MaybeEmitDirectAtomicOperation( auto opcode = primitive_util::IsSignedIntegralType(element_type) ? llvm::AtomicRMWInst::Min : llvm::AtomicRMWInst::UMin; - ir_builder_.CreateAtomicRMW(opcode, output_address, source, - llvm::AtomicOrdering::SequentiallyConsistent); + b_.CreateAtomicRMW(opcode, output_address, source, + llvm::AtomicOrdering::SequentiallyConsistent); return true; } @@ -301,20 +280,20 @@ Status IrEmitter::EmitAtomicOperationUsingCAS(const HloComputation& computation, llvm::Type* element_address_type = element_type->getPointerTo(); int atomic_size = (element_size < 32) ? 32 : element_size; - llvm::Type* atomic_type = ir_builder_.getIntNTy(atomic_size); + llvm::Type* atomic_type = b_.getIntNTy(atomic_size); llvm::Type* atomic_address_type = atomic_type->getPointerTo(output_address_type->getPointerAddressSpace()); // cas_old_output_address and cas_new_output_address point to the scratch // memory where we store the old and new values for the repeated atomicCAS // operations. - llvm::Value* cas_old_output_address = ir_builder_.CreateAlloca( + llvm::Value* cas_old_output_address = b_.CreateAlloca( atomic_type, /*ArraySize=*/nullptr, "cas_old_output_address"); - llvm::Value* cas_new_output_address = ir_builder_.CreateAlloca( + llvm::Value* cas_new_output_address = b_.CreateAlloca( atomic_type, /*ArraySize=*/nullptr, "cas_new_output_address"); // Emit preparation code to the preheader. - llvm::BasicBlock* loop_preheader_bb = ir_builder_.GetInsertBlock(); + llvm::BasicBlock* loop_preheader_bb = b_.GetInsertBlock(); llvm::Value* atomic_memory_address; // binop_output_address points to the scratch memory that stores the @@ -325,77 +304,71 @@ Status IrEmitter::EmitAtomicOperationUsingCAS(const HloComputation& computation, CHECK_EQ((element_size % sizeof(char)), 0); llvm::Type* address_int_type = module_->getDataLayout().getIntPtrType(output_address_type); - atomic_memory_address = - ir_builder_.CreatePtrToInt(output_address, address_int_type); + atomic_memory_address = b_.CreatePtrToInt(output_address, address_int_type); llvm::Value* mask = llvm::ConstantInt::get(address_int_type, 3); - llvm::Value* offset = ir_builder_.CreateAnd(atomic_memory_address, mask); + llvm::Value* offset = b_.CreateAnd(atomic_memory_address, mask); mask = llvm::ConstantInt::get(address_int_type, -4); - atomic_memory_address = ir_builder_.CreateAnd(atomic_memory_address, mask); + atomic_memory_address = b_.CreateAnd(atomic_memory_address, mask); atomic_memory_address = - ir_builder_.CreateIntToPtr(atomic_memory_address, atomic_address_type); - binop_output_address = ir_builder_.CreateAdd( - ir_builder_.CreatePtrToInt(cas_new_output_address, address_int_type), - offset); + b_.CreateIntToPtr(atomic_memory_address, atomic_address_type); + binop_output_address = b_.CreateAdd( + b_.CreatePtrToInt(cas_new_output_address, address_int_type), offset); binop_output_address = - ir_builder_.CreateIntToPtr(binop_output_address, element_address_type); + b_.CreateIntToPtr(binop_output_address, element_address_type); } else { atomic_memory_address = - ir_builder_.CreateBitCast(output_address, atomic_address_type); + b_.CreateBitCast(output_address, atomic_address_type); binop_output_address = - ir_builder_.CreateBitCast(cas_new_output_address, element_address_type); + b_.CreateBitCast(cas_new_output_address, element_address_type); } // Use the value from the memory that atomicCAS operates on to initialize // cas_old_output. llvm::Value* cas_old_output = - ir_builder_.CreateLoad(atomic_memory_address, "cas_old_output"); - ir_builder_.CreateStore(cas_old_output, cas_old_output_address); + b_.CreateLoad(atomic_memory_address, "cas_old_output"); + b_.CreateStore(cas_old_output, cas_old_output_address); llvm::BasicBlock* loop_exit_bb = loop_preheader_bb->splitBasicBlock( - ir_builder_.GetInsertPoint(), "atomic_op_loop_exit"); - llvm::BasicBlock* loop_body_bb = - llvm::BasicBlock::Create(ir_builder_.getContext(), "atomic_op_loop_body", - ir_builder_.GetInsertBlock()->getParent()); - ir_builder_.SetInsertPoint(loop_body_bb); + b_.GetInsertPoint(), "atomic_op_loop_exit"); + llvm::BasicBlock* loop_body_bb = llvm::BasicBlock::Create( + b_.getContext(), "atomic_op_loop_body", b_.GetInsertBlock()->getParent()); + b_.SetInsertPoint(loop_body_bb); // Change preheader's successor from loop_exit_bb to loop_body_bb. loop_preheader_bb->getTerminator()->setSuccessor(0, loop_body_bb); // Emit the body of the loop that repeatedly invokes atomicCAS. // // Use cas_old_output to initialize cas_new_output. - cas_old_output = - ir_builder_.CreateLoad(cas_old_output_address, "cas_old_output"); - ir_builder_.CreateStore(cas_old_output, cas_new_output_address); + cas_old_output = b_.CreateLoad(cas_old_output_address, "cas_old_output"); + b_.CreateStore(cas_old_output, cas_new_output_address); // Emits code to calculate new_output = operation(old_output, source); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( computation, {binop_output_address, source_address}, binop_output_address)); llvm::Value* cas_new_output = - ir_builder_.CreateLoad(cas_new_output_address, "cas_new_output"); + b_.CreateLoad(cas_new_output_address, "cas_new_output"); // Emit code to perform the atomicCAS operation // (cas_old_output, success) = atomicCAS(memory_address, cas_old_output, // cas_new_output); - llvm::Value* ret_value = ir_builder_.CreateAtomicCmpXchg( + llvm::Value* ret_value = b_.CreateAtomicCmpXchg( atomic_memory_address, cas_old_output, cas_new_output, llvm::AtomicOrdering::SequentiallyConsistent, llvm::AtomicOrdering::SequentiallyConsistent); // Extract the memory value returned from atomicCAS and store it as // cas_old_output. - ir_builder_.CreateStore( - ir_builder_.CreateExtractValue(ret_value, 0, "cas_old_output"), - cas_old_output_address); + b_.CreateStore(b_.CreateExtractValue(ret_value, 0, "cas_old_output"), + cas_old_output_address); // Extract the success bit returned from atomicCAS and generate a // conditional branch on the success bit. - ir_builder_.CreateCondBr( - ir_builder_.CreateExtractValue(ret_value, 1, "success"), loop_exit_bb, - loop_body_bb); + b_.CreateCondBr(b_.CreateExtractValue(ret_value, 1, "success"), loop_exit_bb, + loop_body_bb); // Set the insertion point to the exit basic block so that the caller of // this method can continue emitting code to the right place. - SetToFirstInsertPoint(loop_exit_bb, &ir_builder_); + SetToFirstInsertPoint(loop_exit_bb, &b_); return Status::OK(); } @@ -438,32 +411,32 @@ Status IrEmitter::HandleTupleSelect(HloInstruction* tuple_select) { llvm_ir::EmitTupleSelect(GetIrArray(*tuple_select, *tuple_select), GetIrArray(*pred, *tuple_select), GetBasePointer(*on_true), GetBasePointer(*on_false), - &ir_builder_, module_); + &b_, module_); return Status::OK(); } namespace { -llvm::Value* Real(llvm::Value* x, llvm::IRBuilder<>* ir_builder) { - return ir_builder->CreateExtractValue(x, {0}); -} - -llvm::Value* Imag(llvm::Value* x, llvm::IRBuilder<>* ir_builder) { - return ir_builder->CreateExtractValue(x, {1}); -} - -std::pair MultiplyComplex( - llvm::Value* lhs_value, llvm::Value* rhs_value, - llvm::IRBuilder<>* ir_builder) { - llvm::Value* lhs_real = Real(lhs_value, ir_builder); - llvm::Value* lhs_imag = Imag(lhs_value, ir_builder); - llvm::Value* rhs_real = Real(rhs_value, ir_builder); - llvm::Value* rhs_imag = Imag(rhs_value, ir_builder); - llvm::Value* real_result1 = ir_builder->CreateFMul(lhs_real, rhs_real); - llvm::Value* real_result2 = ir_builder->CreateFMul(lhs_imag, rhs_imag); - llvm::Value* real_result = ir_builder->CreateFSub(real_result1, real_result2); - llvm::Value* imag_result1 = ir_builder->CreateFMul(lhs_real, rhs_imag); - llvm::Value* imag_result2 = ir_builder->CreateFMul(lhs_imag, rhs_real); - llvm::Value* imag_result = ir_builder->CreateFAdd(imag_result1, imag_result2); +llvm::Value* Real(llvm::Value* x, llvm::IRBuilder<>* b) { + return b->CreateExtractValue(x, {0}); +} + +llvm::Value* Imag(llvm::Value* x, llvm::IRBuilder<>* b) { + return b->CreateExtractValue(x, {1}); +} + +std::pair MultiplyComplex(llvm::Value* lhs_value, + llvm::Value* rhs_value, + llvm::IRBuilder<>* b) { + llvm::Value* lhs_real = Real(lhs_value, b); + llvm::Value* lhs_imag = Imag(lhs_value, b); + llvm::Value* rhs_real = Real(rhs_value, b); + llvm::Value* rhs_imag = Imag(rhs_value, b); + llvm::Value* real_result1 = b->CreateFMul(lhs_real, rhs_real); + llvm::Value* real_result2 = b->CreateFMul(lhs_imag, rhs_imag); + llvm::Value* real_result = b->CreateFSub(real_result1, real_result2); + llvm::Value* imag_result1 = b->CreateFMul(lhs_real, rhs_imag); + llvm::Value* imag_result2 = b->CreateFMul(lhs_imag, rhs_real); + llvm::Value* imag_result = b->CreateFAdd(imag_result1, imag_result2); return {real_result, imag_result}; } } // namespace @@ -479,25 +452,24 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { const Shape& rhs_shape = rhs_instruction->shape(); // TODO(b/110211620): Convert to use i32 index_type when it is possible. - llvm::Type* index_type = ir_builder_.getInt64Ty(); + llvm::Type* index_type = b_.getInt64Ty(); llvm_ir::IrArray::Index element_index(index_type); if (ShapeUtil::IsScalar(lhs_shape) && ShapeUtil::IsScalar(rhs_shape)) { // If the operands are scalar, don't emit any loops. llvm::Value* lhs_value = - lhs_array.EmitReadArrayElement(/*index=*/element_index, &ir_builder_); + lhs_array.EmitReadArrayElement(/*index=*/element_index, &b_); llvm::Value* rhs_value = - rhs_array.EmitReadArrayElement(/*index=*/element_index, &ir_builder_); + rhs_array.EmitReadArrayElement(/*index=*/element_index, &b_); llvm::Value* result; if (ShapeUtil::ElementIsComplex(lhs_shape)) { - auto value = MultiplyComplex(lhs_value, rhs_value, &ir_builder_); + auto value = MultiplyComplex(lhs_value, rhs_value, &b_); result = llvm::ConstantAggregateZero::get(lhs_array.GetElementLlvmType()); - result = ir_builder_.CreateInsertValue(result, value.first, {0}); - result = ir_builder_.CreateInsertValue(result, value.second, {1}); + result = b_.CreateInsertValue(result, value.first, {0}); + result = b_.CreateInsertValue(result, value.second, {1}); } else { - result = ir_builder_.CreateFMul(lhs_value, rhs_value); + result = b_.CreateFMul(lhs_value, rhs_value); } - target_array.EmitWriteArrayElement(/*index=*/element_index, result, - &ir_builder_); + target_array.EmitWriteArrayElement(/*index=*/element_index, result, &b_); return Status::OK(); } @@ -524,11 +496,11 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { // Create loop nests which loop through the LHS operand dimensions and the RHS // operand dimensions. The reduction dimension of the LHS and RHS are handled // in a separate innermost loop which performs the sum of products. - llvm_ir::ForLoopNest loop_nest(IrName(dot), &ir_builder_); - llvm_ir::IrArray::Index lhs_index = EmitOperandArrayLoopNest( - lhs_array, lhs_reduction_dimension, "lhs", &loop_nest); - llvm_ir::IrArray::Index rhs_index = EmitOperandArrayLoopNest( - rhs_array, rhs_reduction_dimension, "rhs", &loop_nest); + llvm_ir::ForLoopNest loop_nest(IrName(dot), &b_); + llvm_ir::IrArray::Index lhs_index = loop_nest.EmitOperandArrayLoopNest( + lhs_array, /*dimension_to_skip=*/lhs_reduction_dimension, "lhs"); + llvm_ir::IrArray::Index rhs_index = loop_nest.EmitOperandArrayLoopNest( + rhs_array, /*dimension_to_skip=*/rhs_reduction_dimension, "rhs"); // Create the reduction loop which does the sum of products reduction. std::unique_ptr reduction_loop = loop_nest.AddLoop( @@ -548,7 +520,7 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { llvm::Value* accum_address = llvm_ir::EmitAllocaAtFunctionEntry( accum_type, // The pointee type of the alloca instruction. "accum_address", // The name of the alloca instruction. - &ir_builder_); + &b_); // Initialize the accumulator in the preheader to zero. new llvm::StoreInst( @@ -562,27 +534,25 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { // updated_accum = accum + lhs_element * rhs_element // *accum_address = updated_accum TF_RET_CHECK(!reduction_loop->GetBodyBasicBlock()->empty()); - ir_builder_.SetInsertPoint( + b_.SetInsertPoint( &*reduction_loop->GetBodyBasicBlock()->getFirstInsertionPt()); - llvm::Value* lhs_element = - lhs_array.EmitReadArrayElement(lhs_index, &ir_builder_); - llvm::Value* rhs_element = - rhs_array.EmitReadArrayElement(rhs_index, &ir_builder_); - llvm::Value* accum = ir_builder_.CreateLoad(accum_address); + llvm::Value* lhs_element = lhs_array.EmitReadArrayElement(lhs_index, &b_); + llvm::Value* rhs_element = rhs_array.EmitReadArrayElement(rhs_index, &b_); + llvm::Value* accum = b_.CreateLoad(accum_address); llvm::Value* updated_accum; if (ShapeUtil::ElementIsComplex(lhs_shape)) { - auto value = MultiplyComplex(lhs_element, rhs_element, &ir_builder_); - llvm::Value* accum_real = Real(accum, &ir_builder_); - llvm::Value* real_sum = ir_builder_.CreateFAdd(accum_real, value.first); - updated_accum = ir_builder_.CreateInsertValue(accum, real_sum, {0}); - llvm::Value* accum_imag = Imag(accum, &ir_builder_); - llvm::Value* imag_sum = ir_builder_.CreateFAdd(accum_imag, value.second); - updated_accum = ir_builder_.CreateInsertValue(updated_accum, imag_sum, {1}); + auto value = MultiplyComplex(lhs_element, rhs_element, &b_); + llvm::Value* accum_real = Real(accum, &b_); + llvm::Value* real_sum = b_.CreateFAdd(accum_real, value.first); + updated_accum = b_.CreateInsertValue(accum, real_sum, {0}); + llvm::Value* accum_imag = Imag(accum, &b_); + llvm::Value* imag_sum = b_.CreateFAdd(accum_imag, value.second); + updated_accum = b_.CreateInsertValue(updated_accum, imag_sum, {1}); } else { - llvm::Value* product = ir_builder_.CreateFMul(lhs_element, rhs_element); - updated_accum = ir_builder_.CreateFAdd(accum, product); + llvm::Value* product = b_.CreateFMul(lhs_element, rhs_element); + updated_accum = b_.CreateFAdd(accum, product); } - ir_builder_.CreateStore(updated_accum, accum_address); + b_.CreateStore(updated_accum, accum_address); // After the reduction loop exits, store the accumulator into the target // address. The index into the target address is the concatenation of the rhs @@ -599,16 +569,15 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { target_index.push_back(rhs_index[dimension]); } } - SetToFirstInsertPoint(reduction_loop->GetExitBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(reduction_loop->GetExitBasicBlock(), &b_); target_array.EmitWriteArrayElement( target_index, - ir_builder_.CreateLoad( - accum_address), // The value written to the target array. - &ir_builder_); + b_.CreateLoad(accum_address), // The value written to the target array. + &b_); // Set the IR builder insert point to the exit basic block of the outer most // loop. This ensures later instructions are inserted after this loop nest. - ir_builder_.SetInsertPoint(loop_nest.GetOuterLoopExitBasicBlock()); + b_.SetInsertPoint(loop_nest.GetOuterLoopExitBasicBlock()); return Status::OK(); } @@ -650,11 +619,10 @@ Status IrEmitter::HandleReduce(HloInstruction* reduce) { [=](const llvm_ir::IrArray::Index& index) -> StatusOr { // Initialize an accumulator with init_value. llvm::AllocaInst* accumulator_addr = - ir_builder_.CreateAlloca(llvm_ir::PrimitiveTypeToIrType( + b_.CreateAlloca(llvm_ir::PrimitiveTypeToIrType( reduce->shape().element_type(), module_)); - ir_builder_.CreateStore( - ir_builder_.CreateLoad(GetBasePointer(*init_value)), - accumulator_addr); + b_.CreateStore(b_.CreateLoad(GetBasePointer(*init_value)), + accumulator_addr); // The enclosing loops go over all the target elements. Now we have to // compute the actual target element. For this, we build a new loop nest @@ -662,12 +630,12 @@ Status IrEmitter::HandleReduce(HloInstruction* reduce) { // AddLoopsForShapeOnDimensions will return an Index where induction // Value*s are placed for each dimension in dimensions, and all the rest // are nullptrs. - llvm_ir::ForLoopNest loops(IrName(reduce, "inner"), &ir_builder_); + llvm_ir::ForLoopNest loops(IrName(reduce, "inner"), &b_); const llvm_ir::IrArray::Index reduced_dims_index = loops.AddLoopsForShapeOnDimensions(arg->shape(), dimensions, "reduction_dim"); - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &b_); // Build a full index for the input argument, using reduced_dims_index // as the base. In reduced_dims_index only the reduction dimensions are @@ -686,13 +654,12 @@ Status IrEmitter::HandleReduce(HloInstruction* reduce) { // Apply the reduction function to the loaded value. llvm::Value* input_address = - GetIrArray(*arg, *reduce) - .EmitArrayElementAddress(input_index, &ir_builder_); + GetIrArray(*arg, *reduce).EmitArrayElementAddress(input_index, &b_); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( *function, {accumulator_addr, input_address}, accumulator_addr)); - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_); - return ir_builder_.CreateLoad(accumulator_addr); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &b_); + return b_.CreateLoad(accumulator_addr); }); } @@ -705,8 +672,8 @@ Status IrEmitter::HandleFusion(HloInstruction* fusion) { for (HloInstruction* operand : fusion->operands()) { parameter_arrays.push_back(GetIrArray(*operand, *fusion)); } - GpuElementalIrEmitter elemental_emitter(hlo_module_config_, module_, - &ir_builder_, GetNestedComputer()); + GpuElementalIrEmitter elemental_emitter(hlo_module_config_, module_, &b_, + GetNestedComputer()); FusedIrEmitter fused_emitter(parameter_arrays, &elemental_emitter); TF_RETURN_IF_ERROR(fusion->fused_expression_root()->Accept(&fused_emitter)); @@ -736,24 +703,6 @@ Status IrEmitter::HandleOutfeed(HloInstruction*) { return Unimplemented("Outfeed is not supported on GPU."); } -Status IrEmitter::HandleRng(HloInstruction* random) { - ElementalIrEmitter::HloToElementGeneratorMap operand_to_generator; - for (const HloInstruction* operand : random->operands()) { - operand_to_generator[operand] = [=](const llvm_ir::IrArray::Index& index) { - return GetIrArray(*operand, *random) - .EmitReadArrayElement(index, &ir_builder_); - }; - } - // Emits a single-threaded loop because the loop body generated by the element - // generator for Rng can't be parallelized (b/32333178). - return llvm_ir::LoopEmitter( - GpuElementalIrEmitter(hlo_module_config_, module_, &ir_builder_, - GetNestedComputer()) - .MakeElementGenerator(random, operand_to_generator), - GetIrArray(*random, *random), &ir_builder_) - .EmitLoop(IrName(random)); -} - Status IrEmitter::HandleBatchNormInference(HloInstruction*) { return Unimplemented( "The GPU backend does not implement BatchNormInference directly. It " @@ -777,34 +726,9 @@ Status IrEmitter::HandleBatchNormGrad(HloInstruction*) { "to a cudnn CustomCall using CudnnBatchNormRewriter."); } -llvm_ir::IrArray::Index IrEmitter::EmitOperandArrayLoopNest( - const llvm_ir::IrArray& operand_array, int64 reduction_dimension, - tensorflow::StringPiece name_suffix, llvm_ir::ForLoopNest* loop_nest) { - // Prepares the dimension list we will use to emit the loop nest. Outermost - // loops are added first. Add loops in major-to-minor order, and skip the - // reduction dimension. - std::vector dimensions; - const Shape& shape = operand_array.GetShape(); - for (int i = 0; i < LayoutUtil::MinorToMajor(shape).size(); ++i) { - int64 dimension = LayoutUtil::Major(shape.layout(), i); - if (dimension != reduction_dimension) { - dimensions.push_back(dimension); - } - } - - // Create loop nest with one for-loop for each dimension of the - // output. - llvm_ir::IrArray::Index index = - loop_nest->AddLoopsForShapeOnDimensions(shape, dimensions, name_suffix); - // Verify every dimension except the reduction dimension was set in the index. - for (size_t dimension = 0; dimension < index.size(); ++dimension) { - if (dimension == reduction_dimension) { - DCHECK_EQ(nullptr, index[dimension]); - } else { - DCHECK_NE(nullptr, index[dimension]); - } - } - return index; +Status IrEmitter::HandleIota(HloInstruction*) { + // TODO(b/64798317): implement iota on GPU. + return Unimplemented("Iota is not implemented on GPU."); } StatusOr IrEmitter::ComputeNestedElement( @@ -813,16 +737,16 @@ StatusOr IrEmitter::ComputeNestedElement( llvm::Value* return_buffer = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType( computation.root_instruction()->shape().element_type(), module_), - "return_buffer", &ir_builder_); + "return_buffer", &b_); std::vector parameter_buffers; for (llvm::Value* parameter_element : parameter_elements) { parameter_buffers.push_back(llvm_ir::EmitAllocaAtFunctionEntry( - parameter_element->getType(), "parameter_buffer", &ir_builder_)); - ir_builder_.CreateStore(parameter_element, parameter_buffers.back()); + parameter_element->getType(), "parameter_buffer", &b_)); + b_.CreateStore(parameter_element, parameter_buffers.back()); } TF_RETURN_IF_ERROR(EmitCallToNestedComputation(computation, parameter_buffers, return_buffer)); - return ir_builder_.CreateLoad(return_buffer); + return b_.CreateLoad(return_buffer); } } // namespace gpu diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.h b/tensorflow/compiler/xla/service/gpu/ir_emitter.h index d2dd335f10cc8346c5f941e5c8c6b5c403722fa3..80e2a203ac3a1fbe95bf38a886288ea8be130148 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.h @@ -79,7 +79,6 @@ class IrEmitter : public DfsHloVisitorWithDefault { Status HandleCrossReplicaSum(HloInstruction* crs) override; Status HandleInfeed(HloInstruction* infeed) override; Status HandleOutfeed(HloInstruction* outfeed) override; - Status HandleSort(HloInstruction* sort) override; Status HandleSend(HloInstruction* send) override; Status HandleSendDone(HloInstruction* send_done) override; Status HandleRecv(HloInstruction* recv) override; @@ -92,10 +91,10 @@ class IrEmitter : public DfsHloVisitorWithDefault { Status HandleFusion(HloInstruction* fusion) override; Status HandleCall(HloInstruction* call) override; Status HandleCustomCall(HloInstruction* custom_call) override; - Status HandleRng(HloInstruction* random) override; Status HandleBatchNormInference(HloInstruction* batch_norm) override; Status HandleBatchNormTraining(HloInstruction* batch_norm) override; Status HandleBatchNormGrad(HloInstruction* batch_norm) override; + Status HandleIota(HloInstruction* iota) override; Status FinishVisit(HloInstruction* root) override { return Status::OK(); } @@ -162,7 +161,7 @@ class IrEmitter : public DfsHloVisitorWithDefault { // The following fields track the IR emission state. According to LLVM memory // management rules, their memory is owned by the module. - llvm::IRBuilder<> ir_builder_; + llvm::IRBuilder<> b_; // Mapping from HLO to its underlying LLVM value. HloToIrBindings bindings_; @@ -171,17 +170,6 @@ class IrEmitter : public DfsHloVisitorWithDefault { const HloModuleConfig& hlo_module_config_; private: - // Emits a series of nested loops for iterating over an operand array in the - // dot operation. Loops are constructed in major to minor dimension layout - // order. No loop is emitted for the given reduction_dimension. The function - // returns an IrArray index for the given operand_array containing the indvars - // of the loops. All dimensions of the index are filled except for the - // reduction dimension. name_suffix is the string to append to the names of - // LLVM constructs (eg, basic blocks) constructed by this method. - llvm_ir::IrArray::Index EmitOperandArrayLoopNest( - const llvm_ir::IrArray& operand_array, int64 reduction_dimension, - tensorflow::StringPiece name_suffix, llvm_ir::ForLoopNest* loop_nest); - // A helper method for EmitAtomicOperationForNestedComputation. Certain // computations, such as floating-point addition and integer maximization, can // be simply implemented using an LLVM atomic instruction. If "computation" is @@ -198,6 +186,13 @@ class IrEmitter : public DfsHloVisitorWithDefault { llvm::Value* output_address, llvm::Value* source_address); + // A helper method for HandleSort(). It adds the inner comparison loop where + // we compare elements pointed to by 'keys_index' and 'compare_keys_index'. + void EmitCompareLoop(int64 dimension_to_sort, + const llvm_ir::IrArray::Index& keys_index, + const llvm_ir::IrArray::Index& compare_keys_index, + const llvm_ir::IrArray& keys_array); + StatusOr ComputeNestedElement( const HloComputation& computation, tensorflow::gtl::ArraySlice parameter_elements); diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc index c9574c87a3be208915b3d6a32679553eb425d2f0..5c827e5f9cf3e1c04af444dae338a2ec411ce372 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc @@ -70,10 +70,10 @@ llvm::Function* IrEmitterNested::EmitBasePointersForNestedComputation( argument_dereferenceable_bytes.push_back(root_size); } // The base pointer of the memory block for all pre-allocated temp buffers. - argument_types.push_back(ir_builder_.getInt8PtrTy()); + argument_types.push_back(b_.getInt8PtrTy()); llvm::FunctionType* function_type = - llvm::FunctionType::get(ir_builder_.getVoidTy(), argument_types, false); + llvm::FunctionType::get(b_.getVoidTy(), argument_types, false); llvm::Function* function = llvm::Function::Create( function_type, // The function type. llvm::GlobalValue::InternalLinkage, // The linkage type. @@ -96,8 +96,7 @@ llvm::Function* IrEmitterNested::EmitBasePointersForNestedComputation( llvm::BasicBlock::Create(function->getContext(), "entry", function); // Emit a "return void" at entry_bb's end, and sets the insert point before // that return instruction. - ir_builder_.SetInsertPoint( - llvm::ReturnInst::Create(function->getContext(), entry_bb)); + b_.SetInsertPoint(llvm::ReturnInst::Create(function->getContext(), entry_bb)); std::vector non_io_hlos; for (const auto* hlo : nested_computation.instructions()) { @@ -127,20 +126,17 @@ Status IrEmitterNested::EmitTargetElementLoop( target_arrays.push_back(GetIrArray(hlo, hlo, {i})); } TF_RETURN_IF_ERROR( - llvm_ir::LoopEmitter(element_generator, target_arrays, &ir_builder_) - .EmitLoop()); + llvm_ir::LoopEmitter(element_generator, target_arrays, &b_).EmitLoop()); std::vector tuple_operand_ptrs; tuple_operand_ptrs.reserve(num_elems); for (const llvm_ir::IrArray& array : target_arrays) { tuple_operand_ptrs.push_back(array.GetBasePointer()); } - llvm_ir::EmitTuple(GetIrArray(hlo, hlo), tuple_operand_ptrs, &ir_builder_, - module_); + llvm_ir::EmitTuple(GetIrArray(hlo, hlo), tuple_operand_ptrs, &b_, module_); return Status::OK(); } - return llvm_ir::LoopEmitter(element_generator, GetIrArray(hlo, hlo), - &ir_builder_) + return llvm_ir::LoopEmitter(element_generator, GetIrArray(hlo, hlo), &b_) .EmitLoop(); } diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 80208e1c98506a2d69125aa80d08218f4716101f..874c7cfb8ae9c2f23c6af9b28f06395730dccf2d 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -28,11 +28,12 @@ limitations under the License. #include "llvm/IR/Instructions.h" #include "llvm/IR/LLVMContext.h" #include "llvm/IR/Module.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor.h" #include "tensorflow/compiler/xla/service/gpu/backend_configs.pb.h" +#include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" #include "tensorflow/compiler/xla/service/gpu/conditional_thunk.h" #include "tensorflow/compiler/xla/service/gpu/convolution_thunk.h" #include "tensorflow/compiler/xla/service/gpu/copy_thunk.h" @@ -48,6 +49,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/ir_emitter_context.h" #include "tensorflow/compiler/xla/service/gpu/kernel_thunk.h" #include "tensorflow/compiler/xla/service/gpu/memset_thunk.h" +#include "tensorflow/compiler/xla/service/gpu/outfeed_thunk.h" #include "tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h" #include "tensorflow/compiler/xla/service/gpu/partition_assignment.h" #include "tensorflow/compiler/xla/service/gpu/sequential_thunk.h" @@ -58,10 +60,12 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h" +#include "tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h" #include "tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" -#include "tensorflow/compiler/xla/service/llvm_ir/ops.h" +#include "tensorflow/compiler/xla/service/llvm_ir/sort_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h" #include "tensorflow/compiler/xla/service/name_uniquer.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -70,8 +74,10 @@ limitations under the License. #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/window_util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/bits.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/lib/gtl/optional.h" #include "tensorflow/core/platform/logging.h" namespace xla { @@ -79,6 +85,7 @@ namespace gpu { namespace { +using llvm_ir::IrArray; using llvm_ir::IrName; using tensorflow::gtl::ArraySlice; using tensorflow::gtl::InlinedVector; @@ -211,7 +218,7 @@ llvm::Function* IrEmitterUnnested::BuildKernelPrototype( llvm::LLVMContext& context = module->getContext(); llvm::FunctionType* kernel_type = llvm::FunctionType::get( /*Result=*/llvm::Type::getVoidTy(context), - std::vector(args.size(), ir_builder_.getInt8PtrTy()), + std::vector(args.size(), b_.getInt8PtrTy()), /*isVarArg=*/false); llvm::Function* kernel = llvm::Function::Create(kernel_type, llvm::GlobalValue::ExternalLinkage, @@ -226,9 +233,20 @@ llvm::Function* IrEmitterUnnested::BuildKernelPrototype( ++arg_it; kernel->addDereferenceableAttr(arg_no + 1, alloc->size()); + + const int64 alignment = [&] { + if (alloc->is_entry_computation_parameter()) { + return kEntryParameterAlignBytes; + } else if (alloc->is_constant()) { + return kConstantBufferAlignBytes; + } else { + return kXlaAllocatedBufferAlignBytes; + } + }(); + kernel->addParamAttr( - arg_no, llvm::Attribute::get(context, llvm::Attribute::Alignment, - kCudaMallocAlignBytes)); + arg_no, + llvm::Attribute::get(context, llvm::Attribute::Alignment, alignment)); if (alloc->IsPreallocatedTempBuffer()) { fn_arg->setName("temp_buf"); @@ -247,7 +265,7 @@ llvm::Function* IrEmitterUnnested::BuildKernelPrototype( nvvm_annotations_node->addOperand(llvm::MDNode::get( context, {llvm::ConstantAsMetadata::get(kernel), llvm::MDString::get(context, "kernel"), - llvm::ConstantAsMetadata::get(ir_builder_.getInt32(1))})); + llvm::ConstantAsMetadata::get(b_.getInt32(1))})); // Update the insert point to the entry basic block. llvm::BasicBlock* entry_bb = @@ -255,7 +273,7 @@ llvm::Function* IrEmitterUnnested::BuildKernelPrototype( // Emit a "return void" at entry_bb's end, and set the insert point before // that return instruction. - ir_builder_.SetInsertPoint(llvm::ReturnInst::Create(context, entry_bb)); + b_.SetInsertPoint(llvm::ReturnInst::Create(context, entry_bb)); return kernel; } @@ -293,7 +311,7 @@ int ComputeMaxUnrollFactor(const HloInstruction* hlo) { // range of i32. // Otherwise, the return type is i64. llvm::Type* GetIndexTypeForKernel(const HloInstruction* hlo, int64 launch_size, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { // Find the unnested hlo instructon for which the kernel is generated for. const HloInstruction* unnested_hlo = hlo; const HloComputation* computation = hlo->parent(); @@ -314,7 +332,7 @@ llvm::Type* GetIndexTypeForKernel(const HloInstruction* hlo, int64 launch_size, return in_range; }; - llvm::Type* i64_ty = ir_builder->getInt64Ty(); + llvm::Type* i64_ty = b->getInt64Ty(); // Check launch dimension if (!IsInt32(launch_size)) { return i64_ty; @@ -343,7 +361,7 @@ llvm::Type* GetIndexTypeForKernel(const HloInstruction* hlo, int64 launch_size, } } - return ir_builder->getInt32Ty(); + return b->getInt32Ty(); } } // namespace @@ -355,7 +373,8 @@ Status IrEmitterUnnested::DefaultAction(HloInstruction* hlo) { unroll_factor = ComputeMaxUnrollFactor(hlo); } - thunk_sequence_->emplace_back(BuildKernelThunk(hlo, unroll_factor)); + thunk_sequence_->emplace_back(BuildKernelThunk( + hlo, /*implements_whole_instruction=*/true, unroll_factor)); return IrEmitter::DefaultAction(hlo); } @@ -369,7 +388,8 @@ Status IrEmitterUnnested::HandleDot(HloInstruction* dot) { thunk_sequence_->emplace_back(BuildGemmThunk(dot)); return Status::OK(); } - thunk_sequence_->emplace_back(BuildKernelThunk(dot)); + thunk_sequence_->emplace_back( + BuildKernelThunk(dot, /*implements_whole_instruction=*/true)); return IrEmitter::HandleDot(dot); } @@ -379,7 +399,8 @@ Status IrEmitterUnnested::HandleConditional(HloInstruction* conditional) { } Status IrEmitterUnnested::HandleConvolution(HloInstruction* convolution) { - thunk_sequence_->emplace_back(BuildKernelThunk(convolution)); + thunk_sequence_->emplace_back( + BuildKernelThunk(convolution, /*implements_whole_instruction=*/true)); return IrEmitter::HandleConvolution(convolution); } @@ -586,16 +607,17 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { } } CHECK(first_reduce != nullptr); - thunks.push_back(BuildKernelThunk(fusion)); + thunks.push_back( + BuildKernelThunk(fusion, /*implements_whole_instruction=*/false)); thunk_sequence_->emplace_back( MakeUnique(std::move(thunks), fusion)); - std::vector parameter_arrays; + std::vector parameter_arrays; for (HloInstruction* operand : fusion->operands()) { parameter_arrays.push_back(GetIrArray(*operand, *fusion)); } GpuElementalIrEmitter elemental_emitter( - hlo_module_config_, ir_emitter_context_->llvm_module(), - &ir_builder_, GetNestedComputer()); + hlo_module_config_, ir_emitter_context_->llvm_module(), &b_, + GetNestedComputer()); FusedIrEmitter fused_emitter(parameter_arrays, &elemental_emitter); TF_RETURN_IF_ERROR(root->Accept(&fused_emitter)); @@ -660,21 +682,22 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { // touching the un-updated elements. // Set up kernel thunk and fused ir emitter. - thunk_sequence_->emplace_back(BuildKernelThunk(fusion)); - std::vector operand_arrays; + thunk_sequence_->emplace_back( + BuildKernelThunk(fusion, /*implements_whole_instruction=*/true)); + std::vector operand_arrays; for (HloInstruction* operand : fusion->operands()) { operand_arrays.push_back(GetIrArray(*operand, *fusion)); } GpuElementalIrEmitter elemental_emitter(hlo_module_config_, ir_emitter_context_->llvm_module(), - &ir_builder_, GetNestedComputer()); + &b_, GetNestedComputer()); // Shape of the dynamic-update-slice's "update" operand. Shape update_shape = root->operand(1)->shape(); // Array to write into. Because this is an in-place operation, this is the // same as operand 0's array. - llvm_ir::IrArray output_array = GetIrArray(*fusion, *fusion); + IrArray output_array = GetIrArray(*fusion, *fusion); LaunchDimensions launch_dimensions = CalculateLaunchDimensions( update_shape, ir_emitter_context_->device_description()); @@ -685,316 +708,27 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { return llvm_ir::EmitParallelFusedDynamicUpdateSliceInPlace( fusion, operand_arrays, output_array, &elemental_emitter, - launch_dimensions, &ir_builder_); + launch_dimensions, &b_); } + if (ImplementedAsGemm(*fusion)) { thunk_sequence_->emplace_back(BuildGemmThunk(fusion)); return Status::OK(); } - CHECK(fusion->fusion_kind() == HloInstruction::FusionKind::kLoop); - int unroll_factor = ComputeMaxUnrollFactor(fusion); + CHECK_EQ(fusion->fusion_kind(), HloInstruction::FusionKind::kLoop); - thunk_sequence_->emplace_back(BuildKernelThunk(fusion, unroll_factor)); - return IrEmitter::HandleFusion(fusion); -} - -namespace { - -// Returns the indices of the first elements of all consecutive subarrays of the -// given array. For example: -// ConsecutiveSegments({m, m+1, m+2, n, k, k+1}) = {0, 3, 4} -std::vector ConsecutiveSegments(tensorflow::gtl::ArraySlice xs) { - std::vector is = {0}; - for (size_t i = 1; i < xs.size(); ++i) { - if (1 != xs[i] - xs[i - 1]) { - is.push_back(i); - } - } - return is; -} - -// Merges the sequences of dimensions of the given shape which start at the -// given indices `segs`. -Shape MergeDimensions(tensorflow::gtl::ArraySlice segs, - const Shape& shape) { - std::vector dimensions; - for (size_t i = 1; i <= segs.size(); ++i) { - dimensions.push_back(std::accumulate( - shape.dimensions().begin() + segs[i - 1], - shape.dimensions().begin() + - (segs.size() == i ? shape.dimensions().size() : segs[i]), - 1, std::multiplies())); - } - return ShapeUtil::MakeShapeWithDescendingLayout(shape.element_type(), - dimensions); -} - -// Returns whether the given shapes and permutation are a 0-2-1 transpose, and -// if so, the normalized and rank-reduced shapes. The shapes must have the same -// dimensions, so this considers layout only. -// -// This function recognizes higher-rank transposes which are elementwise -// equivalent to a 0-2-1 transpose. -std::tuple IsTranspose021(const Shape& a, const Shape& b) { - CHECK(ShapeUtil::Compatible(a, b)); - std::vector perm(a.dimensions().size()); - { - auto layout_a_orig = LayoutUtil::MinorToMajor(a); - std::vector layout_a(layout_a_orig.rbegin(), layout_a_orig.rend()); - auto layout_b_orig = LayoutUtil::MinorToMajor(b); - std::vector layout_b(layout_b_orig.rbegin(), layout_b_orig.rend()); - for (size_t i = 0; i < perm.size(); ++i) { - perm[i] = PositionInContainer(layout_b, layout_a[i]); - } + if (CheckAndEmitHloWithTile021(fusion)) { + return Status::OK(); } - auto segs = ConsecutiveSegments(perm); - Shape norm_a = - ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout(a); - Shape norm_b = - ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout(b); - if (3 == segs.size() && 0 == perm[0]) { - Shape reduced_a = MergeDimensions(segs, norm_a); - Shape reduced_b = ShapeUtil::MakeShapeWithDescendingLayout( - b.element_type(), - Permute({0, 2, 1}, AsInt64Slice(reduced_a.dimensions()))); - return std::make_tuple(true, reduced_a, reduced_b); - } - return std::make_tuple(false, ShapeUtil::MakeNil(), ShapeUtil::MakeNil()); -} - -// Returns whether the given shapes are potentially of a 0-2-1 transpose. -// As 0-2-1 is a self-inverse permutation, which shape is input or output is -// arbitrary. -bool AreShapesForTranspose021(const Shape& a, const Shape& b) { - return 3 == b.dimensions().size() && - ShapeUtil::Compatible( - ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout(a), - ShapeUtil::PermuteDimensions( - {0, 2, 1}, - ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( - b))); -} - -// Emits a tiled 0-2-1 transpose, assuming both input and output lain out from -// major to minor. The x- and y- dimensions are tiled in square tiles of edge -// length `tile_size`. Each thread block of `tile_size` x `num_rows` threads -// transposes one tile: each thread copies a row from the input to a shared -// memory tile, then copies a column from the shared memory tile to the output. -// -// `tile_size` should usually be same as warp size. -// -// Returns (number of tiles = number of thread blocks needed). -// -// TODO(b/33320379): Here each block transposes 1 tile. It may be more efficient -// to launch fewer blocks so each transposes many tiles, and -// in any case, the number of blocks we can launch is limited. -// -// This is the same algorithm in CUDA: -// https://github.com/tensorflow/tensorflow/blob/d2693c8a70567cc78b2e8a9ac8020d321620ca83/tensorflow/core/kernels/conv_ops_gpu_3.cu.cc#L189 -int64 EmitTranspose021Tiled(llvm_ir::IrArray input, llvm_ir::IrArray output, - const int64 tile_size, const int64 num_rows, - llvm::IRBuilder<>* builder) { - // Adds `addend` to the given `dim` of `index`. - auto offset_dim = [builder](llvm_ir::IrArray::Index index, - llvm::Value* addend, int64 dim) { - index[dim] = builder->CreateAdd(index[dim], addend); - return index; - }; - CHECK(AreShapesForTranspose021(input.GetShape(), output.GetShape())); - - Shape input_shape = - ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( - input.GetShape()); - Shape output_shape = - ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout( - output.GetShape()); - input = input.CastToShape(input_shape, builder); - output = output.CastToShape(output_shape, builder); - - llvm::Type* tile_type = llvm::ArrayType::get( - llvm::ArrayType::get(input.GetElementLlvmType(), tile_size), - // One extra here to avoid share memory bank conflict - tile_size + 1); - auto* tile = new llvm::GlobalVariable( - *builder->GetInsertBlock()->getParent()->getParent(), tile_type, - /*isConstant=*/false, llvm::GlobalValue::PrivateLinkage, - llvm::UndefValue::get(tile_type), "tile", nullptr, - llvm::GlobalValue::NotThreadLocal, - /*AddressSpace=*/3 /* GPU shared memory */); - - // let x = threadIdx.x - llvm::Value* x = llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::nvvm_read_ptx_sreg_tid_x, {}, {}, builder); - llvm_ir::AddRangeMetadata(0, num_rows * tile_size, - static_cast(x)); - x = builder->CreateIntCast(x, builder->getInt64Ty(), /*isSigned=*/true, - "thread.id.x"); - - // computing logical thread ids - // logical_x = x % tile_size - auto logical_x = builder->CreateURem(x, builder->getInt64(tile_size)); - - // logical_y = x / tile_size - auto logical_y = builder->CreateUDiv(x, builder->getInt64(tile_size)); - - // `emit_cp` emits equivalent to following pseudocode: - // if (tile_size == tile_width && tile_size == tile_height) { - // unroll for (i in range(0, tile_size, num_rows)) { - // emit_cp_element(index + {0, i, 0}, y + logical_y); - // } - // } else if (x < tile_width) { - // tile_height_upperbound = ceil(tile_height / num_rows) * num_rows; - // for (i in range(0, tile_height_upperbound, num_rows)) { - // y_loc = i + logical_y; - // if (y_loc < tile_height) - // emit_cp_element(index + {0, i, 0}, y_loc); - // } - // } - // - // We use this to emit both the copy from input to tile and the copy from tile - // to output. - // - // `index` is the origin of the row or column in the input or output array. - // - // `emit_cp_element(index, y)` emits code to copy a single element between the - // tile and the input or output array, where `y` is the `y`-position in the - // tile, whether which is row or column is a function of whether we're copying - // from input or to output, and `index` is the index into the input or output - // array. - auto emit_cp_tile = [builder, tile_size, &offset_dim, num_rows, logical_x, - logical_y]( - std::function - emit_cp_element, - llvm::Value* tile_width, llvm::Value* tile_height, - const llvm_ir::IrArray::Index& index, - const string& loop_name) { - llvm_ir::LlvmIfData if_not_last_row = llvm_ir::EmitIfThenElse( - builder->CreateAnd( - builder->CreateICmpEQ(builder->getInt64(tile_size), tile_width), - builder->CreateICmpEQ(builder->getInt64(tile_size), tile_height)), - "not_last_row", builder); - builder->SetInsertPoint(if_not_last_row.true_block->getTerminator()); - for (int64 i = 0; i < tile_size; i += num_rows) { - auto source_idx = offset_dim(index, builder->getInt64(i), /*dim=*/1); - auto y_loc = builder->CreateAdd(builder->getInt64(i), logical_y); - emit_cp_element(source_idx, y_loc); - } - builder->SetInsertPoint(if_not_last_row.false_block->getTerminator()); - llvm_ir::LlvmIfData if_in_tile = llvm_ir::EmitIfThenElse( - builder->CreateICmpULT(logical_x, tile_width), "x_in_tile", builder); - builder->SetInsertPoint(if_in_tile.true_block->getTerminator()); - - // tile_height_upper_bound = ceil(tile_height / num_rows) * num_rows - auto tile_height_upper_bound = builder->CreateMul( - builder->CreateUDiv( - builder->CreateAdd(tile_height, builder->getInt64(num_rows - 1)), - builder->getInt64(num_rows)), - builder->getInt64(num_rows)); - - auto loop = llvm_ir::ForLoop::EmitForLoop( - loop_name, builder->getInt64(0), tile_height_upper_bound, - builder->getInt64(num_rows), builder); - llvm_ir::SetToFirstInsertPoint(loop->GetHeaderBasicBlock(), builder); - builder->SetInsertPoint(loop->GetBodyBasicBlock()->getTerminator()); - - auto y_loc = builder->CreateAdd(loop->GetIndVarValue(), logical_y); - auto if_y_in_tile = llvm_ir::EmitIfThenElse( - builder->CreateICmpULT(y_loc, tile_height), "y_in_tile", builder); - builder->SetInsertPoint(if_y_in_tile.true_block->getTerminator()); - - emit_cp_element(offset_dim(index, loop->GetIndVarValue(), /*dim=*/1), - y_loc); - builder->SetInsertPoint(if_not_last_row.after_block->getTerminator()); - }; - - auto input_dims_in_tiles = input_shape.dimensions(); - // Unpermuted dimensions are untiled. - for (int i = 1; i < 3; ++i) { - input_dims_in_tiles[i] = - CeilOfRatio(input_dims_in_tiles[i], tile_size); - } - int64 num_tiles = - std::accumulate(input_dims_in_tiles.begin(), input_dims_in_tiles.end(), 1, - std::multiplies()); - const llvm_ir::IrArray::Index input_tile_index( - /*linear=*/builder->CreateIntCast( - llvm_ir::AddRangeMetadata( - 0, num_tiles, - static_cast(llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::nvvm_read_ptx_sreg_ctaid_x, {}, {}, - builder))), - builder->getInt64Ty(), /*isSigned=*/true, "block.id.x"), - ShapeUtil::MakeShapeWithDescendingLayout( - PRED /*arbitrary*/, AsInt64Slice(input_dims_in_tiles)), - builder); - const llvm_ir::IrArray::Index input_tile_origin = ({ - llvm_ir::IrArray::Index index = input_tile_index; - for (int i = 1; i < 3; ++i) { - index[i] = builder->CreateMul(index[i], builder->getInt64(tile_size), - "tile_origin." + std::to_string(i)); - } - index; - }); - const llvm_ir::IrArray::Index input_index = - offset_dim(offset_dim(input_tile_origin, logical_x, /*dim=*/2), logical_y, - /*dim=*/1); - std::vector tile_dims(input_shape.dimensions().size()); - // Only last row or column may not have full size. - for (int i = 1; i < 3; ++i) { - tile_dims[i] = builder->CreateSelect( - builder->CreateICmpEQ(input_tile_index[i], - builder->getInt64(input_dims_in_tiles[i] - 1)), - builder->getInt64(input_shape.dimensions(i) - - (input_dims_in_tiles[i] - 1) * tile_size), - builder->getInt64(tile_size), "tile_size"); - } - - // Load data from input memory to shared memory tile. - emit_cp_tile( - // tile[y, x] = input_array[index] - [builder, tile, &input, logical_x](const llvm_ir::IrArray::Index& index, - llvm::Value* y) { - builder->CreateStore( - input.EmitReadArrayElement(index, builder, "input_element"), - builder->CreateGEP(tile, {builder->getInt64(0), y, logical_x})); - }, - tile_dims[2], tile_dims[1], input_index, "input"); + int unroll_factor = ComputeMaxUnrollFactor(fusion); - // Wait for all threads to reach this point, lest we copy a value from tile to - // output before the other thread copies it from input to tile. - // This is `__syncthreads` in CUDA. - llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::nvvm_barrier0, {}, {}, builder); - - const llvm_ir::IrArray::Index output_tile_index( - Permute({0, 2, 1}, input_tile_index.multidim())); - const llvm_ir::IrArray::Index output_tile_origin( - Permute({0, 2, 1}, input_tile_origin.multidim())); - const llvm_ir::IrArray::Index output_index = - offset_dim(offset_dim(output_tile_origin, logical_x, /*dim=*/2), - logical_y, /*dim=*/1); - - // Store data from shared memory tile to output memory. - emit_cp_tile( - // output_array[index] = tile[x, y] - [builder, tile, &output, logical_x](const llvm_ir::IrArray::Index& index, - llvm::Value* y) { - output.EmitWriteArrayElement( - index, - builder->CreateLoad( - builder->CreateGEP(tile, {builder->getInt64(0), logical_x, y}), - "output_element"), - builder); - }, - tile_dims[1], tile_dims[2], output_index, "output"); - - return num_tiles; + thunk_sequence_->emplace_back(BuildKernelThunk( + fusion, /*implements_whole_instruction=*/true, unroll_factor)); + return IrEmitter::HandleFusion(fusion); } -} // namespace - Status IrEmitterUnnested::HandleCopy(HloInstruction* copy) { if (ImplementedAsHostToDeviceMemcpy(ir_emitter_context_->buffer_assignment(), *copy)) { @@ -1006,25 +740,7 @@ Status IrEmitterUnnested::HandleCopy(HloInstruction* copy) { thunk_sequence_->emplace_back(BuildDeviceToDeviceCopyThunk(copy)); return Status::OK(); } - bool is_transpose_021; - Shape reduced_input_shape, reduced_output_shape; - std::tie(is_transpose_021, reduced_input_shape, reduced_output_shape) = - IsTranspose021(copy->operand(0)->shape(), copy->shape()); - if (is_transpose_021 && - reduced_input_shape.dimensions(1) >= kMinDimensionToTransposeTiled && - reduced_input_shape.dimensions(2) >= kMinDimensionToTransposeTiled) { - thunk_sequence_->emplace_back(BuildKernelThunk(copy)); - VLOG(3) << "Emitting tiled 0-2-1 transposition"; - constexpr int64 tile_size = 32; - constexpr int64 num_rows = 8; - int64 num_tiles = EmitTranspose021Tiled( - GetIrArray(*copy->operand(0), *copy) - .CastToShape(reduced_input_shape, &ir_builder_), - GetIrArray(*copy, *copy) - .CastToShape(reduced_output_shape, &ir_builder_), - tile_size, num_rows, &ir_builder_); - UpdateLaunchDimensions(LaunchDimensions(num_tiles, num_rows * tile_size), - LastThunk(), ir_emitter_context_->llvm_module()); + if (CheckAndEmitHloWithTile021(copy)) { return Status::OK(); } @@ -1032,7 +748,7 @@ Status IrEmitterUnnested::HandleCopy(HloInstruction* copy) { } Status IrEmitterUnnested::EmitExtraOutputsForReduce( - const HloInstruction* reduce, const llvm_ir::IrArray::Index& index, + const HloInstruction* reduce, const IrArray::Index& index, tensorflow::gtl::ArraySlice< std::pair> extra_output_gens) { @@ -1040,11 +756,11 @@ Status IrEmitterUnnested::EmitExtraOutputsForReduce( const HloInstruction* output = reduce->parent()->FusionInstruction(); llvm::Value* extra_output_address = GetIrArray(*output, *output, extra_output_gens[i].second) - .EmitArrayElementAddress(index, &ir_builder_, + .EmitArrayElementAddress(index, &b_, "extra_output_element_address"); TF_ASSIGN_OR_RETURN(llvm::Value* const extra_output_ir_value, extra_output_gens[i].first(index)); - ir_builder_.CreateStore(extra_output_ir_value, extra_output_address); + b_.CreateStore(extra_output_ir_value, extra_output_address); } return Status::OK(); } @@ -1074,12 +790,10 @@ Status IrEmitterUnnested::EmitReductionToScalar( LaunchDimensions launch_dimensions = CalculateLaunchDimensions( tiled_input_shape, ir_emitter_context_->device_description()); - llvm::Type* index_ty = GetIndexTypeForKernel( - reduce, - launch_dimensions.block_count() * launch_dimensions.threads_per_block(), - &ir_builder_); + llvm::Type* index_ty = + GetIndexTypeForKernel(reduce, launch_dimensions.launch_bound(), &b_); - auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + auto index_typed_constant = [&](uint64 c) -> llvm::Constant* { return llvm::ConstantInt::get(index_ty, c); }; @@ -1121,59 +835,57 @@ Status IrEmitterUnnested::EmitReductionToScalar( // // and threads_per_block is a multiple of warpSize. // reduce_kernel<<>>(); // - auto loop_body_emitter = - [=](const llvm_ir::IrArray::Index& tile_index) -> Status { + auto loop_body_emitter = [=](const IrArray::Index& tile_index) -> Status { const int num_reduces = reducers.size(); llvm::Type* element_ir_type = llvm_ir::PrimitiveTypeToIrType(input_shape.element_type(), module_); std::vector partial_reduction_result_addresses; for (int i = 0; i != num_reduces; ++i) { - llvm::Value* partial_reduction_result_address = ir_builder_.CreateAlloca( - element_ir_type, /*ArraySize=*/nullptr, - "partial_reduction_result." + llvm::Twine(i)); - TF_ASSIGN_OR_RETURN( - llvm::Value* const init_ir_value, - init_value_gens[i](llvm_ir::IrArray::Index(index_ty))); - ir_builder_.CreateStore(init_ir_value, partial_reduction_result_address); + llvm::Value* partial_reduction_result_address = + b_.CreateAlloca(element_ir_type, /*ArraySize=*/nullptr, + "partial_reduction_result." + llvm::Twine(i)); + TF_ASSIGN_OR_RETURN(llvm::Value* const init_ir_value, + init_value_gens[i](IrArray::Index(index_ty))); + b_.CreateStore(init_ir_value, partial_reduction_result_address); partial_reduction_result_addresses.push_back( partial_reduction_result_address); } llvm::Value* x_in_tiles = tile_index[0]; - x_in_tiles = ir_builder_.CreateZExtOrTrunc(x_in_tiles, index_ty); + x_in_tiles = b_.CreateZExtOrTrunc(x_in_tiles, index_ty); // Emit an inner for-loop that reduces the elements in the tile. auto emit_tile_element_loop = [=](bool tile_in_bounds) -> Status { std::unique_ptr tile_element_loop = llvm_ir::ForLoop::EmitForLoop( - "element_id_in_tile", index_typed_const(0), - index_typed_const(kTileSize), index_typed_const(1), &ir_builder_); + "element_id_in_tile", index_typed_constant(0), + index_typed_constant(kTileSize), index_typed_constant(1), &b_); // Emit the body of the partial reduction loop. llvm_ir::SetToFirstInsertPoint(tile_element_loop->GetBodyBasicBlock(), - &ir_builder_); - llvm::Value* x = ir_builder_.CreateNSWAdd( - ir_builder_.CreateNSWMul(x_in_tiles, index_typed_const(kTileSize)), + &b_); + llvm::Value* x = b_.CreateNSWAdd( + b_.CreateNSWMul(x_in_tiles, index_typed_constant(kTileSize)), tile_element_loop->GetIndVarValue()); // Unless we know the tile is entirely in bounds, we have to emit a // x-in-bounds check before reading from the input. if (!tile_in_bounds) { llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpULT(x, index_typed_const(num_elems)), - "x_in_bounds", &ir_builder_); + b_.CreateICmpULT(x, index_typed_constant(num_elems)), "x_in_bounds", + &b_); // Emit code that reads the input element and accumulates it to // the partial reduction result. - llvm_ir::SetToFirstInsertPoint(if_data.true_block, &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_data.true_block, &b_); } - llvm_ir::IrArray::Index input_index( - /*linear=*/x, input_shape, &ir_builder_); - llvm::Value* input_address = ir_builder_.CreateAlloca(element_ir_type); + IrArray::Index input_index( + /*linear=*/x, input_shape, &b_); + llvm::Value* input_address = b_.CreateAlloca(element_ir_type); for (int i = 0; i != num_reduces; ++i) { TF_ASSIGN_OR_RETURN(llvm::Value* const input_ir_value, input_gens[i](input_index)); - ir_builder_.CreateStore(input_ir_value, input_address); + b_.CreateStore(input_ir_value, input_address); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( *reducers[i], {partial_reduction_result_addresses[i], input_address}, @@ -1184,49 +896,48 @@ Status IrEmitterUnnested::EmitReductionToScalar( // x_end = kTileSize + x_in_tiles * kTileSize, i.e., the location that's // immediately beyond the tile. - llvm::Value* x_end = ir_builder_.CreateNSWAdd( - index_typed_const(kTileSize), - ir_builder_.CreateNSWMul(x_in_tiles, index_typed_const(kTileSize))); + llvm::Value* x_end = b_.CreateNSWAdd( + index_typed_constant(kTileSize), + b_.CreateNSWMul(x_in_tiles, index_typed_constant(kTileSize))); // The tile is entirely in bound if all_threads_in_bounds or // x_end <= num_elems. - llvm::Value* tile_in_bounds = ir_builder_.CreateOr( - ir_builder_.CreateICmpULE(x_end, index_typed_const(num_elems)), - ir_builder_.getInt1(all_threads_in_bounds)); + llvm::Value* tile_in_bounds = + b_.CreateOr(b_.CreateICmpULE(x_end, index_typed_constant(num_elems)), + b_.getInt1(all_threads_in_bounds)); llvm_ir::LlvmIfData if_tile_in_bounds_data = - llvm_ir::EmitIfThenElse(tile_in_bounds, "tile_in_bounds", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.true_block, - &ir_builder_); + llvm_ir::EmitIfThenElse(tile_in_bounds, "tile_in_bounds", &b_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.true_block, &b_); TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_bounds=*/true)); - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.false_block, - &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.false_block, &b_); TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_bounds=*/false)); // After the if-then-else statement on tile_in_bounds, emit calls to // shfl_down that accumulate the partial reduction results of all threads // from the warp. - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.after_block, - &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.after_block, &b_); int bit_width = llvm_ir::GetSizeInBits(element_ir_type); // bitcast cannot be applied to aggregate types (even packed ones), so we // instead bitcast addresses of load/store to intN* of the same bit-width. llvm::Type* shuffle_ir_type = element_ir_type->isStructTy() - ? ir_builder_.getIntNTy(bit_width) + ? b_.getIntNTy(bit_width) : element_ir_type; for (int shuffle_distance = kWarpSize / 2; shuffle_distance >= 1; shuffle_distance /= 2) { - llvm::Value* result_from_other_lane = ir_builder_.CreateAlloca( - element_ir_type, nullptr, "result_from_other_lane"); + llvm::Value* result_from_other_lane = + b_.CreateAlloca(element_ir_type, nullptr, "result_from_other_lane"); for (int i = 0; i != num_reduces; ++i) { - llvm::Value* partial_reduction_result = ir_builder_.CreateLoad( - ir_builder_.CreateBitCast(partial_reduction_result_addresses[i], - shuffle_ir_type->getPointerTo()), + llvm::Value* partial_reduction_result = b_.CreateLoad( + b_.CreateBitCast(partial_reduction_result_addresses[i], + shuffle_ir_type->getPointerTo()), "partial_reduction_result"); - ir_builder_.CreateStore( - EmitShuffleDown(partial_reduction_result, - ir_builder_.getInt32(shuffle_distance), - &ir_builder_), - ir_builder_.CreateBitCast(result_from_other_lane, - shuffle_ir_type->getPointerTo())); + CHECK_EQ(launch_dimensions.threads_per_block() % kWarpSize, 0) + << "Requires block size a multiple of the warp size, otherwise we " + "will read undefined elements."; + b_.CreateStore( + EmitFullWarpShuffleDown(partial_reduction_result, + b_.getInt32(shuffle_distance), &b_), + b_.CreateBitCast(result_from_other_lane, + shuffle_ir_type->getPointerTo())); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( *reducers[i], {partial_reduction_result_addresses[i], result_from_other_lane}, @@ -1240,24 +951,23 @@ Status IrEmitterUnnested::EmitReductionToScalar( // Emit an atomic operation that accumulates the partial reduction result of // lane 0 (which holds the partially accumulated result for its warp) to the // output element. - llvm::Value* lane_id = ir_builder_.CreateURem( - x_in_tiles, index_typed_const(kWarpSize), "lane_id"); + llvm::Value* lane_id = + b_.CreateURem(x_in_tiles, index_typed_constant(kWarpSize), "lane_id"); llvm_ir::LlvmIfData if_lane_id_is_zero_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpEQ(lane_id, index_typed_const(0)), - "lane_id_is_zero", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, - &ir_builder_); + b_.CreateICmpEQ(lane_id, index_typed_constant(0)), "lane_id_is_zero", + &b_); + llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, &b_); for (int i = 0; i != num_reduces; ++i) { llvm::Value* output_address = GetIrArray(*output, *output, reduce_output_shapes[i]) .EmitArrayElementAddress( - llvm_ir::IrArray::Index( - /*linear=*/ir_builder_.getInt64(0), + IrArray::Index( + /*linear=*/b_.getInt64(0), ShapeUtil::GetSubshape(output->shape(), reduce_output_shapes[i]), - &ir_builder_), - &ir_builder_, "output_element_address"); + &b_), + &b_, "output_element_address"); TF_RETURN_IF_ERROR(EmitAtomicOperationForNestedComputation( *reducers[i], output_address, partial_reduction_result_addresses[i])); } @@ -1271,7 +981,7 @@ Status IrEmitterUnnested::EmitReductionToScalar( static_cast(LastThunk())->thunks().back().get(), ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, - launch_dimensions, &ir_builder_) + launch_dimensions, &b_) .EmitLoop(IrName(reduce), index_ty); } @@ -1284,8 +994,8 @@ Status IrEmitterUnnested::EmitColumnReduction( tensorflow::gtl::ArraySlice< std::pair> extra_output_gens) { - // Divide the input matrix into tiles of size Kx1. For example, when the - // input matrix is 4x4 and K=2, the tiled matrix looks like + // Divide the input matrix into tiles of size KxL. For example, when the + // input matrix is 4x4, K=2, and L=1 the tiled matrix looks like // // 0123 // 0123 @@ -1297,100 +1007,131 @@ Status IrEmitterUnnested::EmitColumnReduction( // // We choose 128 as the tile size based on empirical evidence. It's big enough // to reduce the amount of atomic adds in the end, maximizing the memory - // bandwidth. - constexpr int64 kTileSize = 128; + // bandwidth. A tile width of 2 allows for high memory bandwidth utilization + // on 16b input data. + constexpr int64 kTileHeight = 128; + constexpr int64 kTileWidth = 2; - // If the height is not a multiple of the tile size, we pad the bottom of the + // If the height is not a multiple of kTileHeight, we pad the bottom of the // input matrix. - const int64 height_in_tiles = CeilOfRatio(height, kTileSize); - Shape tiled_input_shape = ShapeUtil::MakeShapeWithLayout( - reduce->shape().element_type(), {height_in_tiles, width}, {1, 0}); + const int64 height_in_tiles = CeilOfRatio(height, kTileHeight); + // If width is not a multiple of kTileWidth the rightmost thread will process + // fewer input elements. + const int64 width_in_tiles = CeilOfRatio(width, kTileWidth); + Shape tiled_input_shape = + ShapeUtil::MakeShapeWithLayout(reduce->shape().element_type(), + {height_in_tiles, width_in_tiles}, {1, 0}); LaunchDimensions launch_dimensions = CalculateLaunchDimensions( tiled_input_shape, ir_emitter_context_->device_description()); // TODO(b/110211620): Convert to use i32 index_type when it is possible. - llvm::Type* index_ty = ir_builder_.getInt64Ty(); + llvm::Type* index_ty = b_.getInt64Ty(); - auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + auto index_typed_constant = [&](uint64 c) -> llvm::Constant* { return llvm::ConstantInt::get(index_ty, c); }; // for (linear_index = threadIdx.x + blockIdx.x * blockDim.x; - // linear_index < height_in_tiles * width; + // linear_index < height_in_tiles * width_in_tiles; // linear_index += blockDim.x * gridDim.x) { - // y_in_tiles = linear_index / width; - // x = linear_index % width; + // y_in_tiles = linear_index / width_in_tiles; + // x_in_tiles = linear_index % width_in_tiles; // - // partial_result = init_value; - // if (height % kTileSize == 0 || - // y_in_tiles * kTileSize + kTileSize <= height) { - // for (element_id_in_tile : range(kTileSize)) { - // y = y_in_tiles * kTileSize + element_id_in_tile; - // partial_result = Reducer(partial_result, input[y][x]); + // partial_results[kTileWidth] = init_values; + // tile_in_y_bounds = height % kTileHeight == 0 || + // y_in_tiles * kTileHeight + kTileHeight <= height; + // tile_in_x_bounds = width % kTileWidth == 0 || + // x_in_tiles * kTileWidth + kTileWidth <= width; + // // The implementation handles y and x bound checks separately. + // if (tile_in_y_bounds && tile_in_x_bounds) { + // for (y_offset : range(kTileHeight)) { + // y = y_in_tiles * kTileHeight + y_offset; + // for (x_offset : range(kTileWidth)) { + // x = x_in_tiles * kTileWidth + x_offset; + // partial_result = Reducer(partial_result[x_offset], input[y][x]); + // } // } // } else { - // for (element_id_in_tile : range(kTileSize)) { - // y = y_in_tiles * kTileSize + element_id_in_tile; - // if (y < height) { - // partial_result = Reducer(partial_result, input[y][x]); + // for (y_offset : range(kTileHeight)) { + // y = y_in_tiles * kTileHeight + y_offset; + // for (y_offset : range(kTileHeight)) { + // x = x_in_tiles * kTileWidth + x_offset; + // if (y < height && x < width) { + // partial_result = Reducer(partial_result, input[y][x]); + // } // } // } // } - // AtomicReducer(&output[x], partial_result); + // for (x_offset : range(kTileWidth)) { + // AtomicReducer(&output[x + x_offset], partial_result[x_offset]); + // } // } - auto loop_body_emitter = - [=](const llvm_ir::IrArray::Index& tile_index) -> Status { + auto loop_body_emitter = [=](const IrArray::Index& tile_index) -> Status { const int num_reduces = reducers.size(); // Emit the loop body that reduces one tile. llvm::Type* element_ir_type = llvm_ir::PrimitiveTypeToIrType(input_shape.element_type(), module_); std::vector partial_reduction_result_addresses; for (int i = 0; i != num_reduces; ++i) { - llvm::Value* partial_reduction_result_address = ir_builder_.CreateAlloca( - element_ir_type, /*ArraySize=*/nullptr, - "partial_reduction_result." + llvm::Twine(i)); - TF_ASSIGN_OR_RETURN( - llvm::Value* const init_ir_value, - init_value_gens[i](llvm_ir::IrArray::Index(index_ty))); - ir_builder_.CreateStore(init_ir_value, partial_reduction_result_address); - partial_reduction_result_addresses.push_back( - partial_reduction_result_address); + for (int x_offset = 0; x_offset < kTileWidth; ++x_offset) { + llvm::Value* partial_reduction_result_address = + b_.CreateAlloca(element_ir_type, /*ArraySize=*/nullptr, + "partial_reduction_result." + + llvm::Twine(i * kTileWidth + x_offset)); + TF_ASSIGN_OR_RETURN(llvm::Value* const init_ir_value, + init_value_gens[i](IrArray::Index(index_ty))); + b_.CreateStore(init_ir_value, partial_reduction_result_address); + partial_reduction_result_addresses.push_back( + partial_reduction_result_address); + } } // Emit an inner for-loop that partially reduces the elements in the given // tile. llvm::Value* y_in_tiles = tile_index[0]; - llvm::Value* x = tile_index[1]; + llvm::Value* x_in_tiles = tile_index[1]; - y_in_tiles = ir_builder_.CreateZExtOrTrunc(y_in_tiles, index_ty); - x = ir_builder_.CreateZExtOrTrunc(x, index_ty); + y_in_tiles = b_.CreateZExtOrTrunc(y_in_tiles, index_ty); + x_in_tiles = b_.CreateZExtOrTrunc(x_in_tiles, index_ty); - auto emit_tile_element_loop = [=](bool tile_in_bounds) -> Status { + auto emit_tile_element_loop = [=](bool tile_in_y_bounds, + bool tile_in_x_bounds) -> Status { std::unique_ptr tile_element_loop = llvm_ir::ForLoop::EmitForLoop( - "element_id_in_tile", index_typed_const(0), - index_typed_const(kTileSize), index_typed_const(1), &ir_builder_); + "element_id_in_tile", index_typed_constant(0), + index_typed_constant(kTileHeight), index_typed_constant(1), &b_); // Emit the body of the partial reduction loop. llvm_ir::SetToFirstInsertPoint(tile_element_loop->GetBodyBasicBlock(), - &ir_builder_); - llvm::Value* y = ir_builder_.CreateNSWAdd( - ir_builder_.CreateNSWMul(y_in_tiles, index_typed_const(kTileSize)), + &b_); + llvm::Value* y = b_.CreateNSWAdd( + b_.CreateNSWMul(y_in_tiles, index_typed_constant(kTileHeight)), tile_element_loop->GetIndVarValue()); - // Unless we know the tile is entirely in bounds, we have to emit a - // y-in-bounds check before reading from the input. - if (!tile_in_bounds) { + // Unless we know that y is in bounds, we have to emit a check before + // reading from the input. + if (!tile_in_y_bounds) { llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpULT(y, index_typed_const(height)), - "y_in_bounds", &ir_builder_); + b_.CreateICmpULT(y, index_typed_constant(height)), "y_in_bounds", + &b_); // Emit code that reads the input element and accumulates it to // the partial reduction result. - llvm_ir::SetToFirstInsertPoint(if_data.true_block, &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_data.true_block, &b_); } - llvm::Value* input_address = ir_builder_.CreateAlloca(element_ir_type); - { + for (int x_offset = 0; x_offset < kTileWidth; ++x_offset) { + llvm::Value* x = b_.CreateNSWAdd( + b_.CreateNSWMul(x_in_tiles, index_typed_constant(kTileWidth)), + index_typed_constant(x_offset)); + // Unless we know that x is in bounds, we have to emit a check before + // reading from the input. + if (!tile_in_x_bounds) { + llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( + b_.CreateICmpULT(x, index_typed_constant(width)), "x_in_bounds", + &b_); + llvm_ir::SetToFirstInsertPoint(if_data.true_block, &b_); + } + llvm::Value* input_address = b_.CreateAlloca(element_ir_type); // {y,x} is an index to input_matrix_shape [height,width]. We need to // convert that to an index to input_shape (the shape of the operand of // "reduce"). This conversion is composed of a transposition from @@ -1406,67 +1147,95 @@ Status IrEmitterUnnested::EmitColumnReduction( const Shape input_matrix_shape = ShapeUtil::MakeShapeWithDescendingLayout(input_shape.element_type(), {height, width}); - const llvm_ir::IrArray::Index input_matrix_index( - {y, x}, input_matrix_shape, &ir_builder_); - const llvm_ir::IrArray::Index input_index = + const IrArray::Index input_matrix_index({y, x}, input_matrix_shape, + &b_); + const IrArray::Index input_index = input_matrix_index .SourceIndexOfReshape(input_matrix_shape, - normalized_input_shape, &ir_builder_) + normalized_input_shape, &b_) .SourceIndexOfTranspose(normalized_input_shape, input_shape, - transpose_dimension_mapping, - &ir_builder_); + transpose_dimension_mapping, &b_); for (int i = 0; i != num_reduces; ++i) { TF_ASSIGN_OR_RETURN(llvm::Value* const input_ir_value, input_gens[i](input_index)); - ir_builder_.CreateStore(input_ir_value, input_address); + b_.CreateStore(input_ir_value, input_address); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( *reducers[i], - {partial_reduction_result_addresses[i], input_address}, - partial_reduction_result_addresses[i])); + {partial_reduction_result_addresses[i * kTileWidth + x_offset], + input_address}, + partial_reduction_result_addresses[i * kTileWidth + x_offset])); + TF_RETURN_IF_ERROR(EmitExtraOutputsForReduce(reduce, input_index, + extra_output_gens)); } - return EmitExtraOutputsForReduce(reduce, input_index, - extra_output_gens); } + return Status::OK(); }; - // y_end = kTileSize + y_in_tiles * kTileSize, i.e., the y location that's - // immediately beyond the tile. - llvm::Value* y_end = ir_builder_.CreateNSWAdd( - index_typed_const(kTileSize), - ir_builder_.CreateNSWMul(y_in_tiles, index_typed_const(kTileSize))); - llvm::Value* tile_in_bounds = ir_builder_.CreateOr( - ir_builder_.CreateICmpULE(y_end, index_typed_const(height)), - ir_builder_.getInt1(height % kTileSize == 0)); - // The tile is entirely in bound if "height" is a multiple of kTileSize or + // y_end = kTileHeight + y_in_tiles * kTileHeight, i.e., the y location + // that's immediately beyond the tile. + llvm::Value* y_end = b_.CreateNSWAdd( + index_typed_constant(kTileHeight), + b_.CreateNSWMul(y_in_tiles, index_typed_constant(kTileHeight))); + // x_end = kTileWidth + x_in_tiles * kTileWidth, i.e., the x location + // that's immediately beyond the tile. + llvm::Value* x_end = b_.CreateNSWAdd( + index_typed_constant(kTileWidth), + b_.CreateNSWMul(x_in_tiles, index_typed_constant(kTileWidth))); + llvm::Value* tile_in_y_bounds = + b_.CreateOr(b_.CreateICmpULE(y_end, index_typed_constant(height)), + b_.getInt1(height % kTileHeight == 0)); + llvm::Value* tile_in_x_bounds = + b_.CreateOr(b_.CreateICmpULE(x_end, index_typed_constant(width)), + b_.getInt1(width % kTileWidth == 0)); + // The tile is in y bounds if "height" is a multiple of kTileHeight or // y_end <= height. - llvm_ir::LlvmIfData if_tile_in_bounds_data = - llvm_ir::EmitIfThenElse(tile_in_bounds, "tile_in_bounds", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.true_block, - &ir_builder_); - TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_bounds=*/true)); - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.false_block, - &ir_builder_); - TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_bounds=*/false)); - - // After the if-then-else statement on tile_in_bounds, emit atomic - // operations to accumulate the partial reduction result to the output - // element. - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.after_block, - &ir_builder_); + llvm_ir::LlvmIfData if_tile_in_y_bounds_data = + llvm_ir::EmitIfThenElse(tile_in_y_bounds, "tile_in_y_bounds", &b_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_y_bounds_data.true_block, &b_); + // The tile is in x bounds if "width" is a multiple of kTileWidth or + // x_end <= width. + llvm_ir::LlvmIfData if_tile_in_x_bounds_data = + llvm_ir::EmitIfThenElse(tile_in_x_bounds, "tile_in_x_bounds", &b_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.true_block, &b_); + TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_y_bounds=*/true, + /*tile_in_x_bounds=*/true)); + llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.false_block, &b_); + TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_y_bounds=*/true, + /*tile_in_x_bounds=*/false)); + llvm_ir::SetToFirstInsertPoint(if_tile_in_y_bounds_data.false_block, &b_); + if_tile_in_x_bounds_data = + llvm_ir::EmitIfThenElse(tile_in_x_bounds, "tile_in_x_bounds", &b_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.true_block, &b_); + TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_y_bounds=*/false, + /*tile_in_x_bounds=*/true)); + llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.false_block, &b_); + TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_y_bounds=*/false, + /*tile_in_x_bounds=*/false)); + + // After the nested if-then-else statement on tile_in_y_bounds and + // tile_in_x_bounds, emit atomic operations to accumulate the partial + // reduction result to the output element. + llvm_ir::SetToFirstInsertPoint(if_tile_in_y_bounds_data.after_block, &b_); const HloInstruction* output = reduce->IsFused() ? reduce->parent()->FusionInstruction() : reduce; for (int i = 0; i != num_reduces; ++i) { - llvm::Value* output_address = - GetIrArray(*output, *output, reduce_output_shapes[i]) - .EmitArrayElementAddress( - llvm_ir::IrArray::Index( - x, - ShapeUtil::GetSubshape(output->shape(), - reduce_output_shapes[i]), - &ir_builder_), - &ir_builder_, "output_element_address"); - TF_RETURN_IF_ERROR(EmitAtomicOperationForNestedComputation( - *reducers[i], output_address, partial_reduction_result_addresses[i])); + for (int x_offset = 0; x_offset < kTileWidth; ++x_offset) { + llvm::Value* x = b_.CreateNSWAdd( + b_.CreateNSWMul(x_in_tiles, index_typed_constant(kTileWidth)), + index_typed_constant(x_offset)); + llvm::Value* output_address = + GetIrArray(*output, *output, reduce_output_shapes[i]) + .EmitArrayElementAddress( + IrArray::Index( + x, + ShapeUtil::GetSubshape(output->shape(), + reduce_output_shapes[i]), + &b_), + &b_, "output_element_address"); + TF_RETURN_IF_ERROR(EmitAtomicOperationForNestedComputation( + *reducers[i], output_address, + partial_reduction_result_addresses[i * kTileWidth + x_offset])); + } } return Status::OK(); }; @@ -1478,7 +1247,7 @@ Status IrEmitterUnnested::EmitColumnReduction( static_cast(LastThunk())->thunks().back().get(), ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, - launch_dimensions, &ir_builder_) + launch_dimensions, &b_) .EmitLoop(IrName(reduce), index_ty); } @@ -1628,28 +1397,25 @@ Status IrEmitterUnnested::EmitRowReduction( {depth / z_tile_size, height, width_in_tiles}, {2, 1, 0}); LaunchDimensions launch_dimensions = CalculateLaunchDimensions( tiled_input_shape, ir_emitter_context_->device_description()); - llvm::Type* index_ty = GetIndexTypeForKernel( - reduce, - launch_dimensions.block_count() * launch_dimensions.threads_per_block(), - &ir_builder_); + llvm::Type* index_ty = + GetIndexTypeForKernel(reduce, launch_dimensions.launch_bound(), &b_); - auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + auto index_typed_constant = [&](uint64 c) -> llvm::Constant* { return llvm::ConstantInt::get(index_ty, c); }; - auto loop_body_emitter = [=](const llvm_ir::IrArray::Index& tile_index) { + auto loop_body_emitter = [=](const IrArray::Index& tile_index) { const int num_reduces = reducers.size(); llvm::Type* element_ir_type = llvm_ir::PrimitiveTypeToIrType( input_shape.element_type(), ir_emitter_context_->llvm_module()); std::vector partial_reduction_result_addresses; for (int i = 0; i != num_reduces; ++i) { - llvm::Value* partial_reduction_result_address = ir_builder_.CreateAlloca( - element_ir_type, /*ArraySize=*/nullptr, - "partial_reduction_result." + llvm::Twine(i)); - TF_ASSIGN_OR_RETURN( - llvm::Value* const init_ir_value, - init_value_gens[i](llvm_ir::IrArray::Index(index_ty))); - ir_builder_.CreateStore(init_ir_value, partial_reduction_result_address); + llvm::Value* partial_reduction_result_address = + b_.CreateAlloca(element_ir_type, /*ArraySize=*/nullptr, + "partial_reduction_result." + llvm::Twine(i)); + TF_ASSIGN_OR_RETURN(llvm::Value* const init_ir_value, + init_value_gens[i](IrArray::Index(index_ty))); + b_.CreateStore(init_ir_value, partial_reduction_result_address); partial_reduction_result_addresses.push_back( partial_reduction_result_address); } @@ -1658,25 +1424,25 @@ Status IrEmitterUnnested::EmitRowReduction( llvm::Value* y = tile_index[1]; llvm::Value* x_tile = tile_index[2]; - x_tile = ir_builder_.CreateZExtOrTrunc(x_tile, index_ty); + x_tile = b_.CreateZExtOrTrunc(x_tile, index_ty); llvm::Value* warp_id = - ir_builder_.CreateUDiv(x_tile, index_typed_const(kWarpSize), "warp_id"); + b_.CreateUDiv(x_tile, index_typed_constant(kWarpSize), "warp_id"); llvm::Value* lane_id = - ir_builder_.CreateURem(x_tile, index_typed_const(kWarpSize), "lane_id"); + b_.CreateURem(x_tile, index_typed_constant(kWarpSize), "lane_id"); // The x-location of the last element in this z-x-tile. // last_x = lane_id + warpSize * (x_tile_size - 1 + warp_id * x_tile_size); - llvm::Value* last_x = ir_builder_.CreateNSWAdd( - lane_id, ir_builder_.CreateNSWMul( - index_typed_const(kWarpSize), - ir_builder_.CreateNSWAdd( - index_typed_const(x_tile_size - 1), - ir_builder_.CreateNSWMul( - warp_id, index_typed_const(x_tile_size))))); + llvm::Value* last_x = b_.CreateNSWAdd( + lane_id, + b_.CreateNSWMul( + index_typed_constant(kWarpSize), + b_.CreateNSWAdd( + index_typed_constant(x_tile_size - 1), + b_.CreateNSWMul(warp_id, index_typed_constant(x_tile_size))))); KernelSupportLibrary ksl( - &ir_builder_, + &b_, /*unroll_mode=*/xla::llvm_ir::UnrollMode::kFullyUnroll, /*prevent_vectorization=*/false); @@ -1685,22 +1451,22 @@ Status IrEmitterUnnested::EmitRowReduction( auto emit_z_x_tile_element_loop = [&](bool x_tile_in_bounds, int64 x_tile_loop_bound) -> Status { auto emit_z_tile_element_loop = [&](llvm::Value* z_indvar) -> Status { - llvm::Value* z = ir_builder_.CreateNSWAdd( + llvm::Value* z = b_.CreateNSWAdd( z_indvar, - ir_builder_.CreateNSWMul(index_typed_const(z_tile_size), z_tile)); + b_.CreateNSWMul(index_typed_constant(z_tile_size), z_tile)); TF_RETURN_IF_ERROR(ksl.For( "x_tile", - /*start=*/index_typed_const(0), - /*end=*/index_typed_const(x_tile_loop_bound), + /*start=*/index_typed_constant(0), + /*end=*/index_typed_constant(x_tile_loop_bound), /*step=*/1, [&](llvm::Value* x_indvar) -> Status { // x = lane_id + // warpSize * (element_id_in_x_tile + warp_id * x_tile_size); - llvm::Value* x = ir_builder_.CreateNSWAdd( + llvm::Value* x = b_.CreateNSWAdd( lane_id, - ir_builder_.CreateNSWMul( - index_typed_const(kWarpSize), - ir_builder_.CreateNSWAdd( - x_indvar, ir_builder_.CreateNSWMul( + b_.CreateNSWMul( + index_typed_constant(kWarpSize), + b_.CreateNSWAdd( + x_indvar, b_.CreateNSWMul( warp_id, llvm::ConstantInt::get( index_ty, x_tile_size))))); @@ -1709,17 +1475,16 @@ Status IrEmitterUnnested::EmitRowReduction( if (!x_tile_in_bounds) { llvm_ir::LlvmIfData if_x_in_bounds_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpULT(x, index_typed_const(width)), - "x_in_bounds", &ir_builder_); - // Points ir_builder_ to the then-block. + b_.CreateICmpULT(x, index_typed_constant(width)), + "x_in_bounds", &b_); + // Points b_ to the then-block. llvm_ir::SetToFirstInsertPoint(if_x_in_bounds_data.true_block, - &ir_builder_); + &b_); } // Emit code that reads the input element and accumulates it // to the partial reduction result. - llvm::Value* input_address = - ir_builder_.CreateAlloca(element_ir_type); + llvm::Value* input_address = b_.CreateAlloca(element_ir_type); { // {z,y,x} is an index to input_3d_tensor_shape // [depth,height,width]. We need to convert that to an index @@ -1737,21 +1502,20 @@ Status IrEmitterUnnested::EmitRowReduction( const Shape input_3d_tensor_shape = ShapeUtil::MakeShapeWithDescendingLayout( input_shape.element_type(), {depth, height, width}); - const llvm_ir::IrArray::Index input_3d_tensor_index( - {z, y, x}, input_3d_tensor_shape, &ir_builder_); - const llvm_ir::IrArray::Index input_index = + const IrArray::Index input_3d_tensor_index( + {z, y, x}, input_3d_tensor_shape, &b_); + const IrArray::Index input_index = input_3d_tensor_index .SourceIndexOfReshape(input_3d_tensor_shape, - normalized_input_shape, - &ir_builder_) + normalized_input_shape, &b_) .SourceIndexOfTranspose( normalized_input_shape, input_shape, - transpose_dimension_mapping, &ir_builder_); + transpose_dimension_mapping, &b_); for (int i = 0; i != num_reduces; ++i) { TF_ASSIGN_OR_RETURN(llvm::Value* const input_ir_value, input_gens[i](input_index)); - ir_builder_.CreateStore(input_ir_value, input_address); + b_.CreateStore(input_ir_value, input_address); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( *reducers[i], {partial_reduction_result_addresses[i], input_address}, @@ -1765,14 +1529,14 @@ Status IrEmitterUnnested::EmitRowReduction( }; return ksl.For("z_tile", - /*start=*/index_typed_const(0), - /*end=*/index_typed_const(z_tile_size), + /*start=*/index_typed_constant(0), + /*end=*/index_typed_constant(z_tile_size), /*step=*/1, emit_z_tile_element_loop); }; - llvm::Value* tile_in_bounds = ir_builder_.CreateOr( - ir_builder_.getInt1(width % (x_tile_size * kWarpSize) == 0), - ir_builder_.CreateICmpULT(last_x, index_typed_const(width))); + llvm::Value* tile_in_bounds = + b_.CreateOr(b_.getInt1(width % (x_tile_size * kWarpSize) == 0), + b_.CreateICmpULT(last_x, index_typed_constant(width))); TF_RETURN_IF_ERROR( ksl.If(tile_in_bounds, @@ -1795,23 +1559,25 @@ Status IrEmitterUnnested::EmitRowReduction( // bitcast cannot be applied to aggregate types (even packed ones), so we // instead bitcast addresses of load/store to intN* of the same bit-width. llvm::Type* shuffle_ir_type = element_ir_type->isStructTy() - ? ir_builder_.getIntNTy(bit_width) + ? b_.getIntNTy(bit_width) : element_ir_type; for (int shuffle_distance = 16; shuffle_distance >= 1; shuffle_distance /= 2) { - llvm::Value* result_from_other_lane = ir_builder_.CreateAlloca( - element_ir_type, nullptr, "result_from_other_lane"); + llvm::Value* result_from_other_lane = + b_.CreateAlloca(element_ir_type, nullptr, "result_from_other_lane"); for (int i = 0; i != num_reduces; ++i) { - llvm::Value* partial_reduction_result = ir_builder_.CreateLoad( - ir_builder_.CreateBitCast(partial_reduction_result_addresses[i], - shuffle_ir_type->getPointerTo()), + llvm::Value* partial_reduction_result = b_.CreateLoad( + b_.CreateBitCast(partial_reduction_result_addresses[i], + shuffle_ir_type->getPointerTo()), "partial_reduction_result"); - ir_builder_.CreateStore( - EmitShuffleDown(partial_reduction_result, - ir_builder_.getInt32(shuffle_distance), - &ir_builder_), - ir_builder_.CreateBitCast(result_from_other_lane, - shuffle_ir_type->getPointerTo())); + CHECK_EQ(launch_dimensions.threads_per_block() % kWarpSize, 0) + << "Requires block size a multiple of the warp size, otherwise we " + "will read undefined elements."; + b_.CreateStore( + EmitFullWarpShuffleDown(partial_reduction_result, + b_.getInt32(shuffle_distance), &b_), + b_.CreateBitCast(result_from_other_lane, + shuffle_ir_type->getPointerTo())); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( *reducers[i], {partial_reduction_result_addresses[i], result_from_other_lane}, @@ -1826,20 +1592,18 @@ Status IrEmitterUnnested::EmitRowReduction( // lane 0 (which holds the partially accumulated result for its warp) to the // output element. llvm_ir::LlvmIfData if_lane_id_is_zero_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpEQ(lane_id, index_typed_const(0)), - "lane_id_is_zero", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, - &ir_builder_); + b_.CreateICmpEQ(lane_id, index_typed_constant(0)), "lane_id_is_zero", + &b_); + llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, &b_); for (int i = 0; i != num_reduces; ++i) { llvm::Value* output_address = GetIrArray(*output, *output, reduce_output_shapes[i]) .EmitArrayElementAddress( - llvm_ir::IrArray::Index( - y, - ShapeUtil::GetSubshape(output->shape(), - reduce_output_shapes[i]), - &ir_builder_), - &ir_builder_, "output_element_address"); + IrArray::Index(y, + ShapeUtil::GetSubshape( + output->shape(), reduce_output_shapes[i]), + &b_), + &b_, "output_element_address"); // We don't need to emit atomic operations if there is only one tile of // results. 'depth' is the z dimension, 'width' is the x dimension. if (z_tile_size >= depth && x_tile_size >= width) { @@ -1863,7 +1627,7 @@ Status IrEmitterUnnested::EmitRowReduction( static_cast(LastThunk())->thunks().back().get(), ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, - launch_dimensions, &ir_builder_) + launch_dimensions, &b_) .EmitLoop(IrName(reduce), index_ty); } @@ -1982,32 +1746,36 @@ Status IrEmitterUnnested::HandleReduce(HloInstruction* reduce) { BuildInitializerThunk(reduce)); std::vector> thunks; thunks.push_back(std::move(initializer_thunk)); - thunks.push_back(BuildKernelThunk(reduce)); + thunks.push_back( + BuildKernelThunk(reduce, /*implements_whole_instruction=*/false)); thunk_sequence_->emplace_back( MakeUnique(std::move(thunks), reduce)); return EmitReductionToVector( - reduce, input->shape(), {[&](const llvm_ir::IrArray::Index& index) { - return GetIrArray(*input, *reduce) - .EmitReadArrayElement(index, &ir_builder_); + reduce, input->shape(), {[&](const IrArray::Index& index) { + return GetIrArray(*input, *reduce).EmitReadArrayElement(index, &b_); }}, - {[&](const llvm_ir::IrArray::Index& index) { + {[&](const IrArray::Index& index) { return GetIrArray(*init_value, *reduce) - .EmitReadArrayElement(index, &ir_builder_); + .EmitReadArrayElement(index, &b_); }}, dimensions_to_reduce, {reducer}, {{}}, {}); } - thunk_sequence_->emplace_back(BuildKernelThunk(reduce)); + thunk_sequence_->emplace_back( + BuildKernelThunk(reduce, /*implements_whole_instruction=*/true)); return IrEmitter::HandleReduce(reduce); } Status IrEmitterUnnested::HandleTuple(HloInstruction* tuple) { bool all_tuple_elements_have_buffer = c_all_of(tuple->operands(), [&](HloInstruction* tuple_element) { - return ir_emitter_context_->buffer_assignment().HasTopLevelAllocation( - tuple_element); + return ir_emitter_context_->buffer_assignment() + .GetUniqueTopLevelSlice(tuple_element) + .ok(); }); + // TODO(b/111689850): This logic isn't quite correct. + // // Tuples (especially tuples that are the final result of a computation) can // be so huge that if we were to emit a kernel that took each tuple element as // a parameter, we would exceed the max allowable number of parameters to a @@ -2015,9 +1783,9 @@ Status IrEmitterUnnested::HandleTuple(HloInstruction* tuple) { // buffer, we collect their buffer addresses in a host array, and then copy // that array to the tuple's buffer. // - // Some tuple elements (e.g. const or bitcast of const) might not have a - // buffer -- their contents are stored in code. In that case, we fall back to - // emitting kernels which have access to their buffer addresses in code. + // Some tuple elements might not have an unambiguous buffer (like the result + // of a select-tuple). In that case, we fall back to emitting kernels which + // have access to their buffer addresses in code. if (all_tuple_elements_have_buffer) { std::vector tuple_element_buffers; for (const HloInstruction* tuple_element : tuple->operands()) { @@ -2027,7 +1795,8 @@ Status IrEmitterUnnested::HandleTuple(HloInstruction* tuple) { tuple_element_buffers, GetAllocationSlice(*tuple), tuple)); return Status::OK(); } - thunk_sequence_->emplace_back(BuildKernelThunk(tuple)); + thunk_sequence_->emplace_back( + BuildKernelThunk(tuple, /*implements_whole_instruction=*/true)); return IrEmitter::HandleTuple(tuple); } @@ -2052,7 +1821,8 @@ Status IrEmitterUnnested::HandleSelectAndScatter( BuildInitializerThunk(select_and_scatter)); std::vector> thunks; thunks.push_back(std::move(initializer_thunk)); - thunks.push_back(BuildKernelThunk(select_and_scatter)); + thunks.push_back(BuildKernelThunk(select_and_scatter, + /*implements_whole_instruction=*/false)); thunk_sequence_->emplace_back( MakeUnique(std::move(thunks), select_and_scatter)); @@ -2065,8 +1835,8 @@ Status IrEmitterUnnested::HandleSelectAndScatter( LaunchDimensions launch_dimensions = CalculateLaunchDimensions( source->shape(), ir_emitter_context_->device_description()); llvm::Type* index_type = GetIndexTypeForKernel( - select_and_scatter, launch_dimensions.launch_bound(), &ir_builder_); - auto index_typed_const = [&](uint64 c) -> llvm::Constant* { + select_and_scatter, launch_dimensions.launch_bound(), &b_); + auto index_typed_constant = [&](uint64 c) -> llvm::Constant* { return llvm::ConstantInt::get(index_type, c); }; @@ -2089,114 +1859,106 @@ Status IrEmitterUnnested::HandleSelectAndScatter( // selected_index = I // initialized_flag = true // output(selected_index) = scatter(output(selected_index), source(S)) - auto loop_body_emitter = - [=](const llvm_ir::IrArray::Index& source_index) -> Status { + auto loop_body_emitter = [=](const IrArray::Index& source_index) -> Status { // Allocate space to keep the currently selected value, its index, and a // boolean flag if the value is initialized. The initialized_flag is set // false. llvm::Value* selected_value_address = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType(operand_element_type, ir_emitter_context_->llvm_module()), - "selected_value_address", &ir_builder_); + "selected_value_address", &b_); llvm::Value* selected_index_address = llvm_ir::EmitAllocaAtFunctionEntryWithCount( - index_type, index_typed_const(rank), "selected_index_address", - &ir_builder_); + index_type, index_typed_constant(rank), "selected_index_address", + &b_); llvm::Value* initialized_flag_address = llvm_ir::EmitAllocaAtFunctionEntry( - ir_builder_.getInt1Ty(), "initialized_flag_address", &ir_builder_); - ir_builder_.CreateStore(ir_builder_.getInt1(false), - initialized_flag_address); + b_.getInt1Ty(), "initialized_flag_address", &b_); + b_.CreateStore(b_.getInt1(false), initialized_flag_address); // Create the inner loop to iterate over the window. - llvm_ir::ForLoopNest window_loops(IrName(select_and_scatter, "inner"), - &ir_builder_, index_type); + llvm_ir::ForLoopNest window_loops(IrName(select_and_scatter, "inner"), &b_, + index_type); std::vector window_size; for (const auto& dim : window.dimensions()) { window_size.push_back(dim.size()); CHECK_GT(dim.size(), 0); } - const llvm_ir::IrArray::Index window_index = window_loops.AddLoopsForShape( + const IrArray::Index window_index = window_loops.AddLoopsForShape( ShapeUtil::MakeShape(operand_element_type, window_size), "window"); llvm_ir::SetToFirstInsertPoint(window_loops.GetInnerLoopBodyBasicBlock(), - &ir_builder_); + &b_); // Compute the operand index to visit and evaluate the condition whether the // operand index is within the bounds. The unsigned comparison includes // checking whether the operand index >= 0. - llvm_ir::IrArray::Index operand_index(index_type, source_index.size()); - llvm::Value* in_bounds_condition = ir_builder_.getInt1(true); + IrArray::Index operand_index(index_type, source_index.size()); + llvm::Value* in_bounds_condition = b_.getInt1(true); for (int64 i = 0; i < rank; ++i) { - llvm::Value* strided_index = ir_builder_.CreateNSWMul( - source_index[i], index_typed_const(window.dimensions(i).stride())); - operand_index[i] = ir_builder_.CreateNSWSub( - ir_builder_.CreateNSWAdd(strided_index, window_index[i]), - index_typed_const(window.dimensions(i).padding_low())); - llvm::Value* index_condition = ir_builder_.CreateICmpULT( + llvm::Value* strided_index = b_.CreateNSWMul( + source_index[i], index_typed_constant(window.dimensions(i).stride())); + operand_index[i] = b_.CreateNSWSub( + b_.CreateNSWAdd(strided_index, window_index[i]), + index_typed_constant(window.dimensions(i).padding_low())); + llvm::Value* index_condition = b_.CreateICmpULT( operand_index[i], - index_typed_const(ShapeUtil::GetDimension(operand->shape(), i))); - in_bounds_condition = - ir_builder_.CreateAnd(in_bounds_condition, index_condition); + index_typed_constant(ShapeUtil::GetDimension(operand->shape(), i))); + in_bounds_condition = b_.CreateAnd(in_bounds_condition, index_condition); } CHECK(in_bounds_condition != nullptr); // Only need to do something if the operand index is within the bounds. // First check if the initialized_flag is set. llvm_ir::LlvmIfData if_in_bounds = - llvm_ir::EmitIfThenElse(in_bounds_condition, "in-bounds", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_in_bounds.true_block, &ir_builder_); + llvm_ir::EmitIfThenElse(in_bounds_condition, "in-bounds", &b_); + llvm_ir::SetToFirstInsertPoint(if_in_bounds.true_block, &b_); llvm_ir::LlvmIfData if_initialized = llvm_ir::EmitIfThenElse( - ir_builder_.CreateLoad(initialized_flag_address), "initialized", - &ir_builder_); + b_.CreateLoad(initialized_flag_address), "initialized", &b_); // If the initialized_flag is false, initialize the selected value and index // with the currently visiting operand. - llvm_ir::SetToFirstInsertPoint(if_initialized.false_block, &ir_builder_); - const auto save_operand_index = [&]( - const llvm_ir::IrArray::Index& operand_index) { + llvm_ir::SetToFirstInsertPoint(if_initialized.false_block, &b_); + const auto save_operand_index = [&](const IrArray::Index& operand_index) { for (int64 i = 0; i < rank; ++i) { llvm::Value* selected_index_address_slot = - ir_builder_.CreateInBoundsGEP(selected_index_address, - {ir_builder_.getInt32(i)}); - ir_builder_.CreateStore(operand_index[i], selected_index_address_slot); + b_.CreateInBoundsGEP(selected_index_address, {b_.getInt32(i)}); + b_.CreateStore(operand_index[i], selected_index_address_slot); } }; - llvm_ir::IrArray operand_array = GetIrArray(*operand, *select_and_scatter); + IrArray operand_array = GetIrArray(*operand, *select_and_scatter); llvm::Value* operand_data = - operand_array.EmitReadArrayElement(operand_index, &ir_builder_); - ir_builder_.CreateStore(operand_data, selected_value_address); + operand_array.EmitReadArrayElement(operand_index, &b_); + b_.CreateStore(operand_data, selected_value_address); save_operand_index(operand_index); - ir_builder_.CreateStore(ir_builder_.getInt1(true), - initialized_flag_address); + b_.CreateStore(b_.getInt1(true), initialized_flag_address); // If the initialized_flag is true, call the `select` function to // potentially update the selected value and index with the currently // visiting operand. - llvm_ir::SetToFirstInsertPoint(if_initialized.true_block, &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_initialized.true_block, &b_); const Shape output_shape = ShapeUtil::MakeShape(PRED, {}); llvm::Value* operand_address = - operand_array.EmitArrayElementAddress(operand_index, &ir_builder_); + operand_array.EmitArrayElementAddress(operand_index, &b_); llvm::Value* select_return_buffer = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType(PRED, ir_emitter_context_->llvm_module()), - "select_return_buffer", &ir_builder_); + "select_return_buffer", &b_); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( *select_and_scatter->select(), {selected_value_address, operand_address}, select_return_buffer)); - llvm::Value* result = ir_builder_.CreateLoad(select_return_buffer); + llvm::Value* result = b_.CreateLoad(select_return_buffer); // If the 'select' function returns false, update the selected value and the // index to the currently visiting operand. - llvm::Value* cond = ir_builder_.CreateICmpNE( + llvm::Value* cond = b_.CreateICmpNE( result, llvm::ConstantInt::get(llvm_ir::PrimitiveTypeToIrType( PRED, ir_emitter_context_->llvm_module()), 0), "boolean_predicate"); llvm_ir::LlvmIfData if_select_lhs = - llvm_ir::EmitIfThenElse(cond, "if-select-lhs", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_select_lhs.false_block, &ir_builder_); - ir_builder_.CreateStore(ir_builder_.CreateLoad(operand_address), - selected_value_address); + llvm_ir::EmitIfThenElse(cond, "if-select-lhs", &b_); + llvm_ir::SetToFirstInsertPoint(if_select_lhs.false_block, &b_); + b_.CreateStore(b_.CreateLoad(operand_address), selected_value_address); save_operand_index(operand_index); // After iterating over the window elements, scatter the source element to @@ -2204,20 +1966,19 @@ Status IrEmitterUnnested::HandleSelectAndScatter( // location is computed by calling the `scatter` function with the source // value and the current output value. llvm_ir::SetToFirstInsertPoint(window_loops.GetOuterLoopExitBasicBlock(), - &ir_builder_); - llvm_ir::IrArray::Index selected_index(operand_index.GetType()); + &b_); + IrArray::Index selected_index(operand_index.GetType()); for (int64 i = 0; i < rank; ++i) { - llvm::Value* selected_index_address_slot = ir_builder_.CreateInBoundsGEP( - selected_index_address, {ir_builder_.getInt32(i)}); - selected_index.push_back( - ir_builder_.CreateLoad(selected_index_address_slot)); + llvm::Value* selected_index_address_slot = + b_.CreateInBoundsGEP(selected_index_address, {b_.getInt32(i)}); + selected_index.push_back(b_.CreateLoad(selected_index_address_slot)); } llvm::Value* source_value_address = GetIrArray(*source, *select_and_scatter) - .EmitArrayElementAddress(source_index, &ir_builder_); + .EmitArrayElementAddress(source_index, &b_); llvm::Value* output_value_address = GetIrArray(*select_and_scatter, *select_and_scatter) - .EmitArrayElementAddress(selected_index, &ir_builder_); + .EmitArrayElementAddress(selected_index, &b_); return EmitAtomicOperationForNestedComputation( *select_and_scatter->scatter(), output_value_address, source_value_address); @@ -2232,7 +1993,7 @@ Status IrEmitterUnnested::HandleSelectAndScatter( static_cast(LastThunk())->thunks().back().get(), ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, source->shape(), - launch_dimensions, &ir_builder_) + launch_dimensions, &b_) .EmitLoop(IrName(select_and_scatter), index_type); } @@ -2259,18 +2020,133 @@ Status IrEmitterUnnested::HandleWhile(HloInstruction* xla_while) { return Status::OK(); } -Status IrEmitterUnnested::HandleRng(HloInstruction* random) { - thunk_sequence_->push_back(BuildKernelThunk(random)); - return IrEmitter::HandleRng(random); +Status IrEmitterUnnested::HandleRng(HloInstruction* rng) { + // Build the kernel to generate the random numbers. + // + // Unroll the kernel so that the duplicated computation that calculates the + // 128 bit sample can be optimized away by LLVM. + thunk_sequence_->emplace_back( + BuildKernelThunk(rng, /*implements_whole_instruction=*/false, + ComputeMaxUnrollFactor(rng))); + ElementalIrEmitter::HloToElementGeneratorMap operand_to_generator; + for (const HloInstruction* operand : rng->operands()) { + operand_to_generator[operand] = [=](const llvm_ir::IrArray::Index& index) { + return GetIrArray(*operand, *rng).EmitReadArrayElement(index, &b_); + }; + } + TF_RETURN_IF_ERROR(EmitTargetElementLoop( + *rng, GpuElementalIrEmitter(hlo_module_config_, module_, &b_, + GetNestedComputer()) + .MakeElementGenerator(rng, operand_to_generator))); + std::unique_ptr rng_thunk = std::move(thunk_sequence_->back()); + thunk_sequence_->pop_back(); + + // Emit a kernel to increment the global state for Philox RNG algorithm. + thunk_sequence_->emplace_back( + BuildKernelThunk(rng, /*implements_whole_instruction=*/false)); + llvm_ir::IncrementVariableForPhiloxRngState(1, module_, &b_); + std::unique_ptr increment_seed_thunk = + std::move(thunk_sequence_->back()); + thunk_sequence_->pop_back(); + + // Build the SequentialThunk for the RNG hlo. + std::vector> thunks; + thunks.reserve(2); + thunks.push_back(std::move(rng_thunk)); + thunks.push_back(std::move(increment_seed_thunk)); + thunk_sequence_->emplace_back( + MakeUnique(std::move(thunks), rng)); + + return Status::OK(); } Status IrEmitterUnnested::HandleSelect(HloInstruction* select) { - thunk_sequence_->push_back(BuildKernelThunk(select)); + thunk_sequence_->push_back( + BuildKernelThunk(select, /*implements_whole_instruction=*/true)); return IrEmitter::HandleSelect(select); } +Status IrEmitterUnnested::HandleSort(HloInstruction* sort) { + std::vector> thunks; + auto keys = sort->operand(0); + auto values = sort->operand_count() > 1 ? sort->operand(1) : nullptr; + ShapeIndex keys_shape_index({}); + ShapeIndex values_shape_index({}); + if (values != nullptr) { + keys_shape_index = ShapeIndex({0}); + values_shape_index = ShapeIndex({1}); + } + auto keys_destination = GetAllocationSlice(*sort, keys_shape_index); + auto values_destination = GetAllocationSlice(*sort, values_shape_index); + + if (keys_destination != GetAllocationSlice(*keys)) { + thunks.push_back(MakeUnique( + /*source_address=*/GetAllocationSlice(*keys), + /*destination_buffer=*/keys_destination, + /*mem_size=*/ShapeUtil::ByteSizeOf(keys->shape()), nullptr)); + } + if (values != nullptr && values_destination != GetAllocationSlice(*values)) { + // TODO(b/26783907): Figure out why we never seem to share buffers for + // key/value sort. + thunks.push_back(MakeUnique( + /*source_address=*/GetAllocationSlice(*values), + /*destination_buffer=*/values_destination, + /*mem_size=*/ShapeUtil::ByteSizeOf(values->shape()), nullptr)); + } + + int64 dimension_to_sort = sort->dimensions(0); + int64 dimension_to_sort_bound = keys->shape().dimensions(dimension_to_sort); + int64 num_stages = tensorflow::Log2Ceiling(dimension_to_sort_bound); + auto index_type = b_.getInt64Ty(); + + // Naive C++ code for the outer loops: + // + // for (int64 stage = 0; stage < Log2Ceiling(dimension_to_sort_bound); + // ++stage) { + // int64 first_xor_mask = (1LL << (stage + 1)) - 1; + // SortInPlace(first_xor_mask); + // for (int64 mask = stage - 1; mask >= 0; --mask) { + // int64 later_xor_mask = 1LL << mask; + // SortInPlace(later_xor_mask); + // } + // } + // + // This follows the algorithm described on Wikipedia: + // https://en.wikipedia.org/wiki/Bitonic_sorter + + for (int64 stage = 0; stage < num_stages; ++stage) { + for (int64 mask = stage; mask >= 0; --mask) { + thunks.push_back( + BuildKernelThunk(sort, /*implements_whole_instruction=*/false)); + LaunchDimensions launch_dimensions = CalculateLaunchDimensions( + keys->shape(), ir_emitter_context_->device_description()); + UpdateLaunchDimensions(launch_dimensions, thunks.back().get(), + ir_emitter_context_->llvm_module()); + + llvm::Value* xor_mask; + if (mask == stage) { + xor_mask = llvm::ConstantInt::get(index_type, (1LL << (stage + 1)) - 1); + } else { + xor_mask = llvm::ConstantInt::get(index_type, 1LL << mask); + } + + TF_RETURN_IF_ERROR(llvm_ir::EmitSortInPlace( + dimension_to_sort, GetIrArray(*sort, *sort, keys_shape_index), + values != nullptr ? tensorflow::gtl::make_optional( + GetIrArray(*sort, *sort, values_shape_index)) + : tensorflow::gtl::nullopt, + IrName(sort), xor_mask, &b_, &launch_dimensions)); + } + } + + thunk_sequence_->emplace_back( + MakeUnique(std::move(thunks), sort)); + return Status::OK(); +} + Status IrEmitterUnnested::HandleTupleSelect(HloInstruction* tuple_select) { - thunk_sequence_->push_back(BuildKernelThunk(tuple_select)); + thunk_sequence_->push_back( + BuildKernelThunk(tuple_select, /*implements_whole_instruction=*/true)); return IrEmitter::HandleTupleSelect(tuple_select); } @@ -2309,12 +2185,12 @@ Status IrEmitterUnnested::HandleCrossReplicaSum(HloInstruction* crs) { thunks.push_back(MakeUnique( /*source_address=*/GetAllocationSlice(*crs->operand(i)), /*destination_buffer=*/tuple_element_buffers.back(), - /*mem_size=*/ShapeUtil::ByteSizeOf(crs->operand(i)->shape()), crs)); + /*mem_size=*/ShapeUtil::ByteSizeOf(crs->operand(i)->shape()), nullptr)); } // Output a tuple of the buffers above. thunks.push_back(MakeUnique(tuple_element_buffers, - GetAllocationSlice(*crs), crs)); + GetAllocationSlice(*crs), nullptr)); thunk_sequence_->push_back( MakeUnique(std::move(thunks), crs)); return Status::OK(); @@ -2329,6 +2205,11 @@ Status IrEmitterUnnested::HandleInfeed(HloInstruction* infeed) { return Status::OK(); } +Status IrEmitterUnnested::HandleOutfeed(HloInstruction* outfeed) { + thunk_sequence_->emplace_back(BuildOutfeedThunk(outfeed)); + return Status::OK(); +} + // Figures out how to access the buffers for all subshapes of hlo's operands and // for hlo itself (i.e. all the buffers produced by HLO). // @@ -2416,11 +2297,6 @@ GetHloBufferSlices(const HloInstruction* hlo, // Adds entries for all subshapes of instr to `slices`. auto add_slices_for = [&](const HloInstruction* instr) { - // GPU constants don't have buffers; don't bother looking for one. - if (instr->IsConstant()) { - return; - } - ShapeUtil::ForEachSubshape( instr->shape(), [&](const Shape& /*shape*/, const ShapeIndex& index) { if (slices.count({instr, index})) { @@ -2448,7 +2324,8 @@ GetHloBufferSlices(const HloInstruction* hlo, } std::unique_ptr IrEmitterUnnested::BuildKernelThunk( - const HloInstruction* inst, int unroll_factor) { + const HloInstruction* inst, bool implements_whole_instruction, + int unroll_factor) { const BufferAssignment& buffer_assn = ir_emitter_context_->buffer_assignment(); @@ -2481,21 +2358,25 @@ std::unique_ptr IrEmitterUnnested::BuildKernelThunk( // We'll pass a pointer to each of the elements of `buffers` to our kernel, in // this order. - std::vector buffers(buffers_needed.begin(), - buffers_needed.end()); - std::sort(buffers.begin(), buffers.end(), + std::vector non_constant_buffers; + c_copy_if(buffers_needed, std::back_inserter(non_constant_buffers), + [](const BufferAllocation* allocation) { + return !allocation->is_constant(); + }); + + std::sort(non_constant_buffers.begin(), non_constant_buffers.end(), [](const BufferAllocation* a, const BufferAllocation* b) { return a->index() < b->index(); }); - llvm::Function* kernel = BuildKernelPrototype(*inst, buffers); + llvm::Function* kernel = BuildKernelPrototype(*inst, non_constant_buffers); // Build a map from a BufferAllocation to the corresponding argument in our // kernel. std::unordered_map kernel_args; { auto arg_it = kernel->arg_begin(); - auto buffers_it = buffers.begin(); + auto buffers_it = non_constant_buffers.begin(); for (; arg_it != kernel->arg_end(); ++arg_it, ++buffers_it) { kernel_args[*buffers_it] = arg_it; } @@ -2513,18 +2394,24 @@ std::unique_ptr IrEmitterUnnested::BuildKernelThunk( << " is found in slice " << slice.ToString() << " at GTE index " << gte_index.ToString(); - llvm::Value* loc = - ir_builder_.CreateInBoundsGEP(kernel_args.at(slice.allocation()), - {ir_builder_.getInt64(slice.offset())}); + llvm::Value* loc; + if (slice.allocation()->is_constant()) { + loc = ir_emitter_context_->llvm_module()->getGlobalVariable( + llvm_ir::AsStringRef(llvm_ir::ConstantBufferAllocationToGlobalName( + *slice.allocation()))); + CHECK_NE(loc, nullptr); + } else { + loc = b_.CreateInBoundsGEP(kernel_args.at(slice.allocation()), + {b_.getInt64(slice.offset())}); + } // If gte_index is nonempty, we have to dereference `loc` to get to the // value we're ultimately interested in. llvm::Type* int8_double_pointer = - llvm::PointerType::get(ir_builder_.getInt8PtrTy(), /*AddressSpace=*/0); + llvm::PointerType::get(b_.getInt8PtrTy(), /*AddressSpace=*/0); for (int64 idx : gte_index) { - loc = ir_builder_.CreateBitCast(loc, int8_double_pointer); - loc = ir_builder_.CreateLoad( - ir_builder_.CreateInBoundsGEP(loc, {ir_builder_.getInt64(idx)})); + loc = b_.CreateBitCast(loc, int8_double_pointer); + loc = b_.CreateLoad(b_.CreateInBoundsGEP(loc, {b_.getInt64(idx)})); } bindings_.BindHloToIrValue(*instr, loc, index); @@ -2536,11 +2423,12 @@ std::unique_ptr IrEmitterUnnested::BuildKernelThunk( bindings_.SetTempBufferBase(kernel_args.at(*temp_buffer)); } else { bindings_.SetTempBufferBase( - llvm::ConstantPointerNull::get(ir_builder_.getInt8PtrTy())); + llvm::ConstantPointerNull::get(b_.getInt8PtrTy())); } - return MakeUnique(buffers, llvm_ir::AsString(kernel->getName()), - inst, unroll_factor); + return MakeUnique( + non_constant_buffers, llvm_ir::AsString(kernel->getName()), + implements_whole_instruction ? inst : nullptr, unroll_factor); } std::unique_ptr IrEmitterUnnested::BuildHostToDeviceCopyThunk( @@ -2574,7 +2462,7 @@ std::unique_ptr IrEmitterUnnested::BuildInfeedThunk( ShapeTree slices(inst->shape()); slices.ForEachMutableElement( - [this, inst](const ShapeIndex& index, BufferAllocation::Slice* slice) { + [&](const ShapeIndex& index, BufferAllocation::Slice* slice) { *slice = ir_emitter_context_->buffer_assignment() .GetUniqueSlice(inst, index) .ConsumeValueOrDie(); @@ -2582,6 +2470,23 @@ std::unique_ptr IrEmitterUnnested::BuildInfeedThunk( return MakeUnique(slices, inst); } +std::unique_ptr IrEmitterUnnested::BuildOutfeedThunk( + const HloInstruction* inst) { + CHECK_EQ(HloOpcode::kOutfeed, inst->opcode()); + + ShapeTree slices(inst->operand(0)->shape()); + slices.ForEachMutableElement( + [&](const ShapeIndex& index, BufferAllocation::Slice* slice) { + auto status_or_slice = + ir_emitter_context_->buffer_assignment().GetUniqueSlice( + inst->operand(0), index); + if (status_or_slice.ok()) { + *slice = status_or_slice.ConsumeValueOrDie(); + } + }); + return MakeUnique(std::move(slices), inst); +} + namespace { double GetScalarConstantAsDouble(const Literal& literal) { switch (literal.shape().element_type()) { @@ -2697,6 +2602,11 @@ StatusOr> IrEmitterUnnested::BuildInitializerThunk( init_value = hlo->operand(init_value->parameter_number()); } + // Initializer thunks don't implement a whole instruction, and we want to + // profile the whole instruction instead of the individual thunks it consists + // of. Therefore we pass nullptr as the HloInstruction* to the thunks we + // generate below. + // // In the common case, the initializer is a constant. In this case, emit a // device-memset call if we can. Currently StreamExecutor only supports // zeroing and 32-bit memsets. @@ -2710,7 +2620,8 @@ StatusOr> IrEmitterUnnested::BuildInitializerThunk( ArraySlice literal_bytes( reinterpret_cast(literal.untyped_data()), num_bytes); if (c_all_of(literal_bytes, [](uint8 byte) { return byte == 0; })) { - return {MakeUnique(GetAllocationSlice(*hlo, index), hlo)}; + return { + MakeUnique(GetAllocationSlice(*hlo, index), nullptr)}; } // If the literal is 8 or 16 bits wide, we can emit a 32-bit memset by @@ -2728,7 +2639,7 @@ StatusOr> IrEmitterUnnested::BuildInitializerThunk( } uint32 pattern32 = uint32{pattern16} | (uint32{pattern16} << 16); return {MakeUnique( - pattern32, GetAllocationSlice(*hlo, index), hlo)}; + pattern32, GetAllocationSlice(*hlo, index), nullptr)}; } // If the literal is an even multiple of 32 bits wide, we can emit a 32-bit @@ -2739,12 +2650,13 @@ StatusOr> IrEmitterUnnested::BuildInitializerThunk( uint32 word; memcpy(&word, literal_bytes.data(), sizeof(word)); return {MakeUnique( - word, GetAllocationSlice(*hlo, index), hlo)}; + word, GetAllocationSlice(*hlo, index), nullptr)}; } } // Otherwise fall back to our slow initializer code. - std::unique_ptr kernel_thunk = BuildKernelThunk(hlo); + std::unique_ptr kernel_thunk = + BuildKernelThunk(hlo, /*implements_whole_instruction=*/false); LaunchDimensions launch_dimensions = CalculateLaunchDimensions(ShapeUtil::GetSubshape(hlo->shape(), index), ir_emitter_context_->device_description()); @@ -2753,15 +2665,24 @@ StatusOr> IrEmitterUnnested::BuildInitializerThunk( // If the init_value was fused into this reduce we have to generate it first. if (fused && init_value_operand->opcode() != HloOpcode::kParameter) { CHECK_EQ(HloOpcode::kConstant, init_value_operand->opcode()); - TF_RETURN_IF_ERROR(HandleConstant(const_cast(init_value))); + + const Literal& literal = init_value_operand->literal(); + llvm::Constant* initializer = + llvm_ir::ConvertLiteralToIrConstant(literal, module_); + + llvm::GlobalVariable* global_for_const = new llvm::GlobalVariable( + *module_, initializer->getType(), + /*isConstant=*/true, llvm::GlobalValue::PrivateLinkage, initializer, + /*Name=*/""); + global_for_const->setAlignment(kConstantBufferAlignBytes); + bindings_.BindHloToIrValue(*init_value_operand, global_for_const); } TF_RETURN_IF_ERROR(ParallelLoopEmitter( - [=](const llvm_ir::IrArray::Index& index) { + [=](const IrArray::Index& index) { return GetIrArray(*init_value, *hlo) - .EmitReadArrayElement(index, &ir_builder_); + .EmitReadArrayElement(index, &b_); }, - GetIrArray(*hlo, *hlo, index), launch_dimensions, - &ir_builder_) + GetIrArray(*hlo, *hlo, index), launch_dimensions, &b_) .EmitLoop(IrName(hlo))); // Clean up state left behind by emitting the loop above. (This is normally @@ -2872,13 +2793,13 @@ std::unique_ptr IrEmitterUnnested::BuildWhileThunk( HloComputation* condition = hlo->while_condition(); IrEmitterUnnested ir_emitter_condition(hlo_module_config_, condition, ir_emitter_context_); - TF_CHECK_OK(condition->root_instruction()->Accept(&ir_emitter_condition)); + TF_CHECK_OK(condition->Accept(&ir_emitter_condition)); // Generate thunk sequence for while 'body'. HloComputation* body = hlo->while_body(); IrEmitterUnnested ir_emitter_body(hlo_module_config_, body, ir_emitter_context_); - TF_CHECK_OK(body->root_instruction()->Accept(&ir_emitter_body)); + TF_CHECK_OK(body->Accept(&ir_emitter_body)); return MakeUnique( GetAllocationSlice(*condition->root_instruction()), // cond result @@ -2896,7 +2817,7 @@ std::unique_ptr IrEmitterUnnested::BuildForThunk( HloComputation* body = hlo->while_body(); IrEmitterUnnested ir_emitter_body(hlo_module_config_, body, ir_emitter_context_); - TF_CHECK_OK(body->root_instruction()->Accept(&ir_emitter_body)); + TF_CHECK_OK(body->Accept(&ir_emitter_body)); return MakeUnique(loop_limit, ir_emitter_body.ConsumeThunkSequence(), hlo); @@ -2912,12 +2833,12 @@ std::unique_ptr IrEmitterUnnested::BuildConditionalThunk( HloComputation* true_computation = hlo->true_computation(); IrEmitterUnnested ir_emitter_true(hlo_module_config_, true_computation, ir_emitter_context_); - TF_CHECK_OK(true_computation->root_instruction()->Accept(&ir_emitter_true)); + TF_CHECK_OK(true_computation->Accept(&ir_emitter_true)); HloComputation* false_computation = hlo->false_computation(); IrEmitterUnnested ir_emitter_false(hlo_module_config_, false_computation, ir_emitter_context_); - TF_CHECK_OK(false_computation->root_instruction()->Accept(&ir_emitter_false)); + TF_CHECK_OK(false_computation->Accept(&ir_emitter_false)); return MakeUnique( GetAllocationSlice(*hlo->operand(0)), @@ -2945,41 +2866,588 @@ Status IrEmitterUnnested::EmitTargetElementLoopInThunk( ir_emitter_context_->llvm_module()); if (!hlo.IsMultiOutputFusion()) { return ParallelLoopEmitter(element_generator, GetIrArray(hlo, hlo), - launch_dimensions, &ir_builder_, unroll_factor) - .EmitLoop(IrName(&hlo), - GetIndexTypeForKernel(&hlo, launch_dimensions.launch_bound(), - &ir_builder_)); + launch_dimensions, &b_, unroll_factor) + .EmitLoop( + IrName(&hlo), + GetIndexTypeForKernel(&hlo, launch_dimensions.launch_bound(), &b_)); } - // For multiple outputs fusion, we need to emit each operand and the root. - std::vector output_arrays; + // For multioutput fusion, we need to emit each operand and the root. + std::vector output_arrays; for (int64 i = 0; i < ShapeUtil::TupleElementCount(hlo.shape()); ++i) { output_arrays.push_back(GetIrArray(hlo, hlo, {i})); } TF_RETURN_IF_ERROR( ParallelLoopEmitter(element_generator, output_arrays, launch_dimensions, - &ir_builder_, unroll_factor) + &b_, unroll_factor) .EmitLoop(IrName(&hlo), GetIndexTypeForKernel( - &hlo, launch_dimensions.launch_bound(), &ir_builder_))); + &hlo, launch_dimensions.launch_bound(), &b_))); std::vector tuple_operand_ptrs; for (int64 i = 0; i < output_arrays.size(); ++i) { tuple_operand_ptrs.push_back(output_arrays[i].GetBasePointer()); } - ir_builder_.SetInsertPoint(ir_builder_.GetInsertBlock()->getTerminator()); - llvm_ir::EmitTuple(GetIrArray(hlo, hlo), tuple_operand_ptrs, &ir_builder_, - module_); + b_.SetInsertPoint(b_.GetInsertBlock()->getTerminator()); + llvm_ir::EmitTuple(GetIrArray(hlo, hlo), tuple_operand_ptrs, &b_, module_); return Status::OK(); } Status IrEmitterUnnested::EmitTargetElementLoop( const HloInstruction& hlo, const llvm_ir::ElementGenerator& element_generator) { - CHECK(Thunk::Kind::kKernel == LastThunk()->kind()); + CHECK_EQ(Thunk::Kind::kKernel, LastThunk()->kind()); return EmitTargetElementLoopInThunk(hlo, element_generator, static_cast(LastThunk())); } +int IrEmitterUnnested::ConstructIrArrayForOutputs( + const HloInstruction& hlo, std::vector* output_arrays) { + int64 num_outputs = 1; + if (hlo.IsMultiOutputFusion()) { + num_outputs = ShapeUtil::TupleElementCount(hlo.shape()); + output_arrays->reserve(num_outputs); + for (int64 i = 0; i < num_outputs; ++i) { + output_arrays->push_back(GetIrArray(hlo, hlo, {i})); + } + } else { + output_arrays->push_back(GetIrArray(hlo, hlo)); + } + return num_outputs; +} + +int IrEmitterUnnested::ConstructIrArrayForInputs( + const HloInstruction& hlo, std::vector* param_arrays) { + int64 num_params = hlo.operands().size(); + param_arrays->reserve(num_params); + for (const HloInstruction* param : hlo.operands()) { + param_arrays->push_back(GetIrArray(*param, hlo)); + } + return num_params; +} + +int IrEmitterUnnested::ConstructOutputReducedShapeAndCastOutputIrArrayToShape( + const HloInstruction& hlo, const std::vector& output_arrays, + tensorflow::gtl::ArraySlice reduced_output_dims, + std::vector* output_reduced_shapes, + std::vector* output_in_reduced_shape_arrays) { + int64 num_outputs = 1; + if (hlo.IsMultiOutputFusion()) { + num_outputs = ShapeUtil::TupleElementCount(hlo.shape()); + output_in_reduced_shape_arrays->reserve(num_outputs); + output_reduced_shapes->reserve(num_outputs); + for (int64 i = 0; i < num_outputs; ++i) { + output_reduced_shapes->push_back(ShapeUtil::MakeShapeWithDescendingLayout( + ShapeUtil::GetSubshape(hlo.shape(), {i}).element_type(), + reduced_output_dims)); + output_in_reduced_shape_arrays->push_back( + output_arrays[i].CastToShape((*output_reduced_shapes)[i], &b_)); + } + } else { + output_reduced_shapes->push_back(ShapeUtil::MakeShapeWithDescendingLayout( + hlo.shape().element_type(), reduced_output_dims)); + output_in_reduced_shape_arrays->push_back( + output_arrays[0].CastToShape((*output_reduced_shapes)[0], &b_)); + } + return num_outputs; +} + +int IrEmitterUnnested::ConstructInputReducedShapeAndCastInputIrArrayToShape( + const HloInstruction& hlo, const std::vector& param_arrays, + const std::vector& param_buffers, + tensorflow::gtl::ArraySlice reduced_output_dims, + std::vector* param_reduced_shapes, + std::vector* param_in_reduced_shape_arrays) { + int64 num_params = hlo.operands().size(); + param_in_reduced_shape_arrays->reserve(num_params); + param_reduced_shapes->reserve(num_params); + for (int64 id = 0; id < num_params; ++id) { + if (param_buffers[id] == nullptr) { + param_reduced_shapes->push_back(Shape()); + param_in_reduced_shape_arrays->push_back(IrArray()); + continue; + } + const HloInstruction* param = hlo.operand(id); + param_reduced_shapes->push_back(ShapeUtil::MakeShapeWithDescendingLayout( + param->shape().element_type(), + Permute({0, 2, 1}, reduced_output_dims))); + param_in_reduced_shape_arrays->push_back( + param_arrays[id].CastToShape((*param_reduced_shapes)[id], &b_)); + } + return num_params; +} + +namespace { + +// Reads thread_idx.x and converts it to a (y,x) coordinate, assuming that the +// thread lives within a square tile of size tile_size (so thread blocks are of +// size tile_size * tile_size). +std::tuple CalculateYXCoordinateWithinTile( + llvm::IRBuilder<>* builder, llvm::Value* tile_size, + int64 threads_per_tile) { + // Calculate the starting element coordinate within a tile for the current + // thread, (y, x) from thread_id. + llvm::Value* thread_id = llvm_ir::EmitCallToIntrinsic( + llvm::Intrinsic::nvvm_read_ptx_sreg_tid_x, {}, {}, builder); + llvm_ir::AddRangeMetadata(0, threads_per_tile, + llvm::cast(thread_id)); + thread_id = builder->CreateIntCast(thread_id, tile_size->getType(), + /*isSigned=*/true, "thread.id.x"); + auto x = builder->CreateURem(thread_id, tile_size); + auto y = builder->CreateUDiv(thread_id, tile_size); + return std::make_tuple(y, x); +} + +// Reads block_idx.x, casts it to type index_ty, and adds the assumption that +// it's in the range [0, num_blocks]. +llvm::Value* GetBlockIdx(llvm::IRBuilder<>* builder, llvm::Type* index_ty, + int64 num_blocks) { + llvm::Value* block_id = llvm_ir::EmitCallToIntrinsic( + llvm::Intrinsic::nvvm_read_ptx_sreg_ctaid_x, {}, {}, builder); + llvm_ir::AddRangeMetadata(0, num_blocks, + llvm::cast(block_id)); + return builder->CreateIntCast(block_id, index_ty, /*isSigned=*/true, + "block.id.x"); +} + +// Emits code to process up to (tile_size/num_rows) elements in a tile, given +// `emit_elem_function` is the function to emit code to process one element, `y` +// and `x` are the coordinates for the first element to process, and `index` is +// the index for the origin of the tile. Emits bounds check to ensure that each +// processed element is within the boundary defined by `tile_width` and +// `tile_height`. +void EmitTiledElementalCodeWithBoundsCheck( + int64 tile_size, int64 num_rows, const IrArray::Index& index, + const string& loop_name, KernelSupportLibrary* ksl, + llvm::IRBuilder<>* builder, llvm::Value* y, llvm::Value* x, + llvm::Value* tile_width, llvm::Value* tile_height, + const std::function& + emit_elem_function) { + llvm::Type* index_ty = tile_width->getType(); + // Emits a constant value with index type. + auto index_typed_constant = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_ty, c); + }; + // Adds `addend` to the given `dim` of `index`. + auto offset_dim = [&](IrArray::Index index, llvm::Value* addend, int64 dim) { + index[dim] = builder->CreateAdd(index[dim], addend); + return index; + }; + + auto emit_full_tile = [&] { + for (int64 i = 0; i < tile_size; i += num_rows) { + auto source_idx = offset_dim(index, index_typed_constant(i), /*dim=*/1); + auto y_loc = builder->CreateAdd(index_typed_constant(i), y); + emit_elem_function(source_idx, y_loc); + } + }; + + auto emit_last_row = [&] { + ksl->IfReturnVoid("x_in_tile", builder->CreateICmpULT(x, tile_width), [&] { + // tile_height_upper_bound = + // ceil(tile_height / num_rows) * num_rows + auto tile_height_upper_bound = builder->CreateMul( + builder->CreateUDiv( + builder->CreateAdd(tile_height, + index_typed_constant(num_rows - 1)), + index_typed_constant(num_rows)), + index_typed_constant(num_rows)); + ksl->ForReturnVoid( + loop_name, /*start=*/index_typed_constant(0), + /*end=*/tile_height_upper_bound, + /*step=*/index_typed_constant(num_rows), [&](llvm::Value* y_indvar) { + auto y_loc = builder->CreateAdd(y_indvar, y); + ksl->IfReturnVoid( + "y_in_tile", builder->CreateICmpULT(y_loc, tile_height), [&] { + emit_elem_function(offset_dim(index, y_indvar, /*dim=*/1), + y_loc); + }); + }); + }); + }; + ksl->IfReturnVoid( + "full_tile", + builder->CreateAnd( + builder->CreateICmpEQ(index_typed_constant(tile_size), tile_width), + builder->CreateICmpEQ(index_typed_constant(tile_size), tile_height)), + emit_full_tile, emit_last_row); +} +} // namespace + +// Emits a kernel for the given hlo instruction using a tiled 0-2-1 transpose +// algorithm to improve the memory access patterns for the input parameters +// which have a shape that is a 0-2-1 transpose of the output tensors. +// +// For the purpose of tiling, the output tensors have a logical shape of three +// components 0-2-1 while the relevant input parameters have a logical shape of +// three components 0-1-2 in the order major to minor. The x- and y- dimensions +// of the tensors are tiled in square tiles of edge length `kTileSize`. Each +// thread block of `kTileSize` x `kNumRows` threads transposes one tile: each +// thread copies kTileSize/kNumRows elements from the input to a shared memory +// tile, then the otherwise "regular hlo kernel" reads from the shared memory +// instead of the original input. +// +// This is similar to the following CUDA algorithm in TensorFlow: +// https://goo.gl/MStRV6. +// +// `kTileSize` should usually be same as warp size. We currently choose 32 for +// `kTileSize` and 4 for `kNumRows`. The CUDA algorithm uses 8 for `kNumRows`. +// +// TODO(b/33320379): Here each block transposes 1 tile. It may be more efficient +// to launch fewer blocks so each transposes many tiles. +LaunchDimensions IrEmitterUnnested::EmitHlo021Tile( + HloInstruction* hlo, tensorflow::gtl::ArraySlice reduced_output_dims, + tensorflow::gtl::ArraySlice tiled_param_ids) { + // Parameters for the tiling algorithm. + constexpr int64 kTileSize = 32; + constexpr int64 kNumRows = 4; + constexpr int64 kThreadsPerTile = kTileSize * kNumRows; + + // Construct IrArrays for the inputs and outputs. + std::vector output_arrays; + int64 num_outputs = ConstructIrArrayForOutputs(*hlo, &output_arrays); + std::vector param_arrays; + int64 num_params = ConstructIrArrayForInputs(*hlo, ¶m_arrays); + + // Allocate shared memory buffers to store the tiled inputs. + std::vector param_shmem_buffers(num_params, nullptr); + for (int64 id : tiled_param_ids) { + const HloInstruction* param = hlo->operand(id); + // Add 1 to the minor dimension to reduce shared memory bank conflicts. + llvm::Type* tile_type = llvm::ArrayType::get( + llvm::ArrayType::get(llvm_ir::PrimitiveTypeToIrType( + param->shape().element_type(), module_), + kTileSize + 1), + kTileSize); + const int kNVPTXSharedMemoryAddrSpace = 3; + auto* tile_base_ptr = new llvm::GlobalVariable( + *b_.GetInsertBlock()->getParent()->getParent(), tile_type, + /*isConstant=*/false, llvm::GlobalValue::PrivateLinkage, + llvm::UndefValue::get(tile_type), + llvm_ir::AsStringRef(IrName(hlo, StrCat("tile", id))), nullptr, + llvm::GlobalValue::NotThreadLocal, kNVPTXSharedMemoryAddrSpace); + param_shmem_buffers[id] = tile_base_ptr; + VLOG(3) << "Added shmem buffer for parameter " << id << ": " + << llvm_ir::DumpToString(*tile_base_ptr); + } + + // The 0-2-1 shape of the tiling scheme is the reduced shape of the HLO result + // for the purpose of tiling. Calculate the logical output dimensions in the + // tile from the reduced output dimensions. + std::vector output_dims_in_tiles = std::vector( + reduced_output_dims.begin(), reduced_output_dims.end()); + CHECK_EQ(output_dims_in_tiles.size(), 3); + for (int i = 1; i < 3; ++i) { + output_dims_in_tiles[i] = + CeilOfRatio(output_dims_in_tiles[i], kTileSize); + } + const int64 num_tiles = + c_accumulate(output_dims_in_tiles, 1, std::multiplies()); + LaunchDimensions launch_dimensions(num_tiles, kThreadsPerTile); + + llvm::Type* index_ty = + GetIndexTypeForKernel(hlo, launch_dimensions.launch_bound(), &b_); + auto index_typed_constant = [&](uint64 c) -> llvm::Constant* { + return llvm::ConstantInt::get(index_ty, c); + }; + + // Cast each output IrArray to its corresponding reduced shape and keep the + // reduced shape live during IR emission. + std::vector output_in_reduced_shape_arrays; + std::vector output_reduced_shapes; + CHECK_EQ(ConstructOutputReducedShapeAndCastOutputIrArrayToShape( + *hlo, output_arrays, reduced_output_dims, &output_reduced_shapes, + &output_in_reduced_shape_arrays), + num_outputs); + + // For each tiled parameter, cast its input IrArray to the corresponding + // reduced shape and keep the reduced shape live during IR emission. + std::vector param_in_reduced_shape_arrays; + std::vector param_reduced_shapes; + CHECK_EQ(ConstructInputReducedShapeAndCastInputIrArrayToShape( + *hlo, param_arrays, param_shmem_buffers, reduced_output_dims, + ¶m_reduced_shapes, ¶m_in_reduced_shape_arrays), + num_params); + + // Calculate the starting element coordinate within a tile for the current + // thread, (y, x) from thread_id. + llvm::Value* x; + llvm::Value* y; + std::tie(y, x) = CalculateYXCoordinateWithinTile( + &b_, index_typed_constant(kTileSize), kThreadsPerTile); + + // Calculate the index for the current output tile from block_id. + const IrArray::Index output_tile_index( + GetBlockIdx(&b_, index_ty, num_tiles), + ShapeUtil::MakeShapeWithDescendingLayout(PRED /*arbitrary*/, + output_dims_in_tiles), + &b_); + + // Output tile origin is the index for the first element of the current output + // tile. + const IrArray::Index output_tile_origin = [&] { + IrArray::Index index = output_tile_index; + for (int i = 1; i < 3; ++i) { + index[i] = + b_.CreateMul(output_tile_index[i], index_typed_constant(kTileSize), + "tile_origin." + std::to_string(i)); + } + return index; + }(); + + // Calculate the input tile origin from the output tile origin. + const IrArray::Index input_tile_origin( + Permute({0, 2, 1}, output_tile_origin.multidim())); + + // Calculate the current output tile bounds in each of the logical dimensions. + std::vector output_tile_bounds(3); + for (int i = 1; i < 3; ++i) { + // Only last row or column may not have full size. + output_tile_bounds[i] = b_.CreateSelect( + b_.CreateICmpEQ(output_tile_index[i], + index_typed_constant(output_dims_in_tiles[i] - 1)), + index_typed_constant(reduced_output_dims[i] - + (output_dims_in_tiles[i] - 1) * kTileSize), + index_typed_constant(kTileSize), "kTileSize"); + } + + KernelSupportLibrary ksl(&b_, llvm_ir::UnrollMode::kDefaultUnroll); + + // Curry a few parameters to EmitTiledElementalCodeWithBoundsCheck. + auto emit_tiled_elemental_code_with_bounds_check = + [&](const IrArray::Index& index, const string& loop_name, + llvm::Value* tile_width, llvm::Value* tile_height, + const std::function& + emit_elem_function) { + EmitTiledElementalCodeWithBoundsCheck( + kTileSize, kNumRows, index, loop_name, &ksl, &b_, y, x, tile_width, + tile_height, emit_elem_function); + }; + + // Adds `addend` to the given `dim` of `index`. + auto offset_dim = [&](IrArray::Index index, llvm::Value* addend, int64 dim) { + index[dim] = b_.CreateAdd(index[dim], addend); + return index; + }; + const IrArray::Index input_index = + offset_dim(offset_dim(input_tile_origin, x, /*dim=*/2), y, /*dim=*/1); + + // Copy input parameter values to shared memory buffers: + // tile[y, x] = input[index] + emit_tiled_elemental_code_with_bounds_check( + input_index, "input", output_tile_bounds[1], output_tile_bounds[2], + [&](const IrArray::Index& index, llvm::Value* y_loc) { + for (int64 id : tiled_param_ids) { + IrArray& input_in_logical_shape = param_in_reduced_shape_arrays[id]; + llvm::Value* shmem_buffer = param_shmem_buffers[id]; + // TODO(jlebar): Add AA metadata to this store. Tile buffers are + // global variables, so LLVM can't infer much about it. + b_.CreateStore( + input_in_logical_shape.EmitReadArrayElement(index, &b_, + "input_element"), + b_.CreateGEP(shmem_buffer, {index_typed_constant(0), y_loc, x})); + } + }); + + // Wait for all threads to reach this point, lest we copy a value from tile to + // output before the other thread copies it from input to tile. + // This is `__syncthreads` in CUDA. + llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::nvvm_barrier0, {}, {}, &b_); + + llvm_ir::TiledParameterInfo tiled_param_info(param_shmem_buffers, y, x); + + const IrArray::Index output_index = + offset_dim(offset_dim(output_tile_origin, x, /*dim=*/2), y, /*dim=*/1); + + // Write to output[index] by emitting code like normal, except that values for + // the tiled parameters are read from the shmem buffers. + if (hlo->opcode() == HloOpcode::kCopy) { + emit_tiled_elemental_code_with_bounds_check( + output_index, "output", output_tile_bounds[2], output_tile_bounds[1], + [&](const IrArray::Index& index, llvm::Value* y_loc) { + // TODO(jlebar): Add AA metadata to this load. + llvm::Instruction* load_from_shmem_buffer = b_.CreateLoad( + b_.CreateGEP(param_shmem_buffers[0], {b_.getInt64(0), x, y_loc}), + "output_element"); + output_in_reduced_shape_arrays[0].EmitWriteArrayElement( + index, load_from_shmem_buffer, &b_); + }); + } else { + CHECK_EQ(hlo->opcode(), HloOpcode::kFusion); + emit_tiled_elemental_code_with_bounds_check( + output_index, "output", output_tile_bounds[2], output_tile_bounds[1], + [&](const IrArray::Index& index, llvm::Value* y_loc) { + GpuElementalIrEmitter elem_emitter(hlo_module_config_, module_, &b_, + GetNestedComputer()); + FusedIrEmitter fused_emitter(param_arrays, &elem_emitter); + tiled_param_info.set_y(y_loc); + fused_emitter.SetTiledParameterInfo(&tiled_param_info); + TF_CHECK_OK(hlo->fused_expression_root()->Accept(&fused_emitter)); + IrArray::Index untiled_index = llvm_ir::GetUnreducedOutputIndex( + index, output_reduced_shapes[0], output_arrays[0].GetShape(), + &b_); + const llvm_ir::ElementGenerator& output_generator = + fused_emitter.GetRootGenerator(); + llvm::Value* output_value = + output_generator(untiled_index).ValueOrDie(); + if (hlo->IsMultiOutputFusion()) { + CHECK(output_value->getType()->isStructTy()); + CHECK_EQ(output_value->getType()->getStructNumElements(), + output_in_reduced_shape_arrays.size()); + for (int64 i = 0; i < output_in_reduced_shape_arrays.size(); ++i) { + output_in_reduced_shape_arrays[i].EmitWriteArrayElement( + index, b_.CreateExtractValue(output_value, i), &b_); + } + } else { + output_in_reduced_shape_arrays[0].EmitWriteArrayElement( + index, output_value, &b_); + } + }); + } + + // For multioutput fusion, emit a tuple with all the individual outputs. + if (hlo->IsMultiOutputFusion()) { + std::vector tuple_operand_ptrs; + for (int64 i = 0; i < output_arrays.size(); ++i) { + tuple_operand_ptrs.push_back(output_arrays[i].GetBasePointer()); + } + llvm_ir::EmitTuple(GetIrArray(*hlo, *hlo), tuple_operand_ptrs, &b_, + module_); + } + + return launch_dimensions; +} + +bool IrEmitterUnnested::CheckAndEmitHloWithTile021(HloInstruction* hlo) { + HloOpcode opcode = hlo->opcode(); + CHECK(opcode == HloOpcode::kFusion || opcode == HloOpcode::kCopy); + CHECK(opcode != HloOpcode::kFusion || + hlo->fusion_kind() == HloInstruction::FusionKind::kLoop) + << "Only loop fusions are supported."; + + const Shape& output_shape = hlo->IsMultiOutputFusion() + ? ShapeUtil::GetSubshape(hlo->shape(), {0}) + : hlo->shape(); + + // If the output_shape is reduced to 021 shape, find all the parameters of the + // hlo that are in the corresponding 012 shape. + std::vector params_012; + optional> reduced_dims_021; + for (int64 operand_idx = 0; operand_idx < hlo->operand_count(); + ++operand_idx) { + HloInstruction* operand = hlo->mutable_operand(operand_idx); + auto find_transpose_result = + llvm_ir::FindTranspose021(operand->shape(), output_shape); + if (!find_transpose_result.has_value()) { + continue; + } + const std::vector& curr_reduced_dims_021 = *find_transpose_result; + if (!reduced_dims_021.has_value()) { + reduced_dims_021 = curr_reduced_dims_021; + } + if (!ContainersEqual(*reduced_dims_021, curr_reduced_dims_021)) { + // There is more than one possible transpose. Instead of picking one + // transpose, we simply give up here. + return false; + } + params_012.push_back(operand_idx); + } + + if (!reduced_dims_021.has_value()) { + return false; + } + + if ((*reduced_dims_021)[1] < kMinDimensionToTransposeTiled || + (*reduced_dims_021)[2] < kMinDimensionToTransposeTiled) { + return false; + } + + // Each of our shared memory tiles has 32*33 elements (so ~4kb, if the + // elements are of size 4 bytes), and CUDA has an architectural limit of 48kb + // shared memory per SM. (This is increased to 96kb in Volta, but we don't + // use this, in part because it eats into our L1 cache space.) + // + // For correctness we need to ensure that we don't make more than 48kb worth + // of shmem tiles per block. And for performance, we'd probably like to use + // significantly less, so that we can fit more than one block at a time on a + // gpu core. + // + // We say without benchmarks that we want at least 3 threads/block, + // corresponding to 3 shmem tiles if the elements are 32 bits wide. We choose + // which params get the shmem transpose treatment arbitrarily; it's not clear + // if there's a Right Choice. + // + // This is only sound if tiled transposes are the only place where we use + // shared memory in fusions. If in the future other fusile ops use shared + // memory, we'll have to adjust this heuristic. + constexpr int kMinBlocksPerCore = 3; + constexpr int64 kShmemPerCore = 48 * 1024; + int64 shmem_used = 0; + for (int64 i = 0; i < params_012.size(); ++i) { + const HloInstruction* operand = hlo->operand(params_012[i]); + shmem_used += + 32 * 33 * + ShapeUtil::ByteSizeOfPrimitiveType(operand->shape().element_type()); + + if (kMinBlocksPerCore * shmem_used > kShmemPerCore) { + // Erase this element and everything after it from params_012. + params_012.resize(i); + break; + } + } + + VLOG(3) << "EmitHlo021Tile Emitting hlo tile 0-2-1" << hlo->ToString(); + thunk_sequence_->emplace_back( + BuildKernelThunk(hlo, /*implements_whole_instruction=*/true)); + const LaunchDimensions launch_dimensions = + EmitHlo021Tile(hlo, *reduced_dims_021, params_012); + UpdateLaunchDimensions(launch_dimensions, LastThunk(), + ir_emitter_context_->llvm_module()); + + return true; +} + +Status IrEmitterUnnested::EmitConstantGlobals() { + for (const BufferAllocation& allocation : + ir_emitter_context_->buffer_assignment().Allocations()) { + if (!allocation.is_constant()) { + continue; + } + + const Literal& literal = llvm_ir::LiteralForConstantAllocation(allocation); + const bool should_emit_initializer = ShouldEmitLiteralInLlvmIr(literal); + llvm::ArrayType* global_type = + llvm::ArrayType::get(b_.getInt8Ty(), allocation.size()); + llvm::Constant* initializer = + should_emit_initializer + ? llvm_ir::ConvertLiteralToIrConstant(literal, module_) + : llvm::ConstantAggregateZero::get(global_type); + if (should_emit_initializer) { + VLOG(3) << "Emitted initializer for constant with shape " + << ShapeUtil::HumanString(literal.shape()); + } + + // These globals will be looked up by name by GpuExecutable so we need to + // give them an external linkage. Not all of their uses are visible in the + // LLVM IR (e.g. TupleThunk) so we can't give then a linkage that merely + // preserves their names (like available_externally), we also need to ensure + // that they stick around even if they're "unused". + // + // We may have to be more more clever here in the future if we notice that + // we're keeping around too many globals because of their linkage. + llvm::GlobalVariable* global_for_const = new llvm::GlobalVariable( + global_type, /*isConstant=*/should_emit_initializer, + llvm::GlobalValue::ExternalLinkage, + /*Initializer=*/initializer, + llvm_ir::AsStringRef( + llvm_ir::ConstantBufferAllocationToGlobalName(allocation))); + global_for_const->setAlignment(kConstantBufferAlignBytes); + ir_emitter_context_->llvm_module()->getGlobalList().push_back( + global_for_const); + } + + return Status::OK(); +} + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h index e8dce1ca539a24f91d6c9e5f3425e085e2d30a5a..525441990795e160ba0e8facb910d5cc9796c4bb 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/ir_emitter.h" #include "tensorflow/compiler/xla/service/gpu/thunk.h" +#include "tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.h" namespace xla { namespace gpu { @@ -73,8 +74,10 @@ class IrEmitterUnnested : public IrEmitter { Status HandleTuple(HloInstruction* tuple) override; Status HandleWhile(HloInstruction* xla_while) override; Status HandleInfeed(HloInstruction* xla_infeed) override; + Status HandleOutfeed(HloInstruction* outfeed) override; Status HandleRng(HloInstruction* random) override; Status HandleSelect(HloInstruction* select) override; + Status HandleSort(HloInstruction* sort) override; Status HandleTupleSelect(HloInstruction* tuple_select) override; Status HandleCrossReplicaSum(HloInstruction* crs) override; Status HandleAfterAll(HloInstruction* gen_token) override; @@ -89,6 +92,9 @@ class IrEmitterUnnested : public IrEmitter { const HloInstruction& hlo, const llvm_ir::ElementGenerator& body_emitter, KernelThunk* thunk); + // Emits LLVM global variables corresponding to constant instructions. + Status EmitConstantGlobals(); + private: // Builds the appropriate thunk for the instruction hlo and returns the owning // pointer to it. The caller needs to make sure `inst` outlives the lifetime @@ -116,7 +122,7 @@ class IrEmitterUnnested : public IrEmitter { // Emits code that reduces a matrix of shape [height x width] to a vector of // [width]. Other parameters have the same meaning as those of // `EmitReductionToVector`. Note that input shape might not be - // [height x width], but can be bitcast to [height x weight] with "height" + // [height x width], but can be bitcast to [height x width] with "height" // being the major dimension. Status EmitColumnReduction( int64 height, int64 width, HloInstruction* reduce, @@ -132,7 +138,7 @@ class IrEmitterUnnested : public IrEmitter { // Emits code that reduces a 3D tensor of shape [depth x height x width] to a // vector of shape [height]. Other parameters have the same meaning as those // of `EmitReductionToVector`. Note that input shape might not be - // [depth x height x width], but can be bitcast to [depth x height x weight] + // [depth x height x width], but can be bitcast to [depth x height x width] // with "depth" being the most major dimension. Status EmitRowReduction( int64 depth, int64 height, int64 width, HloInstruction* reduce, @@ -183,12 +189,56 @@ class IrEmitterUnnested : public IrEmitter { std::pair> extra_output_gens); + // Returns true if a 0-2-1 tiling algorithm is already used to emit the kernel + // for the hlo instruction. + bool CheckAndEmitHloWithTile021(HloInstruction* hlo); + // Emits a kernel for the hlo instruction using a 0-2-1 tiling algorithm and + // returns the launch dimensions for the kernel. This is a helper to support + // the implementation of CheckAndEmitHloWithTile021. + LaunchDimensions EmitHlo021Tile( + HloInstruction* hlo, + tensorflow::gtl::ArraySlice reduced_output_dims, + tensorflow::gtl::ArraySlice tiled_param_ids); + // Generates the IrArray for each output of hlo and returns the number of + // outputs. + int ConstructIrArrayForOutputs(const HloInstruction& hlo, + std::vector* output_arrays); + // Generates the IrArray for each input of hlo and returns the number of + // inputs. + int ConstructIrArrayForInputs(const HloInstruction& hlo, + std::vector* param_arrays); + // For each output of the `hlo` instruction, constructs the reduced shape for + // the output with the given `reduced_output_dims` and cast the original + // output IrArray element in `output_arrays` to the reduced shape. Returns + // the number of outputs. + int ConstructOutputReducedShapeAndCastOutputIrArrayToShape( + const HloInstruction& hlo, + const std::vector& output_arrays, + tensorflow::gtl::ArraySlice reduced_output_dims, + std::vector* output_reduced_shapes, + std::vector* output_in_reduced_shape_arrays); + // For each input of the `hlo` instruction, checks its value in + // `param_buffers` to find out whether the input has a reduced shape. If the + // input has a reduced shape, constructs the reduced shape for the input and + // casts the original input IrArray in `param_arrays` to the reduced shape. + // Return the total number of inputs. + int ConstructInputReducedShapeAndCastInputIrArrayToShape( + const HloInstruction& hlo, + const std::vector& param_arrays, + const std::vector& param_buffers, + tensorflow::gtl::ArraySlice reduced_output_dims, + std::vector* param_reduced_shapes, + std::vector* param_in_reduced_shape_arrays); + // Returns a KernelThunk that invokes the kernel emitted for `inst`. The // caller needs to make sure `inst` outlives the lifetime of the returned // Thunk object. The kernel implementation will be unrolled if unroll_factor - // is greater than one. - std::unique_ptr BuildKernelThunk(const HloInstruction* inst, - int unroll_factor = 1); + // is greater than one. 'implements_whole_instruction' specifies whether this + // KernelThunk implements the whole 'inst' HloInstruction. In some cases + // 'inst' will be implemented by a sequence of Thunks. + std::unique_ptr BuildKernelThunk( + const HloInstruction* inst, bool implements_whole_instruction, + int unroll_factor = 1); // Returns a FftThunk that calls cuFFT to implement `inst`. std::unique_ptr BuildFftThunk(const HloInstruction* inst); @@ -209,10 +259,14 @@ class IrEmitterUnnested : public IrEmitter { std::unique_ptr BuildDeviceToDeviceCopyThunk( const HloInstruction* inst); - // Returns an InfeedThunk that performs device-to-device memcpy to implement + // Returns an InfeedThunk that performs a host-to-device memcpy to implement // `inst`. std::unique_ptr BuildInfeedThunk(const HloInstruction* inst); + // Returns an OutfeedThunk that performs a device-to-host memcpy to implement + // `inst`. + std::unique_ptr BuildOutfeedThunk(const HloInstruction* inst); + // Returns a WhileThunk that invokes thunk sequences for 'condition' and // 'body' sub-computations of while instruction 'hlo'. std::unique_ptr BuildWhileThunk(const HloInstruction* hlo); diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD index 7de8f9e1ee922bdbf65fd1299702482e1843f17e..eb93efc560efbb4c14065ec98b980a1ca78605c6 100644 --- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD +++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/BUILD @@ -17,12 +17,12 @@ cc_library( name = "llvm_gpu_backend", srcs = [ "dump_ir_pass.cc", - "gpu_backend_lib.cc", + "nvptx_backend_lib.cc", "utils.cc", ], hdrs = [ "dump_ir_pass.h", - "gpu_backend_lib.h", + "nvptx_backend_lib.h", "utils.h", ], deps = [ @@ -34,6 +34,7 @@ cc_library( "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", + "@llvm//:amdgpu_code_gen", "@llvm//:analysis", "@llvm//:bit_reader", "@llvm//:bit_writer", diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.cc similarity index 94% rename from tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc rename to tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.cc index a4e4e85bf3d2c197cfc691b7fca0920aa6571729..6c1c20fc0464927054deace8980620c3a9c6f09b 100644 --- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc +++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.h" +#include "tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.h" #include #include @@ -114,20 +114,21 @@ static string GetLibdeviceFilename(const string& libdevice_dir_path, // Gets the GPU name as it's known to LLVM for a given compute capability. If // we see an unrecognized compute capability, we return "sm_30". static string GetSmName(std::pair compute_capability) { - static auto* m = new std::map, int>({{{2, 0}, 20}, - {{2, 1}, 21}, - {{3, 0}, 30}, - {{3, 2}, 32}, - {{3, 5}, 35}, - {{3, 7}, 37}, - {{5, 0}, 50}, - {{5, 2}, 52}, - {{5, 3}, 53}, - {{6, 0}, 60}, - {{6, 1}, 61}, - {{6, 2}, 62}, - // TODO: Change this to 70 once LLVM NVPTX supports it - {{7, 0}, 60}}); + static auto* m = new std::map, int>( + {{{2, 0}, 20}, + {{2, 1}, 21}, + {{3, 0}, 30}, + {{3, 2}, 32}, + {{3, 5}, 35}, + {{3, 7}, 37}, + {{5, 0}, 50}, + {{5, 2}, 52}, + {{5, 3}, 53}, + {{6, 0}, 60}, + {{6, 1}, 61}, + {{6, 2}, 62}, + // TODO: Change this to 70 once LLVM NVPTX supports it + {{7, 0}, 60}}); int sm_version = 30; auto it = m->find(compute_capability); if (it != m->end()) { @@ -206,7 +207,7 @@ std::unique_ptr GetTargetMachine( codegen_opt_level = CodeGenOpt::None; } return WrapUnique(target->createTargetMachine( - triple.str(), llvm_ir::AsStringRef(cpu_name), "+ptx42", target_options, + triple.str(), llvm_ir::AsStringRef(cpu_name), "+ptx60", target_options, Optional(RelocModel), Optional(CMModel), codegen_opt_level)); } @@ -319,8 +320,8 @@ Status LinkLibdeviceIfNecessary(llvm::Module* module, llvm::Linker linker(*module); string libdevice_path = tensorflow::io::JoinPath( - libdevice_dir_path, GetLibdeviceFilename(libdevice_dir_path, - compute_capability)); + libdevice_dir_path, + GetLibdeviceFilename(libdevice_dir_path, compute_capability)); TF_RETURN_IF_ERROR(tensorflow::Env::Default()->FileExists(libdevice_path)); VLOG(1) << "Linking with libdevice from: " << libdevice_path; std::unique_ptr libdevice_module = diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.h b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.h similarity index 90% rename from tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.h rename to tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.h index 0a345191d34e6f40db043c559a67a44a6748321c..54e0e140dea1c3a8b21ffde2950c4bc9b703b71c 100644 --- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.h +++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.h @@ -14,8 +14,8 @@ limitations under the License. ==============================================================================*/ // LLVM-based compiler backend. -#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_LLVM_GPU_BACKEND_GPU_BACKEND_LIB_H_ -#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_LLVM_GPU_BACKEND_GPU_BACKEND_LIB_H_ +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_LLVM_GPU_BACKEND_NVPTX_BACKEND_LIB_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_LLVM_GPU_BACKEND_NVPTX_BACKEND_LIB_H_ #include #include @@ -44,4 +44,4 @@ StatusOr CompileToPtx(llvm::Module* module, } // namespace gpu } // namespace xla -#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_LLVM_GPU_BACKEND_GPU_BACKEND_LIB_H_ +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_LLVM_GPU_BACKEND_NVPTX_BACKEND_LIB_H_ diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc index ea661b3c2cb2c945297ac2098cd1c4009b2e966d..c67dcbce77a19abe60f9f871f7cce09b20d3d455 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc @@ -23,6 +23,8 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/layout_util.h" +#include "tensorflow/compiler/xla/service/gpu/instruction_fusion.h" #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" @@ -71,7 +73,6 @@ bool GpuMultiOutputFusion::ShapesCompatibleForFusion(HloInstruction* instr1, // In that case, the operand of the reduce needs to have the same shape // as the other tuple operands, but also we need to compare the output // shapes of the reduces. - // TODO(tjoerg): Allow differences in fp precision. auto* element_instr_1 = get_element_instr(instr1); auto* element_instr_2 = get_element_instr(instr2); if (element_instr_1->opcode() == HloOpcode::kReduce && @@ -80,8 +81,8 @@ bool GpuMultiOutputFusion::ShapesCompatibleForFusion(HloInstruction* instr1, return false; } // The elementwise output shapes must be the same (including layout). - return ShapeUtil::Equal(get_element_shape(element_instr_1), - get_element_shape(element_instr_2)); + return ShapeUtil::EqualIgnoringFpPrecision( + get_element_shape(element_instr_1), get_element_shape(element_instr_2)); } namespace { @@ -107,6 +108,27 @@ bool IsInputFusibleReduction(HloInstruction* instr) { return IsReductionToVector(*instr); } } + +// The code emitted for reduction suffers from poor data locality if the layouts +// of input parameters differ. In such situtations it is beneficial not to fuse. +// We consider input params with maximum rank only. Params with smaller ranks +// will be broadcasted and have not been observed to cause data locality issues. +// TODO(b/111977086): Improve reduce emitters to remove this limitation. +bool ReduceFriendlyInputLayouts(HloInstruction* instr) { + int64 max_rank = 0; + const Layout* max_rank_layout; + for (HloInstruction* param : instr->fused_parameters()) { + if (ShapeUtil::Rank(param->shape()) > max_rank) { + max_rank = ShapeUtil::Rank(param->shape()); + max_rank_layout = ¶m->shape().layout(); + } + } + return c_all_of(instr->fused_parameters(), [&](HloInstruction* param) { + return (ShapeUtil::Rank(param->shape()) < max_rank) || + (LayoutUtil::Equal(param->shape().layout(), *max_rank_layout)); + }); +} + } // namespace bool GpuMultiOutputFusion::IsFusible(HloInstruction* instr) { @@ -142,16 +164,22 @@ bool GpuMultiOutputFusion::LegalToFuse(HloInstruction* instr1, if (!MultiOutputFusion::LegalToFuse(instr1, instr2)) { return false; } + // If we're fusing fusions only do it if the fusion kind matches. Loop fusions // merge into bigger loop fusions and input (reduce) fusions become fusions // with multiple reduce outputs. We could fuse reduce and loop fusions // together too (the result being an input fusion) if we find cases where this // improves things. CHECK(instr1->opcode() == HloOpcode::kFusion); - if (instr2->opcode() == HloOpcode::kFusion) { - return instr1->fusion_kind() == instr2->fusion_kind(); + if ((instr2->opcode() == HloOpcode::kFusion && + instr1->fusion_kind() != instr2->fusion_kind()) || + (instr2->opcode() != HloOpcode::kFusion && + instr1->fusion_kind() == HloInstruction::FusionKind::kLoop)) { + return false; } - return instr1->fusion_kind() != HloInstruction::FusionKind::kLoop; + + // Do this check last, as it may be expensive. + return !GpuInstructionFusion::FusionWouldBeTooLarge(instr1, instr2); } bool GpuMultiOutputFusion::DoProducerConsumerMultiOutputFusion() { @@ -173,29 +201,41 @@ bool GpuMultiOutputFusion::DoProducerConsumerMultiOutputFusion() { // fusions operands. for (HloInstruction* consumer : computation()->MakeInstructionPostOrder()) { if (consumer->user_count() == 0) { + VLOG(3) << consumer->name() << " has no users."; continue; } if (!IsInputFusibleReduction(consumer)) { + VLOG(3) << consumer->name() << " is not an input-fusable reduction."; continue; } + VLOG(3) << consumer->name() + << " is a fusion candidate. Looking for fuseable operands."; auto consumer_operands = consumer->operands(); for (size_t i = 0; i < consumer_operands.size(); ++i) { HloInstruction* producer = consumer_operands[i]; if (!producer->IsFusable()) { + VLOG(3) << producer->name() << " is not fusable."; continue; } const bool is_loop_fusion = producer->opcode() == HloOpcode::kFusion && producer->fusion_kind() == HloInstruction::FusionKind::kLoop; if (!is_loop_fusion) { + VLOG(3) << producer->name() << " is not a loop fusion."; continue; } if (!ShapesCompatibleForFusion(producer, consumer)) { + VLOG(3) << producer->name() << " has an incompatible shape."; + continue; + } + if (!ReduceFriendlyInputLayouts(producer)) { + VLOG(3) << producer->name() << " has inputs with mixed layouts."; continue; } // If we have already decided to fuse this producer, skip it. if (ContainsKey(to_fuse, producer)) { + VLOG(3) << producer->name() << " will be fused with another consumer."; continue; } // Do not fuse a producer if the other operands of the fusion are @@ -204,6 +244,7 @@ bool GpuMultiOutputFusion::DoProducerConsumerMultiOutputFusion() { return producer != operand && reachability()->IsReachable(producer, operand); })) { + VLOG(3) << producer->name() << " would introduce a cycle when fused."; break; } to_fuse.insert(producer); diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc index 979ea79243818c398b1b130254a41c95ced51830..ec4234b8d9a5da299a9dc574169b0bb5fe6a575f 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/multi_output_fusion.h" +#include "tensorflow/compiler/xla/service/gpu/instruction_fusion.h" #include "tensorflow/compiler/xla/service/hlo_matchers.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -27,7 +28,7 @@ namespace op = xla::testing::opcode_matchers; namespace xla { namespace gpu { -using InstructionFusionTest = HloTestBase; +using MultiOutputFusionTest = HloTestBase; const char kModulePrefix[] = R"( HloModule test_module @@ -40,10 +41,10 @@ const char kModulePrefix[] = R"( scalar_mul_computation { scalar_lhs.1 = f32[] parameter(0) scalar_rhs.1 = f32[] parameter(1) - ROOT mul.1 = f32[] add(scalar_lhs.1, scalar_rhs.1) + ROOT mul.1 = f32[] multiply(scalar_lhs.1, scalar_rhs.1) })"; -TEST_F(InstructionFusionTest, MultiOutputFusionSiblingReduceAndReduceFusion) { +TEST_F(MultiOutputFusionTest, MultiOutputFusionSiblingReduceAndReduceFusion) { // Fusion with reduce instruction root and a sibling reduce instruction // sharing the same input param. auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( @@ -72,7 +73,7 @@ TEST_F(InstructionFusionTest, MultiOutputFusionSiblingReduceAndReduceFusion) { op::Tuple(op::Reduce(), op::Reduce())); } -TEST_F(InstructionFusionTest, MultiOutputFusionDifferentReduceInputShapes) { +TEST_F(MultiOutputFusionTest, MultiOutputFusionDifferentReduceInputShapes) { auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( fused_computation_1 { p1.1 = f32[6400]{0} parameter(1) @@ -99,7 +100,7 @@ TEST_F(InstructionFusionTest, MultiOutputFusionDifferentReduceInputShapes) { ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); } -TEST_F(InstructionFusionTest, MultiOutputFusionDifferentReduceOutputShapes) { +TEST_F(MultiOutputFusionTest, MultiOutputFusionDifferentReduceOutputShapes) { auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( fused_computation_1 { p1.1 = f32[10,10]{1,0} parameter(1) @@ -126,7 +127,7 @@ TEST_F(InstructionFusionTest, MultiOutputFusionDifferentReduceOutputShapes) { ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); } -TEST_F(InstructionFusionTest, MultiOutputFusionSiblingReduceFusions) { +TEST_F(MultiOutputFusionTest, MultiOutputFusionSiblingReduceFusions) { // Two sibling fusions with reduce instruction roots sharing the same input // param. auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( @@ -160,7 +161,7 @@ TEST_F(InstructionFusionTest, MultiOutputFusionSiblingReduceFusions) { op::Tuple(op::Reduce(), op::Reduce())); } -TEST_F(InstructionFusionTest, +TEST_F(MultiOutputFusionTest, MultiOutputFusionSiblingReduceAndReduceMultiOutputFusion) { // Multi-output fusion with two reduce instructions root and a sibling reduce // instruction sharing the same input param. @@ -193,7 +194,7 @@ TEST_F(InstructionFusionTest, op::Tuple(op::Reduce(), op::Reduce(), op::Reduce())); } -TEST_F(InstructionFusionTest, +TEST_F(MultiOutputFusionTest, MultiOutputFusionSiblingFusionCheckAgainstReduceOperand) { // Verify that if we already have a multi-output fusion that we prefer to pick // a reduce op from its operands for checking shape compatibility. @@ -226,7 +227,7 @@ TEST_F(InstructionFusionTest, ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); } -TEST_F(InstructionFusionTest, MultiOutputFusionTwoLoops) { +TEST_F(MultiOutputFusionTest, MultiOutputFusionTwoLoops) { auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( fused_computation_1 { p0.1 = f32[6400]{0} parameter(0) @@ -255,7 +256,7 @@ TEST_F(InstructionFusionTest, MultiOutputFusionTwoLoops) { op::Tuple(op::Multiply(), op::Divide())); } -TEST_F(InstructionFusionTest, ProducerConsumerFusionLoopFusionAndReduce) { +TEST_F(MultiOutputFusionTest, ProducerConsumerFusionLoopFusionAndReduce) { auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( fused_add { p0.1 = f32[2,2,2]{2,1,0} parameter(0) @@ -282,7 +283,7 @@ TEST_F(InstructionFusionTest, ProducerConsumerFusionLoopFusionAndReduce) { op::Tuple(op::Reduce(), op::Add())); } -TEST_F(InstructionFusionTest, ProducerConsumerFusionLoopFusionAndReduceFusion) { +TEST_F(MultiOutputFusionTest, ProducerConsumerFusionLoopFusionAndReduceFusion) { auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( fused_select { p1.1 = f32[2,2,2]{2,1,0} parameter(1) @@ -323,7 +324,7 @@ TEST_F(InstructionFusionTest, ProducerConsumerFusionLoopFusionAndReduceFusion) { op::Tuple(op::Reduce(), op::Reduce(), op::Select())); } -TEST_F(InstructionFusionTest, ProducerConsumerFusionDoNotFuseLoopReduceFusion) { +TEST_F(MultiOutputFusionTest, ProducerConsumerFusionDoNotFuseLoopReduceFusion) { auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( fused_element_wise { p0.1 = f32[2,2,2]{2,1,0} parameter(0) @@ -349,5 +350,128 @@ TEST_F(InstructionFusionTest, ProducerConsumerFusionDoNotFuseLoopReduceFusion) { ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); } +TEST_F(MultiOutputFusionTest, + ProducerConsumerFusionFp16LoopFusionAndReduceFusion) { + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_select { + p1.1 = f16[2,2,2]{2,1,0} parameter(1) + c0 = f16[] constant(0) + broadcast = f16[2,2,2]{2,1,0} broadcast(f16[] c0), dimensions={} + greater-than = pred[2,2,2]{2,1,0} greater-than(f32[2,2,2]{2,1,0} p1.1, f32[2,2,2]{2,1,0} broadcast) + p0.1 = f16[2,2,2]{2,1,0} parameter(0) + ROOT select = f16[2,2,2]{2,1,0} select(pred[2,2,2]{2,1,0} greater-than, f16[2,2,2]{2,1,0} p0.1, f16[2,2,2]{2,1,0} broadcast) + } + fused_reduce { + p0.2 = f16[2,2,2]{2,1,0} parameter(0) + convert = f32[2,2,2]{2,1,0} convert(p0.2) + c1 = f32[] constant(0) + r1 = f32[2,2]{1,0} reduce(convert, c1), dimensions={2}, to_apply=scalar_add_computation + mul = f32[2,2,2]{2,1,0} multiply(convert, convert) + r2 = f32[2,2]{1,0} reduce(mul, c1), dimensions={2}, to_apply=scalar_add_computation + ROOT tuple = (f32[2,2]{1,0}, f32[2,2]{1,0}) tuple(r1, r2) + } + ENTRY reduce { + p0 = f16[2,2,2]{2,1,0} parameter(0) + p1 = f16[2,2,2]{2,1,0} parameter(1) + select = f16[2,2,2]{2,1,0} fusion(p0, p1), kind=kLoop, calls=fused_select + fusion = (f32[2,2]{1,0}, f32[2,2]{1,0}) fusion(select), kind=kInput, calls=fused_reduce + gte0 = f32[2,2]{1,0} get-tuple-element(fusion), index=0 + gte1 = f32[2,2]{1,0} get-tuple-element(fusion), index=1 + ROOT root = (f32[2,2]{1,0}, f32[2,2]{1,0}, f16[2,2,2]{2,1,0}) tuple(gte1, gte1, select) + })")) + .ValueOrDie(); + ASSERT_TRUE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); + SCOPED_TRACE(module->ToString()); + const HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Tuple(op::GetTupleElement(), op::GetTupleElement(), + op::GetTupleElement())); + const HloInstruction* fusion = root->operand(0)->operand(0); + ASSERT_TRUE(fusion->IsMultiOutputFusion()); + EXPECT_THAT(fusion->fused_expression_root(), + op::Tuple(op::Reduce(), op::Reduce(), op::Select())); +} + +TEST_F(MultiOutputFusionTest, + ProducerConsumerFusionReduceUnfriendlyLoopFusion) { + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + mixed_input_layouts_computation { + p0.1 = f16[128,1024,32,32]{1,3,2,0} parameter(0) + p1.1 = f16[128,1024,32,32]{3,2,1,0} parameter(1) + copy = f16[128,1024,32,32]{1,3,2,0} copy(p1.1) + c0 = f16[] constant(0) + broadcast = f16[128,1024,32,32]{1,3,2,0} broadcast(c0), dimensions={} + greater-than = pred[128,1024,32,32]{1,3,2,0} greater-than(copy, broadcast) + ROOT root = f16[128,1024,32,32]{1,3,2,0} select(greater-than, p0.1, broadcast) + } + fused_reduce { + p0.2 = f16[128,1024,32,32]{1,3,2,0} parameter(0) + convert = f32[128,1024,32,32]{1,3,2,0} convert(p0.2) + c0.2 = f32[] constant(0) + ROOT reduce = f32[1024]{0} reduce(convert, c0.2), dimensions={0,2,3}, to_apply=scalar_add_computation + } + ENTRY reduce { + p0 = f16[128,1024,32,32]{3,2,1,0} parameter(0) + p1 = f16[128,1024,32,32]{1,3,2,0} parameter(1) + loop_fusion = f16[128,1024,32,32]{1,3,2,0} fusion(p0, p1), kind=kLoop, calls=mixed_input_layouts_computation + reduce_fusion = f32[1024]{0} fusion(loop_fusion), kind=kInput, calls=fused_reduce + ROOT root = (f32[1024]{0}, f16[128,1024,32,32]{1,3,2,0}) tuple(reduce_fusion, loop_fusion) + })")) + .ValueOrDie(); + ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); +} + +// Check that we limit the number of operands to fusions we create. +TEST_F(MultiOutputFusionTest, AvoidsLargeFusion) { + constexpr int64 kNumParams = 200; + ASSERT_GT(kNumParams, GpuInstructionFusion::kMaxOperandsAndOutputsPerFusion); + + // Compute + // p0 * p1, + // p0 * p1 + p1 * p2 + // p0 * p1 + p1 * p2 + p2 * p3 + // ... + // where each of the (pi * pj)'s is represented as a fusion node so that + // multi-output fusion will pay attention to it. + auto module = CreateNewModule(); + HloComputation::Builder b(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {10, 100}); + + std::vector params; + for (int64 i = 0; i < kNumParams; ++i) { + params.push_back( + b.AddInstruction(HloInstruction::CreateParameter(i, shape, "p"))); + } + + // Creates a fusion node that calculates x*y. + auto make_fusion = [&](HloInstruction* x, HloInstruction* y) { + HloComputation::Builder sub_builder("subcomp"); + auto* p0 = sub_builder.AddInstruction( + HloInstruction::CreateParameter(0, shape, "p")); + auto* p1 = sub_builder.AddInstruction( + HloInstruction::CreateParameter(1, shape, "p")); + sub_builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kMultiply, p0, p1)); + HloComputation* subcomp = + module->AddEmbeddedComputation(sub_builder.Build()); + return HloInstruction::CreateFusion( + shape, HloInstruction::FusionKind::kLoop, {x, y}, subcomp); + }; + + auto* sum = b.AddInstruction(make_fusion(params[0], params[1])); + for (int64 i = 2; i < kNumParams; ++i) { + sum = b.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kAdd, sum, + b.AddInstruction(make_fusion(params[i - 1], params[i])))); + } + auto computation = module->AddEntryComputation(b.Build()); + EXPECT_TRUE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); + SCOPED_TRACE(module->ToString()); + for (const HloInstruction* instr : computation->instructions()) { + EXPECT_LE(instr->operand_count() + ShapeUtil::SubshapeCount(instr->shape()), + GpuInstructionFusion::kMaxOperandsAndOutputsPerFusion) + << instr->ToString(); + } +} + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc similarity index 88% rename from tensorflow/compiler/xla/service/gpu/gpu_compiler.cc rename to tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc index decfc40dafafe875fa02bab6695f5c54e522f267..7a683ede54354245c07ee2559172efe298f34950 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/service/gpu/gpu_compiler.h" +#include "tensorflow/compiler/xla/service/gpu/nvptx_compiler.h" #include #include @@ -50,11 +50,13 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" #include "tensorflow/compiler/xla/service/gpu/ir_emitter_context.h" #include "tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h" -#include "tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.h" +#include "tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/nvptx_backend_lib.h" #include "tensorflow/compiler/xla/service/gpu/multi_output_fusion.h" +#include "tensorflow/compiler/xla/service/gpu/pad_for_tensor_cores.h" #include "tensorflow/compiler/xla/service/gpu/pad_insertion.h" #include "tensorflow/compiler/xla/service/gpu/partition_assignment.h" #include "tensorflow/compiler/xla/service/gpu/stream_assignment.h" +#include "tensorflow/compiler/xla/service/gpu/stream_executor_util.h" #include "tensorflow/compiler/xla/service/gpu/thunk_schedule.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" @@ -96,8 +98,8 @@ limitations under the License. namespace xla { namespace gpu { -/* static */ const char* GpuCompiler::kTargetTriple = "nvptx64-nvidia-cuda"; -/* static */ const char* GpuCompiler::kDataLayout = +/* static */ const char* NVPTXCompiler::kTargetTriple = "nvptx64-nvidia-cuda"; +/* static */ const char* NVPTXCompiler::kDataLayout = "e-i64:64-i128:128-v16:16-v32:32-n16:32:64"; namespace { @@ -199,6 +201,12 @@ Status OptimizeHloModule(HloModule* hlo_module, se::StreamExecutor* stream_exec, pipeline.AddInvariantChecker(); pipeline.AddPass(); pipeline.AddPass(); + if (IsVoltaOrLater(*stream_exec)) { + pipeline.AddPass(); + // PadForTensorCores leaves behind unnecessary tuple/get-tuple-element + // pairs that TupleSimplifier fixes. + pipeline.AddPass(); + } TF_RETURN_IF_ERROR(pipeline.Run(hlo_module).status()); } @@ -354,16 +362,30 @@ void WarnIfBadPtxasVersion(const string& ptxas_path) { return; } + // We need ptxas >= 9.0 as a hard requirement, because we compile targeting + // PTX 6.0. An older ptxas will just fail to compile any of our code. + // // ptxas 9.0 before 9.0.276 and ptxas 9.1 before 9.1.121 miscompile some // address calculations with large offsets (e.g. "load ptr + large_constant"), // b/70245379. - if ((vmaj == 9 && vmin == 0 && vdot < 276) || - (vmaj == 9 && vmin == 1 && vdot < 121)) { - LOG(WARNING) << "*** WARNING *** You are using ptxas " << vmaj << "." - << vmin << "." << vdot - << ", which is in range [9.0.0, 9.0.276) + [9.1.0, 9.1.121). " - "These versions are known to miscompile XLA code, leading " - "to incorrect results or invalid-address errors."; + // + // ptxas 9.1.121 miscompiles some large multioutput fusions, again in a way + // that appears related to address calculations, b/111107644. ptxas 9.2.88 + // appears to work, as far as we can tell. + if (vmaj < 9) { + LOG(ERROR) + << "You are using ptxas 8.x, but XLA requires ptxas 9.x (and strongly " + "prefers >= 9.2.88). Compilation of XLA kernels below will likely " + "fail.\n\nYou do not need to update CUDA; cherry-picking the ptxas " + "binary is sufficient."; + } else if ((vmaj < 9 || vmin < 2 || vdot < 88)) { + LOG(WARNING) + << "*** WARNING *** You are using ptxas " << vmaj << "." << vmin << "." + << vdot + << ", which older than 9.2.88. ptxas 9.x before 9.2.88 is known to " + "miscompile XLA code, leading to incorrect results or " + "invalid-address errors.\n\nYou do not need to update to CUDA " + "9.2.88; cherry-picking the ptxas binary is sufficient."; } } @@ -391,17 +413,18 @@ void WarnIfBadDriverJITVersion() { // - 384.x before 384.108 // - 387.x before 387.40 // - 390.x before 390.10. - auto vmaj = std::get<0>(version); - auto vmin = std::get<1>(version); - if ((vmaj == 384 && vmin < 108) || // - (vmaj == 387 && vmin < 40) || // - (vmaj == 390 && vmin < 10)) { + // + // In addition, only >= 396.20 contains ptxas >= 9.2.88, which contains the + // fix for the "large multioutput fusions" miscompile, b/111107644. + if (version < std::make_tuple(396, 20, 0)) { LOG(WARNING) << "*** WARNING *** Invoking the PTX->SASS JIT from driver version " << se::cuda::DriverVersionToString(version) - << ", which is in range [384.0.0, 384.108.0) + [387.0.0, 387.40.0) + " - "[390.0.0, 390.10.0). These versions are known to miscompile XLA " - "code, leading to incorrect results or invalid-address errors."; + << ", which is older than 396.20.0. These versions are known to " + "miscompile XLA code, leading to incorrect results or " + "invalid-address errors.\nXLA only uses the driver JIT if it " + "cannot find ptxas; you don't need to update your driver if " + "you can point XLA to ptxas 9.2.88 or newer."; } }); } @@ -473,14 +496,14 @@ StatusOr> CompilePtx(const string& ptx, int cc_major, } // namespace -GpuCompiler::GpuCompiler() +NVPTXCompiler::NVPTXCompiler() : pointer_size_(llvm::DataLayout(kDataLayout) .getPointerSize(0 /* default address space */)) {} -StatusOr> GpuCompiler::RunHloPasses( +StatusOr> NVPTXCompiler::RunHloPasses( std::unique_ptr module, se::StreamExecutor* stream_exec, DeviceMemoryAllocator* device_allocator) { - XLA_SCOPED_LOGGING_TIMER("GpuCompiler::RunHloPasses"); + XLA_SCOPED_LOGGING_TIMER("NVPTXCompiler::RunHloPasses"); tracing::ScopedActivity activity("HLO Transforms", module->name(), /*is_expensive=*/true); TF_RETURN_IF_ERROR( @@ -488,10 +511,10 @@ StatusOr> GpuCompiler::RunHloPasses( return std::move(module); } -StatusOr> GpuCompiler::RunBackend( +StatusOr> NVPTXCompiler::RunBackend( std::unique_ptr module, se::StreamExecutor* stream_exec, DeviceMemoryAllocator* device_allocator) { - XLA_SCOPED_LOGGING_TIMER("GpuCompiler::RunBackend"); + XLA_SCOPED_LOGGING_TIMER("NVPTXCompiler::RunBackend"); TF_RET_CHECK(stream_exec != nullptr); @@ -525,11 +548,13 @@ StatusOr> GpuCompiler::RunBackend( // temporary buffers are required to run the computation. TF_ASSIGN_OR_RETURN( std::unique_ptr buffer_assignment, - BufferAssigner::Run(module.get(), hlo_schedule->ConsumeHloOrdering(), - BufferSizeBytesFunction(), - /*color_alignment=*/[](LogicalBuffer::Color) { - return kCudaMallocAlignBytes; - })); + BufferAssigner::Run( + module.get(), hlo_schedule->ConsumeHloOrdering(), + BufferSizeBytesFunction(), + /*color_alignment=*/ + [](LogicalBuffer::Color) { return kXlaAllocatedBufferAlignBytes; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true)); // BufferAssignment::Stats::ToString() and BufferAssignment::ToString() // include headers, so no need for us to print them ourselves. XLA_VLOG_LINES(1, buffer_assignment->GetStats().ToString()); @@ -550,10 +575,12 @@ StatusOr> GpuCompiler::RunBackend( HloComputation* entry_computation = module->entry_computation(); IrEmitterUnnested ir_emitter(module->config(), entry_computation, &ir_emitter_context); + + TF_RETURN_IF_ERROR(ir_emitter.EmitConstantGlobals()); + { - XLA_SCOPED_LOGGING_TIMER("GpuCompiler::RunBackend - IR emission"); - TF_RETURN_IF_ERROR( - entry_computation->root_instruction()->Accept(&ir_emitter)); + XLA_SCOPED_LOGGING_TIMER("NVPTXCompiler::RunBackend - IR emission"); + TF_RETURN_IF_ERROR(entry_computation->Accept(&ir_emitter)); } if (user_pre_optimization_hook_) { @@ -579,7 +606,8 @@ StatusOr> GpuCompiler::RunBackend( } { - XLA_SCOPED_LOGGING_TIMER("GpuCompiler::RunBackend - Running LLVM verifier"); + XLA_SCOPED_LOGGING_TIMER( + "NVPTXCompiler::RunBackend - Running LLVM verifier"); std::string err; llvm::raw_string_ostream err_stream(err); @@ -619,7 +647,7 @@ StatusOr> GpuCompiler::RunBackend( string ptx; { - XLA_SCOPED_LOGGING_TIMER("GpuCompiler::RunBackend - CompileToPtx"); + XLA_SCOPED_LOGGING_TIMER("NVPTXCompiler::RunBackend - CompileToPtx"); TF_ASSIGN_OR_RETURN(ptx, CompileToPtx(&llvm_module, {cc_major, cc_minor}, module->config(), libdevice_dir)); } @@ -688,10 +716,10 @@ StatusOr> GpuCompiler::RunBackend( return std::unique_ptr(gpu_executable); } -std::vector GpuCompiler::CompilePtxOrGetCachedResult(const string& ptx, - int cc_major, - int cc_minor) { - XLA_SCOPED_LOGGING_TIMER("GpuCompiler::CompilePtxOrGetCachedResult"); +std::vector NVPTXCompiler::CompilePtxOrGetCachedResult(const string& ptx, + int cc_major, + int cc_minor) { + XLA_SCOPED_LOGGING_TIMER("NVPTXCompiler::CompilePtxOrGetCachedResult"); tracing::ScopedActivity activity("PTX->CUBIN", /*is_expensive=*/true); bool inserted; decltype(compilation_cache_.begin()) iter; @@ -764,12 +792,14 @@ std::vector GpuCompiler::CompilePtxOrGetCachedResult(const string& ptx, } StatusOr>> -GpuCompiler::CompileAheadOfTime(std::vector> module, - const AotCompilationOptions& options) { - return Unimplemented("not yet implemented: GpuCompiler::CompileAheadOfTime"); +NVPTXCompiler::CompileAheadOfTime( + std::vector> module, + const AotCompilationOptions& options) { + return Unimplemented( + "not yet implemented: NVPTXCompiler::CompileAheadOfTime"); } -se::Platform::Id GpuCompiler::PlatformId() const { +se::Platform::Id NVPTXCompiler::PlatformId() const { return se::cuda::kCudaPlatformId; } @@ -779,7 +809,7 @@ se::Platform::Id GpuCompiler::PlatformId() const { static bool InitModule() { xla::Compiler::RegisterCompilerFactory( stream_executor::cuda::kCudaPlatformId, - []() { return xla::MakeUnique(); }); + []() { return xla::MakeUnique(); }); return true; } static bool module_initialized = InitModule(); diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.h b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.h similarity index 93% rename from tensorflow/compiler/xla/service/gpu/gpu_compiler.h rename to tensorflow/compiler/xla/service/gpu/nvptx_compiler.h index f3b02ae5d8867bdf1d970e809bff95a15d9f54d2..d4d2909f1b2dc57c3ae0f9d67067e533574369dd 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.h +++ b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_GPU_COMPILER_H_ -#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_GPU_COMPILER_H_ +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_NVPTX_COMPILER_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_NVPTX_COMPILER_H_ #include #include @@ -37,10 +37,10 @@ namespace xla { namespace gpu { // The GPU compiler generates efficient GPU executables. -class GpuCompiler : public LLVMCompiler { +class NVPTXCompiler : public LLVMCompiler { public: - GpuCompiler(); - ~GpuCompiler() override {} + NVPTXCompiler(); + ~NVPTXCompiler() override {} // Bring in // StatusOr>> Compile( @@ -64,7 +64,7 @@ class GpuCompiler : public LLVMCompiler { se::Platform::Id PlatformId() const override; HloCostAnalysis::ShapeSizeFunction ShapeSizeBytesFunction() const override { - // Capture just the pointer size, not the entire GpuCompiler object. + // Capture just the pointer size, not the entire NVPTXCompiler object. int64 pointer_size = pointer_size_; return [pointer_size](const Shape& shape) { return ShapeUtil::ByteSizeOf(shape, pointer_size); @@ -146,10 +146,10 @@ class GpuCompiler : public LLVMCompiler { CompilationCacheHash, CompilationCacheEq> compilation_cache_ GUARDED_BY(mutex_); - TF_DISALLOW_COPY_AND_ASSIGN(GpuCompiler); + TF_DISALLOW_COPY_AND_ASSIGN(NVPTXCompiler); }; } // namespace gpu } // namespace xla -#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_GPU_COMPILER_H_ +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_NVPTX_COMPILER_H_ diff --git a/tensorflow/compiler/xla/service/gpu/outfeed_manager.cc b/tensorflow/compiler/xla/service/gpu/outfeed_manager.cc new file mode 100644 index 0000000000000000000000000000000000000000..4aaf0c9e142106a0e74f319d71dad4c4c96d3f08 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/outfeed_manager.cc @@ -0,0 +1,32 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/gpu/outfeed_manager.h" + +#include "tensorflow/compiler/xla/map_util.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace xla { +namespace gpu { + +OutfeedManager* GetOrCreateOutfeedManager() { + static auto* manager = new OutfeedManager; + return manager; +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/outfeed_manager.h b/tensorflow/compiler/xla/service/gpu/outfeed_manager.h new file mode 100644 index 0000000000000000000000000000000000000000..a752eb70119b00e8cca7ddce26da7730ef5db8cb --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/outfeed_manager.h @@ -0,0 +1,69 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_OUTFEED_MANAGER_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_OUTFEED_MANAGER_H_ + +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/service/gpu/xfeed_queue.h" +#include "tensorflow/compiler/xla/shape_tree.h" +#include "tensorflow/core/platform/mutex.h" +#include "tensorflow/core/platform/notification.h" + +namespace xla { +namespace gpu { + +// TODO(b/30467474) Once GPU outfeed implementation settles, consider +// folding back the cpu and gpu outfeed implementations into a generic +// one if possible. + +// Defines a buffer holding the destination for an outfeed in host memory and a +// notification when that triggers when the transfer is done. +class OutfeedBuffer { + public: + OutfeedBuffer(int64 length) : length_(length) {} + + // Waits for the device transfer to be finished. + std::unique_ptr WaitUntilAvailable() { + done_.WaitForNotification(); + return std::move(destination_); + } + + int64 length() const { return length_; } + void set_destination(std::unique_ptr destination) { + destination_ = std::move(destination); + } + Literal* destination() { return destination_.get(); } + + // Callback to signal that this buffer is consumed. + void Done() { done_.Notify(); } + + private: + std::unique_ptr destination_; + const int64 length_; + tensorflow::Notification done_; +}; + +// Manages a thread-safe queue of buffers. The buffers are supposed to be +// produced by the transfer manager and consumed by the device. +using OutfeedManager = XfeedQueue>*>; + +// Singleton creator-or-accessor: Returns the GPU outfeed manager. +OutfeedManager* GetOrCreateOutfeedManager(); + +} // namespace gpu +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_OUTFEED_MANAGER_H_ diff --git a/tensorflow/compiler/xla/service/gpu/outfeed_thunk.cc b/tensorflow/compiler/xla/service/gpu/outfeed_thunk.cc new file mode 100644 index 0000000000000000000000000000000000000000..7986e63f43ee508370f94fdb9057b91bfe4add18 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/outfeed_thunk.cc @@ -0,0 +1,111 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/gpu/outfeed_thunk.h" +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" +#include "tensorflow/compiler/xla/service/gpu/outfeed_manager.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/platform/stream_executor_no_cuda.h" + +namespace xla { +namespace gpu { + +OutfeedThunk::OutfeedThunk(ShapeTree outfeed_slices, + const HloInstruction* hlo_instruction) + : Thunk(Kind::kOutfeed, hlo_instruction), + outfeed_slices_(std::move(outfeed_slices)) {} + +Status OutfeedThunk::ExecuteOnStream( + const BufferAllocations& buffer_allocations, se::Stream* stream, + HloExecutionProfiler* profiler) { + VLOG(2) << "Outfeeding from GPU: " << hlo_instruction()->ToString(); + + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); + OutfeedManager* outfeed_manager = GetOrCreateOutfeedManager(); + ShapeTree>* outfeed_buffers = + outfeed_manager->BlockingGetNextDestination(); + + // Nothing to be done for empty tuples. + if (ShapeUtil::IsEmptyTuple(hlo_instruction()->operand(0)->shape())) { + return Status::OK(); + } + CHECK(ShapeUtil::Compatible(hlo_instruction()->operand(0)->shape(), + outfeed_buffers->shape())); + + TF_RETURN_IF_ERROR(outfeed_buffers->ForEachMutableElementWithStatus( + [&](const ShapeIndex& index, std::unique_ptr* buffer) { + if (!*buffer) { // Tuple pointers. + return Status::OK(); + } + // Allocate storage for the literal data. + const Shape& shape = + ShapeUtil::GetSubshape(outfeed_buffers->shape(), index); + (*buffer)->set_destination(Literal::CreateFromShape(shape)); + + BufferAllocation::Slice slice = outfeed_slices_.element(index); + se::DeviceMemoryBase data_address; + if (slice.allocation()) { + // If we have a static allocation, read it from there. This avoids + // synchronizing the host and device just to read a pointer. + data_address = buffer_allocations.GetDeviceAddress(slice); + } else { + // Otherwise we have to read the tuple pointer first. + CHECK(!index.empty()); + // Copy the parent buffer to the host. + BufferAllocation::Slice tuple_slice = + outfeed_slices_.element(ShapeIndexView(index).ConsumeFront()); + if (!tuple_slice.allocation()) { + return Unimplemented( + "Nested dynamic tuples are not supported on GPU"); + } + se::DeviceMemoryBase tuple_address = + buffer_allocations.GetDeviceAddress(tuple_slice); + CHECK(tuple_slice.size() % sizeof(void*) == 0) + << "Tuple size must be a multiple of pointer size"; + std::vector tuple_element_buffer_addresses(tuple_slice.size() / + sizeof(void*)); + stream->ThenMemcpy(tuple_element_buffer_addresses.data(), + tuple_address, tuple_slice.size()); + TF_RETURN_IF_ERROR(stream->BlockHostUntilDone()); + // The data address is specified by the element of the tuple pointer + // buffer. + data_address = + se::DeviceMemoryBase(tuple_element_buffer_addresses[index.back()], + (*buffer)->length()); + } + + // TODO(b/111309141): Run this on a separate stream so it doesn't block + // the GPU from doing work during the transfer. This could be handled by + // making StreamAssignment do something intelligent with outfeed thunks. + stream + ->ThenMemcpy((*buffer)->destination()->untyped_data(), data_address, + (*buffer)->length()) + .ThenDoHostCallback([buffer]() { (*buffer)->Done(); }); + return Status::OK(); + })); + + Status block_status = stream->BlockHostUntilDone(); + if (!block_status.ok()) { + return InternalError("Failed to complete data transfer on stream %p: %s", + stream, block_status.error_message().c_str()); + } + + VLOG(2) << "Outfeeding from GPU complete"; + return Status::OK(); +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/outfeed_thunk.h b/tensorflow/compiler/xla/service/gpu/outfeed_thunk.h new file mode 100644 index 0000000000000000000000000000000000000000..8ed89f05f0c5bb2e3893e695d413bac3b231112d --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/outfeed_thunk.h @@ -0,0 +1,52 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_OUTFEED_THUNK_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_OUTFEED_THUNK_H_ + +#include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" +#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" +#include "tensorflow/compiler/xla/service/gpu/thunk.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/core/platform/stream_executor_no_cuda.h" + +namespace xla { +namespace gpu { + +// A thunk that outfeeds data. Data must be already resident on the host. This +// thunk performs a host to device copy from the buffer allocated for the +// outfeed op to the host location. +class OutfeedThunk : public Thunk { + public: + // Constructs a OutfeedThunk that copies data to the host-side + // outfeed queue from the buffers in the given shape tree. + OutfeedThunk(ShapeTree outfeed_slices, + const HloInstruction* hlo_instruction); + + OutfeedThunk(const OutfeedThunk&) = delete; + OutfeedThunk& operator=(const OutfeedThunk&) = delete; + + Status ExecuteOnStream(const BufferAllocations& buffer_allocations, + se::Stream* stream, + HloExecutionProfiler* profiler) override; + + private: + const ShapeTree outfeed_slices_; +}; + +} // namespace gpu +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_OUTFEED_THUNK_H_ diff --git a/tensorflow/compiler/xla/service/gpu/pad_for_tensor_cores.cc b/tensorflow/compiler/xla/service/gpu/pad_for_tensor_cores.cc new file mode 100644 index 0000000000000000000000000000000000000000..79f7d31816baf0b95b967771b956a9c06ac81e91 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/pad_for_tensor_cores.cc @@ -0,0 +1,233 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/gpu/pad_for_tensor_cores.h" + +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/compiler/xla/window_util.h" + +namespace xla { +namespace gpu { + +using tensorflow::gtl::ArraySlice; + +// We want the input/output feature counts of an f16 conv to be factors of 8, +// because without this cudnn can't use tensor cores on the conv. +static constexpr int64 kDesiredNumFeaturesFactor = 8; + +// We won't pad a conv if doing so increases the total number of bytes in the +// lhs, rhs, or result by more than this amount. +// +// TODO(jlebar): This number was tuned experimentally. It represents a +// compromise on our current benchmarks; it speeds some up significantly, and +// doesn't slow any down. But we can observe by changing this value that +// there's additional room for speedups. Achieving those speedups without also +// slowing other things down will likely require a more sophisticated heuristic, +// possibly some form of auto-tuning. +static constexpr double kMaxBytesTouchedIncrease = 1.2; + +// Pads the given dimensions in the given shape up to a multiple of +// kDesiredNumFeaturesFactor. +static Shape PadShape(Shape s, ArraySlice dims) { + for (int64 dim : dims) { + int64 dim_to_pad_size = s.dimensions(dim); + int64 new_dim_to_pad_size = + RoundUpToNearest(dim_to_pad_size, kDesiredNumFeaturesFactor); + s.set_dimensions(dim, new_dim_to_pad_size); + } + return s; +} + +// Creates and returns an HLO that zero-pads one or more dimensions in the given +// instruction so that its shape is equal to the given shape. +// +// Padding is added to the end of each relevant dimension. +// +// If the instruction already has the given shape, simply returns it without an +// intervening pad. +static HloInstruction* PadInstruction(HloInstruction* instr, + const Shape& new_shape) { + HloComputation* comp = instr->parent(); + + const Shape& shape = instr->shape(); + auto* zero = comp->AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::Zero(shape.element_type()).CloneToUnique())); + + PaddingConfig pad_config = MakeNoPaddingConfig(ShapeUtil::Rank(shape)); + + bool added_padding = false; + for (int64 dim = 0; dim < ShapeUtil::Rank(shape); ++dim) { + if (shape.dimensions(dim) == new_shape.dimensions(dim)) { + continue; + } + CHECK_GT(new_shape.dimensions(dim), shape.dimensions(dim)); + pad_config.mutable_dimensions(dim)->set_edge_padding_high( + new_shape.dimensions(dim) - shape.dimensions(dim)); + added_padding = true; + } + + if (!added_padding) { + return instr; + } + return comp->AddInstruction( + HloInstruction::CreatePad(new_shape, instr, zero, pad_config)); +} + +// Pads the input/output feature dimensions of the given cudnn convolution +// custom-call to be multiples of kDesiredNumFeaturesFactor. +static StatusOr PadFeaturesDims(HloInstruction* conv) { + CHECK_EQ(0, conv->shape().tuple_shapes(1).dimensions(0)) + << "conv must use 0 scratch bytes, i.e. this pass must be run " + "before CudnnConvolutionAlgorithmPicker."; + + const auto& target = conv->custom_call_target(); + const auto& dnums = conv->convolution_dimension_numbers(); + auto* lhs = conv->mutable_operand(0); + auto* rhs = conv->mutable_operand(1); + const Shape& result_shape = conv->shape().tuple_shapes(0); + + Shape new_lhs_shape = [&] { + if (target == kCudnnConvForwardCallTarget || + target == kCudnnConvBackwardFilterCallTarget) { + // LHS is "input". + return PadShape(lhs->shape(), {dnums.input_feature_dimension()}); + } + CHECK_EQ(target, kCudnnConvBackwardInputCallTarget); + // LHS is "output". + return PadShape(lhs->shape(), {dnums.output_feature_dimension()}); + }(); + + Shape new_rhs_shape = [&] { + if (target == kCudnnConvForwardCallTarget || + target == kCudnnConvBackwardInputCallTarget) { + // RHS is "filter". + return PadShape(rhs->shape(), {dnums.kernel_input_feature_dimension(), + dnums.kernel_output_feature_dimension()}); + } + CHECK_EQ(target, kCudnnConvBackwardFilterCallTarget); + // RHS is "output". + return PadShape(rhs->shape(), {dnums.output_feature_dimension()}); + }(); + + if (ShapeUtil::Equal(lhs->shape(), new_lhs_shape) && + ShapeUtil::Equal(rhs->shape(), new_rhs_shape)) { + VLOG(3) << "No need to pad features of " << conv->ToString(); + return false; + } + + Shape new_result_shape = [&] { + if (target == kCudnnConvForwardCallTarget) { + // Result is "output". + return PadShape(result_shape, {dnums.output_feature_dimension()}); + } + if (target == kCudnnConvBackwardInputCallTarget) { + // Result is "input". + return PadShape(result_shape, {dnums.input_feature_dimension()}); + } + CHECK_EQ(target, kCudnnConvBackwardFilterCallTarget); + // Result is "filter". + return PadShape(result_shape, {dnums.kernel_input_feature_dimension(), + dnums.kernel_output_feature_dimension()}); + }(); + + // Check that padding wouldn't increase the total bytes read/written by this + // operation too much. + auto check_size_increase = [&](const Shape& old_shape, + const Shape& new_shape) { + int64 old_bytes = ShapeUtil::ByteSizeOf(old_shape); + int64 new_bytes = ShapeUtil::ByteSizeOf(new_shape); + if (new_bytes <= old_bytes * kMaxBytesTouchedIncrease) { + return true; + } + VLOG(3) << "Not padding convolution; doing so would change input / result " + "shape from " + << ShapeUtil::HumanString(old_shape) << " to " + << ShapeUtil::HumanString(new_shape) << ", a size increase of " + << new_bytes / static_cast(old_bytes) << "x > " + << kMaxBytesTouchedIncrease << "x: " << conv->ToString(); + return false; + }; + if (!check_size_increase(lhs->shape(), new_lhs_shape) || + !check_size_increase(rhs->shape(), new_rhs_shape) || + !check_size_increase(result_shape, new_result_shape)) { + return false; + } + + // OK, let's do the transformation! + + auto* new_lhs = PadInstruction(lhs, new_lhs_shape); + auto* new_rhs = PadInstruction(rhs, new_rhs_shape); + CHECK(new_lhs != lhs || new_rhs != rhs) + << "We should have had to pad either LHS or RHS."; + + auto add = [&](std::unique_ptr new_instr) { + return conv->parent()->AddInstruction(std::move(new_instr)); + }; + + Shape new_conv_shape = ShapeUtil::MakeTupleShape( + {new_result_shape, ShapeUtil::MakeShape(U8, {0})}); + auto* new_conv = + add(conv->CloneWithNewOperands(new_conv_shape, {new_lhs, new_rhs})); + + // Slice the new conv result if necessary, keeping in mind that new_conv has + // tuple shape (new_result_shape, u8[0]). + if (!ShapeUtil::Equal(result_shape, new_result_shape)) { + std::vector start_indices(result_shape.dimensions_size(), 0); + std::vector end_indices(result_shape.dimensions().begin(), + result_shape.dimensions().end()); + std::vector strides(result_shape.dimensions_size(), 1); + + auto* new_conv_result = add( + HloInstruction::CreateGetTupleElement(new_result_shape, new_conv, 0)); + auto* empty_temp_buffer = + add(HloInstruction::CreateConstant(LiteralUtil::CreateR1({}))); + auto* sliced_result = add(HloInstruction::CreateSlice( + result_shape, new_conv_result, start_indices, end_indices, strides)); + new_conv = + add(HloInstruction::CreateTuple({sliced_result, empty_temp_buffer})); + } + + VLOG(2) << "Padded features of " << conv->ToString() << ", replaced with " + << new_conv->ToString(); + TF_RETURN_IF_ERROR(conv->parent()->ReplaceInstruction(conv, new_conv)); + return true; +} + +static std::vector GetRelevantConvs(HloComputation* comp) { + std::vector convs; + for (HloInstruction* instr : comp->instructions()) { + if (IsCustomCallToDnnConvolution(*instr) && + instr->operand(0)->shape().element_type() == F16) { + convs.push_back(instr); + } + } + return convs; +} + +StatusOr PadForTensorCores::Run(HloModule* module) { + bool changed = false; + for (HloComputation* comp : module->MakeNonfusionComputations()) { + for (HloInstruction* conv : GetRelevantConvs(comp)) { + TF_ASSIGN_OR_RETURN(bool result, PadFeaturesDims(conv)); + changed |= result; + } + } + return changed; +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/pad_for_tensor_cores.h b/tensorflow/compiler/xla/service/gpu/pad_for_tensor_cores.h new file mode 100644 index 0000000000000000000000000000000000000000..192359f026bfb2f1d5436713e4a30725fa0ad6ba --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/pad_for_tensor_cores.h @@ -0,0 +1,45 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_PAD_FOR_TENSOR_CORES_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_PAD_FOR_TENSOR_CORES_H_ + +#include "tensorflow/compiler/xla/service/hlo_pass_interface.h" + +namespace xla { +namespace gpu { + +// Ensures that f16 cudnn convolutions have input/output channel dimensions that +// are multiples of 8, inserting pads/slices as necessary. +// +// This is useful primarily for Volta and newer GPUs, where tensor cores can +// only be used if the channel dims are multiples of 8. It's probably the +// opposite of useful on other GPUs, so you should check what GPU you're +// targeting before running this pass. +// +// TODO(jlebar): Also pad dots. +class PadForTensorCores : public HloPassInterface { + public: + tensorflow::StringPiece name() const override { + return "pad for tensor cores"; + } + + StatusOr Run(HloModule* module) override; +}; + +} // namespace gpu +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_PAD_FOR_TENSOR_CORES_H_ diff --git a/tensorflow/compiler/xla/service/gpu/pad_for_tensor_cores_test.cc b/tensorflow/compiler/xla/service/gpu/pad_for_tensor_cores_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..99e7580b826fc5cd6d98a037a5eb064552952e18 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/pad_for_tensor_cores_test.cc @@ -0,0 +1,164 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/gpu/pad_for_tensor_cores.h" + +#include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" +#include "tensorflow/compiler/xla/service/hlo_matchers.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" +#include "tensorflow/compiler/xla/status_macros.h" +#include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h" +#include "tensorflow/compiler/xla/util.h" + +namespace xla { +namespace gpu { +namespace { + +namespace op = xla::testing::opcode_matchers; +using ::testing::_; + +using PadForTensorCoresTest = HloVerifiedTestBase; + +TEST_F(PadForTensorCoresTest, PadF16ForwardConvInputChannels) { + ParseAndVerifyModule(R"( + HloModule TestModule + + ENTRY TestComputation { + input = f16[10,20,30,41] parameter(0) + filter = f16[2,2,41,40] parameter(1) + ROOT result = (f16[10,20,30,40], u8[0]) custom-call(input, filter), + window={size=2x2}, dim_labels=b01f_01io->b01f, + custom_call_target="__cudnn$convForward" + })"); + EXPECT_TRUE(PadForTensorCores().Run(&module()).ValueOrDie()); + auto* root = module().entry_computation()->root_instruction(); + + SCOPED_TRACE(module().ToString()); + EXPECT_THAT(root, op::CustomCall(kCudnnConvForwardCallTarget, + op::Pad(op::Parameter(0), _), + op::Pad(op::Parameter(1), _))); + EXPECT_TRUE(ShapeUtil::Equal(root->operand(0)->shape(), + ShapeUtil::MakeShape(F16, {10, 20, 30, 48}))); + EXPECT_TRUE(ShapeUtil::Equal(root->operand(1)->shape(), + ShapeUtil::MakeShape(F16, {2, 2, 48, 40}))); +} + +TEST_F(PadForTensorCoresTest, PadF16BackwardInputConvOutputChannels) { + ParseAndVerifyModule(R"( + HloModule TestModule + + ENTRY TestComputation { + output = f16[10,20,30,41] parameter(0) + filter = f16[2,2,40,41] parameter(1) + ROOT result = (f16[10,20,30,40], u8[0]) custom-call(output, filter), + window={size=2x2}, dim_labels=b01f_01io->b01f, + custom_call_target="__cudnn$convBackwardInput" + })"); + EXPECT_TRUE(PadForTensorCores().Run(&module()).ValueOrDie()); + auto* root = module().entry_computation()->root_instruction(); + EXPECT_THAT(root, op::CustomCall(kCudnnConvBackwardInputCallTarget, + op::Pad(op::Parameter(0), _), + op::Pad(op::Parameter(1), _))); + EXPECT_TRUE(ShapeUtil::Equal(root->operand(0)->shape(), + ShapeUtil::MakeShape(F16, {10, 20, 30, 48}))); + EXPECT_TRUE(ShapeUtil::Equal(root->operand(1)->shape(), + ShapeUtil::MakeShape(F16, {2, 2, 40, 48}))); +} + +TEST_F(PadForTensorCoresTest, PadF16ForwardConvOutputChannels) { + ParseAndVerifyModule(R"( + HloModule TestModule + + ENTRY TestComputation { + input = f16[10,20,30,40] parameter(0) + filter = f16[2,2,40,41] parameter(1) + ROOT result = (f16[10,20,30,41], u8[0]) custom-call(input, filter), + window={size=2x2}, dim_labels=b01f_01io->b01f, + custom_call_target="__cudnn$convForward" + })"); + EXPECT_TRUE(PadForTensorCores().Run(&module()).ValueOrDie()); + auto* root = module().entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Tuple(op::Slice(op::GetTupleElement(op::CustomCall( + kCudnnConvForwardCallTarget, op::Parameter(0), + op::Pad(op::Parameter(1), _)))), + _)); +} + +TEST_F(PadForTensorCoresTest, PadF16BackwardInputConvInputChannels) { + ParseAndVerifyModule(R"( + HloModule TestModule + + ENTRY TestComputation { + output = f16[10,20,30,40] parameter(0) + filter = f16[2,2,41,40] parameter(1) + result = (f16[10,20,30,41], u8[0]) custom-call(output, filter), + window={size=2x2}, dim_labels=b01f_01io->b01f, + custom_call_target="__cudnn$convBackwardInput" + ROOT gte = f16[10,20,30,41] get-tuple-element(result), index=0 + })"); + EXPECT_TRUE(PadForTensorCores().Run(&module()).ValueOrDie()); + auto* root = module().entry_computation()->root_instruction(); + EXPECT_THAT(root, op::GetTupleElement(op::Tuple( + op::Slice(op::GetTupleElement(op::CustomCall( + kCudnnConvBackwardInputCallTarget, op::Parameter(0), + op::Pad(op::Parameter(1), _)))), + _))); +} + +TEST_F(PadForTensorCoresTest, PadF16BackwardFilterConvInputChannels) { + ParseAndVerifyModule(R"( + HloModule TestModule + + ENTRY TestComputation { + input = f16[10,20,30,41] parameter(0) + output = f16[10,20,30,40] parameter(1) + result = (f16[2,2,41,40], u8[0]) custom-call(input, output), + window={size=2x2}, dim_labels=b01f_01io->b01f, + custom_call_target="__cudnn$convBackwardFilter" + ROOT gte = f16[2,2,41,40] get-tuple-element(result), index=0 + })"); + EXPECT_TRUE(PadForTensorCores().Run(&module()).ValueOrDie()); + auto* root = module().entry_computation()->root_instruction(); + EXPECT_THAT(root, op::GetTupleElement(op::Tuple( + op::Slice(op::GetTupleElement(op::CustomCall( + kCudnnConvBackwardFilterCallTarget, + op::Pad(op::Parameter(0), _), op::Parameter(1)))), + _))); +} + +TEST_F(PadForTensorCoresTest, PadF16BackwardFilterConvOutputChannels) { + ParseAndVerifyModule(R"( + HloModule TestModule + + ENTRY TestComputation { + input = f16[10,20,30,40] parameter(0) + output = f16[10,20,30,41] parameter(1) + result = (f16[2,2,40,41], u8[0]) custom-call(input, output), + window={size=2x2}, dim_labels=b01f_01io->b01f, + custom_call_target="__cudnn$convBackwardFilter" + ROOT gte = f16[2,2,40,41] get-tuple-element(result), index=0 + })"); + EXPECT_TRUE(PadForTensorCores().Run(&module()).ValueOrDie()); + auto* root = module().entry_computation()->root_instruction(); + EXPECT_THAT(root, op::GetTupleElement(op::Tuple( + op::Slice(op::GetTupleElement(op::CustomCall( + kCudnnConvBackwardFilterCallTarget, + op::Parameter(0), op::Pad(op::Parameter(1), _)))), + _))); +} + +} // anonymous namespace +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/pad_insertion.cc b/tensorflow/compiler/xla/service/gpu/pad_insertion.cc index c8f0d4185c63c5bafca6f30acab31cbe8e987277..b22040eee167e784bed58dbc0d0ad2ae042037f3 100644 --- a/tensorflow/compiler/xla/service/gpu/pad_insertion.cc +++ b/tensorflow/compiler/xla/service/gpu/pad_insertion.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/pad_insertion.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" #include "tensorflow/compiler/xla/service/hlo_creation_utils.h" @@ -68,7 +69,7 @@ HloInstruction* MaybePaddedAndSlicedInput( PrimitiveType element_type = input->shape().element_type(); HloInstruction* padding = computation->AddInstruction(HloInstruction::CreateConstant( - MakeUnique(Literal::Zero(element_type)))); + MakeUnique(LiteralUtil::Zero(element_type)))); input = MakePadHlo(input, padding, padding_config).ValueOrDie(); } @@ -125,7 +126,7 @@ HloInstruction* MaybePaddedKernel(const Window& conv_window, PrimitiveType element_type = kernel->shape().element_type(); HloInstruction* padding = computation->AddInstruction(HloInstruction::CreateConstant( - MakeUnique(Literal::Zero(element_type)))); + MakeUnique(LiteralUtil::Zero(element_type)))); return MakePadHlo(kernel, padding, padding_config).ValueOrDie(); } } // namespace @@ -234,9 +235,9 @@ bool PadInsertion::CanonicalizeBackwardFilterConvolution( // Create a new backward convolution replacing the old one. HloComputation* computation = backward_conv->parent(); HloInstruction* output = backward_conv->mutable_operand(1); - HloInstruction* padding = - computation->AddInstruction(HloInstruction::CreateConstant( - MakeUnique(Literal::Zero(input->shape().element_type())))); + HloInstruction* padding = computation->AddInstruction( + HloInstruction::CreateConstant(MakeUnique( + LiteralUtil::Zero(input->shape().element_type())))); HloInstruction* padded_input = MakePadHlo(input, padding, input_padding_config).ValueOrDie(); diff --git a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc index cd833ec7bd858aabee84ac306d198e80eb112506..3838fee674566196e10ddd98462c1a1aa7835e1a 100644 --- a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc @@ -32,27 +32,27 @@ namespace gpu { ParallelLoopEmitter::ParallelLoopEmitter( BodyEmitter body_emitter, const Shape& shape, - const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* ir_builder, + const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* b, int unroll_factor) - : LoopEmitter(body_emitter, shape, ir_builder), + : LoopEmitter(body_emitter, shape, b), launch_dimensions_(launch_dimensions), unroll_factor_(unroll_factor) {} ParallelLoopEmitter::ParallelLoopEmitter( const llvm_ir::ElementGenerator& target_element_generator, tensorflow::gtl::ArraySlice target_arrays, - const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* ir_builder, + const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* b, int unroll_factor) - : LoopEmitter(target_element_generator, target_arrays, ir_builder), + : LoopEmitter(target_element_generator, target_arrays, b), launch_dimensions_(launch_dimensions), unroll_factor_(unroll_factor) {} ParallelLoopEmitter::ParallelLoopEmitter( const llvm_ir::ElementGenerator& target_element_generator, const llvm_ir::IrArray& target_array, - const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* ir_builder, + const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* b, int unroll_factor) - : LoopEmitter(target_element_generator, target_array, ir_builder), + : LoopEmitter(target_element_generator, target_array, b), launch_dimensions_(launch_dimensions), unroll_factor_(unroll_factor) {} @@ -74,29 +74,27 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( CHECK_NE(index_type, nullptr); std::vector array_indices; llvm::Value* block_id = llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::nvvm_read_ptx_sreg_ctaid_x, {}, {}, ir_builder_); + llvm::Intrinsic::nvvm_read_ptx_sreg_ctaid_x, {}, {}, b_); llvm_ir::AddRangeMetadata(0, launch_dimensions_.block_count(), static_cast(block_id)); - block_id = ir_builder_->CreateZExtOrTrunc(block_id, index_type, "block_id"); + block_id = b_->CreateZExtOrTrunc(block_id, index_type, "block_id"); // Per the PTX documentation: // "It is guaranteed that [...] 0 <= %tid.x < %ntid.x" // // %ntid.x is currently specified as 1024. llvm::Value* thread_id = llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::nvvm_read_ptx_sreg_tid_x, {}, {}, ir_builder_); + llvm::Intrinsic::nvvm_read_ptx_sreg_tid_x, {}, {}, b_); llvm_ir::AddRangeMetadata(0, launch_dimensions_.threads_per_block(), static_cast(thread_id)); - thread_id = - ir_builder_->CreateZExtOrTrunc(thread_id, index_type, "thread_id"); - - llvm::Value* linear_index_base = ir_builder_->CreateAdd( - ir_builder_->CreateMul( - block_id, - llvm::ConstantInt::get(index_type, - launch_dimensions_.threads_per_block()), - "", - /*HasNUW=*/true, /*HasNSW=*/true), + thread_id = b_->CreateZExtOrTrunc(thread_id, index_type, "thread_id"); + + llvm::Value* linear_index_base = b_->CreateAdd( + b_->CreateMul(block_id, + llvm::ConstantInt::get( + index_type, launch_dimensions_.threads_per_block()), + "", + /*HasNUW=*/true, /*HasNSW=*/true), thread_id, "linear_index", /*HasNUW=*/true, /*HasNSW=*/true); // Add an @llvm.assume(linear_index < threads_per_block * num_blocks). @@ -109,41 +107,41 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( // conditions in the same basic block as their operands. llvm_ir::EmitCallToIntrinsic( llvm::Intrinsic::assume, - {ir_builder_->CreateICmpULT( + {b_->CreateICmpULT( linear_index_base, llvm::ConstantInt::get(index_type, launch_dimensions_.threads_per_block() * launch_dimensions_.block_count()), "linear_index_in_range")}, - {}, ir_builder_); + {}, b_); if (unroll_factor_ > 1) { - linear_index_base = ir_builder_->CreateMul( + linear_index_base = b_->CreateMul( linear_index_base, llvm::ConstantInt::get(index_type, unroll_factor_), "linear_index_base", /*HasNUW=*/true, /*HasNSW=*/true); } - array_indices.emplace_back(linear_index_base, shape_, ir_builder_); + array_indices.emplace_back(linear_index_base, shape_, b_); for (int i = 1; i < unroll_factor_; ++i) { - llvm::Value* linear_index = ir_builder_->CreateAdd( - linear_index_base, llvm::ConstantInt::get(index_type, i), - "linear_index", - /*HasNUW=*/true, /*HasNSW=*/true); - array_indices.emplace_back(linear_index, shape_, ir_builder_); + llvm::Value* linear_index = + b_->CreateAdd(linear_index_base, llvm::ConstantInt::get(index_type, i), + "linear_index", + /*HasNUW=*/true, /*HasNSW=*/true); + array_indices.emplace_back(linear_index, shape_, b_); } auto if_in_bounds = llvm_ir::EmitIfThenElse( - ir_builder_->CreateICmpULT( + b_->CreateICmpULT( linear_index_base, llvm::ConstantInt::get(index_type, ShapeUtil::ElementsIn(shape_))), - llvm_ir::IrName(loop_name, "in_bounds"), ir_builder_, false); + llvm_ir::IrName(loop_name, "in_bounds"), b_, false); // Set exit_bb_ to the exit block of the if structure. exit_bb_ = if_in_bounds.after_block; CHECK_NE(nullptr, exit_bb_); // Set IR builder insertion point to the body of the if structure. - llvm_ir::SetToFirstInsertPoint(if_in_bounds.true_block, ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_in_bounds.true_block, b_); return array_indices; } diff --git a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h index 302e1bf1bc8e90f2eebd838f156a1552e86185ac..b82a23419df08cafdc69b6d2f14528484b95dc73 100644 --- a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h +++ b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h @@ -34,13 +34,13 @@ class ParallelLoopEmitter : public llvm_ir::LoopEmitter { // The meanings of other parameters are the same as LoopEmitter. ParallelLoopEmitter(BodyEmitter body_emitter, const Shape& shape, const LaunchDimensions& launch_dimensions, - llvm::IRBuilder<>* ir_builder, int unroll_factor = 1); + llvm::IRBuilder<>* b, int unroll_factor = 1); // Constructs a ParallelLoopEmitter from an element generator that generates // each element of the given target array. ParallelLoopEmitter(const llvm_ir::ElementGenerator& target_element_generator, const llvm_ir::IrArray& target_array, const LaunchDimensions& launch_dimensions, - llvm::IRBuilder<>* ir_builder, int unroll_factor = 1); + llvm::IRBuilder<>* b, int unroll_factor = 1); // Constructs a loop emitter for a loop that generates on element of each of N // arrays on each iteration. @@ -50,7 +50,7 @@ class ParallelLoopEmitter : public llvm_ir::LoopEmitter { ParallelLoopEmitter( const llvm_ir::ElementGenerator& target_element_generator, tensorflow::gtl::ArraySlice target_arrays, - const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* ir_builder, + const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* b, int unroll_factor = 1); ParallelLoopEmitter(const ParallelLoopEmitter&) = delete; diff --git a/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc b/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc index dfdba7d7d9a60458e1b1c90cf9f5017b44b7b801..84285be70a4ba94101040a639c39b3eaecbb5bb3 100644 --- a/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/sequential_thunk.cc @@ -36,12 +36,7 @@ Status SequentialThunk::Initialize(const GpuExecutable& executable, Status SequentialThunk::ExecuteOnStream( const BufferAllocations& buffer_allocations, se::Stream* stream, HloExecutionProfiler* profiler) { - // TODO(b/71544591): We need to potentially measure the total time of the - // sequential thunk. This happens for a reduce op which consists of - // SequentialThunk with a thunk that initializes the output, and another thunk - // that does the actual reduce. Right now, in this case we would only measure - // the time of the last thunk, because both thunks would have the same - // HloInstruction. + auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); for (const auto& thunk : thunks_) { TF_RETURN_IF_ERROR( thunk->ExecuteOnStream(buffer_allocations, stream, profiler)); diff --git a/tensorflow/compiler/xla/service/gpu/stream_assignment.cc b/tensorflow/compiler/xla/service/gpu/stream_assignment.cc index e4cfc6999f2da04dd7e7a34d854fdb3d75b8bfc6..0806dd51614f4d2da12f3fbbc9fb98df5273d5c8 100644 --- a/tensorflow/compiler/xla/service/gpu/stream_assignment.cc +++ b/tensorflow/compiler/xla/service/gpu/stream_assignment.cc @@ -33,13 +33,13 @@ int StreamAssignment::StreamNumberForHlo(const HloInstruction& hlo) const { } void StreamAssignment::AssignStreamToHlo(const HloInstruction* hlo, - int stream_no) { - CHECK_GE(stream_no, 0); - if (stream_no >= stream_count_) { - stream_count_ = stream_no + 1; + int stream_num) { + CHECK_GE(stream_num, 0); + if (stream_num >= stream_count_) { + stream_count_ = stream_num + 1; } - InsertOrDie(&hlo_to_stream_number_, hlo, stream_no); - VLOG(2) << "Assign stream #" << stream_no << " to " << hlo->ToString(); + InsertOrDie(&hlo_to_stream_number_, hlo, stream_num); + VLOG(2) << "Assign stream #" << stream_num << " to " << hlo->ToString(); } namespace { @@ -51,6 +51,12 @@ bool CanRunConcurrently(const HloInstruction& a, const HloInstruction& b, return !reachability.IsConnected(&a, &b); } +constexpr int kInvalidStreamNum = -1; +// Returns true iff `stream_num` is an invalid stream number. +inline bool IsStreamNumValid(int stream_num) { + return stream_num != kInvalidStreamNum; +} + // Returns which existing stream to assign to `hlo`, or -1 if a stream is not // needed. `stream_assignment` is the existing stream assignment for all // instructions topologically before `hlo`. `seen_gemms` contains all GEMMs that @@ -62,7 +68,7 @@ int ComputeStreamToAssign( if (hlo.opcode() == HloOpcode::kParameter || hlo.opcode() == HloOpcode::kConstant) { // kParameter and kConstant do not need a thunk. - return -1; + return kInvalidStreamNum; } if (hlo.GetModule() @@ -75,17 +81,17 @@ int ComputeStreamToAssign( if (!ImplementedAsGemm(hlo)) { // If `hlo` is not implemented as a GEMM, keep it close to its operands to // avoid excessive synchronization. - int stream_no = -1; + int stream_num = -1; for (const auto* operand : hlo.operands()) { if (stream_assignment.HasStreamAssigned(*operand)) { - stream_no = - std::max(stream_no, stream_assignment.StreamNumberForHlo(*operand)); + stream_num = std::max(stream_num, + stream_assignment.StreamNumberForHlo(*operand)); } } - if (stream_no == -1) { - stream_no = 0; + if (!IsStreamNumValid(stream_num)) { + stream_num = 0; } - return stream_no; + return stream_num; } // Assign different streams to concurrent GEMMs. The code below uses a @@ -94,17 +100,17 @@ int ComputeStreamToAssign( // `hlo` a different stream. std::set forbidden_stream_numbers; for (const auto* seen_gemm : seen_gemms) { - int stream_no = stream_assignment.StreamNumberForHlo(*seen_gemm); - if (!forbidden_stream_numbers.count(stream_no) && + int stream_num = stream_assignment.StreamNumberForHlo(*seen_gemm); + if (!forbidden_stream_numbers.count(stream_num) && CanRunConcurrently(*seen_gemm, hlo, reachability)) { - forbidden_stream_numbers.insert(stream_no); + forbidden_stream_numbers.insert(stream_num); } } - for (int stream_no = 0; stream_no < stream_assignment.StreamCount(); - ++stream_no) { - if (!forbidden_stream_numbers.count(stream_no)) { - return stream_no; + for (int stream_num = 0; stream_num < stream_assignment.StreamCount(); + ++stream_num) { + if (!forbidden_stream_numbers.count(stream_num)) { + return stream_num; } } return stream_assignment.StreamCount(); @@ -118,11 +124,27 @@ std::unique_ptr AssignStreams(const HloModule& module) { std::unique_ptr reachability = computation.ComputeReachability(); std::vector seen_gemms; + // The execution of different RNG Hlo instructions in the same module updates + // a common global variable. To avoid a race condition, we simply assign all + // RNG kernels to the same stream to make them run sequentially. + // + // TODO(b/111791052): If we remove such a common variable, we will need to + // clean up the code here. + int stream_num_for_rng = kInvalidStreamNum; for (const auto* hlo : computation.MakeInstructionPostOrder()) { - int stream_no = ComputeStreamToAssign(*hlo, *stream_assignment, - *reachability, seen_gemms); - if (stream_no != -1) { - stream_assignment->AssignStreamToHlo(hlo, stream_no); + // If we ever enable fusion of RNG instructions, we will need to extend this + // code to look inside a fused instruction. + int stream_num = (hlo->opcode() == HloOpcode::kRng && + IsStreamNumValid(stream_num_for_rng)) + ? stream_num_for_rng + : ComputeStreamToAssign(*hlo, *stream_assignment, + *reachability, seen_gemms); + if (IsStreamNumValid(stream_num)) { + stream_assignment->AssignStreamToHlo(hlo, stream_num); + if (hlo->opcode() == HloOpcode::kRng && + !IsStreamNumValid(stream_num_for_rng)) { + stream_num_for_rng = stream_num; + } } if (ImplementedAsGemm(*hlo)) { seen_gemms.push_back(hlo); diff --git a/tensorflow/compiler/xla/service/gpu/stream_executor_util.cc b/tensorflow/compiler/xla/service/gpu/stream_executor_util.cc index a50ddf6ac63c7fa7ccace94bc7f40f438aedccf8..05b305ea4cdfdbaeb42544b626a6b9990bb42f57 100644 --- a/tensorflow/compiler/xla/service/gpu/stream_executor_util.cc +++ b/tensorflow/compiler/xla/service/gpu/stream_executor_util.cc @@ -20,10 +20,17 @@ limitations under the License. namespace xla { namespace gpu { -using stream_executor::dnn::DataLayout; -using stream_executor::dnn::DataLayoutString; -using stream_executor::dnn::FilterLayout; -using stream_executor::dnn::FilterLayoutString; +using se::dnn::DataLayout; +using se::dnn::DataLayoutString; +using se::dnn::FilterLayout; +using se::dnn::FilterLayoutString; + +bool IsVoltaOrLater(const se::StreamExecutor& stream_executor) { + int major, minor; + CHECK(stream_executor.GetDeviceDescription().cuda_compute_capability(&major, + &minor)); + return major >= 7; +} StatusOr> StreamExecutorConvLayoutsToXlaLayouts(const ConvolutionDimensionNumbers& dnums, diff --git a/tensorflow/compiler/xla/service/gpu/stream_executor_util.h b/tensorflow/compiler/xla/service/gpu/stream_executor_util.h index 39a6a38d001f502b2abb8de6efe2ce623b478c71..1fc46bafa10e7ba6c896f081d5c836bd400886c9 100644 --- a/tensorflow/compiler/xla/service/gpu/stream_executor_util.h +++ b/tensorflow/compiler/xla/service/gpu/stream_executor_util.h @@ -17,6 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_STREAM_EXECUTOR_UTIL_H_ #include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" @@ -25,18 +26,20 @@ limitations under the License. namespace xla { namespace gpu { +// Returns true if the given StreamExecutor is for a Volta or newer nvidia GPU. +bool IsVoltaOrLater(const se::StreamExecutor& stream_exec); + // Returns (input, filter, output) XLA Layout protos given the StreamExecutor // layouts. StatusOr> StreamExecutorConvLayoutsToXlaLayouts(const ConvolutionDimensionNumbers& dnums, - stream_executor::dnn::DataLayout input, - stream_executor::dnn::FilterLayout filter, - stream_executor::dnn::DataLayout output); + se::dnn::DataLayout input, + se::dnn::FilterLayout filter, + se::dnn::DataLayout output); // Returns (input, filter, output) StreamExecutor layouts given the XLA layouts. -StatusOr> +StatusOr< + std::tuple> XlaConvLayoutsToStreamExecutorLayouts(const ConvolutionDimensionNumbers& dnums, const Layout& input, const Layout& filter, const Layout& output); diff --git a/tensorflow/compiler/xla/service/gpu/tests/BUILD b/tensorflow/compiler/xla/service/gpu/tests/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..4fad3f46cf953945e4f395e751e5ba76db97ecc4 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/BUILD @@ -0,0 +1,223 @@ +# Description: GPU-specific XLA tests. For example, codegen tests that +# verify the IR emitted. +# +# TODO(jlebar): None of these tests actually use the GPU, so they should not +# need to run on machines with GPUs present. + +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = [":friends"]) + +package_group( + name = "friends", + includes = [ + "//tensorflow/compiler/xla:friends", + ], +) + +# Filegroup used to collect source files for dependency checking. +filegroup( + name = "c_srcs", + data = glob([ + "**/*.cc", + "**/*.h", + ]), +) + +load("//tensorflow:tensorflow.bzl", "tf_cc_test") + +cc_library( + name = "gpu_codegen_test", + testonly = True, + srcs = ["gpu_codegen_test.cc"], + hdrs = ["gpu_codegen_test.h"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", + "//tensorflow/compiler/xla/service:gpu_plugin", + "//tensorflow/compiler/xla/service/gpu:gpu_executable", + "//tensorflow/compiler/xla/tests:filecheck", + "//tensorflow/compiler/xla/tests:llvm_irgen_test_base", + "//tensorflow/core:lib", + ], +) + +tf_cc_test( + name = "gpu_copy_test", + srcs = ["gpu_copy_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_ftz_test", + srcs = ["gpu_ftz_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_index_test", + srcs = ["gpu_index_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla:xla_proto", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_module_config", + "//tensorflow/compiler/xla/service:hlo_parser", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_infeed_test", + srcs = ["infeed_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:test_helpers", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:global_data", + "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client/lib:arithmetic", + "//tensorflow/compiler/xla/tests:client_library_test_base", + "//tensorflow/compiler/xla/tests:literal_test_util", + "//tensorflow/core:lib", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_kernel_tiling_test", + srcs = ["gpu_kernel_tiling_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_module_config", + "//tensorflow/compiler/xla/service:hlo_parser", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_ldg_test", + srcs = ["gpu_ldg_test.cc"], + tags = ["requires-gpu-sm35"], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_noalias_test", + srcs = ["gpu_noalias_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_fusion_test", + srcs = ["gpu_fusion_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla/service:hlo_module_config", + "//tensorflow/compiler/xla/service:hlo_parser", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_unrolling_test", + srcs = ["gpu_unrolling_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla/service:hlo_module_config", + "//tensorflow/compiler/xla/service:hlo_parser", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_alignment_test", + testonly = True, + srcs = ["gpu_alignment_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla/service:gpu_plugin", + "//tensorflow/compiler/xla/service/cpu:custom_call_target_registry", + "//tensorflow/compiler/xla/service/llvm_ir:alias_analysis", + "//tensorflow/compiler/xla/tests:filecheck", + "//tensorflow/compiler/xla/tests:llvm_irgen_test_base", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_alignment_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_alignment_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..672c68e59b59dff19f0c5575db26dea455c45053 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_alignment_test.cc @@ -0,0 +1,54 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include + +#include "tensorflow/compiler/xla/service/cpu/custom_call_target_registry.h" +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/llvm_ir/alias_analysis.h" +#include "tensorflow/compiler/xla/tests/filecheck.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { +namespace { + +class GpuAlignmentTest : public GpuCodegenTest {}; + +TEST_F(GpuAlignmentTest, Test) { + const char* hlo_string = R"( +HloModule GpuAlignmentTest + +ENTRY main { + zero = f32[] constant(0) + tok = token[] after-all() + a = f32[100] parameter(0) + b_tup = (f32[200], token[]) infeed(tok) + b = f32[200] get-tuple-element(b_tup), index=0 + a_padded = f32[150] pad(a, zero), padding=0_50 + b_sliced = f32[150] slice(b), slice={[0:150]} + ROOT c = f32[150] add(a_padded, b_sliced) +} +)"; + + CompileAndVerifyIr(hlo_string, R"( +CHECK: @fusion(i8* align 64 dereferenceable(600) %alloc0, i8* align 16 dereferenceable(400) %alloc1, i8* align 64 dereferenceable(864) %temp_buf) +)"); +} + +} // namespace +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..4b8415fe9106137e588f345a3492f93e46aeb5b6 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.cc @@ -0,0 +1,50 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h" +#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/gpu/gpu_executable.h" +#include "tensorflow/compiler/xla/tests/filecheck.h" +#include "tensorflow/core/platform/logging.h" + +namespace xla { +namespace gpu { + +std::unique_ptr GpuCodegenTest::CreateNewModuleWithFTZ(bool ftz) { + HloModuleConfig config; + auto debug_options = legacy_flags::GetDebugOptionsFromFlags(); + debug_options.set_xla_gpu_ftz(ftz); + debug_options.set_xla_gpu_max_kernel_unroll_factor(1); + // TODO(b/38354253): Change tests to use Parameters instead of Constants. + debug_options.add_xla_disable_hlo_passes("constant_folding"); + config.set_debug_options(debug_options); + + return MakeUnique(TestName(), config); +} + +void GpuCodegenTest::CompileAndVerifyPtx(std::unique_ptr hlo_module, + const string& pattern) { + std::unique_ptr executable = + std::move(CompileToExecutable(std::move(hlo_module)).ValueOrDie()); + string ptx_str = + std::string(static_cast(executable.get())->ptx()); + StatusOr filecheck_result = RunFileCheck(ptx_str, pattern); + ASSERT_TRUE(filecheck_result.ok()); + EXPECT_TRUE(filecheck_result.ValueOrDie()); +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h b/tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h new file mode 100644 index 0000000000000000000000000000000000000000..e4a3573babb7ed746504c1466f85b582aa4d044f --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h @@ -0,0 +1,42 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_TESTS_GPU_CODEGEN_TEST_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_TESTS_GPU_CODEGEN_TEST_H_ + +#include + +#include "tensorflow/compiler/xla/tests/llvm_irgen_test_base.h" + +namespace xla { +namespace gpu { + +// Tests that verify IR or PTX emitted by the GPU backend is as expected. +class GpuCodegenTest : public LlvmIrGenTestBase { + protected: + // Like HloTestBase::CreateNewModule(), with a flag for configuring the ftz + // option. + std::unique_ptr CreateNewModuleWithFTZ(bool ftz); + + // Compiles the given HLO module to PTX and verifies the PTX matches the given + // FileCheck pattern. (See http://llvm.org/docs/CommandGuide/FileCheck.html). + void CompileAndVerifyPtx(std::unique_ptr hlo_module, + const string& pattern); +}; + +} // namespace gpu +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_TESTS_GPU_CODEGEN_TEST_H_ diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_copy_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_copy_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..ce69e058e64aab1f3c292b2ad7c7b529d4666b35 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_copy_test.cc @@ -0,0 +1,59 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include + +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { + +class GpuCopyTest : public GpuCodegenTest {}; + +// The GPU backend should not emit a copy kernel for the kCopy instruction in +// this test. Instead, it should generate a CopyThunk which invokes cuMemcpy at +// runtime. +TEST_F(GpuCopyTest, UseMemcpy) { + HloComputation::Builder builder(TestName()); + + std::unique_ptr literal = + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + HloInstruction* constant = builder.AddInstruction( + HloInstruction::CreateConstant(std::move(literal))); + builder.AddInstruction(HloInstruction::CreateUnary( + constant->shape(), HloOpcode::kCopy, constant)); + + std::unique_ptr computation = builder.Build(); + + auto hlo_module = CreateNewModule(); + hlo_module->AddEntryComputation(std::move(computation)); + + // There should not be any kernel prefixed "copy". + CompileAndVerifyIr(std::move(hlo_module), "; CHECK-NOT: define void @_copy", + /*match_optimized_ir=*/false); +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_ftz_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_ftz_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..177b94934c7f519172508b5cc6e088f908401193 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_ftz_test.cc @@ -0,0 +1,119 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h" + +// Check that the ftz (flush denormals to zero) flag is reflected in PTX as +// expected. + +namespace xla { +namespace gpu { +namespace { + +class GpuFtzTest : public GpuCodegenTest { + public: + explicit GpuFtzTest(bool ftz) : ftz_(ftz) {} + + // Creates an HLO module that performs the given binary operation on some + // data. + std::unique_ptr CreateBinaryOpModule(HloOpcode op) { + HloComputation::Builder builder(TestName()); + + Shape param_shape = ShapeUtil::MakeShapeWithLayout( + F32, /*dimensions=*/{100, 100}, /*minor_to_major=*/{1, 0}); + HloInstruction* x = builder.AddInstruction(HloInstruction::CreateParameter( + /* parameter_number=*/0, param_shape, "x")); + HloInstruction* y = builder.AddInstruction(HloInstruction::CreateParameter( + /* parameter_number=*/1, param_shape, "y")); + builder.AddInstruction(HloInstruction::CreateBinary(param_shape, op, x, y)); + + auto hlo_module = CreateNewModuleWithFTZ(ftz_); + hlo_module->AddEntryComputation(builder.Build()); + return hlo_module; + } + + // Creates an HLO module that performs the given unary operation on some data. + std::unique_ptr CreateUnaryOpModule(HloOpcode op) { + HloComputation::Builder builder(TestName()); + + Shape param_shape = ShapeUtil::MakeShapeWithLayout( + F32, /*dimensions=*/{100, 100}, /*minor_to_major=*/{1, 0}); + HloInstruction* x = builder.AddInstruction(HloInstruction::CreateParameter( + /* parameter_number=*/0, param_shape, "x")); + builder.AddInstruction(HloInstruction::CreateUnary(param_shape, op, x)); + + auto hlo_module = CreateNewModuleWithFTZ(ftz_); + hlo_module->AddEntryComputation(builder.Build()); + return hlo_module; + } + + bool ftz_; +}; + +class GpuFtzEnabledTest : public GpuFtzTest { + public: + GpuFtzEnabledTest() : GpuFtzTest(/*ftz=*/true) {} +}; + +class GpuFtzDisabledTest : public GpuFtzTest { + public: + GpuFtzDisabledTest() : GpuFtzTest(/*ftz=*/false) {} +}; + +// Check that we emit mul.ftz.f32 when in ftz mode, and plain mul.f32 otherwise. +TEST_F(GpuFtzEnabledTest, MultiplyFtz) { + CompileAndVerifyPtx(CreateBinaryOpModule(HloOpcode::kMultiply), R"( + CHECK-NOT: mul.f32 + CHECK: mul.ftz.f32 + CHECK-NOT: mul.f32 + )"); +} +TEST_F(GpuFtzDisabledTest, MultiplyFtz) { + CompileAndVerifyPtx(CreateBinaryOpModule(HloOpcode::kMultiply), R"( + CHECK-NOT: mul.ftz.f32 + CHECK: mul.f32 + CHECK-NOT: mul.ftz.f32 + )"); +} + +// In NVPTX, exp(float) is implemented in libdevice, and consults __nvvm_reflect +// to determine whether or not ftz is enabled. The implementation uses two +// calls to ex2.approx. When ftz is on, we get two calls to the ftz version; +// when ftz is off, we get one call to the ftz version and one call to the +// regular version. +TEST_F(GpuFtzEnabledTest, ExpFtz) { + CompileAndVerifyPtx(CreateUnaryOpModule(HloOpcode::kExp), R"( + CHECK-NOT: ex2.approx.f32 + CHECK: ex2.approx.ftz.f32 + CHECK-NOT: ex2.approx.f32 + CHECK: ex2.approx.ftz.f32 + CHECK-NOT: ex2.approx.f32 + CHECK-NOT: ex2.approx.ftz.f32 + )"); +} + +TEST_F(GpuFtzDisabledTest, ExpFtz) { + CompileAndVerifyPtx(CreateUnaryOpModule(HloOpcode::kExp), R"( + CHECK-NOT: ex2.approx.f32 + CHECK-DAG: ex2.approx.ftz.f32 + CHECK-DAG: ex2.approx.f32 + CHECK-NOT: ex2.approx.f32 + CHECK-NOT: ex2.approx.ftz.f32 + )"); +} + +} // namespace +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_fusion_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..674b436a8e3135a5dfe3731647897696bf1321cd --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_fusion_test.cc @@ -0,0 +1,59 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include + +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/hlo_module_config.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { +namespace { + +class GpuFusionTest : public GpuCodegenTest {}; + +TEST_F(GpuFusionTest, FusedReshape) { + const char* hlo_text = R"( + HloModule test_module + + fused_computation { + p0.param_0 = f32[4,1,1]{2,1,0} parameter(0) + p1.param_1 = f32[4,1]{1,0} parameter(1) + reshape = f32[4,1]{1,0} reshape(p0.param_0) + ROOT add = f32[4,1] add(reshape, p1.param_1) + } + + ENTRY BroadcastIntoAdd { + p0 = f32[4,1,1]{2,1,0} parameter(0) + p1 = f32[4,1]{1,0} parameter(1) + ROOT fusion = f32[4,1]{1,0} fusion(p0, p1), kind=kLoop, + calls=fused_computation + } +)"; + + CompileAndVerifyIr(hlo_text, + R"( +; CHECK-LABEL: @fusion +; CHECK: fadd +; CHECK: } + )"); +} + +} // namespace +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_index_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_index_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..e5958165eff21d82faf821213e50fe30a11059a4 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_index_test.cc @@ -0,0 +1,147 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include + +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_module_config.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/xla.pb.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { + +// This file tests the index expressions used to reference source tensors. When +// the destination tensor and source tensor have compatible shapes, the linear +// index is used to access the source tensor. Otherwise, dimensional indices +// computed from the linear index are used to access the source tensor. + +class GpuIndexTest : public GpuCodegenTest {}; + +TEST_F(GpuIndexTest, CompatibleUseLinearIndex) { + HloComputation::Builder builder(TestName()); + + auto param_shape = ShapeUtil::MakeShape(F32, {5, 7, 2}); + HloInstruction* param_x = builder.AddInstruction( + HloInstruction::CreateParameter(0, param_shape, "x")); + HloInstruction* param_y = builder.AddInstruction( + HloInstruction::CreateParameter(1, param_shape, "y")); + builder.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(PRED, {5, 7, 2}), HloOpcode::kGe, param_x, param_y)); + + auto hlo_module = CreateNewModule(); + hlo_module->AddEntryComputation(builder.Build()); + + // Check the optimized IR as the unoptimized IR contains dead udiv and urem. + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-NOT: udiv +; CHECK-NOT: urem + )", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuIndexTest, CompatibleUseLinearIndexWithReshape) { + HloModuleConfig config; + config.set_debug_options(HloTestBase::GetDebugOptionsForTest()); + auto module = ParseHloString(R"( + HloModule test_module + + ENTRY CompatibleUseLinearIndexWithReshape { + x = f32[5,7,2]{2,1,0} parameter(0) + y = f32[5,14]{1,0} parameter(1) + reshape = f32[5,7,2]{2,1,0} reshape(y) + ROOT gte = pred[5,7,2]{2,1,0} greater-than-or-equal-to(x, reshape) + })", + config) + .ValueOrDie(); + + // Check the optimized IR as the unoptimized IR contains dead udiv and urem. + CompileAndVerifyIr(std::move(module), + R"( +; CHECK-NOT: udiv +; CHECK-NOT: urem + )", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuIndexTest, CompatibleUseLinearIndexWithReshapeAndBroadcast) { + HloModuleConfig config; + config.set_debug_options(HloTestBase::GetDebugOptionsForTest()); + auto module = ParseHloString(R"( + HloModule test_module + + ENTRY CompatibleUseLinearIndexWithReshape { + x = f32[5,7,2]{2,1,0} parameter(0) + y = f32[14]{0} parameter(1) + reshape = f32[7,2]{1,0} reshape(y) + broadcast = f32[5,7,2]{2,1,0} broadcast(reshape), dimensions={1,2} + ROOT gte = pred[5,7,2]{2,1,0} greater-than-or-equal-to(x, broadcast) + })", + config) + .ValueOrDie(); + + // Check the optimized IR reuses the linear index by calculating modulo 14. + CompileAndVerifyIr(std::move(module), + R"( +; CHECK: %[[urem1:.*]] = urem i{{[0-9]*}} %[[linear_index:.*]], 14 +; CHECK: %[[bitcast:.*]] = bitcast i8 addrspace(1)* %[[alloc:.*]] to float addrspace(1)* +; CHECK: %[[idx1:.*]] = zext i{{[0-9]*}} %[[urem1]] to i64 +; CHECK: getelementptr inbounds float, float addrspace(1)* %[[bitcast]], i64 %[[idx1]] + )", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuIndexTest, CompatibleUseLinearIndexWithSizeOneDimensions) { + HloModuleConfig config; + auto debug_options = HloTestBase::GetDebugOptionsForTest(); + debug_options.set_xla_gpu_max_kernel_unroll_factor(1); + config.set_debug_options(debug_options); + + auto module = ParseHloString(R"( + HloModule test_module + + ENTRY CompatibleUseLinearIndexWithSizeOneDimensions { + x = f32[1,1024,1,256]{3,2,1,0} parameter(0) + ROOT y = f16[1,1024,1,256]{2,3,1,0} convert(x) + })", + config) + .ValueOrDie(); + + // Check that the unoptimized IR reuses the linear index. + CompileAndVerifyIr(std::move(module), + R"( +; CHECK-LABEL: @fusion +; CHECK: udiv i32 %[[linear_index:.*]], 262144 +; CHECK: %[[ld_addr:.*]] = getelementptr inbounds float, float* {{.*}}, i32 %[[linear_index]] +; CHECK: load float, float* %[[ld_addr]] +; CHECK: %[[st_addr:.*]] = getelementptr inbounds half, half* {{.*}}, i32 %[[linear_index]] +; CHECK: store half {{.*}}, half* %[[st_addr]] + )", + /*match_optimized_ir=*/false); +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_kernel_tiling_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_kernel_tiling_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..cca35316f0c472d2a17c466f8cd1af7f22575a8b --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_kernel_tiling_test.cc @@ -0,0 +1,177 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include + +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/hlo_module_config.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { +namespace { + +class GpuKernelTilingTest : public GpuCodegenTest { + protected: + GpuKernelTilingTest() { + auto debug_options = HloTestBase::GetDebugOptionsForTest(); + config_.set_debug_options(debug_options); + // Disable layout_assignment to use the preassigned layouts. + debug_options.add_xla_disable_hlo_passes("layout_assignment"); + } + HloModuleConfig config_; +}; + +TEST_F(GpuKernelTilingTest, UnnestedTransposeWithProperDimensionsTiled) { + const char *const kHloString = R"( + HloModule unnested_transpose_1 + + ENTRY unnested_transpose_1 { + para0 = f16[32,3,64]{2,1,0} parameter(0) + ROOT copy1 = f16[32,3,64]{1,0,2} copy(para0) + })"; + + // Check that a call to llvm.nvvm.barrier0 is generated. + auto hlo_module = ParseHloString(kHloString, config_).ValueOrDie(); + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: define void @copy +; CHECK: tail call void @llvm.nvvm.barrier0() +; CHECK: } +)", + /*match_optimized_ir=*/true); + + // Check that the kernel runs correctly. + EXPECT_TRUE(RunAndCompareNoHloPasses(kHloString, ErrorSpec{0.0})); +} + +TEST_F(GpuKernelTilingTest, UnnestedTransposeWithSmallDimensionsNotTiled) { + const char *const kHloString = R"( + HloModule unnested_transpose_2 + + ENTRY unnested_transpose_2 { + para0 = f16[2,3,64]{2,1,0} parameter(0) + ROOT copy1 = f16[2,3,64]{1,0,2} copy(para0) + })"; + + // Check that a call to llvm.nvvm.barrier0 is not generated. + auto hlo_module = ParseHloString(kHloString, config_).ValueOrDie(); + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: define void @copy +; CHECK-NOT: tail call void @llvm.nvvm.barrier0() +; CHECK: } +)", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuKernelTilingTest, SimpleFusionWithTransposeTiled) { + const char *const kHloString = R"( + HloModule multiple_output_fusion_1 + fused_computation.1 { + param0 = f32[4,5,6,7,8]{4,3,2,1,0} parameter(0) + copy = f32[4,5,6,7,8]{2,1,4,3,0} copy(param0) + ROOT convert = f16[4,5,6,7,8]{2,1,4,3,0} convert(copy) + } + + ENTRY copy_in_fusion_run_without_hlo_passes { + para0 = f32[4,5,6,7,8]{4,3,2,1,0} parameter(0) + ROOT fusion.1 = f16[4,5,6,7,8]{2,1,4,3,0} fusion(para0), kind=kLoop, + calls=fused_computation.1 + })"; + + // Check that a call to llvm.nvvm.barrier0 is generated. + auto hlo_module = ParseHloString(kHloString, config_).ValueOrDie(); + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: define void @fusion +; CHECK: tail call void @llvm.nvvm.barrier0() +; CHECK: } +)", + /*match_optimized_ir=*/true); + + // Check that the kernel runs correctly. + EXPECT_TRUE(RunAndCompareNoHloPasses(kHloString, ErrorSpec{0.0})); +} + +TEST_F(GpuKernelTilingTest, MultipleOutputFusionWithOnePossibleTransposeTiled) { + const char *const kHloString = R"( + HloModule multiple_output_fusion_1 + fused_computation.1 { + param0 = f16[8,31,31,65]{3,2,1,0} parameter(0) + param1 = f16[8,31,31,65]{3,2,1,0} parameter(1) + copy0 = f16[8,31,31,65]{2,1,3,0} copy(param0) + copy1 = f16[8,31,31,65]{2,1,3,0} copy(param1) + ROOT tuple1 = (f16[8,31,31,65]{2,1,3,0}, f16[8,31,31,65]{2,1,3,0}) + tuple(copy0, copy1) + } + + ENTRY multiple_output_fusion_1 { + para0 = f16[8,31,31,65]{3,2,1,0} parameter(0) + para1 = f16[8,31,31,65]{3,2,1,0} parameter(1) + ROOT fusion.1 = (f16[8,31,31,65]{2,1,3,0}, f16[8,31,31,65]{2,1,3,0}) + fusion(para0,para1), kind=kLoop, calls=fused_computation.1 + })"; + + // Check that a call to llvm.nvvm.barrier0 is generated. + auto hlo_module = ParseHloString(kHloString, config_).ValueOrDie(); + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: define void @fusion +; CHECK: tail call void @llvm.nvvm.barrier0() +; CHECK: } +)", + /*match_optimized_ir=*/true); + + // Check that the kernel runs correctly. + EXPECT_TRUE(RunAndCompareNoHloPasses(kHloString, ErrorSpec{0.0})); +} + +TEST_F(GpuKernelTilingTest, + MultipleOutputFusionWithTwoPossibleTransposesNotTiled) { + const char *const kHloString = R"( + HloModule multiple_output_fusion_2 + fused_computation.1 { + param0 = f16[8,31,31,65]{3,2,1,0} parameter(0) + param1 = f16[8,31,31,65]{1,3,2,0} parameter(1) + copy2 = f16[8,31,31,65]{2,1,3,0} copy(param0) + copy3 = f16[8,31,31,65]{2,1,3,0} copy(param1) + ROOT tuple1 = (f16[8,31,31,65]{2,1,3,0}, f16[8,31,31,65]{2,1,3,0}) + tuple(copy2, copy3) + } + + ENTRY multiple_output_fusion_2 { + para0 = f16[8,31,31,65]{3,2,1,0} parameter(0) + para1 = f16[8,31,31,65]{1,3,2,0} parameter(1) + ROOT fusion1 = (f16[8,31,31,65]{2,1,3,0}, f16[8,31,31,65]{2,1,3,0}) + fusion(para0,para1), kind=kLoop, calls=fused_computation.1 + })"; + + // Check that a call to llvm.nvvm.barrier0 is not generated. + auto hlo_module = ParseHloString(kHloString, config_).ValueOrDie(); + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: define void @fusion +; CHECK-NOT: tail call void @llvm.nvvm.barrier0() +; CHECK: } +)", + /*match_optimized_ir=*/true); +} + +} // namespace +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_ldg_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_ldg_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..6c9ae7bada5e7545b558b6fcb872ece60850cbe9 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_ldg_test.cc @@ -0,0 +1,141 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// Tests that we emit ld.global.nc (the PTX instruction corresponding to CUDA's +// __ldg builtin) for reads of buffers that don't change during a kernel's +// execution. + +#include +#include + +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { + +class GpuLdgTest : public GpuCodegenTest {}; + +// Parameters are never overwritten, so parameter reads should get ld.global.nc +// reads. +TEST_F(GpuLdgTest, LdgForParamRead) { + HloComputation::Builder builder(TestName()); + + auto shape = ShapeUtil::MakeShape(F32, {2, 2}); + HloInstruction* param = + builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "x")); + builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param, param)); + std::unique_ptr computation = builder.Build(); + + auto hlo_module = CreateNewModule(); + hlo_module->AddEntryComputation(std::move(computation)); + + CompileAndVerifyPtx(std::move(hlo_module), R"( + CHECK-NOT: ld.global.f32 + CHECK: ld.global.nc.f32 + )"); +} + +// Check that reading a buffer produced by a non-parameter HLO also results in +// ld.global.nc, if that buffer isn't modified within the instruction that reads +// it. +TEST_F(GpuLdgTest, LdgForNonParamRead) { + HloComputation::Builder builder(TestName()); + + auto shape = ShapeUtil::MakeShape(F32, {2, 2}); + HloInstruction* param = + builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "x")); + HloInstruction* add = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param, param)); + HloInstruction* square = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kMultiply, add, add)); + builder.AddInstruction(HloInstruction::CreateTuple({add, square})); + std::unique_ptr computation = builder.Build(); + + auto hlo_module = CreateNewModule(); + hlo_module->AddEntryComputation(std::move(computation)); + + CompileAndVerifyPtx(std::move(hlo_module), R"( + CHECK: { + CHECK-NOT: ld.global.f32 + CHECK: ld.global.nc.f32 + CHECK: } + )"); +} + +// Check that reading a buffer that's modified in-place does not produce +// ld.global.nc. +// +// We do this by creating a reduce that feeds into a sin. We don't currently +// fuse sin into reduce, and the sin is elementwise, so it reuses its input +// buffer as its output. +// +// It seems like a fair bet that we won't start fusing sin into the output of +// reduce in the foreseeable future. But if that turns out to be wrong, I give +// you, future reader, permission to delete this test. +TEST_F(GpuLdgTest, NoLdgWhenSharingBuffer) { + auto hlo_module = CreateNewModule(); + HloComputation::Builder builder(TestName()); + + HloComputation* reduce_computation; + { + auto embedded_builder = HloComputation::Builder("add"); + auto lhs = embedded_builder.AddInstruction(HloInstruction::CreateParameter( + 0, ShapeUtil::MakeShape(F32, {}), "lhs")); + auto rhs = embedded_builder.AddInstruction(HloInstruction::CreateParameter( + 1, ShapeUtil::MakeShape(F32, {}), "rhs")); + embedded_builder.AddInstruction( + HloInstruction::CreateBinary(lhs->shape(), HloOpcode::kAdd, lhs, rhs)); + reduce_computation = + hlo_module->AddEmbeddedComputation(embedded_builder.Build()); + } + + auto param_shape = ShapeUtil::MakeShape(F32, {2, 2}); + auto reduce_shape = ShapeUtil::MakeShape(F32, {2}); + HloInstruction* param = builder.AddInstruction( + HloInstruction::CreateParameter(0, param_shape, "x")); + HloInstruction* reduce = builder.AddInstruction(HloInstruction::CreateReduce( + reduce_shape, + builder.AddInstruction(HloInstruction::CreateBinary( + param_shape, HloOpcode::kAdd, param, param)), + builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))), + {0}, reduce_computation)); + builder.AddInstruction( + HloInstruction::CreateUnary(reduce_shape, HloOpcode::kSin, reduce)); + + std::unique_ptr computation = builder.Build(); + hlo_module->AddEntryComputation(std::move(computation)); + + CompileAndVerifyPtx(std::move(hlo_module), R"( + CHECK-LABEL: .entry sin + CHECK: { + CHECK-NOT: ld.global.nc.f32 + CHECK: ld.global.f32 + CHECK: } + )"); +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_noalias_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_noalias_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..c42e5704a4d2e611a203293e60a86ba4104bca46 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_noalias_test.cc @@ -0,0 +1,68 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include + +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { + +class GpuNoAliasTest : public GpuCodegenTest {}; + +TEST_F(GpuNoAliasTest, Concat) { + HloComputation::Builder builder(TestName()); + + auto param_shape = ShapeUtil::MakeShape(F32, {2, 2}); + HloInstruction* param_x = builder.AddInstruction( + HloInstruction::CreateParameter(0, param_shape, "x")); + HloInstruction* param_y = builder.AddInstruction( + HloInstruction::CreateParameter(1, param_shape, "y")); + HloInstruction* concat = + builder.AddInstruction(HloInstruction::CreateConcatenate( + ShapeUtil::MakeShape(F32, {2, 4}), {param_x, param_y}, 1)); + builder.AddInstruction(HloInstruction::CreateConcatenate( + ShapeUtil::MakeShape(F32, {2, 6}), {concat, param_x}, 1)); + + std::unique_ptr computation = builder.Build(); + + auto hlo_module = CreateNewModule(); + hlo_module->AddEntryComputation(std::move(computation)); + + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK: %[[x_gep:.*]] = getelementptr inbounds [2 x [2 x float]], [2 x [2 x float]]* %x{{.*}}, i32 0 +; CHECK: load float, float* %[[x_gep]], {{.*}}, !noalias ![[param_noalias:.*]] +; CHECK: %[[y_gep:.*]] = getelementptr inbounds [2 x [2 x float]], [2 x [2 x float]]* %y{{.*}}, i32 0 +; CHECK: load float, float* %[[y_gep]], {{.*}}, !noalias ![[param_noalias]] +; CHECK: %[[result_ptr:.*]] = bitcast [2 x [6 x float]]* %fusion{{.*}} to float* +; CHECK: %[[result_gep:.*]] = getelementptr inbounds float, float* %[[result_ptr]] +; CHECK: store float {{.*}}, float* %[[result_gep]], !alias.scope ![[param_noalias]] +; CHECK: ![[param_noalias]] = !{![[retval_buffer:.*]]} + )", + /*match_optimized_ir=*/false); +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_unrolling_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_unrolling_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..962293630683fcbbce3941f622061a2ff0f02dda --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_unrolling_test.cc @@ -0,0 +1,185 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include + +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/hlo_module_config.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { +namespace { + +class GpuUnrollingTest : public GpuCodegenTest {}; + +const char *const kAddModule = R"( + HloModule test_module + + fused_computation { + p0.param_0 = f32[2,2]{1,0} parameter(0) + p1.param_1 = f32[2,2]{1,0} parameter(1) + ROOT add = f32[2,2] add(p0.param_0, p1.param_1) + } + + ENTRY BroadcastIntoAdd { + p0 = f32[2,2]{1,0} parameter(0) + p1 = f32[2,2]{1,0} parameter(1) + ROOT fusion = f32[2,2]{1,0} fusion(p0, p1), kind=kLoop, + calls=fused_computation + })"; + +TEST_F(GpuUnrollingTest, DoNotUnroll) { + HloModuleConfig config; + auto debug_options = HloTestBase::GetDebugOptionsForTest(); + debug_options.set_xla_gpu_max_kernel_unroll_factor(1); + config.set_debug_options(debug_options); + auto hlo_module = ParseHloString(kAddModule, config).ValueOrDie(); + + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: @fusion +; CHECK: fadd +; CHECK-NOT: fadd +; CHECK: } + )", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuUnrollingTest, UnrollFourTimes) { + HloModuleConfig config; + auto debug_options = HloTestBase::GetDebugOptionsForTest(); + // We request a factor of 8, but the computation works on 4 elements, limiting + // the maximum unroll factor. + debug_options.set_xla_gpu_max_kernel_unroll_factor(8); + config.set_debug_options(debug_options); + auto hlo_module = ParseHloString(kAddModule, config).ValueOrDie(); + + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: @fusion +; CHECK: fadd +; CHECK: fadd +; CHECK: fadd +; CHECK: fadd +; CHECK-NOT: fadd +; CHECK: } + )", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuUnrollingTest, UnrollDefaultTimes) { + // The default unrolling factor is 4. + HloModuleConfig config; + config.set_debug_options(legacy_flags::GetDebugOptionsFromFlags()); + auto hlo_module = ParseHloString(kAddModule, config).ValueOrDie(); + + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: @fusion +; CHECK: load <4 x float> +; CHECK: fadd +; CHECK: fadd +; CHECK: fadd +; CHECK: fadd +; CHECK-NOT: fadd +; CHECK: store <4 x float> +; CHECK: } + )", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuUnrollingTest, UnrollUnfusedAdd) { + HloModuleConfig config; + auto debug_options = HloTestBase::GetDebugOptionsForTest(); + debug_options.set_xla_gpu_max_kernel_unroll_factor(4); + config.set_debug_options(debug_options); + + const char *const kUnfusedAddModule = R"( + HloModule test_module + + ENTRY AddFunc { + p0 = f32[2,2]{1,0} parameter(0) + p1 = f32[2,2]{1,0} parameter(1) + ROOT add = f32[2,2]{1,0} add(p0, p1) + })"; + auto hlo_module = ParseHloString(kUnfusedAddModule, config).ValueOrDie(); + + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: @add +; CHECK: load <4 x float> +; CHECK: fadd +; CHECK: fadd +; CHECK: fadd +; CHECK: fadd +; CHECK-NOT: fadd +; CHECK: store <4 x float> +; CHECK: } + )", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuUnrollingTest, UnrollMultiOutputFusion) { + HloModuleConfig config; + auto debug_options = HloTestBase::GetDebugOptionsForTest(); + debug_options.set_xla_gpu_max_kernel_unroll_factor(2); + config.set_debug_options(debug_options); + + const char *const kMultiOutputFusionModule = R"( + HloModule test_module + + fused_computation { + p0.param_0 = f32[2,2]{1,0} parameter(0) + p1.param_1 = f32[2,2]{1,0} parameter(1) + add = f32[2,2]{1,0} add(p0.param_0, p1.param_1) + mul = f32[2,2]{1,0} multiply(p0.param_0, p1.param_1) + ROOT tuple = (f32[2,2]{1,0}, f32[2,2]{1,0}) tuple(add, mul) + } + + ENTRY BroadcastIntoAdd { + p0 = f32[2,2]{1,0} parameter(0) + p1 = f32[2,2]{1,0} parameter(1) + ROOT fusion = (f32[2,2]{1,0}, f32[2,2]{1,0}) fusion(p0, p1), kind=kLoop, + calls=fused_computation + })"; + auto hlo_module = + ParseHloString(kMultiOutputFusionModule, config).ValueOrDie(); + + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: @fusion +; CHECK: load <2 x float> +; CHECK: load <2 x float> +; CHECK-NOT: load <2 x float> +; CHECK: fadd +; CHECK: fmul +; CHECK: fadd +; CHECK: fmul +; CHECK: store <2 x float> +; CHECK: store <2 x float> +; CHECK-NOT: store <2 x float> +; CHECK-NOT: fadd +; CHECK-NOT: fmul +; CHECK: } + )", + /*match_optimized_ir=*/true); +} + +} // namespace +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/infeed_test.cc b/tensorflow/compiler/xla/service/gpu/tests/infeed_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..9072b30317d253fd6d50e9d98949cad4eaebfe7b --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/infeed_test.cc @@ -0,0 +1,121 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include + +#include "tensorflow/compiler/xla/client/global_data.h" +#include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/test_helpers.h" +#include "tensorflow/compiler/xla/tests/client_library_test_base.h" +#include "tensorflow/compiler/xla/tests/literal_test_util.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/math/math_util.h" +#include "tensorflow/core/platform/env.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { +namespace { + +class InfeedTest : public ClientLibraryTestBase { + protected: + // Transfers the given literal to the infeed interface of the device, and + // check if the returned data from Infeed HLO is same as the literal. + void TestInfeedRoundTrip(const Literal& literal) { + // TODO(b/30481585) Explicitly reset the Infeed state so that the + // test is not affected by the state from the previous tests. + ASSERT_IS_OK(client_->TransferToInfeed(literal)); + XlaBuilder builder(TestName()); + Infeed(&builder, literal.shape()); + if (ShapeUtil::IsTuple(literal.shape())) { + // TODO(b/30609564): Use ComputeAndCompareLiteral instead. + ComputeAndCompareTuple(&builder, literal, {}); + } else { + ComputeAndCompareLiteral(&builder, literal, {}); + } + } +}; + +TEST_F(InfeedTest, SingleInfeedR0Bool) { + TestInfeedRoundTrip(*LiteralUtil::CreateR0(true)); +} + +TEST_F(InfeedTest, SingleInfeedR1U32) { + TestInfeedRoundTrip(*LiteralUtil::CreateR1({1, 2, 3})); +} + +TEST_F(InfeedTest, SingleInfeedR2F32) { + TestInfeedRoundTrip(*LiteralUtil::CreateR2F32Linspace(0.0, 1.0, 128, 64)); +} + +TEST_F(InfeedTest, SingleInfeedR3F32) { + TestInfeedRoundTrip( + *LiteralUtil::CreateR3({{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, + {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}})); +} + +TEST_F(InfeedTest, SingleInfeedR3F32DifferentLayout) { + const Layout r3_dim0minor = LayoutUtil::MakeLayout({0, 1, 2}); + const Layout r3_dim0major = LayoutUtil::MakeLayout({2, 1, 0}); + + TestInfeedRoundTrip(*LiteralUtil::CreateR3WithLayout( + {{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, + {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}}, + r3_dim0minor)); + + TestInfeedRoundTrip(*LiteralUtil::CreateR3WithLayout( + {{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, + {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}}, + r3_dim0major)); +} + +TEST_F(InfeedTest, SingleInfeedR4S32) { + TestInfeedRoundTrip(*LiteralUtil::CreateR4( + {{{{1, -2}, {-4, 5}, {6, 7}}, {{8, 9}, {10, 11}, {12, 13}}}, + {{{10, 3}, {7, -2}, {3, 6}}, {{2, 5}, {-11, 5}, {-2, -5}}}})); +} + +// Tests that a large infeed can be handled. +TEST_F(InfeedTest, LargeInfeed) { + Array4D array(80, 100, 8, 128); + array.FillIota(1.0f); + TestInfeedRoundTrip(*LiteralUtil::CreateR4FromArray4D(array)); +} + +TEST_F(InfeedTest, SingleInfeedTuple) { + TestInfeedRoundTrip( + *LiteralUtil::MakeTuple({LiteralUtil::CreateR1({1, 2, 3}).get(), + LiteralUtil::CreateR0(false).get()})); +} + +TEST_F(InfeedTest, SingleInfeedEmptyTuple) { + TestInfeedRoundTrip(*LiteralUtil::MakeTuple({})); +} + +// Tests that a large tuple infeed can be handled. +TEST_F(InfeedTest, SingleInfeedLargeTuple) { + Array4D array(40, 100, 8, 128); + array.FillIota(1.0f); + TestInfeedRoundTrip(*LiteralUtil::MakeTuple( + {LiteralUtil::CreateR4FromArray4D(array).get(), + LiteralUtil::CreateR0(5).get()})); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/thunk.cc b/tensorflow/compiler/xla/service/gpu/thunk.cc new file mode 100644 index 0000000000000000000000000000000000000000..c78605cebbc671272b8df9faf0e0cc54be2f5b1c --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/thunk.cc @@ -0,0 +1,59 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/gpu/thunk.h" + +namespace xla { +namespace gpu { + +std::ostream& operator<<(std::ostream& os, Thunk::Kind kind) { + switch (kind) { + case Thunk::kConditional: + return os << "kConditional"; + case Thunk::kConvolution: + return os << "kConvolution"; + case Thunk::kCopy: + return os << "kCopy"; + case Thunk::kCudnnBatchNormBackward: + return os << "kCudnnBatchNormBackward"; + case Thunk::kCudnnBatchNormForwardInference: + return os << "kCudnnBatchNormForwardInference"; + case Thunk::kCudnnBatchNormForwardTraining: + return os << "kCudnnBatchNormForwardTraining"; + case Thunk::kFft: + return os << "kFft"; + case Thunk::kGemm: + return os << "kGemm"; + case Thunk::kInfeed: + return os << "kInfeed"; + case Thunk::kKernel: + return os << "kKernel"; + case Thunk::kMemset32BitValue: + return os << "kMemset32BitValue"; + case Thunk::kMemzero: + return os << "kMemzero"; + case Thunk::kOutfeed: + return os << "kOutfeed"; + case Thunk::kSequential: + return os << "kSequential"; + case Thunk::kTuple: + return os << "kTuple"; + case Thunk::kWhile: + return os << "kWhile"; + } +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/thunk.h b/tensorflow/compiler/xla/service/gpu/thunk.h index 14d41033c2c7681e3262c0674be13b1f3aa83aef..4df0bb005b623e5ac79a4dfcb7c5a8a7a400940c 100644 --- a/tensorflow/compiler/xla/service/gpu/thunk.h +++ b/tensorflow/compiler/xla/service/gpu/thunk.h @@ -41,7 +41,7 @@ class GpuExecutable; // This is thread-compatible. class Thunk { public: - enum class Kind { + enum Kind { kConditional, kConvolution, kCopy, @@ -54,6 +54,7 @@ class Thunk { kKernel, kMemset32BitValue, kMemzero, + kOutfeed, kSequential, kTuple, kWhile, @@ -110,6 +111,8 @@ class Thunk { // A sequence of thunks. using ThunkSequence = std::vector>; +std::ostream& operator<<(std::ostream& os, Thunk::Kind kind); + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/while_thunk.cc b/tensorflow/compiler/xla/service/gpu/while_thunk.cc index 5e13f989c2ffb0396efc94a01783ee91725dbd44..d81d87e7dc54cd752000b85f3ec173d66d7195e4 100644 --- a/tensorflow/compiler/xla/service/gpu/while_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/while_thunk.cc @@ -30,10 +30,14 @@ WhileThunk::WhileThunk( const HloInstruction* hlo) : Thunk(Kind::kWhile, hlo), condition_result_buffer_index_(condition_result_buffer_index), + // Pass nullptr as the HloInstruction* to the condition_thunk_sequence_ + // and body_thunk_sequence_ constructors because these SequentialThunks + // are logically "part of" this WhileThunk, and shouldn't be profiled + // separately from it. condition_thunk_sequence_(MakeUnique( - std::move(*condition_thunk_sequence), hlo)), - body_thunk_sequence_( - MakeUnique(std::move(*body_thunk_sequence), hlo)) {} + std::move(*condition_thunk_sequence), nullptr)), + body_thunk_sequence_(MakeUnique( + std::move(*body_thunk_sequence), nullptr)) {} Status WhileThunk::Initialize(const GpuExecutable& executable, se::StreamExecutor* executor) { @@ -53,6 +57,7 @@ Status WhileThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, while (true) { // Invoke thunk sequence for while 'condition' computation. profiler->StartHloComputation(); + VLOG(3) << "Executing condition computation"; TF_RETURN_IF_ERROR(condition_thunk_sequence_->ExecuteOnStream( buffer_allocations, stream, profiler)); profiler->FinishHloComputation(hlo_instruction()->while_condition()); @@ -60,6 +65,7 @@ Status WhileThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, // Copy the result of condition computation and break the loop if 'false'. bool condition_result; stream->ThenMemcpy(&condition_result, condition_result_data, sizeof(bool)); + VLOG(3) << "condition_result = " << condition_result; Status block_status = stream->BlockHostUntilDone(); if (!block_status.ok()) { return InternalError( @@ -74,6 +80,7 @@ Status WhileThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, // We measure the time of one execution of the while body computation. The // while body may be executed more than once, the last measurement "wins". profiler->StartHloComputation(); + VLOG(3) << "Executing body computation"; // Invoke thunk sequence for while 'body' computation, and pass on // 'profiler' to measure the timing of the thunks in 'body_thunk_sequence_'. TF_RETURN_IF_ERROR(body_thunk_sequence_->ExecuteOnStream(buffer_allocations, diff --git a/tensorflow/compiler/xla/service/gpu/while_transformer.cc b/tensorflow/compiler/xla/service/gpu/while_transformer.cc index 7749201cbceece216a2db2569936949eb7de5125..c5321df6c466fcb3816fb2aedad65b7c3811cb37 100644 --- a/tensorflow/compiler/xla/service/gpu/while_transformer.cc +++ b/tensorflow/compiler/xla/service/gpu/while_transformer.cc @@ -18,7 +18,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" diff --git a/tensorflow/compiler/xla/service/gpu/while_transformer_test.cc b/tensorflow/compiler/xla/service/gpu/while_transformer_test.cc index 2f290f61bd527e9827472a78256f015e066e44be..dbc8442ed2785a112b674632689256c01282156b 100644 --- a/tensorflow/compiler/xla/service/gpu/while_transformer_test.cc +++ b/tensorflow/compiler/xla/service/gpu/while_transformer_test.cc @@ -42,7 +42,7 @@ class WhileTransformerTest : public HloTestBase { const int64 tuple_index, const int64 limit) { auto builder = HloComputation::Builder(TestName() + ".Condition"); auto limit_const = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(limit))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(limit))); auto loop_state = builder.AddInstruction(HloInstruction::CreateParameter( 0, GetLoopStateShape(tuple_index), "loop_state")); auto induction_variable = @@ -65,8 +65,8 @@ class WhileTransformerTest : public HloTestBase { auto induction_variable = builder.AddInstruction(HloInstruction::CreateGetTupleElement( induction_variable_shape_, loop_state, ind_var_tuple_index)); - auto inc = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(increment))); + auto inc = builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR0(increment))); auto add0 = builder.AddInstruction(HloInstruction::CreateBinary( induction_variable->shape(), HloOpcode::kAdd, induction_variable, inc)); // Update data GTE(data_tuple_index). @@ -89,10 +89,12 @@ class WhileTransformerTest : public HloTestBase { const int64 ind_var_tuple_index, const int64 ind_var_init) { auto builder = HloComputation::Builder(TestName() + ".While"); - auto induction_var_init = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(ind_var_init))); - auto data_init = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}))); + auto induction_var_init = + builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR0(ind_var_init))); + auto data_init = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR1( + {0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}))); auto loop_state_init = ind_var_tuple_index == 0 ? builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/gpu/xfeed_queue.h b/tensorflow/compiler/xla/service/gpu/xfeed_queue.h new file mode 100644 index 0000000000000000000000000000000000000000..dd46ff433ba0ad6bfa3999b96845fdaebe148aca --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/xfeed_queue.h @@ -0,0 +1,90 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_XFEED_QUEUE_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_XFEED_QUEUE_H_ + +#include +#include +#include + +#include "tensorflow/core/platform/mutex.h" +#include "tensorflow/core/platform/notification.h" +#include "tensorflow/core/platform/thread_annotations.h" + +namespace xla { +namespace gpu { + +// TODO(b/30467474) Once GPU outfeed implementation settles, consider +// folding back the cpu and gpu outfeed implementations into a generic +// one if possible. + +// Manages a thread-safe queue of buffers. +template +class XfeedQueue { + public: + // Adds a tree of buffers to the queue. The individual buffers correspond to + // the elements of a tuple and may be nullptr if the buffer is a tuple index + // buffer. + void EnqueueDestination(BufferType buffers) { + tensorflow::mutex_lock l(mu_); + enqueued_buffers_.push_back(std::move(buffers)); + cv_.notify_one(); + } + + // Blocks until the queue is non-empty, then returns the buffer at the head of + // the queue. + BufferType BlockingGetNextDestination() { + bool became_empty; + BufferType current_buffer; + { + tensorflow::mutex_lock l(mu_); + while (enqueued_buffers_.empty()) { + cv_.wait(l); + } + current_buffer = std::move(enqueued_buffers_.front()); + enqueued_buffers_.pop_front(); + became_empty = enqueued_buffers_.empty(); + } + if (became_empty) { + for (const auto& callback : on_empty_callbacks_) { + callback(); + } + } + return current_buffer; + } + + void RegisterOnEmptyCallback(std::function callback) { + on_empty_callbacks_.push_back(std::move(callback)); + } + + private: + tensorflow::mutex mu_; + + // Condition variable that is signaled every time a buffer is enqueued. + tensorflow::condition_variable cv_; + + // The queue of trees of buffers. Buffer* queue contents are not owned. + std::deque enqueued_buffers_ GUARDED_BY(mu_); + + // List of callbacks which will be called when 'enqueued_buffers_' becomes + // empty. + std::vector> on_empty_callbacks_; +}; + +} // namespace gpu +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_XFEED_QUEUE_H_ diff --git a/tensorflow/compiler/xla/service/graphviz_example.cc b/tensorflow/compiler/xla/service/graphviz_example.cc index acf661148699dab18916e3065ee647d37fda6208..aa89567ee86e59e197045c0b51eed3b9aa59fef7 100644 --- a/tensorflow/compiler/xla/service/graphviz_example.cc +++ b/tensorflow/compiler/xla/service/graphviz_example.cc @@ -22,6 +22,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" @@ -47,7 +48,7 @@ HloComputation* AddScalarConstantComputation(int64 addend, HloModule* module) { auto x_value = builder.AddInstruction(HloInstruction::CreateParameter( 0, ShapeUtil::MakeShape(F32, {}), "x_value")); auto half = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.5))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.5))); builder.AddInstruction(HloInstruction::CreateBinary( half->shape(), HloOpcode::kAdd, x_value, half)); return module->AddEmbeddedComputation(builder.Build()); @@ -122,7 +123,7 @@ std::unique_ptr MakeBigGraph() { auto rng = builder.AddInstruction( HloInstruction::CreateRng(vshape, RNG_UNIFORM, {param_m, param_m})); auto one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto add_computation = ScalarSumComputation(module.get()); builder.AddInstruction( HloInstruction::CreateReduce(vshape, rng, one, {1}, add_computation)); diff --git a/tensorflow/compiler/xla/service/heap_simulator_test.cc b/tensorflow/compiler/xla/service/heap_simulator_test.cc index 3849b565e3136924b2d2b1929353885f85b1a043..b41dc66fe9f5e869a114be96b7cc01fc1a3d59da 100644 --- a/tensorflow/compiler/xla/service/heap_simulator_test.cc +++ b/tensorflow/compiler/xla/service/heap_simulator_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/buffer_value.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -239,7 +239,7 @@ class HeapSimulatorTest : public HloTestBase { TEST_F(HeapSimulatorTest, ScalarConstant) { auto builder = HloComputation::Builder(TestName()); auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); // Constants aren't assigned. See b/32248867 HeapSimulatorTracker tracker(TestName(), builder.Build(), {const0}); @@ -674,7 +674,7 @@ class HeapAlgorithmTestBase : public ::testing::Test { const BufferValue* DummyBufferValue() { const BufferValue::Id id = buffers_.size(); auto const0 = builder_.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); buffers_.emplace_back(MakeUnique(id, const0, ShapeIndex{})); return buffers_.back().get(); } diff --git a/tensorflow/compiler/xla/service/hlo.proto b/tensorflow/compiler/xla/service/hlo.proto index d2417910606fdd13223076d33ff1bda1dd291d98..63a8a813cddf304e60fa9b4bbf709eca2d7c2cae 100644 --- a/tensorflow/compiler/xla/service/hlo.proto +++ b/tensorflow/compiler/xla/service/hlo.proto @@ -155,6 +155,11 @@ message HloInstructionProto { repeated int64 replica_group_ids = 44; int64 all_reduce_id = 45; string cross_replica_sum_barrier = 46; + + // Whether this Send/Recv instruction transfers data to/from the host. Only + // present for Send and Recv instructions and their SendDone and RecvDone + // partners. + bool is_host_transfer = 47; } // Serialization of HloComputation. @@ -239,8 +244,9 @@ message BufferAllocationProto { int64 index = 1; int64 size = 2; bool is_thread_local = 3; - bool is_reusable = 4; + bool is_tuple = 11; bool is_entry_computation_parameter = 5; + bool is_constant = 12; int64 parameter_number = 6; repeated int64 parameter_shape_index = 10; bool maybe_live_out = 7; diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.h b/tensorflow/compiler/xla/service/hlo_alias_analysis.h index afb0c20f0cdf3eb92f72ab8bc368b4b8d723459e..1fea544730c27efdaa260f55ea81c163165f7ed5 100644 --- a/tensorflow/compiler/xla/service/hlo_alias_analysis.h +++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.h @@ -42,7 +42,7 @@ class HloAliasAnalysis { static StatusOr> Run( HloModule* module, const HloDataflowAnalysis::FusionCanShareBufferFunction& - fusion_can_share_buffer = nullptr); + fusion_can_share_buffer); string ToString() const; diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc index a59bf1750c06c091187b211c8530be126cf5e524..da94ab5346e5628b4a603b3ac2d84071904d1e65 100644 --- a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc +++ b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/flatten_call_graph.h" #include "tensorflow/compiler/xla/service/hlo_graph_dumper.h" #include "tensorflow/compiler/xla/service/hlo_matchers.h" @@ -47,7 +47,9 @@ class HloAliasAnalysisTest : public HloTestBase { // reference to the generated analysis stored in analysis_. HloAliasAnalysis& RunAnalysis() { hlo_graph_dumper::MaybeDumpHloModule(*module_, "Before alias analysis"); - analysis_ = HloAliasAnalysis::Run(module_.get()).ConsumeValueOrDie(); + analysis_ = HloAliasAnalysis::Run(module_.get(), + /*fusion_can_share_buffer=*/nullptr) + .ConsumeValueOrDie(); return *analysis_; } @@ -116,9 +118,9 @@ TEST_F(HloAliasAnalysisTest, BinaryOperation) { // Test the analysis on a single binary operation (Add). auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto add = builder.AddInstruction(HloInstruction::CreateBinary( scalar_shape_, HloOpcode::kAdd, constant1, constant2)); module_->AddEntryComputation(builder.Build()); @@ -228,9 +230,9 @@ TEST_F(HloAliasAnalysisTest, SingleCall) { auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto call = builder.AddInstruction(HloInstruction::CreateCall( scalar_shape_, {constant1, constant2}, called_computation)); module_->AddEntryComputation(builder.Build()); @@ -267,9 +269,9 @@ TEST_F(HloAliasAnalysisTest, ComputationCalledTwice) { auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto call1 = builder.AddInstruction(HloInstruction::CreateCall( scalar_shape_, {constant1, constant2}, called_computation)); auto call2 = builder.AddInstruction(HloInstruction::CreateCall( @@ -346,15 +348,15 @@ TEST_F(HloAliasAnalysisTest, SingleWhile) { auto cond_param = cond_builder.AddInstruction( HloInstruction::CreateParameter(0, tuple_shape, "param")); cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloComputation* condition = module_->AddEmbeddedComputation(cond_builder.Build()); auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto xla_while = builder.AddInstruction( @@ -439,15 +441,15 @@ TEST_F(HloAliasAnalysisTest, SequentialWhiles) { cond_builder.AddInstruction( HloInstruction::CreateParameter(0, tuple_shape, "param")); cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloComputation* condition = module_->AddEmbeddedComputation(cond_builder.Build()); auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto xla_while0 = builder.AddInstruction( @@ -498,7 +500,7 @@ TEST_F(HloAliasAnalysisTest, NestedWhiles) { cond_builder.AddInstruction( HloInstruction::CreateParameter(0, tuple_shape, "param")); cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); return cond_builder.Build(); }; // Build separate condition computations so the call graph is flat. The @@ -543,9 +545,9 @@ TEST_F(HloAliasAnalysisTest, NestedWhiles) { auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto entry_while = builder.AddInstruction( @@ -608,17 +610,17 @@ TEST_F(HloAliasAnalysisTest, SwizzlingWhile) { cond_builder.AddInstruction( HloInstruction::CreateParameter(0, tuple_shape, "param")); auto cond_constant = cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloComputation* condition = module_->AddEmbeddedComputation(cond_builder.Build()); auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto constant3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(3.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(3.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2, constant3})); auto xla_while = builder.AddInstruction( @@ -657,15 +659,15 @@ TEST_F(HloAliasAnalysisTest, TupleSelect) { // Test a kTupleSelect. Non-top-level element flow through the instruction. auto builder = HloComputation::Builder(TestName()); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto constant3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(3.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(3.0))); auto constant4 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(4.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(4.0))); auto tuple1 = builder.AddInstruction(HloInstruction::CreateTuple({constant1})); auto tuple2 = @@ -753,16 +755,16 @@ TEST_F(HloAliasAnalysisTest, TupleSelectToWhile) { auto cond_param = cond_builder.AddInstruction( HloInstruction::CreateParameter(0, tuple_shape, "param")); cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloComputation* condition = module_->AddEmbeddedComputation(cond_builder.Build()); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple1 = builder.AddInstruction(HloInstruction::CreateTuple({constant1})); auto tuple2 = @@ -805,7 +807,7 @@ TEST_F(HloAliasAnalysisTest, Bitcast) { // Bitcasting a value should not produce a new buffer. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto bitcast = builder.AddInstruction(HloInstruction::CreateUnary( scalar_shape_, HloOpcode::kBitcast, constant)); @@ -824,7 +826,7 @@ TEST_F(HloAliasAnalysisTest, BitcastInterference) { // interference. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto bitcast = builder.AddInstruction(HloInstruction::CreateUnary( scalar_shape_, HloOpcode::kBitcast, constant)); builder.AddInstruction(HloInstruction::CreateTuple({constant, bitcast})); @@ -843,13 +845,13 @@ TEST_F(HloAliasAnalysisTest, WhileInterference) { // the other use of the init. auto builder = HloComputation::Builder(TestName()); auto init = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto cond_builder = HloComputation::Builder("condition"); auto cond_param = cond_builder.AddInstruction( HloInstruction::CreateParameter(0, init->shape(), "param")); auto cond_root = cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloComputation* condition = module_->AddEmbeddedComputation(cond_builder.Build()); diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc index e36bef60a3c395af82cd93ef012de7eaf700ed4f..441288da1a6859a3f393a298ee02eb4b435e42e0 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.cc +++ b/tensorflow/compiler/xla/service/hlo_computation.cc @@ -528,8 +528,10 @@ HloInstruction* HloComputation::CreateFusionInstruction( } StatusOr HloComputation::DeepCopyHelper( - HloInstruction* instruction, const ShapeTree* indices_to_copy, - ShapeTree* copies_added, ShapeIndex* index) { + HloInstruction* instruction, ShapeIndex* index, + const std::function< + HloInstruction*(HloInstruction* leaf, const ShapeIndex& leaf_index, + HloComputation* computation)>& copy_leaf) { if (ShapeUtil::IsTuple(instruction->shape())) { std::vector elements; for (int64 i = 0; i < ShapeUtil::TupleElementCount(instruction->shape()); @@ -540,9 +542,8 @@ StatusOr HloComputation::DeepCopyHelper( instruction, i)); index->push_back(i); - TF_ASSIGN_OR_RETURN( - HloInstruction * element, - DeepCopyHelper(gte, indices_to_copy, copies_added, index)); + TF_ASSIGN_OR_RETURN(HloInstruction * element, + DeepCopyHelper(gte, index, copy_leaf)); elements.push_back(element); index->pop_back(); } @@ -556,19 +557,7 @@ StatusOr HloComputation::DeepCopyHelper( // Array shape. TF_RET_CHECK(ShapeUtil::IsArray(instruction->shape())); - if (indices_to_copy == nullptr || indices_to_copy->element(*index)) { - // Use kCopy to copy array elements - HloInstruction* copy = AddInstruction(HloInstruction::CreateUnary( - instruction->shape(), HloOpcode::kCopy, instruction)); - if (copies_added != nullptr) { - *copies_added->mutable_element(*index) = copy; - } - return copy; - } else { - // Elements which are not to be copied are passed through - // transparently. - return instruction; - } + return copy_leaf(instruction, *index, this); } StatusOr HloComputation::DeepCopyInstruction( @@ -590,7 +579,36 @@ StatusOr HloComputation::DeepCopyInstruction( } ShapeIndex index; - return DeepCopyHelper(instruction, indices_to_copy, copies_added, &index); + auto copy_leaf = [indices_to_copy, copies_added]( + HloInstruction* leaf, const ShapeIndex& leaf_index, + HloComputation* computation) { + if (indices_to_copy == nullptr || indices_to_copy->element(leaf_index)) { + HloInstruction* copy = computation->AddInstruction( + HloInstruction::CreateUnary(leaf->shape(), HloOpcode::kCopy, leaf)); + if (copies_added != nullptr) { + *copies_added->mutable_element(leaf_index) = copy; + } + return copy; + } + // Elements which are not to be copied are passed through + // transparently. + return leaf; + }; + return DeepCopyHelper(instruction, &index, copy_leaf); +} + +StatusOr HloComputation::DeepCopyInstructionWithCustomCopier( + HloInstruction* instruction, + const std::function< + HloInstruction*(HloInstruction* leaf, const ShapeIndex& leaf_index, + HloComputation* computation)>& copy_leaf) { + if (instruction->parent() != this) { + return FailedPrecondition( + "Can't deep copy instruction %s: instruction is not in computation %s", + instruction->name().c_str(), name().c_str()); + } + ShapeIndex index; + return DeepCopyHelper(instruction, &index, copy_leaf); } ProgramShape HloComputation::ComputeProgramShape() const { @@ -663,7 +681,7 @@ std::unique_ptr HloComputation::ComputeReachability() inputs.assign(hlo->operands().begin(), hlo->operands().end()); inputs.insert(inputs.end(), hlo->control_predecessors().begin(), hlo->control_predecessors().end()); - result->SetReachabilityToUnion(inputs, hlo); + result->FastSetReachabilityToUnion(inputs, hlo); } return result; } @@ -880,4 +898,13 @@ void HloComputation::UniquifyName(NameUniquer* name_uniquer) { name_ = name_uniquer->GetUniqueName(name_); } +HloInstruction* HloComputation::GetInstructionWithName( + tensorflow::StringPiece name) { + auto instructions_in_computation = instructions(); + auto it = c_find_if(instructions_in_computation, [&](HloInstruction* instr) { + return instr->name() == name; + }); + return it == instructions_in_computation.end() ? nullptr : *it; +} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_computation.h b/tensorflow/compiler/xla/service/hlo_computation.h index c1c3e79ebc789eff0873515c5fffd11089b92043..49ed65910f519810740b89760ad815f287e59a91 100644 --- a/tensorflow/compiler/xla/service/hlo_computation.h +++ b/tensorflow/compiler/xla/service/hlo_computation.h @@ -16,6 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_COMPUTATION_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_COMPUTATION_H_ +#include #include #include #include @@ -254,6 +255,14 @@ class HloComputation { const ShapeTree* indices_to_copy = nullptr, ShapeTree* copies_added = nullptr); + // As above, but uses a custom function to copy the leaf nodes, which could + // create alternative HLOs other than kCopy, or even pass-throughs. + StatusOr DeepCopyInstructionWithCustomCopier( + HloInstruction* instruction, + const std::function< + HloInstruction*(HloInstruction* leaf, const ShapeIndex& leaf_index, + HloComputation* computation)>& copy_leaf); + // Computes and returns the ProgramShape of this computation (shape of // parameters and result with layout). ProgramShape ComputeProgramShape() const; @@ -356,6 +365,10 @@ class HloComputation { unique_id_ = id; } + // Returns the instruction in this computation that has name `name`. Returns + // null if there is no such computation. + HloInstruction* GetInstructionWithName(tensorflow::StringPiece name); + int64 unique_id() const { return unique_id_; } private: @@ -378,8 +391,10 @@ class HloComputation { // Internal helper for recursive copying of an instruction. Creates and // returns a deep copy of the given instruction. StatusOr DeepCopyHelper( - HloInstruction* instruction, const ShapeTree* indices_to_copy, - ShapeTree* copies_added, ShapeIndex* index); + HloInstruction* instruction, ShapeIndex* index, + const std::function< + HloInstruction*(HloInstruction* leaf, const ShapeIndex& leaf_index, + HloComputation* computation)>& copy_leaf); // Internal helper to collect unreachable roots. std::vector CollectUnreachableRoots() const; diff --git a/tensorflow/compiler/xla/service/hlo_computation_test.cc b/tensorflow/compiler/xla/service/hlo_computation_test.cc index a8f3f0e9c2dca8fb97ebc8f8c9dd80fcf7f4de4a..e4c547033139185d5dd4ef37db2d22a6431c1102 100644 --- a/tensorflow/compiler/xla/service/hlo_computation_test.cc +++ b/tensorflow/compiler/xla/service/hlo_computation_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_matchers.h" @@ -118,7 +118,7 @@ TEST_F(HloComputationTest, PostOrderSingleton) { // Test GetInstructionPostOrder for a computation with one instruction. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto module = CreateNewModule(); auto computation = module->AddEntryComputation(builder.Build()); EXPECT_THAT(computation->MakeInstructionPostOrder(), ElementsAre(constant)); @@ -129,7 +129,7 @@ TEST_F(HloComputationTest, PostOrderSimple) { // instructions. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto negate1 = builder.AddInstruction( HloInstruction::CreateUnary(r0f32_, HloOpcode::kNegate, constant)); auto negate2 = builder.AddInstruction( @@ -144,7 +144,7 @@ TEST_F(HloComputationTest, PostOrderTrace) { // Test GetInstructionPostOrder for a computation with a trace instruction. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto negate1 = builder.AddInstruction( HloInstruction::CreateUnary(r0f32_, HloOpcode::kNegate, constant)); auto trace = @@ -163,13 +163,13 @@ TEST_F(HloComputationTest, PostOrderDisconnectedInstructions) { // which are not connected. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto constant3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto constant4 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto module = CreateNewModule(); auto computation = module->AddEntryComputation(builder.Build()); EXPECT_THAT(computation->MakeInstructionPostOrder(), @@ -181,11 +181,11 @@ TEST_F(HloComputationTest, PostOrderWithMultipleRoots) { // which are not connected. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto constant3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto add1 = builder.AddInstruction(HloInstruction::CreateBinary( r0f32_, HloOpcode::kAdd, constant1, constant2)); auto add2 = builder.AddInstruction(HloInstruction::CreateBinary( @@ -205,11 +205,11 @@ TEST_F(HloComputationTest, VisitWithMultipleRoots) { // computation has multiple roots (dead code). auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto constant3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); // Add three disconnected add expressions. builder.AddInstruction(HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, constant1, constant2)); @@ -256,7 +256,7 @@ TEST_F(HloComputationTest, DeepCopyArray) { // Test that DeepCopyInstruction properly copies an array. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.0, 2.0, 3.0}))); + LiteralUtil::CreateR1({1.0, 2.0, 3.0}))); auto module = CreateNewModule(); auto computation = module->AddEntryComputation(builder.Build()); auto copy = computation->DeepCopyInstruction(constant).ValueOrDie(); @@ -268,9 +268,9 @@ TEST_F(HloComputationTest, DeepCopyTuple) { // Test that DeepCopyInstruction properly copies a tuple. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.0, 2.0, 3.0}))); + LiteralUtil::CreateR1({1.0, 2.0, 3.0}))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); @@ -289,7 +289,7 @@ TEST_F(HloComputationTest, DeepCopyArrayAtIndices) { // copy are specified. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.0, 2.0, 3.0}))); + LiteralUtil::CreateR1({1.0, 2.0, 3.0}))); auto computation = builder.Build(); { @@ -314,9 +314,9 @@ TEST_F(HloComputationTest, DeepCopyTupleAtIndices) { // specified by the given indices. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.0, 2.0, 3.0}))); + LiteralUtil::CreateR1({1.0, 2.0, 3.0}))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto computation = builder.Build(); @@ -375,7 +375,7 @@ TEST_F(HloComputationTest, DeepCopyToken) { // Test that DeepCopyInstruction properly handles tokens which should not be // copied. auto builder = HloComputation::Builder(TestName()); - auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = builder.AddInstruction(HloInstruction::CreateToken()); auto module = CreateNewModule(); auto computation = module->AddEntryComputation(builder.Build()); auto copy = computation->DeepCopyInstruction(token).ValueOrDie(); @@ -388,9 +388,9 @@ TEST_F(HloComputationTest, DeepCopyTokenTuple) { // Test that DeepCopyInstruction properly handles tokens which should not be // copied. auto builder = HloComputation::Builder(TestName()); - auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = builder.AddInstruction(HloInstruction::CreateToken()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0))); auto tuple = builder.AddInstruction(HloInstruction::CreateTuple({token, constant})); auto module = CreateNewModule(); @@ -407,7 +407,7 @@ TEST_F(HloComputationTest, CycleDetection) { // Test whether the visitor can detect cycles in the graph. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto negate = builder.AddInstruction( HloInstruction::CreateUnary(r0f32_, HloOpcode::kNegate, constant)); auto add = builder.AddInstruction( @@ -433,7 +433,7 @@ TEST_F(HloComputationTest, RemoveInstructionWithDuplicateOperand) { // twice. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto dead_negate = builder.AddInstruction( HloInstruction::CreateUnary(r0f32_, HloOpcode::kNegate, constant)); auto dead_add = builder.AddInstruction(HloInstruction::CreateBinary( @@ -456,9 +456,9 @@ TEST_F(HloComputationTest, RemoveInstructionWithDuplicateOperand) { TEST_F(HloComputationTest, CloneWithControlDependency) { auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0f))); auto add = builder.AddInstruction(HloInstruction::CreateBinary( r0f32_, HloOpcode::kAdd, constant1, constant2)); @@ -502,9 +502,9 @@ TEST_F(HloComputationTest, Reachability) { // There is a control dependency from 'add' to 'exp'. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0f))); auto add = builder.AddInstruction(HloInstruction::CreateBinary( r0f32_, HloOpcode::kAdd, constant1, constant2)); auto negate = builder.AddInstruction( @@ -607,13 +607,14 @@ TEST_F(HloComputationTest, Stringification) { auto* computation = module->AddEntryComputation(builder.Build()); auto options = HloPrintOptions().set_print_metadata(false); - EXPECT_EQ(computation->ToString(options), - R"(%TransposeDot (x: f32[5,10], y: f32[20,10]) -> f32[5,20] { + const string expected_computation = + R"(%TransposeDot (x: f32[5,10], y: f32[20,10]) -> f32[5,20] { %x = f32[5,10]{1,0} parameter(0) %y = f32[20,10]{1,0} parameter(1) %transpose = f32[10,20]{1,0} transpose(f32[20,10]{1,0} %y), dimensions={1,0} ROOT %dot = f32[5,20]{1,0} dot(f32[5,10]{1,0} %x, f32[10,20]{1,0} %transpose), lhs_contracting_dims={1}, rhs_contracting_dims={0} -})"); +})"; + EXPECT_EQ(computation->ToString(options), expected_computation); } TEST_F(HloComputationTest, StringificationIndent) { @@ -639,13 +640,14 @@ TEST_F(HloComputationTest, StringificationIndent) { auto options = HloPrintOptions().set_print_metadata(false).set_indent_amount(2); - EXPECT_EQ(computation->ToString(options), - R"( %TransposeDot (x: f32[5,10], y: f32[20,10]) -> f32[5,20] { + const string expected_computation = + R"( %TransposeDot (x: f32[5,10], y: f32[20,10]) -> f32[5,20] { %x = f32[5,10]{1,0} parameter(0) %y = f32[20,10]{1,0} parameter(1) %transpose = f32[10,20]{1,0} transpose(f32[20,10]{1,0} %y), dimensions={1,0} ROOT %dot = f32[5,20]{1,0} dot(f32[5,10]{1,0} %x, f32[10,20]{1,0} %transpose), lhs_contracting_dims={1}, rhs_contracting_dims={0} - })"); + })"; + EXPECT_EQ(computation->ToString(options), expected_computation); } TEST_F(HloComputationTest, StringificationCanonical) { @@ -670,21 +672,23 @@ TEST_F(HloComputationTest, StringificationCanonical) { auto* computation = module->AddEntryComputation(builder.Build()); auto options = HloPrintOptions().set_print_metadata(false); - EXPECT_EQ(computation->ToString(options), - R"(%TransposeDot (x: f32[5,10], y: f32[20,10]) -> f32[5,20] { + const string expected_computation1 = + R"(%TransposeDot (x: f32[5,10], y: f32[20,10]) -> f32[5,20] { %x = f32[5,10]{1,0} parameter(0) %y = f32[20,10]{1,0} parameter(1) %transpose = f32[10,20]{1,0} transpose(f32[20,10]{1,0} %y), dimensions={1,0} ROOT %dot = f32[5,20]{1,0} dot(f32[5,10]{1,0} %x, f32[10,20]{1,0} %transpose), lhs_contracting_dims={1}, rhs_contracting_dims={0} -})"); +})"; + EXPECT_EQ(computation->ToString(options), expected_computation1); options = HloPrintOptions().Canonical(); - EXPECT_EQ(computation->ToString(options), R"(TransposeDot { + const string expected_computation2 = R"(TransposeDot { tmp_0 = f32[5,10]{1,0} parameter(0) tmp_1 = f32[20,10]{1,0} parameter(1) tmp_2 = f32[10,20]{1,0} transpose(f32[20,10]{1,0} tmp_1), dimensions={1,0} ROOT tmp_3 = f32[5,20]{1,0} dot(f32[5,10]{1,0} tmp_0, f32[10,20]{1,0} tmp_2), lhs_contracting_dims={1}, rhs_contracting_dims={0} -})"); +})"; + EXPECT_EQ(computation->ToString(options), expected_computation2); } } // namespace diff --git a/tensorflow/compiler/xla/service/hlo_constant_folding.cc b/tensorflow/compiler/xla/service/hlo_constant_folding.cc index 436d103f230e078e62201bff377a5bab0e62f92b..7229031c0c7f8bd374cfb495c7d8c11e9ca8b95e 100644 --- a/tensorflow/compiler/xla/service/hlo_constant_folding.cc +++ b/tensorflow/compiler/xla/service/hlo_constant_folding.cc @@ -21,7 +21,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_evaluator.h" diff --git a/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc b/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc index 5d05ccfc0b223d8749a2577ba1bf96b1ab3e761b..64a42c1efc0c788ae8e66fb72b2d9aecec179082 100644 --- a/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc +++ b/tensorflow/compiler/xla/service/hlo_constant_folding_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_matchers.h" @@ -41,7 +41,7 @@ using HloConstantFoldingTest = HloTestBase; TEST_F(HloConstantFoldingTest, ConvertF32ToS64) { HloComputation::Builder builder(TestName()); HloInstruction* input = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); builder.AddInstruction( HloInstruction::CreateConvert(ShapeUtil::MakeShape(S64, {}), input)); @@ -62,7 +62,7 @@ TEST_F(HloConstantFoldingTest, ConvertF32ToS64) { TEST_F(HloConstantFoldingTest, ConvertS64ToF32) { HloComputation::Builder builder(TestName()); HloInstruction* input = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42))); builder.AddInstruction( HloInstruction::CreateConvert(ShapeUtil::MakeShape(F32, {}), input)); @@ -82,8 +82,8 @@ TEST_F(HloConstantFoldingTest, ConvertS64ToF32) { TEST_F(HloConstantFoldingTest, ConvertF32ArrayToS64Array) { HloComputation::Builder builder(TestName()); - HloInstruction* input = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({42.0f, 19.0f}))); + HloInstruction* input = builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR1({42.0f, 19.0f}))); builder.AddInstruction( HloInstruction::CreateConvert(ShapeUtil::MakeShape(S64, {2}), input)); @@ -120,7 +120,7 @@ TEST_F(HloConstantFoldingTest, Concatenate) { for (auto csize : test_config.concat_sizes) { dimensions[test_config.concat_dimension] = csize; concat_size += csize; - auto literal = Literal::CreateFromDimensions(F32, dimensions); + auto literal = LiteralUtil::CreateFromDimensions(F32, dimensions); HloInstruction* insn = builder.AddInstruction( HloInstruction::CreateConstant(std::move(literal))); operands.push_back(insn); @@ -149,7 +149,7 @@ TEST_F(HloConstantFoldingTest, Slice) { const int64 slice_limits[] = {10, 8, 6, 5, 9}; const int64 slice_strides[] = {1, 1, 1, 1, 1}; TF_ASSERT_OK_AND_ASSIGN(auto literal, - Literal::CreateRandomLiteral( + LiteralUtil::CreateRandomLiteral( ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0)); HloInstruction* literal_instruction = builder.AddInstruction( HloInstruction::CreateConstant(std::move(literal))); @@ -172,7 +172,7 @@ TEST_F(HloConstantFoldingTest, TransposeConstantFold) { HloComputation::Builder builder(TestName()); const int64 dimensions[] = {11, 8, 7, 5, 9}; TF_ASSERT_OK_AND_ASSIGN(auto literal, - Literal::CreateRandomLiteral( + LiteralUtil::CreateRandomLiteral( ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0)); auto literal_clone = literal->Literal::CloneToUnique(); HloInstruction* literal_instruction = builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc index c49cf7f5db5ee9100718fbcd87dc5bdcc175ae5f..1f672502f72f9c658b681383e858995f6e94d2c7 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc @@ -155,6 +155,10 @@ Status HloCostAnalysis::HandleConstant(const HloInstruction*) { return Status::OK(); } +Status HloCostAnalysis::HandleIota(const HloInstruction*) { + return Status::OK(); +} + Status HloCostAnalysis::HandleGetTupleElement(const HloInstruction*) { // GetTupleElement forwards a pointer and does not touch each element in the // output. diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h index 0181138a6dc554438957e8545c66a98d32dd68d5..82d650dc7b2a7fdd7c156d5fadcabd40f5535161 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h @@ -52,6 +52,7 @@ class HloCostAnalysis : public ConstDfsHloVisitor { Status HandleElementwiseUnary(const HloInstruction* hlo) override; Status HandleElementwiseBinary(const HloInstruction* hlo) override; Status HandleConstant(const HloInstruction* constant) override; + Status HandleIota(const HloInstruction* iota) override; Status HandleGetTupleElement( const HloInstruction* get_tuple_element) override; Status HandleSelect(const HloInstruction* hlo) override; diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc index 9fc4c48226fa5307f5e030a612f3957756827e37..2c854eea18642eb7cb081b4fdfe3bc83627e41ae 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc @@ -22,8 +22,8 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/service/local_service.h" #include "tensorflow/compiler/xla/service/service.h" @@ -338,13 +338,13 @@ TEST_F(FusionCostAnalysis, LoopFusion) { // tuple = Tuple({sub, sub, mul, C1}) HloComputation::Builder builder(TestName()); auto c1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2F32Linspace( + HloInstruction::CreateConstant(LiteralUtil::CreateR2F32Linspace( /*from=*/0.0f, /*to=*/1.0f, /*rows=*/2, /*cols=*/2))); auto c2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2F32Linspace( + HloInstruction::CreateConstant(LiteralUtil::CreateR2F32Linspace( /*from=*/1.0f, /*to=*/2.0f, /*rows=*/2, /*cols=*/2))); auto c3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2F32Linspace( + HloInstruction::CreateConstant(LiteralUtil::CreateR2F32Linspace( /*from=*/2.0f, /*to=*/3.0f, /*rows=*/2, /*cols=*/2))); auto add = builder.AddInstruction( HloInstruction::CreateBinary(r2f32, HloOpcode::kAdd, c1, c2)); @@ -391,9 +391,9 @@ TEST_F(FusionCostAnalysis, NoLayout) { HloComputation::Builder builder(TestName()); auto c1 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR4FromArray4D(Array4D(2, 3, 4, 5)))); + LiteralUtil::CreateR4FromArray4D(Array4D(2, 3, 4, 5)))); auto c2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({1, 2, 3}))); auto broadcast = builder.AddInstruction( HloInstruction::CreateBroadcast(shape_without_layout, c2, {1})); diff --git a/tensorflow/compiler/xla/service/hlo_creation_utils.cc b/tensorflow/compiler/xla/service/hlo_creation_utils.cc index 0fb65c845a6d4407c81171f6c1569fee98b1d16d..90d2be118d94d52135820e5b8138fcb06389c684 100644 --- a/tensorflow/compiler/xla/service/hlo_creation_utils.cc +++ b/tensorflow/compiler/xla/service/hlo_creation_utils.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/service/hlo_creation_utils.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/shape_inference.h" @@ -261,9 +262,9 @@ StatusOr PadVectorWithZeros(HloInstruction* operand, padding_config_dim.set_edge_padding_high(zeros_to_append); *padding_config.add_dimensions() = padding_config_dim; - HloInstruction* zero = - computation->AddInstruction(HloInstruction::CreateConstant( - MakeUnique(Literal::Zero(operand->shape().element_type())))); + HloInstruction* zero = computation->AddInstruction( + HloInstruction::CreateConstant(MakeUnique( + LiteralUtil::Zero(operand->shape().element_type())))); return MakePadHlo(operand, zero, padding_config); } @@ -272,7 +273,7 @@ StatusOr BroadcastZeros( ArraySlice broadcast_dimensions) { HloInstruction* zero = computation->AddInstruction(HloInstruction::CreateConstant( - MakeUnique(Literal::Zero(element_type)))); + MakeUnique(LiteralUtil::Zero(element_type)))); return MakeBroadcastHlo(zero, /*broadcast_dimensions=*/{}, /*result_shape_bounds=*/broadcast_dimensions); } diff --git a/tensorflow/compiler/xla/service/hlo_creation_utils_test.cc b/tensorflow/compiler/xla/service/hlo_creation_utils_test.cc index 7e7c4f95fed737f40064224717f409b934e4ff27..60d3e71757d5ce31e025c744e089ff56091d9a43 100644 --- a/tensorflow/compiler/xla/service/hlo_creation_utils_test.cc +++ b/tensorflow/compiler/xla/service/hlo_creation_utils_test.cc @@ -60,8 +60,8 @@ TEST_F(HloCreationUtilsTest, CollapseFirst1Dim) { HloEvaluator evaluator; TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result_literal, evaluator.Evaluate>( - *module, {Literal::CreateR1({3, 4})})); - CHECK_EQ(*result_literal, *Literal::CreateR1({3, 4})); + *module, {LiteralUtil::CreateR1({3, 4})})); + CHECK_EQ(*result_literal, *LiteralUtil::CreateR1({3, 4})); } TEST_F(HloCreationUtilsTest, CollapseFirst2Dims) { @@ -82,10 +82,10 @@ TEST_F(HloCreationUtilsTest, CollapseFirst2Dims) { std::unique_ptr result_literal, evaluator.Evaluate>( *module, - {Literal::CreateR3( + {LiteralUtil::CreateR3( {{{1, 2}, {3, 4}, {5, 6}}, {{-1, -2}, {-3, -4}, {-5, -6}}})})); CHECK_EQ(*result_literal, - *Literal::CreateR2( + *LiteralUtil::CreateR2( {{1, 2}, {3, 4}, {5, 6}, {-1, -2}, {-3, -4}, {-5, -6}})); } @@ -103,10 +103,11 @@ TEST_F(HloCreationUtilsTest, Prepend1DegenerateDim) { entry_computation->set_root_instruction(with_1_degenerate_dim_prepended); HloEvaluator evaluator; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result_literal, - evaluator.Evaluate>( - *module, {Literal::CreateR1({9, 10})})); - CHECK_EQ(*result_literal, *Literal::CreateR2({{9, 10}})); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result_literal, + evaluator.Evaluate>( + *module, {LiteralUtil::CreateR1({9, 10})})); + CHECK_EQ(*result_literal, *LiteralUtil::CreateR2({{9, 10}})); } TEST_F(HloCreationUtilsTest, Prepend2DegenerateDims) { @@ -123,10 +124,11 @@ TEST_F(HloCreationUtilsTest, Prepend2DegenerateDims) { entry_computation->set_root_instruction(with_2_degenerate_dims_prepended); HloEvaluator evaluator; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result_literal, - evaluator.Evaluate>( - *module, {Literal::CreateR1({9, 10})})); - CHECK_EQ(*result_literal, *Literal::CreateR3({{{9, 10}}})); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr result_literal, + evaluator.Evaluate>( + *module, {LiteralUtil::CreateR1({9, 10})})); + CHECK_EQ(*result_literal, *LiteralUtil::CreateR3({{{9, 10}}})); } TEST_F(HloCreationUtilsTest, Prepend2DegenerateDimsToScalar) { @@ -145,8 +147,8 @@ TEST_F(HloCreationUtilsTest, Prepend2DegenerateDimsToScalar) { HloEvaluator evaluator; TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result_literal, evaluator.Evaluate>( - *module, {Literal::CreateR0(9)})); - CHECK_EQ(*result_literal, *Literal::CreateR2({{9}})); + *module, {LiteralUtil::CreateR0(9)})); + CHECK_EQ(*result_literal, *LiteralUtil::CreateR2({{9}})); } TEST_F(HloCreationUtilsTest, ExpandFirstDimInto3Dims) { @@ -166,9 +168,9 @@ TEST_F(HloCreationUtilsTest, ExpandFirstDimInto3Dims) { TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr result_literal, evaluator.Evaluate>( - *module, {Literal::CreateR1({1, 2, 3, 4, 5, 6})})); + *module, {LiteralUtil::CreateR1({1, 2, 3, 4, 5, 6})})); CHECK_EQ(*result_literal, - *Literal::CreateR3({{{1, 2}}, {{3, 4}}, {{5, 6}}})); + *LiteralUtil::CreateR3({{{1, 2}}, {{3, 4}}, {{5, 6}}})); } TEST_F(HloCreationUtilsTest, PadVectorWithZeros) { @@ -188,8 +190,8 @@ TEST_F(HloCreationUtilsTest, PadVectorWithZeros) { HloEvaluator evaluator; TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result_literal, evaluator.Evaluate>( - *module, {Literal::CreateR1({3, 4})})); - CHECK_EQ(*result_literal, *Literal::CreateR1({0, 0, 0, 3, 4, 0})); + *module, {LiteralUtil::CreateR1({3, 4})})); + CHECK_EQ(*result_literal, *LiteralUtil::CreateR1({0, 0, 0, 3, 4, 0})); } TEST_F(HloCreationUtilsTest, BroadcastZeros_S32) { @@ -209,8 +211,8 @@ TEST_F(HloCreationUtilsTest, BroadcastZeros_S32) { HloEvaluator evaluator; TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result_literal, evaluator.Evaluate>( - *module, {Literal::CreateR0(0)})); - CHECK_EQ(*result_literal, *Literal::CreateR2({{0, 0}, {0, 0}})); + *module, {LiteralUtil::CreateR0(0)})); + CHECK_EQ(*result_literal, *LiteralUtil::CreateR2({{0, 0}, {0, 0}})); } TEST_F(HloCreationUtilsTest, BroadcastZeros_F32) { @@ -230,9 +232,9 @@ TEST_F(HloCreationUtilsTest, BroadcastZeros_F32) { HloEvaluator evaluator; TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result_literal, evaluator.Evaluate>( - *module, {Literal::CreateR0(0.0f)})); + *module, {LiteralUtil::CreateR0(0.0f)})); CHECK_EQ(*result_literal, - *Literal::CreateR2({{0.0f, 0.0f}, {0.0f, 0.0f}})); + *LiteralUtil::CreateR2({{0.0f, 0.0f}, {0.0f, 0.0f}})); } } // namespace diff --git a/tensorflow/compiler/xla/service/hlo_cse.cc b/tensorflow/compiler/xla/service/hlo_cse.cc index a0ee8896230d6dcacb5a8eb607fc00ae5226cfa5..06484f4012fc091f70df7bc8ec231ce3fcf89669 100644 --- a/tensorflow/compiler/xla/service/hlo_cse.cc +++ b/tensorflow/compiler/xla/service/hlo_cse.cc @@ -24,7 +24,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_domain_map.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -143,10 +143,8 @@ StatusOr HloCSE::Run(HloModule* module) { if (instruction->operand_count() == 0) { continue; } - // Skip instructions which have side effects or are a domain (which must - // not be CSE-ed). - if (instruction->HasSideEffect() || - instruction->opcode() == HloOpcode::kDomain) { + // Skip instructions which have side effects. + if (instruction->HasSideEffect()) { continue; } diff --git a/tensorflow/compiler/xla/service/hlo_cse_test.cc b/tensorflow/compiler/xla/service/hlo_cse_test.cc index 16db374566c727f1f3efe2a6d419f1f3caf0aaf1..90fbaa37c5a70a78a9a818b4a8968f3406c671b1 100644 --- a/tensorflow/compiler/xla/service/hlo_cse_test.cc +++ b/tensorflow/compiler/xla/service/hlo_cse_test.cc @@ -21,7 +21,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -53,9 +53,9 @@ TEST_F(HloCseTest, CombineTwoConstants) { // Test that two identical constants are commoned. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); builder.AddInstruction(HloInstruction::CreateBinary( constant1->shape(), HloOpcode::kAdd, constant1, constant2)); @@ -72,7 +72,7 @@ TEST_F(HloCseTest, CombineTwoConstants) { EXPECT_EQ(42.0f, constant->literal().Get({})); auto result = ExecuteAndTransfer(std::move(module), {}); - auto expected = Literal::CreateR0(84.0); + auto expected = LiteralUtil::CreateR0(84.0); EXPECT_TRUE(LiteralTestUtil::Near(*expected, *result, ErrorSpec(1e-4))); } @@ -81,10 +81,10 @@ TEST_F(HloCseTest, CombineTwoConstantsDifferentLayoutsAndInsensitive) { // the pass is not layout sensitive. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2WithLayout( + HloInstruction::CreateConstant(LiteralUtil::CreateR2WithLayout( {{1.0, 2.0}, {3.0, 4.0}}, LayoutUtil::MakeLayout({0, 1})))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2WithLayout( + HloInstruction::CreateConstant(LiteralUtil::CreateR2WithLayout( {{1.0, 2.0}, {3.0, 4.0}}, LayoutUtil::MakeLayout({1, 0})))); auto add = builder.AddInstruction(HloInstruction::CreateBinary( constant1->shape(), HloOpcode::kAdd, constant1, constant2)); @@ -104,7 +104,7 @@ TEST_F(HloCseTest, CombineTwoConstantsDifferentLayoutsAndInsensitive) { EXPECT_THAT(add, op::Add(first_operand, first_operand)); auto result = ExecuteAndTransfer(std::move(module), {}); - auto expected = Literal::CreateR2({{2.0, 4.0}, {6.0, 8.0}}); + auto expected = LiteralUtil::CreateR2({{2.0, 4.0}, {6.0, 8.0}}); EXPECT_TRUE(LiteralTestUtil::Near(*expected, *result, ErrorSpec(1e-4))); } @@ -113,10 +113,10 @@ TEST_F(HloCseTest, CombineTwoConstantsDifferentLayoutsAndSensitive) { // if the pass is layout sensitive. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2WithLayout( + HloInstruction::CreateConstant(LiteralUtil::CreateR2WithLayout( {{1.0, 2.0}, {3.0, 4.0}}, LayoutUtil::MakeLayout({0, 1})))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2WithLayout( + HloInstruction::CreateConstant(LiteralUtil::CreateR2WithLayout( {{1.0, 2.0}, {3.0, 4.0}}, LayoutUtil::MakeLayout({1, 0})))); auto add = builder.AddInstruction(HloInstruction::CreateBinary( constant1->shape(), HloOpcode::kAdd, constant1, constant2)); @@ -134,7 +134,7 @@ TEST_F(HloCseTest, CombineTwoConstantsDifferentLayoutsAndSensitive) { EXPECT_THAT(add, op::Add(constant1, constant2)); auto result = ExecuteAndTransfer(std::move(module), {}); - auto expected = Literal::CreateR2({{2.0, 4.0}, {6.0, 8.0}}); + auto expected = LiteralUtil::CreateR2({{2.0, 4.0}, {6.0, 8.0}}); EXPECT_TRUE(LiteralTestUtil::Near(*expected, *result, ErrorSpec(1e-4))); } @@ -144,20 +144,20 @@ TEST_F(HloCseTest, ConstantsSameValueDifferentType) { auto builder = HloComputation::Builder(TestName()); std::vector constants; constants.push_back(builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42)))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42)))); constants.push_back(builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42)))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42)))); constants.push_back(builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0)))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0)))); constants.push_back(builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0)))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0)))); constants.push_back(builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0)))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0)))); constants.push_back(builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f)))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f)))); // Duplicate the float constant to verify something happens. constants.push_back(builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f)))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f)))); const Shape shape_r0 = ShapeUtil::MakeShape(F32, {}); for (int64 i = 0; i < constants.size(); ++i) { @@ -188,13 +188,13 @@ TEST_F(HloCseTest, NonscalarConstants) { // Test that identical nonscalar constants are merged. auto builder = HloComputation::Builder(TestName()); auto common_constant1 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); auto common_constant2 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); // Create a constant which has the same shape but a different value. auto uncommon_constant = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{2.0, 4.0}, {6.0, 8.0}}))); + LiteralUtil::CreateR2({{2.0, 4.0}, {6.0, 8.0}}))); // Tie the constants together with a tuple. This makes it easier to refer to // the constant instructions via their use. @@ -223,7 +223,7 @@ TEST_F(HloCseTest, IdenticalInstructions) { // Test that three identical instructions are commoned. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0))); auto exp1 = builder.AddInstruction(HloInstruction::CreateUnary( constant->shape(), HloOpcode::kExp, constant)); auto exp2 = builder.AddInstruction(HloInstruction::CreateUnary( @@ -239,7 +239,7 @@ TEST_F(HloCseTest, IdenticalInstructions) { EXPECT_EQ(5, computation->instruction_count()); EXPECT_THAT(tuple, op::Tuple(exp1, exp2, exp3)); - HloCSE cse(/*is_layout_sensitive=*/false); + HloCSE cse(/*is_layout_sensitive=*/true); EXPECT_TRUE(cse.Run(module.get()).ValueOrDie()); EXPECT_EQ(3, computation->instruction_count()); @@ -248,12 +248,189 @@ TEST_F(HloCseTest, IdenticalInstructions) { EXPECT_THAT(tuple, op::Tuple(first_operand, first_operand, first_operand)); } +// Test two identical while loops with same inputs +TEST_F(HloCseTest, WhileLoopsIdenticalConditionsAndBodiesSameInput) { + auto module = ParseHloString(R"( + HloModule WhileLoopsIdenticalConditionsAndBodiesSameInput + + %body (param: (f32[], f32[])) -> (f32[], f32[]) { + %param = (f32[], f32[]) parameter(0) + %get-tuple-element = f32[] get-tuple-element((f32[], f32[]) %param), +index=0 %get-tuple-element.1 = f32[] get-tuple-element((f32[], f32[]) %param), +index=1 %add = f32[] add(f32[] %get-tuple-element, f32[] %get-tuple-element.1) + ROOT %tuple = (f32[], f32[]) tuple(f32[] %get-tuple-element, f32[] %add) + } + + %condition (param.1: (f32[], f32[])) -> pred[] { + %param.1 = (f32[], f32[]) parameter(0) + ROOT %constant = pred[] constant(false) + } + + %condition.1 (param.2: (f32[], f32[])) -> pred[] { + %param.2 = (f32[], f32[]) parameter(0) + ROOT %constant.1 = pred[] constant(false) + } + + ENTRY %WhileLoopsIdenticalConditionsAndBodiesSameInput () -> (f32[], f32[]) +{ %constant.2 = f32[] constant(1) %constant.3 = f32[] constant(2) %tuple.1 = +(f32[], f32[]) tuple(f32[] %constant.2, f32[] %constant.3) %while = (f32[], +f32[]) while((f32[], f32[]) %tuple.1), condition=%condition, body=%body ROOT +%while.1 = (f32[], f32[]) while((f32[], f32[]) %tuple.1), +condition=%condition.1, body=%body + } + )") + .ValueOrDie(); + + auto computation = module->entry_computation(); + + EXPECT_EQ(5, computation->instruction_count()); + HloCSE cse(true); + EXPECT_TRUE(cse.Run(module.get()).ValueOrDie()); + EXPECT_EQ(4, computation->instruction_count()); +} + +// Test two while loops with same conditions, same inputs, but different +// bodies +TEST_F(HloCseTest, WhileLoopsIdenticalConditionsSameInputAndDifferentBodies) { + auto module = ParseHloString(R"( + HloModule WhileLoopsIdenticalConditionsSameInputAndDifferentBodies + + %body (param: (f32[], f32[])) -> (f32[], f32[]) { + %param = (f32[], f32[]) parameter(0) + %get-tuple-element = f32[] get-tuple-element((f32[], f32[]) %param), +index=0 %get-tuple-element.1 = f32[] get-tuple-element((f32[], f32[]) %param), +index=1 %add = f32[] add(f32[] %get-tuple-element, f32[] %get-tuple-element.1) + ROOT %tuple = (f32[], f32[]) tuple(f32[] %get-tuple-element, f32[] %add) + } + + %body2 (param.1: (f32[], f32[])) -> (f32[], f32[]) { + %param.1 = (f32[], f32[]) parameter(0) + %get-tuple-element.2 = f32[] get-tuple-element((f32[], f32[]) %param.1), +index=0 %get-tuple-element.3 = f32[] get-tuple-element((f32[], f32[]) %param.1), +index=1 %sub = f32[] subtract(f32[] %get-tuple-element.2, f32[] +%get-tuple-element.3) ROOT %tuple.2 = (f32[], f32[]) tuple(f32[] +%get-tuple-element.2, f32[] %sub) + } + + %condition (param.2: (f32[], f32[])) -> pred[] { + %param.2 = (f32[], f32[]) parameter(0) + ROOT %constant = pred[] constant(false) + } + + %condition.1 (param.3: (f32[], f32[])) -> pred[] { + %param.3 = (f32[], f32[]) parameter(0) + ROOT %constant.1 = pred[] constant(false) + } + + ENTRY %WhileLoopsIdenticalConditionsSameInputAndDifferentBodies () -> +(f32[], f32[]) { %constant.2 = f32[] constant(1) %constant.3 = f32[] constant(2) + %tuple.1 = (f32[], f32[]) tuple(f32[] %constant.2, f32[] %constant.3) + %while = (f32[], f32[]) while((f32[], f32[]) %tuple.1), +condition=%condition, body=%body ROOT %while.1 = (f32[], f32[]) while((f32[], +f32[]) %tuple.1), condition=%condition.1, body=%body2 + } + )") + .ValueOrDie(); + + auto computation = module->entry_computation(); + + EXPECT_EQ(5, computation->instruction_count()); + HloCSE cse(true); + EXPECT_FALSE(cse.Run(module.get()).ValueOrDie()); + EXPECT_EQ(5, computation->instruction_count()); +} + +// Test two identical while loops with different inputs +TEST_F(HloCseTest, WhileLoopsIdenticalConditionsAndBodiesDifferentInput) { + auto module = ParseHloString(R"( + HloModule WhileLoopsIdenticalConditionsAndBodiesDifferentInput + + %body (param: (f32[], f32[])) -> (f32[], f32[]) { + %param = (f32[], f32[]) parameter(0) + %get-tuple-element = f32[] get-tuple-element((f32[], f32[]) %param), +index=0 %get-tuple-element.1 = f32[] get-tuple-element((f32[], f32[]) %param), +index=1 %add = f32[] add(f32[] %get-tuple-element, f32[] %get-tuple-element.1) + ROOT %tuple = (f32[], f32[]) tuple(f32[] %get-tuple-element, f32[] %add) + } + + %condition (param.1: (f32[], f32[])) -> pred[] { + %param.1 = (f32[], f32[]) parameter(0) + ROOT %constant = pred[] constant(false) + } + + %condition.1 (param.2: (f32[], f32[])) -> pred[] { + %param.2 = (f32[], f32[]) parameter(0) + ROOT %constant.1 = pred[] constant(false) + } + + ENTRY %WhileLoopsIdenticalConditionsAndBodiesDifferentInput () -> (f32[], +f32[]) { %constant.2 = f32[] constant(1) %constant.3 = f32[] constant(2) + %tuple.1 = (f32[], f32[]) tuple(f32[] %constant.2, f32[] %constant.3) + %while = (f32[], f32[]) while((f32[], f32[]) %tuple.1), +condition=%condition, body=%body %constant.4 = f32[] constant(1) %constant.5 = +f32[] constant(2) %tuple.2 = (f32[], f32[]) tuple(f32[] %constant.4, f32[] +%constant.5) ROOT %while.1 = (f32[], f32[]) while((f32[], f32[]) %tuple.2), +condition=%condition.1, body=%body + } + + )") + .ValueOrDie(); + + auto computation = module->entry_computation(); + + EXPECT_EQ(8, computation->instruction_count()); + HloCSE cse(true); + EXPECT_FALSE(cse.Run(module.get()).ValueOrDie()); + EXPECT_EQ(8, computation->instruction_count()); +} + +// Test two while loops with identical bodies and same inputs, but different +// conditions +TEST_F(HloCseTest, WhileLoopsIdenticalBodiesAndInputDifferntConditions) { + auto module = ParseHloString(R"( + HloModule WhileLoopsIdenticalBodiesAndInputDifferntConditions + + %body (param: (f32[], f32[])) -> (f32[], f32[]) { + %param = (f32[], f32[]) parameter(0) + %get-tuple-element = f32[] get-tuple-element((f32[], f32[]) %param), +index=0 %get-tuple-element.1 = f32[] get-tuple-element((f32[], f32[]) %param), +index=1 %add = f32[] add(f32[] %get-tuple-element, f32[] %get-tuple-element.1) + ROOT %tuple = (f32[], f32[]) tuple(f32[] %get-tuple-element, f32[] %add) + } + + %condition (param.1: (f32[], f32[])) -> pred[] { + %param.1 = (f32[], f32[]) parameter(0) + ROOT %constant = pred[] constant(false) + } + + %condition.1 (param.2: (f32[], f32[])) -> pred[] { + %param.2 = (f32[], f32[]) parameter(0) + ROOT %constant.1 = pred[] constant(true) + } + + ENTRY %WhileLoopsIdenticalBodiesAndInputDifferntConditions () -> (f32[], +f32[]) { %constant.2 = f32[] constant(1) %constant.3 = f32[] constant(2) + %tuple.1 = (f32[], f32[]) tuple(f32[] %constant.2, f32[] %constant.3) + %while = (f32[], f32[]) while((f32[], f32[]) %tuple.1), +condition=%condition, body=%body ROOT %while.1 = (f32[], f32[]) while((f32[], +f32[]) %tuple.1), condition=%condition.1, body=%body + })") + .ValueOrDie(); + + auto computation = module->entry_computation(); + + EXPECT_EQ(5, computation->instruction_count()); + HloCSE cse(true); + EXPECT_FALSE(cse.Run(module.get()).ValueOrDie()); + EXPECT_EQ(5, computation->instruction_count()); +} + TEST_F(HloCseTest, IdenticalInstructionsDifferentLayoutsSensitive) { // Test that two identical instructions with different layouts are *not* // commoned if the pass is layout sensitive. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); auto exp1 = builder.AddInstruction(HloInstruction::CreateUnary( constant->shape(), HloOpcode::kExp, constant)); @@ -284,7 +461,7 @@ TEST_F(HloCseTest, IdenticalInstructionsDifferentLayoutsInsensitive) { // the pass is layout insensitive. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); auto exp1 = builder.AddInstruction(HloInstruction::CreateUnary( constant->shape(), HloOpcode::kExp, constant)); @@ -362,7 +539,7 @@ TEST_F(HloCseTest, IdenticalExpressions) { // The *1 instructions should be merged with the *2 instructions. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0))); auto negate1 = builder.AddInstruction(HloInstruction::CreateUnary( constant->shape(), HloOpcode::kNegate, constant)); @@ -400,9 +577,9 @@ TEST_F(HloCseTest, DoNotCombineRng) { // Test that two RNG ops are not commoned. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0f))); auto rng1 = builder.AddInstruction(HloInstruction::CreateRng( ShapeUtil::MakeShape(F32, {}), RandomDistribution::RNG_UNIFORM, {constant1, constant2})); @@ -442,9 +619,9 @@ TEST_F(HloCseTest, DoNotCombineCallsToImpureFunctions) { Shape scalar_shape = ShapeUtil::MakeShape(F32, {}); auto builder = HloComputation::Builder(TestName() + "_rng_fun"); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0f))); auto rng = builder.AddInstruction(HloInstruction::CreateRng( scalar_shape, RandomDistribution::RNG_UNIFORM, {constant1, constant2})); auto param = builder.AddInstruction(HloInstruction::CreateParameter( @@ -459,7 +636,7 @@ TEST_F(HloCseTest, DoNotCombineCallsToImpureFunctions) { { auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({5.0f}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({5.0f}))); auto rng1 = builder.AddInstruction( HloInstruction::CreateMap(constant->shape(), {constant}, rng_function)); auto rng2 = builder.AddInstruction( @@ -521,9 +698,9 @@ TEST_F(HloCseTest, ConstantsSameValueInDifferentDomains) { // in this case) are not collapsed. auto builder = HloComputation::Builder(TestName()); builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42))); builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42))); auto module = CreateNewModule(); auto computation = module->AddEntryComputation(builder.Build()); @@ -536,5 +713,40 @@ TEST_F(HloCseTest, ConstantsSameValueInDifferentDomains) { EXPECT_EQ(2, computation->instruction_count()); } +TEST_F(HloCseTest, Domain) { + auto module = ParseHloString(R"( +HloModule module +ENTRY %entry { + %param = f32[] parameter(0), sharding={maximal device=0} + %domain.0 = f32[] domain(%param), + domain={kind="sharding", entry={maximal device=0}, exit={maximal device=1}} + %domain.1 = f32[] domain(%param), + domain={kind="sharding", entry={maximal device=0}, exit={maximal device=1}} + %domain.2 = f32[] domain(%param), + domain={kind="sharding", entry={maximal device=0}, exit={maximal device=2}} + %negate.0 = f32[] negate(%domain.0) + %negate.1 = f32[] negate(%domain.1) + %negate.2 = f32[] negate(%domain.2) + %domain.3 = f32[] domain(%negate.0), + domain={kind="sharding", entry={maximal device=1}, exit={maximal device=0}} + %domain.4 = f32[] domain(%negate.1), + domain={kind="sharding", entry={maximal device=1}, exit={maximal device=0}} + %domain.5 = f32[] domain(%negate.2), + domain={kind="sharding", entry={maximal device=2}, exit={maximal device=0}} + %add = f32[] add(%domain.3, %domain.4) + ROOT %sub = f32[] subtract(%add, %domain.5) +})") + .ValueOrDie(); + + HloCSE cse(/*is_layout_sensitive=*/false); + EXPECT_TRUE(cse.Run(module.get()).ValueOrDie()); + LOG(INFO) << "AAAAA " << module->ToString(); + const HloInstruction* sub = module->entry_computation()->root_instruction(); + const HloInstruction* add = sub->operand(0); + EXPECT_EQ(add->operand(0), add->operand(1)); + EXPECT_NE(add->operand(0), sub->operand(1)); + EXPECT_NE(add->operand(1), sub->operand(1)); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc index de1a32d8bd9217baabda4ab4b02bf28baebad531..bbfb0c253f583b633c4b2c34b2f068b563d3d9e0 100644 --- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc +++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc @@ -1017,19 +1017,17 @@ bool HloDataflowAnalysis::CanShareOperandBufferWithUser( } if (user->opcode() == HloOpcode::kFusion) { + if (fusion_can_share_buffer_ != nullptr) { + return fusion_can_share_buffer_(user, operand); + } // Get the parameter associated with 'operand'; HloInstruction* fusion_param = user->fused_parameter(user->operand_index(operand)); const HloValue& value = GetValueDefinedAt(fusion_param, operand_index); - if (value.uses().size() != 1) { - if (MultiDynamicSliceUseShareSameIndices(value.uses())) { - return true; - } - return false; + if (MultiDynamicSliceUseShareSameIndices(value.uses())) { + return true; } - const HloUse& use = value.uses()[0]; - if (user->fusion_kind() == HloInstruction::FusionKind::kLoop || user->fusion_kind() == HloInstruction::FusionKind::kInput) { if (user->fused_expression_root()->opcode() == @@ -1039,13 +1037,17 @@ bool HloDataflowAnalysis::CanShareOperandBufferWithUser( // Returns true iff there is exactly one use of 'operand' at shape index // 'operand_index', and this singleton use is the fused root at operand // index 0. - return use.instruction == user->fused_expression_root() && - use.operand_number == 0; - } else { - return AreTransitiveUsesElementwiseOrTuple(fusion_param); + if (value.uses().size() == 1) { + const HloUse& use = value.uses()[0]; + return use.instruction == user->fused_expression_root() && + use.operand_number == 0; + } + return false; } - } else if (user->fusion_kind() == HloInstruction::FusionKind::kOutput && - user->fused_expression_root()->opcode() == HloOpcode::kAdd) { + return AreTransitiveUsesElementwiseOrTuple(fusion_param); + } + if (user->fusion_kind() == HloInstruction::FusionKind::kOutput && + user->fused_expression_root()->opcode() == HloOpcode::kAdd) { // Output fusion with kAdd fused root. // Check if one operand of kAdd fused root is kDot or kConvolution. @@ -1066,11 +1068,12 @@ bool HloDataflowAnalysis::CanShareOperandBufferWithUser( // Returns true iff there is exactly one use of 'operand' at shape index // 'operand_index', and this singleton use is the fused root (at operand // index 'other_add_operand_index'). - return use.instruction == user->fused_expression_root() && - use.operand_number == other_add_operand_index; - } else if (fusion_can_share_buffer_ != nullptr && - fusion_can_share_buffer_(user, operand)) { - return true; + if (value.uses().size() == 1) { + const HloUse& use = value.uses()[0]; + return use.instruction == user->fused_expression_root() && + use.operand_number == other_add_operand_index; + } + return false; } } @@ -1081,6 +1084,21 @@ bool HloDataflowAnalysis::CanShareOperandBufferWithUser( std::vector operand_indices = user->OperandIndices(operand); return operand_indices.size() == 1 && operand_indices[0] == 0; } + if (user->opcode() == HloOpcode::kSort) { + // Only valid if there are no other users. + if (operand->users().size() != 1) { + return false; + } + // If we only sort keys, the output of sort is not a tuple, so we can always + // share the buffer. + if (user->operand_count() == 1) { + return true; + } + CHECK(!user_index.empty()); + // Only share with the right tuple element buffer. + std::vector operand_indices = user->OperandIndices(operand); + return operand_indices.size() == 1 && user_index[0] == operand_indices[0]; + } if (user->opcode() == HloOpcode::kCall) { // Get all uses of value defined by 'operand' at 'operand_index'. const auto& uses = GetValueDefinedAt(operand, operand_index).uses(); diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc index f176473366ab74fa532ffb26ffc6adbb9731de67..2ec31a91488805b323549575f9c8d1a92ea9c619 100644 --- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc +++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_dataflow_analysis.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_graph_dumper.h" #include "tensorflow/compiler/xla/service/hlo_matchers.h" @@ -101,9 +101,9 @@ TEST_P(HloDataflowAnalysisTest, BinaryOperation) { // Test the dataflow for a simple binary operation (Add). auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto add = builder.AddInstruction(HloInstruction::CreateBinary( scalar_shape_, HloOpcode::kAdd, constant1, constant2)); module_->AddEntryComputation(builder.Build()); @@ -198,9 +198,9 @@ TEST_P(HloDataflowAnalysisTest, NestedTuple) { // Verify the dataflow through a nested tuple. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto nested_tuple = builder.AddInstruction( @@ -259,9 +259,9 @@ TEST_P(HloDataflowAnalysisTest, SingleCall) { auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto call = builder.AddInstruction(HloInstruction::CreateCall( scalar_shape_, {constant1, constant2}, called_computation)); module_->AddEntryComputation(builder.Build()); @@ -308,9 +308,9 @@ TEST_P(HloDataflowAnalysisTest, ComputationCalledTwiceWithSameArguments) { auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto call1 = builder.AddInstruction(HloInstruction::CreateCall( scalar_shape_, {constant1, constant2}, called_computation)); auto call2 = builder.AddInstruction(HloInstruction::CreateCall( @@ -362,9 +362,9 @@ TEST_P(HloDataflowAnalysisTest, ComputationCalledTwiceWithDifferentArguments) { auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto call1 = builder.AddInstruction(HloInstruction::CreateCall( scalar_shape_, {constant1, constant2}, called_computation)); auto call2 = builder.AddInstruction(HloInstruction::CreateCall( @@ -426,9 +426,9 @@ TEST_P(HloDataflowAnalysisTest, NestedCalls) { auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto call = builder.AddInstruction(HloInstruction::CreateCall( scalar_shape_, {constant1, constant2}, outer_computation)); module_->AddEntryComputation(builder.Build()); @@ -493,15 +493,15 @@ TEST_P(HloDataflowAnalysisTest, SingleWhile) { auto cond_param = cond_builder.AddInstruction( HloInstruction::CreateParameter(0, tuple_shape, "param")); auto cond_constant = cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloComputation* condition = module_->AddEmbeddedComputation(cond_builder.Build()); auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto xla_while = builder.AddInstruction( @@ -594,15 +594,15 @@ TEST_P(HloDataflowAnalysisTest, SequentialWhiles) { cond_builder.AddInstruction( HloInstruction::CreateParameter(0, tuple_shape, "param")); cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloComputation* condition = module_->AddEmbeddedComputation(cond_builder.Build()); auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto xla_while0 = builder.AddInstruction( @@ -653,7 +653,7 @@ TEST_P(HloDataflowAnalysisTest, NestedWhiles) { cond_builder.AddInstruction( HloInstruction::CreateParameter(0, tuple_shape, "param")); cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloComputation* condition = module_->AddEmbeddedComputation(cond_builder.Build()); @@ -691,9 +691,9 @@ TEST_P(HloDataflowAnalysisTest, NestedWhiles) { auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto entry_while = builder.AddInstruction( @@ -780,15 +780,15 @@ TEST_P(HloDataflowAnalysisTest, SwizzlingWhile) { auto cond_param = cond_builder.AddInstruction( HloInstruction::CreateParameter(0, tuple_shape, "param")); cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloComputation* condition = module_->AddEmbeddedComputation(cond_builder.Build()); auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto xla_while = builder.AddInstruction( @@ -840,11 +840,11 @@ TEST_P(HloDataflowAnalysisTest, ArraySelect) { // Test a kSelect of an array value. auto builder = HloComputation::Builder(TestName()); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto select = builder.AddInstruction(HloInstruction::CreateTernary( scalar_shape_, HloOpcode::kSelect, pred, constant1, constant2)); @@ -863,15 +863,15 @@ TEST_P(HloDataflowAnalysisTest, TupleSelect) { // Test a kTupleSelect. Non-top-level element flow through the instruction. auto builder = HloComputation::Builder(TestName()); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto constant3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(3.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(3.0))); auto constant4 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(4.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(4.0))); auto tuple1 = builder.AddInstruction(HloInstruction::CreateTuple({constant1})); auto tuple2 = @@ -939,17 +939,17 @@ TEST_P(HloDataflowAnalysisTest, NestedTupleSelect) { // Test kTupleSelect of a nested tuple. auto builder = HloComputation::Builder(TestName()); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto constant3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(3.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(3.0))); auto constant4 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(4.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(4.0))); auto constant5 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(5.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(5.0))); auto inner_tuple1 = builder.AddInstruction( HloInstruction::CreateTuple({constant2, constant3})); auto tuple1 = builder.AddInstruction( @@ -1025,18 +1025,18 @@ TEST_P(HloDataflowAnalysisTest, TupleSelectToWhile) { cond_builder.AddInstruction( HloInstruction::CreateParameter(0, tuple_shape, "param")); cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloComputation* condition = module_->AddEmbeddedComputation(cond_builder.Build()); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto constant3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(3.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(3.0))); auto tuple1 = builder.AddInstruction(HloInstruction::CreateTuple({constant1})); auto tuple2 = @@ -1088,7 +1088,7 @@ TEST_P(HloDataflowAnalysisTest, BitcastDefinesValue) { // Test the bitcast_defines_value flag to the dataflow analysis. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto bitcast = builder.AddInstruction(HloInstruction::CreateUnary( scalar_shape_, HloOpcode::kBitcast, constant)); @@ -1157,7 +1157,7 @@ TEST_P(HloDataflowAnalysisTest, SendAndSendDone) { auto builder = HloComputation::Builder(TestName()); auto param = builder.AddInstruction( HloInstruction::CreateParameter(0, scalar_shape_, "param0")); - auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = builder.AddInstruction(HloInstruction::CreateToken()); auto send = builder.AddInstruction( HloInstruction::CreateSend(param, token, /*channel_id=*/0)); auto send_done = builder.AddInstruction(HloInstruction::CreateSendDone(send)); @@ -1182,7 +1182,7 @@ TEST_P(HloDataflowAnalysisTest, RecvAndRecvDone) { // Test that a RecvDone forwards its operand tuple element at {0} to element // {0} of the output. auto builder = HloComputation::Builder(TestName()); - auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = builder.AddInstruction(HloInstruction::CreateToken()); auto recv = builder.AddInstruction( HloInstruction::CreateRecv(scalar_shape_, token, /*channel_id=*/0)); auto recv_done = builder.AddInstruction(HloInstruction::CreateRecvDone(recv)); @@ -1309,13 +1309,13 @@ TEST_P(HloDataflowAnalysisTest, WhileParameters_Sequential) { auto body_param = body_builder.AddInstruction( HloInstruction::CreateParameter(0, scalar_shape_, "body_param")); auto constant = body_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto exp = body_builder.AddInstruction( HloInstruction::CreateUnary(scalar_shape_, HloOpcode::kExp, constant)); auto add = body_builder.AddInstruction(HloInstruction::CreateBinary( scalar_shape_, HloOpcode::kAdd, exp, body_param)); auto dead_constant = body_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto dead_negate = body_builder.AddInstruction(HloInstruction::CreateUnary( scalar_shape_, HloOpcode::kNegate, dead_constant)); HloComputation* body = module_->AddEmbeddedComputation( @@ -1325,7 +1325,7 @@ TEST_P(HloDataflowAnalysisTest, WhileParameters_Sequential) { auto cond_param = cond_builder.AddInstruction( HloInstruction::CreateParameter(0, scalar_shape_, "cond_param")); auto cond_constant = cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); HloComputation* condition = module_->AddEmbeddedComputation(cond_builder.Build()); @@ -1576,11 +1576,11 @@ TEST_P(HloDataflowAnalysisTest, ConditionalWithIdentity) { auto builder = HloComputation::Builder(TestName()); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(56.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(56.0f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(12.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(12.0f))); auto conditional = builder.AddInstruction(HloInstruction::CreateConditional( scalar_shape_, pred, constant1, true_computation, constant2, false_computation)); @@ -1667,11 +1667,11 @@ TEST_P(HloDataflowAnalysisTest, ConditionalTakingTupleOperand) { auto builder = HloComputation::Builder(TestName()); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(56.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(56.0f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(12.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(12.0f))); auto tuple_operand = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto conditional = builder.AddInstruction(HloInstruction::CreateConditional( @@ -1797,15 +1797,15 @@ TEST_P(HloDataflowAnalysisTest, NestedConditionals) { // Build entry computation. auto builder = HloComputation::Builder(TestName()); auto pred1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); auto pred2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.1f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.1f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.2f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.2f))); auto constant3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(3.3f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(3.3f))); auto tuple_operand = builder.AddInstruction( HloInstruction::CreateTuple({pred2, constant1, constant2})); auto conditional = builder.AddInstruction(HloInstruction::CreateConditional( @@ -1943,9 +1943,9 @@ TEST_F(DoesNotUseOperandBufferTest, FusedDynamicUpdateSlice) { // Create a DynamicUpdateSlice instruction of tuple element 1. auto starts = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({2}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({2}))); auto update = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({2.f, 2.f, 2.f}))); + LiteralUtil::CreateR1({2.f, 2.f, 2.f}))); auto dynamic_update_slice = builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice( data_shape, gte1, update, starts)); @@ -2048,7 +2048,7 @@ TEST_F(CanShareOperandBufferWithUserTest, Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2}); auto one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto operand = builder.AddInstruction( HloInstruction::CreateBroadcast(data_shape, one, {1})); @@ -2076,7 +2076,7 @@ TEST_F(CanShareOperandBufferWithUserTest, auto param = builder.AddInstruction( HloInstruction::CreateParameter(0, data_shape, "param0")); auto index = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({0, 0}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({0, 0}))); auto ds = builder.AddInstruction( HloInstruction::CreateDynamicSlice(slice_shape, param, index, {1, 2, 2})); @@ -2144,9 +2144,9 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedDynamicUpdateSlice) { // Create a DynamicUpdateSlice instruction of tuple element 1. auto starts = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({2}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({2}))); auto update = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({2.f, 2.f, 2.f}))); + LiteralUtil::CreateR1({2.f, 2.f, 2.f}))); auto dynamic_update_slice = builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice( data_shape, gte1, update, starts)); @@ -2184,9 +2184,9 @@ TEST_F(CanShareOperandBufferWithUserTest, // Create a DynamicUpdateSlice instruction of tuple element 1. auto starts = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({2}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({2}))); auto update = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({2.f, 2.f, 2.f}))); + LiteralUtil::CreateR1({2.f, 2.f, 2.f}))); auto dynamic_update_slice = builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice( data_shape_bf16, convert1, update, starts)); @@ -2232,14 +2232,56 @@ TEST_F(CanShareOperandBufferWithUserTest, DynamicUpdateSliceCanShare) { dataflow_analysis_->CanShareOperandBufferWithUser(starts, {}, dus, {})); } +TEST_F(CanShareOperandBufferWithUserTest, SortCanShare) { + auto builder = HloComputation::Builder(TestName()); + + Shape keys_shape = ShapeUtil::MakeShape(F32, {8}); + auto keys = builder.AddInstruction( + HloInstruction::CreateParameter(0, keys_shape, "keys")); + auto sort = + builder.AddInstruction(HloInstruction::CreateSort(keys_shape, 0, keys)); + + BuildModuleAndRunAnalysis(builder.Build()); + + EXPECT_TRUE( + dataflow_analysis_->CanShareOperandBufferWithUser(keys, {}, sort, {})); +} + +TEST_F(CanShareOperandBufferWithUserTest, SortCanShareWithTupleUser) { + auto builder = HloComputation::Builder(TestName()); + + Shape keys_shape = ShapeUtil::MakeShape(F32, {8}); + Shape values_shape = ShapeUtil::MakeShape(F32, {8}); + auto keys = builder.AddInstruction( + HloInstruction::CreateParameter(0, keys_shape, "keys")); + auto values = builder.AddInstruction( + HloInstruction::CreateParameter(1, values_shape, "values")); + auto sort = builder.AddInstruction(HloInstruction::CreateSort( + ShapeUtil::MakeTupleShape({keys_shape, values_shape}), 0, keys, values)); + + BuildModuleAndRunAnalysis(builder.Build()); + + // The buffer for the keys can be shared with the first tuple entry. + EXPECT_TRUE( + dataflow_analysis_->CanShareOperandBufferWithUser(keys, {}, sort, {0})); + // The buffer for the values can be shared with the second tuple entry. + EXPECT_TRUE( + dataflow_analysis_->CanShareOperandBufferWithUser(values, {}, sort, {1})); + // Verify that the buffers are not shared with the "wrong" tuple entry. + EXPECT_FALSE( + dataflow_analysis_->CanShareOperandBufferWithUser(keys, {}, sort, {1})); + EXPECT_FALSE( + dataflow_analysis_->CanShareOperandBufferWithUser(values, {}, sort, {0})); +} + TEST_F(CanShareOperandBufferWithUserTest, FusedDotAdd) { auto builder = HloComputation::Builder(TestName()); Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2}); auto a = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 0.0}, {0.0, 1.0}}))); + LiteralUtil::CreateR2({{1.0, 0.0}, {0.0, 1.0}}))); auto b = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{2.0, 2.0}, {2.0, 2.0}}))); + LiteralUtil::CreateR2({{2.0, 2.0}, {2.0, 2.0}}))); DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); @@ -2248,7 +2290,7 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedDotAdd) { HloInstruction::CreateDot(data_shape, a, b, dot_dnums)); auto one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto add_operand = builder.AddInstruction( HloInstruction::CreateBroadcast(data_shape, one, {1})); @@ -2270,7 +2312,7 @@ TEST_F(CanShareOperandBufferWithUserTest, OutputFusionCantAliasOperandBuffer) { Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2}); auto one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto operand = builder.AddInstruction( HloInstruction::CreateBroadcast(data_shape, one, {1})); @@ -2278,7 +2320,7 @@ TEST_F(CanShareOperandBufferWithUserTest, OutputFusionCantAliasOperandBuffer) { HloInstruction::CreateReverse(data_shape, operand, {0, 1})); auto two = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{2.0, 2.0}, {2.0, 2.0}}))); + LiteralUtil::CreateR2({{2.0, 2.0}, {2.0, 2.0}}))); auto add = builder.AddInstruction( HloInstruction::CreateBinary(data_shape, HloOpcode::kAdd, reverse, two)); @@ -2298,13 +2340,13 @@ TEST_F(CanShareOperandBufferWithUserTest, FusionCanShareBufferCustomized) { Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2}); auto one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto operand = builder.AddInstruction( HloInstruction::CreateBroadcast(data_shape, one, {1})); auto mul = builder.AddInstruction(HloInstruction::CreateBinary( data_shape, HloOpcode::kMultiply, operand, operand)); auto two = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{2.0, 2.0}, {2.0, 2.0}}))); + LiteralUtil::CreateR2({{2.0, 2.0}, {2.0, 2.0}}))); auto add = builder.AddInstruction( HloInstruction::CreateBinary(data_shape, HloOpcode::kAdd, mul, two)); @@ -2370,7 +2412,7 @@ TEST_F(CanShareOperandBufferWithUserTest, CallToComputationWithFusionRoot) { auto sub_param = sub_builder.AddInstruction( HloInstruction::CreateParameter(0, shape, "sub_param")); auto one = sub_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto ones = sub_builder.AddInstruction( HloInstruction::CreateBroadcast(shape, one, {1})); auto add = sub_builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/hlo_dce_test.cc b/tensorflow/compiler/xla/service/hlo_dce_test.cc index f5524dc6fef3ae11e29011ad7927ee55e1701d76..26e3736e01270dbc6ca67647e814843aba2d1e3d 100644 --- a/tensorflow/compiler/xla/service/hlo_dce_test.cc +++ b/tensorflow/compiler/xla/service/hlo_dce_test.cc @@ -53,9 +53,9 @@ TEST_F(HloDceTest, NoDeadCode) { // Verify that no dead code is removed from a computation with no dead code. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(123.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(123.0f))); builder.AddInstruction(HloInstruction::CreateBinary( constant1->shape(), HloOpcode::kAdd, constant1, constant2)); @@ -74,8 +74,8 @@ TEST_F(HloDceTest, InstructionsWithSideEffect) { // Verify that side-effect instructions (Send in this test) are not removed. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); - auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); + auto token = builder.AddInstruction(HloInstruction::CreateToken()); builder.AddInstruction( HloInstruction::CreateSend(constant, token, /*channel_id=*/0)); builder.AddInstruction(HloInstruction::CreateTuple({})); @@ -127,9 +127,9 @@ TEST_F(HloDceTest, ControlDependencies) { // Verify that instructions with control dependencies are not removed. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(123.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(123.0f))); // Create two dead instructions: a negate and an add. auto dead_negate = builder.AddInstruction(HloInstruction::CreateUnary( @@ -224,7 +224,7 @@ TEST_F(HloDceTest, CalledComputationWithSideEffect) { auto param = cond_builder.AddInstruction( HloInstruction::CreateParameter(0, shape, "cond_param")); auto constant = cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); cond_builder.AddInstruction(HloInstruction::CreateBinary( ShapeUtil::MakeShape(PRED, {}), HloOpcode::kLt, param, constant)); } @@ -235,8 +235,7 @@ TEST_F(HloDceTest, CalledComputationWithSideEffect) { { auto param = body_builder.AddInstruction( HloInstruction::CreateParameter(0, shape, "param")); - auto token = - body_builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = body_builder.AddInstruction(HloInstruction::CreateToken()); auto infeed = body_builder.AddInstruction( HloInstruction::CreateInfeed(shape, token, "")); body_builder.AddInstruction( @@ -280,8 +279,8 @@ TEST_F(HloDceTest, CalledComputationWithNestedSideEffect) { { auto param = nested_callee_builder.AddInstruction( HloInstruction::CreateParameter(0, shape, "param")); - auto token = nested_callee_builder.AddInstruction( - HloInstruction::CreateAfterAll({})); + auto token = + nested_callee_builder.AddInstruction(HloInstruction::CreateToken()); nested_callee_builder.AddInstruction( HloInstruction::CreateOutfeed(shape, param, token, "")); } @@ -346,12 +345,12 @@ TEST_F(HloDceTest, RemoveDeadSubcomputation) { builder.AddInstruction(HloInstruction::CreateParameter( /*parameter_number=*/0, ShapeUtil::MakeShape(F32, {100}), "param0")), builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))), + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))), /*dimensions_to_reduce=*/{0}, reduce_subcomp)); // Add another instruction as the root of the computation. builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))); module->AddEntryComputation(builder.Build()); EXPECT_EQ(module->MakeComputationPostOrder().size(), 2); @@ -387,7 +386,7 @@ TEST_F(HloDceTest, KeepUsedSubcomputation) { builder.AddInstruction(HloInstruction::CreateParameter( /*parameter_number=*/0, ShapeUtil::MakeShape(F32, {100}), "param0")), builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))), + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))), /*dimensions_to_reduce=*/{0}, reduce_subcomp)); // Add another instruction as the root of the computation that also uses @@ -397,7 +396,7 @@ TEST_F(HloDceTest, KeepUsedSubcomputation) { builder.AddInstruction(HloInstruction::CreateParameter( /*parameter_number=*/1, ShapeUtil::MakeShape(F32, {100}), "param1")), builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))), + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))), /*dimensions_to_reduce=*/{0}, reduce_subcomp)); module->AddEntryComputation(builder.Build()); diff --git a/tensorflow/compiler/xla/service/hlo_domain_map.cc b/tensorflow/compiler/xla/service/hlo_domain_map.cc index ebd5adb5d573ce4b556046f85eb26a6ad59efcb9..9e096320db5048457435199627a1ef1fe1572177 100644 --- a/tensorflow/compiler/xla/service/hlo_domain_map.cc +++ b/tensorflow/compiler/xla/service/hlo_domain_map.cc @@ -41,11 +41,15 @@ namespace xla { bool HloDomainMap::InSameDomain(HloInstruction* instruction1, HloInstruction* instruction2) const { - int64 domain_id1 = FindOrDefault(instruction_to_domain_, instruction1, -1); - int64 domain_id2 = FindOrDefault(instruction_to_domain_, instruction2, -1); + int64 domain_id1 = GetDomainId(instruction1); + int64 domain_id2 = GetDomainId(instruction2); return domain_id1 >= 0 && domain_id1 == domain_id2; } +int64 HloDomainMap::GetDomainId(HloInstruction* instruction) const { + return FindOrDefault(instruction_to_domain_, instruction, -1); +} + Status HloDomainMap::TryProcessEmptyDomain(HloInstruction* instruction) { TF_RET_CHECK(instruction->opcode() == HloOpcode::kDomain); // We only check operands, so we are sure to not process the empty domain from @@ -58,6 +62,11 @@ Status HloDomainMap::TryProcessEmptyDomain(HloInstruction* instruction) { TF_RETURN_IF_ERROR(InsertDomain(std::move(domain))); } } + if (instruction == instruction->parent()->root_instruction()) { + auto domain = MakeUnique(); + domain->enter_domains.insert(instruction); + TF_RETURN_IF_ERROR(InsertDomain(std::move(domain))); + } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/hlo_domain_map.h b/tensorflow/compiler/xla/service/hlo_domain_map.h index e62ef763fb3881ab6030b1f6a66266ac80a3d84d..1ca71597253eecfb45ae8f384240033a57045277 100644 --- a/tensorflow/compiler/xla/service/hlo_domain_map.h +++ b/tensorflow/compiler/xla/service/hlo_domain_map.h @@ -65,6 +65,10 @@ class HloDomainMap { // currently processing. bool IsDomainInstruction(HloInstruction* instruction) const; + // Retrieves the domain identifier of the instruction, or -1 in case + // instruction is not found within any domain. + int64 GetDomainId(HloInstruction* instruction) const; + private: HloDomainMap(string domain_kind) : domain_kind_(std::move(domain_kind)) {} diff --git a/tensorflow/compiler/xla/service/hlo_domain_metadata.h b/tensorflow/compiler/xla/service/hlo_domain_metadata.h index aa0308100a21f109579de75788fce7d242d6a6b0..f855f2a1fc944fcc11c9afed278bef4af87813da 100644 --- a/tensorflow/compiler/xla/service/hlo_domain_metadata.h +++ b/tensorflow/compiler/xla/service/hlo_domain_metadata.h @@ -71,12 +71,6 @@ class DomainMetadata { // Returns a string representation of the metadata. virtual string ToString() const = 0; - - // Given a reachable set (the set of instructions which are reachable from - // each other via user/operand pathways, without crossing a kDomain - // instruciton), makes sure that all of them have metadata attributes which - // are coherent with this metadata object. - virtual Status NormalizeInstructions(const Domain& domain) const = 0; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_domain_remover.cc b/tensorflow/compiler/xla/service/hlo_domain_remover.cc index 1d06040b0e7c92b03f4cb5481bdee73a0f74f939..67fad0769f5eb5ceca64ebd2aa78c6469f2c813d 100644 --- a/tensorflow/compiler/xla/service/hlo_domain_remover.cc +++ b/tensorflow/compiler/xla/service/hlo_domain_remover.cc @@ -16,8 +16,8 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_domain_remover.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" -#include "tensorflow/compiler/xla/service/hlo_domain_isolator.h" #include "tensorflow/compiler/xla/service/hlo_domain_map.h" +#include "tensorflow/compiler/xla/service/hlo_domain_verifier.h" #include "tensorflow/compiler/xla/service/hlo_graph_dumper.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" @@ -43,54 +43,16 @@ class HloDomainRemover::RunContext { Status HloDomainRemover::RunContext::VerifyAndNormalizeDomain( const DomainMetadata::Domain& domain) { - // Verify that the whole kDomain frontier bounding the instruction reach set, - // has matching metadata. - // A kDomain instruction has two sides of metadata, a user facing and an - // operand facing. - // A reachable instruction set can make contact with a kDomain instruction on - // a user facing side (the kDomain is operand of the instruction), or on a - // operand facing side (the kDomain is user of the instruction). - // And depending on the contact side, the proper metadata object - // (user_side_metadata() vs. operand_side_metadata()) needs to be used for - // consistency checks. - const DomainMetadata* ref_metadata = nullptr; - VLOG(4) << "Reach set:"; - for (HloInstruction* instruction : domain.instructions) { - VLOG(4) << " " << instruction->name(); - } - VLOG(4) << " Domains:"; - for (HloInstruction* instruction : domain.enter_domains) { - const DomainMetadata& meta = instruction->user_side_metadata(); - VLOG(4) << " User side: " << instruction->name(); - VLOG(4) << " " << meta.ToString(); - if (ref_metadata == nullptr) { - ref_metadata = &meta; - } else { - TF_RET_CHECK(meta.Matches(*ref_metadata)) - << "Metadata mismatch at instruction " << instruction->name() << " : " - << meta.ToString() << " vs " << ref_metadata->ToString(); - } - } - for (HloInstruction* instruction : domain.exit_domains) { - const DomainMetadata& meta = instruction->operand_side_metadata(); - VLOG(4) << " Operand side: " << instruction->name(); - VLOG(4) << " " << meta.ToString(); - if (ref_metadata == nullptr) { - ref_metadata = &meta; - } else { - TF_RET_CHECK(meta.Matches(*ref_metadata)) - << "Metadata mismatch at instruction " << instruction->name() << " : " - << meta.ToString() << " vs " << ref_metadata->ToString(); - } - } + TF_ASSIGN_OR_RETURN(const DomainMetadata* ref_metadata, + HloDomainVerifier::VerifyDomain(domain)); if (ref_metadata != nullptr) { VLOG(4) << "Applying domain normalization: " << ref_metadata->ToString(); - TF_RETURN_IF_ERROR(ref_metadata->NormalizeInstructions(domain)); + TF_RETURN_IF_ERROR(remover_->normalizer_(domain, ref_metadata)); } else { // No kDomain instruction was present within this domain, so call the // generic normalization functions and have them apply their heuristic. VLOG(2) << "Applying domain-less normalization"; - TF_RETURN_IF_ERROR(remover_->normalizer_(domain)); + TF_RETURN_IF_ERROR(remover_->normalizer_(domain, nullptr)); } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/hlo_domain_remover.h b/tensorflow/compiler/xla/service/hlo_domain_remover.h index 0c71dd34fd4d2944037dc965a2c9ad2c592d6e3e..c859e05f02e54d601804b641094ecdd11bbe1aed 100644 --- a/tensorflow/compiler/xla/service/hlo_domain_remover.h +++ b/tensorflow/compiler/xla/service/hlo_domain_remover.h @@ -35,9 +35,10 @@ class HloDomainRemover : public HloPassInterface { // instructions in it with the same attributes (ie, sharding), a normalizer // function is tasked at applying attribute normalization on the instructions // within such domain. - HloDomainRemover( - tensorflow::StringPiece kind, - std::function normalizer) + HloDomainRemover(tensorflow::StringPiece kind, + std::function + normalizer) : kind_(kind.ToString()), normalizer_(std::move(normalizer)) {} tensorflow::StringPiece name() const override { return "domain_remover"; } @@ -48,7 +49,9 @@ class HloDomainRemover : public HloPassInterface { class RunContext; string kind_; - std::function normalizer_; + std::function + normalizer_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_domain_test.cc b/tensorflow/compiler/xla/service/hlo_domain_test.cc index 3859e4cae6e15bdb783277093b80d7822b1f4670..ffc18a0f886df86d87944d9c284a6faf8afe4c60 100644 --- a/tensorflow/compiler/xla/service/hlo_domain_test.cc +++ b/tensorflow/compiler/xla/service/hlo_domain_test.cc @@ -97,12 +97,6 @@ class OpNameMetadata : public DomainMetadata { string ToString() const override { return opname_; } - Status NormalizeInstructions( - const DomainMetadata::Domain& domain) const override { - // For the purposes of this test, nothing to do. - return Status::OK(); - } - static tensorflow::StringPiece KindName() { return "opname"; } private: @@ -124,7 +118,8 @@ std::unique_ptr OpNameDomainCreator(HloInstruction* instruction, std::move(user_side_metadata)); } -Status OpNameDomainNormalizer(const DomainMetadata::Domain& domain) { +Status OpNameDomainNormalizer(const DomainMetadata::Domain& domain, + const DomainMetadata* metadata) { // Nothing to do for the particular use this test make of the OpName domains. return Status::OK(); } @@ -159,7 +154,7 @@ ENTRY entry { EXPECT_FALSE(HasDomainEdge(module, "e", "d")); HloDomainRemover remover(ShardingMetadata::KindName(), - NormalizeShardingDomain); + ShardingMetadata::NormalizeShardingDomain); TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module)); EXPECT_TRUE(remover_changed); @@ -227,7 +222,7 @@ ENTRY entry { EXPECT_FALSE(HasDomainEdge(module, "e", "d")); HloDomainRemover remover(ShardingMetadata::KindName(), - NormalizeShardingDomain); + ShardingMetadata::NormalizeShardingDomain); TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module)); EXPECT_TRUE(remover_changed); @@ -277,7 +272,7 @@ ENTRY entry { LOG(INFO) << "Original module:\n" << module->ToString(); HloDomainRemover remover(ShardingMetadata::KindName(), - NormalizeShardingDomain); + ShardingMetadata::NormalizeShardingDomain); TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module)); EXPECT_FALSE(remover_changed); @@ -324,7 +319,7 @@ ENTRY entry { EXPECT_FALSE(HasDomainEdge(module, "e", "d")); HloDomainRemover sharding_remover(ShardingMetadata::KindName(), - NormalizeShardingDomain); + ShardingMetadata::NormalizeShardingDomain); TF_ASSERT_OK_AND_ASSIGN(bool sharding_remover_changed, sharding_remover.Run(module)); EXPECT_TRUE(sharding_remover_changed); @@ -411,7 +406,7 @@ ENTRY entry { } HloDomainRemover remover(ShardingMetadata::KindName(), - NormalizeShardingDomain); + ShardingMetadata::NormalizeShardingDomain); TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module)); EXPECT_TRUE(remover_changed); @@ -436,6 +431,44 @@ ENTRY entry { HloSharding::AssignDevice(0)})); } +TEST_F(HloDomainTest, EmptyRootDomain) { + const char* const hlo_string = R"( +HloModule Module + +ENTRY entry { + %param = f32[1] parameter(0), sharding={maximal device=0} + %tuple = (f32[1]) tuple(%param), + sharding={maximal device=1} + ROOT %gte = f32[1] get-tuple-element(%tuple), index=0, + sharding={maximal device=1} +})"; + + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); + + HloDomainIsolator isolator(CreateShardingDomain); + TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module)); + EXPECT_TRUE(isolator_changed); + + EXPECT_TRUE(HasDomainEdge(module, "tuple", "param")); + EXPECT_FALSE(HasDomainEdge(module, "gte", "tuple")); + + // Remove %tuple and %gte (tuple simplification) + HloInstruction* gte = FindInstruction(module, "gte"); + HloInstruction* tuple = FindInstruction(module, "tuple"); + module->entry_computation()->set_root_instruction(tuple->mutable_operand(0)); + TF_EXPECT_OK(module->entry_computation()->RemoveInstruction(gte)); + TF_EXPECT_OK(module->entry_computation()->RemoveInstruction(tuple)); + + HloDomainRemover remover(ShardingMetadata::KindName(), + ShardingMetadata::NormalizeShardingDomain); + TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module)); + EXPECT_TRUE(remover_changed); + + const HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_TRUE(root->has_sharding()); + EXPECT_EQ(root->sharding(), HloSharding::AssignDevice(1)); +} + // Tests that text dumps of domain instructions can be parsed back, in the // specific case of null shardings. TEST_F(HloDomainTest, DumpParseNullSharding) { diff --git a/tensorflow/compiler/xla/service/hlo_domain_verifier.cc b/tensorflow/compiler/xla/service/hlo_domain_verifier.cc new file mode 100644 index 0000000000000000000000000000000000000000..751fc677e2d955fd3d9f8970f7c0370a22c054bf --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_domain_verifier.cc @@ -0,0 +1,124 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/hlo_domain_verifier.h" + +#include + +#include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_domain_map.h" +#include "tensorflow/compiler/xla/service/hlo_graph_dumper.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/types.h" + +namespace xla { + +class HloDomainVerifier::RunContext { + public: + RunContext(HloModule* module, HloDomainVerifier* verifier) + : module_(module), verifier_(verifier) {} + + Status Run(); + + private: + // If the verifier caller passed an empty vector for kinds, we collect all the + // avalable domain types. + Status PopulateDomainKinds(); + + HloModule* module_; + HloDomainVerifier* verifier_; +}; + +Status HloDomainVerifier::RunContext::PopulateDomainKinds() { + if (verifier_->kinds_.empty()) { + // The caller specified no domain kinds, collect all the ones available. + std::set kinds; + for (HloComputation* computation : module_->computations()) { + for (HloInstruction* instruction : computation->instructions()) { + if (instruction->opcode() == HloOpcode::kDomain) { + TF_RET_CHECK(instruction->user_side_metadata().Kind() == + instruction->operand_side_metadata().Kind()) + << instruction->ToString(); + kinds.insert(instruction->user_side_metadata().Kind().ToString()); + } + } + } + verifier_->kinds_.insert(verifier_->kinds_.end(), kinds.begin(), + kinds.end()); + } + return Status::OK(); +} + +Status HloDomainVerifier::RunContext::Run() { + VLOG(4) << "Running HLO Domain Verifier"; + TF_RETURN_IF_ERROR(PopulateDomainKinds()); + for (HloComputation* computation : module_->computations()) { + for (auto& kind : verifier_->kinds_) { + // First create the domain instruciton sets. A domain instruction set is + // the set of instructions whose edges never cross a kDomain instruction. + TF_ASSIGN_OR_RETURN(std::unique_ptr domain_map, + HloDomainMap::Create(computation, kind)); + // Verify every domain populated within the map. + for (auto& domain : domain_map->GetDomains()) { + TF_RETURN_IF_ERROR(VerifyDomain(*domain).status()); + } + } + } + return Status::OK(); +} + +StatusOr HloDomainVerifier::Run(HloModule* module) { + RunContext run_context(module, this); + TF_RETURN_IF_ERROR(run_context.Run()); + return false; +} + +StatusOr HloDomainVerifier::VerifyDomain( + const DomainMetadata::Domain& domain) { + const DomainMetadata* ref_metadata = nullptr; + VLOG(4) << "Reach set:"; + for (HloInstruction* instruction : domain.instructions) { + VLOG(4) << " " << instruction->name(); + } + VLOG(4) << " Domains:"; + for (HloInstruction* instruction : domain.enter_domains) { + const DomainMetadata& meta = instruction->user_side_metadata(); + VLOG(4) << " User side: " << instruction->name(); + VLOG(4) << " " << meta.ToString(); + if (ref_metadata == nullptr) { + ref_metadata = &meta; + } else { + TF_RET_CHECK(meta.Matches(*ref_metadata)) + << "Metadata mismatch at instruction " << instruction->name() << " : " + << meta.ToString() << " vs " << ref_metadata->ToString(); + } + } + for (HloInstruction* instruction : domain.exit_domains) { + const DomainMetadata& meta = instruction->operand_side_metadata(); + VLOG(4) << " Operand side: " << instruction->name(); + VLOG(4) << " " << meta.ToString(); + if (ref_metadata == nullptr) { + ref_metadata = &meta; + } else { + TF_RET_CHECK(meta.Matches(*ref_metadata)) + << "Metadata mismatch at instruction " << instruction->name() << " : " + << meta.ToString() << " vs " << ref_metadata->ToString(); + } + } + return ref_metadata; +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_domain_verifier.h b/tensorflow/compiler/xla/service/hlo_domain_verifier.h new file mode 100644 index 0000000000000000000000000000000000000000..8e53cf97f8ba9a88140a909ad20c1a938aec8c1f --- /dev/null +++ b/tensorflow/compiler/xla/service/hlo_domain_verifier.h @@ -0,0 +1,65 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_DOMAIN_VERIFIER_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_DOMAIN_VERIFIER_H_ + +#include +#include + +#include "tensorflow/compiler/xla/service/hlo_domain_map.h" +#include "tensorflow/compiler/xla/service/hlo_domain_metadata.h" +#include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/hlo_pass_interface.h" +#include "tensorflow/core/lib/core/status.h" + +namespace xla { + +// Verifies that the domain instructions are consistent, and the each domain is +// surrounded by the same metadata. +class HloDomainVerifier : public HloPassInterface { + public: + HloDomainVerifier(std::vector kinds) : kinds_(std::move(kinds)) {} + + tensorflow::StringPiece name() const override { return "domain_verifier"; } + + StatusOr Run(HloModule* module) override; + + // Verify that the whole kDomain frontier bounding the instruction reach set, + // has matching metadata. + // A kDomain instruction has two sides of metadata, a user facing and an + // operand facing. + // A reachable instruction set can make contact with a kDomain instruction on + // a user facing side (the kDomain is operand of the instruction), or on a + // operand facing side (the kDomain is user of the instruction). + // And depending on the contact side, the proper metadata object + // (user_side_metadata() vs. operand_side_metadata()) needs to be used for + // consistency checks. + // Returns the DomainMetadata pointer which surrounds the domain, and + // represents the common metadata within such domain. If the returned + // DomainMetadata pointer is nullptr, the input domain had no kDomain + // boundary. + static StatusOr VerifyDomain( + const DomainMetadata::Domain& domain); + + private: + class RunContext; + + std::vector kinds_; +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_DOMAIN_VERIFIER_H_ diff --git a/tensorflow/compiler/xla/service/hlo_element_type_converter.cc b/tensorflow/compiler/xla/service/hlo_element_type_converter.cc index 4ed1508d7067684a15d0fb7d86e69b055bc1333b..c804f4364f6d16d5b8112219ce884495200aa827 100644 --- a/tensorflow/compiler/xla/service/hlo_element_type_converter.cc +++ b/tensorflow/compiler/xla/service/hlo_element_type_converter.cc @@ -21,7 +21,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_evaluator.h" diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc index 47da46bfad646e12b736ecb123f9d3db16ca1990..51353eea6e72d5a131897f3c3ae312046051103e 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc @@ -25,6 +25,7 @@ limitations under the License. #include "tensorflow/compiler/xla/index_util.h" #include "tensorflow/compiler/xla/layout_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/primitive_util.h" @@ -135,7 +136,6 @@ StatusOr> Compare( } // namespace - HloEvaluator::HloEvaluator(int64 max_loop_iterations) : max_loop_iterations_(max_loop_iterations) { typed_visitors_[PRED] = MakeUnique>(this); @@ -330,6 +330,24 @@ StatusOr> HloEvaluator::EvaluateElementwiseUnaryOp( return result; } +StatusOr> HloEvaluator::EvaluateDotOp( + const DotDimensionNumbers& dim_numbers, const Literal& lhs, + const Literal& rhs) { + std::unique_ptr lhs_instr = + HloInstruction::CreateConstant(lhs.CloneToUnique()); + std::unique_ptr rhs_instr = + HloInstruction::CreateConstant(rhs.CloneToUnique()); + + TF_ASSIGN_OR_RETURN( + Shape dot_shape, + ShapeInference::InferDotOpShape(lhs.shape(), rhs.shape(), dim_numbers)); + + std::unique_ptr cloned_instruction = + HloInstruction::CreateDot(dot_shape, lhs_instr.get(), rhs_instr.get(), + dim_numbers); + return Evaluate(cloned_instruction.get()); +} + Status HloEvaluator::HandleParameter(HloInstruction* parameter) { CHECK_LT(parameter->parameter_number(), arg_literals_.size()); const Literal* input_literal = arg_literals_[parameter->parameter_number()]; @@ -382,7 +400,7 @@ Status HloEvaluator::HandleConcatenate(HloInstruction* concatenate) { ShapeUtil::GetDimension(operand_shape, concat_dim); } - auto result_literal = Literal::CreateFromDimensions( + auto result_literal = LiteralUtil::CreateFromDimensions( reference_shape.element_type(), concat_dimensions); DimensionVector source_indices(rank, 0); DimensionVector dest_indices(concat_dimensions.size(), 0); @@ -533,7 +551,7 @@ Status HloEvaluator::HandleTuple(HloInstruction* tuple) { operand_literals.push_back(&GetEvaluatedLiteralFor(operand)); } - evaluated_[tuple] = Literal::MakeTuple(operand_literals); + evaluated_[tuple] = LiteralUtil::MakeTuple(operand_literals); return Status::OK(); } @@ -757,6 +775,12 @@ class OutputWindowIndexToInputIndex { return ArraySlice(input_index_); } + // Returns for a given 'input_dim' the corresponding output dimension index, + // or -1 if 'input_dim' is an elided window dimension. + int64 input_dim_value_to_output_index(int64 input_dim) { + return input_dim_value_to_output_index_[input_dim]; + } + private: // Propagates window dimensions from the output index to input_index_ by // mutating input_index_ in place. @@ -774,7 +798,7 @@ class OutputWindowIndexToInputIndex { // input_dim_value_to_index_vector_[i] tells us how to compute dimension i of // the input index from the output index. See - // PropagateOutputIndexToInputIndex. + // PropagateOutputIndexWindowDimsToInputIndex. std::vector input_dim_value_to_output_index_; // The result computed by this functor. operator() returns an ArraySlice into @@ -827,6 +851,8 @@ Status HloEvaluator::HandleGather(HloInstruction* gather) { // corresponding index in the input shape. std::vector input_index(operand.shape().dimensions_size()); std::vector output_index(gather->shape().dimensions_size()); + std::vector input_gather_index_clamped( + operand.shape().dimensions_size()); OutputGatherIndexToInputIndex output_gather_index_to_input_index( &gather->gather_dimension_numbers(), /*input_shape=*/operand.shape(), @@ -848,14 +874,26 @@ Status HloEvaluator::HandleGather(HloInstruction* gather) { output_index[i] = output_gather_index[i] + output_window_index[i]; DCHECK_LT(output_index[i], shape.dimensions(i)); } + for (int i = 0, e = input_gather_index.size(); i < e; i++) { + int64 output_dim = + output_window_index_to_input_index.input_dim_value_to_output_index(i); + // If 'output_dim' is -1, it means 'i' is an elided window dim. This means + // we set the iteration index to 0, so for the purpose of the following + // calculations we can consider the output dimension size to be 1. + int64 output_dim_size = + output_dim == -1 ? 1 : shape.dimensions(output_dim); + // Clamp the gather index so that the gather region fits in the operand. + // input_gather_index_clamped[i] = clamp(input_gather_index[i], 0, + // operand_shape.dimensions(i) - + // output_dim_size); + input_gather_index_clamped[i] = + std::min(operand_shape.dimensions(i) - output_dim_size, + std::max(0LL, input_gather_index[i])); + } for (int i = 0, e = input_index.size(); i < e; i++) { - // TODO(b/74360564): We should implement whatever out of bounds behavior - // we decide for dynamic-slice here as well. - input_index[i] = (input_gather_index[i] + input_window_index[i]) % - operand_shape.dimensions(i); - if (input_index[i] < 0) { - input_index[i] += operand_shape.dimensions(i); - } + input_index[i] = input_gather_index_clamped[i] + input_window_index[i]; + DCHECK_GE(input_index[i], 0); + DCHECK_LT(input_index[i], operand_shape.dimensions(i)); } TF_RETURN_IF_ERROR( result->CopyElementFrom(operand, input_index, output_index)); @@ -903,7 +941,7 @@ Status HloEvaluator::HandleBroadcast(HloInstruction* broadcast) { } Status HloEvaluator::HandleAfterAll(HloInstruction* token) { - evaluated_[token] = Literal::CreateToken(); + evaluated_[token] = LiteralUtil::CreateToken(); return Status::OK(); } @@ -1084,45 +1122,90 @@ Status HloEvaluator::HandleWhile(HloInstruction* while_hlo) { // hoops to make this work. namespace { template -std::unique_ptr EvaluateSortInternal(HloInstruction* sort, - const Literal& keys_literal, - const Literal& values_literal) { - CHECK_EQ(sort->operand_count(), 2); +StatusOr> EvaluateSortInternal( + HloInstruction* sort, const Literal& keys_literal, + const Literal& values_literal) { + auto rank = ShapeUtil::Rank(keys_literal.shape()); + TF_RET_CHECK( + ShapeUtil::SameDimensions(keys_literal.shape(), values_literal.shape())) + << "Sort keys and values must have the same dimensions"; + TF_RET_CHECK(rank > 0 && rank <= 2) + << "Sort is only supported for rank-1 and rank-2 shapes, rank is: " + << rank; + TF_RET_CHECK(sort->operand_count() == 2) << "Expected key-value sort"; // We need to sort and array of keys and an array of values, where the // sorted order of the values is determined by the keys. The simplest(?) // way to do this is to go to an array-of-pairs representation, sort the // array using the keys, and then go back to pair-of-arrays. VLOG(3) << "HandleSort keys_literal: " << keys_literal.ToString(); VLOG(3) << "HandleSort values_literal: " << values_literal.ToString(); - const auto& keys_data = keys_literal.data(); - const auto& values_data = values_literal.data(); - using kv_pair = std::pair; - std::vector key_value_vector; - CHECK_EQ(keys_data.size(), values_data.size()); - key_value_vector.reserve(keys_data.size()); - for (int i = 0; i < keys_data.size(); ++i) { - key_value_vector.push_back(std::make_pair(keys_data[i], values_data[i])); - } - std::sort(key_value_vector.begin(), key_value_vector.end(), - [](const kv_pair& a, const kv_pair& b) { - return SafeLess(a.first, b.first); - }); - std::vector result_keys; - std::vector result_values; - for (const auto& key_value : key_value_vector) { - result_keys.push_back(key_value.first); - result_values.push_back(key_value.second); + + auto sort_r1 = [](const Literal& keys_literal, + const Literal& values_literal) { + const auto& keys_data = keys_literal.data(); + const auto& values_data = values_literal.data(); + + using kv_pair = std::pair; + std::vector key_value_vector; + CHECK_EQ(keys_data.size(), values_data.size()); + key_value_vector.reserve(keys_data.size()); + for (int i = 0; i < keys_data.size(); ++i) { + key_value_vector.push_back(std::make_pair(keys_data[i], values_data[i])); + } + std::sort(key_value_vector.begin(), key_value_vector.end(), + [](const kv_pair& a, const kv_pair& b) { + return SafeLess(a.first, b.first); + }); + std::vector result_keys; + std::vector result_values; + for (const auto& key_value : key_value_vector) { + result_keys.push_back(key_value.first); + result_values.push_back(key_value.second); + } + auto result_keys_literal = MakeUnique(keys_literal.shape()); + result_keys_literal->PopulateR1( + tensorflow::gtl::ArraySlice(result_keys)); + auto result_values_literal = MakeUnique(values_literal.shape()); + result_values_literal->PopulateR1( + tensorflow::gtl::ArraySlice(result_values)); + return std::make_pair(std::move(result_keys_literal), + std::move(result_values_literal)); + }; + + std::unique_ptr result_tuple; + if (rank == 1) { + auto result_pair = sort_r1(keys_literal, values_literal); + result_tuple = LiteralUtil::MakeTuple( + {result_pair.first.get(), result_pair.second.get()}); + } else { + // For R2 sort, the desired semantics are to sort each matrix row + // independently. + auto keys_result_literal = MakeUnique(keys_literal.shape()); + auto values_result_literal = MakeUnique(values_literal.shape()); + int64 r1_length = keys_literal.shape().dimensions(1); + for (int64 row = 0; row < keys_literal.shape().dimensions(0); ++row) { + TF_ASSIGN_OR_RETURN(auto keys_r1_slice, + keys_literal.Slice({row, 0}, {row + 1, r1_length}) + ->Reshape({r1_length})); + TF_ASSIGN_OR_RETURN(auto values_r1_slice, + values_literal.Slice({row, 0}, {row + 1, r1_length}) + ->Reshape({r1_length})); + auto r1_result_pair = sort_r1(*keys_r1_slice, *values_r1_slice); + TF_ASSIGN_OR_RETURN(auto sorted_keys, + r1_result_pair.first->Reshape({1, r1_length})); + TF_ASSIGN_OR_RETURN(auto sorted_values, + r1_result_pair.second->Reshape({1, r1_length})); + TF_RETURN_IF_ERROR(keys_result_literal->CopySliceFrom( + *sorted_keys, {0, 0}, {row, 0}, {1, r1_length})); + TF_RETURN_IF_ERROR(values_result_literal->CopySliceFrom( + *sorted_values, {0, 0}, {row, 0}, {1, r1_length})); + } + result_tuple = LiteralUtil::MakeTuple( + {keys_result_literal.get(), values_result_literal.get()}); } - auto result_keys_literal = MakeUnique(sort->operand(0)->shape()); - result_keys_literal->PopulateR1( - tensorflow::gtl::ArraySlice(result_keys)); - auto result_values_literal = MakeUnique(sort->operand(1)->shape()); - result_values_literal->PopulateR1( - tensorflow::gtl::ArraySlice(result_values)); - auto result_tuple = Literal::MakeTuple( - {result_keys_literal.get(), result_values_literal.get()}); + VLOG(3) << "HandleSort result_tuple: " << result_tuple->ToString(); - return result_tuple; + return std::move(result_tuple); } template @@ -1166,6 +1249,15 @@ StatusOr> EvaluateSort(HloInstruction* sort, } // namespace Status HloEvaluator::HandleSort(HloInstruction* sort) { + const int64 sort_dim = sort->dimensions(0); + const int64 rank = ShapeUtil::Rank(sort->operand(0)->shape()); + if (sort_dim != rank - 1) { + return Unimplemented( + "Trying to support along dimension %lld, which is not the last " + "dimension", + sort_dim); + } + if (!ShapeUtil::IsTuple(sort->shape())) { return DefaultAction(sort); } else { diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.h b/tensorflow/compiler/xla/service/hlo_evaluator.h index 2850c5cb1a94de0dbab8ba5b27d7e21998794087..a4c37ef32827892194da070ee05ec6dc4f4c306f 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator.h @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_module.h" +#include "tensorflow/compiler/xla/service/shape_inference.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" @@ -115,6 +116,10 @@ class HloEvaluator : public DfsHloVisitorWithDefault { StatusOr> EvaluateElementwiseUnaryOp( HloOpcode opcode, const Literal& operand); + StatusOr> EvaluateDotOp( + const DotDimensionNumbers& dim_numbers, const Literal& lhs, + const Literal& rhs); + protected: // Make HloEvaluatorTypedVisitor a friend because it is logically part of this // class. diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc index 42770d848a83b2e27b87bc963d259e2b7af664a4..cba72469ce73603f05d9957eb64e8519e8b8aec0 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc @@ -21,8 +21,8 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_element_type_converter.h" @@ -112,9 +112,9 @@ class HloEvaluatorTest : public ::testing::WithParamInterface, // Verifies that HloEvaluator evaluates a HLO instruction that performs clamp // with 3 operands. TEST_P(HloEvaluatorTest, DoesClamp) { - auto low = Literal::CreateR2({{0.f, 2.f}, {2.f, 4.f}}); - auto value = Literal::CreateR2({{0.f, 5.f}, {0.f, 4.f}}); - auto high = Literal::CreateR2({{2.f, 4.f}, {4.f, 4.f}}); + auto low = LiteralUtil::CreateR2({{0.f, 2.f}, {2.f, 4.f}}); + auto value = LiteralUtil::CreateR2({{0.f, 5.f}, {0.f, 4.f}}); + auto high = LiteralUtil::CreateR2({{2.f, 4.f}, {4.f, 4.f}}); Shape shape = low->shape(); HloComputation::Builder b(TestName()); @@ -127,15 +127,15 @@ TEST_P(HloEvaluatorTest, DoesClamp) { std::unique_ptr result = Evaluate(); - auto expected = Literal::CreateR2({{0, 4}, {2, 4}}); + auto expected = LiteralUtil::CreateR2({{0, 4}, {2, 4}}); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } TEST_P(HloEvaluatorTest, DISABLED_DoesClampSpecialBroadcast) { - auto low = Literal::CreateR0(0.f); - auto value = Literal::CreateR2({{-1.f, 0.f}, {1.f, 2.f}}); - auto high = Literal::CreateR0(1.f); + auto low = LiteralUtil::CreateR0(0.f); + auto value = LiteralUtil::CreateR2({{-1.f, 0.f}, {1.f, 2.f}}); + auto high = LiteralUtil::CreateR0(1.f); Shape shape = value->shape(); HloComputation::Builder b(TestName()); @@ -148,7 +148,7 @@ TEST_P(HloEvaluatorTest, DISABLED_DoesClampSpecialBroadcast) { std::unique_ptr result = Evaluate(); - auto expected = Literal::CreateR2({{0, 0}, {1, 1}}); + auto expected = LiteralUtil::CreateR2({{0, 0}, {1, 1}}); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -156,9 +156,9 @@ TEST_P(HloEvaluatorTest, DISABLED_DoesClampSpecialBroadcast) { // Verifies that HloEvaluator evaluates a HLO instruction that performs select // with 3 operands. TEST_P(HloEvaluatorTest, DoesSelect) { - auto pred = Literal::CreateR2({{true, false}, {false, true}}); - auto on_true = Literal::CreateR2({{2.f, 4.f}, {4.f, 4.f}}); - auto on_false = Literal::CreateR2({{0.f, 5.f}, {0.f, 4.f}}); + auto pred = LiteralUtil::CreateR2({{true, false}, {false, true}}); + auto on_true = LiteralUtil::CreateR2({{2.f, 4.f}, {4.f, 4.f}}); + auto on_false = LiteralUtil::CreateR2({{0.f, 5.f}, {0.f, 4.f}}); Shape shape = on_true->shape(); HloComputation::Builder b(TestName()); @@ -173,7 +173,7 @@ TEST_P(HloEvaluatorTest, DoesSelect) { std::unique_ptr result = Evaluate({}); - auto expected = Literal::CreateR2({{2, 5}, {0, 4}}); + auto expected = LiteralUtil::CreateR2({{2, 5}, {0, 4}}); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -181,46 +181,46 @@ TEST_P(HloEvaluatorTest, DoesSelect) { // Verifies that HloEvaluator evaluates a HLO instruction that performs // element-wise addition with 2 operands. TEST_P(HloEvaluatorTest, DoesAdd) { - auto lhs = Literal::CreateR2({{1, 0}, {-100, 4}}); - auto rhs = Literal::CreateR2({{2, 4}, {4, 4}}); - auto expected = Literal::CreateR2({{3, 4}, {-96, 8}}); + auto lhs = LiteralUtil::CreateR2({{1, 0}, {-100, 4}}); + auto rhs = LiteralUtil::CreateR2({{2, 4}, {4, 4}}); + auto expected = LiteralUtil::CreateR2({{3, 4}, {-96, 8}}); TestBinaryOp(HloOpcode::kAdd, std::move(expected), std::move(lhs), std::move(rhs)); } // Verifies that HloEvaluator evaluates a HLO instruction that performs // element-wise and with 2 operands. TEST_P(HloEvaluatorTest, DoesAnd) { - auto lhs = Literal::CreateR2({{1, 0}, {-100, 4}}); - auto rhs = Literal::CreateR2({{2, 4}, {4, 4}}); - auto expected = Literal::CreateR2({{0, 0}, {4, 4}}); + auto lhs = LiteralUtil::CreateR2({{1, 0}, {-100, 4}}); + auto rhs = LiteralUtil::CreateR2({{2, 4}, {4, 4}}); + auto expected = LiteralUtil::CreateR2({{0, 0}, {4, 4}}); TestBinaryOp(HloOpcode::kAnd, std::move(expected), std::move(lhs), std::move(rhs)); } // Verifies that HloEvaluator evaluates a HLO instruction that performs // element-wise or with 2 operands. TEST_P(HloEvaluatorTest, DoesOr) { - auto lhs = Literal::CreateR2({{1, 0}, {-100, 4}}); - auto rhs = Literal::CreateR2({{2, 4}, {4, 4}}); - auto expected = Literal::CreateR2({{3, 4}, {-100, 4}}); + auto lhs = LiteralUtil::CreateR2({{1, 0}, {-100, 4}}); + auto rhs = LiteralUtil::CreateR2({{2, 4}, {4, 4}}); + auto expected = LiteralUtil::CreateR2({{3, 4}, {-100, 4}}); TestBinaryOp(HloOpcode::kOr, std::move(expected), std::move(lhs), std::move(rhs)); } // Verifies that HloEvaluator evaluates a HLO instruction that performs // element-wise or with 2 operands. TEST_P(HloEvaluatorTest, DoesXor) { - auto lhs = Literal::CreateR2({{1, 0}, {-100, 4}}); - auto rhs = Literal::CreateR2({{2, 4}, {4, 4}}); - auto expected = Literal::CreateR2({{3, 4}, {-104, 0}}); + auto lhs = LiteralUtil::CreateR2({{1, 0}, {-100, 4}}); + auto rhs = LiteralUtil::CreateR2({{2, 4}, {4, 4}}); + auto expected = LiteralUtil::CreateR2({{3, 4}, {-104, 0}}); TestBinaryOp(HloOpcode::kXor, std::move(expected), std::move(lhs), std::move(rhs)); } // Verifies that HloEvaluator evaluates a HLO instruction that performs // element-wise multiply with 2 operands. TEST_P(HloEvaluatorTest, DoesMultiply) { - auto lhs = Literal::CreateR2({{-1, 0}, {-100, 4}}); - auto rhs = Literal::CreateR2( + auto lhs = LiteralUtil::CreateR2({{-1, 0}, {-100, 4}}); + auto rhs = LiteralUtil::CreateR2( {{std::numeric_limits::min(), 4}, {4, 4}}); - auto expected = Literal::CreateR2( + auto expected = LiteralUtil::CreateR2( {{std::numeric_limits::min(), 0}, {-400, 16}}); TestBinaryOp(HloOpcode::kMultiply, std::move(expected), std::move(lhs), std::move(rhs)); @@ -228,17 +228,17 @@ TEST_P(HloEvaluatorTest, DoesMultiply) { // Verifies that HloEvaluator evaluates a HLO instruction that performs // element-wise divide with 2 operands. TEST_P(HloEvaluatorTest, DoesDivideInt64) { - auto lhs = Literal::CreateR2({{1, 0}, {-100, 4}}); - auto rhs = Literal::CreateR2({{2, 4}, {4, 4}}); - auto expected = Literal::CreateR2({{0, 0}, {-25, 1}}); + auto lhs = LiteralUtil::CreateR2({{1, 0}, {-100, 4}}); + auto rhs = LiteralUtil::CreateR2({{2, 4}, {4, 4}}); + auto expected = LiteralUtil::CreateR2({{0, 0}, {-25, 1}}); TestBinaryOp(HloOpcode::kDivide, std::move(expected), std::move(lhs), std::move(rhs)); } TEST_P(HloEvaluatorTest, DoesDivideDouble) { - auto lhs = Literal::CreateR2({{1.0, 0.0}, {-100.0, 4.0}}); - auto rhs = Literal::CreateR2({{2.2, 4.0}, {4.0, 4.0}}); + auto lhs = LiteralUtil::CreateR2({{1.0, 0.0}, {-100.0, 4.0}}); + auto rhs = LiteralUtil::CreateR2({{2.2, 4.0}, {4.0, 4.0}}); auto expected = - Literal::CreateR2({{0.45454545454545453, 0}, {-25, 1}}); + LiteralUtil::CreateR2({{0.45454545454545453, 0}, {-25, 1}}); TestBinaryOp(HloOpcode::kDivide, std::move(expected), std::move(lhs), std::move(rhs)); } @@ -246,54 +246,54 @@ TEST_P(HloEvaluatorTest, DoesDivideDouble) { // Verifies that HloEvaluator evaluates a HLO instruction that performs // element-wise abs op with 1 operand. TEST_P(HloEvaluatorTest, DoesAbsR2) { - auto operand = Literal::CreateR2({{1, -20}, {-100, 4}}); - auto expected = Literal::CreateR2({{1, 20}, {100, 4}}); + auto operand = LiteralUtil::CreateR2({{1, -20}, {-100, 4}}); + auto expected = LiteralUtil::CreateR2({{1, 20}, {100, 4}}); TestUnaryOp(HloOpcode::kAbs, std::move(expected), std::move(operand)); } TEST_P(HloEvaluatorTest, DoesAbsR0) { - auto operand = Literal::CreateR0(-1.0f); - auto expected = Literal::CreateR0(1.0f); + auto operand = LiteralUtil::CreateR0(-1.0f); + auto expected = LiteralUtil::CreateR0(1.0f); TestUnaryOp(HloOpcode::kAbs, std::move(expected), std::move(operand)); } TEST_P(HloEvaluatorTest, DoesAbsR1WithZeroSize) { - auto operand = Literal::CreateR1({}); - auto expected = Literal::CreateR1({}); + auto operand = LiteralUtil::CreateR1({}); + auto expected = LiteralUtil::CreateR1({}); TestUnaryOp(HloOpcode::kAbs, std::move(expected), std::move(operand)); } TEST_P(HloEvaluatorTest, DoesNegateR2) { - auto operand = Literal::CreateR2( + auto operand = LiteralUtil::CreateR2( {{0, std::numeric_limits::min()}, {-1, 4}}); - auto expected = - Literal::CreateR2({{0, std::numeric_limits::min()}, {1, -4}}); + auto expected = LiteralUtil::CreateR2( + {{0, std::numeric_limits::min()}, {1, -4}}); TestUnaryOp(HloOpcode::kNegate, std::move(expected), std::move(operand)); } TEST_P(HloEvaluatorTest, DoesCosR2) { - auto operand = Literal::CreateR2({{0, M_PI}, {-M_PI, 2 * M_PI}}); - auto expected = Literal::CreateR2({{1, -1}, {-1, 1}}); + auto operand = LiteralUtil::CreateR2({{0, M_PI}, {-M_PI, 2 * M_PI}}); + auto expected = LiteralUtil::CreateR2({{1, -1}, {-1, 1}}); TestUnaryOp(HloOpcode::kCos, std::move(expected), std::move(operand), use_bfloat16_ ? 0.031250 : 9.5367431640625E-7); } TEST_P(HloEvaluatorTest, DoesSinR2) { - auto operand = Literal::CreateR2({{0, M_PI}, {-M_PI, 2 * M_PI}}); - auto expected = Literal::CreateR2({{0, 0}, {0, 0}}); + auto operand = LiteralUtil::CreateR2({{0, M_PI}, {-M_PI, 2 * M_PI}}); + auto expected = LiteralUtil::CreateR2({{0, 0}, {0, 0}}); TestUnaryOp(HloOpcode::kSin, std::move(expected), std::move(operand), use_bfloat16_ ? 0.031250 : 9.5367431640625E-7); } TEST_P(HloEvaluatorTest, DoesNotR2) { auto operand = - Literal::CreateR2({{0, std::numeric_limits::min()}, - {-1, std::numeric_limits::max()}}); + LiteralUtil::CreateR2({{0, std::numeric_limits::min()}, + {-1, std::numeric_limits::max()}}); auto expected = - Literal::CreateR2({{-1, std::numeric_limits::max()}, - {0, std::numeric_limits::min()}}); + LiteralUtil::CreateR2({{-1, std::numeric_limits::max()}, + {0, std::numeric_limits::min()}}); TestUnaryOp(HloOpcode::kNot, std::move(expected), std::move(operand)); } // Verifies that HloEvaluator evaluates a HLO Computation with non-parameter nor // constant operands. TEST_P(HloEvaluatorTest, DoesTraverseInstructions) { - auto lhs = Literal::CreateR2({{1, 0}, {-100, 4}}); - auto rhs = Literal::CreateR2({{2, 4}, {4, 4}}); - auto rhs2 = Literal::CreateR2({{1, -20}, {-100, 4}}); + auto lhs = LiteralUtil::CreateR2({{1, 0}, {-100, 4}}); + auto rhs = LiteralUtil::CreateR2({{2, 4}, {4, 4}}); + auto rhs2 = LiteralUtil::CreateR2({{1, -20}, {-100, 4}}); std::vector args = {lhs.get(), rhs.get(), rhs2.get()}; Shape shape = ShapeUtil::MakeShape(S64, {2, 2}); @@ -314,7 +314,7 @@ TEST_P(HloEvaluatorTest, DoesTraverseInstructions) { std::unique_ptr result = Evaluate(args); - auto expected = Literal::CreateR2({{4, -16}, {-196, 12}}); + auto expected = LiteralUtil::CreateR2({{4, -16}, {-196, 12}}); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -324,7 +324,7 @@ TEST_P(HloEvaluatorTest, DoesReshape) { HloComputation::Builder b(TestName()); const int64 dimensions[] = {11, 8, 7, 5, 9}; TF_ASSERT_OK_AND_ASSIGN(auto literal, - Literal::CreateRandomLiteral( + LiteralUtil::CreateRandomLiteral( ShapeUtil::MakeShape(F32, dimensions), 0.0, 1.0)); auto literal_clone = literal->CloneToUnique(); HloInstruction* literal_instruction = @@ -349,8 +349,8 @@ TEST_P(HloEvaluatorTest, DoesReshape) { // Verifies Broadcast operation is correctly evaluated. TEST_P(HloEvaluatorTest, DoesBroadcast) { HloComputation::Builder b(TestName()); - auto input_literal = Literal::CreateR2({{1, 2}, {3, 4}, {5, 6}}); - auto output_literal = Literal::CreateR3( + auto input_literal = LiteralUtil::CreateR2({{1, 2}, {3, 4}, {5, 6}}); + auto output_literal = LiteralUtil::CreateR3( {{{1, 2}, {3, 4}, {5, 6}}, {{1, 2}, {3, 4}, {5, 6}}}); HloInstruction* literal_instruction = b.AddInstruction( HloInstruction::CreateConstant(std::move(input_literal))); @@ -365,8 +365,8 @@ TEST_P(HloEvaluatorTest, DoesBroadcast) { TEST_P(HloEvaluatorTest, DoesBroadcastScalar) { HloComputation::Builder b(TestName()); - auto input_literal = Literal::CreateR0(111); - auto output_literal = Literal::CreateR2( + auto input_literal = LiteralUtil::CreateR0(111); + auto output_literal = LiteralUtil::CreateR2( {{111, 111}, {111, 111}, {111, 111}, {111, 111}, {111, 111}, {111, 111}}); HloInstruction* literal_instruction = b.AddInstruction( @@ -386,9 +386,9 @@ TEST_P(HloEvaluatorTest, DoesConcatenateSimple) { HloComputation::Builder b(TestName()); HloInstruction* operand1 = b.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{-1, -2}, {100, 200}}))); + LiteralUtil::CreateR2({{-1, -2}, {100, 200}}))); HloInstruction* operand2 = b.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{-2, -3}, {-100, -200}}))); + LiteralUtil::CreateR2({{-2, -3}, {-100, -200}}))); std::vector operands = {operand1, operand2}; @@ -399,8 +399,8 @@ TEST_P(HloEvaluatorTest, DoesConcatenateSimple) { std::unique_ptr result = Evaluate(); - auto expected = - Literal::CreateR2({{-1, -2}, {100, 200}, {-2, -3}, {-100, -200}}); + auto expected = LiteralUtil::CreateR2( + {{-1, -2}, {100, 200}, {-2, -3}, {-100, -200}}); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -408,9 +408,9 @@ TEST_P(HloEvaluatorTest, ConcatenateHandlesShapeWithZeroElement) { HloComputation::Builder b(TestName()); HloInstruction* operand1 = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({100, 200}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({100, 200}))); HloInstruction* operand2 = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({}))); std::vector operands = {operand1, operand2}; @@ -421,16 +421,16 @@ TEST_P(HloEvaluatorTest, ConcatenateHandlesShapeWithZeroElement) { std::unique_ptr result = Evaluate(); - auto expected = Literal::CreateR1({100, 200}); + auto expected = LiteralUtil::CreateR1({100, 200}); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } TEST_P(HloEvaluatorTest, ConvertWithSameLayout) { HloComputation::Builder b(TestName()); - auto input_literal = Literal::CreateR2({{1, 2}, {3, 4}, {5, 6}}); + auto input_literal = LiteralUtil::CreateR2({{1, 2}, {3, 4}, {5, 6}}); auto expected = - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}}); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}}); ASSERT_TRUE(LayoutUtil::LayoutsInShapesEqual(input_literal->shape(), expected->shape())); @@ -447,9 +447,9 @@ TEST_P(HloEvaluatorTest, ConvertWithSameLayout) { TEST_P(HloEvaluatorTest, ConvertWithDifferentLayout) { HloComputation::Builder b(TestName()); - auto input_literal = Literal::CreateR2WithLayout( + auto input_literal = LiteralUtil::CreateR2WithLayout( {{1, 2}, {3, 4}, {5, 6}}, LayoutUtil::MakeLayout({0, 1})); - auto expected = Literal::CreateR2WithLayout( + auto expected = LiteralUtil::CreateR2WithLayout( {{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}}, LayoutUtil::MakeLayout({1, 0})); ASSERT_FALSE(LayoutUtil::LayoutsInShapesEqual(input_literal->shape(), expected->shape())); @@ -478,13 +478,13 @@ PaddingConfig CreatePaddingConfig( } TEST_P(HloEvaluatorTest, Pad2DIntegerArrayWithZeroDimension) { - auto operand = Literal::CreateR2({{}, {}}); + auto operand = LiteralUtil::CreateR2({{}, {}}); HloComputation::Builder b(TestName()); auto operand_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(operand))); constexpr int32 kPadValue = 10; - auto pad_value = Literal::CreateR0(kPadValue); + auto pad_value = LiteralUtil::CreateR0(kPadValue); auto padding_value_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(pad_value))); @@ -496,7 +496,7 @@ TEST_P(HloEvaluatorTest, Pad2DIntegerArrayWithZeroDimension) { std::unique_ptr result = Evaluate(); - auto expected = Literal::CreateR2( + auto expected = LiteralUtil::CreateR2( {{10, 10}, {10, 10}, {10, 10}, {10, 10}, {10, 10}}); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); @@ -506,11 +506,11 @@ TEST_P(HloEvaluatorTest, Pad4DFloatArrayWithInteriorPadding) { HloComputation::Builder b(TestName()); Array4D input_array(3, 2, 1, 1, {1, 2, 3, 4, 5, 6}); - auto input = Literal::CreateR4FromArray4D(input_array); + auto input = LiteralUtil::CreateR4FromArray4D(input_array); HloInstruction* input_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(input))); constexpr float kPadValue = 1.5; - auto pad_value = Literal::CreateR0(kPadValue); + auto pad_value = LiteralUtil::CreateR0(kPadValue); HloInstruction* pad_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(pad_value))); @@ -532,7 +532,7 @@ TEST_P(HloEvaluatorTest, Pad4DFloatArrayWithInteriorPadding) { (*expected_array)(7, 0, 0, 0) = 5.0f; (*expected_array)(7, 2, 0, 0) = 6.0f; - auto expected = Literal::CreateR4FromArray4D(*expected_array); + auto expected = LiteralUtil::CreateR4FromArray4D(*expected_array); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -549,12 +549,12 @@ TEST_P(HloEvaluatorTest, NegativePadding2D) { // } auto input_array = MakeUnique>(4, 3); input_array->FillUnique(1.0f); - auto input = Literal::CreateR2FromArray2D(*input_array); + auto input = LiteralUtil::CreateR2FromArray2D(*input_array); HloInstruction* input_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(input))); auto pad_value_instruction = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.718f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.718f))); auto r2_padding_on_dim0_dim1 = CreatePaddingConfig({{{-1, -2, 0}}, {{-2, 4, 0}}}); @@ -574,7 +574,7 @@ TEST_P(HloEvaluatorTest, NegativePadding2D) { (*expected_array)(0, 2) = 2.718f; (*expected_array)(0, 3) = 2.718f; (*expected_array)(0, 4) = 2.718f; - auto expected = Literal::CreateR2FromArray2D(*expected_array); + auto expected = LiteralUtil::CreateR2FromArray2D(*expected_array); EXPECT_TRUE(LiteralTestUtil::Near(*expected, *result, ErrorSpec(0.031250))); } @@ -590,12 +590,12 @@ TEST_P(HloEvaluatorTest, NegativeAndInteriorPadding2D) { // } auto input_array = MakeUnique>(4, 3); input_array->FillUnique(1.0f); - auto input = Literal::CreateR2FromArray2D(*input_array); + auto input = LiteralUtil::CreateR2FromArray2D(*input_array); HloInstruction* input_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(input))); auto pad_value_instruction = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.718f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.718f))); PaddingConfig padding_config = MakeNoPaddingConfig(2); @@ -613,7 +613,7 @@ TEST_P(HloEvaluatorTest, NegativeAndInteriorPadding2D) { std::unique_ptr result = Evaluate(); auto expected_array = MakeUnique>(0, 9); - auto expected = Literal::CreateR2FromArray2D(*expected_array); + auto expected = LiteralUtil::CreateR2FromArray2D(*expected_array); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -630,13 +630,13 @@ TEST_P(HloEvaluatorTest, DotRank2AndRank1) { // } auto lhs_array = MakeUnique>(4, 1); lhs_array->FillUnique(1.0f); - auto lhs_literal = Literal::CreateR2FromArray2D(*lhs_array); + auto lhs_literal = LiteralUtil::CreateR2FromArray2D(*lhs_array); HloInstruction* lhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal))); // rhs: // f32[2] { 1, 2 }, - auto rhs_literal = Literal::CreateR2({{1, 2}}); + auto rhs_literal = LiteralUtil::CreateR2({{1, 2}}); HloInstruction* rhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal))); @@ -658,7 +658,7 @@ TEST_P(HloEvaluatorTest, DotRank2AndRank1) { {4.f, 8.f}, }); // clang-format on - auto expected = Literal::CreateR2FromArray2D(expected_array); + auto expected = LiteralUtil::CreateR2FromArray2D(expected_array); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -669,7 +669,7 @@ TEST_P(HloEvaluatorTest, DotRank1AndRank2) { // lhs: // f32[3] // { 1, 2, 3 }, - auto lhs_literal = Literal::CreateR1({1, 2, 3}); + auto lhs_literal = LiteralUtil::CreateR1({1, 2, 3}); HloInstruction* lhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal))); @@ -681,7 +681,7 @@ TEST_P(HloEvaluatorTest, DotRank1AndRank2) { // } auto rhs_array = MakeUnique>(3, 2); rhs_array->FillUnique(1.0f); - auto rhs_literal = Literal::CreateR2FromArray2D(*rhs_array); + auto rhs_literal = LiteralUtil::CreateR2FromArray2D(*rhs_array); HloInstruction* rhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal))); @@ -695,7 +695,7 @@ TEST_P(HloEvaluatorTest, DotRank1AndRank2) { std::unique_ptr result = Evaluate(); - auto expected = Literal::CreateR1({22.f, 28.f}); + auto expected = LiteralUtil::CreateR1({22.f, 28.f}); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -712,7 +712,7 @@ TEST_P(HloEvaluatorTest, DotRank2AndRank2) { // } auto lhs_array = MakeUnique>(4, 3); lhs_array->FillUnique(1.0f); - auto lhs_literal = Literal::CreateR2FromArray2D(*lhs_array); + auto lhs_literal = LiteralUtil::CreateR2FromArray2D(*lhs_array); HloInstruction* lhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal))); @@ -724,7 +724,7 @@ TEST_P(HloEvaluatorTest, DotRank2AndRank2) { // } auto rhs_array = MakeUnique>(3, 2); rhs_array->FillUnique(1.0f); - auto rhs_literal = Literal::CreateR2FromArray2D(*rhs_array); + auto rhs_literal = LiteralUtil::CreateR2FromArray2D(*rhs_array); HloInstruction* rhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal))); @@ -744,7 +744,7 @@ TEST_P(HloEvaluatorTest, DotRank2AndRank2) { {94.f, 124.f}, {130.f, 172.f}, }); - auto expected = Literal::CreateR2FromArray2D(expected_array); + auto expected = LiteralUtil::CreateR2FromArray2D(expected_array); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -753,12 +753,12 @@ TEST_P(HloEvaluatorTest, SimpleConv1D) { HloComputation::Builder b(TestName()); Array3D lhs_array = {{{1, 2, 3}}}; - auto lhs_literal = Literal::CreateR3FromArray3D(lhs_array); + auto lhs_literal = LiteralUtil::CreateR3FromArray3D(lhs_array); HloInstruction* lhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal))); Array3D rhs_array = {{{3.f, 4.f}}}; - auto rhs_literal = Literal::CreateR3FromArray3D(rhs_array); + auto rhs_literal = LiteralUtil::CreateR3FromArray3D(rhs_array); HloInstruction* rhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal))); @@ -792,7 +792,7 @@ TEST_P(HloEvaluatorTest, SimpleConv1D) { std::unique_ptr result = Evaluate(); Array3D expected_array = {{{11.f, 18.f, 9.f}}}; - auto expected = Literal::CreateR3FromArray3D(expected_array); + auto expected = LiteralUtil::CreateR3FromArray3D(expected_array); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -809,7 +809,7 @@ TEST_P(HloEvaluatorTest, Simple4x4Conv2DWith2x2Kernel) { {13, 14, 15, 16}, })); // clang-format on - auto lhs_literal = Literal::CreateR4FromArray4D(lhs_array); + auto lhs_literal = LiteralUtil::CreateR4FromArray4D(lhs_array); HloInstruction* lhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal))); @@ -820,7 +820,7 @@ TEST_P(HloEvaluatorTest, Simple4x4Conv2DWith2x2Kernel) { {7, 8}, })); // clang-format on - auto rhs_literal = Literal::CreateR4FromArray4D(rhs_array); + auto rhs_literal = LiteralUtil::CreateR4FromArray4D(rhs_array); HloInstruction* rhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal))); @@ -854,7 +854,7 @@ TEST_P(HloEvaluatorTest, Simple4x4Conv2DWith2x2Kernel) { {149, 160, 171, 80}, })); // clang-format on - auto expected = Literal::CreateR4FromArray4D(expected_array); + auto expected = LiteralUtil::CreateR4FromArray4D(expected_array); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -884,11 +884,11 @@ TEST_P(HloEvaluatorTest, Conv2DGeneralDimensionsReversed) { }}); // clang-format on - auto lhs_literal = Literal::CreateR4FromArray4D(input); + auto lhs_literal = LiteralUtil::CreateR4FromArray4D(input); HloInstruction* lhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal))); - auto rhs_literal = Literal::CreateR4FromArray4D(weight); + auto rhs_literal = LiteralUtil::CreateR4FromArray4D(weight); HloInstruction* rhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal))); rhs_instruction = b.AddInstruction(HloInstruction::CreateReverse( @@ -933,7 +933,7 @@ TEST_P(HloEvaluatorTest, Conv2DGeneralDimensionsReversed) { Array4D expected_array({{{{2514, 2685}}}}); Array4D expected_array_bf16({{{{2512, 2672}}}}); // clang-format on - auto expected = Literal::CreateR4FromArray4D( + auto expected = LiteralUtil::CreateR4FromArray4D( use_bfloat16_ ? expected_array_bf16 : expected_array); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); @@ -964,11 +964,11 @@ TEST_P(HloEvaluatorTest, Conv2DGeneralDimensions) { }}); // clang-format on - auto lhs_literal = Literal::CreateR4FromArray4D(input); + auto lhs_literal = LiteralUtil::CreateR4FromArray4D(input); HloInstruction* lhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal))); - auto rhs_literal = Literal::CreateR4FromArray4D(weight); + auto rhs_literal = LiteralUtil::CreateR4FromArray4D(weight); HloInstruction* rhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal))); @@ -1010,7 +1010,7 @@ TEST_P(HloEvaluatorTest, Conv2DGeneralDimensions) { Array4D expected_array({{{{2514, 2685}}}}); Array4D expected_array_bf16({{{{2512, 2672}}}}); // clang-format on - auto expected = Literal::CreateR4FromArray4D( + auto expected = LiteralUtil::CreateR4FromArray4D( use_bfloat16_ ? expected_array_bf16 : expected_array); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); @@ -1028,7 +1028,7 @@ TEST_P(HloEvaluatorTest, DilatedBaseConv2DWithHighPadding) { {13, 14, 15, 16}, })); // clang-format on - auto lhs_literal = Literal::CreateR4FromArray4D(lhs_array); + auto lhs_literal = LiteralUtil::CreateR4FromArray4D(lhs_array); HloInstruction* lhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal))); @@ -1039,7 +1039,7 @@ TEST_P(HloEvaluatorTest, DilatedBaseConv2DWithHighPadding) { {7, 8}, })); // clang-format on - auto rhs_literal = Literal::CreateR4FromArray4D(rhs_array); + auto rhs_literal = LiteralUtil::CreateR4FromArray4D(rhs_array); HloInstruction* rhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal))); @@ -1074,7 +1074,7 @@ TEST_P(HloEvaluatorTest, DilatedBaseConv2DWithHighPadding) { {91, 112, 98, 120, 105, 128, 112}, {65, 84, 70, 90, 75, 96, 80}, })); - auto expected = Literal::CreateR4FromArray4D(expected_array); + auto expected = LiteralUtil::CreateR4FromArray4D(expected_array); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -1091,7 +1091,7 @@ TEST_P(HloEvaluatorTest, DilatedBaseConv2DWithLowAndHighPadding) { {13, 14, 15, 16}, })); // clang-format on - auto lhs_literal = Literal::CreateR4FromArray4D(lhs_array); + auto lhs_literal = LiteralUtil::CreateR4FromArray4D(lhs_array); HloInstruction* lhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal))); @@ -1102,7 +1102,7 @@ TEST_P(HloEvaluatorTest, DilatedBaseConv2DWithLowAndHighPadding) { {7, 8}, })); // clang-format on - auto rhs_literal = Literal::CreateR4FromArray4D(rhs_array); + auto rhs_literal = LiteralUtil::CreateR4FromArray4D(rhs_array); HloInstruction* rhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal))); @@ -1138,7 +1138,7 @@ TEST_P(HloEvaluatorTest, DilatedBaseConv2DWithLowAndHighPadding) { {104, 91, 112, 98, 120, 105, 128, 112}, {78, 65, 84, 70, 90, 75, 96, 80}, })); - auto expected = Literal::CreateR4FromArray4D(expected_array); + auto expected = LiteralUtil::CreateR4FromArray4D(expected_array); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -1156,7 +1156,7 @@ TEST_P(HloEvaluatorTest, {13, 14, 15, 16}, })); // clang-format on - auto lhs_literal = Literal::CreateR4FromArray4D(lhs_array); + auto lhs_literal = LiteralUtil::CreateR4FromArray4D(lhs_array); HloInstruction* lhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(lhs_literal))); @@ -1167,7 +1167,7 @@ TEST_P(HloEvaluatorTest, {8, 9, 10}, })); // clang-format on - auto rhs_literal = Literal::CreateR4FromArray4D(rhs_array); + auto rhs_literal = LiteralUtil::CreateR4FromArray4D(rhs_array); HloInstruction* rhs_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(rhs_literal))); @@ -1210,7 +1210,7 @@ TEST_P(HloEvaluatorTest, {0, 0, 0}, {91, 98, 105}, })); - auto expected = Literal::CreateR4FromArray4D(expected_array); + auto expected = LiteralUtil::CreateR4FromArray4D(expected_array); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -1225,9 +1225,9 @@ TEST_F(HloEvaluatorPreciseReduceTest, AddReductionPrecisionTest) { constexpr int kNumElements = 1 << 25; // float += 1 saturates at 1<<24 std::vector v(kNumElements, 1.0f); HloInstruction* arg_instruction = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1(v))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1(v))); HloInstruction* init_value = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.f))); HloComputation::Builder add_computation("add"); Shape scalar_shape = ShapeUtil::MakeShape(F32, {}); @@ -1262,9 +1262,9 @@ void BM_ReducePrecisely(int num_iters) { constexpr int kNumElements = 1 << 25; // float += 1 saturates at 1<<24 std::vector v(kNumElements, 1.0f); HloInstruction* arg_instruction = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1(v))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1(v))); auto init_value = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.f))); HloComputation::Builder add_computation("add"); Shape scalar_shape = ShapeUtil::MakeShape(F32, {}); @@ -1299,13 +1299,13 @@ TEST_P(HloEvaluatorTest, ReduceAdd) { // } auto arg_array = MakeUnique>(2, 3); arg_array->FillUnique(1.0f); - auto arg_literal = Literal::CreateR2FromArray2D(*arg_array); + auto arg_literal = LiteralUtil::CreateR2FromArray2D(*arg_array); HloInstruction* arg_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(arg_literal))); auto init_value = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.f))); HloComputation::Builder add_computation("add"); Shape scalar_shape = ShapeUtil::MakeShape(F32, {}); @@ -1326,7 +1326,7 @@ TEST_P(HloEvaluatorTest, ReduceAdd) { std::unique_ptr result = Evaluate(); - auto expected = Literal::CreateR1({6, 18}); + auto expected = LiteralUtil::CreateR1({6, 18}); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -1341,13 +1341,13 @@ TEST_P(HloEvaluatorTest, ReduceWindowMax) { // } auto arg_array = MakeUnique>(2, 3); arg_array->FillUnique(1.0f); - auto arg_literal = Literal::CreateR2FromArray2D(*arg_array); + auto arg_literal = LiteralUtil::CreateR2FromArray2D(*arg_array); HloInstruction* arg_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(arg_literal))); auto init_value = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.f))); HloComputation::Builder max_computation("max"); Shape scalar_shape = ShapeUtil::MakeShape(F32, {}); @@ -1378,7 +1378,7 @@ TEST_P(HloEvaluatorTest, ReduceWindowMax) { std::unique_ptr result = Evaluate(); - auto expected = Literal::CreateR2({{6, 7}}); + auto expected = LiteralUtil::CreateR2({{6, 7}}); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -1392,13 +1392,13 @@ TEST_P(HloEvaluatorTest, ReduceWindowAdd) { // } auto arg_array = MakeUnique>(2, 3); arg_array->FillUnique(1.0f); - auto arg_literal = Literal::CreateR2FromArray2D(*arg_array); + auto arg_literal = LiteralUtil::CreateR2FromArray2D(*arg_array); HloInstruction* arg_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(arg_literal))); auto init_value = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.f))); HloComputation::Builder add_computation("add"); Shape scalar_shape = ShapeUtil::MakeShape(F32, {}); @@ -1435,7 +1435,7 @@ TEST_P(HloEvaluatorTest, ReduceWindowAdd) { std::unique_ptr result = Evaluate(); - auto expected = Literal::CreateR2({{1, 3, 5}, {5, 11, 13}}); + auto expected = LiteralUtil::CreateR2({{1, 3, 5}, {5, 11, 13}}); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *result)); } @@ -1445,13 +1445,13 @@ TEST_P(HloEvaluatorTest, ReduceWindowAdd6D) { // arg: f32[4,4,4,4,4,4] full of ones. Using small dims to limit run-time. std::vector input_dims(6, 4); std::unique_ptr arg_literal = - Literal::CreateFullWithDescendingLayout(input_dims, 1.0f); + LiteralUtil::CreateFullWithDescendingLayout(input_dims, 1.0f); HloInstruction* arg_instruction = b.AddInstruction(HloInstruction::CreateConstant(std::move(arg_literal))); auto init_value = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.f))); HloComputation::Builder add_computation("add"); Shape scalar_shape = ShapeUtil::MakeShape(F32, {}); @@ -1498,7 +1498,7 @@ TEST_P(HloEvaluatorTest, ReduceWindowAdd6D) { std::vector output_dims = {4, 3, 3, 3, 4, 4}; std::unique_ptr result_literal = - Literal::CreateFullWithDescendingLayout(output_dims, 8.0f); + LiteralUtil::CreateFullWithDescendingLayout(output_dims, 8.0f); EXPECT_TRUE(LiteralTestUtil::Equal(*result_literal, *result)); } @@ -1513,7 +1513,8 @@ TEST_P(HloEvaluatorTest, StridedSlice) { // } auto operand_array = MakeUnique>(3, 5); operand_array->FillUnique(1.0f); - auto operand_literal = Literal::CreateR2FromArray2D(*operand_array); + auto operand_literal = + LiteralUtil::CreateR2FromArray2D(*operand_array); HloInstruction* operand = b.AddInstruction( HloInstruction::CreateConstant(std::move(operand_literal))); @@ -1527,7 +1528,7 @@ TEST_P(HloEvaluatorTest, StridedSlice) { std::unique_ptr result = Evaluate(); - auto expected = Literal::CreateR2({ + auto expected = LiteralUtil::CreateR2({ {3}, {19}, }); @@ -1545,13 +1546,14 @@ TEST_P(HloEvaluatorTest, DynamicSlice) { // } auto operand_array = MakeUnique>(2, 4); operand_array->FillUnique(1.0f); - auto operand_literal = Literal::CreateR2FromArray2D(*operand_array); + auto operand_literal = + LiteralUtil::CreateR2FromArray2D(*operand_array); HloInstruction* operand = b.AddInstruction( HloInstruction::CreateConstant(std::move(operand_literal))); auto start_indices = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({0, 1}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({0, 1}))); Shape shape = ShapeUtil::MakeShape(F32, {2, 3}); b.AddInstruction(HloInstruction::CreateDynamicSlice(shape, operand, @@ -1560,7 +1562,7 @@ TEST_P(HloEvaluatorTest, DynamicSlice) { std::unique_ptr result = Evaluate(); - auto expected = Literal::CreateR2({ + auto expected = LiteralUtil::CreateR2({ {2, 3, 4}, {6, 7, 8}, }); @@ -1580,13 +1582,14 @@ TEST_P(HloEvaluatorTest, DynamicSliceModSlice) { // } auto operand_array = MakeUnique>(2, 4); operand_array->FillUnique(1.0f); - auto operand_literal = Literal::CreateR2FromArray2D(*operand_array); + auto operand_literal = + LiteralUtil::CreateR2FromArray2D(*operand_array); HloInstruction* operand = b.AddInstruction( HloInstruction::CreateConstant(std::move(operand_literal))); auto start_indices = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({2, 1}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({2, 1}))); Shape shape = ShapeUtil::MakeShape(F32, {2, 3}); b.AddInstruction(HloInstruction::CreateDynamicSlice(shape, operand, @@ -1595,7 +1598,7 @@ TEST_P(HloEvaluatorTest, DynamicSliceModSlice) { std::unique_ptr result = Evaluate(); - auto expected = Literal::CreateR2({ + auto expected = LiteralUtil::CreateR2({ {2, 3, 4}, {6, 7, 8}, }); @@ -1613,16 +1616,17 @@ TEST_P(HloEvaluatorTest, DynamicSliceUpdate) { // } auto operand_array = MakeUnique>(2, 3); operand_array->FillUnique(1.0); - auto operand_literal = Literal::CreateR2FromArray2D(*operand_array); + auto operand_literal = + LiteralUtil::CreateR2FromArray2D(*operand_array); HloInstruction* operand = b.AddInstruction( HloInstruction::CreateConstant(std::move(operand_literal))); auto start_indices = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({0, 1}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({0, 1}))); auto update = b.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{-2.0, -3.0}, {-6.0, -7.0}}))); + LiteralUtil::CreateR2({{-2.0, -3.0}, {-6.0, -7.0}}))); Shape shape = ShapeUtil::MakeShape(F64, {2, 3}); b.AddInstruction(HloInstruction::CreateDynamicUpdateSlice( @@ -1631,7 +1635,7 @@ TEST_P(HloEvaluatorTest, DynamicSliceUpdate) { std::unique_ptr result = Evaluate(); - auto expected = Literal::CreateR2({ + auto expected = LiteralUtil::CreateR2({ {1, -2, -3}, {5, -6, -7}, }); @@ -1649,12 +1653,13 @@ TEST_P(HloEvaluatorTest, SetAndGetTuples) { // } auto operand_array = MakeUnique>(2, 3); operand_array->FillUnique(1.0); - auto operand_literal2 = Literal::CreateR2FromArray2D(*operand_array); + auto operand_literal2 = + LiteralUtil::CreateR2FromArray2D(*operand_array); HloInstruction* operand2 = b.AddInstruction( HloInstruction::CreateConstant(std::move(operand_literal2))); HloInstruction* operand1 = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({0, 1}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({0, 1}))); auto tuple = b.AddInstruction(HloInstruction::CreateTuple({operand1, operand2})); @@ -1666,7 +1671,7 @@ TEST_P(HloEvaluatorTest, SetAndGetTuples) { std::unique_ptr result = Evaluate(); - auto expected = Literal::CreateR2({ + auto expected = LiteralUtil::CreateR2({ {1, 2, 3}, {5, 6, 7}, }); @@ -1686,9 +1691,9 @@ TEST_P(HloEvaluatorTest, SetAndGetNestedTuples) { operand_array->FillUnique(1.0); HloInstruction* operand2 = b.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2FromArray2D(*operand_array))); + LiteralUtil::CreateR2FromArray2D(*operand_array))); HloInstruction* operand1 = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({0, 1}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({0, 1}))); auto tuple1 = b.AddInstruction(HloInstruction::CreateTuple({operand1, operand2})); @@ -1706,8 +1711,8 @@ TEST_P(HloEvaluatorTest, SetAndGetNestedTuples) { std::unique_ptr result = Evaluate(); auto result_inner_literal = - Literal::CreateR2FromArray2D(*operand_array); - auto expected = Literal::MakeTuple({ + LiteralUtil::CreateR2FromArray2D(*operand_array); + auto expected = LiteralUtil::MakeTuple({ result_inner_literal.get(), result_inner_literal.get(), }); @@ -1735,7 +1740,7 @@ TEST_P(HloEvaluatorTest, Reverse) { {{23.0f}, {24.0f}}}, }); // clang-format on - auto operand_literal = Literal::CreateR4FromArray4D(input); + auto operand_literal = LiteralUtil::CreateR4FromArray4D(input); HloInstruction* operand = b.AddInstruction( HloInstruction::CreateConstant(std::move(operand_literal))); @@ -1746,7 +1751,7 @@ TEST_P(HloEvaluatorTest, Reverse) { std::unique_ptr result = Evaluate(); // clang-format off - auto expected = Literal::CreateR4FromArray4D({ + auto expected = LiteralUtil::CreateR4FromArray4D({ {{{23.0f}, {24.0f}}, {{21.0f}, {22.0f}}, {{19.0f}, {20.0f}}}, @@ -1782,11 +1787,11 @@ TEST_P(HloEvaluatorTest, EvaluateWithSubstitutions) { // Evaluate add with param0 = {1, 2, 3, 4}, square = {10, 20, 30, 40}. HloEvaluator evaluator; auto result = evaluator.EvaluateWithSubstitutions( - add, {{param0, Literal::CreateR1({1, 2, 3, 4}).get()}, - {square, Literal::CreateR1({10, 20, 30, 40}).get()}}); + add, {{param0, LiteralUtil::CreateR1({1, 2, 3, 4}).get()}, + {square, LiteralUtil::CreateR1({10, 20, 30, 40}).get()}}); TF_ASSERT_OK(result.status()); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::CreateR1({11, 22, 33, 44}), *result.ValueOrDie())); + *LiteralUtil::CreateR1({11, 22, 33, 44}), *result.ValueOrDie())); } // Check that EvaluateWithSubstitutions works if one of the operands to the op @@ -1799,18 +1804,18 @@ TEST_P(HloEvaluatorTest, EvaluateWithSubstitutionsWithConstantOperand) { b.AddInstruction(HloInstruction::CreateParameter(0, shape, "param0")); HloInstruction* square = b.AddInstruction(HloInstruction::CreateBinary( shape, HloOpcode::kMultiply, param0, param0)); - HloInstruction* constant = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3, 4}))); + HloInstruction* constant = b.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR1({1, 2, 3, 4}))); HloInstruction* add = b.AddInstruction( HloInstruction::CreateBinary(shape, HloOpcode::kAdd, constant, square)); // Evaluate add with square = {10, 20, 30, 40}. HloEvaluator evaluator; auto result = evaluator.EvaluateWithSubstitutions( - add, {{square, Literal::CreateR1({10, 20, 30, 40}).get()}}); + add, {{square, LiteralUtil::CreateR1({10, 20, 30, 40}).get()}}); TF_ASSERT_OK(result.status()); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::CreateR1({11, 22, 33, 44}), *result.ValueOrDie())); + *LiteralUtil::CreateR1({11, 22, 33, 44}), *result.ValueOrDie())); } TEST_P(HloEvaluatorTest, EvaluateGather_TensorFlowGatherV1) { @@ -1830,11 +1835,12 @@ ENTRY main { )"; ParseAndVerifyModule(hlo_text); std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); - std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); - EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR2({{1, 2, 3}, {7, 8, 9}}), - *Evaluate({operand.get(), gather_indices.get()}))); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + LiteralUtil::CreateR1({0, 2}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *LiteralUtil::CreateR2({{1, 2, 3}, {7, 8, 9}}), + *Evaluate({operand.get(), gather_indices.get()}))); } TEST_P(HloEvaluatorTest, EvaluateGather_TensorFlowGatherV2) { @@ -1854,10 +1860,11 @@ ENTRY main { )"; ParseAndVerifyModule(hlo_text); std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); - std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + LiteralUtil::CreateR1({0, 2}); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::CreateR2({{1, 3}, {4, 6}, {7, 9}}), + *LiteralUtil::CreateR2({{1, 3}, {4, 6}, {7, 9}}), *Evaluate({operand.get(), gather_indices.get()}))); } @@ -1878,11 +1885,11 @@ ENTRY main { )"; ParseAndVerifyModule(hlo_text); std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); std::unique_ptr gather_indices = - Literal::CreateR2({{0, 2}, {2, 1}}); + LiteralUtil::CreateR2({{0, 2}, {2, 1}}); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::CreateR3( + *LiteralUtil::CreateR3( {{{1, 3}, {4, 6}, {7, 9}}, {{3, 2}, {6, 5}, {9, 8}}}), *Evaluate({operand.get(), gather_indices.get()}))); } @@ -1904,13 +1911,13 @@ ENTRY main { )"; ParseAndVerifyModule(hlo_text); std::unique_ptr operand = - Literal::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // - {{-4, 4}, {-5, 5}, {-6, 6}}, // - {{-7, 7}, {-8, 8}, {-9, 9}}}); + LiteralUtil::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); std::unique_ptr gather_indices = - Literal::CreateR2({{0, 0}, {1, 0}}); + LiteralUtil::CreateR2({{0, 0}, {1, 0}}); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR2({{-1, 1}, {-4, 4}}), + LiteralTestUtil::Equal(*LiteralUtil::CreateR2({{-1, 1}, {-4, 4}}), *Evaluate({operand.get(), gather_indices.get()}))); } @@ -1932,13 +1939,13 @@ ENTRY main { )"; ParseAndVerifyModule(hlo_text); std::unique_ptr operand = - Literal::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // - {{-4, 4}, {-5, 5}, {-6, 6}}, // - {{-7, 7}, {-8, 8}, {-9, 9}}}); + LiteralUtil::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); std::unique_ptr gather_indices = - Literal::CreateR2({{0, 0}, {1, 0}}); + LiteralUtil::CreateR2({{0, 0}, {1, 0}}); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR2({{-2, 2}, {-1, 1}}), + LiteralTestUtil::Equal(*LiteralUtil::CreateR2({{-2, 2}, {-1, 1}}), *Evaluate({operand.get(), gather_indices.get()}))); } @@ -1959,10 +1966,11 @@ ENTRY main { )"; ParseAndVerifyModule(hlo_text); std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); - std::unique_ptr gather_indices = Literal::CreateR1({1, 1}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + LiteralUtil::CreateR1({1, 1}); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR2({{5}}), + LiteralTestUtil::Equal(*LiteralUtil::CreateR2({{5}}), *Evaluate({operand.get(), gather_indices.get()}))); } @@ -1983,11 +1991,11 @@ ENTRY main { )"; ParseAndVerifyModule(hlo_text); std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); std::unique_ptr gather_indices = - Literal::CreateR2({{2, 1}, {1, 1}}); + LiteralUtil::CreateR2({{2, 1}, {1, 1}}); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR3({{{8}}, {{5}}}), + LiteralTestUtil::Equal(*LiteralUtil::CreateR3({{{8}}, {{5}}}), *Evaluate({operand.get(), gather_indices.get()}))); } @@ -2007,10 +2015,11 @@ ENTRY main { } )"; ParseAndVerifyModule(hlo_text); - std::unique_ptr operand = Literal::CreateR2({{}, {}, {}}); - std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + std::unique_ptr operand = LiteralUtil::CreateR2({{}, {}, {}}); + std::unique_ptr gather_indices = + LiteralUtil::CreateR1({0, 2}); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR2({{}, {}}), + LiteralTestUtil::Equal(*LiteralUtil::CreateR2({{}, {}}), *Evaluate({operand.get(), gather_indices.get()}))); } @@ -2031,11 +2040,11 @@ ENTRY main { )"; ParseAndVerifyModule(hlo_text); - std::unique_ptr operand = Literal::CreateR1({0, 1, 2}); + std::unique_ptr operand = LiteralUtil::CreateR1({0, 1, 2}); std::unique_ptr gather_indices = - Literal::CreateR3({{{0}, {1}}, {{2}, {1}}}); + LiteralUtil::CreateR3({{{0}, {1}}, {{2}, {1}}}); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR2({{0, 1}, {2, 1}}), + LiteralTestUtil::Equal(*LiteralUtil::CreateR2({{0, 1}, {2, 1}}), *Evaluate({operand.get(), gather_indices.get()}))); } @@ -2043,14 +2052,14 @@ ENTRY main { // element-wise comparison with 2 bfloat16 operands. TEST_P(HloEvaluatorTest, DoesCompareBF16) { // lhs >= rhs - auto lhs = Literal::CreateR2( + auto lhs = LiteralUtil::CreateR2( {{bfloat16(0.25), bfloat16(0.35), bfloat16(0.125)}, {bfloat16(-0.25), bfloat16(-0.35), bfloat16(-0.125)}}); - auto rhs = Literal::CreateR2( + auto rhs = LiteralUtil::CreateR2( {{bfloat16(0.5), bfloat16(0.125), bfloat16(0.125)}, {bfloat16(0.25), bfloat16(-0.375), bfloat16(-0.127)}}); auto expected = - Literal::CreateR2({{false, true, true}, {false, true, true}}); + LiteralUtil::CreateR2({{false, true, true}, {false, true, true}}); TestBinaryOp(HloOpcode::kGe, std::move(expected), std::move(lhs), std::move(rhs)); } diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h index cdbac74ba4c9855ca586c8a6ef37b1e507eedea4..d5b4be7e1284509a4494b0e804e5396c7cfcecc2 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h @@ -16,6 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_EVALUATOR_TYPED_VISITOR_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_EVALUATOR_TYPED_VISITOR_H_ +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/hlo_evaluator.h" #include "tensorflow/compiler/xla/service/shape_inference.h" #include "tensorflow/core/lib/core/casts.h" @@ -300,6 +301,14 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { return HandleFloor(floor); } + Status HandleImag(HloInstruction* imag) override { + TF_ASSIGN_OR_RETURN(parent_->evaluated_[imag], + ElementWiseUnaryOp(imag, [](ElementwiseT elem_operand) { + return std::imag(elem_operand); + })); + return Status::OK(); + } + Status HandleLog(HloInstruction* log) override { TF_ASSIGN_OR_RETURN(parent_->evaluated_[log], ElementWiseUnaryOp(log, [](ElementwiseT elem_operand) { @@ -603,6 +612,14 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { return Status::OK(); } + Status HandleReal(HloInstruction* real) override { + TF_ASSIGN_OR_RETURN(parent_->evaluated_[real], + ElementWiseUnaryOp(real, [](ElementwiseT elem_operand) { + return std::real(elem_operand); + })); + return Status::OK(); + } + template < typename NativeT, typename std::enable_if::value>::type* = nullptr> @@ -1316,7 +1333,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { parent_->GetEvaluatedLiteralFor(operand); auto curr_val = arg_literal.Get(multi_index); - auto curr_val_literal = Literal::CreateR0(curr_val); + auto curr_val_literal = LiteralUtil::CreateR0(curr_val); arg_literals.push_back(std::move(curr_val_literal)); } @@ -1398,25 +1415,48 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { !std::is_same::value>::type* = nullptr> Status HandleSort(HloInstruction* sort) { auto keys = sort->operand(0); - TF_RET_CHECK(ShapeUtil::Rank(keys->shape()) == 1) - << "Sort is only supported for R1 shapes"; + auto rank = ShapeUtil::Rank(keys->shape()); + TF_RET_CHECK(rank > 0 && rank <= 2) + << "Sort is only supported for R1 and R2 shapes"; TF_RET_CHECK(sort->operand_count() == 1) << "Typed visitor does not support key-value sort"; const Literal& keys_literal = parent_->GetEvaluatedLiteralFor(keys); - VLOG(3) << "HandleSort keys_literal: " << keys_literal.ToString(); - const auto& keys_data = keys_literal.data(); - std::vector result_data(keys_data.begin(), keys_data.end()); - std::sort(result_data.begin(), result_data.end(), - [](const ReturnT& a, const ReturnT& b) { - return SafeLess(a, b); - }); - auto result_literal = MakeUnique(sort->shape()); - result_literal->PopulateR1( - tensorflow::gtl::ArraySlice(result_data)); - VLOG(3) << "HandleSort result_literal: " << result_literal->ToString(); - parent_->evaluated_[sort] = std::move(result_literal); + auto sort_r1 = [this](const Literal& keys_literal) { + VLOG(3) << "HandleSort keys_literal: " << keys_literal.ToString(); + const auto& keys_data = keys_literal.data(); + + std::vector result_data(keys_data.begin(), keys_data.end()); + std::sort(result_data.begin(), result_data.end(), + [](const ReturnT& a, const ReturnT& b) { + return SafeLess(a, b); + }); + auto result_literal = MakeUnique(keys_literal.shape()); + result_literal->PopulateR1( + tensorflow::gtl::ArraySlice(result_data)); + VLOG(3) << "HandleSort result_literal: " << result_literal->ToString(); + return result_literal; + }; + + if (rank == 1) { + parent_->evaluated_[sort] = std::move(sort_r1(keys_literal)); + } else { + // For R2 sort, the desired semantics are to sort each matrix row + // independently. + auto result_literal = MakeUnique(keys_literal.shape()); + int64 r1_length = keys->shape().dimensions(1); + for (int64 row = 0; row < keys->shape().dimensions(0); ++row) { + TF_ASSIGN_OR_RETURN(auto r1_slice, + keys_literal.Slice({row, 0}, {row + 1, r1_length}) + ->Reshape({r1_length})); + auto r1_result = sort_r1(*r1_slice); + TF_ASSIGN_OR_RETURN(r1_result, r1_result->Reshape({1, r1_length})); + TF_RETURN_IF_ERROR(result_literal->CopySliceFrom( + *r1_result, {0, 0}, {row, 0}, {1, r1_length})); + } + parent_->evaluated_[sort] = std::move(result_literal); + } return Status::OK(); } @@ -1504,8 +1544,9 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { auto curr_val = arg_literal.Get(input_index); // Evaluate computation with specified literal operands. - auto curr_val_literal = Literal::CreateR0(curr_val); - auto result_val_literal = Literal::CreateR0(result_val); + auto curr_val_literal = LiteralUtil::CreateR0(curr_val); + auto result_val_literal = + LiteralUtil::CreateR0(result_val); std::unique_ptr computed_result = embedded_evaluator @@ -1583,10 +1624,10 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { // Used in the dual IterateThroughWindow lambdas below. Hoisted to avoid // dynamic memory allocations. - auto curr_val_literal = Literal::CreateR0(ReturnT()); - auto selected_val_literal = Literal::CreateR0(ReturnT()); - auto source_literal_scatter = Literal::CreateR0(ReturnT()); - auto scattered_literal = Literal::CreateR0(ReturnT()); + auto curr_val_literal = LiteralUtil::CreateR0(ReturnT()); + auto selected_val_literal = LiteralUtil::CreateR0(ReturnT()); + auto source_literal_scatter = LiteralUtil::CreateR0(ReturnT()); + auto scattered_literal = LiteralUtil::CreateR0(ReturnT()); do { // For each element in `source`, we place a window in `operand`. For each // window placement, we iterate inside the window twice: @@ -1707,9 +1748,9 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { // Evaluate computation with specified literal operands. const auto curr_val_literal = - Literal::CreateR0(curr_val); + LiteralUtil::CreateR0(curr_val); const auto result_val_literal = - Literal::CreateR0(result_val); + LiteralUtil::CreateR0(result_val); std::unique_ptr computed_result = embedded_evaluator .Evaluate( @@ -1754,7 +1795,7 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { return operand_literal.Get(operand_index); }; - auto result = Literal::CreateFromDimensions( + auto result = LiteralUtil::CreateFromDimensions( shape.element_type(), AsInt64Slice(shape.dimensions())); TF_RETURN_IF_ERROR(result->Populate(func)); parent_->evaluated_[slice] = std::move(result); @@ -1956,6 +1997,30 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { return HandleReducePrecision(reduce_precision); } + template ::value || + std::is_same::value || + std::is_same::value>::type* = nullptr> + Status HandleIota(HloInstruction* iota) { + auto result = MakeUnique(iota->shape()); + auto data = result->data(); + std::iota(data.begin(), data.end(), 0); + parent_->evaluated_[iota] = std::move(result); + return Status::OK(); + } + template ::value || + std::is_same::value || + std::is_same::value)>::type* = nullptr> + Status HandleIota(HloInstruction* iota) { + return InvalidArgument("Unsupported type for iota"); + } + Status HandleIota(HloInstruction* iota) override { + return HandleIota(iota); + } + private: // Creates a vector of multipliers which can be used to create a linear index // into shape. @@ -2013,10 +2078,6 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { start_indices_typed.end()); // Clamp the start indices so the slice is in-bounds w.r.t the operand. - - // TODO(b/74360564): This is implementation defined behavior, but is - // currently respected by all implementations. Change this if we ever decide - // to officially document different behavior. for (int64 i = 0; i < start.size(); ++i) { start[i] = std::min( std::max(int64{0}, start[i]), @@ -2050,10 +2111,6 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { start_indices_typed.end()); // Clamp the update start indices so the slice is in-bounds w.r.t the // operand. - - // TODO(b/74360564): This is implementation defined behavior, but is - // currently respected by all implementations. Change this if we ever decide - // to oficially document different behavior. for (int64 i = 0; i < rank; ++i) { start[i] = std::min( std::max(0, start[i]), diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc index 7a1372f929833a16de97c94e12b616e359b36950..fd5085bed234068a1bdf18977b38d92badc02a49 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc @@ -27,7 +27,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_casting_utils.h" #include "tensorflow/compiler/xla/service/hlo_instructions.h" #include "tensorflow/compiler/xla/service/hlo_module.h" @@ -948,6 +948,7 @@ ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) { case HloOpcode::kGe: case HloOpcode::kGt: case HloOpcode::kImag: + case HloOpcode::kIota: case HloOpcode::kIsFinite: case HloOpcode::kLe: case HloOpcode::kLog: diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc index 68f41a1cbb4db228f5dcf8b4a6130f05e81262a8..1d7a062c55696de9db4b187efd86bce191279083 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper_test.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_graph_dumper.h" +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_module.h" @@ -120,7 +121,7 @@ TEST(HloGraphDumperTest, NestedFusion) { TEST(HloGraphDumperTest, Constant) { HloComputation::Builder b("b"); auto instruction = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(-42))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(-42))); instruction->SetAndSanitizeName("i_am_a_constant_root_instruction"); HloModuleConfig config; HloModule m(TestName(), config); diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 6ea302f8b4170ea5043176a58b6f47003a79f5a5..8b9bdd2f46fe8a63b419b45ef2c2a2e025c60c8f 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -22,7 +22,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/protobuf_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor.h" @@ -115,26 +115,27 @@ StatusOr> HloInstruction::CreateFromProto( TF_RET_CHECK(proto.operand_ids_size() == 2) << "Send instruction should have 2 operand but sees " << proto.operand_ids_size(); - instruction = CreateSend(operands(0), operands(1), proto.channel_id()); + instruction = CreateSend(operands(0), operands(1), proto.channel_id(), + proto.is_host_transfer()); break; case HloOpcode::kSendDone: TF_RET_CHECK(proto.operand_ids_size() == 1) << "SendDone instruction should have 1 operand but sees " << proto.operand_ids_size(); - instruction = CreateSendDone(operands(0)); + instruction = CreateSendDone(operands(0), proto.is_host_transfer()); break; case HloOpcode::kRecv: TF_RET_CHECK(proto.operand_ids_size() == 1) << "Recv instruction should have 1 operand but sees " << proto.operand_ids_size(); instruction = CreateRecv(proto.shape().tuple_shapes(0), operands(0), - proto.channel_id()); + proto.channel_id(), proto.is_host_transfer()); break; case HloOpcode::kRecvDone: TF_RET_CHECK(proto.operand_ids_size() == 1) << "RecvDone instruction should have 1 operand but sees " << proto.operand_ids_size(); - instruction = CreateRecvDone(operands(0)); + instruction = CreateRecvDone(operands(0), proto.is_host_transfer()); break; case HloOpcode::kReverse: TF_RET_CHECK(proto.operand_ids_size() == 1) @@ -163,6 +164,20 @@ StatusOr> HloInstruction::CreateFromProto( proto.dimensions().end()), computations(0)); break; + case HloOpcode::kSort: { + TF_RET_CHECK(proto.operand_ids_size() == 1 || + proto.operand_ids_size() == 2) + << "Sort instruction should have 1 or 2 operands but has " + << proto.operand_ids_size(); + TF_RET_CHECK(proto.dimensions().size() == 1) + << "Sort instruction should have 1 dimension"; + HloInstruction* keys = operands(0); + HloInstruction* values = + proto.operand_ids_size() == 2 ? operands(1) : nullptr; + instruction = + CreateSort(proto.shape(), proto.dimensions(0), keys, values); + break; + } case HloOpcode::kTranspose: TF_RET_CHECK(proto.operand_ids_size() == 1) << "Transpose instruction should have 1 operand but sees " @@ -271,7 +286,7 @@ StatusOr> HloInstruction::CreateFromProto( // converted to take tokens. instruction = CreateInfeed(data_shape, proto.infeed_config()); } else { - CHECK_EQ(proto.operand_ids_size(), 2); + CHECK_EQ(proto.operand_ids_size(), 1); instruction = CreateInfeed(data_shape, operands(0), proto.infeed_config()); } @@ -372,6 +387,23 @@ StatusOr> HloInstruction::CreateFromProto( slice_sizes); break; } + case HloOpcode::kGather: { + TF_RET_CHECK(proto.operand_ids_size() == 2) + << "Gather instruction should have 2 operands but sees " + << proto.operand_ids_size(); + TF_RET_CHECK(proto.has_gather_dimension_numbers()) + << "Gather instruction should have GatherDimensionNumbers set."; + std::unique_ptr gather_dimension_numbers = + MakeUnique(proto.gather_dimension_numbers()); + std::vector gather_window_bounds; + for (int64 bound : proto.gather_window_bounds()) { + gather_window_bounds.push_back(bound); + } + instruction = + CreateGather(proto.shape(), operands(0), operands(1), + *gather_dimension_numbers, gather_window_bounds); + break; + } default: { instruction = WrapUnique(new HloInstruction(opcode, proto.shape())); for (const int64 operand_id : proto.operand_ids()) { @@ -413,13 +445,6 @@ StatusOr> HloInstruction::CreateFromProto( instruction->set_sharding(sharding); } - if (proto.has_gather_dimension_numbers()) { - instruction->gather_dimension_numbers_ = - MakeUnique(proto.gather_dimension_numbers()); - } - for (int64 bound : proto.gather_window_bounds()) { - instruction->gather_window_bounds_.push_back(bound); - } return std::move(instruction); } @@ -438,6 +463,11 @@ StatusOr> HloInstruction::CreateFromProto( return MakeUnique(std::move(literal)); } +/* static */ std::unique_ptr HloInstruction::CreateIota( + const Shape& shape) { + return WrapUnique(new HloInstruction(HloOpcode::kIota, shape)); +} + /* static */ std::unique_ptr HloInstruction::CreateGetTupleElement(const Shape& shape, HloInstruction* operand, int64 index) { @@ -651,29 +681,33 @@ HloInstruction::CreateCrossReplicaSum( } /* static */ std::unique_ptr HloInstruction::CreateSend( - HloInstruction* operand, HloInstruction* token, int64 channel_id) { - return MakeUnique(operand, token, channel_id); + HloInstruction* operand, HloInstruction* token, int64 channel_id, + bool is_host_transfer) { + return MakeUnique(operand, token, channel_id, + is_host_transfer); } /* static */ std::unique_ptr HloInstruction::CreateSendDone( - HloInstruction* operand) { + HloInstruction* operand, bool is_host_transfer) { auto send_operand = DynCast(operand); CHECK(send_operand != nullptr) << "SendDone must take the context operand from Send"; - return MakeUnique(send_operand); + return MakeUnique(send_operand, is_host_transfer); } /* static */ std::unique_ptr HloInstruction::CreateRecv( - const Shape& shape, HloInstruction* token, int64 channel_id) { - return MakeUnique(shape, token, channel_id); + const Shape& shape, HloInstruction* token, int64 channel_id, + bool is_host_transfer) { + return MakeUnique(shape, token, channel_id, + is_host_transfer); } /* static */ std::unique_ptr HloInstruction::CreateRecvDone( - HloInstruction* operand) { + HloInstruction* operand, bool is_host_transfer) { auto recv_operand = DynCast(operand); CHECK(recv_operand != nullptr) << "RecvDone must take the context operand from Recv"; - return MakeUnique(recv_operand); + return MakeUnique(recv_operand, is_host_transfer); } /* static */ std::unique_ptr HloInstruction::CreateReverse( @@ -684,6 +718,7 @@ HloInstruction::CreateCrossReplicaSum( /* static */ std::unique_ptr HloInstruction::CreateAfterAll( tensorflow::gtl::ArraySlice operands) { + CHECK(!operands.empty()); auto instruction = WrapUnique( new HloInstruction(HloOpcode::kAfterAll, ShapeUtil::MakeTokenShape())); for (auto operand : operands) { @@ -692,6 +727,11 @@ HloInstruction::CreateCrossReplicaSum( return instruction; } +/* static */ std::unique_ptr HloInstruction::CreateToken() { + return WrapUnique( + new HloInstruction(HloOpcode::kAfterAll, ShapeUtil::MakeTokenShape())); +} + /* static */ std::unique_ptr HloInstruction::CreateWhile( const Shape& shape, HloComputation* condition, HloComputation* body, HloInstruction* init) { @@ -909,13 +949,9 @@ HloInstruction::CreateBroadcastSequence( } /* static */ std::unique_ptr HloInstruction::CreateSort( - const Shape& shape, HloInstruction* keys, HloInstruction* values) { - auto instruction = WrapUnique(new HloInstruction(HloOpcode::kSort, shape)); - instruction->AppendOperand(keys); - if (values) { - instruction->AppendOperand(values); - } - return instruction; + const Shape& shape, int64 dimension, HloInstruction* keys, + HloInstruction* values) { + return MakeUnique(shape, dimension, keys, values); } /* static */ std::unique_ptr HloInstruction::CreateFusion( @@ -962,6 +998,8 @@ bool HloInstruction::HasSideEffectNoRecurse() const { case HloOpcode::kTrace: case HloOpcode::kHostCompute: return true; + case HloOpcode::kCrossReplicaSum: + return all_reduce_id().has_value(); default: return false; } @@ -1020,34 +1058,8 @@ bool HloInstruction::HasSideEffect() const { const Shape& shape, HloInstruction* operand, HloInstruction* gather_indices, const GatherDimensionNumbers& gather_dim_numbers, tensorflow::gtl::ArraySlice window_bounds) { - std::unique_ptr instruction = - WrapUnique(new HloInstruction(HloOpcode::kGather, shape)); - instruction->AppendOperand(operand); - instruction->AppendOperand(gather_indices); - instruction->gather_dimension_numbers_ = - MakeUnique(gather_dim_numbers); - c_copy(window_bounds, std::back_inserter(instruction->gather_window_bounds_)); - return instruction; -} - -/* static */ GatherDimensionNumbers HloInstruction::MakeGatherDimNumbers( - tensorflow::gtl::ArraySlice output_window_dims, - tensorflow::gtl::ArraySlice elided_window_dims, - tensorflow::gtl::ArraySlice gather_dims_to_operand_dims, - int64 index_vector_dim) { - GatherDimensionNumbers gather_dim_numbers; - for (int64 output_window_dim : output_window_dims) { - gather_dim_numbers.add_output_window_dims(output_window_dim); - } - for (int64 elided_window_dim : elided_window_dims) { - gather_dim_numbers.add_elided_window_dims(elided_window_dim); - } - for (int64 gather_dim_to_input_dim : gather_dims_to_operand_dims) { - gather_dim_numbers.add_gather_dims_to_operand_dims(gather_dim_to_input_dim); - } - - gather_dim_numbers.set_index_vector_dim(index_vector_dim); - return gather_dim_numbers; + return MakeUnique(shape, operand, gather_indices, + gather_dim_numbers, window_bounds); } /* static */ std::unique_ptr HloInstruction::CreateDomain( @@ -1110,6 +1122,9 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( case HloOpcode::kHostCompute: case HloOpcode::kPad: case HloOpcode::kDynamicSlice: + case HloOpcode::kSort: + case HloOpcode::kGather: + case HloOpcode::kIota: clone = CloneWithNewOperandsImpl(shape, new_operands, context); break; // Unary ops. @@ -1211,11 +1226,6 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( true_computation(), new_operands[2], false_computation()); break; - case HloOpcode::kGather: - CHECK_EQ(new_operands.size(), 2); - clone = CreateGather(shape, new_operands[0], new_operands[1], - *gather_dimension_numbers_, gather_window_bounds_); - break; case HloOpcode::kDomain: CHECK_EQ(new_operands.size(), 1); clone = @@ -1223,15 +1233,11 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( user_side_metadata_->Clone()); break; case HloOpcode::kAfterAll: - clone = CreateAfterAll(new_operands); - break; - case HloOpcode::kSort: - CHECK(new_operands.size() == 1 || new_operands.size() == 2) - << "Too many operands for sort: " << new_operands.size(); - HloInstruction* keys = new_operands[0]; - HloInstruction* values = - new_operands.size() == 2 ? new_operands[1] : nullptr; - clone = CreateSort(shape, keys, values); + if (new_operands.empty()) { + clone = CreateToken(); + } else { + clone = CreateAfterAll(new_operands); + } break; } SetupDerivedInstruction(clone.get()); @@ -1509,7 +1515,6 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kShiftRightArithmetic: case HloOpcode::kShiftRightLogical: case HloOpcode::kSign: - case HloOpcode::kSort: case HloOpcode::kSin: case HloOpcode::kSubtract: case HloOpcode::kTanh: @@ -1517,9 +1522,7 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kTupleSelect: return true; - // These opcodes have complex or special behavior so just return false. - case HloOpcode::kDomain: - case HloOpcode::kWhile: + // This opcode has complex or special behavior so just return false. case HloOpcode::kAfterAll: return false; @@ -1528,11 +1531,6 @@ bool HloInstruction::IdenticalSlowPath( return protobuf_util::ProtobufEquals(dot_dimension_numbers(), other.dot_dimension_numbers()); - case HloOpcode::kGather: - return protobuf_util::ProtobufEquals(gather_dimension_numbers(), - other.gather_dimension_numbers()) && - gather_window_bounds() == other.gather_window_bounds(); - // Remaining instructions with special values. case HloOpcode::kCall: return eq_computations(to_apply(), other.to_apply()); @@ -1540,6 +1538,18 @@ bool HloInstruction::IdenticalSlowPath( return eq_computations(true_computation(), other.true_computation()) && eq_computations(false_computation(), other.false_computation()); + case HloOpcode::kWhile: { + if (eq_computations(while_body(), other.while_body()) && + eq_computations(while_condition(), other.while_condition())) { + return true; + } + return false; + } + + case HloOpcode::kDomain: + return operand_side_metadata().Matches(other.operand_side_metadata()) && + user_side_metadata().Matches(other.user_side_metadata()); + // Ops migrated to subclasses should never come to this line. // TODO(b/80131774): Remove this switch when migration is complete. case HloOpcode::kBatchNormTraining: @@ -1553,11 +1563,13 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kReverse: case HloOpcode::kConcatenate: case HloOpcode::kReduce: + case HloOpcode::kSort: case HloOpcode::kTranspose: case HloOpcode::kBroadcast: case HloOpcode::kMap: case HloOpcode::kSlice: case HloOpcode::kConstant: + case HloOpcode::kIota: case HloOpcode::kTrace: case HloOpcode::kFusion: case HloOpcode::kRng: @@ -1574,9 +1586,11 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kHostCompute: case HloOpcode::kPad: case HloOpcode::kDynamicSlice: + case HloOpcode::kGather: LOG(FATAL) << "Base class impl called for opcode with subclass: " << opcode(); } + return false; } void HloInstruction::RemoveUser(HloInstruction* user) { @@ -1847,6 +1861,10 @@ bool HloInstruction::IsElementwiseImpl( } } +bool HloInstruction::IsCrossModuleAllReduce() const { + return opcode() == HloOpcode::kCrossReplicaSum && all_reduce_id(); +} + string HloInstruction::ToStringWithCanonicalNameMap( const HloPrintOptions& options, CanonicalNameMap* canonical_name_map) const { @@ -1939,11 +1957,6 @@ std::vector HloInstruction::ExtraAttributesToString( if (dot_dimension_numbers_ != nullptr) { extra.push_back(DotDimensionNumbersToString()); } - if (gather_dimension_numbers_ != nullptr) { - extra.push_back(GatherDimensionNumbersToString()); - extra.push_back( - StrCat("window_bounds={", Join(gather_window_bounds(), ","), "}")); - } if (options.print_subcomputation_mode() == HloPrintOptions::PrintSubcomputationMode::kNameOnly) { @@ -2073,14 +2086,6 @@ HloInstructionProto HloInstruction::ToProto() const { if (dot_dimension_numbers_ != nullptr) { *proto.mutable_dot_dimension_numbers() = *dot_dimension_numbers_; } - if (gather_dimension_numbers_ != nullptr) { - *proto.mutable_gather_dimension_numbers() = *gather_dimension_numbers_; - } - if (opcode() == HloOpcode::kGather) { - for (int64 bound : gather_window_bounds()) { - proto.add_gather_window_bounds(bound); - } - } if (has_sharding()) { *proto.mutable_sharding() = sharding().ToProto(); @@ -2310,6 +2315,8 @@ Status HloInstruction::Visit(DfsHloVisitorBase* visitor) { return visitor->HandleDomain(this); case HloOpcode::kAfterAll: return visitor->HandleAfterAll(this); + case HloOpcode::kIota: + return visitor->HandleIota(this); // These opcodes are not handled here. case HloOpcode::kTrace: @@ -2841,26 +2848,6 @@ std::ostream& operator<<(std::ostream& os, HloInstruction::FusionKind kind) { return os << ToString(kind); } -string HloInstruction::GatherDimensionNumbersToString() const { - CHECK_NE(gather_dimension_numbers_.get(), nullptr); - string output_window_dims = - StrCat("output_window_dims={", - Join(gather_dimension_numbers_->output_window_dims(), ","), "}"); - string elided_window_dims = - StrCat("elided_window_dims={", - Join(gather_dimension_numbers_->elided_window_dims(), ","), "}"); - string gather_dims_to_operand_dims = StrCat( - "gather_dims_to_operand_dims={", - Join(gather_dimension_numbers_->gather_dims_to_operand_dims(), ","), "}"); - string index_vector_dim = StrCat( - "index_vector_dim=", gather_dimension_numbers_->index_vector_dim()); - - return Join>( - {output_window_dims, elided_window_dims, gather_dims_to_operand_dims, - index_vector_dim}, - ", "); -} - bool HloInstruction::CouldBeBitcast() const { switch (opcode_) { case HloOpcode::kTranspose: @@ -3174,4 +3161,14 @@ int64 HloInstruction::slice_sizes(int64 dimension) const { const std::vector& HloInstruction::dynamic_slice_sizes() const { return Cast(this)->dynamic_slice_sizes(); } + +const GatherDimensionNumbers& HloInstruction::gather_dimension_numbers() const { + return Cast(this)->gather_dimension_numbers(); +} + +tensorflow::gtl::ArraySlice HloInstruction::gather_window_bounds() + const { + return Cast(this)->gather_window_bounds(); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h index 34e7dcb43d43483f010f226f00bdf211722f2562..70441b879de6a7fa4af24aa489069c75d7d5cbbd 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.h +++ b/tensorflow/compiler/xla/service/hlo_instruction.h @@ -33,7 +33,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/iterator_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" @@ -346,6 +346,9 @@ class HloInstruction { static std::unique_ptr CreateConstant( std::unique_ptr literal); + // Creates an Iota instruction. + static std::unique_ptr CreateIota(const Shape& shape); + // Creates a get tuple element instruction. static std::unique_ptr CreateGetTupleElement( const Shape& shape, HloInstruction* operand, int64 index); @@ -444,8 +447,7 @@ class HloInstruction { HloComputation* reduce_computation, tensorflow::gtl::ArraySlice replica_group_ids, tensorflow::StringPiece barrier, - const tensorflow::gtl::optional& all_reduce_id = - tensorflow::gtl::nullopt); + const tensorflow::gtl::optional& all_reduce_id); // Creates a conversion instruction, where operand is the data to convert and // shape is the target shape for the conversion. @@ -485,27 +487,30 @@ class HloInstruction { // Creates an asynchronous send instruction with the given channel id, which // initiates sending the operand data to a unique receive instruction in - // another computation that has the same channel id. - static std::unique_ptr CreateSend(HloInstruction* operand, - HloInstruction* token, - int64 channel_id); + // another computation that has the same channel id. If is_host_transfer is + // true, then this Send operation transfers data to the host. + static std::unique_ptr CreateSend( + HloInstruction* operand, HloInstruction* token, int64 channel_id, + bool is_host_transfer = false); // Blocks until data transfer for the Send instruction (operand) is complete. // The operand must be kSend. static std::unique_ptr CreateSendDone( - HloInstruction* operand); + HloInstruction* operand, bool is_host_transfer = false); // Creates an asynchronous receive instruction with the given channel id, // which allocates resources to receive data of the given shape from a unique - // send instruction in another computation that has the same channel id. - static std::unique_ptr CreateRecv(const Shape& shape, - HloInstruction* token, - int64 channel_id); + // send instruction in another computation that has the same channel id. If + // is_host_transfer is true, then this Send operation transfers data from the + // host. + static std::unique_ptr CreateRecv( + const Shape& shape, HloInstruction* token, int64 channel_id, + bool is_host_transfer = false); // Blocks until data transfer for the Recv instruction (operand) is complete // and returns the receive buffer. The operand must be kRecv. static std::unique_ptr CreateRecvDone( - HloInstruction* operand); + HloInstruction* operand, bool is_host_transfer = false); // Creates a slice instruction, where the operand is sliced by the given // start/limit indices. @@ -615,7 +620,7 @@ class HloInstruction { // Creates a sort op, with a keys operand, and an optional values operand. static std::unique_ptr CreateSort( - const Shape& shape, HloInstruction* keys, + const Shape& shape, int64 dimension, HloInstruction* keys, HloInstruction* values = nullptr); // Creates a while instruction, given a condition computation, a body @@ -687,17 +692,18 @@ class HloInstruction { const Shape& shape, HloInstruction* operand, tensorflow::gtl::ArraySlice dimensions); - // Creates a token instruction used for joining or creating new values of - // token type which thread through side-effecting operations. + // Creates a Afterall instruction used for joining or creating new values of + // token type which thread through side-effecting operations. Operands must + // all be tokens, and there must be at least one operand. static std::unique_ptr CreateAfterAll( tensorflow::gtl::ArraySlice operands); - // Creates an instance of GatherDimensionNumbers. - static GatherDimensionNumbers MakeGatherDimNumbers( - tensorflow::gtl::ArraySlice output_window_dims, - tensorflow::gtl::ArraySlice elided_window_dims, - tensorflow::gtl::ArraySlice gather_dims_to_operand_dims, - int64 index_vector_dim); + // Creates an AfterAll instruction which creates a token type out of thin air + // (no operands). This is a separate method from CreateAfterAll to facility + // the removal of operand-less AfterAll instructions. + // TODO(b/110532604): Remove this capability of creating a token from nothing + // when we plumb a primordial token from the entry computation. + static std::unique_ptr CreateToken(); // Returns the opcode for this instruction. HloOpcode opcode() const { return opcode_; } @@ -1073,19 +1079,6 @@ class HloInstruction { // Returns the dump string of the dot dimension numbers. string DotDimensionNumbersToString() const; - const GatherDimensionNumbers& gather_dimension_numbers() const { - CHECK(gather_dimension_numbers_ != nullptr); - return *gather_dimension_numbers_; - } - - tensorflow::gtl::ArraySlice gather_window_bounds() const { - CHECK_EQ(opcode(), HloOpcode::kGather); - return gather_window_bounds_; - } - - // Returns the dump string of the gather dimension numbers. - string GatherDimensionNumbersToString() const; - // Clones the HLO instruction. The clone will have the same opcode, shape, and // operands. After creation the clone has no uses. "this" (the instruction // cloned from) is not changed. Suffix is the string to append to the name of @@ -1140,6 +1133,9 @@ class HloInstruction { // Returns true if this instruction is elementwise on all its operands. bool IsElementwise() const; + // Returns true if this is an cross module all-reduce instrucion. + bool IsCrossModuleAllReduce() const; + // Returns true if this elementwise instruction implicitly broadcasts operand // `operand_idx`. // @@ -1452,6 +1448,12 @@ class HloInstruction { // Delegates to HloDynamicSliceInstruction::dynamic_slice_sizes. const std::vector& dynamic_slice_sizes() const; + + // Delegates to HloGatherInstruction::gather_dimension_numbers. + const GatherDimensionNumbers& gather_dimension_numbers() const; + // Delegates to HloGatherInstruction::gather_window_bounds. + tensorflow::gtl::ArraySlice gather_window_bounds() const; + // Old methods kept for smooth subclassing transition END. protected: @@ -1595,9 +1597,6 @@ class HloInstruction { // Describes the dimension numbers used for a dot. std::unique_ptr dot_dimension_numbers_; - std::unique_ptr gather_dimension_numbers_; - std::vector gather_window_bounds_; - // Used to tag kCopy instructions that are eligible for copy elision. bool copy_elision_allowed_ = true; diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc index d8ca99dfd12ef95ab5e1ea61093d8bf3ea97a5e2..b75a2bd34bc5d3b5b6100515748df787b9e7f08a 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc @@ -20,10 +20,11 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/protobuf_util.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" +#include "tensorflow/compiler/xla/service/hlo_instructions.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test.h" @@ -249,7 +250,7 @@ TEST_F(HloInstructionTest, MultipleUsersAndOperands) { auto param1 = builder.AddInstruction( HloInstruction::CreateParameter(1, r0f32_, "param1")); auto c0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.1f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.1f))); auto addleft = builder.AddInstruction( HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, param0, c0)); auto addright = builder.AddInstruction( @@ -294,7 +295,7 @@ TEST_F(HloInstructionTest, MultipleUsersAndOperandsWithUnaryOps) { auto param1 = builder.AddInstruction( HloInstruction::CreateParameter(1, r0f32_, "param1")); auto c0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.1f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.1f))); auto neg1 = builder.AddInstruction( HloInstruction::CreateUnary(r0f32_, HloOpcode::kNegate, c0)); auto addleft = builder.AddInstruction( @@ -334,7 +335,7 @@ TEST_F(HloInstructionTest, TrivialMap) { auto param = embedded_builder.AddInstruction( HloInstruction::CreateParameter(0, r0f32, "x")); auto value = embedded_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); embedded_builder.AddInstruction( HloInstruction::CreateBinary(r0f32, HloOpcode::kAdd, param, value)); auto add_f32 = module->AddEmbeddedComputation(embedded_builder.Build()); @@ -383,9 +384,9 @@ TEST_F(HloInstructionTest, TrivialReduce) { auto param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, f32a100x10, "p")); auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.1f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.1f))); auto reduce = builder.AddInstruction( HloInstruction::CreateReduce(f32v100, param0, const0, /*dimensions_to_reduce=*/{1}, add_f32)); @@ -626,7 +627,7 @@ TEST_F(HloInstructionTest, SingletonFusionOp) { HloComputation::Builder builder(TestName()); // Create a fusion instruction containing a single unary operation. auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.1f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.1f))); auto exp = builder.AddInstruction( HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, constant)); auto module = CreateNewModule(); @@ -642,9 +643,9 @@ TEST_F(HloInstructionTest, BinaryFusionOp) { HloComputation::Builder builder(TestName()); // Create a fusion instruction containing a single binary operation. auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.1f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.1f))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.1f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.1f))); auto add = builder.AddInstruction(HloInstruction::CreateBinary( r0f32_, HloOpcode::kAdd, constant1, constant2)); auto module = CreateNewModule(); @@ -661,7 +662,7 @@ TEST_F(HloInstructionTest, ChainFusionOp) { HloComputation::Builder builder(TestName()); // Create a chain of fused unary ops. auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.1f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.1f))); auto exp1 = builder.AddInstruction( HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, constant)); auto exp2 = builder.AddInstruction( @@ -682,7 +683,7 @@ TEST_F(HloInstructionTest, PreserveMetadataInFusionAndClone) { HloComputation::Builder builder(TestName()); // Create a chain of fused unary ops. auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.1f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.1f))); auto exp1 = builder.AddInstruction( HloInstruction::CreateUnary(r0f32_, HloOpcode::kExp, constant)); auto exp2 = builder.AddInstruction( @@ -710,13 +711,13 @@ TEST_F(HloInstructionTest, PreserveMetadataInFusionAndClone) { TEST_F(HloInstructionTest, PreserveOutfeedShapeThroughClone) { HloComputation::Builder builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2({ + HloInstruction::CreateConstant(LiteralUtil::CreateR2({ {1, 2}, {3, 4}, }))); auto shape10 = ShapeUtil::MakeShapeWithLayout(F32, {2, 2}, {1, 0}); auto shape01 = ShapeUtil::MakeShapeWithLayout(F32, {2, 2}, {0, 1}); - auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = builder.AddInstruction(HloInstruction::CreateToken()); auto outfeed10 = builder.AddInstruction( HloInstruction::CreateOutfeed(shape10, constant, token, "")); auto outfeed01 = builder.AddInstruction( @@ -732,7 +733,7 @@ TEST_F(HloInstructionTest, PreserveOutfeedShapeThroughClone) { TEST_F(HloInstructionTest, PreserveTupleShapeThroughClone) { HloComputation::Builder builder(TestName()); auto* constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2({ + HloInstruction::CreateConstant(LiteralUtil::CreateR2({ {1, 2}, {3, 4}, }))); @@ -763,7 +764,7 @@ TEST_F(HloInstructionTest, FusionOpWithCalledComputations) { HloComputation::Builder builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.1f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.1f))); auto map_1_x = builder.AddInstruction( HloInstruction::CreateMap(scalar_shape, {constant}, computation_x)); auto map_2_x = builder.AddInstruction( @@ -798,11 +799,11 @@ TEST_F(HloInstructionTest, ComplexFusionOp) { // Notable complexities are repeated operands in the same instruction, // different shapes, use of value in different expressions. auto c1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.1f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.1f))); auto c2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.1f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.1f))); auto c3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(9.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(9.0f))); auto add = builder.AddInstruction( HloInstruction::CreateBinary(r0f32_, HloOpcode::kAdd, c1, c2)); @@ -873,11 +874,11 @@ TEST_F(HloInstructionTest, IdenticalInstructions) { // Create a set of random constant operands to use below. Make them matrices // so dimensions are interesting. auto operand1 = HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}})); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}})); auto operand2 = HloInstruction::CreateConstant( - Literal::CreateR2({{10.0, 20.0}, {30.0, 40.0}})); - auto vector_operand = - HloInstruction::CreateConstant(Literal::CreateR1({42.0, 123.0})); + LiteralUtil::CreateR2({{10.0, 20.0}, {30.0, 40.0}})); + auto vector_operand = HloInstruction::CreateConstant( + LiteralUtil::CreateR1({42.0, 123.0})); Shape shape = operand1->shape(); // Convenient short names for the operands. @@ -1234,9 +1235,9 @@ TEST_F(HloInstructionTest, NestedFusionEquality) { // Build a nested fusion computation. Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2}); auto a = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 0.0}, {0.0, 1.0}}))); + LiteralUtil::CreateR2({{1.0, 0.0}, {0.0, 1.0}}))); auto b = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{2.0, 2.0}, {2.0, 2.0}}))); + LiteralUtil::CreateR2({{2.0, 2.0}, {2.0, 2.0}}))); auto b_t = builder.AddInstruction( HloInstruction::CreateTranspose(data_shape, b, {1, 0})); DotDimensionNumbers dot_dnums; @@ -1245,7 +1246,7 @@ TEST_F(HloInstructionTest, NestedFusionEquality) { auto dot = builder.AddInstruction( HloInstruction::CreateDot(data_shape, a, b_t, dot_dnums)); auto one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto add_operand = builder.AddInstruction( HloInstruction::CreateBroadcast(data_shape, one, {1})); auto add = builder.AddInstruction(HloInstruction::CreateBinary( @@ -1342,7 +1343,7 @@ TEST_F(HloInstructionTest, Stringification) { "condition=%TransposeDot, body=%TransposeDot"); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); HloInstruction* conditional = builder.AddInstruction(HloInstruction::CreateConditional( sout, pred, x, computation, x, computation)); @@ -1369,7 +1370,7 @@ TEST_F(HloInstructionTest, StringifyGather_0) { HloInstruction* gather_instruction = builder.AddInstruction(HloInstruction::CreateGather( gather_result_shape, input, gather_indices, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1405,7 +1406,7 @@ TEST_F(HloInstructionTest, StringifyGather_1) { HloInstruction* gather_instruction = builder.AddInstruction(HloInstruction::CreateGather( gather_result_shape, input, gather_indices, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1455,15 +1456,15 @@ TEST_F(HloInstructionTest, CanonnicalStringificationFusion) { HloInstruction* fusion = computation->CreateFusionInstruction( {dot, reshape}, HloInstruction::FusionKind::kLoop); - EXPECT_EQ( - fusion->ToString(options), + const string expected_fusion = R"(f32[5,20]{1,0} fusion(f32[5,10]{1,0}, f32[20,10]{1,0}), kind=kLoop, calls= { tmp_0 = f32[5,10]{1,0} parameter(0) tmp_1 = f32[20,10]{1,0} parameter(1) tmp_2 = f32[10,20]{1,0} transpose(f32[20,10]{1,0} tmp_1), dimensions={1,0} ROOT tmp_3 = f32[5,20]{1,0} dot(f32[5,10]{1,0} tmp_0, f32[10,20]{1,0} tmp_2), lhs_contracting_dims={1}, rhs_contracting_dims={0} -})"); +})"; + EXPECT_EQ(fusion->ToString(options), expected_fusion); } TEST_F(HloInstructionTest, CanonnicalStringificationWhile) { @@ -1495,8 +1496,8 @@ TEST_F(HloInstructionTest, CanonnicalStringificationWhile) { HloInstruction::CreateWhile(sout, computation, computation, x)); auto options = HloPrintOptions().Canonical(); - EXPECT_EQ(loop->ToString(options), - R"(f32[5,20]{1,0} while(f32[5,10]{1,0}), condition= + const string expected_loop = + R"(f32[5,20]{1,0} while(f32[5,10]{1,0}), condition= { tmp_0 = f32[5,10]{1,0} parameter(0) tmp_1 = f32[20,10]{1,0} parameter(1) @@ -1518,7 +1519,8 @@ TEST_F(HloInstructionTest, CanonnicalStringificationWhile) { tmp_2 = f32[10,20]{1,0} transpose(f32[20,10]{1,0} tmp_1), dimensions={1,0} ROOT tmp_3 = f32[5,20]{1,0} dot(f32[5,10]{1,0} tmp_0, f32[10,20]{1,0} tmp_2), lhs_contracting_dims={1}, rhs_contracting_dims={0} } -})"); +})"; + EXPECT_EQ(loop->ToString(options), expected_loop); } TEST_F(HloInstructionTest, CanonnicalStringificationConditional) { @@ -1550,13 +1552,12 @@ TEST_F(HloInstructionTest, CanonnicalStringificationConditional) { HloInstruction::CreateWhile(sout, computation, computation, x)); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); HloInstruction* conditional = builder.AddInstruction(HloInstruction::CreateConditional( sout, pred, x, computation, x, computation)); auto options = HloPrintOptions().Canonical(); - EXPECT_EQ( - conditional->ToString(options), + const string expected_conditional = R"(f32[5,20]{1,0} conditional(pred[], f32[5,10]{1,0}, f32[5,10]{1,0}), true_computation= { tmp_0 = f32[5,10]{1,0} parameter(0) @@ -1579,7 +1580,8 @@ TEST_F(HloInstructionTest, CanonnicalStringificationConditional) { tmp_2 = f32[10,20]{1,0} transpose(f32[20,10]{1,0} tmp_1), dimensions={1,0} ROOT tmp_3 = f32[5,20]{1,0} dot(f32[5,10]{1,0} tmp_0, f32[10,20]{1,0} tmp_2), lhs_contracting_dims={1}, rhs_contracting_dims={0} } -})"); +})"; + EXPECT_EQ(conditional->ToString(options), expected_conditional); } TEST_F(HloInstructionTest, CheckDeepClone) { diff --git a/tensorflow/compiler/xla/service/hlo_instructions.cc b/tensorflow/compiler/xla/service/hlo_instructions.cc index 7052e236cdab534864d8d4791bcdcfa162a2851d..df26a2c744fbcac814727139e1cf7f23037dcc50 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.cc +++ b/tensorflow/compiler/xla/service/hlo_instructions.cc @@ -17,6 +17,7 @@ limitations under the License. #include +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/hlo_casting_utils.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_module.h" @@ -180,8 +181,11 @@ std::unique_ptr HloFftInstruction::CloneWithNewOperandsImpl( HloSendRecvInstruction::HloSendRecvInstruction(HloOpcode opcode, const Shape& shape, - int64 channel_id) - : HloInstruction(opcode, shape), channel_id_(channel_id) {} + int64 channel_id, + bool is_host_transfer) + : HloInstruction(opcode, shape), + channel_id_(channel_id), + is_host_transfer_(is_host_transfer) {} HloInstructionProto HloSendRecvInstruction::ToProto() const { HloInstructionProto proto = HloInstruction::ToProto(); @@ -191,7 +195,12 @@ HloInstructionProto HloSendRecvInstruction::ToProto() const { std::vector HloSendRecvInstruction::ExtraAttributesToStringImpl( const HloPrintOptions& options) const { - return {StrCat("channel_id=", channel_id_)}; + std::vector attrs; + attrs.push_back(StrCat("channel_id=", channel_id_)); + if (is_host_transfer()) { + attrs.push_back("is_host_transfer=true"); + } + return attrs; } bool HloSendRecvInstruction::IdenticalSlowPath( @@ -204,13 +213,14 @@ bool HloSendRecvInstruction::IdenticalSlowPath( // Send instruction produces a tuple of {aliased operand, U32 context}. HloSendInstruction::HloSendInstruction(HloInstruction* operand, - HloInstruction* token, int64 channel_id) + HloInstruction* token, int64 channel_id, + bool is_host_transfer) : HloSendRecvInstruction( HloOpcode::kSend, ShapeUtil::MakeTupleShape({CHECK_NOTNULL(operand)->shape(), ShapeUtil::MakeShape(U32, {}), ShapeUtil::MakeTokenShape()}), - channel_id) { + channel_id, is_host_transfer) { AppendOperand(operand); AppendOperand(token); } @@ -221,12 +231,14 @@ std::unique_ptr HloSendInstruction::CloneWithNewOperandsImpl( HloCloneContext* context) const { CHECK_EQ(new_operands.size(), 2); return MakeUnique(new_operands[0], new_operands[1], - channel_id()); + channel_id(), is_host_transfer()); } -HloSendDoneInstruction::HloSendDoneInstruction(HloSendInstruction* operand) +HloSendDoneInstruction::HloSendDoneInstruction(HloSendInstruction* operand, + bool is_host_transfer) : HloSendRecvInstruction(HloOpcode::kSendDone, ShapeUtil::MakeTokenShape(), - CHECK_NOTNULL(operand)->channel_id()) { + CHECK_NOTNULL(operand)->channel_id(), + is_host_transfer) { AppendOperand(operand); } @@ -237,17 +249,18 @@ HloSendDoneInstruction::CloneWithNewOperandsImpl( HloCloneContext* context) const { CHECK_EQ(new_operands.size(), 1); return MakeUnique( - Cast(new_operands[0])); + Cast(new_operands[0]), is_host_transfer()); } // Recv instruction produces a tuple of {receive buffer, U32 context}. HloRecvInstruction::HloRecvInstruction(const Shape& shape, - HloInstruction* token, int64 channel_id) + HloInstruction* token, int64 channel_id, + bool is_host_transfer) : HloSendRecvInstruction( HloOpcode::kRecv, ShapeUtil::MakeTupleShape({shape, ShapeUtil::MakeShape(U32, {}), ShapeUtil::MakeTokenShape()}), - channel_id) { + channel_id, is_host_transfer) { AppendOperand(token); } @@ -257,16 +270,18 @@ std::unique_ptr HloRecvInstruction::CloneWithNewOperandsImpl( HloCloneContext* context) const { CHECK_EQ(new_operands.size(), 1); return MakeUnique( - ShapeUtil::GetTupleElementShape(shape, 0), new_operands[0], channel_id()); + ShapeUtil::GetTupleElementShape(shape, 0), new_operands[0], channel_id(), + is_host_transfer()); } -HloRecvDoneInstruction::HloRecvDoneInstruction(HloRecvInstruction* operand) +HloRecvDoneInstruction::HloRecvDoneInstruction(HloRecvInstruction* operand, + bool is_host_transfer) : HloSendRecvInstruction( HloOpcode::kRecvDone, ShapeUtil::MakeTupleShape( {ShapeUtil::GetTupleElementShape(operand->shape(), 0), ShapeUtil::MakeTokenShape()}), - CHECK_NOTNULL(operand)->channel_id()) { + CHECK_NOTNULL(operand)->channel_id(), is_host_transfer) { AppendOperand(operand); } @@ -277,7 +292,7 @@ HloRecvDoneInstruction::CloneWithNewOperandsImpl( HloCloneContext* context) const { CHECK_EQ(new_operands.size(), 1); return MakeUnique( - Cast(new_operands[0])); + Cast(new_operands[0]), is_host_transfer()); } HloAllReduceInstruction::HloAllReduceInstruction( @@ -290,8 +305,6 @@ HloAllReduceInstruction::HloAllReduceInstruction( replica_group_ids_(replica_group_ids.begin(), replica_group_ids.end()), cross_replica_sum_barrier_(barrier.begin(), barrier.end()), all_reduce_id_(all_reduce_id) { - // TODO(b/79737069): Remove the CHECK when supported. - CHECK(!all_reduce_id_); for (auto operand : operands) { AppendOperand(operand); } @@ -468,6 +481,46 @@ std::unique_ptr HloReduceInstruction::CloneWithNewOperandsImpl( shape, new_operands[0], new_operands[1], dimensions(), to_apply()); } +HloSortInstruction::HloSortInstruction(const Shape& shape, int64 dimension, + HloInstruction* keys, + HloInstruction* values) + : HloInstruction(HloOpcode::kSort, shape), dimensions_({dimension}) { + AppendOperand(keys); + if (values) { + AppendOperand(values); + } +} + +HloInstructionProto HloSortInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + for (int64 dimension : dimensions_) { + proto.add_dimensions(dimension); + } + return proto; +} + +std::vector HloSortInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {StrCat("dimensions={", Join(dimensions(), ","), "}")}; +} + +bool HloSortInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = static_cast(other); + return dimensions() == casted_other.dimensions(); +} + +std::unique_ptr HloSortInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + HloInstruction* keys = new_operands[0]; + HloInstruction* values = new_operands.size() == 2 ? new_operands[1] : nullptr; + return MakeUnique(shape, dimensions(0), keys, values); +} + HloTransposeInstruction::HloTransposeInstruction( const Shape& shape, HloInstruction* operand, tensorflow::gtl::ArraySlice dimensions) @@ -766,7 +819,7 @@ string HloConstantInstruction::OperandsToStringWithCanonicalNameMap( HloTraceInstruction::HloTraceInstruction(const string& tag, HloInstruction* operand) : HloInstruction(HloOpcode::kTrace, ShapeUtil::MakeNil()), - literal_(Literal::CreateR1U8(tag)) { + literal_(LiteralUtil::CreateR1U8(tag)) { AppendOperand(operand); operand->set_tracing(this); } @@ -1052,8 +1105,6 @@ HloInstruction* HloFusionInstruction::CloneAndFuseInternal( CHECK_NOTNULL(GetModule())->AddEmbeddedComputation(builder.Build())); clone = fused_expression_root(); } else { - clone = fused_instructions_computation()->AddInstruction( - instruction_to_fuse->Clone(/*suffix=*/"")); // When add_output is false, instruction_to_fuse is necessarily an operand // of the fusion instruction. After fusion this will no longer be the // case. Remove the operand from the operand list and remove its @@ -1063,6 +1114,16 @@ HloInstruction* HloFusionInstruction::CloneAndFuseInternal( bool in_operand_list = std::find(operands().begin(), operands().end(), instruction_to_fuse) != operands().end(); CHECK(add_output || in_operand_list); + if (instruction_to_fuse->opcode() == HloOpcode::kTuple) { + // We assume all uses of a kTuple operation are GTE ops, not another + // fusion node. In this case, we don't need to clone + // 'instruction_to_fuse'. + CHECK(!in_operand_list); + clone = instruction_to_fuse; + } else { + clone = fused_instructions_computation()->AddInstruction( + instruction_to_fuse->Clone(/*suffix=*/"")); + } const std::vector& fused_parameters = fused_instructions_computation()->parameter_instructions(); for (int64 operand_num = 0; operand_num < operand_count(); ++operand_num) { @@ -1159,9 +1220,10 @@ HloInstruction* HloFusionInstruction::CloneAndFuseInternal( } int64 index = tuple_elements.size(); if (instruction_to_fuse->opcode() == HloOpcode::kTuple) { - index -= instruction_to_fuse->operand_count(); + CHECK_EQ(clone, instruction_to_fuse); + index -= clone->operand_count(); std::vector to_be_removed; - for (auto old_gte : instruction_to_fuse->users()) { + for (auto old_gte : clone->users()) { CHECK_EQ(old_gte->opcode(), HloOpcode::kGetTupleElement); int64 old_tuple_index = old_gte->tuple_index(); HloInstruction* new_gte = @@ -1173,7 +1235,6 @@ HloInstruction* HloFusionInstruction::CloneAndFuseInternal( for (auto old_gte : to_be_removed) { TF_CHECK_OK(parent()->RemoveInstruction(old_gte)); } - TF_CHECK_OK(fused_instructions_computation()->RemoveInstruction(clone)); } else { HloInstruction* new_gte = parent()->AddInstruction(HloInstruction::CreateGetTupleElement( @@ -1182,7 +1243,9 @@ HloInstruction* HloFusionInstruction::CloneAndFuseInternal( } } - VLOG(2) << "New clone:\n" << clone->ToString(); + if (clone != instruction_to_fuse) { + VLOG(2) << "New clone:\n" << clone->ToString(); + } return clone; } @@ -1863,4 +1926,93 @@ HloDynamicSliceInstruction::CloneWithNewOperandsImpl( return MakeUnique( shape, new_operands[0], new_operands[1], dynamic_slice_sizes_); } + +HloGatherInstruction::HloGatherInstruction( + const Shape& shape, HloInstruction* operand, HloInstruction* gather_indices, + const GatherDimensionNumbers& gather_dim_numbers, + tensorflow::gtl::ArraySlice window_bounds) + : HloInstruction(HloOpcode::kGather, shape) { + AppendOperand(operand); + AppendOperand(gather_indices); + gather_dimension_numbers_ = + MakeUnique(gather_dim_numbers); + c_copy(window_bounds, std::back_inserter(gather_window_bounds_)); +} + +string HloGatherInstruction::GatherDimensionNumbersToString() const { + CHECK(gather_dimension_numbers_ != nullptr); + string output_window_dims = + StrCat("output_window_dims={", + Join(gather_dimension_numbers_->output_window_dims(), ","), "}"); + string elided_window_dims = + StrCat("elided_window_dims={", + Join(gather_dimension_numbers_->elided_window_dims(), ","), "}"); + string gather_dims_to_operand_dims = StrCat( + "gather_dims_to_operand_dims={", + Join(gather_dimension_numbers_->gather_dims_to_operand_dims(), ","), "}"); + string index_vector_dim = StrCat( + "index_vector_dim=", gather_dimension_numbers_->index_vector_dim()); + + return Join>( + {output_window_dims, elided_window_dims, gather_dims_to_operand_dims, + index_vector_dim}, + ", "); +} + +/* static */ GatherDimensionNumbers HloGatherInstruction::MakeGatherDimNumbers( + tensorflow::gtl::ArraySlice output_window_dims, + tensorflow::gtl::ArraySlice elided_window_dims, + tensorflow::gtl::ArraySlice gather_dims_to_operand_dims, + int64 index_vector_dim) { + GatherDimensionNumbers gather_dim_numbers; + for (int64 output_window_dim : output_window_dims) { + gather_dim_numbers.add_output_window_dims(output_window_dim); + } + for (int64 elided_window_dim : elided_window_dims) { + gather_dim_numbers.add_elided_window_dims(elided_window_dim); + } + for (int64 gather_dim_to_input_dim : gather_dims_to_operand_dims) { + gather_dim_numbers.add_gather_dims_to_operand_dims(gather_dim_to_input_dim); + } + + gather_dim_numbers.set_index_vector_dim(index_vector_dim); + return gather_dim_numbers; +} + +HloInstructionProto HloGatherInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + *proto.mutable_gather_dimension_numbers() = gather_dimension_numbers(); + for (int64 bound : gather_window_bounds()) { + proto.add_gather_window_bounds(bound); + } + return proto; +} + +std::vector HloGatherInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + return {GatherDimensionNumbersToString(), + StrCat("window_bounds={", Join(gather_window_bounds(), ","), "}")}; +} + +bool HloGatherInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = static_cast(other); + return protobuf_util::ProtobufEquals( + gather_dimension_numbers(), + casted_other.gather_dimension_numbers()) && + gather_window_bounds() == casted_other.gather_window_bounds(); +} + +std::unique_ptr HloGatherInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const { + CHECK_EQ(new_operands.size(), 2); + return MakeUnique( + shape, new_operands[0], new_operands[1], gather_dimension_numbers(), + gather_window_bounds()); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_instructions.h b/tensorflow/compiler/xla/service/hlo_instructions.h index df6969c410a7742a9abfff56c3d41864232a8bff..132e767420ce1e9229eb8c545a0f3111ffba48f4 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.h +++ b/tensorflow/compiler/xla/service/hlo_instructions.h @@ -141,12 +141,15 @@ class HloSendRecvInstruction : public HloInstruction { // channel. int64 channel_id() const { return channel_id_; } + // Returns whether this send/recv instruction sends data to/from the host. + bool is_host_transfer() const { return is_host_transfer_; } + // Returns a serialized representation of this instruction. HloInstructionProto ToProto() const override; protected: explicit HloSendRecvInstruction(HloOpcode opcode, const Shape& shape, - int64 channel_id); + int64 channel_id, bool is_host_transfer); private: std::vector ExtraAttributesToStringImpl( @@ -157,12 +160,15 @@ class HloSendRecvInstruction : public HloInstruction { eq_computations) const override; // Represents a unique identifier for each Send/Recv instruction pair. int64 channel_id_; + + // Whether this send/recv instruction sends data to/from the host. + bool is_host_transfer_; }; class HloSendInstruction : public HloSendRecvInstruction { public: explicit HloSendInstruction(HloInstruction* operand, HloInstruction* token, - int64 channel_id); + int64 channel_id, bool is_host_transfer); private: // Implementation for non-common logic of CloneWithNewOperands. @@ -174,7 +180,8 @@ class HloSendInstruction : public HloSendRecvInstruction { class HloSendDoneInstruction : public HloSendRecvInstruction { public: - explicit HloSendDoneInstruction(HloSendInstruction* operand); + explicit HloSendDoneInstruction(HloSendInstruction* operand, + bool is_host_transfer); private: // Implementation for non-common logic of CloneWithNewOperands. @@ -187,7 +194,7 @@ class HloSendDoneInstruction : public HloSendRecvInstruction { class HloRecvInstruction : public HloSendRecvInstruction { public: explicit HloRecvInstruction(const Shape& shape, HloInstruction* token, - int64 channel_id); + int64 channel_id, bool is_host_transfer); private: // Implementation for non-common logic of CloneWithNewOperands. @@ -199,7 +206,8 @@ class HloRecvInstruction : public HloSendRecvInstruction { class HloRecvDoneInstruction : public HloSendRecvInstruction { public: - explicit HloRecvDoneInstruction(HloRecvInstruction* operand); + explicit HloRecvDoneInstruction(HloRecvInstruction* operand, + bool is_host_transfer); private: // Implementation for non-common logic of CloneWithNewOperands. @@ -216,8 +224,7 @@ class HloAllReduceInstruction : public HloInstruction { HloComputation* reduce_computation, tensorflow::gtl::ArraySlice replica_group_ids, tensorflow::StringPiece barrier, - const tensorflow::gtl::optional& all_reduce_id = - tensorflow::gtl::nullopt); + const tensorflow::gtl::optional& all_reduce_id); // Returns the group ids of each replica for CrossReplicaSum op. const std::vector& replica_group_ids() const { @@ -349,6 +356,35 @@ class HloReduceInstruction : public HloInstruction { std::vector dimensions_; }; +class HloSortInstruction : public HloInstruction { + public: + explicit HloSortInstruction(const Shape& shape, int64 dimension, + HloInstruction* keys, + HloInstruction* values = nullptr); + // Returns the dimension sizes or numbers associated with this instruction. + const std::vector& dimensions() const override { return dimensions_; } + int64 dimensions(int64 index) const override { return dimensions()[index]; } + // Returns the sort dimension for this instruction + int64 sort_dimension() { return dimensions(0); } + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + std::vector dimensions_; +}; + class HloTransposeInstruction : public HloInstruction { public: explicit HloTransposeInstruction( @@ -1119,6 +1155,49 @@ class HloDynamicSliceInstruction : public HloInstruction { // ('start' is specified dynamically in the second operand of the operation). std::vector dynamic_slice_sizes_; }; + +class HloGatherInstruction : public HloInstruction { + public: + explicit HloGatherInstruction( + const Shape& shape, HloInstruction* operand, + HloInstruction* gather_indices, + const GatherDimensionNumbers& gather_dim_numbers, + tensorflow::gtl::ArraySlice window_bounds); + const GatherDimensionNumbers& gather_dimension_numbers() const { + CHECK(gather_dimension_numbers_ != nullptr); + return *gather_dimension_numbers_; + } + tensorflow::gtl::ArraySlice gather_window_bounds() const { + return gather_window_bounds_; + } + // Returns the dump string of the gather dimension numbers. + string GatherDimensionNumbersToString() const; + // Returns a serialized representation of this instruction. + HloInstructionProto ToProto() const override; + + // Creates an instance of GatherDimensionNumbers. + static GatherDimensionNumbers MakeGatherDimNumbers( + tensorflow::gtl::ArraySlice output_window_dims, + tensorflow::gtl::ArraySlice elided_window_dims, + tensorflow::gtl::ArraySlice gather_dims_to_operand_dims, + int64 index_vector_dim); + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + std::unique_ptr gather_dimension_numbers_; + std::vector gather_window_bounds_; +}; + } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_INSTRUCTIONS_H_ diff --git a/tensorflow/compiler/xla/service/hlo_liveness_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_liveness_analysis_test.cc index 0275294a1a86cef13e5b267ad578f30cc18858dc..01b625c29ca2823b2a2490b30a9d4d5128b4c22e 100644 --- a/tensorflow/compiler/xla/service/hlo_liveness_analysis_test.cc +++ b/tensorflow/compiler/xla/service/hlo_liveness_analysis_test.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_liveness_analysis.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" diff --git a/tensorflow/compiler/xla/service/hlo_matchers_test.cc b/tensorflow/compiler/xla/service/hlo_matchers_test.cc index 9a3010cf1ff75e840130d8442bbe26d6041cef25..7de59acc1efbc0150b95ebdd85a13ede48eec2f9 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers_test.cc +++ b/tensorflow/compiler/xla/service/hlo_matchers_test.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/service/hlo_matchers.h" +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -75,8 +76,10 @@ TEST(HloMatchersTest, Test) { } TEST(HloMatchersTest, CustomCallMatcher) { - auto c1 = HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3})); - auto c2 = HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3})); + auto c1 = + HloInstruction::CreateConstant(LiteralUtil::CreateR1({1, 2, 3})); + auto c2 = + HloInstruction::CreateConstant(LiteralUtil::CreateR1({1, 2, 3})); auto call = HloInstruction::CreateCustomCall( ShapeUtil::MakeShape(F32, {1}), {c1.get(), c2.get()}, "foo_target"); diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc index 39bc25ba42c2cb6a9f77e2726405311ba13b3edc..55ff073d3faf34aa0f1b8f0886946837e7a49bcc 100644 --- a/tensorflow/compiler/xla/service/hlo_module.cc +++ b/tensorflow/compiler/xla/service/hlo_module.cc @@ -537,10 +537,11 @@ uint64 HloModule::RandomNew64() const { HloComputation* HloModule::GetComputationWithName( tensorflow::StringPiece name) { - auto it = c_find_if(computations(), [&](HloComputation* computation) { + auto computations_in_module = computations(); + auto it = c_find_if(computations_in_module, [&](HloComputation* computation) { return computation->name() == name; }); - return it == computations().end() ? nullptr : *it; + return it == computations_in_module.end() ? nullptr : *it; } /* static */ std::atomic HloModule::next_unique_module_id_(0); diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc index 6bcd7b042dfddfea6ac86365b82f8077be2a6101..10bf9ffd6c1960df5ca2a3555d120b0874407f15 100644 --- a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc +++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc @@ -20,6 +20,8 @@ limitations under the License. #include #include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/hlo_casting_utils.h" +#include "tensorflow/compiler/xla/service/hlo_instructions.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/util.h" @@ -75,10 +77,23 @@ Status HloModuleGroupMetadata::Build() { if (tracked == nullptr) { return Status::OK(); } - // Add the parent computation of this channel instruction and its peer - // computation (both must be while computations) as companions. + + std::vector peers; if (IsChannelInstruction(hlo)) { - HloComputation* peer_computation = PeerComputation(hlo); + peers.push_back(PeerComputation(hlo)); + } else if (hlo->IsCrossModuleAllReduce()) { + for (HloInstruction* instr : GetAllReduceGroup(*hlo->all_reduce_id())) { + if (instr == hlo) { + continue; + } + peers.push_back(instr->parent()); + } + } + + // Add the parent computation of this channel (or all-reduce) instruction + // and its peer computation(s) (both must be while computations) as + // companions. + for (HloComputation* peer_computation : peers) { const TrackedInstruction* peer_tracked = GetTrackedInstruction(peer_computation); TF_RET_CHECK(peer_tracked != nullptr) @@ -162,8 +177,12 @@ bool HloModuleGroupMetadata::IsChannelInstruction( case HloOpcode::kSend: case HloOpcode::kRecv: case HloOpcode::kSendDone: - case HloOpcode::kRecvDone: - return true; + case HloOpcode::kRecvDone: { + const HloSendRecvInstruction* send_recv_instr = + DynCast(instruction); + CHECK(send_recv_instr != nullptr); + return !send_recv_instr->is_host_transfer(); + } default: return false; } @@ -175,7 +194,8 @@ bool HloModuleGroupMetadata::IsCompanionInstruction(HloInstruction* hlo) const { bool HloModuleGroupMetadata::InstructionCommunicates( HloInstruction* hlo) const { - return IsChannelInstruction(hlo) || IsCompanionInstruction(hlo); + return IsChannelInstruction(hlo) || IsCompanionInstruction(hlo) || + hlo->IsCrossModuleAllReduce(); } const HloModuleGroupMetadata::Channel& HloModuleGroupMetadata::GetChannel( @@ -200,6 +220,13 @@ HloComputation* HloModuleGroupMetadata::PeerComputation( } } +const std::vector& HloModuleGroupMetadata::GetAllReduceGroup( + int64 all_reduce_id) const { + auto it = all_reduce_map_.find(all_reduce_id); + CHECK(it != all_reduce_map_.end()); + return it->second; +} + std::vector HloModuleGroupMetadata::GetCompanionsPath(const HloInstruction* hlo) const { std::vector path; @@ -278,10 +305,27 @@ Status HloModuleGroupMetadata::RecordInstructions() { tracked_instructions_[hlo->to_apply()] = TrackedInstruction(hlo, ComputationKind::kCallFunction); } + + // Group cross module all-reduce instructions by the all_reduce id. + if (hlo->IsCrossModuleAllReduce()) { + TF_RET_CHECK(channel_id_map_.find(*hlo->all_reduce_id()) == + channel_id_map_.end()) + << "all_reduce_id " << *hlo->all_reduce_id() + << " is already used by a send/recv instruction"; + all_reduce_map_[*hlo->all_reduce_id()].push_back(hlo); + max_channel_id_ = std::max(max_channel_id_, *hlo->all_reduce_id()); + return Status::OK(); + } + if (!IsChannelInstruction(hlo)) { return Status::OK(); } + TF_RET_CHECK(all_reduce_map_.find(hlo->channel_id()) == + all_reduce_map_.end()) + << "channel id " << hlo->channel_id() + << " is already used by an all-reduce instruction"; + // Add a new channel if needed. if (channel_id_map_.find(hlo->channel_id()) == channel_id_map_.end()) { channels_.emplace_back(); @@ -324,6 +368,7 @@ Status HloModuleGroupMetadata::RecordInstructions() { } } VLOG(2) << "Created " << channels_.size() << " channels"; + VLOG(2) << "Created " << all_reduce_map_.size() << " all-reduce groups"; return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.h b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h index ffde3a332dfc141ca928a44cfdf4686900e9f47b..84f2d3f5fbc1a6ff1df8ba3c0babd122e5701148 100644 --- a/tensorflow/compiler/xla/service/hlo_module_group_metadata.h +++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h @@ -92,7 +92,7 @@ class HloModuleGroupMetadata { ComputationKind kind_ = ComputationKind::kInvalid; }; - // Represents a channel and the 4 instructions that form the channel. + // Represents a channel and the instructions that form the channel. struct Channel { int64 id = -1; HloInstruction* send = nullptr; @@ -118,13 +118,17 @@ class HloModuleGroupMetadata { // comment above on companion instructions. bool IsCompanionInstruction(HloInstruction* hlo) const; - // Returns true if the instruction is either a channel instruction or a - // companion instruction. + // Returns true if the instruction is either a channel instruction, a + // cross-module all-reduce instruction, or a companion instruction. bool InstructionCommunicates(HloInstruction* hlo) const; // Returns the Channel instance for the given channel id. const Channel& GetChannel(int64 channel_id) const; + // Returns the all-reduce instructions with the same all_reduce_id. + const std::vector& GetAllReduceGroup( + int64 all_reduce_id) const; + // Returns the computation that contains the peer channel instructions for // the given instruction. // @@ -187,13 +191,14 @@ class HloModuleGroupMetadata { // Returns all channels in the module group. const std::vector& channels() const { return channels_; } - // Returns the maximum channel id used in the module group. + // Returns the maximum channel id or all_reduce_id used in the module group. int64 max_channel_id() const { return max_channel_id_; } private: Status Build(); - // Record all channel instructions and While instructions. + // Record all channel instructions, cross-module AllReduce instructions, and + // While/Conditional/Call instructions. Status RecordInstructions(); // Verifies the given HloModules are well-formed and follow the specification, @@ -255,6 +260,9 @@ class HloModuleGroupMetadata { // Map from channel ids to the index in channels_. tensorflow::gtl::FlatMap channel_id_map_; + // Map from all-reduce ids to the all reduce instructions. + tensorflow::gtl::FlatMap> all_reduce_map_; + // The maximum channel id used in the module group. int64 max_channel_id_ = -1; diff --git a/tensorflow/compiler/xla/service/hlo_module_group_util.cc b/tensorflow/compiler/xla/service/hlo_module_group_util.cc index 21a9b7291acc9e0066a9061facd13ab5acbf0bac..9fd0ade153109c6c809c37aa08257f83a82c44d5 100644 --- a/tensorflow/compiler/xla/service/hlo_module_group_util.cc +++ b/tensorflow/compiler/xla/service/hlo_module_group_util.cc @@ -56,12 +56,17 @@ std::vector HloModuleGroupUtil::GlobalPredecessors( }; // If the given instruction is a companion instruction, we need to find the - // predecessors of all of its companion instructions. + // predecessors of all of its companion instructions. If the instruction is an + // all-reduce, we need to find the predecessors of all the peer all-reduce + // instructions. std::vector instruction_group; if (metadata_.IsCompanionInstruction(instruction)) { for (HloInstruction* companion : metadata_.Companions(instruction)) { instruction_group.push_back(companion); } + } else if (instruction->IsCrossModuleAllReduce()) { + instruction_group = + metadata_.GetAllReduceGroup(*instruction->all_reduce_id()); } else { instruction_group.push_back(instruction); } @@ -112,12 +117,17 @@ std::vector HloModuleGroupUtil::GlobalSuccessors( }; // If the given instruction is a companion instruction, we need to find the - // successors of all of its companion instructions. + // successors of all of its companion instructions. If the instruction is an + // all-reduce, we need to find the successors of all its peer all-reduce + // instructions. std::vector instruction_group; if (metadata_.IsCompanionInstruction(instruction)) { for (HloInstruction* companion : metadata_.Companions(instruction)) { instruction_group.push_back(companion); } + } else if (instruction->IsCrossModuleAllReduce()) { + instruction_group = + metadata_.GetAllReduceGroup(*instruction->all_reduce_id()); } else { instruction_group.push_back(instruction); } @@ -170,15 +180,17 @@ Status HloModuleGroupUtil::VisitTopologicalOrder( HloInstruction* hlo = stack.top(); // Find the instruction group of the currently visited instruction. The - // instruction group represents all companion instructions of the - // current instruction, and are considered to be a single entity for the - // purpose of the traversal (i.e., they must always be in the same visit - // state). + // instruction group represents all companion instructions of the current + // instruction, or all the all-reduce instructions that belong to the same + // group, or are considered to be a single entity for the purpose of the + // traversal (i.e., they must always be in the same visit state). std::vector instruction_group; if (metadata_.IsCompanionInstruction(hlo)) { for (HloInstruction* companion : metadata_.Companions(hlo)) { instruction_group.push_back(companion); } + } else if (hlo->IsCrossModuleAllReduce()) { + instruction_group = metadata_.GetAllReduceGroup(*hlo->all_reduce_id()); } else { instruction_group.push_back(hlo); } @@ -292,7 +304,7 @@ HloModuleGroupUtil::ComputeReachability( } auto reachability = MakeUnique(post_order); for (HloInstruction* hlo : post_order) { - reachability->SetReachabilityToUnion(GlobalPredecessors(hlo), hlo); + reachability->FastSetReachabilityToUnion(GlobalPredecessors(hlo), hlo); } return std::move(reachability); } diff --git a/tensorflow/compiler/xla/service/hlo_module_test.cc b/tensorflow/compiler/xla/service/hlo_module_test.cc index 7f28a804bfec9c2f1bbb5fa08f7dd4e68be14d35..236f4500860a8673e61cbd2f861a8fc40c7861f7 100644 --- a/tensorflow/compiler/xla/service/hlo_module_test.cc +++ b/tensorflow/compiler/xla/service/hlo_module_test.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_module.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -38,7 +38,7 @@ class HloModuleTest : public HloTestBase { std::unique_ptr CreateConstantComputation() { auto builder = HloComputation::Builder("Constant"); builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); return builder.Build(); } @@ -122,7 +122,7 @@ TEST_F(HloModuleTest, CloneHasFusion) { { auto b = HloComputation::Builder("Entry"); auto input = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); b.AddInstruction( HloInstruction::CreateFusion(r0f32_, HloInstruction::FusionKind::kInput, /*operands=*/{input}, fused_computation)); @@ -173,7 +173,7 @@ TEST_F(HloModuleTest, LargeConstantToString) { auto builder = HloComputation::Builder("Constant"); std::vector values(16, 42.0); builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1(values))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1(values))); module->AddEntryComputation(builder.Build()); EXPECT_EQ( diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h index 39e12c48157992410a5d3b733720d677a1191611..59e9a5a94aa4fc6270bde76c19dbd0d4506a563c 100644 --- a/tensorflow/compiler/xla/service/hlo_opcode.h +++ b/tensorflow/compiler/xla/service/hlo_opcode.h @@ -87,6 +87,7 @@ namespace xla { V(kHostCompute, "host-compute") \ V(kImag, "imag") \ V(kInfeed, "infeed") \ + V(kIota, "iota") \ V(kIsFinite, "is-finite") \ V(kLe, "less-than-or-equal-to", kHloOpcodeIsComparison) \ V(kLog, "log") \ diff --git a/tensorflow/compiler/xla/service/hlo_ordering_test.cc b/tensorflow/compiler/xla/service/hlo_ordering_test.cc index cfe5dace05ac03f1573f90b2ce664c94837837b4..126d3a2d9c70bff1d2a022e395652049768d6d21 100644 --- a/tensorflow/compiler/xla/service/hlo_ordering_test.cc +++ b/tensorflow/compiler/xla/service/hlo_ordering_test.cc @@ -57,7 +57,7 @@ TEST_F(HloOrderingTest, InstructionsInDifferentComputations) { auto builder_c = HloComputation::Builder("C"); HloInstruction* c = builder_c.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); HloComputation* computation_c = module->AddEmbeddedComputation(builder_c.Build()); @@ -145,7 +145,7 @@ TEST_F(HloOrderingTest, InstructionsInWhileComputations) { auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto xla_while = builder.AddInstruction( HloInstruction::CreateWhile(scalar_shape, condition, body, constant)); module->AddEntryComputation(builder.Build()); @@ -208,7 +208,7 @@ TEST_F(HloOrderingTest, ValuesInWhileComputations) { auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto xla_while = builder.AddInstruction( HloInstruction::CreateWhile(scalar_shape, condition, body, constant)); auto add = builder.AddInstruction(HloInstruction::CreateBinary( diff --git a/tensorflow/compiler/xla/service/hlo_parser.cc b/tensorflow/compiler/xla/service/hlo_parser.cc index f192debc9c75e49d0be09c1a069a20343685a134..d71d3c81702fb3d2adae82b1055464e4983eb891 100644 --- a/tensorflow/compiler/xla/service/hlo_parser.cc +++ b/tensorflow/compiler/xla/service/hlo_parser.cc @@ -15,8 +15,10 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_parser.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/hlo_domain_metadata.h" +#include "tensorflow/compiler/xla/service/hlo_instructions.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/hlo_sharding_metadata.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -117,6 +119,7 @@ class HloParser { // Types of attributes. enum class AttrTy { + kBool, kInt64, kInt32, kFloat, @@ -489,6 +492,14 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, HloInstruction::CreateConstant(std::move(literal))); break; } + case HloOpcode::kIota: { + if (!ParseOperands(&operands, /*expected_size=*/0) || + !ParseAttributes(attrs)) { + return false; + } + instruction = builder->AddInstruction(HloInstruction::CreateIota(shape)); + break; + } // Unary ops. case HloOpcode::kAbs: case HloOpcode::kRoundNearestAfz: @@ -621,23 +632,32 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, if (!ParseOperands(&operands) || !ParseAttributes(attrs)) { return false; } - instruction = - builder->AddInstruction(HloInstruction::CreateAfterAll(operands)); + if (operands.empty()) { + instruction = builder->AddInstruction(HloInstruction::CreateToken()); + } else { + instruction = + builder->AddInstruction(HloInstruction::CreateAfterAll(operands)); + } break; } case HloOpcode::kSort: { auto loc = lexer_.GetLoc(); - if (!ParseOperands(&operands) || !ParseAttributes(attrs)) { + + optional> dimensions; + attrs["dimensions"] = {/*required=*/true, AttrTy::kBracedInt64List, + &dimensions}; + if (!ParseOperands(&operands) || !ParseAttributes(attrs) || + dimensions->size() != 1) { return false; } switch (operands.size()) { case 1: - instruction = builder->AddInstruction( - HloInstruction::CreateSort(shape, /*keys=*/operands[0])); + instruction = builder->AddInstruction(HloInstruction::CreateSort( + shape, dimensions->at(0), /*keys=*/operands[0])); break; case 2: instruction = builder->AddInstruction(HloInstruction::CreateSort( - shape, + shape, dimensions->at(0), /*keys=*/operands[0], /*values=*/operands[1])); break; default: @@ -670,18 +690,27 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, } case HloOpcode::kRecv: { optional channel_id; + // If the is_host_transfer attribute is not present then default to false. + optional is_host_transfer = false; attrs["channel_id"] = {/*required=*/true, AttrTy::kInt64, &channel_id}; + attrs["is_host_transfer"] = {/*required=*/false, AttrTy::kBool, + &is_host_transfer}; if (!ParseOperands(&operands, /*expected_size=*/1) || !ParseAttributes(attrs)) { return false; } + // If the is_host_transfer attribute is not present then default to false. instruction = builder->AddInstruction(HloInstruction::CreateRecv( - shape.tuple_shapes(0), operands[0], *channel_id)); + shape.tuple_shapes(0), operands[0], *channel_id, *is_host_transfer)); break; } case HloOpcode::kRecvDone: { optional channel_id; + // If the is_host_transfer attribute is not present then default to false. + optional is_host_transfer = false; attrs["channel_id"] = {/*required=*/true, AttrTy::kInt64, &channel_id}; + attrs["is_host_transfer"] = {/*required=*/false, AttrTy::kBool, + &is_host_transfer}; if (!ParseOperands(&operands, /*expected_size=*/1) || !ParseAttributes(attrs)) { return false; @@ -689,24 +718,32 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, if (channel_id != operands[0]->channel_id()) { return false; } - instruction = - builder->AddInstruction(HloInstruction::CreateRecvDone(operands[0])); + instruction = builder->AddInstruction( + HloInstruction::CreateRecvDone(operands[0], *is_host_transfer)); break; } case HloOpcode::kSend: { optional channel_id; + // If the is_host_transfer attribute is not present then default to false. + optional is_host_transfer = false; attrs["channel_id"] = {/*required=*/true, AttrTy::kInt64, &channel_id}; + attrs["is_host_transfer"] = {/*required=*/false, AttrTy::kBool, + &is_host_transfer}; if (!ParseOperands(&operands, /*expected_size=*/2) || !ParseAttributes(attrs)) { return false; } - instruction = builder->AddInstruction( - HloInstruction::CreateSend(operands[0], operands[1], *channel_id)); + instruction = builder->AddInstruction(HloInstruction::CreateSend( + operands[0], operands[1], *channel_id, *is_host_transfer)); break; } case HloOpcode::kSendDone: { optional channel_id; + // If the is_host_transfer attribute is not present then default to false. + optional is_host_transfer = false; attrs["channel_id"] = {/*required=*/true, AttrTy::kInt64, &channel_id}; + attrs["is_host_transfer"] = {/*required=*/false, AttrTy::kBool, + &is_host_transfer}; if (!ParseOperands(&operands, /*expected_size=*/1) || !ParseAttributes(attrs)) { return false; @@ -714,8 +751,8 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, if (channel_id != operands[0]->channel_id()) { return false; } - instruction = - builder->AddInstruction(HloInstruction::CreateSendDone(operands[0])); + instruction = builder->AddInstruction( + HloInstruction::CreateSendDone(operands[0], *is_host_transfer)); break; } case HloOpcode::kGetTupleElement: { @@ -1095,13 +1132,24 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, } case HloOpcode::kCustomCall: { optional custom_call_target; + optional window; + optional dnums; attrs["custom_call_target"] = {/*required=*/true, AttrTy::kString, &custom_call_target}; + attrs["window"] = {/*required=*/false, AttrTy::kWindow, &window}; + attrs["dim_labels"] = {/*required=*/false, + AttrTy::kConvolutionDimensionNumbers, &dnums}; if (!ParseOperands(&operands) || !ParseAttributes(attrs)) { return false; } instruction = builder->AddInstruction(HloInstruction::CreateCustomCall( shape, operands, *custom_call_target)); + if (window.has_value()) { + instruction->set_window(*window); + } + if (dnums.has_value()) { + instruction->set_convolution_dimension_numbers(*dnums); + } break; } case HloOpcode::kHostCompute: { @@ -1182,11 +1230,12 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, return false; } - GatherDimensionNumbers dim_numbers = HloInstruction::MakeGatherDimNumbers( - /*output_window_dims=*/*output_window_dims, - /*elided_window_dims=*/*elided_window_dims, - /*gather_dims_to_operand_dims=*/*gather_dims_to_operand_dims, - /*index_vector_dim=*/*index_vector_dim); + GatherDimensionNumbers dim_numbers = + HloGatherInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/*output_window_dims, + /*elided_window_dims=*/*elided_window_dims, + /*gather_dims_to_operand_dims=*/*gather_dims_to_operand_dims, + /*index_vector_dim=*/*index_vector_dim); instruction = builder->AddInstruction(HloInstruction::CreateGather( shape, /*operand=*/operands[0], /*gather_indices=*/operands[1], @@ -1609,7 +1658,7 @@ bool HloParser::ParseTupleLiteral(std::unique_ptr* literal, } } } - *literal = Literal::MakeTupleOwned(std::move(elements)); + *literal = LiteralUtil::MakeTupleOwned(std::move(elements)); return ParseToken(TokKind::kRparen, StrCat("expects ')' at the end of the tuple with ", ShapeUtil::TupleElementCount(shape), "elements")); @@ -1637,8 +1686,8 @@ bool HloParser::ParseDenseLiteral(std::unique_ptr* literal, } // Create a literal with the given shape in default layout. - *literal = Literal::CreateFromDimensions(shape.element_type(), - AsInt64Slice(shape.dimensions())); + *literal = LiteralUtil::CreateFromDimensions( + shape.element_type(), AsInt64Slice(shape.dimensions())); tensorflow::int64 nest_level = 0; tensorflow::int64 linear_index = 0; // elems_seen_per_dim[i] is how many elements or sub-arrays we have seen for @@ -2031,6 +2080,14 @@ bool HloParser::ParseAttributeHelper( bool success = [&] { LocTy attr_loc = lexer_.GetLoc(); switch (attr_type) { + case AttrTy::kBool: { + bool result; + if (!ParseBool(&result)) { + return false; + } + static_cast*>(attr_out_ptr)->emplace(result); + return true; + } case AttrTy::kInt64: { tensorflow::int64 result; if (!ParseInt64(&result)) { diff --git a/tensorflow/compiler/xla/service/hlo_parser_test.cc b/tensorflow/compiler/xla/service/hlo_parser_test.cc index 88f3309baa4150c14f44a3db0b412fe80e22293c..1c08c51220e88cdd04b26fe3bcd84d28c4436e85 100644 --- a/tensorflow/compiler/xla/service/hlo_parser_test.cc +++ b/tensorflow/compiler/xla/service/hlo_parser_test.cc @@ -286,6 +286,21 @@ ENTRY %TwoSendRecvBothWayRecvFist.v3 () -> (f32[], token[]) { %send-done = token[] send-done((f32[], u32[], token[]) %send), channel_id=16, sharding={maximal device=0} } +)" +}, +{ +"SendRecvWithHostTransfer", +R"(HloModule HostTransferSendRecv_module + +ENTRY %TwoSendRecvBothWayRecvFist.v3 () -> (f32[], token[]) { + %token = token[] after-all() + %recv = (f32[], u32[], token[]) recv(token[] %token), channel_id=15, is_host_transfer=true + ROOT %recv-done = (f32[], token[]) recv-done((f32[], u32[], token[]) %recv), channel_id=15, is_host_transfer=true + %constant = f32[] constant(2.1), sharding={maximal device=0} + %send = (f32[], u32[], token[]) send(f32[] %constant, token[] %token), channel_id=16, is_host_transfer=true + %send-done = token[] send-done((f32[], u32[], token[]) %send), channel_id=16, is_host_transfer=true +} + )" }, // get-tuple-element @@ -840,7 +855,7 @@ R"(HloModule sort ENTRY Sort { x = f32[1024]{0} parameter(0) - ROOT sorted = f32[1024]{0} sort(x) + ROOT sorted = f32[1024]{0} sort(x), dimensions={0} } )" @@ -853,7 +868,32 @@ R"(HloModule sort ENTRY Sort { keys = f32[1024]{0} parameter(0) values = s32[1024]{0} parameter(1) - ROOT sorted = (f32[1024]{0}, s32[1024]{0}) sort(keys, values) + ROOT sorted = (f32[1024]{0}, s32[1024]{0}) sort(keys, values), dimensions={0} +} + +)" +}, +// R2 Sort (Key) +{ +"SortKeyR2", +R"(HloModule sort + +ENTRY Sort { + x = f32[1024,16]{0,1} parameter(0) + ROOT sorted = f32[1024,16]{0,1} sort(x), dimensions={0} +} + +)" +}, +// R2 Sort (Key, Value) +{ +"SortKeyValueR2", +R"(HloModule sort + +ENTRY Sort { + keys = f32[1024,16]{0,1} parameter(0) + values = s32[1024,16]{0,1} parameter(1) + ROOT sorted = (f32[1024,16]{0,1}, s32[1024,16]{0,1}) sort(keys, values), dimensions={0} } )" @@ -964,6 +1004,28 @@ ENTRY CrossReplicaSumWithSubgroups { ROOT cross-replica-sum = f32[128,32]{0,1} cross-replica-sum(input), replica_group_ids={0,0,1,1}, barrier="abc", to_apply=add } +)" +}, +// Iota +{ +"Iota", +R"(HloModule iota + +ENTRY Iota { + ROOT iota = f32[100]{0} iota() +} + +)" +}, +// custom-call with window and dim_labels +{ +"CustomCallWithWindowAndDimLabels", +R"(HloModule CustomCallWithWindowAndDimLabels + +ENTRY Computation { + ROOT r = f32[100]{0} custom-call(), window={size=2x2}, dim_labels=b01f_01io->b01f, custom_call_target="target" +} + )" } }); diff --git a/tensorflow/compiler/xla/service/hlo_query.cc b/tensorflow/compiler/xla/service/hlo_query.cc index 2418c19f3de7b036d7ef52d3a6db11de6316203b..2a07b6fcbc243d955e136ccdf097c8155a115845 100644 --- a/tensorflow/compiler/xla/service/hlo_query.cc +++ b/tensorflow/compiler/xla/service/hlo_query.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_query.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/service/hlo_reachability_test.cc b/tensorflow/compiler/xla/service/hlo_reachability_test.cc index 657a9ee83d29e72b95660325f9139f44159d6508..585c95972b0e01abc14543205af71b4b0c0bdf3c 100644 --- a/tensorflow/compiler/xla/service/hlo_reachability_test.cc +++ b/tensorflow/compiler/xla/service/hlo_reachability_test.cc @@ -39,15 +39,15 @@ TEST_F(HloReachabilityTest, Reachability) { */ auto builder = HloComputation::Builder(TestName()); auto a = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); auto b = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); auto c = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); auto d = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); auto e = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))); builder.Build(); HloReachabilityMap reachability({a, b, c, d, e}); diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.cc b/tensorflow/compiler/xla/service/hlo_rematerialization.cc index 0b222f43483405cf1d3f711bab3e8390903f8ded..cf0be30c7ad5cbeb7fd3d71c7c649b6b448360b8 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization.cc +++ b/tensorflow/compiler/xla/service/hlo_rematerialization.cc @@ -1202,17 +1202,14 @@ StatusOr HloRematerialization::RematerializeComputation( StatusOr HloRematerialization::Run( HloModule* module, SequentialHloOrdering::HloModuleSequence* sequence, - int64 memory_limit_bytes, RematerializationSizes* sizes) { + int64 memory_limit_bytes, RematerializationSizes* sizes, + CopyInsertion* copy_insertion) { // The sequence is constructed entirely by this method. TF_RET_CHECK(sequence->empty()); VLOG(1) << "HloRematerialization() with memory limit of " << HumanReadableNumBytes(memory_limit_bytes); - if (copy_insertion_) { - TF_RETURN_IF_ERROR(copy_insertion_->Run(module).status()); - } - TF_ASSIGN_OR_RETURN(points_to_analysis_, TuplePointsToAnalysis::Run(module)); // Adjust memory limit to account for the output of the entry @@ -1241,12 +1238,14 @@ StatusOr HloRematerialization::Run( return size_function_(buffer.shape()); }, scheduler_algorithm_)); - if (copy_insertion_) { + if (copy_insertion) { // We run a separate pass of copy elision here because the sequential // ordering from the HLO schedule allows for more copies to be eliminated. + // TODO(b/80249101): Instead of a separate copy elision pass, use the + // ordering from the HLO schedule directly for copy insertion. SequentialHloOrdering ordering(module, *sequence); TF_RETURN_IF_ERROR( - copy_insertion_->RemoveUnnecessaryCopies(ordering, module)); + copy_insertion->RemoveUnnecessaryCopies(ordering, module)); } // Compute peak memory usage of all computations in the module called in a @@ -1352,9 +1351,9 @@ StatusOr HloRematerialization::Run( MemorySchedulerAlgorithm scheduler_algorithm, SequentialHloOrdering::HloModuleSequence* sequence, RematerializationSizes* sizes, CopyInsertion* copy_insertion) { - HloRematerialization remat(std::move(scheduler_algorithm), size_function, - copy_insertion); - return remat.Run(hlo_module, sequence, memory_limit_bytes, sizes); + HloRematerialization remat(scheduler_algorithm, size_function); + return remat.Run(hlo_module, sequence, memory_limit_bytes, sizes, + copy_insertion); } } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.h b/tensorflow/compiler/xla/service/hlo_rematerialization.h index 1c72f42b8c6085de43af766ce8084ca059620e53..2ec004350ad88ff31ece90ec419d90a55b965166 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization.h +++ b/tensorflow/compiler/xla/service/hlo_rematerialization.h @@ -58,8 +58,12 @@ class HloRematerialization { // sizes: Optional outparam that indicates the peak memory usage of the HLO // module before/after rematerialization. // - // copy_insertion: If non-null, run the provided copy insertion pass - // before HLO scheduling. + // copy_insertion: If non-null, run copy elision after scheduling. This + // pass is used to eliminate copies that were inserted by copy insertion + // before HLO scheduling. + // + // TODO(b/80249101): Remove the 'run_copy_elision' parameter when copy + // insertion is integrated with HLO scheduling. // // Returns whether any instructions were rematerialized. If memory use is // already below the given limit then no instructions are rematerialized and @@ -76,11 +80,9 @@ class HloRematerialization { protected: HloRematerialization(MemorySchedulerAlgorithm scheduler_algorithm, - const ShapeSizeFunction& size_function, - CopyInsertion* copy_insertion) + const ShapeSizeFunction& size_function) : scheduler_algorithm_(scheduler_algorithm), - size_function_(size_function), - copy_insertion_(copy_insertion) {} + size_function_(size_function) {} ~HloRematerialization() {} // Runs rematerialization on the given module. Returns whether the module was @@ -89,7 +91,8 @@ class HloRematerialization { // contains the memory-minimizing order in which to emit the HLO instructions. StatusOr Run(HloModule* module, SequentialHloOrdering::HloModuleSequence* sequence, - int64 memory_limit, RematerializationSizes* sizes); + int64 memory_limit, RematerializationSizes* sizes, + CopyInsertion* copy_insertion); // Rematerializes instructions within the given computation. 'order' is the // order in which the computation's instructions will be emitted in the @@ -145,9 +148,6 @@ class HloRematerialization { // uses of the original instruction and the original instruction is // dead. Hence, no net instructions were added. int64 net_instructions_added_ = 0; - - // Copy insertion pass that runs before HLO scheduling. - CopyInsertion* copy_insertion_; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc index fc137c839fc18d01b8aad9e073ad24dc166ebb4a..ac8c97d380953764b66135ad1c5fcee0d481c004 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc +++ b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc @@ -132,7 +132,7 @@ class HloRematerializationTest : public HloTestBase { builder.AddInstruction( HloInstruction::CreateParameter(0, vec1_shape_, "param")); builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); return builder.Build(); } @@ -143,12 +143,11 @@ class HloRematerializationTest : public HloTestBase { StatusOr RunHloRematerialization( int64 memory_limit_bytes, HloModule* module, - SequentialHloOrdering::HloModuleSequence* sequence, - CopyInsertion* copy_insertion = nullptr) { + SequentialHloOrdering::HloModuleSequence* sequence) { TF_EXPECT_OK(verifier().Run(module).status()); return HloRematerialization::RematerializeAndSchedule( ByteSizeOf, memory_limit_bytes, module, DefaultMemoryScheduler, - sequence, /*sizes=*/nullptr, copy_insertion); + sequence, /*sizes=*/nullptr); } // Various shapes used in the canned computations. @@ -227,7 +226,7 @@ TEST_F(HloRematerializationTest, RematerializeAroundWhile) { cond_builder.AddInstruction( HloInstruction::CreateParameter(0, vec1_shape_, "param")); cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); HloComputation* while_cond = module->AddEmbeddedComputation(cond_builder.Build()); @@ -264,7 +263,7 @@ TEST_F(HloRematerializationTest, RematerializeEntryAndWhileBody) { cond_builder.AddInstruction( HloInstruction::CreateParameter(0, vec1_shape_, "param")); cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); HloComputation* while_cond = module->AddEmbeddedComputation(cond_builder.Build()); @@ -288,41 +287,6 @@ TEST_F(HloRematerializationTest, RematerializeEntryAndWhileBody) { EXPECT_EQ(body_computation->instruction_count(), 9); } -// Similar to RematerializeEntryAndWhileBody, except with copy insertion run -// after HLO scheduling. -TEST_F(HloRematerializationTest, RematerializeEntryAndWhileBodyWithCopies) { - auto module = CreateNewModule(); - - auto cond_builder = HloComputation::Builder(TestName() + ".cond"); - cond_builder.AddInstruction( - HloInstruction::CreateParameter(0, vec1_shape_, "param")); - cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); - HloComputation* while_cond = - module->AddEmbeddedComputation(cond_builder.Build()); - - HloComputation* body_computation = module->AddEmbeddedComputation( - MakeRematerializableComputation(/*suffix=*/".body")); - HloComputation* entry_computation = - module->AddEntryComputation(MakeRematerializableWhileComputation( - while_cond, /*while_body=*/body_computation)); - - EXPECT_EQ(entry_computation->instruction_count(), 7); - EXPECT_EQ(body_computation->instruction_count(), 8); - - SequentialHloOrdering::HloModuleSequence sequence; - CopyInsertion copy_insertion; - TF_ASSERT_OK_AND_ASSIGN(bool changed, - RunHloRematerialization( - /*memory_limit_bytes=*/15 * 1024, module.get(), - &sequence, ©_insertion)); - EXPECT_TRUE(changed); - - // Both computations should have rematerialized instructions added. - EXPECT_EQ(entry_computation->instruction_count(), 9); - EXPECT_EQ(body_computation->instruction_count(), 9); -} - // Test rematerialization of a doubly nested computation. All computations // should have an instruction rematerialized. TEST_F(HloRematerializationTest, RematerializeNestedComputations) { @@ -332,7 +296,7 @@ TEST_F(HloRematerializationTest, RematerializeNestedComputations) { cond_builder.AddInstruction( HloInstruction::CreateParameter(0, vec1_shape_, "param")); cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); HloComputation* while_cond = module->AddEmbeddedComputation(cond_builder.Build()); diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc index c6d3909af6103949daf4b0ab6be9b74724461e30..27cc5361cde2fa021b9489f98217ae5648afc2ad 100644 --- a/tensorflow/compiler/xla/service/hlo_scheduling.cc +++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc @@ -567,6 +567,7 @@ StatusOr ScheduleComputationsInModule( sequence[computation] = std::move(one_computation_sequence); } } + VLOG(1) << "Module schedule:\n" << sequence; return sequence; } diff --git a/tensorflow/compiler/xla/service/hlo_scheduling_test.cc b/tensorflow/compiler/xla/service/hlo_scheduling_test.cc index 73f22f81f4e9cf597db8b184642acff2fdaaf2b0..cf9ceed5b2fb49eb91fea96d89c8e1efc2a3dad1 100644 --- a/tensorflow/compiler/xla/service/hlo_scheduling_test.cc +++ b/tensorflow/compiler/xla/service/hlo_scheduling_test.cc @@ -168,8 +168,9 @@ TEST_F(HloSchedulingTest, ListAccountsForSubcomputations) { auto cond_builder = HloComputation::Builder("WhileCond"); HloInstruction* cond_param = cond_builder.AddInstruction( HloInstruction::CreateParameter(0, r1f32, "cond_param")); - HloInstruction* zero_vector = cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2({{0, 0, 0, 0}}))); + HloInstruction* zero_vector = + cond_builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR2({{0, 0, 0, 0}}))); cond_builder.AddInstruction(HloInstruction::CreateBinary( ShapeUtil::MakeShape(PRED, {}), HloOpcode::kNe, cond_param, zero_vector)); auto cond_computation = module->AddEmbeddedComputation(cond_builder.Build()); @@ -179,16 +180,18 @@ TEST_F(HloSchedulingTest, ListAccountsForSubcomputations) { auto body_builder = HloComputation::Builder("WhileBody"); HloInstruction* body_param = body_builder.AddInstruction( HloInstruction::CreateParameter(0, r1f32, "body_param")); - HloInstruction* one_vector = body_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2({{1, 1, 1, 1}}))); + HloInstruction* one_vector = + body_builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR2({{1, 1, 1, 1}}))); body_builder.AddInstruction(HloInstruction::CreateBinary( r1f32, HloOpcode::kSubtract, body_param, one_vector)); auto body_computation = module->AddEmbeddedComputation(body_builder.Build()); // transpose(matrix) + bcast(while) auto builder = HloComputation::Builder(TestName()); - HloInstruction* while_init = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2({{1, 1, 1, 1}}))); + HloInstruction* while_init = + builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR2({{1, 1, 1, 1}}))); // Creates 16 bytes, ignoring subcomputations HloInstruction* while_loop = builder.AddInstruction(HloInstruction::CreateWhile( @@ -199,7 +202,7 @@ TEST_F(HloSchedulingTest, ListAccountsForSubcomputations) { HloInstruction::CreateBroadcast(r2f32, while_loop, {0})); HloInstruction* matrix = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2( + HloInstruction::CreateConstant(LiteralUtil::CreateR2( {{1.0, 2.0, 3.0, 4.0}, {1.0, 2.0, 3.0, 4.0}}))); // Creates 32 bytes HloInstruction* transpose = builder.AddInstruction( @@ -257,7 +260,7 @@ TEST_F(HloSchedulingTest, TuplesAreAccountedCorrectly) { // Wrap lit in abs because constants are considered free by // IgnoreInstruction, and it skews the accounting. auto lit = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1, 1, 1, 1, 1, 1}))); + LiteralUtil::CreateR1({1, 1, 1, 1, 1, 1}))); auto abs_const = builder.AddInstruction( HloInstruction::CreateUnary(r1f32, HloOpcode::kAbs, lit)); @@ -300,11 +303,11 @@ TEST_F(HloSchedulingTest, MultiOutputFusionAccountedCorrectly) { HloComputation::Builder builder(TestName()); auto c1 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1, 1, 1, 1, 1}))); + LiteralUtil::CreateR1({1, 1, 1, 1, 1}))); auto c2 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1, 2, 3, 4, 5}))); + LiteralUtil::CreateR1({1, 2, 3, 4, 5}))); auto c3 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({0, 2, 4, 6, 8}))); + LiteralUtil::CreateR1({0, 2, 4, 6, 8}))); auto add = builder.AddInstruction( HloInstruction::CreateBinary(r1f32, HloOpcode::kAdd, c1, c2)); @@ -354,8 +357,9 @@ TEST_F(HloSchedulingTest, HeapSimulatorAccountsForSubcomputations) { auto cond_builder = HloComputation::Builder("WhileCond"); HloInstruction* cond_param = cond_builder.AddInstruction( HloInstruction::CreateParameter(0, r1f32, "cond_param")); - HloInstruction* zero_vector = cond_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2({{0, 0, 0, 0}}))); + HloInstruction* zero_vector = + cond_builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR2({{0, 0, 0, 0}}))); cond_builder.AddInstruction(HloInstruction::CreateBinary( ShapeUtil::MakeShape(PRED, {}), HloOpcode::kNe, cond_param, zero_vector)); auto cond_computation = module->AddEmbeddedComputation(cond_builder.Build()); @@ -365,15 +369,17 @@ TEST_F(HloSchedulingTest, HeapSimulatorAccountsForSubcomputations) { auto body_builder = HloComputation::Builder("WhileBody"); HloInstruction* body_param = body_builder.AddInstruction( HloInstruction::CreateParameter(0, r1f32, "body_param")); - HloInstruction* one_vector = body_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2({{1, 1, 1, 1}}))); + HloInstruction* one_vector = + body_builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR2({{1, 1, 1, 1}}))); body_builder.AddInstruction(HloInstruction::CreateBinary( r1f32, HloOpcode::kSubtract, body_param, one_vector)); auto body_computation = module->AddEmbeddedComputation(body_builder.Build()); auto builder = HloComputation::Builder(TestName()); - HloInstruction* while_init = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2({{1, 1, 1, 1}}))); + HloInstruction* while_init = + builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR2({{1, 1, 1, 1}}))); // Creates 16 bytes, ignoring subcomputations builder.AddInstruction(HloInstruction::CreateWhile( r1f32, cond_computation, body_computation, while_init)); diff --git a/tensorflow/compiler/xla/service/hlo_sharding.cc b/tensorflow/compiler/xla/service/hlo_sharding.cc index 268b4727bcbed42ba71526f1d5ef5c887e941930..393944c20faa0b09ebc8544543b62566c836739f 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding.cc +++ b/tensorflow/compiler/xla/service/hlo_sharding.cc @@ -60,6 +60,9 @@ HloSharding HloSharding::Tuple( const Shape& tuple_shape, tensorflow::gtl::ArraySlice shardings) { CHECK(ShapeUtil::IsTuple(tuple_shape)) << ShapeUtil::HumanString(tuple_shape); + for (auto& sharding : shardings) { + CHECK(!sharding.IsTuple()) << sharding.ToString(); + } std::vector flattened_list(shardings.begin(), shardings.end()); CHECK_EQ(flattened_list.size(), RequiredLeaves(tuple_shape)) << "Flat list has " << flattened_list.size() << ", required " @@ -67,6 +70,24 @@ HloSharding HloSharding::Tuple( return HloSharding(flattened_list); } +HloSharding HloSharding::SingleTuple(const Shape& tuple_shape, + const HloSharding& sharding) { + CHECK(ShapeUtil::IsTuple(tuple_shape)) << ShapeUtil::HumanString(tuple_shape); + CHECK(!sharding.IsTuple()) << sharding.ToString(); + int64 leaf_count = ShapeUtil::GetLeafCount(tuple_shape); + std::vector flattened_list; + flattened_list.reserve(leaf_count); + for (int64 i = 0; i < leaf_count; ++i) { + flattened_list.push_back(sharding); + } + return HloSharding(flattened_list); +} + +HloSharding HloSharding::Single(const Shape& shape, + const HloSharding& sharding) { + return ShapeUtil::IsTuple(shape) ? SingleTuple(shape, sharding) : sharding; +} + string HloSharding::ToString() const { if (IsTuple()) { std::vector parts; diff --git a/tensorflow/compiler/xla/service/hlo_sharding.h b/tensorflow/compiler/xla/service/hlo_sharding.h index 34324d2058efe804cda486600dabd8a62cb84fda..6f672b0f28d2b85411d70f33da9a9f270aefc0d0 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding.h +++ b/tensorflow/compiler/xla/service/hlo_sharding.h @@ -24,7 +24,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/array.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/protobuf_util.h" #include "tensorflow/compiler/xla/shape_tree.h" #include "tensorflow/compiler/xla/xla_data.pb.h" @@ -80,6 +80,15 @@ class HloSharding { static HloSharding Tuple(const Shape& tuple_shape, tensorflow::gtl::ArraySlice shardings); + // Creates a new sharding for a tuple type, with a single input sharding + // repeated on each leaf. + static HloSharding SingleTuple(const Shape& tuple_shape, + const HloSharding& sharding); + + // If shape is an array, returns sharding, otherwise returns the tuple shaped + // sharding with all the leaf nodes having the same input sharding. + static HloSharding Single(const Shape& shape, const HloSharding& sharding); + // Create a new sharding from a protobuf OpSharding. static StatusOr FromProto(const OpSharding& proto); diff --git a/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc b/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc index 39036e205e76979e7da08246cd030ebd17e52f76..94f5a3b273b2fd7e545472c42f3863f549dd3db1 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc +++ b/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc @@ -88,6 +88,12 @@ std::vector LocatePassThroughDomainLinks( VLOG(2) << " " << instruction->ToString(); } } + if (instruction == instruction->parent()->root_instruction()) { + pass_through.emplace_back(nullptr, instruction); + VLOG(2) << "Found passthrough domain link:"; + VLOG(2) << " "; + VLOG(2) << " " << instruction->ToString(); + } } return pass_through; } @@ -101,8 +107,12 @@ Status FixupPassThroughDomainLinks(const DomainMetadata::Domain& domain, HloInstruction::CreateGetTupleElement(pass_through.operand->shape(), tuple, 0)); gte->set_sharding(sharding); - TF_RETURN_IF_ERROR( - pass_through.operand->ReplaceUseWith(pass_through.user, gte)); + if (pass_through.user != nullptr) { + TF_RETURN_IF_ERROR( + pass_through.operand->ReplaceUseWith(pass_through.user, gte)); + } else { + pass_through.operand->parent()->set_root_instruction(gte); + } } return Status::OK(); } @@ -235,21 +245,6 @@ StatusOr ApplyDomainShardingPass(const DomainMetadata::Domain& domain, Status ApplyDomainSharding(const DomainMetadata::Domain& domain, const HloSharding& sharding) { - // Here is the place to call external sharding normalizers, which are - // implemented in other modules (ie, spatial partitioning). - // The signature of the external normalizer function should be something - // like: - // - // StatusOr Normalizer(const DomainMetadata::Domain&, - // const HloSharding& sharding); - // - // The function should return true if it has processed the domain - // normalization, false if domain was not one recognized by it, or an error. - // We will call the functions in order below, and fall back to local code if - // none of the external normalizers acted on the domain. - // External normalizers should not handle the cases that are already handled - // locally. - // None of the external normalizers handled the domain sharding, try to see // whether this is a single sharding first. auto single_sharding = sharding.ExtractSingleSharding(); @@ -380,25 +375,36 @@ string ShardingMetadata::ToString() const { return sharding_ != nullptr ? sharding_->ToString() : "{}"; } -Status ShardingMetadata::NormalizeInstructions( - const DomainMetadata::Domain& domain) const { - if (sharding_ != nullptr) { - VLOG(4) << "Normalizing sharding to " << sharding_->ToString() << ":"; - TF_RETURN_IF_ERROR(ApplyDomainSharding(domain, *sharding_)); - TF_RETURN_IF_ERROR(FixupPassThroughDomainLinks(domain, *sharding_)); +/*static*/ StatusOr +ShardingMetadata::ToShardingMetadata(const DomainMetadata* metadata) { + if (metadata->Kind() != ShardingMetadata::KindName()) { + return Status( + tensorflow::error::INVALID_ARGUMENT, + "ShardingMetadata normalizer called with incorrect domain metadata"); } - return Status::OK(); + return static_cast(metadata); } -Status NormalizeShardingDomain(const DomainMetadata::Domain& domain) { - TF_ASSIGN_OR_RETURN(std::unique_ptr sharding, - ExtractOriginalCommonSharding(domain.instructions)); - if (sharding != nullptr) { - VLOG(4) << "Normalizing sharding-less domain to " << sharding->ToString() - << ":"; - TF_RETURN_IF_ERROR(ApplyDomainSharding(domain, *sharding)); +Status ShardingMetadata::NormalizeShardingDomain( + const DomainMetadata::Domain& domain, const DomainMetadata* metadata) { + if (metadata != nullptr) { + TF_ASSIGN_OR_RETURN(const auto& sharding_metadata, + ToShardingMetadata(metadata)); + const HloSharding* sharding = sharding_metadata->sharding(); + if (sharding != nullptr) { + VLOG(4) << "Normalizing sharding to " << sharding->ToString() << ":"; + TF_RETURN_IF_ERROR(ApplyDomainSharding(domain, *sharding)); + TF_RETURN_IF_ERROR(FixupPassThroughDomainLinks(domain, *sharding)); + } } else { - VLOG(1) << "Unable to find common sharding"; + TF_ASSIGN_OR_RETURN(std::unique_ptr sharding, + ExtractOriginalCommonSharding(domain.instructions)); + if (sharding != nullptr) { + VLOG(4) << "Normalizing sharding-less domain to " << sharding->ToString(); + TF_RETURN_IF_ERROR(ApplyDomainSharding(domain, *sharding)); + } else { + VLOG(1) << "Unable to find common sharding"; + } } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/hlo_sharding_metadata.h b/tensorflow/compiler/xla/service/hlo_sharding_metadata.h index ec162c34904ee2dfac3daeeee37133282a9c9698..5e01fc0e22ae8f3421c2cb5790adf44b1200a804 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding_metadata.h +++ b/tensorflow/compiler/xla/service/hlo_sharding_metadata.h @@ -38,23 +38,26 @@ class ShardingMetadata : public DomainMetadata { string ToString() const override; - Status NormalizeInstructions( - const DomainMetadata::Domain& domain) const override; + const HloSharding* sharding() const { return sharding_.get(); } static tensorflow::StringPiece KindName() { return "sharding"; } + static StatusOr ToShardingMetadata( + const DomainMetadata* metadata); + + // Apply the specified domain metadata onto the specified domain. If no + // metadata is specified then apply sharding heuristics and normalize the + // instructions whose sharding deviates from the one which is inferred as to + // be the original one. Policy wise, HLO passes are allowed to create new + // unassigned instructions, but if they do create assigned ones, they have to + // conform to the ones around. + static Status NormalizeShardingDomain(const DomainMetadata::Domain& domain, + const DomainMetadata* metadata); + private: std::unique_ptr sharding_; }; -// Within a set of instructions which had common sharding attributes before -// entring the HLO passes pipeline, apply sharding heuristics and normalize the -// instructions whose sharding deviates from the one which is inferred as to be -// the original one. -// Policy wise, HLO passes are allowed to create new unassigned instructions, -// but if they do create assigned ones, they have to conform to the ones around. -Status NormalizeShardingDomain(const DomainMetadata::Domain& domain); - // Given an HLO graph edge between instruction and one of its operands, creates // a ShardingMetadata based kDomain instruction if the sharding between // instruction and operand changes. Returns nullptr if there is no need for a diff --git a/tensorflow/compiler/xla/service/hlo_sharding_test.cc b/tensorflow/compiler/xla/service/hlo_sharding_test.cc index 54b7402b866361748d9eb35182b0bf486c4c9bdc..7baa927d0e2b1abbbb2333633d16dd605ae8c8ef 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding_test.cc +++ b/tensorflow/compiler/xla/service/hlo_sharding_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test.h" diff --git a/tensorflow/compiler/xla/service/hlo_subcomputation_unification_test.cc b/tensorflow/compiler/xla/service/hlo_subcomputation_unification_test.cc index 7b601f9a9578cfa6b293cf7f002255f7db8b1257..45c684d66752862eec301b8943d350804f070309 100644 --- a/tensorflow/compiler/xla/service/hlo_subcomputation_unification_test.cc +++ b/tensorflow/compiler/xla/service/hlo_subcomputation_unification_test.cc @@ -75,7 +75,7 @@ TEST_F(HloSubcomputationUnificationTest, UnifyIdentities) { module->AddEmbeddedComputation(CreateR0S32IdentityComputation()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(5))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(5))); auto x = builder.AddInstruction( HloInstruction::CreateCall(r0s32_, {constant}, callee1)); auto y = builder.AddInstruction( @@ -112,9 +112,9 @@ TEST_F(HloSubcomputationUnificationTest, UnifyAdditions) { module->AddEmbeddedComputation(CreateR0S32AdditionComputation()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(5))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(5))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(3))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(3))); auto x = builder.AddInstruction( HloInstruction::CreateCall(r0s32_, {constant1, constant2}, callee1)); auto y = builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/hlo_tfgraph_builder.cc b/tensorflow/compiler/xla/service/hlo_tfgraph_builder.cc index 3dc733940fc89952bd5e75a9b28d9cbf356f8000..48f676db85ab5e7711d9e9ac900306a9ea85ef10 100644 --- a/tensorflow/compiler/xla/service/hlo_tfgraph_builder.cc +++ b/tensorflow/compiler/xla/service/hlo_tfgraph_builder.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_tfgraph_builder.h" #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/core/framework/attr_value.pb.h" diff --git a/tensorflow/compiler/xla/service/hlo_tfgraph_builder_test.cc b/tensorflow/compiler/xla/service/hlo_tfgraph_builder_test.cc index be156d765dc10d54eaf301e90883babbc5693e28..1e2b31a1f2bb4865faafc3d14e2b194e3aa171a1 100644 --- a/tensorflow/compiler/xla/service/hlo_tfgraph_builder_test.cc +++ b/tensorflow/compiler/xla/service/hlo_tfgraph_builder_test.cc @@ -90,7 +90,7 @@ TEST_F(HloTfGraphBuilderTest, CheckConcatenateDimsAndShapes) { TEST_F(HloTfGraphBuilderTest, CheckScalarValue) { auto builder = HloComputation::Builder("Const"); HloInstruction *instruction = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(123))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(123))); OpMetadata metadata; metadata.set_op_name("x"); metadata.set_op_type("y"); diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc index f89677372944f2708aa678d2a6a53665ae1752ab..25fa319faf13d8bef69381c869f08f4948fc3519 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.cc +++ b/tensorflow/compiler/xla/service/hlo_verifier.cc @@ -119,7 +119,7 @@ Status CheckIsTokenOperand(const HloInstruction* instruction, const HloInstruction* token = instruction->operand(operand_no); if (!ShapeUtil::Equal(token->shape(), ShapeUtil::MakeTokenShape())) { return InternalError( - "Expected operand %lld to be token-shaped, actual shape is" + "Expected operand %lld to be token-shaped, actual shape is " "%s:\n%s", operand_no, ShapeUtil::HumanString(token->shape()).c_str(), instruction->ToString().c_str()); @@ -127,6 +127,22 @@ Status CheckIsTokenOperand(const HloInstruction* instruction, return Status::OK(); } +Status CheckOperandAndParameter(const HloInstruction* instruction, + int64 operand_number, + const HloComputation* computation, + int64 parameter_number) { + const HloInstruction* operand = instruction->operand(operand_number); + const HloInstruction* parameter = + computation->parameter_instruction(parameter_number); + if (!ShapeUtil::Compatible(operand->shape(), parameter->shape())) { + return InternalError("Operand %s shape does not match parameter's %s in %s", + operand->ToString().c_str(), + parameter->ToString().c_str(), + instruction->ToString().c_str()); + } + return Status::OK(); +} + } // namespace Status ShapeVerifier::HandleInfeed(HloInstruction* instruction) { @@ -194,6 +210,12 @@ Status ShapeVerifier::HandleConstant(HloInstruction* constant) { return CheckShape(constant, constant->literal().shape()); } +Status ShapeVerifier::HandleIota(HloInstruction* iota) { + return ShapeUtil::Rank(iota->shape()) == 1 + ? Status::OK() + : InternalError("Iota only supports arrays of rank 1."); +} + Status ShapeVerifier::HandleGetTupleElement(HloInstruction* get_tuple_element) { return CheckShape(get_tuple_element, ShapeInference::InferGetTupleElementShape( @@ -253,8 +275,11 @@ Status ShapeVerifier::HandleParameter(HloInstruction* hlo) { Status ShapeVerifier::HandleFusion(HloInstruction*) { return Status::OK(); } Status ShapeVerifier::HandleCall(HloInstruction* call) { + for (int64 i = 0; i < call->to_apply()->num_parameters(); ++i) { + TF_RETURN_IF_ERROR(CheckOperandAndParameter(call, i, call->to_apply(), i)); + } // The shape of kCall should match the shape of the computation it calls. - return CheckShape(call, call->to_apply()->ComputeProgramShape().result()); + return CheckShape(call, call->to_apply()->root_instruction()->shape()); } Status ShapeVerifier::HandleCustomCall(HloInstruction*) { return Status::OK(); } @@ -323,19 +348,37 @@ Status ShapeVerifier::HandleSelectAndScatter(HloInstruction* instruction) { } Status ShapeVerifier::HandleWhile(HloInstruction* xla_while) { + TF_RETURN_IF_ERROR( + CheckOperandAndParameter(xla_while, 0, xla_while->while_body(), 0)); + TF_RETURN_IF_ERROR( + CheckOperandAndParameter(xla_while, 0, xla_while->while_condition(), 0)); + const Shape& conditional_shape = + xla_while->while_condition()->root_instruction()->shape(); + if (!ShapeUtil::Compatible(conditional_shape, + ShapeUtil::MakeShape(PRED, {}))) { + return InternalError( + "Conditional computation shape does not lead to a scalar predicate " + "shape: %s", + ShapeUtil::HumanString(conditional_shape).c_str()); + } // The shape of kWhile should match the shape of the body computation it // calls. return CheckShape(xla_while, - xla_while->while_body()->ComputeProgramShape().result()); + xla_while->while_body()->root_instruction()->shape()); } Status ShapeVerifier::HandleConditional(HloInstruction* conditional) { + TF_RETURN_IF_ERROR(CheckOperandAndParameter( + conditional, 1, conditional->true_computation(), 0)); + TF_RETURN_IF_ERROR(CheckOperandAndParameter( + conditional, 2, conditional->false_computation(), 0)); + TF_RETURN_IF_ERROR( + CheckShape(conditional, + conditional->true_computation()->root_instruction()->shape())); TF_RETURN_IF_ERROR(CheckShape( conditional, - conditional->true_computation()->ComputeProgramShape().result())); - return CheckShape( - conditional, - conditional->false_computation()->ComputeProgramShape().result()); + conditional->false_computation()->root_instruction()->shape())); + return Status::OK(); } Status ShapeVerifier::HandlePad(HloInstruction* pad) { @@ -345,11 +388,6 @@ Status ShapeVerifier::HandlePad(HloInstruction* pad) { } Status ShapeVerifier::HandleSend(HloInstruction* send) { - TF_RET_CHECK(send->users().size() == 1); - const HloInstruction* send_done = send->users().front(); - TF_RET_CHECK(send_done->opcode() == HloOpcode::kSendDone); - TF_RETURN_IF_ERROR(CheckSameChannel(send, send_done)); - TF_RETURN_IF_ERROR(CheckIsTokenOperand(send, 1)); return CheckShape(send, ShapeUtil::MakeTupleShape({send->operand(0)->shape(), ShapeUtil::MakeShape(U32, {}), @@ -357,34 +395,22 @@ Status ShapeVerifier::HandleSend(HloInstruction* send) { } Status ShapeVerifier::HandleSendDone(HloInstruction* send_done) { - TF_RET_CHECK(send_done->operands().size() == 1); - const HloInstruction* send = send_done->operand(0); - TF_RET_CHECK(send->opcode() == HloOpcode::kSend); - TF_RETURN_IF_ERROR(CheckSameChannel(send, send_done)); - return CheckShape(send_done, ShapeUtil::MakeTokenShape()); } Status ShapeVerifier::HandleRecv(HloInstruction* recv) { - TF_RET_CHECK(recv->users().size() == 1); - const HloInstruction* recv_done = recv->users().front(); - TF_RET_CHECK(recv_done->opcode() == HloOpcode::kRecvDone); - TF_RETURN_IF_ERROR(CheckSameChannel(recv, recv_done)); - TF_RETURN_IF_ERROR(CheckIsTokenOperand(recv, 0)); return CheckShape( recv, ShapeUtil::MakeTupleShape( - {ShapeUtil::GetTupleElementShape(recv_done->shape(), 0), + {ShapeUtil::GetTupleElementShape(recv->shape(), 0), ShapeUtil::MakeShape(U32, {}), ShapeUtil::MakeTokenShape()})); } Status ShapeVerifier::HandleRecvDone(HloInstruction* recv_done) { - TF_RET_CHECK(recv_done->operands().size() == 1); - const HloInstruction* recv = recv_done->operand(0); - TF_RET_CHECK(recv->opcode() == HloOpcode::kRecv); - TF_RETURN_IF_ERROR(CheckSameChannel(recv, recv_done)); - return CheckShape(recv_done, - ShapeUtil::MakeTupleShape({recv->shape().tuple_shapes(0), - ShapeUtil::MakeTokenShape()})); + return CheckShape( + recv_done, + ShapeUtil::MakeTupleShape( + {ShapeUtil::GetTupleElementShape(recv_done->operand(0)->shape(), 0), + ShapeUtil::MakeTokenShape()})); } Status ShapeVerifier::HandleBatchNormTraining( @@ -590,19 +616,6 @@ Status ShapeVerifier::CheckVariadicShape(const HloInstruction* instruction) { instruction->opcode(), instruction->operands())); } -// Checks if the given two instructions shares the same channel id. -Status ShapeVerifier::CheckSameChannel(const HloInstruction* instr1, - const HloInstruction* instr2) { - if (instr1->channel_id() != instr2->channel_id()) { - return InternalError( - "Expected to have the same channel id, actual channel ids are: %s " - "(%lld), %s (%lld)", - instr1->ToString().c_str(), instr1->channel_id(), - instr2->ToString().c_str(), instr2->channel_id()); - } - return Status::OK(); -} - string ComputationsToString( tensorflow::gtl::ArraySlice computations) { return tensorflow::str_util::Join( @@ -802,33 +815,23 @@ Status HloVerifier::CheckWhileInstruction(HloInstruction* instruction) { "While loop must have exactly one operand; had %lld : %s", instruction->operand_count(), instruction->ToString().c_str()); } - auto* init = instruction->operand(0); - auto* cond_param = while_cond->parameter_instruction(0); - if (!ShapeUtil::Compatible(init->shape(), cond_param->shape())) { - return FailedPrecondition( - "While condition's parameter must have the same shape as the " - "loop's 'init'. init: %s, param: %s", - init->ToString().c_str(), cond_param->ToString().c_str()); - } - auto* cond_root = while_cond->root_instruction(); - if (!ShapeUtil::Compatible(cond_root->shape(), - ShapeUtil::MakeShape(PRED, {}))) { - return FailedPrecondition("While condition should have shape PRED: %s", - cond_root->ToString().c_str()); - } - auto* body_param = while_body->parameter_instruction(0); - if (!ShapeUtil::Compatible(init->shape(), body_param->shape())) { + return Status::OK(); +} + +Status HloVerifier::CheckConditionalInstruction(HloInstruction* instruction) { + if (instruction->true_computation()->num_parameters() != 1) { return FailedPrecondition( - "While body's parameter must have the same shape as the loop's" - " 'init'. init: %s, param: %s", - init->ToString().c_str(), body_param->ToString().c_str()); + "True computation %s of %s must have 1 parameter insted of %lld", + instruction->true_computation()->name().c_str(), + instruction->ToString().c_str(), + instruction->true_computation()->num_parameters()); } - auto* body_root = while_body->root_instruction(); - if (!ShapeUtil::Compatible(init->shape(), body_root->shape())) { + if (instruction->false_computation()->num_parameters() != 1) { return FailedPrecondition( - "While body should have same shape as the loop's 'init'." - "init: %s, body: %s", - init->ToString().c_str(), body_root->ToString().c_str()); + "False computation %s of %s must have 1 parameter insted of %lld", + instruction->false_computation()->name().c_str(), + instruction->ToString().c_str(), + instruction->false_computation()->num_parameters()); } return Status::OK(); } @@ -881,10 +884,105 @@ Status VerifyEntryAndExitShapes(const HloModule& module) { return Status::OK(); } +// Checks if the given two instructions share the same channel id. +Status CheckSameChannel(const HloInstruction* instr1, + const HloInstruction* instr2) { + if (instr1->channel_id() != instr2->channel_id()) { + return InternalError( + "Expected to have the same channel id, actual channel ids are: %s " + "(%lld), %s (%lld)", + instr1->ToString().c_str(), instr1->channel_id(), + instr2->ToString().c_str(), instr2->channel_id()); + } + return Status::OK(); +} + +// Checks if the given two instructions have the same is_host_transfer attribute +// value. Intsructions must be send/recv instructions or their 'done' variant. +Status CheckSameIsHostTransfer(const HloInstruction* instr1, + const HloInstruction* instr2) { + const HloSendRecvInstruction* send_recv1 = + DynCast(instr1); + const HloSendRecvInstruction* send_recv2 = + DynCast(instr2); + TF_RET_CHECK(send_recv1 != nullptr); + TF_RET_CHECK(send_recv2 != nullptr); + if (send_recv1->is_host_transfer() != send_recv2->is_host_transfer()) { + return InternalError( + "Expected instructions to have the same is-host-transfer property: %s, " + "%s ", + instr1->ToString().c_str(), instr2->ToString().c_str()); + } + return Status::OK(); +} + +// Checks various invariants of send and recv instructions. +Status VerifySendsAndRecvs(const HloModule& module) { + tensorflow::gtl::FlatMap host_channels; + // Host send/recv instructions must have their own unique channel. + auto check_unique_host_channel = [&](const HloInstruction* instruction) { + const HloSendRecvInstruction* sendrecv = + DynCast(instruction); + if (sendrecv->is_host_transfer()) { + auto it_inserted = + host_channels.insert({sendrecv->channel_id(), sendrecv}); + if (!it_inserted.second) { + return FailedPrecondition( + "Channel %lld is used for multiple host send/recv instructions: %s " + "and " + "%s", + sendrecv->channel_id(), sendrecv->ToString().c_str(), + it_inserted.first->second->ToString().c_str()); + } + } + + return Status::OK(); + }; + + // Send/Recv instruction must have a single user: the corresponding + // SendDone/RecvDone. with matching channel. + for (const HloComputation* computation : module.computations()) { + for (const HloInstruction* instruction : computation->instructions()) { + switch (instruction->opcode()) { + case HloOpcode::kSend: { + TF_RETURN_IF_ERROR(check_unique_host_channel(instruction)); + TF_RET_CHECK(instruction->users().size() == 1); + const HloInstruction* send_done = instruction->users().front(); + TF_RET_CHECK(send_done->opcode() == HloOpcode::kSendDone); + TF_RETURN_IF_ERROR(CheckSameChannel(instruction, send_done)); + TF_RETURN_IF_ERROR(CheckSameIsHostTransfer(instruction, send_done)); + break; + } + case HloOpcode::kRecv: { + TF_RETURN_IF_ERROR(check_unique_host_channel(instruction)); + TF_RET_CHECK(instruction->users().size() == 1); + const HloInstruction* recv_done = instruction->users().front(); + TF_RET_CHECK(recv_done->opcode() == HloOpcode::kRecvDone); + TF_RETURN_IF_ERROR(CheckSameChannel(instruction, recv_done)); + TF_RETURN_IF_ERROR(CheckSameIsHostTransfer(instruction, recv_done)); + break; + } + case HloOpcode::kSendDone: + TF_RET_CHECK(instruction->operands().size() == 1); + TF_RET_CHECK(instruction->operand(0)->opcode() == HloOpcode::kSend); + break; + case HloOpcode::kRecvDone: + TF_RET_CHECK(instruction->operands().size() == 1); + TF_RET_CHECK(instruction->operand(0)->opcode() == HloOpcode::kRecv); + break; + default: + break; + } + } + } + return Status::OK(); +} + } // namespace StatusOr HloVerifier::Run(HloModule* module) { TF_RETURN_IF_ERROR(VerifyHloStructure(module)); + TF_RETURN_IF_ERROR(VerifySendsAndRecvs(*module)); tensorflow::gtl::FlatMap instructions; @@ -924,6 +1022,8 @@ StatusOr HloVerifier::Run(HloModule* module) { << " != " << ShapeUtil::Rank(instruction->operand(0)->shape()); } else if (instruction->opcode() == HloOpcode::kWhile) { TF_RETURN_IF_ERROR(CheckWhileInstruction(instruction)); + } else if (instruction->opcode() == HloOpcode::kConditional) { + TF_RETURN_IF_ERROR(CheckConditionalInstruction(instruction)); } else if (instruction->opcode() != HloOpcode::kRng /* Rng operands are always scalar. */ && instruction->IsElementwise()) { diff --git a/tensorflow/compiler/xla/service/hlo_verifier.h b/tensorflow/compiler/xla/service/hlo_verifier.h index 12c047850ef7299f24c3f004613df3e66e0af8d6..79f7aa9f4ce66cc9b53d016f2e126033492c81e9 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.h +++ b/tensorflow/compiler/xla/service/hlo_verifier.h @@ -37,6 +37,7 @@ class ShapeVerifier : public DfsHloVisitor { Status HandleSelect(HloInstruction* select) override; Status HandleTupleSelect(HloInstruction* tuple_select) override; Status HandleConcatenate(HloInstruction* concatenate) override; + Status HandleIota(HloInstruction* iota) override; Status HandleConvert(HloInstruction* convert) override; Status HandleBitcastConvert(HloInstruction* convert) override; Status HandleCopy(HloInstruction* copy) override; @@ -102,10 +103,6 @@ class ShapeVerifier : public DfsHloVisitor { Status CheckTernaryShape(const HloInstruction* instruction); Status CheckVariadicShape(const HloInstruction* instruction); - // Checks if the given two instructions share the same channel id. - Status CheckSameChannel(const HloInstruction* instr1, - const HloInstruction* instr2); - private: // Whether the inputs and output of an instruction can contain both F32s and // BF16s. Tuples that include both F32s and BF16s are allowed regardless of @@ -146,6 +143,8 @@ class HloVerifier : public HloPassInterface { Status CheckWhileInstruction(HloInstruction* instruction); + Status CheckConditionalInstruction(HloInstruction* instruction); + // Checks that the non-scalar operand shapes are compatible to the output // shape, i.e., that there are no implicit broadcasts of size-one dimensions. Status CheckElementwiseInstruction(HloInstruction* instruction); diff --git a/tensorflow/compiler/xla/service/hlo_verifier_test.cc b/tensorflow/compiler/xla/service/hlo_verifier_test.cc index c92db0be14dceb32ea86521dcc99b8f63738e4a5..04c6ba3eeb92bad2b5b69f7f56e73e1f7a8148aa 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier_test.cc +++ b/tensorflow/compiler/xla/service/hlo_verifier_test.cc @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" @@ -123,5 +124,55 @@ TEST_F(HloVerifierTest, ResetsShapeVerifierState) { EXPECT_FALSE(verifier().Run(module.get()).status().ok()); } +TEST_F(HloVerifierTest, CheckCallOperandParameterShapesMismatch) { + const char* const hlo_string = R"( +HloModule Module + +callme { + ROOT param = (s32[], f32[4]) parameter(0) +} + +ENTRY entry { + p0 = (f32[4], s32[]) parameter(0) + ROOT mycall = (s32[], f32[4]) call(p0), to_apply=callme +} +)"; + TF_ASSERT_OK_AND_ASSIGN(auto module, ParseHloString(hlo_string)); + + auto status = verifier().Run(module.get()).status(); + ASSERT_FALSE(status.ok()); + EXPECT_THAT(status.error_message(), + HasSubstr("shape does not match parameter")); +} + +TEST_F(HloVerifierTest, CheckConditionalOperandParameterShapesMismatch) { + const char* const hlo_string = R"( +HloModule Module + +true_branch { + tparam = (s32[], f32[4]) parameter(0) + ROOT tgte1 = f32[4] get-tuple-element(tparam), index=1 +} + +false_branch { + fparam = (s32[], f32[4]) parameter(0) + ROOT fgte1 = f32[4] get-tuple-element(fparam), index=1 +} + +ENTRY entry { + p0 = (f32[4], s32[]) parameter(0) + constant = pred[] constant(true) + ROOT conditional = f32[4] conditional(constant, p0, p0), + true_computation=true_branch, false_computation=false_branch +} +)"; + TF_ASSERT_OK_AND_ASSIGN(auto module, ParseHloString(hlo_string)); + + auto status = verifier().Run(module.get()).status(); + ASSERT_FALSE(status.ok()); + EXPECT_THAT(status.error_message(), + HasSubstr("shape does not match parameter")); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/implicit_broadcast_remover_test.cc b/tensorflow/compiler/xla/service/implicit_broadcast_remover_test.cc index 8c7b38dd1bf73e0be7b669d7215812aaef1cee17..f85d31d5225b8012b68f851b2bfec219d736ba0d 100644 --- a/tensorflow/compiler/xla/service/implicit_broadcast_remover_test.cc +++ b/tensorflow/compiler/xla/service/implicit_broadcast_remover_test.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/implicit_broadcast_remover.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_matchers.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h" diff --git a/tensorflow/compiler/xla/service/indexed_array_analysis.cc b/tensorflow/compiler/xla/service/indexed_array_analysis.cc index 1985d20578677ae68b244023c4640454b004bf49..8b2df3256776a7d77517daff1fe282b0dbde7045 100644 --- a/tensorflow/compiler/xla/service/indexed_array_analysis.cc +++ b/tensorflow/compiler/xla/service/indexed_array_analysis.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/gtl/flatset.h" #include "tensorflow/core/lib/gtl/inlined_vector.h" +#include "tensorflow/core/lib/gtl/optional.h" #include "tensorflow/core/lib/strings/strcat.h" namespace xla { @@ -160,6 +161,12 @@ StatusOr IndexedArrayAnalysis::ComputeArrayFor( computed_array, ComputeArrayForReshape(instr->shape(), FindOrDie(cache_, instr->operand(0)))); + } else if (instr->opcode() == HloOpcode::kDot) { + TF_ASSIGN_OR_RETURN( + computed_array, + ComputeArrayForDot(instr->shape(), instr->dot_dimension_numbers(), + FindOrDie(cache_, instr->operand(0)), + FindOrDie(cache_, instr->operand(1)))); } else { computed_array = nullptr; } @@ -290,8 +297,7 @@ StatusOr IndexedArrayAnalysis::ComputeArrayForGather( } if (auto* indexed = dynamic_cast(source)) { - auto it = c_find(indexed->output_dims(), source_dim); - if (it != indexed->output_dims().end()) { + if (c_linear_search(indexed->output_dims(), source_dim)) { return FoldGatherOfGather(indexed, indices, source_dim, output_dims, shape); } @@ -956,11 +962,177 @@ IndexedArrayAnalysis::ComputeArrayForElementwiseUnaryOp(HloOpcode opcode, return Construct( new_source, scalar_indexed_const->indices(), scalar_indexed_const->source_dim(), - std::vector(scalar_indexed_const->output_dims().begin(), - scalar_indexed_const->output_dims().end()), + ArraySliceToVector(scalar_indexed_const->output_dims()), scalar_indexed_const->shape()); } +namespace { + +// Returns the non-contracting non-batch dimension (as per `contracting_dims` +// and `batch_dims`) if there is exactly one, otherwise returns nullopt. +gtl::optional GetOnlyNonContractingNonBatchDim( + int64 rank, ArraySlice contracting_dims, + ArraySlice batch_dims) { + gtl::optional result; + for (int64 dim = 0; dim < rank; dim++) { + if (!ArrayContains(contracting_dims, dim) && + !ArrayContains(batch_dims, dim)) { + if (result.has_value()) { + return gtl::nullopt; + } + result = dim; + } + } + return result; +} + +// Returns true if `indexed_array`, which is either the LHS or the RHS of a Dot +// HLO, can be folded into the dot operation. For now these conditions are both +// necessary and sufficient. +// +// `tag` describes the caller. Used only for logging. +// +// `contracting_dims` and `batch_dims` are the contracting and batch dimensions +// of whatever operand `indexed_array` is to the dot (LHS or RHS). +bool CanFoldDotIntoIndexedArray( + tensorflow::StringPiece tag, + Analysis::ScalarIndexedConstantArray* indexed_array, + ArraySlice contracting_dims, ArraySlice batch_dims) { + gtl::optional non_contracting_non_batch_dim = + GetOnlyNonContractingNonBatchDim(ShapeUtil::Rank(indexed_array->shape()), + contracting_dims, batch_dims); + if (!non_contracting_non_batch_dim.has_value()) { + VLOG(3) << tag << ": multiple or no non-contracting non-batch dimensions"; + return false; + } + + if (indexed_array->output_dims().size() != 1 || + indexed_array->output_dims()[0] != *non_contracting_non_batch_dim) { + VLOG(3) << tag << ": output dims != the lhs non-contracting non-batch dim"; + return false; + } + + int64 indexed_array_rank = ShapeUtil::Rank(indexed_array->shape()); + if (indexed_array->source_dim() < (indexed_array_rank - 2)) { + // This restriction can be lifted by inserting reshape nodes. + VLOG(3) << tag + << ": source dim is not in the low two dims, won't be able to form " + "a matmul"; + return false; + } + + return true; +} + +} // namespace + +StatusOr +IndexedArrayAnalysis::ComputeArrayForDotWithIndexedLhs( + const Shape& shape, const DotDimensionNumbers& dim_numbers, + ScalarIndexedConstantArray* lhs, ConstantArray* rhs) { + VLOG(3) << "ComputeArrayForDotWithIndexedLhs(" << ToString(lhs) << " " + << ToString(rhs); + if (!CanFoldDotIntoIndexedArray( + "ComputeArrayForDotWithIndexedLhs", lhs, /*contracting_dims=*/ + AsInt64Slice(dim_numbers.lhs_contracting_dimensions()), + /*batch_dims=*/AsInt64Slice(dim_numbers.lhs_batch_dimensions()))) { + return nullptr; + } + + int64 lhs_rank = ShapeUtil::Rank(lhs->shape()); + DotDimensionNumbers new_dim_numbers = dim_numbers; + new_dim_numbers.set_lhs_contracting_dimensions( + 0, lhs->source_dim() == (lhs_rank - 1) ? (lhs_rank - 2) : (lhs_rank - 1)); + + TF_ASSIGN_OR_RETURN(Literal * literal_for_new_source, + TakeOwnership(HloEvaluator{}.EvaluateDotOp( + new_dim_numbers, lhs->literal(), *rhs->literal()))); + + // The new source dimension is wherever the non-batch non-contracting LHS + // dimension "went". + int64 new_source_dim = dim_numbers.lhs_batch_dimensions_size() + + dim_numbers.rhs_batch_dimensions_size(); + + ConstantArray* new_source = Construct(literal_for_new_source); + return Construct( + new_source, lhs->indices(), new_source_dim, + ArraySliceToVector(lhs->output_dims()), shape); +} + +StatusOr +IndexedArrayAnalysis::ComputeArrayForDotWithIndexedRhs( + const Shape& shape, const DotDimensionNumbers& dim_numbers, + ConstantArray* lhs, ScalarIndexedConstantArray* rhs) { + VLOG(3) << "ComputeArrayForDotWithIndexedRhs(" << ToString(lhs) << " " + << ToString(rhs); + if (!CanFoldDotIntoIndexedArray( + "ComputeArrayForDotWithIndexedRhs", rhs, /*contracting_dims=*/ + AsInt64Slice(dim_numbers.rhs_contracting_dimensions()), + /*batch_dims=*/AsInt64Slice(dim_numbers.rhs_batch_dimensions()))) { + return nullptr; + } + + int64 rhs_rank = ShapeUtil::Rank(rhs->shape()); + + DotDimensionNumbers new_dim_numbers = dim_numbers; + new_dim_numbers.set_rhs_contracting_dimensions( + 0, rhs->source_dim() == (rhs_rank - 1) ? (rhs_rank - 2) : (rhs_rank - 1)); + + TF_ASSIGN_OR_RETURN(Literal * literal_for_new_source, + TakeOwnership(HloEvaluator{}.EvaluateDotOp( + new_dim_numbers, *lhs->literal(), rhs->literal()))); + + // The new source dimension is wherever the non-batch non-contracting RHS + // dimension "went". + int64 new_source_dim = dim_numbers.lhs_batch_dimensions_size() + + dim_numbers.rhs_batch_dimensions_size() + 1; + + ConstantArray* new_source = Construct(literal_for_new_source); + return Construct( + new_source, rhs->indices(), new_source_dim, + ArraySliceToVector(rhs->output_dims()), shape); +} + +StatusOr IndexedArrayAnalysis::ComputeArrayForDot( + const Shape& shape, const DotDimensionNumbers& dim_numbers, Array* lhs, + Array* rhs) { + // Intuitively, if + // + // - The LHS of a dot product is a gathered sequence of rows from a constant + // array (i.e. LHS[I,J] = Const[Indices[I],J]) and the RHS is a constant + // + // OR + // + // - If the RHS of a dot product is a gathered sequence of columns from a + // constant array (i.e. RHS[I,J] = Const[I, Indices[J]]) and the LHS is a + // constant + // + // then the result of the dot product itself is a gather from a constant + // array. E.g. Dot(LHS, ConstRhs) where LHS[I,J] = Const[Indices[I],J] can be + // rewritten as Result where Result[I,J] = Dot(Const, ConstRhs)[Indices[I], + // J]. + // + // We do a general version of this rewrite here. + VLOG(3) << "ComputeArrayForDot(" << ToString(lhs) << " " << ToString(rhs); + if (auto* lhs_indexed_array = + dynamic_cast(lhs)) { + if (auto* rhs_constant = dynamic_cast(rhs)) { + return ComputeArrayForDotWithIndexedLhs(shape, dim_numbers, + lhs_indexed_array, rhs_constant); + } + } + + if (auto* rhs_indexed_array = + dynamic_cast(rhs)) { + if (auto* lhs_constant = dynamic_cast(lhs)) { + return ComputeArrayForDotWithIndexedRhs(shape, dim_numbers, lhs_constant, + rhs_indexed_array); + } + } + + return nullptr; +} + tensorflow::StringPiece IndexedArrayAnalysisPrinterPass::name() const { return "indexed-array-analysis-printer-pass"; } diff --git a/tensorflow/compiler/xla/service/indexed_array_analysis.h b/tensorflow/compiler/xla/service/indexed_array_analysis.h index 8684430231c1929f82508e3675f1c275c42b6149..e923dc39f7f464a8d3c400294499a6f5efda3991 100644 --- a/tensorflow/compiler/xla/service/indexed_array_analysis.h +++ b/tensorflow/compiler/xla/service/indexed_array_analysis.h @@ -268,6 +268,18 @@ class IndexedArrayAnalysis { tensorflow::gtl::ArraySlice window_bounds, Array* source, Array* indices); + StatusOr ComputeArrayForDotWithIndexedLhs( + const Shape& shape, const DotDimensionNumbers& dim_numbers, + ScalarIndexedConstantArray* lhs, ConstantArray* rhs); + + StatusOr ComputeArrayForDotWithIndexedRhs( + const Shape& shape, const DotDimensionNumbers& dim_numbers, + ConstantArray* lhs, ScalarIndexedConstantArray* rhs); + + StatusOr ComputeArrayForDot(const Shape& shape, + const DotDimensionNumbers& dim_numbers, + Array* lhs, Array* rhs); + // This tries to fold a ScalarIndexedArray which has another // ScalarIndexedArray as a source into a ScalarIndexedArray that instead has a // ScalarIndexedArray as indices. If `source` happened to be a diff --git a/tensorflow/compiler/xla/service/indexed_array_analysis_test.cc b/tensorflow/compiler/xla/service/indexed_array_analysis_test.cc index fc2befe05b18651502c42b9892e766145d85f2e8..5f4b42799b1c26ea544f9d4447cc45b5ae9d5a48 100644 --- a/tensorflow/compiler/xla/service/indexed_array_analysis_test.cc +++ b/tensorflow/compiler/xla/service/indexed_array_analysis_test.cc @@ -799,5 +799,170 @@ ENTRY main { AssertArrayForRootExpressionIs(hlo_text, "%add"); } +TEST_F(IndexedArrayAnalysisTest, DotOpBasic_0) { + string hlo_text = R"( +HloModule DotOp + +ENTRY main { + gather_operand = s32[3,4] constant(s32[3,4]{{1,2,3,4},{5,6,7,8},{9,10,11,12}}) + dot_rhs_constant = s32[4,3] constant(s32[4,3]{{1,2,3},{4,5,6},{7,8,9},{10,11,12}}) + indices = s32[5] parameter(0) + dot_lhs = s32[5,4] gather(gather_operand, indices), + output_window_dims={1}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1,4} + ROOT dot = s32[5,3] dot(dot_lhs, dot_rhs_constant), lhs_contracting_dims={1}, rhs_contracting_dims={0} +} +)"; + + AssertArrayWithConstantsForRootExpressionIs(hlo_text, R"( +(scalar-indexed-const + (constant s32[3,3] s32[3,3] { + { 70, 80, 90 }, + { 158, 184, 210 }, + { 246, 288, 330 } }) + %indices 0->[0]))"); +} + +TEST_F(IndexedArrayAnalysisTest, DotOpBasic_1) { + string hlo_text = R"( +HloModule DotOp + +ENTRY main { + gather_operand = s32[3,4] constant(s32[3,4]{{1,2,3,4},{5,6,7,8},{9,10,11,12}}) + dot_rhs_constant = s32[3,3] constant(s32[3,3]{{1,2,3},{4,5,6},{7,8,9}}) + indices = s32[5] parameter(0) + dot_lhs = s32[3,5] gather(gather_operand, indices), + output_window_dims={0}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=1, + window_bounds={3,1} + ROOT dot = s32[5,3] dot(dot_lhs, dot_rhs_constant), lhs_contracting_dims={0}, rhs_contracting_dims={0} +} +)"; + + AssertArrayWithConstantsForRootExpressionIs(hlo_text, R"( +(scalar-indexed-const + (constant s32[4,3] s32[4,3] { + { 84, 99, 114 }, + { 96, 114, 132 }, + { 108, 129, 150 }, + { 120, 144, 168 } }) + %indices 0->[1]))"); +} + +TEST_F(IndexedArrayAnalysisTest, DotOpBasic_2) { + string hlo_text = R"( +HloModule DotOp + +ENTRY main { + gather_operand = s32[3,4] constant(s32[3,4]{{1,2,3,4},{5,6,7,8},{9,10,11,12}}) + dot_lhs_constant = s32[4,3] constant(s32[4,3]{{1,2,3},{4,5,6},{7,8,9},{10,11,12}}) + indices = s32[5] parameter(0) + dot_rhs = s32[3,5] gather(gather_operand, indices), + output_window_dims={0}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=1, + window_bounds={3,1} + ROOT dot = s32[4,5] dot(dot_lhs_constant, dot_rhs), lhs_contracting_dims={1}, rhs_contracting_dims={0} +} +)"; + + AssertArrayWithConstantsForRootExpressionIs(hlo_text, R"( +(scalar-indexed-const + (constant s32[4,4] s32[4,4] { + { 38, 44, 50, 56 }, + { 83, 98, 113, 128 }, + { 128, 152, 176, 200 }, + { 173, 206, 239, 272 } }) + %indices 1->[1]) +)"); +} + +TEST_F(IndexedArrayAnalysisTest, DotOpBasic_3) { + string hlo_text = R"( +HloModule DotOp + +ENTRY main { + gather_operand = s32[4,3] constant(s32[4,3]{{1,2,3},{4,5,6},{7,8,9},{10,11,12}}) + dot_lhs_constant = s32[4,3] constant(s32[4,3]{{1,2,3},{4,5,6},{7,8,9},{10,11,12}}) + indices = s32[5] parameter(0) + dot_rhs = s32[5,3] gather(gather_operand, indices), + output_window_dims={1}, + elided_window_dims={0}, + gather_dims_to_operand_dims={0}, + index_vector_dim=1, + window_bounds={1,3} + ROOT dot = s32[4,5] dot(dot_lhs_constant, dot_rhs), lhs_contracting_dims={1}, rhs_contracting_dims={1} +} +)"; + + AssertArrayWithConstantsForRootExpressionIs(hlo_text, R"( +(scalar-indexed-const + (constant s32[4,4] s32[4,4] { + { 14, 32, 50, 68 }, + { 32, 77, 122, 167 }, + { 50, 122, 194, 266 }, + { 68, 167, 266, 365 } }) + %indices 1->[0]) +)"); +} + +TEST_F(IndexedArrayAnalysisTest, DotOpWithBatch) { + string hlo_text = R"( +HloModule DotOp + +ENTRY main { + gather_operand = s32[2,3,2] constant(s32[2,3,2]{{{1,2},{3,4},{5,6}},{{7,8},{9,10},{11,12}}}) + dot_lhs_constant = s32[2,2,3] constant(s32[2,2,3]{{{1,2,3},{4,5,6}},{{7,8,9},{10,11,12}}}) + indices = s32[4] parameter(0) + dot_rhs = s32[2,3,4] gather(gather_operand, indices), + output_window_dims={0,1}, + elided_window_dims={2}, + gather_dims_to_operand_dims={2}, + index_vector_dim=1, + window_bounds={2,3,1} + ROOT dot = s32[2,2,4] dot(dot_lhs_constant, dot_rhs), + lhs_contracting_dims={2}, rhs_contracting_dims={1}, + lhs_batch_dims={0}, rhs_batch_dims={0} +} +)"; + + AssertArrayWithConstantsForRootExpressionIs(hlo_text, R"( +(scalar-indexed-const + (constant s32[2,2,2] s32[2,2,2] { + { { 22, 28 }, + { 49, 64 } }, + { { 220, 244 }, + { 301, 334 } } }) + %indices 3->[2]) +)"); +} + +TEST_F(IndexedArrayAnalysisTest, DotOpNegative) { + string hlo_text = R"( +HloModule DotOp + +ENTRY main { + gather_operand = s32[3,4] constant(s32[3,4]{{1,2,3,4},{5,6,7,8},{9,10,11,12}}) + dot_rhs_constant = s32[2,3] constant(s32[2,3]{{1,2,3},{4,5,6}}) + indices = s32[2] parameter(0) + dot_lhs = s32[3,2] gather(gather_operand, indices), + output_window_dims={0}, + elided_window_dims={1}, + gather_dims_to_operand_dims={1}, + index_vector_dim=1, + window_bounds={3,1} + ROOT dot = s32[3,3] dot(dot_lhs, dot_rhs_constant), lhs_contracting_dims={1}, rhs_contracting_dims={0} +} +)"; + + AssertArrayWithConstantsForRootExpressionIs(hlo_text, "%dot"); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/inliner_test.cc b/tensorflow/compiler/xla/service/inliner_test.cc index d2af261008f40ee83e0676cfc7e67c45f8be1844..32937b33b3737482f07d4c7607f7f1c5c183a56b 100644 --- a/tensorflow/compiler/xla/service/inliner_test.cc +++ b/tensorflow/compiler/xla/service/inliner_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -51,10 +51,10 @@ TEST_F(InlinerTest, MapMax) { auto max_f32 = max_builder.Build(); auto builder = HloComputation::Builder("MapMaxFunction"); - auto lhs = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3, 4}))); - auto rhs = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({4, 3, 2, 1}))); + auto lhs = builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR1({1, 2, 3, 4}))); + auto rhs = builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR1({4, 3, 2, 1}))); builder.AddInstruction( HloInstruction::CreateMap(lhs->shape(), {lhs, rhs}, max_f32.get())); @@ -70,7 +70,7 @@ TEST_F(InlinerTest, MapMax) { // Verify execution on CPU. auto result = ExecuteAndTransfer(std::move(hlo_module), {}); - auto expected = Literal::CreateR1({4, 3, 3, 4}); + auto expected = LiteralUtil::CreateR1({4, 3, 3, 4}); EXPECT_TRUE(LiteralTestUtil::Equal(*result, *expected)); } @@ -83,12 +83,12 @@ TEST_F(InlinerTest, MapConstant) { HloInstruction::CreateParameter(0, r0f32, "x")); (void)param1; const2_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0f))); auto const2_f32 = const2_builder.Build(); auto builder = HloComputation::Builder("MapConstFunction"); auto lhs = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1, 2, 3, 4}, {5, 6, 7, 8}}))); + LiteralUtil::CreateR2({{1, 2, 3, 4}, {5, 6, 7, 8}}))); builder.AddInstruction( HloInstruction::CreateMap(lhs->shape(), {lhs}, const2_f32.get())); @@ -104,7 +104,7 @@ TEST_F(InlinerTest, MapConstant) { // Verify execution on CPU. auto result = ExecuteAndTransfer(std::move(hlo_module), {}); - auto expected = Literal::CreateR2({{2, 2, 2, 2}, {2, 2, 2, 2}}); + auto expected = LiteralUtil::CreateR2({{2, 2, 2, 2}, {2, 2, 2, 2}}); EXPECT_TRUE(LiteralTestUtil::Equal(*result, *expected)); } @@ -123,10 +123,10 @@ TEST_F(InlinerTest, MapSubtractOppositeOrder) { auto max_f32 = max_builder.Build(); auto builder = HloComputation::Builder("MapSubFunction"); - auto lhs = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3, 4}))); - auto rhs = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({4, 3, 2, 1}))); + auto lhs = builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR1({1, 2, 3, 4}))); + auto rhs = builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR1({4, 3, 2, 1}))); builder.AddInstruction( HloInstruction::CreateMap(lhs->shape(), {lhs, rhs}, max_f32.get())); @@ -142,7 +142,7 @@ TEST_F(InlinerTest, MapSubtractOppositeOrder) { // Verify execution on CPU. auto result = ExecuteAndTransfer(std::move(hlo_module), {}); - auto expected = Literal::CreateR1({3, 1, -1, -3}); + auto expected = LiteralUtil::CreateR1({3, 1, -1, -3}); EXPECT_TRUE(LiteralTestUtil::Equal(*result, *expected)); } diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc index da91262130933b6d47fd95fb30bf89574b9469d6..af07370135ca2b2e53fcbcb53696e0aa12bf7a6f 100644 --- a/tensorflow/compiler/xla/service/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/instruction_fusion.cc @@ -73,6 +73,7 @@ bool IsAlwaysDuplicable(const HloInstruction& instruction) { case HloOpcode::kGt: case HloOpcode::kImag: case HloOpcode::kInfeed: + case HloOpcode::kIota: case HloOpcode::kIsFinite: case HloOpcode::kLe: case HloOpcode::kLt: diff --git a/tensorflow/compiler/xla/service/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/instruction_fusion_test.cc index bb7231c8c868ff2fefa3e88c4be036a89ed29118..9e7a15f0330d3f06779c850a4b575f84fe0b9505 100644 --- a/tensorflow/compiler/xla/service/instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/instruction_fusion_test.cc @@ -167,7 +167,7 @@ TEST_F(InstructionFusionTest, AvoidDuplicationIfNotAllFusable) { builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "1")); HloInstruction* binary1 = builder.AddInstruction( HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); - auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = builder.AddInstruction(HloInstruction::CreateToken()); builder.AddInstruction(HloInstruction::CreateSend(binary1, token, 0)); HloInstruction* unary = builder.AddInstruction( HloInstruction::CreateUnary(shape, HloOpcode::kAbs, binary1)); @@ -356,7 +356,7 @@ TEST_F(InstructionFusionTest, AllowUnaryDuplication) { builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "0")); HloInstruction* unary1 = builder.AddInstruction( HloInstruction::CreateUnary(shape, HloOpcode::kFloor, param0)); - auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = builder.AddInstruction(HloInstruction::CreateToken()); builder.AddInstruction(HloInstruction::CreateSend(unary1, token, 0)); HloInstruction* unary2 = builder.AddInstruction( HloInstruction::CreateUnary(shape, HloOpcode::kAbs, unary1)); @@ -380,7 +380,7 @@ TEST_F(InstructionFusionTest, AllowEffectiveUnaryDuplication) { builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "1")); HloInstruction* binary1 = builder.AddInstruction( HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); - auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = builder.AddInstruction(HloInstruction::CreateToken()); builder.AddInstruction(HloInstruction::CreateSend(binary1, token, 0)); HloInstruction* unary = builder.AddInstruction( HloInstruction::CreateUnary(shape, HloOpcode::kAbs, binary1)); diff --git a/tensorflow/compiler/xla/service/interpreter/BUILD b/tensorflow/compiler/xla/service/interpreter/BUILD index 524d3234eb4eff9c7d000eca1a0d9f5c4fae90af..8652599dc6d48ff8c2aaa703fead161f891a57d1 100644 --- a/tensorflow/compiler/xla/service/interpreter/BUILD +++ b/tensorflow/compiler/xla/service/interpreter/BUILD @@ -74,7 +74,7 @@ cc_library( hdrs = ["executable.h"], deps = [ ":executor", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", diff --git a/tensorflow/compiler/xla/service/interpreter/executable.cc b/tensorflow/compiler/xla/service/interpreter/executable.cc index 9816acf6507a0ed5391cf4f1c94ccd0f27f5227a..8d40c08d555a232b7cf3b81cc0f9970804c2f896 100644 --- a/tensorflow/compiler/xla/service/interpreter/executable.cc +++ b/tensorflow/compiler/xla/service/interpreter/executable.cc @@ -21,7 +21,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc index fedc83c8f8384a75beba7081e7e9c6094249178f..9705687b004976fc5d35ddeb1c2a69c65ed50358 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.cc +++ b/tensorflow/compiler/xla/service/layout_assignment.cc @@ -30,10 +30,12 @@ limitations under the License. #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/computation_layout.h" +#include "tensorflow/compiler/xla/service/hlo_casting_utils.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_dce.h" #include "tensorflow/compiler/xla/service/hlo_graph_dumper.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/hlo_instructions.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/logical_buffer.h" #include "tensorflow/compiler/xla/service/tuple_simplifier.h" @@ -59,7 +61,6 @@ namespace xla { // anonymous namespace, instead of three or four spread all over this file. namespace { - } // namespace std::ostream& operator<<(std::ostream& out, @@ -113,14 +114,18 @@ LayoutConstraints::LayoutConstraints( HloComputation* computation) : points_to_analysis_(points_to_analysis), computation_(computation) { // Gather all array-shaped logical buffers into unconstrained_buffer_ids. - for (LogicalBuffer::Id id = 0; id < points_to_analysis_.num_logical_buffers(); - id++) { - auto& buffer = points_to_analysis_.logical_buffer(id); - // The points to analysis is computed per module, restrict constraints to - // array buffers in this computation. - if (buffer.IsArray() && buffer.instruction()->parent() == computation) { - unconstrained_buffer_ids_.insert(buffer.id()); - } + for (HloInstruction* inst : computation_->instructions()) { + points_to_analysis_.GetPointsToSet(inst).ForEachElement( + [&](const ShapeIndex&, const PointsToSet::BufferList& buffers) { + for (const LogicalBuffer* buffer : buffers) { + // The points to analysis is computed per module, restrict + // constraints to array buffers in this computation. + if (buffer->IsArray() && + buffer->instruction()->parent() == computation) { + unconstrained_buffer_ids_.insert(buffer->id()); + } + } + }); } } @@ -392,6 +397,43 @@ string LayoutConstraints::ToString() const { return output; } +namespace { + +bool IsHostSendRecv(const HloInstruction* instruction) { + const HloSendRecvInstruction* send_recv_instr = + DynCast(instruction); + return send_recv_instr != nullptr && send_recv_instr->is_host_transfer(); +} + +} // namespace + +Status LayoutAssignment::BuildHostChannelConstraints( + HloComputation* computation) { + for (auto* instruction : computation->instructions()) { + const HloSendRecvInstruction* send_recv_instr = + DynCast(instruction); + if (send_recv_instr == nullptr || !send_recv_instr->is_host_transfer()) { + continue; + } + + // For host transfers the Send and Recv instruction carry the layout. + if (instruction->opcode() == HloOpcode::kSend || + instruction->opcode() == HloOpcode::kRecv) { + const Shape& data_shape = + ShapeUtil::GetTupleElementShape(send_recv_instr->shape(), 0); + TF_RET_CHECK(ShapeUtil::IsArray(data_shape)); + TF_RET_CHECK(LayoutUtil::HasLayout(data_shape)); + const Layout* prev_layout = host_channel_constraints_.ConstrainChannel( + send_recv_instr->channel_id(), data_shape.layout()); + TF_RET_CHECK(prev_layout == nullptr) + << "Cannot constrain host transfer layout as it was set to " + << LayoutUtil::HumanString(*prev_layout) << ": " + << send_recv_instr->ToString(); + } + } + return Status::OK(); +} + Status LayoutAssignment::AddMandatoryConstraints( const ComputationLayout* computation_layout, ChannelLayoutConstraints* channel_constraints, HloComputation* computation, @@ -399,6 +441,11 @@ Status LayoutAssignment::AddMandatoryConstraints( VLOG(3) << "Adding mandatory layout constraints to computation " << computation->name(); + auto get_channel_constraints = [&](const HloInstruction* instruction) { + return IsHostSendRecv(instruction) ? &host_channel_constraints_ + : channel_constraints; + }; + // Constrain layouts of instructions which define values with pre-existing // layouts. for (auto* instruction : computation->instructions()) { @@ -435,18 +482,21 @@ Status LayoutAssignment::AddMandatoryConstraints( if (instruction->opcode() == HloOpcode::kSend || instruction->opcode() == HloOpcode::kRecv) { - CHECK(channel_constraints) + CHECK(get_channel_constraints(instruction)) << "Multi-module layout assignment requires ChannelLayoutConstraints"; int64 channel_id = instruction->channel_id(); - if (!channel_constraints->IsChannelConstrained(channel_id)) { + if (!get_channel_constraints(instruction) + ->IsChannelConstrained(channel_id)) { continue; } if (instruction->opcode() == HloOpcode::kSend) { // TODO(b/68493863): Change to use SetOperandLayout(). const Shape send_buffer_shape = instruction->operand(0)->shape(); TF_RET_CHECK(ShapeUtil::IsArray(send_buffer_shape)); - Shape new_buffer_shape = channel_constraints->LayoutShapeForChannel( - send_buffer_shape, instruction->channel_id()); + Shape new_buffer_shape = + get_channel_constraints(instruction) + ->LayoutShapeForChannel(send_buffer_shape, + instruction->channel_id()); TF_RETURN_IF_ERROR(constraints->SetInstructionLayout( new_buffer_shape, instruction->operand(0))); } else { @@ -457,8 +507,9 @@ Status LayoutAssignment::AddMandatoryConstraints( const LogicalBuffer* buffer, constraints->points_to_analysis().GetBufferDefinedAt(instruction, {0})); - Shape new_shape = channel_constraints->LayoutShapeForChannel( - recv_buffer_shape, instruction->channel_id()); + Shape new_shape = get_channel_constraints(instruction) + ->LayoutShapeForChannel( + recv_buffer_shape, instruction->channel_id()); TF_RETURN_IF_ERROR( constraints->SetBufferLayout(new_shape.layout(), *buffer)); } @@ -1535,6 +1586,10 @@ Status LayoutAssignment::RunOnComputation( ChannelLayoutConstraints* channel_constraints) { VLOG(2) << "LayoutAssignment::RunOnComputation(" << computation->name() << ")"; + + // Must be run before clearing layouts. + TF_RETURN_IF_ERROR(BuildHostChannelConstraints(computation)); + TF_RETURN_IF_ERROR(ClearComputationLayouts(computation)); if (computation_layout != nullptr) { auto it = computation_layouts_.find(computation); @@ -1624,14 +1679,20 @@ Status LayoutAssignment::RunOnComputation( Status LayoutAssignment::ConstrainChannelLayouts( HloComputation* computation, ChannelLayoutConstraints* channel_constraints) { + auto get_channel_constraints = [&](const HloInstruction* instruction) { + return IsHostSendRecv(instruction) ? &host_channel_constraints_ + : channel_constraints; + }; // We go through the kRecvDone before. These must either impose their layout, - // of find a matching one already existing (ConstrainChannel() returns + // or find a matching one already existing (ConstrainChannel() returns // nullptr). for (HloInstruction* instruction : computation->instructions()) { if (instruction->opcode() == HloOpcode::kRecvDone) { - const Layout* layout = channel_constraints->ConstrainChannel( - instruction->channel_id(), - ShapeUtil::GetSubshape(instruction->shape(), {0}).layout()); + const Layout* layout = + get_channel_constraints(instruction) + ->ConstrainChannel( + instruction->channel_id(), + ShapeUtil::GetSubshape(instruction->shape(), {0}).layout()); TF_RET_CHECK(layout == nullptr) << instruction->ToString() << " cannot constrain layout as it was set to " @@ -1644,8 +1705,9 @@ Status LayoutAssignment::ConstrainChannelLayouts( for (HloInstruction* instruction : computation->MakeInstructionPostOrder()) { if (instruction->opcode() == HloOpcode::kSend) { HloInstruction* operand = instruction->mutable_operand(0); - const Layout* layout = channel_constraints->ConstrainChannel( - instruction->channel_id(), operand->shape().layout()); + const Layout* layout = get_channel_constraints(instruction) + ->ConstrainChannel(instruction->channel_id(), + operand->shape().layout()); if (layout != nullptr) { // We found an already constrained layout which does not match the one // the kSend wants to impose. Either add a new kCopy, or use the diff --git a/tensorflow/compiler/xla/service/layout_assignment.h b/tensorflow/compiler/xla/service/layout_assignment.h index b75ecb311a07b996562460fc5d6fbd8e70ac056b..f9e8dbea2f8aa224318adf3cf4b5e493792d3093 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.h +++ b/tensorflow/compiler/xla/service/layout_assignment.h @@ -488,6 +488,9 @@ class LayoutAssignment : public HloPassInterface { } } + // Adds constraints related to host Send/Recv instructions. + Status BuildHostChannelConstraints(HloComputation* computation); + // Map containing the layouts of all computations assigned so // far. Computations are handled in a topological sort where computations are // handled before their caller instructions so the layouts of caller @@ -507,6 +510,10 @@ class LayoutAssignment : public HloPassInterface { // computations/instructions. ChannelLayoutConstraints channel_constraints_; + // Layout constraints for send/recv instructions which communicate with the + // host. + ChannelLayoutConstraints host_channel_constraints_; + // The set of HLO instructions which lacked any layout constraint, thus // receiving propagated default layouts. tensorflow::gtl::FlatSet diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc index a673901c756950802884187248f4f0c66aee55ce..a16fa75e3032cfa4257d9b5608dd176fdb4ddbdb 100644 --- a/tensorflow/compiler/xla/service/layout_assignment_test.cc +++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc @@ -21,7 +21,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/algebraic_simplifier.h" #include "tensorflow/compiler/xla/service/computation_layout.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" @@ -141,9 +141,9 @@ TEST_F(LayoutAssignmentTest, FusionInstruction) { std::vector> minor_to_majors = {{0, 1}, {1, 0}}; for (auto& minor_to_major : minor_to_majors) { auto builder = HloComputation::Builder(TestName()); - auto constant_literal1 = Literal::CreateR2WithLayout( + auto constant_literal1 = LiteralUtil::CreateR2WithLayout( {{1.0, 2.0}, {3.0, 4.0}}, LayoutUtil::MakeLayout(minor_to_major)); - auto constant_literal2 = Literal::CreateR2WithLayout( + auto constant_literal2 = LiteralUtil::CreateR2WithLayout( {{5.0, 6.0}, {7.0, 8.0}}, LayoutUtil::MakeLayout(minor_to_major)); Shape ashape = constant_literal1->shape(); @@ -192,10 +192,10 @@ TEST_F(LayoutAssignmentTest, TupleLayout) { // match their source). auto builder = HloComputation::Builder(TestName()); auto constant0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2WithLayout( + HloInstruction::CreateConstant(LiteralUtil::CreateR2WithLayout( {{1.0, 2.0}, {3.0, 4.0}}, LayoutUtil::MakeLayout({0, 1})))); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2WithLayout( + HloInstruction::CreateConstant(LiteralUtil::CreateR2WithLayout( {{1.0, 2.0}, {3.0, 4.0}}, LayoutUtil::MakeLayout({1, 0})))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant0, constant1})); @@ -229,10 +229,10 @@ TEST_F(LayoutAssignmentTest, TupleSelect) { // Verify layouts of a select with tuple operands is assigned properly. auto builder = HloComputation::Builder(TestName()); auto constant0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2WithLayout( + HloInstruction::CreateConstant(LiteralUtil::CreateR2WithLayout( {{1.0, 2.0}, {3.0, 4.0}}, LayoutUtil::MakeLayout({0, 1})))); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2WithLayout( + HloInstruction::CreateConstant(LiteralUtil::CreateR2WithLayout( {{1.0, 2.0}, {3.0, 4.0}}, LayoutUtil::MakeLayout({1, 0})))); auto tuple0 = builder.AddInstruction( HloInstruction::CreateTuple({constant0, constant1})); @@ -240,7 +240,7 @@ TEST_F(LayoutAssignmentTest, TupleSelect) { HloInstruction::CreateTuple({constant0, constant1})); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); auto select = builder.AddInstruction(HloInstruction::CreateTernary( tuple0->shape(), HloOpcode::kSelect, pred, tuple0, tuple1)); @@ -274,7 +274,7 @@ TEST_F(LayoutAssignmentTest, ConflictingLayoutTuple) { // tuple and assigning the layouts of the copied arrays as needed. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); auto inner_tuple = builder.AddInstruction(HloInstruction::CreateTuple({constant})); auto nested_tuple = builder.AddInstruction( @@ -584,7 +584,7 @@ TEST_F(LayoutAssignmentTest, TransposeToBitcastToUser) { auto builder = HloComputation::Builder(TestName()); Shape input_shape = ShapeUtil::MakeShape(F32, {3, 5, 6, 7}); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0f))); auto broadcast = builder.AddInstruction( HloInstruction::CreateBroadcast(input_shape, constant, {})); auto transpose = builder.AddInstruction(HloInstruction::CreateTranspose( @@ -770,8 +770,7 @@ TEST_F(LayoutAssignmentTest, ConditionalAsymmetricLayout) { false_builder.AddInstruction( HloInstruction::CreateParameter(0, tshape, "param")); // Using infeed as layout assignment does not mess up with it. - auto token = - false_builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = false_builder.AddInstruction(HloInstruction::CreateToken()); auto infeed = false_builder.AddInstruction( HloInstruction::CreateInfeed(xshape, token, "")); auto infeed_data = false_builder.AddInstruction( @@ -803,7 +802,7 @@ TEST_F(LayoutAssignmentTest, ConditionalAsymmetricLayout) { TEST_F(LayoutAssignmentTest, InternalErrorOnBitcast) { auto builder = HloComputation::Builder(TestName()); auto constant0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2WithLayout( + HloInstruction::CreateConstant(LiteralUtil::CreateR2WithLayout( {{1.0, 2.0}, {3.0, 4.0}}, LayoutUtil::MakeLayout({0, 1})))); builder.AddInstruction(HloInstruction::CreateUnary( constant0->shape(), HloOpcode::kBitcast, constant0)); diff --git a/tensorflow/compiler/xla/service/llvm_ir/BUILD b/tensorflow/compiler/xla/service/llvm_ir/BUILD index f1e7fc29532ce7e6841010a5258f4000a7c70383..cdd3daf73b8ac1a4d1ec3c81224c2c0bfe8e5811 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/BUILD +++ b/tensorflow/compiler/xla/service/llvm_ir/BUILD @@ -21,6 +21,11 @@ filegroup( ]), ) +load( + "//tensorflow:tensorflow.bzl", + "tf_cc_test", +) + cc_library( name = "alias_analysis", srcs = ["alias_analysis.cc"], @@ -37,12 +42,25 @@ cc_library( ], ) +tf_cc_test( + name = "alias_analysis_test", + srcs = ["alias_analysis_test.cc"], + deps = [ + ":alias_analysis", + "//tensorflow/compiler/xla/service:hlo_parser", + "//tensorflow/compiler/xla/service/cpu:custom_call_target_registry", + "//tensorflow/compiler/xla/service/cpu/tests:cpu_codegen_test", + "//tensorflow/compiler/xla/tests:filecheck", + "//tensorflow/core:test", + ], +) + cc_library( name = "llvm_util", srcs = ["llvm_util.cc"], hdrs = ["llvm_util.h"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", @@ -106,12 +124,31 @@ cc_library( ], ) +cc_library( + name = "kernel_tiling", + srcs = ["kernel_tiling.cc"], + hdrs = ["kernel_tiling.h"], + deps = [ + ":ir_array", + ":llvm_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/core:lib", + "@llvm//:core", + ], +) + cc_library( name = "fused_ir_emitter", srcs = ["fused_ir_emitter.cc"], hdrs = ["fused_ir_emitter.h"], deps = [ ":ir_array", + ":kernel_tiling", ":llvm_util", ":loop_emitter", ":tuple_ops", @@ -127,9 +164,9 @@ cc_library( ) cc_library( - name = "ops", - srcs = ["ops.cc"], - hdrs = ["ops.h"], + name = "dynamic_update_slice_util", + srcs = ["dynamic_update_slice_util.cc"], + hdrs = ["dynamic_update_slice_util.h"], deps = [ ":fused_ir_emitter", ":ir_array", @@ -143,6 +180,23 @@ cc_library( ], ) +cc_library( + name = "sort_util", + srcs = ["sort_util.cc"], + hdrs = ["sort_util.h"], + deps = [ + ":ir_array", + ":llvm_loop", + ":llvm_util", + ":loop_emitter", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla/service/gpu:parallel_loop_emitter", + "//tensorflow/compiler/xla/service/gpu:partition_assignment", + "//tensorflow/core:lib", + "@llvm//:core", + ], +) + cc_library( name = "tuple_ops", srcs = ["tuple_ops.cc"], @@ -169,3 +223,22 @@ cc_library( "@llvm//:core", ], ) + +cc_library( + name = "buffer_assignment_util", + srcs = ["buffer_assignment_util.cc"], + hdrs = ["buffer_assignment_util.h"], + deps = [ + "//tensorflow/compiler/xla/service:buffer_assignment", + ], +) + +cc_library( + name = "math_ops", + srcs = ["math_ops.cc"], + hdrs = ["math_ops.h"], + deps = [ + ":llvm_util", + "@llvm//:core", + ], +) diff --git a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc index f200a08a3cd7e33351ec4607d67d40e7ab28f3b9..e5370eca56f2e3a891523ba2b72961d66ec809aa 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc @@ -28,16 +28,16 @@ namespace llvm_ir { // Sentry allocation used to represent parameters of the entry computation in // alias_scope_metadata_ and noalias_metadata_. static const BufferAllocation* kParameterAllocation = new BufferAllocation( - /*index=*/-1, /*size=*/0, /*is_thread_local=*/false, /*is_reusable=*/false, - LogicalBuffer::Color(0)); + /*index=*/-1, /*size=*/0, LogicalBuffer::Color(0)); void AliasAnalysis::AddAliasingInformationToIrArray(const HloInstruction& hlo, llvm_ir::IrArray* array, const ShapeIndex& index) { BufferAllocation::Slice buffer_slice; - if (hlo.opcode() == HloOpcode::kParameter) { - // Parameters may alias with each other but may not alias with our temporary - // buffers. + if (hlo.opcode() == HloOpcode::kParameter && + hlo.parent() == hlo.parent()->parent()->entry_computation()) { + // Entry computation parameters may alias with each other but may not alias + // with our temporary buffers. buffer_slice = BufferAllocation::Slice(kParameterAllocation, 0, 0); } else { const std::set slices = diff --git a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis_test.cc b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..941d940684651792467a84e816a91533ce11dd63 --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis_test.cc @@ -0,0 +1,83 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include + +#include "tensorflow/compiler/xla/service/cpu/custom_call_target_registry.h" +#include "tensorflow/compiler/xla/service/cpu/tests/cpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/llvm_ir/alias_analysis.h" +#include "tensorflow/compiler/xla/tests/filecheck.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace cpu { +namespace { +class AliasAnalysisTest : public CpuCodegenTest {}; + +void FakeCustomCallTarget(float* out, float** in) {} + +REGISTER_CUSTOM_CALL_TARGET(FakeCustomCallTarget); + +TEST_F(AliasAnalysisTest, EmbeddedComputationParamsMayAliasTemps) { + const char* hlo_string = R"( +HloModule while + +body { + const.0.125 = f32[] constant(0.125) + body.state = f32[] parameter(0) + ROOT add.2.2 = f32[] add(const.0.125, body.state) +} + +condition { + const.100 = f32[] constant(100) + condition.state = f32[] parameter(0) + addend = f32[] custom-call(condition.state), custom_call_target="FakeCustomCallTarget" + add = f32[] add(addend, condition.state) + ROOT greater-than = pred[] greater-than(const.100, add) +} + +ENTRY while3 { + const.0 = f32[] constant(0) + ROOT while = f32[] while(const.0), condition=condition, body=body +} +)"; + + CompileAndVerifyIr(hlo_string, R"( +; CHECK-LABEL: @body(i8* align 4 dereferenceable(4) %retval +; CHECK: %[[add_result:.*]] = fadd fast float %[[fadd_lhs:.*]], %[[fadd_rhs:.*]] +; CHECK: store float %[[add_result]], float* %[[store_dest:.*]], !alias.scope ![[alias_scope_md_for_store:[0-9]+]] +; +; CHECK-LABEL: @condition(i8* align 1 dereferenceable(1) %fusion, i8* noalias %run_options, i8** noalias %params +; CHECK: %[[cond_state_buf_ptr:.*]] = getelementptr inbounds i8*, i8** %params, i64 0 +; CHECK: %[[cond_state_buf_untyped:.*]] = load i8*, i8** %[[cond_state_buf_ptr]] +; CHECK: %[[cond_state_buf_typed:.*]] = bitcast i8* %[[cond_state_buf_untyped]] to float* +; CHECK: load float, float* %[[cond_state_buf_typed]], !alias.scope ![[alias_scope_md_for_store]], !noalias ![[noalias_md_for_load:.*]] +; +; CHECK-LABEL: @while3( + +![[alias_scope_md_for_store]] = !{![[buffer_idx_0:.*]]} +![[buffer_idx_0]] = !{!"buffer: {index:0, offset:0, size:4}", ![[aa_md_root:.*]]} +![[aa_md_root]] = !{!"XLA global AA domain"} +![[buffer_idx_1:.*]] = !{!"buffer: {index:1, offset:0, size:4}", !3} +![[buffer_idx_1_offset_16:.*]] = !{!"buffer: {index:1, offset:16, size:1}", !3} +![[noalias_md_for_load]] = !{![[buffer_idx_1_offset_16]], ![[buffer_idx_1]]} +} +)"); +} + +} // namespace +} // namespace cpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.cc b/tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.cc new file mode 100644 index 0000000000000000000000000000000000000000..4eb5d9fb4750927ca189e02f312b2d6be7fdd418 --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.cc @@ -0,0 +1,59 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h" + +namespace xla { +namespace llvm_ir { +static const HloInstruction& InstrForConstantBufferAllocation( + const BufferAllocation& allocation) { + CHECK(allocation.is_constant()); + HloInstruction* const_instr = nullptr; + for (const auto& buffer_offset_pair : allocation.assigned_buffers()) { + const LogicalBuffer* buffer = buffer_offset_pair.first; + // BufferAssignment may have assigned non-constant instructions to this + // allocation too so we can't CHECK this condition. E.g. for + // + // while(init = constant, body = identity, cond = ...) + // + // the LogicalBuffer for the kWhile instruction will have the same + // BufferAllocation as the LogicalBuffer for the (init) constant. + if (buffer->instruction()->opcode() == HloOpcode::kConstant) { + CHECK_EQ(const_instr, nullptr) + << const_instr->ToString() << " " << buffer->ToString(); + const_instr = buffer->instruction(); + } + } + CHECK_NE(const_instr, nullptr); + return *const_instr; +} + +string ConstantBufferAllocationToGlobalName( + const BufferAllocation& allocation) { + string instr_name = InstrForConstantBufferAllocation(allocation).name(); + for (char& c : instr_name) { + if (c == '.') { + c = '_'; + } + } + return tensorflow::strings::StrCat("buffer_for_", instr_name); +} + +const Literal& LiteralForConstantAllocation( + const BufferAllocation& allocation) { + return InstrForConstantBufferAllocation(allocation).literal(); +} +} // namespace llvm_ir +} // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h b/tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h new file mode 100644 index 0000000000000000000000000000000000000000..bfb6eecb87f6a1b756b3a8da3377f608dd7f0be7 --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h @@ -0,0 +1,34 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_BUFFER_ASSIGNMENT_UTIL_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_BUFFER_ASSIGNMENT_UTIL_H_ + +#include "tensorflow/compiler/xla/service/buffer_assignment.h" + +namespace xla { +namespace llvm_ir { +// In XLA:GPU we map constant buffer allocations to globals in the generated +// LLVM IR. This function gives us the name of the global variable a constant +// buffer is mapped to. Not used on XLA:CPU. +string ConstantBufferAllocationToGlobalName(const BufferAllocation& allocation); + +// Returns the Literal corresponding to `allocation`, which must be a constant +// allocation. +const Literal& LiteralForConstantAllocation(const BufferAllocation& allocation); +} // namespace llvm_ir +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_BUFFER_ASSIGNMENT_UTIL_H_ diff --git a/tensorflow/compiler/xla/service/llvm_ir/ops.cc b/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.cc similarity index 75% rename from tensorflow/compiler/xla/service/llvm_ir/ops.cc rename to tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.cc index 3b298f4746d6177da52ba0227705d07fbeba5c19..27fbb11e2ede66a1268e7e949634b2c7d29cbc1c 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ops.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/service/llvm_ir/ops.h" +#include "tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.h" #include "tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h" #include "tensorflow/compiler/xla/service/gpu/partition_assignment.h" #include "tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h" @@ -38,16 +38,16 @@ bool CanUpdateDynamicSliceInPlace(HloInstruction* dynamic_update_slice, // Emits a sequential loop if launch_dimensions is null. static Status EmitDynamicUpdateSliceInPlaceImpl( const Shape& update_shape, const ElementGenerator& start_indices_generator, - ElementGenerator update_array_generator, const IrArray& output_array, - const gpu::LaunchDimensions* launch_dimensions, - tensorflow::StringPiece name, llvm::IRBuilder<>* ir_builder) { + bool is_signed, ElementGenerator update_array_generator, + const IrArray& output_array, const gpu::LaunchDimensions* launch_dimensions, + tensorflow::StringPiece name, llvm::IRBuilder<>* b) { const Shape& output_shape = output_array.GetShape(); // Read start indices from start_indices_generator. const int64 rank = ShapeUtil::Rank(output_shape); - IrArray::Index start_index(ir_builder->getInt64Ty(), rank); + IrArray::Index start_index(b->getInt64Ty(), rank); for (int64 i = 0; i < rank; ++i) { - IrArray::Index dim_index({ir_builder->getInt64(i)}); + IrArray::Index dim_index({b->getInt64(i)}); TF_ASSIGN_OR_RETURN(start_index[i], start_indices_generator(dim_index)); llvm::Value* output_dim_size = llvm::ConstantInt::get( start_index[i]->getType(), output_shape.dimensions(i)); @@ -56,21 +56,19 @@ static Status EmitDynamicUpdateSliceInPlaceImpl( // Clamp the start index so that the update region fits in the operand. // start_index = clamp(start_index, 0, output_dim_size - update_dim_size) - - // TODO(b/74360564): This is implementation defined behavior, but is - // currently respected by all implementations. Change this if we ever decide - // to oficially document different behavior. - llvm::Value* max_bound = - ir_builder->CreateSub(output_dim_size, update_dim_size); + llvm::Value* max_bound = b->CreateSub(output_dim_size, update_dim_size); llvm::Value* zero = llvm::ConstantInt::get(start_index[i]->getType(), 0); - start_index[i] = ir_builder->CreateSelect( - ir_builder->CreateICmp(llvm::ICmpInst::ICMP_SGE, zero, start_index[i]), - zero, start_index[i]); - - start_index[i] = ir_builder->CreateSelect( - ir_builder->CreateICmp(llvm::ICmpInst::ICMP_SLE, max_bound, - start_index[i]), - max_bound, start_index[i]); + start_index[i] = + b->CreateSelect(b->CreateICmp(is_signed ? llvm::ICmpInst::ICMP_SGE + : llvm::ICmpInst::ICMP_UGE, + zero, start_index[i]), + zero, start_index[i]); + + start_index[i] = + b->CreateSelect(b->CreateICmp(is_signed ? llvm::ICmpInst::ICMP_SLE + : llvm::ICmpInst::ICMP_ULE, + max_bound, start_index[i]), + max_bound, start_index[i]); } auto loop_body_emitter = [&](const IrArray::Index& update_index) -> Status { @@ -81,31 +79,30 @@ static Status EmitDynamicUpdateSliceInPlaceImpl( // IrArray::Index output_index(start_index.GetType(), rank); for (int64 i = 0; i < rank; ++i) { - llvm::Value* start_index0 = ir_builder->CreateSExtOrBitCast( - start_index[i], update_index[i]->getType()); - output_index[i] = ir_builder->CreateAdd(start_index0, update_index[i]); + llvm::Value* start_index0 = + b->CreateSExtOrBitCast(start_index[i], update_index[i]->getType()); + output_index[i] = b->CreateAdd(start_index0, update_index[i]); } // Do output[output_index] = update[update_index]. TF_ASSIGN_OR_RETURN(llvm::Value * update_data, update_array_generator(update_index)); - output_array.EmitWriteArrayElement(output_index, update_data, ir_builder); + output_array.EmitWriteArrayElement(output_index, update_data, b); return Status::OK(); }; if (launch_dimensions != nullptr) { return gpu::ParallelLoopEmitter(loop_body_emitter, update_shape, - *launch_dimensions, ir_builder) + *launch_dimensions, b) .EmitLoop(name); } - return LoopEmitter(loop_body_emitter, update_shape, ir_builder) - .EmitLoop(name); + return LoopEmitter(loop_body_emitter, update_shape, b).EmitLoop(name); } Status EmitDynamicUpdateSliceInPlace( tensorflow::gtl::ArraySlice operand_arrays, const IrArray& output_array, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { VLOG(2) << "EmitDynamicUpdateSliceInPlace for " << name; // No need to use operand_arrays[0], the input array of the @@ -116,15 +113,16 @@ Status EmitDynamicUpdateSliceInPlace( Shape update_shape = update_array.GetShape(); ElementGenerator start_indices_generator = [&](const IrArray::Index& index) { - return start_indices_array.EmitReadArrayElement(index, ir_builder); + return start_indices_array.EmitReadArrayElement(index, b); }; ElementGenerator update_array_generator = [&](const IrArray::Index& index) { - return update_array.EmitReadArrayElement(index, ir_builder); + return update_array.EmitReadArrayElement(index, b); }; + bool is_signed = ShapeUtil::ElementIsSigned(start_indices_array.GetShape()); return EmitDynamicUpdateSliceInPlaceImpl( - update_shape, start_indices_generator, update_array_generator, - output_array, /*launch_dimensions=*/nullptr, name, ir_builder); + update_shape, start_indices_generator, is_signed, update_array_generator, + output_array, /*launch_dimensions=*/nullptr, name, b); } // Shared implementation for EmitFusedDynamicUpdateSliceInPlace and @@ -135,8 +133,7 @@ static Status EmitFusedDynamicUpdateSliceInPlaceImpl( HloInstruction* fusion, tensorflow::gtl::ArraySlice fusion_operand_arrays, const IrArray& fusion_output_array, ElementalIrEmitter* elemental_emitter, - const gpu::LaunchDimensions* launch_dimensions, - llvm::IRBuilder<>* ir_builder) { + const gpu::LaunchDimensions* launch_dimensions, llvm::IRBuilder<>* b) { CHECK_EQ(fusion->opcode(), HloOpcode::kFusion); VLOG(2) << "EmitFusedDynamicUpdateSliceInPlace for " << fusion->ToShortString(); @@ -170,30 +167,30 @@ static Status EmitFusedDynamicUpdateSliceInPlaceImpl( ElementGenerator start_indices_generator = fused_emitter.GetGenerator(start_indices); + bool is_signed = ShapeUtil::ElementIsSigned(start_indices->shape()); return EmitDynamicUpdateSliceInPlaceImpl( - update_shape, start_indices_generator, update_array_generator, - fusion_output_array, launch_dimensions, IrName(fusion), ir_builder); + update_shape, start_indices_generator, is_signed, update_array_generator, + fusion_output_array, launch_dimensions, IrName(fusion), b); } Status EmitFusedDynamicUpdateSliceInPlace( HloInstruction* fusion, tensorflow::gtl::ArraySlice fusion_operand_arrays, const IrArray& fusion_output_array, ElementalIrEmitter* elemental_emitter, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { return EmitFusedDynamicUpdateSliceInPlaceImpl( fusion, fusion_operand_arrays, fusion_output_array, elemental_emitter, - /*launch_dimensions=*/nullptr, ir_builder); + /*launch_dimensions=*/nullptr, b); } Status EmitParallelFusedDynamicUpdateSliceInPlace( HloInstruction* fusion, tensorflow::gtl::ArraySlice fusion_operand_arrays, const IrArray& fusion_output_array, ElementalIrEmitter* elemental_emitter, - const gpu::LaunchDimensions& launch_dimensions, - llvm::IRBuilder<>* ir_builder) { + const gpu::LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* b) { return EmitFusedDynamicUpdateSliceInPlaceImpl( fusion, fusion_operand_arrays, fusion_output_array, elemental_emitter, - &launch_dimensions, ir_builder); + &launch_dimensions, b); } } // namespace llvm_ir diff --git a/tensorflow/compiler/xla/service/llvm_ir/ops.h b/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.h similarity index 91% rename from tensorflow/compiler/xla/service/llvm_ir/ops.h rename to tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.h index 175b081e84d31779b15560cb0998011fe046ca01..3502577d236a099e0b721b98217b758696966821 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ops.h +++ b/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_OPS_H_ -#define TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_OPS_H_ +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_DYNAMIC_UPDATE_SLICE_UTIL_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_DYNAMIC_UPDATE_SLICE_UTIL_H_ #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/elemental_ir_emitter.h" @@ -66,7 +66,7 @@ inline bool CanEmitFusedDynamicUpdateSliceInPlace( Status EmitDynamicUpdateSliceInPlace( tensorflow::gtl::ArraySlice operand_arrays, const IrArray& output_array, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Given a loop-fusion node whose root is a dynamic-update-slice op whose // array-to-be-updated and output share the same buffer slice, emits @@ -76,7 +76,7 @@ Status EmitFusedDynamicUpdateSliceInPlace( HloInstruction* fusion, tensorflow::gtl::ArraySlice fusion_operand_arrays, const IrArray& fusion_output_array, ElementalIrEmitter* elemental_emitter, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Same as EmitFusedDynamicUpdateSliceInPlace, except emits a parallel loop with // the given launch dimensions. @@ -84,10 +84,9 @@ Status EmitParallelFusedDynamicUpdateSliceInPlace( HloInstruction* fusion, tensorflow::gtl::ArraySlice fusion_operand_arrays, const IrArray& fusion_output_array, ElementalIrEmitter* elemental_emitter, - const gpu::LaunchDimensions& launch_dimensions, - llvm::IRBuilder<>* ir_builder); + const gpu::LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* b); } // namespace llvm_ir } // namespace xla -#endif // TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_OPS_H_ +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_DYNAMIC_UPDATE_SLICE_UTIL_H_ diff --git a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc index d909845a3a21fc55e44b0037371fca30e577980f..72ede377e1a505d5e4916915e18827e1a0f3fdf9 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc @@ -52,7 +52,7 @@ Status FusedIrEmitter::DefaultAction(HloInstruction* hlo) { // that would be regenerated without caching. But this might increase the // JIT compilation time. if (generated_value_bb == nullptr || - generated_value_bb == ir_builder_->GetInsertBlock()) { + generated_value_bb == b_->GetInsertBlock()) { VLOG(3) << "The cached generated value is reused."; return generated_value; } @@ -60,8 +60,7 @@ Status FusedIrEmitter::DefaultAction(HloInstruction* hlo) { "a different BB (" << llvm_ir::AsString(generated_value_bb->getName()) << ") from the current insertion block (" - << llvm_ir::AsString(ir_builder_->GetInsertBlock()->getName()) - << ")."; + << llvm_ir::AsString(b_->GetInsertBlock()->getName()) << ")."; } TF_ASSIGN_OR_RETURN( @@ -77,14 +76,14 @@ Status FusedIrEmitter::HandleConstant(HloInstruction* constant) { llvm::Constant* initializer = llvm_ir::ConvertLiteralToIrConstant(literal, module_); llvm::GlobalVariable* global = new llvm::GlobalVariable( - *ir_builder_->GetInsertBlock()->getModule(), initializer->getType(), + *b_->GetInsertBlock()->getModule(), initializer->getType(), /*isConstant=*/true, llvm::GlobalValue::ExternalLinkage, initializer, /*Name=*/""); llvm::Constant* shape_constant = llvm::ConstantExpr::getBitCast( global, llvm_ir::ShapeToIrType(literal.shape(), module_)->getPointerTo()); generators_[constant] = [=](const IrArray::Index& index) { return IrArray(shape_constant, constant->shape()) - .EmitReadArrayElement(index, ir_builder_); + .EmitReadArrayElement(index, b_); }; return Status::OK(); @@ -104,7 +103,7 @@ Status FusedIrEmitter::HandleGetTupleElement( // Emit code to lookup tuple element pointer, and store it in 'gte_values_'. llvm::Value* tuple_element_ptr = llvm_ir::EmitGetTupleElement( get_tuple_element->shape(), get_tuple_element->tuple_index(), - /*alignment=*/1, it->second, ir_builder_, module_); + /*alignment=*/1, it->second, b_, module_); gte_values_.insert(std::make_pair(get_tuple_element, tuple_element_ptr)); // Emit code to read base tuple element array (if non-tuple shaped). if (!ShapeUtil::IsTuple(get_tuple_element->shape())) { @@ -112,16 +111,32 @@ Status FusedIrEmitter::HandleGetTupleElement( [=](const IrArray::Index& index) -> StatusOr { // TODO(b/34080002) Add aliasing information to tuple element IrArray. return IrArray(tuple_element_ptr, get_tuple_element->shape()) - .EmitReadArrayElement(index, ir_builder_); + .EmitReadArrayElement(index, b_); }; } return Status::OK(); } Status FusedIrEmitter::HandleParameter(HloInstruction* parameter) { - generators_[parameter] = [=](const IrArray::Index& index) { + generators_[parameter] = [=](const IrArray::Index& index) -> llvm::Value* { + if (tiled_parameter_info_) { + if (llvm::Value* param_tile_buffer = + tiled_parameter_info_->GetBufferForParameter( + parameter->parameter_number())) { + // TODO(jlebar): Add AA metadata to this load. Tile buffers are global + // variables, so LLVM's points-to analysis doesn't help us much. And we + // want the AA info to be present before address spaces are inferred + // (which is pretty late in the pipeline), so even if we had + // address-space-based AA in LLVM, it wouldn't help us much here. + return b_->CreateLoad( + b_->CreateGEP(param_tile_buffer, {index.GetConstantWithIndexType(0), + tiled_parameter_info_->x(), + tiled_parameter_info_->y()}), + "tiled_buffer"); + } + } return parameter_arrays_[parameter->parameter_number()] - .EmitReadArrayElement(index, ir_builder_); + .EmitReadArrayElement(index, b_); }; // Store ir value for fusion operand associated with fusion parameter to be // accessed by subsequent fused GetTupleElement instructions. @@ -140,11 +155,11 @@ Status FusedIrEmitter::HandleTuple(HloInstruction* tuple) { } generators_[tuple] = [=](const IrArray::Index& index) -> StatusOr { - llvm::Value* ret = llvm::UndefValue::get(llvm::StructType::get( - ir_builder_->getContext(), operand_elemental_ir_types)); + llvm::Value* ret = llvm::UndefValue::get( + llvm::StructType::get(b_->getContext(), operand_elemental_ir_types)); for (size_t i = 0; i < ShapeUtil::TupleElementCount(tuple->shape()); ++i) { TF_ASSIGN_OR_RETURN(llvm::Value * val_i, generators_[operands[i]](index)); - ret = ir_builder_->CreateInsertValue(ret, val_i, i); + ret = b_->CreateInsertValue(ret, val_i, i); } return ret; }; diff --git a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h index b3b6026ef17daa184c0a015fdea618597ef068b3..30471480c4fb3ce3bf3226a28e9d2ffa79ae5f29 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h +++ b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h @@ -25,6 +25,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/elemental_ir_emitter.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h" +#include "tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.h" #include "tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/xla_data.pb.h" @@ -56,8 +57,9 @@ class FusedIrEmitter : public DfsHloVisitorWithDefault { FusedIrEmitter(tensorflow::gtl::ArraySlice parameter_arrays, ElementalIrEmitter* elemental_emitter) : parameter_arrays_(parameter_arrays), + tiled_parameter_info_(nullptr), elemental_emitter_(elemental_emitter), - ir_builder_(elemental_emitter->ir_builder()), + b_(elemental_emitter->b()), module_(elemental_emitter->module()) {} Status DefaultAction(HloInstruction* hlo) override; @@ -86,9 +88,14 @@ class FusedIrEmitter : public DfsHloVisitorWithDefault { return it->second; } + void SetTiledParameterInfo(const llvm_ir::TiledParameterInfo* info) { + tiled_parameter_info_ = info; + } + private: // Arrays of parameters of fusion instruction tensorflow::gtl::ArraySlice parameter_arrays_; + const llvm_ir::TiledParameterInfo* tiled_parameter_info_; ElementalIrEmitter* elemental_emitter_; @@ -96,7 +103,7 @@ class FusedIrEmitter : public DfsHloVisitorWithDefault { const HloInstruction* fused_root_ = nullptr; // Borrowed - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; llvm::Module* module_; // Map from instruction pointers to functions to generate elements of their diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc index ea10cef49a4a9aa048b3e0ea443f052645c4912a..2b6caee6aa72f426cf85c8c56c3ef500ff8c5d3d 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc @@ -31,7 +31,7 @@ namespace llvm_ir { void IrArray::Index::Delinearize(std::vector* multidim, llvm::Value* linear, const Shape& shape, - llvm::IRBuilder<>* ir_builder) const { + llvm::IRBuilder<>* b) const { int64 divisor = 1; const Layout& layout = shape.layout(); for (int64 i = 0; i < layout.minor_to_major_size(); ++i) { @@ -48,10 +48,9 @@ void IrArray::Index::Delinearize(std::vector* multidim, // useful because cuda-memcheck can't help us much in XLA: Most of our // memory lives in one big allocation, so cuda-memcheck can't detect // out-of-bounds accesses. - auto* quot = - ir_builder->CreateUDiv(linear, GetConstantWithIndexType(divisor)); + auto* quot = b->CreateUDiv(linear, GetConstantWithIndexType(divisor)); if (i < layout.minor_to_major_size() - 1) { - (*multidim)[dimension] = ir_builder->CreateURem( + (*multidim)[dimension] = b->CreateURem( quot, GetConstantWithIndexType(size_of_current_dimension)); } else { (*multidim)[dimension] = quot; @@ -61,7 +60,7 @@ void IrArray::Index::Delinearize(std::vector* multidim, } IrArray::Index::Index(llvm::Value* linear, const Shape& shape, - llvm::IRBuilder<>* ir_builder) + llvm::IRBuilder<>* b) : multidim_(ShapeUtil::Rank(shape)), linear_(linear), layout_(shape.layout()), @@ -71,7 +70,7 @@ IrArray::Index::Index(llvm::Value* linear, const Shape& shape, CHECK(LayoutUtil::HasLayout(shape)) << "Shape " << ShapeUtil::HumanStringWithLayout(shape) << " should have a layout."; - Delinearize(&multidim_, linear, shape, ir_builder); + Delinearize(&multidim_, linear, shape, b); } IrArray::Index::Index(tensorflow::gtl::ArraySlice multidim, @@ -94,7 +93,7 @@ IrArray::Index::Index(tensorflow::gtl::ArraySlice multidim, } IrArray::Index::Index(tensorflow::gtl::ArraySlice multidim, - const Shape& shape, llvm::IRBuilder<>* ir_builder) + const Shape& shape, llvm::IRBuilder<>* b) : multidim_(multidim.begin(), multidim.end()), layout_(shape.layout()), dims_(shape.dimensions().begin(), shape.dimensions().end()) { @@ -328,6 +327,7 @@ llvm::Value* IrArray::Index::Linearize( llvm::IRBuilder<>* builder) const { // Each dimension is multiplied by the product of the sizes of all // earlier dimensions and added to the accumulator logical_linear_index. + CHECK_EQ(size(), dimensions.size()); llvm::Value* logical_linear_index = GetConstantWithIndexType(0); int64 multiplier = 1; for (ssize_t i = size() - 1; i >= 0; --i) { @@ -343,7 +343,7 @@ llvm::Value* IrArray::Index::Linearize( } llvm::Value* IrArray::EmitArrayElementAddress( - const IrArray::Index& index, llvm::IRBuilder<>* ir_builder, + const IrArray::Index& index, llvm::IRBuilder<>* b, tensorflow::StringPiece name) const { if (ShapeUtil::IsScalar(*shape_)) { // Special handling of scalars: a scalar pretends to have the same value for @@ -354,12 +354,11 @@ llvm::Value* IrArray::EmitArrayElementAddress( CHECK_EQ(index.size(), ShapeUtil::Rank(*shape_)); if (index.LinearValidOnShape(*shape_)) { - llvm::Module* module = - ir_builder->GetInsertBlock()->getParent()->getParent(); - return ir_builder->CreateInBoundsGEP( - ir_builder->CreateBitCast( - base_ptr_, PrimitiveTypeToIrType(shape_->element_type(), module) - ->getPointerTo()), + llvm::Module* module = b->GetInsertBlock()->getParent()->getParent(); + return b->CreateInBoundsGEP( + b->CreateBitCast(base_ptr_, + PrimitiveTypeToIrType(shape_->element_type(), module) + ->getPointerTo()), {index.linear()}, llvm_ir::AsStringRef(name)); } @@ -385,8 +384,8 @@ llvm::Value* IrArray::EmitArrayElementAddress( int64 dimension = LayoutUtil::Major(shape_->layout(), i); gep_indices.push_back(actual_index[dimension]); } - return ir_builder->CreateInBoundsGEP(base_ptr_, gep_indices, - llvm_ir::AsStringRef(name)); + return b->CreateInBoundsGEP(base_ptr_, gep_indices, + llvm_ir::AsStringRef(name)); } void IrArray::AnnotateLoadStoreInstructionWithMetadata( @@ -402,37 +401,37 @@ void IrArray::AnnotateLoadStoreInstructionWithMetadata( } llvm::Value* IrArray::EmitReadArrayElement(const Index& index, - llvm::IRBuilder<>* ir_builder, + llvm::IRBuilder<>* b, tensorflow::StringPiece name) const { - llvm::Value* element_address = - EmitArrayElementAddress(index, ir_builder, name); - llvm::LoadInst* load = ir_builder->CreateLoad(element_address); + llvm::Value* element_address = EmitArrayElementAddress(index, b, name); + llvm::LoadInst* load = b->CreateLoad(element_address); AnnotateLoadStoreInstructionWithMetadata(load); return load; } void IrArray::EmitWriteArrayElement(const Index& index, llvm::Value* value, - llvm::IRBuilder<>* ir_builder) const { - llvm::Value* element_address = EmitArrayElementAddress(index, ir_builder); - llvm::StoreInst* store = ir_builder->CreateStore(value, element_address); + llvm::IRBuilder<>* b) const { + llvm::Value* element_address = EmitArrayElementAddress(index, b); + llvm::StoreInst* store = b->CreateStore(value, element_address); AnnotateLoadStoreInstructionWithMetadata(store); } IrArray IrArray::CastToShape(const Shape& new_shape, - llvm::IRBuilder<>* ir_builder) const { - llvm::Module* module = ir_builder->GetInsertBlock()->getParent()->getParent(); + llvm::IRBuilder<>* b) const { + llvm::Module* module = b->GetInsertBlock()->getParent()->getParent(); llvm::Type* new_ir_type = llvm_ir::ShapeToIrType(new_shape, module); - return IrArray( - ir_builder->CreatePointerCast(base_ptr_, new_ir_type->getPointerTo()), - new_shape); + IrArray new_irarray( + b->CreatePointerCast(base_ptr_, new_ir_type->getPointerTo()), new_shape); + new_irarray.metadata_ = metadata_; + return new_irarray; } /* static */ IrArray::Index IrArray::BumpIndex(const Index& index, int64 which_dimension, int64 addend, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { Index new_index = index; - new_index[which_dimension] = ir_builder->CreateAdd( + new_index[which_dimension] = b->CreateAdd( index[which_dimension], llvm::ConstantInt::get(index[which_dimension]->getType(), addend), "", /*HasNUW=*/true, diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h index 4648c6d7ac089dbea7e660dd9889d557c8ad7318..28ca793e3eeaed86664bfa6aa859a38f2c4dc6f3 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h +++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h @@ -87,20 +87,19 @@ class IrArray { } // Constructs an index from linear index "linear" and computes the - // multi-dimensional index from "linear" and "shape". "ir_builder" is the IR + // multi-dimensional index from "linear" and "shape". "b" is the IR // builder to emit the index of each dimension in the multi-dimensional // index. // // Precondition: "shape" has a layout. - Index(llvm::Value* linear, const Shape& shape, - llvm::IRBuilder<>* ir_builder); + Index(llvm::Value* linear, const Shape& shape, llvm::IRBuilder<>* b); // Constructs an index from the given multi-dimensional index and the shape // that it indexes into. // // Precondition: "shape" has a layout. Index(tensorflow::gtl::ArraySlice multidim, - const Shape& shape, llvm::IRBuilder<>* ir_builder); + const Shape& shape, llvm::IRBuilder<>* b); // Constructs an index from both a multi-dimensional index and a linear // index. "shape" has the same meaning as that in the constructor that takes @@ -114,19 +113,19 @@ class IrArray { size_t size() const { return multidim().size(); } llvm::Value* operator[](size_t i) const { return multidim()[i]; } - llvm::Value*& operator[](size_t i) { return multidim()[i]; } + llvm::Value*& operator[](size_t i) { return mutable_multidim()[i]; } - void push_back(llvm::Value* value) { multidim().push_back(value); } + void push_back(llvm::Value* value) { mutable_multidim().push_back(value); } void InsertAt(int64 index, llvm::Value* value) { CHECK_LE(index, size()); - multidim().insert(multidim().begin() + index, value); + mutable_multidim().insert(mutable_multidim().begin() + index, value); } using iterator = std::vector::iterator; using const_iterator = std::vector::const_iterator; - iterator begin() { return multidim().begin(); } - iterator end() { return multidim().end(); } + iterator begin() { return mutable_multidim().begin(); } + iterator end() { return mutable_multidim().end(); } const_iterator begin() const { return multidim().begin(); } const_iterator end() const { return multidim().end(); } @@ -185,13 +184,13 @@ class IrArray { private: // Changing the multi-dimensional index invalidates the linear index. - std::vector& multidim() { + std::vector& mutable_multidim() { linear_ = nullptr; return multidim_; } void Delinearize(std::vector* multidim, llvm::Value* linear, - const Shape& shape, llvm::IRBuilder<>* ir_builder) const; + const Shape& shape, llvm::IRBuilder<>* b) const; std::vector multidim_; @@ -240,8 +239,7 @@ class IrArray { // // The optional name is useful for debugging when looking at // the emitted LLVM IR. - llvm::Value* EmitArrayElementAddress(const Index& index, - llvm::IRBuilder<>* ir_builder, + llvm::Value* EmitArrayElementAddress(const Index& index, llvm::IRBuilder<>* b, tensorflow::StringPiece name = "") const; // Attach metadata this IrArray instance knows about to "instruction". @@ -255,18 +253,16 @@ class IrArray { // // The optional name is useful for debugging when looking at // the emitted LLVM IR. - llvm::Value* EmitReadArrayElement(const Index& index, - llvm::IRBuilder<>* ir_builder, + llvm::Value* EmitReadArrayElement(const Index& index, llvm::IRBuilder<>* b, tensorflow::StringPiece name = "") const; // Emit IR to write the given value to the array element at the given index. void EmitWriteArrayElement(const Index& index, llvm::Value* value, - llvm::IRBuilder<>* ir_builder) const; + llvm::IRBuilder<>* b) const; // Returns a new IrArray whose shape is "new_shape" and base pointer is a // bitcast of the base pointer of "this" IrArray. - IrArray CastToShape(const Shape& new_shape, - llvm::IRBuilder<>* ir_builder) const; + IrArray CastToShape(const Shape& new_shape, llvm::IRBuilder<>* b) const; void AddAliasScopeMetadata(llvm::MDNode* alias_scope) { CHECK_NE(alias_scope, nullptr); @@ -312,7 +308,7 @@ class IrArray { // Bumps the "which_dimension" value within the provided index by the provided // addend. static Index BumpIndex(const Index& index, int64 which_dimension, - int64 addend, llvm::IRBuilder<>* ir_builder); + int64 addend, llvm::IRBuilder<>* b); private: // Add the specified LLVM IR metadata to loads/stores associated with this diff --git a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc index 1f6e3c829f890d68aa251b101f0402c120a19d61..b79567369aa532c4963e3941f6cb9844cd1476dd 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc @@ -22,9 +22,9 @@ Status KernelSupportLibrary::For( tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, llvm::Value* step, const std::function& for_body_generator) { - return If(ir_builder_->CreateICmpSLT(start, end), [&]() -> Status { + return If(b_->CreateICmpSLT(start, end), [&]() -> Status { TF_RETURN_IF_ERROR(for_body_generator(start, /*is_first_iteration=*/true)); - return For(name, ir_builder_->CreateAdd(start, step), end, step, + return For(name, b_->CreateAdd(start, step), end, step, [&](llvm::Value* iv) { return for_body_generator(iv, false); }); }); } @@ -37,44 +37,44 @@ Status KernelSupportLibrary::For( if (peel_first_iteration) { return For(name, start, end, step, true, [&](llvm::Value* indvar, bool is_first_iteration) -> Status { - return for_body_generator( - indvar, ir_builder_->getInt1(is_first_iteration)); + return for_body_generator(indvar, + b_->getInt1(is_first_iteration)); }); } else { std::unique_ptr loop = llvm_ir::ForLoop::EmitForLoop( - name, start, end, step, ir_builder_, + name, start, end, step, b_, /*unroll_mode=*/unroll_mode_, /*prevent_vectorization=*/prevent_vectorization_); - ir_builder_->SetInsertPoint(&loop->GetBodyBasicBlock()->back()); + b_->SetInsertPoint(&loop->GetBodyBasicBlock()->back()); TF_RETURN_IF_ERROR( for_body_generator(loop->GetIndVarValue(), - /*is_first_iteration=*/ir_builder_->CreateICmpEQ( + /*is_first_iteration=*/b_->CreateICmpEQ( loop->GetIndVarValue(), start))); - llvm_ir::SetToLastInsertPoint(loop->GetExitBasicBlock(), ir_builder_); + llvm_ir::SetToLastInsertPoint(loop->GetExitBasicBlock(), b_); return Status::OK(); } } Status KernelSupportLibrary::If( - llvm::Value* condition, const std::function& true_block_generator, + tensorflow::StringPiece name, llvm::Value* condition, + const std::function& true_block_generator, const std::function& false_block_generator) { - llvm_ir::LlvmIfData if_data = - llvm_ir::EmitIfThenElse(condition, "", ir_builder_); - ir_builder_->SetInsertPoint(&if_data.true_block->back()); + llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse(condition, name, b_); + b_->SetInsertPoint(&if_data.true_block->back()); TF_RETURN_IF_ERROR(true_block_generator()); - ir_builder_->SetInsertPoint(&if_data.false_block->back()); + b_->SetInsertPoint(&if_data.false_block->back()); TF_RETURN_IF_ERROR(false_block_generator()); - llvm_ir::SetToLastInsertPoint(if_data.after_block, ir_builder_); + llvm_ir::SetToLastInsertPoint(if_data.after_block, b_); return Status::OK(); } void KernelSupportLibrary::EmitAndCallOutlinedKernel( - bool enable_fast_math, bool optimize_for_size, - llvm::IRBuilder<>* ir_builder, tensorflow::StringPiece kernel_name, + bool enable_fast_math, bool optimize_for_size, llvm::IRBuilder<>* b, + tensorflow::StringPiece kernel_name, KernelSupportLibrary::ArgumentVector arguments, const std::function& kernel_body_generator) { - llvm::Module* module = ir_builder->GetInsertBlock()->getModule(); + llvm::Module* module = b->GetInsertBlock()->getModule(); llvm::Function* function = module->getFunction(llvm_ir::AsStringRef(kernel_name)); @@ -97,22 +97,22 @@ void KernelSupportLibrary::EmitAndCallOutlinedKernel( std::back_inserter(arg_types), [](llvm::Value* arg) { return arg->getType(); }); - auto* function_type = llvm::FunctionType::get( - ir_builder->getVoidTy(), arg_types, /*isVarArg=*/false); + auto* function_type = + llvm::FunctionType::get(b->getVoidTy(), arg_types, /*isVarArg=*/false); function = llvm_ir::CreateFunction( function_type, llvm::GlobalValue::InternalLinkage, /*enable_fast_math=*/enable_fast_math, /*optimize_for_size=*/optimize_for_size, kernel_name, module); - llvm::IRBuilder<>::InsertPointGuard guard(*ir_builder); + llvm::IRBuilder<>::InsertPointGuard guard(*b); auto* entry_bb = - llvm::BasicBlock::Create(ir_builder->getContext(), "entry", function); - auto* return_inst = llvm::ReturnInst::Create(ir_builder->getContext(), + llvm::BasicBlock::Create(b->getContext(), "entry", function); + auto* return_inst = llvm::ReturnInst::Create(b->getContext(), /*retVal=*/nullptr, entry_bb); // Set the insert point to before return_inst. - ir_builder->SetInsertPoint(return_inst); + b->SetInsertPoint(return_inst); std::vector arg_values; /* @@ -132,7 +132,7 @@ void KernelSupportLibrary::EmitAndCallOutlinedKernel( VLOG(3) << "Re-using kernel for " << kernel_name; } - ir_builder->CreateCall(function, llvm_ir::AsArrayRef(sanitized_args)); + b->CreateCall(function, llvm_ir::AsArrayRef(sanitized_args)); } } // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h index 6f7a9d94e3b9e59b2dfe12b9673335a904ae78b6..b00f903d56a83c5b76188007702470c44c55c213 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h @@ -30,14 +30,14 @@ namespace xla { // flow more readable. class KernelSupportLibrary { public: - // `ir_builder` is the llvm::IRBuilder instance used to generate LLVM IR. + // `b` is the llvm::IRBuilder instance used to generate LLVM IR. // `unroll_mode` specifies the desired LLVM unrolling behavior for every loop // generated by this instance of KernelSupportLibrary. explicit KernelSupportLibrary( - llvm::IRBuilder<>* ir_builder, + llvm::IRBuilder<>* b, llvm_ir::UnrollMode unroll_mode = llvm_ir::UnrollMode::kNoUnroll, bool prevent_vectorization = true) - : ir_builder_(ir_builder), + : b_(b), unroll_mode_(unroll_mode), prevent_vectorization_(prevent_vectorization) {} @@ -71,18 +71,18 @@ class KernelSupportLibrary { const std::function& for_body_generator) { - return For(name, /*start=*/ir_builder_->getInt64(start), - /*end=*/ir_builder_->getInt64(end), - /*step=*/ir_builder_->getInt64(step), for_body_generator); + return For(name, /*start=*/b_->getInt64(start), + /*end=*/b_->getInt64(end), + /*step=*/b_->getInt64(step), for_body_generator); } void ForReturnVoid( tensorflow::StringPiece name, int64 start, int64 end, int64 step, const std::function& for_body_generator) { - ForReturnVoid(name, /*start=*/ir_builder_->getInt64(start), - /*end=*/ir_builder_->getInt64(end), - /*step=*/ir_builder_->getInt64(step), for_body_generator); + ForReturnVoid(name, /*start=*/b_->getInt64(start), + /*end=*/b_->getInt64(end), + /*step=*/b_->getInt64(step), for_body_generator); } // Generates the following control flow structure if `peel_first_iteration` is @@ -184,17 +184,17 @@ class KernelSupportLibrary { Status For( tensorflow::StringPiece name, int64 start, int64 end, int64 step, const std::function& for_body_generator) { - return For(name, /*start=*/ir_builder_->getInt64(start), - /*end=*/ir_builder_->getInt64(end), - /*step=*/ir_builder_->getInt64(step), for_body_generator); + return For(name, /*start=*/b_->getInt64(start), + /*end=*/b_->getInt64(end), + /*step=*/b_->getInt64(step), for_body_generator); } void ForReturnVoid( tensorflow::StringPiece name, int64 start, int64 end, int64 step, const std::function& for_body_generator) { - ForReturnVoid(name, /*start=*/ir_builder_->getInt64(start), - /*end=*/ir_builder_->getInt64(end), - /*step=*/ir_builder_->getInt64(step), for_body_generator); + ForReturnVoid(name, /*start=*/b_->getInt64(start), + /*end=*/b_->getInt64(end), + /*step=*/b_->getInt64(step), for_body_generator); } // Generates the following control flow structure: @@ -203,16 +203,30 @@ class KernelSupportLibrary { // `true_block_generator()`; // else // `false_block_generator()`; - Status If(llvm::Value* condition, + Status If(tensorflow::StringPiece name, llvm::Value* condition, const std::function& true_block_generator, const std::function& false_block_generator = []() -> Status { return Status::OK(); }); + Status If(llvm::Value* condition, + const std::function& true_block_generator, + const std::function& false_block_generator = + []() -> Status { return Status::OK(); }) { + return If("", condition, true_block_generator, false_block_generator); + } + void IfReturnVoid(llvm::Value* condition, const std::function& true_block_generator, const std::function& false_block_generator = []() { }) { - TF_CHECK_OK(If(condition, + IfReturnVoid("", condition, true_block_generator, false_block_generator); + } + + void IfReturnVoid(tensorflow::StringPiece name, llvm::Value* condition, + const std::function& true_block_generator, + const std::function& false_block_generator = []() { + }) { + TF_CHECK_OK(If(name, condition, [&]() { true_block_generator(); return Status::OK(); @@ -244,41 +258,39 @@ class KernelSupportLibrary { // in a nullptr llvm::Value* in its position to `kernel_body_generator`. // Currently we only support at most one nullptr value in `arguments`. static void EmitAndCallOutlinedKernel( - bool enable_fast_math, bool optimize_for_size, - llvm::IRBuilder<>* ir_builder, tensorflow::StringPiece kernel_name, - ArgumentVector arguments, + bool enable_fast_math, bool optimize_for_size, llvm::IRBuilder<>* b, + tensorflow::StringPiece kernel_name, ArgumentVector arguments, const std::function& kernel_body_generator); // Thin wrappers around the more general EmitAndCallOutlinedKernel above. static void EmitAndCallOutlinedKernel( - bool enable_fast_math, bool optimize_for_size, - llvm::IRBuilder<>* ir_builder, tensorflow::StringPiece kernel_name, - llvm::Value* arg0, llvm::Value* arg1, llvm::Value* arg2, + bool enable_fast_math, bool optimize_for_size, llvm::IRBuilder<>* b, + tensorflow::StringPiece kernel_name, llvm::Value* arg0, llvm::Value* arg1, + llvm::Value* arg2, const std::function& kernel_body_generator) { EmitAndCallOutlinedKernel( - enable_fast_math, optimize_for_size, ir_builder, kernel_name, - {arg0, arg1, arg2}, [&](ArgumentVector args) { + enable_fast_math, optimize_for_size, b, kernel_name, {arg0, arg1, arg2}, + [&](ArgumentVector args) { kernel_body_generator(args[0], args[1], args[2]); }); } static void EmitAndCallOutlinedKernel( - bool enable_fast_math, bool optimize_for_size, - llvm::IRBuilder<>* ir_builder, tensorflow::StringPiece kernel_name, - llvm::Value* arg0, llvm::Value* arg1, llvm::Value* arg2, - llvm::Value* arg3, + bool enable_fast_math, bool optimize_for_size, llvm::IRBuilder<>* b, + tensorflow::StringPiece kernel_name, llvm::Value* arg0, llvm::Value* arg1, + llvm::Value* arg2, llvm::Value* arg3, const std::function& kernel_body_generator) { EmitAndCallOutlinedKernel( - enable_fast_math, optimize_for_size, ir_builder, kernel_name, + enable_fast_math, optimize_for_size, b, kernel_name, {arg0, arg1, arg2, arg3}, [&](ArgumentVector args) { kernel_body_generator(args[0], args[1], args[2], args[3]); }); } private: - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; llvm_ir::UnrollMode unroll_mode_; bool prevent_vectorization_; }; diff --git a/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.cc b/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.cc new file mode 100644 index 0000000000000000000000000000000000000000..35b394127288d816952b48c84b193257bab0bcda --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.cc @@ -0,0 +1,118 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.h" +#include "tensorflow/compiler/xla/layout_util.h" +#include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/compiler/xla/util.h" +#include "tensorflow/core/platform/logging.h" + +namespace xla { +namespace llvm_ir { + +namespace { +// Returns the indices of the first elements of all consecutive subarrays of the +// given array. For example: +// ConsecutiveSegments({m, m+1, m+2, n, k, k+1}) = {0, 3, 4} +std::vector ConsecutiveSegments(tensorflow::gtl::ArraySlice xs) { + std::vector is = {0}; + for (size_t i = 1; i < xs.size(); ++i) { + if (1 != xs[i] - xs[i - 1]) { + is.push_back(i); + } + } + return is; +} + +// Merges the sequences of dimensions of the given shape which start at the +// given indices `segs`. +Shape MergeDimensions(tensorflow::gtl::ArraySlice segs, + const Shape& shape) { + std::vector dimensions; + for (size_t i = 1; i <= segs.size(); ++i) { + dimensions.push_back(std::accumulate( + shape.dimensions().begin() + segs[i - 1], + shape.dimensions().begin() + + (segs.size() == i ? shape.dimensions().size() : segs[i]), + 1, std::multiplies())); + } + return ShapeUtil::MakeShapeWithDescendingLayout(shape.element_type(), + dimensions); +} +} // namespace + +tensorflow::gtl::optional > FindTranspose021( + const Shape& a, const Shape& b) { + if (!ShapeUtil::CompatibleIgnoringElementType(a, b)) { + return tensorflow::gtl::nullopt; + } + + std::vector perm(a.dimensions().size()); + { + auto layout_a_orig = LayoutUtil::MinorToMajor(a); + std::vector layout_a(layout_a_orig.rbegin(), layout_a_orig.rend()); + auto layout_b_orig = LayoutUtil::MinorToMajor(b); + std::vector layout_b(layout_b_orig.rbegin(), layout_b_orig.rend()); + for (size_t i = 0; i < perm.size(); ++i) { + perm[i] = PositionInContainer(layout_b, layout_a[i]); + } + } + auto segs = ConsecutiveSegments(perm); + if ((3 == segs.size() && 0 == perm[0]) || 2 == segs.size()) { + Shape norm_a = + ShapeUtil::MakeShapeWithDescendingLayoutAndSamePhysicalLayout(a); + Shape reduced_a = MergeDimensions(segs, norm_a); + auto reduced_a_dims = reduced_a.dimensions(); + std::vector dims_021; + if (2 == segs.size()) { + // The logical component-0 is of size one. + dims_021 = {1, reduced_a_dims[1], reduced_a_dims[0]}; + } else { + dims_021 = {reduced_a_dims[0], reduced_a_dims[2], reduced_a_dims[1]}; + } + + return dims_021; + } + + return tensorflow::gtl::nullopt; +} + +IrArray::Index GetUnreducedOutputIndex( + const IrArray::Index& reduced_output_index, + const Shape& reduced_output_shape, const Shape& unreduced_output_shape, + llvm::IRBuilder<>* b) { + auto bounds = reduced_output_shape.dimensions(); + auto minor_to_major = reduced_output_shape.layout().minor_to_major(); + llvm::Value* linear_index = reduced_output_index.GetConstantWithIndexType(0); + int64 multiplier = 1; + for (int i = 0; i < reduced_output_index.size(); ++i) { + int64 dim = minor_to_major[i]; + llvm::Value* addend = + b->CreateMul(reduced_output_index[dim], + reduced_output_index.GetConstantWithIndexType(multiplier), + "linearizing", + /*HasNUW=*/true, /*HasNSW=*/true); + linear_index = b->CreateAdd(linear_index, addend, "", + /*HasNUW=*/true, /*HasNSW=*/true); + multiplier *= bounds[dim]; + } + + return IrArray::Index(linear_index, unreduced_output_shape, b); +} + +} // namespace llvm_ir +} // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.h b/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.h new file mode 100644 index 0000000000000000000000000000000000000000..ccb9b8ba3e6b0079664f2da92ce67224e176fa1d --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.h @@ -0,0 +1,80 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_KERNEL_TILING_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_KERNEL_TILING_H_ + +#include "llvm/IR/Value.h" +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h" + +namespace xla { +namespace llvm_ir { + +// About 0-2-1 transpose: +// +// If a shape can be viewed as three logical components 0-1-2 in the order of +// major to minor, a 0-2-1-transpose changes the order of such logical +// components to 0-2-1. We call the shape being transposed the input shape and +// the transposed shape the output shape. The logical view of the input and +// output shapes for the transpose are called the 0-1-2 shape or reduced input +// shape and the 0-2-1 shape or the reduced output shape respectively. The +// original input and output shapes are called the unreduced input and output +// shapes. + +// If `b` is a 0-2-1 transpose of `a` in 0-1-2, return the dimensions for the +// reduced shape of `b` or the 0-2-1 shape. +tensorflow::gtl::optional > FindTranspose021(const Shape& a, + const Shape& b); + +// Return the unreduced output index corresponding to the given reduced output +// index. +IrArray::Index GetUnreducedOutputIndex( + const IrArray::Index& reduced_output_index, + const Shape& reduced_output_shape, const Shape& unreduced_output_shape, + llvm::IRBuilder<>* b); + +// A class to represent information for tiled parameters to support IR emission +// for 021 transpose. +class TiledParameterInfo { + public: + TiledParameterInfo(tensorflow::gtl::ArraySlice param_buffers, + llvm::Value* y, llvm::Value* x) + : param_buffers_(param_buffers), y_(y), x_(x) {} + + llvm::Value* x() const { return x_; } + llvm::Value* y() const { return y_; } + + void set_x(llvm::Value* x) { x_ = x; } + void set_y(llvm::Value* y) { y_ = y; } + + llvm::Value* GetBufferForParameter(int64 index) const { + return param_buffers_[index]; + } + + private: + // Param_buffers_[i] stores the tile buffer for the ith parameter or nullptr + // if the parameter is not tiled. + tensorflow::gtl::ArraySlice param_buffers_; + // The y coordinate within a tile. + llvm::Value* y_; + // The x coordinate within a tile. + llvm::Value* x_; +}; + +} // namespace llvm_ir +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_KERNEL_TILING_H_ diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc index c9ae7d3afd5cdc21157732f6d0dfa824268e86bd..ba7f94834c7fd04d97cec012537244323308b8ce 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc @@ -47,27 +47,27 @@ ForLoop::ForLoop(tensorflow::StringPiece prefix, tensorflow::StringPiece suffix, /* static */ std::unique_ptr ForLoop::EmitForLoop( tensorflow::StringPiece prefix, llvm::Value* start_index, - llvm::Value* end_index, llvm::Value* step, llvm::IRBuilder<>* ir_builder, + llvm::Value* end_index, llvm::Value* step, llvm::IRBuilder<>* b, UnrollMode unroll_mode, bool prevent_vectorization) { std::unique_ptr loop(new ForLoop(prefix, /*suffix=*/"", start_index, end_index, step, unroll_mode, prevent_vectorization)); - loop->Emit(ir_builder); + loop->Emit(b); return loop; } -void ForLoop::Emit(llvm::IRBuilder<>* ir_builder) { +void ForLoop::Emit(llvm::IRBuilder<>* b) { // The preheader block is the block the builder is currently emitting // code into. - preheader_bb_ = ir_builder->GetInsertBlock(); + preheader_bb_ = b->GetInsertBlock(); - llvm::BasicBlock::iterator insert_point = ir_builder->GetInsertPoint(); + llvm::BasicBlock::iterator insert_point = b->GetInsertPoint(); if (insert_point == preheader_bb_->end()) { // We're emitting the loop at the end of a basic block. Verify there is no // terminator (eg, branch) in the basic block. CHECK_EQ(nullptr, preheader_bb_->getTerminator()); - exit_bb_ = CreateLoopBB("loop_exit", ir_builder); + exit_bb_ = CreateLoopBB("loop_exit", b); } else { // We're emitting the loop into the middle of a basic block. splitBasicBlock // requires that this basic block be well-formed (have a terminator). @@ -86,51 +86,50 @@ void ForLoop::Emit(llvm::IRBuilder<>* ir_builder) { insert_before_bb_ = exit_bb_; // Create remaining basic block which form the inside of the loop. - header_bb_ = CreateLoopBB("loop_header", ir_builder); - body_bb_ = CreateLoopBB("loop_body", ir_builder); + header_bb_ = CreateLoopBB("loop_header", b); + body_bb_ = CreateLoopBB("loop_body", b); // Function entry basic block. // Emit alloca for the induction variable. We do this at the entry to the // basic block to ensure the alloc only executes once per function (we could // be emitting a nested loop). llvm::Function* func = preheader_bb_->getParent(); - ir_builder->SetInsertPoint(&func->getEntryBlock(), - func->getEntryBlock().getFirstInsertionPt()); + b->SetInsertPoint(&func->getEntryBlock(), + func->getEntryBlock().getFirstInsertionPt()); llvm::Value* indvar_address = - ir_builder->CreateAlloca(start_index_->getType(), nullptr, - AsStringRef(GetQualifiedName("invar_address"))); + b->CreateAlloca(start_index_->getType(), nullptr, + AsStringRef(GetQualifiedName("invar_address"))); // Preheader basic block. // Initialize induction variable starting index. Create branch to the header. - ir_builder->SetInsertPoint(preheader_bb_); - ir_builder->CreateStore(start_index_, indvar_address); + b->SetInsertPoint(preheader_bb_); + b->CreateStore(start_index_, indvar_address); // The preheader should not have a branch yet. CHECK_EQ(preheader_bb_->getTerminator(), nullptr); - ir_builder->CreateBr(header_bb_); + b->CreateBr(header_bb_); // Header basic block. // Emit the loop conditional branch. Load and compare indvar with ending // index and jump to loop exit if equal. Jump to body otherwise. - ir_builder->SetInsertPoint(header_bb_); - indvar_ = ir_builder->CreateLoad(indvar_address, - AsStringRef(GetQualifiedName("indvar"))); - llvm::Value* exit_cond = ir_builder->CreateICmpUGE(indvar_, end_index_); - ir_builder->CreateCondBr(/*Cond=*/exit_cond, - /*True=*/exit_bb_, /*False=*/body_bb_); + b->SetInsertPoint(header_bb_); + indvar_ = + b->CreateLoad(indvar_address, AsStringRef(GetQualifiedName("indvar"))); + llvm::Value* exit_cond = b->CreateICmpUGE(indvar_, end_index_); + b->CreateCondBr(/*Cond=*/exit_cond, + /*True=*/exit_bb_, /*False=*/body_bb_); // Body basic block. // Increment indvar, store indvar, and jump to header. - ir_builder->SetInsertPoint(body_bb_); + b->SetInsertPoint(body_bb_); llvm::Value* step = step_; llvm::Value* indvar = indvar_; - llvm::Value* indvar_inc = - ir_builder->CreateAdd(indvar, step, "invar.inc", - /*HasNUW=*/true, /*HasNSW=*/true); - ir_builder->CreateStore(indvar_inc, indvar_address); - llvm::BranchInst* back_branch = ir_builder->CreateBr(header_bb_); + llvm::Value* indvar_inc = b->CreateAdd(indvar, step, "invar.inc", + /*HasNUW=*/true, /*HasNSW=*/true); + b->CreateStore(indvar_inc, indvar_address); + llvm::BranchInst* back_branch = b->CreateBr(header_bb_); - std::vector loop_metadata = GetLoopMetadata(ir_builder); + std::vector loop_metadata = GetLoopMetadata(b); if (!loop_metadata.empty()) { llvm::LLVMContext* ctx = &start_index_->getContext(); auto temp_node = llvm::MDNode::getTemporary(*ctx, llvm::None); @@ -141,11 +140,10 @@ void ForLoop::Emit(llvm::IRBuilder<>* ir_builder) { } // Re-point the IR builder to the loop exit block. - ir_builder->SetInsertPoint(exit_bb_); + b->SetInsertPoint(exit_bb_); } -std::vector ForLoop::GetLoopMetadata( - llvm::IRBuilder<>* ir_builder) { +std::vector ForLoop::GetLoopMetadata(llvm::IRBuilder<>* b) { const char* const kLlvmLoopUnrollDisableMDName = "llvm.loop.unroll.disable"; const char* const kLlvmLoopUnrollFullMDName = "llvm.loop.unroll.full"; const char* const kLlvmLoopVectorizeMDName = "llvm.loop.vectorize.enable"; @@ -160,7 +158,7 @@ std::vector ForLoop::GetLoopMetadata( if (prevent_vectorization_) { result.push_back(llvm::MDNode::get( *ctx, {llvm::MDString::get(*ctx, kLlvmLoopVectorizeMDName), - llvm::ConstantAsMetadata::get(ir_builder->getFalse())})); + llvm::ConstantAsMetadata::get(b->getFalse())})); } if (unroll_mode_ == xla::llvm_ir::UnrollMode::kFullyUnroll) { @@ -175,9 +173,8 @@ string ForLoop::GetQualifiedName(tensorflow::StringPiece name) { } llvm::BasicBlock* ForLoop::CreateLoopBB(tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder) { - return CreateBasicBlock(insert_before_bb_, GetQualifiedName(name), - ir_builder); + llvm::IRBuilder<>* b) { + return CreateBasicBlock(insert_before_bb_, GetQualifiedName(name), b); } std::unique_ptr ForLoopNest::AddLoop(tensorflow::StringPiece suffix, @@ -197,12 +194,12 @@ std::unique_ptr ForLoopNest::AddLoop(tensorflow::StringPiece suffix, bool prevent_vectorization) { if (inner_loop_body_bb_ != nullptr) { // Create this loop inside the previous one. - ir_builder_->SetInsertPoint(&*inner_loop_body_bb_->getFirstInsertionPt()); + b_->SetInsertPoint(&*inner_loop_body_bb_->getFirstInsertionPt()); } std::unique_ptr loop(new ForLoop( /*prefix=*/name_, suffix, start_index, end_index, stride, unroll_mode, prevent_vectorization)); - loop->Emit(ir_builder_); + loop->Emit(b_); if (outer_loop_preheader_bb_ == nullptr) { outer_loop_preheader_bb_ = loop->GetPreheaderBasicBlock(); @@ -262,5 +259,35 @@ IrArray::Index ForLoopNest::AddLoopsForShapeOnDimensions( return index; } +IrArray::Index ForLoopNest::EmitOperandArrayLoopNest( + const llvm_ir::IrArray& operand_array, int64 dimension_to_skip, + tensorflow::StringPiece name_suffix) { + // Prepares the dimension list we will use to emit the loop nest. Outermost + // loops are added first. Add loops in major-to-minor order, and skip the + // 'dimension_to_skip' dimension. + std::vector dimensions; + const Shape& shape = operand_array.GetShape(); + for (int64 dimension : LayoutUtil::MinorToMajor(shape)) { + if (dimension != dimension_to_skip) { + dimensions.push_back(dimension); + } + } + + // Create loop nest with one for-loop for each dimension of the + // output. + llvm_ir::IrArray::Index index = + AddLoopsForShapeOnDimensions(shape, dimensions, name_suffix); + // Verify every dimension except the 'dimension_to_skip' dimension was set in + // the index. + for (size_t dimension = 0; dimension < index.size(); ++dimension) { + if (dimension == dimension_to_skip) { + DCHECK_EQ(nullptr, index[dimension]); + } else { + DCHECK_NE(nullptr, index[dimension]); + } + } + return index; +} + } // namespace llvm_ir } // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h index 0dd5b9d3b2656af68f76c2adfcb1f3a1385eeb91..a4fed5c8dc55d38d25031252e3960404a5bf84e6 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h @@ -79,7 +79,7 @@ class ForLoop { // loop. static std::unique_ptr EmitForLoop( tensorflow::StringPiece prefix, llvm::Value* start_index, - llvm::Value* end_index, llvm::Value* step, llvm::IRBuilder<>* ir_builder, + llvm::Value* end_index, llvm::Value* step, llvm::IRBuilder<>* b, UnrollMode unroll_mode = llvm_ir::UnrollMode::kDefaultUnroll, bool prevent_vectorization = false); @@ -138,10 +138,10 @@ class ForLoop { UnrollMode unroll_mode, bool prevent_vectorization); // Emit the loop at the insert point of the builder. - void Emit(llvm::IRBuilder<>* ir_builder); + void Emit(llvm::IRBuilder<>* b); llvm::BasicBlock* CreateLoopBB(tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Creates a name for an LLVM construct, appending prefix_ and suffix_, if // they are set. @@ -149,7 +149,7 @@ class ForLoop { // Return a list of metadata nodes that should be associated with the // llvm::Loop for this `ForLoop`. - std::vector GetLoopMetadata(llvm::IRBuilder<>* ir_builder); + std::vector GetLoopMetadata(llvm::IRBuilder<>* b); string prefix_; string suffix_; @@ -177,19 +177,18 @@ class ForLoop { // A simple class for constructing nested for-loops. class ForLoopNest { public: - explicit ForLoopNest(llvm::IRBuilder<>* ir_builder, - llvm::Type* index_ty = nullptr) - : ForLoopNest(/*name=*/"", ir_builder) { + explicit ForLoopNest(llvm::IRBuilder<>* b, llvm::Type* index_ty = nullptr) + : ForLoopNest(/*name=*/"", b) { SetIndexType(index_ty); } - ForLoopNest(tensorflow::StringPiece name, llvm::IRBuilder<>* ir_builder, + ForLoopNest(tensorflow::StringPiece name, llvm::IRBuilder<>* b, llvm::Type* index_ty = nullptr) : name_(std::string(name)), outer_loop_preheader_bb_(nullptr), outer_loop_exit_bb_(nullptr), inner_loop_body_bb_(nullptr), - ir_builder_(ir_builder) { + b_(b) { SetIndexType(index_ty); } @@ -248,6 +247,17 @@ class ForLoopNest { const Shape& shape, tensorflow::gtl::ArraySlice dimensions, tensorflow::StringPiece suffix); + // Emits a series of nested loops for iterating over an operand array. Loops + // are constructed in major to minor dimension layout order. No loop is + // emitted for the given 'dimension_to_skip'. The function returns an IrArray + // index for the given operand_array containing the indvars of the loops. All + // dimensions of the index are filled except for 'dimension_to_skip'. + // name_suffix is the string to append to the names of LLVM constructs (eg, + // basic blocks) constructed by this method. + IrArray::Index EmitOperandArrayLoopNest(const llvm_ir::IrArray& operand_array, + int64 dimension_to_skip, + tensorflow::StringPiece name_suffix); + // Convenience methods which return particular basic blocks of the outermost // or innermost loops. These methods return nullptr if no loops have been // added yet. @@ -259,7 +269,7 @@ class ForLoopNest { private: void SetIndexType(llvm::Type* index_ty) { - index_type_ = index_ty == nullptr ? ir_builder_->getInt64Ty() : index_ty; + index_type_ = index_ty == nullptr ? b_->getInt64Ty() : index_ty; } llvm::Constant* GetConstantWithIndexType(int64 c) const { @@ -278,7 +288,7 @@ class ForLoopNest { // has been added yet. llvm::BasicBlock* inner_loop_body_bb_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; llvm::Type* index_type_; diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc index 97bacc34b59118e60100e4749638d469a1ef1378..e6126881af8b8123e08a4eaa934b52a7fd378ce6 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc @@ -26,7 +26,7 @@ limitations under the License. #include "llvm/Target/TargetOptions.h" #include "llvm/Transforms/Utils/Cloning.h" #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/name_uniquer.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/types.h" @@ -48,8 +48,8 @@ namespace { // Note, this function is only useful in an insertion context; in a global // (e.g. constants) context it will CHECK fail. -llvm::Module* ModuleFromIRBuilder(llvm::IRBuilder<>* ir_builder) { - auto block = CHECK_NOTNULL(ir_builder->GetInsertBlock()); +llvm::Module* ModuleFromIRBuilder(llvm::IRBuilder<>* b) { + auto block = CHECK_NOTNULL(b->GetInsertBlock()); auto fn = CHECK_NOTNULL(block->getParent()); auto module = CHECK_NOTNULL(fn->getParent()); return module; @@ -87,41 +87,41 @@ llvm::Value* EmitCallToIntrinsic( llvm::Intrinsic::ID intrinsic_id, tensorflow::gtl::ArraySlice operands, tensorflow::gtl::ArraySlice overloaded_types, - llvm::IRBuilder<>* ir_builder) { - llvm::Module* module = ModuleFromIRBuilder(ir_builder); + llvm::IRBuilder<>* b) { + llvm::Module* module = ModuleFromIRBuilder(b); llvm::Function* intrinsic = llvm::Intrinsic::getDeclaration( module, intrinsic_id, AsArrayRef(overloaded_types)); - return ir_builder->CreateCall(intrinsic, AsArrayRef(operands)); + return b->CreateCall(intrinsic, AsArrayRef(operands)); } llvm::Value* EmitFloatMax(llvm::Value* lhs_value, llvm::Value* rhs_value, - llvm::IRBuilder<>* ir_builder) { - if (ir_builder->getFastMathFlags().noNaNs()) { - auto cmp = ir_builder->CreateFCmpUGE(lhs_value, rhs_value); - return ir_builder->CreateSelect(cmp, lhs_value, rhs_value); + llvm::IRBuilder<>* b) { + if (b->getFastMathFlags().noNaNs()) { + auto cmp = b->CreateFCmpUGE(lhs_value, rhs_value); + return b->CreateSelect(cmp, lhs_value, rhs_value); } else { - auto cmp_ge = ir_builder->CreateFCmpOGE(lhs_value, rhs_value); - auto lhs_is_nan = ir_builder->CreateFCmpUNE(lhs_value, lhs_value); - auto sel_lhs = ir_builder->CreateOr(cmp_ge, lhs_is_nan); - return ir_builder->CreateSelect(sel_lhs, lhs_value, rhs_value); + auto cmp_ge = b->CreateFCmpOGE(lhs_value, rhs_value); + auto lhs_is_nan = b->CreateFCmpUNE(lhs_value, lhs_value); + auto sel_lhs = b->CreateOr(cmp_ge, lhs_is_nan); + return b->CreateSelect(sel_lhs, lhs_value, rhs_value); } } llvm::Value* EmitFloatMin(llvm::Value* lhs_value, llvm::Value* rhs_value, - llvm::IRBuilder<>* ir_builder) { - if (ir_builder->getFastMathFlags().noNaNs()) { - auto cmp = ir_builder->CreateFCmpULE(lhs_value, rhs_value); - return ir_builder->CreateSelect(cmp, lhs_value, rhs_value); + llvm::IRBuilder<>* b) { + if (b->getFastMathFlags().noNaNs()) { + auto cmp = b->CreateFCmpULE(lhs_value, rhs_value); + return b->CreateSelect(cmp, lhs_value, rhs_value); } else { - auto cmp_le = ir_builder->CreateFCmpOLE(lhs_value, rhs_value); - auto lhs_is_nan = ir_builder->CreateFCmpUNE(lhs_value, lhs_value); - auto sel_lhs = ir_builder->CreateOr(cmp_le, lhs_is_nan); - return ir_builder->CreateSelect(sel_lhs, lhs_value, rhs_value); + auto cmp_le = b->CreateFCmpOLE(lhs_value, rhs_value); + auto lhs_is_nan = b->CreateFCmpUNE(lhs_value, lhs_value); + auto sel_lhs = b->CreateOr(cmp_le, lhs_is_nan); + return b->CreateSelect(sel_lhs, lhs_value, rhs_value); } } llvm::Value* EmitBufferIndexingGEP(llvm::Value* array, llvm::Value* index, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { llvm::Type* array_type = array->getType(); CHECK(array_type->isPointerTy()); llvm::PointerType* array_type_as_pointer = @@ -131,16 +131,16 @@ llvm::Value* EmitBufferIndexingGEP(llvm::Value* array, llvm::Value* index, << " array=" << llvm_ir::DumpToString(*array) << " index=" << llvm_ir::DumpToString(*index); - return ir_builder->CreateInBoundsGEP( + return b->CreateInBoundsGEP( array_type_as_pointer->getElementType(), array, llvm::isa(array) - ? llvm::ArrayRef({ir_builder->getInt64(0), index}) + ? llvm::ArrayRef({b->getInt64(0), index}) : index); } llvm::Value* EmitBufferIndexingGEP(llvm::Value* array, int64 index, - llvm::IRBuilder<>* ir_builder) { - return EmitBufferIndexingGEP(array, ir_builder->getInt64(index), ir_builder); + llvm::IRBuilder<>* b) { + return EmitBufferIndexingGEP(array, b->getInt64(index), b); } llvm::Type* PrimitiveTypeToIrType(PrimitiveType element_type, @@ -232,14 +232,15 @@ llvm::Type* ShapeToIrType(const Shape& shape, llvm::Module* module) { return result_type; } -StatusOr EncodeSelfDescribingShapeConstant( - const Shape& shape, int32* shape_size, llvm::IRBuilder<>* ir_builder) { +StatusOr EncodeSelfDescribingShapeConstant(const Shape& shape, + int32* shape_size, + llvm::IRBuilder<>* b) { string encoded_shape = shape.SerializeAsString(); if (encoded_shape.size() > std::numeric_limits::max()) { return InternalError("Encoded shape size exceeded int32 size limit."); } *shape_size = static_cast(encoded_shape.size()); - return ir_builder->CreateGlobalStringPtr(llvm_ir::AsStringRef(encoded_shape)); + return b->CreateGlobalStringPtr(llvm_ir::AsStringRef(encoded_shape)); } StatusOr DecodeSelfDescribingShapeConstant(const void* shape_ptr, @@ -262,59 +263,57 @@ llvm::Constant* ConvertLiteralToIrConstant(const Literal& literal, llvm::AllocaInst* EmitAllocaAtFunctionEntry(llvm::Type* type, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder, + llvm::IRBuilder<>* b, int alignment) { - return EmitAllocaAtFunctionEntryWithCount(type, nullptr, name, ir_builder, - alignment); + return EmitAllocaAtFunctionEntryWithCount(type, nullptr, name, b, alignment); } llvm::AllocaInst* EmitAllocaAtFunctionEntryWithCount( llvm::Type* type, llvm::Value* element_count, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder, int alignment) { - llvm::IRBuilder<>::InsertPoint insert_point = ir_builder->saveIP(); - llvm::Function* function = ir_builder->GetInsertBlock()->getParent(); - ir_builder->SetInsertPoint(&function->getEntryBlock(), - function->getEntryBlock().getFirstInsertionPt()); + llvm::IRBuilder<>* b, int alignment) { + llvm::IRBuilder<>::InsertPoint insert_point = b->saveIP(); + llvm::Function* function = b->GetInsertBlock()->getParent(); + b->SetInsertPoint(&function->getEntryBlock(), + function->getEntryBlock().getFirstInsertionPt()); llvm::AllocaInst* alloca = - ir_builder->CreateAlloca(type, element_count, AsStringRef(name)); + b->CreateAlloca(type, element_count, AsStringRef(name)); if (alignment != 0) { alloca->setAlignment(alignment); } - ir_builder->restoreIP(insert_point); + b->restoreIP(insert_point); return alloca; } llvm::BasicBlock* CreateBasicBlock(llvm::BasicBlock* insert_before, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { return llvm::BasicBlock::Create( - /*Context=*/ir_builder->getContext(), + /*Context=*/b->getContext(), /*Name=*/AsStringRef(name), - /*Parent=*/ir_builder->GetInsertBlock()->getParent(), + /*Parent=*/b->GetInsertBlock()->getParent(), /*InsertBefore*/ insert_before); } LlvmIfData EmitIfThenElse(llvm::Value* condition, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder, bool emit_else) { + llvm::IRBuilder<>* b, bool emit_else) { llvm_ir::LlvmIfData if_data; - if_data.if_block = ir_builder->GetInsertBlock(); - if_data.true_block = CreateBasicBlock( - nullptr, tensorflow::strings::StrCat(name, "-true"), ir_builder); + if_data.if_block = b->GetInsertBlock(); + if_data.true_block = + CreateBasicBlock(nullptr, tensorflow::strings::StrCat(name, "-true"), b); if_data.false_block = - emit_else ? CreateBasicBlock(nullptr, - tensorflow::strings::StrCat(name, "-false"), - ir_builder) + emit_else ? CreateBasicBlock( + nullptr, tensorflow::strings::StrCat(name, "-false"), b) : nullptr; // Add a terminator to the if block, if necessary. if (if_data.if_block->getTerminator() == nullptr) { - ir_builder->SetInsertPoint(if_data.if_block); + b->SetInsertPoint(if_data.if_block); if_data.after_block = CreateBasicBlock( - nullptr, tensorflow::strings::StrCat(name, "-after"), ir_builder); - ir_builder->CreateBr(if_data.after_block); + nullptr, tensorflow::strings::StrCat(name, "-after"), b); + b->CreateBr(if_data.after_block); } else { if_data.after_block = if_data.if_block->splitBasicBlock( - ir_builder->GetInsertPoint(), + b->GetInsertPoint(), AsStringRef(tensorflow::strings::StrCat(name, "-after"))); } @@ -322,39 +321,37 @@ LlvmIfData EmitIfThenElse(llvm::Value* condition, tensorflow::StringPiece name, // we're going to replace it with a conditional branch. if_data.if_block->getTerminator()->eraseFromParent(); - ir_builder->SetInsertPoint(if_data.if_block); - ir_builder->CreateCondBr( - condition, if_data.true_block, - emit_else ? if_data.false_block : if_data.after_block); + b->SetInsertPoint(if_data.if_block); + b->CreateCondBr(condition, if_data.true_block, + emit_else ? if_data.false_block : if_data.after_block); - ir_builder->SetInsertPoint(if_data.true_block); - ir_builder->CreateBr(if_data.after_block); + b->SetInsertPoint(if_data.true_block); + b->CreateBr(if_data.after_block); if (emit_else) { - ir_builder->SetInsertPoint(if_data.false_block); - ir_builder->CreateBr(if_data.after_block); + b->SetInsertPoint(if_data.false_block); + b->CreateBr(if_data.after_block); } - ir_builder->SetInsertPoint(if_data.after_block, - if_data.after_block->getFirstInsertionPt()); + b->SetInsertPoint(if_data.after_block, + if_data.after_block->getFirstInsertionPt()); return if_data; } llvm::Value* EmitComparison(llvm::CmpInst::Predicate predicate, llvm::Value* lhs_value, llvm::Value* rhs_value, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { llvm::Value* comparison_result; if (lhs_value->getType()->isIntegerTy()) { - comparison_result = ir_builder->CreateICmp(predicate, lhs_value, rhs_value); + comparison_result = b->CreateICmp(predicate, lhs_value, rhs_value); } else { - comparison_result = ir_builder->CreateFCmp(predicate, lhs_value, rhs_value); + comparison_result = b->CreateFCmp(predicate, lhs_value, rhs_value); } // comparison_result is i1, but the NVPTX codegen incorrectly lowers i1 // arrays. So we extend it to i8 so that it's addressable. - return ir_builder->CreateZExt( - comparison_result, - llvm_ir::PrimitiveTypeToIrType(PRED, ModuleFromIRBuilder(ir_builder))); + return b->CreateZExt(comparison_result, llvm_ir::PrimitiveTypeToIrType( + PRED, ModuleFromIRBuilder(b))); } // Internal helper that is called from emitted code to log an int64 value with a @@ -363,17 +360,14 @@ static void LogS64(const char* tag, int64 value) { LOG(INFO) << tag << " (int64): " << value; } -void EmitLogging(const char* tag, llvm::Value* value, - llvm::IRBuilder<>* ir_builder) { +void EmitLogging(const char* tag, llvm::Value* value, llvm::IRBuilder<>* b) { llvm::FunctionType* log_function_type = llvm::FunctionType::get( - ir_builder->getVoidTy(), - {ir_builder->getInt64Ty(), ir_builder->getInt64Ty()}, /*isVarArg=*/false); - ir_builder->CreateCall( + b->getVoidTy(), {b->getInt64Ty(), b->getInt64Ty()}, /*isVarArg=*/false); + b->CreateCall( log_function_type, - ir_builder->CreateIntToPtr( - ir_builder->getInt64(tensorflow::bit_cast(&LogS64)), - log_function_type->getPointerTo()), - {ir_builder->getInt64(tensorflow::bit_cast(tag)), value}); + b->CreateIntToPtr(b->getInt64(tensorflow::bit_cast(&LogS64)), + log_function_type->getPointerTo()), + {b->getInt64(tensorflow::bit_cast(tag)), value}); } void SetAlignmentMetadataForLoad(llvm::LoadInst* load, uint64_t alignment) { @@ -663,5 +657,56 @@ void InitializeLLVMCommandLineOptions(const HloModuleConfig& config) { } } +std::pair UMulLowHigh32(llvm::IRBuilder<>* b, + llvm::Value* src0, + llvm::Value* src1) { + CHECK_EQ(src0->getType()->getPrimitiveSizeInBits(), 32); + CHECK_EQ(src1->getType()->getPrimitiveSizeInBits(), 32); + llvm::Type* int64_ty = b->getInt64Ty(); + src0 = b->CreateZExt(src0, int64_ty); + src1 = b->CreateZExt(src1, int64_ty); + return SplitInt64ToInt32s(b, b->CreateMul(src0, src1)); +} + +std::pair SplitInt64ToInt32s( + llvm::IRBuilder<>* b, llvm::Value* value_64bits) { + CHECK_EQ(value_64bits->getType()->getPrimitiveSizeInBits(), 64); + llvm::Type* int32_ty = b->getInt32Ty(); + llvm::Value* low_32bits = b->CreateTrunc(value_64bits, int32_ty); + llvm::Value* high_32bits = + b->CreateTrunc(b->CreateLShr(value_64bits, 32), int32_ty); + return std::make_pair(low_32bits, high_32bits); +} + +llvm::GlobalVariable* GetOrCreateVariableForPhiloxRngState( + llvm::Module* module, llvm::IRBuilder<>* b) { + static const char* kPhiloxRngStateVariableName = "philox_rng_state"; + llvm::GlobalVariable* state_ptr = + module->getNamedGlobal(kPhiloxRngStateVariableName); + if (!state_ptr) { + state_ptr = new llvm::GlobalVariable( + /*M=*/*module, + /*Ty=*/b->getInt64Ty(), + /*isConstant=*/false, + /*Linkage=*/llvm::GlobalValue::PrivateLinkage, + /*Initializer=*/b->getInt64(0), + /*Name=*/kPhiloxRngStateVariableName); + } + return state_ptr; +} + +void IncrementVariableForPhiloxRngState(int64 value, llvm::Module* module, + llvm::IRBuilder<>* builder) { + llvm::GlobalVariable* state_ptr = + GetOrCreateVariableForPhiloxRngState(module, builder); + llvm::Value* state_value_old = builder->CreateLoad(state_ptr, "load_state"); + // If the 64-bit value overflows, we use the wraparound value. This should + // be fine in practice as we only add one to the value each time when a RNG is + // executed. + llvm::Value* state_value_new = builder->CreateAdd( + state_value_old, builder->getInt64(value), "inc_state"); + builder->CreateStore(state_value_new, state_ptr); +} + } // namespace llvm_ir } // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h index 4a10ec466dae6fdb56546fb8d8b353dcff6a5b8d..09583985342033d486d50910b6f5ca732a9a3756 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h @@ -27,7 +27,7 @@ limitations under the License. #include "llvm/IR/Module.h" #include "llvm/IR/Value.h" #include "llvm/Support/raw_ostream.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_module_config.h" #include "tensorflow/compiler/xla/types.h" @@ -105,26 +105,26 @@ llvm::Value* EmitCallToIntrinsic( llvm::Intrinsic::ID intrinsic_id, tensorflow::gtl::ArraySlice operands, tensorflow::gtl::ArraySlice overloaded_types, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Emit float max. Emit maxnum intrinsic is fast math is disabled, or // fcmp+select otherwise llvm::Value* EmitFloatMax(llvm::Value* lhs_value, llvm::Value* rhs_value, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Emit float min. Emit minnum intrinsic is fast math is disabled, or // fcmp+select otherwise llvm::Value* EmitFloatMin(llvm::Value* lhs_value, llvm::Value* rhs_value, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Convenience methods for emitting a GEP instruction that indexes into a buffer // (1-dimensional array), equivalent to array[index]. The type is automatically // determined from the element type of the array. The int64 index overload // wraps the index in a i64 llvm::Value. llvm::Value* EmitBufferIndexingGEP(llvm::Value* array, llvm::Value* index, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); llvm::Value* EmitBufferIndexingGEP(llvm::Value* array, int64 index, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Returns the LLVM type which represents the given XLA primitive type. llvm::Type* PrimitiveTypeToIrType(PrimitiveType element_type, @@ -139,8 +139,9 @@ llvm::Type* ShapeToIrType(const Shape& shape, llvm::Module* module); // Returns a value that represents a pointer to a global string constant that // encodes the shape as a serialized protobuf. -StatusOr EncodeSelfDescribingShapeConstant( - const Shape& shape, int32* shape_size, llvm::IRBuilder<>* ir_builder); +StatusOr EncodeSelfDescribingShapeConstant(const Shape& shape, + int32* shape_size, + llvm::IRBuilder<>* b); // Inverses the encoding of a Shape protobuf into an LLVM global variable. // @@ -164,21 +165,21 @@ llvm::Constant* ConvertLiteralToIrConstant(const Literal& literal, // through a loop. llvm::AllocaInst* EmitAllocaAtFunctionEntry(llvm::Type* type, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder, + llvm::IRBuilder<>* b, int alignment = 0); // As EmitAllocaAtFunctionEntry, but allocates element_count entries // instead of a single element. llvm::AllocaInst* EmitAllocaAtFunctionEntryWithCount( llvm::Type* type, llvm::Value* element_count, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder, int alignment = 0); + llvm::IRBuilder<>* b, int alignment = 0); // Creates a basic block with the same context and function as for the // builder. Inserts at the end of the function if insert_before is // null. llvm::BasicBlock* CreateBasicBlock(llvm::BasicBlock* insert_before, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Struct with data on a conditional branch in a diamond shape created // via EmitIfThenElse. @@ -210,13 +211,13 @@ struct LlvmIfData { // block with a terminator. If you need to use this for a // non-terminated block, just make the function able to do that too. LlvmIfData EmitIfThenElse(llvm::Value* condition, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder, bool emit_else = true); + llvm::IRBuilder<>* b, bool emit_else = true); // Emits a compare operation between "lhs" and "rhs" with the given predicate, // and then converts the result to i8 so that it is addressable. llvm::Value* EmitComparison(llvm::CmpInst::Predicate predicate, llvm::Value* lhs, llvm::Value* rhs, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Emits a call that logs the given value with the given tag as a prefix. // The provided tag and value are passed to a runtime logging call that is @@ -228,8 +229,7 @@ llvm::Value* EmitComparison(llvm::CmpInst::Predicate predicate, // Precondition: value must be an int64. // Precondition: tag must be a stable pointer for the lifetime of the generated // program (the constant pointer is burned in to the program). -void EmitLogging(const char* tag, llvm::Value* value, - llvm::IRBuilder<>* ir_builder); +void EmitLogging(const char* tag, llvm::Value* value, llvm::IRBuilder<>* b); // Adds alignment metadata to a load instruction using the given alignment. // The alignment refers to the result of the load, not the load itself. @@ -292,6 +292,27 @@ llvm::Function* CreateFunction(llvm::FunctionType* function_type, // don't start with xla_ to LLVM. void InitializeLLVMCommandLineOptions(const HloModuleConfig& config); +// Zero-extends two 32-bit values to 64 bits, multiplies them, and returns the +// result as a pair of (low 32 bits, high 32 bits). +std::pair UMulLowHigh32(llvm::IRBuilder<>* b, + llvm::Value* src0, + llvm::Value* src1); +// Splits the 64-bit integer value into its high and low 32 bits. +std::pair SplitInt64ToInt32s( + llvm::IRBuilder<>* b, llvm::Value* value_64bits); + +// Checks whether a global variable is already created to represent a +// state passed between RNG calls implemented with Philox algorithm. If not, +// creates such a variable. Returns the global variable. +llvm::GlobalVariable* GetOrCreateVariableForPhiloxRngState( + llvm::Module* module, llvm::IRBuilder<>* b); + +// Adds a value to the global state variable each time when a RNG hlo is +// executed. The value of this global state variable is added to the seed +// of the Philox RNG algorithm so that calling the same RNG Hlo multiple times +// should rarely produce the same result. +void IncrementVariableForPhiloxRngState(int64 value, llvm::Module* module, + llvm::IRBuilder<>* b); } // namespace llvm_ir } // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc index e8b0605b9d75677b34f0973d88d269a5795b7629..36f5fa195224c20e30a14f72b32eb42a681bb5e9 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc @@ -33,26 +33,24 @@ namespace xla { namespace llvm_ir { LoopEmitter::LoopEmitter(const BodyEmitter& body_emitter, const Shape& shape, - llvm::IRBuilder<>* ir_builder) - : body_emitter_(body_emitter), shape_(shape), ir_builder_(ir_builder) {} + llvm::IRBuilder<>* b) + : body_emitter_(body_emitter), shape_(shape), b_(b) {} LoopEmitter::LoopEmitter(const ElementGenerator& target_element_generator, - const IrArray& target_array, - llvm::IRBuilder<>* ir_builder) + const IrArray& target_array, llvm::IRBuilder<>* b) : body_emitter_([=](const llvm_ir::IrArray::Index array_index) -> Status { // Convert target_element_generator to a BodyEmitter. TF_ASSIGN_OR_RETURN(llvm::Value * target_element, target_element_generator(array_index)); - target_array.EmitWriteArrayElement(array_index, target_element, - ir_builder); + target_array.EmitWriteArrayElement(array_index, target_element, b); return Status::OK(); }), shape_(target_array.GetShape()), - ir_builder_(ir_builder) {} + b_(b) {} static LoopEmitter::BodyEmitter MakeBodyEmitterForMultiOutputFusion( const ElementGenerator& target_element_generator, - const std::vector& target_arrays, llvm::IRBuilder<>* ir_builder) { + const std::vector& target_arrays, llvm::IRBuilder<>* b) { return [=](const llvm_ir::IrArray::Index array_index) { TF_ASSIGN_OR_RETURN(llvm::Value * target_element, target_element_generator(array_index)); @@ -64,8 +62,7 @@ static LoopEmitter::BodyEmitter MakeBodyEmitterForMultiOutputFusion( for (int64 i = 0; i < target_arrays.size(); ++i) { target_arrays[i].EmitWriteArrayElement( - array_index, ir_builder->CreateExtractValue(target_element, i), - ir_builder); + array_index, b->CreateExtractValue(target_element, i), b); } return Status::OK(); }; @@ -73,13 +70,12 @@ static LoopEmitter::BodyEmitter MakeBodyEmitterForMultiOutputFusion( LoopEmitter::LoopEmitter(const ElementGenerator& target_element_generator, tensorflow::gtl::ArraySlice target_arrays, - llvm::IRBuilder<>* ir_builder) + llvm::IRBuilder<>* b) : body_emitter_(MakeBodyEmitterForMultiOutputFusion( target_element_generator, - std::vector(target_arrays.begin(), target_arrays.end()), - ir_builder)), + std::vector(target_arrays.begin(), target_arrays.end()), b)), shape_(target_arrays[0].GetShape()), - ir_builder_(ir_builder) { + b_(b) { // Sanity check: In multi-output fusion, all shapes produced must have the // same dimensions. for (const IrArray& array : target_arrays) { @@ -102,7 +98,7 @@ std::vector LoopEmitter::EmitIndexAndSetExitBasicBlock( // Loops are added from outermost to innermost order with the ForLoopNest // class so emit loops in order from most-major dimension down to most-minor // dimension (of the target shape). - ForLoopNest loop_nest(loop_name, ir_builder_); + ForLoopNest loop_nest(loop_name, b_); IrArray::Index array_index(index_type, shape_.dimensions_size()); for (int i = 0; i < LayoutUtil::MinorToMajor(shape_).size(); ++i) { int64 dimension = LayoutUtil::Major(shape_.layout(), i); @@ -116,8 +112,8 @@ std::vector LoopEmitter::EmitIndexAndSetExitBasicBlock( // Set IR builder insertion point to the loop body basic block of the // innermost loop. llvm::BasicBlock* innermost_body_bb = loop_nest.GetInnerLoopBodyBasicBlock(); - ir_builder_->SetInsertPoint(innermost_body_bb, - innermost_body_bb->getFirstInsertionPt()); + b_->SetInsertPoint(innermost_body_bb, + innermost_body_bb->getFirstInsertionPt()); // Set exit_bb_ to the exit block of the loop nest. exit_bb_ = loop_nest.GetOuterLoopExitBasicBlock(); @@ -129,7 +125,7 @@ std::vector LoopEmitter::EmitIndexAndSetExitBasicBlock( Status LoopEmitter::EmitLoop(tensorflow::StringPiece loop_name, llvm::Type* index_type) { if (index_type == nullptr) { - index_type = ir_builder_->getInt64Ty(); + index_type = b_->getInt64Ty(); } for (const IrArray::Index& array_index : @@ -137,10 +133,10 @@ Status LoopEmitter::EmitLoop(tensorflow::StringPiece loop_name, TF_RETURN_IF_ERROR(body_emitter_(array_index)); } - // Set the insertion point of ir_builder_ to the loop exit, so that + // Set the insertion point of b_ to the loop exit, so that // code emitted for later instructions will be correctly placed. if (exit_bb_ != nullptr) { - ir_builder_->SetInsertPoint(exit_bb_); + b_->SetInsertPoint(exit_bb_); } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h index 6be1c2fba2cbd78a02865901ef8c5b7e2b2a74e6..c4f5c82086ccfa233e0be118b1de10cce55a51b1 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h +++ b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h @@ -41,11 +41,11 @@ class LoopEmitter { using BodyEmitter = std::function; LoopEmitter(const BodyEmitter& body_emitter, const Shape& shape, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Constructs a LoopEmitter from an element generator that generates each // element of the given target array. LoopEmitter(const ElementGenerator& target_element_generator, - const IrArray& target_array, llvm::IRBuilder<>* ir_builder); + const IrArray& target_array, llvm::IRBuilder<>* b); // Constructs a LoopEmitter that emits one element into each of N separate // arrays on each iteration of the loop. @@ -54,7 +54,7 @@ class LoopEmitter { // produce an LLVM struct with N elements. LoopEmitter(const ElementGenerator& target_element_generator, tensorflow::gtl::ArraySlice target_arrays, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); LoopEmitter(const LoopEmitter&) = delete; LoopEmitter& operator=(const LoopEmitter&) = delete; @@ -65,8 +65,7 @@ class LoopEmitter { // specifies the element, will return multiple indices if the loop is // unrolled. std::vector EmitIndexAndSetExitBasicBlock() { - return EmitIndexAndSetExitBasicBlock(/*loop_name=*/"", - ir_builder_->getInt64Ty()); + return EmitIndexAndSetExitBasicBlock(/*loop_name=*/"", b_->getInt64Ty()); } virtual std::vector EmitIndexAndSetExitBasicBlock( @@ -87,7 +86,7 @@ class LoopEmitter { // scalar, no loops are emitted and exit_bb_ is nullptr in that case. llvm::BasicBlock* exit_bb_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; }; } // namespace llvm_ir diff --git a/tensorflow/compiler/xla/service/llvm_ir/math_ops.cc b/tensorflow/compiler/xla/service/llvm_ir/math_ops.cc new file mode 100644 index 0000000000000000000000000000000000000000..0e115cdabf4b290617700276dba8f2e5648a7c07 --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/math_ops.cc @@ -0,0 +1,59 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/llvm_ir/math_ops.h" +#include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" + +namespace xla { +namespace llvm_ir { + +llvm::Value* EmitFastTanh(llvm::IRBuilder<>* b, llvm::Value* input) { + llvm::Type* type = input->getType(); + + // Clamp the input to [-9, 9]. + llvm::Value* input_clamped = llvm_ir::EmitFloatMin( + llvm_ir::EmitFloatMax(input, llvm::ConstantFP::get(type, -9.0), b), + llvm::ConstantFP::get(type, 9.0), b); + + static constexpr std::array numerator_coeffs{ + -2.76076847742355e-16f, 2.00018790482477e-13f, -8.60467152213735e-11f, + 5.12229709037114e-08f, 1.48572235717979e-05f, 6.37261928875436e-04f, + 4.89352455891786e-03f}; + + static constexpr std::array denominator_coeffs{ + 1.19825839466702e-06f, 1.18534705686654e-04f, 2.26843463243900e-03f, + 4.89352518554385e-03f}; + + llvm::Value* input_squared = b->CreateFMul(input_clamped, input_clamped); + llvm::Value* numerator = llvm::ConstantFP::get(type, numerator_coeffs[0]); + for (int i = 1; i < numerator_coeffs.size(); i++) { + numerator = b->CreateFAdd(b->CreateFMul(input_squared, numerator), + llvm::ConstantFP::get(type, numerator_coeffs[i])); + } + + numerator = b->CreateFMul(input_clamped, numerator); + + llvm::Value* denominator = llvm::ConstantFP::get(type, denominator_coeffs[0]); + for (int i = 1; i < denominator_coeffs.size(); i++) { + denominator = + b->CreateFAdd(b->CreateFMul(input_squared, denominator), + llvm::ConstantFP::get(type, denominator_coeffs[i])); + } + + return b->CreateFDiv(numerator, denominator); +} + +} // namespace llvm_ir +} // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/math_ops.h b/tensorflow/compiler/xla/service/llvm_ir/math_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..6c8bc3a076367eae2f1829966be2872e5f258178 --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/math_ops.h @@ -0,0 +1,32 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_MATH_OPS_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_MATH_OPS_H_ + +#include "llvm/IR/IRBuilder.h" +#include "llvm/IR/Value.h" + +namespace xla { +namespace llvm_ir { + +// Emits an approximation of tanh. The implementation uses the same rational +// interpolant as implemented in Eigen3. +llvm::Value* EmitFastTanh(llvm::IRBuilder<>* b, llvm::Value* input); + +} // namespace llvm_ir +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_MATH_OPS_H_ diff --git a/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc b/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc new file mode 100644 index 0000000000000000000000000000000000000000..5187948e294334b98d389607437bdf545923da0e --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc @@ -0,0 +1,167 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/llvm_ir/sort_util.h" + +// IWYU pragma: no_include "llvm/IR/Intrinsics.gen.inc" +#include "llvm/IR/BasicBlock.h" +#include "llvm/IR/Constants.h" +#include "llvm/IR/Instructions.h" +#include "llvm/IR/Value.h" +#include "tensorflow/compiler/xla/primitive_util.h" +#include "tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h" +#include "tensorflow/compiler/xla/service/gpu/partition_assignment.h" +#include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h" +#include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h" +#include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" +#include "tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/core/stringpiece.h" +#include "tensorflow/core/lib/gtl/optional.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { +namespace llvm_ir { + +namespace { +// Adds the inner comparison loop where we compare elements pointed to by +// 'keys_index' and 'compare_keys_index'. +void EmitCompareLoop(int64 dimension_to_sort, const IrArray::Index& keys_index, + const IrArray::Index& compare_keys_index, + const IrArray& keys_array, + const tensorflow::gtl::optional& values_array, + llvm::IRBuilder<>* b) { + // if (is_smaller_index && + // compare_keys[dimension_to_sort] < dimension_to_sort_bound) + llvm::Value* is_smaller_index = b->CreateICmpSLT( + keys_index[dimension_to_sort], compare_keys_index[dimension_to_sort]); + int64 dimension_to_sort_bound = + keys_array.GetShape().dimensions(dimension_to_sort); + auto if_data = EmitIfThenElse( + b->CreateAnd(is_smaller_index, + b->CreateICmpSLT(compare_keys_index[dimension_to_sort], + keys_index.GetConstantWithIndexType( + dimension_to_sort_bound))), + "smaller_comparison_index", b, /*emit_else=*/false); + SetToFirstInsertPoint(if_data.true_block, b); + auto key1 = keys_array.EmitReadArrayElement(keys_index, b); + auto key2 = keys_array.EmitReadArrayElement(compare_keys_index, b); + auto key_type = keys_array.GetShape().element_type(); + auto comparison = + primitive_util::IsFloatingPointType(key_type) + // TODO(b/26783907): Figure out how to handle NaNs. + ? b->CreateFCmp(llvm::FCmpInst::FCMP_ULT, key2, key1) + : b->CreateICmp(primitive_util::IsSignedIntegralType(key_type) + ? llvm::ICmpInst::ICMP_SLT + : llvm::ICmpInst::ICMP_ULT, + key2, key1); + // If key2 < key1 + auto if_smaller_data = + EmitIfThenElse(comparison, "is_smaller_than", b, /*emit_else=*/false); + SetToFirstInsertPoint(if_smaller_data.true_block, b); + // Swap key1 with key2. + keys_array.EmitWriteArrayElement(keys_index, key2, b); + keys_array.EmitWriteArrayElement(compare_keys_index, key1, b); + if (values_array.has_value()) { + // Also swap the values. + auto value1 = values_array.value().EmitReadArrayElement(keys_index, b); + auto value2 = + values_array.value().EmitReadArrayElement(compare_keys_index, b); + values_array.value().EmitWriteArrayElement(keys_index, value2, b); + values_array.value().EmitWriteArrayElement(compare_keys_index, value1, b); + } +} +} // namespace + +Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, + const tensorflow::gtl::optional& values_array, + tensorflow::StringPiece name, llvm::Value* xor_mask, + llvm::IRBuilder<>* b, + const gpu::LaunchDimensions* launch_dimensions) { + const Shape& keys_shape = keys_array.GetShape(); + + // TODO(b/26783907): This case can probably be avoided with the Algebraic + // Simplifier. + if (ShapeUtil::IsScalar(keys_shape)) { + return Status::OK(); + } + + // Create loop nests which loop through the operand dimensions. The sort + // dimension is handled in the innermost loop which performs the sorting. + ForLoopNest loop_nest(name, b); + IrArray::Index keys_index = + loop_nest.EmitOperandArrayLoopNest(keys_array, dimension_to_sort, "keys"); + if (loop_nest.GetInnerLoopBodyBasicBlock() != nullptr) { + SetToFirstInsertPoint(loop_nest.GetInnerLoopBodyBasicBlock(), b); + } + + // 'compare_keys_index' is the index of the element that 'keys_index' should + // be compared to. + IrArray::Index compare_keys_index(keys_index.GetType()); + for (size_t dimension = 0; dimension < keys_index.size(); ++dimension) { + if (dimension != dimension_to_sort) { + compare_keys_index.push_back(keys_index[dimension]); + } else { + compare_keys_index.push_back(nullptr); + } + } + + // Naive C++ code for the inner compare loop: + // + // for (int64 i = 0; i < dimension_to_sort_bound; ++i) { + // int64 j = i ^ xor_mask; + // if (i < j && j < dimension_to_sort_bound) { + // int64 min_key = std::min(keys[i], keys[j]); + // keys[j] = std::max(keys[i], keys[j]); + // keys[i] = min_key; + // } + // } + // + // This follows the algorithm described on Wikipedia: + // https://en.wikipedia.org/wiki/Bitonic_sorter + + int64 dimension_to_sort_bound = + keys_array.GetShape().dimensions(dimension_to_sort); + Shape compare_shape = ShapeUtil::MakeShape(keys_shape.element_type(), + {dimension_to_sort_bound}); + auto compare_loop_body_emitter = + [&](const IrArray::Index& compare_index) -> Status { + keys_index[dimension_to_sort] = compare_index[0]; + compare_keys_index[dimension_to_sort] = + b->CreateXor(compare_index[0], xor_mask); + EmitCompareLoop(dimension_to_sort, keys_index, compare_keys_index, + keys_array, values_array, b); + return Status::OK(); + }; + if (launch_dimensions != nullptr) { + TF_RETURN_IF_ERROR(gpu::ParallelLoopEmitter(compare_loop_body_emitter, + compare_shape, + *launch_dimensions, b) + .EmitLoop(name)); + } else { + TF_RETURN_IF_ERROR(LoopEmitter(compare_loop_body_emitter, compare_shape, b) + .EmitLoop(name)); + } + + // Set the IR builder insert point to the exit basic block of the outer most + // loop. This ensures later instructions are inserted after this loop nest. + b->SetInsertPoint(loop_nest.GetOuterLoopExitBasicBlock()); + + return Status::OK(); +} + +} // namespace llvm_ir +} // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/sort_util.h b/tensorflow/compiler/xla/service/llvm_ir/sort_util.h new file mode 100644 index 0000000000000000000000000000000000000000..8458744c6bc0e50a1c1cc8d3e66e29c7d4f74d73 --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/sort_util.h @@ -0,0 +1,41 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_SORT_UTIL_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_SORT_UTIL_H_ + +#include "llvm/IR/Value.h" +#include "tensorflow/compiler/xla/service/gpu/partition_assignment.h" +#include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/core/stringpiece.h" +#include "tensorflow/core/lib/gtl/optional.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { +namespace llvm_ir { +// Emits llvm IR to do pairwise comparisons/swaps in the 'dimension_to_sort' +// dimension of 'keys_array'. All other dimensions are kept as-is. This +// implements the inner loop of BitonicSort. If 'launch_dimensions' is nullptr, +// the inner compare loop will not be parallelized. +Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, + const tensorflow::gtl::optional& values_array, + tensorflow::StringPiece name, llvm::Value* xor_mask, + llvm::IRBuilder<>* b, + const gpu::LaunchDimensions* launch_dimensions); +} // namespace llvm_ir +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_SORT_UTIL_H_ diff --git a/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.cc b/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.cc index 5fc08aab916e377b245b6221108956c06da70767..11ed6ee59f1bf8e7004b8bef7319b37ef41a304c 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.cc @@ -31,12 +31,12 @@ namespace llvm_ir { void EmitTupleSelect(const IrArray& select, const IrArray& pred, llvm::Value* on_true, llvm::Value* on_false, - llvm::IRBuilder<>* ir_builder, llvm::Module* module) { + llvm::IRBuilder<>* b, llvm::Module* module) { CHECK(ShapeUtil::IsScalar(pred.GetShape())); llvm::LoadInst* pred_value = - ir_builder->CreateLoad(pred.GetBasePointer(), "load_predicate_value"); - llvm::Value* pred_cond = ir_builder->CreateICmpNE( + b->CreateLoad(pred.GetBasePointer(), "load_predicate_value"); + llvm::Value* pred_cond = b->CreateICmpNE( pred_value, llvm::ConstantInt::get(PrimitiveTypeToIrType(PRED, module), 0), "boolean_predicate"); @@ -46,47 +46,42 @@ void EmitTupleSelect(const IrArray& select, const IrArray& pred, VLOG(2) << " pred_cond: " << DumpToString(*pred_cond); for (int i = 0; i < ShapeUtil::TupleElementCount(select.GetShape()); ++i) { - llvm::Value* const element_index[] = {ir_builder->getInt64(0), - ir_builder->getInt64(i)}; + llvm::Value* const element_index[] = {b->getInt64(0), b->getInt64(i)}; llvm::Value* on_true_element_address = - ir_builder->CreateInBoundsGEP(on_true, element_index); - llvm::Value* on_true_element = ir_builder->CreateLoad( + b->CreateInBoundsGEP(on_true, element_index); + llvm::Value* on_true_element = b->CreateLoad( on_true_element_address, "on_true_element_" + llvm::Twine(i)); llvm::Value* on_false_element_address = - ir_builder->CreateInBoundsGEP(on_false, element_index); - llvm::Value* on_false_element = ir_builder->CreateLoad( + b->CreateInBoundsGEP(on_false, element_index); + llvm::Value* on_false_element = b->CreateLoad( on_false_element_address, "on_false_element_" + llvm::Twine(i)); llvm::Value* output_element_address = - ir_builder->CreateInBoundsGEP(select.GetBasePointer(), element_index); - ir_builder->CreateStore( - ir_builder->CreateSelect(pred_cond, on_true_element, on_false_element, - "select_output_element_" + llvm::Twine(i)), - output_element_address); + b->CreateInBoundsGEP(select.GetBasePointer(), element_index); + b->CreateStore(b->CreateSelect(pred_cond, on_true_element, on_false_element, + "select_output_element_" + llvm::Twine(i)), + output_element_address); } } void EmitTuple(const IrArray& tuple, tensorflow::gtl::ArraySlice operands, - llvm::IRBuilder<>* ir_builder, llvm::Module* module) { + llvm::IRBuilder<>* b, llvm::Module* module) { for (size_t i = 0; i < operands.size(); ++i) { - auto* store = ir_builder->CreateStore( - ir_builder->CreatePointerCast(operands[i], - PrimitiveTypeToIrType(TUPLE, module)), - ir_builder->CreateInBoundsGEP( - tuple.GetBasePointer(), - {ir_builder->getInt64(0), ir_builder->getInt64(i)})); + auto* store = b->CreateStore( + b->CreatePointerCast(operands[i], PrimitiveTypeToIrType(TUPLE, module)), + b->CreateInBoundsGEP(tuple.GetBasePointer(), + {b->getInt64(0), b->getInt64(i)})); tuple.AnnotateLoadStoreInstructionWithMetadata(store); } } llvm::Value* EmitGetTupleElement(const Shape& target_shape, int64 index, int alignment, llvm::Value* operand, - llvm::IRBuilder<>* ir_builder, - llvm::Module* module) { - llvm::Value* element_ptr = ir_builder->CreateInBoundsGEP( - operand, {ir_builder->getInt64(0), ir_builder->getInt64(index)}); - llvm::LoadInst* src_buffer = ir_builder->CreateLoad(element_ptr); + llvm::IRBuilder<>* b, llvm::Module* module) { + llvm::Value* element_ptr = + b->CreateInBoundsGEP(operand, {b->getInt64(0), b->getInt64(index)}); + llvm::LoadInst* src_buffer = b->CreateLoad(element_ptr); // Mark the loaded pointer as dereferenceable if we know its shape. if (!ShapeUtil::IsOpaque(target_shape)) { @@ -98,7 +93,7 @@ llvm::Value* EmitGetTupleElement(const Shape& target_shape, int64 index, llvm::Type* element_type = ShapeToIrType(target_shape, module); llvm::Value* ret_val = - ir_builder->CreateBitCast(src_buffer, element_type->getPointerTo()); + b->CreateBitCast(src_buffer, element_type->getPointerTo()); return ret_val; } diff --git a/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h b/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h index 352d34ebf839c6c2465abade7c3d3eb3b7a34506..cf6bf5d0b14ba71cbed67f9a1dc728c0eef5e393 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h +++ b/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h @@ -61,13 +61,13 @@ namespace llvm_ir { // output[i] = pred ? tuple_on_true[i] : tuple_on_false[i] void EmitTupleSelect(const IrArray& select, const IrArray& pred, llvm::Value* on_true, llvm::Value* on_false, - llvm::IRBuilder<>* ir_builder, llvm::Module* module); + llvm::IRBuilder<>* b, llvm::Module* module); // A tuple is an array of pointers, one for each operand. Each pointer points to // the output buffer of its corresponding operand. void EmitTuple(const IrArray& tuple, tensorflow::gtl::ArraySlice operands, - llvm::IRBuilder<>* ir_builder, llvm::Module* module); + llvm::IRBuilder<>* b, llvm::Module* module); // A tuple is an array of pointers, one for each operand. Each pointer points to // the output buffer of its corresponding operand. A GetTupleElement instruction @@ -75,8 +75,7 @@ void EmitTuple(const IrArray& tuple, // Returns an llvm value representing a pointer to the tuple element buffer. llvm::Value* EmitGetTupleElement(const Shape& target_shape, int64 index, int alignment, llvm::Value* operand, - llvm::IRBuilder<>* ir_builder, - llvm::Module* module); + llvm::IRBuilder<>* b, llvm::Module* module); } // namespace llvm_ir } // namespace xla diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc index 53efc30c3653879709fceae3dcdd4f679740f622..5e02096ee501b23a7976a50f13bb7e7f3c5e2d34 100644 --- a/tensorflow/compiler/xla/service/local_service.cc +++ b/tensorflow/compiler/xla/service/local_service.cc @@ -20,6 +20,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/executable_build_options.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/backend.h" diff --git a/tensorflow/compiler/xla/service/local_service.h b/tensorflow/compiler/xla/service/local_service.h index 39d6734c3fc06df6832cf67edddbc7c14c815cd1..8f707ea9046a00a15cac469672a7a992f20bf483 100644 --- a/tensorflow/compiler/xla/service/local_service.h +++ b/tensorflow/compiler/xla/service/local_service.h @@ -19,7 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/executable_build_options.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/backend.h" #include "tensorflow/compiler/xla/service/compiler.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" diff --git a/tensorflow/compiler/xla/service/platform_util.cc b/tensorflow/compiler/xla/service/platform_util.cc index 7c63c0acc7764d558b2151190f0fa79fac355cbf..39fe3c7835d1c74c0f1e5bc0ebf5916ec734c24a 100644 --- a/tensorflow/compiler/xla/service/platform_util.cc +++ b/tensorflow/compiler/xla/service/platform_util.cc @@ -75,19 +75,6 @@ PlatformUtil::GetSupportedPlatforms() { auto* platform = platform_pair.second; auto compiler_status = Compiler::GetForPlatform(platform); if (compiler_status.ok()) { - if (platform->VisibleDeviceCount() > 0) { - LOG(INFO) << "platform " << platform->Name() << " present with " - << platform->VisibleDeviceCount() << " visible devices"; - } else { - LOG(WARNING) << "platform " << platform->Name() << " present but no " - << "visible devices found"; - } - // Note: currently we call zero device platforms "supported" on the basis - // that, if the platform support was linked in, it was probably intended - // to be used for execution, and this way we can flag an error. - // - // TODO(b/33730287) If we want an alternative version of this behavior we - // could add an --xla_fallback_to_host flag. platforms.push_back(platform); } else { LOG(INFO) << "platform " << platform->Name() << " present but no " diff --git a/tensorflow/compiler/xla/service/pool.h b/tensorflow/compiler/xla/service/pool.h deleted file mode 100644 index 8e710ebb6dc17e0e204ba6ab3c6c159627cd9d3b..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/pool.h +++ /dev/null @@ -1,84 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_COMPILER_XLA_POOL_H_ -#define TENSORFLOW_COMPILER_XLA_POOL_H_ - -#include -#include - -#include "tensorflow/compiler/xla/ptr_util.h" -#include "tensorflow/core/platform/mutex.h" - -namespace xla { - -// Pool of values, which are created as needed and destroyed when the `Pool` is -// destroyed -template -class Pool { - public: - struct Deleter { - void operator()(T* ptr) { pool->Deallocate(ptr); } - - Pool* pool; - }; - - // A pointer to a taken element of a `Pool` which returns it to the pool on - // destruction - using SmartPtr = std::unique_ptr; - - // Constructs a `Pool` with given factory function, which need not be - // thread-safe. - explicit Pool(std::function()> factory) - : factory_(factory) {} - - explicit Pool() : Pool([]() { return MakeUnique(); }) {} - - // Returns a pointer to a value in the pool, creating a new value if none is - // free. The returned smart pointer returns the element to the pool on - // destruction. - // - // This method is thread-safe. - SmartPtr Allocate() { - tensorflow::mutex_lock lock(mu_); - T* ptr; - if (!xs_.empty()) { - ptr = std::move(xs_.back()).release(); - xs_.pop_back(); - } else { - ptr = factory_().release(); - } - Deleter del = {this}; - return std::unique_ptr(ptr, del); - } - - private: - // Puts a pointer to a value back into the pool, leaving it free for future - // use. - // - // This method is thread-safe. - void Deallocate(T* ptr) { - tensorflow::mutex_lock lock(mu_); - xs_.push_back(std::unique_ptr(ptr)); - } - - const std::function()> factory_ GUARDED_BY(mu_); - std::vector> xs_ GUARDED_BY(mu_); - tensorflow::mutex mu_; -}; - -} // namespace xla - -#endif // TENSORFLOW_COMPILER_XLA_POOL_H_ diff --git a/tensorflow/compiler/xla/service/reshape_mover.cc b/tensorflow/compiler/xla/service/reshape_mover.cc index 49ec38eb62c7b51c7a2d301d882cef032b288036..ca86c5d13e98a98c62d0c9e8e32e28fe99e0fa1f 100644 --- a/tensorflow/compiler/xla/service/reshape_mover.cc +++ b/tensorflow/compiler/xla/service/reshape_mover.cc @@ -38,7 +38,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/reshape_mover.h" #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/util.h" diff --git a/tensorflow/compiler/xla/service/reshape_mover_test.cc b/tensorflow/compiler/xla/service/reshape_mover_test.cc index 13e2d3258e3b92f52320201c382594962c0e3b2b..ad3b662c20ac53b0a6d634b16b3b908f730f3d2d 100644 --- a/tensorflow/compiler/xla/service/reshape_mover_test.cc +++ b/tensorflow/compiler/xla/service/reshape_mover_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/reshape_mover.h" #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -175,8 +175,9 @@ TEST_F(ReshapeMoverTest, EquivalentReshapesMoved) { TEST_F(ReshapeMoverTest, 1ConstantAnd2ReshapesMoved) { HloComputation::Builder builder(TestName()); auto root_shape = ShapeUtil::MakeShape(F32, {2, 3}); - auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{true, true, false}, {false, false, true}}))); + auto const0 = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR2( + {{true, true, false}, {false, false, true}}))); auto param1 = builder.AddInstruction(HloInstruction::CreateParameter( 0, ShapeUtil::MakeShape(F32, {1, 3, 1, 2}), "param1")); @@ -255,12 +256,12 @@ TEST_F(ReshapeMoverTest, 2TrivialConstantReshapeNotMoved) { HloComputation::Builder builder(TestName()); auto root_shape = ShapeUtil::MakeShape(F32, {3, 2}); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}}))); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}}))); auto reshape0 = builder.AddInstruction(HloInstruction::CreateReshape(root_shape, const0)); auto const1 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}}))); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}}))); auto reshape1 = builder.AddInstruction(HloInstruction::CreateReshape(root_shape, const1)); @@ -309,7 +310,7 @@ TEST_F(ReshapeMoverTest, 1NonTrivialReshapeMoved) { auto param0 = builder.AddInstruction(HloInstruction::CreateParameter( 0, ShapeUtil::MakeShape(F32, {1, 3, 1, 2}), "param0")); auto const1 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}}))); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}}))); auto reshape0 = builder.AddInstruction(HloInstruction::CreateReshape(root_shape, param0)); builder.AddInstruction(HloInstruction::CreateBinary( @@ -348,7 +349,7 @@ TEST_F(ReshapeMoverTest, 1NonTrivialReshapeWith1ReshapedConstNotMoved) { auto param0 = builder.AddInstruction(HloInstruction::CreateParameter( 0, ShapeUtil::MakeShape(F32, {1, 3}), "param0")); auto const1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({9, 8, 7}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({9, 8, 7}))); auto reshape0 = builder.AddInstruction(HloInstruction::CreateReshape(root_shape, param0)); auto reshape1 = diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index da3b622bfae8ac5132f9f95070ee41674e79b5b8..ce070bc5b6c3dfc22ffd0922be27f0afd6bff48f 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -37,6 +37,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_proto_util.h" #include "tensorflow/compiler/xla/service/platform_util.h" #include "tensorflow/compiler/xla/service/source_map_util.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/compiler/xla/service/transfer_manager.h" #include "tensorflow/compiler/xla/shape_layout.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -169,7 +170,8 @@ Service::Service(const ServiceOptions& options, Status Service::CreateChannelHandle(const CreateChannelHandleRequest* arg, CreateChannelHandleResponse* result) { - *result->mutable_channel() = channel_tracker_.NewChannel(); + TF_ASSIGN_OR_RETURN(*result->mutable_channel(), + channel_tracker_.NewChannel(arg->channel_type())); return Status::OK(); } @@ -375,7 +377,7 @@ Service::ExecuteParallelAndRegisterResult( ExecutionProfile* profile) { // Streams where the computation are launched, so we can wait on the streams // to complete. - std::vector::SmartPtr> streams; + std::vector streams; std::vector> timers; // Global data handles for the computation results, one for each computation. @@ -402,7 +404,7 @@ Service::ExecuteParallelAndRegisterResult( CHECK_EQ(replicas.size(), arguments[i].size()); std::vector result_buffers; for (int64 replica = 0; replica < replicas.size(); ++replica) { - TF_ASSIGN_OR_RETURN(Pool::SmartPtr stream, + TF_ASSIGN_OR_RETURN(StreamPool::Ptr stream, backend->BorrowStream(replicas[replica])); streams.push_back(std::move(stream)); @@ -514,13 +516,13 @@ StatusOr Service::ExecuteAndRegisterResult( arguments, Backend* backend, const string& result_tag, ExecutionProfile* profile) { // Set up streams. - std::vector::SmartPtr> streams; + std::vector streams; TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*backend, SingleComputationDeviceHandle())); TF_RET_CHECK(!replicas.empty()); for (se::StreamExecutor* executor : replicas) { - TF_ASSIGN_OR_RETURN(Pool::SmartPtr stream, + TF_ASSIGN_OR_RETURN(StreamPool::Ptr stream, backend->BorrowStream(executor)); streams.push_back(std::move(stream)); } @@ -532,7 +534,7 @@ StatusOr Service::ExecuteAndRegisterResult( // Set up run options. std::vector run_options; - for (const Pool::SmartPtr& stream : streams) { + for (const StreamPool::Ptr& stream : streams) { ExecutableRunOptions options; options.set_stream(stream.get()); options.set_device_ordinal(stream->parent()->device_ordinal()); diff --git a/tensorflow/compiler/xla/service/service_executable_run_options.h b/tensorflow/compiler/xla/service/service_executable_run_options.h index 7f3910cdb0366078b97fb5f6a2dc498b37570926..dbfed628bfcabffe66bef41a82e0e2430897d80d 100644 --- a/tensorflow/compiler/xla/service/service_executable_run_options.h +++ b/tensorflow/compiler/xla/service/service_executable_run_options.h @@ -17,7 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_XLA_SERVICE_SERVICE_EXECUTABLE_RUN_OPTIONS_H_ #include "tensorflow/compiler/xla/executable_run_options.h" -#include "tensorflow/compiler/xla/service/pool.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/stream_executor/stream_executor.h" @@ -27,8 +27,7 @@ namespace xla { // data, now only a stream cache for GPU backend. class ServiceExecutableRunOptions { public: - using StreamBorrower = - std::function::SmartPtr>(int)>; + using StreamBorrower = std::function(int)>; ServiceExecutableRunOptions() : ServiceExecutableRunOptions(ExecutableRunOptions()) {} @@ -51,7 +50,7 @@ class ServiceExecutableRunOptions { // Borrows a stream and returns a smart pointer which returns the stream on // destruction. - StatusOr::SmartPtr> BorrowStream(int device_ordinal) const { + StatusOr BorrowStream(int device_ordinal) const { return borrow_stream_ ? borrow_stream_(device_ordinal) : Status(tensorflow::error::UNIMPLEMENTED, "No stream cache"); diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc index 70edf7883f91a0112a9576b639eb0e75b7f471e4..35df792b07022b2338fcecc25eb8a0718626e464 100644 --- a/tensorflow/compiler/xla/service/shape_inference.cc +++ b/tensorflow/compiler/xla/service/shape_inference.cc @@ -222,13 +222,16 @@ StatusOr InferWindowOutputShape(const Shape& base_shape, return shape; case HloOpcode::kReal: case HloOpcode::kImag: - if (!ShapeUtil::ElementIsComplex(shape)) { + if (ShapeUtil::ElementIsComplex(shape)) { + return ShapeUtil::ComplexComponentShape(shape); + } else if (ShapeUtil::ElementIsFloating(shape)) { + return shape; + } else { return InvalidArgument( - "Expected element type in shape to be complex for real/imag " - "operation; got %s.", + "Expected element type in shape to be floating or complex for " + "real/imag operation; got %s.", PrimitiveType_Name(shape.element_type()).c_str()); } - return ShapeUtil::ChangeElementType(shape, F32); case HloOpcode::kAbs: if (ShapeUtil::ElementIsComplex(shape)) { return ShapeUtil::ChangeElementType( @@ -967,6 +970,14 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, if (operand_shapes.size() == 1) { return *operand_shapes[0]; } else if (operand_shapes.size() == 2) { + if (!ShapeUtil::SameDimensions(*operand_shapes[0], + *operand_shapes[1])) { + return InvalidArgument( + "Sort keys and values dimensions must match. " + "Keys shape is: %s\n, Values shape is: %s", + ShapeUtil::HumanString(*operand_shapes[0]).c_str(), + ShapeUtil::HumanString(*operand_shapes[1]).c_str()); + } return ShapeUtil::MakeTupleShape( {*operand_shapes[0], *operand_shapes[1]}); } diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc index bafe14d6f45f851924c37908d4c93bbff2dac459..6046d50c6d41a3956b996a3320848784ffd59068 100644 --- a/tensorflow/compiler/xla/service/shape_inference_test.cc +++ b/tensorflow/compiler/xla/service/shape_inference_test.cc @@ -17,6 +17,7 @@ limitations under the License. #include +#include "tensorflow/compiler/xla/service/hlo_instructions.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/test_helpers.h" @@ -1523,6 +1524,18 @@ TEST_F(ShapeInferenceTest, BadSlice) { << statusor.status(); } +TEST_F(ShapeInferenceTest, BadSort) { + auto keys = ShapeUtil::MakeShape(F32, {4}); + auto values = ShapeUtil::MakeShape(F32, {5}); + StatusOr statusor = + ShapeInference::InferVariadicOpShape(HloOpcode::kSort, {&keys, &values}); + ASSERT_FALSE(statusor.ok()); + + EXPECT_THAT(statusor.status().error_message(), + HasSubstr("dimensions must match")) + << statusor.status(); +} + class GatherShapeInferenceTest : public ShapeInferenceTest { protected: const Shape s64_scalar_ = ShapeUtil::MakeShape(S64, {}); @@ -1543,45 +1556,45 @@ class GatherShapeInferenceTest : public ShapeInferenceTest { }; TEST_F(GatherShapeInferenceTest, TensorFlowGather) { - TF_ASSERT_OK_AND_ASSIGN( - Shape gather_shape, - ShapeInference::InferGatherShape(matrix_64_48_, s64_vector_32_, - HloInstruction::MakeGatherDimNumbers( - /*output_window_dims=*/{0}, - /*elided_window_dims=*/{1}, - /*gather_dims_to_operand_dims=*/{1}, - /*index_vector_dim=*/1), - /*window_bounds=*/{64, 1})); + TF_ASSERT_OK_AND_ASSIGN(Shape gather_shape, + ShapeInference::InferGatherShape( + matrix_64_48_, s64_vector_32_, + HloGatherInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{0}, + /*elided_window_dims=*/{1}, + /*gather_dims_to_operand_dims=*/{1}, + /*index_vector_dim=*/1), + /*window_bounds=*/{64, 1})); EXPECT_TRUE( ShapeUtil::Equal(gather_shape, ShapeUtil::MakeShape(F32, {64, 32}))) << ShapeUtil::HumanString(gather_shape); } TEST_F(GatherShapeInferenceTest, TensorFlowGatherV2) { - TF_ASSERT_OK_AND_ASSIGN( - Shape gather_shape, - ShapeInference::InferGatherShape(matrix_64_48_, s64_vector_32_, - HloInstruction::MakeGatherDimNumbers( - /*output_window_dims=*/{1}, - /*elided_window_dims=*/{0}, - /*gather_dims_to_operand_dims=*/{0}, - /*index_vector_dim=*/1), - /*window_bounds=*/{1, 48})); + TF_ASSERT_OK_AND_ASSIGN(Shape gather_shape, + ShapeInference::InferGatherShape( + matrix_64_48_, s64_vector_32_, + HloGatherInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{1}, + /*elided_window_dims=*/{0}, + /*gather_dims_to_operand_dims=*/{0}, + /*index_vector_dim=*/1), + /*window_bounds=*/{1, 48})); EXPECT_TRUE( ShapeUtil::Equal(gather_shape, ShapeUtil::MakeShape(F32, {32, 48}))) << ShapeUtil::HumanString(gather_shape); } TEST_F(GatherShapeInferenceTest, TensorFlowGatherNd) { - TF_ASSERT_OK_AND_ASSIGN( - Shape gather_shape, - ShapeInference::InferGatherShape(matrix_64_48_, s64_4d_tensor_10_9_8_7_1_, - HloInstruction::MakeGatherDimNumbers( - /*output_window_dims=*/{4}, - /*elided_window_dims=*/{0}, - /*gather_dims_to_operand_dims=*/{0}, - /*index_vector_dim=*/4), - /*window_bounds=*/{1, 48})); + TF_ASSERT_OK_AND_ASSIGN(Shape gather_shape, + ShapeInference::InferGatherShape( + matrix_64_48_, s64_4d_tensor_10_9_8_7_1_, + HloGatherInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{4}, + /*elided_window_dims=*/{0}, + /*gather_dims_to_operand_dims=*/{0}, + /*index_vector_dim=*/4), + /*window_bounds=*/{1, 48})); EXPECT_TRUE(ShapeUtil::Equal(gather_shape, ShapeUtil::MakeShape(F32, {10, 9, 8, 7, 48}))) << ShapeUtil::HumanString(gather_shape); @@ -1592,7 +1605,7 @@ TEST_F(GatherShapeInferenceTest, TensorFlowBatchDynamicSlice) { Shape gather_shape, ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1609,7 +1622,7 @@ TEST_F(GatherShapeInferenceTest, NonDefaultGatherIndicesLeafDim_A) { Shape gather_shape, ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_5_7_6_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1627,7 +1640,7 @@ TEST_F(GatherShapeInferenceTest, NonDefaultGatherIndicesLeafDim_B) { Shape gather_shape, ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_5_10_9_7_6_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1646,7 +1659,7 @@ TEST_F(GatherShapeInferenceTest, NoOutputGatherDims) { Shape gather_shape, ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_vector_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{0, 1, 2, 3, 4}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1664,7 +1677,7 @@ TEST_F(GatherShapeInferenceTest, ScalarGatherIndices) { TF_ASSERT_OK_AND_ASSIGN(Shape gather_shape, ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_scalar_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{0, 1, 2, 3}, /*elided_window_dims=*/{0}, /*gather_dims_to_operand_dims=*/{0}, @@ -1679,10 +1692,11 @@ TEST_F(GatherShapeInferenceTest, ScalarGatherIndices) { TEST_F(GatherShapeInferenceTest, TupleShapedTensorInput) { StatusOr statusor = ShapeInference::InferGatherShape( tuple_shape_, s64_vector_32_, - HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0}, - /*elided_window_dims=*/{1}, - /*gather_dims_to_operand_dims=*/{1}, - /*index_vector_dim=*/1), + HloGatherInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{0}, + /*elided_window_dims=*/{1}, + /*gather_dims_to_operand_dims=*/{1}, + /*index_vector_dim=*/1), /*window_bounds=*/{64, 1}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), @@ -1693,10 +1707,11 @@ TEST_F(GatherShapeInferenceTest, TupleShapedTensorInput) { TEST_F(GatherShapeInferenceTest, TupleShapedGatherIndicesInput) { StatusOr statusor = ShapeInference::InferGatherShape( s64_vector_32_, tuple_shape_, - HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0}, - /*elided_window_dims=*/{1}, - /*gather_dims_to_operand_dims=*/{1}, - /*index_vector_dim=*/0), + HloGatherInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{0}, + /*elided_window_dims=*/{1}, + /*gather_dims_to_operand_dims=*/{1}, + /*index_vector_dim=*/0), /*window_bounds=*/{64, 1}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), @@ -1707,10 +1722,11 @@ TEST_F(GatherShapeInferenceTest, TupleShapedGatherIndicesInput) { TEST_F(GatherShapeInferenceTest, FloatingPointGatherIndicesInput) { StatusOr statusor = ShapeInference::InferGatherShape( s64_vector_32_, vector_32_, - HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0}, - /*elided_window_dims=*/{1}, - /*gather_dims_to_operand_dims=*/{1}, - /*index_vector_dim=*/0), + HloGatherInstruction::MakeGatherDimNumbers( + /*output_window_dims=*/{0}, + /*elided_window_dims=*/{1}, + /*gather_dims_to_operand_dims=*/{1}, + /*index_vector_dim=*/0), /*window_bounds=*/{64, 1}); ASSERT_FALSE(statusor.ok()); EXPECT_THAT(statusor.status().error_message(), @@ -1722,7 +1738,7 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_NonAscendingWindowIndices) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 8, 7}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1739,7 +1755,7 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_RepeatedWindowIndices) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 7}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1756,7 +1772,7 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_WindowIndexOutOfBounds) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 99, 100, 101}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1772,7 +1788,7 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_WindowIndexBarelyOutOfBounds) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 9}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1788,7 +1804,7 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_MismatchingElidedWindowDims) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{4}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1806,7 +1822,7 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_OutOfBoundsWindowToInputMapping) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{0, 1, 2, 3, 19}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1823,7 +1839,7 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_RepeatedWindowToInputMapping) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{0, 1, 2, 3, 3}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1841,7 +1857,7 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_MismatchingGatherToInputMapping) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3}, @@ -1860,7 +1876,7 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_OutOfBoundsGatherToInputMapping) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 7}, @@ -1878,7 +1894,7 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_RepeatedGatherToInputMapping) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 3}, @@ -1896,7 +1912,7 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_NonAscendingElidedWindowDims) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{2, 1}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1911,7 +1927,7 @@ TEST_F(GatherShapeInferenceTest, TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_WindowBoundsTooLarge) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7}, /*elided_window_dims=*/{2}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1928,7 +1944,7 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_MismatchingNumberOfWindowBounds) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1946,7 +1962,7 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_WindowBoundsNot1ForElidedDim) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7}, /*elided_window_dims=*/{1}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, @@ -1962,7 +1978,7 @@ TEST_F(GatherShapeInferenceTest, TEST_F(GatherShapeInferenceTest, OutOfBoundsGatherIndicesLeafDim) { StatusOr statusor = ShapeInference::InferGatherShape( f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_5_7_6_, - HloInstruction::MakeGatherDimNumbers( + HloGatherInstruction::MakeGatherDimNumbers( /*output_window_dims=*/{4, 5, 6, 7, 8}, /*elided_window_dims=*/{}, /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}, diff --git a/tensorflow/compiler/xla/service/stream_pool.cc b/tensorflow/compiler/xla/service/stream_pool.cc new file mode 100644 index 0000000000000000000000000000000000000000..92bb21b816c36df4dee266942a7ce51718efdfd1 --- /dev/null +++ b/tensorflow/compiler/xla/service/stream_pool.cc @@ -0,0 +1,56 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/stream_pool.h" + +#include "tensorflow/compiler/xla/ptr_util.h" + +namespace xla { + +StreamPool::Ptr StreamPool::BorrowStream(se::StreamExecutor* executor) { + std::unique_ptr stream; + { + tensorflow::mutex_lock lock(mu_); + if (!streams_.empty()) { + // Re-use an existing stream from the pool. + stream = std::move(streams_.back()); + streams_.pop_back(); + } + } + + if (!stream) { + // Create a new stream. + stream = MakeUnique(executor); + stream->Init(); + } + + // Return the stream wrapped in Ptr, which has our special deleter semantics. + PtrDeleter deleter = {this}; + return Ptr(stream.release(), deleter); +} + +void StreamPool::ReturnStream(se::Stream* stream) { + if (stream->ok()) { + tensorflow::mutex_lock lock(mu_); + streams_.emplace_back(stream); + } else { + // If the stream has encountered any errors, all subsequent + // operations on it will fail. So just delete the stream, and rely + // on new streams to be created in the future. + delete stream; + } +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/stream_pool.h b/tensorflow/compiler/xla/service/stream_pool.h new file mode 100644 index 0000000000000000000000000000000000000000..7221d323a61593ac4b203a81b6046d81a5beaaf0 --- /dev/null +++ b/tensorflow/compiler/xla/service/stream_pool.h @@ -0,0 +1,64 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_STREAM_POOL_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_STREAM_POOL_H_ + +#include +#include + +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/core/platform/mutex.h" +#include "tensorflow/core/platform/stream_executor_no_cuda.h" + +namespace xla { + +// Pool of stream_executor::Streams, which are created as needed and +// destroyed when the pool is destroyed. +class StreamPool { + public: + struct PtrDeleter { + void operator()(se::Stream* stream) { pool->ReturnStream(stream); } + StreamPool* pool; + }; + + // Stream pointer type returned by BorrowStream, which returns the + // stream to the pool on destruction. + using Ptr = std::unique_ptr; + + StreamPool() {} + + // Returns a pointer to a stream in the pool, creating a new stream + // if none are available in the pool. The returned smart pointer + // returns the stream to the pool on destruction. + // + // This method is thread-safe. + Ptr BorrowStream(se::StreamExecutor* executor); + + private: + // Puts a pointer to a stream back into the pool, leaving it free + // for future use. Streams that have previously encountered errors + // are deleted, and not returned to the pool. + // + // This method is thread-safe. + void ReturnStream(se::Stream* stream); + + tensorflow::mutex mu_; + std::vector> streams_ GUARDED_BY(mu_); +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_STREAM_POOL_H_ diff --git a/tensorflow/compiler/xla/service/stream_pool_test.cc b/tensorflow/compiler/xla/service/stream_pool_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..aaf5c37b0d250f78cb57639255ac9b59e1b462f7 --- /dev/null +++ b/tensorflow/compiler/xla/service/stream_pool_test.cc @@ -0,0 +1,136 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/stream_pool.h" + +#include + +#include "tensorflow/compiler/xla/test_helpers.h" +#include "tensorflow/core/platform/stream_executor_no_cuda.h" + +namespace xla { +namespace { + +class StreamPoolTest : public ::testing::Test { + protected: + std::unique_ptr NewStreamExecutor() { + se::Platform* platform = + se::MultiPlatformManager::PlatformWithName("Host").ConsumeValueOrDie(); + se::StreamExecutorConfig config(/*ordinal=*/0); + return platform->GetUncachedExecutor(config).ConsumeValueOrDie(); + } +}; + +TEST_F(StreamPoolTest, EmptyPool) { StreamPool pool; } + +TEST_F(StreamPoolTest, OneStreamPool) { + std::unique_ptr executor = NewStreamExecutor(); + StreamPool pool; + + // Borrow and return a stream. + StreamPool::Ptr stream1 = pool.BorrowStream(executor.get()); + se::Stream* stream1_ptr = stream1.get(); + EXPECT_TRUE(stream1->ok()); + stream1 = nullptr; + + // Borrow and return another stream. + StreamPool::Ptr stream2 = pool.BorrowStream(executor.get()); + se::Stream* stream2_ptr = stream2.get(); + EXPECT_TRUE(stream2->ok()); + stream2 = nullptr; + + // The underlying streams should be the same, since stream1 was the + // only stream available in the pool when stream2 was borrowed. + EXPECT_EQ(stream1_ptr, stream2_ptr); +} + +TEST_F(StreamPoolTest, TwoStreamPool) { + std::unique_ptr executor = NewStreamExecutor(); + StreamPool pool; + + // Borrow two streams. + StreamPool::Ptr stream1 = pool.BorrowStream(executor.get()); + se::Stream* stream1_ptr = stream1.get(); + EXPECT_TRUE(stream1->ok()); + StreamPool::Ptr stream2 = pool.BorrowStream(executor.get()); + se::Stream* stream2_ptr = stream2.get(); + EXPECT_TRUE(stream2->ok()); + + // The underlying streams should be different, since we haven't + // returned either of them yet. + EXPECT_NE(stream1_ptr, stream2_ptr); + + // Return stream1 and borrow stream3. + stream1 = nullptr; + StreamPool::Ptr stream3 = pool.BorrowStream(executor.get()); + se::Stream* stream3_ptr = stream3.get(); + EXPECT_TRUE(stream3->ok()); + + // stream1 and stream3 should be the same. + EXPECT_EQ(stream1_ptr, stream3_ptr); + EXPECT_NE(stream2_ptr, stream3_ptr); + + // Return stream2, and borrow stream4. + stream2 = nullptr; + StreamPool::Ptr stream4 = pool.BorrowStream(executor.get()); + se::Stream* stream4_ptr = stream4.get(); + EXPECT_TRUE(stream4->ok()); + + // Stream2 and stream4 should be the same. + EXPECT_EQ(stream2_ptr, stream4_ptr); + EXPECT_NE(stream3_ptr, stream4_ptr); +} + +TEST_F(StreamPoolTest, BadStreamDiscarded) { + std::unique_ptr executor = NewStreamExecutor(); + StreamPool pool; + + // Borrow a stream. + StreamPool::Ptr stream1 = pool.BorrowStream(executor.get()); + EXPECT_TRUE(stream1->ok()); + + // Force an error on the stream; here we call a method that requires + // DNN support, which we know the Host platform doesn't support. + stream1->ThenDepthConcatenate({}, {}, nullptr); + EXPECT_FALSE(stream1->ok()); + + // Return stream1 and borrow stream2. + stream1 = nullptr; + StreamPool::Ptr stream2 = pool.BorrowStream(executor.get()); + se::Stream* stream2_ptr = stream2.get(); + EXPECT_TRUE(stream2->ok()); + + // The underlying streams should be different. They would have been + // the same, but since we forced an error on stream1, it cannot be + // put back into the pool. Sadly we can't just check: + // EXPECT_NE(stream1_ptr, stream2_ptr); + // + // The above should hold logically, but it may fail if the new + // stream instance allocated for stream2 happens to reside in the + // same memory address as stream1, which has been deleted. + // + // The check that stream2->ok() serves as a good-enough check. + + // Return stream2 and borrow stream3. The previous error on stream1 + // has no effect on these streams, and they are the same. + stream2 = nullptr; + StreamPool::Ptr stream3 = pool.BorrowStream(executor.get()); + se::Stream* stream3_ptr = stream3.get(); + EXPECT_TRUE(stream3->ok()); + EXPECT_EQ(stream2_ptr, stream3_ptr); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/service/transfer_manager.cc b/tensorflow/compiler/xla/service/transfer_manager.cc index 4c5038a009ba5da4172129980014913f3f4418f4..7232c658b3f0687ac93a83e46a200f88bf202084 100644 --- a/tensorflow/compiler/xla/service/transfer_manager.cc +++ b/tensorflow/compiler/xla/service/transfer_manager.cc @@ -44,6 +44,7 @@ StatusOr> TransferManager::TransferLiteralFromDevice( se::Stream* stream, const ShapedBuffer& device_buffer) { StatusOr> ret; se::Stream* substream = stream->GetOrCreateSubStream(); + substream->ThenWaitFor(stream); auto cleanup = tensorflow::gtl::MakeCleanup( [&]() { stream->ReturnSubStream(substream); }); @@ -64,6 +65,7 @@ Status TransferManager::TransferLiteralToDevice( // Use a substream so that if we are called from a HostCallback we don't // deadlock. se::Stream* substream = stream->GetOrCreateSubStream(); + substream->ThenWaitFor(stream); auto cleanup = tensorflow::gtl::MakeCleanup( [&]() { stream->ReturnSubStream(substream); }); TF_RETURN_IF_ERROR( diff --git a/tensorflow/compiler/xla/service/transfer_manager.h b/tensorflow/compiler/xla/service/transfer_manager.h index e384359642a8fe09e0b8516e342a56259912922a..82c599e482d85fc5bbe5a5a48c6c6b053186803b 100644 --- a/tensorflow/compiler/xla/service/transfer_manager.h +++ b/tensorflow/compiler/xla/service/transfer_manager.h @@ -20,7 +20,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/shaped_buffer.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" @@ -167,16 +167,6 @@ class TransferManager { const se::Platform* platform); protected: - // Transfer a memory block of the given size from 'source' buffer to the - // Infeed interface of the device using the given executor. - // - // size is the size to transfer from source in bytes. - // - // source is the source data that must be in the target-dependent layout that - // the Infeed HLO used in the computation expects. - virtual Status TransferBufferToInfeed(se::StreamExecutor* executor, - int64 size, const void* source) = 0; - // Transfer a memory block of the given size from the device source into the // 'destination' buffer. // diff --git a/tensorflow/compiler/xla/service/transpose_folding_test.cc b/tensorflow/compiler/xla/service/transpose_folding_test.cc index cccb8f2fbb0266bbf1f40b09170938a1e5d3e78d..58f767e913fbc0023e0c45a4f0e82ecefeeef2d6 100644 --- a/tensorflow/compiler/xla/service/transpose_folding_test.cc +++ b/tensorflow/compiler/xla/service/transpose_folding_test.cc @@ -19,8 +19,8 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -160,11 +160,11 @@ TEST_F(TransposeFoldingTest, FuseDotWithConstantOperands) { auto builder = HloComputation::Builder("entry"); // (1.0 + 2.0) * (2.0 - 3.0) HloInstruction* const1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); HloInstruction* const2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); HloInstruction* const3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(3.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(3.0))); HloInstruction* add = builder.AddInstruction(HloInstruction::CreateBinary( const1->shape(), HloOpcode::kAdd, const1, const2)); HloInstruction* sub = builder.AddInstruction(HloInstruction::CreateBinary( diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc index 990dfc410ccf6ab84af00f4a16dc783c11985844..0effdc80a43ed8c7edc7ef06aeef1c03c1c9224d 100644 --- a/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc +++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis.cc @@ -718,6 +718,7 @@ bool TuplePointsToAnalysis::HasUniqueFusedUseOfOperandAt( // root at operand 0 or 1. Or... // (4) The 'user' of 'operand' is DynamicUpdateSlice or While at operand index // 0. +// (5) The 'user' of 'operand' is Sort, and it is the only user. // // (2) and (3) can only be determined if points-to analysis is available. bool TuplePointsToAnalysis::CanShareOperandBufferWithUser( @@ -783,6 +784,21 @@ bool TuplePointsToAnalysis::CanShareOperandBufferWithUser( std::vector operand_indices = user->OperandIndices(operand); return operand_indices.size() == 1 && operand_indices[0] == 0; } + if (user->opcode() == HloOpcode::kSort) { + // Only valid if there are no other users. + if (operand->users().size() != 1) { + return false; + } + // If we only sort keys, the output of sort is not a tuple, so we can always + // share the buffer. + if (user->operand_count() == 1) { + return true; + } + CHECK(!user_index.empty()); + // Only share with the right tuple element buffer. + std::vector operand_indices = user->OperandIndices(operand); + return operand_indices.size() == 1 && user_index[0] == operand_indices[0]; + } if (user->opcode() == HloOpcode::kCall) { // TODO(b/62548313): Remove when buffer assignment is module scoped and // does not assign buffers to calls. diff --git a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc index 226d0af5d27bb37b08747cb86f0bc4bfa6f3db96..2e5f6468044036016c5c9e5013968ac5a1046b7d 100644 --- a/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc +++ b/tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc @@ -124,9 +124,9 @@ class TuplePointsToAnalysisTest : public HloTestBase { TEST_F(TuplePointsToAnalysisTest, SimpleTuple) { auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); @@ -177,14 +177,14 @@ TEST_F(TuplePointsToAnalysisTest, NestedTuple) { // tuple. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto inner_tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto constant3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(3.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(3.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({inner_tuple, constant3})); @@ -238,14 +238,14 @@ TEST_F(TuplePointsToAnalysisTest, GetTupleElement) { // tuple. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto inner_tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto constant3 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(3.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(3.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({inner_tuple, constant3})); @@ -270,7 +270,7 @@ TEST_F(TuplePointsToAnalysisTest, DuplicatedElement) { // Create a tuple which contains duplicate elements. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant, constant, constant})); @@ -291,9 +291,9 @@ TEST_F(TuplePointsToAnalysisTest, TupleCopy) { // the same. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto copy = builder.AddInstruction( @@ -317,8 +317,8 @@ TEST_F(TuplePointsToAnalysisTest, SendAndSendDone) { // Send forwards its operand to the output tuple at {0}. auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); - auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); + auto token = builder.AddInstruction(HloInstruction::CreateToken()); auto send = builder.AddInstruction( HloInstruction::CreateSend(constant, token, /*channel_id=*/0)); auto send_done = builder.AddInstruction(HloInstruction::CreateSendDone(send)); @@ -343,7 +343,7 @@ TEST_F(TuplePointsToAnalysisTest, SendAndSendDone) { TEST_F(TuplePointsToAnalysisTest, RecvAndRecvDone) { // RecvDone forwards its operand tuple element at {0} to the output. auto builder = HloComputation::Builder(TestName()); - auto token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = builder.AddInstruction(HloInstruction::CreateToken()); auto recv = builder.AddInstruction(HloInstruction::CreateRecv( ShapeUtil::MakeShape(F32, {1, 2, 3}), token, /*channel_id=*/0)); auto recv_done = builder.AddInstruction(HloInstruction::CreateRecvDone(recv)); @@ -365,16 +365,16 @@ TEST_F(TuplePointsToAnalysisTest, TupleSelect) { // set containing the union of both sides. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple1 = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto tuple2 = builder.AddInstruction( HloInstruction::CreateTuple({constant2, constant2})); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); auto select = builder.AddInstruction(HloInstruction::CreateTernary( tuple1->shape(), HloOpcode::kTupleSelect, pred, tuple1, tuple2)); @@ -403,7 +403,7 @@ TEST_F(TuplePointsToAnalysisTest, SelectTupleParameters) { auto param1 = builder.AddInstruction( HloInstruction::CreateParameter(1, tuple_shape, "param1")); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); auto select = builder.AddInstruction(HloInstruction::CreateTernary( tuple_shape, HloOpcode::kTupleSelect, pred, param0, param1)); auto copy = builder.AddInstruction( @@ -443,16 +443,16 @@ TEST_F(TuplePointsToAnalysisTest, UnambiguousTupleSelect) { // Select from two identical tuples. The result should not be ambiguous. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto tuple1 = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto tuple2 = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); auto select = builder.AddInstruction(HloInstruction::CreateTernary( tuple1->shape(), HloOpcode::kTupleSelect, pred, tuple1, tuple2)); @@ -474,9 +474,9 @@ TEST_F(TuplePointsToAnalysisTest, NestedTupleSelect) { // the right values. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto inner_tuple1 = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto inner_tuple2 = builder.AddInstruction( @@ -488,7 +488,7 @@ TEST_F(TuplePointsToAnalysisTest, NestedTupleSelect) { builder.AddInstruction(HloInstruction::CreateTuple({inner_tuple2})); auto pred = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); auto select = builder.AddInstruction(HloInstruction::CreateTernary( tuple1->shape(), HloOpcode::kTupleSelect, pred, tuple1, tuple2)); @@ -521,9 +521,9 @@ TEST_F(TuplePointsToAnalysisTest, TupleWithBitcast) { // have the operand of the bitcast in its points-to set. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto bitcast = builder.AddInstruction(HloInstruction::CreateUnary( constant2->shape(), HloOpcode::kBitcast, constant2)); auto tuple = @@ -557,9 +557,10 @@ TEST_F(TuplePointsToAnalysisTest, PointsToTupleConstantElements) { // Construct a tuple constant and kCopy it. Verify the points-to set of the // copy correctly correctly points into the nested elements of the constant. auto builder = HloComputation::Builder(TestName()); - auto tuple_constant = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::MakeTuple({Literal::CreateR2({{1.0}, {2.0}}).get(), - Literal::CreateR1({2.0, 42}).get()}))); + auto tuple_constant = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::MakeTuple( + {LiteralUtil::CreateR2({{1.0}, {2.0}}).get(), + LiteralUtil::CreateR1({2.0, 42}).get()}))); auto copy = builder.AddInstruction(HloInstruction::CreateUnary( tuple_constant->shape(), HloOpcode::kCopy, tuple_constant)); @@ -579,9 +580,9 @@ TEST_F(TuplePointsToAnalysisTest, BufferAliases) { // times. Verify buffer alias sets. auto builder = HloComputation::Builder(TestName()); auto constant1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto constant2 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto inner_tuple = builder.AddInstruction( HloInstruction::CreateTuple({constant1, constant2})); auto tuple = builder.AddInstruction( @@ -620,7 +621,7 @@ class FusionPointsToAnalysisTest : public TuplePointsToAnalysisTest { auto tuple_element1 = builder.AddInstruction( HloInstruction::CreateGetTupleElement(update_shape, tuple_param0, 1)); auto ones = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.f, 1.f, 1.f, 1.f}))); + LiteralUtil::CreateR1({1.f, 1.f, 1.f, 1.f}))); // Create 'update' = Add(GetTupleElement(tuple_param0, 1), ones) auto update = builder.AddInstruction(HloInstruction::CreateBinary( update_shape, HloOpcode::kAdd, tuple_element1, ones)); @@ -868,9 +869,9 @@ TEST_F(DoesNotUseOperandBufferTest, FusedDynamicUpdateSlice) { // Create a DynamicUpdateSlice instruction of tuple element 1. auto starts = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({2}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({2}))); auto update = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({2.f, 2.f, 2.f}))); + LiteralUtil::CreateR1({2.f, 2.f, 2.f}))); auto dynamic_update_slice = builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice( data_shape, gte1, update, starts)); @@ -962,9 +963,9 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedDynamicUpdateSlice) { // Create a DynamicUpdateSlice instruction of tuple element 1. auto starts = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({2}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({2}))); auto update = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({2.f, 2.f, 2.f}))); + LiteralUtil::CreateR1({2.f, 2.f, 2.f}))); auto dynamic_update_slice = builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice( data_shape, gte1, update, starts)); @@ -1011,14 +1012,56 @@ TEST_F(CanShareOperandBufferWithUserTest, DynamicUpdateSliceCanShare) { points_to_analysis_->CanShareOperandBufferWithUser(starts, {}, dus, {})); } +TEST_F(CanShareOperandBufferWithUserTest, SortCanShare) { + auto builder = HloComputation::Builder(TestName()); + + Shape keys_shape = ShapeUtil::MakeShape(F32, {8}); + auto keys = builder.AddInstruction( + HloInstruction::CreateParameter(0, keys_shape, "keys")); + auto sort = + builder.AddInstruction(HloInstruction::CreateSort(keys_shape, 0, keys)); + + BuildModuleAndRunAnalysis(builder.Build()); + + EXPECT_TRUE( + points_to_analysis_->CanShareOperandBufferWithUser(keys, {}, sort, {})); +} + +TEST_F(CanShareOperandBufferWithUserTest, SortCanShareWithTupleUser) { + auto builder = HloComputation::Builder(TestName()); + + Shape keys_shape = ShapeUtil::MakeShape(F32, {8}); + Shape values_shape = ShapeUtil::MakeShape(F32, {8}); + auto keys = builder.AddInstruction( + HloInstruction::CreateParameter(0, keys_shape, "keys")); + auto values = builder.AddInstruction( + HloInstruction::CreateParameter(1, values_shape, "values")); + auto sort = builder.AddInstruction(HloInstruction::CreateSort( + ShapeUtil::MakeTupleShape({keys_shape, values_shape}), 0, keys, values)); + + BuildModuleAndRunAnalysis(builder.Build()); + + // The buffer for the keys can be shared with the first tuple entry. + EXPECT_TRUE( + points_to_analysis_->CanShareOperandBufferWithUser(keys, {}, sort, {0})); + // The buffer for the values can be shared with the second tuple entry. + EXPECT_TRUE(points_to_analysis_->CanShareOperandBufferWithUser(values, {}, + sort, {1})); + // Verify that the buffers are not shared with the "wrong" tuple entry. + EXPECT_FALSE( + points_to_analysis_->CanShareOperandBufferWithUser(keys, {}, sort, {1})); + EXPECT_FALSE(points_to_analysis_->CanShareOperandBufferWithUser(values, {}, + sort, {0})); +} + TEST_F(CanShareOperandBufferWithUserTest, FusedDotAdd) { auto builder = HloComputation::Builder(TestName()); Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2}); auto a = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 0.0}, {0.0, 1.0}}))); + LiteralUtil::CreateR2({{1.0, 0.0}, {0.0, 1.0}}))); auto b = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{2.0, 2.0}, {2.0, 2.0}}))); + LiteralUtil::CreateR2({{2.0, 2.0}, {2.0, 2.0}}))); DotDimensionNumbers dot_dnums; dot_dnums.add_lhs_contracting_dimensions(1); @@ -1027,7 +1070,7 @@ TEST_F(CanShareOperandBufferWithUserTest, FusedDotAdd) { HloInstruction::CreateDot(data_shape, a, b, dot_dnums)); auto one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto add_operand = builder.AddInstruction( HloInstruction::CreateBroadcast(data_shape, one, {1})); @@ -1049,7 +1092,7 @@ TEST_F(CanShareOperandBufferWithUserTest, OutputFusionCantAliasOperandBuffer) { Shape data_shape = ShapeUtil::MakeShape(F32, {2, 2}); auto one = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto operand = builder.AddInstruction( HloInstruction::CreateBroadcast(data_shape, one, {1})); @@ -1057,7 +1100,7 @@ TEST_F(CanShareOperandBufferWithUserTest, OutputFusionCantAliasOperandBuffer) { HloInstruction::CreateReverse(data_shape, operand, {0, 1})); auto two = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{2.0, 2.0}, {2.0, 2.0}}))); + LiteralUtil::CreateR2({{2.0, 2.0}, {2.0, 2.0}}))); auto add = builder.AddInstruction( HloInstruction::CreateBinary(data_shape, HloOpcode::kAdd, reverse, two)); @@ -1122,7 +1165,7 @@ TEST_F(CanShareOperandBufferWithUserTest, CallToComputationWithFusionRoot) { auto sub_param = sub_builder.AddInstruction( HloInstruction::CreateParameter(0, shape, "sub_param")); auto one = sub_builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0))); auto ones = sub_builder.AddInstruction( HloInstruction::CreateBroadcast(shape, one, {1})); auto add = sub_builder.AddInstruction( diff --git a/tensorflow/compiler/xla/service/tuple_simplifier_test.cc b/tensorflow/compiler/xla/service/tuple_simplifier_test.cc index d3635eae81ec7017f9bf6a69250d10716309c9ec..39b693872da6bd985d95c2abc9519662c838a3f5 100644 --- a/tensorflow/compiler/xla/service/tuple_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/tuple_simplifier_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_matchers.h" diff --git a/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc b/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc index 10fc4958fae06414dbe7a3a0a798cb5c6e0f35c2..62af45128ad2fb7bf886bef78ec3ab42529a181e 100644 --- a/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc +++ b/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc @@ -61,6 +61,12 @@ StatusOr WhileLoopConstantSinking::TrySinkingConstantsIntoWhileBody( WhileUtil::GetInvariantGTEsForWhileBody(*while_body)) { int64 index = invariant_gte->tuple_index(); const HloInstruction& invariant_value = *init_value.operand(index); + + // Should have at least one user that's not while_body_root. + if (invariant_gte->user_count() <= 1) { + continue; + } + if (invariant_value.opcode() == HloOpcode::kConstant) { auto* constant_instr = while_body->AddInstruction(invariant_value.Clone(/*suffix=*/".sunk")); diff --git a/tensorflow/compiler/xla/service/while_loop_constant_sinking_test.cc b/tensorflow/compiler/xla/service/while_loop_constant_sinking_test.cc index 393e75803888d8a642881c4d525b170d1e1180ba..266039d2ff8ef4befba0d1023ac1914737207d4f 100644 --- a/tensorflow/compiler/xla/service/while_loop_constant_sinking_test.cc +++ b/tensorflow/compiler/xla/service/while_loop_constant_sinking_test.cc @@ -196,5 +196,50 @@ ENTRY entry { op::GetTupleElement(op::Parameter(0)), op::GetTupleElement(op::Parameter(0)))); } + +TEST_F(WhileLoopConstantSinkingTest, DontCreateDeadConstant) { + const char* const hlo_string = R"( +HloModule ModuleWithWhile + +body { + p_body = (f32[2],f32[2]) parameter(0) + p_body.0 = f32[2] get-tuple-element((f32[2],f32[2]) p_body), index=0 + p_body.1 = f32[2] get-tuple-element((f32[2],f32[2]) p_body), index=1 + + outfeed = token[] outfeed(p_body.0) + ROOT root = (f32[2],f32[2],f32[2]) tuple(p_body.0, p_body.1, p_body.1) +} + +condition { + p_cond = (f32[2],f32[2]) parameter(0) + ROOT result = pred[] constant(true) +} + +ENTRY entry { + const_0 = f32[2] constant({1, 2}) + const_1 = f32[2] constant({2, 1}) + while_init = (f32[2],f32[2]) tuple(const_0, const_1) + ROOT while = (f32[2],f32[2],f32[2]) while(while_init), condition=condition, + body=body +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseHloString(hlo_string)); + + TF_ASSERT_OK_AND_ASSIGN(bool changed, + WhileLoopConstantSinking{}.Run(module.get())); + ASSERT_TRUE(changed); + + auto* while_body = module->GetComputationWithName("body"); + EXPECT_THAT(while_body->root_instruction(), + op::Tuple(op::GetTupleElement(), op::GetTupleElement(), + op::GetTupleElement())); + for (const HloInstruction* inst : while_body->instructions()) { + if (inst->opcode() == HloOpcode::kConstant) { + EXPECT_GT(inst->user_count(), 0); + } + } +} } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/while_loop_invariant_code_motion_test.cc b/tensorflow/compiler/xla/service/while_loop_invariant_code_motion_test.cc index 23519e445ea8a5f578a54708f38059feef3280c0..32e69c335b713c438bd7fcb2053709b0624f58ed 100644 --- a/tensorflow/compiler/xla/service/while_loop_invariant_code_motion_test.cc +++ b/tensorflow/compiler/xla/service/while_loop_invariant_code_motion_test.cc @@ -53,7 +53,7 @@ HloComputation* WhileLoopInvariantCodeMotionTest::MakeAlwaysTrueComputation( builder.AddInstruction( HloInstruction::CreateParameter(0, param_shape, "param")); builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); return module->AddEmbeddedComputation(builder.Build()); } @@ -125,7 +125,7 @@ TEST_F(WhileLoopInvariantCodeMotionTest, HoistInvariantOperationTree) { builder.AddInstruction(HloInstruction::CreateUnary( scalar_s32, HloOpcode::kNegate, mul_result)); HloInstruction* constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(4))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(4))); HloInstruction* sub_result = builder.AddInstruction(HloInstruction::CreateBinary( scalar_s32, HloOpcode::kSubtract, negate_result, constant)); @@ -273,7 +273,7 @@ TEST_F(WhileLoopInvariantCodeMotionTest, DontHoistInstructionWithSideEffects) { HloComputation::Builder builder(TestName()); auto* scalar_param = builder.AddInstruction( HloInstruction::CreateParameter(0, scalar_s32, "param")); - auto* token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto* token = builder.AddInstruction(HloInstruction::CreateToken()); auto* init_value = builder.AddInstruction( HloInstruction::CreateTuple({scalar_param, scalar_param, token})); auto* while_inst = builder.AddInstruction(HloInstruction::CreateWhile( @@ -323,7 +323,7 @@ TEST_F(WhileLoopInvariantCodeMotionTest, DontHoistBitcastAlone) { HloComputation::Builder builder(TestName()); auto* scalar_param = builder.AddInstruction( HloInstruction::CreateParameter(0, scalar_s32, "param")); - auto* token = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto* token = builder.AddInstruction(HloInstruction::CreateToken()); auto* init_value = builder.AddInstruction( HloInstruction::CreateTuple({scalar_param, scalar_param, token})); auto* while_inst = builder.AddInstruction(HloInstruction::CreateWhile( diff --git a/tensorflow/compiler/xla/service/while_loop_simplifier_test.cc b/tensorflow/compiler/xla/service/while_loop_simplifier_test.cc index 3c8304921661a486f283ea8c0009db16a81531a4..2e1571943e537f772ee7dcd95c80ba540445b76e 100644 --- a/tensorflow/compiler/xla/service/while_loop_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/while_loop_simplifier_test.cc @@ -157,7 +157,7 @@ TEST_F(WhileLoopSimplifierTest, auto* while_op = computation->root_instruction(); ASSERT_EQ(while_op->opcode(), HloOpcode::kWhile); auto* true_op = while_op->while_body()->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))); TF_ASSERT_OK(true_op->AddControlDependencyTo( while_op->while_body()->root_instruction())); ASSERT_TRUE(WhileLoopSimplifier().Run(the_module).ValueOrDie()); @@ -175,10 +175,10 @@ TEST_F(WhileLoopSimplifierTest, LoopWithSendNotSimplified) { auto* while_op = computation->root_instruction(); ASSERT_EQ(while_op->opcode(), HloOpcode::kWhile); auto* while_body = while_op->while_body(); - auto* token = while_body->AddInstruction(HloInstruction::CreateAfterAll({})); + auto* token = while_body->AddInstruction(HloInstruction::CreateToken()); auto* send = while_body->AddInstruction(HloInstruction::CreateSend( while_body->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(true))), + HloInstruction::CreateConstant(LiteralUtil::CreateR0(true))), token, /*channel_id=*/0)); while_body->AddInstruction(HloInstruction::CreateSendDone(send)); @@ -192,7 +192,7 @@ TEST_F(WhileLoopSimplifierTest, LoopWithRecvNotSimplified) { auto* while_op = computation->root_instruction(); ASSERT_EQ(while_op->opcode(), HloOpcode::kWhile); auto* while_body = while_op->while_body(); - auto* token = while_body->AddInstruction(HloInstruction::CreateAfterAll({})); + auto* token = while_body->AddInstruction(HloInstruction::CreateToken()); auto* recv = while_body->AddInstruction( HloInstruction::CreateRecv(ShapeUtil::MakeShape(F32, {1}), token, /*channel_id=*/0)); @@ -211,7 +211,7 @@ TEST_F(WhileLoopSimplifierTest, LoopWithInfeedNotSimplified) { auto* while_op = computation->root_instruction(); ASSERT_EQ(while_op->opcode(), HloOpcode::kWhile); auto* while_body = while_op->while_body(); - auto token = while_body->AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = while_body->AddInstruction(HloInstruction::CreateToken()); while_body->AddInstruction(HloInstruction::CreateInfeed( ShapeUtil::MakeShape(F32, {1}), token, "config")); EXPECT_FALSE(WhileLoopSimplifier().Run(the_module).ValueOrDie()); diff --git a/tensorflow/compiler/xla/service/while_util.cc b/tensorflow/compiler/xla/service/while_util.cc index 473eab2ea84eb8faf745cbe299bc80bcc1b62a35..1ef17b9d7d2e769aadf39f8a70f78200b88e9d2c 100644 --- a/tensorflow/compiler/xla/service/while_util.cc +++ b/tensorflow/compiler/xla/service/while_util.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/service/while_util.h" +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_creation_utils.h" #include "tensorflow/compiler/xla/service/tuple_util.h" @@ -38,7 +39,7 @@ static StatusOr WidenWhileCondition( // the root instruction later. We later change the root instruction to // something more appropriate. builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(false))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(false))); return narrow_condition->parent()->AddEmbeddedComputation(builder.Build()); }(); @@ -154,7 +155,7 @@ MakeCountedLoopConditionComputation(const Shape& loop_state_shape, {&loop_state_shape}, scalar_pred, "while_cond")); HloInstruction* trip_count_constant = cond_computation->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(trip_count))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(trip_count))); HloInstruction* param = cond_computation->parameter_instruction(0); TF_ASSIGN_OR_RETURN(HloInstruction * indvar, @@ -175,7 +176,7 @@ static StatusOr> MakeCountedLoopBodyComputation( CreateComputationWithSignature( {&loop_state_shape}, loop_state_shape, "while_body")); HloInstruction* one = body_computation->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); HloInstruction* param = body_computation->parameter_instruction(0); TF_ASSIGN_OR_RETURN(HloInstruction * indvar, MakeGetTupleElementHlo(param, 0)); @@ -203,7 +204,7 @@ static StatusOr MakeInitTupleFromInitValues( std::vector init_values_with_indvar; init_values_with_indvar.reserve(init_values.size() + 1); HloInstruction* zero = computation->AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))); init_values_with_indvar.push_back(zero); c_copy(init_values, std::back_inserter(init_values_with_indvar)); return computation->AddInstruction( diff --git a/tensorflow/compiler/xla/service/zero_sized_hlo_elimination.cc b/tensorflow/compiler/xla/service/zero_sized_hlo_elimination.cc index 44b0ec5cd4c1d406467007fcc530e919d602c438..83d696fe0915086c3c98b6d7cbdaeaeb4d9d0bdb 100644 --- a/tensorflow/compiler/xla/service/zero_sized_hlo_elimination.cc +++ b/tensorflow/compiler/xla/service/zero_sized_hlo_elimination.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/zero_sized_hlo_elimination.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -32,7 +32,8 @@ StatusOr ZeroSizedHloElimination::Run(HloModule* module) { for (HloComputation* comp : module->MakeNonfusionComputations()) { for (HloInstruction* instruction : comp->MakeInstructionPostOrder()) { if (instruction->HasSideEffect() || - !ShapeUtil::IsArray(instruction->shape())) { + !ShapeUtil::IsArray(instruction->shape()) || + instruction->opcode() == HloOpcode::kConstant) { continue; } if (comp->IsRemovable(instruction) && diff --git a/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc b/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc index c6bd013a1aa59fe99f8f80197f04eb1e8a97cbb7..b9ef18892d7aa859f6b0b505db4c004e4f5c5066 100644 --- a/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc +++ b/tensorflow/compiler/xla/service/zero_sized_hlo_elimination_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_module.h" @@ -67,12 +67,19 @@ TEST_F(ZeroSizedHloEliminationTest, DoesNotEliminateParameter) { } TEST_F(ZeroSizedHloEliminationTest, DoesNotEliminateSideEffects) { - auto token = builder_.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token = builder_.AddInstruction(HloInstruction::CreateToken()); builder_.AddInstruction( HloInstruction::CreateSend(zero_sized_param_, token, 0)); TF_ASSERT_OK_AND_ASSIGN(bool changed, RunZeroSizedElimination()); EXPECT_FALSE(changed); } +TEST_F(ZeroSizedHloEliminationTest, DoesNotEliminateConstant) { + builder_.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR1({}))); + TF_ASSERT_OK_AND_ASSIGN(bool changed, RunZeroSizedElimination()); + EXPECT_FALSE(changed); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/shape_layout.cc b/tensorflow/compiler/xla/shape_layout.cc index 7ee366b27a82bdbcb7a63a57ea80194db8ca7df4..caad31d6ce7ce35fa362ec364b0d7f1d95973715 100644 --- a/tensorflow/compiler/xla/shape_layout.cc +++ b/tensorflow/compiler/xla/shape_layout.cc @@ -67,6 +67,14 @@ void ShapeLayout::ResetLayout(const Layout& layout) { TF_CHECK_OK(ShapeUtil::ValidateShape(shape_)); } +void ShapeLayout::ResetLayout(const Layout& layout, + ShapeIndexView shape_index) { + CHECK(ShapeUtil::IsTuple(shape_)); + *ShapeUtil::GetMutableSubshape(&shape_, shape_index)->mutable_layout() = + layout; + TF_CHECK_OK(ShapeUtil::ValidateShape(shape_)); +} + bool ShapeLayout::operator==(const ShapeLayout& other) const { return ShapeUtil::Equal(shape_, other.shape_); } diff --git a/tensorflow/compiler/xla/shape_layout.h b/tensorflow/compiler/xla/shape_layout.h index 36806da599cc9b27286e67c128bb7f496f29c105..214cf98854938414c23c5031f4114016140ae9a7 100644 --- a/tensorflow/compiler/xla/shape_layout.h +++ b/tensorflow/compiler/xla/shape_layout.h @@ -72,6 +72,10 @@ class ShapeLayout { // tuple. void ResetLayout(const Layout& layout); + // Resets the layout on the shape at the provided ShapeIndex to the provided + // layout. Shape must be a tuple. + void ResetLayout(const Layout& layout, ShapeIndexView shape_index); + // Returns a string representation of this object. string ToString() const { return ShapeUtil::HumanStringWithLayout(shape_); } diff --git a/tensorflow/compiler/xla/shape_tree.h b/tensorflow/compiler/xla/shape_tree.h index 4aacc87b78e2c271829cdf397cd69bfb490125b8..c74dd648addd70633edc2ec10a60879a00942716 100644 --- a/tensorflow/compiler/xla/shape_tree.h +++ b/tensorflow/compiler/xla/shape_tree.h @@ -44,10 +44,6 @@ struct ShapeTreeNode { // Data corresponding to this node. std::pair data; - // Children of this node, as indices into the container's nodes_ array. - std::vector children; - - // Tells whether this is a leaf node. bool is_leaf = true; explicit ShapeTreeNode(ShapeIndex index) @@ -56,6 +52,20 @@ struct ShapeTreeNode { : data(std::move(index), std::move(data)) {} }; +// Internal representation of an index table entry. +struct IndexTableEntry { + // Index of the node in the ShapeTreeNode vector. + uint32 index; + // Index of the first child in a IndexTableEntry vector. In the index + // table all children entries for a given node will be placed next to each + // other. This allows us to use a single field to index them. + uint32 children_start; +#ifndef NDEBUG + // Number of children, used for bounds checking. + uint32 children_count; +#endif +}; + } // namespace internal template @@ -84,6 +94,7 @@ template class ShapeTree { public: using Node = internal::ShapeTreeNode; + using Index = internal::IndexTableEntry; // Default constructor creates a tree with a nil shape (i.e. an empty tuple). ShapeTree() : ShapeTree(ShapeUtil::MakeNil()) {} @@ -267,11 +278,12 @@ class ShapeTree { private: // Initialize node->children based on 'shape'. All children are assigned the // the given 'init_value'. - void InitChildren(const Shape& shape, const T& init_value, Node* node); + void InitChildren(const Shape& shape, const T& init_value, Node* node, + Index* index); // Initialize node->children based on 'shape'. All children have // default-constructed data values. - void InitChildren(const Shape& shape, Node* node); + void InitChildren(const Shape& shape, Node* node, Index* index); // Returns the number of subshapes, including interior nodes, in shape. int64 CountSubshapes(const Shape& shape); @@ -291,6 +303,9 @@ class ShapeTree { // The nodes in this shape tree. std::vector nodes_; + // Index table for node lookups. + std::vector index_table_; + // If we own our Shape, this field contains it, and shape_ is a pointer into // here. Otherwise if we don't own our shape, this is nullptr. std::shared_ptr shape_storage_; @@ -373,36 +388,74 @@ int64 ShapeTree::CountSubshapes(const Shape& shape) { template void ShapeTree::InitChildren(const Shape& shape, const T& init_value, - Node* node) { + Node* node, Index* index) { if (ShapeUtil::IsTuple(shape)) { const int64 size = ShapeUtil::TupleElementCount(shape); - node->children.reserve(size); +#ifndef NDEBUG + index->children_count = size; +#endif node->is_leaf = false; ShapeIndex shape_index = node->data.first; shape_index.push_back(0); + + // At the end of the index_table, reserve a continuous space to hold the + // children of current node. In order to enforce the invariant that all + // children of a given node are placed together, we need to do the + // reservation before we recurse into any of its children. + int64 children_start_position = index_table_.size(); + index_table_.resize(index_table_.size() + size); + for (int i = 0; i < size; ++i) { shape_index[shape_index.size() - 1] = i; - node->children.push_back(nodes_.size()); + index_table_[children_start_position + i].index = nodes_.size(); + // The first child of the node in the index table is placed at the end of + // the table. + index_table_[children_start_position + i].children_start = + index_table_.size(); nodes_.emplace_back(shape_index, init_value); - InitChildren(shape.tuple_shapes(i), init_value, &nodes_.back()); + InitChildren(shape.tuple_shapes(i), init_value, &nodes_.back(), + &index_table_[children_start_position + i]); } + } else { +#ifndef NDEBUG + index->children_count = 0; +#endif } } template -void ShapeTree::InitChildren(const Shape& shape, Node* node) { +void ShapeTree::InitChildren(const Shape& shape, Node* node, Index* index) { if (ShapeUtil::IsTuple(shape)) { const int64 size = ShapeUtil::TupleElementCount(shape); - node->children.reserve(size); +#ifndef NDEBUG + index->children_count = size; +#endif node->is_leaf = false; ShapeIndex shape_index = node->data.first; shape_index.push_back(0); + + // At the end of the index_table, reserve a continuous space to hold the + // children of current node. In order to enforce the invariant that all + // children of a given node are placed together, we need to do the + // reservation before we recurse into any of its children. + int64 children_start_position = index_table_.size(); + index_table_.resize(index_table_.size() + size); + for (int i = 0; i < size; ++i) { shape_index[shape_index.size() - 1] = i; - node->children.push_back(nodes_.size()); + index_table_[children_start_position + i].index = nodes_.size(); + // The first child of the node in the index table is placed at the end of + // the table. + index_table_[children_start_position + i].children_start = + index_table_.size(); nodes_.emplace_back(shape_index); - InitChildren(shape.tuple_shapes(i), &nodes_.back()); + InitChildren(shape.tuple_shapes(i), &nodes_.back(), + &index_table_[children_start_position + i]); } + } else { +#ifndef NDEBUG + index->children_count = 0; +#endif } } @@ -413,24 +466,36 @@ ShapeTree::ShapeTree(Shape shape) // The shape_ field is just used to hold the structure of the shape. // It should not be relied upon to store layout information. LayoutUtil::ClearLayout(shape_storage_.get()); - nodes_.reserve(CountSubshapes(*shape_)); + const int64 count = CountSubshapes(*shape_); + nodes_.reserve(count); nodes_.emplace_back(ShapeIndex{}); - InitChildren(*shape_, &nodes_[0]); + + index_table_.reserve(count); + index_table_.emplace_back(Index{0, 1}); + InitChildren(*shape_, &nodes_[0], &index_table_[0]); } template ShapeTree::ShapeTree(const Shape* shape) : shape_(shape) { - nodes_.reserve(CountSubshapes(*shape_)); + const int64 count = CountSubshapes(*shape_); + nodes_.reserve(count); nodes_.emplace_back(ShapeIndex{}); - InitChildren(*shape_, &nodes_[0]); + + index_table_.reserve(count); + index_table_.emplace_back(Index{0, 1}); + InitChildren(*shape_, &nodes_[0], &index_table_[0]); } template ShapeTree::ShapeTree(const std::shared_ptr& shape) : shape_storage_(shape), shape_(shape_storage_.get()) { - nodes_.reserve(CountSubshapes(*shape_)); + const int64 count = CountSubshapes(*shape_); + nodes_.reserve(count); nodes_.emplace_back(ShapeIndex{}); - InitChildren(*shape_, &nodes_[0]); + + index_table_.reserve(count); + index_table_.emplace_back(Index{0, 1}); + InitChildren(*shape_, &nodes_[0], &index_table_[0]); } template @@ -440,26 +505,38 @@ ShapeTree::ShapeTree(Shape shape, const T& init_value) // The shape_ field is just used to hold the structure of the shape. // It should not be relied upon to store layout information. LayoutUtil::ClearLayout(shape_storage_.get()); - nodes_.reserve(CountSubshapes(*shape_)); + const int64 count = CountSubshapes(*shape_); + nodes_.reserve(count); nodes_.emplace_back(ShapeIndex{}, init_value); - InitChildren(*shape_, init_value, &nodes_[0]); + + index_table_.reserve(count); + index_table_.emplace_back(Index{0, 1}); + InitChildren(*shape_, init_value, &nodes_[0], &index_table_[0]); } template ShapeTree::ShapeTree(const Shape* shape, const T& init_value) : shape_(shape) { - nodes_.reserve(CountSubshapes(*shape_)); + const int64 count = CountSubshapes(*shape_); + nodes_.reserve(count); nodes_.emplace_back(ShapeIndex{}, init_value); - InitChildren(*shape_, init_value, &nodes_[0]); + + index_table_.reserve(count); + index_table_.emplace_back(Index{0, 1}); + InitChildren(*shape_, init_value, &nodes_[0], &index_table_[0]); } template ShapeTree::ShapeTree(const std::shared_ptr& shape, const T& init_value) : shape_storage_(shape), shape_(shape_storage_.get()) { - nodes_.reserve(CountSubshapes(*shape_)); + const int64 count = CountSubshapes(*shape_); + nodes_.reserve(count); nodes_.emplace_back(ShapeIndex{}, init_value); - InitChildren(*shape_, init_value, &nodes_[0]); + + index_table_.reserve(count); + index_table_.emplace_back(Index{0, 1}); + InitChildren(*shape_, init_value, &nodes_[0], &index_table_[0]); } template @@ -474,13 +551,16 @@ T* ShapeTree::mutable_element(ShapeIndexView index) { template internal::ShapeTreeNode* ShapeTree::Lookup(ShapeIndexView index) { - Node* node = &nodes_[0]; + Index* iter = &index_table_[0]; for (const int64 i : index) { CHECK_GE(i, 0); - CHECK_LT(i, node->children.size()); - node = &nodes_[node->children[i]]; +#ifndef NDEBUG + CHECK_LT(i, iter->children_count); +#endif + iter = &index_table_[iter->children_start + i]; } - return node; + + return &nodes_[iter->index]; } template diff --git a/tensorflow/compiler/xla/shape_tree_test.cc b/tensorflow/compiler/xla/shape_tree_test.cc index 51de82e95746281ed6e587b545dc933b48ce1ad4..4391078b6484f25ba81aefa2c1d1f69d7d2774f4 100644 --- a/tensorflow/compiler/xla/shape_tree_test.cc +++ b/tensorflow/compiler/xla/shape_tree_test.cc @@ -227,14 +227,16 @@ TEST_F(ShapeTreeTest, NestedTupleShape) { TEST_F(ShapeTreeTest, InvalidIndexingTuple) { ShapeTree shape_tree{tuple_shape_}; - +#ifndef NDEBUG EXPECT_DEATH(shape_tree.element({4}), ""); +#endif } TEST_F(ShapeTreeTest, InvalidIndexingNestedTuple) { ShapeTree shape_tree{nested_tuple_shape_}; - +#ifndef NDEBUG EXPECT_DEATH(shape_tree.element({0, 0}), ""); +#endif } TEST_F(ShapeTreeTest, ShapeTreeOfNonCopyableType) { @@ -602,12 +604,15 @@ void BM_Iterate(int iters, int depth, int fan_out) { } } -BENCHMARK(BM_Construct)->ArgPair(2, 8); -BENCHMARK(BM_ConstructUnowned)->ArgPair(2, 8); -BENCHMARK(BM_Copy)->ArgPair(2, 8); -BENCHMARK(BM_Move)->ArgPair(2, 8); -BENCHMARK(BM_ForEach)->ArgPair(2, 8); -BENCHMARK(BM_Iterate)->ArgPair(2, 8); +#define BENCHMARK_WITH_ARGS(name) \ + BENCHMARK(name)->ArgPair(2, 8)->ArgPair(1, 1000) + +BENCHMARK_WITH_ARGS(BM_Construct); +BENCHMARK_WITH_ARGS(BM_ConstructUnowned); +BENCHMARK_WITH_ARGS(BM_Copy); +BENCHMARK_WITH_ARGS(BM_Move); +BENCHMARK_WITH_ARGS(BM_ForEach); +BENCHMARK_WITH_ARGS(BM_Iterate); } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index 56d24423c428d32c1c65ed7a47aab9691a846559..ec901af1e2057449452c4c65243593b016a26f61 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -46,28 +46,14 @@ namespace xla { using ::tensorflow::strings::StrAppend; using ::tensorflow::strings::StrCat; -string ShapeIndex::ToString() const { - return StrCat("{", tensorflow::str_util::Join(indices_, ","), "}"); -} +string ShapeIndex::ToString() const { return ShapeIndexView(*this).ToString(); } string ShapeIndexView::ToString() const { - return StrCat("{", - tensorflow::str_util::Join( - tensorflow::gtl::make_range(begin_, end_), ","), - "}"); + return StrCat("{", tensorflow::str_util::Join(indices_, ","), "}"); } bool ShapeIndexView::operator==(const ShapeIndexView& other) const { - if (size() != other.size()) { - return false; - } - for (auto it = begin(), other_it = other.begin(); it != end(); - ++it, ++other_it) { - if (*it != *other_it) { - return false; - } - } - return true; + return indices_ == other.indices_; } bool ShapeIndexView::operator!=(const ShapeIndexView& other) const { @@ -696,7 +682,7 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { CompatibleIgnoringElementType); } else { // Opaque, token, etc types are vacuously compatible. - return true; + return lhs.element_type() == rhs.element_type(); } } @@ -711,7 +697,7 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { CompatibleIgnoringFpPrecision); } else { // Opaque, token, etc types are vacuously compatible. - return true; + return lhs.element_type() == rhs.element_type(); } } @@ -897,40 +883,51 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { } int64 shape_size = [&shape]() { - int64 shape_size; if (LayoutUtil::IsSparseArray(shape)) { - shape_size = LayoutUtil::MaxSparseElements(shape.layout()); - if (shape_size < 0) { - return shape_size; + int64 max_sparse_elements = LayoutUtil::MaxSparseElements(shape.layout()); + if (max_sparse_elements < 0) { + return max_sparse_elements; + } + int64 sparse_elements_size = MultiplyWithoutOverflow( + max_sparse_elements, ByteSizeOfPrimitiveType(shape.element_type())); + if (sparse_elements_size < 0) { + return sparse_elements_size; } - shape_size = MultiplyWithoutOverflow(shape_size, ShapeUtil::Rank(shape)); - if (shape_size < 0) { - return shape_size; + int64 sparse_indices_size = + MultiplyWithoutOverflow(max_sparse_elements, ShapeUtil::Rank(shape)); + if (sparse_indices_size < 0) { + return sparse_indices_size; } - shape_size = MultiplyWithoutOverflow(shape_size, sizeof(int64)); - if (shape_size < 0) { - return shape_size; + sparse_indices_size = + MultiplyWithoutOverflow(sparse_indices_size, sizeof(int64)); + if (sparse_indices_size < 0) { + return sparse_indices_size; + } + // At this point, both sparse_indices_size and sparse_elements_size are + // non-negative, so we can easily check if adding them wraps. + if (static_cast(sparse_elements_size) + + static_cast(sparse_indices_size) > + INT64_MAX) { + return static_cast(-1); } } - shape_size = 1; - // This is intentionally unconditional: even if the shape is sparse, we want // to verify the densified version has a reasonable size. + int64 dense_shape_size = 1; if (shape.dimensions().empty()) { - return shape_size; + return dense_shape_size; } for (int64 dim : shape.dimensions()) { - shape_size = MultiplyWithoutOverflow(shape_size, dim); - if (shape_size < 0) { - return shape_size; + dense_shape_size = MultiplyWithoutOverflow(dense_shape_size, dim); + if (dense_shape_size < 0) { + return dense_shape_size; } } - shape_size = MultiplyWithoutOverflow( - shape_size, ByteSizeOfPrimitiveType(shape.element_type())); - - return shape_size; + dense_shape_size = MultiplyWithoutOverflow( + dense_shape_size, ByteSizeOfPrimitiveType(shape.element_type())); + return dense_shape_size; }(); if (shape_size < 0) { @@ -1126,12 +1123,41 @@ Status ForEachMutableSubshapeHelper( for (auto dim : Permute(permutation, shape.dimensions())) { new_shape.add_dimensions(dim); } + + // If `shape` has a layout, by contract we choose a new layout such that the + // transpose defined by this permutation is a bitcast. + // + // Some formalism helps to understand the correct way to do this. We're going + // to do algebra in the group of permutations of the dimensions of `shape`. + // + // Since the order of `shape`'s dimensions is not permuted relative to itself, + // `shape`'s list of dimensions is isomorphic to the identity I. + // + // Let `shape`'s layout be L. A layout is a permutation which maps a + // minor-to-major physical layout to the order of a shape's logical dims. + // Therefore inverse of a layout maps from logical to physical dims, and so + // the physical layout of I is simply L'.I = L', where L' is the inverse of L. + // + // Let the argument `permutation` be P. This is a permutation over `shape`'s + // dimensions, so our return value will be a shape with dims P.I = P. Our + // goal is to construct a layout permutation L* that we can apply to P such + // that that the physical dimension ordering of the returned shape is the same + // as that of the original shape, namely L'. + // + // Our returned shape has dims P and layout L*, so its in-memory layout is + // L*'.P. Setting this equal to L' and solving for L*, we get: + // + // L*'.P = L' => + // L*' = L'P' => + // L* = P.L + // if (shape.has_layout()) { CHECK(LayoutUtil::IsDenseArray(shape)); Layout* new_layout = new_shape.mutable_layout(); new_layout->set_format(DENSE); new_layout->clear_minor_to_major(); - for (auto index : Permute(permutation, shape.layout().minor_to_major())) { + for (auto index : ComposePermutations( + permutation, AsInt64Slice(shape.layout().minor_to_major()))) { new_layout->add_minor_to_major(index); } if (shape.layout().padded_dimensions_size() > 0) { @@ -1141,6 +1167,13 @@ Status ForEachMutableSubshapeHelper( new_layout->add_padded_dimensions(dim); } } + // The permutation accepted by TransposeIsBitcast is the inverse of the + // permutation here. + CHECK(TransposeIsBitcast(shape, new_shape, InversePermutation(permutation))) + << "shape=" << HumanStringWithLayout(shape) + << ", new_shape=" << HumanStringWithLayout(new_shape) + << ", permutation={" << tensorflow::str_util::Join(permutation, ",") + << "}"; } return new_shape; } diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h index 5ae04451d32bd733dce55c4a56f5ebc1882d9fbd..d6f17fc965d24bbbbd083b8dd0ec11a59e49ed4e 100644 --- a/tensorflow/compiler/xla/shape_util.h +++ b/tensorflow/compiler/xla/shape_util.h @@ -31,6 +31,7 @@ limitations under the License. #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/core/threadpool.h" #include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/lib/gtl/inlined_vector.h" #include "tensorflow/core/lib/gtl/optional.h" #include "tensorflow/core/platform/cpu_info.h" #include "tensorflow/core/platform/env.h" @@ -73,10 +74,12 @@ class ShapeIndex { // push_front is O(n^2), but shapes don't usually have a ton of dimensions. void push_front(int64 value) { indices_.insert(indices_.begin(), value); } - std::vector::const_iterator begin() const { return indices_.begin(); } - std::vector::const_iterator end() const { return indices_.end(); } - std::vector::iterator begin() { return indices_.begin(); } - std::vector::iterator end() { return indices_.end(); } + using container_type = tensorflow::gtl::InlinedVector; + + container_type::const_iterator begin() const { return indices_.begin(); } + container_type::const_iterator end() const { return indices_.end(); } + container_type::iterator begin() { return indices_.begin(); } + container_type::iterator end() { return indices_.end(); } const int64* data() const { return indices_.data(); } @@ -97,7 +100,7 @@ class ShapeIndex { string ToString() const; private: - std::vector indices_; + container_type indices_; }; // A view into a ShapeIndex as above, with the cheap/easy ability to consume the @@ -110,31 +113,33 @@ class ShapeIndex { class ShapeIndexView { public: ShapeIndexView(const ShapeIndex& shape_index, int64 offset = 0) - : ShapeIndexView(shape_index.data() + offset, - shape_index.data() + shape_index.size()) { + : indices_(shape_index.data() + offset, shape_index.size() - offset) { CHECK_LE(offset, shape_index.size()); } - ShapeIndexView(std::initializer_list indices) - : ShapeIndexView(indices.begin(), indices.end()) {} + ShapeIndexView(std::initializer_list indices) : indices_(indices) {} ShapeIndexView(const ShapeIndexView& other) = default; using iterator = const int64*; - iterator begin() const { return begin_; } - iterator end() const { return end_; } - int64 size() const { return std::distance(begin_, end_); } - bool empty() const { return begin_ == end_; } + iterator begin() const { return indices_.begin(); } + iterator end() const { return indices_.end(); } + int64 size() const { return indices_.size(); } + bool empty() const { return indices_.empty(); } int64 front() const { CHECK(!empty()); - return *begin_; + return indices_.front(); } ShapeIndexView ConsumeFront() const { - CHECK(!empty()); - auto new_begin = begin_; - ++new_begin; - return ShapeIndexView(new_begin, end_); + ShapeIndexView result = *this; + result.indices_.pop_front(); + return result; + } + ShapeIndexView ConsumeBack() const { + ShapeIndexView result = *this; + result.indices_.pop_back(); + return result; } - ShapeIndex ToShapeIndex() const { return ShapeIndex(begin_, end_); } + ShapeIndex ToShapeIndex() const { return ShapeIndex(begin(), end()); } bool operator==(const ShapeIndexView& other) const; bool operator!=(const ShapeIndexView& other) const; @@ -142,10 +147,7 @@ class ShapeIndexView { string ToString() const; private: - ShapeIndexView(iterator begin, iterator end) : begin_(begin), end_(end) {} - - iterator begin_; - iterator end_; + tensorflow::gtl::ArraySlice indices_; }; std::ostream& operator<<(std::ostream& out, const ShapeIndex& shape_index); @@ -530,7 +532,13 @@ class ShapeUtil { static bool HasDegenerateDimensions(const Shape& shape); // Permutes the dimensions by the given permutation, so - // return_value.dimensions[permutation[i]] = argument.dimensions[i] + // return_value.dimensions[permutation[i]] = argument.dimensions[i]. + // + // Postcondition: For any valid permutation, + // + // !HasLayout(shape) || + // TransposeIsBitcast(shape, PermuteDimensions(permutation, shape), + // InversePermutation(permutation)). static Shape PermuteDimensions(tensorflow::gtl::ArraySlice permutation, const Shape& shape); diff --git a/tensorflow/compiler/xla/shape_util_test.cc b/tensorflow/compiler/xla/shape_util_test.cc index b6f30af381dd8d24ff28fdf7f729d6cb3df46ec9..e5dd62ae9a3dd9b961a7ae03a99c19220dbd43e7 100644 --- a/tensorflow/compiler/xla/shape_util_test.cc +++ b/tensorflow/compiler/xla/shape_util_test.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/shape_util.h" +#include #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/test.h" @@ -22,12 +23,23 @@ limitations under the License. #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/lib/strings/strcat.h" namespace xla { namespace { using ::testing::ElementsAre; +TEST(ShapeUtilTest, ShapeIndexViewTest) { + ShapeIndex index = {1, 2, 3, 4}; + ShapeIndexView index_view(index, 1); + EXPECT_EQ(3, index_view.size()); + EXPECT_EQ(ShapeIndexView({2, 3, 4}), index_view); + EXPECT_EQ(ShapeIndexView({3, 4}), index_view.ConsumeFront()); + EXPECT_EQ(ShapeIndexView({2, 3}), index_view.ConsumeBack()); +} + TEST(ShapeUtilTest, GetDimensionHelperCanNegativeIndex) { Shape matrix = ShapeUtil::MakeShape(F32, {2, 3}); EXPECT_EQ(3, ShapeUtil::GetDimension(matrix, -1)); @@ -322,6 +334,17 @@ TEST(ShapeUtilTest, IncompatibleScalarVsTuple) { EXPECT_FALSE(ShapeUtil::CompatibleIgnoringFpPrecision(shape2, shape1)); } +TEST(ShapeUtilTest, OpaqueVsArray) { + Shape shape1 = ShapeUtil::MakeShape(F32, {5, 7}); + Shape shape2 = ShapeUtil::MakeOpaqueShape(); + EXPECT_FALSE(ShapeUtil::Compatible(shape1, shape2)); + EXPECT_FALSE(ShapeUtil::Compatible(shape2, shape1)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringFpPrecision(shape1, shape2)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringFpPrecision(shape2, shape1)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringElementType(shape1, shape2)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringElementType(shape2, shape1)); +} + TEST(ShapeUtilTest, CompareShapesWithPaddedDimensionsMismatch) { Shape shape1 = ShapeUtil::MakeShape(F32, {20, 30}); shape1.mutable_layout()->add_padded_dimensions(10); @@ -821,6 +844,28 @@ TEST(ShapeUtilTest, HasDegenerateDimensions) { ShapeUtil::HasDegenerateDimensions(ShapeUtil::MakeShape(F32, {3, 0, 5}))); } +TEST(ShapeUtilTest, PermuteDimensionsLayout) { + std::vector layout(3); + std::iota(layout.begin(), layout.end(), 0); + do { + Shape s = ShapeUtil::MakeShapeWithLayout(F32, {10, 100, 1000}, layout); + SCOPED_TRACE(tensorflow::strings::StrCat("s=", ShapeUtil::HumanString(s))); + + std::vector permutation(3); + std::iota(permutation.begin(), permutation.end(), 0); + do { + SCOPED_TRACE(tensorflow::strings::StrCat( + "permutation=", tensorflow::str_util::Join(permutation, ","))); + + // TransposeIsBitcast takes the inverse of the permutation that + // PermuteDimensions takes. + EXPECT_TRUE(ShapeUtil::TransposeIsBitcast( + s, ShapeUtil::PermuteDimensions(permutation, s), + InversePermutation(permutation))); + } while (std::next_permutation(permutation.begin(), permutation.end())); + } while (std::next_permutation(layout.begin(), layout.end())); +} + TEST(AlgebraicSimplifierTest, ReshapeIsBitcast_3x2x2_6x2_Dim0IsMostMinor) { EXPECT_FALSE(ShapeUtil::ReshapeIsBitcast( ShapeUtil::MakeShapeWithLayout(F32, {3, 2, 2}, {0, 1, 2}), diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index 02f6fc3a27152afb6085494887f0777c23030263..42d52aee780e2aade0f2ed3597e653567b8da49b 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -65,6 +65,7 @@ cc_library( srcs = ["test_utils.cc"], hdrs = ["test_utils.h"], deps = [ + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:util", @@ -88,6 +89,7 @@ cc_library( "//tensorflow/compiler/xla:array3d", "//tensorflow/compiler/xla:array4d", "//tensorflow/compiler/xla:error_spec", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_comparison", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:test", @@ -152,8 +154,8 @@ tf_cc_binary( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla/client:client_library", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service/cpu:cpu_compiler", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/core:lib", @@ -179,6 +181,7 @@ cc_library( "//tensorflow/compiler/xla:array3d", "//tensorflow/compiler/xla:array4d", "//tensorflow/compiler/xla:execution_options_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", @@ -189,8 +192,8 @@ cc_library( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:interpreter_plugin", # reference backend "//tensorflow/compiler/xla/service:platform_util", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -209,6 +212,7 @@ cc_library( deps = [ ":codegen_test_base", ":filecheck", + "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/service:llvm_compiler", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/core:test", @@ -258,7 +262,7 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:computation_placer", "//tensorflow/compiler/xla/service:device_memory_allocator", "//tensorflow/compiler/xla/service:local_service", @@ -286,8 +290,8 @@ xla_test( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -302,7 +306,7 @@ xla_test( "enable_for_xla_interpreter", ], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", @@ -310,8 +314,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", @@ -330,8 +334,8 @@ xla_test( "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", @@ -345,16 +349,16 @@ xla_test( "enable_for_xla_interpreter", ], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:platform_util", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -372,9 +376,10 @@ xla_test( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:platform_util", + "//tensorflow/compiler/xla/service:stream_pool", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:test_utils", "//tensorflow/core:lib", @@ -391,8 +396,8 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -406,7 +411,7 @@ xla_test( tags = ["enable_for_xla_interpreter"], deps = [ "//tensorflow/compiler/xla:array2d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", @@ -415,9 +420,9 @@ xla_test( "//tensorflow/compiler/xla:xla_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -435,14 +440,14 @@ xla_test( tags = ["optonly"], deps = [ "//tensorflow/compiler/xla:array2d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -460,9 +465,9 @@ xla_test( deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", @@ -479,8 +484,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -497,8 +502,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -515,9 +520,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -531,6 +536,7 @@ xla_test( srcs = ["scalar_computations_test.cc"], shard_count = 32, deps = [ + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -538,8 +544,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -557,8 +563,8 @@ xla_test( "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -573,7 +579,7 @@ xla_test( "enable_for_xla_interpreter", ], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", @@ -581,8 +587,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -599,7 +605,7 @@ xla_test( "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array3d", "//tensorflow/compiler/xla:array4d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", @@ -607,8 +613,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -633,7 +639,7 @@ xla_test( deps = [ ":client_library_test_base", ":literal_test_util", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", ], @@ -645,7 +651,7 @@ xla_test( tags = ["enable_for_xla_interpreter"], deps = [ "//tensorflow/compiler/xla:array2d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", @@ -653,7 +659,7 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:reduce_precision_insertion", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -676,8 +682,8 @@ xla_test( "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -697,7 +703,7 @@ xla_test( "//tensorflow/compiler/xla:execution_options_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:test", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/tests:xla_internal_test_main", ], @@ -720,8 +726,8 @@ xla_test( "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -744,8 +750,8 @@ xla_test( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -764,11 +770,12 @@ xla_test( "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array3d", "//tensorflow/compiler/xla:array4d", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -780,7 +787,7 @@ xla_test( CONVOLUTION_TEST_DEPS = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", @@ -789,7 +796,7 @@ CONVOLUTION_TEST_DEPS = [ "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -827,13 +834,13 @@ xla_test( deps = [ "//tensorflow/compiler/xla:array3d", "//tensorflow/compiler/xla:array4d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -856,8 +863,8 @@ xla_test( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -874,7 +881,7 @@ xla_test( ":test_utils", "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", @@ -885,10 +892,10 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/lib:math", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", @@ -907,7 +914,7 @@ xla_test( ":test_utils", "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", @@ -918,9 +925,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", @@ -940,12 +947,12 @@ xla_test( ], deps = [ ":test_utils", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -966,7 +973,7 @@ xla_test( "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -985,8 +992,8 @@ xla_test( "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array3d", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1007,7 +1014,7 @@ xla_test( "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:computation_placer", "//tensorflow/compiler/xla/service:device_memory_allocator", "//tensorflow/compiler/xla/service:local_service", @@ -1031,14 +1038,15 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla:array2d", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1058,9 +1066,9 @@ xla_test( "//tensorflow/compiler/xla:array3d", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1079,6 +1087,7 @@ xla_test( deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", @@ -1088,9 +1097,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1115,9 +1124,9 @@ xla_test_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1149,16 +1158,16 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla:array2d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1176,10 +1185,10 @@ xla_test( deps = [ ":client_library_test_base", "//tensorflow/compiler/xla:array2d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1228,12 +1237,13 @@ xla_test( "enable_for_xla_interpreter", ], deps = [ + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1246,11 +1256,12 @@ xla_test( name = "custom_call_test", srcs = ["custom_call_test.cc"], deps = [ + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service/cpu:custom_call_target_registry", "//tensorflow/compiler/xla/tests:client_library_test_base", @@ -1273,8 +1284,8 @@ xla_test( "//tensorflow/compiler/xla:array4d", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1291,12 +1302,13 @@ xla_test( deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1316,8 +1328,8 @@ xla_test( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1334,8 +1346,8 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1351,8 +1363,8 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1368,7 +1380,7 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla:array2d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", @@ -1376,8 +1388,8 @@ xla_test( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -1391,14 +1403,14 @@ xla_test( name = "prng_test", srcs = ["prng_test.cc"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -1416,6 +1428,7 @@ xla_test( deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", @@ -1426,8 +1439,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1446,7 +1459,7 @@ xla_test( "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1469,9 +1482,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1495,8 +1508,8 @@ xla_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1515,8 +1528,8 @@ xla_test( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1530,15 +1543,15 @@ xla_test( name = "cross_replica_sum_test", srcs = ["cross_replica_sum_test.cc"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", @@ -1560,7 +1573,7 @@ xla_test( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1574,15 +1587,15 @@ xla_test( name = "compilation_cache_test", srcs = ["compilation_cache_test.cc"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla:xla_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -1600,8 +1613,8 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1614,7 +1627,7 @@ xla_test( name = "compute_constant_test", srcs = ["compute_constant_test.cc"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -1623,8 +1636,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:global_data", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1644,8 +1657,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -1661,8 +1674,8 @@ xla_test( deps = [ ":client_library_test_base", "//tensorflow/compiler/xla/client:global_data", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", ], @@ -1675,8 +1688,8 @@ xla_test( deps = [ ":client_library_test_base", "//tensorflow/compiler/xla/client:global_data", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", ], @@ -1689,15 +1702,15 @@ xla_test( "enable_for_xla_interpreter", ], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:protobuf_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:hlo_proto", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1714,7 +1727,7 @@ xla_test( "enable_for_xla_interpreter", ], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", @@ -1731,6 +1744,7 @@ tf_cc_test( srcs = ["llvm_compiler_test.cc"], tags = ["requires-gpu-sm35"], deps = [ + "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla/service:backend", "//tensorflow/compiler/xla/service:cpu_plugin", @@ -1751,7 +1765,7 @@ xla_test( name = "round_trip_packed_literal_test", srcs = ["round_trip_packed_literal_test.cc"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:packed_literal_reader", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", @@ -1774,14 +1788,14 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla:array2d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/service:hlo_runner", @@ -1802,14 +1816,14 @@ xla_test( srcs = ["multioutput_fusion_test.cc"], deps = [ "//tensorflow/compiler/xla:array2d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_runner", "//tensorflow/compiler/xla/service:platform_util", @@ -1842,11 +1856,11 @@ xla_test( name = "local_client_allocation_test", srcs = ["local_client_allocation_test.cc"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:local_service", "//tensorflow/compiler/xla/service:shaped_buffer", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1865,7 +1879,7 @@ xla_test( shard_count = 30, tags = ["optonly"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", @@ -1873,8 +1887,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:device_memory_allocator", "//tensorflow/compiler/xla/service:local_service", "//tensorflow/compiler/xla/service:platform_util", @@ -1890,6 +1904,16 @@ xla_test( ], ) +xla_test( + name = "outfeed_in_nested_computation_test", + srcs = ["outfeed_in_nested_computation_test.cc"], + deps = [ + "//tensorflow/compiler/xla/tests:local_client_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:test", + ], +) + tf_cc_test( name = "hlo_metadata_test", srcs = [ @@ -1899,7 +1923,7 @@ tf_cc_test( ":local_client_test_base", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:cpu_plugin", "//tensorflow/compiler/xla/service:local_service", "//tensorflow/core:test_main", @@ -1911,7 +1935,7 @@ xla_test( srcs = ["round_trip_transfer_test.cc"], deps = [ "//tensorflow/compiler/xla:array4d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:xla_data_proto", @@ -1932,7 +1956,7 @@ xla_test( deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", @@ -1941,8 +1965,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1955,7 +1979,7 @@ xla_test( name = "deep_graph_test", srcs = ["deep_graph_test.cc"], deps = [ - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", ], @@ -1980,7 +2004,7 @@ xla_test( ":literal_test_util", ":local_client_test_base", ":xla_internal_test_main", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:types", @@ -1988,6 +2012,7 @@ xla_test( "//tensorflow/compiler/xla/service:device_memory_allocator", "//tensorflow/compiler/xla/service:generic_transfer_manager", "//tensorflow/compiler/xla/service:shaped_buffer", + "//tensorflow/compiler/xla/service:stream_pool", "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", "//tensorflow/core:test", @@ -2040,10 +2065,30 @@ xla_test( ":local_client_test_base", ":test_utils", "//tensorflow/compiler/xla:shape_util", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", ], ) + +xla_test( + name = "iota_test", + srcs = ["iota_test.cc"], + blacklisted_backends = [ + "cpu", + "gpu", + ], + tags = [ + "enable_for_xla_interpreter", + ], + deps = [ + ":client_library_test_base", + ":literal_test_util", + ":xla_internal_test_main", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/core:lib", + "//tensorflow/core:test", + ], +) diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc index 3bdf98544affca11fd825e28d20f4903188fe920..74f2e36f826cd82ce4015df857f3de67950beaeb 100644 --- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc +++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc @@ -24,9 +24,9 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" @@ -225,7 +225,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantU64s) { 0x8000000000000000LL, 0x8000000000000000LL, 1}; - std::unique_ptr lhs_literal = Literal::CreateR1({lhs}); + std::unique_ptr lhs_literal = LiteralUtil::CreateR1({lhs}); auto lhs_param = Parameter(&b, 0, lhs_literal->shape(), "lhs_param"); std::unique_ptr lhs_data = client_->TransferToServer(*lhs_literal).ConsumeValueOrDie(); @@ -239,7 +239,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantU64s) { 0, 1, 0x8000000000000000LL}; - std::unique_ptr rhs_literal = Literal::CreateR1({rhs}); + std::unique_ptr rhs_literal = LiteralUtil::CreateR1({rhs}); auto rhs_param = Parameter(&b, 1, rhs_literal->shape(), "rhs_param"); std::unique_ptr rhs_data = client_->TransferToServer(*rhs_literal).ConsumeValueOrDie(); @@ -265,7 +265,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantS64s) { 1, 0, -1}; - std::unique_ptr lhs_literal = Literal::CreateR1({lhs}); + std::unique_ptr lhs_literal = LiteralUtil::CreateR1({lhs}); auto lhs_param = Parameter(&b, 0, lhs_literal->shape(), "lhs_param"); std::unique_ptr lhs_data = client_->TransferToServer(*lhs_literal).ConsumeValueOrDie(); @@ -278,7 +278,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantS64s) { 0x7FFFFFFFFFFFFFFLL, 0x7FFFFFFFFFFFFFFFLL, 0x7FFFFFFFFFFFFFFFLL}; - std::unique_ptr rhs_literal = Literal::CreateR1({rhs}); + std::unique_ptr rhs_literal = LiteralUtil::CreateR1({rhs}); auto rhs_param = Parameter(&b, 1, rhs_literal->shape(), "rhs_param"); std::unique_ptr rhs_data = client_->TransferToServer(*rhs_literal).ConsumeValueOrDie(); @@ -303,13 +303,13 @@ TEST_P(ArrayElementwiseOpTestParamCount, AddManyValues) { b_values.push_back(2 * i / static_cast(count + 2)); } - std::unique_ptr a_literal = Literal::CreateR1({a_values}); + std::unique_ptr a_literal = LiteralUtil::CreateR1({a_values}); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); auto a_constant = ConstantR1(&builder, a_values); auto a_param = Parameter(&builder, 0, a_literal->shape(), "a_param"); - std::unique_ptr b_literal = Literal::CreateR1({b_values}); + std::unique_ptr b_literal = LiteralUtil::CreateR1({b_values}); std::unique_ptr b_data = client_->TransferToServer(*b_literal).ConsumeValueOrDie(); auto b_constant = Parameter(&builder, 1, a_literal->shape(), "b_param"); @@ -1426,7 +1426,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, PowSpecialF32) { std::vector values = {1.0f, 2.0f, 3.2f, -4.0f}; std::vector exponents = {0.0f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f}; - std::unique_ptr param_literal = Literal::CreateR1(values); + std::unique_ptr param_literal = LiteralUtil::CreateR1(values); std::unique_ptr param_data = client_->TransferToServer(*param_literal).ConsumeValueOrDie(); @@ -1454,10 +1454,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, PowOfExpF32) { std::vector values0 = {1.0f, 2.0f, 3.2f, -4.0f, 0.0f, 5.7f}; std::vector values1 = {0.0f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f}; - std::unique_ptr literal0 = Literal::CreateR1(values0); + std::unique_ptr literal0 = LiteralUtil::CreateR1(values0); std::unique_ptr data0 = client_->TransferToServer(*literal0).ConsumeValueOrDie(); - std::unique_ptr literal1 = Literal::CreateR1(values1); + std::unique_ptr literal1 = LiteralUtil::CreateR1(values1); std::unique_ptr data1 = client_->TransferToServer(*literal1).ConsumeValueOrDie(); auto param0 = Parameter(&b, 0, literal0->shape(), "param0"); @@ -1479,10 +1479,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, LogOfPowerF32) { std::vector values0 = {1.0f, 2.0f, 3.2f, 4.0f, 0.5f, 5.7f}; std::vector values1 = {0.0f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f}; - std::unique_ptr literal0 = Literal::CreateR1(values0); + std::unique_ptr literal0 = LiteralUtil::CreateR1(values0); std::unique_ptr data0 = client_->TransferToServer(*literal0).ConsumeValueOrDie(); - std::unique_ptr literal1 = Literal::CreateR1(values1); + std::unique_ptr literal1 = LiteralUtil::CreateR1(values1); std::unique_ptr data1 = client_->TransferToServer(*literal1).ConsumeValueOrDie(); auto param0 = Parameter(&b, 0, literal0->shape(), "param0"); @@ -1504,10 +1504,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, MulOfExpF32) { std::vector values0 = {1.0f, 2.0f, 3.2f, -4.0f, 0.0f, 5.7f}; std::vector values1 = {0.0f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f}; - std::unique_ptr literal0 = Literal::CreateR1(values0); + std::unique_ptr literal0 = LiteralUtil::CreateR1(values0); std::unique_ptr data0 = client_->TransferToServer(*literal0).ConsumeValueOrDie(); - std::unique_ptr literal1 = Literal::CreateR1(values1); + std::unique_ptr literal1 = LiteralUtil::CreateR1(values1); std::unique_ptr data1 = client_->TransferToServer(*literal1).ConsumeValueOrDie(); auto param0 = Parameter(&b, 0, literal0->shape(), "param0"); @@ -1529,10 +1529,10 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivOfExpF32) { std::vector values0 = {1.0f, 2.0f, 3.2f, -4.0f, 0.0f, 5.7f}; std::vector values1 = {0.0f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f}; - std::unique_ptr literal0 = Literal::CreateR1(values0); + std::unique_ptr literal0 = LiteralUtil::CreateR1(values0); std::unique_ptr data0 = client_->TransferToServer(*literal0).ConsumeValueOrDie(); - std::unique_ptr literal1 = Literal::CreateR1(values1); + std::unique_ptr literal1 = LiteralUtil::CreateR1(values1); std::unique_ptr data1 = client_->TransferToServer(*literal1).ConsumeValueOrDie(); auto param0 = Parameter(&b, 0, literal0->shape(), "param0"); @@ -1555,15 +1555,15 @@ XLA_TEST_F(ArrayElementwiseOpTest, Div3_lhs_F32) { std::vector values1 = {0.1f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f}; std::vector values2 = {0.1f, 1.1f, 6.9f, 12.5f, -15.0f, -0.5f}; - std::unique_ptr literal0 = Literal::CreateR1(values0); + std::unique_ptr literal0 = LiteralUtil::CreateR1(values0); std::unique_ptr data0 = client_->TransferToServer(*literal0).ConsumeValueOrDie(); - std::unique_ptr literal1 = Literal::CreateR1(values1); + std::unique_ptr literal1 = LiteralUtil::CreateR1(values1); std::unique_ptr data1 = client_->TransferToServer(*literal1).ConsumeValueOrDie(); - std::unique_ptr literal2 = Literal::CreateR1(values2); + std::unique_ptr literal2 = LiteralUtil::CreateR1(values2); std::unique_ptr data2 = client_->TransferToServer(*literal2).ConsumeValueOrDie(); auto param0 = Parameter(&b, 0, literal0->shape(), "param0"); @@ -1587,15 +1587,15 @@ XLA_TEST_F(ArrayElementwiseOpTest, Div3_rhs_F32) { std::vector values1 = {0.1f, 1.0f, 2.0f, 0.5f, -1.0f, -0.5f}; std::vector values2 = {0.1f, 1.1f, 6.9f, 12.5f, -15.0f, -0.5f}; - std::unique_ptr literal0 = Literal::CreateR1(values0); + std::unique_ptr literal0 = LiteralUtil::CreateR1(values0); std::unique_ptr data0 = client_->TransferToServer(*literal0).ConsumeValueOrDie(); - std::unique_ptr literal1 = Literal::CreateR1(values1); + std::unique_ptr literal1 = LiteralUtil::CreateR1(values1); std::unique_ptr data1 = client_->TransferToServer(*literal1).ConsumeValueOrDie(); - std::unique_ptr literal2 = Literal::CreateR1(values2); + std::unique_ptr literal2 = LiteralUtil::CreateR1(values2); std::unique_ptr data2 = client_->TransferToServer(*literal2).ConsumeValueOrDie(); @@ -1620,15 +1620,15 @@ XLA_TEST_F(ArrayElementwiseOpTest, DivOfPowerF32) { std::vector values1 = {0.1f, 1.0f, 2.0f, 0.5f, 1.0f, 0.5f}; std::vector values2 = {0.1f, 1.1f, 6.9f, 9.5f, -11.0f, -0.5f}; - std::unique_ptr literal0 = Literal::CreateR1(values0); + std::unique_ptr literal0 = LiteralUtil::CreateR1(values0); std::unique_ptr data0 = client_->TransferToServer(*literal0).ConsumeValueOrDie(); - std::unique_ptr literal1 = Literal::CreateR1(values1); + std::unique_ptr literal1 = LiteralUtil::CreateR1(values1); std::unique_ptr data1 = client_->TransferToServer(*literal1).ConsumeValueOrDie(); - std::unique_ptr literal2 = Literal::CreateR1(values2); + std::unique_ptr literal2 = LiteralUtil::CreateR1(values2); std::unique_ptr data2 = client_->TransferToServer(*literal2).ConsumeValueOrDie(); @@ -1654,19 +1654,19 @@ XLA_TEST_F(ArrayElementwiseOpTest, Div4F32) { std::vector values2 = {0.1f, 1.1f, 6.9f, 12.5f, -15.0f, -0.5f}; std::vector values3 = {2.1f, 3.1f, 9.9f, -4.5f, -11.0f, -21.5f}; - std::unique_ptr literal0 = Literal::CreateR1(values0); + std::unique_ptr literal0 = LiteralUtil::CreateR1(values0); std::unique_ptr data0 = client_->TransferToServer(*literal0).ConsumeValueOrDie(); - std::unique_ptr literal1 = Literal::CreateR1(values1); + std::unique_ptr literal1 = LiteralUtil::CreateR1(values1); std::unique_ptr data1 = client_->TransferToServer(*literal1).ConsumeValueOrDie(); - std::unique_ptr literal2 = Literal::CreateR1(values2); + std::unique_ptr literal2 = LiteralUtil::CreateR1(values2); std::unique_ptr data2 = client_->TransferToServer(*literal2).ConsumeValueOrDie(); - std::unique_ptr literal3 = Literal::CreateR1(values3); + std::unique_ptr literal3 = LiteralUtil::CreateR1(values3); std::unique_ptr data3 = client_->TransferToServer(*literal3).ConsumeValueOrDie(); @@ -2101,12 +2101,12 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoParametersF32s) { XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR1({1.1f, 2.2f, 3.3f, 5.5f}); + LiteralUtil::CreateR1({1.1f, 2.2f, 3.3f, 5.5f}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); std::unique_ptr param1_literal = - Literal::CreateR1({7.2f, 2.3f, 3.4f, 5.6f}); + LiteralUtil::CreateR1({7.2f, 2.3f, 3.4f, 5.6f}); std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); @@ -2123,12 +2123,12 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoParametersZeroElementF32s) { XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR3FromArray3D(Array3D(0, 7, 0)); + LiteralUtil::CreateR3FromArray3D(Array3D(0, 7, 0)); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); std::unique_ptr param1_literal = - Literal::CreateR3FromArray3D(Array3D(0, 7, 0)); + LiteralUtil::CreateR3FromArray3D(Array3D(0, 7, 0)); std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); @@ -2145,7 +2145,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddParameterToConstantF32s) { XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR1({1.1f, 2.2f, 3.3f, 5.5f}); + LiteralUtil::CreateR1({1.1f, 2.2f, 3.3f, 5.5f}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -2201,7 +2201,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, TanhF32sVector) { // the input tensor is large enough to exercise the vectorized tanh // implementation on XLA CPU. XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateR1( + auto input_literal = LiteralUtil::CreateR1( {1.02, -0.32, 0.85, 0.90, 1.23, -0.91, -0.49, 0.80, -0.67, 0.16, -0.07, 0.39, -0.41, 0.04, 1.36, 1.25, 0.41, 0.65, -1.08, 0.32, -1.45, -0.77, -1.09, 0.91, -1.03, -0.30, -1.11, -1.17, 1.50, -0.85, @@ -2243,7 +2243,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, ExpF32sVector) { // Just to help make sense of the scales here -- exp(89) saturates float32 and // exp(-10) is smaller than our error spec. - std::unique_ptr input_literal = Literal::CreateR1( + std::unique_ptr input_literal = LiteralUtil::CreateR1( {1.02, -0.32, 0.85, 0.9, 1.23, -0.91, -0.49, 0.8, -1.31, -1.44, -0.13, -1.31, -0.79, 1.41, 1.21, 1.05, -195.6, -194.5, -193.4, -192.3, -191.2, -190.1, -189.0, -187.9, -19.6, -18.5, -17.4, @@ -2277,7 +2277,7 @@ XLA_TEST_F(ArrayElementwiseOpTest, LogF32sVector) { // implementation on XLA CPU. XlaBuilder builder(TestName()); - std::unique_ptr input_literal = Literal::CreateR1( + std::unique_ptr input_literal = LiteralUtil::CreateR1( {-1.29, -1.41, -1.25, -13.5, -11.7, -17.9, -198, -167, 1.29, 1.41, 1.25, 13.5, 11.7, 17.9, 198, 167, 1.27e+03, 1.33e+03, 1.74e+03, 1.6e+04, 1.84e+04, @@ -2469,9 +2469,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, Compare1DTo2DS32Eq) { auto cmp_dim_1 = Eq(v, m, /*broadcast_dimensions=*/{0}); Tuple(&builder, {cmp_dim_0, cmp_dim_1}); - auto expected = Literal::MakeTuple( - {Literal::CreateR2({{true, true}, {true, false}}).get(), - Literal::CreateR2({{true, false}, {false, false}}).get()}); + auto expected = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR2({{true, true}, {true, false}}).get(), + LiteralUtil::CreateR2({{true, false}, {false, false}}).get()}); ComputeAndCompareTuple(&builder, *expected, {}, error_spec_); } @@ -2825,8 +2825,9 @@ XLA_TEST_F(ArrayElementwiseOpTest, R4_16x16x2x2_Plus_R1_16) { std::iota(r1.begin(), r1.end(), 1.0); XlaBuilder builder(TestName()); - std::unique_ptr a_literal = Literal::CreateR4FromArray4DWithLayout( - r4, LayoutUtil::MakeLayout({0, 1, 2, 3})); + std::unique_ptr a_literal = + LiteralUtil::CreateR4FromArray4DWithLayout( + r4, LayoutUtil::MakeLayout({0, 1, 2, 3})); auto a = ConstantLiteral(&builder, *a_literal); auto b = ConstantR1(&builder, r1); Add(a, b, {1}); @@ -2887,8 +2888,8 @@ XLA_TEST_F(ArrayElementwiseOpTest, NonIdentityBroadcastOfSameRankIsDisallowed) { // broadcast. XLA_TEST_F(ArrayElementwiseOpTest, ImplictBroadcastInFusedExpressions) { XlaBuilder builder(TestName()); - auto x_literal = Literal::CreateR1({1, 2, 3}); - auto y_literal = Literal::CreateR1({4, 5}); + auto x_literal = LiteralUtil::CreateR1({1, 2, 3}); + auto y_literal = LiteralUtil::CreateR1({4, 5}); auto x_data = client_->TransferToServer(*x_literal).ConsumeValueOrDie(); auto y_data = client_->TransferToServer(*y_literal).ConsumeValueOrDie(); diff --git a/tensorflow/compiler/xla/tests/axpy_simple_test.cc b/tensorflow/compiler/xla/tests/axpy_simple_test.cc index 8d15b7841bc7298cd6865d8689cc496c0459e4b9..caeb0bf49a0dde9eeac02037b2ea04fd024d100c 100644 --- a/tensorflow/compiler/xla/tests/axpy_simple_test.cc +++ b/tensorflow/compiler/xla/tests/axpy_simple_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" diff --git a/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc b/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc index 8c227df7f04e79ccc332062d0889d282c0f5e40f..af0b8522394a0c591e6c42ad12db8853ef66243c 100644 --- a/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc +++ b/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc @@ -19,8 +19,8 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" diff --git a/tensorflow/compiler/xla/tests/batch_normalization_test.cc b/tensorflow/compiler/xla/tests/batch_normalization_test.cc index 217673c8cbc212958fe79b67546f28b0be091803..d372d1ca434b1da416f671060f9461cf07aa5fc4 100644 --- a/tensorflow/compiler/xla/tests/batch_normalization_test.cc +++ b/tensorflow/compiler/xla/tests/batch_normalization_test.cc @@ -22,9 +22,9 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/math.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -63,7 +63,7 @@ class BatchNormalizationTest {5.0f, 4.4f}, // p2 }); input_array_.FillWithPZ(pz); - input_literal_ = std::move(*Literal::CreateR4FromArray4D(input_array_)); + input_literal_ = std::move(*LiteralUtil::CreateR4FromArray4D(input_array_)); CHECK_EQ(kSamples, input_array_.planes()); CHECK_EQ(kZ, input_array_.depth()); CHECK_EQ(kY, input_array_.height()); @@ -242,12 +242,12 @@ XLA_TEST_P(BatchNormalizationTest, BasicTraining) { BatchNormTraining(operand, scale, offset, /*epsilon=*/0.001, kFeatureIndex); - auto expected = Literal::MakeTuple( - {Literal::CreateR4({{{{-1.6f, -2.0f}}, {{0.1f, 0.6f}}}, - {{{1.9f, 3.3f}}, {{3.7f, 6.0f}}}}) + auto expected = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR4({{{{-1.6f, -2.0f}}, {{0.1f, 0.6f}}}, + {{{1.9f, 3.3f}}, {{3.7f, 6.0f}}}}) .get(), - Literal::CreateR1({4, 5}).get(), - Literal::CreateR1({5, 5}).get()}); + LiteralUtil::CreateR1({4, 5}).get(), + LiteralUtil::CreateR1({5, 5}).get()}); ComputeAndCompareTuple(&builder, *expected, {}, ErrorSpec(0.1)); } @@ -267,12 +267,12 @@ XLA_TEST_P(BatchNormalizationTest, BasicTrainingOnDimension2) { BatchNormTraining(operand, scale, offset, /*epsilon=*/0.001, kFeatureIndex); - auto expected = Literal::MakeTuple( - {Literal::CreateR4({{{{-1.6f}, {-2.0f}}, {{0.1f}, {0.6f}}}, - {{{1.9f}, {3.3f}}, {{3.7f}, {6.0f}}}}) + auto expected = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR4({{{{-1.6f}, {-2.0f}}, {{0.1f}, {0.6f}}}, + {{{1.9f}, {3.3f}}, {{3.7f}, {6.0f}}}}) .get(), - Literal::CreateR1({4, 5}).get(), - Literal::CreateR1({5, 5}).get()}); + LiteralUtil::CreateR1({4, 5}).get(), + LiteralUtil::CreateR1({5, 5}).get()}); ComputeAndCompareTuple(&builder, *expected, {}, ErrorSpec(0.1)); } @@ -298,11 +298,11 @@ XLA_TEST_P(BatchNormalizationTest, TrainingWithFeatureOnLowDimension) { BatchNormTraining(h0, h1, h2, /*epsilon=*/1, kFeatureIndex); - auto expected = Literal::MakeTuple( - {Literal::CreateR3FromArray3D(Array3D(260, 2, 2, 1.0f)) + auto expected = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR3FromArray3D(Array3D(260, 2, 2, 1.0f)) .get(), - Literal::CreateR1(std::vector(260, 1.0f)).get(), - Literal::CreateR1(std::vector(260, 0.0f)).get()}); + LiteralUtil::CreateR1(std::vector(260, 1.0f)).get(), + LiteralUtil::CreateR1(std::vector(260, 0.0f)).get()}); ComputeAndCompareTuple(&builder, *expected, {operand.get(), scale.get(), offset.get()}, @@ -331,11 +331,12 @@ XLA_TEST_P(BatchNormalizationTest, LargeEpsilonTest) { BatchNormTraining(h0, h1, h2, /*epsilon=*/-100, kFeatureIndex); - auto expected = Literal::MakeTuple( - {Literal::CreateR3FromArray3D({{{-3.0f}, {-1.0f}, {1.0f}, {3.0f}}}) + auto expected = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR3FromArray3D( + {{{-3.0f}, {-1.0f}, {1.0f}, {3.0f}}}) .get(), - Literal::CreateR1(std::vector(1, 15.0f)).get(), - Literal::CreateR1(std::vector(1, 125.0f)).get()}); + LiteralUtil::CreateR1(std::vector(1, 15.0f)).get(), + LiteralUtil::CreateR1(std::vector(1, 125.0f)).get()}); ComputeAndCompareTuple(&builder, *expected, {operand.get(), scale.get(), offset.get()}, @@ -362,12 +363,12 @@ XLA_TEST_P(BatchNormalizationTest, BatchNormGradBasic) { BatchNormGrad(operand, scale, mean, var, grad_output, /*epsilon=*/0.0, kFeatureIndex); - auto expected = Literal::MakeTuple( - {Literal::CreateR4({{{{-3.f}, {-3.f}}, {{-1.f}, {-1.f}}}, - {{{1.f}, {1.f}}, {{3.f}, {3.f}}}}) + auto expected = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR4({{{{-3.f}, {-3.f}}, {{-1.f}, {-1.f}}}, + {{{1.f}, {1.f}}, {{3.f}, {3.f}}}}) .get(), - Literal::CreateR1({0, 0}).get(), - Literal::CreateR1({16, 20}).get()}); + LiteralUtil::CreateR1({0, 0}).get(), + LiteralUtil::CreateR1({16, 20}).get()}); ComputeAndCompareTuple(&builder, *expected, {}, ErrorSpec(0.1)); } @@ -513,11 +514,12 @@ XLA_TEST_P(BatchNormTestManySizes, RandomizedTrainingTests) { auto normalized = *ReferenceUtil::BatchNorm4D(input_array, mean4D, var4D, scale4D, offset4D, epsilon); - auto expected_normalized = Literal::CreateR4FromArray4D(normalized); + auto expected_normalized = + LiteralUtil::CreateR4FromArray4D(normalized); - auto offset_literal = Literal::CreateR1(offset); - auto scale_literal = Literal::CreateR1(scale); - auto input_literal = Literal::CreateR4FromArray4D(input_array); + auto offset_literal = LiteralUtil::CreateR1(offset); + auto scale_literal = LiteralUtil::CreateR1(scale); + auto input_literal = LiteralUtil::CreateR4FromArray4D(input_array); auto input_activations = Parameter(&builder, 0, input_literal->shape(), "input"); @@ -526,9 +528,9 @@ XLA_TEST_P(BatchNormTestManySizes, RandomizedTrainingTests) { auto offset_activations = Parameter(&builder, 2, offset_literal->shape(), "scale"); - auto expected = Literal::MakeTuple({expected_normalized.get(), - Literal::CreateR1(mean).get(), - Literal::CreateR1(var).get()}); + auto expected = LiteralUtil::MakeTuple( + {expected_normalized.get(), LiteralUtil::CreateR1(mean).get(), + LiteralUtil::CreateR1(var).get()}); std::unique_ptr input_data = client_->TransferToServer(*input_literal).ConsumeValueOrDie(); @@ -613,11 +615,11 @@ XLA_TEST_P(BatchNormTestManySizes, RandomizedInferencingTests) { auto normalized = *ReferenceUtil::BatchNorm4D(input_array, mean4D, var4D, scale4D, offset4D, epsilon); - auto offset_literal = Literal::CreateR1(offset); - auto scale_literal = Literal::CreateR1(scale); - auto mean_literal = Literal::CreateR1(mean); - auto var_literal = Literal::CreateR1(var); - auto input_literal = Literal::CreateR4FromArray4D(input_array); + auto offset_literal = LiteralUtil::CreateR1(offset); + auto scale_literal = LiteralUtil::CreateR1(scale); + auto mean_literal = LiteralUtil::CreateR1(mean); + auto var_literal = LiteralUtil::CreateR1(var); + auto input_literal = LiteralUtil::CreateR4FromArray4D(input_array); auto input_activations = Parameter(&builder, 0, input_literal->shape(), "input"); @@ -800,14 +802,14 @@ XLA_TEST_P(BatchNormTestManySizes, RandomizedGradTests) { }); auto expected_grad_activation = - Literal::CreateR4FromArray4D(grad_activation); + LiteralUtil::CreateR4FromArray4D(grad_activation); - auto input_literal = Literal::CreateR4FromArray4D(input_array); - auto scale_literal = Literal::CreateR1(scale); - auto mean_literal = Literal::CreateR1(mean); - auto var_literal = Literal::CreateR1(var); + auto input_literal = LiteralUtil::CreateR4FromArray4D(input_array); + auto scale_literal = LiteralUtil::CreateR1(scale); + auto mean_literal = LiteralUtil::CreateR1(mean); + auto var_literal = LiteralUtil::CreateR1(var); auto grad_output_literal = - Literal::CreateR4FromArray4D(grad_output_array); + LiteralUtil::CreateR4FromArray4D(grad_output_array); auto input_parameter = Parameter(&builder, 0, input_literal->shape(), "input"); @@ -833,9 +835,9 @@ XLA_TEST_P(BatchNormTestManySizes, RandomizedGradTests) { grad_output_parameter, epsilon, feature_index); auto expected = - Literal::MakeTuple({expected_grad_activation.get(), - Literal::CreateR1(grad_scale).get(), - Literal::CreateR1(grad_offset).get()}); + LiteralUtil::MakeTuple({expected_grad_activation.get(), + LiteralUtil::CreateR1(grad_scale).get(), + LiteralUtil::CreateR1(grad_offset).get()}); // Run all HLO passes during this test. In particular, ClientLibraryTestBase // disables constant folding, but we want it enabled for our zero-sized tensor diff --git a/tensorflow/compiler/xla/tests/bfloat16_test.cc b/tensorflow/compiler/xla/tests/bfloat16_test.cc index f40d03bea79de2a78814a0ad9f6cae6098d1449b..6c20f654fe3df6a28e9633cd832c11b487894bad 100644 --- a/tensorflow/compiler/xla/tests/bfloat16_test.cc +++ b/tensorflow/compiler/xla/tests/bfloat16_test.cc @@ -21,8 +21,8 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -95,18 +95,18 @@ XLA_TEST_F(Bfloat16Test, BatchNormTraining) { BatchNormTraining(operand, scale, offset, /*epsilon=*/0.001, kFeatureIndex); - auto expected = Literal::MakeTuple( - {Literal::CreateR4( + auto expected = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR4( {{{{static_cast(-1.6875f)}, {static_cast(-2.04f)}}, {{static_cast(0.105f)}, {static_cast(0.66f)}}}, {{{static_cast(1.89f)}, {static_cast(3.35f)}}, {{static_cast(3.7f)}, {static_cast(6.04f)}}}}) .get(), - Literal::CreateR1( + LiteralUtil::CreateR1( {static_cast(4), static_cast(5)}) .get(), - Literal::CreateR1( + LiteralUtil::CreateR1( {static_cast(5), static_cast(5)}) .get()}); @@ -139,17 +139,17 @@ XLA_TEST_F(Bfloat16Test, BatchNormGrad) { BatchNormGrad(operand, scale, mean, var, grad_output, /*epsilon=*/0.0, kFeatureIndex); - auto expected = Literal::MakeTuple( - {Literal::CreateR4( + auto expected = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR4( {{{{static_cast(-3.f)}, {static_cast(-3.f)}}, {{static_cast(-1.f)}, {static_cast(-1.f)}}}, {{{static_cast(1.f)}, {static_cast(1.f)}}, {{static_cast(3.f)}, {static_cast(3.f)}}}}) .get(), - Literal::CreateR1( + LiteralUtil::CreateR1( {static_cast(0), static_cast(0)}) .get(), - Literal::CreateR1( + LiteralUtil::CreateR1( {static_cast(16), static_cast(20)}) .get()}); diff --git a/tensorflow/compiler/xla/tests/binop_scaling_test.cc b/tensorflow/compiler/xla/tests/binop_scaling_test.cc index 20cb989751ad69e2f3cf97c87c43293951f599ab..0d7a3aa46a9c12c19d954c11ae3a2cccbed886ef 100644 --- a/tensorflow/compiler/xla/tests/binop_scaling_test.cc +++ b/tensorflow/compiler/xla/tests/binop_scaling_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" diff --git a/tensorflow/compiler/xla/tests/bitcast_convert_test.cc b/tensorflow/compiler/xla/tests/bitcast_convert_test.cc index d531e8fa82e47f7bcd278f10da2c205e44db0ac1..c6b5108fe9e5bcf843982676d822f1942359da71 100644 --- a/tensorflow/compiler/xla/tests/bitcast_convert_test.cc +++ b/tensorflow/compiler/xla/tests/bitcast_convert_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" diff --git a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc index 91aba9a8de3f1fe098e8bc8cc9d5378fa67b8385..1d28e85b16596b0ec2717138fb2081878203e8b2 100644 --- a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc +++ b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc @@ -20,7 +20,8 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" @@ -58,7 +59,7 @@ class BroadcastSimpleTest : public ClientLibraryTestBase { Array3D* r3_array, float start, float end, int seed) { *r3_shape = ShapeUtil::MakeShapeWithLayout(F32, bounds, minor_to_major); r3_array->FillRandom(start, end, seed); - auto r3_data = Literal::CreateR3FromArray3D(*r3_array)->Relayout( + auto r3_data = LiteralUtil::CreateR3FromArray3D(*r3_array)->Relayout( LayoutUtil::MakeLayout(minor_to_major)); std::unique_ptr r3_global_data = client_->TransferToServer(*r3_data).ConsumeValueOrDie(); @@ -71,7 +72,7 @@ class BroadcastSimpleTest : public ClientLibraryTestBase { Array2D* r2_array, float start, float end, int seed) { *r2_shape = ShapeUtil::MakeShapeWithLayout(F32, bounds, minor_to_major); r2_array->FillRandom(start, end, seed); - auto r2_data = Literal::CreateR2FromArray2D(*r2_array)->Relayout( + auto r2_data = LiteralUtil::CreateR2FromArray2D(*r2_array)->Relayout( LayoutUtil::MakeLayout(minor_to_major)); std::unique_ptr r2_global_data = client_->TransferToServer(*r2_data).ConsumeValueOrDie(); @@ -290,13 +291,13 @@ XLA_TEST_F(BroadcastSimpleTest, InDimensionAndDegenerateBroadcasting) { XlaBuilder b(TestName()); Add(ConstantR2(&b, {{1.0, 5.0}}), - ConstantLiteral(&b, *Literal::CreateR3( + ConstantLiteral(&b, *LiteralUtil::CreateR3( {{{2.0}, {3.0}, {4.0}}, {{5.0}, {6.0}, {7.0}}})), /*broadcast_dimensions=*/{1, 2}); auto expected = - Literal::CreateR3({{{3.0, 7.0}, {4.0, 8.0}, {5.0, 9.0}}, - {{6.0, 10.0}, {7.0, 11.0}, {8.0, 12.0}}}); + LiteralUtil::CreateR3({{{3.0, 7.0}, {4.0, 8.0}, {5.0, 9.0}}, + {{6.0, 10.0}, {7.0, 11.0}, {8.0, 12.0}}}); ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001)); } @@ -365,7 +366,7 @@ XLA_TEST_P(BroadcastR3ImplicitTest, Doit) { } } } - auto expected = Literal::CreateR3FromArray3D(expected_array); + auto expected = LiteralUtil::CreateR3FromArray3D(expected_array); ComputeAndCompareLiteral( &builder, *expected, {r3_implicit_global_data.get(), r3_global_data.get()}, @@ -390,7 +391,7 @@ XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_1_2) { Add(r3h, r1h); auto expected = - Literal::CreateR3({{{2, 3}, {4, 5}}, {{7, 8}, {9, 10}}}); + LiteralUtil::CreateR3({{{2, 3}, {4, 5}}, {{7, 8}, {9, 10}}}); ComputeAndCompareLiteral(&b, *expected, {r3.get(), r1.get()}, ErrorSpec(0.0001)); @@ -398,39 +399,40 @@ XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_1_2) { XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0_1) { XlaBuilder b(TestName()); - auto r1 = ConstantLiteral(&b, *Literal::CreateR3({{{1, 2}}})); + auto r1 = ConstantLiteral(&b, *LiteralUtil::CreateR3({{{1, 2}}})); auto r3 = ConstantLiteral( - &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + &b, *LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); Add(r3, r1); auto expected = - Literal::CreateR3({{{2, 4}, {4, 6}}, {{6, 8}, {8, 10}}}); + LiteralUtil::CreateR3({{{2, 4}, {4, 6}}, {{6, 8}, {8, 10}}}); ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001)); } XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0_2) { XlaBuilder b(TestName()); - auto r1 = ConstantLiteral(&b, *Literal::CreateR3({{{1}, {2}}})); + auto r1 = ConstantLiteral(&b, *LiteralUtil::CreateR3({{{1}, {2}}})); auto r3 = ConstantLiteral( - &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + &b, *LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); Add(r3, r1); auto expected = - Literal::CreateR3({{{2, 3}, {5, 6}}, {{6, 7}, {9, 10}}}); + LiteralUtil::CreateR3({{{2, 3}, {5, 6}}, {{6, 7}, {9, 10}}}); ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001)); } XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0) { XlaBuilder b(TestName()); - auto r1 = ConstantLiteral(&b, *Literal::CreateR3({{{1, 2}, {3, 4}}})); + auto r1 = + ConstantLiteral(&b, *LiteralUtil::CreateR3({{{1, 2}, {3, 4}}})); auto r3 = ConstantLiteral( - &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + &b, *LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); Add(r3, r1); auto expected = - Literal::CreateR3({{{2, 4}, {6, 8}}, {{6, 8}, {10, 12}}}); + LiteralUtil::CreateR3({{{2, 4}, {6, 8}}, {{6, 8}, {10, 12}}}); ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001)); } @@ -438,40 +440,40 @@ XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0) { XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_1) { XlaBuilder b(TestName()); auto r1 = - ConstantLiteral(&b, *Literal::CreateR3({{{1, 2}}, {{3, 4}}})); + ConstantLiteral(&b, *LiteralUtil::CreateR3({{{1, 2}}, {{3, 4}}})); auto r3 = ConstantLiteral( - &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + &b, *LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); Add(r3, r1); auto expected = - Literal::CreateR3({{{2, 4}, {4, 6}}, {{8, 10}, {10, 12}}}); + LiteralUtil::CreateR3({{{2, 4}, {4, 6}}, {{8, 10}, {10, 12}}}); ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001)); } XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_2) { XlaBuilder b(TestName()); - auto r1 = - ConstantLiteral(&b, *Literal::CreateR3({{{1}, {2}}, {{3}, {4}}})); + auto r1 = ConstantLiteral( + &b, *LiteralUtil::CreateR3({{{1}, {2}}, {{3}, {4}}})); auto r3 = ConstantLiteral( - &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + &b, *LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); Add(r3, r1); auto expected = - Literal::CreateR3({{{2, 3}, {5, 6}}, {{8, 9}, {11, 12}}}); + LiteralUtil::CreateR3({{{2, 3}, {5, 6}}, {{8, 9}, {11, 12}}}); ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001)); } XLA_TEST_F(BroadcastSimpleTest, Add3DTo3DDegenerate_0_1_2) { XlaBuilder b(TestName()); - auto r1 = ConstantLiteral(&b, *Literal::CreateR3({{{1}}})); + auto r1 = ConstantLiteral(&b, *LiteralUtil::CreateR3({{{1}}})); auto r3 = ConstantLiteral( - &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + &b, *LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); Add(r3, r1); auto expected = - Literal::CreateR3({{{2, 3}, {4, 5}}, {{6, 7}, {8, 9}}}); + LiteralUtil::CreateR3({{{2, 3}, {4, 5}}, {{6, 7}, {8, 9}}}); ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001)); } @@ -612,7 +614,7 @@ XLA_TEST_P(BroadcastR2ImplicitTest, Doit) { *v = ApplyOpToFloats(spec.op2, tmp, v3); }); - auto expected = Literal::CreateR2FromArray2D(expected_array); + auto expected = LiteralUtil::CreateR2FromArray2D(expected_array); ComputeAndCompareLiteral( &builder, *expected, {r2_implicit_global_data1.get(), r2_global_data.get(), @@ -626,22 +628,24 @@ INSTANTIATE_TEST_CASE_P(BroadcastR2ImplicitTestInstances, XLA_TEST_F(BroadcastSimpleTest, Add2DTo2DDegenerate_0) { XlaBuilder b(TestName()); - auto r1 = ConstantLiteral(&b, *Literal::CreateR2({{1, 2}})); - auto r2 = ConstantLiteral(&b, *Literal::CreateR2({{1, 2}, {3, 4}})); + auto r1 = ConstantLiteral(&b, *LiteralUtil::CreateR2({{1, 2}})); + auto r2 = + ConstantLiteral(&b, *LiteralUtil::CreateR2({{1, 2}, {3, 4}})); Add(r2, r1); - auto expected = Literal::CreateR2({{2, 4}, {4, 6}}); + auto expected = LiteralUtil::CreateR2({{2, 4}, {4, 6}}); ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001)); } XLA_TEST_F(BroadcastSimpleTest, Add2DTo2DDegenerate_1) { XlaBuilder b(TestName()); - auto r1 = ConstantLiteral(&b, *Literal::CreateR2({{1}, {2}})); - auto r2 = ConstantLiteral(&b, *Literal::CreateR2({{1, 2}, {3, 4}})); + auto r1 = ConstantLiteral(&b, *LiteralUtil::CreateR2({{1}, {2}})); + auto r2 = + ConstantLiteral(&b, *LiteralUtil::CreateR2({{1, 2}, {3, 4}})); Add(r2, r1); - auto expected = Literal::CreateR2({{2, 3}, {5, 6}}); + auto expected = LiteralUtil::CreateR2({{2, 3}, {5, 6}}); ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001)); } @@ -650,11 +654,11 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDim0) { XlaBuilder b(TestName()); auto r1 = ConstantR1(&b, {10, 20}); auto r3 = ConstantLiteral( - &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + &b, *LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); Add(r3, r1, {0}); - auto expected = - Literal::CreateR3({{{11, 12}, {13, 14}}, {{25, 26}, {27, 28}}}); + auto expected = LiteralUtil::CreateR3( + {{{11, 12}, {13, 14}}, {{25, 26}, {27, 28}}}); ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001)); } @@ -663,11 +667,11 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDim1) { XlaBuilder b(TestName()); auto r1 = ConstantR1(&b, {10, 20}); auto r3 = ConstantLiteral( - &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + &b, *LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); Add(r1, r3, {1}); - auto expected = - Literal::CreateR3({{{11, 12}, {23, 24}}, {{15, 16}, {27, 28}}}); + auto expected = LiteralUtil::CreateR3( + {{{11, 12}, {23, 24}}, {{15, 16}, {27, 28}}}); ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001)); } @@ -676,11 +680,11 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDim2) { XlaBuilder b(TestName()); auto r1 = ConstantR1(&b, {10, 20}); auto r3 = ConstantLiteral( - &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + &b, *LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); Add(r1, r3, {2}); - auto expected = - Literal::CreateR3({{{11, 22}, {13, 24}}, {{15, 26}, {17, 28}}}); + auto expected = LiteralUtil::CreateR3( + {{{11, 22}, {13, 24}}, {{15, 26}, {17, 28}}}); ComputeAndCompareLiteral(&b, *expected, {}, ErrorSpec(0.0001)); } @@ -691,7 +695,7 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDimAll) { auto r1_1 = ConstantR1(&b, {100, 200}); auto r1_2 = ConstantR1(&b, {10, 20}); auto r3 = ConstantLiteral( - &b, *Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); + &b, *LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}})); for (int i = 0; i < 3; ++i) { r3 = Add(r1_0, r3, {0}); r3 = Add(r3, r1_1, {1}); @@ -699,7 +703,7 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDimAll) { } r3 = Mul(r3, ConstantR0(&b, -2)); - auto expected = Literal::CreateR3( + auto expected = LiteralUtil::CreateR3( {{{-6 * 1110 - 2, -6 * 1120 - 4}, {-6 * 1210 - 6, -6 * 1220 - 8}}, {{-6 * 2110 - 10, -6 * 2120 - 12}, {-6 * 2210 - 14, -6 * 2220 - 16}}}); @@ -720,7 +724,7 @@ XLA_TEST_F(BroadcastSimpleTest, Add1DTo3DInDimAllWithScalarBroadcast) { } r3 = Mul(r3, ConstantR0(&b, -1)); - auto expected = Literal::CreateR3( + auto expected = LiteralUtil::CreateR3( {{{-3 * 1110 - 3, -3 * 1120 - 3}, {-3 * 1210 - 3, -3 * 1220 - 3}}, {{-3 * 2110 - 3, -3 * 2120 - 3}, {-3 * 2210 - 3, -3 * 2220 - 3}}}); @@ -733,7 +737,7 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidBinaryAndDegenerateBroadcasting) { XlaBuilder b(TestName()); Add(ConstantR2(&b, {{1.0, 5.0}, {1.0, 5.0}}), - ConstantLiteral(&b, *Literal::CreateR3( + ConstantLiteral(&b, *LiteralUtil::CreateR3( {{{2.0}, {3.0}, {4.0}}, {{5.0}, {6.0}, {7.0}}})), /*broadcast_dimensions=*/{1, 2}); diff --git a/tensorflow/compiler/xla/tests/broadcast_test.cc b/tensorflow/compiler/xla/tests/broadcast_test.cc index 51b9f0d3e330e73f5d110f0a62f824179d5c7cf7..c7b94b5bbaaa512ad36056f9e68a87cc706c24b1 100644 --- a/tensorflow/compiler/xla/tests/broadcast_test.cc +++ b/tensorflow/compiler/xla/tests/broadcast_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -37,7 +37,7 @@ XLA_TEST_F(BroadcastTest, BroadcastScalarToScalar) { // Test degenerate case of broadcasting a scalar into a scalar. auto builder = HloComputation::Builder(TestName()); auto input = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0))); builder.AddInstruction(HloInstruction::CreateBroadcast( ShapeUtil::MakeShape(F32, {}), input, {})); @@ -46,14 +46,14 @@ XLA_TEST_F(BroadcastTest, BroadcastScalarToScalar) { hlo_module->AddEntryComputation(builder.Build()); auto result = ExecuteAndTransfer(std::move(hlo_module), {}); - EXPECT_TRUE(LiteralTestUtil::Near(*Literal::CreateR0(42.0), *result, - error_spec_)); + EXPECT_TRUE(LiteralTestUtil::Near(*LiteralUtil::CreateR0(42.0), + *result, error_spec_)); } XLA_TEST_F(BroadcastTest, BroadcastScalarTo2D) { auto builder = HloComputation::Builder(TestName()); auto input = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0))); builder.AddInstruction(HloInstruction::CreateBroadcast( ShapeUtil::MakeShape(F32, {2, 2}), input, {})); @@ -63,14 +63,14 @@ XLA_TEST_F(BroadcastTest, BroadcastScalarTo2D) { auto result = ExecuteAndTransfer(std::move(hlo_module), {}); EXPECT_TRUE(LiteralTestUtil::Near( - *Literal::CreateR2({{42.0, 42.0}, {42.0, 42.0}}), *result, + *LiteralUtil::CreateR2({{42.0, 42.0}, {42.0, 42.0}}), *result, error_spec_)); } XLA_TEST_F(BroadcastTest, BroadcastVectorTo2D) { auto builder = HloComputation::Builder(TestName()); auto input = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.0, 2.0, 3.0}))); + LiteralUtil::CreateR1({1.0, 2.0, 3.0}))); // Broadcast vector in both dimension 0 and dimension 1. Join them in a tuple // to enable testing of the results. @@ -86,18 +86,18 @@ XLA_TEST_F(BroadcastTest, BroadcastVectorTo2D) { auto result = ExecuteAndTransfer(std::move(hlo_module), {}); EXPECT_TRUE(LiteralTestUtil::Near( - *Literal::CreateR2({{1.0, 1.0}, {2.0, 2.0}, {3.0, 3.0}}), + *LiteralUtil::CreateR2({{1.0, 1.0}, {2.0, 2.0}, {3.0, 3.0}}), LiteralSlice(*result, {0}), error_spec_)); EXPECT_TRUE(LiteralTestUtil::Near( - *Literal::CreateR2({{1.0, 2.0, 3.0}, {1.0, 2.0, 3.0}}), + *LiteralUtil::CreateR2({{1.0, 2.0, 3.0}, {1.0, 2.0, 3.0}}), LiteralSlice(*result, {1}), error_spec_)); } XLA_TEST_F(BroadcastTest, Broadcast2DTo2D) { auto builder = HloComputation::Builder(TestName()); auto input = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); builder.AddInstruction(HloInstruction::CreateBroadcast( ShapeUtil::MakeShape(F32, {2, 2}), input, {0, 1})); @@ -106,9 +106,9 @@ XLA_TEST_F(BroadcastTest, Broadcast2DTo2D) { hlo_module->AddEntryComputation(builder.Build()); auto result = ExecuteAndTransfer(std::move(hlo_module), {}); - EXPECT_TRUE( - LiteralTestUtil::Near(*Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}), - *result, error_spec_)); + EXPECT_TRUE(LiteralTestUtil::Near( + *LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}), *result, + error_spec_)); } XLA_TEST_F(BroadcastTest, Broadcast2DTo2DTranspose) { @@ -116,7 +116,7 @@ XLA_TEST_F(BroadcastTest, Broadcast2DTo2DTranspose) { // the dimensions, ie transpose. auto builder = HloComputation::Builder(TestName()); auto input = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); builder.AddInstruction(HloInstruction::CreateBroadcast( ShapeUtil::MakeShape(F32, {2, 2}), input, {1, 0})); @@ -125,15 +125,15 @@ XLA_TEST_F(BroadcastTest, Broadcast2DTo2DTranspose) { hlo_module->AddEntryComputation(builder.Build()); auto result = ExecuteAndTransfer(std::move(hlo_module), {}); - EXPECT_TRUE( - LiteralTestUtil::Near(*Literal::CreateR2({{1.0, 3.0}, {2.0, 4.0}}), - *result, error_spec_)); + EXPECT_TRUE(LiteralTestUtil::Near( + *LiteralUtil::CreateR2({{1.0, 3.0}, {2.0, 4.0}}), *result, + error_spec_)); } XLA_TEST_F(BroadcastTest, Broadcast2DTo3D) { auto builder = HloComputation::Builder(TestName()); auto input = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}))); builder.AddInstruction(HloInstruction::CreateBroadcast( ShapeUtil::MakeShape(F32, {2, 3, 2}), input, {0, 2})); @@ -143,15 +143,15 @@ XLA_TEST_F(BroadcastTest, Broadcast2DTo3D) { auto result = ExecuteAndTransfer(std::move(hlo_module), {}); EXPECT_TRUE(LiteralTestUtil::Near( - *Literal::CreateR3({{{1.0, 2.0}, {1.0, 2.0}, {1.0, 2.0}}, - {{3.0, 4.0}, {3.0, 4.0}, {3.0, 4.0}}}), + *LiteralUtil::CreateR3({{{1.0, 2.0}, {1.0, 2.0}, {1.0, 2.0}}, + {{3.0, 4.0}, {3.0, 4.0}, {3.0, 4.0}}}), *result, error_spec_)); } TEST_F(BroadcastTest, Broadcast_R1_2_To_R4_2x2x3x3) { auto builder = HloComputation::Builder(TestName()); auto input = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1.0, 2.0}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({1.0, 2.0}))); // Broadcast vector in dimension 1. builder.AddInstruction(HloInstruction::CreateBroadcast( @@ -166,8 +166,9 @@ TEST_F(BroadcastTest, Broadcast_R1_2_To_R4_2x2x3x3) { Array2D pz({{1, 2}, {1, 2}}); expected.FillWithPZ(pz); - EXPECT_TRUE(LiteralTestUtil::Near( - *Literal::CreateR4FromArray4D(expected), *result, error_spec_)); + EXPECT_TRUE( + LiteralTestUtil::Near(*LiteralUtil::CreateR4FromArray4D(expected), + *result, error_spec_)); } TEST_F(BroadcastTest, Broadcast_R1_1025_To_R4_3x3x3x1025) { @@ -176,7 +177,7 @@ TEST_F(BroadcastTest, Broadcast_R1_1025_To_R4_3x3x3x1025) { int64 r1_size = input_data.size(); std::iota(input_data.begin(), input_data.end(), 0.0f); auto input = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1(input_data))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1(input_data))); // Broadcast vector in dimension 3. builder.AddInstruction(HloInstruction::CreateBroadcast( @@ -196,8 +197,9 @@ TEST_F(BroadcastTest, Broadcast_R1_1025_To_R4_3x3x3x1025) { } expected.FillWithYX(yx); - EXPECT_TRUE(LiteralTestUtil::Near( - *Literal::CreateR4FromArray4D(expected), *result, error_spec_)); + EXPECT_TRUE( + LiteralTestUtil::Near(*LiteralUtil::CreateR4FromArray4D(expected), + *result, error_spec_)); } XLA_TEST_F(BroadcastTest, Broadcast_R1_64_To_R4_32x64x7x7) { @@ -207,7 +209,7 @@ XLA_TEST_F(BroadcastTest, Broadcast_R1_64_To_R4_32x64x7x7) { std::vector r1_array(64, 42.0); auto input = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1(r1_array))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1(r1_array))); // Broadcast vector in dimension 1. builder.AddInstruction(HloInstruction::CreateBroadcast( @@ -218,14 +220,14 @@ XLA_TEST_F(BroadcastTest, Broadcast_R1_64_To_R4_32x64x7x7) { hlo_module->AddEntryComputation(builder.Build()); auto result = ExecuteAndTransfer(std::move(hlo_module), {}); - EXPECT_TRUE(LiteralTestUtil::Near(*Literal::CreateR4FromArray4D(r4_array), + EXPECT_TRUE(LiteralTestUtil::Near(*LiteralUtil::CreateR4FromArray4D(r4_array), *result, error_spec_)); } TEST_F(BroadcastTest, Broadcast_R0_to_R4_64x64x3x3) { auto builder = HloComputation::Builder(TestName()); auto input = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1.0f))); builder.AddInstruction(HloInstruction::CreateBroadcast( ShapeUtil::MakeShape(F32, {64, 64, 3, 3}), input, {})); @@ -238,15 +240,16 @@ TEST_F(BroadcastTest, Broadcast_R0_to_R4_64x64x3x3) { Array4D expected(64, 64, 3, 3); expected.Fill(1.0f); - EXPECT_TRUE(LiteralTestUtil::Near( - *Literal::CreateR4FromArray4D(expected), *result, error_spec_)); + EXPECT_TRUE( + LiteralTestUtil::Near(*LiteralUtil::CreateR4FromArray4D(expected), + *result, error_spec_)); } TEST_F(BroadcastTest, Broadcast_R2_2x2_To_R4_3x3x2x2) { auto builder = HloComputation::Builder(TestName()); Array2D to_broadcast({{1.0f, 2.0f}, {3.0f, 4.0f}}); auto input = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2FromArray2D(to_broadcast))); + LiteralUtil::CreateR2FromArray2D(to_broadcast))); // Broadcast vector in dimensions 2 and 3. builder.AddInstruction(HloInstruction::CreateBroadcast( @@ -260,8 +263,9 @@ TEST_F(BroadcastTest, Broadcast_R2_2x2_To_R4_3x3x2x2) { Array4D expected(3, 3, 2, 2); expected.FillWithYX(to_broadcast); - EXPECT_TRUE(LiteralTestUtil::Near( - *Literal::CreateR4FromArray4D(expected), *result, error_spec_)); + EXPECT_TRUE( + LiteralTestUtil::Near(*LiteralUtil::CreateR4FromArray4D(expected), + *result, error_spec_)); } TEST_F(BroadcastTest, Broadcast_R3_2x3x4_to_R4_2x3x4x5) { @@ -280,7 +284,7 @@ TEST_F(BroadcastTest, Broadcast_R3_2x3x4_to_R4_2x3x4x5) { } } auto input = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR3FromArray3D(input_vals))); + LiteralUtil::CreateR3FromArray3D(input_vals))); // Broadcast vector in dimensions 2 and 3. builder.AddInstruction(HloInstruction::CreateBroadcast( @@ -291,8 +295,9 @@ TEST_F(BroadcastTest, Broadcast_R3_2x3x4_to_R4_2x3x4x5) { hlo_module->AddEntryComputation(builder.Build()); auto result = ExecuteAndTransfer(std::move(hlo_module), {}); - EXPECT_TRUE(LiteralTestUtil::Near( - *Literal::CreateR4FromArray4D(expected), *result, error_spec_)); + EXPECT_TRUE( + LiteralTestUtil::Near(*LiteralUtil::CreateR4FromArray4D(expected), + *result, error_spec_)); } } // namespace diff --git a/tensorflow/compiler/xla/tests/call_test.cc b/tensorflow/compiler/xla/tests/call_test.cc index bc64a19ce22072152216a7c150fbd16480d261fb..b1d18210eaafdfec0920c0cccaa0dfdbd6de5609 100644 --- a/tensorflow/compiler/xla/tests/call_test.cc +++ b/tensorflow/compiler/xla/tests/call_test.cc @@ -16,8 +16,9 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test_helpers.h" @@ -76,7 +77,8 @@ class CallOpTest : public ClientLibraryTestBase { XLA_TEST_F(CallOpTest, CallR0F32IdentityScalar) { XlaBuilder builder(TestName()); XlaComputation callee = CreateR0F32IdentityComputation(); - auto constant = ConstantLiteral(&builder, *Literal::CreateR0(42.0)); + auto constant = + ConstantLiteral(&builder, *LiteralUtil::CreateR0(42.0)); Call(&builder, callee, {constant}); ComputeAndCompareR0(&builder, 42.0, {}, ErrorSpec(0.01f)); @@ -85,8 +87,8 @@ XLA_TEST_F(CallOpTest, CallR0F32IdentityScalar) { XLA_TEST_F(CallOpTest, CallR1S0F32AddArray) { XlaBuilder builder(TestName()); XlaComputation callee = CreateR1S0F32AdditionComputation(); - auto x = ConstantLiteral(&builder, *Literal::CreateR1({})); - auto y = ConstantLiteral(&builder, *Literal::CreateR1({})); + auto x = ConstantLiteral(&builder, *LiteralUtil::CreateR1({})); + auto y = ConstantLiteral(&builder, *LiteralUtil::CreateR1({})); Call(&builder, callee, {x, y}); ComputeAndCompareR1(&builder, {}, {}, ErrorSpec(0.01f)); @@ -95,8 +97,10 @@ XLA_TEST_F(CallOpTest, CallR1S0F32AddArray) { XLA_TEST_F(CallOpTest, CallR1S2F32AddArray) { XlaBuilder builder(TestName()); XlaComputation callee = CreateR1S2F32AdditionComputation(); - auto x = ConstantLiteral(&builder, *Literal::CreateR1({1.0f, 2.0f})); - auto y = ConstantLiteral(&builder, *Literal::CreateR1({2.0f, 3.0f})); + auto x = + ConstantLiteral(&builder, *LiteralUtil::CreateR1({1.0f, 2.0f})); + auto y = + ConstantLiteral(&builder, *LiteralUtil::CreateR1({2.0f, 3.0f})); Call(&builder, callee, {x, y}); ComputeAndCompareR1(&builder, {3.0f, 5.0f}, {}, ErrorSpec(0.01f)); @@ -129,15 +133,15 @@ XLA_TEST_F(CallOpTest, CallTreeTwoDeepBranchFactorThree) { TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr start, - client_->TransferToServer(*Literal::CreateR0(1.0f))); + client_->TransferToServer(*LiteralUtil::CreateR0(1.0f))); ComputeAndCompareR0(&builder3, 10.0f, {start.get()}, ErrorSpec(0.0f)); } XLA_TEST_F(CallOpTest, CallR0F32Tuple) { XlaBuilder builder(TestName()); XlaComputation callee = CreateR0F32TupleComputation(); - auto elem = Literal::CreateR0(42.0); - auto tuple = Literal::MakeTuple({elem.get()}); + auto elem = LiteralUtil::CreateR0(42.0); + auto tuple = LiteralUtil::MakeTuple({elem.get()}); Call(&builder, callee, {ConstantLiteral(&builder, *elem)}); ComputeAndCompareTuple(&builder, *tuple, {}, ErrorSpec(0.01f)); diff --git a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc index 1ad57c075b22c7730ffd8d1beeab60c9d5dc7458..a4eb57fc7b9abd460a7d158d0dc629eba88018cd 100644 --- a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc +++ b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc @@ -17,8 +17,8 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" @@ -36,7 +36,7 @@ class CheckExecutionArityTest : public ClientLibraryTestBase {}; TEST_F(CheckExecutionArityTest, TwoParamComputationNumArguments) { XlaBuilder builder("add_two_params"); - auto param_literal = Literal::CreateR1({1.1f, 2.2f}); + auto param_literal = LiteralUtil::CreateR1({1.1f, 2.2f}); auto p0 = Parameter(&builder, 0, param_literal->shape(), "param0"); auto p1 = Parameter(&builder, 1, param_literal->shape(), "param1"); @@ -85,12 +85,12 @@ XLA_TEST_F(CheckExecutionArityTest, CheckArgumentShapes) { ASSERT_IS_OK(computation_status.status()); auto computation = computation_status.ConsumeValueOrDie(); - auto f32_literal = Literal::CreateR0(1.1f); + auto f32_literal = LiteralUtil::CreateR0(1.1f); auto f32_data = client_->TransferToServer(*f32_literal).ConsumeValueOrDie(); - auto f32_4_literal = Literal::CreateR1({1.0f, 2.0f, 3.0f, 4.0f}); + auto f32_4_literal = LiteralUtil::CreateR1({1.0f, 2.0f, 3.0f, 4.0f}); auto f32_4_data = client_->TransferToServer(*f32_4_literal).ConsumeValueOrDie(); - auto u8_4_literal = Literal::CreateR1U8("hola"); + auto u8_4_literal = LiteralUtil::CreateR1U8("hola"); auto u8_4_data = client_->TransferToServer(*u8_4_literal).ConsumeValueOrDie(); // Match diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc index dafd6ebabbe6edafc1c926677b3ea00e775be010..59d917054be2ebe3a25f902f51972a682a5231b6 100644 --- a/tensorflow/compiler/xla/tests/client_library_test_base.cc +++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/ptr_util.h" @@ -157,7 +157,7 @@ string ClientLibraryTestBase::ExecuteToString( void ClientLibraryTestBase::ComputeAndCompareR1( XlaBuilder* builder, const tensorflow::core::Bitmap& expected, tensorflow::gtl::ArraySlice arguments) { - std::unique_ptr expected_literal = Literal::CreateR1(expected); + std::unique_ptr expected_literal = LiteralUtil::CreateR1(expected); ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal, arguments); } @@ -273,10 +273,16 @@ Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus( const Shape* shape_with_layout) { std::vector arguments(arguments_passed_in.begin(), arguments_passed_in.end()); + + // Transfer and use elements of arguments_, if the AddParam() API was used. + std::vector> owning_arguments; if (!arguments_.empty()) { CHECK(arguments.empty()); for (const auto& argument : arguments_) { - arguments.push_back(argument.get()); + owning_arguments.push_back( + client_->TransferToServer(MaybeConvertLiteralToBfloat16(argument)) + .ValueOrDie()); + arguments.push_back(owning_arguments.back().get()); } } @@ -295,7 +301,7 @@ Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus( std::unique_ptr converted_expected; Shape layout_shape; if (use_bfloat16_) { - converted_expected = Literal::ConvertF32ToBF16(expected); + converted_expected = LiteralUtil::ConvertF32ToBF16(expected); expected_ptr = converted_expected.get(); if (shape_with_layout != nullptr) { layout_shape = *shape_with_layout; @@ -331,10 +337,16 @@ Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus( ErrorSpec error, const Shape* shape_with_layout) { std::vector arguments(arguments_passed_in.begin(), arguments_passed_in.end()); + + // Transfer and use elements of arguments_, if the AddParam() API was used. + std::vector> owning_arguments; if (!arguments_.empty()) { CHECK(arguments.empty()); for (const auto& argument : arguments_) { - arguments.push_back(argument.get()); + owning_arguments.push_back( + client_->TransferToServer(MaybeConvertLiteralToBfloat16(argument)) + .ValueOrDie()); + arguments.push_back(owning_arguments.back().get()); } } @@ -347,7 +359,7 @@ Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus( std::unique_ptr converted_expected; Shape layout_shape; if (use_bfloat16_) { - converted_expected = Literal::ConvertF32ToBF16(expected); + converted_expected = LiteralUtil::ConvertF32ToBF16(expected); expected_ptr = converted_expected.get(); if (shape_with_layout != nullptr) { layout_shape = *shape_with_layout; @@ -389,7 +401,7 @@ void ClientLibraryTestBase::ComputeAndCompareR1U8( auto actual = actual_status.ConsumeValueOrDie(); // Turn the expected value into a literal. - std::unique_ptr expected_literal = Literal::CreateR1U8(expected); + std::unique_ptr expected_literal = LiteralUtil::CreateR1U8(expected); VLOG(1) << "expected: " << expected_literal->ToString(); VLOG(1) << "actual: " << actual->ToString(); @@ -454,6 +466,14 @@ ClientLibraryTestBase::ComputeValueAndReference( // function. std::vector> argument_data; std::vector> ref_argument_data; + + // Use `arguments_` if the AddParam() API was used. Otherwise, use + // plain `arguments`. + if (!arguments_.empty()) { + CHECK_EQ(arguments.size(), 0); + arguments = arguments_; + } + for (const auto& arg : arguments) { TF_ASSIGN_OR_RETURN(auto data, client_->TransferToServer(arg.Clone())); TF_ASSIGN_OR_RETURN(auto ref_data, ref_client_->TransferToServer(arg)); @@ -552,16 +572,16 @@ ClientLibraryTestBase::CreatePatternedMatrixWithZeroPadding(int rows, int cols, XlaOp ClientLibraryTestBase::AddParam(const Literal& argument, XlaBuilder* builder) { - XlaOp data_handle; - arguments_.push_back(CreateParameterAndTransferLiteral( - arguments_.size(), argument, "", builder, &data_handle)); - return data_handle; + arguments_.push_back(argument.Clone()); + return Parameter(builder, /*parameter_number=*/arguments_.size() - 1, + MaybeConvertShapeToBfloat16(argument.shape()), ""); } XlaOp ClientLibraryTestBase::CreateConstantFromLiteral(const Literal& literal, XlaBuilder* builder) { - return ConstantLiteral( - builder, use_bfloat16_ ? *Literal::ConvertF32ToBF16(literal) : literal); + return ConstantLiteral(builder, use_bfloat16_ + ? *LiteralUtil::ConvertF32ToBF16(literal) + : literal); } std::unique_ptr @@ -574,22 +594,39 @@ ClientLibraryTestBase::CreateParameterAndTransferLiteral(int64 parameter_number, nullptr, builder, data_handle); } +Shape ClientLibraryTestBase::MaybeConvertShapeToBfloat16(const Shape& shape) { + if (!use_bfloat16_) { + return shape; + } + Shape new_shape = shape; + ShapeUtil::ForEachMutableSubshape(&new_shape, + [](Shape* subshape, const ShapeIndex&) { + if (subshape->element_type() == F32) { + subshape->set_element_type(BF16); + } + }); + return new_shape; +} + +Literal ClientLibraryTestBase::MaybeConvertLiteralToBfloat16( + const Literal& literal) { + if (use_bfloat16_) { + return std::move(*LiteralUtil::ConvertF32ToBF16(literal)); + } + return literal.Clone(); +} + std::unique_ptr ClientLibraryTestBase::CreateParameterAndTransferLiteral( int64 parameter_number, const Literal& literal, const string& name, const DeviceHandle* device_handle, XlaBuilder* builder, XlaOp* data_handle) { - const Literal* param_literal = &literal; - std::unique_ptr converted_literal; - if (use_bfloat16_) { - converted_literal = Literal::ConvertF32ToBF16(literal); - param_literal = converted_literal.get(); - } + Literal param_literal = MaybeConvertLiteralToBfloat16(literal); std::unique_ptr data = - client_->TransferToServer(*param_literal, device_handle) + client_->TransferToServer(param_literal, device_handle) .ConsumeValueOrDie(); *data_handle = - Parameter(builder, parameter_number, param_literal->shape(), name); + Parameter(builder, parameter_number, param_literal.shape(), name); return data; } diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h index 5361ae6783c4c103cf923ffbda066165545c39a1..4a6e8a31241d39db21935576d57f0acb17caef11 100644 --- a/tensorflow/compiler/xla/tests/client_library_test_base.h +++ b/tensorflow/compiler/xla/tests/client_library_test_base.h @@ -26,8 +26,9 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/global_data.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/statusor.h" @@ -284,7 +285,7 @@ class ClientLibraryTestBase : public ::testing::Test { template XlaOp AddParam(const Array& argument, XlaBuilder* builder) { - return AddParam(*Literal::CreateFromArray(argument), builder); + return AddParam(*LiteralUtil::CreateFromArray(argument), builder); } // Creates a constant instruction with the given literal. When the @@ -299,13 +300,14 @@ class ClientLibraryTestBase : public ::testing::Test { template XlaOp CreateConstantFromArray(const Array& array, XlaBuilder* builder) { - return CreateConstantFromLiteral(*Literal::CreateFromArray(array), builder); + return CreateConstantFromLiteral(*LiteralUtil::CreateFromArray(array), + builder); } // Same as CreateConstantFromArray, but for scalars. template XlaOp CreateConstantFromScalar(NativeT value, XlaBuilder* builder) { - return CreateConstantFromLiteral(*Literal::CreateR0(value), + return CreateConstantFromLiteral(*LiteralUtil::CreateR0(value), builder); } @@ -397,12 +399,16 @@ class ClientLibraryTestBase : public ::testing::Test { const string& error_message)>& verify_output, const Shape* output_with_layout = nullptr); + // Converts an f32 shape/literal to bf16 if use_bfloat16_ is true. + Literal MaybeConvertLiteralToBfloat16(const Literal& literal); + Shape MaybeConvertShapeToBfloat16(const Shape& shape); + // Whether to run tests with all float-type input/output converted to // bfloat16. bool use_bfloat16_ = false; // Arguments to be passed to the computation when it runs. - std::vector> arguments_; + std::vector arguments_; }; template @@ -410,7 +416,7 @@ void ClientLibraryTestBase::ComputeAndCompareR0( XlaBuilder* builder, NativeT expected, tensorflow::gtl::ArraySlice arguments) { std::unique_ptr expected_literal = - Literal::CreateR0(expected); + LiteralUtil::CreateR0(expected); ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal, arguments); } @@ -426,7 +432,7 @@ void ClientLibraryTestBase::ComputeAndCompareR0( std::is_same::value, "Float or complex type required when specifying an ErrorSpec"); std::unique_ptr expected_literal = - Literal::CreateR0(expected); + LiteralUtil::CreateR0(expected); ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal, arguments, error); } @@ -436,7 +442,7 @@ void ClientLibraryTestBase::ComputeAndCompareR1( XlaBuilder* builder, tensorflow::gtl::ArraySlice expected, tensorflow::gtl::ArraySlice arguments) { std::unique_ptr expected_literal = - Literal::CreateR1(expected); + LiteralUtil::CreateR1(expected); ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal, arguments); } @@ -452,7 +458,7 @@ void ClientLibraryTestBase::ComputeAndCompareR1( std::is_same::value, "Float or complex type required when specifying an ErrorSpec"); std::unique_ptr expected_literal = - Literal::CreateR1(expected); + LiteralUtil::CreateR1(expected); ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal, arguments, error); } @@ -462,7 +468,7 @@ void ClientLibraryTestBase::ComputeAndCompareR2( XlaBuilder* builder, const Array2D& expected, tensorflow::gtl::ArraySlice arguments) { std::unique_ptr expected_literal = - Literal::CreateR2FromArray2D(expected); + LiteralUtil::CreateR2FromArray2D(expected); ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal, arguments); } @@ -478,7 +484,7 @@ void ClientLibraryTestBase::ComputeAndCompareR2( std::is_same::value, "Float or complex type required when specifying an ErrorSpec"); std::unique_ptr expected_literal = - Literal::CreateR2FromArray2D(expected); + LiteralUtil::CreateR2FromArray2D(expected); ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal, arguments, error); } @@ -488,7 +494,7 @@ void ClientLibraryTestBase::ComputeAndCompareR3( XlaBuilder* builder, const Array3D& expected, tensorflow::gtl::ArraySlice arguments) { std::unique_ptr expected_literal = - Literal::CreateR3FromArray3D(expected); + LiteralUtil::CreateR3FromArray3D(expected); ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal, arguments); } @@ -504,7 +510,7 @@ void ClientLibraryTestBase::ComputeAndCompareR3( std::is_same::value, "Float or complex type required when specifying an ErrorSpec"); std::unique_ptr expected_literal = - Literal::CreateR3FromArray3D(expected); + LiteralUtil::CreateR3FromArray3D(expected); ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal, arguments, error); } @@ -514,7 +520,7 @@ void ClientLibraryTestBase::ComputeAndCompareR4( XlaBuilder* builder, const Array4D& expected, tensorflow::gtl::ArraySlice arguments) { std::unique_ptr expected_literal = - Literal::CreateR4FromArray4D(expected); + LiteralUtil::CreateR4FromArray4D(expected); ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal, arguments); } @@ -530,7 +536,7 @@ void ClientLibraryTestBase::ComputeAndCompareR4( std::is_same::value, "Float or complex type required when specifying an ErrorSpec"); std::unique_ptr expected_literal = - Literal::CreateR4FromArray4D(expected); + LiteralUtil::CreateR4FromArray4D(expected); ClientLibraryTestBase::ComputeAndCompareLiteral(builder, *expected_literal, arguments, error); } @@ -539,9 +545,9 @@ template std::unique_ptr ClientLibraryTestBase::CreateR0Parameter( NativeT value, int64 parameter_number, const string& name, XlaBuilder* builder, XlaOp* data_handle) { - std::unique_ptr literal = Literal::CreateR0(value); + std::unique_ptr literal = LiteralUtil::CreateR0(value); if (use_bfloat16_ && literal->shape().element_type() == F32) { - literal = Literal::ConvertF32ToBF16(*literal); + literal = LiteralUtil::ConvertF32ToBF16(*literal); } std::unique_ptr data = client_->TransferToServer(*literal).ConsumeValueOrDie(); @@ -553,9 +559,9 @@ template std::unique_ptr ClientLibraryTestBase::CreateR1Parameter( tensorflow::gtl::ArraySlice values, int64 parameter_number, const string& name, XlaBuilder* builder, XlaOp* data_handle) { - std::unique_ptr literal = Literal::CreateR1(values); + std::unique_ptr literal = LiteralUtil::CreateR1(values); if (use_bfloat16_ && literal->shape().element_type() == F32) { - literal = Literal::ConvertF32ToBF16(*literal); + literal = LiteralUtil::ConvertF32ToBF16(*literal); } std::unique_ptr data = client_->TransferToServer(*literal).ConsumeValueOrDie(); @@ -567,9 +573,9 @@ template std::unique_ptr ClientLibraryTestBase::CreateR2Parameter( const Array2D& array_2d, int64 parameter_number, const string& name, XlaBuilder* builder, XlaOp* data_handle) { - std::unique_ptr literal = Literal::CreateR2FromArray2D(array_2d); + std::unique_ptr literal = LiteralUtil::CreateR2FromArray2D(array_2d); if (use_bfloat16_ && literal->shape().element_type() == F32) { - literal = Literal::ConvertF32ToBF16(*literal); + literal = LiteralUtil::ConvertF32ToBF16(*literal); } std::unique_ptr data = client_->TransferToServer(*literal).ConsumeValueOrDie(); @@ -581,9 +587,9 @@ template std::unique_ptr ClientLibraryTestBase::CreateR3Parameter( const Array3D& array_3d, int64 parameter_number, const string& name, XlaBuilder* builder, XlaOp* data_handle) { - std::unique_ptr literal = Literal::CreateR3FromArray3D(array_3d); + std::unique_ptr literal = LiteralUtil::CreateR3FromArray3D(array_3d); if (use_bfloat16_ && literal->shape().element_type() == F32) { - literal = Literal::ConvertF32ToBF16(*literal); + literal = LiteralUtil::ConvertF32ToBF16(*literal); } std::unique_ptr data = client_->TransferToServer(*literal).ConsumeValueOrDie(); diff --git a/tensorflow/compiler/xla/tests/client_test.cc b/tensorflow/compiler/xla/tests/client_test.cc index 831b863998f1cab31d37aa4474be45d8531075ac..c898dacf489db97223e2918414daf5de88bece64 100644 --- a/tensorflow/compiler/xla/tests/client_test.cc +++ b/tensorflow/compiler/xla/tests/client_test.cc @@ -18,8 +18,8 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" @@ -56,7 +56,7 @@ XLA_TEST_F(ClientTest, ExecuteWithLayout) { client_->Execute(computation, {}, &execution_options)); std::unique_ptr expected_literal = - Literal::CreateR2WithLayout( + LiteralUtil::CreateR2WithLayout( {{11, 22}, {33, 44}}, LayoutUtil::MakeLayout(transfer_layout)); TF_ASSERT_OK_AND_ASSIGN( @@ -112,9 +112,9 @@ XLA_TEST_F(ClientTest, DISABLED_ON_GPU(ExecuteParallel)) { XlaComputation add_with_one_arg, mul_with_two_args, dot_with_one_arg; Shape shape = ShapeUtil::MakeShape(S32, {2, 2}); - TF_ASSERT_OK_AND_ASSIGN( - std::unique_ptr const_arg, - client_->TransferToServer(*Literal::CreateR2({{5, 6}, {7, 8}}))); + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr const_arg, + client_->TransferToServer( + *LiteralUtil::CreateR2({{5, 6}, {7, 8}}))); XlaBuilder b(TestName() + ".add"); Add(Parameter(&b, 0, shape, "param_0"), @@ -136,7 +136,7 @@ XLA_TEST_F(ClientTest, DISABLED_ON_GPU(ExecuteParallel)) { TF_ASSERT_OK_AND_ASSIGN(auto results, client_->ExecuteParallel(computation_instances)); - auto expected_result = Literal::CreateR2({{6, 8}, {10, 12}}); + auto expected_result = LiteralUtil::CreateR2({{6, 8}, {10, 12}}); TF_ASSERT_OK_AND_ASSIGN( auto result_literal, diff --git a/tensorflow/compiler/xla/tests/compilation_cache_test.cc b/tensorflow/compiler/xla/tests/compilation_cache_test.cc index eb211dd8ff376fb0da03b3e68be1d849970d96fd..7c52c9fbbb57f9291ea9f0966e2efa715819fb67 100644 --- a/tensorflow/compiler/xla/tests/compilation_cache_test.cc +++ b/tensorflow/compiler/xla/tests/compilation_cache_test.cc @@ -19,9 +19,9 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" @@ -50,7 +50,7 @@ class CompilationCacheTest : public ClientLibraryTestBase { &execution_profile) .ConsumeValueOrDie(); EXPECT_TRUE(LiteralTestUtil::Near( - *Literal::CreateR0(expected_result), *result, error_spec_)); + *LiteralUtil::CreateR0(expected_result), *result, error_spec_)); EXPECT_EQ(expect_cache_hit, execution_profile.compilation_cache_hit()); } @@ -67,7 +67,7 @@ class CompilationCacheTest : public ClientLibraryTestBase { std::unique_ptr result = client_->Transfer(*data_handle).ConsumeValueOrDie(); EXPECT_TRUE(LiteralTestUtil::Near( - *Literal::CreateR2(expected_result), *result, error_spec_)); + *LiteralUtil::CreateR2(expected_result), *result, error_spec_)); EXPECT_EQ(expect_cache_hit, execution_profile.compilation_cache_hit()); } @@ -89,13 +89,13 @@ XLA_TEST_F(CompilationCacheTest, DISABLED_ComputationCalledMultipleTimes) { XLA_TEST_F(CompilationCacheTest, DISABLED_ComputationCalledWithDifferentParameters) { std::unique_ptr data_42 = - client_->TransferToServer(*Literal::CreateR0(42.0f)) + client_->TransferToServer(*LiteralUtil::CreateR0(42.0f)) .ConsumeValueOrDie(); std::unique_ptr data_123 = - client_->TransferToServer(*Literal::CreateR0(123.0f)) + client_->TransferToServer(*LiteralUtil::CreateR0(123.0f)) .ConsumeValueOrDie(); std::unique_ptr data_456 = - client_->TransferToServer(*Literal::CreateR0(456.0f)) + client_->TransferToServer(*LiteralUtil::CreateR0(456.0f)) .ConsumeValueOrDie(); XlaBuilder builder(TestName()); @@ -143,12 +143,12 @@ XLA_TEST_F(CompilationCacheTest, DISABLED_DifferentParameterLayouts) { // layouts. Use these arrays as parameters to a simple computation. If the // layout of the array changes then computation should be recompiled (cache // miss). - auto rowmaj_array = Literal::CreateR2WithLayout( + auto rowmaj_array = LiteralUtil::CreateR2WithLayout( {{1.0f, 2.0f}, {3.0f, 4.0f}}, LayoutUtil::MakeLayout({1, 0})); auto rowmaj_handle = client_->TransferToServer(*rowmaj_array).ConsumeValueOrDie(); - auto colmaj_array = Literal::CreateR2WithLayout( + auto colmaj_array = LiteralUtil::CreateR2WithLayout( {{1.0f, 2.0f}, {3.0f, 4.0f}}, LayoutUtil::MakeLayout({0, 1})); auto colmaj_handle = client_->TransferToServer(*colmaj_array).ConsumeValueOrDie(); diff --git a/tensorflow/compiler/xla/tests/compute_constant_test.cc b/tensorflow/compiler/xla/tests/compute_constant_test.cc index 1a396b090c615dbd829964bd68ebda74df29c71e..5a06d061f0d83fff547502495ff8ab13fb421b70 100644 --- a/tensorflow/compiler/xla/tests/compute_constant_test.cc +++ b/tensorflow/compiler/xla/tests/compute_constant_test.cc @@ -19,10 +19,10 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/global_data.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" @@ -207,7 +207,7 @@ TEST_F(ComputeConstantTest, NonScalarAdd) { TF_ASSERT_OK_AND_ASSIGN(auto computed, ComputeConstantLiteral(client, computation, &b)); std::unique_ptr expected_literal = - Literal::CreateR1({4, 6}); + LiteralUtil::CreateR1({4, 6}); EXPECT_TRUE(LiteralTestUtil::Equal(*expected_literal, *computed)); } } @@ -221,7 +221,7 @@ TEST_F(ComputeConstantTest, IntegerDivide) { TF_ASSERT_OK_AND_ASSIGN(auto computed, ComputeConstantLiteral(client, computation, &b)); - std::unique_ptr expected_literal = Literal::CreateR0(5); + std::unique_ptr expected_literal = LiteralUtil::CreateR0(5); EXPECT_TRUE(LiteralTestUtil::Equal(*expected_literal, *computed)); } } @@ -242,8 +242,8 @@ XLA_TEST_F(ComputeConstantTest, Layout) { &b, &layout_proto)); std::unique_ptr expected_literal = - Literal::CreateR2WithLayout({{11, 22}, {33, 44}}, - LayoutUtil::MakeLayout(layout)); + LiteralUtil::CreateR2WithLayout( + {{11, 22}, {33, 44}}, LayoutUtil::MakeLayout(layout)); ASSERT_TRUE(LiteralTestUtil::EqualShapesAndLayouts( expected_literal->shape(), computed->shape())); EXPECT_TRUE(LiteralTestUtil::Equal(*expected_literal, *computed)); diff --git a/tensorflow/compiler/xla/tests/concat_test.cc b/tensorflow/compiler/xla/tests/concat_test.cc index 1161b560b7b0756556911812666c6f4fe9179f72..be017477d84eb9faf5aa79dcdf54d6b6aaf6fd8e 100644 --- a/tensorflow/compiler/xla/tests/concat_test.cc +++ b/tensorflow/compiler/xla/tests/concat_test.cc @@ -19,8 +19,8 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/array3d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" @@ -534,8 +534,8 @@ TEST_P(ConcatR2BinaryTest, DoIt) { // concat XLA_TEST_F(ConcatTest, ConcatOperandsOfSameOperand) { auto f32_scalar = ShapeUtil::MakeShape(xla::F32, {}); - auto x_literal = Literal::CreateR0(2.f); - auto y_literal = Literal::CreateR0(3.f); + auto x_literal = LiteralUtil::CreateR0(2.f); + auto y_literal = LiteralUtil::CreateR0(3.f); auto x_data = client_->TransferToServer(*x_literal).ConsumeValueOrDie(); auto y_data = client_->TransferToServer(*y_literal).ConsumeValueOrDie(); @@ -556,9 +556,9 @@ XLA_TEST_F(ConcatTest, ConcatOperandsOfSameOperand) { // produces the correct result in rank 1. XLA_TEST_F(ConcatTest, ConcatBroadcastArgument) { auto f32_scalar = ShapeUtil::MakeShape(xla::F32, {}); - auto x_literal = Literal::CreateR1({2.0f, 3.0f, 5.0f, 6.0f}); - auto y_literal = Literal::CreateR0(1.5f); - auto z_literal = Literal::CreateR0(5.5f); + auto x_literal = LiteralUtil::CreateR1({2.0f, 3.0f, 5.0f, 6.0f}); + auto y_literal = LiteralUtil::CreateR0(1.5f); + auto z_literal = LiteralUtil::CreateR0(5.5f); auto x_data = client_->TransferToServer(*x_literal).ConsumeValueOrDie(); auto y_data = client_->TransferToServer(*y_literal).ConsumeValueOrDie(); auto z_data = client_->TransferToServer(*z_literal).ConsumeValueOrDie(); @@ -584,9 +584,9 @@ XLA_TEST_F(ConcatTest, ConcatBroadcastArgument) { XLA_TEST_F(ConcatTest, ConcatBroadcastArgumentR3) { auto f32_scalar = ShapeUtil::MakeShape(xla::F32, {}); Array3D x3d(3, 5, 7, 3.14f); - auto x_literal = Literal::CreateR3FromArray3D(x3d); - auto y_literal = Literal::CreateR0(1.5f); - auto z_literal = Literal::CreateR0(5.5f); + auto x_literal = LiteralUtil::CreateR3FromArray3D(x3d); + auto y_literal = LiteralUtil::CreateR0(1.5f); + auto z_literal = LiteralUtil::CreateR0(5.5f); auto x_data = client_->TransferToServer(*x_literal).ConsumeValueOrDie(); auto y_data = client_->TransferToServer(*y_literal).ConsumeValueOrDie(); auto z_data = client_->TransferToServer(*z_literal).ConsumeValueOrDie(); diff --git a/tensorflow/compiler/xla/tests/conditional_test.cc b/tensorflow/compiler/xla/tests/conditional_test.cc index ee3c83039bfc13f6ad78111d92ba0f8387a3ade3..b27c1044baf2c0002f166c53a81e4361c60d012a 100644 --- a/tensorflow/compiler/xla/tests/conditional_test.cc +++ b/tensorflow/compiler/xla/tests/conditional_test.cc @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" @@ -172,88 +172,95 @@ class ConditionalOpTest : public ClientLibraryTestBase { // Test true and false computations that do not take any parameters. XLA_TEST_F(ConditionalOpTest, Parameters0) { XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, true); + XlaOp pred; + auto pred_arg = CreateR0Parameter(true, 0, "pred", &builder, &pred); auto operands = Tuple(&builder, {}); auto true_computation = CreateR0ConstantComputation(56.0f); auto false_computation = CreateR0ConstantComputation(12.0f); Conditional(pred, operands, true_computation, operands, false_computation); - ComputeAndCompareR0(&builder, 56.0f, {}, error_spec_); + ComputeAndCompareR0(&builder, 56.0f, {pred_arg.get()}, error_spec_); } // Test true and false computations that take in 1 parameter. XLA_TEST_F(ConditionalOpTest, Parameters1) { XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, false); + XlaOp pred; + auto pred_arg = CreateR0Parameter(false, 0, "pred", &builder, &pred); auto operand1 = ConstantR0(&builder, 56.0f); auto operand2 = ConstantR0(&builder, 12.0f); auto identity = CreateR0IdentityComputation(); Conditional(pred, operand1, identity, operand2, identity); - ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); + ComputeAndCompareR0(&builder, 12.0f, {pred_arg.get()}, error_spec_); } // Test conditional with two different computations in the true and false cases // that take in different arguments. XLA_TEST_F(ConditionalOpTest, DiffComputationsDiffArgs) { XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, false); + XlaOp pred; + auto pred_arg = CreateR0Parameter(false, 0, "pred", &builder, &pred); auto operand1 = ConstantR0(&builder, 56.4f); auto operand2 = ConstantR0(&builder, 12.6f); Conditional(pred, operand1, CreateR0CeilComputation(), operand2, CreateR0FloorComputation()); - ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); + ComputeAndCompareR0(&builder, 12.0f, {pred_arg.get()}, error_spec_); } // Test conditional with two different computations in the true and false cases // that take in the same arguments. XLA_TEST_F(ConditionalOpTest, DiffComputationsSameArg) { XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, false); + XlaOp pred; + auto pred_arg = CreateR0Parameter(false, 0, "pred", &builder, &pred); auto operand = ConstantR0(&builder, 12.6f); Conditional(pred, operand, CreateR0CeilComputation(), operand, CreateR0FloorComputation()); - ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); + ComputeAndCompareR0(&builder, 12.0f, {pred_arg.get()}, error_spec_); } // Test conditional with the same computation in the true and false cases but // take in different arguments. XLA_TEST_F(ConditionalOpTest, SameComputationDiffArgs) { XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, false); + XlaOp pred; + auto pred_arg = CreateR0Parameter(false, 0, "pred", &builder, &pred); auto operand1 = ConstantR0(&builder, 56.4f); auto operand2 = ConstantR0(&builder, 12.6f); auto floor = CreateR0FloorComputation(); Conditional(pred, operand1, floor, operand2, floor); - ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); + ComputeAndCompareR0(&builder, 12.0f, {pred_arg.get()}, error_spec_); } // Test conditional with the same computation in the true and false cases that // take in the same arguments. XLA_TEST_F(ConditionalOpTest, SameComputationSameArg) { XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, false); + XlaOp pred; + auto pred_arg = CreateR0Parameter(false, 0, "pred", &builder, &pred); auto operand = ConstantR0(&builder, 12.6f); auto floor = CreateR0FloorComputation(); Conditional(pred, operand, floor, operand, floor); - ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); + ComputeAndCompareR0(&builder, 12.0f, {pred_arg.get()}, error_spec_); } // Test conditional with different instances of the same computation in the true // and false cases. XLA_TEST_F(ConditionalOpTest, SameComputationDiffInstances) { XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, false); + XlaOp pred; + auto pred_arg = CreateR0Parameter(false, 0, "pred", &builder, &pred); auto operand1 = ConstantR0(&builder, 56.4f); auto operand2 = ConstantR0(&builder, 12.6f); Conditional(pred, operand1, CreateR0FloorComputation(), operand2, CreateR0FloorComputation()); - ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); + ComputeAndCompareR0(&builder, 12.0f, {pred_arg.get()}, error_spec_); } // Test the case when a call invokes a computation that contains a conditional. @@ -268,75 +275,83 @@ XLA_TEST_F(ConditionalOpTest, ConditionalWithCall) { auto inner_builder_result = inner_builder.Build(); XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, false); + XlaOp pred; + auto pred_arg = CreateR0Parameter(false, 0, "pred", &builder, &pred); auto operand1 = ConstantR0(&builder, 56.4f); auto operand2 = ConstantR0(&builder, 12.6f); Call(&builder, inner_builder_result.ConsumeValueOrDie(), {pred, operand1, operand2}); - ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); + ComputeAndCompareR0(&builder, 12.0f, {pred_arg.get()}, error_spec_); } // Test true and false computations that take in 2 parameters and predicate is // true. XLA_TEST_F(ConditionalOpTest, Parameters2TrueBranch) { XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, true); + XlaOp pred; + auto pred_arg = CreateR0Parameter(true, 0, "pred", &builder, &pred); auto operand1 = ConstantR0(&builder, 56.0f); auto operand2 = ConstantR0(&builder, 12.0f); auto operands = Tuple(&builder, {operand1, operand2}); Conditional(pred, operands, CreateR0TupleAddComputation(), operands, CreateR0TupleSubComputation()); - ComputeAndCompareR0(&builder, 68.0f, {}, error_spec_); + ComputeAndCompareR0(&builder, 68.0f, {pred_arg.get()}, error_spec_); } // Test true and false computations that take in 2 parameters and predicate is // false. XLA_TEST_F(ConditionalOpTest, Parameters2FalseBranch) { XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, false); + XlaOp pred; + auto pred_arg = CreateR0Parameter(false, 0, "pred", &builder, &pred); auto operand1 = ConstantR0(&builder, 56.0f); auto operand2 = ConstantR0(&builder, 12.0f); auto operands = Tuple(&builder, {operand1, operand2}); Conditional(pred, operands, CreateR0TupleAddComputation(), operands, CreateR0TupleSubComputation()); - ComputeAndCompareR0(&builder, 44.0f, {}, error_spec_); + ComputeAndCompareR0(&builder, 44.0f, {pred_arg.get()}, error_spec_); } // Test true and false computations that take in 2 array parameters and // predicate is true. XLA_TEST_F(ConditionalOpTest, Parameters2ArrayTrueBranch) { XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, true); + XlaOp pred; + auto pred_arg = CreateR0Parameter(true, 0, "pred", &builder, &pred); auto operand1 = ConstantR1(&builder, {24.0f, 56.0f}); auto operand2 = ConstantR1(&builder, {10.0f, 11.0f}); auto operands = Tuple(&builder, {operand1, operand2}); Conditional(pred, operands, CreateR1TupleAddComputation(), operands, CreateR1TupleSubComputation()); - ComputeAndCompareR1(&builder, {34.0f, 67.0f}, {}, error_spec_); + ComputeAndCompareR1(&builder, {34.0f, 67.0f}, {pred_arg.get()}, + error_spec_); } // Test true and false computations that take in 2 array parameters and // predicate is false. XLA_TEST_F(ConditionalOpTest, Parameters2ArrayFalseBranch) { XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, false); + XlaOp pred; + auto pred_arg = CreateR0Parameter(false, 0, "pred", &builder, &pred); auto operand1 = ConstantR1(&builder, {24.0f, 56.0f}); auto operand2 = ConstantR1(&builder, {10.0f, 11.0f}); auto operands = Tuple(&builder, {operand1, operand2}); Conditional(pred, operands, CreateR1TupleAddComputation(), operands, CreateR1TupleSubComputation()); - ComputeAndCompareR1(&builder, {14.0f, 45.0f}, {}, error_spec_); + ComputeAndCompareR1(&builder, {14.0f, 45.0f}, {pred_arg.get()}, + error_spec_); } // Test true and false computations that return a tuple of scalars. XLA_TEST_F(ConditionalOpTest, ReturnTupleOfScalars) { XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, false); + XlaOp pred; + auto pred_arg = CreateR0Parameter(false, 0, "pred", &builder, &pred); auto operands = Tuple(&builder, {ConstantR0(&builder, 12.2f), ConstantR0(&builder, 25.6f)}); Conditional(pred, operands, CreateR0TupleCeilComputation(), operands, @@ -344,15 +359,16 @@ XLA_TEST_F(ConditionalOpTest, ReturnTupleOfScalars) { ComputeAndCompareTuple( &builder, - *Literal::MakeTuple({Literal::CreateR0(12.0f).get(), - Literal::CreateR0(25.0f).get()}), - {}, error_spec_); + *LiteralUtil::MakeTuple({LiteralUtil::CreateR0(12.0f).get(), + LiteralUtil::CreateR0(25.0f).get()}), + {pred_arg.get()}, error_spec_); } // Test true and false computations that return a tuple of arrays. XLA_TEST_F(ConditionalOpTest, ReturnTupleOfArrays) { XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, true); + XlaOp pred; + auto pred_arg = CreateR0Parameter(true, 0, "pred", &builder, &pred); auto operands = Tuple(&builder, {ConstantR1(&builder, {12.2f, 15.8f}), ConstantR1(&builder, {25.6f, 29.2f})}); @@ -361,9 +377,10 @@ XLA_TEST_F(ConditionalOpTest, ReturnTupleOfArrays) { ComputeAndCompareTuple( &builder, - *Literal::MakeTuple({Literal::CreateR1({13.0f, 16.0f}).get(), - Literal::CreateR1({26.0f, 30.0f}).get()}), - {}, error_spec_); + *LiteralUtil::MakeTuple( + {LiteralUtil::CreateR1({13.0f, 16.0f}).get(), + LiteralUtil::CreateR1({26.0f, 30.0f}).get()}), + {pred_arg.get()}, error_spec_); } // Test true and false computations that return a tuple of a predicate, a @@ -392,17 +409,19 @@ XLA_TEST_F(ConditionalOpTest, ReturnTupleofPredicateScalarArray) { EXPECT_IS_OK(false_builder_result.status()); XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, true); + XlaOp pred; + auto pred_arg = CreateR0Parameter(true, 0, "pred", &builder, &pred); auto operands = Tuple(&builder, {}); Conditional(pred, operands, true_builder_result.ConsumeValueOrDie(), operands, false_builder_result.ConsumeValueOrDie()); ComputeAndCompareTuple( &builder, - *Literal::MakeTuple({Literal::CreateR0(true).get(), - Literal::CreateR0(12.2f).get(), - Literal::CreateR1({12.8f, 14.6f}).get()}), - {}, error_spec_); + *LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(true).get(), + LiteralUtil::CreateR0(12.2f).get(), + LiteralUtil::CreateR1({12.8f, 14.6f}).get()}), + {pred_arg.get()}, error_spec_); } // Test true and false computations that return a nested tuple. @@ -436,21 +455,24 @@ XLA_TEST_F(ConditionalOpTest, ReturnNestedTuple) { EXPECT_IS_OK(false_builder_result.status()); XlaBuilder builder(TestName()); - auto pred = ConstantR0(&builder, false); + XlaOp pred; + auto pred_arg = CreateR0Parameter(false, 0, "pred", &builder, &pred); auto operands = Tuple(&builder, {}); Conditional(pred, operands, true_builder_result.ConsumeValueOrDie(), operands, false_builder_result.ConsumeValueOrDie()); ComputeAndCompareTuple( &builder, - *Literal::MakeTuple( - {Literal::MakeTuple({Literal::CreateR0(46.6f).get(), - Literal::CreateR1({54.4f, 58.4f}).get()}) + *LiteralUtil::MakeTuple( + {LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(46.6f).get(), + LiteralUtil::CreateR1({54.4f, 58.4f}).get()}) .get(), - Literal::MakeTuple({Literal::CreateR1({62.1f, 67.4f}).get(), - Literal::CreateR0(9.3f).get()}) + LiteralUtil::MakeTuple( + {LiteralUtil::CreateR1({62.1f, 67.4f}).get(), + LiteralUtil::CreateR0(9.3f).get()}) .get()}), - {}, error_spec_); + {pred_arg.get()}, error_spec_); } // Test conditional that takes in scalar operands in the form of external @@ -511,8 +533,9 @@ XLA_TEST_F(ConditionalOpTest, NestedConditionals) { EXPECT_IS_OK(inner_builder_result.status()); XlaBuilder builder(TestName()); - auto pred1 = ConstantR0(&builder, true); - auto pred2 = ConstantR0(&builder, false); + XlaOp pred1, pred2; + auto pred1_arg = CreateR0Parameter(true, 0, "pred1", &builder, &pred1); + auto pred2_arg = CreateR0Parameter(false, 1, "pred2", &builder, &pred2); auto operand1 = ConstantR0(&builder, 1.1f); auto operand2 = ConstantR0(&builder, 12.2f); auto operand3 = ConstantR0(&builder, 43.3f); @@ -520,7 +543,8 @@ XLA_TEST_F(ConditionalOpTest, NestedConditionals) { Conditional(pred1, tuple_operand, inner_builder_result.ConsumeValueOrDie(), operand3, CreateR0IdentityComputation()); - ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); + ComputeAndCompareR0(&builder, 12.0f, + {pred1_arg.get(), pred2_arg.get()}, error_spec_); } XLA_TEST_F(ConditionalOpTest, ConditionalInNestedComputation) { @@ -539,13 +563,14 @@ XLA_TEST_F(ConditionalOpTest, ConditionalInNestedComputation) { EXPECT_IS_OK(inner_builder_result.status()); XlaBuilder builder(TestName()); - auto pred2 = ConstantR0(&builder, false); + XlaOp pred; + auto pred_arg = CreateR0Parameter(false, 0, "pred", &builder, &pred); auto operand1 = ConstantR0(&builder, 1.1f); auto operand2 = ConstantR0(&builder, 12.2f); - auto tuple_operand = Tuple(&builder, {pred2, operand1, operand2}); + auto tuple_operand = Tuple(&builder, {pred, operand1, operand2}); Call(&builder, inner_builder_result.ConsumeValueOrDie(), {tuple_operand}); - ComputeAndCompareR0(&builder, 12.0f, {}, error_spec_); + ComputeAndCompareR0(&builder, 12.0f, {pred_arg.get()}, error_spec_); } // Test a mismatch in the shape of the true operand and true computation. @@ -600,16 +625,17 @@ XLA_TEST_F(ConditionalOpTest, SwappedInputsInSequentialConditionals) { auto test_swap = [&](float a, float b) { XlaBuilder builder(TestName()); - auto x = ConstantR0(&builder, a); - auto y = ConstantR0(&builder, b); + XlaOp x, y; + auto x_arg = CreateR0Parameter(a, 0, "x", &builder, &x); + auto y_arg = CreateR0Parameter(b, 1, "y", &builder, &y); auto tuple_operand = Tuple(&builder, {x, y}); Call(&builder, main, {tuple_operand}); ComputeAndCompareTuple( &builder, - *Literal::MakeTuple({Literal::CreateR0(a).get(), - Literal::CreateR0(b).get()}), - {}, error_spec_); + *LiteralUtil::MakeTuple({LiteralUtil::CreateR0(a).get(), + LiteralUtil::CreateR0(b).get()}), + {x_arg.get(), y_arg.get()}, error_spec_); }; test_swap(3.11f, 9.4f); diff --git a/tensorflow/compiler/xla/tests/constants_test.cc b/tensorflow/compiler/xla/tests/constants_test.cc index cc5d3b11767457444d4c199943e689f082d5b199..49375748319ad5fe40db507a034ec4b07adb7e84 100644 --- a/tensorflow/compiler/xla/tests/constants_test.cc +++ b/tensorflow/compiler/xla/tests/constants_test.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array3d.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" @@ -110,8 +110,8 @@ TEST_F(ConstantsTest, Small_2x2) { TEST_F(ConstantsTest, Empty_3x0x2) { XlaBuilder builder(TestName()); - ConstantLiteral( - &builder, *Literal::CreateR3FromArray3D(Array3D(3, 0, 2))); + ConstantLiteral(&builder, *LiteralUtil::CreateR3FromArray3D( + Array3D(3, 0, 2))); ComputeAndCompareR3(&builder, Array3D(3, 0, 2), {}); } @@ -126,7 +126,7 @@ TEST_F(ConstantsTest, Small_2x2x2) { {{5.f, 6.f}, // y0 {7.f, 8.f}}, // y1 }); - ConstantLiteral(&builder, *Literal::CreateR3FromArray3D(array3d)); + ConstantLiteral(&builder, *LiteralUtil::CreateR3FromArray3D(array3d)); ComputeAndCompareR3(&builder, array3d, {}); } @@ -141,7 +141,7 @@ TEST_F(ConstantsTest, Small_3x2x1x1) { }); input_array.FillWithPZ(pz); std::unique_ptr input_literal = - Literal::CreateR4FromArray4D(input_array); + LiteralUtil::CreateR4FromArray4D(input_array); { XlaBuilder builder(TestName()); @@ -159,22 +159,23 @@ TEST_F(ConstantsTest, Small_3x2x1x1) { // TODO(b/29263943): Support tuple constants. TEST_F(ConstantsTest, DISABLED_TupleConstant) { XlaBuilder builder(TestName()); - ConstantLiteral(&builder, *Literal::MakeTuple( - {Literal::CreateR2({{1.0}, {2.0}}).get(), - Literal::CreateR1({2.0, 42}).get()})); + ConstantLiteral(&builder, + *LiteralUtil::MakeTuple( + {LiteralUtil::CreateR2({{1.0}, {2.0}}).get(), + LiteralUtil::CreateR1({2.0, 42}).get()})); std::unique_ptr result = ExecuteAndTransfer(&builder, {}).ConsumeValueOrDie(); - LiteralTestUtil::ExpectR2Near( - {{1.0}, {2.0}}, LiteralSlice(*result, {0}), error_spec_); - LiteralTestUtil::ExpectR1Near( - {2.0, 42.0}, LiteralSlice(*result, {1}), error_spec_); + LiteralTestUtil::ExpectR2Near({{1.0}, {2.0}}, + LiteralSlice(*result, {0}), error_spec_); + LiteralTestUtil::ExpectR1Near({2.0, 42.0}, LiteralSlice(*result, {1}), + error_spec_); } TEST_F(ConstantsTest, Token) { XlaBuilder builder(TestName()); - ConstantLiteral(&builder, *Literal::CreateToken()); + ConstantLiteral(&builder, *LiteralUtil::CreateToken()); // TODO(b/80000000): tokens cannot be returned from computations. Tuple(&builder, {}); TF_ASSERT_OK(Execute(&builder, {}).status()); diff --git a/tensorflow/compiler/xla/tests/convert_test.cc b/tensorflow/compiler/xla/tests/convert_test.cc index 292942a49e2f0c4b077dc71c9d0e730909689e3a..1adc68cc4839dcd7d89741ec016f27bc9047c9a5 100644 --- a/tensorflow/compiler/xla/tests/convert_test.cc +++ b/tensorflow/compiler/xla/tests/convert_test.cc @@ -13,13 +13,14 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include #include #include #include #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" @@ -52,13 +53,67 @@ TEST_F(ConvertTest, ConvertR1S32ToR1S32) { ComputeAndCompareR1(&builder, expected, {}); } +TEST_F(ConvertTest, ConvertR1S32ToR1U32) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {42, 64}); + ConvertElementType(a, U32); + + std::vector expected = {42, 64}; + ComputeAndCompareR1(&builder, expected, {}); +} + +TEST_F(ConvertTest, ConvertR1S32ToR1PRED) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {42, 0, -64}); + ConvertElementType(a, PRED); + + std::array expected = {true, false, true}; + ComputeAndCompareR1(&builder, expected, {}); +} + +TEST_F(ConvertTest, ConvertR1U32ToR1U32) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {42, 64}); + ConvertElementType(a, U32); + + std::vector expected = {42, 64}; + ComputeAndCompareR1(&builder, expected, {}); +} + +TEST_F(ConvertTest, ConvertR1U32ToR1S32) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {42, 64}); + ConvertElementType(a, S32); + + std::vector expected = {42, 64}; + ComputeAndCompareR1(&builder, expected, {}); +} + +TEST_F(ConvertTest, ConvertR1U32ToR1PRED) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {42, 0, 64}); + ConvertElementType(a, PRED); + + std::array expected = {true, false, true}; + ComputeAndCompareR1(&builder, expected, {}); +} + TEST_F(ConvertTest, ConvertR1F32ToR1F32) { XlaBuilder builder(TestName()); auto a = ConstantR1(&builder, {42.0f, 64.0f}); ConvertElementType(a, F32); std::vector expected = {42.0f, 64.0f}; - ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); + ComputeAndCompareR1(&builder, expected, {}); +} + +TEST_F(ConvertTest, ConvertR1F32ToR1PRED) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {42.0f, 0.0f, 64.0f}); + ConvertElementType(a, PRED); + + std::array expected = {true, false, true}; + ComputeAndCompareR1(&builder, expected, {}); } TEST_F(ConvertTest, ConvertR1S32ToR1F32) { @@ -67,7 +122,7 @@ TEST_F(ConvertTest, ConvertR1S32ToR1F32) { ConvertElementType(a, F32); std::vector expected = {42.0f, 64.0f}; - ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); + ComputeAndCompareR1(&builder, expected, {}); } TEST_F(ConvertTest, ConvertR1PREDToR1S32) { @@ -79,6 +134,15 @@ TEST_F(ConvertTest, ConvertR1PREDToR1S32) { ComputeAndCompareR1(&builder, expected, {}); } +TEST_F(ConvertTest, ConvertR1PREDToR1U32) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {true, false, true}); + ConvertElementType(a, U32); + + std::vector expected = {1, 0, 1}; + ComputeAndCompareR1(&builder, expected, {}); +} + TEST_F(ConvertTest, ConvertR1PREDToR1F32) { XlaBuilder builder(TestName()); auto a = ConstantR1(&builder, {true, false, true}); @@ -94,7 +158,7 @@ XLA_TEST_F(ConvertTest, ConvertR1S0S32ToR1S0F32) { ConvertElementType(a, F32); std::vector expected = {}; - ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); + ComputeAndCompareR1(&builder, expected, {}); } TEST_F(ConvertTest, ConvertR1F32ToR1S32) { @@ -145,7 +209,7 @@ XLA_TEST_F(ConvertTest, ConvertR1S64ToR1F32) { static_cast(0x8000008000000000LL), static_cast(0x8000010000000000LL), }; - std::unique_ptr arg_literal = Literal::CreateR1({arg}); + std::unique_ptr arg_literal = LiteralUtil::CreateR1({arg}); auto arg_param = Parameter(&builder, 0, arg_literal->shape(), "arg_param"); std::unique_ptr arg_data = client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); @@ -164,7 +228,7 @@ XLA_TEST_F(ConvertTest, ConvertR1U32ToR1F32) { std::vector arg{0, 1, 0x1000, 0x7fffffff, 0x80000000, 0x80000001, 0x80000002, 0x80000003, 0x80000080, 0x80000081, 0x80000082, 0xFFFFFFFF}; - std::unique_ptr arg_literal = Literal::CreateR1({arg}); + std::unique_ptr arg_literal = LiteralUtil::CreateR1({arg}); auto arg_param = Parameter(&builder, 0, arg_literal->shape(), "arg_param"); std::unique_ptr arg_data = client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); @@ -182,7 +246,7 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1U32) { XlaBuilder builder(TestName()); std::vector arg{0.0f, 1.0f, 16777216.0f, 16777218.0f, 2147483647.0f, 4294967040.0f}; - std::unique_ptr arg_literal = Literal::CreateR1({arg}); + std::unique_ptr arg_literal = LiteralUtil::CreateR1({arg}); auto arg_param = Parameter(&builder, 0, arg_literal->shape(), "arg_param"); std::unique_ptr arg_data = client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); @@ -199,7 +263,7 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1U32) { XLA_TEST_F(ConvertTest, ConvertR1U32ToR1S64) { XlaBuilder builder(TestName()); std::vector arg{0, 1, 0x1000, 0x7fffffff, 0x80000082, 0xFFFFFFFF}; - std::unique_ptr arg_literal = Literal::CreateR1({arg}); + std::unique_ptr arg_literal = LiteralUtil::CreateR1({arg}); auto arg_param = Parameter(&builder, 0, arg_literal->shape(), "arg_param"); std::unique_ptr arg_data = client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); @@ -216,7 +280,7 @@ XLA_TEST_F(ConvertTest, ConvertR1U32ToR1S64) { XLA_TEST_F(ConvertTest, ConvertR1S32ToR1S64) { XlaBuilder builder(TestName()); std::vector arg{0, 1, 0x1000, -1, -0x1000}; - std::unique_ptr arg_literal = Literal::CreateR1({arg}); + std::unique_ptr arg_literal = LiteralUtil::CreateR1({arg}); auto arg_param = Parameter(&builder, 0, arg_literal->shape(), "arg_param"); std::unique_ptr arg_data = client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); @@ -253,7 +317,7 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1S64) { 9223370937343148032.f, -9223371487098961920.f, -9223370937343148032.f}; - std::unique_ptr arg_literal = Literal::CreateR1({arg}); + std::unique_ptr arg_literal = LiteralUtil::CreateR1({arg}); auto arg_param = Parameter(&builder, 0, arg_literal->shape(), "arg_param"); std::unique_ptr arg_data = client_->TransferToServer(*arg_literal).ConsumeValueOrDie(); @@ -391,7 +455,7 @@ XLA_TEST_F(ConvertTest, ConvertR1F16ToR1F32) { TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr dot_lhs_handle, - client_->TransferToServer(*Literal::CreateR1(input))); + client_->TransferToServer(*LiteralUtil::CreateR1(input))); XlaBuilder builder(TestName()); ConvertElementType( @@ -411,7 +475,7 @@ XLA_TEST_F(ConvertTest, ConvertR1F32ToR1F16) { TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr dot_lhs_handle, - client_->TransferToServer(*Literal::CreateR1(input))); + client_->TransferToServer(*LiteralUtil::CreateR1(input))); XlaBuilder builder(TestName()); ConvertElementType( diff --git a/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc b/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc index 7605ebf4c0eacd7f44e867e23dbc27c6c1bc3e93..7b6bbc4f571af2e11306f95c24e243e78e0f4f4e 100644 --- a/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc +++ b/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/statusor.h" @@ -93,7 +93,8 @@ XLA_TEST_F(ConvolutionDimensionNumbersTest, auto weight_array = MakeUnique>(4, 3, 1, 1); weight_array->FillWithMultiples(0.2); auto weight_data = - client_->TransferToServer(*Literal::CreateR4FromArray4D(*weight_array)) + client_ + ->TransferToServer(*LiteralUtil::CreateR4FromArray4D(*weight_array)) .ConsumeValueOrDie(); XlaBuilder builder(TestName()); diff --git a/tensorflow/compiler/xla/tests/convolution_test.cc b/tensorflow/compiler/xla/tests/convolution_test.cc index 0f6d54d042dd6af6d82e1eea93a66c2e9be53639..5ed8122e0073bde77bb2507a0ddd89c4365627c9 100644 --- a/tensorflow/compiler/xla/tests/convolution_test.cc +++ b/tensorflow/compiler/xla/tests/convolution_test.cc @@ -23,9 +23,9 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -123,8 +123,8 @@ class Convolve_1x1x1x2_1x1x1x2_Valid : public ConvolutionTest { })); ComputeAndCompare(&builder, - {std::move(*Literal::CreateFromArray(input_data)), - std::move(*Literal::CreateFromArray(filter_data))}, + {std::move(*LiteralUtil::CreateFromArray(input_data)), + std::move(*LiteralUtil::CreateFromArray(filter_data))}, error_spec_); } }; @@ -157,8 +157,8 @@ class Convolve_1x1x4x4_1x1x2x2_Valid : public ConvolutionTest { {7.0f, 8.0f}, })); ComputeAndCompare(&builder, - {std::move(*Literal::CreateFromArray(input_data)), - std::move(*Literal::CreateFromArray(filter_data))}, + {std::move(*LiteralUtil::CreateFromArray(input_data)), + std::move(*LiteralUtil::CreateFromArray(filter_data))}, error_spec_); } }; @@ -192,8 +192,8 @@ class Convolve_1x1x4x4_1x1x2x2_Same : public ConvolutionTest { })); ComputeAndCompare(&builder, - {std::move(*Literal::CreateFromArray(input_data)), - std::move(*Literal::CreateFromArray(filter_data))}, + {std::move(*LiteralUtil::CreateFromArray(input_data)), + std::move(*LiteralUtil::CreateFromArray(filter_data))}, error_spec_); } }; @@ -224,8 +224,8 @@ class Convolve_1x1x4x4_1x1x3x3_Same : public ConvolutionTest { {{5.0f, 6.0f, 7.0f}, {8.0f, 9.0f, 10.0f}, {11.0f, 12.0f, 13.0f}})); // clang-format on ComputeAndCompare(&builder, - {std::move(*Literal::CreateFromArray(input_data)), - std::move(*Literal::CreateFromArray(filter_data))}, + {std::move(*LiteralUtil::CreateFromArray(input_data)), + std::move(*LiteralUtil::CreateFromArray(filter_data))}, error_spec_); } }; @@ -249,10 +249,10 @@ XLA_TEST_F(ConvolutionTest, Convolve1D_1x2x5_1x2x2_Valid) { Array3D expected({{{510, 610, 710, 810}}}); auto input_literal = - client_->TransferToServer(*Literal::CreateR3FromArray3D(input)) + client_->TransferToServer(*LiteralUtil::CreateR3FromArray3D(input)) .ConsumeValueOrDie(); auto filter_literal = - client_->TransferToServer(*Literal::CreateR3FromArray3D(filter)) + client_->TransferToServer(*LiteralUtil::CreateR3FromArray3D(filter)) .ConsumeValueOrDie(); ComputeAndCompareR3(&builder, expected, @@ -284,10 +284,10 @@ class Convolve1D_1x2x5_1x2x2_WithRHSDilation : public ConvolutionTest { Array3D expected({{{570.0f, 670.0f, 770.0f}}}); auto input_literal = - client_->TransferToServer(*Literal::CreateR3FromArray3D(input)) + client_->TransferToServer(*LiteralUtil::CreateR3FromArray3D(input)) .ConsumeValueOrDie(); auto filter_literal = - client_->TransferToServer(*Literal::CreateR3FromArray3D(filter)) + client_->TransferToServer(*LiteralUtil::CreateR3FromArray3D(filter)) .ConsumeValueOrDie(); ComputeAndCompareR3(&builder, expected, @@ -319,10 +319,10 @@ XLA_TEST_F(ConvolutionTest, Convolve1D_1x2x5_1x2x2_WithLHSDilation) { Array3D expected({{{190, 320, 230, 380, 270, 440, 310, 500}}}); auto input_literal = - client_->TransferToServer(*Literal::CreateR3FromArray3D(input)) + client_->TransferToServer(*LiteralUtil::CreateR3FromArray3D(input)) .ConsumeValueOrDie(); auto filter_literal = - client_->TransferToServer(*Literal::CreateR3FromArray3D(filter)) + client_->TransferToServer(*LiteralUtil::CreateR3FromArray3D(filter)) .ConsumeValueOrDie(); ComputeAndCompareR3(&builder, expected, @@ -350,10 +350,10 @@ XLA_TEST_F(ConvolutionTest, Convolve1D_1x2x5_1x2x2_WithLHSAndRHSDilation) { Array3D expected({{{510, 0, 610, 0, 710, 0, 810}}}); auto input_literal = - client_->TransferToServer(*Literal::CreateR3FromArray3D(input)) + client_->TransferToServer(*LiteralUtil::CreateR3FromArray3D(input)) .ConsumeValueOrDie(); auto filter_literal = - client_->TransferToServer(*Literal::CreateR3FromArray3D(filter)) + client_->TransferToServer(*LiteralUtil::CreateR3FromArray3D(filter)) .ConsumeValueOrDie(); ComputeAndCompareR3(&builder, expected, @@ -386,10 +386,10 @@ class Convolve1D_1x2x5_1x2x2_WithPadding : public ConvolutionTest { {{{0.0f, 260.0f, 510.0f, 610.0f, 710.0f, 810.0f, 350.0f, 0.0f}}}); auto input_literal = - client_->TransferToServer(*Literal::CreateR3FromArray3D(input)) + client_->TransferToServer(*LiteralUtil::CreateR3FromArray3D(input)) .ConsumeValueOrDie(); auto filter_literal = - client_->TransferToServer(*Literal::CreateR3FromArray3D(filter)) + client_->TransferToServer(*LiteralUtil::CreateR3FromArray3D(filter)) .ConsumeValueOrDie(); ComputeAndCompareR3(&builder, expected, @@ -434,15 +434,15 @@ XLA_TEST_F(ConvolutionTest, Convolve3D_1x4x2x3x3_2x2x2x3x3_Valid) { std::vector input_elems(ShapeUtil::ElementsIn(input_shape)); iota(input_elems.begin(), input_elems.end(), 1.0f); - auto input_r1 = Literal::CreateR1(input_elems); + auto input_r1 = LiteralUtil::CreateR1(input_elems); auto input_r5 = input_r1->Reshape(input_dims).ConsumeValueOrDie(); std::vector filter_elems(ShapeUtil::ElementsIn(filter_shape)); iota(filter_elems.begin(), filter_elems.end(), 1.0f); - auto filter_r1 = Literal::CreateR1(filter_elems); + auto filter_r1 = LiteralUtil::CreateR1(filter_elems); auto filter_r5 = filter_r1->Reshape(filter_dims).ConsumeValueOrDie(); - auto expected_r1 = Literal::CreateR1( + auto expected_r1 = LiteralUtil::CreateR1( {19554, 19962, 20370, 22110, 22590, 23070, 34890, 35730, 36570, 37446, 38358, 39270, 50226, 51498, 52770, 52782, 54126, 55470}); auto expected_r5 = expected_r1->Reshape({1, 3, 1, 2, 3}).ConsumeValueOrDie(); @@ -497,15 +497,15 @@ class Convolve2D_1x3x3x5_3x3x5x5_Valid : public ConvolutionTest { std::vector input_elems(ShapeUtil::ElementsIn(input_shape)); iota_int_init_value(input_elems, 1); - auto input_r1 = Literal::CreateR1(input_elems); + auto input_r1 = LiteralUtil::CreateR1(input_elems); auto input_r4 = input_r1->Reshape(input_dims).ConsumeValueOrDie(); std::vector filter_elems(ShapeUtil::ElementsIn(filter_shape)); iota_int_init_value(filter_elems, 1); - auto filter_r1 = Literal::CreateR1(filter_elems); + auto filter_r1 = LiteralUtil::CreateR1(filter_elems); auto filter_r4 = filter_r1->Reshape(filter_dims).ConsumeValueOrDie(); - auto expected_r1 = Literal::CreateR1( + auto expected_r1 = LiteralUtil::CreateR1( {static_cast(92115), static_cast(93150), static_cast(94185)}); auto expected_r4 = expected_r1->Reshape({1, 1, 1, 3}).ConsumeValueOrDie(); @@ -561,8 +561,8 @@ XLA_TEST_P(ConvolveWithAndWithoutCanonicalization, expected_result.Fill(0); ComputeAndCompare(&builder, - {std::move(*Literal::CreateFromArray(param0)), - std::move(*Literal::CreateFromArray(param1))}, + {std::move(*LiteralUtil::CreateFromArray(param0)), + std::move(*LiteralUtil::CreateFromArray(param1))}, error_spec_); } @@ -617,18 +617,18 @@ class Convolve1D1WindowTestBase std::vector input_elems(ShapeUtil::ElementsIn(input_shape), static_cast(1.0f)); - auto input_r1 = Literal::CreateR1(input_elems); + auto input_r1 = LiteralUtil::CreateR1(input_elems); auto input_r3 = input_r1->Reshape(input_dims).ConsumeValueOrDie(); std::vector filter_elems(ShapeUtil::ElementsIn(filter_shape), static_cast(1.0f)); - auto filter_r1 = Literal::CreateR1(filter_elems); + auto filter_r1 = LiteralUtil::CreateR1(filter_elems); auto filter_r3 = filter_r1->Reshape(filter_dims).ConsumeValueOrDie(); std::vector expect_elems(batch * output_feature * num_windows, static_cast(window_size * input_feature)); - auto expected_r1 = Literal::CreateR1(expect_elems); + auto expected_r1 = LiteralUtil::CreateR1(expect_elems); auto expected_r3 = expected_r1->Reshape({batch, num_windows, output_feature}) .ConsumeValueOrDie(); @@ -737,8 +737,8 @@ XLA_TEST_F(ConvolutionTest, Convolve_bf16_1x1x1x2_1x1x1x2_Valid) { })); ComputeAndCompare(&builder, - {std::move(*Literal::CreateFromArray(input_data)), - std::move(*Literal::CreateFromArray(filter_data))}, + {std::move(*LiteralUtil::CreateFromArray(input_data)), + std::move(*LiteralUtil::CreateFromArray(filter_data))}, error_spec_); } @@ -761,8 +761,8 @@ XLA_TEST_F(ConvolutionTest, NoCudnnAlgorithmPicker) { filter_data.FillIota(10); ComputeAndCompare(&builder, - {std::move(*Literal::CreateFromArray(input_data)), - std::move(*Literal::CreateFromArray(filter_data))}); + {std::move(*LiteralUtil::CreateFromArray(input_data)), + std::move(*LiteralUtil::CreateFromArray(filter_data))}); } } // namespace diff --git a/tensorflow/compiler/xla/tests/convolution_variants_test.cc b/tensorflow/compiler/xla/tests/convolution_variants_test.cc index c31d033bb0f0e52d40251c4d7b64d52f42d29dc6..6784c16715da72d337edf70fa51db42c59404136 100644 --- a/tensorflow/compiler/xla/tests/convolution_variants_test.cc +++ b/tensorflow/compiler/xla/tests/convolution_variants_test.cc @@ -27,8 +27,8 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" @@ -1333,17 +1333,17 @@ XLA_TEST_F(ConvolutionVariantsTest, BackwardFilterEvenPadding1D) { XLA_TEST_F(ConvolutionVariantsTest, BackwardInputEvenPadding3D) { XlaBuilder builder(TestName()); - auto gradients_flat = Literal::CreateR1({1}); + auto gradients_flat = LiteralUtil::CreateR1({1}); auto gradients_literal = gradients_flat->Reshape({1, 1, 1, 1, 1}).ConsumeValueOrDie(); auto gradients = ConstantLiteral(&builder, *gradients_literal); - auto weights_flat = Literal::CreateR1({1, 10, 100}); + auto weights_flat = LiteralUtil::CreateR1({1, 10, 100}); auto weights_literal = weights_flat->Reshape({1, 1, 1, 1, 3}).ConsumeValueOrDie(); auto weights = ConstantLiteral(&builder, *weights_literal); - auto expected_flat = Literal::CreateR1({10}); + auto expected_flat = LiteralUtil::CreateR1({10}); auto expected_literal = expected_flat->Reshape({1, 1, 1, 1, 1}).ConsumeValueOrDie(); @@ -1357,17 +1357,17 @@ XLA_TEST_F(ConvolutionVariantsTest, BackwardInputEvenPadding3D) { XLA_TEST_F(ConvolutionVariantsTest, BackwardFilterEvenPadding3D) { XlaBuilder builder(TestName()); - auto activations_flat = Literal::CreateR1({1, 2, 3, 4}); + auto activations_flat = LiteralUtil::CreateR1({1, 2, 3, 4}); auto activations_literal = activations_flat->Reshape({1, 1, 1, 1, 4}).ConsumeValueOrDie(); auto activations = ConstantLiteral(&builder, *activations_literal); - auto gradients_flat = Literal::CreateR1({100, 10, 1}); + auto gradients_flat = LiteralUtil::CreateR1({100, 10, 1}); auto gradients_literal = gradients_flat->Reshape({1, 1, 1, 1, 3}).ConsumeValueOrDie(); auto gradients = ConstantLiteral(&builder, *gradients_literal); - auto expected_flat = Literal::CreateR1({13, 24, 130}); + auto expected_flat = LiteralUtil::CreateR1({13, 24, 130}); auto expected_literal = expected_flat->Reshape({1, 1, 1, 1, 3}).ConsumeValueOrDie(); diff --git a/tensorflow/compiler/xla/tests/copy_test.cc b/tensorflow/compiler/xla/tests/copy_test.cc index fef42885e516fa8c8f87756d7a953fe5f37a630f..5ef273e5a26ea8a16db864974c9bfa2c296cbce8 100644 --- a/tensorflow/compiler/xla/tests/copy_test.cc +++ b/tensorflow/compiler/xla/tests/copy_test.cc @@ -17,8 +17,8 @@ limitations under the License. #include #include "tensorflow/compiler/xla/array2d.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" @@ -58,37 +58,38 @@ class CopyOpTest : public HloTestBase { }; XLA_TEST_F(CopyOpTest, CopyR0Bool) { - TestCopyOp(*Literal::CreateR0(true)); + TestCopyOp(*LiteralUtil::CreateR0(true)); } XLA_TEST_F(CopyOpTest, CopyR1S0U32) { - TestCopyOp(*Literal::CreateR1({})); + TestCopyOp(*LiteralUtil::CreateR1({})); } XLA_TEST_F(CopyOpTest, CopyR1S3U32) { - TestCopyOp(*Literal::CreateR1({1, 2, 3})); + TestCopyOp(*LiteralUtil::CreateR1({1, 2, 3})); } XLA_TEST_F(CopyOpTest, CopyR3F32_2x2x3) { - TestCopyOp(*Literal::CreateR3({{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, - {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}})); + TestCopyOp( + *LiteralUtil::CreateR3({{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, + {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}})); } XLA_TEST_F(CopyOpTest, CopyR4S32_2x2x3x2) { - TestCopyOp(*Literal::CreateR4( + TestCopyOp(*LiteralUtil::CreateR4( {{{{1, -2}, {-4, 5}, {6, 7}}, {{8, 9}, {10, 11}, {12, 13}}}, {{{10, 3}, {7, -2}, {3, 6}}, {{2, 5}, {-11, 5}, {-2, -5}}}})); } XLA_TEST_F(CopyOpTest, CopyR4S32_0x2x3x2) { - TestCopyOp(*Literal::CreateR4FromArray4D(Array4D(0, 2, 3, 2))); + TestCopyOp(*LiteralUtil::CreateR4FromArray4D(Array4D(0, 2, 3, 2))); } XLA_TEST_F(CopyOpTest, CopyParameterScalar) { auto builder = HloComputation::Builder(TestName()); // Copy literal to device to use as parameter. - auto literal = Literal::CreateR0(42.0); + auto literal = LiteralUtil::CreateR0(42.0); Shape shape = literal->shape(); auto param0 = builder.AddInstruction( @@ -109,7 +110,7 @@ XLA_TEST_F(CopyOpTest, CopyParameterScalar) { XLA_TEST_F(CopyOpTest, CopyConstantR2Twice) { auto builder = HloComputation::Builder(TestName()); - auto literal = Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + auto literal = LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); auto constant = builder.AddInstruction( HloInstruction::CreateConstant(std::move(literal))); @@ -131,7 +132,7 @@ XLA_TEST_F(CopyOpTest, CopyConstantR2DifferentLayouts) { HloComputation::Builder builder(TestName()); std::unique_ptr literal = - Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); // Reverse the minor-to-major order of the literal. Layout* literal_layout = literal->mutable_shape_do_not_use()->mutable_layout(); @@ -168,7 +169,7 @@ void CopyOpTest::TestCopyConstantLayout021(size_t n1, size_t n2, size_t n3) { HloComputation::Builder builder(TestName()); - std::unique_ptr literal = Literal::CreateR3FromArray3D(a); + std::unique_ptr literal = LiteralUtil::CreateR3FromArray3D(a); HloInstruction* constant = builder.AddInstruction( HloInstruction::CreateConstant(std::move(literal))); @@ -202,7 +203,7 @@ void CopyOpTest::TestCopyConstantLayoutR4( HloComputation::Builder builder(TestName()); - std::unique_ptr literal = Literal::CreateR4FromArray4D(a); + std::unique_ptr literal = LiteralUtil::CreateR4FromArray4D(a); HloInstruction* constant = builder.AddInstruction( HloInstruction::CreateConstant(std::move(literal))); diff --git a/tensorflow/compiler/xla/tests/cross_replica_sum_test.cc b/tensorflow/compiler/xla/tests/cross_replica_sum_test.cc index b151187c4b8f01c5b46ccadf27d2e22a7c902e98..d12a4e7fcd7813775a81677bcaa07af60ff9b477 100644 --- a/tensorflow/compiler/xla/tests/cross_replica_sum_test.cc +++ b/tensorflow/compiler/xla/tests/cross_replica_sum_test.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test.h" @@ -45,7 +45,7 @@ XLA_TEST_F(TrivialCrossReplicaSumTest, OneOperand) { })"; auto module = ParseHloString(module_str, GetModuleConfigForTest()).ValueOrDie(); - auto literal = Literal::CreateR1({1, 2, 3}); + auto literal = LiteralUtil::CreateR1({1, 2, 3}); EXPECT_EQ(*literal, *ExecuteAndTransfer(std::move(module), {literal.get()})); } @@ -66,10 +66,10 @@ XLA_TEST_F(TrivialCrossReplicaSumTest, MultipleOperands) { })"; auto module = ParseHloString(module_str, GetModuleConfigForTest()).ValueOrDie(); - auto literal0 = Literal::CreateR1({1, 2, 3}); - auto literal1 = Literal::CreateR1({10, 20}); + auto literal0 = LiteralUtil::CreateR1({1, 2, 3}); + auto literal1 = LiteralUtil::CreateR1({10, 20}); EXPECT_EQ( - *Literal::MakeTuple({literal0.get(), literal1.get()}), + *LiteralUtil::MakeTuple({literal0.get(), literal1.get()}), *ExecuteAndTransfer(std::move(module), {literal0.get(), literal1.get()})); } @@ -93,9 +93,9 @@ XLA_TEST_F(TrivialCrossReplicaSumTest, ConstantOperand) { })"; auto module = ParseHloString(module_str, GetModuleConfigForTest()).ValueOrDie(); - auto literal0 = Literal::CreateR1({1, 2, 3}); - auto literal1 = Literal::CreateR1({10, 20}); - EXPECT_EQ(*Literal::MakeTuple({literal0.get(), literal1.get()}), + auto literal0 = LiteralUtil::CreateR1({1, 2, 3}); + auto literal1 = LiteralUtil::CreateR1({10, 20}); + EXPECT_EQ(*LiteralUtil::MakeTuple({literal0.get(), literal1.get()}), *ExecuteAndTransfer(std::move(module), {literal0.get()})); } diff --git a/tensorflow/compiler/xla/tests/custom_call_test.cc b/tensorflow/compiler/xla/tests/custom_call_test.cc index d1516a28b0bb3857d9aee0922a252e25a8f9d2d5..13c777835eb2d2519d39205cdc96f0aac4850c7d 100644 --- a/tensorflow/compiler/xla/tests/custom_call_test.cc +++ b/tensorflow/compiler/xla/tests/custom_call_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/cpu/custom_call_target_registry.h" @@ -74,7 +74,7 @@ XLA_TEST_F(CustomCallTest, DISABLED_ON_GPU(CustomCallR0F32Add2)) { auto builder = HloComputation::Builder(TestName()); auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0f))); builder.AddInstruction( HloInstruction::CreateCustomCall(r0f32_, {constant}, "R0F32Add2")); @@ -95,7 +95,7 @@ XLA_TEST_F(CustomCallTest, DISABLED_ON_GPU(CustomCallR2F32Reduce)) { array(1, 1) = 4.0f; auto constant = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2FromArray2D(array))); + HloInstruction::CreateConstant(LiteralUtil::CreateR2FromArray2D(array))); builder.AddInstruction( HloInstruction::CreateCustomCall(r0f32_, {constant}, "R2F32ReduceSum")); @@ -111,7 +111,7 @@ XLA_TEST_F(CustomCallTest, auto b = HloComputation::Builder(TestName()); auto input = b.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2FromArray2D( + HloInstruction::CreateConstant(LiteralUtil::CreateR2FromArray2D( Array2D{{1.0f, 2.0f}, {3.0f, 4.0f}}))); auto incremented = b.AddInstruction(HloInstruction::CreateCustomCall( ShapeUtil::MakeShape(F32, {1, 2, 2}), {input}, "Add1ToValues")); diff --git a/tensorflow/compiler/xla/tests/deallocation_test.cc b/tensorflow/compiler/xla/tests/deallocation_test.cc index d4b3aac85bff283515088f6e61c9d2bad11f60d3..5f234f36a8543ad408fb3430b27844beb16a54b5 100644 --- a/tensorflow/compiler/xla/tests/deallocation_test.cc +++ b/tensorflow/compiler/xla/tests/deallocation_test.cc @@ -17,8 +17,8 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/test_helpers.h" diff --git a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc index acba67491d25007ab774530fd7ca236a4363b6f0..2db6503afab748d7b778e26b2f9350ac64c7778b 100644 --- a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc +++ b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc @@ -18,9 +18,9 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" @@ -171,7 +171,7 @@ TEST_F(DeconstructTupleTest, DeconstructNonTuple) { XLA_TEST_F(DeconstructTupleTest, DeconstructTupleFromParam) { XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR1({3.14f, -100.25f}); + LiteralUtil::CreateR1({3.14f, -100.25f}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); auto p = Parameter(&builder, 0, ShapeUtil::MakeShape(F32, {2}), "param0"); diff --git a/tensorflow/compiler/xla/tests/deep_graph_test.cc b/tensorflow/compiler/xla/tests/deep_graph_test.cc index 810947ab01b69b10b6ae60c551bd7aba10a6313d..3f3e8ab712fea14be9e4a7015effdf8ce518309b 100644 --- a/tensorflow/compiler/xla/tests/deep_graph_test.cc +++ b/tensorflow/compiler/xla/tests/deep_graph_test.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" namespace xla { diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc index cf2e645d472efab9ca649dbde6602fd4f205d924..cfd36abf47c0e510b41b4ce8dfba077f4119a6c2 100644 --- a/tensorflow/compiler/xla/tests/dot_operation_test.cc +++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/array3d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -67,15 +67,16 @@ XLA_TEST_F(DotOperationTest, DotOfInputTupleElem) { XlaOp param; auto param_data = CreateParameterAndTransferLiteral( 0, - *Literal::MakeTuple({Literal::CreateR2({{1, 2}, {3, 4}}).get(), - Literal::CreateR2({{5, 6}, {7, 8}}).get()}), + *LiteralUtil::MakeTuple( + {LiteralUtil::CreateR2({{1, 2}, {3, 4}}).get(), + LiteralUtil::CreateR2({{5, 6}, {7, 8}}).get()}), "arg0", &builder, ¶m); auto lhs = GetTupleElement(param, 0); auto rhs = GetTupleElement(param, 1); Dot(lhs, rhs); ComputeAndCompareLiteral(&builder, - *Literal::CreateR2({{19, 22}, {43, 50}}), + *LiteralUtil::CreateR2({{19, 22}, {43, 50}}), {param_data.get()}); } @@ -194,11 +195,11 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, FusedDot) { auto lhs_handle = this->client_ - ->TransferToServer(*Literal::CreateR2FromArray2D( + ->TransferToServer(*LiteralUtil::CreateR2FromArray2D( {{1.0f, 2.0f, 3.0f, 4.0f}, {-1.0f, -2.0f, -3.0f, -4.0f}})) .ConsumeValueOrDie(); auto rhs_handle = this->client_ - ->TransferToServer(*Literal::CreateR2FromArray2D( + ->TransferToServer(*LiteralUtil::CreateR2FromArray2D( {{1.0f}, {2.0f}, {3.0f}, {4.0f}})) .ConsumeValueOrDie(); @@ -217,14 +218,14 @@ class SquareMatrixDot : public DotOperationTest { void TestImpl(bool lhs_row_major, bool rhs_row_major) { auto lhs_handle = client_ - ->TransferToServer(*Literal::CreateFromArrayWithLayout( + ->TransferToServer(*LiteralUtil::CreateFromArrayWithLayout( {{1.0f, 2.0f}, {3.0f, -4.0f}}, LayoutUtil::MakeLayout( MinorToMajorForIsRowMajor(lhs_row_major)))) .ConsumeValueOrDie(); auto rhs_handle = client_ - ->TransferToServer(*Literal::CreateFromArrayWithLayout( + ->TransferToServer(*LiteralUtil::CreateFromArrayWithLayout( {{1.0f, 6.0f}, {7.0f, -4.0f}}, LayoutUtil::MakeLayout( MinorToMajorForIsRowMajor(rhs_row_major)))) @@ -286,9 +287,10 @@ void ParametricDotTest::TestImpl() { std::unique_ptr> dot_lhs_data = MakeLinspaceArray2D(0.0, 1.0, param.m, param.k); - std::unique_ptr dot_lhs_lit = Literal::CreateR2FromArray2DWithLayout( - *dot_lhs_data, LayoutUtil::MakeLayout( - MinorToMajorForIsRowMajor(param.dot_lhs_row_major))); + std::unique_ptr dot_lhs_lit = + LiteralUtil::CreateR2FromArray2DWithLayout( + *dot_lhs_data, LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor( + param.dot_lhs_row_major))); std::unique_ptr dot_lhs_handle = client_->TransferToServer(*dot_lhs_lit).ConsumeValueOrDie(); @@ -297,7 +299,7 @@ void ParametricDotTest::TestImpl() { Layout rhs_layout = LayoutUtil::MakeLayout( MinorToMajorForIsRowMajor(param.dot_rhs_row_major)); std::unique_ptr dot_rhs_lit = - Literal::CreateR2FromArray2DWithLayout(*dot_rhs_data, rhs_layout); + LiteralUtil::CreateR2FromArray2DWithLayout(*dot_rhs_data, rhs_layout); std::unique_ptr dot_rhs_handle = client_->TransferToServer(*dot_rhs_lit).ConsumeValueOrDie(); @@ -307,7 +309,7 @@ void ParametricDotTest::TestImpl() { if (param.has_addend) { addend_data = MakeLinspaceArray2D(0.0, 1.0, param.m, param.n); - addend_lit = Literal::CreateR2FromArray2DWithLayout( + addend_lit = LiteralUtil::CreateR2FromArray2DWithLayout( *addend_data, LayoutUtil::MakeLayout( MinorToMajorForIsRowMajor(param.addend_row_major))); addend_handle = client_->TransferToServer(*addend_lit).ConsumeValueOrDie(); @@ -476,14 +478,14 @@ class NonsquareMatrixDot : public DotOperationTest { void TestImpl(bool lhs_row_major, bool rhs_row_major) { auto lhs_handle = client_ - ->TransferToServer(*Literal::CreateFromArrayWithLayout( + ->TransferToServer(*LiteralUtil::CreateFromArrayWithLayout( {{1.0f, 2.0f, 3.0f}, {3.0f, -4.0f, -1.0f}}, LayoutUtil::MakeLayout( MinorToMajorForIsRowMajor(lhs_row_major)))) .ConsumeValueOrDie(); auto rhs_handle = client_ - ->TransferToServer(*Literal::CreateFromArrayWithLayout( + ->TransferToServer(*LiteralUtil::CreateFromArrayWithLayout( {{1.0f, 6.0f}, {2.0f, 3.0f}, {7.0f, -4.0f}}, LayoutUtil::MakeLayout( MinorToMajorForIsRowMajor(rhs_row_major)))) @@ -510,12 +512,12 @@ XLA_TYPED_TEST(NonsquareMatrixDot, TestTT) { this->TestImpl(true, true); } XLA_TEST_F(DotOperationTest, MatrixVectorC64) { auto lhs_handle = client_ - ->TransferToServer(*Literal::CreateR2WithLayout( + ->TransferToServer(*LiteralUtil::CreateR2WithLayout( {{1.0, 2.0, 3.0, -4.0}}, LayoutUtil::MakeLayout({1, 0}))) .ConsumeValueOrDie(); auto rhs_handle = client_ - ->TransferToServer(*Literal::CreateR2WithLayout( + ->TransferToServer(*LiteralUtil::CreateR2WithLayout( {{1.0, 1.0}, {2.0, 2.0}, {3.0, 3.0}, {-4.0, 4.0}}, LayoutUtil::MakeLayout({1, 0}))) .ConsumeValueOrDie(); @@ -583,7 +585,7 @@ XLA_TYPED_TEST(DotOperationTestForBatchMatMul, Types) { Reshape(out_flat, {0, 1, 2}, {2, 2, 2, 2}); auto x_data = this->client_ - ->TransferToServer(*Literal::CreateR4FromArray4D( + ->TransferToServer(*LiteralUtil::CreateR4FromArray4D( {{{{1000.0f, 100.0f}, {10.0f, 1.0f}}, {{2000.0f, 200.0f}, {20.0f, 2.0f}}}, {{{3000.0f, 300.0f}, {30.0f, 3.0f}}, @@ -591,7 +593,7 @@ XLA_TYPED_TEST(DotOperationTestForBatchMatMul, Types) { .ConsumeValueOrDie(); auto y_data = this->client_ - ->TransferToServer(*Literal::CreateR4FromArray4D( + ->TransferToServer(*LiteralUtil::CreateR4FromArray4D( {{{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}}, {{{11.0f, 22.0f}, {33.0f, 44.0f}}, {{55.0f, 66.0f}, {77.0f, 88.0f}}}})) @@ -629,13 +631,13 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, GeneralMatMul) { auto x_data = this->client_ - ->TransferToServer(*Literal::CreateR3FromArray3D( + ->TransferToServer(*LiteralUtil::CreateR3FromArray3D( {{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}})) .ConsumeValueOrDie(); auto y_data = this->client_ - ->TransferToServer(*Literal::CreateR3FromArray3D( + ->TransferToServer(*LiteralUtil::CreateR3FromArray3D( {{{1.0f, 0.0f}, {0.0f, 1.0f}}, {{1.0f, 0.0f}, {0.0f, 1.0f}}})) .ConsumeValueOrDie(); @@ -664,15 +666,17 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, TransposeFolding) { } auto lhs_handle = this->client_ - ->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( - *lhs, LayoutUtil::MakeLayout( - MinorToMajorForIsRowMajor(row_major)))) + ->TransferToServer( + *LiteralUtil::CreateR2FromArray2DWithLayout( + *lhs, LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(row_major)))) .ConsumeValueOrDie(); auto rhs_handle = this->client_ - ->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( - *rhs, LayoutUtil::MakeLayout( - MinorToMajorForIsRowMajor(row_major)))) + ->TransferToServer( + *LiteralUtil::CreateR2FromArray2DWithLayout( + *rhs, LayoutUtil::MakeLayout( + MinorToMajorForIsRowMajor(row_major)))) .ConsumeValueOrDie(); XlaBuilder builder(this->TestName()); @@ -733,15 +737,15 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, TF_ASSERT_OK_AND_ASSIGN( auto arg_0_value, this->client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_0_value_array))); + *LiteralUtil::CreateR2FromArray2D(*arg_0_value_array))); TF_ASSERT_OK_AND_ASSIGN( auto arg_1_value, this->client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_1_value_array))); + *LiteralUtil::CreateR2FromArray2D(*arg_1_value_array))); TF_ASSERT_OK_AND_ASSIGN( auto arg_2_value, this->client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_2_value_array))); + *LiteralUtil::CreateR2FromArray2D(*arg_2_value_array))); Array2D expected({{53.0f, 74.0f}, {45.0f, 66.0f}}); this->template ComputeAndCompareR2( @@ -782,15 +786,15 @@ XLA_TYPED_TEST(DotOperationTest_F16F32F64, TF_ASSERT_OK_AND_ASSIGN( auto arg_0_value, this->client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_0_value_array))); + *LiteralUtil::CreateR2FromArray2D(*arg_0_value_array))); TF_ASSERT_OK_AND_ASSIGN( auto arg_1_value, this->client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_1_value_array))); + *LiteralUtil::CreateR2FromArray2D(*arg_1_value_array))); TF_ASSERT_OK_AND_ASSIGN( auto arg_2_value, this->client_->TransferToServer( - *Literal::CreateR2FromArray2D(*arg_2_value_array))); + *LiteralUtil::CreateR2FromArray2D(*arg_2_value_array))); Array2D expected({{38.0f, 36.0f}, {93.0f, 91.0f}}); this->template ComputeAndCompareR2( diff --git a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc index f3c258a4d4c446c465320ac16ef7c72e299a51a8..7f6f203a1ba48e0053f799c58bbbeae87aef1f7f 100644 --- a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc +++ b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" #include "tensorflow/compiler/xla/service/local_service.h" @@ -124,11 +124,11 @@ class DynamicSliceTest : public ClientLibraryTestBase { // vector is special so that it cannot be an ArraySlice, which // is what the code below wants. So instead we do this. Literal input_values = - std::move(*Literal::CreateR1(input_values_int) + std::move(*LiteralUtil::CreateR1(input_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); Literal expected_values = - std::move(*Literal::CreateR1(expected_values_int) + std::move(*LiteralUtil::CreateR1(expected_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); @@ -150,11 +150,11 @@ class DynamicSliceTest : public ClientLibraryTestBase { const std::vector& slice_sizes, const Array2D& expected_values_int) { Literal input_values = - std::move(*Literal::CreateR2FromArray2D(input_values_int) + std::move(*LiteralUtil::CreateR2FromArray2D(input_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); Literal expected_values = - std::move(*Literal::CreateR2FromArray2D(expected_values_int) + std::move(*LiteralUtil::CreateR2FromArray2D(expected_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); @@ -176,11 +176,11 @@ class DynamicSliceTest : public ClientLibraryTestBase { const std::vector& slice_sizes, const Array3D& expected_values_int) { Literal input_values = - std::move(*Literal::CreateR3FromArray3D(input_values_int) + std::move(*LiteralUtil::CreateR3FromArray3D(input_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); Literal expected_values = - std::move(*Literal::CreateR3FromArray3D(expected_values_int) + std::move(*LiteralUtil::CreateR3FromArray3D(expected_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); @@ -202,18 +202,28 @@ XLA_TEST_F(DynamicSliceTest, Int32R1) { TestR1(); } XLA_TEST_F(DynamicSliceTest, Int32R1OOB) { TestR1OOB(); } XLA_TEST_F(DynamicSliceTest, Int64R1) { TestR1(); } XLA_TEST_F(DynamicSliceTest, UInt64R1) { TestR1(); } +XLA_TEST_F(DynamicSliceTest, UInt32R1OOB) { + RunR1({0, 1, 2, 3, 4}, {2147483648u}, {2}, {3, 4}); +} XLA_TEST_F(DynamicSliceTest, Int32R2BF16) { TestR2(); } XLA_TEST_F(DynamicSliceTest, Int32R2) { TestR2(); } XLA_TEST_F(DynamicSliceTest, Int32R2OOB) { TestR2OOB(); } XLA_TEST_F(DynamicSliceTest, Int64R2) { TestR2(); } XLA_TEST_F(DynamicSliceTest, UInt64R2) { TestR2(); } +XLA_TEST_F(DynamicSliceTest, UInt32R2OOB) { + RunR2({{0, 1}, {2, 3}}, {2147483648u, 0}, {1, 1}, {{2}}); +} XLA_TEST_F(DynamicSliceTest, Int32R3BF16) { TestR3(); } XLA_TEST_F(DynamicSliceTest, Int32R3) { TestR3(); } XLA_TEST_F(DynamicSliceTest, Int32R3OOB) { TestR3OOB(); } XLA_TEST_F(DynamicSliceTest, Int64R3) { TestR3(); } XLA_TEST_F(DynamicSliceTest, UInt64R3) { TestR3(); } +XLA_TEST_F(DynamicSliceTest, UInt32R3OOB) { + RunR3({{{0, 1}, {2, 3}}, {{4, 5}, {6, 7}}}, + {2147483648u, 0, 2147483648u}, {1, 1, 1}, {{{5}}}); +} XLA_TEST_F(DynamicSliceTest, Int32R1Pred) { // Slice at dimension start. @@ -349,15 +359,15 @@ class DynamicUpdateSliceTest : public ClientLibraryTestBase { void RunR0(int input_value_int, int update_value_int, const std::vector slice_starts, int expected_value_int) { Literal input_value = - std::move(*Literal::CreateR0(input_value_int) + std::move(*LiteralUtil::CreateR0(input_value_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); Literal update_value = - std::move(*Literal::CreateR0(update_value_int) + std::move(*LiteralUtil::CreateR0(update_value_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); Literal expected_value = - std::move(*Literal::CreateR0(expected_value_int) + std::move(*LiteralUtil::CreateR0(expected_value_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); @@ -380,15 +390,15 @@ class DynamicUpdateSliceTest : public ClientLibraryTestBase { const std::vector slice_starts, tensorflow::gtl::ArraySlice expected_values_int) { Literal input_values = - std::move(*Literal::CreateR1(input_values_int) + std::move(*LiteralUtil::CreateR1(input_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); Literal update_values = - std::move(*Literal::CreateR1(update_values_int) + std::move(*LiteralUtil::CreateR1(update_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); Literal expected_values = - std::move(*Literal::CreateR1(expected_values_int) + std::move(*LiteralUtil::CreateR1(expected_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); @@ -411,15 +421,15 @@ class DynamicUpdateSliceTest : public ClientLibraryTestBase { const std::vector slice_starts, const Array2D& expected_values_int) { Literal input_values = - std::move(*Literal::CreateR2FromArray2D(input_values_int) + std::move(*LiteralUtil::CreateR2FromArray2D(input_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); Literal update_values = - std::move(*Literal::CreateR2FromArray2D(update_values_int) + std::move(*LiteralUtil::CreateR2FromArray2D(update_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); Literal expected_values = - std::move(*Literal::CreateR2FromArray2D(expected_values_int) + std::move(*LiteralUtil::CreateR2FromArray2D(expected_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); @@ -442,15 +452,15 @@ class DynamicUpdateSliceTest : public ClientLibraryTestBase { const std::vector slice_starts, const Array3D& expected_values_int) { Literal input_values = - std::move(*Literal::CreateR3FromArray3D(input_values_int) + std::move(*LiteralUtil::CreateR3FromArray3D(input_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); Literal update_values = - std::move(*Literal::CreateR3FromArray3D(update_values_int) + std::move(*LiteralUtil::CreateR3FromArray3D(update_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); Literal expected_values = - std::move(*Literal::CreateR3FromArray3D(expected_values_int) + std::move(*LiteralUtil::CreateR3FromArray3D(expected_values_int) ->Convert(primitive_util::NativeToPrimitiveType()) .ValueOrDie()); @@ -520,7 +530,7 @@ class DynamicUpdateSliceTest : public ClientLibraryTestBase { template void DumpArray(const string& name, const Array3D values) { std::unique_ptr literal = - Literal::CreateR3FromArray3D(values); + LiteralUtil::CreateR3FromArray3D(values); LOG(INFO) << name << ":" << literal->ToString(); } }; @@ -530,21 +540,32 @@ XLA_TEST_F(DynamicUpdateSliceTest, Int32R0) { TestR0(); } XLA_TEST_F(DynamicUpdateSliceTest, Int64R0) { TestR0(); } XLA_TEST_F(DynamicUpdateSliceTest, UInt64R0) { TestR0(); } -// TODO(b/71820067): The CPU parallel backend failed for this on 2018-01-10. XLA_TEST_F(DynamicUpdateSliceTest, Int32R1BF16) { TestR1(); } XLA_TEST_F(DynamicUpdateSliceTest, Int32R1) { TestR1(); } XLA_TEST_F(DynamicUpdateSliceTest, Int64R1) { TestR1(); } XLA_TEST_F(DynamicUpdateSliceTest, UInt64R1) { TestR1(); } +XLA_TEST_F(DynamicUpdateSliceTest, UInt32R1OOB) { + RunR1({0, 1, 2, 3, 4}, {5, 6}, {2147483648u}, {0, 1, 2, 5, 6}); +} XLA_TEST_F(DynamicUpdateSliceTest, Int32R2BF16) { TestR2(); } XLA_TEST_F(DynamicUpdateSliceTest, Int32R2) { TestR2(); } XLA_TEST_F(DynamicUpdateSliceTest, Int64R2) { TestR2(); } XLA_TEST_F(DynamicUpdateSliceTest, UInt64R2) { TestR2(); } +XLA_TEST_F(DynamicUpdateSliceTest, UInt32R2OOB) { + RunR2({{0, 1}, {2, 3}}, {{4}}, {2147483648u, 0}, + {{0, 1}, {4, 3}}); +} XLA_TEST_F(DynamicUpdateSliceTest, Int32R3BF16) { TestR3(); } XLA_TEST_F(DynamicUpdateSliceTest, Int32R3) { TestR3(); } XLA_TEST_F(DynamicUpdateSliceTest, Int64R3) { TestR3(); } XLA_TEST_F(DynamicUpdateSliceTest, UInt64R3) { TestR3(); } +XLA_TEST_F(DynamicUpdateSliceTest, UInt32R3OOB) { + RunR3({{{0, 1}, {2, 3}}, {{4, 5}, {6, 7}}}, {{{8}}}, + {2147483648u, 0, 2147483648u}, + {{{0, 1}, {2, 3}}, {{4, 8}, {6, 7}}}); +} XLA_TEST_F(DynamicUpdateSliceTest, Int32OOBBF16) { TestOOB(); } XLA_TEST_F(DynamicUpdateSliceTest, Int32OOB) { TestOOB(); } @@ -695,7 +716,7 @@ void BM_DynamicSlice(int num_iters) { XlaBuilder builder("DynamicSlice"); // Create input as a constant: shape [1, 2, 3, 4] - auto input_literal = Literal::CreateR4( + auto input_literal = LiteralUtil::CreateR4( {{{{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}, {{13, 14, 15, 16}, {17, 18, 19, 20}, {21, 22, 23, 24}}}}); auto input = ConstantLiteral(&builder, *input_literal); @@ -715,7 +736,7 @@ void BM_DynamicSlice(int num_iters) { start_indices_shape, &allocator, /*device_ordinal=*/0) .ConsumeValueOrDie(); - auto start_indices_literal = Literal::CreateR1({0, 1, 2, 3}); + auto start_indices_literal = LiteralUtil::CreateR1({0, 1, 2, 3}); auto stream = client->mutable_backend()->BorrowStream(device_ordinal).ValueOrDie(); ASSERT_IS_OK(transfer_manager->TransferLiteralToDevice( diff --git a/tensorflow/compiler/xla/tests/execution_profile_test.cc b/tensorflow/compiler/xla/tests/execution_profile_test.cc index ddc6a7db18760bf951023f0a684d78739f3e869d..5116e60ca63ef5f94b25b15e6616086fb9e44bbb 100644 --- a/tensorflow/compiler/xla/tests/execution_profile_test.cc +++ b/tensorflow/compiler/xla/tests/execution_profile_test.cc @@ -14,8 +14,8 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/client/global_data.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/test_macros.h" #include "tensorflow/core/platform/test.h" @@ -31,7 +31,7 @@ XLA_TEST_F(ExecutionProfileTest, ExecuteWithExecutionProfile) { TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr input, client_->TransferToServer( - *Literal::CreateR2F32Linspace(1e0, 1e5, 256, 256))); + *LiteralUtil::CreateR2F32Linspace(1e0, 1e5, 256, 256))); XlaBuilder b(TestName() + ".add"); Dot(Parameter(&b, 0, shape, "param_0"), Parameter(&b, 1, shape, "param_1")); diff --git a/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc b/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc index 74cf8b213e0a03394c84008e7a2919e1a5bf1af2..bf1de02ba9dbd97db9ee31484402fe9b92385219 100644 --- a/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc +++ b/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" @@ -39,7 +39,7 @@ class ExhaustiveF32ElementwiseOpTest XlaBuilder builder(TestName()); std::unique_ptr input_literal = - Literal::CreateFromDimensions(F32, {input_size}); + LiteralUtil::CreateFromDimensions(F32, {input_size}); for (int64 i = begin; i < end; i++) { if (i >= known_incorrect_range.first && i < known_incorrect_range.second) { diff --git a/tensorflow/compiler/xla/tests/filecheck.cc b/tensorflow/compiler/xla/tests/filecheck.cc index 93d1c921c4a138cda55ed7338b8e3aa82518d114..dcb469087e0064d17ce3b04fdeaf0b6136069a55 100644 --- a/tensorflow/compiler/xla/tests/filecheck.cc +++ b/tensorflow/compiler/xla/tests/filecheck.cc @@ -76,6 +76,11 @@ StatusOr RunFileCheck(const string& input, const string& pattern) { XLA_LOG_LINES(tensorflow::WARNING, input); LOG(WARNING) << "FileCheck pattern was:"; XLA_LOG_LINES(tensorflow::WARNING, pattern); + } else if (!standard_error.empty()) { + LOG(INFO) << "FileCheck stderr:"; + XLA_LOG_LINES(tensorflow::INFO, standard_error); + LOG(INFO) << "FileCheck input was:"; + XLA_LOG_LINES(tensorflow::INFO, input); } return succeeded; } diff --git a/tensorflow/compiler/xla/tests/floor_ceil_test.cc b/tensorflow/compiler/xla/tests/floor_ceil_test.cc index 30dc639f117b9871238f0bf1628502cf8bef2e0c..39cc6c5927f1d416e31f689487efc10c20371abe 100644 --- a/tensorflow/compiler/xla/tests/floor_ceil_test.cc +++ b/tensorflow/compiler/xla/tests/floor_ceil_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" diff --git a/tensorflow/compiler/xla/tests/fmax_test.cc b/tensorflow/compiler/xla/tests/fmax_test.cc index 0254ae1baaa864b38c3b217a5c2026d34b7f7d12..c5bbbe778df15d63a2586bd6291a7a33fc82aa52 100644 --- a/tensorflow/compiler/xla/tests/fmax_test.cc +++ b/tensorflow/compiler/xla/tests/fmax_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/core/platform/test.h" diff --git a/tensorflow/compiler/xla/tests/fusion_test.cc b/tensorflow/compiler/xla/tests/fusion_test.cc index f7f9a87413ee3cae50b3aa6518293827d40837ca..792be0d3fcd55621b9f8cdf0fdc28f7bb49294d1 100644 --- a/tensorflow/compiler/xla/tests/fusion_test.cc +++ b/tensorflow/compiler/xla/tests/fusion_test.cc @@ -25,8 +25,8 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" @@ -90,7 +90,7 @@ class FusionTest : public HloTestBase { HloInstruction* hlos[4]; for (int i = 0; i < Arity; ++i) { hlos[i + 1] = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2FromArray2D(operand_data[i]))); + LiteralUtil::CreateR2FromArray2D(operand_data[i]))); } auto answer_shape = ShapeUtil::MakeShape(prim_type, {test_width, test_height}); @@ -116,7 +116,7 @@ class FusionTest : public HloTestBase { ArraySlice(hlos, 0, Arity + 1), HloInstruction::FusionKind::kLoop); - auto expected = Literal::CreateR2FromArray2D(answer_data); + auto expected = LiteralUtil::CreateR2FromArray2D(answer_data); auto actual = ExecuteAndTransfer(std::move(hlo_module), {}); if (primitive_util::IsFloatingPointType(prim_type)) { EXPECT_TRUE(LiteralTestUtil::Near(*expected, *actual, ErrorSpec(1e-4))); @@ -187,27 +187,28 @@ XLA_TEST_F(FusionTest, Test) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0}, {2.0}, {3.0}}))); + LiteralUtil::CreateR2({{1.0}, {2.0}, {3.0}}))); auto const1 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{-1.0}, {-1.0}, {-1.0}}))); + LiteralUtil::CreateR2({{-1.0}, {-1.0}, {-1.0}}))); auto add2 = builder.AddInstruction(HloInstruction::CreateBinary( ShapeUtil::MakeShape(F32, {3, 1}), HloOpcode::kAdd, const0, const1)); auto reshape3 = builder.AddInstruction(HloInstruction::CreateTranspose( ShapeUtil::MakeShape(F32, {1, 3}), add2, {1, 0})); auto const4 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.62, 2.72, 3.14}}))); + LiteralUtil::CreateR2({{1.62, 2.72, 3.14}}))); auto concat5 = builder.AddInstruction(HloInstruction::CreateConcatenate( ShapeUtil::MakeShape(F32, {2, 3}), {reshape3, const4}, 0)); auto const6 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 1.0, 1.0}, {0.0, 0.0, 0.0}}))); + LiteralUtil::CreateR2({{1.0, 1.0, 1.0}, {0.0, 0.0, 0.0}}))); auto negate7 = builder.AddInstruction(HloInstruction::CreateUnary( ShapeUtil::MakeShape(F32, {2, 3}), HloOpcode::kNegate, const6)); auto add8 = builder.AddInstruction(HloInstruction::CreateBinary( ShapeUtil::MakeShape(F32, {2, 3}), HloOpcode::kAdd, concat5, negate7)); auto const9 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{0.5, 0.5, 0.5}, {0.5, 0.5, 0.5}}))); - auto const10 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{true, false, true}, {false, true, false}}))); + LiteralUtil::CreateR2({{0.5, 0.5, 0.5}, {0.5, 0.5, 0.5}}))); + auto const10 = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR2( + {{true, false, true}, {false, true, false}}))); auto select11 = builder.AddInstruction( HloInstruction::CreateTernary(ShapeUtil::MakeShape(F32, {2, 3}), HloOpcode::kSelect, const10, add8, const9)); @@ -223,7 +224,7 @@ XLA_TEST_F(FusionTest, Test) { HloInstruction::FusionKind::kLoop); EXPECT_TRUE(LiteralTestUtil::Near( - *Literal::CreateR2({{0.5}, {2.72}}), + *LiteralUtil::CreateR2({{0.5}, {2.72}}), *ExecuteAndTransfer(std::move(hlo_module), {}), ErrorSpec(1e-4))); } @@ -234,11 +235,11 @@ XLA_TEST_F(FusionTest, Parameter) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1.0, 2.0, 3.0}}))); + LiteralUtil::CreateR2({{1.0, 2.0, 3.0}}))); auto copy1 = builder.AddInstruction(HloInstruction::CreateUnary( ShapeUtil::MakeShape(F32, {1, 3}), HloOpcode::kCopy, const0)); auto const2 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{-2.0, -2.0, -2.0}}))); + LiteralUtil::CreateR2({{-2.0, -2.0, -2.0}}))); // add3 = copy1 + const2 = const0 + const2 = {1,2,3} + {-2,-2,-2} = {-1,0,+1} auto add3 = builder.AddInstruction(HloInstruction::CreateBinary( ShapeUtil::MakeShape(F32, {1, 3}), HloOpcode::kAdd, copy1, const2)); @@ -249,7 +250,7 @@ XLA_TEST_F(FusionTest, Parameter) { HloInstruction::FusionKind::kLoop); EXPECT_TRUE(LiteralTestUtil::Near( - *Literal::CreateR2({{-1.0, 0.0, 1.0}}), + *LiteralUtil::CreateR2({{-1.0, 0.0, 1.0}}), *ExecuteAndTransfer(std::move(hlo_module), {}), ErrorSpec(1e-4))); } @@ -270,7 +271,7 @@ XLA_TEST_F(FusionTest, RandomizedParallelPartition) { auto hlo_module = CreateNewModule(); auto two = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(2.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(2.0))); auto x = builder.AddInstruction(HloInstruction::CreateBroadcast(shape, two, {})); auto y = builder.AddInstruction( @@ -293,9 +294,9 @@ XLA_TEST_F(FusionTest, BroadcastIntoBinaryOp) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const_vector = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR1({1.0, 2.0, 3.0}))); + LiteralUtil::CreateR1({1.0, 2.0, 3.0}))); auto const_array = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{-1.0, -2.0, -4.0}, {10.0, 20.0, 30.0}}))); + LiteralUtil::CreateR2({{-1.0, -2.0, -4.0}, {10.0, 20.0, 30.0}}))); auto broadcast = builder.AddInstruction( HloInstruction::CreateBroadcast(const_array->shape(), const_vector, {1})); // add2 = broadcast(const_vector) + const_array @@ -309,7 +310,7 @@ XLA_TEST_F(FusionTest, BroadcastIntoBinaryOp) { HloInstruction::FusionKind::kLoop); EXPECT_TRUE(LiteralTestUtil::Near( - *Literal::CreateR2({{0.0, 0.0, -1.0}, {11.0, 22.0, 33.0}}), + *LiteralUtil::CreateR2({{0.0, 0.0, -1.0}, {11.0, 22.0, 33.0}}), *ExecuteAndTransfer(std::move(hlo_module), {}), ErrorSpec(1e-4))); } @@ -317,14 +318,14 @@ XLA_TEST_F(FusionTest, ReshapeToScalar) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto single_element_array = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR2({{5}}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR2({{5}}))); auto reshape = builder.AddInstruction(HloInstruction::CreateReshape( ShapeUtil::MakeShape(S32, {}), single_element_array)); hlo_module->AddEntryComputation(builder.Build()) ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape}, HloInstruction::FusionKind::kLoop); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR0(5), + LiteralTestUtil::Equal(*LiteralUtil::CreateR0(5), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -332,14 +333,14 @@ XLA_TEST_F(FusionTest, Reshape_3by2_1by2by3) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1, 2}, {3, 4}, {5, 6}}))); + LiteralUtil::CreateR2({{1, 2}, {3, 4}, {5, 6}}))); auto reshape1 = builder.AddInstruction(HloInstruction::CreateReshape( ShapeUtil::MakeShape(S32, {1, 2, 3}), const0)); hlo_module->AddEntryComputation(builder.Build()) ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1}, HloInstruction::FusionKind::kLoop); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::CreateR3({{{1, 2, 3}, {4, 5, 6}}}), + *LiteralUtil::CreateR3({{{1, 2, 3}, {4, 5, 6}}}), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -347,14 +348,14 @@ XLA_TEST_F(FusionTest, Reshape_1by2by3_3by2) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR3({{{1, 2, 3}, {4, 5, 6}}}))); + LiteralUtil::CreateR3({{{1, 2, 3}, {4, 5, 6}}}))); auto reshape1 = builder.AddInstruction( HloInstruction::CreateReshape(ShapeUtil::MakeShape(S32, {3, 2}), const0)); hlo_module->AddEntryComputation(builder.Build()) ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1}, HloInstruction::FusionKind::kLoop); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::CreateR2({{1, 2}, {3, 4}, {5, 6}}), + *LiteralUtil::CreateR2({{1, 2}, {3, 4}, {5, 6}}), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -362,14 +363,14 @@ XLA_TEST_F(FusionTest, Reshape_1by1by1_) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR3({{{7}}}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR3({{{7}}}))); auto reshape1 = builder.AddInstruction( HloInstruction::CreateReshape(ShapeUtil::MakeShape(S32, {}), const0)); hlo_module->AddEntryComputation(builder.Build()) ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1}, HloInstruction::FusionKind::kLoop); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR0(7), + LiteralTestUtil::Equal(*LiteralUtil::CreateR0(7), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -377,14 +378,14 @@ XLA_TEST_F(FusionTest, Reshape__1by1by1) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(7))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(7))); auto reshape1 = builder.AddInstruction(HloInstruction::CreateReshape( ShapeUtil::MakeShape(S32, {1, 1, 1}), const0)); hlo_module->AddEntryComputation(builder.Build()) ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1}, HloInstruction::FusionKind::kLoop); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR3({{{7}}}), + LiteralTestUtil::Equal(*LiteralUtil::CreateR3({{{7}}}), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -392,14 +393,14 @@ XLA_TEST_F(FusionTest, Reshape__) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(7))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(7))); auto reshape1 = builder.AddInstruction( HloInstruction::CreateReshape(ShapeUtil::MakeShape(S32, {}), const0)); hlo_module->AddEntryComputation(builder.Build()) ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1}, HloInstruction::FusionKind::kLoop); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR0(7), + LiteralTestUtil::Equal(*LiteralUtil::CreateR0(7), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -407,14 +408,14 @@ XLA_TEST_F(FusionTest, Reshape_3by3_3by3) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}))); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}))); auto reshape1 = builder.AddInstruction( HloInstruction::CreateReshape(ShapeUtil::MakeShape(S32, {3, 3}), const0)); hlo_module->AddEntryComputation(builder.Build()) ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1}, HloInstruction::FusionKind::kLoop); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}), + *LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -422,14 +423,14 @@ XLA_TEST_F(FusionTest, Transpose_2by3) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}}))); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}}))); auto reshape1 = builder.AddInstruction(HloInstruction::CreateTranspose( ShapeUtil::MakeShape(S32, {3, 2}), const0, {1, 0})); hlo_module->AddEntryComputation(builder.Build()) ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1}, HloInstruction::FusionKind::kLoop); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::CreateR2({{1, 4}, {2, 5}, {3, 6}}), + *LiteralUtil::CreateR2({{1, 4}, {2, 5}, {3, 6}}), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -437,14 +438,14 @@ XLA_TEST_F(FusionTest, Transpose_3by3) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}))); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}))); auto reshape1 = builder.AddInstruction(HloInstruction::CreateTranspose( ShapeUtil::MakeShape(S32, {3, 3}), const0, {1, 0})); hlo_module->AddEntryComputation(builder.Build()) ->CreateFusionInstruction(/*instructions_to_fuse=*/{reshape1}, HloInstruction::FusionKind::kLoop); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::CreateR2({{1, 4, 7}, {2, 5, 8}, {3, 6, 9}}), + *LiteralUtil::CreateR2({{1, 4, 7}, {2, 5, 8}, {3, 6, 9}}), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -452,7 +453,7 @@ XLA_TEST_F(FusionTest, Reverse) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({1, 2, 3}))); auto reverse1 = builder.AddInstruction(HloInstruction::CreateReverse( ShapeUtil::MakeShape(S32, {3}), const0, {0})); hlo_module->AddEntryComputation(builder.Build()) @@ -460,7 +461,7 @@ XLA_TEST_F(FusionTest, Reverse) { HloInstruction::FusionKind::kLoop); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR1({3, 2, 1}), + LiteralTestUtil::Equal(*LiteralUtil::CreateR1({3, 2, 1}), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -468,7 +469,7 @@ XLA_TEST_F(FusionTest, ReverseNegate) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({1, 2, 3}))); auto reverse1 = builder.AddInstruction(HloInstruction::CreateReverse( ShapeUtil::MakeShape(S32, {3}), const0, {0})); auto negate2 = builder.AddInstruction(HloInstruction::CreateUnary( @@ -478,7 +479,7 @@ XLA_TEST_F(FusionTest, ReverseNegate) { HloInstruction::FusionKind::kLoop); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR1({-3, -2, -1}), + LiteralTestUtil::Equal(*LiteralUtil::CreateR1({-3, -2, -1}), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -486,7 +487,7 @@ XLA_TEST_F(FusionTest, BroadcastNegate) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); auto broadcast1 = builder.AddInstruction(HloInstruction::CreateBroadcast( ShapeUtil::MakeShape(S32, {2}), const0, {})); auto negate2 = builder.AddInstruction(HloInstruction::CreateUnary( @@ -496,15 +497,15 @@ XLA_TEST_F(FusionTest, BroadcastNegate) { HloInstruction::FusionKind::kLoop); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR1({-1, -1}), + LiteralTestUtil::Equal(*LiteralUtil::CreateR1({-1, -1}), *ExecuteAndTransfer(std::move(hlo_module), {}))); } XLA_TEST_F(FusionTest, SliceNegate) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); - auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3, 4}))); + auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR1({1, 2, 3, 4}))); auto slice1 = builder.AddInstruction(HloInstruction::CreateSlice( ShapeUtil::MakeShape(S32, {2}), const0, {0}, {4}, {2})); auto negate2 = builder.AddInstruction(HloInstruction::CreateUnary( @@ -514,17 +515,17 @@ XLA_TEST_F(FusionTest, SliceNegate) { HloInstruction::FusionKind::kLoop); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR1({-1, -3}), + LiteralTestUtil::Equal(*LiteralUtil::CreateR1({-1, -3}), *ExecuteAndTransfer(std::move(hlo_module), {}))); } XLA_TEST_F(FusionTest, DynamicSliceNegate) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); - auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3, 4}))); + auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR1({1, 2, 3, 4}))); auto const1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({1}))); auto dynamic_slice2 = builder.AddInstruction(HloInstruction::CreateDynamicSlice( ShapeUtil::MakeShape(S32, {2}), const0, const1, {2})); @@ -536,15 +537,15 @@ XLA_TEST_F(FusionTest, DynamicSliceNegate) { HloInstruction::FusionKind::kLoop); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR1({-2, -3}), + LiteralTestUtil::Equal(*LiteralUtil::CreateR1({-2, -3}), *ExecuteAndTransfer(std::move(hlo_module), {}))); } XLA_TEST_F(FusionTest, ReshapeNegate) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); - auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 3, 4}))); + auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR1({1, 2, 3, 4}))); auto reshape1 = builder.AddInstruction( HloInstruction::CreateReshape(ShapeUtil::MakeShape(S32, {2, 2}), const0)); auto negate2 = builder.AddInstruction(HloInstruction::CreateUnary( @@ -553,16 +554,16 @@ XLA_TEST_F(FusionTest, ReshapeNegate) { ->CreateFusionInstruction(/*instructions_to_fuse=*/{negate2, reshape1}, HloInstruction::FusionKind::kLoop); - EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR2({{-1, -2}, {-3, -4}}), - *ExecuteAndTransfer(std::move(hlo_module), {}))); + EXPECT_TRUE(LiteralTestUtil::Equal( + *LiteralUtil::CreateR2({{-1, -2}, {-3, -4}}), + *ExecuteAndTransfer(std::move(hlo_module), {}))); } XLA_TEST_F(FusionTest, TransposeNegate) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{1, 2}, {3, 4}}))); + LiteralUtil::CreateR2({{1, 2}, {3, 4}}))); auto transpose1 = builder.AddInstruction(HloInstruction::CreateTranspose( ShapeUtil::MakeShape(S32, {2, 2}), const0, {1, 0})); auto negate2 = builder.AddInstruction(HloInstruction::CreateUnary( @@ -571,9 +572,9 @@ XLA_TEST_F(FusionTest, TransposeNegate) { ->CreateFusionInstruction(/*instructions_to_fuse=*/{negate2, transpose1}, HloInstruction::FusionKind::kLoop); - EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR2({{-1, -3}, {-2, -4}}), - *ExecuteAndTransfer(std::move(hlo_module), {}))); + EXPECT_TRUE(LiteralTestUtil::Equal( + *LiteralUtil::CreateR2({{-1, -3}, {-2, -4}}), + *ExecuteAndTransfer(std::move(hlo_module), {}))); } std::unique_ptr MakeReduceTestComputation() { @@ -591,10 +592,10 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(Reduce)) { auto hlo_module = CreateNewModule(); auto builder = HloComputation::Builder(TestName()); - auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 4, 8}))); + auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR1({1, 2, 4, 8}))); auto const1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))); auto reduce2 = builder.AddInstruction(HloInstruction::CreateReduce( ShapeUtil::MakeShape(S32, {}), const0, const1, {0}, hlo_module->AddEmbeddedComputation(MakeReduceTestComputation()))); @@ -603,7 +604,7 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(Reduce)) { HloInstruction::FusionKind::kLoop); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR0(15), + LiteralTestUtil::Equal(*LiteralUtil::CreateR0(15), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -611,10 +612,10 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(ReduceImplicitBroadcast)) { auto hlo_module = CreateNewModule(); auto builder = HloComputation::Builder(TestName()); - auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({1, 2, 4, 8}))); + auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR1({1, 2, 4, 8}))); auto const1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))); auto reduce2 = builder.AddInstruction(HloInstruction::CreateReduce( ShapeUtil::MakeShape(S32, {}), const0, const1, {0}, hlo_module->AddEmbeddedComputation(MakeReduceTestComputation()))); @@ -625,7 +626,7 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(ReduceImplicitBroadcast)) { HloInstruction::FusionKind::kLoop); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR0(-15), + LiteralTestUtil::Equal(*LiteralUtil::CreateR0(-15), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -633,9 +634,9 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(ReduceWindow)) { auto builder = HloComputation::Builder(TestName()); auto hlo_module = CreateNewModule(); auto const0 = builder.AddInstruction(HloInstruction::CreateConstant( - Literal::CreateR2({{2, 3, 5}, {7, 11, 13}, {17, 19, 23}}))); + LiteralUtil::CreateR2({{2, 3, 5}, {7, 11, 13}, {17, 19, 23}}))); auto const1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(1))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(1))); Window window; ASSERT_TRUE( tensorflow::protobuf::TextFormat::ParseFromString("dimensions:{\n" @@ -675,7 +676,7 @@ XLA_TEST_F(FusionTest, DISABLED_ON_CPU(ReduceWindow)) { HloInstruction::FusionKind::kLoop); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::CreateR2({{462, 2145}, {24871, 62491}}), + *LiteralUtil::CreateR2({{462, 2145}, {24871, 62491}}), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -687,9 +688,9 @@ XLA_TEST_F(FusionTest, SharedConstant) { auto builder = HloComputation::Builder(TestName()); auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({0}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({0}))); auto const1 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR1({2}))); + HloInstruction::CreateConstant(LiteralUtil::CreateR1({2}))); auto add1 = builder.AddInstruction(HloInstruction::CreateBinary( ShapeUtil::MakeShape(S32, {1}), HloOpcode::kAdd, const1, const0)); auto add2 = builder.AddInstruction(HloInstruction::CreateBinary( @@ -711,7 +712,7 @@ XLA_TEST_F(FusionTest, SharedConstant) { EXPECT_EQ(entry_comp->root_instruction()->fused_instruction_count(), 6); EXPECT_TRUE( - LiteralTestUtil::Equal(*Literal::CreateR1({8}), + LiteralTestUtil::Equal(*LiteralUtil::CreateR1({8}), *ExecuteAndTransfer(std::move(hlo_module), {}))); } @@ -784,7 +785,7 @@ ENTRY main { )"; std::unique_ptr operand = - Literal::CreateR2({{0., 0.}, {1., 0.}}); + LiteralUtil::CreateR2({{0., 0.}, {1., 0.}}); HloModuleConfig config; config.set_debug_options(GetDebugOptionsForTest()); TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, @@ -794,10 +795,50 @@ ENTRY main { test_runner_.Execute(std::move(module), {operand.get()}, /*run_hlo_passes=*/false)); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::CreateR3({{{0.}, {0.76159415595}}, {{0.}, {0.}}}), + *LiteralUtil::CreateR3({{{0.}, {0.76159415595}}, {{0.}, {0.}}}), *result)); } +class FusionClientLibraryTest : public ClientLibraryTestBase {}; + +XLA_TEST_F(FusionClientLibraryTest, ManyLayoutTransformations) { + // On the GPU backend, it's possible to have too many transposes within one + // fusion, causing the kernel to run out shared memory and thus not compile. + // We want to check that doesn't happen. + // + // To do this, we create a computation that computes + // + // P0 + P0*P1*P1 + P0*P2*P2 ... + // + // where even parameters have layout 1 and odd parameters have layout 2. + // + // Our goal is to tempt the backend into creating one giant multi-output + // fusion for the whole computation, including the transposes. Currently + // multi-output fusion only fuses fusions, so each of the terms in the sum + // needs to be a fusion itself, thus the contortions above. + constexpr int kNumParams = 25; + XlaBuilder b("ManyLayoutTransformations"); + + // This test produces values that overflow int32, which is UB, so use uint32, + // where overflow is OK. + Array2D arr(32, 32); + arr.FillUnique(); + std::unique_ptr l1 = LiteralUtil::CreateR2FromArray2D(arr)->Relayout( + LayoutUtil::MakeLayout({0, 1})); + + std::unique_ptr l2 = LiteralUtil::CreateR2FromArray2D(arr)->Relayout( + LayoutUtil::MakeLayout({1, 0})); + + XlaOp p0 = AddParam(*l1, &b); + XlaOp sum = p0; + for (int i = 1; i < kNumParams; ++i) { + auto pN = AddParam((i % 2 == 0 ? *l1 : *l2), &b); + sum = sum + p0 * pN * pN; + } + + ComputeAndCompare(&b, {}); +} + void BM_ParallelFusion(int num_iters) { // Simple element-wise computation to benchmark parallel task partitioning. tensorflow::testing::StopTiming(); @@ -838,19 +879,19 @@ void BM_ParallelFusion(int num_iters) { // Transfer literals to device. auto param0_literal = - Literal::CreateR2F32Linspace(1.0, 2.0, param0_dim0, param0_dim1); + LiteralUtil::CreateR2F32Linspace(1.0, 2.0, param0_dim0, param0_dim1); ScopedShapedBuffer buffer0 = client->LiteralToShapedBuffer(*param0_literal, device_ordinal) .ConsumeValueOrDie(); auto param1_literal = - Literal::CreateR2F32Linspace(1.0, 2.0, param1_dim0, param1_dim1); + LiteralUtil::CreateR2F32Linspace(1.0, 2.0, param1_dim0, param1_dim1); ScopedShapedBuffer buffer1 = client->LiteralToShapedBuffer(*param1_literal, device_ordinal) .ConsumeValueOrDie(); auto param2_literal = - Literal::CreateR2F32Linspace(1.0, 2.0, param2_dim0, param2_dim1); + LiteralUtil::CreateR2F32Linspace(1.0, 2.0, param2_dim0, param2_dim1); ScopedShapedBuffer buffer2 = client->LiteralToShapedBuffer(*param2_literal, device_ordinal) .ConsumeValueOrDie(); diff --git a/tensorflow/compiler/xla/tests/gather_operation_test.cc b/tensorflow/compiler/xla/tests/gather_operation_test.cc index b8404826b161b9edbbd260d73c175cce935ace91..b77bece85ad1b2192b04330af9e60d3a424b59f4 100644 --- a/tensorflow/compiler/xla/tests/gather_operation_test.cc +++ b/tensorflow/compiler/xla/tests/gather_operation_test.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -22,9 +22,6 @@ limitations under the License. #include "tensorflow/compiler/xla/tests/hlo_test_base.h" #include "tensorflow/compiler/xla/tests/test_macros.h" -// NB! TODO(b/74360564): These tests do not test out of bounds behavior since -// that hasn't been specced yet. - namespace xla { namespace { @@ -63,8 +60,9 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); - std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + LiteralUtil::CreateR1({0, 2}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -84,8 +82,9 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); - std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + LiteralUtil::CreateR1({0, 2}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -105,9 +104,9 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); std::unique_ptr gather_indices = - Literal::CreateR2({{0, 2}, {2, 1}}); + LiteralUtil::CreateR2({{0, 2}, {2, 1}}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -127,9 +126,9 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); std::unique_ptr gather_indices = - Literal::CreateR3({{{0, 2}, {2, 1}}, {{1, 2}, {2, 0}}}); + LiteralUtil::CreateR3({{{0, 2}, {2, 1}}, {{1, 2}, {2, 0}}}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -149,9 +148,9 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); std::unique_ptr gather_indices = - Literal::CreateR3({{{0, 2}, {2, 1}}, {{1, 2}, {2, 0}}}); + LiteralUtil::CreateR3({{{0, 2}, {2, 1}}, {{1, 2}, {2, 0}}}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -171,11 +170,11 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // - {{-4, 4}, {-5, 5}, {-6, 6}}, // - {{-7, 7}, {-8, 8}, {-9, 9}}}); + LiteralUtil::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); std::unique_ptr gather_indices = - Literal::CreateR2({{0, 0}, {1, 0}}); + LiteralUtil::CreateR2({{0, 0}, {1, 0}}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -195,11 +194,11 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // - {{-4, 4}, {-5, 5}, {-6, 6}}, // - {{-7, 7}, {-8, 8}, {-9, 9}}}); + LiteralUtil::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); std::unique_ptr gather_indices = - Literal::CreateR2({{0, 0}, {1, 0}}); + LiteralUtil::CreateR2({{0, 0}, {1, 0}}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -219,8 +218,9 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); - std::unique_ptr gather_indices = Literal::CreateR1({1, 1}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + LiteralUtil::CreateR1({1, 1}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -240,9 +240,9 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); std::unique_ptr gather_indices = - Literal::CreateR2({{2, 1}, {1, 1}}); + LiteralUtil::CreateR2({{2, 1}, {1, 1}}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -261,18 +261,15 @@ ENTRY main { window_bounds={1, 0} } )"; - std::unique_ptr operand = Literal::CreateR2({{}, {}, {}}); - std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + std::unique_ptr operand = LiteralUtil::CreateR2({{}, {}, {}}); + std::unique_ptr gather_indices = + LiteralUtil::CreateR1({0, 2}); RunTest(hlo_text, operand.get(), gather_indices.get()); } XLA_TEST_F(GatherOperationTest, OutOfBoundsIndex) { // Out of bounds indices must not crash, and the indices in range should // produce the same values across all backends. - // - // TODO(b/74360564): Once we have a well defined semantics for OOB accesses, - // we should get rid of the mask and check that backends produce the same - // value for OOB indices too. const string hlo_text = R"( HloModule BatchDynamicSlice @@ -286,29 +283,45 @@ ENTRY main { gather_dims_to_operand_dims={0,1}, index_vector_dim=1, window_bounds={1,1} - gather_reshaped = s32[6]{0} reshape(gather) - in_bounds_mask = s32[6]{0} parameter(2) - ROOT result = s32[6]{0} multiply(gather_reshaped, in_bounds_mask) + ROOT result = s32[6]{0} reshape(gather) } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); - std::unique_ptr gather_indices = Literal::CreateR2( + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = LiteralUtil::CreateR2( {{2, 7}, {2, 1}, {1, 1}, {5, 1}, {2147483647, 1}, {1, 2}}); - std::unique_ptr in_bounds_mask = - Literal::CreateR1({0, 1, 1, 0, 0, 1}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, OutOfBoundsUnsignedIndex) { + // Out of bounds indices must not crash, and the indices in range should + // produce the same values across all backends. - RunTest(hlo_text, - {operand.get(), gather_indices.get(), in_bounds_mask.get()}); + const string hlo_text = R"( +HloModule BatchDynamicSlice + +ENTRY main { + operand = s32[3,3]{1,0} parameter(0) + indices = u32[6,2]{1,0} parameter(1) + gather = s32[6,1,1]{2,1,0} gather(operand, indices), + output_window_dims={1,2}, + elided_window_dims={}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=1, + window_bounds={1,1} + ROOT result = s32[6]{0} reshape(gather) +} +)"; + std::unique_ptr operand = + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = LiteralUtil::CreateR2( + {{2, 7}, {2, 1}, {1, 1}, {5, 1}, {2147483648u, 1}, {1, 2}}); + RunTest(hlo_text, operand.get(), gather_indices.get()); } XLA_TEST_F(GatherOperationTest, NegativeIndex) { // Negative indices must not crash, and the indices in range should produce // the same values across all backends. - // - // TODO(b/74360564): Once we have a well defined semantics for negative - // accesses, we should get rid of the mask and check that backends produce the - // same value for negative indices too. const string hlo_text = R"( HloModule BatchDynamicSlice @@ -322,20 +335,40 @@ ENTRY main { gather_dims_to_operand_dims={0,1}, index_vector_dim=1, window_bounds={1,1} - gather_reshaped = s32[6]{0} reshape(gather) - in_bounds_mask = s32[6]{0} parameter(2) - ROOT result = s32[6]{0} multiply(gather_reshaped, in_bounds_mask) + ROOT result = s32[6]{0} reshape(gather) } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); - std::unique_ptr gather_indices = Literal::CreateR2( + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = LiteralUtil::CreateR2( {{2, -1}, {2, 1}, {1, 1}, {-500, 1}, {-2147483648, 1}, {1, 2}}); - std::unique_ptr in_bounds_mask = - Literal::CreateR1({0, 1, 1, 0, 0, 1}); + RunTest(hlo_text, operand.get(), gather_indices.get()); +} + +XLA_TEST_F(GatherOperationTest, NegativeIndexIntoUnsignedOperand) { + // Negative indices must not crash, and the indices in range should produce + // the same values across all backends. - RunTest(hlo_text, - {operand.get(), gather_indices.get(), in_bounds_mask.get()}); + const string hlo_text = R"( +HloModule BatchDynamicSlice + +ENTRY main { + operand = u32[3,3]{1,0} parameter(0) + indices = s32[6,2]{1,0} parameter(1) + gather = u32[6,1,1]{2,1,0} gather(operand, indices), + output_window_dims={1,2}, + elided_window_dims={}, + gather_dims_to_operand_dims={0,1}, + index_vector_dim=1, + window_bounds={1,1} + ROOT result = u32[6]{0} reshape(gather) +} +)"; + std::unique_ptr operand = + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = LiteralUtil::CreateR2( + {{2, -1}, {2, 1}, {1, 1}, {-500, 1}, {-2147483648, 1}, {1, 2}}); + RunTest(hlo_text, operand.get(), gather_indices.get()); } XLA_TEST_F(GatherOperationTest, OneScalarIndex) { @@ -353,9 +386,9 @@ ENTRY main { window_bounds={1,3,2} } )"; - std::unique_ptr operand = Literal::CreateR3( + std::unique_ptr operand = LiteralUtil::CreateR3( {{{1, 2}, {3, 4}, {5, 6}}, {{7, 8}, {9, 10}, {11, 12}}}); - std::unique_ptr gather_indices = Literal::CreateR0(1); + std::unique_ptr gather_indices = LiteralUtil::CreateR0(1); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -374,8 +407,8 @@ ENTRY main { window_bounds={1} } )"; - std::unique_ptr operand = Literal::CreateR1({1, 2, 3, 4}); - std::unique_ptr gather_indices = Literal::CreateR0(1); + std::unique_ptr operand = LiteralUtil::CreateR1({1, 2, 3, 4}); + std::unique_ptr gather_indices = LiteralUtil::CreateR0(1); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -395,8 +428,8 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); - std::unique_ptr gather_indices = Literal::CreateR1({}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = LiteralUtil::CreateR1({}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -419,8 +452,9 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); - std::unique_ptr gather_indices = Literal::CreateR1({0, 2}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + LiteralUtil::CreateR1({0, 2}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -443,9 +477,9 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); std::unique_ptr gather_indices = - Literal::CreateR2({{0, 2}, {2, 1}}); + LiteralUtil::CreateR2({{0, 2}, {2, 1}}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -468,9 +502,9 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); std::unique_ptr gather_indices = - Literal::CreateR3({{{0, 2}, {2, 1}}, {{1, 2}, {2, 0}}}); + LiteralUtil::CreateR3({{{0, 2}, {2, 1}}, {{1, 2}, {2, 0}}}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -493,11 +527,11 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // - {{-4, 4}, {-5, 5}, {-6, 6}}, // - {{-7, 7}, {-8, 8}, {-9, 9}}}); + LiteralUtil::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); std::unique_ptr gather_indices = - Literal::CreateR2({{0, 0}, {1, 0}}); + LiteralUtil::CreateR2({{0, 0}, {1, 0}}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -521,11 +555,11 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // - {{-4, 4}, {-5, 5}, {-6, 6}}, // - {{-7, 7}, {-8, 8}, {-9, 9}}}); + LiteralUtil::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); std::unique_ptr gather_indices = - Literal::CreateR2({{0, 0}, {1, 0}}); + LiteralUtil::CreateR2({{0, 0}, {1, 0}}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -548,8 +582,9 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); - std::unique_ptr gather_indices = Literal::CreateR1({1, 1}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr gather_indices = + LiteralUtil::CreateR1({1, 1}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -572,9 +607,9 @@ ENTRY main { } )"; std::unique_ptr operand = - Literal::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); std::unique_ptr gather_indices = - Literal::CreateR2({{2, 1}, {1, 1}}); + LiteralUtil::CreateR2({{2, 1}, {1, 1}}); RunTest(hlo_text, operand.get(), gather_indices.get()); } @@ -609,12 +644,13 @@ XLA_TEST_F(GatherClientLibraryTest, DISABLED_ON_GPU(Basic)) { Gather(operand, indices, dim_numbers, {1, 3}); std::vector expected = {}; - TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr operand_arg, - client_->TransferToServer(*Literal::CreateR2( - {{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}))); + TF_ASSERT_OK_AND_ASSIGN( + std::unique_ptr operand_arg, + client_->TransferToServer( + *LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}))); TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr indices_arg, - client_->TransferToServer(*Literal::CreateR1({0, 2}))); + client_->TransferToServer(*LiteralUtil::CreateR1({0, 2}))); TF_ASSERT_OK_AND_ASSIGN(std::vector devices, client_->GetDeviceHandles(1)); xla::ExecutionOptions execution_options = CreateDefaultExecutionOptions(); diff --git a/tensorflow/compiler/xla/tests/half_test.cc b/tensorflow/compiler/xla/tests/half_test.cc index fd8511884907ae500d8256c3250fe779f8eba83a..51450314b611b49c643fb6fd5b0c0d2e7205a2d2 100644 --- a/tensorflow/compiler/xla/tests/half_test.cc +++ b/tensorflow/compiler/xla/tests/half_test.cc @@ -16,8 +16,8 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/test_helpers.h" @@ -48,7 +48,8 @@ class UnaryOpTest : public HalfTestBase, public ::testing::WithParamInterface {}; XLA_TEST_P(UnaryOpTest, Ops) { - std::vector x({half(1.4), half(-2.3), half(3.2), half(-4.1)}); + std::vector x({half(1.4), half(-2.3), half(3.2), half(-4.1), half(9.0), + half(42.0), half(-9.0), half(-100.0)}); XlaBuilder builder(TestName()); XlaOp x_opnd; auto x_data = CreateR1Parameter(x, /*parameter_number=*/0, "x", diff --git a/tensorflow/compiler/xla/tests/hlo_metadata_test.cc b/tensorflow/compiler/xla/tests/hlo_metadata_test.cc index 4d82442f7e3630c115eff1f17544e2b892c5e7eb..5511190caf95544e2ac48d91c0a138db06a2544c 100644 --- a/tensorflow/compiler/xla/tests/hlo_metadata_test.cc +++ b/tensorflow/compiler/xla/tests/hlo_metadata_test.cc @@ -14,7 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/service/local_service.h" #include "tensorflow/compiler/xla/test_helpers.h" #include "tensorflow/compiler/xla/tests/local_client_test_base.h" diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc index 242cc5db11ff2bdf69209df7537216573d8afbf3..b662e837168c8b16daea0181786be19fa0237a8c 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.cc +++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc @@ -276,9 +276,10 @@ StatusOr<::testing::AssertionResult> HloTestBase::RunAndCompareInternal( HloComputation* HloTestBase::FindComputation(HloModule* module, tensorflow::StringPiece name) { - auto it = c_find_if(module->computations(), + auto computations = module->computations(); + auto it = c_find_if(computations, [&](HloComputation* c) { return c->name() == name; }); - if (it == module->computations().end()) { + if (it == computations.end()) { return nullptr; } return *it; @@ -287,9 +288,10 @@ HloComputation* HloTestBase::FindComputation(HloModule* module, HloInstruction* HloTestBase::FindInstruction(HloModule* module, tensorflow::StringPiece name) { for (const HloComputation* c : module->computations()) { - auto it = c_find_if(c->instructions(), + auto instructions = c->instructions(); + auto it = c_find_if(instructions, [&](HloInstruction* i) { return i->name() == name; }); - if (it != c->instructions().end()) { + if (it != instructions.end()) { return *it; } } diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.h b/tensorflow/compiler/xla/tests/hlo_test_base.h index 9009d67cea6840235d63724ef76d777c8f693d33..66719b1460063a61541535ff7507468ae0ca1ada 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.h +++ b/tensorflow/compiler/xla/tests/hlo_test_base.h @@ -200,6 +200,13 @@ class HloTestBase : public ::testing::Test { ->ResetLayout(layout); } + void ForceResultLayout(HloModule* module, const Layout& layout, + ShapeIndexView shape_index) { + module->mutable_entry_computation_layout() + ->mutable_result_layout() + ->ResetLayout(layout, shape_index); + } + // Convenience method to clear the layout of the computation result in // 'module'. void ForceClearResultLayout(HloModule* module) { diff --git a/tensorflow/compiler/xla/tests/iota_test.cc b/tensorflow/compiler/xla/tests/iota_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..f950aa1e8fe745075234a5ebff52d92be7378a5d --- /dev/null +++ b/tensorflow/compiler/xla/tests/iota_test.cc @@ -0,0 +1,61 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include + +#include "tensorflow/compiler/xla/tests/client_library_test_base.h" +#include "tensorflow/core/lib/core/errors.h" + +namespace xla { +namespace { + +class IotaTest : public ClientLibraryTestBase { + public: + explicit IotaTest(se::Platform* platform = nullptr) + : ClientLibraryTestBase(platform) {} + template + std::vector GetExpected(const int64 num_elements) { + std::vector result(num_elements); + std::iota(result.begin(), result.end(), 0); + return result; + } +}; + +TEST_F(IotaTest, SimpleR1) { + for (int num_elements = 1; num_elements < 10000001; num_elements *= 10) { + { + XlaBuilder builder(TestName() + "_f32"); + IotaGen(&builder, F32, num_elements); + ComputeAndCompareR1(&builder, GetExpected(num_elements), {}, + ErrorSpec{0.0001}); + } + { + XlaBuilder builder(TestName() + "_u32"); + IotaGen(&builder, U32, num_elements); + ComputeAndCompareR1(&builder, GetExpected(num_elements), + {}); + } + { + XlaBuilder builder(TestName() + "_s32"); + IotaGen(&builder, S32, num_elements); + ComputeAndCompareR1(&builder, GetExpected(num_elements), + {}); + } + } +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/tests/literal_test_util.h b/tensorflow/compiler/xla/tests/literal_test_util.h index d1b8a6cf0b2552f1b7d95a2560d502da14ddc39a..31a099c15f1f20457c90de97054f68a31eb49011 100644 --- a/tensorflow/compiler/xla/tests/literal_test_util.h +++ b/tensorflow/compiler/xla/tests/literal_test_util.h @@ -25,6 +25,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array3d.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/error_spec.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/test_helpers.h" @@ -154,20 +155,20 @@ class LiteralTestUtil { template /* static */ void LiteralTestUtil::ExpectR0Equal(NativeT expected, const LiteralSlice& actual) { - EXPECT_TRUE(Equal(*Literal::CreateR0(expected), actual)); + EXPECT_TRUE(Equal(*LiteralUtil::CreateR0(expected), actual)); } template /* static */ void LiteralTestUtil::ExpectR1Equal( tensorflow::gtl::ArraySlice expected, const LiteralSlice& actual) { - EXPECT_TRUE(Equal(*Literal::CreateR1(expected), actual)); + EXPECT_TRUE(Equal(*LiteralUtil::CreateR1(expected), actual)); } template /* static */ void LiteralTestUtil::ExpectR2Equal( std::initializer_list> expected, const LiteralSlice& actual) { - EXPECT_TRUE(Equal(*Literal::CreateR2(expected), actual)); + EXPECT_TRUE(Equal(*LiteralUtil::CreateR2(expected), actual)); } template @@ -175,46 +176,46 @@ template std::initializer_list>> expected, const LiteralSlice& actual) { - EXPECT_TRUE(Equal(*Literal::CreateR3(expected), actual)); + EXPECT_TRUE(Equal(*LiteralUtil::CreateR3(expected), actual)); } template /* static */ void LiteralTestUtil::ExpectR2EqualArray2D( const Array2D& expected, const LiteralSlice& actual) { - EXPECT_TRUE(Equal(*Literal::CreateR2FromArray2D(expected), actual)); + EXPECT_TRUE(Equal(*LiteralUtil::CreateR2FromArray2D(expected), actual)); } template /* static */ void LiteralTestUtil::ExpectR3EqualArray3D( const Array3D& expected, const LiteralSlice& actual) { - EXPECT_TRUE(Equal(*Literal::CreateR3FromArray3D(expected), actual)); + EXPECT_TRUE(Equal(*LiteralUtil::CreateR3FromArray3D(expected), actual)); } template /* static */ void LiteralTestUtil::ExpectR4EqualArray4D( const Array4D& expected, const LiteralSlice& actual) { - EXPECT_TRUE(Equal(*Literal::CreateR4FromArray4D(expected), actual)); + EXPECT_TRUE(Equal(*LiteralUtil::CreateR4FromArray4D(expected), actual)); } template /* static */ void LiteralTestUtil::ExpectR0Near(NativeT expected, const LiteralSlice& actual, const ErrorSpec& error) { - EXPECT_TRUE(Near(*Literal::CreateR0(expected), actual, error)); + EXPECT_TRUE(Near(*LiteralUtil::CreateR0(expected), actual, error)); } template /* static */ void LiteralTestUtil::ExpectR1Near( tensorflow::gtl::ArraySlice expected, const LiteralSlice& actual, const ErrorSpec& error) { - EXPECT_TRUE(Near(*Literal::CreateR1(expected), actual, error)); + EXPECT_TRUE(Near(*LiteralUtil::CreateR1(expected), actual, error)); } template /* static */ void LiteralTestUtil::ExpectR2Near( std::initializer_list> expected, const LiteralSlice& actual, const ErrorSpec& error) { - EXPECT_TRUE(Near(*Literal::CreateR2(expected), actual, error)); + EXPECT_TRUE(Near(*LiteralUtil::CreateR2(expected), actual, error)); } template @@ -222,7 +223,7 @@ template std::initializer_list>> expected, const LiteralSlice& actual, const ErrorSpec& error) { - EXPECT_TRUE(Near(*Literal::CreateR3(expected), actual, error)); + EXPECT_TRUE(Near(*LiteralUtil::CreateR3(expected), actual, error)); } template @@ -231,28 +232,28 @@ template std::initializer_list>>> expected, const LiteralSlice& actual, const ErrorSpec& error) { - EXPECT_TRUE(Near(*Literal::CreateR4(expected), actual, error)); + EXPECT_TRUE(Near(*LiteralUtil::CreateR4(expected), actual, error)); } template /* static */ void LiteralTestUtil::ExpectR2NearArray2D( const Array2D& expected, const LiteralSlice& actual, const ErrorSpec& error) { - EXPECT_TRUE(Near(*Literal::CreateR2FromArray2D(expected), actual, error)); + EXPECT_TRUE(Near(*LiteralUtil::CreateR2FromArray2D(expected), actual, error)); } template /* static */ void LiteralTestUtil::ExpectR3NearArray3D( const Array3D& expected, const LiteralSlice& actual, const ErrorSpec& error) { - EXPECT_TRUE(Near(*Literal::CreateR3FromArray3D(expected), actual, error)); + EXPECT_TRUE(Near(*LiteralUtil::CreateR3FromArray3D(expected), actual, error)); } template /* static */ void LiteralTestUtil::ExpectR4NearArray4D( const Array4D& expected, const LiteralSlice& actual, const ErrorSpec& error) { - EXPECT_TRUE(Near(*Literal::CreateR4FromArray4D(expected), actual, error)); + EXPECT_TRUE(Near(*LiteralUtil::CreateR4FromArray4D(expected), actual, error)); } } // namespace xla diff --git a/tensorflow/compiler/xla/tests/literal_test_util_test.cc b/tensorflow/compiler/xla/tests/literal_test_util_test.cc index bbac7285aefbb1f028fad152e4b7fe6af01e9f6d..f297b2b847f570d26e71ddcd8e34bc626f982e1f 100644 --- a/tensorflow/compiler/xla/tests/literal_test_util_test.cc +++ b/tensorflow/compiler/xla/tests/literal_test_util_test.cc @@ -31,8 +31,9 @@ namespace xla { namespace { TEST(LiteralTestUtilTest, ComparesEqualTuplesEqual) { - std::unique_ptr literal = Literal::MakeTuple({ - Literal::CreateR0(42).get(), Literal::CreateR0(64).get(), + std::unique_ptr literal = LiteralUtil::MakeTuple({ + LiteralUtil::CreateR0(42).get(), + LiteralUtil::CreateR0(64).get(), }); EXPECT_TRUE(LiteralTestUtil::Equal(*literal, *literal)); } @@ -42,11 +43,13 @@ TEST(LiteralTestUtilTest, ComparesUnequalTuplesUnequal) { // un-fail an assertion failure. The CHECK-failure is death, so we can make a // death assertion. auto unequal_things_are_equal = [] { - std::unique_ptr lhs = Literal::MakeTuple({ - Literal::CreateR0(42).get(), Literal::CreateR0(64).get(), + std::unique_ptr lhs = LiteralUtil::MakeTuple({ + LiteralUtil::CreateR0(42).get(), + LiteralUtil::CreateR0(64).get(), }); - std::unique_ptr rhs = Literal::MakeTuple({ - Literal::CreateR0(64).get(), Literal::CreateR0(42).get(), + std::unique_ptr rhs = LiteralUtil::MakeTuple({ + LiteralUtil::CreateR0(64).get(), + LiteralUtil::CreateR0(42).get(), }); CHECK(LiteralTestUtil::Equal(*lhs, *rhs)) << "LHS and RHS are unequal"; }; @@ -55,8 +58,8 @@ TEST(LiteralTestUtilTest, ComparesUnequalTuplesUnequal) { TEST(LiteralTestUtilTest, ExpectNearFailurePlacesResultsInTemporaryDirectory) { auto dummy_lambda = [] { - auto two = Literal::CreateR0(2); - auto four = Literal::CreateR0(4); + auto two = LiteralUtil::CreateR0(2); + auto four = LiteralUtil::CreateR0(4); ErrorSpec error(0.001); CHECK(LiteralTestUtil::Near(*two, *four, error)) << "two is not near four"; }; @@ -98,8 +101,8 @@ TEST(LiteralTestUtilTest, ExpectNearFailurePlacesResultsInTemporaryDirectory) { } TEST(LiteralTestUtilTest, NotEqualHasValuesInMessage) { - auto expected = Literal::CreateR1({1, 2, 3}); - auto actual = Literal::CreateR1({4, 5, 6}); + auto expected = LiteralUtil::CreateR1({1, 2, 3}); + auto actual = LiteralUtil::CreateR1({4, 5, 6}); ::testing::AssertionResult result = LiteralTestUtil::Equal(*expected, *actual); EXPECT_THAT(result.message(), ::testing::HasSubstr("expected: {1, 2, 3}")); @@ -107,25 +110,26 @@ TEST(LiteralTestUtilTest, NotEqualHasValuesInMessage) { } TEST(LiteralTestUtilTest, NearComparatorR1) { - auto a = - Literal::CreateR1({0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}); - auto b = - Literal::CreateR1({0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}); + auto a = LiteralUtil::CreateR1( + {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}); + auto b = LiteralUtil::CreateR1( + {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}); EXPECT_TRUE(LiteralTestUtil::Near(*a, *b, ErrorSpec{0.0001})); } TEST(LiteralTestUtilTest, NearComparatorR1Nan) { - auto a = - Literal::CreateR1({0.0, 0.1, 0.2, 0.3, NAN, 0.5, 0.6, 0.7, 0.8}); - auto b = - Literal::CreateR1({0.0, 0.1, 0.2, 0.3, NAN, 0.5, 0.6, 0.7, 0.8}); + auto a = LiteralUtil::CreateR1( + {0.0, 0.1, 0.2, 0.3, NAN, 0.5, 0.6, 0.7, 0.8}); + auto b = LiteralUtil::CreateR1( + {0.0, 0.1, 0.2, 0.3, NAN, 0.5, 0.6, 0.7, 0.8}); EXPECT_TRUE(LiteralTestUtil::Near(*a, *b, ErrorSpec{0.0001})); } TEST(LiteralTestUtil, NearComparatorDifferentLengths) { - auto a = - Literal::CreateR1({0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}); - auto b = Literal::CreateR1({0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7}); + auto a = LiteralUtil::CreateR1( + {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}); + auto b = + LiteralUtil::CreateR1({0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7}); EXPECT_FALSE(LiteralTestUtil::Near(*a, *b, ErrorSpec{0.0001})); EXPECT_FALSE(LiteralTestUtil::Near(*b, *a, ErrorSpec{0.0001})); } diff --git a/tensorflow/compiler/xla/tests/llvm_compiler_test.cc b/tensorflow/compiler/xla/tests/llvm_compiler_test.cc index 082bc34136e004795ce300c66591758f47c665fe..e719da54d45d3e6eb3f3e14d3fa3076db2081e04 100644 --- a/tensorflow/compiler/xla/tests/llvm_compiler_test.cc +++ b/tensorflow/compiler/xla/tests/llvm_compiler_test.cc @@ -14,9 +14,10 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/service/llvm_compiler.h" +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/backend.h" #include "tensorflow/compiler/xla/service/cpu/cpu_compiler.h" -#include "tensorflow/compiler/xla/service/gpu/gpu_compiler.h" +#include "tensorflow/compiler/xla/service/gpu/nvptx_compiler.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/platform_util.h" #include "tensorflow/compiler/xla/test_helpers.h" @@ -64,7 +65,7 @@ class LLVMCompilerTest : public ::testing::Test { // Create HLO module, and run the compiler. auto builder = HloComputation::Builder(TestName()); builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0))); auto hlo_module = CreateNewModule(); hlo_module->AddEntryComputation(builder.Build()); @@ -86,7 +87,7 @@ class LLVMCompilerTest : public ::testing::Test { void TestMultiModuleCompilation(LLVMCompiler *compiler) { HloComputation::Builder builder(TestName()); builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42.0))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42.0))); std::unique_ptr hlo_module = CreateNewModule(); hlo_module->AddEntryComputation(builder.Build()); @@ -144,7 +145,7 @@ TEST_F(CpuCompilerTest, HooksTest) { } TEST_F(GpuCompilerTest, HooksTest) { - gpu::GpuCompiler compiler; + gpu::NVPTXCompiler compiler; TestCompilerHooks(&compiler); } @@ -154,7 +155,7 @@ TEST_F(CpuCompilerTest, MultiModuleCompilation) { } TEST_F(GpuCompilerTest, MultModuleCompilation) { - gpu::GpuCompiler compiler; + gpu::NVPTXCompiler compiler; TestMultiModuleCompilation(&compiler); } } // namespace diff --git a/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc b/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc index 2c45f19c090d2690878430363bf0d20252b2f3df..6fc11150978931f980349799372872f9fb68f292 100644 --- a/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc +++ b/tensorflow/compiler/xla/tests/llvm_irgen_test_base.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" #include "tensorflow/compiler/xla/tests/filecheck.h" #include "tensorflow/core/lib/core/status_test_util.h" @@ -25,28 +26,28 @@ limitations under the License. namespace xla { -void LLVMIRGenTestBase::SetIrHook(bool match_optimized_ir) { +void LlvmIrGenTestBase::SetIrHook(bool match_optimized_ir) { auto llvm_compiler = GetLLVMCompiler(); using std::placeholders::_1; // Add the IR inspection hook to the LLVM compiler. if (match_optimized_ir) { llvm_compiler->SetPostOptimizationHook( - std::bind(&LLVMIRGenTestBase::IrHook, this, _1)); + std::bind(&LlvmIrGenTestBase::IrHook, this, _1)); } else { llvm_compiler->SetPreOptimizationHook( - std::bind(&LLVMIRGenTestBase::IrHook, this, _1)); + std::bind(&LlvmIrGenTestBase::IrHook, this, _1)); } } -void LLVMIRGenTestBase::ResetIrHook() { +void LlvmIrGenTestBase::ResetIrHook() { auto llvm_compiler = GetLLVMCompiler(); llvm_compiler->RemovePreOptimizationHook(); llvm_compiler->RemovePostOptimizationHook(); } -void LLVMIRGenTestBase::CompileAndVerifyIr( +void LlvmIrGenTestBase::CompileAndVerifyIr( std::unique_ptr hlo_module, const string& pattern, bool match_optimized_ir) { SetIrHook(match_optimized_ir); @@ -58,7 +59,17 @@ void LLVMIRGenTestBase::CompileAndVerifyIr( EXPECT_TRUE(filecheck_result.ValueOrDie()); } -void LLVMIRGenTestBase::CompileAheadOfTimeAndVerifyIr( +void LlvmIrGenTestBase::CompileAndVerifyIr(const string& hlo_text, + const string& expected_llvm_ir, + bool match_optimized_ir) { + HloModuleConfig config; + config.set_debug_options(GetDebugOptionsForTest()); + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseHloString(hlo_text, config)); + CompileAndVerifyIr(std::move(module), expected_llvm_ir, match_optimized_ir); +} + +void LlvmIrGenTestBase::CompileAheadOfTimeAndVerifyIr( std::unique_ptr hlo_module, const AotCompilationOptions& options, const string& pattern, bool match_optimized_ir) { SetIrHook(match_optimized_ir); @@ -71,11 +82,11 @@ void LLVMIRGenTestBase::CompileAheadOfTimeAndVerifyIr( EXPECT_TRUE(filecheck_result.ValueOrDie()); } -LLVMCompiler* LLVMIRGenTestBase::GetLLVMCompiler() { +LLVMCompiler* LlvmIrGenTestBase::GetLLVMCompiler() { return static_cast(backend().compiler()); } -Status LLVMIRGenTestBase::IrHook(const llvm::Module& module) { +Status LlvmIrGenTestBase::IrHook(const llvm::Module& module) { ir_ = llvm_ir::DumpModuleToString(module); return Status::OK(); } diff --git a/tensorflow/compiler/xla/tests/llvm_irgen_test_base.h b/tensorflow/compiler/xla/tests/llvm_irgen_test_base.h index 74cbb5f5df662992046a5b0f9a31e52879f375ad..018f9546afc3e408686a9ac75a74320a05b27182 100644 --- a/tensorflow/compiler/xla/tests/llvm_irgen_test_base.h +++ b/tensorflow/compiler/xla/tests/llvm_irgen_test_base.h @@ -24,7 +24,7 @@ limitations under the License. namespace xla { // Tests that verify IR emitted by the CPU/GPU backend is as expected. -class LLVMIRGenTestBase : public CodegenTestBase { +class LlvmIrGenTestBase : public CodegenTestBase { protected: // Compiles the given HLO module to LLVM IR and verifies the IR matches the // given pattern. `pattern` is in the FileCheck pattern matching syntax @@ -38,6 +38,12 @@ class LLVMIRGenTestBase : public CodegenTestBase { void CompileAndVerifyIr(std::unique_ptr hlo_module, const string& pattern, bool match_optimized_ir); + // A thin wrapper around CompileAndVerifyIr that parses `hlo_text` to create + // an HLO module. + void CompileAndVerifyIr(const string& hlo_text, + const string& expected_llvm_ir, + bool match_optimized_ir = false); + // Compiles the given HLO module to LLVM IR and verifies the IR matches the // given pattern. `pattern` is in the FileCheck pattern matching syntax // (http://llvm.org/docs/CommandGuide/FileCheck.html). diff --git a/tensorflow/compiler/xla/tests/local_client_allocation_test.cc b/tensorflow/compiler/xla/tests/local_client_allocation_test.cc index 9191be9fd905ab2e0c661042b042c8233d39e4a1..e2cd5bcc5a95f692dcf4a43d717252bfe876aa81 100644 --- a/tensorflow/compiler/xla/tests/local_client_allocation_test.cc +++ b/tensorflow/compiler/xla/tests/local_client_allocation_test.cc @@ -16,8 +16,8 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/local_service.h" #include "tensorflow/compiler/xla/service/shaped_buffer.h" #include "tensorflow/compiler/xla/statusor.h" @@ -45,7 +45,7 @@ XLA_TEST_F(LocalClientAllocationTest, AddVectors) { TestAllocator* allocator = GetOrCreateAllocator(local_client_->platform()); auto x_array = - LiteralToShapedBuffer(*Literal::CreateR1({0.0f, 1.0f, 2.0f})); + LiteralToShapedBuffer(*LiteralUtil::CreateR1({0.0f, 1.0f, 2.0f})); int64 allocation_count_before = allocator_->allocation_count(); diff --git a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc index 70612e7c49d2815096cc54fd6ae796148249b4db..74494e60e883417d5772ce71544715aef5ef3ef2 100644 --- a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc +++ b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc @@ -21,8 +21,8 @@ limitations under the License. #include "llvm/ADT/Triple.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/cpu/cpu_compiler.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" #include "tensorflow/compiler/xla/types.h" @@ -92,9 +92,10 @@ int main(int argc, char** argv) { // It's lame to hard-code the buffer assignments, but we need // local_client_aot_test.cc to be able to easily invoke the function. CHECK_EQ(result->result_buffer_index(), 1); - CHECK_EQ(result->buffer_sizes().size(), 2); + CHECK_EQ(result->buffer_sizes().size(), 3); CHECK_EQ(result->buffer_sizes()[0], -1); // param buffer CHECK_EQ(result->buffer_sizes()[1], sizeof(float)); // result buffer + CHECK_EQ(result->buffer_sizes()[2], -1); // const buffer if (triple.isOSBinFormatELF()) { // Check the ELF magic. CHECK_EQ(result->object_file_data()[0], 0x7F); diff --git a/tensorflow/compiler/xla/tests/local_client_execute_test.cc b/tensorflow/compiler/xla/tests/local_client_execute_test.cc index 2c6393794ef1b1558f5e651b5cb7bfa2afa961de..1a823cf189b310c62c735419936544ea99fcfbaf 100644 --- a/tensorflow/compiler/xla/tests/local_client_execute_test.cc +++ b/tensorflow/compiler/xla/tests/local_client_execute_test.cc @@ -19,9 +19,9 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" #include "tensorflow/compiler/xla/service/local_service.h" #include "tensorflow/compiler/xla/service/platform_util.h" @@ -68,7 +68,7 @@ XLA_TEST_F(LocalClientExecuteTest, AddScalars) { auto y = ConstantR0(&builder, 123.0f); Add(x, y); - auto x_value = LiteralToShapedBuffer(*Literal::CreateR0(42.0f)); + auto x_value = LiteralToShapedBuffer(*LiteralUtil::CreateR0(42.0f)); ScopedShapedBuffer result = ExecuteLocallyOrDie(builder.Build().ValueOrDie(), {&x_value}); LiteralTestUtil::ExpectR0Near(165.f, *ShapedBufferToLiteral(result), @@ -81,7 +81,7 @@ XLA_TEST_F(LocalClientExecuteTest, AddZeroElementVectors) { auto y = ConstantR1(&builder, {}); Add(x, y); - auto x_array = LiteralToShapedBuffer(*Literal::CreateR1({})); + auto x_array = LiteralToShapedBuffer(*LiteralUtil::CreateR1({})); ScopedShapedBuffer result = ExecuteLocallyOrDie(builder.Build().ValueOrDie(), {&x_array}); LiteralTestUtil::ExpectR1Near({}, *ShapedBufferToLiteral(result), @@ -95,7 +95,7 @@ XLA_TEST_F(LocalClientExecuteTest, AddVectors) { Add(x, y); auto x_array = - LiteralToShapedBuffer(*Literal::CreateR1({0.0f, 1.0f, 2.0f})); + LiteralToShapedBuffer(*LiteralUtil::CreateR1({0.0f, 1.0f, 2.0f})); ScopedShapedBuffer result = ExecuteLocallyOrDie(builder.Build().ValueOrDie(), {&x_array}); LiteralTestUtil::ExpectR1Near( @@ -109,7 +109,7 @@ XLA_TEST_F(LocalClientExecuteTest, AddVectorsWithProfile) { Add(x, y); auto x_array = - LiteralToShapedBuffer(*Literal::CreateR1({0.0f, 1.0f, 2.0f})); + LiteralToShapedBuffer(*LiteralUtil::CreateR1({0.0f, 1.0f, 2.0f})); ExecutionProfile profile; ScopedShapedBuffer result = ExecuteLocallyOrDie( builder.Build().ValueOrDie(), {&x_array}, DefaultExecutableBuildOptions(), @@ -128,13 +128,13 @@ XLA_TEST_F(LocalClientExecuteTest, AddArraysWithDifferentInputLayouts) { auto computation = builder.Build().ConsumeValueOrDie(); // Create x as a col-major array. - auto x_array = LiteralToShapedBuffer(*Literal::CreateR2WithLayout( + auto x_array = LiteralToShapedBuffer(*LiteralUtil::CreateR2WithLayout( {{1.0f, 2.0f}, {3.0f, 4.0f}}, LayoutUtil::MakeLayout({0, 1}))); EXPECT_TRUE(LayoutUtil::Equal(x_array.on_device_shape().layout(), LayoutUtil::MakeLayout({0, 1}))); // Create y as a row-major array. - auto y_array = LiteralToShapedBuffer(*Literal::CreateR2WithLayout( + auto y_array = LiteralToShapedBuffer(*LiteralUtil::CreateR2WithLayout( {{10.0f, 20.0f}, {30.0f, 40.0f}}, LayoutUtil::MakeLayout({1, 0}))); EXPECT_TRUE(LayoutUtil::Equal(y_array.on_device_shape().layout(), LayoutUtil::MakeLayout({1, 0}))); @@ -161,9 +161,9 @@ XLA_TEST_F(LocalClientExecuteTest, AddArraysWithDifferentOutputLayouts) { auto computation = builder.Build().ConsumeValueOrDie(); auto x_array = LiteralToShapedBuffer( - *Literal::CreateR2({{1.0f, 2.0f}, {3.0f, 4.0f}})); + *LiteralUtil::CreateR2({{1.0f, 2.0f}, {3.0f, 4.0f}})); auto y_array = LiteralToShapedBuffer( - *Literal::CreateR2({{10.0f, 20.0f}, {30.0f, 40.0f}})); + *LiteralUtil::CreateR2({{10.0f, 20.0f}, {30.0f, 40.0f}})); // Run with col-major result layout. ScopedShapedBuffer result_colmaj = ExecuteLocallyOrDie( @@ -198,9 +198,9 @@ XLA_TEST_F(LocalClientExecuteTest, TupleResult) { auto computation = builder.Build().ConsumeValueOrDie(); auto x_array = LiteralToShapedBuffer( - *Literal::CreateR2({{1.0f, 2.0f}, {3.0f, 4.0f}})); + *LiteralUtil::CreateR2({{1.0f, 2.0f}, {3.0f, 4.0f}})); auto y_array = LiteralToShapedBuffer( - *Literal::CreateR2({{10.0f, 20.0f}, {30.0f, 40.0f}})); + *LiteralUtil::CreateR2({{10.0f, 20.0f}, {30.0f, 40.0f}})); ScopedShapedBuffer result = ExecuteLocallyOrDie(computation, {&x_array, &y_array}); @@ -226,9 +226,9 @@ XLA_TEST_F(LocalClientExecuteTest, NestedTupleResult) { auto computation = builder.Build().ConsumeValueOrDie(); auto x_array = LiteralToShapedBuffer( - *Literal::CreateR2({{1.0f, 2.0f}, {3.0f, 4.0f}})); + *LiteralUtil::CreateR2({{1.0f, 2.0f}, {3.0f, 4.0f}})); auto y_array = LiteralToShapedBuffer( - *Literal::CreateR2({{10.0f, 20.0f}, {30.0f, 40.0f}})); + *LiteralUtil::CreateR2({{10.0f, 20.0f}, {30.0f, 40.0f}})); ScopedShapedBuffer result = ExecuteLocallyOrDie(computation, {&x_array, &y_array}); @@ -255,7 +255,7 @@ XLA_TEST_F(LocalClientExecuteTest, TupleResultWithLayout) { Tuple(&builder, {x, y}); auto array = LiteralToShapedBuffer( - *Literal::CreateR2({{1.0f, 2.0f}, {3.0f, 4.0f}})); + *LiteralUtil::CreateR2({{1.0f, 2.0f}, {3.0f, 4.0f}})); ExecutableBuildOptions options = DefaultExecutableBuildOptions(); Shape shape_with_layout = ShapeUtil::MakeTupleShape( @@ -298,12 +298,12 @@ XLA_TEST_F(LocalClientExecuteTest, TupleArguments) { Tuple(&builder, {array_sum, vector_diff}); auto computation = builder.Build().ConsumeValueOrDie(); - auto x_literal = Literal::MakeTuple( - {Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}).get(), - Literal::CreateR1({42.0, 75.0, 123.0}).get()}); - auto y_literal = Literal::MakeTuple( - {Literal::CreateR1({2.0, 4.0, 6.0}).get(), - Literal::CreateR2({{55.0, 44.0}, {33.0, 22.0}}).get()}); + auto x_literal = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}).get(), + LiteralUtil::CreateR1({42.0, 75.0, 123.0}).get()}); + auto y_literal = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR1({2.0, 4.0, 6.0}).get(), + LiteralUtil::CreateR2({{55.0, 44.0}, {33.0, 22.0}}).get()}); auto x_buffer = LiteralToShapedBuffer(*x_literal); auto y_buffer = LiteralToShapedBuffer(*y_literal); @@ -344,12 +344,12 @@ XLA_TEST_F(LocalClientExecuteTest, NestedTupleArgument) { Tuple(&builder, {negate_array, vector_sum}); auto computation = builder.Build().ConsumeValueOrDie(); - auto arg_literal = Literal::MakeTuple( - {Literal::MakeTuple( - {Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}).get(), - Literal::CreateR1({42.0, 75.0, 123.0}).get()}) + auto arg_literal = LiteralUtil::MakeTuple( + {LiteralUtil::MakeTuple( + {LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}).get(), + LiteralUtil::CreateR1({42.0, 75.0, 123.0}).get()}) .get(), - Literal::CreateR1({222.0, -2.0, 10.0}).get()}); + LiteralUtil::CreateR1({222.0, -2.0, 10.0}).get()}); auto arg_buffer = LiteralToShapedBuffer(*arg_literal); ScopedShapedBuffer result = ExecuteLocallyOrDie(computation, {&arg_buffer}); @@ -377,9 +377,9 @@ XLA_TEST_F(LocalClientExecuteTest, PassingTupleResultBackIntoComputation) { Tuple(&builder, {Neg(element_0), Add(element_1, element_1)}); auto computation = builder.Build().ConsumeValueOrDie(); - auto arg_literal = Literal::MakeTuple( - {Literal::CreateR2({{1.0, 2.0}, {3.0, 4.0}}).get(), - Literal::CreateR2({{11.0, 3.0}, {4.0, 5.0}}).get()}); + auto arg_literal = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}).get(), + LiteralUtil::CreateR2({{11.0, 3.0}, {4.0, 5.0}}).get()}); auto arg_buffer = LiteralToShapedBuffer(*arg_literal); ScopedShapedBuffer result_0 = ExecuteLocallyOrDie(computation, {&arg_buffer}); @@ -429,10 +429,10 @@ XLA_TEST_F(LocalClientExecuteTest, LargeTuple) { // -tuple_index}. std::vector> arg_elements; for (int i = 0; i < kElementCount; ++i) { - arg_elements.push_back(Literal::CreateR1({1.0f * i, -1.0f * i})); + arg_elements.push_back(LiteralUtil::CreateR1({1.0f * i, -1.0f * i})); } std::unique_ptr arg_literal = - Literal::MakeTupleOwned(std::move(arg_elements)); + LiteralUtil::MakeTupleOwned(std::move(arg_elements)); auto arg_buffer = LiteralToShapedBuffer(*arg_literal); ScopedShapedBuffer result = ExecuteLocallyOrDie(computation, {&arg_buffer}); @@ -480,12 +480,13 @@ XLA_TEST_F(LocalClientExecuteTest, LargeNestedTuple) { for (int i = 0; i < kFanout; ++i) { std::vector> inner_tuple_elements; for (int j = 0; j < kFanout; ++j) { - inner_tuple_elements.push_back(Literal::CreateR0(i + j)); + inner_tuple_elements.push_back(LiteralUtil::CreateR0(i + j)); } outer_tuple_elements.push_back( - Literal::MakeTupleOwned(std::move(inner_tuple_elements))); + LiteralUtil::MakeTupleOwned(std::move(inner_tuple_elements))); } - auto arg_literal = Literal::MakeTupleOwned(std::move(outer_tuple_elements)); + auto arg_literal = + LiteralUtil::MakeTupleOwned(std::move(outer_tuple_elements)); auto arg_buffer = LiteralToShapedBuffer(*arg_literal); ScopedShapedBuffer result = ExecuteLocallyOrDie(computation, {&arg_buffer}); @@ -524,11 +525,11 @@ XLA_TEST_F(LocalClientExecuteTest, DeepTuple) { auto computation = builder.Build().ConsumeValueOrDie(); // Construct the argument to pass to the computation. - std::unique_ptr arg_literal = Literal::CreateR0(123.0); + std::unique_ptr arg_literal = LiteralUtil::CreateR0(123.0); for (int i = 0; i < kTupleDepth; ++i) { std::vector> arg_vector; arg_vector.push_back(std::move(arg_literal)); - arg_literal = Literal::MakeTupleOwned(std::move(arg_vector)); + arg_literal = LiteralUtil::MakeTupleOwned(std::move(arg_vector)); } auto arg_buffer = LiteralToShapedBuffer(*arg_literal); @@ -551,7 +552,7 @@ XLA_TEST_F(LocalClientExecuteTest, InvalidNumberOfArguments) { Add(x, y); auto x_array = - LiteralToShapedBuffer(*Literal::CreateR1({1.0f, 2.0f, 3.0f})); + LiteralToShapedBuffer(*LiteralUtil::CreateR1({1.0f, 2.0f, 3.0f})); auto execute_status = ExecuteLocally(builder.Build().ValueOrDie(), {&x_array}); @@ -567,7 +568,7 @@ XLA_TEST_F(LocalClientExecuteTest, IncorrectArgumentShape) { Neg(x); auto x_array = LiteralToShapedBuffer( - *Literal::CreateR2({{0.0f, 1.0f}, {2.0f, 3.0f}})); + *LiteralUtil::CreateR2({{0.0f, 1.0f}, {2.0f, 3.0f}})); auto execute_status = ExecuteLocally(builder.Build().ValueOrDie(), {&x_array}); @@ -584,7 +585,7 @@ XLA_TEST_F(LocalClientExecuteTest, InvalidResultLayout) { Neg(x); auto x_array = LiteralToShapedBuffer( - *Literal::CreateR2({{0.0f, 1.0f}, {2.0f, 3.0f}})); + *LiteralUtil::CreateR2({{0.0f, 1.0f}, {2.0f, 3.0f}})); auto execute_status = ExecuteLocally( builder.Build().ValueOrDie(), {&x_array}, DefaultExecutableBuildOptions().set_result_layout( @@ -767,7 +768,7 @@ XLA_TEST_F(LocalClientExecuteTest, CompileExecutable) { executable_status.ConsumeValueOrDie(); auto x_array = - LiteralToShapedBuffer(*Literal::CreateR1({0.0f, 1.0f, 2.0f})); + LiteralToShapedBuffer(*LiteralUtil::CreateR1({0.0f, 1.0f, 2.0f})); ScopedShapedBuffer result = executable->Run({&x_array}, DefaultExecutableRunOptions()) .ConsumeValueOrDie(); @@ -795,29 +796,29 @@ XLA_TEST_F(LocalClientExecuteTest, ShapeBufferToLiteralConversion) { }; // Array shapes. - test_to_device_and_back(*Literal::CreateR0(42.0)); - test_to_device_and_back(*Literal::CreateR0(true)); - test_to_device_and_back(*Literal::CreateR1({1.0, 42.0, 744.4})); + test_to_device_and_back(*LiteralUtil::CreateR0(42.0)); + test_to_device_and_back(*LiteralUtil::CreateR0(true)); + test_to_device_and_back(*LiteralUtil::CreateR1({1.0, 42.0, 744.4})); test_to_device_and_back( - *Literal::CreateR2({{1.0, 2.0, 3.0}, {44.0, 0.1, -3}})); - test_to_device_and_back(*Literal::CreateR2({{2, 1}, {4444, 56}})); + *LiteralUtil::CreateR2({{1.0, 2.0, 3.0}, {44.0, 0.1, -3}})); + test_to_device_and_back(*LiteralUtil::CreateR2({{2, 1}, {4444, 56}})); // Null shape (empty tuple). - test_to_device_and_back(*Literal::MakeTuple({})); + test_to_device_and_back(*LiteralUtil::MakeTuple({})); // Non-nested tuples. test_to_device_and_back( - *Literal::MakeTuple({Literal::CreateR0(12223.0).get()})); + *LiteralUtil::MakeTuple({LiteralUtil::CreateR0(12223.0).get()})); test_to_device_and_back( - *Literal::MakeTuple({Literal::CreateR1({1.0, -42.0}).get(), - Literal::CreateR0(123456.0).get()})); + *LiteralUtil::MakeTuple({LiteralUtil::CreateR1({1.0, -42.0}).get(), + LiteralUtil::CreateR0(123456.0).get()})); // Nested tuple. - test_to_device_and_back(*Literal::MakeTuple( - {Literal::MakeTuple({Literal::CreateR1({1.0, -42.0}).get(), - Literal::CreateR0(123456.0).get()}) + test_to_device_and_back(*LiteralUtil::MakeTuple( + {LiteralUtil::MakeTuple({LiteralUtil::CreateR1({1.0, -42.0}).get(), + LiteralUtil::CreateR0(123456.0).get()}) .get(), - Literal::CreateR0(false).get()})); + LiteralUtil::CreateR0(false).get()})); } XLA_TEST_F(LocalClientExecuteTest, ShapeBufferToLiteralConversion64bit) { @@ -835,13 +836,13 @@ XLA_TEST_F(LocalClientExecuteTest, ShapeBufferToLiteralConversion64bit) { }; test_to_device_and_back( - *Literal::CreateR2({{1.0, 2.0, 3.0}, {44.0, 0.1, -3}})); - test_to_device_and_back(*Literal::CreateR2({{2, 1}, {4444, 56}})); + *LiteralUtil::CreateR2({{1.0, 2.0, 3.0}, {44.0, 0.1, -3}})); + test_to_device_and_back(*LiteralUtil::CreateR2({{2, 1}, {4444, 56}})); test_to_device_and_back( - *Literal::CreateR2({{20000000000ULL, 1}, {4444, 56}})); - test_to_device_and_back( - *Literal::MakeTuple({Literal::CreateR1({1.0, -42.0}).get(), - Literal::CreateR0(123456789000LL).get()})); + *LiteralUtil::CreateR2({{20000000000ULL, 1}, {4444, 56}})); + test_to_device_and_back(*LiteralUtil::MakeTuple( + {LiteralUtil::CreateR1({1.0, -42.0}).get(), + LiteralUtil::CreateR0(123456789000LL).get()})); } XLA_TEST_F(LocalClientExecuteTest, InfeedTest) { @@ -860,7 +861,7 @@ XLA_TEST_F(LocalClientExecuteTest, InfeedTest) { })); ASSERT_IS_OK(local_client_->TransferToInfeedLocal( - *Literal::CreateR1({-5.0, 123.0, 42.0}), + *LiteralUtil::CreateR1({-5.0, 123.0, 42.0}), local_client_->default_device_ordinal())); // Join the thread. @@ -869,9 +870,7 @@ XLA_TEST_F(LocalClientExecuteTest, InfeedTest) { LiteralTestUtil::ExpectR1Equal({-4.0, 125.0, 45.0}, *result); } -// TODO(b/34359662): Support infeed/outfeed on GPU and CPU parallel. -// 2017-10-18. -XLA_TEST_F(LocalClientExecuteTest, DISABLED_ON_GPU(InfeedOutfeedTest)) { +XLA_TEST_F(LocalClientExecuteTest, InfeedOutfeedTest) { XlaBuilder builder(TestName()); const Shape shape = ShapeUtil::MakeShape(F32, {3}); auto in = Infeed(&builder, shape); @@ -885,7 +884,7 @@ XLA_TEST_F(LocalClientExecuteTest, DISABLED_ON_GPU(InfeedOutfeedTest)) { [&] { ExecuteLocallyOrDie(builder.Build().ValueOrDie(), {}); })); ASSERT_IS_OK(local_client_->TransferToInfeedLocal( - *Literal::CreateR1({-5.0, 123.0, 42.0}), + *LiteralUtil::CreateR1({-5.0, 123.0, 42.0}), local_client_->default_device_ordinal())); TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, @@ -920,7 +919,7 @@ void BM_LocalClientOverhead(int num_iters) { transfer_manager ->AllocateScopedShapedBuffer(shape, &allocator, /*device_ordinal=*/0) .ConsumeValueOrDie(); - auto literal = Literal::CreateR2({{0, 0, 0}, {0, 0, 0}}); + auto literal = LiteralUtil::CreateR2({{0, 0, 0}, {0, 0, 0}}); auto stream = client->mutable_backend()->BorrowStream(device_ordinal).ValueOrDie(); ASSERT_IS_OK(transfer_manager->TransferLiteralToDevice(stream.get(), *literal, diff --git a/tensorflow/compiler/xla/tests/local_client_test_base.cc b/tensorflow/compiler/xla/tests/local_client_test_base.cc index c31ba0e713a45d18b60bfdb9a47545cf34220333..eaddf756dbc913dd9668cd22228fbd18c2c33309 100644 --- a/tensorflow/compiler/xla/tests/local_client_test_base.cc +++ b/tensorflow/compiler/xla/tests/local_client_test_base.cc @@ -20,6 +20,7 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/tests/local_client_test_base.h b/tensorflow/compiler/xla/tests/local_client_test_base.h index 258226523d830b40ecaa761df95988dc90f5ca47..b4477e9a6b23363ee3a1380f9f98f4b8226f6920 100644 --- a/tensorflow/compiler/xla/tests/local_client_test_base.h +++ b/tensorflow/compiler/xla/tests/local_client_test_base.h @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" #include "tensorflow/compiler/xla/service/local_service.h" #include "tensorflow/compiler/xla/service/platform_util.h" diff --git a/tensorflow/compiler/xla/tests/log_test.cc b/tensorflow/compiler/xla/tests/log_test.cc index cdf70ee4185be2ecd9dcb2d21fbd98c2ab6cc0ad..2d622242e657ce032a17f7b26c94227d343e2a38 100644 --- a/tensorflow/compiler/xla/tests/log_test.cc +++ b/tensorflow/compiler/xla/tests/log_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" diff --git a/tensorflow/compiler/xla/tests/map_test.cc b/tensorflow/compiler/xla/tests/map_test.cc index 1b3bc9d5040e1382f534e00ea2679ebbd48ceb59..0732e195d44d738b264361e43d38259c26a4116e 100644 --- a/tensorflow/compiler/xla/tests/map_test.cc +++ b/tensorflow/compiler/xla/tests/map_test.cc @@ -19,9 +19,9 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" @@ -169,7 +169,7 @@ class MapTest : public ClientLibraryTestBase { TEST_F(MapTest, MapEachElemPlusOneR0) { // Applies lambda (x) (+ x 1)) to an input scalar. XlaBuilder builder(TestName()); - std::unique_ptr param0_literal = Literal::CreateR0(42.0); + std::unique_ptr param0_literal = LiteralUtil::CreateR0(42.0); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -183,7 +183,7 @@ TEST_F(MapTest, MapEachElemPlusOneR0) { XLA_TEST_F(MapTest, MapEachElemPlusOneR1S0) { // Maps (lambda (x) (+ x 1)) onto an input R1F32 vector of length 0. XlaBuilder builder(TestName()); - std::unique_ptr param0_literal = Literal::CreateR1({}); + std::unique_ptr param0_literal = LiteralUtil::CreateR1({}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -198,7 +198,7 @@ TEST_F(MapTest, MapEachElemPlusOneR1S4) { // Maps (lambda (x) (+ x 1)) onto an input R1F32 vector of length 4. XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR1({2.2f, 3.3f, 4.4f, 5.5f}); + LiteralUtil::CreateR1({2.2f, 3.3f, 4.4f, 5.5f}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -212,7 +212,7 @@ TEST_F(MapTest, MapEachElemPlusOneR1S4) { TEST_F(MapTest, MapEachF32ElementToS32Constant) { XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR1({2.2f, 3.3f, 4.4f, 5.5f}); + LiteralUtil::CreateR1({2.2f, 3.3f, 4.4f, 5.5f}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -225,7 +225,7 @@ TEST_F(MapTest, MapEachF32ElementToS32Constant) { TEST_F(MapTest, MapEachF32ElementToU32Constant) { XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR1({2.2f, 3.3f, 4.4f, 5.5f}); + LiteralUtil::CreateR1({2.2f, 3.3f, 4.4f, 5.5f}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -239,7 +239,7 @@ TEST_F(MapTest, MapEachElemLongerChainR1) { // Maps (lambda (x) (* (+ x 1) x)) onto an input R1F32 vector. XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR1({2.6f, -5.1f, 0.1f, 0.2f, 999.0f, 255.5f}); + LiteralUtil::CreateR1({2.6f, -5.1f, 0.1f, 0.2f, 999.0f, 255.5f}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -255,7 +255,7 @@ XLA_TEST_F(MapTest, MapMultipleMapsR1S0) { // Maps (lambda (x) (+ x 1)) onto an input R1F32 vector of length 0, and then // maps (lambda (x) (* x 2)) on the result. XlaBuilder builder(TestName()); - std::unique_ptr param0_literal = Literal::CreateR1({}); + std::unique_ptr param0_literal = LiteralUtil::CreateR1({}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -272,7 +272,7 @@ TEST_F(MapTest, MapMultipleMapsR1S4) { // maps (lambda (x) (* x 2)) on the result. XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR1({2.2f, 3.3f, 4.4f, 5.5f}); + LiteralUtil::CreateR1({2.2f, 3.3f, 4.4f, 5.5f}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -287,7 +287,7 @@ TEST_F(MapTest, MapMultipleMapsR1S4) { TEST_F(MapTest, MapEachElemPlusOneR2) { // Maps (lambda (x) (+ x 1)) onto an input R2F32 vector. XlaBuilder builder(TestName()); - std::unique_ptr param0_literal = Literal::CreateR2( + std::unique_ptr param0_literal = LiteralUtil::CreateR2( {{13.25f, 14.0f}, {-7.1f, -7.2f}, {-8.8f, 8.8f}}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -343,11 +343,11 @@ TEST_F(MapTest, MapBinaryAdder) { // Maps (lambda (x y) (+ x y)) onto two R1F32 vectors. XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR1({2.2f, 3.3f, 4.4f, 5.5f}); + LiteralUtil::CreateR1({2.2f, 3.3f, 4.4f, 5.5f}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); std::unique_ptr param1_literal = - Literal::CreateR1({5.1f, 4.4f, -0.1f, -5.5f}); + LiteralUtil::CreateR1({5.1f, 4.4f, -0.1f, -5.5f}); std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); @@ -365,12 +365,12 @@ TEST_F(MapTest, MapBinaryAdder) { // for Map that used to fail in shape inference (b/28989438). XLA_TEST_F(MapTest, AddWithMixedLayouts) { XlaBuilder builder(TestName()); - std::unique_ptr param0_literal = Literal::CreateR2WithLayout( + std::unique_ptr param0_literal = LiteralUtil::CreateR2WithLayout( {{1, 2}, {3, 4}}, LayoutUtil::MakeLayout({1, 0})); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); - std::unique_ptr param1_literal = Literal::CreateR2WithLayout( + std::unique_ptr param1_literal = LiteralUtil::CreateR2WithLayout( {{10, 20}, {30, 40}}, LayoutUtil::MakeLayout({0, 1})); std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); @@ -392,12 +392,12 @@ XLA_TEST_F(MapTest, AddWithMixedLayouts) { XLA_TEST_F(MapTest, AddR3_3x0x2) { XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR3FromArray3D(Array3D(3, 0, 2)); + LiteralUtil::CreateR3FromArray3D(Array3D(3, 0, 2)); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); std::unique_ptr param1_literal = - Literal::CreateR3FromArray3D(Array3D(3, 0, 2)); + LiteralUtil::CreateR3FromArray3D(Array3D(3, 0, 2)); std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); @@ -414,15 +414,15 @@ TEST_F(MapTest, MapTernaryAdder) { // Maps (lambda (x y z) (+ x y z)) onto three R1F32 vectors. XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR1({2.2f, 3.3f, 4.4f, 5.5f}); + LiteralUtil::CreateR1({2.2f, 3.3f, 4.4f, 5.5f}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); std::unique_ptr param1_literal = - Literal::CreateR1({5.1f, 4.4f, -0.1f, -5.5f}); + LiteralUtil::CreateR1({5.1f, 4.4f, -0.1f, -5.5f}); std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); std::unique_ptr param2_literal = - Literal::CreateR1({-10.0f, -100.0f, -900.0f, -400.0f}); + LiteralUtil::CreateR1({-10.0f, -100.0f, -900.0f, -400.0f}); std::unique_ptr param2_data = client_->TransferToServer(*param2_literal).ConsumeValueOrDie(); @@ -476,11 +476,11 @@ TEST_F(MapTest, MapOperantionWithBuildError) { auto error_add = sub_builder->BuildAndNoteError(); std::unique_ptr param0_literal = - Literal::CreateR1({2.2f, 3.3f, 4.4f, 5.5f}); + LiteralUtil::CreateR1({2.2f, 3.3f, 4.4f, 5.5f}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); std::unique_ptr param1_literal = - Literal::CreateR1({5.1f, 4.4f, -0.1f, -5.5f}); + LiteralUtil::CreateR1({5.1f, 4.4f, -0.1f, -5.5f}); std::unique_ptr param1_data = client_->TransferToServer(*param1_literal).ConsumeValueOrDie(); @@ -513,8 +513,8 @@ TEST_F(MapTestWithFullOpt, MapScalarPower) { Pow(x, y); auto power = sub_builder->BuildAndNoteError(); - std::unique_ptr param0_literal = Literal::CreateR0(2.0f); - std::unique_ptr param1_literal = Literal::CreateR0(5.0f); + std::unique_ptr param0_literal = LiteralUtil::CreateR0(2.0f); + std::unique_ptr param1_literal = LiteralUtil::CreateR0(5.0f); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); std::unique_ptr param1_data = @@ -540,8 +540,8 @@ TEST_F(MapTestWithFullOpt, MapSubtractOppositeOrder) { Sub(y, x); // note that this is y - x, not x - y auto sub_opposite = sub_builder->BuildAndNoteError(); - std::unique_ptr param0_literal = Literal::CreateR0(2.0f); - std::unique_ptr param1_literal = Literal::CreateR0(5.0f); + std::unique_ptr param0_literal = LiteralUtil::CreateR0(2.0f); + std::unique_ptr param1_literal = LiteralUtil::CreateR0(5.0f); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); std::unique_ptr param1_data = @@ -565,7 +565,7 @@ TEST_F(MapTestWithFullOpt, MapSquare) { Mul(x, x); auto square = sub_builder->BuildAndNoteError(); - std::unique_ptr param0_literal = Literal::CreateR0(10.0f); + std::unique_ptr param0_literal = LiteralUtil::CreateR0(10.0f); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); diff --git a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc index 17b1807f44a457786906afc15d8d410f6cf2d4cd..da8c42d465340f2af3d6acd2c3676b69512f193f 100644 --- a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc +++ b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc @@ -19,9 +19,9 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -63,8 +63,8 @@ XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, ExpTwoByTwoValues) { Exp(data); std::unique_ptr expected = - Literal::CreateR2FromArray2D({{2.71828f, 1.00000f}, // row 0 - {0.36788f, 1.64872f}}); // row 1 + LiteralUtil::CreateR2FromArray2D({{2.71828f, 1.00000f}, // row 0 + {0.36788f, 1.64872f}}); // row 1 this->ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-5)); } @@ -92,8 +92,8 @@ XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MapTwoByTwo) { Map(&builder, {data}, add_half, {0, 1}); std::unique_ptr expected = - Literal::CreateR2FromArray2D({{1.5f, 0.5f}, // row 0 - {-0.5f, 1.0f}}); // row 1 + LiteralUtil::CreateR2FromArray2D({{1.5f, 0.5f}, // row 0 + {-0.5f, 1.0f}}); // row 1 this->ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-5)); } @@ -111,8 +111,8 @@ XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MaxTwoByTwoValues) { Max(lhs, rhs); std::unique_ptr expected = - Literal::CreateR2FromArray2D({{7.0f, 6.0f}, // row 0 - {3.0f, -4.0f}}); // row 1 + LiteralUtil::CreateR2FromArray2D({{7.0f, 6.0f}, // row 0 + {3.0f, -4.0f}}); // row 1 this->ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-6)); } @@ -200,12 +200,14 @@ class MatOpsDotAddTest TF_ASSERT_OK_AND_ASSIGN( auto lhs_handle, - client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( - lhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); + client_->TransferToServer( + *LiteralUtil::CreateR2FromArray2DWithLayout( + lhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); TF_ASSERT_OK_AND_ASSIGN( auto rhs_handle, - client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout( - rhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); + client_->TransferToServer( + *LiteralUtil::CreateR2FromArray2DWithLayout( + rhs, LayoutUtil::MakeLayout(minor_to_major(row_major))))); XlaBuilder builder(TestName()); auto lhs_arg = Parameter(&builder, 0, lhs_shape, "lhs"); diff --git a/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc b/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc index e576f000ef23e761d6fa818457eec2144d4bcb00..955dbef6dcd28421fb351c6ee064ac53eda1fd08 100644 --- a/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc +++ b/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/array3d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" diff --git a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc index 6597748c8d1f45391799dbe384a5afc0284de2dd..eb06b115daa96bccd73de30bb7fa30733a6fd947 100644 --- a/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc +++ b/tensorflow/compiler/xla/tests/multioutput_fusion_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" @@ -60,7 +60,7 @@ class MultiOutputFusionTest : public HloTestBase { const Shape elem_shape2 = ShapeUtil::MakeShape(F32, {size, size}); auto const0 = builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(8.0f))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(8.0f))); auto param0 = builder.AddInstruction( HloInstruction::CreateParameter(0, elem_shape0, "0")); @@ -105,8 +105,9 @@ class MultiOutputFusionTest : public HloTestBase { Literal expect(ShapeUtil::MakeShape(F32, {size, size})); expect.PopulateWithValue(size * 1.5f * 3.5f); - auto actual = ExecuteAndTransfer( - std::move(hlo_module), {Literal::CreateR0(-9.0f).get(), &arg1}); + auto actual = + ExecuteAndTransfer(std::move(hlo_module), + {LiteralUtil::CreateR0(-9.0f).get(), &arg1}); EXPECT_TRUE(LiteralTestUtil::Near(expect, *actual, error_spec_)); } @@ -165,7 +166,8 @@ class MultiOutputFusionTest : public HloTestBase { Literal input1(ShapeUtil::MakeShape(F64, {size})); input1.PopulateWithValue(1.); - Literal expect = std::move(*Literal::CreateR1({size * 1.5f * 3.5f})); + Literal expect = + std::move(*LiteralUtil::CreateR1({size * 1.5f * 3.5f})); auto actual = ExecuteAndTransfer(std::move(hlo_module), {&input0, &input1}); EXPECT_TRUE(LiteralTestUtil::Near(expect, *actual, error_spec_)); } @@ -198,16 +200,16 @@ XLA_TEST_F(MultiOutputFusionTest, FusionNodeIsRoot) { auto module = HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); - auto param = Literal::MakeTupleOwned( - Literal::MakeTupleOwned( - Literal::MakeTupleOwned(Literal::CreateR0(42)), - Literal::CreateR0(1.0)), - Literal::MakeTupleOwned(Literal::CreateR0(3.0), - Literal::CreateR0(4))); + auto param = LiteralUtil::MakeTupleOwned( + LiteralUtil::MakeTupleOwned( + LiteralUtil::MakeTupleOwned(LiteralUtil::CreateR0(42)), + LiteralUtil::CreateR0(1.0)), + LiteralUtil::MakeTupleOwned(LiteralUtil::CreateR0(3.0), + LiteralUtil::CreateR0(4))); std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::MakeTupleOwned(Literal::CreateR0(42)), *result)); + *LiteralUtil::MakeTupleOwned(LiteralUtil::CreateR0(42)), *result)); } XLA_TEST_F(MultiOutputFusionTest, MultiOutputLoopFusion) { @@ -232,7 +234,7 @@ XLA_TEST_F(MultiOutputFusionTest, MultiOutputLoopFusion) { auto module = HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); - auto param = Literal::CreateR1({1.0, 2.0, 3.0, -1.0}); + auto param = LiteralUtil::CreateR1({1.0, 2.0, 3.0, -1.0}); std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); LiteralTestUtil::ExpectR1Equal({0.0, 4.0, 9.0, 1.0}, *result); @@ -265,7 +267,7 @@ XLA_TEST_F(MultiOutputFusionTest, MultiOutputLoopFeedingMap) { auto module = HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); - auto param = Literal::CreateR1({1.0, 2.0, 3.0}); + auto param = LiteralUtil::CreateR1({1.0, 2.0, 3.0}); std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); LiteralTestUtil::ExpectR1Equal({0.0, 4.0, 9.0}, *result); @@ -308,12 +310,14 @@ XLA_TEST_F(MultiOutputFusionTest, auto module = HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); - auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); + auto param = + LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::MakeTupleOwned(Literal::CreateR2({{3, 7}, {11, 15}}), - Literal::CreateR2({{5, 16}, {36, 64}})), + *LiteralUtil::MakeTupleOwned( + LiteralUtil::CreateR2({{3, 7}, {11, 15}}), + LiteralUtil::CreateR2({{5, 16}, {36, 64}})), *result)); } @@ -338,12 +342,14 @@ XLA_TEST_F(MultiOutputFusionTest, auto module = HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); - auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); + auto param = + LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::MakeTupleOwned(Literal::CreateR2({{6, 8}, {10, 12}}), - Literal::CreateR2({{25, 36}, {49, 64}})), + *LiteralUtil::MakeTupleOwned( + LiteralUtil::CreateR2({{6, 8}, {10, 12}}), + LiteralUtil::CreateR2({{25, 36}, {49, 64}})), *result)); } @@ -369,13 +375,14 @@ XLA_TEST_F(MultiOutputFusionTest, auto module = HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); - auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); + auto param = + LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::MakeTupleOwned(Literal::CreateR1({14, 22}), - Literal::CreateR1({36, 64}), - Literal::CreateR1({66, 138})), + *LiteralUtil::MakeTupleOwned(LiteralUtil::CreateR1({14, 22}), + LiteralUtil::CreateR1({36, 64}), + LiteralUtil::CreateR1({66, 138})), *result)); } @@ -401,14 +408,15 @@ XLA_TEST_F(MultiOutputFusionTest, auto module = HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); - auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); + auto param = + LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::MakeTupleOwned( - Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}), - Literal::CreateR2({{3, 7}, {11, 15}}), - Literal::CreateR2({{5, 16}, {36, 64}})), + *LiteralUtil::MakeTupleOwned( + LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}), + LiteralUtil::CreateR2({{3, 7}, {11, 15}}), + LiteralUtil::CreateR2({{5, 16}, {36, 64}})), *result)); } @@ -434,14 +442,16 @@ XLA_TEST_F(MultiOutputFusionTest, auto module = HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); - auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); + auto param = + LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::MakeTupleOwned( - Literal::CreateR2({{6, 8}, {10, 12}}), - Literal::CreateR3({{{1, 4}, {9, 16}}, {{25, 36}, {49, 64}}}), - Literal::CreateR2({{25, 36}, {49, 64}})), + *LiteralUtil::MakeTupleOwned( + LiteralUtil::CreateR2({{6, 8}, {10, 12}}), + LiteralUtil::CreateR3( + {{{1, 4}, {9, 16}}, {{25, 36}, {49, 64}}}), + LiteralUtil::CreateR2({{25, 36}, {49, 64}})), *result)); } @@ -468,14 +478,16 @@ XLA_TEST_F(MultiOutputFusionTest, auto module = HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); - auto param = Literal::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); + auto param = + LiteralUtil::CreateR3({{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::MakeTupleOwned( - Literal::CreateR1({14, 22}), - Literal::CreateR3({{{1, 4}, {9, 16}}, {{25, 36}, {49, 64}}}), - Literal::CreateR3( + *LiteralUtil::MakeTupleOwned( + LiteralUtil::CreateR1({14, 22}), + LiteralUtil::CreateR3( + {{{1, 4}, {9, 16}}, {{25, 36}, {49, 64}}}), + LiteralUtil::CreateR3( {{{5, 10}, {15, 20}}, {{25, 30}, {35, 40}}})), *result)); } @@ -502,15 +514,16 @@ XLA_TEST_F(MultiOutputFusionTest, auto module = HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); - auto param = Literal::CreateR3({{{0, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); - auto init1 = Literal::CreateR0(5); - auto init2 = Literal::CreateR0(6); + auto param = + LiteralUtil::CreateR3({{{0, 2}, {3, 4}}, {{5, 6}, {7, 8}}}); + auto init1 = LiteralUtil::CreateR0(5); + auto init2 = LiteralUtil::CreateR0(6); std::unique_ptr result = ExecuteNoHloPasses( std::move(module), {param.get(), init1.get(), init2.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::MakeTupleOwned( - Literal::CreateR2({{167, 172}, {176, 180}}), - Literal::CreateR2({{6, 6}, {6, 8}})), + *LiteralUtil::MakeTupleOwned( + LiteralUtil::CreateR2({{167, 172}, {176, 180}}), + LiteralUtil::CreateR2({{6, 6}, {6, 8}})), *result)); } @@ -537,19 +550,20 @@ XLA_TEST_F(MultiOutputFusionTest, auto module = HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); - auto param = Literal::CreateR3( + auto param = LiteralUtil::CreateR3( {{{Eigen::half(1), Eigen::half(2)}, {Eigen::half(3), Eigen::half(4)}}, {{Eigen::half(5), Eigen::half(6)}, {Eigen::half(7), Eigen::half(8)}}}); std::unique_ptr result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::MakeTupleOwned( - Literal::CreateR2({{3, 7}, {11, 15}}), - Literal::CreateR2({{5, 16}, {36, 64}}), - Literal::CreateR3({{{Eigen::half(1), Eigen::half(2)}, - {Eigen::half(3), Eigen::half(4)}}, - {{Eigen::half(5), Eigen::half(6)}, - {Eigen::half(7), Eigen::half(8)}}})), + *LiteralUtil::MakeTupleOwned( + LiteralUtil::CreateR2({{3, 7}, {11, 15}}), + LiteralUtil::CreateR2({{5, 16}, {36, 64}}), + LiteralUtil::CreateR3( + {{{Eigen::half(1), Eigen::half(2)}, + {Eigen::half(3), Eigen::half(4)}}, + {{Eigen::half(5), Eigen::half(6)}, + {Eigen::half(7), Eigen::half(8)}}})), *result)); } diff --git a/tensorflow/compiler/xla/tests/outfeed_in_nested_computation_test.cc b/tensorflow/compiler/xla/tests/outfeed_in_nested_computation_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..cea7006526f0c56ade3cedead489ea12c0ab3922 --- /dev/null +++ b/tensorflow/compiler/xla/tests/outfeed_in_nested_computation_test.cc @@ -0,0 +1,168 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/tests/local_client_test_base.h" +#include "tensorflow/core/lib/core/status_test_util.h" + +namespace xla { +namespace { + +// Tests that ensure outfeed instructions that are contained in nested +// computations in non-root positions are executed. + +class LocalClientExecuteTest : public LocalClientTestBase {}; + +TEST_F(LocalClientExecuteTest, OutfeedInWhile) { + XlaBuilder b(TestName()); + + Shape state_tuple_array_shape = ShapeUtil::MakeShape(xla::S32, {10, 5}); + Shape int_shape = ShapeUtil::MakeShape(xla::S32, {}); + Shape state_tuple_shape = + ShapeUtil::MakeTupleShape({int_shape, state_tuple_array_shape}); + Shape xfeed_shape = ShapeUtil::MakeShape(xla::S32, {2}); + + XlaOp some_buffer = Broadcast(ConstantR0(&b, 0), {10, 5}); + XlaOp num_iter = Infeed(&b, int_shape); + XlaOp init_tuple = Tuple(&b, {num_iter, some_buffer}); + + TF_ASSERT_OK_AND_ASSIGN(XlaComputation loop_cond, [&] { + // Condition: iteration variable > 0 + XlaBuilder cond_builder("loop_condition"); + XlaOp state_tuple = Parameter(&cond_builder, 0, state_tuple_shape, "state"); + XlaOp loop_counter = GetTupleElement(state_tuple, 0); + Outfeed(loop_counter, int_shape, ""); + Gt(loop_counter, ConstantR0(&cond_builder, 0)); + return cond_builder.Build(); + }()); + + TF_ASSERT_OK_AND_ASSIGN(XlaComputation loop_body, [&] { + XlaBuilder body_builder("loop_body"); + XlaOp state_tuple = Parameter(&body_builder, 0, state_tuple_shape, "state"); + XlaOp loop_counter = GetTupleElement(state_tuple, 0); + XlaOp buffer_inside = GetTupleElement(state_tuple, 1); + + // Read some stuff from Infeed. + XlaOp some_input = Infeed(&body_builder, xfeed_shape); + XlaOp sum = Add(some_input, Broadcast(loop_counter, {2})); + Outfeed(sum, xfeed_shape, ""); + + XlaOp iter_left = Sub(loop_counter, ConstantR0(&body_builder, 1)); + + Tuple(&body_builder, {iter_left, buffer_inside}); + return body_builder.Build(); + }()); + + // Build loop. + XlaOp result_tuple = While(loop_cond, loop_body, init_tuple); + GetTupleElement(result_tuple, 0); + TF_ASSERT_OK_AND_ASSIGN(XlaComputation computation, b.Build()); + + std::unique_ptr comp_result; + std::unique_ptr thread( + tensorflow::Env::Default()->StartThread( + tensorflow::ThreadOptions(), "execute_thread", [&] { + comp_result = local_client_->ExecuteAndTransfer(computation, {}) + .ConsumeValueOrDie(); + })); + + VLOG(1) << "Transferring trip count to computation"; + // Transfer number of iterations to Infeed. + TF_ASSERT_OK( + local_client_->TransferToInfeed(*LiteralUtil::CreateR0(1))); + + // Pick up value from outfeed + { + VLOG(1) << "Reading from condition outfeed"; + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr r, + local_client_->TransferFromOutfeed(&int_shape)); + EXPECT_EQ(r->Get({}), 1); + } + + VLOG(1) << "Writing data to infeed"; + // Transfer some stuff to Infeed for use inside of loop. + TF_ASSERT_OK(local_client_->TransferToInfeed( + *LiteralUtil::CreateR1({10, 20}))); + + // Pick up value from outfeed + { + VLOG(1) << "Reading from body outfeed"; + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr r, + local_client_->TransferFromOutfeed(&xfeed_shape)); + EXPECT_EQ(r->Get({0}), 11); + EXPECT_EQ(r->Get({1}), 21); + } + + { + VLOG(1) << "Reading from condition outfeed"; + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr r, + local_client_->TransferFromOutfeed(&int_shape)); + EXPECT_EQ(r->Get({}), 0); + } + + // Joins the thread + thread.reset(); + + EXPECT_EQ(comp_result->Get({}), 0); +} + +TEST_F(LocalClientExecuteTest, OutfeedInConditional) { + XlaBuilder b(TestName()); + + Shape condition_shape = ShapeUtil::MakeShape(xla::PRED, {}); + Shape result_shape = ShapeUtil::MakeShape(xla::PRED, {}); + + TF_ASSERT_OK_AND_ASSIGN(XlaComputation true_computation, [&] { + XlaBuilder inner_builder("true_computation"); + XlaOp param = Parameter(&inner_builder, 0, result_shape, "param"); + Outfeed(param, result_shape, ""); + Or(param, param); + return inner_builder.Build(); + }()); + + TF_ASSERT_OK_AND_ASSIGN(XlaComputation false_computation, [&] { + XlaBuilder inner_builder("false_computation"); + Parameter(&inner_builder, 0, result_shape, "param"); + return inner_builder.Build(); + }()); + + XlaOp pred = Infeed(&b, condition_shape); + Conditional(/*predicate=*/pred, /*true_operand=*/pred, + /*true_computation=*/true_computation, /*false_operand=*/pred, + /*false_computation=*/false_computation); + + TF_ASSERT_OK_AND_ASSIGN(XlaComputation computation, b.Build()); + + std::unique_ptr comp_result; + std::unique_ptr thread( + tensorflow::Env::Default()->StartThread( + tensorflow::ThreadOptions(), "execute_thread", [&] { + comp_result = local_client_->ExecuteAndTransfer(computation, {}) + .ConsumeValueOrDie(); + })); + + TF_ASSERT_OK( + local_client_->TransferToInfeed(*LiteralUtil::CreateR0(true))); + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr r, + local_client_->TransferFromOutfeed(&result_shape)); + + EXPECT_EQ(r->Get({}), true); + + // Join the thread + thread.reset(); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/tests/pad_test.cc b/tensorflow/compiler/xla/tests/pad_test.cc index 2e5081bbcb64ea9416c5a9731dba43891ecceedf..ca21b0b2ba590a6daadf2c8d3d9ad213514b0f0f 100644 --- a/tensorflow/compiler/xla/tests/pad_test.cc +++ b/tensorflow/compiler/xla/tests/pad_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" @@ -93,8 +93,8 @@ XLA_TEST_P(PadTestFloat, Pad1DS0ToS0Array) { dimension->set_edge_padding_high(0); dimension->set_interior_padding(0); - Pad(AddParam(*Literal::CreateR1({}), &b), - AddParam(*Literal::CreateR0(0.1), &b), padding_config); + Pad(AddParam(*LiteralUtil::CreateR1({}), &b), + AddParam(*LiteralUtil::CreateR0(0.1), &b), padding_config); ComputeAndCompareR1(&b, {}, {}, DefaultErrorSpec()); } @@ -108,8 +108,8 @@ XLA_TEST_P(PadTestFloat, Pad1DS0ToS5Array) { dimension->set_edge_padding_high(4); dimension->set_interior_padding(7); - Pad(AddParam(*Literal::CreateR1({}), &b), - AddParam(*Literal::CreateR0(0.1), &b), padding_config); + Pad(AddParam(*LiteralUtil::CreateR1({}), &b), + AddParam(*LiteralUtil::CreateR0(0.1), &b), padding_config); ComputeAndCompareR1(&b, std::vector(5, 0.1), {}, DefaultErrorSpec()); } @@ -123,8 +123,8 @@ XLA_TEST_P(PadTestFloat, Pad1DS3Array) { dimension->set_edge_padding_high(0); dimension->set_interior_padding(1); - Pad(AddParam(*Literal::CreateR1({1, 2, 3}), &b), - AddParam(*Literal::CreateR0(0.1), &b), padding_config); + Pad(AddParam(*LiteralUtil::CreateR1({1, 2, 3}), &b), + AddParam(*LiteralUtil::CreateR0(0.1), &b), padding_config); std::vector expected({0.1, 0.1, 0.1, 1, 0.1, 2, 0.1, 3}); ComputeAndCompareR1(&b, expected, {}, DefaultErrorSpec()); } @@ -132,7 +132,8 @@ XLA_TEST_P(PadTestFloat, Pad1DS3Array) { XLA_TEST_P(PadTestFloat, Pad4D_2x0x3x2_FloatArray) { XlaBuilder b(TestName()); Pad(AddParam(Array4D(2, 0, 3, 2), &b), - AddParam(*Literal::CreateR0(1.5), &b), r4_padding_on_dim0_dim1_); + AddParam(*LiteralUtil::CreateR0(1.5), &b), + r4_padding_on_dim0_dim1_); ComputeAndCompareR4(&b, Array4D(5, 2, 3, 2, 1.5f), {}, DefaultErrorSpec()); } @@ -147,7 +148,7 @@ TEST_P(PadTestFloat, Pad4DFloat_1x1x3x2_Array) { }); input->FillWithYX(input_xy); - Pad(AddParam(*input, &b), AddParam(*Literal::CreateR0(1.5), &b), + Pad(AddParam(*input, &b), AddParam(*LiteralUtil::CreateR0(1.5), &b), r4_padding_on_dim0_dim1_); auto expected = MakeUnique>(2, 3, 3, 2); @@ -166,7 +167,8 @@ TEST_P(PadTestFloat, Pad4DFloatArrayWithInteriorPadding) { const float pad_value = 1.5f; Array4D input(3, 2, 1, 1, {1, 2, 3, 4, 5, 6}); - Pad(AddParam(input, &b), AddParam(*Literal::CreateR0(pad_value), &b), + Pad(AddParam(input, &b), + AddParam(*LiteralUtil::CreateR0(pad_value), &b), r4_padding_on_dim0_dim1_); auto expected = MakeUnique>(8, 5, 1, 1); @@ -205,11 +207,11 @@ TEST_P(PadTestFloat, Pad4DFloatArrayMinorFirstSmall) { const float pad_value = -5.123f; Array4D input_array(1, 1, 2, 3, {1, 2, 3, 4, 5, 6}); - auto input = Literal::CreateR4FromArray4D(input_array); + auto input = LiteralUtil::CreateR4FromArray4D(input_array); input = input->Relayout(layout); - Pad(AddParam(*input, &b), AddParam(*Literal::CreateR0(pad_value), &b), - padding_config); + Pad(AddParam(*input, &b), + AddParam(*LiteralUtil::CreateR0(pad_value), &b), padding_config); Array4D expected_array(1, 1, 5, 8); expected_array.Fill(pad_value); @@ -251,11 +253,11 @@ XLA_TEST_P(PadTestFloat, Pad4DFloatArrayMinorFirstNonTrivialMinorDimensions) { input_array(0, 0, 0, 0) = 1.0f; input_array(0, 24, 6, 6) = 2.0f; input_array(0, 17, 2, 5) = 3.0f; - auto input = Literal::CreateR4FromArray4D(input_array); + auto input = LiteralUtil::CreateR4FromArray4D(input_array); input = input->Relayout(layout); - Pad(AddParam(*input, &b), AddParam(*Literal::CreateR0(pad_value), &b), - padding_config); + Pad(AddParam(*input, &b), + AddParam(*LiteralUtil::CreateR0(pad_value), &b), padding_config); Array4D expected_array(1, 25, 17, 11); expected_array.Fill(pad_value); @@ -329,7 +331,7 @@ XLA_TEST_P(PadTestFloat, Large2DPad) { padding_config.mutable_dimensions(dim)->set_edge_padding_high(58 + 100 * dim); } - Pad(input, AddParam(*Literal::CreateR0(0.0f), &b), padding_config); + Pad(input, AddParam(*LiteralUtil::CreateR0(0.0f), &b), padding_config); auto expected = ReferenceUtil::PadArray2D(*ones, padding_config, 0.0f); ComputeAndCompareR2(&b, *expected, {}, DefaultErrorSpec()); @@ -351,7 +353,8 @@ XLA_TEST_P(PadTestFloat, AllTypes2DPad) { padding_config.mutable_dimensions(1)->set_edge_padding_low(6); padding_config.mutable_dimensions(1)->set_edge_padding_high(4); padding_config.mutable_dimensions(1)->set_interior_padding(2); - Pad(input, AddParam(*Literal::CreateR0(3.14f), &b), padding_config); + Pad(input, AddParam(*LiteralUtil::CreateR0(3.14f), &b), + padding_config); auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 3.14f); ComputeAndCompareR2(&b, *expected, {}, DefaultErrorSpec()); @@ -376,7 +379,8 @@ XLA_TEST_P(PadTestFloat, High2DPad) { padding_config.mutable_dimensions(dim)->set_interior_padding( interior_padding); } - Pad(input, AddParam(*Literal::CreateR0(2.718f), &b), padding_config); + Pad(input, AddParam(*LiteralUtil::CreateR0(2.718f), &b), + padding_config); auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 2.718f); @@ -403,7 +407,8 @@ XLA_TEST_P(PadTestFloat, NegativePadding2D) { padding_config.mutable_dimensions(dim)->set_interior_padding( interior_padding); } - Pad(input, AddParam(*Literal::CreateR0(2.718f), &b), padding_config); + Pad(input, AddParam(*LiteralUtil::CreateR0(2.718f), &b), + padding_config); auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 2.718f); @@ -430,7 +435,8 @@ XLA_TEST_P(PadTestFloat, NegativeAndInteriorPadding2D) { padding_config.mutable_dimensions(dim)->set_interior_padding( interior_padding[dim]); } - Pad(input, AddParam(*Literal::CreateR0(2.718f), &b), padding_config); + Pad(input, AddParam(*LiteralUtil::CreateR0(2.718f), &b), + padding_config); auto expected = ReferenceUtil::PadArray2D(*operand, padding_config, 2.718f); @@ -446,12 +452,13 @@ XLA_TEST_P(PadTestFloat, ReducePad) { XlaComputation add = CreateScalarAddComputation(FloatType(), &b); auto reduce = - Reduce(input, AddParam(*Literal::CreateR0(0.0), &b), add, {0}); + Reduce(input, AddParam(*LiteralUtil::CreateR0(0.0), &b), add, {0}); PaddingConfig padding_config = MakeNoPaddingConfig(3); padding_config.mutable_dimensions(0)->set_edge_padding_low(1); padding_config.mutable_dimensions(0)->set_edge_padding_high(1); - Pad(reduce, AddParam(*Literal::CreateR0(0.0f), &b), padding_config); + Pad(reduce, AddParam(*LiteralUtil::CreateR0(0.0f), &b), + padding_config); Array3D expected({{{0.0, 0.0}, {0.0, 0.0}}, {{2.0, 2.0}, {2.0, 2.0}}, diff --git a/tensorflow/compiler/xla/tests/params_test.cc b/tensorflow/compiler/xla/tests/params_test.cc index 2620063aa492902a705690d28d8124d16184d635..f6c762e7a4bee91a26c4c2e033c3717fef6d91d0 100644 --- a/tensorflow/compiler/xla/tests/params_test.cc +++ b/tensorflow/compiler/xla/tests/params_test.cc @@ -21,10 +21,10 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" @@ -42,7 +42,8 @@ class ParamsTest : public ClientLibraryTestBase {}; XLA_TEST_F(ParamsTest, ConstantR0F32Param) { XlaBuilder builder(TestName()); - std::unique_ptr param0_literal = Literal::CreateR0(3.14159f); + std::unique_ptr param0_literal = + LiteralUtil::CreateR0(3.14159f); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -54,7 +55,7 @@ XLA_TEST_F(ParamsTest, ConstantR0F32Param) { XLA_TEST_F(ParamsTest, ConstantR1S0F32Param) { XlaBuilder builder(TestName()); - std::unique_ptr param0_literal = Literal::CreateR1({}); + std::unique_ptr param0_literal = LiteralUtil::CreateR1({}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -67,7 +68,7 @@ XLA_TEST_F(ParamsTest, ConstantR1S0F32Param) { XLA_TEST_F(ParamsTest, ConstantR1S2F32Param) { XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR1({3.14f, -100.25f}); + LiteralUtil::CreateR1({3.14f, -100.25f}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -80,7 +81,7 @@ XLA_TEST_F(ParamsTest, ConstantR1S2F32Param) { XLA_TEST_F(ParamsTest, ConstantR1U8Param) { XlaBuilder builder(TestName()); string str("hello world"); - std::unique_ptr param0_literal = Literal::CreateR1U8(str); + std::unique_ptr param0_literal = LiteralUtil::CreateR1U8(str); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -94,7 +95,7 @@ XLA_TEST_F(ParamsTest, ConstantR1U8Param) { XLA_TEST_F(ParamsTest, ConstantR2_3x0_F32Param) { XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR2FromArray2D(Array2D(3, 0)); + LiteralUtil::CreateR2FromArray2D(Array2D(3, 0)); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -106,7 +107,7 @@ XLA_TEST_F(ParamsTest, ConstantR2_3x0_F32Param) { XLA_TEST_F(ParamsTest, ConstantR2F32Param) { XlaBuilder builder(TestName()); - std::unique_ptr param0_literal = Literal::CreateR2( + std::unique_ptr param0_literal = LiteralUtil::CreateR2( {{3.14f, -100.25f}, {7e8f, 7e-9f}, {30.3f, -100.0f}}); std::unique_ptr param0_data = client_->TransferToServer(*param0_literal).ConsumeValueOrDie(); @@ -122,12 +123,12 @@ XLA_TEST_F(ParamsTest, ConstantR2F32Param) { XLA_TEST_F(ParamsTest, TwoParameters) { XlaBuilder builder(TestName()); - std::unique_ptr literal0 = Literal::CreateR1({1, 2}); + std::unique_ptr literal0 = LiteralUtil::CreateR1({1, 2}); std::unique_ptr param0_data = client_->TransferToServer(*literal0).ConsumeValueOrDie(); auto param0 = Parameter(&builder, 0, literal0->shape(), "param0"); - std::unique_ptr literal1 = Literal::CreateR1({10, 20}); + std::unique_ptr literal1 = LiteralUtil::CreateR1({10, 20}); std::unique_ptr param1_data = client_->TransferToServer(*literal1).ConsumeValueOrDie(); auto param1 = Parameter(&builder, 1, literal1->shape(), "param1"); @@ -153,7 +154,7 @@ XLA_TEST_F(ParamsTest, TwoParameters) { XLA_TEST_F(ParamsTest, MissingParameter) { // Test that an error is returned when a computation with an incomplete set of // parameters (parameter numbers not contiguous from 0) is executed. - std::unique_ptr literal = Literal::CreateR0(3.14159f); + std::unique_ptr literal = LiteralUtil::CreateR0(3.14159f); std::unique_ptr data = client_->TransferToServer(*literal).ConsumeValueOrDie(); @@ -167,12 +168,12 @@ XLA_TEST_F(ParamsTest, MissingParameter) { XLA_TEST_F(ParamsTest, UnusedParameter) { XlaBuilder builder(TestName()); - std::unique_ptr literal0 = Literal::CreateR1({1, 2}); + std::unique_ptr literal0 = LiteralUtil::CreateR1({1, 2}); std::unique_ptr param0_data = client_->TransferToServer(*literal0).ConsumeValueOrDie(); Parameter(&builder, 0, literal0->shape(), "param0"); - std::unique_ptr literal1 = Literal::CreateR1({10, 20}); + std::unique_ptr literal1 = LiteralUtil::CreateR1({10, 20}); std::unique_ptr param1_data = client_->TransferToServer(*literal1).ConsumeValueOrDie(); Parameter(&builder, 1, literal1->shape(), "param1"); @@ -187,11 +188,12 @@ XLA_TEST_F(ParamsTest, UnusedParametersInUnusedExpression) { // unused expression. XlaBuilder builder(TestName()); - std::unique_ptr literal0 = Literal::CreateR1({1, 2}); + std::unique_ptr literal0 = LiteralUtil::CreateR1({1, 2}); std::unique_ptr param0_data = client_->TransferToServer(*literal0).ConsumeValueOrDie(); - std::unique_ptr literal1 = Literal::CreateR1({10, 20, 30}); + std::unique_ptr literal1 = + LiteralUtil::CreateR1({10, 20, 30}); std::unique_ptr param1_data = client_->TransferToServer(*literal1).ConsumeValueOrDie(); @@ -231,7 +233,7 @@ XLA_TEST_F(ParamsTest, HundredLargeR1Parameters) { std::vector sum_value = {{entry0, entry1}}; sum_value.resize(size); - std::unique_ptr literal = Literal::CreateR1(sum_value); + std::unique_ptr literal = LiteralUtil::CreateR1(sum_value); param_data_owner.push_back( client_->TransferToServer(*literal).ConsumeValueOrDie()); XlaOp param = Parameter(&builder, i, literal->shape(), "param"); @@ -266,7 +268,7 @@ XLA_TEST_F(ParamsTest, constexpr int kParamCount = 3000; for (int i = 0; i < kParamCount; ++i) { target += i; - std::unique_ptr literal = Literal::CreateR0(i); + std::unique_ptr literal = LiteralUtil::CreateR0(i); param_data_owner.push_back( std::move(client_->TransferToServer(*literal)).ValueOrDie()); XlaOp param = Parameter(&builder, i, literal->shape(), "param"); @@ -298,7 +300,7 @@ XLA_TEST_F(ParamsTest, DISABLED_ON_CPU(DISABLED_ON_GPU( std::vector params; for (int i = 0; i < kParamCount; ++i) { target += i; - std::unique_ptr literal = Literal::CreateR1({i, i}); + std::unique_ptr literal = LiteralUtil::CreateR1({i, i}); param_data_owner.push_back( std::move(client_->TransferToServer(*literal)).ValueOrDie()); XlaOp param = Parameter(&builder, i, literal->shape(), "param"); @@ -322,10 +324,10 @@ XLA_TEST_F(ParamsTest, DISABLED_ON_CPU(DISABLED_ON_GPU( std::vector> elements; std::vector ptrs; for (int i = 0; i < kParamCount; ++i) { - elements.push_back(Literal::CreateR1({target + i, target + i})); + elements.push_back(LiteralUtil::CreateR1({target + i, target + i})); ptrs.push_back(elements.back().get()); } - ComputeAndCompareTuple(&builder, *Literal::MakeTuple(ptrs), param_data); + ComputeAndCompareTuple(&builder, *LiteralUtil::MakeTuple(ptrs), param_data); } // Test large number of parameters flowing into a while-loop. @@ -354,7 +356,7 @@ XLA_TEST_F(ParamsTest, std::vector params; std::vector parameter_shapes; for (int i = 0; i < kParamCount; ++i) { - std::unique_ptr literal = Literal::CreateR1({i, i}); + std::unique_ptr literal = LiteralUtil::CreateR1({i, i}); param_data_owner.push_back( std::move(client_->TransferToServer(*literal)).ValueOrDie()); XlaOp param = Parameter(&builder, i, literal->shape(), "param"); @@ -364,7 +366,7 @@ XLA_TEST_F(ParamsTest, // Add bool parameter for the loop condition. Use a parameter HLO instead of a // constant because DCE may eliminate the while-body otherwise. - std::unique_ptr bool_literal = Literal::CreateR0(false); + std::unique_ptr bool_literal = LiteralUtil::CreateR0(false); param_data_owner.push_back( std::move(client_->TransferToServer(*bool_literal)).ValueOrDie()); XlaOp bool_param = @@ -421,10 +423,10 @@ XLA_TEST_F(ParamsTest, std::vector> elements; std::vector ptrs; for (int i = 0; i < kParamCount; ++i) { - elements.push_back(Literal::CreateR1({i, i})); + elements.push_back(LiteralUtil::CreateR1({i, i})); ptrs.push_back(elements.back().get()); } - ComputeAndCompareTuple(&builder, *Literal::MakeTuple(ptrs), param_data); + ComputeAndCompareTuple(&builder, *LiteralUtil::MakeTuple(ptrs), param_data); } #endif @@ -441,9 +443,9 @@ XLA_TEST_F(ParamsTest, TupleOfR1ParametersAddedTogether) { std::unique_ptr data = client_ - ->TransferToServer(*Literal::MakeTuple({ - Literal::CreateR1({1, 2, 3}).get(), - Literal::CreateR1({4, 5, 6}).get(), + ->TransferToServer(*LiteralUtil::MakeTuple({ + LiteralUtil::CreateR1({1, 2, 3}).get(), + LiteralUtil::CreateR1({4, 5, 6}).get(), })) .ConsumeValueOrDie(); @@ -455,7 +457,7 @@ XLA_TEST_F(ParamsTest, TupleOfR1ParametersAddedTogether) { // Verifies that passing a 2x2 with {0, 1} layout returns the same value back // when (transferred to the server and) passed through a parameter. XLA_TEST_F(ParamsTest, R2_2x2_Layout_01) { - std::unique_ptr literal = Literal::CreateR2WithLayout( + std::unique_ptr literal = LiteralUtil::CreateR2WithLayout( {{1, 2}, {3, 4}}, LayoutUtil::MakeLayout({0, 1})); XlaBuilder builder(TestName()); Parameter(&builder, 0, literal->shape(), "input"); @@ -467,7 +469,7 @@ XLA_TEST_F(ParamsTest, R2_2x2_Layout_01) { // As above, but for {1, 0} layout. XLA_TEST_F(ParamsTest, R2_2x2_Layout_10) { - std::unique_ptr literal = Literal::CreateR2WithLayout( + std::unique_ptr literal = LiteralUtil::CreateR2WithLayout( {{1, 3}, {2, 4}}, LayoutUtil::MakeLayout({1, 0})); XlaBuilder builder(TestName()); Parameter(&builder, 0, literal->shape(), "input"); @@ -478,7 +480,7 @@ XLA_TEST_F(ParamsTest, R2_2x2_Layout_10) { } XLA_TEST_F(ParamsTest, R2_2x2_TryToPassReverseLayoutToParameter) { - std::unique_ptr literal = Literal::CreateR2({ + std::unique_ptr literal = LiteralUtil::CreateR2({ {1, 3}, {2, 4}, }); diff --git a/tensorflow/compiler/xla/tests/pred_test.cc b/tensorflow/compiler/xla/tests/pred_test.cc index 5c351b2d113709105244de4aafa49d7cc535ced1..2fc7f816b56db6f57ca835d1847476b6d622ce5e 100644 --- a/tensorflow/compiler/xla/tests/pred_test.cc +++ b/tensorflow/compiler/xla/tests/pred_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/test.h" diff --git a/tensorflow/compiler/xla/tests/prng_test.cc b/tensorflow/compiler/xla/tests/prng_test.cc index 8e163e885d0d6315341c213577a3beb0180b679a..029af69573e458a45cf1e446e942c7401cd9e629 100644 --- a/tensorflow/compiler/xla/tests/prng_test.cc +++ b/tensorflow/compiler/xla/tests/prng_test.cc @@ -17,8 +17,8 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test.h" @@ -177,7 +177,7 @@ XLA_TEST_F(PrngTest, Uniformity108) { EXPECT_LT(UniformChiSquared(108, 256), 132.144); } XLA_TEST_F(PrngTest, Uniformity256) { - EXPECT_LT(UniformChiSquared(256, 256), 293.248); + EXPECT_LT(UniformChiSquared(256, 512), 293.248); } XLA_TEST_F(PrngTest, MapUsingRng) { @@ -193,7 +193,7 @@ XLA_TEST_F(PrngTest, MapUsingRng) { XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR1({2.2f, 5.3f, 4.4f, 5.5f}); + LiteralUtil::CreateR1({2.2f, 5.3f, 4.4f, 5.5f}); TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr param0_data, client_->TransferToServer(*param0_literal)); diff --git a/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc b/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc index 526a38e8d1dbed9cdd4a31bfbec49bc5c6bb174b..fab2a65de109c670a6854c0fc1118162acf3d312 100644 --- a/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc +++ b/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test_helpers.h" diff --git a/tensorflow/compiler/xla/tests/reduce_hlo_test.cc b/tensorflow/compiler/xla/tests/reduce_hlo_test.cc index 9052b188ed09a715b6ad7c3a40dc853d02cdd70c..a080dd1732bde21712cf47b4b57538cf4040f30e 100644 --- a/tensorflow/compiler/xla/tests/reduce_hlo_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_hlo_test.cc @@ -95,21 +95,21 @@ XLA_TEST_P(ReduceWithLayoutTest, DISABLED_ON_GPU(Reduce)) { *reduce_input_shape->mutable_layout() = LayoutUtil::MakeLayout(reduce_layout.input_minor_to_major); - std::unique_ptr reduce_input = - Literal::CreateR4({{ /*i0=0*/ - {/*i1=0*/ - {-0.246092796, -0.179497838, -0.161181688}, - {-0.151643038, -0.240213156, -0.198156}}, - {/*i1=1*/ - {-0.14222312, -0.162200093, -0.193907976}, - {-0.239411, -0.198166847, -0.172471642}}}, - { /*i0=1*/ - {/*i1=0*/ - {-0.22965157, -0.218723893, -0.129257083}, - {-0.188762426, -0.16123569, -0.181166649}}, - {/*i1=1*/ - {-0.241772294, -0.245131493, -0.160247207}, - {-0.179881215, -0.23383224, -0.121976733}}}}); + std::unique_ptr reduce_input = LiteralUtil::CreateR4( + {{ /*i0=0*/ + {/*i1=0*/ + {-0.246092796, -0.179497838, -0.161181688}, + {-0.151643038, -0.240213156, -0.198156}}, + {/*i1=1*/ + {-0.14222312, -0.162200093, -0.193907976}, + {-0.239411, -0.198166847, -0.172471642}}}, + { /*i0=1*/ + {/*i1=0*/ + {-0.22965157, -0.218723893, -0.129257083}, + {-0.188762426, -0.16123569, -0.181166649}}, + {/*i1=1*/ + {-0.241772294, -0.245131493, -0.160247207}, + {-0.179881215, -0.23383224, -0.121976733}}}}); EXPECT_TRUE(RunAndCompareNoHloPasses(std::move(module), ErrorSpec(1e-5))); } diff --git a/tensorflow/compiler/xla/tests/reduce_precision_test.cc b/tensorflow/compiler/xla/tests/reduce_precision_test.cc index 4c1aa121067eed465c6128ea7a34e0284f7af43e..531648fe3eb8e3941c5e3c012847ee68c616590f 100644 --- a/tensorflow/compiler/xla/tests/reduce_precision_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_precision_test.cc @@ -22,9 +22,9 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/reduce_precision_insertion.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" @@ -230,7 +230,8 @@ XLA_TEST_P(ReducePrecisionAccuracyTest, ReducePrecisionF32) { XlaBuilder builder(TestName()); - std::unique_ptr a_literal = Literal::CreateR1({input_values}); + std::unique_ptr a_literal = + LiteralUtil::CreateR1({input_values}); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); auto a = Parameter(&builder, 0, a_literal->shape(), "a"); @@ -253,7 +254,7 @@ XLA_TEST_F(ReducePrecisionInsertionTest, DISABLED_ON_INTERPRETER(ReducePrecisionBeforeFusion)) { XlaBuilder builder(TestName()); - std::unique_ptr a_literal = Literal::CreateR1({1.00001}); + std::unique_ptr a_literal = LiteralUtil::CreateR1({1.00001}); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); auto a = Parameter(&builder, 0, a_literal->shape(), "a"); @@ -282,7 +283,7 @@ XLA_TEST_F(ReducePrecisionInsertionTest, DISABLED_ON_INTERPRETER(ReducePrecisionSkippedAfterFusion)) { XlaBuilder builder(TestName()); - std::unique_ptr a_literal = Literal::CreateR1({1.00001}); + std::unique_ptr a_literal = LiteralUtil::CreateR1({1.00001}); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); auto a = Parameter(&builder, 0, a_literal->shape(), "a"); @@ -308,7 +309,7 @@ XLA_TEST_F(ReducePrecisionInsertionTest, DISABLED_ON_INTERPRETER(ReducePrecisionAddedAfterFusion)) { XlaBuilder builder(TestName()); - std::unique_ptr a_literal = Literal::CreateR1({1.00001}); + std::unique_ptr a_literal = LiteralUtil::CreateR1({1.00001}); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); auto a = Parameter(&builder, 0, a_literal->shape(), "a"); @@ -332,7 +333,7 @@ XLA_TEST_F(ReducePrecisionInsertionTest, DISABLED_ON_INTERPRETER(ReducePrecisionSkippedFusionContains)) { XlaBuilder builder(TestName()); - std::unique_ptr a_literal = Literal::CreateR1({1.00001}); + std::unique_ptr a_literal = LiteralUtil::CreateR1({1.00001}); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); auto a = Parameter(&builder, 0, a_literal->shape(), "a"); @@ -357,7 +358,7 @@ XLA_TEST_F(ReducePrecisionInsertionTest, DISABLED_ON_INTERPRETER(ReducePrecisionAddedFusionContains)) { XlaBuilder builder(TestName()); - std::unique_ptr a_literal = Literal::CreateR1({1.00001}); + std::unique_ptr a_literal = LiteralUtil::CreateR1({1.00001}); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); auto a = Parameter(&builder, 0, a_literal->shape(), "a"); diff --git a/tensorflow/compiler/xla/tests/reduce_test.cc b/tensorflow/compiler/xla/tests/reduce_test.cc index c9f57cbb16729627a5e9ad3d49438295a286989e..2065271a7f686c52c88df80b0efe8f2e1542d198 100644 --- a/tensorflow/compiler/xla/tests/reduce_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_test.cc @@ -37,8 +37,8 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/reference_util.h" @@ -67,12 +67,12 @@ class ReduceTest : public ClientLibraryTestBase { ReduceTest() { // Implementation note: laid out z >> y >> x by default. // clang-format off - literal_2d_ = Literal::CreateR2({ + literal_2d_ = LiteralUtil::CreateR2({ // x0 x1 x2 { 1.f, 2.f, 3.f}, // y0 { 4.f, 5.f, 6.f}, // y1 }); - literal_3d_ = Literal::CreateR3Projected({ + literal_3d_ = LiteralUtil::CreateR3Projected({ // x0 x1 x2 { 1.f, 2.f, 3.f}, // y0 { 4.f, 5.f, 6.f}, // y1 @@ -101,7 +101,7 @@ class ReduceTest : public ClientLibraryTestBase { } } std::unique_ptr input_literal = - Literal::CreateR1(AsSlice(input_data)); + LiteralUtil::CreateR1(AsSlice(input_data)); std::unique_ptr input_global_data = client_->TransferToServer(*input_literal).ConsumeValueOrDie(); @@ -125,15 +125,15 @@ class ReduceTest : public ClientLibraryTestBase { XlaComputation reduce; if (and_reduce) { init_value = ConstantR0(&builder, true); - reduce = CreateScalarAndComputation(&builder); + reduce = CreateScalarAndComputation(PRED, &builder); } else { init_value = ConstantR0(&builder, false); - reduce = CreateScalarOrComputation(&builder); + reduce = CreateScalarOrComputation(PRED, &builder); } Reduce(pred_values, init_value, reduce, /*dimensions_to_reduce=*/{0}); - std::unique_ptr input_literal = Literal::CreateR1(input_data); + std::unique_ptr input_literal = LiteralUtil::CreateR1(input_data); std::unique_ptr input_global_data = client_->TransferToServer(*input_literal).ConsumeValueOrDie(); @@ -163,10 +163,10 @@ class ReduceTest : public ClientLibraryTestBase { XlaComputation reduce_op; if (and_reduce) { init_value = ConstantR0(&builder, true); - reduce_op = CreateScalarAndComputation(&builder); + reduce_op = CreateScalarAndComputation(PRED, &builder); } else { init_value = ConstantR0(&builder, false); - reduce_op = CreateScalarOrComputation(&builder); + reduce_op = CreateScalarOrComputation(PRED, &builder); } Reduce(input_pred, init_value, reduce_op, @@ -175,7 +175,7 @@ class ReduceTest : public ClientLibraryTestBase { Array2D input_data(rows, cols); input_data.FillRandom(0, 1); std::unique_ptr input_literal = - Literal::CreateR2FromArray2D(input_data); + LiteralUtil::CreateR2FromArray2D(input_data); input_literal = input_literal->Relayout(LayoutUtil::MakeLayout({minor, major})); std::unique_ptr input_global_data = @@ -209,7 +209,7 @@ class ReduceTest : public ClientLibraryTestBase { Array2D input_data(rows, cols); input_data.FillRandom(3.14f, 0.04); std::unique_ptr input_literal = - Literal::CreateR2FromArray2D(input_data); + LiteralUtil::CreateR2FromArray2D(input_data); input_literal = input_literal->Relayout(LayoutUtil::MakeLayout({minor, major})); std::unique_ptr input_global_data = @@ -237,7 +237,7 @@ class ReduceTest : public ClientLibraryTestBase { Array2D input_data(rows, cols); input_data.FillRandom(3.14f, 0.04); std::unique_ptr input_literal = - Literal::CreateR2FromArray2D(input_data); + LiteralUtil::CreateR2FromArray2D(input_data); input_literal = input_literal->Relayout(LayoutUtil::MakeLayout({minor, major})); std::unique_ptr input_global_data = @@ -295,7 +295,7 @@ class ReduceTest : public ClientLibraryTestBase { Array2D input_data(rows, cols); input_data.FillUnique(initial_value); std::unique_ptr input_literal = - Literal::CreateR2FromArray2D(input_data); + LiteralUtil::CreateR2FromArray2D(input_data); input_literal = input_literal->Relayout(LayoutUtil::MakeLayout({minor, major})); std::unique_ptr input_global_data = @@ -450,7 +450,7 @@ XLA_TEST_F(ReduceTest, ReduceElementwiseR2_111x50_To_R1) { Array2D input_data(rows, cols); input_data.FillRandom(3.14f, 0.04); std::unique_ptr input_literal = - Literal::CreateR2FromArray2D(input_data); + LiteralUtil::CreateR2FromArray2D(input_data); input_literal = input_literal->Relayout(LayoutUtil::MakeLayout({0, 1})); std::unique_ptr input_global_data = client_->TransferToServer(*input_literal).ConsumeValueOrDie(); @@ -482,7 +482,7 @@ XLA_TEST_F(ReduceTest, TransposeAndReduceElementwiseR2_111x50_To_R1) { Array2D input_data(rows, cols); input_data.FillRandom(3.14f, 0.04); std::unique_ptr input_literal = - Literal::CreateR2FromArray2D(input_data); + LiteralUtil::CreateR2FromArray2D(input_data); input_literal = input_literal->Relayout(LayoutUtil::MakeLayout({0, 1})); std::unique_ptr input_global_data = client_->TransferToServer(*input_literal).ConsumeValueOrDie(); @@ -531,7 +531,7 @@ XLA_TEST_F(ReduceTest, Reshape_111x2x25Reduce_111x50_To_R1) { Array3D input_data(rows, 2, cols / 2); input_data.FillRandom(3.14f, 0.04); std::unique_ptr input_literal = - Literal::CreateR3FromArray3D(input_data); + LiteralUtil::CreateR3FromArray3D(input_data); std::unique_ptr input_global_data = client_->TransferToServer(*input_literal).ConsumeValueOrDie(); @@ -594,7 +594,7 @@ XLA_TEST_F(ReduceTest, MaxReduce2DToR0) { auto max = CreateScalarMaxComputation(F32, &builder); Array2D input(300, 250); input.FillRandom(214.0f); - auto input_literal = Literal::CreateR2FromArray2D(input); + auto input_literal = LiteralUtil::CreateR2FromArray2D(input); Reduce(ConstantLiteral(&builder, *input_literal), ConstantR0(&builder, FLT_MIN), max, {0, 1}); auto input_max = FLT_MIN; @@ -609,7 +609,7 @@ XLA_TEST_F(ReduceTest, MinReduce2DToR0) { auto min = CreateScalarMinComputation(F32, &builder); Array2D input(150, 130); input.FillRandom(214.0f); - auto input_literal = Literal::CreateR2FromArray2D(input); + auto input_literal = LiteralUtil::CreateR2FromArray2D(input); Reduce(ConstantLiteral(&builder, *input_literal), ConstantR0(&builder, FLT_MAX), min, {0, 1}); @@ -623,7 +623,7 @@ XLA_TEST_F(ReduceTest, UnsignedInt_MinReduce) { XlaBuilder builder(TestName()); Array2D input({{1}, {2}}); auto min = CreateScalarMinComputation(U32, &builder); - auto input_literal = Literal::CreateR2FromArray2D(input); + auto input_literal = LiteralUtil::CreateR2FromArray2D(input); auto initial_value = ConstantR0(&builder, std::numeric_limits::max()); @@ -635,7 +635,7 @@ XLA_TEST_F(ReduceTest, UnsignedInt_MaxReduce) { XlaBuilder builder(TestName()); Array2D input({{1}, {2}}); auto max = CreateScalarMaxComputation(U32, &builder); - auto input_literal = Literal::CreateR2FromArray2D(input); + auto input_literal = LiteralUtil::CreateR2FromArray2D(input); auto initial_value = ConstantR0(&builder, std::numeric_limits::min()); @@ -798,13 +798,17 @@ XLA_TEST_F(ReduceTest, VectorizedReduce_Min) { XLA_TEST_F(ReduceTest, VectorizedReduce_BooleanAnd) { RunVectorizedReduceTestForType( - static_cast(CreateScalarAndComputation), + static_cast([](XlaBuilder* builder) { + return CreateScalarAndComputation(PRED, builder); + }), [](bool a, bool b) { return a && b; }, true); } XLA_TEST_F(ReduceTest, VectorizedReduce_BooleanOr) { RunVectorizedReduceTestForType( - static_cast(CreateScalarOrComputation), + static_cast([](XlaBuilder* builder) { + return CreateScalarOrComputation(PRED, builder); + }), [](bool a, bool b) { return a || b; }, false); } @@ -818,7 +822,7 @@ XLA_TEST_P(ReduceR3ToR2Test, ReduceR3ToR2) { // input_array.FillRandom(3.14f, 0.05); input_array.Fill(1.0f); - auto input_literal = Literal::CreateR3FromArray3D(input_array); + auto input_literal = LiteralUtil::CreateR3FromArray3D(input_array); input_literal = input_literal->Relayout(LayoutUtil::MakeLayout(GetParam().layout)); std::unique_ptr input_data = @@ -872,7 +876,8 @@ XLA_TEST_F(ReduceTest, DISABLED_ON_GPU(OperationOnConstantAsInitValue)) { auto a = ConstantR0(&builder, 2.0f); auto a2 = Abs(a); - std::unique_ptr b_literal = Literal::CreateR1({1.0f, 4.0f}); + std::unique_ptr b_literal = + LiteralUtil::CreateR1({1.0f, 4.0f}); std::unique_ptr b_data = client_->TransferToServer(*b_literal).ConsumeValueOrDie(); auto b = Parameter(&builder, 0, b_literal->shape(), "b"); @@ -900,7 +905,7 @@ class ReduceInitializerTest : public ReduceTest { auto init = ConstantR0(&builder, initializer); std::vector input_arr(num_elems, std::numeric_limits::lowest()); - auto input_literal = Literal::CreateR1(input_arr); + auto input_literal = LiteralUtil::CreateR1(input_arr); auto input_data = client_->TransferToServer(*input_literal).ConsumeValueOrDie(); Reduce(Parameter(&builder, 0, input_literal->shape(), "input"), init, @@ -950,10 +955,11 @@ XLA_TEST_F(ReduceTest, ReduceIdentity) { float operand[] = {42.0f}; float init = 58.5f; float expected = 42.0f; - std::unique_ptr input_literal = Literal::CreateR1(operand); + std::unique_ptr input_literal = + LiteralUtil::CreateR1(operand); std::unique_ptr input_global_data = client_->TransferToServer(*input_literal).ConsumeValueOrDie(); - std::unique_ptr input_literal2 = Literal::CreateR0(init); + std::unique_ptr input_literal2 = LiteralUtil::CreateR0(init); std::unique_ptr input_global_data2 = client_->TransferToServer(*input_literal2).ConsumeValueOrDie(); ComputeAndCompareR0( @@ -961,5 +967,32 @@ XLA_TEST_F(ReduceTest, ReduceIdentity) { ErrorSpec(0.0001)); } +XLA_TEST_F(ReduceTest, AndReduceU64) { + XlaBuilder builder(TestName()); + Array2D initializer = {{0x123456789ABCDEF0LL, 0x3BCDEF12A4567890LL}, + {0XFFFFFFFFFFFFFFD6LL, 101}, + {1, 0XFFFFFFFFFFFFFFFFLL}}; + auto reducer = CreateScalarAndComputation(U64, &builder); + auto m = ConstantR2FromArray2D(&builder, initializer); + Reduce(m, ConstantR0(&builder, 0xFFFFFFFFFFFFFFFFLL), reducer, {1}); + + std::vector expected = {0x1204461080145890LL, 68, 1}; + ComputeAndCompareR1(&builder, expected, {}); +} + +XLA_TEST_F(ReduceTest, OrReduceU64) { + XlaBuilder builder(TestName()); + Array2D initializer = {{0x123456789ABCDEF0LL, 0x3BCDEF12A4567890LL}, + {0xFFFFFFFFFFFFFFD6LL, 101}, + {1, 0xCAFEBEEFABABABABLL}}; + auto reducer = CreateScalarOrComputation(U64, &builder); + auto m = ConstantR2FromArray2D(&builder, initializer); + Reduce(m, ConstantR0(&builder, 0), reducer, {1}); + + std::vector expected = {0X3BFDFF7ABEFEFEF0LL, 0XFFFFFFFFFFFFFFF7LL, + 0xCAFEBEEFABABABABLL}; + ComputeAndCompareR1(&builder, expected, {}); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc index 741974480c6a862a7794aa6257f131a5893e963d..1bd6fdab31d6c3516339bdb98459ffe3bbdef1d1 100644 --- a/tensorflow/compiler/xla/tests/reduce_window_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc @@ -24,8 +24,8 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" @@ -70,8 +70,8 @@ class ReduceWindowTest : public ::testing::WithParamInterface, tensorflow::gtl::ArraySlice window_dimensions, tensorflow::gtl::ArraySlice window_strides, Padding padding) { - auto init = - CreateConstantFromLiteral(*Literal::CreateR0(0.0f), &builder_); + auto init = CreateConstantFromLiteral(*LiteralUtil::CreateR0(0.0f), + &builder_); ReduceWindow(input, init, CreateScalarAddComputation(FloatType(), &builder_), window_dimensions, window_strides, padding); @@ -81,7 +81,8 @@ class ReduceWindowTest : public ::testing::WithParamInterface, tensorflow::gtl::ArraySlice window_dimensions, tensorflow::gtl::ArraySlice window_strides, Padding padding) { - auto init = CreateConstantFromLiteral(Literal::MinValue(F32), &builder_); + auto init = + CreateConstantFromLiteral(LiteralUtil::MinValue(F32), &builder_); ReduceWindow(input, init, CreateScalarMaxComputation(FloatType(), &builder_), window_dimensions, window_strides, padding); @@ -91,7 +92,8 @@ class ReduceWindowTest : public ::testing::WithParamInterface, tensorflow::gtl::ArraySlice window_dimensions, tensorflow::gtl::ArraySlice window_strides, Padding padding) { - auto init = CreateConstantFromLiteral(Literal::MaxValue(F32), &builder_); + auto init = + CreateConstantFromLiteral(LiteralUtil::MaxValue(F32), &builder_); ReduceWindow(input, init, CreateScalarMinComputation(FloatType(), &builder_), window_dimensions, window_strides, padding); @@ -102,9 +104,9 @@ class ReduceWindowTest : public ::testing::WithParamInterface, TEST_P(ReduceWindowTest, MismatchedRanksGivesErrorStatus) { const auto input = CreateConstantFromLiteral( - *Literal::CreateR1({1, 1, 1, 1}), &builder_); + *LiteralUtil::CreateR1({1, 1, 1, 1}), &builder_); const auto init_value = - CreateConstantFromLiteral(*Literal::CreateR0(0), &builder_); + CreateConstantFromLiteral(*LiteralUtil::CreateR0(0), &builder_); TF_ASSERT_OK(builder_.first_error()); ReduceWindow(input, init_value, CreateScalarAddComputation(FloatType(), &builder_), @@ -119,32 +121,32 @@ TEST_P(ReduceWindowTest, MismatchedRanksGivesErrorStatus) { // Regression test for b/68964348. TEST_P(ReduceWindowTest, R0ReduceWindow) { const auto input = - CreateConstantFromLiteral(*Literal::CreateR0(42.0), &builder_); + CreateConstantFromLiteral(*LiteralUtil::CreateR0(42.0), &builder_); const auto init = - CreateConstantFromLiteral(*Literal::CreateR0(1.0), &builder_); + CreateConstantFromLiteral(*LiteralUtil::CreateR0(1.0), &builder_); ReduceWindow(input, init, CreateScalarAddComputation(FloatType(), &builder_), /*window_dimensions=*/{}, /*window_strides=*/{}, Padding::kSame); - ComputeAndCompareLiteral(&builder_, *Literal::CreateR0(43.0), {}, + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateR0(43.0), {}, ErrorSpec(0.00001)); } TEST_P(ReduceWindowTest, Min3In5Stride2) { const auto input = CreateConstantFromLiteral( - *Literal::CreateR1({10000, 1000, 100, 10, 1}), &builder_); + *LiteralUtil::CreateR1({10000, 1000, 100, 10, 1}), &builder_); ReduceWindowMin(input, {3}, {2}, Padding::kValid); - ComputeAndCompareLiteral(&builder_, *Literal::CreateR1({100, 1}), {}, - ErrorSpec(0.00001)); + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateR1({100, 1}), + {}, ErrorSpec(0.00001)); } TEST_P(ReduceWindowTest, Min3In5Stride1WithSamePadding) { const auto input = CreateConstantFromLiteral( - *Literal::CreateR1({10000, 1000, 100, 10, 1}), &builder_); + *LiteralUtil::CreateR1({10000, 1000, 100, 10, 1}), &builder_); ReduceWindowMin(input, /*window_dimensions=*/{3}, /*window_strides=*/{1}, Padding::kSame); ComputeAndCompareLiteral(&builder_, - *Literal::CreateR1({1000, 100, 10, 1, 1}), {}, - ErrorSpec(0.00001)); + *LiteralUtil::CreateR1({1000, 100, 10, 1, 1}), + {}, ErrorSpec(0.00001)); } XLA_TEST_P(ReduceWindowTest, ZeroElementSmall) { @@ -156,7 +158,7 @@ XLA_TEST_P(ReduceWindowTest, ZeroElementSmall) { auto res = ReferenceUtil::ReduceWindow4DAdd(input_array, 0.0f, {1, 1, 2, 1}, {1, 1, 1, 1}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*res), {}, + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateFromArray(*res), {}, DefaultErrorSpec()); } @@ -171,7 +173,7 @@ TEST_P(ReduceWindowTest, NonSquareSmall) { auto res = ReferenceUtil::ReduceWindow4DAdd(input_array, 0.0f, {1, 1, 2, 1}, {1, 1, 1, 1}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*res), {}, + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateFromArray(*res), {}, DefaultErrorSpec()); } @@ -185,7 +187,7 @@ TEST_P(ReduceWindowTest, MiddleDimsSmall) { auto res = ReferenceUtil::ReduceWindow4DAdd(input_array, 0.0f, {1, 1, 1, 1}, {1, 2, 2, 1}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*res), {}, + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateFromArray(*res), {}, DefaultErrorSpec()); } @@ -202,7 +204,7 @@ TEST_P(ReduceWindowTest, Along2ndMinorDim) { auto res = ReferenceUtil::ReduceWindow4DAdd( input_array, 0.0f, {1, 1, lrn_diameter, 1}, {1, 1, 1, 1}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*res), {}, + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateFromArray(*res), {}, DefaultErrorSpec()); } @@ -224,8 +226,8 @@ TEST_P(ReduceWindowTest, AmongMajor2Dims) { input_array, 0.0f, {win_len, win_len, 1, 1}, {win_stride, win_stride, 1, 1}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*result), {}, - DefaultErrorSpec()); + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateFromArray(*result), + {}, DefaultErrorSpec()); } TEST_P(ReduceWindowTest, AmongMajor2DimsMediumSize) { @@ -247,8 +249,8 @@ TEST_P(ReduceWindowTest, AmongMajor2DimsMediumSize) { input_array, 0.0f, {win_len, win_len, 1, 1}, {win_stride, win_stride, 1, 1}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*result), {}, - DefaultErrorSpec()); + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateFromArray(*result), + {}, DefaultErrorSpec()); } // Tests the super windowing logic w.r.t handling prime number of windows in a @@ -272,8 +274,8 @@ TEST_P(ReduceWindowTest, PrimeWindowsInReductionDimension) { input_array, 0.0f, {win_len, win_len, 1, 1}, {win_stride, win_stride, 1, 1}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*result), {}, - DefaultErrorSpec()); + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateFromArray(*result), + {}, DefaultErrorSpec()); } TEST_P(ReduceWindowTest, ReduceAlongLaneDimension) { @@ -289,8 +291,8 @@ TEST_P(ReduceWindowTest, ReduceAlongLaneDimension) { auto result = ReferenceUtil::ReduceWindow4DAdd( input_array, 0.0f, {1, 1, 1, 11}, {1, 1, 1, 1}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*result), {}, - DefaultErrorSpec()); + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateFromArray(*result), + {}, DefaultErrorSpec()); } // Tests a reduction function that is not a simple add/min/max/etc. @@ -308,12 +310,12 @@ XLA_TEST_P(ReduceWindowTest, NonstandardReduceFunction) { auto lhs = Parameter(b.get(), 0, scalar, "lhs"); auto rhs = Parameter(b.get(), 1, scalar, "rhs"); Min(Add(lhs, rhs), - CreateConstantFromLiteral(*Literal::CreateR0(8.0f), b.get())); + CreateConstantFromLiteral(*LiteralUtil::CreateR0(8.0f), b.get())); XlaComputation reduce_fn = b->BuildAndNoteError(); ReduceWindow( input, - CreateConstantFromLiteral(*Literal::CreateR0(0.0f), &builder_), + CreateConstantFromLiteral(*LiteralUtil::CreateR0(0.0f), &builder_), reduce_fn, /*window_dimensions=*/{1, 1, 2, 1}, /*window_strides=*/{1, 1, 1, 1}, padding); @@ -327,15 +329,15 @@ XLA_TEST_P(ReduceWindowTest, NonstandardReduceFunction) { /*window=*/{1, 1, 2, 1}, /*stride=*/{1, 1, 1, 1}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*expected), {}, - DefaultErrorSpec()); + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateFromArray(*expected), + {}, DefaultErrorSpec()); } TEST_P(ReduceWindowTest, R4UnitWindow) { Array4D input_array(13, 12, 8, 15); input_array.FillRandom(2.f, 2.f); std::unique_ptr input_literal = - Literal::CreateR4FromArray4DWithLayout( + LiteralUtil::CreateR4FromArray4DWithLayout( input_array, LayoutUtil::MakeLayout({0, 3, 2, 1})); XlaOp input; auto input_data = CreateParameterAndTransferLiteral( @@ -347,7 +349,7 @@ TEST_P(ReduceWindowTest, R4UnitWindow) { auto res = ReferenceUtil::ReduceWindow4DAdd(input_array, 0.0f, {1, 1, 7, 1}, {1, 4, 1, 1}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*res), + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateFromArray(*res), {input_data.get()}, DefaultErrorSpec()); } @@ -376,7 +378,7 @@ XLA_TEST_P(ReduceWindowTest, R6Add) { auto shape = ShapeUtil::MakeShape(F32, input_dims); std::unique_ptr arg_literal = - Literal::CreateFullWithDescendingLayout(input_dims, 1.0f); + LiteralUtil::CreateFullWithDescendingLayout(input_dims, 1.0f); const auto input = CreateConstantFromLiteral(*arg_literal, &builder_); @@ -385,7 +387,7 @@ XLA_TEST_P(ReduceWindowTest, R6Add) { std::vector output_dims = {8, 8, 6, 6, 8, 8}; std::unique_ptr expected = - Literal::CreateFullWithDescendingLayout(output_dims, 9.0f); + LiteralUtil::CreateFullWithDescendingLayout(output_dims, 9.0f); ComputeAndCompareLiteral(&builder_, *expected, {}, DefaultErrorSpec()); } @@ -394,7 +396,7 @@ XLA_TEST_P(ReduceWindowTest, R4SecondMinorStride) { Array4D input_array(2, 1, 27, 119); input_array.FillRandom(2.0f); std::unique_ptr input_literal = - Literal::CreateR4FromArray4DWithLayout( + LiteralUtil::CreateR4FromArray4DWithLayout( input_array, LayoutUtil::MakeLayout({3, 2, 1, 0})); XlaOp input; auto input_data = CreateParameterAndTransferLiteral( @@ -408,7 +410,7 @@ XLA_TEST_P(ReduceWindowTest, R4SecondMinorStride) { auto res = ReferenceUtil::ReduceWindow4DAdd( input_array, 0.0f, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*res), + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateFromArray(*res), {input_data.get()}, DefaultErrorSpec()); } @@ -416,7 +418,7 @@ XLA_TEST_P(ReduceWindowTest, R4SecondMinorUnitStride) { Array4D input_array(3, 2, 4, 64); input_array.FillRandom(2.0f); std::unique_ptr input_literal = - Literal::CreateR4FromArray4DWithLayout( + LiteralUtil::CreateR4FromArray4DWithLayout( input_array, LayoutUtil::MakeLayout({3, 2, 1, 0})); XlaOp input; auto input_data = CreateParameterAndTransferLiteral( @@ -430,7 +432,7 @@ XLA_TEST_P(ReduceWindowTest, R4SecondMinorUnitStride) { auto res = ReferenceUtil::ReduceWindow4DAdd( input_array, 0.0f, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*res), + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateFromArray(*res), {input_data.get()}, DefaultErrorSpec()); } @@ -438,7 +440,7 @@ XLA_TEST_P(ReduceWindowTest, R4SecondMinorWin) { Array4D input_array(1, 3, 12, 200); input_array.FillRandom(2.0f); std::unique_ptr input_literal = - Literal::CreateR4FromArray4DWithLayout( + LiteralUtil::CreateR4FromArray4DWithLayout( input_array, LayoutUtil::MakeLayout({3, 2, 1, 0})); XlaOp input; auto input_data = CreateParameterAndTransferLiteral( @@ -452,7 +454,7 @@ XLA_TEST_P(ReduceWindowTest, R4SecondMinorWin) { auto res = ReferenceUtil::ReduceWindow4DAdd( input_array, 0.0f, {1, 1, win_len, 1}, {1, 1, stride, 1}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*res), + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateFromArray(*res), {input_data.get()}, DefaultErrorSpec()); } @@ -473,18 +475,18 @@ TEST_P(ReduceWindowTest, AmongMajor2DimsMultipleMinor) { auto result = ReferenceUtil::ReduceWindow4DAdd( input_array, 0.0f, {win_len, win_len, 1, 1}, {win_stride, win_stride, 1, 1}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*result), {}, - DefaultErrorSpec()); + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateFromArray(*result), + {}, DefaultErrorSpec()); } XLA_TEST_P(ReduceWindowTest, Add24In1152_NoOverlap) { std::vector input_vector(128 * 9, 1); const auto input = CreateConstantFromLiteral( - *Literal::CreateR1(input_vector), &builder_); + *LiteralUtil::CreateR1(input_vector), &builder_); ReduceWindowAdd(input, {32}, {128}, Padding::kValid); ComputeAndCompareLiteral( &builder_, - *Literal::CreateR1({32, 32, 32, 32, 32, 32, 32, 32, 32}), {}, + *LiteralUtil::CreateR1({32, 32, 32, 32, 32, 32, 32, 32, 32}), {}, DefaultErrorSpec()); } @@ -499,9 +501,9 @@ XLA_TEST_P(ReduceWindowTest, Add128In128Stride128) { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}; const auto input = CreateConstantFromLiteral( - *Literal::CreateR1(input_vector), &builder_); + *LiteralUtil::CreateR1(input_vector), &builder_); ReduceWindowAdd(input, {128}, {128}, Padding::kValid); - ComputeAndCompareLiteral(&builder_, *Literal::CreateR1({1088}), {}, + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateR1({1088}), {}, DefaultErrorSpec()); } @@ -516,9 +518,9 @@ XLA_TEST_P(ReduceWindowTest, Add128In128) { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}; const auto input = CreateConstantFromLiteral( - *Literal::CreateR1(input_vector), &builder_); + *LiteralUtil::CreateR1(input_vector), &builder_); ReduceWindowAdd(input, {128}, {1}, Padding::kValid); - ComputeAndCompareLiteral(&builder_, *Literal::CreateR1({1088}), {}, + ComputeAndCompareLiteral(&builder_, *LiteralUtil::CreateR1({1088}), {}, DefaultErrorSpec()); } @@ -535,14 +537,15 @@ TEST_P(ReduceWindowTest, R2ReduceWindowInceptionFromBroadcast) { auto res = ReferenceUtil::ReduceWindow2DAdd( input_array, 0.0f, {win_len, win_len}, {stride, stride}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*res), - {}, DefaultErrorSpec()); + ComputeAndCompareLiteral(&builder_, + *LiteralUtil::CreateFromArray(*res), {}, + DefaultErrorSpec()); } TEST_P(ReduceWindowTest, R2ReduceWindowNonOverlappingFromBroadcast) { Array2D input_array(6, 4, 1.0f); XlaOp input = Broadcast( - CreateConstantFromLiteral(Literal::One(F32), &builder_), {6, 4}); + CreateConstantFromLiteral(LiteralUtil::One(F32), &builder_), {6, 4}); Padding padding = Padding::kSame; ReduceWindowAdd(input, {4, 2}, {3, 3}, padding); @@ -550,8 +553,9 @@ TEST_P(ReduceWindowTest, R2ReduceWindowNonOverlappingFromBroadcast) { auto res = ReferenceUtil::ReduceWindow2DAdd(input_array, 0.0f, {4, 2}, {3, 3}, padding); - ComputeAndCompareLiteral(&builder_, *Literal::CreateFromArray(*res), - {}, DefaultErrorSpec()); + ComputeAndCompareLiteral(&builder_, + *LiteralUtil::CreateFromArray(*res), {}, + DefaultErrorSpec()); } INSTANTIATE_TEST_CASE_P(ReduceWindowTestInstance, ReduceWindowTest, @@ -609,7 +613,7 @@ class R4ReduceWindowTest : public ReduceWindowTestBase, param.base_bounds[2], param.base_bounds[3]); input.FillIota(1); std::unique_ptr input_literal = - Literal::CreateR4FromArray4DWithLayout( + LiteralUtil::CreateR4FromArray4DWithLayout( input, LayoutUtil::MakeLayout(param.layout)); XlaOp parameter; auto input_arg = CreateParameterAndTransferLiteral(0, *input_literal, "p0", @@ -621,7 +625,7 @@ class R4ReduceWindowTest : public ReduceWindowTestBase, } auto init_value = - CreateConstantFromLiteral(*Literal::CreateR0(kInitValue), &b); + CreateConstantFromLiteral(*LiteralUtil::CreateR0(kInitValue), &b); CHECK(param.reducer == kAdd || param.reducer == kMax); auto computation = param.reducer == kAdd ? CreateScalarAddComputation(FloatType(), &b) @@ -647,7 +651,7 @@ class R4ReduceWindowTest : public ReduceWindowTestBase, /*stride=*/param.strides, /*padding=*/padding); std::unique_ptr expected_literal = - Literal::CreateFromArray(*expected); + LiteralUtil::CreateFromArray(*expected); const Shape& expected_shape_with_layout = ShapeUtil::MakeShapeWithLayout( input_literal->shape().element_type(), AsInt64Slice(expected_literal->shape().dimensions()), param.layout); @@ -959,14 +963,14 @@ TEST_P(R3ReduceWindowTest, Add) { Array3D input(param.base_bounds[0], param.base_bounds[1], param.base_bounds[2], 1.0f); std::unique_ptr input_literal = - Literal::CreateR3FromArray3DWithLayout( + LiteralUtil::CreateR3FromArray3DWithLayout( input, LayoutUtil::MakeLayout(param.layout)); XlaOp parameter; auto input_arg = CreateParameterAndTransferLiteral(0, *input_literal, "p0", &b, ¶meter); auto init_value = - CreateConstantFromLiteral(*Literal::CreateR0(kInitValue), &b); + CreateConstantFromLiteral(*LiteralUtil::CreateR0(kInitValue), &b); ReduceWindow(/*operand=*/parameter, /*init_value=*/init_value, /*computation=*/CreateScalarAddComputation(FloatType(), &b), @@ -977,7 +981,7 @@ TEST_P(R3ReduceWindowTest, Add) { /*operand=*/input, /*init=*/kInitValue, /*window=*/param.window_bounds, /*stride=*/param.strides, /*padding=*/param.padding); - ComputeAndCompareLiteral(&b, *Literal::CreateFromArray(*expected), + ComputeAndCompareLiteral(&b, *LiteralUtil::CreateFromArray(*expected), {input_arg.get()}, DefaultErrorSpec()); } @@ -1093,7 +1097,7 @@ class R2ReduceWindowTest : public ReduceWindowTestBase, const float kInitValue = 0.0f; Array2D input(param.base_bounds[0], param.base_bounds[1], 1.0f); std::unique_ptr input_literal = - Literal::CreateR2FromArray2DWithLayout( + LiteralUtil::CreateR2FromArray2DWithLayout( input, LayoutUtil::MakeLayout(param.layout)); XlaOp parameter; @@ -1107,7 +1111,7 @@ class R2ReduceWindowTest : public ReduceWindowTestBase, ? CreateScalarAddComputation(FloatType(), &b) : CreateScalarMaxComputation(FloatType(), &b); auto init_value = - CreateConstantFromLiteral(*Literal::CreateR0(kInitValue), &b); + CreateConstantFromLiteral(*LiteralUtil::CreateR0(kInitValue), &b); ReduceWindowWithGeneralPadding( /*operand=*/parameter, /*init_value=*/init_value, @@ -1123,7 +1127,7 @@ class R2ReduceWindowTest : public ReduceWindowTestBase, /*window=*/param.window_bounds, /*stride=*/param.strides, /*padding=*/padding); - ComputeAndCompareLiteral(&b, *Literal::CreateFromArray(*expected), + ComputeAndCompareLiteral(&b, *LiteralUtil::CreateFromArray(*expected), {input_arg.get()}, DefaultErrorSpec()); } }; @@ -1292,7 +1296,7 @@ TEST_P(R1ReduceWindowTest, DoIt) { std::vector input_vector(param.base_bounds[0]); std::iota(std::begin(input_vector), std::end(input_vector), 0); std::unique_ptr input_literal = - Literal::CreateR1(tensorflow::gtl::ArraySlice(input_vector)); + LiteralUtil::CreateR1(tensorflow::gtl::ArraySlice(input_vector)); XlaOp parameter; auto input_arg = CreateParameterAndTransferLiteral(0, *input_literal, "p0", &b, ¶meter); @@ -1304,7 +1308,7 @@ TEST_P(R1ReduceWindowTest, DoIt) { ? CreateScalarAddComputation(FloatType(), &b) : CreateScalarMaxComputation(FloatType(), &b); auto init_value = - CreateConstantFromLiteral(*Literal::CreateR0(kInitValue), &b); + CreateConstantFromLiteral(*LiteralUtil::CreateR0(kInitValue), &b); ReduceWindowWithGeneralPadding( /*operand=*/parameter, /*init_value=*/init_value, @@ -1323,7 +1327,7 @@ TEST_P(R1ReduceWindowTest, DoIt) { /*stride=*/param.strides, /*padding=*/padding); - ComputeAndCompareLiteral(&b, *Literal::CreateR1(*expected), + ComputeAndCompareLiteral(&b, *LiteralUtil::CreateR1(*expected), {input_arg.get()}, DefaultErrorSpec()); } diff --git a/tensorflow/compiler/xla/tests/replay_test.cc b/tensorflow/compiler/xla/tests/replay_test.cc index bebd814fa8b863428750dc12a93d1ef5ad7e6685..d8914513819415368a628eab1f482f9644dd46b1 100644 --- a/tensorflow/compiler/xla/tests/replay_test.cc +++ b/tensorflow/compiler/xla/tests/replay_test.cc @@ -17,9 +17,9 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/protobuf_util.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -91,10 +91,10 @@ XLA_TEST_F(ReplayTest, XPlusYReplayWithParameters) { // Run it. std::unique_ptr x_data = - client_->TransferToServer(*Literal::CreateR0(2)) + client_->TransferToServer(*LiteralUtil::CreateR0(2)) .ConsumeValueOrDie(); std::unique_ptr y_data = - client_->TransferToServer(*Literal::CreateR0(3)) + client_->TransferToServer(*LiteralUtil::CreateR0(3)) .ConsumeValueOrDie(); std::unique_ptr literal = client_ diff --git a/tensorflow/compiler/xla/tests/reshape_motion_test.cc b/tensorflow/compiler/xla/tests/reshape_motion_test.cc index 5812fe442b25da1b7e34494d00fe8025d29b2802..368f5583c9ce3773e57b858ff7606f679346529a 100644 --- a/tensorflow/compiler/xla/tests/reshape_motion_test.cc +++ b/tensorflow/compiler/xla/tests/reshape_motion_test.cc @@ -22,9 +22,9 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" diff --git a/tensorflow/compiler/xla/tests/reshape_test.cc b/tensorflow/compiler/xla/tests/reshape_test.cc index d3d6c3c7d703161e433740acbbd58d51ba1434af..382d1b1ae741285dcd1f7761edb82a5c333887af 100644 --- a/tensorflow/compiler/xla/tests/reshape_test.cc +++ b/tensorflow/compiler/xla/tests/reshape_test.cc @@ -22,8 +22,8 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/reference_util.h" @@ -55,39 +55,39 @@ XLA_TEST_P(ReshapeTest, CollapseTrivial1x1) { XlaBuilder builder(TestName()); Array2D input_array(1, 1); input_array.Fill(1.0f); - auto input_literal = Literal::CreateR2FromArray2D(input_array); + auto input_literal = LiteralUtil::CreateR2FromArray2D(input_array); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "parameter", &builder, ¶meter); Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); - auto expected_literal = Literal::CreateR1({1.0f}); + auto expected_literal = LiteralUtil::CreateR1({1.0f}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } XLA_TEST_P(ReshapeTest, CollapseTrivialR1EmptyDims) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateR1({1.0f}); + auto input_literal = LiteralUtil::CreateR1({1.0f}); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "parameter", &builder, ¶meter); Collapse(/*operand=*/parameter, /*dimensions=*/{}); - auto expected_literal = Literal::CreateR1({1.0f}); + auto expected_literal = LiteralUtil::CreateR1({1.0f}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } XLA_TEST_P(ReshapeTest, CollapseTrivialR1OnlyDim) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateR1({1.0f}); + auto input_literal = LiteralUtil::CreateR1({1.0f}); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "parameter", &builder, ¶meter); Collapse(/*operand=*/parameter, /*dimensions=*/{0}); - auto expected_literal = Literal::CreateR1({1.0f}); + auto expected_literal = LiteralUtil::CreateR1({1.0f}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -97,7 +97,7 @@ XLA_TEST_P(ReshapeTest, SingleElementArrayToScalar) { XlaBuilder builder(TestName()); Array2D input_array(1, 1); input_array.Fill(1.0f); - auto input_literal = Literal::CreateR2FromArray2D(input_array); + auto input_literal = LiteralUtil::CreateR2FromArray2D(input_array); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "parameter", &builder, ¶meter); @@ -105,7 +105,7 @@ XLA_TEST_P(ReshapeTest, SingleElementArrayToScalar) { /*new_sizes=*/{}); auto new_shape = builder.GetShape(reshape).ConsumeValueOrDie(); - auto expected_literal = Literal::CreateR0(1.0f); + auto expected_literal = LiteralUtil::CreateR0(1.0f); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -113,14 +113,14 @@ XLA_TEST_P(ReshapeTest, SingleElementArrayToScalar) { XLA_TEST_P(ReshapeTest, ScalarToSingleElementArray) { XlaBuilder builder(TestName()); - std::unique_ptr param0_literal = Literal::CreateR0(1.0f); + std::unique_ptr param0_literal = LiteralUtil::CreateR0(1.0f); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *param0_literal, "param0", &builder, ¶meter); auto a = Neg(parameter); Reshape(/*operand=*/a, /*dimensions=*/{}, /*new_sizes=*/{1}); - auto expected_literal = Literal::CreateR1({-1.0f}); + auto expected_literal = LiteralUtil::CreateR1({-1.0f}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -128,12 +128,12 @@ XLA_TEST_P(ReshapeTest, ScalarToSingleElementArray) { XLA_TEST_P(ReshapeTest, Trivial0x3) { XlaBuilder builder(TestName()); Array2D input_array(0, 3); - auto input_literal = Literal::CreateR2FromArray2D(input_array); + auto input_literal = LiteralUtil::CreateR2FromArray2D(input_array); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); - auto expected_literal = Literal::CreateR1({}); + auto expected_literal = LiteralUtil::CreateR1({}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -142,12 +142,12 @@ XLA_TEST_P(ReshapeTest, Trivial0x3WithParameter) { XlaBuilder builder(TestName()); std::unique_ptr param0_literal = - Literal::CreateR2FromArray2D(Array2D(0, 3)); + LiteralUtil::CreateR2FromArray2D(Array2D(0, 3)); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *param0_literal, "param0", &builder, ¶meter); Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); - auto expected_literal = Literal::CreateR1({}); + auto expected_literal = LiteralUtil::CreateR1({}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -155,12 +155,12 @@ XLA_TEST_P(ReshapeTest, Trivial0x3WithParameter) { XLA_TEST_P(ReshapeTest, Trivial3x0) { XlaBuilder builder(TestName()); Array2D input_array(3, 0); - auto input_literal = Literal::CreateR2FromArray2D(input_array); + auto input_literal = LiteralUtil::CreateR2FromArray2D(input_array); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); - auto expected_literal = Literal::CreateR1({}); + auto expected_literal = LiteralUtil::CreateR1({}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -168,12 +168,12 @@ XLA_TEST_P(ReshapeTest, Trivial3x0) { // Collapses a 2-dimensional row vector to 1 dimension. XLA_TEST_P(ReshapeTest, Trivial1x3) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateR2({{1.0f, 2.0f, 3.0f}}); + auto input_literal = LiteralUtil::CreateR2({{1.0f, 2.0f, 3.0f}}); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); - auto expected_literal = Literal::CreateR1({1.0f, 2.0f, 3.0f}); + auto expected_literal = LiteralUtil::CreateR1({1.0f, 2.0f, 3.0f}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -181,12 +181,12 @@ XLA_TEST_P(ReshapeTest, Trivial1x3) { // Collapses a 2-dimensional column vector to 1 dimension. XLA_TEST_P(ReshapeTest, Trivial3x1) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateR2({{1.0f}, {2.0f}, {3.0f}}); + auto input_literal = LiteralUtil::CreateR2({{1.0f}, {2.0f}, {3.0f}}); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Collapse(/*operand=*/parameter, /*dimensions=*/{0, 1}); - auto expected_literal = Literal::CreateR1({1.0f, 2.0f, 3.0f}); + auto expected_literal = LiteralUtil::CreateR1({1.0f, 2.0f, 3.0f}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -194,13 +194,13 @@ XLA_TEST_P(ReshapeTest, Trivial3x1) { // Splits an empty vector into an empty matrix. XLA_TEST_P(ReshapeTest, R1ToR2_0_To_2x0) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateR1({}); + auto input_literal = LiteralUtil::CreateR1({}); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Reshape(/*operand=*/parameter, /*dimensions=*/{0}, /*new_sizes=*/{2, 0}); - auto expected_literal = Literal::CreateR2({{}, {}}); + auto expected_literal = LiteralUtil::CreateR2({{}, {}}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -209,14 +209,14 @@ XLA_TEST_P(ReshapeTest, R1ToR2_0_To_2x0) { XLA_TEST_P(ReshapeTest, R1ToR2_6_To_2x3) { XlaBuilder builder(TestName()); auto input_literal = - Literal::CreateR1({1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}); + LiteralUtil::CreateR1({1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Reshape(/*operand=*/parameter, /*dimensions=*/{0}, /*new_sizes=*/{2, 3}); auto expected_literal = - Literal::CreateR2({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}); + LiteralUtil::CreateR2({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -224,13 +224,13 @@ XLA_TEST_P(ReshapeTest, R1ToR2_6_To_2x3) { // Transposes a 2x0 array to a 0x2 array. XLA_TEST_P(ReshapeTest, Reshape0x2To2x0) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateFromArray(Array2D(0, 2)); + auto input_literal = LiteralUtil::CreateFromArray(Array2D(0, 2)); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, /*new_sizes=*/{2, 0}); - auto expected_literal = Literal::CreateR2({{}, {}}); + auto expected_literal = LiteralUtil::CreateR2({{}, {}}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -239,7 +239,7 @@ XLA_TEST_P(ReshapeTest, Reshape0x2To2x0) { XLA_TEST_P(ReshapeTest, ReshapeRowToCol) { XlaBuilder builder(TestName()); auto simple = MakeLinspaceArray2D(1.0f, 3.0f, 1, 3); - auto input_literal = Literal::CreateFromArray(*simple); + auto input_literal = LiteralUtil::CreateFromArray(*simple); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); @@ -247,7 +247,7 @@ XLA_TEST_P(ReshapeTest, ReshapeRowToCol) { /*new_sizes=*/{3, 1}); auto expected = ReferenceUtil::TransposeArray2D(*simple); - auto expected_literal = Literal::CreateFromArray(*expected); + auto expected_literal = LiteralUtil::CreateFromArray(*expected); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -256,7 +256,7 @@ XLA_TEST_P(ReshapeTest, ReshapeRowToCol) { XLA_TEST_P(ReshapeTest, TransposeAsReshape) { XlaBuilder builder(TestName()); auto a4x3 = MakeLinspaceArray2D(1.0f, 12.0f, 4, 3); - auto input_literal = Literal::CreateFromArray(*a4x3); + auto input_literal = LiteralUtil::CreateFromArray(*a4x3); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); @@ -264,7 +264,7 @@ XLA_TEST_P(ReshapeTest, TransposeAsReshape) { /*new_sizes=*/{3, 4}); auto expected = ReferenceUtil::TransposeArray2D(*a4x3); - auto expected_literal = Literal::CreateFromArray(*expected); + auto expected_literal = LiteralUtil::CreateFromArray(*expected); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -272,12 +272,12 @@ XLA_TEST_P(ReshapeTest, TransposeAsReshape) { // Transposes a 0x4 array with XlaBuilder::Transpose. XLA_TEST_P(ReshapeTest, Transpose0x4) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateFromArray(Array2D(0, 4)); + auto input_literal = LiteralUtil::CreateFromArray(Array2D(0, 4)); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Transpose(parameter, {1, 0}); - auto expected_literal = Literal::CreateR2({{}, {}, {}, {}}); + auto expected_literal = LiteralUtil::CreateR2({{}, {}, {}, {}}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -286,14 +286,14 @@ XLA_TEST_P(ReshapeTest, Transpose0x4) { XLA_TEST_P(ReshapeTest, Transpose4x3) { XlaBuilder builder(TestName()); auto a4x3 = MakeLinspaceArray2D(1.0f, 12.0f, 4, 3); - auto input_literal = Literal::CreateFromArray(*a4x3); + auto input_literal = LiteralUtil::CreateFromArray(*a4x3); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Transpose(parameter, {1, 0}); auto expected = ReferenceUtil::TransposeArray2D(*a4x3); - auto expected_literal = Literal::CreateFromArray(*expected); + auto expected_literal = LiteralUtil::CreateFromArray(*expected); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -302,26 +302,27 @@ XLA_TEST_P(ReshapeTest, Transpose4x3) { // rearrangement of the originals (split), but no reordering (no shuffle). XLA_TEST_P(ReshapeTest, ReshapeSplitNoShuffleZeroElements) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateFromArray(Array2D(6, 0)); + auto input_literal = LiteralUtil::CreateFromArray(Array2D(6, 0)); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1}, /*new_sizes=*/{2, 3, 0, 0}); - auto expected_literal = Literal::CreateFromArray(Array4D(2, 3, 0, 0)); + auto expected_literal = + LiteralUtil::CreateFromArray(Array4D(2, 3, 0, 0)); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } XLA_TEST_P(ReshapeTest, ReshapeR4ToR2ZeroElements) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateFromArray(Array4D(2, 3, 4, 0)); + auto input_literal = LiteralUtil::CreateFromArray(Array4D(2, 3, 4, 0)); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1, 2, 3}, /*new_sizes=*/{24, 0}); - auto expected_literal = Literal::CreateFromArray(Array2D(24, 0)); + auto expected_literal = LiteralUtil::CreateFromArray(Array2D(24, 0)); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -331,7 +332,7 @@ XLA_TEST_P(ReshapeTest, ReshapeR4ToR2ZeroElements) { XLA_TEST_P(ReshapeTest, ReshapeSplitNoShuffle) { XlaBuilder builder(TestName()); auto a4x3 = MakeLinspaceArray2D(1.0f, 12.0f, 4, 3); - auto input_literal = Literal::CreateFromArray(*a4x3); + auto input_literal = LiteralUtil::CreateFromArray(*a4x3); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); @@ -339,20 +340,20 @@ XLA_TEST_P(ReshapeTest, ReshapeSplitNoShuffle) { /*new_sizes=*/{2, 6}); auto expected = MakeLinspaceArray2D(1.0f, 12.0f, 2, 6); - auto expected_literal = Literal::CreateFromArray(*expected); + auto expected_literal = LiteralUtil::CreateFromArray(*expected); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } XLA_TEST_P(ReshapeTest, ReshapeSplitAndShuffleZeroElements) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateFromArray(Array2D(0, 6)); + auto input_literal = LiteralUtil::CreateFromArray(Array2D(0, 6)); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Reshape(/*operand=*/parameter, /*dimensions=*/{1, 0}, /*new_sizes=*/{3, 0}); - auto expected_literal = Literal::CreateFromArray(Array2D(3, 0)); + auto expected_literal = LiteralUtil::CreateFromArray(Array2D(3, 0)); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -362,7 +363,7 @@ XLA_TEST_P(ReshapeTest, ReshapeSplitAndShuffleZeroElements) { XLA_TEST_P(ReshapeTest, ReshapeSplitAndShuffle) { XlaBuilder builder(TestName()); auto a4x3 = MakeLinspaceArray2D(1.0f, 12.0f, 4, 3); - auto input_literal = Literal::CreateFromArray(*a4x3); + auto input_literal = LiteralUtil::CreateFromArray(*a4x3); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); @@ -370,7 +371,7 @@ XLA_TEST_P(ReshapeTest, ReshapeSplitAndShuffle) { /*new_sizes=*/{2, 6}); Array2D expected({{1.0f, 4.0f, 7.0f, 10.0f, 2.0f, 5.0f}, {8.0f, 11.0f, 3.0f, 6.0f, 9.0f, 12.0f}}); - auto expected_literal = Literal::CreateFromArray(expected); + auto expected_literal = LiteralUtil::CreateFromArray(expected); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -388,13 +389,13 @@ static Array3D ArrayForDocR3Tests() { XLA_TEST_P(ReshapeTest, DocR3_R1_Collapse_012) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateFromArray(ArrayForDocR3Tests()); + auto input_literal = LiteralUtil::CreateFromArray(ArrayForDocR3Tests()); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1, 2}, /*new_sizes=*/{24}); - auto expected_literal = Literal::CreateR1( + auto expected_literal = LiteralUtil::CreateR1( {10, 11, 12, 15, 16, 17, 20, 21, 22, 25, 26, 27, 30, 31, 32, 35, 36, 37, 40, 41, 42, 45, 46, 47}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, @@ -403,33 +404,33 @@ XLA_TEST_P(ReshapeTest, DocR3_R1_Collapse_012) { XLA_TEST_P(ReshapeTest, DocR3_R2_Collapse_012_Refine_83) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateFromArray(ArrayForDocR3Tests()); + auto input_literal = LiteralUtil::CreateFromArray(ArrayForDocR3Tests()); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Reshape(/*operand=*/parameter, /*dimensions=*/{0, 1, 2}, /*new_sizes=*/{8, 3}); - auto expected_literal = Literal::CreateR2({{10, 11, 12}, - {15, 16, 17}, - {20, 21, 22}, - {25, 26, 27}, - {30, 31, 32}, - {35, 36, 37}, - {40, 41, 42}, - {45, 46, 47}}); + auto expected_literal = LiteralUtil::CreateR2({{10, 11, 12}, + {15, 16, 17}, + {20, 21, 22}, + {25, 26, 27}, + {30, 31, 32}, + {35, 36, 37}, + {40, 41, 42}, + {45, 46, 47}}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } XLA_TEST_P(ReshapeTest, DocR3_R1_Collapse_120) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateFromArray(ArrayForDocR3Tests()); + auto input_literal = LiteralUtil::CreateFromArray(ArrayForDocR3Tests()); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Reshape(/*operand=*/parameter, /*dimensions=*/{1, 2, 0}, /*new_sizes=*/{24}); - auto expected_literal = Literal::CreateR1( + auto expected_literal = LiteralUtil::CreateR1( {10, 20, 30, 40, 11, 21, 31, 41, 12, 22, 32, 42, 15, 25, 35, 45, 16, 26, 36, 46, 17, 27, 37, 47}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, @@ -438,33 +439,33 @@ XLA_TEST_P(ReshapeTest, DocR3_R1_Collapse_120) { XLA_TEST_P(ReshapeTest, DocR3_R2_Collapse_120_Refine_83) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateFromArray(ArrayForDocR3Tests()); + auto input_literal = LiteralUtil::CreateFromArray(ArrayForDocR3Tests()); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Reshape(/*operand=*/parameter, /*dimensions=*/{1, 2, 0}, /*new_sizes=*/{8, 3}); - auto expected_literal = Literal::CreateR2({{10, 20, 30}, - {40, 11, 21}, - {31, 41, 12}, - {22, 32, 42}, - {15, 25, 35}, - {45, 16, 26}, - {36, 46, 17}, - {27, 37, 47}}); + auto expected_literal = LiteralUtil::CreateR2({{10, 20, 30}, + {40, 11, 21}, + {31, 41, 12}, + {22, 32, 42}, + {15, 25, 35}, + {45, 16, 26}, + {36, 46, 17}, + {27, 37, 47}}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } XLA_TEST_P(ReshapeTest, DocR3_R3_Collapse_120_Refine_262) { XlaBuilder builder(TestName()); - auto input_literal = Literal::CreateFromArray(ArrayForDocR3Tests()); + auto input_literal = LiteralUtil::CreateFromArray(ArrayForDocR3Tests()); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Reshape(/*operand=*/parameter, /*dimensions=*/{1, 2, 0}, /*new_sizes=*/{2, 6, 2}); - auto expected_literal = Literal::CreateR3( + auto expected_literal = LiteralUtil::CreateR3( {{{10, 20}, {30, 40}, {11, 21}, {31, 41}, {12, 22}, {32, 42}}, {{15, 25}, {35, 45}, {16, 26}, {36, 46}, {17, 27}, {37, 47}}}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, @@ -491,12 +492,12 @@ XLA_TEST_P(ReshapeTest, FullyConnectedCollapse) { Array4D t2x2x2x3(2, 2, 2, 3); auto filler2x3 = MakeLinspaceArray2D(1.0f, 6.0f, 2, 3); t2x2x2x3.FillWithYX(*filler2x3); - auto input_literal = Literal::CreateFromArray(t2x2x2x3); + auto input_literal = LiteralUtil::CreateFromArray(t2x2x2x3); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); Collapse(/*operand=*/parameter, /*dimensions=*/{1, 2, 3}); - auto expected_literal = Literal::CreateR2( + auto expected_literal = LiteralUtil::CreateR2( {{1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}}); @@ -516,7 +517,7 @@ XLA_TEST_P(ReshapeTest, FullyConnectedCollapseDesugared) { t(1, 0, 0, 1) = 5; t(1, 0, 1, 0) = 6; t(1, 0, 1, 1) = 7; - auto input_literal = Literal::CreateFromArray(t); + auto input_literal = LiteralUtil::CreateFromArray(t); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &builder, ¶meter); @@ -524,7 +525,7 @@ XLA_TEST_P(ReshapeTest, FullyConnectedCollapseDesugared) { /*new_sizes=*/{2, 4}); auto expected_literal = - Literal::CreateR2({{0, 1, 2, 3}, {4, 5, 6, 7}}); + LiteralUtil::CreateR2({{0, 1, 2, 3}, {4, 5, 6, 7}}); ComputeAndCompareLiteral(&builder, *expected_literal, {input.get()}, zero_error_spec_); } @@ -545,7 +546,7 @@ XLA_TEST_P(ReshapeTest, ToScalar) { &b, ¶meter); Reshape(parameter, dimensions, {}); - auto expected_literal = Literal::CreateR0(83.0f); + auto expected_literal = LiteralUtil::CreateR0(83.0f); ComputeAndCompareLiteral(&b, *expected_literal, {input.get()}, zero_error_spec_); } @@ -553,7 +554,7 @@ XLA_TEST_P(ReshapeTest, ToScalar) { XLA_TEST_P(ReshapeTest, BadDimensions) { XlaBuilder b(TestName()); - auto input_literal = Literal::CreateR1({1.0f}); + auto input_literal = LiteralUtil::CreateR1({1.0f}); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &b, ¶meter); @@ -565,7 +566,7 @@ XLA_TEST_P(ReshapeTest, BadDimensions) { XLA_TEST_P(ReshapeTest, BadNewSizes) { XlaBuilder b(TestName()); - auto input_literal = Literal::CreateR1({1.0f, 2.0f}); + auto input_literal = LiteralUtil::CreateR1({1.0f, 2.0f}); XlaOp parameter; auto input = CreateParameterAndTransferLiteral(0, *input_literal, "input", &b, ¶meter); @@ -577,7 +578,8 @@ XLA_TEST_P(ReshapeTest, BadNewSizes) { XLA_TEST_P(ReshapeTest, R4Dim0MinorLayoutToR2Dim0MajorLayout) { XlaBuilder builder(TestName()); // clang-format off - auto input_literal = Literal::CreateR4FromArray4DWithLayout(Array4D{ + auto input_literal = LiteralUtil::CreateR4FromArray4DWithLayout( + Array4D{ { { {0, 1}, @@ -622,16 +624,16 @@ XLA_TEST_P(ReshapeTest, R4Dim0MinorLayoutToR2Dim0MajorLayout) { ->ExecuteAndTransfer(computation, {input.get()}, &execution_options) .ConsumeValueOrDie(); std::unique_ptr expected = - Literal::CreateR2FromArray2D(expected_array); + LiteralUtil::CreateR2FromArray2D(expected_array); if (use_bfloat16()) { - expected = Literal::ConvertF32ToBF16(*expected); + expected = LiteralUtil::ConvertF32ToBF16(*expected); } EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *actual)); } XLA_TEST_P(ReshapeTest, R2ToR4_3x8_To_3x2x1x4) { XlaBuilder builder(TestName()); - std::unique_ptr input_literal = Literal::CreateR2({ + std::unique_ptr input_literal = LiteralUtil::CreateR2({ {0, 1, 2, 3, 4, 5, 6, 7}, {100, 101, 102, 103, 104, 105, 106, 107}, {200, 201, 202, 203, 204, 205, 206, 207}, @@ -642,7 +644,7 @@ XLA_TEST_P(ReshapeTest, R2ToR4_3x8_To_3x2x1x4) { Reshape(parameter, /*dimensions=*/{0, 1}, /*new_sizes=*/{3, 2, 1, 4}); // clang-format off - auto expected_literal = Literal::CreateR4({ + auto expected_literal = LiteralUtil::CreateR4({ {{{0, 1, 2, 3}}, {{4, 5, 6, 7}}}, {{{100, 101, 102, 103}}, @@ -658,7 +660,7 @@ XLA_TEST_P(ReshapeTest, R2ToR4_3x8_To_3x2x1x4) { // Tests R2->R4 reshape with the reshape dimensions {1, 0}. XLA_TEST_P(ReshapeTest, R2ToR4_3x8_To_3x2x1x4_Dimensions_10) { XlaBuilder builder(TestName()); - std::unique_ptr input_literal = Literal::CreateR2({ + std::unique_ptr input_literal = LiteralUtil::CreateR2({ {0, 1, 2, 3, 4, 5, 6, 7}, {100, 101, 102, 103, 104, 105, 106, 107}, {200, 201, 202, 203, 204, 205, 206, 207}, @@ -669,7 +671,7 @@ XLA_TEST_P(ReshapeTest, R2ToR4_3x8_To_3x2x1x4_Dimensions_10) { Reshape(parameter, /*dimensions=*/{1, 0}, /*new_sizes=*/{3, 2, 1, 4}); // clang-format off - auto expected_literal = Literal::CreateR4({ + auto expected_literal = LiteralUtil::CreateR4({ {{{0, 100, 200, 1}}, {{101, 201, 2, 102}}}, {{{202, 3, 103, 203}}, @@ -691,7 +693,7 @@ XLA_TEST_P(ReshapeTest, R4ToR2_2x1x1x1_To_2x1) { [&rng, &distribution](tensorflow::gtl::ArraySlice /* indices */, float* cell) { *cell = distribution(rng); }); std::unique_ptr input_literal = - Literal::CreateR4FromArray4DWithLayout( + LiteralUtil::CreateR4FromArray4DWithLayout( input, LayoutUtil::MakeLayout({3, 2, 1, 0})); XlaOp parameter; auto input_data = CreateParameterAndTransferLiteral( @@ -699,7 +701,7 @@ XLA_TEST_P(ReshapeTest, R4ToR2_2x1x1x1_To_2x1) { Reshape(parameter, /*dimensions=*/{0, 1, 2, 3}, /*new_sizes=*/{2, 1}); std::unique_ptr expected = - Literal::ReshapeSlice({2, 1}, {1, 0}, *input_literal); + LiteralUtil::ReshapeSlice({2, 1}, {1, 0}, *input_literal); ComputeAndCompareLiteral(&builder, *expected, {input_data.get()}, zero_error_spec_); } @@ -713,7 +715,7 @@ XLA_TEST_P(ReshapeTest, R4ToR2_2x1x4x1_To_4x2) { [&rng, &distribution](tensorflow::gtl::ArraySlice /* indices */, float* cell) { *cell = distribution(rng); }); std::unique_ptr input_literal = - Literal::CreateR4FromArray4DWithLayout( + LiteralUtil::CreateR4FromArray4DWithLayout( input, LayoutUtil::MakeLayout({3, 2, 1, 0})); XlaOp parameter; auto input_data = CreateParameterAndTransferLiteral( @@ -721,7 +723,7 @@ XLA_TEST_P(ReshapeTest, R4ToR2_2x1x4x1_To_4x2) { Reshape(parameter, /*dimensions=*/{0, 1, 2, 3}, /*new_sizes=*/{4, 2}); std::unique_ptr expected = - Literal::ReshapeSlice({4, 2}, {1, 0}, *input_literal); + LiteralUtil::ReshapeSlice({4, 2}, {1, 0}, *input_literal); ComputeAndCompareLiteral(&builder, *expected, {input_data.get()}, zero_error_spec_); } @@ -736,7 +738,7 @@ XLA_TEST_P(ReshapeTest, R4ToR2_5x10x2x3_To_5x60_Dimensions_0213) { [&rng, &distribution](tensorflow::gtl::ArraySlice /* indices */, float* cell) { *cell = distribution(rng); }); std::unique_ptr input_literal = - Literal::CreateR4FromArray4DWithLayout( + LiteralUtil::CreateR4FromArray4DWithLayout( input, LayoutUtil::MakeLayout({3, 2, 1, 0})); XlaOp parameter; auto input_data = CreateParameterAndTransferLiteral( @@ -749,7 +751,7 @@ XLA_TEST_P(ReshapeTest, R4ToR2_5x10x2x3_To_5x60_Dimensions_0213) { expected_array(indices[0], indices[2] * 30 + indices[1] * 3 + indices[3]) = *cell; }); - auto expected = Literal::CreateR2FromArray2D(expected_array); + auto expected = LiteralUtil::CreateR2FromArray2D(expected_array); ComputeAndCompareLiteral(&builder, *expected, {input_data.get()}, zero_error_spec_); } @@ -763,7 +765,7 @@ XLA_TEST_P(ReshapeTest, NoopReshape) { [&rng, &distribution](tensorflow::gtl::ArraySlice /* indices */, float* cell) { *cell = distribution(rng); }); std::unique_ptr input_literal = - Literal::CreateR4FromArray4DWithLayout( + LiteralUtil::CreateR4FromArray4DWithLayout( input_array, LayoutUtil::MakeLayout({1, 2, 3, 0})); XlaOp parameter; auto input_data = CreateParameterAndTransferLiteral( @@ -785,7 +787,7 @@ XLA_TEST_P(ReshapeTest, NoopReshape) { // Since the reshape is a no-op, verify that it does not change the underlying // data. if (use_bfloat16()) { - auto expected = Literal::ConvertF32ToBF16(*input_literal); + auto expected = LiteralUtil::ConvertF32ToBF16(*input_literal); EXPECT_EQ(expected->data(), output_literal->data()); } else { EXPECT_EQ(input_literal->data(), output_literal->data()); @@ -794,7 +796,7 @@ XLA_TEST_P(ReshapeTest, NoopReshape) { XLA_TEST_P(ReshapeTest, R4ToR4Reshape_Trivial) { XlaBuilder builder(TestName()); - auto literal_1x2x3x4 = Literal::CreateR4( + auto literal_1x2x3x4 = LiteralUtil::CreateR4( {{{{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}, {{13, 14, 15, 16}, {17, 18, 19, 20}, {21, 22, 23, 24}}}}); @@ -808,7 +810,7 @@ XLA_TEST_P(ReshapeTest, R4ToR4Reshape_Trivial) { } XLA_TEST_P(ReshapeTest, R4ToR4Reshape) { - auto literal_1x2x3x4 = Literal::CreateR4( + auto literal_1x2x3x4 = LiteralUtil::CreateR4( {{{{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}, {{13, 14, 15, 16}, {17, 18, 19, 20}, {21, 22, 23, 24}}}}); @@ -820,7 +822,7 @@ XLA_TEST_P(ReshapeTest, R4ToR4Reshape) { /*new_sizes=*/{2, 4, 3, 1}); // clang-format off - auto expected_2x4x3x1 = Literal::CreateR4( + auto expected_2x4x3x1 = LiteralUtil::CreateR4( {{{{1}, {5}, {9}}, {{2}, {6}, {10}}, {{3}, {7}, {11}}, @@ -844,7 +846,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeSimple) { [&rng, &distribution](tensorflow::gtl::ArraySlice /* indices */, float* cell) { *cell = distribution(rng); }); std::unique_ptr input_literal = - Literal::CreateR4FromArray4DWithLayout( + LiteralUtil::CreateR4FromArray4DWithLayout( input, LayoutUtil::MakeLayout({3, 2, 1, 0})); XlaBuilder builder(TestName()); XlaOp parameter; @@ -854,7 +856,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeSimple) { /*new_sizes=*/new_bounds); std::unique_ptr expected = - Literal::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal) + LiteralUtil::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal) ->Relayout(LayoutUtil::MakeLayout({3, 2, 1, 0})); // Specify the requested output shape explicitly to ensure that this reshape @@ -873,7 +875,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeMajorFirstEffectiveR2) { [&rng, &distribution](tensorflow::gtl::ArraySlice /* indices */, float* cell) { *cell = distribution(rng); }); std::unique_ptr input_literal = - Literal::CreateR4FromArray4DWithLayout( + LiteralUtil::CreateR4FromArray4DWithLayout( input, LayoutUtil::MakeLayout({3, 2, 1, 0})); XlaBuilder builder(TestName()); XlaOp parameter; @@ -883,7 +885,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeMajorFirstEffectiveR2) { /*new_sizes=*/new_bounds); std::unique_ptr expected = - Literal::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal) + LiteralUtil::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal) ->Relayout(LayoutUtil::MakeLayout({3, 2, 1, 0})); // Specify the requested output shape explicitly to ensure that this reshape @@ -902,7 +904,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeMajorFirstMinorEffectiveR1) { [&rng, &distribution](tensorflow::gtl::ArraySlice /* indices */, float* cell) { *cell = distribution(rng); }); std::unique_ptr input_literal = - Literal::CreateR4FromArray4DWithLayout( + LiteralUtil::CreateR4FromArray4DWithLayout( input, LayoutUtil::MakeLayout({3, 2, 1, 0})); XlaBuilder builder(TestName()); XlaOp parameter; @@ -912,7 +914,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeMajorFirstMinorEffectiveR1) { /*new_sizes=*/new_bounds); std::unique_ptr expected = - Literal::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal) + LiteralUtil::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal) ->Relayout(LayoutUtil::MakeLayout({3, 2, 1, 0})); // Specify the requested output shape explicitly to ensure that this reshape @@ -932,7 +934,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeMajorFirstMinorEffectiveR1InR2) { [&rng, &distribution](tensorflow::gtl::ArraySlice /* indices */, float* cell) { *cell = distribution(rng); }); std::unique_ptr input_literal = - Literal::CreateR4FromArray4DWithLayout( + LiteralUtil::CreateR4FromArray4DWithLayout( input, LayoutUtil::MakeLayout({3, 2, 1, 0})); XlaBuilder builder(TestName()); XlaOp parameter; @@ -942,7 +944,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeMajorFirstMinorEffectiveR1InR2) { /*new_sizes=*/new_bounds); std::unique_ptr expected = - Literal::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal) + LiteralUtil::ReshapeSlice(new_bounds, {2, 3, 1, 0}, *input_literal) ->Relayout(LayoutUtil::MakeLayout({3, 2, 1, 0})); // Specify the requested output shape explicitly to ensure that this reshape @@ -961,7 +963,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeTrivialR2) { [&rng, &distribution](tensorflow::gtl::ArraySlice /* indices */, float* cell) { *cell = distribution(rng); }); std::unique_ptr input_literal = - Literal::CreateR4FromArray4DWithLayout( + LiteralUtil::CreateR4FromArray4DWithLayout( input, LayoutUtil::MakeLayout({0, 1, 2, 3})); XlaBuilder builder(TestName()); XlaOp parameter; @@ -971,7 +973,7 @@ XLA_TEST_P(ReshapeTest, R4TwoMinorTransposeTrivialR2) { /*new_sizes=*/new_bounds); std::unique_ptr expected = - Literal::ReshapeSlice(new_bounds, {1, 0, 2, 3}, *input_literal) + LiteralUtil::ReshapeSlice(new_bounds, {1, 0, 2, 3}, *input_literal) ->Relayout(input_literal->shape().layout()); // Specify the requested output shape explicitly to ensure that this reshape diff --git a/tensorflow/compiler/xla/tests/reverse_test.cc b/tensorflow/compiler/xla/tests/reverse_test.cc index 662bc42224851ac19c690129f525953e6d410a55..41e49b4003236d55d85592315652a0ddefd5c485 100644 --- a/tensorflow/compiler/xla/tests/reverse_test.cc +++ b/tensorflow/compiler/xla/tests/reverse_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" @@ -82,7 +82,7 @@ TEST_P(FloatReverseTest, Reverses) { std::vector input_vector( ShapeUtil::ElementsIn(ShapeUtil::MakeShape(F32, spec.input_dims))); std::iota(input_vector.begin(), input_vector.end(), 0.0); - auto r1_literal = Literal::CreateR1(input_vector); + auto r1_literal = LiteralUtil::CreateR1(input_vector); auto input_literal = r1_literal->Reshape(spec.input_dims).ConsumeValueOrDie(); XlaBuilder builder(TestName()); diff --git a/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc b/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc index 7cfca781acda15879075f4386c2096e537877aac..a620fe19085d98c8b6642b25b159d6c2308bdae2 100644 --- a/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc +++ b/tensorflow/compiler/xla/tests/round_trip_packed_literal_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/packed_literal_reader.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc b/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc index f334a8c1318a59bbfdd27dd1a63ed162600089ce..a8193c2eac05ba4f0df339909f3e82a28ac35253 100644 --- a/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc +++ b/tensorflow/compiler/xla/tests/round_trip_transfer_test.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" @@ -46,61 +46,62 @@ class RoundTripTransferTest : public ClientLibraryTestBase { }; TEST_F(RoundTripTransferTest, R0S32) { - RoundTripTest(*Literal::CreateR0(42)); + RoundTripTest(*LiteralUtil::CreateR0(42)); } TEST_F(RoundTripTransferTest, R0F32) { - RoundTripTest(*Literal::CreateR0(42.0)); + RoundTripTest(*LiteralUtil::CreateR0(42.0)); } TEST_F(RoundTripTransferTest, R1F32_Len0) { - RoundTripTest(*Literal::CreateR1({})); + RoundTripTest(*LiteralUtil::CreateR1({})); } TEST_F(RoundTripTransferTest, R1F32_Len2) { - RoundTripTest(*Literal::CreateR1({42.0, 64.0})); + RoundTripTest(*LiteralUtil::CreateR1({42.0, 64.0})); } TEST_F(RoundTripTransferTest, R1F32_Len256) { std::vector values(256); std::iota(values.begin(), values.end(), 1.0); - RoundTripTest(*Literal::CreateR1(values)); + RoundTripTest(*LiteralUtil::CreateR1(values)); } TEST_F(RoundTripTransferTest, R1F32_Len1024) { std::vector values(1024); std::iota(values.begin(), values.end(), 1.0); - RoundTripTest(*Literal::CreateR1(values)); + RoundTripTest(*LiteralUtil::CreateR1(values)); } TEST_F(RoundTripTransferTest, R1F32_Len1025) { std::vector values(1025); std::iota(values.begin(), values.end(), 1.0); - RoundTripTest(*Literal::CreateR1(values)); + RoundTripTest(*LiteralUtil::CreateR1(values)); } TEST_F(RoundTripTransferTest, R1F32_Len4096) { std::vector values(4096); std::iota(values.begin(), values.end(), 1.0); - RoundTripTest(*Literal::CreateR1(values)); + RoundTripTest(*LiteralUtil::CreateR1(values)); } TEST_F(RoundTripTransferTest, R2F32_Len10x0) { - RoundTripTest(*Literal::CreateR2FromArray2D(Array2D(10, 0))); + RoundTripTest( + *LiteralUtil::CreateR2FromArray2D(Array2D(10, 0))); } TEST_F(RoundTripTransferTest, R2F32_Len2x2) { - RoundTripTest(*Literal::CreateR2({{42.0, 64.0}, {77.0, 88.0}})); + RoundTripTest(*LiteralUtil::CreateR2({{42.0, 64.0}, {77.0, 88.0}})); } TEST_F(RoundTripTransferTest, R3F32) { RoundTripTest( - *Literal::CreateR3({{{1.0, 2.0}, {1.0, 2.0}, {1.0, 2.0}}, - {{3.0, 4.0}, {3.0, 4.0}, {3.0, 4.0}}})); + *LiteralUtil::CreateR3({{{1.0, 2.0}, {1.0, 2.0}, {1.0, 2.0}}, + {{3.0, 4.0}, {3.0, 4.0}, {3.0, 4.0}}})); } TEST_F(RoundTripTransferTest, R4F32) { - RoundTripTest(*Literal::CreateR4({{ + RoundTripTest(*LiteralUtil::CreateR4({{ {{10, 11, 12, 13}, {14, 15, 16, 17}}, {{18, 19, 20, 21}, {22, 23, 24, 25}}, {{26, 27, 28, 29}, {30, 31, 32, 33}}, @@ -108,33 +109,36 @@ TEST_F(RoundTripTransferTest, R4F32) { } TEST_F(RoundTripTransferTest, EmptyTuple) { - RoundTripTest(*Literal::MakeTuple({})); + RoundTripTest(*LiteralUtil::MakeTuple({})); } TEST_F(RoundTripTransferTest, TupleOfR1F32) { - RoundTripTest(*Literal::MakeTuple({Literal::CreateR1({1, 2}).get(), - Literal::CreateR1({3, 4}).get()})); + RoundTripTest( + *LiteralUtil::MakeTuple({LiteralUtil::CreateR1({1, 2}).get(), + LiteralUtil::CreateR1({3, 4}).get()})); } TEST_F(RoundTripTransferTest, TupleOfR1F32_Len0_Len2) { - RoundTripTest(*Literal::MakeTuple({Literal::CreateR1({}).get(), - Literal::CreateR1({3, 4}).get()})); + RoundTripTest( + *LiteralUtil::MakeTuple({LiteralUtil::CreateR1({}).get(), + LiteralUtil::CreateR1({3, 4}).get()})); } TEST_F(RoundTripTransferTest, TupleOfR0F32AndR1S32) { - RoundTripTest(*Literal::MakeTuple({Literal::CreateR0(1.0).get(), - Literal::CreateR1({2, 3}).get()})); + RoundTripTest( + *LiteralUtil::MakeTuple({LiteralUtil::CreateR0(1.0).get(), + LiteralUtil::CreateR1({2, 3}).get()})); } // Below two tests are added to identify the cost of large data transfers. TEST_F(RoundTripTransferTest, R2F32_Large) { - RoundTripTest(*Literal::CreateR2F32Linspace(-1.0f, 1.0f, 512, 512)); + RoundTripTest(*LiteralUtil::CreateR2F32Linspace(-1.0f, 1.0f, 512, 512)); } TEST_F(RoundTripTransferTest, R4F32_Large) { Array4D array4d(2, 2, 256, 256); array4d.FillWithMultiples(1.0f); - RoundTripTest(*Literal::CreateR4FromArray4D(array4d)); + RoundTripTest(*LiteralUtil::CreateR4FromArray4D(array4d)); } } // namespace diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc index 3afd8c8fc88a3879cc524c2d1680e8b176b55f81..e42c71eb284deb2e50d6ea4b47fa707e4bc14ffc 100644 --- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc +++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc @@ -19,8 +19,9 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" @@ -162,7 +163,7 @@ XLA_TEST_F(ScalarComputationsTest, CastS64ToF32) { ConvertElementType(a, F32); int64 value = 3LL << 35; - std::unique_ptr a_literal = Literal::CreateR0(value); + std::unique_ptr a_literal = LiteralUtil::CreateR0(value); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); ComputeAndCompareR0(&builder, static_cast(value), @@ -226,9 +227,9 @@ XLA_TEST_F(ScalarComputationsTest, MulThreeScalarsS32) { XLA_TEST_F(ScalarComputationsTest, MulThreeScalarsF32Params) { XlaBuilder builder(TestName()); - std::unique_ptr a_literal = Literal::CreateR0(2.1f); - std::unique_ptr b_literal = Literal::CreateR0(5.5f); - std::unique_ptr c_literal = Literal::CreateR0(0.5f); + std::unique_ptr a_literal = LiteralUtil::CreateR0(2.1f); + std::unique_ptr b_literal = LiteralUtil::CreateR0(5.5f); + std::unique_ptr c_literal = LiteralUtil::CreateR0(0.5f); std::unique_ptr a_data = client_->TransferToServer(*a_literal).ConsumeValueOrDie(); @@ -375,8 +376,8 @@ XLA_TEST_F(ScalarComputationsTest, DivU32s) { for (uint32 divisor : vals) { if (divisor != 0) { for (uint32 dividend : vals) { - auto dividend_literal = Literal::CreateR0(dividend); - auto divisor_literal = Literal::CreateR0(divisor); + auto dividend_literal = LiteralUtil::CreateR0(dividend); + auto divisor_literal = LiteralUtil::CreateR0(divisor); TF_ASSERT_OK_AND_ASSIGN(auto dividend_data, client_->TransferToServer(*dividend_literal)); TF_ASSERT_OK_AND_ASSIGN(auto divisor_data, @@ -387,7 +388,8 @@ XLA_TEST_F(ScalarComputationsTest, DivU32s) { {dividend_data.get(), divisor_data.get()}, &execution_options_) .ConsumeValueOrDie(); - auto expected_literal = Literal::CreateR0(dividend / divisor); + auto expected_literal = + LiteralUtil::CreateR0(dividend / divisor); EXPECT_TRUE(LiteralTestUtil::Equal(*expected_literal, *actual_literal)); } } @@ -416,8 +418,8 @@ XLA_TEST_F(ScalarComputationsTest, RemU32s) { for (uint32 divisor : vals) { if (divisor != 0) { for (uint32 dividend : vals) { - auto dividend_literal = Literal::CreateR0(dividend); - auto divisor_literal = Literal::CreateR0(divisor); + auto dividend_literal = LiteralUtil::CreateR0(dividend); + auto divisor_literal = LiteralUtil::CreateR0(divisor); TF_ASSERT_OK_AND_ASSIGN(auto dividend_data, client_->TransferToServer(*dividend_literal)); TF_ASSERT_OK_AND_ASSIGN(auto divisor_data, @@ -428,7 +430,8 @@ XLA_TEST_F(ScalarComputationsTest, RemU32s) { {dividend_data.get(), divisor_data.get()}, &execution_options_) .ConsumeValueOrDie(); - auto expected_literal = Literal::CreateR0(dividend % divisor); + auto expected_literal = + LiteralUtil::CreateR0(dividend % divisor); EXPECT_TRUE(LiteralTestUtil::Equal(*expected_literal, *actual_literal)); } } @@ -440,7 +443,7 @@ XLA_TEST_F(ScalarComputationsTest, RemainderTwoScalarsNonConstDividendS32) { auto x = Parameter(&builder, 0, ShapeUtil::MakeShape(S32, {}), "x"); Rem(x, ConstantR0(&builder, 80000)); - std::unique_ptr literal = Literal::CreateR0(87919); + std::unique_ptr literal = LiteralUtil::CreateR0(87919); TF_ASSERT_OK_AND_ASSIGN(auto input_data, client_->TransferToServer(*literal)); ComputeAndCompareR0(&builder, 7919, {input_data.get()}); } diff --git a/tensorflow/compiler/xla/tests/select_and_scatter_test.cc b/tensorflow/compiler/xla/tests/select_and_scatter_test.cc index 0a173fbbbd5cb5e5005728331561008b8b29af26..e3d4f98dd7432d1dce7e697586e8b17105dc82e7 100644 --- a/tensorflow/compiler/xla/tests/select_and_scatter_test.cc +++ b/tensorflow/compiler/xla/tests/select_and_scatter_test.cc @@ -22,10 +22,10 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" diff --git a/tensorflow/compiler/xla/tests/select_test.cc b/tensorflow/compiler/xla/tests/select_test.cc index 59409ab26e1c19a8271318c18e19caa7b8ddc3b7..1c01402798658877889527a5dd02d5c74787ff99 100644 --- a/tensorflow/compiler/xla/tests/select_test.cc +++ b/tensorflow/compiler/xla/tests/select_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" diff --git a/tensorflow/compiler/xla/tests/slice_test.cc b/tensorflow/compiler/xla/tests/slice_test.cc index 3e5c01d6d47cc3f3b7d46ce300fe26c5ec9e63fa..b8ad6668f80a3002eff3cc458997966ee67c8d4b 100644 --- a/tensorflow/compiler/xla/tests/slice_test.cc +++ b/tensorflow/compiler/xla/tests/slice_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" @@ -170,7 +170,7 @@ XLA_TEST_F(SliceTest, StridedSliceR4WithOutputLayout) { values.FillRandom(3.14f); auto expected = ReferenceUtil::Slice4D(values, {{0, 0, 0, 0}}, {{2, 4, 6, 8}}, /*strides=*/{{1, 1, 2, 1}}); - auto expected_literal = Literal::CreateR4FromArray4DWithLayout( + auto expected_literal = LiteralUtil::CreateR4FromArray4DWithLayout( *expected, LayoutUtil::MakeLayout({0, 1, 2, 3})); XlaBuilder builder(TestName()); auto original = ConstantR4FromArray4D(&builder, values); @@ -197,7 +197,7 @@ class SliceR1Test : public ClientLibraryTestBase, // vector. tensorflow::gtl::InlinedVector input(spec.input_dim0); std::iota(input.begin(), input.end(), NativeT()); - auto literal = Literal::CreateR1(input); + auto literal = LiteralUtil::CreateR1(input); XlaBuilder builder(TestName()); auto original = Parameter(&builder, 0, literal->shape(), "p0"); @@ -344,7 +344,11 @@ INSTANTIATE_TEST_CASE_P( R1Spec{1024 * 1024 + 71, 3, 1024 * 512 - 9, 2}, R1Spec{1024 * 1024 + 71, 3, 1024 * 512 - 9, 8}, R1Spec{1024 * 1024 + 71, 3, 1024 * 512 - 9, 7}, - R1Spec{1024 * 1024 + 71, 3, 1024 * 512 - 9, 125} + R1Spec{1024 * 1024 + 71, 3, 1024 * 512 - 9, 125}, + R1Spec{16 * 1024 * 1024, 0, 16 * 1024 * 1024, 4097}, + R1Spec{16 * 1024 * 1024, 0, 16 * 1024 * 1024, 4093}, + R1Spec{16 * 1024 * 1024, 12 * 1024 + 17, 16 * 1024 * 1024 - 231, 4097}, + R1Spec{16 * 1024 * 1024, 12 * 1024 + 17, 16 * 1024 * 1024 - 231, 4093} ), SliceR1TestDataToString ); @@ -368,7 +372,7 @@ XLA_TEST_P(SliceR2Test, DoIt) { const R2Spec& spec = GetParam(); Array2D input(spec.input_dim0, spec.input_dim1); input.FillUnique(); - auto literal = Literal::CreateR2FromArray2DWithLayout( + auto literal = LiteralUtil::CreateR2FromArray2DWithLayout( input, LayoutUtil::MakeLayout(spec.layout)); XlaBuilder builder(TestName()); @@ -463,7 +467,7 @@ class SliceR4Test : public ClientLibraryTestBase, auto expected = ReferenceUtil::Slice4D( values, spec.slice_starts, spec.slice_limits, spec.slice_strides); XlaBuilder builder(TestName()); - auto literal = Literal::CreateR4FromArray4DWithLayout( + auto literal = LiteralUtil::CreateR4FromArray4DWithLayout( values, LayoutUtil::MakeLayout(spec.input_layout)); auto parameter = Parameter(&builder, 0, literal->shape(), "p0"); TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr arg, diff --git a/tensorflow/compiler/xla/tests/test_utils.cc b/tensorflow/compiler/xla/tests/test_utils.cc index 20c7c30878a2821915d47bcf9fa1cc53907df9da..2647937013222ccfdae98b0c1d141f461020b5c9 100644 --- a/tensorflow/compiler/xla/tests/test_utils.cc +++ b/tensorflow/compiler/xla/tests/test_utils.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/tests/test_utils.h" +#include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/service/hlo_dataflow_analysis.h" #include "tensorflow/compiler/xla/service/hlo_verifier.h" @@ -110,7 +111,7 @@ StatusOr> MakeFakeLiteralInternal( MakeFakeLiteralInternal(element_shape, engine)); elements.push_back(std::move(element)); } - return Literal::MakeTupleOwned(std::move(elements)); + return LiteralUtil::MakeTupleOwned(std::move(elements)); } if (engine == nullptr) { return Literal::CreateFromShape(shape); @@ -220,7 +221,7 @@ std::unique_ptr MakeRandomNonwrappingSliceIndex( start_indices[i] = generator(*engine); } } - return Literal::CreateR1(start_indices); + return LiteralUtil::CreateR1(start_indices); } // Use dataflow analysis on each parameter to see if there are uses that would @@ -318,9 +319,9 @@ StatusOr> CreateLiteralForConstrainedUses( } else if (needs_constant != nullptr) { switch (constant_type) { case ConstantType::kZero: - return Literal::Zero(param.shape().element_type()).CloneToUnique(); + return LiteralUtil::Zero(param.shape().element_type()).CloneToUnique(); case ConstantType::kOne: - return Literal::One(param.shape().element_type()).CloneToUnique(); + return LiteralUtil::One(param.shape().element_type()).CloneToUnique(); case ConstantType::kUnknown: // We want the identity element for the computation, but we don't really // know what it is - so any value we generate will be just as wrong. diff --git a/tensorflow/compiler/xla/tests/test_utils.h b/tensorflow/compiler/xla/tests/test_utils.h index a8689f64981569ceb7c8a712f8ece00c99e8cf2d..e59f215a9a3ace80d7a23e1bbc40970c7a63ea0d 100644 --- a/tensorflow/compiler/xla/tests/test_utils.h +++ b/tensorflow/compiler/xla/tests/test_utils.h @@ -21,7 +21,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/xla_data.pb.h" diff --git a/tensorflow/compiler/xla/tests/test_utils_test.cc b/tensorflow/compiler/xla/tests/test_utils_test.cc index 8f424ae81f592bfd8accd8decb8fc363f7561c73..a2f0338e25977d7c76dbc48b3afc649b77ba4ee2 100644 --- a/tensorflow/compiler/xla/tests/test_utils_test.cc +++ b/tensorflow/compiler/xla/tests/test_utils_test.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/tests/test_utils.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/local_client_test_base.h" diff --git a/tensorflow/compiler/xla/tests/token_hlo_test.cc b/tensorflow/compiler/xla/tests/token_hlo_test.cc index e9008fa48aa7d0158bd2221791be23c128859098..2bdbd08309a81b201fc224110805549f7fb5bb55 100644 --- a/tensorflow/compiler/xla/tests/token_hlo_test.cc +++ b/tensorflow/compiler/xla/tests/token_hlo_test.cc @@ -31,21 +31,21 @@ class TokenHloTest : public HloTestBase {}; XLA_TEST_F(TokenHloTest, SingleTokenInstruction) { std::unique_ptr module = CreateNewModule(); auto builder = HloComputation::Builder(TestName()); - builder.AddInstruction(HloInstruction::CreateAfterAll({})); + builder.AddInstruction(HloInstruction::CreateToken()); module->AddEntryComputation(builder.Build()); TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, Execute(std::move(module), {})); - EXPECT_TRUE(LiteralTestUtil::Equal(*result, *Literal::CreateToken())); + EXPECT_TRUE(LiteralTestUtil::Equal(*result, *LiteralUtil::CreateToken())); } XLA_TEST_F(TokenHloTest, TokenTree) { std::unique_ptr module = CreateNewModule(); auto builder = HloComputation::Builder(TestName()); - auto token0 = builder.AddInstruction(HloInstruction::CreateAfterAll({})); - auto token1 = builder.AddInstruction(HloInstruction::CreateAfterAll({})); - auto token2 = builder.AddInstruction(HloInstruction::CreateAfterAll({})); + auto token0 = builder.AddInstruction(HloInstruction::CreateToken()); + auto token1 = builder.AddInstruction(HloInstruction::CreateToken()); + auto token2 = builder.AddInstruction(HloInstruction::CreateToken()); builder.AddInstruction( HloInstruction::CreateAfterAll({token0, token0, token1, token2})); @@ -53,7 +53,7 @@ XLA_TEST_F(TokenHloTest, TokenTree) { TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, Execute(std::move(module), {})); - EXPECT_TRUE(LiteralTestUtil::Equal(*result, *Literal::CreateToken())); + EXPECT_TRUE(LiteralTestUtil::Equal(*result, *LiteralUtil::CreateToken())); } XLA_TEST_F(TokenHloTest, InvalidTokenShapedEntryParameter) { @@ -64,7 +64,7 @@ XLA_TEST_F(TokenHloTest, InvalidTokenShapedEntryParameter) { builder.AddInstruction( HloInstruction::CreateParameter(1, ShapeUtil::MakeTokenShape(), "p1")); builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(42))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(42))); module->AddEntryComputation(builder.Build()); Status status = HloVerifier().Run(module.get()).status(); @@ -98,7 +98,7 @@ XLA_TEST_F(TokenHloTest, InvalidOperandToTokenInstruction) { HloInstruction::CreateParameter(0, ShapeUtil::MakeShape(F32, {}), "p0")); builder.AddInstruction(HloInstruction::CreateAfterAll({param})); builder.AddInstruction( - HloInstruction::CreateConstant(Literal::CreateR0(123))); + HloInstruction::CreateConstant(LiteralUtil::CreateR0(123))); module->AddEntryComputation(builder.Build()); Status status = HloVerifier().Run(module.get()).status(); @@ -184,7 +184,7 @@ ENTRY %TokenInConditional (param.3: pred[]) -> s32[] { TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr module, HloRunner::CreateModuleFromString(module_string, debug_options)); - auto arg = Literal::CreateR0(true); + auto arg = LiteralUtil::CreateR0(true); TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, Execute(std::move(module), {arg.get()})); EXPECT_EQ(42, result->Get({})); @@ -195,7 +195,7 @@ ENTRY %TokenInConditional (param.3: pred[]) -> s32[] { TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr module, HloRunner::CreateModuleFromString(module_string, debug_options)); - auto arg = Literal::CreateR0(false); + auto arg = LiteralUtil::CreateR0(false); TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr result, Execute(std::move(module), {arg.get()})); EXPECT_EQ(7, result->Get({})); diff --git a/tensorflow/compiler/xla/tests/transfer_manager_test.cc b/tensorflow/compiler/xla/tests/transfer_manager_test.cc index 86babb58c9d4515935a5904e04e8fea1074a2812..125513ddfd16cb4e742e7d589e22b721307621ee 100644 --- a/tensorflow/compiler/xla/tests/transfer_manager_test.cc +++ b/tensorflow/compiler/xla/tests/transfer_manager_test.cc @@ -18,10 +18,11 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" #include "tensorflow/compiler/xla/service/generic_transfer_manager.h" #include "tensorflow/compiler/xla/service/shaped_buffer.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" @@ -60,7 +61,7 @@ class TransferManagerTest : public LocalClientTestBase { } protected: - Backend::StreamPtr stream_ptr_; + StreamPool::Ptr stream_ptr_; se::Stream* stream_; private: @@ -68,7 +69,7 @@ class TransferManagerTest : public LocalClientTestBase { }; XLA_TEST_F(TransferManagerTest, TransferR0U32) { - std::unique_ptr literal = Literal::CreateR0(42); + std::unique_ptr literal = LiteralUtil::CreateR0(42); const Shape& shape = literal->shape(); auto device_buffer = AllocateDeviceBuffer(shape); @@ -84,7 +85,7 @@ XLA_TEST_F(TransferManagerTest, TransferR0U32) { XLA_TEST_F(TransferManagerTest, TransferR1F32) { std::unique_ptr literal = - Literal::CreateR1({1.25f, 2.5f, -17.0f, -20.125f}); + LiteralUtil::CreateR1({1.25f, 2.5f, -17.0f, -20.125f}); const Shape& shape = literal->shape(); auto device_buffer = AllocateDeviceBuffer(shape); @@ -102,7 +103,7 @@ XLA_TEST_F(TransferManagerTest, TransferR1F32) { XLA_TEST_F(TransferManagerTest, TransferR1LargeF32) { std::vector test_vector(1024 * 1024); std::iota(test_vector.begin(), test_vector.end(), 0); - std::unique_ptr literal = Literal::CreateR1(test_vector); + std::unique_ptr literal = LiteralUtil::CreateR1(test_vector); const Shape& shape = literal->shape(); auto device_buffer = AllocateDeviceBuffer(shape); @@ -118,7 +119,7 @@ XLA_TEST_F(TransferManagerTest, TransferR1LargeF32) { XLA_TEST_F(TransferManagerTest, TransferR1U8) { const char* test_string = "0123456789abcdef"; - std::unique_ptr literal = Literal::CreateR1U8(test_string); + std::unique_ptr literal = LiteralUtil::CreateR1U8(test_string); const Shape& shape = literal->shape(); auto device_buffer = AllocateDeviceBuffer(shape); @@ -134,7 +135,7 @@ XLA_TEST_F(TransferManagerTest, TransferR1U8) { XLA_TEST_F(TransferManagerTest, TransferR2F32) { std::unique_ptr literal = - Literal::CreateR2({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}); + LiteralUtil::CreateR2({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}); const Shape& shape = literal->shape(); auto device_buffer = AllocateDeviceBuffer(shape); @@ -151,7 +152,7 @@ XLA_TEST_F(TransferManagerTest, TransferR2F32) { XLA_TEST_F(TransferManagerTest, TransferR2F32AndChangeLayoutTransferringToDevice) { - std::unique_ptr literal = Literal::CreateR2WithLayout( + std::unique_ptr literal = LiteralUtil::CreateR2WithLayout( {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, LayoutUtil::MakeLayout({0, 1})); const Shape ondevice_shape = ShapeUtil::MakeShapeWithLayout(F32, {2, 3}, {1, 0}); @@ -172,10 +173,10 @@ XLA_TEST_F(TransferManagerTest, } XLA_TEST_F(TransferManagerTest, TransferTuple) { - std::unique_ptr literal = Literal::MakeTuple( - {Literal::CreateR0(123.0f).get(), - Literal::CreateR2({{1.0f, 2.0f}, {4.0f, 5.0f}}).get(), - Literal::CreateR1({44.0f, -10.0f, 3333333.3f}).get()}); + std::unique_ptr literal = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(123.0f).get(), + LiteralUtil::CreateR2({{1.0f, 2.0f}, {4.0f, 5.0f}}).get(), + LiteralUtil::CreateR1({44.0f, -10.0f, 3333333.3f}).get()}); auto device_buffer = AllocateDeviceBuffer(literal->shape()); // Round trip literal through device. @@ -189,7 +190,7 @@ XLA_TEST_F(TransferManagerTest, TransferTuple) { } XLA_TEST_F(TransferManagerTest, TransferEmptyTuple) { - std::unique_ptr literal = Literal::MakeTuple({}); + std::unique_ptr literal = LiteralUtil::MakeTuple({}); auto device_buffer = AllocateDeviceBuffer(literal->shape()); // Round trip literal through device. @@ -203,13 +204,13 @@ XLA_TEST_F(TransferManagerTest, TransferEmptyTuple) { } XLA_TEST_F(TransferManagerTest, TransferNestedTuple) { - std::unique_ptr literal = Literal::MakeTuple( - {Literal::CreateR0(123.0f).get(), - Literal::MakeTuple( - {Literal::CreateR2({{1.0f, 2.0f}, {4.0f, 5.0f}}).get(), - Literal::CreateR1({44.0f, -10.0f, 3333333.3f}).get()}) + std::unique_ptr literal = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(123.0f).get(), + LiteralUtil::MakeTuple( + {LiteralUtil::CreateR2({{1.0f, 2.0f}, {4.0f, 5.0f}}).get(), + LiteralUtil::CreateR1({44.0f, -10.0f, 3333333.3f}).get()}) .get(), - Literal::CreateR1({-10.0f, 123.0f}).get()}); + LiteralUtil::CreateR1({-10.0f, 123.0f}).get()}); auto device_buffer = AllocateDeviceBuffer(literal->shape()); // Round trip literal through device. @@ -223,7 +224,7 @@ XLA_TEST_F(TransferManagerTest, TransferNestedTuple) { } XLA_TEST_F(TransferManagerTest, TransferComplexValue) { - std::unique_ptr literal = Literal::CreateR1( + std::unique_ptr literal = LiteralUtil::CreateR1( {complex64(1.0f, 2.0f), complex64(42.0f, -123.4f)}); auto device_buffer = AllocateDeviceBuffer(literal->shape()); @@ -238,12 +239,12 @@ XLA_TEST_F(TransferManagerTest, TransferComplexValue) { } XLA_TEST_F(TransferManagerTest, TransferComplexValueInTuple) { - std::unique_ptr literal = Literal::MakeTuple( - {Literal::CreateR1( + std::unique_ptr literal = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR1( {complex64(1.0f, 2.0f), complex64(42.0f, -123.4f)}) .get(), - Literal::CreateR1({1, 2, 3, 4, 5, 6}).get(), - Literal::CreateR0(complex64(0.3f, -0.4f)).get()}); + LiteralUtil::CreateR1({1, 2, 3, 4, 5, 6}).get(), + LiteralUtil::CreateR0(complex64(0.3f, -0.4f)).get()}); auto device_buffer = AllocateDeviceBuffer(literal->shape()); // Round trip literal through device. @@ -265,25 +266,25 @@ XLA_TEST_F(TransferManagerTest, TransferTokenFromDevice) { TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr result, transfer_manager_->TransferLiteralFromDevice(stream_, device_buffer)); - EXPECT_TRUE(LiteralTestUtil::Equal(*Literal::CreateToken(), *result)); + EXPECT_TRUE(LiteralTestUtil::Equal(*LiteralUtil::CreateToken(), *result)); } XLA_TEST_F(TransferManagerTest, MultiStreamRoundTripSoak) { const int64 kIterationCount = 5000; - std::unique_ptr literal1 = Literal::MakeTuple( - {Literal::CreateR0(123.0f).get(), - Literal::MakeTuple( - {Literal::CreateR2({{1.0f, 2.0f}, {4.0f, 5.0f}}).get(), - Literal::CreateR1({44.0f, -10.0f, 3333333.3f}).get()}) + std::unique_ptr literal1 = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(123.0f).get(), + LiteralUtil::MakeTuple( + {LiteralUtil::CreateR2({{1.0f, 2.0f}, {4.0f, 5.0f}}).get(), + LiteralUtil::CreateR1({44.0f, -10.0f, 3333333.3f}).get()}) .get(), - Literal::CreateR1({-10.0f, 123.0f}).get()}); - std::unique_ptr literal2 = Literal::MakeTuple( - {Literal::CreateR0(456.0f).get(), - Literal::MakeTuple( - {Literal::CreateR2({{5.0f, 7.0f}, {9.0f, 4.0f}}).get(), - Literal::CreateR1({44.0f, -11.0f, 3333333.3f}).get()}) + LiteralUtil::CreateR1({-10.0f, 123.0f}).get()}); + std::unique_ptr literal2 = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(456.0f).get(), + LiteralUtil::MakeTuple( + {LiteralUtil::CreateR2({{5.0f, 7.0f}, {9.0f, 4.0f}}).get(), + LiteralUtil::CreateR1({44.0f, -11.0f, 3333333.3f}).get()}) .get(), - Literal::CreateR1({-98.0f, 153.0f}).get()}); + LiteralUtil::CreateR1({-98.0f, 153.0f}).get()}); auto device_buffer1 = AllocateDeviceBuffer(literal1->shape()); auto device_buffer2 = AllocateDeviceBuffer(literal2->shape()); @@ -325,10 +326,10 @@ class TransferDeviceToHostBenchmark : public TransferManagerTest { std::vector> tuple_elements; for (int i = 0; i < num_tuple_elements; ++i) { tuple_elements.push_back( - Literal::CreateR2F32Linspace(0.0f, 1.0f, array_size, array_size)); + LiteralUtil::CreateR2F32Linspace(0.0f, 1.0f, array_size, array_size)); } std::unique_ptr literal = - Literal::MakeTupleOwned(std::move(tuple_elements)); + LiteralUtil::MakeTupleOwned(std::move(tuple_elements)); auto device_buffer = AllocateDeviceBuffer(literal->shape()); TF_CHECK_OK(transfer_manager_->TransferLiteralToDevice(stream_, *literal, device_buffer)); @@ -357,10 +358,10 @@ class TransferHostToDeviceBenchmark : public TransferManagerTest { std::vector> tuple_elements; for (int i = 0; i < num_tuple_elements; ++i) { tuple_elements.push_back( - Literal::CreateR2F32Linspace(0.0f, 1.0f, array_size, array_size)); + LiteralUtil::CreateR2F32Linspace(0.0f, 1.0f, array_size, array_size)); } std::unique_ptr literal = - Literal::MakeTupleOwned(std::move(tuple_elements)); + LiteralUtil::MakeTupleOwned(std::move(tuple_elements)); auto device_buffer = AllocateDeviceBuffer(literal->shape()); tensorflow::testing::StartTiming(); for (int i = 0; i < iters; ++i) { diff --git a/tensorflow/compiler/xla/tests/transpose_test.cc b/tensorflow/compiler/xla/tests/transpose_test.cc index 6ebb4324f8d20ed9f8886d92b0513441685ed19b..fbe9d1b64aa0c06d65b547c45cfa981800d40ff3 100644 --- a/tensorflow/compiler/xla/tests/transpose_test.cc +++ b/tensorflow/compiler/xla/tests/transpose_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc index ec11508891d13f8032a1ebec388c756cf6d752c7..2fd70b72b52f360fc74a73cd13d401b7dac6e708 100644 --- a/tensorflow/compiler/xla/tests/tuple_test.cc +++ b/tensorflow/compiler/xla/tests/tuple_test.cc @@ -18,8 +18,8 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" @@ -29,6 +29,7 @@ limitations under the License. #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" #include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/test.h" namespace xla { @@ -49,10 +50,10 @@ XLA_TEST_F(TupleTest, TupleConstant) { {1.1f, 2.2f, 3.5f}, // row 0 {4.8f, 5.0f, 6.7f}, // row 1 }; - auto value = - Literal::MakeTuple({Literal::CreateR0(constant_scalar).get(), - Literal::CreateR1(constant_vector).get(), - Literal::CreateR2(constant_matrix).get()}); + auto value = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(constant_scalar).get(), + LiteralUtil::CreateR1(constant_vector).get(), + LiteralUtil::CreateR2(constant_matrix).get()}); ConstantLiteral(&builder, *value); ComputeAndCompareTuple(&builder, *value, {}, error_spec_); @@ -64,9 +65,9 @@ XLA_TEST_F(TupleTest, TupleScalarConstant) { const float constant_scalar1 = 7.3f; const float constant_scalar2 = 1.2f; - auto value = - Literal::MakeTuple({Literal::CreateR0(constant_scalar1).get(), - Literal::CreateR0(constant_scalar2).get()}); + auto value = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(constant_scalar1).get(), + LiteralUtil::CreateR0(constant_scalar2).get()}); ConstantLiteral(&builder, *value); ComputeAndCompareTuple(&builder, *value, {}, error_spec_); @@ -86,10 +87,10 @@ XLA_TEST_F(TupleTest, TupleCreate) { ConstantR1(&builder, constant_vector), ConstantR2(&builder, constant_matrix)}); - auto expected = - Literal::MakeTuple({Literal::CreateR0(constant_scalar).get(), - Literal::CreateR1(constant_vector).get(), - Literal::CreateR2(constant_matrix).get()}); + auto expected = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0(constant_scalar).get(), + LiteralUtil::CreateR1(constant_vector).get(), + LiteralUtil::CreateR2(constant_matrix).get()}); ComputeAndCompareTuple(&builder, *expected, {}, error_spec_); } @@ -100,8 +101,9 @@ XLA_TEST_F(TupleTest, TupleCreateWithZeroElementEntry) { Tuple(&builder, {ConstantR0(&builder, 7.0), ConstantR1(&builder, {})}); - auto expected = Literal::MakeTuple({Literal::CreateR0(7.0).get(), - Literal::CreateR1({}).get()}); + auto expected = + LiteralUtil::MakeTuple({LiteralUtil::CreateR0(7.0).get(), + LiteralUtil::CreateR1({}).get()}); ComputeAndCompareTuple(&builder, *expected, {}, error_spec_); } @@ -109,7 +111,7 @@ XLA_TEST_F(TupleTest, TupleCreateWithZeroElementEntry) { XLA_TEST_F(TupleTest, EmptyTupleCreate) { XlaBuilder builder(TestName()); Tuple(&builder, {}); - auto expected = Literal::MakeTuple({}); + auto expected = LiteralUtil::MakeTuple({}); ComputeAndCompareTuple(&builder, *expected, {}, error_spec_); } @@ -193,9 +195,9 @@ XLA_TEST_F(TupleTest, TupleGTEToTuple) { ConstantR2(&builder, constant_matrix)}); Tuple(&builder, {GetTupleElement(tuple_data, 1), GetTupleElement(tuple_data, 0)}); - auto expected = - Literal::MakeTuple({Literal::CreateR2(constant_matrix).get(), - Literal::CreateR1(constant_vector).get()}); + auto expected = LiteralUtil::MakeTuple( + {LiteralUtil::CreateR2(constant_matrix).get(), + LiteralUtil::CreateR1(constant_vector).get()}); ComputeAndCompareTuple(&builder, *expected, {}, error_spec_); } @@ -216,8 +218,8 @@ XLA_TEST_F(TupleTest, SelectBetweenPredTuples) { auto v2_v1 = Tuple(&b, {v2_gt, v1_gt}); // {true, false} Select(direction ? v1_gt : v2_gt, v1_v2, v2_v1); auto expected = - Literal::MakeTuple({Literal::CreateR0(direction).get(), - Literal::CreateR0(!direction).get()}); + LiteralUtil::MakeTuple({LiteralUtil::CreateR0(direction).get(), + LiteralUtil::CreateR0(!direction).get()}); ComputeAndCompareTuple(&b, *expected, {v1_data.get(), v2_data.get()}, error_spec_); @@ -284,8 +286,9 @@ XLA_TEST_F(TupleTest, SelectBetweenTuplesOnFalse) { ConstantR1(&builder, vec1)}); Select(ConstantR0(&builder, false), tuple12, tuple21); - auto expected = Literal::MakeTuple({Literal::CreateR1(vec2).get(), - Literal::CreateR1(vec1).get()}); + auto expected = + LiteralUtil::MakeTuple({LiteralUtil::CreateR1(vec2).get(), + LiteralUtil::CreateR1(vec1).get()}); ComputeAndCompareTuple(&builder, *expected, {}, error_spec_); } @@ -328,8 +331,9 @@ XLA_TEST_F(TupleTest, SelectBetweenTuplesOnTrue) { ConstantR1(&builder, vec1)}); Select(ConstantR0(&builder, true), tuple12, tuple21); - auto expected = Literal::MakeTuple({Literal::CreateR1(vec1).get(), - Literal::CreateR1(vec2).get()}); + auto expected = + LiteralUtil::MakeTuple({LiteralUtil::CreateR1(vec1).get(), + LiteralUtil::CreateR1(vec2).get()}); ComputeAndCompareTuple(&builder, *expected, {}, error_spec_); } @@ -403,8 +407,9 @@ XLA_TEST_F(TupleTest, SelectBetweenTuplesReuseConstants) { Select(ConstantR0(&builder, false), tuple12, tuple21); - auto expected = Literal::MakeTuple({Literal::CreateR1(vec2).get(), - Literal::CreateR1(vec1).get()}); + auto expected = + LiteralUtil::MakeTuple({LiteralUtil::CreateR1(vec2).get(), + LiteralUtil::CreateR1(vec1).get()}); ComputeAndCompareTuple(&builder, *expected, {}, error_spec_); } @@ -414,13 +419,13 @@ XLA_TEST_F(TupleTest, NestedTuples) { ConstantR0(&builder, 42.0)}); Tuple(&builder, {inner_tuple, ConstantR1(&builder, {22.0, 44.0})}); - auto expected_v1 = Literal::CreateR1({1.0, 2.0}); - auto expected_s = Literal::CreateR0(42.0); + auto expected_v1 = LiteralUtil::CreateR1({1.0, 2.0}); + auto expected_s = LiteralUtil::CreateR0(42.0); auto expected_inner_tuple = - Literal::MakeTuple({expected_v1.get(), expected_s.get()}); - auto expected_v2 = Literal::CreateR1({22.0, 44.0}); + LiteralUtil::MakeTuple({expected_v1.get(), expected_s.get()}); + auto expected_v2 = LiteralUtil::CreateR1({22.0, 44.0}); auto expected = - Literal::MakeTuple({expected_inner_tuple.get(), expected_v2.get()}); + LiteralUtil::MakeTuple({expected_inner_tuple.get(), expected_v2.get()}); ComputeAndCompareTuple(&builder, *expected, {}, error_spec_); } @@ -440,14 +445,14 @@ XLA_TEST_F(TupleTest, GetTupleElementOfNestedTuple) { std::unique_ptr data = client_ - ->TransferToServer(*Literal::MakeTuple({ - Literal::MakeTuple( + ->TransferToServer(*LiteralUtil::MakeTuple({ + LiteralUtil::MakeTuple( { - Literal::CreateR1({1.0, 2.0, 3.0}).get(), - Literal::CreateR1({4.0, 5.0, 6.0}).get(), + LiteralUtil::CreateR1({1.0, 2.0, 3.0}).get(), + LiteralUtil::CreateR1({4.0, 5.0, 6.0}).get(), }) .get(), - Literal::CreateR1({7.0, 8.0, 9.0}).get(), + LiteralUtil::CreateR1({7.0, 8.0, 9.0}).get(), })) .ConsumeValueOrDie(); @@ -478,11 +483,12 @@ XLA_TEST_F(TupleTest, ComplexTuples) { std::unique_ptr arg0 = client_ - ->TransferToServer(*Literal::MakeTuple( - {Literal::CreateR0({1, 2}).get(), - Literal::MakeTuple( - {Literal::CreateR1({{10, 20}, {30, 40}}).get(), - Literal::CreateR2( + ->TransferToServer(*LiteralUtil::MakeTuple( + {LiteralUtil::CreateR0({1, 2}).get(), + LiteralUtil::MakeTuple( + {LiteralUtil::CreateR1({{10, 20}, {30, 40}}) + .get(), + LiteralUtil::CreateR2( {{{100, 200}, {300, 400}}, {{1000, 2000}, {3000, 4000}}, {{10000, 20000}, {30000, 40000}}}) @@ -491,11 +497,13 @@ XLA_TEST_F(TupleTest, ComplexTuples) { .ConsumeValueOrDie(); std::unique_ptr arg1 = client_ - ->TransferToServer(*Literal::CreateR1({{1, 2}, {1, -2}})) + ->TransferToServer( + *LiteralUtil::CreateR1({{1, 2}, {1, -2}})) .ConsumeValueOrDie(); - auto sum = Literal::CreateR2({{{111, 222}, {331, 442}}, - {{1011, 2022}, {3031, 4042}}, - {{10011, 20022}, {30031, 40042}}}); + auto sum = + LiteralUtil::CreateR2({{{111, 222}, {331, 442}}, + {{1011, 2022}, {3031, 4042}}, + {{10011, 20022}, {30031, 40042}}}); auto prod = MakeUnique(sum->shape()); ASSERT_TRUE(prod->Populate( [&sum](tensorflow::gtl::ArraySlice indexes) { @@ -505,9 +513,9 @@ XLA_TEST_F(TupleTest, ComplexTuples) { : complex64(1, -2)); }) .ok()); - auto expected = - Literal::MakeTuple({Literal::MakeTuple({prod.get(), sum.get()}).get(), - Literal::CreateR0({123, 456}).get()}); + auto expected = LiteralUtil::MakeTuple( + {LiteralUtil::MakeTuple({prod.get(), sum.get()}).get(), + LiteralUtil::CreateR0({123, 456}).get()}); ComputeAndCompareTuple(&builder, *expected, {arg0.get(), arg1.get()}, error_spec_); } @@ -530,12 +538,59 @@ XLA_TEST_F(TupleHloTest, DISABLED_ON_INTERPRETER(BitcastAfterGTE)) { auto module = HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) .ValueOrDie(); - auto param = Literal::MakeTupleOwned(Literal::CreateR1({1, 2, 3})); + auto param = + LiteralUtil::MakeTupleOwned(LiteralUtil::CreateR1({1, 2, 3})); auto result = ExecuteNoHloPasses(std::move(module), {param.get()}); EXPECT_TRUE(LiteralTestUtil::Equal( - *Literal::MakeTupleOwned(Literal::CreateR2({{1, 2, 3}})), + *LiteralUtil::MakeTupleOwned(LiteralUtil::CreateR2({{1, 2, 3}})), *result)); } +// Disabled on interpreter due to lack of outfeed. +XLA_TEST_F(TupleHloTest, + DISABLED_ON_INTERPRETER(NonAmbiguousTopLevelAllocation)) { + const char* testcase = R"( + HloModule tuple + + ENTRY main { + a = f32[2] parameter(0) + b = f32[2] parameter(1) + c = f32[2] parameter(2) + d = f32[2] parameter(3) + cond = pred[] parameter(4) + + tup0 = (f32[2],f32[2]) tuple(a, b) + tup1 = (f32[2],f32[2]) tuple(c, d) + + s = (f32[2],f32[2]) tuple-select(cond, tup0, tup1) + gte = f32[2] get-tuple-element(s), index=0 + tuple = (f32[2]) tuple(gte) + token = token[] after-all() + ROOT outfeed = token[] outfeed(tuple, token) + } + )"; + auto module = + HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) + .ValueOrDie(); + auto param0 = LiteralUtil::CreateR1({1, 2}); + auto param1 = LiteralUtil::CreateR1({2, 3}); + auto param4 = LiteralUtil::CreateR0(false); + // Put execution on a separate thread so we can block on outfeed. + std::unique_ptr thread( + tensorflow::Env::Default()->StartThread( + tensorflow::ThreadOptions(), "execute_thread", [&] { + TF_EXPECT_OK(Execute(std::move(module), + {param0.get(), param1.get(), param1.get(), + param0.get(), param4.get()}) + .status()); + })); + auto expected = + LiteralUtil::MakeTupleOwned(LiteralUtil::CreateR1({2, 3})); + auto literal = MakeUnique(); + TF_EXPECT_OK(backend().transfer_manager()->TransferLiteralFromOutfeed( + backend().default_stream_executor(), expected->shape(), literal.get())); + EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *literal)); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/unary_op_test.cc b/tensorflow/compiler/xla/tests/unary_op_test.cc index 929b1ca7fb93c545265bf85fec1ed7dc845405b2..20ae68ab74026936c43e5f525eb796eb402a19cb 100644 --- a/tensorflow/compiler/xla/tests/unary_op_test.cc +++ b/tensorflow/compiler/xla/tests/unary_op_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" @@ -101,7 +101,7 @@ void UnaryOpTest::AbsTestHelper() { Abs(arg); std::unique_ptr expected = - Literal::CreateR1({2, 25, 0, 0.5, inf(), inf()}); + LiteralUtil::CreateR1({2, 25, 0, 0.5, inf(), inf()}); ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-6f)); } @@ -113,7 +113,7 @@ void UnaryOpTest::SignTestHelper() { {{-2, 0}, {0, 25}, {0, 0}, {static_cast(-0.0), 0}, {-1, 1}}); Sign(arg); - std::unique_ptr expected = Literal::CreateR1( + std::unique_ptr expected = LiteralUtil::CreateR1( {{-1, 0}, {0, 1}, {0, 0}, {0, 0}, {-std::sqrt(0.5f), std::sqrt(0.5f)}}); ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-6f)); } @@ -128,7 +128,7 @@ void UnaryOpTest::SignAbsTestHelper() { Sub(Mul(sign, ConvertElementType(abs, C64)), arg); std::unique_ptr expected = - Literal::CreateR1({0, 0, 0, 0}); + LiteralUtil::CreateR1({0, 0, 0, 0}); ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-6f)); } @@ -173,7 +173,7 @@ XLA_TEST_F(UnaryOpTest, SignTestR0) { Add(Add(sgnf0, sgnf), ConvertElementType(sgni, F32)), C64)); std::unique_ptr expected = - Literal::CreateR0({-2.6f, 0.8f}); + LiteralUtil::CreateR0({-2.6f, 0.8f}); ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-6f)); } diff --git a/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc b/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc index ea3aba6df1d3fbd492a23b280309322b8524c0bf..ef1b1445bbe555da00db4446d59439b752735a80 100644 --- a/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc +++ b/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array3d.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/compiler/xla/tests/test_macros.h" diff --git a/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc b/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc index 79bae22dac9599a38c73ea1dc2e6b4856395ff79..3848ec1684cdc9186e14ac0b60315b7520d127f3 100644 --- a/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc +++ b/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc @@ -21,8 +21,8 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test_helpers.h" diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc index bbd67cd8d7c433550deefc38ce28b2b732d354aa..c81c27891c29394fe01116ca22fa678b0a409c62 100644 --- a/tensorflow/compiler/xla/tests/while_test.cc +++ b/tensorflow/compiler/xla/tests/while_test.cc @@ -20,9 +20,9 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/platform_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -347,8 +347,8 @@ TEST_F(WhileTest, WhileWithVectorResultIntoTuple) { // the sum will increase by 1.0. It will first be >15.5 when the elements // have all reached 2.0. auto expected_data = - Literal::CreateR1({2.f, 2.f, 2.f, 2.f, 2.f, 2.f, 2.f, 2.f}); - auto expected = Literal::MakeTuple({expected_data.get()}); + LiteralUtil::CreateR1({2.f, 2.f, 2.f, 2.f, 2.f, 2.f, 2.f, 2.f}); + auto expected = LiteralUtil::MakeTuple({expected_data.get()}); VLOG(2) << "expected = " << ShapeUtil::HumanString(expected->shape()); ComputeAndCompareTuple(&builder, *expected, {}, ErrorSpec(0.0001)); } @@ -397,12 +397,13 @@ TEST_F(WhileTest, WhileWithPermutationAndTupleResult) { << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); - auto expected_counter = Literal::CreateR0(N); - auto expected_w1 = Literal::CreateR1({1.0f, 1.0f, 1.0f}); - auto expected_w2 = Literal::CreateR1({2.0f, 2.0f, 2.0f}); - auto expected_w3 = Literal::CreateR1({3.0f, 3.0f, 3.0f}); - auto expected = Literal::MakeTuple({expected_counter.get(), expected_w2.get(), - expected_w3.get(), expected_w1.get()}); + auto expected_counter = LiteralUtil::CreateR0(N); + auto expected_w1 = LiteralUtil::CreateR1({1.0f, 1.0f, 1.0f}); + auto expected_w2 = LiteralUtil::CreateR1({2.0f, 2.0f, 2.0f}); + auto expected_w3 = LiteralUtil::CreateR1({3.0f, 3.0f, 3.0f}); + auto expected = + LiteralUtil::MakeTuple({expected_counter.get(), expected_w2.get(), + expected_w3.get(), expected_w1.get()}); VLOG(2) << "expected = " << ShapeUtil::HumanString(expected->shape()); ComputeAndCompareTuple(&builder, *expected, {}, ErrorSpec(0.0001)); } @@ -506,11 +507,11 @@ TEST_F(WhileTest, WhileWithTupleResult) { << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); - auto expected_counter = Literal::CreateR0(5); - auto expected_data = Literal::CreateR1( + auto expected_counter = LiteralUtil::CreateR0(5); + auto expected_data = LiteralUtil::CreateR1( {5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f}); auto expected = - Literal::MakeTuple({expected_counter.get(), expected_data.get()}); + LiteralUtil::MakeTuple({expected_counter.get(), expected_data.get()}); VLOG(2) << "expected = " << ShapeUtil::HumanString(expected->shape()); ComputeAndCompareTuple(&builder, *expected, {}, ErrorSpec(0.0001)); } @@ -554,10 +555,10 @@ TEST_F(WhileTest, WhileWithPredicateTupleResult) { << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); - auto expected_counter = Literal::CreateR0(5); - auto expected_predicate = Literal::CreateR0(true); - auto expected = - Literal::MakeTuple({expected_counter.get(), expected_predicate.get()}); + auto expected_counter = LiteralUtil::CreateR0(5); + auto expected_predicate = LiteralUtil::CreateR0(true); + auto expected = LiteralUtil::MakeTuple( + {expected_counter.get(), expected_predicate.get()}); ComputeAndCompareTuple(&builder, *expected, {}, ErrorSpec(0)); } @@ -599,10 +600,10 @@ TEST_F(WhileTest, WhileWithTupleConstantScalarResult) { << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); - auto expected_counter = Literal::CreateR0(5); - auto expected_data = Literal::CreateR0(7); + auto expected_counter = LiteralUtil::CreateR0(5); + auto expected_data = LiteralUtil::CreateR0(7); auto expected = - Literal::MakeTuple({expected_counter.get(), expected_data.get()}); + LiteralUtil::MakeTuple({expected_counter.get(), expected_data.get()}); VLOG(2) << "expected = " << ShapeUtil::HumanString(expected->shape()); ComputeAndCompareTuple(&builder, *expected, {}, ErrorSpec(0.0001)); } @@ -882,11 +883,11 @@ XLA_TEST_F(WhileTest, WhileWithDynamicUpdateSlice) { << ShapeUtil::HumanString( builder.GetShape(result).ConsumeValueOrDie()); - auto expected_counter = Literal::CreateR0(5); - auto expected_data = Literal::CreateR1( + auto expected_counter = LiteralUtil::CreateR0(5); + auto expected_data = LiteralUtil::CreateR1( {1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f}); auto expected = - Literal::MakeTuple({expected_counter.get(), expected_data.get()}); + LiteralUtil::MakeTuple({expected_counter.get(), expected_data.get()}); VLOG(2) << "expected = " << ShapeUtil::HumanString(expected->shape()); ComputeAndCompareTuple(&builder, *expected, {}, ErrorSpec(0.0001)); } @@ -974,12 +975,12 @@ TEST_F(WhileTest, WhileThatSwapsParameterWithTupleElement) { TF_ASSERT_OK_AND_ASSIGN(auto body_computation, body.Build()); While(cond_computation, body_computation, t); - auto expected_element = Literal::CreateR1({1, 1}); + auto expected_element = LiteralUtil::CreateR1({1, 1}); auto expected = - Literal::MakeTuple({expected_element.get(), expected_element.get()}); + LiteralUtil::MakeTuple({expected_element.get(), expected_element.get()}); TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr parameter_data, - client_->TransferToServer(*Literal::CreateR1({42, 42}))); + client_->TransferToServer(*LiteralUtil::CreateR1({42, 42}))); ComputeAndCompareTuple(&outer, *expected, {parameter_data.get()}, ErrorSpec(1e-6)); } @@ -1004,7 +1005,7 @@ TEST_F(WhileTest, WhileThatSwapsParameterWithBroadcast) { TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr parameter_data, - client_->TransferToServer(*Literal::CreateR1({42, 42}))); + client_->TransferToServer(*LiteralUtil::CreateR1({42, 42}))); ComputeAndCompareR1(&outer, {1.0f, 1.0f}, {parameter_data.get()}, ErrorSpec(1e-6)); } @@ -1030,7 +1031,7 @@ TEST_F(WhileTest, WhileThatTurnsScalarParameterToTupleElement) { TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr parameter_data, - client_->TransferToServer(*Literal::CreateR0(42))); + client_->TransferToServer(*LiteralUtil::CreateR0(42))); ComputeAndCompareR0(&outer, 43.0f, {parameter_data.get()}, ErrorSpec(1e-6)); } @@ -1069,11 +1070,11 @@ TEST_F(WhileTest, WhileWithMixedTupleElements) { TF_ASSERT_OK_AND_ASSIGN( std::unique_ptr parameter_data, - client_->TransferToServer(*Literal::CreateR0(1))); + client_->TransferToServer(*LiteralUtil::CreateR0(1))); - auto add1 = Literal::CreateR0(15); - auto add2 = Literal::CreateR0(16); - auto expected = Literal::MakeTuple({add1.get(), add2.get()}); + auto add1 = LiteralUtil::CreateR0(15); + auto add2 = LiteralUtil::CreateR0(16); + auto expected = LiteralUtil::MakeTuple({add1.get(), add2.get()}); ComputeAndCompareTuple(&outer, *expected, {parameter_data.get()}, ErrorSpec(1e-6)); } @@ -1226,9 +1227,9 @@ TEST_F(WhileTest, WhileWithLoopInvariantOperation) { auto while_instruction = While(condition, body, init); GetTupleElement(while_instruction, 3); - TF_ASSERT_OK_AND_ASSIGN(auto param_value, - client_->TransferToServer(*Literal::CreateR2( - {{1.0, 2.0}, {-1.0, -2.0}}))); + TF_ASSERT_OK_AND_ASSIGN( + auto param_value, client_->TransferToServer(*LiteralUtil::CreateR2( + {{1.0, 2.0}, {-1.0, -2.0}}))); ComputeAndCompareR2( &builder, {{-0.76159416, -0.96402758}, {0.76159416, 0.96402758}}, diff --git a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc index 7dba058d407758b42365c3b6883e5e0891e1ab6c..0ee8e68c88011d53ab6484e0bd81eb969304b6fb 100644 --- a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc +++ b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc @@ -18,10 +18,11 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/service/platform_util.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/test_macros.h" @@ -79,7 +80,9 @@ struct ParsedProfileOutputLine { Status ParseOneProfileOutputLine( const string& line, bool expect_hlo, - gtl::FlatMap* parsed_results) { + gtl::FlatMap* parsed_results, + tensorflow::gtl::ArraySlice opcodes_to_ignore = + {}) { string separator = "[^:]*:: +"; string match_percentage = "\\d+\\.\\d\\d%"; string match_cycles = "(\\d+) cycles +\\( *(" + match_percentage + ")\\)"; @@ -113,7 +116,9 @@ Status ParseOneProfileOutputLine( ", Regexp: ", regexp_pattern); } - InsertOrDie(parsed_results, parsed_line.opcode, parsed_line); + if (!c_linear_search(opcodes_to_ignore, parsed_line.opcode)) { + InsertOrDie(parsed_results, parsed_line.opcode, parsed_line); + } return Status::OK(); } @@ -129,7 +134,7 @@ void ExecuteAndFetchProfile(string* profile_output, LocalClient* client, DeviceMemoryAllocator* allocator = backend->memory_allocator(); auto* transfer_manager = backend->transfer_manager(); TF_ASSERT_OK_AND_ASSIGN( - Backend::StreamPtr stream_ptr, + StreamPool::Ptr stream_ptr, backend->BorrowStream(backend->default_device_ordinal())); TF_ASSERT_OK_AND_ASSIGN( @@ -267,7 +272,7 @@ XLA_TEST_F(HloProfileTest, ProfileWhileComputation) { auto matrix = GetTupleElement(state, 1); auto next_iteration = Add(GetTupleElement(state, 0), ConstantR0(&builder, 1)); - Tuple(&builder, {next_iteration, Add(matrix, matrix)}); + Tuple(&builder, {next_iteration, Mul(matrix, matrix)}); TF_ASSERT_OK_AND_ASSIGN(body, builder.Build()); } @@ -289,36 +294,50 @@ XLA_TEST_F(HloProfileTest, ProfileWhileComputation) { tensorflow::str_util::Split(profile_output, '\n'); auto while_body_profile_start = - std::find_if(profile_output_lines.begin(), profile_output_lines.end(), + c_find_if(profile_output_lines, [](tensorflow::StringPiece s) { + return tensorflow::str_util::StartsWith(s, + "Execution profile for body"); + }); + + ASSERT_NE(while_body_profile_start, profile_output_lines.cend()); + + auto while_body_profile_end = + std::find_if(while_body_profile_start, profile_output_lines.end(), [](tensorflow::StringPiece s) { return tensorflow::str_util::StartsWith( - s, "Execution profile for body"); + s, "********** microseconds report **********"); }); - ASSERT_NE(while_body_profile_start, profile_output_lines.end()); + // We emit a blank line before the "********** microseconds report **********" + // line. + while_body_profile_end--; - gtl::FlatMap parsed_profile_lines; + ASSERT_NE(while_body_profile_end, profile_output_lines.end()); - TF_ASSERT_OK( - ParseOneProfileOutputLine(*std::next(while_body_profile_start, 1), - /*expect_hlo=*/false, &parsed_profile_lines)); + gtl::FlatMap parsed_profile_lines; - TF_ASSERT_OK( - ParseOneProfileOutputLine(*std::next(while_body_profile_start, 2), - /*expect_hlo=*/true, &parsed_profile_lines)); + for (auto while_body_profile_i = while_body_profile_start + 1; + while_body_profile_i != while_body_profile_end; while_body_profile_i++) { + // There are multiple "get-tuple-element" instructions in the while body so + // we ignore them -- we don't want parsed_profile_lines to be a multi-map. + TF_ASSERT_OK(ParseOneProfileOutputLine( + *while_body_profile_i, + /*expect_hlo=*/while_body_profile_i != (while_body_profile_start + 1), + &parsed_profile_lines, {"get-tuple-element"})); + } TF_ASSERT_OK_AND_ASSIGN(ParsedProfileOutputLine total_while_body_profile, MaybeFind(parsed_profile_lines, "[total]")); - TF_ASSERT_OK_AND_ASSIGN(ParsedProfileOutputLine dot_profile, - MaybeFind(parsed_profile_lines, "add")); + TF_ASSERT_OK_AND_ASSIGN(ParsedProfileOutputLine multiply_profile, + MaybeFind(parsed_profile_lines, "multiply")); EXPECT_GT(total_while_body_profile.cycles, 0); EXPECT_EQ(total_while_body_profile.opcode, "[total]"); EXPECT_EQ(total_while_body_profile.cycles_percentage, "100.00%"); - EXPECT_GT(total_while_body_profile.cycles, dot_profile.cycles); - EXPECT_NE(dot_profile.cycles_percentage, "0.00%"); - EXPECT_NE(dot_profile.cycles_percentage, "100.00%"); + EXPECT_GT(total_while_body_profile.cycles, multiply_profile.cycles); + EXPECT_NE(multiply_profile.cycles_percentage, "0.00%"); + EXPECT_NE(multiply_profile.cycles_percentage, "100.00%"); } } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/text_literal_reader.cc b/tensorflow/compiler/xla/text_literal_reader.cc index 56702feab9a4e8d00df3a165ab994aef2d42d830..897123d7606db60abc1105b03beb3f23ab249579 100644 --- a/tensorflow/compiler/xla/text_literal_reader.cc +++ b/tensorflow/compiler/xla/text_literal_reader.cc @@ -20,7 +20,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" diff --git a/tensorflow/compiler/xla/text_literal_reader.h b/tensorflow/compiler/xla/text_literal_reader.h index e45e5291c9b10803f5e5008b72c7dd0116a0dea0..708e8c80d8b5c09454eb64d4e12df51a5b7ea628 100644 --- a/tensorflow/compiler/xla/text_literal_reader.h +++ b/tensorflow/compiler/xla/text_literal_reader.h @@ -18,7 +18,7 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" diff --git a/tensorflow/compiler/xla/text_literal_reader_test.cc b/tensorflow/compiler/xla/text_literal_reader_test.cc index 23070b663870a2b78b38663e09a32fcb28d9c2dc..92f9b4f9f0efa2dc08287bdcbefc88f879164308 100644 --- a/tensorflow/compiler/xla/text_literal_reader_test.cc +++ b/tensorflow/compiler/xla/text_literal_reader_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/types.h" diff --git a/tensorflow/compiler/xla/text_literal_writer.cc b/tensorflow/compiler/xla/text_literal_writer.cc index 373c0d2d8d8ab05dec11e51f265d41b91e7920bf..24e0784741a4c9779b0adb7a7740c3d6e2fb033a 100644 --- a/tensorflow/compiler/xla/text_literal_writer.cc +++ b/tensorflow/compiler/xla/text_literal_writer.cc @@ -17,7 +17,7 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" diff --git a/tensorflow/compiler/xla/text_literal_writer.h b/tensorflow/compiler/xla/text_literal_writer.h index 0a1235b5e04675da0f412bafab6c4ecf04367787..159ac1b7e1b6f9c07dac795fb640cd0b2d284bcb 100644 --- a/tensorflow/compiler/xla/text_literal_writer.h +++ b/tensorflow/compiler/xla/text_literal_writer.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_TEXT_LITERAL_WRITER_H_ #define TENSORFLOW_COMPILER_XLA_TEXT_LITERAL_WRITER_H_ -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/core/status.h" diff --git a/tensorflow/compiler/xla/text_literal_writer_test.cc b/tensorflow/compiler/xla/text_literal_writer_test.cc index 70cf2fb1b8a1b4f2ecfdaeaef3a00ddc974e2652..4ea02faffcd52065b05c0444202bd1a3d9d87ee6 100644 --- a/tensorflow/compiler/xla/text_literal_writer_test.cc +++ b/tensorflow/compiler/xla/text_literal_writer_test.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/test_helpers.h" @@ -30,8 +31,9 @@ namespace xla { namespace { TEST(TextLiteralWriterTest, WritesFloatLiteral) { - auto literal = Literal::CreateR2({ - {3.14, 2.17}, {1.23, 4.56}, + auto literal = LiteralUtil::CreateR2({ + {3.14, 2.17}, + {1.23, 4.56}, }); string path = tensorflow::io::JoinPath(tensorflow::testing::TmpDir(), "/whatever"); diff --git a/tensorflow/compiler/xla/tools/BUILD b/tensorflow/compiler/xla/tools/BUILD index e4a052c8f1c0009619c3a94606f6384d04006e4e..d7cabbe876c662fc71237a0fb62141c93e69d14b 100644 --- a/tensorflow/compiler/xla/tools/BUILD +++ b/tensorflow/compiler/xla/tools/BUILD @@ -37,6 +37,7 @@ cc_library( "//tensorflow/compiler/xla/client", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", "//tensorflow/compiler/xla/service", "//tensorflow/compiler/xla/service:hlo_proto", @@ -74,7 +75,7 @@ cc_library( srcs = ["replay_computation.cc"], deps = [ "//tensorflow/compiler/xla:execution_options_util", - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -84,6 +85,7 @@ cc_library( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:testing", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/service:hlo_proto", @@ -123,7 +125,7 @@ tf_cc_binary( name = "show_literal", srcs = ["show_literal.cc"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/core:lib", @@ -145,7 +147,7 @@ tf_cc_binary( name = "show_text_literal", srcs = ["show_text_literal.cc"], deps = [ - "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:text_literal_reader", "//tensorflow/compiler/xla:types", @@ -164,6 +166,7 @@ tf_cc_binary( "//tensorflow/compiler/xla/client", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service", "//tensorflow/compiler/xla/service:hlo_proto", "//tensorflow/compiler/xla/service:interpreter_plugin", @@ -181,6 +184,7 @@ tf_cc_binary( "//tensorflow/compiler/xla/client", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_proto", @@ -198,6 +202,7 @@ tf_cc_binary( "//tensorflow/compiler/xla/client", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", "//tensorflow/compiler/xla/service", "//tensorflow/compiler/xla/service:hlo_graph_dumper", diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc index befb55453777dce30af89bcaad2ffe1647097576..f20dcef382b86d27d7c176ae7e4132ad1db7b901 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc @@ -31,6 +31,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/service/service.h" diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc index cfb8f37487d6499b803438a135be54524fcf17d2..f0af0580c1fbca455c6ed5f87f82971faee50a06 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/service/service.h" diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc index 5dd5150be339846d0775880931f615b92c5b08d8..f03e1b1f965af761c101555fd0275bc0425b9cf0 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/service/service.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc index a5dce20456c6a2402f425ebb3d575d1bb625f839..dc5c106d02cb679f3e6f5b2bea40bbb42f8bd1cc 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc @@ -29,6 +29,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/service/service.h" diff --git a/tensorflow/compiler/xla/tools/replay_computation.cc b/tensorflow/compiler/xla/tools/replay_computation.cc index 3a7917cf3043de8a77f189f011bdeb3e8d2ddf3c..3bb2f3c0007bbe92aed6a995790284c89719be91 100644 --- a/tensorflow/compiler/xla/tools/replay_computation.cc +++ b/tensorflow/compiler/xla/tools/replay_computation.cc @@ -42,8 +42,9 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/lib/testing.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/execution_options_util.h" -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/gpu/infeed_manager.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" diff --git a/tensorflow/compiler/xla/tools/show_literal.cc b/tensorflow/compiler/xla/tools/show_literal.cc index fe8e72ba32bb4493b2751cfdfeb977f271092f9c..51909190a3ef20c3df78d08796e88bdbb650609d 100644 --- a/tensorflow/compiler/xla/tools/show_literal.cc +++ b/tensorflow/compiler/xla/tools/show_literal.cc @@ -21,7 +21,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/core/status.h" diff --git a/tensorflow/compiler/xla/tools/show_text_literal.cc b/tensorflow/compiler/xla/tools/show_text_literal.cc index 8525873e913185554d18df8c8c3584bfcdcdcabe..48c837481181f6ad8f864569fd62e0e23fa02ecd 100644 --- a/tensorflow/compiler/xla/tools/show_text_literal.cc +++ b/tensorflow/compiler/xla/tools/show_text_literal.cc @@ -20,7 +20,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/text_literal_reader.h" #include "tensorflow/compiler/xla/types.h" diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h index b23b968aae6ed8d6fb2b9f61ea5db2690eb5246c..5ae099a4622bb7116c7a17f93060b699ead6e3a6 100644 --- a/tensorflow/compiler/xla/util.h +++ b/tensorflow/compiler/xla/util.h @@ -500,17 +500,17 @@ bool c_is_sorted(const C& c, Compare&& comp) { } template -auto c_adjacent_find(const C& c) -> decltype(std::begin(c)) { +auto c_adjacent_find(C& c) -> decltype(std::begin(c)) { return std::adjacent_find(std::begin(c), std::end(c)); } template -auto c_find_if(const C& c, Pred&& pred) -> decltype(std::begin(c)) { +auto c_find_if(C& c, Pred&& pred) -> decltype(std::begin(c)) { return std::find_if(std::begin(c), std::end(c), std::forward(pred)); } template -auto c_find(const C& c, Value&& value) -> decltype(std::begin(c)) { +auto c_find(C& c, Value&& value) -> decltype(std::begin(c)) { return std::find(std::begin(c), std::end(c), std::forward(value)); } @@ -562,6 +562,11 @@ void EraseAt(C* c, int64 index) { c->erase(c->begin() + index); } +template +std::vector ArraySliceToVector(tensorflow::gtl::ArraySlice slice) { + return std::vector(slice.begin(), slice.end()); +} + template std::vector InlinedVectorToVector( const tensorflow::gtl::InlinedVector& inlined_vector) { diff --git a/tensorflow/compiler/xla/xla.proto b/tensorflow/compiler/xla/xla.proto index 6f07e4606bef015214f2c564515c8258a906205b..10c0adc6707f01fcee87303a6e2ec5c570601309 100644 --- a/tensorflow/compiler/xla/xla.proto +++ b/tensorflow/compiler/xla/xla.proto @@ -293,6 +293,7 @@ message ComputationStatsResponse { } message CreateChannelHandleRequest { + ChannelHandle.ChannelType channel_type = 1; } message CreateChannelHandleResponse { diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto index c7472173a705b7a6e1bee2f5221f23db0a77991d..0b300dc7b2d03cc8e1564f78412cc610cff518cd 100644 --- a/tensorflow/compiler/xla/xla_data.proto +++ b/tensorflow/compiler/xla/xla_data.proto @@ -308,6 +308,22 @@ message DeviceHandle { // Send instructions will be blocked until the data is transferred. message ChannelHandle { int64 handle = 1; + enum ChannelType { + // Invalid primitive type to serve as default. + CHANNEL_TYPE_INVALID = 0; + + // A channel for sending data between devices. + DEVICE_TO_DEVICE = 1; + + // A channel for sending data from the device to the host. Can only be used + // with a Send operation. + DEVICE_TO_HOST = 2; + + // A channel for sending data from the host to the device. Can only be used + // with a Recv operation. + HOST_TO_DEVICE = 3; + } + ChannelType type = 2; } // DeviceAssignmentProto is a serialized form of DeviceAssignment class, which diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index c039624daa65174b0550ff6a304947e37cf58e1d..6a4e252b44881c679350e121b1793e3b797f0785 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -7,7 +7,6 @@ package(default_visibility = ["//tensorflow:__subpackages__"]) load("//third_party/mpi:mpi.bzl", "if_mpi") load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda") -load("@local_config_tensorrt//:build_defs.bzl", "if_tensorrt") load("//tensorflow:tensorflow.bzl", "if_not_windows") load("//tensorflow:tensorflow.bzl", "if_not_windows_cuda") @@ -27,7 +26,6 @@ py_library( "//tensorflow/contrib/bayesflow:bayesflow_py", "//tensorflow/contrib/boosted_trees:init_py", "//tensorflow/contrib/checkpoint/python:checkpoint", - "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip", "//tensorflow/contrib/cluster_resolver:cluster_resolver_py", "//tensorflow/contrib/coder:coder_py", "//tensorflow/contrib/compiler:compiler_py", @@ -114,9 +112,7 @@ py_library( "//tensorflow/contrib/util:util_py", "//tensorflow/python:util", "//tensorflow/python/estimator:estimator_py", - ] + if_mpi(["//tensorflow/contrib/mpi_collectives:mpi_collectives_py"]) + if_tensorrt([ - "//tensorflow/contrib/tensorrt:init_py", - ]) + select({ + ] + if_mpi(["//tensorflow/contrib/mpi_collectives:mpi_collectives_py"]) + select({ "//tensorflow:with_kafka_support_windows_override": [], "//tensorflow:with_kafka_support": [ "//tensorflow/contrib/kafka", @@ -134,6 +130,11 @@ py_library( "//tensorflow/contrib/bigtable", # depends on bigtable "//tensorflow/contrib/cloud:cloud_py", # doesn't compile on Windows "//tensorflow/contrib/ffmpeg:ffmpeg_ops_py", + # TODO(aaroey): tensorrt dependency has to appear before tflite so the + # build can resolve its flatbuffers symbols within the tensorrt library. + # This is an issue with the tensorrt static library and will be fixed by + # the next tensorrt release, so fix the order here after that. + "//tensorflow/contrib/tensorrt:init_py", # doesn't compile on windows "//tensorflow/contrib/lite/python:lite", # unix dependency, need to fix code ]), ) diff --git a/tensorflow/contrib/android/cmake/src/main/AndroidManifest.xml b/tensorflow/contrib/android/cmake/src/main/AndroidManifest.xml index bced47e046db889366bf88e563d086a8c367431a..c17110a78be49f70ef108be79a624d87ad9ed28d 100644 --- a/tensorflow/contrib/android/cmake/src/main/AndroidManifest.xml +++ b/tensorflow/contrib/android/cmake/src/main/AndroidManifest.xml @@ -1,6 +1,10 @@ + + diff --git a/tensorflow/contrib/autograph/README.md b/tensorflow/contrib/autograph/README.md index 7e26f4711851138c1834f881621ebfa227a85821..cc54da4daa9a5bb4e64145963ffec63021d08876 100644 --- a/tensorflow/contrib/autograph/README.md +++ b/tensorflow/contrib/autograph/README.md @@ -1,10 +1,10 @@ # AutoGraph -IMPORTANT: AutoGraph is alpha software, and under active development. Expect rough edges and bugs, but if you try it, we appreciate early feedback! We'd also love contributions ([please see our contributing guidelines](CONTRIBUTING.md) and our [style guide](STYLE_GUIDE.md)). +IMPORTANT: AutoGraph is beta software, and under active development. Expect rough edges and bugs, but if you try it, we appreciate early feedback! We'd also love contributions ([please see our contributing guidelines](CONTRIBUTING.md) and our [style guide](STYLE_GUIDE.md)). AutoGraph is a Python to TensorFlow compiler. -With AutoGraph, you can write [Eager style](https://www.tensorflow.org/guide/eager) code in a concise manner, and run it as a TensorFlow graph. AutoGraph uses source code transformation and partial evaluation to generate Python code that builds an equivalent TensorFlow subgraph. The result is code that behaves like ops and can be freely combined with other TensorFlow ops. +With AutoGraph, you can write [Eager style](https://www.tensorflow.org/guide/eager) code in a concise manner, and run it as a TensorFlow graph. AutoGraph uses source code transformation and partial evaluation to generate Python code that builds an equivalent TensorFlow subgraph. The result is code that behaves like ops and can be freely combined with other TensorFlow ops. [Please see this file for which parts of the Python language we currently support](LIMITATIONS.md). For example, this Python function: @@ -68,12 +68,21 @@ Then import the `autograph` module from `tf.contrib`: from tensorflow.contrib import autograph as ag ``` -### Interactive demo notebooks +### Related links -For more extensive examples, check out these interactive notebooks: +Articles: - * [RNN trained using Keras and Estimators](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/rnn_keras_estimator.ipynb) + * [TensorFlow blog post](https://medium.com/tensorflow/autograph-converts-python-into-tensorflow-graphs-b2a871f87ec7) + +Interactive notebooks: + + * [Quick guide](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/guide/autograph.ipynb) + * [RNN trained using Keras and Estimators](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/rnn_keras_estimator.ipynb) * [Demo from the TF Dev Summit 2018](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb) + * [Basic control flow speed test](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/ag_vs_eager_collatz_speed_test.ipynb) + * [MNIST training speed test](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/ag_vs_eager_mnist_speed_test.ipynb) + * [Basic algorithm samples](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/algorithms.ipynb) + * [Introductory workshop support notebook](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/workshop.ipynb) ## Using with annotations diff --git a/tensorflow/contrib/autograph/__init__.py b/tensorflow/contrib/autograph/__init__.py index 361cf2d77c7e46912d5bff5881df2ffa897c5179..26e7a4a4d38e264486c981e6fc4c547bcc53b302 100644 --- a/tensorflow/contrib/autograph/__init__.py +++ b/tensorflow/contrib/autograph/__init__.py @@ -22,17 +22,21 @@ from __future__ import division from __future__ import print_function # TODO(mdan): Bring only the relevant symbols to the top level. -from tensorflow.contrib.autograph import utils from tensorflow.contrib.autograph import operators +from tensorflow.contrib.autograph import utils +from tensorflow.contrib.autograph.core.errors import GraphConstructionError +from tensorflow.contrib.autograph.core.errors import TfRuntimeError +from tensorflow.contrib.autograph.core.errors import improved_errors +from tensorflow.contrib.autograph.impl.api import RunMode from tensorflow.contrib.autograph.impl.api import convert from tensorflow.contrib.autograph.impl.api import converted_call from tensorflow.contrib.autograph.impl.api import do_not_convert -from tensorflow.contrib.autograph.impl.api import RunMode from tensorflow.contrib.autograph.impl.api import to_code from tensorflow.contrib.autograph.impl.api import to_graph from tensorflow.contrib.autograph.lang.directives import set_element_type from tensorflow.contrib.autograph.lang.directives import set_loop_options from tensorflow.contrib.autograph.lang.special_functions import stack +from tensorflow.contrib.autograph.lang.special_functions import tensor_list from tensorflow.contrib.autograph.pyct.transformer import AutographParseError from tensorflow.python.util.all_util import remove_undocumented @@ -46,10 +50,15 @@ _allowed_symbols = [ 'to_graph', # Overloaded operators 'operators', + # Errors + 'improved_errors', + 'GraphConstructionError', + 'TfRuntimeError', # Python language "extensions" 'set_element_type', 'set_loop_options', 'stack', + 'tensor_list', # Exceptions 'AutographParseError', # Utilities: to be removed diff --git a/tensorflow/contrib/autograph/converters/BUILD b/tensorflow/contrib/autograph/converters/BUILD index b2e2e27673dafe290cef40a9fe0a834bfe1ea61f..7cbba7168383f3d0cdc80fda9908cb7d70836bb4 100644 --- a/tensorflow/contrib/autograph/converters/BUILD +++ b/tensorflow/contrib/autograph/converters/BUILD @@ -21,16 +21,18 @@ py_library( "break_statements.py", "builtin_functions.py", "call_trees.py", + "conditional_expressions.py", "continue_statements.py", "control_flow.py", "decorators.py", - "ifexp.py", - "list_comprehension.py", + "directives.py", + "error_handlers.py", + "list_comprehensions.py", "lists.py", "logical_expressions.py", "name_scopes.py", + "return_statements.py", "side_effect_guards.py", - "single_return.py", "slices.py", ], srcs_version = "PY2AND3", @@ -94,6 +96,17 @@ py_test( ], ) +py_test( + name = "conditional_expressions_test", + srcs = ["conditional_expressions_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", + "//tensorflow/python:client_testlib", + ], +) + py_test( name = "continue_statements_test", srcs = ["continue_statements_test.py"], @@ -131,6 +144,18 @@ py_test( ], ) +py_test( + name = "directives_test", + srcs = ["directives_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":converters", + "//tensorflow/contrib/autograph/core:test_lib", + "//tensorflow/contrib/autograph/lang", + "//tensorflow/python:client_testlib", + ], +) + py_test( name = "name_scopes_test", srcs = ["name_scopes_test.py"], @@ -143,8 +168,8 @@ py_test( ) py_test( - name = "list_comprehension_test", - srcs = ["list_comprehension_test.py"], + name = "list_comprehensions_test", + srcs = ["list_comprehensions_test.py"], srcs_version = "PY2AND3", deps = [ ":converters", @@ -179,11 +204,6 @@ py_test( name = "side_effect_guards_test", srcs = ["side_effect_guards_test.py"], srcs_version = "PY2AND3", - tags = [ - # TODO(mdan): Fix. - "flaky", - "notap", - ], deps = [ ":converters", "//tensorflow/contrib/autograph/core:test_lib", @@ -192,8 +212,8 @@ py_test( ) py_test( - name = "single_return_test", - srcs = ["single_return_test.py"], + name = "return_statements_test", + srcs = ["return_statements_test.py"], srcs_version = "PY2AND3", deps = [ ":converters", @@ -204,8 +224,8 @@ py_test( ) py_test( - name = "ifexp_test", - srcs = ["ifexp_test.py"], + name = "error_handlers_test", + srcs = ["error_handlers_test.py"], srcs_version = "PY2AND3", deps = [ ":converters", diff --git a/tensorflow/contrib/autograph/converters/__init__.py b/tensorflow/contrib/autograph/converters/__init__.py index e4e8eda42f655e204310eaa9defdd5c90bf06e15..6325ac78dc3a08d14c1abf5e0f1ae60258639162 100644 --- a/tensorflow/contrib/autograph/converters/__init__.py +++ b/tensorflow/contrib/autograph/converters/__init__.py @@ -18,5 +18,15 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -# TODO(mdan): Define a base transformer class that can recognize skip_processing -# TODO(mdan): All converters are incomplete, especially those that change blocks +# Naming conventions: +# * each converter should specialize on a single idiom; be consistent with +# the Python reference for naming +# * all converters inherit core.converter.Base +# * module names describe the idiom that the converter covers, plural +# * the converter class is named consistent with the module, singular and +# includes the word Transformer +# +# Example: +# +# lists.py +# class ListTransformer(converter.Base) diff --git a/tensorflow/contrib/autograph/converters/asserts.py b/tensorflow/contrib/autograph/converters/asserts.py index e664a403a5fb800e7d0dddfa5695330927aaf4e0..af2f20f267d5cc64a6e9507a08c44f7e52245c28 100644 --- a/tensorflow/contrib/autograph/converters/asserts.py +++ b/tensorflow/contrib/autograph/converters/asserts.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Converts Assert statements to their corresponding TF calls.""" +"""Converts assert statements to their corresponding TF calls.""" from __future__ import absolute_import from __future__ import division @@ -24,8 +24,8 @@ from tensorflow.contrib.autograph.core import converter from tensorflow.contrib.autograph.pyct import templates -class AssertsTransformer(converter.Base): - """Transforms Print nodes to Call so they can be handled as functions.""" +class AssertTransformer(converter.Base): + """Transforms Assert nodes to Call so they can be handled as functions.""" def visit_Assert(self, node): self.generic_visit(node) @@ -46,4 +46,4 @@ class AssertsTransformer(converter.Base): def transform(node, ctx): - return AssertsTransformer(ctx).visit(node) + return AssertTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/asserts_test.py b/tensorflow/contrib/autograph/converters/asserts_test.py index 2cd0e626bc4552bd40bc94b890fdcc7efcafb3f3..38faba45df6746d56933a1647594af133b671628 100644 --- a/tensorflow/contrib/autograph/converters/asserts_test.py +++ b/tensorflow/contrib/autograph/converters/asserts_test.py @@ -32,10 +32,10 @@ class AssertsTest(converter_testing.TestCase): def test_fn(a): assert a > 0 - node = self.parse_and_analyze(test_fn, {}) - node = asserts.transform(node, self.ctx) + node, ctx = self.prepare(test_fn, {}) + node = asserts.transform(node, ctx) - self.assertTrue(isinstance(node.body[0].body[0].value, gast.Call)) + self.assertTrue(isinstance(node.body[0].value, gast.Call)) if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/converters/break_statements.py b/tensorflow/contrib/autograph/converters/break_statements.py index a990e359a2a25a57ee2a4f8a866350633f3b9ea8..180779670d91abd7d395bda0b63f592967c5015b 100644 --- a/tensorflow/contrib/autograph/converters/break_statements.py +++ b/tensorflow/contrib/autograph/converters/break_statements.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Canonicalizes break statements by de-sugaring into a control boolean.""" +"""Lowers break statements to conditionals.""" from __future__ import absolute_import from __future__ import division @@ -24,20 +24,25 @@ from tensorflow.contrib.autograph.pyct import templates from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno -# Tags for local state. -BREAK_USED = 'break_used' -CONTROL_VAR_NAME = 'control_var_name' +class _Break(object): + def __init__(self): + self.used = False + self.control_var_name = None -class BreakStatementTransformer(converter.Base): + def __repr__(self): + return 'used: %s, var: %s' % (self.used, self.control_var_name) + + +class BreakTransformer(converter.Base): """Canonicalizes break statements into additional conditionals.""" def visit_Break(self, node): - self.set_local(BREAK_USED, True) - var_name = self.get_local(CONTROL_VAR_NAME) + self.state[_Break].used = True + var_name = self.state[_Break].control_var_name # TODO(mdan): This will fail when expanded inside a top-level else block. template = """ - var_name = True + var_name = tf.constant(True) continue """ return templates.replace(template, var_name=var_name) @@ -57,12 +62,12 @@ class BreakStatementTransformer(converter.Base): block=block) return node - def _track_body(self, nodes, break_var): - self.enter_local_scope() - self.set_local(CONTROL_VAR_NAME, break_var) + def _process_body(self, nodes, break_var): + self.state[_Break].enter() + self.state[_Break].control_var_name = break_var nodes = self.visit_block(nodes) - break_used = self.get_local(BREAK_USED, False) - self.exit_local_scope() + break_used = self.state[_Break].used + self.state[_Break].exit() return nodes, break_used def visit_While(self, node): @@ -70,7 +75,7 @@ class BreakStatementTransformer(converter.Base): break_var = self.ctx.namer.new_symbol('break_', scope.referenced) node.test = self.visit(node.test) - node.body, break_used = self._track_body(node.body, break_var) + node.body, break_used = self._process_body(node.body, break_var) # A break in the else clause applies to the containing scope. node.orelse = self.visit_block(node.orelse) @@ -80,7 +85,7 @@ class BreakStatementTransformer(converter.Base): guarded_orelse = self._guard_if_present(node.orelse, break_var) template = """ - var_name = False + var_name = tf.constant(False) while test and not var_name: body else: @@ -101,7 +106,7 @@ class BreakStatementTransformer(converter.Base): node.target = self.visit(node.target) node.iter = self.visit(node.iter) - node.body, break_used = self._track_body(node.body, break_var) + node.body, break_used = self._process_body(node.body, break_var) # A break in the else clause applies to the containing scope. node.orelse = self.visit_block(node.orelse) @@ -117,7 +122,7 @@ class BreakStatementTransformer(converter.Base): # the control variable is marked as used. # TODO(mdan): Use a marker instead, e.g. ag__.condition_loop_on(var_name) template = """ - var_name = False + var_name = tf.constant(False) for target in iter_: (var_name,) body @@ -138,4 +143,4 @@ class BreakStatementTransformer(converter.Base): def transform(node, ctx): - return BreakStatementTransformer(ctx).visit(node) + return BreakTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/break_statements_test.py b/tensorflow/contrib/autograph/converters/break_statements_test.py index dcff1c54c2f9300d58d217517e108d634ae85fb4..fcae7d68c0f90817e001b45fa86ca6be08456027 100644 --- a/tensorflow/contrib/autograph/converters/break_statements_test.py +++ b/tensorflow/contrib/autograph/converters/break_statements_test.py @@ -20,12 +20,19 @@ from __future__ import print_function from tensorflow.contrib.autograph.converters import break_statements from tensorflow.contrib.autograph.core import converter_testing +from tensorflow.python.eager import context as tfe_ctx +from tensorflow.python.framework import constant_op from tensorflow.python.platform import test class BreakCanonicalizationTest(converter_testing.TestCase): - def test_basic_while(self): + def assertTransformedEquivalent(self, test_fn, *inputs): + with self.converted(test_fn, break_statements, {}, + constant_op.constant) as result: + self.assertEqual(test_fn(*inputs), result.test_fn(*inputs)) + + def test_while_loop(self): def test_fn(x): v = [] @@ -36,15 +43,12 @@ class BreakCanonicalizationTest(converter_testing.TestCase): v.append(x) return v - node = self.parse_and_analyze(test_fn, {}) - node = break_statements.transform(node, self.ctx) - - with self.compiled(node) as result: - self.assertEqual([], result.test_fn(0)) - self.assertEqual([], result.test_fn(1)) - self.assertEqual([3], result.test_fn(4)) + with tfe_ctx.eager_mode(): + self.assertTransformedEquivalent(test_fn, 0) + self.assertTransformedEquivalent(test_fn, 1) + self.assertTransformedEquivalent(test_fn, 4) - def test_basic_for(self): + def test_for_loop(self): def test_fn(a): v = [] @@ -55,18 +59,13 @@ class BreakCanonicalizationTest(converter_testing.TestCase): v.append(x) return v - node = self.parse_and_analyze(test_fn, {}) - node = break_statements.transform(node, self.ctx) - - with self.compiled(node) as result: + with self.converted(test_fn, break_statements, {}, + constant_op.constant) as result: # The break is incompletely canonicalized. The loop will not interrupt, # but the section following the break will be skipped. - self.assertEqual([], result.test_fn([])) - self.assertEqual([3, 3], result.test_fn([4, 4])) - self.assertEqual([3], result.test_fn([4, 5])) self.assertEqual([3], result.test_fn([5, 4])) - def test_deeply_nested(self): + def test_nested(self): def test_fn(x): v = [] @@ -83,13 +82,10 @@ class BreakCanonicalizationTest(converter_testing.TestCase): v.append(x) return v, u, w - node = self.parse_and_analyze(test_fn, {}) - node = break_statements.transform(node, self.ctx) - - with self.compiled(node) as result: - self.assertEqual(([], [], []), result.test_fn(0)) - self.assertEqual(([2, 1], [2], [0]), result.test_fn(3)) - self.assertEqual(([10, 9, 8, 7], [10, 8], [6]), result.test_fn(11)) + with tfe_ctx.eager_mode(): + self.assertTransformedEquivalent(test_fn, 0) + self.assertTransformedEquivalent(test_fn, 3) + self.assertTransformedEquivalent(test_fn, 11) def test_nested_loops(self): @@ -109,16 +105,13 @@ class BreakCanonicalizationTest(converter_testing.TestCase): v.append(x) return v, u - node = self.parse_and_analyze(test_fn, {}) - node = break_statements.transform(node, self.ctx) - - with self.compiled(node) as result: - self.assertEqual(([], []), result.test_fn(0)) - self.assertEqual(([1], []), result.test_fn(2)) - self.assertEqual(([2, 1], [1]), result.test_fn(3)) - self.assertEqual(([4, 3, 2, 1], [3, 1]), result.test_fn(5)) + with tfe_ctx.eager_mode(): + self.assertTransformedEquivalent(test_fn, 0) + self.assertTransformedEquivalent(test_fn, 2) + self.assertTransformedEquivalent(test_fn, 3) + self.assertTransformedEquivalent(test_fn, 5) - def test_loop_else(self): + def test_loop_orelse(self): def test_fn(x): v = [] @@ -134,13 +127,10 @@ class BreakCanonicalizationTest(converter_testing.TestCase): v.append(x) return v, u - node = self.parse_and_analyze(test_fn, {}) - node = break_statements.transform(node, self.ctx) - - with self.compiled(node) as result: - self.assertEqual(([], []), result.test_fn(0)) - self.assertEqual(([], [1]), result.test_fn(2)) - self.assertEqual(([2], [1]), result.test_fn(3)) + with tfe_ctx.eager_mode(): + self.assertTransformedEquivalent(test_fn, 0) + self.assertTransformedEquivalent(test_fn, 2) + self.assertTransformedEquivalent(test_fn, 3) if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/converters/builtin_functions_test.py b/tensorflow/contrib/autograph/converters/builtin_functions_test.py index e9000e518ce14f9e0ea486d5b3e374439b8c78ca..d5c3e2c250cc1ee0205fd1941040bf70de4a149a 100644 --- a/tensorflow/contrib/autograph/converters/builtin_functions_test.py +++ b/tensorflow/contrib/autograph/converters/builtin_functions_test.py @@ -18,8 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import sys - import six from tensorflow.contrib.autograph.converters import builtin_functions @@ -36,55 +34,39 @@ class BuiltinFunctionsTest(converter_testing.TestCase): def test_fn(a): return len(a) - node = self.parse_and_analyze(test_fn, {'len': len}) - node = builtin_functions.transform(node, self.ctx) - - with self.compiled(node, array_ops.shape) as result: + with self.converted(test_fn, builtin_functions, {'len': len}, + array_ops.shape) as result: with self.test_session() as sess: - self.assertEqual(3, - sess.run( - result.test_fn(constant_op.constant([0, 0, 0])))) - - self.assertEqual(3, result.test_fn([0, 0, 0])) + ops = result.test_fn(constant_op.constant([0, 0, 0])) + self.assertEqual(sess.run(ops), 3) def test_print(self): - def test_fn(a): - print(a) + if six.PY2: + return - node = self.parse_and_analyze(test_fn, {'print': print}) - node = builtin_functions.transform(node, self.ctx) + def test_fn(a): + return print(a) - with self.compiled(node) as result: + with self.converted(test_fn, builtin_functions, {'print': print}) as result: with self.test_session() as sess: - try: - out_capturer = six.StringIO() - sys.stdout = out_capturer - result.test_fn(constant_op.constant('a')) - sess.run(sess.graph.get_operations()) - self.assertEqual(out_capturer.getvalue(), 'a\n') - finally: - sys.stdout = sys.__stdout__ + with self.assertPrints('a\n'): + sess.run(result.test_fn('a')) - def test_print_with_op_multiple_values(self): + def test_print_multiple_values(self): - def test_fn(a, b, c): - print(a, b, c) + if six.PY2: + return - node = self.parse_and_analyze(test_fn, {'print': print}) - node = builtin_functions.transform(node, self.ctx) + def test_fn(a, b, c): + return print(a, b, c) - with self.compiled(node) as result: + with self.converted(test_fn, builtin_functions, {'print': print}) as result: with self.test_session() as sess: - try: - out_capturer = six.StringIO() - sys.stdout = out_capturer - result.test_fn( - constant_op.constant('a'), constant_op.constant(1), [2, 3]) - sess.run(sess.graph.get_operations()) - self.assertEqual(out_capturer.getvalue(), 'a 1 [2, 3]\n') - finally: - sys.stdout = sys.__stdout__ + with self.assertPrints('a 1 [2, 3]\n'): + sess.run( + result.test_fn( + constant_op.constant('a'), constant_op.constant(1), [2, 3])) if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/converters/call_trees_test.py b/tensorflow/contrib/autograph/converters/call_trees_test.py index 27d8281b856f505062ceacc8ad50c8cbc2ce6c81..8cdba659eee264717204cc6048bbe0b8bbfe245f 100644 --- a/tensorflow/contrib/autograph/converters/call_trees_test.py +++ b/tensorflow/contrib/autograph/converters/call_trees_test.py @@ -36,37 +36,34 @@ class CallTreesTest(converter_testing.TestCase): def test_fn_1(_): raise ValueError('This should not be called in the compiled version.') - def renamed_test_fn_1(a): + def other_test_fn_1(a): return a + 1 def test_fn_2(a): return test_fn_1(a) + 1 - node = self.parse_and_analyze(test_fn_2, {'test_fn_1': test_fn_1}) - node = call_trees.transform(node, self.ctx) + ns = {'test_fn_1': test_fn_1} + node, ctx = self.prepare(test_fn_2, ns) + node = call_trees.transform(node, ctx) - with self.compiled(node) as result: - # Only test_fn_2 is transformed, so we'll insert renamed_test_fn_1 - # manually. - result.renamed_test_fn_1 = renamed_test_fn_1 - self.assertEquals(3, result.test_fn_2(1)) + with self.compiled(node, ns) as result: + new_name, _ = ctx.namer.compiled_function_name(('test_fn_1',)) + setattr(result, new_name, other_test_fn_1) + self.assertEquals(result.test_fn_2(1), 3) def test_dynamic_function(self): def test_fn_1(): - raise ValueError('This should be masked by the mock.') + raise ValueError('This should be masked by the mock in self.compiled.') def test_fn_2(f): return f() + 3 - node = self.parse_and_analyze(test_fn_2, {}) - node = call_trees.transform(node, self.ctx) - - with self.compiled(node) as result: + with self.converted(test_fn_2, call_trees, {}) as result: # 10 = 7 (from the mock) + 3 (from test_fn_2) self.assertEquals(10, result.test_fn_2(test_fn_1)) - def test_simple_methods(self): + def test_basic_method(self): class TestClass(object): @@ -76,49 +73,43 @@ class CallTreesTest(converter_testing.TestCase): def test_fn_2(self, a): return self.test_fn_1(a) + 1 - node = self.parse_and_analyze( - TestClass.test_fn_2, {'TestClass': TestClass}, + ns = {'TestClass': TestClass} + node, ctx = self.prepare( + TestClass.test_fn_2, + ns, namer=converter_testing.FakeNoRenameNamer(), arg_types={'self': (TestClass.__name__, TestClass)}) - node = call_trees.transform(node, self.ctx) + node = call_trees.transform(node, ctx) - with self.compiled(node) as result: + with self.compiled(node, ns) as result: tc = TestClass() self.assertEquals(3, result.test_fn_2(tc, 1)) - def test_py_func_wrap_no_retval(self): + def test_py_func_no_retval(self): def test_fn(a): setattr(a, 'foo', 'bar') - node = self.parse_and_analyze(test_fn, {'setattr': setattr}) - node = call_trees.transform(node, self.ctx) - - with self.compiled(node) as result: + with self.converted(test_fn, call_trees, {'setattr': setattr}) as result: with self.test_session() as sess: - # The function has no return value, so we do some tricks to grab the - # generated py_func node and ensure its effect only happens at graph - # execution. class Dummy(object): pass a = Dummy() result.test_fn(a) + py_func_op, = sess.graph.get_operations() self.assertFalse(hasattr(a, 'foo')) - sess.run(sess.graph.get_operations()[0]) + sess.run(py_func_op) self.assertEquals('bar', a.foo) - def test_py_func_wrap_known_function(self): + def test_py_func_known_function(self): def test_fn(): return np.random.binomial(2, 0.5) - node = self.parse_and_analyze(test_fn, {'np': np}) - node = call_trees.transform(node, self.ctx) - - with self.compiled(node, dtypes.int64) as result: - result.np = np + with self.converted(test_fn, call_trees, {'np': np}, + dtypes.int64) as result: with self.test_session() as sess: self.assertTrue(isinstance(result.test_fn(), ops.Tensor)) self.assertIn(sess.run(result.test_fn()), (0, 1, 2)) @@ -130,22 +121,17 @@ class CallTreesTest(converter_testing.TestCase): a = math_ops.add(a, constant_op.constant(1)) return a - node = self.parse_and_analyze( - test_fn, { - 'math_ops': math_ops, - 'constant_op': constant_op - }, + ns = {'math_ops': math_ops, 'constant_op': constant_op} + node, ctx = self.prepare( + test_fn, + ns, arg_types=set(((math_ops.__name__,), (constant_op.__name__,)))) - node = call_trees.transform(node, self.ctx) + node = call_trees.transform(node, ctx) - with self.compiled(node) as result: - result.math_ops = math_ops - result.constant_op = constant_op + with self.compiled(node, ns) as result: with self.test_session() as sess: - # Not renamed, because the converter doesn't rename the definition - # itself (the caller is responsible for that). result_tensor = result.test_fn(constant_op.constant(1)) - self.assertEquals(3, sess.run(result_tensor)) + self.assertEquals(sess.run(result_tensor), 3) if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/converters/conditional_expressions.py b/tensorflow/contrib/autograph/converters/conditional_expressions.py new file mode 100644 index 0000000000000000000000000000000000000000..63f649dfdf5f740ba66260a51175a0ec2b716ea3 --- /dev/null +++ b/tensorflow/contrib/autograph/converters/conditional_expressions.py @@ -0,0 +1,129 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Converts the ternary conditional operator.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.core import converter +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import templates +from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno + + +class _FunctionDefs(object): + + def __init__(self): + self.nodes = [] + + +class _Statement(object): + + def __init__(self): + self.scope = None + + +class ConditionalExpressionTransformer(converter.Base): + """Converts contitional expressions to functional form.""" + + def _postprocess_statement(self, node): + """Inserts any separate functions that node may use.""" + replacements = [] + for def_node in self.state[_FunctionDefs].nodes: + replacements.extend(def_node) + replacements.append(node) + node = replacements + # The corresponding enter is called by self.visit_block (see _process_block) + self.state[_FunctionDefs].exit() + return node, None + + def _create_branch(self, expr, name_stem): + scope = self.state[_Statement].scope + name = self.ctx.namer.new_symbol(name_stem, scope.referenced) + template = """ + def name(): + return expr, + """ + node = templates.replace(template, name=name, expr=expr) + self.state[_FunctionDefs].nodes.append(node) + return name + + def visit_IfExp(self, node): + if anno.hasanno(node.test, anno.Basic.QN): + name_root = anno.getanno(node.test, anno.Basic.QN).ssf() + else: + name_root = 'ifexp' + + true_fn_name = self._create_branch(node.body, '%s_true' % name_root) + false_fn_name = self._create_branch(node.orelse, '%s_false' % name_root) + + return templates.replace_as_expression( + 'ag__.utils.run_cond(test, true_fn_name, false_fn_name)', + test=node.test, + true_fn_name=true_fn_name, + false_fn_name=false_fn_name) + + def _process_block(self, scope, block): + self.state[_Statement].enter() + self.state[_Statement].scope = scope + block = self.visit_block( + block, + before_visit=self.state[_FunctionDefs].enter, + after_visit=self._postprocess_statement) + self.state[_Statement].exit() + return block + + def visit_FunctionDef(self, node): + node.args = self.generic_visit(node.args) + node.decorator_list = self.visit_block(node.decorator_list) + node.body = self._process_block( + anno.getanno(node, anno.Static.SCOPE), node.body) + return node + + def visit_For(self, node): + node.target = self.visit(node.target) + node.body = self._process_block( + anno.getanno(node, NodeAnno.BODY_SCOPE), node.body) + node.orelse = self._process_block( + anno.getanno(node, NodeAnno.ORELSE_SCOPE), node.orelse) + return node + + def visit_While(self, node): + node.test = self.visit(node.test) + node.body = self._process_block( + anno.getanno(node, NodeAnno.BODY_SCOPE), node.body) + node.orelse = self._process_block( + anno.getanno(node, NodeAnno.ORELSE_SCOPE), node.orelse) + return node + + def visit_If(self, node): + node.test = self.visit(node.test) + node.body = self._process_block( + anno.getanno(node, NodeAnno.BODY_SCOPE), node.body) + node.orelse = self._process_block( + anno.getanno(node, NodeAnno.ORELSE_SCOPE), node.orelse) + return node + + def visit_With(self, node): + node.items = self.visit_block(node.items) + node.body = self._process_block( + anno.getanno(node, NodeAnno.BODY_SCOPE), node.body) + return node + + +def transform(node, ctx): + node = ConditionalExpressionTransformer(ctx).visit(node) + return node diff --git a/tensorflow/contrib/autograph/converters/conditional_expressions_test.py b/tensorflow/contrib/autograph/converters/conditional_expressions_test.py new file mode 100644 index 0000000000000000000000000000000000000000..95a3108741800c5fe504690f92876fa63edd8651 --- /dev/null +++ b/tensorflow/contrib/autograph/converters/conditional_expressions_test.py @@ -0,0 +1,53 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for conditional_expressions module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.converters import conditional_expressions +from tensorflow.contrib.autograph.core import converter_testing +from tensorflow.python.platform import test + + +class ConditionalExpressionsTest(converter_testing.TestCase): + + def assertTransformedEquivalent(self, test_fn, *inputs): + ns = {} + with self.converted(test_fn, conditional_expressions, ns) as result: + self.assertEqual(test_fn(*inputs), result.test_fn(*inputs)) + + def test_basic(self): + + def test_fn(x): + return 1 if x else 0 + + self.assertTransformedEquivalent(test_fn, 0) + self.assertTransformedEquivalent(test_fn, 3) + + def test_nested_orelse(self): + + def test_fn(x): + y = x * x if x > 0 else x if x else 1 + return y + + self.assertTransformedEquivalent(test_fn, -2) + self.assertTransformedEquivalent(test_fn, 0) + self.assertTransformedEquivalent(test_fn, 2) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/autograph/converters/continue_statements.py b/tensorflow/contrib/autograph/converters/continue_statements.py index 958bde0a58764e705c35ab73ce879b2c11ce7cdc..0476e97c15e33dcfc09b3555cf8dc7ff3fd7ce19 100644 --- a/tensorflow/contrib/autograph/converters/continue_statements.py +++ b/tensorflow/contrib/autograph/converters/continue_statements.py @@ -37,7 +37,7 @@ class ContinueCanonicalizationTransformer(converter.Base): def visit_Continue(self, node): self.set_local(CONTINUE_USED, True) template = """ - var_name = True + var_name = tf.constant(True) """ return templates.replace( template, var_name=self.get_local(CONTROL_VAR_NAME)) @@ -92,7 +92,7 @@ class ContinueCanonicalizationTransformer(converter.Base): if self.get_local(CONTINUE_USED, False): template = """ - var_name = False + var_name = tf.constant(False) """ control_var_init = templates.replace(template, var_name=continue_var) nodes = control_var_init + nodes diff --git a/tensorflow/contrib/autograph/converters/continue_statements_test.py b/tensorflow/contrib/autograph/converters/continue_statements_test.py index 2ce1837972c50bbc4921487a290f5cb2f782b5f3..37c15211b4fe266e57879249fe7e060ded44dc1f 100644 --- a/tensorflow/contrib/autograph/converters/continue_statements_test.py +++ b/tensorflow/contrib/autograph/converters/continue_statements_test.py @@ -20,12 +20,19 @@ from __future__ import print_function from tensorflow.contrib.autograph.converters import continue_statements from tensorflow.contrib.autograph.core import converter_testing +from tensorflow.python.eager import context as tfe_ctx +from tensorflow.python.framework import constant_op from tensorflow.python.platform import test class ContinueCanonicalizationTest(converter_testing.TestCase): - def test_basic_continue(self): + def assertTransformedEquivalent(self, test_fn, *inputs): + with self.converted(test_fn, continue_statements, {}, + constant_op.constant) as result: + self.assertEqual(test_fn(*inputs), result.test_fn(*inputs)) + + def test_basic(self): def test_fn(x): v = [] @@ -36,17 +43,13 @@ class ContinueCanonicalizationTest(converter_testing.TestCase): v.append(x) return v - node = self.parse_and_analyze(test_fn, {}) - node = continue_statements.transform(node, self.ctx) - - with self.compiled(node) as result: - self.assertEqual(test_fn(0), result.test_fn(0)) - self.assertEqual(test_fn(1), result.test_fn(1)) - self.assertEqual(test_fn(2), result.test_fn(2)) - self.assertEqual(test_fn(3), result.test_fn(3)) - self.assertEqual(test_fn(4), result.test_fn(4)) + with tfe_ctx.eager_mode(): + self.assertTransformedEquivalent(test_fn, 0) + self.assertTransformedEquivalent(test_fn, 1) + self.assertTransformedEquivalent(test_fn, 3) + self.assertTransformedEquivalent(test_fn, 4) - def test_basic_continue_for_loop(self): + def test_for_loop(self): def test_fn(a): v = [] @@ -57,16 +60,13 @@ class ContinueCanonicalizationTest(converter_testing.TestCase): v.append(x) return v - node = self.parse_and_analyze(test_fn, {}) - node = continue_statements.transform(node, self.ctx) + with tfe_ctx.eager_mode(): + self.assertTransformedEquivalent(test_fn, []) + self.assertTransformedEquivalent(test_fn, [1]) + self.assertTransformedEquivalent(test_fn, [2]) + self.assertTransformedEquivalent(test_fn, [1, 2, 3]) - with self.compiled(node) as result: - self.assertEqual(test_fn([]), result.test_fn([])) - self.assertEqual(test_fn([1]), result.test_fn([1])) - self.assertEqual(test_fn([2]), result.test_fn([2])) - self.assertEqual(test_fn([1, 2, 3]), result.test_fn([1, 2, 3])) - - def test_continue_deeply_nested(self): + def test_nested(self): def test_fn(x): v = [] @@ -83,15 +83,11 @@ class ContinueCanonicalizationTest(converter_testing.TestCase): v.append(x) return v, u, w - node = self.parse_and_analyze(test_fn, {}) - node = continue_statements.transform(node, self.ctx) - - with self.compiled(node) as result: - self.assertEqual(test_fn(0), result.test_fn(0)) - self.assertEqual(test_fn(1), result.test_fn(1)) - self.assertEqual(test_fn(2), result.test_fn(2)) - self.assertEqual(test_fn(3), result.test_fn(3)) - self.assertEqual(test_fn(4), result.test_fn(4)) + with tfe_ctx.eager_mode(): + self.assertTransformedEquivalent(test_fn, 0) + self.assertTransformedEquivalent(test_fn, 1) + self.assertTransformedEquivalent(test_fn, 3) + self.assertTransformedEquivalent(test_fn, 4) if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/converters/control_flow.py b/tensorflow/contrib/autograph/converters/control_flow.py index f4a87106279d5658ecaa90a577cbe741711ba22e..5a5a6ad63a777f463e80e061d4870f2ee7491c39 100644 --- a/tensorflow/contrib/autograph/converters/control_flow.py +++ b/tensorflow/contrib/autograph/converters/control_flow.py @@ -25,8 +25,7 @@ from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import ast_util from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import templates -from tensorflow.contrib.autograph.pyct.static_analysis import cfg -from tensorflow.contrib.autograph.pyct.static_analysis.annos import NodeAnno +from tensorflow.contrib.autograph.pyct.static_analysis import annos class SymbolNamer(object): @@ -47,6 +46,7 @@ class SymbolNamer(object): class ControlFlowTransformer(converter.Base): """Transforms control flow structures like loops an conditionals.""" + def _create_cond_branch(self, body_name, aliased_orig_names, aliased_new_names, body, returns): if aliased_orig_names: @@ -90,55 +90,51 @@ class ControlFlowTransformer(converter.Base): return templates.replace( template, test=test, body_name=body_name, orelse_name=orelse_name) - def visit_If(self, node): - self.generic_visit(node) + def _fmt_symbol_list(self, symbol_set): + if not symbol_set: + return 'no variables' + return ', '.join(map(str, symbol_set)) - body_scope = anno.getanno(node, NodeAnno.BODY_SCOPE) - orelse_scope = anno.getanno(node, NodeAnno.ORELSE_SCOPE) - body_defs = body_scope.created | body_scope.modified - orelse_defs = orelse_scope.created | orelse_scope.modified - live = anno.getanno(node, 'live_out') - - # We'll need to check if we're closing over variables that are defined - # elsewhere in the function - # NOTE: we can only detect syntactic closure in the scope - # of the code passed in. If the AutoGraph'd function itself closes - # over other variables, this analysis won't take that into account. - defined = anno.getanno(node, 'defined_in') - - # We only need to return variables that are - # - modified by one or both branches - # - live (or has a live parent) at the end of the conditional - modified = [] - for def_ in body_defs | orelse_defs: - def_with_parents = set((def_,)) | def_.support_set - if live & def_with_parents: - modified.append(def_) - - # We need to check if live created variables are balanced - # in both branches - created = live & (body_scope.created | orelse_scope.created) - - # The if statement is illegal if there are variables that are created, - # that are also live, but both branches don't create them. - if created: - if created != (body_scope.created & live): - raise ValueError( - 'The main branch does not create all live symbols that the else ' - 'branch does.') - if created != (orelse_scope.created & live): - raise ValueError( - 'The else branch does not create all live symbols that the main ' - 'branch does.') - - # Alias the closure variables inside the conditional functions - # to avoid errors caused by the local variables created in the branch - # functions. + def visit_If(self, node): + node = self.generic_visit(node) + + body_scope = anno.getanno(node, annos.NodeAnno.BODY_SCOPE) + orelse_scope = anno.getanno(node, annos.NodeAnno.ORELSE_SCOPE) + defined_in = anno.getanno(node, anno.Static.DEFINED_VARS_IN) + live_out = anno.getanno(node, anno.Static.LIVE_VARS_OUT) + + modified_in_cond = body_scope.modified | orelse_scope.modified + returned_from_cond = set() + for s in modified_in_cond: + if s in live_out: + returned_from_cond.add(s) + elif s.is_composite(): + # Special treatment for compound objects: if any of their owner entities + # are live, then they are outputs as well. + if any(owner in live_out for owner in s.owner_set): + returned_from_cond.add(s) + + need_alias_in_body = body_scope.modified & defined_in + need_alias_in_orelse = orelse_scope.modified & defined_in + + created_in_body = body_scope.modified & returned_from_cond - defined_in + created_in_orelse = orelse_scope.modified & returned_from_cond - defined_in + + if created_in_body != created_in_orelse: + raise ValueError( + 'if statement may not initialize all variables: the true branch' + ' creates %s, while the false branch creates %s. Make sure all' + ' these variables are initialized either in both' + ' branches or before the if statement.' % + (self._fmt_symbol_list(created_in_body), + self._fmt_symbol_list(created_in_orelse))) + + # Alias the closure variables inside the conditional functions, to allow + # the functions access to the respective variables. # We will alias variables independently for body and orelse scope, # because different branches might write different variables. - aliased_body_orig_names = tuple(body_scope.modified - body_scope.created) - aliased_orelse_orig_names = tuple(orelse_scope.modified - - orelse_scope.created) + aliased_body_orig_names = tuple(need_alias_in_body) + aliased_orelse_orig_names = tuple(need_alias_in_orelse) aliased_body_new_names = tuple( self.ctx.namer.new_symbol(s.ssf(), body_scope.referenced) for s in aliased_body_orig_names) @@ -153,58 +149,47 @@ class ControlFlowTransformer(converter.Base): node_body = ast_util.rename_symbols(node.body, alias_body_map) node_orelse = ast_util.rename_symbols(node.orelse, alias_orelse_map) - if not modified: + returned_from_cond = tuple(returned_from_cond) + if returned_from_cond: + if len(returned_from_cond) == 1: + # TODO(mdan): Move this quirk into the operator implementation. + cond_results = returned_from_cond[0] + else: + cond_results = gast.Tuple([s.ast() for s in returned_from_cond], None) + + returned_from_body = tuple( + alias_body_map[s] if s in need_alias_in_body else s + for s in returned_from_cond) + returned_from_orelse = tuple( + alias_orelse_map[s] if s in need_alias_in_orelse else s + for s in returned_from_cond) + + else: # When the cond would return no value, we leave the cond called without # results. That in turn should trigger the side effect guards. The # branch functions will return a dummy value that ensures cond # actually has some return value as well. - results = None - elif len(modified) == 1: - results = modified[0] - else: - results = gast.Tuple([s.ast() for s in modified], None) + cond_results = None + # TODO(mdan): This doesn't belong here; it's specific to the operator. + returned_from_body = templates.replace_as_expression('tf.constant(1)') + returned_from_orelse = templates.replace_as_expression('tf.constant(1)') body_name = self.ctx.namer.new_symbol('if_true', body_scope.referenced) orelse_name = self.ctx.namer.new_symbol('if_false', orelse_scope.referenced) - if modified: - - def build_returns(aliased_names, alias_map, scope): - """Builds list of return variables for a branch of a conditional.""" - returns = [] - for s in modified: - if s in aliased_names: - returns.append(alias_map[s]) - else: - if s not in scope.created | defined: - raise ValueError( - 'Attempting to return variable "%s" from the true branch of ' - 'a conditional, but it was not closed over, or created in ' - 'this branch.' % str(s)) - else: - returns.append(s) - return tuple(returns) - - body_returns = build_returns(aliased_body_orig_names, alias_body_map, - body_scope) - orelse_returns = build_returns(aliased_orelse_orig_names, - alias_orelse_map, orelse_scope) - - else: - body_returns = orelse_returns = templates.replace('tf.ones(())')[0].value body_def = self._create_cond_branch( body_name, - aliased_orig_names=tuple(aliased_body_orig_names), - aliased_new_names=tuple(aliased_body_new_names), + aliased_orig_names=aliased_body_orig_names, + aliased_new_names=aliased_body_new_names, body=node_body, - returns=body_returns) + returns=returned_from_body) orelse_def = self._create_cond_branch( orelse_name, - aliased_orig_names=tuple(aliased_orelse_orig_names), - aliased_new_names=tuple(aliased_orelse_new_names), + aliased_orig_names=aliased_orelse_orig_names, + aliased_new_names=aliased_orelse_new_names, body=node_orelse, - returns=orelse_returns) - cond_expr = self._create_cond_expr(results, node.test, body_name, + returns=returned_from_orelse) + cond_expr = self._create_cond_expr(cond_results, node.test, body_name, orelse_name) return body_def + orelse_def + cond_expr @@ -212,11 +197,11 @@ class ControlFlowTransformer(converter.Base): def visit_While(self, node): self.generic_visit(node) - body_scope = anno.getanno(node, NodeAnno.BODY_SCOPE) + body_scope = anno.getanno(node, annos.NodeAnno.BODY_SCOPE) body_closure = body_scope.modified - body_scope.created all_referenced = body_scope.referenced - cond_scope = anno.getanno(node, NodeAnno.COND_SCOPE) + cond_scope = anno.getanno(node, annos.NodeAnno.COND_SCOPE) cond_closure = set() for s in cond_scope.referenced: for root in s.support_set: @@ -277,7 +262,7 @@ class ControlFlowTransformer(converter.Base): def visit_For(self, node): self.generic_visit(node) - body_scope = anno.getanno(node, NodeAnno.BODY_SCOPE) + body_scope = anno.getanno(node, annos.NodeAnno.BODY_SCOPE) body_closure = body_scope.modified - body_scope.created all_referenced = body_scope.referenced @@ -331,7 +316,5 @@ class ControlFlowTransformer(converter.Base): def transform(node, ctx): - cfg.run_analyses(node, cfg.Liveness(ctx.info)) - cfg.run_analyses(node, cfg.Defined(ctx.info)) node = ControlFlowTransformer(ctx).visit(node) return node diff --git a/tensorflow/contrib/autograph/converters/control_flow_test.py b/tensorflow/contrib/autograph/converters/control_flow_test.py index 735eb92a0dd06ee7fd621b92b1a8f894e09cee4a..ade35014263c3ae4ec14b40ee0f2507b70627d41 100644 --- a/tensorflow/contrib/autograph/converters/control_flow_test.py +++ b/tensorflow/contrib/autograph/converters/control_flow_test.py @@ -20,16 +20,23 @@ from __future__ import print_function from tensorflow.contrib.autograph.converters import control_flow from tensorflow.contrib.autograph.core import converter_testing +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import control_flow_ops from tensorflow.python.platform import test class ControlFlowTest(converter_testing.TestCase): - def test_simple_while(self): + def assertTransformedResult(self, test_fn, inputs, expected): + if not isinstance(inputs, tuple): + inputs = (inputs,) + with self.converted(test_fn, control_flow, {}, + constant_op.constant) as result: + with self.test_session() as sess: + self.assertEqual(sess.run(result.test_fn(*inputs)), expected) + + def test_while_basic(self): def test_fn(n): i = 0 @@ -39,29 +46,18 @@ class ControlFlowTest(converter_testing.TestCase): i += 1 return s, i, n - node = self.parse_and_analyze(test_fn, {}) - node = control_flow.transform(node, self.ctx) - - with self.compiled(node) as result: - with self.test_session() as sess: - self.assertEqual((10, 5, 5), - sess.run(result.test_fn(constant_op.constant(5)))) + self.assertTransformedResult(test_fn, constant_op.constant(5), (10, 5, 5)) - def test_while_single_var(self): + def test_while_single_output(self): def test_fn(n): while n > 0: n -= 1 return n - node = self.parse_and_analyze(test_fn, {}) - node = control_flow.transform(node, self.ctx) + self.assertTransformedResult(test_fn, constant_op.constant(5), 0) - with self.compiled(node) as result: - with self.test_session() as sess: - self.assertEqual(0, sess.run(result.test_fn(constant_op.constant(5)))) - - def test_simple_if(self): + def test_if_basic(self): def test_fn(n): a = 0 @@ -72,114 +68,85 @@ class ControlFlowTest(converter_testing.TestCase): b = 2 * n return a, b - node = self.parse_and_analyze(test_fn, {}) - node = control_flow.transform(node, self.ctx) + self.assertTransformedResult(test_fn, constant_op.constant(1), (-1, 0)) + self.assertTransformedResult(test_fn, constant_op.constant(-1), (0, -2)) + + def test_if_complex_outputs(self): + + class TestClass(object): - with self.compiled(node) as result: + def __init__(self, a, b): + self.a = a + self.b = b + + def test_fn(n, obj): + obj.a = 0 + obj.b = 0 + if n > 0: + obj.a = -n + else: + obj.b = 2 * n + return obj + + with self.converted(test_fn, control_flow, {}) as result: with self.test_session() as sess: - self.assertEqual((-1, 0), - sess.run(result.test_fn(constant_op.constant(1)))) - self.assertEqual((0, -2), - sess.run(result.test_fn(constant_op.constant(-1)))) + res_obj = result.test_fn(constant_op.constant(1), TestClass(0, 0)) + self.assertEqual(sess.run((res_obj.a, res_obj.b)), (-1, 0)) + res_obj = result.test_fn(constant_op.constant(-1), TestClass(0, 0)) + self.assertEqual(sess.run((res_obj.a, res_obj.b)), (0, -2)) - def test_if_single_var(self): + def test_if_single_output(self): def test_fn(n): if n > 0: n = -n return n - node = self.parse_and_analyze(test_fn, {}) - node = control_flow.transform(node, self.ctx) + self.assertTransformedResult(test_fn, constant_op.constant(1), -1) - with self.compiled(node) as result: - with self.test_session() as sess: - self.assertEqual(-1, sess.run(result.test_fn(constant_op.constant(1)))) - - def test_imbalanced_aliasing(self): + def test_if_semi(self): def test_fn(n): if n > 0: n = 3 return n - node = self.parse_and_analyze(test_fn, {}) - node = control_flow.transform(node, self.ctx) - - with self.compiled(node, control_flow_ops.cond) as result: - with self.test_session() as sess: - self.assertEqual(3, sess.run(result.test_fn(constant_op.constant(2)))) - self.assertEqual(-3, sess.run(result.test_fn(constant_op.constant(-3)))) + self.assertTransformedResult(test_fn, constant_op.constant(2), 3) + self.assertTransformedResult(test_fn, constant_op.constant(-3), -3) - def test_ignore_unread_variable(self): + def test_if_local_var(self): def test_fn(n): - b = 3 # pylint: disable=unused-variable if n > 0: b = 4 + n = b + 1 return n - node = self.parse_and_analyze(test_fn, {}) - node = control_flow.transform(node, self.ctx) + self.assertTransformedResult(test_fn, constant_op.constant(1), 5) + self.assertTransformedResult(test_fn, constant_op.constant(-1), -1) - with self.compiled(node, control_flow_ops.cond, array_ops.ones) as result: - with self.test_session() as sess: - self.assertEqual(3, sess.run(result.test_fn(constant_op.constant(3)))) - self.assertEqual(-3, sess.run(result.test_fn(constant_op.constant(-3)))) + def test_if_no_outputs(self): - def test_handle_temp_variable(self): + def test_fn(n): + if n > 0: + b = 4 # pylint:disable=unused-variable + return n - def test_fn_using_temp(x, y, w): - if x < y: - z = x + y - else: - w = 2 - tmp = w - z = x - tmp - return z, w + # Without side effect guards, the if statement will stage a cond, + # but that will be pruned at execution. + self.assertTransformedResult(test_fn, constant_op.constant(1), 1) + self.assertTransformedResult(test_fn, constant_op.constant(-1), -1) - node = self.parse_and_analyze(test_fn_using_temp, {}) - node = control_flow.transform(node, self.ctx) + def test_if_imbalanced_outputs(self): - with self.compiled(node, control_flow_ops.cond, array_ops.ones) as result: - with self.test_session() as sess: - z, w = sess.run( - result.test_fn_using_temp( - constant_op.constant(-3), constant_op.constant(3), - constant_op.constant(3))) - self.assertEqual(0, z) - self.assertEqual(3, w) - z, w = sess.run( - result.test_fn_using_temp( - constant_op.constant(3), constant_op.constant(-3), - constant_op.constant(3))) - self.assertEqual(1, z) - self.assertEqual(2, w) - - def test_fn_ignoring_temp(x, y, w): - if x < y: - z = x + y - else: - w = 2 - tmp = w - z = x - tmp - return z + def test_fn(n): + if n > 0: + b = 4 + return b - node = self.parse_and_analyze(test_fn_ignoring_temp, {}) - node = control_flow.transform(node, self.ctx) - - with self.compiled(node, control_flow_ops.cond, array_ops.ones) as result: - with self.test_session() as sess: - z = sess.run( - result.test_fn_ignoring_temp( - constant_op.constant(-3), constant_op.constant(3), - constant_op.constant(3))) - self.assertEqual(0, z) - z = sess.run( - result.test_fn_ignoring_temp( - constant_op.constant(3), constant_op.constant(-3), - constant_op.constant(3))) - self.assertEqual(1, z) + node, ctx = self.prepare(test_fn, {}) + with self.assertRaises(transformer.AutographParseError): + control_flow.transform(node, ctx) def test_simple_for(self): @@ -191,22 +158,11 @@ class ControlFlowTest(converter_testing.TestCase): s2 += e * e return s1, s2 - node = self.parse_and_analyze(test_fn, {}) - node = control_flow.transform(node, self.ctx) + self.assertTransformedResult(test_fn, constant_op.constant([1, 3]), (4, 10)) + empty_vector = constant_op.constant([], shape=(0,), dtype=dtypes.int32) + self.assertTransformedResult(test_fn, empty_vector, (0, 0)) - with self.compiled(node) as result: - with self.test_session() as sess: - l = [1, 2, 3] - self.assertEqual( - test_fn(l), sess.run(result.test_fn(constant_op.constant(l)))) - l = [] - self.assertEqual( - test_fn(l), - sess.run( - result.test_fn( - constant_op.constant(l, shape=(0,), dtype=dtypes.int32)))) - - def test_for_single_var(self): + def test_for_single_output(self): def test_fn(l): s = 0 @@ -214,22 +170,11 @@ class ControlFlowTest(converter_testing.TestCase): s += e return s - node = self.parse_and_analyze(test_fn, {}) - node = control_flow.transform(node, self.ctx) + self.assertTransformedResult(test_fn, constant_op.constant([1, 3]), 4) + empty_vector = constant_op.constant([], shape=(0,), dtype=dtypes.int32) + self.assertTransformedResult(test_fn, empty_vector, 0) - with self.compiled(node) as result: - with self.test_session() as sess: - l = [1, 2, 3] - self.assertEqual( - test_fn(l), sess.run(result.test_fn(constant_op.constant(l)))) - l = [] - self.assertEqual( - test_fn(l), - sess.run( - result.test_fn( - constant_op.constant(l, shape=(0,), dtype=dtypes.int32)))) - - def test_for_with_iterated_expression(self): + def test_for_iterated_expression(self): eval_count = [0] @@ -243,14 +188,13 @@ class ControlFlowTest(converter_testing.TestCase): s += e return s - node = self.parse_and_analyze(test_fn, {'count_evals': count_evals}) - node = control_flow.transform(node, self.ctx) + ns = {'count_evals': count_evals} + node, ctx = self.prepare(test_fn, ns) + node = control_flow.transform(node, ctx) - with self.compiled(node) as result: - result.count_evals = count_evals - self.assertEqual(test_fn(5), result.test_fn(5)) - # count_evals ran twice, once for test_fn and another for result.test_fn - self.assertEqual(eval_count[0], 2) + with self.compiled(node, ns) as result: + self.assertEqual(result.test_fn(5), 10) + self.assertEqual(eval_count[0], 1) if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/converters/decorators_test.py b/tensorflow/contrib/autograph/converters/decorators_test.py index d41c7fde2474803a438100e7e00ce8e9f675de45..095abc5edc02de55cd0b28d9aa9f9c4e7cec13c3 100644 --- a/tensorflow/contrib/autograph/converters/decorators_test.py +++ b/tensorflow/contrib/autograph/converters/decorators_test.py @@ -61,13 +61,13 @@ class DecoratorsTest(converter_testing.TestCase): 'simple_decorator': simple_decorator, 'converter_testing': converter_testing, } - node = self.parse_and_analyze( + node, ctx = self.prepare( f, namespace, recursive=False, autograph_decorators=autograph_decorators) - node = decorators.transform(node, self.ctx) - import_line = '\n'.join(self.ctx.program.additional_imports) + node = decorators.transform(node, ctx) + import_line = '\n'.join(ctx.program.additional_imports) result, _ = compiler.ast_to_object(node, source_prefix=import_line) return getattr(result, f.__name__) @@ -76,11 +76,8 @@ class DecoratorsTest(converter_testing.TestCase): def test_fn(a): return a - node = self.parse_and_analyze(test_fn, {}) - node = decorators.transform(node, self.ctx) - result, _ = compiler.ast_to_object(node) - - self.assertEqual(1, result.test_fn(1)) + with self.converted(test_fn, decorators, {}) as result: + self.assertEqual(1, result.test_fn(1)) def test_function(self): @@ -124,7 +121,7 @@ class DecoratorsTest(converter_testing.TestCase): return b + 11 return inner_fn(a) - # Expected to fail because simple_decorator cannot be imported. + # Expected to fail because simple_decorator could not be imported. with self.assertRaises(transformer.AutographParseError): test_fn(1) diff --git a/tensorflow/contrib/autograph/converters/directives.py b/tensorflow/contrib/autograph/converters/directives.py new file mode 100644 index 0000000000000000000000000000000000000000..ccdf79d47be65dd777a7ae3a226246a62e274430 --- /dev/null +++ b/tensorflow/contrib/autograph/converters/directives.py @@ -0,0 +1,108 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Handles directives. + +This converter removes the directive functions from the code and moves the +information they specify into AST annotations. It is a specialized form of +static analysis, one that is specific to AutoGraph. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gast + +from tensorflow.contrib.autograph.core import converter +from tensorflow.contrib.autograph.lang import directives +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.python.util import tf_inspect + +ENCLOSING_LOOP = 'enclosing_loop' + + +def _map_args(call_node, function): + """Maps AST call nodes to the actual function's arguments. + + Args: + call_node: ast.Call + function: Callable[..., Any], the actual function matching call_node + Returns: + Dict[Text, ast.AST], mapping each of the function's argument names to + the respective AST node. + """ + args = call_node.args + kwds = {kwd.arg: kwd.value for kwd in call_node.keywords} + return tf_inspect.getcallargs(function, *args, **kwds) + + +class DirectivesTransformer(converter.Base): + """Parses compiler directives and converts them into AST annotations.""" + + def _process_symbol_directive(self, call_node, directive): + if len(call_node.args) < 1: + raise ValueError('"%s" requires a positional first argument' + ' as the target' % directive.__name__) + target = call_node.args[0] + defs = anno.getanno(target, anno.Static.ORIG_DEFINITIONS) + for def_ in defs: + def_.directives[directive] = _map_args(call_node, directive) + return call_node + + def _process_statement_directive(self, call_node, directive): + if self.local_scope_level < 1: + raise ValueError( + '"%s" must be used inside a statement' % directive.__name__) + target = self.get_local(ENCLOSING_LOOP) + node_anno = anno.getanno(target, converter.AgAnno.DIRECTIVES, {}) + node_anno[directive] = _map_args(call_node, directive) + anno.setanno(target, converter.AgAnno.DIRECTIVES, node_anno) + return call_node + + def visit_Expr(self, node): + if isinstance(node.value, gast.Call): + call_node = node.value + if anno.hasanno(call_node.func, 'live_val'): + live_val = anno.getanno(call_node.func, 'live_val') + + if live_val is directives.set_element_type: + call_node = self._process_symbol_directive(call_node, live_val) + elif live_val is directives.set_loop_options: + call_node = self._process_statement_directive(call_node, live_val) + else: + return self.generic_visit(node) + + return None # Directive calls are not output in the generated code. + return self.generic_visit(node) + + # TODO(mdan): This will be insufficient for other control flow. + # That means that if we ever have a directive that affects things other than + # loops, we'll need support for parallel scopes, or have multiple converters. + def _track_and_visit_loop(self, node): + self.enter_local_scope() + self.set_local(ENCLOSING_LOOP, node) + node = self.generic_visit(node) + self.exit_local_scope() + return node + + def visit_While(self, node): + return self._track_and_visit_loop(node) + + def visit_For(self, node): + return self._track_and_visit_loop(node) + + +def transform(node, ctx): + return DirectivesTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/directives_test.py b/tensorflow/contrib/autograph/converters/directives_test.py new file mode 100644 index 0000000000000000000000000000000000000000..a573ba5850609f65ea60432470485c523cd3da3b --- /dev/null +++ b/tensorflow/contrib/autograph/converters/directives_test.py @@ -0,0 +1,78 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for directives module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.converters import directives as directives_converter +from tensorflow.contrib.autograph.core import converter_testing +from tensorflow.contrib.autograph.core.converter import AgAnno +from tensorflow.contrib.autograph.lang import directives +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.python.platform import test + + +class DirectivesTest(converter_testing.TestCase): + + def test_local_target(self): + + def test_fn(): + l = [] + string_var = 0 + directives.set_element_type(l, 'a', string_var) + + node, ctx = self.prepare(test_fn, {'directives': directives}) + node = directives_converter.transform(node, ctx) + + def_, = anno.getanno(node.body[0].targets[0], + anno.Static.DEFINITIONS) + d = def_.directives[directives.set_element_type] + self.assertEqual(d['dtype'].s, 'a') + self.assertEqual(d['shape'].id, 'string_var') + + def test_argument_target(self): + + def test_fn(a): + directives.set_element_type(a, 1, shape=2) + + node, ctx = self.prepare(test_fn, {'directives': directives}) + node = directives_converter.transform(node, ctx) + + def_, = anno.getanno(node.args.args[0], anno.Static.DEFINITIONS) + d = def_.directives[directives.set_element_type] + self.assertEqual(d['dtype'].n, 1) + self.assertEqual(d['shape'].n, 2) + + def test_loop_target(self): + + def test_fn(): + a = True + while True: + directives.set_loop_options(parallel_iterations=10, back_prop=a) + + node, ctx = self.prepare(test_fn, {'directives': directives}) + node = directives_converter.transform(node, ctx) + + d = anno.getanno(node.body[1], AgAnno.DIRECTIVES) + d = d[directives.set_loop_options] + self.assertEqual(d['parallel_iterations'].n, 10) + self.assertEqual(d['back_prop'].id, 'a') + self.assertEqual(d['swap_memory'], directives.UNSPECIFIED) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/autograph/converters/error_handlers.py b/tensorflow/contrib/autograph/converters/error_handlers.py new file mode 100644 index 0000000000000000000000000000000000000000..3f2366215268cffe1aa2c55a174dbdba6127d701 --- /dev/null +++ b/tensorflow/contrib/autograph/converters/error_handlers.py @@ -0,0 +1,52 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Wraps function bodies with a try/except to rewrite error tracebacks. + +Only adds try/except wrappers to functions that have the anno.Basic.ORIGIN +annotation because these are the functions originally written by the user. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.core import converter +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import templates + + +class ErrorRewritingTransformer(converter.Base): + """Possibly wraps the body of a function in a try/except. + + Only wraps functions that were originally defined by the user, detected by + checking for the anno.Basic.ORIGIN annotation. + """ + + def visit_FunctionDef(self, node): + node = self.generic_visit(node) + + if anno.hasanno(node, anno.Basic.ORIGIN): + template = """ + try: + body + except: + ag__.rewrite_graph_construction_error(ag_source_map__) + """ + node.body = templates.replace(template, body=node.body) + return node + + +def transform(node, ctx): + return ErrorRewritingTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/error_handlers_test.py b/tensorflow/contrib/autograph/converters/error_handlers_test.py new file mode 100644 index 0000000000000000000000000000000000000000..cd74e5f18f76d0c531f487bc0c736b421c9c3fb4 --- /dev/null +++ b/tensorflow/contrib/autograph/converters/error_handlers_test.py @@ -0,0 +1,57 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for error_handlers module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.converters import error_handlers +from tensorflow.contrib.autograph.core import converter_testing +from tensorflow.contrib.autograph.core import errors +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import origin_info +from tensorflow.python.platform import test + + +class ErrorHandlersTest(converter_testing.TestCase): + + def test_basic(self): + + def test_fn(): + raise ValueError() + + node, ctx = self.prepare(test_fn, {}) + anno.setanno(node, anno.Basic.ORIGIN, + origin_info.OriginInfo(None, None, None)) + node = error_handlers.transform(node, ctx) + with self.compiled(node, {}) as result: + with self.assertRaises(errors.GraphConstructionError): + # Here we just assert that the handler works. Its correctness is + # verified by errors_test.py. + result.test_fn() + + def test_no_origin_annotation(self): + + def test_fn(): + raise ValueError() + + with self.converted(test_fn, error_handlers, {}) as result: + with self.assertRaises(ValueError): + result.test_fn() + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/autograph/converters/ifexp.py b/tensorflow/contrib/autograph/converters/ifexp.py deleted file mode 100644 index e996138498ab2b7efa76671d8cc67fd4c6a9d9b8..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/autograph/converters/ifexp.py +++ /dev/null @@ -1,49 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Canonicalizes the ternary conditional operator.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.autograph.core import converter -from tensorflow.contrib.autograph.pyct import templates - - -class IfExp(converter.Base): - """Canonicalizes all IfExp nodes into plain conditionals.""" - - def visit_IfExp(self, node): - template = """ - ag__.utils.run_cond(test, lambda: (body,), lambda: (orelse,)) - """ - desugared_ifexp = templates.replace_as_expression( - template, test=node.test, body=node.body, orelse=node.orelse) - return desugared_ifexp - - -def transform(node, ctx): - """Desugar IfExp nodes into plain conditionals. - - Args: - node: ast.AST, the node to transform - ctx: converter.EntityContext - - Returns: - new_node: an AST with no IfExp nodes, only conditionals. - """ - - node = IfExp(ctx).visit(node) - return node diff --git a/tensorflow/contrib/autograph/converters/ifexp_test.py b/tensorflow/contrib/autograph/converters/ifexp_test.py deleted file mode 100644 index cdd5a2f591edc1138df1c165577ed375131ddf09..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/autograph/converters/ifexp_test.py +++ /dev/null @@ -1,106 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for ifexp module.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.autograph import utils -from tensorflow.contrib.autograph.converters import ifexp -from tensorflow.contrib.autograph.core import converter_testing -from tensorflow.python.platform import test - - -class IfExpTest(converter_testing.TestCase): - - def compiled_fn(self, test_fn, *args): - node = self.parse_and_analyze(test_fn, {}) - node = ifexp.transform(node, self.ctx) - module = self.compiled(node, *args) - return module - - def test_simple(self): - - def test_fn(x): - return 1 if x else 0 - - with self.compiled_fn(test_fn) as result: - result.autograph_util = utils - for x in [0, 1]: - self.assertEqual(test_fn(x), result.test_fn(x)) - - def test_fn(self): - - def f(x): - return 3 * x - - def test_fn(x): - y = f(x * x if x > 0 else x) - return y - - with self.compiled_fn(test_fn) as result: - result.autograph_util = utils - result.f = f - for x in [-2, 2]: - self.assertEqual(test_fn(x), result.test_fn(x)) - - def test_exp(self): - - def test_fn(x): - return x * x if x > 0 else x - - with self.compiled_fn(test_fn) as result: - result.autograph_util = utils - for x in [-2, 2]: - self.assertEqual(test_fn(x), result.test_fn(x)) - - def test_nested(self): - - def test_fn(x): - return x * x if x > 0 else x if x else 1 - - with self.compiled_fn(test_fn) as result: - result.autograph_util = utils - for x in [-2, 0, 2]: - self.assertEqual(test_fn(x), result.test_fn(x)) - - def test_in_cond(self): - - def test_fn(x): - if x > 0: - return x * x if x < 5 else x * x * x - return -x - - with self.compiled_fn(test_fn) as result: - result.autograph_util = utils - for x in [-2, 2, 5]: - self.assertEqual(test_fn(x), result.test_fn(x)) - - def test_assign_in_cond(self): - - def test_fn(x): - if x > 0: - x = -x if x < 5 else x - return x - - with self.compiled_fn(test_fn) as result: - result.autograph_util = utils - for x in [-2, 2, 5]: - self.assertEqual(test_fn(x), result.test_fn(x)) - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/autograph/converters/list_comprehension.py b/tensorflow/contrib/autograph/converters/list_comprehension.py deleted file mode 100644 index c4a13ee822ab84706df83256d9e9684c3f7dacba..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/autograph/converters/list_comprehension.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Canonicalizing list comprehensions into for and if statements. - -e.g. -result = [x * x for x in xs] - -becomes - -result = [] -for x in xs: - elt = x * x - result.append(elt) -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import gast - -from tensorflow.contrib.autograph.core import converter -from tensorflow.contrib.autograph.pyct import parser -from tensorflow.contrib.autograph.pyct import templates - - -class ListCompCanonicalizationTransformer(converter.Base): - """NodeTransformer to canonicalize list comprehensions.""" - - def make_update_list_node(self, list_, elt): - return templates.replace('list_.append(elt)', list_=list_, elt=elt)[0] - - def instantiate_list_node(self): - return parser.parse_str('[]').body[0].value - - def visit_Assign(self, node): - if not isinstance(node.value, gast.ListComp): - return node - if len(node.targets) > 1: - raise ValueError('Only support single assignment.') - return self.canonicalize_listcomp(node.targets[0], node.value) - - def canonicalize_listcomp(self, result_node, list_comp_node): - - make_list = templates.replace( - 'list_ = create_list', - list_=result_node, - create_list=self.instantiate_list_node()) - loop_body = self.make_update_list_node(result_node, list_comp_node.elt) - - for gen in reversed(list_comp_node.generators): - for gen_if in reversed(gen.ifs): - loop_body = templates.replace( - 'if test: loop_body', test=gen_if, loop_body=loop_body) - loop_body = templates.replace( - 'for target in iter_: loop_body', - iter_=gen.iter, - target=gen.target, - loop_body=loop_body) - - return make_list + loop_body - - -def transform(node, ctx): - return ListCompCanonicalizationTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/list_comprehensions.py b/tensorflow/contrib/autograph/converters/list_comprehensions.py new file mode 100644 index 0000000000000000000000000000000000000000..ecf4628816201a0a6ef4ca14b0f351d818d905b3 --- /dev/null +++ b/tensorflow/contrib/autograph/converters/list_comprehensions.py @@ -0,0 +1,82 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Lowers list comprehensions into for and if statements. + +Example: + + result = [x * x for x in xs] + +becomes + + result = [] + for x in xs: + elt = x * x + result.append(elt) +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gast + +from tensorflow.contrib.autograph.core import converter +from tensorflow.contrib.autograph.pyct import templates + + +# TODO(mdan): This should covert directly to operator calls. + + +class ListCompTransformer(converter.Base): + """Lowers list comprehensions into standard control flow.""" + + def visit_Assign(self, node): + if not isinstance(node.value, gast.ListComp): + return self.generic_visit(node) + if len(node.targets) > 1: + raise NotImplementedError('multiple assignments') + + target, = node.targets + list_comp_node = node.value + + template = """ + target = [] + """ + initialization = templates.replace(template, target=target) + + template = """ + target.append(elt) + """ + body = templates.replace(template, target=target, elt=list_comp_node.elt) + + for gen in reversed(list_comp_node.generators): + for gen_if in reversed(gen.ifs): + template = """ + if test: + body + """ + body = templates.replace(template, test=gen_if, body=body) + template = """ + for target in iter_: + body + """ + body = templates.replace( + template, iter_=gen.iter, target=gen.target, body=body) + + return initialization + body + + +def transform(node, ctx): + return ListCompTransformer(ctx).visit(node) diff --git a/tensorflow/contrib/autograph/converters/list_comprehension_test.py b/tensorflow/contrib/autograph/converters/list_comprehensions_test.py similarity index 59% rename from tensorflow/contrib/autograph/converters/list_comprehension_test.py rename to tensorflow/contrib/autograph/converters/list_comprehensions_test.py index 2bbee93412ce3174a14f3d60af9435dcf3b82cc6..59b5ce9ca052bd1f2201285bef90f398b35e536c 100644 --- a/tensorflow/contrib/autograph/converters/list_comprehension_test.py +++ b/tensorflow/contrib/autograph/converters/list_comprehensions_test.py @@ -12,33 +12,31 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for list_comprehension module.""" +"""Tests for list_comprehensions module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph.converters import list_comprehension +from tensorflow.contrib.autograph.converters import list_comprehensions from tensorflow.contrib.autograph.core import converter_testing from tensorflow.python.platform import test class ListCompTest(converter_testing.TestCase): + def assertTransformedEquivalent(self, test_fn, *inputs): + with self.converted(test_fn, list_comprehensions, {}) as result: + self.assertEqual(test_fn(*inputs), result.test_fn(*inputs)) + def test_basic(self): def test_fn(l): s = [e * e for e in l] return s - node = self.parse_and_analyze(test_fn, {}) - node = list_comprehension.transform(node, self.ctx) - - with self.compiled(node) as result: - l = [1, 2, 3] - self.assertEqual(test_fn(l), result.test_fn(l)) - l = [] - self.assertEqual(test_fn(l), result.test_fn(l)) + self.assertTransformedEquivalent(test_fn, []) + self.assertTransformedEquivalent(test_fn, [1, 2, 3]) def test_multiple_generators(self): @@ -46,29 +44,17 @@ class ListCompTest(converter_testing.TestCase): s = [e * e for sublist in l for e in sublist] return s - node = self.parse_and_analyze(test_fn, {}) - node = list_comprehension.transform(node, self.ctx) + self.assertTransformedEquivalent(test_fn, []) + self.assertTransformedEquivalent(test_fn, [[1], [2], [3]]) - with self.compiled(node) as result: - l = [[1], [2], [3]] - self.assertEqual(test_fn(l), result.test_fn(l)) - l = [] - self.assertEqual(test_fn(l), result.test_fn(l)) - - def test_conds(self): + def test_cond(self): def test_fn(l): s = [e * e for e in l if e > 1] return s - node = self.parse_and_analyze(test_fn, {}) - node = list_comprehension.transform(node, self.ctx) - - with self.compiled(node) as result: - l = [1, 2, 3] - self.assertEqual(test_fn(l), result.test_fn(l)) - l = [] - self.assertEqual(test_fn(l), result.test_fn(l)) + self.assertTransformedEquivalent(test_fn, []) + self.assertTransformedEquivalent(test_fn, [1, 2, 3]) if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/converters/lists.py b/tensorflow/contrib/autograph/converters/lists.py index d77a04479826779b8aa859d70f2f7ff51138f841..a02fc827b8bd92b36549599b5433118fcd9a28cf 100644 --- a/tensorflow/contrib/autograph/converters/lists.py +++ b/tensorflow/contrib/autograph/converters/lists.py @@ -33,6 +33,7 @@ from __future__ import print_function import gast from tensorflow.contrib.autograph.core import converter +from tensorflow.contrib.autograph.lang import directives from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import templates @@ -88,12 +89,12 @@ class ListTransformer(converter.Base): scope = anno.getanno(node, NodeAnno.ARGS_SCOPE) target_node = node.func.value - # Attempt to use a related name if can get one. Otherwise use something + # Attempt to use a related name if one exists. Otherwise use something # generic. if anno.hasanno(target_node, anno.Basic.QN): target_name = anno.getanno(target_node, anno.Basic.QN).ssf() else: - target_name = 'list' + target_name = 'list_' pop_var_name = self.ctx.namer.new_symbol(target_name, scope.referenced) pop_uses = self.get_local(POP_USES, []) @@ -104,9 +105,10 @@ class ListTransformer(converter.Base): def _replace_stack_call(self, node): assert len(node.args) == 1 - dtype = anno.getanno( + dtype = self.get_definition_directive( node.args[0], - 'element_type', + directives.set_element_type, + 'dtype', default=templates.replace_as_expression('None')) template = """ ag__.list_stack( @@ -134,7 +136,10 @@ class ListTransformer(converter.Base): node = self._replace_append_call(node) elif func_name == 'pop' and (len(node.args) <= 1): node = self._replace_pop_call(node) - elif func_name == 'stack' and (len(node.args) == 1): + elif (func_name == 'stack' and (len(node.args) == 1) and + (not node.keywords or node.keywords[0].arg == 'strict')): + # This avoids false positives with keyword args. + # TODO(mdan): handle kwargs properly. node = self._replace_stack_call(node) return node @@ -146,15 +151,22 @@ class ListTransformer(converter.Base): pop_element = original_call_node.args[0] else: pop_element = parser.parse_expression('None') + # The call will be something like "target.pop()", and the dtype is hooked to # target, hence the func.value. - dtype = anno.getanno( + # TODO(mdan): For lists of lists, this won't work. + # The reason why it won't work is because it's unclear how to annotate + # the list as a "list of lists with a certain element type" when using + # operations like `l.pop().pop()`. + dtype = self.get_definition_directive( original_call_node.func.value, - 'element_type', + directives.set_element_type, + 'dtype', default=templates.replace_as_expression('None')) - shape = anno.getanno( + shape = self.get_definition_directive( original_call_node.func.value, - 'element_shape', + directives.set_element_type, + 'shape', default=templates.replace_as_expression('None')) template = """ diff --git a/tensorflow/contrib/autograph/converters/lists_test.py b/tensorflow/contrib/autograph/converters/lists_test.py index ea04097b28deedd705164bd95ab62dba3e3c7834..996e99ee61b3713a03ff167b892101fca35eaeac 100644 --- a/tensorflow/contrib/autograph/converters/lists_test.py +++ b/tensorflow/contrib/autograph/converters/lists_test.py @@ -18,9 +18,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph import utils from tensorflow.contrib.autograph.converters import lists from tensorflow.contrib.autograph.core import converter_testing +from tensorflow.contrib.autograph.lang import directives +from tensorflow.contrib.autograph.lang import special_functions +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import parser from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops @@ -28,6 +31,9 @@ from tensorflow.python.ops import list_ops from tensorflow.python.platform import test +tf = None # Will be replaced by a mock. + + class ListTest(converter_testing.TestCase): def test_empty_list(self): @@ -35,10 +41,7 @@ class ListTest(converter_testing.TestCase): def test_fn(): return [] - node = self.parse_and_analyze(test_fn, {}) - node = lists.transform(node, self.ctx) - - with self.compiled(node) as result: + with self.converted(test_fn, lists, {}) as result: tl = result.test_fn() # Empty tensor lists cannot be evaluated or stacked. self.assertTrue(isinstance(tl, ops.Tensor)) @@ -49,27 +52,19 @@ class ListTest(converter_testing.TestCase): def test_fn(): return [1, 2, 3] - node = self.parse_and_analyze(test_fn, {}) - node = lists.transform(node, self.ctx) - - with self.compiled(node) as result: - with self.test_session() as sess: - tl = result.test_fn() - r = list_ops.tensor_list_stack(tl, dtypes.int32) - self.assertAllEqual(sess.run(r), [1, 2, 3]) + with self.converted(test_fn, lists, {}) as result: + self.assertAllEqual(result.test_fn(), [1, 2, 3]) def test_list_append(self): def test_fn(): - l = [1] + l = special_functions.tensor_list([1]) l.append(2) l.append(3) return l - node = self.parse_and_analyze(test_fn, {}) - node = lists.transform(node, self.ctx) - - with self.compiled(node) as result: + ns = {'special_functions': special_functions} + with self.converted(test_fn, lists, ns) as result: with self.test_session() as sess: tl = result.test_fn() r = list_ops.tensor_list_stack(tl, dtypes.int32) @@ -78,24 +73,21 @@ class ListTest(converter_testing.TestCase): def test_list_pop(self): def test_fn(): - l = [1, 2, 3] - utils.set_element_type(l, dtypes.int32, ()) + l = special_functions.tensor_list([1, 2, 3]) s = l.pop() return s, l - node = self.parse_and_analyze( - test_fn, - { - 'utils': utils, - 'dtypes': dtypes - }, - include_type_analysis=True, - ) - node = lists.transform(node, self.ctx) - - with self.compiled(node) as result: - result.utils = utils - result.dtypes = dtypes + ns = {'special_functions': special_functions} + node, ctx = self.prepare(test_fn, ns) + def_, = anno.getanno(node.body[0].targets[0], + anno.Static.ORIG_DEFINITIONS) + def_.directives[directives.set_element_type] = { + 'dtype': parser.parse_expression('tf.int32'), + 'shape': parser.parse_expression('()'), + } + node = lists.transform(node, ctx) + + with self.compiled(node, ns, dtypes.int32) as result: with self.test_session() as sess: ts, tl = result.test_fn() r = list_ops.tensor_list_stack(tl, dtypes.int32) @@ -108,10 +100,7 @@ class ListTest(converter_testing.TestCase): s = l.pop().pop() return s - node = self.parse_and_analyze(test_fn, {}) - node = lists.transform(node, self.ctx) - - with self.compiled(node) as result: + with self.converted(test_fn, lists, {}) as result: test_input = [1, 2, [1, 2, 3]] # TODO(mdan): Pass a list of lists of tensor when we fully support that. # For now, we just pass a regular Python list of lists just to verify that @@ -120,29 +109,24 @@ class ListTest(converter_testing.TestCase): def test_list_stack(self): - tf = None # Will be replaced with a mock. - def test_fn(): l = [1, 2, 3] - utils.set_element_type(l, dtypes.int32) return tf.stack(l) - node = self.parse_and_analyze( - test_fn, - { - 'utils': utils, - 'dtypes': dtypes - }, - include_type_analysis=True, - ) - node = lists.transform(node, self.ctx) - - with self.compiled(node, array_ops.stack, dtypes.int32) as result: - result.utils = utils - result.dtypes = dtypes + node, ctx = self.prepare(test_fn, {}) + def_, = anno.getanno(node.body[0].targets[0], + anno.Static.ORIG_DEFINITIONS) + def_.directives[directives.set_element_type] = { + 'dtype': parser.parse_expression('tf.int32') + } + node = lists.transform(node, ctx) + + with self.compiled(node, {}, array_ops.stack, dtypes.int32) as result: with self.test_session() as sess: self.assertAllEqual(sess.run(result.test_fn()), [1, 2, 3]) + # TODO(mdan): Add a test with tf.stack with axis kwarg. + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/autograph/converters/logical_expressions_test.py b/tensorflow/contrib/autograph/converters/logical_expressions_test.py index 48186024a9da7b41fa7ff9a8ab18f3477ba09c8f..ca07de5e8a1f870391ecbe41bf1341dc52c25347 100644 --- a/tensorflow/contrib/autograph/converters/logical_expressions_test.py +++ b/tensorflow/contrib/autograph/converters/logical_expressions_test.py @@ -31,10 +31,8 @@ class GradientsFunctionTest(converter_testing.TestCase): def test_fn(a, b): return a == b - node = self.parse_and_analyze(test_fn, {}) - node = logical_expressions.transform(node, self.ctx) - - with self.compiled(node, math_ops.equal) as result: + with self.converted(test_fn, logical_expressions, {}, + math_ops.equal) as result: with self.test_session() as sess: self.assertTrue(sess.run(result.test_fn(1, 1))) self.assertFalse(sess.run(result.test_fn(1, 2))) @@ -44,11 +42,8 @@ class GradientsFunctionTest(converter_testing.TestCase): def test_fn(a, b, c): return (a or b) and (a or b or c) - node = self.parse_and_analyze(test_fn, {}) - node = logical_expressions.transform(node, self.ctx) - - with self.compiled(node, math_ops.logical_or, - math_ops.logical_and) as result: + with self.converted(test_fn, logical_expressions, {}, math_ops.logical_or, + math_ops.logical_and) as result: with self.test_session() as sess: self.assertTrue(sess.run(result.test_fn(True, False, True))) diff --git a/tensorflow/contrib/autograph/converters/name_scopes_test.py b/tensorflow/contrib/autograph/converters/name_scopes_test.py index 444d0bcd469f35689d078debe3622f930dbac723..a329b0db70e2c6559fa5cf36694cf808fa28a6cb 100644 --- a/tensorflow/contrib/autograph/converters/name_scopes_test.py +++ b/tensorflow/contrib/autograph/converters/name_scopes_test.py @@ -31,17 +31,13 @@ class FunctionNameScopeTransformer(converter_testing.TestCase): def test_fn(l): """This should stay here.""" - a = 5 + a = 1 l += a return l - node = self.parse_and_analyze(test_fn, {}) - node = name_scopes.transform(node, self.ctx) - - with self.compiled(node, ops.name_scope) as result: + with self.converted(test_fn, name_scopes, {}, ops.name_scope) as result: result_op = result.test_fn(constant_op.constant(1)) self.assertIn('test_fn/', result_op.op.name) - self.assertEqual('This should stay here.', result.test_fn.__doc__) def test_long_docstring(self): @@ -54,13 +50,12 @@ class FunctionNameScopeTransformer(converter_testing.TestCase): Returns: l """ - return l - - node = self.parse_and_analyze(test_fn, {}) - node = name_scopes.transform(node, self.ctx) + return l + 1 - with self.compiled(node, ops.name_scope) as result: - self.assertIn('Multi-line', result.test_fn.__doc__) + with self.converted(test_fn, name_scopes, {}, ops.name_scope) as result: + result_op = result.test_fn(constant_op.constant(1)) + self.assertIn('test_fn/', result_op.op.name) + self.assertIn('Multi-line docstring.', result.test_fn.__doc__) self.assertIn('Returns:', result.test_fn.__doc__) def test_nested_functions(self): @@ -68,21 +63,16 @@ class FunctionNameScopeTransformer(converter_testing.TestCase): def test_fn(l): def inner_fn(i): - return i ** 2 - - l += 4 - return inner_fn(l) + return i + 1 - node = self.parse_and_analyze(test_fn, {}) - node = name_scopes.transform(node, self.ctx) + l += 1 + return l, inner_fn(l) - with self.compiled(node, ops.name_scope) as result: - result_op = result.test_fn(constant_op.constant(1)) - first_result_input_name = result_op.op.inputs[0].name - second_result_input_name = result_op.op.inputs[1].name - self.assertIn('test_fn/', first_result_input_name) - self.assertNotIn('inner_fn', first_result_input_name) - self.assertIn('test_fn/inner_fn/', second_result_input_name) + with self.converted(test_fn, name_scopes, {}, ops.name_scope) as result: + first, second = result.test_fn(constant_op.constant(1)) + self.assertIn('test_fn/', first.op.name) + self.assertNotIn('inner_fn', first.op.name) + self.assertIn('test_fn/inner_fn/', second.op.name) def test_method(self): @@ -91,48 +81,20 @@ class FunctionNameScopeTransformer(converter_testing.TestCase): def test_fn(self, l): def inner_fn(i): - return i ** 2 - - l += 4 - return inner_fn(l) + return i + 1 - # Note that 'TestClass' was needed in the namespace here. - node = self.parse_and_analyze( - TestClass, {'TestClass': TestClass}, owner_type=TestClass) - node = name_scopes.transform(node, self.ctx) + l += 1 + return l, inner_fn(l) - with self.compiled(node, ops.name_scope) as result: - result_op = result.TestClass().test_fn(constant_op.constant(1)) - first_result_input_name = result_op.op.inputs[0].name - second_result_input_name = result_op.op.inputs[1].name - self.assertIn('TestClass/test_fn/', first_result_input_name) - self.assertNotIn('inner_fn', first_result_input_name) - self.assertIn('TestClass/test_fn/inner_fn/', second_result_input_name) + ns = {'TestClass': TestClass} + node, ctx = self.prepare(TestClass, ns, owner_type=TestClass) + node = name_scopes.transform(node, ctx) - def test_operator(self): - - class TestClass(object): - - def __call__(self, l): - - def inner_fn(i): - return i ** 2 - - l += 4 - return inner_fn(l) - - # Note that 'TestClass' was needed in the namespace here. - node = self.parse_and_analyze( - TestClass.__call__, {'TestClass': TestClass}, owner_type=TestClass) - node = name_scopes.transform(node, self.ctx) - - with self.compiled(node, ops.name_scope) as result: - result_op = result.__call__(TestClass(), constant_op.constant(1)) - first_result_input_name = result_op.op.inputs[0].name - second_result_input_name = result_op.op.inputs[1].name - self.assertIn('call__/', first_result_input_name) - self.assertNotIn('inner_fn', first_result_input_name) - self.assertIn('call__/inner_fn/', second_result_input_name) + with self.compiled(node, {}, ops.name_scope) as result: + first, second = result.TestClass().test_fn(constant_op.constant(1)) + self.assertIn('TestClass/test_fn/', first.op.name) + self.assertNotIn('inner_fn', first.op.name) + self.assertIn('TestClass/test_fn/inner_fn/', second.op.name) if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/converters/single_return.py b/tensorflow/contrib/autograph/converters/return_statements.py similarity index 100% rename from tensorflow/contrib/autograph/converters/single_return.py rename to tensorflow/contrib/autograph/converters/return_statements.py diff --git a/tensorflow/contrib/autograph/converters/return_statements_test.py b/tensorflow/contrib/autograph/converters/return_statements_test.py new file mode 100644 index 0000000000000000000000000000000000000000..3c7c8c8a2586c6716e78960ee964ff3b0735fa47 --- /dev/null +++ b/tensorflow/contrib/autograph/converters/return_statements_test.py @@ -0,0 +1,167 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for return_statements module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.converters import return_statements +from tensorflow.contrib.autograph.core import converter_testing +from tensorflow.python.framework import ops +from tensorflow.python.platform import test + + +class SingleReturnTest(converter_testing.TestCase): + + def assertTransformedEquivalent(self, test_fn, *inputs): + ns = {'ops': ops} + with self.converted(test_fn, return_statements, ns) as result: + self.assertEqual(test_fn(*inputs), result.test_fn(*inputs)) + + def test_straightline(self): + + def test_fn(x): + return x * x + + self.assertTransformedEquivalent(test_fn, 2) + + def test_conditional(self): + + def test_fn(x): + if x > 0: + return x + else: + return x * x + + self.assertTransformedEquivalent(test_fn, 2) + self.assertTransformedEquivalent(test_fn, -2) + + def test_missing_orelse(self): + + def test_fn(x): + if x > 0: + return x + + node, ctx = self.prepare(test_fn, {}) + with self.assertRaises(ValueError): + return_statements.transform(node, ctx) + + def test_missing_orelse_recovrable(self): + + def test_fn(x): + if x > 0: + return x + return x * x + + self.assertTransformedEquivalent(test_fn, 2) + self.assertTransformedEquivalent(test_fn, -2) + + def test_missing_branch_return_recoverable(self): + + def test_fn(x): + if x < 0: + x *= x + else: + return x + return x + + self.assertTransformedEquivalent(test_fn, 2) + self.assertTransformedEquivalent(test_fn, -2) + + def test_conditional_nested(self): + + def test_fn(x): + if x > 0: + if x < 5: + return x + else: + return x * x + else: + return x * x * x + + self.assertTransformedEquivalent(test_fn, 2) + self.assertTransformedEquivalent(test_fn, -2) + self.assertTransformedEquivalent(test_fn, 5) + + def test_context_manager(self): + + def test_fn(x): + with ops.name_scope(''): + return x * x + + self.assertTransformedEquivalent(test_fn, 2) + self.assertTransformedEquivalent(test_fn, -2) + + def test_context_manager_in_conditional(self): + + def test_fn(x): + if x > 0: + with ops.name_scope(''): + return x * x + else: + return x + + self.assertTransformedEquivalent(test_fn, 2) + self.assertTransformedEquivalent(test_fn, -2) + + def text_conditional_in_context_manager(self): + + def test_fn(x): + with ops.name_scope(''): + if x > 0: + return x * x + else: + return x + + self.assertTransformedEquivalent(test_fn, 2) + self.assertTransformedEquivalent(test_fn, -2) + + def test_no_return(self): + + def test_fn(x): + x *= x + + self.assertTransformedEquivalent(test_fn, 2) + + def test_nested_functions(self): + + def test_fn(x): + + def inner_fn(y): + if y > 0: + return y * y + else: + return y + + return inner_fn(x) + + self.assertTransformedEquivalent(test_fn, 2) + self.assertTransformedEquivalent(test_fn, -2) + + def test_loop(self): + + def test_fn(x): + for _ in range(10): + return x + return x + + node, ctx = self.prepare(test_fn, {}) + with self.assertRaises(ValueError): + return_statements.transform(node, ctx) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/autograph/converters/side_effect_guards_test.py b/tensorflow/contrib/autograph/converters/side_effect_guards_test.py index a7ad8efed4c88e15ce9dc14cb02e5e035602013d..bee512abbc2e115d69bc9a5d53b6c54d428cc73a 100644 --- a/tensorflow/contrib/autograph/converters/side_effect_guards_test.py +++ b/tensorflow/contrib/autograph/converters/side_effect_guards_test.py @@ -25,140 +25,138 @@ from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import state_ops -from tensorflow.python.ops import variables +from tensorflow.python.ops import variable_scope from tensorflow.python.platform import test +tf = None # Will be replaced by a mock. + + class SideEffectGuardsTest(converter_testing.TestCase): def test_side_effect_on_return_only_variable(self): - tf = None - def test_fn(a): tf.assign(a, a + 1) return a - node = self.parse_and_analyze(test_fn, {}) - node = side_effect_guards.transform(node, self.ctx) + node, ctx = self.prepare(test_fn, {}) + node = side_effect_guards.transform(node, ctx) - with self.compiled(node, state_ops.assign) as result: - self.assertEqual(len(node.body[0].body), 1) + self.assertEqual(len(node.body), 1) + + with self.compiled(node, {}, state_ops.assign) as result: with self.test_session() as sess: - v = variables.Variable(2) + v = variable_scope.get_variable('test', initializer=2) sess.run(v.initializer) - # NOTE: We don't expect the assignment to execute in this case, because - # variables cannot be reliably guarded. - self.assertEqual(2, sess.run(result.test_fn(v))) + sess.run(result.test_fn(v)) + # TODO(mdan): Add support for this use case. + # Right now the variable `a` is not conditioned on the `assign` because + # there's no way to add control dependencies to a variable object. + self.assertEqual(2, sess.run(v)) def test_side_effect_on_used_variable(self): - tf = None - def test_fn(a): tf.assign(a, a + 1) return a + 1 - node = self.parse_and_analyze(test_fn, {}) - node = side_effect_guards.transform(node, self.ctx) + node, ctx = self.prepare(test_fn, {}) + node = side_effect_guards.transform(node, ctx) - with self.compiled(node, state_ops.assign) as result: - self.assertEqual(len(node.body[0].body), 1) + self.assertEqual(len(node.body), 1) + + with self.compiled(node, {}, state_ops.assign) as result: with self.test_session() as sess: - v = variables.Variable(2) + v = variable_scope.get_variable('test', initializer=2) sess.run(v.initializer) - # NOTE: Unlike test_side_effect_on_return_only_variable, the variable - # was used in the local scope and so we could catch the assign's side - # effect. - self.assertEqual(4, sess.run(result.test_fn(v))) + sess.run(result.test_fn(v)) + # TODO(mdan): Ensure the result of test_fn(v) is also deterministic. + # Right now it's 3 or 4 based on whether the read is synchronized. + self.assertEqual(3, sess.run(v)) def test_side_effect_on_tensor(self): - tf = None - def test_fn(a): tf.Assert(a > 0, ['expected in throw']) return a - node = self.parse_and_analyze(test_fn, {}) - node = side_effect_guards.transform(node, self.ctx) + node, ctx = self.prepare(test_fn, {}) + node = side_effect_guards.transform(node, ctx) - with self.compiled(node, control_flow_ops.Assert) as result: - self.assertEqual(len(node.body[0].body), 1) + self.assertEqual(len(node.body), 1) + + with self.compiled(node, {}, control_flow_ops.Assert) as result: with self.test_session() as sess: - # NOTE: In this case we can also capture the side effect because the - # argument is a tensor ans we can wrap it inside an identity. with self.assertRaisesRegexp(errors_impl.InvalidArgumentError, 'expected in throw'): sess.run(result.test_fn(constant_op.constant(-1))) def test_multiline_block(self): - tf = None - def test_fn(a): - tf.assign(a, a + 1) + tf.assign_add(a, 1) b = a + 1 - tf.assign(a, b + 1) - c = b + 1 - d = c + 1 - return d + tf.assign_add(a, 1) + b += 1 + return b - node = self.parse_and_analyze(test_fn, {}) - node = side_effect_guards.transform(node, self.ctx) + node, ctx = self.prepare(test_fn, {}) + node = side_effect_guards.transform(node, ctx) - with self.compiled(node, state_ops.assign) as result: - self.assertEqual(len(node.body[0].body), 1) + self.assertEqual(len(node.body), 1) + + with self.compiled(node, {}, state_ops.assign_add) as result: with self.test_session() as sess: - v = variables.Variable(2) + v = variable_scope.get_variable('test', initializer=2) sess.run(v.initializer) - self.assertEqual(6, sess.run(result.test_fn(v))) + sess.run(result.test_fn(v)) + # TODO(mdan): Ensure the result of test_fn(v) is also deterministic. + self.assertEqual(4, sess.run(v)) def test_multiline_nested_block(self): - tf = None - def test_fn(a): with tf.name_scope('foo'): tf.assign(a, a + 1) b = a + 1 - c = b + 1 - d = c + 1 - return d + return b - node = self.parse_and_analyze(test_fn, {}) - node = side_effect_guards.transform(node, self.ctx) + node, ctx = self.prepare(test_fn, {}) + node = side_effect_guards.transform(node, ctx) - with self.compiled(node, state_ops.assign, ops.name_scope) as result: - self.assertEqual(len(node.body[0].body[0].body), 1) + self.assertEqual(len(node.body[0].body), 1) + + with self.compiled(node, {}, state_ops.assign, ops.name_scope) as result: with self.test_session() as sess: - v = variables.Variable(2) + v = variable_scope.get_variable('test', initializer=2) sess.run(v.initializer) - self.assertEqual(6, sess.run(result.test_fn(v))) + sess.run(result.test_fn(v)) + # TODO(mdan): Ensure the result of test_fn(v) is also deterministic. + self.assertEqual(3, sess.run(v)) def test_multiline_block_unsafe(self): - tf = None - def test_fn(a): tf.assign(a, a + 1) b = a + 1 - tf.assign(a, a + 1) + tf.assign_add(a, 1) c = b + 1 - d = c + 1 - return d + return c + + node, ctx = self.prepare(test_fn, {}) + node = side_effect_guards.transform(node, ctx) - node = self.parse_and_analyze(test_fn, {}) - node = side_effect_guards.transform(node, self.ctx) + self.assertEqual(len(node.body), 1) - with self.compiled(node, state_ops.assign) as result: - self.assertEqual(len(node.body[0].body), 1) + with self.compiled(node, {}, state_ops.assign, + state_ops.assign_add) as result: with self.test_session() as sess: - v = variables.Variable(2) + v = variable_scope.get_variable('test', initializer=2) sess.run(v.initializer) - # NOTE: This intentionally highlights the flakiness. The test should be - # tightened down once that is solved. - self.assertTrue(sess.run(result.test_fn(v)) in (6, 7)) + sess.run(result.test_fn(v)) + # TODO(mdan): Ensure the result of test_fn(v) is also deterministic. + self.assertEqual(4, sess.run(v)) if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/converters/single_return_test.py b/tensorflow/contrib/autograph/converters/single_return_test.py deleted file mode 100644 index 1f0de4310e370235a4a7bfeaa61bd519a81aff47..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/autograph/converters/single_return_test.py +++ /dev/null @@ -1,189 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for single_return module.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.autograph.converters import single_return -from tensorflow.contrib.autograph.core import converter_testing -from tensorflow.python.framework.ops import name_scope -from tensorflow.python.platform import test - - -class SingleReturnTest(converter_testing.TestCase): - - def compiled_fn(self, test_fn, *args): - node = self.parse_and_analyze(test_fn, {}) - node = single_return.transform(node, self.ctx) - module = self.compiled(node, *args) - return module - - def test_noop(self): - # Noop - def test_fn(x): - return x - - with self.compiled_fn(test_fn) as result: - self.assertEqual(test_fn(2.0), result.test_fn(2.0)) - - def test_return_expression(self): - # ANF - def test_fn(x): - return x * x - - with self.compiled_fn(test_fn) as result: - x = 2 - self.assertEqual(test_fn(x), result.test_fn(x)) - - def test_merge(self): - # Simple merge - def test_fn(x): - if x > 0: - return x - else: - return x * x - - with self.compiled_fn(test_fn) as result: - for x in [-2, 2]: - self.assertEqual(test_fn(x), result.test_fn(x)) - - def test_orphan_branch(self): - - def test_fn(x): - if x > 0: - return x - - with self.assertRaises(ValueError): - self.compiled_fn(test_fn) - - def test_lift_body_into_false_branch(self): - - def test_fn(x): - if x > 0: - return x - return x * x - - with self.compiled_fn(test_fn) as result: - for x in [-2, 2]: - self.assertEqual(test_fn(x), result.test_fn(x)) - - def test_lift_body_into_true_branch(self): - - def test_fn(x): - if x < 0: - x *= x - else: - # TODO(alexbw): linter bug here that requires us suppress this warning. - return x # pylint: disable=undefined-loop-variable - return x - - with self.compiled_fn(test_fn) as result: - for x in [-2, 2]: - self.assertEqual(test_fn(x), result.test_fn(x)) - - def test_nested_if(self): - - def test_fn(x): - if x > 0: - if x < 5: - return x - else: - return x * x - else: - return x * x * x - - with self.compiled_fn(test_fn) as result: - for x in [-2, 2, 5]: - self.assertEqual(test_fn(x), result.test_fn(x)) - - def test_context_manager(self): - - def test_fn(x): - - with name_scope(''): - return x * x - - with self.compiled_fn(test_fn) as result: - result.name_scope = name_scope - for x in [-2, 2]: - self.assertEqual(test_fn(x), result.test_fn(x)) - - def test_context_manager_in_conditional(self): - - def test_fn(x): - if x > 0: - with name_scope(''): - return x * x - else: - return x - - with self.compiled_fn(test_fn, name_scope) as result: - result.name_scope = name_scope - for x in [-2, 2]: - self.assertEqual(test_fn(x), result.test_fn(x)) - - def text_conditional_in_context_manager(self): - - def test_fn(x): - with name_scope(''): - if x > 0: - return x * x - else: - return x - - with self.compiled_fn(test_fn) as result: - result.name_scope = name_scope - for x in [-2, 2]: - self.assertEqual(test_fn(x), result.test_fn(x)) - - def test_no_return(self): - - def test_fn(x): - x *= x - - with self.compiled_fn(test_fn) as result: - self.assertEqual(test_fn(2), result.test_fn(2)) - - def test_nested_functiondefs(self): - - def test_fn(x): - - def inner_fn(y): - if y > 0: - return y * y - else: - return y - - return inner_fn(x) - - with self.compiled_fn(test_fn) as result: - for x in [-2, 2]: - self.assertEqual(test_fn(x), result.test_fn(x)) - - def test_loop(self): - - def test_fn(x): - for _ in range(10): - return x - return x - - with self.assertRaises(ValueError): - self.compiled_fn(test_fn) - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/autograph/converters/slices.py b/tensorflow/contrib/autograph/converters/slices.py index 3f5fc57125a8b65faf1e3a377d7984ff05b3245c..c527f98613a2ffebf35141d4dac85e972a89c93b 100644 --- a/tensorflow/contrib/autograph/converters/slices.py +++ b/tensorflow/contrib/autograph/converters/slices.py @@ -21,7 +21,7 @@ from __future__ import print_function import gast from tensorflow.contrib.autograph.core import converter -from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.lang import directives from tensorflow.contrib.autograph.pyct import templates @@ -36,12 +36,14 @@ class SliceTransformer(converter.Base): def _process_single_assignment(self, target, value): if not isinstance(target, gast.Subscript): return None + if not isinstance(target.slice, gast.Index): + return None template = """ target = ag__.set_item(target, key, item) """ return templates.replace( - template, target=target.value, key=target.slice, item=value) + template, target=target.value, key=target.slice.value, item=value) def visit_Assign(self, node): node = self.generic_visit(node) @@ -56,17 +58,17 @@ class SliceTransformer(converter.Base): def visit_Subscript(self, node): node = self.generic_visit(node) if not isinstance(node.slice, gast.Index): - # TODO(mdan): It might make more sense to wave them through. - raise NotImplementedError('non-index slice') + return node if not isinstance(node.ctx, gast.Load): # Index writes are handled at a higher level, one at which the rvalue is # also available. return node - dtype = anno.getanno( + dtype = self.get_definition_directive( node.value, - 'element_type', + directives.set_element_type, + 'dtype', default=templates.replace_as_expression('None')) template = """ @@ -76,7 +78,7 @@ class SliceTransformer(converter.Base): opts=ag__.GetItemOpts(element_dtype=dtype)) """ return templates.replace_as_expression( - template, target=node.value, key=node.slice, dtype=dtype) + template, target=node.value, key=node.slice.value, dtype=dtype) def transform(node, ctx): diff --git a/tensorflow/contrib/autograph/converters/slices_test.py b/tensorflow/contrib/autograph/converters/slices_test.py index df9a4c8bab66f24374605b45bc90bc2730431323..c822d53a4a2810755fd6841af85544dd8fc76a5e 100644 --- a/tensorflow/contrib/autograph/converters/slices_test.py +++ b/tensorflow/contrib/autograph/converters/slices_test.py @@ -18,9 +18,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.autograph import utils from tensorflow.contrib.autograph.converters import slices from tensorflow.contrib.autograph.core import converter_testing +from tensorflow.contrib.autograph.lang import directives +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import parser +from tensorflow.contrib.autograph.pyct import transformer from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import list_ops @@ -32,28 +35,42 @@ class SliceTest(converter_testing.TestCase): def test_index_access(self): def test_fn(l): - utils.set_element_type(l, dtypes.int32) return l[1] - node = self.parse_and_analyze( - test_fn, - { - 'utils': utils, - 'dtypes': dtypes - }, - include_type_analysis=True, - ) - node = slices.transform(node, self.ctx) - - with self.compiled(node, dtypes.int32) as result: - result.utils = utils - result.dtypes = dtypes + node, ctx = self.prepare(test_fn, {}) + def_, = anno.getanno(node.args.args[0], anno.Static.DEFINITIONS) + def_.directives[directives.set_element_type] = { + 'dtype': parser.parse_expression('tf.int32') + } + node = slices.transform(node, ctx) + + with self.compiled(node, {}, dtypes.int32) as result: with self.test_session() as sess: tl = list_ops.tensor_list_from_tensor( [1, 2], element_shape=constant_op.constant([], dtype=dtypes.int32)) y = result.test_fn(tl) self.assertEqual(2, sess.run(y)) + def test_index_access_multiple_definitions(self): + + def test_fn(l): + if l: + l = [] + return l[1] + + node, ctx = self.prepare(test_fn, {}) + def_, = anno.getanno(node.args.args[0], anno.Static.DEFINITIONS) + def_.directives[directives.set_element_type] = { + 'dtype': parser.parse_expression('tf.int32') + } + def_, = anno.getanno(node.body[0].body[0].targets[0], + anno.Static.DEFINITIONS) + def_.directives[directives.set_element_type] = { + 'dtype': parser.parse_expression('tf.float32') + } + with self.assertRaises(transformer.AutographParseError): + slices.transform(node, ctx) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/autograph/core/BUILD b/tensorflow/contrib/autograph/core/BUILD index 833f9dced81bd651244d281322c830bb1c88b259..1873045a921f8af6068d8fccca6a5625b2aedcf8 100644 --- a/tensorflow/contrib/autograph/core/BUILD +++ b/tensorflow/contrib/autograph/core/BUILD @@ -19,6 +19,7 @@ py_library( srcs = [ "config.py", "converter.py", + "errors.py", "naming.py", ], srcs_version = "PY2AND3", @@ -30,6 +31,31 @@ py_library( ], ) +py_test( + name = "errors_test", + srcs = ["errors_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":core", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:math_ops", + "//tensorflow/python:random_ops", + ], +) + +py_test( + name = "naming_test", + srcs = ["naming_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":core", + "//tensorflow/python:client_testlib", + ], +) + py_library( name = "test_lib", srcs = [ @@ -47,13 +73,3 @@ py_library( "@six_archive//:six", ], ) - -py_test( - name = "naming_test", - srcs = ["naming_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":core", - "//tensorflow/python:client_testlib", - ], -) diff --git a/tensorflow/contrib/autograph/core/converter.py b/tensorflow/contrib/autograph/core/converter.py index 54e6aa0f3bbb9059e044861362407cb5050240b4..a93e4a806469db63e7d767563e64dadfe71f50ee 100644 --- a/tensorflow/contrib/autograph/core/converter.py +++ b/tensorflow/contrib/autograph/core/converter.py @@ -64,15 +64,29 @@ from __future__ import division from __future__ import print_function import collections +from enum import Enum + from tensorflow.contrib.autograph.core import config from tensorflow.contrib.autograph.core import naming +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import ast_util +from tensorflow.contrib.autograph.pyct import cfg +from tensorflow.contrib.autograph.pyct import compiler +from tensorflow.contrib.autograph.pyct import qual_names from tensorflow.contrib.autograph.pyct import transformer +from tensorflow.contrib.autograph.pyct.static_analysis import activity +from tensorflow.contrib.autograph.pyct.static_analysis import live_values +from tensorflow.contrib.autograph.pyct.static_analysis import liveness +from tensorflow.contrib.autograph.pyct.static_analysis import reaching_definitions +from tensorflow.contrib.autograph.pyct.static_analysis import type_info # TODO(mdan): These contexts can be refactored into first class objects. # For example, we could define Program and Entity abstractions that hold on # to the actual entity and have conversion methods. +# TODO(mdan): Add a test specific to this converter. + class ProgramContext(object): """ProgramContext keeps track of converting function hierarchies. @@ -197,6 +211,46 @@ class Base(transformer.Base): self._used = False self._ast_depth = 0 + def get_definition_directive(self, node, directive, arg, default): + """Returns the unique directive for a symbol, or a default if none exist. + + See lang/directives.py for details on directives. + + Args: + node: ast.AST + directive: Callable[..., Any] + arg: str + default: Any + + Raises: + ValueError: if conflicting annotations have been found + """ + defs = anno.getanno(node, anno.Static.ORIG_DEFINITIONS, ()) + if not defs: + return default + + # TODO(mdan): Simplify this. + arg_values = [] + for def_ in defs: + if (directive not in def_.directives or + arg not in arg not in def_.directives[directive]): + continue + arg_value = def_.directives[directive][arg] + for prev_value in arg_values: + if not ast_util.matches(arg_value, prev_value): + qn = anno.getanno(node, anno.Basic.QN) + raise ValueError('%s has ambiguous annotations for %s(%s): %s, %s' % + (qn, directive.__name__, arg, + compiler.ast_to_source(arg_value).strip(), + compiler.ast_to_source(prev_value).strip())) + arg_values.append(arg_value) + + if not arg_values: + return default + + arg_value, = arg_values + return arg_value + def visit(self, node): if not self._ast_depth: if self._used: @@ -208,3 +262,69 @@ class Base(transformer.Base): return super(Base, self).visit(node) finally: self._ast_depth -= 1 + + +class AnnotatedDef(reaching_definitions.Definition): + + def __init__(self): + super(AnnotatedDef, self).__init__() + self.directives = {} + + +class AgAnno(Enum): + """Annotation labels specific to AutoGraph. See anno.py.""" + + DIRECTIVES = 'User directives associated with the annotated statement.' + + def __repr__(self): + return self.name + + +def standard_analysis(node, context, is_initial=False): + """Performs a complete static analysis of the given code. + + Args: + node: ast.AST + context: converter.EntityContext + is_initial: bool, whether this is the initial analysis done on the input + source code + + Returns: + ast.AST, same as node, with the static analysis annotations added + """ + # TODO(mdan): Clear static analysis here. + # TODO(mdan): Consider not running all analyses every time. + # TODO(mdan): Don't return a node because it's modified by reference. + graphs = cfg.build(node) + node = qual_names.resolve(node) + node = activity.resolve(node, context.info, None) + node = reaching_definitions.resolve(node, context.info, graphs, AnnotatedDef) + node = liveness.resolve(node, context.info, graphs) + node = live_values.resolve(node, context.info, config.PYTHON_LITERALS) + node = type_info.resolve(node, context.info) + # This second call allows resolving first-order class attributes. + node = live_values.resolve(node, context.info, config.PYTHON_LITERALS) + if is_initial: + anno.dup( + node, + { + anno.Static.DEFINITIONS: anno.Static.ORIG_DEFINITIONS, + }, + ) + return node + + +def apply_(node, context, converter_module): + """Applies a converter to an AST. + + Args: + node: ast.AST + context: converter.EntityContext + converter_module: converter.Base + + Returns: + ast.AST, the result of applying converter to node + """ + node = standard_analysis(node, context) + node = converter_module.transform(node, context) + return node diff --git a/tensorflow/contrib/autograph/core/converter_testing.py b/tensorflow/contrib/autograph/core/converter_testing.py index 0e46aacc1216d2dbd9d34ad0e72ca8251094bddc..5ee2c3fffd7474cb8ca28349385a9d543e92a72d 100644 --- a/tensorflow/contrib/autograph/core/converter_testing.py +++ b/tensorflow/contrib/autograph/core/converter_testing.py @@ -20,19 +20,19 @@ from __future__ import print_function import contextlib import imp +import sys + +import six from tensorflow.contrib.autograph import operators from tensorflow.contrib.autograph import utils from tensorflow.contrib.autograph.core import config from tensorflow.contrib.autograph.core import converter +from tensorflow.contrib.autograph.core import errors from tensorflow.contrib.autograph.pyct import compiler from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import pretty_printer -from tensorflow.contrib.autograph.pyct import qual_names from tensorflow.contrib.autograph.pyct import transformer -from tensorflow.contrib.autograph.pyct.static_analysis import activity -from tensorflow.contrib.autograph.pyct.static_analysis import live_values -from tensorflow.contrib.autograph.pyct.static_analysis import type_info from tensorflow.python.platform import test @@ -74,7 +74,17 @@ class TestCase(test.TestCase): """Base class for unit tests in this module. Contains relevant utilities.""" @contextlib.contextmanager - def compiled(self, node, *symbols): + def assertPrints(self, expected_result): + try: + out_capturer = six.StringIO() + sys.stdout = out_capturer + yield + self.assertEqual(out_capturer.getvalue(), expected_result) + finally: + sys.stdout = sys.__stdout__ + + @contextlib.contextmanager + def compiled(self, node, namespace, *symbols): source = None self.dynamic_calls = [] @@ -84,12 +94,17 @@ class TestCase(test.TestCase): return 7 try: - result, source = compiler.ast_to_object(node) + result, source = compiler.ast_to_object(node, include_source_map=True) + result.tf = self.make_fake_mod('fake_tf', *symbols) fake_ag = self.make_fake_mod('fake_ag', converted_call) fake_ag.__dict__.update(operators.__dict__) fake_ag.__dict__['utils'] = utils + fake_ag.__dict__['rewrite_graph_construction_error'] = ( + errors.rewrite_graph_construction_error) result.__dict__['ag__'] = fake_ag + for k, v in namespace.items(): + result.__dict__[k] = v yield result except Exception: # pylint:disable=broad-except if source is None: @@ -98,6 +113,13 @@ class TestCase(test.TestCase): print('Offending compiled code:\n%s' % source) raise + @contextlib.contextmanager + def converted(self, entity, converter_module, namespace, *tf_symbols): + node, ctx = self.prepare(entity, namespace) + node = converter_module.transform(node, ctx) + with self.compiled(node, namespace, *tf_symbols) as result: + yield result + def make_fake_mod(self, name, *symbols): fake_mod = imp.new_module(name) for s in symbols: @@ -114,17 +136,16 @@ class TestCase(test.TestCase): for k, v in ns.items(): setattr(module, k, v) - def parse_and_analyze(self, - test_fn, - namespace, - namer=None, - arg_types=None, - include_type_analysis=True, - owner_type=None, - recursive=True, - autograph_decorators=()): + def prepare(self, + test_fn, + namespace, + namer=None, + arg_types=None, + owner_type=None, + recursive=True, + autograph_decorators=()): node, source = parser.parse_entity(test_fn) - + node = node.body[0] if namer is None: namer = FakeNamer() program_ctx = converter.ProgramContext( @@ -141,12 +162,5 @@ class TestCase(test.TestCase): arg_types=arg_types, owner_type=owner_type) ctx = converter.EntityContext(namer, entity_info, program_ctx) - - node = qual_names.resolve(node) - node = activity.resolve(node, entity_info) - node = live_values.resolve(node, entity_info, {}) - if include_type_analysis: - node = type_info.resolve(node, entity_info) - node = live_values.resolve(node, entity_info, {}) - self.ctx = ctx - return node + node = converter.standard_analysis(node, ctx, is_initial=True) + return node, ctx diff --git a/tensorflow/contrib/autograph/core/errors.py b/tensorflow/contrib/autograph/core/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..c219b372c13f2870ebde2d35c50dcc1fb270490c --- /dev/null +++ b/tensorflow/contrib/autograph/core/errors.py @@ -0,0 +1,274 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Error rewriting logic. + +Contains the functions responsible for rewriting tracebacks of errors raised +in AutoGraph (AG) code to refer to user written code, so that errors only refer +to the original user code. + +When 'user code' is used in comments it refers to the original source code that +the user wrote and is converting using AutoGraph. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import contextlib +import logging +import sys +import traceback + +from tensorflow.contrib.autograph.pyct import origin_info +from tensorflow.python.framework import errors_impl +from tensorflow.python.util import tf_inspect + + +# TODO(mdan): Add a superclass common to all errors. + + +class GraphConstructionError(Exception): + """Error for graph construction errors from AutoGraph generated code.""" + + def __init__(self, original_error, custom_traceback): + self.original_error = original_error + self.custom_traceback = custom_traceback + super(GraphConstructionError, self).__init__() + + def __str__(self): + traceback_str = ''.join(traceback.format_list(self.custom_traceback)) + return ('Traceback (most recent call last):\n' + traceback_str + '\n' + str( + self.original_error) + '\n') + + +class TfRuntimeError(Exception): + """Error wrapper for runtime errors raised by AutoGraph generated code.""" + + def __init__(self, op_name, op_message, custom_traceback): + self.op_name = op_name + self.op_message = op_message + self.custom_traceback = custom_traceback + super(TfRuntimeError, self).__init__() + + def __str__(self): + message = '%s\n\nCaused by op %r, defined at:\n' % (self.op_message, + self.op_name) + return message + ''.join(traceback.format_list(self.custom_traceback)) + + +def _rewrite_tb(source_map, tb, filter_function_name=None): + """Rewrites code references in a traceback. + + Args: + source_map: Dict[origin_info.LineLocation, origin_info.OriginInfo], mapping + locations to their origin + tb: List[Tuple[Text, Text, Text, Text]], consistent with + traceback.extract_tb + filter_function_name: Optional[Text], allows restricting restricts the + frames to rewrite to a particular function name + Returns: + List[Tuple[Text, Text, Text, Text]], the rewritten traceback + """ + new_tb = [] + for frame in tb: + filename, lineno, function_name, _ = frame + loc = origin_info.LineLocation(filename, lineno) + origin = source_map.get(loc) + # TODO(mdan): We shouldn't need the function name at all. + # filename + lineno should be sufficient, even if there are multiple source + # maps. + if origin is not None: + if filter_function_name == function_name or filter_function_name is None: + new_tb.append(origin.as_frame()) + else: + new_tb.append(frame) + else: + new_tb.append(frame) + return new_tb + + +# TODO(znado): Make more robust to name changes in the rewriting logic. +def _remove_rewrite_frames(tb): + """Remove stack frames containing the error rewriting logic.""" + cleaned_tb = [] + for f in tb: + if 'ag__.rewrite_graph_construction_error' not in f[3]: + cleaned_tb.append(f) + return cleaned_tb + + +# TODO(mdan): rename to raise_* +def rewrite_graph_construction_error(source_map): + """Rewrites errors raised by non-AG APIs inside AG generated code. + + This is called from the except handler inside an AutoGraph generated function + (that is, during exception handling). Only rewrites the frames corresponding + to the function that this is called from, so each function is responsible + to call this to have its own frames rewritten. + + This function always raises an error. + + Args: + source_map: Dict[origin_info.Location, origin_info.OriginInfo], the source + map belonging to the calling function + + Raises: + GraphConstructionError: The rewritten underlying error. + Exception: The underlying error, if it could not be rewritten. + """ + error_info = sys.exc_info() + _, original_error, e_traceback = error_info + assert original_error is not None + try: + _, _, _, func_name, _, _ = tf_inspect.stack()[1] + if isinstance(original_error, GraphConstructionError): + # TODO(mdan): This is incomplete. + # The error might have bubbled through a non-converted function. + cleaned_traceback = traceback.extract_tb(e_traceback) + previous_traceback = original_error.custom_traceback + cleaned_traceback = [cleaned_traceback[0]] + previous_traceback + else: + cleaned_traceback = traceback.extract_tb(e_traceback) + + # Remove the frame corresponding to this function call. + cleaned_traceback = cleaned_traceback[1:] + + cleaned_traceback = _rewrite_tb(source_map, cleaned_traceback, func_name) + + if isinstance(original_error, GraphConstructionError): + original_error.custom_traceback = cleaned_traceback + new_error = original_error + else: + new_error = GraphConstructionError(original_error, cleaned_traceback) + except Exception: + logging.exception('Error while rewriting AutoGraph error:') + # TODO(mdan): Should reraise here, removing the top frame as well. + raise original_error + else: + raise new_error + finally: + # Addresses warning https://docs.python.org/2/library/sys.html#sys.exc_info. + del e_traceback + + +# TODO(mdan): This should be consistent with rewrite_graph_construction_error +# Both should either raise or return. +def rewrite_tf_runtime_error(error, source_map): + """Rewrites TensorFlow runtime errors raised by ops created in AG code. + + Args: + error: tf.OpError + source_map: Dict[origin_info.LineLocation, origin_info.OriginInfo] + + Returns: + TfRuntimeError, the rewritten underlying error. + """ + # Check for cases where we leave a user method and re-enter it in the + # traceback. This is done by looking at the function names when the + # filenames are from any files the user code is in. If we find a case where + # we return to a user method after leaving it then we cut out the frames in + # between because we assume this means these in between frames are from + # internal AutoGraph code that shouldn't be included. + # + # An example of this is: + # + # File "file1.py", line 57, in my_func + # ... + # File "control_flow_ops.py", line 231, in cond + # ... + # File "control_flow_ops.py", line 1039, in inner_cond + # ... + # File "file1.py", line 68, in my_func + # ... + # + # Where we would remove the control_flow_ops.py frames because we re-enter + # my_func in file1.py. + # + # The source map keys are (file_path, line_number) so get the set of all user + # file_paths. + try: + all_user_files = set(loc.filename for loc in source_map) + cleaned_traceback = [] + last_user_frame_index = None + last_user_user_file_path = None + last_user_user_fn_name = None + # TODO(mdan): Simplify this logic. + for fi, frame in enumerate(error.op.traceback): + frame_file_path, lineno, _, _ = frame + lineno -= 1 # Frame line numbers are 1-based. + src_map_key = origin_info.LineLocation(frame_file_path, lineno) + if frame_file_path in all_user_files: + if src_map_key in source_map: + original_fn_name = source_map[src_map_key].function_name + if (last_user_frame_index is not None and + last_user_user_file_path == frame_file_path): + if last_user_user_fn_name == original_fn_name: + cleaned_traceback = cleaned_traceback[:last_user_frame_index] + else: + cleaned_traceback = cleaned_traceback[:last_user_frame_index + 1] + last_user_user_fn_name = original_fn_name + else: + last_user_user_fn_name = None + last_user_frame_index = fi + last_user_user_file_path = frame_file_path + cleaned_traceback.append(frame) + + cleaned_traceback = _rewrite_tb(source_map, cleaned_traceback) + + op_name = error.op.name + op_message = error.message + rewritten_error = TfRuntimeError(op_name, op_message, cleaned_traceback) + return rewritten_error + except Exception: # pylint: disable=broad-except + logging.exception('Error while rewriting AutoGraph error:') + return error + + +# TODO(znado): Add arg to enable different levels of error rewriting. +@contextlib.contextmanager +def improved_errors(converted_function): + """Context manager that rewrites runtime errors. + + This context manager will rewrite runtime errors so that their traceback + is relative to the original code before conversion. + + Use with the output of to_graph, and wrap the execution of respective ops. + Example: + + converted_my_func = ag.to_graph(my_func) + ops = converted_my_func(...) + + with ag.improved_errors(converted_my_func): + sess.run(ops) + + Args: + converted_function: Callable[..., Any], the output of a to_graph call + + Yields: + None + + Raises: + TfRuntimeError: if any OpError originates in the converted code, it will + be wrapped into a TfRuntimeError + ValueError: If converted_function is not generated by AutoGraph + """ + if (getattr(converted_function, 'ag_source_map', None) is None or + not isinstance(converted_function.ag_source_map, dict)): + raise ValueError( + 'converted_function must be the result of an autograph.to_graph call') + try: + yield + except errors_impl.OpError as e: + raise rewrite_tf_runtime_error(e, converted_function.ag_source_map) diff --git a/tensorflow/contrib/autograph/core/errors_test.py b/tensorflow/contrib/autograph/core/errors_test.py new file mode 100644 index 0000000000000000000000000000000000000000..c0e2c74e47ddfb8ee812d6d839b06784e7a01dba --- /dev/null +++ b/tensorflow/contrib/autograph/core/errors_test.py @@ -0,0 +1,104 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for errors module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.core import errors +from tensorflow.contrib.autograph.pyct import origin_info +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors as tf_errors +from tensorflow.python.ops import array_ops +from tensorflow.python.platform import test +from tensorflow.python.util import tf_inspect + + +def zero_div(): + x = array_ops.constant(10, dtype=dtypes.int32) + return x // 0 + + +def zero_div_caller(): + return zero_div() + + +class RuntimeErrorsTest(test.TestCase): + + def fake_origin(self, function, line_offset): + _, lineno = tf_inspect.getsourcelines(function) + filename = tf_inspect.getsourcefile(function) + lineno += line_offset + loc = origin_info.LineLocation(filename, lineno) + origin = origin_info.OriginInfo(loc, 'test_function_name', 'test_code') + return loc, origin + + def test_improved_errors_basic(self): + loc, origin = self.fake_origin(zero_div, 2) + zero_div_caller.ag_source_map = {loc: origin} + + ops = zero_div_caller() + with self.assertRaises(errors.TfRuntimeError) as cm: + with errors.improved_errors(zero_div_caller): + with self.test_session() as sess: + sess.run(ops) + + for frame in cm.exception.custom_traceback: + _, _, function_name, _ = frame + self.assertNotEqual('zero_div', function_name) + self.assertIn(origin.as_frame(), set(cm.exception.custom_traceback)) + + def test_improved_errors_no_matching_lineno(self): + loc, origin = self.fake_origin(zero_div, -1) + zero_div_caller.ag_source_map = {loc: origin} + + ops = zero_div_caller() + with self.assertRaises(errors.TfRuntimeError) as cm: + with errors.improved_errors(zero_div_caller): + with self.test_session() as sess: + sess.run(ops) + + all_function_names = set() + for frame in cm.exception.custom_traceback: + _, _, function_name, _ = frame + all_function_names.add(function_name) + self.assertNotEqual('test_function_name', function_name) + self.assertIn('zero_div', all_function_names) + + def test_improved_errors_failures(self): + loc, _ = self.fake_origin(zero_div, 2) + zero_div_caller.ag_source_map = {loc: 'bogus object'} + + ops = zero_div_caller() + with self.assertRaises(tf_errors.InvalidArgumentError): + with errors.improved_errors(zero_div_caller): + with self.test_session() as sess: + sess.run(ops) + + def test_improved_errors_validation(self): + with self.assertRaisesRegexp( + ValueError, + 'converted_function must be the result of an autograph.to_graph call'): + errors.improved_errors(zero_div).__enter__() + with self.assertRaisesRegexp( + ValueError, + 'converted_function must be the result of an autograph.to_graph call'): + zero_div_caller.ag_source_map = 'not a dict' + errors.improved_errors(zero_div_caller).__enter__() + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/autograph/examples/integration_tests/BUILD b/tensorflow/contrib/autograph/examples/integration_tests/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..d20c17b63b923458952dbfdb1e07e808cf6a36ff --- /dev/null +++ b/tensorflow/contrib/autograph/examples/integration_tests/BUILD @@ -0,0 +1,41 @@ +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("//tensorflow:tensorflow.bzl", "py_test") + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) + +py_test( + name = "keras_test", + srcs = [ + "keras_test.py", + ], + srcs_version = "PY2AND3", + tags = ["no_windows"], + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_test( + name = "list_literals_test", + srcs = [ + "list_literals_test.py", + ], + srcs_version = "PY2AND3", + tags = ["no_windows"], + deps = [ + "//tensorflow:tensorflow_py", + ], +) diff --git a/tensorflow/contrib/autograph/examples/integration_tests/keras_test.py b/tensorflow/contrib/autograph/examples/integration_tests/keras_test.py new file mode 100644 index 0000000000000000000000000000000000000000..73125eb452fc3f3f94a8323d677341345931c4ea --- /dev/null +++ b/tensorflow/contrib/autograph/examples/integration_tests/keras_test.py @@ -0,0 +1,62 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Keras integration tests.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from tensorflow.contrib import autograph + + +class MinimalKeras(tf.keras.Model): + + def call(self, x): + return x * 3 + + +class ModelWithStaticConditional(object): + + def __init__(self, initial): + self.initial = initial + if self.initial: + self.h = 15 + + @autograph.convert() + def call(self): + x = 10 + if self.initial: + x += self.h + return x + + +class KerasTest(tf.test.TestCase): + + def test_basic(self): + MinimalKeras() + + def test_conditional_attributes_False(self): + model = ModelWithStaticConditional(False) + self.assertEqual(model.call(), 10) + + def test_conditional_attributes_True(self): + model = ModelWithStaticConditional(True) + self.assertEqual(model.call(), 25) + + +if __name__ == '__main__': + tf.test.main() diff --git a/tensorflow/contrib/autograph/core/annos.py b/tensorflow/contrib/autograph/examples/integration_tests/list_literals_test.py similarity index 63% rename from tensorflow/contrib/autograph/core/annos.py rename to tensorflow/contrib/autograph/examples/integration_tests/list_literals_test.py index b8937ce36a9631739ab3d7e65a4dad4124406a00..680b6dbaf07fc10e11dfa1e9d3a075624024c103 100644 --- a/tensorflow/contrib/autograph/core/annos.py +++ b/tensorflow/contrib/autograph/examples/integration_tests/list_literals_test.py @@ -12,28 +12,30 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Annotations specific to AutoGraph.""" +"""Tests of functions that use list literals.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -from enum import Enum +import tensorflow as tf +from tensorflow.contrib import autograph as ag -class NoValue(Enum): - def __repr__(self): - return self.name +def list_used_as_tuple(): + return tf.constant([1, 2, 3]) -class NodeAnno(NoValue): - """Additional annotations used by AutoGraph converters. +class ListLiteralsTest(tf.test.TestCase): - These are in addition to the basic annotations declared in pyct/anno.py and - pyct/static_analysis/annos.py. - """ + def test_basic(self): + converted = ag.to_graph(list_used_as_tuple) + result = converted() - # The directives collection - see directives.py - DIRECTIVES = ( - 'Dict depicting static directive calls. See the directives converter.') + with self.test_session() as sess: + self.assertAllEqual(sess.run(result), [1, 2, 3]) + + +if __name__ == '__main__': + tf.test.main() diff --git a/tensorflow/contrib/autograph/examples/notebooks/ag_vs_eager_collatz_speed_test.ipynb b/tensorflow/contrib/autograph/examples/notebooks/ag_vs_eager_collatz_speed_test.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c10a5741f640be5ab7d2604dd32f2f4d6ddf1a22 --- /dev/null +++ b/tensorflow/contrib/autograph/examples/notebooks/ag_vs_eager_collatz_speed_test.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aQkTGc-d8I1k" + }, + "source": [ + "This notebook runs a basic speed test for a simple algorithm that implements the process described in Collatz Conjecture.\n", + "\n", + "https://en.wikipedia.org/wiki/Collatz_conjecture" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "x5ChBlH09jk_" + }, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "X-QAUpWdPxUh" + }, + "outputs": [], + "source": [ + "!pip install -U -q tf-nightly" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "wiKQu3w05eCa" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "import tensorflow as tf\n", + "from tensorflow.contrib import autograph as ag\n", + "from tensorflow.python.eager import context" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_cRFTcwT9mnn" + }, + "source": [ + "### Plotting helpers" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "ww7rc0GQ9pMu" + }, + "outputs": [], + "source": [ + "def plot_results(counts, times, title):\n", + " plt.plot(counts, np.array(times) * 1000., 'o')\n", + " plt.ylabel('Time (milliseconds)')\n", + " plt.xlabel('Collatz counter')\n", + " plt.title(title)\n", + " plt.ylim(0, 30)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ESZGw9s9-Y5_" + }, + "source": [ + "### Collatz function definition" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "qeunWm9m-dT7" + }, + "outputs": [], + "source": [ + "def collatz(a):\n", + " count = 0\n", + " while a \u003e 1.1:\n", + " if a % 2 \u003c 0.1:\n", + " a //= 2\n", + " else:\n", + " a = 3 * a + 1\n", + " count += 1\n", + " return count\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nnFmPDvScsDo" + }, + "source": [ + "# AutoGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 301 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 9153, + "status": "ok", + "timestamp": 1531757473651, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "6fU4vlxYcsDe", + "outputId": "11b50f28-aced-4506-a743-4b749e9645c3" + }, + "outputs": [ + { + "data": { + "image/png": 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lMv17dSJwt0YLQDqUpE6azaETRXwW/2yr9mEt7DMgR8KQcFKGmk9aOr2yqRvn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\n", + "text/plain": [ + "\u003cmatplotlib.figure.Figure at 0x7fc3b259add0\u003e" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "counts = []\n", + "times = []\n", + "for n in np.logspace(0, 7, 50):\n", + "\n", + " with tf.Graph().as_default():\n", + " tf_collatz = ag.to_graph(collatz)\n", + " count = tf_collatz(tf.constant(n, dtype=tf.float32))\n", + " with tf.Session() as sess:\n", + " count_value = sess.run(count)\n", + "\n", + " res = %timeit -n10 -r1 -o -q sess.run(count)\n", + " counts.append(count_value)\n", + " times.append(res.best)\n", + " \n", + "plot_results(counts, times, 'AutoGraph')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RRENYzLRF_f3" + }, + "source": [ + "# Eager" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 301 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 5003, + "status": "ok", + "timestamp": 1531757478713, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "dhDf8LLdF_f-", + "outputId": "3de0a5a5-7a11-4b41-8ab0-e4e21ce8d59b" + }, + "outputs": [ + { + "data": { + "image/png": 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7Uhz1nKzbTSEhITh06BBEUURxcTH+/d//HePGjVO6NiIisyxNCU7WJSskVq5cibNnz0Kv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\n", + "text/plain": [ + "\u003cmatplotlib.figure.Figure at 0x7fc3af690a50\u003e" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "with context.eager_mode():\n", + "\n", + " counts = []\n", + " times = [] \n", + " for n in np.logspace(0, 7, 50):\n", + "\n", + " n_tensor = tf.constant(n, dtype=tf.float32)\n", + " count = collatz(n_tensor)\n", + "\n", + " res = %timeit -n10 -r1 -o -q collatz(n_tensor)\n", + " times.append(res.best)\n", + " counts.append(count)\n", + " \n", + "plot_results(counts, times, 'Eager')\n" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "x5ChBlH09jk_", + "_cRFTcwT9mnn" + ], + "default_view": {}, + "last_runtime": { + "build_target": "", + "kind": "local" + }, + "name": "Autograph vs. Eager Collatz speed test", + "provenance": [ + { + "file_id": "0B8bm7KvwJklpMUQtbnVpYkdJUjRtOTRyWVVfSEhpRl9HYm5n", + "timestamp": 1531512047714 + } + ], + "version": "0.3.2", + "views": {} + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow/contrib/autograph/examples/notebooks/ag_vs_eager_mnist_speed_test.ipynb b/tensorflow/contrib/autograph/examples/notebooks/ag_vs_eager_mnist_speed_test.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..952ec091fb1883e4f17314efa8c458bfe7f01eda --- /dev/null +++ b/tensorflow/contrib/autograph/examples/notebooks/ag_vs_eager_mnist_speed_test.ipynb @@ -0,0 +1,652 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "etTmZVFN8fYO" + }, + "source": [ + "This notebook runs a basic speed test for a short training loop of a neural network training on the MNIST dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eqOvRhOz8SWs" + }, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "nHY0tntRizGb" + }, + "outputs": [], + "source": [ + "!pip install -U -q tf-nightly" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "Pa2qpEmoVOGe" + }, + "outputs": [], + "source": [ + "import gzip\n", + "import os\n", + "import shutil\n", + "import time\n", + "\n", + "import numpy as np\n", + "import six\n", + "from six.moves import urllib\n", + "import tensorflow as tf\n", + "\n", + "from tensorflow.contrib import autograph as ag\n", + "from tensorflow.contrib.eager.python import tfe\n", + "from tensorflow.python.eager import context\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PZWxEJFM9A7b" + }, + "source": [ + "### Testing boilerplate" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "kfZk9EFZ5TeQ" + }, + "outputs": [], + "source": [ + "# Test-only parameters. Test checks successful completion not correctness. \n", + "burn_ins = 1\n", + "trials = 1\n", + "max_steps = 2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "k0GKbZBJ9Gt9" + }, + "source": [ + "### Speed test configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "gWXV8WHn43iZ" + }, + "outputs": [], + "source": [ + "#@test {\"skip\": true} \n", + "burn_ins = 3\n", + "trials = 10\n", + "max_steps = 500\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kZV_3pGy8033" + }, + "source": [ + "### Data source setup" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "YfnHJbBOBKae" + }, + "outputs": [], + "source": [ + "def download(directory, filename):\n", + " filepath = os.path.join(directory, filename)\n", + " if tf.gfile.Exists(filepath):\n", + " return filepath\n", + " if not tf.gfile.Exists(directory):\n", + " tf.gfile.MakeDirs(directory)\n", + " url = 'https://storage.googleapis.com/cvdf-datasets/mnist/' + filename + '.gz'\n", + " zipped_filepath = filepath + '.gz'\n", + " print('Downloading %s to %s' % (url, zipped_filepath))\n", + " urllib.request.urlretrieve(url, zipped_filepath)\n", + " with gzip.open(zipped_filepath, 'rb') as f_in, open(filepath, 'wb') as f_out:\n", + " shutil.copyfileobj(f_in, f_out)\n", + " os.remove(zipped_filepath)\n", + " return filepath\n", + "\n", + "\n", + "def dataset(directory, images_file, labels_file):\n", + " images_file = download(directory, images_file)\n", + " labels_file = download(directory, labels_file)\n", + "\n", + " def decode_image(image):\n", + " # Normalize from [0, 255] to [0.0, 1.0]\n", + " image = tf.decode_raw(image, tf.uint8)\n", + " image = tf.cast(image, tf.float32)\n", + " image = tf.reshape(image, [784])\n", + " return image / 255.0\n", + "\n", + " def decode_label(label):\n", + " label = tf.decode_raw(label, tf.uint8)\n", + " label = tf.reshape(label, [])\n", + " return tf.to_int32(label)\n", + "\n", + " images = tf.data.FixedLengthRecordDataset(\n", + " images_file, 28 * 28, header_bytes=16).map(decode_image)\n", + " labels = tf.data.FixedLengthRecordDataset(\n", + " labels_file, 1, header_bytes=8).map(decode_label)\n", + " return tf.data.Dataset.zip((images, labels))\n", + "\n", + "\n", + "def mnist_train(directory):\n", + " return dataset(directory, 'train-images-idx3-ubyte',\n", + " 'train-labels-idx1-ubyte')\n", + "\n", + "def mnist_test(directory):\n", + " return dataset(directory, 't10k-images-idx3-ubyte', 't10k-labels-idx1-ubyte')\n", + "\n", + "def setup_mnist_data(is_training, hp, batch_size):\n", + " if is_training:\n", + " ds = mnist_train('/tmp/autograph_mnist_data')\n", + " ds = ds.cache()\n", + " ds = ds.shuffle(batch_size * 10)\n", + " else:\n", + " ds = mnist_test('/tmp/autograph_mnist_data')\n", + " ds = ds.cache()\n", + " ds = ds.repeat()\n", + " ds = ds.batch(batch_size)\n", + " return ds\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "qzkZyZcS9THu" + }, + "source": [ + "### Keras model definition" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "x_MU13boiok2" + }, + "outputs": [], + "source": [ + "def mlp_model(input_shape):\n", + " model = tf.keras.Sequential((\n", + " tf.keras.layers.Dense(100, activation='relu', input_shape=input_shape),\n", + " tf.keras.layers.Dense(100, activation='relu'),\n", + " tf.keras.layers.Dense(10, activation='softmax')))\n", + " model.build()\n", + " return model\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "DXt4GoTxtvn2" + }, + "source": [ + "# AutoGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "W51sfbONiz_5" + }, + "outputs": [], + "source": [ + "def predict(m, x, y):\n", + " y_p = m(x)\n", + " losses = tf.keras.losses.categorical_crossentropy(y, y_p)\n", + " l = tf.reduce_mean(losses)\n", + " accuracies = tf.keras.metrics.categorical_accuracy(y, y_p)\n", + " accuracy = tf.reduce_mean(accuracies)\n", + " return l, accuracy\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "CsAD0ajbi9iZ" + }, + "outputs": [], + "source": [ + "def fit(m, x, y, opt):\n", + " l, accuracy = predict(m, x, y)\n", + " opt.minimize(l)\n", + " return l, accuracy\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "RVw57HdTjPzi" + }, + "outputs": [], + "source": [ + "def get_next_batch(ds):\n", + " itr = ds.make_one_shot_iterator()\n", + " image, label = itr.get_next()\n", + " x = tf.to_float(tf.reshape(image, (-1, 28 * 28)))\n", + " y = tf.one_hot(tf.squeeze(label), 10)\n", + " return x, y\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "UUI0566FjZPx" + }, + "outputs": [], + "source": [ + "def train(train_ds, test_ds, hp):\n", + " m = mlp_model((28 * 28,))\n", + " opt = tf.train.MomentumOptimizer(hp.learning_rate, 0.9)\n", + "\n", + " train_losses = []\n", + " test_losses = []\n", + " train_accuracies = []\n", + " test_accuracies = []\n", + " ag.set_element_type(train_losses, tf.float32)\n", + " ag.set_element_type(test_losses, tf.float32)\n", + " ag.set_element_type(train_accuracies, tf.float32)\n", + " ag.set_element_type(test_accuracies, tf.float32)\n", + "\n", + " i = tf.constant(0)\n", + " while i \u003c hp.max_steps:\n", + " train_x, train_y = get_next_batch(train_ds)\n", + " test_x, test_y = get_next_batch(test_ds)\n", + " step_train_loss, step_train_accuracy = fit(m, train_x, train_y, opt)\n", + " step_test_loss, step_test_accuracy = predict(m, test_x, test_y)\n", + "\n", + " train_losses.append(step_train_loss)\n", + " test_losses.append(step_test_loss)\n", + " train_accuracies.append(step_train_accuracy)\n", + " test_accuracies.append(step_test_accuracy)\n", + "\n", + " i += 1\n", + " return (ag.stack(train_losses), ag.stack(test_losses),\n", + " ag.stack(train_accuracies), ag.stack(test_accuracies))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 215 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 12156, + "status": "ok", + "timestamp": 1531752050611, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "K1m8TwOKjdNd", + "outputId": "bd5746f2-bf91-44aa-9eff-38eb11ced33f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Duration:', 0.6226680278778076)\n", + "('Duration:', 0.6082069873809814)\n", + "('Duration:', 0.6223258972167969)\n", + "('Duration:', 0.6176440715789795)\n", + "('Duration:', 0.6309840679168701)\n", + "('Duration:', 0.6180410385131836)\n", + "('Duration:', 0.6219630241394043)\n", + "('Duration:', 0.6183009147644043)\n", + "('Duration:', 0.6176400184631348)\n", + "('Duration:', 0.6476900577545166)\n", + "('Mean duration:', 0.62254641056060789, '+/-', 0.0099792188690656976)\n" + ] + } + ], + "source": [ + "#@test {\"timeout\": 90}\n", + "with tf.Graph().as_default():\n", + " hp = tf.contrib.training.HParams(\n", + " learning_rate=0.05,\n", + " max_steps=max_steps,\n", + " )\n", + " train_ds = setup_mnist_data(True, hp, 500)\n", + " test_ds = setup_mnist_data(False, hp, 100)\n", + " tf_train = ag.to_graph(train)\n", + " losses = tf_train(train_ds, test_ds, hp)\n", + "\n", + " with tf.Session() as sess:\n", + " durations = []\n", + " for t in range(burn_ins + trials):\n", + " sess.run(tf.global_variables_initializer())\n", + "\n", + " start = time.time()\n", + " (train_losses, test_losses, train_accuracies,\n", + " test_accuracies) = sess.run(losses)\n", + "\n", + " if t \u003c burn_ins:\n", + " continue\n", + "\n", + " duration = time.time() - start\n", + " durations.append(duration)\n", + " print('Duration:', duration)\n", + "\n", + " print('Mean duration:', np.mean(durations), '+/-', np.std(durations))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "A06kdgtZtlce" + }, + "source": [ + "# Eager" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "hBKOKGrWty4e" + }, + "outputs": [], + "source": [ + "def predict(m, x, y):\n", + " y_p = m(x)\n", + " losses = tf.keras.losses.categorical_crossentropy(tf.cast(y, tf.float32), y_p)\n", + " l = tf.reduce_mean(losses)\n", + " accuracies = tf.keras.metrics.categorical_accuracy(y, y_p)\n", + " accuracy = tf.reduce_mean(accuracies)\n", + " return l, accuracy\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "HCgTZ0MTt6vt" + }, + "outputs": [], + "source": [ + "def train(ds, hp):\n", + " m = mlp_model((28 * 28,))\n", + " opt = tf.train.MomentumOptimizer(hp.learning_rate, 0.9)\n", + "\n", + " train_losses = []\n", + " test_losses = []\n", + " train_accuracies = []\n", + " test_accuracies = []\n", + "\n", + " i = 0\n", + " train_test_itr = tfe.Iterator(ds)\n", + " for (train_x, train_y), (test_x, test_y) in train_test_itr:\n", + " train_x = tf.to_float(tf.reshape(train_x, (-1, 28 * 28)))\n", + " train_y = tf.one_hot(tf.squeeze(train_y), 10)\n", + " test_x = tf.to_float(tf.reshape(test_x, (-1, 28 * 28)))\n", + " test_y = tf.one_hot(tf.squeeze(test_y), 10)\n", + "\n", + " if i \u003e hp.max_steps:\n", + " break\n", + "\n", + " with tf.GradientTape() as tape:\n", + " step_train_loss, step_train_accuracy = predict(m, train_x, train_y)\n", + " grad = tape.gradient(step_train_loss, m.variables)\n", + " opt.apply_gradients(zip(grad, m.variables))\n", + " step_test_loss, step_test_accuracy = predict(m, test_x, test_y)\n", + "\n", + " train_losses.append(step_train_loss)\n", + " test_losses.append(step_test_loss)\n", + " train_accuracies.append(step_train_accuracy)\n", + " test_accuracies.append(step_test_accuracy)\n", + "\n", + " i += 1\n", + " return train_losses, test_losses, train_accuracies, test_accuracies\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 215 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 52499, + "status": "ok", + "timestamp": 1531752103279, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "plv_yrn_t8Dy", + "outputId": "55d5ab3d-252d-48ba-8fb4-20ec3c3e6d00" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Duration:', 3.9973549842834473)\n", + "('Duration:', 4.018772125244141)\n", + "('Duration:', 3.9740989208221436)\n", + "('Duration:', 3.9922947883605957)\n", + "('Duration:', 3.9795801639556885)\n", + "('Duration:', 3.966722011566162)\n", + "('Duration:', 3.986541986465454)\n", + "('Duration:', 3.992305040359497)\n", + "('Duration:', 4.012261867523193)\n", + "('Duration:', 4.004716157913208)\n", + "('Mean duration:', 3.9924648046493529, '+/-', 0.015681688635624851)\n" + ] + } + ], + "source": [ + "#@test {\"timeout\": 90}\n", + "with context.eager_mode():\n", + " durations = []\n", + " for t in range(burn_ins + trials):\n", + " hp = tf.contrib.training.HParams(\n", + " learning_rate=0.05,\n", + " max_steps=max_steps,\n", + " )\n", + " train_ds = setup_mnist_data(True, hp, 500)\n", + " test_ds = setup_mnist_data(False, hp, 100)\n", + " ds = tf.data.Dataset.zip((train_ds, test_ds))\n", + " start = time.time()\n", + " (train_losses, test_losses, train_accuracies,\n", + " test_accuracies) = train(ds, hp)\n", + " \n", + " train_losses[-1].numpy()\n", + " test_losses[-1].numpy()\n", + " train_accuracies[-1].numpy()\n", + " test_accuracies[-1].numpy()\n", + "\n", + " if t \u003c burn_ins:\n", + " continue\n", + "\n", + " duration = time.time() - start\n", + " durations.append(duration)\n", + " print('Duration:', duration)\n", + "\n", + " print('Mean duration:', np.mean(durations), '+/-', np.std(durations))\n" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "eqOvRhOz8SWs", + "PZWxEJFM9A7b", + "kZV_3pGy8033" + ], + "default_view": {}, + "name": "Autograph vs. Eager MNIST speed test", + "provenance": [ + { + "file_id": "1tAQW5tHUgAc8M4-iwwJm6Xs6dV9nEqtD", + "timestamp": 1530297010607 + }, + { + "file_id": "18dCjshrmHiPTIe1CNsL8tnpdGkuXgpM9", + "timestamp": 1530289467317 + }, + { + "file_id": "1DcfimonWU11tmyivKBGVrbpAl3BIOaRG", + "timestamp": 1522272821237 + }, + { + "file_id": "1wCZUh73zTNs1jzzYjqoxMIdaBWCdKJ2K", + "timestamp": 1522238054357 + }, + { + "file_id": "1_HpC-RrmIv4lNaqeoslUeWaX8zH5IXaJ", + "timestamp": 1521743157199 + }, + { + "file_id": "1mjO2fQ2F9hxpAzw2mnrrUkcgfb7xSGW-", + "timestamp": 1520522344607 + } + ], + "version": "0.3.2", + "views": {} + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow/contrib/autograph/examples/notebooks/algorithms.ipynb b/tensorflow/contrib/autograph/examples/notebooks/algorithms.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..bf824e2760e694ae3c00c9f08d9aa5d5522a9b84 --- /dev/null +++ b/tensorflow/contrib/autograph/examples/notebooks/algorithms.ipynb @@ -0,0 +1,1512 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b9R-4ezU3NH0" + }, + "source": [ + "## AutoGraph: examples of simple algorithms\n", + "\n", + "This notebook shows how you can use AutoGraph to compile simple algorithms and run them in TensorFlow.\n", + "\n", + "It requires the nightly build of TensorFlow, which is installed below." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "TuWj26KWz1fZ" + }, + "outputs": [], + "source": [ + "!pip install -U -q tf-nightly" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3kudk1elq0Gh" + }, + "source": [ + "### Fibonacci numbers\n", + "\n", + "https://en.wikipedia.org/wiki/Fibonacci_number" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 197 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 7512, + "status": "ok", + "timestamp": 1532101577266, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "H7olFlMXqrHe", + "outputId": "472dbfe0-9449-4f93-e908-1a0785188a92" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 : 1\n", + "1 : 2\n", + "2 : 3\n", + "3 : 5\n", + "4 : 8\n", + "5 : 13\n", + "6 : 21\n", + "7 : 34\n", + "8 : 55\n", + "9 : 89\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.contrib import autograph as ag\n", + "\n", + "\n", + "def fib(n):\n", + " f1 = 0\n", + " f2 = 1\n", + " for i in range(n):\n", + " tmp = f2\n", + " f2 = f2 + f1\n", + " f1 = tmp\n", + " print(i, ': ', f2)\n", + " return f2\n", + "\n", + "\n", + "with tf.Graph().as_default():\n", + " final_fib = ag.to_graph(fib)(tf.constant(10))\n", + " with tf.Session() as sess:\n", + " sess.run(final_fib)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "p8zZyj-tq4K3" + }, + "source": [ + "#### Generated code" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 541 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 103, + "status": "ok", + "timestamp": 1532101577412, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "UeWjK8rHq6Cj", + "outputId": "73ece895-12fb-489a-e52c-032945d7ed7a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "from __future__ import print_function\n", + "import tensorflow as tf\n", + "\n", + "def tf__fib(n):\n", + " try:\n", + " with tf.name_scope('fib'):\n", + " f1 = 0\n", + " f2 = 1\n", + "\n", + " def extra_test(f1_1, f2_1):\n", + " with tf.name_scope('extra_test'):\n", + " return True\n", + "\n", + " def loop_body(i, f1_1, f2_1):\n", + " with tf.name_scope('loop_body'):\n", + " tmp = f2_1\n", + " f2_1 = f2_1 + f1_1\n", + " f1_1 = tmp\n", + " with ag__.utils.control_dependency_on_returns(ag__.utils.\n", + " dynamic_print(i, ': ', f2_1)):\n", + " f2, i_1 = ag__.utils.alias_tensors(f2_1, i)\n", + " return f1_1, f2\n", + " f1, f2 = ag__.for_stmt(ag__.utils.dynamic_builtin(range, n),\n", + " extra_test, loop_body, (f1, f2))\n", + " return f2\n", + " except:\n", + " ag__.rewrite_graph_construction_error(ag_source_map__)\n", + "\n" + ] + } + ], + "source": [ + "print(ag.to_code(fib))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eIfVy6ZTrFEH" + }, + "source": [ + "### Fizz Buzz\n", + "\n", + "https://en.wikipedia.org/wiki/Fizz_buzz" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 125 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 233, + "status": "ok", + "timestamp": 1532101577681, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "33CAheYsrEQ7", + "outputId": "82a493ee-15b5-419d-8c9c-5f4159090a05" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Buzz\n", + "11\n", + "Fizz\n", + "13\n", + "14\n", + "FizzBuzz\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.contrib import autograph as ag\n", + "\n", + "def fizzbuzz(i, n):\n", + " while i \u003c n:\n", + " msg = ''\n", + " if i % 3 == 0:\n", + " msg += 'Fizz'\n", + " if i % 5 == 0:\n", + " msg += 'Buzz'\n", + " if msg == '':\n", + " msg = tf.as_string(i)\n", + " print(msg)\n", + " i += 1\n", + " return i\n", + "\n", + "with tf.Graph().as_default():\n", + " final_i = ag.to_graph(fizzbuzz)(tf.constant(10), tf.constant(16))\n", + " with tf.Session() as sess:\n", + " sess.run(final_i)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Lkq3DBGOv3fA" + }, + "source": [ + "#### Generated code" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 1081 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 289, + "status": "ok", + "timestamp": 1532101578003, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "bBhFIIaZrxvx", + "outputId": "d076a7ea-e643-4689-f90a-57f5d086dedc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "from __future__ import print_function\n", + "import tensorflow as tf\n", + "\n", + "def tf__fizzbuzz(i, n):\n", + " try:\n", + " with tf.name_scope('fizzbuzz'):\n", + "\n", + " def loop_test(i_1):\n", + " with tf.name_scope('loop_test'):\n", + " return tf.less(i_1, n)\n", + "\n", + " def loop_body(i_1):\n", + " with tf.name_scope('loop_body'):\n", + " msg = ''\n", + "\n", + " def if_true():\n", + " with tf.name_scope('if_true'):\n", + " msg_1, = msg,\n", + " msg_1 += 'Fizz'\n", + " return msg_1,\n", + "\n", + " def if_false():\n", + " with tf.name_scope('if_false'):\n", + " return msg,\n", + " msg = ag__.utils.run_cond(tf.equal(i_1 % 3, 0), if_true, if_false)\n", + "\n", + " def if_true_1():\n", + " with tf.name_scope('if_true_1'):\n", + " msg_2, = msg,\n", + " msg_2 += 'Buzz'\n", + " return msg_2,\n", + "\n", + " def if_false_1():\n", + " with tf.name_scope('if_false_1'):\n", + " return msg,\n", + " msg = ag__.utils.run_cond(tf.equal(i_1 % 5, 0), if_true_1, if_false_1\n", + " )\n", + "\n", + " def if_true_2():\n", + " with tf.name_scope('if_true_2'):\n", + " msg_3, = msg,\n", + " msg_3 = tf.as_string(i_1)\n", + " return msg_3,\n", + "\n", + " def if_false_2():\n", + " with tf.name_scope('if_false_2'):\n", + " return msg,\n", + " msg = ag__.utils.run_cond(tf.equal(msg, ''), if_true_2, if_false_2)\n", + " with ag__.utils.control_dependency_on_returns(ag__.utils.\n", + " dynamic_print(msg)):\n", + " msg_4 = ag__.utils.alias_tensors(msg)\n", + " i_1 += 1\n", + " return i_1,\n", + " i = ag__.while_stmt(loop_test, loop_body, (i,), (tf, n, ag__, i))\n", + " return i\n", + " except:\n", + " ag__.rewrite_graph_construction_error(ag_source_map__)\n", + "\n" + ] + } + ], + "source": [ + "print(ag.to_code(fizzbuzz))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "BNRtprSvwJgk" + }, + "source": [ + "### Conway's Game of Life\n", + "\n", + "https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "r8_0ioEuAI-a" + }, + "source": [ + "#### Testing boilerplate" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "7moIlf8VABkl" + }, + "outputs": [], + "source": [ + "NUM_STEPS = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "QlEvfIQPAYF5" + }, + "source": [ + "#### Game of Life for AutoGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "5pCK2qQSAAK4" + }, + "outputs": [], + "source": [ + "#@test {\"skip\": true} \n", + "NUM_STEPS = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 308 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 14892, + "status": "ok", + "timestamp": 1532101593030, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + 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ims.append([im])\n", + "\n", + " try:\n", + " ani = anim.ArtistAnimation(\n", + " fig, ims, interval=100, blit=True, repeat_delay=5000)\n", + " plt.close()\n", + "\n", + " display.display(display.HTML(ani.to_html5_video()))\n", + " except RuntimeError:\n", + " print('Coult not render animation:')\n", + " traceback.print_exc()\n", + "\n", + "\n", + "def gol_episode(board):\n", + " directions = tf.constant(\n", + " ((-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)))\n", + "\n", + " new_board = []\n", + " ag.set_element_type(new_board, tf.int32)\n", + "\n", + " for i in range(len(board)):\n", + " for j in range(len(board[i])):\n", + " num_neighbors = 0\n", + " for d in directions:\n", + " ni = i + d[0]\n", + " nj = j + d[1]\n", + " if ni \u003e= 0 and nj \u003e= 0 and ni \u003c len(board) and nj \u003c len(board[i]):\n", + " num_neighbors += board[ni][nj]\n", + " \n", + " new_cell = 0\n", + " if num_neighbors == 2:\n", + " new_cell = board[i][j]\n", + " elif num_neighbors == 3:\n", + " new_cell = 1\n", + " \n", + " new_board.append(new_cell)\n", + " final_board = ag.stack(new_board)\n", + " final_board = tf.reshape(final_board, board.shape)\n", + " return final_board\n", + " \n", + "\n", + "def gol(initial_board):\n", + " board = initial_board\n", + " boards = []\n", + " ag.set_element_type(boards, tf.int32)\n", + " # We are being explicit about tensor constants to ensure the loop\n", + " # is not unrolled in the graph. This may change in the future.\n", + " for i in range(tf.constant(NUM_STEPS)):\n", + " board = gol_episode(board)\n", + " boards.append(board)\n", + " boards = ag.stack(boards)\n", + " render(boards)\n", + " return tf.no_op()\n", + " \n", + "\n", + "with tf.Graph().as_default():\n", + " # Gosper glider gun\n", + " # Adapted from http://www.cplusplus.com/forum/lounge/75168/\n", + " _ = 0\n", + " initial_board = tf.constant((\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,1,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_,_,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,1,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_ ),\n", + " ( _,1,1,_,_,_,_,_,_,_,_,1,_,_,_,_,_,1,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,1,1,_,_,_,_,_,_,_,_,1,_,_,_,1,_,1,1,_,_,_,_,1,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,1,_,_,_,_,_,1,_,_,_,_,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ))\n", + " initial_board = tf.pad(initial_board, ((0, 20), (0, 10)))\n", + " \n", + " tf_gol = ag.to_graph(gol)\n", + " game_ops = tf_gol(initial_board)\n", + " with tf.Session() as sess:\n", + " sess.run(game_ops)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "7NgrSPCZxs3h" + }, + "source": [ + "#### Generated code" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 2323 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 753, + "status": "ok", + "timestamp": 1532101593840, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "hIGYeX0Cxs3i", + "outputId": "e0b62eb1-3e12-4e53-dc54-8a3fa56d823d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "from __future__ import print_function\n", + "import tensorflow as tf\n", + "\n", + "def tf__gol_episode(board):\n", + " try:\n", + " with tf.name_scope('gol_episode'):\n", + " directions = tf.constant(((-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1),\n", + " (1, -1), (1, 0), (1, 1)))\n", + " new_board = ag__.new_list([])\n", + "\n", + " def extra_test_2(new_board_2):\n", + " with tf.name_scope('extra_test_2'):\n", + " return True\n", + "\n", + " def loop_body_2(i, new_board_2):\n", + " with tf.name_scope('loop_body_2'):\n", + "\n", + " def extra_test_1(new_board_1):\n", + " with tf.name_scope('extra_test_1'):\n", + " return True\n", + "\n", + " def loop_body_1(j, new_board_1):\n", + " with tf.name_scope('loop_body_1'):\n", + " num_neighbors = 0\n", + "\n", + " def extra_test(num_neighbors_2):\n", + " with tf.name_scope('extra_test'):\n", + " return True\n", + "\n", + " def loop_body(d, num_neighbors_2):\n", + " with tf.name_scope('loop_body'):\n", + " ni = i + ag__.get_item(d, (0), opts=ag__.GetItemOpts(\n", + " element_dtype=None))\n", + " nj = j + ag__.get_item(d, (1), opts=ag__.GetItemOpts(\n", + " element_dtype=None))\n", + "\n", + " def if_true():\n", + " with tf.name_scope('if_true'):\n", + " num_neighbors_1, = num_neighbors_2,\n", + " num_neighbors_1 += ag__.get_item(ag__.get_item(board,\n", + " (ni), opts=ag__.GetItemOpts(element_dtype=None)),\n", + " (nj), opts=ag__.GetItemOpts(element_dtype=None))\n", + " return num_neighbors_1,\n", + "\n", + " def if_false():\n", + " with tf.name_scope('if_false'):\n", + " return num_neighbors_2,\n", + " num_neighbors_2 = ag__.utils.run_cond(tf.logical_and(tf.\n", + " greater_equal(ni, 0), tf.logical_and(tf.greater_equal\n", + " (nj, 0), tf.logical_and(tf.less(ni, ag__.utils.\n", + " dynamic_builtin(len, board)), tf.less(nj, ag__.utils.\n", + " dynamic_builtin(len, ag__.get_item(board, (i), opts=\n", + " ag__.GetItemOpts(element_dtype=None))))))), if_true,\n", + " if_false)\n", + " return num_neighbors_2,\n", + " num_neighbors = ag__.for_stmt(directions, extra_test,\n", + " loop_body, (num_neighbors,))\n", + " new_cell = 0\n", + "\n", + " def if_true_2():\n", + " with tf.name_scope('if_true_2'):\n", + " new_cell_2, = new_cell,\n", + " new_cell_2 = ag__.get_item(ag__.get_item(board, (i), opts\n", + " =ag__.GetItemOpts(element_dtype=None)), (j), opts=\n", + " ag__.GetItemOpts(element_dtype=None))\n", + " return new_cell_2,\n", + "\n", + " def if_false_2():\n", + " with tf.name_scope('if_false_2'):\n", + " new_cell_3, = new_cell,\n", + "\n", + " def if_true_1():\n", + " with tf.name_scope('if_true_1'):\n", + " new_cell_1, = new_cell_3,\n", + " new_cell_1 = 1\n", + " return new_cell_1,\n", + "\n", + " def if_false_1():\n", + " with tf.name_scope('if_false_1'):\n", + " return new_cell_3,\n", + " new_cell_3 = ag__.utils.run_cond(tf.equal(num_neighbors, \n", + " 3), if_true_1, if_false_1)\n", + " return new_cell_3,\n", + " new_cell = ag__.utils.run_cond(tf.equal(num_neighbors, 2),\n", + " if_true_2, if_false_2)\n", + " new_board_1 = ag__.list_append(new_board_1, new_cell)\n", + " return new_board_1,\n", + " new_board_2 = ag__.for_stmt(ag__.utils.dynamic_builtin(range,\n", + " ag__.utils.dynamic_builtin(len, ag__.get_item(board, (i),\n", + " opts=ag__.GetItemOpts(element_dtype=None)))), extra_test_1,\n", + " loop_body_1, (new_board_2,))\n", + " return new_board_2,\n", + " new_board = ag__.for_stmt(ag__.utils.dynamic_builtin(range, ag__.\n", + " utils.dynamic_builtin(len, board)), extra_test_2, loop_body_2, (\n", + " new_board,))\n", + " final_board = ag__.list_stack(new_board, opts=ag__.ListStackOpts(\n", + " element_dtype=tf.int32, original_call=ag.stack))\n", + " final_board = tf.reshape(final_board, board.shape)\n", + " return final_board\n", + " except:\n", + " ag__.rewrite_graph_construction_error(ag_source_map__)\n", + "\n", + "def tf__gol(initial_board):\n", + " try:\n", + " with tf.name_scope('gol'):\n", + " board = initial_board\n", + " boards = ag__.new_list([])\n", + "\n", + " def extra_test(board_1, boards_1):\n", + " with tf.name_scope('extra_test'):\n", + " return True\n", + "\n", + " def loop_body(i, board_1, boards_1):\n", + " with tf.name_scope('loop_body'):\n", + " board_1 = tf__gol_episode(board_1)\n", + " boards_1 = ag__.list_append(boards_1, board_1)\n", + " return board_1, boards_1\n", + " board, boards = ag__.for_stmt(ag__.utils.dynamic_builtin(range, tf.\n", + " constant(NUM_STEPS)), extra_test, loop_body, (board, boards))\n", + " boards = ag__.list_stack(boards, opts=ag__.ListStackOpts(\n", + " element_dtype=tf.int32, original_call=ag.stack))\n", + " with ag__.utils.control_dependency_on_returns(render(boards)):\n", + " boards_2 = ag__.utils.alias_tensors(boards)\n", + " return tf.no_op()\n", + " except:\n", + " ag__.rewrite_graph_construction_error(ag_source_map__)\n", + "\n" + ] + } + ], + "source": [ + "print(ag.to_code(gol))" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "p8zZyj-tq4K3", + "Lkq3DBGOv3fA", + "r8_0ioEuAI-a", + "7NgrSPCZxs3h" + ], + "default_view": {}, + "last_runtime": { + "build_target": "", + "kind": "local" + }, + "name": "Simple algorithms using AutoGraph", + "provenance": [ + { + "file_id": "19q8KdVF8Cb_fDd13i-WDOG_6n_QGNW5-", + "timestamp": 1528465909719 + } + ], + "version": "0.3.2", + "views": {} + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb b/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb index 0702273fac15da61a72d66d8344a5add32ad12a6..7e9cc54d4cafa64e4cd3b48f9376b1b2b4d3575e 100644 --- a/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb +++ b/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb @@ -1,49 +1,20 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "Dev Summit 2018 - Autograph", - "version": "0.3.2", - "views": {}, - "default_view": {}, - "provenance": [ - { - "file_id": "1wCZUh73zTNs1jzzYjqoxMIdaBWCdKJ2K", - "timestamp": 1522238054357 - }, - { - "file_id": "1_HpC-RrmIv4lNaqeoslUeWaX8zH5IXaJ", - "timestamp": 1521743157199 - }, - { - "file_id": "1mjO2fQ2F9hxpAzw2mnrrUkcgfb7xSGW-", - "timestamp": 1520522344607 - } - ], - "collapsed_sections": [] - }, - "kernelspec": { - "name": "python2", - "display_name": "Python 2" - } - }, "cells": [ { + "cell_type": "markdown", "metadata": { - "id": "g7nGs4mzVUHP", - "colab_type": "text" + "colab_type": "text", + "id": "g7nGs4mzVUHP" }, - "cell_type": "markdown", "source": [ - "# Experimental: TF Autograph\n", + "# Experimental: TF AutoGraph\n", "**TensorFlow Dev Summit, 2018.**\n", "\n", - "This interactive notebook demonstrates **autograph**, an experimental source-code transformation library to automatically convert TF.Eager and Python code to TensorFlow graphs.\n", + "This interactive notebook demonstrates **AutoGraph**, an experimental source-code transformation library to automatically convert Python, TensorFlow and NumPy code to TensorFlow graphs.\n", "\n", "**Note: this is pre-alpha software!** The notebook works best with Python 2, for now.\n", "\n", - "> ![alt text](https://lh3.googleusercontent.com/QOvy0clmg7siaVKzwmSPAjicWWNQ0OeyaB16plDjSJMf35WD3vLjF6mz4CGrhSHw60HnlZPJjkyDCBzw5XOI0oBGSewyYw=s688)\n", + "\u003e ![alt text](https://lh3.googleusercontent.com/QOvy0clmg7siaVKzwmSPAjicWWNQ0OeyaB16plDjSJMf35WD3vLjF6mz4CGrhSHw60HnlZPJjkyDCBzw5XOI0oBGSewyYw=s688)\n", "\n", "### Table of Contents\n", "1. _Write Eager code that is fast and scalable._\n", @@ -53,37 +24,39 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "uFcgBENZqkB2", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "uFcgBENZqkB2" }, - "cell_type": "code", + "outputs": [], "source": [ "# Install TensorFlow; note that Colab notebooks run remotely, on virtual\n", "# instances provided by Google.\n", "!pip install -U -q tf-nightly" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "Pa2qpEmoVOGe", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "Pa2qpEmoVOGe" }, - "cell_type": "code", + "outputs": [], "source": [ "import os\n", "import time\n", @@ -96,170 +69,172 @@ "import six\n", "\n", "from google.colab import widgets" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "ZVKfj5ttVkqz", - "colab_type": "text" + "colab_type": "text", + "id": "ZVKfj5ttVkqz" }, - "cell_type": "markdown", "source": [ "# 1. Write Eager code that is fast and scalable\n", "\n", "TF.Eager gives you more flexibility while coding, but at the cost of losing the benefits of TensorFlow graphs. For example, Eager does not currently support distributed training, exporting models, and a variety of memory and computation optimizations.\n", "\n", - "Autograph gives you the best of both worlds: write your code in an Eager style, and we will automatically transform it into the equivalent TF graph code. The graph code can be executed eagerly (as a single op), included as part of a larger graph, or exported." + "AutoGraph gives you the best of both worlds: you can write your code in an Eager style, and we will automatically transform it into the equivalent TF graph code. The graph code can be executed eagerly (as a single op), included as part of a larger graph, or exported." ] }, { + "cell_type": "markdown", "metadata": { - "id": "snaZRFdWd9ym", - "colab_type": "text" + "colab_type": "text", + "id": "snaZRFdWd9ym" }, - "cell_type": "markdown", "source": [ - "For example, autograph can convert a function like this:" + "For example, AutoGraph can convert a function like this:" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "9__n8cSIeDnD", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "9__n8cSIeDnD" }, - "cell_type": "code", + "outputs": [], "source": [ "def g(x):\n", - " if x > 0:\n", + " if x \u003e 0:\n", " x = x * x\n", " else:\n", " x = 0\n", " return x" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "gq0eQcuReHET", - "colab_type": "text" + "colab_type": "text", + "id": "gq0eQcuReHET" }, - "cell_type": "markdown", "source": [ "... into a TF graph-building function:" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "sELSn599ePUF", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, - "output_extras": [ - {} - ], - "base_uri": "https://localhost:8080/", - "height": 413 + "height": 431 }, - "outputId": "bb0c7216-1ca3-4da1-d1fb-589902cdcd1a", + "colab_type": "code", "executionInfo": { + "elapsed": 69, "status": "ok", - "timestamp": 1522345737505, - "user_tz": 240, - "elapsed": 243, + "timestamp": 1531750911837, "user": { - "displayName": "Dan Moldovan", - "photoUrl": "//lh5.googleusercontent.com/-Rneh8xjecyk/AAAAAAAAAAI/AAAAAAAACB4/c5vwsJpbktY/s50-c-k-no/photo.jpg", - "userId": "112023154726779574577" - } - } + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "sELSn599ePUF", + "outputId": "2858bde5-ae05-4c32-be01-7770ac914f02" }, - "cell_type": "code", - "source": [ - "print(autograph.to_code(g))" - ], - "execution_count": 0, "outputs": [ { + "name": "stdout", "output_type": "stream", "text": [ "from __future__ import print_function\n", "import tensorflow as tf\n", - "from tensorflow.contrib.autograph.impl import api as autograph_api\n", - "from tensorflow.contrib.autograph import utils as autograph_utils\n", "\n", "def tf__g(x):\n", - " with tf.name_scope('g'):\n", + " try:\n", + " with tf.name_scope('g'):\n", "\n", - " def if_true():\n", - " with tf.name_scope('if_true'):\n", - " x_1, = x,\n", - " x_1 = x_1 * x_1\n", - " return x_1,\n", + " def if_true():\n", + " with tf.name_scope('if_true'):\n", + " x_1, = x,\n", + " x_1 = x_1 * x_1\n", + " return x_1,\n", "\n", - " def if_false():\n", - " with tf.name_scope('if_false'):\n", - " x_1, = x,\n", - " x_1 = 0\n", - " return x_1,\n", - " x = autograph_utils.run_cond(tf.greater(x, 0), if_true, if_false)\n", - " return x\n", + " def if_false():\n", + " with tf.name_scope('if_false'):\n", + " x_2, = x,\n", + " x_2 = 0\n", + " return x_2,\n", + " x = ag__.utils.run_cond(tf.greater(x, 0), if_true, if_false)\n", + " return x\n", + " except:\n", + " ag__.rewrite_graph_construction_error(ag_source_map__)\n", "\n" - ], - "name": "stdout" + ] } + ], + "source": [ + "print(autograph.to_code(g))" ] }, { + "cell_type": "markdown", "metadata": { - "id": "j74n-8hEe6dk", - "colab_type": "text" + "colab_type": "text", + "id": "j74n-8hEe6dk" }, - "cell_type": "markdown", "source": [ "You can then use the converted function as you would any regular TF op -- you can pass `Tensor` arguments and it will return `Tensor`s:" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "AkVaY0-dfEbH", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, - "output_extras": [ - {} - ], - "base_uri": "https://localhost:8080/", "height": 53 }, - "outputId": "4ffe3757-c44d-424c-c2a8-7ddc973bfcce", + "colab_type": "code", "executionInfo": { + "elapsed": 83, "status": "ok", - "timestamp": 1522345737841, - "user_tz": 240, - "elapsed": 257, + "timestamp": 1531750911965, "user": { - "displayName": "Dan Moldovan", - "photoUrl": "//lh5.googleusercontent.com/-Rneh8xjecyk/AAAAAAAAAAI/AAAAAAAACB4/c5vwsJpbktY/s50-c-k-no/photo.jpg", - "userId": "112023154726779574577" - } - } + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "AkVaY0-dfEbH", + "outputId": "f04541ad-b1d3-4663-bf27-4d902648283d" }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "g(9) = 81\n", + "tf_g(9) = 81\n" + ] + } + ], "source": [ "tf_g = autograph.to_graph(g)\n", "\n", @@ -272,77 +247,72 @@ "\n", " print('g(9) = %s' % g(9))\n", " print('tf_g(9) = %s' % tf_g_result)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "g(9) = 81\n", - "tf_g(9) = 81\n" - ], - "name": "stdout" - } ] }, { + "cell_type": "markdown", "metadata": { - "id": "trrHQBM1VnD0", - "colab_type": "text" + "colab_type": "text", + "id": "trrHQBM1VnD0" }, - "cell_type": "markdown", "source": [ "# 2. Case study: complex control flow\n", "\n", - "Autograph can convert a large chunk of the Python language into graph-equivalent code, and we're adding new supported language features all the time. In this section, we'll give you a taste of some of the functionality in autograph.\n", - "Autograph will automatically convert most Python control flow statements into their correct graph equivalent.\n", + "Autograph can convert a large subset of the Python language into graph-equivalent code, and we're adding new supported language features all the time. In this section, we'll give you a taste of some of the functionality in AutoGraph.\n", + "AutoGraph will automatically convert most Python control flow statements into their graph equivalent.\n", " " ] }, { + "cell_type": "markdown", "metadata": { - "id": "u0YG3DPgZxoW", - "colab_type": "text" + "colab_type": "text", + "id": "u0YG3DPgZxoW" }, - "cell_type": "markdown", "source": [ "We support common statements like `while`, `for`, `if`, `break`, `return` and more. You can even nest them as much as you like. Imagine trying to write the graph version of this code by hand:" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "xJYDzOcrZ8pI", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, - "output_extras": [ - {} - ], - "base_uri": "https://localhost:8080/", "height": 35 }, - "outputId": "6c244ee4-b141-4ad6-eefa-cfffa71f33c6", + "colab_type": "code", "executionInfo": { + "elapsed": 169, "status": "ok", - "timestamp": 1522345738402, - "user_tz": 240, - "elapsed": 483, + "timestamp": 1531750912183, "user": { - "displayName": "Dan Moldovan", - "photoUrl": "//lh5.googleusercontent.com/-Rneh8xjecyk/AAAAAAAAAAI/AAAAAAAACB4/c5vwsJpbktY/s50-c-k-no/photo.jpg", - "userId": "112023154726779574577" - } - } + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "xJYDzOcrZ8pI", + "outputId": "f392b475-bf87-4d90-919d-44f895ee9fc7" }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sum of even numbers: 42\n" + ] + } + ], "source": [ "def sum_even(numbers):\n", " s = 0\n", " for n in numbers:\n", - " if n % 2 > 0:\n", + " if n % 2 \u003e 0:\n", " continue\n", " s += n\n", " return s\n", @@ -358,77 +328,74 @@ " \n", "# Uncomment the line below to print the generated graph code\n", "# print(autograph.to_code(sum_even))" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Sum of even numbers: 42\n" - ], - "name": "stdout" - } ] }, { + "cell_type": "markdown", "metadata": { - "id": "_YXo4KOcbKrn", - "colab_type": "text" + "colab_type": "text", + "id": "_YXo4KOcbKrn" }, - "cell_type": "markdown", "source": [ "Try replacing the `continue` in the above code with `break` -- Autograph supports that as well!" ] }, { + "cell_type": "markdown", "metadata": { - "id": "xHmC0rBIavW_", - "colab_type": "text" + "colab_type": "text", + "id": "xHmC0rBIavW_" }, - "cell_type": "markdown", "source": [ "The Python code above is much more readable than the matching graph code. Autograph takes care of tediously converting every piece of Python code into the matching TensorFlow graph version for you, so that you can quickly write maintainable code, but still benefit from the optimizations and deployment benefits of graphs." ] }, { + "cell_type": "markdown", "metadata": { - "id": "UEHWGpBXbS7g", - "colab_type": "text" + "colab_type": "text", + "id": "UEHWGpBXbS7g" }, - "cell_type": "markdown", "source": [ "Let's try some other useful Python constructs, like `print` and `assert`. We automatically convert Python `assert` statements into the equivalent `tf.Assert` code. " ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "qUU57xlEbauI", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, - "output_extras": [ - {} - ], - "base_uri": "https://localhost:8080/", "height": 53 }, - "outputId": "add3db4a-2077-4dd5-f7a7-a5b5a4529c26", + "colab_type": "code", "executionInfo": { + "elapsed": 56, "status": "ok", - "timestamp": 1522345738697, - "user_tz": 240, - "elapsed": 253, + "timestamp": 1531750912292, "user": { - "displayName": "Dan Moldovan", - "photoUrl": "//lh5.googleusercontent.com/-Rneh8xjecyk/AAAAAAAAAAI/AAAAAAAACB4/c5vwsJpbktY/s50-c-k-no/photo.jpg", - "userId": "112023154726779574577" - } - } + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "qUU57xlEbauI", + "outputId": "c9cd536a-4a95-4eb0-98c0-aafce5d79580" }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Got error message: assertion failed: [Do not pass zero!]\n", + "\t [[{{node f/Assert/Assert}} = Assert[T=[DT_STRING], summarize=3, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](f/NotEqual, f/Assert/Assert/data_0)]]\n" + ] + } + ], "source": [ "def f(x):\n", " assert x != 0, 'Do not pass zero!'\n", @@ -444,61 +411,35 @@ " \n", "# Uncomment the line below to print the generated graph code\n", "# print(autograph.to_code(f))" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Got error message: assertion failed: [Do not pass zero!]\n", - "\t [[Node: f/Assert/Assert = Assert[T=[DT_STRING], summarize=3, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](f/NotEqual, f/Assert/Assert/data_0)]]\n" - ], - "name": "stdout" - } ] }, { + "cell_type": "markdown", "metadata": { - "id": "w5hBZaVJbck4", - "colab_type": "text" + "colab_type": "text", + "id": "w5hBZaVJbck4" }, - "cell_type": "markdown", "source": [ "You can also use `print` functions in-graph:" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "6NdzRKLEboRv", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 - }, - "output_extras": [ - {} - ], - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "outputId": "fb82dfc3-790f-4127-87f6-361805be9e9b", - "executionInfo": { - "status": "ok", - "timestamp": 1522345739013, - "user_tz": 240, - "elapsed": 247, - "user": { - "displayName": "Dan Moldovan", - "photoUrl": "//lh5.googleusercontent.com/-Rneh8xjecyk/AAAAAAAAAAI/AAAAAAAACB4/c5vwsJpbktY/s50-c-k-no/photo.jpg", - "userId": "112023154726779574577" } - } + }, + "colab_type": "code", + "id": "6NdzRKLEboRv" }, - "cell_type": "code", + "outputs": [], "source": [ "def print_sign(n):\n", - " if n >= 0:\n", + " if n \u003e= 0:\n", " print(n, 'is positive!')\n", " else:\n", " print(n, 'is negative!')\n", @@ -512,62 +453,58 @@ " \n", "# Uncomment the line below to print the generated graph code\n", "# print(autograph.to_code(print_sign))" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "1 is positive!\n" - ], - "name": "stdout" - } ] }, { + "cell_type": "markdown", "metadata": { - "id": "9u_Z3i3AivLA", - "colab_type": "text" + "colab_type": "text", + "id": "9u_Z3i3AivLA" }, - "cell_type": "markdown", "source": [ - "We can convert lists to TensorArray, so appending to lists also works, with a few modifications:" + "Appending to lists also works, with a few modifications:" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "MjhCQJVuiTNR", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, - "output_extras": [ - {} - ], - "base_uri": "https://localhost:8080/", "height": 35 }, - "outputId": "dc320b87-595b-4392-d29c-994486fd8a0a", + "colab_type": "code", "executionInfo": { + "elapsed": 148, "status": "ok", - "timestamp": 1522345744470, - "user_tz": 240, - "elapsed": 5391, + "timestamp": 1531750912595, "user": { - "displayName": "Dan Moldovan", - "photoUrl": "//lh5.googleusercontent.com/-Rneh8xjecyk/AAAAAAAAAAI/AAAAAAAACB4/c5vwsJpbktY/s50-c-k-no/photo.jpg", - "userId": "112023154726779574577" - } - } + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "MjhCQJVuiTNR", + "outputId": "96bf9131-c7c1-4359-ee82-9c38575e7ab4" }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 2 3 4]\n" + ] + } + ], "source": [ "def f(n):\n", " numbers = []\n", " # We ask you to tell us about the element dtype.\n", - " autograph.utils.set_element_type(numbers, tf.int32)\n", + " autograph.set_element_type(numbers, tf.int32)\n", " for i in range(n):\n", " numbers.append(i)\n", " return autograph.stack(numbers) # Stack the list so that it can be used as a Tensor\n", @@ -580,65 +517,62 @@ " \n", "# Uncomment the line below to print the generated graph code\n", "# print(autograph.to_code(f))" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[0 1 2 3 4]\n" - ], - "name": "stdout" - } ] }, { + "cell_type": "markdown", "metadata": { - "id": "UdG8ZFrkTAF2", - "colab_type": "text" + "colab_type": "text", + "id": "UdG8ZFrkTAF2" }, - "cell_type": "markdown", "source": [ "And all of these functionalities, and more, can be composed into more complicated code:\n" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "DVs6wt8NKaGQ", - "colab_type": "code", + "cellView": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, - "output_extras": [ - {} - ], - "base_uri": "https://localhost:8080/", "height": 53 }, - "cellView": "code", - "outputId": "0a4b8d08-8f65-4bbc-85ba-dc4c60563519", + "colab_type": "code", "executionInfo": { + "elapsed": 555, "status": "ok", - "timestamp": 1522345745186, - "user_tz": 240, - "elapsed": 658, + "timestamp": 1531750913176, "user": { - "displayName": "Dan Moldovan", - "photoUrl": "//lh5.googleusercontent.com/-Rneh8xjecyk/AAAAAAAAAAI/AAAAAAAACB4/c5vwsJpbktY/s50-c-k-no/photo.jpg", - "userId": "112023154726779574577" - } - } + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "DVs6wt8NKaGQ", + "outputId": "8729229c-4f08-4640-d3a1-0d3f9c697a87" }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The prime numbers less than 50 are:\n", + "[ 2 3 5 7 11 13 17 19 23 29 31 37 41 43 47]\n" + ] + } + ], "source": [ "def print_primes(n):\n", " \"\"\"Returns all the prime numbers less than n.\"\"\"\n", - " assert n > 0\n", + " assert n \u003e 0\n", " \n", " primes = []\n", - " autograph.utils.set_element_type(primes, tf.int32)\n", + " autograph.set_element_type(primes, tf.int32)\n", " for i in range(2, n):\n", " is_prime = True\n", " for k in range(2, i):\n", @@ -663,45 +597,36 @@ " \n", "# Uncomment the line below to print the generated graph code\n", "# print(autograph.to_code(print_primes))" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "The prime numbers less than 50 are:\n", - "[ 2 3 5 7 11 13 17 19 23 29 31 37 41 43 47]\n" - ], - "name": "stdout" - } ] }, { + "cell_type": "markdown", "metadata": { - "id": "JQ8kQT99VqDk", - "colab_type": "text" + "colab_type": "text", + "id": "JQ8kQT99VqDk" }, - "cell_type": "markdown", "source": [ "# 3. Case study: training MNIST with Keras\n", "\n", - "As we've seen, writing control flow in Autograph is easy. So running a training loop in graph should be easy as well!\n", + "As we've seen, writing control flow in AutoGraph is easy. So running a training loop in graph should be easy as well!\n", "\n", "Here, we show an example of such a training loop for a simple Keras model that trains on MNIST." ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "0CrtGWgwuLJr", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "0CrtGWgwuLJr" }, - "cell_type": "code", + "outputs": [], "source": [ "import gzip\n", "import shutil\n", @@ -754,66 +679,67 @@ "\n", "def mnist_test(directory):\n", " return dataset(directory, 't10k-images-idx3-ubyte', 't10k-labels-idx1-ubyte')" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "2zu1U9Nqir6L", - "colab_type": "text" + "colab_type": "text", + "id": "2zu1U9Nqir6L" }, - "cell_type": "markdown", "source": [ "First, we'll define a small three-layer neural network using the Keras API" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "x_MU13boiok2", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "x_MU13boiok2" }, - "cell_type": "code", + "outputs": [], "source": [ "def mlp_model(input_shape):\n", - " model = tf.keras.Sequential([\n", + " model = tf.keras.Sequential((\n", " tf.keras.layers.Dense(100, activation='relu', input_shape=input_shape),\n", " tf.keras.layers.Dense(100, activation='relu'),\n", - " tf.keras.layers.Dense(10, activation='softmax')])\n", + " tf.keras.layers.Dense(10, activation='softmax'),\n", + " ))\n", " model.build()\n", " return model" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "Wuqg3H8mi0Xj", - "colab_type": "text" + "colab_type": "text", + "id": "Wuqg3H8mi0Xj" }, - "cell_type": "markdown", "source": [ "Let's connect the model definition (here abbreviated as `m`) to a loss function, so that we can train our model." ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "W51sfbONiz_5", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "W51sfbONiz_5" }, - "cell_type": "code", + "outputs": [], "source": [ "def predict(m, x, y):\n", " y_p = m(x)\n", @@ -822,63 +748,63 @@ " accuracies = tf.keras.metrics.categorical_accuracy(y, y_p)\n", " accuracy = tf.reduce_mean(accuracies)\n", " return l, accuracy" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "035tNWQki9tr", - "colab_type": "text" + "colab_type": "text", + "id": "035tNWQki9tr" }, - "cell_type": "markdown", "source": [ "Now the final piece of the problem specification (before loading data, and clicking everything together) is backpropagating the loss through the model, and optimizing the weights using the gradient." ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "CsAD0ajbi9iZ", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "CsAD0ajbi9iZ" }, - "cell_type": "code", + "outputs": [], "source": [ "def fit(m, x, y, opt):\n", " l, accuracy = predict(m, x, y)\n", " opt.minimize(l)\n", " return l, accuracy" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "PcVRIacKjSwb", - "colab_type": "text" + "colab_type": "text", + "id": "PcVRIacKjSwb" }, - "cell_type": "markdown", "source": [ "These are some utility functions to download data and generate batches for training" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "RVw57HdTjPzi", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "RVw57HdTjPzi" }, - "cell_type": "code", + "outputs": [], "source": [ "def setup_mnist_data(is_training, hp, batch_size):\n", " if is_training:\n", @@ -896,16 +822,14 @@ " x = tf.to_float(tf.reshape(image, (-1, 28 * 28)))\n", " y = tf.one_hot(tf.squeeze(label), 10)\n", " return x, y" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "2zEJH5XNjgFz", - "colab_type": "text" + "colab_type": "text", + "id": "2zEJH5XNjgFz" }, - "cell_type": "markdown", "source": [ "This function specifies the main training loop. We instantiate the model (using the code above), instantiate an optimizer (here we'll use SGD with momentum, nothing too fancy), and we'll instantiate some lists to keep track of training and test loss and accuracy over time.\n", "\n", @@ -913,33 +837,35 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "UUI0566FjZPx", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "UUI0566FjZPx" }, - "cell_type": "code", + "outputs": [], "source": [ "def train(train_ds, test_ds, hp):\n", " m = mlp_model((28 * 28,))\n", " opt = tf.train.MomentumOptimizer(hp.learning_rate, 0.9)\n", + "\n", " train_losses = []\n", - " train_losses = autograph.utils.set_element_type(train_losses, tf.float32)\n", + " autograph.set_element_type(train_losses, tf.float32)\n", " test_losses = []\n", - " test_losses = autograph.utils.set_element_type(test_losses, tf.float32)\n", + " autograph.set_element_type(test_losses, tf.float32)\n", " train_accuracies = []\n", - " train_accuracies = autograph.utils.set_element_type(train_accuracies,\n", - " tf.float32)\n", + " autograph.set_element_type(train_accuracies, tf.float32)\n", " test_accuracies = []\n", - " test_accuracies = autograph.utils.set_element_type(test_accuracies,\n", - " tf.float32)\n", - " i = tf.constant(0)\n", - " while i < hp.max_steps:\n", + " autograph.set_element_type(test_accuracies, tf.float32)\n", + "\n", + " i = 0\n", + " while i \u003c hp.max_steps:\n", " train_x, train_y = get_next_batch(train_ds)\n", " test_x, test_y = get_next_batch(test_ds)\n", " step_train_loss, step_train_accuracy = fit(m, train_x, train_y, opt)\n", @@ -956,173 +882,144 @@ " return (autograph.stack(train_losses), autograph.stack(test_losses),\n", " autograph.stack(train_accuracies),\n", " autograph.stack(test_accuracies))" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "cYiUQ1ppkHzk", - "colab_type": "text" + "colab_type": "text", + "id": "cYiUQ1ppkHzk" }, - "cell_type": "markdown", "source": [ "Everything is ready to go, let's train the model and plot its performance!" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "K1m8TwOKjdNd", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, - "output_extras": [ - {}, - {}, - {} - ], - "base_uri": "https://localhost:8080/", - "height": 988 + "height": 585 }, - "outputId": "f9d3eef3-5bea-45c1-ddf9-4edee73e4436", + "colab_type": "code", "executionInfo": { + "elapsed": 17094, "status": "ok", - "timestamp": 1522345800262, - "user_tz": 240, - "elapsed": 52391, + "timestamp": 1531750930585, "user": { - "displayName": "Dan Moldovan", - "photoUrl": "//lh5.googleusercontent.com/-Rneh8xjecyk/AAAAAAAAAAI/AAAAAAAACB4/c5vwsJpbktY/s50-c-k-no/photo.jpg", - "userId": "112023154726779574577" - } - } + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "K1m8TwOKjdNd", + "outputId": "9f63da19-c3bf-498b-cf00-29090bf3b4f0" }, - "cell_type": "code", - "source": [ - "with tf.Graph().as_default():\n", - " hp = tf.contrib.training.HParams(\n", - " learning_rate=0.05,\n", - " max_steps=500,\n", - " )\n", - " train_ds = setup_mnist_data(True, hp, 50)\n", - " test_ds = setup_mnist_data(False, hp, 1000)\n", - " tf_train = autograph.to_graph(train)\n", - " (train_losses, test_losses, train_accuracies,\n", - " test_accuracies) = tf_train(train_ds, test_ds, hp)\n", - "\n", - " with tf.Session() as sess:\n", - " sess.run(tf.global_variables_initializer())\n", - " (train_losses, test_losses, train_accuracies,\n", - " test_accuracies) = sess.run([train_losses, test_losses, train_accuracies,\n", - " test_accuracies])\n", - " plt.title('MNIST train/test losses')\n", - " plt.plot(train_losses, label='train loss')\n", - " plt.plot(test_losses, label='test loss')\n", - " plt.legend()\n", - " plt.xlabel('Training step')\n", - " plt.ylabel('Loss')\n", - " plt.show()\n", - " plt.title('MNIST train/test accuracies')\n", - " plt.plot(train_accuracies, label='train accuracy')\n", - " plt.plot(test_accuracies, label='test accuracy')\n", - " plt.legend(loc='lower right')\n", - " plt.xlabel('Training step')\n", - " plt.ylabel('Accuracy')\n", - " plt.show()" - ], - "execution_count": 0, "outputs": [ { - "output_type": "stream", - "text": [ - "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz to /tmp/autograph_mnist_data/train-images-idx3-ubyte.gz\n", - "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz to /tmp/autograph_mnist_data/train-labels-idx1-ubyte.gz\n", - "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz to /tmp/autograph_mnist_data/t10k-images-idx3-ubyte.gz\n", - "Downloading https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz to /tmp/autograph_mnist_data/t10k-labels-idx1-ubyte.gz\n", - "Step 0 train loss: 2.244329 test loss: 2.2499208 train accuracy: 0.12 test accuracy: 0.161\n", - "Step 50 train loss: 0.64771986 test loss: 0.56013924 train accuracy: 0.82 test accuracy: 0.836\n", - "Step 100 train loss: 0.49011207 test loss: 0.42143965 train accuracy: 0.84 test accuracy: 0.879\n", - "Step 150 train loss: 0.3768609 test loss: 0.39319593 train accuracy: 0.88 test accuracy: 0.883\n", - "Step 200 train loss: 0.36007702 test loss: 0.37089333 train accuracy: 0.9 test accuracy: 0.881\n", - "Step 250 train loss: 0.182115 test loss: 0.28543878 train accuracy: 0.94 test accuracy: 0.915\n", - "Step 300 train loss: 0.2119576 test loss: 0.22305593 train accuracy: 0.92 test accuracy: 0.93\n", - "Step 350 train loss: 0.12932214 test loss: 0.29057172 train accuracy: 0.96 test accuracy: 0.906\n", - "Step 400 train loss: 0.22937602 test loss: 0.2200287 train accuracy: 0.92 test accuracy: 0.925\n", - "Step 450 train loss: 0.23444137 test loss: 0.19857481 train accuracy: 0.94 test accuracy: 0.94\n" - ], - "name": "stdout" - }, - { - "output_type": "display_data", "data": { - "image/png": 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PDu/a0FgAQAysvImIhrM1a1ajvb0Nixe/DABIJMzu0pNP/jquvvq/cNpps3D66bN63E+u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fICcIgjiRSalgTJ48GbNnz467ffny5Th8+DAWL16MJ598Eo899lhSry9wIjhB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"text/plain": [ - "" + "\u003cmatplotlib.figure.Figure at 0x7f97f1e98d90\u003e" ] }, "metadata": { "tags": [] - } + }, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "image/png": 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cyF8Xe3P+RCkpJprmulWcyzb4wlqmXE3ewTEKEpebgSpE3gEBSDMsUDXkQKT2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\nH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mjPakO1MT/AmSac1pSwy5kTc/enoRna3WjNtLCm2JLnYeyq7D6V86gW4YPPDi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"text/plain": [ - "" + "\u003cmatplotlib.figure.Figure at 0x7f97f1330850\u003e" ] }, "metadata": { "tags": [] - } + }, + "output_type": "display_data" } + ], + "source": [ + "def plot(train, test, label):\n", + " plt.title('MNIST model %s' % label)\n", + " plt.plot(train, label='train %s' % label)\n", + " plt.plot(test, label='test %s' % label)\n", + " plt.legend()\n", + " plt.xlabel('Training step')\n", + " plt.ylabel(label.capitalize())\n", + " plt.show()\n", + " \n", + "\n", + "with tf.Graph().as_default():\n", + " hp = tf.contrib.training.HParams(\n", + " learning_rate=0.05,\n", + " max_steps=tf.constant(500),\n", + " )\n", + " train_ds = setup_mnist_data(True, hp, 50)\n", + " test_ds = setup_mnist_data(False, hp, 1000)\n", + " tf_train = autograph.to_graph(train)\n", + " all_losses = tf_train(train_ds, test_ds, hp)\n", + "\n", + " with tf.Session() as sess:\n", + " sess.run(tf.global_variables_initializer())\n", + " (train_losses, test_losses, train_accuracies,\n", + " test_accuracies) = sess.run(all_losses)\n", + " \n", + " plot(train_losses, test_losses, 'loss')\n", + " plot(train_accuracies, test_accuracies, 'accuracy')" ] }, { + "cell_type": "markdown", "metadata": { - "id": "HNqUFL4deCsL", - "colab_type": "text" + "colab_type": "text", + "id": "HNqUFL4deCsL" }, - "cell_type": "markdown", "source": [ "# 4. Case study: building an RNN\n" ] }, { + "cell_type": "markdown", "metadata": { - "id": "YkC1k4HEQ7rw", - "colab_type": "text" + "colab_type": "text", + "id": "YkC1k4HEQ7rw" }, - "cell_type": "markdown", "source": [ "In this exercise we build and train a model similar to the RNNColorbot model that was used in the main Eager notebook. The model is adapted for converting and training in graph mode." ] }, { + "cell_type": "markdown", "metadata": { - "id": "7nkPDl5CTCNb", - "colab_type": "text" + "colab_type": "text", + "id": "7nkPDl5CTCNb" }, - "cell_type": "markdown", "source": [ "To get started, we load the colorbot dataset. The code is identical to that used in the other exercise and its details are unimportant." ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "A0uREmVXCQEw", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "A0uREmVXCQEw" }, - "cell_type": "code", + "outputs": [], "source": [ "def parse(line):\n", " \"\"\"Parses a line from the colors dataset.\n", @@ -1137,7 +1034,7 @@ " A tuple of three tensors (rgb, chars, length), of shapes: (batch_size, 3),\n", " (batch_size, max_sequence_length, 256) and respectively (batch_size).\n", " \"\"\"\n", - " items = tf.string_split([line], \",\").values\n", + " items = tf.string_split(tf.expand_dims(line, 0), \",\").values\n", " rgb = tf.string_to_number(items[1:], out_type=tf.float32) / 255.0\n", " color_name = items[0]\n", " chars = tf.one_hot(tf.decode_raw(color_name, tf.uint8), depth=256)\n", @@ -1169,23 +1066,21 @@ " dataset = dataset.repeat()\n", " if training:\n", " dataset = dataset.shuffle(buffer_size=3000)\n", - " dataset = dataset.padded_batch(batch_size, padded_shapes=([None], [None, None], []))\n", + " dataset = dataset.padded_batch(batch_size, padded_shapes=((None,), (None, None), ()))\n", " return dataset\n", "\n", "\n", - "train_url = \"https://raw.githubusercontent.com/random-forests/tensorflow-workshop/master/extras/colorbot/data/train.csv\"\n", - "test_url = \"https://raw.githubusercontent.com/random-forests/tensorflow-workshop/master/extras/colorbot/data/test.csv\"\n", + "train_url = \"https://raw.githubusercontent.com/random-forests/tensorflow-workshop/master/archive/extras/colorbot/data/train.csv\"\n", + "test_url = \"https://raw.githubusercontent.com/random-forests/tensorflow-workshop/master/archive/extras/colorbot/data/test.csv\"\n", "data_dir = \"tmp/rnn/data\"" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "waZ89t3DTUla", - "colab_type": "text" + "colab_type": "text", + "id": "waZ89t3DTUla" }, - "cell_type": "markdown", "source": [ "Next, we set up the RNNColobot model, which is very similar to the one we used in the main exercise.\n", "\n", @@ -1193,17 +1088,19 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "9v8AJouiC44V", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "9v8AJouiC44V" }, - "cell_type": "code", + "outputs": [], "source": [ "def model_components():\n", " lower_cell = tf.contrib.rnn.LSTMBlockCell(256)\n", @@ -1227,12 +1124,13 @@ " Returns:\n", " A Tensor of shape (max_sequence_length, batch_size, output_size).\n", " \"\"\"\n", - " hidden_outputs = []\n", - " autograph.utils.set_element_type(hidden_outputs, tf.float32)\n", + " hidden_outputs = tf.TensorArray(tf.float32, size=0, dynamic_size=True)\n", " state, output = cell.zero_state(batch_size, tf.float32)\n", + " initial_state_shape = state.shape\n", + " initial_output_shape = output.shape\n", " n = tf.shape(chars)[0]\n", " i = 0\n", - " while i < n:\n", + " while i \u003c n:\n", " ch = chars[i]\n", " cell_output, (state, output) = cell.call(ch, (state, output))\n", " hidden_outputs.append(cell_output)\n", @@ -1261,50 +1159,51 @@ " A Tensor of shape (batch_size, 3) - the model predictions.\n", " \"\"\"\n", " (chars, length) = inputs\n", - " chars_time_major = tf.transpose(chars, [1, 0, 2])\n", + " chars_time_major = tf.transpose(chars, (1, 0, 2))\n", " chars_time_major.set_shape((None, batch_size, 256))\n", "\n", " hidden_outputs = rnn_layer(chars_time_major, lower_cell, batch_size, training)\n", " final_outputs = rnn_layer(hidden_outputs, upper_cell, batch_size, training)\n", "\n", " # Grab just the end-of-sequence from each output.\n", - " indices = tf.stack([length - 1, range(batch_size)], axis=1)\n", + " indices = tf.stack((length - 1, range(batch_size)), axis=1)\n", " sequence_ends = tf.gather_nd(final_outputs, indices)\n", + " sequence_ends.set_shape((batch_size, 128))\n", " return relu_layer(sequence_ends)\n", "\n", "def loss_fn(labels, predictions):\n", " return tf.reduce_mean((predictions - labels) ** 2)" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "JjK4gXFvFsf4", - "colab_type": "text" + "colab_type": "text", + "id": "JjK4gXFvFsf4" }, - "cell_type": "markdown", "source": [ "The train and test functions are also similar to the ones used in the Eager notebook. Since the network requires a fixed batch size, we'll train in a single shot, rather than by epoch." ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "ZWQMExk0S6X6", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "ZWQMExk0S6X6" }, - "cell_type": "code", + "outputs": [], "source": [ "def train(optimizer, train_data, lower_cell, upper_cell, relu_layer, batch_size, num_steps):\n", " iterator = train_data.make_one_shot_iterator()\n", " step = 0\n", - " while step < num_steps:\n", + " while step \u003c num_steps:\n", " labels, chars, sequence_length = iterator.get_next()\n", " predictions = model((chars, sequence_length), lower_cell, upper_cell, relu_layer, batch_size, training=True)\n", " loss = loss_fn(labels, predictions)\n", @@ -1319,7 +1218,7 @@ " total_loss = 0.0\n", " iterator = eval_data.make_one_shot_iterator()\n", " step = 0\n", - " while step < num_steps:\n", + " while step \u003c num_steps:\n", " labels, chars, sequence_length = iterator.get_next()\n", " predictions = model((chars, sequence_length), lower_cell, upper_cell, relu_layer, batch_size, training=False)\n", " total_loss += loss_fn(labels, predictions)\n", @@ -1340,16 +1239,14 @@ " # Here, we create a no_op that will drive the execution of all other code in\n", " # this function. Autograph will add the necessary control dependencies.\n", " return tf.no_op()" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "iopcs5hXG2od", - "colab_type": "text" + "colab_type": "text", + "id": "iopcs5hXG2od" }, - "cell_type": "markdown", "source": [ "Finally, we add code to run inference on a single input, which we'll read from the input.\n", "\n", @@ -1357,17 +1254,19 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "DyU0wnnAFEYj", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "DyU0wnnAFEYj" }, - "cell_type": "code", + "outputs": [], "source": [ "@autograph.do_not_convert(run_as=autograph.RunMode.PY_FUNC)\n", "def draw_prediction(color_name, pred):\n", @@ -1389,16 +1288,14 @@ " draw_prediction(color_name, pred)\n", " # Create an op that will drive the entire function.\n", " return tf.no_op()" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "Nt0Kv5OCHip0", - "colab_type": "text" + "colab_type": "text", + "id": "Nt0Kv5OCHip0" }, - "cell_type": "markdown", "source": [ "Finally, we put everything together.\n", "\n", @@ -1406,218 +1303,132 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "-GmWa0GtYWdh", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, - "output_extras": [ - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {} - ], - "base_uri": "https://localhost:8080/", - "height": 668 + "height": 415 }, - "outputId": "61f4af1d-c81e-44db-9079-1a7b8ed8ce58", + "colab_type": "code", "executionInfo": { + "elapsed": 15536, "status": "ok", - "timestamp": 1522345877153, - "user_tz": 240, - "elapsed": 75500, + "timestamp": 1531750946373, "user": { - "displayName": "Dan Moldovan", - "photoUrl": "//lh5.googleusercontent.com/-Rneh8xjecyk/AAAAAAAAAAI/AAAAAAAACB4/c5vwsJpbktY/s50-c-k-no/photo.jpg", - "userId": "112023154726779574577" - } - } + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "-GmWa0GtYWdh", + "outputId": "2e7a9856-9809-43a3-8b43-3c8514ea43e9" }, - "cell_type": "code", - "source": [ - "def run_input_loop(sess, inference_ops, color_name_placeholder):\n", - " \"\"\"Helper function that reads from input and calls the inference ops in a loop.\"\"\"\n", - "\n", - " tb = widgets.TabBar([\"RNN Colorbot\"])\n", - " while True:\n", - " with tb.output_to(0):\n", - " try:\n", - " color_name = six.moves.input(\"Give me a color name (or press 'enter' to exit): \")\n", - " except (EOFError, KeyboardInterrupt):\n", - " break\n", - " if not color_name:\n", - " break\n", - " with tb.output_to(0):\n", - " tb.clear_tab()\n", - " sess.run(inference_ops, {color_name_placeholder: color_name})\n", - " plt.show()\n", - "\n", - "with tf.Graph().as_default():\n", - " # Read the data.\n", - " batch_size = 64\n", - " train_data = load_dataset(data_dir, train_url, batch_size)\n", - " eval_data = load_dataset(data_dir, test_url, 50, training=False)\n", - " \n", - " # Create the model components.\n", - " lower_cell, upper_cell, relu_layer = model_components()\n", - " # Create the helper placeholder for inference.\n", - " color_name_placeholder = tf.placeholder(tf.string, shape=())\n", - " \n", - " # Compile the train / test code.\n", - " tf_train_model = autograph.to_graph(train_model)\n", - " train_model_ops = tf_train_model(\n", - " train_data, eval_data, batch_size, lower_cell, upper_cell, relu_layer, train_steps=100)\n", - " \n", - " # Compile the inference code.\n", - " tf_inference = autograph.to_graph(inference)\n", - " inference_ops = tf_inference(color_name_placeholder, lower_cell, upper_cell, relu_layer)\n", - " \n", - " with tf.Session() as sess:\n", - " sess.run(tf.global_variables_initializer())\n", - " \n", - " # Run training and testing.\n", - " sess.run(train_model_ops)\n", - " \n", - " # Run the inference loop.\n", - " run_input_loop(sess, inference_ops, color_name_placeholder)" - ], - "execution_count": 0, "outputs": [ { + "name": "stdout", "output_type": "stream", "text": [ - "('Successfully downloaded', 'train.csv', 28010L, 'bytes.')\n", - "('Successfully downloaded', 'test.csv', 2414L, 'bytes.')\n", - "Step 0 train loss 0.37890616\n", - "Step 10 train loss 0.18515904\n", - "Step 20 train loss 0.0892782\n", - "Step 30 train loss 0.07883155\n", - "Step 40 train loss 0.08585831\n", - "Step 50 train loss 0.09302989\n", - "Step 60 train loss 0.089012615\n", - "Step 70 train loss 0.07275697\n", - "Step 80 train loss 0.06644974\n", - "Step 90 train loss 0.0854013\n", - "Test loss 0.13216865Colorbot is ready to generate colors!\n", - "\n", + "Test loss 0.138294\n", + "Colorbot is ready to generate colors!\n", "\n", "\n" - ], - "name": "stdout" + ] }, { - "output_type": "display_data", "data": { - "text/plain": [ - "" - ], "text/html": [ - "" + "\u003clink rel=stylesheet type=text/css href='/nbextensions/google.colab/tabbar.css'\u003e\u003c/link\u003e" + ], + "text/plain": [ + "\u003cIPython.core.display.HTML at 0x7f97ee42bb90\u003e" ] }, "metadata": { "tags": [ "outputarea_id1" ] - 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"window[\"a6045498-8903-11e8-99f9-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"a6045497-8903-11e8-99f9-c8d3ffb5fbe0\"]);\n", + "//# sourceURL=js_3a3123cadb" ], "text/plain": [ - "" + "\u003cIPython.core.display.Javascript at 0x7f97ee2aba50\u003e" ] }, "metadata": { @@ -1661,17 +1472,17 @@ "id1_content_0", "outputarea_id1" ] - } + }, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { "application/javascript": [ - "window[\"b1070f38-3379-11e8-ac70-0242ac110002\"] = window[\"id1\"].setSelectedTabIndex(0);\n", - "//# sourceURL=js_d53293d4a7" + "window[\"a6045499-8903-11e8-99f9-c8d3ffb5fbe0\"] = window[\"id1\"].setSelectedTabIndex(0);\n", + "//# sourceURL=js_1a0e1f7d6f" ], "text/plain": [ - "" + "\u003cIPython.core.display.Javascript at 0x7f97ee2ab890\u003e" ] }, "metadata": { @@ -1679,17 +1490,17 @@ "id1_content_0", "outputarea_id1" ] - } + }, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { "application/javascript": [ - "window[\"c6d90d5c-3379-11e8-ac70-0242ac110002\"] = google.colab.output.setActiveOutputArea(window[\"b105b28c-3379-11e8-ac70-0242ac110002\"]);\n", - "//# sourceURL=js_3000dc2c05" + "window[\"a8e54762-8903-11e8-99f9-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"a6045496-8903-11e8-99f9-c8d3ffb5fbe0\"]);\n", + "//# sourceURL=js_6213539615" ], "text/plain": [ - "" + "\u003cIPython.core.display.Javascript at 0x7f97ee2abad0\u003e" ] }, "metadata": { @@ -1697,17 +1508,17 @@ "id1_content_0", "outputarea_id1" ] - } + }, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { "application/javascript": [ - "window[\"c6da872c-3379-11e8-ac70-0242ac110002\"] = google.colab.output.getActiveOutputArea();\n", - "//# sourceURL=js_4136f669a3" + "window[\"a8e54763-8903-11e8-99f9-c8d3ffb5fbe0\"] = google.colab.output.getActiveOutputArea();\n", + "//# sourceURL=js_0bd7f95c6e" ], "text/plain": [ - "" + "\u003cIPython.core.display.Javascript at 0x7f97ee2ab950\u003e" ] }, "metadata": { @@ -1715,17 +1526,17 @@ "id1_content_0", "outputarea_id1" ] - } + }, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { "application/javascript": [ - "window[\"c6dac868-3379-11e8-ac70-0242ac110002\"] = document.querySelector(\"#id1_content_0\");\n", - "//# sourceURL=js_2f70dd9aee" + "window[\"a8e54764-8903-11e8-99f9-c8d3ffb5fbe0\"] = document.querySelector(\"#id1_content_0\");\n", + "//# sourceURL=js_215f004f6b" ], "text/plain": [ - "" + "\u003cIPython.core.display.Javascript at 0x7f97ee2abb10\u003e" ] }, "metadata": { @@ -1733,17 +1544,17 @@ "id1_content_0", "outputarea_id1" ] - } + }, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { "application/javascript": [ - "window[\"c6db07d8-3379-11e8-ac70-0242ac110002\"] = google.colab.output.setActiveOutputArea(window[\"c6dac868-3379-11e8-ac70-0242ac110002\"]);\n", - "//# sourceURL=js_7226726048" + "window[\"a8e54765-8903-11e8-99f9-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"a8e54764-8903-11e8-99f9-c8d3ffb5fbe0\"]);\n", + "//# sourceURL=js_a06186c8ad" ], "text/plain": [ - "" + "\u003cIPython.core.display.Javascript at 0x7f97ee2aba90\u003e" ] }, "metadata": { @@ -1751,17 +1562,17 @@ "id1_content_0", "outputarea_id1" ] - } + }, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { "application/javascript": [ - "window[\"c6dcc6fe-3379-11e8-ac70-0242ac110002\"] = window[\"id1\"].setSelectedTabIndex(0);\n", - "//# sourceURL=js_72e7709865" + "window[\"a8e54766-8903-11e8-99f9-c8d3ffb5fbe0\"] = window[\"id1\"].setSelectedTabIndex(0);\n", + "//# sourceURL=js_383fbaae67" ], "text/plain": [ - "" + "\u003cIPython.core.display.Javascript at 0x7f97ee2abc50\u003e" ] }, "metadata": { @@ -1769,14 +1580,14 @@ "id1_content_0", "outputarea_id1" ] - } + }, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { - "image/png": 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google.colab.output.getActiveOutputArea();\n", - "//# sourceURL=js_984c56b816" + "window[\"a8e54768-8903-11e8-99f9-c8d3ffb5fbe0\"] = google.colab.output.getActiveOutputArea();\n", + "//# sourceURL=js_ae2887f57d" ], "text/plain": [ - "" + "\u003cIPython.core.display.Javascript at 0x7f97ea9efb50\u003e" ] }, "metadata": { @@ -1821,17 +1632,17 @@ "id1_content_0", "outputarea_id1" ] - } + }, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { "application/javascript": [ - "window[\"c708dec4-3379-11e8-ac70-0242ac110002\"] = document.querySelector(\"#id1_content_0\");\n", - "//# sourceURL=js_e0451a1217" + "window[\"a8e54769-8903-11e8-99f9-c8d3ffb5fbe0\"] = document.querySelector(\"#id1_content_0\");\n", + "//# sourceURL=js_608805a786" ], "text/plain": [ - "" + "\u003cIPython.core.display.Javascript at 0x7f97ea9ef710\u003e" ] }, "metadata": { @@ -1839,17 +1650,17 @@ "id1_content_0", "outputarea_id1" ] - } + }, + "output_type": "display_data" }, { - "output_type": 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+ { + "data": { + "application/javascript": [ + "window[\"a9e9b8b1-8903-11e8-99f9-c8d3ffb5fbe0\"] = window[\"a9e9b8b0-8903-11e8-99f9-c8d3ffb5fbe0\"].remove();\n", + "//# sourceURL=js_016ae4bf21" + ], + "text/plain": [ + "\u003cIPython.core.display.Javascript at 0x7f97ea9ef250\u003e" + ] + }, + "metadata": { + "tags": [ + "id1_content_0", + "outputarea_id1", + "user_output" + ] + }, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "window[\"a9e9b8b2-8903-11e8-99f9-c8d3ffb5fbe0\"] = jQuery(\".id_45185901 span\");\n", + "//# sourceURL=js_e666f179bc" + ], + "text/plain": [ + "\u003cIPython.core.display.Javascript at 0x7f97ea9ef550\u003e" + ] + }, + "metadata": { + "tags": [ + "id1_content_0", + "outputarea_id1", + "user_output" + ] + }, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "window[\"a9e9b8b3-8903-11e8-99f9-c8d3ffb5fbe0\"] = window[\"a9e9b8b2-8903-11e8-99f9-c8d3ffb5fbe0\"].text(\"Give me a color name (or press 'enter' to exit): \");\n", + "//# sourceURL=js_cbb9d14aec" + ], + "text/plain": [ + "\u003cIPython.core.display.Javascript at 0x7f97ea9ef1d0\u003e" + ] + }, + "metadata": { + "tags": [ + "id1_content_0", + "outputarea_id1", + "user_output" + ] + }, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { "application/javascript": [ - "window[\"c7baac12-3379-11e8-ac70-0242ac110002\"] = google.colab.output.setActiveOutputArea(window[\"c70842c0-3379-11e8-ac70-0242ac110002\"]);\n", - "//# sourceURL=js_cdd622e58f" + "window[\"a9e9b8b4-8903-11e8-99f9-c8d3ffb5fbe0\"] = google.colab.output.setActiveOutputArea(window[\"a8e54768-8903-11e8-99f9-c8d3ffb5fbe0\"]);\n", + "//# sourceURL=js_2967a79665" ], "text/plain": [ - "" + "\u003cIPython.core.display.Javascript at 0x7f97ea9ef1d0\u003e" ] }, "metadata": { @@ -1900,21 +1836,98 @@ "id1_content_0", "outputarea_id1" ] - } + }, + "output_type": "display_data" } + ], + "source": [ + "def run_input_loop(sess, inference_ops, color_name_placeholder):\n", + " \"\"\"Helper function that reads from input and calls the inference ops in a loop.\"\"\"\n", + "\n", + " tb = widgets.TabBar([\"RNN Colorbot\"])\n", + " while True:\n", + " with tb.output_to(0):\n", + " try:\n", + " color_name = six.moves.input(\"Give me a color name (or press 'enter' to exit): \")\n", + " except (EOFError, KeyboardInterrupt):\n", + " break\n", + " if not color_name:\n", + " break\n", + " with tb.output_to(0):\n", + " tb.clear_tab()\n", + " sess.run(inference_ops, {color_name_placeholder: color_name})\n", + " plt.show()\n", + "\n", + "with tf.Graph().as_default():\n", + " # Read the data.\n", + " batch_size = 64\n", + " train_data = load_dataset(data_dir, train_url, batch_size)\n", + " eval_data = load_dataset(data_dir, test_url, 50, training=False)\n", + " \n", + " # Create the model components.\n", + " lower_cell, upper_cell, relu_layer = model_components()\n", + " # Create the helper placeholder for inference.\n", + " color_name_placeholder = tf.placeholder(tf.string, shape=())\n", + " \n", + " # Compile the train / test code.\n", + " tf_train_model = autograph.to_graph(train_model)\n", + " train_model_ops = tf_train_model(\n", + " train_data, eval_data, batch_size, lower_cell, upper_cell, relu_layer, train_steps=100)\n", + " \n", + " # Compile the inference code.\n", + " tf_inference = autograph.to_graph(inference)\n", + " inference_ops = tf_inference(color_name_placeholder, lower_cell, upper_cell, relu_layer)\n", + " \n", + " with tf.Session() as sess:\n", + " sess.run(tf.global_variables_initializer())\n", + " \n", + " # Run training and testing.\n", + " sess.run(train_model_ops)\n", + " \n", + " # Run the inference loop.\n", + " run_input_loop(sess, inference_ops, color_name_placeholder)" ] }, { + "cell_type": "markdown", "metadata": { - "id": "AHJ2c47U-A5W", - "colab_type": "text" + "colab_type": "text", + "id": "AHJ2c47U-A5W" }, - "cell_type": "markdown", "source": [ "# Where do we go next?\n", "\n", - "Autograph is available in tensorflow.contrib, but it's still in its early stages. We're excited about the possibilities it brings — write your machine learning code in the flexible Eager style, but still enjoy all the benefits that come with running in graph mode. A beta version will be available soon -- stay tuned!" + "AutoGraph is still in its early stages, but is available in [tensorflow.contrib](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/autograph). We're excited about the possibilities it brings. New versions will be available soon — stay tuned!" ] } - ] + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "default_view": {}, + "name": "Dev Summit 2018 - Autograph", + "provenance": [ + { + "file_id": "1wCZUh73zTNs1jzzYjqoxMIdaBWCdKJ2K", + "timestamp": 1522238054357 + }, + { + "file_id": "1_HpC-RrmIv4lNaqeoslUeWaX8zH5IXaJ", + "timestamp": 1521743157199 + }, + { + "file_id": "1mjO2fQ2F9hxpAzw2mnrrUkcgfb7xSGW-", + "timestamp": 1520522344607 + } + ], + "version": "0.3.2", + "views": {} + }, + "kernelspec": { + "display_name": "Python 2", + "name": "python2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/tensorflow/contrib/autograph/examples/notebooks/graph_vs_ag_vs_eager_sum_speed_test.ipynb b/tensorflow/contrib/autograph/examples/notebooks/graph_vs_ag_vs_eager_sum_speed_test.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..32742bec7ee4a412aabb6640b5a1329353ebfc9d --- /dev/null +++ b/tensorflow/contrib/autograph/examples/notebooks/graph_vs_ag_vs_eager_sum_speed_test.ipynb @@ -0,0 +1,519 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "moMkWaT_TTHi" + }, + "source": [ + "This Colab illustrates the differing overhead* between a custom, vectorized graph operation and a loop over a tensor\n", + "that computes the same function. The loop is implemented in TensorFlow Eager mode using Python syntax and control-flow, and using AutoGraph which takes a python function and converts it into graph mode. In AutoGraph the Python loop is converted into a tf.while_loop.\n", + "\n", + "The actual computation, summing a small number of scalar values, takes very little time to compute, so the graphs below are showing the overhead of the differing approaches. As such, this is more of a \"micro-benchmark\" than a representation of real-world performance of the three approaches.\n", + "\n", + "*Note the differing scales of the included plots" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "a0X_rfvuav98" + }, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "EdxWv4Vn0ync" + }, + "outputs": [], + "source": [ + "!pip install -U -q tf-nightly" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "erq3_S7QsjkU" + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import\n", + "from __future__ import division\n", + "from __future__ import print_function\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "import math\n", + "import time\n", + "import random\n", + "from colabtools import adhoc_import\n", + "from tensorflow.contrib import autograph as ag\n", + "from tensorflow.python.framework import function" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1JgnsXooa2RP" + }, + "source": [ + "### Testing boilerplate" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "UyD5LLjVZzny" + }, + "outputs": [], + "source": [ + "# Test-only parameters. Test checks successful completion not correctness. \n", + "burn_ins = 1\n", + "trials = 1\n", + "batches = 2\n", + "max_elements = 2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4_NBL0RQa8gY" + }, + "source": [ + "### Speed comparison parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "Yq6daecyiJV5" + }, + "outputs": [], + "source": [ + "#@test {\"skip\": true} \n", + "burn_ins = 3 # Batches not counted in the average\n", + "trials = 10 # Batches run per vector-size (and averaged)\n", + "batches = 1000 # Number of random vectors summed over per trial\n", + "max_elements = 100 # Vectors of size 0 to this-1 will be executed and plotted" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "fiR8m13CbKH2" + }, + "source": [ + "### Random input" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "d8vrTlyNXuxc" + }, + "outputs": [], + "source": [ + "# Construct a random num x 1 tensor\n", + "def get_elements(num):\n", + " return tf.random_uniform(shape=(num, 1), maxval=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ILJ6SbF3bXFQ" + }, + "source": [ + "## Graph mode" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "vovRf597X55n" + }, + "outputs": [], + "source": [ + "def tf_sum(elements):\n", + " # Using custom vectorized op\n", + " return tf.reduce_sum(elements)\n", + "\n", + "def run_trial(num):\n", + " elements = get_elements(num)\n", + " return tf_sum(elements)\n", + "\n", + "\n", + "\n", + "graph_means = []\n", + "for num in range(max_elements):\n", + " with tf.Graph().as_default():\n", + " durations = []\n", + " foo = run_trial(num)\n", + " \n", + " with tf.Session() as sess:\n", + " \n", + " for _ in range(burn_ins):\n", + " for _ in range(batches):\n", + " sess.run(foo)\n", + " \n", + " for _ in range(trials):\n", + " \n", + " start = time.time()\n", + " for _ in range(batches):\n", + " sess.run(foo)\n", + " \n", + " duration = time.time() - start\n", + " durations.append(duration) \n", + " \n", + " graph_means.append(np.mean(durations)) " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 301 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 278, + "status": "ok", + "timestamp": 1532447361278, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "Jm9Blkyx90Eq", + "outputId": "d83cd51f-7e56-4d73-f7df-bb157dee46df" + }, + "outputs": [ + { + "data": { + "image/png": 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yBLDSqPFwsyczr6xFj5pSgleAu8n3Pm0cUKmQgzYaUFpRxf7TmeiqjCSmF13X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\nH4gzFtcS9o9MAAAAAElFTkSuQmCC\n", + "text/plain": [ + "\u003cmatplotlib.figure.Figure at 0x7f47b20dd690\u003e" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(graph_means)\n", + "plt.ylabel('Time (seconds)')\n", + "plt.xlabel('Length of vector')\n", + "_ = plt.title('Time to sum the elements of 1000 vectors (vectorized TF operation)')\n", + "_ = plt.ylim(ymin=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4KZg2WXjbhg5" + }, + "source": [ + "## AutoGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "UQJBQWbCbinm" + }, + "outputs": [], + "source": [ + "# Sum written using for loop and converted with AutoGraph\n", + "def sum_all(elements):\n", + " sum_ = 0.0\n", + " length = len(elements)\n", + " for i in tf.range(length): \n", + " sum_ += elements[i][0]\n", + " return sum_\n", + "\n", + "def run_trial(num):\n", + " elements = get_elements(num)\n", + " return sum_all(elements)\n", + " \n", + "ag_means = []\n", + "ag_run_trial = ag.to_graph(run_trial)\n", + "\n", + "for num in range(max_elements):\n", + " with tf.Graph().as_default():\n", + " durations = []\n", + " foo = ag_run_trial(num)\n", + " with tf.Session() as sess:\n", + " for _ in range(burn_ins):\n", + " for _ in range(batches):\n", + " sess.run(foo)\n", + " \n", + " for _ in range(trials):\n", + " start = time.time()\n", + " for _ in range(batches):\n", + " sess.run(foo)\n", + " \n", + " duration = time.time() - start\n", + " durations.append(duration)\n", + " ag_means.append(np.mean(durations))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 301 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 310, + "status": "ok", + "timestamp": 1532448438694, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "DLDOmrRW99v5", + "outputId": "ae0e0573-39db-4004-a064-efc618dbf867" + }, + "outputs": [ + { + "data": { + "image/png": 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R4OLiwl/+8hfatWtnzZCEEHVIid7I+0sOk55dxIrt5xkW3YK4bkHorhazds9F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\n", + "text/plain": [ + "\u003cmatplotlib.figure.Figure at 0x7f47b218dbd0\u003e" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(ag_means)\n", + "plt.ylabel('Time(s)')\n", + "plt.xlabel('Length of vector')\n", + "_ = plt.title('Time to sum the elements of 1000 vectors (AutoGraph)')\n", + "_ = plt.ylim(ymin=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "d7IAJ6Bwbk9t" + }, + "source": [ + "## Eager" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "XMu5-12yoOzY" + }, + "outputs": [], + "source": [ + "from tensorflow.python.eager import context" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "_vt9MzpyjQ4T" + }, + "outputs": [], + "source": [ + "# Sum written using for loop and run with tf.eager\n", + "def sum_all(elements):\n", + " sum_ = 0.0\n", + " length = elements.shape[0]\n", + " for i in tf.range(length): \n", + " sum_ += elements[i][0]\n", + " return sum_\n", + "\n", + "eager_means = []\n", + "for num in range(max_elements):\n", + " with context.eager_mode():\n", + " durations = []\n", + " for i in range(trials + burn_ins):\n", + " \n", + " start = time.time()\n", + " for _ in range(batches):\n", + " run_trial(num)\n", + " \n", + " if i \u003c burn_ins:\n", + " continue\n", + " \n", + " duration = time.time() - start\n", + " durations.append(duration)\n", + " eager_means.append(np.mean(durations))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 301 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 422, + "status": "ok", + "timestamp": 1532460024499, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "5gHVdMlD-A-T", + "outputId": "3b581cb7-7ef9-489c-92f1-3e52c0c2dc8a" + }, + "outputs": [ + { + "data": { + "image/png": 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hs+n8FZdBYbGOXj6uTBzc/ran9hZ1kyQJIcQdOZt8hU9+PkZ2fgkALg5WDOrp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\nRK5CYII=\n", + "text/plain": [ + "\u003cmatplotlib.figure.Figure at 0x7f47b8e3bd90\u003e" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(eager_means)\n", + "plt.ylabel('Time(s)')\n", + "plt.xlabel('Length of vector')\n", + "_ = plt.title('Time to sum the elements of 1000 vectors (Eager)')\n", + "_ = plt.ylim(ymin=0)" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "default_view": {}, + "name": "Autograph vs. Eager vs Graph sum", + "provenance": [ + { + "file_id": "1olZkm32B7n7pQwlIAXR0_w8fZhRHCtkX", + "timestamp": 1531755808890 + } + ], + "version": "0.3.2", + "views": {} + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow/contrib/autograph/examples/notebooks/workshop.ipynb b/tensorflow/contrib/autograph/examples/notebooks/workshop.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e7dfb13e15a8c30fd905f0ed9db9f0f67d9b6e88 --- /dev/null +++ b/tensorflow/contrib/autograph/examples/notebooks/workshop.ipynb @@ -0,0 +1,1129 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "u3B7Uh50lozN" + }, + "outputs": [], + "source": [ + "!pip install -U -q tf-nightly" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "qWUV0FYjDSKj" + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.contrib import autograph\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kGXS3UWBBNoc" + }, + "source": [ + "# 1. AutoGraph writes graph code for you\n", + "\n", + "[AutoGraph](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/README.md) helps you write complicated graph code using just plain Python -- behind the scenes, AutoGraph automatically transforms your code into the equivalent TF graph code. We support a large chunk of the Python language, which is growing. [Please see this document for what we currently support, and what we're working on](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/LIMITATIONS.md).\n", + "\n", + "Here's a quick example of how it works:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "aA3gOodCBkOw" + }, + "outputs": [], + "source": [ + "# Autograph can convert functions like this...\n", + "def g(x):\n", + " if x \u003e 0:\n", + " x = x * x\n", + " else:\n", + " x = 0.0\n", + " return x\n", + "\n", + "# ...into graph-building functions like this:\n", + "def tf_g(x):\n", + " with tf.name_scope('g'):\n", + "\n", + " def if_true():\n", + " with tf.name_scope('if_true'):\n", + " x_1, = x,\n", + " x_1 = x_1 * x_1\n", + " return x_1,\n", + "\n", + " def if_false():\n", + " with tf.name_scope('if_false'):\n", + " x_1, = x,\n", + " x_1 = 0.0\n", + " return x_1,\n", + "\n", + " x = autograph_utils.run_cond(tf.greater(x, 0), if_true, if_false)\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "I1RtBvoKBxq5" + }, + "outputs": [], + "source": [ + "# You can run your plain-Python code in graph mode,\n", + "# and get the same results out, but with all the benfits of graphs:\n", + "print('Original value: %2.2f' % g(9.0))\n", + "\n", + "# Generate a graph-version of g and call it:\n", + "tf_g = autograph.to_graph(g)\n", + "\n", + "with tf.Graph().as_default():\n", + " # The result works like a regular op: takes tensors in, returns tensors.\n", + " # You can inspect the graph using tf.get_default_graph().as_graph_def()\n", + " g_ops = tf_g(tf.constant(9.0))\n", + " with tf.Session() as sess:\n", + " print('Autograph value: %2.2f\\n' % sess.run(g_ops))\n", + "\n", + "\n", + "# You can view, debug and tweak the generated code:\n", + "print(autograph.to_code(g))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "m-jWmsCmByyw" + }, + "source": [ + "#### Automatically converting complex control flow\n", + "\n", + "AutoGraph can convert a large chunk of the Python language into equivalent graph-construction code, and we're adding new supported language features all the time. In this section, we'll give you a taste of some of the functionality in AutoGraph.\n", + "AutoGraph will automatically convert most Python control flow statements into their correct graph equivalent. \n", + " \n", + "We support common statements like `while`, `for`, `if`, `break`, `return` and more. You can even nest them as much as you like. Imagine trying to write the graph version of this code by hand:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "toxKBOXbB1ro" + }, + "outputs": [], + "source": [ + "# Continue in a loop\n", + "def f(l):\n", + " s = 0\n", + " for c in l:\n", + " if c % 2 \u003e 0:\n", + " continue\n", + " s += c\n", + " return s\n", + "\n", + "print('Original value: %d' % f([10,12,15,20]))\n", + "\n", + "tf_f = autograph.to_graph(f)\n", + "with tf.Graph().as_default():\n", + " with tf.Session():\n", + " print('Graph value: %d\\n\\n' % tf_f(tf.constant([10,12,15,20])).eval())\n", + "\n", + "print(autograph.to_code(f))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "FUJJ-WTdCGeq" + }, + "source": [ + "Try replacing the `continue` in the above code with `break` -- AutoGraph supports that as well! \n", + " \n", + "Let's try some other useful Python constructs, like `print` and `assert`. We automatically convert Python `assert` statements into the equivalent `tf.Assert` code. " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "IAOgh62zCPZ4" + }, + "outputs": [], + "source": [ + "def f(x):\n", + " assert x != 0, 'Do not pass zero!'\n", + " return x * x\n", + "\n", + "tf_f = autograph.to_graph(f)\n", + "with tf.Graph().as_default():\n", + " with tf.Session():\n", + " try:\n", + " print(tf_f(tf.constant(0)).eval())\n", + " except tf.errors.InvalidArgumentError as e:\n", + " print('Got error message:\\n%s' % e.message)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "KRu8iIPBCQr5" + }, + "source": [ + "You can also use plain Python `print` functions in in-graph" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "ySTsuxnqCTQi" + }, + "outputs": [], + "source": [ + "def f(n):\n", + " if n \u003e= 0:\n", + " while n \u003c 5:\n", + " n += 1\n", + " print(n)\n", + " return n\n", + "\n", + "tf_f = autograph.to_graph(f)\n", + "with tf.Graph().as_default():\n", + " with tf.Session():\n", + " tf_f(tf.constant(0)).eval()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "NqF0GT-VCVFh" + }, + "source": [ + "Appending to lists in loops also works (we create a tensor list ops behind the scenes)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "ABX070KwCczR" + }, + "outputs": [], + "source": [ + "def f(n):\n", + " z = []\n", + " # We ask you to tell us the element dtype of the list\n", + " autograph.set_element_type(z, tf.int32)\n", + " for i in range(n):\n", + " z.append(i)\n", + " # when you're done with the list, stack it\n", + " # (this is just like np.stack)\n", + " return autograph.stack(z)\n", + "\n", + "tf_f = autograph.to_graph(f)\n", + "with tf.Graph().as_default():\n", + " with tf.Session():\n", + " print(tf_f(tf.constant(3)).eval())\n", + "\n", + "print('\\n\\n'+autograph.to_code(f))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "iu5IF7n2Df7C" + }, + "outputs": [], + "source": [ + "def fizzbuzz(num):\n", + " if num % 3 == 0 and num % 5 == 0:\n", + " print('FizzBuzz')\n", + " elif num % 3 == 0:\n", + " print('Fizz')\n", + " elif num % 5 == 0:\n", + " print('Buzz')\n", + " else:\n", + " print(num)\n", + " return num" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "EExAjWuwDPpR" + }, + "outputs": [], + "source": [ + "tf_g = autograph.to_graph(fizzbuzz)\n", + "\n", + "with tf.Graph().as_default():\n", + " # The result works like a regular op: takes tensors in, returns tensors.\n", + " # You can inspect the graph using tf.get_default_graph().as_graph_def()\n", + " g_ops = tf_g(tf.constant(15))\n", + " with tf.Session() as sess:\n", + " sess.run(g_ops) \n", + " \n", + "# You can view, debug and tweak the generated code:\n", + "print('\\n')\n", + "print(autograph.to_code(fizzbuzz))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "SzpKGzVpBkph" + }, + "source": [ + "# De-graphify Exercises\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "8k23dxcSmmXq" + }, + "source": [ + "#### Easy print statements" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "dE1Vsmp-mlpK" + }, + "outputs": [], + "source": [ + "# See what happens when you turn AutoGraph off.\n", + "# Do you see the type or the value of x when you print it?\n", + "\n", + "# @autograph.convert()\n", + "def square_log(x):\n", + " x = x * x\n", + " print('Squared value of x =', x)\n", + " return x\n", + "\n", + "\n", + "with tf.Graph().as_default():\n", + " with tf.Session() as sess:\n", + " print(sess.run(square_log(tf.constant(4))))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_R-Q7BbxmkBF" + }, + "source": [ + "#### Convert the TensorFlow code into Python code for AutoGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "SwA11tO-yCvg" + }, + "outputs": [], + "source": [ + "def square_if_positive(x):\n", + " x = tf.cond(tf.greater(x, 0), lambda: x * x, lambda: x)\n", + " return x\n", + "\n", + "with tf.Session() as sess:\n", + " print(sess.run(square_if_positive(tf.constant(4))))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "GPmx4CNhyPI_" + }, + "outputs": [], + "source": [ + "@autograph.convert()\n", + "def square_if_positive(x):\n", + "\n", + " pass # TODO: fill it in!\n", + "\n", + "\n", + "with tf.Session() as sess:\n", + " print(sess.run(square_if_positive(tf.constant(4))))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "qqsjik-QyA9R" + }, + "source": [ + "#### Uncollapse to see answer" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "DaSmaWUEvMRv" + }, + "outputs": [], + "source": [ + "# Simple cond\n", + "@autograph.convert()\n", + "def square_if_positive(x):\n", + " if x \u003e 0:\n", + " x = x * x\n", + " return x\n", + "\n", + "with tf.Graph().as_default(): \n", + " with tf.Session() as sess:\n", + " print(sess.run(square_if_positive(tf.constant(4))))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "qj7am2I_xvTJ" + }, + "source": [ + "#### Nested If statement" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "4yyNOf-Twr6s" + }, + "outputs": [], + "source": [ + "def nearest_odd_square(x):\n", + "\n", + " def if_positive():\n", + " x1 = x * x\n", + " x1 = tf.cond(tf.equal(x1 % 2, 0), lambda: x1 + 1, lambda: x1)\n", + " return x1,\n", + "\n", + " x = tf.cond(tf.greater(x, 0), if_positive, lambda: x)\n", + " return x\n", + "\n", + "with tf.Graph().as_default():\n", + " with tf.Session() as sess:\n", + " print(sess.run(nearest_odd_square(tf.constant(4))))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "hqmh5b2VyU9w" + }, + "outputs": [], + "source": [ + "@autograph.convert()\n", + "def nearest_odd_square(x):\n", + "\n", + " pass # TODO: fill it in!\n", + "\n", + "\n", + "with tf.Session() as sess:\n", + " print(sess.run(nearest_odd_square(tf.constant(4))))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b9AXIkNLxp6J" + }, + "source": [ + "#### Uncollapse to reveal answer" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "8RlCVEpNxD91" + }, + "outputs": [], + "source": [ + "@autograph.convert()\n", + "def nearest_odd_square(x):\n", + " if x \u003e 0:\n", + " x = x * x\n", + " if x % 2 == 0:\n", + " x = x + 1\n", + " return x\n", + "\n", + "with tf.Graph().as_default():\n", + " with tf.Session() as sess:\n", + " print(sess.run(nearest_odd_square(tf.constant(4))))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jXAxjeBr1qWK" + }, + "source": [ + "#### Convert a while loop" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "kWkv7anlxoee" + }, + "outputs": [], + "source": [ + "# Convert a while loop\n", + "def square_until_stop(x, y):\n", + " x = tf.while_loop(lambda x: tf.less(x, y), lambda x: x * x, [x])\n", + " return x\n", + "\n", + "with tf.Graph().as_default():\n", + " with tf.Session() as sess:\n", + " print(sess.run(square_until_stop(tf.constant(4), tf.constant(100))))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "zVUsc1eA1u2K" + }, + "outputs": [], + "source": [ + "@autograph.convert()\n", + "def square_until_stop(x, y):\n", + "\n", + " pass # TODO: fill it in!\n", + "\n", + "\n", + "with tf.Graph().as_default():\n", + " with tf.Session() as sess:\n", + " print(sess.run(square_until_stop(tf.constant(4), tf.constant(100))))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "L2psuzPI02S9" + }, + "source": [ + "#### Uncollapse for the answer\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "ucmZyQVL03bF" + }, + "outputs": [], + "source": [ + "@autograph.convert()\n", + "def square_until_stop(x, y):\n", + " while x \u003c y:\n", + " x = x * x\n", + " return x\n", + "\n", + "with tf.Graph().as_default():\n", + " with tf.Session() as sess:\n", + " print(sess.run(square_until_stop(tf.constant(4), tf.constant(100))))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "FXB0Zbwl13PY" + }, + "source": [ + "#### Nested loop and conditional" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "clGymxdf15Ig" + }, + "outputs": [], + "source": [ + "@autograph.convert()\n", + "def argwhere_cumsum(x, threshold):\n", + " current_sum = 0.0\n", + " idx = 0\n", + "\n", + " for i in range(len(x)):\n", + " idx = i\n", + " if current_sum \u003e= threshold:\n", + " break\n", + " current_sum += x[i]\n", + " return idx\n", + "\n", + "n = 10\n", + "with tf.Graph().as_default():\n", + " with tf.Session() as sess:\n", + " idx = argwhere_cumsum(tf.ones(n), tf.constant(float(n / 2)))\n", + " print(sess.run(idx))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "i7PF-uId9lp5" + }, + "outputs": [], + "source": [ + "@autograph.convert()\n", + "def argwhere_cumsum(x, threshold):\n", + "\n", + " pass # TODO: fill it in!\n", + "\n", + "\n", + "n = 10\n", + "with tf.Graph().as_default():\n", + " with tf.Session() as sess:\n", + " idx = argwhere_cumsum(tf.ones(n), tf.constant(float(n / 2)))\n", + " print(sess.run(idx))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "weKFXAb615Vp" + }, + "source": [ + "#### Uncollapse to see answer" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "1sjaFcL717Ig" + }, + "outputs": [], + "source": [ + "@autograph.convert()\n", + "def argwhere_cumsum(x, threshold):\n", + " current_sum = 0.0\n", + " idx = 0\n", + " for i in range(len(x)):\n", + " idx = i\n", + " if current_sum \u003e= threshold:\n", + " break\n", + " current_sum += x[i]\n", + " return idx\n", + "\n", + "n = 10\n", + "with tf.Graph().as_default(): \n", + " with tf.Session() as sess:\n", + " idx = argwhere_cumsum(tf.ones(n), tf.constant(float(n / 2)))\n", + " print(sess.run(idx))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4LfnJjm0Bm0B" + }, + "source": [ + "# 3. Training MNIST in-graph\n", + "\n", + "Writing control flow in AutoGraph is easy, so running a training loop in a TensorFlow graph should be easy as well! \n", + "\n", + "Here, we show an example of training a simple Keras model on MNIST, where the entire training process -- loading batches, calculating gradients, updating parameters, calculating validation accuracy, and repeating until convergence -- is done in-graph." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Em5dzSUOtLRP" + }, + "source": [ + "#### Download data" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "xqoxumv0ssQW" + }, + "outputs": [], + "source": [ + "import gzip\n", + "import os\n", + "import shutil\n", + "\n", + "from six.moves import urllib\n", + "\n", + "\n", + "def download(directory, filename):\n", + " filepath = os.path.join(directory, filename)\n", + " if tf.gfile.Exists(filepath):\n", + " return filepath\n", + " if not tf.gfile.Exists(directory):\n", + " tf.gfile.MakeDirs(directory)\n", + " url = 'https://storage.googleapis.com/cvdf-datasets/mnist/' + filename + '.gz'\n", + " zipped_filepath = filepath + '.gz'\n", + " print('Downloading %s to %s' % (url, zipped_filepath))\n", + " urllib.request.urlretrieve(url, zipped_filepath)\n", + " with gzip.open(zipped_filepath, 'rb') as f_in, open(filepath, 'wb') as f_out:\n", + " shutil.copyfileobj(f_in, f_out)\n", + " os.remove(zipped_filepath)\n", + " return filepath\n", + "\n", + "\n", + "def dataset(directory, images_file, labels_file):\n", + " images_file = download(directory, images_file)\n", + " labels_file = download(directory, labels_file)\n", + "\n", + " def decode_image(image):\n", + " # Normalize from [0, 255] to [0.0, 1.0]\n", + " image = tf.decode_raw(image, tf.uint8)\n", + " image = tf.cast(image, tf.float32)\n", + " image = tf.reshape(image, [784])\n", + " return image / 255.0\n", + "\n", + " def decode_label(label):\n", + " label = tf.decode_raw(label, tf.uint8)\n", + " label = tf.reshape(label, [])\n", + " return tf.to_int32(label)\n", + "\n", + " images = tf.data.FixedLengthRecordDataset(\n", + " images_file, 28 * 28, header_bytes=16).map(decode_image)\n", + " labels = tf.data.FixedLengthRecordDataset(\n", + " labels_file, 1, header_bytes=8).map(decode_label)\n", + " return tf.data.Dataset.zip((images, labels))\n", + "\n", + "\n", + "def mnist_train(directory):\n", + " return dataset(directory, 'train-images-idx3-ubyte',\n", + " 'train-labels-idx1-ubyte')\n", + "\n", + "def mnist_test(directory):\n", + " return dataset(directory, 't10k-images-idx3-ubyte', 't10k-labels-idx1-ubyte')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "znmy4l8ntMvW" + }, + "source": [ + "#### Define the model" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "Pe-erWQdBoC5" + }, + "outputs": [], + "source": [ + "def mlp_model(input_shape):\n", + " model = tf.keras.Sequential((\n", + " tf.keras.layers.Dense(100, activation='relu', input_shape=input_shape),\n", + " tf.keras.layers.Dense(100, activation='relu'),\n", + " tf.keras.layers.Dense(10, activation='softmax')))\n", + " model.build()\n", + " return model\n", + "\n", + "\n", + "def predict(m, x, y):\n", + " y_p = m(x)\n", + " losses = tf.keras.losses.categorical_crossentropy(y, y_p)\n", + " l = tf.reduce_mean(losses)\n", + " accuracies = tf.keras.metrics.categorical_accuracy(y, y_p)\n", + " accuracy = tf.reduce_mean(accuracies)\n", + " return l, accuracy\n", + "\n", + "\n", + "def fit(m, x, y, opt):\n", + " l, accuracy = predict(m, x, y)\n", + " opt.minimize(l)\n", + " return l, accuracy\n", + "\n", + "\n", + "def setup_mnist_data(is_training, hp, batch_size):\n", + " if is_training:\n", + " ds = mnist_train('/tmp/autograph_mnist_data')\n", + " ds = ds.shuffle(batch_size * 10)\n", + " else:\n", + " ds = mnist_test('/tmp/autograph_mnist_data')\n", + " ds = ds.repeat()\n", + " ds = ds.batch(batch_size)\n", + " return ds\n", + "\n", + "\n", + "def get_next_batch(ds):\n", + " itr = ds.make_one_shot_iterator()\n", + " image, label = itr.get_next()\n", + " x = tf.to_float(tf.reshape(image, (-1, 28 * 28)))\n", + " y = tf.one_hot(tf.squeeze(label), 10)\n", + " return x, y" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "oeYV6mKnJGMr" + }, + "source": [ + "#### Define the training loop" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "3xtg_MMhJETd" + }, + "outputs": [], + "source": [ + "def train(train_ds, test_ds, hp):\n", + " m = mlp_model((28 * 28,))\n", + " opt = tf.train.MomentumOptimizer(hp.learning_rate, 0.9)\n", + "\n", + " # We'd like to save our losses to a list. In order for AutoGraph\n", + " # to convert these lists into their graph equivalent,\n", + " # we need to specify the element type of the lists.\n", + " train_losses = []\n", + " test_losses = []\n", + " train_accuracies = []\n", + " test_accuracies = []\n", + " autograph.set_element_type(train_losses, tf.float32)\n", + " autograph.set_element_type(test_losses, tf.float32)\n", + " autograph.set_element_type(train_accuracies, tf.float32)\n", + " autograph.set_element_type(test_accuracies, tf.float32)\n", + "\n", + " # This entire training loop will be run in-graph.\n", + " i = tf.constant(0)\n", + " while i \u003c hp.max_steps:\n", + " train_x, train_y = get_next_batch(train_ds)\n", + " test_x, test_y = get_next_batch(test_ds)\n", + "\n", + " step_train_loss, step_train_accuracy = fit(m, train_x, train_y, opt)\n", + " step_test_loss, step_test_accuracy = predict(m, test_x, test_y)\n", + "\n", + " if i % (hp.max_steps // 10) == 0:\n", + " print('Step', i, 'train loss:', step_train_loss, 'test loss:',\n", + " step_test_loss, 'train accuracy:', step_train_accuracy,\n", + " 'test accuracy:', step_test_accuracy)\n", + "\n", + " train_losses.append(step_train_loss)\n", + " test_losses.append(step_test_loss)\n", + " train_accuracies.append(step_train_accuracy)\n", + " test_accuracies.append(step_test_accuracy)\n", + "\n", + " i += 1\n", + "\n", + " # We've recorded our loss values and accuracies\n", + " # to a list in a graph with AutoGraph's help.\n", + " # In order to return the values as a Tensor,\n", + " # we need to stack them before returning them.\n", + " return (\n", + " autograph.stack(train_losses),\n", + " autograph.stack(test_losses),\n", + " autograph.stack(train_accuracies),\n", + " autograph.stack(test_accuracies),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "HYh6MSZyJOag" + }, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + " hp = tf.contrib.training.HParams(\n", + " learning_rate=0.05,\n", + " max_steps=500,\n", + " )\n", + " train_ds = setup_mnist_data(True, hp, 50)\n", + " test_ds = setup_mnist_data(False, hp, 1000)\n", + " tf_train = autograph.to_graph(train)\n", + " loss_tensors = tf_train(train_ds, test_ds, hp)\n", + "\n", + " with tf.Session() as sess:\n", + " sess.run(tf.global_variables_initializer())\n", + " (\n", + " train_losses,\n", + " test_losses,\n", + " train_accuracies,\n", + " test_accuracies\n", + " ) = sess.run(loss_tensors)\n", + "\n", + " plt.title('MNIST train/test losses')\n", + " plt.plot(train_losses, label='train loss')\n", + " plt.plot(test_losses, label='test loss')\n", + " plt.legend()\n", + " plt.xlabel('Training step')\n", + " plt.ylabel('Loss')\n", + " plt.show()\n", + " plt.title('MNIST train/test accuracies')\n", + " plt.plot(train_accuracies, label='train accuracy')\n", + " plt.plot(test_accuracies, label='test accuracy')\n", + " plt.legend(loc='lower right')\n", + " plt.xlabel('Training step')\n", + " plt.ylabel('Accuracy')\n", + " plt.show()" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "qqsjik-QyA9R", + "b9AXIkNLxp6J", + "L2psuzPI02S9", + "weKFXAb615Vp", + "Em5dzSUOtLRP" + ], + "default_view": {}, + "name": "AutoGraph Workshop.ipynb", + "provenance": [ + { + "file_id": "1kE2gz_zuwdYySL4K2HQSz13uLCYi-fYP", + "timestamp": 1530563781803 + } + ], + "version": "0.3.2", + "views": {} + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow/contrib/autograph/impl/api.py b/tensorflow/contrib/autograph/impl/api.py index c7401c7df126b73ca22cdaf74a2f1fd6149d7545..0adff76a9f2cae9480954a32dd1a81458bab37f7 100644 --- a/tensorflow/contrib/autograph/impl/api.py +++ b/tensorflow/contrib/autograph/impl/api.py @@ -23,7 +23,6 @@ from functools import wraps from enum import Enum # pylint:disable=g-bad-import-order -import gast import six # pylint:enable=g-bad-import-order @@ -99,6 +98,7 @@ def do_not_convert(run_as=RunMode.GRAPH, return_dtypes=None): Returns: A decorator that wraps the original function. """ + def decorator(f): """Decorator implementation.""" @@ -109,8 +109,7 @@ def do_not_convert(run_as=RunMode.GRAPH, return_dtypes=None): @wraps(f) def py_func_wrapper(*args, **kwargs): if kwargs: - raise NotImplementedError( - 'RunMode.PY_FUNC does not yet support kwargs') + raise NotImplementedError('RunMode.PY_FUNC does not yet support kwargs') # TODO(mdan): Add support for kwargs. return py_func.wrap_py_func( f, return_dtypes, args, kwargs, use_dummy_return=not return_dtypes) @@ -231,7 +230,10 @@ def to_graph(e, Returns: A function with a signature identical to `o`, but which when executed it - creates TF a graph that has the same functionality as the original entity. + creates TF a graph that has the same functionality as the original entity. + Raises: + ValueError: If the converted function defines or refers to symbol names that + are reserved for AutoGraph. """ program_ctx = converter.ProgramContext( recursive=recursive, @@ -242,24 +244,41 @@ def to_graph(e, _, name, namespace = conversion.entity_to_graph(e, program_ctx, arg_values, arg_types) - module = gast.Module([]) + nodes = [] for dep in reversed(program_ctx.dependency_cache.values()): - module.body.append(dep) - compiled_node, compiled_src = compiler.ast_to_object( - module, source_prefix=program_ctx.required_imports) + nodes.extend(dep) + compiled_module, compiled_src = compiler.ast_to_object( + nodes, + source_prefix=program_ctx.required_imports, + include_source_map=True) # The compiled code should see everything the entry entity saw. # TODO(mdan): This might not work well if the call tree spans modules? for key, val in namespace.items(): # Avoid overwriting entities that have been transformed. - if key not in compiled_node.__dict__: - compiled_node.__dict__[key] = val - compiled_fn = getattr(compiled_node, name) + if key not in compiled_module.__dict__: + compiled_module.__dict__[key] = val + compiled = getattr(compiled_module, name) + + # Need this so the source_mapping attribute is available for the context + # manager to access for runtime errors. + # + # Note that compiler.ast_to_object attaches the source map 'ag_source_map__' + # symbol to the compiled module. + # TODO(mdan): Record this statically in the generated code. + # TODO(mdan): Rename this attribute to 'autograph_info__' + source_map_attribute_name = 'ag_source_map' + if getattr(compiled, source_map_attribute_name, None) is not None: + raise ValueError('cannot convert %s because is has an attribute ' + '"%s", which is reserved for AutoGraph.' % + (compiled, source_map_attribute_name)) + setattr(compiled, source_map_attribute_name, + compiled_module.__dict__['ag_source_map__']) if verbose: logging.info('Compiled output of %s:\n\n%s\n', e, compiled_src) - return compiled_fn + return compiled def to_code(e, diff --git a/tensorflow/contrib/autograph/impl/api_test.py b/tensorflow/contrib/autograph/impl/api_test.py index 994309333209586001c9369322ec3ddeee0a508e..754baa87b0c3e4fa071923686078ac7235076533 100644 --- a/tensorflow/contrib/autograph/impl/api_test.py +++ b/tensorflow/contrib/autograph/impl/api_test.py @@ -206,8 +206,8 @@ class ApiTest(test.TestCase): return x with self.test_session() as sess: - x = api.converted_call( - test_fn, False, False, {}, constant_op.constant(-1)) + x = api.converted_call(test_fn, False, False, {}, + constant_op.constant(-1)) self.assertEqual(1, sess.run(x)) def test_converted_call_method(self): @@ -274,12 +274,26 @@ class ApiTest(test.TestCase): return self.x with self.test_session() as sess: - tc = api.converted_call( - TestClass, False, False, {}, constant_op.constant(-1)) + tc = api.converted_call(TestClass, False, False, {}, + constant_op.constant(-1)) # tc is now a converted object. x = tc.test_method() self.assertEqual(1, sess.run(x)) + def test_converted_call_already_converted(self): + + def f(x): + return x == 0 + + with self.test_session() as sess: + x = api.converted_call(f, False, False, {}, constant_op.constant(0)) + self.assertTrue(sess.run(x)) + + converted_f = api.to_graph(f) + x = api.converted_call(converted_f, False, False, {}, + constant_op.constant(0)) + self.assertTrue(sess.run(x)) + def test_to_graph_basic(self): def test_fn(x, s): @@ -305,6 +319,13 @@ class ApiTest(test.TestCase): # Just check that it is parseable Python code. self.assertIsNotNone(parser.parse_str(compiled_code)) + def test_source_map_attribute_present(self): + + def test_fn(y): + return y**2 + + self.assertTrue(hasattr(api.to_graph(test_fn), 'ag_source_map')) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/autograph/impl/conversion.py b/tensorflow/contrib/autograph/impl/conversion.py index 776d19f672ebbd6b88985dda157434f2046d87e7..afb10d4d8bec648a43615762f780f989eb8de950 100644 --- a/tensorflow/contrib/autograph/impl/conversion.py +++ b/tensorflow/contrib/autograph/impl/conversion.py @@ -28,26 +28,28 @@ from tensorflow.contrib.autograph.converters import asserts from tensorflow.contrib.autograph.converters import break_statements from tensorflow.contrib.autograph.converters import builtin_functions from tensorflow.contrib.autograph.converters import call_trees +from tensorflow.contrib.autograph.converters import conditional_expressions from tensorflow.contrib.autograph.converters import continue_statements from tensorflow.contrib.autograph.converters import control_flow from tensorflow.contrib.autograph.converters import decorators -from tensorflow.contrib.autograph.converters import ifexp +from tensorflow.contrib.autograph.converters import directives +from tensorflow.contrib.autograph.converters import error_handlers from tensorflow.contrib.autograph.converters import lists from tensorflow.contrib.autograph.converters import logical_expressions from tensorflow.contrib.autograph.converters import name_scopes +from tensorflow.contrib.autograph.converters import return_statements from tensorflow.contrib.autograph.converters import side_effect_guards -from tensorflow.contrib.autograph.converters import single_return from tensorflow.contrib.autograph.converters import slices from tensorflow.contrib.autograph.core import config from tensorflow.contrib.autograph.core import converter +from tensorflow.contrib.autograph.core import errors from tensorflow.contrib.autograph.pyct import ast_util from tensorflow.contrib.autograph.pyct import inspect_utils +from tensorflow.contrib.autograph.pyct import origin_info from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import templates from tensorflow.contrib.autograph.pyct import transformer -from tensorflow.contrib.autograph.pyct.static_analysis import activity -from tensorflow.contrib.autograph.pyct.static_analysis import live_values -from tensorflow.contrib.autograph.pyct.static_analysis import type_info from tensorflow.python.util import tf_inspect @@ -69,6 +71,8 @@ def is_whitelisted_for_graph(o): for prefix, in config.DEFAULT_UNCOMPILED_MODULES: if m.__name__.startswith(prefix): return True + if hasattr(o, 'autograph_info__'): + return True return False @@ -119,7 +123,16 @@ def entity_to_graph(o, program_ctx, arg_values, arg_types): 'Entity "%s" has unsupported type "%s". Only functions and classes are ' 'supported for now.' % (o, type(o))) + # TODO(mdan): This is temporary. it should be created using a converter. + # TODO(mdan): The attribute should be added with a helper, not directly. + # The helper can ensure there are no collisions. + template = ''' + entity.autograph_info__ = {} + ''' + node.extend(templates.replace(template, entity=name)) + program_ctx.add_to_cache(o, node) + if program_ctx.recursive: while True: candidate = None @@ -157,12 +170,13 @@ def class_to_graph(c, program_ctx): program_ctx=program_ctx, arg_values={}, arg_types={'self': (c.__name__, c)}, - owner_type=c) + owner_type=c, + rewrite_errors=False) if class_namespace is None: class_namespace = namespace else: class_namespace.update(namespace) - converted_members[m] = node + converted_members[m] = node[0] namer = program_ctx.new_namer(class_namespace) class_name = namer.compiled_class_name(c.__name__, c) @@ -173,10 +187,10 @@ def class_to_graph(c, program_ctx): # program_ctx.update_name_map(namer)). output_nodes = [] renames = {} - bases = [] + base_names = [] for base in c.__bases__: if isinstance(object, base): - bases.append('object') + base_names.append('object') continue if is_whitelisted_for_graph(base): alias = namer.new_symbol(base.__name__, ()) @@ -188,28 +202,28 @@ def class_to_graph(c, program_ctx): else: # This will trigger a conversion into a class with this name. alias = namer.compiled_class_name(base.__name__, base) - bases.append(alias) + base_names.append(alias) renames[qual_names.QN(base.__name__)] = qual_names.QN(alias) program_ctx.update_name_map(namer) # Generate the definition of the converted class. - output_nodes.append( - gast.ClassDef( - class_name, - bases=bases, - keywords=[], - body=list(converted_members.values()), - decorator_list=[])) - node = gast.Module(output_nodes) - + bases = [gast.Name(n, gast.Load(), None) for n in base_names] + class_def = gast.ClassDef( + class_name, + bases=bases, + keywords=[], + body=list(converted_members.values()), + decorator_list=[]) # Make a final pass to replace references to the class or its base classes. # Most commonly, this occurs when making super().__init__() calls. # TODO(mdan): Making direct references to superclass' superclass will fail. - node = qual_names.resolve(node) + class_def = qual_names.resolve(class_def) renames[qual_names.QN(c.__name__)] = qual_names.QN(class_name) - node = ast_util.rename_symbols(node, renames) + class_def = ast_util.rename_symbols(class_def, renames) - return node, class_name, class_namespace + output_nodes.append(class_def) + + return output_nodes, class_name, class_namespace def _add_reserved_symbol(namespace, name, entity): @@ -231,6 +245,8 @@ def _add_self_references(namespace, autograph_module): ag_internal = imp.new_module('autograph') ag_internal.converted_call = autograph_module.converted_call ag_internal.utils = utils + ag_internal.rewrite_graph_construction_error = ( + errors.rewrite_graph_construction_error) # TODO(mdan): Add safeguards against name clashes. # We don't want to create a submodule because we want the operators to be # accessible as ag__. @@ -239,11 +255,17 @@ def _add_self_references(namespace, autograph_module): _add_reserved_symbol(namespace, 'ag__', ag_internal) -def function_to_graph(f, program_ctx, arg_values, arg_types, owner_type=None): +def function_to_graph(f, + program_ctx, + arg_values, + arg_types, + owner_type=None, + rewrite_errors=True): """Specialization of `entity_to_graph` for callable functions.""" + node, source = parser.parse_entity(f) node = node.body[0] - + origin_info.resolve(node, source, f) namespace = inspect_utils.getnamespace(f) _add_self_references(namespace, program_ctx.autograph_module) namer = program_ctx.new_namer(namespace) @@ -256,38 +278,29 @@ def function_to_graph(f, program_ctx, arg_values, arg_types, owner_type=None): arg_types=arg_types, owner_type=owner_type) context = converter.EntityContext(namer, entity_info, program_ctx) - node = node_to_graph(node, context) + node = node_to_graph(node, context, rewrite_errors=rewrite_errors) - # TODO(mdan): This somewhat duplicates the call rename logic in call_treest.py + # TODO(mdan): This somewhat duplicates the call rename logic in call_trees.py new_name, did_rename = namer.compiled_function_name(f.__name__, f, owner_type) if not did_rename: new_name = f.__name__ if node.name != f.__name__: raise NotImplementedError('Strange corner case. Send us offending code!') - node.name = new_name + program_ctx.update_name_map(namer) # TODO(mdan): Use this at compilation. - return node, new_name, namespace - - -def _apply_transformer(node, context, converter_module): - # TODO(mdan): Clear static analysis here. - node = qual_names.resolve(node) - node = activity.resolve(node, context.info, None) - node = live_values.resolve(node, context.info, config.PYTHON_LITERALS) - node = type_info.resolve(node, context.info) - node = converter_module.transform(node, context) - return node + return [node], new_name, namespace -def node_to_graph(node, context): +def node_to_graph(node, context, rewrite_errors=True): """Convert Python code to equivalent TF graph mode code. Args: node: AST, the code to convert. context: converter.EntityContext + rewrite_errors: Boolean, whether or not to rewrite the error traceback. Returns: A tuple (node, deps): @@ -295,28 +308,33 @@ def node_to_graph(node, context): * deps: A set of strings, the fully qualified names of entity dependencies that this node has. """ - # TODO(mdan): Verify arguments for correctness. + # TODO(mdan): Insert list_comprehensions somewhere. - node = _apply_transformer(node, context, ifexp) + node = converter.standard_analysis(node, context, is_initial=True) # Past this point, line numbers are no longer accurate so we ignore the # source. # TODO(mdan): Is it feasible to reconstruct intermediate source code? context.info.source_code = None - node = _apply_transformer(node, context, decorators) - node = _apply_transformer(node, context, break_statements) - node = _apply_transformer(node, context, asserts) + + node = converter.apply_(node, context, decorators) + node = converter.apply_(node, context, directives) + node = converter.apply_(node, context, break_statements) + node = converter.apply_(node, context, asserts) # Note: sequencing continue canonicalization before for loop one avoids # dealing with the extra loop increment operation that the for # canonicalization creates. - node = _apply_transformer(node, context, continue_statements) + node = converter.apply_(node, context, continue_statements) context.info.namespace['len'] = len - node = _apply_transformer(node, context, single_return) - node = _apply_transformer(node, context, lists) - node = _apply_transformer(node, context, slices) - node = _apply_transformer(node, context, builtin_functions) - node = _apply_transformer(node, context, call_trees) - node = _apply_transformer(node, context, control_flow) - node = _apply_transformer(node, context, logical_expressions) - node = _apply_transformer(node, context, side_effect_guards) - node = _apply_transformer(node, context, name_scopes) + node = converter.apply_(node, context, return_statements) + node = converter.apply_(node, context, lists) + node = converter.apply_(node, context, slices) + node = converter.apply_(node, context, builtin_functions) + node = converter.apply_(node, context, call_trees) + node = converter.apply_(node, context, control_flow) + node = converter.apply_(node, context, conditional_expressions) + node = converter.apply_(node, context, logical_expressions) + node = converter.apply_(node, context, side_effect_guards) + node = converter.apply_(node, context, name_scopes) + if rewrite_errors: + node = converter.apply_(node, context, error_handlers) return node diff --git a/tensorflow/contrib/autograph/impl/conversion_test.py b/tensorflow/contrib/autograph/impl/conversion_test.py index f5279298afdcd406a9a6762e58367cea8ca63141..1c5d4d09c4e74a3f88b47186aa563419aa7dcb07 100644 --- a/tensorflow/contrib/autograph/impl/conversion_test.py +++ b/tensorflow/contrib/autograph/impl/conversion_test.py @@ -60,10 +60,11 @@ class ConversionTest(test.TestCase): return a + b program_ctx = self._simple_program_ctx() - ast, name, ns = conversion.entity_to_graph(f, program_ctx, None, None) - self.assertTrue(isinstance(ast, gast.FunctionDef), ast) + nodes, name, ns = conversion.entity_to_graph(f, program_ctx, None, None) + fn_node, _ = nodes + self.assertIsInstance(fn_node, gast.FunctionDef) self.assertEqual('tf__f', name) - self.assertTrue(ns['b'] is b) + self.assertIs(ns['b'], b) def test_entity_to_graph_call_tree(self): @@ -78,12 +79,11 @@ class ConversionTest(test.TestCase): self.assertTrue(f in program_ctx.dependency_cache) self.assertTrue(g in program_ctx.dependency_cache) - self.assertEqual('tf__f', program_ctx.dependency_cache[f].name) - # need the extra .body[0] in order to step past the with tf.name_scope('f') - # that is added automatically - self.assertEqual( - 'tf__g', program_ctx.dependency_cache[f].body[0].body[0].value.func.id) - self.assertEqual('tf__g', program_ctx.dependency_cache[g].name) + f_node = program_ctx.dependency_cache[f][0] + g_node = program_ctx.dependency_cache[g][0] + self.assertEqual('tf__f', f_node.name) + self.assertEqual('tf__g', f_node.body[0].body[0].body[0].value.func.id) + self.assertEqual('tf__g', g_node.name) def test_entity_to_graph_class_hierarchy(self): @@ -115,10 +115,12 @@ class ConversionTest(test.TestCase): self.assertTrue(TestBase in program_ctx.dependency_cache) self.assertTrue(TestSubclass in program_ctx.dependency_cache) + # The returned nodes will include: + # , , self.assertEqual('TfTestBase', - program_ctx.dependency_cache[TestBase].body[-1].name) + program_ctx.dependency_cache[TestBase][-2].name) self.assertEqual('TfTestSubclass', - program_ctx.dependency_cache[TestSubclass].body[-1].name) + program_ctx.dependency_cache[TestSubclass][-2].name) def test_entity_to_graph_class_hierarchy_whitelisted(self): @@ -137,10 +139,11 @@ class ConversionTest(test.TestCase): self.assertTrue(TestSubclass in program_ctx.dependency_cache) self.assertFalse(training.Model in program_ctx.dependency_cache) self.assertEqual( - 'Model', - program_ctx.dependency_cache[TestSubclass].body[0].names[0].name) + 'Model', program_ctx.dependency_cache[TestSubclass][0].names[0].name) + # The returned nodes will include: + # , , self.assertEqual('TfTestSubclass', - program_ctx.dependency_cache[TestSubclass].body[-1].name) + program_ctx.dependency_cache[TestSubclass][-2].name) def test_entity_to_graph_lambda(self): f = lambda a: a diff --git a/tensorflow/contrib/autograph/lang/special_functions.py b/tensorflow/contrib/autograph/lang/special_functions.py index 11135295a7966bc5d693676fcc71fe43791f2e99..6149cbbd6c9214fb6989bdcae430286445b1db28 100644 --- a/tensorflow/contrib/autograph/lang/special_functions.py +++ b/tensorflow/contrib/autograph/lang/special_functions.py @@ -26,6 +26,43 @@ from __future__ import print_function from tensorflow.contrib.autograph.operators import data_structures +def tensor_list(elements, + element_dtype=None, + element_shape=None, + use_tensor_array=False): + """Creates an tensor list and populates it with the given elements. + + This function provides a more uniform access to tensor lists and tensor + arrays, and allows optional initialization. + + Note: this function is a simplified wrapper. If you need greater control, + it is recommended to use the underlying implementation directly. + + Args: + elements: Iterable[tf.Tensor, ...], the elements to initially fill the list + with + element_dtype: Optional[tf.DType], data type for the elements in the list; + required if the list is empty + element_shape: Optional[tf.TensorShape], shape for the elements in the list; + required if the list is empty + use_tensor_array: bool, whether to use the more compatible but restrictive + tf.TensorArray implementation + Returns: + Union[tf.Tensor, tf.TensorArray], the new list. + Raises: + ValueError: for invalid arguments + """ + if not (elements or (element_dtype and element_shape)): + raise ValueError( + 'element_dtype and element_shape are required for empty lists') + if use_tensor_array: + return data_structures.tf_tensor_array_new(elements, element_dtype, + element_shape) + else: + return data_structures.tf_tensor_list_new(elements, element_dtype, + element_shape) + + def stack(list_or_tensor, element_dtype=None, strict=True): """Stacks the input, if it admits the notion of stacking. diff --git a/tensorflow/contrib/autograph/lang/special_functions_test.py b/tensorflow/contrib/autograph/lang/special_functions_test.py index a49cb6407517b634e0f1259fccda03d4ed18e83f..db492cc5c689155bf7d426cbfee320130f4bda9f 100644 --- a/tensorflow/contrib/autograph/lang/special_functions_test.py +++ b/tensorflow/contrib/autograph/lang/special_functions_test.py @@ -28,7 +28,23 @@ from tensorflow.python.platform import test class SpecialFunctionsTest(test.TestCase): - def test_basic(self): + def test_tensor_list_from_elements(self): + elements = [constant_op.constant([1, 2]), constant_op.constant([3, 4])] + + l = special_functions.tensor_list(elements) + sl = list_ops.tensor_list_stack(l, element_dtype=dtypes.int32) + with self.test_session() as sess: + self.assertAllEqual(sess.run(sl), [[1, 2], [3, 4]]) + + def test_tensor_list_array_from_elements(self): + elements = [constant_op.constant([1, 2]), constant_op.constant([3, 4])] + + l = special_functions.tensor_list(elements, use_tensor_array=True) + sl = l.stack() + with self.test_session() as sess: + self.assertAllEqual(sess.run(sl), [[1, 2], [3, 4]]) + + def test_stack(self): self.assertEqual(special_functions.stack(1, strict=False), 1) self.assertListEqual( special_functions.stack([1, 2, 3], strict=False), [1, 2, 3]) diff --git a/tensorflow/contrib/autograph/operators/__init__.py b/tensorflow/contrib/autograph/operators/__init__.py index c900fd6af2ea5dfb419f731ee8d8822d68424b27..392cb60bcc44c0f554defcddc50c4afbdaa25067 100644 --- a/tensorflow/contrib/autograph/operators/__init__.py +++ b/tensorflow/contrib/autograph/operators/__init__.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""This module implements operators that we overload. +"""This module implements operators that AutoGraph overloads. Note that "operator" is used loosely here, and includes control structures like conditionals and loops, implemented in functional form, using for example diff --git a/tensorflow/contrib/autograph/operators/data_structures.py b/tensorflow/contrib/autograph/operators/data_structures.py index 06d8727b0fcc30b532b3f11281cd1a83c51ac8bc..cc0a3c35448980945f2975f829f9d9421afdb65d 100644 --- a/tensorflow/contrib/autograph/operators/data_structures.py +++ b/tensorflow/contrib/autograph/operators/data_structures.py @@ -28,7 +28,6 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import list_ops from tensorflow.python.ops import tensor_array_ops -from tensorflow.python.ops import variables # TODO(mdan): Once control flow supports objects, repackage as a class. @@ -48,29 +47,101 @@ def new_list(iterable=None): else: elements = () - # TODO(mdan): Extend these criteria. - if any(isinstance(el, variables.Variable) for el in elements): + if elements: + # When the list contains elements, it is assumed to be a "Python" lvalue + # list. return _py_list_new(elements) - return _tf_tensor_list_new(elements) + return tf_tensor_list_new(elements) -def _tf_tensor_list_new(elements): +def tf_tensor_array_new(elements, element_dtype=None, element_shape=None): """Overload of new_list that stages a Tensor list creation.""" elements = tuple(ops.convert_to_tensor(el) for el in elements) + + all_dtypes = set(el.dtype for el in elements) + if len(all_dtypes) == 1: + inferred_dtype, = tuple(all_dtypes) + if element_dtype is not None and element_dtype != inferred_dtype: + raise ValueError( + 'incompatible dtype; specified: {}, inferred from {}: {}'.format( + element_dtype, elements, inferred_dtype)) + elif len(all_dtypes) > 1: + raise ValueError( + 'TensorArray requires all elements to have the same dtype:' + ' {}'.format(elements)) + else: + if element_dtype is None: + raise ValueError('dtype is required to create an empty TensorArray') + + all_shapes = set(tuple(el.shape.as_list()) for el in elements) + if len(all_shapes) == 1: + inferred_shape, = tuple(all_shapes) + if element_shape is not None and element_shape != inferred_shape: + raise ValueError( + 'incompatible shape; specified: {}, inferred from {}: {}'.format( + element_shape, elements, inferred_shape)) + elif len(all_shapes) > 1: + raise ValueError( + 'TensorArray requires all elements to have the same shape:' + ' {}'.format(elements)) + # TODO(mdan): We may want to allow different shapes with infer_shape=False. + else: + inferred_shape = None + + if element_dtype is None: + element_dtype = inferred_dtype + if element_shape is None: + element_shape = inferred_shape + + l = tensor_array_ops.TensorArray( + dtype=element_dtype, + size=len(elements), + dynamic_size=True, + infer_shape=(element_shape is None), + element_shape=element_shape) + for i, el in enumerate(elements): + l = l.write(i, el) + return l + + +def tf_tensor_list_new(elements, element_dtype=None, element_shape=None): + """Overload of new_list that stages a Tensor list creation.""" + elements = tuple(ops.convert_to_tensor(el) for el in elements) + all_dtypes = set(el.dtype for el in elements) if len(all_dtypes) == 1: - element_dtype = tuple(all_dtypes)[0] + inferred_dtype = tuple(all_dtypes)[0] + if element_dtype is not None and element_dtype != inferred_dtype: + raise ValueError( + 'incompatible dtype; specified: {}, inferred from {}: {}'.format( + element_dtype, elements, inferred_dtype)) else: # Heterogeneous lists are ok. - element_dtype = dtypes.variant + if element_dtype is not None: + raise ValueError( + 'specified dtype {} is inconsistent with that of elements {}'.format( + element_dtype, elements)) + inferred_dtype = dtypes.variant - # TODO(mdan): This may fail for elements of variable shapes. all_shapes = set(tuple(el.shape.as_list()) for el in elements) if len(all_shapes) == 1: - element_shape = array_ops.shape(elements[0]) + inferred_shape = array_ops.shape(elements[0]) + if element_shape is not None and element_shape != inferred_shape: + raise ValueError( + 'incompatible shape; specified: {}, inferred from {}: {}'.format( + element_shape, elements, inferred_shape)) else: # Heterogeneous lists are ok. - element_shape = constant_op.constant(-1) # unknown shape, by convention + if element_shape is not None: + raise ValueError( + 'specified shape {} is inconsistent with that of elements {}'.format( + element_shape, elements)) + inferred_shape = constant_op.constant(-1) # unknown shape, by convention + + if element_dtype is None: + element_dtype = inferred_dtype + if element_shape is None: + element_shape = inferred_shape l = list_ops.empty_tensor_list( element_shape=element_shape, element_dtype=element_dtype) diff --git a/tensorflow/contrib/autograph/operators/data_structures_test.py b/tensorflow/contrib/autograph/operators/data_structures_test.py index 8bbb52d6c10b241ec754c7dea599fa15a869595f..7ea11a839b6070f6c6dfdd8a8f7939923a7d9eaa 100644 --- a/tensorflow/contrib/autograph/operators/data_structures_test.py +++ b/tensorflow/contrib/autograph/operators/data_structures_test.py @@ -37,10 +37,51 @@ class ListTest(test.TestCase): def test_new_list_tensor(self): l = data_structures.new_list([3, 4, 5]) + self.assertAllEqual(l, [3, 4, 5]) + + def test_tf_tensor_list_new(self): + l = data_structures.tf_tensor_list_new([3, 4, 5]) t = list_ops.tensor_list_stack(l, element_dtype=dtypes.int32) with self.test_session() as sess: self.assertAllEqual(sess.run(t), [3, 4, 5]) + def test_tf_tensor_list_new_illegal_input(self): + with self.assertRaises(ValueError): + data_structures.tf_tensor_list_new([3, 4.0]) + # TODO(mdan): It might make more sense to type cast in this case. + with self.assertRaises(ValueError): + data_structures.tf_tensor_list_new([3, 4], element_dtype=dtypes.float32) + # Tensor lists do support heterogeneous lists. + self.assertIsNot(data_structures.tf_tensor_list_new([3, [4, 5]]), None) + with self.assertRaises(ValueError): + data_structures.tf_tensor_list_new([3, 4], element_shape=(2,)) + with self.assertRaises(ValueError): + data_structures.tf_tensor_list_new([], element_shape=(2,)) + with self.assertRaises(ValueError): + data_structures.tf_tensor_list_new([], element_dtype=dtypes.float32) + + def test_tf_tensor_array_new(self): + l = data_structures.tf_tensor_array_new([3, 4, 5]) + t = l.stack() + with self.test_session() as sess: + self.assertAllEqual(sess.run(t), [3, 4, 5]) + + def test_tf_tensor_array_new_illegal_input(self): + with self.assertRaises(ValueError): + data_structures.tf_tensor_array_new([3, 4.0]) + with self.assertRaises(ValueError): + data_structures.tf_tensor_array_new([3, 4], element_dtype=dtypes.float32) + with self.assertRaises(ValueError): + data_structures.tf_tensor_array_new([3, [4, 5]]) + with self.assertRaises(ValueError): + data_structures.tf_tensor_array_new([3, 4], element_shape=(2,)) + with self.assertRaises(ValueError): + data_structures.tf_tensor_array_new([], element_shape=(2,)) + # TAs can infer the shape. + self.assertIsNot( + data_structures.tf_tensor_array_new([], element_dtype=dtypes.float32), + None) + def test_append_tensor_list(self): l = data_structures.new_list() x = constant_op.constant([1, 2, 3]) diff --git a/tensorflow/contrib/autograph/pyct/BUILD b/tensorflow/contrib/autograph/pyct/BUILD index a49a4ed05ca99a5c9784cfc132784890e63a94de..ddadc6b96e8eb5417bfa1676ae304f7cbdedd92b 100644 --- a/tensorflow/contrib/autograph/pyct/BUILD +++ b/tensorflow/contrib/autograph/pyct/BUILD @@ -25,6 +25,7 @@ py_library( "cfg.py", "compiler.py", "inspect_utils.py", + "origin_info.py", "parser.py", "pretty_printer.py", "qual_names.py", @@ -98,6 +99,16 @@ py_test( ], ) +py_test( + name = "origin_info_test", + srcs = ["origin_info_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":pyct", + "//tensorflow/python:client_testlib", + ], +) + py_test( name = "parser_test", srcs = ["parser_test.py"], diff --git a/tensorflow/contrib/autograph/pyct/anno.py b/tensorflow/contrib/autograph/pyct/anno.py index ae861627fd65cca057e7bf1af41424e605d4b7a1..1a52110ef36bbc0888e03cc25b3717822cb75c16 100644 --- a/tensorflow/contrib/autograph/pyct/anno.py +++ b/tensorflow/contrib/autograph/pyct/anno.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Handling annotations on AST nodes. +"""AST node annotation support. Adapted from Tangent. """ @@ -21,37 +21,90 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from enum import Enum +import enum +# pylint:disable=g-bad-import-order +import gast +# pylint:enable=g-bad-import-order -class NoValue(Enum): + +# TODO(mdan): Shorten the names. +# These names are heavily used, and anno.blaa +# TODO(mdan): Replace the attr-dict mechanism with a more typed solution. + + +class NoValue(enum.Enum): def __repr__(self): return self.name class Basic(NoValue): - """Container for annotation keys. + """Container for basic annotation keys. The enum values are used strictly for documentation purposes. """ - QN = 'Qualified name, as it appeared in the code.' + QN = 'Qualified name, as it appeared in the code. See qual_names.py.' SKIP_PROCESSING = ( 'This node should be preserved as is and not processed any further.') INDENT_BLOCK_REMAINDER = ( - 'When a node is annotated with this, the remainder of the block should ' - 'be indented below it. The annotation contains a tuple ' - '(new_body, name_map), where `new_body` is the new indented block and ' - '`name_map` allows renaming symbols.') + 'When a node is annotated with this, the remainder of the block should' + ' be indented below it. The annotation contains a tuple' + ' (new_body, name_map), where `new_body` is the new indented block and' + ' `name_map` allows renaming symbols.') + ORIGIN = ('Information about the source code that converted code originated' + ' from. See origin_information.py.') + + +class Static(NoValue): + """Container for static analysis annotation keys. + + The enum values are used strictly for documentation purposes. + """ + + # Deprecated - use reaching definitions instead. + # Symbols + # These flags are boolean. + IS_LOCAL = 'Symbol is local to the function scope being analyzed.' + IS_PARAM = 'Symbol is a parameter to the function being analyzed.' + + # Scopes + # Scopes are represented by objects of type activity.Scope. + SCOPE = 'The scope for the annotated node. See activity.py.' + # TODO(mdan): Drop these in favor of accessing the child's SCOPE. + ARGS_SCOPE = 'The scope for the argument list of a function call.' + COND_SCOPE = 'The scope for the test node of a conditional statement.' + BODY_SCOPE = ( + 'The scope for the main body of a statement (True branch for if ' + 'statements, main body for loops).') + ORELSE_SCOPE = ( + 'The scope for the orelse body of a statement (False branch for if ' + 'statements, orelse body for loops).') + + # Static analysis annotations. + DEFINITIONS = ( + 'Reaching definition information. See reaching_definitions.py.') + ORIG_DEFINITIONS = ( + 'The value of DEFINITIONS that applied to the original code before any' + ' conversion.') + DEFINED_VARS_IN = ( + 'Symbols defined when entering the node. See reaching_definitions.py.') + LIVE_VARS_OUT = ('Symbols live when exiting the node. See liveness.py.') FAIL = object() +def keys(node, field_name='___pyct_anno'): + if not hasattr(node, field_name): + return frozenset() + return frozenset(getattr(node, field_name).keys()) + + def getanno(node, key, default=FAIL, field_name='___pyct_anno'): - if (default is FAIL or - (hasattr(node, field_name) and (key in getattr(node, field_name)))): + if (default is FAIL or (hasattr(node, field_name) and + (key in getattr(node, field_name)))): return getattr(node, field_name)[key] else: return default @@ -86,3 +139,19 @@ def copyanno(from_node, to_node, key, field_name='___pyct_anno'): key, getanno(from_node, key, field_name=field_name), field_name=field_name) + + +def dup(node, copy_map, field_name='___pyct_anno'): + """Recursively copies annotations in an AST tree. + + Args: + node: ast.AST + copy_map: Dict[Hashable, Hashable], maps a source anno key to a destination + key. All annotations with the source key will be copied to identical + annotations with the destination key. + field_name: str + """ + for n in gast.walk(node): + for k in copy_map: + if hasanno(n, k, field_name): + setanno(n, copy_map[k], getanno(n, k, field_name), field_name) diff --git a/tensorflow/contrib/autograph/pyct/anno_test.py b/tensorflow/contrib/autograph/pyct/anno_test.py index f2c0c8cf05ca4b3671eb653ce56f6da61de54aee..5ef4da61a3627f9c0bc615ce5cb56052a28c64d1 100644 --- a/tensorflow/contrib/autograph/pyct/anno_test.py +++ b/tensorflow/contrib/autograph/pyct/anno_test.py @@ -32,22 +32,27 @@ class AnnoTest(test.TestCase): def test_basic(self): node = ast.Name() + self.assertEqual(anno.keys(node), set()) self.assertFalse(anno.hasanno(node, 'foo')) with self.assertRaises(AttributeError): anno.getanno(node, 'foo') anno.setanno(node, 'foo', 3) + + self.assertEqual(anno.keys(node), {'foo'}) self.assertTrue(anno.hasanno(node, 'foo')) self.assertEqual(anno.getanno(node, 'foo'), 3) self.assertEqual(anno.getanno(node, 'bar', default=7), 7) anno.delanno(node, 'foo') + + self.assertEqual(anno.keys(node), set()) self.assertFalse(anno.hasanno(node, 'foo')) with self.assertRaises(AttributeError): anno.getanno(node, 'foo') self.assertIsNone(anno.getanno(node, 'foo', default=None)) - def test_copyanno(self): + def test_copy(self): node_1 = ast.Name() anno.setanno(node_1, 'foo', 3) @@ -58,6 +63,22 @@ class AnnoTest(test.TestCase): self.assertTrue(anno.hasanno(node_2, 'foo')) self.assertFalse(anno.hasanno(node_2, 'bar')) + def test_duplicate(self): + node = ast.If( + test=ast.Num(1), + body=[ast.Expr(ast.Name('bar', ast.Load()))], + orelse=[]) + anno.setanno(node, 'spam', 1) + anno.setanno(node, 'ham', 1) + anno.setanno(node.body[0], 'ham', 1) + + anno.dup(node, {'spam': 'eggs'}) + + self.assertTrue(anno.hasanno(node, 'spam')) + self.assertTrue(anno.hasanno(node, 'ham')) + self.assertTrue(anno.hasanno(node, 'eggs')) + self.assertFalse(anno.hasanno(node.body[0], 'eggs')) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/autograph/pyct/ast_util.py b/tensorflow/contrib/autograph/pyct/ast_util.py index c4f82d11708393a6029d3f17be428b47eb9342ff..d7453b078197cd6f1c0521b861e96dd28d287cab 100644 --- a/tensorflow/contrib/autograph/pyct/ast_util.py +++ b/tensorflow/contrib/autograph/pyct/ast_util.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Copy an AST tree, discarding annotations.""" +"""AST manipulation utilities.""" from __future__ import absolute_import from __future__ import division @@ -26,47 +26,53 @@ from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import parser -class CleanCopier(gast.NodeVisitor): - """Copies AST nodes. +class CleanCopier(object): + """NodeTransformer-like visitor that copies an AST.""" - The copied nodes will ignore almost all fields that are prefixed by '__'. - Exceptions make some annotations. - """ + def __init__(self, preserve_annos): + super(CleanCopier, self).__init__() + self.preserve_annos = preserve_annos - # TODO(mdan): Parametrize which annotations get carried over. + def copy(self, node): + """Returns a deep copy of node (excluding some fields, see copy_clean).""" + + if isinstance(node, list): + return [self.copy(n) for n in node] + elif isinstance(node, tuple): + return tuple(self.copy(n) for n in node) + elif not isinstance(node, (gast.AST, ast.AST)): + # Assuming everything that's not an AST, list or tuple is a value type + # and may simply be assigned. + return node + + assert isinstance(node, (gast.AST, ast.AST)) - def generic_visit(self, node): new_fields = {} for f in node._fields: - if f.startswith('__'): - continue - if not hasattr(node, f): - continue - v = getattr(node, f) - if isinstance(v, list): - v = [self.generic_visit(n) for n in v] - elif isinstance(v, tuple): - v = tuple(self.generic_visit(n) for n in v) - elif isinstance(v, (gast.AST, ast.AST)): - v = self.generic_visit(v) - else: - # Assume everything else is a value type. - pass - new_fields[f] = v + if not f.startswith('__') and hasattr(node, f): + new_fields[f] = self.copy(getattr(node, f)) new_node = type(node)(**new_fields) - if anno.hasanno(node, anno.Basic.SKIP_PROCESSING): - anno.setanno(new_node, anno.Basic.SKIP_PROCESSING, True) + + if self.preserve_annos: + for k in self.preserve_annos: + anno.copyanno(node, new_node, k) return new_node -def copy_clean(node): - copier = CleanCopier() - if isinstance(node, list): - return [copier.visit(n) for n in node] - elif isinstance(node, tuple): - return tuple(copier.visit(n) for n in node) - else: - return copier.visit(node) +def copy_clean(node, preserve_annos=None): + """Creates a deep copy of an AST. + + The copy will not include fields that are prefixed by '__', with the + exception of user-specified annotations. + + Args: + node: ast.AST + preserve_annos: Optional[Set[Hashable]], annotation keys to include in the + copy + Returns: + ast.AST + """ + return CleanCopier(preserve_annos).copy(node) class SymbolRenamer(gast.NodeTransformer): @@ -78,7 +84,11 @@ class SymbolRenamer(gast.NodeTransformer): def _process(self, node): qn = anno.getanno(node, anno.Basic.QN) if qn in self.name_map: - return gast.Name(str(self.name_map[qn]), node.ctx, None) + new_node = gast.Name(str(self.name_map[qn]), node.ctx, None) + # All annotations get carried over. + for k in anno.keys(node): + anno.copyanno(node, new_node, k) + return new_node return self.generic_visit(node) def visit_Name(self, node): @@ -92,6 +102,7 @@ class SymbolRenamer(gast.NodeTransformer): def rename_symbols(node, name_map): + """Renames symbols in an AST. Requires qual_names annotations.""" renamer = SymbolRenamer(name_map) if isinstance(node, list): return [renamer.visit(n) for n in node] @@ -101,6 +112,7 @@ def rename_symbols(node, name_map): def keywords_to_dict(keywords): + """Converts a list of ast.keyword objects to a dict.""" keys = [] values = [] for kw in keywords: @@ -110,10 +122,7 @@ def keywords_to_dict(keywords): class PatternMatcher(gast.NodeVisitor): - """Matches a node against a pattern represented by a node. - - The pattern may contain wildcards represented by the symbol '_'. - """ + """Matches a node against a pattern represented by a node.""" def __init__(self, pattern): self.pattern = pattern @@ -177,9 +186,128 @@ class PatternMatcher(gast.NodeVisitor): def matches(node, pattern): + """Basic pattern matcher for AST. + + The pattern may contain wildcards represented by the symbol '_'. A node + matches a pattern if for every node in the tree, either there is a node of + the same type in pattern, or a Name node with id='_'. + + Args: + node: ast.AST + pattern: ast.AST + Returns: + bool + """ if isinstance(pattern, str): pattern = parser.parse_expression(pattern) matcher = PatternMatcher(pattern) matcher.visit(node) return matcher.matches + +# TODO(mdan): Once we have error tracing, we may be able to just go to SSA. +def apply_to_single_assignments(targets, values, apply_fn): + """Applies a function to each individual assignment. + + This function can process a possibly-unpacked (e.g. a, b = c, d) assignment. + It tries to break down the unpacking if possible. In effect, it has the same + effect as passing the assigned values in SSA form to apply_fn. + + Examples: + + The following will result in apply_fn(a, c), apply_fn(b, d): + + a, b = c, d + + The following will result in apply_fn(a, c[0]), apply_fn(b, c[1]): + + a, b = c + + The following will result in apply_fn(a, (b, c)): + + a = b, c + + It uses the visitor pattern to allow subclasses to process single + assignments individually. + + Args: + targets: Union[List[ast.AST, ...], Tuple[ast.AST, ...], ast.AST, should be + used with the targets field of an ast.Assign node + values: ast.AST + apply_fn: Callable[[ast.AST, ast.AST], None], called with the + respective nodes of each single assignment + """ + if not isinstance(targets, (list, tuple)): + targets = (targets,) + for target in targets: + if isinstance(target, (gast.Tuple, gast.List)): + for i in range(len(target.elts)): + target_el = target.elts[i] + if isinstance(values, (gast.Tuple, gast.List)): + value_el = values.elts[i] + else: + idx = parser.parse_expression(str(i)) + value_el = gast.Subscript(values, gast.Index(idx), ctx=gast.Load()) + apply_to_single_assignments(target_el, value_el, apply_fn) + else: + apply_fn(target, values) + + +def parallel_walk(node, other): + """Walks two ASTs in parallel. + + The two trees must have identical structure. + + Args: + node: Union[ast.AST, Iterable[ast.AST]] + other: Union[ast.AST, Iterable[ast.AST]] + Yields: + Tuple[ast.AST, ast.AST] + Raises: + ValueError: if the two trees don't have identical structure. + """ + if isinstance(node, (list, tuple)): + node_stack = list(node) + else: + node_stack = [node] + + if isinstance(other, (list, tuple)): + other_stack = list(other) + else: + other_stack = [other] + + while node_stack and other_stack: + assert len(node_stack) == len(other_stack) + n = node_stack.pop() + o = other_stack.pop() + + if (not isinstance(n, (ast.AST, gast.AST)) or + not isinstance(o, (ast.AST, gast.AST)) or + n.__class__.__name__ != o.__class__.__name__): + raise ValueError('inconsistent nodes: {} and {}'.format(n, o)) + + yield n, o + + for f in n._fields: + n_child = getattr(n, f, None) + o_child = getattr(o, f, None) + if f.startswith('__') or n_child is None or o_child is None: + continue + + if isinstance(n_child, (list, tuple)): + if (not isinstance(o_child, (list, tuple)) or + len(n_child) != len(o_child)): + raise ValueError( + 'inconsistent values for field {}: {} and {}'.format( + f, n_child, o_child)) + node_stack.extend(n_child) + other_stack.extend(o_child) + + elif isinstance(n_child, (gast.AST, ast.AST)): + node_stack.append(n_child) + other_stack.append(o_child) + + elif n_child != o_child: + raise ValueError( + 'inconsistent values for field {}: {} and {}'.format( + f, n_child, o_child)) diff --git a/tensorflow/contrib/autograph/pyct/ast_util_test.py b/tensorflow/contrib/autograph/pyct/ast_util_test.py index 3afa04a50685d19c90944c14ed39f9d3ad35e486..2293c89720a54f7495670c6f28b00f716cad70db 100644 --- a/tensorflow/contrib/autograph/pyct/ast_util_test.py +++ b/tensorflow/contrib/autograph/pyct/ast_util_test.py @@ -19,7 +19,10 @@ from __future__ import division from __future__ import print_function import ast +import collections +import textwrap +from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import ast_util from tensorflow.contrib.autograph.pyct import compiler from tensorflow.contrib.autograph.pyct import parser @@ -29,62 +32,75 @@ from tensorflow.python.platform import test class AstUtilTest(test.TestCase): - def test_rename_symbols(self): - node = ast.Tuple([ - ast.Name('a', ast.Load()), - ast.Name('b', ast.Load()), - ast.Attribute(ast.Name('b', None), 'c', ast.Store()), - ast.Attribute( - ast.Attribute(ast.Name('b', None), 'c', ast.Load()), 'd', None) - ], None) + def setUp(self): + super(AstUtilTest, self).setUp() + self._invocation_counts = collections.defaultdict(lambda: 0) + + def test_rename_symbols_basic(self): + node = parser.parse_str('a + b') node = qual_names.resolve(node) + node = ast_util.rename_symbols( - node, { - qual_names.QN('a'): - qual_names.QN('renamed_a'), - qual_names.QN(qual_names.QN('b'), attr='c'): - qual_names.QN('renamed_b_c'), - }) - - self.assertEqual(node.elts[0].id, 'renamed_a') - self.assertTrue(isinstance(node.elts[0].ctx, ast.Load)) - self.assertEqual(node.elts[1].id, 'b') - self.assertEqual(node.elts[2].id, 'renamed_b_c') - self.assertTrue(isinstance(node.elts[2].ctx, ast.Store)) - self.assertEqual(node.elts[3].value.id, 'renamed_b_c') - self.assertTrue(isinstance(node.elts[3].value.ctx, ast.Load)) + node, {qual_names.QN('a'): qual_names.QN('renamed_a')}) + + self.assertIsInstance(node.body[0].value.left.id, str) + source = compiler.ast_to_source(node) + self.assertEqual(source.strip(), 'renamed_a + b') + + def test_rename_symbols_attributes(self): + node = parser.parse_str('b.c = b.c.d') + node = qual_names.resolve(node) + + node = ast_util.rename_symbols( + node, {qual_names.from_str('b.c'): qual_names.QN('renamed_b_c')}) + + source = compiler.ast_to_source(node) + self.assertEqual(source.strip(), 'renamed_b_c = renamed_b_c.d') + + def test_rename_symbols_annotations(self): + node = parser.parse_str('a[i]') + node = qual_names.resolve(node) + anno.setanno(node, 'foo', 'bar') + orig_anno = anno.getanno(node, 'foo') + + node = ast_util.rename_symbols(node, + {qual_names.QN('a'): qual_names.QN('b')}) + + self.assertIs(anno.getanno(node, 'foo'), orig_anno) def test_copy_clean(self): - ret = ast.Return( - ast.BinOp( - op=ast.Add(), - left=ast.Name(id='a', ctx=ast.Load()), - right=ast.Num(1))) - setattr(ret, '__foo', 'bar') - node = ast.FunctionDef( - name='f', - args=ast.arguments( - args=[ast.Name(id='a', ctx=ast.Param())], - vararg=None, - kwarg=None, - defaults=[]), - body=[ret], - decorator_list=[], - returns=None) + node = parser.parse_str( + textwrap.dedent(""" + def f(a): + return a + 1 + """)) + setattr(node.body[0], '__foo', 'bar') new_node = ast_util.copy_clean(node) - self.assertFalse(node is new_node) - self.assertFalse(ret is new_node.body[0]) + self.assertIsNot(new_node, node) + self.assertIsNot(new_node.body[0], node.body[0]) self.assertFalse(hasattr(new_node.body[0], '__foo')) + def test_copy_clean_preserves_annotations(self): + node = parser.parse_str( + textwrap.dedent(""" + def f(a): + return a + 1 + """)) + anno.setanno(node.body[0], 'foo', 'bar') + anno.setanno(node.body[0], 'baz', 1) + new_node = ast_util.copy_clean(node, preserve_annos={'foo'}) + self.assertEqual(anno.getanno(new_node.body[0], 'foo'), 'bar') + self.assertFalse(anno.hasanno(new_node.body[0], 'baz')) + def test_keywords_to_dict(self): keywords = parser.parse_expression('f(a=b, c=1, d=\'e\')').keywords d = ast_util.keywords_to_dict(keywords) # Make sure we generate a usable dict node by attaching it to a variable and # compiling everything. - output = parser.parse_str('b = 3') - output.body += (ast.Assign([ast.Name(id='d', ctx=ast.Store())], d),) - result, _ = compiler.ast_to_object(output) - self.assertDictEqual(result.d, {'a': 3, 'c': 1, 'd': 'e'}) + node = parser.parse_str('def f(b): pass').body[0] + node.body.append(ast.Return(d)) + result, _ = compiler.ast_to_object(node) + self.assertDictEqual(result.f(3), {'a': 3, 'c': 1, 'd': 'e'}) def assertMatch(self, target_str, pattern_str): node = parser.parse_expression(target_str) @@ -113,6 +129,68 @@ class AstUtilTest(test.TestCase): self.assertNoMatch('super(Foo, self).__init__()', 'super(Bar, _).__init__(_)') + def _mock_apply_fn(self, target, source): + target = compiler.ast_to_source(target) + source = compiler.ast_to_source(source) + self._invocation_counts[(target.strip(), source.strip())] += 1 + + def test_apply_to_single_assignments_dynamic_unpack(self): + node = parser.parse_str('a, b, c = d') + node = node.body[0] + ast_util.apply_to_single_assignments(node.targets, node.value, + self._mock_apply_fn) + self.assertDictEqual(self._invocation_counts, { + ('a', 'd[0]'): 1, + ('b', 'd[1]'): 1, + ('c', 'd[2]'): 1, + }) + + def test_apply_to_single_assignments_static_unpack(self): + node = parser.parse_str('a, b, c = d, e, f') + node = node.body[0] + ast_util.apply_to_single_assignments(node.targets, node.value, + self._mock_apply_fn) + self.assertDictEqual(self._invocation_counts, { + ('a', 'd'): 1, + ('b', 'e'): 1, + ('c', 'f'): 1, + }) + + def test_parallel_walk(self): + node = parser.parse_str( + textwrap.dedent(""" + def f(a): + return a + 1 + """)) + for child_a, child_b in ast_util.parallel_walk(node, node): + self.assertEqual(child_a, child_b) + + def test_parallel_walk_inconsistent_trees(self): + node_1 = parser.parse_str( + textwrap.dedent(""" + def f(a): + return a + 1 + """)) + node_2 = parser.parse_str( + textwrap.dedent(""" + def f(a): + return a + (a * 2) + """)) + node_3 = parser.parse_str( + textwrap.dedent(""" + def f(a): + return a + 2 + """)) + with self.assertRaises(ValueError): + for _ in ast_util.parallel_walk(node_1, node_2): + pass + # There is not particular reason to reject trees that differ only in the + # value of a constant. + # TODO(mdan): This should probably be allowed. + with self.assertRaises(ValueError): + for _ in ast_util.parallel_walk(node_1, node_3): + pass + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/autograph/pyct/cfg.py b/tensorflow/contrib/autograph/pyct/cfg.py index 666328781f683c9457f6892c0a26088c33ba94a7..ba51dcf285036220e01b89e8beeb9aec8ffe36be 100644 --- a/tensorflow/contrib/autograph/pyct/cfg.py +++ b/tensorflow/contrib/autograph/pyct/cfg.py @@ -64,11 +64,17 @@ class Node(object): self.prev = frozenset(self.prev) def __repr__(self): + if isinstance(self.ast_node, gast.FunctionDef): + return 'def %s' % self.ast_node.name + elif isinstance(self.ast_node, gast.withitem): + return compiler.ast_to_source(self.ast_node.context_expr).strip() return compiler.ast_to_source(self.ast_node).strip() class Graph( - collections.namedtuple('Graph', ['entry', 'exit', 'error', 'index'])): + collections.namedtuple( + 'Graph', + ['entry', 'exit', 'error', 'index', 'stmt_prev', 'stmt_next'])): """A Control Flow Graph. The CFG maintains an index to allow looking up a CFG node by the AST node to @@ -82,6 +88,11 @@ class Graph( because these are shared, and wiring them would create a reverse path from normal control flow into the error nodes, which we want to avoid. + The graph also maintains edges corresponding to higher level statements + like for-else loops. A node is considered successor of a statement if there + is an edge from a node that is lexically a child of that statement to a node + that is not. Statement predecessors are analogously defined. + Attributes: entry: Node, the entry node exit: FrozenSet[Node, ...], the exit nodes @@ -89,6 +100,10 @@ class Graph( error (errors propagated from function calls are not accounted) index: Dict[ast.Node, Node], mapping AST nodes to the respective CFG node + stmt_prev: Dict[ast.Node, FrozenSet[Node, ...]], mapping statement AST + nodes to their predecessor CFG nodes + stmt_next: Dict[ast.Node, FrozenSet[Node, ...]], mapping statement AST + nodes to their successor CFG nodes """ def __repr__(self): @@ -96,9 +111,8 @@ class Graph( for node in self.index.values(): result += ' %s [label="%s"];\n' % (id(node), node) for node in self.index.values(): - if node.next: - result += ' %s -> {%s};\n' % (id(node), ', '.join( - repr(id(n)) for n in node.next)) + for next_ in node.next: + result += ' %s -> %s;\n' % (id(node), id(next_)) result += '}' return result @@ -108,6 +122,8 @@ class _WalkMode(Enum): REVERSE = 2 +# TODO(mdan): Rename to DataFlowAnalyzer. +# TODO(mdan): Consider specializations that use gen/kill/transfer abstractions. class GraphVisitor(object): """Base class for a CFG visitors. @@ -130,26 +146,22 @@ class GraphVisitor(object): out: Dict[Node, Any], stores node-keyed state during a visit """ - def reset(self): - self.in_ = { - node: self.init_state(node) for node in self.graph.index.values() - } - self.out = { - node: self.init_state(node) for node in self.graph.index.values() - } + def __init__(self, graph): + self.graph = graph + self.reset() def init_state(self, node): """State initialization function. Optional to overload. An in/out state slot will be created for each node in the graph. Subclasses - may overload this to control what that is initialized to. + must overload this to control what that is initialized to. Args: node: Node """ - del node - return None + raise NotImplementedError('Subclasses must implement this.') + # TODO(mdan): Rename to flow? def visit_node(self, node): """Visitor function. @@ -161,6 +173,14 @@ class GraphVisitor(object): """ raise NotImplementedError('Subclasses must implement this.') + def reset(self): + self.in_ = { + node: self.init_state(node) for node in self.graph.index.values() + } + self.out = { + node: self.init_state(node) for node in self.graph.index.values() + } + def _visit_internal(self, mode): """Visits the CFG, depth-first.""" assert mode in (_WalkMode.FORWARD, _WalkMode.REVERSE) @@ -169,7 +189,6 @@ class GraphVisitor(object): elif mode == _WalkMode.REVERSE: open_ = list(self.graph.exit) closed = set() - self.reset() while open_: node = open_.pop(0) @@ -186,12 +205,10 @@ class GraphVisitor(object): if should_revisit or next_ not in closed: open_.append(next_) - def visit_forward(self, graph): - self.graph = graph + def visit_forward(self): self._visit_internal(_WalkMode.FORWARD) - def visit_reverse(self, graph): - self.graph = graph + def visit_reverse(self): self._visit_internal(_WalkMode.REVERSE) @@ -244,8 +261,16 @@ class GraphBuilder(object): # TODO(mdan): Too many primitives. Use classes. self.leaves = set() + # Note: This mechanism requires that nodes are added in lexical order (top + # to bottom, depth first). + self.active_stmts = set() + self.owners = {} # type: Set[any] + self.forward_edges = set() # type: Tuple[Node, Node] # (from, to) + self.finally_sections = {} - self.finally_section_subgraphs = {} # Values are [begin_node, exit_nodes] + # Dict values represent (entry, exits) + self.finally_section_subgraphs = { + } # type: Dict[ast.AST, Tuple[Node, Set[Node]]] # Whether the guard section can be reached from the statement that precedes # it. self.finally_section_has_direct_flow = {} @@ -275,6 +300,7 @@ class GraphBuilder(object): if isinstance(first, Node): first.next.add(second) second.prev.add(first) + self.forward_edges.add((first, second)) else: for node in first: self._connect_nodes(node, second) @@ -285,6 +311,7 @@ class GraphBuilder(object): raise ValueError('%s added twice' % ast_node) node = Node(next_=set(), prev=set(), ast_node=ast_node) self.node_index[ast_node] = node + self.owners[node] = frozenset(self.active_stmts) if self.head is None: self.head = node @@ -299,6 +326,25 @@ class GraphBuilder(object): return node + def begin_statement(self, stmt): + """Marks the beginning of a statement. + + Args: + stmt: Hashable, a key by which the statement can be identified in + the CFG's stmt_prev and stmt_next attributes + """ + self.active_stmts.add(stmt) + + def end_statement(self, stmt): + """Marks the end of a statement. + + Args: + stmt: Hashable, a key by which the statement can be identified in + the CFG's stmt_prev and stmt_next attributes; must match a key + previously passed to begin_statement. + """ + self.active_stmts.remove(stmt) + def add_ordinary_node(self, ast_node): """Grows the graph by adding an ordinary CFG node. @@ -505,11 +551,35 @@ class GraphBuilder(object): for node in self.node_index.values(): node.freeze() + # Build the statement edges. + stmt_next = {} + stmt_prev = {} + for node, _ in self.forward_edges: + for stmt in self.owners[node]: + if stmt not in stmt_next: + stmt_next[stmt] = set() + if stmt not in stmt_prev: + stmt_prev[stmt] = set() + for first, second in self.forward_edges: + stmts_exited = self.owners[first] - self.owners[second] + for stmt in stmts_exited: + stmt_next[stmt].add(second) + stmts_entered = self.owners[second] - self.owners[first] + for stmt in stmts_entered: + stmt_prev[stmt].add(first) + for stmt in stmt_next: + stmt_next[stmt] = frozenset(stmt_next[stmt]) + for stmt in stmt_prev: + stmt_prev[stmt] = frozenset(stmt_prev[stmt]) + + # Construct the final graph object. result = Graph( entry=self.head, exit=self.leaves, error=self.errors, - index=self.node_index) + index=self.node_index, + stmt_prev=stmt_prev, + stmt_next=stmt_next) # Reset the state. self.reset() @@ -523,8 +593,6 @@ class AstToCfg(gast.NodeVisitor): A separate CFG will be constructed for each function. """ - # TODO(mdan): Figure out how to deal with closures. - def __init__(self): super(AstToCfg, self).__init__() @@ -577,6 +645,13 @@ class AstToCfg(gast.NodeVisitor): self.builder.add_continue_node(node, try_node, guards) def visit_FunctionDef(self, node): + # We also keep the FunctionDef node in the CFG. This allows us to determine + # things like reaching definitions via closure. Note that the function body + # will be stored in a separate graph, because function definitions are not + # the same as function calls. + if self.builder is not None: + self.builder.add_ordinary_node(node) + self.builder_stack.append(self.builder) self.builder = GraphBuilder(node) @@ -622,7 +697,7 @@ class AstToCfg(gast.NodeVisitor): ) if try_node is None: raise ValueError('%s that is not enclosed by any FunctionDef' % node) - self.builder.add_error_node(node, try_node, guards) + self.builder.add_error_node(node, guards) def visit_Assert(self, node): # Ignoring the effect of exceptions. @@ -637,6 +712,7 @@ class AstToCfg(gast.NodeVisitor): # targets of jump statements like break/continue/etc. Since there is no # statement that can interrupt a conditional, we don't need to track their # lexical scope. That may change in the future. + self.builder.begin_statement(node) self.builder.enter_cond_section(node) self._process_basic_statement(node.test) @@ -650,8 +726,10 @@ class AstToCfg(gast.NodeVisitor): self.visit(stmt) self.builder.exit_cond_section(node) + self.builder.end_statement(node) def visit_While(self, node): + self.builder.begin_statement(node) self._enter_lexical_scope(node) self.builder.enter_section(node) @@ -670,8 +748,10 @@ class AstToCfg(gast.NodeVisitor): self.visit(stmt) self.builder.exit_section(node) + self.builder.end_statement(node) def visit_For(self, node): + self.builder.begin_statement(node) self._enter_lexical_scope(node) self.builder.enter_section(node) @@ -693,6 +773,7 @@ class AstToCfg(gast.NodeVisitor): self.visit(stmt) self.builder.exit_section(node) + self.builder.end_statement(node) def visit_Break(self, node): self._process_exit_statement(node, gast.While, gast.For) @@ -722,12 +803,13 @@ class AstToCfg(gast.NodeVisitor): def visit_With(self, node): # TODO(mdan): Mark the context manager's exit call as exit guard. - self._process_basic_statement(node.items) + for item in node.items: + self._process_basic_statement(item) for stmt in node.body: self.visit(stmt) def build(node): - builder = AstToCfg() - builder.visit(node) - return builder.cfgs + visitor = AstToCfg() + visitor.visit(node) + return visitor.cfgs diff --git a/tensorflow/contrib/autograph/pyct/cfg_test.py b/tensorflow/contrib/autograph/pyct/cfg_test.py index 00afadd5212a3aba8f25cd9a6f111d292635bbce..9d0a85d615cc5a7dcebf405aebdbfe409be0b5cf 100644 --- a/tensorflow/contrib/autograph/pyct/cfg_test.py +++ b/tensorflow/contrib/autograph/pyct/cfg_test.py @@ -25,9 +25,13 @@ from tensorflow.python.platform import test class CountingVisitor(cfg.GraphVisitor): - def __init__(self): + def __init__(self, graph): + super(CountingVisitor, self).__init__(graph) self.counts = {} + def init_state(self, _): + return None + def visit_node(self, node): self.counts[node.ast_node] = self.counts.get(node.ast_node, 0) + 1 return False # visit only once @@ -51,8 +55,8 @@ class GraphVisitorTest(test.TestCase): graphs, node = self._build_cfg(test_fn) graph, = graphs.values() - visitor = CountingVisitor() - visitor.visit_forward(graph) + visitor = CountingVisitor(graph) + visitor.visit_forward() fn_node = node.body[0] self.assertEqual(visitor.counts[fn_node.args], 1) @@ -74,8 +78,8 @@ class GraphVisitorTest(test.TestCase): graphs, node = self._build_cfg(test_fn) graph, = graphs.values() - visitor = CountingVisitor() - visitor.visit_reverse(graph) + visitor = CountingVisitor(graph) + visitor.visit_reverse() fn_node = node.body[0] self.assertEqual(visitor.counts[fn_node.args], 1) @@ -94,7 +98,7 @@ class AstToCfgTest(test.TestCase): return cfgs def _repr_set(self, node_set): - return set(repr(n) for n in node_set) + return frozenset(repr(n) for n in node_set) def _as_set(self, elements): if elements is None: @@ -110,14 +114,35 @@ class AstToCfgTest(test.TestCase): matched = False for cfg_node in graph.index.values(): if repr(cfg_node) == node_repr: - if (self._as_set(prev) == set(map(repr, cfg_node.prev)) and - self._as_set(next_) == set(map(repr, cfg_node.next))): + if (self._as_set(prev) == frozenset(map(repr, cfg_node.prev)) and + self._as_set(next_) == frozenset(map(repr, cfg_node.next))): matched = True break if not matched: self.fail( 'match failed for node "%s" in graph:\n%s' % (node_repr, graph)) + def assertStatementEdges(self, graph, edges): + """Tests whether the CFG contains the specified statement edges.""" + for prev_node_reprs, node_repr, next_node_reprs in edges: + matched = False + partial_matches = [] + self.assertSetEqual( + frozenset(graph.stmt_next.keys()), frozenset(graph.stmt_prev.keys())) + for stmt_ast_node in graph.stmt_next: + ast_repr = '%s:%s' % (stmt_ast_node.__class__.__name__, + stmt_ast_node.lineno) + if ast_repr == node_repr: + actual_next = frozenset(map(repr, graph.stmt_next[stmt_ast_node])) + actual_prev = frozenset(map(repr, graph.stmt_prev[stmt_ast_node])) + partial_matches.append((actual_prev, node_repr, actual_next)) + if (self._as_set(prev_node_reprs) == actual_prev and + self._as_set(next_node_reprs) == actual_next): + matched = True + break + if not matched: + self.fail('edges mismatch for %s: %s' % (node_repr, partial_matches)) + def test_straightline(self): def test_fn(a): @@ -171,7 +196,7 @@ class AstToCfgTest(test.TestCase): ), ) - def test_branch_straightline(self): + def test_if_straightline(self): def test_fn(a): if a > 0: @@ -189,6 +214,10 @@ class AstToCfgTest(test.TestCase): ('(a > 0)', 'a += -1', None), ), ) + self.assertStatementEdges( + graph, + (('a', 'If:2', None),), + ) def test_branch_nested(self): @@ -219,6 +248,14 @@ class AstToCfgTest(test.TestCase): ('(a > 2)', 'a = 4', None), ), ) + self.assertStatementEdges( + graph, + ( + ('a', 'If:2', None), + ('(a > 0)', 'If:3', None), + ('(a > 0)', 'If:8', None), + ), + ) def test_branch_straightline_semi(self): @@ -236,6 +273,10 @@ class AstToCfgTest(test.TestCase): ('(a > 0)', 'a = 1', None), ), ) + self.assertStatementEdges( + graph, + (('a', 'If:2', None),), + ) def test_branch_return(self): @@ -257,6 +298,10 @@ class AstToCfgTest(test.TestCase): ('a = 1', 'a = 2', None), ), ) + self.assertStatementEdges( + graph, + (('a', 'If:2', 'a = 2'),), + ) def test_branch_return_minimal(self): @@ -273,6 +318,10 @@ class AstToCfgTest(test.TestCase): ('(a > 0)', 'return', None), ), ) + self.assertStatementEdges( + graph, + (('a', 'If:2', None),), + ) def test_while_straightline(self): @@ -291,6 +340,10 @@ class AstToCfgTest(test.TestCase): ('(a > 0)', 'a = 2', None), ), ) + self.assertStatementEdges( + graph, + (('a', 'While:2', 'a = 2'),), + ) def test_while_else_straightline(self): @@ -312,6 +365,10 @@ class AstToCfgTest(test.TestCase): ('a = 2', 'a = 3', None), ), ) + self.assertStatementEdges( + graph, + (('a', 'While:2', 'a = 3'),), + ) def test_while_else_continue(self): @@ -339,6 +396,13 @@ class AstToCfgTest(test.TestCase): ('a = 2', 'a = 3', None), ), ) + self.assertStatementEdges( + graph, + ( + ('a', 'While:2', 'a = 3'), + ('(a > 0)', 'If:3', ('a = 1', '(a > 0)')), + ), + ) def test_while_else_break(self): @@ -364,6 +428,13 @@ class AstToCfgTest(test.TestCase): (('break', 'a = 2'), 'a = 3', None), ), ) + self.assertStatementEdges( + graph, + ( + ('a', 'While:2', 'a = 3'), + ('(a > 0)', 'If:3', ('a = 1', 'a = 3')), + ), + ) def test_while_else_return(self): @@ -389,6 +460,13 @@ class AstToCfgTest(test.TestCase): ('a = 2', 'a = 3', None), ), ) + self.assertStatementEdges( + graph, + ( + ('a', 'While:2', 'a = 3'), + ('(a > 0)', 'If:3', 'a = 1'), + ), + ) def test_while_nested_straightline(self): @@ -411,6 +489,13 @@ class AstToCfgTest(test.TestCase): ('(a > 0)', 'a = 3', None), ), ) + self.assertStatementEdges( + graph, + ( + ('a', 'While:2', 'a = 3'), + ('(a > 0)', 'While:3', 'a = 2'), + ), + ) def test_while_nested_continue(self): @@ -437,6 +522,14 @@ class AstToCfgTest(test.TestCase): ('(a > 0)', 'a = 3', None), ), ) + self.assertStatementEdges( + graph, + ( + ('a', 'While:2', 'a = 3'), + ('(a > 0)', 'While:3', 'a = 2'), + ('(a > 1)', 'If:4', ('a = 1', '(a > 1)')), + ), + ) def test_while_nested_break(self): @@ -451,16 +544,21 @@ class AstToCfgTest(test.TestCase): graph, = self._build_cfg(test_fn).values() - self.assertGraphMatches( + self.assertGraphMatches(graph, ( + (('a', 'a = 2'), '(a > 0)', ('(a > 1)', 'a = 3')), + (('(a > 0)', 'a = 1'), '(a > 1)', ('(a > 2)', 'a = 2')), + ('(a > 1)', '(a > 2)', ('break', 'a = 1')), + ('(a > 2)', 'break', 'a = 2'), + ('(a > 2)', 'a = 1', '(a > 1)'), + (('(a > 1)', 'break'), 'a = 2', '(a > 0)'), + ('(a > 0)', 'a = 3', None), + )) + self.assertStatementEdges( graph, ( - (('a', 'a = 2'), '(a > 0)', ('(a > 1)', 'a = 3')), - (('(a > 0)', 'a = 1'), '(a > 1)', ('(a > 2)', 'a = 2')), - ('(a > 1)', '(a > 2)', ('break', 'a = 1')), - ('(a > 2)', 'break', 'a = 2'), - ('(a > 2)', 'a = 1', '(a > 1)'), - (('(a > 1)', 'break'), 'a = 2', '(a > 0)'), - ('(a > 0)', 'a = 3', None), + ('a', 'While:2', 'a = 3'), + ('(a > 0)', 'While:3', 'a = 2'), + ('(a > 1)', 'If:4', ('a = 1', 'a = 2')), ), ) @@ -481,6 +579,10 @@ class AstToCfgTest(test.TestCase): ('range(0, a)', 'a = 2', None), ), ) + self.assertStatementEdges( + graph, + (('a', 'For:2', 'a = 2'),), + ) def test_for_else_straightline(self): @@ -502,6 +604,10 @@ class AstToCfgTest(test.TestCase): ('a = 2', 'a = 3', None), ), ) + self.assertStatementEdges( + graph, + (('a', 'For:2', 'a = 3'),), + ) def test_for_else_continue(self): @@ -530,6 +636,13 @@ class AstToCfgTest(test.TestCase): ('a = 2', 'a = 3', None), ), ) + self.assertStatementEdges( + graph, + ( + ('a', 'For:2', 'a = 3'), + ('range(0, a)', 'If:3', ('a = 1', 'range(0, a)')), + ), + ) def test_for_else_break(self): @@ -555,6 +668,13 @@ class AstToCfgTest(test.TestCase): (('break', 'a = 2'), 'a = 3', None), ), ) + self.assertStatementEdges( + graph, + ( + ('a', 'For:2', 'a = 3'), + ('range(0, a)', 'If:3', ('a = 1', 'a = 3')), + ), + ) def test_for_else_return(self): @@ -580,6 +700,13 @@ class AstToCfgTest(test.TestCase): ('a = 2', 'a = 3', None), ), ) + self.assertStatementEdges( + graph, + ( + ('a', 'For:2', 'a = 3'), + ('range(0, a)', 'If:3', 'a = 1'), + ), + ) def test_for_nested_straightline(self): @@ -602,6 +729,13 @@ class AstToCfgTest(test.TestCase): ('range(0, a)', 'a = 3', None), ), ) + self.assertStatementEdges( + graph, + ( + ('a', 'For:2', 'a = 3'), + ('range(0, a)', 'For:3', 'a = 2'), + ), + ) def test_for_nested_continue(self): @@ -629,6 +763,14 @@ class AstToCfgTest(test.TestCase): ('range(0, a)', 'a = 3', None), ), ) + self.assertStatementEdges( + graph, + ( + ('a', 'For:2', 'a = 3'), + ('range(0, a)', 'For:3', 'a = 2'), + ('range(1, a)', 'If:4', ('b += 1', 'range(1, a)')), + ), + ) def test_for_nested_break(self): @@ -655,6 +797,14 @@ class AstToCfgTest(test.TestCase): ('range(0, a)', 'a = 3', None), ), ) + self.assertStatementEdges( + graph, + ( + ('a', 'For:2', 'a = 3'), + ('range(0, a)', 'For:3', 'a = 2'), + ('range(1, a)', 'If:4', ('b += 1', 'a = 2')), + ), + ) def test_complex(self): @@ -704,6 +854,17 @@ class AstToCfgTest(test.TestCase): ('range(1, a)', 'a = 3', None), ), ) + self.assertStatementEdges( + graph, + ( + ('b = 0', 'While:3', 'range(1, a)'), + ('(a > 0)', 'For:4', 'a = 2'), + ('range(0, a)', 'If:5', ('(a > 3)', 'a = 2')), + ('(a > 2)', 'If:7', ('b += 1', 'a = 2', 'range(0, a)')), + ('(a > 3)', 'If:8', ('a = 2', 'range(0, a)')), + ('(a > 0)', 'For:17', 'a = 3'), + ), + ) def test_finally_straightline(self): @@ -785,6 +946,24 @@ class AstToCfgTest(test.TestCase): ), ) + def test_with_straightline(self): + + def test_fn(a): + with max(a) as b: + a = 0 + return b + + graph, = self._build_cfg(test_fn).values() + + self.assertGraphMatches( + graph, + ( + ('a', 'max(a)', 'a = 0'), + ('max(a)', 'a = 0', 'return b'), + ('a = 0', 'return b', None), + ), + ) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/autograph/pyct/common_transformers/BUILD b/tensorflow/contrib/autograph/pyct/common_transformers/BUILD index ca1441cf6f8bb034c95b37fcdd9e8158d1db2e39..a0938b3e5f0e52532f63fea6fb4c3e478fc51d93 100644 --- a/tensorflow/contrib/autograph/pyct/common_transformers/BUILD +++ b/tensorflow/contrib/autograph/pyct/common_transformers/BUILD @@ -24,6 +24,7 @@ py_library( deps = [ "//tensorflow/contrib/autograph/pyct", "@gast_archive//:gast", + "@six_archive//:six", ], ) diff --git a/tensorflow/contrib/autograph/pyct/common_transformers/anf.py b/tensorflow/contrib/autograph/pyct/common_transformers/anf.py index cc039986c219db1febfe610a5078e26eeb2d5a83..e42f679cfe31f919e10f7baf409247014b3cf386 100644 --- a/tensorflow/contrib/autograph/pyct/common_transformers/anf.py +++ b/tensorflow/contrib/autograph/pyct/common_transformers/anf.py @@ -12,12 +12,24 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Conversion to A-normal form.""" +"""Conversion to A-normal form. + +The general idea of A-normal form is that every intermediate value is +explicitly named with a variable. For more, see +https://en.wikipedia.org/wiki/A-normal_form. + +The specific converters used here are based on Python AST semantics as +documented at https://greentreesnakes.readthedocs.io/en/latest/. +""" from __future__ import absolute_import from __future__ import division from __future__ import print_function +import gast +import six + +from tensorflow.contrib.autograph.pyct import templates from tensorflow.contrib.autograph.pyct import transformer @@ -32,26 +44,375 @@ class DummyGensym(object): # * the symbols generated so far self._idx = 0 - def new_name(self, stem): + def new_name(self, stem='tmp'): self._idx += 1 return stem + '_' + str(1000 + self._idx) class AnfTransformer(transformer.Base): - """Performs the actual conversion.""" + """Performs the conversion to A-normal form (ANF).""" - # TODO(mdan): Link to a reference. - # TODO(mdan): Implement. + # The algorithm is a postorder recursive tree walk. Any given node A may, in + # general, require creation of a series B of Assign statements, which compute + # and explicitly name the intermediate values needed to compute the value of + # A. If A was already a statement, it can be replaced with the sequence B + + # [A]. If A was an expression, B needs to be propagated up the tree until a + # statement is encountered. Since the `ast.NodeTransformer` framework makes + # no provision for subtraversals returning side information, this class + # accumulates the sequence B in an instance variable. - def __init__(self, entity_info): - """Creates a transformer. + # The only other subtlety is that some Python statements (like `if`) have both + # expression fields (`test`) and statement list fields (`body` and `orelse`). + # Any additional assignments needed to name all the intermediate values in the + # `test` can be prepended to the `if` node, but assignments produced by + # processing the `body` and the `orelse` need to be kept together with them, + # and not accidentally lifted out of the `if`. + + def __init__(self, entity_info, gensym_source=None): + """Creates an ANF transformer. Args: entity_info: transformer.EntityInfo + gensym_source: An optional object with the same interface as `DummyGensym` + for generating unique names """ super(AnfTransformer, self).__init__(entity_info) - self._gensym = DummyGensym(entity_info) + if gensym_source is None: + self._gensym = DummyGensym(entity_info) + else: + self._gensym = gensym_source(entity_info) + self._pending_statements = [] + + def _consume_pending_statements(self): + ans = self._pending_statements + self._pending_statements = [] + return ans + + def _add_pending_statement(self, stmt): + self._pending_statements.append(stmt) + + _trivial_nodes = ( + # Non-nodes that show up as AST fields + bool, six.string_types, + # Leaf nodes that are already in A-normal form + gast.expr_context, gast.Name, gast.Num, gast.Str, gast.Bytes, + gast.NameConstant, gast.Ellipsis, + # Binary operators + gast.Add, gast.Sub, gast.Mult, gast.Div, gast.Mod, gast.Pow, gast.LShift, + gast.RShift, gast.BitOr, gast.BitXor, gast.BitAnd, gast.FloorDiv, + # Unary operators + gast.Invert, gast.Not, gast.UAdd, gast.USub, + # Comparison operators + gast.Eq, gast.NotEq, gast.Lt, gast.LtE, gast.Gt, gast.GtE, + gast.Is, gast.IsNot, gast.In, gast.NotIn, + ) + + def _is_node_trivial(self, node): + if node is None: + return True + elif isinstance(node, self._trivial_nodes): + return True + elif isinstance(node, gast.keyword): + return self._is_node_trivial(node.value) + elif isinstance(node, (gast.Starred, gast.withitem, gast.slice)): + return self._are_children_trivial(node) + return False + + def _are_children_trivial(self, node): + for field in node._fields: + if not field.startswith('__'): + if not self._is_node_trivial(getattr(node, field)): + return False + return True + + def _ensure_node_is_trivial(self, node): + if node is None: + return node + elif isinstance(node, self._trivial_nodes): + return node + elif isinstance(node, list): + # If something's field was actually a list, e.g., variadic arguments. + return [self._ensure_node_is_trivial(n) for n in node] + elif isinstance(node, gast.keyword): + node.value = self._ensure_node_is_trivial(node.value) + return node + elif isinstance(node, (gast.Starred, gast.withitem, gast.slice)): + return self._ensure_fields_trivial(node) + elif isinstance(node, gast.expr): + temp_name = self._gensym.new_name() + temp_assign = templates.replace( + 'temp_name = expr', temp_name=temp_name, expr=node)[0] + self._add_pending_statement(temp_assign) + answer = templates.replace('temp_name', temp_name=temp_name)[0] + return answer + else: + raise ValueError('Do not know how to treat {}'.format(node)) + + def _ensure_fields_trivial(self, node): + for field in node._fields: + if field.startswith('__'): + continue + setattr(node, field, self._ensure_node_is_trivial(getattr(node, field))) + return node + + def _visit_strict_statement(self, node, trivialize_children=True): + assert not self._pending_statements + node = self.generic_visit(node) + if trivialize_children: + self._ensure_fields_trivial(node) + results = self._consume_pending_statements() + results.append(node) + return results + + def _visit_strict_expression(self, node): + node = self.generic_visit(node) + self._ensure_fields_trivial(node) + return node + + # Note on code order: These are listed in the same order as the grammar + # elements on https://github.com/serge-sans-paille/gast + + # FunctionDef, AsyncFunctionDef, and ClassDef should be correct by default. + + def visit_Return(self, node): + return self._visit_strict_statement(node) + + def visit_Delete(self, node): + return self._visit_strict_statement(node, trivialize_children=False) + + def visit_Assign(self, node): + return self._visit_strict_statement(node, trivialize_children=False) + + def visit_AugAssign(self, node): + return self._visit_strict_statement(node, trivialize_children=False) + + def visit_Print(self, node): + return self._visit_strict_statement(node) + + def visit_For(self, node): + assert not self._pending_statements + # It's important to visit node.iter first, because any statements created + # thereby need to live outside the body. + self.visit(node.iter) + node.iter = self._ensure_node_is_trivial(node.iter) + iter_stmts = self._consume_pending_statements() + # This generic_visit will revisit node.iter, but that is both correct and + # cheap because by this point node.iter is trivial. + node = self.generic_visit(node) + assert not self._pending_statements + iter_stmts.append(node) + return iter_stmts + + def visit_AsyncFor(self, node): + if not self._are_children_trivial(node): + msg = ('Nontrivial AsyncFor nodes not supported yet ' + '(need to think through the semantics).') + raise ValueError(msg) + return self.generic_visit(node) + + def visit_While(self, node): + if not self._is_node_trivial(node.test): + msg = ('While with nontrivial test not supported yet ' + '(need to avoid precomputing the test).') + raise ValueError(msg) + return self.generic_visit(node) + + def visit_If(self, node): + assert not self._pending_statements + # It's important to visit node.test first, because any statements created + # thereby need to live outside the body. + self.visit(node.test) + node.test = self._ensure_node_is_trivial(node.test) + condition_stmts = self._consume_pending_statements() + # This generic_visit will revisit node.test, but that is both correct and + # cheap because by this point node.test is trivial. + node = self.generic_visit(node) + assert not self._pending_statements + condition_stmts.append(node) + return condition_stmts + + def visit_With(self, node): + assert not self._pending_statements + # It's important to visit node.items first, because any statements created + # thereby need to live outside the body. + for item in node.items: + self.visit(item) + node.items = [self._ensure_node_is_trivial(n) for n in node.items] + contexts_stmts = self._consume_pending_statements() + # This generic_visit will revisit node.items, but that is both correct and + # cheap because by this point node.items is trivial. + node = self.generic_visit(node) + assert not self._pending_statements + contexts_stmts.append(node) + return contexts_stmts + + def visit_AsyncWith(self, node): + if not self._are_children_trivial(node): + msg = ('Nontrivial AsyncWith nodes not supported yet ' + '(need to think through the semantics).') + raise ValueError(msg) + return self.generic_visit(node) + + def visit_Raise(self, node): + return self._visit_strict_statement(node) + + # Try should be correct by default. + + def visit_Assert(self, node): + if not self._are_children_trivial(node): + msg = ('Nontrivial Assert nodes not supported yet ' + '(need to avoid computing the test when assertions are off, and ' + 'avoid computing the irritant when the assertion does not fire).') + raise ValueError(msg) + return self.generic_visit(node) + + # Import and ImportFrom should be correct by default. + + def visit_Exec(self, node): + return self._visit_strict_statement(node) + + # Global and Nonlocal should be correct by default. + + def visit_Expr(self, node): + return self._visit_strict_statement(node, trivialize_children=False) + + # Pass, Break, and Continue should be correct by default. + + def visit_BoolOp(self, node): + if not self._are_children_trivial(node): + msg = ('Nontrivial BoolOp nodes not supported yet ' + '(need to preserve short-circuiting semantics).') + raise ValueError(msg) + return self.generic_visit(node) + + def visit_BinOp(self, node): + return self._visit_strict_expression(node) + + def visit_UnaryOp(self, node): + return self._visit_strict_expression(node) + + def visit_Lambda(self, node): + if not self._are_children_trivial(node): + msg = ('Nontrivial Lambda nodes not supported ' + '(cannot insert statements into lambda bodies).') + raise ValueError(msg) + return self.generic_visit(node) + + def visit_IfExp(self, node): + if not self._are_children_trivial(node): + msg = ('Nontrivial IfExp nodes not supported yet ' + '(need to convert to If statement, to evaluate branches lazily ' + 'and insert statements into them).') + raise ValueError(msg) + return self.generic_visit(node) + + def visit_Dict(self, node): + return self._visit_strict_expression(node) + + def visit_Set(self, node): + return self._visit_strict_expression(node) + + def visit_ListComp(self, node): + msg = ('ListComp nodes not supported ' + '(need to convert to a form that tolerates ' + 'assignment statements in clause bodies).') + raise ValueError(msg) + + def visit_SetComp(self, node): + msg = ('SetComp nodes not supported ' + '(need to convert to a form that tolerates ' + 'assignment statements in clause bodies).') + raise ValueError(msg) + + def visit_DictComp(self, node): + msg = ('DictComp nodes not supported ' + '(need to convert to a form that tolerates ' + 'assignment statements in clause bodies).') + raise ValueError(msg) + + def visit_GeneratorExp(self, node): + msg = ('GeneratorExp nodes not supported ' + '(need to convert to a form that tolerates ' + 'assignment statements in clause bodies).') + raise ValueError(msg) + + def visit_Await(self, node): + if not self._are_children_trivial(node): + msg = ('Nontrivial Await nodes not supported yet ' + '(need to think through the semantics).') + raise ValueError(msg) + return self.generic_visit(node) + + def visit_Yield(self, node): + return self._visit_strict_expression(node) + + def visit_YieldFrom(self, node): + if not self._are_children_trivial(node): + msg = ('Nontrivial YieldFrom nodes not supported yet ' + '(need to unit-test them in Python 2).') + raise ValueError(msg) + return self.generic_visit(node) + + def visit_Compare(self, node): + if len(node.ops) > 1: + msg = ('Multi-ary compare nodes not supported yet ' + '(need to preserve short-circuiting semantics).') + raise ValueError(msg) + return self._visit_strict_expression(node) + + def visit_Call(self, node): + return self._visit_strict_expression(node) + + def visit_Repr(self, node): + if not self._are_children_trivial(node): + msg = ('Nontrivial Repr nodes not supported yet ' + '(need to research their syntax and semantics).') + raise ValueError(msg) + return self.generic_visit(node) + + def visit_FormattedValue(self, node): + if not self._are_children_trivial(node): + msg = ('Nontrivial FormattedValue nodes not supported yet ' + '(need to unit-test them in Python 2).') + raise ValueError(msg) + return self.generic_visit(node) + + def visit_JoinedStr(self, node): + if not self._are_children_trivial(node): + msg = ('Nontrivial JoinedStr nodes not supported yet ' + '(need to unit-test them in Python 2).') + raise ValueError(msg) + return self.generic_visit(node) + + def visit_Attribute(self, node): + return self._visit_strict_expression(node) + + def visit_Subscript(self, node): + return self._visit_strict_expression(node) + + # Starred and Name are correct by default, because the right thing to do is to + # just recur. + + def visit_List(self, node): + return self._visit_strict_expression(node) + + def visit_Tuple(self, node): + return self._visit_strict_expression(node) + + +def transform(node, entity_info, gensym_source=None): + """Converts the given node to A-normal form (ANF). + + The general idea of A-normal form: https://en.wikipedia.org/wiki/A-normal_form + The specific converters used here are based on Python AST semantics as + documented at https://greentreesnakes.readthedocs.io/en/latest/. -def transform(node, entity_info): - return AnfTransformer(entity_info).visit(node) + Args: + node: The node to transform. + entity_info: transformer.EntityInfo. TODO(mdan): What information does this + argument provide? + gensym_source: An optional object with the same interface as `DummyGensym` + for generating unique names. + """ + return AnfTransformer(entity_info, gensym_source=gensym_source).visit(node) diff --git a/tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py b/tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py index 81983a5ecb7b8c6216285409f854e27b7154a08b..951974820c784974cb5bb2320adbb2b07f9332df 100644 --- a/tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py +++ b/tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py @@ -18,6 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import textwrap + from tensorflow.contrib.autograph.pyct import compiler from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import transformer @@ -25,6 +27,22 @@ from tensorflow.contrib.autograph.pyct.common_transformers import anf from tensorflow.python.platform import test +class DummyGensym(object): + """A dumb gensym that suffixes a stem by sequential numbers from 1000.""" + + def __init__(self, entity_info): + del entity_info + # A proper implementation needs to account for: + # * entity_info.namespace + # * all the symbols defined in the AST + # * the symbols generated so far + self._idx = 0 + + def new_name(self, stem='tmp'): + self._idx += 1 + return stem + '_' + str(1000 + self._idx) + + class AnfTransformerTest(test.TestCase): def _simple_source_info(self): @@ -37,17 +55,349 @@ class AnfTransformerTest(test.TestCase): owner_type=None) def test_basic(self): - def test_function(): a = 0 return a - node, _ = parser.parse_entity(test_function) - node = anf.transform(node, self._simple_source_info()) + node = anf.transform(node.body[0], self._simple_source_info()) result, _ = compiler.ast_to_object(node) - self.assertEqual(test_function(), result.test_function()) + def assert_same_ast(self, expected_node, node, msg=None): + expected_source = compiler.ast_to_source(expected_node, indentation=' ') + expected_str = textwrap.dedent(expected_source).strip() + got_source = compiler.ast_to_source(node, indentation=' ') + got_str = textwrap.dedent(got_source).strip() + self.assertEqual(expected_str, got_str, msg=msg) + + def assert_body_anfs_as_expected(self, expected_fn, test_fn): + # Testing the code bodies only. Wrapping them in functions so the + # syntax highlights nicely, but Python doesn't try to execute the + # statements. + exp_node, _ = parser.parse_entity(expected_fn) + node, _ = parser.parse_entity(test_fn) + node = anf.transform( + node, self._simple_source_info(), gensym_source=DummyGensym) + exp_name = exp_node.body[0].name + # Ignoring the function names in the result because they can't be + # the same (because both functions have to exist in the same scope + # at the same time). + node.body[0].name = exp_name + self.assert_same_ast(exp_node, node) + # Check that ANF is idempotent + node_repeated = anf.transform( + node, self._simple_source_info(), gensym_source=DummyGensym) + self.assert_same_ast(node_repeated, node) + + def test_binop_basic(self): + + def test_function(x, y, z): + a = x + y + z + return a + + def expected_result(x, y, z): + tmp_1001 = x + y + a = tmp_1001 + z + return a + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_if_basic(self): + + def test_function(a, b, c, e, f, g): + if a + b + c: + d = e + f + g + return d + + def expected_result(a, b, c, e, f, g): + tmp_1001 = a + b + tmp_1002 = tmp_1001 + c + if tmp_1002: + tmp_1003 = e + f + d = tmp_1003 + g + return d + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_nested_binop_and_return(self): + + def test_function(b, c, d, e): + return (2 * b + c) + (d + e) + + def expected_result(b, c, d, e): + tmp_1001 = 2 * b + tmp_1002 = tmp_1001 + c + tmp_1003 = d + e + tmp_1004 = tmp_1002 + tmp_1003 + return tmp_1004 + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_function_call_and_expr(self): + + def test_function(call_something, a, b, y, z, c, d, e, f, g, h, i): + call_something(a + b, y * z, kwarg=c + d, *(e + f), **(g + h + i)) + + def expected_result(call_something, a, b, y, z, c, d, e, f, g, h, i): + tmp_1001 = g + h + tmp_1002 = a + b + tmp_1003 = y * z + tmp_1004 = e + f + tmp_1005 = c + d + tmp_1006 = tmp_1001 + i + call_something(tmp_1002, tmp_1003, kwarg=tmp_1005, *tmp_1004, **tmp_1006) + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_with_and_print(self): + + def test_function(a, b, c): + with a + b + c as d: + print(2 * d + 1) + + def expected_result(a, b, c): + tmp_1001 = a + b + tmp_1002 = tmp_1001 + c + with tmp_1002 as d: + tmp_1003 = 2 * d + tmp_1004 = tmp_1003 + 1 + print(tmp_1004) + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_local_definition_and_binary_compare(self): + + def test_function(): + def foo(a, b): + return 2 * a < b + return foo + + def expected_result(): + def foo(a, b): + tmp_1001 = 2 * a + tmp_1002 = tmp_1001 < b + return tmp_1002 + return foo + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_list_literal(self): + + def test_function(a, b, c, d, e, f): + return [a + b, c + d, e + f] + + def expected_result(a, b, c, d, e, f): + tmp_1001 = a + b + tmp_1002 = c + d + tmp_1003 = e + f + tmp_1004 = [tmp_1001, tmp_1002, tmp_1003] + return tmp_1004 + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_tuple_literal_and_unary(self): + + def test_function(a, b, c, d, e, f): + return (a + b, -(c + d), e + f) + + def expected_result(a, b, c, d, e, f): + tmp_1001 = c + d + tmp_1002 = a + b + tmp_1003 = -tmp_1001 + tmp_1004 = e + f + tmp_1005 = (tmp_1002, tmp_1003, tmp_1004) + return tmp_1005 + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_set_literal(self): + + def test_function(a, b, c, d, e, f): + return set(a + b, c + d, e + f) + + def expected_result(a, b, c, d, e, f): + tmp_1001 = a + b + tmp_1002 = c + d + tmp_1003 = e + f + tmp_1004 = set(tmp_1001, tmp_1002, tmp_1003) + return tmp_1004 + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_dict_literal_and_repr(self): + + def test_function(foo, bar, baz): + return repr({foo + bar + baz: 7 | 8}) + + def expected_result(foo, bar, baz): + tmp_1001 = foo + bar + tmp_1002 = tmp_1001 + baz + tmp_1003 = 7 | 8 + tmp_1004 = {tmp_1002: tmp_1003} + tmp_1005 = repr(tmp_1004) + return tmp_1005 + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_field_read_and_write(self): + + def test_function(a, d): + a.b.c = d.e.f + 3 + + def expected_result(a, d): + tmp_1001 = a.b + tmp_1002 = d.e + tmp_1003 = tmp_1002.f + tmp_1001.c = tmp_1003 + 3 + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_subscript_read_and_write(self): + + def test_function(a, b, c, d, e, f): + a[b][c] = d[e][f] + 3 + + def expected_result(a, b, c, d, e, f): + tmp_1001 = a[b] + tmp_1002 = d[e] + tmp_1003 = tmp_1002[f] + tmp_1001[c] = tmp_1003 + 3 + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_augassign_and_delete(self): + + def test_function(a, x, y, z): + a += x + y + z + del a + del z[y][x] + + def expected_result(a, x, y, z): + tmp_1001 = x + y + a += tmp_1001 + z + del a + tmp_1002 = z[y] + del tmp_1002[x] + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_raise_yield_and_raise(self): + + def test_function(a, c, some_computed, exception): + yield a ** c + raise some_computed('complicated' + exception) + + def expected_result(a, c, some_computed, exception): + tmp_1001 = a ** c + yield tmp_1001 + tmp_1002 = 'complicated' + exception + tmp_1003 = some_computed(tmp_1002) + raise tmp_1003 + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_with_and_if_with_expressions(self): + + def test_function(foo, bar, function, quux, quozzle, w, x, y, z): + with foo + bar: + function(x + y) + if quux + quozzle: + function(z / w) + + def expected_result(foo, bar, function, quux, quozzle, w, x, y, z): + tmp_1001 = foo + bar + with tmp_1001: + tmp_1002 = x + y + function(tmp_1002) + tmp_1003 = quux + quozzle + if tmp_1003: + tmp_1004 = z / w + function(tmp_1004) + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_exec(self): + + def test_function(): + # The point is to test A-normal form conversion of exec + # pylint: disable=exec-used + exec('computed' + 5 + 'stuff', globals(), locals()) + + def expected_result(): + # pylint: disable=exec-used + tmp_1001 = 'computed' + 5 + tmp_1002 = tmp_1001 + 'stuff' + tmp_1003 = globals() + tmp_1004 = locals() + exec(tmp_1002, tmp_1003, tmp_1004) + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_simple_while_and_assert(self): + + def test_function(foo, quux): + while foo: + assert quux + foo = foo + 1 * 3 + + def expected_result(foo, quux): + while foo: + assert quux + tmp_1001 = 1 * 3 + foo = foo + tmp_1001 + + self.assert_body_anfs_as_expected(expected_result, test_function) + + def test_for(self): + + def test_function(compute, something, complicated, foo): + for foo in compute(something + complicated): + bar = foo + 1 * 3 + return bar + + def expected_result(compute, something, complicated, foo): + tmp_1001 = something + complicated + tmp_1002 = compute(tmp_1001) + for foo in tmp_1002: + tmp_1003 = 1 * 3 + bar = foo + tmp_1003 + return bar + + self.assert_body_anfs_as_expected(expected_result, test_function) + + # This test collects several examples where the definition of A-normal form + # implemented by this transformer is questionable. Mostly it's here to spell + # out what the definition is in these cases. + def test_controversial(self): + + def test_function(b, c, d, f): + a = c + d + a.b = c + d + a[b] = c + d + a += c + d + a, b = c + a, b = c, d + a = f(c) + a = f(c + d) + a[b + d] = f.e(c + d) + + def expected_result(b, c, d, f): + a = c + d + a.b = c + d # Should be a.b = tmp? (Definitely not tmp = c + d) + a[b] = c + d # Should be a[b] = tmp? (Definitely not tmp = c + d) + a += c + d # Should be a += tmp? (Definitely not tmp = c + d) + a, b = c # Should be a = c[0], b = c[1]? Or not? + a, b = c, d # Should be a = c, b = d? Or not? + a = f(c) + tmp_1001 = c + d + a = f(tmp_1001) + tmp_1002 = b + d + tmp_1003 = f.e + tmp_1004 = c + d + a[tmp_1002] = tmp_1003(tmp_1004) # Or should be a[tmp1] = tmp2? + + self.assert_body_anfs_as_expected(expected_result, test_function) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/autograph/pyct/compiler.py b/tensorflow/contrib/autograph/pyct/compiler.py index 24c4517afa89147101f80af3ef60237132c1144c..f9cee109624dafd4da4a0981c5f8fda0a5d8a5e7 100644 --- a/tensorflow/contrib/autograph/pyct/compiler.py +++ b/tensorflow/contrib/autograph/pyct/compiler.py @@ -30,46 +30,112 @@ import tempfile import astor import gast +from tensorflow.contrib.autograph.pyct import origin_info + def ast_to_source(node, indentation=' '): - """Return the source code of given AST.""" - if isinstance(node, gast.AST): - node = gast.gast_to_ast(node) + """Return the source code of given AST. + + Args: + node: The code to compile, as an AST object. + indentation: The string to use for indentation. + + Returns: + code: The source code generated from the AST object + source_mapping: A mapping between the user and AutoGraph generated code. + """ + if not isinstance(node, (list, tuple)): + node = (node,) generator = astor.codegen.SourceGenerator(indentation, False, astor.string_repr.pretty_string) - generator.visit(node) - generator.result.append('\n') + + for n in node: + if isinstance(n, gast.AST): + n = gast.gast_to_ast(n) + generator.visit(n) + generator.result.append('\n') + # In some versions of Python, literals may appear as actual values. This # ensures everything is string. code = map(str, generator.result) - return astor.source_repr.pretty_source(code).lstrip() + code = astor.source_repr.pretty_source(code).lstrip() + return code -def ast_to_object( - node, indentation=' ', source_prefix=None, delete_on_exit=True): + +def ast_to_object(nodes, + indentation=' ', + include_source_map=False, + source_prefix=None, + delete_on_exit=True): """Return the Python objects represented by given AST. Compiling the AST code this way ensures that the source code is readable by e.g. `pdb` or `inspect`. Args: - node: The code to compile, as an AST object. - indentation: The string to use for indentation. - source_prefix: Optional string to print as-is into the source file. - delete_on_exit: Whether to delete the temporary file used for compilation - on exit. + nodes: Union[ast.AST, Iterable[ast.AST]], the code to compile, as an AST + object. + indentation: Text, the string to use for indentation. + include_source_map: bool, whether to attach a source map to the compiled + object. Also see origin_info.py. + source_prefix: Optional[Text], string to print as-is into the source file. + delete_on_exit: bool, whether to delete the temporary file used for + compilation on exit. Returns: - A module object containing the compiled source code. + compiled_nodes: A module object containing the compiled source code. + source: The source code of the compiled object + Raises: + ValueError: If ag_source_map__ is already in the namespace of the compiled + nodes. """ - source = ast_to_source(node, indentation) + if not isinstance(nodes, (list, tuple)): + nodes = (nodes,) + + source = ast_to_source(nodes, indentation=indentation) + + if source_prefix: + source = source_prefix + '\n' + source with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f: module_name = os.path.basename(f.name[:-3]) - if source_prefix: - f.write(source_prefix) - f.write('\n') f.write(source) + + if isinstance(nodes, (list, tuple)): + indices = range(-len(nodes), 0) + else: + indices = (-1,) + + if include_source_map: + source_map = origin_info.source_map(nodes, source, f.name, indices) + + # TODO(mdan): Try flush() and delete=False instead. if delete_on_exit: atexit.register(lambda: os.remove(f.name)) - return imp.load_source(module_name, f.name), source + compiled_nodes = imp.load_source(module_name, f.name) + + # TODO(znado): Clean this up so we don't need to attach it to the namespace. + # TODO(znado): This does not work for classes because their methods share a + # namespace. + # This attaches the source map which is needed for error handling. Note that + # api.to_graph copies this source map into an attribute of the function. + # + # We need this so the ag_source_map__ variable is available to the call to + # rewrite_graph_construction_error in the except block inside each function + # that handles graph construction errors. + # + # We cannot get the rewritten function name until it is too late so templating + # is hard, and this cleanly fixes the + # issues encountered with nested functions because this is attached to the + # outermost one. + if include_source_map: + # TODO(mdan): This name should be decided by the caller. + source_map_name = 'ag_source_map__' + if source_map_name in compiled_nodes.__dict__: + raise ValueError('cannot convert %s because is has namespace attribute ' + '"%s", which is reserved for AutoGraph.' % + (compiled_nodes, source_map_name)) + compiled_nodes.__dict__[source_map_name] = source_map + + return compiled_nodes, source diff --git a/tensorflow/contrib/autograph/pyct/compiler_test.py b/tensorflow/contrib/autograph/pyct/compiler_test.py index 98cdc1506b6aced603df99662f1468687a55f92c..cf783da6a3e540c6901a5fe9a5e4afdb6b1cfc03 100644 --- a/tensorflow/contrib/autograph/pyct/compiler_test.py +++ b/tensorflow/contrib/autograph/pyct/compiler_test.py @@ -59,14 +59,14 @@ class CompilerTest(test.TestCase): value=gast.Str('c')) ]) + source = compiler.ast_to_source(node, indentation=' ') self.assertEqual( textwrap.dedent(""" if 1: a = b else: a = 'c' - """).strip(), - compiler.ast_to_source(node, indentation=' ').strip()) + """).strip(), source.strip()) def test_ast_to_object(self): node = gast.FunctionDef( diff --git a/tensorflow/contrib/autograph/pyct/origin_info.py b/tensorflow/contrib/autograph/pyct/origin_info.py new file mode 100644 index 0000000000000000000000000000000000000000..1aad2f47dfa66cc1bfd3a7a66d31b03e2aa0d09e --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/origin_info.py @@ -0,0 +1,173 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Container for origin source code information before AutoGraph compilation.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections + +import gast + +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import ast_util +from tensorflow.contrib.autograph.pyct import parser +from tensorflow.python.util import tf_inspect + + +class LineLocation( + collections.namedtuple('LineLocation', ('filename', 'lineno'))): + """Similar to Location, but without column information. + + Attributes: + filename: Text + lineno: int, 1-based + """ + pass + + +class Location( + collections.namedtuple('Location', ('filename', 'lineno', 'col_offset'))): + """Encodes code location information. + + Attributes: + filename: Text + lineno: int, 1-based + col_offset: int + """ + + @property + def line_loc(self): + return LineLocation(self.filename, self.lineno) + + +class OriginInfo( + collections.namedtuple( + 'OriginInfo', + ('loc', 'function_name', 'source_code_line'))): + """Container for information about the source code before conversion. + + Attributes: + loc: Location + function_name: Optional[Text] + source_code_line: Text + """ + + def as_frame(self): + """Returns a 4-tuple consistent with the return of traceback.extract_tb.""" + return (self.loc.filename, self.loc.lineno, self.function_name, + self.source_code_line) + + +# TODO(mdan): This source map should be a class - easier to refer to. +def source_map(nodes, code, filename, indices_in_code): + """Creates a source map between an annotated AST and the code it compiles to. + + Args: + nodes: Iterable[ast.AST, ...] + code: Text + filename: Optional[Text] + indices_in_code: Union[int, Iterable[int, ...]], the positions at which + nodes appear in code. The parser always returns a module when parsing + code. This argument indicates the position in that module's body at + which the corresponding of node should appear. + + Returns: + Dict[CodeLocation, OriginInfo], mapping locations in code to locations + indicated by origin annotations in node. + """ + reparsed_nodes = parser.parse_str(code) + reparsed_nodes = [reparsed_nodes.body[i] for i in indices_in_code] + + resolve(reparsed_nodes, code) + result = {} + + for before, after in ast_util.parallel_walk(nodes, reparsed_nodes): + # Note: generated code might not be mapped back to its origin. + # TODO(mdan): Generated code should always be mapped to something. + origin_info = anno.getanno(before, anno.Basic.ORIGIN, default=None) + final_info = anno.getanno(after, anno.Basic.ORIGIN, default=None) + if origin_info is None or final_info is None: + continue + + line_loc = LineLocation(filename, final_info.loc.lineno) + + existing_origin = result.get(line_loc) + if existing_origin is not None: + # Overlaps may exist because of child nodes, but almost never to + # different line locations. Exception make decorated functions, where + # both lines are mapped to the same line in the AST. + + # Line overlaps: keep bottom node. + if existing_origin.loc.line_loc == origin_info.loc.line_loc: + if existing_origin.loc.lineno >= origin_info.loc.lineno: + continue + + # In case of overlaps, keep the leftmost node. + if existing_origin.loc.col_offset <= origin_info.loc.col_offset: + continue + + result[line_loc] = origin_info + + return result + + +# TODO(znado): Consider refactoring this into a Visitor. +# TODO(mdan): Does this work correctly with inner functions? +def resolve(nodes, source, function=None): + """Adds an origin information to all nodes inside the body of function. + + Args: + nodes: Union[ast.AST, Iterable[ast.AST, ...]] + source: Text, the source code string for the function whose body nodes will + be annotated. + function: Callable, the function that will have all nodes inside of it + annotation with an OriginInfo annotation with key anno.Basic.ORIGIN. If + it is None then only the line numbers and column offset will be set in the + annotation, with the rest of the information being None. + + Returns: + A tuple of the AST node for function and a String containing its source + code. + """ + if not isinstance(nodes, (list, tuple)): + nodes = (nodes,) + + if function: + _, function_lineno = tf_inspect.getsourcelines(function) + function_filepath = tf_inspect.getsourcefile(function) + else: + function_lineno = None + function_filepath = None + + source_lines = source.split('\n') + for node in nodes: + for n in gast.walk(node): + if not hasattr(n, 'lineno'): + continue + + lineno_in_body = n.lineno + + source_code_line = source_lines[lineno_in_body - 1] + if function: + source_lineno = function_lineno + lineno_in_body + function_name = function.__name__ + else: + source_lineno = lineno_in_body + function_name = None + + location = Location(function_filepath, source_lineno, n.col_offset) + origin = OriginInfo(location, function_name, source_code_line) + anno.setanno(n, anno.Basic.ORIGIN, origin) diff --git a/tensorflow/contrib/autograph/pyct/origin_info_test.py b/tensorflow/contrib/autograph/pyct/origin_info_test.py new file mode 100644 index 0000000000000000000000000000000000000000..6d7d8b1622a2ddb1a1d0eaeec50bdfaf38f05182 --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/origin_info_test.py @@ -0,0 +1,101 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for origin_info module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import compiler +from tensorflow.contrib.autograph.pyct import origin_info +from tensorflow.contrib.autograph.pyct import parser +from tensorflow.python.platform import test + + +class OriginInfoTest(test.TestCase): + + def test_source_map(self): + + def test_fn(x): + if x > 0: + x += 1 + return x + + node, source = parser.parse_entity(test_fn) + fn_node = node.body[0] + origin_info.resolve(fn_node, source) + + # Insert a traced line. + new_node = parser.parse_str('x = abs(x)').body[0] + anno.copyanno(fn_node.body[0], new_node, anno.Basic.ORIGIN) + fn_node.body.insert(0, new_node) + + # Insert an untraced line. + fn_node.body.insert(0, parser.parse_str('x = 0').body[0]) + + modified_source = compiler.ast_to_source(fn_node) + + source_map = origin_info.source_map(fn_node, modified_source, + 'test_filename', [0]) + + loc = origin_info.LineLocation('test_filename', 1) + origin = source_map[loc] + self.assertEqual(origin.source_code_line, 'def test_fn(x):') + self.assertEqual(origin.loc.lineno, 1) + + # The untraced line, inserted second. + loc = origin_info.LineLocation('test_filename', 2) + self.assertFalse(loc in source_map) + + # The traced line, inserted first. + loc = origin_info.LineLocation('test_filename', 3) + origin = source_map[loc] + self.assertEqual(origin.source_code_line, ' if x > 0:') + self.assertEqual(origin.loc.lineno, 2) + + loc = origin_info.LineLocation('test_filename', 4) + origin = source_map[loc] + self.assertEqual(origin.source_code_line, ' if x > 0:') + self.assertEqual(origin.loc.lineno, 2) + + def test_resolve(self): + + def test_fn(x): + """Docstring.""" + return x # comment + + node, source = parser.parse_entity(test_fn) + fn_node = node.body[0] + origin_info.resolve(fn_node, source) + + origin = anno.getanno(fn_node, anno.Basic.ORIGIN) + self.assertEqual(origin.loc.lineno, 1) + self.assertEqual(origin.loc.col_offset, 0) + self.assertEqual(origin.source_code_line, 'def test_fn(x):') + + origin = anno.getanno(fn_node.body[0], anno.Basic.ORIGIN) + self.assertEqual(origin.loc.lineno, 2) + self.assertEqual(origin.loc.col_offset, 2) + self.assertEqual(origin.source_code_line, ' """Docstring."""') + + origin = anno.getanno(fn_node.body[1], anno.Basic.ORIGIN) + self.assertEqual(origin.loc.lineno, 3) + self.assertEqual(origin.loc.col_offset, 2) + self.assertEqual(origin.source_code_line, ' return x # comment') + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/autograph/pyct/parser.py b/tensorflow/contrib/autograph/pyct/parser.py index c961efa892df6a21804dae8f52ef64bf99cd409e..112ed46a1e487a7904e79267c1ce7db0ad914552 100644 --- a/tensorflow/contrib/autograph/pyct/parser.py +++ b/tensorflow/contrib/autograph/pyct/parser.py @@ -37,6 +37,7 @@ def parse_entity(entity): def parse_str(src): """Returns the AST of given piece of code.""" + # TODO(mdan): This should exclude the module things are autowrapped in. return gast.parse(src) diff --git a/tensorflow/contrib/autograph/pyct/qual_names.py b/tensorflow/contrib/autograph/pyct/qual_names.py index da07013cf4f4309c0e24adda3017575d942861b7..fb81404edc1994309f5108fc7e7ba368a1ea3ccb 100644 --- a/tensorflow/contrib/autograph/pyct/qual_names.py +++ b/tensorflow/contrib/autograph/pyct/qual_names.py @@ -30,6 +30,7 @@ import collections import gast from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import parser class Symbol(collections.namedtuple('Symbol', ['name'])): @@ -89,7 +90,8 @@ class QN(object): if not isinstance(base, (str, StringLiteral, NumberLiteral)): # TODO(mdan): Require Symbol instead of string. raise ValueError( - 'For simple QNs, base must be a string or a Literal object.') + 'for simple QNs, base must be a string or a Literal object;' + ' got instead "%s"' % type(base)) assert '.' not in base and '[' not in base and ']' not in base self._parent = None self.qn = (base,) @@ -112,6 +114,22 @@ class QN(object): raise ValueError('Cannot get parent of simple name "%s".' % self.qn[0]) return self._parent + @property + def owner_set(self): + """Returns all the symbols (simple or composite) that own this QN. + + In other words, if this symbol was modified, the symbols in the owner set + may also be affected. + + Examples: + 'a.b[c.d]' has two owners, 'a' and 'a.b' + """ + owners = set() + if self.has_attr() or self.has_subscript(): + owners.add(self.parent) + owners.update(self.parent.owner_set) + return owners + @property def support_set(self): """Returns the set of simple symbols that this QN relies on. @@ -122,7 +140,7 @@ class QN(object): Examples: 'a.b' has only one support symbol, 'a' - 'a[i]' has two roots, 'a' and 'i' + 'a[i]' has two support symbols, 'a' and 'i' """ # TODO(mdan): This might be the set of Name nodes in the AST. Track those? roots = set() @@ -231,3 +249,9 @@ class QnResolver(gast.NodeTransformer): def resolve(node): return QnResolver().visit(node) + + +def from_str(qn_str): + node = parser.parse_expression(qn_str) + node = resolve(node) + return anno.getanno(node, anno.Basic.QN) diff --git a/tensorflow/contrib/autograph/pyct/qual_names_test.py b/tensorflow/contrib/autograph/pyct/qual_names_test.py index 264afd508cdb847315c486806b531dc1483ef622..c793c2bb39df19f1af9b74f33323dbd4c985ee0d 100644 --- a/tensorflow/contrib/autograph/pyct/qual_names_test.py +++ b/tensorflow/contrib/autograph/pyct/qual_names_test.py @@ -30,6 +30,15 @@ from tensorflow.python.platform import test class QNTest(test.TestCase): + def test_from_str(self): + a = QN('a') + b = QN('b') + a_dot_b = QN(a, attr='b') + a_sub_b = QN(a, subscript=b) + self.assertEqual(qual_names.from_str('a.b'), a_dot_b) + self.assertEqual(qual_names.from_str('a'), a) + self.assertEqual(qual_names.from_str('a[b]'), a_sub_b) + def test_basic(self): a = QN('a') self.assertEqual(a.qn, ('a',)) diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/BUILD b/tensorflow/contrib/autograph/pyct/static_analysis/BUILD index bcf2dacec2062704805f1d72ec27a243159d13c1..92eacba3fd53602ce238dfd7115ff0c3da9b1fc8 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/BUILD +++ b/tensorflow/contrib/autograph/pyct/static_analysis/BUILD @@ -19,8 +19,9 @@ py_library( srcs = [ "activity.py", "annos.py", - "cfg.py", "live_values.py", + "liveness.py", + "reaching_definitions.py", "type_info.py", ], srcs_version = "PY2AND3", @@ -28,6 +29,7 @@ py_library( deps = [ "//tensorflow/contrib/autograph/pyct", "//tensorflow/contrib/autograph/utils", + "//tensorflow/python:util", "@gast_archive//:gast", ], ) @@ -46,23 +48,32 @@ py_test( ) py_test( - name = "cfg_test", - srcs = ["cfg_test.py"], + name = "live_values_test", + srcs = ["live_values_test.py"], srcs_version = "PY2AND3", tags = ["no_windows"], deps = [ ":static_analysis", "//tensorflow/contrib/autograph/pyct", "//tensorflow/python:client_testlib", - "@gast_archive//:gast", ], ) py_test( - name = "live_values_test", - srcs = ["live_values_test.py"], + name = "liveness_test", + srcs = ["liveness_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":static_analysis", + "//tensorflow/contrib/autograph/pyct", + "//tensorflow/python:client_testlib", + ], +) + +py_test( + name = "reaching_definitions_test", + srcs = ["reaching_definitions_test.py"], srcs_version = "PY2AND3", - tags = ["no_windows"], deps = [ ":static_analysis", "//tensorflow/contrib/autograph/pyct", diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/__init__.py b/tensorflow/contrib/autograph/pyct/static_analysis/__init__.py index c325e19f28376da3be6db4b00b9f664eac047af2..9a82de735dc663f6a824488e4c5864943cecc3d4 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/__init__.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/__init__.py @@ -18,10 +18,14 @@ This module contains utilities to help annotate AST nodes with as much runtime information as can be possibly extracted without actually executing the code, under that assumption that the context in which the code will run is known. -Note: It's a fair bet that this analysis cannot be reused across contexts -without re-running it. In most cases, the context usually means referenced -modules, which should be static enough to allow reuse, but that is not being -reliably verified. +Overall, the different analyses have the functions listed below: + + * activity: inventories symbols read, written to, params, etc. at different + levels + * liveness, reaching_definitions: dataflow analyses based on the program's CFG + and using the symbol information gathered by activity analysis + * live_values, type_info: type and value inference based on dataflow + analysis and context information """ from __future__ import absolute_import diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/activity.py b/tensorflow/contrib/autograph/pyct/static_analysis/activity.py index 4d7b0cbb7b8f6ee5bd64553644dc3ec9b8bca95b..a0182da9d132f50f290f4ba4896484815efb1286 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/activity.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/activity.py @@ -12,7 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Activity analysis.""" +"""Activity analysis. + +Requires qualified name annotations (see qual_names.py). +""" from __future__ import absolute_import from __future__ import division @@ -59,9 +62,10 @@ class Scope(object): self.parent = parent self.add_unknown_symbols = add_unknown_symbols self.modified = set() + # TODO(mdan): Completely remove this. self.created = set() self.used = set() - self.params = set() + self.params = {} self.returned = set() # TODO(mdan): Rename to `locals` @@ -106,37 +110,23 @@ class Scope(object): self.modified |= other.modified self.created |= other.created self.used |= other.used - self.params |= other.params + self.params.update(other.params) self.returned |= other.returned def has(self, name): - if name in self.modified or name in self.params: + if name in self.modified: return True elif self.parent is not None: return self.parent.has(name) return False - def is_modified_since_entry(self, name): - if name in self.modified: - return True - elif self.parent is not None and not self.isolated: - return self.parent.is_modified_since_entry(name) - return False - - def is_param(self, name): - if name in self.params: - return True - elif self.parent is not None and not self.isolated: - return self.parent.is_param(name) - return False - def mark_read(self, name): self.used.add(name) if self.parent is not None and name not in self.created: self.parent.mark_read(name) - def mark_param(self, name): - self.params.add(name) + def mark_param(self, name, owner): + self.params[name] = owner def mark_creation(self, name, writes_create_symbol=False): """Mark a qualified name as created.""" @@ -226,37 +216,56 @@ class ActivityAnalyzer(transformer.Base): elif isinstance(node.ctx, gast.Param): # Param contexts appear in function defs, so they have the meaning of # defining a variable. - # TODO(mdan): This may be incorrect with nested functions. - # For nested functions, we'll have to add the notion of hiding args from - # the parent scope, not writing to them. - self.scope.mark_creation(qn) - self.scope.mark_param(qn) + self.scope.mark_write(qn) + self.scope.mark_param(qn, self.enclosing_entities[-1]) else: raise ValueError('Unknown context %s for node %s.' % (type(node.ctx), qn)) anno.setanno(node, NodeAnno.IS_LOCAL, self.scope.has(qn)) - anno.setanno(node, NodeAnno.IS_MODIFIED_SINCE_ENTRY, - self.scope.is_modified_since_entry(qn)) - anno.setanno(node, NodeAnno.IS_PARAM, self.scope.is_param(qn)) if self._in_return_statement: self.scope.mark_returned(qn) + def _enter_scope(self, isolated): + self.scope = Scope(self.scope, isolated=isolated) + + def _exit_scope(self): + self.scope = self.scope.parent + + def _process_statement(self, node): + self._enter_scope(False) + node = self.generic_visit(node) + anno.setanno(node, anno.Static.SCOPE, self.scope) + self._exit_scope() + return node + + def visit_Expr(self, node): + return self._process_statement(node) + + def visit_Return(self, node): + self._in_return_statement = True + node = self._process_statement(node) + self._in_return_statement = False + return node + + def visit_Assign(self, node): + return self._process_statement(node) + def visit_AugAssign(self, node): # Special rules for AugAssign. In Assign, the target is only written, # but in AugAssig (e.g. a += b), the target is both read and written. self._in_aug_assign = True - self.generic_visit(node) + node = self._process_statement(node) self._in_aug_assign = False return node def visit_Name(self, node): - self.generic_visit(node) + node = self.generic_visit(node) self._track_symbol(node) return node def visit_Attribute(self, node): - self.generic_visit(node) + node = self.generic_visit(node) if self._in_constructor and self._node_sets_self_attribute(node): self._track_symbol( node, composite_writes_alter_parent=True, writes_create_symbol=True) @@ -265,44 +274,38 @@ class ActivityAnalyzer(transformer.Base): return node def visit_Subscript(self, node): - self.generic_visit(node) + node = self.generic_visit(node) # Subscript writes (e.g. a[b] = "value") are considered to modify # both the element itself (a[b]) and its parent (a). - self._track_symbol(node, composite_writes_alter_parent=True) + self._track_symbol(node) return node def visit_Print(self, node): - current_scope = self.scope - args_scope = Scope(current_scope) - self.scope = args_scope - for n in node.values: - self.visit(n) - anno.setanno(node, NodeAnno.ARGS_SCOPE, args_scope) - self.scope = current_scope + self._enter_scope(False) + node.values = self.visit_block(node.values) + anno.setanno(node, anno.Static.SCOPE, self.scope) + anno.setanno(node, NodeAnno.ARGS_SCOPE, self.scope) + self._exit_scope() return node + def visit_Assert(self, node): + return self._process_statement(node) + def visit_Call(self, node): - current_scope = self.scope - args_scope = Scope(current_scope, isolated=False) - self.scope = args_scope - for n in node.args: - self.visit(n) + self._enter_scope(False) + node.args = self.visit_block(node.args) + node.keywords = self.visit_block(node.keywords) # TODO(mdan): Account starargs, kwargs - for n in node.keywords: - self.visit(n) - anno.setanno(node, NodeAnno.ARGS_SCOPE, args_scope) - self.scope = current_scope - self.visit(node.func) + anno.setanno(node, NodeAnno.ARGS_SCOPE, self.scope) + self._exit_scope() + node.func = self.visit(node.func) return node def _process_block_node(self, node, block, scope_name): - current_scope = self.scope - block_scope = Scope(current_scope, isolated=False) - self.scope = block_scope - for n in block: - self.visit(n) - anno.setanno(node, scope_name, block_scope) - self.scope = current_scope + self._enter_scope(False) + block = self.visit_block(block) + anno.setanno(node, scope_name, self.scope) + self._exit_scope() return node def _process_parallel_blocks(self, parent, children): @@ -321,94 +324,75 @@ class ActivityAnalyzer(transformer.Base): self.scope.merge_from(after_child) return parent + def visit_arguments(self, node): + return self._process_statement(node) + def visit_FunctionDef(self, node): - if self.scope: - qn = qual_names.QN(node.name) - self.scope.mark_write(qn) - current_scope = self.scope - body_scope = Scope(current_scope, isolated=True) - self.scope = body_scope - self.generic_visit(node) - anno.setanno(node, NodeAnno.BODY_SCOPE, body_scope) - self.scope = current_scope + # The FunctionDef node itself has a Scope object that tracks the creation + # of its name, along with the usage of any decorator accompany it. + self._enter_scope(False) + node.decorator_list = self.visit_block(node.decorator_list) + self.scope.mark_write(qual_names.QN(node.name)) + anno.setanno(node, anno.Static.SCOPE, self.scope) + self._exit_scope() + + # A separate Scope tracks the actual function definition. + self._enter_scope(True) + node.args = self.visit(node.args) + + # Track the body separately. This is for compatibility reasons, it may not + # be strictly needed. + self._enter_scope(False) + node.body = self.visit_block(node.body) + anno.setanno(node, NodeAnno.BODY_SCOPE, self.scope) + self._exit_scope() + + self._exit_scope() return node def visit_With(self, node): - current_scope = self.scope - with_scope = Scope(current_scope, isolated=False) - self.scope = with_scope - self.generic_visit(node) - anno.setanno(node, NodeAnno.BODY_SCOPE, with_scope) - self.scope = current_scope + self._enter_scope(False) + node = self.generic_visit(node) + anno.setanno(node, NodeAnno.BODY_SCOPE, self.scope) + self._exit_scope() return node - def visit_If(self, node): - current_scope = self.scope - cond_scope = Scope(current_scope, isolated=False) - self.scope = cond_scope - self.visit(node.test) - anno.setanno(node, NodeAnno.COND_SCOPE, cond_scope) - self.scope = current_scope + def visit_withitem(self, node): + return self._process_statement(node) + def visit_If(self, node): + self._enter_scope(False) + node.test = self.visit(node.test) + anno.setanno(node, NodeAnno.COND_SCOPE, self.scope) + anno.setanno(node.test, anno.Static.SCOPE, self.scope) + self._exit_scope() node = self._process_parallel_blocks(node, ((node.body, NodeAnno.BODY_SCOPE), (node.orelse, NodeAnno.ORELSE_SCOPE))) return node def visit_For(self, node): - self.visit(node.target) - self.visit(node.iter) + self._enter_scope(False) + node.target = self.visit(node.target) + node.iter = self.visit(node.iter) + anno.setanno(node.iter, anno.Static.SCOPE, self.scope) + self._exit_scope() node = self._process_parallel_blocks(node, ((node.body, NodeAnno.BODY_SCOPE), (node.orelse, NodeAnno.ORELSE_SCOPE))) return node def visit_While(self, node): - current_scope = self.scope - cond_scope = Scope(current_scope, isolated=False) - self.scope = cond_scope - self.visit(node.test) - anno.setanno(node, NodeAnno.COND_SCOPE, cond_scope) - self.scope = current_scope - + self._enter_scope(False) + node.test = self.visit(node.test) + anno.setanno(node, NodeAnno.COND_SCOPE, self.scope) + anno.setanno(node.test, anno.Static.SCOPE, self.scope) + self._exit_scope() node = self._process_parallel_blocks(node, ((node.body, NodeAnno.BODY_SCOPE), (node.orelse, NodeAnno.ORELSE_SCOPE))) return node - def visit_Return(self, node): - self._in_return_statement = True - node = self.generic_visit(node) - self._in_return_statement = False - return node - - -def get_read(node, context): - """Return the variable names as QNs (qual_names.py) read by this statement.""" - analyzer = ActivityAnalyzer(context, None, True) - analyzer.visit(node) - return analyzer.scope.used - - -def get_updated(node, context): - """Return the variable names created or mutated by this statement. - - This function considers assign statements, augmented assign statements, and - the targets of for loops, as well as function arguments. - For example, `x[0] = 2` will return `x`, `x, y = 3, 4` will return `x` and - `y`, `for i in range(x)` will return `i`, etc. - Args: - node: An AST node - context: An EntityContext instance - - Returns: - A set of variable names (QNs, see qual_names.py) of all the variables - created or mutated. - """ - analyzer = ActivityAnalyzer(context, None, True) - analyzer.visit(node) - return analyzer.scope.created | analyzer.scope.modified - def resolve(node, context, parent_scope=None): return ActivityAnalyzer(context, parent_scope).visit(node) diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py index bc22be0a270bbc9c361aea6d6d9c255ea51796e8..e940516190182a905f5747ffdd66533567bac76b 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/activity_test.py @@ -52,18 +52,18 @@ class ScopeTest(test.TestCase): other = activity.Scope(None) other.copy_from(scope) - self.assertTrue(QN('foo') in other.created) + self.assertTrue(QN('foo') in other.modified) scope.mark_write(QN('bar')) scope.copy_from(other) - self.assertFalse(QN('bar') in scope.created) + self.assertFalse(QN('bar') in scope.modified) scope.mark_write(QN('bar')) scope.merge_from(other) - self.assertTrue(QN('bar') in scope.created) - self.assertFalse(QN('bar') in other.created) + self.assertTrue(QN('bar') in scope.modified) + self.assertFalse(QN('bar') in other.modified) def test_copy_of(self): scope = activity.Scope(None) @@ -157,7 +157,8 @@ class ActivityAnalyzerTest(test.TestCase): """Assert the scope contains specific used, modified & created variables.""" self.assertSymbolSetsAre(used, scope.used, 'read') self.assertSymbolSetsAre(modified, scope.modified, 'modified') - self.assertSymbolSetsAre(created, scope.created, 'created') + # Created is deprecated, we're no longer verifying it. + # self.assertSymbolSetsAre(created, scope.created, 'created') def test_print_statement(self): @@ -215,12 +216,6 @@ class ActivityAnalyzerTest(test.TestCase): (), (), ) - self.assertScopeIsRmc( - anno.getanno(call_node, NodeAnno.ARGS_SCOPE).parent, - ('a', 'a.b', 'a.c', 'a.d', 'foo'), - ('a.c',), - ('a',), - ) def test_call_args_subscripts(self): @@ -241,12 +236,6 @@ class ActivityAnalyzerTest(test.TestCase): (), (), ) - self.assertScopeIsRmc( - anno.getanno(call_node, NodeAnno.ARGS_SCOPE).parent, - ('a', 'a[0]', 'a[b]', 'a[c]', 'b', 'c', 'foo'), - ('b', 'c'), - ('a', 'b', 'c'), - ) def test_while(self): @@ -362,20 +351,20 @@ class ActivityAnalyzerTest(test.TestCase): self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.BODY_SCOPE), ('a', 'b', 'c', 'a[c]'), - ('a', 'a[b]', 'd'), + ('a[b]', 'd'), ('d',), ) # TODO(mdan): Should subscript writes (a[0] = 1) be considered to read "a"? self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.ORELSE_SCOPE), ('a', 'e'), - ('a', 'a[0]', 'd'), + ('a[0]', 'd'), ('d',), ) self.assertScopeIsRmc( anno.getanno(if_node, NodeAnno.ORELSE_SCOPE).parent, ('a', 'b', 'c', 'd', 'e', 'a[c]'), - ('a', 'd', 'a[b]', 'a[0]'), + ('d', 'a[b]', 'a[0]'), ('a', 'b', 'c', 'd', 'e'), ) @@ -415,10 +404,6 @@ class ActivityAnalyzerTest(test.TestCase): node, _ = self._parse_and_analyze(test_fn) fn_def_node = node.body[0].body[0] - self.assertScopeIsRmc( - anno.getanno(fn_def_node, - NodeAnno.BODY_SCOPE).parent, ('b', 'i', 'f', 'c', 'a'), - ('f', 'b', 'c', 'i'), ('f', 'a', 'b', 'c', 'i')) self.assertScopeIsRmc( anno.getanno(fn_def_node, NodeAnno.BODY_SCOPE), ('x', 'y'), ('y',), ( 'x', @@ -452,7 +437,7 @@ class ActivityAnalyzerTest(test.TestCase): self.assertScopeIsRmc( anno.getanno(fn_node, NodeAnno.BODY_SCOPE), ('a', 'a[0]'), - ('a', 'a[0]'), + ('a[0]',), ('a',), ) @@ -518,47 +503,6 @@ class ActivityAnalyzerTest(test.TestCase): anno.getanno(fn_node, NodeAnno.BODY_SCOPE), ('b',), (('')), (('a', 'b'))) - def test_get_read(self): - - def test_fn(x, y): - z = test_fn(x, y) - return z - - node, ctx = self._parse_and_analyze(test_fn) - node = node.body[0].body[0] - read_vars = activity.get_read(node, ctx) - self.assertEqual(read_vars, set(map(qual_names.QN, ('test_fn', 'x', 'y')))) - - def test_fn2(x, y, z): - z += test_fn2(x, y, z) - return z - - node, ctx = self._parse_and_analyze(test_fn2) - node = node.body[0].body[0] - read_vars = activity.get_read(node, ctx) - self.assertEqual(read_vars, - set(map(qual_names.QN, ('test_fn2', 'x', 'y', 'z')))) - - def test_get_updated(self): - - def test_fn(x, y): - z = test_fn(x, y) - return z - - node, ctx = self._parse_and_analyze(test_fn) - node = node.body[0].body[0] - updated_vars = activity.get_updated(node, ctx) - self.assertEqual(updated_vars, set(map(qual_names.QN, ('z')))) - - def test_fn2(x, y, z): - z += test_fn2(x, y, z) - return z - - node, ctx = self._parse_and_analyze(test_fn2) - node = node.body[0].body[0] - updated_vars = activity.get_updated(node, ctx) - self.assertEqual(updated_vars, set(map(qual_names.QN, ('z')))) - if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/annos.py b/tensorflow/contrib/autograph/pyct/static_analysis/annos.py index b929b35b79200b0968c9c4f26b10cda28763773a..5eefecf278992f73464817585a3498de4c031978 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/annos.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/annos.py @@ -21,6 +21,9 @@ from __future__ import print_function from enum import Enum +# TODO(mdan): Remove. + + class NoValue(Enum): def __repr__(self): @@ -50,10 +53,3 @@ class NodeAnno(NoValue): ORELSE_SCOPE = ( 'The scope for the orelse body of a statement (False branch for if ' 'statements, orelse body for loops).') - - # Type and Value annotations - # Type annotations are represented by objects of type type_info.Type. - STATIC_INFO = ( - 'The type or value information that should be asserted about the entity ' - 'referenced by the symbol holding this annotation, irrespective of the ' - 'execution context.') diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py b/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py deleted file mode 100644 index 4acc4ed66a62b0ccd407d39b1abda00c4c88a9a1..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/autograph/pyct/static_analysis/cfg.py +++ /dev/null @@ -1,446 +0,0 @@ -# Copyright 2016 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Control flow graph analysis. - -Given a Python AST we construct a control flow graph, with edges both to the -next and previous statements (so it can easily walk the graph both ways). Its -nodes contain the AST of the statements. It can then perform forward or backward -analysis on this CFG. -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from collections import namedtuple -import functools -import operator - -import gast - -from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct.static_analysis import activity - - -class CfgNode(object): - """A node in the CFG.""" - __slots__ = ['next', 'value', 'prev'] - - def __init__(self, value): - self.next = set() - self.prev = set() - self.value = value - - -class Cfg(namedtuple('Cfg', ['entry', 'exit'])): - """A Control Flow Graph. - - Each statement is represented as a node. For control flow statements such - as conditionals and loops the conditional itself is a node which either - branches or cycles, respectively. - Attributes: - entry: The entry node, which contains the `gast.arguments` node of the - function definition. - exit: The exit node. This node is special because it has no value (i.e. no - corresponding AST node). This is because Python functions can have - multiple return statements. - """ - pass - - -class CfgBuilder(gast.NodeVisitor): - """Construct a control flow graph. - - Construct a CFG starting from a FunctionDef node. - Usage: - cfg_obj = CfgBuilder().build_cfg(fndef_node) - """ - - def __init__(self): - # The current leaves of the CFG - self.current_leaves = [] - # TODO(alexbw): generalize to break, return, continue, yield, etc. - # A stack of lists, tracking continue statements - self.continue_ = [] - # A stack of lists tracking break nodes - self.break_ = [] - - def set_current_leaves(self, cfg_node): - """Link this cfg_node to the current leaves. - - This is the central function for building the CFG. It links the current - head cfg_nodes to the passed cfg_node. It then resets the head to the - passed cfg_node. - - Args: - cfg_node: A CfgNode instance. - """ - for head in self.current_leaves: - head.next.add(cfg_node) - # While we're linking the CFG forward, add backlinks - cfg_node.prev.add(head) - self.current_leaves = [cfg_node] - - def build_cfg(self, node): - """Build a CFG for a function. - - Implementation of building a CFG for dataflow analysis. See, e.g.: - https://www.seas.harvard.edu/courses/cs252/2011sp/slides/Lec02-Dataflow.pdf - - Args: - node: A function definition the body of which to analyze. - Returns: - A CFG object. - Raises: - TypeError: If the input is not a function definition. - """ - if not isinstance(node, gast.FunctionDef): - raise TypeError('input must be a function definition') - entry_cfg_node = CfgNode(node.args) - self.current_leaves = [entry_cfg_node] - self.visit_statements(node.body) - exit_cfg_node = CfgNode(None) - self.set_current_leaves(exit_cfg_node) - return Cfg(entry_cfg_node, exit_cfg_node) - - def visit_statements(self, nodes): - for node in nodes: - # Check for control flow - if isinstance(node, (gast.For, gast.While, gast.If, gast.Try, gast.Break, - gast.Continue, gast.With)): - self.visit(node) - else: - expr = CfgNode(node) - self.set_current_leaves(expr) - - def generic_visit(self, node): - raise ValueError('unknown control flow') - - def visit_If(self, node): - # TODO(alexbw): change this to use immutable tuples instead of lists - # The current head will hold the conditional - test = CfgNode(node.test) - self.set_current_leaves(test) - # Handle the body - self.visit_statements(node.body) - body_exit = self.current_leaves - self.current_leaves = [test] - # Handle the orelse - self.visit_statements(node.orelse) - self.current_leaves.extend(body_exit) - - def visit_While(self, node): - test = CfgNode(node.test) - self.set_current_leaves(test) - # Start a new level of nesting - self.break_.append([]) - self.continue_.append([]) - # Handle the body - self.visit_statements(node.body) - body_exit = self.current_leaves - self.current_leaves.extend(self.continue_.pop()) - self.set_current_leaves(test) - # Handle the orelse - self.visit_statements(node.orelse) - # The break statements and the test go to the next node - self.current_leaves.extend(self.break_.pop()) - # Body and orelse statements can reach out of the loop - self.current_leaves.extend(body_exit) - - def visit_For(self, node): - iter_ = CfgNode(node.iter) - self.set_current_leaves(iter_) - self.break_.append([]) - self.continue_.append([]) - self.visit_statements(node.body) - body_exit = self.current_leaves - self.current_leaves.extend(self.continue_.pop()) - self.set_current_leaves(iter_) - # Handle the orelse - self.visit_statements(node.orelse) - # The break statements and the test go to the next node - self.current_leaves.extend(self.break_.pop()) - # Body and orelse statements can reach out of the loop - self.current_leaves.extend(body_exit) - - def visit_Break(self, node): - self.break_[-1].extend(self.current_leaves) - self.current_leaves[:] = [] - - def visit_Continue(self, node): - self.continue_[-1].extend(self.current_leaves) - self.current_leaves[:] = [] - - def visit_Try(self, node): - self.visit_statements(node.body) - body = self.current_leaves - handlers = [] - for handler in node.handlers: - self.current_leaves = body[:] - self.visit_statements(handler.body) - handlers.extend(self.current_leaves) - self.current_leaves = body - self.visit_statements(node.orelse) - self.current_leaves = handlers + self.current_leaves - self.visit_statements(node.finalbody) - - def visit_With(self, node): - for item in node.items: - self.set_current_leaves(CfgNode(item)) - self.visit_statements(node.body) - - -# TODO(alexbw): once CFG analysis occurs at a block level, -# this extra class will not be necessary -class PropagateAnalysis(gast.NodeVisitor): - """Port analysis annotations from statements to their enclosing blocks.""" - - def __init__(self, analysis): - self.transfer_fn = analysis.transfer_fn - self.in_label = analysis.in_label - self.out_label = analysis.out_label - super(PropagateAnalysis, self).__init__() - - def visit_If(self, node): - # Depth-first. - self.generic_visit(node) - incoming = anno.getanno(node.body[0], self.in_label) - incoming |= anno.getanno(node.test, self.in_label) - outgoing = anno.getanno(node.body[-1], self.out_label) - outgoing |= anno.getanno(node.test, self.out_label) - if node.orelse: - orelse_outgoing = anno.getanno(node.orelse[-1], self.out_label) - outgoing = self.transfer_fn(outgoing, orelse_outgoing) - anno.setanno(node, self.in_label, incoming) - anno.setanno(node, self.out_label, outgoing) - - def visit_For(self, node): - self.generic_visit(node) - incoming = set(anno.getanno(node.body[0], self.in_label)) - incoming -= set((anno.getanno(node.target, anno.Basic.QN),)) - outgoing = anno.getanno(node.body[-1], self.out_label) - if node.orelse: - orelse_outgoing = anno.getanno(node.orelse[-1], self.out_label) - outgoing = self.transfer_fn(outgoing, orelse_outgoing) - anno.setanno(node, self.in_label, frozenset(incoming)) - anno.setanno(node, self.out_label, outgoing) - - def visit_While(self, node): - self.generic_visit(node) - incoming = anno.getanno(node.body[0], self.in_label) - incoming |= anno.getanno(node.test, self.in_label) - outgoing = anno.getanno(node.body[-1], self.out_label) - if node.orelse: - orelse_outgoing = anno.getanno(node.orelse[-1], self.out_label) - outgoing = self.transfer_fn(outgoing, orelse_outgoing) - anno.setanno(node, self.in_label, incoming) - anno.setanno(node, self.out_label, outgoing) - - def visit_With(self, node): - self.generic_visit(node) - incoming = anno.getanno(node.body[0], self.in_label) - for item in node.items: - incoming |= anno.getanno(item, self.in_label) - outgoing = anno.getanno(node.body[-1], self.out_label) - anno.setanno(node, self.in_label, incoming) - anno.setanno(node, self.out_label, outgoing) - - -# TODO(alexbw): Abstract the CFG walking machinery into a superclass -# which is parameterized on which fields it selects when walking. -# TODO(alexbw): Abstract the application of dataflow analysis -class Forward(object): - """Forward analysis on CFG. - - Args: - label: A name for this analysis e.g. 'active' for activity analysis. The AST - nodes in the CFG will be given annotations 'name_in', 'name_out', - 'name_gen' and 'name_kill' which contain the incoming values, outgoing - values, values generated by the statement, and values deleted by the - statement respectively. - transfer_fn: Either the AND or OR operator. If the AND operator is used it - turns into forward must analysis (i.e. a value will only be carried - forward if it appears on all incoming paths). The OR operator means that - forward may analysis is done (i.e. the union of incoming values will be - taken). - """ - - def __init__(self, label, source_info, transfer_fn=operator.or_): - self.transfer_fn = transfer_fn - self.source_info = source_info - self.out_label = label + '_out' - self.in_label = label + '_in' - self.gen_label = label + '_gen' - self.kill_label = label + '_kill' - - # TODO(alexbw): see if we can simplify by visiting breadth-first - def visit(self, node): - """Depth-first walking the CFG, applying dataflow info propagation.""" - # node.value is None only for the exit CfgNode. - if not node.value: - return - - if anno.hasanno(node.value, self.out_label): - before = hash(anno.getanno(node.value, self.out_label)) - else: - before = None - preds = [ - anno.getanno(pred.value, self.out_label) - for pred in node.prev - if anno.hasanno(pred.value, self.out_label) - ] - if preds: - incoming = functools.reduce(self.transfer_fn, preds[1:], preds[0]) - else: - incoming = frozenset() - anno.setanno(node.value, self.in_label, incoming) - gen, kill = self.get_gen_kill(node, incoming) - anno.setanno(node.value, self.gen_label, gen) - anno.setanno(node.value, self.kill_label, kill) - anno.setanno(node.value, self.out_label, (incoming - kill) | gen) - - if hash(anno.getanno(node.value, self.out_label)) != before: - for succ in node.next: - self.visit(succ) - - def get_gen_kill(self, cfg_node, incoming): - """Calculate Gen and Kill properties of a CFG node in dataflow analysis. - - A function which takes the CFG node as well as a set of incoming - values. It must return a set of newly generated values by the statement as - well as a set of deleted (killed) values. - - Args: - cfg_node: A CfgNode instance. - incoming: - """ - raise NotImplementedError() - - -class Backward(Forward): - """Backward analysis on CFG.""" - - def visit(self, cfg_node): - # cfg_node.value is None for the exit node, which will be visited only once - if not cfg_node.value: - for pred in cfg_node.prev: - self.visit(pred) - return - - if anno.hasanno(cfg_node.value, self.in_label): - before = hash(anno.getanno(cfg_node.value, self.in_label)) - else: - before = None - succs = [ - anno.getanno(succ.value, self.in_label) - for succ in cfg_node.next - if anno.hasanno(succ.value, self.in_label) - ] - if succs: - incoming = functools.reduce(self.transfer_fn, succs[1:], succs[0]) - else: - incoming = frozenset() - anno.setanno(cfg_node.value, self.out_label, incoming) - gen, kill = self.get_gen_kill(cfg_node, incoming) - anno.setanno(cfg_node.value, self.gen_label, gen) - anno.setanno(cfg_node.value, self.kill_label, kill) - anno.setanno(cfg_node.value, self.in_label, (incoming - kill) | gen) - if hash(anno.getanno(cfg_node.value, self.in_label)) != before: - for pred in cfg_node.prev: - self.visit(pred) - - -def run_analyses(node, analyses): - """Perform dataflow analysis on all functions within an AST. - - Args: - node: An AST node on which to run dataflow analysis. - analyses: Either an instance of the Forward or Backward dataflow analysis - class, or a list or tuple of them. - - Returns: - node: The node, but now with annotations on the AST nodes containing the - results of the dataflow analyses. - """ - if not isinstance(analyses, (tuple, list)): - analyses = (analyses,) - for analysis in analyses: - if not isinstance(analysis, (Forward, Backward)): - raise TypeError('not a valid forward analysis object') - - for child_node in gast.walk(node): - if isinstance(child_node, gast.FunctionDef): - cfg_obj = CfgBuilder().build_cfg(child_node) - for analysis in analyses: - if isinstance(analysis, Backward): - analysis.visit(cfg_obj.exit) - elif isinstance(analysis, Forward): - analysis.visit(cfg_obj.entry) - for analysis in analyses: - PropagateAnalysis(analysis).visit(node) - return node - - -class Liveness(Backward): - """Perform a liveness analysis. - - Each statement is annotated with a set of variables that may be used - later in the program. - """ - - def __init__(self, source_info): - super(Liveness, self).__init__('live', source_info) - - def get_gen_kill(self, node, _): - # A variable's parents are live if it is live - # e.g. x is live if x.y is live. This means gen needs to return - # all parents of a variable (if it's an Attribute or Subscript). - # This doesn't apply to kill (e.g. del x.y doesn't affect liveness of x) - gen = activity.get_read(node.value, self.source_info) - gen = functools.reduce(lambda left, right: left | right.support_set, gen, - gen) - kill = activity.get_updated(node.value, self.source_info) - return gen, kill - - -class ReachingDefinitions(Forward): - """Perform reaching definition analysis. - - Each statement is annotated with a set of (variable, definition) pairs. - """ - - def __init__(self, source_info): - super(ReachingDefinitions, self).__init__('definitions', source_info) - - def get_gen_kill(self, node, incoming): - definitions = activity.get_updated(node.value, self.source_info) - gen = frozenset((id_, node.value) for id_ in definitions) - kill = frozenset(def_ for def_ in incoming if def_[0] in definitions) - return gen, kill - - -class Defined(Forward): - """Perform defined variable analysis. - - Each statement is annotated with a set of variables which are guaranteed to - be defined at that point. - """ - - def __init__(self, source_info): - super(Defined, self).__init__( - 'defined', source_info, transfer_fn=operator.and_) - - def get_gen_kill(self, node, _): - gen = activity.get_updated(node.value, self.source_info) - return gen, frozenset() diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/cfg_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/cfg_test.py deleted file mode 100644 index 428ebbedca85f9b94b4b1db0f3b36a334126196b..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/autograph/pyct/static_analysis/cfg_test.py +++ /dev/null @@ -1,303 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Tests for cfg module.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import functools - -import gast - -from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import parser -from tensorflow.contrib.autograph.pyct import qual_names -from tensorflow.contrib.autograph.pyct import transformer -from tensorflow.contrib.autograph.pyct.static_analysis import cfg -from tensorflow.python.platform import test - - -class CFGTest(test.TestCase): - - def _parse_and_analyze(self, test_fn): - node, source = parser.parse_entity(test_fn) - entity_info = transformer.EntityInfo( - source_code=source, - source_file=None, - namespace={}, - arg_values=None, - arg_types=None, - owner_type=None) - node = qual_names.resolve(node) - return node, entity_info - - def _check_anno_matches(self, node, anno_name, var_names): - if isinstance(var_names, str): - var_names = (var_names,) - qual_vars = set() - for var_name in var_names: - if isinstance(var_name, str): - if '[' in var_name or ']' in var_name: - raise ValueError('Annotation matching not supported with subscript.') - if '.' not in var_name: - qual_vars.add(qual_names.QN(var_name)) - else: - attrs = var_name.split('.') - this_qn = functools.reduce(qual_names.QN, attrs[1:], - qual_names.QN(attrs[0])) - qual_vars.add(this_qn) - self.assertEqual(anno.getanno(node, anno_name), qual_vars) - - def test_reaching(self): - - def f(x): - print(x) - while True: - x = x - x = x - return x - - node, ctx = self._parse_and_analyze(f) - cfg.run_analyses(node, cfg.ReachingDefinitions(ctx)) - body = node.body[0].body - # Only the argument reaches the expression - def_in = anno.getanno(body[0], 'definitions_in') - # One element, x, from arguments - self.assertEqual(set(type(d[1]) for d in def_in), set((gast.arguments,))) - - while_body = body[1].body - def_in = anno.getanno(while_body[0], 'definitions_in') - # One definition, two possible sources. - # - One from an assignment (if the loop is entered) - # - The other from the arguments (if loop is not entered) - self.assertEqual( - set(type(d[1]) for d in def_in), set((gast.arguments, gast.Assign))) - - def_in = anno.getanno(while_body[1], 'definitions_in') - # If we've reached this line, the only reaching definition of x is the - # Assign node in previous line - self.assertEqual(set(type(d[1]) for d in def_in), set((gast.Assign,))) - - def_in = anno.getanno(body[2], 'definitions_in') - # Same situation as while_body[0] - self.assertEqual( - set(type(d[1]) for d in def_in), set((gast.arguments, gast.Assign))) - - def test_defined(self): - - def f(x): - if x: - y = 2 # pylint: disable=unused-variable - return x - - node, ctx = self._parse_and_analyze(f) - cfg.run_analyses(node, cfg.Defined(ctx)) - body = node.body[0].body - # only x is for sure defined at the end - self._check_anno_matches(body[1], 'defined_in', 'x') - # at the end of the if body both x and y are defined - if_body = body[0].body - self._check_anno_matches(if_body[0], 'defined_out', ('x', 'y')) - - def _get_live_annotated_fnbody(self, f): - node, ctx = self._parse_and_analyze(f) - cfg.run_analyses(node, cfg.Liveness(ctx)) - body = node.body[0].body - return body - - def test_live_straightline(self): - - def f1(x): - a = g(x) # pylint: disable=undefined-variable - b = h(a) # pylint: disable=undefined-variable, unused-variable - return x - - body = self._get_live_annotated_fnbody(f1) - self._check_anno_matches(body[1], 'live_in', ('a', 'h', 'x')) - self._check_anno_matches(body[2], 'live_in', ('x')) - self._check_anno_matches(body[0], 'live_in', ('g', 'h', 'x')) - self._check_anno_matches(body[2], 'live_out', ()) - - def test_live_stacked_conds_with_else(self): - - def f2(x, a): # pylint: disable=unused-argument - if a > 0: # x should not be live - x = 0 - if a > 1: - x = 1 - else: - x = 2 - - body = self._get_live_annotated_fnbody(f2) - self._check_anno_matches(body[0], 'live_in', ('a')) - self._check_anno_matches(body[1], 'live_in', ('a')) - - def test_live_stacked_conds(self): - - def f3(x, a): - if a > 0: # x and a should be live - x = 0 - if a > 1: # x and a should be live_in - x = 1 - return x # x should be live - - body = self._get_live_annotated_fnbody(f3) - self._check_anno_matches(body[0], 'live_in', ('a', 'x')) - self._check_anno_matches(body[1], 'live_in', ('a', 'x')) - self._check_anno_matches(body[2], 'live_in', ('x')) - - def test_live_possibly_unused_cond(self): - - def f4(x, a): - if a > 0: # x should be live - x = 0 - x += 1 - - body = self._get_live_annotated_fnbody(f4) - self._check_anno_matches(body[0], 'live_in', ('x', 'a')) - self._check_anno_matches(body[1], 'live_in', ('x')) - - def test_live_attribute_in_cond(self): - - def f5(x, a): - if a > 0: # x.y should be live - x.y = 0 - return x.y - - body = self._get_live_annotated_fnbody(f5) - self._check_anno_matches(body[0], 'live_in', ('x', 'x.y', 'a')) - - def test_live_noop(self): - - def f6(x): - return x # should this cause x.* to be live? - - body = self._get_live_annotated_fnbody(f6) - self._check_anno_matches(body[0], 'live_in', ('x')) - - def test_live_loop(self): - - def f7(x, n): - for i in range(n): - x += i - return x - - body = self._get_live_annotated_fnbody(f7) - self._check_anno_matches(body[0], 'live_in', ('x', 'n', 'range')) - self._check_anno_matches(body[1], 'live_in', ('x')) - - def test_live_context_manager(self): - - def f8(x, f): - with f: - x += 1 - - body = self._get_live_annotated_fnbody(f8) - self._check_anno_matches(body[0], 'live_in', ('f', 'x')) - - def test_node_equality(self): - node_a = gast.parse('y = x').body[0] - node_b = gast.parse('y = x').body[0] - self.assertNotEqual(node_a, node_b) - - def test_nested_functions_defined(self): - - def f(x): - y = x * 2 - - def g(z): - return z + y - - return g(x) - - node, ctx = self._parse_and_analyze(f) - cfg.run_analyses(node, cfg.Defined(ctx)) - - body = node.body[0].body - self.assertEqual( - anno.getanno(body[2], 'defined_in'), - frozenset(map(qual_names.QN, ('g', 'x', 'y')))) - - # TODO(alexbw): CFG analysis doesn't currently cross FunctionDef boundaries. - # NOTE: 'z' is easy to find, but 'y' is not identified as - # defined, because CFG analysis is applied with each function separately. - # fndef_body = body[1].body - # self.assertEqual( - # anno.getanno(fndef_body[0], 'defined_in'), - # frozenset(map(qual_names.QN, ('z', 'y')))) - - def test_nested_functions_dont_leak_definitions(self): - - def f(x): - print(x) - - def g(): - y = 2 - return y - - return g() # y is not defined here - - node, ctx = self._parse_and_analyze(f) - cfg.run_analyses(node, cfg.Defined(ctx)) - body = node.body[0].body - self.assertEqual( - anno.getanno(body[2], 'defined_in'), - frozenset(map(qual_names.QN, ('x', 'g')))) - - def test_loop_else(self): - - # Disabling useless-else-on-loop error, because 'break' and 'continue' - # canonicalization are a separate analysis pass, and here we test - # the CFG analysis in isolation. - def for_orelse(x): - y = 0 - for i in range(len(x)): - x += i - else: # pylint: disable=useless-else-on-loop - y = 1 - return x, y - - def while_orelse(x, i): - y = 0 - while x < 10: - x += i - else: # pylint: disable=useless-else-on-loop - y = 1 - return x, y - - for f in (for_orelse, while_orelse): - node, ctx = self._parse_and_analyze(f) - cfg.run_analyses(node, cfg.ReachingDefinitions(ctx)) - body = node.body[0].body - return_node = body[-1] - reaching_defs = anno.getanno(return_node, 'definitions_in') - - # Y could be defined by Assign(Num(0)) or Assign(Num(1)) - # X could be defined as an argument or an AugAssign. - y_defs = [node for var, node in reaching_defs if str(var) == 'y'] - x_defs = [node for var, node in reaching_defs if str(var) == 'x'] - - self.assertEqual(set((gast.Assign,)), set(type(def_) for def_ in y_defs)) - self.assertEqual(set((0, 1)), set(def_.value.n for def_ in y_defs)) - self.assertEqual(len(y_defs), 2) - self.assertEqual( - set((gast.arguments, gast.AugAssign)), - set(type(def_) for def_ in x_defs)) - self.assertEqual(len(x_defs), 2) - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py b/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py index 9ccb98f79adbe5410a7554548ee75ab95345962d..2d8f922a4589e45ab7e4f20f800e0ffef3d7f0a5 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py @@ -16,7 +16,7 @@ Live values are extracted from the known execution context. -Requires activity analysis annotations. +Requires activity and reaching definitions analyses. """ from __future__ import absolute_import @@ -45,14 +45,12 @@ class LiveValueResolver(transformer.Base): def visit_Name(self, node): self.generic_visit(node) if isinstance(node.ctx, gast.Load): - assert anno.hasanno(node, NodeAnno.IS_LOCAL), node - symbol_is_local = anno.getanno(node, NodeAnno.IS_LOCAL) - assert anno.hasanno(node, NodeAnno.IS_MODIFIED_SINCE_ENTRY), node - symbol_is_modified = anno.getanno(node, NodeAnno.IS_MODIFIED_SINCE_ENTRY) - assert anno.hasanno(node, NodeAnno.IS_PARAM), node - symbol_is_param = anno.getanno(node, NodeAnno.IS_PARAM) - - if not symbol_is_local and not symbol_is_param: + defs = anno.getanno(node, anno.Static.DEFINITIONS, ()) + + is_defined = bool(defs) + has_single_def = len(defs) == 1 + + if not is_defined: if node.id in self.literals: anno.setanno(node, 'live_val', self.literals[node.id]) elif node.id in self.entity_info.namespace: @@ -79,11 +77,13 @@ class LiveValueResolver(transformer.Base): # TODO(mdan): Attempt to trace its value through the local chain. # TODO(mdan): Use type annotations as fallback. - if not symbol_is_modified: - if node.id in self.entity_info.arg_values: - obj = self.entity_info.arg_values[node.id] - anno.setanno(node, 'live_val', obj) - anno.setanno(node, 'fqn', (obj.__class__.__name__,)) + if has_single_def: + def_, = defs + if def_.param_of is self.enclosing_entities[0]: + if node.id in self.entity_info.arg_values: + obj = self.entity_info.arg_values[node.id] + anno.setanno(node, 'live_val', obj) + anno.setanno(node, 'fqn', (obj.__class__.__name__,)) return node def visit_Attribute(self, node): @@ -91,12 +91,20 @@ class LiveValueResolver(transformer.Base): if anno.hasanno(node.value, 'live_val'): assert anno.hasanno(node.value, 'fqn') parent_object = anno.getanno(node.value, 'live_val') - if not hasattr(parent_object, node.attr): - raise AttributeError('%s has no attribute %s' % (parent_object, - node.attr)) + anno.setanno(node, 'parent_type', type(parent_object)) - anno.setanno(node, 'live_val', getattr(parent_object, node.attr)) anno.setanno(node, 'fqn', anno.getanno(node.value, 'fqn') + (node.attr,)) + if hasattr(parent_object, node.attr): + # This can happen when the attribute's creation and use depend on the + # same static condition, for example: + # + # if cond: + # foo.bar = baz + # if cond: + # x = foo.bar + # + anno.setanno(node, 'live_val', getattr(parent_object, node.attr)) + # TODO(mdan): Investigate the role built-in annotations can play here. elif anno.hasanno(node.value, 'type'): parent_type = anno.getanno(node.value, 'type') diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/live_values_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/live_values_test.py index 38af79277779f77ffe31c2f6e26ae88f3e1a7ae9..fe3051179cd93ddd2627802dd2536bb50f17fb7f 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/live_values_test.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/live_values_test.py @@ -21,11 +21,13 @@ from __future__ import print_function import six from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import cfg from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import qual_names from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis import activity from tensorflow.contrib.autograph.pyct.static_analysis import live_values +from tensorflow.contrib.autograph.pyct.static_analysis import reaching_definitions from tensorflow.contrib.autograph.pyct.static_analysis import type_info from tensorflow.python.framework import constant_op from tensorflow.python.platform import test @@ -48,7 +50,10 @@ class LiveValuesResolverTest(test.TestCase): arg_types=arg_types, owner_type=None) node = qual_names.resolve(node) + graphs = cfg.build(node) node = activity.resolve(node, entity_info) + node = reaching_definitions.resolve(node, entity_info, graphs, + reaching_definitions.Definition) node = live_values.resolve(node, entity_info, literals) node = type_info.resolve(node, entity_info) node = live_values.resolve(node, entity_info, literals) diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/liveness.py b/tensorflow/contrib/autograph/pyct/static_analysis/liveness.py new file mode 100644 index 0000000000000000000000000000000000000000..bf29d868a2e4d2a4c7dd1057c0ed93e54d01d750 --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/static_analysis/liveness.py @@ -0,0 +1,200 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Live variable analysis. + +This analysis attaches a set containing the live symbols that are live at the +exit of control flow statements. + +Requires activity analysis. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gast + +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import cfg +from tensorflow.contrib.autograph.pyct import transformer +from tensorflow.contrib.autograph.pyct.static_analysis import annos + + +class Analyzer(cfg.GraphVisitor): + """CFG visitor that performs liveness analysis at statement level.""" + + def __init__(self, graph): + super(Analyzer, self).__init__(graph) + # This allows communicating that nodes generate extra symbols, + # e.g. those that a function definition closes over. + self.extra_gen = {} + + def init_state(self, _): + return set() + + def visit_node(self, node): + prev_live_in = self.in_[node] + + if anno.hasanno(node.ast_node, anno.Static.SCOPE): + node_scope = anno.getanno(node.ast_node, anno.Static.SCOPE) + + gen = node_scope.used | self.extra_gen.get(node.ast_node, frozenset()) + # TODO(mdan): verify whether composites' parents need to be added. + # E.g. if x.y is live whether x needs to be added. Theoretically the + # activity analysis should have both so that wouldn't be needed. + kill = node_scope.modified + + live_out = set() + for n in node.next: + live_out |= self.in_[n] + live_in = gen | (live_out - kill) + + else: + # Nodes that don't have a scope annotation are assumed not to touch any + # symbols. + # This Name node below is a literal name, e.g. False + assert isinstance(node.ast_node, + (gast.Name, gast.Continue, gast.Break)), type( + node.ast_node) + live_in = prev_live_in + live_out = live_in + + self.in_[node] = live_in + self.out[node] = live_out + + # TODO(mdan): Move this to the superclass? + return prev_live_in != live_in + + +class WholeTreeAnalyzer(transformer.Base): + """Runs liveness analysis on each of the functions defined in the AST. + + If a function defined other local functions, those will have separate CFGs. + However, dataflow analysis needs to tie up these CFGs to properly emulate the + effect of closures. In the case of liveness, the parent function's live + variables must account for the variables that are live at the entry of each + subfunction. For example: + + def foo(): + # baz is live here + def bar(): + print(baz) + + This analyzer runs liveness analysis on each individual function, accounting + for the effect above. + """ + + def __init__(self, source_info, graphs): + super(WholeTreeAnalyzer, self).__init__(source_info) + self.graphs = graphs + self.current_analyzer = None + self.analyzers = {} + + def visit_FunctionDef(self, node): + parent_analyzer = self.current_analyzer + subgraph = self.graphs[node] + + # Postorder tree processing makes this a bit complicated: + # 1. construct an analyzer object and put it on stack + # 2. recursively walk the subtree; this will initialize the analyzer's + # in_ state properly (done in a block below) + # 3. run the final analysis + analyzer = Analyzer(subgraph) + self.current_analyzer = analyzer + node = self.generic_visit(node) + analyzer.visit_reverse() + + if parent_analyzer is not None: + # Wire the state between the two subgraphs' analyzers. + child_in_state = analyzer.in_[subgraph.entry] + # Exception: symbols modified in the child function are local to it + body_scope = anno.getanno(node, annos.NodeAnno.BODY_SCOPE) + for qn in body_scope.modified: + # Note: a function modifying the symbol doesn't make that symbol + # live at the function's entry. In fact when that happens it is + # probably a case of undefined assignment, like this: + # + # bar = 0 + # def foo(): + # print(bar) # bar is undefined here! + # bar = 1 + # + # Hence we use discard and not remove below. + child_in_state.discard(qn) + parent_analyzer.extra_gen[node] = frozenset(child_in_state,) + + self.analyzers[node] = analyzer + self.current_analyzer = parent_analyzer + return node + + def visit_nonlocal(self, node): + raise NotImplementedError() + + def visit_global(self, node): + raise NotImplementedError() + + +class Annotator(transformer.Base): + """AST visitor that annotates each control flow block with live symbols.""" + + # Note: additional nodes may be added as needed. + + def __init__(self, source_info, cross_function_analyzer): + super(Annotator, self).__init__(source_info) + self.cross_function_analyzer = cross_function_analyzer + self.current_analyzer = None + + def visit_FunctionDef(self, node): + parent_analyzer = self.current_analyzer + self.current_analyzer = self.cross_function_analyzer.analyzers[node] + + node = self.generic_visit(node) + self.current_analyzer = parent_analyzer + return node + + def _aggregate_successors_live_in(self, node): + successors = self.current_analyzer.graph.stmt_next[node] + node_live_out = set() + for s in successors: + node_live_out.update(self.current_analyzer.in_[s]) + anno.setanno(node, anno.Static.LIVE_VARS_OUT, frozenset(node_live_out)) + node = self.generic_visit(node) + return node + + def visit_If(self, node): + return self._aggregate_successors_live_in(node) + + def visit_For(self, node): + return self._aggregate_successors_live_in(node) + + def visit_While(self, node): + return self._aggregate_successors_live_in(node) + + +def resolve(node, source_info, graphs): + """Resolves the live symbols at the exit of control flow statements. + + Args: + node: ast.AST + source_info: transformer.SourceInfo + graphs: Dict[ast.FunctionDef, cfg.Graph] + Returns: + ast.AST + """ + cross_function_analyzer = WholeTreeAnalyzer(source_info, graphs) + node = cross_function_analyzer.visit(node) + visitor = Annotator(source_info, cross_function_analyzer) + node = visitor.visit(node) + return node diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/liveness_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/liveness_test.py new file mode 100644 index 0000000000000000000000000000000000000000..d53adb28af03f0de14f319f642ee82928a480e3a --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/static_analysis/liveness_test.py @@ -0,0 +1,149 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for liveness module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import cfg +from tensorflow.contrib.autograph.pyct import parser +from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import transformer +from tensorflow.contrib.autograph.pyct.static_analysis import activity +from tensorflow.contrib.autograph.pyct.static_analysis import liveness +from tensorflow.python.platform import test + + +class LivenessTest(test.TestCase): + + def _parse_and_analyze(self, test_fn): + node, source = parser.parse_entity(test_fn) + entity_info = transformer.EntityInfo( + source_code=source, + source_file=None, + namespace={}, + arg_values=None, + arg_types=None, + owner_type=None) + node = qual_names.resolve(node) + node = activity.resolve(node, entity_info) + graphs = cfg.build(node) + liveness.resolve(node, entity_info, graphs) + return node + + def assertHasLiveOut(self, node, expected): + live_out = anno.getanno(node, anno.Static.LIVE_VARS_OUT) + live_out_str = set(str(v) for v in live_out) + if not expected: + expected = () + if not isinstance(expected, tuple): + expected = (expected,) + self.assertSetEqual(live_out_str, set(expected)) + + def test_stacked_if(self): + + def test_fn(x, a): + if a > 0: + x = 0 + if a > 1: + x = 1 + return x + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + + self.assertHasLiveOut(fn_body[0], ('a', 'x')) + self.assertHasLiveOut(fn_body[1], 'x') + + def test_stacked_if_else(self): + + def test_fn(x, a): + if a > 0: + x = 0 + if a > 1: + x = 1 + else: + x = 2 + return x + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + + self.assertHasLiveOut(fn_body[0], 'a') + self.assertHasLiveOut(fn_body[1], 'x') + + def test_for_basic(self): + + def test_fn(x, a): + for i in range(a): + x += i + return x + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + + self.assertHasLiveOut(fn_body[0], 'x') + + def test_attributes(self): + + def test_fn(x, a): + if a > 0: + x.y = 0 + return x.y + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + + self.assertHasLiveOut(fn_body[0], ('x.y', 'x')) + + def test_nested_functions(self): + + def test_fn(a, b): + if b: + a = [] + + def foo(): + return a + + foo() + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + + self.assertHasLiveOut(fn_body[0], 'a') + + def test_nested_functions_isolation(self): + + def test_fn(b): + if b: + a = 0 # pylint:disable=unused-variable + + def child(): + max(a) # pylint:disable=used-before-assignment + a = 1 + return a + + child() + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + + self.assertHasLiveOut(fn_body[0], 'max') + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/reaching_definitions.py b/tensorflow/contrib/autograph/pyct/static_analysis/reaching_definitions.py new file mode 100644 index 0000000000000000000000000000000000000000..9a84f1231cb71745f778285f30ada151a7c1accd --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/static_analysis/reaching_definitions.py @@ -0,0 +1,301 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Reaching definition analysis. + +This analysis attaches a set of a Definition objects to each symbol, one +for each distinct definition that may reach it. The Definition objects are +mutable and may be used by subsequent analyses to further annotate data like +static type and value information. +The analysis also attaches the set of the symbols defined at the entry of +control flow statements. + +Requires activity analysis. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gast + +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import cfg +from tensorflow.contrib.autograph.pyct import transformer +from tensorflow.contrib.autograph.pyct.static_analysis import annos + + +class Definition(object): + """Definition objects describe a unique definition of a variable. + + Subclasses of this may be used by passing an appropriate factory fuction to + resolve. + + Attributes: + param_of: Optional[ast.AST] + """ + + def __init__(self): + self.param_of = None + + def __repr__(self): + return '%s[%d]' % (self.__class__.__name__, id(self)) + + +class _NodeState(object): + """Abstraction for the state of the CFG walk for reaching definition analysis. + + This is a value type. Only implements the strictly necessary operators. + + Attributes: + value: Dict[qual_names.QN, Set[Definition, ...]], the defined symbols and + their possible definitions + """ + + def __init__(self, init_from=None): + if init_from: + if isinstance(init_from, _NodeState): + self.value = { + s: set(other_infos) for s, other_infos in init_from.value.items() + } + elif isinstance(init_from, dict): + self.value = {s: set((init_from[s],)) for s in init_from} + else: + assert False, init_from + else: + self.value = {} + + def __eq__(self, other): + if frozenset(self.value.keys()) != frozenset(other.value.keys()): + return False + ret = all(self.value[s] == other.value[s] for s in self.value) + return ret + + def __ne__(self, other): + return not self.__eq__(other) + + def __or__(self, other): + assert isinstance(other, _NodeState) + result = _NodeState(self) + for s, other_infos in other.value.items(): + if s in result.value: + result.value[s].update(other_infos) + else: + result.value[s] = set(other_infos) + return result + + def __sub__(self, other): + assert isinstance(other, set) + result = _NodeState(self) + for s in other: + result.value.pop(s, None) + return result + + def __repr__(self): + return 'NodeState[%s]=%s' % (id(self), repr(self.value)) + + +class Analyzer(cfg.GraphVisitor): + """CFG visitor that determines reaching definitions at statement level.""" + + def __init__(self, graph, definition_factory): + self._definition_factory = definition_factory + super(Analyzer, self).__init__(graph) + # This allows communicating that nodes have extra reaching definitions, + # e.g. those that a function closes over. + self.extra_in = {} + + self.gen_map = {} + + def init_state(self, _): + return _NodeState() + + def visit_node(self, node): + prev_defs_out = self.out[node] + + defs_in = _NodeState(self.extra_in.get(node.ast_node, None)) + for n in node.prev: + defs_in |= self.out[n] + + if anno.hasanno(node.ast_node, anno.Static.SCOPE): + node_scope = anno.getanno(node.ast_node, anno.Static.SCOPE) + # The definition objects created by each node must be singletons because + # their ids are used in equality checks. + if node not in self.gen_map: + node_symbols = {} + for s in node_scope.modified: + def_ = self._definition_factory() + if s in node_scope.params: + def_.param_of = node_scope.params[s] + node_symbols[s] = def_ + self.gen_map[node] = _NodeState(node_symbols) + + gen = self.gen_map[node] + kill = node_scope.modified + defs_out = gen | (defs_in - kill) + + else: + # Nodes that don't have a scope annotation are assumed not to touch any + # symbols. + # This Name node below is a literal name, e.g. False + # This can also happen if activity.py forgot to annotate the node with a + # scope object. + assert isinstance( + node.ast_node, + (gast.Name, gast.Break, gast.Continue, gast.Raise)), (node.ast_node, + node) + defs_out = defs_in + + self.in_[node] = defs_in + self.out[node] = defs_out + + # TODO(mdan): Move this to the superclass? + return prev_defs_out != defs_out + + +class TreeAnnotator(transformer.Base): + """AST visitor that annotates each symbol name with its reaching definitions. + + Simultaneously, the visitor runs the dataflow analysis on each function node, + accounting for the effect of closures. For example: + + def foo(): + bar = 1 + def baz(): + # bar = 1 reaches here + """ + + def __init__(self, source_info, graphs, definition_factory): + super(TreeAnnotator, self).__init__(source_info) + self.definition_factory = definition_factory + self.graphs = graphs + self.current_analyzer = None + self.current_cfg_node = None + + def visit_FunctionDef(self, node): + parent_analyzer = self.current_analyzer + subgraph = self.graphs[node] + + # Preorder tree processing: + # 1. if this is a child function, the parent was already analyzed and it + # has the proper state value for the subgraph's entry + # 2. analyze the current function body + # 2. recursively walk the subtree; child functions will be processed + analyzer = Analyzer(subgraph, self.definition_factory) + if parent_analyzer is not None: + # Wire the state between the two subgraphs' analyzers. + parent_out_state = parent_analyzer.out[parent_analyzer.graph.index[node]] + # Exception: symbols modified in the child function are local to it + body_scope = anno.getanno(node, annos.NodeAnno.BODY_SCOPE) + parent_out_state -= body_scope.modified + analyzer.extra_in[node.args] = parent_out_state + + # Complete the analysis for the local function and annotate its body. + analyzer.visit_forward() + + # Recursively process any remaining subfunctions. + self.current_analyzer = analyzer + # Note: not visiting name, decorator_list and returns because they don't + # apply to this anlysis. + # TODO(mdan): Should we still process the function name? + node.args = self.visit(node.args) + node.body = self.visit_block(node.body) + self.current_analyzer = parent_analyzer + + return node + + def visit_nonlocal(self, node): + raise NotImplementedError() + + def visit_global(self, node): + raise NotImplementedError() + + def visit_Name(self, node): + if self.current_analyzer is None: + # Names may appear outside function defs - for example in class + # definitions. + return node + + analyzer = self.current_analyzer + cfg_node = self.current_cfg_node + + assert cfg_node is not None, 'name node outside of any statement?' + + qn = anno.getanno(node, anno.Basic.QN) + if isinstance(node.ctx, gast.Load): + anno.setanno(node, anno.Static.DEFINITIONS, + tuple(analyzer.in_[cfg_node].value.get(qn, ()))) + else: + anno.setanno(node, anno.Static.DEFINITIONS, + tuple(analyzer.out[cfg_node].value.get(qn, ()))) + + return node + + def _aggregate_predecessors_defined_in(self, node): + preds = self.current_analyzer.graph.stmt_prev[node] + node_defined_in = set() + for p in preds: + node_defined_in |= set(self.current_analyzer.out[p].value.keys()) + anno.setanno(node, anno.Static.DEFINED_VARS_IN, frozenset(node_defined_in)) + + def visit_If(self, node): + self._aggregate_predecessors_defined_in(node) + return self.generic_visit(node) + + def visit_For(self, node): + self._aggregate_predecessors_defined_in(node) + + # Manually accounting for the shortcoming described in + # cfg.AstToCfg.visit_For. + parent = self.current_cfg_node + self.current_cfg_node = self.current_analyzer.graph.index[node.iter] + node.target = self.visit(node.target) + self.current_cfg_node = parent + + node.iter = self.visit(node.iter) + node.body = self.visit_block(node.body) + node.orelse = self.visit_block(node.orelse) + + return node + + def visit_While(self, node): + self._aggregate_predecessors_defined_in(node) + return self.generic_visit(node) + + def visit(self, node): + parent = self.current_cfg_node + + if (self.current_analyzer is not None and + node in self.current_analyzer.graph.index): + self.current_cfg_node = self.current_analyzer.graph.index[node] + node = super(TreeAnnotator, self).visit(node) + + self.current_cfg_node = parent + return node + + +def resolve(node, source_info, graphs, definition_factory): + """Resolves reaching definitions for each symbol. + + Args: + node: ast.AST + source_info: transformer.SourceInfo + graphs: Dict[ast.FunctionDef, cfg.Graph] + definition_factory: Callable[[], Definition] + Returns: + ast.AST + """ + visitor = TreeAnnotator(source_info, graphs, definition_factory) + node = visitor.visit(node) + return node diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/reaching_definitions_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/reaching_definitions_test.py new file mode 100644 index 0000000000000000000000000000000000000000..243fe804b229686f33a4964b16c987c673a97c4b --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/static_analysis/reaching_definitions_test.py @@ -0,0 +1,263 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for reaching_definitions module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import cfg +from tensorflow.contrib.autograph.pyct import parser +from tensorflow.contrib.autograph.pyct import qual_names +from tensorflow.contrib.autograph.pyct import transformer +from tensorflow.contrib.autograph.pyct.static_analysis import activity +from tensorflow.contrib.autograph.pyct.static_analysis import reaching_definitions +from tensorflow.python.platform import test + + +class DefinitionInfoTest(test.TestCase): + + def _parse_and_analyze(self, test_fn): + node, source = parser.parse_entity(test_fn) + entity_info = transformer.EntityInfo( + source_code=source, + source_file=None, + namespace={}, + arg_values=None, + arg_types=None, + owner_type=None) + node = qual_names.resolve(node) + node = activity.resolve(node, entity_info) + graphs = cfg.build(node) + node = reaching_definitions.resolve(node, entity_info, graphs, + reaching_definitions.Definition) + return node + + def assertHasDefs(self, node, num): + defs = anno.getanno(node, anno.Static.DEFINITIONS) + self.assertEqual(len(defs), num) + for r in defs: + self.assertIsInstance(r, reaching_definitions.Definition) + + def assertHasDefinedIn(self, node, expected): + defined_in = anno.getanno(node, anno.Static.DEFINED_VARS_IN) + defined_in_str = set(str(v) for v in defined_in) + if not expected: + expected = () + if not isinstance(expected, tuple): + expected = (expected,) + self.assertSetEqual(defined_in_str, set(expected)) + + def assertSameDef(self, first, second): + self.assertHasDefs(first, 1) + self.assertHasDefs(second, 1) + self.assertIs( + anno.getanno(first, anno.Static.DEFINITIONS)[0], + anno.getanno(second, anno.Static.DEFINITIONS)[0]) + + def assertNotSameDef(self, first, second): + self.assertHasDefs(first, 1) + self.assertHasDefs(second, 1) + self.assertIsNot( + anno.getanno(first, anno.Static.DEFINITIONS)[0], + anno.getanno(second, anno.Static.DEFINITIONS)[0]) + + def test_conditional(self): + + def test_fn(a, b): + a = [] + if b: + a = [] + return a + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + + self.assertHasDefs(fn_body[0].targets[0], 1) + self.assertHasDefs(fn_body[1].test, 1) + self.assertHasDefs(fn_body[1].body[0].targets[0], 1) + self.assertHasDefs(fn_body[2].value, 2) + + self.assertHasDefinedIn(fn_body[1], ('a', 'b')) + + def test_while(self): + + def test_fn(a): + max(a) + while True: + a = a + a = a + return a + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + + self.assertHasDefs(fn_body[0].value.args[0], 1) + self.assertHasDefs(fn_body[1].body[0].targets[0], 1) + self.assertHasDefs(fn_body[1].body[1].targets[0], 1) + self.assertHasDefs(fn_body[1].body[1].value, 1) + # The loop does have an invariant test, but the CFG doesn't know that. + self.assertHasDefs(fn_body[1].body[0].value, 2) + self.assertHasDefs(fn_body[2].value, 2) + + def test_while_else(self): + + def test_fn(x, i): + y = 0 + while x: + x += i + if i: + break + else: + y = 1 + return x, y + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + + self.assertHasDefs(fn_body[0].targets[0], 1) + self.assertHasDefs(fn_body[1].test, 2) + self.assertHasDefs(fn_body[1].body[0].target, 1) + self.assertHasDefs(fn_body[1].body[1].test, 1) + self.assertHasDefs(fn_body[1].orelse[0].targets[0], 1) + self.assertHasDefs(fn_body[2].value.elts[0], 2) + self.assertHasDefs(fn_body[2].value.elts[1], 2) + + def test_for_else(self): + + def test_fn(x, i): + y = 0 + for i in x: + x += i + if i: + break + else: + continue + else: + y = 1 + return x, y + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + + self.assertHasDefs(fn_body[0].targets[0], 1) + self.assertHasDefs(fn_body[1].target, 1) + self.assertHasDefs(fn_body[1].body[0].target, 1) + self.assertHasDefs(fn_body[1].body[1].test, 1) + self.assertHasDefs(fn_body[1].orelse[0].targets[0], 1) + self.assertHasDefs(fn_body[2].value.elts[0], 2) + self.assertHasDefs(fn_body[2].value.elts[1], 2) + + def test_nested_functions(self): + + def test_fn(a, b): + a = [] + if b: + a = [] + + def foo(): + return a + + foo() + + return a + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + def_of_a_in_if = fn_body[1].body[0].targets[0] + + self.assertHasDefs(fn_body[0].targets[0], 1) + self.assertHasDefs(fn_body[1].test, 1) + self.assertHasDefs(def_of_a_in_if, 1) + self.assertHasDefs(fn_body[2].value, 2) + + inner_fn_body = fn_body[1].body[1].body + self.assertSameDef(inner_fn_body[0].value, def_of_a_in_if) + + def test_nested_functions_isolation(self): + + def test_fn(a): + a = 0 + + def child(): + a = 1 + return a + + child() + return a + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + + parent_return = fn_body[3] + child_return = fn_body[1].body[1] + # The assignment `a = 1` makes `a` local to `child`. + self.assertNotSameDef(parent_return.value, child_return.value) + + def test_function_call_in_with(self): + + def foo(_): + pass + + def test_fn(a): + with foo(a): + return a + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + + self.assertHasDefs(fn_body[0].items[0].context_expr.func, 0) + self.assertHasDefs(fn_body[0].items[0].context_expr.args[0], 1) + + def test_mutation_subscript(self): + + def test_fn(a): + l = [] + l[0] = a + return l + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + + creation = fn_body[0].targets[0] + mutation = fn_body[1].targets[0].value + use = fn_body[2].value + self.assertSameDef(creation, mutation) + self.assertSameDef(creation, use) + + def test_replacement(self): + + def foo(a): + return a + + def test_fn(a): + a = foo(a) + return a + + node = self._parse_and_analyze(test_fn) + fn_body = node.body[0].body + + param = node.body[0].args.args[0] + source = fn_body[0].value.args[0] + target = fn_body[0].targets[0] + retval = fn_body[1].value + self.assertSameDef(param, source) + self.assertNotSameDef(source, target) + self.assertSameDef(target, retval) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/type_info.py b/tensorflow/contrib/autograph/pyct/static_analysis/type_info.py index a229c288a83e516fc02f3af8df2046c5365e569c..835d5199fa1a5c145e29a413d4d23b4138a3c1cd 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/type_info.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/type_info.py @@ -43,9 +43,8 @@ from __future__ import print_function import gast -from tensorflow.contrib.autograph import utils from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import parser +from tensorflow.contrib.autograph.pyct import ast_util from tensorflow.contrib.autograph.pyct import transformer from tensorflow.python.util import tf_inspect @@ -166,7 +165,6 @@ class TypeInfoResolver(transformer.Base): definition = self.scope.getval(qn) anno.copyanno(definition, node, 'type') anno.copyanno(definition, node, 'type_fqn') - anno.setanno(node, 'definition', definition) # TODO(mdan): Remove this when the directives module is in. anno.copyanno(definition, node, 'element_type') @@ -198,52 +196,18 @@ class TypeInfoResolver(transformer.Base): def visit_With(self, node): for item in node.items: if item.optional_vars is not None: - self.apply_to_single_assignments((item.optional_vars,), - item.context_expr, - self._process_variable_assignment) + ast_util.apply_to_single_assignments((item.optional_vars,), + item.context_expr, + self._process_variable_assignment) self.generic_visit(node) return node def visit_Assign(self, node): self.generic_visit(node) - self.apply_to_single_assignments( - node.targets, node.value, self._process_variable_assignment) + ast_util.apply_to_single_assignments(node.targets, node.value, + self._process_variable_assignment) return node - # TODO(mdan): Remove as soon as the new directives module is ready. - def visit_Call(self, node): - if anno.hasanno(node.func, 'live_val'): - # Symbols targeted by the "set_type" marker function are assigned the data - # type that it specified. - if anno.getanno(node.func, 'live_val') is utils.set_element_type: - - if len(node.args) < 2 or len(node.args) > 3: - raise ValueError('"%s" must have either two or three parameters' - % self.context.type_annotation_func) - if len(node.args) == 2: - target_arg, type_arg = node.args - shape_arg = parser.parse_expression('None') - else: - target_arg, type_arg, shape_arg = node.args - if not anno.hasanno(target_arg, anno.Basic.QN): - raise ValueError('the first argument of "%s" must by a symbol' % - utils.set_element_type) - # TODO(mdan): This is vulnerable to symbol renaming. - element_type = type_arg - element_shape = shape_arg - - target_symbol = anno.getanno(target_arg, anno.Basic.QN) - # Find the definition of this symbol and annotate it with the given - # data type. That in turn will cause future uses of the symbol - # to receive the same type annotation. - definition = self.scope.getval(target_symbol) - anno.setanno(node, 'element_type', element_type) - anno.setanno(node, 'element_shape', element_shape) - anno.setanno(definition, 'element_type', element_type) - anno.setanno(definition, 'element_shape', element_shape) - # TODO(mdan): Should we update references between definition and here? - return self.generic_visit(node) - def resolve(node, context): return TypeInfoResolver(context).visit(node) diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/type_info_test.py b/tensorflow/contrib/autograph/pyct/static_analysis/type_info_test.py index 32b1148ab21809514bc09a31e26f0219017bd088..404311ba242cf0359cf5695dfe3eeaf9cb858eb8 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/type_info_test.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/type_info_test.py @@ -19,11 +19,13 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import cfg from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import qual_names from tensorflow.contrib.autograph.pyct import transformer from tensorflow.contrib.autograph.pyct.static_analysis import activity from tensorflow.contrib.autograph.pyct.static_analysis import live_values +from tensorflow.contrib.autograph.pyct.static_analysis import reaching_definitions from tensorflow.contrib.autograph.pyct.static_analysis import type_info from tensorflow.python.client import session from tensorflow.python.platform import test @@ -69,7 +71,10 @@ class TypeInfoResolverTest(test.TestCase): arg_types=arg_types, owner_type=None) node = qual_names.resolve(node) + graphs = cfg.build(node) node = activity.resolve(node, entity_info) + node = reaching_definitions.resolve(node, entity_info, graphs, + reaching_definitions.Definition) node = live_values.resolve(node, entity_info, {}) node = type_info.resolve(node, entity_info) node = live_values.resolve(node, entity_info, {}) diff --git a/tensorflow/contrib/autograph/pyct/templates.py b/tensorflow/contrib/autograph/pyct/templates.py index 9c479ebc2fa83d27dc363ae306daedb556734a1f..5831d57ceb58d4b291a4f52bbf4282e107104219 100644 --- a/tensorflow/contrib/autograph/pyct/templates.py +++ b/tensorflow/contrib/autograph/pyct/templates.py @@ -26,6 +26,7 @@ import textwrap import gast +from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import ast_util from tensorflow.contrib.autograph.pyct import parser from tensorflow.contrib.autograph.pyct import qual_names @@ -43,39 +44,65 @@ class ReplaceTransformer(gast.NodeTransformer): """ self.replacements = replacements self.in_replacements = False + self.preserved_annos = { + anno.Basic.ORIGIN, + anno.Basic.SKIP_PROCESSING, + anno.Static.ORIG_DEFINITIONS, + } + + def _prepare_replacement(self, replaced, key): + """Prepares a replacement AST that's safe to swap in for a node. + + Args: + replaced: ast.AST, the node being replaced + key: Hashable, the key of the replacement AST + Returns: + ast.AST, the replacement AST + """ + repl = self.replacements[key] + + new_nodes = ast_util.copy_clean(repl, preserve_annos=self.preserved_annos) + if isinstance(new_nodes, gast.AST): + new_nodes = [new_nodes] + + return new_nodes def visit_Expr(self, node): - if (isinstance(node.value, gast.Name) and - node.value.id in self.replacements): - return self.visit(node.value) - self.generic_visit(node) - return node + # When replacing a placeholder with an entire statement, the replacement + # must stand on its own and not be wrapped in an Expr. + new_value = self.visit(node.value) + if new_value is node.value: + return node + return new_value def visit_keyword(self, node): - if node.arg in self.replacements: - repl = self.replacements[node.arg] - if isinstance(repl, gast.keyword): - return repl - elif (isinstance(repl, (list, tuple)) and repl and - all(isinstance(r, gast.keyword) for r in repl)): - return repl - # TODO(mdan): We may allow replacing with a string as well. - # For example, if one wanted to replace foo with bar in foo=baz, then - # we could allow changing just node arg, so that we end up with bar=baz. - raise ValueError( - 'a keyword argument may only be replaced by another keyword or a ' - 'non-empty list of keywords. Found: %s' % repl) - return self.generic_visit(node) + if node.arg not in self.replacements: + return self.generic_visit(node) + + repl = self._prepare_replacement(node, node.arg) + if isinstance(repl, gast.keyword): + return repl + elif (repl and isinstance(repl, (list, tuple)) and + all(isinstance(r, gast.keyword) for r in repl)): + return repl + # TODO(mdan): We may allow replacing with a string as well. + # For example, if one wanted to replace foo with bar in foo=baz, then + # we could allow changing just node arg, so that we end up with bar=baz. + raise ValueError( + 'a keyword argument may only be replaced by another keyword or a ' + 'non-empty list of keywords. Found: %s' % repl) def visit_FunctionDef(self, node): node = self.generic_visit(node) - if node.name in self.replacements: - repl = self.replacements[node.name] - if not isinstance(repl, (gast.Name, ast.Name)): - raise ValueError( - 'a function name can only be replaced by a Name node. Found: %s' % - repl) - node.name = repl.id + if node.name not in self.replacements: + return node + + repl = self.replacements[node.name] + if not isinstance(repl, (gast.Name, ast.Name)): + raise ValueError( + 'a function name can only be replaced by a Name node. Found: %s' % + repl) + node.name = repl.id return node def _check_has_context(self, node): @@ -113,8 +140,8 @@ class ReplaceTransformer(gast.NodeTransformer): def _set_inner_child_context(self, node, ctx): if isinstance(node, gast.Attribute): - self._set_inner_child_context(node.value, ctx) - node.ctx = gast.Load() + self._set_inner_child_context(node.value, gast.Load()) + node.ctx = ctx elif isinstance(node, gast.Tuple): for e in node.elts: self._set_inner_child_context(e, ctx) @@ -148,6 +175,7 @@ class ReplaceTransformer(gast.NodeTransformer): node = self.generic_visit(node) if node.attr not in self.replacements: return node + repl = self.replacements[node.attr] if not isinstance(repl, gast.Name): raise ValueError( @@ -159,9 +187,7 @@ class ReplaceTransformer(gast.NodeTransformer): if node.id not in self.replacements: return node - new_nodes = ast_util.copy_clean(self.replacements[node.id]) - if isinstance(new_nodes, gast.AST): - new_nodes = [new_nodes] + new_nodes = self._prepare_replacement(node, node.id) # Preserve the target context. for n in new_nodes: @@ -182,7 +208,7 @@ class ReplaceTransformer(gast.NodeTransformer): def _convert_to_ast(n): - """Convert from a known data type to AST.""" + """Converts from a known data type to AST.""" if isinstance(n, str): # Note: the node will receive the ctx value from the template, see # ReplaceTransformer.visit_Name. @@ -197,7 +223,7 @@ def _convert_to_ast(n): def replace(template, **replacements): - """Replace placeholders in a Python template. + """Replaces placeholders in a Python template. AST Name and Tuple nodes always receive the context that inferred from the template. However, when replacing more complex nodes (that can potentially diff --git a/tensorflow/contrib/autograph/pyct/templates_test.py b/tensorflow/contrib/autograph/pyct/templates_test.py index a01f8bf04c4faa6ec1779e0fb306155d99f5bd09..77e8ff62fd8665e095cfb410a2aa418e9f9bd52b 100644 --- a/tensorflow/contrib/autograph/pyct/templates_test.py +++ b/tensorflow/contrib/autograph/pyct/templates_test.py @@ -97,6 +97,19 @@ class TemplatesTest(test.TestCase): with self.assertRaises(ValueError): templates.replace(template, foo=1) + def test_replace_attribute_context(self): + template = """ + def test_fn(foo): + foo = 0 + """ + + node = templates.replace( + template, + foo=parser.parse_expression('a.b.c'))[0] + self.assertIsInstance(node.body[0].targets[0].ctx, gast.Store) + self.assertIsInstance(node.body[0].targets[0].value.ctx, gast.Load) + self.assertIsInstance(node.body[0].targets[0].value.value.ctx, gast.Load) + def test_replace_call_keyword(self): template = """ def test_fn(): @@ -151,17 +164,13 @@ class TemplatesTest(test.TestCase): self.assertEqual(node.func.id, 'bar') self.assertEqual(node.func.args[0].id, 'baz') - def replace_as_expression_restrictions(self): + def test_replace_as_expression_restrictions(self): template = """ foo(a) bar(b) """ with self.assertRaises(ValueError): templates.replace_as_expression(template) - with self.assertRaises(ValueError): - templates.replace('') - with self.assertRaises(ValueError): - templates.replace('a = b') if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/pyct/transformer.py b/tensorflow/contrib/autograph/pyct/transformer.py index 76558118308c31a2c1a770cad814e96abd6a6063..969ca12244148b346ba3160fba124384a9641a05 100644 --- a/tensorflow/contrib/autograph/pyct/transformer.py +++ b/tensorflow/contrib/autograph/pyct/transformer.py @@ -59,6 +59,103 @@ class EntityInfo(object): self.owner_type = owner_type +class _StateStack(object): + """Typed stack abstraction. + + This class provides syntactic sugar for a stack of objects of known + type. It allows accessing attributes of the object at the top of the stack + directly against this object, which allows for very terse syntax. + + For example, this code: + + stack = _StateStack(Foo) + stack.enter() + stack.bar + + Is equivalent to: + + stack = [] + stack.append(Foo()) + foo = stack[-1] + foo.bar + + See _State for more on how this is used. + + Attributes: + type: Any, the type of objects that this stack holds + level: int, the current stack depth + value: Any, the instance of the object at the top of the stack + """ + + def __init__(self, type_): + # Because we override __setattr__, we need to attach these attributes using + # the superclass' setattr. + object.__setattr__(self, 'type', type_) + object.__setattr__(self, '_stack', []) + self.enter() + + def enter(self): + self._stack.append(self.type()) + + def exit(self): + return self._stack.pop() + + @property + def level(self): + return len(self._stack) + + @property + def value(self): + return self._stack[-1] + + def __getattr__(self, key): + return getattr(self._stack[-1], key) + + def __setattr__(self, key, value): + setattr(self._stack[-1], key, value) + + +class _State(object): + """Supporting class for nested scope variable space for converter.Base. + + This structure offers syntactic sugar over a dict of stacks of objects + of known type. These structures are useful to keep state during AST walks. + Multiple different scopes can be tracked in parallel. For example: + + s = _State() + + s[foo].enter() + s[bar].enter() # this will not affect s[foo] + + Element access has special semantics: + * keys are a data type + * element values are _StateStack(type=key) objects + * missing elements are automatically added, similarly to defaultdict + + For example, the following block : + + _State s + s[Foo] + + Is equivalent to: + + s = {} + if Foo not in s: + s[Foo] = Foo() + s[Foo] + + See Base for how it's used. + """ + + def __init__(self): + self._value = {} + + def __getitem__(self, key): + if key not in self._value: + self._value[key] = _StateStack(key) + return self._value[key] + + class Base(gast.NodeTransformer): """Base class for general-purpose code transformers transformers. @@ -71,6 +168,27 @@ class Base(gast.NodeTransformer): (possibly nested) scopes, use enter/exit_local_scope and set/get_local. You must call enter/exit_local_scope manually, but the transformer detects when they are not properly paired. + + The transformer allows keeping state across calls to visit_* that is local to + arbitrary nodes and their descendants, using the self.state attribute. + Multiple independent scopes are allowed and automatically constructed. + + For example, to keep track of the If node that encloses any Name node, one can + write: + + class FooType(object): + + def __init__(self): + self.foo_property = None + + class DummyTransformer(Base): + + def visit_If(self, node): + self.state[FooType].enter() + self.state[FooType].foo_property = node + + def visit_Name(self, node): + self.state[FooType].foo_property # will hold the innermost enclosing if """ # TODO(mdan): Document all extra features. @@ -92,6 +210,12 @@ class Base(gast.NodeTransformer): self._local_scope_state = [] self.enter_local_scope() + # Allows scoping of local variables to keep state across calls to visit_* + # methods. Multiple scope hierchies may exist and are keyed by tag. A scope + # is valid at one or more nodes and all its children. Scopes created in + # child nodes supersede their parent. Scopes are isolated from one another. + self.state = _State() + @property def enclosing_entities(self): return tuple(self._enclosing_entities) @@ -101,7 +225,9 @@ class Base(gast.NodeTransformer): return len(self._local_scope_state) def enter_local_scope(self, inherit=None): - """Marks entry into a new local scope. + """Deprecated. Use self.state instead. + + Marks entry into a new local scope. Args: inherit: Optional enumerable of variable names to copy from the @@ -116,7 +242,9 @@ class Base(gast.NodeTransformer): self._local_scope_state.append(scope_entered) def exit_local_scope(self, keep=None): - """Marks exit from the current local scope. + """Deprecated. Use self.state instead. + + Marks exit from the current local scope. Args: keep: Optional enumerable of variable names to copy into the @@ -133,9 +261,11 @@ class Base(gast.NodeTransformer): return scope_left def set_local(self, name, value): + """Deprecated. Use self.state instead.""" self._local_scope_state[-1][name] = value def get_local(self, name, default=None): + """Deprecated. Use self.state instead.""" return self._local_scope_state[-1].get(name, default) def debug_print(self, node): @@ -216,7 +346,7 @@ class Base(gast.NodeTransformer): node_destination = new_destination return results - # TODO(mdan): Once we have error tracing, we may be able to just go to SSA. + # TODO(mdan): Remove. def apply_to_single_assignments(self, targets, values, apply_fn): """Applies a function to each individual assignment. @@ -266,19 +396,38 @@ class Base(gast.NodeTransformer): def _get_source(self, node): try: - return compiler.ast_to_source(node) - except AssertionError: + source, _ = compiler.ast_to_source(node) + return source + # pylint: disable=broad-except + # This function is used for error reporting. If an exception occurs here, + # it should be suppressed, in favor of emitting as informative a message + # about the original error as possible. + except Exception: return '' def visit(self, node): + if not isinstance(node, gast.AST): + # This is not that uncommon a mistake: various node bodies are lists, for + # example, posing a land mine for transformers that need to recursively + # call `visit`. The error needs to be raised before the exception handler + # below is installed, because said handler will mess up if `node` is not, + # in fact, a node. + msg = ( + 'invalid value for "node": expected "ast.AST", got "{}"; to' + ' visit lists of nodes, use "visit_block" instead').format(type(node)) + raise ValueError(msg) + source_code = self.entity_info.source_code source_file = self.entity_info.source_file did_enter_function = False local_scope_size_at_entry = len(self._local_scope_state) + processing_expr_node = False try: if isinstance(node, (gast.FunctionDef, gast.ClassDef, gast.Lambda)): did_enter_function = True + elif isinstance(node, gast.Expr): + processing_expr_node = True if did_enter_function: self._enclosing_entities.append(node) @@ -287,9 +436,23 @@ class Base(gast.NodeTransformer): self._lineno = node.lineno self._col_offset = node.col_offset + if processing_expr_node: + entry_expr_value = node.value + if not anno.hasanno(node, anno.Basic.SKIP_PROCESSING): result = super(Base, self).visit(node) + # Adjust for consistency: replacing the value of an Expr with + # an Assign node removes the need for the Expr node. + if processing_expr_node: + if isinstance(result, gast.Expr) and result.value != entry_expr_value: + # When the replacement is a list, it is assumed that the list came + # from a template that contained a number of statements, which + # themselves are standalone and don't require an enclosing Expr. + if isinstance(result.value, + (list, tuple, gast.Assign, gast.AugAssign)): + result = result.value + # On exception, the local scope integrity is not guaranteed. if did_enter_function: self._enclosing_entities.pop() diff --git a/tensorflow/contrib/autograph/pyct/transformer_test.py b/tensorflow/contrib/autograph/pyct/transformer_test.py index baf04653ae862b0159fb50a1c67fa675ceb74b9a..a37e922a1de902106dd3a11f20a14ddde8f6675e 100644 --- a/tensorflow/contrib/autograph/pyct/transformer_test.py +++ b/tensorflow/contrib/autograph/pyct/transformer_test.py @@ -93,6 +93,83 @@ class TransformerTest(test.TestCase): inner_function, lambda_node), anno.getanno(lambda_expr, 'enclosing_entities')) + def assertSameAnno(self, first, second, key): + self.assertIs(anno.getanno(first, key), anno.getanno(second, key)) + + def assertDifferentAnno(self, first, second, key): + self.assertIsNot(anno.getanno(first, key), anno.getanno(second, key)) + + def test_state_tracking(self): + + class LoopState(object): + pass + + class CondState(object): + pass + + class TestTransformer(transformer.Base): + + def visit(self, node): + anno.setanno(node, 'loop_state', self.state[LoopState].value) + anno.setanno(node, 'cond_state', self.state[CondState].value) + return super(TestTransformer, self).visit(node) + + def visit_While(self, node): + self.state[LoopState].enter() + node = self.generic_visit(node) + self.state[LoopState].exit() + return node + + def visit_If(self, node): + self.state[CondState].enter() + node = self.generic_visit(node) + self.state[CondState].exit() + return node + + tr = TestTransformer(self._simple_source_info()) + + def test_function(a): + a = 1 + while a: + _ = 'a' + if a > 2: + _ = 'b' + while True: + raise '1' + if a > 3: + _ = 'c' + while True: + raise '1' + + node, _ = parser.parse_entity(test_function) + node = tr.visit(node) + + fn_body = node.body[0].body + outer_while_body = fn_body[1].body + self.assertSameAnno(fn_body[0], outer_while_body[0], 'cond_state') + self.assertDifferentAnno(fn_body[0], outer_while_body[0], 'loop_state') + + first_if_body = outer_while_body[1].body + self.assertDifferentAnno(outer_while_body[0], first_if_body[0], + 'cond_state') + self.assertSameAnno(outer_while_body[0], first_if_body[0], 'loop_state') + + first_inner_while_body = first_if_body[1].body + self.assertSameAnno(first_if_body[0], first_inner_while_body[0], + 'cond_state') + self.assertDifferentAnno(first_if_body[0], first_inner_while_body[0], + 'loop_state') + + second_if_body = outer_while_body[2].body + self.assertDifferentAnno(first_if_body[0], second_if_body[0], 'cond_state') + self.assertSameAnno(first_if_body[0], second_if_body[0], 'loop_state') + + second_inner_while_body = second_if_body[1].body + self.assertDifferentAnno(first_inner_while_body[0], + second_inner_while_body[0], 'cond_state') + self.assertDifferentAnno(first_inner_while_body[0], + second_inner_while_body[0], 'loop_state') + def test_local_scope_info_stack(self): class TestTransformer(transformer.Base): @@ -205,6 +282,88 @@ class TransformerTest(test.TestCase): self.assertTrue(isinstance(node.body[1].body[0], gast.Assign)) self.assertTrue(isinstance(node.body[1].body[1], gast.Return)) + def test_robust_error_on_list_visit(self): + + class BrokenTransformer(transformer.Base): + + def visit_If(self, node): + # This is broken because visit expects a single node, not a list, and + # the body of an if is a list. + # Importantly, the default error handling in visit also expects a single + # node. Therefore, mistakes like this need to trigger a type error + # before the visit called here installs its error handler. + # That type error can then be caught by the enclosing call to visit, + # and correctly blame the If node. + self.visit(node.body) + return node + + def test_function(x): + if x > 0: + return x + + tr = BrokenTransformer(self._simple_source_info()) + + node, _ = parser.parse_entity(test_function) + with self.assertRaises(transformer.AutographParseError) as cm: + node = tr.visit(node) + obtained_message = str(cm.exception) + expected_message = r'expected "ast.AST", got "\<(type|class) \'list\'\>"' + self.assertRegexpMatches(obtained_message, expected_message) + # The exception should point at the if statement, not any place else. Could + # also check the stack trace. + self.assertTrue( + 'Occurred at node:\nIf' in obtained_message, obtained_message) + self.assertTrue( + 'Occurred at node:\nFunctionDef' not in obtained_message, + obtained_message) + self.assertTrue( + 'Occurred at node:\nReturn' not in obtained_message, obtained_message) + + def test_robust_error_on_ast_corruption(self): + # A child class should not be able to be so broken that it causes the error + # handling in `transformer.Base` to raise an exception. Why not? Because + # then the original error location is dropped, and an error handler higher + # up in the call stack gives misleading information. + + # Here we test that the error handling in `visit` completes, and blames the + # correct original exception, even if the AST gets corrupted. + + class NotANode(object): + pass + + class BrokenTransformer(transformer.Base): + + def visit_If(self, node): + node.body = NotANode() + raise ValueError('I blew up') + + def test_function(x): + if x > 0: + return x + + tr = BrokenTransformer(self._simple_source_info()) + + node, _ = parser.parse_entity(test_function) + with self.assertRaises(transformer.AutographParseError) as cm: + node = tr.visit(node) + obtained_message = str(cm.exception) + # The message should reference the exception actually raised, not anything + # from the exception handler. + expected_substring = 'I blew up' + self.assertTrue(expected_substring in obtained_message, obtained_message) + # Expect the exception to have failed to parse the corrupted AST + self.assertTrue( + '' in obtained_message, + obtained_message) + # The exception should point at the if statement, not any place else. Could + # also check the stack trace. + self.assertTrue( + 'Occurred at node:\nIf' in obtained_message, obtained_message) + self.assertTrue( + 'Occurred at node:\nFunctionDef' not in obtained_message, + obtained_message) + self.assertTrue( + 'Occurred at node:\nReturn' not in obtained_message, obtained_message) if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/autograph/utils/BUILD b/tensorflow/contrib/autograph/utils/BUILD index d82c17bf2afd01aedf4344f983b02c09abcb9bad..d2b399f19b63bfaa20d334df78ae60d50f6ca6e7 100644 --- a/tensorflow/contrib/autograph/utils/BUILD +++ b/tensorflow/contrib/autograph/utils/BUILD @@ -28,7 +28,6 @@ py_library( "tensor_list.py", "testing.py", "type_check.py", - "type_hints.py", ], srcs_version = "PY2AND3", visibility = ["//tensorflow:__subpackages__"], diff --git a/tensorflow/contrib/autograph/utils/__init__.py b/tensorflow/contrib/autograph/utils/__init__.py index 817d4126d106487e1fea3e442712a69bbfccd7f3..57b5f747417613a5dd5bce08e4a9e9ef98442cf6 100644 --- a/tensorflow/contrib/autograph/utils/__init__.py +++ b/tensorflow/contrib/autograph/utils/__init__.py @@ -30,4 +30,3 @@ from tensorflow.contrib.autograph.utils.py_func import wrap_py_func from tensorflow.contrib.autograph.utils.tensor_list import dynamic_list_append from tensorflow.contrib.autograph.utils.testing import fake_tf from tensorflow.contrib.autograph.utils.type_check import is_tensor -from tensorflow.contrib.autograph.utils.type_hints import set_element_type diff --git a/tensorflow/contrib/autograph/utils/builtins.py b/tensorflow/contrib/autograph/utils/builtins.py index 998087e056c2cd264399982220d6e0528aab9edb..ccbe5fc9541dfad561d8eab730e2b15f6250ceb2 100644 --- a/tensorflow/contrib/autograph/utils/builtins.py +++ b/tensorflow/contrib/autograph/utils/builtins.py @@ -27,6 +27,7 @@ from tensorflow.contrib.autograph.utils import type_check from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops +from tensorflow.python.ops import list_ops from tensorflow.python.ops import logging_ops from tensorflow.python.ops import math_ops @@ -50,15 +51,22 @@ def dynamic_builtin(f, *args, **kwargs): def dynamic_len(list_or_tensor): """Implementation of len using dynamic dispatch.""" - if tensor_util.is_tensor(list_or_tensor): + if _is_tensor_list(list_or_tensor): + return list_ops.tensor_list_length(list_or_tensor) + elif tensor_util.is_tensor(list_or_tensor): shape = list_or_tensor.shape - if not shape: + if not shape.ndims: raise ValueError( 'len requires non-zero rank for tensor "%s"' % list_or_tensor) return array_ops.shape(list_or_tensor)[0] return len(list_or_tensor) +def _is_tensor_list(list_or_tensor): + return (tensor_util.is_tensor(list_or_tensor) + and list_or_tensor.dtype == dtypes.variant) + + def dynamic_int(num_or_tensor, **kwargs): """Implementation of int() using dynamic dispatch.""" if tensor_util.is_tensor(num_or_tensor): diff --git a/tensorflow/contrib/autograph/utils/builtins_test.py b/tensorflow/contrib/autograph/utils/builtins_test.py index 0c2312178a921037fa419818bf309d671c33914d..b4821f36fcab8c201956e366d394bababb9f02b6 100644 --- a/tensorflow/contrib/autograph/utils/builtins_test.py +++ b/tensorflow/contrib/autograph/utils/builtins_test.py @@ -33,7 +33,8 @@ class BuiltinsTest(test.TestCase): def test_dynamic_len_tf_scalar(self): a = constant_op.constant(1) - with self.assertRaises(ValueError): + with self.assertRaisesRegexp(ValueError, + 'len requires non-zero rank for tensor.*'): with self.test_session() as sess: sess.run(builtins.dynamic_builtin(len, a)) diff --git a/tensorflow/contrib/batching/python/ops/batch_ops.py b/tensorflow/contrib/batching/python/ops/batch_ops.py index 47b80bdf4ad88ebce3603a14ea2aa3cbe5bd345f..55faad983f2bcf2f3fa633669bd371608e2e925b 100644 --- a/tensorflow/contrib/batching/python/ops/batch_ops.py +++ b/tensorflow/contrib/batching/python/ops/batch_ops.py @@ -58,8 +58,6 @@ def batch_function(num_batch_threads, max_batch_size, batch_timeout_micros, allowed_batch_sizes=None, - grad_timeout_micros=60 * 1000 * 1000, - unbatch_timeout_micros=60 * 1000 * 1000, max_enqueued_batches=10): """Batches the computation done by the decorated function. @@ -94,10 +92,6 @@ def batch_function(num_batch_threads, does nothing. Otherwise, supplies a list of batch sizes, causing the op to pad batches up to one of those sizes. The entries must increase monotonically, and the final entry must equal max_batch_size. - grad_timeout_micros: The timeout to use for the gradient. See the - documentation of the unbatch op for more details. Defaults to 60s. - unbatch_timeout_micros: The timeout to use for unbatching. See the - documentation of the unbatch op for more details. Defaults to 60s. max_enqueued_batches: The maximum depth of the batch queue. Defaults to 10. Returns: diff --git a/tensorflow/contrib/bigtable/BUILD b/tensorflow/contrib/bigtable/BUILD index 5c15d21e35557ba5ff25d9d943aae2809eddba4a..71538e0770dcb436c8ff1571c22e950336328357 100644 --- a/tensorflow/contrib/bigtable/BUILD +++ b/tensorflow/contrib/bigtable/BUILD @@ -31,6 +31,7 @@ tf_custom_op_py_library( srcs_version = "PY2AND3", deps = [ ":bigtable_ops", + "//tensorflow/contrib/data/python/ops:interleave_ops", "//tensorflow/contrib/util:util_py", "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:platform", @@ -39,18 +40,24 @@ tf_custom_op_py_library( ], ) +KERNEL_FILES = [ + "kernels/bigtable_kernels.cc", + "kernels/bigtable_lookup_dataset_op.cc", + "kernels/bigtable_prefix_key_dataset_op.cc", + "kernels/bigtable_range_key_dataset_op.cc", + "kernels/bigtable_sample_keys_dataset_op.cc", + "kernels/bigtable_sample_key_pairs_dataset_op.cc", + "kernels/bigtable_scan_dataset_op.cc", +] + tf_custom_op_library( name = "python/ops/_bigtable.so", - srcs = [ - "kernels/bigtable_kernels.cc", - "kernels/bigtable_lookup_dataset_op.cc", - "kernels/bigtable_prefix_key_dataset_op.cc", - "kernels/bigtable_range_key_dataset_op.cc", - "kernels/bigtable_scan_dataset_op.cc", + srcs = KERNEL_FILES + [ "ops/bigtable_ops.cc", ], deps = [ ":bigtable_lib_cc", + ":bigtable_range_helpers", "@com_github_googlecloudplatform_google_cloud_cpp//google/cloud/bigtable:bigtable_client", ], ) @@ -69,15 +76,10 @@ tf_gen_op_libs( tf_kernel_library( name = "bigtable_kernels", - srcs = [ - "kernels/bigtable_kernels.cc", - "kernels/bigtable_lookup_dataset_op.cc", - "kernels/bigtable_prefix_key_dataset_op.cc", - "kernels/bigtable_range_key_dataset_op.cc", - "kernels/bigtable_scan_dataset_op.cc", - ], + srcs = KERNEL_FILES, deps = [ ":bigtable_lib_cc", + ":bigtable_range_helpers", "//tensorflow/core:framework_headers_lib", "//third_party/eigen3", "@com_github_googlecloudplatform_google_cloud_cpp//google/cloud/bigtable:bigtable_client", @@ -96,6 +98,15 @@ cc_library( ], ) +cc_library( + name = "bigtable_range_helpers", + srcs = ["kernels/bigtable_range_helpers.cc"], + hdrs = ["kernels/bigtable_range_helpers.h"], + deps = [ + "//tensorflow/core:framework_headers_lib", + ], +) + cc_library( name = "bigtable_test_client", srcs = ["kernels/test_kernels/bigtable_test_client.cc"], @@ -120,6 +131,17 @@ tf_cc_test( ], ) +tf_cc_test( + name = "bigtable_range_helpers_test", + size = "small", + srcs = ["kernels/bigtable_range_helpers_test.cc"], + deps = [ + ":bigtable_range_helpers", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + tf_gen_op_wrapper_py( name = "bigtable_test_ops", deps = [":bigtable_test_ops_op_lib"], @@ -168,11 +190,6 @@ tf_custom_op_py_library( srcs_version = "PY2AND3", deps = [ ":bigtable_test_ops", - # "//tensorflow/contrib/util:util_py", - # "//tensorflow/python:framework_for_generated_wrappers", - # "//tensorflow/python:platform", - # "//tensorflow/python:util", - # "//tensorflow/python/data", ], ) diff --git a/tensorflow/contrib/bigtable/README.md b/tensorflow/contrib/bigtable/README.md index ef3c60069e8a97f7a13457156d20f3f7a4f7eccb..d7c71a20ed4ba6a55dc0356ab5a3d096ed042e59 100644 --- a/tensorflow/contrib/bigtable/README.md +++ b/tensorflow/contrib/bigtable/README.md @@ -1,10 +1,344 @@ # Bigtable # -[Google Cloud Bigtable](https://cloud.google.com/bigtable/) is a high +[Cloud Bigtable](https://cloud.google.com/bigtable/) is a high performance storage system that can store and serve training data. This contrib package contains an experimental integration with TensorFlow. > **Status: Highly experimental.** The current implementation is very much in > flux. Please use at your own risk! :-) - +The TensorFlow integration with Cloud Bigtable is optimized for common +TensorFlow usage and workloads. It is currently optimized for reading from Cloud +Bigtable at high speed, in particular to feed modern accelerators. For +general-purpose Cloud Bigtable +APIs, see the [official Cloud Bigtable client library documentation][clientdoc]. + +[clientdoc]: https://cloud.google.com/bigtable/docs/reference/libraries + +## Sample Use + +There are three main reading styles supported by the `BigtableTable` class: + + 1. **Reading keys**: Read only the row keys in a table. Keys are returned in + sorted order from the table. Most key reading operations retrieve all keys + in a contiguous range, however the `sample_keys` operation skips keys, and + operates on the whole table (and not a contiguous subset). + 2. **Retrieving a row's values**: Given a row key, look up the data associated + with a defined set of columns. This operation takes advantage of Cloud + Bigtable's low-latency and excellent support for random access. + 3. **Scanning ranges**: Given a contiguous range of rows retrieve both the row + key and the data associated with a fixed set of columns. This operation + takes advantage of Cloud Bigtable's high throughput scans, and is the most + efficient way to read data. + +When using the Cloud Bigtable API, the workflow is: + + 1. Create a `BigtableClient` object. + 2. Use the `BigtableClient` to create `BigtableTable` objects corresponding to + each table in the Cloud Bigtable instance you would like to access. + 3. Call methods on the `BigtableTable` object to create `tf.data.Dataset`s to + retrieve data. + +The following is an example for how to read all row keys with the prefix +`train-`. + +```python +import tensorflow as tf + +GCP_PROJECT_ID = '' +BIGTABLE_INSTANCE_ID = '' +BIGTABLE_TABLE_NAME = '' +PREFIX = 'train-' + +def main(): + client = tf.contrib.cloud.BigtableClient(GCP_PROJECT_ID, BIGTABLE_INSTANCE_ID) + table = client.table(BIGTABLE_TABLE_NAME) + dataset = table.keys_by_prefix_dataset(PREFIX) + iterator = dataset.make_initializable_iterator() + get_next_op = iterator.get_next() + + with tf.Session() as sess: + print('Initializing the iterator.') + sess.run(iterator.initializer) + print('Retrieving rows:') + row_index = 0 + while True: + try: + row_key = sess.run(get_next_op) + print('Row key %d: %s' % (row_index, row_key)) + row_index += 1 + except tf.errors.OutOfRangeError: + print('Finished reading data!') + break + +if __name__ == '__main__': + main() + +``` + +### Reading row keys + +Read only the row keys in a table. Keys are returned in sorted order from the +table. Most key reading operations retrieve all keys in a contiguous range, +however the `sample_keys` operation skips keys, and operates on the whole table +(and not a contiguous subset). + +There are 3 methods to retrieve row keys: + + - `table.keys_by_range_dataset(start, end)`: Retrieve row keys starting with + `start`, and ending with `end`. The range is "half-open", and thus it + includes `start` if `start` is present in the table. It does not include + `end`. + - `table.keys_by_prefix_dataset(prefix)`: Retrieves all row keys that start + with `prefix`. It includes the row key `prefix` if present in the table. + - `table.sample_keys()`: Retrieves a sampling of keys from the underlying + table. This is often useful in conjunction with parallel scans. + +### Reading cell values given a row key + +Given a dataset producing row keys, you can use the `table.lookup_columns` +transformation to retrieve values. Example: + +```python +key_dataset = tf.data.Dataset.from_tensor_slices([ + 'row_key_1', + 'other_row_key', + 'final_row_key', +]) +values_dataset = key_dataset.apply( + table.lookup_columns(('my_column_family', 'column_name'), + ('other_cf', 'col'))) +training_data = values_dataset.map(my_parsing_function) # ... +``` + +### Scanning ranges +Given a contiguous range of rows retrieve both the row key and the data +associated with a fixed set of columns. Scanning is the most efficient way to +retrieve data from Cloud Bigtable and is thus a very common API for high +performance data pipelines. To construct a scanning `tf.data.Dataset` from a +`BigtableTable` object, call one of the following methods: + + - `table.scan_prefix(prefix, ...)` + - `table.scan_range(start, end, ...)` + - `table.parallel_scan_prefix(prefix, ...)` + - `table.parallel_scan_range(start, end, ...)` + +Aside from the specification of the contiguous range of rows, they all take the +following arguments: + + - `probability`: (Optional.) A float between 0 (exclusive) and 1 (inclusive). + A non-1 value indicates to probabilistically sample rows with the + provided probability. + - `columns`: The columns to read. (See below.) + - `**kwargs`: The columns to read. (See below.) + +In addition the two parallel operations accept the following optional argument: +`num_parallel_scans` which configures the number of parallel Cloud Bigtable scan +operations to run. A reasonable default is automatically chosen for small +Cloud Bigtable clusters. If you have a large cluster, or an extremely demanding +workload, you can tune this value to optimize performance. + +#### Specifying columns to read when scanning + +All of the scan operations allow you to specify the column family and columns +in the same ways. + +##### Using `columns` + +The first way to specify the data to read is via the `columns` parameter. The +value should be a tuple (or list of tuples) of strings. The first string in the +tuple is the column family, and the second string in the tuple is the column +qualifier. + +##### Using `**kwargs` + +The second way to specify the data to read is via the `**kwargs` parameter, +which you can use to specify keyword arguments corresponding to the columns that +you want to read. The keyword to use is the column family name, and the argument +value should be either a string, or a tuple of strings, specifying the column +qualifiers (column names). + +Although using `**kwargs` has the advantage of requiring less typing, it is not +future-proof in all cases. (If we add a new parameter to the scan functions that +has the same name as your column family, your code will break.) + +##### Examples + +Below are two equivalent snippets for how to specify which columns to read: + +```python +ds1 = table.scan_range("row_start", "row_end", columns=[("cfa", "c1"), + ("cfa", "c2"), + ("cfb", "c3")]) +ds2 = table.scan_range("row_start", "row_end", cfa=["c1", "c2"], cfb="c3") +``` + +In this example, we are reading 3 columns from a total of 2 column families. +From the `cfa` column family, we are reading columns `c1`, and `c2`. From the +second column family (`cfb`), we are reading `c3`. Both `ds1` and `ds2` will +output elements of the following types (`tf.string`, `tf.string`, `tf.string`, +`tf.string`). The first `tf.string` is the row key, the second `tf.string` is +the latest data in cell `cfa:c1`, the third corresponds to `cfa:c2`, and the +final one is `cfb:c3`. + +#### Determinism when scanning + +While the non-parallel scan operations are fully deterministic, the parallel +scan operations are not. If you would like to scan in parallel without losing +determinism, you can build up the `parallel_interleave` yourself. As an example, +say we wanted to scan all rows between `training_data_00000`, and +`training_data_90000`, we can use the following code snippet: + +```python +table = # ... +columns = [('cf1', 'col1'), ('cf1', 'col2')] +NUM_PARALLEL_READS = # ... +ds = tf.data.Dataset.range(9).shuffle(10) +def interleave_fn(index): + # Given a starting index, create 2 strings to be the start and end + start_idx = index + end_idx = index + 1 + start_idx_str = tf.as_string(start_idx * 10000, width=5, fill='0') + end_idx_str = tf.as_string(end_idx * 10000, width=5, fill='0') + start = tf.string_join(['training_data_', start_idx_str]) + end = tf.string_join(['training_data_', end_idx_str]) + return table.scan_range(start_idx, end_idx, columns=columns) +ds = ds.apply(tf.contrib.data.parallel_interleave( + interleave_fn, cycle_length=NUM_PARALLEL_READS, prefetch_input_elements=1)) +``` + +> Note: you should divide up the key range into more sub-ranges for increased +> parallelism. + +## Writing to Cloud Bigtable + +In order to simplify getting started, this package provides basic support for +writing data into Cloud Bigtable. + +> Note: The implementation is not optimized for performance! Please consider +> using alternative frameworks such as Apache Beam / Cloud Dataflow for +> production workloads. + +Below is an example for how to write a trivial dataset into Cloud Bigtable. + +```python +import tensorflow as tf + +GCP_PROJECT_ID = '' +BIGTABLE_INSTANCE_ID = '' +BIGTABLE_TABLE_NAME = '' +COLUMN_FAMILY = '' +COLUMN_QUALIFIER = '' + +def make_dataset(): + """Makes a dataset to write to Cloud Bigtable.""" + return tf.data.Dataset.from_tensor_slices([ + 'training_data_1', + 'training_data_2', + 'training_data_3', + ]) + +def make_row_key_dataset(): + """Makes a dataset of strings used for row keys. + + The strings are of the form: `fake-data-` followed by a sequential counter. + For example, this dataset would contain the following elements: + + - fake-data-00000001 + - fake-data-00000002 + - ... + - fake-data-23498103 + """ + counter_dataset = tf.contrib.data.Counter() + width = 8 + row_key_prefix = 'fake-data-' + ds = counter_dataset.map(lambda index: tf.as_string(index, + width=width, + fill='0')) + ds = ds.map(lambda idx_str: tf.string_join([row_key_prefix, idx_str])) + return ds + + +def main(): + client = tf.contrib.cloud.BigtableClient(GCP_PROJECT_ID, BIGTABLE_INSTANCE_ID) + table = client.table(BIGTABLE_TABLE_NAME) + dataset = make_dataset() + index_dataset = make_row_key_dataset() + aggregate_dataset = tf.data.Dataset.zip((index_dataset, dataset)) + write_op = table.write(aggregate_dataset, column_families=[COLUMN_FAMILY], + columns=[COLUMN_QUALIFIER]) + + with tf.Session() as sess: + print('Starting transfer.') + sess.run(write_op) + print('Transfer complete.') + +if __name__ == '__main__': + main() +``` + +## Sample applications and architectures + +While most machine learning applications are well suited by a high performance +distributed file system, there are certain applications where using Cloud +Bigtable works extremely well. + +### Perfect Shuffling + +Normally, training data is stored in flat files, and a combination of +(1) `tf.data.Dataset.interleave` (or `parallel_interleave`), (2) +`tf.data.Dataset.shuffle`, and (3) writing the data in an unsorted order in the +data files in the first place, provides enough randomization to ensure models +train efficiently. However, if you would like perfect shuffling, you can use +Cloud Bigtable's low-latency random access capabilities. Create a +`tf.data.Dataset` that generates the keys in a perfectly random order (or read +all the keys into memory and use a shuffle buffer sized to fit all of them for a +perfect random shuffle using `tf.data.Dataset.shuffle`), and then use +`lookup_columns` to retrieve the training data. + +### Distributed Reinforcement Learning + +Sophisticated reinforcement learning algorithms are commonly trained across a +distributed cluster. (See [IMPALA by DeepMind][impala].) One part of the cluster +runs self-play, while the other part of the cluster learns a new version of the +model based on the training data generated by self-play. The new model version +is then distributed to the self-play half of the cluster, and new training data +is generated to continue the cycle. + +In such a configuration, because there is value in training on the freshest +examples, a storage service like Cloud Bigtable can be used to store and +serve the generated training data. When using Cloud Bigtable, there is no need +to aggregate the examples into large batch files, but the examples can instead +be written as soon as they are generated, and then retrieved at high speed. + +[impala]: https://arxiv.org/abs/1802.01561 + +## Common Gotchas! + +### gRPC Certificates + +If you encounter a log line that includes the following: + +``` +"description":"Failed to load file", [...], +"filename":"/usr/share/grpc/roots.pem" +``` + +you likely need to copy the [gRPC roots.pem file][grpcPem] to +`/usr/share/grpc/roots.pem` on your local machine. + +[grpcPem]: https://github.com/grpc/grpc/blob/master/etc/roots.pem + +### Permission denied errors + +The TensorFlow Cloud Bigtable client will search for credentials to use in the +process's environment. It will use the first credentials it finds if multiple +are available. + + - **Compute Engine**: When running on Compute Engine, the client will often use + the service account from the virtual machine's metadata service. Be sure to + authorize your Compute Engine VM to have access to the Cloud Bigtable service + when creating your VM. + - **Cloud TPU**: Your Cloud TPUs run with the designated Cloud TPU service + account dedicated to your GCP project. Ensure the service account has been + authorized via the Cloud Console to access your Cloud Bigtable instances. diff --git a/tensorflow/contrib/bigtable/__init__.py b/tensorflow/contrib/bigtable/__init__.py index 7df054637cdab32f2dd6201dd3488a90495e1cf5..b7d89c98420ab3ac1465bba718f8257ce2312467 100644 --- a/tensorflow/contrib/bigtable/__init__.py +++ b/tensorflow/contrib/bigtable/__init__.py @@ -18,7 +18,7 @@ This contrib package allows TensorFlow to interface directly with Cloud Bigtable for high-speed data loading. @@BigtableClient -@@BigTable +@@BigtableTable """ @@ -26,14 +26,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigTable from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigtableClient +from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigtableTable from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ - 'BigTable', 'BigtableClient', + 'BigtableTable', ] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc b/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc index f43b44f2cb412244c47d7feea388b6c1eea417f9..a6755a3496f3e1720f1c8c67f75521f2380a9845 100644 --- a/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc +++ b/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc @@ -40,7 +40,16 @@ class BigtableClientOp : public OpKernel { if (connection_pool_size_ == -1) { connection_pool_size_ = 100; } - OP_REQUIRES(ctx, connection_pool_size_ > 0, + + OP_REQUIRES_OK(ctx, ctx->GetAttr("max_receive_message_size", + &max_receive_message_size_)); + // If left unset by the client code, set it to a default of 100. Note: the + // cloud-cpp default of 4 concurrent connections is far too low for high + // performance streaming. + if (max_receive_message_size_ == -1) { + max_receive_message_size_ = 1 << 24; // 16 MBytes + } + OP_REQUIRES(ctx, max_receive_message_size_ > 0, errors::InvalidArgument("connection_pool_size must be > 0")); } @@ -67,7 +76,15 @@ class BigtableClientOp : public OpKernel { cinfo_.container(), cinfo_.name(), &resource, [this, ctx]( BigtableClientResource** ret) EXCLUSIVE_LOCKS_REQUIRED(mu_) { - auto client_options = google::cloud::bigtable::ClientOptions(); + auto client_options = + google::cloud::bigtable::ClientOptions() + .set_connection_pool_size(connection_pool_size_) + .set_data_endpoint("batch-bigtable.googleapis.com"); + auto channel_args = client_options.channel_arguments(); + channel_args.SetMaxReceiveMessageSize( + max_receive_message_size_); + channel_args.SetUserAgentPrefix("tensorflow"); + client_options.set_channel_arguments(channel_args); std::shared_ptr client = google::cloud::bigtable::CreateDefaultDataClient( project_id_, instance_id_, std::move(client_options)); @@ -87,6 +104,7 @@ class BigtableClientOp : public OpKernel { string project_id_; string instance_id_; int64 connection_pool_size_; + int32 max_receive_message_size_; mutex mu_; ContainerInfo cinfo_ GUARDED_BY(mu_); @@ -240,6 +258,12 @@ class ToBigtableOp : public AsyncOpKernel { grpc::Status mutation_status; std::vector<::google::cloud::bigtable::FailedMutation> failures = resource->table().BulkApply(std::move(mutation), mutation_status); + if (!mutation_status.ok()) { + LOG(ERROR) << "Failure applying mutation: " + << mutation_status.error_code() << " - " + << mutation_status.error_message() << " (" + << mutation_status.error_details() << ")."; + } if (!failures.empty()) { for (const auto& failure : failures) { LOG(ERROR) << "Failure applying mutation on row (" @@ -252,7 +276,7 @@ class ToBigtableOp : public AsyncOpKernel { } OP_REQUIRES_ASYNC( ctx, failures.empty() && mutation_status.ok(), - errors::Unknown("Failure while writing to BigTable: ", + errors::Unknown("Failure while writing to Cloud Bigtable: ", mutation_status.error_code(), " - ", mutation_status.error_message(), " (", mutation_status.error_details(), diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_lib.cc b/tensorflow/contrib/bigtable/kernels/bigtable_lib.cc index 2514575f30831bdcfab87eba07511fd309e8b1c2..67bf14c17646cff81af707405b66c9fba2ded0bd 100644 --- a/tensorflow/contrib/bigtable/kernels/bigtable_lib.cc +++ b/tensorflow/contrib/bigtable/kernels/bigtable_lib.cc @@ -27,10 +27,10 @@ Status GrpcStatusToTfStatus(const ::grpc::Status& status) { status.error_code() == ::grpc::StatusCode::OUT_OF_RANGE) { grpc_code = ::grpc::StatusCode::INTERNAL; } - return Status( - static_cast<::tensorflow::error::Code>(status.error_code()), - strings::StrCat("Error reading from BigTable: ", status.error_message(), - " (Details: ", status.error_details(), ")")); + return Status(static_cast<::tensorflow::error::Code>(status.error_code()), + strings::StrCat("Error reading from Cloud Bigtable: ", + status.error_message(), + " (Details: ", status.error_details(), ")")); } string RegexFromStringSet(const std::vector& strs) { diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_lib.h b/tensorflow/contrib/bigtable/kernels/bigtable_lib.h index 12d8256dea72e443826675765369ac6daa99a0ca..a2a5df1037a00ccfdff1910dd950d7b012e684e2 100644 --- a/tensorflow/contrib/bigtable/kernels/bigtable_lib.h +++ b/tensorflow/contrib/bigtable/kernels/bigtable_lib.h @@ -58,7 +58,8 @@ class BigtableTableResource : public ResourceBase { BigtableTableResource(BigtableClientResource* client, string table_name) : client_(client), table_name_(std::move(table_name)), - table_(client->get_client(), table_name_) { + table_(client->get_client(), table_name_, + google::cloud::bigtable::AlwaysRetryMutationPolicy()) { client_->Ref(); } diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_range_helpers.cc b/tensorflow/contrib/bigtable/kernels/bigtable_range_helpers.cc new file mode 100644 index 0000000000000000000000000000000000000000..51965f6214413c08453473e71c30eecbd8925a64 --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/bigtable_range_helpers.cc @@ -0,0 +1,68 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/bigtable/kernels/bigtable_range_helpers.h" + +#include "tensorflow/core/platform/logging.h" + +namespace tensorflow { + +namespace { + +string MakePrefixEndKey(const string& prefix) { + string end = prefix; + while (true) { + if (end.empty()) { + return end; + } + ++end[end.size() - 1]; + if (end[end.size() - 1] == 0) { + // Handle wraparound case. + end = end.substr(0, end.size() - 1); + } else { + return end; + } + } +} + +} // namespace + +/* static */ MultiModeKeyRange MultiModeKeyRange::FromPrefix(string prefix) { + string end = MakePrefixEndKey(prefix); + VLOG(1) << "Creating MultiModeKeyRange from Prefix: " << prefix + << ", with end key: " << end; + return MultiModeKeyRange(std::move(prefix), std::move(end)); +} + +/* static */ MultiModeKeyRange MultiModeKeyRange::FromRange(string begin, + string end) { + return MultiModeKeyRange(std::move(begin), std::move(end)); +} + +const string& MultiModeKeyRange::begin_key() const { return begin_; } + +const string& MultiModeKeyRange::end_key() const { return end_; } + +bool MultiModeKeyRange::contains_key(StringPiece key) const { + if (StringPiece(begin_) > key) { + return false; + } + if (StringPiece(end_) <= key && !end_.empty()) { + return false; + } + return true; +} + +} // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_range_helpers.h b/tensorflow/contrib/bigtable/kernels/bigtable_range_helpers.h new file mode 100644 index 0000000000000000000000000000000000000000..44c628e366c26b88011642f1e8e8d8e74b4698fd --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/bigtable_range_helpers.h @@ -0,0 +1,67 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_BIGTABLE_KERNELS_BIGTABLE_RANGE_HELPERS_H_ +#define TENSORFLOW_CONTRIB_BIGTABLE_KERNELS_BIGTABLE_RANGE_HELPERS_H_ + +#include + +#include "tensorflow/core/lib/core/stringpiece.h" +#include "tensorflow/core/platform/types.h" + +namespace tensorflow { + +// Represents a continuous range of keys defined by either a prefix or a range. +// +// Ranges are represented as "half-open", where the beginning key is included +// in the range, and the end_key is the first excluded key after the range. +// +// The range of keys can be specified either by a key prefix, or by an explicit +// begin key and end key. All methods on this class are valid no matter which +// way the range was specified. +// +// Example: +// MultiModeKeyRange range = MultiModeKeyRange::FromPrefix("myPrefix"); +// if (range.contains_key("myPrefixedKey")) { +// LOG(INFO) << "range from " << range.begin_key() << " to " +// << range.end_key() << "contains \"myPrefixedKey\""; +// } +// if (!range.contains_key("randomKey")) { +// LOG(INFO) << "range does not contain \"randomKey\""; +// } +// range = MultiModeKeyRange::FromRange("a_start_key", "z_end_key"); +class MultiModeKeyRange { + public: + static MultiModeKeyRange FromPrefix(string prefix); + static MultiModeKeyRange FromRange(string begin, string end); + + // The first valid key in the range. + const string& begin_key() const; + // The first invalid key after the valid range. + const string& end_key() const; + // Returns true if the provided key is a part of the range, false otherwise. + bool contains_key(StringPiece key) const; + + private: + MultiModeKeyRange(string begin, string end) + : begin_(std::move(begin)), end_(std::move(end)) {} + + const string begin_; + const string end_; +}; + +} // namespace tensorflow + +#endif // TENSORFLOW_CONTRIB_BIGTABLE_KERNELS_BIGTABLE_RANGE_HELPERS_H_ diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_range_helpers_test.cc b/tensorflow/contrib/bigtable/kernels/bigtable_range_helpers_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..1bfc547271d5e58a9145b73356b2b558dc1af9f1 --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/bigtable_range_helpers_test.cc @@ -0,0 +1,107 @@ +/* Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/bigtable/kernels/bigtable_range_helpers.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { +namespace { + +TEST(MultiModeKeyRangeTest, SimplePrefix) { + MultiModeKeyRange r = MultiModeKeyRange::FromPrefix("prefix"); + EXPECT_EQ("prefix", r.begin_key()); + EXPECT_EQ("prefiy", r.end_key()); + EXPECT_TRUE(r.contains_key("prefixed_key")); + EXPECT_FALSE(r.contains_key("not-prefixed-key")); + EXPECT_FALSE(r.contains_key("prefi")); + EXPECT_FALSE(r.contains_key("prefiy")); + EXPECT_FALSE(r.contains_key("early")); + EXPECT_FALSE(r.contains_key("")); +} + +TEST(MultiModeKeyRangeTest, Range) { + MultiModeKeyRange r = MultiModeKeyRange::FromRange("a", "b"); + EXPECT_EQ("a", r.begin_key()); + EXPECT_EQ("b", r.end_key()); + EXPECT_TRUE(r.contains_key("a")); + EXPECT_TRUE(r.contains_key("ab")); + EXPECT_FALSE(r.contains_key("b")); + EXPECT_FALSE(r.contains_key("bc")); + EXPECT_FALSE(r.contains_key("A")); + EXPECT_FALSE(r.contains_key("B")); + EXPECT_FALSE(r.contains_key("")); +} + +TEST(MultiModeKeyRangeTest, InvertedRange) { + MultiModeKeyRange r = MultiModeKeyRange::FromRange("b", "a"); + EXPECT_FALSE(r.contains_key("a")); + EXPECT_FALSE(r.contains_key("b")); + EXPECT_FALSE(r.contains_key("")); +} + +TEST(MultiModeKeyRangeTest, EmptyPrefix) { + MultiModeKeyRange r = MultiModeKeyRange::FromPrefix(""); + EXPECT_EQ("", r.begin_key()); + EXPECT_EQ("", r.end_key()); + EXPECT_TRUE(r.contains_key("")); + EXPECT_TRUE(r.contains_key("a")); + EXPECT_TRUE(r.contains_key("z")); + EXPECT_TRUE(r.contains_key("A")); + EXPECT_TRUE(r.contains_key("ZZZZZZ")); +} + +TEST(MultiModeKeyRangeTest, HalfRange) { + MultiModeKeyRange r = MultiModeKeyRange::FromRange("start", ""); + EXPECT_EQ("start", r.begin_key()); + EXPECT_EQ("", r.end_key()); + EXPECT_TRUE(r.contains_key("start")); + EXPECT_TRUE(r.contains_key("starting")); + EXPECT_TRUE(r.contains_key("z-end")); + EXPECT_FALSE(r.contains_key("")); + EXPECT_FALSE(r.contains_key("early")); +} + +TEST(MultiModeKeyRangeTest, PrefixWrapAround) { + string prefix = "abc\xff"; + MultiModeKeyRange r = MultiModeKeyRange::FromPrefix(prefix); + EXPECT_EQ(prefix, r.begin_key()); + EXPECT_EQ("abd", r.end_key()); + + EXPECT_TRUE(r.contains_key("abc\xff\x07")); + EXPECT_TRUE(r.contains_key("abc\xff\x15")); + EXPECT_TRUE(r.contains_key("abc\xff\x61")); + EXPECT_TRUE(r.contains_key("abc\xff\xff")); + EXPECT_FALSE(r.contains_key("abc\0")); + EXPECT_FALSE(r.contains_key("abd")); +} + +TEST(MultiModeKeyRangeTest, PrefixSignedWrapAround) { + string prefix = "abc\x7f"; + MultiModeKeyRange r = MultiModeKeyRange::FromPrefix(prefix); + EXPECT_EQ(prefix, r.begin_key()); + EXPECT_EQ("abc\x80", r.end_key()); + + EXPECT_TRUE(r.contains_key("abc\x7f\x07")); + EXPECT_TRUE(r.contains_key("abc\x7f\x15")); + EXPECT_TRUE(r.contains_key("abc\x7f\x61")); + EXPECT_TRUE(r.contains_key("abc\x7f\xff")); + EXPECT_FALSE(r.contains_key("abc\0")); + EXPECT_FALSE(r.contains_key("abc\x01")); + EXPECT_FALSE(r.contains_key("abd")); + EXPECT_FALSE(r.contains_key("ab\x80")); +} + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_sample_key_pairs_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_sample_key_pairs_dataset_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..a1a63a975afd62325e01586542006058fa2c83bc --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/bigtable_sample_key_pairs_dataset_op.cc @@ -0,0 +1,200 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/bigtable/kernels/bigtable_lib.h" +#include "tensorflow/contrib/bigtable/kernels/bigtable_range_helpers.h" +#include "tensorflow/core/framework/op_kernel.h" + +namespace tensorflow { +namespace { + +class BigtableSampleKeyPairsDatasetOp : public DatasetOpKernel { + public: + using DatasetOpKernel::DatasetOpKernel; + + void MakeDataset(OpKernelContext* ctx, DatasetBase** output) override { + string prefix; + OP_REQUIRES_OK(ctx, ParseScalarArgument(ctx, "prefix", &prefix)); + + string start_key; + OP_REQUIRES_OK(ctx, + ParseScalarArgument(ctx, "start_key", &start_key)); + string end_key; + OP_REQUIRES_OK(ctx, ParseScalarArgument(ctx, "end_key", &end_key)); + + BigtableTableResource* resource; + OP_REQUIRES_OK(ctx, + LookupResource(ctx, HandleFromInput(ctx, 0), &resource)); + + OP_REQUIRES(ctx, prefix.empty() || start_key.empty(), + errors::InvalidArgument( + "Only one of prefix and start_key can be provided")); + if (!prefix.empty()) { + OP_REQUIRES(ctx, end_key.empty(), + errors::InvalidArgument( + "If prefix is specified, end_key must be empty.")); + } + + *output = new Dataset(ctx, resource, std::move(prefix), + std::move(start_key), std::move(end_key)); + } + + private: + class Dataset : public GraphDatasetBase { + public: + explicit Dataset(OpKernelContext* ctx, BigtableTableResource* table, + string prefix, string start_key, string end_key) + : GraphDatasetBase(ctx), + table_(table), + key_range_(MakeMultiModeKeyRange( + std::move(prefix), std::move(start_key), std::move(end_key))) { + table_->Ref(); + } + + ~Dataset() override { table_->Unref(); } + + std::unique_ptr MakeIteratorInternal( + const string& prefix) const override { + return std::unique_ptr(new Iterator( + {this, strings::StrCat(prefix, "::BigtableSampleKeyPairsDataset")})); + } + + const DataTypeVector& output_dtypes() const override { + static DataTypeVector* dtypes = + new DataTypeVector({DT_STRING, DT_STRING}); + return *dtypes; + } + + const std::vector& output_shapes() const override { + static std::vector* shapes = + new std::vector({{}, {}}); + return *shapes; + } + + string DebugString() const override { + return "BigtableSampleKeyPairsDatasetOp::Dataset"; + } + + private: + static MultiModeKeyRange MakeMultiModeKeyRange(string prefix, + string start_key, + string end_key) { + if (!start_key.empty()) { + return MultiModeKeyRange::FromRange(std::move(start_key), + std::move(end_key)); + } + return MultiModeKeyRange::FromPrefix(std::move(prefix)); + } + + BigtableTableResource& table() const { return *table_; } + + class Iterator : public DatasetIterator { + public: + explicit Iterator(const Params& params) + : DatasetIterator(params) {} + + // Computes split points (`keys_`) to use when scanning the table. + // + // Initialize first retrieves the sample keys from the table (`row_keys`), + // as these often form good split points within the table. We then iterate + // over them, and copy them to `keys_` if they fall within the requested + // range to scan (`dataset()->key_range_`). Because the requested range + // might start between elements of the sampled keys list, care is taken to + // ensure we don't accidentally miss any subsets of the requested range by + // including `begin_key()` and `end_key()` as appropriate. + Status Initialize(IteratorContext* ctx) override { + grpc::Status status; + std::vector row_keys = + dataset()->table().table().SampleRows(status); + if (!status.ok()) { + return GrpcStatusToTfStatus(status); + } + + for (size_t i = 0; i < row_keys.size(); ++i) { + string row_key(row_keys[i].row_key); + if (dataset()->key_range_.contains_key(row_key)) { + // First key: check to see if we need to add the begin_key. + if (keys_.empty() && dataset()->key_range_.begin_key() != row_key) { + keys_.push_back(dataset()->key_range_.begin_key()); + } + keys_.push_back(std::move(row_key)); + } else if (!keys_.empty()) { + // If !keys_.empty(), then we have found at least one element of + // `row_keys` that is within our requested range + // (`dataset()->key_range_`). Because `row_keys` is sorted, if we + // have found an element that's not within our key range, then we + // are after our requested range (ranges are contiguous) and can end + // iteration early. + break; + } + } + + // Handle the case where we skip over the selected range entirely. + if (keys_.empty()) { + keys_.push_back(dataset()->key_range_.begin_key()); + } + + // Last key: check to see if we need to add the end_key. + if (keys_.back() != dataset()->key_range_.end_key()) { + keys_.push_back(dataset()->key_range_.end_key()); + } + return Status::OK(); + } + + Status GetNextInternal(IteratorContext* ctx, + std::vector* out_tensors, + bool* end_of_sequence) override { + mutex_lock l(mu_); + if (index_ > keys_.size() - 2) { + *end_of_sequence = true; + return Status::OK(); + } + + *end_of_sequence = false; + out_tensors->emplace_back(ctx->allocator({}), DT_STRING, + TensorShape({})); + out_tensors->back().scalar()() = keys_[index_]; + + out_tensors->emplace_back(ctx->allocator({}), DT_STRING, + TensorShape({})); + out_tensors->back().scalar()() = keys_[index_ + 1]; + ++index_; + + return Status::OK(); + } + + private: + mutex mu_; + size_t index_ GUARDED_BY(mu_) = 0; + // Note: we store the keys_ on the iterator instead of the dataset + // because we want to re-sample the row keys in case there have been + // tablet rebalancing operations since the dataset was created. + // + // Note: keys_ is readonly after Initialize, and thus does not need a + // guarding lock. + std::vector keys_; + }; + + BigtableTableResource* const table_; + const MultiModeKeyRange key_range_; + }; +}; + +REGISTER_KERNEL_BUILDER( + Name("BigtableSampleKeyPairsDataset").Device(DEVICE_CPU), + BigtableSampleKeyPairsDatasetOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_sample_keys_dataset_op.cc b/tensorflow/contrib/bigtable/kernels/bigtable_sample_keys_dataset_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..a5a47cfe2dcf7c4034e0d5bc7d9a73ef9c1dc94e --- /dev/null +++ b/tensorflow/contrib/bigtable/kernels/bigtable_sample_keys_dataset_op.cc @@ -0,0 +1,113 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/bigtable/kernels/bigtable_lib.h" +#include "tensorflow/core/framework/op_kernel.h" + +namespace tensorflow { +namespace { + +class BigtableSampleKeysDatasetOp : public DatasetOpKernel { + public: + using DatasetOpKernel::DatasetOpKernel; + + void MakeDataset(OpKernelContext* ctx, DatasetBase** output) override { + BigtableTableResource* resource; + OP_REQUIRES_OK(ctx, + LookupResource(ctx, HandleFromInput(ctx, 0), &resource)); + *output = new Dataset(ctx, resource); + } + + private: + class Dataset : public GraphDatasetBase { + public: + explicit Dataset(OpKernelContext* ctx, BigtableTableResource* table) + : GraphDatasetBase(ctx), table_(table) { + table_->Ref(); + } + + ~Dataset() override { table_->Unref(); } + + std::unique_ptr MakeIteratorInternal( + const string& prefix) const override { + return std::unique_ptr(new Iterator( + {this, strings::StrCat(prefix, "::BigtableSampleKeysDataset")})); + } + + const DataTypeVector& output_dtypes() const override { + static DataTypeVector* dtypes = new DataTypeVector({DT_STRING}); + return *dtypes; + } + + const std::vector& output_shapes() const override { + static std::vector* shapes = + new std::vector({{}}); + return *shapes; + } + + string DebugString() const override { + return "BigtableRangeKeyDatasetOp::Dataset"; + } + + BigtableTableResource* table() const { return table_; } + + private: + class Iterator : public DatasetIterator { + public: + explicit Iterator(const Params& params) + : DatasetIterator(params) {} + + Status Initialize(IteratorContext* ctx) override { + ::grpc::Status status; + row_keys_ = dataset()->table()->table().SampleRows(status); + if (!status.ok()) { + row_keys_.clear(); + return GrpcStatusToTfStatus(status); + } + return Status::OK(); + } + + Status GetNextInternal(IteratorContext* ctx, + std::vector* out_tensors, + bool* end_of_sequence) override { + mutex_lock l(mu_); + if (index_ < row_keys_.size()) { + out_tensors->emplace_back(ctx->allocator({}), DT_STRING, + TensorShape({})); + out_tensors->back().scalar()() = + string(row_keys_[index_].row_key); + *end_of_sequence = false; + index_++; + } else { + *end_of_sequence = true; + } + return Status::OK(); + } + + private: + mutex mu_; + size_t index_ = 0; + std::vector<::google::cloud::bigtable::RowKeySample> row_keys_; + }; + + BigtableTableResource* const table_; + }; +}; + +REGISTER_KERNEL_BUILDER(Name("BigtableSampleKeysDataset").Device(DEVICE_CPU), + BigtableSampleKeysDatasetOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.cc b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.cc index c164682508cd1ef6ec04162b5206a88628fa5221..f083ce6f44b3c2a83d9b5d3235056eb94c4be4a8 100644 --- a/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.cc +++ b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client.cc @@ -63,24 +63,29 @@ class SampleRowKeysResponse : public grpc::ClientReaderInterface< bool NextMessageSize(uint32_t* sz) override { mutex_lock l(mu_); - if (sent_first_message_) { - return false; + mutex_lock l2(client_->mu_); + if (num_messages_sent_ * 2 < client_->table_.rows.size()) { + *sz = 10000; // A sufficiently high enough value to not worry about. + return true; } - *sz = 10000; // A sufficiently high enough value to not worry about. - return true; + return false; } bool Read(google::bigtable::v2::SampleRowKeysResponse* resp) override { + // Send every other key from the table. mutex_lock l(mu_); - if (sent_first_message_) { - return false; - } - sent_first_message_ = true; - mutex_lock l2(client_->mu_); *resp = google::bigtable::v2::SampleRowKeysResponse(); - resp->set_row_key(client_->table_.rows.begin()->first); - resp->set_offset_bytes(0); + auto itr = client_->table_.rows.begin(); + for (uint64 i = 0; i < 2 * num_messages_sent_; ++i) { + ++itr; + if (itr == client_->table_.rows.end()) { + return false; + } + } + resp->set_row_key(itr->first); + resp->set_offset_bytes(100 * num_messages_sent_); + num_messages_sent_++; return true; } @@ -90,7 +95,7 @@ class SampleRowKeysResponse : public grpc::ClientReaderInterface< private: mutex mu_; - bool sent_first_message_ GUARDED_BY(mu_) = false; + int64 num_messages_sent_ GUARDED_BY(mu_) = 0; BigtableTestClient* client_; // Not owned. }; diff --git a/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client_test.cc b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client_test.cc index d6b396471941eaa0ca1c13a7386503ed3861e087..32611e2590d9a81f46d0b9dfc09fe7e0068e9671 100644 --- a/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client_test.cc +++ b/tensorflow/contrib/bigtable/kernels/test_kernels/bigtable_test_client_test.cc @@ -286,5 +286,60 @@ TEST(BigtableTestClientTest, RowKeys) { EXPECT_TRUE(rows.Finish().ok()); } +TEST(BigtableTestClientTest, SampleKeys) { + std::shared_ptr<::google::cloud::bigtable::DataClient> client_ptr = + std::make_shared(); + ::google::cloud::bigtable::noex::Table table(client_ptr, "test_table"); + + WriteCell("r1", "f1", "c1", "v1", &table); + WriteCell("r2", "f1", "c1", "v2", &table); + WriteCell("r3", "f1", "c1", "v3", &table); + WriteCell("r4", "f1", "c1", "v4", &table); + WriteCell("r5", "f1", "c1", "v5", &table); + + grpc::Status status; + auto resp = table.SampleRows(status); + EXPECT_TRUE(status.ok()); + EXPECT_EQ(3, resp.size()); + EXPECT_EQ("r1", string(resp[0].row_key)); + EXPECT_EQ(0, resp[0].offset_bytes); + EXPECT_EQ("r3", string(resp[1].row_key)); + EXPECT_EQ(100, resp[1].offset_bytes); + EXPECT_EQ("r5", string(resp[2].row_key)); + EXPECT_EQ(200, resp[2].offset_bytes); +} + +TEST(BigtableTestClientTest, SampleKeysShort) { + std::shared_ptr<::google::cloud::bigtable::DataClient> client_ptr = + std::make_shared(); + ::google::cloud::bigtable::noex::Table table(client_ptr, "test_table"); + + WriteCell("r1", "f1", "c1", "v1", &table); + + grpc::Status status; + auto resp = table.SampleRows(status); + EXPECT_TRUE(status.ok()); + EXPECT_EQ(1, resp.size()); + EXPECT_EQ("r1", string(resp[0].row_key)); +} + +TEST(BigtableTestClientTest, SampleKeysEvenNumber) { + std::shared_ptr<::google::cloud::bigtable::DataClient> client_ptr = + std::make_shared(); + ::google::cloud::bigtable::noex::Table table(client_ptr, "test_table"); + + WriteCell("r1", "f1", "c1", "v1", &table); + WriteCell("r2", "f1", "c1", "v2", &table); + WriteCell("r3", "f1", "c1", "v3", &table); + WriteCell("r4", "f1", "c1", "v4", &table); + + grpc::Status status; + auto resp = table.SampleRows(status); + EXPECT_TRUE(status.ok()); + EXPECT_EQ(2, resp.size()); + EXPECT_EQ("r1", string(resp[0].row_key)); + EXPECT_EQ("r3", string(resp[1].row_key)); +} + } // namespace } // namespace tensorflow diff --git a/tensorflow/contrib/bigtable/ops/bigtable_ops.cc b/tensorflow/contrib/bigtable/ops/bigtable_ops.cc index c7ff012ec89db74848b513d614de49664b5724d8..416b719e30aa5f2504449d151a48e95c9105c68b 100644 --- a/tensorflow/contrib/bigtable/ops/bigtable_ops.cc +++ b/tensorflow/contrib/bigtable/ops/bigtable_ops.cc @@ -23,6 +23,7 @@ REGISTER_OP("BigtableClient") .Attr("project_id: string") .Attr("instance_id: string") .Attr("connection_pool_size: int") + .Attr("max_receive_message_size: int = -1") .Attr("container: string = ''") .Attr("shared_name: string = ''") .Output("client: resource") @@ -71,6 +72,23 @@ REGISTER_OP("BigtableRangeKeyDataset") // stateful to inhibit constant folding. .SetShapeFn(shape_inference::ScalarShape); +REGISTER_OP("BigtableSampleKeysDataset") + .Input("table: resource") + .Output("handle: variant") + .SetIsStateful() // TODO(b/65524810): Source dataset ops must be marked + // stateful to inhibit constant folding. + .SetShapeFn(shape_inference::ScalarShape); + +REGISTER_OP("BigtableSampleKeyPairsDataset") + .Input("table: resource") + .Input("prefix: string") + .Input("start_key: string") + .Input("end_key: string") + .Output("handle: variant") + .SetIsStateful() // TODO(b/65524810): Source dataset ops must be marked + // stateful to inhibit constant folding. + .SetShapeFn(shape_inference::ScalarShape); + // TODO(saeta): Support continuing despite bad data (e.g. empty string, or // skip incomplete row.) REGISTER_OP("BigtableScanDataset") diff --git a/tensorflow/contrib/bigtable/python/kernel_tests/bigtable_ops_test.py b/tensorflow/contrib/bigtable/python/kernel_tests/bigtable_ops_test.py index d33a66f2dfbecd0dc1082fd98973660ce9a93931..e36f7f32c61b50047c0d9137427f2a24462b1c9a 100644 --- a/tensorflow/contrib/bigtable/python/kernel_tests/bigtable_ops_test.py +++ b/tensorflow/contrib/bigtable/python/kernel_tests/bigtable_ops_test.py @@ -21,8 +21,10 @@ from __future__ import print_function from tensorflow.contrib import bigtable from tensorflow.contrib.bigtable.ops import gen_bigtable_ops from tensorflow.contrib.bigtable.ops import gen_bigtable_test_ops +from tensorflow.contrib.bigtable.python.ops import bigtable_api from tensorflow.contrib.util import loader from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import errors from tensorflow.python.platform import resource_loader from tensorflow.python.platform import test from tensorflow.python.util import compat @@ -31,6 +33,10 @@ _bigtable_so = loader.load_op_library( resource_loader.get_path_to_datafile("_bigtable_test.so")) +def _ListOfTuplesOfStringsToBytes(values): + return [(compat.as_bytes(i[0]), compat.as_bytes(i[1])) for i in values] + + class BigtableOpsTest(test.TestCase): COMMON_ROW_KEYS = ["r1", "r2", "r3"] COMMON_VALUES = ["v1", "v2", "v3"] @@ -38,7 +44,7 @@ class BigtableOpsTest(test.TestCase): def setUp(self): self._client = gen_bigtable_test_ops.bigtable_test_client() table = gen_bigtable_ops.bigtable_table(self._client, "testtable") - self._table = bigtable.BigTable("testtable", None, table) + self._table = bigtable.BigtableTable("testtable", None, table) def _makeSimpleDataset(self): output_rows = dataset_ops.Dataset.from_tensor_slices(self.COMMON_ROW_KEYS) @@ -99,12 +105,18 @@ class BigtableOpsTest(test.TestCase): def testScanPrefixListCol(self): self.runScanTest(self._table.scan_prefix("r", cf1=["c1"])) + def testScanPrefixTupleCol(self): + self.runScanTest(self._table.scan_prefix("r", columns=("cf1", "c1"))) + def testScanRangeStringCol(self): self.runScanTest(self._table.scan_range("r1", "r4", cf1="c1")) def testScanRangeListCol(self): self.runScanTest(self._table.scan_range("r1", "r4", cf1=["c1"])) + def testScanRangeTupleCol(self): + self.runScanTest(self._table.scan_range("r1", "r4", columns=("cf1", "c1"))) + def testLookup(self): ds = self._table.keys_by_prefix_dataset("r") ds = ds.apply(self._table.lookup_columns(cf1="c1")) @@ -127,6 +139,134 @@ class BigtableOpsTest(test.TestCase): "Unequal values at step %d: want: %s, got: %s" % (i, compat.as_bytes(elem[1]), compat.as_bytes(output[1]))) + def testSampleKeys(self): + ds = self._table.sample_keys() + itr = ds.make_initializable_iterator() + n = itr.get_next() + expected_key = self.COMMON_ROW_KEYS[0] + with self.test_session() as sess: + self._writeCommonValues(sess) + sess.run(itr.initializer) + output = sess.run(n) + self.assertEqual( + compat.as_bytes(self.COMMON_ROW_KEYS[0]), compat.as_bytes(output), + "Unequal keys: want: %s, got: %s" % (compat.as_bytes( + self.COMMON_ROW_KEYS[0]), compat.as_bytes(output))) + output = sess.run(n) + self.assertEqual( + compat.as_bytes(self.COMMON_ROW_KEYS[2]), compat.as_bytes(output), + "Unequal keys: want: %s, got: %s" % (compat.as_bytes( + self.COMMON_ROW_KEYS[2]), compat.as_bytes(output))) + with self.assertRaises(errors.OutOfRangeError): + sess.run(n) + + def runSampleKeyPairsTest(self, ds, expected_key_pairs): + itr = ds.make_initializable_iterator() + n = itr.get_next() + with self.test_session() as sess: + self._writeCommonValues(sess) + sess.run(itr.initializer) + for i, elems in enumerate(expected_key_pairs): + output = sess.run(n) + self.assertEqual( + compat.as_bytes(elems[0]), compat.as_bytes(output[0]), + "Unequal key pair (first element) at step %d; want: %s, got %s" % + (i, compat.as_bytes(elems[0]), compat.as_bytes(output[0]))) + self.assertEqual( + compat.as_bytes(elems[1]), compat.as_bytes(output[1]), + "Unequal key pair (second element) at step %d; want: %s, got %s" % + (i, compat.as_bytes(elems[1]), compat.as_bytes(output[1]))) + with self.assertRaises(errors.OutOfRangeError): + sess.run(n) + + def testSampleKeyPairsSimplePrefix(self): + ds = bigtable_api._BigtableSampleKeyPairsDataset( + self._table, prefix="r", start="", end="") + expected_key_pairs = [("r", "r1"), ("r1", "r3"), ("r3", "s")] + self.runSampleKeyPairsTest(ds, expected_key_pairs) + + def testSampleKeyPairsSimpleRange(self): + ds = bigtable_api._BigtableSampleKeyPairsDataset( + self._table, prefix="", start="r1", end="r3") + expected_key_pairs = [("r1", "r3")] + self.runSampleKeyPairsTest(ds, expected_key_pairs) + + def testSampleKeyPairsSkipRangePrefix(self): + ds = bigtable_api._BigtableSampleKeyPairsDataset( + self._table, prefix="r2", start="", end="") + expected_key_pairs = [("r2", "r3")] + self.runSampleKeyPairsTest(ds, expected_key_pairs) + + def testSampleKeyPairsSkipRangeRange(self): + ds = bigtable_api._BigtableSampleKeyPairsDataset( + self._table, prefix="", start="r2", end="r3") + expected_key_pairs = [("r2", "r3")] + self.runSampleKeyPairsTest(ds, expected_key_pairs) + + def testSampleKeyPairsOffsetRanges(self): + ds = bigtable_api._BigtableSampleKeyPairsDataset( + self._table, prefix="", start="r2", end="r4") + expected_key_pairs = [("r2", "r3"), ("r3", "r4")] + self.runSampleKeyPairsTest(ds, expected_key_pairs) + + def testSampleKeyPairEverything(self): + ds = bigtable_api._BigtableSampleKeyPairsDataset( + self._table, prefix="", start="", end="") + expected_key_pairs = [("", "r1"), ("r1", "r3"), ("r3", "")] + self.runSampleKeyPairsTest(ds, expected_key_pairs) + + def testSampleKeyPairsPrefixAndStartKey(self): + ds = bigtable_api._BigtableSampleKeyPairsDataset( + self._table, prefix="r", start="r1", end="") + itr = ds.make_initializable_iterator() + with self.test_session() as sess: + with self.assertRaises(errors.InvalidArgumentError): + sess.run(itr.initializer) + + def testSampleKeyPairsPrefixAndEndKey(self): + ds = bigtable_api._BigtableSampleKeyPairsDataset( + self._table, prefix="r", start="", end="r3") + itr = ds.make_initializable_iterator() + with self.test_session() as sess: + with self.assertRaises(errors.InvalidArgumentError): + sess.run(itr.initializer) + + def testParallelScanPrefix(self): + ds = self._table.parallel_scan_prefix(prefix="r", cf1="c1") + itr = ds.make_initializable_iterator() + n = itr.get_next() + with self.test_session() as sess: + self._writeCommonValues(sess) + sess.run(itr.initializer) + expected_values = list(zip(self.COMMON_ROW_KEYS, self.COMMON_VALUES)) + actual_values = [] + for _ in range(len(expected_values)): + output = sess.run(n) + actual_values.append(output) + with self.assertRaises(errors.OutOfRangeError): + sess.run(n) + self.assertItemsEqual( + _ListOfTuplesOfStringsToBytes(expected_values), + _ListOfTuplesOfStringsToBytes(actual_values)) + + def testParallelScanRange(self): + ds = self._table.parallel_scan_range(start="r1", end="r4", cf1="c1") + itr = ds.make_initializable_iterator() + n = itr.get_next() + with self.test_session() as sess: + self._writeCommonValues(sess) + sess.run(itr.initializer) + expected_values = list(zip(self.COMMON_ROW_KEYS, self.COMMON_VALUES)) + actual_values = [] + for _ in range(len(expected_values)): + output = sess.run(n) + actual_values.append(output) + with self.assertRaises(errors.OutOfRangeError): + sess.run(n) + self.assertItemsEqual( + _ListOfTuplesOfStringsToBytes(expected_values), + _ListOfTuplesOfStringsToBytes(actual_values)) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/bigtable/python/ops/bigtable_api.py b/tensorflow/contrib/bigtable/python/ops/bigtable_api.py index 39c58ba6659e5e637c31dce419c34bcce9c09838..fd30aa8bbb962257c1ef5ac07e047fffca88c4bc 100644 --- a/tensorflow/contrib/bigtable/python/ops/bigtable_api.py +++ b/tensorflow/contrib/bigtable/python/ops/bigtable_api.py @@ -28,8 +28,10 @@ from __future__ import division from __future__ import print_function from six import iteritems +from six import string_types from tensorflow.contrib.bigtable.ops import gen_bigtable_ops +from tensorflow.contrib.data.python.ops import interleave_ops from tensorflow.contrib.util import loader from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest @@ -49,7 +51,11 @@ class BigtableClient(object): `table` method to open a Bigtable Table. """ - def __init__(self, project_id, instance_id, connection_pool_size=None): + def __init__(self, + project_id, + instance_id, + connection_pool_size=None, + max_receive_message_size=None): """Creates a BigtableClient that can be used to open connections to tables. Args: @@ -57,6 +63,8 @@ class BigtableClient(object): instance_id: A string representing the Bigtable instance to connect to. connection_pool_size: (Optional.) A number representing the number of concurrent connections to the Cloud Bigtable service to make. + max_receive_message_size: (Optional.) The maximum bytes received in a + single gRPC response. Raises: ValueError: if the arguments are invalid (e.g. wrong type, or out of @@ -74,13 +82,19 @@ class BigtableClient(object): connection_pool_size = -1 elif connection_pool_size < 1: raise ValueError("`connection_pool_size` must be positive") + + if max_receive_message_size is None: + max_receive_message_size = -1 + elif max_receive_message_size < 1: + raise ValueError("`max_receive_message_size` must be positive") + self._connection_pool_size = connection_pool_size - self._resource = gen_bigtable_ops.bigtable_client(project_id, instance_id, - connection_pool_size) + self._resource = gen_bigtable_ops.bigtable_client( + project_id, instance_id, connection_pool_size, max_receive_message_size) def table(self, name, snapshot=None): - """Opens a table and returns a `BigTable` object. + """Opens a table and returns a `BigtableTable` object. Args: name: A `tf.string` `tf.Tensor` name of the table to open. @@ -88,19 +102,20 @@ class BigtableClient(object): request the creation of a snapshot. (Note: currently unimplemented.) Returns: - A `BigTable` python object representing the operations available on the - table. + A `BigtableTable` python object representing the operations available on + the table. """ # TODO(saeta): Implement snapshot functionality. table = gen_bigtable_ops.bigtable_table(self._resource, name) - return BigTable(name, snapshot, table) + return BigtableTable(name, snapshot, table) -class BigTable(object): - """BigTable is the entrypoint for reading and writing data in Cloud Bigtable. +class BigtableTable(object): + """BigtableTable is the entrypoint for reading and writing data in Cloud + Bigtable. - This BigTable class is the python representation of the Cloud Bigtable table - within TensorFlow. Methods on this class allow data to be read from and + This BigtableTable class is the Python representation of the Cloud Bigtable + table within TensorFlow. Methods on this class allow data to be read from and written to the Cloud Bigtable service in flexible and high performance manners. """ @@ -205,6 +220,18 @@ class BigTable(object): """ return _BigtablePrefixKeyDataset(self, prefix) + def sample_keys(self): + """Retrieves a sampling of row keys from the Bigtable table. + + This dataset is most often used in conjunction with + @{tf.contrib.data.parallel_interleave} to construct a set of ranges for + scanning in parallel. + + Returns: + A @{tf.data.Dataset} returning string row keys. + """ + return _BigtableSampleKeysDataset(self) + def scan_prefix(self, prefix, probability=None, columns=None, **kwargs): """Retrieves row (including values) from the Bigtable service. @@ -227,9 +254,11 @@ class BigTable(object): Note: only the latest value of a cell will be retrieved. Args: - prefix: The prefix all row keys muat match to be retrieved for prefix- + prefix: The prefix all row keys must match to be retrieved for prefix- based scans. - probability: Probabilistically sample rows. + probability: (Optional.) A float between 0 (exclusive) and 1 (inclusive). + A non-1 value indicates to probabilistically sample rows with the + provided probability. columns: The columns to read. Note: most commonly, they are expressed as kwargs. Use the columns value if you are using column families that are reserved. The value of columns and kwargs are merged. Columns is a list @@ -244,26 +273,8 @@ class BigTable(object): Raises: ValueError: If the configured probability is unexpected. """ - if probability is None: - probability = 1.0 - if isinstance(probability, float) and (probability <= 0.0 or - probability > 1.0): - raise ValueError("probability must be in the range (0, 1].") - - normalized = columns - if normalized is None: - normalized = [] - if isinstance(normalized, tuple): - normalized = list(normalized) - for key, value in iteritems(kwargs): - if key == "name": - continue - if isinstance(value, str): - normalized.append((key, value)) - continue - for col in value: - normalized.append((key, col)) - + probability = _normalize_probability(probability) + normalized = _normalize_columns(columns, kwargs) return _BigtableScanDataset(self, prefix, "", "", normalized, probability) def scan_range(self, start, end, probability=None, columns=None, **kwargs): @@ -290,7 +301,9 @@ class BigTable(object): Args: start: The start of the range when scanning by range. end: (Optional.) The end of the range when scanning by range. - probability: Probabilistically sample rows. + probability: (Optional.) A float between 0 (exclusive) and 1 (inclusive). + A non-1 value indicates to probabilistically sample rows with the + provided probability. columns: The columns to read. Note: most commonly, they are expressed as kwargs. Use the columns value if you are using column families that are reserved. The value of columns and kwargs are merged. Columns is a list @@ -305,27 +318,129 @@ class BigTable(object): Raises: ValueError: If the configured probability is unexpected. """ - if probability is None: - probability = 1.0 - if isinstance(probability, float) and (probability <= 0.0 or - probability > 1.0): - raise ValueError("probability must be in the range (0, 1].") + probability = _normalize_probability(probability) + normalized = _normalize_columns(columns, kwargs) + return _BigtableScanDataset(self, "", start, end, normalized, probability) - normalized = columns - if normalized is None: - normalized = [] - if isinstance(normalized, tuple): - normalized = list(normalized) - for key, value in iteritems(kwargs): - if key == "name": - continue - if isinstance(value, str): - normalized.append((key, value)) - continue - for col in value: - normalized.append((key, col)) + def parallel_scan_prefix(self, + prefix, + num_parallel_scans=None, + probability=None, + columns=None, + **kwargs): + """Retrieves row (including values) from the Bigtable service at high speed. - return _BigtableScanDataset(self, "", start, end, normalized, probability) + Rows with row-key prefixed by `prefix` will be retrieved. This method is + similar to `scan_prefix`, but by constrast performs multiple sub-scans in + parallel in order to achieve higher performance. + + Note: The dataset produced by this method is not deterministic! + + Specifying the columns to retrieve for each row is done by either using + kwargs or in the columns parameter. To retrieve values of the columns "c1", + and "c2" from the column family "cfa", and the value of the column "c3" + from column family "cfb", the following datasets (`ds1`, and `ds2`) are + equivalent: + + ``` + table = # ... + ds1 = table.parallel_scan_prefix("row_prefix", columns=[("cfa", "c1"), + ("cfa", "c2"), + ("cfb", "c3")]) + ds2 = table.parallel_scan_prefix("row_prefix", cfa=["c1", "c2"], cfb="c3") + ``` + + Note: only the latest value of a cell will be retrieved. + + Args: + prefix: The prefix all row keys must match to be retrieved for prefix- + based scans. + num_parallel_scans: (Optional.) The number of concurrent scans against the + Cloud Bigtable instance. + probability: (Optional.) A float between 0 (exclusive) and 1 (inclusive). + A non-1 value indicates to probabilistically sample rows with the + provided probability. + columns: The columns to read. Note: most commonly, they are expressed as + kwargs. Use the columns value if you are using column families that are + reserved. The value of columns and kwargs are merged. Columns is a list + of tuples of strings ("column_family", "column_qualifier"). + **kwargs: The column families and columns to read. Keys are treated as + column_families, and values can be either lists of strings, or strings + that are treated as the column qualifier (column name). + + Returns: + A @{tf.data.Dataset} returning the row keys and the cell contents. + + Raises: + ValueError: If the configured probability is unexpected. + """ + probability = _normalize_probability(probability) + normalized = _normalize_columns(columns, kwargs) + ds = _BigtableSampleKeyPairsDataset(self, prefix, "", "") + return self._make_parallel_scan_dataset(ds, num_parallel_scans, probability, + normalized) + + def parallel_scan_range(self, + start, + end, + num_parallel_scans=None, + probability=None, + columns=None, + **kwargs): + """Retrieves rows (including values) from the Bigtable service. + + Rows with row-keys between `start` and `end` will be retrieved. This method + is similar to `scan_range`, but by constrast performs multiple sub-scans in + parallel in order to achieve higher performance. + + Note: The dataset produced by this method is not deterministic! + + Specifying the columns to retrieve for each row is done by either using + kwargs or in the columns parameter. To retrieve values of the columns "c1", + and "c2" from the column family "cfa", and the value of the column "c3" + from column family "cfb", the following datasets (`ds1`, and `ds2`) are + equivalent: + + ``` + table = # ... + ds1 = table.parallel_scan_range("row_start", + "row_end", + columns=[("cfa", "c1"), + ("cfa", "c2"), + ("cfb", "c3")]) + ds2 = table.parallel_scan_range("row_start", "row_end", + cfa=["c1", "c2"], cfb="c3") + ``` + + Note: only the latest value of a cell will be retrieved. + + Args: + start: The start of the range when scanning by range. + end: (Optional.) The end of the range when scanning by range. + num_parallel_scans: (Optional.) The number of concurrent scans against the + Cloud Bigtable instance. + probability: (Optional.) A float between 0 (exclusive) and 1 (inclusive). + A non-1 value indicates to probabilistically sample rows with the + provided probability. + columns: The columns to read. Note: most commonly, they are expressed as + kwargs. Use the columns value if you are using column families that are + reserved. The value of columns and kwargs are merged. Columns is a list + of tuples of strings ("column_family", "column_qualifier"). + **kwargs: The column families and columns to read. Keys are treated as + column_families, and values can be either lists of strings, or strings + that are treated as the column qualifier (column name). + + Returns: + A @{tf.data.Dataset} returning the row keys and the cell contents. + + Raises: + ValueError: If the configured probability is unexpected. + """ + probability = _normalize_probability(probability) + normalized = _normalize_columns(columns, kwargs) + ds = _BigtableSampleKeyPairsDataset(self, "", start, end) + return self._make_parallel_scan_dataset(ds, num_parallel_scans, probability, + normalized) def write(self, dataset, column_families, columns, timestamp=None): """Writes a dataset to the table. @@ -372,6 +487,89 @@ class BigTable(object): columns, timestamp) + def _make_parallel_scan_dataset(self, ds, num_parallel_scans, + normalized_probability, normalized_columns): + """Builds a parallel dataset from a given range. + + Args: + ds: A `_BigtableSampleKeyPairsDataset` returning ranges of keys to use. + num_parallel_scans: The number of concurrent parallel scans to use. + normalized_probability: A number between 0 and 1 for the keep probability. + normalized_columns: The column families and column qualifiers to retrieve. + + Returns: + A @{tf.data.Dataset} representing the result of the parallel scan. + """ + if num_parallel_scans is None: + num_parallel_scans = 50 + + ds = ds.shuffle(buffer_size=10000) # TODO(saeta): Make configurable. + + def _interleave_fn(start, end): + return _BigtableScanDataset( + self, + prefix="", + start=start, + end=end, + normalized=normalized_columns, + probability=normalized_probability) + + # Note prefetch_input_elements must be set in order to avoid rpc timeouts. + ds = ds.apply( + interleave_ops.parallel_interleave( + _interleave_fn, + cycle_length=num_parallel_scans, + sloppy=True, + prefetch_input_elements=1)) + return ds + + +def _normalize_probability(probability): + if probability is None: + probability = 1.0 + if isinstance(probability, float) and (probability <= 0.0 or + probability > 1.0): + raise ValueError("probability must be in the range (0, 1].") + return probability + + +def _normalize_columns(columns, provided_kwargs): + """Converts arguments (columns, and kwargs dict) to C++ representation. + + Args: + columns: a datastructure containing the column families and qualifier to + retrieve. Valid types include (1) None, (2) list of tuples, (3) a tuple of + strings. + provided_kwargs: a dictionary containing the column families and qualifiers + to retrieve + + Returns: + A list of pairs of column family+qualifier to retrieve. + + Raises: + ValueError: If there are no cells to retrieve or the columns are in an + incorrect format. + """ + normalized = columns + if normalized is None: + normalized = [] + if isinstance(normalized, tuple): + if len(normalized) == 2: + normalized = [normalized] + else: + raise ValueError("columns was a tuple of inappropriate length") + for key, value in iteritems(provided_kwargs): + if key == "name": + continue + if isinstance(value, string_types): + normalized.append((key, value)) + continue + for col in value: + normalized.append((key, col)) + if not normalized: + raise ValueError("At least one column + column family must be specified.") + return normalized + class _BigtableKeyDataset(dataset_ops.Dataset): """_BigtableKeyDataset is an abstract class representing the keys of a table. @@ -429,6 +627,20 @@ class _BigtableRangeKeyDataset(_BigtableKeyDataset): end_key=self._end) +class _BigtableSampleKeysDataset(_BigtableKeyDataset): + """_BigtableSampleKeysDataset represents a sampling of row keys. + """ + + # TODO(saeta): Expose the data size offsets into the keys. + + def __init__(self, table): + super(_BigtableSampleKeysDataset, self).__init__(table) + + def _as_variant_tensor(self): + return gen_bigtable_ops.bigtable_sample_keys_dataset( + table=self._table._resource) # pylint: disable=protected-access + + class _BigtableLookupDataset(dataset_ops.Dataset): """_BigtableLookupDataset represents a dataset that retrieves values for keys. """ @@ -497,3 +709,34 @@ class _BigtableScanDataset(dataset_ops.Dataset): column_families=self._column_families, columns=self._columns, probability=self._probability) + + +class _BigtableSampleKeyPairsDataset(dataset_ops.Dataset): + """_BigtableKeyRangeDataset returns key pairs from the Bigtable. + """ + + def __init__(self, table, prefix, start, end): + self._table = table + self._prefix = prefix + self._start = start + self._end = end + + @property + def output_classes(self): + return (ops.Tensor, ops.Tensor) + + @property + def output_shapes(self): + return (tensor_shape.TensorShape([]), tensor_shape.TensorShape([])) + + @property + def output_types(self): + return (dtypes.string, dtypes.string) + + def _as_variant_tensor(self): + # pylint: disable=protected-access + return gen_bigtable_ops.bigtable_sample_key_pairs_dataset( + table=self._table._resource, + prefix=self._prefix, + start_key=self._start, + end_key=self._end) diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/BUILD b/tensorflow/contrib/boosted_trees/estimator_batch/BUILD index ef0e80cd0997bc0e95cd0d150e87db144a2dde44..f4a375328eb9cdbe17682637c2f20e3aa8a1e0ca 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/BUILD +++ b/tensorflow/contrib/boosted_trees/estimator_batch/BUILD @@ -147,6 +147,7 @@ py_library( deps = [ ":distillation_loss", ":estimator_utils", + ":model", ":trainer_hooks", "//tensorflow/contrib/boosted_trees:gbdt_batch", "//tensorflow/contrib/boosted_trees:model_ops_py", diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py b/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py index 62f1f4122b05b56a708823df4246d618bd3fa5d4..78232fa0a6e2311c13d4f35acffc3486a9a28803 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py @@ -32,6 +32,7 @@ from tensorflow.python.framework import ops from tensorflow.python.platform import gfile from tensorflow.python.saved_model import loader as saved_model_loader from tensorflow.python.saved_model import tag_constants +from tensorflow.python.util import compat _SPARSE_FLOAT_FEATURE_NAME_TEMPLATE = "%s_%d" @@ -88,10 +89,12 @@ def make_custom_export_strategy(name, len(sparse_float_indices), len(sparse_int_indices)) sorted_by_importance = sorted( feature_importances.items(), key=lambda x: -x[1]) - assets_dir = os.path.join(result_dir, "assets.extra") + assets_dir = os.path.join( + compat.as_bytes(result_dir), compat.as_bytes("assets.extra")) gfile.MakeDirs(assets_dir) - with gfile.GFile(os.path.join(assets_dir, "feature_importances"), - "w") as f: + with gfile.GFile(os.path.join( + compat.as_bytes(assets_dir), + compat.as_bytes("feature_importances")), "w") as f: f.write("\n".join("%s, %f" % (k, v) for k, v in sorted_by_importance)) return result_dir diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator.py b/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator.py index 7eb429b636a5193a124dd9b0c020dae6cac910cb..dbfa69edcbf9e59fedc068b8ee516b92e2c03f4f 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator.py @@ -26,6 +26,7 @@ from __future__ import print_function import six from tensorflow.contrib import layers +from tensorflow.contrib.boosted_trees.estimator_batch import model from tensorflow.contrib.boosted_trees.estimator_batch import distillation_loss from tensorflow.contrib.boosted_trees.estimator_batch import estimator_utils from tensorflow.contrib.boosted_trees.estimator_batch import trainer_hooks @@ -34,6 +35,7 @@ from tensorflow.contrib.boosted_trees.python.training.functions import gbdt_batc from tensorflow.contrib.layers.python.layers import optimizers from tensorflow.contrib.learn.python.learn.estimators import estimator from tensorflow.contrib.learn.python.learn.estimators import head as head_lib +from tensorflow.python.estimator import estimator as core_estimator from tensorflow.contrib.learn.python.learn.estimators import model_fn from tensorflow.python.feature_column import feature_column as feature_column_lib from tensorflow.python.framework import ops @@ -62,27 +64,29 @@ def _add_hidden_layer_summary(value, tag): summary.histogram("%s_activation" % tag, value) -def _dnn_tree_combined_model_fn(features, - labels, - mode, - head, - dnn_hidden_units, - dnn_feature_columns, - tree_learner_config, - num_trees, - tree_examples_per_layer, - config=None, - dnn_optimizer="Adagrad", - dnn_activation_fn=nn.relu, - dnn_dropout=None, - dnn_input_layer_partitioner=None, - dnn_input_layer_to_tree=True, - dnn_steps_to_train=10000, - predict_with_tree_only=False, - tree_feature_columns=None, - tree_center_bias=False, - dnn_to_tree_distillation_param=None, - use_core_versions=False): +def _dnn_tree_combined_model_fn( + features, + labels, + mode, + head, + dnn_hidden_units, + dnn_feature_columns, + tree_learner_config, + num_trees, + tree_examples_per_layer, + config=None, + dnn_optimizer="Adagrad", + dnn_activation_fn=nn.relu, + dnn_dropout=None, + dnn_input_layer_partitioner=None, + dnn_input_layer_to_tree=True, + dnn_steps_to_train=10000, + predict_with_tree_only=False, + tree_feature_columns=None, + tree_center_bias=False, + dnn_to_tree_distillation_param=None, + use_core_versions=False, + output_type=model.ModelBuilderOutputType.MODEL_FN_OPS): """DNN and GBDT combined model_fn. Args: @@ -156,6 +160,10 @@ def _dnn_tree_combined_model_fn(features, partitioned_variables.min_max_variable_partitioner( max_partitions=config.num_ps_replicas, min_slice_size=64 << 20)) + if (output_type == model.ModelBuilderOutputType.ESTIMATOR_SPEC and + not use_core_versions): + raise ValueError("You must use core versions with Estimator Spec") + with variable_scope.variable_scope( dnn_parent_scope, values=tuple(six.itervalues(features)), @@ -235,7 +243,8 @@ def _dnn_tree_combined_model_fn(features, learner_config=tree_learner_config, feature_columns=tree_feature_columns, logits_dimension=head.logits_dimension, - features=tree_features) + features=tree_features, + use_core_columns=use_core_versions) with ops.name_scope("gbdt"): predictions_dict = gbdt_model.predict(mode) @@ -284,63 +293,96 @@ def _dnn_tree_combined_model_fn(features, del loss return control_flow_ops.no_op() - if use_core_versions: - model_fn_ops = head.create_estimator_spec( - features=features, - mode=mode, - labels=labels, - train_op_fn=_no_train_op_fn, - logits=tree_train_logits) - dnn_train_op = head.create_estimator_spec( - features=features, - mode=mode, - labels=labels, - train_op_fn=_dnn_train_op_fn, - logits=dnn_logits) - dnn_train_op = estimator_utils.estimator_spec_to_model_fn_ops( - dnn_train_op).train_op + if tree_center_bias: + num_trees += 1 + finalized_trees, attempted_trees = gbdt_model.get_number_of_trees_tensor() - tree_train_op = head.create_estimator_spec( - features=tree_features, - mode=mode, - labels=labels, - train_op_fn=_tree_train_op_fn, - logits=tree_train_logits) - tree_train_op = estimator_utils.estimator_spec_to_model_fn_ops( - tree_train_op).train_op + if output_type == model.ModelBuilderOutputType.MODEL_FN_OPS: + if use_core_versions: + model_fn_ops = head.create_estimator_spec( + features=features, + mode=mode, + labels=labels, + train_op_fn=_no_train_op_fn, + logits=tree_train_logits) + dnn_train_op = head.create_estimator_spec( + features=features, + mode=mode, + labels=labels, + train_op_fn=_dnn_train_op_fn, + logits=dnn_logits) + dnn_train_op = estimator_utils.estimator_spec_to_model_fn_ops( + dnn_train_op).train_op - model_fn_ops = estimator_utils.estimator_spec_to_model_fn_ops(model_fn_ops) - else: - model_fn_ops = head.create_model_fn_ops( + tree_train_op = head.create_estimator_spec( + features=tree_features, + mode=mode, + labels=labels, + train_op_fn=_tree_train_op_fn, + logits=tree_train_logits) + tree_train_op = estimator_utils.estimator_spec_to_model_fn_ops( + tree_train_op).train_op + + model_fn_ops = estimator_utils.estimator_spec_to_model_fn_ops( + model_fn_ops) + else: + model_fn_ops = head.create_model_fn_ops( + features=features, + mode=mode, + labels=labels, + train_op_fn=_no_train_op_fn, + logits=tree_train_logits) + dnn_train_op = head.create_model_fn_ops( + features=features, + mode=mode, + labels=labels, + train_op_fn=_dnn_train_op_fn, + logits=dnn_logits).train_op + tree_train_op = head.create_model_fn_ops( + features=tree_features, + mode=mode, + labels=labels, + train_op_fn=_tree_train_op_fn, + logits=tree_train_logits).train_op + + # Add the hooks + model_fn_ops.training_hooks.extend([ + trainer_hooks.SwitchTrainOp(dnn_train_op, dnn_steps_to_train, + tree_train_op), + trainer_hooks.StopAfterNTrees(num_trees, attempted_trees, + finalized_trees) + ]) + return model_fn_ops + + elif output_type == model.ModelBuilderOutputType.ESTIMATOR_SPEC: + fusion_spec = head.create_estimator_spec( features=features, mode=mode, labels=labels, train_op_fn=_no_train_op_fn, logits=tree_train_logits) - dnn_train_op = head.create_model_fn_ops( + dnn_spec = head.create_estimator_spec( features=features, mode=mode, labels=labels, train_op_fn=_dnn_train_op_fn, - logits=dnn_logits).train_op - tree_train_op = head.create_model_fn_ops( + logits=dnn_logits) + tree_spec = head.create_estimator_spec( features=tree_features, mode=mode, labels=labels, train_op_fn=_tree_train_op_fn, - logits=tree_train_logits).train_op - - if tree_center_bias: - num_trees += 1 - finalized_trees, attempted_trees = gbdt_model.get_number_of_trees_tensor() - - model_fn_ops.training_hooks.extend([ - trainer_hooks.SwitchTrainOp(dnn_train_op, dnn_steps_to_train, - tree_train_op), - trainer_hooks.StopAfterNTrees(num_trees, attempted_trees, finalized_trees) - ]) + logits=tree_train_logits) - return model_fn_ops + training_hooks = [ + trainer_hooks.SwitchTrainOp(dnn_spec.train_op, dnn_steps_to_train, + tree_spec.train_op), + trainer_hooks.StopAfterNTrees(num_trees, attempted_trees, + finalized_trees) + ] + fusion_spec = fusion_spec._replace(training_hooks=training_hooks + + list(fusion_spec.training_hooks)) + return fusion_spec class DNNBoostedTreeCombinedClassifier(estimator.Estimator): @@ -697,3 +739,100 @@ class DNNBoostedTreeCombinedEstimator(estimator.Estimator): model_dir=model_dir, config=config, feature_engineering_fn=feature_engineering_fn) + + +class CoreDNNBoostedTreeCombinedEstimator(core_estimator.Estimator): + """Initializes a core version of DNNBoostedTreeCombinedEstimator. + + Args: + dnn_hidden_units: List of hidden units per layer for DNN. + dnn_feature_columns: An iterable containing all the feature columns + used by the model's DNN. + tree_learner_config: A config for the tree learner. + num_trees: Number of trees to grow model to after training DNN. + tree_examples_per_layer: Number of examples to accumulate before + growing the tree a layer. This value has a big impact on model + quality and should be set equal to the number of examples in + training dataset if possible. It can also be a function that computes + the number of examples based on the depth of the layer that's + being built. + head: `Head` instance. + model_dir: Directory for model exports. + config: `RunConfig` of the estimator. + dnn_optimizer: string, `Optimizer` object, or callable that defines the + optimizer to use for training the DNN. If `None`, will use the Adagrad + optimizer with default learning rate. + dnn_activation_fn: Activation function applied to each layer of the DNN. + If `None`, will use `tf.nn.relu`. + dnn_dropout: When not `None`, the probability to drop out a given + unit in the DNN. + dnn_input_layer_partitioner: Partitioner for input layer of the DNN. + Defaults to `min_max_variable_partitioner` with `min_slice_size` + 64 << 20. + dnn_input_layer_to_tree: Whether to provide the DNN's input layer + as a feature to the tree. + dnn_steps_to_train: Number of steps to train dnn for before switching + to gbdt. + predict_with_tree_only: Whether to use only the tree model output as the + final prediction. + tree_feature_columns: An iterable containing all the feature columns + used by the model's boosted trees. If dnn_input_layer_to_tree is + set to True, these features are in addition to dnn_feature_columns. + tree_center_bias: Whether a separate tree should be created for + first fitting the bias. + dnn_to_tree_distillation_param: A Tuple of (float, loss_fn), where the + float defines the weight of the distillation loss, and the loss_fn, for + computing distillation loss, takes dnn_logits, tree_logits and weight + tensor. If the entire tuple is None, no distillation will be applied. If + only the loss_fn is None, we will take the sigmoid/softmax cross entropy + loss be default. When distillation is applied, `predict_with_tree_only` + will be set to True. + """ + + def __init__(self, + dnn_hidden_units, + dnn_feature_columns, + tree_learner_config, + num_trees, + tree_examples_per_layer, + head, + model_dir=None, + config=None, + dnn_optimizer="Adagrad", + dnn_activation_fn=nn.relu, + dnn_dropout=None, + dnn_input_layer_partitioner=None, + dnn_input_layer_to_tree=True, + dnn_steps_to_train=10000, + predict_with_tree_only=False, + tree_feature_columns=None, + tree_center_bias=False, + dnn_to_tree_distillation_param=None): + + def _model_fn(features, labels, mode, config): + return _dnn_tree_combined_model_fn( + features=features, + labels=labels, + mode=mode, + head=head, + dnn_hidden_units=dnn_hidden_units, + dnn_feature_columns=dnn_feature_columns, + tree_learner_config=tree_learner_config, + num_trees=num_trees, + tree_examples_per_layer=tree_examples_per_layer, + config=config, + dnn_optimizer=dnn_optimizer, + dnn_activation_fn=dnn_activation_fn, + dnn_dropout=dnn_dropout, + dnn_input_layer_partitioner=dnn_input_layer_partitioner, + dnn_input_layer_to_tree=dnn_input_layer_to_tree, + dnn_steps_to_train=dnn_steps_to_train, + predict_with_tree_only=predict_with_tree_only, + tree_feature_columns=tree_feature_columns, + tree_center_bias=tree_center_bias, + dnn_to_tree_distillation_param=dnn_to_tree_distillation_param, + output_type=model.ModelBuilderOutputType.ESTIMATOR_SPEC, + use_core_versions=True) + + super(CoreDNNBoostedTreeCombinedEstimator, self).__init__( + model_fn=_model_fn, model_dir=model_dir, config=config) diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator_test.py b/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator_test.py index 9b7acfa664b0398216b5a7fb904960d8363929d6..839eedd3a87ccaa1faecd1966fe5907d682cac02 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator_test.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator_test.py @@ -28,10 +28,11 @@ from tensorflow.python.estimator.canned import head as head_lib from tensorflow.python.feature_column import feature_column_lib as core_feature_column from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops.losses import losses from tensorflow.python.platform import googletest - +from tensorflow.python.training import checkpoint_utils def _train_input_fn(): features = { @@ -156,5 +157,72 @@ class DNNBoostedTreeCombinedTest(test_util.TensorFlowTestCase): classifier.evaluate(input_fn=_eval_input_fn, steps=1) +class CoreDNNBoostedTreeCombinedTest(test_util.TensorFlowTestCase): + + def _assert_checkpoint(self, model_dir, global_step): + reader = checkpoint_utils.load_checkpoint(model_dir) + self.assertEqual(global_step, reader.get_tensor(ops.GraphKeys.GLOBAL_STEP)) + + def testTrainEvaluateInferDoesNotThrowErrorWithNoDnnInput(self): + head_fn = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS) + + learner_config = learner_pb2.LearnerConfig() + learner_config.num_classes = 2 + learner_config.constraints.max_tree_depth = 3 + model_dir = tempfile.mkdtemp() + config = run_config.RunConfig() + + est = estimator.CoreDNNBoostedTreeCombinedEstimator( + head=head_fn, + dnn_hidden_units=[1], + dnn_feature_columns=[core_feature_column.numeric_column("x")], + tree_learner_config=learner_config, + num_trees=1, + tree_examples_per_layer=3, + model_dir=model_dir, + config=config, + dnn_steps_to_train=10, + dnn_input_layer_to_tree=False, + tree_feature_columns=[core_feature_column.numeric_column("x")]) + + # Train for a few steps. + est.train(input_fn=_train_input_fn, steps=1000) + # 10 steps for dnn, 3 for 1 tree of depth 3 + 1 after the tree finished + self._assert_checkpoint(est.model_dir, global_step=14) + res = est.evaluate(input_fn=_eval_input_fn, steps=1) + self.assertLess(0.5, res["auc"]) + est.predict(input_fn=_eval_input_fn) + + def testTrainEvaluateInferDoesNotThrowErrorWithDnnInput(self): + head_fn = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS) + + learner_config = learner_pb2.LearnerConfig() + learner_config.num_classes = 2 + learner_config.constraints.max_tree_depth = 3 + model_dir = tempfile.mkdtemp() + config = run_config.RunConfig() + + est = estimator.CoreDNNBoostedTreeCombinedEstimator( + head=head_fn, + dnn_hidden_units=[1], + dnn_feature_columns=[core_feature_column.numeric_column("x")], + tree_learner_config=learner_config, + num_trees=1, + tree_examples_per_layer=3, + model_dir=model_dir, + config=config, + dnn_steps_to_train=10, + dnn_input_layer_to_tree=True, + tree_feature_columns=[]) + + # Train for a few steps. + est.train(input_fn=_train_input_fn, steps=1000) + res = est.evaluate(input_fn=_eval_input_fn, steps=1) + self.assertLess(0.5, res["auc"]) + est.predict(input_fn=_eval_input_fn) + + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py b/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py index 9c36c302210185bc390751a0229a61f2f8cd91b8..2df879f924d735c5bcd0d354159c825dee3afda8 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py @@ -22,9 +22,16 @@ from tensorflow.contrib.boosted_trees.estimator_batch import model from tensorflow.contrib.boosted_trees.python.utils import losses from tensorflow.contrib.learn.python.learn.estimators import estimator from tensorflow.contrib.learn.python.learn.estimators import head as head_lib +from tensorflow.python.estimator import estimator as core_estimator from tensorflow.python.ops import math_ops +# ================== Old estimator interface=================================== +# The estimators below were designed for old feature columns and old estimator +# interface. They can be used with new feature columns and losses by setting +# use_core_libs = True. + + class GradientBoostedDecisionTreeClassifier(estimator.Estimator): """An estimator using gradient boosted decision trees.""" @@ -269,3 +276,251 @@ class GradientBoostedDecisionTreeEstimator(estimator.Estimator): model_dir=model_dir, config=config, feature_engineering_fn=feature_engineering_fn) + + +class GradientBoostedDecisionTreeRanker(estimator.Estimator): + """A ranking estimator using gradient boosted decision trees.""" + + def __init__( + self, + learner_config, + examples_per_layer, + head, + ranking_model_pair_keys, + num_trees=None, + feature_columns=None, + weight_column_name=None, + model_dir=None, + config=None, + label_keys=None, + feature_engineering_fn=None, + logits_modifier_function=None, + center_bias=False, + use_core_libs=False, + output_leaf_index=False, + ): + """Initializes a GradientBoostedDecisionTreeRanker instance. + + This is an estimator that can be trained off the pairwise data and can be + used for inference on non-paired data. This is essentially LambdaMart. + Args: + learner_config: A config for the learner. + examples_per_layer: Number of examples to accumulate before growing a + layer. It can also be a function that computes the number of examples + based on the depth of the layer that's being built. + head: `Head` instance. + ranking_model_pair_keys: Keys to distinguish between features + for left and right part of the training pairs for ranking. For example, + for an Example with features "a.f1" and "b.f1", the keys would be + ("a", "b"). + num_trees: An int, number of trees to build. + feature_columns: A list of feature columns. + weight_column_name: Name of the column for weights, or None if not + weighted. + model_dir: Directory for model exports, etc. + config: `RunConfig` object to configure the runtime settings. + label_keys: Optional list of strings with size `[n_classes]` defining the + label vocabulary. Only supported for `n_classes` > 2. + feature_engineering_fn: Feature engineering function. Takes features and + labels which are the output of `input_fn` and returns features and + labels which will be fed into the model. + logits_modifier_function: A modifier function for the logits. + center_bias: Whether a separate tree should be created for first fitting + the bias. + use_core_libs: Whether feature columns and loss are from the core (as + opposed to contrib) version of tensorflow. + output_leaf_index: whether to output leaf indices along with predictions + during inference. The leaf node indexes are available in predictions + dict by the key 'leaf_index'. It is a Tensor of rank 2 and its shape is + [batch_size, num_trees]. + For example, + result_iter = classifier.predict(...) + for result_dict in result_iter: + # access leaf index list by result_dict["leaf_index"] + # which contains one leaf index per tree + + Raises: + ValueError: If learner_config is not valid. + """ + super(GradientBoostedDecisionTreeRanker, self).__init__( + model_fn=model.ranking_model_builder, + params={ + 'head': head, + 'n_classes': 2, + 'feature_columns': feature_columns, + 'learner_config': learner_config, + 'num_trees': num_trees, + 'weight_column_name': weight_column_name, + 'examples_per_layer': examples_per_layer, + 'center_bias': center_bias, + 'logits_modifier_function': logits_modifier_function, + 'use_core_libs': use_core_libs, + 'output_leaf_index': output_leaf_index, + 'ranking_model_pair_keys': ranking_model_pair_keys, + }, + model_dir=model_dir, + config=config, + feature_engineering_fn=feature_engineering_fn) + +# ================== New Estimator interface=================================== +# The estimators below use new core Estimator interface and must be used with +# new feature columns and heads. + + +class CoreGradientBoostedDecisionTreeEstimator(core_estimator.Estimator): + """An estimator using gradient boosted decision trees. + + Useful for training with user specified `Head`. + """ + + def __init__(self, + learner_config, + examples_per_layer, + head, + num_trees=None, + feature_columns=None, + weight_column_name=None, + model_dir=None, + config=None, + label_keys=None, + feature_engineering_fn=None, + logits_modifier_function=None, + center_bias=True, + output_leaf_index=False): + """Initializes a core version of GradientBoostedDecisionTreeEstimator. + + Args: + learner_config: A config for the learner. + examples_per_layer: Number of examples to accumulate before growing a + layer. It can also be a function that computes the number of examples + based on the depth of the layer that's being built. + head: `Head` instance. + num_trees: An int, number of trees to build. + feature_columns: A list of feature columns. + weight_column_name: Name of the column for weights, or None if not + weighted. + model_dir: Directory for model exports, etc. + config: `RunConfig` object to configure the runtime settings. + label_keys: Optional list of strings with size `[n_classes]` defining the + label vocabulary. Only supported for `n_classes` > 2. + feature_engineering_fn: Feature engineering function. Takes features and + labels which are the output of `input_fn` and returns features and + labels which will be fed into the model. + logits_modifier_function: A modifier function for the logits. + center_bias: Whether a separate tree should be created for first fitting + the bias. + output_leaf_index: whether to output leaf indices along with predictions + during inference. The leaf node indexes are available in predictions + dict by the key 'leaf_index'. For example, + result_dict = classifier.predict(...) + for example_prediction_result in result_dict: + # access leaf index list by example_prediction_result["leaf_index"] + # which contains one leaf index per tree + """ + + def _model_fn(features, labels, mode, config): + return model.model_builder( + features=features, + labels=labels, + mode=mode, + config=config, + params={ + 'head': head, + 'feature_columns': feature_columns, + 'learner_config': learner_config, + 'num_trees': num_trees, + 'weight_column_name': weight_column_name, + 'examples_per_layer': examples_per_layer, + 'center_bias': center_bias, + 'logits_modifier_function': logits_modifier_function, + 'use_core_libs': True, + 'output_leaf_index': output_leaf_index, + }, + output_type=model.ModelBuilderOutputType.ESTIMATOR_SPEC) + + super(CoreGradientBoostedDecisionTreeEstimator, self).__init__( + model_fn=_model_fn, model_dir=model_dir, config=config) + + +class CoreGradientBoostedDecisionTreeRanker(core_estimator.Estimator): + """A ranking estimator using gradient boosted decision trees.""" + + def __init__( + self, + learner_config, + examples_per_layer, + head, + ranking_model_pair_keys, + num_trees=None, + feature_columns=None, + weight_column_name=None, + model_dir=None, + config=None, + label_keys=None, + logits_modifier_function=None, + center_bias=False, + output_leaf_index=False, + ): + """Initializes a GradientBoostedDecisionTreeRanker instance. + + This is an estimator that can be trained off the pairwise data and can be + used for inference on non-paired data. This is essentially LambdaMart. + Args: + learner_config: A config for the learner. + examples_per_layer: Number of examples to accumulate before growing a + layer. It can also be a function that computes the number of examples + based on the depth of the layer that's being built. + head: `Head` instance. + ranking_model_pair_keys: Keys to distinguish between features + for left and right part of the training pairs for ranking. For example, + for an Example with features "a.f1" and "b.f1", the keys would be + ("a", "b"). + num_trees: An int, number of trees to build. + feature_columns: A list of feature columns. + weight_column_name: Name of the column for weights, or None if not + weighted. + model_dir: Directory for model exports, etc. + config: `RunConfig` object to configure the runtime settings. + label_keys: Optional list of strings with size `[n_classes]` defining the + label vocabulary. Only supported for `n_classes` > 2. + logits_modifier_function: A modifier function for the logits. + center_bias: Whether a separate tree should be created for first fitting + the bias. + output_leaf_index: whether to output leaf indices along with predictions + during inference. The leaf node indexes are available in predictions + dict by the key 'leaf_index'. It is a Tensor of rank 2 and its shape is + [batch_size, num_trees]. + For example, + result_iter = classifier.predict(...) + for result_dict in result_iter: + # access leaf index list by result_dict["leaf_index"] + # which contains one leaf index per tree + + Raises: + ValueError: If learner_config is not valid. + """ + + def _model_fn(features, labels, mode, config): + return model.ranking_model_builder( + features=features, + labels=labels, + mode=mode, + config=config, + params={ + 'head': head, + 'n_classes': 2, + 'feature_columns': feature_columns, + 'learner_config': learner_config, + 'num_trees': num_trees, + 'weight_column_name': weight_column_name, + 'examples_per_layer': examples_per_layer, + 'center_bias': center_bias, + 'logits_modifier_function': logits_modifier_function, + 'use_core_libs': True, + 'output_leaf_index': output_leaf_index, + 'ranking_model_pair_keys': ranking_model_pair_keys, + }, + output_type=model.ModelBuilderOutputType.ESTIMATOR_SPEC) + + super(CoreGradientBoostedDecisionTreeRanker, self).__init__( + model_fn=_model_fn, model_dir=model_dir, config=config) diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/estimator_test.py b/tensorflow/contrib/boosted_trees/estimator_batch/estimator_test.py index 75ef1b050028b6462b255827c06e836e5c481844..9e9febbbef662a594d3589b501e9ae0eea0af196 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/estimator_test.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/estimator_test.py @@ -37,12 +37,31 @@ def _train_input_fn(): return features, label +def _ranking_train_input_fn(): + features = { + "a.f1": constant_op.constant([[3.], [0.3], [1.]]), + "a.f2": constant_op.constant([[0.1], [3.], [1.]]), + "b.f1": constant_op.constant([[13.], [0.4], [5.]]), + "b.f2": constant_op.constant([[1.], [3.], [0.01]]), + } + label = constant_op.constant([[0], [0], [1]], dtype=dtypes.int32) + return features, label + + def _eval_input_fn(): features = {"x": constant_op.constant([[1.], [2.], [2.]])} label = constant_op.constant([[0], [1], [1]], dtype=dtypes.int32) return features, label +def _infer_ranking_train_input_fn(): + features = { + "f1": constant_op.constant([[3.], [2], [1.]]), + "f2": constant_op.constant([[0.1], [3.], [1.]]) + } + return features, None + + class BoostedTreeEstimatorTest(test_util.TensorFlowTestCase): def setUp(self): @@ -155,6 +174,89 @@ class BoostedTreeEstimatorTest(test_util.TensorFlowTestCase): regressor.evaluate(input_fn=_eval_input_fn, steps=1) regressor.export(self._export_dir_base) + def testRankingDontThrowExceptionForForEstimator(self): + learner_config = learner_pb2.LearnerConfig() + learner_config.num_classes = 2 + learner_config.constraints.max_tree_depth = 1 + model_dir = tempfile.mkdtemp() + config = run_config.RunConfig() + + head_fn = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS) + + model = estimator.GradientBoostedDecisionTreeRanker( + head=head_fn, + learner_config=learner_config, + num_trees=1, + examples_per_layer=3, + model_dir=model_dir, + config=config, + use_core_libs=True, + feature_columns=[ + core_feature_column.numeric_column("f1"), + core_feature_column.numeric_column("f2") + ], + ranking_model_pair_keys=("a", "b")) + + model.fit(input_fn=_ranking_train_input_fn, steps=1000) + model.evaluate(input_fn=_ranking_train_input_fn, steps=1) + model.predict(input_fn=_infer_ranking_train_input_fn) + + +class CoreGradientBoostedDecisionTreeEstimators(test_util.TensorFlowTestCase): + + def testTrainEvaluateInferDoesNotThrowError(self): + head_fn = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS) + + learner_config = learner_pb2.LearnerConfig() + learner_config.num_classes = 2 + learner_config.constraints.max_tree_depth = 1 + model_dir = tempfile.mkdtemp() + config = run_config.RunConfig() + + est = estimator.CoreGradientBoostedDecisionTreeEstimator( + head=head_fn, + learner_config=learner_config, + num_trees=1, + examples_per_layer=3, + model_dir=model_dir, + config=config, + feature_columns=[core_feature_column.numeric_column("x")]) + + # Train for a few steps. + est.train(input_fn=_train_input_fn, steps=1000) + est.evaluate(input_fn=_eval_input_fn, steps=1) + est.predict(input_fn=_eval_input_fn) + + def testRankingDontThrowExceptionForForEstimator(self): + learner_config = learner_pb2.LearnerConfig() + learner_config.num_classes = 2 + learner_config.constraints.max_tree_depth = 1 + model_dir = tempfile.mkdtemp() + config = run_config.RunConfig() + + head_fn = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS) + + est = estimator.CoreGradientBoostedDecisionTreeRanker( + head=head_fn, + learner_config=learner_config, + num_trees=1, + examples_per_layer=3, + model_dir=model_dir, + config=config, + feature_columns=[ + core_feature_column.numeric_column("f1"), + core_feature_column.numeric_column("f2") + ], + ranking_model_pair_keys=("a", "b")) + + # Train for a few steps. + est.train(input_fn=_ranking_train_input_fn, steps=1000) + est.evaluate(input_fn=_ranking_train_input_fn, steps=1) + est.predict(input_fn=_infer_ranking_train_input_fn) + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/model.py b/tensorflow/contrib/boosted_trees/estimator_batch/model.py index 1ee891198939e53fc5913104b2c2e65dc977823f..161cc42cb0fe93c18722923095edf7228b5b378c 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/model.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/model.py @@ -20,6 +20,7 @@ from __future__ import print_function import copy +from tensorflow.contrib import learn from tensorflow.contrib.boosted_trees.estimator_batch import estimator_utils from tensorflow.contrib.boosted_trees.estimator_batch import trainer_hooks from tensorflow.contrib.boosted_trees.python.ops import model_ops @@ -28,8 +29,17 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import state_ops from tensorflow.python.training import training_util +class ModelBuilderOutputType(object): + MODEL_FN_OPS = 0 + ESTIMATOR_SPEC = 1 -def model_builder(features, labels, mode, params, config): + +def model_builder(features, + labels, + mode, + params, + config, + output_type=ModelBuilderOutputType.MODEL_FN_OPS): """Multi-machine batch gradient descent tree model. Args: @@ -49,6 +59,8 @@ def model_builder(features, labels, mode, params, config): * center_bias: Whether a separate tree should be created for first fitting the bias. config: `RunConfig` of the estimator. + output_type: Whether to return ModelFnOps (old interface) or EstimatorSpec + (new interface). Returns: A `ModelFnOps` object. @@ -115,29 +127,264 @@ def model_builder(features, labels, mode, params, config): return update_op create_estimator_spec_op = getattr(head, "create_estimator_spec", None) - if use_core_libs and callable(create_estimator_spec_op): - model_fn_ops = head.create_estimator_spec( + + training_hooks = [] + if num_trees: + if center_bias: + num_trees += 1 + + finalized_trees, attempted_trees = gbdt_model.get_number_of_trees_tensor() + training_hooks.append( + trainer_hooks.StopAfterNTrees(num_trees, attempted_trees, + finalized_trees)) + + if output_type == ModelBuilderOutputType.MODEL_FN_OPS: + if use_core_libs and callable(create_estimator_spec_op): + model_fn_ops = head.create_estimator_spec( + features=features, + mode=mode, + labels=labels, + train_op_fn=_train_op_fn, + logits=logits) + model_fn_ops = estimator_utils.estimator_spec_to_model_fn_ops( + model_fn_ops) + else: + model_fn_ops = head.create_model_fn_ops( + features=features, + mode=mode, + labels=labels, + train_op_fn=_train_op_fn, + logits=logits) + + if output_leaf_index and gbdt_batch.LEAF_INDEX in predictions_dict: + model_fn_ops.predictions[gbdt_batch.LEAF_INDEX] = predictions_dict[ + gbdt_batch.LEAF_INDEX] + + model_fn_ops.training_hooks.extend(training_hooks) + return model_fn_ops + elif output_type == ModelBuilderOutputType.ESTIMATOR_SPEC: + assert callable(create_estimator_spec_op) + estimator_spec = head.create_estimator_spec( features=features, mode=mode, labels=labels, train_op_fn=_train_op_fn, logits=logits) - model_fn_ops = estimator_utils.estimator_spec_to_model_fn_ops(model_fn_ops) + + estimator_spec = estimator_spec._replace( + training_hooks=training_hooks + list(estimator_spec.training_hooks)) + return estimator_spec + + return model_fn_ops + + +def ranking_model_builder(features, + labels, + mode, + params, + config, + output_type=ModelBuilderOutputType.MODEL_FN_OPS): + """Multi-machine batch gradient descent tree model for ranking. + + Args: + features: `Tensor` or `dict` of `Tensor` objects. + labels: Labels used to train on. + mode: Mode we are in. (TRAIN/EVAL/INFER) + params: A dict of hyperparameters. + The following hyperparameters are expected: + * head: A `Head` instance. + * learner_config: A config for the learner. + * feature_columns: An iterable containing all the feature columns used by + the model. + * examples_per_layer: Number of examples to accumulate before growing a + layer. It can also be a function that computes the number of examples + based on the depth of the layer that's being built. + * weight_column_name: The name of weight column. + * center_bias: Whether a separate tree should be created for first fitting + the bias. + * ranking_model_pair_keys (Optional): Keys to distinguish between features + for left and right part of the training pairs for ranking. For example, + for an Example with features "a.f1" and "b.f1", the keys would be + ("a", "b"). + config: `RunConfig` of the estimator. + output_type: Whether to return ModelFnOps (old interface) or EstimatorSpec + (new interface). + + + Returns: + A `ModelFnOps` object. + Raises: + ValueError: if inputs are not valid. + """ + head = params["head"] + learner_config = params["learner_config"] + examples_per_layer = params["examples_per_layer"] + feature_columns = params["feature_columns"] + weight_column_name = params["weight_column_name"] + num_trees = params["num_trees"] + use_core_libs = params["use_core_libs"] + logits_modifier_function = params["logits_modifier_function"] + output_leaf_index = params["output_leaf_index"] + ranking_model_pair_keys = params["ranking_model_pair_keys"] + + if features is None: + raise ValueError("At least one feature must be specified.") + + if config is None: + raise ValueError("Missing estimator RunConfig.") + + center_bias = params["center_bias"] + + if isinstance(features, ops.Tensor): + features = {features.name: features} + + # Make a shallow copy of features to ensure downstream usage + # is unaffected by modifications in the model function. + training_features = copy.copy(features) + training_features.pop(weight_column_name, None) + global_step = training_util.get_global_step() + with ops.device(global_step.device): + ensemble_handle = model_ops.tree_ensemble_variable( + stamp_token=0, + tree_ensemble_config="", # Initialize an empty ensemble. + name="ensemble_model") + + # Extract the features. + if mode == learn.ModeKeys.TRAIN or mode == learn.ModeKeys.EVAL: + # For ranking pairwise training, we extract two sets of features. + if len(ranking_model_pair_keys) != 2: + raise ValueError("You must provide keys for ranking.") + left_pair_key = ranking_model_pair_keys[0] + right_pair_key = ranking_model_pair_keys[1] + if left_pair_key is None or right_pair_key is None: + raise ValueError("Both pair keys should be provided for ranking.") + + features_1 = {} + features_2 = {} + for name in training_features: + feature = training_features[name] + new_name = name[2:] + if name.startswith(left_pair_key + "."): + features_1[new_name] = feature + else: + assert name.startswith(right_pair_key + ".") + features_2[new_name] = feature + + main_features = features_1 + supplementary_features = features_2 else: - model_fn_ops = head.create_model_fn_ops( - features=features, - mode=mode, - labels=labels, - train_op_fn=_train_op_fn, - logits=logits) - if output_leaf_index and gbdt_batch.LEAF_INDEX in predictions_dict: - model_fn_ops.predictions[gbdt_batch.LEAF_INDEX] = predictions_dict[ - gbdt_batch.LEAF_INDEX] + # For non-ranking or inference ranking, we have only 1 set of features. + main_features = training_features + + # Create GBDT model. + gbdt_model_main = gbdt_batch.GradientBoostedDecisionTreeModel( + is_chief=config.is_chief, + num_ps_replicas=config.num_ps_replicas, + ensemble_handle=ensemble_handle, + center_bias=center_bias, + examples_per_layer=examples_per_layer, + learner_config=learner_config, + feature_columns=feature_columns, + logits_dimension=head.logits_dimension, + features=main_features, + use_core_columns=use_core_libs, + output_leaf_index=output_leaf_index) + + with ops.name_scope("gbdt", "gbdt_optimizer"): + # Logits for inference. + if mode == learn.ModeKeys.INFER: + predictions_dict = gbdt_model_main.predict(mode) + logits = predictions_dict[gbdt_batch.PREDICTIONS] + if logits_modifier_function: + logits = logits_modifier_function(logits, features, mode) + else: + gbdt_model_supplementary = gbdt_batch.GradientBoostedDecisionTreeModel( + is_chief=config.is_chief, + num_ps_replicas=config.num_ps_replicas, + ensemble_handle=ensemble_handle, + center_bias=center_bias, + examples_per_layer=examples_per_layer, + learner_config=learner_config, + feature_columns=feature_columns, + logits_dimension=head.logits_dimension, + features=supplementary_features, + use_core_columns=use_core_libs, + output_leaf_index=output_leaf_index) + + # Logits for train and eval. + if not supplementary_features: + raise ValueError("Features for ranking must be specified.") + + predictions_dict_1 = gbdt_model_main.predict(mode) + predictions_1 = predictions_dict_1[gbdt_batch.PREDICTIONS] + + predictions_dict_2 = gbdt_model_supplementary.predict(mode) + predictions_2 = predictions_dict_2[gbdt_batch.PREDICTIONS] + + logits = predictions_1 - predictions_2 + if logits_modifier_function: + logits = logits_modifier_function(logits, features, mode) + + predictions_dict = predictions_dict_1 + predictions_dict[gbdt_batch.PREDICTIONS] = logits + + def _train_op_fn(loss): + """Returns the op to optimize the loss.""" + update_op = gbdt_model_main.train(loss, predictions_dict, labels) + with ops.control_dependencies( + [update_op]), (ops.colocate_with(global_step)): + update_op = state_ops.assign_add(global_step, 1).op + return update_op + + create_estimator_spec_op = getattr(head, "create_estimator_spec", None) + + training_hooks = [] if num_trees: if center_bias: num_trees += 1 - finalized_trees, attempted_trees = gbdt_model.get_number_of_trees_tensor() - model_fn_ops.training_hooks.append( + + finalized_trees, attempted_trees = ( + gbdt_model_main.get_number_of_trees_tensor()) + training_hooks.append( trainer_hooks.StopAfterNTrees(num_trees, attempted_trees, finalized_trees)) + + if output_type == ModelBuilderOutputType.MODEL_FN_OPS: + if use_core_libs and callable(create_estimator_spec_op): + model_fn_ops = head.create_estimator_spec( + features=features, + mode=mode, + labels=labels, + train_op_fn=_train_op_fn, + logits=logits) + model_fn_ops = estimator_utils.estimator_spec_to_model_fn_ops( + model_fn_ops) + else: + model_fn_ops = head.create_model_fn_ops( + features=features, + mode=mode, + labels=labels, + train_op_fn=_train_op_fn, + logits=logits) + + if output_leaf_index and gbdt_batch.LEAF_INDEX in predictions_dict: + model_fn_ops.predictions[gbdt_batch.LEAF_INDEX] = predictions_dict[ + gbdt_batch.LEAF_INDEX] + + model_fn_ops.training_hooks.extend(training_hooks) + return model_fn_ops + + elif output_type == ModelBuilderOutputType.ESTIMATOR_SPEC: + assert callable(create_estimator_spec_op) + estimator_spec = head.create_estimator_spec( + features=features, + mode=mode, + labels=labels, + train_op_fn=_train_op_fn, + logits=logits) + + estimator_spec = estimator_spec._replace( + training_hooks=training_hooks + list(estimator_spec.training_hooks)) + return estimator_spec + return model_fn_ops diff --git a/tensorflow/contrib/boosted_trees/examples/boston.py b/tensorflow/contrib/boosted_trees/examples/boston.py index e9dbdb0fd784052eeb36ac1aa9342165ef2ac0a7..54c4ff059e3408d2cb8fc689a9ae877f57485f58 100644 --- a/tensorflow/contrib/boosted_trees/examples/boston.py +++ b/tensorflow/contrib/boosted_trees/examples/boston.py @@ -45,6 +45,7 @@ from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientB from tensorflow.contrib.boosted_trees.proto import learner_pb2 from tensorflow.contrib.layers.python.layers import feature_column from tensorflow.contrib.learn import learn_runner +from tensorflow.python.util import compat _BOSTON_NUM_FEATURES = 13 @@ -79,7 +80,8 @@ def _convert_fn(dtec, sorted_feature_names, num_dense, num_sparse_float, num_sparse_int, export_dir, unused_eval_result): universal_format = custom_export_strategy.convert_to_universal_format( dtec, sorted_feature_names, num_dense, num_sparse_float, num_sparse_int) - with tf.gfile.GFile(os.path.join(export_dir, "tree_proto"), "w") as f: + with tf.gfile.GFile(os.path.join( + compat.as_bytes(export_dir), compat.as_bytes("tree_proto")), "w") as f: f.write(str(universal_format)) diff --git a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc index 0b28f81e7ca9a1228adc5bde19c429265e0aa9b8..5b4be2f25838d5405a8148ea20cb0f759cd3a8fb 100644 --- a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc +++ b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc @@ -241,6 +241,11 @@ class CreateQuantileAccumulatorOp : public OpKernel { // other exceptions. If one already exists, it unrefs the new one. const Tensor* stamp_token_t; OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t)); + // An epsilon value of zero could cause perfoamance issues and is therefore, + // disallowed. + OP_REQUIRES( + context, epsilon_ > 0, + errors::InvalidArgument("An epsilon value of zero is not allowed.")); auto result = new QuantileStreamResource(epsilon_, num_quantiles_, max_elements_, generate_quantiles_, stamp_token_t->scalar()()); diff --git a/tensorflow/contrib/boosted_trees/kernels/training_ops.cc b/tensorflow/contrib/boosted_trees/kernels/training_ops.cc index 1bfeed306641111718984b2097512e5ec3fa8630..6d9a6ee5a0d05465459393c4339558f1ca38d417 100644 --- a/tensorflow/contrib/boosted_trees/kernels/training_ops.cc +++ b/tensorflow/contrib/boosted_trees/kernels/training_ops.cc @@ -372,12 +372,18 @@ class GrowTreeEnsembleOp : public OpKernel { return; } + // Get the max tree depth. + const Tensor* max_tree_depth_t; + OP_REQUIRES_OK(context, + context->input("max_tree_depth", &max_tree_depth_t)); + const int32 max_tree_depth = max_tree_depth_t->scalar()(); + // Update and retrieve the growable tree. // If the tree is fully built and dropout was applied, it also adjusts the // weights of dropped and the last tree. boosted_trees::trees::DecisionTreeConfig* const tree_config = UpdateAndRetrieveGrowableTree(ensemble_resource, learning_rate, - dropout_seed); + dropout_seed, max_tree_depth); // Split tree nodes. for (auto& split_entry : best_splits) { @@ -494,7 +500,8 @@ class GrowTreeEnsembleOp : public OpKernel { boosted_trees::trees::DecisionTreeConfig* UpdateAndRetrieveGrowableTree( boosted_trees::models::DecisionTreeEnsembleResource* const ensemble_resource, - const float learning_rate, const uint64 dropout_seed) { + const float learning_rate, const uint64 dropout_seed, + const int32 max_tree_depth) { const auto num_trees = ensemble_resource->num_trees(); if (num_trees <= 0 || ensemble_resource->LastTreeMetadata()->is_finalized()) { @@ -506,8 +513,7 @@ class GrowTreeEnsembleOp : public OpKernel { tree_config->add_nodes()->mutable_leaf(); boosted_trees::trees::DecisionTreeMetadata* const tree_metadata = ensemble_resource->LastTreeMetadata(); - tree_metadata->set_is_finalized( - learner_config_.constraints().max_tree_depth() <= 1); + tree_metadata->set_is_finalized(max_tree_depth <= 1); tree_metadata->set_num_tree_weight_updates(1); } else { // The growable tree is by definition the last tree in the ensemble. @@ -518,8 +524,7 @@ class GrowTreeEnsembleOp : public OpKernel { << num_trees - 1 << " of ensemble of " << num_trees << " trees."; // Update growable tree metadata. tree_metadata->set_num_layers_grown(new_num_layers); - tree_metadata->set_is_finalized( - new_num_layers >= learner_config_.constraints().max_tree_depth()); + tree_metadata->set_is_finalized(new_num_layers >= max_tree_depth); } UpdateTreeWeightsIfDropout(ensemble_resource, dropout_seed); return ensemble_resource->LastTree(); diff --git a/tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_stream.h b/tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_stream.h index c120dd8a6c156ec9eb7ba0b6c552f5138bd21a16..f19e5116f5865777ab65e1add2777ac41105acc0 100644 --- a/tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_stream.h +++ b/tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_stream.h @@ -58,6 +58,8 @@ namespace quantiles { // Compute: O(n * log(1/eps * log(eps * n))). // Memory: O(1/eps * log^2(eps * n)) <- for one worker streaming through the // entire dataset. +// An epsilon value of zero would make the algorithm extremely inefficent and +// therefore, is disallowed. template > class WeightedQuantilesStream { @@ -69,6 +71,9 @@ class WeightedQuantilesStream { explicit WeightedQuantilesStream(double eps, int64 max_elements) : eps_(eps), buffer_(1LL, 2LL), finalized_(false) { + // See the class documentation. An epsilon value of zero could cause + // perfoamance issues. + QCHECK(eps > 0) << "An epsilon value of zero is not allowed."; std::tie(max_levels_, block_size_) = GetQuantileSpecs(eps, max_elements); buffer_ = Buffer(block_size_, max_elements); summary_levels_.reserve(max_levels_); diff --git a/tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_summary.h b/tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_summary.h index a7e7bfc13cadcea4d29d33e0dbd955bdad6ffcb9..69bb8fd4ada861a42a0ccc3f287a47d91be5c879 100644 --- a/tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_summary.h +++ b/tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_summary.h @@ -51,7 +51,7 @@ class WeightedQuantilesSummary { SummaryEntry() { memset(this, 0, sizeof(*this)); - value = 0; + value = ValueType(); weight = 0; min_rank = 0; max_rank = 0; diff --git a/tensorflow/contrib/boosted_trees/lib/utils/batch_features.cc b/tensorflow/contrib/boosted_trees/lib/utils/batch_features.cc index 35b059f3496dbc8fb2b3d4fe6ec6b55a9d73dd0c..4fab2b0b7deb6ff2e353d758dc068aa28d44d5ae 100644 --- a/tensorflow/contrib/boosted_trees/lib/utils/batch_features.cc +++ b/tensorflow/contrib/boosted_trees/lib/utils/batch_features.cc @@ -16,6 +16,7 @@ #include "tensorflow/contrib/boosted_trees/lib/utils/batch_features.h" #include "tensorflow/contrib/boosted_trees/lib/utils/macros.h" #include "tensorflow/contrib/boosted_trees/lib/utils/tensor_utils.h" +#include "tensorflow/core/lib/core/errors.h" namespace tensorflow { namespace boosted_trees { @@ -96,9 +97,11 @@ Status BatchFeatures::Initialize( "Sparse float feature shape incompatible with batch size.")); auto tensor_shape = TensorShape({shape_flat(0), shape_flat(1)}); auto order_dims = sparse::SparseTensor::VarDimArray({0, 1}); - sparse_float_feature_columns_.emplace_back(sparse_float_feature_indices, - sparse_float_feature_values, - tensor_shape, order_dims); + sparse::SparseTensor sparse_tensor; + TF_RETURN_IF_ERROR(sparse::SparseTensor::Create( + sparse_float_feature_indices, sparse_float_feature_values, tensor_shape, + order_dims, &sparse_tensor)); + sparse_float_feature_columns_.push_back(std::move(sparse_tensor)); } // Read sparse int features. @@ -136,9 +139,11 @@ Status BatchFeatures::Initialize( "Sparse int feature shape incompatible with batch size.")); auto tensor_shape = TensorShape({shape_flat(0), shape_flat(1)}); auto order_dims = sparse::SparseTensor::VarDimArray({0, 1}); - sparse_int_feature_columns_.emplace_back(sparse_int_feature_indices, - sparse_int_feature_values, - tensor_shape, order_dims); + sparse::SparseTensor sparse_tensor; + TF_RETURN_IF_ERROR(sparse::SparseTensor::Create( + sparse_int_feature_indices, sparse_int_feature_values, tensor_shape, + order_dims, &sparse_tensor)); + sparse_int_feature_columns_.push_back(std::move(sparse_tensor)); } return Status::OK(); } diff --git a/tensorflow/contrib/boosted_trees/lib/utils/examples_iterable_test.cc b/tensorflow/contrib/boosted_trees/lib/utils/examples_iterable_test.cc index d8a608864834b17886313a368221fbf94e31c98e..30c37435fe16ef29a9e29202850501098e9ac7f8 100644 --- a/tensorflow/contrib/boosted_trees/lib/utils/examples_iterable_test.cc +++ b/tensorflow/contrib/boosted_trees/lib/utils/examples_iterable_test.cc @@ -43,27 +43,35 @@ TEST_F(ExamplesIterableTest, Iterate) { test::AsTensor({0, 0, 2, 0, 3, 0, 4, 0}, {4, 2}); auto sparse_float_values1 = test::AsTensor({-3.0f, 0.0f, 5.0f, 0.0f}); auto sparse_float_shape1 = TensorShape({8, 1}); - sparse::SparseTensor sparse_float_tensor1( - sparse_float_indices1, sparse_float_values1, sparse_float_shape1); + sparse::SparseTensor sparse_float_tensor1; + TF_ASSERT_OK( + sparse::SparseTensor::Create(sparse_float_indices1, sparse_float_values1, + sparse_float_shape1, &sparse_float_tensor1)); auto sparse_float_indices2 = test::AsTensor( {0, 1, 1, 0, 2, 1, 3, 0, 4, 1, 5, 0, 5, 1, 7, 0}, {8, 2}); auto sparse_float_values2 = test::AsTensor({1.f, 4.0f, 3.f, 7.0f, 4.3f, 9.0f, 0.8f, -4.0f}); auto sparse_float_shape2 = TensorShape({8, 2}); - sparse::SparseTensor sparse_float_tensor2( - sparse_float_indices2, sparse_float_values2, sparse_float_shape2); + sparse::SparseTensor sparse_float_tensor2; + TF_ASSERT_OK( + sparse::SparseTensor::Create(sparse_float_indices2, sparse_float_values2, + sparse_float_shape2, &sparse_float_tensor2)); auto sparse_int_indices1 = test::AsTensor({0, 0, 0, 1, 1, 0, 3, 0, 3, 1, 7, 0}, {6, 2}); auto sparse_int_values1 = test::AsTensor({1, 8, 0, 2, 0, 5}); auto sparse_int_shape1 = TensorShape({8, 2}); - sparse::SparseTensor sparse_int_tensor1( - sparse_int_indices1, sparse_int_values1, sparse_int_shape1); + sparse::SparseTensor sparse_int_tensor1; + TF_ASSERT_OK( + sparse::SparseTensor::Create(sparse_int_indices1, sparse_int_values1, + sparse_int_shape1, &sparse_int_tensor1)); auto sparse_int_indices2 = test::AsTensor({1, 0, 2, 0, 3, 0, 4, 0}, {4, 2}); auto sparse_int_values2 = test::AsTensor({7, 13, 4, 0}); auto sparse_int_shape2 = TensorShape({8, 1}); - sparse::SparseTensor sparse_int_tensor2( - sparse_int_indices2, sparse_int_values2, sparse_int_shape2); + sparse::SparseTensor sparse_int_tensor2; + TF_ASSERT_OK( + sparse::SparseTensor::Create(sparse_int_indices2, sparse_int_values2, + sparse_int_shape2, &sparse_int_tensor2)); auto validate_example_features = [](int64 example_idx, const Example& example) { diff --git a/tensorflow/contrib/boosted_trees/ops/training_ops.cc b/tensorflow/contrib/boosted_trees/ops/training_ops.cc index f63c199ad6146c23c22437ffe2287a77ee91ca44..22ac9edb72ea91ecef6fd1dff9f399b3c9020083 100644 --- a/tensorflow/contrib/boosted_trees/ops/training_ops.cc +++ b/tensorflow/contrib/boosted_trees/ops/training_ops.cc @@ -56,6 +56,7 @@ REGISTER_OP("GrowTreeEnsemble") .Input("next_stamp_token: int64") .Input("learning_rate: float") .Input("dropout_seed: int64") + .Input("max_tree_depth: int32") .Input("partition_ids: num_handlers * int32") .Input("gains: num_handlers * float") .Input("splits: num_handlers * string") @@ -67,6 +68,8 @@ REGISTER_OP("GrowTreeEnsemble") TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused_input)); // Dropout seed. TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused_input)); + // Maximum tree depth. + TF_RETURN_IF_ERROR(c->WithRank(c->input(5), 0, &unused_input)); return Status::OK(); }) .Doc(R"doc( diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py index 3e524efbeac74ff754d63cae92b3e194411cb2de..e39e1de8d1954c7f4dcab87d7727a64affa13c8c 100644 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py +++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py @@ -296,7 +296,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE, growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE, # Dropout does not change anything here, tree is not finalized. - dropout_probability=0.5).SerializeToString() + dropout_probability=0.5) # Prepare handler inputs. # Note that handlers 1 & 3 have the same gain but different splits. @@ -321,9 +321,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): ], gains=[handler1_gains, handler2_gains, handler3_gains], splits=[handler1_split, handler2_split, handler3_split], - learner_config=learner_config, + learner_config=learner_config.SerializeToString(), dropout_seed=123, - center_bias=True) + center_bias=True, + max_tree_depth=learner_config.constraints.max_tree_depth) session.run(grow_op) # Expect the simpler split from handler 1 to be chosen. @@ -443,7 +444,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE, growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE, # Dropout does not change anything here - tree is not finalized. - dropout_probability=0.5).SerializeToString() + dropout_probability=0.5) # Prepare handler inputs. # Handler 1 only has a candidate for partition 1, handler 2 has candidates @@ -472,9 +473,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): ], gains=[handler1_gains, handler2_gains, handler3_gains], splits=[handler1_split, handler2_split, handler3_split], - learner_config=learner_config, + learner_config=learner_config.SerializeToString(), dropout_seed=123, - center_bias=True) + center_bias=True, + max_tree_depth=learner_config.constraints.max_tree_depth) session.run(grow_op) # Expect the split for partition 1 to be chosen from handler 1 and @@ -632,8 +634,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): max_depth=1, min_node_weight=0, pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE, - growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE).SerializeToString( - ) + growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE) # Prepare handler inputs. handler1_partitions = np.array([0], dtype=np.int32) @@ -657,9 +658,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): ], gains=[handler1_gains, handler2_gains, handler3_gains], splits=[handler1_split, handler2_split, handler3_split], - learner_config=learner_config, + learner_config=learner_config.SerializeToString(), dropout_seed=123, - center_bias=True) + center_bias=True, + max_tree_depth=learner_config.constraints.max_tree_depth) session.run(grow_op) # Expect a new tree to be added with the split from handler 1. @@ -773,8 +775,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): max_depth=1, min_node_weight=0, pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE, - growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE).SerializeToString( - ) + growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE) # Prepare handler inputs. # All handlers have negative gain. @@ -794,9 +795,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): partition_ids=[handler1_partitions, handler2_partitions], gains=[handler1_gains, handler2_gains], splits=[handler1_split, handler2_split], - learner_config=learner_config, + learner_config=learner_config.SerializeToString(), dropout_seed=123, - center_bias=True) + center_bias=True, + max_tree_depth=learner_config.constraints.max_tree_depth) session.run(grow_op) # Expect the ensemble to be empty. @@ -839,8 +841,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): max_depth=1, min_node_weight=0, pruning_mode=learner_pb2.LearnerConfig.POST_PRUNE, - growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE).SerializeToString( - ) + growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE) # Prepare handler inputs. # Note that handlers 1 & 3 have the same gain but different splits. @@ -865,9 +866,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): ], gains=[handler1_gains, handler2_gains, handler3_gains], splits=[handler1_split, handler2_split, handler3_split], - learner_config=learner_config, + learner_config=learner_config.SerializeToString(), dropout_seed=123, - center_bias=True) + center_bias=True, + max_tree_depth=learner_config.constraints.max_tree_depth) session.run(grow_op) # Expect the simpler split from handler 1 to be chosen. @@ -946,8 +948,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): max_depth=2, min_node_weight=0, pruning_mode=learner_pb2.LearnerConfig.POST_PRUNE, - growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE).SerializeToString( - ) + growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE) # Prepare handler inputs. # All handlers have negative gain. @@ -967,9 +968,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): partition_ids=[handler1_partitions, handler2_partitions], gains=[handler1_gains, handler2_gains], splits=[handler1_split, handler2_split], - learner_config=learner_config, + learner_config=learner_config.SerializeToString(), dropout_seed=123, - center_bias=True) + center_bias=True, + max_tree_depth=learner_config.constraints.max_tree_depth) session.run(grow_op) # Expect the split from handler 2 to be chosen despite the negative gain. @@ -1048,9 +1050,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): partition_ids=[handler1_partitions], gains=[handler1_gains], splits=[handler1_split], - learner_config=learner_config, + learner_config=learner_config.SerializeToString(), dropout_seed=123, - center_bias=True) + center_bias=True, + max_tree_depth=learner_config.constraints.max_tree_depth) session.run(grow_op) # Expect the ensemble to be empty as post-pruning will prune @@ -1094,8 +1097,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): max_depth=2, min_node_weight=0, pruning_mode=learner_pb2.LearnerConfig.POST_PRUNE, - growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE).SerializeToString( - ) + growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE) # Prepare handler inputs. # Second handler has positive gain. @@ -1115,9 +1117,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): partition_ids=[handler1_partitions, handler2_partitions], gains=[handler1_gains, handler2_gains], splits=[handler1_split, handler2_split], - learner_config=learner_config, + learner_config=learner_config.SerializeToString(), dropout_seed=123, - center_bias=True) + center_bias=True, + max_tree_depth=learner_config.constraints.max_tree_depth) session.run(grow_op) # Expect the split from handler 2 to be chosen despite the negative gain. @@ -1194,9 +1197,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): partition_ids=[handler1_partitions], gains=[handler1_gains], splits=[handler1_split], - learner_config=learner_config, + learner_config=learner_config.SerializeToString(), dropout_seed=123, - center_bias=True) + center_bias=True, + max_tree_depth=learner_config.constraints.max_tree_depth) session.run(grow_op) # Expect the negative gain split of partition 1 to be pruned and the @@ -1335,7 +1339,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE, growing_mode=learner_pb2.LearnerConfig.LAYER_BY_LAYER, # Dropout will have no effect, since the tree will not be fully grown. - dropout_probability=1.0).SerializeToString() + dropout_probability=1.0) # Prepare handler inputs. # Handler 1 only has a candidate for partition 1, handler 2 has candidates @@ -1364,9 +1368,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): ], gains=[handler1_gains, handler2_gains, handler3_gains], splits=[handler1_split, handler2_split, handler3_split], - learner_config=learner_config, + learner_config=learner_config.SerializeToString(), dropout_seed=123, - center_bias=True) + center_bias=True, + max_tree_depth=learner_config.constraints.max_tree_depth) session.run(grow_op) # Expect the split for partition 1 to be chosen from handler 1 and @@ -1543,7 +1548,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): min_node_weight=0, pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE, growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE, - dropout_probability=1.0).SerializeToString() + dropout_probability=1.0) # Prepare handler inputs. handler1_partitions = np.array([0], dtype=np.int32) @@ -1567,9 +1572,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): ], gains=[handler1_gains, handler2_gains, handler3_gains], splits=[handler1_split, handler2_split, handler3_split], - learner_config=learner_config, + learner_config=learner_config.SerializeToString(), dropout_seed=123, - center_bias=True) + center_bias=True, + max_tree_depth=learner_config.constraints.max_tree_depth) session.run(grow_op) # Expect a new tree to be added with the split from handler 1. @@ -1669,7 +1675,6 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE) learner_config.constraints.max_number_of_unique_feature_columns = 3 - learner_config = learner_config.SerializeToString() # Prepare handler inputs. handler1_partitions = np.array([0], dtype=np.int32) handler1_gains = np.array([7.62], dtype=np.float32) @@ -1692,9 +1697,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): ], gains=[handler1_gains, handler2_gains, handler3_gains], splits=[handler1_split, handler2_split, handler3_split], - learner_config=learner_config, + learner_config=learner_config.SerializeToString(), dropout_seed=123, - center_bias=True) + center_bias=True, + max_tree_depth=learner_config.constraints.max_tree_depth) session.run(grow_op) _, serialized = session.run( diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py index 1ee7f2395ea2ad71a7d380a1cc8f9a77bd4782b3..19e053fcb629c73c00cbfcf6f9afee75b10e5f15 100644 --- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py +++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py @@ -287,7 +287,8 @@ class GradientBoostedDecisionTreeModel(object): loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS, feature_columns=None, use_core_columns=False, - output_leaf_index=False): + output_leaf_index=False, + output_leaf_index_modes=None): """Construct a new GradientBoostedDecisionTreeModel function. Args: @@ -307,6 +308,9 @@ class GradientBoostedDecisionTreeModel(object): used. output_leaf_index: A boolean variable indicating whether to output leaf index into predictions dictionary. + output_leaf_index_modes: A list of modes from (TRAIN, EVAL, INFER) which + dictates when leaf indices will be outputted. By default, leaf indices + are only outputted in INFER mode. Raises: ValueError: if inputs are not valid. @@ -376,6 +380,8 @@ class GradientBoostedDecisionTreeModel(object): self._learner_config = learner_config self._feature_columns = feature_columns self._learner_config_serialized = learner_config.SerializeToString() + self._max_tree_depth = variables.Variable( + initial_value=self._learner_config.constraints.max_tree_depth) self._attempted_trees = variables.Variable( initial_value=array_ops.zeros([], dtypes.int64), trainable=False, @@ -404,7 +410,16 @@ class GradientBoostedDecisionTreeModel(object): self._learner_config.multi_class_strategy == learner_pb2.LearnerConfig.TREE_PER_CLASS and learner_config.num_classes == 2) + + if output_leaf_index_modes is None: + output_leaf_index_modes = [learn.ModeKeys.INFER] + elif not all( + mode in (learn.ModeKeys.TRAIN, learn.ModeKeys.EVAL, + learn.ModeKeys.INFER) for mode in output_leaf_index_modes): + raise ValueError("output_leaf_index_modes should only contain ModeKeys.") + self._output_leaf_index = output_leaf_index + self._output_leaf_index_modes = output_leaf_index_modes def _predict_and_return_dict(self, ensemble_handle, ensemble_stamp, mode): """Runs prediction and returns a dictionary of the prediction results. @@ -435,8 +450,7 @@ class GradientBoostedDecisionTreeModel(object): # the right stamp. with ops.control_dependencies(ensemble_stats): leaf_index = None - # Only used in infer (predict), not used in train and eval. - if self._output_leaf_index and mode == learn.ModeKeys.INFER: + if self._output_leaf_index and mode in self._output_leaf_index_modes: predictions, _, leaf_index = ( prediction_ops).gradient_trees_prediction_verbose( ensemble_handle, @@ -508,9 +522,6 @@ class GradientBoostedDecisionTreeModel(object): if not input_deps: raise ValueError("No input tensors for prediction.") - if any(i.device != input_deps[0].device for i in input_deps): - raise ValueError("All input tensors should be on the same device.") - # Get most current model stamp. ensemble_stamp = model_ops.tree_ensemble_stamp_token(self._ensemble_handle) @@ -1042,7 +1053,8 @@ class GradientBoostedDecisionTreeModel(object): splits=split_info_list, learner_config=self._learner_config_serialized, dropout_seed=dropout_seed, - center_bias=self._center_bias) + center_bias=self._center_bias, + max_tree_depth=self._max_tree_depth) def _grow_ensemble_not_ready_fn(): # Don't grow the ensemble, just update the stamp. @@ -1056,7 +1068,8 @@ class GradientBoostedDecisionTreeModel(object): splits=[], learner_config=self._learner_config_serialized, dropout_seed=dropout_seed, - center_bias=self._center_bias) + center_bias=self._center_bias, + max_tree_depth=self._max_tree_depth) def _grow_ensemble_fn(): # Conditionally grow an ensemble depending on whether the splits @@ -1096,6 +1109,9 @@ class GradientBoostedDecisionTreeModel(object): def get_number_of_trees_tensor(self): return self._finalized_trees, self._attempted_trees + def get_max_tree_depth(self): + return self._max_tree_depth + def train(self, loss, predictions_dict, labels): """Updates the accumalator stats and grows the ensemble. diff --git a/tensorflow/contrib/boosted_trees/python/utils/losses.py b/tensorflow/contrib/boosted_trees/python/utils/losses.py index ab7ac2aba605db22a8ed370049b27d55cf1d413a..b5ebaf1999519f65110e8164fa20bace5ecc3ef6 100644 --- a/tensorflow/contrib/boosted_trees/python/utils/losses.py +++ b/tensorflow/contrib/boosted_trees/python/utils/losses.py @@ -23,6 +23,12 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn +from tensorflow.python.ops.losses import losses + + +def per_example_squared_hinge_loss(labels, weights, predictions): + loss = losses.hinge_loss(labels=labels, logits=predictions, weights=weights) + return math_ops.square(loss), control_flow_ops.no_op() def per_example_logistic_loss(labels, weights, predictions): @@ -126,7 +132,7 @@ def per_example_squared_loss(labels, weights, predictions): def per_example_exp_loss(labels, weights, predictions, name=None, eps=0.1): - """Exponential loss given labels, example weights and predictions. + """Trimmed exponential loss given labels, example weights and predictions. Note that this is only for binary classification. If logistic loss tries to make sure that the classifier is certain of its @@ -211,3 +217,62 @@ def per_example_exp_loss(labels, weights, predictions, name=None, eps=0.1): unweighted_loss = exp_with_logits( name=name, eps=eps, labels=labels, logits=predictions) return unweighted_loss * weights, control_flow_ops.no_op() + + +def per_example_full_exp_loss(labels, weights, predictions, name=None): + """Full exponential loss given labels, example weights and predictions. + + Note that this is only for binary classification. + The loss returns is exp(-targets*logits), where targets are converted to -1 + and 1. + + Args: + labels: Rank 2 (N, D) tensor of per-example labels. + weights: Rank 2 (N, 1) tensor of per-example weights. + predictions: Rank 2 (N, D) tensor of per-example predictions. + name: A name for the operation (optional). + + Returns: + loss: A Rank 2 (N, 1) tensor of per-example exp loss + update_op: An update operation to update the loss's internal state. + """ + + def full_exp_with_logits(name, labels=None, logits=None): + """Computes exponential loss given `logits`. + + Args: + name: A name for the operation (optional). + labels: A `Tensor` of the same type and shape as `logits`. + logits: A `Tensor` of type `float32` or `float64`. + + Returns: + A `Tensor` of the same shape as `logits` with the componentwise + exponential losses. + + Raises: + ValueError: If `logits` and `labels` do not have the same shape. + """ + with ops.name_scope(name, "exp_loss", [logits, labels]) as name: + logits = ops.convert_to_tensor(logits, name="logits") + labels = ops.convert_to_tensor(labels, name="labels") + try: + labels.get_shape().merge_with(logits.get_shape()) + except ValueError: + raise ValueError("logits and labels must have the same shape (%s vs %s)" + % (logits.get_shape(), labels.get_shape())) + + # Default threshold of 0 to switch between classes + zeros = array_ops.zeros_like(logits, dtype=logits.dtype) + ones = array_ops.ones_like(logits, dtype=logits.dtype) + neg_ones = -array_ops.ones_like(logits, dtype=logits.dtype) + + # Convert labels to 1 and -1 + cond_labels = (labels > zeros) + labels_converted = array_ops.where(cond_labels, ones, neg_ones) + + return math_ops.exp(-1.0 * logits * labels_converted) + + labels = math_ops.to_float(labels) + unweighted_loss = full_exp_with_logits( + name=name, labels=labels, logits=predictions) + return unweighted_loss * weights, control_flow_ops.no_op() diff --git a/tensorflow/contrib/checkpoint/python/containers.py b/tensorflow/contrib/checkpoint/python/containers.py index 4d3d5312993740636709cb732c0b8e3e2626262d..242c1e8ba45e0b2f6f9a1a51695b824546382666 100644 --- a/tensorflow/contrib/checkpoint/python/containers.py +++ b/tensorflow/contrib/checkpoint/python/containers.py @@ -35,9 +35,9 @@ class UniqueNameTracker(data_structures.CheckpointableDataStructure): self.slotdeps = tf.contrib.checkpoint.UniqueNameTracker() slotdeps = self.slotdeps slots = [] - slots.append(slotdeps.track(tfe.Variable(3.), "x")) # Named "x" - slots.append(slotdeps.track(tfe.Variable(4.), "y")) - slots.append(slotdeps.track(tfe.Variable(5.), "x")) # Named "x_1" + slots.append(slotdeps.track(tf.Variable(3.), "x")) # Named "x" + slots.append(slotdeps.track(tf.Variable(4.), "y")) + slots.append(slotdeps.track(tf.Variable(5.), "x")) # Named "x_1" ``` """ diff --git a/tensorflow/contrib/cloud/README.md b/tensorflow/contrib/cloud/README.md index 134ce057f4334096b4fbbec29cc85f0ea42c9f86..a80d8965f3b562cadaff8caad8d40c7b98afa78f 100644 --- a/tensorflow/contrib/cloud/README.md +++ b/tensorflow/contrib/cloud/README.md @@ -1,8 +1,8 @@ # Cloud # -## BigTable ## +## Cloud Bigtable ## -[Google Cloud BigTable](https://cloud.google.com/bigtable/) is a high +[Google Cloud Bigtable](https://cloud.google.com/bigtable/) is a high performance storage system that can store and serve training data. This contrib package contains an experimental integration with TensorFlow. diff --git a/tensorflow/contrib/cloud/__init__.py b/tensorflow/contrib/cloud/__init__.py index af81106a6848bfd8c91108b56c8150d47c3eb501..8efd259946b7696e66b83a3b0aa451543c107467 100644 --- a/tensorflow/contrib/cloud/__init__.py +++ b/tensorflow/contrib/cloud/__init__.py @@ -25,8 +25,8 @@ from tensorflow.contrib.cloud.python.ops.bigquery_reader_ops import * from tensorflow.contrib.cloud.python.ops.gcs_config_ops import * if os.name != 'nt': - from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigTable from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigtableClient + from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigtableTable del os @@ -34,8 +34,8 @@ from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ 'BigQueryReader', - 'BigTable', 'BigtableClient', + 'BigtableTable', 'BlockCacheParams', 'configure_colab_session', 'configure_gcs', diff --git a/tensorflow/contrib/cluster_resolver/BUILD b/tensorflow/contrib/cluster_resolver/BUILD index c239e6f8f960910cee14e1df7c4678c643496f54..707f6211846ca0310bde297603928e9ec5bb471c 100644 --- a/tensorflow/contrib/cluster_resolver/BUILD +++ b/tensorflow/contrib/cluster_resolver/BUILD @@ -12,6 +12,15 @@ licenses(["notice"]) # Apache 2.0 py_library( name = "cluster_resolver_pip", + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + ":cluster_resolver_py", + ], +) + +py_library( + name = "cluster_resolver_py", srcs = [ "__init__.py", "python/training/__init__.py", @@ -19,7 +28,7 @@ py_library( srcs_version = "PY2AND3", visibility = ["//visibility:public"], deps = [ - ":cluster_resolver_py", + ":base_cluster_resolver_py", ":gce_cluster_resolver_py", ":tpu_cluster_resolver_py", "//tensorflow/python:util", @@ -27,7 +36,7 @@ py_library( ) py_library( - name = "cluster_resolver_py", + name = "base_cluster_resolver_py", srcs = ["python/training/cluster_resolver.py"], srcs_version = "PY2AND3", deps = [ @@ -40,7 +49,7 @@ py_library( srcs = ["python/training/gce_cluster_resolver.py"], srcs_version = "PY2AND3", deps = [ - ":cluster_resolver_py", + ":base_cluster_resolver_py", "//tensorflow/python:training", ], ) @@ -50,13 +59,13 @@ py_library( srcs = ["python/training/tpu_cluster_resolver.py"], srcs_version = "PY2AND3", deps = [ - ":cluster_resolver_py", + ":base_cluster_resolver_py", "//tensorflow/python:training", ], ) tf_py_test( - name = "cluster_resolver_py_test", + name = "base_cluster_resolver_py_test", srcs = ["python/training/cluster_resolver_test.py"], additional_deps = [ ":cluster_resolver_py", diff --git a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py index 8f521ffee4d31e090c13bac98290656d6e1d330e..f9dc3effd075d7e0add07aa77039824031976772 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py +++ b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py @@ -259,11 +259,11 @@ class TPUClusterResolver(ClusterResolver): if 'state' in response and response['state'] != 'READY': raise RuntimeError('TPU "%s" is not yet ready; state: "%s"' % - (self._tpu, response['state'])) + (compat.as_text(self._tpu), response['state'])) if 'health' in response and response['health'] != 'HEALTHY': - raise RuntimeError('TPU "%s" is unhealthy: "%s"' % (self._tpu, - response['health'])) + raise RuntimeError('TPU "%s" is unhealthy: "%s"' % + (compat.as_text(self._tpu), response['health'])) if 'networkEndpoints' in response: worker_list = [ diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt index a0a5b0e00c1979ebf8850408785135b9ceac7d2a..f6c928e2be62e7292c6feaa3bb26fd463320158b 100644 --- a/tensorflow/contrib/cmake/CMakeLists.txt +++ b/tensorflow/contrib/cmake/CMakeLists.txt @@ -145,26 +145,41 @@ if(WIN32) # temporary fix for #18241 add_definitions(-DEIGEN_DEFAULT_DENSE_INDEX_TYPE=std::int64_t) endif() - add_definitions(-DNOMINMAX -D_WIN32_WINNT=0x0A00 -DLANG_CXX11) - add_definitions(-DWIN32 -DOS_WIN -D_MBCS -DWIN32_LEAN_AND_MEAN -DNOGDI -DPLATFORM_WINDOWS) + add_definitions(-DNOMINMAX -D_WIN32_WINNT=0x0A00) + add_definitions(-DWIN32_LEAN_AND_MEAN -DNOGDI -DPLATFORM_WINDOWS) add_definitions(-DTENSORFLOW_USE_EIGEN_THREADPOOL -DEIGEN_HAS_C99_MATH) add_definitions(-DTF_COMPILE_LIBRARY) - add_definitions(/bigobj /nologo /EHsc /GF /MP /Gm-) + add_compile_options(/bigobj /GF /MP /Gm-) # Suppress warnings to reduce build log size. - add_definitions(/wd4267 /wd4244 /wd4800 /wd4503 /wd4554 /wd4996 /wd4348 /wd4018) - add_definitions(/wd4099 /wd4146 /wd4267 /wd4305 /wd4307) - add_definitions(/wd4715 /wd4722 /wd4723 /wd4838 /wd4309 /wd4334) - add_definitions(/wd4003 /wd4244 /wd4267 /wd4503 /wd4506 /wd4800 /wd4996) + add_compile_options(/wd4267 /wd4244 /wd4800 /wd4503 /wd4554 /wd4996 /wd4348 /wd4018) + add_compile_options(/wd4099 /wd4146 /wd4267 /wd4305 /wd4307) + add_compile_options(/wd4715 /wd4722 /wd4723 /wd4838 /wd4309 /wd4334) + add_compile_options(/wd4003 /wd4244 /wd4267 /wd4503 /wd4506 /wd4800 /wd4996) # Suppress linker warnings. set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} /ignore:4049 /ignore:4197 /ignore:4217 /ignore:4221") set(CMAKE_MODULE_LINKER_FLAGS "${CMAKE_MODULE_LINKER_FLAGS} /ignore:4049 /ignore:4197 /ignore:4217 /ignore:4221") set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /ignore:4049 /ignore:4197 /ignore:4217 /ignore:4221") - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /MP") set(CMAKE_CXX_FLAGS_DEBUG "/D_DEBUG /MDd /Ob2") set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /D_ITERATOR_DEBUG_LEVEL=0") set(CMAKE_CXX_FLAGS_MINSIZEREL "${CMAKE_CXX_FLAGS_MINSIZEREL} /D_ITERATOR_DEBUG_LEVEL=0") set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} /D_ITERATOR_DEBUG_LEVEL=0") + set(compiler_flags + CMAKE_CXX_FLAGS + CMAKE_CXX_FLAGS_DEBUG + CMAKE_CXX_FLAGS_RELEASE + CMAKE_C_FLAGS + CMAKE_C_FLAGS_DEBUG + CMAKE_C_FLAGS_RELEASE + ) + # No exception + foreach(flag ${compiler_flags}) + string(REPLACE "/EHsc" "/EHs-c-" ${flag} "${${flag}}") + endforeach() + add_definitions(/D_HAS_EXCEPTIONS=0) + # Suppress 'noexcept used with no exception handling mode specified' warning + add_compile_options(/wd4577) + # Try to avoid flaky failures due to failed generation of generate.stamp files. set(CMAKE_SUPPRESS_REGENERATION ON) endif() @@ -379,16 +394,20 @@ if (tensorflow_ENABLE_GPU) # by default we assume compute cabability 3.5 and 5.2. If you change this change it in # CUDA_NVCC_FLAGS and cuda_config.h below - set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode arch=compute_30,code=\"sm_30,compute_30\";-gencode arch=compute_35,code=\"sm_35,compute_35\";-gencode arch=compute_52,code=\"sm_52,compute_52\") + set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode arch=compute_37,code=\"sm_37,compute_37\") + set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode arch=compute_52,code=\"sm_52,compute_52\") + set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode arch=compute_60,code=\"sm_60,compute_60\") + set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode arch=compute_61,code=\"sm_61,compute_61\") + set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode arch=compute_70,code=\"sm_70,compute_70\") set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};--include-path ${PROJECT_BINARY_DIR}/$\{build_configuration\};--expt-relaxed-constexpr) set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-ftz=true) # Flush denormals to zero set(CUDA_INCLUDE ${CUDA_TOOLKIT_TARGET_DIR} ${CUDA_TOOLKIT_TARGET_DIR}/extras/CUPTI/include) include_directories(${CUDA_INCLUDE}) if (WIN32) - add_definitions(-DGOOGLE_CUDA=1 -DTF_EXTRA_CUDA_CAPABILITIES=3.0,3.5,5.2) + add_definitions(-DGOOGLE_CUDA=1 -DTF_EXTRA_CUDA_CAPABILITIES=3.7,5.2,6.0,6.1,7.0) else (WIN32) - # Without these double quotes, cmake in Linux makes it "-DTF_EXTRA_CUDA_CAPABILITIES=3.0, -D3.5, -D5.2" for cc, which incurs build breaks - add_definitions(-DGOOGLE_CUDA=1 -D"TF_EXTRA_CUDA_CAPABILITIES=3.0,3.5,5.2") + # Without these double quotes, cmake in Linux makes it "-DTF_EXTRA_CUDA_CAPABILITIES=3.7, -D5.2, ..." for cc, which incurs build breaks + add_definitions(-DGOOGLE_CUDA=1 -D"TF_EXTRA_CUDA_CAPABILITIES=3.7,5.2,6.0,6.1,7.0") endif (WIN32) if (WIN32) @@ -437,7 +456,7 @@ if (tensorflow_ENABLE_GPU) FILE(WRITE ${tensorflow_source_dir}/third_party/gpus/cuda/cuda_config.h "#ifndef CUDA_CUDA_CONFIG_H_\n" "#define CUDA_CUDA_CONFIG_H_\n" - "#define TF_CUDA_CAPABILITIES CudaVersion(\"3.0\"),CudaVersion(\"3.5\"),CudaVersion(\"5.2\")\n" + "#define TF_CUDA_CAPABILITIES CudaVersion(\"3.7\"),CudaVersion(\"5.2\"),CudaVersion(\"6.0\"),CudaVersion(\"6.1\"),CudaVersion(\"7.0\")\n" "#define TF_CUDA_VERSION \"64_${short_CUDA_VER}\"\n" "#define TF_CUDNN_VERSION \"64_${tensorflow_CUDNN_VERSION}\"\n" "#define TF_CUDA_TOOLKIT_PATH \"${CUDA_TOOLKIT_ROOT_DIR}\"\n" @@ -452,7 +471,6 @@ if (tensorflow_ENABLE_GPU) ${CUDA_TOOLKIT_TARGET_DIR}/include/cuComplex.h ${CUDA_TOOLKIT_TARGET_DIR}/include/cublas_v2.h ${CUDA_TOOLKIT_TARGET_DIR}/include/cusolverDn.h - ${CUDA_TOOLKIT_TARGET_DIR}/include/cuda_fp16.h ${CUDA_TOOLKIT_TARGET_DIR}/include/device_functions.h ${CUDA_TOOLKIT_TARGET_DIR}/include/cufft.h ${CUDA_TOOLKIT_TARGET_DIR}/include/curand.h diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt index a5eba5a8c94d6ddfa820ae371841f764b628c4b5..75e00f32675df1b7e523bc7e8bb44fa584b79347 100644 --- a/tensorflow/contrib/cmake/python_modules.txt +++ b/tensorflow/contrib/cmake/python_modules.txt @@ -14,6 +14,7 @@ tensorflow/examples/tutorials tensorflow/examples/tutorials/mnist tensorflow/python tensorflow/python/client +tensorflow/python/compat tensorflow/python/data tensorflow/python/data/ops tensorflow/python/data/util @@ -61,6 +62,8 @@ tensorflow/python/saved_model tensorflow/python/summary tensorflow/python/summary/writer tensorflow/python/tools +tensorflow/python/tools/api +tensorflow/python/tools/api/generator tensorflow/python/training tensorflow/python/training/checkpointable tensorflow/python/user_ops @@ -68,7 +71,6 @@ tensorflow/python/util tensorflow/python/util/protobuf tensorflow/tools tensorflow/tools/api -tensorflow/tools/api/generator tensorflow/tools/graph_transforms tensorflow/contrib tensorflow/contrib/all_reduce diff --git a/tensorflow/contrib/cmake/tf_core_framework.cmake b/tensorflow/contrib/cmake/tf_core_framework.cmake index 872b016d2b6c1b8fb5875c9568a1b7b6201507c0..067c299a71cd4ac96878bcf27b4453466785e4ba 100644 --- a/tensorflow/contrib/cmake/tf_core_framework.cmake +++ b/tensorflow/contrib/cmake/tf_core_framework.cmake @@ -49,48 +49,43 @@ function(RELATIVE_PROTOBUF_GENERATE_CPP SRCS HDRS ROOT_DIR) set(${HDRS} ${${HDRS}} PARENT_SCOPE) endfunction() -function(RELATIVE_PROTOBUF_GENERATE_GRPC_CPP SRCS HDRS ROOT_DIR) - if(NOT ARGN) - message(SEND_ERROR "Error: RELATIVE_PROTOBUF_GENERATE_GRPC_CPP() called without any proto files") - return() - endif() - - set(${SRCS}) - set(${HDRS}) - foreach(FIL ${ARGN}) - set(ABS_FIL ${ROOT_DIR}/${FIL}) - get_filename_component(FIL_WE ${FIL} NAME_WE) - get_filename_component(FIL_DIR ${ABS_FIL} PATH) - file(RELATIVE_PATH REL_DIR ${ROOT_DIR} ${FIL_DIR}) - - list(APPEND ${SRCS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.cc") - list(APPEND ${HDRS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.h") - list(APPEND ${SRCS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.cc") - list(APPEND ${HDRS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.h") - - # We adust the path of the gRPC code generation accordingly. - if(WIN32) - set(GRPC_PROTOC_PLUGIN_PATH ${GRPC_BUILD}/Release/grpc_cpp_plugin.exe) - else() - set(GRPC_PROTOC_PLUGIN_PATH ${GRPC_BUILD}/grpc_cpp_plugin) +if(NOT WIN32) + function(RELATIVE_PROTOBUF_GENERATE_GRPC_CPP SRCS HDRS ROOT_DIR) + if(NOT ARGN) + message(SEND_ERROR "Error: RELATIVE_PROTOBUF_GENERATE_GRPC_CPP() called without any proto files") + return() endif() - add_custom_command( - OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.cc" - "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.h" - "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.cc" - "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.h" - COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} - ARGS --grpc_out ${CMAKE_CURRENT_BINARY_DIR} --cpp_out ${CMAKE_CURRENT_BINARY_DIR} --plugin=protoc-gen-grpc=${GRPC_PROTOC_PLUGIN_PATH} -I ${ROOT_DIR} ${ABS_FIL} -I ${PROTOBUF_INCLUDE_DIRS} - DEPENDS ${ABS_FIL} protobuf grpc - COMMENT "Running C++ protocol buffer grpc compiler on ${FIL}" - VERBATIM ) - endforeach() - - set_source_files_properties(${${SRCS}} ${${HDRS}} PROPERTIES GENERATED TRUE) - set(${SRCS} ${${SRCS}} PARENT_SCOPE) - set(${HDRS} ${${HDRS}} PARENT_SCOPE) -endfunction() + set(${SRCS}) + set(${HDRS}) + foreach(FIL ${ARGN}) + set(ABS_FIL ${ROOT_DIR}/${FIL}) + get_filename_component(FIL_WE ${FIL} NAME_WE) + get_filename_component(FIL_DIR ${ABS_FIL} PATH) + file(RELATIVE_PATH REL_DIR ${ROOT_DIR} ${FIL_DIR}) + + list(APPEND ${SRCS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.cc") + list(APPEND ${HDRS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.h") + list(APPEND ${SRCS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.cc") + list(APPEND ${HDRS} "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.h") + + add_custom_command( + OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.cc" + "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.grpc.pb.h" + "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.cc" + "${CMAKE_CURRENT_BINARY_DIR}/${REL_DIR}/${FIL_WE}.pb.h" + COMMAND ${PROTOBUF_PROTOC_EXECUTABLE} + ARGS --grpc_out ${CMAKE_CURRENT_BINARY_DIR} --cpp_out ${CMAKE_CURRENT_BINARY_DIR} --plugin protoc-gen-grpc=${GRPC_BUILD}/grpc_cpp_plugin -I ${ROOT_DIR} ${ABS_FIL} -I ${PROTOBUF_INCLUDE_DIRS} + DEPENDS ${ABS_FIL} protobuf grpc + COMMENT "Running C++ protocol buffer grpc compiler on ${FIL}" + VERBATIM ) + endforeach() + + set_source_files_properties(${${SRCS}} ${${HDRS}} PROPERTIES GENERATED TRUE) + set(${SRCS} ${${SRCS}} PARENT_SCOPE) + set(${HDRS} ${${HDRS}} PARENT_SCOPE) + endfunction() +endif() function(RELATIVE_PROTOBUF_TEXT_GENERATE_CPP SRCS HDRS ROOT_DIR) if(NOT ARGN) @@ -180,14 +175,17 @@ RELATIVE_PROTOBUF_TEXT_GENERATE_CPP(PROTO_TEXT_SRCS PROTO_TEXT_HDRS ${tensorflow_source_dir} ${tf_proto_text_srcs} ) -file(GLOB_RECURSE tf_protos_grpc_cc_srcs RELATIVE ${tensorflow_source_dir} - "${tensorflow_source_dir}/tensorflow/core/debug/*.proto" - "${tensorflow_source_dir}/tensorflow/core/protobuf/master_service.proto" -) -RELATIVE_PROTOBUF_GENERATE_GRPC_CPP(PROTO_GRPC_SRCS PROTO_GRPC_HDRS - ${tensorflow_source_dir} ${tf_protos_grpc_cc_srcs} -) -add_library(tf_protos_cc ${PROTO_GRPC_SRCS} ${PROTO_GRPC_HDRS} ${PROTO_SRCS} ${PROTO_HDRS}) +if(WIN32) + add_library(tf_protos_cc ${PROTO_SRCS} ${PROTO_HDRS}) +else() + file(GLOB_RECURSE tf_protos_grpc_cc_srcs RELATIVE ${tensorflow_source_dir} + "${tensorflow_source_dir}/tensorflow/core/debug/*.proto" + ) + RELATIVE_PROTOBUF_GENERATE_GRPC_CPP(PROTO_GRPC_SRCS PROTO_GRPC_HDRS + ${tensorflow_source_dir} ${tf_protos_grpc_cc_srcs} + ) + add_library(tf_protos_cc ${PROTO_GRPC_SRCS} ${PROTO_GRPC_HDRS} ${PROTO_SRCS} ${PROTO_HDRS}) +endif() ######################################################## # tf_core_lib library diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake index 844f62649d970506f1b4b4c5718fab8d1f0856e1..7b892ba248bc43cd885f295288c677ac97efaa06 100644 --- a/tensorflow/contrib/cmake/tf_core_kernels.cmake +++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake @@ -68,6 +68,7 @@ if(tensorflow_BUILD_CONTRIB_KERNELS) "${tensorflow_source_dir}/tensorflow/contrib/coder/kernels/range_coder_ops.cc" "${tensorflow_source_dir}/tensorflow/contrib/coder/kernels/range_coder_ops_util.cc" "${tensorflow_source_dir}/tensorflow/contrib/coder/ops/coder_ops.cc" + "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/assert_next_dataset_op.cc" "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/csv_dataset_op.cc" "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/directed_interleave_dataset_op.cc" "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc" diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake index e3b59001bcb4f081eb2db3443ee9ad714c822ac8..32b185f07b6ba836ffb47e85beff6fb2481fdc3e 100755 --- a/tensorflow/contrib/cmake/tf_python.cmake +++ b/tensorflow/contrib/cmake/tf_python.cmake @@ -736,8 +736,8 @@ endif() # Generate API __init__.py files. ######################################################## -# Parse tensorflow/tools/api/generator/BUILD to get list of generated files. -FILE(READ ${tensorflow_source_dir}/tensorflow/tools/api/generator/api_gen.bzl api_generator_BUILD_text) +# Parse tensorflow/python/tools/api/generator/BUILD to get list of generated files. +FILE(READ ${tensorflow_source_dir}/tensorflow/python/tools/api/generator/api_gen.bzl api_generator_BUILD_text) STRING(REGEX MATCH "# BEGIN GENERATED FILES.*# END GENERATED FILES" api_init_files_text ${api_generator_BUILD_text}) string(REPLACE "# BEGIN GENERATED FILES" "" api_init_files_text ${api_init_files_text}) string(REPLACE "# END GENERATED FILES" "" api_init_files_text ${api_init_files_text}) @@ -781,7 +781,7 @@ if (tensorflow_ENABLE_MKL_SUPPORT) # Run create_python_api.py to generate API init files. COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}/tf_python PATH=${PY_RUNTIME_ENV} ${PYTHON_EXECUTABLE} - "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/tools/api/generator/create_python_api.py" + "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/tools/api/generator/create_python_api.py" "--root_init_template=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/api_template.__init__.py" "--apidir=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow" "--package=tensorflow.python" @@ -803,7 +803,7 @@ else (tensorflow_ENABLE_MKL_SUPPORT) # Run create_python_api.py to generate API init files. COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}/tf_python ${PYTHON_EXECUTABLE} - "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/tools/api/generator/create_python_api.py" + "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/tools/api/generator/create_python_api.py" "--root_init_template=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/api_template.__init__.py" "--apidir=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow" "--package=tensorflow.python" @@ -824,8 +824,8 @@ add_dependencies(tf_python_api tf_python_ops) # Generate API __init__.py files for tf.estimator. ######################################################## -# Parse tensorflow/tools/api/generator/BUILD to get list of generated files. -FILE(READ ${tensorflow_source_dir}/tensorflow/tools/api/generator/api_gen.bzl api_generator_BUILD_text) +# Parse tensorflow/python/tools/api/generator/BUILD to get list of generated files. +FILE(READ ${tensorflow_source_dir}/tensorflow/python/tools/api/generator/api_gen.bzl api_generator_BUILD_text) STRING(REGEX MATCH "# BEGIN GENERATED ESTIMATOR FILES.*# END GENERATED ESTIMATOR FILES" api_init_files_text ${api_generator_BUILD_text}) string(REPLACE "# BEGIN GENERATED ESTIMATOR FILES" "" api_init_files_text ${api_init_files_text}) string(REPLACE "# END GENERATED ESTIMATOR FILES" "" api_init_files_text ${api_init_files_text}) @@ -849,10 +849,11 @@ add_custom_command( # Run create_python_api.py to generate API init files. COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}/tf_python ${PYTHON_EXECUTABLE} - "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/tools/api/generator/create_python_api.py" + "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/tools/api/generator/create_python_api.py" "--apidir=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/estimator/api" "--package=tensorflow.python.estimator" "--apiname=estimator" + "--output_package=tensorflow.python.estimator.api" "${estimator_api_init_list_file}" COMMENT "Generating __init__.py files for Python API." diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake index eb9482dc25f2be8ce46cc38bf3dd28889b09a9d4..b2330c4e340d531f70234de812ab6f6b2e5c1160 100644 --- a/tensorflow/contrib/cmake/tf_tests.cmake +++ b/tensorflow/contrib/cmake/tf_tests.cmake @@ -193,6 +193,7 @@ if (tensorflow_BUILD_PYTHON_TESTS) # flaky test "${tensorflow_source_dir}/tensorflow/python/profiler/internal/run_metadata_test.py" "${tensorflow_source_dir}/tensorflow/python/profiler/model_analyzer_test.py" + "${tensorflow_source_dir}/tensorflow/python/data/kernel_tests/map_dataset_op_test.py" # Fails because uses data dependencies with bazel "${tensorflow_source_dir}/tensorflow/python/saved_model/saved_model_test.py" "${tensorflow_source_dir}/tensorflow/contrib/image/python/kernel_tests/sparse_image_warp_test.py" @@ -216,7 +217,8 @@ if (tensorflow_BUILD_PYTHON_TESTS) ${tensorflow_source_dir}/tensorflow/python/kernel_tests/duplicate_op_test.py ${tensorflow_source_dir}/tensorflow/python/kernel_tests/invalid_op_test.py ${tensorflow_source_dir}/tensorflow/python/kernel_tests/ackermann_test.py - + # Tests too large to run. + ${tensorflow_source_dir}/tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py ) if (WIN32) set(tf_test_src_py_exclude diff --git a/tensorflow/contrib/copy_graph/python/util/copy_elements.py b/tensorflow/contrib/copy_graph/python/util/copy_elements.py index a0dd3881a86c19e47ccb65f84a2477a55626b81c..6c9ab6aeb87fd39b22ab4f28d69b432b15899a13 100644 --- a/tensorflow/contrib/copy_graph/python/util/copy_elements.py +++ b/tensorflow/contrib/copy_graph/python/util/copy_elements.py @@ -18,7 +18,7 @@ These functions allow for recursive copying of elements (ops and variables) from one graph to another. The copied elements are initialized inside a user-specified scope in the other graph. There are separate functions to copy ops and variables. -There is also a function to retrive the copied version of an op from the +There is also a function to retrieve the copied version of an op from the first graph inside a scope in the second graph. @@copy_op_to_graph @@ -77,7 +77,7 @@ def copy_variable_to_graph(org_instance, to_graph, scope=''): else: collections.append(scope + '/' + name) - #See if its trainable. + #See if it's trainable. trainable = ( org_instance in org_instance.graph.get_collection( ops.GraphKeys.TRAINABLE_VARIABLES)) @@ -162,7 +162,7 @@ def copy_op_to_graph(org_instance, to_graph, variables, scope=''): if isinstance(org_instance, ops.Tensor): - #If its a Tensor, it is one of the outputs of the underlying + #If it's a Tensor, it is one of the outputs of the underlying #op. Therefore, copy the op itself and return the appropriate #output. op = org_instance.op @@ -219,8 +219,10 @@ def copy_op_to_graph(org_instance, to_graph, variables, scope=''): op_def) #Use Graph's hidden methods to add the op to_graph._record_op_seen_by_control_dependencies(new_op) - for device_function in reversed(to_graph._device_function_stack): + # pylint: disable=protected-access + for device_function in to_graph._device_functions_outer_to_inner: new_op._set_device(device_function(new_op)) + # pylint: enable=protected-access return new_op diff --git a/tensorflow/contrib/crf/__init__.py b/tensorflow/contrib/crf/__init__.py index 046c509626bc2eb20a65c0b38495ff37c294e0e1..615e62b16f1906dafa22a12cc7275a2335e8df88 100644 --- a/tensorflow/contrib/crf/__init__.py +++ b/tensorflow/contrib/crf/__init__.py @@ -20,6 +20,7 @@ See the @{$python/contrib.crf} guide. @@crf_decode @@crf_log_likelihood @@crf_log_norm +@@crf_multitag_sequence_score @@crf_sequence_score @@crf_unary_score @@CrfDecodeBackwardRnnCell @@ -36,6 +37,7 @@ from tensorflow.contrib.crf.python.ops.crf import crf_binary_score from tensorflow.contrib.crf.python.ops.crf import crf_decode from tensorflow.contrib.crf.python.ops.crf import crf_log_likelihood from tensorflow.contrib.crf.python.ops.crf import crf_log_norm +from tensorflow.contrib.crf.python.ops.crf import crf_multitag_sequence_score from tensorflow.contrib.crf.python.ops.crf import crf_sequence_score from tensorflow.contrib.crf.python.ops.crf import crf_unary_score from tensorflow.contrib.crf.python.ops.crf import CrfDecodeBackwardRnnCell diff --git a/tensorflow/contrib/crf/python/kernel_tests/crf_test.py b/tensorflow/contrib/crf/python/kernel_tests/crf_test.py index 74f2ec22ffaab1654e5cd38169258fb87d307ad4..f56a973f6f80b81697e9f58578e60a2efb90154e 100644 --- a/tensorflow/contrib/crf/python/kernel_tests/crf_test.py +++ b/tensorflow/contrib/crf/python/kernel_tests/crf_test.py @@ -31,6 +31,15 @@ from tensorflow.python.platform import test class CrfTest(test.TestCase): + def calculateSequenceScore(self, inputs, transition_params, tag_indices, + sequence_lengths): + expected_unary_score = sum( + inputs[i][tag_indices[i]] for i in range(sequence_lengths)) + expected_binary_score = sum( + transition_params[tag_indices[i], tag_indices[i + 1]] + for i in range(sequence_lengths - 1)) + return expected_unary_score + expected_binary_score + def testCrfSequenceScore(self): transition_params = np.array( [[-3, 5, -2], [3, 4, 1], [1, 2, 1]], dtype=np.float32) @@ -60,14 +69,55 @@ class CrfTest(test.TestCase): transition_params=constant_op.constant(transition_params)) sequence_score = array_ops.squeeze(sequence_score, [0]) tf_sequence_score = sess.run(sequence_score) - expected_unary_score = sum(inputs[i][tag_indices[i]] - for i in range(sequence_lengths)) - expected_binary_score = sum( - transition_params[tag_indices[i], tag_indices[i + 1]] - for i in range(sequence_lengths - 1)) - expected_sequence_score = expected_unary_score + expected_binary_score + expected_sequence_score = self.calculateSequenceScore( + inputs, transition_params, tag_indices, sequence_lengths) self.assertAllClose(tf_sequence_score, expected_sequence_score) + def testCrfMultiTagSequenceScore(self): + transition_params = np.array( + [[-3, 5, -2], [3, 4, 1], [1, 2, 1]], dtype=np.float32) + # Test both the length-1 and regular cases. + sequence_lengths_list = [ + np.array(3, dtype=np.int32), + np.array(1, dtype=np.int32) + ] + inputs_list = [ + np.array([[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]], + dtype=np.float32), + np.array([[4, 5, -3]], + dtype=np.float32), + ] + tag_bitmap_list = [ + np.array( + [[True, True, False], [True, False, True], [False, True, True], + [True, False, True]], + dtype=np.bool), + np.array([[True, True, False]], dtype=np.bool) + ] + for sequence_lengths, inputs, tag_bitmap in zip( + sequence_lengths_list, inputs_list, tag_bitmap_list): + with self.test_session() as sess: + sequence_score = crf.crf_multitag_sequence_score( + inputs=array_ops.expand_dims(inputs, 0), + tag_bitmap=array_ops.expand_dims(tag_bitmap, 0), + sequence_lengths=array_ops.expand_dims(sequence_lengths, 0), + transition_params=constant_op.constant(transition_params)) + sequence_score = array_ops.squeeze(sequence_score, [0]) + tf_sum_sequence_score = sess.run(sequence_score) + all_indices_list = [ + single_index_bitmap.nonzero()[0] + for single_index_bitmap in tag_bitmap[:sequence_lengths] + ] + expected_sequence_scores = [ + self.calculateSequenceScore(inputs, transition_params, indices, + sequence_lengths) + for indices in itertools.product(*all_indices_list) + ] + expected_log_sum_exp_sequence_scores = np.logaddexp.reduce( + expected_sequence_scores) + self.assertAllClose(tf_sum_sequence_score, + expected_log_sum_exp_sequence_scores) + def testCrfUnaryScore(self): inputs = np.array( [[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]], dtype=np.float32) diff --git a/tensorflow/contrib/crf/python/ops/crf.py b/tensorflow/contrib/crf/python/ops/crf.py index 2d2cbdc1990ed9d8e58c0032cbc141a52271838f..8a7ff61bc8391efe453ee37019c23bd6ccbdf066 100644 --- a/tensorflow/contrib/crf/python/ops/crf.py +++ b/tensorflow/contrib/crf/python/ops/crf.py @@ -67,7 +67,7 @@ __all__ = [ "crf_sequence_score", "crf_log_norm", "crf_log_likelihood", "crf_unary_score", "crf_binary_score", "CrfForwardRnnCell", "viterbi_decode", "crf_decode", "CrfDecodeForwardRnnCell", - "CrfDecodeBackwardRnnCell" + "CrfDecodeBackwardRnnCell", "crf_multitag_sequence_score" ] @@ -114,6 +114,56 @@ def crf_sequence_score(inputs, tag_indices, sequence_lengths, false_fn=_multi_seq_fn) +def crf_multitag_sequence_score(inputs, tag_bitmap, sequence_lengths, + transition_params): + """Computes the unnormalized score of all tag sequences matching tag_bitmap. + + tag_bitmap enables more than one tag to be considered correct at each time + step. This is useful when an observed output at a given time step is + consistent with more than one tag, and thus the log likelihood of that + observation must take into account all possible consistent tags. + + Using one-hot vectors in tag_bitmap gives results identical to + crf_sequence_score. + + Args: + inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials + to use as input to the CRF layer. + tag_bitmap: A [batch_size, max_seq_len, num_tags] boolean tensor + representing all active tags at each index for which to calculate the + unnormalized score. + sequence_lengths: A [batch_size] vector of true sequence lengths. + transition_params: A [num_tags, num_tags] transition matrix. + Returns: + sequence_scores: A [batch_size] vector of unnormalized sequence scores. + """ + + # If max_seq_len is 1, we skip the score calculation and simply gather the + # unary potentials of all active tags. + def _single_seq_fn(): + filtered_inputs = array_ops.where( + tag_bitmap, inputs, + array_ops.fill(array_ops.shape(inputs), float("-inf"))) + return math_ops.reduce_logsumexp( + filtered_inputs, axis=[1, 2], keepdims=False) + + def _multi_seq_fn(): + # Compute the logsumexp of all scores of sequences matching the given tags. + filtered_inputs = array_ops.where( + tag_bitmap, inputs, + array_ops.fill(array_ops.shape(inputs), float("-inf"))) + return crf_log_norm( + inputs=filtered_inputs, + sequence_lengths=sequence_lengths, + transition_params=transition_params) + + return utils.smart_cond( + pred=math_ops.equal(inputs.shape[1].value or array_ops.shape(inputs)[1], + 1), + true_fn=_single_seq_fn, + false_fn=_multi_seq_fn) + + def crf_log_norm(inputs, sequence_lengths, transition_params): """Computes the normalization for a CRF. diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py index 156538b4e01bf1a1ccca0fca1e309b1d37b6dbc0..7878e46e88b2ea8b0012768342c218baeda80eaa 100644 --- a/tensorflow/contrib/data/__init__.py +++ b/tensorflow/contrib/data/__init__.py @@ -34,6 +34,7 @@ See @{$guide/datasets$Importing Data} for an overview. @@batch_and_drop_remainder @@bucket_by_sequence_length @@choose_from_datasets +@@copy_to_device @@dense_to_sparse_batch @@enumerate_dataset @@ -51,6 +52,7 @@ See @{$guide/datasets$Importing Data} for an overview. @@prefetch_to_device @@read_batch_features @@rejection_resample +@@reduce_dataset @@sample_from_datasets @@scan @@shuffle_and_repeat @@ -76,6 +78,7 @@ from tensorflow.contrib.data.python.ops.counter import Counter from tensorflow.contrib.data.python.ops.enumerate_ops import enumerate_dataset from tensorflow.contrib.data.python.ops.error_ops import ignore_errors from tensorflow.contrib.data.python.ops.get_single_element import get_single_element +from tensorflow.contrib.data.python.ops.get_single_element import reduce_dataset from tensorflow.contrib.data.python.ops.grouping import bucket_by_sequence_length from tensorflow.contrib.data.python.ops.grouping import group_by_reducer from tensorflow.contrib.data.python.ops.grouping import group_by_window @@ -86,6 +89,7 @@ from tensorflow.contrib.data.python.ops.interleave_ops import sample_from_datase from tensorflow.contrib.data.python.ops.interleave_ops import sloppy_interleave from tensorflow.contrib.data.python.ops.iterator_ops import CheckpointInputPipelineHook from tensorflow.contrib.data.python.ops.iterator_ops import make_saveable_from_iterator +from tensorflow.contrib.data.python.ops.prefetching_ops import copy_to_device from tensorflow.contrib.data.python.ops.prefetching_ops import prefetch_to_device from tensorflow.contrib.data.python.ops.random_ops import RandomDataset from tensorflow.contrib.data.python.ops.readers import CsvDataset diff --git a/tensorflow/contrib/data/kernels/BUILD b/tensorflow/contrib/data/kernels/BUILD index 7b69e10441eba3e38c979d5715c16699ac2710ed..566cbb246a104d1e6cfc284d220ca8386b8897e1 100644 --- a/tensorflow/contrib/data/kernels/BUILD +++ b/tensorflow/contrib/data/kernels/BUILD @@ -70,9 +70,20 @@ cc_library( ], ) +cc_library( + name = "assert_next_dataset_op", + srcs = ["assert_next_dataset_op.cc"], + deps = [ + "//tensorflow/core:framework_headers_lib", + "//third_party/eigen3", + "@protobuf_archive//:protobuf_headers", + ], +) + cc_library( name = "dataset_kernels", deps = [ + ":assert_next_dataset_op", ":csv_dataset_op", ":directed_interleave_dataset_op", ":ignore_errors_dataset_op", diff --git a/tensorflow/contrib/data/kernels/assert_next_dataset_op.cc b/tensorflow/contrib/data/kernels/assert_next_dataset_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..95b8e1f7fd487119d77a5f708de42b014c55f79d --- /dev/null +++ b/tensorflow/contrib/data/kernels/assert_next_dataset_op.cc @@ -0,0 +1,152 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include + +#include "tensorflow/core/framework/dataset.h" +#include "tensorflow/core/framework/partial_tensor_shape.h" +#include "tensorflow/core/framework/tensor.h" + +namespace tensorflow { +namespace { + +// See documentation in ../ops/dataset_ops.cc for a high-level +// description of the following op. +class AssertNextDatasetOp : public UnaryDatasetOpKernel { + public: + explicit AssertNextDatasetOp(OpKernelConstruction* ctx) + : UnaryDatasetOpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_types_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_)); + } + + protected: + void MakeDataset(OpKernelContext* ctx, DatasetBase* input, + DatasetBase** output) override { + std::vector transformations; + OP_REQUIRES_OK(ctx, ParseVectorArgument(ctx, "transformations", + &transformations)); + *output = + new Dataset(ctx, input, transformations, output_types_, output_shapes_); + } + + private: + class Dataset : public GraphDatasetBase { + public: + Dataset(OpKernelContext* ctx, const DatasetBase* input, + const std::vector& transformations, + const DataTypeVector& output_types, + const std::vector& output_shapes) + : GraphDatasetBase(ctx), + input_(input), + transformations_(transformations), + output_types_(output_types), + output_shapes_(output_shapes) { + input_->Ref(); + } + + ~Dataset() override { input_->Unref(); } + + std::unique_ptr MakeIteratorInternal( + const string& prefix) const override { + return std::unique_ptr( + new Iterator({this, strings::StrCat(prefix, "::Assert")})); + } + + const DataTypeVector& output_dtypes() const override { + return output_types_; + } + const std::vector& output_shapes() const override { + return output_shapes_; + } + + string DebugString() const override { + return "AssertNextDatasetOp::Dataset"; + } + + protected: + Status AsGraphDefInternal(OpKernelContext* ctx, DatasetGraphDefBuilder* b, + Node** output) const override { + Node* input_graph_node = nullptr; + TF_RETURN_IF_ERROR(b->AddParentDataset(ctx, input_, &input_graph_node)); + Node* transformations_node = nullptr; + TF_RETURN_IF_ERROR(b->AddVector(transformations_, &transformations_node)); + TF_RETURN_IF_ERROR(b->AddDataset( + this, {input_graph_node, transformations_node}, output)); + return Status::OK(); + } + + private: + class Iterator : public DatasetIterator { + public: + explicit Iterator(const Params& params) + : DatasetIterator(params) {} + + Status Initialize(IteratorContext* ctx) override { + std::vector tokens = + str_util::Split(prefix(), ':', str_util::SkipEmpty()); + if (dataset()->transformations_.size() > tokens.size() - 2) { + return errors::InvalidArgument( + "Asserted next ", dataset()->transformations_.size(), + " transformations but encountered only ", tokens.size() - 2, "."); + } + int n = tokens.size(); + for (size_t i = 0; i < dataset()->transformations_.size(); ++i) { + if (dataset()->transformations_[i] != tokens[n - 2 - i]) { + return errors::InvalidArgument( + "Asserted ", dataset()->transformations_[i], + " transformation at offset ", i, " but encountered ", + tokens[n - 2 - i], " transformation instead."); + } + } + return dataset()->input_->MakeIterator(ctx, prefix(), &input_impl_); + } + + Status GetNextInternal(IteratorContext* ctx, + std::vector* out_tensors, + bool* end_of_sequence) override { + return input_impl_->GetNext(ctx, out_tensors, end_of_sequence); + } + + protected: + Status SaveInternal(IteratorStateWriter* writer) override { + TF_RETURN_IF_ERROR(SaveParent(writer, input_impl_)); + return Status::OK(); + } + + Status RestoreInternal(IteratorContext* ctx, + IteratorStateReader* reader) override { + TF_RETURN_IF_ERROR(RestoreParent(ctx, reader, input_impl_)); + return Status::OK(); + } + + private: + std::unique_ptr input_impl_; + }; + + const DatasetBase* input_; + const std::vector transformations_; + const DataTypeVector output_types_; + const std::vector output_shapes_; + }; + + DataTypeVector output_types_; + std::vector output_shapes_; +}; + +REGISTER_KERNEL_BUILDER(Name("AssertNextDataset").Device(DEVICE_CPU), + AssertNextDatasetOp); + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/contrib/data/kernels/csv_dataset_op.cc b/tensorflow/contrib/data/kernels/csv_dataset_op.cc index 4657807785d58727d34f37172bd30c56a5b7cde6..f7e3ed886c6655cdc07e08bbe2fbe82e671a6802 100644 --- a/tensorflow/contrib/data/kernels/csv_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/csv_dataset_op.cc @@ -18,7 +18,10 @@ limitations under the License. #include "tensorflow/core/framework/dataset.h" #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/shape_inference.h" +#include "tensorflow/core/lib/io/inputstream_interface.h" #include "tensorflow/core/lib/io/random_inputstream.h" +#include "tensorflow/core/lib/io/zlib_compression_options.h" +#include "tensorflow/core/lib/io/zlib_inputstream.h" namespace tensorflow { namespace { @@ -37,6 +40,10 @@ class CSVDatasetOp : public DatasetOpKernel { ctx, filenames_tensor->dims() <= 1, errors::InvalidArgument("`filenames` must be a scalar or a vector.")); + string compression_type; + OP_REQUIRES_OK(ctx, ParseScalarArgument(ctx, "compression_type", + &compression_type)); + OpInputList record_defaults_list; OP_REQUIRES_OK(ctx, ctx->input_list("record_defaults", &record_defaults_list)); @@ -86,6 +93,19 @@ class CSVDatasetOp : public DatasetOpKernel { filenames.push_back(filenames_tensor->flat()(i)); } + io::ZlibCompressionOptions zlib_compression_options = + io::ZlibCompressionOptions::DEFAULT(); + if (compression_type == "ZLIB") { + zlib_compression_options = io::ZlibCompressionOptions::DEFAULT(); + } else if (compression_type == "GZIP") { + zlib_compression_options = io::ZlibCompressionOptions::GZIP(); + } else { + OP_REQUIRES(ctx, compression_type.empty(), + errors::InvalidArgument( + "Unsupported compression_type: ", compression_type, ".")); + } + zlib_compression_options.input_buffer_size = buffer_size; + std::vector select_cols; select_cols.reserve(select_cols_tensor->NumElements()); for (int i = 0; i < select_cols_tensor->NumElements(); ++i) { @@ -103,7 +123,8 @@ class CSVDatasetOp : public DatasetOpKernel { ctx, select_cols.empty() || select_cols.front() >= 0, errors::InvalidArgument("select_cols should be non-negative indices")); - *output = new Dataset(ctx, std::move(filenames), header, buffer_size, + *output = new Dataset(ctx, std::move(filenames), header, + std::move(compression_type), zlib_compression_options, output_types_, output_shapes_, std::move(record_defaults), std::move(select_cols), use_quote_delim, delim[0], std::move(na_value)); @@ -113,21 +134,24 @@ class CSVDatasetOp : public DatasetOpKernel { class Dataset : public GraphDatasetBase { public: Dataset(OpKernelContext* ctx, std::vector filenames, bool header, - int64 buffer_size, const DataTypeVector& output_types, + string compression_type, io::ZlibCompressionOptions options, + const DataTypeVector& output_types, const std::vector& output_shapes, std::vector record_defaults, std::vector select_cols, bool use_quote_delim, char delim, string na_value) : GraphDatasetBase(ctx), filenames_(std::move(filenames)), header_(header), - buffer_size_(buffer_size), out_type_(output_types), output_shapes_(output_shapes), record_defaults_(std::move(record_defaults)), select_cols_(std::move(select_cols)), use_quote_delim_(use_quote_delim), delim_(delim), - na_value_(std::move(na_value)) {} + na_value_(std::move(na_value)), + use_compression_(!compression_type.empty()), + compression_type_(std::move(compression_type)), + options_(options) {} std::unique_ptr MakeIteratorInternal( const string& prefix) const override { @@ -146,10 +170,45 @@ class CSVDatasetOp : public DatasetOpKernel { protected: Status AsGraphDefInternal(DatasetGraphDefBuilder* b, Node** output) const override { - // TODO(rachelim): Implement this - std::vector input_tensors; - TF_RETURN_IF_ERROR(b->AddDataset(this, input_tensors, output)); - return errors::Unimplemented("CSVDataset: AsGraphDefInternal"); + Node* filenames = nullptr; + Node* compression_type = nullptr; + Node* buffer_size = nullptr; + Node* header = nullptr; + Node* delim = nullptr; + Node* use_quote_delim = nullptr; + Node* na_value = nullptr; + Node* select_cols = nullptr; + + std::vector record_defaults; + record_defaults.reserve(record_defaults_.size()); + for (const Tensor& t : record_defaults_) { + Node* node; + TF_RETURN_IF_ERROR(b->AddTensor(t, &node)); + record_defaults.emplace_back(node); + } + + TF_RETURN_IF_ERROR(b->AddVector(filenames_, &filenames)); + TF_RETURN_IF_ERROR(b->AddScalar(compression_type_, &compression_type)); + TF_RETURN_IF_ERROR( + b->AddScalar(options_.input_buffer_size, &buffer_size)); + TF_RETURN_IF_ERROR(b->AddScalar(header_, &header)); + + string delim_string(1, delim_); + TF_RETURN_IF_ERROR(b->AddScalar(delim_string, &delim)); + TF_RETURN_IF_ERROR(b->AddScalar(use_quote_delim_, &use_quote_delim)); + TF_RETURN_IF_ERROR(b->AddScalar(na_value_, &na_value)); + TF_RETURN_IF_ERROR(b->AddVector(select_cols_, &select_cols)); + + TF_RETURN_IF_ERROR(b->AddDataset( + this, + {std::make_pair(0, filenames), std::make_pair(1, compression_type), + std::make_pair(2, buffer_size), std::make_pair(3, header), + std::make_pair(4, delim), std::make_pair(5, use_quote_delim), + std::make_pair(6, na_value), + std::make_pair(7, select_cols)}, // Single tensor inputs + {std::make_pair(8, record_defaults)}, // Tensor list inputs + {}, output)); + return Status::OK(); } private: @@ -201,14 +260,58 @@ class CSVDatasetOp : public DatasetOpKernel { protected: Status SaveInternal(IteratorStateWriter* writer) override { mutex_lock l(mu_); - // TODO(rachelim): Implement save - return errors::Unimplemented("CSVDataset: SaveInternal"); + TF_RETURN_IF_ERROR(writer->WriteScalar(full_name("current_file_index"), + current_file_index_)); + // `input_stream_` is empty if + // 1. GetNext has not been called even once. + // 2. All files have been read and the iterator has been exhausted. + if (input_stream_ && num_buffer_reads_ > 0) { + TF_RETURN_IF_ERROR(writer->WriteScalar(full_name("pos"), pos_)); + // If num_buffer_reads_ == 0, the buffer hasn't been filled even once. + TF_RETURN_IF_ERROR(writer->WriteScalar(full_name("num_buffer_reads"), + num_buffer_reads_)); + } + return Status::OK(); } + Status RestoreInternal(IteratorContext* ctx, IteratorStateReader* reader) override { mutex_lock l(mu_); - // TODO(rachelim): Implement restore - return errors::Unimplemented("CSVDataset: RestoreInternal"); + ResetStreamsLocked(); + int64 current_file_index; + TF_RETURN_IF_ERROR(reader->ReadScalar(full_name("current_file_index"), + ¤t_file_index)); + current_file_index_ = size_t(current_file_index); + // The keys "pos" and "num_buffer_reads" are written only if + // the iterator was saved with an open, partially read file. + if (reader->Contains(full_name("pos"))) { + int64 pos, num_buffer_reads; + TF_RETURN_IF_ERROR(reader->ReadScalar(full_name("pos"), &pos)); + TF_RETURN_IF_ERROR(reader->ReadScalar(full_name("num_buffer_reads"), + &num_buffer_reads)); + + TF_RETURN_IF_ERROR(SetupStreamsLocked(ctx->env())); + + num_buffer_reads_ = size_t(num_buffer_reads - 1); + + // Restores the most recently held buffer + Status s = input_stream_->SkipNBytes( + num_buffer_reads_ * dataset()->options_.input_buffer_size); + if (!s.ok() && !errors::IsOutOfRange(s)) { + // We might get out of range error here if the size of the file + // is not an exact multiple of the buffer size, and the last buffer + // read is < buffer_size. This is valid and we do not surface the + // error. + return s; + } + + Status s2 = FillBuffer(&buffer_); + if (!s2.ok() && !errors::IsOutOfRange(s2)) { + return s2; + } + pos_ = size_t(pos); + } + return Status::OK(); } private: @@ -510,7 +613,9 @@ class CSVDatasetOp : public DatasetOpKernel { Status FillBuffer(string* result) EXCLUSIVE_LOCKS_REQUIRED(mu_) { result->clear(); - Status s = input_stream_->ReadNBytes(dataset()->buffer_size_, result); + ++num_buffer_reads_; + Status s = input_stream_->ReadNBytes( + dataset()->options_.input_buffer_size, result); if (errors::IsOutOfRange(s) && !result->empty()) { // Ignore OutOfRange error when ReadNBytes read < N bytes. @@ -675,10 +780,20 @@ class CSVDatasetOp : public DatasetOpKernel { // Actually move on to next file. TF_RETURN_IF_ERROR(env->NewRandomAccessFile( dataset()->filenames_[current_file_index_], &file_)); - input_stream_.reset( - new io::RandomAccessInputStream(file_.get(), false)); + random_access_input_stream_ = + std::make_shared(file_.get(), false); + + if (dataset()->use_compression_) { + input_stream_ = std::make_shared( + random_access_input_stream_.get(), + dataset()->options_.input_buffer_size, + dataset()->options_.input_buffer_size, dataset()->options_); + } else { + input_stream_ = random_access_input_stream_; + } buffer_.clear(); pos_ = 0; + num_buffer_reads_ = 0; if (dataset()->header_) { // Read one line, but don't include it. Pass nullptrs as dummy // pointers to objects that shouldn't be invoked anyway @@ -704,8 +819,10 @@ class CSVDatasetOp : public DatasetOpKernel { string buffer_ GUARDED_BY(mu_); // Maintain our own buffer size_t pos_ GUARDED_BY( mu_); // Index into the buffer must be maintained between iters - std::unique_ptr input_stream_ + size_t num_buffer_reads_ GUARDED_BY(mu_); + std::shared_ptr random_access_input_stream_ GUARDED_BY(mu_); + std::shared_ptr input_stream_ GUARDED_BY(mu_); size_t current_file_index_ GUARDED_BY(mu_) = 0; std::unique_ptr file_ GUARDED_BY(mu_); // must outlive input_stream_ @@ -713,7 +830,6 @@ class CSVDatasetOp : public DatasetOpKernel { const std::vector filenames_; const bool header_; - const int64 buffer_size_; const DataTypeVector out_type_; const std::vector output_shapes_; const std::vector record_defaults_; @@ -721,6 +837,9 @@ class CSVDatasetOp : public DatasetOpKernel { const bool use_quote_delim_; const char delim_; const string na_value_; + const bool use_compression_; + const string compression_type_; + const io::ZlibCompressionOptions options_; }; // class Dataset DataTypeVector output_types_; diff --git a/tensorflow/contrib/data/kernels/prefetching_kernels.cc b/tensorflow/contrib/data/kernels/prefetching_kernels.cc index b3d464d7165d53cf198072e06214f7d5e982073d..32f03ca68364e40c6fd6769f05d0566f50119240 100644 --- a/tensorflow/contrib/data/kernels/prefetching_kernels.cc +++ b/tensorflow/contrib/data/kernels/prefetching_kernels.cc @@ -15,6 +15,7 @@ limitations under the License. #include #include "tensorflow/core/common_runtime/process_function_library_runtime.h" +#include "tensorflow/core/framework/dataset.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/resource_op_kernel.h" @@ -23,6 +24,7 @@ limitations under the License. #include "tensorflow/core/util/device_name_utils.h" namespace tensorflow { +namespace { struct BufferElement { // The producer sets `status` if getting the input element fails. @@ -473,4 +475,466 @@ class IteratorGetDeviceOp : public OpKernel { REGISTER_KERNEL_BUILDER(Name("IteratorGetDevice").Device(DEVICE_CPU), IteratorGetDeviceOp); +Status VerifyTypesMatch(const DataTypeVector& expected, + const DataTypeVector& received) { + if (expected.size() != received.size()) { + return errors::InvalidArgument( + "Number of components does not match: expected ", expected.size(), + " types but got ", received.size(), "."); + } + for (size_t i = 0; i < expected.size(); ++i) { + if (expected[i] != received[i]) { + return errors::InvalidArgument("Data type mismatch at component ", i, + ": expected ", DataTypeString(expected[i]), + " but got ", DataTypeString(received[i]), + "."); + } + } + return Status::OK(); +} + +Status VerifyShapesCompatible(const std::vector& expected, + const std::vector& received) { + if (expected.size() != received.size()) { + return errors::InvalidArgument( + "Number of components does not match: expected ", expected.size(), + " shapes but got ", received.size(), "."); + } + for (size_t i = 0; i < expected.size(); ++i) { + if (!expected[i].IsCompatibleWith(received[i])) { + return errors::InvalidArgument("Incompatible shapes at component ", i, + ": expected ", expected[i].DebugString(), + " but got ", received[i].DebugString(), + "."); + } + } + + return Status::OK(); +} + +string SanitizeThreadSuffix(string suffix) { + string clean; + for (int i = 0; i < suffix.size(); ++i) { + const char ch = suffix[i]; + if ((ch >= 'a' && ch <= 'z') || (ch >= 'A' && ch <= 'Z') || + (ch >= '0' && ch <= '9') || ch == '_' || ch == '-') { + clean += ch; + } else { + clean += '_'; + } + } + return clean; +} + +class MultiDeviceIterator : public ResourceBase { + public: + MultiDeviceIterator(const DataTypeVector& output_types, + const std::vector& output_shapes, + const std::vector& devices, + std::unique_ptr flib_def, + std::unique_ptr pflr, + FunctionLibraryRuntime* lib) + : output_types_(output_types), + output_shapes_(output_shapes), + devices_(devices), + flib_def_(std::move(flib_def)), + pflr_(std::move(pflr)), + lib_(lib) { + buffer_.resize(devices_.size()); + } + + string DebugString() override { + return strings::StrCat("MultiDeviceIterator"); + } + + Status Init(std::unique_ptr iterator, int64* incarnation_id) { + mutex_lock l(mu_); + if (iterator) { + TF_RETURN_IF_ERROR( + VerifyTypesMatch(output_types_, iterator->output_dtypes())); + TF_RETURN_IF_ERROR( + VerifyShapesCompatible(output_shapes_, iterator->output_shapes())); + } + host_iterator_.reset(iterator.release()); + incarnation_id_++; + *incarnation_id = incarnation_id_; + max_buffer_size_ = 0; + num_elements_ = 0; + buffer_.clear(); + buffer_.resize(devices_.size()); + return Status::OK(); + } + + Status GetNextFromShard(IteratorContext* ctx, int shard_num, + int64 incarnation_id, + std::vector* out_tensors, + bool* end_of_sequence) { + // TODO(rohanj): This might potentially strand elements in other shards. + // Opportunity to do smarter locking semantics. + mutex_lock l(mu_); + // Make sure we're in the right incarnation. + if (incarnation_id != incarnation_id_) { + return errors::InvalidArgument( + "Current incarnation: ", incarnation_id_, + "; Supplied incarnation: ", incarnation_id); + } + // Then look it up in the buffer. + if (!buffer_[shard_num].empty()) { + const HostBufferElement& elem = buffer_[shard_num].front(); + *out_tensors = elem.value; + *end_of_sequence = elem.end_of_sequence; + Status s = elem.status; + buffer_[shard_num].pop_front(); + return s; + } + std::shared_ptr captured_iterator(host_iterator_); + if (captured_iterator) { + if (lib_ != nullptr) { + ctx->set_lib(lib_); + } + while (true) { + HostBufferElement elem; + elem.status = + captured_iterator->GetNext(ctx, &elem.value, &elem.end_of_sequence); + int buffer_index = num_elements_ % devices_.size(); + num_elements_++; + if (buffer_index == shard_num) { + out_tensors->swap(elem.value); + *end_of_sequence = elem.end_of_sequence; + return elem.status; + } else { + buffer_[buffer_index].push_back(std::move(elem)); + // TODO(rohanj): Put an upper bound to buffer size. + if (buffer_[buffer_index].size() > max_buffer_size_) { + max_buffer_size_ = buffer_[buffer_index].size(); + VLOG(1) << "MultiDeviceIterator: Max buffer size increased to: " + << max_buffer_size_; + } + } + } + } else { + return errors::FailedPrecondition("Iterator not initialized"); + } + return Status::OK(); + } + + const DataTypeVector& output_types() const { return output_types_; } + + const std::vector& output_shapes() const { + return output_shapes_; + } + + std::shared_ptr function_library() { + tf_shared_lock l(mu_); + return lib_def_; + } + + private: + struct HostBufferElement { + Status status; + bool end_of_sequence; + std::vector value; + }; + + mutex mu_; + const DataTypeVector output_types_; + const std::vector output_shapes_; + const std::vector devices_; + int64 num_elements_ GUARDED_BY(mu_) = 0; + int64 max_buffer_size_ GUARDED_BY(mu_) = 0; + int64 incarnation_id_ GUARDED_BY(mu_) = 0; + std::vector> buffer_ GUARDED_BY(mu_); + std::unique_ptr flib_def_; + std::unique_ptr pflr_; + FunctionLibraryRuntime* lib_ = nullptr; // not owned. + std::shared_ptr host_iterator_; + std::shared_ptr lib_def_ GUARDED_BY(mu_); +}; + +// Just creates a MultiDeviceIterator and returns it. +class MultiDeviceIteratorHandleOp : public OpKernel { + public: + explicit MultiDeviceIteratorHandleOp(OpKernelConstruction* ctx) + : OpKernel(ctx), graph_def_version_(ctx->graph_def_version()) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_types_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("shared_name", &name_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("container", &container_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("devices", &devices_)); + } + + // The resource is deleted from the resource manager only when it is private + // to kernel. + ~MultiDeviceIteratorHandleOp() override { + if (resource_ != nullptr) { + resource_->Unref(); + if (cinfo_.resource_is_private_to_kernel()) { + if (!cinfo_.resource_manager() + ->template Delete(cinfo_.container(), + cinfo_.name()) + .ok()) { + // Do nothing; the resource can have been deleted by session resets. + } + } + } + } + + void Compute(OpKernelContext* context) override LOCKS_EXCLUDED(mu_) { + { + mutex_lock l(mu_); + if (resource_ == nullptr) { + FunctionLibraryRuntime* lib; + std::unique_ptr flib_def(nullptr); + std::unique_ptr pflr(nullptr); + OP_REQUIRES_OK(context, context->function_library()->Clone( + &flib_def, &pflr, &lib)); + ResourceMgr* mgr = context->resource_manager(); + OP_REQUIRES_OK(context, cinfo_.Init(mgr, def())); + + MultiDeviceIterator* resource; + OP_REQUIRES_OK( + context, + mgr->LookupOrCreate( + cinfo_.container(), cinfo_.name(), &resource, + [this, lib, &flib_def, &pflr](MultiDeviceIterator** ret) + EXCLUSIVE_LOCKS_REQUIRED(mu_) { + *ret = new MultiDeviceIterator( + output_types_, output_shapes_, devices_, + std::move(flib_def), std::move(pflr), lib); + return Status::OK(); + })); + + Status s = VerifyResource(resource); + if (TF_PREDICT_FALSE(!s.ok())) { + resource->Unref(); + context->SetStatus(s); + return; + } + + resource_ = resource; + } + } + OP_REQUIRES_OK(context, MakeResourceHandleToOutput( + context, 0, cinfo_.container(), cinfo_.name(), + MakeTypeIndex())); + } + + private: + // During the first Compute(), resource is either created or looked up using + // shared_name. In the latter case, the resource found should be verified if + // it is compatible with this op's configuration. The verification may fail in + // cases such as two graphs asking queues of the same shared name to have + // inconsistent capacities. + Status VerifyResource(MultiDeviceIterator* resource) { + TF_RETURN_IF_ERROR( + VerifyTypesMatch(output_types_, resource->output_types())); + TF_RETURN_IF_ERROR( + VerifyShapesCompatible(output_shapes_, resource->output_shapes())); + return Status::OK(); + } + + mutex mu_; + ContainerInfo cinfo_; // Written once under mu_ then constant afterwards. + MultiDeviceIterator* resource_ GUARDED_BY(mu_) = nullptr; + DataTypeVector output_types_; + std::vector output_shapes_; + const int graph_def_version_; + string name_; + string container_; + std::vector devices_; +}; + +REGISTER_KERNEL_BUILDER(Name("MultiDeviceIterator").Device(DEVICE_CPU), + MultiDeviceIteratorHandleOp); + +// Calls init on the MultiDeviceIterator. +class MultiDeviceIteratorInitOp : public OpKernel { + public: + explicit MultiDeviceIteratorInitOp(OpKernelConstruction* ctx) + : OpKernel(ctx) {} + + void Compute(OpKernelContext* ctx) override { + DatasetBase* dataset; + OP_REQUIRES_OK(ctx, GetDatasetFromVariantTensor(ctx->input(0), &dataset)); + MultiDeviceIterator* resource; + OP_REQUIRES_OK(ctx, + LookupResource(ctx, HandleFromInput(ctx, 1), &resource)); + core::ScopedUnref unref(resource); + + IteratorContext iter_ctx = dataset::MakeIteratorContext(ctx); + std::unique_ptr iterator; + OP_REQUIRES_OK(ctx, + dataset->MakeIterator(&iter_ctx, "Iterator", &iterator)); + int64 incarnation_id; + OP_REQUIRES_OK(ctx, resource->Init(std::move(iterator), &incarnation_id)); + Tensor tensor_incarnation_id(DT_INT64, TensorShape({})); + tensor_incarnation_id.scalar()() = incarnation_id; + OP_REQUIRES_OK(ctx, + ctx->set_output("incarnation_id", tensor_incarnation_id)); + } +}; + +REGISTER_KERNEL_BUILDER(Name("MultiDeviceIteratorInit").Device(DEVICE_CPU), + MultiDeviceIteratorInitOp); + +// Calls GetNextFromShard(shard) and returns a vector of Tensors as output. +// TODO(rohanj): Implement using BackgroundWorker that Derek built? +class MultiDeviceIteratorGetNextFromShardOp : public AsyncOpKernel { + public: + explicit MultiDeviceIteratorGetNextFromShardOp(OpKernelConstruction* ctx) + : AsyncOpKernel(ctx), + thread_pool_(new thread::ThreadPool( + ctx->env(), ThreadOptions(), + strings::StrCat("multi_device_iterator_get_next_thread_", + SanitizeThreadSuffix(name())), + 1 /* num_threads */, false /* low_latency_hint */)) {} + + void ComputeAsync(OpKernelContext* ctx, DoneCallback done) override { + const Tensor* tensor_shard_num; + OP_REQUIRES_OK_ASYNC(ctx, ctx->input("shard_num", &tensor_shard_num), done); + int32 shard_num = tensor_shard_num->scalar()(); + + const Tensor* tensor_incarnation_id; + OP_REQUIRES_OK_ASYNC( + ctx, ctx->input("incarnation_id", &tensor_incarnation_id), done); + int64 incarnation_id = tensor_incarnation_id->scalar()(); + + MultiDeviceIterator* iterator; + OP_REQUIRES_OK_ASYNC( + ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &iterator), done); + thread_pool_->Schedule(std::bind( + [ctx, iterator, shard_num, incarnation_id](DoneCallback done) { + std::vector components; + bool end_of_sequence = false; + + IteratorContext::Params params; + params.env = ctx->env(); + params.runner = *(ctx->runner()); + params.function_library = iterator->function_library(); + DeviceBase* device = ctx->function_library()->device(); + params.allocator_getter = [device](AllocatorAttributes attrs) { + return device->GetAllocator(attrs); + }; + IteratorContext iter_ctx(std::move(params)); + + Status s = + iterator->GetNextFromShard(&iter_ctx, shard_num, incarnation_id, + &components, &end_of_sequence); + iterator->Unref(); + + if (!s.ok()) { + ctx->SetStatus(s); + } else if (end_of_sequence) { + ctx->SetStatus(errors::OutOfRange("End of sequence")); + } else { + for (int i = 0; i < components.size(); ++i) { + // TODO(mrry): Check that the shapes match the shape attrs. + ctx->set_output(i, components[i]); + } + } + done(); + }, + std::move(done))); + } + + private: + std::unique_ptr thread_pool_; +}; + +REGISTER_KERNEL_BUILDER( + Name("MultiDeviceIteratorGetNextFromShard").Device(DEVICE_CPU), + MultiDeviceIteratorGetNextFromShardOp); + +class MultiDeviceIteratorToStringHandleOp : public OpKernel { + public: + explicit MultiDeviceIteratorToStringHandleOp(OpKernelConstruction* ctx) + : OpKernel(ctx) {} + + void Compute(OpKernelContext* ctx) override { + const Tensor& resource_handle_t = ctx->input(0); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(resource_handle_t.shape()), + errors::InvalidArgument("resource_handle must be a scalar")); + + // Validate that the handle corresponds to a real resource, and + // that it is an MultiDeviceIterator. + MultiDeviceIterator* resource; + OP_REQUIRES_OK(ctx, + LookupResource(ctx, HandleFromInput(ctx, 0), &resource)); + resource->Unref(); + + Tensor* string_handle_t; + OP_REQUIRES_OK(ctx, + ctx->allocate_output(0, TensorShape({}), &string_handle_t)); + string_handle_t->scalar()() = + resource_handle_t.scalar()().SerializeAsString(); + } +}; + +REGISTER_KERNEL_BUILDER( + Name("MultiDeviceIteratorToStringHandle").Device(DEVICE_CPU), + MultiDeviceIteratorToStringHandleOp); + +class MultiDeviceIteratorFromStringHandleOp : public OpKernel { + public: + explicit MultiDeviceIteratorFromStringHandleOp(OpKernelConstruction* ctx) + : OpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_types_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_)); + OP_REQUIRES( + ctx, + output_types_.empty() || output_shapes_.empty() || + output_types_.size() == output_shapes_.size(), + errors::InvalidArgument("If both 'output_types' and 'output_shapes' " + "are set, they must have the same length.")); + } + + void Compute(OpKernelContext* ctx) override { + const Tensor& string_handle_t = ctx->input(0); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(string_handle_t.shape()), + errors::InvalidArgument("string_handle must be a scalar")); + + ResourceHandle resource_handle; + OP_REQUIRES( + ctx, + resource_handle.ParseFromString(string_handle_t.scalar()()), + errors::InvalidArgument( + "Could not parse string_handle as a valid ResourceHandle")); + + OP_REQUIRES( + ctx, resource_handle.device() == ctx->device()->attributes().name(), + errors::InvalidArgument("Attempted create an iterator on device \"", + ctx->device()->attributes().name(), + "\" from handle defined on device \"", + resource_handle.device(), "\"")); + + // Validate that the handle corresponds to a real resource, and + // that it is an MultiDeviceIterator. + MultiDeviceIterator* resource; + OP_REQUIRES_OK(ctx, LookupResource(ctx, resource_handle, &resource)); + core::ScopedUnref unref_iterator(resource); + if (!output_types_.empty()) { + OP_REQUIRES_OK(ctx, + VerifyTypesMatch(output_types_, resource->output_types())); + } + if (!output_shapes_.empty()) { + OP_REQUIRES_OK(ctx, VerifyShapesCompatible(output_shapes_, + resource->output_shapes())); + } + + Tensor* resource_handle_t; + OP_REQUIRES_OK( + ctx, ctx->allocate_output(0, TensorShape({}), &resource_handle_t)); + resource_handle_t->scalar()() = resource_handle; + } + + private: + DataTypeVector output_types_; + std::vector output_shapes_; +}; + +REGISTER_KERNEL_BUILDER( + Name("MultiDeviceIteratorFromStringHandle").Device(DEVICE_CPU), + MultiDeviceIteratorFromStringHandleOp); + +} // anonymous namespace } // namespace tensorflow diff --git a/tensorflow/contrib/data/ops/dataset_ops.cc b/tensorflow/contrib/data/ops/dataset_ops.cc index 8413fcaf872f49f654c6a1327a14d5c44bdd815a..66a7c7fdcd5e0ab77596177c209470e17f63bc10 100644 --- a/tensorflow/contrib/data/ops/dataset_ops.cc +++ b/tensorflow/contrib/data/ops/dataset_ops.cc @@ -36,6 +36,7 @@ data_input_datasets: `N` datasets with the same type that will be interleaved REGISTER_OP("CSVDataset") .Input("filenames: string") + .Input("compression_type: string") .Input("buffer_size: int64") .Input("header: bool") .Input("field_delim: string") @@ -52,17 +53,18 @@ REGISTER_OP("CSVDataset") shape_inference::ShapeHandle unused; // `filenames` must be a scalar or a vector. TF_RETURN_IF_ERROR(c->WithRankAtMost(c->input(0), 1, &unused)); - // `buffer_size`, `header`, `field_delim`, `use_quote_delim`, - // `na_value` must be scalars + // `compression_type`, `buffer_size`, `header`, `field_delim`, + // `use_quote_delim`, `na_value` must be scalars TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused)); TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused)); TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused)); TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused)); TF_RETURN_IF_ERROR(c->WithRank(c->input(5), 0, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(6), 0, &unused)); // `select_cols` must be a vector - TF_RETURN_IF_ERROR(c->WithRank(c->input(6), 1, &unused)); - // `record_defaults` must be a list of scalars...? - for (size_t i = 7; i < c->num_inputs(); ++i) { + TF_RETURN_IF_ERROR(c->WithRank(c->input(7), 1, &unused)); + // `record_defaults` must be lists of scalars + for (size_t i = 8; i < c->num_inputs(); ++i) { TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 1, &unused)); } return shape_inference::ScalarShape(c); @@ -143,6 +145,80 @@ Resets the FunctionBufferingResource. function_buffer_resource: The FunctionBufferingResource handle. )doc"); +REGISTER_OP("MultiDeviceIterator") + .Output("handle: resource") + .Attr("devices: list(string) >= 1") + .Attr("shared_name: string") + .Attr("container: string") + .Attr("output_types: list(type) >= 1") + .Attr("output_shapes: list(shape) >= 1") + .Doc(R"doc( +Creates a MultiDeviceIterator resource. + +handle: Handle to the resource created. +devices: A list of devices the iterator works across. +shared_name: If non-empty, this resource will be shared under the given name + across multiple sessions. +container: If non-empty, this resource is placed in the given container. + Otherwise, a default container is used. +output_types: The type list for the return values. +output_shapes: The list of shapes being produced. +)doc"); + +REGISTER_OP("MultiDeviceIteratorInit") + .Input("dataset: variant") + .Input("multi_device_iterator: resource") + .Output("incarnation_id: int64") + .Doc(R"doc( +Initializes the multi device iterator with the given dataset. +incarnation_id: An int64 indicating which incarnation of the MultiDeviceIterator + is running. +dataset: Dataset to be iterated upon. +multi_device_iterator: A MultiDeviceIteratorResource. +)doc"); + +REGISTER_OP("MultiDeviceIteratorGetNextFromShard") + .Input("multi_device_iterator: resource") + .Input("shard_num: int32") + .Input("incarnation_id: int64") + .Output("components: output_types") + .Attr("output_types: list(type) >= 1") + .Attr("output_shapes: list(shape) >= 1") + .Doc(R"doc( +Gets next element for the provided shard number. + +multi_device_iterator: A MultiDeviceIterator resource. +shard_num: Integer representing which shard to fetch data for. +incarnation_id: Which incarnation of the MultiDeviceIterator is running. +components: Result of the get_next on the dataset. +output_types: The type list for the return values. +output_shapes: The list of shapes being produced. +)doc"); + +REGISTER_OP("MultiDeviceIteratorToStringHandle") + .Input("multi_device_iterator: resource") + .Output("string_handle: string") + .Doc(R"doc( +Produces a string handle for the given MultiDeviceIterator. + +multi_device_iterator: A MultiDeviceIterator resource. +string_handle: A string representing the resource. +)doc"); + +REGISTER_OP("MultiDeviceIteratorFromStringHandle") + .Input("string_handle: string") + .Output("multi_device_iterator: resource") + .Attr("output_types: list(type) >= 0 = []") + .Attr("output_shapes: list(shape) >= 0 = []") + .Doc(R"doc( +Generates a MultiDeviceIterator resource from its provided string handle. + +string_handle: String representing the resource. +multi_device_iterator: A MultiDeviceIterator resource. +output_types: The type list for the return values. +output_shapes: The list of shapes being produced. +)doc"); + REGISTER_OP("ThreadPoolDataset") .Input("input_dataset: variant") .Input("thread_pool: resource") @@ -175,4 +251,17 @@ display_name: A human-readable name for the threads that may be visible in some visualizations. )doc"); +REGISTER_OP("AssertNextDataset") + .Input("input_dataset: variant") + .Input("transformations: string") + .Output("handle: variant") + .Attr("output_types: list(type) >= 1") + .Attr("output_shapes: list(shape) >= 1") + .SetShapeFn([](shape_inference::InferenceContext* c) { + shape_inference::ShapeHandle unused; + // transformations should be a vector. + TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &unused)); + return shape_inference::ScalarShape(c); + }); + } // namespace tensorflow diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index 079c8bbd8ee4360a847bda14d17a0b48a14c45a5..2de1a79d28c16706e3c237d62935212ce387c776 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -60,7 +60,7 @@ py_test( py_test( name = "csv_dataset_op_test", - size = "small", + size = "medium", srcs = ["csv_dataset_op_test.py"], srcs_version = "PY2AND3", tags = ["no_pip"], @@ -121,6 +121,7 @@ py_test( srcs = ["get_single_element_test.py"], deps = [ "//tensorflow/contrib/data/python/ops:get_single_element", + "//tensorflow/contrib/data/python/ops:grouping", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", @@ -128,6 +129,7 @@ py_test( "//tensorflow/python:errors", "//tensorflow/python:sparse_tensor", "//tensorflow/python/data/ops:dataset_ops", + "@absl_py//absl/testing:parameterized", ], ) @@ -190,6 +192,7 @@ py_test( deps = [ "//tensorflow/contrib/data/python/ops:batching", "//tensorflow/contrib/data/python/ops:error_ops", + "//tensorflow/contrib/data/python/ops:optimization", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:errors", @@ -208,10 +211,10 @@ py_test( srcs_version = "PY2AND3", deps = [ "//tensorflow/contrib/data/python/ops:optimization", - "//tensorflow/core:protos_all_py", "//tensorflow/python:client_testlib", "//tensorflow/python:errors", "//tensorflow/python/data/ops:dataset_ops", + "@absl_py//absl/testing:parameterized", ], ) @@ -229,9 +232,16 @@ cuda_py_test( "//tensorflow/python:framework_test_lib", "//tensorflow/python:function", "//tensorflow/python:resource_variable_ops", + "//tensorflow/python/compat:compat", "//tensorflow/python/data/ops:dataset_ops", "//tensorflow/python/data/ops:iterator_ops", ], + tags = [ + "manual", + "no_oss", + "no_windows_gpu" + + "notap", + ], ) py_test( @@ -378,6 +388,7 @@ py_test( "//tensorflow/python:sparse_tensor", "//tensorflow/python/data/ops:dataset_ops", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/contrib/data/python/kernel_tests/csv_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/csv_dataset_op_test.py index df115175f5046803ada036563be1ca802f7ad0cd..2a0e64caeb61c5a7d45669783ace4588746c19e3 100644 --- a/tensorflow/contrib/data/python/kernel_tests/csv_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/csv_dataset_op_test.py @@ -18,10 +18,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import gzip import os import string import tempfile import time +import zlib import numpy as np @@ -62,18 +64,29 @@ class CsvDatasetOpTest(test.TestCase): op2 = sess.run(next2) self.assertAllEqual(op1, op2) - def setup_files(self, inputs, linebreak='\n'): + def _setup_files(self, inputs, linebreak='\n', compression_type=None): filenames = [] for i, ip in enumerate(inputs): fn = os.path.join(self.get_temp_dir(), 'temp_%d.csv' % i) - with open(fn, 'wb') as f: - f.write(linebreak.join(ip).encode('utf-8')) + contents = linebreak.join(ip).encode('utf-8') + if compression_type is None: + with open(fn, 'wb') as f: + f.write(contents) + elif compression_type == 'GZIP': + with gzip.GzipFile(fn, 'wb') as f: + f.write(contents) + elif compression_type == 'ZLIB': + contents = zlib.compress(contents) + with open(fn, 'wb') as f: + f.write(contents) + else: + raise ValueError('Unsupported compression_type', compression_type) filenames.append(fn) return filenames def _make_test_datasets(self, inputs, **kwargs): # Test by comparing its output to what we could get with map->decode_csv - filenames = self.setup_files(inputs) + filenames = self._setup_files(inputs) dataset_expected = core_readers.TextLineDataset(filenames) dataset_expected = dataset_expected.map( lambda l: parsing_ops.decode_csv(l, **kwargs)) @@ -112,15 +125,18 @@ class CsvDatasetOpTest(test.TestCase): except errors.OutOfRangeError: break - def _test_dataset(self, - inputs, - expected_output=None, - expected_err_re=None, - linebreak='\n', - **kwargs): + def _test_dataset( + self, + inputs, + expected_output=None, + expected_err_re=None, + linebreak='\n', + compression_type=None, # Used for both setup and parsing + **kwargs): """Checks that elements produced by CsvDataset match expected output.""" # Convert str type because py3 tf strings are bytestrings - filenames = self.setup_files(inputs, linebreak) + filenames = self._setup_files(inputs, linebreak, compression_type) + kwargs['compression_type'] = compression_type with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: dataset = readers.CsvDataset(filenames, **kwargs) @@ -174,7 +190,7 @@ class CsvDatasetOpTest(test.TestCase): def testCsvDataset_ignoreErrWithUnescapedQuotes(self): record_defaults = [['']] * 3 inputs = [['1,"2"3",4', '1,"2"3",4",5,5', 'a,b,"c"d"', 'e,f,g']] - filenames = self.setup_files(inputs) + filenames = self._setup_files(inputs) with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: dataset = readers.CsvDataset(filenames, record_defaults=record_defaults) @@ -184,7 +200,7 @@ class CsvDatasetOpTest(test.TestCase): def testCsvDataset_ignoreErrWithUnquotedQuotes(self): record_defaults = [['']] * 3 inputs = [['1,2"3,4', 'a,b,c"d', '9,8"7,6,5', 'e,f,g']] - filenames = self.setup_files(inputs) + filenames = self._setup_files(inputs) with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: dataset = readers.CsvDataset(filenames, record_defaults=record_defaults) @@ -355,7 +371,7 @@ class CsvDatasetOpTest(test.TestCase): '1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19', '1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19' ]] - file_path = self.setup_files(data) + file_path = self._setup_files(data) with ops.Graph().as_default() as g: ds = readers.make_csv_dataset( @@ -432,14 +448,29 @@ class CsvDatasetOpTest(test.TestCase): record_defaults=record_defaults, buffer_size=0) - def testCsvDataset_withBufferSize(self): + def _test_dataset_on_buffer_sizes(self, + inputs, + expected, + linebreak, + record_defaults, + compression_type=None, + num_sizes_to_test=20): + # Testing reading with a range of buffer sizes that should all work. + for i in list(range(1, 1 + num_sizes_to_test)) + [None]: + self._test_dataset( + inputs, + expected, + linebreak=linebreak, + compression_type=compression_type, + record_defaults=record_defaults, + buffer_size=i) + + def testCsvDataset_withLF(self): record_defaults = [['NA']] * 3 inputs = [['abc,def,ghi', '0,1,2', ',,']] expected = [['abc', 'def', 'ghi'], ['0', '1', '2'], ['NA', 'NA', 'NA']] - for i in range(20): - # Test a range of buffer sizes that should all work - self._test_dataset( - inputs, expected, record_defaults=record_defaults, buffer_size=i + 1) + self._test_dataset_on_buffer_sizes( + inputs, expected, linebreak='\n', record_defaults=record_defaults) def testCsvDataset_withCR(self): # Test that when the line separator is '\r', parsing works with all buffer @@ -447,14 +478,8 @@ class CsvDatasetOpTest(test.TestCase): record_defaults = [['NA']] * 3 inputs = [['abc,def,ghi', '0,1,2', ',,']] expected = [['abc', 'def', 'ghi'], ['0', '1', '2'], ['NA', 'NA', 'NA']] - for i in range(20): - # Test a range of buffer sizes that should all work - self._test_dataset( - inputs, - expected, - linebreak='\r', - record_defaults=record_defaults, - buffer_size=i + 1) + self._test_dataset_on_buffer_sizes( + inputs, expected, linebreak='\r', record_defaults=record_defaults) def testCsvDataset_withCRLF(self): # Test that when the line separator is '\r\n', parsing works with all buffer @@ -462,29 +487,15 @@ class CsvDatasetOpTest(test.TestCase): record_defaults = [['NA']] * 3 inputs = [['abc,def,ghi', '0,1,2', ',,']] expected = [['abc', 'def', 'ghi'], ['0', '1', '2'], ['NA', 'NA', 'NA']] - for i in range(20): - # Test a range of buffer sizes that should all work - self._test_dataset( - inputs, - expected, - linebreak='\r\n', - record_defaults=record_defaults, - buffer_size=i + 1) + self._test_dataset_on_buffer_sizes( + inputs, expected, linebreak='\r\n', record_defaults=record_defaults) def testCsvDataset_withBufferSizeAndQuoted(self): record_defaults = [['NA']] * 3 inputs = [['"\n\n\n","\r\r\r","abc"', '"0","1","2"', '"","",""']] expected = [['\n\n\n', '\r\r\r', 'abc'], ['0', '1', '2'], ['NA', 'NA', 'NA']] - for i in range(20): - # Test a range of buffer sizes that should all work - self._test_dataset( - inputs, - expected, - linebreak='\n', - record_defaults=record_defaults, - buffer_size=i + 1) - self._test_dataset( + self._test_dataset_on_buffer_sizes( inputs, expected, linebreak='\n', record_defaults=record_defaults) def testCsvDataset_withCRAndQuoted(self): @@ -494,15 +505,7 @@ class CsvDatasetOpTest(test.TestCase): inputs = [['"\n\n\n","\r\r\r","abc"', '"0","1","2"', '"","",""']] expected = [['\n\n\n', '\r\r\r', 'abc'], ['0', '1', '2'], ['NA', 'NA', 'NA']] - for i in range(20): - # Test a range of buffer sizes that should all work - self._test_dataset( - inputs, - expected, - linebreak='\r', - record_defaults=record_defaults, - buffer_size=i + 1) - self._test_dataset( + self._test_dataset_on_buffer_sizes( inputs, expected, linebreak='\r', record_defaults=record_defaults) def testCsvDataset_withCRLFAndQuoted(self): @@ -512,17 +515,33 @@ class CsvDatasetOpTest(test.TestCase): inputs = [['"\n\n\n","\r\r\r","abc"', '"0","1","2"', '"","",""']] expected = [['\n\n\n', '\r\r\r', 'abc'], ['0', '1', '2'], ['NA', 'NA', 'NA']] - for i in range(20): - # Test a range of buffer sizes that should all work - self._test_dataset( - inputs, - expected, - linebreak='\r\n', - record_defaults=record_defaults, - buffer_size=i + 1) - self._test_dataset( + self._test_dataset_on_buffer_sizes( inputs, expected, linebreak='\r\n', record_defaults=record_defaults) + def testCsvDataset_withGzipCompressionType(self): + record_defaults = [['NA']] * 3 + inputs = [['"\n\n\n","\r\r\r","abc"', '"0","1","2"', '"","",""']] + expected = [['\n\n\n', '\r\r\r', 'abc'], ['0', '1', '2'], + ['NA', 'NA', 'NA']] + self._test_dataset_on_buffer_sizes( + inputs, + expected, + linebreak='\r\n', + compression_type='GZIP', + record_defaults=record_defaults) + + def testCsvDataset_withZlibCompressionType(self): + record_defaults = [['NA']] * 3 + inputs = [['"\n\n\n","\r\r\r","abc"', '"0","1","2"', '"","",""']] + expected = [['\n\n\n', '\r\r\r', 'abc'], ['0', '1', '2'], + ['NA', 'NA', 'NA']] + self._test_dataset_on_buffer_sizes( + inputs, + expected, + linebreak='\r\n', + compression_type='ZLIB', + record_defaults=record_defaults) + class CsvDatasetBenchmark(test.Benchmark): """Benchmarks for the various ways of creating a dataset from CSV files. diff --git a/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py b/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py index 87b7c6ddb7afcbaaf8fe97cd8be87e6f5af8cd4d..e6883d53e02c0f96d966a52abfe2f9b4118f2e12 100644 --- a/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py @@ -17,9 +17,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized +import numpy as np + from tensorflow.contrib.data.python.ops import get_single_element +from tensorflow.contrib.data.python.ops import grouping from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import sparse_tensor @@ -27,40 +30,69 @@ from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class GetSingleElementTest(test.TestCase): +class GetSingleElementTest(test.TestCase, parameterized.TestCase): - def testGetSingleElement(self): - skip_value = array_ops.placeholder(dtypes.int64, shape=[]) - take_value = array_ops.placeholder_with_default( - constant_op.constant(1, dtype=dtypes.int64), shape=[]) + @parameterized.named_parameters( + ("Zero", 0, 1), + ("Five", 5, 1), + ("Ten", 10, 1), + ("Empty", 100, 1, errors.InvalidArgumentError, "Dataset was empty."), + ("MoreThanOne", 0, 2, errors.InvalidArgumentError, + "Dataset had more than one element."), + ) + def testGetSingleElement(self, skip, take, error=None, error_msg=None): + skip_t = array_ops.placeholder(dtypes.int64, shape=[]) + take_t = array_ops.placeholder(dtypes.int64, shape=[]) def make_sparse(x): x_1d = array_ops.reshape(x, [1]) x_2d = array_ops.reshape(x, [1, 1]) return sparse_tensor.SparseTensor(x_2d, x_1d, x_1d) - dataset = (dataset_ops.Dataset.range(100) - .skip(skip_value) - .map(lambda x: (x * x, make_sparse(x))) - .take(take_value)) - + dataset = dataset_ops.Dataset.range(100).skip(skip_t).map( + lambda x: (x * x, make_sparse(x))).take(take_t) element = get_single_element.get_single_element(dataset) with self.test_session() as sess: - for x in [0, 5, 10]: - dense_val, sparse_val = sess.run(element, feed_dict={skip_value: x}) - self.assertEqual(x * x, dense_val) - self.assertAllEqual([[x]], sparse_val.indices) - self.assertAllEqual([x], sparse_val.values) - self.assertAllEqual([x], sparse_val.dense_shape) - - with self.assertRaisesRegexp(errors.InvalidArgumentError, - "Dataset was empty."): - sess.run(element, feed_dict={skip_value: 100}) - - with self.assertRaisesRegexp(errors.InvalidArgumentError, - "Dataset had more than one element."): - sess.run(element, feed_dict={skip_value: 0, take_value: 2}) + if error is None: + dense_val, sparse_val = sess.run( + element, feed_dict={ + skip_t: skip, + take_t: take + }) + self.assertEqual(skip * skip, dense_val) + self.assertAllEqual([[skip]], sparse_val.indices) + self.assertAllEqual([skip], sparse_val.values) + self.assertAllEqual([skip], sparse_val.dense_shape) + else: + with self.assertRaisesRegexp(error, error_msg): + sess.run(element, feed_dict={skip_t: skip, take_t: take}) + + @parameterized.named_parameters( + ("SumZero", 0), + ("SumOne", 1), + ("SumFive", 5), + ("SumTen", 10), + ) + def testReduceDataset(self, stop): + def init_fn(_): + return np.int64(0) + + def reduce_fn(state, value): + return state + value + + def finalize_fn(state): + return state + + sum_reducer = grouping.Reducer(init_fn, reduce_fn, finalize_fn) + + stop_t = array_ops.placeholder(dtypes.int64, shape=[]) + dataset = dataset_ops.Dataset.range(stop_t) + element = get_single_element.reduce_dataset(dataset, sum_reducer) + + with self.test_session() as sess: + value = sess.run(element, feed_dict={stop_t: stop}) + self.assertEqual(stop * (stop - 1) / 2, value) if __name__ == "__main__": diff --git a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py index a075dfd8b56079c7b2509bb5795521b8b9eb3127..48adc98e9a4caee1651d5c7bca9dd813f11dfb01 100644 --- a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py @@ -17,6 +17,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import hashlib import itertools import os import time @@ -25,6 +26,7 @@ import numpy as np from tensorflow.contrib.data.python.ops import batching from tensorflow.contrib.data.python.ops import error_ops +from tensorflow.contrib.data.python.ops import optimization from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops @@ -32,9 +34,12 @@ from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import io_ops +from tensorflow.python.ops import math_ops from tensorflow.python.platform import test from tensorflow.python.util import compat +_NUMPY_RANDOM_SEED = 42 + class MapDatasetTest(test.TestCase): @@ -78,15 +83,17 @@ class MapDatasetTest(test.TestCase): def write_string_to_file(value, filename): with open(filename, "w") as f: f.write(value) - filenames = [os.path.join(self.get_temp_dir(), "file_%d.txt" % i) - for i in range(5)] + + filenames = [ + os.path.join(self.get_temp_dir(), "file_%d.txt" % i) for i in range(5) + ] for filename in filenames: write_string_to_file(filename, filename) dataset = ( dataset_ops.Dataset.from_tensor_slices(filenames).map( - io_ops.read_file, num_parallel_calls=2).prefetch(2).apply( - error_ops.ignore_errors())) + io_ops.read_file, + num_parallel_calls=2).prefetch(2).apply(error_ops.ignore_errors())) iterator = dataset.make_initializable_iterator() init_op = iterator.initializer get_next = iterator.get_next() @@ -142,80 +149,164 @@ class MapDatasetTest(test.TestCase): class MapDatasetBenchmark(test.Benchmark): + # The purpose of this benchmark is to compare the performance of chaining vs + # fusing of the map and batch transformations across various configurations. + # + # NOTE: It is recommended to build the benchmark with + # `-c opt --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-gmlt` + # and execute it on a machine with at least 32 CPU cores. def benchmarkMapAndBatch(self): - small = itertools.product([1, 4], [1, 4], [1, 4], [16, 64], [100]) - large = itertools.product([16, 64], [16, 64], [16, 64], [256, 1024], [10]) - - num_iters = 100 - - def benchmark(series): - - for num_calls, inter_op, element_size, batch_size, num_steps in series: - dataset = dataset_ops.Dataset.from_tensors( - np.random.randint(100, size=element_size)).repeat().map( - lambda x: x, - num_parallel_calls=num_calls).batch(batch_size=batch_size) - iterator = dataset.make_one_shot_iterator() - get_next = iterator.get_next() - fused_dataset = dataset_ops.Dataset.from_tensors( - np.random.randint(100, size=element_size)).repeat(None).apply( - batching.map_and_batch( - lambda x: x, - num_parallel_calls=num_calls, - batch_size=batch_size)) - fused_iterator = fused_dataset.make_one_shot_iterator() - fused_get_next = fused_iterator.get_next() + # Sequential pipeline configurations. + seq_elem_size_series = itertools.product([1], [1], [1, 2, 4, 8], [16]) + seq_batch_size_series = itertools.product([1], [1], [1], [8, 16, 32, 64]) + + # Parallel pipeline configuration. + par_elem_size_series = itertools.product([32], [32], [1, 2, 4, 8], [256]) + par_batch_size_series = itertools.product([32], [32], [1], + [128, 256, 512, 1024]) + par_num_calls_series = itertools.product([8, 16, 32, 64], [32], [1], [512]) + par_inter_op_series = itertools.product([32], [8, 16, 32, 64], [1], [512]) + + def name(method, label, num_calls, inter_op, element_size, batch_size): + return ("%s_id_%s_num_calls_%d_inter_op_%d_elem_size_%d_batch_size_%d" % ( + method, + hashlib.sha1(label).hexdigest(), + num_calls, + inter_op, + element_size, + batch_size, + )) + + def benchmark(label, series): + + print("%s:" % label) + for num_calls, inter_op, element_size, batch_size in series: + + num_iters = 1024 // ( + (element_size * batch_size) // min(num_calls, inter_op)) + k = 1024 * 1024 + dataset = dataset_ops.Dataset.from_tensors((np.random.rand( + element_size, 4 * k), np.random.rand(4 * k, 1))).repeat() + + chained_dataset = dataset.map( + math_ops.matmul, + num_parallel_calls=num_calls).batch(batch_size=batch_size) + chained_iterator = chained_dataset.make_one_shot_iterator() + chained_get_next = chained_iterator.get_next() - fused_deltas = [] + chained_deltas = [] with session.Session( config=config_pb2.ConfigProto( - inter_op_parallelism_threads=inter_op)) as sess: - + inter_op_parallelism_threads=inter_op, + use_per_session_threads=True)) as sess: for _ in range(5): - sess.run(fused_get_next) + sess.run(chained_get_next.op) for _ in range(num_iters): start = time.time() - for _ in range(num_steps): - sess.run(fused_get_next) + sess.run(chained_get_next.op) end = time.time() - fused_deltas.append(end - start) + chained_deltas.append(end - start) - chained_deltas = [] + fused_dataset = dataset = dataset.apply( + batching.map_and_batch( + math_ops.matmul, + num_parallel_calls=num_calls, + batch_size=batch_size)) + fused_iterator = fused_dataset.make_one_shot_iterator() + fused_get_next = fused_iterator.get_next() + + fused_deltas = [] with session.Session( config=config_pb2.ConfigProto( - inter_op_parallelism_threads=inter_op)) as sess: + inter_op_parallelism_threads=inter_op, + use_per_session_threads=True)) as sess: + for _ in range(5): - sess.run(get_next) + sess.run(fused_get_next.op) for _ in range(num_iters): start = time.time() - for _ in range(num_steps): - sess.run(get_next) + sess.run(fused_get_next.op) end = time.time() - chained_deltas.append(end - start) + fused_deltas.append(end - start) - chained_wall_time = np.median(chained_deltas) / num_iters - fused_wall_time = np.median(fused_deltas) / num_iters print( "batch size: %d, num parallel calls: %d, inter-op parallelism: %d, " - "element size: %d, chained wall time: %f, fused wall time: %f" % - (batch_size, num_calls, inter_op, element_size, chained_wall_time, - fused_wall_time)) + "element size: %d, num iters: %d\nchained wall time: %f (median), " + "%f (mean), %f (stddev), %f (min), %f (max)\n fused wall time: " + "%f (median), %f (mean), %f (stddev), %f (min), %f (max)\n " + "chained/fused: %.2fx (median), %.2fx (mean)" % + (batch_size, num_calls, inter_op, element_size, num_iters, + np.median(chained_deltas), np.mean(chained_deltas), + np.std(chained_deltas), np.min(chained_deltas), + np.max(chained_deltas), np.median(fused_deltas), + np.mean(fused_deltas), np.std(fused_deltas), np.min(fused_deltas), + np.max(fused_deltas), + np.median(chained_deltas) / np.median(fused_deltas), + np.mean(chained_deltas) / np.mean(fused_deltas))) self.report_benchmark( iters=num_iters, - wall_time=chained_wall_time, - name="chained_batch_size_%d_num_calls_%d_inter_op_%d_elem_size_%d" - % (batch_size, num_calls, inter_op, element_size)) + wall_time=np.median(chained_deltas), + name=name("chained", label, num_calls, inter_op, element_size, + batch_size)) self.report_benchmark( iters=num_iters, - wall_time=fused_wall_time, - name="fused_batch_size_%d_num_calls_%d_inter_op_%d_elem_size_%d" - % (batch_size, num_calls, inter_op, element_size)) + wall_time=np.median(fused_deltas), + name=name("fused", label, num_calls, inter_op, element_size, + batch_size)) + + print("") + + np.random.seed(_NUMPY_RANDOM_SEED) + benchmark("Sequential element size evaluation", seq_elem_size_series) + benchmark("Sequential batch size evaluation", seq_batch_size_series) + benchmark("Parallel element size evaluation", par_elem_size_series) + benchmark("Parallel batch size evaluation", par_batch_size_series) + benchmark("Transformation parallelism evaluation", par_num_calls_series) + benchmark("Threadpool size evaluation", par_inter_op_series) + + # This benchmark compares the performance of pipeline with multiple chained + # maps with and without map fusion. + def benchmarkChainOfMaps(self): + chain_lengths = [0, 1, 2, 5, 10, 20, 50] + for chain_length in chain_lengths: + self._benchmarkChainOfMaps(chain_length, False) + self._benchmarkChainOfMaps(chain_length, True) + + def _benchmarkChainOfMaps(self, chain_length, optimize_dataset): + with ops.Graph().as_default(): + dataset = dataset_ops.Dataset.from_tensors(0).repeat(None) + for _ in range(chain_length): + dataset = dataset.map(lambda x: x) + if optimize_dataset: + dataset = dataset.apply(optimization.optimize(["map_fusion"])) + + iterator = dataset.make_one_shot_iterator() + next_element = iterator.get_next() + + with session.Session() as sess: + for _ in range(5): + sess.run(next_element.op) + deltas = [] + for _ in range(100): + start = time.time() + for _ in range(100): + sess.run(next_element.op) + end = time.time() + deltas.append(end - start) + + median_wall_time = np.median(deltas) / 100 + opt_mark = "opt" if optimize_dataset else "no-opt" + print("Map dataset {} chain length: {} Median wall time: {}".format( + opt_mark, chain_length, median_wall_time)) + self.report_benchmark( + iters=1000, + wall_time=median_wall_time, + name="benchmark_map_dataset_chain_latency_{}_{}".format( + opt_mark, chain_length)) - benchmark(small) - benchmark(large) if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py index e35be8a23f3706bd170c09b967b4f419fc9a626e..d8156dc9c7bf187d7399aede44c41c8c50670248 100644 --- a/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py @@ -17,60 +17,148 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized + from tensorflow.contrib.data.python.ops import optimization -from tensorflow.core.framework import graph_pb2 from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import errors from tensorflow.python.platform import test -class OptimizeDatasetTest(test.TestCase): +class OptimizeDatasetTest(test.TestCase, parameterized.TestCase): + + def testAssertSuffix(self): + dataset = dataset_ops.Dataset.from_tensors(0).apply( + optimization.assert_next(["Map"])).map(lambda x: x) + iterator = dataset.make_one_shot_iterator() + get_next = iterator.get_next() + + with self.test_session() as sess: + self.assertEqual(0, sess.run(get_next)) + + def testAssertSuffixInvalid(self): + dataset = dataset_ops.Dataset.from_tensors(0).apply( + optimization.assert_next(["Whoops"])).map(lambda x: x) + iterator = dataset.make_one_shot_iterator() + get_next = iterator.get_next() + + with self.test_session() as sess: + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + "Asserted Whoops transformation at offset 0 but encountered " + "Map transformation instead." + ): + sess.run(get_next) + + def testAssertSuffixShort(self): + dataset = dataset_ops.Dataset.from_tensors(0).apply( + optimization.assert_next(["Map", "Whoops"])).map(lambda x: x) + iterator = dataset.make_one_shot_iterator() + get_next = iterator.get_next() + + with self.test_session() as sess: + with self.assertRaisesRegexp( + errors.InvalidArgumentError, + "Asserted next 2 transformations but encountered only 1."): + sess.run(get_next) def testDefaultOptimizations(self): - dataset = dataset_ops.Dataset.range(10).map(lambda x: x * x).batch( - 10).apply(optimization.optimize()) + dataset = dataset_ops.Dataset.range(10).apply( + optimization.assert_next( + ["Map", "Batch"])).map(lambda x: x * x).batch(10).apply( + optimization.optimize()) iterator = dataset.make_one_shot_iterator() get_next = iterator.get_next() with self.test_session() as sess: - graph = graph_pb2.GraphDef().FromString( - sess.run(dataset._as_serialized_graph())) - self.assertTrue( - all([node.op != "MapAndBatchDatasetV2" for node in graph.node])) self.assertAllEqual([x * x for x in range(10)], sess.run(get_next)) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) def testEmptyOptimizations(self): - dataset = dataset_ops.Dataset.range(10).map(lambda x: x * x).batch( - 10).apply(optimization.optimize([])) + dataset = dataset_ops.Dataset.range(10).apply( + optimization.assert_next( + ["Map", "Batch"])).map(lambda x: x * x).batch(10).apply( + optimization.optimize([])) iterator = dataset.make_one_shot_iterator() get_next = iterator.get_next() with self.test_session() as sess: - graph = graph_pb2.GraphDef().FromString( - sess.run(dataset._as_serialized_graph())) - self.assertTrue( - all([node.op != "MapAndBatchDatasetV2" for node in graph.node])) self.assertAllEqual([x * x for x in range(10)], sess.run(get_next)) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) def testOptimization(self): - dataset = dataset_ops.Dataset.range(10).map(lambda x: x * x).batch( - 10).apply(optimization.optimize(["map_and_batch_fusion"])) + dataset = dataset_ops.Dataset.range(10).apply( + optimization.assert_next( + ["MapAndBatch"])).map(lambda x: x * x).batch(10).apply( + optimization.optimize(["map_and_batch_fusion"])) iterator = dataset.make_one_shot_iterator() get_next = iterator.get_next() with self.test_session() as sess: - graph = graph_pb2.GraphDef().FromString( - sess.run(dataset._as_serialized_graph())) - self.assertTrue( - any([node.op == "MapAndBatchDatasetV2" for node in graph.node])) self.assertAllEqual([x * x for x in range(10)], sess.run(get_next)) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) + def testFunctionLibraryDefinitionModification(self): + dataset = dataset_ops.Dataset.from_tensors(0).map(lambda x: x).apply( + optimization.optimize(["_test_only_function_rename"])) + iterator = dataset.make_one_shot_iterator() + get_next = iterator.get_next() + + with self.test_session() as sess: + with self.assertRaisesRegexp(errors.NotFoundError, + "Function .* is not defined."): + sess.run(get_next) + + @staticmethod + def map_functions(): + identity = lambda x: x + increment = lambda x: x + 1 + + def increment_and_square(x): + y = x + 1 + return y * y + + functions = [identity, increment, increment_and_square] + tests = [] + + for fun1 in functions: + for fun2 in functions: + tests.append(([fun1, fun2],)) + for fun3 in functions: + tests.append(([fun1, fun2, fun3],)) + + swap = lambda x, n: (n, x) + tests.append(([lambda x: (x, 42), swap],)) + tests.append(([lambda x: (x, 42), swap, swap],)) + return tuple(tests) + + @parameterized.parameters(*map_functions.__func__()) + def testMapFusion(self, functions): + dataset = dataset_ops.Dataset.range(5).apply( + optimization.assert_next(["Map", "Prefetch"])) + for function in functions: + dataset = dataset.map(function) + + dataset = dataset.prefetch(0).apply(optimization.optimize(["map_fusion"])) + iterator = dataset.make_one_shot_iterator() + get_next = iterator.get_next() + with self.test_session() as sess: + for x in range(5): + result = sess.run(get_next) + r = x + for function in functions: + if isinstance(r, tuple): + r = function(*r) # Pass tuple as multiple arguments. + else: + r = function(r) + self.assertAllEqual(r, result) + + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py index 40a8e4667678710251a25f906a917ca1eadd21c2..2da6131e8e60ca53723da7f66a7ee52151640129 100644 --- a/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py @@ -21,6 +21,7 @@ import threading from tensorflow.contrib.data.python.ops import prefetching_ops from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.compat import compat from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.framework import constant_op @@ -30,6 +31,7 @@ from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import test_util +from tensorflow.python.ops import array_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.platform import test @@ -86,8 +88,7 @@ class PrefetchingKernelsOpsTest(test.TestCase): return (prefetch_op, reset_op, destroy_op) def _prefetch_fn_helper_one_shot(self, buffer_name, device0, device1): - worker_config = config_pb2.ConfigProto() - worker_config.device_count["CPU"] = 2 + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) ds, ds_iterator = self._create_ds_and_iterator(device0, initializable=False) prefetch_op, _, destroy_op = self._create_ops(ds, ds_iterator, buffer_name, @@ -126,8 +127,7 @@ class PrefetchingKernelsOpsTest(test.TestCase): "/job:localhost/replica:0/task:0/gpu:0") def testReinitialization(self): - worker_config = config_pb2.ConfigProto() - worker_config.device_count["CPU"] = 2 + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) device0 = "/job:localhost/replica:0/task:0/cpu:0" device1 = "/job:localhost/replica:0/task:0/cpu:1" @@ -167,8 +167,7 @@ class PrefetchingKernelsOpsTest(test.TestCase): sess.run(destroy_op) def testReinitializationOutOfRange(self): - worker_config = config_pb2.ConfigProto() - worker_config.device_count["CPU"] = 2 + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) device0 = "/job:localhost/replica:0/task:0/cpu:0" device1 = "/job:localhost/replica:0/task:0/cpu:1" @@ -271,8 +270,7 @@ class PrefetchToDeviceTest(test.TestCase): self.assertEqual(dtypes.int64, next_element.dtype) self.assertEqual([], next_element.shape) - worker_config = config_pb2.ConfigProto() - worker_config.device_count["CPU"] = 2 + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) with self.test_session(config=worker_config) as sess: for i in range(10): self.assertEqual(i, sess.run(next_element)) @@ -332,8 +330,7 @@ class PrefetchToDeviceTest(test.TestCase): self.assertEqual(dtypes.int64, next_element["a"].dtype) self.assertEqual([], next_element["a"].shape) - worker_config = config_pb2.ConfigProto() - worker_config.device_count["CPU"] = 2 + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) with self.test_session(config=worker_config) as sess: for i in range(10): self.assertEqual({"a": i}, sess.run(next_element)) @@ -366,8 +363,7 @@ class PrefetchToDeviceTest(test.TestCase): next_element = iterator.get_next() self.assertEqual(dtypes.int64, next_element.dtype) - worker_config = config_pb2.ConfigProto() - worker_config.device_count["CPU"] = 2 + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) with self.test_session(config=worker_config) as sess: for i in range(10): actual = sess.run(next_element) @@ -417,8 +413,7 @@ class PrefetchToDeviceTest(test.TestCase): self.assertEqual(dtypes.int64, next_element.dtype) self.assertEqual([], next_element.shape) - worker_config = config_pb2.ConfigProto() - worker_config.device_count["CPU"] = 2 + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) with self.test_session(config=worker_config) as sess: sess.run(iterator.initializer) for i in range(5): @@ -451,5 +446,617 @@ class PrefetchToDeviceTest(test.TestCase): sess.run(next_element) +class CopyToDeviceTest(test.TestCase): + + def testCopyToDevice(self): + host_dataset = dataset_ops.Dataset.range(10) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/cpu:1")) + + with ops.device("/cpu:1"): + iterator = device_dataset.make_one_shot_iterator() + next_element = iterator.get_next() + + self.assertEqual(host_dataset.output_types, device_dataset.output_types) + self.assertEqual(host_dataset.output_types, iterator.output_types) + self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes) + self.assertEqual(host_dataset.output_shapes, iterator.output_shapes) + self.assertEqual(host_dataset.output_classes, device_dataset.output_classes) + self.assertEqual(host_dataset.output_classes, iterator.output_classes) + + self.assertEqual(dtypes.int64, next_element.dtype) + self.assertEqual([], next_element.shape) + + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) + with self.test_session(config=worker_config) as sess: + for i in range(10): + self.assertEqual(i, sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyToDeviceInt32(self): + host_dataset = dataset_ops.Dataset.from_tensors([0, 1, 2, 3]) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/cpu:1")) + + with ops.device("/cpu:1"): + iterator = device_dataset.make_one_shot_iterator() + next_element = iterator.get_next() + + self.assertEqual(host_dataset.output_types, device_dataset.output_types) + self.assertEqual(host_dataset.output_types, iterator.output_types) + self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes) + self.assertEqual(host_dataset.output_shapes, iterator.output_shapes) + self.assertEqual(host_dataset.output_classes, device_dataset.output_classes) + self.assertEqual(host_dataset.output_classes, iterator.output_classes) + + self.assertEqual(dtypes.int32, next_element.dtype) + self.assertEqual((4,), next_element.shape) + + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) + with self.test_session(config=worker_config) as sess: + self.assertAllEqual([0, 1, 2, 3], sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyToSameDevice(self): + host_dataset = dataset_ops.Dataset.range(10) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/cpu:0")) + + with ops.device("/cpu:0"): + iterator = device_dataset.make_one_shot_iterator() + next_element = iterator.get_next() + + self.assertEqual(host_dataset.output_types, device_dataset.output_types) + self.assertEqual(host_dataset.output_types, iterator.output_types) + self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes) + self.assertEqual(host_dataset.output_shapes, iterator.output_shapes) + self.assertEqual(host_dataset.output_classes, device_dataset.output_classes) + self.assertEqual(host_dataset.output_classes, iterator.output_classes) + + self.assertEqual(dtypes.int64, next_element.dtype) + self.assertEqual([], next_element.shape) + + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) + with self.test_session(config=worker_config) as sess: + for i in range(10): + self.assertEqual(i, sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyToDeviceWithPrefetch(self): + host_dataset = dataset_ops.Dataset.range(10) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/cpu:1")).prefetch(1) + + with ops.device("/cpu:1"): + iterator = device_dataset.make_one_shot_iterator() + next_element = iterator.get_next() + + self.assertEqual(host_dataset.output_types, device_dataset.output_types) + self.assertEqual(host_dataset.output_types, iterator.output_types) + self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes) + self.assertEqual(host_dataset.output_shapes, iterator.output_shapes) + self.assertEqual(host_dataset.output_classes, device_dataset.output_classes) + self.assertEqual(host_dataset.output_classes, iterator.output_classes) + + self.assertEqual(dtypes.int64, next_element.dtype) + self.assertEqual([], next_element.shape) + + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) + with self.test_session(config=worker_config) as sess: + for i in range(10): + self.assertEqual(i, sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyDictToDevice(self): + host_dataset = dataset_ops.Dataset.range(10).map(lambda x: {"a": x}) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/cpu:1")) + + with ops.device("/cpu:1"): + iterator = device_dataset.make_one_shot_iterator() + next_element = iterator.get_next() + + self.assertEqual(host_dataset.output_types, device_dataset.output_types) + self.assertEqual(host_dataset.output_types, iterator.output_types) + self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes) + self.assertEqual(host_dataset.output_shapes, iterator.output_shapes) + self.assertEqual(host_dataset.output_classes, device_dataset.output_classes) + self.assertEqual(host_dataset.output_classes, iterator.output_classes) + + self.assertEqual(dtypes.int64, next_element["a"].dtype) + self.assertEqual([], next_element["a"].shape) + + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) + with self.test_session(config=worker_config) as sess: + for i in range(10): + self.assertEqual({"a": i}, sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyDictToDeviceWithPrefetch(self): + host_dataset = dataset_ops.Dataset.range(10).map(lambda x: {"a": x}) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/cpu:1")).prefetch(1) + + with ops.device("/cpu:1"): + iterator = device_dataset.make_one_shot_iterator() + next_element = iterator.get_next() + + self.assertEqual(host_dataset.output_types, device_dataset.output_types) + self.assertEqual(host_dataset.output_types, iterator.output_types) + self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes) + self.assertEqual(host_dataset.output_shapes, iterator.output_shapes) + self.assertEqual(host_dataset.output_classes, device_dataset.output_classes) + self.assertEqual(host_dataset.output_classes, iterator.output_classes) + + self.assertEqual(dtypes.int64, next_element["a"].dtype) + self.assertEqual([], next_element["a"].shape) + + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) + with self.test_session(config=worker_config) as sess: + for i in range(10): + self.assertEqual({"a": i}, sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopySparseTensorsToDevice(self): + + def make_tensor(i): + return sparse_tensor.SparseTensorValue( + indices=[[0, 0]], values=(i * [1]), dense_shape=[2, 2]) + + host_dataset = dataset_ops.Dataset.range(10).map(make_tensor) + + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/cpu:1")) + + with ops.device("/cpu:1"): + iterator = device_dataset.make_one_shot_iterator() + next_element = iterator.get_next() + + self.assertEqual(host_dataset.output_types, device_dataset.output_types) + self.assertEqual(host_dataset.output_types, iterator.output_types) + self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes) + self.assertEqual(host_dataset.output_shapes, iterator.output_shapes) + self.assertEqual(host_dataset.output_classes, device_dataset.output_classes) + self.assertEqual(host_dataset.output_classes, iterator.output_classes) + + self.assertEqual(dtypes.int64, next_element.dtype) + + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) + with self.test_session(config=worker_config) as sess: + for i in range(10): + actual = sess.run(next_element) + self.assertAllEqual([i], actual.values) + self.assertAllEqual([[0, 0]], actual.indices) + self.assertAllEqual([2, 2], actual.dense_shape) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopySparseTensorsToDeviceWithPrefetch(self): + + def make_tensor(i): + return sparse_tensor.SparseTensorValue( + indices=[[0, 0]], values=(i * [1]), dense_shape=[2, 2]) + + host_dataset = dataset_ops.Dataset.range(10).map(make_tensor) + + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/cpu:1")).prefetch(1) + + with ops.device("/cpu:1"): + iterator = device_dataset.make_one_shot_iterator() + next_element = iterator.get_next() + + self.assertEqual(host_dataset.output_types, device_dataset.output_types) + self.assertEqual(host_dataset.output_types, iterator.output_types) + self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes) + self.assertEqual(host_dataset.output_shapes, iterator.output_shapes) + self.assertEqual(host_dataset.output_classes, device_dataset.output_classes) + self.assertEqual(host_dataset.output_classes, iterator.output_classes) + + self.assertEqual(dtypes.int64, next_element.dtype) + + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) + with self.test_session(config=worker_config) as sess: + for i in range(10): + actual = sess.run(next_element) + self.assertAllEqual([i], actual.values) + self.assertAllEqual([[0, 0]], actual.indices) + self.assertAllEqual([2, 2], actual.dense_shape) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyToDeviceGpu(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + host_dataset = dataset_ops.Dataset.range(10) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/gpu:0")) + + with ops.device("/gpu:0"): + iterator = device_dataset.make_initializable_iterator() + next_element = iterator.get_next() + + with self.test_session() as sess: + sess.run(iterator.initializer) + for i in range(10): + self.assertEqual(i, sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyToDeviceGpuWithPrefetch(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + host_dataset = dataset_ops.Dataset.range(10) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/gpu:0")).prefetch(1) + + with ops.device("/gpu:0"): + iterator = device_dataset.make_initializable_iterator() + next_element = iterator.get_next() + + with self.test_session() as sess: + sess.run(iterator.initializer) + for i in range(10): + self.assertEqual(i, sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyToDeviceGpuInt32(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + host_dataset = dataset_ops.Dataset.from_tensors([0, 1, 2, 3]) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/gpu:0")) + + with ops.device("/gpu:0"): + iterator = device_dataset.make_initializable_iterator() + next_element = iterator.get_next() + + with self.test_session() as sess: + sess.run(iterator.initializer) + self.assertAllEqual([0, 1, 2, 3], sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyToDeviceGpuInt32AndPrefetch(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + host_dataset = dataset_ops.Dataset.from_tensors([0, 1, 2, 3]) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/gpu:0")).prefetch(1) + + with ops.device("/gpu:0"): + iterator = device_dataset.make_initializable_iterator() + next_element = iterator.get_next() + + with self.test_session() as sess: + sess.run(iterator.initializer) + self.assertAllEqual([0, 1, 2, 3], sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyToDeviceGpuStrings(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + host_dataset = dataset_ops.Dataset.from_tensors(["a", "b", "c"]) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/gpu:0")) + + with ops.device("/gpu:0"): + iterator = device_dataset.make_initializable_iterator() + next_element = iterator.get_next() + + with self.test_session() as sess: + sess.run(iterator.initializer) + self.assertAllEqual([b"a", b"b", b"c"], sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyToDeviceGpuStringsAndPrefetch(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + host_dataset = dataset_ops.Dataset.from_tensors(["a", "b", "c"]) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/gpu:0")) + + with ops.device("/gpu:0"): + iterator = device_dataset.make_initializable_iterator() + next_element = iterator.get_next() + + with self.test_session() as sess: + sess.run(iterator.initializer) + self.assertAllEqual([b"a", b"b", b"c"], sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyToDevicePingPongCPUGPU(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + with compat.forward_compatibility_horizon(2018, 8, 4): + host_dataset = dataset_ops.Dataset.range(10) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/gpu:0", source_device="/cpu:0")) + back_to_cpu_dataset = device_dataset.apply( + prefetching_ops.copy_to_device("/cpu:0", source_device="/gpu:0")) + + with ops.device("/cpu:0"): + iterator = back_to_cpu_dataset.make_initializable_iterator() + next_element = iterator.get_next() + + with self.test_session() as sess: + sess.run(iterator.initializer) + for i in range(10): + self.assertEqual(i, sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyToDeviceWithReInit(self): + host_dataset = dataset_ops.Dataset.range(10) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/cpu:1")) + + with ops.device("/cpu:1"): + iterator = device_dataset.make_initializable_iterator() + next_element = iterator.get_next() + + self.assertEqual(host_dataset.output_types, device_dataset.output_types) + self.assertEqual(host_dataset.output_types, iterator.output_types) + self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes) + self.assertEqual(host_dataset.output_shapes, iterator.output_shapes) + self.assertEqual(host_dataset.output_classes, device_dataset.output_classes) + self.assertEqual(host_dataset.output_classes, iterator.output_classes) + + self.assertEqual(dtypes.int64, next_element.dtype) + self.assertEqual([], next_element.shape) + + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) + with self.test_session(config=worker_config) as sess: + sess.run(iterator.initializer) + for i in range(5): + self.assertEqual(i, sess.run(next_element)) + sess.run(iterator.initializer) + for i in range(10): + self.assertEqual(i, sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyToDeviceWithReInitAndPrefetch(self): + host_dataset = dataset_ops.Dataset.range(10) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/cpu:1")).prefetch(1) + + with ops.device("/cpu:1"): + iterator = device_dataset.make_initializable_iterator() + next_element = iterator.get_next() + + self.assertEqual(host_dataset.output_types, device_dataset.output_types) + self.assertEqual(host_dataset.output_types, iterator.output_types) + self.assertEqual(host_dataset.output_shapes, device_dataset.output_shapes) + self.assertEqual(host_dataset.output_shapes, iterator.output_shapes) + self.assertEqual(host_dataset.output_classes, device_dataset.output_classes) + self.assertEqual(host_dataset.output_classes, iterator.output_classes) + + self.assertEqual(dtypes.int64, next_element.dtype) + self.assertEqual([], next_element.shape) + + worker_config = config_pb2.ConfigProto(device_count={"CPU": 2}) + with self.test_session(config=worker_config) as sess: + sess.run(iterator.initializer) + for i in range(5): + self.assertEqual(i, sess.run(next_element)) + sess.run(iterator.initializer) + for i in range(10): + self.assertEqual(i, sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyToDeviceGpuWithReInit(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + host_dataset = dataset_ops.Dataset.range(10) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/gpu:0")) + + with ops.device("/gpu:0"): + iterator = device_dataset.make_initializable_iterator() + next_element = iterator.get_next() + + with self.test_session() as sess: + sess.run(iterator.initializer) + for i in range(5): + self.assertEqual(i, sess.run(next_element)) + sess.run(iterator.initializer) + for i in range(10): + self.assertEqual(i, sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + def testCopyToDeviceGpuWithReInitAndPrefetch(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + host_dataset = dataset_ops.Dataset.range(10) + device_dataset = host_dataset.apply( + prefetching_ops.copy_to_device("/gpu:0")).prefetch(1) + + with ops.device("/gpu:0"): + iterator = device_dataset.make_initializable_iterator() + next_element = iterator.get_next() + + with self.test_session() as sess: + sess.run(iterator.initializer) + for i in range(5): + self.assertEqual(i, sess.run(next_element)) + sess.run(iterator.initializer) + for i in range(10): + self.assertEqual(i, sess.run(next_element)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(next_element) + + +class MultiDeviceIteratorTest(test.TestCase): + + def testBasic(self): + dataset = dataset_ops.Dataset.range(10) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:1", "/cpu:2"]) + elem_on_1, elem_on_2 = multi_device_iterator.get_next() + + config = config_pb2.ConfigProto(device_count={"CPU": 3}) + with self.test_session(config=config) as sess: + sess.run(multi_device_iterator.initializer) + for i in range(0, 10, 2): + self.assertEqual(i, sess.run(elem_on_1)) + self.assertEqual(i + 1, sess.run(elem_on_2)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(elem_on_1) + sess.run(elem_on_2) + + def testOneOnSameDevice(self): + with ops.device("/cpu:0"): + dataset = dataset_ops.Dataset.range(10) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:0", "/cpu:1"]) + elem_on_1, elem_on_2 = multi_device_iterator.get_next() + + config = config_pb2.ConfigProto(device_count={"CPU": 2}) + with self.test_session(config=config) as sess: + sess.run(multi_device_iterator.initializer) + for i in range(0, 10, 2): + self.assertEqual(i, sess.run(elem_on_1)) + self.assertEqual(i + 1, sess.run(elem_on_2)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(elem_on_1) + sess.run(elem_on_2) + + def testRepeatDevices(self): + with ops.device("/cpu:0"): + dataset = dataset_ops.Dataset.range(20) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:1", "/cpu:2", "/cpu:1", "/cpu:2"]) + elements = multi_device_iterator.get_next() + elem_on_1, elem_on_2, elem_on_3, elem_on_4 = elements + + config = config_pb2.ConfigProto(device_count={"CPU": 3}) + with self.test_session(config=config) as sess: + sess.run(multi_device_iterator.initializer) + for i in range(0, 20, 4): + self.assertEqual(i, sess.run(elem_on_1)) + self.assertEqual(i + 1, sess.run(elem_on_2)) + self.assertEqual(i + 2, sess.run(elem_on_3)) + self.assertEqual(i + 3, sess.run(elem_on_4)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(elem_on_1) + sess.run(elem_on_2) + sess.run(elem_on_3) + sess.run(elem_on_4) + + def testNotFullyDivisible(self): + dataset = dataset_ops.Dataset.range(9) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:1", "/cpu:2"]) + elem_on_1, elem_on_2 = multi_device_iterator.get_next() + + config = config_pb2.ConfigProto(device_count={"CPU": 3}) + with self.test_session(config=config) as sess: + sess.run(multi_device_iterator.initializer) + for i in range(0, 8, 2): + self.assertEqual(i, sess.run(elem_on_1)) + self.assertEqual(i + 1, sess.run(elem_on_2)) + self.assertEqual(8, sess.run(elem_on_1)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(elem_on_1) + sess.run(elem_on_2) + + def testUneven(self): + dataset = dataset_ops.Dataset.range(10) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:1", "/cpu:2"]) + elem_on_1, elem_on_2 = multi_device_iterator.get_next() + + config = config_pb2.ConfigProto(device_count={"CPU": 3}) + with self.test_session(config=config) as sess: + sess.run(multi_device_iterator.initializer) + for i in range(0, 10, 2): + self.assertEqual(i, sess.run(elem_on_1)) + for i in range(0, 10, 2): + self.assertEqual(i + 1, sess.run(elem_on_2)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(elem_on_1) + sess.run(elem_on_2) + + def testMultipleInitializations(self): + with ops.device("/cpu:0"): + epoch = array_ops.placeholder(dtypes.int64, shape=[]) + dataset1 = dataset_ops.Dataset.from_tensors(epoch).repeat(1000) + dataset2 = dataset_ops.Dataset.range(1000) + dataset = dataset_ops.Dataset.zip((dataset1, dataset2)) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:1", "/cpu:2"], prefetch_buffer_size=4) + elem_on_1, elem_on_2 = multi_device_iterator.get_next() + init_op = multi_device_iterator.initializer + + config = config_pb2.ConfigProto(device_count={"CPU": 3}) + with self.test_session(config=config) as sess: + for i in range(1000): + sess.run(init_op, feed_dict={epoch: i}) + self.assertEqual([(i, 0), (i, 1)], sess.run([elem_on_1, elem_on_2])) + + def testBasicGpu(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + with compat.forward_compatibility_horizon(2018, 8, 4): + dataset = dataset_ops.Dataset.range(10) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:1", "/gpu:0"]) + elem_on_1, elem_on_2 = multi_device_iterator.get_next() + + config = config_pb2.ConfigProto(device_count={"CPU": 2, "GPU": 1}) + with self.test_session(config=config) as sess: + sess.run(multi_device_iterator.initializer) + for i in range(0, 10, 2): + self.assertEqual(i, sess.run(elem_on_1)) + self.assertEqual(i + 1, sess.run(elem_on_2)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(elem_on_1) + sess.run(elem_on_2) + + def testUnevenGpu(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + with compat.forward_compatibility_horizon(2018, 8, 4): + dataset = dataset_ops.Dataset.range(10) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:1", "/gpu:0"]) + elem_on_1, elem_on_2 = multi_device_iterator.get_next() + + config = config_pb2.ConfigProto(device_count={"CPU": 2, "GPU": 1}) + with self.test_session(config=config) as sess: + sess.run(multi_device_iterator.initializer) + for i in range(0, 10, 2): + self.assertEqual(i, sess.run(elem_on_1)) + for i in range(0, 10, 2): + self.assertEqual(i + 1, sess.run(elem_on_2)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(elem_on_1) + sess.run(elem_on_2) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py index 9df403ef50e459d94b8edf3f651c7c95baf3ec42..851a33dfc849a2d935887def44734aace5dcaf7f 100644 --- a/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/reader_dataset_ops_test.py @@ -17,13 +17,16 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import gzip import os +import zlib import numpy as np from tensorflow.contrib.data.python.kernel_tests import reader_dataset_ops_test_base from tensorflow.contrib.data.python.ops import readers from tensorflow.python.data.ops import readers as core_readers +from tensorflow.python.data.util import nest from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors @@ -182,264 +185,363 @@ class ReadBatchFeaturesTest( class MakeCsvDatasetTest(test.TestCase): - COLUMN_TYPES = [ - dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64, dtypes.string - ] - COLUMNS = ["col%d" % i for i in range(len(COLUMN_TYPES))] - DEFAULT_VALS = [[], [], [], [], ["NULL"]] - DEFAULTS = [ - constant_op.constant([], dtype=dtypes.int32), - constant_op.constant([], dtype=dtypes.int64), - constant_op.constant([], dtype=dtypes.float32), - constant_op.constant([], dtype=dtypes.float64), - constant_op.constant(["NULL"], dtype=dtypes.string) - ] - LABEL = COLUMNS[0] - - def setUp(self): - super(MakeCsvDatasetTest, self).setUp() - self._num_files = 2 - self._num_records = 11 - self._test_filenames = self._create_files() - - def _csv_values(self, fileno, recordno): - return [ - fileno, - recordno, - fileno * recordno * 0.5, - fileno * recordno + 0.5, - "record %d" % recordno if recordno % 2 == 1 else "", - ] + def _make_csv_dataset(self, filenames, batch_size, num_epochs=1, **kwargs): + return readers.make_csv_dataset( + filenames, batch_size=batch_size, num_epochs=num_epochs, **kwargs) - def _write_file(self, filename, rows): - for i in range(len(rows)): - if isinstance(rows[i], list): - rows[i] = ",".join(str(v) if v is not None else "" for v in rows[i]) - fn = os.path.join(self.get_temp_dir(), filename) - f = open(fn, "w") - f.write("\n".join(rows)) - f.close() - return fn - - def _create_file(self, fileno, header=True): - rows = [] - if header: - rows.append(self.COLUMNS) - for recno in range(self._num_records): - rows.append(self._csv_values(fileno, recno)) - return self._write_file("csv_file%d.csv" % fileno, rows) - - def _create_files(self): + def _setup_files(self, inputs, linebreak="\n", compression_type=None): filenames = [] - for i in range(self._num_files): - filenames.append(self._create_file(i)) + for i, ip in enumerate(inputs): + fn = os.path.join(self.get_temp_dir(), "temp_%d.csv" % i) + contents = linebreak.join(ip).encode("utf-8") + if compression_type is None: + with open(fn, "wb") as f: + f.write(contents) + elif compression_type == "GZIP": + with gzip.GzipFile(fn, "wb") as f: + f.write(contents) + elif compression_type == "ZLIB": + contents = zlib.compress(contents) + with open(fn, "wb") as f: + f.write(contents) + else: + raise ValueError("Unsupported compression_type", compression_type) + filenames.append(fn) return filenames - def _make_csv_dataset( - self, - filenames, - defaults, - column_names=COLUMNS, - label_name=LABEL, - select_cols=None, - batch_size=1, - num_epochs=1, - shuffle=False, - shuffle_seed=None, - header=True, - na_value="", - ): - return readers.make_csv_dataset( - filenames, - batch_size=batch_size, - column_names=column_names, - column_defaults=defaults, - label_name=label_name, - num_epochs=num_epochs, - shuffle=shuffle, - shuffle_seed=shuffle_seed, - header=header, - na_value=na_value, - select_columns=select_cols, - ) - - def _next_actual_batch(self, file_indices, batch_size, num_epochs, defaults): - features = {col: list() for col in self.COLUMNS} + def _next_expected_batch(self, expected_output, expected_keys, batch_size, + num_epochs): + features = {k: [] for k in expected_keys} for _ in range(num_epochs): - for i in file_indices: - for j in range(self._num_records): - values = self._csv_values(i, j) - for n, v in enumerate(values): - if v == "": # pylint: disable=g-explicit-bool-comparison - values[n] = defaults[n][0] - values[-1] = values[-1].encode("utf-8") - - # Regroup lists by column instead of row - for n, col in enumerate(self.COLUMNS): - features[col].append(values[n]) - if len(list(features.values())[0]) == batch_size: - yield features - features = {col: list() for col in self.COLUMNS} - - def _run_actual_batch(self, outputs, sess): - features, labels = sess.run(outputs) - batch = [features[k] for k in self.COLUMNS if k != self.LABEL] - batch.append(labels) - return batch - - def _verify_records( + for values in expected_output: + for n, key in enumerate(expected_keys): + features[key].append(values[n]) + if len(features[expected_keys[0]]) == batch_size: + yield features + features = {k: [] for k in expected_keys} + if features[expected_keys[0]]: # Leftover from the last batch + yield features + + def _verify_output( self, sess, dataset, - file_indices, - defaults=tuple(DEFAULT_VALS), - label_name=LABEL, - batch_size=1, - num_epochs=1, + batch_size, + num_epochs, + label_name, + expected_output, + expected_keys, ): - iterator = dataset.make_one_shot_iterator() - get_next = iterator.get_next() + nxt = dataset.make_one_shot_iterator().get_next() - for expected_features in self._next_actual_batch(file_indices, batch_size, - num_epochs, defaults): - actual_features = sess.run(get_next) + for expected_features in self._next_expected_batch( + expected_output, + expected_keys, + batch_size, + num_epochs, + ): + actual_features = sess.run(nxt) if label_name is not None: expected_labels = expected_features.pop(label_name) - # Compare labels self.assertAllEqual(expected_labels, actual_features[1]) - actual_features = actual_features[0] # Extract features dict from tuple + actual_features = actual_features[0] for k in expected_features.keys(): # Compare features self.assertAllEqual(expected_features[k], actual_features[k]) with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - - def testMakeCSVDataset(self): - defaults = self.DEFAULTS - - with ops.Graph().as_default() as g: - with self.test_session(graph=g) as sess: - # Basic test: read from file 0. - dataset = self._make_csv_dataset(self._test_filenames[0], defaults) - self._verify_records(sess, dataset, [0]) - with ops.Graph().as_default() as g: - with self.test_session(graph=g) as sess: - # Basic test: read from file 1. - dataset = self._make_csv_dataset(self._test_filenames[1], defaults) - self._verify_records(sess, dataset, [1]) + sess.run(nxt) + + def _test_dataset(self, + inputs, + expected_output, + expected_keys, + batch_size=1, + num_epochs=1, + label_name=None, + **kwargs): + """Checks that elements produced by CsvDataset match expected output.""" + # Convert str type because py3 tf strings are bytestrings + filenames = self._setup_files( + inputs, compression_type=kwargs.get("compression_type", None)) with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: - # Read from both files. - dataset = self._make_csv_dataset(self._test_filenames, defaults) - self._verify_records(sess, dataset, range(self._num_files)) - with ops.Graph().as_default() as g: - with self.test_session(graph=g) as sess: - # Read from both files. Exercise the `batch` and `num_epochs` parameters - # of make_csv_dataset and make sure they work. dataset = self._make_csv_dataset( - self._test_filenames, defaults, batch_size=2, num_epochs=10) - self._verify_records( - sess, dataset, range(self._num_files), batch_size=2, num_epochs=10) + filenames, + batch_size=batch_size, + num_epochs=num_epochs, + label_name=label_name, + **kwargs) + self._verify_output(sess, dataset, batch_size, num_epochs, label_name, + expected_output, expected_keys) + + def testMakeCSVDataset(self): + """Tests making a CSV dataset with keys and defaults provided.""" + record_defaults = [ + constant_op.constant([], dtypes.int32), + constant_op.constant([], dtypes.int64), + constant_op.constant([], dtypes.float32), + constant_op.constant([], dtypes.float64), + constant_op.constant([], dtypes.string) + ] + + column_names = ["col%d" % i for i in range(5)] + inputs = [[",".join(x for x in column_names), "0,1,2,3,4", "5,6,7,8,9"], [ + ",".join(x for x in column_names), "10,11,12,13,14", "15,16,17,18,19" + ]] + expected_output = [[0, 1, 2, 3, b"4"], [5, 6, 7, 8, b"9"], + [10, 11, 12, 13, b"14"], [15, 16, 17, 18, b"19"]] + label = "col0" + + self._test_dataset( + inputs, + expected_output=expected_output, + expected_keys=column_names, + column_names=column_names, + label_name=label, + batch_size=1, + num_epochs=1, + shuffle=False, + header=True, + column_defaults=record_defaults, + ) + + def testMakeCSVDataset_withBatchSizeAndEpochs(self): + """Tests making a CSV dataset with keys and defaults provided.""" + record_defaults = [ + constant_op.constant([], dtypes.int32), + constant_op.constant([], dtypes.int64), + constant_op.constant([], dtypes.float32), + constant_op.constant([], dtypes.float64), + constant_op.constant([], dtypes.string) + ] + + column_names = ["col%d" % i for i in range(5)] + inputs = [[",".join(x for x in column_names), "0,1,2,3,4", "5,6,7,8,9"], [ + ",".join(x for x in column_names), "10,11,12,13,14", "15,16,17,18,19" + ]] + expected_output = [[0, 1, 2, 3, b"4"], [5, 6, 7, 8, b"9"], + [10, 11, 12, 13, b"14"], [15, 16, 17, 18, b"19"]] + label = "col0" + + self._test_dataset( + inputs, + expected_output=expected_output, + expected_keys=column_names, + column_names=column_names, + label_name=label, + batch_size=3, + num_epochs=10, + shuffle=False, + header=True, + column_defaults=record_defaults, + ) - def testMakeCSVDataset_withBadColumns(self): + def testMakeCSVDataset_withCompressionType(self): + """Tests `compression_type` argument.""" + record_defaults = [ + constant_op.constant([], dtypes.int32), + constant_op.constant([], dtypes.int64), + constant_op.constant([], dtypes.float32), + constant_op.constant([], dtypes.float64), + constant_op.constant([], dtypes.string) + ] + + column_names = ["col%d" % i for i in range(5)] + inputs = [[",".join(x for x in column_names), "0,1,2,3,4", "5,6,7,8,9"], [ + ",".join(x for x in column_names), "10,11,12,13,14", "15,16,17,18,19" + ]] + expected_output = [[0, 1, 2, 3, b"4"], [5, 6, 7, 8, b"9"], + [10, 11, 12, 13, b"14"], [15, 16, 17, 18, b"19"]] + label = "col0" + + for compression_type in ("GZIP", "ZLIB"): + self._test_dataset( + inputs, + expected_output=expected_output, + expected_keys=column_names, + column_names=column_names, + label_name=label, + batch_size=1, + num_epochs=1, + shuffle=False, + header=True, + column_defaults=record_defaults, + compression_type=compression_type, + ) + + def testMakeCSVDataset_withBadInputs(self): """Tests that exception is raised when input is malformed. """ - dupe_columns = self.COLUMNS[:-1] + self.COLUMNS[:1] - defaults = self.DEFAULTS + record_defaults = [ + constant_op.constant([], dtypes.int32), + constant_op.constant([], dtypes.int64), + constant_op.constant([], dtypes.float32), + constant_op.constant([], dtypes.float64), + constant_op.constant([], dtypes.string) + ] + + column_names = ["col%d" % i for i in range(5)] + inputs = [[",".join(x for x in column_names), "0,1,2,3,4", "5,6,7,8,9"], [ + ",".join(x for x in column_names), "10,11,12,13,14", "15,16,17,18,19" + ]] + filenames = self._setup_files(inputs) # Duplicate column names with self.assertRaises(ValueError): self._make_csv_dataset( - self._test_filenames, defaults, column_names=dupe_columns) + filenames, + batch_size=1, + column_defaults=record_defaults, + label_name="col0", + column_names=column_names * 2) # Label key not one of column names with self.assertRaises(ValueError): self._make_csv_dataset( - self._test_filenames, defaults, label_name="not_a_real_label") + filenames, + batch_size=1, + column_defaults=record_defaults, + label_name="not_a_real_label", + column_names=column_names) def testMakeCSVDataset_withNoLabel(self): - """Tests that CSV datasets can be created when no label is specified. - """ - defaults = self.DEFAULTS - with ops.Graph().as_default() as g: - with self.test_session(graph=g) as sess: - # Read from both files. Make sure this works with no label key supplied. - dataset = self._make_csv_dataset( - self._test_filenames, - defaults, - batch_size=2, - num_epochs=10, - label_name=None) - self._verify_records( - sess, - dataset, - range(self._num_files), - batch_size=2, - num_epochs=10, - label_name=None) + """Tests making a CSV dataset with no label provided.""" + record_defaults = [ + constant_op.constant([], dtypes.int32), + constant_op.constant([], dtypes.int64), + constant_op.constant([], dtypes.float32), + constant_op.constant([], dtypes.float64), + constant_op.constant([], dtypes.string) + ] + + column_names = ["col%d" % i for i in range(5)] + inputs = [[",".join(x for x in column_names), "0,1,2,3,4", "5,6,7,8,9"], [ + ",".join(x for x in column_names), "10,11,12,13,14", "15,16,17,18,19" + ]] + expected_output = [[0, 1, 2, 3, b"4"], [5, 6, 7, 8, b"9"], + [10, 11, 12, 13, b"14"], [15, 16, 17, 18, b"19"]] + + self._test_dataset( + inputs, + expected_output=expected_output, + expected_keys=column_names, + column_names=column_names, + batch_size=1, + num_epochs=1, + shuffle=False, + header=True, + column_defaults=record_defaults, + ) def testMakeCSVDataset_withNoHeader(self): """Tests that datasets can be created from CSV files with no header line. """ - defaults = self.DEFAULTS - file_without_header = self._create_file( - len(self._test_filenames), header=False) - with ops.Graph().as_default() as g: - with self.test_session(graph=g) as sess: - dataset = self._make_csv_dataset( - file_without_header, - defaults, - batch_size=2, - num_epochs=10, - header=False, - ) - self._verify_records( - sess, - dataset, - [len(self._test_filenames)], - batch_size=2, - num_epochs=10, - ) + record_defaults = [ + constant_op.constant([], dtypes.int32), + constant_op.constant([], dtypes.int64), + constant_op.constant([], dtypes.float32), + constant_op.constant([], dtypes.float64), + constant_op.constant([], dtypes.string) + ] + + column_names = ["col%d" % i for i in range(5)] + inputs = [["0,1,2,3,4", "5,6,7,8,9"], ["10,11,12,13,14", "15,16,17,18,19"]] + expected_output = [[0, 1, 2, 3, b"4"], [5, 6, 7, 8, b"9"], + [10, 11, 12, 13, b"14"], [15, 16, 17, 18, b"19"]] + label = "col0" + + self._test_dataset( + inputs, + expected_output=expected_output, + expected_keys=column_names, + column_names=column_names, + label_name=label, + batch_size=1, + num_epochs=1, + shuffle=False, + header=False, + column_defaults=record_defaults, + ) def testMakeCSVDataset_withTypes(self): """Tests that defaults can be a dtype instead of a Tensor for required vals. """ - defaults = [d for d in self.COLUMN_TYPES[:-1]] - defaults.append(constant_op.constant(["NULL"], dtype=dtypes.string)) - with ops.Graph().as_default() as g: - with self.test_session(graph=g) as sess: - dataset = self._make_csv_dataset(self._test_filenames, defaults) - self._verify_records(sess, dataset, range(self._num_files)) + record_defaults = [ + dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64, + dtypes.string + ] + + column_names = ["col%d" % i for i in range(5)] + inputs = [[",".join(x[0] for x in column_names), "0,1,2,3,4", "5,6,7,8,9"], + [ + ",".join(x[0] for x in column_names), "10,11,12,13,14", + "15,16,17,18,19" + ]] + expected_output = [[0, 1, 2, 3, b"4"], [5, 6, 7, 8, b"9"], + [10, 11, 12, 13, b"14"], [15, 16, 17, 18, b"19"]] + label = "col0" + + self._test_dataset( + inputs, + expected_output=expected_output, + expected_keys=column_names, + column_names=column_names, + label_name=label, + batch_size=1, + num_epochs=1, + shuffle=False, + header=True, + column_defaults=record_defaults, + ) def testMakeCSVDataset_withNoColNames(self): """Tests that datasets can be created when column names are not specified. In that case, we should infer the column names from the header lines. """ - defaults = self.DEFAULTS - with ops.Graph().as_default() as g: - with self.test_session(graph=g) as sess: - # Read from both files. Exercise the `batch` and `num_epochs` parameters - # of make_csv_dataset and make sure they work. - dataset = self._make_csv_dataset( - self._test_filenames, - defaults, - column_names=None, - batch_size=2, - num_epochs=10) - self._verify_records( - sess, dataset, range(self._num_files), batch_size=2, num_epochs=10) + record_defaults = [ + constant_op.constant([], dtypes.int32), + constant_op.constant([], dtypes.int64), + constant_op.constant([], dtypes.float32), + constant_op.constant([], dtypes.float64), + constant_op.constant([], dtypes.string) + ] + + column_names = ["col%d" % i for i in range(5)] + inputs = [[",".join(x for x in column_names), "0,1,2,3,4", "5,6,7,8,9"], [ + ",".join(x for x in column_names), "10,11,12,13,14", "15,16,17,18,19" + ]] + expected_output = [[0, 1, 2, 3, b"4"], [5, 6, 7, 8, b"9"], + [10, 11, 12, 13, b"14"], [15, 16, 17, 18, b"19"]] + label = "col0" + + self._test_dataset( + inputs, + expected_output=expected_output, + expected_keys=column_names, + label_name=label, + batch_size=1, + num_epochs=1, + shuffle=False, + header=True, + column_defaults=record_defaults, + ) def testMakeCSVDataset_withTypeInferenceMismatch(self): # Test that error is thrown when num fields doesn't match columns + column_names = ["col%d" % i for i in range(5)] + inputs = [[",".join(x for x in column_names), "0,1,2,3,4", "5,6,7,8,9"], [ + ",".join(x for x in column_names), "10,11,12,13,14", "15,16,17,18,19" + ]] + filenames = self._setup_files(inputs) with self.assertRaises(ValueError): self._make_csv_dataset( - self._test_filenames, - column_names=self.COLUMNS + ["extra_name"], - defaults=None, + filenames, + column_names=column_names + ["extra_name"], + column_defaults=None, batch_size=2, num_epochs=10) @@ -448,197 +550,215 @@ class MakeCsvDatasetTest(test.TestCase): In that case, we should infer the types from the first N records. """ - # Test that it works with standard test files (with header, etc) - with ops.Graph().as_default() as g: - with self.test_session(graph=g) as sess: - dataset = self._make_csv_dataset( - self._test_filenames, defaults=None, batch_size=2, num_epochs=10) - self._verify_records( - sess, - dataset, - range(self._num_files), - batch_size=2, - num_epochs=10, - defaults=[[], [], [], [], [""]]) - - def testMakeCSVDataset_withTypeInferenceTricky(self): - # Test on a deliberately tricky file (type changes as we read more rows, and - # there are null values) - fn = os.path.join(self.get_temp_dir(), "file.csv") - expected_dtypes = [ - dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float32, - dtypes.string, dtypes.string - ] - col_names = ["col%d" % i for i in range(len(expected_dtypes))] - rows = [[None, None, None, "NAN", "", - "a"], [1, 2**31 + 1, 2**64, 123, "NAN", ""], - ['"123"', 2, 2**64, 123.4, "NAN", '"cd,efg"']] - expected = [[0, 0, 0, 0, "", "a"], [1, 2**31 + 1, 2**64, 123, "", ""], - [123, 2, 2**64, 123.4, "", "cd,efg"]] - for row in expected: - row[-1] = row[-1].encode("utf-8") # py3 expects byte strings - row[-2] = row[-2].encode("utf-8") # py3 expects byte strings - self._write_file("file.csv", [col_names] + rows) + column_names = ["col%d" % i for i in range(5)] + str_int32_max = str(2**33) + inputs = [[ + ",".join(x for x in column_names), + "0,%s,2.0,3e50,rabbit" % str_int32_max + ]] + expected_output = [[0, 2**33, 2.0, 3e50, b"rabbit"]] + label = "col0" - with ops.Graph().as_default() as g: - with self.test_session(graph=g) as sess: - dataset = self._make_csv_dataset( - fn, - defaults=None, - column_names=None, - label_name=None, - na_value="NAN", - ) - features = dataset.make_one_shot_iterator().get_next() - # Check that types match - for i in range(len(expected_dtypes)): - print(features["col%d" % i].dtype, expected_dtypes[i]) - assert features["col%d" % i].dtype == expected_dtypes[i] - for i in range(len(rows)): - assert sess.run(features) == dict(zip(col_names, expected[i])) - - def testMakeCSVDataset_withTypeInferenceAllTypes(self): - # Test that we make the correct inference for all types with fallthrough - fn = os.path.join(self.get_temp_dir(), "file.csv") - expected_dtypes = [ - dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64, - dtypes.string, dtypes.string + self._test_dataset( + inputs, + expected_output=expected_output, + expected_keys=column_names, + column_names=column_names, + label_name=label, + batch_size=1, + num_epochs=1, + shuffle=False, + header=True, + ) + + def testMakeCSVDataset_withTypeInferenceFallthrough(self): + """Tests that datasets can be created when no defaults are specified. + + Tests on a deliberately tricky file. + """ + column_names = ["col%d" % i for i in range(5)] + str_int32_max = str(2**33) + inputs = [[ + ",".join(x for x in column_names), + ",,,,", + "0,0,0.0,0.0,0.0", + "0,%s,2.0,3e50,rabbit" % str_int32_max, + ",,,,", + ]] + expected_output = [[0, 0, 0, 0, b""], [0, 0, 0, 0, b"0.0"], + [0, 2**33, 2.0, 3e50, b"rabbit"], [0, 0, 0, 0, b""]] + label = "col0" + + self._test_dataset( + inputs, + expected_output=expected_output, + expected_keys=column_names, + column_names=column_names, + label_name=label, + batch_size=1, + num_epochs=1, + shuffle=False, + header=True, + ) + + def testMakeCSVDataset_withSelectCols(self): + record_defaults = [ + constant_op.constant([], dtypes.int32), + constant_op.constant([], dtypes.int64), + constant_op.constant([], dtypes.float32), + constant_op.constant([], dtypes.float64), + constant_op.constant([], dtypes.string) ] - col_names = ["col%d" % i for i in range(len(expected_dtypes))] - rows = [[1, 2**31 + 1, 1.0, 4e40, "abc", ""]] - expected = [[ - 1, 2**31 + 1, 1.0, 4e40, "abc".encode("utf-8"), "".encode("utf-8") + column_names = ["col%d" % i for i in range(5)] + str_int32_max = str(2**33) + inputs = [[ + ",".join(x for x in column_names), + "0,%s,2.0,3e50,rabbit" % str_int32_max ]] - self._write_file("file.csv", [col_names] + rows) + expected_output = [[0, 2**33, 2.0, 3e50, b"rabbit"]] - with ops.Graph().as_default() as g: - with self.test_session(graph=g) as sess: - dataset = self._make_csv_dataset( - fn, - defaults=None, - column_names=None, - label_name=None, - na_value="NAN", - ) - features = dataset.make_one_shot_iterator().get_next() - # Check that types match - for i in range(len(expected_dtypes)): - self.assertAllEqual(features["col%d" % i].dtype, expected_dtypes[i]) - for i in range(len(rows)): - self.assertAllEqual( - sess.run(features), dict(zip(col_names, expected[i]))) + select_cols = [1, 3, 4] + self._test_dataset( + inputs, + expected_output=[[x[i] for i in select_cols] for x in expected_output], + expected_keys=[column_names[i] for i in select_cols], + column_names=column_names, + column_defaults=[record_defaults[i] for i in select_cols], + batch_size=1, + num_epochs=1, + shuffle=False, + header=True, + select_columns=select_cols, + ) + + # Can still do inference without provided defaults + self._test_dataset( + inputs, + expected_output=[[x[i] for i in select_cols] for x in expected_output], + expected_keys=[column_names[i] for i in select_cols], + column_names=column_names, + batch_size=1, + num_epochs=1, + shuffle=False, + header=True, + select_columns=select_cols, + ) + + # Can still do column name inference + self._test_dataset( + inputs, + expected_output=[[x[i] for i in select_cols] for x in expected_output], + expected_keys=[column_names[i] for i in select_cols], + batch_size=1, + num_epochs=1, + shuffle=False, + header=True, + select_columns=select_cols, + ) + + # Can specify column names instead of indices + self._test_dataset( + inputs, + expected_output=[[x[i] for i in select_cols] for x in expected_output], + expected_keys=[column_names[i] for i in select_cols], + column_names=column_names, + batch_size=1, + num_epochs=1, + shuffle=False, + header=True, + select_columns=[column_names[i] for i in select_cols], + ) def testMakeCSVDataset_withSelectColsError(self): - data = [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] - col_names = ["col%d" % i for i in range(5)] - fn = self._write_file("file.csv", [col_names] + data) + record_defaults = [ + constant_op.constant([], dtypes.int32), + constant_op.constant([], dtypes.int64), + constant_op.constant([], dtypes.float32), + constant_op.constant([], dtypes.float64), + constant_op.constant([], dtypes.string) + ] + column_names = ["col%d" % i for i in range(5)] + str_int32_max = str(2**33) + inputs = [[ + ",".join(x for x in column_names), + "0,%s,2.0,3e50,rabbit" % str_int32_max + ]] + + select_cols = [1, 3, 4] + filenames = self._setup_files(inputs) + with self.assertRaises(ValueError): # Mismatch in number of defaults and number of columns selected, # should raise an error self._make_csv_dataset( - fn, - defaults=[[0]] * 5, - column_names=col_names, - label_name=None, - select_cols=[1, 3]) + filenames, + batch_size=1, + column_defaults=record_defaults, + column_names=column_names, + select_columns=select_cols) + with self.assertRaises(ValueError): # Invalid column name should raise an error self._make_csv_dataset( - fn, - defaults=[[0]], - column_names=col_names, + filenames, + batch_size=1, + column_defaults=[[0]], + column_names=column_names, label_name=None, - select_cols=["invalid_col_name"]) - - def testMakeCSVDataset_withSelectCols(self): - data = [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] - col_names = ["col%d" % i for i in range(5)] - fn = self._write_file("file.csv", [col_names] + data) - # If select_cols is specified, should only yield a subset of columns - with ops.Graph().as_default() as g: - with self.test_session(graph=g) as sess: - dataset = self._make_csv_dataset( - fn, - defaults=[[0], [0]], - column_names=col_names, - label_name=None, - select_cols=[1, 3]) - expected = [[1, 3], [6, 8]] - features = dataset.make_one_shot_iterator().get_next() - for i in range(len(data)): - self.assertAllEqual( - sess.run(features), - dict(zip([col_names[1], col_names[3]], expected[i]))) - # Can still do default inference with select_cols - with ops.Graph().as_default() as g: - with self.test_session(graph=g) as sess: - dataset = self._make_csv_dataset( - fn, - defaults=None, - column_names=col_names, - label_name=None, - select_cols=[1, 3]) - expected = [[1, 3], [6, 8]] - features = dataset.make_one_shot_iterator().get_next() - for i in range(len(data)): - self.assertAllEqual( - sess.run(features), - dict(zip([col_names[1], col_names[3]], expected[i]))) - # Can still do column name inference - with ops.Graph().as_default() as g: - with self.test_session(graph=g) as sess: - dataset = self._make_csv_dataset( - fn, - defaults=None, - column_names=None, - label_name=None, - select_cols=[1, 3]) - expected = [[1, 3], [6, 8]] - features = dataset.make_one_shot_iterator().get_next() - for i in range(len(data)): - self.assertAllEqual( - sess.run(features), - dict(zip([col_names[1], col_names[3]], expected[i]))) - # Can specify column names instead of indices - with ops.Graph().as_default() as g: - with self.test_session(graph=g) as sess: - dataset = self._make_csv_dataset( - fn, - defaults=None, - column_names=None, - label_name=None, - select_cols=[col_names[1], col_names[3]]) - expected = [[1, 3], [6, 8]] - features = dataset.make_one_shot_iterator().get_next() - for i in range(len(data)): - self.assertAllEqual( - sess.run(features), - dict(zip([col_names[1], col_names[3]], expected[i]))) + select_columns=["invalid_col_name"]) def testMakeCSVDataset_withShuffle(self): - total_records = self._num_files * self._num_records - defaults = self.DEFAULTS + record_defaults = [ + constant_op.constant([], dtypes.int32), + constant_op.constant([], dtypes.int64), + constant_op.constant([], dtypes.float32), + constant_op.constant([], dtypes.float64), + constant_op.constant([], dtypes.string) + ] + + def str_series(st): + return ",".join(str(i) for i in range(st, st + 5)) + + column_names = ["col%d" % i for i in range(5)] + inputs = [ + [",".join(x for x in column_names) + ] + [str_series(5 * i) for i in range(15)], + [",".join(x for x in column_names)] + + [str_series(5 * i) for i in range(15, 20)], + ] + + filenames = self._setup_files(inputs) + + total_records = 20 for batch_size in [1, 2]: with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: # Test that shuffling with the same seed produces the same result dataset1 = self._make_csv_dataset( - self._test_filenames, - defaults, + filenames, + column_defaults=record_defaults, + column_names=column_names, batch_size=batch_size, + header=True, shuffle=True, - shuffle_seed=5) + shuffle_seed=5, + num_epochs=2, + ) dataset2 = self._make_csv_dataset( - self._test_filenames, - defaults, + filenames, + column_defaults=record_defaults, + column_names=column_names, batch_size=batch_size, + header=True, shuffle=True, - shuffle_seed=5) + shuffle_seed=5, + num_epochs=2, + ) outputs1 = dataset1.make_one_shot_iterator().get_next() outputs2 = dataset2.make_one_shot_iterator().get_next() for _ in range(total_records // batch_size): - batch1 = self._run_actual_batch(outputs1, sess) - batch2 = self._run_actual_batch(outputs2, sess) + batch1 = nest.flatten(sess.run(outputs1)) + batch2 = nest.flatten(sess.run(outputs2)) for i in range(len(batch1)): self.assertAllEqual(batch1[i], batch2[i]) @@ -646,23 +766,31 @@ class MakeCsvDatasetTest(test.TestCase): with self.test_session(graph=g) as sess: # Test that shuffling with a different seed produces different results dataset1 = self._make_csv_dataset( - self._test_filenames, - defaults, + filenames, + column_defaults=record_defaults, + column_names=column_names, batch_size=batch_size, + header=True, shuffle=True, - shuffle_seed=5) + shuffle_seed=5, + num_epochs=2, + ) dataset2 = self._make_csv_dataset( - self._test_filenames, - defaults, + filenames, + column_defaults=record_defaults, + column_names=column_names, batch_size=batch_size, + header=True, shuffle=True, - shuffle_seed=6) + shuffle_seed=6, + num_epochs=2, + ) outputs1 = dataset1.make_one_shot_iterator().get_next() outputs2 = dataset2.make_one_shot_iterator().get_next() all_equal = False for _ in range(total_records // batch_size): - batch1 = self._run_actual_batch(outputs1, sess) - batch2 = self._run_actual_batch(outputs2, sess) + batch1 = nest.flatten(sess.run(outputs1)) + batch2 = nest.flatten(sess.run(outputs2)) for i in range(len(batch1)): all_equal = all_equal and np.array_equal(batch1[i], batch2[i]) self.assertFalse(all_equal) @@ -874,6 +1002,5 @@ class MakeTFRecordDatasetTest( self._shuffle_test(batch_size, num_epochs, num_parallel_reads, seed=21345) - if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/BUILD b/tensorflow/contrib/data/python/kernel_tests/serialization/BUILD index 686788522acdf1c5e91132c38bdc81d10d2a0cc2..3c3f23f9a984c702abfdacf11bef0e5d4066782f 100644 --- a/tensorflow/contrib/data/python/kernel_tests/serialization/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/BUILD @@ -72,6 +72,20 @@ py_test( ], ) +py_test( + name = "csv_dataset_serialization_test", + size = "small", + srcs = ["csv_dataset_serialization_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + ":dataset_serialization_test_base", + "//tensorflow/contrib/data/python/ops:readers", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_ops", + ], +) + py_test( name = "dataset_constructor_serialization_test", size = "medium", diff --git a/tensorflow/contrib/data/python/kernel_tests/serialization/csv_dataset_serialization_test.py b/tensorflow/contrib/data/python/kernel_tests/serialization/csv_dataset_serialization_test.py new file mode 100644 index 0000000000000000000000000000000000000000..247f2046ea313f97bdbda1674765f12406258509 --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/serialization/csv_dataset_serialization_test.py @@ -0,0 +1,73 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for the CsvDataset serialization.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gzip +import os + +from tensorflow.contrib.data.python.kernel_tests.serialization import dataset_serialization_test_base +from tensorflow.contrib.data.python.ops import readers +from tensorflow.python.platform import test + + +class CsvDatasetSerializationTest( + dataset_serialization_test_base.DatasetSerializationTestBase): + + def setUp(self): + self._num_cols = 7 + self._num_rows = 10 + self._num_epochs = 14 + self._num_outputs = self._num_rows * self._num_epochs + + inputs = [ + ",".join(str(self._num_cols * j + i) + for i in range(self._num_cols)) + for j in range(self._num_rows) + ] + contents = "\n".join(inputs).encode("utf-8") + + self._filename = os.path.join(self.get_temp_dir(), "file.csv") + self._compressed = os.path.join(self.get_temp_dir(), + "comp.csv") # GZip compressed + + with open(self._filename, "wb") as f: + f.write(contents) + with gzip.GzipFile(self._compressed, "wb") as f: + f.write(contents) + + def ds_func(self, **kwargs): + compression_type = kwargs.get("compression_type", None) + if compression_type == "GZIP": + filename = self._compressed + elif compression_type is None: + filename = self._filename + else: + raise ValueError("Invalid compression type:", compression_type) + + return readers.CsvDataset(filename, **kwargs).repeat(self._num_epochs) + + def testSerializationCore(self): + defs = [[0]] * self._num_cols + self.run_core_tests( + lambda: self.ds_func(record_defaults=defs, buffer_size=2), + lambda: self.ds_func(record_defaults=defs, buffer_size=12), + self._num_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py index 5590a4bf783d12b0d0710c0130b0b1df921c9baa..8b2f84649486e35e1067f5f9cbe4a7abec71e080 100644 --- a/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py @@ -17,6 +17,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized import numpy as np from tensorflow.contrib.data.python.ops import sliding @@ -29,28 +30,45 @@ from tensorflow.python.ops import math_ops from tensorflow.python.platform import test -class SlideDatasetTest(test.TestCase): - - def testSlideDataset(self): - """Test an dataset that maps a TF function across its input elements.""" +class SlideDatasetTest(test.TestCase, parameterized.TestCase): + + @parameterized.parameters( + (20, 14, 7, 1), + (20, 17, 9, 1), + (20, 14, 14, 1), + (20, 10, 14, 1), + (20, 14, 19, 1), + (20, 4, 1, 2), + (20, 2, 1, 6), + (20, 4, 7, 2), + (20, 2, 7, 6), + (1, 10, 4, 1), + (0, 10, 4, 1), + ) + def testSlideDataset(self, count, window_size, window_shift, window_stride): + """Tests a dataset that slides a window its input elements.""" components = (np.arange(7), np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], np.array(37.0) * np.arange(7)) - count = array_ops.placeholder(dtypes.int64, shape=[]) - window_size = array_ops.placeholder(dtypes.int64, shape=[]) - stride = array_ops.placeholder(dtypes.int64, shape=[]) + count_t = array_ops.placeholder(dtypes.int64, shape=[]) + window_size_t = array_ops.placeholder(dtypes.int64, shape=[]) + window_shift_t = array_ops.placeholder(dtypes.int64, shape=[]) + window_stride_t = array_ops.placeholder(dtypes.int64, shape=[]) def _map_fn(x, y, z): return math_ops.square(x), math_ops.square(y), math_ops.square(z) # The pipeline is TensorSliceDataset -> MapDataset(square_3) -> - # RepeatDataset(count) -> _SlideDataset(window_size, stride). - iterator = (dataset_ops.Dataset.from_tensor_slices(components) - .map(_map_fn) - .repeat(count) - .apply(sliding.sliding_window_batch(window_size, stride)) - .make_initializable_iterator()) + # RepeatDataset(count) -> + # _SlideDataset(window_size, window_shift, window_stride). + iterator = ( + dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) + .repeat(count).apply( + sliding.sliding_window_batch( + window_size=window_size_t, + window_shift=window_shift_t, + window_stride=window_stride_t)).make_initializable_iterator()) init_op = iterator.initializer get_next = iterator.get_next() @@ -58,90 +76,126 @@ class SlideDatasetTest(test.TestCase): [t.shape.as_list() for t in get_next]) with self.test_session() as sess: - # stride < window_size. - # Slide over a finite input, where the window_size divides the - # total number of elements. - sess.run(init_op, feed_dict={count: 20, window_size: 14, stride: 7}) - # Same formula with convolution layer. - num_batches = (20 * 7 - 14) // 7 + 1 - for i in range(num_batches): - result = sess.run(get_next) - for component, result_component in zip(components, result): - for j in range(14): - self.assertAllEqual(component[(i*7 + j) % 7]**2, - result_component[j]) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - # Slide over a finite input, where the window_size does not - # divide the total number of elements. - sess.run(init_op, feed_dict={count: 20, window_size: 17, stride: 9}) - num_batches = (20 * 7 - 17) // 9 + 1 + sess.run( + init_op, + feed_dict={ + count_t: count, + window_size_t: window_size, + window_shift_t: window_shift, + window_stride_t: window_stride + }) + num_batches = (count * 7 - ( + (window_size - 1) * window_stride + 1)) // window_shift + 1 for i in range(num_batches): result = sess.run(get_next) for component, result_component in zip(components, result): - for j in range(17): - self.assertAllEqual(component[(i*9 + j) % 7]**2, - result_component[j]) + for j in range(window_size): + self.assertAllEqual( + component[(i * window_shift + j * window_stride) % 7]**2, + result_component[j]) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) - # stride == window_size. - sess.run(init_op, feed_dict={count: 20, window_size: 14, stride: 14}) - num_batches = 20 * 7 // 14 - for i in range(num_batches): - result = sess.run(get_next) - for component, result_component in zip(components, result): - for j in range(14): - self.assertAllEqual(component[(i*14 + j) % 7]**2, - result_component[j]) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + @parameterized.parameters( + (20, 14, 7, 1), + (20, 17, 9, 1), + (20, 14, 14, 1), + (20, 10, 14, 1), + (20, 14, 19, 1), + (20, 4, 1, 2), + (20, 2, 1, 6), + (20, 4, 7, 2), + (20, 2, 7, 6), + (1, 10, 4, 1), + (0, 10, 4, 1), + ) + def testSlideDatasetDeprecated(self, count, window_size, stride, + window_stride): + """Tests a dataset that slides a window its input elements.""" + components = (np.arange(7), + np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis], + np.array(37.0) * np.arange(7)) - # stride > window_size. - sess.run(init_op, feed_dict={count: 20, window_size: 10, stride: 14}) - num_batches = 20 * 7 // 14 - for i in range(num_batches): - result = sess.run(get_next) - for component, result_component in zip(components, result): - for j in range(10): - self.assertAllEqual(component[(i*14 + j) % 7]**2, - result_component[j]) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) - # Drop the last batch which is smaller than window_size. - sess.run(init_op, feed_dict={count: 20, window_size: 14, stride: 19}) - num_batches = (20 * 7 - 7) // 19 # = 19 * 7 // 19 - for i in range(num_batches): - result = sess.run(get_next) - for component, result_component in zip(components, result): - for j in range(14): - self.assertAllEqual(component[(i*19 + j) % 7]**2, - result_component[j]) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + count_t = array_ops.placeholder(dtypes.int64, shape=[]) + window_size_t = array_ops.placeholder(dtypes.int64, shape=[]) + stride_t = array_ops.placeholder(dtypes.int64, shape=[]) + window_stride_t = array_ops.placeholder(dtypes.int64, shape=[]) - # Slide over a finite input, which is less than window_size, - # should fail straight away. - sess.run(init_op, feed_dict={count: 1, window_size: 10, stride: 4}) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + def _map_fn(x, y, z): + return math_ops.square(x), math_ops.square(y), math_ops.square(z) - sess.run(init_op, feed_dict={count: 1, window_size: 10, stride: 8}) - with self.assertRaises(errors.OutOfRangeError): - sess.run(get_next) + # The pipeline is TensorSliceDataset -> MapDataset(square_3) -> + # RepeatDataset(count) -> _SlideDataset(window_size, stride, window_stride). + iterator = ( + dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn) + .repeat(count).apply( + sliding.sliding_window_batch( + window_size=window_size_t, + stride=stride_t, + window_stride=window_stride_t)).make_initializable_iterator()) + init_op = iterator.initializer + get_next = iterator.get_next() - # Slide over an empty input should fail straight away. - sess.run(init_op, feed_dict={count: 0, window_size: 8, stride: 4}) + self.assertEqual([[None] + list(c.shape[1:]) for c in components], + [t.shape.as_list() for t in get_next]) + + with self.test_session() as sess: + sess.run( + init_op, + feed_dict={ + count_t: count, + window_size_t: window_size, + stride_t: stride, + window_stride_t: window_stride + }) + num_batches = (count * 7 - ( + (window_size - 1) * window_stride + 1)) // stride + 1 + for i in range(num_batches): + result = sess.run(get_next) + for component, result_component in zip(components, result): + for j in range(window_size): + self.assertAllEqual( + component[(i * stride + j * window_stride) % 7]**2, + result_component[j]) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) - # Empty window_size should be an initialization time error. - with self.assertRaises(errors.InvalidArgumentError): - sess.run(init_op, feed_dict={count: 14, window_size: 0, stride: 0}) + @parameterized.parameters( + (14, 0, 3, 1), + (14, 3, 0, 1), + (14, 3, 3, 0), + ) + def testSlideDatasetInvalid(self, count, window_size, window_shift, + window_stride): + count_t = array_ops.placeholder(dtypes.int64, shape=[]) + window_size_t = array_ops.placeholder(dtypes.int64, shape=[]) + window_shift_t = array_ops.placeholder(dtypes.int64, shape=[]) + window_stride_t = array_ops.placeholder(dtypes.int64, shape=[]) + + iterator = ( + dataset_ops.Dataset.range(10).map(lambda x: x).repeat(count_t).apply( + sliding.sliding_window_batch( + window_size=window_size_t, + window_shift=window_shift_t, + window_stride=window_stride_t)).make_initializable_iterator()) + init_op = iterator.initializer - # Invalid stride should be an initialization time error. + with self.test_session() as sess: with self.assertRaises(errors.InvalidArgumentError): - sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 0}) + sess.run( + init_op, + feed_dict={ + count_t: count, + window_size_t: window_size, + window_shift_t: window_shift, + window_stride_t: window_stride + }) + + def testSlideDatasetValueError(self): + with self.assertRaises(ValueError): + dataset_ops.Dataset.range(10).map(lambda x: x).apply( + sliding.sliding_window_batch( + window_size=1, stride=1, window_shift=1, window_stride=1)) def assertSparseValuesEqual(self, a, b): self.assertAllEqual(a.indices, b.indices) @@ -155,7 +209,8 @@ class SlideDatasetTest(test.TestCase): indices=[[0]], values=(i * [1]), dense_shape=[1]) iterator = dataset_ops.Dataset.range(10).map(_sparse).apply( - sliding.sliding_window_batch(5, 3)).make_initializable_iterator() + sliding.sliding_window_batch( + window_size=5, window_shift=3)).make_initializable_iterator() init_op = iterator.initializer get_next = iterator.get_next() @@ -183,7 +238,8 @@ class SlideDatasetTest(test.TestCase): dense_shape=[i]) iterator = dataset_ops.Dataset.range(10).map(_sparse).apply( - sliding.sliding_window_batch(5, 3)).make_initializable_iterator() + sliding.sliding_window_batch( + window_size=5, window_shift=3)).make_initializable_iterator() init_op = iterator.initializer get_next = iterator.get_next() @@ -213,11 +269,11 @@ class SlideDatasetTest(test.TestCase): return sparse_tensor.SparseTensorValue( indices=[[0]], values=(i * [1]), dense_shape=[1]) - iterator = (dataset_ops.Dataset.range(10) - .map(_sparse) - .apply(sliding.sliding_window_batch(4, 2)) - .apply(sliding.sliding_window_batch(3, 1)) - .make_initializable_iterator()) + iterator = ( + dataset_ops.Dataset.range(10).map(_sparse).apply( + sliding.sliding_window_batch(window_size=4, window_shift=2)).apply( + sliding.sliding_window_batch(window_size=3, window_shift=1)) + .make_initializable_iterator()) init_op = iterator.initializer get_next = iterator.get_next() @@ -226,9 +282,9 @@ class SlideDatasetTest(test.TestCase): # Slide: 1st batch. actual = sess.run(get_next) expected = sparse_tensor.SparseTensorValue( - indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], - [1, 0, 0], [1, 1, 0], [1, 2, 0], [1, 3, 0], - [2, 0, 0], [2, 1, 0], [2, 2, 0], [2, 3, 0]], + indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], [1, 0, 0], + [1, 1, 0], [1, 2, 0], [1, 3, 0], [2, 0, 0], [2, 1, 0], + [2, 2, 0], [2, 3, 0]], values=[0, 1, 2, 3, 2, 3, 4, 5, 4, 5, 6, 7], dense_shape=[3, 4, 1]) self.assertTrue(sparse_tensor.is_sparse(actual)) @@ -236,9 +292,9 @@ class SlideDatasetTest(test.TestCase): # Slide: 2nd batch. actual = sess.run(get_next) expected = sparse_tensor.SparseTensorValue( - indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], - [1, 0, 0], [1, 1, 0], [1, 2, 0], [1, 3, 0], - [2, 0, 0], [2, 1, 0], [2, 2, 0], [2, 3, 0]], + indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], [1, 0, 0], + [1, 1, 0], [1, 2, 0], [1, 3, 0], [2, 0, 0], [2, 1, 0], + [2, 2, 0], [2, 3, 0]], values=[2, 3, 4, 5, 4, 5, 6, 7, 6, 7, 8, 9], dense_shape=[3, 4, 1]) self.assertTrue(sparse_tensor.is_sparse(actual)) @@ -253,10 +309,11 @@ class SlideDatasetTest(test.TestCase): yield [4.0, 5.0, 6.0] yield [7.0, 8.0, 9.0, 10.0] - iterator = (dataset_ops.Dataset.from_generator(generator, dtypes.float32, - output_shapes=[None]) - .apply(sliding.sliding_window_batch(3, 1)) - .make_initializable_iterator()) + iterator = ( + dataset_ops.Dataset.from_generator( + generator, dtypes.float32, output_shapes=[None]).apply( + sliding.sliding_window_batch(window_size=3, window_shift=1)) + .make_initializable_iterator()) next_element = iterator.get_next() with self.test_session() as sess: diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD index 160d7fe22a9f127f7ee23d7a988c22cc4430ce11..1ad021ea037add48afee5bdfda9eea18485eca5d 100644 --- a/tensorflow/contrib/data/python/ops/BUILD +++ b/tensorflow/contrib/data/python/ops/BUILD @@ -28,10 +28,12 @@ py_library( srcs = ["get_single_element.py"], srcs_version = "PY2AND3", deps = [ + ":grouping", "//tensorflow/python:dataset_ops_gen", "//tensorflow/python/data/ops:dataset_ops", "//tensorflow/python/data/util:nest", "//tensorflow/python/data/util:sparse", + "//third_party/py/numpy", ], ) @@ -129,6 +131,7 @@ py_library( "//tensorflow/python/data/util:convert", "//tensorflow/python/data/util:nest", "//tensorflow/python/data/util:sparse", + "//third_party/py/numpy", ], ) diff --git a/tensorflow/contrib/data/python/ops/batching.py b/tensorflow/contrib/data/python/ops/batching.py index a4914f4cde71925af477636c91d98b54ce0cce0e..42fc20ec015a078ef8cd42065196f45438f19785 100644 --- a/tensorflow/contrib/data/python/ops/batching.py +++ b/tensorflow/contrib/data/python/ops/batching.py @@ -515,10 +515,7 @@ def batch_and_drop_remainder(batch_size): def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" - # TODO(jsimsa): Switch to using `batch(..., drop_remainder=True)` any time - # after 6/30/2018. - batched = dataset.batch(batch_size) - return _filter_irregular_batches(batch_size)(batched) + return dataset.batch(batch_size, drop_remainder=True) return _apply_fn @@ -553,11 +550,9 @@ def padded_batch_and_drop_remainder(batch_size, def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" - # TODO(jsimsa): Switch to using `padded_batch(..., drop_remainder=True)` - # any time after 6/30/2018. - batched = dataset.padded_batch( - batch_size, padded_shapes=padded_shapes, padding_values=padding_values) - return _filter_irregular_batches(batch_size)(batched) + return dataset.padded_batch( + batch_size, padded_shapes=padded_shapes, padding_values=padding_values, + drop_remainder=True) return _apply_fn diff --git a/tensorflow/contrib/data/python/ops/get_single_element.py b/tensorflow/contrib/data/python/ops/get_single_element.py index 0f4cd8e20c5727a5bcfa1dce4dadbfa8f90bd551..ef9284456eb35099db804e0680abfacd6384d503 100644 --- a/tensorflow/contrib/data/python/ops/get_single_element.py +++ b/tensorflow/contrib/data/python/ops/get_single_element.py @@ -17,6 +17,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import numpy as np + +from tensorflow.contrib.data.python.ops import grouping from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.util import nest from tensorflow.python.data.util import sparse @@ -68,3 +71,30 @@ def get_single_element(dataset): return sparse.deserialize_sparse_tensors( nested_ret, dataset.output_types, dataset.output_shapes, dataset.output_classes) + + +def reduce_dataset(dataset, reducer): + """Returns the result of reducing the `dataset` using `reducer`. + + Args: + dataset: A @{tf.data.Dataset} object. + reducer: A @{tf.contrib.data.Reducer} object representing the reduce logic. + + Returns: + A nested structure of @{tf.Tensor} objects, corresponding to the result + of reducing `dataset` using `reducer`. + + Raises: + TypeError: if `dataset` is not a `tf.data.Dataset` object. + """ + if not isinstance(dataset, dataset_ops.Dataset): + raise TypeError("`dataset` must be a `tf.data.Dataset` object.") + + # The sentinel dataset is used in case the reduced dataset is empty. + sentinel_dataset = dataset_ops.Dataset.from_tensors( + reducer.finalize_func(reducer.init_func(np.int64(0)))) + reduced_dataset = dataset.apply( + grouping.group_by_reducer(lambda x: np.int64(0), reducer)) + + return get_single_element( + reduced_dataset.concatenate(sentinel_dataset).take(1)) diff --git a/tensorflow/contrib/data/python/ops/optimization.py b/tensorflow/contrib/data/python/ops/optimization.py index cf896572262929add5ac34d4fc8e4192c1049da3..018c5115e1d5599e48bf99ccf832c7962794fc40 100644 --- a/tensorflow/contrib/data/python/ops/optimization.py +++ b/tensorflow/contrib/data/python/ops/optimization.py @@ -18,12 +18,34 @@ from __future__ import division from __future__ import print_function from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import +from tensorflow.contrib.data.python.ops import gen_dataset_ops as contrib_gen_dataset_ops from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import gen_dataset_ops +# TODO(jsimsa): Support RE matching for both individual transformation (e.g. to +# account for indexing) and transformation sequence. +def assert_next(transformations): + """A transformation that asserts which transformations happen next. + + Args: + transformations: A `tf.string` vector `tf.Tensor` identifying the + transformations that are expected to happen next. + + Returns: + A `Dataset` transformation function, which can be passed to + @{tf.data.Dataset.apply}. + """ + + def _apply_fn(dataset): + """Function from `Dataset` to `Dataset` that applies the transformation.""" + return _AssertNextDataset(dataset, transformations) + + return _apply_fn + + def optimize(optimizations=None): """A transformation that applies optimizations. @@ -44,6 +66,37 @@ def optimize(optimizations=None): return _apply_fn +class _AssertNextDataset(dataset_ops.Dataset): + """A `Dataset` that asserts which transformations happen next.""" + + def __init__(self, input_dataset, transformations): + """See `assert_next()` for details.""" + super(_AssertNextDataset, self).__init__() + self._input_dataset = input_dataset + if transformations is None: + raise ValueError("At least one transformation should be specified") + self._transformations = ops.convert_to_tensor( + transformations, dtype=dtypes.string, name="transformations") + + def _as_variant_tensor(self): + return contrib_gen_dataset_ops.assert_next_dataset( + self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access + self._transformations, + **dataset_ops.flat_structure(self)) + + @property + def output_classes(self): + return self._input_dataset.output_classes + + @property + def output_shapes(self): + return self._input_dataset.output_shapes + + @property + def output_types(self): + return self._input_dataset.output_types + + class _OptimizeDataset(dataset_ops.Dataset): """A `Dataset` that acts as an identity, and applies optimizations.""" diff --git a/tensorflow/contrib/data/python/ops/prefetching_ops.py b/tensorflow/contrib/data/python/ops/prefetching_ops.py index 21fc17102e16a1f98f2c2e8aa0aeec89989edf67..0edd7c9fe974784f199c272a649b302e72d8c218 100644 --- a/tensorflow/contrib/data/python/ops/prefetching_ops.py +++ b/tensorflow/contrib/data/python/ops/prefetching_ops.py @@ -26,10 +26,15 @@ from tensorflow.python.data.ops import iterator_ops from tensorflow.python.data.util import nest from tensorflow.python.data.util import sparse from tensorflow.python.eager import context +from tensorflow.python.framework import device as framework_device from tensorflow.python.framework import dtypes from tensorflow.python.framework import function from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import functional_ops from tensorflow.python.ops import gen_dataset_ops as core_gen_dataset_ops +from tensorflow.python.ops import resource_variable_ops def function_buffering_resource(string_arg, @@ -345,3 +350,348 @@ def prefetch_to_device(device, buffer_size=None): return _PrefetchToDeviceDataset(dataset, device, buffer_size) return _apply_fn + + +def copy_to_device(target_device, source_device="/cpu:0"): + """A transformation that copies dataset elements to the given `target_device`. + + Args: + target_device: The name of a device to which elements will be copied. + source_device: The original device on which `input_dataset` will be placed. + + Returns: + A `Dataset` transformation function, which can be passed to + @{tf.data.Dataset.apply}. + """ + + def _apply_fn(dataset): + return _CopyToDeviceDataset( + dataset, target_device=target_device, source_device=source_device) + + return _apply_fn + + +# TODO(rohanj): Use the _input_hostmem attr on the RemoteCall ops to indicate +# all inputs to the Op are in host memory, thereby avoiding some unnecessary +# Sends and Recvs. +class _CopyToDeviceDataset(dataset_ops.Dataset): + """A `Dataset` that copies elements to another device.""" + + def __init__(self, input_dataset, target_device, source_device="/cpu:0"): + """Constructs a _CopyToDeviceDataset. + + Args: + input_dataset: `Dataset` to be copied + target_device: The name of the device to which elements would be copied. + source_device: Device where input_dataset would be placed. + """ + self._input_dataset = input_dataset + self._target_device = target_device + spec = framework_device.DeviceSpec().from_string(self._target_device) + self._is_gpu_target = (spec.device_type == "GPU") + self._source_device_string = source_device + self._source_device = ops.convert_to_tensor(source_device) + + self._flat_output_shapes = nest.flatten( + sparse.as_dense_shapes(self._input_dataset.output_shapes, + self._input_dataset.output_classes)) + self._flat_output_types = nest.flatten( + sparse.as_dense_types(self._input_dataset.output_types, + self._input_dataset.output_classes)) + + @function.Defun() + def _init_func(): + """Creates an iterator for the input dataset. + + Returns: + A `string` tensor that encapsulates the iterator created. + """ + # pylint: disable=protected-access + ds_variant = self._input_dataset._as_variant_tensor() + resource = core_gen_dataset_ops.anonymous_iterator( + output_types=self._flat_output_types, + output_shapes=self._flat_output_shapes) + with ops.control_dependencies( + [core_gen_dataset_ops.make_iterator(ds_variant, resource)]): + return core_gen_dataset_ops.iterator_to_string_handle(resource) + + @function.Defun() + def _remote_init_func(): + return functional_ops.remote_call( + target=self._source_device, + args=_init_func.captured_inputs, + Tout=[dtypes.string], + f=_init_func) + + self._init_func = _remote_init_func + self._init_captured_args = _remote_init_func.captured_inputs + + @function.Defun(dtypes.string) + def _next_func(string_handle): + """Calls get_next for created iterator. + + Args: + string_handle: An iterator string handle created by _init_func + Returns: + The elements generated from `input_dataset` + """ + with ops.device(self._source_device_string): + iterator = iterator_ops.Iterator.from_string_handle( + string_handle, self.output_types, self.output_shapes, + self.output_classes) + ret = iterator.get_next() + return nest.flatten(sparse.serialize_sparse_tensors(ret)) + + @function.Defun(dtypes.string) + def _remote_next_func(string_handle): + return functional_ops.remote_call( + target=self._source_device, + args=[string_handle] + _next_func.captured_inputs, + Tout=self._flat_output_types, + f=_next_func) + + self._next_func = _remote_next_func + self._next_captured_args = _remote_next_func.captured_inputs + + @function.Defun(dtypes.string) + def _finalize_func(string_handle): + """Destroys the iterator resource created. + + Args: + string_handle: An iterator string handle created by _init_func + Returns: + Tensor constant 0 + """ + iterator_resource = core_gen_dataset_ops.iterator_from_string_handle_v2( + string_handle, + output_types=self._flat_output_types, + output_shapes=self._flat_output_shapes) + with ops.control_dependencies([ + resource_variable_ops.destroy_resource_op( + iterator_resource, ignore_lookup_error=True)]): + return array_ops.constant(0, dtypes.int64) + + @function.Defun(dtypes.string) + def _remote_finalize_func(string_handle): + return functional_ops.remote_call( + target=self._source_device, + args=[string_handle] + _finalize_func.captured_inputs, + Tout=[dtypes.int64], + f=_finalize_func) + + self._finalize_func = _remote_finalize_func + self._finalize_captured_args = _remote_finalize_func.captured_inputs + + g = ops.get_default_graph() + _remote_init_func.add_to_graph(g) + _remote_next_func.add_to_graph(g) + _remote_finalize_func.add_to_graph(g) + # pylint: enable=protected-scope + + # The one_shot_iterator implementation needs a 0 arg _make_dataset function + # that thereby captures all the inputs required to create the dataset. Since + # there are strings that are inputs to the GeneratorDataset which can't be + # placed on a GPU, this fails for the GPU case. Therefore, disabling it for + # GPU + def make_one_shot_iterator(self): + if self._is_gpu_target: + raise ValueError("Cannot create a one shot iterator when using " + "`tf.contrib.data.copy_to_device()` on GPU. Please use " + "`Dataset.make_initializable_iterator()` instead.") + else: + return super(_CopyToDeviceDataset, self).make_one_shot_iterator() + + def _as_variant_tensor(self): + with ops.device(self._target_device): + return core_gen_dataset_ops.generator_dataset( + self._init_captured_args, + self._next_captured_args, + self._finalize_captured_args, + init_func=self._init_func, + next_func=self._next_func, + finalize_func=self._finalize_func, + output_types=self._flat_output_types, + output_shapes=self._flat_output_shapes) + + @property + def output_types(self): + return self._input_dataset.output_types + + @property + def output_shapes(self): + return self._input_dataset.output_shapes + + @property + def output_classes(self): + return self._input_dataset.output_classes + + +class _PerDeviceGenerator(dataset_ops.Dataset): + """A `dummy` generator dataset.""" + + def __init__(self, shard_num, multi_device_iterator_resource, incarnation_id, + source_device, target_device, output_shapes, output_types, + output_classes): + self._target_device = target_device + self._output_types = output_types + self._output_shapes = output_shapes + self._output_classes = output_classes + self._flat_output_shapes = nest.flatten( + sparse.as_dense_shapes(self._output_shapes, self._output_classes)) + self._flat_output_types = nest.flatten( + sparse.as_dense_types(self._output_types, self._output_classes)) + + multi_device_iterator_string_handle = ( + gen_dataset_ops.multi_device_iterator_to_string_handle( + multi_device_iterator_resource)) + + @function.Defun() + def _init_func(): + return multi_device_iterator_string_handle + + @function.Defun() + def _remote_init_func(): + return functional_ops.remote_call( + target=source_device, + args=_init_func.captured_inputs, + Tout=[dtypes.string], + f=_init_func) + + self._init_func = _remote_init_func + self._init_captured_args = _remote_init_func.captured_inputs + + @function.Defun(dtypes.string) + def _next_func(string_handle): + multi_device_iterator = ( + gen_dataset_ops.multi_device_iterator_from_string_handle( + string_handle=string_handle, + output_types=self._flat_output_types, + output_shapes=self._flat_output_shapes)) + return gen_dataset_ops.multi_device_iterator_get_next_from_shard( + multi_device_iterator=multi_device_iterator, + shard_num=shard_num, + incarnation_id=incarnation_id, + output_types=self._flat_output_types, + output_shapes=self._flat_output_shapes) + + @function.Defun(dtypes.string) + def _remote_next_func(string_handle): + return functional_ops.remote_call( + target=source_device, + args=[string_handle] + _next_func.captured_inputs, + Tout=self._flat_output_types, + f=_next_func) + + self._next_func = _remote_next_func + self._next_captured_args = _remote_next_func.captured_inputs + + @function.Defun(dtypes.string) + def _finalize_func(unused_string_handle): + return array_ops.constant(0, dtypes.int64) + + @function.Defun(dtypes.string) + def _remote_finalize_func(string_handle): + return functional_ops.remote_call( + target=source_device, + args=[string_handle] + _finalize_func.captured_inputs, + Tout=[dtypes.int64], + f=_finalize_func) + + self._finalize_func = _remote_finalize_func + self._finalize_captured_args = _remote_finalize_func.captured_inputs + + def _as_variant_tensor(self): + with ops.device(self._target_device): + return core_gen_dataset_ops.generator_dataset( + self._init_captured_args, + self._next_captured_args, + self._finalize_captured_args, + init_func=self._init_func, + next_func=self._next_func, + finalize_func=self._finalize_func, + output_types=self._flat_output_types, + output_shapes=self._flat_output_shapes) + + @property + def output_types(self): + return self._output_types + + @property + def output_shapes(self): + return self._output_shapes + + @property + def output_classes(self): + return self._output_classes + + +class MultiDeviceIterator(object): + """An iterator over multiple devices.""" + + def __init__(self, + dataset, + devices, + prefetch_buffer_size=1, + source_device="/cpu:0"): + self._dataset = dataset + self._devices = devices + self._source_device = source_device + self._source_device_tensor = ops.convert_to_tensor(source_device) + + self._flat_output_shapes = nest.flatten( + sparse.as_dense_shapes(self._dataset.output_shapes, + self._dataset.output_classes)) + self._flat_output_types = nest.flatten( + sparse.as_dense_types(self._dataset.output_types, + self._dataset.output_classes)) + + # Create the MultiDeviceIterator. + with ops.device(self._source_device): + self._multi_device_iterator_resource = ( + gen_dataset_ops.multi_device_iterator( + devices=self._devices, + shared_name="", + container="", + output_types=self._flat_output_types, + output_shapes=self._flat_output_shapes)) + + # The incarnation ID is used to ensure consistency between the per-device + # iterators and the multi-device iterator. + self._incarnation_id = gen_dataset_ops.multi_device_iterator_init( + self._dataset._as_variant_tensor(), # pylint: disable=protected-access + self._multi_device_iterator_resource) + + # TODO(rohanj): Explore the possibility of the MultiDeviceIterator to + # initialize the device side of the pipeline. This would allow the + # MultiDeviceIterator to choose, for example, to move some transformations + # into the device side from its input. It might be useful in rewriting. + # Create the per device iterators. + self._device_iterators = [] + i = 0 + for device in self._devices: + ds = _PerDeviceGenerator( + i, self._multi_device_iterator_resource, self._incarnation_id, + self._source_device_tensor, device, self._dataset.output_shapes, + self._dataset.output_types, self._dataset.output_classes) + ds = ds.prefetch(prefetch_buffer_size) + with ops.device(device): + self._device_iterators.append(ds.make_initializable_iterator()) + i += 1 + + device_iterator_initializers = [ + iterator.initializer for iterator in self._device_iterators + ] + self._initializer = control_flow_ops.group(*device_iterator_initializers) + + def get_next(self): + result = [] + i = 0 + for device in self._devices: + with ops.device(device): + result.append(self._device_iterators[i].get_next()) + i += 1 + return result + + @property + def initializer(self): + return self._initializer diff --git a/tensorflow/contrib/data/python/ops/readers.py b/tensorflow/contrib/data/python/ops/readers.py index 83095c7ba1c6465d18490e5197f71bf7f1fe2497..f018dd02e6ae9de69c7364677e1756d1e11bf484 100644 --- a/tensorflow/contrib/data/python/ops/readers.py +++ b/tensorflow/contrib/data/python/ops/readers.py @@ -326,6 +326,7 @@ def make_csv_dataset( num_parallel_parser_calls=2, sloppy=False, num_rows_for_inference=100, + compression_type=None, ): """Reads CSV files into a dataset. @@ -399,6 +400,8 @@ def make_csv_dataset( num_rows_for_inference: Number of rows of a file to use for type inference if record_defaults is not provided. If None, reads all the rows of all the files. Defaults to 100. + compression_type: (Optional.) A `tf.string` scalar evaluating to one of + `""` (no compression), `"ZLIB"`, or `"GZIP"`. Defaults to no compression. Returns: A dataset, where each element is a (features, labels) tuple that corresponds @@ -461,7 +464,9 @@ def make_csv_dataset( use_quote_delim=use_quote_delim, na_value=na_value, select_cols=select_columns, - header=header) + header=header, + compression_type=compression_type, + ) def map_fn(*columns): """Organizes columns into a features dictionary. @@ -505,6 +510,7 @@ class CsvDataset(dataset_ops.Dataset): def __init__(self, filenames, record_defaults, + compression_type=None, buffer_size=None, header=False, field_delim=",", @@ -540,11 +546,11 @@ class CsvDataset(dataset_ops.Dataset): The expected output of its iterations is: ```python - next = dataset.make_one_shot_iterator().get_next() + next_element = dataset.make_one_shot_iterator().get_next() with tf.Session() as sess: while True: try: - print(sess.run(nxt)) + print(sess.run(next_element)) except tf.errors.OutOfRangeError: break @@ -562,6 +568,9 @@ class CsvDataset(dataset_ops.Dataset): both this and `select_columns` are specified, these must have the same lengths, and `column_defaults` is assumed to be sorted in order of increasing column index. + compression_type: (Optional.) A `tf.string` scalar evaluating to one of + `""` (no compression), `"ZLIB"`, or `"GZIP"`. Defaults to no + compression. buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes to buffer while reading files. Defaults to 4MB. header: (Optional.) A `tf.bool` scalar indicating whether the CSV file(s) @@ -581,6 +590,11 @@ class CsvDataset(dataset_ops.Dataset): super(CsvDataset, self).__init__() self._filenames = ops.convert_to_tensor( filenames, dtype=dtypes.string, name="filenames") + self._compression_type = convert.optional_param_to_tensor( + "compression_type", + compression_type, + argument_default="", + argument_dtype=dtypes.string) record_defaults = [ constant_op.constant([], dtype=x) if x in _ACCEPTABLE_CSV_TYPES else x for x in record_defaults @@ -621,6 +635,7 @@ class CsvDataset(dataset_ops.Dataset): use_quote_delim=self._use_quote_delim, na_value=self._na_value, select_cols=self._select_cols, + compression_type=self._compression_type, ) @property diff --git a/tensorflow/contrib/data/python/ops/sliding.py b/tensorflow/contrib/data/python/ops/sliding.py index 3f3c5ca17cf6ae22a719ed1d593d98eec37413fb..e9dd74530ac64cd414d53eab5294eaa95c919131 100644 --- a/tensorflow/contrib/data/python/ops/sliding.py +++ b/tensorflow/contrib/data/python/ops/sliding.py @@ -23,25 +23,29 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import gen_dataset_ops +from tensorflow.python.util import deprecation class _SlideDataset(dataset_ops.Dataset): """A `Dataset` that passes a sliding window over its input.""" - def __init__(self, input_dataset, window_size, stride=1): + def __init__(self, input_dataset, window_size, window_shift, window_stride): """See `sliding_window_batch` for details.""" super(_SlideDataset, self).__init__() self._input_dataset = input_dataset self._window_size = ops.convert_to_tensor( - window_size, dtype=dtypes.int64, name="window_size") - self._stride = ops.convert_to_tensor( - stride, dtype=dtypes.int64, name="stride") + window_size, dtype=dtypes.int64, name="window_stride") + self._window_stride = ops.convert_to_tensor( + window_stride, dtype=dtypes.int64, name="window_stride") + self._window_shift = ops.convert_to_tensor( + window_shift, dtype=dtypes.int64, name="window_shift") def _as_variant_tensor(self): return gen_dataset_ops.slide_dataset( self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access window_size=self._window_size, - stride=self._stride, + window_shift=self._window_shift, + window_stride=self._window_stride, **dataset_ops.flat_structure(self)) @property @@ -61,38 +65,63 @@ class _SlideDataset(dataset_ops.Dataset): return self._input_dataset.output_types -def sliding_window_batch(window_size, stride=1): - """A sliding window with size of `window_size` and step of `stride`. +@deprecation.deprecated_args( + None, "stride is deprecated, use window_shift instead", "stride") +def sliding_window_batch(window_size, + stride=None, + window_shift=None, + window_stride=1): + """A sliding window over a dataset. - This transformation passes a sliding window over this dataset. The - window size is `window_size` and step size is `stride`. If the left - elements cannot fill up the sliding window, this transformation will - drop the final smaller element. For example: + This transformation passes a sliding window over this dataset. The window size + is `window_size`, the stride of the input elements is `window_stride`, and the + shift between consecutive windows is `window_shift`. If the remaining elements + cannot fill up the sliding window, this transformation will drop the final + smaller element. For example: ```python # NOTE: The following examples use `{ ... }` to represent the # contents of a dataset. a = { [1], [2], [3], [4], [5], [6] } - a.apply(tf.contrib.data.sliding_window_batch(window_size=3, stride=2)) == - { - [[1], [2], [3]], - [[3], [4], [5]], - } + a.apply(sliding_window_batch(window_size=3)) == + { [[1], [2], [3]], [[2], [3], [4]], [[3], [4], [5]], [[4], [5], [6]] } + + a.apply(sliding_window_batch(window_size=3, window_shift=2)) == + { [[1], [2], [3]], [[3], [4], [5]] } + + a.apply(sliding_window_batch(window_size=3, window_stride=2)) == + { [[1], [3], [5]], [[2], [4], [6]] } ``` Args: window_size: A `tf.int64` scalar `tf.Tensor`, representing the number of - elements in the sliding window. + elements in the sliding window. It must be positive. stride: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the - steps moving the sliding window forward for one iteration. The default - is `1`. It must be positive. + forward shift of the sliding window in each iteration. The default is `1`. + It must be positive. Deprecated alias for `window_shift`. + window_shift: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the + forward shift of the sliding window in each iteration. The default is `1`. + It must be positive. + window_stride: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the + stride of the input elements in the sliding window. The default is `1`. + It must be positive. Returns: A `Dataset` transformation function, which can be passed to @{tf.data.Dataset.apply}. + + Raises: + ValueError: if invalid arguments are provided. """ + if stride is None and window_shift is None: + window_shift = 1 + elif stride is not None and window_shift is None: + window_shift = stride + elif stride is not None and window_shift is not None: + raise ValueError("Cannot specify both `stride` and `window_shift`") + def _apply_fn(dataset): - return _SlideDataset(dataset, window_size, stride) + return _SlideDataset(dataset, window_size, window_shift, window_stride) return _apply_fn diff --git a/tensorflow/contrib/distribute/BUILD b/tensorflow/contrib/distribute/BUILD index 74b2cd90a187159fd2da8ce236c14e813cc43c49..1126f76f5854932bcb6a9550c100768069bbd1cc 100644 --- a/tensorflow/contrib/distribute/BUILD +++ b/tensorflow/contrib/distribute/BUILD @@ -30,6 +30,7 @@ py_library( "//tensorflow/contrib/distribute/python:monitor", "//tensorflow/contrib/distribute/python:one_device_strategy", "//tensorflow/contrib/distribute/python:step_fn", + "//tensorflow/contrib/distribute/python:tpu_strategy", "//tensorflow/python:training", "//tensorflow/python:util", ], diff --git a/tensorflow/contrib/distribute/README.md b/tensorflow/contrib/distribute/README.md index 44a4481021c380e72b535cf0aca39df2bf04d3b7..2f5dd10550d0771d0cd3c2501d0456dc95077386 100644 --- a/tensorflow/contrib/distribute/README.md +++ b/tensorflow/contrib/distribute/README.md @@ -116,8 +116,6 @@ in the input function gives a solid boost in performance. When using ## Caveats This feature is in early stages and there are a lot of improvements forthcoming: -* Metrics are not yet supported during distributed training. They are still -supported during the evaluation. * Summaries are only computed in the first tower in `MirroredStrategy`. * Evaluation is not yet distributed. * Eager support is in the works; performance can be more challenging with eager diff --git a/tensorflow/contrib/distribute/__init__.py b/tensorflow/contrib/distribute/__init__.py index 76711baf3a11c8978fbb5770ec173ff74a153158..2e2c3be853cc5503c86121c142394d49e5037405 100644 --- a/tensorflow/contrib/distribute/__init__.py +++ b/tensorflow/contrib/distribute/__init__.py @@ -24,6 +24,7 @@ from tensorflow.contrib.distribute.python.mirrored_strategy import MirroredStrat from tensorflow.contrib.distribute.python.monitor import Monitor from tensorflow.contrib.distribute.python.one_device_strategy import OneDeviceStrategy from tensorflow.contrib.distribute.python.step_fn import * +from tensorflow.contrib.distribute.python.tpu_strategy import TPUStrategy from tensorflow.python.training.distribute import * from tensorflow.python.util.all_util import remove_undocumented @@ -41,6 +42,7 @@ _allowed_symbols = [ 'StandardInputStep', 'StandardSingleLossStep', 'TowerContext', + 'TPUStrategy', 'get_cross_tower_context', 'get_distribution_strategy', 'get_loss_reduction', diff --git a/tensorflow/contrib/distribute/python/BUILD b/tensorflow/contrib/distribute/python/BUILD index eba0dd0ea330e29db0ea8e68ee14767fcb8ddad0..cbe741de5a67c231c0982d6d389b3591cff001ec 100644 --- a/tensorflow/contrib/distribute/python/BUILD +++ b/tensorflow/contrib/distribute/python/BUILD @@ -100,6 +100,23 @@ py_library( ], ) +py_library( + name = "parameter_server_strategy", + srcs = ["parameter_server_strategy.py"], + visibility = ["//tensorflow:internal"], + deps = [ + ":cross_tower_ops", + ":mirrored_strategy", + ":values", + "//tensorflow/core:protos_all_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:framework_ops", + "//tensorflow/python:resource_variable_ops", + "//tensorflow/python:training", + "//tensorflow/python:util", + ], +) + py_library( name = "one_device_strategy", srcs = ["one_device_strategy.py"], @@ -207,6 +224,35 @@ py_test( ], ) +py_test( + name = "parameter_server_strategy_test", + srcs = ["parameter_server_strategy_test.py"], + srcs_version = "PY2AND3", + tags = [ + "no_pip", + ], + deps = [ + ":combinations", + ":multi_worker_test_base", + ":parameter_server_strategy", + "//tensorflow/core:protos_all_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:framework_ops", + "//tensorflow/python:gradients", + "//tensorflow/python:layers", + "//tensorflow/python:session", + "//tensorflow/python:training", + "//tensorflow/python:variable_scope", + "//tensorflow/python:variables", + "//tensorflow/python/eager:context", + "//tensorflow/python/estimator:run_config", + "@absl_py//absl/testing:parameterized", + ], +) + cuda_py_test( name = "mirrored_strategy_multigpu_test", srcs = ["mirrored_strategy_multigpu_test.py"], @@ -587,6 +633,7 @@ cuda_py_test( ], tags = [ "multi_and_single_gpu", + "no_windows_gpu", "notsan", ], ) @@ -609,3 +656,40 @@ cuda_py_test( "no_pip", ], ) + +cuda_py_test( + name = "warm_starting_util_test", + size = "medium", + srcs = ["warm_starting_util_test.py"], + additional_deps = [ + ":combinations", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_ops", + "//tensorflow/python:training", + "//tensorflow/python:variable_scope", + "//tensorflow/python:variables", + ], + tags = [ + "multi_and_single_gpu", + "no_pip", + ], +) + +cuda_py_test( + name = "checkpoint_utils_test", + size = "medium", + srcs = ["checkpoint_utils_test.py"], + additional_deps = [ + ":combinations", + "//tensorflow/python:client_testlib", + "//tensorflow/python:checkpoint_utils_test", + "//tensorflow/python:framework_ops", + "//tensorflow/python:training", + "//tensorflow/python:variable_scope", + "//tensorflow/python:variables", + ], + tags = [ + "multi_and_single_gpu", + "no_pip", + ], +) diff --git a/tensorflow/contrib/distribute/python/checkpoint_utils_test.py b/tensorflow/contrib/distribute/python/checkpoint_utils_test.py new file mode 100644 index 0000000000000000000000000000000000000000..bcb977f64073b1d15ef5c872eb0d6b09d5307b54 --- /dev/null +++ b/tensorflow/contrib/distribute/python/checkpoint_utils_test.py @@ -0,0 +1,78 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for checkpoint_utils.init_from_checkpoint with Distribution Strategy. + +These tests are located here instead of as part of +`python.training.CheckpointsTest` because they need access to distribution +strategies which are only present in contrib right now. +TODO(priyag): Move the tests to core `python.training.CheckpointsTest` when +distribution strategy moves out of contrib. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from absl.testing import parameterized + +from tensorflow.contrib.distribute.python import combinations +from tensorflow.python.framework import ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.training import checkpoint_utils +from tensorflow.python.training import checkpoint_utils_test + + +class CheckpointUtilsWithDistributionStrategyTest( + test.TestCase, parameterized.TestCase): + + @combinations.generate(combinations.combine( + distribution=[combinations.default_strategy, + combinations.one_device_strategy, + combinations.mirrored_strategy_with_gpu_and_cpu, + combinations.mirrored_strategy_with_two_gpus], + in_tower_mode=[True, False], + mode=["graph"])) + def testInitFromCheckpoint(self, distribution, in_tower_mode): + checkpoint_dir = self.get_temp_dir() + with self.test_session() as session: + v1_value, v2_value, _, _ = checkpoint_utils_test._create_checkpoints( + session, checkpoint_dir) + + def init_and_verify(g): + v1 = variable_scope.get_variable("new_var1", [1, 10]) + v2 = variable_scope.get_variable( + "new_var2", [10, 10], + synchronization=variable_scope.VariableSynchronization.ON_READ, + aggregation=variable_scope.VariableAggregation.MEAN) + checkpoint_utils.init_from_checkpoint(checkpoint_dir, { + "var1": "new_var1", + "var2": "new_var2" + }) + with self.test_session(graph=g) as session: + session.run(variables.global_variables_initializer()) + self.assertAllEqual(v1_value, self.evaluate(v1)) + self.assertAllEqual(v2_value, self.evaluate(v2)) + + with ops.Graph().as_default() as g, distribution.scope(): + if in_tower_mode: + distribution.call_for_each_tower(init_and_verify, g) + else: + init_and_verify(g) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/distribute/python/cross_tower_ops.py b/tensorflow/contrib/distribute/python/cross_tower_ops.py index b0baf0dad1d55eafac5338d1eb43465927e428a1..b6037d2133e23841a7804ed84bca302faa9574e3 100644 --- a/tensorflow/contrib/distribute/python/cross_tower_ops.py +++ b/tensorflow/contrib/distribute/python/cross_tower_ops.py @@ -28,18 +28,37 @@ from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import device_util +def check_destinations(destinations): + """Checks whether `destinations` is not None and not empty. + + Args: + destinations: a DistributedValues, Variable, string or a list of strings. + + Returns: + Boolean indicating whether `destinations` is not None and not empty. + """ + # Calling bool() on a ResourceVariable is not allowed. + if isinstance(destinations, resource_variable_ops.ResourceVariable): + return bool(destinations.device) + return bool(destinations) + + def validate_destinations(destinations): - if not isinstance(destinations, - (value_lib.DistributedValues, six.string_types, list)): + if not isinstance( + destinations, + (value_lib.DistributedValues, resource_variable_ops.ResourceVariable, + six.string_types, list)): raise ValueError("destinations must be one of a `DistributedValues` object," - " a device string, a list of device strings or None") + " a tf.Variable object, a device string, a list of device " + "strings or None") - if not destinations: + if not check_destinations(destinations): raise ValueError("destinations can not be empty") @@ -59,6 +78,8 @@ def _validate_value_destination_pairs(value_destination_pairs): def get_devices_from(destinations): if isinstance(destinations, value_lib.DistributedValues): return list(destinations.devices) + elif isinstance(destinations, resource_variable_ops.ResourceVariable): + return [destinations.device] elif isinstance(destinations, six.string_types): return [device_util.resolve(destinations)] else: @@ -225,7 +246,10 @@ class ReductionToOneDeviceCrossTowerOps(CrossTowerOps): super(ReductionToOneDeviceCrossTowerOps, self).__init__() def _reduce(self, aggregation, per_device_value, destinations): - devices = get_devices_from(destinations or per_device_value) + if check_destinations(destinations): + devices = get_devices_from(destinations) + else: + devices = get_devices_from(per_device_value) reduce_to_device = self.reduce_to_device or devices[0] reduced = _simple_reduce(per_device_value, reduce_to_device, self.accumulation_fn, aggregation) @@ -508,7 +532,10 @@ class AllReduceCrossTowerOps(CrossTowerOps): logging.WARN, "Efficient allreduce is not supported for IndexedSlices.", 10) - devices = get_devices_from(destinations or per_device_value) + if check_destinations(destinations): + devices = get_devices_from(destinations) + else: + devices = get_devices_from(per_device_value) reduce_to_device = devices[0] reduced = _simple_reduce(per_device_value, reduce_to_device, math_ops.add_n, aggregation) diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy.py b/tensorflow/contrib/distribute/python/mirrored_strategy.py index dcbc6b0878b89cbb5b9779de315429e6f9478d15..eb2d102012217026f6edb2256ae05b5ce4e4301e 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy.py @@ -20,7 +20,6 @@ from __future__ import print_function import contextlib import threading -import six from tensorflow.contrib.distribute.python import cross_tower_ops as cross_tower_ops_lib from tensorflow.contrib.distribute.python import shared_variable_creator @@ -60,6 +59,156 @@ class _RequestedStop(Exception): pass +# Make _call_for_each_tower and _reduce_non_distributed_value not members of +# MirroredStrategy so that they are generally not allowed to use anything +# specific to MirroredStrategy and thus can be shared with other distribution +# strategies. + + +# TODO(yuefengz): maybe create a common class for those who need to call this +# _call_for_each_tower. +def _call_for_each_tower(distribution, fn, *args, **kwargs): + """Run `fn` in separate threads, once per tower/worker device. + + Args: + distribution: the DistributionStrategy object. + fn: function to run (will be run once per device, each in its own thread). + *args: positional arguments for `fn` + **kwargs: keyword arguments for `fn`. + `"run_concurrently"`: Boolean indicating whether executions of `fn` + can be run concurrently (under eager execution only), defaults to + `True`. + + Returns: + Merged return value of `fn` across all towers. + + Raises: + RuntimeError: If fn() calls get_tower_context().merge_call() a different + number of times from the available devices. + """ + run_concurrently = kwargs.pop("run_concurrently", True) + if not context.executing_eagerly(): + # Lots of TF library code isn't thread-safe in graph mode, and + # there is little to be gained by turning on multithreading when + # constructing a graph. + run_concurrently = False + # Needed for per-thread device, etc. contexts in graph mode. + ops.get_default_graph().switch_to_thread_local() + elif run_concurrently is None: + run_concurrently = True + + coord = coordinator.Coordinator(clean_stop_exception_types=(_RequestedStop,)) + + shared_variable_store = {} + + # TODO(isaprykin): Create these threads once instead of during every run() + # call. + threads = [] + for index, d in enumerate(distribution.worker_devices): + variable_creator_fn = shared_variable_creator.make_fn( + shared_variable_store, index) + t = MirroredStrategy._MirroredTowerThread( # pylint: disable=protected-access + distribution, coord, d, variable_creator_fn, fn, + *values.select_device(d, args), **values.select_device(d, kwargs)) + threads.append(t) + + for t in threads: + t.start() + + # When `fn` starts `should_run` event is set on _MirroredTowerThread + # (`MTT`) threads. The execution waits until + # `MTT.has_paused` is set, which indicates that either `fn` is + # complete or a `get_tower_context().merge_call()` is called. If `fn` is + # complete, then `MTT.done` is set to True. Otherwise, arguments + # of `get_tower_context().merge_call` from all paused threads are grouped + # and the `merge_fn` is performed. Results of the + # `get_tower_context().merge_call` are then set to `MTT.merge_result`. + # Each such `get_tower_context().merge_call` call returns the + # `MTT.merge_result` for that thread when `MTT.should_run` event + # is reset again. Execution of `fn` resumes. + + try: + with coord.stop_on_exception(): + all_done = False + while not all_done and not coord.should_stop(): + done = [] + if run_concurrently: + for t in threads: + t.should_run.set() + for t in threads: + t.has_paused.wait() + t.has_paused.clear() + if coord.should_stop(): + return None + done.append(t.done) + else: + for t in threads: + t.should_run.set() + t.has_paused.wait() + t.has_paused.clear() + if coord.should_stop(): + return None + done.append(t.done) + if coord.should_stop(): + return None + all_done = all(done) + if not all_done: + if any(done): + raise RuntimeError("Some towers made a different number of " + "tower_context().merge_call() calls.") + # get_tower_context().merge_call() case + merge_args = values.regroup({t.device: t.merge_args for t in threads}) + merge_kwargs = values.regroup( + {t.device: t.merge_kwargs for t in threads}) + # We capture the name_scope of the MTT when we call merge_fn + # to ensure that if we have opened a name scope in the MTT, + # it will be respected when executing the merge function. We only + # capture the name_scope from the first MTT and assume it is + # the same for all other MTTs. + mtt_captured_name_scope = threads[0].captured_name_scope + with ops.name_scope(mtt_captured_name_scope): + merge_result = threads[0].merge_fn(distribution, *merge_args, + **merge_kwargs) + for t in threads: + t.merge_result = values.select_device(t.device, merge_result) + finally: + for t in threads: + t.should_run.set() + coord.join(threads) + + return values.regroup({t.device: t.main_result for t in threads}) + + +def _reduce_non_distributed_value(distribution, aggregation, value, + destinations): + """Reduce a non-DistributedValue `value` to `destinations`.""" + if isinstance(value, values.DistributedValues): + raise ValueError("You are passing a `DistributedValue` to " + "`_reduce_non_distributed_value`, which is not allowed.") + + if value == 0: + return 0 + if aggregation == variable_scope.VariableAggregation.MEAN: + return distribution.broadcast(value, destinations) + + cross_tower_ops_lib.validate_destinations(destinations) + if (len(distribution.worker_devices) != 1 or + not cross_tower_ops_lib.check_destinations(destinations)): + raise ValueError("A non-DistributedValues value cannot be reduced with the " + "given aggregation.") + # TODO(anjalisridhar): Moves these methods to a device utility file? + devices = cross_tower_ops_lib.get_devices_from(destinations) + if len(devices) == 1: + with ops.device(devices[0]): + return array_ops.identity(value) + else: + value_updates = {} + for d in devices: + with ops.device(d): + value_updates[d] = array_ops.identity(value) + return values.Mirrored(value_updates) + + class MirroredStrategy(distribute_lib.DistributionStrategy): """Mirrors vars to distribute across multiple devices on a single machine. @@ -198,116 +347,7 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): self._devices) def _call_for_each_tower(self, fn, *args, **kwargs): - """Run `fn` in separate threads, once per tower/worker device. - - Args: - fn: function to run (will be run once per device, each in its own thread). - *args: positional arguments for `fn` - **kwargs: keyword arguments for `fn`. - `"run_concurrently"`: Boolean indicating whether executions of `fn` - can be run concurrently (under eager execution only), defaults to - `True`. - - Returns: - Merged return value of `fn` across all towers. - - Raises: - RuntimeError: If fn() calls get_tower_context().merge_call() a different - number of times for when called for different devices. - """ - run_concurrently = kwargs.pop("run_concurrently", True) - if not context.executing_eagerly(): - # Lots of TF library code isn't thread-safe in graph mode, and - # there is little to be gained by turning on multithreading when - # constructing a graph. - run_concurrently = False - # Needed for per-thread device, etc. contexts in graph mode. - ops.get_default_graph().switch_to_thread_local() - elif run_concurrently is None: - run_concurrently = True - - coord = coordinator.Coordinator( - clean_stop_exception_types=(_RequestedStop,)) - - shared_variable_store = {} - - # TODO(isaprykin): Create these threads once instead of during every run() - # call. - threads = [] - for index, d in enumerate(self._devices): - variable_creator_fn = shared_variable_creator.make_fn( - shared_variable_store, index) - t = MirroredStrategy._MirroredTowerThread( - self, coord, d, variable_creator_fn, fn, - *values.select_device(d, args), **values.select_device(d, kwargs)) - threads.append(t) - - for t in threads: - t.start() - - # When `fn` starts `should_run` event is set on _MirroredTowerThread - # (`MTT`) threads. The execution waits until - # `MTT.has_paused` is set, which indicates that either `fn` is - # complete or a `get_tower_context().merge_call()` is called. If `fn` is - # complete, then `MTT.done` is set to True. Otherwise, arguments - # of `get_tower_context().merge_call` from all paused threads are grouped - # and the `merge_fn` is performed. Results of the - # `get_tower_context().merge_call` are then set to `MTT.merge_result`. - # Each such `get_tower_context().merge_call` call returns the - # `MTT.merge_result` for that thread when `MTT.should_run` event - # is reset again. Execution of `fn` resumes. - - try: - with coord.stop_on_exception(): - all_done = False - while not all_done and not coord.should_stop(): - done = [] - if run_concurrently: - for t in threads: - t.should_run.set() - for t in threads: - t.has_paused.wait() - t.has_paused.clear() - if coord.should_stop(): - return None - done.append(t.done) - else: - for t in threads: - t.should_run.set() - t.has_paused.wait() - t.has_paused.clear() - if coord.should_stop(): - return None - done.append(t.done) - if coord.should_stop(): - return None - all_done = all(done) - if not all_done: - if any(done): - raise RuntimeError("Some towers made a different number of " - "tower_context().merge_call() calls.") - # get_tower_context().merge_call() case - merge_args = values.regroup( - {t.device: t.merge_args for t in threads}) - merge_kwargs = values.regroup( - {t.device: t.merge_kwargs for t in threads}) - # We capture the name_scope of the MTT when we call merge_fn - # to ensure that if we have opened a name scope in the MTT, - # it will be respected when executing the merge function. We only - # capture the name_scope from the first MTT and assume it is - # the same for all other MTTs. - mtt_captured_name_scope = threads[0].captured_name_scope - with ops.name_scope(mtt_captured_name_scope): - merge_result = threads[0].merge_fn( - self, *merge_args, **merge_kwargs) - for t in threads: - t.merge_result = values.select_device(t.device, merge_result) - finally: - for t in threads: - t.should_run.set() - coord.join(threads) - - return values.regroup({t.device: t.main_result for t in threads}) + return _call_for_each_tower(self, fn, *args, **kwargs) def map(self, map_over, fn, *args, **kwargs): # TODO(josh11b): In eager mode, use one thread per device. @@ -337,29 +377,9 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): def _reduce(self, aggregation, value, destinations): assert not isinstance(value, values.Mirrored) - if not isinstance(value, values.PerDevice): - if value == 0: - return 0 - if aggregation == variable_scope.VariableAggregation.MEAN: - return self._broadcast(value, destinations) - - cross_tower_ops_lib.validate_destinations(destinations) - if len(self._devices) == 1: - if destinations: - # TODO(anjalisridhar): Moves these methods to a device utility file? - devices = cross_tower_ops_lib.get_devices_from(destinations) - if len(devices) == 1: - with ops.device(devices[0]): - return array_ops.identity(value) - else: - value_updates = {} - for d in devices: - with ops.device(d): - value_updates[d] = array_ops.identity(value) - return values.Mirrored(value_updates) - raise ValueError("A non PerDevice value cannot be reduced with the given " - "aggregation.") - + if not isinstance(value, values.DistributedValues): + return _reduce_non_distributed_value(self, aggregation, value, + destinations) return self._get_cross_tower_ops().reduce( aggregation, value, destinations=destinations) @@ -433,15 +453,8 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): def _get_devices_from(self, colocate_with=None): if colocate_with is None: return self._devices - elif isinstance(colocate_with, values.DistributedValues): - # pylint: disable=protected-access - return list(colocate_with._index.keys()) - elif isinstance(colocate_with, six.string_types): - return [device_util.resolve(colocate_with)] - elif isinstance(colocate_with, list): - return [device_util.resolve(d) for d in colocate_with] else: - return colocate_with + return cross_tower_ops_lib.get_devices_from(colocate_with) class _MirroredTowerThread(threading.Thread): """A thread that runs() a function on a device.""" diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py index b597bce035493891c3f492bca04abda60c6e8e22..aab7119901023affaad954c4c4ca7678a2ffee06 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py @@ -491,13 +491,14 @@ class MirroredStrategyVariableCreationTest(test.TestCase): components_mean = {} def model_fn(device_id): - tower_context = distribute_lib.get_tower_context() - with tower_context.tower_local_var_scope( - variable_scope.VariableAggregation.SUM): - v_sum = variable_scope.variable(1.0) - with tower_context.tower_local_var_scope( - variable_scope.VariableAggregation.MEAN): - v_mean = variable_scope.variable(4.0) + v_sum = variable_scope.variable( + 1.0, + synchronization=variable_scope.VariableSynchronization.ON_READ, + aggregation=variable_scope.VariableAggregation.SUM) + v_mean = variable_scope.variable( + 4.0, + synchronization=variable_scope.VariableSynchronization.ON_READ, + aggregation=variable_scope.VariableAggregation.MEAN) self.assertTrue(isinstance(v_sum, values.TowerLocalVariable)) self.assertTrue(isinstance(v_mean, values.TowerLocalVariable)) updates = [v_sum.assign_add(2.0 + device_id), @@ -700,10 +701,10 @@ class MirroredStrategyVariableCreationTest(test.TestCase): with context.graph_mode(): def model_fn(): - tower_context = distribute_lib.get_tower_context() - with tower_context.tower_local_var_scope( - variable_scope.VariableAggregation.SUM): - v_sum = variable_scope.variable(1.0) + v_sum = variable_scope.variable( + 1.0, + synchronization=variable_scope.VariableSynchronization.ON_READ, + aggregation=variable_scope.VariableAggregation.SUM) self.assertTrue(isinstance(v_sum, values.TowerLocalVariable)) return v_sum @@ -791,8 +792,8 @@ class MirroredVariableUpdateTest(test.TestCase): return mirrored_var.assign(5.0) with self.assertRaisesRegexp( - ValueError, "A non PerDevice value cannot be reduced with the given " - "aggregation."): + ValueError, "A non-DistributedValues value cannot be reduced with " + "the given aggregation."): self.evaluate(dist.unwrap(dist.call_for_each_tower(model_fn))) @test_util.run_in_graph_and_eager_modes(config=config) @@ -922,5 +923,118 @@ class MirroredVariableUpdateTest(test.TestCase): self.assertEquals(4.5, self.evaluate(mirrored_var)) +class MirroredAndTowerLocalVariableInitializerTest(test.TestCase): + config = config_pb2.ConfigProto() + config.allow_soft_placement = True + + def testAssignMirroredVarInitializer(self): + # This test is not eager compatible since in eager variables are initialized + # upon construction instead of once the initialization op is run. + with context.graph_mode(): + def var_fn(): + v = variable_scope.variable(1.0, name="foo") + return v + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with dist.scope(): + mirrored_var = dist.call_for_each_tower(var_fn) + self.assertIsInstance(mirrored_var, values.MirroredVariable) + self.assertFalse(self.evaluate(mirrored_var.is_initialized())) + self.evaluate(mirrored_var.initializer) + self.assertTrue(self.evaluate(mirrored_var.is_initialized())) + + def testAssignTowerLocalVarInitializer(self): + # This test is not eager compatible since in eager variables are initialized + # upon construction instead of once the initialization op is run. + with context.graph_mode(): + def model_fn(): + v_sum = variable_scope.variable( + 1.0, + synchronization=variable_scope.VariableSynchronization.ON_READ, + aggregation=variable_scope.VariableAggregation.SUM) + self.assertTrue(isinstance(v_sum, values.TowerLocalVariable)) + return v_sum + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with dist.scope(): + tower_local_var = dist.call_for_each_tower(model_fn) + self.assertTrue(isinstance(tower_local_var, values.TowerLocalVariable)) + self.assertFalse(self.evaluate(tower_local_var.is_initialized())) + self.evaluate(tower_local_var.initializer) + self.assertTrue(self.evaluate(tower_local_var.is_initialized())) + + +class TowerLocalVariableAssignTest(test.TestCase): + config = config_pb2.ConfigProto() + config.allow_soft_placement = True + + def _skip_eager_if_gpus_less_than(self, num_gpus): + if context.num_gpus() < num_gpus and context.executing_eagerly(): + self.skipTest("Enough GPUs not available for this test in eager mode.") + + @test_util.run_in_graph_and_eager_modes(config=config) + def testAssignTowerLocalVarSumAggregation(self): + self._skip_eager_if_gpus_less_than(1) + def model_fn(): + v_sum = variable_scope.variable( + 1.0, + synchronization=variable_scope.VariableSynchronization.ON_READ, + aggregation=variable_scope.VariableAggregation.SUM) + return v_sum + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with dist.scope(): + tower_local_var = dist.call_for_each_tower(model_fn, + run_concurrently=False) + self.assertTrue(isinstance(tower_local_var, values.TowerLocalVariable)) + self.evaluate(variables.global_variables_initializer()) + # Each tower has a value of 1.0 assigned to it in tower context. + # When we read the value using `read_var` we should see the SUM of each of + # values on each of the towers. + self.assertEqual(2.0, self.evaluate(dist.read_var(tower_local_var))) + # Assigning 6.0 in cross tower context will assign a value of + # 6.0/num_towers to each tower. + tlv_ops = tower_local_var.assign(6.0) + self.evaluate(tlv_ops) + # On reading the tower local var we should get the assigned value back. + # The value on all the towers are added before being returned by + # `read_var`. + self.assertEqual(6.0, self.evaluate(dist.read_var(tower_local_var))) + + @test_util.run_in_graph_and_eager_modes(config=config) + def testAssignTowerLocalVarMeanAggregation(self): + self._skip_eager_if_gpus_less_than(1) + def model_fn(): + v_sum = variable_scope.variable( + 1.0, + synchronization=variable_scope.VariableSynchronization.ON_READ, + aggregation=variable_scope.VariableAggregation.MEAN) + return v_sum + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with dist.scope(): + tower_local_var = dist.call_for_each_tower(model_fn, + run_concurrently=False) + self.assertTrue(isinstance(tower_local_var, values.TowerLocalVariable)) + self.evaluate(variables.global_variables_initializer()) + # Each tower has a value of 1.0 assigned to it in tower context. + # When we read the value using `read_var` we should see the MEAN of values + # on all towers which is the value assigned in tower context. + self.assertEqual(1.0, self.evaluate(dist.read_var(tower_local_var))) + tlv_ops = tower_local_var.assign(6.0) + self.evaluate(tlv_ops) + # On reading the tower local var we should get the MEAN of all values + # which is equal to the value assigned. + self.assertEqual(6.0, self.evaluate(dist.read_var(tower_local_var))) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/distribute/python/multi_worker_strategy.py b/tensorflow/contrib/distribute/python/multi_worker_strategy.py index 0f21a427320510635279f80c11711e81715ec37c..cbfe5df61d1ee6fa1eb9275b715b0721d678a46f 100644 --- a/tensorflow/contrib/distribute/python/multi_worker_strategy.py +++ b/tensorflow/contrib/distribute/python/multi_worker_strategy.py @@ -46,7 +46,7 @@ class MultiWorkerMirroredStrategy(MirroredStrategy): * **In-graph replication**: the `client` creates a single `tf.Graph` that specifies tasks for devices on all workers. The `client` then creates a client session which will talk to the `master` service of a `worker`. Then - the `master` will parition the graph and distribute the work to all + the `master` will partition the graph and distribute the work to all participating workers. * **Worker**: A `worker` is a TensorFlow `task` that usually maps to one physical machine. We will have multiple `worker`s with different `task` diff --git a/tensorflow/contrib/distribute/python/multi_worker_test_base.py b/tensorflow/contrib/distribute/python/multi_worker_test_base.py index f659be5f42594b275af06435cb0c228e5d594ac9..fa479918bd48224d042725566ec905018b974f45 100644 --- a/tensorflow/contrib/distribute/python/multi_worker_test_base.py +++ b/tensorflow/contrib/distribute/python/multi_worker_test_base.py @@ -28,23 +28,39 @@ from tensorflow.python.eager import test from tensorflow.python.framework import test_util +def create_in_process_cluster(num_workers, num_ps): + """Create an in-process cluster that consists of only standard server.""" + # Leave some memory for cuda runtime. + gpu_mem_frac = 0.7 / num_workers + worker_config = config_pb2.ConfigProto() + worker_config.gpu_options.per_process_gpu_memory_fraction = gpu_mem_frac + + ps_config = config_pb2.ConfigProto() + ps_config.device_count['GPU'] = 0 + + # Create in-process servers. Once an in-process tensorflow server is created, + # there is no way to terminate it. So we create one cluster per test process. + # We could've started the server in another process, we could then kill that + # process to terminate the server. The reasons why we don't want multiple + # processes are + # 1) it is more difficult to manage these processes + # 2) there is something global in CUDA such that if we initialize CUDA in the + # parent process, the child process cannot initialize it again and thus cannot + # use GPUs (https://stackoverflow.com/questions/22950047). + return test_util.create_local_cluster( + num_workers, + num_ps=num_ps, + worker_config=worker_config, + ps_config=ps_config) + + class MultiWorkerTestBase(test.TestCase): """Base class for testing multi node strategy and dataset.""" @classmethod def setUpClass(cls): """Create a local cluster with 2 workers.""" - num_workers = 2 - # Leave some memory for cuda runtime. - gpu_mem_frac = 0.7 / num_workers - default_config = config_pb2.ConfigProto() - default_config.gpu_options.per_process_gpu_memory_fraction = gpu_mem_frac - - # The local cluster takes some portion of the local GPUs and there is no way - # for the cluster to terminate unless using multiple processes. Therefore, - # we have to only create only one cluster throughout a test process. - workers, _ = test_util.create_local_cluster( - num_workers, num_ps=0, worker_config=default_config) + workers, _ = create_in_process_cluster(num_workers=2, num_ps=0) cls._master_target = workers[0].target @contextlib.contextmanager diff --git a/tensorflow/contrib/distribute/python/parameter_server_strategy.py b/tensorflow/contrib/distribute/python/parameter_server_strategy.py new file mode 100644 index 0000000000000000000000000000000000000000..9bcf6f8bac1d0d694381a12e7609a87e8025fa63 --- /dev/null +++ b/tensorflow/contrib/distribute/python/parameter_server_strategy.py @@ -0,0 +1,355 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Classes implementing a multi-worker ps DistributionStrategy.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import json +import os + +from tensorflow.contrib.distribute.python import cross_tower_ops as cross_tower_ops_lib +from tensorflow.contrib.distribute.python import mirrored_strategy +from tensorflow.contrib.distribute.python import values +from tensorflow.core.protobuf import cluster_pb2 +from tensorflow.python.framework import device as tf_device +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.training import device_setter +from tensorflow.python.training import device_util +from tensorflow.python.training import distribute as distribute_lib +from tensorflow.python.training import server_lib +from tensorflow.python.util import nest + +_LOCAL_CPU = "/device:CPU:0" +_LOCAL_GPU_0 = "/device:GPU:0" + + +def _normalize_cluster_spec(cluster_spec): + """Makes `cluster_spec` into a `ClusterSpec` object.""" + if isinstance(cluster_spec, (dict, cluster_pb2.ClusterDef)): + return server_lib.ClusterSpec(cluster_spec) + elif not isinstance(cluster_spec, server_lib.ClusterSpec): + raise ValueError( + "`cluster_spec' should be dict or a `tf.train.ClusterSpec` or a " + "`tf.train.ClusterDef` object") + return cluster_spec + + +# TODO(yuefengz): maybe cache variables on local CPU. +# TODO(yuefengz): we may want to set session options to disallow communication +# between workers. +class ParameterServerStrategy(distribute_lib.DistributionStrategy): + """A parameter server DistributionStrategy. + + This strategy class works for both local training and between-graph replicated + training for multiple workers. If `cluster_spec` is specified, either passed + in to __init__() method or parsed from the + ["TF_CONFIG" environment + variable](https://www.tensorflow.org/api_docs/python/tf/estimator/RunConfig), + variables and updates to those variables are assigned to parameter servers and + other operations are assigned to workers. If `cluster_spec` is not set, it + becomes local training where variables are assigned to local CPU or the only + GPU. When each worker has more than one GPU, operations will be replicated on + these GPUs. In both cases, operations are replicated but variables are not and + these workers share a common view for which paramater server a variable is + assigned to. + + This class assumes between-graph replication will be used and works on a graph + for a particular worker. + + It is expected to call `call_for_each_tower(fn, *args, **kwargs)` for any + operations which potentially can be replicated across towers (i.e. multiple + GPUs) even if there is only CPU or one GPU. When defining the `fn`, extra + caution needs to be taken: + + 1) Always use @{tf.get_variable} instead of @{tf.Variable} which is not able + to refer to the same variable on different towers. + + 2) It is generally not recommended to open a device scope under the strategy's + scope. A device scope (i.e. calling @{tf.device}) will be merged with or + override the device for operations but will not change the device for + variables. + + 3) It is also not recommended to open a colocation scope (i.e. calling + @{tf.colocate_with}) under the strategy's scope. For colocating variables, + use `distribution.colocate_vars_with` instead. Colocation of ops will possibly + create conflicts of device assignement. + """ + + def __init__(self, + num_gpus_per_worker=0, + cluster_spec=None, + task_type=None, + task_id=None): + """Initiailizes this strategy. + + Args: + num_gpus_per_worker: number of local GPUs or GPUs per worker. + cluster_spec: a dict, ClusterDef or ClusterSpec object specifying the + cluster configurations. + task_type: the current task type. + task_id: the current task id. + """ + super(ParameterServerStrategy, self).__init__() + self._num_gpus_per_worker = num_gpus_per_worker + if cluster_spec: + cluster_spec = _normalize_cluster_spec(cluster_spec) + self._cluster_spec = cluster_spec + + # We typically don't need to do all-reduce in this strategy. + self._cross_tower_ops = ( + cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps( + reduce_to_device=_LOCAL_CPU)) + + self._initialize_devices(num_gpus_per_worker, cluster_spec, task_type, + task_id) + + def _initialize_devices(self, num_gpus_per_worker, cluster_spec, task_type, + task_id): + """Initialize internal devices. + + It creates variable devices and compute devices. Variables and operations + will be assigned to them respectively. We have one compute device per tower. + The variable device is a device function or device string. The default + variable device assigns variables to parameter servers in a round-robin + fashion. + + Args: + num_gpus_per_worker: number of local GPUs or GPUs per worker. + cluster_spec: a dict, ClusterDef or ClusterSpec object specifying the + cluster configurations. + task_type: the current task type. + task_id: the current task id. + + Raises: + ValueError: if the cluster_spec doesn't have ps jobs. + """ + self._task_type = task_type or "worker" + self._task_id = task_id or 0 + self._worker_device = "/job:%s/task:%d" % (self._task_type, self._task_id) + + # TODO(yuefengz): maybe clearer to split it into two classes, one for + # the distribuetd case and one for the local case, once we have the factory + # class/method. + + # Define compute devices which is a list of device strings and one for each + # tower. When there are GPUs, replicate operations on these GPUs. Otherwise, + # place operations on CPU. + if cluster_spec is None: + # Local mode. + if num_gpus_per_worker > 0: + self._compute_devices = list( + map("/device:GPU:{}".format, range(num_gpus_per_worker))) + else: + self._compute_devices = [_LOCAL_CPU] + else: + # Distributed mode. + if num_gpus_per_worker > 0: + self._compute_devices = [ + "%s/device:GPU:%d" % (self._worker_device, i) + for i in range(num_gpus_per_worker) + ] + else: + self._compute_devices = [self._worker_device] + + self._compute_devices = list( + map(device_util.resolve, self._compute_devices)) + self._canonical_compute_device_set = set(self._compute_devices) + + # Define variable device which is a device string in the local case and a + # device function in the distributed case. It is used to open a device scope + # where varibles are defined. + # The `_parameter_devices` is needed for the `parameter_devices` property + # and is a list of all variable devices. + if cluster_spec is None: + # Local mode. If there is only one GPU, put everything on that GPU. + # Otherwise, place variables on CPU. + if num_gpus_per_worker == 1: + assert len(list(self._compute_devices)) == 1 + self._variable_device = _LOCAL_GPU_0 + self._parameter_devices = [_LOCAL_GPU_0] + else: + self._variable_device = _LOCAL_CPU + self._parameter_devices = [_LOCAL_CPU] + else: + # Distributed mode. Place variables on ps jobs in a round-robin fashion. + # Note that devices returned from `replica_device_setter` are not + # canonical and therefore we don't canonicalize all variable devices to + # make them consistent. + # TODO(yuefengz): support passing a strategy object to control variable + # assignment. + # TODO(yuefengz): merge the logic of replica_device_setter into this + # class. + num_ps_replicas = len(cluster_spec.as_dict().get("ps", [])) + if num_ps_replicas == 0: + raise ValueError("The cluster spec needs to have `ps` jobs.") + self._variable_device = device_setter.replica_device_setter( + ps_tasks=num_ps_replicas, + worker_device=self._worker_device, + merge_devices=True, + cluster=cluster_spec) + + # Parameter devices are all tasks of the "ps" job. + self._parameter_devices = map("/job:ps/task:{}".format, + range(num_ps_replicas)) + + # Define the default device in cross-tower mode. In the distributed case, we + # set the default device to the corresponding worker to prevent these ops + # from being placed on other workers. + if cluster_spec is None: + self._default_device = None + else: + self._default_device = self._worker_device + + def distribute_dataset(self, dataset_fn): + """Distributes the dataset to each local GPU.""" + return values.PerDeviceDataset( + self._call_dataset_fn(dataset_fn), self._compute_devices, True) + + def _broadcast(self, tensor, destinations): + if not cross_tower_ops_lib.check_destinations(destinations): + destinations = self._compute_devices + return self._cross_tower_ops.broadcast(tensor, destinations) + + # TODO(yuefengz): not all ops in device_setter.STANDARD_PS_OPS will go through + # this creator, such as "MutableHashTable". + def _create_variable(self, next_creator, *args, **kwargs): + if "colocate_with" in kwargs: + with ops.device(None): + with ops.colocate_with(kwargs["colocate_with"]): + return next_creator(*args, **kwargs) + + with ops.colocate_with(None, ignore_existing=True): + with ops.device(self._variable_device): + return next_creator(*args, **kwargs) + + def _call_for_each_tower(self, fn, *args, **kwargs): + # pylint: disable=protected-access + return mirrored_strategy._call_for_each_tower(self, fn, *args, **kwargs) + + def _verify_destinations_not_different_worker(self, destinations): + if destinations is None: + return + for d in cross_tower_ops_lib.get_devices_from(destinations): + d_spec = tf_device.DeviceSpec.from_string(d) + if d_spec.job == self._task_type and d_spec.task != self._task_id: + raise ValueError( + "Cannot reduce to another worker: %r, current worker is %r" % + (d, self._worker_device)) + + def _reduce(self, aggregation, value, destinations): + self._verify_destinations_not_different_worker(destinations) + if not isinstance(value, values.DistributedValues): + # pylint: disable=protected-access + return mirrored_strategy._reduce_non_distributed_value( + self, aggregation, value, destinations) + + return self._cross_tower_ops.reduce( + aggregation, value, destinations=destinations) + + def _batch_reduce(self, aggregation, value_destination_pairs): + for _, destinations in value_destination_pairs: + self._verify_destinations_not_different_worker(destinations) + return self._cross_tower_ops.batch_reduce(aggregation, + value_destination_pairs) + + def _select_single_value(self, structured): + """Select any single values in `structured`.""" + + def _select_fn(x): # pylint: disable=g-missing-docstring + if isinstance(x, values.Mirrored): + if len(x.devices) == 1: + return list(x._index.values())[0] # pylint: disable=protected-access + else: + raise ValueError( + "You cannot update variable with a Mirrored object with multiple " + "components %r when using ParameterServerStrategy. You must " + "specify a single value or a Mirrored with a single value." % x) + elif isinstance(x, values.PerDevice): + raise ValueError( + "You cannot update variable with a PerDevice object %r when using " + "ParameterServerStrategy. You must specify a single value or a " + "Mirrored with a single value" % x) + else: + return x + + return nest.map_structure(_select_fn, structured) + + def _update(self, var, fn, *args, **kwargs): + if not isinstance(var, resource_variable_ops.ResourceVariable): + raise ValueError( + "You can not update `var` %r. It must be a Variable." % var) + with ops.colocate_with(var), distribute_lib.UpdateContext(var.device): + return fn(var, *self._select_single_value(args), + **self._select_single_value(kwargs)) + + # TODO(yuefengz): does it need to call _select_single_value? + def _update_non_slot(self, colocate_with, fn, *args, **kwargs): + with ops.device( + colocate_with.device), distribute_lib.UpdateContext(colocate_with): + return fn(*args, **kwargs) + + def _unwrap(self, val): + if isinstance(val, values.DistributedValues): + # Return in a deterministic order. + if set(val.devices) == self._canonical_compute_device_set: + return [val.get(device=d) for d in self._compute_devices] + return [val.get(device=d) for d in sorted(val.devices)] + return [val] + + def read_var(self, var): + # No need to distinguish between normal variables and tower-local variables. + return array_ops.identity(var) + + def configure(self, session_config=None): + del session_config + + # Use TF_CONFIG to get the cluster spec and the current job. + tf_config = json.loads(os.environ.get("TF_CONFIG", "{}")) + cluster_spec = _normalize_cluster_spec(tf_config.get("cluster", {})) + + task_env = tf_config.get("task", {}) + if task_env: + task_type = task_env.get("type", "worker") + task_id = int(task_env.get("index", "0")) + else: + task_type = "worker" + task_id = None + + # Set the devices if cluster_spec is defined in TF_CONFIG but not passed in + # the constructor. + if not self._cluster_spec and cluster_spec: + self._cluster_spec = cluster_spec + self._initialize_devices(self._num_gpus_per_worker, cluster_spec, + task_type, task_id) + + @property + def num_towers(self): + return len(self._compute_devices) + + @property + def worker_devices(self): + # Make a copy to prevent users from accidentally mutating our copy. + return list(self._compute_devices) + + @property + def parameter_devices(self): + return list(self._parameter_devices) + + def non_slot_devices(self, var_list): + return min(var_list, key=lambda x: x.name) diff --git a/tensorflow/contrib/distribute/python/parameter_server_strategy_test.py b/tensorflow/contrib/distribute/python/parameter_server_strategy_test.py new file mode 100644 index 0000000000000000000000000000000000000000..ad538b9e8ee99d3658ef3dbfad9fbe66bcfd2b6d --- /dev/null +++ b/tensorflow/contrib/distribute/python/parameter_server_strategy_test.py @@ -0,0 +1,455 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for ParameterServerStrategy.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import contextlib +import json +import threading +from absl.testing import parameterized + +from tensorflow.contrib.distribute.python import combinations +from tensorflow.contrib.distribute.python import multi_worker_test_base +from tensorflow.contrib.distribute.python import parameter_server_strategy +from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.client import session +from tensorflow.python.eager import context +from tensorflow.python.estimator import run_config +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import ops +from tensorflow.python.layers import core +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import gradients +from tensorflow.python.ops import variable_scope +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.training import device_util +from tensorflow.python.training import distribute as distribute_lib + + +class ParameterServerStrategyTest(test.TestCase, parameterized.TestCase): + + @classmethod + def setUpClass(cls): + cls._workers, cls._ps = multi_worker_test_base.create_in_process_cluster( + num_workers=3, num_ps=2) + + def setUp(self): + self._result = 0 + self._lock = threading.Lock() + self._init_condition = threading.Condition() + self._init_reached = 0 + self._finish_condition = threading.Condition() + self._finish_reached = 0 + + def _get_ps_distribution_strategy(self, task_type, task_index, num_gpus=0): + tf_config = { + 'cluster': { + run_config.TaskType.WORKER: [ + 'fake_worker_0', 'fake_worker_1', 'fake_worker_2' + ], + run_config.TaskType.PS: ['fake_ps_0', 'fake_ps_1'] + }, + 'task': { + 'type': task_type, + 'index': task_index + } + } + distribution = parameter_server_strategy.ParameterServerStrategy( + num_gpus_per_worker=num_gpus) + with self._lock: + # Accessing environment variables should be protected by locks because + # environment variables are shared by all threads. + with test.mock.patch.dict('os.environ', + {'TF_CONFIG': json.dumps(tf_config)}): + distribution.configure() + return distribution + + @contextlib.contextmanager + def _test_session(self, target): + config = config_pb2.ConfigProto(allow_soft_placement=True) + config.graph_options.optimizer_options.opt_level = -1 + with session.Session(graph=None, config=config, target=target) as sess: + yield sess + + def _test_device_assignment_distributed(self, d, num_gpus=0): + with ops.Graph().as_default(), \ + self._test_session(target=self._workers[0].target) as sess, \ + d.scope(): + + # Define a variable outside the call_for_each_tower scope. This is not + # recommended. + n = variable_scope.get_variable('n', initializer=10.0) + self.assertEqual(n.device, '/job:ps/task:0') + + def model_fn(): + if num_gpus == 0: + last_part_device = 'device:CPU:0' + else: + last_part_device = ( + 'device:GPU:%d' % distribute_lib.get_tower_context().tower_id) + + a = constant_op.constant(1.0) + b = constant_op.constant(2.0) + c = a + b + self.assertEqual(a.device, + '/job:worker/replica:0/task:1/%s' % last_part_device) + self.assertEqual(b.device, + '/job:worker/replica:0/task:1/%s' % last_part_device) + self.assertEqual(c.device, + '/job:worker/replica:0/task:1/%s' % last_part_device) + + # The device scope is ignored for variables but not for normal ops. + with ops.device('/job:worker/task:0'): + x = variable_scope.get_variable('x', initializer=10.0) + x_add = x.assign_add(c) + e = a + c + # The variable x is on the task 1 since the device_function has been + # called once before the model_fn. + self.assertEqual(x.device, '/job:ps/task:1') + self.assertEqual(x_add.device, x.device) + self.assertEqual(e.device, + '/job:worker/replica:0/task:0/%s' % last_part_device) + + # The colocate_vars_with can override the distribution's device. + with d.colocate_vars_with(x): + y = variable_scope.get_variable('y', initializer=20.0) + y_add = y.assign_add(x_add) + self.assertEqual(y.device, '/job:ps/task:1') + self.assertEqual(y_add.device, y.device) + self.assertEqual(y.device, x.device) + + z = variable_scope.get_variable('z', initializer=10.0) + self.assertEqual(z.device, '/job:ps/task:0') + self.assertNotEqual(z.device, x.device) + + with ops.control_dependencies([y_add]): + z_add = z.assign_add(y) + with ops.control_dependencies([z_add]): + f = z + c + self.assertEqual(f.device, + '/job:worker/replica:0/task:1/%s' % last_part_device) + + # The device scope would merge with the default worker device. + with ops.device('/CPU:1'): + g = e + 1.0 + self.assertEqual(g.device, '/job:worker/replica:0/task:1/device:CPU:1') + + # Ths ops.colocate_with will be ignored when defining a variale but not + # for a normal tensor. + with ops.colocate_with(x): + u = variable_scope.get_variable('u', initializer=30.0) + v = variable_scope.get_variable('v', initializer=30.0) + h = f + 1.0 + self.assertIn('/job:ps/', u.device) + self.assertIn('/job:ps/', v.device) + # u and v are on different parameter servers. + self.assertTrue(u.device != x.device or v.device != x.device) + self.assertTrue(u.device == x.device or v.device == x.device) + # Here h is not on one worker. Note h.device is canonical while x.device + # is not but. + self.assertIn('/job:ps/', h.device) + return y_add, z_add, f + + y, z, f = d.call_for_each_tower(model_fn) + self.assertNotEqual(y, None) + self.assertNotEqual(z, None) + self.assertNotEqual(f, None) + + if context.num_gpus() >= 1 and num_gpus <= 1: + variables.global_variables_initializer().run() + y_val, z_val, f_val = sess.run([y, z, f]) + self.assertEqual(y_val, 33.0) + self.assertEqual(z_val, 43.0) + self.assertEqual(f_val, 46.0) + + @combinations.generate( + combinations.combine(mode=['graph'], num_gpus=[0, 1, 2])) + def testDeviceAssignmentDistributed(self, num_gpus): + d = self._get_ps_distribution_strategy('worker', 1, num_gpus=num_gpus) + self._test_device_assignment_distributed(d, num_gpus=num_gpus) + + def _test_device_assignment_local(self, + d, + compute_device='CPU', + variable_device='CPU', + num_gpus=0): + with ops.Graph().as_default(), \ + self._test_session(target=self._workers[0].target) as sess, \ + d.scope(): + + def model_fn(): + if 'CPU' in compute_device: + tower_compute_device = '/device:CPU:0' + else: + tower_compute_device = ( + '/device:GPU:%d' % distribute_lib.get_tower_context().tower_id) + tower_compute_device = device_util.canonicalize(tower_compute_device) + + if 'CPU' in variable_device: + tower_variable_device = '/device:CPU:0' + else: + tower_variable_device = ( + '/device:GPU:%d' % distribute_lib.get_tower_context().tower_id) + tower_variable_device = device_util.canonicalize(tower_variable_device) + + a = constant_op.constant(1.0) + b = constant_op.constant(2.0) + c = a + b + self.assertEqual(a.device, tower_compute_device) + self.assertEqual(b.device, tower_compute_device) + self.assertEqual(c.device, tower_compute_device) + + # The device scope is ignored for variables but not for normal ops. + with ops.device('/device:GPU:2'): + x = variable_scope.get_variable('x', initializer=10.0) + x_add = x.assign_add(c) + e = a + c + self.assertEqual( + device_util.canonicalize(x.device), tower_variable_device) + self.assertEqual(x_add.device, x.device) + self.assertEqual(e.device, device_util.canonicalize('/device:GPU:2')) + + # The colocate_vars_with can override the distribution's device. + with d.colocate_vars_with(x): + y = variable_scope.get_variable('y', initializer=20.0) + y_add = y.assign_add(x_add) + self.assertEqual( + device_util.canonicalize(y.device), tower_variable_device) + self.assertEqual(y_add.device, y.device) + self.assertEqual(y.device, x.device) + + z = variable_scope.get_variable('z', initializer=10.0) + self.assertEqual( + device_util.canonicalize(z.device), tower_variable_device) + + with ops.control_dependencies([y_add]): + z_add = z.assign_add(y) + with ops.control_dependencies([z_add]): + f = z + c + self.assertEqual(f.device, tower_compute_device) + + # The device scope would merge with the default worker device. + with ops.device('/CPU:1'): + g = e + 1.0 + self.assertEqual(g.device, device_util.canonicalize('/device:CPU:1')) + + # Ths ops.colocate_with will be ignored when defining a variale but not + # for a normal tensor. + with ops.colocate_with(x): + u = variable_scope.get_variable('u', initializer=30.0) + h = f + 1.0 + self.assertEqual( + device_util.canonicalize(u.device), tower_variable_device) + self.assertEqual(device_util.canonicalize(x.device), h.device) + return y_add, z_add, f + + y, z, f = d.call_for_each_tower(model_fn) + self.assertNotEqual(y, None) + self.assertNotEqual(z, None) + self.assertNotEqual(f, None) + + if context.num_gpus() >= 1 and num_gpus <= 1: + variables.global_variables_initializer().run() + y_val, z_val, f_val = sess.run([y, z, f]) + self.assertEqual(y_val, 33.0) + self.assertEqual(z_val, 43.0) + self.assertEqual(f_val, 46.0) + + def testDeviceAssignmentLocal(self): + distribution = parameter_server_strategy.ParameterServerStrategy( + num_gpus_per_worker=0) + self._test_device_assignment_local( + distribution, compute_device='CPU', variable_device='CPU', num_gpus=0) + + distribution = parameter_server_strategy.ParameterServerStrategy( + num_gpus_per_worker=1) + self._test_device_assignment_local( + distribution, compute_device='GPU', variable_device='GPU', num_gpus=1) + + distribution = parameter_server_strategy.ParameterServerStrategy( + num_gpus_per_worker=2) + self._test_device_assignment_local( + distribution, compute_device='GPU', variable_device='CPU', num_gpus=2) + + def _test_simple_increment(self, d, task_type, task_index, master_target): + if hasattr(d, '_cluster_spec') and d._cluster_spec: + num_workers = len(d._cluster_spec.as_dict().get('worker', + ['dummy_worker'])) + else: + num_workers = 1 + with ops.Graph().as_default(), \ + self._test_session(target=master_target) as sess, \ + d.scope(): + + def model_fn(): + x = variable_scope.get_variable('x', initializer=10.0) + y = variable_scope.get_variable('y', initializer=20.0) + + x_add = x.assign_add(1.0, use_locking=True) + y_add = y.assign_add(1.0, use_locking=True) + + train_op = control_flow_ops.group([x_add, y_add]) + return x, y, train_op + + x, y, train_op = d.call_for_each_tower(model_fn) + train_op = d.group(d.unwrap(train_op)) + + if context.num_gpus() < d._num_gpus_per_worker: + return True + + if task_index == 0: + variables.global_variables_initializer().run() + + # Workers waiting for chief worker's initializing variables. + self._init_condition.acquire() + self._init_reached += 1 + while self._init_reached != num_workers: + self._init_condition.wait() + self._init_condition.notify_all() + self._init_condition.release() + + sess.run(train_op) + + # Wait for other workers to finish training. + self._finish_condition.acquire() + self._finish_reached += 1 + while self._finish_reached != num_workers: + self._finish_condition.wait() + self._finish_condition.notify_all() + self._finish_condition.release() + + x_val, y_val = sess.run([x, y]) + self.assertEqual(x_val, 10.0 + 1.0 * num_workers * d.num_towers) + self.assertEqual(y_val, 20.0 + 1.0 * num_workers * d.num_towers) + return (x_val == 10.0 + 1.0 * num_workers * d.num_towers and + y_val == 20.0 + 1.0 * num_workers * d.num_towers) + + def _test_minimize_loss_graph(self, d, task_type, task_index, master_target): + with ops.Graph().as_default(), \ + self._test_session(target=master_target) as sess, \ + d.scope(): + l = core.Dense(1, use_bias=False) + + def loss_fn(x): + y = array_ops.reshape(l(x), []) - constant_op.constant(1.) + return y * y + + # TODO(yuefengz, apassos): eager.backprop.implicit_grad is not safe for + # multiple graphs (b/111216820). + def grad_fn(x): + loss = loss_fn(x) + var_list = ( + variables.trainable_variables() + ops.get_collection( + ops.GraphKeys.TRAINABLE_RESOURCE_VARIABLES)) + grads = gradients.gradients(loss, var_list) + ret = list(zip(grads, var_list)) + return ret + + def update(v, g): + return v.assign_sub(0.05 * g, use_locking=True) + + one = d.broadcast(constant_op.constant([[1.]])) + + def step(): + """Perform one optimization step.""" + # Run forward & backward to get gradients, variables list. + g_v = d.call_for_each_tower(grad_fn, one) + # Update the variables using the gradients and the update() function. + before_list = [] + after_list = [] + for g, v in g_v: + fetched = d.read_var(v) + before_list.append(fetched) + with ops.control_dependencies([fetched]): + # TODO(yuefengz): support non-Mirrored variable as destinations. + g = d.reduce( + variable_scope.VariableAggregation.SUM, g, destinations=v) + with ops.control_dependencies(d.unwrap(d.update(v, update, g))): + after_list.append(d.read_var(v)) + return before_list, after_list + + before_out, after_out = step() + + if context.num_gpus() < d._num_gpus_per_worker: + return True + + if task_index == 0: + variables.global_variables_initializer().run() + + # Workers waiting for chief worker's initializing variables. + self._init_condition.acquire() + self._init_reached += 1 + while self._init_reached != 3: + self._init_condition.wait() + self._init_condition.notify_all() + self._init_condition.release() + + for i in range(10): + b, a = sess.run((before_out, after_out)) + if i == 0: + before, = b + after, = a + + error_before = abs(before - 1) + error_after = abs(after - 1) + # Error should go down + self.assertLess(error_after, error_before) + return error_after < error_before + + def _run_client(self, index, model_fn, num_gpus): + task_type = run_config.TaskType.WORKER + result = model_fn( + self._get_ps_distribution_strategy(task_type, index, num_gpus=num_gpus), + task_type, index, self._workers[index].target) + if result: + with self._lock: + self._result += 1 + + def _run_multiple_clients(self, num_clients, model_fn, num_gpus=0): + threads = [] + for i in range(num_clients): + t = threading.Thread( + target=self._run_client, args=(i, model_fn, num_gpus)) + t.start() + threads.append(t) + for t in threads: + t.join() + + def testSimpleBetweenGraph(self): + self._run_multiple_clients(3, self._test_simple_increment) + self.assertEqual(self._result, 3) + + @combinations.generate( + combinations.combine(mode=['graph'], num_gpus=[0, 1, 2])) + def testLocalSimpleIncrement(self, num_gpus): + d = parameter_server_strategy.ParameterServerStrategy( + num_gpus_per_worker=num_gpus) + self._test_simple_increment(d, 'dummy_worker', 0, '') + + @combinations.generate( + combinations.combine(mode=['graph'], num_gpus=[0, 1, 2])) + def testMinimizeLossGraph(self, num_gpus): + self._run_multiple_clients( + 3, self._test_minimize_loss_graph, num_gpus=num_gpus) + self.assertEqual(self._result, 3) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py index b36ac563d29fc9157873796a845fefba3651edda..4018b1e02339e377acc0594407a4f89791ff57af 100644 --- a/tensorflow/contrib/distribute/python/values.py +++ b/tensorflow/contrib/distribute/python/values.py @@ -30,10 +30,10 @@ from tensorflow.contrib.distribute.python import prefetching_ops_v2 from tensorflow.python.eager import context from tensorflow.python.framework import device as tf_device from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.training import device_util from tensorflow.python.training import distribute as distribute_lib @@ -78,6 +78,13 @@ class DistributedValues(object): def devices(self): return list(self._index.keys()) + @property + def is_tensor_like(self): + for v in self._index.values(): + if not tensor_util.is_tensor(v): + return False + return True + def __str__(self): return "%s:%s" % (self.__class__.__name__, self._index) @@ -197,11 +204,54 @@ class DistributedVariable(DistributedDelegate): # to the container without introducing a reference cycle. for v in six.itervalues(index): v._distributed_container = weakref.ref(self) # pylint: disable=protected-access + # tf.keras keeps track of variables initialized using this attribute. When + # tf.keras gets the default session, it initializes all uninitialized vars. + # We need to make _keras_initialized a member of DistributedVariable because + # without this it will use `__getattr__` which will delegate to a component + # variable. + self._keras_initialized = False + # Typically, a `DistributedVariable`'s initializer is composed of the + # initializers of the components variables. However, in some cases, such as + # when restoring from a checkpoint, we may set the _initializer_op + # property on the entire `DistributedVariable`. + self._initializer_op = None super(DistributedVariable, self).__init__(index) + def is_initialized(self, name=None): + """Identifies if all the component variables are initialized. + + Args: + name: Name of the final `logical_and` op. + + Returns: + The op that evaluates to True or False depending on if all the + component variables are initialized. + """ + # We have to cast the self._index.values() to a `list` because when we + # use `model_to_estimator` to run tf.keras models, self._index.values() is + # of type `dict_values` and not `list`. + values_list = list(self._index.values()) + result = values_list[0].is_initialized() + # We iterate through the list of values except the last one to allow us to + # name the final `logical_and` op the same name that is passed by the user + # to the `is_initialized` op. For distributed variables, the + # `is_initialized` op is a `logical_and` op. + for v in values_list[1:-1]: + result = math_ops.logical_and(result, v.is_initialized()) + result = math_ops.logical_and(result, values_list[-1].is_initialized(), + name=name) + return result + @property def initializer(self): - return control_flow_ops.group([v.initializer for v in self._index.values()]) + if self._initializer_op: + init_op = self._initializer_op + else: + # return grouped ops of all the var initializations of component values of + # the mirrored variable + init_op = control_flow_ops.group( + [v.initializer for v in self._index.values()]) + return init_op @property def graph(self): @@ -320,6 +370,7 @@ class MirroredVariable(DistributedVariable, Mirrored, return distribute_lib.get_distribution_strategy().update( self, f, *args, **kwargs) else: + _assert_tower_context() # We are calling an assign function on the mirrored variable in tower # context. # We reduce the value we want to assign/add/sub. More details about how we @@ -330,23 +381,27 @@ class MirroredVariable(DistributedVariable, Mirrored, raise ValueError("You must specify an aggregation method to update a " "MirroredVariable in Tower Context.") - def merge_fn(strategy, value): + def merge_fn(strategy, value, *other_args, **other_kwargs): return strategy.update( self, f, strategy.reduce( - aggregation=self._aggregation, value=value, destinations=self)) + aggregation=self._aggregation, value=value, destinations=self), + *other_args, **other_kwargs) return distribute_lib.get_tower_context().merge_call(merge_fn, *args, **kwargs) def assign_sub(self, *args, **kwargs): - return self._assign_func(f=state_ops.assign_sub, *args, **kwargs) + assign_sub_fn = lambda var, *a, **kw: var.assign_sub(*a, **kw) + return self._assign_func(f=assign_sub_fn, *args, **kwargs) def assign_add(self, *args, **kwargs): - return self._assign_func(f=state_ops.assign_add, *args, **kwargs) + assign_add_fn = lambda var, *a, **kw: var.assign_add(*a, **kw) + return self._assign_func(f=assign_add_fn, *args, **kwargs) def assign(self, *args, **kwargs): - return self._assign_func(f=state_ops.assign, *args, **kwargs) + assign_fn = lambda var, *a, **kw: var.assign(*a, **kw) + return self._assign_func(f=assign_fn, *args, **kwargs) @property def aggregation(self): @@ -412,14 +467,7 @@ class _TowerLocalSaveable(saver.BaseSaverBuilder.SaveableObject): def restore(self, restored_tensors, restored_shapes): """Restore the same value into all variables.""" tensor, = restored_tensors - # To preserve the sum across save and restore, we have to divide the - # total across all devices when restoring a variable that was summed - # when saving. - if self._tower_local_variable.aggregation == vs.VariableAggregation.SUM: - tensor *= 1. / len(self._tower_local_variable.devices) - return control_flow_ops.group([ - _assign_on_device(d, v, tensor) - for d, v in six.iteritems(self._tower_local_variable._index)]) # pylint: disable=protected-access + return self._tower_local_variable.assign(tensor) def _assert_tower_context(): @@ -446,8 +494,19 @@ class TowerLocalVariable(DistributedVariable, PerDevice, return self.get().assign_add(*args, **kwargs) def assign(self, *args, **kwargs): - _assert_tower_context() - return self.get().assign(*args, **kwargs) + if distribute_lib.get_cross_tower_context(): + # To preserve the sum across save and restore, we have to divide the + # total across all devices when restoring a variable that was summed + # when saving. + tensor = args[0] + if self._aggregation == vs.VariableAggregation.SUM: + tensor *= 1. / len(self.devices) + return control_flow_ops.group( + [_assign_on_device(d, v, tensor) + for d, v in six.iteritems(self._index)]) + else: + _assert_tower_context() + return self.get().assign(*args, **kwargs) @property def aggregation(self): diff --git a/tensorflow/contrib/distribute/python/values_test.py b/tensorflow/contrib/distribute/python/values_test.py index 8e44f2fea16ac851c124b573948ee14ec0640556..91a43d499933c77de846085e0f12abf3064b0499 100644 --- a/tensorflow/contrib/distribute/python/values_test.py +++ b/tensorflow/contrib/distribute/python/values_test.py @@ -32,6 +32,7 @@ from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import random_ops @@ -79,6 +80,30 @@ class DistributedValuesTest(test.TestCase): with self.assertRaises(AssertionError): v = values.DistributedValues({"/device:cpu:0": 42}) + def testIsTensorLike(self): + with context.graph_mode(), \ + ops.Graph().as_default(), \ + ops.device("/device:CPU:0"): + one = constant_op.constant(1) + two = constant_op.constant(2) + v = values.DistributedValues({"/device:CPU:0": one, "/device:GPU:0": two}) + self.assertEqual(two, v.get("/device:GPU:0")) + self.assertEqual(one, v.get()) + self.assertTrue(v.is_tensor_like) + self.assertTrue(tensor_util.is_tensor(v)) + + def testIsTensorLikeWithAConstant(self): + with context.graph_mode(), \ + ops.Graph().as_default(), \ + ops.device("/device:CPU:0"): + one = constant_op.constant(1) + two = 2.0 + v = values.DistributedValues({"/device:CPU:0": one, "/device:GPU:0": two}) + self.assertEqual(two, v.get("/device:GPU:0")) + self.assertEqual(one, v.get()) + self.assertFalse(v.is_tensor_like) + self.assertFalse(tensor_util.is_tensor(v)) + class DistributedDelegateTest(test.TestCase): diff --git a/tensorflow/contrib/distribute/python/warm_starting_util_test.py b/tensorflow/contrib/distribute/python/warm_starting_util_test.py new file mode 100644 index 0000000000000000000000000000000000000000..d8bacdb338d93a169a26a55d8ee5f5f9f0d59fce --- /dev/null +++ b/tensorflow/contrib/distribute/python/warm_starting_util_test.py @@ -0,0 +1,97 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for warm_starting_util with Distribution Strategy. + +These tests are located here instead of as part of `WarmStartingUtilTest` +because they need access to distribution strategies which are only present in +contrib right now. +TODO(priyag): Move the tests to core `WarmStartingUtilTest` when distribution +strategy moves out of contrib. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +from absl.testing import parameterized + +from tensorflow.contrib.distribute.python import combinations +from tensorflow.python.framework import ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.training import saver as saver_lib +from tensorflow.python.training import warm_starting_util as ws_util + + +class WarmStartingUtilWithDistributionStrategyTest( + test.TestCase, parameterized.TestCase): + + @combinations.generate(combinations.combine( + distribution=[combinations.default_strategy, + combinations.one_device_strategy, + combinations.mirrored_strategy_with_gpu_and_cpu, + combinations.mirrored_strategy_with_two_gpus], + save_with_distribution=[True, False], + restore_with_distribution=[True, False], + mode=["graph"])) + def testWarmStart(self, distribution, save_with_distribution, + restore_with_distribution): + + var_name = "v" + original_value = [[1., 2.], [3., 4.]] + + # Create variable and save checkpoint from which to warm-start. + def create_var(g): + with self.test_session(graph=g) as sess: + var = variable_scope.get_variable(var_name, initializer=original_value) + sess.run(variables.global_variables_initializer()) + saver = saver_lib.Saver() + ckpt_prefix = os.path.join(self.get_temp_dir(), "model") + saver.save(sess, ckpt_prefix, global_step=0) + return var, sess.run(var) + + if save_with_distribution: + with ops.Graph().as_default() as g, distribution.scope(): + _, prev_init_val = create_var(g) + else: + with ops.Graph().as_default() as g: + _, prev_init_val = create_var(g) + + # Verify we initialized the values correctly. + self.assertAllEqual(original_value, prev_init_val) + + def warm_start(g): + with self.test_session(graph=g) as sess: + # Initialize with zeros. + var = variable_scope.get_variable( + var_name, initializer=[[0., 0.], [0., 0.]]) + ws_util.warm_start(self.get_temp_dir()) + sess.run(variables.global_variables_initializer()) + # Verify weights were correctly warm-started to previous values. + self.assertAllEqual(original_value, self.evaluate(var)) + + # Warm start in a new graph. + if restore_with_distribution: + with ops.Graph().as_default() as g, distribution.scope(): + warm_start(g) + else: + with ops.Graph().as_default() as g: + warm_start(g) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py b/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py index b8f2a4b2c731bdaee78692c036fb9f2fba4e3760..296e66f2b24fecf2142066727b5b12ee5cbd0379 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py @@ -514,9 +514,8 @@ def masked_autoregressive_default_template( Masked Autoencoder for Distribution Estimation. In _International Conference on Machine Learning_, 2015. https://arxiv.org/abs/1502.03509 """ - - with ops.name_scope(name, "masked_autoregressive_default_template", - values=[log_scale_min_clip, log_scale_max_clip]): + name = name or "masked_autoregressive_default_template" + with ops.name_scope(name, values=[log_scale_min_clip, log_scale_max_clip]): def _fn(x): """MADE parameterized via `masked_autoregressive_default_template`.""" # TODO(b/67594795): Better support of dynamic shape. @@ -552,8 +551,7 @@ def masked_autoregressive_default_template( else _clip_by_value_preserve_grad) log_scale = which_clip(log_scale, log_scale_min_clip, log_scale_max_clip) return shift, log_scale - return template_ops.make_template( - "masked_autoregressive_default_template", _fn) + return template_ops.make_template(name, _fn) @deprecation.deprecated( diff --git a/tensorflow/contrib/distributions/python/ops/quantized_distribution.py b/tensorflow/contrib/distributions/python/ops/quantized_distribution.py index ef3bdfa75fcaa8df17db1238ceadadf788601356..18a0f754e6e618f240db109f593a80dec57e200b 100644 --- a/tensorflow/contrib/distributions/python/ops/quantized_distribution.py +++ b/tensorflow/contrib/distributions/python/ops/quantized_distribution.py @@ -326,6 +326,21 @@ class QuantizedDistribution(distributions.Distribution): graph_parents=graph_parents, name=name) + @property + def distribution(self): + """Base distribution, p(x).""" + return self._dist + + @property + def low(self): + """Lowest value that quantization returns.""" + return self._low + + @property + def high(self): + """Highest value that quantization returns.""" + return self._high + def _batch_shape_tensor(self): return self.distribution.batch_shape_tensor() @@ -569,8 +584,3 @@ class QuantizedDistribution(distributions.Distribution): dependencies = [distribution_util.assert_integer_form( value, message="value has non-integer components.")] return control_flow_ops.with_dependencies(dependencies, value) - - @property - def distribution(self): - """Base distribution, p(x).""" - return self._dist diff --git a/tensorflow/contrib/eager/python/datasets.py b/tensorflow/contrib/eager/python/datasets.py index 58c548d798178a2848006cbf301f7d5cb2143f24..e31dbbe80f9634e8e45ec91bf395eab82942c8ce 100644 --- a/tensorflow/contrib/eager/python/datasets.py +++ b/tensorflow/contrib/eager/python/datasets.py @@ -18,33 +18,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import threading - from tensorflow.contrib.data.python.ops import prefetching_ops from tensorflow.python.data.ops import iterator_ops -from tensorflow.python.data.util import nest -from tensorflow.python.data.util import sparse from tensorflow.python.eager import context -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.ops import gen_dataset_ops -from tensorflow.python.ops import resource_variable_ops from tensorflow.python.training.checkpointable import base as checkpointable from tensorflow.python.training.saver import BaseSaverBuilder -_uid_counter = 0 -_uid_lock = threading.Lock() - - -def _generate_shared_name(prefix): - with _uid_lock: - global _uid_counter - uid = _uid_counter - _uid_counter += 1 - return "{}{}".format(prefix, uid) - class Iterator(iterator_ops.EagerIterator, checkpointable.CheckpointableBase): """An iterator producing tf.Tensor objects from a tf.data.Dataset. @@ -80,38 +61,18 @@ class Iterator(iterator_ops.EagerIterator, checkpointable.CheckpointableBase): "`tf.contrib.eager.Iterator`. Use `for ... in dataset:` to iterate " "over the dataset instead.") - super(Iterator, self).__init__(dataset) if not context.context().device_spec.device_type: is_remote_device = False else: is_remote_device = context.context().device_spec.device_type != "CPU" - self._buffer_resource_handle = None if is_remote_device: - with ops.device("/device:CPU:0"): - iter_string_handle = gen_dataset_ops.iterator_to_string_handle( - self._resource) - - @function.Defun(dtypes.string) - def remote_fn(h): - remote_iterator = iterator_ops.Iterator.from_string_handle( - h, self.output_types, self.output_shapes, self.output_classes) - return remote_iterator.get_next() - - remote_fn.add_to_graph(None) - target = constant_op.constant("/device:CPU:0") - with ops.device(self._device): - self._buffer_resource_handle = prefetching_ops.function_buffering_resource( # pylint: disable=line-too-long - string_arg=iter_string_handle, - output_types=self._flat_output_types, - f=remote_fn, - target_device=target, - buffer_size=10, - container="", - shared_name=_generate_shared_name( - "contrib_eager_iterator_function_buffer_resource")) - self._buffer_resource_deleter = resource_variable_ops.EagerResourceDeleter( # pylint: disable=line-too-long - handle=self._buffer_resource_handle, - handle_device=self._device) + with ops.device(None): + # Let the placer figure out where to place the various functions etc. + # created by the CopyToDeviceDataset. + dataset = dataset.apply(prefetching_ops.copy_to_device( + context.context().device_name)) + dataset = dataset.prefetch(1) + super(Iterator, self).__init__(dataset) def _next_internal(self): """Returns a nested structure of `tf.Tensor`s containing the next element. @@ -120,16 +81,7 @@ class Iterator(iterator_ops.EagerIterator, checkpointable.CheckpointableBase): # that there is no more data to iterate over. # TODO(b/77291417): Fix with context.execution_mode(context.SYNC): - if self._buffer_resource_handle is not None: - with ops.device(self._device): - ret = prefetching_ops.function_buffering_resource_get_next( - function_buffer_resource=self._buffer_resource_handle, - output_types=self._flat_output_types) - return sparse.deserialize_sparse_tensors( - nest.pack_sequence_as(self._output_types, ret), self._output_types, - self._output_shapes, self._output_classes) - else: - return super(Iterator, self)._next_internal() + return super(Iterator, self)._next_internal() # TODO(shivaniagrawal): Expose checkpointable stateful objects from dataset # attributes(potential). diff --git a/tensorflow/contrib/eager/python/datasets_test.py b/tensorflow/contrib/eager/python/datasets_test.py index 68bec9aee894edd60a025ac1cf87ca3e010db842..acc605247faffcf7ba83891dacdab13fc8c8574a 100644 --- a/tensorflow/contrib/eager/python/datasets_test.py +++ b/tensorflow/contrib/eager/python/datasets_test.py @@ -193,6 +193,20 @@ class IteratorTest(test.TestCase): x = math_ops.add(x, x) self.assertAllEqual([0., 2.], x.numpy()) + def testGpuTensor(self): + ds = Dataset.from_tensors([0., 1.]) + with ops.device(test.gpu_device_name()): + for x in ds: + y = math_ops.add(x, x) + self.assertAllEqual([0., 2.], y.numpy()) + + def testGpuDefinedDataset(self): + with ops.device(test.gpu_device_name()): + ds = Dataset.from_tensors([0., 1.]) + for x in ds: + y = math_ops.add(x, x) + self.assertAllEqual([0., 2.], y.numpy()) + def testTensorsExplicitPrefetchToDevice(self): ds = Dataset.from_tensor_slices([0., 1.]) ds = ds.apply(prefetching_ops.prefetch_to_device(test.gpu_device_name())) diff --git a/tensorflow/contrib/eager/python/examples/densenet/BUILD b/tensorflow/contrib/eager/python/examples/densenet/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..2dc196f550a10367066730f6f042c4ed69533ec3 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/densenet/BUILD @@ -0,0 +1,48 @@ +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//tensorflow:internal"]) + +load("//tensorflow:tensorflow.bzl", "cuda_py_test") + +py_binary( + name = "densenet", + srcs = ["densenet.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + "//tensorflow/contrib/eager/python:tfe", + ], +) + +cuda_py_test( + name = "densenet_test", + size = "large", + srcs = ["densenet_test.py"], + additional_deps = [ + ":densenet", + "//tensorflow/contrib/eager/python:tfe", + "//tensorflow:tensorflow_py", + ], + tags = [ + "no_pip", + "optonly", + ], +) + +cuda_py_test( + name = "densenet_graph_test", + size = "large", + srcs = ["densenet_graph_test.py"], + additional_deps = [ + ":densenet", + "//third_party/py/numpy", + "//tensorflow:tensorflow_py", + ], + tags = [ + "no_pip", + "noasan", + "nomsan", + "notsan", + "optonly", + ], +) diff --git a/tensorflow/contrib/eager/python/examples/densenet/densenet.py b/tensorflow/contrib/eager/python/examples/densenet/densenet.py new file mode 100644 index 0000000000000000000000000000000000000000..6de4e6940094849b5cf6f977e351aef525c77cc2 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/densenet/densenet.py @@ -0,0 +1,296 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Densely Connected Convolutional Networks. + +Reference [ +Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993) + +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +l2 = tf.keras.regularizers.l2 + + +class ConvBlock(tf.keras.Model): + """Convolutional Block consisting of (batchnorm->relu->conv). + + Arguments: + num_filters: number of filters passed to a convolutional layer. + data_format: "channels_first" or "channels_last" + bottleneck: if True, then a 1x1 Conv is performed followed by 3x3 Conv. + weight_decay: weight decay + dropout_rate: dropout rate. + """ + + def __init__(self, num_filters, data_format, bottleneck, weight_decay=1e-4, + dropout_rate=0): + super(ConvBlock, self).__init__() + self.bottleneck = bottleneck + + axis = -1 if data_format == "channels_last" else 1 + inter_filter = num_filters * 4 + # don't forget to set use_bias=False when using batchnorm + self.conv2 = tf.keras.layers.Conv2D(num_filters, + (3, 3), + padding="same", + use_bias=False, + data_format=data_format, + kernel_initializer="he_normal", + kernel_regularizer=l2(weight_decay)) + self.batchnorm1 = tf.keras.layers.BatchNormalization(axis=axis) + self.dropout = tf.keras.layers.Dropout(dropout_rate) + + if self.bottleneck: + self.conv1 = tf.keras.layers.Conv2D(inter_filter, + (1, 1), + padding="same", + use_bias=False, + data_format=data_format, + kernel_initializer="he_normal", + kernel_regularizer=l2(weight_decay)) + self.batchnorm2 = tf.keras.layers.BatchNormalization(axis=axis) + + def call(self, x, training=True): + output = self.batchnorm1(x, training=training) + + if self.bottleneck: + output = self.conv1(tf.nn.relu(output)) + output = self.batchnorm2(output, training=training) + + output = self.conv2(tf.nn.relu(output)) + output = self.dropout(output, training=training) + + return output + + +class TransitionBlock(tf.keras.Model): + """Transition Block to reduce the number of features. + + Arguments: + num_filters: number of filters passed to a convolutional layer. + data_format: "channels_first" or "channels_last" + weight_decay: weight decay + dropout_rate: dropout rate. + """ + + def __init__(self, num_filters, data_format, + weight_decay=1e-4, dropout_rate=0): + super(TransitionBlock, self).__init__() + axis = -1 if data_format == "channels_last" else 1 + + self.batchnorm = tf.keras.layers.BatchNormalization(axis=axis) + self.conv = tf.keras.layers.Conv2D(num_filters, + (1, 1), + padding="same", + use_bias=False, + data_format=data_format, + kernel_initializer="he_normal", + kernel_regularizer=l2(weight_decay)) + self.avg_pool = tf.keras.layers.AveragePooling2D(data_format=data_format) + + def call(self, x, training=True): + output = self.batchnorm(x, training=training) + output = self.conv(tf.nn.relu(output)) + output = self.avg_pool(output) + return output + + +class DenseBlock(tf.keras.Model): + """Dense Block consisting of ConvBlocks where each block's + output is concatenated with its input. + + Arguments: + num_layers: Number of layers in each block. + growth_rate: number of filters to add per conv block. + data_format: "channels_first" or "channels_last" + bottleneck: boolean, that decides which part of ConvBlock to call. + weight_decay: weight decay + dropout_rate: dropout rate. + """ + + def __init__(self, num_layers, growth_rate, data_format, bottleneck, + weight_decay=1e-4, dropout_rate=0): + super(DenseBlock, self).__init__() + self.num_layers = num_layers + self.axis = -1 if data_format == "channels_last" else 1 + + self.blocks = [] + for _ in range(int(self.num_layers)): + self.blocks.append(ConvBlock(growth_rate, + data_format, + bottleneck, + weight_decay, + dropout_rate)) + + def call(self, x, training=True): + for i in range(int(self.num_layers)): + output = self.blocks[i](x, training=training) + x = tf.concat([x, output], axis=self.axis) + + return x + + +class DenseNet(tf.keras.Model): + """Creating the Densenet Architecture. + + Arguments: + depth_of_model: number of layers in the model. + growth_rate: number of filters to add per conv block. + num_of_blocks: number of dense blocks. + output_classes: number of output classes. + num_layers_in_each_block: number of layers in each block. + If -1, then we calculate this by (depth-3)/4. + If positive integer, then the it is used as the + number of layers per block. + If list or tuple, then this list is used directly. + data_format: "channels_first" or "channels_last" + bottleneck: boolean, to decide which part of conv block to call. + compression: reducing the number of inputs(filters) to the transition block. + weight_decay: weight decay + rate: dropout rate. + pool_initial: If True add a 7x7 conv with stride 2 followed by 3x3 maxpool + else, do a 3x3 conv with stride 1. + include_top: If true, GlobalAveragePooling Layer and Dense layer are + included. + """ + + def __init__(self, depth_of_model, growth_rate, num_of_blocks, + output_classes, num_layers_in_each_block, data_format, + bottleneck=True, compression=0.5, weight_decay=1e-4, + dropout_rate=0, pool_initial=False, include_top=True): + super(DenseNet, self).__init__() + self.depth_of_model = depth_of_model + self.growth_rate = growth_rate + self.num_of_blocks = num_of_blocks + self.output_classes = output_classes + self.num_layers_in_each_block = num_layers_in_each_block + self.data_format = data_format + self.bottleneck = bottleneck + self.compression = compression + self.weight_decay = weight_decay + self.dropout_rate = dropout_rate + self.pool_initial = pool_initial + self.include_top = include_top + + # deciding on number of layers in each block + if isinstance(self.num_layers_in_each_block, list) or isinstance( + self.num_layers_in_each_block, tuple): + self.num_layers_in_each_block = list(self.num_layers_in_each_block) + else: + if self.num_layers_in_each_block == -1: + if self.num_of_blocks != 3: + raise ValueError( + "Number of blocks must be 3 if num_layers_in_each_block is -1") + if (self.depth_of_model - 4) % 3 == 0: + num_layers = (self.depth_of_model - 4) / 3 + if self.bottleneck: + num_layers //= 2 + self.num_layers_in_each_block = [num_layers] * self.num_of_blocks + else: + raise ValueError("Depth must be 3N+4 if num_layer_in_each_block=-1") + else: + self.num_layers_in_each_block = [ + self.num_layers_in_each_block] * self.num_of_blocks + + axis = -1 if self.data_format == "channels_last" else 1 + + # setting the filters and stride of the initial covn layer. + if self.pool_initial: + init_filters = (7, 7) + stride = (2, 2) + else: + init_filters = (3, 3) + stride = (1, 1) + + self.num_filters = 2 * self.growth_rate + + # first conv and pool layer + self.conv1 = tf.keras.layers.Conv2D(self.num_filters, + init_filters, + strides=stride, + padding="same", + use_bias=False, + data_format=self.data_format, + kernel_initializer="he_normal", + kernel_regularizer=l2( + self.weight_decay)) + if self.pool_initial: + self.pool1 = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), + strides=(2, 2), + padding="same", + data_format=self.data_format) + self.batchnorm1 = tf.keras.layers.BatchNormalization(axis=axis) + + self.batchnorm2 = tf.keras.layers.BatchNormalization(axis=axis) + + # last pooling and fc layer + if self.include_top: + self.last_pool = tf.keras.layers.GlobalAveragePooling2D( + data_format=self.data_format) + self.classifier = tf.keras.layers.Dense(self.output_classes) + + # calculating the number of filters after each block + num_filters_after_each_block = [self.num_filters] + for i in range(1, self.num_of_blocks): + temp_num_filters = num_filters_after_each_block[i-1] + ( + self.growth_rate * self.num_layers_in_each_block[i-1]) + # using compression to reduce the number of inputs to the + # transition block + temp_num_filters = int(temp_num_filters * compression) + num_filters_after_each_block.append(temp_num_filters) + + # dense block initialization + self.dense_blocks = [] + self.transition_blocks = [] + for i in range(self.num_of_blocks): + self.dense_blocks.append(DenseBlock(self.num_layers_in_each_block[i], + self.growth_rate, + self.data_format, + self.bottleneck, + self.weight_decay, + self.dropout_rate)) + if i+1 < self.num_of_blocks: + self.transition_blocks.append( + TransitionBlock(num_filters_after_each_block[i+1], + self.data_format, + self.weight_decay, + self.dropout_rate)) + + def call(self, x, training=True): + output = self.conv1(x) + + if self.pool_initial: + output = self.batchnorm1(output, training=training) + output = tf.nn.relu(output) + output = self.pool1(output) + + for i in range(self.num_of_blocks - 1): + output = self.dense_blocks[i](output, training=training) + output = self.transition_blocks[i](output, training=training) + + output = self.dense_blocks[ + self.num_of_blocks - 1](output, training=training) + output = self.batchnorm2(output, training=training) + output = tf.nn.relu(output) + + if self.include_top: + output = self.last_pool(output) + output = self.classifier(output) + + return output diff --git a/tensorflow/contrib/eager/python/examples/densenet/densenet_graph_test.py b/tensorflow/contrib/eager/python/examples/densenet/densenet_graph_test.py new file mode 100644 index 0000000000000000000000000000000000000000..bd0057fb1a0175a805a0f7a1e4dcaa2bdc3c435a --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/densenet/densenet_graph_test.py @@ -0,0 +1,149 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests and Benchmarks for Densenet model under graph execution.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import time +import numpy as np +import tensorflow as tf + +from tensorflow.contrib.eager.python.examples.densenet import densenet + + +def data_format(): + return 'channels_first' if tf.test.is_gpu_available() else 'channels_last' + + +def image_shape(batch_size): + if data_format() == 'channels_first': + return [batch_size, 3, 224, 224] + return [batch_size, 224, 224, 3] + + +def random_batch(batch_size): + images = np.random.rand(*image_shape(batch_size)).astype(np.float32) + num_classes = 1000 + labels = np.random.randint( + low=0, high=num_classes, size=[batch_size]).astype(np.int32) + one_hot = np.zeros((batch_size, num_classes)).astype(np.float32) + one_hot[np.arange(batch_size), labels] = 1. + return images, one_hot + + +class DensenetGraphTest(tf.test.TestCase): + + def testApply(self): + depth = 7 + growth_rate = 2 + num_blocks = 3 + output_classes = 10 + num_layers_in_each_block = -1 + batch_size = 1 + with tf.Graph().as_default(): + images = tf.placeholder(tf.float32, image_shape(None)) + model = densenet.DenseNet(depth, growth_rate, num_blocks, + output_classes, num_layers_in_each_block, + data_format(), bottleneck=True, compression=0.5, + weight_decay=1e-4, dropout_rate=0, + pool_initial=False, include_top=True) + predictions = model(images, training=False) + + init = tf.global_variables_initializer() + + with tf.Session() as sess: + sess.run(init) + np_images, _ = random_batch(batch_size) + out = sess.run(predictions, feed_dict={images: np_images}) + self.assertAllEqual([batch_size, output_classes], out.shape) + + +class DensenetBenchmark(tf.test.Benchmark): + + def __init__(self): + self.depth = 121 + self.growth_rate = 32 + self.num_blocks = 4 + self.output_classes = 1000 + self.num_layers_in_each_block = [6, 12, 24, 16] + + def _report(self, label, start, num_iters, batch_size): + avg_time = (time.time() - start) / num_iters + dev = 'gpu' if tf.test.is_gpu_available() else 'cpu' + name = 'graph_%s_%s_batch_%d_%s' % (label, dev, batch_size, data_format()) + extras = {'examples_per_sec': batch_size / avg_time} + self.report_benchmark( + iters=num_iters, wall_time=avg_time, name=name, extras=extras) + + def benchmark_graph_apply(self): + with tf.Graph().as_default(): + images = tf.placeholder(tf.float32, image_shape(None)) + model = densenet.DenseNet(self.depth, self.growth_rate, self.num_blocks, + self.output_classes, + self.num_layers_in_each_block, data_format(), + bottleneck=True, compression=0.5, + weight_decay=1e-4, dropout_rate=0, + pool_initial=True, include_top=True) + predictions = model(images, training=False) + + init = tf.global_variables_initializer() + + batch_size = 64 + with tf.Session() as sess: + sess.run(init) + np_images, _ = random_batch(batch_size) + num_burn, num_iters = (3, 30) + for _ in range(num_burn): + sess.run(predictions, feed_dict={images: np_images}) + start = time.time() + for _ in range(num_iters): + sess.run(predictions, feed_dict={images: np_images}) + self._report('apply', start, num_iters, batch_size) + + def benchmark_graph_train(self): + for batch_size in [16, 32, 64]: + with tf.Graph().as_default(): + np_images, np_labels = random_batch(batch_size) + dataset = tf.data.Dataset.from_tensors((np_images, np_labels)).repeat() + (images, labels) = dataset.make_one_shot_iterator().get_next() + + model = densenet.DenseNet(self.depth, self.growth_rate, self.num_blocks, + self.output_classes, + self.num_layers_in_each_block, data_format(), + bottleneck=True, compression=0.5, + weight_decay=1e-4, dropout_rate=0, + pool_initial=True, include_top=True) + logits = model(images, training=True) + loss = tf.losses.softmax_cross_entropy( + logits=logits, onehot_labels=labels) + optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0) + train_op = optimizer.minimize(loss) + + init = tf.global_variables_initializer() + with tf.Session() as sess: + sess.run(init) + (num_burn, num_iters) = (5, 10) + for _ in range(num_burn): + sess.run(train_op) + start = time.time() + for _ in range(num_iters): + sess.run(train_op) + self._report('train', start, num_iters, batch_size) + + +if __name__ == '__main__': + tf.test.main() diff --git a/tensorflow/contrib/eager/python/examples/densenet/densenet_test.py b/tensorflow/contrib/eager/python/examples/densenet/densenet_test.py new file mode 100644 index 0000000000000000000000000000000000000000..4f19711fb87d6b5558302fd69104aca7e2cf403e --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/densenet/densenet_test.py @@ -0,0 +1,310 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests and Benchmarks for Densenet model.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import gc +import time +import tensorflow as tf +import tensorflow.contrib.eager as tfe + +from tensorflow.contrib.eager.python.examples.densenet import densenet +from tensorflow.python.client import device_lib + + +class DensenetTest(tf.test.TestCase): + + def test_bottleneck_true(self): + depth = 7 + growth_rate = 2 + num_blocks = 3 + output_classes = 10 + num_layers_in_each_block = -1 + batch_size = 1 + data_format = ('channels_first') if tf.test.is_gpu_available() else ( + 'channels_last') + + model = densenet.DenseNet(depth, growth_rate, num_blocks, + output_classes, num_layers_in_each_block, + data_format, bottleneck=True, compression=0.5, + weight_decay=1e-4, dropout_rate=0, + pool_initial=False, include_top=True) + + if data_format == 'channels_last': + rand_input = tf.random_uniform((batch_size, 32, 32, 3)) + else: + rand_input = tf.random_uniform((batch_size, 3, 32, 32)) + output_shape = model(rand_input).shape + self.assertEqual(output_shape, (batch_size, output_classes)) + + def test_bottleneck_false(self): + depth = 7 + growth_rate = 2 + num_blocks = 3 + output_classes = 10 + num_layers_in_each_block = -1 + batch_size = 1 + data_format = ('channels_first') if tf.test.is_gpu_available() else ( + 'channels_last') + + model = densenet.DenseNet(depth, growth_rate, num_blocks, + output_classes, num_layers_in_each_block, + data_format, bottleneck=False, compression=0.5, + weight_decay=1e-4, dropout_rate=0, + pool_initial=False, include_top=True) + + if data_format == 'channels_last': + rand_input = tf.random_uniform((batch_size, 32, 32, 3)) + else: + rand_input = tf.random_uniform((batch_size, 3, 32, 32)) + output_shape = model(rand_input).shape + self.assertEqual(output_shape, (batch_size, output_classes)) + + def test_pool_initial_true(self): + depth = 7 + growth_rate = 2 + num_blocks = 4 + output_classes = 10 + num_layers_in_each_block = [1, 2, 2, 1] + batch_size = 1 + data_format = ('channels_first') if tf.test.is_gpu_available() else ( + 'channels_last') + + model = densenet.DenseNet(depth, growth_rate, num_blocks, + output_classes, num_layers_in_each_block, + data_format, bottleneck=True, compression=0.5, + weight_decay=1e-4, dropout_rate=0, + pool_initial=True, include_top=True) + + if data_format == 'channels_last': + rand_input = tf.random_uniform((batch_size, 32, 32, 3)) + else: + rand_input = tf.random_uniform((batch_size, 3, 32, 32)) + output_shape = model(rand_input).shape + self.assertEqual(output_shape, (batch_size, output_classes)) + + +def compute_gradients(model, images, labels): + with tf.GradientTape() as tape: + logits = model(images, training=True) + loss = tf.losses.softmax_cross_entropy( + logits=logits, onehot_labels=labels) + tf.contrib.summary.scalar(name='loss', tensor=loss) + return tape.gradient(loss, model.variables) + + +def apply_gradients(model, optimizer, gradients): + optimizer.apply_gradients(zip(gradients, model.variables)) + + +def device_and_data_format(): + return ('/gpu:0', + 'channels_first') if tf.test.is_gpu_available() else ('/cpu:0', + 'channels_last') + + +def random_batch(batch_size, data_format): + shape = (3, 224, 224) if data_format == 'channels_first' else (224, 224, 3) + shape = (batch_size,) + shape + + num_classes = 1000 + images = tf.random_uniform(shape) + labels = tf.random_uniform( + [batch_size], minval=0, maxval=num_classes, dtype=tf.int32) + one_hot = tf.one_hot(labels, num_classes) + + return images, one_hot + + +class MockIterator(object): + + def __init__(self, tensors): + self._tensors = [tf.identity(x) for x in tensors] + + def next(self): + return self._tensors + + +class DensenetBenchmark(tf.test.Benchmark): + + def __init__(self): + self.depth = 121 + self.growth_rate = 32 + self.num_blocks = 4 + self.output_classes = 1000 + self.num_layers_in_each_block = [6, 12, 24, 16] + + def _train_batch_sizes(self): + """Choose batch sizes based on GPU capability.""" + for device in device_lib.list_local_devices(): + if tf.DeviceSpec.from_string(device.name).device_type == 'GPU': + if 'K20' in device.physical_device_desc: + return (16,) + if 'P100' in device.physical_device_desc: + return (16, 32, 64) + + if tf.DeviceSpec.from_string(device.name).device_type == 'TPU': + return (32,) + return (16, 32) + + def _report(self, label, start, num_iters, device, batch_size, data_format): + avg_time = (time.time() - start) / num_iters + dev = tf.DeviceSpec.from_string(device).device_type.lower() + name = '%s_%s_batch_%d_%s' % (label, dev, batch_size, data_format) + extras = {'examples_per_sec': batch_size / avg_time} + self.report_benchmark( + iters=num_iters, wall_time=avg_time, name=name, extras=extras) + + def _force_device_sync(self): + # If this function is called in the context of a non-CPU device + # (e.g., inside a 'with tf.device("/gpu:0")' block) + # then this will force a copy from CPU->NON_CPU_DEVICE->CPU, + # which forces a sync. This is a roundabout way, yes. + tf.constant(1.).cpu() + + def _benchmark_eager_apply(self, label, device_and_format, defun=False, + execution_mode=None, compiled=False): + with tfe.execution_mode(execution_mode): + device, data_format = device_and_format + model = densenet.DenseNet(self.depth, self.growth_rate, self.num_blocks, + self.output_classes, + self.num_layers_in_each_block, data_format, + bottleneck=True, compression=0.5, + weight_decay=1e-4, dropout_rate=0, + pool_initial=True, include_top=True) + if defun: + model.call = tfe.defun(model.call, compiled=compiled) + batch_size = 64 + num_burn = 5 + num_iters = 30 + with tf.device(device): + images, _ = random_batch(batch_size, data_format) + for _ in xrange(num_burn): + model(images, training=False).cpu() + if execution_mode: + tfe.async_wait() + gc.collect() + start = time.time() + for _ in xrange(num_iters): + model(images, training=False).cpu() + if execution_mode: + tfe.async_wait() + self._report(label, start, num_iters, device, batch_size, data_format) + + def benchmark_eager_apply_sync(self): + self._benchmark_eager_apply('eager_apply', device_and_data_format(), + defun=False) + + def benchmark_eager_apply_async(self): + self._benchmark_eager_apply( + 'eager_apply_async', device_and_data_format(), defun=False, + execution_mode=tfe.ASYNC) + + def benchmark_eager_apply_with_defun(self): + self._benchmark_eager_apply('eager_apply_with_defun', + device_and_data_format(), defun=True) + + def _benchmark_eager_train(self, + label, + make_iterator, + device_and_format, + defun=False, + execution_mode=None, + compiled=False): + with tfe.execution_mode(execution_mode): + device, data_format = device_and_format + for batch_size in self._train_batch_sizes(): + (images, labels) = random_batch(batch_size, data_format) + model = densenet.DenseNet(self.depth, self.growth_rate, self.num_blocks, + self.output_classes, + self.num_layers_in_each_block, data_format, + bottleneck=True, compression=0.5, + weight_decay=1e-4, dropout_rate=0, + pool_initial=True, include_top=True) + optimizer = tf.train.GradientDescentOptimizer(0.1) + apply_grads = apply_gradients + if defun: + model.call = tfe.defun(model.call, compiled=compiled) + apply_grads = tfe.defun(apply_gradients, compiled=compiled) + + num_burn = 3 + num_iters = 10 + with tf.device(device): + iterator = make_iterator((images, labels)) + for _ in xrange(num_burn): + (images, labels) = iterator.next() + apply_grads(model, optimizer, + compute_gradients(model, images, labels)) + if execution_mode: + tfe.async_wait() + self._force_device_sync() + gc.collect() + + start = time.time() + for _ in xrange(num_iters): + (images, labels) = iterator.next() + apply_grads(model, optimizer, + compute_gradients(model, images, labels)) + if execution_mode: + tfe.async_wait() + self._force_device_sync() + self._report(label, start, num_iters, device, batch_size, data_format) + + def benchmark_eager_train_sync(self): + self._benchmark_eager_train('eager_train', MockIterator, + device_and_data_format(), defun=False) + + def benchmark_eager_train_async(self): + self._benchmark_eager_train( + 'eager_train_async', + MockIterator, + device_and_data_format(), + defun=False, + execution_mode=tfe.ASYNC) + + def benchmark_eager_train_with_defun(self): + self._benchmark_eager_train( + 'eager_train_with_defun', MockIterator, + device_and_data_format(), defun=True) + + def benchmark_eager_train_datasets(self): + + def make_iterator(tensors): + with tf.device('/device:CPU:0'): + ds = tf.data.Dataset.from_tensors(tensors).repeat() + return tfe.Iterator(ds) + + self._benchmark_eager_train( + 'eager_train_dataset', make_iterator, + device_and_data_format(), defun=False) + + def benchmark_eager_train_datasets_with_defun(self): + + def make_iterator(tensors): + with tf.device('/device:CPU:0'): + ds = tf.data.Dataset.from_tensors(tensors).repeat() + return tfe.Iterator(ds) + + self._benchmark_eager_train( + 'eager_train_dataset_with_defun', make_iterator, + device_and_data_format(), defun=True) + + +if __name__ == '__main__': + tf.enable_eager_execution() + tf.test.main() diff --git a/tensorflow/contrib/eager/python/examples/gan/mnist.py b/tensorflow/contrib/eager/python/examples/gan/mnist.py index cc9cf53410f641cc3303b4450e9eaa1301904a64..9a4217929916c258b7e8f2e5b3add2905d20d1da 100644 --- a/tensorflow/contrib/eager/python/examples/gan/mnist.py +++ b/tensorflow/contrib/eager/python/examples/gan/mnist.py @@ -29,7 +29,6 @@ import time import tensorflow as tf -import tensorflow.contrib.eager as tfe from tensorflow.examples.tutorials.mnist import input_data layers = tf.keras.layers @@ -214,7 +213,7 @@ def train_one_epoch(generator, discriminator, generator_optimizer, total_generator_loss = 0.0 total_discriminator_loss = 0.0 - for (batch_index, images) in enumerate(tfe.Iterator(dataset)): + for (batch_index, images) in enumerate(dataset): with tf.device('/cpu:0'): tf.assign_add(step_counter, 1) @@ -227,7 +226,10 @@ def train_one_epoch(generator, discriminator, generator_optimizer, maxval=1., seed=batch_index) - with tf.GradientTape(persistent=True) as g: + # we can use 2 tapes or a single persistent tape. + # Using two tapes is memory efficient since intermediate tensors can be + # released between the two .gradient() calls below + with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape: generated_images = generator(noise) tf.contrib.summary.image( 'generated_images', @@ -243,9 +245,10 @@ def train_one_epoch(generator, discriminator, generator_optimizer, generator_loss_val = generator_loss(discriminator_gen_outputs) total_generator_loss += generator_loss_val - generator_grad = g.gradient(generator_loss_val, generator.variables) - discriminator_grad = g.gradient(discriminator_loss_val, - discriminator.variables) + generator_grad = gen_tape.gradient(generator_loss_val, + generator.variables) + discriminator_grad = disc_tape.gradient(discriminator_loss_val, + discriminator.variables) generator_optimizer.apply_gradients( zip(generator_grad, generator.variables)) @@ -261,7 +264,7 @@ def train_one_epoch(generator, discriminator, generator_optimizer, def main(_): (device, data_format) = ('/gpu:0', 'channels_first') - if FLAGS.no_gpu or tfe.num_gpus() <= 0: + if FLAGS.no_gpu or tf.contrib.eager.num_gpus() <= 0: (device, data_format) = ('/cpu:0', 'channels_last') print('Using device %s, and data format %s.' % (device, data_format)) @@ -287,7 +290,7 @@ def main(_): latest_cpkt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir) if latest_cpkt: print('Using latest checkpoint at ' + latest_cpkt) - checkpoint = tfe.Checkpoint(**model_objects) + checkpoint = tf.train.Checkpoint(**model_objects) # Restore variables on creation if a checkpoint exists. checkpoint.restore(latest_cpkt) diff --git a/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb b/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..44ff43a1112e771eb6c91c398286a003e17632e0 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb @@ -0,0 +1,733 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "0TD5ZrvEMbhZ" + }, + "source": [ + "##### Copyright 2018 The TensorFlow Authors.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\").\n", + "\n", + "# DCGAN: An example with tf.keras and eager\n", + "\n", + "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\u003ctd\u003e\n", + "\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb\"\u003e\n", + " \u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e \n", + "\u003c/td\u003e\u003ctd\u003e\n", + "\u003ca target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb\"\u003e\u003cimg width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\u003c/td\u003e\u003c/table\u003e" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ITZuApL56Mny" + }, + "source": [ + "This notebook demonstrates how to generate images of handwritten digits using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager). To do so, we use Deep Convolutional Generative Adverserial Networks ([DCGAN](https://arxiv.org/pdf/1511.06434.pdf)).\n", + "\n", + "This model takes about ~30 seconds per epoch (using tf.contrib.eager.defun to create graph functions) to train on a single Tesla K80 on Colab, as of July 2018.\n", + "\n", + "Below is the output generated after training the generator and discriminator models for 150 epochs.\n", + "\n", + "![sample output](https://tensorflow.org/images/gan/dcgan.gif)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "u_2z-B3piVsw" + }, + "outputs": [], + "source": [ + "# to generate gifs\n", + "!pip install imageio" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "e1_Y75QXJS6h" + }, + "source": [ + "## Import TensorFlow and enable eager execution" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "YfIk2es3hJEd" + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "# Import TensorFlow \u003e= 1.9 and enable eager execution\n", + "import tensorflow as tf\n", + "tf.enable_eager_execution()\n", + "\n", + "import os\n", + "import time\n", + "import numpy as np\n", + "import glob\n", + "import matplotlib.pyplot as plt\n", + "import PIL\n", + "import imageio\n", + "from IPython import display" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iYn4MdZnKCey" + }, + "source": [ + "## Load the dataset\n", + "\n", + "We are going to use the MNIST dataset to train the generator and the discriminator. The generator will then generate handwritten digits." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "a4fYMGxGhrna" + }, + "outputs": [], + "source": [ + "(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "NFC2ghIdiZYE" + }, + "outputs": [], + "source": [ + "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')\n", + "# We are normalizing the images to the range of [-1, 1]\n", + "train_images = (train_images - 127.5) / 127.5" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "S4PIDhoDLbsZ" + }, + "outputs": [], + "source": [ + "BUFFER_SIZE = 60000\n", + "BATCH_SIZE = 256" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PIGN6ouoQxt3" + }, + "source": [ + "## Use tf.data to create batches and shuffle the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "-yKCCQOoJ7cn" + }, + "outputs": [], + "source": [ + "train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "THY-sZMiQ4UV" + }, + "source": [ + "## Write the generator and discriminator models\n", + "\n", + "* **Generator** \n", + " * It is responsible for **creating convincing images that are good enough to fool the discriminator**.\n", + " * It consists of Conv2DTranspose (Upsampling) layers. We start with a fully connected layer and upsample the image 2 times so as to reach the desired image size (mnist image size) which is (28, 28, 1). \n", + " * We use **leaky relu** activation except for the **last layer** which uses **tanh** activation.\n", + " \n", + "* **Discriminator**\n", + " * **The discriminator is responsible for classifying the fake images from the real images.**\n", + " * In other words, the discriminator is given generated images (from the generator) and the real MNIST images. The job of the discriminator is to classify these images into fake (generated) and real (MNIST images).\n", + " * **Basically the generator should be good enough to fool the discriminator that the generated images are real**." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "VGLbvBEmjK0a" + }, + "outputs": [], + "source": [ + "class Generator(tf.keras.Model):\n", + " def __init__(self):\n", + " super(Generator, self).__init__()\n", + " self.fc1 = tf.keras.layers.Dense(7*7*64, use_bias=False)\n", + " self.batchnorm1 = tf.keras.layers.BatchNormalization()\n", + " \n", + " self.conv1 = tf.keras.layers.Conv2DTranspose(64, (5, 5), strides=(1, 1), padding='same', use_bias=False)\n", + " self.batchnorm2 = tf.keras.layers.BatchNormalization()\n", + " \n", + " self.conv2 = tf.keras.layers.Conv2DTranspose(32, (5, 5), strides=(2, 2), padding='same', use_bias=False)\n", + " self.batchnorm3 = tf.keras.layers.BatchNormalization()\n", + " \n", + " self.conv3 = tf.keras.layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False)\n", + "\n", + " def call(self, x, training=True):\n", + " x = self.fc1(x)\n", + " x = self.batchnorm1(x, training=training)\n", + " x = tf.nn.relu(x)\n", + "\n", + " x = tf.reshape(x, shape=(-1, 7, 7, 64))\n", + "\n", + " x = self.conv1(x)\n", + " x = self.batchnorm2(x, training=training)\n", + " x = tf.nn.relu(x)\n", + "\n", + " x = self.conv2(x)\n", + " x = self.batchnorm3(x, training=training)\n", + " x = tf.nn.relu(x)\n", + "\n", + " x = tf.nn.tanh(self.conv3(x)) \n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "bkOfJxk5j5Hi" + }, + "outputs": [], + "source": [ + "class Discriminator(tf.keras.Model):\n", + " def __init__(self):\n", + " super(Discriminator, self).__init__()\n", + " self.conv1 = tf.keras.layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same')\n", + " self.conv2 = tf.keras.layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same')\n", + " self.dropout = tf.keras.layers.Dropout(0.3)\n", + " self.flatten = tf.keras.layers.Flatten()\n", + " self.fc1 = tf.keras.layers.Dense(1)\n", + "\n", + " def call(self, x, training=True):\n", + " x = tf.nn.leaky_relu(self.conv1(x))\n", + " x = self.dropout(x, training=training)\n", + " x = tf.nn.leaky_relu(self.conv2(x))\n", + " x = self.dropout(x, training=training)\n", + " x = self.flatten(x)\n", + " x = self.fc1(x)\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "gDkA05NE6QMs" + }, + "outputs": [], + "source": [ + "generator = Generator()\n", + "discriminator = Discriminator()" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "k1HpMSLImuRi" + }, + "outputs": [], + "source": [ + "# Defun gives 10 secs/epoch performance boost\n", + "generator.call = tf.contrib.eager.defun(generator.call)\n", + "discriminator.call = tf.contrib.eager.defun(discriminator.call)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "0FMYgY_mPfTi" + }, + "source": [ + "## Define the loss functions and the optimizer\n", + "\n", + "* **Discriminator loss**\n", + " * The discriminator loss function takes 2 inputs; **real images, generated images**\n", + " * real_loss is a sigmoid cross entropy loss of the **real images** and an **array of ones (since these are the real images)**\n", + " * generated_loss is a sigmoid cross entropy loss of the **generated images** and an **array of zeros (since these are the fake images)**\n", + " * Then the total_loss is the sum of real_loss and the generated_loss\n", + " \n", + "* **Generator loss**\n", + " * It is a sigmoid cross entropy loss of the generated images and an **array of ones**\n", + " \n", + "\n", + "* The discriminator and the generator optimizers are different since we will train them separately." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "wkMNfBWlT-PV" + }, + "outputs": [], + "source": [ + "def discriminator_loss(real_output, generated_output):\n", + " # [1,1,...,1] with real output since it is true and we want\n", + " # our generated examples to look like it\n", + " real_loss = tf.losses.sigmoid_cross_entropy(multi_class_labels=tf.ones_like(real_output), logits=real_output)\n", + "\n", + " # [0,0,...,0] with generated images since they are fake\n", + " generated_loss = tf.losses.sigmoid_cross_entropy(multi_class_labels=tf.zeros_like(generated_output), logits=generated_output)\n", + "\n", + " total_loss = real_loss + generated_loss\n", + "\n", + " return total_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "90BIcCKcDMxz" + }, + "outputs": [], + "source": [ + "def generator_loss(generated_output):\n", + " return tf.losses.sigmoid_cross_entropy(tf.ones_like(generated_output), generated_output)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "iWCn_PVdEJZ7" + }, + "outputs": [], + "source": [ + "discriminator_optimizer = tf.train.AdamOptimizer(1e-4)\n", + "generator_optimizer = tf.train.AdamOptimizer(1e-4)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Rw1fkAczTQYh" + }, + "source": [ + "## Training\n", + "\n", + "* We start by iterating over the dataset\n", + "* The generator is given **noise as an input** which when passed through the generator model will output a image looking like a handwritten digit\n", + "* The discriminator is given the **real MNIST images as well as the generated images (from the generator)**.\n", + "* Next, we calculate the generator and the discriminator loss.\n", + "* Then, we calculate the gradients of loss with respect to both the generator and the discriminator variables (inputs) and apply those to the optimizer.\n", + "\n", + "## Generate Images\n", + "\n", + "* After training, its time to generate some images!\n", + "* We start by creating noise array as an input to the generator\n", + "* The generator will then convert the noise into handwritten images.\n", + "* Last step is to plot the predictions and **voila!**" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "NS2GWywBbAWo" + }, + "outputs": [], + "source": [ + "EPOCHS = 150\n", + "noise_dim = 100\n", + "num_examples_to_generate = 100\n", + "\n", + "# keeping the random vector constant for generation (prediction) so\n", + "# it will be easier to see the improvement of the gan.\n", + "random_vector_for_generation = tf.random_normal([num_examples_to_generate,\n", + " noise_dim])" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "RmdVsmvhPxyy" + }, + "outputs": [], + "source": [ + "def generate_and_save_images(model, epoch, test_input):\n", + " # make sure the training parameter is set to False because we\n", + " # don't want to train the batchnorm layer when doing inference.\n", + " predictions = model(test_input, training=False)\n", + "\n", + " fig = plt.figure(figsize=(10,10))\n", + " \n", + " for i in range(predictions.shape[0]):\n", + " plt.subplot(10, 10, i+1)\n", + " plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')\n", + " plt.axis('off')\n", + " \n", + " # tight_layout minimizes the overlap between 2 sub-plots\n", + " plt.tight_layout()\n", + " plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "2M7LmLtGEMQJ" + }, + "outputs": [], + "source": [ + "def train(dataset, epochs, noise_dim): \n", + " for epoch in range(epochs):\n", + " start = time.time()\n", + " \n", + " for images in dataset:\n", + " # generating noise from a uniform distribution\n", + " noise = tf.random_normal([BATCH_SIZE, noise_dim])\n", + " \n", + " with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n", + " generated_images = generator(noise, training=True)\n", + " \n", + " real_output = discriminator(images, training=True)\n", + " generated_output = discriminator(generated_images, training=True)\n", + " \n", + " gen_loss = generator_loss(generated_output)\n", + " disc_loss = discriminator_loss(real_output, generated_output)\n", + " \n", + " gradients_of_generator = gen_tape.gradient(gen_loss, generator.variables)\n", + " gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.variables)\n", + " \n", + " generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.variables))\n", + " discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.variables))\n", + "\n", + " \n", + " if epoch % 10 == 0:\n", + " display.clear_output(wait=True)\n", + " generate_and_save_images(generator,\n", + " epoch + 1,\n", + " random_vector_for_generation)\n", + "\n", + " print ('Time taken for epoch {} is {} sec'.format(epoch + 1,\n", + " time.time()-start))\n", + " # generating after the final epoch\n", + " generate_and_save_images(generator,\n", + " epochs,\n", + " random_vector_for_generation)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "Ly3UN0SLLY2l" + }, + "outputs": [], + "source": [ + "train(train_dataset, EPOCHS, noise_dim)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "P4M_vIbUi7c0" + }, + "source": [ + "# Display an image using the epoch number" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "WfO5wCdclHGL" + }, + "outputs": [], + "source": [ + "def display_image(epoch_no):\n", + " plt.figure(figsize=(15,15))\n", + " plt.imshow(np.array(PIL.Image.open('image_at_epoch_{:04d}.png'.format(epoch_no))))\n", + " plt.axis('off')" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "5x3q9_Oe5q0A" + }, + "outputs": [], + "source": [ + "display_image(EPOCHS)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "NywiH3nL8guF" + }, + "source": [ + "## Generate a GIF of all the saved images." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "xmO0Dmu2WICn" + }, + "source": [ + "\u003c!-- TODO(markdaoust): Remove the hack when Ipython version is updated --\u003e\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "IGKQgENQ8lEI" + }, + "outputs": [], + "source": [ + "with imageio.get_writer('dcgan.gif', mode='I') as writer:\n", + " filenames = glob.glob('image*.png')\n", + " filenames = sorted(filenames)\n", + " for filename in filenames:\n", + " image = imageio.imread(filename)\n", + " writer.append_data(image)\n", + " # this is a hack to display the gif inside the notebook\n", + " os.system('mv dcgan.gif dcgan.gif.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "uV0yiKpzNP1b" + }, + "outputs": [], + "source": [ + "display.Image(filename=\"dcgan.gif.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "4UJjSnIMOzOJ" + }, + "outputs": [], + "source": [ + "" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "default_view": {}, + "name": "dcgan.ipynb", + "private_outputs": true, + "provenance": [ + { + "file_id": "1eb0NOTQapkYs3X0v-zL1x5_LFKgDISnp", + "timestamp": 1527173385672 + } + ], + "toc_visible": true, + "version": "0.3.2", + "views": {} + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb b/tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1a5a186e7a3e456cc43f8091370d3eeb795d5e0e --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb @@ -0,0 +1,1184 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "image_captioning_with_attention.ipynb", + "version": "0.3.2", + "views": {}, + "default_view": {}, + "provenance": [ + { + "file_id": "1HI8OK2sMjcx9CTWVn0122QAHOuXaOaMg", + "timestamp": 1530222436922 + } + ], + "private_outputs": true, + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "metadata": { + "id": "K2s1A9eLRPEj", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "##### Copyright 2018 The TensorFlow Authors.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\").\n" + ] + }, + { + "metadata": { + "id": "Cffg2i257iMS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Image Captioning with Attention\n", + "\n", + "
\n", + "\n", + " Run in Google Colab \n", + "\n", + "View source on GitHub
" + ] + }, + { + "metadata": { + "id": "QASbY_HGo4Lq", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Image captioning is the task of generating a caption for an image. Given an image like this:\n", + "\n", + "![Man Surfing](https://tensorflow.org/images/surf.jpg) \n", + "\n", + "[Image Source](https://commons.wikimedia.org/wiki/Surfing#/media/File:Surfing_in_Hawaii.jpg), License: Public Domain\n", + "\n", + "Our goal is generate a caption, such as \"a surfer riding on a wave\". Here, we'll use an attention based model. This enables us to see which parts of the image the model focuses on as it generates a caption.\n", + "\n", + "![Prediction](https://tensorflow.org/images/imcap_prediction.png)\n", + "\n", + "This model architecture below is similar to [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://arxiv.org/abs/1502.03044). \n", + "\n", + "The code uses [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager), which you can learn more about in the linked guides.\n", + "\n", + "This notebook is an end-to-end example. If you run it, it will download the [MS-COCO](http://cocodataset.org/#home) dataset, preprocess and cache a subset of the images using Inception V3, train an encoder-decoder model, and use it to generate captions on new images.\n", + "\n", + "The code requires TensorFlow version >=1.9. If you're running this in [Colab]()\n", + "\n", + "In this example, we're training on a relatively small amount of data as an example. On a single P100 GPU, this example will take about ~2 hours to train. We train on the first 30,000 captions (corresponding to about ~20,000 images depending on shuffling, as there are multiple captions per image in the dataset)\n" + ] + }, + { + "metadata": { + "id": "U8l4RJ0XRPEm", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Import TensorFlow and enable eager execution\n", + "# This code requires TensorFlow version >=1.9\n", + "import tensorflow as tf\n", + "tf.enable_eager_execution()\n", + "\n", + "# We'll generate plots of attention in order to see which parts of an image\n", + "# our model focuses on during captioning\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit-learn includes many helpful utilities\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.utils import shuffle\n", + "\n", + "import re\n", + "import numpy as np\n", + "import os\n", + "import time\n", + "import json\n", + "from glob import glob\n", + "from PIL import Image\n", + "import pickle" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "b6qbGw8MRPE5", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Download and prepare the MS-COCO dataset\n", + "\n", + "We will use the [MS-COCO dataset](http://cocodataset.org/#home) to train our model. This dataset contains >82,000 images, each of which has been annotated with at least 5 different captions. The code code below will download and extract the dataset automatically. \n", + "\n", + "**Caution: large download ahead**. We'll use the training set, it's a 13GB file." + ] + }, + { + "metadata": { + "id": "krQuPYTtRPE7", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "annotation_zip = tf.keras.utils.get_file('captions.zip', \n", + " cache_subdir=os.path.abspath('.'),\n", + " origin = 'http://images.cocodataset.org/annotations/annotations_trainval2014.zip',\n", + " extract = True)\n", + "annotation_file = os.path.dirname(annotation_zip)+'/annotations/captions_train2014.json'\n", + "\n", + "name_of_zip = 'train2014.zip'\n", + "if not os.path.exists(os.path.abspath('.') + '/' + name_of_zip):\n", + " image_zip = tf.keras.utils.get_file(name_of_zip, \n", + " cache_subdir=os.path.abspath('.'),\n", + " origin = 'http://images.cocodataset.org/zips/train2014.zip',\n", + " extract = True)\n", + " PATH = os.path.dirname(image_zip)+'/train2014/'\n", + "else:\n", + " PATH = os.path.abspath('.')+'/train2014/'" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "aANEzb5WwSzg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Optionally, limit the size of the training set for faster training\n", + "For this example, we'll select a subset of 30,000 captions and use these and the corresponding images to train our model. As always, captioning quality will improve if you choose to use more data." + ] + }, + { + "metadata": { + "id": "4G3b8x8_RPFD", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# read the json file\n", + "with open(annotation_file, 'r') as f:\n", + " annotations = json.load(f)\n", + "\n", + "# storing the captions and the image name in vectors\n", + "all_captions = []\n", + "all_img_name_vector = []\n", + "\n", + "for annot in annotations['annotations']:\n", + " caption = ' ' + annot['caption'] + ' '\n", + " image_id = annot['image_id']\n", + " full_coco_image_path = PATH + 'COCO_train2014_' + '%012d.jpg' % (image_id)\n", + " \n", + " all_img_name_vector.append(full_coco_image_path)\n", + " all_captions.append(caption)\n", + "\n", + "# shuffling the captions and image_names together\n", + "# setting a random state\n", + "train_captions, img_name_vector = shuffle(all_captions,\n", + " all_img_name_vector,\n", + " random_state=1)\n", + "\n", + "# selecting the first 30000 captions from the shuffled set\n", + "num_examples = 30000\n", + "train_captions = train_captions[:num_examples]\n", + "img_name_vector = img_name_vector[:num_examples]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mPBMgK34RPFL", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "len(train_captions), len(all_captions)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "8cSW4u-ORPFQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Preprocess the images using InceptionV3\n", + "Next, we will use InceptionV3 (pretrained on Imagenet) to classify each image. We will extract features from the last convolutional layer. \n", + "\n", + "First, we will need to convert the images into the format inceptionV3 expects by:\n", + "* Resizing the image to (299, 299)\n", + "* Using the [preprocess_input](https://www.tensorflow.org/api_docs/python/tf/keras/applications/inception_v3/preprocess_input) method to place the pixels in the range of -1 to 1 (to match the format of the images used to train InceptionV3)." + ] + }, + { + "metadata": { + "id": "zXR0217aRPFR", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def load_image(image_path):\n", + " img = tf.read_file(image_path)\n", + " img = tf.image.decode_jpeg(img, channels=3)\n", + " img = tf.image.resize_images(img, (299, 299))\n", + " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", + " return img, image_path" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "MDvIu4sXRPFV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Initialize InceptionV3 and load the pretrained Imagenet weights\n", + "\n", + "To do so, we'll create a tf.keras model where the output layer is the last convolutional layer in the InceptionV3 architecture. \n", + "* Each image is forwarded through the network and the vector that we get at the end is stored in a dictionary (image_name --> feature_vector). \n", + "* We use the last convolutional layer because we are using attention in this example. The shape of the output of this layer is ```8x8x2048```. \n", + "* We avoid doing this during training so it does not become a bottleneck. \n", + "* After all the images are passed through the network, we pickle the dictionary and save it to disk." + ] + }, + { + "metadata": { + "id": "RD3vW4SsRPFW", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "image_model = tf.keras.applications.InceptionV3(include_top=False, \n", + " weights='imagenet')\n", + "new_input = image_model.input\n", + "hidden_layer = image_model.layers[-1].output\n", + "\n", + "image_features_extract_model = tf.keras.Model(new_input, hidden_layer)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "rERqlR3WRPGO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Caching the features extracted from InceptionV3\n", + "\n", + "We will pre-process each image with InceptionV3 and cache the output to disk. Caching the output in RAM would be faster but memory intensive, requiring 8 \\* 8 \\* 2048 floats per image. At the time of writing, this would exceed the memory limitations of Colab (although these may change, an instance appears to have about 12GB of memory currently). \n", + "\n", + "Performance could be improved with a more sophisticated caching strategy (e.g., by sharding the images to reduce random access disk I/O) at the cost of more code.\n", + "\n", + "This will take about 10 minutes to run in Colab with a GPU. If you'd like to see a progress bar, you could: install [tqdm](https://github.com/tqdm/tqdm) (```!pip install tqdm```), then change this line: \n", + "\n", + "```for img, path in image_dataset:``` \n", + "\n", + "to:\n", + "\n", + "```for img, path in tqdm(image_dataset):```." + ] + }, + { + "metadata": { + "id": "Dx_fvbVgRPGQ", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# getting the unique images\n", + "encode_train = sorted(set(img_name_vector))\n", + "\n", + "# feel free to change the batch_size according to your system configuration\n", + "image_dataset = tf.data.Dataset.from_tensor_slices(\n", + " encode_train).map(load_image).batch(16)\n", + "\n", + "for img, path in image_dataset:\n", + " batch_features = image_features_extract_model(img)\n", + " batch_features = tf.reshape(batch_features, \n", + " (batch_features.shape[0], -1, batch_features.shape[3]))\n", + "\n", + " for bf, p in zip(batch_features, path):\n", + " path_of_feature = p.numpy().decode(\"utf-8\")\n", + " np.save(path_of_feature, bf.numpy())" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "nyqH3zFwRPFi", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Preprocess and tokenize the captions\n", + "\n", + "* First, we'll tokenize the captions (e.g., by splitting on spaces). This will give us a vocabulary of all the unique words in the data (e.g., \"surfing\", \"football\", etc).\n", + "* Next, we'll limit the vocabulary size to the top 5,000 words to save memory. We'll replace all other words with the token \"UNK\" (for unknown).\n", + "* Finally, we create a word --> index mapping and vice-versa.\n", + "* We will then pad all sequences to the be same length as the longest one. " + ] + }, + { + "metadata": { + "id": "HZfK8RhQRPFj", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# This will find the maximum length of any caption in our dataset\n", + "def calc_max_length(tensor):\n", + " return max(len(t) for t in tensor)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "oJGE34aiRPFo", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# The steps above is a general process of dealing with text processing\n", + "\n", + "# choosing the top 5000 words from the vocabulary\n", + "top_k = 5000\n", + "tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=top_k, \n", + " oov_token=\"\", \n", + " filters='!\"#$%&()*+.,-/:;=?@[\\]^_`{|}~ ')\n", + "tokenizer.fit_on_texts(train_captions)\n", + "train_seqs = tokenizer.texts_to_sequences(train_captions)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "8Q44tNQVRPFt", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "tokenizer.word_index = {key:value for key, value in tokenizer.word_index.items() if value <= top_k}\n", + "# putting token in the word2idx dictionary\n", + "tokenizer.word_index[tokenizer.oov_token] = top_k + 1\n", + "tokenizer.word_index[''] = 0" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "0fpJb5ojRPFv", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# creating the tokenized vectors\n", + "train_seqs = tokenizer.texts_to_sequences(train_captions)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "olQArbgbRPF1", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# creating a reverse mapping (index -> word)\n", + "index_word = {value:key for key, value in tokenizer.word_index.items()}" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "AidglIZVRPF4", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# padding each vector to the max_length of the captions\n", + "# if the max_length parameter is not provided, pad_sequences calculates that automatically\n", + "cap_vector = tf.keras.preprocessing.sequence.pad_sequences(train_seqs, padding='post')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "gL0wkttkRPGA", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# calculating the max_length \n", + "# used to store the attention weights\n", + "max_length = calc_max_length(train_seqs)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "M3CD75nDpvTI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Split the data into training and testing" + ] + }, + { + "metadata": { + "id": "iS7DDMszRPGF", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Create training and validation sets using 80-20 split\n", + "img_name_train, img_name_val, cap_train, cap_val = train_test_split(img_name_vector, \n", + " cap_vector, \n", + " test_size=0.2, \n", + " random_state=0)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "XmViPkRFRPGH", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "len(img_name_train), len(cap_train), len(img_name_val), len(cap_val)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "uEWM9xrYcg45", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Our images and captions are ready! Next, let's create a tf.data dataset to use for training our model.\n", + "\n" + ] + }, + { + "metadata": { + "id": "Q3TnZ1ToRPGV", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# feel free to change these parameters according to your system's configuration\n", + "\n", + "BATCH_SIZE = 64\n", + "BUFFER_SIZE = 1000\n", + "embedding_dim = 256\n", + "units = 512\n", + "vocab_size = len(tokenizer.word_index)\n", + "# shape of the vector extracted from InceptionV3 is (64, 2048)\n", + "# these two variables represent that\n", + "features_shape = 2048\n", + "attention_features_shape = 64" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "SmZS2N0bXG3T", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# loading the numpy files \n", + "def map_func(img_name, cap):\n", + " img_tensor = np.load(img_name.decode('utf-8')+'.npy')\n", + " return img_tensor, cap" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "FDF_Nm3tRPGZ", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "dataset = tf.data.Dataset.from_tensor_slices((img_name_train, cap_train))\n", + "\n", + "# using map to load the numpy files in parallel\n", + "# NOTE: Be sure to set num_parallel_calls to the number of CPU cores you have\n", + "# https://www.tensorflow.org/api_docs/python/tf/py_func\n", + "dataset = dataset.map(lambda item1, item2: tf.py_func(\n", + " map_func, [item1, item2], [tf.float32, tf.int32]), num_parallel_calls=8)\n", + "\n", + "# shuffling and batching\n", + "dataset = dataset.shuffle(BUFFER_SIZE)\n", + "# https://www.tensorflow.org/api_docs/python/tf/contrib/data/batch_and_drop_remainder\n", + "dataset = dataset.batch(BATCH_SIZE)\n", + "dataset = dataset.prefetch(1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "nrvoDphgRPGd", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Model\n", + "\n", + "Fun fact, the decoder below is identical to the one in the example for [Neural Machine Translation with Attention]( https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb).\n", + "\n", + "The model architecture is inspired by the [Show, Attend and Tell](https://arxiv.org/pdf/1502.03044.pdf) paper.\n", + "\n", + "* In this example, we extract the features from the lower convolutional layer of InceptionV3 giving us a vector of shape (8, 8, 2048). \n", + "* We squash that to a shape of (64, 2048).\n", + "* This vector is then passed through the CNN Encoder(which consists of a single Fully connected layer).\n", + "* The RNN(here GRU) attends over the image to predict the next word." + ] + }, + { + "metadata": { + "id": "AAppCGLKRPGd", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def gru(units):\n", + " # If you have a GPU, we recommend using the CuDNNGRU layer (it provides a \n", + " # significant speedup).\n", + " if tf.test.is_gpu_available():\n", + " return tf.keras.layers.CuDNNGRU(units, \n", + " return_sequences=True, \n", + " return_state=True, \n", + " recurrent_initializer='glorot_uniform')\n", + " else:\n", + " return tf.keras.layers.GRU(units, \n", + " return_sequences=True, \n", + " return_state=True, \n", + " recurrent_activation='sigmoid', \n", + " recurrent_initializer='glorot_uniform')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ja2LFTMSdeV3", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "class BahdanauAttention(tf.keras.Model):\n", + " def __init__(self, units):\n", + " super(BahdanauAttention, self).__init__()\n", + " self.W1 = tf.keras.layers.Dense(units)\n", + " self.W2 = tf.keras.layers.Dense(units)\n", + " self.V = tf.keras.layers.Dense(1)\n", + " \n", + " def call(self, features, hidden):\n", + " # features(CNN_encoder output) shape == (batch_size, 64, embedding_dim)\n", + " \n", + " # hidden shape == (batch_size, hidden_size)\n", + " # hidden_with_time_axis shape == (batch_size, 1, hidden_size)\n", + " hidden_with_time_axis = tf.expand_dims(hidden, 1)\n", + " \n", + " # score shape == (batch_size, 64, hidden_size)\n", + " score = tf.nn.tanh(self.W1(features) + self.W2(hidden_with_time_axis))\n", + " \n", + " # attention_weights shape == (batch_size, 64, 1)\n", + " # we get 1 at the last axis because we are applying score to self.V\n", + " attention_weights = tf.nn.softmax(self.V(score), axis=1)\n", + " \n", + " # context_vector shape after sum == (batch_size, hidden_size)\n", + " context_vector = attention_weights * features\n", + " context_vector = tf.reduce_sum(context_vector, axis=1)\n", + " \n", + " return context_vector, attention_weights" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "AZ7R1RxHRPGf", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "class CNN_Encoder(tf.keras.Model):\n", + " # Since we have already extracted the features and dumped it using pickle\n", + " # This encoder passes those features through a Fully connected layer\n", + " def __init__(self, embedding_dim):\n", + " super(CNN_Encoder, self).__init__()\n", + " # shape after fc == (batch_size, 64, embedding_dim)\n", + " self.fc = tf.keras.layers.Dense(embedding_dim)\n", + " \n", + " def call(self, x):\n", + " x = self.fc(x)\n", + " x = tf.nn.relu(x)\n", + " return x" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "V9UbGQmERPGi", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "class RNN_Decoder(tf.keras.Model):\n", + " def __init__(self, embedding_dim, units, vocab_size):\n", + " super(RNN_Decoder, self).__init__()\n", + " self.units = units\n", + "\n", + " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", + " self.gru = gru(self.units)\n", + " self.fc1 = tf.keras.layers.Dense(self.units)\n", + " self.fc2 = tf.keras.layers.Dense(vocab_size)\n", + " \n", + " self.attention = BahdanauAttention(self.units)\n", + " \n", + " def call(self, x, features, hidden):\n", + " # defining attention as a separate model\n", + " context_vector, attention_weights = self.attention(features, hidden)\n", + " \n", + " # x shape after passing through embedding == (batch_size, 1, embedding_dim)\n", + " x = self.embedding(x)\n", + " \n", + " # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\n", + " x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\n", + " \n", + " # passing the concatenated vector to the GRU\n", + " output, state = self.gru(x)\n", + " \n", + " # shape == (batch_size, max_length, hidden_size)\n", + " x = self.fc1(output)\n", + " \n", + " # x shape == (batch_size * max_length, hidden_size)\n", + " x = tf.reshape(x, (-1, x.shape[2]))\n", + " \n", + " # output shape == (batch_size * max_length, vocab)\n", + " x = self.fc2(x)\n", + "\n", + " return x, state, attention_weights\n", + "\n", + " def reset_state(self, batch_size):\n", + " return tf.zeros((batch_size, self.units))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Qs_Sr03wRPGk", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "encoder = CNN_Encoder(embedding_dim)\n", + "decoder = RNN_Decoder(embedding_dim, units, vocab_size)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-bYN7xA0RPGl", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "optimizer = tf.train.AdamOptimizer()\n", + "\n", + "# We are masking the loss calculated for padding\n", + "def loss_function(real, pred):\n", + " mask = 1 - np.equal(real, 0)\n", + " loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=real, logits=pred) * mask\n", + " return tf.reduce_mean(loss_)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "PHod7t72RPGn", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Training\n", + "\n", + "* We extract the features stored in the respective `.npy` files and then pass those features through the encoder.\n", + "* The encoder output, hidden state(initialized to 0) and the decoder input (which is the start token) is passed to the decoder.\n", + "* The decoder returns the predictions and the decoder hidden state.\n", + "* The decoder hidden state is then passed back into the model and the predictions are used to calculate the loss.\n", + "* Use teacher forcing to decide the next input to the decoder.\n", + "* Teacher forcing is the technique where the target word is passed as the next input to the decoder.\n", + "* The final step is to calculate the gradients and apply it to the optimizer and backpropagate.\n" + ] + }, + { + "metadata": { + "id": "Vt4WZ5mhJE-E", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# adding this in a separate cell because if you run the training cell \n", + "# many times, the loss_plot array will be reset\n", + "loss_plot = []" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "UlA4VIQpRPGo", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "EPOCHS = 20\n", + "\n", + "for epoch in range(EPOCHS):\n", + " start = time.time()\n", + " total_loss = 0\n", + " \n", + " for (batch, (img_tensor, target)) in enumerate(dataset):\n", + " loss = 0\n", + " \n", + " # initializing the hidden state for each batch\n", + " # because the captions are not related from image to image\n", + " hidden = decoder.reset_state(batch_size=target.shape[0])\n", + "\n", + " dec_input = tf.expand_dims([tokenizer.word_index['']] * BATCH_SIZE, 1)\n", + " \n", + " with tf.GradientTape() as tape:\n", + " features = encoder(img_tensor)\n", + " \n", + " for i in range(1, target.shape[1]):\n", + " # passing the features through the decoder\n", + " predictions, hidden, _ = decoder(dec_input, features, hidden)\n", + "\n", + " loss += loss_function(target[:, i], predictions)\n", + " \n", + " # using teacher forcing\n", + " dec_input = tf.expand_dims(target[:, i], 1)\n", + " \n", + " total_loss += (loss / int(target.shape[1]))\n", + " \n", + " variables = encoder.variables + decoder.variables\n", + " \n", + " gradients = tape.gradient(loss, variables) \n", + " \n", + " optimizer.apply_gradients(zip(gradients, variables), tf.train.get_or_create_global_step())\n", + " \n", + " if batch % 100 == 0:\n", + " print ('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1, \n", + " batch, \n", + " loss.numpy() / int(target.shape[1])))\n", + " # storing the epoch end loss value to plot later\n", + " loss_plot.append(total_loss / len(cap_vector))\n", + " \n", + " print ('Epoch {} Loss {:.6f}'.format(epoch + 1, \n", + " total_loss/len(cap_vector)))\n", + " print ('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "1Wm83G-ZBPcC", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "plt.plot(loss_plot)\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Loss')\n", + "plt.title('Loss Plot')\n", + "plt.show()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "xGvOcLQKghXN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Caption!\n", + "\n", + "* The evaluate function is similar to the training loop, except we don't use teacher forcing here. The input to the decoder at each time step is its previous predictions along with the hidden state and the encoder output.\n", + "* Stop predicting when the model predicts the end token.\n", + "* And store the attention weights for every time step." + ] + }, + { + "metadata": { + "id": "RCWpDtyNRPGs", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def evaluate(image):\n", + " attention_plot = np.zeros((max_length, attention_features_shape))\n", + "\n", + " hidden = decoder.reset_state(batch_size=1)\n", + "\n", + " temp_input = tf.expand_dims(load_image(image)[0], 0)\n", + " img_tensor_val = image_features_extract_model(temp_input)\n", + " img_tensor_val = tf.reshape(img_tensor_val, (img_tensor_val.shape[0], -1, img_tensor_val.shape[3]))\n", + "\n", + " features = encoder(img_tensor_val)\n", + "\n", + " dec_input = tf.expand_dims([tokenizer.word_index['']], 0)\n", + " result = []\n", + "\n", + " for i in range(max_length):\n", + " predictions, hidden, attention_weights = decoder(dec_input, features, hidden)\n", + "\n", + " attention_plot[i] = tf.reshape(attention_weights, (-1, )).numpy()\n", + "\n", + " predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy()\n", + " result.append(index_word[predicted_id])\n", + "\n", + " if index_word[predicted_id] == '':\n", + " return result, attention_plot\n", + "\n", + " dec_input = tf.expand_dims([predicted_id], 0)\n", + "\n", + " attention_plot = attention_plot[:len(result), :]\n", + " return result, attention_plot" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "fD_y7PD6RPGt", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def plot_attention(image, result, attention_plot):\n", + " temp_image = np.array(Image.open(image))\n", + "\n", + " fig = plt.figure(figsize=(10, 10))\n", + " \n", + " len_result = len(result)\n", + " for l in range(len_result):\n", + " temp_att = np.resize(attention_plot[l], (8, 8))\n", + " ax = fig.add_subplot(len_result//2, len_result//2, l+1)\n", + " ax.set_title(result[l])\n", + " img = ax.imshow(temp_image)\n", + " ax.imshow(temp_att, cmap='gray', alpha=0.6, extent=img.get_extent())\n", + "\n", + " plt.tight_layout()\n", + " plt.show()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "io7ws3ReRPGv", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# captions on the validation set\n", + "rid = np.random.randint(0, len(img_name_val))\n", + "image = img_name_val[rid]\n", + "real_caption = ' '.join([index_word[i] for i in cap_val[rid] if i not in [0]])\n", + "result, attention_plot = evaluate(image)\n", + "\n", + "print ('Real Caption:', real_caption)\n", + "print ('Prediction Caption:', ' '.join(result))\n", + "plot_attention(image, result, attention_plot)\n", + "# opening the image\n", + "Image.open(img_name_val[rid])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Rprk3HEvZuxb", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Try it on your own images\n", + "For fun, below we've provided a method you can use to caption your own images with the model we've just trained. Keep in mind, it was trained on a relatively small amount of data, and your images may be different from the training data (so be prepared for weird results!)\n" + ] + }, + { + "metadata": { + "id": "9Psd1quzaAWg", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "image_url = 'https://tensorflow.org/images/surf.jpg'\n", + "image_extension = image_url[-4:]\n", + "image_path = tf.keras.utils.get_file('image'+image_extension, \n", + " origin=image_url)\n", + "\n", + "result, attention_plot = evaluate(image_path)\n", + "print ('Prediction Caption:', ' '.join(result))\n", + "plot_attention(image_path, result, attention_plot)\n", + "# opening the image\n", + "Image.open(image_path)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VJZXyJco6uLO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Next steps\n", + "\n", + "Congrats! You've just trained an image captioning model with attention. Next, we recommend taking a look at this example [Neural Machine Translation with Attention]( https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb). It uses a similar architecture to translate between Spanish and English sentences. You can also experiment with training the code in this notebook on a different dataset." + ] + } + ] +} diff --git a/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb b/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b173f856c641b4d7dca96adda113f904c97a25a7 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb @@ -0,0 +1,689 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hcD2nPQvPOFM" + }, + "source": [ + "##### Copyright 2018 The TensorFlow Authors.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\").\n", + "\n", + "# Text Generation using a RNN\n", + "\n", + "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\u003ctd\u003e\n", + "\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb\"\u003e\n", + " \u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e \n", + "\u003c/td\u003e\u003ctd\u003e\n", + "\u003ca target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb\"\u003e\u003cimg width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on Github\u003c/a\u003e\u003c/td\u003e\u003c/table\u003e" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "BwpJ5IffzRG6" + }, + "source": [ + "This notebook demonstrates how to generate text using an RNN using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager). If you like, you can write a similar [model](https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/8.1-text-generation-with-lstm.ipynb) using less code. Here, we show a lower-level impementation that's useful to understand as prework before diving in to deeper examples in a similar, like [Neural Machine Translation with Attention](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb).\n", + "\n", + "This notebook is an end-to-end example. When you run it, it will download a dataset of Shakespeare's writing. We'll use a collection of plays, borrowed from Andrej Karpathy's excellent [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/). The notebook will train a model, and use it to generate sample output.\n", + " \n", + "Here is the output(with start string='w') after training a single layer GRU for 30 epochs with the default settings below:\n", + "\n", + "```\n", + "were to the death of him\n", + "And nothing of the field in the view of hell,\n", + "When I said, banish him, I will not burn thee that would live.\n", + "\n", + "HENRY BOLINGBROKE:\n", + "My gracious uncle--\n", + "\n", + "DUKE OF YORK:\n", + "As much disgraced to the court, the gods them speak,\n", + "And now in peace himself excuse thee in the world.\n", + "\n", + "HORTENSIO:\n", + "Madam, 'tis not the cause of the counterfeit of the earth,\n", + "And leave me to the sun that set them on the earth\n", + "And leave the world and are revenged for thee.\n", + "\n", + "GLOUCESTER:\n", + "I would they were talking with the very name of means\n", + "To make a puppet of a guest, and therefore, good Grumio,\n", + "Nor arm'd to prison, o' the clouds, of the whole field,\n", + "With the admire\n", + "With the feeding of thy chair, and we have heard it so,\n", + "I thank you, sir, he is a visor friendship with your silly your bed.\n", + "\n", + "SAMPSON:\n", + "I do desire to live, I pray: some stand of the minds, make thee remedies\n", + "With the enemies of my soul.\n", + "\n", + "MENENIUS:\n", + "I'll keep the cause of my mistress.\n", + "\n", + "POLIXENES:\n", + "My brother Marcius!\n", + "\n", + "Second Servant:\n", + "Will't ple\n", + "```\n", + "\n", + "Of course, while some of the sentences are grammatical, most do not make sense. But, consider:\n", + "\n", + "* Our model is character based (when we began training, it did not yet know how to spell a valid English word, or that words were even a unit of text).\n", + "\n", + "* The structure of the output resembles a play (blocks begin with a speaker name, in all caps similar to the original text). Sentences generally end with a period. If you look at the text from a distance (or don't read the invididual words too closely, it appears as if it's an excerpt from a play).\n", + "\n", + "As a next step, you can experiment training the model on a different dataset - any large text file(ASCII) will do, and you can modify a single line of code below to make that change. Have fun!\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "R3p22DBDsaCA" + }, + "source": [ + "## Install unidecode library\n", + "A helpful library to convert unicode to ASCII." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "wZ6LOM12wKGH" + }, + "outputs": [], + "source": [ + "!pip install unidecode" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "WGyKZj3bzf9p" + }, + "source": [ + "## Import tensorflow and enable eager execution." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "yG_n40gFzf9s" + }, + "outputs": [], + "source": [ + "# Import TensorFlow \u003e= 1.9 and enable eager execution\n", + "import tensorflow as tf\n", + "\n", + "# Note: Once you enable eager execution, it cannot be disabled. \n", + "tf.enable_eager_execution()\n", + "\n", + "import numpy as np\n", + "import re\n", + "import random\n", + "import unidecode\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "EHDoRoc5PKWz" + }, + "source": [ + "## Download the dataset\n", + "\n", + "In this example, we will use the [shakespeare dataset](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt). You can use any other dataset that you like.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "pD_55cOxLkAb" + }, + "outputs": [], + "source": [ + "path_to_file = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "UHjdCjDuSvX_" + }, + "source": [ + "## Read the dataset\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "-E5JvY3wzf94" + }, + "outputs": [], + "source": [ + "text = unidecode.unidecode(open(path_to_file).read())\n", + "# length of text is the number of characters in it\n", + "print (len(text))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Il9ww98izf-D" + }, + "source": [ + "Creating dictionaries to map from characters to their indices and vice-versa, which will be used to vectorize the inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "IalZLbvOzf-F" + }, + "outputs": [], + "source": [ + "# unique contains all the unique characters in the file\n", + "unique = sorted(set(text))\n", + "\n", + "# creating a mapping from unique characters to indices\n", + "char2idx = {u:i for i, u in enumerate(unique)}\n", + "idx2char = {i:u for i, u in enumerate(unique)}" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "1v_qUYfAzf-I" + }, + "outputs": [], + "source": [ + "# setting the maximum length sentence we want for a single input in characters\n", + "max_length = 100\n", + "\n", + "# length of the vocabulary in chars\n", + "vocab_size = len(unique)\n", + "\n", + "# the embedding dimension \n", + "embedding_dim = 256\n", + "\n", + "# number of RNN (here GRU) units\n", + "units = 1024\n", + "\n", + "# batch size \n", + "BATCH_SIZE = 64\n", + "\n", + "# buffer size to shuffle our dataset\n", + "BUFFER_SIZE = 10000" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "LFjSVAlWzf-N" + }, + "source": [ + "## Creating the input and output tensors\n", + "\n", + "Vectorizing the input and the target text because our model cannot understand strings only numbers.\n", + "\n", + "But first, we need to create the input and output vectors.\n", + "Remember the max_length we set above, we will use it here. We are creating **max_length** chunks of input, where each input vector is all the characters in that chunk except the last and the target vector is all the characters in that chunk except the first.\n", + "\n", + "For example, consider that the string = 'tensorflow' and the max_length is 9\n", + "\n", + "So, the `input = 'tensorflo'` and `output = 'ensorflow'`\n", + "\n", + "After creating the vectors, we convert each character into numbers using the **char2idx** dictionary we created above." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "0UHJDA39zf-O" + }, + "outputs": [], + "source": [ + "input_text = []\n", + "target_text = []\n", + "\n", + "for f in range(0, len(text)-max_length, max_length):\n", + " inps = text[f:f+max_length]\n", + " targ = text[f+1:f+1+max_length]\n", + "\n", + " input_text.append([char2idx[i] for i in inps])\n", + " target_text.append([char2idx[t] for t in targ])\n", + " \n", + "print (np.array(input_text).shape)\n", + "print (np.array(target_text).shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "MJdfPmdqzf-R" + }, + "source": [ + "## Creating batches and shuffling them using tf.data" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "p2pGotuNzf-S" + }, + "outputs": [], + "source": [ + "dataset = tf.data.Dataset.from_tensor_slices((input_text, target_text)).shuffle(BUFFER_SIZE)\n", + "dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(BATCH_SIZE))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "m8gPwEjRzf-Z" + }, + "source": [ + "## Creating the model\n", + "\n", + "We use the Model Subclassing API which gives us full flexibility to create the model and change it however we like. We use 3 layers to define our model.\n", + "\n", + "* Embedding layer\n", + "* GRU layer (you can use an LSTM layer here)\n", + "* Fully connected layer" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "P3KTiiInzf-a" + }, + "outputs": [], + "source": [ + "class Model(tf.keras.Model):\n", + " def __init__(self, vocab_size, embedding_dim, units, batch_size):\n", + " super(Model, self).__init__()\n", + " self.units = units\n", + " self.batch_sz = batch_size\n", + "\n", + " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", + "\n", + " if tf.test.is_gpu_available():\n", + " self.gru = tf.keras.layers.CuDNNGRU(self.units, \n", + " return_sequences=True, \n", + " return_state=True, \n", + " recurrent_initializer='glorot_uniform')\n", + " else:\n", + " self.gru = tf.keras.layers.GRU(self.units, \n", + " return_sequences=True, \n", + " return_state=True, \n", + " recurrent_activation='sigmoid', \n", + " recurrent_initializer='glorot_uniform')\n", + "\n", + " self.fc = tf.keras.layers.Dense(vocab_size)\n", + " \n", + " def call(self, x, hidden):\n", + " x = self.embedding(x)\n", + "\n", + " # output shape == (batch_size, max_length, hidden_size) \n", + " # states shape == (batch_size, hidden_size)\n", + "\n", + " # states variable to preserve the state of the model\n", + " # this will be used to pass at every step to the model while training\n", + " output, states = self.gru(x, initial_state=hidden)\n", + "\n", + "\n", + " # reshaping the output so that we can pass it to the Dense layer\n", + " # after reshaping the shape is (batch_size * max_length, hidden_size)\n", + " output = tf.reshape(output, (-1, output.shape[2]))\n", + "\n", + " # The dense layer will output predictions for every time_steps(max_length)\n", + " # output shape after the dense layer == (max_length * batch_size, vocab_size)\n", + " x = self.fc(output)\n", + "\n", + " return x, states" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "trpqTWyvk0nr" + }, + "source": [ + "## Call the model and set the optimizer and the loss function" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "7t2XrzEOzf-e" + }, + "outputs": [], + "source": [ + "model = Model(vocab_size, embedding_dim, units, BATCH_SIZE)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "dkjWIATszf-h" + }, + "outputs": [], + "source": [ + "optimizer = tf.train.AdamOptimizer()\n", + "\n", + "# using sparse_softmax_cross_entropy so that we don't have to create one-hot vectors\n", + "def loss_function(real, preds):\n", + " return tf.losses.sparse_softmax_cross_entropy(labels=real, logits=preds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "lPrP0XMUzf-p" + }, + "source": [ + "## Train the model\n", + "\n", + "Here we will use a custom training loop with the help of GradientTape()\n", + "\n", + "* We initialize the hidden state of the model with zeros and shape == (batch_size, number of rnn units). We do this by calling the function defined while creating the model.\n", + "\n", + "* Next, we iterate over the dataset(batch by batch) and calculate the **predictions and the hidden states** associated with that input.\n", + "\n", + "* There are a lot of interesting things happening here.\n", + " * The model gets hidden state(initialized with 0), lets call that **H0** and the first batch of input, lets call that **I0**.\n", + " * The model then returns the predictions **P1** and **H1**.\n", + " * For the next batch of input, the model receives **I1** and **H1**.\n", + " * The interesting thing here is that we pass **H1** to the model with **I1** which is how the model learns. The context learned from batch to batch is contained in the **hidden state**.\n", + " * We continue doing this until the dataset is exhausted and then we start a new epoch and repeat this.\n", + "\n", + "* After calculating the predictions, we calculate the **loss** using the loss function defined above. Then we calculate the gradients of the loss with respect to the model variables(input)\n", + "\n", + "* Finally, we take a step in that direction with the help of the optimizer using the apply_gradients function.\n", + "\n", + "Note:- If you are running this notebook in Colab which has a **Tesla K80 GPU** it takes about 23 seconds per epoch.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "d4tSNwymzf-q" + }, + "outputs": [], + "source": [ + "# Training step\n", + "\n", + "EPOCHS = 30\n", + "\n", + "for epoch in range(EPOCHS):\n", + " start = time.time()\n", + " \n", + " # initializing the hidden state at the start of every epoch\n", + " hidden = model.reset_states()\n", + " \n", + " for (batch, (inp, target)) in enumerate(dataset):\n", + " with tf.GradientTape() as tape:\n", + " # feeding the hidden state back into the model\n", + " # This is the interesting step\n", + " predictions, hidden = model(inp, hidden)\n", + " \n", + " # reshaping the target because that's how the \n", + " # loss function expects it\n", + " target = tf.reshape(target, (-1,))\n", + " loss = loss_function(target, predictions)\n", + " \n", + " grads = tape.gradient(loss, model.variables)\n", + " optimizer.apply_gradients(zip(grads, model.variables), global_step=tf.train.get_or_create_global_step())\n", + "\n", + " if batch % 100 == 0:\n", + " print ('Epoch {} Batch {} Loss {:.4f}'.format(epoch+1,\n", + " batch,\n", + " loss))\n", + " \n", + " print ('Epoch {} Loss {:.4f}'.format(epoch+1, loss))\n", + " print('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "DjGz1tDkzf-u" + }, + "source": [ + "## Predicting using our trained model\n", + "\n", + "The below code block is used to generated the text\n", + "\n", + "* We start by choosing a start string and initializing the hidden state and setting the number of characters we want to generate.\n", + "\n", + "* We get predictions using the start_string and the hidden state\n", + "\n", + "* Then we use a multinomial distribution to calculate the index of the predicted word. **We use this predicted word as our next input to the model**\n", + "\n", + "* **The hidden state returned by the model is fed back into the model so that it now has more context rather than just one word.** After we predict the next word, the modified hidden states are again fed back into the model, which is how it learns as it gets more context from the previously predicted words.\n", + "\n", + "* If you see the predictions, the model knows when to capitalize, make paragraphs and the text follows a shakespeare style of writing which is pretty awesome!" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "WvuwZBX5Ogfd" + }, + "outputs": [], + "source": [ + "# Evaluation step(generating text using the model learned)\n", + "\n", + "# number of characters to generate\n", + "num_generate = 1000\n", + "\n", + "# You can change the start string to experiment\n", + "start_string = 'Q'\n", + "# converting our start string to numbers(vectorizing!) \n", + "input_eval = [char2idx[s] for s in start_string]\n", + "input_eval = tf.expand_dims(input_eval, 0)\n", + "\n", + "# empty string to store our results\n", + "text_generated = ''\n", + "\n", + "# low temperatures results in more predictable text.\n", + "# higher temperatures results in more surprising text\n", + "# experiment to find the best setting\n", + "temperature = 1.0\n", + "\n", + "# hidden state shape == (batch_size, number of rnn units); here batch size == 1\n", + "hidden = [tf.zeros((1, units))]\n", + "for i in range(num_generate):\n", + " predictions, hidden = model(input_eval, hidden)\n", + "\n", + " # using a multinomial distribution to predict the word returned by the model\n", + " predictions = predictions / temperature\n", + " predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy()\n", + " \n", + " # We pass the predicted word as the next input to the model\n", + " # along with the previous hidden state\n", + " input_eval = tf.expand_dims([predicted_id], 0)\n", + " \n", + " text_generated += idx2char[predicted_id]\n", + "\n", + "print (start_string + text_generated)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AM2Uma_-yVIq" + }, + "source": [ + "## Next steps\n", + "\n", + "* Change the start string to a different character, or the start of a sentence.\n", + "* Experiment with training on a different, or with different parameters. [Project Gutenberg](http://www.gutenberg.org/ebooks/100), for example, contains a large collection of books.\n", + "* Experiment with the temperature parameter.\n", + "* Add another RNN layer.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "gtEd86sX5cB2" + }, + "outputs": [], + "source": [ + "" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "default_view": {}, + "name": "text_generation.ipynb", + "private_outputs": true, + "provenance": [], + "toc_visible": true, + "version": "0.3.2", + "views": {} + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow/contrib/eager/python/examples/l2hmc/README.md b/tensorflow/contrib/eager/python/examples/l2hmc/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f171806e379da7213b6ee33e0d454056068fe7a5 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/l2hmc/README.md @@ -0,0 +1,53 @@ +# L2HMC with TensorFlow eager execution + +This folder contains an implementation of [L2HMC](https://arxiv.org/pdf/1711.09268.pdf) adapted from the released implementation by the authors. The presented implementation runs in both eager and graph mode. +With eager execution enabled, longer sample chains can be handled compared to graph mode, since no graph is explicitly stored. Moreover, with eager execution enabled, there is no need to use a `tf.while_loop`. + +## What is L2HMC? +L2HMC is an adaptive Markov Chain Monte Carlo (MCMC) algorithm that learns a non-volume preserving transformation +for a Hamiltonian Monte Carlo (HMC) sampling algorithm. More specifically, the non-volume preserving +transformation is learned with neural nets instantiated within Normalizing Flows +(real-NVPs). + +## Content + +- `l2hmc.py`: Dynamics definitions and example energy functions, +including the 2D strongly correlated Gaussian and the rough well energy function, +- `l2hmc_test.py`: Unit tests and benchmarks for training a sampler on the energy functions in both eager and graph mode. +- `neural_nets.py`: The neural net for learning the kernel on the 2D strongly correlated example. +- `main.py`: Run to train a samplers on 2D energy landscapes. + +## To run +- Make sure you have installed TensorFlow 1.9+ or the latest `tf-nightly` or `tf-nightly-gpu` pip package. +- Execute the command + +```bash +python main.py --train_dir ${PWD}/dump --use_defun +``` + +Specifying the optional argument `train_dir` will store event files for +tensorboard and a plot of sampled chain from the trained sampler. + +Specifying the optional argument `use_defun` will let the program use compiled +graphs when running specific sections and improve the overall speed. + +## Boosting Performance with `tfe.defun` +Currently, some models may experience increased overhead with eager execution enabled. +To improve performance, we could wrap certain functions with the decorator `@tfe.defun`. +For example, we could wrap the function that does the sampling step: + +```python +@tfe.defun +def apply_transition(old_sample): + new_sample = ... + return new_sample +``` + +We could also explicitly wrap the desired function with `tfe.defun`: + +```python +apply_transition = tfe.defun(apply_transition) +``` + +## Reference +Generalizing Hamiltonian Monte Carlo with Neural Networks. Levy, Daniel, Hoffman, Matthew D, and Sohl-Dickstein, Jascha. International Conference on Learning Representations (ICLR), 2018. diff --git a/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc.py b/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc.py index 729d8525fab31ee214178ca1bcb18dbd069f767a..14b8324e488a864cb23ff2507fab1c53c0583bc0 100644 --- a/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc.py +++ b/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc.py @@ -32,20 +32,28 @@ from tensorflow.contrib.eager.python.examples.l2hmc import neural_nets class Dynamics(tf.keras.Model): - """Dynamics engine of naive L2HMC sampler. - - Args: - x_dim: dimensionality of observed data - loglikelihood_fn: log-likelihood function of conditional probability - n_steps: number of leapfrog steps within each transition - eps: initial value learnable scale of step size - """ - - def __init__(self, x_dim, loglikelihood_fn, n_steps=25, eps=.1): + """Dynamics engine of naive L2HMC sampler.""" + + def __init__(self, + x_dim, + minus_loglikelihood_fn, + n_steps=25, + eps=.1, + np_seed=1): + """Initialization. + + Args: + x_dim: dimensionality of observed data + minus_loglikelihood_fn: log-likelihood function of conditional probability + n_steps: number of leapfrog steps within each transition + eps: initial value learnable scale of step size + np_seed: Random seed for numpy; used to control sampled masks. + """ super(Dynamics, self).__init__() + npr.seed(np_seed) self.x_dim = x_dim - self.potential = loglikelihood_fn + self.potential = minus_loglikelihood_fn self.n_steps = n_steps self._construct_time() @@ -54,7 +62,7 @@ class Dynamics(tf.keras.Model): self.position_fn = neural_nets.GenericNet(x_dim, factor=2.) self.momentum_fn = neural_nets.GenericNet(x_dim, factor=1.) - self.eps = tfe.Variable( + self.eps = tf.Variable( initial_value=eps, name="eps", dtype=tf.float32, trainable=True) def apply_transition(self, position): @@ -68,8 +76,8 @@ class Dynamics(tf.keras.Model): position, forward=False) # Decide direction uniformly - forward_mask = tf.cast( - tf.random_uniform(shape=[tf.shape(position)[0]]) > .5, tf.float32) + batch_size = tf.shape(position)[0] + forward_mask = tf.cast(tf.random_uniform((batch_size,)) > .5, tf.float32) backward_mask = 1. - forward_mask # Obtain proposed states @@ -108,7 +116,6 @@ class Dynamics(tf.keras.Model): position_post, momentum_post, logdet = lf_fn(position_post, momentum_post, i) sumlogdet += logdet - accept_prob = self._compute_accept_prob(position, momentum, position_post, momentum_post, sumlogdet) @@ -125,17 +132,17 @@ class Dynamics(tf.keras.Model): sumlogdet += logdet position, logdet = self._update_position_forward(position, momentum, t, - mask) + mask, mask_inv) sumlogdet += logdet position, logdet = self._update_position_forward(position, momentum, t, - mask_inv) + mask_inv, mask) sumlogdet += logdet momentum, logdet = self._update_momentum_forward(position, momentum, t) sumlogdet += logdet - return position, momentum, tf.reduce_sum(sumlogdet, axis=1) + return position, momentum, sumlogdet def _backward_lf(self, position, momentum, i): """One backward augmented leapfrog step. See Appendix A in paper.""" @@ -149,17 +156,17 @@ class Dynamics(tf.keras.Model): sumlogdet += logdet position, logdet = self._update_position_backward(position, momentum, t, - mask) + mask_inv, mask) sumlogdet += logdet position, logdet = self._update_position_backward(position, momentum, t, - mask_inv) + mask, mask_inv) sumlogdet += logdet momentum, logdet = self._update_momentum_backward(position, momentum, t) sumlogdet += logdet - return position, momentum, tf.reduce_sum(sumlogdet, axis=1) + return position, momentum, sumlogdet def _update_momentum_forward(self, position, momentum, t): """Update v in the forward leapfrog step.""" @@ -172,12 +179,11 @@ class Dynamics(tf.keras.Model): momentum * tf.exp(scale) - .5 * self.eps * (tf.exp(transformed) * grad - translation)) - return momentum, scale + return momentum, tf.reduce_sum(scale, axis=1) - def _update_position_forward(self, position, momentum, t, mask): + def _update_position_forward(self, position, momentum, t, mask, mask_inv): """Update x in the forward leapfrog step.""" - mask_inv = 1. - mask scale, translation, transformed = self.position_fn( [momentum, mask * position, t]) scale *= self.eps @@ -186,8 +192,7 @@ class Dynamics(tf.keras.Model): mask * position + mask_inv * (position * tf.exp(scale) + self.eps * (tf.exp(transformed) * momentum + translation))) - - return position, mask_inv * scale + return position, tf.reduce_sum(mask_inv * scale, axis=1) def _update_momentum_backward(self, position, momentum, t): """Update v in the backward leapfrog step. Inverting the forward update.""" @@ -200,21 +205,20 @@ class Dynamics(tf.keras.Model): tf.exp(scale) * (momentum + .5 * self.eps * (tf.exp(transformed) * grad - translation))) - return momentum, scale + return momentum, tf.reduce_sum(scale, axis=1) - def _update_position_backward(self, position, momentum, t, mask): + def _update_position_backward(self, position, momentum, t, mask, mask_inv): """Update x in the backward leapfrog step. Inverting the forward update.""" - mask_inv = 1. - mask scale, translation, transformed = self.position_fn( - [momentum, mask_inv * position, t]) + [momentum, mask * position, t]) scale *= -self.eps transformed *= self.eps position = ( - mask_inv * position + mask * tf.exp(scale) * - (position - self.eps * tf.exp(transformed) * momentum + translation)) + mask * position + mask_inv * tf.exp(scale) * + (position - self.eps * (tf.exp(transformed) * momentum + translation))) - return position, mask * scale + return position, tf.reduce_sum(mask_inv * scale, axis=1) def _compute_accept_prob(self, position, momentum, position_post, momentum_post, sumlogdet): @@ -222,8 +226,10 @@ class Dynamics(tf.keras.Model): old_hamil = self.hamiltonian(position, momentum) new_hamil = self.hamiltonian(position_post, momentum_post) + prob = tf.exp(tf.minimum(old_hamil - new_hamil + sumlogdet, 0.)) - return tf.exp(tf.minimum(old_hamil - new_hamil + sumlogdet, 0.)) + # Ensure numerical stability as well as correct gradients + return tf.where(tf.is_finite(prob), prob, tf.zeros_like(prob)) def _construct_time(self): """Convert leapfrog step index into sinusoidal time.""" @@ -248,6 +254,8 @@ class Dynamics(tf.keras.Model): self.masks = [] for _ in range(self.n_steps): + # Need to use npr here because tf would generated different random + # values across different `sess.run` idx = npr.permutation(np.arange(self.x_dim))[:self.x_dim // 2] mask = np.zeros((self.x_dim,)) mask[idx] = 1. @@ -273,19 +281,15 @@ class Dynamics(tf.keras.Model): def grad_potential(self, position, check_numerics=True): """Get gradient of potential function at current location.""" - if not tf.executing_eagerly(): - # TODO(lxuechen): Change this to tfe.gradients_function when it works - grad = tf.gradients(self.potential(position), position)[0] - else: + if tf.executing_eagerly(): grad = tfe.gradients_function(self.potential)(position)[0] - - if check_numerics: - return tf.check_numerics(grad, message="gradient of potential") + else: + grad = tf.gradients(self.potential(position), position)[0] return grad -# Examples of unnormalized log density/probabilities +# Examples of unnormalized log densities def get_scg_energy_fn(): """Get energy function for 2d strongly correlated Gaussian.""" @@ -295,32 +299,53 @@ def get_scg_energy_fn(): sigma_inv = tf.matrix_inverse(sigma) def energy(x): - """Unnormalized log density/energy of 2d strongly correlated Gaussian.""" + """Unnormalized minus log density of 2d strongly correlated Gaussian.""" xmmu = x - mu return .5 * tf.diag_part( tf.matmul(tf.matmul(xmmu, sigma_inv), tf.transpose(xmmu))) - return energy + return energy, mu, sigma -def get_multivariate_gaussian_energy_fn(x_dim=2): - """Get energy function for 2d strongly correlated Gaussian.""" - - mu = tf.random_normal(shape=[x_dim]) - # Lower triangularize and positive diagonal - l = tf.sigmoid( - tf.matrix_band_part(tf.random_normal(shape=[x_dim, x_dim]), -1, 0)) - # Exploit Cholesky decomposition - sigma = tf.matmul(l, tf.transpose(l)) - sigma *= 100. # Small covariance causes extreme numerical instability - sigma_inv = tf.matrix_inverse(sigma) +def get_rw_energy_fn(): + """Get energy function for rough well distribution.""" + # For small eta, the density underlying the rough-well energy is very close to + # a unit Gaussian; however, the gradient is greatly affected by the small + # cosine perturbations + eta = 1e-2 + mu = tf.constant([0., 0.]) + sigma = tf.constant([[1., 0.], [0., 1.]]) def energy(x): - """Unnormalized log density/energy of 2d strongly correlated Gaussian.""" + ip = tf.reduce_sum(x**2., axis=1) + return .5 * ip + eta * tf.reduce_sum(tf.cos(x / eta), axis=1) - xmmu = x - mu - return .5 * tf.diag_part( - tf.matmul(tf.matmul(xmmu, sigma_inv), tf.transpose(xmmu))) + return energy, mu, sigma + + +# Loss function +def compute_loss(dynamics, x, scale=.1, eps=1e-4): + """Compute loss defined in equation (8).""" + + z = tf.random_normal(tf.shape(x)) # Auxiliary variable + x_, _, x_accept_prob, x_out = dynamics.apply_transition(x) + z_, _, z_accept_prob, _ = dynamics.apply_transition(z) + + # Add eps for numerical stability; following released impl + x_loss = tf.reduce_sum((x - x_)**2, axis=1) * x_accept_prob + eps + z_loss = tf.reduce_sum((z - z_)**2, axis=1) * z_accept_prob + eps + + loss = tf.reduce_mean( + (1. / x_loss + 1. / z_loss) * scale - (x_loss + z_loss) / scale, axis=0) + + return loss, x_out, x_accept_prob + + +def loss_and_grads(dynamics, x, loss_fn=compute_loss): + """Obtain loss value and gradients.""" + with tf.GradientTape() as tape: + loss_val, out, accept_prob = loss_fn(dynamics, x) + grads = tape.gradient(loss_val, dynamics.trainable_variables) - return energy + return loss_val, grads, out, accept_prob diff --git a/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc_test.py b/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc_test.py index e33b4cae4c73388dfd78542c9907953f137ad710..955747988536bd21d52df66a35af4aa31b3f7688 100644 --- a/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc_test.py +++ b/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc_test.py @@ -37,63 +37,37 @@ def get_default_hparams(): n_warmup_iters=3) -# Relevant functions for benchmarking -def compute_loss(dynamics, x, scale=.1, eps=1e-4): - """Compute loss defined in equation (8).""" - - z = tf.random_normal(tf.shape(x)) - x_, _, x_accept_prob, x_out = dynamics.apply_transition(x) - z_, _, z_accept_prob, _ = dynamics.apply_transition(z) - - # Add eps for numerical stability; following released impl - x_loss = tf.reduce_sum((x - x_)**2, axis=1) * x_accept_prob + eps - z_loss = tf.reduce_sum((z - z_)**2, axis=1) * z_accept_prob + eps - - loss = tf.reduce_mean( - (1. / x_loss + 1. / z_loss) * scale - (x_loss + z_loss) / scale, axis=0) - - return loss, x_out - - -def loss_and_grads(dynamics, x, loss_fn=compute_loss): - """Obtain loss value and gradients.""" - - with tf.GradientTape() as tape: - loss_val, x_out = loss_fn(dynamics, x) - grads = tape.gradient(loss_val, dynamics.variables) - - return loss_val, grads, x_out - - -def warmup(dynamics, optimizer, n_iters=1, n_samples=200, loss_fn=compute_loss): +def warmup(dynamics, + optimizer, + n_iters=1, + n_samples=200, + loss_fn=l2hmc.compute_loss): """Warmup optimization to reduce overhead.""" samples = tf.random_normal( shape=[n_samples, dynamics.x_dim], dtype=tf.float32) for _ in range(n_iters): - _, grads, samples = loss_and_grads(dynamics, samples, loss_fn=loss_fn) + _, grads, samples, _ = l2hmc.loss_and_grads( + dynamics, samples, loss_fn=loss_fn) optimizer.apply_gradients(zip(grads, dynamics.variables)) def fit(dynamics, samples, optimizer, - loss_fn=compute_loss, + loss_fn=l2hmc.compute_loss, n_iters=5000, verbose=True, - logdir=None, - decay_lr=True): + logdir=None): """Fit L2HMC sampler with given log-likelihood function.""" if logdir: summary_writer = tf.contrib.summary.create_file_writer(logdir) for i in range(n_iters): - loss, grads, samples = loss_and_grads(dynamics, samples, loss_fn=loss_fn) - # TODO(lxuechen): Proper learning rate decay - if decay_lr: - grads = [grad * .96**(i // 1000) for grad in grads] + loss, grads, samples, _ = l2hmc.loss_and_grads( + dynamics, samples, loss_fn=loss_fn) optimizer.apply_gradients(zip(grads, dynamics.variables)) if verbose: print("Iteration %d: loss %.4f" % (i, loss)) @@ -112,9 +86,10 @@ class L2hmcTest(tf.test.TestCase): # Eager mode testing hparams = get_default_hparams() + energy_fn, _, _ = l2hmc.get_scg_energy_fn() dynamics = l2hmc.Dynamics( x_dim=hparams.x_dim, - loglikelihood_fn=l2hmc.get_scg_energy_fn(), + minus_loglikelihood_fn=energy_fn, n_steps=hparams.n_steps, eps=hparams.eps) samples = tf.random_normal(shape=[hparams.n_samples, hparams.x_dim]) @@ -127,9 +102,10 @@ class L2hmcTest(tf.test.TestCase): # Graph mode testing with tf.Graph().as_default(): + energy_fn, _, _ = l2hmc.get_scg_energy_fn() dynamics = l2hmc.Dynamics( x_dim=hparams.x_dim, - loglikelihood_fn=l2hmc.get_scg_energy_fn(), + minus_loglikelihood_fn=energy_fn, n_steps=hparams.n_steps, eps=hparams.eps) x = tf.placeholder(tf.float32, shape=[None, hparams.x_dim]) @@ -150,32 +126,20 @@ class L2hmcTest(tf.test.TestCase): class L2hmcBenchmark(tf.test.Benchmark): """Eager and graph benchmarks for l2hmc.""" - def _get_energy_fn(self): - """Get specific energy function according to FLAGS.""" - - if FLAGS.energy_fn == "scg": - energy_fn = l2hmc.get_scg_energy_fn() - elif FLAGS.energy_fn == "multivariate_gaussian": - energy_fn = l2hmc.get_multivariate_gaussian_energy_fn(x_dim=FLAGS.x_dim) - else: - raise ValueError("No such energy function %s" % FLAGS.energy_fn) - - return energy_fn - def benchmark_graph(self): """Benchmark Graph performance.""" hparams = get_default_hparams() tf.reset_default_graph() with tf.Graph().as_default(): - energy_fn = self._get_energy_fn() + energy_fn, _, _ = l2hmc.get_scg_energy_fn() dynamics = l2hmc.Dynamics( x_dim=hparams.x_dim, - loglikelihood_fn=energy_fn, + minus_loglikelihood_fn=energy_fn, n_steps=hparams.n_steps, eps=hparams.eps) x = tf.placeholder(tf.float32, shape=[None, hparams.x_dim]) - loss, x_out = compute_loss(dynamics, x) + loss, x_out, _ = l2hmc.compute_loss(dynamics, x) global_step = tf.Variable(0., name="global_step", trainable=False) learning_rate = tf.train.exponential_decay( @@ -183,7 +147,11 @@ class L2hmcBenchmark(tf.test.Benchmark): optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) train_op = optimizer.minimize(loss, global_step=global_step) - with tf.Session() as sess: + # Single thread; fairer comparison against eager + session_conf = tf.ConfigProto( + intra_op_parallelism_threads=1, inter_op_parallelism_threads=1) + + with tf.Session(config=session_conf) as sess: sess.run(tf.global_variables_initializer()) # Warmup to reduce initialization effect when timing @@ -218,14 +186,14 @@ class L2hmcBenchmark(tf.test.Benchmark): """Benchmark Eager performance.""" hparams = get_default_hparams() - energy_fn = self._get_energy_fn() + energy_fn, _, _ = l2hmc.get_scg_energy_fn() dynamics = l2hmc.Dynamics( x_dim=hparams.x_dim, - loglikelihood_fn=energy_fn, + minus_loglikelihood_fn=energy_fn, n_steps=hparams.n_steps, eps=hparams.eps) optimizer = tf.train.AdamOptimizer(learning_rate=hparams.learning_rate) - loss_fn = tfe.defun(compute_loss) if defun else compute_loss + loss_fn = tfe.defun(l2hmc.compute_loss) if defun else l2hmc.compute_loss # Warmup to reduce initialization effect when timing warmup(dynamics, optimizer, n_iters=hparams.n_warmup_iters, loss_fn=loss_fn) @@ -234,12 +202,7 @@ class L2hmcBenchmark(tf.test.Benchmark): samples = tf.random_normal( shape=[hparams.n_samples, hparams.x_dim], dtype=tf.float32) start_time = time.time() - fit(dynamics, - samples, - optimizer, - loss_fn=loss_fn, - n_iters=hparams.n_iters, - decay_lr=True) + fit(dynamics, samples, optimizer, loss_fn=loss_fn, n_iters=hparams.n_iters) wall_time = time.time() - start_time examples_per_sec = hparams.n_samples / wall_time @@ -251,14 +214,8 @@ class L2hmcBenchmark(tf.test.Benchmark): wall_time=wall_time) del dynamics - del loss_fn if __name__ == "__main__": - tf.flags.DEFINE_string("energy_fn", "scg", - ("The energy function/unnormalized log-probability. " - "Either be `scg` or `multivariate_gaussian`")) - tf.flags.DEFINE_integer("x_dim", 2, "Dimensionality of observation space.") - FLAGS = tf.flags.FLAGS tf.enable_eager_execution() tf.test.main() diff --git a/tensorflow/contrib/eager/python/examples/l2hmc/main.py b/tensorflow/contrib/eager/python/examples/l2hmc/main.py new file mode 100644 index 0000000000000000000000000000000000000000..45e1f98429f48749d374c2aefd8874690c3830ad --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/l2hmc/main.py @@ -0,0 +1,235 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""L2HMC on simple Gaussian mixture model with TensorFlow eager.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys + +from absl import flags +import numpy as np +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.l2hmc import l2hmc +try: + import matplotlib.pyplot as plt # pylint: disable=g-import-not-at-top + HAS_MATPLOTLIB = True +except ImportError: + HAS_MATPLOTLIB = False +tfe = tf.contrib.eager + + +def main(_): + tf.enable_eager_execution() + global_step = tf.train.get_or_create_global_step() + global_step.assign(1) + + energy_fn, mean, covar = { + "scg": l2hmc.get_scg_energy_fn(), + "rw": l2hmc.get_rw_energy_fn() + }[FLAGS.energy_fn] + + x_dim = 2 + train_iters = 5000 + eval_iters = 2000 + eps = 0.1 + n_steps = 10 # Chain length + n_samples = 200 + record_loss_every = 100 + + dynamics = l2hmc.Dynamics( + x_dim=x_dim, minus_loglikelihood_fn=energy_fn, n_steps=n_steps, eps=eps) + learning_rate = tf.train.exponential_decay( + 1e-3, global_step, 1000, 0.96, staircase=True) + optimizer = tf.train.AdamOptimizer(learning_rate) + checkpointer = tf.train.Checkpoint( + optimizer=optimizer, dynamics=dynamics, global_step=global_step) + + if FLAGS.train_dir: + summary_writer = tf.contrib.summary.create_file_writer(FLAGS.train_dir) + if FLAGS.restore: + latest_path = tf.train.latest_checkpoint(FLAGS.train_dir) + checkpointer.restore(latest_path) + print("Restored latest checkpoint at path:\"{}\" ".format(latest_path)) + sys.stdout.flush() + + if not FLAGS.restore: + # Training + if FLAGS.use_defun: + # Use `tfe.deun` to boost performance when there are lots of small ops + loss_fn = tfe.defun(l2hmc.compute_loss) + else: + loss_fn = l2hmc.compute_loss + + samples = tf.random_normal(shape=[n_samples, x_dim]) + for i in range(1, train_iters + 1): + loss, samples, accept_prob = train_one_iter( + dynamics, + samples, + optimizer, + loss_fn=loss_fn, + global_step=global_step) + + if i % record_loss_every == 0: + print("Iteration {}, loss {:.4f}, x_accept_prob {:.4f}".format( + i, loss.numpy(), + accept_prob.numpy().mean())) + if FLAGS.train_dir: + with summary_writer.as_default(): + with tf.contrib.summary.always_record_summaries(): + tf.contrib.summary.scalar("Training loss", loss, step=global_step) + print("Training complete.") + sys.stdout.flush() + + if FLAGS.train_dir: + saved_path = checkpointer.save( + file_prefix=os.path.join(FLAGS.train_dir, "ckpt")) + print("Saved checkpoint at path: \"{}\" ".format(saved_path)) + sys.stdout.flush() + + # Evaluation + if FLAGS.use_defun: + # Use tfe.deun to boost performance when there are lots of small ops + apply_transition = tfe.defun(dynamics.apply_transition) + else: + apply_transition = dynamics.apply_transition + + samples = tf.random_normal(shape=[n_samples, x_dim]) + samples_history = [] + for i in range(eval_iters): + samples_history.append(samples.numpy()) + _, _, _, samples = apply_transition(samples) + samples_history = np.array(samples_history) + print("Sampling complete.") + sys.stdout.flush() + + # Mean and covariance of target distribution + mean = mean.numpy() + covar = covar.numpy() + ac_spectrum = compute_ac_spectrum(samples_history, mean, covar) + print("First 25 entries of the auto-correlation spectrum: {}".format( + ac_spectrum[:25])) + ess = compute_ess(ac_spectrum) + print("Effective sample size per Metropolis-Hastings step: {}".format(ess)) + sys.stdout.flush() + + if FLAGS.train_dir: + # Plot autocorrelation spectrum in tensorboard + plot_step = tfe.Variable(1, trainable=False, dtype=tf.int64) + + for ac in ac_spectrum: + with summary_writer.as_default(): + with tf.contrib.summary.always_record_summaries(): + tf.contrib.summary.scalar("Autocorrelation", ac, step=plot_step) + plot_step.assign(plot_step + n_steps) + + if HAS_MATPLOTLIB: + # Choose a single chain and plot the trajectory + single_chain = samples_history[:, 0, :] + xs = single_chain[:100, 0] + ys = single_chain[:100, 1] + plt.figure() + plt.plot(xs, ys, color="orange", marker="o", alpha=0.6) # Trained chain + plt.savefig(os.path.join(FLAGS.train_dir, "single_chain.png")) + + +def train_one_iter(dynamics, + x, + optimizer, + loss_fn=l2hmc.compute_loss, + global_step=None): + """Train the sampler for one iteration.""" + loss, grads, out, accept_prob = l2hmc.loss_and_grads( + dynamics, x, loss_fn=loss_fn) + optimizer.apply_gradients( + zip(grads, dynamics.trainable_variables), global_step=global_step) + + return loss, out, accept_prob + + +def compute_ac_spectrum(samples_history, target_mean, target_covar): + """Compute autocorrelation spectrum. + + Follows equation 15 from the L2HMC paper. + + Args: + samples_history: Numpy array of shape [T, B, D], where T is the total + number of time steps, B is the batch size, and D is the dimensionality + of sample space. + target_mean: 1D Numpy array of the mean of target(true) distribution. + target_covar: 2D Numpy array representing a symmetric matrix for variance. + Returns: + Autocorrelation spectrum, Numpy array of shape [T-1]. + """ + + # Using numpy here since eager is a bit slow due to the loop + time_steps = samples_history.shape[0] + trace = np.trace(target_covar) + + rhos = [] + for t in range(time_steps - 1): + rho_t = 0. + for tau in range(time_steps - t): + v_tau = samples_history[tau, :, :] - target_mean + v_tau_plus_t = samples_history[tau + t, :, :] - target_mean + # Take dot product over observation dims and take mean over batch dims + rho_t += np.mean(np.sum(v_tau * v_tau_plus_t, axis=1)) + + rho_t /= trace * (time_steps - t) + rhos.append(rho_t) + + return np.array(rhos) + + +def compute_ess(ac_spectrum): + """Compute the effective sample size based on autocorrelation spectrum. + + This follows equation 16 from the L2HMC paper. + + Args: + ac_spectrum: Autocorrelation spectrum + Returns: + The effective sample size + """ + # Cutoff from the first value less than 0.05 + cutoff = np.argmax(ac_spectrum[1:] < .05) + if cutoff == 0: + cutoff = len(ac_spectrum) + ess = 1. / (1. + 2. * np.sum(ac_spectrum[1:cutoff])) + return ess + + +if __name__ == "__main__": + flags.DEFINE_string( + "train_dir", + default=None, + help="[Optional] Directory to store the training information") + flags.DEFINE_boolean( + "restore", + default=False, + help="[Optional] Restore the latest checkpoint from `train_dir` if True") + flags.DEFINE_boolean( + "use_defun", + default=False, + help="[Optional] Use `tfe.defun` to boost performance") + flags.DEFINE_string( + "energy_fn", + default="scg", + help="[Optional] The energy function used for experimentation" + "Other options include `rw`") + FLAGS = flags.FLAGS + tf.app.run(main) diff --git a/tensorflow/contrib/eager/python/examples/l2hmc/neural_nets.py b/tensorflow/contrib/eager/python/examples/l2hmc/neural_nets.py index e230ad5e259df5b450897bd815e901e3934cd293..68e0bc31239007e3b1b8451cf1d6e7592c6ca030 100644 --- a/tensorflow/contrib/eager/python/examples/l2hmc/neural_nets.py +++ b/tensorflow/contrib/eager/python/examples/l2hmc/neural_nets.py @@ -25,7 +25,6 @@ from __future__ import division from __future__ import print_function import tensorflow as tf -import tensorflow.contrib.eager as tfe class GenericNet(tf.keras.Model): @@ -47,13 +46,13 @@ class GenericNet(tf.keras.Model): # Scale self.scale_layer = _custom_dense(x_dim, .001) - self.coeff_scale = tfe.Variable( + self.coeff_scale = tf.Variable( initial_value=tf.zeros([1, x_dim]), name='coeff_scale', trainable=True) # Translation self.translation_layer = _custom_dense(x_dim, factor=.001) # Transformation self.transformation_layer = _custom_dense(x_dim, .001) - self.coeff_transformation = tfe.Variable( + self.coeff_transformation = tf.Variable( initial_value=tf.zeros([1, x_dim]), name='coeff_transformation', trainable=True) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb index 34ce5e0cc349bfe71f2e6faad497e6c149754d14..1f66d7e75299df0c7db9bc8ec67cb6c0b5d4de40 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb @@ -42,10 +42,10 @@ "# Neural Machine Translation with Attention\n", "\n", "
\n", - "\n", - " Run in Google Colab \n", + "\n", + " Run in Google Colab \n", "\n", - "View source on GitHub
" + "View source on GitHub" ] }, { diff --git a/tensorflow/contrib/eager/python/examples/notebooks/automatic_differentiation.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/automatic_differentiation.ipynb index a18882fafa192bc4d4277d9d76fcd676b8295e04..7c0f9b5b8161a763c4153ebdeece7e0d1b90b384 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/automatic_differentiation.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/automatic_differentiation.ipynb @@ -75,10 +75,10 @@ "cell_type": "markdown", "source": [ "
\n", - "\n", - " Run in Google Colab\n", + "\n", + " Run in Google Colab\n", "\n", - "View source on GitHub
" + "View source on GitHub" ] }, { diff --git a/tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb index 54fbf2a7e18da0e8ec21ff6e01ea13b3a6a57ca4..a0bbbb612381c5eb386b04fd7bb9914eb01f4c8e 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb @@ -75,10 +75,10 @@ "cell_type": "markdown", "source": [ "
\n", - "\n", - " Run in Google Colab\n", + "\n", + " Run in Google Colab\n", "\n", - "View source on GitHub
" + "View source on GitHub" ] }, { diff --git a/tensorflow/contrib/eager/python/examples/notebooks/custom_training.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/custom_training.ipynb index 0a781d215308f04290aac2a74b5f0b1faf8b5406..5f1b48fa0d4aea06adab19a0e561923e1f557e50 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/custom_training.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/custom_training.ipynb @@ -75,10 +75,10 @@ "cell_type": "markdown", "source": [ "
\n", - "\n", - " Run in Google Colab\n", + "\n", + " Run in Google Colab\n", "\n", - "View source on GitHub
" + "View source on GitHub" ] }, { @@ -118,7 +118,6 @@ "cell_type": "code", "source": [ "import tensorflow as tf\n", - "tfe = tf.contrib.eager # Shorthand for some symbols\n", "\n", "tf.enable_eager_execution()" ], @@ -184,7 +183,7 @@ }, "cell_type": "code", "source": [ - "v = tfe.Variable(1.0)\n", + "v = tf.Variable(1.0)\n", "assert v.numpy() == 1.0\n", "\n", "# Re-assign the value\n", @@ -258,8 +257,8 @@ " def __init__(self):\n", " # Initialize variable to (5.0, 0.0)\n", " # In practice, these should be initialized to random values.\n", - " self.W = tfe.Variable(5.0)\n", - " self.b = tfe.Variable(0.0)\n", + " self.W = tf.Variable(5.0)\n", + " self.b = tf.Variable(0.0)\n", " \n", " def __call__(self, x):\n", " return self.W * x + self.b\n", diff --git a/tensorflow/contrib/eager/python/examples/notebooks/eager_basics.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/eager_basics.ipynb index b37a18c9a6091c927767a814c1131ef5739c810b..f1e13de5dec2fbda126caeb355494875317e3373 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/eager_basics.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/eager_basics.ipynb @@ -75,10 +75,10 @@ "cell_type": "markdown", "source": [ "
\n", - "\n", - " Run in Google Colab\n", + "\n", + " Run in Google Colab\n", "\n", - "View source on GitHub
" + "View source on GitHub" ] }, { diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py index b14ef1df8ff4c660b9b6f2abfd5df6572d10b1e8..07d8788882c2d831dfb041fe7409af51857190bf 100644 --- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py +++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py @@ -29,6 +29,7 @@ import tensorflow.contrib.eager as tfe from tensorflow.contrib.eager.python.examples.resnet50 import resnet50 from tensorflow.contrib.summary import summary_test_util from tensorflow.python.client import device_lib +from tensorflow.python.eager import tape def device_and_data_format(): @@ -49,13 +50,21 @@ def random_batch(batch_size, data_format): return images, one_hot -def compute_gradients(model, images, labels): - with tf.GradientTape() as tape: +def compute_gradients(model, images, labels, num_replicas=1): + with tf.GradientTape() as grad_tape: logits = model(images, training=True) loss = tf.losses.softmax_cross_entropy( logits=logits, onehot_labels=labels) tf.contrib.summary.scalar(name='loss', tensor=loss) - return tape.gradient(loss, model.variables) + if num_replicas != 1: + loss /= num_replicas + + # TODO(b/110991947): We can mistakenly trace the gradient call in + # multi-threaded environment. Explicitly disable recording until + # this is fixed. + with tape.stop_recording(): + grads = grad_tape.gradient(loss, model.variables) + return grads def apply_gradients(model, optimizer, gradients): @@ -188,11 +197,14 @@ class ResNet50Benchmarks(tf.test.Benchmark): return (32,) return (16, 32) - def _report(self, label, start, num_iters, device, batch_size, data_format): + def _report(self, label, start, num_iters, device, batch_size, data_format, + num_replicas=1): avg_time = (time.time() - start) / num_iters dev = tf.DeviceSpec.from_string(device).device_type.lower() - name = '%s_%s_batch_%d_%s' % (label, dev, batch_size, data_format) - extras = {'examples_per_sec': batch_size / avg_time} + replica_str = '' if num_replicas == 1 else 'replicas_%d_' % num_replicas + name = '%s_%s_batch_%d_%s%s' % (label, dev, batch_size, + replica_str, data_format) + extras = {'examples_per_sec': (num_replicas * batch_size) / avg_time} self.report_benchmark( iters=num_iters, wall_time=avg_time, name=name, extras=extras) diff --git a/tensorflow/contrib/eager/python/examples/revnet/BUILD b/tensorflow/contrib/eager/python/examples/revnet/BUILD index 81c9facfb5f00c45c8f26c1cd4284b98fb73dd23..4f0d46b1bae3760a63b2abe871034bdedf258f07 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/BUILD +++ b/tensorflow/contrib/eager/python/examples/revnet/BUILD @@ -43,6 +43,27 @@ py_library( ], ) +py_library( + name = "resnet_preprocessing", + srcs = ["resnet_preprocessing.py"], + srcs_version = "PY2AND3", + tags = ["local"], + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "imagenet_input", + srcs = ["imagenet_input.py"], + srcs_version = "PY2AND3", + tags = ["local"], + deps = [ + ":resnet_preprocessing", + "//tensorflow:tensorflow_py", + ], +) + # Tests cuda_py_test( name = "ops_test", @@ -78,7 +99,7 @@ cuda_py_test( "//tensorflow:tensorflow_py", ], tags = [ - "no_pip", + "no_pip", # depends on blocks_test, which is not available in pip package "optonly", ], ) @@ -113,3 +134,39 @@ py_binary( "//tensorflow:tensorflow_py", ], ) + +py_binary( + name = "main_estimator", + srcs = ["main_estimator.py"], + srcs_version = "PY2AND3", + deps = [ + ":cifar_input", + ":main", + ":revnet", + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "main_estimator_lib", + srcs = ["main_estimator.py"], + srcs_version = "PY2AND3", + deps = [ + ":cifar_input", + ":main", + ":revnet", + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "main_estimator_tpu_lib", + srcs = ["main_estimator_tpu.py"], + srcs_version = "PY2AND3", + deps = [ + ":cifar_input", + ":main", + ":revnet", + "//tensorflow:tensorflow_py", + ], +) diff --git a/tensorflow/contrib/eager/python/examples/revnet/README.md b/tensorflow/contrib/eager/python/examples/revnet/README.md index 21fc44febc8abdc30daad1b35d8434b083360bdf..2875d0ffb330c2593a7f293f417a5d1ce8322624 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/README.md +++ b/tensorflow/contrib/eager/python/examples/revnet/README.md @@ -1,18 +1,21 @@ # RevNet with TensorFlow eager execution -This folder contains an TensorFlow eager implementation of the [Reversible Residual Network](https://arxiv.org/pdf/1707.04585.pdf) adapted from the released implementation by the authors. The presented implementation can be ran both in eager and graph mode. The code is considerably simplified with `tf.GradientTape`. Moreover, we reduce the step of reconstructing the outputs. This saves us from using `tf.stop_gradient` and makes the model run faster. +This folder contains a TensorFlow eager implementation of the [Reversible Residual Network](https://arxiv.org/pdf/1707.04585.pdf) adapted from the released implementation by the authors. The presented implementation can be ran both in eager and graph mode. The code is considerably simplified with `tf.GradientTape`. Moreover, we reduce the step of reconstructing the outputs. This saves us from using `tf.stop_gradient` and makes the model run faster. ## Content - `revnet.py`: The RevNet model. - `blocks.py`: The relevant reversible blocks. +- `ops.py`: Auxiliary downsampling operation. - `cifar_tfrecords.py`: Script to generate the TFRecords for both CIFAR-10 and CIFAR-100. - `cifar_input.py`: Script to read from TFRecords and generate dataset objects with the `tf.data` API. - `config.py`: Configuration file for network architectures and training hyperparameters. - `main.py`: Main training and evaluation script. -- `ops.py`: Auxiliary downsampling operation. +- `main_estimator.py`: Script to train RevNet models on CIFAR-10 and CIFAR-100 with the `tf.estimator` API. +- `main_estimator_tpu.py`: Script to train RevNet models on ImageNet with TPU estimators on Cloud TPUs. +- `resnet_preprocessing.py`, `imagenet_input.py`: Boilerplate to read ImageNet data from TFRecords. -## To run +## Train on CIFAR-10/CIFAR-100 - Make sure you have installed TensorFlow 1.9+ or the latest `tf-nightly` or `tf-nightly-gpu` pip package in order to access the eager execution feature. @@ -24,7 +27,7 @@ python cifar_tfrecords.py --data_dir ${PWD}/cifar to download the cifar dataset and convert them to TFRecords. This produces TFRecord files for both CIFAR-10 and CIFAR-100. -- To train a model run +- To train a model, run ```bash python main.py --data_dir ${PWD}/cifar @@ -34,8 +37,63 @@ python main.py --data_dir ${PWD}/cifar - `train_dir`: Directory to store eventfiles and checkpoints. - `restore`: Restore the latest checkpoint. - `validate`: Use validation set for training monitoring. - - `manual_grad`: Use the manually defined gradient map given by the authors. - - `dataset`: Use either `cifar-10` or `cifar-100` + - `dataset`: Use either `cifar-10` or `cifar-100`. + - `config`: RevNet configuration. + - `use_defun`: Use `tfe.defun` to boost performance. + +- To train a model with estimators in graph-mode, run + +```bash +python main_estimator.py --data_dir ${PWD}/cifar +``` + +- Optional arguments for `main.py` include + - `model_dir`: Directory to store eventfiles and checkpoints. + - `dataset`: Use either `cifar-10` or `cifar-100`. + - `config`: RevNet configuration. + - `export`: Export the model for serving if True. + +## Speed up with `tfe.defun` +Even though the speed difference between pure eager execution and graph-mode execution is noticeable, +the difference between fully "defunned" model training and graph-mode +training is negligible. + +## Train on ImageNet with Cloud TPUs +The standard way to train models on Cloud TPUs is via TPU estimators and graph-mode +execution. Models built with the `tf.keras` API are fully compatible with TPU estimators. + +### Setup a Google Cloud project + +Follow the instructions at the [Quickstart Guide](https://cloud.google.com/tpu/docs/quickstart) +to get a GCE VM with access to Cloud TPU. + +To run this model, you will need: + +* A GCE VM instance with an associated Cloud TPU resource +* A GCS bucket to store your training checkpoints +* (Optional): The ImageNet training and validation data preprocessed into + TFRecord format, and stored in GCS. + +### Format the data + +The data is expected to be formatted in TFRecord format, as generated by [this +script](https://github.com/tensorflow/tpu/blob/master/tools/datasets/imagenet_to_gcs.py). + +If you do not have ImageNet dataset prepared, you can use a randomly generated +fake dataset to test the model. It is located at +`gs://cloud-tpu-test-datasets/fake_imagenet`. + +### Start training + +Train the model by executing the following command (substituting the appropriate +values): + +```bash +python main_estimator_tpu.py \ + --tpu=$TPU_NAME \ + --data_dir=$DATA_DIR \ + --model_dir=$MODEL_DIR +``` ## Performance - With the current implementation, RevNet-38 achieves >92% on CIFAR-10 and >71% on CIFAR-100. diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks.py b/tensorflow/contrib/eager/python/examples/revnet/blocks.py index 306096e9f8c4da0ed7f893ae75067cd24e7274b1..f61354bc38a9fcb941f186cac4eac8097eea742d 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/blocks.py +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks.py @@ -24,6 +24,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import functools +import operator + import tensorflow as tf from tensorflow.contrib.eager.python.examples.revnet import ops @@ -45,7 +48,7 @@ class RevBlock(tf.keras.Model): bottleneck=False, fused=True, dtype=tf.float32): - """Initialize RevBlock. + """Initialization. Args: n_res: number of residual blocks @@ -88,49 +91,27 @@ class RevBlock(tf.keras.Model): h = block(h, training=training) return h - def backward_grads_and_vars(self, x, y, dy, training=True): + def backward_grads(self, x, y, dy, training=True): """Apply reversible block backward to outputs.""" grads_all = [] - vars_all = [] - for i in reversed(range(len(self.blocks))): block = self.blocks[i] if i == 0: # First block usually contains downsampling that can't be reversed - with tf.GradientTape() as tape: - x = tf.identity(x) - tape.watch(x) - y = block(x, training=training) - - grads_combined = tape.gradient( - y, [x] + block.trainable_variables, output_gradients=dy) - dy = grads_combined[0] - grads_all += grads_combined[1:] - vars_all += block.trainable_variables + dy, grads = block.backward_grads_with_downsample( + x, y, dy, training=True) else: - y, dy, grads, vars_ = block.backward_grads_and_vars( - y, dy, training=training) - grads_all += grads - vars_all += vars_ + y, dy, grads = block.backward_grads(y, dy, training=training) + grads_all = grads + grads_all - return dy, grads_all, vars_all + return dy, grads_all class _Residual(tf.keras.Model): """Single residual block contained in a _RevBlock. Each `_Residual` object has two _ResidualInner objects, corresponding to the `F` and `G` functions in the paper. - - Args: - filters: output filter size - strides: length 2 list/tuple of integers for height and width strides - input_shape: length 3 list/tuple of integers - batch_norm_first: whether to apply activation and batch norm before conv - data_format: tensor data format, "NCHW"/"NHWC", - bottleneck: use bottleneck residual if True - fused: use fused batch normalization if True - dtype: float16, float32, or float64 """ def __init__(self, @@ -142,6 +123,18 @@ class _Residual(tf.keras.Model): bottleneck=False, fused=True, dtype=tf.float32): + """Initialization. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC", + bottleneck: use bottleneck residual if True + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + """ super(_Residual, self).__init__() self.filters = filters @@ -174,10 +167,9 @@ class _Residual(tf.keras.Model): fused=fused, dtype=dtype) - def call(self, x, training=True, concat=True): + def call(self, x, training=True): """Apply residual block to inputs.""" - - x1, x2 = tf.split(x, num_or_size_splits=2, axis=self.axis) + x1, x2 = x f_x2 = self.f(x2, training=training) x1_down = ops.downsample( x1, self.filters // 2, self.strides, axis=self.axis) @@ -186,172 +178,327 @@ class _Residual(tf.keras.Model): y1 = f_x2 + x1_down g_y1 = self.g(y1, training=training) y2 = g_y1 + x2_down - if not concat: # For correct backward grads - return y1, y2 - return tf.concat([y1, y2], axis=self.axis) + return y1, y2 - def backward_grads_and_vars(self, y, dy, training=True): + def backward_grads(self, y, dy, training=True): """Manually compute backward gradients given input and output grads.""" - dy1, dy2 = tf.split(dy, num_or_size_splits=2, axis=self.axis) - - with tf.GradientTape(persistent=True) as tape: - y = tf.identity(y) - tape.watch(y) - y1, y2 = tf.split(y, num_or_size_splits=2, axis=self.axis) - z1 = y1 - gz1 = self.g(z1, training=training) - x2 = y2 - gz1 + dy1, dy2 = dy + y1, y2 = y + + with tf.GradientTape() as gtape: + gtape.watch(y1) + gy1 = self.g(y1, training=training) + grads_combined = gtape.gradient( + gy1, [y1] + self.g.trainable_variables, output_gradients=dy2) + dg = grads_combined[1:] + dx1 = dy1 + grads_combined[0] + # This doesn't affect eager execution, but improves memory efficiency with + # graphs + with tf.control_dependencies(dg + [dx1]): + x2 = y2 - gy1 + + with tf.GradientTape() as ftape: + ftape.watch(x2) fx2 = self.f(x2, training=training) - x1 = z1 - fx2 + grads_combined = ftape.gradient( + fx2, [x2] + self.f.trainable_variables, output_gradients=dx1) + df = grads_combined[1:] + dx2 = dy2 + grads_combined[0] + # Same behavior as above + with tf.control_dependencies(df + [dx2]): + x1 = y1 - fx2 - grads_combined = tape.gradient( - gz1, [z1] + self.g.trainable_variables, output_gradients=dy2) - dz1 = dy1 + grads_combined[0] + x = x1, x2 + dx = dx1, dx2 + grads = df + dg + + return x, dx, grads + + def backward_grads_with_downsample(self, x, y, dy, training=True): + """Manually compute backward gradients given input and output grads.""" + # Splitting this from `backward_grads` for better readability + x1, x2 = x + y1, _ = y + dy1, dy2 = dy + + with tf.GradientTape() as gtape: + gtape.watch(y1) + gy1 = self.g(y1, training=training) + grads_combined = gtape.gradient( + gy1, [y1] + self.g.trainable_variables, output_gradients=dy2) dg = grads_combined[1:] - dx1 = dz1 + dz1 = dy1 + grads_combined[0] - grads_combined = tape.gradient( + # dx1 need one more step to backprop through downsample + with tf.GradientTape() as x1tape: + x1tape.watch(x1) + z1 = ops.downsample(x1, self.filters // 2, self.strides, axis=self.axis) + dx1 = x1tape.gradient(z1, x1, output_gradients=dz1) + + with tf.GradientTape() as ftape: + ftape.watch(x2) + fx2 = self.f(x2, training=training) + grads_combined = ftape.gradient( fx2, [x2] + self.f.trainable_variables, output_gradients=dz1) - dx2 = dy2 + grads_combined[0] - df = grads_combined[1:] + dx2, df = grads_combined[0], grads_combined[1:] - del tape + # dx2 need one more step to backprop through downsample + with tf.GradientTape() as x2tape: + x2tape.watch(x2) + z2 = ops.downsample(x2, self.filters // 2, self.strides, axis=self.axis) + dx2 += x2tape.gradient(z2, x2, output_gradients=dy2) + dx = dx1, dx2 grads = df + dg - vars_ = self.f.trainable_variables + self.g.trainable_variables - x = tf.concat([x1, x2], axis=self.axis) - dx = tf.concat([dx1, dx2], axis=self.axis) + return dx, grads - return x, dx, grads, vars_ - -def _BottleneckResidualInner(filters, - strides, - input_shape, - batch_norm_first=True, - data_format="channels_first", - fused=True, - dtype=tf.float32): +# Ideally, the following should be wrapped in `tf.keras.Sequential`, however +# there are subtle issues with its placeholder insertion policy and batch norm +class _BottleneckResidualInner(tf.keras.Model): """Single bottleneck residual inner function contained in _Resdual. Corresponds to the `F`/`G` functions in the paper. Suitable for training on ImageNet dataset. - - Args: - filters: output filter size - strides: length 2 list/tuple of integers for height and width strides - input_shape: length 3 list/tuple of integers - batch_norm_first: whether to apply activation and batch norm before conv - data_format: tensor data format, "NCHW"/"NHWC" - fused: use fused batch normalization if True - dtype: float16, float32, or float64 - - Returns: - A keras model """ - axis = 1 if data_format == "channels_first" else 3 - model = tf.keras.Sequential() - if batch_norm_first: - model.add( - tf.keras.layers.BatchNormalization( - axis=axis, input_shape=input_shape, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters // 4, - kernel_size=1, - strides=strides, - input_shape=input_shape, - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype)) - - model.add( - tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters // 4, - kernel_size=3, - strides=(1, 1), - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype)) - - model.add( - tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters, - kernel_size=1, - strides=(1, 1), - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype)) + def __init__(self, + filters, + strides, + input_shape, + batch_norm_first=True, + data_format="channels_first", + fused=True, + dtype=tf.float32): + """Initialization. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC" + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + """ + super(_BottleneckResidualInner, self).__init__() + axis = 1 if data_format == "channels_first" else 3 + if batch_norm_first: + self.batch_norm_0 = tf.keras.layers.BatchNormalization( + axis=axis, input_shape=input_shape, fused=fused, dtype=dtype) + self.conv2d_1 = tf.keras.layers.Conv2D( + filters=filters // 4, + kernel_size=1, + strides=strides, + input_shape=input_shape, + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + + self.batch_norm_1 = tf.keras.layers.BatchNormalization( + axis=axis, fused=fused, dtype=dtype) + self.conv2d_2 = tf.keras.layers.Conv2D( + filters=filters // 4, + kernel_size=3, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + + self.batch_norm_2 = tf.keras.layers.BatchNormalization( + axis=axis, fused=fused, dtype=dtype) + self.conv2d_3 = tf.keras.layers.Conv2D( + filters=filters, + kernel_size=1, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + + self.batch_norm_first = batch_norm_first + + def call(self, x, training=True): + net = x + if self.batch_norm_first: + net = self.batch_norm_0(net, training=training) + net = tf.nn.relu(net) + net = self.conv2d_1(net) + + net = self.batch_norm_1(net, training=training) + net = tf.nn.relu(net) + net = self.conv2d_2(net) - return model + net = self.batch_norm_2(net, training=training) + net = tf.nn.relu(net) + net = self.conv2d_3(net) + return net -def _ResidualInner(filters, - strides, - input_shape, - batch_norm_first=True, - data_format="channels_first", - fused=True, - dtype=tf.float32): + +class _ResidualInner(tf.keras.Model): """Single residual inner function contained in _ResdualBlock. Corresponds to the `F`/`G` functions in the paper. - - Args: - filters: output filter size - strides: length 2 list/tuple of integers for height and width strides - input_shape: length 3 list/tuple of integers - batch_norm_first: whether to apply activation and batch norm before conv - data_format: tensor data format, "NCHW"/"NHWC" - fused: use fused batch normalization if True - dtype: float16, float32, or float64 - - Returns: - A keras model """ - axis = 1 if data_format == "channels_first" else 3 - model = tf.keras.Sequential() - if batch_norm_first: - model.add( - tf.keras.layers.BatchNormalization( - axis=axis, input_shape=input_shape, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters, - kernel_size=3, - strides=strides, - input_shape=input_shape, - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype)) - - model.add( - tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters, - kernel_size=3, - strides=(1, 1), - data_format=data_format, - use_bias=False, + def __init__(self, + filters, + strides, + input_shape, + batch_norm_first=True, + data_format="channels_first", + fused=True, + dtype=tf.float32): + """Initialization. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC" + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + """ + super(_ResidualInner, self).__init__() + axis = 1 if data_format == "channels_first" else 3 + if batch_norm_first: + self.batch_norm_0 = tf.keras.layers.BatchNormalization( + axis=axis, input_shape=input_shape, fused=fused, dtype=dtype) + self.conv2d_1 = tf.keras.layers.Conv2D( + filters=filters, + kernel_size=3, + strides=strides, + input_shape=input_shape, + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + + self.batch_norm_1 = tf.keras.layers.BatchNormalization( + axis=axis, fused=fused, dtype=dtype) + self.conv2d_2 = tf.keras.layers.Conv2D( + filters=filters, + kernel_size=3, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + + self.batch_norm_first = batch_norm_first + + def call(self, x, training=True): + net = x + if self.batch_norm_first: + net = self.batch_norm_0(net, training=training) + net = tf.nn.relu(net) + net = self.conv2d_1(net) + + net = self.batch_norm_1(net, training=training) + net = tf.nn.relu(net) + net = self.conv2d_2(net) + + return net + + +class InitBlock(tf.keras.Model): + """Initial block of RevNet.""" + + def __init__(self, config): + """Initialization. + + Args: + config: tf.contrib.training.HParams object; specifies hyperparameters + """ + super(InitBlock, self).__init__() + self.config = config + self.axis = 1 if self.config.data_format == "channels_first" else 3 + self.conv2d = tf.keras.layers.Conv2D( + filters=self.config.init_filters, + kernel_size=self.config.init_kernel, + strides=(self.config.init_stride, self.config.init_stride), + data_format=self.config.data_format, + use_bias=False, + padding="SAME", + input_shape=self.config.input_shape, + dtype=self.config.dtype) + self.batch_norm = tf.keras.layers.BatchNormalization( + axis=self.axis, fused=self.config.fused, dtype=self.config.dtype) + self.activation = tf.keras.layers.Activation("relu") + + if self.config.init_max_pool: + self.max_pool = tf.keras.layers.MaxPooling2D( + pool_size=(3, 3), + strides=(2, 2), padding="SAME", - dtype=dtype)) + data_format=self.config.data_format, + dtype=self.config.dtype) + + def call(self, x, training=True): + net = x + net = self.conv2d(net) + net = self.batch_norm(net, training=training) + net = self.activation(net) + + if self.config.init_max_pool: + net = self.max_pool(net) + + return tf.split(net, num_or_size_splits=2, axis=self.axis) - return model + +class FinalBlock(tf.keras.Model): + """Final block of RevNet.""" + + def __init__(self, config): + """Initialization. + + Args: + config: tf.contrib.training.HParams object; specifies hyperparameters + + Raises: + ValueError: Unsupported data format + """ + super(FinalBlock, self).__init__() + self.config = config + self.axis = 1 if self.config.data_format == "channels_first" else 3 + + f = self.config.filters[-1] # Number of filters + r = functools.reduce(operator.mul, self.config.strides, 1) # Reduce ratio + r *= self.config.init_stride + if self.config.init_max_pool: + r *= 2 + + if self.config.data_format == "channels_first": + w, h = self.config.input_shape[1], self.config.input_shape[2] + input_shape = (f, w // r, h // r) + elif self.config.data_format == "channels_last": + w, h = self.config.input_shape[0], self.config.input_shape[1] + input_shape = (w // r, h // r, f) + else: + raise ValueError("Data format should be either `channels_first`" + " or `channels_last`") + self.batch_norm = tf.keras.layers.BatchNormalization( + axis=self.axis, + input_shape=input_shape, + fused=self.config.fused, + dtype=self.config.dtype) + self.activation = tf.keras.layers.Activation("relu") + self.global_avg_pool = tf.keras.layers.GlobalAveragePooling2D( + data_format=self.config.data_format, dtype=self.config.dtype) + self.dense = tf.keras.layers.Dense( + self.config.n_classes, dtype=self.config.dtype) + + def call(self, x, training=True): + net = tf.concat(x, axis=self.axis) + net = self.batch_norm(net, training=training) + net = self.activation(net) + net = self.global_avg_pool(net) + net = self.dense(net) + + return net diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py b/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py index d74785c8fe1c170ee95172974141c1cfe18b9502..fda9020ddf79cd3fd59611d03c1a4202a4901337 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py @@ -116,70 +116,13 @@ def _validate_block_call_channels_first(block_factory, test): class RevBlockTest(tf.test.TestCase): - def test_call_channels_first(self): - """Test `call` function with `channels_first` data format.""" - if not tf.test.is_gpu_available(): - self.skipTest("GPU not available") - - with tf.device("/gpu:0"): # Default NCHW format - input_shape = (128, 8, 8) - data_shape = (16,) + input_shape - x = tf.random_normal(shape=data_shape) - - # Stride of 1 - block = blocks.RevBlock( - n_res=3, filters=128, strides=(1, 1), input_shape=input_shape) - y_tr, y_ev = block(x, training=True), block(x, training=False) - self.assertEqual(y_tr.shape, y_ev.shape) - self.assertEqual(y_ev.shape, (16, 128, 8, 8)) - self.assertNotAllClose(y_tr, y_ev) - - # Stride of 2 - block = blocks.RevBlock( - n_res=3, filters=128, strides=(2, 2), input_shape=input_shape) - y_tr, y_ev = block(x, training=True), block(x, training=False) - self.assertEqual(y_tr.shape, y_ev.shape) - self.assertEqual(y_ev.shape, [16, 128, 4, 4]) - self.assertNotAllClose(y_tr, y_ev) - - def test_call_channels_last(self): - """Test `call` function with `channels_last` data format.""" - with tf.device("/cpu:0"): # NHWC format - input_shape = (8, 8, 128) - data_shape = (16,) + input_shape - x = tf.random_normal(shape=data_shape) - - # Stride 1 - block = blocks.RevBlock( - n_res=3, - filters=128, - strides=(1, 1), - input_shape=input_shape, - data_format="channels_last") - y_tr, y_ev = block(x, training=True), block(x, training=False) - self.assertEqual(y_tr.shape, y_ev.shape) - self.assertEqual(y_ev.shape, (16, 8, 8, 128)) - self.assertNotAllClose(y_tr, y_ev) - - # Stride of 2 - block = blocks.RevBlock( - n_res=3, - filters=128, - strides=(2, 2), - input_shape=input_shape, - data_format="channels_last") - y_tr, y_ev = block(x, training=True), block(x, training=False) - self.assertEqual(y_tr.shape, y_ev.shape) - self.assertEqual(y_ev.shape, (16, 4, 4, 128)) - self.assertNotAllClose(y_tr, y_ev) - def _check_grad_angle(self, grads, grads_true, atol=1e0): """Check the angle between two list of vectors are all close.""" for g1, g2 in zip(grads, grads_true): degree = compute_degree(g1, g2) self.assertLessEqual(degree, atol) - def test_backward_grads_and_vars_channels_first(self): + def test_backward_grads_channels_first(self): """Test `backward` function with `channels_first` data format.""" if not tf.test.is_gpu_available(): self.skipTest("GPU not available") @@ -190,6 +133,7 @@ class RevBlockTest(tf.test.TestCase): data_shape = (16,) + input_shape x = tf.random_normal(shape=data_shape, dtype=tf.float64) dy = tf.random_normal(shape=data_shape, dtype=tf.float64) + dy1, dy2 = tf.split(dy, num_or_size_splits=2, axis=1) block = blocks.RevBlock( n_res=3, filters=128, @@ -199,9 +143,14 @@ class RevBlockTest(tf.test.TestCase): dtype=tf.float64) with tf.GradientTape() as tape: tape.watch(x) - y = block(x, training=True) + x1, x2 = tf.split(x, num_or_size_splits=2, axis=1) + y1, y2 = block((x1, x2), training=True) + y = tf.concat((y1, y2), axis=1) # Compute grads from reconstruction - dx, dw, vars_ = block.backward_grads_and_vars(x, y, dy, training=True) + (dx1, dx2), dw = block.backward_grads( + x=(x1, x2), y=(y1, y2), dy=(dy1, dy2), training=True) + dx = tf.concat((dx1, dx2), axis=1) + vars_ = block.trainable_variables # Compute true grads grads = tape.gradient(y, [x] + vars_, output_gradients=dy) dx_true, dw_true = grads[0], grads[1:] @@ -213,6 +162,7 @@ class RevBlockTest(tf.test.TestCase): # Stride 2 x = tf.random_normal(shape=data_shape, dtype=tf.float64) dy = tf.random_normal(shape=(16, 128, 4, 4), dtype=tf.float64) + dy1, dy2 = tf.split(dy, num_or_size_splits=2, axis=1) block = blocks.RevBlock( n_res=3, filters=128, @@ -222,9 +172,14 @@ class RevBlockTest(tf.test.TestCase): dtype=tf.float64) with tf.GradientTape() as tape: tape.watch(x) - y = block(x, training=True) + x1, x2 = tf.split(x, num_or_size_splits=2, axis=1) + y1, y2 = block((x1, x2), training=True) + y = tf.concat((y1, y2), axis=1) # Compute grads from reconstruction - dx, dw, vars_ = block.backward_grads_and_vars(x, y, dy, training=True) + (dx1, dx2), dw = block.backward_grads( + x=(x1, x2), y=(y1, y2), dy=(dy1, dy2), training=True) + dx = tf.concat((dx1, dx2), axis=1) + vars_ = block.trainable_variables # Compute true grads grads = tape.gradient(y, [x] + vars_, output_gradients=dy) dx_true, dw_true = grads[0], grads[1:] @@ -236,16 +191,7 @@ class RevBlockTest(tf.test.TestCase): class _ResidualTest(tf.test.TestCase): - def test_call(self): - """Test `call` function. - - Varying downsampling and data format options. - """ - - _validate_block_call_channels_first(blocks._Residual, self) - _validate_block_call_channels_last(blocks._Residual, self) - - def test_backward_grads_and_vars_channels_first(self): + def test_backward_grads_channels_first(self): """Test `backward_grads` function with `channels_first` data format.""" if not tf.test.is_gpu_available(): self.skipTest("GPU not available") @@ -256,6 +202,7 @@ class _ResidualTest(tf.test.TestCase): # Use double precision for testing x_true = tf.random_normal(shape=data_shape, dtype=tf.float64) dy = tf.random_normal(shape=data_shape, dtype=tf.float64) + dy1, dy2 = tf.split(dy, num_or_size_splits=2, axis=1) residual = blocks._Residual( filters=128, strides=(1, 1), @@ -264,16 +211,19 @@ class _ResidualTest(tf.test.TestCase): dtype=tf.float64) with tf.GradientTape() as tape: - x_true = tf.identity(x_true) tape.watch(x_true) - y = residual(x_true, training=True) + x1_true, x2_true = tf.split(x_true, num_or_size_splits=2, axis=1) + y1, y2 = residual((x1_true, x2_true), training=True) + y = tf.concat((y1, y2), axis=1) # Gradients computed due to reversibility - x, dx, dw, vars_ = residual.backward_grads_and_vars( - y, dy=dy, training=True) - + (x1, x2), (dx1, dx2), dw = residual.backward_grads( + y=(y1, y2), dy=(dy1, dy2), training=True) + x = tf.concat((x1, x2), axis=1) + dx = tf.concat((dx1, dx2), axis=1) # True gradients computed by the tape - grads = tape.gradient(y, [x_true] + vars_, output_gradients=dy) + grads = tape.gradient( + y, [x_true] + residual.trainable_variables, output_gradients=dy) dx_true, dw_true = grads[0], grads[1:] self.assertAllClose(x_true, x) diff --git a/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py b/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py index b6d4c35bfd21f9d651c4f059c019cf2e585da8b2..e9672f13e1587c96cea0fc7dd58b66ef256296cd 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py +++ b/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py @@ -111,6 +111,6 @@ def get_ds_from_tfrecords(data_dir, }[split] dataset = dataset.shuffle(size) - dataset = dataset.batch(batch_size) + dataset = dataset.batch(batch_size, drop_remainder=True) return dataset diff --git a/tensorflow/contrib/eager/python/examples/revnet/config.py b/tensorflow/contrib/eager/python/examples/revnet/config.py index 3d93fa955a29718fdec52b04500c41f77351dd8d..29f1db0e0367515757413c8e47f7b7280fc4cfbb 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/config.py +++ b/tensorflow/contrib/eager/python/examples/revnet/config.py @@ -27,17 +27,17 @@ from __future__ import division from __future__ import print_function import tensorflow as tf -tfe = tf.contrib.eager def get_hparams_cifar_38(): """RevNet-38 configurations for CIFAR-10/CIFAR-100.""" config = tf.contrib.training.HParams() + config.add_hparam("num_train_images", 50000) + config.add_hparam("num_eval_images", 10000) config.add_hparam("init_filters", 32) config.add_hparam("init_kernel", 3) config.add_hparam("init_stride", 1) - config.add_hparam("n_classes", 10) config.add_hparam("n_rev_blocks", 3) config.add_hparam("n_res", [3, 3, 3]) config.add_hparam("filters", [32, 64, 112]) @@ -46,7 +46,7 @@ def get_hparams_cifar_38(): config.add_hparam("bottleneck", False) config.add_hparam("fused", True) config.add_hparam("init_max_pool", False) - if tfe.num_gpus() > 0: + if tf.test.is_gpu_available(): config.add_hparam("input_shape", (3, 32, 32)) config.add_hparam("data_format", "channels_first") else: @@ -68,9 +68,22 @@ def get_hparams_cifar_38(): config.add_hparam("div255", True) # This is imprecise, when training with validation set, # we only have 40k images in training data - config.add_hparam("iters_per_epoch", 50000 // config.batch_size) + config.add_hparam("iters_per_epoch", + config.num_train_images // config.batch_size) config.add_hparam("epochs", config.max_train_iter // config.iters_per_epoch) + # Customized TPU hyperparameters due to differing batch size caused by + # TPU architecture specifics + # Suggested batch sizes to reduce overhead from excessive tensor padding + # https://cloud.google.com/tpu/docs/troubleshooting + config.add_hparam("tpu_batch_size", 1024) + config.add_hparam("tpu_eval_batch_size", 1024) + config.add_hparam("tpu_iters_per_epoch", + config.num_train_images // config.tpu_batch_size) + config.add_hparam("tpu_epochs", + config.max_train_iter // config.tpu_iters_per_epoch) + config.add_hparam("tpu_eval_steps", + config.num_eval_images // config.tpu_eval_batch_size) return config @@ -98,15 +111,18 @@ def get_hparams_imagenet_56(): """RevNet-56 configurations for ImageNet.""" config = tf.contrib.training.HParams() + config.add_hparam("n_classes", 1000) + config.add_hparam("dataset", "ImageNet") + config.add_hparam("num_train_images", 1281167) + config.add_hparam("num_eval_images", 50000) config.add_hparam("init_filters", 128) config.add_hparam("init_kernel", 7) config.add_hparam("init_stride", 2) - config.add_hparam("n_classes", 1000) config.add_hparam("n_rev_blocks", 4) config.add_hparam("n_res", [2, 2, 2, 2]) config.add_hparam("filters", [128, 256, 512, 832]) config.add_hparam("strides", [1, 2, 2, 2]) - config.add_hparam("batch_size", 16) + config.add_hparam("batch_size", 256) config.add_hparam("bottleneck", True) config.add_hparam("fused", True) config.add_hparam("init_max_pool", True) @@ -116,6 +132,9 @@ def get_hparams_imagenet_56(): else: config.add_hparam("input_shape", (224, 224, 3)) config.add_hparam("data_format", "channels_last") + # Due to bottleneck residual blocks + filters = [f * 4 for f in config.filters] + config.filters = filters # Training details config.add_hparam("weight_decay", 1e-4) @@ -125,16 +144,32 @@ def get_hparams_imagenet_56(): config.add_hparam("max_train_iter", 600000) config.add_hparam("seed", 1234) config.add_hparam("shuffle", True) - config.add_hparam("log_every", 50) - config.add_hparam("save_every", 50) + config.add_hparam("log_every", 500) + config.add_hparam("save_every", 500) config.add_hparam("dtype", tf.float32) - config.add_hparam("eval_batch_size", 1000) + config.add_hparam("eval_batch_size", 256) config.add_hparam("div255", True) - # TODO(lxuechen): Update this according to ImageNet data - config.add_hparam("iters_per_epoch", 50000 // config.batch_size) + config.add_hparam("iters_per_epoch", + config.num_train_images // config.batch_size) config.add_hparam("epochs", config.max_train_iter // config.iters_per_epoch) - # Due to bottleneck residual blocks - filters = [f * 4 for f in config.filters] - config.filters = filters + + # Customized TPU hyperparameters due to differing batch size caused by + # TPU architecture specifics + # Suggested batch sizes to reduce overhead from excessive tensor padding + # https://cloud.google.com/tpu/docs/troubleshooting + config.add_hparam("tpu_batch_size", 1024) + config.add_hparam("tpu_eval_batch_size", 1024) + config.add_hparam("tpu_iters_per_epoch", + config.num_train_images // config.tpu_batch_size) + config.add_hparam("tpu_epochs", + config.max_train_iter // config.tpu_iters_per_epoch) + config.add_hparam("tpu_eval_steps", + config.num_eval_images // config.tpu_eval_batch_size) + return config + + +def get_hparams_imagenet_104(): + config = get_hparams_imagenet_56() + config.n_res = [2, 2, 11, 2] return config diff --git a/tensorflow/contrib/eager/python/examples/revnet/imagenet_input.py b/tensorflow/contrib/eager/python/examples/revnet/imagenet_input.py new file mode 100644 index 0000000000000000000000000000000000000000..e81351b1b14dbf6973e7430c369774339e2dcdd8 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/imagenet_input.py @@ -0,0 +1,230 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Efficient ImageNet input pipeline using tf.data.Dataset.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import functools +import os + +import tensorflow as tf + +from tensorflow.contrib.eager.python.examples.revnet import resnet_preprocessing + + +def image_serving_input_fn(): + """Serving input fn for raw images.""" + + def _preprocess_image(image_bytes): + """Preprocess a single raw image.""" + image = resnet_preprocessing.preprocess_image( + image_bytes=image_bytes, is_training=False) + return image + + image_bytes_list = tf.placeholder( + shape=[None], + dtype=tf.string, + ) + images = tf.map_fn( + _preprocess_image, image_bytes_list, back_prop=False, dtype=tf.float32) + return tf.estimator.export.ServingInputReceiver( + images, {'image_bytes': image_bytes_list}) + + +class ImageNetInput(object): + """Generates ImageNet input_fn for training or evaluation. + + The training data is assumed to be in TFRecord format with keys as specified + in the dataset_parser below, sharded across 1024 files, named sequentially: + train-00000-of-01024 + train-00001-of-01024 + ... + train-01023-of-01024 + + The validation data is in the same format but sharded in 128 files. + + The format of the data required is created by the script at: + https://github.com/tensorflow/tpu/blob/master/tools/datasets/imagenet_to_gcs.py + + Args: + is_training: `bool` for whether the input is for training + data_dir: `str` for the directory of the training and validation data; + if 'null' (the literal string 'null', not None), then construct a null + pipeline, consisting of empty images. + use_bfloat16: If True, use bfloat16 precision; else use float32. + transpose_input: 'bool' for whether to use the double transpose trick + num_cores: `int` for the number of TPU cores + """ + + def __init__(self, is_training, + use_bfloat16, + data_dir, + num_cores=8, + num_parallel_calls=64, + image_size=224, + transpose_input=False, + cache=False): + self.image_preprocessing_fn = resnet_preprocessing.preprocess_image + self.is_training = is_training + self.use_bfloat16 = use_bfloat16 + self.data_dir = data_dir + self.num_cores = num_cores + self.num_parallel_calls = num_parallel_calls + if self.data_dir == 'null' or self.data_dir == '': + self.data_dir = None + self.transpose_input = transpose_input + self.image_size = image_size + self.cache = cache + + def set_shapes(self, batch_size, images, labels): + """Statically set the batch_size dimension.""" + if self.transpose_input: + images.set_shape(images.get_shape().merge_with( + tf.TensorShape([None, None, None, batch_size]))) + labels.set_shape(labels.get_shape().merge_with( + tf.TensorShape([batch_size]))) + else: + images.set_shape(images.get_shape().merge_with( + tf.TensorShape([batch_size, None, None, None]))) + labels.set_shape(labels.get_shape().merge_with( + tf.TensorShape([batch_size]))) + + return images, labels + + def dataset_parser(self, value): + """Parse an ImageNet record from a serialized string Tensor.""" + keys_to_features = { + 'image/encoded': tf.FixedLenFeature((), tf.string, ''), + 'image/format': tf.FixedLenFeature((), tf.string, 'jpeg'), + 'image/class/label': tf.FixedLenFeature([], tf.int64, -1), + 'image/class/text': tf.FixedLenFeature([], tf.string, ''), + 'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32), + 'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32), + 'image/object/bbox/xmax': tf.VarLenFeature(dtype=tf.float32), + 'image/object/bbox/ymax': tf.VarLenFeature(dtype=tf.float32), + 'image/object/class/label': tf.VarLenFeature(dtype=tf.int64), + } + + parsed = tf.parse_single_example(value, keys_to_features) + image_bytes = tf.reshape(parsed['image/encoded'], shape=[]) + + image = self.image_preprocessing_fn( + image_bytes=image_bytes, + is_training=self.is_training, + image_size=self.image_size, + use_bfloat16=self.use_bfloat16) + + # Subtract one so that labels are in [0, 1000). + label = tf.cast( + tf.reshape(parsed['image/class/label'], shape=[]), dtype=tf.int32) - 1 + + return image, label + + def input_fn(self, params): + """Input function which provides a single batch for train or eval. + + Args: + params: `dict` of parameters passed from the `TPUEstimator`. + `params['batch_size']` is always provided and should be used as the + effective batch size. + + Returns: + A `tf.data.Dataset` object. + """ + if self.data_dir is None: + tf.logging.info('Using fake input.') + return self.input_fn_null(params) + + # Retrieves the batch size for the current shard. The # of shards is + # computed according to the input pipeline deployment. See + # tf.contrib.tpu.RunConfig for details. + batch_size = params['batch_size'] + + # Shuffle the filenames to ensure better randomization. + file_pattern = os.path.join( + self.data_dir, 'train-*' if self.is_training else 'validation-*') + dataset = tf.data.Dataset.list_files(file_pattern, shuffle=self.is_training) + + if self.is_training and not self.cache: + dataset = dataset.repeat() + + def fetch_dataset(filename): + buffer_size = 8 * 1024 * 1024 # 8 MiB per file + dataset = tf.data.TFRecordDataset(filename, buffer_size=buffer_size) + return dataset + + # Read the data from disk in parallel + dataset = dataset.apply( + tf.contrib.data.parallel_interleave( + fetch_dataset, cycle_length=self.num_parallel_calls, sloppy=True)) + if self.cache: + dataset = dataset.cache().apply( + tf.contrib.data.shuffle_and_repeat(1024 * 16)) + else: + dataset = dataset.shuffle(1024) + + # Use the fused map-and-batch operation. + # + # For XLA, we must used fixed shapes. Because we repeat the source training + # dataset indefinitely, we can use `drop_remainder=True` to get fixed-size + # batches without dropping any training examples. + # + # When evaluating, `drop_remainder=True` prevents accidentally evaluating + # the same image twice by dropping the final batch if it is less than a full + # batch size. As long as this validation is done with consistent batch size, + # exactly the same images will be used. + dataset = dataset.apply( + tf.contrib.data.map_and_batch( + self.dataset_parser, batch_size=batch_size, + num_parallel_batches=self.num_cores, drop_remainder=True)) + + # Transpose for performance on TPU + if self.transpose_input: + dataset = dataset.map( + lambda images, labels: (tf.transpose(images, [1, 2, 3, 0]), labels), + num_parallel_calls=self.num_cores) + + # Assign static batch size dimension + dataset = dataset.map(functools.partial(self.set_shapes, batch_size)) + + # Prefetch overlaps in-feed with training + dataset = dataset.prefetch(tf.contrib.data.AUTOTUNE) + return dataset + + def input_fn_null(self, params): + """Input function which provides null (black) images.""" + batch_size = params['batch_size'] + dataset = tf.data.Dataset.range(1).repeat().map(self._get_null_input) + dataset = dataset.prefetch(batch_size) + + dataset = dataset.apply( + tf.contrib.data.batch_and_drop_remainder(batch_size)) + if self.transpose_input: + dataset = dataset.map( + lambda images, labels: (tf.transpose(images, [1, 2, 3, 0]), labels), + num_parallel_calls=8) + + dataset = dataset.map(functools.partial(self.set_shapes, batch_size)) + + dataset = dataset.prefetch(32) # Prefetch overlaps in-feed with training + tf.logging.info('Input dataset: %s', str(dataset)) + return dataset + + def _get_null_input(self, _): + null_image = tf.zeros([224, 224, 3], tf.bfloat16 + if self.use_bfloat16 else tf.float32) + return (null_image, tf.constant(0, tf.int32)) diff --git a/tensorflow/contrib/eager/python/examples/revnet/main.py b/tensorflow/contrib/eager/python/examples/revnet/main.py index e2f43b03f90ef6db01db1f85943e10ce8c9b582a..b702e91f92220c2a9003a1b82411131332012a9e 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/main.py +++ b/tensorflow/contrib/eager/python/examples/revnet/main.py @@ -29,10 +29,18 @@ from tensorflow.contrib.eager.python.examples.revnet import revnet tfe = tf.contrib.eager +def apply_gradients(optimizer, grads, vars_, global_step=None): + """Functional style apply_grads for `tfe.defun`.""" + optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) + + def main(_): """Eager execution workflow with RevNet trained on CIFAR-10.""" - config = get_config() - ds_train, ds_train_one_shot, ds_validation, ds_test = get_datasets(config) + tf.enable_eager_execution() + + config = get_config(config_name=FLAGS.config, dataset=FLAGS.dataset) + ds_train, ds_train_one_shot, ds_validation, ds_test = get_datasets( + data_dir=FLAGS.data_dir, config=config) model = revnet.RevNet(config=config) global_step = tf.train.get_or_create_global_step() # Ensure correct summary global_step.assign(1) @@ -43,6 +51,14 @@ def main(_): checkpointer = tf.train.Checkpoint( optimizer=optimizer, model=model, optimizer_step=global_step) + if FLAGS.use_defun: + model.call = tfe.defun(model.call) + model.compute_gradients = tfe.defun(model.compute_gradients) + model.get_moving_stats = tfe.defun(model.get_moving_stats) + model.restore_moving_stats = tfe.defun(model.restore_moving_stats) + global apply_gradients # pylint:disable=global-variable-undefined + apply_gradients = tfe.defun(apply_gradients) + if FLAGS.train_dir: summary_writer = tf.contrib.summary.create_file_writer(FLAGS.train_dir) if FLAGS.restore: @@ -52,46 +68,37 @@ def main(_): "with global_step: {}".format(latest_path, global_step.numpy())) sys.stdout.flush() - if FLAGS.manual_grad: - print("Using manual gradients.") - else: - print("Not using manual gradients.") - sys.stdout.flush() - for x, y in ds_train: train_one_iter(model, x, y, optimizer, global_step=global_step) if global_step.numpy() % config.log_every == 0: - it_train = ds_train_one_shot.make_one_shot_iterator() it_test = ds_test.make_one_shot_iterator() - acc_train, loss_train = evaluate(model, it_train) acc_test, loss_test = evaluate(model, it_test) if FLAGS.validate: + it_train = ds_train_one_shot.make_one_shot_iterator() it_validation = ds_validation.make_one_shot_iterator() + acc_train, loss_train = evaluate(model, it_train) acc_validation, loss_validation = evaluate(model, it_validation) print("Iter {}, " "training set accuracy {:.4f}, loss {:.4f}; " - "validation set accuracy {:.4f}, loss {:4.f}" + "validation set accuracy {:.4f}, loss {:.4f}; " "test accuracy {:.4f}, loss {:.4f}".format( global_step.numpy(), acc_train, loss_train, acc_validation, loss_validation, acc_test, loss_test)) else: - print("Iter {}, " - "training set accuracy {:.4f}, loss {:.4f}; " - "test accuracy {:.4f}, loss {:.4f}".format( - global_step.numpy(), acc_train, loss_train, acc_test, - loss_test)) + print("Iter {}, test accuracy {:.4f}, loss {:.4f}".format( + global_step.numpy(), acc_test, loss_test)) sys.stdout.flush() if FLAGS.train_dir: with summary_writer.as_default(): with tf.contrib.summary.always_record_summaries(): - tf.contrib.summary.scalar("Training accuracy", acc_train) tf.contrib.summary.scalar("Test accuracy", acc_test) - tf.contrib.summary.scalar("Training loss", loss_train) tf.contrib.summary.scalar("Test loss", loss_test) if FLAGS.validate: + tf.contrib.summary.scalar("Training accuracy", acc_train) + tf.contrib.summary.scalar("Training loss", loss_train) tf.contrib.summary.scalar("Validation accuracy", acc_validation) tf.contrib.summary.scalar("Validation loss", loss_validation) @@ -103,34 +110,38 @@ def main(_): sys.stdout.flush() -def get_config(): +def get_config(config_name="revnet-38", dataset="cifar-10"): """Return configuration.""" - print("Config: {}".format(FLAGS.config)) + print("Config: {}".format(config_name)) sys.stdout.flush() config = { "revnet-38": config_.get_hparams_cifar_38(), "revnet-110": config_.get_hparams_cifar_110(), "revnet-164": config_.get_hparams_cifar_164(), - }[FLAGS.config] + }[config_name] - if FLAGS.dataset == "cifar-100": - config.n_classes = 100 + if dataset == "cifar-10": + config.add_hparam("n_classes", 10) + config.add_hparam("dataset", "cifar-10") + else: + config.add_hparam("n_classes", 100) + config.add_hparam("dataset", "cifar-100") return config -def get_datasets(config): +def get_datasets(data_dir, config): """Return dataset.""" - if FLAGS.data_dir is None: + if data_dir is None: raise ValueError("No supplied data directory") - if not os.path.exists(FLAGS.data_dir): - raise ValueError("Data directory {} does not exist".format(FLAGS.data_dir)) - if FLAGS.dataset not in ["cifar-10", "cifar-100"]: - raise ValueError("Unknown dataset {}".format(FLAGS.dataset)) + if not os.path.exists(data_dir): + raise ValueError("Data directory {} does not exist".format(data_dir)) + if config.dataset not in ["cifar-10", "cifar-100"]: + raise ValueError("Unknown dataset {}".format(config.dataset)) - print("Training on {} dataset.".format(FLAGS.dataset)) + print("Training on {} dataset.".format(config.dataset)) sys.stdout.flush() - data_dir = os.path.join(FLAGS.data_dir, FLAGS.dataset) + data_dir = os.path.join(data_dir, config.dataset) if FLAGS.validate: # 40k Training set ds_train = cifar_input.get_ds_from_tfrecords( @@ -168,7 +179,7 @@ def get_datasets(config): prefetch=config.batch_size) ds_validation = None - # Always compute loss and accuracy on whole training and test set + # Always compute loss and accuracy on whole test set ds_train_one_shot = cifar_input.get_ds_from_tfrecords( data_dir=data_dir, split="train_all", @@ -196,19 +207,15 @@ def get_datasets(config): def train_one_iter(model, inputs, labels, optimizer, global_step=None): """Train for one iteration.""" - if FLAGS.manual_grad: - grads, vars_, loss = model.compute_gradients(inputs, labels, training=True) - optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) - else: # For correctness validation - with tf.GradientTape() as tape: - logits, _ = model(inputs, training=True) - loss = model.compute_loss(logits=logits, labels=labels) - tf.logging.info("Logits are placed on device: {}".format(logits.device)) - grads = tape.gradient(loss, model.trainable_variables) - optimizer.apply_gradients( - zip(grads, model.trainable_variables), global_step=global_step) + logits, saved_hiddens = model(inputs, training=True) + values = model.get_moving_stats() + grads, loss = model.compute_gradients(saved_hiddens, labels) + # Restore moving averages when executing eagerly to avoid updating twice + model.restore_moving_stats(values) + apply_gradients( + optimizer, grads, model.trainable_variables, global_step=global_step) - return loss.numpy() + return logits, loss def evaluate(model, iterator): @@ -241,16 +248,18 @@ if __name__ == "__main__": "validate", default=False, help="[Optional] Use the validation set or not for hyperparameter search") - flags.DEFINE_boolean( - "manual_grad", - default=False, - help="[Optional] Use manual gradient graph to save memory") flags.DEFINE_string( "dataset", default="cifar-10", help="[Optional] The dataset used; either `cifar-10` or `cifar-100`") flags.DEFINE_string( - "config", default="revnet-38", help="[Optional] Architecture of network.") + "config", + default="revnet-38", + help="[Optional] Architecture of network. " + "Other options include `revnet-110` and `revnet-164`") + flags.DEFINE_boolean( + "use_defun", + default=False, + help="[Optional] Use `tfe.defun` to boost performance.") FLAGS = flags.FLAGS - tf.enable_eager_execution() tf.app.run(main) diff --git a/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py b/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py new file mode 100644 index 0000000000000000000000000000000000000000..3a17eb30da3b989acb0b33f2fcb730da76546c18 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py @@ -0,0 +1,200 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Estimator workflow with RevNet train on CIFAR-10.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from absl import flags +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import cifar_input +from tensorflow.contrib.eager.python.examples.revnet import main as main_ +from tensorflow.contrib.eager.python.examples.revnet import revnet + + +def model_fn(features, labels, mode, params): + """Function specifying the model that is required by the `tf.estimator` API. + + Args: + features: Input images + labels: Labels of images + mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT' + params: A dictionary of extra parameter that might be passed + + Returns: + An instance of `tf.estimator.EstimatorSpec` + """ + + inputs = features + if isinstance(inputs, dict): + inputs = features["image"] + + config = params["config"] + model = revnet.RevNet(config=config) + + if mode == tf.estimator.ModeKeys.TRAIN: + global_step = tf.train.get_or_create_global_step() + learning_rate = tf.train.piecewise_constant( + global_step, config.lr_decay_steps, config.lr_list) + optimizer = tf.train.MomentumOptimizer( + learning_rate, momentum=config.momentum) + logits, saved_hidden = model(inputs, training=True) + grads, loss = model.compute_gradients(saved_hidden, labels, training=True) + with tf.control_dependencies(model.get_updates_for(inputs)): + train_op = optimizer.apply_gradients( + zip(grads, model.trainable_variables), global_step=global_step) + + return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op) + else: + logits, _ = model(inputs, training=False) + predictions = tf.argmax(logits, axis=1) + probabilities = tf.nn.softmax(logits) + + if mode == tf.estimator.ModeKeys.EVAL: + loss = model.compute_loss(labels=labels, logits=logits) + return tf.estimator.EstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops={ + "accuracy": + tf.metrics.accuracy(labels=labels, predictions=predictions) + }) + + else: # mode == tf.estimator.ModeKeys.PREDICT + result = { + "classes": predictions, + "probabilities": probabilities, + } + + return tf.estimator.EstimatorSpec( + mode=mode, + predictions=predictions, + export_outputs={ + "classify": tf.estimator.export.PredictOutput(result) + }) + + +def get_input_fn(config, data_dir, split): + """Get the input function that is required by the `tf.estimator` API. + + Args: + config: Customized hyperparameters + data_dir: Directory where the data is stored + split: One of `train`, `validation`, `train_all`, and `test` + + Returns: + Input function required by the `tf.estimator` API + """ + + data_dir = os.path.join(data_dir, config.dataset) + # Fix split-dependent hyperparameters + if split == "train_all" or split == "train": + data_aug = True + batch_size = config.batch_size + epochs = config.epochs + shuffle = True + prefetch = config.batch_size + else: + data_aug = False + batch_size = config.eval_batch_size + epochs = 1 + shuffle = False + prefetch = config.eval_batch_size + + def input_fn(): + """Input function required by the `tf.estimator.Estimator` API.""" + return cifar_input.get_ds_from_tfrecords( + data_dir=data_dir, + split=split, + data_aug=data_aug, + batch_size=batch_size, + epochs=epochs, + shuffle=shuffle, + prefetch=prefetch, + data_format=config.data_format) + + return input_fn + + +def main(_): + tf.logging.set_verbosity(tf.logging.INFO) + + # RevNet specific configuration + config = main_.get_config(config_name=FLAGS.config, dataset=FLAGS.dataset) + + # Estimator specific configuration + run_config = tf.estimator.RunConfig( + model_dir=FLAGS.model_dir, # Directory for storing checkpoints + tf_random_seed=config.seed, + save_summary_steps=config.log_every, + save_checkpoints_steps=config.log_every, + session_config=None, # Using default + keep_checkpoint_max=100, + keep_checkpoint_every_n_hours=10000, # Using default + log_step_count_steps=config.log_every, + train_distribute=None # Default not use distribution strategy + ) + + # Construct estimator + revnet_estimator = tf.estimator.Estimator( + model_fn=model_fn, + model_dir=FLAGS.model_dir, + config=run_config, + params={"config": config}) + + # Construct input functions + train_input_fn = get_input_fn( + config=config, data_dir=FLAGS.data_dir, split="train_all") + eval_input_fn = get_input_fn( + config=config, data_dir=FLAGS.data_dir, split="test") + + # Train and evaluate estimator + revnet_estimator.train(input_fn=train_input_fn) + revnet_estimator.evaluate(input_fn=eval_input_fn) + + if FLAGS.export: + input_shape = (None,) + config.input_shape + inputs = tf.placeholder(tf.float32, shape=input_shape) + input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({ + "image": inputs + }) + revnet_estimator.export_savedmodel(FLAGS.model_dir, input_fn) + + +if __name__ == "__main__": + flags.DEFINE_string( + "data_dir", default=None, help="Directory to load tfrecords") + flags.DEFINE_string( + "model_dir", + default=None, + help="[Optional] Directory to store the training information") + flags.DEFINE_string( + "dataset", + default="cifar-10", + help="[Optional] The dataset used; either `cifar-10` or `cifar-100`") + flags.DEFINE_boolean( + "export", + default=False, + help="[Optional] Export the model for serving if True") + flags.DEFINE_string( + "config", + default="revnet-38", + help="[Optional] Architecture of network. " + "Other options include `revnet-110` and `revnet-164`") + FLAGS = flags.FLAGS + tf.app.run() diff --git a/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py b/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py new file mode 100644 index 0000000000000000000000000000000000000000..f0aad9b11088e72e9027e3ba59c1924ace9ee558 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py @@ -0,0 +1,319 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Cloud TPU Estimator workflow with RevNet train on CIFAR-10.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import time + +from absl import flags +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import cifar_input +from tensorflow.contrib.eager.python.examples.revnet import main as main_ +from tensorflow.contrib.eager.python.examples.revnet import revnet +from tensorflow.contrib.training.python.training import evaluation +from tensorflow.python.estimator import estimator as estimator_ + + +def model_fn(features, labels, mode, params): + """Model function required by the `tf.contrib.tpu.TPUEstimator` API. + + Args: + features: Input images + labels: Labels of images + mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT' + params: A dictionary of extra parameter that might be passed + + Returns: + An instance of `tf.contrib.tpu.TPUEstimatorSpec` + """ + + inputs = features + if isinstance(inputs, dict): + inputs = features["image"] + + config = params["config"] + model = revnet.RevNet(config=config) + + if mode == tf.estimator.ModeKeys.TRAIN: + global_step = tf.train.get_or_create_global_step() + learning_rate = tf.train.piecewise_constant( + global_step, config.lr_decay_steps, config.lr_list) + optimizer = tf.train.MomentumOptimizer( + learning_rate, momentum=config.momentum) + + if FLAGS.use_tpu: + optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer) + + logits, saved_hidden = model(inputs, training=True) + grads, loss = model.compute_gradients(saved_hidden, labels, training=True) + train_op = optimizer.apply_gradients( + zip(grads, model.trainable_variables), global_step=global_step) + + return tf.contrib.tpu.TPUEstimatorSpec( + mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op) + + elif mode == tf.estimator.ModeKeys.EVAL: + logits, _ = model(inputs, training=False) + loss = model.compute_loss(labels=labels, logits=logits) + + def metric_fn(labels, logits): + predictions = tf.argmax(logits, axis=1) + accuracy = tf.metrics.accuracy(labels=labels, predictions=predictions) + return { + "accuracy": accuracy, + } + + return tf.contrib.tpu.TPUEstimatorSpec( + mode=mode, loss=loss, eval_metrics=(metric_fn, [labels, logits])) + + else: # Predict or export + logits, _ = model(inputs, training=False) + predictions = { + "classes": tf.argmax(logits, axis=1), + "probabilities": tf.nn.softmax(logits), + } + + return tf.contrib.tpu.TPUEstimatorSpec( + mode=mode, + predictions=predictions, + export_outputs={ + "classify": tf.estimator.export.PredictOutput(predictions) + }) + + +def get_input_fn(config, data_dir, split): + """Get the input function required by the `tf.contrib.tpu.TPUEstimator` API. + + Args: + config: Customized hyperparameters + data_dir: Directory where the data is stored + split: One of `train`, `validation`, `train_all`, and `test` + + Returns: + Input function required by the `tf.contrib.tpu.TPUEstimator` API + """ + + data_dir = os.path.join(data_dir, config.dataset) + # Fix split-dependent hyperparameters + if split == "train_all" or split == "train": + data_aug = True + epochs = config.tpu_epochs + shuffle = True + else: + data_aug = False + epochs = 1 + shuffle = False + + def input_fn(params): + """Input function required by the `tf.contrib.tpu.TPUEstimator` API.""" + batch_size = params["batch_size"] + return cifar_input.get_ds_from_tfrecords( + data_dir=data_dir, + split=split, + data_aug=data_aug, + batch_size=batch_size, # per-shard batch size + epochs=epochs, + shuffle=shuffle, + prefetch=batch_size, # per-shard batch size + data_format=config.data_format) + + return input_fn + + +def main(_): + tf.logging.set_verbosity(tf.logging.INFO) + + # RevNet specific configuration + config = main_.get_config(config_name=FLAGS.config, dataset=FLAGS.dataset) + + if FLAGS.use_tpu: + tf.logging.info("Using TPU.") + tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( + FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) + else: + tpu_cluster_resolver = None + + # TPU specific configuration + tpu_config = tf.contrib.tpu.TPUConfig( + # Recommended to be set as number of global steps for next checkpoint + iterations_per_loop=FLAGS.iterations_per_loop, + num_shards=FLAGS.num_shards) + + # Estimator specific configuration + run_config = tf.contrib.tpu.RunConfig( + cluster=tpu_cluster_resolver, + model_dir=FLAGS.model_dir, + session_config=tf.ConfigProto( + allow_soft_placement=True, log_device_placement=False), + tpu_config=tpu_config, + ) + + # Construct TPU Estimator + estimator = tf.contrib.tpu.TPUEstimator( + model_fn=model_fn, + use_tpu=FLAGS.use_tpu, + train_batch_size=config.tpu_batch_size, + eval_batch_size=config.tpu_eval_batch_size, + config=run_config, + params={"config": config}) + + # Construct input functions + train_input_fn = get_input_fn( + config=config, data_dir=FLAGS.data_dir, split="train_all") + eval_input_fn = get_input_fn( + config=config, data_dir=FLAGS.data_dir, split="test") + + # Disabling a range within an else block currently doesn't work + # due to https://github.com/PyCQA/pylint/issues/872 + # pylint: disable=protected-access + if FLAGS.mode == "eval": + # TPUEstimator.evaluate *requires* a steps argument. + # Note that the number of examples used during evaluation is + # --eval_steps * --batch_size. + # So if you change --batch_size then change --eval_steps too. + eval_steps = 10000 // config.tpu_eval_batch_size + + # Run evaluation when there's a new checkpoint + for ckpt in evaluation.checkpoints_iterator( + FLAGS.model_dir, timeout=FLAGS.eval_timeout): + tf.logging.info("Starting to evaluate.") + try: + start_timestamp = time.time() # This time will include compilation time + eval_results = estimator.evaluate( + input_fn=eval_input_fn, steps=eval_steps, checkpoint_path=ckpt) + elapsed_time = int(time.time() - start_timestamp) + tf.logging.info("Eval results: %s. Elapsed seconds: %d" % + (eval_results, elapsed_time)) + + # Terminate eval job when final checkpoint is reached + current_step = int(os.path.basename(ckpt).split("-")[1]) + if current_step >= config.max_train_iter: + tf.logging.info( + "Evaluation finished after training step %d" % current_step) + break + + except tf.errors.NotFoundError: + # Since the coordinator is on a different job than the TPU worker, + # sometimes the TPU worker does not finish initializing until long after + # the CPU job tells it to start evaluating. In this case, the checkpoint + # file could have been deleted already. + tf.logging.info( + "Checkpoint %s no longer exists, skipping checkpoint" % ckpt) + + else: # FLAGS.mode == 'train' or FLAGS.mode == 'train_and_eval' + current_step = estimator_._load_global_step_from_checkpoint_dir( + FLAGS.model_dir) + tf.logging.info("Training for %d steps . Current" + " step %d." % (config.max_train_iter, current_step)) + + start_timestamp = time.time() # This time will include compilation time + if FLAGS.mode == "train": + estimator.train(input_fn=train_input_fn, max_steps=config.max_train_iter) + else: + eval_steps = 10000 // config.tpu_eval_batch_size + assert FLAGS.mode == "train_and_eval" + while current_step < config.max_train_iter: + # Train for up to steps_per_eval number of steps. + # At the end of training, a checkpoint will be written to --model_dir. + next_checkpoint = min(current_step + FLAGS.steps_per_eval, + config.max_train_iter) + estimator.train(input_fn=train_input_fn, max_steps=next_checkpoint) + current_step = next_checkpoint + + # Evaluate the model on the most recent model in --model_dir. + # Since evaluation happens in batches of --eval_batch_size, some images + # may be consistently excluded modulo the batch size. + tf.logging.info("Starting to evaluate.") + eval_results = estimator.evaluate( + input_fn=eval_input_fn, steps=eval_steps) + tf.logging.info("Eval results: %s" % eval_results) + + elapsed_time = int(time.time() - start_timestamp) + tf.logging.info("Finished training up to step %d. Elapsed seconds %d." % + (config.max_train_iter, elapsed_time)) + # pylint: enable=protected-access + + +if __name__ == "__main__": + # Cloud TPU Cluster Resolver flags + flags.DEFINE_string( + "tpu", + default=None, + help="The Cloud TPU to use for training. This should be either the name " + "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 " + "url.") + flags.DEFINE_string( + "tpu_zone", + default=None, + help="[Optional] GCE zone where the Cloud TPU is located in. If not " + "specified, we will attempt to automatically detect the GCE project from " + "metadata.") + flags.DEFINE_string( + "gcp_project", + default=None, + help="[Optional] Project name for the Cloud TPU-enabled project. If not " + "specified, we will attempt to automatically detect the GCE project from " + "metadata.") + + # Model specific parameters + flags.DEFINE_string( + "data_dir", default=None, help="Directory to load tfrecords") + flags.DEFINE_string( + "model_dir", + default=None, + help="[Optional] Directory to store the model information") + flags.DEFINE_string( + "dataset", + default="cifar-10", + help="[Optional] The dataset used; either `cifar-10` or `cifar-100`") + flags.DEFINE_string( + "config", + default="revnet-38", + help="[Optional] Architecture of network. " + "Other options include `revnet-110` and `revnet-164`") + flags.DEFINE_boolean( + "use_tpu", default=True, help="[Optional] Whether to use TPU") + flags.DEFINE_integer( + "num_shards", default=8, help="Number of shards (TPU chips).") + flags.DEFINE_integer( + "iterations_per_loop", + default=100, + help=( + "Number of steps to run on TPU before feeding metrics to the CPU." + " If the number of iterations in the loop would exceed the number of" + " train steps, the loop will exit before reaching" + " --iterations_per_loop. The larger this value is, the higher the" + " utilization on the TPU.")) + flags.DEFINE_string( + "mode", + default="train_and_eval", + help="[Optional] Mode to run: train, eval, train_and_eval") + flags.DEFINE_integer( + "eval_timeout", 60 * 60 * 24, + "Maximum seconds between checkpoints before evaluation terminates.") + flags.DEFINE_integer( + "steps_per_eval", + default=1000, + help=( + "Controls how often evaluation is performed. Since evaluation is" + " fairly expensive, it is advised to evaluate as infrequently as" + " possible (i.e. up to --train_steps, which evaluates the model only" + " after finishing the entire training regime).")) + FLAGS = flags.FLAGS + tf.app.run() diff --git a/tensorflow/contrib/eager/python/examples/revnet/resnet_preprocessing.py b/tensorflow/contrib/eager/python/examples/revnet/resnet_preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..21a1ab85d46cde11453e1f693cc4aabbbf3c90ed --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/resnet_preprocessing.py @@ -0,0 +1,190 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""ImageNet preprocessing for ResNet.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +IMAGE_SIZE = 224 +CROP_PADDING = 32 + + +def distorted_bounding_box_crop(image_bytes, + bbox, + min_object_covered=0.1, + aspect_ratio_range=(0.75, 1.33), + area_range=(0.05, 1.0), + max_attempts=100, + scope=None): + """Generates cropped_image using one of the bboxes randomly distorted. + + See `tf.image.sample_distorted_bounding_box` for more documentation. + + Args: + image_bytes: `Tensor` of binary image data. + bbox: `Tensor` of bounding boxes arranged `[1, num_boxes, coords]` + where each coordinate is [0, 1) and the coordinates are arranged + as `[ymin, xmin, ymax, xmax]`. If num_boxes is 0 then use the whole + image. + min_object_covered: An optional `float`. Defaults to `0.1`. The cropped + area of the image must contain at least this fraction of any bounding + box supplied. + aspect_ratio_range: An optional list of `float`s. The cropped area of the + image must have an aspect ratio = width / height within this range. + area_range: An optional list of `float`s. The cropped area of the image + must contain a fraction of the supplied image within in this range. + max_attempts: An optional `int`. Number of attempts at generating a cropped + region of the image of the specified constraints. After `max_attempts` + failures, return the entire image. + scope: Optional `str` for name scope. + Returns: + cropped image `Tensor` + """ + with tf.name_scope(scope, 'distorted_bounding_box_crop', [image_bytes, bbox]): + shape = tf.image.extract_jpeg_shape(image_bytes) + sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box( + shape, + bounding_boxes=bbox, + min_object_covered=min_object_covered, + aspect_ratio_range=aspect_ratio_range, + area_range=area_range, + max_attempts=max_attempts, + use_image_if_no_bounding_boxes=True) + bbox_begin, bbox_size, _ = sample_distorted_bounding_box + + # Crop the image to the specified bounding box. + offset_y, offset_x, _ = tf.unstack(bbox_begin) + target_height, target_width, _ = tf.unstack(bbox_size) + crop_window = tf.stack([offset_y, offset_x, target_height, target_width]) + image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3) + + return image + + +def _at_least_x_are_equal(a, b, x): + """At least `x` of `a` and `b` `Tensors` are equal.""" + match = tf.equal(a, b) + match = tf.cast(match, tf.int32) + return tf.greater_equal(tf.reduce_sum(match), x) + + +def _decode_and_random_crop(image_bytes, image_size): + """Make a random crop of image_size.""" + bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4]) + image = distorted_bounding_box_crop( + image_bytes, + bbox, + min_object_covered=0.1, + aspect_ratio_range=(3. / 4, 4. / 3.), + area_range=(0.08, 1.0), + max_attempts=10, + scope=None) + original_shape = tf.image.extract_jpeg_shape(image_bytes) + bad = _at_least_x_are_equal(original_shape, tf.shape(image), 3) + + image = tf.cond( + bad, + lambda: _decode_and_center_crop(image_bytes, image_size), + lambda: tf.image.resize_bicubic([image], # pylint: disable=g-long-lambda + [image_size, image_size])[0]) + + return image + + +def _decode_and_center_crop(image_bytes, image_size): + """Crops to center of image with padding then scales image_size.""" + shape = tf.image.extract_jpeg_shape(image_bytes) + image_height = shape[0] + image_width = shape[1] + + padded_center_crop_size = tf.cast( + ((image_size / (image_size + CROP_PADDING)) * + tf.cast(tf.minimum(image_height, image_width), tf.float32)), + tf.int32) + + offset_height = ((image_height - padded_center_crop_size) + 1) // 2 + offset_width = ((image_width - padded_center_crop_size) + 1) // 2 + crop_window = tf.stack([offset_height, offset_width, + padded_center_crop_size, padded_center_crop_size]) + image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3) + image = tf.image.resize_bicubic([image], [image_size, image_size])[0] + + return image + + +def _flip(image): + """Random horizontal image flip.""" + image = tf.image.random_flip_left_right(image) + return image + + +def preprocess_for_train(image_bytes, use_bfloat16, image_size=IMAGE_SIZE): + """Preprocesses the given image for evaluation. + + Args: + image_bytes: `Tensor` representing an image binary of arbitrary size. + use_bfloat16: `bool` for whether to use bfloat16. + image_size: image size. + + Returns: + A preprocessed image `Tensor`. + """ + image = _decode_and_random_crop(image_bytes, image_size) + image = _flip(image) + image = tf.reshape(image, [image_size, image_size, 3]) + image = tf.image.convert_image_dtype( + image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32) + return image + + +def preprocess_for_eval(image_bytes, use_bfloat16, image_size=IMAGE_SIZE): + """Preprocesses the given image for evaluation. + + Args: + image_bytes: `Tensor` representing an image binary of arbitrary size. + use_bfloat16: `bool` for whether to use bfloat16. + image_size: image size. + + Returns: + A preprocessed image `Tensor`. + """ + image = _decode_and_center_crop(image_bytes, image_size) + image = tf.reshape(image, [image_size, image_size, 3]) + image = tf.image.convert_image_dtype( + image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32) + return image + + +def preprocess_image(image_bytes, + is_training=False, + use_bfloat16=False, + image_size=IMAGE_SIZE): + """Preprocesses the given image. + + Args: + image_bytes: `Tensor` representing an image binary of arbitrary size. + is_training: `bool` for whether the preprocessing is for training. + use_bfloat16: `bool` for whether to use bfloat16. + image_size: image size. + + Returns: + A preprocessed image `Tensor`. + """ + if is_training: + return preprocess_for_train(image_bytes, use_bfloat16, image_size) + else: + return preprocess_for_eval(image_bytes, use_bfloat16, image_size) diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet.py b/tensorflow/contrib/eager/python/examples/revnet/revnet.py index af0d20fa729836b12036d5d54a9b5b0b68d719d2..1f2cb14972f0b92d29489adff8f94e790e1ec4ed 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet.py @@ -24,10 +24,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import functools -import operator - -import six import tensorflow as tf from tensorflow.contrib.eager.python.examples.revnet import blocks @@ -45,71 +41,10 @@ class RevNet(tf.keras.Model): self.axis = 1 if config.data_format == "channels_first" else 3 self.config = config - self._init_block = self._construct_init_block() + self._init_block = blocks.InitBlock(config=self.config) + self._final_block = blocks.FinalBlock(config=self.config) self._block_list = self._construct_intermediate_blocks() - self._final_block = self._construct_final_block() - - def _construct_init_block(self): - init_block = tf.keras.Sequential( - [ - tf.keras.layers.Conv2D( - filters=self.config.init_filters, - kernel_size=self.config.init_kernel, - strides=(self.config.init_stride, self.config.init_stride), - data_format=self.config.data_format, - use_bias=False, - padding="SAME", - input_shape=self.config.input_shape, - dtype=self.config.dtype), - tf.keras.layers.BatchNormalization( - axis=self.axis, - fused=self.config.fused, - dtype=self.config.dtype), - tf.keras.layers.Activation("relu"), - ], - name="init") - if self.config.init_max_pool: - init_block.add( - tf.keras.layers.MaxPooling2D( - pool_size=(3, 3), - strides=(2, 2), - padding="SAME", - data_format=self.config.data_format, - dtype=self.config.dtype)) - return init_block - - def _construct_final_block(self): - f = self.config.filters[-1] # Number of filters - r = functools.reduce(operator.mul, self.config.strides, 1) # Reduce ratio - r *= self.config.init_stride - if self.config.init_max_pool: - r *= 2 - - if self.config.data_format == "channels_first": - w, h = self.config.input_shape[1], self.config.input_shape[2] - input_shape = (f, w // r, h // r) - elif self.config.data_format == "channels_last": - w, h = self.config.input_shape[0], self.config.input_shape[1] - input_shape = (w // r, h // r, f) - else: - raise ValueError("Data format should be either `channels_first`" - " or `channels_last`") - - final_block = tf.keras.Sequential( - [ - tf.keras.layers.BatchNormalization( - axis=self.axis, - input_shape=input_shape, - fused=self.config.fused, - dtype=self.config.dtype), - tf.keras.layers.Activation("relu"), - tf.keras.layers.GlobalAveragePooling2D( - data_format=self.config.data_format, dtype=self.config.dtype), - tf.keras.layers.Dense( - self.config.n_classes, dtype=self.config.dtype) - ], - name="final") - return final_block + self._moving_average_variables = [] def _construct_intermediate_blocks(self): # Precompute input shape after initial block @@ -193,109 +128,90 @@ class RevNet(tf.keras.Model): return tf.reduce_mean(cross_ent) - def compute_gradients(self, inputs, labels, training=True, l2_reg=True): + def compute_gradients(self, saved_hidden, labels, training=True, l2_reg=True): """Manually computes gradients. - When eager execution is enabled, this method also SILENTLY updates the - running averages of batch normalization when `training` is set to True. + This method silently updates the running averages of batch normalization. Args: - inputs: Image tensor, either NHWC or NCHW, conforming to `data_format` + saved_hidden: List of hidden states Tensors labels: One-hot labels for classification training: Use the mini-batch stats in batch norm if set to True l2_reg: Apply l2 regularization Returns: - list of tuples each being (grad, var) for optimizer to use + A tuple with the first entry being a list of all gradients and the second + being the loss """ - # Run forward pass to record hidden states; avoid updating running averages - vars_and_vals = self.get_moving_stats() - _, saved_hidden = self.call(inputs, training=training) - self.restore_moving_stats(vars_and_vals) - - grads_all = [] - vars_all = [] + def _defunable_pop(l): + """Functional style list pop that works with `tfe.defun`.""" + t, l = l[-1], l[:-1] + return t, l - # Manually backprop through last block + # Backprop through last block x = saved_hidden[-1] with tf.GradientTape() as tape: - x = tf.identity(x) tape.watch(x) - # Running stats updated below logits = self._final_block(x, training=training) loss = self.compute_loss(logits, labels) - grads_combined = tape.gradient(loss, [x] + self._final_block.trainable_variables) - dy, grads_ = grads_combined[0], grads_combined[1:] - grads_all += grads_ - vars_all += self._final_block.trainable_variables + dy, final_grads = grads_combined[0], grads_combined[1:] - # Manually backprop through intermediate blocks + # Backprop through intermediate blocks + intermediate_grads = [] for block in reversed(self._block_list): - y = saved_hidden.pop() + y, saved_hidden = _defunable_pop(saved_hidden) x = saved_hidden[-1] - dy, grads, vars_ = block.backward_grads_and_vars( - x, y, dy, training=training) - grads_all += grads - vars_all += vars_ - - # Manually backprop through first block - saved_hidden.pop() - x = saved_hidden.pop() - assert not saved_hidden # Cleared after backprop + dy, grads = block.backward_grads(x, y, dy, training=training) + intermediate_grads = grads + intermediate_grads + # Backprop through first block + _, saved_hidden = _defunable_pop(saved_hidden) + x, saved_hidden = _defunable_pop(saved_hidden) + assert not saved_hidden with tf.GradientTape() as tape: - x = tf.identity(x) - # Running stats updated below y = self._init_block(x, training=training) - - grads_all += tape.gradient( + init_grads = tape.gradient( y, self._init_block.trainable_variables, output_gradients=dy) - vars_all += self._init_block.trainable_variables - # Apply weight decay + # Ordering match up with `model.trainable_variables` + grads_all = init_grads + final_grads + intermediate_grads if l2_reg: - grads_all = self._apply_weight_decay(grads_all, vars_all) + grads_all = self._apply_weight_decay(grads_all) - return grads_all, vars_all, loss + return grads_all, loss - def _apply_weight_decay(self, grads, vars_): + def _apply_weight_decay(self, grads): """Update gradients to reflect weight decay.""" - # Don't decay bias return [ g + self.config.weight_decay * v if v.name.endswith("kernel:0") else g - for g, v in zip(grads, vars_) + for g, v in zip(grads, self.trainable_variables) ] def get_moving_stats(self): - """Get moving averages of batch normalization. - - This is needed to avoid updating the running average twice in one iteration. - - Returns: - A dictionary mapping variables for batch normalization moving averages - to their current values. - """ - vars_and_vals = {} - - def _is_moving_var(v): + """Get moving averages of batch normalization.""" + device = "/gpu:0" if tf.test.is_gpu_available() else "/cpu:0" + with tf.device(device): + return [v.read_value() for v in self.moving_average_variables] + + def restore_moving_stats(self, values): + """Restore moving averages of batch normalization.""" + device = "/gpu:0" if tf.test.is_gpu_available() else "/cpu:0" + with tf.device(device): + for var_, val in zip(self.moving_average_variables, values): + var_.assign(val) + + @property + def moving_average_variables(self): + """Get all variables that are batch norm moving averages.""" + + def _is_moving_avg(v): n = v.name return n.endswith("moving_mean:0") or n.endswith("moving_variance:0") - for v in filter(_is_moving_var, self.variables): - vars_and_vals[v] = v.read_value() + if not self._moving_average_variables: + self._moving_average_variables = filter(_is_moving_avg, self.variables) - return vars_and_vals - - def restore_moving_stats(self, vars_and_vals): - """Restore moving averages of batch normalization. - - This is needed to avoid updating the running average twice in one iteration. - - Args: - vars_and_vals: The dictionary mapping variables to their previous values. - """ - for var_, val in six.iteritems(vars_and_vals): - var_.assign(val) + return self._moving_average_variables diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py index b2ac4b67c926951672996df5564b9b57def0ea13..84b2ddf0de0739936d458ae1bce832cfbb167d64 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py @@ -31,10 +31,13 @@ tfe = tf.contrib.eager def train_one_iter(model, inputs, labels, optimizer, global_step=None): """Train for one iteration.""" - grads, vars_, loss = model.compute_gradients(inputs, labels, training=True) - optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) + logits, saved_hidden = model(inputs) + grads, loss = model.compute_gradients( + saved_hidden=saved_hidden, labels=labels) + optimizer.apply_gradients( + zip(grads, model.trainable_variables), global_step=global_step) - return loss + return logits, loss class RevNetTest(tf.test.TestCase): @@ -42,6 +45,8 @@ class RevNetTest(tf.test.TestCase): def setUp(self): super(RevNetTest, self).setUp() config = config_.get_hparams_cifar_38() + config.add_hparam("n_classes", 10) + config.add_hparam("dataset", "cifar-10") # Reconstruction could cause numerical error, use double precision for tests config.dtype = tf.float64 config.fused = False # Fused batch norm does not support tf.float64 @@ -93,9 +98,10 @@ class RevNetTest(tf.test.TestCase): def test_compute_gradients(self): """Test `compute_gradients` function.""" - self.model(self.x, training=False) # Initialize model - grads, vars_, loss = self.model.compute_gradients( - inputs=self.x, labels=self.t, training=True, l2_reg=True) + _, saved_hidden = self.model(self.x) # Initialize model + grads, loss = self.model.compute_gradients( + saved_hidden=saved_hidden, labels=self.t) + vars_ = self.model.trainable_variables self.assertTrue(isinstance(grads, list)) self.assertTrue(isinstance(vars_, list)) self.assertEqual(len(grads), len(vars_)) @@ -104,7 +110,7 @@ class RevNetTest(tf.test.TestCase): # Compare against the true gradient computed by the tape with tf.GradientTape() as tape: - logits, _ = self.model(self.x, training=True) + logits, _ = self.model(self.x) loss_true = self.model.compute_loss(logits=logits, labels=self.t) grads_true = tape.gradient(loss_true, vars_) self.assertAllClose(loss, loss_true) @@ -119,7 +125,9 @@ class RevNetTest(tf.test.TestCase): def test_compute_gradients_defun(self): """Test `compute_gradients` function with defun.""" compute_gradients = tfe.defun(self.model.compute_gradients) - grads, vars_, _ = compute_gradients(self.x, self.t, training=True) + _, saved_hidden = self.model(self.x) + grads, _ = compute_gradients(saved_hidden=saved_hidden, labels=self.t) + vars_ = self.model.trainable_variables self.assertTrue(isinstance(grads, list)) self.assertTrue(isinstance(vars_, list)) self.assertEqual(len(grads), len(vars_)) @@ -131,6 +139,9 @@ class RevNetTest(tf.test.TestCase): """Test model training in graph mode.""" with tf.Graph().as_default(): config = config_.get_hparams_cifar_38() + config.add_hparam("n_classes", 10) + config.add_hparam("dataset", "cifar-10") + x = tf.random_normal( shape=(self.config.batch_size,) + self.config.input_shape) t = tf.random_uniform( @@ -138,17 +149,13 @@ class RevNetTest(tf.test.TestCase): minval=0, maxval=self.config.n_classes, dtype=tf.int32) - global_step = tfe.Variable(0., trainable=False) + global_step = tf.Variable(0., trainable=False) model = revnet.RevNet(config=config) - model(x) - updates = model.get_updates_for(x) - - x_ = tf.identity(x) - grads_all, vars_all, _ = model.compute_gradients(x_, t, training=True) + _, saved_hidden = model(x) + grads, _ = model.compute_gradients(saved_hidden=saved_hidden, labels=t) optimizer = tf.train.AdamOptimizer(learning_rate=1e-3) - with tf.control_dependencies(updates): - train_op = optimizer.apply_gradients( - zip(grads_all, vars_all), global_step=global_step) + train_op = optimizer.apply_gradients( + zip(grads, model.trainable_variables), global_step=global_step) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) diff --git a/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py b/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py index c2340a293a80924f2dfa90e2fb23134b0f1feb6b..15776c694e92825895437a4c1547699f6d9269fb 100644 --- a/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py +++ b/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py @@ -310,12 +310,12 @@ def main(_): with tf.device("/device:GPU:0" if have_gpu else None): # Make learning_rate a Variable so it can be included in the checkpoint # and we can resume training with the last saved learning_rate. - learning_rate = tfe.Variable(20.0, name="learning_rate") + learning_rate = tf.Variable(20.0, name="learning_rate") model = PTBModel(corpus.vocab_size(), FLAGS.embedding_dim, FLAGS.hidden_dim, FLAGS.num_layers, FLAGS.dropout, use_cudnn_rnn) optimizer = tf.train.GradientDescentOptimizer(learning_rate) - checkpoint = tfe.Checkpoint( + checkpoint = tf.train.Checkpoint( learning_rate=learning_rate, model=model, # GradientDescentOptimizer has no state to checkpoint, but noting it # here lets us swap in an optimizer that does. diff --git a/tensorflow/contrib/eager/python/examples/sagan/sagan.py b/tensorflow/contrib/eager/python/examples/sagan/sagan.py index 561be36c911d7145e2d4a5ed12eccd8ceb054f45..81304149851675e07a3c7f9ad92697da2017022b 100644 --- a/tensorflow/contrib/eager/python/examples/sagan/sagan.py +++ b/tensorflow/contrib/eager/python/examples/sagan/sagan.py @@ -62,7 +62,7 @@ class SelfAttentionModule(tf.keras.Model): kernel_size=1, strides=(1, 1), data_format=data_format) - self.scale = tfe.Variable(0., trainable=True) + self.scale = tf.Variable(0., trainable=True) def call(self, x): f = self.f(x) diff --git a/tensorflow/contrib/eager/python/examples/workshop/1_basic.ipynb b/tensorflow/contrib/eager/python/examples/workshop/1_basic.ipynb index 3e7abe952d63610b14967d41be0a36430fcd29c6..75cb3f8227fe90223734f422e458f15810b8089a 100644 --- a/tensorflow/contrib/eager/python/examples/workshop/1_basic.ipynb +++ b/tensorflow/contrib/eager/python/examples/workshop/1_basic.ipynb @@ -210,7 +210,7 @@ "a = tf.constant(0.0)\n", "b = tf.constant(1.0)\n", "epsilon = tf.constant(0.001)\n", - "x = bisecting_line_search(test_f, a, b, epsilon)\n", + "x = bisecting_line_search(test_f, a, b, epsilon)\n" ], "execution_count": 0, "outputs": [] @@ -279,4 +279,4 @@ ] } ] -} \ No newline at end of file +} diff --git a/tensorflow/contrib/eager/python/examples/workshop/2_models.ipynb b/tensorflow/contrib/eager/python/examples/workshop/2_models.ipynb index 4f1410e00bb986f68f3c4c8494aa97bf66284510..f3a65f5aab1fe683565caf21dcfa8054045fd759 100644 --- a/tensorflow/contrib/eager/python/examples/workshop/2_models.ipynb +++ b/tensorflow/contrib/eager/python/examples/workshop/2_models.ipynb @@ -69,7 +69,7 @@ "cell_type": "code", "source": [ "# Creating variables\n", - "v = tfe.Variable(1.0)\n", + "v = tf.Variable(1.0)\n", "v" ], "execution_count": 2, diff --git a/tensorflow/contrib/eager/python/metrics_impl.py b/tensorflow/contrib/eager/python/metrics_impl.py index efa6ba062631500bd7cd16620ebec23d15b93b62..6efafccd6b93ad58da395e0b2e1e647809af62ad 100644 --- a/tensorflow/contrib/eager/python/metrics_impl.py +++ b/tensorflow/contrib/eager/python/metrics_impl.py @@ -291,8 +291,6 @@ class Metric(checkpointable.CheckpointableBase): class Mean(Metric): """Computes the (weighted) mean of the given values.""" - # TODO(josh11b): Maybe have a dtype argument that defaults to tf.float64? - # Or defaults to type of the input if it is tf.float32, else tf.float64? def __init__(self, name=None, dtype=dtypes.float64, use_global_variables=False): @@ -377,7 +375,7 @@ class Accuracy(Mean): array_ops.shape(labels), array_ops.shape(predictions), message="Shapes of labels and predictions are unequal") matches = math_ops.equal(labels, predictions) - matches = math_ops.cast(matches, dtypes.float64) + matches = math_ops.cast(matches, self.dtype) super(Accuracy, self).call(matches, weights=weights) if weights is None: return labels, predictions @@ -421,7 +419,7 @@ class CategoricalAccuracy(Mean): labels = math_ops.argmax(labels, axis=-1) predictions = math_ops.argmax(predictions, axis=-1) matches = math_ops.equal(labels, predictions) - matches = math_ops.cast(matches, dtypes.float64) + matches = math_ops.cast(matches, self.dtype) super(CategoricalAccuracy, self).call(matches, weights=weights) if weights is None: return labels, predictions @@ -472,7 +470,7 @@ class BinaryAccuracy(Mean): predictions = ops.convert_to_tensor(predictions) predictions = predictions > self.threshold matches = math_ops.equal(labels, predictions) - matches = math_ops.cast(matches, dtypes.float64) + matches = math_ops.cast(matches, self.dtype) super(BinaryAccuracy, self).call(matches, weights=weights) if weights is None: return labels, predictions @@ -520,7 +518,7 @@ class SparseAccuracy(Mean): predictions = math_ops.argmax(predictions, axis=-1) labels = math_ops.cast(labels, dtypes.int64) matches = math_ops.equal(labels, predictions) - matches = math_ops.cast(matches, dtypes.float64) + matches = math_ops.cast(matches, self.dtype) super(SparseAccuracy, self).call(matches, weights=weights) if weights is None: return labels, predictions diff --git a/tensorflow/contrib/eager/python/saver.py b/tensorflow/contrib/eager/python/saver.py index fdaca90fd13576e6ca8a3408aaf528dbc2384b0c..d70930864784b3e48140da27ca33ff13f593e663 100644 --- a/tensorflow/contrib/eager/python/saver.py +++ b/tensorflow/contrib/eager/python/saver.py @@ -125,8 +125,8 @@ class Saver(object): Args: var_list: The list of variables that will be saved and restored. Either a - list of `tfe.Variable` objects, or a dictionary mapping names to - `tfe.Variable` objects. + list of `tf.Variable` objects, or a dictionary mapping names to + `tf.Variable` objects. Raises: RuntimeError: if invoked when eager execution has not been enabled. diff --git a/tensorflow/contrib/eager/python/tfe.py b/tensorflow/contrib/eager/python/tfe.py index ca6430253b67d825290b6a376ba3f29b3ae67577..2f0ab616e40560e21dfe19fffb0010f724e48ecd 100644 --- a/tensorflow/contrib/eager/python/tfe.py +++ b/tensorflow/contrib/eager/python/tfe.py @@ -34,6 +34,7 @@ To use, at program startup, call `tfe.enable_eager_execution()`. @@run @@enable_eager_execution +@@enable_remote_eager_execution @@custom_gradient @@ -114,6 +115,7 @@ from tensorflow.python.eager.execution_callbacks import inf_nan_callback from tensorflow.python.eager.execution_callbacks import nan_callback from tensorflow.python.eager.execution_callbacks import seterr from tensorflow.python.framework.ops import enable_eager_execution +from tensorflow.python.framework.ops import enable_eager_execution_internal as enable_remote_eager_execution from tensorflow.python.framework.ops import eager_run as run from tensorflow.python.framework.test_util import run_in_graph_and_eager_modes as run_test_in_graph_and_eager_modes from tensorflow.python.framework.test_util import run_all_in_graph_and_eager_modes as run_all_tests_in_graph_and_eager_modes diff --git a/tensorflow/contrib/eager/python/tfe_test.py b/tensorflow/contrib/eager/python/tfe_test.py index db50b33af2e4f1cc6575d4b0d416d6d2669b5c35..4454abfb9667f824b9de0100bb81bae24ad5f7a6 100644 --- a/tensorflow/contrib/eager/python/tfe_test.py +++ b/tensorflow/contrib/eager/python/tfe_test.py @@ -27,7 +27,6 @@ from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import numerics -from tensorflow.python.ops import variables from tensorflow.python.platform import test from tensorflow.python.summary import summary from tensorflow.python.summary.writer import writer @@ -45,12 +44,6 @@ class TFETest(test_util.TensorFlowTestCase): r'indices = 7 is not in \[0, 3\)'): array_ops.gather([0, 1, 2], 7) - def testVariableError(self): - with self.assertRaisesRegexp( - RuntimeError, - r'Variable not supported when eager execution is enabled'): - variables.Variable(initial_value=1.0) - def testGradients(self): def square(x): diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD index 30d297a5fb2dd2f844093d790d051a79105984dd..349f48f7f788b458af2639f7ad4cc4cd904465b4 100644 --- a/tensorflow/contrib/estimator/BUILD +++ b/tensorflow/contrib/estimator/BUILD @@ -18,6 +18,7 @@ py_library( ":boosted_trees", ":dnn", ":dnn_linear_combined", + ":early_stopping", ":export", ":extenders", ":head", @@ -27,7 +28,8 @@ py_library( ":multi_head", ":replicate_model_fn", ":rnn", - "//tensorflow/python:util", + ":saved_model_estimator", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -53,22 +55,10 @@ py_test( deps = [ ":baseline", ":head", - "//tensorflow/python:check_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:platform", - "//tensorflow/python:session", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variables", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:export_export", "//tensorflow/python/estimator:metric_keys", "//tensorflow/python/estimator:numpy_io", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", "//third_party/py/numpy", "@six_archive//:six", ], @@ -95,11 +85,8 @@ py_test( ], deps = [ ":boosted_trees", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:numpy_io", - "//tensorflow/python/feature_column", "//third_party/py/numpy", ], ) @@ -109,7 +96,7 @@ py_library( srcs = ["python/estimator/dnn.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:nn", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator", "//tensorflow/python/estimator:dnn", ], @@ -128,16 +115,11 @@ py_test( deps = [ ":dnn", ":head", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:platform", - "//tensorflow/python:summary", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:dnn_testing_utils", "//tensorflow/python/estimator:export_export", "//tensorflow/python/estimator:numpy_io", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", "//third_party/py/numpy", "@six_archive//:six", ], @@ -148,7 +130,7 @@ py_library( srcs = ["python/estimator/dnn_linear_combined.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:nn", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator", "//tensorflow/python/estimator:dnn_linear_combined", ], @@ -167,18 +149,12 @@ py_test( deps = [ ":dnn_linear_combined", ":head", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:nn", - "//tensorflow/python:platform", - "//tensorflow/python:summary", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:dnn_testing_utils", "//tensorflow/python/estimator:export_export", "//tensorflow/python/estimator:linear_testing_utils", "//tensorflow/python/estimator:numpy_io", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", "//third_party/py/numpy", "@six_archive//:six", ], @@ -191,10 +167,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:clip_ops", - "//tensorflow/python:framework_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/estimator:util", @@ -210,18 +183,11 @@ py_test( tags = ["notsan"], # b/62863147 deps = [ ":extenders", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/contrib/data/python/ops:dataset_ops", "//tensorflow/contrib/predictor", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:framework_ops", - "//tensorflow/python:metrics", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:training", - "//tensorflow/python:variables", "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/estimator:linear", - "//tensorflow/python/feature_column", "//third_party/py/numpy", ], ) @@ -245,21 +211,11 @@ py_test( tags = ["notsan"], # b/62863147 deps = [ ":export", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:metrics", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:session", - "//tensorflow/python:state_ops", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python:variables", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator", "//tensorflow/python/estimator:export_export", "//tensorflow/python/estimator:export_output", "//tensorflow/python/estimator:model_fn", - "//tensorflow/python/saved_model:loader", - "//tensorflow/python/saved_model:tag_constants", ], ) @@ -270,25 +226,12 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:lookup_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:metrics", - "//tensorflow/python:nn", - "//tensorflow/python:sparse_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:summary", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:export_output", "//tensorflow/python/estimator:head", "//tensorflow/python/estimator:metric_keys", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/ops/losses", - "//tensorflow/python/saved_model:signature_constants", ], ) @@ -299,25 +242,10 @@ py_test( srcs_version = "PY2AND3", deps = [ ":head", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:string_ops", - "//tensorflow/python:training", - "//tensorflow/python:variables", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:metric_keys", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/ops/losses", - "//tensorflow/python/saved_model:signature_constants", "//third_party/py/numpy", "@six_archive//:six", ], @@ -330,8 +258,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:framework_ops", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:estimator_py", ], ) @@ -344,10 +271,7 @@ py_test( tags = ["notsan"], deps = [ ":hooks", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:training", - "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:estimator_py", "//third_party/py/numpy", "@six_archive//:six", @@ -376,16 +300,11 @@ py_test( deps = [ ":head", ":linear", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:platform", - "//tensorflow/python:summary", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:export_export", "//tensorflow/python/estimator:linear_testing_utils", "//tensorflow/python/estimator:numpy_io", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", "//third_party/py/numpy", "@six_archive//:six", ], @@ -398,8 +317,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:framework_ops", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:dnn", "//tensorflow/python/estimator:linear", ], @@ -412,9 +330,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":logit_fns", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:session", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:model_fn", ], ) @@ -426,18 +342,11 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:array_ops", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:metrics", - "//tensorflow/python:summary", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:export_output", "//tensorflow/python/estimator:head", "//tensorflow/python/estimator:metric_keys", "//tensorflow/python/estimator:model_fn", - "//tensorflow/python/saved_model:signature_constants", "@six_archive//:six", ], ) @@ -450,15 +359,10 @@ py_test( deps = [ ":head", ":multi_head", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:framework_ops", - "//tensorflow/python:string_ops", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:metric_keys", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/saved_model:signature_constants", "//third_party/py/numpy", "@six_archive//:six", ], @@ -471,24 +375,10 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:device", - "//tensorflow/python:device_lib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:platform", - "//tensorflow/python:sparse_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:state_ops", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python:variable_scope", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:export_output", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/estimator:util", - "//tensorflow/python/ops/losses", "@six_archive//:six", ], ) @@ -499,6 +389,7 @@ cuda_py_test( srcs = ["python/estimator/replicate_model_fn_test.py"], additional_deps = [ "@absl_py//absl/testing:parameterized", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator", "//tensorflow/python/estimator:dnn", "//tensorflow/python/estimator:export_export", @@ -507,21 +398,6 @@ cuda_py_test( "//tensorflow/python/estimator:numpy_io", "//tensorflow/python/estimator:optimizers", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", - "//tensorflow/python/saved_model:signature_constants", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:math_ops", - "//tensorflow/python:metrics", - "//tensorflow/python:platform", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python:variables", ":replicate_model_fn", ], tags = [ @@ -537,22 +413,11 @@ py_library( srcs_version = "PY2AND3", deps = [ ":extenders", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/contrib/feature_column:feature_column_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:framework_ops", - "//tensorflow/python:init_ops", - "//tensorflow/python:layers", - "//tensorflow/python:partitioned_variables", - "//tensorflow/python:rnn", - "//tensorflow/python:rnn_cell", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", "//tensorflow/python/estimator", "//tensorflow/python/estimator:head", "//tensorflow/python/estimator:optimizers", - "//tensorflow/python/feature_column", "@six_archive//:six", ], ) @@ -571,22 +436,73 @@ py_test( deps = [ ":head", ":rnn", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/contrib/data", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:check_ops", + "//tensorflow/python/estimator:numpy_io", + "//tensorflow/python/estimator:parsing_utils", + "//third_party/py/numpy", + "@six_archive//:six", + ], +) + +py_library( + name = "early_stopping", + srcs = ["python/estimator/early_stopping.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py_no_contrib", + "//tensorflow/python/estimator", + ], +) + +py_test( + name = "early_stopping_test", + srcs = ["python/estimator/early_stopping_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":early_stopping", + "//tensorflow:tensorflow_py_no_contrib", + "//tensorflow/python/estimator", + "@absl_py//absl/testing:parameterized", + ], +) + +py_library( + name = "saved_model_estimator", + srcs = ["python/estimator/saved_model_estimator.py"], + deps = [ + ":export", + "//tensorflow/python:framework_ops", + "//tensorflow/python:platform", + "//tensorflow/python:training", + "//tensorflow/python/estimator", + "//tensorflow/python/estimator:export", + "//tensorflow/python/estimator:model_fn", + "//tensorflow/python/saved_model", + ], +) + +py_test( + name = "saved_model_estimator_test", + size = "medium", + srcs = ["python/estimator/saved_model_estimator_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":export", + ":saved_model_estimator", + "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", + "//tensorflow/python:control_flow_ops", "//tensorflow/python:framework_ops", - "//tensorflow/python:lib", - "//tensorflow/python:math_ops", + "//tensorflow/python:metrics", + "//tensorflow/python:platform", "//tensorflow/python:state_ops", - "//tensorflow/python:summary", "//tensorflow/python:training", "//tensorflow/python:variables", - "//tensorflow/python/estimator:numpy_io", - "//tensorflow/python/estimator:parsing_utils", - "//tensorflow/python/feature_column", - "//third_party/py/numpy", - "@six_archive//:six", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/estimator", + "//tensorflow/python/estimator:export_export", + "//tensorflow/python/estimator:export_output", + "//tensorflow/python/estimator:model_fn", ], ) diff --git a/tensorflow/contrib/estimator/__init__.py b/tensorflow/contrib/estimator/__init__.py index 788ac5ca7046d6dd30a3d5520b243944532622fa..e1453ae1d04ebd8d72f812b51480f0b05f7a5416 100644 --- a/tensorflow/contrib/estimator/__init__.py +++ b/tensorflow/contrib/estimator/__init__.py @@ -23,6 +23,7 @@ from tensorflow.contrib.estimator.python.estimator.baseline import * from tensorflow.contrib.estimator.python.estimator.boosted_trees import * from tensorflow.contrib.estimator.python.estimator.dnn import * from tensorflow.contrib.estimator.python.estimator.dnn_linear_combined import * +from tensorflow.contrib.estimator.python.estimator.early_stopping import * from tensorflow.contrib.estimator.python.estimator.export import * from tensorflow.contrib.estimator.python.estimator.extenders import * from tensorflow.contrib.estimator.python.estimator.head import * @@ -32,6 +33,8 @@ from tensorflow.contrib.estimator.python.estimator.logit_fns import * from tensorflow.contrib.estimator.python.estimator.multi_head import * from tensorflow.contrib.estimator.python.estimator.replicate_model_fn import * from tensorflow.contrib.estimator.python.estimator.rnn import * +from tensorflow.contrib.estimator.python.estimator.saved_model_estimator import * +from tensorflow.python.estimator.export.export import * from tensorflow.python.util.all_util import remove_undocumented # pylint: enable=unused-import,line-too-long,wildcard-import @@ -63,6 +66,15 @@ _allowed_symbols = [ 'RNNEstimator', 'export_saved_model_for_mode', 'export_all_saved_models', + 'make_early_stopping_hook', + 'read_eval_metrics', + 'stop_if_lower_hook', + 'stop_if_higher_hook', + 'stop_if_no_increase_hook', + 'stop_if_no_decrease_hook', + 'build_raw_supervised_input_receiver_fn', + 'build_supervised_input_receiver_fn_from_input_fn', + 'SavedModelEstimator' ] remove_undocumented(__name__, allowed_exception_list=_allowed_symbols) diff --git a/tensorflow/contrib/estimator/python/estimator/baseline_test.py b/tensorflow/contrib/estimator/python/estimator/baseline_test.py index d0e3e670f7332811c1bfdaea65b0308ce59ade59..505c94e97192afdd4e2ce9af2abb9825320751f2 100644 --- a/tensorflow/contrib/estimator/python/estimator/baseline_test.py +++ b/tensorflow/contrib/estimator/python/estimator/baseline_test.py @@ -113,6 +113,8 @@ class BaselineEstimatorEvaluationTest(test.TestCase): self.assertDictEqual({ metric_keys.MetricKeys.LOSS: 18., metric_keys.MetricKeys.LOSS_MEAN: 9., + metric_keys.MetricKeys.PREDICTION_MEAN: 13., + metric_keys.MetricKeys.LABEL_MEAN: 10., ops.GraphKeys.GLOBAL_STEP: 100 }, eval_metrics) @@ -141,6 +143,8 @@ class BaselineEstimatorEvaluationTest(test.TestCase): self.assertDictEqual({ metric_keys.MetricKeys.LOSS: 27., metric_keys.MetricKeys.LOSS_MEAN: 9., + metric_keys.MetricKeys.PREDICTION_MEAN: 13., + metric_keys.MetricKeys.LABEL_MEAN: 10., ops.GraphKeys.GLOBAL_STEP: 100 }, eval_metrics) @@ -166,7 +170,9 @@ class BaselineEstimatorEvaluationTest(test.TestCase): self.assertItemsEqual( (metric_keys.MetricKeys.LOSS, metric_keys.MetricKeys.LOSS_MEAN, - ops.GraphKeys.GLOBAL_STEP), eval_metrics.keys()) + metric_keys.MetricKeys.PREDICTION_MEAN, + metric_keys.MetricKeys.LABEL_MEAN, ops.GraphKeys.GLOBAL_STEP), + eval_metrics.keys()) # Logit is bias which is [46, 58] self.assertAlmostEqual(0, eval_metrics[metric_keys.MetricKeys.LOSS]) diff --git a/tensorflow/contrib/estimator/python/estimator/boosted_trees.py b/tensorflow/contrib/estimator/python/estimator/boosted_trees.py index 43bfcffd790e7b3c716c3f70820851a8819af225..7ed77bcce6f00ed13e9952951800f1017d582f19 100644 --- a/tensorflow/contrib/estimator/python/estimator/boosted_trees.py +++ b/tensorflow/contrib/estimator/python/estimator/boosted_trees.py @@ -50,7 +50,8 @@ class _BoostedTreesEstimator(estimator.Estimator): tree_complexity=0., min_node_weight=0., config=None, - center_bias=False): + center_bias=False, + pruning_mode='none'): """Initializes a `BoostedTreesEstimator` instance. Args: @@ -89,13 +90,18 @@ class _BoostedTreesEstimator(estimator.Estimator): regression problems, the first node will return the mean of the labels. For binary classification problems, it will return a logit for a prior probability of label 1. + pruning_mode: one of 'none', 'pre', 'post' to indicate no pruning, pre- + pruning (do not split a node if not enough gain is observed) and post + pruning (build the tree up to a max depth and then prune branches with + negative gain). For pre and post pruning, you MUST provide + tree_complexity >0. """ # pylint:disable=protected-access # HParams for the model. tree_hparams = canned_boosted_trees._TreeHParams( n_trees, max_depth, learning_rate, l1_regularization, l2_regularization, - tree_complexity, min_node_weight, center_bias) + tree_complexity, min_node_weight, center_bias, pruning_mode) def _model_fn(features, labels, mode, config): return canned_boosted_trees._bt_model_fn( @@ -129,7 +135,8 @@ def boosted_trees_classifier_train_in_memory( min_node_weight=0., config=None, train_hooks=None, - center_bias=False): + center_bias=False, + pruning_mode='none'): """Trains a boosted tree classifier with in memory dataset. Example: @@ -208,6 +215,11 @@ def boosted_trees_classifier_train_in_memory( regression problems, the first node will return the mean of the labels. For binary classification problems, it will return a logit for a prior probability of label 1. + pruning_mode: one of 'none', 'pre', 'post' to indicate no pruning, pre- + pruning (do not split a node if not enough gain is observed) and post + pruning (build the tree up to a max depth and then prune branches with + negative gain). For pre and post pruning, you MUST provide + tree_complexity >0. Returns: a `BoostedTreesClassifier` instance created with the given arguments and @@ -228,7 +240,7 @@ def boosted_trees_classifier_train_in_memory( # HParams for the model. tree_hparams = canned_boosted_trees._TreeHParams( n_trees, max_depth, learning_rate, l1_regularization, l2_regularization, - tree_complexity, min_node_weight, center_bias) + tree_complexity, min_node_weight, center_bias, pruning_mode) def _model_fn(features, labels, mode, config): return canned_boosted_trees._bt_model_fn( @@ -269,7 +281,8 @@ def boosted_trees_regressor_train_in_memory( min_node_weight=0., config=None, train_hooks=None, - center_bias=False): + center_bias=False, + pruning_mode='none'): """Trains a boosted tree regressor with in memory dataset. Example: @@ -341,6 +354,11 @@ def boosted_trees_regressor_train_in_memory( regression problems, the first node will return the mean of the labels. For binary classification problems, it will return a logit for a prior probability of label 1. + pruning_mode: one of 'none', 'pre', 'post' to indicate no pruning, pre- + pruning (do not split a node if not enough gain is observed) and post + pruning (build the tree up to a max depth and then prune branches with + negative gain). For pre and post pruning, you MUST provide + tree_complexity >0. Returns: a `BoostedTreesClassifier` instance created with the given arguments and @@ -360,7 +378,7 @@ def boosted_trees_regressor_train_in_memory( # HParams for the model. tree_hparams = canned_boosted_trees._TreeHParams( n_trees, max_depth, learning_rate, l1_regularization, l2_regularization, - tree_complexity, min_node_weight, center_bias) + tree_complexity, min_node_weight, center_bias, pruning_mode) def _model_fn(features, labels, mode, config): return canned_boosted_trees._bt_model_fn( diff --git a/tensorflow/contrib/estimator/python/estimator/boosted_trees_test.py b/tensorflow/contrib/estimator/python/estimator/boosted_trees_test.py index 999c2aa5e28242f996e12da3807a74c6acf31df9..b1581f37509b5dc2bec98942e88c024905f25d93 100644 --- a/tensorflow/contrib/estimator/python/estimator/boosted_trees_test.py +++ b/tensorflow/contrib/estimator/python/estimator/boosted_trees_test.py @@ -136,6 +136,49 @@ class BoostedTreesEstimatorTest(test_util.TensorFlowTestCase): eval_res = est.evaluate(input_fn=input_fn, steps=1) self.assertAllClose(eval_res['average_loss'], 0.614642) + def testTrainAndEvaluateEstimatorWithPrePruning(self): + input_fn = _make_train_input_fn(is_classification=False) + + est = boosted_trees._BoostedTreesEstimator( + feature_columns=self._feature_columns, + n_batches_per_layer=1, + n_trees=2, + head=self._head, + max_depth=5, + tree_complexity=0.001, + pruning_mode='pre') + + num_steps = 100 + # Train for a few steps, and validate final checkpoint. + est.train(input_fn, steps=num_steps) + # We stop actually after 2*depth*n_trees steps (via a hook) because we still + # could not grow 2 trees of depth 5 (due to pre-pruning). + self._assert_checkpoint( + est.model_dir, global_step=21, finalized_trees=0, attempted_layers=21) + eval_res = est.evaluate(input_fn=input_fn, steps=1) + self.assertAllClose(eval_res['average_loss'], 3.83943) + + def testTrainAndEvaluateEstimatorWithPostPruning(self): + input_fn = _make_train_input_fn(is_classification=False) + + est = boosted_trees._BoostedTreesEstimator( + feature_columns=self._feature_columns, + n_batches_per_layer=1, + n_trees=2, + head=self._head, + max_depth=5, + tree_complexity=0.001, + pruning_mode='post') + + # It will stop after 10 steps because of the max depth and num trees. + num_steps = 100 + # Train for a few steps, and validate final checkpoint. + est.train(input_fn, steps=num_steps) + self._assert_checkpoint( + est.model_dir, global_step=10, finalized_trees=2, attempted_layers=10) + eval_res = est.evaluate(input_fn=input_fn, steps=1) + self.assertAllClose(eval_res['average_loss'], 2.37652) + def testInferEstimator(self): train_input_fn = _make_train_input_fn(is_classification=False) predict_input_fn = numpy_io.numpy_input_fn( @@ -231,6 +274,31 @@ class BoostedTreesEstimatorTest(test_util.TensorFlowTestCase): self.assertAllClose([[0], [1], [1], [0], [0]], [pred['class_ids'] for pred in predictions]) + def testBinaryClassifierTrainInMemoryAndEvalAndInferWithPrePruning(self): + train_input_fn = _make_train_input_fn(is_classification=True) + predict_input_fn = numpy_io.numpy_input_fn( + x=FEATURES_DICT, y=None, batch_size=1, num_epochs=1, shuffle=False) + + est = boosted_trees.boosted_trees_classifier_train_in_memory( + train_input_fn=train_input_fn, + feature_columns=self._feature_columns, + n_trees=1, + max_depth=5, + pruning_mode='pre', + tree_complexity=0.01) + # We stop actually after 2*depth*n_trees steps (via a hook) because we still + # could not grow 1 trees of depth 5 (due to pre-pruning). + self._assert_checkpoint( + est.model_dir, global_step=11, finalized_trees=0, attempted_layers=11) + + # Check evaluate and predict. + eval_res = est.evaluate(input_fn=train_input_fn, steps=1) + self.assertAllClose(eval_res['accuracy'], 1.0) + # Validate predictions. + predictions = list(est.predict(input_fn=predict_input_fn)) + self.assertAllClose([[0], [1], [1], [0], [0]], + [pred['class_ids'] for pred in predictions]) + def testBinaryClassifierTrainInMemoryWithDataset(self): train_input_fn = _make_train_input_fn_dataset(is_classification=True) predict_input_fn = numpy_io.numpy_input_fn( diff --git a/tensorflow/contrib/estimator/python/estimator/early_stopping.py b/tensorflow/contrib/estimator/python/estimator/early_stopping.py new file mode 100644 index 0000000000000000000000000000000000000000..3eab21d5acaf26f14a73e7fa8e9c50fffc22fe9c --- /dev/null +++ b/tensorflow/contrib/estimator/python/estimator/early_stopping.py @@ -0,0 +1,469 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utilities for early stopping.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import operator +import os + +from tensorflow.python.estimator import estimator as estimator_lib +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import init_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.platform import gfile +from tensorflow.python.platform import tf_logging +from tensorflow.python.summary import summary_iterator +from tensorflow.python.training import basic_session_run_hooks +from tensorflow.python.training import session_run_hook +from tensorflow.python.training import training_util + +_EVENT_FILE_GLOB_PATTERN = 'events.out.tfevents.*' + + +def make_early_stopping_hook(estimator, + should_stop_fn, + run_every_secs=60, + run_every_steps=None): + """Creates early-stopping hook. + + Returns a `SessionRunHook` that stops training when `should_stop_fn` returns + `True`. + + Usage example: + + ```python + estimator = ... + hook = early_stopping.make_early_stopping_hook( + estimator, should_stop_fn=make_stop_fn(...)) + train_spec = tf.estimator.TrainSpec(..., hooks=[hook]) + tf.estimator.train_and_evaluate(estimator, train_spec, ...) + ``` + + Args: + estimator: A `tf.estimator.Estimator` instance. + should_stop_fn: `callable`, function that takes no arguments and returns a + `bool`. If the function returns `True`, stopping will be initiated by the + chief. + run_every_secs: If specified, calls `should_stop_fn` at an interval of + `run_every_secs` seconds. Defaults to 60 seconds. Either this or + `run_every_steps` must be set. + run_every_steps: If specified, calls `should_stop_fn` every + `run_every_steps` steps. Either this or `run_every_secs` must be set. + + Returns: + A `SessionRunHook` that periodically executes `should_stop_fn` and initiates + early stopping if the function returns `True`. + + Raises: + TypeError: If `estimator` is not of type `tf.estimator.Estimator`. + ValueError: If both `run_every_secs` and `run_every_steps` are set. + """ + if not isinstance(estimator, estimator_lib.Estimator): + raise TypeError('`estimator` must have type `tf.estimator.Estimator`. ' + 'Got: {}'.format(type(estimator))) + + if run_every_secs is not None and run_every_steps is not None: + raise ValueError('Only one of `run_every_secs` and `run_every_steps` must ' + 'be set.') + + if estimator.config.is_chief: + return _StopOnPredicateHook(should_stop_fn, run_every_secs, run_every_steps) + else: + return _CheckForStoppingHook() + + +def stop_if_higher_hook(estimator, + metric_name, + threshold, + eval_dir=None, + min_steps=0, + run_every_secs=60, + run_every_steps=None): + """Creates hook to stop if the given metric is higher than the threshold. + + Usage example: + + ```python + estimator = ... + # Hook to stop training if accuracy becomes higher than 0.9. + hook = early_stopping.stop_if_higher_hook(estimator, "accuracy", 0.9) + train_spec = tf.estimator.TrainSpec(..., hooks=[hook]) + tf.estimator.train_and_evaluate(estimator, train_spec, ...) + ``` + + Args: + estimator: A `tf.estimator.Estimator` instance. + metric_name: `str`, metric to track. "loss", "accuracy", etc. + threshold: Numeric threshold for the given metric. + eval_dir: If set, directory containing summary files with eval metrics. By + default, `estimator.eval_dir()` will be used. + min_steps: `int`, stop is never requested if global step is less than this + value. Defaults to 0. + run_every_secs: If specified, calls `should_stop_fn` at an interval of + `run_every_secs` seconds. Defaults to 60 seconds. Either this or + `run_every_steps` must be set. + run_every_steps: If specified, calls `should_stop_fn` every + `run_every_steps` steps. Either this or `run_every_secs` must be set. + + Returns: + An early-stopping hook of type `SessionRunHook` that periodically checks + if the given metric is higher than specified threshold and initiates + early stopping if true. + """ + return _stop_if_threshold_crossed_hook( + estimator=estimator, + metric_name=metric_name, + threshold=threshold, + higher_is_better=True, + eval_dir=eval_dir, + min_steps=min_steps, + run_every_secs=run_every_secs, + run_every_steps=run_every_steps) + + +def stop_if_lower_hook(estimator, + metric_name, + threshold, + eval_dir=None, + min_steps=0, + run_every_secs=60, + run_every_steps=None): + """Creates hook to stop if the given metric is lower than the threshold. + + Usage example: + + ```python + estimator = ... + # Hook to stop training if loss becomes lower than 100. + hook = early_stopping.stop_if_lower_hook(estimator, "loss", 100) + train_spec = tf.estimator.TrainSpec(..., hooks=[hook]) + tf.estimator.train_and_evaluate(estimator, train_spec, ...) + ``` + + Args: + estimator: A `tf.estimator.Estimator` instance. + metric_name: `str`, metric to track. "loss", "accuracy", etc. + threshold: Numeric threshold for the given metric. + eval_dir: If set, directory containing summary files with eval metrics. By + default, `estimator.eval_dir()` will be used. + min_steps: `int`, stop is never requested if global step is less than this + value. Defaults to 0. + run_every_secs: If specified, calls `should_stop_fn` at an interval of + `run_every_secs` seconds. Defaults to 60 seconds. Either this or + `run_every_steps` must be set. + run_every_steps: If specified, calls `should_stop_fn` every + `run_every_steps` steps. Either this or `run_every_secs` must be set. + + Returns: + An early-stopping hook of type `SessionRunHook` that periodically checks + if the given metric is lower than specified threshold and initiates + early stopping if true. + """ + return _stop_if_threshold_crossed_hook( + estimator=estimator, + metric_name=metric_name, + threshold=threshold, + higher_is_better=False, + eval_dir=eval_dir, + min_steps=min_steps, + run_every_secs=run_every_secs, + run_every_steps=run_every_steps) + + +def stop_if_no_increase_hook(estimator, + metric_name, + max_steps_without_increase, + eval_dir=None, + min_steps=0, + run_every_secs=60, + run_every_steps=None): + """Creates hook to stop if metric does not increase within given max steps. + + Usage example: + + ```python + estimator = ... + # Hook to stop training if accuracy does not increase in over 100000 steps. + hook = early_stopping.stop_if_no_increase_hook(estimator, "accuracy", 100000) + train_spec = tf.estimator.TrainSpec(..., hooks=[hook]) + tf.estimator.train_and_evaluate(estimator, train_spec, ...) + ``` + + Args: + estimator: A `tf.estimator.Estimator` instance. + metric_name: `str`, metric to track. "loss", "accuracy", etc. + max_steps_without_increase: `int`, maximum number of training steps with no + increase in the given metric. + eval_dir: If set, directory containing summary files with eval metrics. By + default, `estimator.eval_dir()` will be used. + min_steps: `int`, stop is never requested if global step is less than this + value. Defaults to 0. + run_every_secs: If specified, calls `should_stop_fn` at an interval of + `run_every_secs` seconds. Defaults to 60 seconds. Either this or + `run_every_steps` must be set. + run_every_steps: If specified, calls `should_stop_fn` every + `run_every_steps` steps. Either this or `run_every_secs` must be set. + + Returns: + An early-stopping hook of type `SessionRunHook` that periodically checks + if the given metric shows no increase over given maximum number of + training steps, and initiates early stopping if true. + """ + return _stop_if_no_metric_improvement_hook( + estimator=estimator, + metric_name=metric_name, + max_steps_without_improvement=max_steps_without_increase, + higher_is_better=True, + eval_dir=eval_dir, + min_steps=min_steps, + run_every_secs=run_every_secs, + run_every_steps=run_every_steps) + + +def stop_if_no_decrease_hook(estimator, + metric_name, + max_steps_without_decrease, + eval_dir=None, + min_steps=0, + run_every_secs=60, + run_every_steps=None): + """Creates hook to stop if metric does not decrease within given max steps. + + Usage example: + + ```python + estimator = ... + # Hook to stop training if loss does not decrease in over 100000 steps. + hook = early_stopping.stop_if_no_decrease_hook(estimator, "loss", 100000) + train_spec = tf.estimator.TrainSpec(..., hooks=[hook]) + tf.estimator.train_and_evaluate(estimator, train_spec, ...) + ``` + + Args: + estimator: A `tf.estimator.Estimator` instance. + metric_name: `str`, metric to track. "loss", "accuracy", etc. + max_steps_without_decrease: `int`, maximum number of training steps with no + decrease in the given metric. + eval_dir: If set, directory containing summary files with eval metrics. By + default, `estimator.eval_dir()` will be used. + min_steps: `int`, stop is never requested if global step is less than this + value. Defaults to 0. + run_every_secs: If specified, calls `should_stop_fn` at an interval of + `run_every_secs` seconds. Defaults to 60 seconds. Either this or + `run_every_steps` must be set. + run_every_steps: If specified, calls `should_stop_fn` every + `run_every_steps` steps. Either this or `run_every_secs` must be set. + + Returns: + An early-stopping hook of type `SessionRunHook` that periodically checks + if the given metric shows no decrease over given maximum number of + training steps, and initiates early stopping if true. + """ + return _stop_if_no_metric_improvement_hook( + estimator=estimator, + metric_name=metric_name, + max_steps_without_improvement=max_steps_without_decrease, + higher_is_better=False, + eval_dir=eval_dir, + min_steps=min_steps, + run_every_secs=run_every_secs, + run_every_steps=run_every_steps) + + +def read_eval_metrics(eval_dir): + """Helper to read eval metrics from eval summary files. + + Args: + eval_dir: Directory containing summary files with eval metrics. + + Returns: + A `dict` with global steps mapping to `dict` of metric names and values. + """ + eval_metrics_dict = {} + for event in _summaries(eval_dir): + if not event.HasField('summary'): + continue + metrics = {} + for value in event.summary.value: + if value.HasField('simple_value'): + metrics[value.tag] = value.simple_value + if metrics: + eval_metrics_dict[event.step] = metrics + return eval_metrics_dict + + +def _stop_if_threshold_crossed_hook(estimator, metric_name, threshold, + higher_is_better, eval_dir, min_steps, + run_every_secs, run_every_steps): + """Creates early-stopping hook to stop training if threshold is crossed.""" + + if eval_dir is None: + eval_dir = estimator.eval_dir() + + is_lhs_better = operator.gt if higher_is_better else operator.lt + greater_or_lesser = 'greater than' if higher_is_better else 'less than' + + def stop_if_threshold_crossed_fn(): + """Returns `True` if the given metric crosses specified threshold.""" + + eval_results = read_eval_metrics(eval_dir) + + for step, metrics in eval_results.items(): + if step < min_steps: + continue + val = metrics[metric_name] + if is_lhs_better(val, threshold): + tf_logging.info( + 'At step %s, metric "%s" has value %s which is %s the configured ' + 'threshold (%s) for early stopping.', step, metric_name, val, + greater_or_lesser, threshold) + return True + return False + + return make_early_stopping_hook( + estimator=estimator, + should_stop_fn=stop_if_threshold_crossed_fn, + run_every_secs=run_every_secs, + run_every_steps=run_every_steps) + + +def _stop_if_no_metric_improvement_hook( + estimator, metric_name, max_steps_without_improvement, higher_is_better, + eval_dir, min_steps, run_every_secs, run_every_steps): + """Returns hook to stop training if given metric shows no improvement.""" + + if eval_dir is None: + eval_dir = estimator.eval_dir() + + is_lhs_better = operator.gt if higher_is_better else operator.lt + increase_or_decrease = 'increase' if higher_is_better else 'decrease' + + def stop_if_no_metric_improvement_fn(): + """Returns `True` if metric does not improve within max steps.""" + + eval_results = read_eval_metrics(eval_dir) + + best_val = None + best_val_step = None + for step, metrics in eval_results.items(): + if step < min_steps: + continue + val = metrics[metric_name] + if best_val is None or is_lhs_better(val, best_val): + best_val = val + best_val_step = step + if step - best_val_step >= max_steps_without_improvement: + tf_logging.info( + 'No %s in metric "%s" for %s steps, which is greater than or equal ' + 'to max steps (%s) configured for early stopping.', + increase_or_decrease, metric_name, step - best_val_step, + max_steps_without_improvement) + return True + return False + + return make_early_stopping_hook( + estimator=estimator, + should_stop_fn=stop_if_no_metric_improvement_fn, + run_every_secs=run_every_secs, + run_every_steps=run_every_steps) + + +def _summaries(eval_dir): + """Yields `tensorflow.Event` protos from event files in the eval dir. + + Args: + eval_dir: Directory containing summary files with eval metrics. + + Yields: + `tensorflow.Event` object read from the event files. + """ + if gfile.Exists(eval_dir): + for event_file in gfile.Glob( + os.path.join(eval_dir, _EVENT_FILE_GLOB_PATTERN)): + for event in summary_iterator.summary_iterator(event_file): + yield event + + +def _get_or_create_stop_var(): + with variable_scope.variable_scope( + name_or_scope='signal_early_stopping', + values=[], + reuse=variable_scope.AUTO_REUSE): + return variable_scope.get_variable( + name='STOP', + shape=[], + dtype=dtypes.bool, + initializer=init_ops.constant_initializer(False), + collections=[ops.GraphKeys.GLOBAL_VARIABLES], + trainable=False) + + +class _StopOnPredicateHook(session_run_hook.SessionRunHook): + """Hook that requests stop when `should_stop_fn` returns `True`.""" + + def __init__(self, should_stop_fn, run_every_secs=60, run_every_steps=None): + if not callable(should_stop_fn): + raise TypeError('`should_stop_fn` must be callable.') + + self._should_stop_fn = should_stop_fn + self._timer = basic_session_run_hooks.SecondOrStepTimer( + every_secs=run_every_secs, every_steps=run_every_steps) + self._global_step_tensor = None + self._stop_var = None + self._stop_op = None + + def begin(self): + self._global_step_tensor = training_util.get_global_step() + self._stop_var = _get_or_create_stop_var() + self._stop_op = state_ops.assign(self._stop_var, True) + + def before_run(self, run_context): + del run_context + return session_run_hook.SessionRunArgs(self._global_step_tensor) + + def after_run(self, run_context, run_values): + global_step = run_values.results + if self._timer.should_trigger_for_step(global_step): + self._timer.update_last_triggered_step(global_step) + if self._should_stop_fn(): + tf_logging.info('Requesting early stopping at global step %d', + global_step) + run_context.session.run(self._stop_op) + run_context.request_stop() + + +class _CheckForStoppingHook(session_run_hook.SessionRunHook): + """Hook that requests stop if stop is requested by `_StopOnPredicateHook`.""" + + def __init__(self): + self._stop_var = None + + def begin(self): + self._stop_var = _get_or_create_stop_var() + + def before_run(self, run_context): + del run_context + return session_run_hook.SessionRunArgs(self._stop_var) + + def after_run(self, run_context, run_values): + should_early_stop = run_values.results + if should_early_stop: + tf_logging.info('Early stopping requested, suspending run.') + run_context.request_stop() diff --git a/tensorflow/contrib/estimator/python/estimator/early_stopping_test.py b/tensorflow/contrib/estimator/python/estimator/early_stopping_test.py new file mode 100644 index 0000000000000000000000000000000000000000..e4bfd4b446b9413bd1627ef6904ff2dc9f1a9120 --- /dev/null +++ b/tensorflow/contrib/estimator/python/estimator/early_stopping_test.py @@ -0,0 +1,246 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for early_stopping.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import tempfile + +from absl.testing import parameterized +from tensorflow.contrib.estimator.python.estimator import early_stopping +from tensorflow.python.estimator import estimator +from tensorflow.python.estimator import run_config +from tensorflow.python.framework import ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.platform import test +from tensorflow.python.training import monitored_session +from tensorflow.python.training import training_util + + +class _FakeRunConfig(run_config.RunConfig): + + def __init__(self, is_chief): + super(_FakeRunConfig, self).__init__() + self._is_chief = is_chief + + @property + def is_chief(self): + return self._is_chief + + +def _dummy_model_fn(features, labels, params): + _, _, _ = features, labels, params + + +class _FakeEstimator(estimator.Estimator): + """Fake estimator for testing.""" + + def __init__(self, config): + super(_FakeEstimator, self).__init__( + model_fn=_dummy_model_fn, config=config) + + +def _write_events(eval_dir, params): + """Test helper to write events to summary files.""" + for steps, loss, accuracy in params: + estimator._write_dict_to_summary(eval_dir, { + 'loss': loss, + 'accuracy': accuracy, + }, steps) + + +class ReadEvalMetricsTest(test.TestCase): + + def test_read_eval_metrics(self): + eval_dir = tempfile.mkdtemp() + _write_events( + eval_dir, + [ + # steps, loss, accuracy + (1000, 1, 2), + (2000, 3, 4), + (3000, 5, 6), + ]) + self.assertEqual({ + 1000: { + 'loss': 1, + 'accuracy': 2 + }, + 2000: { + 'loss': 3, + 'accuracy': 4 + }, + 3000: { + 'loss': 5, + 'accuracy': 6 + }, + }, early_stopping.read_eval_metrics(eval_dir)) + + def test_read_eval_metrics_when_no_events(self): + eval_dir = tempfile.mkdtemp() + self.assertTrue(os.path.exists(eval_dir)) + + # No error should be raised when eval directory exists with no event files. + self.assertEqual({}, early_stopping.read_eval_metrics(eval_dir)) + + os.rmdir(eval_dir) + self.assertFalse(os.path.exists(eval_dir)) + + # No error should be raised when eval directory does not exist. + self.assertEqual({}, early_stopping.read_eval_metrics(eval_dir)) + + +class EarlyStoppingHooksTest(test.TestCase, parameterized.TestCase): + + def setUp(self): + config = _FakeRunConfig(is_chief=True) + self._estimator = _FakeEstimator(config=config) + eval_dir = self._estimator.eval_dir() + os.makedirs(eval_dir) + _write_events( + eval_dir, + [ + # steps, loss, accuracy + (1000, 0.8, 0.5), + (2000, 0.7, 0.6), + (3000, 0.4, 0.7), + (3500, 0.41, 0.68), + ]) + + def run_session(self, hooks, should_stop): + hooks = hooks if isinstance(hooks, list) else [hooks] + with ops.Graph().as_default(): + training_util.create_global_step() + no_op = control_flow_ops.no_op() + with monitored_session.SingularMonitoredSession(hooks=hooks) as mon_sess: + mon_sess.run(no_op) + self.assertEqual(mon_sess.should_stop(), should_stop) + + @parameterized.parameters((0.8, 0, False), (0.6, 4000, False), (0.6, 0, True)) + def test_stop_if_higher_hook(self, threshold, min_steps, should_stop): + self.run_session( + early_stopping.stop_if_higher_hook( + self._estimator, + metric_name='accuracy', + threshold=threshold, + min_steps=min_steps), should_stop) + + @parameterized.parameters((0.3, 0, False), (0.5, 4000, False), (0.5, 0, True)) + def test_stop_if_lower_hook(self, threshold, min_steps, should_stop): + self.run_session( + early_stopping.stop_if_lower_hook( + self._estimator, + metric_name='loss', + threshold=threshold, + min_steps=min_steps), should_stop) + + @parameterized.parameters((1500, 0, False), (500, 4000, False), + (500, 0, True)) + def test_stop_if_no_increase_hook(self, max_steps, min_steps, should_stop): + self.run_session( + early_stopping.stop_if_no_increase_hook( + self._estimator, + metric_name='accuracy', + max_steps_without_increase=max_steps, + min_steps=min_steps), should_stop) + + @parameterized.parameters((1500, 0, False), (500, 4000, False), + (500, 0, True)) + def test_stop_if_no_decrease_hook(self, max_steps, min_steps, should_stop): + self.run_session( + early_stopping.stop_if_no_decrease_hook( + self._estimator, + metric_name='loss', + max_steps_without_decrease=max_steps, + min_steps=min_steps), should_stop) + + @parameterized.parameters((1500, 0.3, False), (1500, 0.5, True), + (500, 0.3, True)) + def test_multiple_hooks(self, max_steps, loss_threshold, should_stop): + self.run_session([ + early_stopping.stop_if_no_decrease_hook( + self._estimator, + metric_name='loss', + max_steps_without_decrease=max_steps), + early_stopping.stop_if_lower_hook( + self._estimator, metric_name='loss', threshold=loss_threshold) + ], should_stop) + + @parameterized.parameters(False, True) + def test_make_early_stopping_hook(self, should_stop): + self.run_session([ + early_stopping.make_early_stopping_hook( + self._estimator, should_stop_fn=lambda: should_stop) + ], should_stop) + + def test_make_early_stopping_hook_typeerror(self): + with self.assertRaises(TypeError): + early_stopping.make_early_stopping_hook( + estimator=object(), should_stop_fn=lambda: True) + + def test_make_early_stopping_hook_valueerror(self): + with self.assertRaises(ValueError): + early_stopping.make_early_stopping_hook( + self._estimator, + should_stop_fn=lambda: True, + run_every_secs=60, + run_every_steps=100) + + +class StopOnPredicateHookTest(test.TestCase): + + def test_stop(self): + hook = early_stopping._StopOnPredicateHook( + should_stop_fn=lambda: False, run_every_secs=0) + with ops.Graph().as_default(): + training_util.create_global_step() + no_op = control_flow_ops.no_op() + with monitored_session.SingularMonitoredSession(hooks=[hook]) as mon_sess: + mon_sess.run(no_op) + self.assertFalse(mon_sess.should_stop()) + self.assertFalse(mon_sess.raw_session().run(hook._stop_var)) + + hook = early_stopping._StopOnPredicateHook( + should_stop_fn=lambda: True, run_every_secs=0) + with ops.Graph().as_default(): + training_util.create_global_step() + no_op = control_flow_ops.no_op() + with monitored_session.SingularMonitoredSession(hooks=[hook]) as mon_sess: + mon_sess.run(no_op) + self.assertTrue(mon_sess.should_stop()) + self.assertTrue(mon_sess.raw_session().run(hook._stop_var)) + + +class CheckForStoppingHookTest(test.TestCase): + + def test_stop(self): + hook = early_stopping._CheckForStoppingHook() + with ops.Graph().as_default(): + no_op = control_flow_ops.no_op() + assign_op = state_ops.assign(early_stopping._get_or_create_stop_var(), + True) + with monitored_session.SingularMonitoredSession(hooks=[hook]) as mon_sess: + mon_sess.run(no_op) + self.assertFalse(mon_sess.should_stop()) + mon_sess.run(assign_op) + self.assertTrue(mon_sess.should_stop()) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/estimator/python/estimator/head.py b/tensorflow/contrib/estimator/python/estimator/head.py index c9d86ef4ab89950b0c7b0414ba60d9e0a1cbe476..34f765d56546d3cd10fcde5ac444a221c73602cd 100644 --- a/tensorflow/contrib/estimator/python/estimator/head.py +++ b/tensorflow/contrib/estimator/python/estimator/head.py @@ -943,20 +943,30 @@ class _MultiLabelHead(head_lib._Head): # pylint:disable=protected-access class_probabilities = array_ops.slice( probabilities, begin=begin, size=size) class_labels = array_ops.slice(labels, begin=begin, size=size) - prob_key = keys.PROBABILITY_MEAN_AT_CLASS % class_id + if self._label_vocabulary is None: + prob_key = keys.PROBABILITY_MEAN_AT_CLASS % class_id + else: + prob_key = ( + keys.PROBABILITY_MEAN_AT_NAME % self._label_vocabulary[class_id]) metric_ops[head_lib._summary_key(self._name, prob_key)] = ( # pylint:disable=protected-access head_lib._predictions_mean( # pylint:disable=protected-access predictions=class_probabilities, weights=weights, name=prob_key)) - auc_key = keys.AUC_AT_CLASS % class_id + if self._label_vocabulary is None: + auc_key = keys.AUC_AT_CLASS % class_id + else: + auc_key = keys.AUC_AT_NAME % self._label_vocabulary[class_id] metric_ops[head_lib._summary_key(self._name, auc_key)] = ( # pylint:disable=protected-access head_lib._auc( # pylint:disable=protected-access labels=class_labels, predictions=class_probabilities, weights=weights, name=auc_key)) - auc_pr_key = keys.AUC_PR_AT_CLASS % class_id + if self._label_vocabulary is None: + auc_pr_key = keys.AUC_PR_AT_CLASS % class_id + else: + auc_pr_key = keys.AUC_PR_AT_NAME % self._label_vocabulary[class_id] metric_ops[head_lib._summary_key(self._name, auc_pr_key)] = ( # pylint:disable=protected-access head_lib._auc( # pylint:disable=protected-access labels=class_labels, diff --git a/tensorflow/contrib/estimator/python/estimator/head_test.py b/tensorflow/contrib/estimator/python/estimator/head_test.py index 7b884402d4650636bc9fe053994246aabb9c312d..2d367adb47080a630d1d2ef5ecfd4e8d5d0377d9 100644 --- a/tensorflow/contrib/estimator/python/estimator/head_test.py +++ b/tensorflow/contrib/estimator/python/estimator/head_test.py @@ -694,12 +694,14 @@ class MultiLabelHead(test.TestCase): # this assert tests that the algorithm remains consistent. keys.AUC: 0.3333, keys.AUC_PR: 0.7639, - keys.PROBABILITY_MEAN_AT_CLASS % 0: np.sum(_sigmoid(logits[:, 0])) / 2., - keys.AUC_AT_CLASS % 0: 0., - keys.AUC_PR_AT_CLASS % 0: 1., - keys.PROBABILITY_MEAN_AT_CLASS % 1: np.sum(_sigmoid(logits[:, 1])) / 2., - keys.AUC_AT_CLASS % 1: 1., - keys.AUC_PR_AT_CLASS % 1: 1., + keys.PROBABILITY_MEAN_AT_NAME % 'a': + np.sum(_sigmoid(logits[:, 0])) / 2., + keys.AUC_AT_NAME % 'a': 0., + keys.AUC_PR_AT_NAME % 'a': 1., + keys.PROBABILITY_MEAN_AT_NAME % 'b': + np.sum(_sigmoid(logits[:, 1])) / 2., + keys.AUC_AT_NAME % 'b': 1., + keys.AUC_PR_AT_NAME % 'b': 1., } self._test_eval( diff --git a/tensorflow/contrib/estimator/python/estimator/hooks.py b/tensorflow/contrib/estimator/python/estimator/hooks.py index ddd6aa442f82bad2d4714dbcdc85b20b34773068..caadafdfa6972c141d32a705e62a98d220cace41 100644 --- a/tensorflow/contrib/estimator/python/estimator/hooks.py +++ b/tensorflow/contrib/estimator/python/estimator/hooks.py @@ -189,7 +189,7 @@ class InMemoryEvaluatorHook(training.SessionRunHook): init_fn=feed_variables, copy_from_scaffold=self._scaffold) with self._graph.as_default(): - return self._estimator._evaluate_run( + self._estimator._evaluate_run( checkpoint_path=None, scaffold=scaffold, update_op=self._update_op, diff --git a/tensorflow/contrib/estimator/python/estimator/hooks_test.py b/tensorflow/contrib/estimator/python/estimator/hooks_test.py index 95ae971852ee6dffb6174fc243686721c30ef685..ee88d5ecf50aa15b2faa0f3e136c686b5b0ef62a 100644 --- a/tensorflow/contrib/estimator/python/estimator/hooks_test.py +++ b/tensorflow/contrib/estimator/python/estimator/hooks_test.py @@ -102,6 +102,7 @@ class InMemoryEvaluatorHookTest(test.TestCase): self.assertTrue(os.path.isdir(estimator.eval_dir())) step_keyword_to_value = summary_step_keyword_to_value_mapping( estimator.eval_dir()) + # 4.5 = sum(range(10))/10 # before training self.assertEqual(4.5, step_keyword_to_value[0]['mean_of_features']) @@ -110,6 +111,7 @@ class InMemoryEvaluatorHookTest(test.TestCase): self.assertEqual(4.5, step_keyword_to_value[8]['mean_of_features']) # end self.assertEqual(4.5, step_keyword_to_value[10]['mean_of_features']) + self.assertEqual(set([0, 4, 8, 10]), set(step_keyword_to_value.keys())) def test_uses_latest_variable_value(self): diff --git a/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py new file mode 100644 index 0000000000000000000000000000000000000000..b0082f7e550b069c072654e3c3fec8f917a84478 --- /dev/null +++ b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py @@ -0,0 +1,449 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Class that creates an Estimator from a SavedModel.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import six + +from tensorflow.python.estimator import estimator as estimator_lib +from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.estimator.export import export as export_lib +from tensorflow.python.estimator.export import export_output +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.saved_model import constants +from tensorflow.python.saved_model import loader_impl +from tensorflow.python.saved_model import signature_constants +from tensorflow.python.training import checkpoint_utils +from tensorflow.python.training import monitored_session +from tensorflow.python.training import training_util + + +class SavedModelEstimator(estimator_lib.Estimator): + """Create an Estimator from a SavedModel. + + Only SavedModels exported with + `tf.contrib.estimator.export_all_saved_models()` or + `tf.estimator.Estimator.export_savedmodel()` are supported for this class. + + Example with `tf.estimator.DNNClassifier`: + + **Step 1: Create and train DNNClassifier.** + + ```python + feature1 = tf.feature_column.embedding_column( + tf.feature_column.categorical_column_with_vocabulary_list( + key='feature1', vocabulary_list=('green', 'yellow')), dimension=1) + feature2 = tf.feature_column.numeric_column(key='feature2', default_value=0.0) + + classifier = tf.estimator.DNNClassifier( + hidden_units=[4,2], feature_columns=[feature1, feature2]) + + def input_fn(): + features = {'feature1': tf.constant(['green', 'green', 'yellow']), + 'feature2': tf.constant([3.5, 4.2, 6.1])} + label = tf.constant([1., 0., 0.]) + return tf.data.Dataset.from_tensors((features, label)).repeat() + + classifier.train(input_fn=input_fn, steps=10) + ``` + + **Step 2: Export classifier.** + First, build functions that specify the expected inputs. + + ```python + # During train and evaluation, both the features and labels should be defined. + supervised_input_receiver_fn = ( + tf.contrib.estimator.build_raw_supervised_input_receiver_fn( + {'feature1': tf.placeholder(dtype=tf.string, shape=[None]), + 'feature2': tf.placeholder(dtype=tf.float32, shape=[None])}, + tf.placeholder(dtype=tf.float32, shape=[None]))) + + # During predict mode, expect to receive a `tf.Example` proto, so a parsing + # function is used. + serving_input_receiver_fn = ( + tf.estimator.export.build_parsing_serving_input_receiver_fn( + tf.feature_column.make_parse_example_spec([feature1, feature2]))) + ``` + + Next, export the model as a SavedModel. A timestamped directory will be + created (for example `/tmp/export_all/1234567890`). + + ```python + # Option 1: Save all modes (train, eval, predict) + export_dir = tf.contrib.estimator.export_all_saved_models( + classifier, '/tmp/export_all', + {tf.estimator.ModeKeys.TRAIN: supervised_input_receiver_fn, + tf.estimator.ModeKeys.EVAL: supervised_input_receiver_fn, + tf.estimator.ModeKeys.PREDICT: serving_input_receiver_fn}) + + # Option 2: Only export predict mode + export_dir = classifier.export_savedmodel( + '/tmp/export_predict', serving_input_receiver_fn) + ``` + + **Step 3: Create a SavedModelEstimator from the exported SavedModel.** + + ```python + est = tf.contrib.estimator.SavedModelEstimator(export_dir) + + # If all modes were exported, you can immediately evaluate and predict, or + # continue training. Otherwise only predict is available. + eval_results = est.evaluate(input_fn=input_fn, steps=1) + print(eval_results) + + est.train(input_fn=input_fn, steps=20) + + def predict_input_fn(): + example = tf.train.Example() + example.features.feature['feature1'].bytes_list.value.extend(['yellow']) + example.features.feature['feature2'].float_list.value.extend([1.]) + return {'inputs':tf.constant([example.SerializeToString()])} + + predictions = est.predict(predict_input_fn) + print(next(predictions)) + ``` + """ + + def __init__(self, saved_model_dir, model_dir=None): + """Initialize a SavedModelEstimator. + + The SavedModelEstimator loads its model function and variable values from + the graphs defined in the SavedModel. There is no option to pass in + `RunConfig` or `params` arguments, because the model function graph is + defined statically in the SavedModel. + + Args: + saved_model_dir: Directory containing SavedModel protobuf and subfolders. + model_dir: Directory to save new checkpoints during training. + + Raises: + NotImplementedError: If a DistributionStrategy is defined in the config. + Unless the SavedModelEstimator is subclassed, this shouldn't happen. + """ + checkpoint = estimator_lib._get_saved_model_ckpt(saved_model_dir) # pylint: disable=protected-access + vars_to_warm_start = [name for name, _ in + checkpoint_utils.list_variables(checkpoint)] + warm_start_settings = estimator_lib.WarmStartSettings( + ckpt_to_initialize_from=checkpoint, + vars_to_warm_start=vars_to_warm_start) + + super(SavedModelEstimator, self).__init__( + model_fn=self._model_fn_from_saved_model, model_dir=model_dir, + warm_start_from=warm_start_settings) + if self._distribution is not None: + raise NotImplementedError( + 'SavedModelEstimator currently does not support ' + 'DistributionStrategy.') + self.saved_model_dir = saved_model_dir + self.saved_model_loader = loader_impl.SavedModelLoader(saved_model_dir) + self._available_modes = self._extract_available_modes() + + def _extract_available_modes(self): + """Return list of modes found in SavedModel.""" + available_modes = [] + logging.info('Checking available modes for SavedModelEstimator.') + for mode in [model_fn_lib.ModeKeys.TRAIN, model_fn_lib.ModeKeys.EVAL, + model_fn_lib.ModeKeys.PREDICT]: + try: + self._get_meta_graph_def_for_mode(mode) + except RuntimeError: + logging.warning('%s mode not found in SavedModel.' % mode) + continue + + if self._get_signature_def_for_mode(mode) is not None: + available_modes.append(mode) + + logging.info('Available modes for Estimator: %s' % available_modes) + return available_modes + + def _validate_mode(self, mode): + """Make sure that mode can be run using the SavedModel.""" + if mode not in self._available_modes: + raise RuntimeError('%s mode is not available in the SavedModel. Use ' + 'saved_model_cli to check that the Metagraph for this ' + 'mode has been exported.' % mode) + + def _get_meta_graph_def_for_mode(self, mode): + tags = model_fn_lib.EXPORT_TAG_MAP[mode] + return self.saved_model_loader.get_meta_graph_def_from_tags(tags) + + def _get_signature_def_for_mode(self, mode): + meta_graph_def = self._get_meta_graph_def_for_mode(mode) + sig_def_key = (signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY + if mode == model_fn_lib.ModeKeys.PREDICT else mode) + if sig_def_key not in meta_graph_def.signature_def: + logging.warning('Metagraph for mode %s was found, but SignatureDef with' + ' key \"%s\" is missing.' % (mode, sig_def_key)) + return None + return meta_graph_def.signature_def[sig_def_key] + + def _create_and_assert_global_step(self, graph): + # Do nothing here. The global step variable will be created/loaded from the + # SavedModel. If a global step variable were created here, the result + # will be two duplicate global step variables, causing issues during + # the warm-start phase. + # Due to the global variable being created in the model function, this may + # cause issues when running DistributionStrategy. Thus, DistributionStrategy + # is not yet supported with SavedModelEstimator. + return None + + def _model_fn_from_saved_model(self, features, labels, mode): + """Load a SavedModel graph and return an EstimatorSpec.""" + # TODO(kathywu): Model function loads placeholders from the graph. Calling + # export_all_saved_models creates another placeholder for the inputs, on top + # of the original placeholders. There should be a way to avoid this. + self._validate_mode(mode) + + g = ops.get_default_graph() + if training_util.get_global_step(g) is not None: + raise RuntimeError( + 'Graph must not contain a global step tensor before the SavedModel is' + ' loaded. Please make sure that the input function does not create a ' + 'global step.') + + # Extract SignatureDef for information about the input and output tensors. + signature_def = self._get_signature_def_for_mode(mode) + + # Generate input map for replacing the inputs in the SavedModel graph with + # the provided features and labels. + input_map = _generate_input_map(signature_def, features, labels) + + # Create a list of the names of output tensors. When the graph is loaded, + # names of the output tensors may be remapped. This ensures that the correct + # tensors are returned in the EstimatorSpec. + output_tensor_names = [ + value.name for value in six.itervalues(signature_def.outputs)] + + # Load the graph. `output_tensors` contains output `Tensors` in the same + # same order as the `output_tensor_names` list. + tags = model_fn_lib.EXPORT_TAG_MAP[mode] + _, output_tensors = self.saved_model_loader.load_graph( + g, tags, input_map=input_map, return_elements=output_tensor_names) + + # Create a scaffold from the MetaGraphDef that contains ops to initialize + # the graph. This should mirror the steps from _add_meta_graph_for_mode(), + # which creates a MetaGraphDef from the EstimatorSpec's scaffold. + scaffold = monitored_session.Scaffold( + local_init_op=loader_impl._get_main_op_tensor( # pylint: disable=protected-access + self._get_meta_graph_def_for_mode(mode))) + + # Ensure that a global step tensor has been created. + global_step_tensor = training_util.get_global_step(g) + training_util.assert_global_step(global_step_tensor) + + # Extract values to return in the EstimatorSpec. + output_map = dict(zip(output_tensor_names, output_tensors)) + outputs = {key: output_map[value.name] + for key, value in six.iteritems(signature_def.outputs)} + + loss, predictions, metrics = _validate_and_extract_outputs( + mode, outputs, signature_def.method_name) + + train_op = ops.get_collection(constants.TRAIN_OP_KEY) + if len(train_op) > 1: + raise RuntimeError('Multiple ops found in the train_op collection.') + train_op = None if not train_op else train_op[0] + + _clear_saved_model_collections() + return model_fn_lib.EstimatorSpec( + scaffold=scaffold, + mode=mode, + loss=loss, + train_op=train_op, + predictions=predictions, + eval_metric_ops=metrics) + + +def _clear_saved_model_collections(): + """Clear collections that are expected empty when exporting a SavedModel. + + The SavedModel builder uses these collections to track ops necessary to + restore the graph state. These collections are expected to be empty before + MetaGraphs are added to the builder. + """ + del ops.get_collection_ref(constants.ASSETS_KEY)[:] + del ops.get_collection_ref(constants.LEGACY_INIT_OP_KEY)[:] + del ops.get_collection_ref(constants.MAIN_OP_KEY)[:] + del ops.get_collection_ref(constants.TRAIN_OP_KEY)[:] + + +def _generate_input_map(signature_def, features, labels): + """Return dict mapping an input tensor name to a feature or label tensor. + + Args: + signature_def: SignatureDef loaded from SavedModel + features: A `Tensor`, `SparseTensor`, or dict of string to `Tensor` or + `SparseTensor`, specifying the features to be passed to the model. + labels: A `Tensor`, `SparseTensor`, or dict of string to `Tensor` or + `SparseTensor`, specifying the labels to be passed to the model. May be + `None`. + + Returns: + dict mapping string names of inputs to features or labels tensors + + Raises: + ValueError: if SignatureDef inputs are not completely mapped by the input + features and labels. + """ + # pylint: disable=protected-access + if not isinstance(features, dict): + features = {export_lib._SINGLE_FEATURE_DEFAULT_NAME: features} + if labels is not None and not isinstance(labels, dict): + labels = {export_lib._SINGLE_LABEL_DEFAULT_NAME: labels} + # pylint: enable=protected-access + + inputs = signature_def.inputs + input_map = {} + for key, tensor_info in six.iteritems(inputs): + input_name = tensor_info.name + if ':' in input_name: + input_name = input_name[:input_name.find(':')] + + # When tensors are used as control inputs for operations, their names are + # prepended with a '^' character in the GraphDef. To handle possible control + # flow edge cases, control input names must be included in the input map. + control_dependency_name = '^' + input_name + + if key in features: + _check_same_dtype_and_shape(features[key], tensor_info, key) + input_map[input_name] = input_map[control_dependency_name] = features[key] + elif labels is not None and key in labels: + _check_same_dtype_and_shape(labels[key], tensor_info, key) + input_map[input_name] = input_map[control_dependency_name] = labels[key] + else: + raise ValueError( + 'Key \"%s\" not found in features or labels passed in to the model ' + 'function. All required keys: %s' % (key, inputs.keys())) + + return input_map + + +def _check_same_dtype_and_shape(tensor, tensor_info, name): + """Validate that tensor has the same properties as the TensorInfo proto. + + Args: + tensor: a `Tensor` object. + tensor_info: a `TensorInfo` proto. + name: Name of the input (to identify Tensor if an error is raised). + + Raises: + ValueError: If the tensor shape or dtype don't match the TensorInfo + """ + dtype_error = (tensor.dtype != dtypes.DType(tensor_info.dtype)) + shape_error = not tensor.shape.is_compatible_with(tensor_info.tensor_shape) + + if dtype_error or shape_error: + msg = 'Tensor shape and/or dtype validation failed for input %s:' % name + if dtype_error: + msg += ('\n\tExpected dtype: %s, Got: %s' + % (dtypes.DType(tensor_info.dtype), tensor.dtype)) + if shape_error: + msg += ('\n\tExpected shape: %s, Got: %s' + % (tensor_shape.TensorShape(tensor_info.tensor_shape), + tensor.shape)) + + raise ValueError(msg) + + +def _extract_eval_metrics(output_dict): + """Return a eval metric dict extracted from the output_dict. + + Eval metrics consist of a value tensor and an update op. Both must be in the + passed-in tensor dictionary for an eval metric to be added to the returned + dictionary. + + Args: + output_dict: a dict that maps strings to tensors. + + Returns: + dict mapping strings to (value, update_op) tuples. + """ + # pylint: disable=protected-access + metric_ops = {} + separator_char = export_output._SupervisedOutput._SEPARATOR_CHAR + + for key, tensor in six.iteritems(output_dict): + split_key = key.split(separator_char) + + # The metric name may contain the separator character, so recreate its name. + metric_name = separator_char.join(split_key[:-1]) + + if split_key[0] == export_output._SupervisedOutput.METRICS_NAME: + # If the key ends with the value suffix, and there is a corresponding + # key ending with the update_op suffix, then add tensors to metrics dict. + if split_key[-1] == export_output._SupervisedOutput.METRIC_VALUE_SUFFIX: + update_op = ''.join( + [metric_name, separator_char, + export_output._SupervisedOutput.METRIC_UPDATE_SUFFIX]) + if update_op in output_dict: + update_op_tensor = output_dict[update_op] + metric_ops[metric_name] = (tensor, update_op_tensor) + + # pylint: enable=protected-access + return metric_ops + + +def _validate_and_extract_outputs(mode, output_dict, method_name): + """Extract values from SignatureDef output dictionary. + + Args: + mode: One of the modes enumerated in `tf.estimator.ModeKeys`. + output_dict: dict of string SignatureDef keys to `Tensor`. + method_name: Method name of the SignatureDef as a string. + + Returns: + Tuple of ( + loss: `Tensor` object, + predictions: dictionary mapping string keys to `Tensor` objects, + metrics: dictionary mapping string keys to a tuple of two `Tensor` objects + ) + + Raises: + RuntimeError: raised if SignatureDef has an invalid method name for the mode + """ + # pylint: disable=protected-access + loss, predictions, metrics = None, None, None + + if mode == model_fn_lib.ModeKeys.PREDICT: + predictions = output_dict + else: + # Validate that the SignatureDef's method name matches the expected name for + # the given mode. + expected_method_name = signature_constants.SUPERVISED_TRAIN_METHOD_NAME + if mode == model_fn_lib.ModeKeys.EVAL: + expected_method_name = signature_constants.SUPERVISED_EVAL_METHOD_NAME + if method_name != expected_method_name: + raise RuntimeError( + 'Invalid SignatureDef method name for mode %s.\n\tExpected: %s\n\t' + 'Got: %s\nPlease ensure that the SavedModel was exported with ' + '`tf.contrib.estimator.export_all_saved_models()`.' % + (mode, expected_method_name, method_name)) + + # Extract loss, metrics and predictions from the output dict. + loss = output_dict[export_output._SupervisedOutput.LOSS_NAME] + metrics = _extract_eval_metrics(output_dict) + predictions = { + key: value for key, value in six.iteritems(output_dict) + if key.split(export_output._SupervisedOutput._SEPARATOR_CHAR)[0] == ( + export_output._SupervisedOutput.PREDICTIONS_NAME)} + + # pylint: enable=protected-access + return loss, predictions, metrics diff --git a/tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py new file mode 100644 index 0000000000000000000000000000000000000000..718da1367ce69285f37269c5631fa0be2b050c97 --- /dev/null +++ b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py @@ -0,0 +1,369 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for SavedModelEstimator.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import shutil +import tempfile + +from tensorflow.contrib.estimator.python.estimator import export as contrib_export +from tensorflow.contrib.estimator.python.estimator import saved_model_estimator +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.estimator import estimator +from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.estimator.export import export +from tensorflow.python.estimator.export import export_output +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import metrics as metrics_lib +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.training import monitored_session +from tensorflow.python.training import training + + +def dummy_input_fn(): + return dataset_ops.Dataset.from_tensors(( + {'x': constant_op.constant([[1], [-2]], dtype=dtypes.int64)}, + constant_op.constant([[4], [-3]], dtype=dtypes.float32))).repeat() + + +def dummy_input_fn_features_only(): + return dataset_ops.Dataset.from_tensors( + {'x': constant_op.constant([[5], [6]], dtype=dtypes.int64)}).repeat() + + +def dummy_supervised_receiver_fn(): + feature_spec = { + 'x': array_ops.placeholder( + dtype=dtypes.int64, shape=(2, 1), name='feature_x'), + } + label_spec = array_ops.placeholder( + dtype=dtypes.float32, shape=[2, 1], name='truth') + return export.build_raw_supervised_input_receiver_fn( + feature_spec, label_spec) + + +def dummy_serving_receiver_fn(): + feature_spec = {'x': array_ops.placeholder( + dtype=dtypes.int64, shape=(2, 1), name='feature_x'),} + return export.build_raw_serving_input_receiver_fn(feature_spec) + + +def model_fn_diff_modes(features, labels, mode): + _, _ = features, labels + v = variables.Variable(21, name='some_var') + train_op = None + loss = constant_op.constant(104) + if mode == model_fn_lib.ModeKeys.TRAIN: + loss = constant_op.constant(105) + predictions = constant_op.constant([501]) + train_op = control_flow_ops.group( + state_ops.assign_add(training.get_global_step(), 1), + state_ops.assign_add(v, 3)) + elif mode == model_fn_lib.ModeKeys.EVAL: + loss = constant_op.constant(106) + predictions = constant_op.constant([502]) + else: + loss = constant_op.constant(107) + predictions = constant_op.constant([503]) + return model_fn_lib.EstimatorSpec( + mode, + loss=loss, + train_op=train_op, + eval_metric_ops={ + 'abs_err': metrics_lib.mean_absolute_error( + constant_op.constant(0), predictions)}, + predictions=predictions) + + +class SavedModelEstimatorTest(test.TestCase): + + def setUp(self): + self.tmpdirs = [] + + def tearDown(self): + for tmpdir in self.tmpdirs: + # gfile.DeleteRecursively fails in the windows cmake test, so use shutil. + shutil.rmtree(tmpdir, ignore_errors=True) + self.tmpdirs = [] + + def _get_tmp_dir(self): + tmpdir = tempfile.mkdtemp() + self.tmpdirs.append(tmpdir) + return tmpdir + + def _export_estimator(self, train=True, evaluate=True, predict=True, + model_fn=model_fn_diff_modes): + est = estimator.Estimator(model_fn, self._get_tmp_dir()) + est.train(input_fn=dummy_input_fn, steps=10) + + input_receiver_fn_map = {} + if train: + input_receiver_fn_map[model_fn_lib.ModeKeys.TRAIN] = ( + dummy_supervised_receiver_fn()) + if evaluate: + input_receiver_fn_map[model_fn_lib.ModeKeys.EVAL] = ( + dummy_supervised_receiver_fn()) + if predict: + input_receiver_fn_map[model_fn_lib.ModeKeys.PREDICT] = ( + dummy_serving_receiver_fn()) + + export_base_path = self._get_tmp_dir() + export_dir = contrib_export.export_all_saved_models( + est, export_base_path, input_receiver_fn_map) + return export_dir + + def test_load_all_modes(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + sme.train(input_fn=dummy_input_fn, steps=1) + sme.train(input_fn=dummy_input_fn, steps=2) + self.assertEqual(13, sme.get_variable_value('global_step')) + self.assertEqual(60, sme.get_variable_value('some_var')) + + eval_results = sme.evaluate(dummy_input_fn, steps=5) + + self.assertEqual(13, eval_results['global_step']) + self.assertEqual(106, eval_results['loss']) + self.assertEqual(502, eval_results['metrics/abs_err']) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + def test_load_all_modes_no_train(self): + """Ensure that all functions can be used without requiring a ckpt.""" + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + eval_results = sme.evaluate(dummy_input_fn, steps=5) + self.assertEqual(10, eval_results['global_step']) + self.assertEqual(106, eval_results['loss']) + self.assertEqual(502, eval_results['metrics/abs_err']) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + def test_partial_exported_estimator(self): + sme1 = saved_model_estimator.SavedModelEstimator( + self._export_estimator(train=False, predict=False), self._get_tmp_dir()) + sme1.evaluate(dummy_input_fn, steps=5) + with self.assertRaisesRegexp(RuntimeError, 'train mode is not available'): + sme1.train(input_fn=dummy_input_fn, steps=1) + with self.assertRaisesRegexp(RuntimeError, 'infer mode is not available'): + next(sme1.predict(dummy_input_fn_features_only)) + + sme2 = saved_model_estimator.SavedModelEstimator( + self._export_estimator(evaluate=False), self._get_tmp_dir()) + sme2.train(input_fn=dummy_input_fn, steps=1) + next(sme2.predict(dummy_input_fn_features_only)) + with self.assertRaisesRegexp(RuntimeError, 'eval mode is not available'): + sme2.evaluate(dummy_input_fn, steps=5) + + def test_with_incorrect_input(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + + def bad_shape_input_fn(): + return dataset_ops.Dataset.from_tensors(( + {'x': constant_op.constant([1, 2], dtype=dtypes.int64)}, + constant_op.constant([1, 2], dtype=dtypes.float32))) + + with self.assertRaisesRegexp(ValueError, 'Expected shape'): + sme.train(bad_shape_input_fn, steps=1) + + def bad_dtype_input_fn(): + return dataset_ops.Dataset.from_tensors(( + {'x': constant_op.constant([[1], [1]], dtype=dtypes.int32)}, + constant_op.constant([[1], [1]], dtype=dtypes.int64))) + + with self.assertRaisesRegexp(ValueError, 'Expected dtype'): + sme.train(bad_dtype_input_fn, steps=1) + + def test_input_fn_with_global_step(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + + def bad_input_fn(): + training.get_or_create_global_step() + return dataset_ops.Dataset.from_tensors(( + {'x': constant_op.constant([[1], [1]], dtype=dtypes.int64)}, + constant_op.constant([[1], [1]], dtype=dtypes.float32))) + + with self.assertRaisesRegexp(RuntimeError, + 'Graph must not contain a global step tensor'): + sme.train(bad_input_fn, steps=1) + + def test_re_export_saved_model_serving_only(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + sme.train(dummy_input_fn, steps=3) + self.assertEqual(13, sme.get_variable_value('global_step')) + self.assertEqual(60, sme.get_variable_value('some_var')) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + # Export SavedModel, and test that the variable and prediction values are + # the same. + sme_export_dir = sme.export_savedmodel( + self._get_tmp_dir(), dummy_serving_receiver_fn()) + + sme2 = saved_model_estimator.SavedModelEstimator( + sme_export_dir, self._get_tmp_dir()) + self.assertEqual(60, sme.get_variable_value('some_var')) + self.assertEqual(13, sme.get_variable_value('global_step')) + + predictions = next(sme2.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + def test_re_export_saved_model(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + self.assertDictEqual( + {'loss': 106, 'metrics/abs_err': 502, 'global_step': 10}, + sme.evaluate(dummy_input_fn, steps=1)) + + sme.train(dummy_input_fn, steps=3) + self.assertDictEqual( + {'loss': 106, 'metrics/abs_err': 502, 'global_step': 13}, + sme.evaluate(dummy_input_fn, steps=1)) + self.assertEqual(60, sme.get_variable_value('some_var')) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + # Export SavedModel for all modes + input_receiver_fn_map = { + model_fn_lib.ModeKeys.TRAIN: dummy_supervised_receiver_fn(), + model_fn_lib.ModeKeys.EVAL: dummy_supervised_receiver_fn(), + model_fn_lib.ModeKeys.PREDICT: dummy_serving_receiver_fn()} + sme_export_dir = contrib_export.export_all_saved_models( + sme, self._get_tmp_dir(), input_receiver_fn_map) + + sme2 = saved_model_estimator.SavedModelEstimator( + sme_export_dir, self._get_tmp_dir()) + self.assertDictEqual( + {'loss': 106, 'metrics/abs_err': 502, 'global_step': 13}, + sme.evaluate(dummy_input_fn, steps=1)) + self.assertEqual(60, sme.get_variable_value('some_var')) + + sme.train(dummy_input_fn, steps=7) + self.assertEqual(20, sme.get_variable_value('global_step')) + + predictions = next(sme2.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + def test_load_saved_model_from_serving_only(self): + def model_fn(features, labels, mode): + _, _ = features, labels + return model_fn_lib.EstimatorSpec( + mode, + loss=constant_op.constant([103]), + train_op=state_ops.assign_add(training.get_global_step(), 1), + predictions=constant_op.constant([502]), + export_outputs={'test': export_output.ClassificationOutput( + constant_op.constant([[32.]]))}) + + est = estimator.Estimator(model_fn, self._get_tmp_dir()) + est.train(input_fn=dummy_input_fn, steps=10) + + def serving_input_receiver_fn(): + return export.ServingInputReceiver( + {'test-features': constant_op.constant([[1], [1]])}, + array_ops.placeholder(dtype=dtypes.string)) + + export_dir = est.export_savedmodel( + self._get_tmp_dir(), serving_input_receiver_fn) + + sme = saved_model_estimator.SavedModelEstimator( + export_dir, self._get_tmp_dir()) + + def input_fn(): + return {'inputs': constant_op.constant('someinputstr')} + + prediction = next(sme.predict(input_fn)) + self.assertDictEqual({'scores': 32}, prediction) + + def test_with_local_init_op(self): + def model_fn(features, labels, mode): + _, _ = features, labels + v = variables.Variable(21, name='some_var') + scaffold = monitored_session.Scaffold( + local_init_op=state_ops.assign_add(v, -3).op + ) + return model_fn_lib.EstimatorSpec( + mode, + scaffold=scaffold, + train_op=state_ops.assign_add(training.get_global_step(), 1), + loss=array_ops.identity(v)) + export_dir = self._export_estimator(predict=False, model_fn=model_fn) + sme = saved_model_estimator.SavedModelEstimator( + export_dir, self._get_tmp_dir()) + + eval_results1 = sme.evaluate(dummy_input_fn, steps=2) + self.assertEqual(15, eval_results1['loss']) + + sme.train(dummy_input_fn, steps=1) + self.assertEqual(15, sme.get_variable_value('some_var')) + + eval_results2 = sme.evaluate(dummy_input_fn, steps=5) + self.assertEqual(12, eval_results2['loss']) + + def test_with_working_input_fn(self): + def model_fn(features, labels, mode): + loss = None + if labels is not None: + loss = labels[0][0] + labels[1][0] + return model_fn_lib.EstimatorSpec( + mode, + loss=loss, + train_op=state_ops.assign_add(training.get_global_step(), 1), + predictions={'features_0': array_ops.identity([features['x'][0][0]]), + 'features_1': array_ops.identity([features['x'][1][0]])}) + + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(model_fn=model_fn), self._get_tmp_dir()) + eval_results = sme.evaluate(dummy_input_fn, steps=1) + self.assertEqual(1, eval_results['loss']) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'features_0': 5, 'features_1': 6}, predictions) + + def test_control_dependency(self): + # Control dependencies are saved with "^" appended to the start of the input + # name. The input map must include control dependencies as well. + def model_fn(features, labels, mode): + _ = labels + with ops.control_dependencies([features['x']]): + loss = features['x'][1][0] + return model_fn_lib.EstimatorSpec( + mode, + loss=loss, + train_op=state_ops.assign_add(training.get_global_step(), 1)) + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(train=False, predict=False, model_fn=model_fn), + self._get_tmp_dir()) + sme.evaluate(dummy_input_fn, steps=1) # Should run without error + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/framework/python/ops/variables.py b/tensorflow/contrib/framework/python/ops/variables.py index e8e318001972934c7d2154bc14744823a3ba09f9..322d5c335e6a77c46c7ce5dd795e21a2d5a1f8f9 100644 --- a/tensorflow/contrib/framework/python/ops/variables.py +++ b/tensorflow/contrib/framework/python/ops/variables.py @@ -34,6 +34,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope +from tensorflow.python.ops import variables from tensorflow.python.platform import resource_loader from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import saver as tf_saver @@ -199,10 +200,20 @@ def global_variable(initial_value, @contrib_add_arg_scope -def variable(name, shape=None, dtype=None, initializer=None, - regularizer=None, trainable=True, collections=None, - caching_device=None, device=None, - partitioner=None, custom_getter=None, use_resource=None): +def variable(name, + shape=None, + dtype=None, + initializer=None, + regularizer=None, + trainable=True, + collections=None, + caching_device=None, + device=None, + partitioner=None, + custom_getter=None, + use_resource=None, + synchronization=variables.VariableSynchronization.AUTO, + aggregation=variables.VariableAggregation.NONE): """Gets an existing variable with these parameters or creates a new one. Args: @@ -228,6 +239,15 @@ def variable(name, shape=None, dtype=None, initializer=None, custom_getter: Callable that allows overwriting the internal get_variable method and has to have the same signature. use_resource: If `True` use a ResourceVariable instead of a Variable. + synchronization: Indicates when a distributed a variable will be + aggregated. Accepted values are constants defined in the class + @{tf.VariableSynchronization}. By default the synchronization is set to + `AUTO` and the current `DistributionStrategy` chooses + when to synchronize. If `synchronization` is set to `ON_READ`, + `trainable` must not be set to `True`. + aggregation: Indicates how a distributed variable will be aggregated. + Accepted values are constants defined in the class + @{tf.VariableAggregation}. Returns: The created or existing variable. @@ -242,21 +262,36 @@ def variable(name, shape=None, dtype=None, initializer=None, getter = functools.partial(custom_getter, reuse=variable_scope.get_variable_scope().reuse) with ops.device(device or ''): - return getter(name, shape=shape, dtype=dtype, - initializer=initializer, - regularizer=regularizer, - trainable=trainable, - collections=collections, - caching_device=caching_device, - partitioner=partitioner, - use_resource=use_resource) + return getter( + name, + shape=shape, + dtype=dtype, + initializer=initializer, + regularizer=regularizer, + trainable=trainable, + collections=collections, + caching_device=caching_device, + partitioner=partitioner, + use_resource=use_resource, + synchronization=synchronization, + aggregation=aggregation) @contrib_add_arg_scope -def model_variable(name, shape=None, dtype=dtypes.float32, initializer=None, - regularizer=None, trainable=True, collections=None, - caching_device=None, device=None, partitioner=None, - custom_getter=None, use_resource=None): +def model_variable(name, + shape=None, + dtype=dtypes.float32, + initializer=None, + regularizer=None, + trainable=True, + collections=None, + caching_device=None, + device=None, + partitioner=None, + custom_getter=None, + use_resource=None, + synchronization=variables.VariableSynchronization.AUTO, + aggregation=variables.VariableAggregation.NONE): """Gets an existing model variable with these parameters or creates a new one. Args: @@ -283,18 +318,36 @@ def model_variable(name, shape=None, dtype=dtypes.float32, initializer=None, custom_getter: Callable that allows overwriting the internal get_variable method and has to have the same signature. use_resource: If `True` use a ResourceVariable instead of a Variable. + synchronization: Indicates when a distributed a variable will be + aggregated. Accepted values are constants defined in the class + @{tf.VariableSynchronization}. By default the synchronization is set to + `AUTO` and the current `DistributionStrategy` chooses + when to synchronize. If `synchronization` is set to `ON_READ`, + `trainable` must not be set to `True`. + aggregation: Indicates how a distributed variable will be aggregated. + Accepted values are constants defined in the class + @{tf.VariableAggregation}. Returns: The created or existing variable. """ collections = list(collections or []) collections += [ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.MODEL_VARIABLES] - var = variable(name, shape=shape, dtype=dtype, - initializer=initializer, regularizer=regularizer, - trainable=trainable, collections=collections, - caching_device=caching_device, device=device, - partitioner=partitioner, custom_getter=custom_getter, - use_resource=use_resource) + var = variable( + name, + shape=shape, + dtype=dtype, + initializer=initializer, + regularizer=regularizer, + trainable=trainable, + collections=collections, + caching_device=caching_device, + device=device, + partitioner=partitioner, + custom_getter=custom_getter, + use_resource=use_resource, + synchronization=synchronization, + aggregation=aggregation) return var diff --git a/tensorflow/contrib/framework/python/ops/variables_test.py b/tensorflow/contrib/framework/python/ops/variables_test.py index 7e0c7dbec1d9266b53a169fe83b88d1e3af77d04..3c44630a51deb8a468165e8da458600665d0ada1 100644 --- a/tensorflow/contrib/framework/python/ops/variables_test.py +++ b/tensorflow/contrib/framework/python/ops/variables_test.py @@ -106,8 +106,9 @@ class LocalVariableTest(test.TestCase): def testResourceVariable(self): a = variables_lib2.local_variable(0) b = variables_lib2.local_variable(0, use_resource=True) - self.assertEqual(type(a), variables_lib.Variable) - self.assertEqual(type(b), resource_variable_ops.ResourceVariable) + self.assertTrue(isinstance(a, variables_lib.Variable)) + self.assertFalse(isinstance(a, resource_variable_ops.ResourceVariable)) + self.assertTrue(isinstance(b, resource_variable_ops.ResourceVariable)) class GlobalVariableTest(test.TestCase): @@ -176,8 +177,9 @@ class GlobalVariableTest(test.TestCase): def testResourceVariable(self): a = variables_lib2.global_variable(0) b = variables_lib2.global_variable(0, use_resource=True) - self.assertEqual(type(a), variables_lib.Variable) - self.assertEqual(type(b), resource_variable_ops.ResourceVariable) + self.assertTrue(isinstance(a, variables_lib.Variable)) + self.assertFalse(isinstance(a, resource_variable_ops.ResourceVariable)) + self.assertTrue(isinstance(b, resource_variable_ops.ResourceVariable)) class GlobalStepTest(test.TestCase): diff --git a/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc b/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc index 2458f7554afdc12709571c551a8323cda7fa5c17..0ccb4583ab653bc2ef6c5c810c902a9332e82df9 100644 --- a/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc +++ b/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc @@ -135,9 +135,12 @@ class FusedConv2DBiasActivationOp : public OpKernel { context->GetAttr("activation_mode", &activation_mode_str)); OP_REQUIRES_OK(context, GetActivationModeFromString(activation_mode_str, &activation_mode_)); - OP_REQUIRES(context, activation_mode_ == ActivationMode::RELU, - errors::InvalidArgument("Current implementation only supports " - "RELU as the activation function.")); + OP_REQUIRES(context, + activation_mode_ == ActivationMode::RELU || + activation_mode_ == ActivationMode::NONE, + errors::InvalidArgument( + "Current implementation only supports RELU or NONE " + "as the activation function.")); cudnn_use_autotune_ = CudnnUseAutotune(); } @@ -440,6 +443,8 @@ void LaunchFusedConv2DBiasActivationOp:: : dnn::DataLayout::kBatchDepthYX; constexpr auto filter_layout = is_int8x4 ? dnn::FilterLayout::kOutputInputYX4 : dnn::FilterLayout::kOutputInputYX; + constexpr auto compute_data_format = + is_int8x4 ? FORMAT_NCHW_VECT_C : FORMAT_NCHW; dnn::BatchDescriptor conv_input_desc; conv_input_desc.set_count(batch_size) @@ -526,6 +531,7 @@ void LaunchFusedConv2DBiasActivationOp:: batch_size, conv_input_depth, {{conv_input_rows, conv_input_cols}}, + compute_data_format, output_depth, {{filter_rows, filter_cols}}, // TODO(yangzihao): Add support for arbitrary dilations for fused conv. @@ -538,6 +544,18 @@ void LaunchFusedConv2DBiasActivationOp:: activation_mode, }; + dnn::ActivationMode dnn_activation_mode; + switch (activation_mode) { + case ActivationMode::NONE: + dnn_activation_mode = dnn::ActivationMode::kNone; + break; + case ActivationMode::RELU: + dnn_activation_mode = dnn::ActivationMode::kRelu; + break; + default: + LOG(FATAL) << "Activation mode " << activation_mode << " not supported"; + } + dnn::AlgorithmConfig algorithm_config; if (cudnn_use_autotune && !AutoTuneConvBiasActivation::GetInstance()->Find( fused_conv_parameters, &algorithm_config)) { @@ -558,10 +576,9 @@ void LaunchFusedConv2DBiasActivationOp:: ->ThenFusedConvolveWithAlgorithm( conv_input_desc, conv_input_ptr, conv_input_scale, filter_desc, filter_ptr, conv_desc, side_input_ptr, - side_input_scale, bias_desc, bias_ptr, - dnn::ActivationMode::kRelu, output_desc, &output_ptr, - &scratch_allocator, dnn::AlgorithmConfig(profile_algorithm), - &profile_result) + side_input_scale, bias_desc, bias_ptr, dnn_activation_mode, + output_desc, &output_ptr, &scratch_allocator, + dnn::AlgorithmConfig(profile_algorithm), &profile_result) .ok(); if (cudnn_launch_status) { if (profile_result.is_valid()) { @@ -597,7 +614,7 @@ void LaunchFusedConv2DBiasActivationOp:: ->ThenFusedConvolveWithAlgorithm( conv_input_desc, conv_input_ptr, conv_input_scale, filter_desc, filter_ptr, conv_desc, side_input_ptr, side_input_scale, - bias_desc, bias_ptr, dnn::ActivationMode::kRelu, output_desc, + bias_desc, bias_ptr, dnn_activation_mode, output_desc, &output_ptr, &scratch_allocator, algorithm_config, /*output_profile_result=*/nullptr) .ok(); diff --git a/tensorflow/contrib/fused_conv/kernels/fused_conv_ops_gpu.h b/tensorflow/contrib/fused_conv/kernels/fused_conv_ops_gpu.h index ba52697679dafc239b1dac5562573b3589877a8c..b9c131a2e91469c52931080d8a5af90247bd16f0 100644 --- a/tensorflow/contrib/fused_conv/kernels/fused_conv_ops_gpu.h +++ b/tensorflow/contrib/fused_conv/kernels/fused_conv_ops_gpu.h @@ -29,13 +29,13 @@ namespace tensorflow { class FusedConvParameters : public ConvParameters { public: FusedConvParameters(int64 batch, int64 in_depths, const SpatialArray& in, - int64 out_depths, const SpatialArray& filter, - const SpatialArray& dilation, const SpatialArray& stride, - const SpatialArray& padding, DataType dtype, - int device_id, bool has_side_input, + TensorFormat data_format, int64 out_depths, + const SpatialArray& filter, const SpatialArray& dilation, + const SpatialArray& stride, const SpatialArray& padding, + DataType dtype, int device_id, bool has_side_input, ActivationMode activation_mode) - : ConvParameters(batch, in_depths, in, out_depths, filter, dilation, - stride, padding, dtype, device_id), + : ConvParameters(batch, in_depths, in, data_format, out_depths, filter, + dilation, stride, padding, dtype, device_id), activation_mode_(activation_mode), has_side_input_(has_side_input) { hash_code_ = Hash64Combine(hash_code_, has_side_input); diff --git a/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc b/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc index bafd1d59418f0ba47ebbdaabbf06f8e5471fc1a1..410571f3783263152fda93980580182eb666886d 100644 --- a/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc +++ b/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc @@ -44,7 +44,7 @@ REGISTER_OP("FusedConv2DBiasActivation") .Attr(GetPaddingAttrString()) .Attr("data_format: {'NHWC', 'NCHW', 'NCHW_VECT_C'} = 'NHWC'") .Attr("filter_format: {'HWIO', 'OIHW', 'OIHW_VECT_I'} = 'HWIO'") - .Attr("activation_mode: {'Relu'} = 'Relu'") + .Attr("activation_mode: {'Relu', 'None'} = 'Relu'") .Attr("dilations: list(int) = [1, 1, 1, 1]") .SetShapeFn([](shape_inference::InferenceContext* c) { using shape_inference::ShapeHandle; @@ -144,7 +144,7 @@ REGISTER_OP("FusedConv2DBiasActivation") `qint8 [ output_channels, input_channels / 4, kernel_height, kernel_width, input_channels % 4 ]` activation_mode: The activation applied to the output. - Currently must be "Relu". + Must be "Relu" or "None". dilations: 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined diff --git a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op.py b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op.py index 983b6dc8e5a1512ba81ecbc8d5ca5adaea09afe4..cdc07b935dcc42ce3c0cef6bb8f4a126fe82c883 100644 --- a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op.py +++ b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op.py @@ -66,8 +66,10 @@ def fused_conv2d_bias_activation(conv_input, This is optional and defaults to 0. side_input: A `Tensor` of the format specified by `data_format`. This is useful for implementing ResNet blocks. - activation_mode: (optional) currently must be the default "Relu". - Note that in qint8 mode, it also clips to 127, so acts like ReluX. + activation_mode: (optional) currently supports the default "Relu", or + "None" activation function. + Note: in qint8 mode, "None" actually clips to the range [-128, 127], + while "Relu" clips to the range [0, 127]. data_format: Specifies the data format. Possible values are: "NHWC" float [batch, height, width, channels] diff --git a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py index 4d62ac65ff619f98a18387058fdc8a0eade0d8f8..0185ef662c2ed05b1ceaf0e3e8071bad4c0d1a0a 100644 --- a/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py +++ b/tensorflow/contrib/fused_conv/python/ops/fused_conv2d_bias_activation_op_test.py @@ -622,7 +622,7 @@ def HwioToOihw(in_tensor): def SimulateFusedConv2dBiasActivationInt8(conv_input_scale, conv_input, kernel, padding, strides, side_input_scale, - side_input, biases): + side_input, biases, apply_relu): """Simulates the int8 fused 2-D convolution op using separate float ops. The arguments and return values have the same format, meanings and @@ -636,6 +636,9 @@ def SimulateFusedConv2dBiasActivationInt8(conv_input_scale, conv_input, kernel, side_input_scale: A scalar 'float'. side_input: A `Tensor` of type `qint8` in NCHW_VECT_C layout. biases: A `Tensor` of type `float32` in NCHW layout. + apply_relu: A boolean to specify whether to apply "Relu" activation function + that clips outputs to the range [0, 127], or "None" activation that clips + to the range [-128, 127]. Returns: A `Tensor` of type `qint8` in NCHW_VECT_C layout. """ @@ -649,10 +652,12 @@ def SimulateFusedConv2dBiasActivationInt8(conv_input_scale, conv_input, kernel, conv_and_side_inputs = conv_result + side_input_scale * NchwVectCToNchw( gen_array_ops.dequantize(side_input, -128, 127)) - logit = nn_ops.bias_add(conv_and_side_inputs, biases, data_format="NCHW") + output = nn_ops.bias_add(conv_and_side_inputs, biases, data_format="NCHW") + if apply_relu: + output = nn_ops.relu(output) result, _, _ = gen_array_ops.quantize_v2( - NchwToNchwVectC(nn_ops.relu(logit)), -128, 127, dtypes.qint8) + NchwToNchwVectC(output), -128, 127, dtypes.qint8) return result @@ -795,7 +800,7 @@ class FusedConvInt8Tests(test.TestCase): }, ] - def runTest(self, test_param): + def runTest(self, test_param, apply_relu): batch_size = test_param["batch_size"] input_channels = test_param["input_channels"] output_channels = test_param["output_channels"] @@ -831,8 +836,8 @@ class FusedConvInt8Tests(test.TestCase): vertical_stride, padding_type) output_width = CalculateConvolvedOutputDim(input_width, filter_width, horizontal_stride, padding_type) - tf_logging.info("output_height=", output_height, ", output_width=", - output_width) + tf_logging.info("output_height=", output_height, ", output_width=", + output_width) side_input, _, _ = gen_array_ops.quantize_v2( random_ops.random_uniform( @@ -858,12 +863,13 @@ class FusedConvInt8Tests(test.TestCase): conv_input_scale=conv_input_scale, side_input_scale=side_input_scale, side_input=side_input, + activation_mode="Relu" if apply_relu else "None", data_format="NCHW_VECT_C", filter_format="OIHW_VECT_I") expected = SimulateFusedConv2dBiasActivationInt8( conv_input_scale, conv_input, kernel, padding_type, strides, - side_input_scale, side_input, biases) + side_input_scale, side_input, biases, apply_relu) with self.test_session(use_gpu=True) as sess: actual_y, expected_y = sess.run([actual, expected]) @@ -877,8 +883,9 @@ class FusedConvInt8Tests(test.TestCase): tf_logging.info("int8 test skipped because not run with --config=cuda or " "no GPUs with compute capability >= 6.1 are available.") return - for test_param in self._test_params: - self.runTest(test_param) + for apply_relu in [True, False]: + for test_param in self._test_params: + self.runTest(test_param, apply_relu) if __name__ == "__main__": diff --git a/tensorflow/contrib/gan/BUILD b/tensorflow/contrib/gan/BUILD index b305f37791d71f5a6edeada2bb710a2e5f23087d..053d4e3e977ed1baed8ceeca1a983e999b1ad1ff 100644 --- a/tensorflow/contrib/gan/BUILD +++ b/tensorflow/contrib/gan/BUILD @@ -42,9 +42,12 @@ py_library( "//tensorflow/contrib/training:training_py", "//tensorflow/python:array_ops", "//tensorflow/python:check_ops", + "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", "//tensorflow/python:init_ops", + "//tensorflow/python:random_ops", "//tensorflow/python:training", + "//tensorflow/python:training_util", "//tensorflow/python:variable_scope", "//tensorflow/python/ops/distributions", "//tensorflow/python/ops/losses", @@ -54,26 +57,31 @@ py_library( py_test( name = "train_test", srcs = ["python/train_test.py"], + shard_count = 50, srcs_version = "PY2AND3", tags = ["notsan"], deps = [ - ":features", ":namedtuples", + ":random_tensor_pool", ":train", "//tensorflow/contrib/framework:framework_py", + "//tensorflow/contrib/layers:layers_py", "//tensorflow/contrib/slim:learning", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", + "//tensorflow/python:math_ops", "//tensorflow/python:random_ops", "//tensorflow/python:random_seed", "//tensorflow/python:training", + "//tensorflow/python:training_util", "//tensorflow/python:variable_scope", "//tensorflow/python:variables", "//tensorflow/python/ops/distributions", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) @@ -188,10 +196,16 @@ py_test( srcs = ["python/losses/python/tuple_losses_test.py"], srcs_version = "PY2AND3", deps = [ + ":losses_impl", + ":namedtuples", ":tuple_losses", + "//tensorflow/contrib/layers:layers_py", + "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", + "//tensorflow/python:math_ops", + "//tensorflow/python:variable_scope", "//tensorflow/python:variables", "//third_party/py/numpy", ], @@ -248,12 +262,15 @@ py_library( py_test( name = "random_tensor_pool_test", srcs = ["python/features/python/random_tensor_pool_test.py"], + shard_count = 6, srcs_version = "PY2AND3", deps = [ ":random_tensor_pool", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", "//third_party/py/numpy", ], ) @@ -344,9 +361,11 @@ py_library( "//tensorflow/python:image_ops", "//tensorflow/python:linalg_ops", "//tensorflow/python:math_ops", + "//tensorflow/python:nn", "//tensorflow/python:nn_ops", "//tensorflow/python:platform", "//tensorflow/python:util", + "@six_archive//:six", ], ) @@ -470,12 +489,12 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - ":head", ":namedtuples", ":summaries", ":train", "//tensorflow/contrib/framework:framework_py", "//tensorflow/python:framework_ops", + "//tensorflow/python:metrics", "//tensorflow/python:util", "//tensorflow/python:variable_scope", "//tensorflow/python/estimator", @@ -498,16 +517,19 @@ py_test( "//tensorflow/core:protos_all_py", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", "//tensorflow/python:dtypes", "//tensorflow/python:framework_ops", + "//tensorflow/python:math_ops", + "//tensorflow/python:metrics", "//tensorflow/python:parsing_ops", "//tensorflow/python:summary", "//tensorflow/python:training", - "//tensorflow/python/estimator:head", + "//tensorflow/python:training_util", + "//tensorflow/python:variable_scope", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/estimator:numpy_io", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", "@six_archive//:six", ], ) diff --git a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py index 4092b320042162e4eb4c5f4879c2c3ea5dc14fc9..8e4affb9b4f95bf5afab0f50c86954e60a942279 100644 --- a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py +++ b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py @@ -24,11 +24,11 @@ import enum from tensorflow.contrib.framework.python.ops import variables as variable_lib from tensorflow.contrib.gan.python import namedtuples as tfgan_tuples from tensorflow.contrib.gan.python import train as tfgan_train -from tensorflow.contrib.gan.python.estimator.python import head as head_lib from tensorflow.contrib.gan.python.eval.python import summaries as tfgan_summaries from tensorflow.python.estimator import estimator from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.framework import ops +from tensorflow.python.ops import metrics as metrics_lib from tensorflow.python.ops import variable_scope from tensorflow.python.util import tf_inspect as inspect @@ -154,94 +154,93 @@ class GANEstimator(estimator.Estimator): use_loss_summaries: If `True`, add loss summaries. If `False`, does not. If `None`, uses defaults. config: `RunConfig` object to configure the runtime settings. + + Raises: + ValueError: If loss functions aren't callable. + ValueError: If `use_loss_summaries` isn't boolean or `None`. + ValueError: If `get_hooks_fn` isn't callable or `None`. """ - # TODO(joelshor): Explicitly validate inputs. + if not callable(generator_loss_fn): + raise ValueError('generator_loss_fn must be callable.') + if not callable(discriminator_loss_fn): + raise ValueError('discriminator_loss_fn must be callable.') + if use_loss_summaries not in [True, False, None]: + raise ValueError('use_loss_summaries must be True, False or None.') + if get_hooks_fn is not None and not callable(get_hooks_fn): + raise TypeError('get_hooks_fn must be callable.') def _model_fn(features, labels, mode): - gopt = (generator_optimizer() if callable(generator_optimizer) else - generator_optimizer) - dopt = (discriminator_optimizer() if callable(discriminator_optimizer) - else discriminator_optimizer) - gan_head = head_lib.gan_head( - generator_loss_fn, discriminator_loss_fn, gopt, dopt, - use_loss_summaries, get_hooks_fn=get_hooks_fn, - get_eval_metric_ops_fn=get_eval_metric_ops_fn) - return _gan_model_fn( - features, labels, mode, generator_fn, discriminator_fn, gan_head, + """GANEstimator model function.""" + if mode not in [model_fn_lib.ModeKeys.TRAIN, model_fn_lib.ModeKeys.EVAL, + model_fn_lib.ModeKeys.PREDICT]: + raise ValueError('Mode not recognized: %s' % mode) + real_data = labels # rename inputs for clarity + generator_inputs = features # rename inputs for clarity + + # Make GANModel, which encapsulates the GAN model architectures. + gan_model = _get_gan_model( + mode, generator_fn, discriminator_fn, real_data, generator_inputs, add_summaries) + # Make the EstimatorSpec, which incorporates the GANModel, losses, eval + # metrics, and optimizers (if required). + return _get_estimator_spec( + mode, gan_model, generator_loss_fn, discriminator_loss_fn, + get_eval_metric_ops_fn, generator_optimizer, discriminator_optimizer, + get_hooks_fn) + super(GANEstimator, self).__init__( model_fn=_model_fn, model_dir=model_dir, config=config) -def _gan_model_fn( - features, - labels, - mode, - generator_fn, - discriminator_fn, - head, - add_summaries=None, - generator_scope_name='Generator'): - """The `model_fn` for the GAN estimator. - - We make the following convention: - features -> TFGAN's `generator_inputs` - labels -> TFGAN's `real_data` - - Args: - features: A dictionary to feed to generator. In the unconditional case, - this might be just `noise`. In the conditional GAN case, this - might be the generator's conditioning. The `generator_fn` determines - what the required keys are. - labels: Real data. Can be any structure, as long as `discriminator_fn` - can accept it for the first argument. - mode: Defines whether this is training, evaluation or prediction. - See `ModeKeys`. - generator_fn: A python lambda that takes `generator_inputs` as inputs and - returns the outputs of the GAN generator. - discriminator_fn: A python lambda that takes `real_data`/`generated data` - and `generator_inputs`. Outputs a Tensor in the range [-inf, inf]. - head: A `Head` instance suitable for GANs. - add_summaries: `None`, a single `SummaryType`, or a list of `SummaryType`. - generator_scope_name: The name of the generator scope. We need this to be - the same for GANModels produced by TFGAN's `train.gan_model` and the - manually constructed ones for predictions. - - Returns: - `ModelFnOps` - - Raises: - ValueError: If `labels` isn't `None` during prediction. - """ - real_data = labels - generator_inputs = features - - if mode == model_fn_lib.ModeKeys.TRAIN: - gan_model = _make_train_gan_model( - generator_fn, discriminator_fn, real_data, generator_inputs, - generator_scope_name, add_summaries) - elif mode == model_fn_lib.ModeKeys.EVAL: - gan_model = _make_eval_gan_model( - generator_fn, discriminator_fn, real_data, generator_inputs, - generator_scope_name, add_summaries) - else: +def _get_gan_model( + mode, generator_fn, discriminator_fn, real_data, generator_inputs, + add_summaries, generator_scope='Generator'): + """Makes the GANModel tuple, which encapsulates the GAN model architecture.""" + if mode == model_fn_lib.ModeKeys.PREDICT: if real_data is not None: raise ValueError('`labels` must be `None` when mode is `predict`. ' 'Instead, found %s' % real_data) gan_model = _make_prediction_gan_model( - generator_inputs, generator_fn, generator_scope_name) + generator_inputs, generator_fn, generator_scope) + else: # model_fn_lib.ModeKeys.TRAIN or model_fn_lib.ModeKeys.EVAL + gan_model = _make_gan_model( + generator_fn, discriminator_fn, real_data, generator_inputs, + generator_scope, add_summaries, mode) - return head.create_estimator_spec( - features=None, - mode=mode, - logits=gan_model, - labels=None) + return gan_model + + +def _get_estimator_spec( + mode, gan_model, generator_loss_fn, discriminator_loss_fn, + get_eval_metric_ops_fn, generator_optimizer, discriminator_optimizer, + get_hooks_fn=None): + """Get the EstimatorSpec for the current mode.""" + if mode == model_fn_lib.ModeKeys.PREDICT: + estimator_spec = model_fn_lib.EstimatorSpec( + mode=mode, predictions=gan_model.generated_data) + else: + gan_loss = tfgan_tuples.GANLoss( + generator_loss=generator_loss_fn(gan_model), + discriminator_loss=discriminator_loss_fn(gan_model)) + if mode == model_fn_lib.ModeKeys.EVAL: + estimator_spec = _get_eval_estimator_spec( + gan_model, gan_loss, get_eval_metric_ops_fn) + else: # model_fn_lib.ModeKeys.TRAIN: + gopt = (generator_optimizer() if callable(generator_optimizer) else + generator_optimizer) + dopt = (discriminator_optimizer() if callable(discriminator_optimizer) + else discriminator_optimizer) + get_hooks_fn = get_hooks_fn or tfgan_train.get_sequential_train_hooks() + estimator_spec = _get_train_estimator_spec( + gan_model, gan_loss, gopt, dopt, get_hooks_fn) + + return estimator_spec def _make_gan_model(generator_fn, discriminator_fn, real_data, generator_inputs, generator_scope, add_summaries, mode): - """Make a `GANModel`, and optionally pass in `mode`.""" + """Construct a `GANModel`, and optionally pass in `mode`.""" # If network functions have an argument `mode`, pass mode to it. if 'mode' in inspect.getargspec(generator_fn).args: generator_fn = functools.partial(generator_fn, mode=mode) @@ -264,22 +263,6 @@ def _make_gan_model(generator_fn, discriminator_fn, real_data, return gan_model -def _make_train_gan_model(generator_fn, discriminator_fn, real_data, - generator_inputs, generator_scope, add_summaries): - """Make a `GANModel` for training.""" - return _make_gan_model(generator_fn, discriminator_fn, real_data, - generator_inputs, generator_scope, add_summaries, - model_fn_lib.ModeKeys.TRAIN) - - -def _make_eval_gan_model(generator_fn, discriminator_fn, real_data, - generator_inputs, generator_scope, add_summaries): - """Make a `GANModel` for evaluation.""" - return _make_gan_model(generator_fn, discriminator_fn, real_data, - generator_inputs, generator_scope, add_summaries, - model_fn_lib.ModeKeys.EVAL) - - def _make_prediction_gan_model(generator_inputs, generator_fn, generator_scope): """Make a `GANModel` from just the generator.""" # If `generator_fn` has an argument `mode`, pass mode to it. @@ -303,3 +286,46 @@ def _make_prediction_gan_model(generator_inputs, generator_fn, generator_scope): discriminator_variables=None, discriminator_scope=None, discriminator_fn=None) + + +def _get_eval_estimator_spec(gan_model, gan_loss, get_eval_metric_ops_fn=None, + name=None): + """Return an EstimatorSpec for the eval case.""" + scalar_loss = gan_loss.generator_loss + gan_loss.discriminator_loss + with ops.name_scope(None, 'metrics', + [gan_loss.generator_loss, + gan_loss.discriminator_loss]): + def _summary_key(head_name, val): + return '%s/%s' % (val, head_name) if head_name else val + eval_metric_ops = { + _summary_key(name, 'generator_loss'): + metrics_lib.mean(gan_loss.generator_loss), + _summary_key(name, 'discriminator_loss'): + metrics_lib.mean(gan_loss.discriminator_loss) + } + if get_eval_metric_ops_fn is not None: + custom_eval_metric_ops = get_eval_metric_ops_fn(gan_model) + if not isinstance(custom_eval_metric_ops, dict): + raise TypeError('get_eval_metric_ops_fn must return a dict, ' + 'received: {}'.format(custom_eval_metric_ops)) + eval_metric_ops.update(custom_eval_metric_ops) + return model_fn_lib.EstimatorSpec( + mode=model_fn_lib.ModeKeys.EVAL, + predictions=gan_model.generated_data, + loss=scalar_loss, + eval_metric_ops=eval_metric_ops) + + +def _get_train_estimator_spec( + gan_model, gan_loss, generator_optimizer, discriminator_optimizer, + get_hooks_fn, train_op_fn=tfgan_train.gan_train_ops): + """Return an EstimatorSpec for the train case.""" + scalar_loss = gan_loss.generator_loss + gan_loss.discriminator_loss + train_ops = train_op_fn(gan_model, gan_loss, generator_optimizer, + discriminator_optimizer) + training_hooks = get_hooks_fn(train_ops) + return model_fn_lib.EstimatorSpec( + loss=scalar_loss, + mode=model_fn_lib.ModeKeys.TRAIN, + train_op=train_ops.global_step_inc_op, + training_hooks=training_hooks) diff --git a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py index 955482599b372be3f0d0cbc81451c514958d0eb1..9ac9c6ca9ca86a8a9abe9c0f6ebc4cdf5dd2cfb1 100644 --- a/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py +++ b/tensorflow/contrib/gan/python/estimator/python/gan_estimator_test.py @@ -21,30 +21,30 @@ from __future__ import print_function import shutil import tempfile +from absl.testing import parameterized import numpy as np import six from tensorflow.contrib import layers -from tensorflow.contrib.gan.python import namedtuples +from tensorflow.contrib.gan.python import namedtuples as tfgan_tuples from tensorflow.contrib.gan.python.estimator.python import gan_estimator_impl as estimator from tensorflow.contrib.gan.python.losses.python import tuple_losses as losses from tensorflow.contrib.learn.python.learn.learn_io import graph_io from tensorflow.core.example import example_pb2 from tensorflow.core.example import feature_pb2 from tensorflow.python.estimator import model_fn as model_fn_lib -from tensorflow.python.estimator.canned import head as head_lib from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops -from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics as metrics_lib from tensorflow.python.ops import parsing_ops +from tensorflow.python.ops import variable_scope from tensorflow.python.platform import test from tensorflow.python.summary.writer import writer_cache from tensorflow.python.training import input as input_lib from tensorflow.python.training import learning_rate_decay -from tensorflow.python.training import monitored_session from tensorflow.python.training import training from tensorflow.python.training import training_util @@ -60,120 +60,109 @@ def discriminator_fn(data, unused_conditioning, mode): return layers.fully_connected(data, 1) -def mock_head(testcase, expected_generator_inputs, expected_real_data, - generator_scope_name): - """Returns a mock head that validates logits values and variable names.""" - discriminator_scope_name = 'Discriminator' # comes from TFGAN defaults - generator_var_names = set([ - '%s/fully_connected/weights:0' % generator_scope_name, - '%s/fully_connected/biases:0' % generator_scope_name]) - discriminator_var_names = set([ - '%s/fully_connected/weights:0' % discriminator_scope_name, - '%s/fully_connected/biases:0' % discriminator_scope_name]) - - def _create_estimator_spec(features, mode, logits, labels): - gan_model = logits # renaming for clarity - is_predict = mode == model_fn_lib.ModeKeys.PREDICT - testcase.assertIsNone(features) - testcase.assertIsNone(labels) - testcase.assertIsInstance(gan_model, namedtuples.GANModel) - - trainable_vars = ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES) - expected_var_names = (generator_var_names if is_predict else - generator_var_names | discriminator_var_names) - testcase.assertItemsEqual(expected_var_names, - [var.name for var in trainable_vars]) - - assertions = [] - def _or_none(x): - return None if is_predict else x - testcase.assertEqual(expected_generator_inputs, gan_model.generator_inputs) - # TODO(joelshor): Add check on `generated_data`. - testcase.assertItemsEqual( - generator_var_names, - set([x.name for x in gan_model.generator_variables])) - testcase.assertEqual(generator_scope_name, gan_model.generator_scope.name) - testcase.assertEqual(_or_none(expected_real_data), gan_model.real_data) - # TODO(joelshor): Add check on `discriminator_real_outputs`. - # TODO(joelshor): Add check on `discriminator_gen_outputs`. - if is_predict: - testcase.assertIsNone(gan_model.discriminator_scope) - else: - testcase.assertEqual(discriminator_scope_name, - gan_model.discriminator_scope.name) - - with ops.control_dependencies(assertions): - if mode == model_fn_lib.ModeKeys.TRAIN: - return model_fn_lib.EstimatorSpec( - mode=mode, loss=array_ops.zeros([]), - train_op=control_flow_ops.no_op(), training_hooks=[]) - elif mode == model_fn_lib.ModeKeys.EVAL: - return model_fn_lib.EstimatorSpec( - mode=mode, predictions=gan_model.generated_data, - loss=array_ops.zeros([])) - elif mode == model_fn_lib.ModeKeys.PREDICT: - return model_fn_lib.EstimatorSpec( - mode=mode, predictions=gan_model.generated_data) - else: - testcase.fail('Invalid mode: {}'.format(mode)) - - head = test.mock.NonCallableMagicMock(spec=head_lib._Head) - head.create_estimator_spec = test.mock.MagicMock( - wraps=_create_estimator_spec) - - return head - - -class GANModelFnTest(test.TestCase): - """Tests that _gan_model_fn passes expected logits to mock head.""" - - def setUp(self): - self._model_dir = tempfile.mkdtemp() - - def tearDown(self): - if self._model_dir: - writer_cache.FileWriterCache.clear() - shutil.rmtree(self._model_dir) +class GetGANModelTest(test.TestCase, parameterized.TestCase): + """Tests that `GetGANModel` produces the correct model.""" - def _test_logits_helper(self, mode): - """Tests that the expected logits are passed to mock head.""" + @parameterized.named_parameters( + ('train', model_fn_lib.ModeKeys.TRAIN), + ('eval', model_fn_lib.ModeKeys.EVAL), + ('predict', model_fn_lib.ModeKeys.PREDICT)) + def test_get_gan_model(self, mode): with ops.Graph().as_default(): - training_util.get_or_create_global_step() - generator_inputs = {'x': array_ops.zeros([5, 4])} - real_data = (None if mode == model_fn_lib.ModeKeys.PREDICT else - array_ops.zeros([5, 4])) - generator_scope_name = 'generator' - head = mock_head(self, - expected_generator_inputs=generator_inputs, - expected_real_data=real_data, - generator_scope_name=generator_scope_name) - estimator_spec = estimator._gan_model_fn( - features=generator_inputs, - labels=real_data, - mode=mode, - generator_fn=generator_fn, - discriminator_fn=discriminator_fn, - generator_scope_name=generator_scope_name, - head=head) - with monitored_session.MonitoredTrainingSession( - checkpoint_dir=self._model_dir) as sess: - if mode == model_fn_lib.ModeKeys.TRAIN: - sess.run(estimator_spec.train_op) - elif mode == model_fn_lib.ModeKeys.EVAL: - sess.run(estimator_spec.loss) - elif mode == model_fn_lib.ModeKeys.PREDICT: - sess.run(estimator_spec.predictions) - else: - self.fail('Invalid mode: {}'.format(mode)) - - def test_logits_predict(self): - self._test_logits_helper(model_fn_lib.ModeKeys.PREDICT) - - def test_logits_eval(self): - self._test_logits_helper(model_fn_lib.ModeKeys.EVAL) - - def test_logits_train(self): - self._test_logits_helper(model_fn_lib.ModeKeys.TRAIN) + generator_inputs = {'x': array_ops.ones([3, 4])} + real_data = (array_ops.zeros([3, 4]) if + mode != model_fn_lib.ModeKeys.PREDICT else None) + gan_model = estimator._get_gan_model( + mode, generator_fn, discriminator_fn, real_data, generator_inputs, + add_summaries=False) + + self.assertEqual(generator_inputs, gan_model.generator_inputs) + self.assertIsNotNone(gan_model.generated_data) + self.assertEqual(2, len(gan_model.generator_variables)) # 1 FC layer + self.assertIsNotNone(gan_model.generator_fn) + if mode == model_fn_lib.ModeKeys.PREDICT: + self.assertIsNone(gan_model.real_data) + self.assertIsNone(gan_model.discriminator_real_outputs) + self.assertIsNone(gan_model.discriminator_gen_outputs) + self.assertIsNone(gan_model.discriminator_variables) + self.assertIsNone(gan_model.discriminator_scope) + self.assertIsNone(gan_model.discriminator_fn) + else: + self.assertIsNotNone(gan_model.real_data) + self.assertIsNotNone(gan_model.discriminator_real_outputs) + self.assertIsNotNone(gan_model.discriminator_gen_outputs) + self.assertEqual(2, len(gan_model.discriminator_variables)) # 1 FC layer + self.assertIsNotNone(gan_model.discriminator_scope) + self.assertIsNotNone(gan_model.discriminator_fn) + + +def get_dummy_gan_model(): + # TODO(joelshor): Find a better way of creating a variable scope. + with variable_scope.variable_scope('generator') as gen_scope: + gen_var = variable_scope.get_variable('dummy_var', initializer=0.0) + with variable_scope.variable_scope('discriminator') as dis_scope: + dis_var = variable_scope.get_variable('dummy_var', initializer=0.0) + return tfgan_tuples.GANModel( + generator_inputs=None, + generated_data=array_ops.ones([3, 4]), + generator_variables=[gen_var], + generator_scope=gen_scope, + generator_fn=None, + real_data=array_ops.zeros([3, 4]), + discriminator_real_outputs=array_ops.ones([1, 2, 3]) * dis_var, + discriminator_gen_outputs=array_ops.ones([1, 2, 3]) * gen_var * dis_var, + discriminator_variables=[dis_var], + discriminator_scope=dis_scope, + discriminator_fn=None) + + +def dummy_loss_fn(gan_model): + return math_ops.reduce_sum(gan_model.discriminator_real_outputs - + gan_model.discriminator_gen_outputs) + + +def get_metrics(gan_model): + return { + 'mse_custom_metric': metrics_lib.mean_squared_error( + gan_model.real_data, gan_model.generated_data) + } + + +class GetEstimatorSpecTest(test.TestCase, parameterized.TestCase): + """Tests that the EstimatorSpec is constructed appropriately.""" + + @classmethod + def setUpClass(cls): + cls._generator_optimizer = training.GradientDescentOptimizer(1.0) + cls._discriminator_optimizer = training.GradientDescentOptimizer(1.0) + + @parameterized.named_parameters( + ('train', model_fn_lib.ModeKeys.TRAIN), + ('eval', model_fn_lib.ModeKeys.EVAL), + ('predict', model_fn_lib.ModeKeys.PREDICT)) + def test_get_estimator_spec(self, mode): + with ops.Graph().as_default(): + self._gan_model = get_dummy_gan_model() + spec = estimator._get_estimator_spec( + mode, + self._gan_model, + generator_loss_fn=dummy_loss_fn, + discriminator_loss_fn=dummy_loss_fn, + get_eval_metric_ops_fn=get_metrics, + generator_optimizer=self._generator_optimizer, + discriminator_optimizer=self._discriminator_optimizer) + + self.assertEqual(mode, spec.mode) + if mode == model_fn_lib.ModeKeys.PREDICT: + self.assertEqual(self._gan_model.generated_data, spec.predictions) + elif mode == model_fn_lib.ModeKeys.TRAIN: + self.assertShapeEqual(np.array(0), spec.loss) # must be a scalar + self.assertIsNotNone(spec.train_op) + self.assertIsNotNone(spec.training_hooks) + elif mode == model_fn_lib.ModeKeys.EVAL: + self.assertEqual(self._gan_model.generated_data, spec.predictions) + self.assertShapeEqual(np.array(0), spec.loss) # must be a scalar + self.assertIsNotNone(spec.eval_metric_ops) # TODO(joelshor): Add pandas test. @@ -195,12 +184,6 @@ class GANEstimatorIntegrationTest(test.TestCase): lr = learning_rate_decay.exponential_decay(1.0, gstep, 10, 0.9) return training.GradientDescentOptimizer(lr) - def get_metrics(gan_model): - return { - 'mse_custom_metric': metrics_lib.mean_squared_error( - gan_model.real_data, gan_model.generated_data) - } - gopt = make_opt if lr_decay else training.GradientDescentOptimizer(1.0) dopt = make_opt if lr_decay else training.GradientDescentOptimizer(1.0) est = estimator.GANEstimator( diff --git a/tensorflow/contrib/gan/python/estimator/python/head_impl.py b/tensorflow/contrib/gan/python/estimator/python/head_impl.py index d1441e1eb2aae0fb7d1771110f969bf727ebbb14..1a0ee6dfc498eb6dc8c97411589d9e35bc352062 100644 --- a/tensorflow/contrib/gan/python/estimator/python/head_impl.py +++ b/tensorflow/contrib/gan/python/estimator/python/head_impl.py @@ -27,16 +27,21 @@ from tensorflow.python.estimator.canned import head from tensorflow.python.estimator.export import export_output from tensorflow.python.framework import ops from tensorflow.python.ops import metrics as metrics_lib +from tensorflow.python.util import deprecation __all__ = [ 'GANHead', 'gan_head', ] + def _summary_key(head_name, val): return '%s/%s' % (val, head_name) if head_name else val +@deprecation.deprecated( + None, 'Please use tf.contrib.gan.GANEstimator without explicitly making a ' + 'GANHead.') def gan_head(generator_loss_fn, discriminator_loss_fn, generator_optimizer, discriminator_optimizer, use_loss_summaries=True, get_hooks_fn=tfgan_train.get_sequential_train_hooks(), @@ -77,6 +82,9 @@ def gan_head(generator_loss_fn, discriminator_loss_fn, generator_optimizer, class GANHead(head._Head): # pylint: disable=protected-access """`Head` for a GAN.""" + @deprecation.deprecated( + None, 'Please use tf.contrib.gan.GANEstimator without explicitly making ' + 'a GANHead.') def __init__(self, generator_loss_fn, discriminator_loss_fn, generator_optimizer, discriminator_optimizer, use_loss_summaries=True, @@ -108,7 +116,7 @@ class GANHead(head._Head): # pylint: disable=protected-access raise TypeError('generator_loss_fn must be callable.') if not callable(discriminator_loss_fn): raise TypeError('discriminator_loss_fn must be callable.') - if not use_loss_summaries in [True, False, None]: + if use_loss_summaries not in [True, False, None]: raise ValueError('use_loss_summaries must be True, False or None.') if get_hooks_fn is not None and not callable(get_hooks_fn): raise TypeError('get_hooks_fn must be callable.') diff --git a/tensorflow/contrib/gan/python/estimator/python/head_test.py b/tensorflow/contrib/gan/python/estimator/python/head_test.py index 5309d87765694fa476dae006105e842420a7c437..8205bc889dc01c8680e2139393d65723280cfbd0 100644 --- a/tensorflow/contrib/gan/python/estimator/python/head_test.py +++ b/tensorflow/contrib/gan/python/estimator/python/head_test.py @@ -67,7 +67,7 @@ class GANHeadTest(test.TestCase): generator_optimizer=training.GradientDescentOptimizer(1.0), discriminator_optimizer=training.GradientDescentOptimizer(1.0), get_eval_metric_ops_fn=self.get_metrics) - self.assertTrue(isinstance(self.gan_head, head.GANHead)) + self.assertIsInstance(self.gan_head, head.GANHead) def get_metrics(self, gan_model): self.assertTrue(isinstance(gan_model, tfgan_tuples.GANModel)) diff --git a/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py b/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py index 9e4ec59e7098443efc53506a4ba159e84b5c1618..ca2d724b49db25191b5744e10b48c66b6bdeb120 100644 --- a/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py +++ b/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py @@ -36,16 +36,15 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import random_ops +from tensorflow.python.util import nest __all__ = [ 'tensor_pool', ] -def _to_tuple(x): - if isinstance(x, (list, tuple)): - return tuple(x) - return (x,) +def _to_list(x): + return [x] if isinstance(x, ops.Tensor) else list(x) def tensor_pool(input_values, @@ -63,8 +62,8 @@ def tensor_pool(input_values, `pool_size` = 0 or `pooling_probability` = 0. Args: - input_values: A `Tensor`, or a list or tuple of `Tensor`s from which to read - values to be pooled. + input_values: An arbitrarily nested structure of `tf.Tensors`, from which to + read values to be pooled. pool_size: An integer specifying the maximum size of the pool. Defaults to 50. pooling_probability: A float `Tensor` specifying the probability of getting @@ -72,9 +71,10 @@ def tensor_pool(input_values, name: A string prefix for the name scope for all tensorflow ops. Returns: - A `Tensor`, or a list or tuple of `Tensor`s (according to the type ofx - `input_values`) which is with given probability either the `input_values` or - a randomly chosen sample that was previously inserted in the pool. + A nested structure of `Tensor` objects with the same structure as + `input_values`. With the given probability, the Tensor values are either the + same as in `input_values` or a randomly chosen sample that was previously + inserted in the pool. Raises: ValueError: If `pool_size` is negative. @@ -86,11 +86,10 @@ def tensor_pool(input_values, return input_values original_input_values = input_values - input_values = _to_tuple(input_values) + input_values = nest.flatten(input_values) - with ops.name_scope( - '{}_pool_queue'.format(name), - values=input_values + (pooling_probability,)): + with ops.name_scope('{}_pool_queue'.format(name), + values=input_values + [pooling_probability]): pool_queue = data_flow_ops.RandomShuffleQueue( capacity=pool_size, min_after_dequeue=0, @@ -112,10 +111,10 @@ def tensor_pool(input_values, def _get_input_value_pooled(): enqueue_op = pool_queue.enqueue(input_values) with ops.control_dependencies([enqueue_op]): - return tuple(array_ops.identity(v) for v in input_values) + return [array_ops.identity(v) for v in input_values] def _get_random_pool_value_and_enqueue_input(): - dequeue_values = _to_tuple(pool_queue.dequeue()) + dequeue_values = _to_list(pool_queue.dequeue()) with ops.control_dependencies(dequeue_values): enqueue_op = pool_queue.enqueue(input_values) with ops.control_dependencies([enqueue_op]): @@ -124,7 +123,7 @@ def tensor_pool(input_values, return control_flow_ops.cond(prob, lambda: dequeue_values, lambda: input_values) - output_values = _to_tuple(control_flow_ops.cond( + output_values = _to_list(control_flow_ops.cond( pool_queue.size() < pool_size, _get_input_value_pooled, _get_random_pool_value_and_enqueue_input)) @@ -132,8 +131,4 @@ def tensor_pool(input_values, for input_value, output_value in zip(input_values, output_values): output_value.set_shape(input_value.shape) - if isinstance(original_input_values, list): - return list(output_values) - elif isinstance(original_input_values, tuple): - return output_values - return output_values[0] + return nest.pack_sequence_as(original_input_values, output_values) diff --git a/tensorflow/contrib/gan/python/features/python/random_tensor_pool_test.py b/tensorflow/contrib/gan/python/features/python/random_tensor_pool_test.py index d8cf549cf71838178c9da01df462d41d81595fe5..08584dcd656e3e7a079a3fa36f44742b5eac1178 100644 --- a/tensorflow/contrib/gan/python/features/python/random_tensor_pool_test.py +++ b/tensorflow/contrib/gan/python/features/python/random_tensor_pool_test.py @@ -21,7 +21,9 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.gan.python.features.python.random_tensor_pool_impl import tensor_pool +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.platform import test @@ -111,6 +113,23 @@ class TensorPoolTest(test.TestCase): self.assertEqual(len(outs), len(input_values)) self.assertEqual(outs[1] - outs[0], 1) + def test_pool_preserves_shape(self): + t = constant_op.constant(1) + input_values = [[t, t, t], (t, t), t] + output_values = tensor_pool(input_values, pool_size=5) + print('stuff: ', output_values) + # Overall shape. + self.assertIsInstance(output_values, list) + self.assertEqual(3, len(output_values)) + # Shape of first element. + self.assertIsInstance(output_values[0], list) + self.assertEqual(3, len(output_values[0])) + # Shape of second element. + self.assertIsInstance(output_values[1], tuple) + self.assertEqual(2, len(output_values[1])) + # Shape of third element. + self.assertIsInstance(output_values[2], ops.Tensor) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/gan/python/losses/python/losses_impl.py b/tensorflow/contrib/gan/python/losses/python/losses_impl.py index 1ba3a641671c7f2a411a0c5f99228ca16eee1080..d3897483740faafa62befbaf873886139f1482d2 100644 --- a/tensorflow/contrib/gan/python/losses/python/losses_impl.py +++ b/tensorflow/contrib/gan/python/losses/python/losses_impl.py @@ -949,6 +949,11 @@ def cycle_consistency_loss(data_x, * loss = (loss_x2x + loss_y2y) / 2 where `loss` is the final result. + For the L1-norm, we follow the original implementation: + https://github.com/junyanz/CycleGAN/blob/master/models/cycle_gan_model.lua + we use L1-norm of pixel-wise error normalized by data size such that + `cycle_loss_weight` can be specified independent of image size. + See https://arxiv.org/abs/1703.10593 for more details. Args: @@ -965,19 +970,12 @@ def cycle_consistency_loss(data_x, A scalar `Tensor` of cycle consistency loss. """ - def _partial_cycle_consistency_loss(data, reconstructed_data): - # Following the original implementation - # https://github.com/junyanz/CycleGAN/blob/master/models/cycle_gan_model.lua - # use L1-norm of pixel-wise error normalized by data size so that - # `cycle_loss_weight` can be specified independent of image size. - return math_ops.reduce_mean(math_ops.abs(data - reconstructed_data)) - with ops.name_scope( scope, 'cycle_consistency_loss', values=[data_x, reconstructed_data_x, data_y, reconstructed_data_y]): - loss_x2x = _partial_cycle_consistency_loss(data_x, reconstructed_data_x) - loss_y2y = _partial_cycle_consistency_loss(data_y, reconstructed_data_y) + loss_x2x = losses.absolute_difference(data_x, reconstructed_data_x) + loss_y2y = losses.absolute_difference(data_y, reconstructed_data_y) loss = (loss_x2x + loss_y2y) / 2.0 if add_summaries: summary.scalar('cycle_consistency_loss_x2x', loss_x2x) diff --git a/tensorflow/contrib/gan/python/losses/python/tuple_losses_impl.py b/tensorflow/contrib/gan/python/losses/python/tuple_losses_impl.py index dcc3f94c2d6b9e5e44036e7cc1a9d1bb39104fb5..221c70c38bd432a6be7f6cda9c6700aa2255821f 100644 --- a/tensorflow/contrib/gan/python/losses/python/tuple_losses_impl.py +++ b/tensorflow/contrib/gan/python/losses/python/tuple_losses_impl.py @@ -80,6 +80,9 @@ __all__ = [ 'mutual_information_penalty', 'combine_adversarial_loss', 'cycle_consistency_loss', + 'stargan_generator_loss_wrapper', + 'stargan_discriminator_loss_wrapper', + 'stargan_gradient_penalty_wrapper' ] @@ -277,3 +280,86 @@ def cycle_consistency_loss(cyclegan_model, scope=None, add_summaries=False): cyclegan_model.model_x2y.generator_inputs, cyclegan_model.reconstructed_x, cyclegan_model.model_y2x.generator_inputs, cyclegan_model.reconstructed_y, scope, add_summaries) + + +def stargan_generator_loss_wrapper(loss_fn): + """Convert a generator loss function to take a StarGANModel. + + The new function has the same name as the original one. + + Args: + loss_fn: A python function taking Discriminator's real/fake prediction for + generated data. + + Returns: + A new function that takes a StarGANModel namedtuple and returns the same + loss. + """ + + def new_loss_fn(stargan_model, **kwargs): + return loss_fn( + stargan_model.discriminator_generated_data_source_predication, **kwargs) + + new_docstring = """The stargan_model version of %s.""" % loss_fn.__name__ + new_loss_fn.__docstring__ = new_docstring + new_loss_fn.__name__ = loss_fn.__name__ + new_loss_fn.__module__ = loss_fn.__module__ + return new_loss_fn + + +def stargan_discriminator_loss_wrapper(loss_fn): + """Convert a discriminator loss function to take a StarGANModel. + + The new function has the same name as the original one. + + Args: + loss_fn: A python function taking Discriminator's real/fake prediction for + real data and generated data. + + Returns: + A new function that takes a StarGANModel namedtuple and returns the same + loss. + """ + + def new_loss_fn(stargan_model, **kwargs): + return loss_fn( + stargan_model.discriminator_input_data_source_predication, + stargan_model.discriminator_generated_data_source_predication, **kwargs) + + new_docstring = """The stargan_model version of %s.""" % loss_fn.__name__ + new_loss_fn.__docstring__ = new_docstring + new_loss_fn.__name__ = loss_fn.__name__ + new_loss_fn.__module__ = loss_fn.__module__ + return new_loss_fn + + +def stargan_gradient_penalty_wrapper(loss_fn): + """Convert a gradient penalty function to take a StarGANModel. + + The new function has the same name as the original one. + + Args: + loss_fn: A python function taking real_data, generated_data, + generator_inputs for Discriminator's condition (i.e. number of domains), + discriminator_fn, and discriminator_scope. + + Returns: + A new function that takes a StarGANModel namedtuple and returns the same + loss. + """ + + def new_loss_fn(stargan_model, **kwargs): + num_domains = stargan_model.input_data_domain_label.shape.as_list()[-1] + return loss_fn( + real_data=stargan_model.input_data, + generated_data=stargan_model.generated_data, + generator_inputs=num_domains, + discriminator_fn=stargan_model.discriminator_fn, + discriminator_scope=stargan_model.discriminator_scope, + **kwargs) + + new_docstring = """The stargan_model version of %s.""" % loss_fn.__name__ + new_loss_fn.__docstring__ = new_docstring + new_loss_fn.__name__ = loss_fn.__name__ + new_loss_fn.__module__ = loss_fn.__module__ + return new_loss_fn diff --git a/tensorflow/contrib/gan/python/losses/python/tuple_losses_test.py b/tensorflow/contrib/gan/python/losses/python/tuple_losses_test.py index aa1ef11172dee6799994b87f70a3883cd67fd15b..a559bbfa11367afd7dfe6a72d2ce2cc9d7ba1f16 100644 --- a/tensorflow/contrib/gan/python/losses/python/tuple_losses_test.py +++ b/tensorflow/contrib/gan/python/losses/python/tuple_losses_test.py @@ -22,10 +22,15 @@ import collections import numpy as np +from tensorflow.contrib import layers from tensorflow.contrib.gan.python import namedtuples +from tensorflow.contrib.gan.python.losses.python import losses_impl as tfgan_losses_impl from tensorflow.contrib.gan.python.losses.python import tuple_losses_impl as tfgan_losses from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -129,6 +134,9 @@ manual_tests = [ 'mutual_information_penalty', 'wasserstein_gradient_penalty', 'cycle_consistency_loss', + 'stargan_generator_loss_wrapper', + 'stargan_discriminator_loss_wrapper', + 'stargan_gradient_penalty_wrapper' ] discriminator_keyword_args = { @@ -175,6 +183,112 @@ class CycleConsistencyLossTest(test.TestCase): self.assertNear(5.0, loss.eval(), 1e-5) +class StarGANLossWrapperTest(test.TestCase): + + def setUp(self): + + super(StarGANLossWrapperTest, self).setUp() + + self.input_data = array_ops.ones([1, 2, 2, 3]) + self.input_data_domain_label = constant_op.constant([[0, 1]]) + self.generated_data = array_ops.ones([1, 2, 2, 3]) + self.discriminator_input_data_source_predication = array_ops.ones([1]) + self.discriminator_generated_data_source_predication = array_ops.ones([1]) + + def _discriminator_fn(inputs, num_domains): + """Differentiable dummy discriminator for StarGAN.""" + hidden = layers.flatten(inputs) + output_src = math_ops.reduce_mean(hidden, axis=1) + output_cls = layers.fully_connected( + inputs=hidden, + num_outputs=num_domains, + activation_fn=None, + normalizer_fn=None, + biases_initializer=None) + return output_src, output_cls + + with variable_scope.variable_scope('discriminator') as dis_scope: + pass + + self.model = namedtuples.StarGANModel( + input_data=self.input_data, + input_data_domain_label=self.input_data_domain_label, + generated_data=self.generated_data, + generated_data_domain_target=None, + reconstructed_data=None, + discriminator_input_data_source_predication=self. + discriminator_input_data_source_predication, + discriminator_generated_data_source_predication=self. + discriminator_generated_data_source_predication, + discriminator_input_data_domain_predication=None, + discriminator_generated_data_domain_predication=None, + generator_variables=None, + generator_scope=None, + generator_fn=None, + discriminator_variables=None, + discriminator_scope=dis_scope, + discriminator_fn=_discriminator_fn) + + self.discriminator_fn = _discriminator_fn + self.discriminator_scope = dis_scope + + def test_stargan_generator_loss_wrapper(self): + """Test StarGAN generator loss wrapper.""" + loss_fn = tfgan_losses_impl.wasserstein_generator_loss + wrapped_loss_fn = tfgan_losses.stargan_generator_loss_wrapper(loss_fn) + + loss_result_tensor = loss_fn( + self.discriminator_generated_data_source_predication) + wrapped_loss_result_tensor = wrapped_loss_fn(self.model) + + with self.test_session() as sess: + sess.run(variables.global_variables_initializer()) + loss_result, wrapped_loss_result = sess.run( + [loss_result_tensor, wrapped_loss_result_tensor]) + self.assertAlmostEqual(loss_result, wrapped_loss_result) + + def test_stargan_discriminator_loss_wrapper(self): + """Test StarGAN discriminator loss wrapper.""" + loss_fn = tfgan_losses_impl.wasserstein_discriminator_loss + wrapped_loss_fn = tfgan_losses.stargan_discriminator_loss_wrapper(loss_fn) + + loss_result_tensor = loss_fn( + self.discriminator_generated_data_source_predication, + self.discriminator_generated_data_source_predication) + wrapped_loss_result_tensor = wrapped_loss_fn(self.model) + + with self.test_session() as sess: + sess.run(variables.global_variables_initializer()) + loss_result, wrapped_loss_result = sess.run( + [loss_result_tensor, wrapped_loss_result_tensor]) + self.assertAlmostEqual(loss_result, wrapped_loss_result) + + def test_stargan_gradient_penalty_wrapper(self): + """Test StaGAN gradient penalty wrapper. + + Notes: + The random interpolates are handled by given setting the reconstruction to + be the same as the input. + + """ + loss_fn = tfgan_losses_impl.wasserstein_gradient_penalty + wrapped_loss_fn = tfgan_losses.stargan_gradient_penalty_wrapper(loss_fn) + + loss_result_tensor = loss_fn( + real_data=self.input_data, + generated_data=self.generated_data, + generator_inputs=self.input_data_domain_label.shape.as_list()[-1], + discriminator_fn=self.discriminator_fn, + discriminator_scope=self.discriminator_scope) + wrapped_loss_result_tensor = wrapped_loss_fn(self.model) + + with self.test_session() as sess: + sess.run(variables.global_variables_initializer()) + loss_result, wrapped_loss_result = sess.run( + [loss_result_tensor, wrapped_loss_result_tensor]) + self.assertAlmostEqual(loss_result, wrapped_loss_result) + + if __name__ == '__main__': for loss_name in tfgan_losses.__all__: if loss_name in manual_tests: continue diff --git a/tensorflow/contrib/gan/python/namedtuples.py b/tensorflow/contrib/gan/python/namedtuples.py index 25cfeafeec9000b0dc3849ebe646e59c1b4d1cc3..a462b68e28be989eee04fe4ec5ee902d75e5d909 100644 --- a/tensorflow/contrib/gan/python/namedtuples.py +++ b/tensorflow/contrib/gan/python/namedtuples.py @@ -25,12 +25,12 @@ from __future__ import print_function import collections - __all__ = [ 'GANModel', 'InfoGANModel', 'ACGANModel', 'CycleGANModel', + 'StarGANModel', 'GANLoss', 'CycleGANLoss', 'GANTrainOps', @@ -136,6 +136,54 @@ class CycleGANModel( """ +class StarGANModel( + collections.namedtuple('StarGANModel', ( + 'input_data', + 'input_data_domain_label', + 'generated_data', + 'generated_data_domain_target', + 'reconstructed_data', + 'discriminator_input_data_source_predication', + 'discriminator_generated_data_source_predication', + 'discriminator_input_data_domain_predication', + 'discriminator_generated_data_domain_predication', + 'generator_variables', + 'generator_scope', + 'generator_fn', + 'discriminator_variables', + 'discriminator_scope', + 'discriminator_fn', + ))): + """A StarGANModel contains all the pieces needed for StarGAN training. + + Args: + input_data: The real images that need to be transferred by the generator. + input_data_domain_label: The real domain labels associated with the real + images. + generated_data: The generated images produced by the generator. It has the + same shape as the input_data. + generated_data_domain_target: The target domain that the generated images + belong to. It has the same shape as the input_data_domain_label. + reconstructed_data: The reconstructed images produced by the G(enerator). + reconstructed_data = G(G(input_data, generated_data_domain_target), + input_data_domain_label). + discriminator_input_data_source: The discriminator's output for predicting + the source (real/generated) of input_data. + discriminator_generated_data_source: The discriminator's output for + predicting the source (real/generated) of generated_data. + discriminator_input_data_domain_predication: The discriminator's output for + predicting the domain_label for the input_data. + discriminator_generated_data_domain_predication: The discriminatorr's output + for predicting the domain_target for the generated_data. + generator_variables: A list of all generator variables. + generator_scope: Variable scope all generator variables live in. + generator_fn: The generator function. + discriminator_variables: A list of all discriminator variables. + discriminator_scope: Variable scope all discriminator variables live in. + discriminator_fn: The discriminator function. + """ + + class GANLoss( collections.namedtuple('GANLoss', ( 'generator_loss', diff --git a/tensorflow/contrib/gan/python/train.py b/tensorflow/contrib/gan/python/train.py index 6fa43059f3125daea080f780210223363d0a89f9..03f52d214b5ac2fef075fb66018f88d2be5c1941 100644 --- a/tensorflow/contrib/gan/python/train.py +++ b/tensorflow/contrib/gan/python/train.py @@ -34,15 +34,20 @@ from __future__ import print_function from tensorflow.contrib.framework.python.ops import variables as variables_lib from tensorflow.contrib.gan.python import losses as tfgan_losses from tensorflow.contrib.gan.python import namedtuples +from tensorflow.contrib.gan.python.losses.python import losses_impl as tfgan_losses_impl from tensorflow.contrib.slim.python.slim import learning as slim_learning from tensorflow.contrib.training.python.training import training +from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import init_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops.distributions import distribution as ds from tensorflow.python.ops.losses import losses +from tensorflow.python.summary import summary from tensorflow.python.training import session_run_hook from tensorflow.python.training import sync_replicas_optimizer from tensorflow.python.training import training_util @@ -53,6 +58,7 @@ __all__ = [ 'infogan_model', 'acgan_model', 'cyclegan_model', + 'stargan_model', 'gan_loss', 'cyclegan_loss', 'gan_train_ops', @@ -123,16 +129,9 @@ def gan_model( discriminator_variables = variables_lib.get_trainable_variables(dis_scope) return namedtuples.GANModel( - generator_inputs, - generated_data, - generator_variables, - gen_scope, - generator_fn, - real_data, - discriminator_real_outputs, - discriminator_gen_outputs, - discriminator_variables, - dis_scope, + generator_inputs, generated_data, generator_variables, gen_scope, + generator_fn, real_data, discriminator_real_outputs, + discriminator_gen_outputs, discriminator_variables, dis_scope, discriminator_fn) @@ -201,8 +200,7 @@ def infogan_model( # Get model-specific variables. generator_variables = variables_lib.get_trainable_variables(gen_scope) - discriminator_variables = variables_lib.get_trainable_variables( - disc_scope) + discriminator_variables = variables_lib.get_trainable_variables(disc_scope) return namedtuples.InfoGANModel( generator_inputs, @@ -279,12 +277,12 @@ def acgan_model( generator_inputs = _convert_tensor_or_l_or_d(generator_inputs) generated_data = generator_fn(generator_inputs) with variable_scope.variable_scope(discriminator_scope) as dis_scope: - with ops.name_scope(dis_scope.name+'/generated/'): + with ops.name_scope(dis_scope.name + '/generated/'): (discriminator_gen_outputs, discriminator_gen_classification_logits ) = _validate_acgan_discriminator_outputs( discriminator_fn(generated_data, generator_inputs)) with variable_scope.variable_scope(dis_scope, reuse=True): - with ops.name_scope(dis_scope.name+'/real/'): + with ops.name_scope(dis_scope.name + '/real/'): real_data = ops.convert_to_tensor(real_data) (discriminator_real_outputs, discriminator_real_classification_logits ) = _validate_acgan_discriminator_outputs( @@ -297,8 +295,7 @@ def acgan_model( # Get model-specific variables. generator_variables = variables_lib.get_trainable_variables(gen_scope) - discriminator_variables = variables_lib.get_trainable_variables( - dis_scope) + discriminator_variables = variables_lib.get_trainable_variables(dis_scope) return namedtuples.ACGANModel( generator_inputs, generated_data, generator_variables, gen_scope, @@ -379,6 +376,108 @@ def cyclegan_model( reconstructed_y) +def stargan_model(generator_fn, + discriminator_fn, + input_data, + input_data_domain_label, + generator_scope='Generator', + discriminator_scope='Discriminator'): + """Returns a StarGAN model outputs and variables. + + See https://arxiv.org/abs/1711.09020 for more details. + + Args: + generator_fn: A python lambda that takes `inputs` and `targets` as inputs + and returns 'generated_data' as the transformed version of `input` based + on the `target`. `input` has shape (n, h, w, c), `targets` has shape (n, + num_domains), and `generated_data` has the same shape as `input`. + discriminator_fn: A python lambda that takes `inputs` and `num_domains` as + inputs and returns a tuple (`source_prediction`, `domain_prediction`). + `source_prediction` represents the source(real/generated) prediction by + the discriminator, and `domain_prediction` represents the domain + prediction/classification by the discriminator. `source_prediction` has + shape (n) and `domain_prediction` has shape (n, num_domains). + input_data: Tensor or a list of tensor of shape (n, h, w, c) representing + the real input images. + input_data_domain_label: Tensor or a list of tensor of shape (batch_size, + num_domains) representing the domain label associated with the real + images. + generator_scope: Optional generator variable scope. Useful if you want to + reuse a subgraph that has already been created. + discriminator_scope: Optional discriminator variable scope. Useful if you + want to reuse a subgraph that has already been created. + + Returns: + StarGANModel nametuple return the tensor that are needed to compute the + loss. + + Raises: + ValueError: If the shape of `input_data_domain_label` is not rank 2 or fully + defined in every dimensions. + """ + + # Convert to tensor. + input_data = _convert_tensor_or_l_or_d(input_data) + input_data_domain_label = _convert_tensor_or_l_or_d(input_data_domain_label) + + # Convert list of tensor to a single tensor if applicable. + if isinstance(input_data, (list, tuple)): + input_data = array_ops.concat( + [ops.convert_to_tensor(x) for x in input_data], 0) + if isinstance(input_data_domain_label, (list, tuple)): + input_data_domain_label = array_ops.concat( + [ops.convert_to_tensor(x) for x in input_data_domain_label], 0) + + # Get batch_size, num_domains from the labels. + input_data_domain_label.shape.assert_has_rank(2) + input_data_domain_label.shape.assert_is_fully_defined() + batch_size, num_domains = input_data_domain_label.shape.as_list() + + # Transform input_data to random target domains. + with variable_scope.variable_scope(generator_scope) as generator_scope: + generated_data_domain_target = _generate_stargan_random_domain_target( + batch_size, num_domains) + generated_data = generator_fn(input_data, generated_data_domain_target) + + # Transform generated_data back to the original input_data domain. + with variable_scope.variable_scope(generator_scope, reuse=True): + reconstructed_data = generator_fn(generated_data, input_data_domain_label) + + # Predict source and domain for the generated_data using the discriminator. + with variable_scope.variable_scope( + discriminator_scope) as discriminator_scope: + disc_gen_data_source_pred, disc_gen_data_domain_pred = discriminator_fn( + generated_data, num_domains) + + # Predict source and domain for the input_data using the discriminator. + with variable_scope.variable_scope(discriminator_scope, reuse=True): + disc_input_data_source_pred, disc_input_data_domain_pred = discriminator_fn( + input_data, num_domains) + + # Collect trainable variables from the neural networks. + generator_variables = variables_lib.get_trainable_variables(generator_scope) + discriminator_variables = variables_lib.get_trainable_variables( + discriminator_scope) + + # Create the StarGANModel namedtuple. + return namedtuples.StarGANModel( + input_data=input_data, + input_data_domain_label=input_data_domain_label, + generated_data=generated_data, + generated_data_domain_target=generated_data_domain_target, + reconstructed_data=reconstructed_data, + discriminator_input_data_source_predication=disc_input_data_source_pred, + discriminator_generated_data_source_predication=disc_gen_data_source_pred, + discriminator_input_data_domain_predication=disc_input_data_domain_pred, + discriminator_generated_data_domain_predication=disc_gen_data_domain_pred, + generator_variables=generator_variables, + generator_scope=generator_scope, + generator_fn=generator_fn, + discriminator_variables=discriminator_variables, + discriminator_scope=discriminator_scope, + discriminator_fn=discriminator_fn) + + def _validate_aux_loss_weight(aux_loss_weight, name='aux_loss_weight'): if isinstance(aux_loss_weight, ops.Tensor): aux_loss_weight.shape.assert_is_compatible_with([]) @@ -419,33 +518,42 @@ def _tensor_pool_adjusted_model(model, tensor_pool_fn): Raises: ValueError: If tensor pool does not support the `model`. """ - if tensor_pool_fn is None: - return model - - pooled_generated_data, pooled_generator_inputs = tensor_pool_fn( - (model.generated_data, model.generator_inputs)) - if isinstance(model, namedtuples.GANModel): + pooled_generator_inputs, pooled_generated_data = tensor_pool_fn( + (model.generator_inputs, model.generated_data)) with variable_scope.variable_scope(model.discriminator_scope, reuse=True): dis_gen_outputs = model.discriminator_fn(pooled_generated_data, pooled_generator_inputs) - return model._replace(discriminator_gen_outputs=dis_gen_outputs) + return model._replace( + generator_inputs=pooled_generator_inputs, + generated_data=pooled_generated_data, + discriminator_gen_outputs=dis_gen_outputs) elif isinstance(model, namedtuples.ACGANModel): + pooled_generator_inputs, pooled_generated_data = tensor_pool_fn( + (model.generator_inputs, model.generated_data)) with variable_scope.variable_scope(model.discriminator_scope, reuse=True): - (dis_pooled_gen_outputs, - dis_pooled_gen_classification_logits) = model.discriminator_fn( + (pooled_discriminator_gen_outputs, + pooled_discriminator_gen_classification_logits) = model.discriminator_fn( pooled_generated_data, pooled_generator_inputs) return model._replace( - discriminator_gen_outputs=dis_pooled_gen_outputs, + generator_inputs=pooled_generator_inputs, + generated_data=pooled_generated_data, + discriminator_gen_outputs=pooled_discriminator_gen_outputs, discriminator_gen_classification_logits= - dis_pooled_gen_classification_logits) + pooled_discriminator_gen_classification_logits) elif isinstance(model, namedtuples.InfoGANModel): + pooled_generator_inputs, pooled_generated_data, pooled_structured_input = ( + tensor_pool_fn((model.generator_inputs, model.generated_data, + model.structured_generator_inputs))) with variable_scope.variable_scope(model.discriminator_scope, reuse=True): - (dis_pooled_gen_outputs, + (pooled_discriminator_gen_outputs, pooled_predicted_distributions) = model.discriminator_and_aux_fn( pooled_generated_data, pooled_generator_inputs) return model._replace( - discriminator_gen_outputs=dis_pooled_gen_outputs, + generator_inputs=pooled_generator_inputs, + generated_data=pooled_generated_data, + structured_generator_inputs=pooled_structured_input, + discriminator_gen_outputs=pooled_discriminator_gen_outputs, predicted_distributions=pooled_predicted_distributions) else: raise ValueError('Tensor pool does not support `model`: %s.' % type(model)) @@ -512,8 +620,8 @@ def gan_loss( `model` isn't an `InfoGANModel`. """ # Validate arguments. - gradient_penalty_weight = _validate_aux_loss_weight(gradient_penalty_weight, - 'gradient_penalty_weight') + gradient_penalty_weight = _validate_aux_loss_weight( + gradient_penalty_weight, 'gradient_penalty_weight') mutual_information_penalty_weight = _validate_aux_loss_weight( mutual_information_penalty_weight, 'infogan_weight') aux_cond_generator_weight = _validate_aux_loss_weight( @@ -537,33 +645,38 @@ def gan_loss( 'is provided, `model` must be an `ACGANModel`. Instead, was %s.' % type(model)) + # Optionally create pooled model. + pooled_model = (_tensor_pool_adjusted_model(model, tensor_pool_fn) if + tensor_pool_fn else model) + # Create standard losses. gen_loss = generator_loss_fn(model, add_summaries=add_summaries) - dis_loss = discriminator_loss_fn( - _tensor_pool_adjusted_model(model, tensor_pool_fn), - add_summaries=add_summaries) + dis_loss = discriminator_loss_fn(pooled_model, add_summaries=add_summaries) # Add optional extra losses. if _use_aux_loss(gradient_penalty_weight): gp_loss = tfgan_losses.wasserstein_gradient_penalty( - model, + pooled_model, epsilon=gradient_penalty_epsilon, target=gradient_penalty_target, one_sided=gradient_penalty_one_sided, add_summaries=add_summaries) dis_loss += gradient_penalty_weight * gp_loss if _use_aux_loss(mutual_information_penalty_weight): - info_loss = tfgan_losses.mutual_information_penalty( + gen_info_loss = tfgan_losses.mutual_information_penalty( model, add_summaries=add_summaries) - dis_loss += mutual_information_penalty_weight * info_loss - gen_loss += mutual_information_penalty_weight * info_loss + dis_info_loss = (gen_info_loss if tensor_pool_fn is None else + tfgan_losses.mutual_information_penalty( + pooled_model, add_summaries=add_summaries)) + gen_loss += mutual_information_penalty_weight * gen_info_loss + dis_loss += mutual_information_penalty_weight * dis_info_loss if _use_aux_loss(aux_cond_generator_weight): ac_gen_loss = tfgan_losses.acgan_generator_loss( model, add_summaries=add_summaries) gen_loss += aux_cond_generator_weight * ac_gen_loss if _use_aux_loss(aux_cond_discriminator_weight): ac_disc_loss = tfgan_losses.acgan_discriminator_loss( - model, add_summaries=add_summaries) + pooled_model, add_summaries=add_summaries) dis_loss += aux_cond_discriminator_weight * ac_disc_loss # Gathers auxiliary losses. if model.generator_scope: @@ -631,8 +744,8 @@ def cyclegan_loss( generator_loss_fn=generator_loss_fn, discriminator_loss_fn=discriminator_loss_fn, **kwargs) - return partial_loss._replace( - generator_loss=partial_loss.generator_loss + aux_loss) + return partial_loss._replace(generator_loss=partial_loss.generator_loss + + aux_loss) with ops.name_scope('cyclegan_loss_x2y'): loss_x2y = _partial_loss(model.model_x2y) @@ -642,6 +755,130 @@ def cyclegan_loss( return namedtuples.CycleGANLoss(loss_x2y, loss_y2x) +def stargan_loss( + model, + generator_loss_fn=tfgan_losses.stargan_generator_loss_wrapper( + tfgan_losses_impl.wasserstein_generator_loss), + discriminator_loss_fn=tfgan_losses.stargan_discriminator_loss_wrapper( + tfgan_losses_impl.wasserstein_discriminator_loss), + gradient_penalty_weight=10.0, + gradient_penalty_epsilon=1e-10, + gradient_penalty_target=1.0, + gradient_penalty_one_sided=False, + reconstruction_loss_fn=losses.absolute_difference, + reconstruction_loss_weight=10.0, + classification_loss_fn=losses.softmax_cross_entropy, + classification_loss_weight=1.0, + classification_one_hot=True, + add_summaries=True): + """StarGAN Loss. + + The four major part can be found here: http://screen/tMRMBAohDYG. + + Args: + model: (StarGAN) Model output of the stargan_model() function call. + generator_loss_fn: The loss function on the generator. Takes a + `StarGANModel` named tuple. + discriminator_loss_fn: The loss function on the discriminator. Takes a + `StarGANModel` namedtuple. + gradient_penalty_weight: (float) Gradient penalty weight. Default to 10 per + the original paper https://arxiv.org/abs/1711.09020. Set to 0 or None to + turn off gradient penalty. + gradient_penalty_epsilon: (float) A small positive number added for + numerical stability when computing the gradient norm. + gradient_penalty_target: (float, or tf.float `Tensor`) The target value of + gradient norm. Defaults to 1.0. + gradient_penalty_one_sided: (bool) If `True`, penalty proposed in + https://arxiv.org/abs/1709.08894 is used. Defaults to `False`. + reconstruction_loss_fn: The reconstruction loss function. Default to L1-norm + and the function must conform to the `tf.losses` API. + reconstruction_loss_weight: Reconstruction loss weight. Default to 10.0. + classification_loss_fn: The loss function on the discriminator's ability to + classify domain of the input. Default to one-hot softmax cross entropy + loss, and the function must conform to the `tf.losses` API. + classification_loss_weight: (float) Classification loss weight. Default to + 1.0. + classification_one_hot: (bool) If the label is one hot representation. + Default to True. If False, classification classification_loss_fn need to + be sigmoid cross entropy loss instead. + add_summaries: (bool) Add the loss to the summary + + Returns: + GANLoss namedtuple where we have generator loss and discriminator loss. + + Raises: + ValueError: If input StarGANModel.input_data_domain_label does not have rank + 2, or dimension 2 is not defined. + """ + + def _classification_loss_helper(true_labels, predict_logits, scope_name): + """Classification Loss Function Helper. + + Args: + true_labels: Tensor of shape [batch_size, num_domains] representing the + label where each row is an one-hot vector. + predict_logits: Tensor of shape [batch_size, num_domains] representing the + predicted label logit, which is UNSCALED output from the NN. + scope_name: (string) Name scope of the loss component. + + Returns: + Single scalar tensor representing the classification loss. + """ + + with ops.name_scope(scope_name, values=(true_labels, predict_logits)): + + loss = classification_loss_fn( + onehot_labels=true_labels, logits=predict_logits) + + if not classification_one_hot: + loss = math_ops.reduce_sum(loss, axis=1) + loss = math_ops.reduce_mean(loss) + + if add_summaries: + summary.scalar(scope_name, loss) + + return loss + + # Check input shape. + model.input_data_domain_label.shape.assert_has_rank(2) + model.input_data_domain_label.shape[1:].assert_is_fully_defined() + + # Adversarial Loss. + generator_loss = generator_loss_fn(model, add_summaries=add_summaries) + discriminator_loss = discriminator_loss_fn(model, add_summaries=add_summaries) + + # Gradient Penalty. + if _use_aux_loss(gradient_penalty_weight): + gradient_penalty_fn = tfgan_losses.stargan_gradient_penalty_wrapper( + tfgan_losses_impl.wasserstein_gradient_penalty) + discriminator_loss += gradient_penalty_fn( + model, + epsilon=gradient_penalty_epsilon, + target=gradient_penalty_target, + one_sided=gradient_penalty_one_sided, + add_summaries=add_summaries) * gradient_penalty_weight + + # Reconstruction Loss. + reconstruction_loss = reconstruction_loss_fn(model.input_data, + model.reconstructed_data) + generator_loss += reconstruction_loss * reconstruction_loss_weight + if add_summaries: + summary.scalar('reconstruction_loss', reconstruction_loss) + + # Classification Loss. + generator_loss += _classification_loss_helper( + true_labels=model.generated_data_domain_target, + predict_logits=model.discriminator_generated_data_domain_predication, + scope_name='generator_classification_loss') * classification_loss_weight + discriminator_loss += _classification_loss_helper( + true_labels=model.input_data_domain_label, + predict_logits=model.discriminator_input_data_domain_predication, + scope_name='discriminator_classification_loss' + ) * classification_loss_weight + + return namedtuples.GANLoss(generator_loss, discriminator_loss) + + def _get_update_ops(kwargs, gen_scope, dis_scope, check_for_unused_ops=True): """Gets generator and discriminator update ops. @@ -822,12 +1059,14 @@ def get_sequential_train_hooks(train_steps=namedtuples.GANTrainSteps(1, 1)): Returns: A function that takes a GANTrainOps tuple and returns a list of hooks. """ + def get_hooks(train_ops): generator_hook = RunTrainOpsHook(train_ops.generator_train_op, train_steps.generator_train_steps) discriminator_hook = RunTrainOpsHook(train_ops.discriminator_train_op, train_steps.discriminator_train_steps) return [generator_hook, discriminator_hook] + return get_hooks @@ -881,23 +1120,23 @@ def get_joint_train_hooks(train_steps=namedtuples.GANTrainSteps(1, 1)): d_hook = RunTrainOpsHook(d_op, num_d_steps) return [joint_hook, g_hook, d_hook] + return get_hooks # TODO(joelshor): This function currently returns the global step. Find a # good way for it to return the generator, discriminator, and final losses. -def gan_train( - train_ops, - logdir, - get_hooks_fn=get_sequential_train_hooks(), - master='', - is_chief=True, - scaffold=None, - hooks=None, - chief_only_hooks=None, - save_checkpoint_secs=600, - save_summaries_steps=100, - config=None): +def gan_train(train_ops, + logdir, + get_hooks_fn=get_sequential_train_hooks(), + master='', + is_chief=True, + scaffold=None, + hooks=None, + chief_only_hooks=None, + save_checkpoint_secs=600, + save_summaries_steps=100, + config=None): """A wrapper around `contrib.training.train` that uses GAN hooks. Args: @@ -943,8 +1182,7 @@ def gan_train( config=config) -def get_sequential_train_steps( - train_steps=namedtuples.GANTrainSteps(1, 1)): +def get_sequential_train_steps(train_steps=namedtuples.GANTrainSteps(1, 1)): """Returns a thin wrapper around slim.learning.train_step, for GANs. This function is to provide support for the Supervisor. For new code, please @@ -1042,3 +1280,19 @@ def _validate_acgan_discriminator_outputs(discriminator_output): 'A discriminator function for ACGAN must output a tuple ' 'consisting of (discrimination logits, classification logits).') return a, b + + +def _generate_stargan_random_domain_target(batch_size, num_domains): + """Generate random domain label. + + Args: + batch_size: (int) Number of random domain label. + num_domains: (int) Number of domains representing with the label. + + Returns: + Tensor of shape (batch_size, num_domains) representing random label. + """ + domain_idx = random_ops.random_uniform( + [batch_size], minval=0, maxval=num_domains, dtype=dtypes.int32) + + return array_ops.one_hot(domain_idx, num_domains) diff --git a/tensorflow/contrib/gan/python/train_test.py b/tensorflow/contrib/gan/python/train_test.py index 3ebbe55d059e5e72607bc4efdbf95a6c96d99f11..58f348034fdcaadd8d738517aef2a7e2f0172c13 100644 --- a/tensorflow/contrib/gan/python/train_test.py +++ b/tensorflow/contrib/gan/python/train_test.py @@ -18,8 +18,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized import numpy as np +from tensorflow.contrib import layers from tensorflow.contrib.framework.python.ops import variables as variables_lib from tensorflow.contrib.gan.python import namedtuples from tensorflow.contrib.gan.python import train @@ -30,6 +32,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables @@ -84,19 +87,59 @@ class InfoGANDiscriminator(object): def acgan_discriminator_model(inputs, _, num_classes=10): - return (discriminator_model(inputs, _), array_ops.one_hot( - # TODO(haeusser): infer batch size from input - random_ops.random_uniform([3], maxval=num_classes, dtype=dtypes.int32), - num_classes)) + return ( + discriminator_model(inputs, _), + array_ops.one_hot( + # TODO(haeusser): infer batch size from input + random_ops.random_uniform( + [3], maxval=num_classes, dtype=dtypes.int32), + num_classes)) class ACGANDiscriminator(object): def __call__(self, inputs, _, num_classes=10): - return (discriminator_model(inputs, _), array_ops.one_hot( - # TODO(haeusser): infer batch size from input - random_ops.random_uniform([3], maxval=num_classes, dtype=dtypes.int32), - num_classes)) + return ( + discriminator_model(inputs, _), + array_ops.one_hot( + # TODO(haeusser): infer batch size from input + random_ops.random_uniform( + [3], maxval=num_classes, dtype=dtypes.int32), + num_classes)) + + +def stargan_generator_model(inputs, _): + """Dummy generator for StarGAN.""" + + return variable_scope.get_variable('dummy_g', initializer=0.5) * inputs + + +class StarGANGenerator(object): + + def __call__(self, inputs, _): + return stargan_generator_model(inputs, _) + + +def stargan_discriminator_model(inputs, num_domains): + """Differentiable dummy discriminator for StarGAN.""" + + hidden = layers.flatten(inputs) + + output_src = math_ops.reduce_mean(hidden, axis=1) + + output_cls = layers.fully_connected( + inputs=hidden, + num_outputs=num_domains, + activation_fn=None, + normalizer_fn=None, + biases_initializer=None) + return output_src, output_cls + + +class StarGANDiscriminator(object): + + def __call__(self, inputs, num_domains): + return stargan_discriminator_model(inputs, num_domains) def get_gan_model(): @@ -122,8 +165,7 @@ def get_gan_model(): def get_callable_gan_model(): ganmodel = get_gan_model() return ganmodel._replace( - generator_fn=Generator(), - discriminator_fn=Discriminator()) + generator_fn=Generator(), discriminator_fn=Discriminator()) def create_gan_model(): @@ -242,69 +284,84 @@ def create_callable_cyclegan_model(): data_y=array_ops.ones([1, 2])) -def get_sync_optimizer(): - return sync_replicas_optimizer.SyncReplicasOptimizer( - gradient_descent.GradientDescentOptimizer(learning_rate=1.0), - replicas_to_aggregate=1) +def get_stargan_model(): + """Similar to get_gan_model().""" + # TODO(joelshor): Find a better way of creating a variable scope. + with variable_scope.variable_scope('generator') as gen_scope: + pass + with variable_scope.variable_scope('discriminator') as dis_scope: + pass + return namedtuples.StarGANModel( + input_data=array_ops.ones([1, 2, 2, 3]), + input_data_domain_label=array_ops.ones([1, 2]), + generated_data=array_ops.ones([1, 2, 2, 3]), + generated_data_domain_target=array_ops.ones([1, 2]), + reconstructed_data=array_ops.ones([1, 2, 2, 3]), + discriminator_input_data_source_predication=array_ops.ones([1]), + discriminator_generated_data_source_predication=array_ops.ones([1]), + discriminator_input_data_domain_predication=array_ops.ones([1, 2]), + discriminator_generated_data_domain_predication=array_ops.ones([1, 2]), + generator_variables=None, + generator_scope=gen_scope, + generator_fn=stargan_generator_model, + discriminator_variables=None, + discriminator_scope=dis_scope, + discriminator_fn=stargan_discriminator_model) -def get_tensor_pool_fn(pool_size): +def get_callable_stargan_model(): + model = get_stargan_model() + return model._replace( + generator_fn=StarGANGenerator(), discriminator_fn=StarGANDiscriminator()) - def tensor_pool_fn_impl(input_values): - return random_tensor_pool.tensor_pool(input_values, pool_size=pool_size) - return tensor_pool_fn_impl +def create_stargan_model(): + return train.stargan_model( + stargan_generator_model, stargan_discriminator_model, + array_ops.ones([1, 2, 2, 3]), array_ops.ones([1, 2])) -def get_tensor_pool_fn_for_infogan(pool_size): +def create_callable_stargan_model(): + return train.stargan_model(StarGANGenerator(), StarGANDiscriminator(), + array_ops.ones([1, 2, 2, 3]), + array_ops.ones([1, 2])) - def tensor_pool_fn_impl(input_values): - generated_data, generator_inputs = input_values - output_values = random_tensor_pool.tensor_pool( - [generated_data] + generator_inputs, pool_size=pool_size) - return output_values[0], output_values[1:] - return tensor_pool_fn_impl +def get_sync_optimizer(): + return sync_replicas_optimizer.SyncReplicasOptimizer( + gradient_descent.GradientDescentOptimizer(learning_rate=1.0), + replicas_to_aggregate=1) -class GANModelTest(test.TestCase): +class GANModelTest(test.TestCase, parameterized.TestCase): """Tests for `gan_model`.""" - def _test_output_type_helper(self, create_fn, tuple_type): - self.assertTrue(isinstance(create_fn(), tuple_type)) - - def test_output_type_gan(self): - self._test_output_type_helper(get_gan_model, namedtuples.GANModel) - - def test_output_type_callable_gan(self): - self._test_output_type_helper(get_callable_gan_model, namedtuples.GANModel) - - def test_output_type_infogan(self): - self._test_output_type_helper(get_infogan_model, namedtuples.InfoGANModel) - - def test_output_type_callable_infogan(self): - self._test_output_type_helper( - get_callable_infogan_model, namedtuples.InfoGANModel) - - def test_output_type_acgan(self): - self._test_output_type_helper(get_acgan_model, namedtuples.ACGANModel) - - def test_output_type_callable_acgan(self): - self._test_output_type_helper( - get_callable_acgan_model, namedtuples.ACGANModel) - - def test_output_type_cyclegan(self): - self._test_output_type_helper(get_cyclegan_model, namedtuples.CycleGANModel) - - def test_output_type_callable_cyclegan(self): - self._test_output_type_helper(get_callable_cyclegan_model, - namedtuples.CycleGANModel) + @parameterized.named_parameters( + ('gan', get_gan_model, namedtuples.GANModel), + ('callable_gan', get_callable_gan_model, namedtuples.GANModel), + ('infogan', get_infogan_model, namedtuples.InfoGANModel), + ('callable_infogan', get_callable_infogan_model, + namedtuples.InfoGANModel), + ('acgan', get_acgan_model, namedtuples.ACGANModel), + ('callable_acgan', get_callable_acgan_model, namedtuples.ACGANModel), + ('cyclegan', get_cyclegan_model, namedtuples.CycleGANModel), + ('callable_cyclegan', get_callable_cyclegan_model, + namedtuples.CycleGANModel), + ('stargan', get_stargan_model, namedtuples.StarGANModel), + ('callabel_stargan', get_callable_stargan_model, namedtuples.StarGANModel) + ) + def test_output_type(self, create_fn, expected_tuple_type): + """Test that output type is as expected.""" + self.assertIsInstance(create_fn(), expected_tuple_type) def test_no_shape_check(self): + def dummy_generator_model(_): return (None, None) + def dummy_discriminator_model(data, conditioning): # pylint: disable=unused-argument return 1 + with self.assertRaisesRegexp(AttributeError, 'object has no attribute'): train.gan_model( dummy_generator_model, @@ -320,52 +377,182 @@ class GANModelTest(test.TestCase): check_shapes=False) -class GANLossTest(test.TestCase): - """Tests for `gan_loss`.""" +class StarGANModelTest(test.TestCase): + """Tests for `stargan_model`.""" + + @staticmethod + def create_input_and_label_tensor(batch_size, img_size, c_size, num_domains): + input_tensor_list = [] + label_tensor_list = [] + for _ in range(num_domains): + input_tensor_list.append( + random_ops.random_uniform((batch_size, img_size, img_size, c_size))) + domain_idx = random_ops.random_uniform( + [batch_size], minval=0, maxval=num_domains, dtype=dtypes.int32) + label_tensor_list.append(array_ops.one_hot(domain_idx, num_domains)) + return input_tensor_list, label_tensor_list + + def test_generate_stargan_random_domain_target(self): + batch_size = 8 + domain_numbers = 3 + + target_tensor = train._generate_stargan_random_domain_target( + batch_size, domain_numbers) + + with self.test_session() as sess: + targets = sess.run(target_tensor) + self.assertTupleEqual((batch_size, domain_numbers), targets.shape) + for target in targets: + self.assertEqual(1, np.sum(target)) + self.assertEqual(1, np.max(target)) + + def test_stargan_model_output_type(self): + batch_size = 2 + img_size = 16 + c_size = 3 + num_domains = 5 + + input_tensor, label_tensor = StarGANModelTest.create_input_and_label_tensor( + batch_size, img_size, c_size, num_domains) + model = train.stargan_model( + generator_fn=stargan_generator_model, + discriminator_fn=stargan_discriminator_model, + input_data=input_tensor, + input_data_domain_label=label_tensor) + + self.assertIsInstance(model, namedtuples.StarGANModel) + self.assertTrue(isinstance(model.discriminator_variables, list)) + self.assertTrue(isinstance(model.generator_variables, list)) + self.assertIsInstance(model.discriminator_scope, + variable_scope.VariableScope) + self.assertTrue(model.generator_scope, variable_scope.VariableScope) + self.assertTrue(callable(model.discriminator_fn)) + self.assertTrue(callable(model.generator_fn)) + + def test_stargan_model_generator_output(self): + batch_size = 2 + img_size = 16 + c_size = 3 + num_domains = 5 + + input_tensor, label_tensor = StarGANModelTest.create_input_and_label_tensor( + batch_size, img_size, c_size, num_domains) + model = train.stargan_model( + generator_fn=stargan_generator_model, + discriminator_fn=stargan_discriminator_model, + input_data=input_tensor, + input_data_domain_label=label_tensor) - # Test output type. - def _test_output_type_helper(self, get_gan_model_fn): - loss = train.gan_loss(get_gan_model_fn(), add_summaries=True) - self.assertTrue(isinstance(loss, namedtuples.GANLoss)) - self.assertGreater(len(ops.get_collection(ops.GraphKeys.SUMMARIES)), 0) - - def test_output_type_gan(self): - self._test_output_type_helper(get_gan_model) + with self.test_session(use_gpu=True) as sess: - def test_output_type_callable_gan(self): - self._test_output_type_helper(get_callable_gan_model) + sess.run(variables.global_variables_initializer()) - def test_output_type_infogan(self): - self._test_output_type_helper(get_infogan_model) + input_data, generated_data, reconstructed_data = sess.run( + [model.input_data, model.generated_data, model.reconstructed_data]) + self.assertTupleEqual( + (batch_size * num_domains, img_size, img_size, c_size), + input_data.shape) + self.assertTupleEqual( + (batch_size * num_domains, img_size, img_size, c_size), + generated_data.shape) + self.assertTupleEqual( + (batch_size * num_domains, img_size, img_size, c_size), + reconstructed_data.shape) + + def test_stargan_model_discriminator_output(self): + batch_size = 2 + img_size = 16 + c_size = 3 + num_domains = 5 + + input_tensor, label_tensor = StarGANModelTest.create_input_and_label_tensor( + batch_size, img_size, c_size, num_domains) + model = train.stargan_model( + generator_fn=stargan_generator_model, + discriminator_fn=stargan_discriminator_model, + input_data=input_tensor, + input_data_domain_label=label_tensor) - def test_output_type_callable_infogan(self): - self._test_output_type_helper(get_callable_infogan_model) + with self.test_session(use_gpu=True) as sess: - def test_output_type_acgan(self): - self._test_output_type_helper(get_acgan_model) + sess.run(variables.global_variables_initializer()) - def test_output_type_callable_acgan(self): - self._test_output_type_helper(get_callable_acgan_model) + disc_input_data_source_pred, disc_gen_data_source_pred = sess.run([ + model.discriminator_input_data_source_predication, + model.discriminator_generated_data_source_predication + ]) + self.assertEqual(1, len(disc_input_data_source_pred.shape)) + self.assertEqual(batch_size * num_domains, + disc_input_data_source_pred.shape[0]) + self.assertEqual(1, len(disc_gen_data_source_pred.shape)) + self.assertEqual(batch_size * num_domains, + disc_gen_data_source_pred.shape[0]) + + input_label, disc_input_label, gen_label, disc_gen_label = sess.run([ + model.input_data_domain_label, + model.discriminator_input_data_domain_predication, + model.generated_data_domain_target, + model.discriminator_generated_data_domain_predication + ]) + self.assertTupleEqual((batch_size * num_domains, num_domains), + input_label.shape) + self.assertTupleEqual((batch_size * num_domains, num_domains), + disc_input_label.shape) + self.assertTupleEqual((batch_size * num_domains, num_domains), + gen_label.shape) + self.assertTupleEqual((batch_size * num_domains, num_domains), + disc_gen_label.shape) + + +class GANLossTest(test.TestCase, parameterized.TestCase): + """Tests for `gan_loss`.""" - def test_output_type_cyclegan(self): - loss = train.cyclegan_loss(create_cyclegan_model(), add_summaries=True) - self.assertIsInstance(loss, namedtuples.CycleGANLoss) + @parameterized.named_parameters( + ('gan', get_gan_model), + ('callable_gan', get_callable_gan_model), + ('infogan', get_infogan_model), + ('callable_infogan', get_callable_infogan_model), + ('acgan', get_acgan_model), + ('callable_acgan', get_callable_acgan_model), + ) + def test_output_type(self, get_gan_model_fn): + """Test output type.""" + loss = train.gan_loss(get_gan_model_fn(), add_summaries=True) + self.assertIsInstance(loss, namedtuples.GANLoss) self.assertGreater(len(ops.get_collection(ops.GraphKeys.SUMMARIES)), 0) - def test_output_type_callable_cyclegan(self): - loss = train.cyclegan_loss( - create_callable_cyclegan_model(), add_summaries=True) + @parameterized.named_parameters( + ('cyclegan', create_cyclegan_model), + ('callable_cyclegan', create_callable_cyclegan_model), + ) + def test_cyclegan_output_type(self, get_gan_model_fn): + loss = train.cyclegan_loss(get_gan_model_fn(), add_summaries=True) self.assertIsInstance(loss, namedtuples.CycleGANLoss) self.assertGreater(len(ops.get_collection(ops.GraphKeys.SUMMARIES)), 0) - # Test gradient penalty option. - def _test_grad_penalty_helper(self, create_gan_model_fn, one_sided=False): + @parameterized.named_parameters( + ('gan', create_gan_model, False), + ('gan_one_sided', create_gan_model, True), + ('callable_gan', create_callable_gan_model, False), + ('callable_gan_one_sided', create_callable_gan_model, True), + ('infogan', create_infogan_model, False), + ('infogan_one_sided', create_infogan_model, True), + ('callable_infogan', create_callable_infogan_model, False), + ('callable_infogan_one_sided', create_callable_infogan_model, True), + ('acgan', create_acgan_model, False), + ('acgan_one_sided', create_acgan_model, True), + ('callable_acgan', create_callable_acgan_model, False), + ('callable_acgan_one_sided', create_callable_acgan_model, True), + ) + def test_grad_penalty(self, create_gan_model_fn, one_sided): + """Test gradient penalty option.""" model = create_gan_model_fn() loss = train.gan_loss(model) - loss_gp = train.gan_loss(model, - gradient_penalty_weight=1.0, - gradient_penalty_one_sided=one_sided) - self.assertTrue(isinstance(loss_gp, namedtuples.GANLoss)) + loss_gp = train.gan_loss( + model, + gradient_penalty_weight=1.0, + gradient_penalty_one_sided=one_sided) + self.assertIsInstance(loss_gp, namedtuples.GANLoss) # Check values. with self.test_session(use_gpu=True) as sess: @@ -376,58 +563,28 @@ class GANLossTest(test.TestCase): [loss.discriminator_loss, loss_gp.discriminator_loss]) self.assertEqual(loss_gen_np, loss_gen_gp_np) - self.assertTrue(loss_dis_np < loss_dis_gp_np) - - def test_grad_penalty_gan(self): - self._test_grad_penalty_helper(create_gan_model) - - def test_grad_penalty_callable_gan(self): - self._test_grad_penalty_helper(create_callable_gan_model) - - def test_grad_penalty_infogan(self): - self._test_grad_penalty_helper(create_infogan_model) - - def test_grad_penalty_callable_infogan(self): - self._test_grad_penalty_helper(create_callable_infogan_model) - - def test_grad_penalty_acgan(self): - self._test_grad_penalty_helper(create_acgan_model) - - def test_grad_penalty_callable_acgan(self): - self._test_grad_penalty_helper(create_callable_acgan_model) - - def test_grad_penalty_one_sided_gan(self): - self._test_grad_penalty_helper(create_gan_model, one_sided=True) - - def test_grad_penalty_one_sided_callable_gan(self): - self._test_grad_penalty_helper(create_callable_gan_model, one_sided=True) - - def test_grad_penalty_one_sided_infogan(self): - self._test_grad_penalty_helper(create_infogan_model, one_sided=True) - - def test_grad_penalty_one_sided_callable_infogan(self): - self._test_grad_penalty_helper( - create_callable_infogan_model, one_sided=True) - - def test_grad_penalty_one_sided_acgan(self): - self._test_grad_penalty_helper(create_acgan_model, one_sided=True) - - def test_grad_penalty_one_sided_callable_acgan(self): - self._test_grad_penalty_helper(create_callable_acgan_model, one_sided=True) - - # Test mutual information penalty option. - def _test_mutual_info_penalty_helper(self, create_gan_model_fn): - train.gan_loss(create_gan_model_fn(), - mutual_information_penalty_weight=constant_op.constant(1.0)) - - def test_mutual_info_penalty_infogan(self): - self._test_mutual_info_penalty_helper(get_infogan_model) - - def test_mutual_info_penalty_callable_infogan(self): - self._test_mutual_info_penalty_helper(get_callable_infogan_model) - - # Test regularization loss. - def _test_regularization_helper(self, get_gan_model_fn): + self.assertLess(loss_dis_np, loss_dis_gp_np) + + @parameterized.named_parameters( + ('infogan', get_infogan_model), + ('callable_infogan', get_callable_infogan_model), + ) + def test_mutual_info_penalty(self, create_gan_model_fn): + """Test mutual information penalty option.""" + train.gan_loss( + create_gan_model_fn(), + mutual_information_penalty_weight=constant_op.constant(1.0)) + + @parameterized.named_parameters( + ('gan', get_gan_model), + ('callable_gan', get_callable_gan_model), + ('infogan', get_infogan_model), + ('callable_infogan', get_callable_infogan_model), + ('acgan', get_acgan_model), + ('callable_acgan', get_callable_acgan_model), + ) + def test_regularization_helper(self, get_gan_model_fn): + """Test regularization loss.""" # Evaluate losses without regularization. no_reg_loss = train.gan_loss(get_gan_model_fn()) with self.test_session(use_gpu=True): @@ -435,11 +592,11 @@ class GANLossTest(test.TestCase): no_reg_loss_dis_np = no_reg_loss.discriminator_loss.eval() with ops.name_scope(get_gan_model_fn().generator_scope.name): - ops.add_to_collection( - ops.GraphKeys.REGULARIZATION_LOSSES, constant_op.constant(3.0)) + ops.add_to_collection(ops.GraphKeys.REGULARIZATION_LOSSES, + constant_op.constant(3.0)) with ops.name_scope(get_gan_model_fn().discriminator_scope.name): - ops.add_to_collection( - ops.GraphKeys.REGULARIZATION_LOSSES, constant_op.constant(2.0)) + ops.add_to_collection(ops.GraphKeys.REGULARIZATION_LOSSES, + constant_op.constant(2.0)) # Check that losses now include the correct regularization values. reg_loss = train.gan_loss(get_gan_model_fn()) @@ -447,63 +604,47 @@ class GANLossTest(test.TestCase): reg_loss_gen_np = reg_loss.generator_loss.eval() reg_loss_dis_np = reg_loss.discriminator_loss.eval() - self.assertTrue(3.0, reg_loss_gen_np - no_reg_loss_gen_np) - self.assertTrue(3.0, reg_loss_dis_np - no_reg_loss_dis_np) - - def test_regularization_gan(self): - self._test_regularization_helper(get_gan_model) + self.assertEqual(3.0, reg_loss_gen_np - no_reg_loss_gen_np) + self.assertEqual(2.0, reg_loss_dis_np - no_reg_loss_dis_np) - def test_regularization_callable_gan(self): - self._test_regularization_helper(get_callable_gan_model) - - def test_regularization_infogan(self): - self._test_regularization_helper(get_infogan_model) - - def test_regularization_callable_infogan(self): - self._test_regularization_helper(get_callable_infogan_model) - - def test_regularization_acgan(self): - self._test_regularization_helper(get_acgan_model) - - def test_regularization_callable_acgan(self): - self._test_regularization_helper(get_callable_acgan_model) - - # Test that ACGan models work. - def _test_acgan_helper(self, create_gan_model_fn): + @parameterized.named_parameters( + ('notcallable', create_acgan_model), + ('callable', create_callable_acgan_model), + ) + def test_acgan(self, create_gan_model_fn): + """Test that ACGAN models work.""" model = create_gan_model_fn() loss = train.gan_loss(model) loss_ac_gen = train.gan_loss(model, aux_cond_generator_weight=1.0) loss_ac_dis = train.gan_loss(model, aux_cond_discriminator_weight=1.0) - self.assertTrue(isinstance(loss, namedtuples.GANLoss)) - self.assertTrue(isinstance(loss_ac_gen, namedtuples.GANLoss)) - self.assertTrue(isinstance(loss_ac_dis, namedtuples.GANLoss)) + self.assertIsInstance(loss, namedtuples.GANLoss) + self.assertIsInstance(loss_ac_gen, namedtuples.GANLoss) + self.assertIsInstance(loss_ac_dis, namedtuples.GANLoss) # Check values. with self.test_session(use_gpu=True) as sess: variables.global_variables_initializer().run() - loss_gen_np, loss_ac_gen_gen_np, loss_ac_dis_gen_np = sess.run( - [loss.generator_loss, - loss_ac_gen.generator_loss, - loss_ac_dis.generator_loss]) - loss_dis_np, loss_ac_gen_dis_np, loss_ac_dis_dis_np = sess.run( - [loss.discriminator_loss, - loss_ac_gen.discriminator_loss, - loss_ac_dis.discriminator_loss]) - - self.assertTrue(loss_gen_np < loss_dis_np) + loss_gen_np, loss_ac_gen_gen_np, loss_ac_dis_gen_np = sess.run([ + loss.generator_loss, loss_ac_gen.generator_loss, + loss_ac_dis.generator_loss + ]) + loss_dis_np, loss_ac_gen_dis_np, loss_ac_dis_dis_np = sess.run([ + loss.discriminator_loss, loss_ac_gen.discriminator_loss, + loss_ac_dis.discriminator_loss + ]) + + self.assertLess(loss_gen_np, loss_dis_np) self.assertTrue(np.isscalar(loss_ac_gen_gen_np)) self.assertTrue(np.isscalar(loss_ac_dis_gen_np)) self.assertTrue(np.isscalar(loss_ac_gen_dis_np)) self.assertTrue(np.isscalar(loss_ac_dis_dis_np)) - def test_acgan(self): - self._test_acgan_helper(create_acgan_model) - - def test_callable_acgan(self): - self._test_acgan_helper(create_callable_acgan_model) - - # Test that CycleGan models work. - def _test_cyclegan_helper(self, create_gan_model_fn): + @parameterized.named_parameters( + ('notcallable', create_cyclegan_model), + ('callable', create_callable_cyclegan_model), + ) + def test_cyclegan(self, create_gan_model_fn): + """Test that CycleGan models work.""" model = create_gan_model_fn() loss = train.cyclegan_loss(model) self.assertIsInstance(loss, namedtuples.CycleGANLoss) @@ -524,14 +665,86 @@ class GANLossTest(test.TestCase): self.assertTrue(np.isscalar(loss_y2x_gen_np)) self.assertTrue(np.isscalar(loss_y2x_dis_np)) - def test_cyclegan(self): - self._test_cyclegan_helper(create_cyclegan_model) + @parameterized.named_parameters( + ('notcallable', create_stargan_model), + ('callable', create_callable_stargan_model), + ) + def test_stargan(self, create_gan_model_fn): + + model = create_gan_model_fn() + model_loss = train.stargan_loss(model) + + self.assertIsInstance(model_loss, namedtuples.GANLoss) + + with self.test_session() as sess: + + sess.run(variables.global_variables_initializer()) + + gen_loss, disc_loss = sess.run( + [model_loss.generator_loss, model_loss.discriminator_loss]) + + self.assertTrue(np.isscalar(gen_loss)) + self.assertTrue(np.isscalar(disc_loss)) + + @parameterized.named_parameters( + ('gan', create_gan_model), + ('callable_gan', create_callable_gan_model), + ('infogan', create_infogan_model), + ('callable_infogan', create_callable_infogan_model), + ('acgan', create_acgan_model), + ('callable_acgan', create_callable_acgan_model), + ) + def test_tensor_pool(self, create_gan_model_fn): + """Test tensor pool option.""" + model = create_gan_model_fn() + tensor_pool_fn = lambda x: random_tensor_pool.tensor_pool(x, pool_size=5) + loss = train.gan_loss(model, tensor_pool_fn=tensor_pool_fn) + self.assertIsInstance(loss, namedtuples.GANLoss) + + # Check values. + with self.test_session(use_gpu=True) as sess: + variables.global_variables_initializer().run() + for _ in range(10): + sess.run([loss.generator_loss, loss.discriminator_loss]) + + def test_discriminator_only_sees_pool(self): + """Checks that discriminator only sees pooled values.""" + def checker_gen_fn(_): + return constant_op.constant(0.0) + model = train.gan_model( + checker_gen_fn, + discriminator_model, + real_data=array_ops.zeros([]), + generator_inputs=random_ops.random_normal([])) + def tensor_pool_fn(_): + return (random_ops.random_uniform([]), random_ops.random_uniform([])) + def checker_dis_fn(inputs, _): + """Discriminator that checks that it only sees pooled Tensors.""" + self.assertFalse(constant_op.is_constant(inputs)) + return inputs + model = model._replace( + discriminator_fn=checker_dis_fn) + train.gan_loss(model, tensor_pool_fn=tensor_pool_fn) + + def test_doesnt_crash_when_in_nested_scope(self): + with variable_scope.variable_scope('outer_scope'): + gan_model = train.gan_model( + generator_model, + discriminator_model, + real_data=array_ops.zeros([1, 2]), + generator_inputs=random_ops.random_normal([1, 2])) + + # This should work inside a scope. + train.gan_loss(gan_model, gradient_penalty_weight=1.0) - def test_callable_cyclegan(self): - self._test_cyclegan_helper(create_callable_cyclegan_model) + # This should also work outside a scope. + train.gan_loss(gan_model, gradient_penalty_weight=1.0) - def _check_tensor_pool_adjusted_model_outputs(self, tensor1, tensor2, - pool_size): + +class TensorPoolAdjusteModelTest(test.TestCase): + + def _check_tensor_pool_adjusted_model_outputs( + self, tensor1, tensor2, pool_size): history_values = [] with self.test_session(use_gpu=True) as sess: variables.global_variables_initializer().run() @@ -548,115 +761,66 @@ class GANLossTest(test.TestCase): # pool). self.assertTrue(any([(v == t2).all() for v in history_values])) - # Test `_tensor_pool_adjusted_model` for gan model. - def test_tensor_pool_adjusted_model_gan(self): - model = create_gan_model() - - new_model = train._tensor_pool_adjusted_model(model, None) + def _make_new_model_and_check(self, model, pool_size): + pool_fn = lambda x: random_tensor_pool.tensor_pool(x, pool_size=pool_size) + new_model = train._tensor_pool_adjusted_model(model, pool_fn) # 'Generator/dummy_g:0' and 'Discriminator/dummy_d:0' self.assertEqual(2, len(ops.get_collection(ops.GraphKeys.VARIABLES))) - self.assertIs(new_model.discriminator_gen_outputs, - model.discriminator_gen_outputs) - - pool_size = 5 - new_model = train._tensor_pool_adjusted_model( - model, get_tensor_pool_fn(pool_size=pool_size)) self.assertIsNot(new_model.discriminator_gen_outputs, model.discriminator_gen_outputs) + + return new_model + + def test_tensor_pool_adjusted_model_gan(self): + """Test `_tensor_pool_adjusted_model` for gan model.""" + pool_size = 5 + model = create_gan_model() + new_model = self._make_new_model_and_check(model, pool_size) + # Check values. self._check_tensor_pool_adjusted_model_outputs( model.discriminator_gen_outputs, new_model.discriminator_gen_outputs, pool_size) - # Test _tensor_pool_adjusted_model for infogan model. def test_tensor_pool_adjusted_model_infogan(self): + """Test _tensor_pool_adjusted_model for infogan model.""" + pool_size = 5 model = create_infogan_model() + new_model = self._make_new_model_and_check(model, pool_size) - pool_size = 5 - new_model = train._tensor_pool_adjusted_model( - model, get_tensor_pool_fn_for_infogan(pool_size=pool_size)) - # 'Generator/dummy_g:0' and 'Discriminator/dummy_d:0' - self.assertEqual(2, len(ops.get_collection(ops.GraphKeys.VARIABLES))) - self.assertIsNot(new_model.discriminator_gen_outputs, - model.discriminator_gen_outputs) + # Check values. self.assertIsNot(new_model.predicted_distributions, model.predicted_distributions) - # Check values. self._check_tensor_pool_adjusted_model_outputs( model.discriminator_gen_outputs, new_model.discriminator_gen_outputs, pool_size) - # Test _tensor_pool_adjusted_model for acgan model. def test_tensor_pool_adjusted_model_acgan(self): + """Test _tensor_pool_adjusted_model for acgan model.""" + pool_size = 5 model = create_acgan_model() + new_model = self._make_new_model_and_check(model, pool_size) - pool_size = 5 - new_model = train._tensor_pool_adjusted_model( - model, get_tensor_pool_fn(pool_size=pool_size)) - # 'Generator/dummy_g:0' and 'Discriminator/dummy_d:0' - self.assertEqual(2, len(ops.get_collection(ops.GraphKeys.VARIABLES))) - self.assertIsNot(new_model.discriminator_gen_outputs, - model.discriminator_gen_outputs) + # Check values. self.assertIsNot(new_model.discriminator_gen_classification_logits, model.discriminator_gen_classification_logits) - # Check values. self._check_tensor_pool_adjusted_model_outputs( model.discriminator_gen_outputs, new_model.discriminator_gen_outputs, pool_size) - # Test tensor pool. - def _test_tensor_pool_helper(self, create_gan_model_fn): - model = create_gan_model_fn() - if isinstance(model, namedtuples.InfoGANModel): - tensor_pool_fn = get_tensor_pool_fn_for_infogan(pool_size=5) - else: - tensor_pool_fn = get_tensor_pool_fn(pool_size=5) - loss = train.gan_loss(model, tensor_pool_fn=tensor_pool_fn) - self.assertTrue(isinstance(loss, namedtuples.GANLoss)) - - # Check values. - with self.test_session(use_gpu=True) as sess: - variables.global_variables_initializer().run() - for _ in range(10): - sess.run([loss.generator_loss, loss.discriminator_loss]) - - def test_tensor_pool_gan(self): - self._test_tensor_pool_helper(create_gan_model) - - def test_tensor_pool_callable_gan(self): - self._test_tensor_pool_helper(create_callable_gan_model) - - def test_tensor_pool_infogan(self): - self._test_tensor_pool_helper(create_infogan_model) - - def test_tensor_pool_callable_infogan(self): - self._test_tensor_pool_helper(create_callable_infogan_model) - - def test_tensor_pool_acgan(self): - self._test_tensor_pool_helper(create_acgan_model) - - def test_tensor_pool_callable_acgan(self): - self._test_tensor_pool_helper(create_callable_acgan_model) - - def test_doesnt_crash_when_in_nested_scope(self): - with variable_scope.variable_scope('outer_scope'): - gan_model = train.gan_model( - generator_model, - discriminator_model, - real_data=array_ops.zeros([1, 2]), - generator_inputs=random_ops.random_normal([1, 2])) - - # This should work inside a scope. - train.gan_loss(gan_model, gradient_penalty_weight=1.0) - # This should also work outside a scope. - train.gan_loss(gan_model, gradient_penalty_weight=1.0) - - -class GANTrainOpsTest(test.TestCase): +class GANTrainOpsTest(test.TestCase, parameterized.TestCase): """Tests for `gan_train_ops`.""" - def _test_output_type_helper(self, create_gan_model_fn): + @parameterized.named_parameters( + ('gan', create_gan_model), + ('callable_gan', create_callable_gan_model), + ('infogan', create_infogan_model), + ('callable_infogan', create_callable_infogan_model), + ('acgan', create_acgan_model), + ('callable_acgan', create_callable_acgan_model), + ) + def test_output_type(self, create_gan_model_fn): model = create_gan_model_fn() loss = train.gan_loss(model) @@ -670,28 +834,24 @@ class GANTrainOpsTest(test.TestCase): summarize_gradients=True, colocate_gradients_with_ops=True) - self.assertTrue(isinstance(train_ops, namedtuples.GANTrainOps)) - - def test_output_type_gan(self): - self._test_output_type_helper(create_gan_model) - - def test_output_type_callable_gan(self): - self._test_output_type_helper(create_callable_gan_model) - - def test_output_type_infogan(self): - self._test_output_type_helper(create_infogan_model) - - def test_output_type_callable_infogan(self): - self._test_output_type_helper(create_callable_infogan_model) - - def test_output_type_acgan(self): - self._test_output_type_helper(create_acgan_model) - - def test_output_type_callable_acgan(self): - self._test_output_type_helper(create_callable_acgan_model) + self.assertIsInstance(train_ops, namedtuples.GANTrainOps) # TODO(joelshor): Add a test to check that custom update op is run. - def _test_unused_update_ops(self, create_gan_model_fn, provide_update_ops): + @parameterized.named_parameters( + ('gan', create_gan_model, False), + ('gan_provideupdates', create_gan_model, True), + ('callable_gan', create_callable_gan_model, False), + ('callable_gan_provideupdates', create_callable_gan_model, True), + ('infogan', create_infogan_model, False), + ('infogan_provideupdates', create_infogan_model, True), + ('callable_infogan', create_callable_infogan_model, False), + ('callable_infogan_provideupdates', create_callable_infogan_model, True), + ('acgan', create_acgan_model, False), + ('acgan_provideupdates', create_acgan_model, True), + ('callable_acgan', create_callable_acgan_model, False), + ('callable_acgan_provideupdates', create_callable_acgan_model, True), + ) + def test_unused_update_ops(self, create_gan_model_fn, provide_update_ops): model = create_gan_model_fn() loss = train.gan_loss(model) @@ -707,8 +867,11 @@ class GANTrainOpsTest(test.TestCase): # Add an update op outside the generator and discriminator scopes. if provide_update_ops: - kwargs = {'update_ops': - [constant_op.constant(1.0), gen_update_op, dis_update_op]} + kwargs = { + 'update_ops': [ + constant_op.constant(1.0), gen_update_op, dis_update_op + ] + } else: ops.add_to_collection(ops.GraphKeys.UPDATE_OPS, constant_op.constant(1.0)) kwargs = {} @@ -717,8 +880,8 @@ class GANTrainOpsTest(test.TestCase): d_opt = gradient_descent.GradientDescentOptimizer(1.0) with self.assertRaisesRegexp(ValueError, 'There are unused update ops:'): - train.gan_train_ops(model, loss, g_opt, d_opt, - check_for_unused_update_ops=True, **kwargs) + train.gan_train_ops( + model, loss, g_opt, d_opt, check_for_unused_update_ops=True, **kwargs) train_ops = train.gan_train_ops( model, loss, g_opt, d_opt, check_for_unused_update_ops=False, **kwargs) @@ -735,44 +898,16 @@ class GANTrainOpsTest(test.TestCase): self.assertEqual(1, gen_update_count.eval()) self.assertEqual(1, dis_update_count.eval()) - def test_unused_update_ops_gan(self): - self._test_unused_update_ops(create_gan_model, False) - - def test_unused_update_ops_gan_provideupdates(self): - self._test_unused_update_ops(create_gan_model, True) - - def test_unused_update_ops_callable_gan(self): - self._test_unused_update_ops(create_callable_gan_model, False) - - def test_unused_update_ops_callable_gan_provideupdates(self): - self._test_unused_update_ops(create_callable_gan_model, True) - - def test_unused_update_ops_infogan(self): - self._test_unused_update_ops(create_infogan_model, False) - - def test_unused_update_ops_infogan_provideupdates(self): - self._test_unused_update_ops(create_infogan_model, True) - - def test_unused_update_ops_callable_infogan(self): - self._test_unused_update_ops(create_callable_infogan_model, False) - - def test_unused_update_ops_callable_infogan_provideupdates(self): - self._test_unused_update_ops(create_callable_infogan_model, True) - - def test_unused_update_ops_acgan(self): - self._test_unused_update_ops(create_acgan_model, False) - - def test_unused_update_ops_acgan_provideupdates(self): - self._test_unused_update_ops(create_acgan_model, True) - - def test_unused_update_ops_callable_acgan(self): - self._test_unused_update_ops(create_callable_acgan_model, False) - - def test_unused_update_ops_callable_acgan_provideupdates(self): - self._test_unused_update_ops(create_callable_acgan_model, True) - - def _test_sync_replicas_helper( - self, create_gan_model_fn, create_global_step=False): + @parameterized.named_parameters( + ('gan', create_gan_model, False), + ('callable_gan', create_callable_gan_model, False), + ('infogan', create_infogan_model, False), + ('callable_infogan', create_callable_infogan_model, False), + ('acgan', create_acgan_model, False), + ('callable_acgan', create_callable_acgan_model, False), + ('gan_canbeint32', create_gan_model, True), + ) + def test_sync_replicas(self, create_gan_model_fn, create_global_step): model = create_gan_model_fn() loss = train.gan_loss(model) num_trainable_vars = len(variables_lib.get_trainable_variables()) @@ -785,11 +920,8 @@ class GANTrainOpsTest(test.TestCase): g_opt = get_sync_optimizer() d_opt = get_sync_optimizer() train_ops = train.gan_train_ops( - model, - loss, - generator_optimizer=g_opt, - discriminator_optimizer=d_opt) - self.assertTrue(isinstance(train_ops, namedtuples.GANTrainOps)) + model, loss, generator_optimizer=g_opt, discriminator_optimizer=d_opt) + self.assertIsInstance(train_ops, namedtuples.GANTrainOps) # No new trainable variables should have been added. self.assertEqual(num_trainable_vars, len(variables_lib.get_trainable_variables())) @@ -827,29 +959,8 @@ class GANTrainOpsTest(test.TestCase): coord.request_stop() coord.join(g_threads + d_threads) - def test_sync_replicas_gan(self): - self._test_sync_replicas_helper(create_gan_model) - - def test_sync_replicas_callable_gan(self): - self._test_sync_replicas_helper(create_callable_gan_model) - - def test_sync_replicas_infogan(self): - self._test_sync_replicas_helper(create_infogan_model) - def test_sync_replicas_callable_infogan(self): - self._test_sync_replicas_helper(create_callable_infogan_model) - - def test_sync_replicas_acgan(self): - self._test_sync_replicas_helper(create_acgan_model) - - def test_sync_replicas_callable_acgan(self): - self._test_sync_replicas_helper(create_callable_acgan_model) - - def test_global_step_can_be_int32(self): - self._test_sync_replicas_helper(create_gan_model, create_global_step=True) - - -class GANTrainTest(test.TestCase): +class GANTrainTest(test.TestCase, parameterized.TestCase): """Tests for `gan_train`.""" def _gan_train_ops(self, generator_add, discriminator_add): @@ -860,12 +971,20 @@ class GANTrainTest(test.TestCase): # joint training. train_ops = namedtuples.GANTrainOps( generator_train_op=step.assign_add(generator_add, use_locking=True), - discriminator_train_op=step.assign_add(discriminator_add, - use_locking=True), + discriminator_train_op=step.assign_add( + discriminator_add, use_locking=True), global_step_inc_op=step.assign_add(1)) return train_ops - def _test_run_helper(self, create_gan_model_fn): + @parameterized.named_parameters( + ('gan', create_gan_model), + ('callable_gan', create_callable_gan_model), + ('infogan', create_infogan_model), + ('callable_infogan', create_callable_infogan_model), + ('acgan', create_acgan_model), + ('callable_acgan', create_callable_acgan_model), + ) + def test_run_helper(self, create_gan_model_fn): random_seed.set_random_seed(1234) model = create_gan_model_fn() loss = train.gan_loss(model) @@ -881,30 +1000,15 @@ class GANTrainTest(test.TestCase): self.assertTrue(np.isscalar(final_step)) self.assertEqual(2, final_step) - def test_run_gan(self): - self._test_run_helper(create_gan_model) - - def test_run_callable_gan(self): - self._test_run_helper(create_callable_gan_model) - - def test_run_infogan(self): - self._test_run_helper(create_infogan_model) - - def test_run_callable_infogan(self): - self._test_run_helper(create_callable_infogan_model) - - def test_run_acgan(self): - self._test_run_helper(create_acgan_model) - - def test_run_callable_acgan(self): - self._test_run_helper(create_callable_acgan_model) - - # Test multiple train steps. - def _test_multiple_steps_helper(self, get_hooks_fn_fn): + @parameterized.named_parameters( + ('seq_train_steps', train.get_sequential_train_hooks), + ('efficient_seq_train_steps', train.get_joint_train_hooks), + ) + def test_multiple_steps(self, get_hooks_fn_fn): + """Test multiple train steps.""" train_ops = self._gan_train_ops(generator_add=10, discriminator_add=100) train_steps = namedtuples.GANTrainSteps( - generator_train_steps=3, - discriminator_train_steps=4) + generator_train_steps=3, discriminator_train_steps=4) final_step = train.gan_train( train_ops, get_hooks_fn=get_hooks_fn_fn(train_steps), @@ -914,12 +1018,6 @@ class GANTrainTest(test.TestCase): self.assertTrue(np.isscalar(final_step)) self.assertEqual(1 + 3 * 10 + 4 * 100, final_step) - def test_multiple_steps_seq_train_steps(self): - self._test_multiple_steps_helper(train.get_sequential_train_hooks) - - def test_multiple_steps_efficient_seq_train_steps(self): - self._test_multiple_steps_helper(train.get_joint_train_hooks) - def test_supervisor_run_gan_model_train_ops_multiple_steps(self): step = training_util.create_global_step() train_ops = namedtuples.GANTrainOps( @@ -927,8 +1025,7 @@ class GANTrainTest(test.TestCase): discriminator_train_op=constant_op.constant(2.0), global_step_inc_op=step.assign_add(1)) train_steps = namedtuples.GANTrainSteps( - generator_train_steps=3, - discriminator_train_steps=4) + generator_train_steps=3, discriminator_train_steps=4) final_loss = slim_learning.train( train_op=train_ops, @@ -940,10 +1037,18 @@ class GANTrainTest(test.TestCase): self.assertEqual(17.0, final_loss) -class PatchGANTest(test.TestCase): +class PatchGANTest(test.TestCase, parameterized.TestCase): """Tests that functions work on PatchGAN style output.""" - def _test_patchgan_helper(self, create_gan_model_fn): + @parameterized.named_parameters( + ('gan', create_gan_model), + ('callable_gan', create_callable_gan_model), + ('infogan', create_infogan_model), + ('callable_infogan', create_callable_infogan_model), + ('acgan', create_acgan_model), + ('callable_acgan', create_callable_acgan_model), + ) + def test_patchgan(self, create_gan_model_fn): """Ensure that patch-based discriminators work end-to-end.""" random_seed.set_random_seed(1234) model = create_gan_model_fn() @@ -960,24 +1065,6 @@ class PatchGANTest(test.TestCase): self.assertTrue(np.isscalar(final_step)) self.assertEqual(2, final_step) - def test_patchgan_gan(self): - self._test_patchgan_helper(create_gan_model) - - def test_patchgan_callable_gan(self): - self._test_patchgan_helper(create_callable_gan_model) - - def test_patchgan_infogan(self): - self._test_patchgan_helper(create_infogan_model) - - def test_patchgan_callable_infogan(self): - self._test_patchgan_helper(create_callable_infogan_model) - - def test_patchgan_acgan(self): - self._test_patchgan_helper(create_acgan_model) - - def test_patchgan_callable_acgan(self): - self._test_patchgan_helper(create_callable_acgan_model) - if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/gdr/gdr_memory_manager.cc b/tensorflow/contrib/gdr/gdr_memory_manager.cc index 1435e19109ca2f3bbd6ce70e6e5f26a92dfc2713..f3bbf6b4d78b50b11e23abd584bacff8f3d877c7 100644 --- a/tensorflow/contrib/gdr/gdr_memory_manager.cc +++ b/tensorflow/contrib/gdr/gdr_memory_manager.cc @@ -33,10 +33,11 @@ limitations under the License. #include "tensorflow/core/common_runtime/bfc_allocator.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/dma_helper.h" +#include "tensorflow/core/common_runtime/pool_allocator.h" +#include "tensorflow/core/common_runtime/process_state.h" #if GOOGLE_CUDA #include "tensorflow/core/common_runtime/gpu/gpu_process_state.h" #include "tensorflow/core/common_runtime/gpu/gpu_util.h" -#include "tensorflow/core/common_runtime/process_state.h" #endif // GOOGLE_CUDA #include "tensorflow/core/framework/allocator_registry.h" #include "tensorflow/core/lib/core/status.h" @@ -182,28 +183,25 @@ class GdrMemoryManager : public RemoteMemoryManager { TF_DISALLOW_COPY_AND_ASSIGN(GdrMemoryManager); }; -// TODO(byronyi): remove this class duplicated from the one in -// common/runtime/gpu/pool_allocator.h when it is available in common_runtime -class BasicCPUAllocator : public SubAllocator { - public: - ~BasicCPUAllocator() override {} - - void* Alloc(size_t alignment, size_t num_bytes) override { - return port::AlignedMalloc(num_bytes, alignment); - } - void Free(void* ptr, size_t) override { port::AlignedFree(ptr); } -}; - // TODO(byronyi): remove this class and its registration when the default -// cpu_allocator() returns visitable allocator +// cpu_allocator() returns visitable allocator, or cpu_allocator() is no +// longer in use. class BFCRdmaAllocator : public BFCAllocator { public: BFCRdmaAllocator() - : BFCAllocator(new BasicCPUAllocator(), 1LL << 36, true, "cpu_rdma_bfc") { + : BFCAllocator(new BasicCPUAllocator(port::kNUMANoAffinity), 1LL << 36, + true, "cpu_rdma_bfc") {} +}; +class BFCRdmaAllocatorFactory : public AllocatorFactory { + public: + Allocator* CreateAllocator() override { return new BFCRdmaAllocator; } + + virtual SubAllocator* CreateSubAllocator(int numa_node) { + return new BasicCPUAllocator(numa_node); } }; -REGISTER_MEM_ALLOCATOR("BFCRdmaAllocator", 101, BFCRdmaAllocator); +REGISTER_MEM_ALLOCATOR("BFCRdmaAllocator", 101, BFCRdmaAllocatorFactory); GdrMemoryManager::GdrMemoryManager(const string& host, const string& port) : host_(host), @@ -276,8 +274,8 @@ Status GdrMemoryManager::Init() { Allocator* allocators[] = { #if GOOGLE_CUDA GPUProcessState::singleton()->GetCUDAHostAllocator(0), - ProcessState::singleton()->GetCPUAllocator(0), #endif // GOOGLE_CUDA + ProcessState::singleton()->GetCPUAllocator(0), cpu_allocator(), }; diff --git a/tensorflow/contrib/graph_editor/reroute.py b/tensorflow/contrib/graph_editor/reroute.py index 95c02a64d47c26e731ef2628fb551529e9bc3f4d..d42e0c01f455f861e9ccdbfb79aefab762e61abe 100644 --- a/tensorflow/contrib/graph_editor/reroute.py +++ b/tensorflow/contrib/graph_editor/reroute.py @@ -208,9 +208,9 @@ def _reroute_ts(ts0, ts1, mode, can_modify=None, cannot_modify=None): def swap_ts(ts0, ts1, can_modify=None, cannot_modify=None): """For each tensor's pair, swap the end of (t0,t1). - B0 B1 B0 B1 - | | => X - A0 A1 A0 A1 + B0 B1 B0 B1 + | | => X + A0 A1 A0 A1 Args: ts0: an object convertible to a list of `tf.Tensor`. @@ -233,9 +233,9 @@ def swap_ts(ts0, ts1, can_modify=None, cannot_modify=None): def reroute_ts(ts0, ts1, can_modify=None, cannot_modify=None): """For each tensor's pair, replace the end of t1 by the end of t0. - B0 B1 B0 B1 - | | => |/ - A0 A1 A0 A1 + B0 B1 B0 B1 + | | => |/ + A0 A1 A0 A1 The end of the tensors in ts1 are left dangling. diff --git a/tensorflow/contrib/kafka/ops/kafka_ops.cc b/tensorflow/contrib/kafka/ops/kafka_ops.cc new file mode 100644 index 0000000000000000000000000000000000000000..8cdf16103bab2b22d51c144d21a589e1e39f2f0b --- /dev/null +++ b/tensorflow/contrib/kafka/ops/kafka_ops.cc @@ -0,0 +1,44 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" + +namespace tensorflow { + +REGISTER_OP("KafkaDataset") + .Input("topics: string") + .Input("servers: string") + .Input("group: string") + .Input("eof: bool") + .Input("timeout: int64") + .Output("handle: variant") + .SetIsStateful() + .SetShapeFn(shape_inference::ScalarShape) + .Doc(R"doc( +Creates a dataset that emits the messages of one or more Kafka topics. + +topics: A `tf.string` tensor containing one or more subscriptions, + in the format of [topic:partition:offset:length], + by default length is -1 for unlimited. +servers: A list of bootstrap servers. +group: The consumer group id. +eof: If True, the kafka reader will stop on EOF. +timeout: The timeout value for the Kafka Consumer to wait + (in millisecond). +)doc"); + +} // namespace tensorflow diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py index beeabd6b65631cad88efd10d5faee1917e162e41..dd602cf3a9b7826a19408a78ef543bb0c4fbf84e 100644 --- a/tensorflow/contrib/layers/python/layers/layers.py +++ b/tensorflow/contrib/layers/python/layers/layers.py @@ -1702,19 +1702,22 @@ def _inner_flatten(inputs, new_rank, output_collections=None, scope=None): return utils.collect_named_outputs(output_collections, sc, flattened) -def _model_variable_getter(getter, - name, - shape=None, - dtype=None, - initializer=None, - regularizer=None, - trainable=True, - collections=None, - caching_device=None, - partitioner=None, - rename=None, - use_resource=None, - **_): +def _model_variable_getter( + getter, + name, + shape=None, + dtype=None, + initializer=None, + regularizer=None, + trainable=True, + collections=None, + caching_device=None, + partitioner=None, + rename=None, + use_resource=None, + synchronization=tf_variables.VariableSynchronization.AUTO, + aggregation=tf_variables.VariableAggregation.NONE, + **_): """Getter that uses model_variable for compatibility with core layers.""" short_name = name.split('/')[-1] if rename and short_name in rename: @@ -1732,7 +1735,9 @@ def _model_variable_getter(getter, caching_device=caching_device, partitioner=partitioner, custom_getter=getter, - use_resource=use_resource) + use_resource=use_resource, + synchronization=synchronization, + aggregation=aggregation) def _build_variable_getter(rename=None): diff --git a/tensorflow/contrib/layers/python/layers/rev_block_lib.py b/tensorflow/contrib/layers/python/layers/rev_block_lib.py index 0e35b1aa8bf682c1b4f7e8d974d3e8fad69e33cb..dad3da3748097c26e07b4abe0495f62a18aad369 100644 --- a/tensorflow/contrib/layers/python/layers/rev_block_lib.py +++ b/tensorflow/contrib/layers/python/layers/rev_block_lib.py @@ -514,15 +514,15 @@ def _recompute_grad(fn, args, use_data_dep=_USE_DEFAULT, tupleize_grads=False): original_vars = set(tape.watched_variables()) # Backward pass - def grad_fn(*output_grads, **kwargs): + def _grad_fn(output_grads, variables=None): """Recompute outputs for gradient computation.""" - variables = [] + variables = variables or [] if original_vars: - variables = kwargs["variables"] - if set(variables) != original_vars: - raise ValueError(_WRONG_VARS_ERR) - del kwargs - inputs = list(args) + assert variables, ("Fn created variables but the variables were not " + "passed to the gradient fn.") + if set(variables) != original_vars: + raise ValueError(_WRONG_VARS_ERR) + inputs = [array_ops.identity(x) for x in list(args)] # Recompute outputs with framework_ops.control_dependencies(output_grads): if use_data_dep_: @@ -538,7 +538,7 @@ def _recompute_grad(fn, args, use_data_dep=_USE_DEFAULT, tupleize_grads=False): if original_vars != recompute_vars: raise ValueError(_WRONG_VARS_ERR) - if not (isinstance(outputs, list) or isinstance(outputs, tuple)): + if not isinstance(outputs, (list, tuple)): outputs = [outputs] outputs = list(outputs) grads = gradients_impl.gradients(outputs, inputs + variables, @@ -554,6 +554,16 @@ def _recompute_grad(fn, args, use_data_dep=_USE_DEFAULT, tupleize_grads=False): grad_vars = grads[len(inputs):] return grad_inputs, grad_vars + # custom_gradient inspects the signature of the function to determine + # whether the user expects variables passed in the grad_fn. If the function + # created variables, the grad_fn should accept the "variables" kwarg. + if original_vars: + def grad_fn(*output_grads, **kwargs): + return _grad_fn(output_grads, kwargs["variables"]) + else: + def grad_fn(*output_grads): + return _grad_fn(output_grads) + return outputs, grad_fn return fn_with_recompute(*args) diff --git a/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py b/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py index bc09ba8d439808c1582f207a99504012afcf33a6..d5971fb9d8e2fbc1e14fd24fc79e7981a284a418 100644 --- a/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py +++ b/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py @@ -372,6 +372,26 @@ class RecomputeTest(test.TestCase): self.assertEqual(2, len(update_ops)) self.assertEqual([False, True], kwarg_values) + def testWithoutVariables(self): + + def concat_n(layer_list, num_inputs): + return math_ops.reduce_sum( + array_ops.concat([x for x in layer_list[-num_inputs:]], axis=-1), + axis=1, keepdims=True) + + @rev_block_lib.recompute_grad + def concat_n_wrap(*args): + return concat_n(args, 3) + + # DenseNet-style layers + layer_list = [random_ops.random_uniform((4, 8))] + for _ in range(5): + layer_list.append(math_ops.sqrt(concat_n_wrap(*layer_list))) + + grads = gradients_impl.gradients(layer_list[-1], layer_list[0]) + with self.test_session() as sess: + sess.run(grads) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py index 339c4e0e360ed9ef9906f0e51b64a0dc13826259..ded93d4a7fb473c0c5df446ea89c5ab7784e9f3c 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/head.py +++ b/tensorflow/contrib/learn/python/learn/estimators/head.py @@ -563,10 +563,10 @@ def _mean_squared_loss(labels, logits, weights=None): labels = ops.convert_to_tensor(labels) # To prevent broadcasting inside "-". if len(labels.get_shape()) == 1: - labels = array_ops.expand_dims(labels, dim=(1,)) + labels = array_ops.expand_dims(labels, axis=(1,)) # TODO(zakaria): make sure it does not recreate the broadcast bug. if len(logits.get_shape()) == 1: - logits = array_ops.expand_dims(logits, dim=(1,)) + logits = array_ops.expand_dims(logits, axis=(1,)) logits.get_shape().assert_is_compatible_with(labels.get_shape()) loss = math_ops.square(logits - math_ops.to_float(labels), name=name) return _compute_weighted_loss(loss, weights) @@ -579,10 +579,10 @@ def _poisson_loss(labels, logits, weights=None): labels = ops.convert_to_tensor(labels) # To prevent broadcasting inside "-". if len(labels.get_shape()) == 1: - labels = array_ops.expand_dims(labels, dim=(1,)) + labels = array_ops.expand_dims(labels, axis=(1,)) # TODO(zakaria): make sure it does not recreate the broadcast bug. if len(logits.get_shape()) == 1: - logits = array_ops.expand_dims(logits, dim=(1,)) + logits = array_ops.expand_dims(logits, axis=(1,)) logits.get_shape().assert_is_compatible_with(labels.get_shape()) loss = nn.log_poisson_loss(labels, logits, compute_full_loss=True, name=name) @@ -797,7 +797,7 @@ def _log_loss_with_two_classes(labels, logits, weights=None): # TODO(ptucker): This will break for dynamic shapes. # sigmoid_cross_entropy_with_logits requires [batch_size, 1] labels. if len(labels.get_shape()) == 1: - labels = array_ops.expand_dims(labels, dim=(1,)) + labels = array_ops.expand_dims(labels, axis=(1,)) loss = nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits, name=name) return _compute_weighted_loss(loss, weights) diff --git a/tensorflow/contrib/learn/python/learn/estimators/run_config.py b/tensorflow/contrib/learn/python/learn/estimators/run_config.py index 14ee2ba6094760d52180d6de7763ea88b8ee98c8..7cb87619d960a03f342c7441730aaf2c4f15eb38 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/run_config.py +++ b/tensorflow/contrib/learn/python/learn/estimators/run_config.py @@ -240,6 +240,7 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig): keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, log_step_count_steps=100, + protocol=None, evaluation_master='', model_dir=None, session_config=None): @@ -289,6 +290,8 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig): session_config: a ConfigProto used to set session parameters, or None. Note - using this argument, it is easy to provide settings which break otherwise perfectly good models. Use with care. + protocol: An optional argument which specifies the protocol used when + starting server. None means default to grpc. """ # Neither parent class calls super().__init__(), so here we have to # manually call their __init__() methods. @@ -313,6 +316,7 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig): self._save_summary_steps = save_summary_steps self._save_checkpoints_secs = save_checkpoints_secs self._log_step_count_steps = log_step_count_steps + self._protocol = protocol self._session_config = session_config if save_checkpoints_secs == RunConfig._USE_DEFAULT: if save_checkpoints_steps is None: diff --git a/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py b/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py index 5e7b422e3cc368a22eb94ed470297ae78293c4eb..e74244720896a835174f54bb97049c1d9b1c92f8 100644 --- a/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py +++ b/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py @@ -625,11 +625,13 @@ def attention_decoder(decoder_inputs, v = [] attention_vec_size = attn_size # Size of query vectors for attention. for a in xrange(num_heads): - k = variable_scope.get_variable("AttnW_%d" % a, - [1, 1, attn_size, attention_vec_size]) + k = variable_scope.get_variable( + "AttnW_%d" % a, [1, 1, attn_size, attention_vec_size], + dtype=dtype) hidden_features.append(nn_ops.conv2d(hidden, k, [1, 1, 1, 1], "SAME")) v.append( - variable_scope.get_variable("AttnV_%d" % a, [attention_vec_size])) + variable_scope.get_variable( + "AttnV_%d" % a, [attention_vec_size], dtype=dtype)) state = initial_state @@ -647,11 +649,13 @@ def attention_decoder(decoder_inputs, with variable_scope.variable_scope("Attention_%d" % a): y = Linear(query, attention_vec_size, True)(query) y = array_ops.reshape(y, [-1, 1, 1, attention_vec_size]) + y = math_ops.cast(y, dtype) # Attention mask is a softmax of v^T * tanh(...). s = math_ops.reduce_sum(v[a] * math_ops.tanh(hidden_features[a] + y), [2, 3]) - a = nn_ops.softmax(s) + a = nn_ops.softmax(math_ops.cast(s, dtype=dtypes.float32)) # Now calculate the attention-weighted vector d. + a = math_ops.cast(a, dtype) d = math_ops.reduce_sum( array_ops.reshape(a, [-1, attn_length, 1, 1]) * hidden, [1, 2]) ds.append(array_ops.reshape(d, [-1, attn_size])) @@ -681,6 +685,7 @@ def attention_decoder(decoder_inputs, raise ValueError("Could not infer input size from input: %s" % inp.name) inputs = [inp] + attns + inputs = [math_ops.cast(e, dtype) for e in inputs] x = Linear(inputs, input_size, True)(inputs) # Run the RNN. cell_output, state = cell(x, state) @@ -693,6 +698,7 @@ def attention_decoder(decoder_inputs, attns = attention(state) with variable_scope.variable_scope("AttnOutputProjection"): + cell_output = math_ops.cast(cell_output, dtype) inputs = [cell_output] + attns output = Linear(inputs, output_size, True)(inputs) if loop_function is not None: diff --git a/tensorflow/contrib/linear_optimizer/BUILD b/tensorflow/contrib/linear_optimizer/BUILD index 5b89c6cef9fa9fdef7c26ddee1efa03f3056d881..fe0ba19fcbe90edbeb1445e1fea77c36cf3ba170 100644 --- a/tensorflow/contrib/linear_optimizer/BUILD +++ b/tensorflow/contrib/linear_optimizer/BUILD @@ -41,6 +41,7 @@ py_test( size = "medium", srcs = ["python/kernel_tests/sdca_ops_test.py"], srcs_version = "PY2AND3", + tags = ["no_windows_gpu"], deps = [ ":sdca_ops_py", ":sparse_feature_column_py", diff --git a/tensorflow/contrib/lite/BUILD b/tensorflow/contrib/lite/BUILD index 73f5c1448d91c573efed34c6aaaf5c28feac6555..7d7dd6b7088f457b1a14a3ff30b7eef98c00d18a 100644 --- a/tensorflow/contrib/lite/BUILD +++ b/tensorflow/contrib/lite/BUILD @@ -47,6 +47,10 @@ cc_test( name = "arena_planner_test", size = "small", srcs = ["arena_planner_test.cc"], + tags = [ + "no_oss", + "tflite_not_portable", + ], deps = [ ":arena_planner", "//tensorflow/contrib/lite/testing:util", @@ -146,6 +150,7 @@ cc_library( ":memory_planner", ":schema_fbs_version", ":simple_memory_arena", + ":string", ":util", "//tensorflow/contrib/lite/kernels:eigen_support", "//tensorflow/contrib/lite/kernels:gemm_support", @@ -199,6 +204,7 @@ cc_test( name = "graph_info_test", size = "small", srcs = ["graph_info_test.cc"], + tags = ["no_oss"], deps = [ ":framework", ":string_util", @@ -243,6 +249,7 @@ cc_test( name = "op_resolver_test", size = "small", srcs = ["op_resolver_test.cc"], + tags = ["no_oss"], deps = [ ":framework", "//tensorflow/contrib/lite/testing:util", @@ -275,6 +282,7 @@ cc_test( name = "util_test", size = "small", srcs = ["util_test.cc"], + tags = ["no_oss"], deps = [ ":context", ":util", diff --git a/tensorflow/contrib/lite/Makefile b/tensorflow/contrib/lite/Makefile index a616138d3321d43f66a2b430f7df609a13b9caf6..df5954744a41191d922e91553303e052969c24fb 100644 --- a/tensorflow/contrib/lite/Makefile +++ b/tensorflow/contrib/lite/Makefile @@ -82,8 +82,9 @@ endif # Settings for the host compiler. CXX := $(CC_PREFIX) ${TARGET_TOOLCHAIN_PREFIX}g++ -CXXFLAGS += --std=c++11 -O3 -DNDEBUG +CXXFLAGS += -O3 -DNDEBUG CCFLAGS := ${CXXFLAGS} +CXXFLAGS += --std=c++11 CC := $(CC_PREFIX) ${TARGET_TOOLCHAIN_PREFIX}gcc AR := $(CC_PREFIX) ${TARGET_TOOLCHAIN_PREFIX}ar CFLAGS := diff --git a/tensorflow/contrib/lite/allocation.cc b/tensorflow/contrib/lite/allocation.cc index c42622ff02fc2837b61b35f19e834276c0518d1e..ef6c14f08532a8d25ab9be6000bc0f24559074d2 100644 --- a/tensorflow/contrib/lite/allocation.cc +++ b/tensorflow/contrib/lite/allocation.cc @@ -99,7 +99,9 @@ FileCopyAllocation::FileCopyAllocation(const char* filename, filename); return; } - copied_buffer_ = std::move(buffer); + // Versions of GCC before 6.2.0 don't support std::move from non-const + // char[] to const char[] unique_ptrs. + copied_buffer_.reset(const_cast(buffer.release())); } FileCopyAllocation::~FileCopyAllocation() {} diff --git a/tensorflow/contrib/lite/allocation.h b/tensorflow/contrib/lite/allocation.h index 68aee2e64473320c461ec8b3f194904e7b8da43c..827ea86503f910714971e2b138295b9a5809dfd5 100644 --- a/tensorflow/contrib/lite/allocation.h +++ b/tensorflow/contrib/lite/allocation.h @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/error_reporter.h" #include "tensorflow/contrib/lite/simple_memory_arena.h" +#include "tensorflow/contrib/lite/string.h" namespace tflite { diff --git a/tensorflow/contrib/lite/arena_planner.cc b/tensorflow/contrib/lite/arena_planner.cc index 4257e754ad5c30e17ec8ba8d5c6e69b5c5bcd728..02442575b3aeed04ac6569440dd52a4d5ddd4d98 100644 --- a/tensorflow/contrib/lite/arena_planner.cc +++ b/tensorflow/contrib/lite/arena_planner.cc @@ -17,14 +17,6 @@ limitations under the License. namespace tflite { -namespace { - -// Memory allocation tuning -constexpr const int kDefaultArenaAlignment = 64; -constexpr const int kDefaultTensorAlignment = 4; - -} // namespace - struct AllocationInfo { // The node index requesting this allocation. int node; @@ -36,12 +28,16 @@ struct AllocationInfo { ArenaPlanner::ArenaPlanner(TfLiteContext* context, std::unique_ptr graph_info, - bool preserve_inputs) + bool preserve_inputs, bool preserve_intermediates, + int tensor_alignment) : context_(context), graph_info_(std::move(graph_info)), arena_(kDefaultArenaAlignment), persistent_arena_(kDefaultArenaAlignment), - preserve_inputs_(preserve_inputs) {} + preserve_inputs_(preserve_inputs), + preserve_intermediates_(preserve_intermediates), + tensor_alignment_(tensor_alignment) {} + ArenaPlanner::~ArenaPlanner() {} int64_t ArenaPlanner::BasePointer(TfLiteAllocationType type) { @@ -164,13 +160,15 @@ TfLiteStatus ArenaPlanner::PlanAllocations() { // Then update the ref-counts of the node's inputs, and if necessary queue // them for deallocation. - TfLiteIntArray* node_inputs = node.inputs; - for (int j = 0; j < node_inputs->size; ++j) { - int tensor_index = node_inputs->data[j]; - if (tensor_index != kOptionalTensor) { - refcounts[tensor_index]--; - if (refcounts[tensor_index] == 0) { - TF_LITE_ENSURE_STATUS(deallocate(i, tensor_index)); + if (!preserve_intermediates_) { + TfLiteIntArray* node_inputs = node.inputs; + for (int j = 0; j < node_inputs->size; ++j) { + int tensor_index = node_inputs->data[j]; + if (tensor_index != kOptionalTensor) { + refcounts[tensor_index]--; + if (refcounts[tensor_index] == 0) { + TF_LITE_ENSURE_STATUS(deallocate(i, tensor_index)); + } } } } @@ -261,14 +259,12 @@ TfLiteStatus ArenaPlanner::ResolveTensorAllocation(int tensor_index) { TfLiteStatus ArenaPlanner::CalculateTensorAllocation(int tensor_index) { TfLiteTensor& tensor = *graph_info_->tensor(tensor_index); if (tensor.allocation_type == kTfLiteArenaRw) { - TF_LITE_ENSURE_STATUS(arena_.Allocate(context_, kDefaultTensorAlignment, - tensor.bytes, - &allocs_[tensor_index])); + TF_LITE_ENSURE_STATUS(arena_.Allocate( + context_, tensor_alignment_, tensor.bytes, &allocs_[tensor_index])); } if (tensor.allocation_type == kTfLiteArenaRwPersistent) { - TF_LITE_ENSURE_STATUS( - persistent_arena_.Allocate(context_, kDefaultTensorAlignment, - tensor.bytes, &allocs_[tensor_index])); + TF_LITE_ENSURE_STATUS(persistent_arena_.Allocate( + context_, tensor_alignment_, tensor.bytes, &allocs_[tensor_index])); } return kTfLiteOk; } diff --git a/tensorflow/contrib/lite/arena_planner.h b/tensorflow/contrib/lite/arena_planner.h index 1d84950e91bc48fd1c1a7e5b2d9063e20dea0718..55003cf4e92d9ca79416c0f9f7a0c57e828af4ee 100644 --- a/tensorflow/contrib/lite/arena_planner.h +++ b/tensorflow/contrib/lite/arena_planner.h @@ -25,6 +25,10 @@ limitations under the License. namespace tflite { +// Memory allocation tuning +constexpr const int kDefaultArenaAlignment = 64; +constexpr const int kDefaultTensorAlignment = 64; + struct AllocationInfo; // A memory planner that makes all the allocations using arenas. @@ -47,7 +51,8 @@ class ArenaPlanner : public MemoryPlanner { // graph will not share memory with any other tensor, effectively preserving // them until the end of inference. ArenaPlanner(TfLiteContext* context, std::unique_ptr graph_info, - bool preserve_inputs); + bool preserve_inputs, bool preserve_intermediates, + int tensor_alignment = kDefaultTensorAlignment); ~ArenaPlanner() override; ArenaPlanner(const ArenaPlanner&) = delete; ArenaPlanner& operator=(const ArenaPlanner&) = delete; @@ -104,7 +109,17 @@ class ArenaPlanner : public MemoryPlanner { // declared as kTfLiteArenaRwPersistent. SimpleMemoryArena persistent_arena_; + // Ensure that the memory self-allocated for inputs is never reused by the + // allocator. This allows for example, multiple runs without getting + // unpredictable results. bool preserve_inputs_; + + // If true, then no overlapping of memory areas is done, meaning intermediates + // results can be queried after running (modulo running delegates). + bool preserve_intermediates_; + + // Number of bytes that tensor buffers should be aligned to. + int tensor_alignment_; }; } // namespace tflite diff --git a/tensorflow/contrib/lite/arena_planner_test.cc b/tensorflow/contrib/lite/arena_planner_test.cc index f5bd1932f976f5c7d0f0d14bbaf9ca3807dfd3b0..7d7c41289cad95b73423a7218bf1e0516b2e87a2 100644 --- a/tensorflow/contrib/lite/arena_planner_test.cc +++ b/tensorflow/contrib/lite/arena_planner_test.cc @@ -24,6 +24,8 @@ limitations under the License. namespace tflite { namespace { +constexpr const int kTensorAlignment = 4; + // A simple op to be used in tests, as syntactic sugar. class TestOp { public: @@ -156,7 +158,7 @@ class ArenaPlannerTest : public ::testing::Test { context_.ReportError = ReportError; planner_.reset(new ArenaPlanner( &context_, std::unique_ptr(new TestGraphInfo(graph)), - preserve_inputs)); + preserve_inputs, /*preserve intermediates*/ false, kTensorAlignment)); CHECK(planner_->ResetAllocations() == kTfLiteOk); CHECK(planner_->PlanAllocations() == kTfLiteOk); } @@ -178,8 +180,8 @@ class ArenaPlannerTest : public ::testing::Test { const TfLiteTensor& tensor = (*graph_->tensors())[tensor_index]; int64_t offset = GetOffset(tensor_index) + tensor.bytes; // We must make sure the offset is aligned to kDefaultArenaAlignment. - if (offset % 4 != 0) { - offset += 4 - offset % 4; + if (offset % kTensorAlignment != 0) { + offset += kTensorAlignment - offset % kTensorAlignment; } return offset; }; diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index 5543acc1f5dabaa8a54ec4d1f2027bc66a00f6db..422584c0eac6e703257bc58f138695d8f580a126 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -1,193 +1,218 @@ """Generate Flatbuffer binary from json.""" + load( "//tensorflow:tensorflow.bzl", + "tf_cc_shared_object", "tf_cc_test", ) def tflite_copts(): - """Defines compile time flags.""" - copts = [ - "-DFARMHASH_NO_CXX_STRING", - ] + select({ - str(Label("//tensorflow:android_arm64")): [ - "-std=c++11", - "-O3", - ], - str(Label("//tensorflow:android_arm")): [ - "-mfpu=neon", - "-mfloat-abi=softfp", - "-std=c++11", - "-O3", - ], - str(Label("//tensorflow:android_x86")): [ - "-DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK", - ], - str(Label("//tensorflow:ios_x86_64")): [ - "-msse4.1", - ], - "//conditions:default": [], - }) + select({ - str(Label("//tensorflow:with_default_optimizations")): [], - "//conditions:default": ["-DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK"], - }) + """Defines compile time flags.""" + copts = [ + "-DFARMHASH_NO_CXX_STRING", + ] + select({ + str(Label("//tensorflow:android_arm64")): [ + "-std=c++11", + "-O3", + ], + str(Label("//tensorflow:android_arm")): [ + "-mfpu=neon", + "-mfloat-abi=softfp", + "-std=c++11", + "-O3", + ], + str(Label("//tensorflow:android_x86")): [ + "-DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK", + ], + str(Label("//tensorflow:ios_x86_64")): [ + "-msse4.1", + ], + str(Label("//tensorflow:windows")): [ + "/DTF_COMPILE_LIBRARY", + ], + "//conditions:default": [], + }) + select({ + str(Label("//tensorflow:with_default_optimizations")): [], + "//conditions:default": ["-DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK"], + }) - return copts + return copts LINKER_SCRIPT = "//tensorflow/contrib/lite/java/src/main/native:version_script.lds" def tflite_linkopts_unstripped(): - """Defines linker flags to reduce size of TFLite binary. + """Defines linker flags to reduce size of TFLite binary. - These are useful when trying to investigate the relative size of the - symbols in TFLite. + These are useful when trying to investigate the relative size of the + symbols in TFLite. - Returns: - a select object with proper linkopts - """ - return select({ - "//tensorflow:android": [ - "-Wl,--no-export-dynamic", # Only inc syms referenced by dynamic obj. - "-Wl,--exclude-libs,ALL", # Exclude syms in all libs from auto export. - "-Wl,--gc-sections", # Eliminate unused code and data. - "-Wl,--as-needed", # Don't link unused libs. - ], - "//tensorflow/contrib/lite:mips": [], - "//tensorflow/contrib/lite:mips64": [], - "//conditions:default": [ - "-Wl,--icf=all", # Identical code folding. - ], - }) + Returns: + a select object with proper linkopts + """ + return select({ + "//tensorflow:android": [ + "-Wl,--no-export-dynamic", # Only inc syms referenced by dynamic obj. + "-Wl,--exclude-libs,ALL", # Exclude syms in all libs from auto export. + "-Wl,--gc-sections", # Eliminate unused code and data. + "-Wl,--as-needed", # Don't link unused libs. + ], + "//tensorflow:darwin": [], + "//tensorflow/contrib/lite:mips": [], + "//tensorflow/contrib/lite:mips64": [], + "//conditions:default": [ + "-Wl,--icf=all", # Identical code folding. + ], + }) def tflite_jni_linkopts_unstripped(): - """Defines linker flags to reduce size of TFLite binary with JNI. + """Defines linker flags to reduce size of TFLite binary with JNI. - These are useful when trying to investigate the relative size of the - symbols in TFLite. + These are useful when trying to investigate the relative size of the + symbols in TFLite. - Returns: - a select object with proper linkopts - """ - return select({ - "//tensorflow:android": [ - "-Wl,--gc-sections", # Eliminate unused code and data. - "-Wl,--as-needed", # Don't link unused libs. - ], - "//tensorflow/contrib/lite:mips": [], - "//tensorflow/contrib/lite:mips64": [], - "//conditions:default": [ - "-Wl,--icf=all", # Identical code folding. - ], - }) + Returns: + a select object with proper linkopts + """ + return select({ + "//tensorflow:android": [ + "-Wl,--gc-sections", # Eliminate unused code and data. + "-Wl,--as-needed", # Don't link unused libs. + ], + "//tensorflow:darwin": [], + "//tensorflow/contrib/lite:mips": [], + "//tensorflow/contrib/lite:mips64": [], + "//conditions:default": [ + "-Wl,--icf=all", # Identical code folding. + ], + }) def tflite_linkopts(): - """Defines linker flags to reduce size of TFLite binary.""" - return tflite_linkopts_unstripped() + select({ - "//tensorflow:android": [ - "-s", # Omit symbol table. - ], - "//conditions:default": [], - }) + """Defines linker flags to reduce size of TFLite binary.""" + return tflite_linkopts_unstripped() + select({ + "//tensorflow:android": [ + "-s", # Omit symbol table. + ], + "//conditions:default": [], + }) def tflite_jni_linkopts(): - """Defines linker flags to reduce size of TFLite binary with JNI.""" - return tflite_jni_linkopts_unstripped() + select({ - "//tensorflow:android": [ - "-s", # Omit symbol table. - "-latomic", # Required for some uses of ISO C++11 in x86. - ], - "//conditions:default": [], - }) + """Defines linker flags to reduce size of TFLite binary with JNI.""" + return tflite_jni_linkopts_unstripped() + select({ + "//tensorflow:android": [ + "-s", # Omit symbol table. + "-latomic", # Required for some uses of ISO C++11 in x86. + ], + "//conditions:default": [], + }) + +def tflite_jni_binary( + name, + copts = tflite_copts(), + linkopts = tflite_jni_linkopts(), + linkscript = LINKER_SCRIPT, + linkshared = 1, + linkstatic = 1, + deps = []): + """Builds a jni binary for TFLite.""" + linkopts = linkopts + [ + "-Wl,--version-script", # Export only jni functions & classes. + "$(location {})".format(linkscript), + ] + native.cc_binary( + name = name, + copts = copts, + linkshared = linkshared, + linkstatic = linkstatic, + deps = deps + [linkscript], + linkopts = linkopts, + ) -def tflite_jni_binary(name, - copts=tflite_copts(), - linkopts=tflite_jni_linkopts(), - linkscript=LINKER_SCRIPT, - linkshared=1, - linkstatic=1, - deps=[]): - """Builds a jni binary for TFLite.""" - linkopts = linkopts + [ - "-Wl,--version-script", # Export only jni functions & classes. - "$(location {})".format(linkscript), - ] - native.cc_binary( - name=name, - copts=copts, - linkshared=linkshared, - linkstatic=linkstatic, - deps= deps + [linkscript], - linkopts=linkopts) +def tflite_cc_shared_object( + name, + copts = tflite_copts(), + linkopts = [], + linkstatic = 1, + deps = []): + """Builds a shared object for TFLite.""" + tf_cc_shared_object( + name = name, + copts = copts, + linkstatic = linkstatic, + linkopts = linkopts + tflite_jni_linkopts(), + framework_so = [], + deps = deps, + ) def tf_to_tflite(name, src, options, out): - """Convert a frozen tensorflow graphdef to TF Lite's flatbuffer. + """Convert a frozen tensorflow graphdef to TF Lite's flatbuffer. - Args: - name: Name of rule. - src: name of the input graphdef file. - options: options passed to TOCO. - out: name of the output flatbuffer file. - """ + Args: + name: Name of rule. + src: name of the input graphdef file. + options: options passed to TOCO. + out: name of the output flatbuffer file. + """ - toco_cmdline = " ".join([ - "//tensorflow/contrib/lite/toco:toco", - "--input_format=TENSORFLOW_GRAPHDEF", - "--output_format=TFLITE", - ("--input_file=$(location %s)" % src), - ("--output_file=$(location %s)" % out), - ] + options ) - native.genrule( - name = name, - srcs=[src], - outs=[out], - cmd = toco_cmdline, - tools= ["//tensorflow/contrib/lite/toco:toco"], - ) + toco_cmdline = " ".join([ + "//tensorflow/contrib/lite/toco:toco", + "--input_format=TENSORFLOW_GRAPHDEF", + "--output_format=TFLITE", + ("--input_file=$(location %s)" % src), + ("--output_file=$(location %s)" % out), + ] + options) + native.genrule( + name = name, + srcs = [src], + outs = [out], + cmd = toco_cmdline, + tools = ["//tensorflow/contrib/lite/toco:toco"], + ) def tflite_to_json(name, src, out): - """Convert a TF Lite flatbuffer to JSON. + """Convert a TF Lite flatbuffer to JSON. - Args: - name: Name of rule. - src: name of the input flatbuffer file. - out: name of the output JSON file. - """ + Args: + name: Name of rule. + src: name of the input flatbuffer file. + out: name of the output JSON file. + """ - flatc = "@flatbuffers//:flatc" - schema = "//tensorflow/contrib/lite/schema:schema.fbs" - native.genrule( - name = name, - srcs = [schema, src], - outs = [out], - cmd = ("TMP=`mktemp`; cp $(location %s) $${TMP}.bin &&" + - "$(location %s) --raw-binary --strict-json -t" + - " -o /tmp $(location %s) -- $${TMP}.bin &&" + - "cp $${TMP}.json $(location %s)") - % (src, flatc, schema, out), - tools = [flatc], - ) + flatc = "@flatbuffers//:flatc" + schema = "//tensorflow/contrib/lite/schema:schema.fbs" + native.genrule( + name = name, + srcs = [schema, src], + outs = [out], + cmd = ("TMP=`mktemp`; cp $(location %s) $${TMP}.bin &&" + + "$(location %s) --raw-binary --strict-json -t" + + " -o /tmp $(location %s) -- $${TMP}.bin &&" + + "cp $${TMP}.json $(location %s)") % + (src, flatc, schema, out), + tools = [flatc], + ) def json_to_tflite(name, src, out): - """Convert a JSON file to TF Lite's flatbuffer. + """Convert a JSON file to TF Lite's flatbuffer. - Args: - name: Name of rule. - src: name of the input JSON file. - out: name of the output flatbuffer file. - """ + Args: + name: Name of rule. + src: name of the input JSON file. + out: name of the output flatbuffer file. + """ - flatc = "@flatbuffers//:flatc" - schema = "//tensorflow/contrib/lite/schema:schema_fbs" - native.genrule( - name = name, - srcs = [schema, src], - outs = [out], - cmd = ("TMP=`mktemp`; cp $(location %s) $${TMP}.json &&" + - "$(location %s) --raw-binary --unknown-json --allow-non-utf8 -b" + - " -o /tmp $(location %s) $${TMP}.json &&" + - "cp $${TMP}.bin $(location %s)") - % (src, flatc, schema, out), - tools = [flatc], - ) + flatc = "@flatbuffers//:flatc" + schema = "//tensorflow/contrib/lite/schema:schema_fbs" + native.genrule( + name = name, + srcs = [schema, src], + outs = [out], + cmd = ("TMP=`mktemp`; cp $(location %s) $${TMP}.json &&" + + "$(location %s) --raw-binary --unknown-json --allow-non-utf8 -b" + + " -o /tmp $(location %s) $${TMP}.json &&" + + "cp $${TMP}.bin $(location %s)") % + (src, flatc, schema, out), + tools = [flatc], + ) # This is the master list of generated examples that will be made into tests. A # function called make_XXX_tests() must also appear in generate_examples.py. @@ -195,7 +220,7 @@ def json_to_tflite(name, src, out): def generated_test_models(): return [ "add", - "arg_max", + "arg_min_max", "avg_pool", "batch_to_space_nd", "concat", @@ -222,6 +247,7 @@ def generated_test_models(): "local_response_norm", "log_softmax", "log", + "logical_or", "lstm", "max_pool", "maximum", @@ -230,10 +256,14 @@ def generated_test_models(): "mul", "neg", "not_equal", + "one_hot", + "pack", "pad", "padv2", - # "prelu", + "prelu", "pow", + "reduce_max", + #"reduce_prod", # disabled due to b/111823366 "relu", "relu1", "relu6", @@ -257,63 +287,63 @@ def generated_test_models(): "tile", "topk", "transpose", - "transpose_conv", + #"transpose_conv", # disabled due to b/111213074 "where", ] def gen_zip_test(name, test_name, **kwargs): - """Generate a zipped-example test and its dependent zip files. + """Generate a zipped-example test and its dependent zip files. - Args: - name: Resulting cc_test target name - test_name: Test targets this model. Comes from the list above. - **kwargs: tf_cc_test kwargs. - """ - gen_zipped_test_file( - name = "zip_%s" % test_name, - file = "%s.zip" % test_name, - ) - tf_cc_test(name, **kwargs) + Args: + name: Resulting cc_test target name + test_name: Test targets this model. Comes from the list above. + **kwargs: tf_cc_test kwargs. + """ + gen_zipped_test_file( + name = "zip_%s" % test_name, + file = "%s.zip" % test_name, + ) + tf_cc_test(name, **kwargs) def gen_zipped_test_file(name, file): - """Generate a zip file of tests by using :generate_examples. + """Generate a zip file of tests by using :generate_examples. - Args: - name: Name of output. We will produce "`file`.files" as a target. - file: The name of one of the generated_examples targets, e.g. "transpose" - """ - toco = "//tensorflow/contrib/lite/toco:toco" - native.genrule( - name = file + ".files", - cmd = ("$(locations :generate_examples) --toco $(locations %s) " % toco - + " --zip_to_output " + file + " $(@D)"), - outs = [file], - tools = [ - ":generate_examples", - toco, - ], - ) + Args: + name: Name of output. We will produce "`file`.files" as a target. + file: The name of one of the generated_examples targets, e.g. "transpose" + """ + toco = "//tensorflow/contrib/lite/toco:toco" + native.genrule( + name = file + ".files", + cmd = ("$(locations :generate_examples) --toco $(locations %s) " % toco + + " --zip_to_output " + file + " $(@D)"), + outs = [file], + tools = [ + ":generate_examples", + toco, + ], + ) - native.filegroup( - name = name, - srcs = [file], - ) + native.filegroup( + name = name, + srcs = [file], + ) def gen_selected_ops(name, model): - """Generate the library that includes only used ops. + """Generate the library that includes only used ops. - Args: - name: Name of the generated library. - model: TFLite model to interpret. - """ - out = name + "_registration.cc" - tool = "//tensorflow/contrib/lite/tools:generate_op_registrations" - tflite_path = "//tensorflow/contrib/lite" - native.genrule( - name = name, - srcs = [model], - outs = [out], - cmd = ("$(location %s) --input_model=$(location %s) --output_registration=$(location %s) --tflite_path=%s") - % (tool, model, out, tflite_path[2:]), - tools = [tool], - ) + Args: + name: Name of the generated library. + model: TFLite model to interpret. + """ + out = name + "_registration.cc" + tool = "//tensorflow/contrib/lite/tools:generate_op_registrations" + tflite_path = "//tensorflow/contrib/lite" + native.genrule( + name = name, + srcs = [model], + outs = [out], + cmd = ("$(location %s) --input_model=$(location %s) --output_registration=$(location %s) --tflite_path=%s") % + (tool, model, out, tflite_path[2:]), + tools = [tool], + ) diff --git a/tensorflow/contrib/lite/build_ios_universal_lib.sh b/tensorflow/contrib/lite/build_ios_universal_lib.sh index e9531aef19f04adf719156aa3e874dc5ce6e2b04..31df43a1754bd753a82a613dc15704aaa056a87e 100755 --- a/tensorflow/contrib/lite/build_ios_universal_lib.sh +++ b/tensorflow/contrib/lite/build_ios_universal_lib.sh @@ -21,7 +21,7 @@ cd "$SCRIPT_DIR/../../.." # Build library for supported architectures and packs them in a fat binary. make_library() { - for arch in x86_64 i386 armv7 armv7s arm64 + for arch in x86_64 armv7 armv7s arm64 do make -f tensorflow/contrib/lite/Makefile TARGET=IOS IOS_ARCH=${arch} \ -j 8 \ @@ -29,7 +29,6 @@ make_library() { done lipo \ tensorflow/contrib/lite/gen/lib/ios_x86_64/${1} \ - tensorflow/contrib/lite/gen/lib/ios_i386/${1} \ tensorflow/contrib/lite/gen/lib/ios_armv7/${1} \ tensorflow/contrib/lite/gen/lib/ios_armv7s/${1} \ tensorflow/contrib/lite/gen/lib/ios_arm64/${1} \ diff --git a/tensorflow/contrib/lite/builtin_op_data.h b/tensorflow/contrib/lite/builtin_op_data.h index cda889bf502a535eac4249bbae645359cdb2135d..70178b2faabe85f8a53a94c2b5d2e3ea40c8ba05 100644 --- a/tensorflow/contrib/lite/builtin_op_data.h +++ b/tensorflow/contrib/lite/builtin_op_data.h @@ -249,6 +249,10 @@ typedef struct { TfLiteType output_type; } TfLiteArgMaxParams; +typedef struct { + TfLiteType output_type; +} TfLiteArgMinParams; + typedef struct { TfLitePadding padding; int stride_width; @@ -263,6 +267,25 @@ typedef struct { TfLiteType out_type; } TfLiteShapeParams; +typedef struct { + // Parameters supported by version 1: + float min; + float max; + int num_bits; + + // Parameters supported by version 2: + bool narrow_range; +} TfLiteFakeQuantParams; + +typedef struct { + int values_count; + int axis; +} TfLitePackParams; + +typedef struct { + int axis; +} TfLiteOneHotParams; + #ifdef __cplusplus } // extern "C" #endif // __cplusplus diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h index a44e9182302d19acd1e1c183ed388531eec11d93..0b6568fd2fec583914de1d1594f29912425d8b40 100644 --- a/tensorflow/contrib/lite/builtin_ops.h +++ b/tensorflow/contrib/lite/builtin_ops.h @@ -104,6 +104,13 @@ typedef enum { kTfLiteBuiltinRsqrt = 76, kTfLiteBuiltinShape = 77, kTfLiteBuiltinPow = 78, + kTfLiteBuiltinArgMin = 79, + kTfLiteBuiltinFakeQuant = 80, + kTfLiteBuiltinReduceProd = 81, + kTfLiteBuiltinReduceMax = 82, + kTfLiteBuiltinPack = 83, + kTfLiteBuiltinLogicalOr = 84, + kTfLiteBuiltinOneHot = 85, } TfLiteBuiltinOperator; #ifdef __cplusplus diff --git a/tensorflow/contrib/lite/context.h b/tensorflow/contrib/lite/context.h index 1ff8843fa78f48fc74b4d7e7d0cc4ae2a0d255af..5bc20106d31357e2da3f005baee0f8d134d37be2 100644 --- a/tensorflow/contrib/lite/context.h +++ b/tensorflow/contrib/lite/context.h @@ -29,6 +29,9 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_CONTEXT_H_ #define TENSORFLOW_CONTRIB_LITE_CONTEXT_H_ +#if defined(_MSC_VER) +#include +#endif #include #include #include @@ -180,7 +183,11 @@ typedef union { uint8_t* uint8; bool* b; int16_t* i16; +#if defined(_MSC_VER) + _Fcomplex* c64; +#else _Complex float* c64; +#endif } TfLitePtrUnion; // Memory allocation strategies. kTfLiteMmapRo is for read-only memory-mapped @@ -464,6 +471,12 @@ typedef struct _TfLiteDelegate { } TfLiteDelegate; // WARNING: This is an experimental interface that is subject to change. +// +// Currently, TfLiteDelegateParams has to be allocated in a way that it's +// trivially destructable. It will be stored as `builtin_data` field in +// `TfLiteNode` of the delegate node. +// +// See also the `CreateDelegateParams` function in `interpreter.cc` details. typedef struct { TfLiteDelegate* delegate; TfLiteIntArray* nodes_to_replace; diff --git a/tensorflow/contrib/lite/delegates/eager/BUILD b/tensorflow/contrib/lite/delegates/eager/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..a28707382ebaac421a077432a6efd4ea1f6bb0fb --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/BUILD @@ -0,0 +1,134 @@ +# +# This is a TF Lite delegate that is powered by TensorFlow's Eager. +# +package(default_visibility = [ + "//visibility:public", +]) + +licenses(["notice"]) # Apache 2.0 + +cc_library( + name = "buffer_map", + srcs = ["buffer_map.cc"], + hdrs = ["buffer_map.h"], + deps = [ + ":util", + "//tensorflow/c:c_api_internal", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:kernel_api", + "//tensorflow/core:framework", + "//tensorflow/core:protos_all_cc", + ], +) + +cc_test( + name = "buffer_map_test", + size = "small", + srcs = ["buffer_map_test.cc"], + tags = [ + "no_oss", + "tflite_not_portable", + ], + deps = [ + ":buffer_map", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:util", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", + ], +) + +cc_library( + name = "delegate_data", + srcs = ["delegate_data.cc"], + hdrs = ["delegate_data.h"], + deps = [ + ":buffer_map", + "//tensorflow/core:core_cpu", + "//tensorflow/core:lib", + "//tensorflow/core/common_runtime/eager:context", + ], +) + +cc_test( + name = "delegate_data_test", + size = "small", + srcs = ["delegate_data_test.cc"], + tags = [ + "no_oss", + "tflite_not_portable", + ], + deps = [ + ":delegate_data", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:util", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", + ], +) + +cc_library( + name = "kernel", + srcs = ["kernel.cc"], + hdrs = ["kernel.h"], + deps = [ + ":delegate_data", + ":util", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:kernel_api", + "//tensorflow/contrib/lite/kernels:kernel_util", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core/common_runtime/eager:context", + "//tensorflow/core/common_runtime/eager:execute", + "//tensorflow/core/common_runtime/eager:tensor_handle", + "@flatbuffers", + ], +) + +cc_test( + name = "kernel_test", + size = "small", + srcs = ["kernel_test.cc"], + tags = [ + "no_oss", + "tflite_not_portable", + ], + deps = [ + ":delegate_data", + ":kernel", + "//tensorflow/contrib/lite/kernels:test_util", + "//tensorflow/contrib/lite/testing:util", + "@com_google_absl//absl/memory", + "@com_google_googletest//:gtest", + "@flatbuffers", + ], +) + +cc_library( + name = "util", + srcs = ["util.cc"], + hdrs = ["util.h"], + deps = [ + "//tensorflow/c:c_api_internal", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:kernel_api", + "//tensorflow/core:framework", + "//tensorflow/core:lib", + ], +) + +cc_test( + name = "util_test", + size = "small", + srcs = ["util_test.cc"], + tags = [ + "no_oss", + "tflite_not_portable", + ], + deps = [ + ":util", + "//tensorflow/contrib/lite/testing:util", + "//tensorflow/core:lib", + "@com_google_googletest//:gtest", + ], +) diff --git a/tensorflow/contrib/lite/delegates/eager/buffer_map.cc b/tensorflow/contrib/lite/delegates/eager/buffer_map.cc new file mode 100644 index 0000000000000000000000000000000000000000..e5a19c39976969a0b05b28596c6d7d5ebe7c7782 --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/buffer_map.cc @@ -0,0 +1,111 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/delegates/eager/buffer_map.h" + +#include "tensorflow/c/c_api_internal.h" +#include "tensorflow/contrib/lite/delegates/eager/util.h" +#include "tensorflow/core/framework/allocation_description.pb.h" +#include "tensorflow/core/framework/log_memory.h" + +namespace tflite { +namespace eager { +namespace { +// A tensor buffer that is allocated, deallocated and populated by TF Lite. +class TfLiteTensorBuffer : public tensorflow::TensorBuffer { + public: + explicit TfLiteTensorBuffer(const TfLiteTensor* tensor) { + len_ = tensor->bytes; + // TODO(ahentz): if we can guarantee that TF Lite allocated tensors with + // the same alignment as TensorFlow (EIGEN_MAX_ALIGN_BYTES), then we can + // potentially eliminate the copy below. + data_ = + tensorflow::cpu_allocator()->AllocateRaw(EIGEN_MAX_ALIGN_BYTES, len_); + if (data_ != nullptr) { + if (tensorflow::LogMemory::IsEnabled()) { + tensorflow::LogMemory::RecordRawAllocation( + "TfLiteTensorBuffer_New", + tensorflow::LogMemory::EXTERNAL_TENSOR_ALLOCATION_STEP_ID, len_, + data_, tensorflow::cpu_allocator()); + } + std::memcpy(data_, tensor->data.raw, tensor->bytes); + } + } + + ~TfLiteTensorBuffer() override { + if (tensorflow::LogMemory::IsEnabled() && data_ != nullptr) { + tensorflow::LogMemory::RecordRawDeallocation( + "TfLiteTensorBuffer_Delete", + tensorflow::LogMemory::EXTERNAL_TENSOR_ALLOCATION_STEP_ID, data_, + tensorflow::cpu_allocator(), false); + } + tensorflow::cpu_allocator()->DeallocateRaw(data_); + } + + void* data() const override { return data_; } + size_t size() const override { return len_; } + + TensorBuffer* root_buffer() override { return this; } + void FillAllocationDescription( + tensorflow::AllocationDescription* proto) const override { + tensorflow::int64 rb = size(); + proto->set_requested_bytes(rb); + proto->set_allocator_name(tensorflow::cpu_allocator()->Name()); + } + + // Prevents input forwarding from mutating this buffer. + bool OwnsMemory() const override { return false; } + + private: + void* data_; + size_t len_; +}; +} // namespace + +BufferMap::BufferMap() {} + +BufferMap::~BufferMap() {} + +bool BufferMap::HasTensor(int tensor_index) const { + return id_to_tensor_.count(tensor_index) != 0; +} + +tensorflow::Tensor BufferMap::GetTensor(int tensor_index) const { + return id_to_tensor_.at(tensor_index); +} + +void BufferMap::SetFromTfLite(int tensor_index, const TfLiteTensor* tensor) { + tensorflow::TensorShape shape; + int num_dims = tensor->dims->size; + for (int i = 0; i < num_dims; ++i) { + shape.AddDim(tensor->dims->data[i]); + } + // TODO(ahentz): we assume this is a new tensor and allocate a new buffer + // for it. This is not always the best approach. For example, this might + // be a reallocation after resizing tensors. In that case we would be + // preferable to somehow reuse the buffer. + auto* buf = new TfLiteTensorBuffer(tensor); + tensorflow::Tensor t = tensorflow::TensorCApi::MakeTensor( + GetTensorFlowDataType(tensor->type), shape, buf); + buf->Unref(); + + SetFromTensorFlow(tensor_index, std::move(t)); +} + +void BufferMap::SetFromTensorFlow(int tensor_index, tensorflow::Tensor tensor) { + id_to_tensor_[tensor_index] = std::move(tensor); +} + +} // namespace eager +} // namespace tflite diff --git a/tensorflow/contrib/lite/delegates/eager/buffer_map.h b/tensorflow/contrib/lite/delegates/eager/buffer_map.h new file mode 100644 index 0000000000000000000000000000000000000000..a28329ae7d14e3e0214c6602b28b09c43876bbf0 --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/buffer_map.h @@ -0,0 +1,61 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_BUFFER_MAP_H_ +#define TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_BUFFER_MAP_H_ + +#include + +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/core/framework/tensor.h" + +namespace tflite { +namespace eager { + +// Maps a TF Lite tensor index into a TensorFlow tensor. +// +// The TF Lite interpreter assigns integer indices to each of its tensors, but +// the Eager delegate deals in terms of TensorFlow tensors. This class maps +// from indices to tensors and allows the creation of new tensors to be +// associated with a given index. +class BufferMap { + public: + BufferMap(); + ~BufferMap(); + + // Returns true if the given 'tensor_index' has a corresponding + // tensorflow::Tensor. + bool HasTensor(int tensor_index) const; + + // Returns the tensorflow::Tensor associated with the given 'tensor_index'. + // Precondition: HasTensor() is true. + tensorflow::Tensor GetTensor(int tensor_index) const; + + // Associates the given tensorflow::Tensor with the given 'tensor_index'. + // Note that tensorflow Tensors share data buffers, so this method is only a + // shallow copy. + void SetFromTensorFlow(int tensor_index, tensorflow::Tensor tensor); + + // Same as above but creates a new tensorflow::Tensor with a copy of the + // given TfLiteTensor's data. + void SetFromTfLite(int tensor_index, const TfLiteTensor* tensor); + + private: + std::map id_to_tensor_; +}; + +} // namespace eager +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_BUFFER_MAP_H_ diff --git a/tensorflow/contrib/lite/delegates/eager/buffer_map_test.cc b/tensorflow/contrib/lite/delegates/eager/buffer_map_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..dcb3f6c94150892f565380ff0598a7a28f9399b1 --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/buffer_map_test.cc @@ -0,0 +1,174 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/delegates/eager/buffer_map.h" + +#include +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/testing/util.h" +#include "tensorflow/contrib/lite/util.h" + +namespace tflite { +namespace eager { +namespace { + +using ::testing::ElementsAre; + +// A bit of RAII to simplify handling of TfLiteTensors in the tests. +using UniqueTfLiteTensor = + std::unique_ptr>; + +template +UniqueTfLiteTensor MakeLiteTensor(const std::vector& shape, + const std::vector& data) { + auto tensor = UniqueTfLiteTensor(new TfLiteTensor, [](TfLiteTensor* t) { + TfLiteTensorDataFree(t); + TfLiteIntArrayFree(t->dims); + delete t; + }); + tensor->allocation_type = kTfLiteDynamic; + tensor->type = typeToTfLiteType(); + tensor->dims = ConvertVectorToTfLiteIntArray(shape); + tensor->data.raw = nullptr; + TfLiteTensorRealloc(data.size() * sizeof(T), tensor.get()); + memcpy(tensor->data.raw, data.data(), data.size() * sizeof(T)); + return tensor; +} + +template +tensorflow::Tensor MakeTensor(const std::vector& shape, + const std::vector& data) { + BufferMap buffer_map; // BufferMap is the easiest way to build the tensor. + UniqueTfLiteTensor t1 = MakeLiteTensor(shape, data); + buffer_map.SetFromTfLite(0, t1.get()); + return buffer_map.GetTensor(0); +} + +std::vector GetTensorShape(const tensorflow::Tensor& t) { + std::vector shape(t.dims()); + for (int i = 0; i < t.dims(); ++i) { + shape[i] = t.dim_size(i); + } + return shape; +} + +template +std::vector GetTensorData(const tensorflow::Tensor& t) { + const T* data = t.flat().data(); + return std::vector(data, data + t.NumElements()); +} + +TEST(BufferMapTest, EmptyBuffer) { + BufferMap buffer_map; + EXPECT_FALSE(buffer_map.HasTensor(0)); +} + +TEST(BufferMapTest, SetFromTfLite) { + BufferMap buffer_map; + + UniqueTfLiteTensor t = + MakeLiteTensor({1, 2, 1, 3}, {0, 0, 0, 0.123f, 0, 0}); + buffer_map.SetFromTfLite(0, t.get()); + ASSERT_TRUE(buffer_map.HasTensor(0)); + + EXPECT_THAT(GetTensorData(buffer_map.GetTensor(0)), + ElementsAre(0, 0, 0, 0.123f, 0, 0)); + + // Also check details of the tensor. + tensorflow::Tensor out_tensor = buffer_map.GetTensor(0); + ASSERT_EQ(out_tensor.dtype(), tensorflow::DT_FLOAT); + ASSERT_EQ(out_tensor.NumElements(), 6); + ASSERT_THAT(GetTensorShape(out_tensor), ElementsAre(1, 2, 1, 3)); +} + +TEST(BufferMapTest, SetFromTfLiteTwice) { + UniqueTfLiteTensor t1 = + MakeLiteTensor({1, 2, 1, 3}, {0, 0, 0, 0.123f, 0, 0}); + UniqueTfLiteTensor t2 = + MakeLiteTensor({1, 2, 4}, {0, 0, 0, 3, 0, 0, 1, 2}); + + BufferMap buffer_map; + buffer_map.SetFromTfLite(0, t1.get()); + buffer_map.SetFromTfLite(0, t2.get()); + + EXPECT_THAT(GetTensorData(buffer_map.GetTensor(0)), + ElementsAre(0, 0, 0, 3, 0, 0, 1, 2)); +} + +TEST(BufferMapTest, SetFromTensorFlow) { + tensorflow::Tensor t1 = + MakeTensor({1, 2, 1, 3}, {0, 0, 0, 0.123f, 0, 0}); + + BufferMap buffer_map; + buffer_map.SetFromTensorFlow(0, t1); + + EXPECT_THAT(GetTensorData(buffer_map.GetTensor(0)), + ElementsAre(0, 0, 0, 0.123f, 0, 0)); + + // Also check details of the tensor. + tensorflow::Tensor out_tensor = buffer_map.GetTensor(0); + ASSERT_EQ(out_tensor.dtype(), tensorflow::DT_FLOAT); + ASSERT_EQ(out_tensor.NumElements(), 6); + ASSERT_THAT(GetTensorShape(out_tensor), ElementsAre(1, 2, 1, 3)); +} + +TEST(BufferMapTest, SetFromTensorFlowTwice) { + tensorflow::Tensor t1 = + MakeTensor({1, 2, 1, 3}, {0, 0, 0, 0.123f, 0, 0}); + tensorflow::Tensor t2 = MakeTensor({1, 2, 4}, {0, 0, 0, 3, 0, 0, 1, 2}); + BufferMap buffer_map; + buffer_map.SetFromTensorFlow(0, t1); + buffer_map.SetFromTensorFlow(0, t2); + + EXPECT_THAT(GetTensorData(buffer_map.GetTensor(0)), + ElementsAre(0, 0, 0, 3, 0, 0, 1, 2)); +} + +TEST(BufferMapTest, TfLiteOverwritesTensorFlow) { + tensorflow::Tensor t1 = + MakeTensor({1, 2, 1, 3}, {0, 0, 0, 0.123f, 0, 0}); + UniqueTfLiteTensor t2 = + MakeLiteTensor({1, 2, 4}, {0, 0, 0, 3, 0, 0, 1, 2}); + + BufferMap buffer_map; + buffer_map.SetFromTensorFlow(0, t1); + buffer_map.SetFromTfLite(0, t2.get()); + + EXPECT_THAT(GetTensorData(buffer_map.GetTensor(0)), + ElementsAre(0, 0, 0, 3, 0, 0, 1, 2)); +} + +TEST(BufferMapTest, TensorFlowOverwritesTfLite) { + tensorflow::Tensor t1 = + MakeTensor({1, 2, 1, 3}, {0, 0, 0, 0.123f, 0, 0}); + UniqueTfLiteTensor t2 = + MakeLiteTensor({1, 2, 4}, {0, 0, 0, 3, 0, 0, 1, 2}); + BufferMap buffer_map; + buffer_map.SetFromTfLite(0, t2.get()); + buffer_map.SetFromTensorFlow(0, t1); + + EXPECT_THAT(GetTensorData(buffer_map.GetTensor(0)), + ElementsAre(0, 0, 0, 0.123f, 0, 0)); +} + +} // namespace +} // namespace eager +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/delegates/eager/delegate_data.cc b/tensorflow/contrib/lite/delegates/eager/delegate_data.cc new file mode 100644 index 0000000000000000000000000000000000000000..0fd5c976f8ca9be16f7e3c5e610573755b40c506 --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/delegate_data.cc @@ -0,0 +1,47 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/delegates/eager/delegate_data.h" + +#include "tensorflow/core/common_runtime/device_factory.h" +#include "tensorflow/core/lib/core/status.h" + +namespace tflite { +namespace eager { +tensorflow::Status DelegateData::Create(std::unique_ptr* data) { + std::vector devices; + + TF_RETURN_IF_ERROR(tensorflow::DeviceFactory::AddDevices( + tensorflow::SessionOptions(), "/job:localhost/replica:0/task:0", + &devices)); + + std::unique_ptr device_mgr( + new tensorflow::DeviceMgr(devices)); + // Note that Rendezvous is ref-counted so it will be automatically deleted. + tensorflow::Rendezvous* rendezvous = + new tensorflow::IntraProcessRendezvous(device_mgr.get()); + data->reset(new DelegateData(new tensorflow::EagerContext( + tensorflow::SessionOptions(), + tensorflow::ContextDevicePlacementPolicy::DEVICE_PLACEMENT_SILENT, + /*async=*/false, std::move(device_mgr), rendezvous))); + return tensorflow::Status(); +} + +DelegateData::DelegateData(tensorflow::EagerContext* eager_context) + : eager_context_(eager_context) {} + +DelegateData::~DelegateData() {} + +} // namespace eager +} // namespace tflite diff --git a/tensorflow/contrib/lite/delegates/eager/delegate_data.h b/tensorflow/contrib/lite/delegates/eager/delegate_data.h new file mode 100644 index 0000000000000000000000000000000000000000..8a0e8ba8bf213341d9da15613ea40e1f903f8bb6 --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/delegate_data.h @@ -0,0 +1,48 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_DELEGATE_DATA_H_ +#define TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_DELEGATE_DATA_H_ + +#include "tensorflow/contrib/lite/delegates/eager/buffer_map.h" +#include "tensorflow/core/common_runtime/eager/context.h" + +namespace tflite { +namespace eager { + +// Data kept by the Eager delegate for the lifetime of an Interpreter. +class DelegateData { + public: + // Create a new DelegateData, initialized with a newly-created EagerContext. + static tensorflow::Status Create(std::unique_ptr* data); + + ~DelegateData(); + + // The EagerContext that is required for execution of Eager Ops. + tensorflow::EagerContext* GetEagerContext() { return eager_context_.get(); } + + // Map from TF Lite tensor index to TensorFlow tensor. + BufferMap* GetBufferMap() { return &buffer_map_; } + + private: + explicit DelegateData(tensorflow::EagerContext* eager_context); + + std::unique_ptr eager_context_; + BufferMap buffer_map_; +}; + +} // namespace eager +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_DELEGATE_DATA_H_ diff --git a/tensorflow/contrib/lite/delegates/eager/delegate_data_test.cc b/tensorflow/contrib/lite/delegates/eager/delegate_data_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..30251b8f82cf623b4c45854f7f2f6e5e2c008af0 --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/delegate_data_test.cc @@ -0,0 +1,44 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/delegates/eager/delegate_data.h" + +#include +#include +#include "tensorflow/contrib/lite/testing/util.h" + +namespace tflite { +namespace eager { +namespace { + +TEST(DelegateDataTest, Basic) { + std::unique_ptr data; + // We only check for success because it is hard to make initialization fail. + // It only happens if we manage to not link the CPU device factory into the + // binary. + EXPECT_TRUE(DelegateData::Create(&data).ok()); + + EXPECT_NE(data->GetEagerContext(), nullptr); + EXPECT_NE(data->GetBufferMap(), nullptr); +} + +} // namespace +} // namespace eager +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/delegates/eager/kernel.cc b/tensorflow/contrib/lite/delegates/eager/kernel.cc new file mode 100644 index 0000000000000000000000000000000000000000..172798180762f87e1c080be7788db661a63208b5 --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/kernel.cc @@ -0,0 +1,289 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/delegates/eager/kernel.h" + +#include "third_party/flatbuffers/include/flatbuffers/flexbuffers.h" +#include "tensorflow/contrib/lite/builtin_ops.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/context_util.h" +#include "tensorflow/contrib/lite/delegates/eager/delegate_data.h" +#include "tensorflow/contrib/lite/delegates/eager/util.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/core/common_runtime/eager/context.h" +#include "tensorflow/core/common_runtime/eager/execute.h" +#include "tensorflow/core/common_runtime/eager/tensor_handle.h" +#include "tensorflow/core/framework/node_def.pb.h" + +// Note: this is part of TF Lite's Eager delegation code which is to be +// completed soon. + +// This is the TF Lite op that is created by the eager delegate to handle +// execution of a supported subgraph. The usual flow is that the delegate +// informs the interpreter of supported nodes in a graph, and each supported +// subgraph is replaced with one instance of this kernel. +// +// The kernel is initialized with TfLiteDelegateParams from which we retrieve +// the global EagerContext and BufferMap, as well as a list of inputs and +// outputs to the subgraph. Those are used to build the OpData, with a list of +// TensorFlow Ops that should be executed in order (which we call an OpNode). +// +// For each node included in the subgraph, we query the interpreter and +// retrieve the associated NodeDef, which is then used to configure the +// corresponding TensorFlow/Eager Op. + +namespace tflite { +namespace eager { +namespace kernel { + +// Controls the lifetime of tensor handles in a vector. +class VectorOfHandles { + public: + explicit VectorOfHandles(int num_elements) : vector_(num_elements, nullptr) {} + + ~VectorOfHandles() { + for (auto* handle : vector_) { + if (handle) handle->Unref(); + } + } + + tensorflow::gtl::InlinedVector* GetVector() { + return &vector_; + } + + tensorflow::TensorHandle* GetHandle(int index) { return vector_[index]; } + + private: + tensorflow::gtl::InlinedVector vector_; +}; + +// Executes the TensorFlow op given by 'op_name', with the attributes specified +// in 'nodedef'. Inputs and outputs are given as indices into the 'buffer_map'. +tensorflow::Status ExecuteEagerOp(tensorflow::EagerContext* eager_context, + BufferMap* buffer_map, const string& op_name, + const tensorflow::NodeDef& nodedef, + const std::vector& inputs, + const std::vector& outputs) { + const tensorflow::AttrTypeMap* attr_types; + TF_RETURN_WITH_CONTEXT_IF_ERROR( + tensorflow::AttrTypeMapForOp(op_name.c_str(), &attr_types), + " (while processing attributes of '", op_name, "')"); + + tensorflow::EagerOperation op(eager_context, op_name.c_str(), attr_types); + for (const auto& attr : nodedef.attr()) { + op.MutableAttrs()->Set(attr.first, attr.second); + } + + for (int input_index : inputs) { + if (!buffer_map->HasTensor(input_index)) { + return tensorflow::errors::Internal( + "Cannot read from invalid tensor index ", input_index); + } + auto* handle = new tensorflow::TensorHandle( + buffer_map->GetTensor(input_index), nullptr, nullptr, nullptr); + op.AddInput(handle); + handle->Unref(); + } + + int num_retvals = outputs.size(); + VectorOfHandles retvals(num_retvals); + TF_RETURN_WITH_CONTEXT_IF_ERROR( + EagerExecute(&op, retvals.GetVector(), &num_retvals), + " (while executing '", op_name, "' via Eager)"); + + if (num_retvals != outputs.size()) { + return tensorflow::errors::Internal( + "Unexpected number of outputs from EagerExecute"); + } + + for (int i = 0; i < num_retvals; ++i) { + const tensorflow::Tensor* tensor = nullptr; + TF_RETURN_IF_ERROR(retvals.GetHandle(i)->Tensor(&tensor)); + buffer_map->SetFromTensorFlow(outputs[i], *tensor); + } + + return tensorflow::Status::OK(); +} + +// A single node within the larger 'op'. Note that this kernel executes many +// TensorFlow ops within a single TF Lite op. +struct OpNode { + // The name of the TensorFlow op to execute. + string name; + // The corresponding NodeDef, containing the attributes for the op. + tensorflow::NodeDef nodedef; + // List of inputs, as TF Lite tensor indices. + std::vector inputs; + // List of outputs, as TF Lite tensor indices. + std::vector outputs; +}; + +// The Larger 'op', which contains all the nodes in a supported subgraph. +struct OpData { + tensorflow::EagerContext* eager_context; + BufferMap* buffer_map; + std::vector nodes; + std::vector subgraph_inputs; + std::vector subgraph_outputs; +}; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + auto* op_data = new OpData; + + const TfLiteDelegateParams* params = + reinterpret_cast(buffer); + CHECK(params); + CHECK(params->delegate); + CHECK(params->delegate->data_); + op_data->eager_context = + reinterpret_cast(params->delegate->data_) + ->GetEagerContext(); + op_data->buffer_map = + reinterpret_cast(params->delegate->data_)->GetBufferMap(); + + CHECK(params->output_tensors); + for (auto tensor_index : TfLiteIntArrayView(params->output_tensors)) { + op_data->subgraph_outputs.push_back(tensor_index); + } + + CHECK(params->input_tensors); + for (auto tensor_index : TfLiteIntArrayView(params->input_tensors)) { + op_data->subgraph_inputs.push_back(tensor_index); + } + + CHECK(params->nodes_to_replace); + for (auto node_index : TfLiteIntArrayView(params->nodes_to_replace)) { + TfLiteNode* node; + TfLiteRegistration* reg; + context->GetNodeAndRegistration(context, node_index, &node, ®); + + op_data->nodes.push_back(OpNode()); + OpNode& node_data = op_data->nodes.back(); + + node_data.name = ""; + if (node->custom_initial_data) { + // The flexbuffer contains a vector where the first elements is the + // op name and the second is a serialized NodeDef. + const flexbuffers::Vector& v = + flexbuffers::GetRoot( + reinterpret_cast(node->custom_initial_data), + node->custom_initial_data_size) + .AsVector(); + + node_data.name = v[0].AsString().str(); + if (!node_data.nodedef.ParseFromString(v[1].AsString().str())) { + // We will just leave the nodedef empty and error out in Eval(). + node_data.nodedef.Clear(); + } + } + + for (auto input_index : TfLiteIntArrayView(node->inputs)) { + node_data.inputs.push_back(input_index); + } + for (auto output_index : TfLiteIntArrayView(node->outputs)) { + node_data.outputs.push_back(output_index); + } + } + + return op_data; +} + +void Free(TfLiteContext* context, void* buffer) { + delete reinterpret_cast(buffer); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + const auto* op_data = reinterpret_cast(node->user_data); + TF_LITE_ENSURE_MSG( + context, op_data->eager_context != nullptr, + "Failed to initialize eager context. This often happens when a CPU " + "device has not been registered, presumably because some symbols from " + "tensorflow/core:core_cpu_impl were not linked into the binary."); + + // Whenever we find a constant tensor, insert it in the buffer map. + BufferMap* buffer_map = op_data->buffer_map; + for (auto tensor_index : op_data->subgraph_inputs) { + TfLiteTensor* tensor = &context->tensors[tensor_index]; + if (IsConstantTensor(tensor)) { + if (!buffer_map->HasTensor(tensor_index)) { + buffer_map->SetFromTfLite(tensor_index, tensor); + } + } + } + + // All output tensors are allocated by TensorFlow/Eager, so we + // mark them as kTfLiteDynamic. + for (auto tensor_index : op_data->subgraph_outputs) { + SetTensorToDynamic(&context->tensors[tensor_index]); + } + + return kTfLiteOk; +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + const auto* op_data = reinterpret_cast(node->user_data); + BufferMap* buffer_map = op_data->buffer_map; + tensorflow::EagerContext* eager_context = op_data->eager_context; + + // Insert a tensor in the buffer map for all inputs that are not constant. + // Constants were handled in Prepare() already. + for (auto tensor_index : op_data->subgraph_inputs) { + TfLiteTensor* tensor = &context->tensors[tensor_index]; + if (!IsConstantTensor(tensor)) { + buffer_map->SetFromTfLite(tensor_index, tensor); + } + } + + // Execute the TensorFlow Ops sequentially. + for (const auto& node_data : op_data->nodes) { + if (node_data.nodedef.op().empty()) { + context->ReportError(context, "Invalid NodeDef in Eager op '%s'", + node_data.name.c_str()); + return kTfLiteError; + } + auto status = + ExecuteEagerOp(eager_context, buffer_map, node_data.name, + node_data.nodedef, node_data.inputs, node_data.outputs); + TF_LITE_ENSURE_OK(context, ConvertStatus(context, status)); + } + + for (auto tensor_index : op_data->subgraph_outputs) { + if (!buffer_map->HasTensor(tensor_index)) { + context->ReportError(context, "Cannot write to invalid tensor index %d", + tensor_index); + return kTfLiteError; + } + + TfLiteTensor* tensor = &context->tensors[tensor_index]; + TF_LITE_ENSURE_OK( + context, + CopyShape(context, buffer_map->GetTensor(tensor_index), tensor)); + tensor->buffer_handle = tensor_index; + tensor->data_is_stale = true; + } + + return kTfLiteOk; +} + +} // namespace kernel + +TfLiteRegistration GetKernel() { + TfLiteRegistration registration{&kernel::Init, &kernel::Free, + &kernel::Prepare, &kernel::Eval, + nullptr, kTfLiteBuiltinDelegate}; + return registration; +} + +} // namespace eager +} // namespace tflite diff --git a/tensorflow/contrib/lite/delegates/eager/kernel.h b/tensorflow/contrib/lite/delegates/eager/kernel.h new file mode 100644 index 0000000000000000000000000000000000000000..100672c82dcd3eaee17325f3b712140b081e8efe --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/kernel.h @@ -0,0 +1,34 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_KERNEL_H_ +#define TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_KERNEL_H_ + +#include "tensorflow/contrib/lite/context.h" + +namespace tflite { +namespace eager { + +// Return the registration object used to initialize and execute ops that will +// be delegated to TensorFlow's Eager runtime. This TF Lite op is created by +// the eager delegate to handle execution of a supported subgraph. The usual +// flow is that the delegate informs the interpreter of supported nodes in a +// graph, and each supported subgraph is replaced with one instance of this +// kernel. +TfLiteRegistration GetKernel(); + +} // namespace eager +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_KERNEL_H_ diff --git a/tensorflow/contrib/lite/delegates/eager/kernel_test.cc b/tensorflow/contrib/lite/delegates/eager/kernel_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..7d9dddef93346c8e20df0d3f84ece6197a605c86 --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/kernel_test.cc @@ -0,0 +1,351 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/delegates/eager/kernel.h" + +#include +#include +#include "absl/memory/memory.h" +#include "third_party/flatbuffers/include/flatbuffers/flexbuffers.h" +#include "tensorflow/contrib/lite/delegates/eager/delegate_data.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/testing/util.h" + +namespace tflite { +namespace eager { +namespace { + +using tensorflow::protobuf::TextFormat; +using ::testing::ContainsRegex; +using ::testing::ElementsAre; + +// We will use these are custom_names, so they need to be static. +static const char kIdentity[] = "Identity"; +static const char kUnpack[] = "Unpack"; +static const char kAdd[] = "Add"; +static const char kMul[] = "Mul"; + +TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteDelegate* delegate, + const std::vector& supported_nodes) { + TfLiteIntArray* size_and_nodes = + ConvertVectorToTfLiteIntArray(supported_nodes); + TF_LITE_ENSURE_STATUS(context->ReplaceSubgraphsWithDelegateKernels( + context, eager::GetKernel(), size_and_nodes, delegate)); + TfLiteIntArrayFree(size_and_nodes); + return kTfLiteOk; +} + +class KernelTest : public ::testing::Test { + public: + KernelTest() { + CHECK(DelegateData::Create(&delegate_data_).ok()); + interpreter_.reset(new Interpreter(&error_reporter_)); + } + + bool Invoke() { return interpreter_->Invoke() == kTfLiteOk; } + + void SetValues(int tensor_index, const std::vector& values) { + float* v = interpreter_->typed_tensor(tensor_index); + for (float f : values) { + *v++ = f; + } + } + + std::vector GetValues(int tensor_index) { + TfLiteTensor* o = interpreter_->tensor(tensor_index); + return std::vector(o->data.f, o->data.f + o->bytes / sizeof(float)); + } + + void SetShape(int tensor_index, const std::vector& values) { + ASSERT_EQ(interpreter_->ResizeInputTensor(tensor_index, values), kTfLiteOk); + ASSERT_EQ(interpreter_->AllocateTensors(), kTfLiteOk); + } + + std::vector GetShape(int tensor_index) { + std::vector result; + auto* dims = interpreter_->tensor(tensor_index)->dims; + for (int i = 0; i < dims->size; ++i) { + result.push_back(dims->data[i]); + } + return result; + } + + template + void ConfigureDelegate(T prepare_function) { + delegate_.data_ = delegate_data_.get(); + delegate_.FreeBufferHandle = nullptr; + delegate_.Prepare = prepare_function; + delegate_.CopyFromBufferHandle = [](TfLiteDelegate* delegate, + TfLiteBufferHandle buffer_handle, + void* data, size_t size) { + auto* delegate_data = reinterpret_cast(delegate->data_); + tensorflow::StringPiece values = + delegate_data->GetBufferMap()->GetTensor(buffer_handle).tensor_data(); + memcpy(data, values.data(), values.size()); + return kTfLiteOk; + }; + CHECK(interpreter_->ModifyGraphWithDelegate( + &delegate_, /*allow_dynamic_tensors=*/true) == kTfLiteOk); + } + + void AddOp(const char* name, const std::vector& inputs, + const std::vector& outputs) { + auto attr = [](const string& key, const string& value) { + return " attr{ key: '" + key + "' value {" + value + "}}"; + }; + + string attributes; + if (name == string(kUnpack)) { + attributes = attr("T", "type: DT_FLOAT") + attr("num", "i: 2") + + attr("axis", "i: 0"); + } else if (name == string(kIdentity)) { + attributes = attr("T", "type: DT_FLOAT"); + } else if (name == string(kAdd)) { + attributes = attr("T", "type: DT_FLOAT"); + } else if (name == string(kMul)) { + attributes = attr("T", "type: DT_FLOAT"); + } + AddTfOp(name, attributes, inputs, outputs); + } + + void AddTensors(int num_tensors, const std::vector& inputs, + const std::vector& outputs) { + interpreter_->AddTensors(num_tensors); + for (int i = 0; i < num_tensors; ++i) { + TfLiteQuantizationParams quant; + CHECK_EQ(interpreter_->SetTensorParametersReadWrite(i, kTfLiteFloat32, + /*name=*/"", + /*dims=*/{3}, quant), + kTfLiteOk); + } + + CHECK_EQ(interpreter_->SetInputs(inputs), kTfLiteOk); + CHECK_EQ(interpreter_->SetOutputs(outputs), kTfLiteOk); + } + + const TestErrorReporter& error_reporter() const { return error_reporter_; } + + void AddTfLiteOp(const char* name, const std::vector& inputs, + const std::vector& outputs) { + CHECK_EQ(string(name), kMul); // can only add MUL + static TfLiteRegistration reg = {nullptr, nullptr, nullptr, nullptr}; + reg.builtin_code = BuiltinOperator_MUL; + reg.prepare = [](TfLiteContext* context, TfLiteNode* node) { + auto* i0 = &context->tensors[node->inputs->data[0]]; + auto* o = &context->tensors[node->outputs->data[0]]; + return context->ResizeTensor(context, o, TfLiteIntArrayCopy(i0->dims)); + }; + reg.invoke = [](TfLiteContext* context, TfLiteNode* node) { + auto* i0 = &context->tensors[node->inputs->data[0]]; + auto* i1 = &context->tensors[node->inputs->data[1]]; + auto* o = &context->tensors[node->outputs->data[0]]; + for (int i = 0; i < o->bytes / sizeof(float); ++i) { + o->data.f[i] = i0->data.f[i] * i1->data.f[i]; + } + return kTfLiteOk; + }; + + CHECK_EQ(interpreter_->AddNodeWithParameters(inputs, outputs, nullptr, 0, + nullptr, ®), + kTfLiteOk); + } + + private: + void AddTfOp(const char* name, const string& nodedef_str, + const std::vector& inputs, + const std::vector& outputs) { + static TfLiteRegistration reg = {nullptr, nullptr, nullptr, nullptr}; + reg.builtin_code = BuiltinOperator_CUSTOM; + reg.custom_name = name; + + tensorflow::NodeDef nodedef; + CHECK(TextFormat::ParseFromString(nodedef_str + " op: '" + name + "'", + &nodedef)); + string serialized_nodedef; + CHECK(nodedef.SerializeToString(&serialized_nodedef)); + flexbuffers::Builder fbb; + fbb.Vector([&]() { + fbb.String(nodedef.op()); + fbb.String(serialized_nodedef); + }); + fbb.Finish(); + + flexbuffers_.push_back(fbb.GetBuffer()); + auto& buffer = flexbuffers_.back(); + CHECK_EQ(interpreter_->AddNodeWithParameters( + inputs, outputs, reinterpret_cast(buffer.data()), + buffer.size(), nullptr, ®), + kTfLiteOk); + } + + std::unique_ptr interpreter_; + std::unique_ptr delegate_data_; + TfLiteDelegate delegate_; + std::vector> flexbuffers_; + TestErrorReporter error_reporter_; +}; + +TEST_F(KernelTest, FullGraph) { + // Define the graph. + AddTensors(9, {0, 3}, {8}); + + AddOp(kUnpack, {0}, {1, 2}); + AddOp(kUnpack, {3}, {4, 5}); + AddOp(kAdd, {1, 4}, {6}); + AddOp(kAdd, {2, 5}, {7}); + AddOp(kMul, {6, 7}, {8}); + + // Apply Delegate. + ConfigureDelegate([](TfLiteContext* context, TfLiteDelegate* delegate) { + return GenericPrepare(context, delegate, {0, 1, 2, 3, 4}); + }); + + // Define inputs. + SetShape(0, {2, 2, 1}); + SetValues(0, {1.1f, 2.2f, 3.3f, 4.4f}); + SetShape(3, {2, 2, 1}); + SetValues(3, {1.1f, 2.2f, 3.3f, 4.4f}); + + ASSERT_TRUE(Invoke()); + + ASSERT_THAT(GetShape(8), ElementsAre(2, 1)); + ASSERT_THAT(GetValues(8), ElementsAre(14.52f, 38.72f)); +} + +TEST_F(KernelTest, BadTensorFlowOp) { + AddTensors(2, {0}, {1}); + AddOp("NonExistentOp", {0}, {1}); + + ConfigureDelegate([](TfLiteContext* context, TfLiteDelegate* delegate) { + return GenericPrepare(context, delegate, {0}); + }); + + SetShape(0, {2, 2, 1}); + SetValues(0, {1.1f, 2.2f, 3.3f, 4.4f}); + + ASSERT_FALSE(Invoke()); + ASSERT_THAT(error_reporter().error_messages(), + ContainsRegex("while processing attributes of 'NonExistentOp'")); +} + +TEST_F(KernelTest, BadNumberOfOutputs) { + AddTensors(3, {0}, {1, 2}); + AddOp(kIdentity, {0}, {1, 2}); + + ConfigureDelegate([](TfLiteContext* context, TfLiteDelegate* delegate) { + return GenericPrepare(context, delegate, {0}); + }); + + SetShape(0, {2, 2, 1}); + SetValues(0, {1.1f, 2.2f, 3.3f, 4.4f}); + + ASSERT_FALSE(Invoke()); + ASSERT_THAT(error_reporter().error_messages(), + ContainsRegex("Unexpected number of outputs")); +} + +TEST_F(KernelTest, IncompatibleNodeDef) { + AddTensors(2, {0}, {1}); + + // Cast is a TF op, but we don't add the proper nodedef to it in AddOp. + AddOp("Cast", {0}, {1}); + + ConfigureDelegate([](TfLiteContext* context, TfLiteDelegate* delegate) { + return GenericPrepare(context, delegate, {0}); + }); + + SetShape(0, {2, 2, 1}); + SetValues(0, {1.1f, 2.2f, 3.3f, 4.4f}); + + ASSERT_FALSE(Invoke()); + ASSERT_THAT(error_reporter().error_messages(), + ContainsRegex("while executing 'Cast' via Eager")); +} + +TEST_F(KernelTest, WrongSetOfNodes) { + AddTensors(4, {0}, {3}); + AddOp(kUnpack, {0}, {1, 2}); + AddTfLiteOp(kMul, {1, 2}, {3}); + + // Specify that kMul (#1) is supported when it actually isn't. + ConfigureDelegate([](TfLiteContext* context, TfLiteDelegate* delegate) { + return GenericPrepare(context, delegate, {0, 1}); + }); + + SetShape(0, {2, 2, 1}); + SetValues(0, {1.1f, 2.2f, 3.3f, 4.4f}); + + ASSERT_FALSE(Invoke()); + ASSERT_THAT(error_reporter().error_messages(), + ContainsRegex("Invalid NodeDef in Eager op")); +} + +TEST_F(KernelTest, MixedGraph) { + AddTensors(9, {0, 3}, {8}); + + AddOp(kUnpack, {0}, {1, 2}); + AddOp(kUnpack, {3}, {4, 5}); + AddOp(kAdd, {1, 4}, {6}); + AddOp(kAdd, {2, 5}, {7}); + AddTfLiteOp(kMul, {6, 7}, {8}); + + ConfigureDelegate([](TfLiteContext* context, TfLiteDelegate* delegate) { + return GenericPrepare(context, delegate, {0, 1, 2, 3}); + }); + + SetShape(0, {2, 2, 1}); + SetValues(0, {1.1f, 2.2f, 3.3f, 4.4f}); + SetShape(3, {2, 2, 1}); + SetValues(3, {1.1f, 2.2f, 3.3f, 4.4f}); + + ASSERT_TRUE(Invoke()); + + ASSERT_THAT(GetShape(8), ElementsAre(2, 1)); + ASSERT_THAT(GetValues(8), ElementsAre(14.52f, 38.72f)); +} + +TEST_F(KernelTest, SplitGraph) { + AddTensors(10, {0}, {9}); + + AddOp(kUnpack, {0}, {1, 2}); + AddOp(kAdd, {1, 2}, {3}); + AddOp(kUnpack, {3}, {4, 5}); + + AddTfLiteOp(kMul, {4, 5}, {6}); + + AddOp(kUnpack, {6}, {7, 8}); + AddOp(kAdd, {7, 8}, {9}); + + ConfigureDelegate([](TfLiteContext* context, TfLiteDelegate* delegate) { + return GenericPrepare(context, delegate, {0, 1, 2, 4, 5}); + }); + + SetShape(0, {2, 2, 2, 1}); + SetValues(0, {3.0f, 1.0f, 0.5f, -1.0f, 0.0f, 1.0f, 1.5f, 3.0f}); + + ASSERT_TRUE(Invoke()); + + ASSERT_THAT(GetShape(9), ElementsAre(1)); + ASSERT_THAT(GetValues(9), ElementsAre(10.0f)); +} + +} // namespace +} // namespace eager +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/delegates/eager/util.cc b/tensorflow/contrib/lite/delegates/eager/util.cc new file mode 100644 index 0000000000000000000000000000000000000000..4426c653e6ff80aac52b50e06a3005173490433d --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/util.cc @@ -0,0 +1,72 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/delegates/eager/util.h" + +namespace tflite { +namespace eager { + +TfLiteStatus ConvertStatus(TfLiteContext* context, + const tensorflow::Status& status) { + if (!status.ok()) { + context->ReportError(context, "%s", status.error_message().c_str()); + return kTfLiteError; + } + return kTfLiteOk; +} + +TfLiteStatus CopyShape(TfLiteContext* context, const tensorflow::Tensor& src, + TfLiteTensor* tensor) { + int num_dims = src.dims(); + TfLiteIntArray* shape = TfLiteIntArrayCreate(num_dims); + for (int j = 0; j < num_dims; ++j) { + // We need to cast from TensorFlow's int64 to TF Lite's int32. Let's + // make sure there's no overflow. + if (src.dim_size(j) >= std::numeric_limits::max()) { + context->ReportError(context, + "Dimension value in TensorFlow shape is larger than " + "supported by TF Lite"); + TfLiteIntArrayFree(shape); + return kTfLiteError; + } + shape->data[j] = static_cast(src.dim_size(j)); + } + return context->ResizeTensor(context, tensor, shape); +} + +TF_DataType GetTensorFlowDataType(TfLiteType type) { + switch (type) { + case kTfLiteNoType: + return TF_FLOAT; + case kTfLiteFloat32: + return TF_FLOAT; + case kTfLiteInt16: + return TF_INT16; + case kTfLiteInt32: + return TF_INT32; + case kTfLiteUInt8: + return TF_UINT8; + case kTfLiteInt64: + return TF_INT64; + case kTfLiteComplex64: + return TF_COMPLEX64; + case kTfLiteString: + return TF_STRING; + case kTfLiteBool: + return TF_BOOL; + } +} + +} // namespace eager +} // namespace tflite diff --git a/tensorflow/contrib/lite/delegates/eager/util.h b/tensorflow/contrib/lite/delegates/eager/util.h new file mode 100644 index 0000000000000000000000000000000000000000..a9407be071192e9b7f25f95df9e76a5f44e7c9e3 --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/util.h @@ -0,0 +1,42 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_UTIL_H_ +#define TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_UTIL_H_ + +#include "tensorflow/c/c_api_internal.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/lib/core/status.h" + +namespace tflite { +namespace eager { + +// Converts a tensorflow:Status into a TfLiteStatus. If the original status +// represented an error, reports it using the given 'context'. +TfLiteStatus ConvertStatus(TfLiteContext* context, + const tensorflow::Status& status); + +// Copies the given shape of the given 'src' into a TF Lite 'tensor'. Logs an +// error and returns kTfLiteError if the shape can't be converted. +TfLiteStatus CopyShape(TfLiteContext* context, const tensorflow::Tensor& src, + TfLiteTensor* tensor); + +// Returns the TF C API Data type that corresponds to the given TfLiteType. +TF_DataType GetTensorFlowDataType(TfLiteType type); + +} // namespace eager +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_UTIL_H_ diff --git a/tensorflow/contrib/lite/delegates/eager/util_test.cc b/tensorflow/contrib/lite/delegates/eager/util_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..c4fbf5412776a2c5743e8d72fc6729cfd709c545 --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/util_test.cc @@ -0,0 +1,113 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/delegates/eager/util.h" + +#include + +#include +#include +#include "tensorflow/contrib/lite/testing/util.h" + +namespace tflite { +namespace eager { +namespace { + +using tensorflow::DT_FLOAT; +using tensorflow::Tensor; +using ::testing::ElementsAre; + +struct TestContext : public TfLiteContext { + string error; + std::vector new_size; +}; + +void ReportError(TfLiteContext* context, const char* format, ...) { + TestContext* c = static_cast(context); + const size_t kBufferSize = 1024; + char temp_buffer[kBufferSize]; + + va_list args; + va_start(args, format); + vsnprintf(temp_buffer, kBufferSize, format, args); + va_end(args); + + c->error = temp_buffer; +} + +TfLiteStatus ResizeTensor(TfLiteContext* context, TfLiteTensor* tensor, + TfLiteIntArray* new_size) { + TestContext* c = static_cast(context); + c->new_size.clear(); + for (int i = 0; i < new_size->size; ++i) { + c->new_size.push_back(new_size->data[i]); + } + TfLiteIntArrayFree(new_size); + return kTfLiteOk; +} + +TEST(UtilTest, ConvertStatus) { + TestContext context; + context.ReportError = ReportError; + + EXPECT_EQ(ConvertStatus(&context, tensorflow::errors::Internal("Some Error")), + kTfLiteError); + EXPECT_EQ(context.error, "Some Error"); + + context.error.clear(); + EXPECT_EQ(ConvertStatus(&context, tensorflow::Status()), kTfLiteOk); + EXPECT_TRUE(context.error.empty()); +} + +TEST(UtilTest, CopyShape) { + TestContext context; + context.ReportError = ReportError; + context.ResizeTensor = ResizeTensor; + + TfLiteTensor dst; + + EXPECT_EQ(CopyShape(&context, Tensor(), &dst), kTfLiteOk); + EXPECT_THAT(context.new_size, ElementsAre(0)); + + EXPECT_EQ(CopyShape(&context, Tensor(DT_FLOAT, {1, 2}), &dst), kTfLiteOk); + EXPECT_THAT(context.new_size, ElementsAre(1, 2)); + + EXPECT_EQ(CopyShape(&context, Tensor(DT_FLOAT, {1LL << 44, 2}), &dst), + kTfLiteError); + EXPECT_EQ(context.error, + "Dimension value in TensorFlow shape is larger than supported by " + "TF Lite"); +} + +TEST(UtilTest, TypeConversions) { + EXPECT_EQ(TF_FLOAT, GetTensorFlowDataType(kTfLiteNoType)); + EXPECT_EQ(TF_FLOAT, GetTensorFlowDataType(kTfLiteFloat32)); + EXPECT_EQ(TF_INT16, GetTensorFlowDataType(kTfLiteInt16)); + EXPECT_EQ(TF_INT32, GetTensorFlowDataType(kTfLiteInt32)); + EXPECT_EQ(TF_UINT8, GetTensorFlowDataType(kTfLiteUInt8)); + EXPECT_EQ(TF_INT64, GetTensorFlowDataType(kTfLiteInt64)); + EXPECT_EQ(TF_COMPLEX64, GetTensorFlowDataType(kTfLiteComplex64)); + EXPECT_EQ(TF_STRING, GetTensorFlowDataType(kTfLiteString)); + EXPECT_EQ(TF_BOOL, GetTensorFlowDataType(kTfLiteBool)); +} + +} // namespace +} // namespace eager +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/delegates/nnapi/BUILD b/tensorflow/contrib/lite/delegates/nnapi/BUILD index 35a8f6ca4166e373ea1a0af5d4a013327b30d2b6..091f8fbce734b466de33bb4b84e5e0fc3e4a71ef 100644 --- a/tensorflow/contrib/lite/delegates/nnapi/BUILD +++ b/tensorflow/contrib/lite/delegates/nnapi/BUILD @@ -22,6 +22,7 @@ tf_cc_test( name = "nnapi_delegate_test", size = "small", srcs = ["nnapi_delegate_test.cc"], + tags = ["no_oss"], deps = [ ":nnapi_delegate", "//tensorflow/contrib/lite:framework", diff --git a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc index fd798c209e5112235cf6e351e231d4096006a8b0..60855eb8edc4fb708d76b1e3a4ac37d462a64465 100644 --- a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc @@ -436,7 +436,6 @@ class NNAPIDelegateKernel { } break; case kTfLiteBuiltinSqueeze: - // Squeeze requires NNAPI1.1. if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11) { return [](TfLiteContext* context, NNAPIOpBuilder* builder, TfLiteNode* node) -> ANeuralNetworksOperationType { @@ -452,6 +451,240 @@ class NNAPIDelegateKernel { } else { return nullptr; } + case kTfLiteBuiltinL2Normalization: { + auto builtin = + reinterpret_cast(node->builtin_data); + if (builtin->activation != kTfLiteActNone) { + // NNAPI does not support activations + return nullptr; + } + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_L2_NORMALIZATION; + }; + } + case kTfLiteBuiltinLocalResponseNormalization: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + node->builtin_data); + builder->AddScalarInt32Operand(builtin->radius); + builder->AddScalarFloat32Operand(builtin->bias); + builder->AddScalarFloat32Operand(builtin->alpha); + builder->AddScalarFloat32Operand(builtin->beta); + return ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION; + }; + } else { + // TODO(miaowang): clean-up code and return early in the unsupported + // case. + return nullptr; + } + break; + case kTfLiteBuiltinLshProjection: + if (version == 1) { + // NNAPI does not support sparse projection correctly (b/111751836). + if (reinterpret_cast(node->builtin_data) + ->type == kTfLiteLshProjectionSparse) { + return nullptr; + } + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + node->builtin_data); + builder->AddScalarInt32Operand(builtin->type); + return ANEURALNETWORKS_LSH_PROJECTION; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinConcatenation: + if (version == 1 && + reinterpret_cast(node->builtin_data) + ->activation == kTfLiteActNone) { + if (context->tensors[node->inputs->data[0]].type == kTfLiteUInt8) { + // NNAPI only support concatenating quantized tensor of the same + // scale and offset. + auto first_param = context->tensors[node->inputs->data[0]].params; + for (int i = 0; i < node->inputs->size; i++) { + auto curr_param = context->tensors[node->inputs->data[i]].params; + if (curr_param.scale != first_param.scale || + curr_param.zero_point != first_param.zero_point) { + return nullptr; + } + } + } + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + node->builtin_data); + builder->AddScalarInt32Operand(builtin->axis); + return ANEURALNETWORKS_CONCATENATION; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinDequantize: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_DEQUANTIZE; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinFloor: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_FLOOR; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinRelu: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_RELU; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinReluN1To1: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_RELU1; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinRelu6: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_RELU6; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinLogistic: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_LOGISTIC; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinTanh: + // TODO(miaowang): add additional checks for the parameters. + if (version == 1 && + context->tensors[node->inputs->data[0]].type == kTfLiteFloat32) { + // NNAPI only support float tanh. + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_TANH; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinSub: + if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11 && + context->tensors[node->inputs->data[0]].type == kTfLiteFloat32) { + // NNAPI only support float sub. + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_SUB; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinDiv: + if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11 && + context->tensors[node->inputs->data[0]].type == kTfLiteFloat32) { + // NNAPI only support float div. + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_DIV; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinPad: + if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11 && + node->inputs->size == 2 && + context->tensors[node->inputs->data[0]].type == kTfLiteFloat32) { + // NNAPI does not support specifying the padding value. + // NNAPI pads physical zero for quantized tensors, so only delegate + // float pad to NNAPI. + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_PAD; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinSpaceToBatchNd: + if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_SPACE_TO_BATCH_ND; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinStridedSlice: + if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + builder->AddScalarInt32Operand(builtin->begin_mask); + builder->AddScalarInt32Operand(builtin->end_mask); + builder->AddScalarInt32Operand(builtin->shrink_axis_mask); + return ANEURALNETWORKS_STRIDED_SLICE; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinTranspose: + // Note that the permutation input tensor value dictates the output + // dimensions. + // TODO(b/110888333): Support dynamically-sized tensors in delegates. + if ((version == 1) && + (kAndroidSdkVersion >= kMinSdkVersionForNNAPI11) && + (node->inputs->size > 1) && + (context->tensors[node->inputs->data[1]].allocation_type == + kTfLiteMmapRo)) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_TRANSPOSE; + }; + } else { + return nullptr; + } break; default: return nullptr; diff --git a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc index aad10c9ce730a2e90481a123a1e3e323cfb2bd42..b7b159c59f2f81b055d5d06436b70331cff3dea8 100644 --- a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc +++ b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc @@ -27,14 +27,20 @@ using ::testing::ElementsAreArray; // TODO(b/110368244): figure out how to share the existing tests in kernels/ but // with the delegation on. Also, add more unit tests to improve code coverage. -class FloatAddOpModel : public SingleOpModel { +class SingleOpModelWithNNAPI : public SingleOpModel { + public: + SingleOpModelWithNNAPI() { + this->SetApplyDelegate([](Interpreter* interpreter) { + interpreter->ModifyGraphWithDelegate(NnApiDelegate(), false); + }); + } +}; + +class FloatAddOpModel : public SingleOpModelWithNNAPI { public: FloatAddOpModel(const TensorData& input1, const TensorData& input2, const TensorData& output, ActivationFunctionType activation_type) { - this->SetApplyDelegate([](Interpreter* interpreter) { - interpreter->ModifyGraphWithDelegate(NnApiDelegate()); - }); input1_ = AddInput(input1); input2_ = AddInput(input2); output_ = AddOutput(output); @@ -76,14 +82,11 @@ TEST(NNAPIDelegate, AddWithRelu) { EXPECT_THAT(m.GetOutput(), ElementsAreArray({0.0, 0.4, 1.0, 1.3})); } -class FloatMulOpModel : public SingleOpModel { +class FloatMulOpModel : public SingleOpModelWithNNAPI { public: FloatMulOpModel(const TensorData& input1, const TensorData& input2, const TensorData& output, ActivationFunctionType activation_type) { - this->SetApplyDelegate([](Interpreter* interpreter) { - interpreter->ModifyGraphWithDelegate(NnApiDelegate()); - }); input1_ = AddInput(input1); input2_ = AddInput(input2); output_ = AddOutput(output); @@ -114,15 +117,11 @@ TEST(NNAPIDelegate, MulWithNoActivation) { ElementsAreArray(ArrayFloatNear({-0.2, 0.04, 0.21, 0.4}))); } -class FloatPoolingOpModel : public SingleOpModel { +class FloatPoolingOpModel : public SingleOpModelWithNNAPI { public: FloatPoolingOpModel(BuiltinOperator type, const TensorData& input, int filter_width, int filter_height, const TensorData& output) { - this->SetApplyDelegate([](Interpreter* interpreter) { - interpreter->ModifyGraphWithDelegate(NnApiDelegate()); - }); - input_ = AddInput(input); output_ = AddOutput(output); @@ -185,7 +184,7 @@ TEST(NNAPIDelegate, L2PoolWithNoActivation) { EXPECT_THAT(m.GetOutput(), ElementsAreArray({3.5, 6.5})); } -class BaseConvolutionOpModel : public SingleOpModel { +class BaseConvolutionOpModel : public SingleOpModelWithNNAPI { public: BaseConvolutionOpModel( const TensorData& input, const TensorData& filter, @@ -193,10 +192,6 @@ class BaseConvolutionOpModel : public SingleOpModel { enum Padding padding = Padding_VALID, enum ActivationFunctionType activation = ActivationFunctionType_NONE, int dilation_width_factor = 1, int dilation_height_factor = 1) { - this->SetApplyDelegate([](Interpreter* interpreter) { - interpreter->ModifyGraphWithDelegate(NnApiDelegate()); - }); - input_ = AddInput(input); filter_ = AddInput(filter); @@ -344,14 +339,10 @@ TEST(NNAPIDelegate, Conv2DWithNoActivation) { })); } -class DepthwiseConvolutionOpModel : public SingleOpModel { +class DepthwiseConvolutionOpModel : public SingleOpModelWithNNAPI { public: DepthwiseConvolutionOpModel(const TensorData& input, const TensorData& filter, const TensorData& output) { - this->SetApplyDelegate([](Interpreter* interpreter) { - interpreter->ModifyGraphWithDelegate(NnApiDelegate()); - }); - input_ = AddInput(input); filter_ = AddInput(filter); @@ -426,15 +417,11 @@ TEST(NNAPIDelegate, DepthwiseConv2DWithNoActivation) { })); } -class FloatFullyConnectedOpModel : public SingleOpModel { +class FloatFullyConnectedOpModel : public SingleOpModelWithNNAPI { public: FloatFullyConnectedOpModel(int units, int batches, const TensorData& input, const TensorData& output = {TensorType_FLOAT32}) : batches_(batches), units_(units) { - this->SetApplyDelegate([](Interpreter* interpreter) { - interpreter->ModifyGraphWithDelegate(NnApiDelegate()); - }); - int total_input_size = 1; for (int i = 0; i < input.shape.size(); ++i) { total_input_size *= input.shape[i]; @@ -515,14 +502,10 @@ TEST(NNAPIDelegate, FullyConnectedSimpleTest) { EXPECT_THAT(m.GetOutput(), ElementsAre(24, 25, 26, 58, 59, 60)); } -class SoftmaxOpModel : public SingleOpModel { +class SoftmaxOpModel : public SingleOpModelWithNNAPI { public: SoftmaxOpModel(int batches, int size, float beta) : batches_(batches), input_size_(size), beta_(beta) { - this->SetApplyDelegate([](Interpreter* interpreter) { - interpreter->ModifyGraphWithDelegate(NnApiDelegate()); - }); - input_ = AddInput(TensorType_FLOAT32); output_ = AddOutput(TensorType_FLOAT32); SetBuiltinOp(BuiltinOperator_SOFTMAX, BuiltinOptions_SoftmaxOptions, @@ -566,14 +549,10 @@ TEST(NNAPIDelegate, SoftmaxSimpleTest) { 1e-6))); } -class ReshapeOpModel : public SingleOpModel { +class ReshapeOpModel : public SingleOpModelWithNNAPI { public: ReshapeOpModel(std::initializer_list input_shape, std::initializer_list new_shape) { - this->SetApplyDelegate([](Interpreter* interpreter) { - interpreter->ModifyGraphWithDelegate(NnApiDelegate()); - }); - input_ = AddInput(TensorType_FLOAT32); new_shape_ = AddInput(TensorType_INT32); output_ = AddOutput(TensorType_FLOAT32); @@ -605,14 +584,10 @@ TEST(NNAPIDelegate, ReshapeSimpleTest) { EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 2, 2})); } -class SqueezeOpModel : public SingleOpModel { +class SqueezeOpModel : public SingleOpModelWithNNAPI { public: SqueezeOpModel(const TensorData& input, const TensorData& output, std::initializer_list axis) { - this->SetApplyDelegate([](Interpreter* interpreter) { - interpreter->ModifyGraphWithDelegate(NnApiDelegate()); - }); - input_ = AddInput(input); output_ = AddOutput(output); SetBuiltinOp( @@ -666,6 +641,988 @@ TEST(NNAPIDelegate, SqueezeWithAxisTest) { 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0})); } +class L2NormOpModel : public SingleOpModelWithNNAPI { + public: + L2NormOpModel(const TensorData& input, const TensorData& output, + ActivationFunctionType activation_type) { + input_ = AddInput(input); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_L2_NORMALIZATION, BuiltinOptions_L2NormOptions, + CreateL2NormOptions(builder_, activation_type).Union()); + BuildInterpreter({GetShape(input_)}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input_; + int new_shape_; + int output_; +}; + +TEST(NNAPIDelegate, L2NormSimpleTest) { + std::initializer_list data = {-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}; + L2NormOpModel m({TensorType_FLOAT32, {1, 1, 1, 6}}, + {TensorType_FLOAT32, {1, 1, 1, 6}}, + ActivationFunctionType_NONE); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 1, 6})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray({-0.55, 0.3, 0.35, 0.6, -0.35, 0.05})); +} + +class TransposeSimpleModel : public SingleOpModelWithNNAPI { + public: + TransposeSimpleModel(std::initializer_list input_shape, + std::initializer_list perm_shape, + std::initializer_list perm) { + input_ = AddInput(TensorType_FLOAT32); + perm_ = AddConstInput(TensorType_INT32, perm, perm_shape); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp(BuiltinOperator_TRANSPOSE, BuiltinOptions_TransposeOptions, + CreateTransposeOptions(builder_).Union()); + BuildInterpreter({input_shape, perm_shape}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input_; + int perm_; + int output_; +}; + +TEST(NNAPIDelegate, TransposeSimpleTest) { + TransposeSimpleModel m({2, 3, 4}, {3}, {2, 0, 1}); + m.SetInput({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, + 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4, 2, 3})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray({0, 4, 8, 12, 16, 20, 1, 5, 9, 13, 17, 21, + 2, 6, 10, 14, 18, 22, 3, 7, 11, 15, 19, 23})); +} + +class FloatSubOpModel : public SingleOpModelWithNNAPI { + public: + FloatSubOpModel(const TensorData& input1, const TensorData& input2, + const TensorData& output, + ActivationFunctionType activation_type) { + input1_ = AddInput(input1); + input2_ = AddInput(input2); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_SUB, BuiltinOptions_SubOptions, + CreateMulOptions(builder_, activation_type).Union()); + BuildInterpreter({GetShape(input1_), GetShape(input2_)}); + } + + int input1() { return input1_; } + int input2() { return input2_; } + + std::vector GetOutput() { return ExtractVector(output_); } + + protected: + int input1_; + int input2_; + int output_; +}; + +TEST(NNAPIDelegate, SubWithNoActivation) { + FloatSubOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-2.0, 0.2, 0.7, 0.8}); + m.PopulateTensor(m.input2(), {0.1, 0.2, 0.3, 0.5}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({-2.1, 0.0, 0.4, 0.3}))); +} + +class FloatDivOpModel : public SingleOpModelWithNNAPI { + public: + FloatDivOpModel(const TensorData& input1, const TensorData& input2, + const TensorData& output, + ActivationFunctionType activation_type) { + input1_ = AddInput(input1); + input2_ = AddInput(input2); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_DIV, BuiltinOptions_DivOptions, + CreateMulOptions(builder_, activation_type).Union()); + BuildInterpreter({GetShape(input1_), GetShape(input2_)}); + } + + int input1() { return input1_; } + int input2() { return input2_; } + + std::vector GetOutput() { return ExtractVector(output_); } + + protected: + int input1_; + int input2_; + int output_; +}; + +TEST(NNAPIDelegate, DivWithNoActivation) { + FloatDivOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-2.0, 0.2, 0.8, 0.8}); + m.PopulateTensor(m.input2(), {0.1, 0.2, 0.4, 0.2}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({-20, 1, 2, 4}))); +} + +class BaseConcatenationOpModel : public SingleOpModelWithNNAPI { + public: + BaseConcatenationOpModel() {} + BaseConcatenationOpModel(const TensorData& input_template, int axis, + int num_inputs) { + std::vector> all_input_shapes; + for (int i = 0; i < num_inputs; ++i) { + all_input_shapes.push_back(input_template.shape); + AddInput(input_template); + } + output_ = AddOutput({input_template.type, /*shape=*/{}, input_template.min, + input_template.max}); + SetBuiltinOp( + BuiltinOperator_CONCATENATION, BuiltinOptions_ConcatenationOptions, + CreateConcatenationOptions(builder_, axis, ActivationFunctionType_NONE) + .Union()); + BuildInterpreter(all_input_shapes); + } + + protected: + int output_; +}; + +class ConcatenationOpModel : public BaseConcatenationOpModel { + public: + using BaseConcatenationOpModel::BaseConcatenationOpModel; + void SetInput(int index, std::initializer_list data) { + PopulateTensor(index, data); + } + std::vector GetOutput() { return ExtractVector(output_); } +}; + +TEST(NNAPIDelegate, ConcatenationThreeDimensionalOneInput) { + ConcatenationOpModel m0({TensorType_FLOAT32, {2, 1, 2}}, /*axis=*/1, + /*num_inputs=*/1); + m0.SetInput(0, {1.0f, 3.0f, 4.0f, 7.0f}); + m0.Invoke(); + EXPECT_THAT(m0.GetOutput(), ElementsAreArray({1, 3, 4, 7})); +} + +TEST(NNAPIDelegate, ConcatenationFourInputs) { + ConcatenationOpModel m0({TensorType_FLOAT32, {2, 1, 2}}, /*axis=*/2, + /*num_inputs=*/4); + m0.SetInput(0, {1.0f, 3.0f, 4.0f, 7.0f}); + m0.SetInput(1, {1.1f, 3.1f, 4.1f, 7.1f}); + m0.SetInput(2, {1.2f, 3.2f, 4.2f, 7.2f}); + m0.SetInput(3, {1.3f, 3.3f, 4.3f, 7.3f}); + m0.Invoke(); + EXPECT_THAT(m0.GetOutput(), + ElementsAreArray({ + 1.0f, 3.0f, 1.1f, 3.1f, 1.2f, 3.2f, 1.3f, 3.3f, // + 4.0f, 7.0f, 4.1f, 7.1f, 4.2f, 7.2f, 4.3f, 7.3f, // + })); +} + +class QuantizedConcatenationOpModel : public BaseConcatenationOpModel { + public: + using BaseConcatenationOpModel::BaseConcatenationOpModel; + QuantizedConcatenationOpModel(const std::vector& input_template, + int axis, int num_inputs, + const TensorData& output_template) { + std::vector> all_input_shapes; + CHECK_EQ(input_template.size(), num_inputs); + for (int i = 0; i < num_inputs; ++i) { + all_input_shapes.push_back(input_template[i].shape); + AddInput(input_template[i]); + } + output_ = AddOutput({output_template.type, /*shape=*/{}, + output_template.min, output_template.max}); + SetBuiltinOp( + BuiltinOperator_CONCATENATION, BuiltinOptions_ConcatenationOptions, + CreateConcatenationOptions(builder_, axis, ActivationFunctionType_NONE) + .Union()); + BuildInterpreter(all_input_shapes); + } + void SetInput(int index, std::initializer_list data) { + QuantizeAndPopulate(index, data); + } + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetDequantizedOutput() { + return Dequantize(ExtractVector(output_), + GetScale(output_), GetZeroPoint(output_)); + } +}; + +TEST(NNAPIDelegate, ConcatenationFourInputsQuantized) { + QuantizedConcatenationOpModel m0({TensorType_UINT8, {2, 1, 2}, -12.7, 12.8}, + /*axis=*/2, + /*num_inputs=*/4); + + m0.SetInput(0, {1.0f, 3.0f, 4.0f, 7.0f}); + m0.SetInput(1, {1.1f, 3.1f, 4.1f, 7.1f}); + m0.SetInput(2, {1.2f, 3.2f, 4.2f, 7.2f}); + m0.SetInput(3, {1.3f, 3.3f, 4.3f, 7.3f}); + m0.Invoke(); + EXPECT_THAT(m0.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({ + 1.0f, 3.0f, 1.1f, 3.1f, 1.2f, 3.2f, 1.3f, 3.3f, // + 4.0f, 7.0f, 4.1f, 7.1f, 4.2f, 7.2f, 4.3f, 7.3f, // + }))); + EXPECT_THAT(m0.GetOutput(), ElementsAreArray({ + 137, 157, 138, 158, 139, 159, 140, 160, // + 167, 197, 168, 198, 169, 199, 170, 200, // + })); +} + +TEST(NNAPIDelegate, ConcatenationFourInputsQuantizedMixedRange) { + QuantizedConcatenationOpModel m0({{TensorType_UINT8, {2, 1, 2}, -10.7, 10.8}, + {TensorType_UINT8, {2, 1, 2}, 0, 12.8}, + {TensorType_UINT8, {2, 1, 2}, -11, 11.8}, + {TensorType_UINT8, {2, 1, 2}, 0, 7.4}}, + /*axis=*/2, /*num_inputs=*/4, + {TensorType_UINT8, {2, 1, 2}, -12.7, 12.8}); + + m0.SetInput(0, {1.0f, 3.0f, 4.0f, 7.0f}); + m0.SetInput(1, {1.1f, 3.1f, 4.1f, 7.1f}); + m0.SetInput(2, {1.2f, 3.2f, 4.2f, 7.2f}); + m0.SetInput(3, {1.3f, 3.3f, 4.3f, 7.3f}); + m0.Invoke(); + EXPECT_THAT(m0.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({ + 1.0f, 3.0f, 1.1f, 3.1f, 1.2f, 3.2f, 1.3f, 3.3f, // + 4.0f, 7.0f, 4.1f, 7.1f, 4.2f, 7.2f, 4.3f, 7.3f, // + }))); + EXPECT_THAT(m0.GetOutput(), ElementsAreArray({ + 137, 157, 138, 158, 139, 159, 140, 160, // + 167, 197, 168, 198, 169, 199, 170, 200, // + })); +} + +class DequantizeOpModel : public SingleOpModelWithNNAPI { + public: + DequantizeOpModel(std::initializer_list shape, float min, float max) { + input_ = AddInput({TensorType_UINT8, shape, min, max}); + output_ = AddOutput({TensorType_FLOAT32, shape}); + SetBuiltinOp(BuiltinOperator_DEQUANTIZE, BuiltinOptions_DequantizeOptions, + CreateDequantizeOptions(builder_).Union()); + + BuildInterpreter({GetShape(input_)}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + private: + int input_; + int output_; +}; + +TEST(NNAPIDelegate, DequantizeFourDimensional) { + DequantizeOpModel m({2, 5}, -63.5, 64); + + m.SetInput({0, 1, 2, 3, 4, 251, 252, 253, 254, 255}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64}))); +} + +class FloorOpModel : public SingleOpModelWithNNAPI { + public: + FloorOpModel(std::initializer_list input_shape, TensorType input_type) { + input_ = AddInput(TensorType_FLOAT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp(BuiltinOperator_FLOOR, BuiltinOptions_NONE, 0); + BuildInterpreter({ + input_shape, + }); + } + + int input() { return input_; } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input_; + int output_; +}; + +TEST(NNAPIDelegate, FloorSingleDim) { + FloorOpModel model({2}, TensorType_FLOAT32); + model.PopulateTensor(model.input(), {8.5, 0.0}); + model.Invoke(); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({8, 0})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2})); +} + +TEST(NNAPIDelegate, FloorMultiDims) { + FloorOpModel model({2, 1, 1, 5}, TensorType_FLOAT32); + model.PopulateTensor(model.input(), { + 0.0001, + 8.0001, + 0.9999, + 9.9999, + 0.5, + -0.0001, + -8.0001, + -0.9999, + -9.9999, + -0.5, + }); + model.Invoke(); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({0, 8, 0, 9, 0, -1, -9, -1, -10, -1})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 1, 1, 5})); +} + +class LocalResponseNormOpModel : public SingleOpModelWithNNAPI { + public: + LocalResponseNormOpModel(std::initializer_list input_shape, int radius, + float bias, float alpha, float beta) { + input_ = AddInput(TensorType_FLOAT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp(BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, + BuiltinOptions_LocalResponseNormalizationOptions, + CreateLocalResponseNormalizationOptions(builder_, radius, bias, + alpha, beta) + .Union()); + BuildInterpreter({input_shape}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + private: + int input_; + int output_; +}; + +TEST(NNAPIDelegate, LocalResponseNormSameAsL2Norm) { + LocalResponseNormOpModel m({1, 1, 1, 6}, /*radius=*/20, /*bias=*/0.0, + /*alpha=*/1.0, /*beta=*/0.5); + m.SetInput({-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}); + m.Invoke(); + // The result is every input divided by 2. + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear({-0.55, 0.3, 0.35, 0.6, -0.35, 0.05}))); +} + +TEST(NNAPIDelegate, LocalResponseNormWithAlpha) { + LocalResponseNormOpModel m({1, 1, 1, 6}, /*radius=*/20, /*bias=*/0.0, + /*alpha=*/4.0, /*beta=*/0.5); + m.SetInput({-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}); + m.Invoke(); + // The result is every input divided by 3. + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear( + {-0.275, 0.15, 0.175, 0.3, -0.175, 0.025}))); +} + +TEST(NNAPIDelegate, LocalResponseNormWithBias) { + LocalResponseNormOpModel m({1, 1, 1, 6}, /*radius=*/20, /*bias=*/9.0, + /*alpha=*/4.0, /*beta=*/0.5); + m.SetInput({-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}); + m.Invoke(); + // The result is every input divided by 5. + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear({-0.22, 0.12, 0.14, 0.24, -0.14, 0.02}))); +} + +TEST(NNAPIDelegate, LocalResponseNormSmallRadius) { + LocalResponseNormOpModel m({1, 1, 1, 6}, /*radius=*/2, /*bias=*/9.0, + /*alpha=*/4.0, /*beta=*/0.5); + m.SetInput({-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}); + m.Invoke(); + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {-0.264926, 0.125109, 0.140112, 0.267261, -0.161788, 0.0244266}))); +} + +class LSHProjectionOpModel : public SingleOpModelWithNNAPI { + public: + LSHProjectionOpModel(LSHProjectionType type, + std::initializer_list hash_shape, + std::initializer_list input_shape, + std::initializer_list weight_shape) { + hash_ = AddInput(TensorType_FLOAT32); + input_ = AddInput(TensorType_INT32); + if (weight_shape.size() > 0) { + weight_ = AddInput(TensorType_FLOAT32); + } + output_ = AddOutput(TensorType_INT32); + + SetBuiltinOp(BuiltinOperator_LSH_PROJECTION, + BuiltinOptions_LSHProjectionOptions, + CreateLSHProjectionOptions(builder_, type).Union()); + if (weight_shape.size() > 0) { + BuildInterpreter({hash_shape, input_shape, weight_shape}); + } else { + BuildInterpreter({hash_shape, input_shape}); + } + + output_size_ = 1; + for (int i : hash_shape) { + output_size_ *= i; + if (type == LSHProjectionType_SPARSE) { + break; + } + } + } + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + void SetHash(std::initializer_list data) { + PopulateTensor(hash_, data); + } + + void SetWeight(std::initializer_list f) { PopulateTensor(weight_, f); } + + std::vector GetOutput() { return ExtractVector(output_); } + + private: + int input_; + int hash_; + int weight_; + int output_; + + int output_size_; +}; + +TEST(NNAPIDelegate, LSHProjectionDense1DInputs) { + LSHProjectionOpModel m(LSHProjectionType_DENSE, {3, 2}, {5}, {5}); + + m.SetInput({12345, 54321, 67890, 9876, -12345678}); + m.SetHash({0.123, 0.456, -0.321, 1.234, 5.678, -4.321}); + m.SetWeight({1.0, 1.0, 1.0, 1.0, 1.0}); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAre(0, 0, 0, 1, 0, 0)); +} + +TEST(NNAPIDelegate, LSHProjectionSparse1DInputs) { + LSHProjectionOpModel m(LSHProjectionType_SPARSE, {3, 2}, {5}, {}); + + m.SetInput({12345, 54321, 67890, 9876, -12345678}); + m.SetHash({0.123, 0.456, -0.321, 1.234, 5.678, -4.321}); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAre(0 + 0, 4 + 1, 8 + 0)); +} + +TEST(NNAPIDelegate, LSHProjectionSparse3DInputs) { + LSHProjectionOpModel m(LSHProjectionType_SPARSE, {3, 2}, {5, 2, 2}, {5}); + + m.SetInput({1234, 2345, 3456, 1234, 4567, 5678, 6789, 4567, 7891, 8912, + 9123, 7890, -987, -876, -765, -987, -543, -432, -321, -543}); + m.SetHash({0.123, 0.456, -0.321, 1.234, 5.678, -4.321}); + m.SetWeight({0.12, 0.34, 0.56, 0.67, 0.78}); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAre(0 + 2, 4 + 1, 8 + 1)); +} + +class BaseActivationsOpModel : public SingleOpModelWithNNAPI { + public: + // Most activations don't take any options, so this constructor works for + // them. + BaseActivationsOpModel(BuiltinOperator type, TensorData input) { + input_ = AddInput(input); + if (input.type == TensorType_UINT8) { + output_ = AddOutput({input.type, {}, 0, 0, 1. / 256}); + } else { + output_ = AddOutput({input.type, {}}); + } + SetBuiltinOp(type, BuiltinOptions_NONE, 0); + BuildInterpreter({GetShape(input_)}); + } + + BaseActivationsOpModel(BuiltinOperator type, const TensorData& input, + const TensorData& output) { + input_ = AddInput(input); + output_ = AddOutput(output); + SetBuiltinOp(type, BuiltinOptions_NONE, 0); + BuildInterpreter({GetShape(input_)}); + } + + protected: + int input_; + int output_; +}; + +class FloatActivationsOpModel : public BaseActivationsOpModel { + public: + using BaseActivationsOpModel::BaseActivationsOpModel; + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + std::vector GetOutput() { return ExtractVector(output_); } +}; + +const float kQuantizedTolerance = 2 * (1. / 256); + +class QuantizedActivationsOpModel : public BaseActivationsOpModel { + public: + using BaseActivationsOpModel::BaseActivationsOpModel; + + template + void SetInput(std::initializer_list data) { + QuantizeAndPopulate(input_, data); + } + template + + std::vector GetOutput() { + return ExtractVector(output_); + } + template + std::vector GetDequantizedOutput() { + return Dequantize(ExtractVector(output_), GetScale(output_), + GetZeroPoint(output_)); + } +}; + +TEST(NNAPIDelegate, Relu) { + FloatActivationsOpModel m(BuiltinOperator_RELU, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}); + m.SetInput({ + 0, -6, 2, 4, // + 3, -2, 10, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 0, 0, 2, 4, // + 3, 0, 10, 1, // + })); +} + +TEST(NNAPIDelegate, Relu1) { + FloatActivationsOpModel m(BuiltinOperator_RELU_N1_TO_1, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}); + m.SetInput({ + 0.0, -0.6, 0.2, -0.4, // + 0.3, -2.0, 1.1, -0.1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 0.0, -0.6, 0.2, -0.4, // + 0.3, -1.0, 1.0, -0.1, // + })); +} + +TEST(NNAPIDelegate, Relu6) { + FloatActivationsOpModel m(BuiltinOperator_RELU6, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}); + m.SetInput({ + 0, -6, 2, 4, // + 3, -2, 10, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 0, 0, 2, 4, // + 3, 0, 6, 1, // + })); +} + +TEST(NNAPIDelegate, Tanh) { + FloatActivationsOpModel m(BuiltinOperator_TANH, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}); + m.SetInput({ + 0, -6, 2, 4, // + 3, -2, 10, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 0, -0.9999877, 0.9640275, 0.999329, // + 0.99505475, -0.9640275, 1, 0.7615941, // + }))); +} + +TEST(NNAPIDelegate, LogisticFloat) { + FloatActivationsOpModel m(BuiltinOperator_LOGISTIC, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}); + m.SetInput({ + 0, -6, 2, 4, // + 3, -2, 10, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 0.5, 0.002473, 0.880797, 0.982014, // + 0.952574, 0.119203, 0.999955, 0.731059, // + }))); +} + +TEST(NNAPIDelegate, LogisticQuantized) { + QuantizedActivationsOpModel m( + BuiltinOperator_LOGISTIC, + /*input=*/{TensorType_UINT8, {1, 2, 4, 1}, -10, 10}); + m.SetInput({ + 0, -6, 2, 4, // + 3, -2, 10, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + 0.5, 0.002473, 0.880797, 0.982014, // + 0.952574, 0.119203, 0.999955, 0.731059, // + }, + kQuantizedTolerance))); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray({128, 1, 227, 251, 244, 32, 255, 188})); +} + +#if 0 +class ResizeBilinearOpModel : public SingleOpModelWithNNAPI { + public: + ResizeBilinearOpModel(const TensorData& input, + std::initializer_list size_data = {}) { + bool const_size = size_data.size() != 0; + input_ = AddInput(input); + if (const_size) { + size_ = AddConstInput(TensorType_INT32, size_data, {2}); + } else { + size_ = AddInput({TensorType_INT32, {2}}); + } + output_ = AddOutput(input.type); + SetBuiltinOp(BuiltinOperator_RESIZE_BILINEAR, + BuiltinOptions_ResizeBilinearOptions, + CreateResizeBilinearOptions(builder_).Union()); + if (const_size) { + BuildInterpreter({GetShape(input_)}); + } else { + BuildInterpreter({GetShape(input_), GetShape(size_)}); + } + } + + template + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + void SetSize(std::initializer_list data) { PopulateTensor(size_, data); } + + template + std::vector GetOutput() { + return ExtractVector(output_); + } + + private: + int input_; + int size_; + int output_; +}; + +TEST(NNAPIDelegate, ResizeBilinearHorizontal) { + ResizeBilinearOpModel m({TensorType_FLOAT32, {1, 1, 2, 1}}); + m.SetInput({3, 6}); + m.SetSize({1, 3}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 5, 6}))); + + ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 1, 2, 1}}, {1, 3}); + const_m.SetInput({3, 6}); + const_m.Invoke(); + EXPECT_THAT(const_m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 5, 6}))); +} + +TEST(NNAPIDelegate, ResizeBilinearVertical) { + ResizeBilinearOpModel m({TensorType_FLOAT32, {1, 2, 1, 1}}); + m.SetInput({3, 9}); + m.SetSize({3, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 7, 9}))); + + ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 2, 1, 1}}, {3, 1}); + const_m.SetInput({3, 9}); + const_m.Invoke(); + EXPECT_THAT(const_m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 7, 9}))); +} + +TEST(NNAPIDelegate, ResizeBilinearTwoDimensional) { + ResizeBilinearOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}); + m.SetInput({ + 3, 6, // + 9, 12 // + }); + m.SetSize({3, 3}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 5, 6, // + 7, 9, 10, // + 9, 11, 12, // + }))); + + ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 2, 2, 1}}, {3, 3}); + const_m.SetInput({ + 3, 6, // + 9, 12 // + }); + const_m.Invoke(); + EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 5, 6, // + 7, 9, 10, // + 9, 11, 12, // + }))); +} +#endif + +template +class PadOpModel : public SingleOpModelWithNNAPI { + public: + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + void SetQuantizedInput(std::initializer_list data) { + QuantizeAndPopulate(input_, data); + } + + void SetQuantizedPadValue(float data) { + QuantizeAndPopulate(constant_values_, {data}); + } + + void SetPaddings(std::initializer_list paddings) { + PopulateTensor(paddings_, paddings); + } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + std::vector GetDequantizedOutput() { + return Dequantize(ExtractVector(output_), + GetScale(output_), GetZeroPoint(output_)); + } + + protected: + int input_; + int output_; + int paddings_; + int constant_values_; +}; + +class PadOpConstModel : public PadOpModel { + public: + PadOpConstModel(const TensorData& input, + std::initializer_list paddings_shape, + std::initializer_list paddings, + const TensorData& output) { + input_ = AddInput(input); + paddings_ = AddConstInput(TensorType_INT32, paddings, paddings_shape); + output_ = AddOutput(output); + + SetBuiltinOp(BuiltinOperator_PAD, BuiltinOptions_PadOptions, + CreatePadOptions(builder_).Union()); + BuildInterpreter({input.shape}); + } +}; + +TEST(NNAPIDelegate, PadAdvancedConstTest) { + PadOpConstModel m({TensorType_FLOAT32, {1, 2, 3, 1}}, {4, 2}, + {0, 0, 0, 2, 1, 3, 0, 0}, {TensorType_FLOAT32}); + m.SetInput({1, 2, 3, 4, 5, 6}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray({0, 1, 2, 3, 0, 0, 0, 0, 4, 5, 6, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0})); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 7, 1})); +} + +class SpaceToBatchNDOpModel : public SingleOpModelWithNNAPI { + public: + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + void SetBlockShape(std::initializer_list data) { + PopulateTensor(block_shape_, data); + } + + void SetPaddings(std::initializer_list data) { + PopulateTensor(paddings_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + protected: + int input_; + int block_shape_; + int paddings_; + int output_; +}; + +class SpaceToBatchNDOpConstModel : public SpaceToBatchNDOpModel { + public: + SpaceToBatchNDOpConstModel(std::initializer_list input_shape, + std::initializer_list block_shape, + std::initializer_list paddings) { + input_ = AddInput(TensorType_FLOAT32); + block_shape_ = AddConstInput(TensorType_INT32, block_shape, {2}); + paddings_ = AddConstInput(TensorType_INT32, paddings, {2, 2}); + output_ = AddOutput(TensorType_FLOAT32); + + SetBuiltinOp(BuiltinOperator_SPACE_TO_BATCH_ND, + BuiltinOptions_SpaceToBatchNDOptions, + CreateSpaceToBatchNDOptions(builder_).Union()); + BuildInterpreter({input_shape}); + } +}; + +TEST(NNAPIDelegate, SpaceToBatchNDSimpleConstTest) { + SpaceToBatchNDOpConstModel m({1, 4, 4, 1}, {2, 2}, {0, 0, 0, 0}); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4, 2, 2, 1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 3, 9, 11, 2, 4, 10, 12, 5, 7, + 13, 15, 6, 8, 14, 16})); +} + +TEST(NNAPIDelegate, SpaceToBatchNDMultipleInputBatchesConstTest) { + SpaceToBatchNDOpConstModel m({2, 2, 4, 1}, {2, 2}, {0, 0, 0, 0}); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({8, 1, 2, 1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 3, 9, 11, 2, 4, 10, 12, 5, 7, + 13, 15, 6, 8, 14, 16})); +} + +TEST(NNAPIDelegate, SpaceToBatchNDSimplePaddingConstTest) { + SpaceToBatchNDOpConstModel m({1, 5, 2, 1}, {3, 2}, {1, 0, 2, 0}); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 2, 1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 0, 0, 0, 5, 0, 0, 0, 6, 0, 1, 0, 7, + 0, 2, 0, 8, 0, 3, 0, 9, 0, 4, 0, 10, + })); +} + +TEST(NNAPIDelegate, SpaceToBatchNDComplexPaddingConstTest) { + SpaceToBatchNDOpConstModel m({1, 4, 2, 1}, {3, 2}, {1, 1, 2, 4}); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 4, 1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, + 0, 1, 0, 0, 0, 7, 0, 0, 0, 2, 0, 0, 0, 8, 0, 0, + 0, 3, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, + })); +} + +template +class StridedSliceOpModel : public SingleOpModelWithNNAPI { + public: + StridedSliceOpModel(std::initializer_list input_shape, + std::initializer_list begin_shape, + std::initializer_list end_shape, + std::initializer_list strides_shape, int begin_mask, + int end_mask, int ellipsis_mask, int new_axis_mask, + int shrink_axis_mask) { + input_ = AddInput(tensor_input_type); + begin_ = AddInput(TensorType_INT32); + end_ = AddInput(TensorType_INT32); + strides_ = AddInput(TensorType_INT32); + output_ = AddOutput(tensor_input_type); + SetBuiltinOp( + BuiltinOperator_STRIDED_SLICE, BuiltinOptions_StridedSliceOptions, + CreateStridedSliceOptions(builder_, begin_mask, end_mask, ellipsis_mask, + new_axis_mask, shrink_axis_mask) + .Union()); + BuildInterpreter({input_shape, begin_shape, end_shape, strides_shape}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + void SetBegin(std::initializer_list data) { + PopulateTensor(begin_, data); + } + void SetEnd(std::initializer_list data) { + PopulateTensor(end_, data); + } + void SetStrides(std::initializer_list data) { + PopulateTensor(strides_, data); + } + + std::vector GetOutput() { + return ExtractVector(output_); + } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input_; + int begin_; + int end_; + int strides_; + int output_; +}; + +TEST(NNAPIDelegate, StridedSliceIn2D) { + StridedSliceOpModel<> m({2, 3}, {2}, {2}, {2}, 0, 0, 0, 0, 0); + m.SetInput({1, 2, 3, 4, 5, 6}); + m.SetBegin({1, 0}); + m.SetEnd({2, 2}); + m.SetStrides({1, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({4, 5})); +} + +TEST(NNAPIDelegate, StridedSliceIn2D_ShrinkAxis_NegativeSlice) { + // This is equivalent to tf.range(4)[:, tf.newaxis][-2, -1]. + StridedSliceOpModel<> m({4, 1}, {2}, {2}, {2}, 0, 0, 0, 0, 3); + m.SetInput({0, 1, 2, 3}); + m.SetBegin({-2, -1}); + m.SetEnd({-1, 0}); + m.SetStrides({1, 1}); + + m.Invoke(); + EXPECT_TRUE(m.GetOutputShape().empty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({2})); +} + +TEST(NNAPIDelegate, StridedSliceIn2D_ShrinkAxisMask) { + StridedSliceOpModel<> m({2, 3}, {2}, {2}, {2}, 0, 0, 0, 0, 3); + m.SetInput({1, 2, 3, 4, 5, 6}); + m.SetBegin({0, 0}); + m.SetEnd({1, 1}); + m.SetStrides({1, 1}); + m.Invoke(); + EXPECT_TRUE(m.GetOutputShape().empty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1})); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/download_dependencies.sh b/tensorflow/contrib/lite/download_dependencies.sh index 840015a7fad173dbd2ea353786871dd4e89abb98..8c7df474d55a85d7a6659b436e33ebf7632ab960 100755 --- a/tensorflow/contrib/lite/download_dependencies.sh +++ b/tensorflow/contrib/lite/download_dependencies.sh @@ -35,7 +35,7 @@ GOOGLETEST_URL="https://github.com/google/googletest/archive/release-1.8.0.tar.g ABSL_URL="$(grep -o 'https://github.com/abseil/abseil-cpp/.*tar.gz' "${BZL_FILE_PATH}" | head -n1)" NEON_2_SSE_URL="https://github.com/intel/ARM_NEON_2_x86_SSE/archive/master.zip" FARMHASH_URL="https://mirror.bazel.build/github.com/google/farmhash/archive/816a4ae622e964763ca0862d9dbd19324a1eaf45.tar.gz" -FLATBUFFERS_URL="https://github.com/google/flatbuffers/archive/master.zip" +FLATBUFFERS_URL="https://github.com/google/flatbuffers/archive/v1.8.0.zip" FFT2D_URL="https://mirror.bazel.build/www.kurims.kyoto-u.ac.jp/~ooura/fft.tgz" # TODO(petewarden): Some new code in Eigen triggers a clang bug with iOS arm64, diff --git a/tensorflow/contrib/lite/examples/android/app/README.md b/tensorflow/contrib/lite/examples/android/app/README.md new file mode 100644 index 0000000000000000000000000000000000000000..cbdeeac8790d93210a6c637953605b4ca270d3f6 --- /dev/null +++ b/tensorflow/contrib/lite/examples/android/app/README.md @@ -0,0 +1,19 @@ +# TF Lite Android App Example + +## Building from Source with Bazel + +1. Install [Bazel](https://docs.bazel.build/versions/master/install.html), the Android NDK and SDK. The recommended versions are specified on this [webpage](https://www.tensorflow.org/mobile/tflite/demo_android#build_tensorflow_lite_and_the_demo_app_from_source). + +2. Build this demo app with Bazel. The demo needs C++11. We configure the fat_apk_cpu flag to package support for 4 hardware variants. You may replace it with --config=android_arm64 on a 64-bit device and --config=android_arm for 32-bit device: + + ```shell + bazel build -c opt --cxxopt='--std=c++11' --fat_apk_cpu=x86,x86_64,arm64-v8a,armeabi-v7a \ + //tensorflow/contrib/lite/examples/android:tflite_demo + ``` + +3. Install the demo on a + [debug-enabled device](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android#install): + + ```shell + adb install bazel-bin/tensorflow/contrib/lite/examples/android/tflite_demo.apk + ``` diff --git a/tensorflow/contrib/lite/examples/android/app/build.gradle b/tensorflow/contrib/lite/examples/android/app/build.gradle index 1ffb9dd377730bb3dc872cbf1548fa29ffaa0949..eb7fd705e18f53eb026600207faefa3d2bb072af 100644 --- a/tensorflow/contrib/lite/examples/android/app/build.gradle +++ b/tensorflow/contrib/lite/examples/android/app/build.gradle @@ -51,7 +51,7 @@ apply from: "download-models.gradle" dependencies { compile fileTree(dir: 'libs', include: ['*.jar']) - androidTestCompile('com.androidx.test.espresso:espresso-core:2.2.2', { + androidTestCompile('androidx.test.espresso:espresso-core:3.1.0-alpha3', { exclude group: 'com.android.support', module: 'support-annotations' }) compile 'org.tensorflow:tensorflow-lite:0.0.0-nightly' diff --git a/tensorflow/contrib/lite/examples/android/app/src/main/assets/pets_labels_list.txt b/tensorflow/contrib/lite/examples/android/app/src/main/assets/pets_labels_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..d581f733e48ff8c2ba88162ee56b5e9d12aec7de --- /dev/null +++ b/tensorflow/contrib/lite/examples/android/app/src/main/assets/pets_labels_list.txt @@ -0,0 +1,38 @@ +??? +Abyssinian +american_bulldog +american_pit_bull_terrier +basset_hound +beagle +Bengal +Birman +Bombay +boxer +British_Shorthair +chihuahua +Egyptian_Mau +english_cocker_spaniel +english_setter +german_shorthaired +great_pyrenees +havanese +japanese_chin +keeshond +leonberger +Maine_Coon +miniature_pinscher +newfoundland +Persian +pomeranian +pug +Ragdoll +Russian_Blue +saint_bernard +samoyed +scottish_terrier +shiba_inu +Siamese +Sphynx +staffordshire_bull_terrier +wheaten_terrier +yorkshire_terrier diff --git a/tensorflow/contrib/lite/examples/label_image/BUILD b/tensorflow/contrib/lite/examples/label_image/BUILD index c61445114ecc6dfbe4f2b6ab666b28a8aa746be3..fc55a78019b4a12b24231034a7e4b912869389f2 100644 --- a/tensorflow/contrib/lite/examples/label_image/BUILD +++ b/tensorflow/contrib/lite/examples/label_image/BUILD @@ -63,6 +63,7 @@ cc_test( data = [ "testdata/grace_hopper.bmp", ], + tags = ["no_oss"], deps = [ ":bitmap_helpers", "@com_google_googletest//:gtest", diff --git a/tensorflow/contrib/lite/experimental/c/BUILD b/tensorflow/contrib/lite/experimental/c/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..50f8da66d06abaf0637866e85c04e80fee042071 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/BUILD @@ -0,0 +1,59 @@ +package(default_visibility = ["//visibility:private"]) + +licenses(["notice"]) # Apache 2.0 + +load( + "//tensorflow/contrib/lite:build_def.bzl", + "tflite_cc_shared_object", + "tflite_copts", + "tflite_jni_binary", +) + +tflite_cc_shared_object( + name = "libtensorflowlite_c.so", + linkopts = select({ + "//tensorflow:darwin": [ + "-Wl,-exported_symbols_list", # This line must be directly followed by the exported_symbols.lds file + "$(location //tensorflow/contrib/lite/experimental/c:exported_symbols.lds)", + "-Wl,-install_name,@rpath/libtensorflowlite_c.so", + ], + "//tensorflow:windows": [], + "//conditions:default": [ + "-z defs", + "-Wl,--version-script", # This line must be directly followed by the version_script.lds file + "$(location //tensorflow/contrib/lite/experimental/c:version_script.lds)", + ], + }), + deps = [ + ":c_api", + ":exported_symbols.lds", + ":version_script.lds", + ], +) + +cc_library( + name = "c_api", + srcs = ["c_api.cc"], + hdrs = ["c_api.h"], + copts = tflite_copts(), + deps = [ + "//tensorflow/contrib/lite:context", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:schema_fbs_version", + "//tensorflow/contrib/lite/kernels:builtin_ops", + ], +) + +cc_test( + name = "c_api_test", + size = "small", + srcs = ["c_api_test.cc"], + data = ["//tensorflow/contrib/lite:testdata/add.bin"], + deps = [ + ":c_api", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:kernel_api", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", + ], +) diff --git a/tensorflow/contrib/lite/experimental/c/c_api.cc b/tensorflow/contrib/lite/experimental/c/c_api.cc new file mode 100644 index 0000000000000000000000000000000000000000..9d29e8b3e055e86a9e68285d81de742e36452215 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/c_api.cc @@ -0,0 +1,122 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/experimental/c/c_api.h" + +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/model.h" + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +struct _TFL_Interpreter { + std::unique_ptr impl; +}; + +// LINT.IfChange + +TFL_Interpreter* TFL_NewInterpreter(const void* model_data, + int32_t model_size) { + auto model = tflite::FlatBufferModel::BuildFromBuffer( + static_cast(model_data), static_cast(model_size)); + if (!model) { + return nullptr; + } + + tflite::ops::builtin::BuiltinOpResolver resolver; + tflite::InterpreterBuilder builder(*model, resolver); + std::unique_ptr interpreter_impl; + if (builder(&interpreter_impl) != kTfLiteOk) { + return nullptr; + } + + return new TFL_Interpreter{std::move(interpreter_impl)}; +} + +void TFL_DeleteInterpreter(TFL_Interpreter* interpreter) { delete interpreter; } + +int32_t TFL_InterpreterGetInputTensorCount(const TFL_Interpreter* interpreter) { + return static_cast(interpreter->impl->inputs().size()); +} + +TFL_Tensor* TFL_InterpreterGetInputTensor(const TFL_Interpreter* interpreter, + int32_t input_index) { + return interpreter->impl->tensor(interpreter->impl->inputs()[input_index]); +} + +TFL_Status TFL_InterpreterResizeInputTensor(TFL_Interpreter* interpreter, + int32_t input_index, + const int* input_dims, + int32_t input_dims_size) { + std::vector dims{input_dims, input_dims + input_dims_size}; + return interpreter->impl->ResizeInputTensor( + interpreter->impl->inputs()[input_index], dims); +} + +TFL_Status TFL_InterpreterAllocateTensors(TFL_Interpreter* interpreter) { + return interpreter->impl->AllocateTensors(); +} + +TFL_Status TFL_InterpreterInvoke(TFL_Interpreter* interpreter) { + return interpreter->impl->Invoke(); +} + +int32_t TFL_InterpreterGetOutputTensorCount( + const TFL_Interpreter* interpreter) { + return static_cast(interpreter->impl->outputs().size()); +} + +const TFL_Tensor* TFL_InterpreterGetOutputTensor( + const TFL_Interpreter* interpreter, int32_t output_index) { + return interpreter->impl->tensor(interpreter->impl->outputs()[output_index]); +} + +TFL_Type TFL_TensorType(const TFL_Tensor* tensor) { return tensor->type; } + +int32_t TFL_TensorNumDims(const TFL_Tensor* tensor) { + return tensor->dims->size; +} + +int32_t TFL_TensorDim(const TFL_Tensor* tensor, int32_t dim_index) { + return tensor->dims->data[dim_index]; +} + +size_t TFL_TensorByteSize(const TFL_Tensor* tensor) { return tensor->bytes; } + +TFL_Status TFL_TensorCopyFromBuffer(TFL_Tensor* tensor, const void* input_data, + int32_t input_data_size) { + if (tensor->bytes != static_cast(input_data_size)) { + return kTfLiteError; + } + memcpy(tensor->data.raw, input_data, input_data_size); + return kTfLiteOk; +} + +TFL_Status TFL_TensorCopyToBuffer(const TFL_Tensor* tensor, void* output_data, + int32_t output_data_size) { + if (tensor->bytes != static_cast(output_data_size)) { + return kTfLiteError; + } + memcpy(output_data, tensor->data.raw, output_data_size); + return kTfLiteOk; +} + +// LINT.ThenChange(//tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts/Interpreter.cs) + +#ifdef __cplusplus +} // extern "C" +#endif // __cplusplus diff --git a/tensorflow/contrib/lite/experimental/c/c_api.h b/tensorflow/contrib/lite/experimental/c/c_api.h new file mode 100644 index 0000000000000000000000000000000000000000..070f1add13c9904e1a2b3736001ada0e274fdc55 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/c_api.h @@ -0,0 +1,149 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_C_C_API_H_ +#define TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_C_C_API_H_ + +#include + +// Eventually the various C APIs defined in context.h will be migrated into +// the appropriate /c/c_api*.h header. For now, we pull in existing definitions +// for convenience. +#include "tensorflow/contrib/lite/context.h" + +// -------------------------------------------------------------------------- +// Experimental C API for TensorFlowLite. +// +// The API leans towards simplicity and uniformity instead of convenience, as +// most usage will be by language-specific wrappers. +// +// Conventions: +// * We use the prefix TFL_ for everything in the API. + +#ifdef SWIG +#define TFL_CAPI_EXPORT +#else +#if defined(_WIN32) +#ifdef TF_COMPILE_LIBRARY +#define TFL_CAPI_EXPORT __declspec(dllexport) +#else +#define TFL_CAPI_EXPORT __declspec(dllimport) +#endif // TF_COMPILE_LIBRARY +#else +#define TFL_CAPI_EXPORT __attribute__((visibility("default"))) +#endif // _WIN32 +#endif // SWIG + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +typedef TfLiteTensor TFL_Tensor; +typedef TfLiteStatus TFL_Status; +typedef TfLiteType TFL_Type; + +// -------------------------------------------------------------------------- +// TFL_Interpreter provides inference from a provided model. +typedef struct _TFL_Interpreter TFL_Interpreter; + +// Returns an interpreter for the provided model, or null on failure. +// +// NOTE: The client *must* explicitly allocate tensors before attempting to +// access input tensor data or invoke the interpreter. +TFL_CAPI_EXPORT extern TFL_Interpreter* TFL_NewInterpreter( + const void* model_data, int32_t model_size); + +// Destroys the interpreter. +TFL_CAPI_EXPORT extern void TFL_DeleteInterpreter(TFL_Interpreter* interpreter); + +// Returns the number of input tensors associated with the model. +TFL_CAPI_EXPORT extern int TFL_InterpreterGetInputTensorCount( + const TFL_Interpreter* interpreter); + +// Returns the tensor associated with the input index. +// REQUIRES: 0 <= input_index < TFL_InterpreterGetInputTensorCount(tensor) +TFL_CAPI_EXPORT extern TFL_Tensor* TFL_InterpreterGetInputTensor( + const TFL_Interpreter* interpreter, int32_t input_index); + +// Attempts to resize the specified input tensor. +// NOTE: After a resize, the client *must* explicitly allocate tensors before +// attempting to access the resized tensor data or invoke the interpreter. +// REQUIRES: 0 <= input_index < TFL_InterpreterGetInputTensorCount(tensor) +TFL_CAPI_EXPORT extern TFL_Status TFL_InterpreterResizeInputTensor( + TFL_Interpreter* interpreter, int32_t input_index, const int* input_dims, + int32_t input_dims_size); + +// Updates allocations for all tensors, resizing dependent tensors using the +// specified input tensor dimensionality. +// +// This is a relatively expensive operation, and need only be called after +// creating the graph and/or resizing any inputs. +TFL_CAPI_EXPORT extern TFL_Status TFL_InterpreterAllocateTensors( + TFL_Interpreter* interpreter); + +// Runs inference for the loaded graph. +// +// NOTE: It is possible that the interpreter is not in a ready state to +// evaluate (e.g., if a ResizeInputTensor() has been performed without a call to +// AllocateTensors()). +TFL_CAPI_EXPORT extern TFL_Status TFL_InterpreterInvoke( + TFL_Interpreter* interpreter); + +// Returns the number of output tensors associated with the model. +TFL_CAPI_EXPORT extern int32_t TFL_InterpreterGetOutputTensorCount( + const TFL_Interpreter* interpreter); + +// Returns the tensor associated with the output index. +// REQUIRES: 0 <= input_index < TFL_InterpreterGetOutputTensorCount(tensor) +TFL_CAPI_EXPORT extern const TFL_Tensor* TFL_InterpreterGetOutputTensor( + const TFL_Interpreter* interpreter, int32_t output_index); + +// -------------------------------------------------------------------------- +// TFL_Tensor wraps data associated with a graph tensor. +// +// Note that, while the TFL_Tensor struct is not currently opaque, and its +// fields can be accessed directly, these methods are still convenient for +// language bindings. In the future the tensor struct will likely be made opaque +// in the public API. + +// Returns the type of a tensor element. +TFL_CAPI_EXPORT extern TFL_Type TFL_TensorType(const TFL_Tensor* tensor); + +// Returns the number of dimensions that the tensor has. +TFL_CAPI_EXPORT extern int32_t TFL_TensorNumDims(const TFL_Tensor* tensor); + +// Returns the length of the tensor in the "dim_index" dimension. +// REQUIRES: 0 <= dim_index < TFLiteTensorNumDims(tensor) +TFL_CAPI_EXPORT extern int32_t TFL_TensorDim(const TFL_Tensor* tensor, + int32_t dim_index); + +// Returns the size of the underlying data in bytes. +TFL_CAPI_EXPORT extern size_t TFL_TensorByteSize(const TFL_Tensor* tensor); + +// Copies from the provided input buffer into the tensor's buffer. +// REQUIRES: input_data_size == TFL_TensorByteSize(tensor) +TFL_CAPI_EXPORT extern TFL_Status TFL_TensorCopyFromBuffer( + TFL_Tensor* tensor, const void* input_data, int32_t input_data_size); + +// Copies to the provided output buffer from the tensor's buffer. +// REQUIRES: output_data_size == TFL_TensorByteSize(tensor) +TFL_CAPI_EXPORT extern TFL_Status TFL_TensorCopyToBuffer( + const TFL_Tensor* output_tensor, void* output_data, + int32_t output_data_size); + +#ifdef __cplusplus +} // extern "C" +#endif // __cplusplus + +#endif // TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_C_C_API_H_ diff --git a/tensorflow/contrib/lite/experimental/c/c_api_test.cc b/tensorflow/contrib/lite/experimental/c/c_api_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..bc925e00a6096c5e8abcc0fa68b335c4db4401c3 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/c_api_test.cc @@ -0,0 +1,84 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include + +#include "tensorflow/contrib/lite/experimental/c/c_api.h" + +#include +#include "tensorflow/contrib/lite/allocation.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/testing/util.h" + +namespace { + +TEST(CApiSimple, Smoke) { + tflite::FileCopyAllocation model_file( + "tensorflow/contrib/lite/testdata/add.bin", + tflite::DefaultErrorReporter()); + + TFL_Interpreter* interpreter = + TFL_NewInterpreter(model_file.base(), model_file.bytes()); + ASSERT_NE(interpreter, nullptr); + ASSERT_EQ(TFL_InterpreterAllocateTensors(interpreter), kTfLiteOk); + + ASSERT_EQ(TFL_InterpreterGetInputTensorCount(interpreter), 1); + ASSERT_EQ(TFL_InterpreterGetOutputTensorCount(interpreter), 1); + + std::array input_dims = {2}; + ASSERT_EQ(TFL_InterpreterResizeInputTensor(interpreter, 0, input_dims.data(), + input_dims.size()), + kTfLiteOk); + ASSERT_EQ(TFL_InterpreterAllocateTensors(interpreter), kTfLiteOk); + + TFL_Tensor* input_tensor = TFL_InterpreterGetInputTensor(interpreter, 0); + ASSERT_NE(input_tensor, nullptr); + EXPECT_EQ(TFL_TensorType(input_tensor), kTfLiteFloat32); + EXPECT_EQ(TFL_TensorNumDims(input_tensor), 1); + EXPECT_EQ(TFL_TensorDim(input_tensor, 0), 2); + EXPECT_EQ(TFL_TensorByteSize(input_tensor), sizeof(float) * 2); + + std::array input = {1.f, 3.f}; + ASSERT_EQ(TFL_TensorCopyFromBuffer(input_tensor, input.data(), + input.size() * sizeof(float)), + kTfLiteOk); + + ASSERT_EQ(TFL_InterpreterInvoke(interpreter), kTfLiteOk); + + const TFL_Tensor* output_tensor = + TFL_InterpreterGetOutputTensor(interpreter, 0); + ASSERT_NE(output_tensor, nullptr); + EXPECT_EQ(TFL_TensorType(output_tensor), kTfLiteFloat32); + EXPECT_EQ(TFL_TensorNumDims(output_tensor), 1); + EXPECT_EQ(TFL_TensorDim(output_tensor, 0), 2); + EXPECT_EQ(TFL_TensorByteSize(output_tensor), sizeof(float) * 2); + + std::array output; + ASSERT_EQ(TFL_TensorCopyToBuffer(output_tensor, output.data(), + output.size() * sizeof(float)), + kTfLiteOk); + EXPECT_EQ(output[0], 3.f); + EXPECT_EQ(output[1], 9.f); + + TFL_DeleteInterpreter(interpreter); +} + +} // namespace + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/experimental/c/exported_symbols.lds b/tensorflow/contrib/lite/experimental/c/exported_symbols.lds new file mode 100644 index 0000000000000000000000000000000000000000..a3ddc6bc8d370b1715fb1ebf2a66122296330249 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/exported_symbols.lds @@ -0,0 +1 @@ +_TFL_* diff --git a/tensorflow/contrib/lite/experimental/c/version_script.lds b/tensorflow/contrib/lite/experimental/c/version_script.lds new file mode 100644 index 0000000000000000000000000000000000000000..c0c8a2bca19afed186e6f8c72a58989a79c7b251 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/version_script.lds @@ -0,0 +1,9 @@ +VERS_1.0 { + # Export symbols in c_api.h. + global: + *TFL_*; + + # Hide everything else. + local: + *; +}; diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/.gitignore b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/.gitignore new file mode 100644 index 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a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples/HelloTFLite/Scripts/HelloTFLite.cs b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples/HelloTFLite/Scripts/HelloTFLite.cs new file mode 100644 index 0000000000000000000000000000000000000000..abca8144998367eadaeb0b75d85bb0f6cf3a2057 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples/HelloTFLite/Scripts/HelloTFLite.cs @@ -0,0 +1,70 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +using System; +using System.Collections; +using System.Collections.Generic; +using System.Linq; +using TensorFlowLite; +using UnityEngine; + +/// +/// Simple example demonstrating use of the experimental C# bindings for TensorFlowLite. +/// +public class HelloTFLite : MonoBehaviour { + + [Tooltip("Configurable TFLite model.")] + public TextAsset model; + + [Tooltip("Configurable TFLite input tensor data.")] + public float[] inputs; + + private Interpreter interpreter; + private float[] outputs; + + void Start () { + interpreter = new Interpreter(model.bytes); + Debug.LogFormat("InputCount: {0}, OutputCount: {1}", + interpreter.GetInputTensorCount(), + interpreter.GetOutputTensorCount()); + } + + void Update () { + if (inputs == null) { + return; + } + + if (outputs == null || outputs.Length != inputs.Length) { + interpreter.ResizeInputTensor(0, new int[]{inputs.Length}); + interpreter.AllocateTensors(); + outputs = new float[inputs.Length]; + } + + interpreter.SetInputTensorData(0, inputs); + interpreter.Invoke(); + interpreter.GetOutputTensorData(0, outputs); + + Debug.LogFormat("Input: {0}, Output: {1}", + ArrayToString(inputs), + ArrayToString(outputs)); + } + + void OnDestroy() { + interpreter.Dispose(); + } + + private static string ArrayToString(float[] values) { + return string.Join(",", values.Select(x => x.ToString()).ToArray()); + } +} diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples/HelloTFLite/Scripts/HelloTFLite.cs.meta b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples/HelloTFLite/Scripts/HelloTFLite.cs.meta new file mode 100644 index 0000000000000000000000000000000000000000..ba83f45084bb624e5e7777684b0fda98b4d46688 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples/HelloTFLite/Scripts/HelloTFLite.cs.meta @@ -0,0 +1,11 @@ +fileFormatVersion: 2 +guid: 899510441e0ca4be0879d3055e467878 +MonoImporter: + externalObjects: {} + serializedVersion: 2 + defaultReferences: [] + executionOrder: 0 + icon: {instanceID: 0} + userData: + assetBundleName: + assetBundleVariant: diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK.meta b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK.meta new file mode 100644 index 0000000000000000000000000000000000000000..bf5ce15c6a6932398d798d193b54f4ecfd8ba2d8 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK.meta @@ -0,0 +1,8 @@ +fileFormatVersion: 2 +guid: 16dad1655bcdc48f7b325a2a634b9c69 +folderAsset: yes +DefaultImporter: + externalObjects: {} + userData: + assetBundleName: + assetBundleVariant: diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts.meta b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts.meta new file mode 100644 index 0000000000000000000000000000000000000000..22ed2c466bde1668595967f7a07f34a9193aaec8 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts.meta @@ -0,0 +1,8 @@ +fileFormatVersion: 2 +guid: d70863368f8904d509a9b73d3a555914 +folderAsset: yes +DefaultImporter: + externalObjects: {} + userData: + assetBundleName: + assetBundleVariant: diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts/Interpreter.cs b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts/Interpreter.cs new file mode 100644 index 0000000000000000000000000000000000000000..ab966bae2efb9431e2f9f35dc818d130aabd71f6 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts/Interpreter.cs @@ -0,0 +1,145 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +using System; +using System.Runtime.InteropServices; + +using TFL_Interpreter = System.IntPtr; +using TFL_Tensor = System.IntPtr; + +namespace TensorFlowLite +{ + /// + /// Simple C# bindings for the experimental TensorFlowLite C API. + /// + public class Interpreter : IDisposable + { + private const string TensorFlowLibrary = "tensorflowlite_c"; + + private TFL_Interpreter handle; + + public Interpreter(byte[] modelData) { + GCHandle modelDataHandle = GCHandle.Alloc(modelData, GCHandleType.Pinned); + IntPtr modelDataPtr = modelDataHandle.AddrOfPinnedObject(); + handle = TFL_NewInterpreter(modelDataPtr, modelData.Length); + if (handle == IntPtr.Zero) throw new Exception("Failed to create TensorFlowLite Interpreter"); + } + + ~Interpreter() { + Dispose(); + } + + public void Dispose() { + if (handle != IntPtr.Zero) TFL_DeleteInterpreter(handle); + handle = IntPtr.Zero; + } + + public void Invoke() { + ThrowIfError(TFL_InterpreterInvoke(handle)); + } + + public int GetInputTensorCount() { + return TFL_InterpreterGetInputTensorCount(handle); + } + + public void SetInputTensorData(int inputTensorIndex, Array inputTensorData) { + GCHandle tensorDataHandle = GCHandle.Alloc(inputTensorData, GCHandleType.Pinned); + IntPtr tensorDataPtr = tensorDataHandle.AddrOfPinnedObject(); + TFL_Tensor tensor = TFL_InterpreterGetInputTensor(handle, inputTensorIndex); + ThrowIfError(TFL_TensorCopyFromBuffer( + tensor, tensorDataPtr, Buffer.ByteLength(inputTensorData))); + } + + public void ResizeInputTensor(int inputTensorIndex, int[] inputTensorShape) { + ThrowIfError(TFL_InterpreterResizeInputTensor( + handle, inputTensorIndex, inputTensorShape, inputTensorShape.Length)); + } + + public void AllocateTensors() { + ThrowIfError(TFL_InterpreterAllocateTensors(handle)); + } + + public int GetOutputTensorCount() { + return TFL_InterpreterGetOutputTensorCount(handle); + } + + public void GetOutputTensorData(int outputTensorIndex, Array outputTensorData) { + GCHandle tensorDataHandle = GCHandle.Alloc(outputTensorData, GCHandleType.Pinned); + IntPtr tensorDataPtr = tensorDataHandle.AddrOfPinnedObject(); + TFL_Tensor tensor = TFL_InterpreterGetOutputTensor(handle, outputTensorIndex); + ThrowIfError(TFL_TensorCopyToBuffer( + tensor, tensorDataPtr, Buffer.ByteLength(outputTensorData))); + } + + private static void ThrowIfError(int resultCode) { + if (resultCode != 0) throw new Exception("TensorFlowLite operation failed."); + } + + #region Externs + + [DllImport (TensorFlowLibrary)] + private static extern unsafe TFL_Interpreter TFL_NewInterpreter( + IntPtr model_data, + int model_size); + + [DllImport (TensorFlowLibrary)] + private static extern unsafe void TFL_DeleteInterpreter(TFL_Interpreter interpreter); + + [DllImport (TensorFlowLibrary)] + private static extern unsafe int TFL_InterpreterGetInputTensorCount( + TFL_Interpreter interpreter); + + [DllImport (TensorFlowLibrary)] + private static extern unsafe TFL_Tensor TFL_InterpreterGetInputTensor( + TFL_Interpreter interpreter, + int input_index); + + [DllImport (TensorFlowLibrary)] + private static extern unsafe int TFL_InterpreterResizeInputTensor( + TFL_Interpreter interpreter, + int input_index, + int[] input_dims, + int input_dims_size); + + [DllImport (TensorFlowLibrary)] + private static extern unsafe int TFL_InterpreterAllocateTensors( + 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+ m_InitializeOnStartup: 1 + m_TestMode: 0 + m_IosGameId: + m_AndroidGameId: + m_GameIds: {} + m_GameId: + PerformanceReportingSettings: + m_Enabled: 0 diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/README.md b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0b3813fccb10c3a89fb462f9ab6bb81c6a9a147a --- /dev/null +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/README.md @@ -0,0 +1,24 @@ +# TF Lite Experimental Unity Plugin + +This directoryy contains an experimental sample Unity (2017) Plugin, based on +the experimental TF Lite C API. The sample demonstrates running inference within +Unity by way of a C# `Interpreter` wrapper. + +Note that the native TF Lite plugin(s) *must* be built before using the Unity +Plugin, and placed in Assets/TensorFlowLite/SDK/Plugins/. For the editor (note +that this has only been tested on Linux; the syntax may differ on Mac/Windows): + +```sh +bazel build -c opt --cxxopt=--std=c++11 \ + //tensorflow/contrib/lite/experimental/c:libtensorflowlite_c.so +``` + +and for Android: + +```sh +bazel build -c opt --cxxopt=--std=c++11 \ + --crosstool_top=//external:android/crosstool \ + --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \ + --cpu=armeabi-v7a \ + //tensorflow/contrib/lite/experimental/c:libtensorflowlite_c.so +``` diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/UnityPackageManager/manifest.json b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/UnityPackageManager/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..526aca60573f334a6b6bd536fa5be9c26d678e0f --- /dev/null +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/UnityPackageManager/manifest.json @@ -0,0 +1,4 @@ +{ + "dependencies": { + } +} diff --git a/tensorflow/contrib/lite/g3doc/README.md b/tensorflow/contrib/lite/g3doc/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e3db4784815b7562588d3afbd34f837b101f0977 --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/README.md @@ -0,0 +1,4 @@ +This is a *work-in-progress* TF Lite subsite for: +https://www.tensorflow.org/mobile + +DO NOT PUBLISH diff --git a/tensorflow/contrib/lite/g3doc/_book.yaml b/tensorflow/contrib/lite/g3doc/_book.yaml new file mode 100644 index 0000000000000000000000000000000000000000..98abd5743b2412399496f2fb3a70cd25d8597bca --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/_book.yaml @@ -0,0 +1,58 @@ +upper_tabs: +# Tabs left of dropdown menu +- include: /_upper_tabs_left.yaml +# Dropdown menu +- name: Ecosystem + path: /ecosystem + is_default: True + menu: + - include: /ecosystem/_menu_toc.yaml + lower_tabs: + # Subsite tabs + other: + - name: Guide + contents: + - title: Overview + path: /mobile/overview + - title: Developer Guide + path: /mobile/devguide + - title: Android Demo App + path: /mobile/demo_android + - title: iOS Demo App + path: /mobile/demo_ios + - title: Performance + path: /mobile/performance + - break: True + - title: TensorFlow Lite APIs + path: /mobile/apis + - title: Custom operators + path: /mobile/custom_operators + - title: TensorFlow Lite Ops Versioning + path: /mobile/ops_versioning + - title: TensorFlow Lite Compatibility Guide + path: /mobile/tf_ops_compatibility + - title: List of Hosted Models + path: /mobile/models + - title: TensorFlow Lite for iOS + path: /mobile/ios + - title: TensorFlow Lite for Raspberry Pi + path: /mobile/rpi + + - heading: TF Mobile + status: deprecated + - title: Overview + path: /mobile/tfmobile/ + - title: Building TensorFlow on Android + path: /mobile/tfmobile/android_build + - title: Building TensorFlow on IOS + path: /mobile/tfmobile/ios_build + - title: Integrating TensorFlow libraries + path: /mobile/tfmobile/linking_libs + - title: Preparing models for mobile deployment + path: /mobile/tfmobile/prepare_models + - title: Optimizing for mobile + path: /mobile/tfmobile/optimizing + + - name: API + contents: + - include: /mobile/api_docs/python/_toc.yaml diff --git a/tensorflow/contrib/lite/g3doc/_index.yaml b/tensorflow/contrib/lite/g3doc/_index.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9119e49117ffbda268f36324072d30ffd83c9e6c --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/_index.yaml @@ -0,0 +1,67 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml +description: +landing_page: + rows: + - heading: TensorFlow Lite is a lightweight solution for mobile and embedded devices. + items: + - description: > + TensorFlow Lite is TensorFlow’s lightweight solution for mobile and + embedded devices. It enables on-device machine learning inference with + low latency and a small binary size. TensorFlow Lite also supports + hardware acceleration with the + Android Neural Networks API. + list: + - heading: Key point 1 + description: > + [high-level overview] + icon: + icon_name: chevron_right + foreground: theme + background: grey + - heading: Key point 2 + description: > + [high-level overview] + icon: + icon_name: chevron_right + foreground: theme + background: grey + - heading: Key point 3 + description: > + [high-level overview] + icon: + icon_name: chevron_right + foreground: theme + background: grey + - code_block: | + 
+        $ toco --input_file=$(pwd)/mobilenet_v1_1.0_224/frozen_graph.pb \
+               --input_format=TENSORFLOW_GRAPHDEF \
+               --output_format=TFLITE \
+               --output_file=/tmp/mobilenet_v1_1.0_224.tflite \
+               --inference_type=FLOAT \
+               --input_type=FLOAT \
+               --input_arrays=input \
+               --output_arrays=MobilenetV1/Predictions/Reshape_1 \
+               --input_shapes=1,224,224,3
+
+ + - classname: devsite-landing-row-cards + items: + - heading: Using TensorFlow Lite on Android + image_path: /ecosystem/images/tf-logo-card-16x9.png + path: https://medium.com/tensorflow/using-tensorflow-lite-on-android-9bbc9cb7d69d + buttons: + - label: Read on TensorFlow blog + path: https://medium.com/tensorflow/using-tensorflow-lite-on-android-9bbc9cb7d69d + - heading: TensorFlow Lite at the Dev Summit + youtube_id: FAMfy7izB6A + buttons: + - label: Watch the video + path: https://www.youtube.com/watch?v=FAMfy7izB6A + - heading: TensorFlow Lite on GitHub + image_path: /ecosystem/images/github-card-16x9.png + path: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite + buttons: + - label: View on GitHub + path: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite diff --git a/tensorflow/contrib/lite/g3doc/_project.yaml b/tensorflow/contrib/lite/g3doc/_project.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b39666516baab42d289e4d40077c2877ed65d396 --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/_project.yaml @@ -0,0 +1,10 @@ +name: TensorFlow Lite +breadcrumb_name: Mobile +home_url: /mobile/ +parent_project_metadata_path: /_project.yaml +description: > + TensorFlow Lite is a lightweight solution for mobile and embedded devices. +use_site_branding: True +hide_from_products_list: True +content_license: cc3-apache2 +buganizer_id: 316308 diff --git a/tensorflow/contrib/lite/g3doc/api_docs/python/_toc.yaml b/tensorflow/contrib/lite/g3doc/api_docs/python/_toc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1e1c44c6929571144d8cf0b54463c48e37466022 --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/api_docs/python/_toc.yaml @@ -0,0 +1,6 @@ +# Automatically generated file; please do not edit +toc: + - title: TensorFlow Lite + section: + - title: Overview + path: /mobile/api_docs/python/ diff --git a/tensorflow/contrib/lite/g3doc/api_docs/python/index.md b/tensorflow/contrib/lite/g3doc/api_docs/python/index.md new file mode 100644 index 0000000000000000000000000000000000000000..70031a3c3d26eb6557014879cc92288cd22331eb --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/api_docs/python/index.md @@ -0,0 +1,10 @@ +Project: /mobile/_project.yaml +Book: /mobile/_book.yaml +page_type: reference + + + + +# All symbols in TensorFlow Lite + +TEMP PAGE diff --git a/tensorflow/contrib/lite/g3doc/apis.md b/tensorflow/contrib/lite/g3doc/apis.md index a591a353dd8f0ac94ecaa3f12e1aa1c57566ef69..776803da8c7126c6198e3740448888119df030b9 100644 --- a/tensorflow/contrib/lite/g3doc/apis.md +++ b/tensorflow/contrib/lite/g3doc/apis.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # TensorFlow Lite APIs TensorFlow Lite provides programming APIs in C++ and Java, and in both cases @@ -53,6 +56,7 @@ typedef enum { ``` Failures can be easily verified with: + ```c++ if (status != kTfLiteOk) { // ... error handling here ... diff --git a/tensorflow/contrib/lite/g3doc/custom_operators.md b/tensorflow/contrib/lite/g3doc/custom_operators.md index 972e57f73e82961ebc5e341dd7a41bc00acc5d21..d979353bb3550fe53d86b2e6c76702a3970b01fe 100644 --- a/tensorflow/contrib/lite/g3doc/custom_operators.md +++ b/tensorflow/contrib/lite/g3doc/custom_operators.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # How to use custom operators TensorFlow Lite currently supports a subset of TensorFlow operators. However, it @@ -89,3 +92,83 @@ builtins.AddCustom("Sin", Register_SIN()); Note that a similar process as above can be followed for supporting for a set of operations instead of a single operator. + +## Best Practices for writing custom operators + +1. Optimize memory allocations and de-allocations cautiously. It is more + efficient to allocate memory in Prepare() instead of Invoke(), and allocate + memory before a loop instead of in every iteration. Use temporary tensors + data rather than mallocing yourself (see item 2). Use pointers/references + instead of copying as much as possible. + +2. If a data structure will persist during the entire operation, we advise + pre-allocating the memory using temporary tensors. You may need to use + OpData struct to reference the tensor indices in other functions. See + example in the + [kernel for convolution](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/kernels/conv.cc). + A sample code snippet is below + + ``` + auto* op_data = reinterpret_cast(node->user_data); + TfLiteIntArrayFree(node->temporaries); + node->temporaries = TfLiteIntArrayCreate(1); + node->temporaries->data[0] = op_data->temp_tensor_index; + TfLiteTensor* temp_tensor = &context->tensors[op_data->temp_tensor_index]; + temp_tensor->type = kTfLiteFloat32; + temp_tensor->allocation_type = kTfLiteArenaRw; + ``` + +3. If it doesn't cost too much wasted memory, prefer using a static fixed size + array (or in Resize() pre-allocated std::vector) rather than using a + dynamically allocating std::vector every iteration of execution. + +4. Avoid instantiating standard library container templates that don't already + exist, because they affect binary size. For example, if you need a std::map + in your operation that doesn't exist in other kernels, using a std::vector + with direct indexing mapping could work while keeping the binary size small. + See what other kernels use to gain insight (or ask). + +5. Check the pointer to the memory returned by malloc. If this pointer is + nullptr, no operations should be performed using that pointer. If you + malloc() in a function and have an error exit, deallocate memory before you + exit. + +6. Use TF_LITE_ENSURE(context, condition) to check for a specific condition. + Your code must not leave memory hanging when TF_LITE_ENSURE is done, i.e., + these should be done before any resources are allocated that will leak. + +## Special TF Graph Attributes + +When Toco convertes a TF graph into TFLite format, it makes some assumption +about custom operations that might be not correct. In this case, the generated +graph can be not executable. + +It is possible to add aditional information about your custom op output to TF +graph before it is converted. The following attributes are supported: + +- **_output_quantized** a boolean attribute, true if the operation outputs are + quantized +- **_output_types** a list of types for output tensors +- **_output_shapes** a list of shapes for output tensors + +### Setting the Attributes + +This is an example how the attributes can be set: + +```python +frozen_graph_def = tf.graph_util.convert_variables_to_constants(...) +for node in frozen_graph_def.node: + if node.op == 'sin': + node.attr['_output_types'].list.type.extend([ + types_pb2.DT_FLOAT, + ]) + node.attr['_output_shapes'].list.shape.extend([ + tf.TensorShape([10]), + ]) + node.attr['_output_quantized'].b = False +tflite_model = tf.contrib.lite.toco_convert( + frozen_graph_def,...) +``` + +**Note:** After the attributes are set, the graph can not be executed by +Tensorflow, therefore it should be done just before the conversion. diff --git a/tensorflow/docs_src/mobile/tflite/demo_android.md b/tensorflow/contrib/lite/g3doc/demo_android.md similarity index 98% rename from tensorflow/docs_src/mobile/tflite/demo_android.md rename to tensorflow/contrib/lite/g3doc/demo_android.md index fdf0bcf3c1135f0e702c7dda4d1d608a26169470..d79a2696b4e9cc10480aa67c7eaec5a356eff596 100644 --- a/tensorflow/docs_src/mobile/tflite/demo_android.md +++ b/tensorflow/contrib/lite/g3doc/demo_android.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Android Demo App An example Android application using TensorFLow Lite is available diff --git a/tensorflow/docs_src/mobile/tflite/demo_ios.md b/tensorflow/contrib/lite/g3doc/demo_ios.md similarity index 97% rename from tensorflow/docs_src/mobile/tflite/demo_ios.md rename to tensorflow/contrib/lite/g3doc/demo_ios.md index 3be21da89f9e53d324c2ade0cb937f4b5b30fad4..a554898899e67a6bc2bc52733f5301767bc1c06a 100644 --- a/tensorflow/docs_src/mobile/tflite/demo_ios.md +++ b/tensorflow/contrib/lite/g3doc/demo_ios.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # iOS Demo App The TensorFlow Lite demo is a camera app that continuously classifies whatever diff --git a/tensorflow/docs_src/mobile/tflite/devguide.md b/tensorflow/contrib/lite/g3doc/devguide.md similarity index 91% rename from tensorflow/docs_src/mobile/tflite/devguide.md rename to tensorflow/contrib/lite/g3doc/devguide.md index b168d6c18366708ebaa7216481d262b02051168d..dc9cc98c0821edff57cb9428a50637a15211cfda 100644 --- a/tensorflow/docs_src/mobile/tflite/devguide.md +++ b/tensorflow/contrib/lite/g3doc/devguide.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Developer Guide Using a TensorFlow Lite model in your mobile app requires multiple @@ -56,7 +59,7 @@ both floating point and quantized inference. A developer may choose to train a custom model using Tensorflow (see the [TensorFlow tutorials](../../tutorials/) for examples of building and training models). If you have already written a model, the first step is to export this -to a @{tf.GraphDef} file. This is required because some formats do not store the +to a `tf.GraphDef` file. This is required because some formats do not store the model structure outside the code, and we must communicate with other parts of the framework. See [Exporting the Inference Graph](https://github.com/tensorflow/models/blob/master/research/slim/README.md) @@ -71,12 +74,12 @@ grow in future Tensorflow Lite releases. ## 2. Convert the model format The model generated (or downloaded) in the previous step is a *standard* -Tensorflow model and you should now have a .pb or .pbtxt @{tf.GraphDef} file. +Tensorflow model and you should now have a .pb or .pbtxt `tf.GraphDef` file. Models generated with transfer learning (re-training) or custom models must be converted—but, we must first freeze the graph to convert the model to the Tensorflow Lite format. This process uses several model formats: -* @{tf.GraphDef} (.pb) —A protobuf that represents the TensorFlow training or +* `tf.GraphDef` (.pb) —A protobuf that represents the TensorFlow training or computation graph. It contains operators, tensors, and variables definitions. * *CheckPoint* (.ckpt) —Serialized variables from a TensorFlow graph. Since this does not contain a graph structure, it cannot be interpreted by itself. @@ -143,11 +146,11 @@ containing the model architecture. The [frozen_graph.pb](https://storage.googlea file used here is available for download. `output_file` is where the TensorFlow Lite model will get generated. The `input_type` and `inference_type` arguments should be set to `FLOAT`, unless converting a -@{$performance/quantization$quantized model}. Setting the `input_array`, -`output_array`, and `input_shape` arguments are not as straightforward. The -easiest way to find these values is to explore the graph using Tensorboard. Reuse -the arguments for specifying the output nodes for inference in the -`freeze_graph` step. +quantized model. +Setting the `input_array`, `output_array`, and `input_shape` arguments are not as +straightforward. The easiest way to find these values is to explore the graph +using Tensorboard. Reuse the arguments for specifying the output nodes for +inference in the `freeze_graph` step. It is also possible to use the Tensorflow Optimizing Converter with protobufs from either Python or from the command line (see the @@ -204,16 +207,16 @@ The open source Android demo app uses the JNI interface and is available [on GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/app). You can also download a [prebuilt APK](http://download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk). -See the @{$tflite/demo_android} guide for details. +See the Android demo guide for details. -The @{$mobile/android_build} guide has instructions for installing TensorFlow on -Android and setting up `bazel` and Android Studio. +The Android mobile guide has instructions for +installing TensorFlow on Android and setting up `bazel` and Android Studio. ### iOS To integrate a TensorFlow model in an iOS app, see the [TensorFlow Lite for iOS](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/g3doc/ios.md) -guide and @{$tflite/demo_ios} guide. +guide and iOS demo guide. #### Core ML support diff --git a/tensorflow/contrib/lite/g3doc/ios.md b/tensorflow/contrib/lite/g3doc/ios.md index e0358a444d6dffc377bf13ee72ba5477359d6e07..d78d373ccfea074872773693c562253b202a646b 100644 --- a/tensorflow/contrib/lite/g3doc/ios.md +++ b/tensorflow/contrib/lite/g3doc/ios.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # TensorFlow Lite for iOS ## Building diff --git a/tensorflow/contrib/lite/g3doc/models.md b/tensorflow/contrib/lite/g3doc/models.md index c1c8ef049f693dae038e5e0ca242b9219329cc50..3292aece0e76244a61613b514457edf479858fdb 100644 --- a/tensorflow/contrib/lite/g3doc/models.md +++ b/tensorflow/contrib/lite/g3doc/models.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # List of Hosted Models ## Image classification (Float Models) @@ -39,22 +42,22 @@ single thread large core. Model Name | Paper_Model_Files | Model_Size | Top-1 Accuracy | Top-5 Accuracy | TF Lite Performance ------------------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | ---------: | -------------: | -------------: | ------------------: -Mobilenet_0.25_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_128_quant.tgz) | 0.5 Mb | 39.9% | 65.8% | 3.7 ms -Mobilenet_0.25_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_160_quant.tgz) | 0.5 Mb | 43.5% | 69.1% | 5.5 ms -Mobilenet_0.25_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_192_quant.tgz) | 0.5 Mb | 45.8% | 71.9% | 7.9 ms -Mobilenet_0.25_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_224_quant.tgz) | 0.5 Mb | 48.2% | 73.8% | 10.4 ms -Mobilenet_0.50_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_128_quant.tgz) | 1.4 Mb | 54.9% | 78.9% | 8.8 ms -Mobilenet_0.50_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_160_quant.tgz) | 1.4 Mb | 57.7% | 81.3% | 13.0 ms -Mobilenet_0.50_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_192_quant.tgz) | 1.4 Mb | 60.4% | 83.2% | 18.3 ms -Mobilenet_0.50_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_224_quant.tgz) | 1.4 Mb | 62.2% | 84.5% | 24.7 ms -Mobilenet_0.75_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_128_quant.tgz) | 2.6 Mb | 59.8% | 82.8% | 16.2 ms -Mobilenet_0.75_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_160_quant.tgz) | 2.6 Mb | 63.9% | 85.5% | 24.3 ms -Mobilenet_0.75_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_192_quant.tgz) | 2.6 Mb | 66.2% | 87.1% | 33.8 ms -Mobilenet_0.75_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_224_quant.tgz) | 2.6 Mb | 67.9% | 88.1% | 45.4 ms -Mobilenet_1.0_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_128_quant.tgz) | 4.3 Mb | 64.0% | 85.5% | 24.9 ms -Mobilenet_1.0_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_160_quant.tgz) | 4.3 Mb | 67.3% | 87.7% | 37.4 ms -Mobilenet_1.0_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_192_quant.tgz) | 4.3 Mb | 69.0% | 88.9% | 51.9 ms -Mobilenet_1.0_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224_quant.tgz) | 4.3 Mb | 69.7% | 89.5% | 70.2 ms +Mobilenet_0.25_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_0.25_128_quant.tgz) | 0.5 Mb | 39.7% | 65.8% | 3.7 ms +Mobilenet_0.25_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_0.25_160_quant.tgz) | 0.5 Mb | 41.9% | 69.1% | 5.5 ms +Mobilenet_0.25_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_0.25_192_quant.tgz) | 0.5 Mb | 45.3% | 71.9% | 7.9 ms +Mobilenet_0.25_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_0.25_224_quant.tgz) | 0.5 Mb | 46.4% | 73.8% | 10.4 ms +Mobilenet_0.50_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_0.5_128_quant.tgz) | 1.4 Mb | 54.1% | 78.9% | 8.8 ms +Mobilenet_0.50_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_0.5_160_quant.tgz) | 1.4 Mb | 57.6% | 81.3% | 13.0 ms +Mobilenet_0.50_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_0.5_192_quant.tgz) | 1.4 Mb | 59.1% | 83.2% | 18.3 ms +Mobilenet_0.50_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_0.5_224_quant.tgz) | 1.4 Mb | 61.0% | 84.5% | 24.7 ms +Mobilenet_0.75_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_0.75_128_quant.tgz) | 2.6 Mb | 52.5% | 82.8% | 16.2 ms +Mobilenet_0.75_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_0.75_160_quant.tgz) | 2.6 Mb | 63.6% | 85.5% | 24.3 ms +Mobilenet_0.75_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_0.75_192_quant.tgz) | 2.6 Mb | 61.1% | 87.1% | 33.8 ms +Mobilenet_0.75_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_0.75_224_quant.tgz) | 2.6 Mb | 66.7% | 88.1% | 45.4 ms +Mobilenet_1.0_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_1.0_128_quant.tgz) | 4.3 Mb | 62.7% | 85.5% | 24.9 ms +Mobilenet_1.0_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_1.0_160_quant.tgz) | 4.3 Mb | 66.6% | 87.7% | 37.4 ms +Mobilenet_1.0_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_1.0_192_quant.tgz) | 4.3 Mb | 69.2% | 88.9% | 51.9 ms +Mobilenet_1.0_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_07_12/mobilenet_v1_1.0_224_quant.tgz) | 4.3 Mb | 69.3% | 89.5% | 70.2 ms ## Other models diff --git a/tensorflow/contrib/lite/g3doc/ops_versioning.md b/tensorflow/contrib/lite/g3doc/ops_versioning.md index bd2f797e6c5b05f52bec9fc34f1b8011aca70330..b06f4fd3b893e5e5977f92de26109a6dd264531f 100644 --- a/tensorflow/contrib/lite/g3doc/ops_versioning.md +++ b/tensorflow/contrib/lite/g3doc/ops_versioning.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # TensorFlow Lite Ops Versioning This document describes TensorFlow Lite's op versioning schema. Op diff --git a/tensorflow/docs_src/mobile/tflite/index.md b/tensorflow/contrib/lite/g3doc/overview.md similarity index 93% rename from tensorflow/docs_src/mobile/tflite/index.md rename to tensorflow/contrib/lite/g3doc/overview.md index 3d1733024e493042a2cc85aa9f2fec4b75eefa94..be60d7941ade824ee201bfd05400fb3e4e9fae7e 100644 --- a/tensorflow/docs_src/mobile/tflite/index.md +++ b/tensorflow/contrib/lite/g3doc/overview.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Introduction to TensorFlow Lite TensorFlow Lite is TensorFlow’s lightweight solution for mobile and embedded @@ -70,10 +73,9 @@ There are several factors which are fueling interest in this domain: We believe the next wave of machine learning applications will have significant processing on mobile and embedded devices. -## TensorFlow Lite developer preview highlights +## TensorFlow Lite highlights -TensorFlow Lite is available as a developer preview and includes the -following: +TensorFlow Lite provides: - A set of core operators, both quantized and float, many of which have been tuned for mobile platforms. These can be used to create and run custom @@ -129,9 +131,6 @@ following: - Java and C++ API support -Note: This is a developer release, and it’s likely that there will be changes in -the API in upcoming versions. We do not guarantee backward or forward -compatibility with this release. ## Getting Started @@ -201,9 +200,5 @@ possible performance for a particular model on a particular device. ## Next Steps -For the developer preview, most of our documentation is on GitHub. Please take a -look at the [TensorFlow Lite -repository](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite) -on GitHub for more information and for code samples, demo applications, and -more. - +The TensorFlow Lite [GitHub repository](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite). +contains additional docs, code samples, and demo applications. diff --git a/tensorflow/docs_src/mobile/tflite/performance.md b/tensorflow/contrib/lite/g3doc/performance.md similarity index 98% rename from tensorflow/docs_src/mobile/tflite/performance.md rename to tensorflow/contrib/lite/g3doc/performance.md index 79bacaaa1b889a8711e5c09c7fd4e4912e70d3bd..613e9f97c38942f20d3ca44cdc69e72b35c8608f 100644 --- a/tensorflow/docs_src/mobile/tflite/performance.md +++ b/tensorflow/contrib/lite/g3doc/performance.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Performance This document lists TensorFlow Lite performance benchmarks when running well diff --git a/tensorflow/contrib/lite/g3doc/rpi.md b/tensorflow/contrib/lite/g3doc/rpi.md index ab50789307414255bccd84d4cfcb6ddecc25ba08..cdc9172d873bfd32811ca69901ed2e4eedf902a3 100644 --- a/tensorflow/contrib/lite/g3doc/rpi.md +++ b/tensorflow/contrib/lite/g3doc/rpi.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # TensorFlow Lite for Raspberry Pi ## Cross compiling diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md index dcd17bbeabda08eaf86f8d5ac7f26cea0d3719a3..aa65ec99887a61df658dd7add7b5cc3b91d81846 100644 --- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md +++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # TensorFlow Lite & TensorFlow Compatibility Guide TensorFlow Lite supports a number of TensorFlow operations used in common @@ -42,6 +45,7 @@ counterparts: *as long as the input tensor is 4D (1 batch + 2 spatial + 1 other) and the crops attribute is not used* * [tf.exp](https://www.tensorflow.org/api_docs/python/tf/exp) +* [tf.fake_quant*](https://www.tensorflow.org/api_docs/python/tf/fake_quant_with_min_max_args) * [tf.matmul](https://www.tensorflow.org/api_docs/python/tf/matmul) - *as long as the second argument is constant and transposition is not used* * [tf.nn.avg_pool](https://www.tensorflow.org/api_docs/python/tf/nn/avg_pool) @@ -58,6 +62,7 @@ counterparts: * [tf.nn.softmax](https://www.tensorflow.org/api_docs/python/tf/nn/softmax) - *as long as tensors are 2D and axis is the last dimension* * [tf.nn.top_k](https://www.tensorflow.org/api_docs/python/tf/nn/top_k) +* [tf.one_hot](https://www.tensorflow.org/api_docs/python/tf/one_hot) * [tf.pad](https://www.tensorflow.org/api_docs/python/tf/pad) - *as long as mode and constant_values are not used* * [tf.reduce_mean](https://www.tensorflow.org/api_docs/python/tf/reduce_mean) - @@ -790,6 +795,54 @@ Outputs { } ``` +**ARG_MAX** + +``` +Inputs { + 0: a tensor + 1: a tensor +} +Outputs { + 0: A tensor of indices of maximum values. +} +``` + +**ARG_MIN** + +``` +Inputs { + 0: a tensor + 1: a tensor +} +Outputs { + 0: A tensor of indices of minium values. +} +``` + +**PACK** + +``` +Inputs { + 0: a list of tensors. + 1: an integer. +} +Outputs { + 0: A tensor of stacked tensors. +} +``` + +**LOGICAL_OR** + +``` +Inputs { + 0: a list of tensors. + 1: a list of tensors. +} +Outputs { + 0: A tensor of logical_or output tensors. +} +``` + And these are TensorFlow Lite operations that are present but not ready for custom models yet: diff --git a/tensorflow/docs_src/mobile/android_build.md b/tensorflow/contrib/lite/g3doc/tfmobile/android_build.md similarity index 97% rename from tensorflow/docs_src/mobile/android_build.md rename to tensorflow/contrib/lite/g3doc/tfmobile/android_build.md index f4b07db4591dddcfbf3633f471072f4a0eea9843..76e16fc9db27782fe0f9454ba463722f4bf6eb4b 100644 --- a/tensorflow/docs_src/mobile/android_build.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/android_build.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Building TensorFlow on Android To get you started working with TensorFlow on Android, we'll walk through two @@ -91,7 +94,8 @@ using [ADB](https://developer.android.com/studio/command-line/adb.html). This requires some knowledge of build systems and Android developer tools, but we'll guide you through the basics here. -- First, follow our instructions for @{$install/install_sources$installing from sources}. +- First, follow our instructions for + installing from sources. This will also guide you through installing Bazel and cloning the TensorFlow code. diff --git a/tensorflow/docs_src/mobile/mobile_intro.md b/tensorflow/contrib/lite/g3doc/tfmobile/index.md similarity index 86% rename from tensorflow/docs_src/mobile/mobile_intro.md rename to tensorflow/contrib/lite/g3doc/tfmobile/index.md index baad4433083d18a19ea3dd5ec0c1bae498ac2da9..bd047bfceceddfd0b5a9fd0c83cb47a339299abf 100644 --- a/tensorflow/docs_src/mobile/mobile_intro.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/index.md @@ -1,4 +1,45 @@ -# Introduction to TensorFlow Mobile +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + +# Overview + +TensorFlow was designed to be a good deep learning solution for mobile +platforms. Currently we have two solutions for deploying machine learning +applications on mobile and embedded devices: TensorFlow for Mobile and +TensorFlow Lite. + +## TensorFlow Lite versus TensorFlow Mobile + +Here are a few of the differences between the two: + +- TensorFlow Lite is an evolution of TensorFlow Mobile. In most cases, apps + developed with TensorFlow Lite will have a smaller binary size, fewer + dependencies, and better performance. + +- TensorFlow Lite is in developer preview, so not all use cases are covered yet. + We expect you to use TensorFlow Mobile to cover production cases. + +- TensorFlow Lite supports only a limited set of operators, so not all models + will work on it by default. TensorFlow for Mobile has a fuller set of + supported functionality. + +TensorFlow Lite provides better performance and a small binary size on mobile +platforms as well as the ability to leverage hardware acceleration if available +on their platforms. In addition, it has many fewer dependencies so it can be +built and hosted on simpler, more constrained device scenarios. TensorFlow Lite +also allows targeting accelerators through the [Neural Networks +API](https://developer.android.com/ndk/guides/neuralnetworks/index.html). + +TensorFlow Lite currently has coverage for a limited set of operators. While +TensorFlow for Mobile supports only a constrained set of ops by default, in +principle if you use an arbitrary operator in TensorFlow, it can be customized +to build that kernel. Thus use cases which are not currently supported by +TensorFlow Lite should continue to use TensorFlow for Mobile. As TensorFlow Lite +evolves, it will gain additional operators, and the decision will be easier to +make. + + +## Introduction to TensorFlow Mobile TensorFlow was designed from the ground up to be a good deep learning solution for mobile platforms like Android and iOS. This mobile guide should help you @@ -167,7 +208,7 @@ interesting products possible. TensorFlow runs on Ubuntu Linux, Windows 10, and OS X. For a list of all supported operating systems and instructions to install TensorFlow, see -@{$install$Installing Tensorflow}. +Installing Tensorflow. Note that some of the sample code we provide for mobile TensorFlow requires you to compile TensorFlow from source, so you’ll need more than just `pip install` @@ -241,8 +282,3 @@ results you’ll see. It’s common for an algorithm to get great training accur numbers but then fail to be useful within a real application because there’s a mismatch between the dataset and real usage. Prototype end-to-end usage as soon as possible to create a consistent user experience. - -## Next Steps - -We suggest you get started by building one of our demos for -@{$mobile/android_build$Android} or @{$mobile/ios_build$iOS}. diff --git a/tensorflow/docs_src/mobile/ios_build.md b/tensorflow/contrib/lite/g3doc/tfmobile/ios_build.md similarity index 98% rename from tensorflow/docs_src/mobile/ios_build.md rename to tensorflow/contrib/lite/g3doc/tfmobile/ios_build.md index 4c84a1214a26eeb90c1b6a186a369212377b06cd..6223707892ce7b288ecabf932b33cd39860446a6 100644 --- a/tensorflow/docs_src/mobile/ios_build.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/ios_build.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Building TensorFlow on iOS ## Using CocoaPods diff --git a/tensorflow/docs_src/mobile/linking_libs.md b/tensorflow/contrib/lite/g3doc/tfmobile/linking_libs.md similarity index 83% rename from tensorflow/docs_src/mobile/linking_libs.md rename to tensorflow/contrib/lite/g3doc/tfmobile/linking_libs.md index efef5dd0daa0b267d8384d32d62d9ce0226dc102..4c2071ed053125cfa643ed785fe302198f734ead 100644 --- a/tensorflow/docs_src/mobile/linking_libs.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/linking_libs.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Integrating TensorFlow libraries Once you have made some progress on a model that addresses the problem you’re @@ -14,11 +17,11 @@ TensorFlow mobile demo apps. After you've managed to build the examples, you'll probably want to call TensorFlow from one of your existing applications. The very easiest way to do -this is to use the Pod installation steps described -@{$mobile/ios_build#using_cocoapods$here}, but if you want to build TensorFlow -from source (for example to customize which operators are included) you'll need -to break out TensorFlow as a framework, include the right header files, and link -against the built libraries and dependencies. +this is to use the Pod installation steps described in +Building TensorFlow on iOS, but if you want to build +TensorFlow from source (for example to customize which operators are included) +you'll need to break out TensorFlow as a framework, include the right header +files, and link against the built libraries and dependencies. ### Android @@ -82,10 +85,12 @@ recompile of the core. To achieve this capability, TensorFlow uses a registration pattern in a lot of places. In the code, it looks like this: - class MulKernel : OpKernel { - Status Compute(OpKernelContext* context) { … } - }; - REGISTER_KERNEL(MulKernel, “Mul”); +``` +class MulKernel : OpKernel { + Status Compute(OpKernelContext* context) { … } +}; +REGISTER_KERNEL(MulKernel, “Mul”); +``` This would be in a standalone `.cc` file linked into your application, either as part of the main set of kernels or as a separate custom library. The magic @@ -101,15 +106,17 @@ doesn’t offer a good mechanism for doing this sort of registration, so we have to resort to some tricky code. Under the hood, the macro is implemented so that it produces something like this: - class RegisterMul { - public: - RegisterMul() { - global_kernel_registry()->Register(“Mul”, [](){ - return new MulKernel() - }); - } - }; - RegisterMul g_register_mul; +``` +class RegisterMul { + public: + RegisterMul() { + global_kernel_registry()->Register(“Mul”, [](){ + return new MulKernel() + }); + } +}; +RegisterMul g_register_mul; +``` This sets up a class `RegisterMul` with a constructor that tells the global kernel registry what function to call when somebody asks it how to create a @@ -176,8 +183,10 @@ have an experimental script at [rename_protobuf.sh](https://github.com/tensorflo You need to run this as part of the makefile build, after you’ve downloaded all the dependencies: - tensorflow/contrib/makefile/download_dependencies.sh - tensorflow/contrib/makefile/rename_protobuf.sh +``` +tensorflow/contrib/makefile/download_dependencies.sh +tensorflow/contrib/makefile/rename_protobuf.sh +``` ## Calling the TensorFlow API @@ -193,18 +202,20 @@ use case, while on iOS and Raspberry Pi you call directly into the C++ API. Here’s what a typical Inference Library sequence looks like on Android: - // Load the model from disk. - TensorFlowInferenceInterface inferenceInterface = - new TensorFlowInferenceInterface(assetManager, modelFilename); +``` +// Load the model from disk. +TensorFlowInferenceInterface inferenceInterface = +new TensorFlowInferenceInterface(assetManager, modelFilename); - // Copy the input data into TensorFlow. - inferenceInterface.feed(inputName, floatValues, 1, inputSize, inputSize, 3); +// Copy the input data into TensorFlow. +inferenceInterface.feed(inputName, floatValues, 1, inputSize, inputSize, 3); - // Run the inference call. - inferenceInterface.run(outputNames, logStats); +// Run the inference call. +inferenceInterface.run(outputNames, logStats); - // Copy the output Tensor back into the output array. - inferenceInterface.fetch(outputName, outputs); +// Copy the output Tensor back into the output array. +inferenceInterface.fetch(outputName, outputs); +``` You can find the source of this code in the [Android examples](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowImageClassifier.java#L107). @@ -212,27 +223,29 @@ You can find the source of this code in the [Android examples](https://github.co Here’s the equivalent code for iOS and Raspberry Pi: - // Load the model. - PortableReadFileToProto(file_path, &tensorflow_graph); - - // Create a session from the model. - tensorflow::Status s = session->Create(tensorflow_graph); - if (!s.ok()) { - LOG(FATAL) << "Could not create TensorFlow Graph: " << s; - } - - // Run the model. - std::string input_layer = "input"; - std::string output_layer = "output"; - std::vector outputs; - tensorflow::Status run_status = session->Run({{input_layer, image_tensor}}, +``` +// Load the model. +PortableReadFileToProto(file_path, &tensorflow_graph); + +// Create a session from the model. +tensorflow::Status s = session->Create(tensorflow_graph); +if (!s.ok()) { + LOG(FATAL) << "Could not create TensorFlow Graph: " << s; +} + +// Run the model. +std::string input_layer = "input"; +std::string output_layer = "output"; +std::vector outputs; +tensorflow::Status run_status = session->Run({\{input_layer, image_tensor}}, {output_layer}, {}, &outputs); - if (!run_status.ok()) { - LOG(FATAL) << "Running model failed: " << run_status; - } +if (!run_status.ok()) { + LOG(FATAL) << "Running model failed: " << run_status; +} - // Access the output data. - tensorflow::Tensor* output = &outputs[0]; +// Access the output data. +tensorflow::Tensor* output = &outputs[0]; +``` This is all based on the [iOS sample code](https://www.tensorflow.org/code/tensorflow/examples/ios/simple/RunModelViewController.mm), diff --git a/tensorflow/docs_src/mobile/optimizing.md b/tensorflow/contrib/lite/g3doc/tfmobile/optimizing.md similarity index 98% rename from tensorflow/docs_src/mobile/optimizing.md rename to tensorflow/contrib/lite/g3doc/tfmobile/optimizing.md index 778e4d3a6233c3bec70b830bc998013745a1f0ba..a0192c3541483437b817e22eb92193bd7bcb4c28 100644 --- a/tensorflow/docs_src/mobile/optimizing.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/optimizing.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Optimizing for mobile There are some special issues that you have to deal with when you’re trying to @@ -77,7 +80,7 @@ out of a mobile device's memory faster. To understand how large your network will be on disk, start by looking at the size on disk of your `GraphDef` file after you’ve run `freeze_graph` and -`strip_unused_nodes` on it (see @{$mobile/prepare_models$Preparing models} for +`strip_unused_nodes` on it (see Preparing models for more details on these tools), since then it should only contain inference-related nodes. To double-check that your results are as expected, run the `summarize_graph` tool to see how many parameters are in constants: @@ -103,7 +106,8 @@ you multiply the number of const parameters by four, you should get something that’s close to the size of the file on disk. You can often get away with only eight-bits per parameter with very little loss of accuracy in the final result, so if your file size is too large you can try using -@{$performance/quantization$quantize_weights} to transform the parameters down. +quantize_weights +to transform the parameters down. bazel build tensorflow/tools/graph_transforms:transform_graph && \ bazel-bin/tensorflow/tools/graph_transforms/transform_graph \ @@ -292,7 +296,8 @@ run it on a 64-bit ARM device: You can interpret the results in exactly the same way as the desktop version above. If you have any trouble figuring out what the right input and output -names and types are, take a look at the @{$mobile/prepare_models$Preparing models} +names and types are, take a look at the +Preparing models page for details about detecting these for your model, and look at the `summarize_graph` tool which may give you helpful information. diff --git a/tensorflow/docs_src/mobile/prepare_models.md b/tensorflow/contrib/lite/g3doc/tfmobile/prepare_models.md similarity index 98% rename from tensorflow/docs_src/mobile/prepare_models.md rename to tensorflow/contrib/lite/g3doc/tfmobile/prepare_models.md index 2b84dbb97388b16c6a4ae1d3472e0b1a993285f0..6b4e4a92bd9262139be3cf650b7d16714ee3a277 100644 --- a/tensorflow/docs_src/mobile/prepare_models.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/prepare_models.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Preparing models for mobile deployment The requirements for storing model information during training are very @@ -255,8 +258,8 @@ The criteria for including ops and types fall into several categories: These ops are trimmed by default to optimize for inference on mobile, but it is possible to alter some build files to change the default. After alternating the build files, you will need to recompile TensorFlow. See below for more details -on how to do this, and also see @{$mobile/optimizing#binary_size$Optimizing} for -more on reducing your binary size. +on how to do this, and also see optimizing binary size +for more on reducing your binary size. ### Locate the implementation diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc index 521216a4f1e84582731a1782f74ce981106f636b..e38597495dc7e860209026631c2d386f690b6461 100644 --- a/tensorflow/contrib/lite/interpreter.cc +++ b/tensorflow/contrib/lite/interpreter.cc @@ -40,6 +40,19 @@ class NNAPIDelegate {}; namespace { +TfLiteStatus ReportOpError(TfLiteContext* context, const TfLiteNode& node, + const TfLiteRegistration& registration, + int node_index, const char* message) { + context->ReportError( + context, "Node number %d (%s) %s.\n", node_index, + registration.custom_name + ? registration.custom_name + : EnumNameBuiltinOperator( + static_cast(registration.builtin_code)), + message); + return kTfLiteError; +} + // Stub method which returns kTfLiteError when the function is forbidden. // We're registrating this function to several different function to save // compiled binary size. Please note the restrictions: @@ -121,9 +134,7 @@ Interpreter::Interpreter(ErrorReporter* error_reporter) context_.SetExternalContext = SetExternalContext; // Invalid to call these these except from TfLiteDelegate - SetForbiddenContextFunction(&context_.GetNodeAndRegistration); - SetForbiddenContextFunction(&context_.ReplaceSubgraphsWithDelegateKernels); - SetForbiddenContextFunction(&context_.GetExecutionPlan); + SwitchToKernelContext(); // Reserve some space for the tensors to avoid excessive resizing. tensors_.reserve(kTensorsReservedCapacity); @@ -268,8 +279,9 @@ TfLiteStatus Interpreter::ReplaceSubgraphsWithDelegateKernels( int node_index; TfLiteDelegateParams* params = CreateDelegateParams(delegate, subgraph); - AddNodeWithParameters(subgraph.input_tensors, subgraph.output_tensors, - nullptr, 0, params, ®istration, &node_index); + TF_LITE_ENSURE_STATUS(AddNodeWithParameters( + subgraph.input_tensors, subgraph.output_tensors, nullptr, 0, params, + ®istration, &node_index)); // Initialize the output tensors's delegate-related fields. for (int tensor_index : subgraph.output_tensors) { @@ -441,6 +453,13 @@ TfLiteStatus Interpreter::AllocateTensors() { TF_LITE_ENSURE_STATUS(PrepareOpsAndTensors()); state_ = kStateInvokable; + + // Reset the variable tensors to zero after (re)allocating the tensors. + // Developers shouldn't rely on the side effect of this function to reset + // variable tesnsors. They should call `ResetVariableTensorsToZero` directly + // instead. + ResetVariableTensorsToZero(); + return kTfLiteOk; } @@ -565,7 +584,8 @@ TfLiteStatus Interpreter::PrepareOpsStartingAt( nodes_and_registration_[node_index].second; EnsureTensorsVectorCapacity(); if (OpPrepare(registration, &node) == kTfLiteError) { - return kTfLiteError; + return ReportOpError(&context_, node, registration, node_index, + "failed to prepare"); } *last_execution_plan_index_prepared = execution_plan_index; @@ -584,7 +604,7 @@ TfLiteStatus Interpreter::PrepareOpsAndTensors() { if (!memory_planner_) { memory_planner_.reset(new ArenaPlanner( &context_, std::unique_ptr(new InterpreterInfo(this)), - /*preserve_inputs=*/true)); + /*preserve_inputs=*/true, /*preserve_intermediates*/ false)); memory_planner_->PlanAllocations(); } @@ -665,7 +685,8 @@ TfLiteStatus Interpreter::Invoke() { EnsureTensorsVectorCapacity(); tensor_resized_since_op_invoke_ = false; if (OpInvoke(registration, &node) == kTfLiteError) { - status = kTfLiteError; + status = ReportOpError(&context_, node, registration, node_index, + "failed to invoke"); } // Force execution prep for downstream ops if the latest op triggered the @@ -905,6 +926,19 @@ void Interpreter::SetNumThreads(int num_threads) { } } +void Interpreter::SwitchToDelegateContext() { + context_.GetNodeAndRegistration = GetNodeAndRegistration; + context_.ReplaceSubgraphsWithDelegateKernels = + ReplaceSubgraphsWithDelegateKernels; + context_.GetExecutionPlan = GetExecutionPlan; +} + +void Interpreter::SwitchToKernelContext() { + SetForbiddenContextFunction(&context_.GetNodeAndRegistration); + SetForbiddenContextFunction(&context_.ReplaceSubgraphsWithDelegateKernels); + SetForbiddenContextFunction(&context_.GetExecutionPlan); +} + TfLiteStatus Interpreter::ModifyGraphWithDelegate(TfLiteDelegate* delegate, bool allow_dynamic_tensors) { if (!allow_dynamic_tensors) { @@ -931,17 +965,12 @@ TfLiteStatus Interpreter::ModifyGraphWithDelegate(TfLiteDelegate* delegate, // TODO(aselle): Consider if it is worth storing pointers to delegates. // Setup additional context interface. - context_.GetNodeAndRegistration = GetNodeAndRegistration; - context_.ReplaceSubgraphsWithDelegateKernels = - ReplaceSubgraphsWithDelegateKernels; - context_.GetExecutionPlan = GetExecutionPlan; + SwitchToDelegateContext(); TfLiteStatus status = delegate->Prepare(&context_, delegate); // Remove additional context info. - SetForbiddenContextFunction(&context_.GetNodeAndRegistration); - SetForbiddenContextFunction(&context_.ReplaceSubgraphsWithDelegateKernels); - SetForbiddenContextFunction(&context_.GetExecutionPlan); + SwitchToKernelContext(); TF_LITE_ENSURE_OK(&context_, status); diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h index b69c50fbfce131f6862dc6e91387035e3d3bb7d8..be149a8cc0e642d10b270ba617cd8d6be29430b2 100644 --- a/tensorflow/contrib/lite/interpreter.h +++ b/tensorflow/contrib/lite/interpreter.h @@ -63,6 +63,10 @@ template <> constexpr TfLiteType typeToTfLiteType>() { return kTfLiteComplex64; } +template <> +constexpr TfLiteType typeToTfLiteType() { + return kTfLiteString; +} // Forward declare since NNAPIDelegate uses Interpreter. class NNAPIDelegate; @@ -107,7 +111,7 @@ class Interpreter { // processing this model will be forwarded to the error_reporter object. // // Note, if error_reporter is nullptr, then a default StderrReporter is - // used. + // used. Ownership of 'error_reporter' remains with the caller. explicit Interpreter(ErrorReporter* error_reporter = DefaultErrorReporter()); ~Interpreter(); @@ -412,6 +416,13 @@ class Interpreter { private: friend class InterpreterTest; + // Prevent 'context_' from accessing functions that are only available to + // delegated kernels. + void SwitchToKernelContext(); + + // Add delegate-only functions to 'context_'. + void SwitchToDelegateContext(); + // Give 'op_reg' a chance to initialize itself using the contents of // 'buffer'. void* OpInit(const TfLiteRegistration& op_reg, const char* buffer, @@ -498,6 +509,7 @@ class Interpreter { // Update the execution graph to replace some of the nodes with stub // nodes. Specifically any node index that has `nodes[index]==1` will be // slated for replacement with a delegate kernel specified by registration. + // Ownership of 'nodes_to_replace' and 'delegate' remains with the caller. // WARNING: This is an experimental interface that is subject to change. TfLiteStatus ReplaceSubgraphsWithDelegateKernels( TfLiteRegistration registration, const TfLiteIntArray* nodes_to_replace, @@ -625,7 +637,7 @@ class Interpreter { bool tensor_resized_since_op_invoke_ = false; // Profiler for this interpreter instance. - profiling::Profiler* profiler_; + profiling::Profiler* profiler_ = nullptr; // List of active external contexts. TfLiteExternalContext* external_contexts_[kTfLiteMaxExternalContexts]; diff --git a/tensorflow/contrib/lite/interpreter_test.cc b/tensorflow/contrib/lite/interpreter_test.cc index 4fa97512fca186fce8a2ec6514488b77c6d6511d..2bf598bad71b87afaa22c1eb95474c49386c122f 100644 --- a/tensorflow/contrib/lite/interpreter_test.cc +++ b/tensorflow/contrib/lite/interpreter_test.cc @@ -57,6 +57,22 @@ TEST(BasicInterpreter, InvokeInvalidModel) { ASSERT_EQ(interpreter.Invoke(), kTfLiteOk); } +TEST(BasicInterpreter, TestAllocateTensorsResetVariableTensors) { + Interpreter interpreter; + int tensor_index; + ASSERT_EQ(interpreter.AddTensors(1, &tensor_index), kTfLiteOk); + constexpr int kTensorSize = 16; + interpreter.SetTensorParametersReadWrite(tensor_index, kTfLiteFloat32, "", + {kTensorSize}, {}, true); + interpreter.SetVariables({tensor_index}); + ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk); + TfLiteTensor* tensor = interpreter.tensor(tensor_index); + // Ensure that variable tensors are reset to zero. + for (int i = 0; i < kTensorSize; ++i) { + ASSERT_EQ(tensor->data.f[i], 0.0f); + } +} + // Test size accessor functions. TEST(BasicInterpreter, TestSizeFunctions) { Interpreter interpreter; @@ -631,18 +647,6 @@ TEST(BasicInterpreter, AllocateTwice) { ASSERT_EQ(old_tensor1_ptr, interpreter.tensor(1)->data.raw); } -struct TestErrorReporter : public ErrorReporter { - int Report(const char* format, va_list args) override { - char buffer[1024]; - int size = vsnprintf(buffer, sizeof(buffer), format, args); - all_reports += buffer; - calls++; - return size; - } - int calls = 0; - std::string all_reports; -}; - TEST(BasicInterpreter, TestNullErrorReporter) { TestErrorReporter reporter; Interpreter interpreter; @@ -652,8 +656,9 @@ TEST(BasicInterpreter, TestCustomErrorReporter) { TestErrorReporter reporter; Interpreter interpreter(&reporter); ASSERT_NE(interpreter.Invoke(), kTfLiteOk); - ASSERT_EQ(reporter.all_reports, "Invoke called on model that is not ready."); - ASSERT_EQ(reporter.calls, 1); + ASSERT_EQ(reporter.error_messages(), + "Invoke called on model that is not ready."); + ASSERT_EQ(reporter.num_calls(), 1); } TEST(BasicInterpreter, TestUnsupportedDelegateFunctions) { diff --git a/tensorflow/contrib/lite/java/AndroidManifest.xml b/tensorflow/contrib/lite/java/AndroidManifest.xml index f705feacbec38ab5152ce52b701320d8f1cd8d3d..b91c6d149a213926be90b9b131bd632d4f79a0fc 100644 --- a/tensorflow/contrib/lite/java/AndroidManifest.xml +++ b/tensorflow/contrib/lite/java/AndroidManifest.xml @@ -1,7 +1,12 @@ - - + package="org.tensorflow.lite"> + + + + + diff --git a/tensorflow/contrib/lite/java/BUILD b/tensorflow/contrib/lite/java/BUILD index 593af81a18a1e20a41dcc8d9bb3a1d815876e294..098ba7e7731d833678fbd5eab9cce3f022570f23 100644 --- a/tensorflow/contrib/lite/java/BUILD +++ b/tensorflow/contrib/lite/java/BUILD @@ -69,6 +69,7 @@ java_test( size = "small", srcs = ["src/test/java/org/tensorflow/lite/TensorFlowLiteTest.java"], javacopts = JAVACOPTS, + tags = ["no_oss"], test_class = "org.tensorflow.lite.TensorFlowLiteTest", deps = [ ":libtensorflowlite_jni.so", @@ -83,6 +84,7 @@ java_test( size = "small", srcs = ["src/test/java/org/tensorflow/lite/DataTypeTest.java"], javacopts = JAVACOPTS, + tags = ["no_oss"], test_class = "org.tensorflow.lite.DataTypeTest", deps = [ ":libtensorflowlite_jni.so", @@ -105,6 +107,7 @@ java_test( "src/testdata/with_custom_op.lite", ], javacopts = JAVACOPTS, + tags = ["no_oss"], test_class = "org.tensorflow.lite.NativeInterpreterWrapperTest", deps = [ ":libtensorflowlite_jni.so", @@ -124,6 +127,7 @@ java_test( "src/testdata/mobilenet.tflite.bin", ], javacopts = JAVACOPTS, + tags = ["no_oss"], test_class = "org.tensorflow.lite.InterpreterTest", visibility = ["//visibility:private"], deps = [ @@ -142,6 +146,7 @@ java_test( "src/testdata/add.bin", ], javacopts = JAVACOPTS, + tags = ["no_oss"], test_class = "org.tensorflow.lite.TensorTest", deps = [ ":tensorflowlitelib", diff --git a/tensorflow/contrib/lite/java/demo/app/build.gradle b/tensorflow/contrib/lite/java/demo/app/build.gradle index 49868c5a7566c8c537ac2ae9e0a4acc2c872ecbf..92f04c651c0488a5202def593774890630c8631f 100644 --- a/tensorflow/contrib/lite/java/demo/app/build.gradle +++ b/tensorflow/contrib/lite/java/demo/app/build.gradle @@ -44,7 +44,7 @@ repositories { dependencies { compile fileTree(dir: 'libs', include: ['*.jar']) - androidTestCompile('com.androidx.test.espresso:espresso-core:2.2.2', { + androidTestCompile('androidx.test.espresso:espresso-core:3.1.0-alpha3', { exclude group: 'com.android.support', module: 'support-annotations' }) compile 'com.android.support:appcompat-v7:25.2.0' diff --git a/tensorflow/contrib/lite/java/ovic/BUILD b/tensorflow/contrib/lite/java/ovic/BUILD index f232b00045cf1df6a31ada80af4cc5885a4c0099..06f46fb92394b19415ddb95dcf8c798753b630e3 100644 --- a/tensorflow/contrib/lite/java/ovic/BUILD +++ b/tensorflow/contrib/lite/java/ovic/BUILD @@ -18,6 +18,7 @@ java_test( "//tensorflow/contrib/lite/java/ovic/src/testdata:ovic_testdata", ], javacopts = JAVACOPTS, + tags = ["no_oss"], test_class = "org.tensorflow.ovic.OvicClassifierTest", visibility = ["//visibility:public"], deps = [ diff --git a/tensorflow/contrib/lite/java/ovic/demo/app/build.gradle b/tensorflow/contrib/lite/java/ovic/demo/app/build.gradle index 3f32d62e5c08419c6413fffe09b64356edcac836..2a08608bbb121a2e279bbd16de6a014e68848796 100644 --- a/tensorflow/contrib/lite/java/ovic/demo/app/build.gradle +++ b/tensorflow/contrib/lite/java/ovic/demo/app/build.gradle @@ -43,7 +43,7 @@ repositories { dependencies { compile fileTree(dir: 'libs', include: ['*.jar']) - androidTestCompile('com.androidx.test.espresso:espresso-core:2.2.2', { + androidTestCompile('androidx.test.espresso:espresso-core:3.1.0-alpha3', { exclude group: 'com.android.support', module: 'support-annotations' }) compile 'com.android.support:appcompat-v7:25.2.0' diff --git a/tensorflow/contrib/lite/java/ovic/src/test/java/org/tensorflow/ovic/OvicClassifierTest.java b/tensorflow/contrib/lite/java/ovic/src/test/java/org/tensorflow/ovic/OvicClassifierTest.java index 56f3e7604a5b172e907edbe862b017957594397f..1587c3c56f45c0baddfa75286c979fe0c0edffcc 100644 --- a/tensorflow/contrib/lite/java/ovic/src/test/java/org/tensorflow/ovic/OvicClassifierTest.java +++ b/tensorflow/contrib/lite/java/ovic/src/test/java/org/tensorflow/ovic/OvicClassifierTest.java @@ -127,12 +127,8 @@ public final class OvicClassifierTest { try { testResult = classifier.classifyByteBuffer(testImage); fail(); - } catch (RuntimeException e) { - assertThat(e) - .hasMessageThat() - .contains( - "Failed to get input dimensions. 0-th input should have 49152 bytes, " - + "but found 150528 bytes."); + } catch (IllegalArgumentException e) { + // Success. } } diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/DataType.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/DataType.java index 75334cd96e8daadc356dadea063eee30ef6d5245..94a1ec65d64b6493cdb309fc0c19155eb9cb26cb 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/DataType.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/DataType.java @@ -27,10 +27,7 @@ enum DataType { UINT8(3), /** 64-bit signed integer. */ - INT64(4), - - /** A {@link ByteBuffer}. */ - BYTEBUFFER(999); + INT64(4); private final int value; @@ -69,8 +66,6 @@ enum DataType { return 1; case INT64: return 8; - case BYTEBUFFER: - return 1; } throw new IllegalArgumentException( "DataType error: DataType " + this + " is not supported yet"); @@ -87,8 +82,6 @@ enum DataType { return "byte"; case INT64: return "long"; - case BYTEBUFFER: - return "ByteBuffer"; } throw new IllegalArgumentException( "DataType error: DataType " + this + " is not supported yet"); diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java index 4e22a68bf2e5e9cdc7783ffd829e124023a05479..7002f826775b216e0a27ebe00f30680c9ce362bb 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java @@ -165,20 +165,7 @@ public final class Interpreter implements AutoCloseable { if (wrapper == null) { throw new IllegalStateException("Internal error: The Interpreter has already been closed."); } - Tensor[] tensors = wrapper.run(inputs); - if (outputs == null || tensors == null || outputs.size() > tensors.length) { - throw new IllegalArgumentException("Output error: Outputs do not match with model outputs."); - } - final int size = tensors.length; - for (Integer idx : outputs.keySet()) { - if (idx == null || idx < 0 || idx >= size) { - throw new IllegalArgumentException( - String.format( - "Output error: Invalid index of output %d (should be in range [0, %d))", - idx, size)); - } - tensors[idx].copyTo(outputs.get(idx)); - } + wrapper.run(inputs, outputs); } /** @@ -251,8 +238,10 @@ public final class Interpreter implements AutoCloseable { /** Release resources associated with the {@code Interpreter}. */ @Override public void close() { - wrapper.close(); - wrapper = null; + if (wrapper != null) { + wrapper.close(); + wrapper = null; + } } @Override diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java index 80de88b6a1cd75b033e116f76f5612ee66e48f03..767a220f8cd5381ce10e044553317b1cb05ba17b 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java @@ -15,10 +15,10 @@ limitations under the License. package org.tensorflow.lite; -import java.lang.reflect.Array; import java.nio.ByteBuffer; import java.nio.ByteOrder; import java.nio.MappedByteBuffer; +import java.util.Arrays; import java.util.HashMap; import java.util.Map; @@ -40,6 +40,8 @@ final class NativeInterpreterWrapper implements AutoCloseable { modelHandle = createModel(modelPath, errorHandle); interpreterHandle = createInterpreter(modelHandle, errorHandle, numThreads); isMemoryAllocated = true; + inputTensors = new Tensor[getInputCount(interpreterHandle)]; + outputTensors = new Tensor[getOutputCount(interpreterHandle)]; } /** @@ -72,6 +74,8 @@ final class NativeInterpreterWrapper implements AutoCloseable { modelHandle = createModelWithBuffer(modelByteBuffer, errorHandle); interpreterHandle = createInterpreter(modelHandle, errorHandle, numThreads); isMemoryAllocated = true; + inputTensors = new Tensor[getInputCount(interpreterHandle)]; + outputTensors = new Tensor[getOutputCount(interpreterHandle)]; } /** Releases resources associated with this {@code NativeInterpreterWrapper}. */ @@ -85,75 +89,63 @@ final class NativeInterpreterWrapper implements AutoCloseable { inputsIndexes = null; outputsIndexes = null; isMemoryAllocated = false; + Arrays.fill(inputTensors, null); + Arrays.fill(outputTensors, null); } /** Sets inputs, runs model inference and returns outputs. */ - Tensor[] run(Object[] inputs) { + void run(Object[] inputs, Map outputs) { + inferenceDurationNanoseconds = -1; if (inputs == null || inputs.length == 0) { throw new IllegalArgumentException("Input error: Inputs should not be null or empty."); } - int[] dataTypes = new int[inputs.length]; - Object[] sizes = new Object[inputs.length]; - int[] numsOfBytes = new int[inputs.length]; + if (outputs == null || outputs.isEmpty()) { + throw new IllegalArgumentException("Input error: Outputs should not be null or empty."); + } + + // TODO(b/80431971): Remove implicit resize after deprecating multi-dimensional array inputs. + // Rather than forcing an immediate resize + allocation if an input's shape differs, we first + // flush all resizes, avoiding redundant allocations. for (int i = 0; i < inputs.length; ++i) { - DataType dataType = dataTypeOf(inputs[i]); - dataTypes[i] = dataType.getNumber(); - if (dataType == DataType.BYTEBUFFER) { - ByteBuffer buffer = (ByteBuffer) inputs[i]; - if (buffer == null || !buffer.isDirect() || buffer.order() != ByteOrder.nativeOrder()) { - throw new IllegalArgumentException( - "Input error: ByteBuffer should be a direct ByteBuffer that uses " - + "ByteOrder.nativeOrder()."); - } - numsOfBytes[i] = buffer.limit(); - sizes[i] = getInputDims(interpreterHandle, i, numsOfBytes[i]); - } else if (isNonEmptyArray(inputs[i])) { - int[] dims = shapeOf(inputs[i]); - sizes[i] = dims; - numsOfBytes[i] = dataType.elemByteSize() * numElements(dims); - } else { - throw new IllegalArgumentException( - String.format( - "Input error: %d-th element of the %d inputs is not an array or a ByteBuffer.", - i, inputs.length)); + Tensor tensor = getInputTensor(i); + int[] newShape = tensor.getInputShapeIfDifferent(inputs[i]); + if (newShape != null) { + resizeInput(i, newShape); } } - inferenceDurationNanoseconds = -1; - long[] outputsHandles = - run( - interpreterHandle, - errorHandle, - sizes, - dataTypes, - numsOfBytes, - inputs, - this, - isMemoryAllocated); - if (outputsHandles == null || outputsHandles.length == 0) { - throw new IllegalStateException("Internal error: Interpreter has no outputs."); + + if (!isMemoryAllocated) { + allocateTensors(interpreterHandle, errorHandle); + isMemoryAllocated = true; + // Allocation can trigger dynamic resizing of output tensors, so clear the + // output tensor cache. + Arrays.fill(outputTensors, null); } - isMemoryAllocated = true; - Tensor[] outputs = new Tensor[outputsHandles.length]; - for (int i = 0; i < outputsHandles.length; ++i) { - outputs[i] = Tensor.fromHandle(outputsHandles[i]); + + for (int i = 0; i < inputs.length; ++i) { + getInputTensor(i).setTo(inputs[i]); + } + + long inferenceStartNanos = System.nanoTime(); + run(interpreterHandle, errorHandle); + long inferenceDurationNanoseconds = System.nanoTime() - inferenceStartNanos; + + for (Map.Entry output : outputs.entrySet()) { + getOutputTensor(output.getKey()).copyTo(output.getValue()); } - return outputs; + + // Only set if the entire operation succeeds. + this.inferenceDurationNanoseconds = inferenceDurationNanoseconds; } - private static native long[] run( - long interpreterHandle, - long errorHandle, - Object[] sizes, - int[] dtypes, - int[] numsOfBytes, - Object[] values, - NativeInterpreterWrapper wrapper, - boolean memoryAllocated); + private static native boolean run(long interpreterHandle, long errorHandle); /** Resizes dimensions of a specific input. */ void resizeInput(int idx, int[] dims) { if (resizeInput(interpreterHandle, errorHandle, idx, dims)) { isMemoryAllocated = false; + // Resizing will invalidate the Tensor's shape, so invalidate the Tensor handle. + inputTensors[idx] = null; } } @@ -212,78 +204,6 @@ final class NativeInterpreterWrapper implements AutoCloseable { } } - static int numElements(int[] shape) { - if (shape == null) { - return 0; - } - int n = 1; - for (int i = 0; i < shape.length; i++) { - n *= shape[i]; - } - return n; - } - - static boolean isNonEmptyArray(Object o) { - return (o != null && o.getClass().isArray() && Array.getLength(o) != 0); - } - - /** Returns the type of the data. */ - static DataType dataTypeOf(Object o) { - if (o != null) { - Class c = o.getClass(); - while (c.isArray()) { - c = c.getComponentType(); - } - if (float.class.equals(c)) { - return DataType.FLOAT32; - } else if (int.class.equals(c)) { - return DataType.INT32; - } else if (byte.class.equals(c)) { - return DataType.UINT8; - } else if (long.class.equals(c)) { - return DataType.INT64; - } else if (ByteBuffer.class.isInstance(o)) { - return DataType.BYTEBUFFER; - } - } - throw new IllegalArgumentException( - "DataType error: cannot resolve DataType of " + o.getClass().getName()); - } - - /** Returns the shape of an object as an int array. */ - static int[] shapeOf(Object o) { - int size = numDimensions(o); - int[] dimensions = new int[size]; - fillShape(o, 0, dimensions); - return dimensions; - } - - static int numDimensions(Object o) { - if (o == null || !o.getClass().isArray()) { - return 0; - } - if (Array.getLength(o) == 0) { - throw new IllegalArgumentException("Array lengths cannot be 0."); - } - return 1 + numDimensions(Array.get(o, 0)); - } - - static void fillShape(Object o, int dim, int[] shape) { - if (shape == null || dim == shape.length) { - return; - } - final int len = Array.getLength(o); - if (shape[dim] == 0) { - shape[dim] = len; - } else if (shape[dim] != len) { - throw new IllegalArgumentException( - String.format("Mismatched lengths (%d and %d) in dimension %d", shape[dim], len, dim)); - } - for (int i = 0; i < len; ++i) { - fillShape(Array.get(o, i), dim + 1, shape); - } - } - /** * Gets the last inference duration in nanoseconds. It returns null if there is no previous * inference run or the last inference run failed. @@ -293,40 +213,55 @@ final class NativeInterpreterWrapper implements AutoCloseable { } /** - * Gets the dimensions of an input. It throws IllegalArgumentException if input index is invalid. + * Gets the quantization zero point of an output. + * + * @throws IllegalArgumentException if the output index is invalid. */ - int[] getInputDims(int index) { - return getInputDims(interpreterHandle, index, -1); + int getOutputQuantizationZeroPoint(int index) { + return getOutputQuantizationZeroPoint(interpreterHandle, index); } /** - * Gets the dimensions of an input. If numBytes >= 0, it will check whether num of bytes match the - * input. + * Gets the quantization scale of an output. + * + * @throws IllegalArgumentException if the output index is invalid. */ - private static native int[] getInputDims(long interpreterHandle, int inputIdx, int numBytes); - - /** Gets the type of an output. It throws IllegalArgumentException if output index is invalid. */ - String getOutputDataType(int index) { - int type = getOutputDataType(interpreterHandle, index); - return DataType.fromNumber(type).toStringName(); + float getOutputQuantizationScale(int index) { + return getOutputQuantizationScale(interpreterHandle, index); } /** - * Gets the quantization zero point of an output. + * Gets the input {@link Tensor} for the provided input index. * - * @throws IllegalArgumentExeption if the output index is invalid. + * @throws IllegalArgumentException if the input index is invalid. */ - int getOutputQuantizationZeroPoint(int index) { - return getOutputQuantizationZeroPoint(interpreterHandle, index); + Tensor getInputTensor(int index) { + if (index < 0 || index >= inputTensors.length) { + throw new IllegalArgumentException("Invalid input Tensor index: " + index); + } + Tensor inputTensor = inputTensors[index]; + if (inputTensor == null) { + inputTensor = + inputTensors[index] = Tensor.fromHandle(getInputTensor(interpreterHandle, index)); + } + return inputTensor; } /** - * Gets the quantization scale of an output. + * Gets the output {@link Tensor} for the provided output index. * - * @throws IllegalArgumentExeption if the output index is invalid. + * @throws IllegalArgumentException if the output index is invalid. */ - float getOutputQuantizationScale(int index) { - return getOutputQuantizationScale(interpreterHandle, index); + Tensor getOutputTensor(int index) { + if (index < 0 || index >= outputTensors.length) { + throw new IllegalArgumentException("Invalid output Tensor index: " + index); + } + Tensor outputTensor = outputTensors[index]; + if (outputTensor == null) { + outputTensor = + outputTensors[index] = Tensor.fromHandle(getOutputTensor(interpreterHandle, index)); + } + return outputTensor; } private static native int getOutputDataType(long interpreterHandle, int outputIdx); @@ -343,18 +278,30 @@ final class NativeInterpreterWrapper implements AutoCloseable { private long modelHandle; - private int inputSize; - private long inferenceDurationNanoseconds = -1; private ByteBuffer modelByteBuffer; + // Lazily constructed maps of input and output names to input and output Tensor indexes. private Map inputsIndexes; - private Map outputsIndexes; + // Lazily constructed and populated arrays of input and output Tensor wrappers. + private final Tensor[] inputTensors; + private final Tensor[] outputTensors; + private boolean isMemoryAllocated = false; + private static native long allocateTensors(long interpreterHandle, long errorHandle); + + private static native long getInputTensor(long interpreterHandle, int inputIdx); + + private static native long getOutputTensor(long interpreterHandle, int outputIdx); + + private static native int getInputCount(long interpreterHandle); + + private static native int getOutputCount(long interpreterHandle); + private static native String[] getInputNames(long interpreterHandle); private static native String[] getOutputNames(long interpreterHandle); diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Tensor.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Tensor.java index b2a3e04c55d86a33307e48571d50a72e0fa461ac..2403570c527e762f6782e313731e383feeeef46d 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Tensor.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Tensor.java @@ -15,6 +15,7 @@ limitations under the License. package org.tensorflow.lite; +import java.lang.reflect.Array; import java.nio.ByteBuffer; import java.nio.ByteOrder; import java.util.Arrays; @@ -31,43 +32,179 @@ final class Tensor { return new Tensor(nativeHandle); } + /** Returns the {@link DataType} of elements stored in the Tensor. */ + public DataType dataType() { + return dtype; + } + + /** Returns the size, in bytes, of the tensor data. */ + public int numBytes() { + return numBytes(nativeHandle); + } + + /** + * Returns the shape of + * the Tensor, i.e., the sizes of each dimension. + * + * @return an array where the i-th element is the size of the i-th dimension of the tensor. + */ + public int[] shape() { + return shapeCopy; + } + + /** + * Copies the contents of the provided {@code src} object to the Tensor. + * + *

The {@code src} should either be a (multi-dimensional) array with a shape matching that of + * this tensor, or a {@link ByteByffer} of compatible primitive type with a matching flat size. + * + * @throws IllegalArgumentException if the tensor is a scalar or if {@code src} is not compatible + * with the tensor (for example, mismatched data types or shapes). + */ + void setTo(Object src) { + throwExceptionIfTypeIsIncompatible(src); + if (isByteBuffer(src)) { + ByteBuffer srcBuffer = (ByteBuffer) src; + // For direct ByteBuffer instances we support zero-copy. Note that this assumes the caller + // retains ownership of the source buffer until inference has completed. + if (srcBuffer.isDirect() && srcBuffer.order() == ByteOrder.nativeOrder()) { + writeDirectBuffer(nativeHandle, srcBuffer); + } else { + buffer().put(srcBuffer); + } + return; + } + writeMultiDimensionalArray(nativeHandle, src); + } + /** * Copies the contents of the tensor to {@code dst} and returns {@code dst}. * * @param dst the destination buffer, either an explicitly-typed array or a {@link ByteBuffer}. * @throws IllegalArgumentException if {@code dst} is not compatible with the tensor (for example, * mismatched data types or shapes). - * @throws BufferOverflowException If {@code dst} is a ByteBuffer with insufficient space for the - * data in this tensor. */ - T copyTo(T dst) { + Object copyTo(Object dst) { + throwExceptionIfTypeIsIncompatible(dst); if (dst instanceof ByteBuffer) { ByteBuffer dstByteBuffer = (ByteBuffer) dst; dstByteBuffer.put(buffer()); return dst; } - if (NativeInterpreterWrapper.dataTypeOf(dst) != dtype) { + readMultiDimensionalArray(nativeHandle, dst); + return dst; + } + + /** Returns the provided buffer's shape if specified and different from this Tensor's shape. */ + // TODO(b/80431971): Remove this method after deprecating multi-dimensional array inputs. + int[] getInputShapeIfDifferent(Object input) { + // Implicit resizes based on ByteBuffer capacity isn't supported, so short-circuit that path. + // The ByteBuffer's size will be validated against this Tensor's size in {@link #setTo(Object)}. + if (isByteBuffer(input)) { + return null; + } + int[] inputShape = shapeOf(input); + if (Arrays.equals(shapeCopy, inputShape)) { + return null; + } + return inputShape; + } + + /** Returns the type of the data. */ + static DataType dataTypeOf(Object o) { + if (o != null) { + Class c = o.getClass(); + while (c.isArray()) { + c = c.getComponentType(); + } + if (float.class.equals(c)) { + return DataType.FLOAT32; + } else if (int.class.equals(c)) { + return DataType.INT32; + } else if (byte.class.equals(c)) { + return DataType.UINT8; + } else if (long.class.equals(c)) { + return DataType.INT64; + } + } + throw new IllegalArgumentException( + "DataType error: cannot resolve DataType of " + o.getClass().getName()); + } + + /** Returns the shape of an object as an int array. */ + static int[] shapeOf(Object o) { + int size = numDimensions(o); + int[] dimensions = new int[size]; + fillShape(o, 0, dimensions); + return dimensions; + } + + /** Returns the number of dimensions of a multi-dimensional array, otherwise 0. */ + static int numDimensions(Object o) { + if (o == null || !o.getClass().isArray()) { + return 0; + } + if (Array.getLength(o) == 0) { + throw new IllegalArgumentException("Array lengths cannot be 0."); + } + return 1 + numDimensions(Array.get(o, 0)); + } + + /** Recursively populates the shape dimensions for a given (multi-dimensional) array. */ + static void fillShape(Object o, int dim, int[] shape) { + if (shape == null || dim == shape.length) { + return; + } + final int len = Array.getLength(o); + if (shape[dim] == 0) { + shape[dim] = len; + } else if (shape[dim] != len) { + throw new IllegalArgumentException( + String.format("Mismatched lengths (%d and %d) in dimension %d", shape[dim], len, dim)); + } + for (int i = 0; i < len; ++i) { + fillShape(Array.get(o, i), dim + 1, shape); + } + } + + private void throwExceptionIfTypeIsIncompatible(Object o) { + if (isByteBuffer(o)) { + ByteBuffer oBuffer = (ByteBuffer) o; + if (oBuffer.capacity() != numBytes()) { + throw new IllegalArgumentException( + String.format( + "Cannot convert between a TensorFlowLite buffer with %d bytes and a " + + "ByteBuffer with %d bytes.", + numBytes(), oBuffer.capacity())); + } + return; + } + DataType oType = dataTypeOf(o); + if (oType != dtype) { throw new IllegalArgumentException( String.format( - "Output error: Cannot convert an TensorFlowLite tensor with type %s to a Java " - + "object of type %s (which is compatible with the TensorFlowLite type %s)", - dtype, dst.getClass().getName(), NativeInterpreterWrapper.dataTypeOf(dst))); + "Cannot convert between a TensorFlowLite tensor with type %s and a Java " + + "object of type %s (which is compatible with the TensorFlowLite type %s).", + dtype, o.getClass().getName(), oType)); } - int[] dstShape = NativeInterpreterWrapper.shapeOf(dst); - if (!Arrays.equals(dstShape, shapeCopy)) { + + int[] oShape = shapeOf(o); + if (!Arrays.equals(oShape, shapeCopy)) { throw new IllegalArgumentException( String.format( - "Output error: Shape of output target %s does not match with the shape of the " - + "Tensor %s.", - Arrays.toString(dstShape), Arrays.toString(shapeCopy))); + "Cannot copy between a TensorFlowLite tensor with shape %s and a Java object " + + "with shape %s.", + Arrays.toString(shapeCopy), Arrays.toString(oShape))); } - readMultiDimensionalArray(nativeHandle, dst); - return dst; } - final long nativeHandle; - final DataType dtype; - final int[] shapeCopy; + private static boolean isByteBuffer(Object o) { + return o instanceof ByteBuffer; + } + + private final long nativeHandle; + private final DataType dtype; + private final int[] shapeCopy; private Tensor(long nativeHandle) { this.nativeHandle = nativeHandle; @@ -81,11 +218,17 @@ final class Tensor { private static native ByteBuffer buffer(long handle); + private static native void writeDirectBuffer(long handle, ByteBuffer src); + private static native int dtype(long handle); private static native int[] shape(long handle); - private static native void readMultiDimensionalArray(long handle, Object value); + private static native int numBytes(long handle); + + private static native void readMultiDimensionalArray(long handle, Object dst); + + private static native void writeMultiDimensionalArray(long handle, Object src); static { TensorFlowLite.init(); diff --git a/tensorflow/contrib/lite/java/src/main/native/BUILD b/tensorflow/contrib/lite/java/src/main/native/BUILD index 4399ed202597082fba36c04a744bf6378e4539a2..4b4e1c21d818dc56803ff31d83d19dea2ac08707 100644 --- a/tensorflow/contrib/lite/java/src/main/native/BUILD +++ b/tensorflow/contrib/lite/java/src/main/native/BUILD @@ -11,7 +11,6 @@ licenses(["notice"]) # Apache 2.0 cc_library( name = "native_framework_only", srcs = [ - "duration_utils_jni.cc", "exception_jni.cc", "nativeinterpreterwrapper_jni.cc", "tensor_jni.cc", diff --git a/tensorflow/contrib/lite/java/src/main/native/duration_utils_jni.cc b/tensorflow/contrib/lite/java/src/main/native/duration_utils_jni.cc deleted file mode 100644 index 0e08a04370592f6e3c92b5811fa7e163f808e03c..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/lite/java/src/main/native/duration_utils_jni.cc +++ /dev/null @@ -1,38 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include -#include - -namespace tflite { - -// Gets the elapsed wall-clock timespec. -timespec getCurrentTime() { - timespec time; - clock_gettime(CLOCK_MONOTONIC, &time); - return time; -} - -// Computes the time diff from two timespecs. Returns '-1' if 'stop' is earlier -// than 'start'. -jlong timespec_diff_nanoseconds(struct timespec* start, struct timespec* stop) { - jlong result = stop->tv_sec - start->tv_sec; - if (result < 0) return -1; - result = 1000000000 * result + (stop->tv_nsec - start->tv_nsec); - if (result < 0) return -1; - return result; -} - -} // namespace tflite diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc index 31f7b58fbc30cab9e6cb813094ea4b2627ba5cba..fdcf00a0a08459d8d669f1def3ae2eb21dbd31c3 100644 --- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc +++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc @@ -16,9 +16,6 @@ limitations under the License. #include "tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h" namespace { -const int kByteBufferValue = 999; -const int kBufferSize = 256; - tflite::Interpreter* convertLongToInterpreter(JNIEnv* env, jlong handle) { if (handle == 0) { throwException(env, kIllegalArgumentException, @@ -62,22 +59,6 @@ std::vector convertJIntArrayToVector(JNIEnv* env, jintArray inputs) { return outputs; } -bool isByteBuffer(jint data_type) { return data_type == kByteBufferValue; } - -TfLiteType resolveDataType(jint data_type) { - switch (data_type) { - case 1: - return kTfLiteFloat32; - case 2: - return kTfLiteInt32; - case 3: - return kTfLiteUInt8; - case 4: - return kTfLiteInt64; - default: - return kTfLiteNoType; - } -} int getDataType(TfLiteType data_type) { switch (data_type) { @@ -108,64 +89,6 @@ void printDims(char* buffer, int max_size, int* dims, int num_dims) { } } -TfLiteStatus checkInputs(JNIEnv* env, tflite::Interpreter* interpreter, - const int input_size, jintArray data_types, - jintArray nums_of_bytes, jobjectArray values, - jobjectArray sizes) { - if (input_size != interpreter->inputs().size()) { - throwException(env, kIllegalArgumentException, - "Input error: Expected num of inputs is %d but got %d", - interpreter->inputs().size(), input_size); - return kTfLiteError; - } - if (input_size != env->GetArrayLength(data_types) || - input_size != env->GetArrayLength(nums_of_bytes) || - input_size != env->GetArrayLength(values)) { - throwException(env, kIllegalArgumentException, - "Internal error: Arrays in arguments should be of the same " - "length, but got %d sizes, %d data_types, %d nums_of_bytes, " - "and %d values", - input_size, env->GetArrayLength(data_types), - env->GetArrayLength(nums_of_bytes), - env->GetArrayLength(values)); - return kTfLiteError; - } - for (int i = 0; i < input_size; ++i) { - int input_idx = interpreter->inputs()[i]; - TfLiteTensor* target = interpreter->tensor(input_idx); - jintArray dims = - static_cast(env->GetObjectArrayElement(sizes, i)); - int num_dims = static_cast(env->GetArrayLength(dims)); - if (target->dims->size != num_dims) { - throwException(env, kIllegalArgumentException, - "Input error: %d-th input should have %d dimensions, but " - "found %d dimensions", - i, target->dims->size, num_dims); - return kTfLiteError; - } - jint* ptr = env->GetIntArrayElements(dims, nullptr); - for (int j = 1; j < num_dims; ++j) { - if (target->dims->data[j] != ptr[j]) { - std::unique_ptr expected_dims(new char[kBufferSize]); - std::unique_ptr obtained_dims(new char[kBufferSize]); - printDims(expected_dims.get(), kBufferSize, target->dims->data, - num_dims); - printDims(obtained_dims.get(), kBufferSize, ptr, num_dims); - throwException(env, kIllegalArgumentException, - "Input error: %d-th input dimension should be [%s], but " - "found [%s]", - i, expected_dims.get(), obtained_dims.get()); - env->ReleaseIntArrayElements(dims, ptr, JNI_ABORT); - return kTfLiteError; - } - } - env->ReleaseIntArrayElements(dims, ptr, JNI_ABORT); - env->DeleteLocalRef(dims); - if (env->ExceptionCheck()) return kTfLiteError; - } - return kTfLiteOk; -} - // Checks whether there is any difference between dimensions of a tensor and a // given dimensions. Returns true if there is difference, else false. bool areDimsDifferent(JNIEnv* env, TfLiteTensor* tensor, jintArray dims) { @@ -188,74 +111,6 @@ bool areDimsDifferent(JNIEnv* env, TfLiteTensor* tensor, jintArray dims) { return false; } -bool areInputDimensionsTheSame(JNIEnv* env, tflite::Interpreter* interpreter, - int input_size, jobjectArray sizes) { - if (interpreter->inputs().size() != input_size) { - return false; - } - for (int i = 0; i < input_size; ++i) { - int input_idx = interpreter->inputs()[i]; - jintArray dims = - static_cast(env->GetObjectArrayElement(sizes, i)); - TfLiteTensor* target = interpreter->tensor(input_idx); - if (areDimsDifferent(env, target, dims)) return false; - env->DeleteLocalRef(dims); - if (env->ExceptionCheck()) return false; - } - return true; -} - -TfLiteStatus resizeInputs(JNIEnv* env, tflite::Interpreter* interpreter, - int input_size, jobjectArray sizes) { - for (int i = 0; i < input_size; ++i) { - int input_idx = interpreter->inputs()[i]; - jintArray dims = - static_cast(env->GetObjectArrayElement(sizes, i)); - TfLiteStatus status = interpreter->ResizeInputTensor( - input_idx, convertJIntArrayToVector(env, dims)); - if (status != kTfLiteOk) { - return status; - } - env->DeleteLocalRef(dims); - if (env->ExceptionCheck()) return kTfLiteError; - } - return kTfLiteOk; -} - -TfLiteStatus setInputs(JNIEnv* env, tflite::Interpreter* interpreter, - int input_size, jintArray data_types, - jintArray nums_of_bytes, jobjectArray values) { - jint* data_type = env->GetIntArrayElements(data_types, nullptr); - jint* num_bytes = env->GetIntArrayElements(nums_of_bytes, nullptr); - for (int i = 0; i < input_size; ++i) { - int input_idx = interpreter->inputs()[i]; - TfLiteTensor* target = interpreter->tensor(input_idx); - jobject value = env->GetObjectArrayElement(values, i); - bool is_byte_buffer = isByteBuffer(data_type[i]); - if (is_byte_buffer) { - writeByteBuffer(env, value, &(target->data.raw), - static_cast(num_bytes[i])); - } else { - TfLiteType type = resolveDataType(data_type[i]); - if (type != target->type) { - throwException(env, kIllegalArgumentException, - "Input error: DataType (%d) of input data does not " - "match with the DataType (%d) of model inputs.", - type, target->type); - return kTfLiteError; - } - writeMultiDimensionalArray(env, value, target->type, target->dims->size, - &(target->data.raw), - static_cast(num_bytes[i])); - } - env->DeleteLocalRef(value); - if (env->ExceptionCheck()) return kTfLiteError; - } - env->ReleaseIntArrayElements(data_types, data_type, JNI_ABORT); - env->ReleaseIntArrayElements(nums_of_bytes, num_bytes, JNI_ABORT); - return kTfLiteOk; -} - // TODO(yichengfan): evaluate the benefit to use tflite verifier. bool VerifyModel(const void* buf, size_t len) { flatbuffers::Verifier verifier(static_cast(buf), len); @@ -287,6 +142,64 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputNames(JNIEnv* env, return names; } +JNIEXPORT void JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_allocateTensors( + JNIEnv* env, jclass clazz, jlong handle, jlong error_handle) { + tflite::Interpreter* interpreter = convertLongToInterpreter(env, handle); + if (interpreter == nullptr) return; + BufferErrorReporter* error_reporter = + convertLongToErrorReporter(env, error_handle); + if (error_reporter == nullptr) return; + + if (interpreter->AllocateTensors() != kTfLiteOk) { + throwException( + env, kIllegalStateException, + "Internal error: Unexpected failure when preparing tensor allocations:" + " %s", + error_reporter->CachedErrorMessage()); + } +} + +JNIEXPORT jlong JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputTensor(JNIEnv* env, + jclass clazz, + jlong handle, + jint index) { + tflite::Interpreter* interpreter = convertLongToInterpreter(env, handle); + if (interpreter == nullptr) return 0; + return reinterpret_cast( + interpreter->tensor(interpreter->inputs()[index])); +} + +JNIEXPORT jlong JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputTensor(JNIEnv* env, + jclass clazz, + jlong handle, + jint index) { + tflite::Interpreter* interpreter = convertLongToInterpreter(env, handle); + if (interpreter == nullptr) return 0; + return reinterpret_cast( + interpreter->tensor(interpreter->outputs()[index])); +} + +JNIEXPORT jint JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputCount(JNIEnv* env, + jclass clazz, + jlong handle) { + tflite::Interpreter* interpreter = convertLongToInterpreter(env, handle); + if (interpreter == nullptr) return 0; + return static_cast(interpreter->inputs().size()); +} + +JNIEXPORT jint JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputCount(JNIEnv* env, + jclass clazz, + jlong handle) { + tflite::Interpreter* interpreter = convertLongToInterpreter(env, handle); + if (interpreter == nullptr) return 0; + return static_cast(interpreter->outputs().size()); +} + JNIEXPORT jobjectArray JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputNames(JNIEnv* env, jclass clazz, @@ -424,124 +337,32 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_createInterpreter( // allocates memory status = interpreter->AllocateTensors(); if (status != kTfLiteOk) { - throwException(env, kNullPointerException, - "Internal error: Cannot allocate memory for the interpreter:" - " %s", - error_reporter->CachedErrorMessage()); + throwException( + env, kIllegalStateException, + "Internal error: Unexpected failure when preparing tensor allocations:" + " %s", + error_reporter->CachedErrorMessage()); return 0; } return reinterpret_cast(interpreter.release()); } // Sets inputs, runs inference, and returns outputs as long handles. -JNIEXPORT jlongArray JNICALL -Java_org_tensorflow_lite_NativeInterpreterWrapper_run( - JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle, - jobjectArray sizes, jintArray data_types, jintArray nums_of_bytes, - jobjectArray values, jobject wrapper, jboolean memory_allocated) { +JNIEXPORT void JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_run( + JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle) { tflite::Interpreter* interpreter = convertLongToInterpreter(env, interpreter_handle); - if (interpreter == nullptr) return nullptr; + if (interpreter == nullptr) return; BufferErrorReporter* error_reporter = convertLongToErrorReporter(env, error_handle); - if (error_reporter == nullptr) return nullptr; - const int input_size = env->GetArrayLength(sizes); - // validates inputs - TfLiteStatus status = checkInputs(env, interpreter, input_size, data_types, - nums_of_bytes, values, sizes); - if (status != kTfLiteOk) return nullptr; - if (!memory_allocated || - !areInputDimensionsTheSame(env, interpreter, input_size, sizes)) { - // resizes inputs - status = resizeInputs(env, interpreter, input_size, sizes); - if (status != kTfLiteOk) { - throwException(env, kNullPointerException, - "Internal error: Can not resize the input: %s", - error_reporter->CachedErrorMessage()); - return nullptr; - } - // allocates memory - status = interpreter->AllocateTensors(); - if (status != kTfLiteOk) { - throwException(env, kNullPointerException, - "Internal error: Can not allocate memory for the given " - "inputs: %s", - error_reporter->CachedErrorMessage()); - return nullptr; - } - } - // sets inputs - status = setInputs(env, interpreter, input_size, data_types, nums_of_bytes, - values); - if (status != kTfLiteOk) return nullptr; - timespec beforeInference = ::tflite::getCurrentTime(); - // runs inference + if (error_reporter == nullptr) return; + if (interpreter->Invoke() != kTfLiteOk) { throwException(env, kIllegalArgumentException, "Internal error: Failed to run on the given Interpreter: %s", error_reporter->CachedErrorMessage()); - return nullptr; - } - timespec afterInference = ::tflite::getCurrentTime(); - jclass wrapper_clazz = env->GetObjectClass(wrapper); - jfieldID fid = - env->GetFieldID(wrapper_clazz, "inferenceDurationNanoseconds", "J"); - if (env->ExceptionCheck()) { - env->ExceptionClear(); - } else if (fid != nullptr) { - env->SetLongField( - wrapper, fid, - ::tflite::timespec_diff_nanoseconds(&beforeInference, &afterInference)); - } - // returns outputs - const std::vector& results = interpreter->outputs(); - if (results.empty()) { - throwException( - env, kIllegalArgumentException, - "Internal error: The Interpreter does not have any outputs."); - return nullptr; - } - jlongArray outputs = env->NewLongArray(results.size()); - size_t size = results.size(); - for (int i = 0; i < size; ++i) { - TfLiteTensor* source = interpreter->tensor(results[i]); - jlong output = reinterpret_cast(source); - env->SetLongArrayRegion(outputs, i, 1, &output); - } - return outputs; -} - -JNIEXPORT jintArray JNICALL -Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputDims( - JNIEnv* env, jclass clazz, jlong handle, jint input_idx, jint num_bytes) { - tflite::Interpreter* interpreter = convertLongToInterpreter(env, handle); - if (interpreter == nullptr) return nullptr; - const int idx = static_cast(input_idx); - if (input_idx < 0 || input_idx >= interpreter->inputs().size()) { - throwException(env, kIllegalArgumentException, - "Input error: Out of range: Failed to get %d-th input out of" - " %d inputs", - input_idx, interpreter->inputs().size()); - return nullptr; - } - TfLiteTensor* target = interpreter->tensor(interpreter->inputs()[idx]); - int size = target->dims->size; - if (num_bytes >= 0) { // verifies num of bytes matches if num_bytes if valid. - int expected_num_bytes = elementByteSize(target->type); - for (int i = 0; i < size; ++i) { - expected_num_bytes *= target->dims->data[i]; - } - if (num_bytes != expected_num_bytes) { - throwException(env, kIllegalArgumentException, - "Input error: Failed to get input dimensions. %d-th input " - "should have %d bytes, but found %d bytes.", - idx, expected_num_bytes, num_bytes); - return nullptr; - } + return; } - jintArray outputs = env->NewIntArray(size); - env->SetIntArrayRegion(outputs, 0, size, &(target->dims->data[0])); - return outputs; } JNIEXPORT jint JNICALL diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h index 128ece49811a112684dac7b36810e920eeeb7351..618fba480e4a1c4a1ff8531cb3fbc29fcb8191d8 100644 --- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h +++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h @@ -29,15 +29,63 @@ limitations under the License. namespace tflite { // This is to be provided at link-time by a library. extern std::unique_ptr CreateOpResolver(); -extern timespec getCurrentTime(); -extern jlong timespec_diff_nanoseconds(struct timespec* start, - struct timespec* stop); } // namespace tflite #ifdef __cplusplus extern "C" { #endif // __cplusplus +/* + * Class: org_tensorflow_lite_NativeInterpreterWrapper + * Method: allocateTensors + * Signature: (JJ)V + */ +JNIEXPORT void JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_allocateTensors( + JNIEnv* env, jclass clazz, jlong handle, jlong error_handle); + +/* + * Class: org_tensorflow_lite_NativeInterpreterWrapper + * Method: getInputTensor + * Signature: (JI)J + */ +JNIEXPORT jlong JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputTensor(JNIEnv* env, + jclass clazz, + jlong handle, + jint index); + +/* + * Class: org_tensorflow_lite_NativeInterpreterWrapper + * Method: getOutputTensor + * Signature: (JI)J + */ +JNIEXPORT jlong JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputTensor(JNIEnv* env, + jclass clazz, + jlong handle, + jint index); + +/* + * Class: org_tensorflow_lite_NativeInterpreterWrapper + * Method: getInputCount + * Signature: (J)I + */ +JNIEXPORT jint JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputCount(JNIEnv* env, + jclass clazz, + jlong handle); + +/* + * Class: org_tensorflow_lite_NativeInterpreterWrapper + * Method: getOutputCount + * Signature: (J)I + */ +JNIEXPORT jint JNICALL +Java_org_tensorflow_lite_NativeInterpreterWrapper_getOutputCount(JNIEnv* env, + jclass clazz, + jlong handle); + /* * Class: org_tensorflow_lite_NativeInterpreterWrapper * Method: @@ -118,28 +166,11 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_createInterpreter( /* * Class: org_tensorflow_lite_NativeInterpreterWrapper - * Method: - * Signature: - * (JJ[Ljava/lang/Object;[I[I[Ljava/lang/Object;Ljava/lang/Object;Z)[J - */ -JNIEXPORT jlongArray JNICALL -Java_org_tensorflow_lite_NativeInterpreterWrapper_run( - JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle, - jobjectArray sizes, jintArray data_types, jintArray nums_of_bytes, - jobjectArray values, jobject wrapper, jboolean memory_allocated); - -/* - * Class: org_tensorflow_lite_NativeInterpreterWrapper - * Method: - * Signature: (JII)[I - * - * Gets input dimensions. If num_bytes is non-negative, it will check whether - * num_bytes matches num of bytes required by the input, and return null and - * throw IllegalArgumentException if not. + * Method: run + * Signature: (JJ)V */ -JNIEXPORT jintArray JNICALL -Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputDims( - JNIEnv* env, jclass clazz, jlong handle, jint input_idx, jint num_bytes); +JNIEXPORT void JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_run( + JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle); /* * Class: org_tensorflow_lite_NativeInterpreterWrapper diff --git a/tensorflow/contrib/lite/java/src/main/native/tensor_jni.cc b/tensorflow/contrib/lite/java/src/main/native/tensor_jni.cc index 08b4d042803708830221d5e25fe4463366a4c99a..7ff96a3172dcf020b34fcbe7491c9022fc7f51de 100644 --- a/tensorflow/contrib/lite/java/src/main/native/tensor_jni.cc +++ b/tensorflow/contrib/lite/java/src/main/native/tensor_jni.cc @@ -29,6 +29,35 @@ TfLiteTensor* convertLongToTensor(JNIEnv* env, jlong handle) { return reinterpret_cast(handle); } +size_t elementByteSize(TfLiteType data_type) { + // The code in this file makes the assumption that the + // TensorFlow TF_DataTypes and the Java primitive types + // have the same byte sizes. Validate that: + switch (data_type) { + case kTfLiteFloat32: + static_assert(sizeof(jfloat) == 4, + "Interal error: Java float not compatible with " + "kTfLiteFloat"); + return 4; + case kTfLiteInt32: + static_assert(sizeof(jint) == 4, + "Interal error: Java int not compatible with kTfLiteInt"); + return 4; + case kTfLiteUInt8: + static_assert(sizeof(jbyte) == 1, + "Interal error: Java byte not compatible with " + "kTfLiteUInt8"); + return 1; + case kTfLiteInt64: + static_assert(sizeof(jlong) == 8, + "Interal error: Java long not compatible with " + "kTfLiteInt64"); + return 8; + default: + return 0; + } +} + size_t writeOneDimensionalArray(JNIEnv* env, jobject object, TfLiteType type, void* dst, size_t dst_size) { jarray array = static_cast(object); @@ -141,48 +170,6 @@ size_t readMultiDimensionalArray(JNIEnv* env, TfLiteType data_type, char* src, } } -} // namespace - -size_t elementByteSize(TfLiteType data_type) { - // The code in this file makes the assumption that the - // TensorFlow TF_DataTypes and the Java primitive types - // have the same byte sizes. Validate that: - switch (data_type) { - case kTfLiteFloat32: - static_assert(sizeof(jfloat) == 4, - "Interal error: Java float not compatible with " - "kTfLiteFloat"); - return 4; - case kTfLiteInt32: - static_assert(sizeof(jint) == 4, - "Interal error: Java int not compatible with kTfLiteInt"); - return 4; - case kTfLiteUInt8: - static_assert(sizeof(jbyte) == 1, - "Interal error: Java byte not compatible with " - "kTfLiteUInt8"); - return 1; - case kTfLiteInt64: - static_assert(sizeof(jlong) == 8, - "Interal error: Java long not compatible with " - "kTfLiteInt64"); - return 8; - default: - return 0; - } -} - -size_t writeByteBuffer(JNIEnv* env, jobject object, char** dst, int dst_size) { - char* buf = static_cast(env->GetDirectBufferAddress(object)); - if (!buf) { - throwException(env, kIllegalArgumentException, - "Input ByteBuffer is not a direct buffer"); - return 0; - } - *dst = buf; - return dst_size; -} - size_t writeMultiDimensionalArray(JNIEnv* env, jobject src, TfLiteType type, int dims_left, char** dst, int dst_size) { if (dims_left <= 1) { @@ -203,16 +190,37 @@ size_t writeMultiDimensionalArray(JNIEnv* env, jobject src, TfLiteType type, } } +} // namespace + JNIEXPORT jobject JNICALL Java_org_tensorflow_lite_Tensor_buffer(JNIEnv* env, jclass clazz, jlong handle) { TfLiteTensor* tensor = convertLongToTensor(env, handle); if (tensor == nullptr) return nullptr; - + if (tensor->data.raw == nullptr) { + throwException(env, kIllegalArgumentException, + "Internal error: Tensor hasn't been allocated."); + return nullptr; + } return env->NewDirectByteBuffer(static_cast(tensor->data.raw), static_cast(tensor->bytes)); } +JNIEXPORT void JNICALL Java_org_tensorflow_lite_Tensor_writeDirectBuffer( + JNIEnv* env, jclass clazz, jlong handle, jobject src) { + TfLiteTensor* tensor = convertLongToTensor(env, handle); + if (tensor == nullptr) return; + + char* src_data_raw = static_cast(env->GetDirectBufferAddress(src)); + if (!src_data_raw) { + throwException(env, kIllegalArgumentException, + "Input ByteBuffer is not a direct buffer"); + return; + } + + tensor->data.raw = src_data_raw; +} + JNIEXPORT void JNICALL Java_org_tensorflow_lite_Tensor_readMultiDimensionalArray(JNIEnv* env, jclass clazz, @@ -230,6 +238,27 @@ Java_org_tensorflow_lite_Tensor_readMultiDimensionalArray(JNIEnv* env, num_dims, static_cast(value)); } +JNIEXPORT void JNICALL +Java_org_tensorflow_lite_Tensor_writeMultiDimensionalArray(JNIEnv* env, + jclass clazz, + jlong handle, + jobject src) { + TfLiteTensor* tensor = convertLongToTensor(env, handle); + if (tensor == nullptr) return; + if (tensor->data.raw == nullptr) { + throwException(env, kIllegalArgumentException, + "Internal error: Target Tensor hasn't been allocated."); + return; + } + if (tensor->dims->size == 0) { + throwException(env, kIllegalArgumentException, + "Internal error: Cannot copy empty/scalar Tensors."); + return; + } + writeMultiDimensionalArray(env, src, tensor->type, tensor->dims->size, + &tensor->data.raw, tensor->bytes); +} + JNIEXPORT jint JNICALL Java_org_tensorflow_lite_Tensor_dtype(JNIEnv* env, jclass clazz, jlong handle) { @@ -247,3 +276,11 @@ Java_org_tensorflow_lite_Tensor_shape(JNIEnv* env, jclass clazz, jlong handle) { env->SetIntArrayRegion(result, 0, num_dims, tensor->dims->data); return result; } + +JNIEXPORT jint JNICALL Java_org_tensorflow_lite_Tensor_numBytes(JNIEnv* env, + jclass clazz, + jlong handle) { + const TfLiteTensor* tensor = convertLongToTensor(env, handle); + if (tensor == nullptr) return 0; + return static_cast(tensor->bytes); +} diff --git a/tensorflow/contrib/lite/java/src/main/native/tensor_jni.h b/tensorflow/contrib/lite/java/src/main/native/tensor_jni.h index 9ba95d9ac402662e6de69e3da8a60a6e841f97d6..06e2546af8400de117ed6923a1d1bd67bcb998e2 100644 --- a/tensorflow/contrib/lite/java/src/main/native/tensor_jni.h +++ b/tensorflow/contrib/lite/java/src/main/native/tensor_jni.h @@ -32,6 +32,14 @@ JNIEXPORT jobject JNICALL Java_org_tensorflow_lite_Tensor_buffer(JNIEnv* env, jclass clazz, jlong handle); +/* + * Class: org_tensorflow_lite_Tensor + * Method: writeDirectBuffer + * Signature: (JLjava/nio/ByteBuffer;) + */ +JNIEXPORT void JNICALL Java_org_tensorflow_lite_Tensor_writeDirectBuffer( + JNIEnv* env, jclass clazz, jlong handle, jobject src); + /* * Class: org_tensorflow_lite_Tensor * Method: dtype @@ -50,6 +58,15 @@ JNIEXPORT jintArray JNICALL Java_org_tensorflow_lite_Tensor_shape(JNIEnv* env, jclass clazz, jlong handle); +/* + * Class: org_tensorflow_lite_Tensor + * Method: numBytes + * Signature: (J)I + */ +JNIEXPORT jint JNICALL Java_org_tensorflow_lite_Tensor_numBytes(JNIEnv* env, + jclass clazz, + jlong handle); + /* * Class: org_tensorflow_lite_Tensor * Method: readMultiDimensionalArray @@ -59,23 +76,18 @@ JNIEXPORT void JNICALL Java_org_tensorflow_lite_Tensor_readMultiDimensionalArray(JNIEnv* env, jclass clazz, jlong handle, - jobject value); + jobject dst); /* - * Finds the size of each data type. - */ -size_t elementByteSize(TfLiteType data_type); - -/* - * Writes data of a ByteBuffer into dest. - */ -size_t writeByteBuffer(JNIEnv* env, jobject object, char** dst, int dst_size); - -/* - * Writes a multi-dimensional array into dest. + * Class: org_tensorflow_lite_Tensor + * Method: writeMultidimensionalArray + * Signature: (JLjava/lang/Object;) */ -size_t writeMultiDimensionalArray(JNIEnv* env, jobject src, TfLiteType type, - int dims_left, char** dst, int dst_size); +JNIEXPORT void JNICALL +Java_org_tensorflow_lite_Tensor_writeMultiDimensionalArray(JNIEnv* env, + jclass clazz, + jlong handle, + jobject src); #ifdef __cplusplus } // extern "C" diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java index e6deadffe2d7a110ff742b05a5bf06fa1bc67de9..d66a73db94f06776fe2a7310ed0837941aba87c4 100644 --- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java +++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java @@ -221,7 +221,9 @@ public final class InterpreterTest { assertThat(e) .hasMessageThat() .contains( - "DataType (2) of input data does not match with the DataType (1) of model inputs."); + "Cannot convert between a TensorFlowLite tensor with type " + + "FLOAT32 and a Java object of type [[[[I (which is compatible with the" + + " TensorFlowLite type INT32)"); } interpreter.close(); } @@ -241,8 +243,8 @@ public final class InterpreterTest { assertThat(e) .hasMessageThat() .contains( - "Cannot convert an TensorFlowLite tensor with type " - + "FLOAT32 to a Java object of type [[[[I (which is compatible with the" + "Cannot convert between a TensorFlowLite tensor with type " + + "FLOAT32 and a Java object of type [[[[I (which is compatible with the" + " TensorFlowLite type INT32)"); } interpreter.close(); @@ -329,4 +331,11 @@ public final class InterpreterTest { interpreter.close(); fileChannel.close(); } + + @Test + public void testRedundantClose() throws Exception { + Interpreter interpreter = new Interpreter(MODEL_FILE); + interpreter.close(); + interpreter.close(); + } } diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java index 029e5853e2f843fc38eeca0ffa9bb3a82390093b..9c4a5acd797ec3476f44fb203901c9ba0429ab26 100644 --- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java +++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java @@ -20,6 +20,8 @@ import static org.junit.Assert.fail; import java.nio.ByteBuffer; import java.nio.ByteOrder; +import java.util.HashMap; +import java.util.Map; import org.junit.Test; import org.junit.runner.RunWith; import org.junit.runners.JUnit4; @@ -101,10 +103,10 @@ public final class NativeInterpreterWrapperTest { float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; float[][][][] fourD = {threeD, threeD}; Object[] inputs = {fourD}; - Tensor[] outputs = wrapper.run(inputs); - assertThat(outputs.length).isEqualTo(1); float[][][][] parsedOutputs = new float[2][8][8][3]; - outputs[0].copyTo(parsedOutputs); + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutputs); + wrapper.run(inputs, outputs); float[] outputOneD = parsedOutputs[0][0][0]; float[] expected = {3.69f, -19.62f, 23.43f}; assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); @@ -119,11 +121,11 @@ public final class NativeInterpreterWrapperTest { float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; float[][][][] fourD = {threeD, threeD}; Object[] inputs = {fourD}; - Tensor[] outputs = wrapper.run(inputs); - assertThat(outputs).hasLength(1); ByteBuffer parsedOutput = ByteBuffer.allocateDirect(2 * 8 * 8 * 3 * 4).order(ByteOrder.nativeOrder()); - outputs[0].copyTo(parsedOutput); + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutput); + wrapper.run(inputs, outputs); float[] outputOneD = { parsedOutput.getFloat(0), parsedOutput.getFloat(4), parsedOutput.getFloat(8) }; @@ -140,17 +142,16 @@ public final class NativeInterpreterWrapperTest { float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; float[][][][] fourD = {threeD, threeD}; Object[] inputs = {fourD}; - Tensor[] outputs = wrapper.run(inputs); - assertThat(outputs.length).isEqualTo(1); float[][][][] parsedOutputs = new float[2][8][8][3]; - outputs[0].copyTo(parsedOutputs); + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutputs); + wrapper.run(inputs, outputs); float[] outputOneD = parsedOutputs[0][0][0]; float[] expected = {3.69f, -19.62f, 23.43f}; assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); - outputs = wrapper.run(inputs); - assertThat(outputs.length).isEqualTo(1); parsedOutputs = new float[2][8][8][3]; - outputs[0].copyTo(parsedOutputs); + outputs.put(0, parsedOutputs); + wrapper.run(inputs, outputs); outputOneD = parsedOutputs[0][0][0]; assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); wrapper.close(); @@ -164,10 +165,10 @@ public final class NativeInterpreterWrapperTest { int[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; int[][][][] fourD = {threeD, threeD}; Object[] inputs = {fourD}; - Tensor[] outputs = wrapper.run(inputs); - assertThat(outputs.length).isEqualTo(1); int[][][][] parsedOutputs = new int[2][4][4][12]; - outputs[0].copyTo(parsedOutputs); + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutputs); + wrapper.run(inputs, outputs); int[] outputOneD = parsedOutputs[0][0][0]; int[] expected = {3, 7, -4, 3, 7, -4, 3, 7, -4, 3, 7, -4}; assertThat(outputOneD).isEqualTo(expected); @@ -182,10 +183,10 @@ public final class NativeInterpreterWrapperTest { long[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; long[][][][] fourD = {threeD, threeD}; Object[] inputs = {fourD}; - Tensor[] outputs = wrapper.run(inputs); - assertThat(outputs.length).isEqualTo(1); long[][][][] parsedOutputs = new long[2][4][4][12]; - outputs[0].copyTo(parsedOutputs); + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutputs); + wrapper.run(inputs, outputs); long[] outputOneD = parsedOutputs[0][0][0]; long[] expected = {-892834092L, 923423L, 2123918239018L, -892834092L, 923423L, 2123918239018L, -892834092L, 923423L, 2123918239018L, -892834092L, 923423L, 2123918239018L}; @@ -203,10 +204,10 @@ public final class NativeInterpreterWrapperTest { Object[] inputs = {fourD}; int[] inputDims = {2, 8, 8, 3}; wrapper.resizeInput(0, inputDims); - Tensor[] outputs = wrapper.run(inputs); - assertThat(outputs.length).isEqualTo(1); byte[][][][] parsedOutputs = new byte[2][4][4][12]; - outputs[0].copyTo(parsedOutputs); + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutputs); + wrapper.run(inputs, outputs); byte[] outputOneD = parsedOutputs[0][0][0]; byte[] expected = {(byte) 0xe0, 0x4f, (byte) 0xd0, (byte) 0xe0, 0x4f, (byte) 0xd0, (byte) 0xe0, 0x4f, (byte) 0xd0, (byte) 0xe0, 0x4f, (byte) 0xd0}; @@ -229,13 +230,14 @@ public final class NativeInterpreterWrapperTest { } } } + bbuf.rewind(); Object[] inputs = {bbuf}; int[] inputDims = {2, 8, 8, 3}; wrapper.resizeInput(0, inputDims); - Tensor[] outputs = wrapper.run(inputs); - assertThat(outputs.length).isEqualTo(1); byte[][][][] parsedOutputs = new byte[2][4][4][12]; - outputs[0].copyTo(parsedOutputs); + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutputs); + wrapper.run(inputs, outputs); byte[] outputOneD = parsedOutputs[0][0][0]; byte[] expected = { (byte) 0xe0, 0x4f, (byte) 0xd0, (byte) 0xe0, 0x4f, (byte) 0xd0, @@ -261,21 +263,22 @@ public final class NativeInterpreterWrapperTest { } } Object[] inputs = {bbuf}; + float[][][][] parsedOutputs = new float[4][8][8][3]; + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutputs); try { - wrapper.run(inputs); + wrapper.run(inputs, outputs); fail(); } catch (IllegalArgumentException e) { assertThat(e) .hasMessageThat() .contains( - "Failed to get input dimensions. 0-th input should have 768 bytes, but found 3072 bytes"); + "Cannot convert between a TensorFlowLite buffer with 768 bytes and a " + + "ByteBuffer with 3072 bytes."); } int[] inputDims = {4, 8, 8, 3}; wrapper.resizeInput(0, inputDims); - Tensor[] outputs = wrapper.run(inputs); - assertThat(outputs.length).isEqualTo(1); - float[][][][] parsedOutputs = new float[4][8][8][3]; - outputs[0].copyTo(parsedOutputs); + wrapper.run(inputs, outputs); float[] outputOneD = parsedOutputs[0][0][0]; float[] expected = {3.69f, -19.62f, 23.43f}; assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder(); @@ -288,14 +291,18 @@ public final class NativeInterpreterWrapperTest { ByteBuffer bbuf = ByteBuffer.allocateDirect(2 * 7 * 8 * 3); bbuf.order(ByteOrder.nativeOrder()); Object[] inputs = {bbuf}; + Map outputs = new HashMap<>(); + ByteBuffer parsedOutput = ByteBuffer.allocateDirect(2 * 7 * 8 * 3); + outputs.put(0, parsedOutput); try { - wrapper.run(inputs); + wrapper.run(inputs, outputs); fail(); } catch (IllegalArgumentException e) { assertThat(e) .hasMessageThat() .contains( - "Failed to get input dimensions. 0-th input should have 192 bytes, but found 336 bytes."); + "Cannot convert between a TensorFlowLite buffer with 192 bytes and a " + + "ByteBuffer with 336 bytes."); } wrapper.close(); } @@ -308,14 +315,18 @@ public final class NativeInterpreterWrapperTest { int[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; int[][][][] fourD = {threeD, threeD}; Object[] inputs = {fourD}; + int[][][][] parsedOutputs = new int[2][8][8][3]; + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutputs); try { - wrapper.run(inputs); + wrapper.run(inputs, outputs); fail(); } catch (IllegalArgumentException e) { assertThat(e) .hasMessageThat() .contains( - "DataType (2) of input data does not match with the DataType (1) of model inputs."); + "Cannot convert between a TensorFlowLite tensor with type FLOAT32 and a Java object " + + "of type [[[[I (which is compatible with the TensorFlowLite type INT32)"); } wrapper.close(); } @@ -329,8 +340,11 @@ public final class NativeInterpreterWrapperTest { float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; float[][][][] fourD = {threeD, threeD}; Object[] inputs = {fourD}; + float[][][][] parsedOutputs = new float[2][8][8][3]; + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutputs); try { - wrapper.run(inputs); + wrapper.run(inputs, outputs); fail(); } catch (IllegalArgumentException e) { assertThat(e).hasMessageThat().contains("Invalid handle to Interpreter."); @@ -342,7 +356,7 @@ public final class NativeInterpreterWrapperTest { NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); try { Object[] inputs = {}; - wrapper.run(inputs); + wrapper.run(inputs, null); fail(); } catch (IllegalArgumentException e) { assertThat(e).hasMessageThat().contains("Inputs should not be null or empty."); @@ -358,11 +372,14 @@ public final class NativeInterpreterWrapperTest { float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; float[][][][] fourD = {threeD, threeD}; Object[] inputs = {fourD, fourD}; + float[][][][] parsedOutputs = new float[2][8][8][3]; + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutputs); try { - wrapper.run(inputs); + wrapper.run(inputs, outputs); fail(); } catch (IllegalArgumentException e) { - assertThat(e).hasMessageThat().contains("Expected num of inputs is 1 but got 2"); + assertThat(e).hasMessageThat().contains("Invalid input Tensor index: 1"); } wrapper.close(); } @@ -374,13 +391,18 @@ public final class NativeInterpreterWrapperTest { float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD}; float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; Object[] inputs = {threeD}; + float[][][][] parsedOutputs = new float[2][8][8][3]; + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutputs); try { - wrapper.run(inputs); + wrapper.run(inputs, outputs); fail(); } catch (IllegalArgumentException e) { assertThat(e) .hasMessageThat() - .contains("0-th input should have 4 dimensions, but found 3 dimensions"); + .contains( + "Cannot copy between a TensorFlowLite tensor with shape [8, 7, 3] and a " + + "Java object with shape [2, 8, 8, 3]."); } wrapper.close(); } @@ -393,91 +415,22 @@ public final class NativeInterpreterWrapperTest { float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; float[][][][] fourD = {threeD, threeD}; Object[] inputs = {fourD}; + float[][][][] parsedOutputs = new float[2][8][8][3]; + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutputs); try { - wrapper.run(inputs); + wrapper.run(inputs, outputs); fail(); } catch (IllegalArgumentException e) { assertThat(e) .hasMessageThat() - .contains("0-th input dimension should be [?,8,8,3], but found [?,8,7,3]"); + .contains( + "Cannot copy between a TensorFlowLite tensor with shape [2, 8, 7, 3] and a " + + "Java object with shape [2, 8, 8, 3]."); } wrapper.close(); } - @Test - public void testNumElements() { - int[] shape = {2, 3, 4}; - int num = NativeInterpreterWrapper.numElements(shape); - assertThat(num).isEqualTo(24); - shape = null; - num = NativeInterpreterWrapper.numElements(shape); - assertThat(num).isEqualTo(0); - } - - @Test - public void testIsNonEmtpyArray() { - assertThat(NativeInterpreterWrapper.isNonEmptyArray(null)).isFalse(); - assertThat(NativeInterpreterWrapper.isNonEmptyArray(3.2)).isFalse(); - int[] emptyArray = {}; - assertThat(NativeInterpreterWrapper.isNonEmptyArray(emptyArray)).isFalse(); - int[] validArray = {9, 5, 2, 1}; - assertThat(NativeInterpreterWrapper.isNonEmptyArray(validArray)).isTrue(); - } - - @Test - public void testDataTypeOf() { - float[] testEmtpyArray = {}; - DataType dataType = NativeInterpreterWrapper.dataTypeOf(testEmtpyArray); - assertThat(dataType).isEqualTo(DataType.FLOAT32); - float[] testFloatArray = {0.783f, 0.251f}; - dataType = NativeInterpreterWrapper.dataTypeOf(testFloatArray); - assertThat(dataType).isEqualTo(DataType.FLOAT32); - float[][] testMultiDimArray = {testFloatArray, testFloatArray, testFloatArray}; - dataType = NativeInterpreterWrapper.dataTypeOf(testFloatArray); - assertThat(dataType).isEqualTo(DataType.FLOAT32); - try { - double[] testDoubleArray = {0.783, 0.251}; - NativeInterpreterWrapper.dataTypeOf(testDoubleArray); - fail(); - } catch (IllegalArgumentException e) { - assertThat(e).hasMessageThat().contains("cannot resolve DataType of"); - } - try { - Float[] testBoxedArray = {0.783f, 0.251f}; - NativeInterpreterWrapper.dataTypeOf(testBoxedArray); - fail(); - } catch (IllegalArgumentException e) { - assertThat(e).hasMessageThat().contains("cannot resolve DataType of [Ljava.lang.Float;"); - } - } - - @Test - public void testNumDimensions() { - int scalar = 1; - assertThat(NativeInterpreterWrapper.numDimensions(scalar)).isEqualTo(0); - int[][] array = {{2, 4}, {1, 9}}; - assertThat(NativeInterpreterWrapper.numDimensions(array)).isEqualTo(2); - try { - int[] emptyArray = {}; - NativeInterpreterWrapper.numDimensions(emptyArray); - fail(); - } catch (IllegalArgumentException e) { - assertThat(e).hasMessageThat().contains("Array lengths cannot be 0."); - } - } - - @Test - public void testFillShape() { - int[][][] array = {{{23}, {14}, {87}}, {{12}, {42}, {31}}}; - int num = NativeInterpreterWrapper.numDimensions(array); - int[] shape = new int[num]; - NativeInterpreterWrapper.fillShape(array, 0, shape); - assertThat(num).isEqualTo(3); - assertThat(shape[0]).isEqualTo(2); - assertThat(shape[1]).isEqualTo(3); - assertThat(shape[2]).isEqualTo(1); - } - @Test public void testGetInferenceLatency() { NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); @@ -486,8 +439,10 @@ public final class NativeInterpreterWrapperTest { float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; float[][][][] fourD = {threeD, threeD}; Object[] inputs = {fourD}; - Tensor[] outputs = wrapper.run(inputs); - assertThat(outputs.length).isEqualTo(1); + float[][][][] parsedOutputs = new float[2][8][8][3]; + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutputs); + wrapper.run(inputs, outputs); assertThat(wrapper.getLastNativeInferenceDurationNanoseconds()).isGreaterThan(0L); wrapper.close(); } @@ -507,13 +462,14 @@ public final class NativeInterpreterWrapperTest { float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; float[][][][] fourD = {threeD, threeD}; Object[] inputs = {fourD}; + float[][][][] parsedOutputs = new float[2][8][8][3]; + Map outputs = new HashMap<>(); + outputs.put(0, parsedOutputs); try { - wrapper.run(inputs); + wrapper.run(inputs, outputs); fail(); } catch (IllegalArgumentException e) { - assertThat(e) - .hasMessageThat() - .contains("0-th input dimension should be [?,8,8,3], but found [?,8,7,3]"); + // Expected. } assertThat(wrapper.getLastNativeInferenceDurationNanoseconds()).isNull(); wrapper.close(); @@ -523,41 +479,7 @@ public final class NativeInterpreterWrapperTest { public void testGetInputDims() { NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); int[] expectedDims = {1, 8, 8, 3}; - assertThat(wrapper.getInputDims(0)).isEqualTo(expectedDims); - wrapper.close(); - } - - @Test - public void testGetInputDimsOutOfRange() { - NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); - try { - wrapper.getInputDims(-1); - fail(); - } catch (IllegalArgumentException e) { - assertThat(e).hasMessageThat().contains("Out of range"); - } - try { - wrapper.getInputDims(1); - fail(); - } catch (IllegalArgumentException e) { - assertThat(e).hasMessageThat().contains("Out of range"); - } - wrapper.close(); - } - - @Test - public void testGetOutputDataType() { - NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH); - assertThat(wrapper.getOutputDataType(0)).contains("float"); - wrapper.close(); - wrapper = new NativeInterpreterWrapper(LONG_MODEL_PATH); - assertThat(wrapper.getOutputDataType(0)).contains("long"); - wrapper.close(); - wrapper = new NativeInterpreterWrapper(INT_MODEL_PATH); - assertThat(wrapper.getOutputDataType(0)).contains("int"); - wrapper.close(); - wrapper = new NativeInterpreterWrapper(BYTE_MODEL_PATH); - assertThat(wrapper.getOutputDataType(0)).contains("byte"); + assertThat(wrapper.getInputTensor(0).shape()).isEqualTo(expectedDims); wrapper.close(); } diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/TensorTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/TensorTest.java index dd9d37eedafaa8250f5f926375edcf7cb3b730a0..71ef04494357e8b951cbbbd2c68385b17c472736 100644 --- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/TensorTest.java +++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/TensorTest.java @@ -18,9 +18,10 @@ package org.tensorflow.lite; import static com.google.common.truth.Truth.assertThat; import static org.junit.Assert.fail; -import java.nio.BufferOverflowException; import java.nio.ByteBuffer; import java.nio.ByteOrder; +import java.util.HashMap; +import java.util.Map; import org.junit.After; import org.junit.Before; import org.junit.Test; @@ -35,7 +36,7 @@ public final class TensorTest { "tensorflow/contrib/lite/java/src/testdata/add.bin"; private NativeInterpreterWrapper wrapper; - private long nativeHandle; + private Tensor tensor; @Before public void setUp() { @@ -45,8 +46,10 @@ public final class TensorTest { float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD}; float[][][][] fourD = {threeD, threeD}; Object[] inputs = {fourD}; - Tensor[] outputs = wrapper.run(inputs); - nativeHandle = outputs[0].nativeHandle; + Map outputs = new HashMap<>(); + outputs.put(0, new float[2][8][8][3]); + wrapper.run(inputs, outputs); + tensor = wrapper.getOutputTensor(0); } @After @@ -55,17 +58,16 @@ public final class TensorTest { } @Test - public void testFromHandle() throws Exception { - Tensor tensor = Tensor.fromHandle(nativeHandle); + public void testBasic() throws Exception { assertThat(tensor).isNotNull(); int[] expectedShape = {2, 8, 8, 3}; - assertThat(tensor.shapeCopy).isEqualTo(expectedShape); - assertThat(tensor.dtype).isEqualTo(DataType.FLOAT32); + assertThat(tensor.shape()).isEqualTo(expectedShape); + assertThat(tensor.dataType()).isEqualTo(DataType.FLOAT32); + assertThat(tensor.numBytes()).isEqualTo(2 * 8 * 8 * 3 * 4); } @Test public void testCopyTo() { - Tensor tensor = Tensor.fromHandle(nativeHandle); float[][][][] parsedOutputs = new float[2][8][8][3]; tensor.copyTo(parsedOutputs); float[] outputOneD = parsedOutputs[0][0][0]; @@ -75,7 +77,6 @@ public final class TensorTest { @Test public void testCopyToByteBuffer() { - Tensor tensor = Tensor.fromHandle(nativeHandle); ByteBuffer parsedOutput = ByteBuffer.allocateDirect(2 * 8 * 8 * 3 * 4).order(ByteOrder.nativeOrder()); tensor.copyTo(parsedOutput); @@ -89,19 +90,17 @@ public final class TensorTest { @Test public void testCopyToInvalidByteBuffer() { - Tensor tensor = Tensor.fromHandle(nativeHandle); ByteBuffer parsedOutput = ByteBuffer.allocateDirect(3 * 4).order(ByteOrder.nativeOrder()); try { tensor.copyTo(parsedOutput); fail(); - } catch (BufferOverflowException e) { + } catch (IllegalArgumentException e) { // Expected. } } @Test public void testCopyToWrongType() { - Tensor tensor = Tensor.fromHandle(nativeHandle); int[][][][] parsedOutputs = new int[2][8][8][3]; try { tensor.copyTo(parsedOutputs); @@ -110,15 +109,13 @@ public final class TensorTest { assertThat(e) .hasMessageThat() .contains( - "Cannot convert an TensorFlowLite tensor with type " - + "FLOAT32 to a Java object of type [[[[I (which is compatible with the TensorFlowLite " - + "type INT32)"); + "Cannot convert between a TensorFlowLite tensor with type FLOAT32 and a Java object " + + "of type [[[[I (which is compatible with the TensorFlowLite type INT32)"); } } @Test public void testCopyToWrongShape() { - Tensor tensor = Tensor.fromHandle(nativeHandle); float[][][][] parsedOutputs = new float[1][8][8][3]; try { tensor.copyTo(parsedOutputs); @@ -127,8 +124,104 @@ public final class TensorTest { assertThat(e) .hasMessageThat() .contains( - "Shape of output target [1, 8, 8, 3] does not match " - + "with the shape of the Tensor [2, 8, 8, 3]."); + "Cannot copy between a TensorFlowLite tensor with shape [2, 8, 8, 3] " + + "and a Java object with shape [1, 8, 8, 3]."); + } + } + + @Test + public void testSetTo() { + float[][][][] input = new float[2][8][8][3]; + float[][][][] output = new float[2][8][8][3]; + ByteBuffer inputByteBuffer = + ByteBuffer.allocateDirect(2 * 8 * 8 * 3 * 4).order(ByteOrder.nativeOrder()); + + input[0][0][0][0] = 2.0f; + tensor.setTo(input); + tensor.copyTo(output); + assertThat(output[0][0][0][0]).isEqualTo(2.0f); + + inputByteBuffer.putFloat(0, 3.0f); + tensor.setTo(inputByteBuffer); + tensor.copyTo(output); + assertThat(output[0][0][0][0]).isEqualTo(3.0f); + } + + @Test + public void testSetToInvalidByteBuffer() { + ByteBuffer input = ByteBuffer.allocateDirect(3 * 4).order(ByteOrder.nativeOrder()); + try { + tensor.setTo(input); + fail(); + } catch (IllegalArgumentException e) { + // Success. + } + } + + @Test + public void testGetInputShapeIfDifferent() { + ByteBuffer bytBufferInput = ByteBuffer.allocateDirect(3 * 4).order(ByteOrder.nativeOrder()); + assertThat(tensor.getInputShapeIfDifferent(bytBufferInput)).isNull(); + + float[][][][] sameShapeInput = new float[2][8][8][3]; + assertThat(tensor.getInputShapeIfDifferent(sameShapeInput)).isNull(); + + float[][][][] differentShapeInput = new float[1][8][8][3]; + assertThat(tensor.getInputShapeIfDifferent(differentShapeInput)) + .isEqualTo(new int[] {1, 8, 8, 3}); + } + + @Test + public void testDataTypeOf() { + float[] testEmptyArray = {}; + DataType dataType = Tensor.dataTypeOf(testEmptyArray); + assertThat(dataType).isEqualTo(DataType.FLOAT32); + float[] testFloatArray = {0.783f, 0.251f}; + dataType = Tensor.dataTypeOf(testFloatArray); + assertThat(dataType).isEqualTo(DataType.FLOAT32); + float[][] testMultiDimArray = {testFloatArray, testFloatArray, testFloatArray}; + dataType = Tensor.dataTypeOf(testFloatArray); + assertThat(dataType).isEqualTo(DataType.FLOAT32); + try { + double[] testDoubleArray = {0.783, 0.251}; + Tensor.dataTypeOf(testDoubleArray); + fail(); + } catch (IllegalArgumentException e) { + assertThat(e).hasMessageThat().contains("cannot resolve DataType of"); + } + try { + Float[] testBoxedArray = {0.783f, 0.251f}; + Tensor.dataTypeOf(testBoxedArray); + fail(); + } catch (IllegalArgumentException e) { + assertThat(e).hasMessageThat().contains("cannot resolve DataType of [Ljava.lang.Float;"); } } + + @Test + public void testNumDimensions() { + int scalar = 1; + assertThat(Tensor.numDimensions(scalar)).isEqualTo(0); + int[][] array = {{2, 4}, {1, 9}}; + assertThat(Tensor.numDimensions(array)).isEqualTo(2); + try { + int[] emptyArray = {}; + Tensor.numDimensions(emptyArray); + fail(); + } catch (IllegalArgumentException e) { + assertThat(e).hasMessageThat().contains("Array lengths cannot be 0."); + } + } + + @Test + public void testFillShape() { + int[][][] array = {{{23}, {14}, {87}}, {{12}, {42}, {31}}}; + int num = Tensor.numDimensions(array); + int[] shape = new int[num]; + Tensor.fillShape(array, 0, shape); + assertThat(num).isEqualTo(3); + assertThat(shape[0]).isEqualTo(2); + assertThat(shape[1]).isEqualTo(3); + assertThat(shape[2]).isEqualTo(1); + } } diff --git a/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java b/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java index 3aef0c3bb6cc4748de0e55d31f0215a77320ae69..c23521c0774ebab01f38db8b416020ae5755cee9 100644 --- a/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java +++ b/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java @@ -58,7 +58,7 @@ public class TestHelper { */ public static int[] getInputDims(Interpreter interpreter, int index) { if (interpreter != null && interpreter.wrapper != null) { - return interpreter.wrapper.getInputDims(index); + return interpreter.wrapper.getInputTensor(index).shape(); } else { throw new IllegalArgumentException( "Interpreter has not initialized;" + " Failed to get input dimensions."); @@ -77,7 +77,7 @@ public class TestHelper { */ public static String getOutputDataType(Interpreter interpreter, int index) { if (interpreter != null && interpreter.wrapper != null) { - return interpreter.wrapper.getOutputDataType(index); + return interpreter.wrapper.getOutputTensor(index).dataType().toStringName(); } else { throw new IllegalArgumentException( "Interpreter has not initialized;" + " Failed to get output data type."); diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index 27b8a16e1522de4d31b2870e6130fb3281941a05..329c98f91e90134e1dff58427102776fd6b7a73b 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -12,7 +12,10 @@ tf_cc_test( name = "optional_tensor_test", size = "small", srcs = ["optional_tensor_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -46,11 +49,18 @@ cc_library( hdrs = [ "eigen_support.h", ], - copts = tflite_copts(), + copts = tflite_copts() + [ + "-Wno-error=reorder", + ] + select({ + "//tensorflow:ios": ["-Wno-error=invalid-partial-specialization"], + "//conditions:default": [ + ], + }), deps = [ ":op_macros", + "//tensorflow/contrib/lite:arena_planner", "//tensorflow/contrib/lite:context", - "//third_party/eigen3", + "//tensorflow/contrib/lite/kernels/internal:optimized", ], ) @@ -106,7 +116,10 @@ tf_cc_test( name = "kernel_util_test", size = "small", srcs = ["kernel_util_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":kernel_util", "//tensorflow/contrib/lite/testing:util", @@ -118,6 +131,7 @@ tf_cc_test( name = "test_util_test", size = "small", srcs = ["test_util_test.cc"], + tags = ["no_oss"], deps = [ ":test_util", "//tensorflow/contrib/lite/testing:util", @@ -130,7 +144,7 @@ cc_library( srcs = [ "activations.cc", "add.cc", - "arg_max.cc", + "arg_min_max.cc", "audio_spectrogram.cc", "basic_rnn.cc", "batch_to_space_nd.cc", @@ -149,18 +163,22 @@ cc_library( "embedding_lookup_sparse.cc", "exp.cc", "expand_dims.cc", + "fake_quant.cc", "floor.cc", "fully_connected.cc", "gather.cc", "hashtable_lookup.cc", "l2norm.cc", "local_response_norm.cc", + "logical.cc", "lsh_projection.cc", "lstm.cc", "maximum_minimum.cc", "mfcc.cc", "mul.cc", "neg.cc", + "one_hot.cc", + "pack.cc", "pad.cc", "pooling.cc", "pow.cc", @@ -225,7 +243,10 @@ tf_cc_test( name = "audio_spectrogram_test", size = "small", srcs = ["audio_spectrogram_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -239,7 +260,10 @@ tf_cc_test( name = "mfcc_test", size = "small", srcs = ["mfcc_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -253,7 +277,10 @@ tf_cc_test( name = "detection_postprocess_test", size = "small", srcs = ["detection_postprocess_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -290,10 +317,11 @@ tf_cc_test( ) tf_cc_test( - name = "arg_max_test", + name = "arg_min_max_test", size = "small", - srcs = ["arg_max_test.cc"], + srcs = ["arg_min_max_test.cc"], tags = [ + "no_oss", "tflite_not_portable_ios", ], deps = [ @@ -308,7 +336,10 @@ tf_cc_test( name = "div_test", size = "small", srcs = ["div_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -321,7 +352,10 @@ tf_cc_test( name = "sub_test", size = "small", srcs = ["sub_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -334,7 +368,10 @@ tf_cc_test( name = "transpose_test", size = "small", srcs = ["transpose_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -349,7 +386,10 @@ tf_cc_test( name = "space_to_batch_nd_test", size = "small", srcs = ["space_to_batch_nd_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -362,7 +402,10 @@ tf_cc_test( name = "batch_to_space_nd_test", size = "small", srcs = ["batch_to_space_nd_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -375,7 +418,10 @@ tf_cc_test( name = "cast_test", size = "small", srcs = ["cast_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -428,7 +474,10 @@ tf_cc_test( name = "dequantize_test", size = "small", srcs = ["dequantize_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -455,7 +504,10 @@ tf_cc_test( name = "bidirectional_sequence_lstm_test", size = "small", srcs = ["bidirectional_sequence_lstm_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -468,7 +520,10 @@ tf_cc_test( name = "floor_test", size = "small", srcs = ["floor_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -481,7 +536,10 @@ tf_cc_test( name = "elementwise_test", size = "small", srcs = ["elementwise_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -494,7 +552,10 @@ tf_cc_test( name = "unidirectional_sequence_lstm_test", size = "small", srcs = ["unidirectional_sequence_lstm_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -508,6 +569,7 @@ tf_cc_test( size = "small", srcs = ["bidirectional_sequence_rnn_test.cc"], tags = [ + "no_oss", "tflite_not_portable", ], deps = [ @@ -522,7 +584,10 @@ tf_cc_test( name = "unidirectional_sequence_rnn_test", size = "small", srcs = ["unidirectional_sequence_rnn_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -548,7 +613,26 @@ tf_cc_test( name = "exp_test", size = "small", srcs = ["exp_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + +tf_cc_test( + name = "fake_quant_test", + size = "small", + srcs = ["fake_quant_test.cc"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -561,7 +645,10 @@ tf_cc_test( name = "maximum_minimum_test", size = "small", srcs = ["maximum_minimum_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -574,7 +661,10 @@ tf_cc_test( name = "reduce_test", size = "small", srcs = ["reduce_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -600,7 +690,10 @@ tf_cc_test( name = "pad_test", size = "small", srcs = ["pad_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -626,7 +719,10 @@ tf_cc_test( name = "gather_test", size = "small", srcs = ["gather_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:builtin_op_data", @@ -640,7 +736,10 @@ tf_cc_test( name = "topk_v2_test", size = "small", srcs = ["topk_v2_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:builtin_op_data", @@ -761,7 +860,10 @@ tf_cc_test( name = "log_softmax_test", size = "small", srcs = ["log_softmax_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -842,7 +944,10 @@ tf_cc_test( name = "split_test", size = "small", srcs = ["split_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -855,7 +960,10 @@ tf_cc_test( name = "squeeze_test", size = "small", srcs = ["squeeze_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -868,7 +976,10 @@ tf_cc_test( name = "strided_slice_test", size = "small", srcs = ["strided_slice_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -881,7 +992,10 @@ tf_cc_test( name = "tile_test", size = "small", srcs = ["tile_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:builtin_op_data", @@ -898,6 +1012,7 @@ tf_cc_test( "comparisons_test.cc", ], tags = [ + "no_oss", "tflite_not_portable_ios", ], deps = [ @@ -912,7 +1027,10 @@ tf_cc_test( name = "neg_test", size = "small", srcs = ["neg_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", @@ -928,6 +1046,7 @@ tf_cc_test( "select_test.cc", ], tags = [ + "no_oss", "tflite_not_portable_ios", ], deps = [ @@ -945,6 +1064,7 @@ tf_cc_test( "slice_test.cc", ], tags = [ + "no_oss", "tflite_not_portable_ios", ], deps = [ @@ -959,12 +1079,14 @@ tf_cc_test( name = "transpose_conv_test", size = "small", srcs = ["transpose_conv_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite/kernels:test_util", - "@com_google_absl//absl/memory", "@com_google_googletest//:gtest", ], ) @@ -973,7 +1095,10 @@ tf_cc_test( name = "expand_dims_test", size = "small", srcs = ["expand_dims_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:builtin_op_data", @@ -987,7 +1112,10 @@ tf_cc_test( name = "sparse_to_dense_test", size = "small", srcs = ["sparse_to_dense_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:builtin_op_data", @@ -1001,7 +1129,10 @@ tf_cc_test( name = "shape_test", size = "small", srcs = ["shape_test.cc"], - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":builtin_ops", "//tensorflow/contrib/lite:builtin_op_data", @@ -1015,6 +1146,50 @@ tf_cc_test( name = "pow_test", size = "small", srcs = ["pow_test.cc"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:builtin_op_data", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + +tf_cc_test( + name = "pack_test", + size = "small", + srcs = ["pack_test.cc"], + tags = ["tflite_not_portable_ios"], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:builtin_op_data", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + +tf_cc_test( + name = "one_hot_test", + size = "small", + srcs = ["one_hot_test.cc"], + tags = ["tflite_not_portable_ios"], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + +tf_cc_test( + name = "logical_test", + size = "small", + srcs = ["logical_test.cc"], tags = ["tflite_not_portable_ios"], deps = [ ":builtin_ops", diff --git a/tensorflow/contrib/lite/kernels/activations.cc b/tensorflow/contrib/lite/kernels/activations.cc index 99f81c4a8a78ab0b2a24955d77f25ed09da13b84..6e13b8c667c5c5188c9e1bc753346f231ae8e1b0 100644 --- a/tensorflow/contrib/lite/kernels/activations.cc +++ b/tensorflow/contrib/lite/kernels/activations.cc @@ -12,7 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include @@ -186,8 +185,8 @@ TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* output = GetOutput(context, node, 0); TF_LITE_ENSURE_EQ(context, input->type, output->type); - TF_LITE_ENSURE(context, - NumDimensions(input) == 2 || NumDimensions(input) == 4); + const int num_dims = NumDimensions(input); + TF_LITE_ENSURE(context, num_dims == 1 || num_dims == 2 || num_dims == 4); if (input->type == kTfLiteUInt8) { TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); @@ -365,13 +364,9 @@ TfLiteStatus SigmoidEval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } -// Takes a 2D tensor and perform softmax along the second dimension. -void Softmax2DFloat(const TfLiteTensor* input, TfLiteTensor* output, - TfLiteSoftmaxParams* params) { - const int batch_size = input->dims->data[0]; - const int input_size = input->dims->data[1]; - float* in = input->data.f; - float* out = output->data.f; +// Performs softmax along the input of size (input_size * batch_size). +void Softmax(const float* in, const int input_size, const int batch_size, + const float beta, float* out) { TF_LITE_ASSERT(input_size > 0); // For each batch @@ -385,7 +380,7 @@ void Softmax2DFloat(const TfLiteTensor* input, TfLiteTensor* output, // Compute the normalized sum of exps. float exp_sum = 0.0; for (int i = 0; i < input_size; i++) { - out[i] = std::exp((in[i] - max_coeff) * params->beta); + out[i] = std::exp((in[i] - max_coeff) * beta); exp_sum += out[i]; } @@ -401,6 +396,33 @@ void Softmax2DFloat(const TfLiteTensor* input, TfLiteTensor* output, } } +// Takes a 1D tensor and performs softmax along it. +void Softmax1DFloat(const TfLiteTensor* input, TfLiteTensor* output, + TfLiteSoftmaxParams* params) { + const int input_size = input->dims->data[0]; + Softmax(input->data.f, input_size, 1, params->beta, output->data.f); +} + +// Takes a 2D tensor and perform softmax along the last dimension. +void Softmax2DFloat(const TfLiteTensor* input, TfLiteTensor* output, + TfLiteSoftmaxParams* params) { + const int batch_size = input->dims->data[0]; + const int input_size = input->dims->data[1]; + Softmax(input->data.f, input_size, batch_size, params->beta, output->data.f); +} + +void Softmax1DQuantized(const TfLiteTensor* input, TfLiteTensor* output, + TfLiteSoftmaxParams* params, OpData* data) { + // TODO(ahentz): this is arguably a dirty trick. Since the implementation + // always traverses the last dimension of a 4D tensor, we will pretend our 1D + // tensor is 4D in a special way. We will convert a (Y) shape into a (1, + // 1, 1, Y) shape. + const int input_size = input->dims->data[0]; + optimized_ops::Softmax( + GetTensorData(input), GetTensorShape({1, 1, 1, input_size}), + data->input_multiplier, data->input_left_shift, data->diff_min, + GetTensorData(output), GetTensorShape({1, 1, 1, input_size})); +} void Softmax2DQuantized(const TfLiteTensor* input, TfLiteTensor* output, TfLiteSoftmaxParams* params, OpData* data) { // TODO(ahentz): this is arguably a dirty trick. Since the implementation @@ -443,6 +465,10 @@ TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) { // dimensions. switch (input->type) { case kTfLiteFloat32: { + if (NumDimensions(input) == 1) { + Softmax1DFloat(input, output, params); + return kTfLiteOk; + } if (NumDimensions(input) == 2) { Softmax2DFloat(input, output, params); return kTfLiteOk; @@ -452,11 +478,15 @@ TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } context->ReportError( - context, "Only 2D and 4D tensors supported currently, got %dD.", + context, "Only 1D, 2D and 4D tensors supported currently, got %dD.", NumDimensions(input)); return kTfLiteError; } case kTfLiteUInt8: { + if (NumDimensions(input) == 1) { + Softmax1DQuantized(input, output, params, data); + return kTfLiteOk; + } if (NumDimensions(input) == 2) { Softmax2DQuantized(input, output, params, data); return kTfLiteOk; diff --git a/tensorflow/contrib/lite/kernels/activations_test.cc b/tensorflow/contrib/lite/kernels/activations_test.cc index 587e1303da6afed1fc711100f457f1bf62b0b7e1..083cdf78d76991b89c4c2caf03dcb6db404a2578 100644 --- a/tensorflow/contrib/lite/kernels/activations_test.cc +++ b/tensorflow/contrib/lite/kernels/activations_test.cc @@ -339,6 +339,29 @@ TEST(QuantizedActivationsOpTest, Softmax4D) { kQuantizedTolerance))); } +TEST(FloatActivationsOpTest, Softmax1D) { + FloatActivationsOpModel m(0.1, + /*input=*/{TensorType_FLOAT32, {8}}); + m.SetInput({0, -6, 2, 4, 3, -2, 10, 1}); + m.Invoke(); + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {.09752, .05352, .11911, .14548, .13164, .07984, .26509, .10778}))); +} + +TEST(QuantizedActivationsOpTest, Softmax1D) { + QuantizedActivationsOpModel m(0.1, + /*input=*/{TensorType_UINT8, {8}, -10, 10}); + m.SetInput({0, -6, 2, 4, 3, -2, 10, 1}); + m.Invoke(); + EXPECT_THAT( + m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({0.09766, 0.05469, 0.12109, 0.14453, + 0.13281, 0.07813, 0.26563, 0.10938}, + kQuantizedTolerance))); +} + TEST(FloatActivationsOpTest, Softmax2D) { FloatActivationsOpModel m(0.1, /*input=*/{TensorType_FLOAT32, {2, 4}}); diff --git a/tensorflow/contrib/lite/kernels/add.cc b/tensorflow/contrib/lite/kernels/add.cc index f44d531cbfa9ed41f881380752558555aab97b4d..af9b5c7013afc5d32d01cba07492a282727b3e12 100644 --- a/tensorflow/contrib/lite/kernels/add.cc +++ b/tensorflow/contrib/lite/kernels/add.cc @@ -110,15 +110,12 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { QuantizeMultiplierSmallerThanOneExp( real_input1_multiplier, &data->input1_multiplier, &data->input1_shift); - data->input1_shift *= -1; QuantizeMultiplierSmallerThanOneExp( real_input2_multiplier, &data->input2_multiplier, &data->input2_shift); - data->input2_shift *= -1; QuantizeMultiplierSmallerThanOneExp( real_output_multiplier, &data->output_multiplier, &data->output_shift); - data->output_shift *= -1; CalculateActivationRangeUint8(params->activation, output, &data->output_activation_min, @@ -152,14 +149,14 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { CheckedLog2(output->params.scale, &output_scale_log2_rounded); TF_LITE_ENSURE(context, output_scale_is_pot); - data->input1_shift = output_scale_log2_rounded - input1_scale_log2_rounded; - data->input2_shift = output_scale_log2_rounded - input2_scale_log2_rounded; + data->input1_shift = input1_scale_log2_rounded - output_scale_log2_rounded; + data->input2_shift = input2_scale_log2_rounded - output_scale_log2_rounded; // Shifting of one input is supported. The graph quantization should ensure // that the other input matches the output. TF_LITE_ENSURE(context, data->input1_shift == 0 || data->input2_shift == 0); - TF_LITE_ENSURE(context, data->input1_shift >= 0); - TF_LITE_ENSURE(context, data->input2_shift >= 0); + TF_LITE_ENSURE(context, data->input1_shift <= 0); + TF_LITE_ENSURE(context, data->input2_shift <= 0); CalculateActivationRangeQuantized(context, params->activation, output, &data->output_activation_min, @@ -173,24 +170,27 @@ template void EvalAdd(TfLiteContext* context, TfLiteNode* node, TfLiteAddParams* params, const OpData* data, const TfLiteTensor* input1, const TfLiteTensor* input2, TfLiteTensor* output) { -#define TF_LITE_ADD(type, opname, data_type) \ - data_type output_activation_min, output_activation_max; \ - CalculateActivationRange(params->activation, &output_activation_min, \ - &output_activation_max); \ - type::opname(GetTensorData(input1), GetTensorDims(input1), \ - GetTensorData(input2), GetTensorDims(input2), \ - output_activation_min, output_activation_max, \ - GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_ADD(type, opname, data_type) \ + data_type output_activation_min, output_activation_max; \ + CalculateActivationRange(params->activation, &output_activation_min, \ + &output_activation_max); \ + tflite::ArithmeticParams op_params; \ + SetActivationParams(output_activation_min, output_activation_max, \ + &op_params); \ + type::opname(op_params, GetTensorShape(input1), \ + GetTensorData(input1), GetTensorShape(input2), \ + GetTensorData(input2), GetTensorShape(output), \ + GetTensorData(output)) if (output->type == kTfLiteInt32) { if (kernel_type == kReference) { if (data->requires_broadcast) { - TF_LITE_ADD(reference_ops, BroadcastAdd, int32_t); + TF_LITE_ADD(reference_ops, BroadcastAdd4DSlow, int32_t); } else { TF_LITE_ADD(reference_ops, Add, int32_t); } } else { if (data->requires_broadcast) { - TF_LITE_ADD(optimized_ops, BroadcastAdd, int32_t); + TF_LITE_ADD(optimized_ops, BroadcastAdd4DSlow, int32_t); } else { TF_LITE_ADD(optimized_ops, Add, int32_t); } @@ -198,13 +198,13 @@ void EvalAdd(TfLiteContext* context, TfLiteNode* node, TfLiteAddParams* params, } else if (output->type == kTfLiteFloat32) { if (kernel_type == kReference) { if (data->requires_broadcast) { - TF_LITE_ADD(reference_ops, BroadcastAdd, float); + TF_LITE_ADD(reference_ops, BroadcastAdd4DSlow, float); } else { TF_LITE_ADD(reference_ops, Add, float); } } else { if (data->requires_broadcast) { - TF_LITE_ADD(optimized_ops, BroadcastAdd, float); + TF_LITE_ADD(optimized_ops, BroadcastAdd4DSlow, float); } else { TF_LITE_ADD(optimized_ops, Add, float); } @@ -220,30 +220,43 @@ TfLiteStatus EvalAddQuantized(TfLiteContext* context, TfLiteNode* node, const TfLiteTensor* input2, TfLiteTensor* output) { if (output->type == kTfLiteUInt8) { -#define TF_LITE_ADD(type, opname) \ - type::opname( \ - data->left_shift, GetTensorData(input1), GetTensorDims(input1), \ - data->input1_offset, data->input1_multiplier, data->input1_shift, \ - GetTensorData(input2), GetTensorDims(input2), \ - data->input2_offset, data->input2_multiplier, data->input2_shift, \ - data->output_offset, data->output_multiplier, data->output_shift, \ - data->output_activation_min, data->output_activation_max, \ - GetTensorData(output), GetTensorDims(output)); +#define TF_LITE_ADD(type, opname) \ + tflite::ArithmeticParams op_params; \ + op_params.left_shift = data->left_shift; \ + op_params.input1_offset = data->input1_offset; \ + op_params.input1_multiplier = data->input1_multiplier; \ + op_params.input1_shift = data->input1_shift; \ + op_params.input2_offset = data->input2_offset; \ + op_params.input2_multiplier = data->input2_multiplier; \ + op_params.input2_shift = data->input2_shift; \ + op_params.output_offset = data->output_offset; \ + op_params.output_multiplier = data->output_multiplier; \ + op_params.output_shift = data->output_shift; \ + SetActivationParams(data->output_activation_min, \ + data->output_activation_max, &op_params); \ + type::opname(op_params, GetTensorShape(input1), \ + GetTensorData(input1), GetTensorShape(input2), \ + GetTensorData(input2), GetTensorShape(output), \ + GetTensorData(output)) // The quantized version of Add doesn't support activations, so we // always use BroadcastAdd. if (kernel_type == kReference) { - TF_LITE_ADD(reference_ops, BroadcastAdd); + TF_LITE_ADD(reference_ops, BroadcastAdd4DSlow); } else { - TF_LITE_ADD(optimized_ops, BroadcastAdd); + TF_LITE_ADD(optimized_ops, BroadcastAdd4DSlow); } #undef TF_LITE_ADD } else if (output->type == kTfLiteInt16) { -#define TF_LITE_ADD(type, opname) \ - type::opname(GetTensorData(input1), GetTensorDims(input1), \ - data->input1_shift, GetTensorData(input2), \ - GetTensorDims(input2), data->input2_shift, \ - data->output_activation_min, data->output_activation_max, \ - GetTensorData(output), GetTensorDims(output)); +#define TF_LITE_ADD(type, opname) \ + tflite::ArithmeticParams op_params; \ + op_params.input1_shift = data->input1_shift; \ + op_params.input2_shift = data->input2_shift; \ + SetActivationParams(data->output_activation_min, \ + data->output_activation_max, &op_params); \ + type::opname(op_params, GetTensorShape(input1), \ + GetTensorData(input1), GetTensorShape(input2), \ + GetTensorData(input2), GetTensorShape(output), \ + GetTensorData(output)) // The quantized version of Add doesn't support activations, so we // always use BroadcastAdd. if (kernel_type == kReference) { diff --git a/tensorflow/contrib/lite/kernels/arg_max.cc b/tensorflow/contrib/lite/kernels/arg_min_max.cc similarity index 70% rename from tensorflow/contrib/lite/kernels/arg_max.cc rename to tensorflow/contrib/lite/kernels/arg_min_max.cc index 26f57e88962116f446e72fbc164d2747e8b633b4..4f30d09030fb8d26c08090b180fdd352a967807f 100644 --- a/tensorflow/contrib/lite/kernels/arg_max.cc +++ b/tensorflow/contrib/lite/kernels/arg_min_max.cc @@ -23,7 +23,7 @@ limitations under the License. namespace tflite { namespace ops { namespace builtin { -namespace arg_max { +namespace arg_min_max { constexpr int kInputTensor = 0; constexpr int kAxis = 1; @@ -80,30 +80,39 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { return context->ResizeTensor(context, output, output_size); } +template +std::function GetComparefunction(bool is_arg_max) { + if (is_arg_max) { + return std::greater(); + } else { + return std::less(); + } +} + // The current impl actually ignores the axis argument. // Only determine the index of the maximum value in the last dimension. -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node, bool is_arg_max) { const TfLiteTensor* input = GetInput(context, node, kInputTensor); const TfLiteTensor* axis = GetInput(context, node, kAxis); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); -#define TF_LITE_ARG_MAX(data_type, axis_type, output_type) \ - optimized_ops::ArgMax(GetTensorData(axis), \ - GetTensorData(input), GetTensorDims(input), \ - GetTensorData(output), \ - GetTensorDims(output)) +#define TF_LITE_ARG_MIN_MAX(data_type, axis_type, output_type) \ + optimized_ops::ArgMinMax( \ + GetTensorData(axis), GetTensorData(input), \ + GetTensorDims(input), GetTensorData(output), \ + GetTensorDims(output), GetComparefunction(is_arg_max)) if (axis->type == kTfLiteInt32) { switch (output->type) { case kTfLiteInt32: { switch (input->type) { case kTfLiteFloat32: - TF_LITE_ARG_MAX(float, int32_t, int32_t); + TF_LITE_ARG_MIN_MAX(float, int32_t, int32_t); break; case kTfLiteUInt8: - TF_LITE_ARG_MAX(uint8_t, int32_t, int32_t); + TF_LITE_ARG_MIN_MAX(uint8_t, int32_t, int32_t); break; case kTfLiteInt32: - TF_LITE_ARG_MAX(int32_t, int32_t, int32_t); + TF_LITE_ARG_MIN_MAX(int32_t, int32_t, int32_t); break; default: return kTfLiteError; @@ -112,13 +121,13 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { case kTfLiteInt64: { switch (input->type) { case kTfLiteFloat32: - TF_LITE_ARG_MAX(float, int32_t, int64_t); + TF_LITE_ARG_MIN_MAX(float, int32_t, int64_t); break; case kTfLiteUInt8: - TF_LITE_ARG_MAX(uint8_t, int32_t, int64_t); + TF_LITE_ARG_MIN_MAX(uint8_t, int32_t, int64_t); break; case kTfLiteInt32: - TF_LITE_ARG_MAX(int32_t, int32_t, int64_t); + TF_LITE_ARG_MIN_MAX(int32_t, int32_t, int64_t); break; default: return kTfLiteError; @@ -132,13 +141,13 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { case kTfLiteInt32: { switch (input->type) { case kTfLiteFloat32: - TF_LITE_ARG_MAX(float, int64_t, int32_t); + TF_LITE_ARG_MIN_MAX(float, int64_t, int32_t); break; case kTfLiteUInt8: - TF_LITE_ARG_MAX(uint8_t, int64_t, int32_t); + TF_LITE_ARG_MIN_MAX(uint8_t, int64_t, int32_t); break; case kTfLiteInt32: - TF_LITE_ARG_MAX(int32_t, int64_t, int32_t); + TF_LITE_ARG_MIN_MAX(int32_t, int64_t, int32_t); break; default: return kTfLiteError; @@ -147,13 +156,13 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { case kTfLiteInt64: { switch (input->type) { case kTfLiteFloat32: - TF_LITE_ARG_MAX(float, int64_t, int64_t); + TF_LITE_ARG_MIN_MAX(float, int64_t, int64_t); break; case kTfLiteUInt8: - TF_LITE_ARG_MAX(uint8_t, int64_t, int64_t); + TF_LITE_ARG_MIN_MAX(uint8_t, int64_t, int64_t); break; case kTfLiteInt32: - TF_LITE_ARG_MAX(int32_t, int64_t, int64_t); + TF_LITE_ARG_MIN_MAX(int32_t, int64_t, int64_t); break; default: return kTfLiteError; @@ -163,16 +172,30 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteError; } } -#undef TF_LITE_ARG_MAX +#undef TF_LITE_ARG_MIN_MAX return kTfLiteOk; } -} // namespace arg_max +TfLiteStatus ArgMinEval(TfLiteContext* context, TfLiteNode* node) { + return Eval(context, node, false); +} + +TfLiteStatus ArgMaxEval(TfLiteContext* context, TfLiteNode* node) { + return Eval(context, node, true); +} + +} // namespace arg_min_max TfLiteRegistration* Register_ARG_MAX() { - static TfLiteRegistration r = {nullptr, nullptr, arg_max::Prepare, - arg_max::Eval}; + static TfLiteRegistration r = {nullptr, nullptr, arg_min_max::Prepare, + arg_min_max::ArgMaxEval}; + return &r; +} + +TfLiteRegistration* Register_ARG_MIN() { + static TfLiteRegistration r = {nullptr, nullptr, arg_min_max::Prepare, + arg_min_max::ArgMinEval}; return &r; } diff --git a/tensorflow/contrib/lite/kernels/arg_max_test.cc b/tensorflow/contrib/lite/kernels/arg_min_max_test.cc similarity index 52% rename from tensorflow/contrib/lite/kernels/arg_max_test.cc rename to tensorflow/contrib/lite/kernels/arg_min_max_test.cc index 31b15fe19ab87027c28bde9eaff7d88d03b2c213..90e5fdc532c821691aaeca6e6faa4c24919ca2c8 100644 --- a/tensorflow/contrib/lite/kernels/arg_max_test.cc +++ b/tensorflow/contrib/lite/kernels/arg_min_max_test.cc @@ -24,16 +24,13 @@ namespace { using ::testing::ElementsAreArray; template -class ArgMaxOpModel : public SingleOpModel { +class ArgBaseOpModel : public SingleOpModel { public: - ArgMaxOpModel(std::initializer_list input_shape, TensorType input_type, - TensorType output_type, TensorType index_output_type) { + ArgBaseOpModel(std::initializer_list input_shape, TensorType input_type, + TensorType output_type, TensorType index_output_type) { input_ = AddInput(input_type); axis_ = AddInput(TensorType_INT32); output_ = AddOutput(output_type); - SetBuiltinOp(BuiltinOperator_ARG_MAX, BuiltinOptions_ArgMaxOptions, - CreateArgMaxOptions(builder_, index_output_type).Union()); - BuildInterpreter({input_shape, {1, 1, 1, 1}}); } int input() { return input_; } @@ -42,12 +39,42 @@ class ArgMaxOpModel : public SingleOpModel { std::vector GetOutput() { return ExtractVector(output_); } std::vector GetOutputShape() { return GetTensorShape(output_); } - private: + protected: int input_; int axis_; int output_; }; +template +class ArgMaxOpModel : public ArgBaseOpModel { + public: + ArgMaxOpModel(std::initializer_list input_shape, TensorType input_type, + TensorType output_type, TensorType index_output_type) + : ArgBaseOpModel(input_shape, input_type, output_type, + index_output_type) { + ArgBaseOpModel::SetBuiltinOp( + BuiltinOperator_ARG_MAX, BuiltinOptions_ArgMaxOptions, + CreateArgMaxOptions(ArgBaseOpModel::builder_, index_output_type) + .Union()); + ArgBaseOpModel::BuildInterpreter({input_shape, {1, 1, 1, 1}}); + } +}; + +template +class ArgMinOpModel : public ArgBaseOpModel { + public: + ArgMinOpModel(std::initializer_list input_shape, TensorType input_type, + TensorType output_type, TensorType index_output_type) + : ArgBaseOpModel(input_shape, input_type, output_type, + index_output_type) { + ArgBaseOpModel::SetBuiltinOp( + BuiltinOperator_ARG_MIN, BuiltinOptions_ArgMinOptions, + CreateArgMinOptions(ArgBaseOpModel::builder_, index_output_type) + .Union()); + ArgBaseOpModel::BuildInterpreter({input_shape, {1, 1, 1, 1}}); + } +}; + TEST(ArgMaxOpTest, GetMaxArgFloat) { ArgMaxOpModel model({1, 1, 1, 4}, TensorType_FLOAT32, TensorType_INT32, TensorType_INT32); @@ -96,6 +123,54 @@ TEST(ArgMaxOpTest, GetMaxArgOutput64) { EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 2, 1})); } +TEST(ArgMinOpTest, GetMinArgFloat) { + ArgMinOpModel model({1, 1, 1, 4}, TensorType_FLOAT32, + TensorType_INT32, TensorType_INT32); + model.PopulateTensor(model.input(), {0.1, 0.9, 0.7, 0.3}); + // Currently only support the last dimension. + model.PopulateTensor(model.axis(), {3}); + model.Invoke(); + + EXPECT_THAT(model.GetOutput(), ElementsAreArray({0})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 1})); +} + +TEST(ArgMinOpTest, GetMinArgInt) { + ArgMinOpModel model({1, 1, 1, 4}, TensorType_INT32, TensorType_INT32, + TensorType_INT32); + model.PopulateTensor(model.input(), {1, 9, 7, 3}); + // Currently only support the last dimension. + model.PopulateTensor(model.axis(), {3}); + model.Invoke(); + + EXPECT_THAT(model.GetOutput(), ElementsAreArray({0})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 1})); +} + +TEST(ArgMinOpTest, GetMinArgMulDimensions) { + ArgMinOpModel model({1, 1, 2, 4}, TensorType_INT32, TensorType_INT32, + TensorType_INT32); + model.PopulateTensor(model.input(), {1, 2, 7, 8, 1, 9, 7, 3}); + // Currently only support the last dimension. + model.PopulateTensor(model.axis(), {3}); + model.Invoke(); + + EXPECT_THAT(model.GetOutput(), ElementsAreArray({0, 0})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 2, 1})); +} + +TEST(ArgMinOpTest, GetMinArgOutput64) { + ArgMinOpModel model({1, 1, 2, 4}, TensorType_INT32, TensorType_INT64, + TensorType_INT64); + model.PopulateTensor(model.input(), {10, 2, 7, 8, 1, 9, 7, 3}); + // Currently only support the last dimension. + model.PopulateTensor(model.axis(), {3}); + model.Invoke(); + + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 2, 1})); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc index 3425288f027a6fd9eb65f730bc7d039c832ace1c..a11a59aa050675314ac8b1316cdd0f15c81b8b15 100644 --- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include @@ -276,27 +275,33 @@ TfLiteStatus CheckLstmTensorDimensions( TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, TfLiteNode* node, int n_input, int n_output, int n_cell) { - CheckLstmTensorDimensions( - context, node, n_input, n_output, n_cell, kFwInputToInputWeightsTensor, - kFwInputToForgetWeightsTensor, kFwInputToCellWeightsTensor, - kFwInputToOutputWeightsTensor, kFwRecurrentToInputWeightsTensor, - kFwRecurrentToForgetWeightsTensor, kFwRecurrentToCellWeightsTensor, - kFwRecurrentToOutputWeightsTensor, kFwCellToInputWeightsTensor, - kFwCellToForgetWeightsTensor, kFwCellToOutputWeightsTensor, - kFwInputGateBiasTensor, kFwForgetGateBiasTensor, kFwCellGateBiasTensor, - kFwOutputGateBiasTensor, kFwProjectionWeightsTensor, - kFwProjectionBiasTensor); - - CheckLstmTensorDimensions( - context, node, n_input, n_output, n_cell, kBwInputToInputWeightsTensor, - kBwInputToForgetWeightsTensor, kBwInputToCellWeightsTensor, - kBwInputToOutputWeightsTensor, kBwRecurrentToInputWeightsTensor, - kBwRecurrentToForgetWeightsTensor, kBwRecurrentToCellWeightsTensor, - kBwRecurrentToOutputWeightsTensor, kBwCellToInputWeightsTensor, - kBwCellToForgetWeightsTensor, kBwCellToOutputWeightsTensor, - kBwInputGateBiasTensor, kBwForgetGateBiasTensor, kBwCellGateBiasTensor, - kBwOutputGateBiasTensor, kBwProjectionWeightsTensor, - kBwProjectionBiasTensor); + TF_LITE_ENSURE_OK( + context, + CheckLstmTensorDimensions( + context, node, n_input, n_output, n_cell, + kFwInputToInputWeightsTensor, kFwInputToForgetWeightsTensor, + kFwInputToCellWeightsTensor, kFwInputToOutputWeightsTensor, + kFwRecurrentToInputWeightsTensor, kFwRecurrentToForgetWeightsTensor, + kFwRecurrentToCellWeightsTensor, kFwRecurrentToOutputWeightsTensor, + kFwCellToInputWeightsTensor, kFwCellToForgetWeightsTensor, + kFwCellToOutputWeightsTensor, kFwInputGateBiasTensor, + kFwForgetGateBiasTensor, kFwCellGateBiasTensor, + kFwOutputGateBiasTensor, kFwProjectionWeightsTensor, + kFwProjectionBiasTensor)); + + TF_LITE_ENSURE_OK( + context, + CheckLstmTensorDimensions( + context, node, n_input, n_output, n_cell, + kBwInputToInputWeightsTensor, kBwInputToForgetWeightsTensor, + kBwInputToCellWeightsTensor, kBwInputToOutputWeightsTensor, + kBwRecurrentToInputWeightsTensor, kBwRecurrentToForgetWeightsTensor, + kBwRecurrentToCellWeightsTensor, kBwRecurrentToOutputWeightsTensor, + kBwCellToInputWeightsTensor, kBwCellToForgetWeightsTensor, + kBwCellToOutputWeightsTensor, kBwInputGateBiasTensor, + kBwForgetGateBiasTensor, kBwCellGateBiasTensor, + kBwOutputGateBiasTensor, kBwProjectionWeightsTensor, + kBwProjectionBiasTensor)); // Check if Forward and Backward tensors match along required dimensions. return kTfLiteOk; @@ -334,7 +339,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { const int n_fw_output = fw_recurrent_to_output_weights->dims->data[1]; // Check that input tensor dimensions matches with each other. - CheckInputTensorDimensions(context, node, n_input, n_fw_output, n_fw_cell); + TF_LITE_ENSURE_OK( + context, CheckInputTensorDimensions(context, node, n_input, n_fw_output, + n_fw_cell)); // Get the pointer to output, state and scratch buffer tensors. TfLiteTensor* fw_output = GetOutput(context, node, kFwOutputTensor); @@ -404,7 +411,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { const int n_bw_output = bw_recurrent_to_output_weights->dims->data[1]; // Check that input tensor dimensions matches with each other. - CheckInputTensorDimensions(context, node, n_input, n_bw_output, n_bw_cell); + TF_LITE_ENSURE_OK( + context, CheckInputTensorDimensions(context, node, n_input, n_bw_output, + n_bw_cell)); // Get the pointer to output, output_state and cell_state buffer tensors. TfLiteTensor* bw_output = GetOutput(context, node, kBwOutputTensor); diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc index aa24c1f34cd1e8c02a6a75b62fbe5f3c629498ca..517309a226bcfb717186be8c1d02d68e3b337f8e 100644 --- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc @@ -12,7 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/concatenation.cc b/tensorflow/contrib/lite/kernels/concatenation.cc index 45ea8d00498455be98467f2f1addc8ad7dcf35fa..ad211e9c67eed9ca70fcdd51171fdb70bd89b27c 100644 --- a/tensorflow/contrib/lite/kernels/concatenation.cc +++ b/tensorflow/contrib/lite/kernels/concatenation.cc @@ -12,7 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/conv.cc b/tensorflow/contrib/lite/kernels/conv.cc index 0321b2e2a0088bdb09b2c3c61827be8064fe939b..6f174763dfab9845d991b930e44b07a95e00d824 100644 --- a/tensorflow/contrib/lite/kernels/conv.cc +++ b/tensorflow/contrib/lite/kernels/conv.cc @@ -12,7 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include @@ -418,6 +417,7 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node, filter_data = GetTensorData(filter); } multithreaded_ops::Conv( + *eigen_support::GetThreadPoolDevice(context), GetTensorData(input), GetTensorDims(input), filter_data, GetTensorDims(filter), GetTensorData(bias), GetTensorDims(bias), params->stride_width, params->stride_height, diff --git a/tensorflow/contrib/lite/kernels/depthwise_conv.cc b/tensorflow/contrib/lite/kernels/depthwise_conv.cc index 16e5f1d065d8ea6d187c5e368d6c9385fe62514b..21518156b851892f50c62df7901d71c41fd733f7 100644 --- a/tensorflow/contrib/lite/kernels/depthwise_conv.cc +++ b/tensorflow/contrib/lite/kernels/depthwise_conv.cc @@ -12,7 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/div.cc b/tensorflow/contrib/lite/kernels/div.cc index bc5c3783fd63451fd6d600df2d8e93f740c68e95..d7420ddd8e41a57c901527884e942d444e543aa6 100644 --- a/tensorflow/contrib/lite/kernels/div.cc +++ b/tensorflow/contrib/lite/kernels/div.cc @@ -78,29 +78,44 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { } template -void EvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLiteDivParams* params, const OpData* data, - const TfLiteTensor* input1, const TfLiteTensor* input2, - TfLiteTensor* output) { - float output_activation_min, output_activation_max; - CalculateActivationRange(params->activation, &output_activation_min, - &output_activation_max); -#define TF_LITE_DIV(type, opname) \ - type::opname(GetTensorData(input1), GetTensorDims(input1), \ - GetTensorData(input2), GetTensorDims(input2), \ - output_activation_min, output_activation_max, \ - GetTensorData(output), GetTensorDims(output)) - if (kernel_type == kReference) { - if (data->requires_broadcast) { - TF_LITE_DIV(reference_ops, BroadcastDiv); +void EvalDiv(TfLiteContext* context, TfLiteNode* node, TfLiteDivParams* params, + const OpData* data, const TfLiteTensor* input1, + const TfLiteTensor* input2, TfLiteTensor* output) { +#define TF_LITE_DIV(type, opname, data_type) \ + data_type output_activation_min, output_activation_max; \ + CalculateActivationRange(params->activation, &output_activation_min, \ + &output_activation_max); \ + type::opname(GetTensorData(input1), GetTensorDims(input1), \ + GetTensorData(input2), GetTensorDims(input2), \ + output_activation_min, output_activation_max, \ + GetTensorData(output), GetTensorDims(output)) + if (output->type == kTfLiteInt32) { + if (kernel_type == kReference) { + if (data->requires_broadcast) { + TF_LITE_DIV(reference_ops, BroadcastDiv, int32_t); + } else { + TF_LITE_DIV(reference_ops, Div, int32_t); + } } else { - TF_LITE_DIV(reference_ops, Div); + if (data->requires_broadcast) { + TF_LITE_DIV(optimized_ops, BroadcastDiv, int32_t); + } else { + TF_LITE_DIV(optimized_ops, Div, int32_t); + } } - } else { - if (data->requires_broadcast) { - TF_LITE_DIV(optimized_ops, BroadcastDiv); + } else if (output->type == kTfLiteFloat32) { + if (kernel_type == kReference) { + if (data->requires_broadcast) { + TF_LITE_DIV(reference_ops, BroadcastDiv, float); + } else { + TF_LITE_DIV(reference_ops, Div, float); + } } else { - TF_LITE_DIV(optimized_ops, Div); + if (data->requires_broadcast) { + TF_LITE_DIV(optimized_ops, BroadcastDiv, float); + } else { + TF_LITE_DIV(optimized_ops, Div, float); + } } } #undef TF_LITE_DIV @@ -115,11 +130,12 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - if (output->type == kTfLiteFloat32) { - EvalFloat(context, node, params, data, input1, input2, output); + if (output->type == kTfLiteFloat32 || output->type == kTfLiteInt32) { + EvalDiv(context, node, params, data, input1, input2, output); } else { context->ReportError( - context, "Div only supports FLOAT32 and quantized UINT8 now, got %d.", + context, + "Div only supports FLOAT32, INT32 and quantized UINT8 now, got %d.", output->type); return kTfLiteError; } diff --git a/tensorflow/contrib/lite/kernels/div_test.cc b/tensorflow/contrib/lite/kernels/div_test.cc index 276b8289fbc1b4dcbf4624b76b854300d0fd4912..97aa2fe04e27416b99f48ab61ece54b745597ae3 100644 --- a/tensorflow/contrib/lite/kernels/div_test.cc +++ b/tensorflow/contrib/lite/kernels/div_test.cc @@ -52,6 +52,13 @@ class FloatDivOpModel : public BaseDivOpModel { std::vector GetOutput() { return ExtractVector(output_); } }; +class IntegerDivOpModel : public BaseDivOpModel { + public: + using BaseDivOpModel::BaseDivOpModel; + + std::vector GetOutput() { return ExtractVector(output_); } +}; + TEST(FloatDivOpTest, NoActivation) { FloatDivOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}, {TensorType_FLOAT32, {1, 2, 2, 1}}, @@ -75,7 +82,7 @@ TEST(FloatDivOpTest, ActivationRELU_N1_TO_1) { } TEST(FloatDivOpTest, VariousInputShapes) { - std::vector> test_shapes = { + std::vector> test_shapes = { {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; for (int i = 0; i < test_shapes.size(); ++i) { FloatDivOpModel m({TensorType_FLOAT32, test_shapes[i]}, @@ -92,7 +99,7 @@ TEST(FloatDivOpTest, VariousInputShapes) { } TEST(FloatDivOpTest, WithBroadcast) { - std::vector> test_shapes = { + std::vector> test_shapes = { {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; for (int i = 0; i < test_shapes.size(); ++i) { FloatDivOpModel m({TensorType_FLOAT32, test_shapes[i]}, @@ -108,6 +115,56 @@ TEST(FloatDivOpTest, WithBroadcast) { } } +TEST(IntegerDivOpTest, NoActivation) { + IntegerDivOpModel m({TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}}, + ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-2, 2, -15, 8}); + m.PopulateTensor(m.input2(), {5, -2, -3, 5}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({0, -1, 5, 1})); +} + +TEST(IntegerDivOpTest, ActivationRELU_N1_TO_1) { + IntegerDivOpModel m({TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}}, + ActivationFunctionType_RELU_N1_TO_1); + m.PopulateTensor(m.input1(), {-2, 2, -12, 8}); + m.PopulateTensor(m.input2(), {1, 2, -15, 5}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1, 1, 0, 1})); +} + +TEST(IntegerDivOpTest, VariousInputShapes) { + std::vector> test_shapes = { + {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; + for (int i = 0; i < test_shapes.size(); ++i) { + IntegerDivOpModel m({TensorType_INT32, test_shapes[i]}, + {TensorType_INT32, test_shapes[i]}, + {TensorType_INT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-20, 2, 3, 8, 11, -20}); + m.PopulateTensor(m.input2(), {1, 2, 6, 5, -11, -1}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({-20, 1, 0, 1, -1, 20})) + << "With shape number " << i; + } +} + +TEST(IntegerDivOpTest, WithBroadcast) { + std::vector> test_shapes = { + {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; + for (int i = 0; i < test_shapes.size(); ++i) { + IntegerDivOpModel m({TensorType_INT32, test_shapes[i]}, + {TensorType_INT32, {}}, // always a scalar + {TensorType_INT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-20, 21, 7, 8, 11, -123}); + m.PopulateTensor(m.input2(), {3}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({-6, 7, 2, 2, 3, -41})) + << "With shape number " << i; + } +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/eigen_support.cc b/tensorflow/contrib/lite/kernels/eigen_support.cc index 94927cb53df8033e55e647e19fb19afd7def788f..e542ad076528fa30152abba074a5c7dcd6ca1f48 100644 --- a/tensorflow/contrib/lite/kernels/eigen_support.cc +++ b/tensorflow/contrib/lite/kernels/eigen_support.cc @@ -14,14 +14,49 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/kernels/eigen_support.h" -#include "third_party/eigen3/Eigen/Core" +#include + +#include "tensorflow/contrib/lite/arena_planner.h" +#include "tensorflow/contrib/lite/kernels/internal/optimized/eigen_spatial_convolutions.h" #include "tensorflow/contrib/lite/kernels/op_macros.h" namespace tflite { namespace eigen_support { namespace { +#ifndef EIGEN_DONT_ALIGN +// Eigen may require buffers to be algiend to 16, 32 or 64 bytes depending on +// hardware architecture and build configurations. +// If the static assertion fails, try to increase `kDefaultTensorAlignment` to +// in `arena_planner.h` to 32 or 64. +static_assert( + kDefaultTensorAlignment % EIGEN_MAX_ALIGN_BYTES == 0, + "kDefaultArenaAlignment doesn't comply with Eigen alignment requirement."); +#endif // EIGEN_DONT_ALIGN + +// We have a single global threadpool for all convolution operations. This means +// that inferences started from different threads may block each other, but +// since the underlying resource of CPU cores should be consumed by the +// operations anyway, it shouldn't affect overall performance. +class EigenThreadPoolWrapper : public Eigen::ThreadPoolInterface { + public: + // Takes ownership of 'pool' + explicit EigenThreadPoolWrapper(Eigen::ThreadPool* pool) : pool_(pool) {} + ~EigenThreadPoolWrapper() override {} + + void Schedule(std::function fn) override { + pool_->Schedule(std::move(fn)); + } + int NumThreads() const override { return pool_->NumThreads(); } + int CurrentThreadId() const override { return pool_->CurrentThreadId(); } + + private: + std::unique_ptr pool_; +}; + struct RefCountedEigenContext : public TfLiteExternalContext { + std::unique_ptr thread_pool_wrapper; + std::unique_ptr device; int num_references = 0; }; @@ -30,8 +65,26 @@ RefCountedEigenContext* GetEigenContext(TfLiteContext* context) { context->GetExternalContext(context, kTfLiteEigenContext)); } +void InitDevice(TfLiteContext* context, RefCountedEigenContext* ptr) { + int num_threads = 4; + if (context->recommended_num_threads != -1) { + num_threads = context->recommended_num_threads; + } + ptr->device.reset(); // destroy before we invalidate the thread pool + ptr->thread_pool_wrapper.reset( + new EigenThreadPoolWrapper(new Eigen::ThreadPool(num_threads))); + ptr->device.reset( + new Eigen::ThreadPoolDevice(ptr->thread_pool_wrapper.get(), num_threads)); +} + TfLiteStatus Refresh(TfLiteContext* context) { Eigen::setNbThreads(context->recommended_num_threads); + + auto* ptr = GetEigenContext(context); + if (ptr != nullptr) { + InitDevice(context, ptr); + } + return kTfLiteOk; } @@ -47,6 +100,7 @@ void IncrementUsageCounter(TfLiteContext* context) { ptr->type = kTfLiteEigenContext; ptr->Refresh = Refresh; ptr->num_references = 0; + InitDevice(context, ptr); context->SetExternalContext(context, kTfLiteEigenContext, ptr); } ptr->num_references++; @@ -65,5 +119,14 @@ void DecrementUsageCounter(TfLiteContext* context) { } } +const Eigen::ThreadPoolDevice* GetThreadPoolDevice(TfLiteContext* context) { + auto* ptr = GetEigenContext(context); + if (ptr == nullptr) { + TF_LITE_FATAL( + "Call to GetFromContext() not preceded by IncrementUsageCounter()"); + } + return ptr->device.get(); +} + } // namespace eigen_support } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/eigen_support.h b/tensorflow/contrib/lite/kernels/eigen_support.h index d47e691123282a8a8cc53c29be1d95af037e3939..ec77856b1054e85c405193c6f44dc6e74b58a645 100644 --- a/tensorflow/contrib/lite/kernels/eigen_support.h +++ b/tensorflow/contrib/lite/kernels/eigen_support.h @@ -17,6 +17,10 @@ limitations under the License. #include "tensorflow/contrib/lite/context.h" +namespace EigenForTFLite { +class ThreadPoolDevice; +} + namespace tflite { namespace eigen_support { @@ -28,6 +32,9 @@ void IncrementUsageCounter(TfLiteContext* context); // usages all temporary Eigen objects will be deleted. void DecrementUsageCounter(TfLiteContext* context); +const EigenForTFLite::ThreadPoolDevice* GetThreadPoolDevice( + TfLiteContext* context); + } // namespace eigen_support } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/embedding_lookup.cc b/tensorflow/contrib/lite/kernels/embedding_lookup.cc index 0ba170a4da7b7f0d7afa8b425027b03185d3a559..b2dff87e6296c6038241c704d9158e174501f026 100644 --- a/tensorflow/contrib/lite/kernels/embedding_lookup.cc +++ b/tensorflow/contrib/lite/kernels/embedding_lookup.cc @@ -29,7 +29,6 @@ limitations under the License. // When indices are out of bound, the ops will not succeed. // -#include #include #include #include @@ -112,8 +111,9 @@ TfLiteStatus EvalHybrid(TfLiteContext* context, TfLiteNode* node, // TODO(alanchiao): refactor scalar multiply into separate function // for ease of adding a neon equivalent if ever necessary. for (int j = 0; j < col_size; j++) { + const int8_t* value_ptr = reinterpret_cast(value->data.uint8); output->data.f[j + i * col_size] = - value->data.uint8[j + idx * col_size] * scaling_factor; + value_ptr[j + idx * col_size] * scaling_factor; } } } diff --git a/tensorflow/contrib/lite/kernels/embedding_lookup_test.cc b/tensorflow/contrib/lite/kernels/embedding_lookup_test.cc index 04657fd86323ef1c58d069c06097c7665f55cc87..4a88d168c60203f10802e634def9b1d1316c9c6d 100644 --- a/tensorflow/contrib/lite/kernels/embedding_lookup_test.cc +++ b/tensorflow/contrib/lite/kernels/embedding_lookup_test.cc @@ -107,9 +107,9 @@ TEST(HybridEmbeddingLookupHybridOpTest, Simple2DTest) { HybridEmbeddingLookupOpModel m({3}, {3, 8}); m.SetInput({1, 0, 2}); m.SetWeight({ - 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 - 1.00, 1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 - 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 + 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 + 1.00, -1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 + 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 }); m.Invoke(); @@ -117,9 +117,9 @@ TEST(HybridEmbeddingLookupHybridOpTest, Simple2DTest) { EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear( { - 1.00, 1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 - 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 - 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 + 1.00, -1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 + 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 + 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 }, 7.41e-03))); } @@ -128,9 +128,9 @@ TEST(HybridEmbeddingLookupHybridOpTest, Simple3DTest) { HybridEmbeddingLookupOpModel m({3}, {3, 2, 4}); m.SetInput({1, 0, 2}); m.SetWeight({ - 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 - 1.00, 1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 - 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 + 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 + 1.00, -1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 + 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 }); m.Invoke(); @@ -138,9 +138,9 @@ TEST(HybridEmbeddingLookupHybridOpTest, Simple3DTest) { EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear( { - 1.00, 1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 - 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 - 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 + 1.00, -1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 + 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 + 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 }, 7.41e-03))); } @@ -149,9 +149,9 @@ TEST(HybridEmbeddingLookupHybridOpTest, Simple4DTest) { HybridEmbeddingLookupOpModel m({3}, {3, 2, 2, 2}); m.SetInput({1, 0, 2}); m.SetWeight({ - 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 - 1.00, 1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 - 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 + 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 + 1.00, -1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 + 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 }); m.Invoke(); @@ -159,9 +159,9 @@ TEST(HybridEmbeddingLookupHybridOpTest, Simple4DTest) { EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear( { - 1.00, 1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 - 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 - 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 + 1.00, -1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 + 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 + 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 }, 7.41e-03))); } diff --git a/tensorflow/contrib/lite/kernels/fake_quant.cc b/tensorflow/contrib/lite/kernels/fake_quant.cc new file mode 100644 index 0000000000000000000000000000000000000000..0ef1a50b308b2e8a781bc9ed7195c22e627ea2de --- /dev/null +++ b/tensorflow/contrib/lite/kernels/fake_quant.cc @@ -0,0 +1,92 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace fake_quant { + +// This file has reference implementation of FakeQuant. +enum KernelType { + kReference, +}; + +struct OpContext { + OpContext(TfLiteContext* context, TfLiteNode* node) { + input = GetInput(context, node, 0); + output = GetOutput(context, node, 0); + } + const TfLiteTensor* input; + TfLiteTensor* output; +}; + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + const auto* params = + reinterpret_cast(node->builtin_data); + + if (params->narrow_range) { + context->ReportError( + context, + "narrow_range FakeQuant is not currently supported at runtime. " + "narrow_range is only meant to be applied to weights, not activations"); + return kTfLiteError; + } + + OpContext op_context(context, node); + TfLiteIntArray* output_dims = TfLiteIntArrayCopy(op_context.input->dims); + op_context.output->type = op_context.input->type; + return context->ResizeTensor(context, op_context.output, output_dims); +} + +template +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + OpContext op_context(context, node); + + const auto* params = + reinterpret_cast(node->builtin_data); + + reference_ops::FakeQuant(GetTensorData(op_context.input), + GetTensorDims(op_context.input), params->min, + params->max, params->num_bits, + GetTensorData(op_context.output), + GetTensorDims(op_context.output)); + + return kTfLiteOk; +} + +} // namespace fake_quant + +TfLiteRegistration* Register_FAKE_QUANT_REF() { + static TfLiteRegistration r = {nullptr, nullptr, fake_quant::Prepare, + fake_quant::Eval}; + return &r; +} + +TfLiteRegistration* Register_FAKE_QUANT() { return Register_FAKE_QUANT_REF(); } + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/fake_quant_test.cc b/tensorflow/contrib/lite/kernels/fake_quant_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..11a02f7ed7474e05b887955c111179d2d403f0e6 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/fake_quant_test.cc @@ -0,0 +1,112 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +class FakeQuantOpModel : public SingleOpModel { + public: + FakeQuantOpModel(const TensorData& input, const TensorType& output, float min, + float max, int num_bits) { + input_ = AddInput(input); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_FAKE_QUANT, BuiltinOptions_FakeQuantOptions, + CreateFakeQuantOptions(builder_, min, max, num_bits).Union()); + BuildInterpreter({GetShape(input_)}); + } + + template + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + template + std::vector GetOutput() { + return ExtractVector(output_); + } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + protected: + int input_; + int output_; +}; + +TEST(FakeQuantOpTest, FloatPositiveRange8Test) { + std::initializer_list data = {0.0, 1.0, 0.25, + 0.50, 0.4444444, 0.00001}; + FakeQuantOpModel m({TensorType_FLOAT32, {3, 1, 2}}, TensorType_FLOAT32, 0.0f, + 1.0f, 8); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1, 2})); + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear({0, 1, 0.25098, 0.498039, 0.443137, 0}))); +} + +TEST(FakeQuantOpTest, FloatNegativeRange8Test) { + std::initializer_list data = {0.0, -0.9, 0.25, + 0.50, 0.4444444, -0.00001}; + FakeQuantOpModel m({TensorType_FLOAT32, {3, 1, 2}}, TensorType_FLOAT32, -0.9f, + 0.9f, 8); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1, 2})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {0, -0.896471, 0.247059, 0.501176, 0.444706, 0}))); +} + +TEST(FakeQuantOpTest, FloatPositiveRange16Test) { + std::initializer_list data = {0.0, 1.0, 0.25, + 0.50, 0.4444444, 0.00001}; + FakeQuantOpModel m({TensorType_FLOAT32, {3, 1, 2}}, TensorType_FLOAT32, 0.0f, + 1.0f, 16); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1, 2})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {0, 1, 0.250004, 0.500008, 0.44445, 1.5259e-05}))); +} + +TEST(FakeQuantOpTest, FloatNegativeRange16Test) { + std::initializer_list data = {0.0, -0.9, 0.25, + 0.50, 0.4444444, -0.00001}; + FakeQuantOpModel m({TensorType_FLOAT32, {3, 1, 2}}, TensorType_FLOAT32, -0.9f, + 0.9f, 16); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1, 2})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {0, -0.900014, 0.249998, 0.499995, 0.444431, 0}))); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/fully_connected.cc b/tensorflow/contrib/lite/kernels/fully_connected.cc index 3b203dd480f95c5dc70a69aafce0bac6ab2cbc06..bc370608c092eeb5312dc40b56f47740f473c8ae 100644 --- a/tensorflow/contrib/lite/kernels/fully_connected.cc +++ b/tensorflow/contrib/lite/kernels/fully_connected.cc @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include @@ -71,7 +70,7 @@ void* Init(TfLiteContext* context, const char* buffer, size_t length) { // Instead, we allocate a new object to carry information from Prepare() to // Eval(). gemm_support::IncrementUsageCounter(context); - auto* op_data = new OpData; + auto* op_data = new OpData(); context->AddTensors(context, 1, &op_data->input_quantized_index); return op_data; } diff --git a/tensorflow/contrib/lite/kernels/hashtable_lookup.cc b/tensorflow/contrib/lite/kernels/hashtable_lookup.cc index 41211d41aa85a5a2da6ae96dc6f0337c54fb1a45..f37c66acb33eb9995772e595b84df6616e8d9e6a 100644 --- a/tensorflow/contrib/lite/kernels/hashtable_lookup.cc +++ b/tensorflow/contrib/lite/kernels/hashtable_lookup.cc @@ -31,7 +31,6 @@ limitations under the License. // Each item indicates whether the corresponding lookup has a returned value. // 0 for missing key, 1 for found key. -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD index 7962fcbc9d6c839ea11d7355e955239194442e03..3a855fe3ddaa7e7de0134f8dfee1ccf67168541a 100644 --- a/tensorflow/contrib/lite/kernels/internal/BUILD +++ b/tensorflow/contrib/lite/kernels/internal/BUILD @@ -232,6 +232,7 @@ cc_library( cc_test( name = "tensor_test", srcs = ["tensor_test.cc"], + tags = ["no_oss"], deps = [ ":reference", "@com_google_googletest//:gtest", @@ -260,6 +261,7 @@ cc_library( cc_test( name = "quantization_util_test", srcs = ["quantization_util_test.cc"], + tags = ["no_oss"], deps = [ ":quantization_util", "@com_google_googletest//:gtest", @@ -505,7 +507,10 @@ cc_test( "//conditions:default": [], }), linkstatic = 1, - tags = ["tflite_not_portable_ios"], + tags = [ + "no_oss", + "tflite_not_portable_ios", + ], deps = [ ":tensor_utils", "//tensorflow/contrib/lite:builtin_op_data", @@ -517,6 +522,7 @@ cc_test( cc_test( name = "depthwiseconv_float_test", srcs = ["depthwiseconv_float_test.cc"], + tags = ["no_oss"], deps = [ ":optimized_base", ":reference_base", @@ -529,6 +535,7 @@ cc_test( cc_test( name = "depthwiseconv_quantized_test", srcs = ["depthwiseconv_quantized_test.cc"], + tags = ["no_oss"], deps = [ ":optimized_base", ":reference_base", @@ -541,7 +548,10 @@ cc_test( cc_test( name = "resize_bilinear_test", srcs = ["resize_bilinear_test.cc"], - tags = ["tflite_not_portable"], + tags = [ + "no_oss", + "tflite_not_portable", + ], deps = [ ":optimized_base", ":reference_base", @@ -557,6 +567,7 @@ cc_test( srcs = [ "softmax_quantized_test.cc", ], + tags = ["no_oss"], deps = [ ":optimized_base", ":quantization_util", @@ -572,7 +583,10 @@ cc_test( srcs = [ "logsoftmax_quantized_test.cc", ], - tags = ["tflite_not_portable"], + tags = [ + "no_oss", + "tflite_not_portable", + ], deps = [ ":optimized_base", ":quantization_util", @@ -585,6 +599,7 @@ cc_test( cc_test( name = "log_quantized_test", srcs = ["log_quantized_test.cc"], + tags = ["no_oss"], deps = [ ":optimized_base", ":reference_base", @@ -611,6 +626,7 @@ cc_library( cc_test( name = "batch_to_space_nd_test", srcs = ["batch_to_space_nd_test.cc"], + tags = ["no_oss"], deps = [ ":optimized_base", "@com_google_googletest//:gtest_main", diff --git a/tensorflow/contrib/lite/kernels/internal/common.h b/tensorflow/contrib/lite/kernels/internal/common.h index b86ca49c116875672c4516a2a47f7dae511a7116..310a8980e6943db3804b0671a21ccf0e6ce34c28 100644 --- a/tensorflow/contrib/lite/kernels/internal/common.h +++ b/tensorflow/contrib/lite/kernels/internal/common.h @@ -127,6 +127,139 @@ int CountLeadingZeros(T integer_input) { return leading_zeros; } +// DO NOT USE THIS STRUCT FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING +// BROADCASTING. +// +// NdArrayDesc describes the shape and memory layout of an N-dimensional +// rectangular array of numbers. +// +// NdArrayDesc is basically identical to Dims defined in types.h. +// However, as Dims is to be deprecated, this class exists as an adaptor +// to enable simple unoptimized implementations of element-wise broadcasting +// operations. +template +struct NdArrayDesc { + // The "extent" of each dimension. Indices along dimension d must be in the + // half-open interval [0, extents[d]). + int extents[N]; + + // The number of *elements* (not bytes) between consecutive indices of each + // dimension. + int strides[N]; +}; + +// DO NOT USE THIS FUNCTION FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING +// BROADCASTING. +// +// Same as Offset(), except takes as NdArrayDesc instead of Dims. +inline int SubscriptToIndex(const NdArrayDesc<4>& desc, int i0, int i1, int i2, + int i3) { + TFLITE_DCHECK(i0 >= 0 && i0 < desc.extents[0]); + TFLITE_DCHECK(i1 >= 0 && i1 < desc.extents[1]); + TFLITE_DCHECK(i2 >= 0 && i2 < desc.extents[2]); + TFLITE_DCHECK(i3 >= 0 && i3 < desc.extents[3]); + return i0 * desc.strides[0] + i1 * desc.strides[1] + i2 * desc.strides[2] + + i3 * desc.strides[3]; +} + +// Given the dimensions of the operands for an element-wise binary broadcast, +// adjusts them so that they can be directly iterated over with simple loops. +// Returns the adjusted dims as instances of NdArrayDesc in 'desc0_out' and +// 'desc1_out'. 'desc0_out' and 'desc1_out' cannot be nullptr. +// +// This function assumes that the two input shapes are compatible up to +// broadcasting and the shorter one has already been prepended with 1s to be the +// same length. E.g., if shape0 is (1, 16, 16, 64) and shape1 is (1, 64), +// shape1 must already have been prepended to be (1, 1, 1, 64). Recall that +// Dims refer to shapes in reverse order. In this case, input0_dims will be +// (64, 16, 16, 1) and input1_dims will be (64, 1, 1, 1). +// +// When two shapes are compatible up to broadcasting, for each dimension d, +// the input extents are either equal, or one of them is 1. +// +// This function performs the following for each dimension d: +// - If the extents are equal, then do nothing since the loop that walks over +// both of the input arrays is correct. +// - Otherwise, one (and only one) of the extents must be 1. Say extent0 is 1 +// and extent1 is e1. Then set extent0 to e1 and stride0 *to 0*. This allows +// array0 to be referenced *at any index* in dimension d and still access the +// same slice. +template +inline void NdArrayDescsForElementwiseBroadcast(const Dims& input0_dims, + const Dims& input1_dims, + NdArrayDesc* desc0_out, + NdArrayDesc* desc1_out) { + TFLITE_DCHECK(desc0_out != nullptr); + TFLITE_DCHECK(desc1_out != nullptr); + + // Copy dims to desc. + for (int i = 0; i < N; ++i) { + desc0_out->extents[i] = input0_dims.sizes[i]; + desc0_out->strides[i] = input0_dims.strides[i]; + desc1_out->extents[i] = input1_dims.sizes[i]; + desc1_out->strides[i] = input1_dims.strides[i]; + } + + // Walk over each dimension. If the extents are equal do nothing. + // Otherwise, set the desc with extent 1 to have extent equal to the other and + // stride 0. + for (int i = 0; i < N; ++i) { + const int extent0 = ArraySize(input0_dims, i); + const int extent1 = ArraySize(input1_dims, i); + if (extent0 != extent1) { + if (extent0 == 1) { + desc0_out->strides[i] = 0; + desc0_out->extents[i] = extent1; + } else { + TFLITE_DCHECK_EQ(extent1, 1); + desc1_out->strides[i] = 0; + desc1_out->extents[i] = extent0; + } + } + } +} + +template +inline void NdArrayDescsForElementwiseBroadcast( + const RuntimeShape& input0_shape, const RuntimeShape& input1_shape, + NdArrayDesc* desc0_out, NdArrayDesc* desc1_out) { + TFLITE_DCHECK(desc0_out != nullptr); + TFLITE_DCHECK(desc1_out != nullptr); + + auto extended_input0_shape = RuntimeShape::ExtendedShape(N, input0_shape); + auto extended_input1_shape = RuntimeShape::ExtendedShape(N, input1_shape); + + // Copy dims to desc, calculating strides. + int desc0_stride = 1; + int desc1_stride = 1; + for (int i = N - 1; i >= 0; --i) { + desc0_out->extents[i] = extended_input0_shape.Dims(i); + desc0_out->strides[i] = desc0_stride; + desc0_stride *= extended_input0_shape.Dims(i); + desc1_out->extents[i] = extended_input1_shape.Dims(i); + desc1_out->strides[i] = desc1_stride; + desc1_stride *= extended_input1_shape.Dims(i); + } + + // Walk over each dimension. If the extents are equal do nothing. + // Otherwise, set the desc with extent 1 to have extent equal to the other and + // stride 0. + for (int i = 0; i < N; ++i) { + const int extent0 = extended_input0_shape.Dims(i); + const int extent1 = extended_input1_shape.Dims(i); + if (extent0 != extent1) { + if (extent0 == 1) { + desc0_out->strides[i] = 0; + desc0_out->extents[i] = extent1; + } else { + TFLITE_DCHECK_EQ(extent1, 1); + desc1_out->strides[i] = 0; + desc1_out->extents[i] = extent0; + } + } + } +} + } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_COMMON_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc index a0e382edb6efe467c7b16624cf1760b0d1c6d760..200f2f151582c2361dd2403164d0bbe119cbed72 100644 --- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc @@ -255,14 +255,6 @@ void LstmStep( output_state_ptr); } -// TODO(alanchiao): move this to tensor_utils. -void VectorMultiply(const int8_t* vector, const int v_size, const float scale, - float* result) { - for (int i = 0; i < v_size; ++i) { - *result++ = scale * *vector++; - } -} - void LstmStep( const float* input_ptr_batch, const int8_t* input_to_input_weights_ptr, float input_to_input_weights_scale, @@ -415,8 +407,9 @@ void LstmStep( // For each batch and cell: update input gate. if (!use_cifg) { if (use_peephole && !is_cell_state_all_zeros) { - VectorMultiply(cell_to_input_weights_ptr, n_cell, - cell_to_input_weights_scale, recovered_cell_weights); + tensor_utils::VectorScalarMultiply(cell_to_input_weights_ptr, n_cell, + cell_to_input_weights_scale, + recovered_cell_weights); tensor_utils::VectorBatchVectorCwiseProductAccumulate( recovered_cell_weights, n_cell, cell_state_ptr, n_batch, input_gate_scratch); @@ -427,8 +420,9 @@ void LstmStep( // For each batch and cell: update forget gate. if (use_peephole && !is_cell_state_all_zeros) { - VectorMultiply(cell_to_forget_weights_ptr, n_cell, - cell_to_forget_weights_scale, recovered_cell_weights); + tensor_utils::VectorScalarMultiply(cell_to_forget_weights_ptr, n_cell, + cell_to_forget_weights_scale, + recovered_cell_weights); tensor_utils::VectorBatchVectorCwiseProductAccumulate( recovered_cell_weights, n_cell, cell_state_ptr, n_batch, forget_gate_scratch); @@ -459,8 +453,9 @@ void LstmStep( tensor_utils::IsZeroVector(cell_state_ptr, n_batch * n_cell); // For each batch and cell: update the output gate. if (use_peephole && !is_cell_state_all_zeros) { - VectorMultiply(cell_to_output_weights_ptr, n_cell, - cell_to_output_weights_scale, recovered_cell_weights); + tensor_utils::VectorScalarMultiply(cell_to_output_weights_ptr, n_cell, + cell_to_output_weights_scale, + recovered_cell_weights); tensor_utils::VectorBatchVectorCwiseProductAccumulate( recovered_cell_weights, n_cell, cell_state_ptr, n_batch, output_gate_scratch); diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h index 7816752132761d9523ffc1f45b3740c0817ed402..d5503073a7cfc0be137fde104815ca1a2a6bb438 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h @@ -55,15 +55,262 @@ inline void Relu(const float* input_data, const Dims<4>& input_dims, DimsToShape(output_dims)); } +// legacy, for compatibility with old checked-in code +template +void Add(const float* input1_data, const Dims<4>& input1_dims, + const float* input2_data, const Dims<4>& input2_dims, + float* output_data, const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + tflite::ArithmeticParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +inline void Add(int left_shift, const uint8* input1_data, + const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, + const uint8* input2_data, const Dims<4>& input2_dims, + int32 input2_offset, int32 input2_multiplier, int input2_shift, + int32 output_offset, int32 output_multiplier, int output_shift, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + + tflite::ArithmeticParams op_params; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +void Add(const int32* input1_data, const Dims<4>& input1_dims, + const int32* input2_data, const Dims<4>& input2_dims, + int32* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("Add/int32"); + TFLITE_DCHECK(Ac == FusedActivationFunctionType::kNone); + + tflite::ArithmeticParams op_params; + op_params.quantized_activation_min = std::numeric_limits::min(); + op_params.quantized_activation_max = std::numeric_limits::max(); + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T output_activation_min, T output_activation_max, + T* output_data, const Dims<4>& output_dims) { + tflite::ArithmeticParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + BroadcastAdd4DSlow(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +template +inline void BroadcastAdd(int left_shift, const uint8* input1_data, + const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, + const uint8* input2_data, const Dims<4>& input2_dims, + int32 input2_offset, int32 input2_multiplier, + int input2_shift, int32 output_offset, + int32 output_multiplier, int output_shift, + int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + + tflite::ArithmeticParams op_params; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + BroadcastAdd4DSlow(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +template +inline void BroadcastAddFivefold( + int y0, int y1, int y2, int y3, int y4, int left_shift, + const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, const uint8* input2_data, + const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier, + int input2_shift, int32 output_offset, int32 output_multiplier, + int output_shift, int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + tflite::ArithmeticParams op_params; + op_params.broadcast_category = + tflite::BroadcastableOpCategory::kFirstInputBroadcastsFast; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + op_params.broadcast_shape[4] = y0; + op_params.broadcast_shape[3] = y1; + op_params.broadcast_shape[2] = y2; + op_params.broadcast_shape[1] = y3; + op_params.broadcast_shape[0] = y4; + BroadcastAddFivefold(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +// legacy, for compatibility with old checked-in code +template +void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T* output_data, const Dims<4>& output_dims) { + T output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + BroadcastAdd(input1_data, input1_dims, input2_data, input2_dims, + output_activation_min, output_activation_max, output_data, + output_dims); +} + +template +inline void Add(const int16* input1_data, const Dims<4>& input1_dims, + int input1_shift, const int16* input2_data, + const Dims<4>& input2_dims, int input2_shift, + int16 output_activation_min, int16 output_activation_max, + int16* output_data, const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, -32768); + TFLITE_DCHECK_EQ(output_activation_max, 32767); + } + + tflite::ArithmeticParams op_params; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +inline void Sub(const float* input1_data, const Dims<4>& input1_dims, + const float* input2_data, const Dims<4>& input2_dims, + float* output_data, const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(FusedActivationFunctionType::kNone, + &output_activation_min, &output_activation_max); + tflite::ArithmeticParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + Sub(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +void Sub(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, + const Dims<4>& input2_dims, T* output_data, + const Dims<4>& output_dims) { + T output_activation_min, output_activation_max; + GetActivationMinMax(FusedActivationFunctionType::kNone, + &output_activation_min, &output_activation_max); + tflite::ArithmeticParams op_params; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + Sub(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + inline void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride_width, int stride_height, int pad_width, int pad_height, int kwidth, int kheight, float output_activation_min, float output_activation_max, float* output_data, const Dims<4>& output_dims) { - AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, DimsToShape(output_dims)); + tflite::PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = kheight; + params.filter_width = kwidth; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.float_activation_min = output_activation_min; + params.float_activation_max = output_activation_max; + AveragePool(params, DimsToShape(input_dims), input_data, + DimsToShape(output_dims), output_data); } // legacy, for compatibility with old checked-in code @@ -96,10 +343,17 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int32 output_activation_min, int32 output_activation_max, uint8* output_data, const Dims<4>& output_dims) { - AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); + tflite::PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = filter_height; + params.filter_width = filter_width; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.quantized_activation_min = output_activation_min; + params.quantized_activation_max = output_activation_max; + AveragePool(params, DimsToShape(input_dims), input_data, + DimsToShape(output_dims), output_data); } // legacy, for compatibility with old checked-in code @@ -140,9 +394,17 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, int pad_height, int kwidth, int kheight, float output_activation_min, float output_activation_max, float* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, DimsToShape(output_dims)); + tflite::PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = kheight; + params.filter_width = kwidth; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.float_activation_min = output_activation_min; + params.float_activation_max = output_activation_max; + MaxPool(params, DimsToShape(input_dims), input_data, DimsToShape(output_dims), + output_data); } // legacy, for compatibility with old checked-in code @@ -172,10 +434,17 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int pad_height, int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, uint8* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); + PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = filter_height; + params.filter_width = filter_width; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.quantized_activation_min = output_activation_min; + params.quantized_activation_max = output_activation_max; + MaxPool(params, DimsToShape(input_dims), input_data, DimsToShape(output_dims), + output_data); } // legacy, for compatibility with old checked-in code @@ -215,10 +484,17 @@ inline void L2Pool(const float* input_data, const Dims<4>& input_dims, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, float* output_data, const Dims<4>& output_dims) { - L2Pool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); + PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = filter_height; + params.filter_width = filter_width; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.float_activation_min = output_activation_min; + params.float_activation_max = output_activation_max; + L2Pool(params, DimsToShape(input_dims), input_data, DimsToShape(output_dims), + output_data); } // legacy, for compatibility with old checked-in code diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/multithreaded_conv.h b/tensorflow/contrib/lite/kernels/internal/optimized/multithreaded_conv.h index 27d9224512a835ea58911031f1b4d6dcf5482ba9..4a3545d47aca7d649061d39cbc23fa7ddf208156 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/multithreaded_conv.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/multithreaded_conv.h @@ -35,35 +35,6 @@ limitations under the License. namespace tflite { namespace multithreaded_ops { -class EigenThreadPoolWrapper : public Eigen::ThreadPoolInterface { - public: - explicit EigenThreadPoolWrapper(Eigen::ThreadPool* pool) : pool_(pool) {} - ~EigenThreadPoolWrapper() override {} - - void Schedule(std::function fn) override { - pool_->Schedule(std::move(fn)); - } - int NumThreads() const override { return pool_->NumThreads(); } - int CurrentThreadId() const override { return pool_->CurrentThreadId(); } - - private: - Eigen::ThreadPool* pool_ = nullptr; -}; - -// We have a single global threadpool for all convolution operations. This means -// that inferences started from different threads may block each other, but -// since the underlying resource of CPU cores should be consumed by the -// operations anyway, it shouldn't affect overall performance. -const Eigen::ThreadPoolDevice& GetThreadPoolDevice() { - const int thread_count = 4; - static Eigen::ThreadPool* tp = new Eigen::ThreadPool(thread_count); - static EigenThreadPoolWrapper* thread_pool_wrapper = - new EigenThreadPoolWrapper(tp); - static Eigen::ThreadPoolDevice* device = - new Eigen::ThreadPoolDevice(thread_pool_wrapper, thread_count); - return *device; -} - // Shorthands for the types we need when interfacing with the EigenTensor // library. typedef Eigen::TensorMap< @@ -113,14 +84,13 @@ class EigenTensorConvFunctor { } public: - void operator()(const T* input_data, T* im2col_buffer, int input_batches, - int input_height, int input_width, int input_depth, - const T* filter_data, int filter_height, int filter_width, - int filter_count, int stride_rows, int stride_cols, - int pad_width, int pad_height, TfLitePadding padding, - T* output_data, int output_height, int output_width) { - const Eigen::ThreadPoolDevice& device = GetThreadPoolDevice(); - + void operator()(const Eigen::ThreadPoolDevice& device, const T* input_data, + T* im2col_buffer, int input_batches, int input_height, + int input_width, int input_depth, const T* filter_data, + int filter_height, int filter_width, int filter_count, + int stride_rows, int stride_cols, int pad_width, + int pad_height, TfLitePadding padding, T* output_data, + int output_height, int output_width) { const bool is_1x1_kernel = (filter_height == 1 && filter_width == 1 && stride_rows == 1 && stride_cols == 1); if (is_1x1_kernel) { @@ -162,11 +132,11 @@ class EigenTensorConvFunctor { } }; -inline void Conv(const float* input_data, const Dims<4>& input_dims, - const float* filter_data, const Dims<4>& filter_dims, - const float* bias_data, const Dims<4>& bias_dims, - int stride_width, int stride_height, int pad_width, - int pad_height, TfLitePadding padding, +inline void Conv(const Eigen::ThreadPoolDevice& device, const float* input_data, + const Dims<4>& input_dims, const float* filter_data, + const Dims<4>& filter_dims, const float* bias_data, + const Dims<4>& bias_dims, int stride_width, int stride_height, + int pad_width, int pad_height, TfLitePadding padding, float output_activation_min, float output_activation_max, float* output_data, const Dims<4>& output_dims, float* im2col_data, const Dims<4>& im2col_dims) { @@ -180,10 +150,11 @@ inline void Conv(const float* input_data, const Dims<4>& input_dims, const int output_height = ArraySize(output_dims, 2); const int output_width = ArraySize(output_dims, 1); EigenTensorConvFunctor conv_functor; - conv_functor(input_data, im2col_data, batches, input_height, input_width, - input_depth, filter_data, filter_height, filter_width, - output_depth, stride_height, stride_width, pad_height, pad_width, - padding, output_data, output_height, output_width); + conv_functor(device, input_data, im2col_data, batches, input_height, + input_width, input_depth, filter_data, filter_height, + filter_width, output_depth, stride_height, stride_width, + pad_height, pad_width, padding, output_data, output_height, + output_width); optimized_ops::AddBiasAndEvalActivationFunction( bias_data, bias_dims, output_data, output_dims, output_activation_min, diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc index 5ba7e2af9b8f2beeee151e219997b68f5c7a6bce..420bc68b43dd8c135e95badcc7d18935449cfc73 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.cc @@ -55,83 +55,33 @@ void NeonMatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows, const int postamble_start = m_cols - (m_cols & (kFloatWeightsPerNeonLane - 1)); - // The arrays used to cache the vector. - void* aligned_vector_cache_free = nullptr; - float32x4_t* vector_cache_float32x4 = - reinterpret_cast(aligned_alloc( - sizeof(float32x4_t), (postamble_start >> 2) * sizeof(float32x4_t), - &aligned_vector_cache_free)); - - const int kUnrollSize = 2; for (int b = 0; b < n_batch; b++) { float* result_in_batch = result + b * m_rows * result_stride; const float* vector_in_batch = vector + b * m_cols; + const float* matrix_row = matrix; - const float* matrix_ptr0 = matrix; - // If there is only 1 row, we don't want to assign an illegal pointer. - const float* matrix_ptr1 = nullptr; - if (m_rows > 1) { - matrix_ptr1 = matrix + m_cols; - } - - // Cache the vector. - for (int c = 0; c < postamble_start; c += kFloatWeightsPerNeonLane) { - vector_cache_float32x4[c >> 2] = vld1q_f32(vector_in_batch + c); - } - - // Main matrix by vector multiplication loop, which handles two rows of - // matrix by vector multiplication. - for (int r = 0; r < (m_rows & ~(kUnrollSize - 1)); r += kUnrollSize) { - float32x4_t acc0_32x4 = vmovq_n_f32(0.0); - float32x4_t acc1_32x4 = vmovq_n_f32(0.0); + // Main matrix by vector multiplication loop + for (int r = 0; r < m_rows; r++) { + float32x4_t acc_32x4 = vmovq_n_f32(0.0); for (int c = 0; c < postamble_start; c += kFloatWeightsPerNeonLane) { - float32x4_t temp = vector_cache_float32x4[c >> 2]; - // Load 4 float values from vector1 and vector2 and accumulator. - float32x4_t v0_f32x4 = vld1q_f32(matrix_ptr0 + c); - float32x4_t v1_f32x4 = vld1q_f32(matrix_ptr1 + c); - // Vector multiply-accumulate 4 float - acc0_32x4 = vmlaq_f32(acc0_32x4, v0_f32x4, temp); - acc1_32x4 = vmlaq_f32(acc1_32x4, v1_f32x4, temp); + // Load 4 float values from vector and matrix row. + float32x4_t vector_f32x4 = vld1q_f32(vector_in_batch + c); + float32x4_t matrix_f32x4 = vld1q_f32(matrix_row + c); + // Multiply the vector and matrix row and add to accumulator. + acc_32x4 = vmlaq_f32(acc_32x4, matrix_f32x4, vector_f32x4); } // Add the 4 intermediate sum values to get the final dot-prod value for // this column. *result_in_batch += - (vgetq_lane_f32(acc0_32x4, 0) + vgetq_lane_f32(acc0_32x4, 1) + - vgetq_lane_f32(acc0_32x4, 2) + vgetq_lane_f32(acc0_32x4, 3)); - *(result_in_batch + result_stride) += - (vgetq_lane_f32(acc1_32x4, 0) + vgetq_lane_f32(acc1_32x4, 1) + - vgetq_lane_f32(acc1_32x4, 2) + vgetq_lane_f32(acc1_32x4, 3)); + (vgetq_lane_f32(acc_32x4, 0) + vgetq_lane_f32(acc_32x4, 1) + + vgetq_lane_f32(acc_32x4, 2) + vgetq_lane_f32(acc_32x4, 3)); for (int c = postamble_start; c < m_cols; c++) { - *result_in_batch += matrix_ptr0[c] * vector_in_batch[c]; - *(result_in_batch + result_stride) += - matrix_ptr1[c] * vector_in_batch[c]; + *result_in_batch += matrix_row[c] * vector_in_batch[c]; } - matrix_ptr0 += kUnrollSize * m_cols; - matrix_ptr1 += kUnrollSize * m_cols; - result_in_batch += kUnrollSize * result_stride; - } - for (int r = (m_rows & ~(kUnrollSize - 1)); r < m_rows; r++) { - float32x4_t acc0_32x4 = vmovq_n_f32(0.0); - for (int c = 0; c < postamble_start; c += kFloatWeightsPerNeonLane) { - float32x4_t temp = vector_cache_float32x4[c >> 2]; - // Load 4 float values from vector1 and vector2 and accumulator. - float32x4_t v0_f32x4 = vld1q_f32(matrix_ptr0 + c); - // Vector multiply-accumulate 4 float - acc0_32x4 = vmlaq_f32(acc0_32x4, v0_f32x4, temp); - } - // Add the 4 intermediate sum values to get the final dot-prod value for - // this column. - *result_in_batch += - (vgetq_lane_f32(acc0_32x4, 0) + vgetq_lane_f32(acc0_32x4, 1) + - vgetq_lane_f32(acc0_32x4, 2) + vgetq_lane_f32(acc0_32x4, 3)); - for (int c = postamble_start; c < m_cols; c++) { - *result_in_batch += matrix_ptr0[c] * vector_in_batch[c]; - } - matrix_ptr0 += m_cols; + matrix_row += m_cols; result_in_batch += result_stride; } } - free(aligned_vector_cache_free); } void NeonMatrixBatchVectorMultiplyAccumulate( @@ -296,17 +246,6 @@ void NeonVectorBatchVectorCwiseProductAccumulate(const float* vector, const int postamble_start = v_size - (v_size & (kFloatWeightsPerNeonLane - 1)); - // The arrays used to cache the vector. - void* aligned_vector_cache_free = nullptr; - float32x4_t* vector_cache_float32x4 = - reinterpret_cast(aligned_alloc( - sizeof(float32x4_t), (postamble_start >> 2) * sizeof(float32x4_t), - &aligned_vector_cache_free)); - - for (int v = 0; v < postamble_start; v += kFloatWeightsPerNeonLane) { - vector_cache_float32x4[v >> 2] = vld1q_f32(vector + v); - } - float* result_ptr = result; const float* batch_vector_ptr = batch_vector; for (int b = 0; b < n_batch; b++) { @@ -314,9 +253,9 @@ void NeonVectorBatchVectorCwiseProductAccumulate(const float* vector, // Load from memory to vectors. float32x4_t result_f32x4 = vld1q_f32(result_ptr + v); float32x4_t batch_vector_f32x4 = vld1q_f32(batch_vector_ptr + v); + float32x4_t vector_f32x4 = vld1q_f32(vector + v); // Multiply-accumulate. - result_f32x4 = vmlaq_f32(result_f32x4, batch_vector_f32x4, - vector_cache_float32x4[v >> 2]); + result_f32x4 = vmlaq_f32(result_f32x4, batch_vector_f32x4, vector_f32x4); // Store. vst1q_f32(result_ptr + v, result_f32x4); } @@ -328,7 +267,6 @@ void NeonVectorBatchVectorCwiseProductAccumulate(const float* vector, result_ptr += v_size; batch_vector_ptr += v_size; } - free(aligned_vector_cache_free); } void NeonSub1Vector(const float* vector, int v_size, float* result) { @@ -404,6 +342,77 @@ void NeonClipVector(const float* vector, int v_size, float abs_limit, } } +void NeonVectorScalarMultiply(const int8_t* vector, const int v_size, + const float scale, float* result) { + // Here the assumption is that each buffer is 4-byte aligned. + const int kWeightsPerUint32 = 4; + TFLITE_CHECK_EQ((intptr_t)(&vector[0]) & (kWeightsPerUint32 - 1), 0); + // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main + // vectorized loop, and we need to process sequentially. postamble_start shows + // the start index where this should happen. + const int kWeightsPerNeonLane = 16; + const int postamble_start = v_size - (v_size & (kWeightsPerNeonLane - 1)); + + // Create a vector of 4 floats with the scale value. + const float32x4_t scale_f32x4 = vdupq_n_f32(scale); + int v = 0; + for (; v < postamble_start; v += kWeightsPerNeonLane) { + // Load int8 values, sixteen at a time. + const int8x16_t v_i8x16 = vld1q_s8(vector + v); + // Split it into two components of size eight. + const int8x8_t v0_i8x8 = vget_low_s8(v_i8x16); + const int8x8_t v1_i8x8 = vget_high_s8(v_i8x16); + // Convert both components to int16 first. + const int16x8_t v0_i16x8 = vmovl_s8(v0_i8x8); + const int16x8_t v1_i16x8 = vmovl_s8(v1_i8x8); + // Split each of them into two components each. + const int16x4_t v0_i16x4 = vget_low_s16(v0_i16x8); + const int16x4_t v1_i16x4 = vget_high_s16(v0_i16x8); + const int16x4_t v2_i16x4 = vget_low_s16(v1_i16x8); + const int16x4_t v3_i16x4 = vget_high_s16(v1_i16x8); + // Convert these to int32 and then to float. + float32x4_t v0_f32x4 = vcvtq_f32_s32(vmovl_s16(v0_i16x4)); + float32x4_t v1_f32x4 = vcvtq_f32_s32(vmovl_s16(v1_i16x4)); + float32x4_t v2_f32x4 = vcvtq_f32_s32(vmovl_s16(v2_i16x4)); + float32x4_t v3_f32x4 = vcvtq_f32_s32(vmovl_s16(v3_i16x4)); + // Vector multiply four floats at a time. + v0_f32x4 = vmulq_f32(v0_f32x4, scale_f32x4); + v1_f32x4 = vmulq_f32(v1_f32x4, scale_f32x4); + v2_f32x4 = vmulq_f32(v2_f32x4, scale_f32x4); + v3_f32x4 = vmulq_f32(v3_f32x4, scale_f32x4); + // Store the results. + vst1q_f32(result + v, v0_f32x4); + vst1q_f32(result + v + 4, v1_f32x4); + vst1q_f32(result + v + 8, v2_f32x4); + vst1q_f32(result + v + 12, v3_f32x4); + } + + if (v_size - postamble_start >= (kWeightsPerNeonLane >> 1)) { + // Load eight int8 values, if there is at least eight remaining. + const int8x8_t v_i8x8 = vld1_s8(vector + v); + // Convert them to int16 first. + const int16x8_t v_i16x8 = vmovl_s8(v_i8x8); + // Split it into two components. + const int16x4_t v0_i16x4 = vget_low_s16(v_i16x8); + const int16x4_t v1_i16x4 = vget_high_s16(v_i16x8); + // Convert the components two floats. + float32x4_t v0_f32x4 = vcvtq_f32_s32(vmovl_s16(v0_i16x4)); + float32x4_t v1_f32x4 = vcvtq_f32_s32(vmovl_s16(v1_i16x4)); + // Vector multiply four floats at a time. + v0_f32x4 = vmulq_f32(v0_f32x4, scale_f32x4); + v1_f32x4 = vmulq_f32(v1_f32x4, scale_f32x4); + // Store the results. + vst1q_f32(result + v, v0_f32x4); + vst1q_f32(result + v + 4, v1_f32x4); + v += (kWeightsPerNeonLane >> 1); + } + + // Postamble loop. + for (; v < v_size; v++) { + result[v] = scale * vector[v]; + } +} + void NeonSymmetricQuantizeFloats(const float* values, const int size, int8_t* quantized_values, float* min, float* max, float* scaling_factor) { diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h index 7a5a8fc54123946229963abd1720030d0bb358bf..63c89d1eeef47b206fc871929f1fb1295b2f70ff 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h @@ -105,16 +105,20 @@ bool IsZeroVector(const float* vector, int v_size) { return NEON_OR_PORTABLE(IsZeroVector, vector, v_size); } +void VectorScalarMultiply(const int8_t* vector, int v_size, float scale, + float* result) { + NEON_OR_PORTABLE(VectorScalarMultiply, vector, v_size, scale, result); +} void ClipVector(const float* vector, int v_size, float abs_limit, float* result) { NEON_OR_PORTABLE(ClipVector, vector, v_size, abs_limit, result); } void SymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float* min, float* max, - float* scaling_factor) { - NEON_OR_PORTABLE(SymmetricQuantizeFloats, values, size, quantized_values, min, - max, scaling_factor); + int8_t* quantized_values, float* min_value, + float* max_value, float* scaling_factor) { + NEON_OR_PORTABLE(SymmetricQuantizeFloats, values, size, quantized_values, + min_value, max_value, scaling_factor); } void VectorShiftLeft(float* vector, int v_size, float shift_value) { diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index 8597707b24325588b1b4dc4f4ac68ccfa9cecd36..78567d52eaab779c724d3e3d04fbaf92fe6e589b 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -41,10 +41,13 @@ namespace optimized_ops { // Unoptimized reference ops: using reference_ops::ArgMax; +using reference_ops::ArgMinMax; +using reference_ops::BroadcastAdd4DSlow; using reference_ops::BroadcastGreater; using reference_ops::BroadcastGreaterEqual; using reference_ops::BroadcastLess; using reference_ops::BroadcastLessEqual; +using reference_ops::BroadcastSub4DSlow; using reference_ops::Concatenation; using reference_ops::DepthConcatenation; using reference_ops::Dequantize; @@ -216,98 +219,6 @@ SaturatingRoundingMultiplyByPOTParam( SaturatingRoundingMultiplyByPOTParam(a.raw(), exponent)); } -// DO NOT USE THIS STRUCT FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING ELEMENT-WISE -// BROADCASTING. -// -// NdArrayDesc describes the shape and memory layout of an N-dimensional -// rectangular array of numbers. -// -// NdArrayDesc is basically identical to Dims defined in types.h. -// However, as Dims is to be deprecated, this class exists as an adaptor -// to enable simple unoptimized implementations of element-wise broadcasting -// operations. -template -struct NdArrayDesc { - // The "extent" of each dimension. Indices along dimension d must be in the - // half-open interval [0, extents[d]). - int extents[N]; - - // The number of *elements* (not bytes) between consecutive indices of each - // dimension. - int strides[N]; -}; - -// DO NOT USE THIS FUNCTION FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING -// ELEMENT-WISE BROADCASTING. -// -// Same as Offset(), except takes as NdArrayDesc instead of Dims. -inline int SubscriptToIndex(const NdArrayDesc<4>& desc, int i0, int i1, int i2, - int i3) { - TFLITE_DCHECK(i0 >= 0 && i0 < desc.extents[0]); - TFLITE_DCHECK(i1 >= 0 && i1 < desc.extents[1]); - TFLITE_DCHECK(i2 >= 0 && i2 < desc.extents[2]); - TFLITE_DCHECK(i3 >= 0 && i3 < desc.extents[3]); - return i0 * desc.strides[0] + i1 * desc.strides[1] + i2 * desc.strides[2] + - i3 * desc.strides[3]; -} - -// Given the dimensions of the operands for an element-wise binary broadcast, -// adjusts them so that they can be directly iterated over with simple loops. -// Returns the adjusted dims as instances of NdArrayDesc in 'desc0_out' and -// 'desc1_out'. 'desc0_out' and 'desc1_out' cannot be nullptr. -// -// This function assumes that the two input shapes are compatible up to -// broadcasting and the shorter one has already been prepended with 1s to be the -// same length. E.g., if shape0 is (1, 16, 16, 64) and shape1 is (1, 64), -// shape1 must already have been prepended to be (1, 1, 1, 64). Recall that -// Dims refer to shapes in reverse order. In this case, input0_dims will be -// (64, 16, 16, 1) and input1_dims will be (64, 1, 1, 1). -// -// When two shapes are compatible up to broadcasting, for each dimension d, -// the input extents are either equal, or one of them is 1. -// -// This function performs the following for each dimension d: -// - If the extents are equal, then do nothing since the loop that walks over -// both of the input arrays is correct. -// - Otherwise, one (and only one) of the extents must be 1. Say extent0 is 1 -// and extent1 is e1. Then set extent0 to e1 and stride0 *to 0*. This allows -// array0 to be referenced *at any index* in dimension d and still access the -// same slice. -template -inline void NdArrayDescsForElementwiseBroadcast(const Dims& input0_dims, - const Dims& input1_dims, - NdArrayDesc* desc0_out, - NdArrayDesc* desc1_out) { - TFLITE_DCHECK(desc0_out != nullptr); - TFLITE_DCHECK(desc1_out != nullptr); - - // Copy dims to desc. - for (int i = 0; i < N; ++i) { - desc0_out->extents[i] = input0_dims.sizes[i]; - desc0_out->strides[i] = input0_dims.strides[i]; - desc1_out->extents[i] = input1_dims.sizes[i]; - desc1_out->strides[i] = input1_dims.strides[i]; - } - - // Walk over each dimension. If the extents are equal do nothing. - // Otherwise, set the desc with extent 1 to have extent equal to the other and - // stride 0. - for (int i = 0; i < N; ++i) { - const int extent0 = ArraySize(input0_dims, i); - const int extent1 = ArraySize(input1_dims, i); - if (extent0 != extent1) { - if (extent0 == 1) { - desc0_out->strides[i] = 0; - desc0_out->extents[i] = extent1; - } else { - TFLITE_DCHECK_EQ(extent1, 1); - desc1_out->strides[i] = 0; - desc1_out->extents[i] = extent0; - } - } - } -} - inline bool AreSameDims(const Dims<4>& dims1, const Dims<4>& dims2) { for (int i = 0; i < 4; i++) { if (dims1.sizes[i] != dims2.sizes[i]) { @@ -2477,20 +2388,17 @@ inline void L2Normalization(const uint8* input_data, } } -inline void Add(const float* input1_data, const Dims<4>& input1_dims, - const float* input2_data, const Dims<4>& input2_dims, - float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const float* input1_data, + const RuntimeShape& input2_shape, const float* input2_data, + const RuntimeShape& output_shape, float* output_data) { gemmlowp::ScopedProfilingLabel label("Add"); - TFLITE_DCHECK(IsPackedWithoutStrides(input1_dims)); - TFLITE_DCHECK(IsPackedWithoutStrides(input2_dims)); - TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); int i = 0; - const int size = MatchingFlatSize(input1_dims, input2_dims, output_dims); + const int size = MatchingFlatSize(input1_shape, input2_shape, output_shape); #ifdef USE_NEON - const auto activation_min = vdupq_n_f32(output_activation_min); - const auto activation_max = vdupq_n_f32(output_activation_max); + const auto activation_min = vdupq_n_f32(params.float_activation_min); + const auto activation_max = vdupq_n_f32(params.float_activation_max); for (; i <= size - 16; i += 16) { auto a10 = vld1q_f32(input1_data + i); auto a11 = vld1q_f32(input1_data + i + 4); @@ -2529,29 +2437,26 @@ inline void Add(const float* input1_data, const Dims<4>& input1_dims, for (; i < size; i++) { auto x = input1_data[i] + input2_data[i]; - output_data[i] = ActivationFunctionWithMinMax(x, output_activation_min, - output_activation_max); + output_data[i] = ActivationFunctionWithMinMax( + x, params.float_activation_min, params.float_activation_max); } } // Element-wise add that can often be used for inner loop of broadcast add as // well as the non-broadcast add. -inline void AddElementwise(int size, int left_shift, const uint8* input1_data, - int32 input1_offset, int32 input1_multiplier, - int input1_shift, const uint8* input2_data, - int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, - int32 output_multiplier, int output_shift, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data) { +inline void AddElementwise(int size, const ArithmeticParams& params, + const uint8* input1_data, const uint8* input2_data, + uint8* output_data) { int i = 0; - TFLITE_DCHECK_GT(input1_offset, -256); - TFLITE_DCHECK_GT(input2_offset, -256); - TFLITE_DCHECK_LT(input1_offset, 256); - TFLITE_DCHECK_LT(input2_offset, 256); + TFLITE_DCHECK_GT(params.input1_offset, -256); + TFLITE_DCHECK_GT(params.input2_offset, -256); + TFLITE_DCHECK_LT(params.input1_offset, 256); + TFLITE_DCHECK_LT(params.input2_offset, 256); #ifdef USE_NEON - const auto output_activation_min_vector = vdup_n_u8(output_activation_min); - const auto output_activation_max_vector = vdup_n_u8(output_activation_max); + const auto output_activation_min_vector = + vdup_n_u8(params.quantized_activation_min); + const auto output_activation_max_vector = + vdup_n_u8(params.quantized_activation_max); for (; i <= size - 8; i += 8) { const auto input1_val_original = vld1_u8(input1_data + i); const auto input2_val_original = vld1_u8(input2_data + i); @@ -2560,9 +2465,9 @@ inline void AddElementwise(int size, int left_shift, const uint8* input1_data, const auto input2_val_s16 = vreinterpretq_s16_u16(vmovl_u8(input2_val_original)); const auto input1_val = - vaddq_s16(input1_val_s16, vdupq_n_s16(input1_offset)); + vaddq_s16(input1_val_s16, vdupq_n_s16(params.input1_offset)); const auto input2_val = - vaddq_s16(input2_val_s16, vdupq_n_s16(input2_offset)); + vaddq_s16(input2_val_s16, vdupq_n_s16(params.input2_offset)); const auto input1_val_high = vget_high_s16(input1_val); const auto input1_val_low = vget_low_s16(input1_val); const auto input2_val_high = vget_high_s16(input2_val); @@ -2571,32 +2476,32 @@ inline void AddElementwise(int size, int left_shift, const uint8* input1_data, auto x12 = vmovl_s16(input1_val_high); auto x21 = vmovl_s16(input2_val_low); auto x22 = vmovl_s16(input2_val_high); - const auto left_shift_dup = vdupq_n_s32(left_shift); + const auto left_shift_dup = vdupq_n_s32(params.left_shift); x11 = vshlq_s32(x11, left_shift_dup); x12 = vshlq_s32(x12, left_shift_dup); x21 = vshlq_s32(x21, left_shift_dup); x22 = vshlq_s32(x22, left_shift_dup); - x11 = vqrdmulhq_n_s32(x11, input1_multiplier); - x12 = vqrdmulhq_n_s32(x12, input1_multiplier); - x21 = vqrdmulhq_n_s32(x21, input2_multiplier); - x22 = vqrdmulhq_n_s32(x22, input2_multiplier); - const auto input1_shift_dup = vdupq_n_s32(-input1_shift); - const auto input2_shift_dup = vdupq_n_s32(-input2_shift); + x11 = vqrdmulhq_n_s32(x11, params.input1_multiplier); + x12 = vqrdmulhq_n_s32(x12, params.input1_multiplier); + x21 = vqrdmulhq_n_s32(x21, params.input2_multiplier); + x22 = vqrdmulhq_n_s32(x22, params.input2_multiplier); + const auto input1_shift_dup = vdupq_n_s32(params.input1_shift); + const auto input2_shift_dup = vdupq_n_s32(params.input2_shift); x11 = vshlq_s32(x11, input1_shift_dup); x12 = vshlq_s32(x12, input1_shift_dup); x21 = vshlq_s32(x21, input2_shift_dup); x22 = vshlq_s32(x22, input2_shift_dup); auto s1 = vaddq_s32(x11, x21); auto s2 = vaddq_s32(x12, x22); - s1 = vqrdmulhq_n_s32(s1, output_multiplier); - s2 = vqrdmulhq_n_s32(s2, output_multiplier); + s1 = vqrdmulhq_n_s32(s1, params.output_multiplier); + s2 = vqrdmulhq_n_s32(s2, params.output_multiplier); using gemmlowp::RoundingDivideByPOT; - s1 = RoundingDivideByPOT(s1, output_shift); - s2 = RoundingDivideByPOT(s2, output_shift); + s1 = RoundingDivideByPOT(s1, -params.output_shift); + s2 = RoundingDivideByPOT(s2, -params.output_shift); const auto s1_narrowed = vmovn_s32(s1); const auto s2_narrowed = vmovn_s32(s2); const auto s = vaddq_s16(vcombine_s16(s1_narrowed, s2_narrowed), - vdupq_n_s16(output_offset)); + vdupq_n_s16(params.output_offset)); const auto clamped = vmax_u8(output_activation_min_vector, vmin_u8(output_activation_max_vector, vqmovun_s16(s))); @@ -2605,101 +2510,74 @@ inline void AddElementwise(int size, int left_shift, const uint8* input1_data, #endif // NEON for (; i < size; ++i) { - const int32 input1_val = input1_offset + input1_data[i]; - const int32 input2_val = input2_offset + input2_data[i]; - const int32 shifted_input1_val = input1_val * (1 << left_shift); - const int32 shifted_input2_val = input2_val * (1 << left_shift); + const int32 input1_val = params.input1_offset + input1_data[i]; + const int32 input2_val = params.input2_offset + input2_data[i]; + const int32 shifted_input1_val = input1_val * (1 << params.left_shift); + const int32 shifted_input2_val = input2_val * (1 << params.left_shift); const int32 scaled_input1_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, - kReverseShift * input1_shift); + shifted_input1_val, params.input1_multiplier, params.input1_shift); const int32 scaled_input2_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, - kReverseShift * input2_shift); + shifted_input2_val, params.input2_multiplier, params.input2_shift); const int32 raw_sum = scaled_input1_val + scaled_input2_val; const int32 raw_output = MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, output_multiplier, kReverseShift * output_shift) + - output_offset; - const int32 clamped_output = std::min( - output_activation_max, std::max(output_activation_min, raw_output)); + raw_sum, params.output_multiplier, params.output_shift) + + params.output_offset; + const int32 clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); output_data[i] = static_cast(clamped_output); } } -// legacy, for compatibility with old checked-in code -template -void Add(const float* input1_data, const Dims<4>& input1_dims, - const float* input2_data, const Dims<4>& input2_dims, - float* output_data, const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - - Add(input1_data, input1_dims, input2_data, input2_dims, output_activation_min, - output_activation_max, output_data, output_dims); -} - -template -inline void Add(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, int input2_shift, - int32 output_offset, int32 output_multiplier, int output_shift, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const uint8* input1_data, + const RuntimeShape& input2_shape, const uint8* input2_data, + const RuntimeShape& output_shape, uint8* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); gemmlowp::ScopedProfilingLabel label("Add/8bit"); - const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); - TFLITE_DCHECK(IsPackedWithoutStrides(input1_dims)); - TFLITE_DCHECK(IsPackedWithoutStrides(input2_dims)); - TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); - - TFLITE_DCHECK_GT(input1_offset, -256); - TFLITE_DCHECK_GT(input2_offset, -256); - TFLITE_DCHECK_LT(input1_offset, 256); - TFLITE_DCHECK_LT(input2_offset, 256); - AddElementwise(flat_size, left_shift, input1_data, input1_offset, - input1_multiplier, input1_shift, input2_data, input2_offset, - input2_multiplier, input2_shift, output_offset, - output_multiplier, output_shift, output_activation_min, - output_activation_max, output_data); + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, output_shape); + + TFLITE_DCHECK_GT(params.input1_offset, -256); + TFLITE_DCHECK_GT(params.input2_offset, -256); + TFLITE_DCHECK_LT(params.input1_offset, 256); + TFLITE_DCHECK_LT(params.input2_offset, 256); + AddElementwise(flat_size, params, input1_data, input2_data, output_data); } -inline void Add(const int16* input1_data, const Dims<4>& input1_dims, - int input1_shift, const int16* input2_data, - const Dims<4>& input2_dims, int input2_shift, - int16 output_activation_min, int16 output_activation_max, - int16* output_data, const Dims<4>& output_dims) { +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const int16* input1_data, + const RuntimeShape& input2_shape, const int16* input2_data, + const RuntimeShape& output_shape, int16* output_data) { gemmlowp::ScopedProfilingLabel label("Add/Int16"); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - - const int flat_size = MatchingFlatSize(output_dims, input1_dims, input2_dims); - - TFLITE_DCHECK(input1_shift == 0 || input2_shift == 0); - TFLITE_DCHECK_GE(input1_shift, 0); - TFLITE_DCHECK_GE(input2_shift, 0); + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + + const int input1_shift = params.input1_shift; + const int flat_size = + MatchingFlatSize(output_shape, input1_shape, input2_shape); + const int16 output_activation_min = params.quantized_activation_min; + const int16 output_activation_max = params.quantized_activation_max; + + TFLITE_DCHECK(input1_shift == 0 || params.input2_shift == 0); + TFLITE_DCHECK_LE(input1_shift, 0); + TFLITE_DCHECK_LE(params.input2_shift, 0); const int16* not_shift_input = input1_shift == 0 ? input1_data : input2_data; const int16* shift_input = input1_shift == 0 ? input2_data : input1_data; - const int input_shift = input1_shift == 0 ? input2_shift : input1_shift; + const int input_right_shift = + input1_shift == 0 ? -params.input2_shift : -input1_shift; for (int i = 0; i < flat_size; i++) { // F0 uses 0 integer bits, range [-1, 1]. using F0 = gemmlowp::FixedPoint; F0 input_ready_scaled = F0::FromRaw(not_shift_input[i]); - F0 scaled_input = - F0::FromRaw(gemmlowp::RoundingDivideByPOT(shift_input[i], input_shift)); + F0 scaled_input = F0::FromRaw( + gemmlowp::RoundingDivideByPOT(shift_input[i], input_right_shift)); F0 result = gemmlowp::SaturatingAdd(scaled_input, input_ready_scaled); const int16 raw_output = result.raw(); const int16 clamped_output = std::min( @@ -2708,195 +2586,59 @@ inline void Add(const int16* input1_data, const Dims<4>& input1_dims, } } -inline void Add(const int32* input1_data, const Dims<4>& input1_dims, - const int32* input2_data, const Dims<4>& input2_dims, - int32 output_activation_min, int32 output_activation_max, - int32* output_data, const Dims<4>& output_dims) { +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const int32* input1_data, + const RuntimeShape& input2_shape, const int32* input2_data, + const RuntimeShape& output_shape, int32* output_data) { gemmlowp::ScopedProfilingLabel label("Add/int32"); - const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] + input2_data[i], output_activation_min, - output_activation_max); - } -} - -template -inline void Add(const int16* input1_data, const Dims<4>& input1_dims, - int input1_shift, const int16* input2_data, - const Dims<4>& input2_dims, int input2_shift, - int16 output_activation_min, int16 output_activation_max, - int16* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, -32768); - TFLITE_DCHECK_EQ(output_activation_max, 32767); - } - - Add(input1_data, input1_dims, input1_shift, input2_data, input2_dims, - input2_shift, output_activation_min, output_activation_max, output_data, - output_dims); -} - -template -void Add(const int32* input1_data, const Dims<4>& input1_dims, - const int32* input2_data, const Dims<4>& input2_dims, - int32* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Add/int32"); - TFLITE_DCHECK(Ac == FusedActivationFunctionType::kNone); - - auto input1_map = MapAsVector(input1_data, input1_dims); - auto input2_map = MapAsVector(input2_data, input2_dims); - auto output_map = MapAsVector(output_data, output_dims); - if (AreSameDims(input1_dims, input2_dims)) { + auto input1_map = MapAsVector(input1_data, input1_shape); + auto input2_map = MapAsVector(input2_data, input2_shape); + auto output_map = MapAsVector(output_data, output_shape); + if (input1_shape == input2_shape) { output_map.array() = input1_map.array() + input2_map.array(); - } else if (FlatSize(input2_dims) == 1) { + } else if (input2_shape.FlatSize() == 1) { auto scalar = input2_data[0]; output_map.array() = input1_map.array() + scalar; - } else if (FlatSize(input1_dims) == 1) { + } else if (input1_shape.FlatSize() == 1) { auto scalar = input1_data[0]; output_map.array() = scalar + input2_map.array(); } else { // Should not come here. TFLITE_DCHECK(false); } + output_map = output_map.cwiseMax(params.quantized_activation_min); + output_map = output_map.cwiseMin(params.quantized_activation_max); } -// TODO(jiawen): We can implement BroadcastAdd on buffers of arbitrary -// dimensionality if the runtime code does a single loop over one dimension -// that handles broadcasting as the base case. The code generator would then -// generate max(D1, D2) nested for loops. -// TODO(benoitjacob): BroadcastAdd is intentionally duplicated from -// reference_ops.h. Once an optimized version is implemented and NdArrayDesc -// is no longer referenced in this file, move NdArrayDesc from types.h to -// reference_ops.h. -template -void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T output_activation_min, T output_activation_max, - T* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastAdd"); - - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - output_data[Offset(output_dims, c, x, y, b)] = - ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, c, x, y, b)] + - input2_data[SubscriptToIndex(desc2, c, x, y, b)], - output_activation_min, output_activation_max); - } - } - } - } -} - -// legacy, for compatibility with old checked-in code -template -void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T* output_data, const Dims<4>& output_dims) { - T output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - - BroadcastAdd(input1_data, input1_dims, input2_data, input2_dims, - output_activation_min, output_activation_max, output_data, - output_dims); -} - -inline void BroadcastAdd(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, - int32 output_multiplier, int output_shift, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastAddGeneric/8bit"); - - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - const int32 input1_val = - input1_offset + input1_data[SubscriptToIndex(desc1, c, x, y, b)]; - const int32 input2_val = - input2_offset + input2_data[SubscriptToIndex(desc2, c, x, y, b)]; - const int32 shifted_input1_val = input1_val * (1 << left_shift); - const int32 shifted_input2_val = input2_val * (1 << left_shift); - const int32 scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, - kReverseShift * input1_shift); - const int32 scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, - kReverseShift * input2_shift); - const int32 raw_sum = scaled_input1_val + scaled_input2_val; - const int32 raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, output_multiplier, kReverseShift * output_shift) + - output_offset; - const int32 clamped_output = - std::min(output_activation_max, - std::max(output_activation_min, raw_output)); - output_data[Offset(output_dims, c, x, y, b)] = - static_cast(clamped_output); - } - } - } - } -} - -inline void BroadcastAddFivefold( - int y0, int y1, int y2, int y3, int y4, int left_shift, - const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, const uint8* input2_data, - const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, int32 output_multiplier, - int output_shift, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { +inline void BroadcastAddFivefold(const ArithmeticParams& unswitched_params, + const RuntimeShape& unswitched_input1_shape, + const uint8* unswitched_input1_data, + const RuntimeShape& unswitched_input2_shape, + const uint8* unswitched_input2_data, + const RuntimeShape& output_shape, + uint8* output_data) { gemmlowp::ScopedProfilingLabel label("BroadcastAddFivefold/8bit"); + ArithmeticParams switched_params = unswitched_params; + switched_params.input1_offset = unswitched_params.input2_offset; + switched_params.input1_multiplier = unswitched_params.input2_multiplier; + switched_params.input1_shift = unswitched_params.input2_shift; + switched_params.input2_offset = unswitched_params.input1_offset; + switched_params.input2_multiplier = unswitched_params.input1_multiplier; + switched_params.input2_shift = unswitched_params.input1_shift; + + const bool use_unswitched = + unswitched_params.broadcast_category == + tflite::BroadcastableOpCategory::kFirstInputBroadcastsFast; + + const ArithmeticParams& params = + use_unswitched ? unswitched_params : switched_params; + const uint8* input1_data = + use_unswitched ? unswitched_input1_data : unswitched_input2_data; + const uint8* input2_data = + use_unswitched ? unswitched_input2_data : unswitched_input1_data; + // Fivefold nested loops. The second input resets its position for each // iteration of the second loop. The first input resets its position at the // beginning of the fourth loop. The innermost loop is an elementwise add of @@ -2904,82 +2646,29 @@ inline void BroadcastAddFivefold( uint8* output_data_ptr = output_data; const uint8* input1_data_ptr = input1_data; const uint8* input2_data_reset = input2_data; - for (int i4 = 0; i4 < y4; ++i4) { + int y0 = params.broadcast_shape[0]; + int y1 = params.broadcast_shape[1]; + int y2 = params.broadcast_shape[2]; + int y3 = params.broadcast_shape[3]; + int y4 = params.broadcast_shape[4]; + for (int i0 = 0; i0 < y0; ++i0) { const uint8* input2_data_ptr; - for (int i3 = 0; i3 < y3; ++i3) { + for (int i1 = 0; i1 < y1; ++i1) { input2_data_ptr = input2_data_reset; for (int i2 = 0; i2 < y2; ++i2) { - for (int i1 = 0; i1 < y1; ++i1) { - AddElementwise( - y0, left_shift, input1_data_ptr, input1_offset, input1_multiplier, - input1_shift, input2_data_ptr, input2_offset, input2_multiplier, - input2_shift, output_offset, output_multiplier, output_shift, - output_activation_min, output_activation_max, output_data_ptr); - input2_data_ptr += y0; - output_data_ptr += y0; + for (int i3 = 0; i3 < y3; ++i3) { + AddElementwise(y4, params, input1_data_ptr, input2_data_ptr, + output_data_ptr); + input2_data_ptr += y4; + output_data_ptr += y4; } - input1_data_ptr += y0; + input1_data_ptr += y4; } } input2_data_reset = input2_data_ptr; } } -template -inline void BroadcastAdd(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, - int32 output_multiplier, int output_shift, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - BroadcastAdd(left_shift, input1_data, input1_dims, input1_offset, - input1_multiplier, input1_shift, input2_data, input2_dims, - input2_offset, input2_multiplier, input2_shift, output_offset, - output_multiplier, output_shift, output_activation_min, - output_activation_max, output_data, output_dims); -} - -template -inline void BroadcastAddFivefold( - int y0, int y1, int y2, int y3, int y4, int left_shift, - const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, const uint8* input2_data, - const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, int32 output_multiplier, - int output_shift, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - BroadcastAddFivefold(y0, y1, y2, y3, y4, left_shift, input1_data, input1_dims, - input1_offset, input1_multiplier, input1_shift, - input2_data, input2_dims, input2_offset, - input2_multiplier, input2_shift, output_offset, - output_multiplier, output_shift, output_activation_min, - output_activation_max, output_data, output_dims); -} - inline void Mul(const float* input1_data, const Dims<4>& input1_dims, const float* input2_data, const Dims<4>& input2_dims, float output_activation_min, float output_activation_max, @@ -3053,6 +2742,20 @@ void Mul(const float* input1_data, const Dims<4>& input1_dims, output_activation_max, output_data, output_dims); } +inline void Mul(const int32* input1_data, const Dims<4>& input1_dims, + const int32* input2_data, const Dims<4>& input2_dims, + int32 output_activation_min, int32 output_activation_max, + int32* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("Mul/int32"); + + const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] * input2_data[i], output_activation_min, + output_activation_max); + } +} + template void Mul(const int32* input1_data, const Dims<4>& input1_dims, const int32* input2_data, const Dims<4>& input2_dims, @@ -3290,122 +2993,78 @@ void BroadcastDiv(const T* input1_data, const Dims<4>& input1_dims, } // TODO(aselle): This is not actually optimized yet. -inline void Sub(const float* input1_data, const Dims<4>& input1_dims, - const float* input2_data, const Dims<4>& input2_dims, - float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Sub"); - const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); +inline void SubNonBroadcast(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const float* input1_data, + const RuntimeShape& input2_shape, + const float* input2_data, + const RuntimeShape& output_shape, + float* output_data) { + gemmlowp::ScopedProfilingLabel label("SubNonBroadcast"); + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, output_shape); for (int i = 0; i < flat_size; ++i) { output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] - input2_data[i], output_activation_min, - output_activation_max); + input1_data[i] - input2_data[i], params.float_activation_min, + params.float_activation_max); } } -// TODO(jiawen): We can implement BroadcastSub on buffers of arbitrary -// dimensionality if the runtime code does a single loop over one dimension -// that handles broadcasting as the base case. The code generator would then -// generate max(D1, D2) nested for loops. -// TODO(benoitjacob): BroadcastSub is intentionally duplicated from -// reference_ops.h. Once an optimized version is implemented and NdArrayDesc -// is no longer referenced in this file, move NdArrayDesc from types.h to -// reference_ops.h. -template -void BroadcastSub(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T output_activation_min, T output_activation_max, - T* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastSub"); - - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - output_data[Offset(output_dims, c, x, y, b)] = - ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, c, x, y, b)] - - input2_data[SubscriptToIndex(desc2, c, x, y, b)], - output_activation_min, output_activation_max); - } - } - } +inline void SubWithActivation(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const int32* input1_data, + const RuntimeShape& input2_shape, + const int32* input2_data, + const RuntimeShape& output_shape, + int32* output_data) { + gemmlowp::ScopedProfilingLabel label("SubWithActivation/int32"); + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, input2_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] - input2_data[i], params.quantized_activation_min, + params.quantized_activation_max); } } -inline void BroadcastSub(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, - int32 output_multiplier, int output_shift, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastSub/8bit"); +inline void SubWithActivation(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const float* input1_data, + const RuntimeShape& input2_shape, + const float* input2_data, + const RuntimeShape& output_shape, + float* output_data) { + gemmlowp::ScopedProfilingLabel label("SubWithActivation/float"); + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, input2_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] - input2_data[i], params.float_activation_min, + params.float_activation_max); + } +} - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); +template +void Sub(const ArithmeticParams& params, const RuntimeShape& input1_shape, + const T* input1_data, const RuntimeShape& input2_shape, + const T* input2_data, const RuntimeShape& output_shape, + T* output_data) { + gemmlowp::ScopedProfilingLabel label("Sub"); - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - const int32 input1_val = - input1_offset + input1_data[SubscriptToIndex(desc1, c, x, y, b)]; - const int32 input2_val = - input2_offset + input2_data[SubscriptToIndex(desc2, c, x, y, b)]; - const int32 shifted_input1_val = input1_val * (1 << left_shift); - const int32 shifted_input2_val = input2_val * (1 << left_shift); - const int32 scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, - kReverseShift * input1_shift); - const int32 scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, - kReverseShift * input2_shift); - const int32 raw_sub = scaled_input1_val - scaled_input2_val; - const int32 raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sub, output_multiplier, kReverseShift * output_shift) + - output_offset; - const int32 clamped_output = - std::min(output_activation_max, - std::max(output_activation_min, raw_output)); - output_data[Offset(output_dims, c, x, y, b)] = - static_cast(clamped_output); - } - } - } + auto input1_map = MapAsVector(input1_data, input1_shape); + auto input2_map = MapAsVector(input2_data, input2_shape); + auto output_map = MapAsVector(output_data, output_shape); + if (input1_shape == input2_shape) { + output_map.array() = input1_map.array() - input2_map.array(); + } else if (input1_shape.FlatSize() == 1) { + auto scalar = input1_data[0]; + output_map.array() = scalar - input2_map.array(); + } else if (input2_shape.FlatSize() == 1) { + auto scalar = input2_data[0]; + output_map.array() = input1_map.array() - scalar; + } else { + BroadcastSub4DSlow(params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data); } } @@ -3771,21 +3430,20 @@ inline int NodeOffset(int b, int h, int w, int height, int width) { return (b * height + h) * width + w; } -inline void AveragePool(const float* input_data, - const RuntimeShape& input_shape, int stride_width, - int stride_height, int pad_width, int pad_height, - int kwidth, int kheight, float output_activation_min, - float output_activation_max, float* output_data, - const RuntimeShape& output_shape) { +inline void AveragePool(const PoolParams& params, + const RuntimeShape& input_shape, + const float* input_data, + const RuntimeShape& output_shape, float* output_data) { gemmlowp::ScopedProfilingLabel label("AveragePool"); TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); const int input_height = input_shape.Dims(1); const int input_width = input_shape.Dims(2); const int output_height = output_shape.Dims(1); const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; // TODO(benoitjacob) make this a proper reference impl without Eigen! const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); @@ -3800,12 +3458,15 @@ inline void AveragePool(const float* input_data, for (int w = 0; w < input_width; ++w) { // (h_start, h_end) * (w_start, w_end) is the range that the input // vector projects to. - int hpad = h + pad_height; - int wpad = w + pad_width; - int h_start = - (hpad < kheight) ? 0 : (hpad - kheight) / stride_height + 1; + int hpad = h + params.padding_values.height; + int wpad = w + params.padding_values.width; + int h_start = (hpad < params.filter_height) + ? 0 + : (hpad - params.filter_height) / stride_height + 1; int h_end = std::min(hpad / stride_height + 1, output_height); - int w_start = (wpad < kwidth) ? 0 : (wpad - kwidth) / stride_width + 1; + int w_start = (wpad < params.filter_width) + ? 0 + : (wpad - params.filter_width) / stride_width + 1; int w_end = std::min(wpad / stride_width + 1, output_width); // compute elementwise sum for (int ph = h_start; ph < h_end; ++ph) { @@ -3823,29 +3484,21 @@ inline void AveragePool(const float* input_data, TFLITE_DCHECK_GT(out_count.minCoeff(), 0); out_mat.array().rowwise() /= out_count.transpose().array(); - for (int b = 0; b < batches; ++b) { - for (int y = 0; y < output_height; ++y) { - for (int x = 0; x < output_width; ++x) { - for (int c = 0; c < depth; ++c) { - output_data[Offset(output_shape, b, y, x, c)] = - ActivationFunctionWithMinMax( - output_data[Offset(output_shape, b, y, x, c)], - output_activation_min, output_activation_max); - } - } - } + const int flat_size = output_shape.FlatSize(); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax(output_data[i], + params.float_activation_min, + params.float_activation_max); } } -inline void AveragePool(const uint8* input_data, - const RuntimeShape& input_shape, int stride_width, - int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const RuntimeShape& output_shape) { +inline void AveragePool(const PoolParams& params, + const RuntimeShape& input_shape, + const uint8* input_data, + const RuntimeShape& output_shape, uint8* output_data) { gemmlowp::ScopedProfilingLabel label("AveragePool/8bit"); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); const int batches = MatchingDim(input_shape, 0, output_shape, 0); @@ -3854,17 +3507,21 @@ inline void AveragePool(const uint8* input_data, const int input_width = input_shape.Dims(2); const int output_height = output_shape.Dims(1); const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; const int filter_x_start = std::max(0, -in_x_origin); const int filter_x_end = - std::min(filter_width, input_width - in_x_origin); + std::min(params.filter_width, input_width - in_x_origin); const int filter_y_start = std::max(0, -in_y_origin); const int filter_y_end = - std::min(filter_height, input_height - in_y_origin); + std::min(params.filter_height, input_height - in_y_origin); const int filter_count = (filter_x_end - filter_x_start) * (filter_y_end - filter_y_start); // 1280 required by Inception v3 @@ -3912,18 +3569,18 @@ inline void AveragePool(const uint8* input_data, output_data + Offset(output_shape, batch, out_y, out_x, 0); int channel = 0; #ifdef USE_NEON -#define AVGPOOL_DIVIDING_BY(FILTER_COUNT) \ - if (filter_count == FILTER_COUNT) { \ - for (; channel <= depth - 8; channel += 8) { \ - uint16 buf[8]; \ - for (int i = 0; i < 8; i++) { \ - buf[i] = (acc[channel + i] + FILTER_COUNT / 2) / FILTER_COUNT; \ - } \ - uint8x8_t buf8 = vqmovn_u16(vld1q_u16(buf)); \ - buf8 = vmin_u8(buf8, vdup_n_u8(output_activation_max)); \ - buf8 = vmax_u8(buf8, vdup_n_u8(output_activation_min)); \ - vst1_u8(output_ptr + channel, buf8); \ - } \ +#define AVGPOOL_DIVIDING_BY(FILTER_COUNT) \ + if (filter_count == FILTER_COUNT) { \ + for (; channel <= depth - 8; channel += 8) { \ + uint16 buf[8]; \ + for (int i = 0; i < 8; i++) { \ + buf[i] = (acc[channel + i] + FILTER_COUNT / 2) / FILTER_COUNT; \ + } \ + uint8x8_t buf8 = vqmovn_u16(vld1q_u16(buf)); \ + buf8 = vmin_u8(buf8, vdup_n_u8(params.quantized_activation_max)); \ + buf8 = vmax_u8(buf8, vdup_n_u8(params.quantized_activation_min)); \ + vst1_u8(output_ptr + channel, buf8); \ + } \ } AVGPOOL_DIVIDING_BY(9) AVGPOOL_DIVIDING_BY(15) @@ -3934,15 +3591,15 @@ inline void AveragePool(const uint8* input_data, buf[i] = (acc[channel + i] + filter_count / 2) / filter_count; } uint8x8_t buf8 = vqmovn_u16(vld1q_u16(buf)); - buf8 = vmin_u8(buf8, vdup_n_u8(output_activation_max)); - buf8 = vmax_u8(buf8, vdup_n_u8(output_activation_min)); + buf8 = vmin_u8(buf8, vdup_n_u8(params.quantized_activation_max)); + buf8 = vmax_u8(buf8, vdup_n_u8(params.quantized_activation_min)); vst1_u8(output_ptr + channel, buf8); } #endif for (; channel < depth; ++channel) { uint16 a = (acc[channel] + filter_count / 2) / filter_count; - a = std::max(a, output_activation_min); - a = std::min(a, output_activation_max); + a = std::max(a, params.quantized_activation_min); + a = std::min(a, params.quantized_activation_max); output_ptr[channel] = static_cast(a); } } @@ -3950,20 +3607,19 @@ inline void AveragePool(const uint8* input_data, } } -inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, - int stride_width, int stride_height, int pad_width, - int pad_height, int kwidth, int kheight, - float output_activation_min, float output_activation_max, - float* output_data, const RuntimeShape& output_shape) { +inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, + const float* input_data, const RuntimeShape& output_shape, + float* output_data) { gemmlowp::ScopedProfilingLabel label("MaxPool"); TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); const int input_height = input_shape.Dims(1); const int input_width = input_shape.Dims(2); const int output_height = output_shape.Dims(1); const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape); @@ -3974,12 +3630,15 @@ inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, for (int w = 0; w < input_width; ++w) { // (h_start, h_end) * (w_start, w_end) is the range that the input // vector projects to. - int hpad = h + pad_height; - int wpad = w + pad_width; - int h_start = - (hpad < kheight) ? 0 : (hpad - kheight) / stride_height + 1; + int hpad = h + params.padding_values.height; + int wpad = w + params.padding_values.width; + int h_start = (hpad < params.filter_height) + ? 0 + : (hpad - params.filter_height) / stride_height + 1; int h_end = std::min(hpad / stride_height + 1, output_height); - int w_start = (wpad < kwidth) ? 0 : (wpad - kwidth) / stride_width + 1; + int w_start = (wpad < params.filter_width) + ? 0 + : (wpad - params.filter_width) / stride_width + 1; int w_end = std::min(wpad / stride_width + 1, output_width); // compute elementwise sum for (int ph = h_start; ph < h_end; ++ph) { @@ -3994,28 +3653,20 @@ inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, } } } - - for (int b = 0; b < batches; ++b) { - for (int y = 0; y < output_height; ++y) { - for (int x = 0; x < output_width; ++x) { - for (int c = 0; c < depth; ++c) { - output_data[Offset(output_shape, b, y, x, c)] = - ActivationFunctionWithMinMax( - output_data[Offset(output_shape, b, y, x, c)], - output_activation_min, output_activation_max); - } - } - } + const int flat_size = output_shape.FlatSize(); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax(output_data[i], + params.float_activation_min, + params.float_activation_max); } } -inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const RuntimeShape& output_shape) { +inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, + const uint8* input_data, const RuntimeShape& output_shape, + uint8* output_data) { gemmlowp::ScopedProfilingLabel label("MaxPool/8bit"); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); const int batches = MatchingDim(input_shape, 0, output_shape, 0); @@ -4024,17 +3675,21 @@ inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, const int input_width = input_shape.Dims(2); const int output_height = output_shape.Dims(1); const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; const int filter_x_start = std::max(0, -in_x_origin); const int filter_x_end = - std::min(filter_width, input_width - in_x_origin); + std::min(params.filter_width, input_width - in_x_origin); const int filter_y_start = std::max(0, -in_y_origin); const int filter_y_end = - std::min(filter_height, input_height - in_y_origin); + std::min(params.filter_height, input_height - in_y_origin); // 2048 required by Inception v3 static constexpr int kAccBufferMaxSize = 2048; TFLITE_DCHECK_LE(depth, kAccBufferMaxSize); @@ -4077,21 +3732,21 @@ inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, #ifdef USE_NEON for (; channel <= depth - 16; channel += 16) { uint8x16_t a = vld1q_u8(acc + channel); - a = vminq_u8(a, vdupq_n_u8(output_activation_max)); - a = vmaxq_u8(a, vdupq_n_u8(output_activation_min)); + a = vminq_u8(a, vdupq_n_u8(params.quantized_activation_max)); + a = vmaxq_u8(a, vdupq_n_u8(params.quantized_activation_min)); vst1q_u8(output_ptr + channel, a); } for (; channel <= depth - 8; channel += 8) { uint8x8_t a = vld1_u8(acc + channel); - a = vmin_u8(a, vdup_n_u8(output_activation_max)); - a = vmax_u8(a, vdup_n_u8(output_activation_min)); + a = vmin_u8(a, vdup_n_u8(params.quantized_activation_max)); + a = vmax_u8(a, vdup_n_u8(params.quantized_activation_min)); vst1_u8(output_ptr + channel, a); } #endif for (; channel < depth; ++channel) { uint8 a = acc[channel]; - a = std::max(a, output_activation_min); - a = std::min(a, output_activation_max); + a = std::max(a, params.quantized_activation_min); + a = std::min(a, params.quantized_activation_max); output_ptr[channel] = static_cast(a); } } @@ -4099,11 +3754,9 @@ inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, } } -inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - float output_activation_min, float output_activation_max, - float* output_data, const RuntimeShape& output_shape) { +inline void L2Pool(const PoolParams& params, const RuntimeShape& input_shape, + const float* input_data, const RuntimeShape& output_shape, + float* output_data) { gemmlowp::ScopedProfilingLabel label("L2Pool"); TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); @@ -4112,6 +3765,8 @@ inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, const int input_width = input_shape.Dims(2); const int output_height = output_shape.Dims(1); const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; // Actually carry out L2 Pool. Code is written in forward mode: we go through // the input values once, and write to all the pooled regions that it maps to. const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape); @@ -4126,15 +3781,17 @@ inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, for (int w = 0; w < input_width; ++w) { // (h_start, h_end) * (w_start, w_end) is the range that the input // vector projects to. - const int hpad = h + pad_height; - const int wpad = w + pad_width; - const int h_start = (hpad < filter_height) - ? 0 - : (hpad - filter_height) / stride_height + 1; + const int hpad = h + params.padding_values.height; + const int wpad = w + params.padding_values.width; + const int h_start = + (hpad < params.filter_height) + ? 0 + : (hpad - params.filter_height) / stride_height + 1; const int h_end = std::min(hpad / stride_height + 1, output_height); - const int w_start = (wpad < filter_width) - ? 0 - : (wpad - filter_width) / stride_width + 1; + const int w_start = + (wpad < params.filter_width) + ? 0 + : (wpad - params.filter_width) / stride_width + 1; const int w_end = std::min(wpad / stride_width + 1, output_width); // pre-compute square const int in_offset = w + input_width * (h + input_height * b); @@ -4155,6 +3812,13 @@ inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, out_count = out_count.array().inverse(); out_mat = (out_mat.array().rowwise() * out_count.transpose().array()).cwiseSqrt(); + + const int flat_size = output_shape.FlatSize(); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax(output_data[i], + params.float_activation_min, + params.float_activation_max); + } } inline void LocalResponseNormalization(const float* input_data, @@ -5842,63 +5506,6 @@ inline void Slice(const T* input_data, const Dims<4>& input_dims, } } -template -void GenericBroadcastSub(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("GenericBroadcastSub"); - - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - output_data[Offset(output_dims, c, x, y, b)] = - input1_data[SubscriptToIndex(desc1, c, x, y, b)] - - input2_data[SubscriptToIndex(desc2, c, x, y, b)]; - } - } - } - } -} - -template -void Sub(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, - const Dims<4>& input2_dims, T* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Sub"); - - auto input1_map = MapAsVector(input1_data, input1_dims); - auto input2_map = MapAsVector(input2_data, input2_dims); - auto output_map = MapAsVector(output_data, output_dims); - if (AreSameDims(input1_dims, input2_dims)) { - output_map.array() = input1_map.array() - input2_map.array(); - } else if (FlatSize(input1_dims) == 1) { - auto scalar = input1_data[0]; - output_map.array() = scalar - input2_map.array(); - } else if (FlatSize(input2_dims) == 1) { - auto scalar = input2_data[0]; - output_map.array() = input1_map.array() - scalar; - } else { - GenericBroadcastSub(input1_data, input1_dims, input2_data, input2_dims, - output_data, output_dims); - } -} - template void TensorFlowMinimum(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, T* output_data, diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h b/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h index f14667090f5c3867c7992211272063239f3b92aa..010b40b901e2821c36367da7e2c987fac113de11 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h @@ -19,6 +19,10 @@ limitations under the License. // structure. #include "tensorflow/contrib/lite/builtin_op_data.h" +#if defined(_MSC_VER) +#define __restrict__ __restrict +#endif + #ifndef USE_NEON #if defined(__ARM_NEON__) || defined(__ARM_NEON) #define USE_NEON @@ -124,6 +128,12 @@ void PortableCopyVector(const float* vector, int v_size, float* result); // Fill vector with 0.f. void PortableZeroVector(float* vector, int v_size); +// Multiply all elements of vector with a scalar. +void PortableVectorScalarMultiply(const int8_t* vector, int v_size, float scale, + float* result); +void NeonVectorScalarMultiply(const int8_t* vector, int v_size, float scale, + float* result); + // Limit a float input f between +abs_limit and -abs_limit. float PortableClip(float f, float abs_limit); diff --git a/tensorflow/contrib/lite/kernels/internal/quantization_util.h b/tensorflow/contrib/lite/kernels/internal/quantization_util.h index 525857a2e6f73276d0a6e64770947169033c7667..9b3f1823dc7e08562d8906346bc44e4478642ddc 100644 --- a/tensorflow/contrib/lite/kernels/internal/quantization_util.h +++ b/tensorflow/contrib/lite/kernels/internal/quantization_util.h @@ -28,8 +28,9 @@ namespace tflite { // Given the min and max values of a float array, return // reasonable quantization parameters to use for this array. template -QuantizationParams ChooseQuantizationParams(double rmin, double rmax) { - const T qmin = std::numeric_limits::min(); +QuantizationParams ChooseQuantizationParams(double rmin, double rmax, + bool narrow_range) { + const T qmin = std::numeric_limits::min() + (narrow_range ? 1 : 0); const T qmax = std::numeric_limits::max(); const double qmin_double = qmin; const double qmax_double = qmax; @@ -97,6 +98,11 @@ QuantizationParams ChooseQuantizationParams(double rmin, double rmax) { return quantization_params; } +template +QuantizationParams ChooseQuantizationParams(double rmin, double rmax) { + return ChooseQuantizationParams(rmin, rmax, false); +} + // Converts a floating-point number to an integer. For all inputs x where // static_cast(x) is legal according to the C++ standard, the result // is identical to that cast (i.e. the result is x with its fractional part diff --git a/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h index 878b2441b4f2828a014673f5bd80fb8aa29514db..bcf5e4e4f6593ec9bce7acd1fb7082955276ca32 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h @@ -63,15 +63,257 @@ inline void Relu6(const float* input_data, const Dims<4>& input_dims, DimsToShape(output_dims)); } +template +inline void Add(int left_shift, const uint8* input1_data, + const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, + const uint8* input2_data, const Dims<4>& input2_dims, + int32 input2_offset, int32 input2_multiplier, int input2_shift, + int32 output_offset, int32 output_multiplier, int output_shift, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + + tflite::ArithmeticParams op_params; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +void Add(const int32* input1_data, const Dims<4>& input1_dims, + const int32* input2_data, const Dims<4>& input2_dims, + int32* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("Add/int32"); + TFLITE_DCHECK(Ac == FusedActivationFunctionType::kNone); + + tflite::ArithmeticParams op_params; + op_params.quantized_activation_min = std::numeric_limits::min(); + op_params.quantized_activation_max = std::numeric_limits::max(); + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +inline void BroadcastAdd(int left_shift, const uint8* input1_data, + const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, + const uint8* input2_data, const Dims<4>& input2_dims, + int32 input2_offset, int32 input2_multiplier, + int input2_shift, int32 output_offset, + int32 output_multiplier, int output_shift, + int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + + tflite::ArithmeticParams op_params; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + BroadcastAdd4DSlow(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +template +void Add(const float* input1_data, const Dims<4>& input1_dims, + const float* input2_data, const Dims<4>& input2_dims, + float* output_data, const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + tflite::ArithmeticParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T output_activation_min, T output_activation_max, + T* output_data, const Dims<4>& output_dims) { + tflite::ArithmeticParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + BroadcastAdd4DSlow(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +template +inline void BroadcastAddFivefold( + int y0, int y1, int y2, int y3, int y4, int left_shift, + const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, const uint8* input2_data, + const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier, + int input2_shift, int32 output_offset, int32 output_multiplier, + int output_shift, int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + tflite::ArithmeticParams op_params; + op_params.broadcast_category = + tflite::BroadcastableOpCategory::kFirstInputBroadcastsFast; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + op_params.broadcast_shape[4] = y0; + op_params.broadcast_shape[3] = y1; + op_params.broadcast_shape[2] = y2; + op_params.broadcast_shape[1] = y3; + op_params.broadcast_shape[0] = y4; + BroadcastAddFivefold(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +// legacy, for compatibility with old checked-in code +template +void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T* output_data, const Dims<4>& output_dims) { + T output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + BroadcastAdd(input1_data, input1_dims, input2_data, input2_dims, + output_activation_min, output_activation_max, output_data, + output_dims); +} + +template +inline void Add(const int16* input1_data, const Dims<4>& input1_dims, + int input1_shift, const int16* input2_data, + const Dims<4>& input2_dims, int input2_shift, + int16 output_activation_min, int16 output_activation_max, + int16* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, -32768); + TFLITE_DCHECK_EQ(output_activation_max, 32767); + } + + tflite::ArithmeticParams op_params; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +inline void Sub(const float* input1_data, const Dims<4>& input1_dims, + const float* input2_data, const Dims<4>& input2_dims, + float* output_data, const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(FusedActivationFunctionType::kNone, + &output_activation_min, &output_activation_max); + tflite::ArithmeticParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + Sub(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +void Sub(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, + const Dims<4>& input2_dims, T* output_data, + const Dims<4>& output_dims) { + tflite::ArithmeticParams op_params; + op_params.quantized_activation_min = std::numeric_limits::min(); + op_params.quantized_activation_max = std::numeric_limits::max(); + Sub(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + inline void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride_width, int stride_height, int pad_width, int pad_height, int kwidth, int kheight, float output_activation_min, float output_activation_max, float* output_data, const Dims<4>& output_dims) { - AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, DimsToShape(output_dims)); + tflite::PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = kheight; + params.filter_width = kwidth; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.float_activation_min = output_activation_min; + params.float_activation_max = output_activation_max; + AveragePool(params, DimsToShape(input_dims), input_data, + DimsToShape(output_dims), output_data); } // legacy, for compatibility with old checked-in code @@ -104,10 +346,17 @@ inline void AveragePool(const uint8* input_data, const Dims<4>& input_dims, int32 output_activation_min, int32 output_activation_max, uint8* output_data, const Dims<4>& output_dims) { - AveragePool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); + tflite::PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = filter_height; + params.filter_width = filter_width; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.quantized_activation_min = output_activation_min; + params.quantized_activation_max = output_activation_max; + AveragePool(params, DimsToShape(input_dims), input_data, + DimsToShape(output_dims), output_data); } // legacy, for compatibility with old checked-in code @@ -148,9 +397,17 @@ inline void MaxPool(const float* input_data, const Dims<4>& input_dims, int pad_height, int kwidth, int kheight, float output_activation_min, float output_activation_max, float* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, kwidth, kheight, output_activation_min, - output_activation_max, output_data, DimsToShape(output_dims)); + tflite::PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = kheight; + params.filter_width = kwidth; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.float_activation_min = output_activation_min; + params.float_activation_max = output_activation_max; + MaxPool(params, DimsToShape(input_dims), input_data, DimsToShape(output_dims), + output_data); } // legacy, for compatibility with old checked-in code @@ -180,10 +437,17 @@ inline void MaxPool(const uint8* input_data, const Dims<4>& input_dims, int pad_height, int filter_width, int filter_height, int32 output_activation_min, int32 output_activation_max, uint8* output_data, const Dims<4>& output_dims) { - MaxPool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); + PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = filter_height; + params.filter_width = filter_width; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.quantized_activation_min = output_activation_min; + params.quantized_activation_max = output_activation_max; + MaxPool(params, DimsToShape(input_dims), input_data, DimsToShape(output_dims), + output_data); } // legacy, for compatibility with old checked-in code @@ -223,10 +487,17 @@ inline void L2Pool(const float* input_data, const Dims<4>& input_dims, int pad_height, int filter_width, int filter_height, float output_activation_min, float output_activation_max, float* output_data, const Dims<4>& output_dims) { - L2Pool(input_data, DimsToShape(input_dims), stride_width, stride_height, - pad_width, pad_height, filter_width, filter_height, - output_activation_min, output_activation_max, output_data, - DimsToShape(output_dims)); + PoolParams params; + params.stride_height = stride_height; + params.stride_width = stride_width; + params.filter_height = filter_height; + params.filter_width = filter_width; + params.padding_values.height = pad_height; + params.padding_values.width = pad_width; + params.float_activation_min = output_activation_min; + params.float_activation_max = output_activation_max; + L2Pool(params, DimsToShape(input_dims), input_data, DimsToShape(output_dims), + output_data); } // legacy, for compatibility with old checked-in code diff --git a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc index ccf112c990f3b5cba755a9b29aadd5aa82104849..6bd88b5596bc0f7c425745012b7b4a091b64afbb 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include #include +#include #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/kernels/activation_functor.h" @@ -38,14 +39,14 @@ bool PortableIsZeroVector(const float* vector, int v_size) { void PortableSymmetricQuantizeFloats(const float* values, const int size, int8_t* quantized_values, - float* __restrict__ min, - float* __restrict__ max, + float* __restrict__ min_value, + float* __restrict__ max_value, float* __restrict__ scaling_factor) { auto minmax = std::minmax_element(values, values + size); - *min = *minmax.first; - *max = *minmax.second; + *min_value = *minmax.first; + *max_value = *minmax.second; const int kScale = 127; - const float range = std::max(std::abs(*min), std::abs(*max)); + const float range = std::max(std::abs(*min_value), std::abs(*max_value)); if (range == 0) { memset(quantized_values, 0, size * sizeof(int8_t)); *scaling_factor = 1; @@ -195,6 +196,13 @@ void PortableZeroVector(float* vector, int v_size) { memset(vector, 0, v_size * sizeof(float)); } +void PortableVectorScalarMultiply(const int8_t* vector, const int v_size, + const float scale, float* result) { + for (int v = 0; v < v_size; ++v) { + *result++ = scale * *vector++; + } +} + void PortableClipVector(const float* vector, int v_size, float abs_limit, float* result) { for (int v = 0; v < v_size; v++) { diff --git a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h index d2e1fecd25cf3d11d3daffcc566dc1d5df97128c..a375aaffa67ac19975cc8e371be11547d689dc72 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h @@ -19,6 +19,10 @@ limitations under the License. // structure. #include "tensorflow/contrib/lite/builtin_op_data.h" +#if defined(_MSC_VER) +#define __restrict__ __restrict +#endif + namespace tflite { namespace tensor_utils { @@ -28,8 +32,8 @@ float PortableClip(float f, float abs_limit); bool PortableIsZeroVector(const float* vector, int v_size); void PortableSymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float* min, - float* max, float* scaling_factor); + int8_t* quantized_values, float* min_value, + float* max_value, float* scaling_factor); // Multiply a matrix by a batch vector, and store results in a batch-size // vector. @@ -96,6 +100,10 @@ void PortableSub1Vector(const float* vector, int v_size, float* result); // Fill vector with 0.f. void PortableZeroVector(float* vector, int v_size); +// Multiply all elements of vector with a scalar. +void PortableVectorScalarMultiply(const int8_t* vector, int v_size, float scale, + float* result); + // Clip elements of a vector using a abs_limit value. void PortableClipVector(const float* vector, int v_size, float abs_limit, float* result); @@ -199,6 +207,12 @@ void ZeroVector(float* vector, int v_size) { PortableZeroVector(vector, v_size); } +// Multiply all elements of vector with a scalar. +void VectorScalarMultiply(const int8_t* vector, int v_size, float scale, + float* result) { + PortableVectorScalarMultiply(vector, v_size, scale, result); +} + void ClipVector(const float* vector, int v_size, float abs_limit, float* result) { PortableClipVector(vector, v_size, abs_limit, result); diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 9357e7407eb83fe8ea3486dfdde8742fc6323ee9..714613b96e11d417cb962eb76baee794556d12eb 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -158,98 +158,6 @@ SaturatingRoundingMultiplyByPOTParam( SaturatingRoundingMultiplyByPOTParam(a.raw(), exponent)); } -// DO NOT USE THIS STRUCT FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING ELEMENT-WISE -// BROADCASTING. -// -// NdArrayDesc describes the shape and memory layout of an N-dimensional -// rectangular array of numbers. -// -// NdArrayDesc is basically identical to Dims defined in types.h. -// However, as Dims is to be deprecated, this class exists as an adaptor -// to enable simple unoptimized implementations of element-wise broadcasting -// operations. -template -struct NdArrayDesc { - // The "extent" of each dimension. Indices along dimension d must be in the - // half-open interval [0, extents[d]). - int extents[N]; - - // The number of *elements* (not bytes) between consecutive indices of each - // dimension. - int strides[N]; -}; - -// DO NOT USE THIS FUNCTION FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING -// ELEMENT-WISE BROADCASTING. -// -// Same as Offset(), except takes as NdArrayDesc instead of Dims. -inline int SubscriptToIndex(const NdArrayDesc<4>& desc, int i0, int i1, int i2, - int i3) { - TFLITE_DCHECK(i0 >= 0 && i0 < desc.extents[0]); - TFLITE_DCHECK(i1 >= 0 && i1 < desc.extents[1]); - TFLITE_DCHECK(i2 >= 0 && i2 < desc.extents[2]); - TFLITE_DCHECK(i3 >= 0 && i3 < desc.extents[3]); - return i0 * desc.strides[0] + i1 * desc.strides[1] + i2 * desc.strides[2] + - i3 * desc.strides[3]; -} - -// Given the dimensions of the operands for an element-wise binary broadcast, -// adjusts them so that they can be directly iterated over with simple loops. -// Returns the adjusted dims as instances of NdArrayDesc in 'desc0_out' and -// 'desc1_out'. 'desc0_out' and 'desc1_out' cannot be nullptr. -// -// This function assumes that the two input shapes are compatible up to -// broadcasting and the shorter one has already been prepended with 1s to be the -// same length. E.g., if shape0 is (1, 16, 16, 64) and shape1 is (1, 64), -// shape1 must already have been prepended to be (1, 1, 1, 64). Recall that -// Dims refer to shapes in reverse order. In this case, input0_dims will be -// (64, 16, 16, 1) and input1_dims will be (64, 1, 1, 1). -// -// When two shapes are compatible up to broadcasting, for each dimension d, -// the input extents are either equal, or one of them is 1. -// -// This function performs the following for each dimension d: -// - If the extents are equal, then do nothing since the loop that walks over -// both of the input arrays is correct. -// - Otherwise, one (and only one) of the extents must be 1. Say extent0 is 1 -// and extent1 is e1. Then set extent0 to e1 and stride0 *to 0*. This allows -// array0 to be referenced *at any index* in dimension d and still access the -// same slice. -template -inline void NdArrayDescsForElementwiseBroadcast(const Dims& input0_dims, - const Dims& input1_dims, - NdArrayDesc* desc0_out, - NdArrayDesc* desc1_out) { - TFLITE_DCHECK(desc0_out != nullptr); - TFLITE_DCHECK(desc1_out != nullptr); - - // Copy dims to desc. - for (int i = 0; i < N; ++i) { - desc0_out->extents[i] = input0_dims.sizes[i]; - desc0_out->strides[i] = input0_dims.strides[i]; - desc1_out->extents[i] = input1_dims.sizes[i]; - desc1_out->strides[i] = input1_dims.strides[i]; - } - - // Walk over each dimension. If the extents are equal do nothing. - // Otherwise, set the desc with extent 1 to have extent equal to the other and - // stride 0. - for (int i = 0; i < N; ++i) { - const int extent0 = ArraySize(input0_dims, i); - const int extent1 = ArraySize(input1_dims, i); - if (extent0 != extent1) { - if (extent0 == 1) { - desc0_out->strides[i] = 0; - desc0_out->extents[i] = extent1; - } else { - TFLITE_DCHECK_EQ(extent1, 1); - desc1_out->strides[i] = 0; - desc1_out->extents[i] = extent0; - } - } - } -} - inline void Conv(const float* input_data, const Dims<4>& input_dims, const float* filter_data, const Dims<4>& filter_dims, const float* bias_data, const Dims<4>& bias_dims, @@ -1065,114 +973,108 @@ inline void L2Normalization(const uint8* input_data, } template -inline void Add(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T output_activation_min, T output_activation_max, - T* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const T* input1_data, + const RuntimeShape& input2_shape, const T* input2_data, + const RuntimeShape& output_shape, T* output_data) { + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, output_shape); for (int i = 0; i < flat_size; ++i) { output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] + input2_data[i], output_activation_min, - output_activation_max); + input1_data[i] + input2_data[i], params.quantized_activation_min, + params.quantized_activation_max); } } -// legacy, for compatibility with old checked-in code -template -void Add(const float* input1_data, const Dims<4>& input1_dims, - const float* input2_data, const Dims<4>& input2_dims, - float* output_data, const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - - Add(input1_data, input1_dims, input2_data, input2_dims, output_activation_min, - output_activation_max, output_data, output_dims); +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const float* input1_data, + const RuntimeShape& input2_shape, const float* input2_data, + const RuntimeShape& output_shape, float* output_data) { + const int size = MatchingFlatSize(input1_shape, input2_shape, output_shape); + for (int i = 0; i < size; i++) { + auto x = input1_data[i] + input2_data[i]; + output_data[i] = ActivationFunctionWithMinMax( + x, params.float_activation_min, params.float_activation_max); + } } -template -inline void Add(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, int input2_shift, - int32 output_offset, int32 output_multiplier, int output_shift, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - const int batches = - MatchingArraySize(input1_dims, 3, input2_dims, 3, output_dims, 3); - const int height = - MatchingArraySize(input1_dims, 2, input2_dims, 2, output_dims, 2); - const int width = - MatchingArraySize(input1_dims, 1, input2_dims, 1, output_dims, 1); - const int depth = - MatchingArraySize(input1_dims, 0, input2_dims, 0, output_dims, 0); - for (int b = 0; b < batches; ++b) { - for (int y = 0; y < height; ++y) { - for (int x = 0; x < width; ++x) { - for (int c = 0; c < depth; ++c) { - const int32 input1_val = - input1_offset + input1_data[Offset(input1_dims, c, x, y, b)]; - const int32 input2_val = - input2_offset + input2_data[Offset(input2_dims, c, x, y, b)]; - const int32 shifted_input1_val = input1_val * (1 << left_shift); - const int32 shifted_input2_val = input2_val * (1 << left_shift); - const int32 scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, - kReverseShift * input1_shift); - const int32 scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, - kReverseShift * input2_shift); - const int32 raw_sum = scaled_input1_val + scaled_input2_val; - const int32 raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, output_multiplier, kReverseShift * output_shift) + - output_offset; - const int32 clamped_output = - std::min(output_activation_max, - std::max(output_activation_min, raw_output)); - output_data[Offset(output_dims, c, x, y, b)] = - static_cast(clamped_output); - } - } - } +// Element-wise add that can often be used for inner loop of broadcast add as +// well as the non-broadcast add. +inline void AddElementwise(int size, const ArithmeticParams& params, + const uint8* input1_data, const uint8* input2_data, + uint8* output_data) { + TFLITE_DCHECK_GT(params.input1_offset, -256); + TFLITE_DCHECK_GT(params.input2_offset, -256); + TFLITE_DCHECK_LT(params.input1_offset, 256); + TFLITE_DCHECK_LT(params.input2_offset, 256); + + for (int i = 0; i < size; ++i) { + const int32 input1_val = params.input1_offset + input1_data[i]; + const int32 input2_val = params.input2_offset + input2_data[i]; + const int32 shifted_input1_val = input1_val * (1 << params.left_shift); + const int32 shifted_input2_val = input2_val * (1 << params.left_shift); + const int32 scaled_input1_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input1_val, params.input1_multiplier, params.input1_shift); + const int32 scaled_input2_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input2_val, params.input2_multiplier, params.input2_shift); + const int32 raw_sum = scaled_input1_val + scaled_input2_val; + const int32 raw_output = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + raw_sum, params.output_multiplier, params.output_shift) + + params.output_offset; + const int32 clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + output_data[i] = static_cast(clamped_output); } } -inline void Add(const int16* input1_data, const Dims<4>& input1_dims, - int input1_shift, const int16* input2_data, - const Dims<4>& input2_dims, int input2_shift, - int16 output_activation_min, int16 output_activation_max, - int16* output_data, const Dims<4>& output_dims) { - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - - const int flat_size = MatchingFlatSize(output_dims, input1_dims, input2_dims); - - TFLITE_DCHECK(input1_shift == 0 || input2_shift == 0); - TFLITE_DCHECK_GE(input1_shift, 0); - TFLITE_DCHECK_GE(input2_shift, 0); +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const uint8* input1_data, + const RuntimeShape& input2_shape, const uint8* input2_data, + const RuntimeShape& output_shape, uint8* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, output_shape); + + TFLITE_DCHECK_GT(params.input1_offset, -256); + TFLITE_DCHECK_GT(params.input2_offset, -256); + TFLITE_DCHECK_LT(params.input1_offset, 256); + TFLITE_DCHECK_LT(params.input2_offset, 256); + AddElementwise(flat_size, params, input1_data, input2_data, output_data); +} + +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const int16* input1_data, + const RuntimeShape& input2_shape, const int16* input2_data, + const RuntimeShape& output_shape, int16* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + + const int input1_shift = params.input1_shift; + const int flat_size = + MatchingFlatSize(output_shape, input1_shape, input2_shape); + const int16 output_activation_min = params.quantized_activation_min; + const int16 output_activation_max = params.quantized_activation_max; + + TFLITE_DCHECK(input1_shift == 0 || params.input2_shift == 0); + TFLITE_DCHECK_LE(input1_shift, 0); + TFLITE_DCHECK_LE(params.input2_shift, 0); const int16* not_shift_input = input1_shift == 0 ? input1_data : input2_data; const int16* shift_input = input1_shift == 0 ? input2_data : input1_data; - const int input_shift = input1_shift == 0 ? input2_shift : input1_shift; + const int input_right_shift = + input1_shift == 0 ? -params.input2_shift : -input1_shift; for (int i = 0; i < flat_size; i++) { // F0 uses 0 integer bits, range [-1, 1]. using F0 = gemmlowp::FixedPoint; F0 input_ready_scaled = F0::FromRaw(not_shift_input[i]); - F0 scaled_input = - F0::FromRaw(gemmlowp::RoundingDivideByPOT(shift_input[i], input_shift)); + F0 scaled_input = F0::FromRaw( + gemmlowp::RoundingDivideByPOT(shift_input[i], input_right_shift)); F0 result = gemmlowp::SaturatingAdd(scaled_input, input_ready_scaled); const int16 raw_output = result.raw(); const int16 clamped_output = std::min( @@ -1181,42 +1083,28 @@ inline void Add(const int16* input1_data, const Dims<4>& input1_dims, } } -template -inline void Add(const int16* input1_data, const Dims<4>& input1_dims, - int input1_shift, const int16* input2_data, - const Dims<4>& input2_dims, int input2_shift, - int16 output_activation_min, int16 output_activation_max, - int16* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, -32768); - TFLITE_DCHECK_EQ(output_activation_max, 32767); - } - - Add(input1_data, input1_dims, input1_shift, input2_data, input2_dims, - input2_shift, output_activation_min, output_activation_max, output_data, - output_dims); -} - // TODO(jiawen): We can implement BroadcastAdd on buffers of arbitrary // dimensionality if the runtime code does a single loop over one dimension // that handles broadcasting as the base case. The code generator would then // generate max(D1, D2) nested for loops. -template -void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T output_activation_min, T output_activation_max, - T* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastAdd"); - +// TODO(benoitjacob): BroadcastAdd is intentionally duplicated from +// reference_ops.h. Once an optimized version is implemented and NdArrayDesc +// is no longer referenced in this file, move NdArrayDesc from types.h to +// reference_ops.h. +inline void BroadcastAdd4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const float* input1_data, + const RuntimeShape& input2_shape, + const float* input2_data, + const RuntimeShape& output_shape, + float* output_data) { + gemmlowp::ScopedProfilingLabel label("BroadcastAdd4DSlow/float"); NdArrayDesc<4> desc1; NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); // In Tensorflow, the dimensions are canonically named (batch_number, row, // col, channel), with extents (batches, height, width, depth), with the @@ -1229,49 +1117,77 @@ void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, // We name our variables by their Tensorflow convention, but generate C code // nesting loops such that the innermost loop has the smallest stride for the // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - output_data[Offset(output_dims, c, x, y, b)] = + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { + output_data[Offset(extended_output_shape, b, y, x, c)] = ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, c, x, y, b)] + - input2_data[SubscriptToIndex(desc2, c, x, y, b)], - output_activation_min, output_activation_max); + input1_data[SubscriptToIndex(desc1, b, y, x, c)] + + input2_data[SubscriptToIndex(desc2, b, y, x, c)], + params.float_activation_min, params.float_activation_max); } } } } } -// legacy, for compatibility with old checked-in code -template -void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T* output_data, const Dims<4>& output_dims) { - T output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); +inline void BroadcastAdd4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const int32* input1_data, + const RuntimeShape& input2_shape, + const int32* input2_data, + const RuntimeShape& output_shape, + int32* output_data) { + gemmlowp::ScopedProfilingLabel label("BroadcastAdd4DSlow/int32"); + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); - BroadcastAdd(input1_data, input1_dims, input2_data, input2_dims, - output_activation_min, output_activation_max, output_data, - output_dims); + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { + output_data[Offset(extended_output_shape, b, y, x, c)] = + ActivationFunctionWithMinMax( + input1_data[SubscriptToIndex(desc1, b, y, x, c)] + + input2_data[SubscriptToIndex(desc2, b, y, x, c)], + params.quantized_activation_min, + params.quantized_activation_max); + } + } + } + } } -inline void BroadcastAdd(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, - int32 output_multiplier, int output_shift, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastAdd/8bit"); - +inline void BroadcastAdd4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const uint8* input1_data, + const RuntimeShape& input2_shape, + const uint8* input2_data, + const RuntimeShape& output_shape, + uint8* output_data) { + gemmlowp::ScopedProfilingLabel label("BroadcastAdd4DSlow/uint8"); NdArrayDesc<4> desc1; NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); // In Tensorflow, the dimensions are canonically named (batch_number, row, // col, channel), with extents (batches, height, width, depth), with the @@ -1284,33 +1200,37 @@ inline void BroadcastAdd(int left_shift, const uint8* input1_data, // We name our variables by their Tensorflow convention, but generate C code // nesting loops such that the innermost loop has the smallest stride for the // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { const int32 input1_val = - input1_offset + input1_data[SubscriptToIndex(desc1, c, x, y, b)]; + params.input1_offset + + input1_data[SubscriptToIndex(desc1, b, y, x, c)]; const int32 input2_val = - input2_offset + input2_data[SubscriptToIndex(desc2, c, x, y, b)]; - const int32 shifted_input1_val = input1_val * (1 << left_shift); - const int32 shifted_input2_val = input2_val * (1 << left_shift); + params.input2_offset + + input2_data[SubscriptToIndex(desc2, b, y, x, c)]; + const int32 shifted_input1_val = + input1_val * (1 << params.left_shift); + const int32 shifted_input2_val = + input2_val * (1 << params.left_shift); const int32 scaled_input1_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, - kReverseShift * input1_shift); + shifted_input1_val, params.input1_multiplier, + params.input1_shift); const int32 scaled_input2_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, - kReverseShift * input2_shift); + shifted_input2_val, params.input2_multiplier, + params.input2_shift); const int32 raw_sum = scaled_input1_val + scaled_input2_val; const int32 raw_output = MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, output_multiplier, kReverseShift * output_shift) + - output_offset; + raw_sum, params.output_multiplier, params.output_shift) + + params.output_offset; const int32 clamped_output = - std::min(output_activation_max, - std::max(output_activation_min, raw_output)); - output_data[Offset(output_dims, c, x, y, b)] = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + output_data[Offset(extended_output_shape, b, y, x, c)] = static_cast(clamped_output); } } @@ -1318,121 +1238,67 @@ inline void BroadcastAdd(int left_shift, const uint8* input1_data, } } -inline void BroadcastAddFivefold( - int y0, int y1, int y2, int y3, int y4, int left_shift, - const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, const uint8* input2_data, - const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, int32 output_multiplier, - int output_shift, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastAddFivefold/8bit"); - - int sb1 = y0; - int sa2 = y0; - int sb2 = y0 * y1; - int sa3 = y0 * y2; - int sa4 = y0 * y2 * y3; - int sb4 = y0 * y1 * y2; - +inline void BroadcastAddFivefold(const ArithmeticParams& unswitched_params, + const RuntimeShape& unswitched_input1_shape, + const uint8* unswitched_input1_data, + const RuntimeShape& unswitched_input2_shape, + const uint8* unswitched_input2_data, + const RuntimeShape& output_shape, + uint8* output_data) { + ArithmeticParams switched_params = unswitched_params; + switched_params.input1_offset = unswitched_params.input2_offset; + switched_params.input1_multiplier = unswitched_params.input2_multiplier; + switched_params.input1_shift = unswitched_params.input2_shift; + switched_params.input2_offset = unswitched_params.input1_offset; + switched_params.input2_multiplier = unswitched_params.input1_multiplier; + switched_params.input2_shift = unswitched_params.input1_shift; + + const bool use_unswitched = + unswitched_params.broadcast_category == + tflite::BroadcastableOpCategory::kFirstInputBroadcastsFast; + + const ArithmeticParams& params = + use_unswitched ? unswitched_params : switched_params; + const uint8* input1_data = + use_unswitched ? unswitched_input1_data : unswitched_input2_data; + const uint8* input2_data = + use_unswitched ? unswitched_input2_data : unswitched_input1_data; + + // Fivefold nested loops. The second input resets its position for each + // iteration of the second loop. The first input resets its position at the + // beginning of the fourth loop. The innermost loop is an elementwise add of + // sections of the arrays. uint8* output_data_ptr = output_data; - for (int i4 = 0; i4 < y4; ++i4) { - for (int i3 = 0; i3 < y3; ++i3) { + const uint8* input1_data_ptr = input1_data; + const uint8* input2_data_reset = input2_data; + int y0 = params.broadcast_shape[0]; + int y1 = params.broadcast_shape[1]; + int y2 = params.broadcast_shape[2]; + int y3 = params.broadcast_shape[3]; + int y4 = params.broadcast_shape[4]; + for (int i0 = 0; i0 < y0; ++i0) { + const uint8* input2_data_ptr; + for (int i1 = 0; i1 < y1; ++i1) { + input2_data_ptr = input2_data_reset; for (int i2 = 0; i2 < y2; ++i2) { - for (int i1 = 0; i1 < y1; ++i1) { - for (int i0 = 0; i0 < y0; ++i0) { - const int32 input1_val = - input1_offset + - input1_data[i4 * sa4 + i3 * sa3 + i2 * sa2 + i0]; - const int32 input2_val = - input2_offset + - input2_data[i4 * sb4 + i2 * sb2 + i1 * sb1 + i0]; - const int32 shifted_input1_val = input1_val * (1 << left_shift); - const int32 shifted_input2_val = input2_val * (1 << left_shift); - const int32 scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, - kReverseShift * input1_shift); - const int32 scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, - kReverseShift * input2_shift); - const int32 raw_sum = scaled_input1_val + scaled_input2_val; - const int32 raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, output_multiplier, kReverseShift * output_shift) + - output_offset; - const int32 clamped_output = - std::min(output_activation_max, - std::max(output_activation_min, raw_output)); - *output_data_ptr = static_cast(clamped_output); - ++output_data_ptr; - } + for (int i3 = 0; i3 < y3; ++i3) { + AddElementwise(y4, params, input1_data_ptr, input2_data_ptr, + output_data_ptr); + input2_data_ptr += y4; + output_data_ptr += y4; } + input1_data_ptr += y4; } } + input2_data_reset = input2_data_ptr; } } -template -inline void BroadcastAdd(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, - int32 output_multiplier, int output_shift, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - BroadcastAdd(left_shift, input1_data, input1_dims, input1_offset, - input1_multiplier, input1_shift, input2_data, input2_dims, - input2_offset, input2_multiplier, input2_shift, output_offset, - output_multiplier, output_shift, output_activation_min, - output_activation_max, output_data, output_dims); -} - -template -inline void BroadcastAddFivefold( - int y0, int y1, int y2, int y3, int y4, int left_shift, - const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, const uint8* input2_data, - const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, int32 output_multiplier, - int output_shift, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - BroadcastAddFivefold(y0, y1, y2, y3, y4, left_shift, input1_data, input1_dims, - input1_offset, input1_multiplier, input1_shift, - input2_data, input2_dims, input2_offset, - input2_multiplier, input2_shift, output_offset, - output_multiplier, output_shift, output_activation_min, - output_activation_max, output_data, output_dims); -} - -inline void Mul(const float* input1_data, const Dims<4>& input1_dims, - const float* input2_data, const Dims<4>& input2_dims, - float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { +template +inline void Mul(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T output_activation_min, T output_activation_max, + T* output_data, const Dims<4>& output_dims) { const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); for (int i = 0; i < flat_size; ++i) { output_data[i] = ActivationFunctionWithMinMax( @@ -1653,10 +1519,11 @@ void BroadcastDiv(const T* input1_data, const Dims<4>& input1_dims, } } -inline void Div(const float* input1_data, const Dims<4>& input1_dims, - const float* input2_data, const Dims<4>& input2_dims, - float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { +template +inline void Div(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T output_activation_min, T output_activation_max, + T* output_data, const Dims<4>& output_dims) { const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); for (int i = 0; i < flat_size; ++i) { output_data[i] = ActivationFunctionWithMinMax( @@ -1665,15 +1532,35 @@ inline void Div(const float* input1_data, const Dims<4>& input1_dims, } } -inline void Sub(const float* input1_data, const Dims<4>& input1_dims, - const float* input2_data, const Dims<4>& input2_dims, - float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); +inline void SubNonBroadcast(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const float* input1_data, + const RuntimeShape& input2_shape, + const float* input2_data, + const RuntimeShape& output_shape, + float* output_data) { + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, output_shape); for (int i = 0; i < flat_size; ++i) { output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] - input2_data[i], output_activation_min, - output_activation_max); + input1_data[i] - input2_data[i], params.float_activation_min, + params.float_activation_max); + } +} + +inline void SubNonBroadcast(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const int32* input1_data, + const RuntimeShape& input2_shape, + const int32* input2_data, + const RuntimeShape& output_shape, + int32* output_data) { + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, output_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] - input2_data[i], params.quantized_activation_min, + params.quantized_activation_max); } } @@ -1681,16 +1568,24 @@ inline void Sub(const float* input1_data, const Dims<4>& input1_dims, // dimensionality if the runtime code does a single loop over one dimension // that handles broadcasting as the base case. The code generator would then // generate max(D1, D2) nested for loops. -template -void BroadcastSub(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T output_activation_min, T output_activation_max, - T* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastSub"); - +// TODO(benoitjacob): BroadcastSub is intentionally duplicated from +// reference_ops.h. Once an optimized version is implemented and NdArrayDesc +// is no longer referenced in this file, move NdArrayDesc from types.h to +// reference_ops.h. +inline void BroadcastSub4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const float* input1_data, + const RuntimeShape& input2_shape, + const float* input2_data, + const RuntimeShape& output_shape, + float* output_data) { + gemmlowp::ScopedProfilingLabel label("BroadcastAdd4DSlow/float"); NdArrayDesc<4> desc1; NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); // In Tensorflow, the dimensions are canonically named (batch_number, row, // col, channel), with extents (batches, height, width, depth), with the @@ -1703,36 +1598,35 @@ void BroadcastSub(const T* input1_data, const Dims<4>& input1_dims, // We name our variables by their Tensorflow convention, but generate C code // nesting loops such that the innermost loop has the smallest stride for the // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - output_data[Offset(output_dims, c, x, y, b)] = + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { + output_data[Offset(extended_output_shape, b, y, x, c)] = ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, c, x, y, b)] - - input2_data[SubscriptToIndex(desc2, c, x, y, b)], - output_activation_min, output_activation_max); + input1_data[SubscriptToIndex(desc1, b, y, x, c)] - + input2_data[SubscriptToIndex(desc2, b, y, x, c)], + params.float_activation_min, params.float_activation_max); } } } } } -inline void BroadcastSub(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, - int32 output_multiplier, int output_shift, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastSub/8bit"); - +inline void BroadcastSub4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const uint8* input1_data, + const RuntimeShape& input2_shape, + const uint8* input2_data, + const RuntimeShape& output_shape, + uint8* output_data) { + gemmlowp::ScopedProfilingLabel label("BroadcastAdd4DSlow/uint8"); NdArrayDesc<4> desc1; NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); // In Tensorflow, the dimensions are canonically named (batch_number, row, // col, channel), with extents (batches, height, width, depth), with the @@ -1745,33 +1639,37 @@ inline void BroadcastSub(int left_shift, const uint8* input1_data, // We name our variables by their Tensorflow convention, but generate C code // nesting loops such that the innermost loop has the smallest stride for the // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { const int32 input1_val = - input1_offset + input1_data[SubscriptToIndex(desc1, c, x, y, b)]; + params.input1_offset + + input1_data[SubscriptToIndex(desc1, b, y, x, c)]; const int32 input2_val = - input2_offset + input2_data[SubscriptToIndex(desc2, c, x, y, b)]; - const int32 shifted_input1_val = input1_val * (1 << left_shift); - const int32 shifted_input2_val = input2_val * (1 << left_shift); + params.input2_offset + + input2_data[SubscriptToIndex(desc2, b, y, x, c)]; + const int32 shifted_input1_val = + input1_val * (1 << params.left_shift); + const int32 shifted_input2_val = + input2_val * (1 << params.left_shift); const int32 scaled_input1_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, - kReverseShift * input1_shift); + shifted_input1_val, params.input1_multiplier, + params.input1_shift); const int32 scaled_input2_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, - kReverseShift * input2_shift); + shifted_input2_val, params.input2_multiplier, + params.input2_shift); const int32 raw_sub = scaled_input1_val - scaled_input2_val; const int32 raw_output = MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sub, output_multiplier, kReverseShift * output_shift) + - output_offset; + raw_sub, params.output_multiplier, params.output_shift) + + params.output_offset; const int32 clamped_output = - std::min(output_activation_max, - std::max(output_activation_min, raw_output)); - output_data[Offset(output_dims, c, x, y, b)] = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + output_data[Offset(extended_output_shape, b, y, x, c)] = static_cast(clamped_output); } } @@ -1779,6 +1677,156 @@ inline void BroadcastSub(int left_shift, const uint8* input1_data, } } +inline void BroadcastSub4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const int32* input1_data, + const RuntimeShape& input2_shape, + const int32* input2_data, + const RuntimeShape& output_shape, + int32* output_data) { + gemmlowp::ScopedProfilingLabel label("BroadcastAdd4DSlow/int32"); + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { + output_data[Offset(extended_output_shape, b, y, x, c)] = + ActivationFunctionWithMinMax( + input1_data[SubscriptToIndex(desc1, b, y, x, c)] - + input2_data[SubscriptToIndex(desc2, b, y, x, c)], + params.quantized_activation_min, + params.quantized_activation_max); + } + } + } + } +} + +template +void BroadcastSub4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const T* input1_data, + const RuntimeShape& input2_shape, const T* input2_data, + const RuntimeShape& output_shape, T* output_data) { + gemmlowp::ScopedProfilingLabel label("BroadcastAdd4DSlow/templated"); + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { + output_data[Offset(extended_output_shape, b, y, x, c)] = + ActivationFunctionWithMinMax( + input1_data[SubscriptToIndex(desc1, b, y, x, c)] - + input2_data[SubscriptToIndex(desc2, b, y, x, c)], + params.quantized_activation_min, + params.quantized_activation_max); + } + } + } + } +} + +template +void Sub(const ArithmeticParams& params, const RuntimeShape& input1_shape, + const T* input1_data, const RuntimeShape& input2_shape, + const T* input2_data, const RuntimeShape& output_shape, + T* output_data) { + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { + output_data[Offset(extended_output_shape, b, y, x, c)] = + input1_data[SubscriptToIndex(desc1, b, y, x, c)] - + input2_data[SubscriptToIndex(desc2, b, y, x, c)]; + } + } + } + } +} + +inline void SubWithActivation(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const int32* input1_data, + const RuntimeShape& input2_shape, + const int32* input2_data, + const RuntimeShape& output_shape, + int32* output_data) { + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, input2_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] - input2_data[i], params.quantized_activation_min, + params.quantized_activation_max); + } +} + +inline void SubWithActivation(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const float* input1_data, + const RuntimeShape& input2_shape, + const float* input2_data, + const RuntimeShape& output_shape, + float* output_data) { + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, input2_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] - input2_data[i], params.float_activation_min, + params.float_activation_max); + } +} + template void Concatenation(int concat_dim, const Scalar* const* input_data, const Dims<4>* const* input_dims, int inputs_count, @@ -1812,6 +1860,26 @@ void Concatenation(int concat_dim, const Scalar* const* input_data, } } +template +void Pack(int dim, const Scalar* const* input_data, + const Dims<4>* const* input_dims, int inputs_count, + Scalar* output_data, const Dims<4>& output_dims) { + TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); + int outer_size = 1; + for (int i = dim + 1; i < 4; i++) { + outer_size *= output_dims.sizes[i]; + } + Scalar* output_ptr = output_data; + const int copy_size = FlatSize(**input_dims) / outer_size; + for (int k = 0; k < outer_size; k++) { + for (int i = 0; i < inputs_count; ++i) { + memcpy(output_ptr, input_data[i] + k * copy_size, + copy_size * sizeof(Scalar)); + output_ptr += copy_size; + } + } +} + // TODO(prabhumk): This is the same as the optimized implementation. // TODO(prabhumk): The quantized implementation of concatentation isn't fully // quantized as it takes scale as a floating point value. This should be fixed @@ -2273,13 +2341,10 @@ inline int NodeOffset(int b, int h, int w, int height, int width) { return (b * height + h) * width + w; } -inline void AveragePool(const float* input_data, - const RuntimeShape& input_shape, int stride_width, - int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, - float output_activation_min, - float output_activation_max, float* output_data, - const RuntimeShape& output_shape) { +inline void AveragePool(const PoolParams& params, + const RuntimeShape& input_shape, + const float* input_data, + const RuntimeShape& output_shape, float* output_data) { TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); const int batches = MatchingDim(input_shape, 0, output_shape, 0); @@ -2288,20 +2353,24 @@ inline void AveragePool(const float* input_data, const int input_width = input_shape.Dims(2); const int output_height = output_shape.Dims(1); const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; // Compute the boundaries of the filter region clamped so as to // ensure that the filter window fits in the input array. const int filter_x_start = std::max(0, -in_x_origin); const int filter_x_end = - std::min(filter_width, input_width - in_x_origin); + std::min(params.filter_width, input_width - in_x_origin); const int filter_y_start = std::max(0, -in_y_origin); const int filter_y_end = - std::min(filter_height, input_height - in_y_origin); + std::min(params.filter_height, input_height - in_y_origin); float total = 0.f; float filter_count = 0; for (int filter_y = filter_y_start; filter_y < filter_y_end; @@ -2317,22 +2386,20 @@ inline void AveragePool(const float* input_data, } const float average = total / filter_count; output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - ActivationFunctionWithMinMax(average, output_activation_min, - output_activation_max); + ActivationFunctionWithMinMax(average, params.float_activation_min, + params.float_activation_max); } } } } } -inline void AveragePool(const uint8* input_data, - const RuntimeShape& input_shape, int stride_width, - int stride_height, int pad_width, int pad_height, - int filter_width, int filter_height, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const RuntimeShape& output_shape) { - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); +inline void AveragePool(const PoolParams& params, + const RuntimeShape& input_shape, + const uint8* input_data, + const RuntimeShape& output_shape, uint8* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); const int batches = MatchingDim(input_shape, 0, output_shape, 0); @@ -2341,20 +2408,24 @@ inline void AveragePool(const uint8* input_data, const int input_width = input_shape.Dims(2); const int output_height = output_shape.Dims(1); const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; // Compute the boundaries of the filter region clamped so as to // ensure that the filter window fits in the input array. const int filter_x_start = std::max(0, -in_x_origin); const int filter_x_end = - std::min(filter_width, input_width - in_x_origin); + std::min(params.filter_width, input_width - in_x_origin); const int filter_y_start = std::max(0, -in_y_origin); const int filter_y_end = - std::min(filter_height, input_height - in_y_origin); + std::min(params.filter_height, input_height - in_y_origin); int32 acc = 0; int filter_count = 0; for (int filter_y = filter_y_start; filter_y < filter_y_end; @@ -2369,8 +2440,8 @@ inline void AveragePool(const uint8* input_data, } } acc = (acc + filter_count / 2) / filter_count; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); + acc = std::max(acc, params.quantized_activation_min); + acc = std::min(acc, params.quantized_activation_max); output_data[Offset(output_shape, batch, out_y, out_x, channel)] = static_cast(acc); } @@ -2379,11 +2450,9 @@ inline void AveragePool(const uint8* input_data, } } -inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - float output_activation_min, float output_activation_max, - float* output_data, const RuntimeShape& output_shape) { +inline void L2Pool(const PoolParams& params, const RuntimeShape& input_shape, + const float* input_data, const RuntimeShape& output_shape, + float* output_data) { TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); const int batches = MatchingDim(input_shape, 0, output_shape, 0); @@ -2392,20 +2461,24 @@ inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, const int input_width = input_shape.Dims(2); const int output_height = output_shape.Dims(1); const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; // Compute the boundaries of the filter region clamped so as to // ensure that the filter window fits in the input array. const int filter_x_start = std::max(0, -in_x_origin); const int filter_x_end = - std::min(filter_width, input_width - in_x_origin); + std::min(params.filter_width, input_width - in_x_origin); const int filter_y_start = std::max(0, -in_y_origin); const int filter_y_end = - std::min(filter_height, input_height - in_y_origin); + std::min(params.filter_height, input_height - in_y_origin); float sum_squares = 0.f; int filter_count = 0; for (int filter_y = filter_y_start; filter_y < filter_y_end; @@ -2422,19 +2495,18 @@ inline void L2Pool(const float* input_data, const RuntimeShape& input_shape, } const float l2pool_result = std::sqrt(sum_squares / filter_count); output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - ActivationFunctionWithMinMax(l2pool_result, output_activation_min, - output_activation_max); + ActivationFunctionWithMinMax(l2pool_result, + params.float_activation_min, + params.float_activation_max); } } } } } -inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - float output_activation_min, float output_activation_max, - float* output_data, const RuntimeShape& output_shape) { +inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, + const float* input_data, const RuntimeShape& output_shape, + float* output_data) { TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); const int batches = MatchingDim(input_shape, 0, output_shape, 0); @@ -2443,20 +2515,24 @@ inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, const int input_width = input_shape.Dims(2); const int output_height = output_shape.Dims(1); const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; // Compute the boundaries of the filter region clamped so as to // ensure that the filter window fits in the input array. const int filter_x_start = std::max(0, -in_x_origin); const int filter_x_end = - std::min(filter_width, input_width - in_x_origin); + std::min(params.filter_width, input_width - in_x_origin); const int filter_y_start = std::max(0, -in_y_origin); const int filter_y_end = - std::min(filter_height, input_height - in_y_origin); + std::min(params.filter_height, input_height - in_y_origin); float max = std::numeric_limits::lowest(); for (int filter_y = filter_y_start; filter_y < filter_y_end; ++filter_y) { @@ -2470,22 +2546,21 @@ inline void MaxPool(const float* input_data, const RuntimeShape& input_shape, } } output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - ActivationFunctionWithMinMax(max, output_activation_min, - output_activation_max); + ActivationFunctionWithMinMax(max, params.float_activation_min, + params.float_activation_max); } } } } } -inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, - int stride_width, int stride_height, int pad_width, - int pad_height, int filter_width, int filter_height, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const RuntimeShape& output_shape) { - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - TFLITE_DCHECK_GE(output_activation_min, 0); - TFLITE_DCHECK_LE(output_activation_max, 255); +inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, + const uint8* input_data, const RuntimeShape& output_shape, + uint8* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + TFLITE_DCHECK_GE(params.quantized_activation_min, 0); + TFLITE_DCHECK_LE(params.quantized_activation_max, 255); TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); const int batches = MatchingDim(input_shape, 0, output_shape, 0); @@ -2494,20 +2569,24 @@ inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, const int input_width = input_shape.Dims(2); const int output_height = output_shape.Dims(1); const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; for (int batch = 0; batch < batches; ++batch) { for (int out_y = 0; out_y < output_height; ++out_y) { for (int out_x = 0; out_x < output_width; ++out_x) { for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; // Compute the boundaries of the filter region clamped so as to // ensure that the filter window fits in the input array. const int filter_x_start = std::max(0, -in_x_origin); const int filter_x_end = - std::min(filter_width, input_width - in_x_origin); + std::min(params.filter_width, input_width - in_x_origin); const int filter_y_start = std::max(0, -in_y_origin); const int filter_y_end = - std::min(filter_height, input_height - in_y_origin); + std::min(params.filter_height, input_height - in_y_origin); uint8 max = 0; for (int filter_y = filter_y_start; filter_y < filter_y_end; ++filter_y) { @@ -2520,8 +2599,8 @@ inline void MaxPool(const uint8* input_data, const RuntimeShape& input_shape, input_data[Offset(input_shape, batch, in_y, in_x, channel)]); } } - max = std::max(max, output_activation_min); - max = std::min(max, output_activation_max); + max = std::max(max, params.quantized_activation_min); + max = std::min(max, params.quantized_activation_max); output_data[Offset(output_shape, batch, out_y, out_x, channel)] = static_cast(max); } @@ -3205,7 +3284,8 @@ inline void SpaceToBatchND(const T* input_data, const Dims<4>& input_dims, const Dims<4>& block_shape_dims, const int32* paddings_data, const Dims<4>& paddings_dims, T* output_data, - const Dims<4>& output_dims) { + const Dims<4>& output_dims, + const int32_t pad_value) { const int output_batch_size = ArraySize(output_dims, 3); const int output_height = ArraySize(output_dims, 2); const int output_width = ArraySize(output_dims, 1); @@ -3230,7 +3310,7 @@ inline void SpaceToBatchND(const T* input_data, const Dims<4>& input_dims, padding_top + input_height || out_w * block_shape_width + shift_w < padding_left || out_w * block_shape_width + shift_w >= padding_left + input_width) { - memset(out, 0, depth * sizeof(T)); + memset(out, pad_value, depth * sizeof(T)); } else { const T* in = input_data + @@ -3245,6 +3325,17 @@ inline void SpaceToBatchND(const T* input_data, const Dims<4>& input_dims, } } +template +inline void SpaceToBatchND(const T* input_data, const Dims<4>& input_dims, + const int32* block_shape_data, + const Dims<4>& block_shape_dims, + const int32* paddings_data, + const Dims<4>& paddings_dims, T* output_data, + const Dims<4>& output_dims) { + SpaceToBatchND(input_data, input_dims, block_shape_data, block_shape_dims, + paddings_data, paddings_dims, output_data, output_dims, 0); +} + template inline void BatchToSpaceND(const T* input_data, const Dims<4>& input_dims, const int32* block_shape_data, @@ -3455,9 +3546,9 @@ inline bool Reduce(const In* input_data, const int* input_dims, const int* output_dims, const int input_num_dims, const int output_num_dims, const int* axis, const int num_axis, int* input_iter, - Out reducer(Out current, const In in), Out* output_data) { + Out reducer(const Out current, const In in), + Out* output_data) { // Reset input iterator. - TFLITE_DCHECK(input_num_dims > 0); for (int idx = 0; idx < input_num_dims; ++idx) { input_iter[idx] = 0; } @@ -3473,11 +3564,16 @@ inline bool Reduce(const In* input_data, const int* input_dims, return true; } -inline bool ResolveAxis(const int num_dims, const int* axis, const int num_axis, - int* out_axis, int* out_num_axis) { +inline bool ResolveAxis(const int num_dims, const int* axis, + const int64_t num_axis, int* out_axis, + int* out_num_axis) { *out_num_axis = 0; // Just in case. + // Short-circuit axis resolution for scalars; the axis will go unused. + if (num_dims == 0) { + return true; + } // o(n^2) is fine since out_num_axis should be really small, mostly <= 4 - for (int idx = 0; idx < num_axis; ++idx) { + for (int64_t idx = 0; idx < num_axis; ++idx) { // Handle negative index. int current = axis[idx] < 0 ? (axis[idx] + num_dims) : axis[idx]; TFLITE_DCHECK(current >= 0 && current < num_dims); @@ -3503,7 +3599,7 @@ inline bool ReduceSumImpl(const In* input_data, const int* input_dims, const int output_num_dims, const int* axis, const int num_axis, int* input_iter, Out* output_data) { - auto reducer = [](Out current, const In in) -> Out { + auto reducer = [](const Out current, const In in) -> Out { const Out actual_in = static_cast(in); return current + actual_in; }; @@ -3512,6 +3608,24 @@ inline bool ReduceSumImpl(const In* input_data, const int* input_dims, output_data); } +template +inline bool InitTensorDataForReduce(const int* dims, const int num_dims, + const T init_value, T* data) { + size_t num_elements = 1; + for (int idx = 0; idx < num_dims; ++idx) { + size_t current = static_cast(dims[idx]); + // Overflow prevention. + if (num_elements > std::numeric_limits::max() / current) { + return false; + } + num_elements *= current; + } + for (size_t idx = 0; idx < num_elements; ++idx) { + data[idx] = init_value; + } + return true; +} + // Computes the sum of elements across dimensions given in axis. template inline bool Sum(const T* input_data, const int* input_dims, @@ -3520,17 +3634,9 @@ inline bool Sum(const T* input_data, const int* input_dims, const int* axis, const int num_axis_dimensions, bool keep_dims, int* temp_index, int* resolved_axis) { // Reset output data. - size_t num_outputs = 1; - for (int idx = 0; idx < output_num_dims; ++idx) { - size_t current = static_cast(output_dims[idx]); - // Overflow prevention. - if (num_outputs > std::numeric_limits::max() / current) { - return false; - } - num_outputs *= current; - } - for (size_t idx = 0; idx < num_outputs; ++idx) { - output_data[idx] = T(); + if (!InitTensorDataForReduce(output_dims, output_num_dims, static_cast(0), + output_data)) { + return false; } // Resolve axis. @@ -3545,6 +3651,61 @@ inline bool Sum(const T* input_data, const int* input_dims, num_resolved_axis, temp_index, output_data); } +// Computes the max of elements across dimensions given in axis. +template +inline bool ReduceMax(const T* input_data, const int* input_dims, + const int input_num_dims, T* output_data, + const int* output_dims, const int output_num_dims, + const int* axis, const int64_t num_axis_dimensions, + bool keep_dims, int* temp_index, int* resolved_axis) { + T init_value = std::numeric_limits::lowest(); + // Reset output data. + if (!InitTensorDataForReduce(output_dims, output_num_dims, init_value, + output_data)) { + return false; + } + + // Resolve axis. + int num_resolved_axis = 0; + if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, + &num_resolved_axis)) { + return false; + } + + auto reducer = [](const T current, const T in) -> T { + return (in > current) ? in : current; + }; + return Reduce(input_data, input_dims, output_dims, input_num_dims, + output_num_dims, resolved_axis, num_resolved_axis, + temp_index, reducer, output_data); +} + +// Computes the prod of elements across dimensions given in axis. +template +inline bool ReduceProd(const T* input_data, const int* input_dims, + const int input_num_dims, T* output_data, + const int* output_dims, const int output_num_dims, + const int* axis, const int64_t num_axis_dimensions, + bool keep_dims, int* temp_index, int* resolved_axis) { + // Reset output data. + if (!InitTensorDataForReduce(output_dims, output_num_dims, static_cast(1), + output_data)) { + return false; + } + + // Resolve axis. + int num_resolved_axis = 0; + if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, + &num_resolved_axis)) { + return false; + } + + auto reducer = [](const T current, const T in) -> T { return in * current; }; + return Reduce(input_data, input_dims, output_dims, input_num_dims, + output_num_dims, resolved_axis, num_resolved_axis, + temp_index, reducer, output_data); +} + // Computes the mean of elements across dimensions given in axis. // It does so in two stages, first calculates the sum of elements along the axis // then divides it by the number of element in axis. @@ -3636,38 +3797,6 @@ inline void Mean(const T* input_data, const Dims<4>& input_dims, } } -template -void Sub(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, - const Dims<4>& input2_dims, T* output_data, - const Dims<4>& output_dims) { - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - output_data[Offset(output_dims, c, x, y, b)] = - input1_data[SubscriptToIndex(desc1, c, x, y, b)] - - input2_data[SubscriptToIndex(desc2, c, x, y, b)]; - } - } - } - } -} - template void TensorFlowMinimum(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, T* output_data, @@ -3717,9 +3846,9 @@ void TensorFlowMaximumMinimum(const T* input1_data, const Dims<4>& input1_dims, } } -template -void ArgMax(const T3* axis, const T1* input_data, const Dims<4>& input_dims, - T2* output_data, const Dims<4>& output_dims) { +template +void ArgMinMax(const T3* axis, const T1* input_data, const Dims<4>& input_dims, + T2* output_data, const Dims<4>& output_dims, const Cmp& cmp) { // The current ArgMax implemention can only determine the index of the maximum // value in the last dimension. So the axis argument is ignored. @@ -3732,19 +3861,28 @@ void ArgMax(const T3* axis, const T1* input_data, const Dims<4>& input_dims, const int depth = ArraySize(input_dims, 0); for (int i = 0; i < outer_size; ++i) { - auto max_value = input_data[i * depth]; - int max_index = 0; + auto min_max_value = input_data[i * depth]; + int min_max_index = 0; for (int d = 1; d < depth; ++d) { const auto& curr_value = input_data[i * depth + d]; - if (curr_value > max_value) { - max_value = curr_value; - max_index = d; + if (cmp(curr_value, min_max_value)) { + min_max_value = curr_value; + min_max_index = d; } } - output_data[i] = max_index; + output_data[i] = min_max_index; } } +// TODO(renjieliu): Remove this one. +template +void ArgMax(const T3* axis, const T1* input_data, + const tflite::Dims<4>& input_dims, T2* output_data, + const tflite::Dims<4>& output_dims) { + ArgMinMax(axis, input_data, input_dims, output_data, output_dims, + std::greater()); +} + template void Transpose(const T* input, const Dims<4>& input_dims, T* output, const Dims<4>& output_dims, const int* permuted_axes) { @@ -4117,6 +4255,38 @@ inline void BroadcastPow(const T* input1_data, const Dims<4>& input1_dims, } } +inline void Logical(const bool* input1_data, const Dims<4>& input1_dims, + const bool* input2_data, const Dims<4>& input2_dims, + bool* output_data, const Dims<4>& output_dims, + const std::function& func) { + const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = func(input1_data[i], input2_data[i]); + } +} + +inline void BroadcastLogical(const bool* input1_data, + const Dims<4>& input1_dims, + const bool* input2_data, + const Dims<4>& input2_dims, bool* output_data, + const Dims<4>& output_dims, + const std::function& func) { + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); + for (int b = 0; b < ArraySize(output_dims, 3); ++b) { + for (int y = 0; y < ArraySize(output_dims, 2); ++y) { + for (int x = 0; x < ArraySize(output_dims, 1); ++x) { + for (int c = 0; c < ArraySize(output_dims, 0); ++c) { + output_data[Offset(output_dims, c, x, y, b)] = + func(input1_data[SubscriptToIndex(desc1, c, x, y, b)], + input2_data[SubscriptToIndex(desc2, c, x, y, b)]); + } + } + } + } +} + } // namespace reference_ops } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/spectrogram.cc b/tensorflow/contrib/lite/kernels/internal/spectrogram.cc index 4eddf7bf0a2cbca695dae20ba8ba56a9cd72e4ba..20abcb725859d03f83c969369bddf1429895e0ba 100644 --- a/tensorflow/contrib/lite/kernels/internal/spectrogram.cc +++ b/tensorflow/contrib/lite/kernels/internal/spectrogram.cc @@ -43,13 +43,13 @@ bool Spectrogram::Initialize(int window_length, int step_length) { return Initialize(window, step_length); } -inline int Log2Floor(uint n) { +inline int Log2Floor(uint32_t n) { if (n == 0) return -1; int log = 0; - uint value = n; + uint32_t value = n; for (int i = 4; i >= 0; --i) { int shift = (1 << i); - uint x = value >> shift; + uint32_t x = value >> shift; if (x != 0) { value = x; log += shift; @@ -58,7 +58,7 @@ inline int Log2Floor(uint n) { return log; } -inline int Log2Ceiling(uint n) { +inline int Log2Ceiling(uint32_t n) { int floor = Log2Floor(n); if (n == (n & ~(n - 1))) // zero or a power of two return floor; @@ -66,7 +66,7 @@ inline int Log2Ceiling(uint n) { return floor + 1; } -inline uint NextPowerOfTwo(uint value) { +inline uint32_t NextPowerOfTwo(uint32_t value) { int exponent = Log2Ceiling(value); // DCHECK_LT(exponent, std::numeric_limits::digits); return 1 << exponent; diff --git a/tensorflow/contrib/lite/kernels/internal/tensor_utils.h b/tensorflow/contrib/lite/kernels/internal/tensor_utils.h index 5160e22307ae0894fabd0e9c4f7b9cd38b00840e..1ff8cfe39c9aed7e9241815dc8eff7ab4d9fd585 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/tensor_utils.h @@ -17,6 +17,10 @@ limitations under the License. #include "tensorflow/contrib/lite/builtin_op_data.h" +#if defined(_MSC_VER) +#define __restrict__ __restrict +#endif + namespace tflite { namespace tensor_utils { @@ -31,8 +35,8 @@ bool IsZeroVector(const float* vector, int v_size); // It also outputs the range (min, max) of the floating point buffer, and the // scaling factor used to quantize the values. void SymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float* min, float* max, - float* scaling_factor); + int8_t* quantized_values, float* min_value, + float* max_value, float* scaling_factor); // Multiplies a matrix by a "batched" vector (i.e. a matrix with a batch // dimension composed by input vectors independent from each other). The result @@ -124,6 +128,10 @@ void Sub1Vector(const float* vector, int v_size, float* result); // Fill vector with 0.f. void ZeroVector(float* vector, int v_size); +// Multiply all elements of vector with a scalar. +void VectorScalarMultiply(const int8_t* vector, int v_size, float scale, + float* result); + // Clip elements of a vector using a abs_limit value. void ClipVector(const float* vector, int v_size, float abs_limit, float* result); diff --git a/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc b/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc index aa0d49ae4db6b4952b5864166f4a13459763cf44..372a6efec5c69e53d558edf8c822f638a4d33d81 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc @@ -32,6 +32,22 @@ TEST(uKernels, ClipTest) { {0.0, -0.5, 1.0, -1.5, 2.0, -2.0, 2.0, -2.0, 2.0, -2.0}))); } +TEST(uKernels, VectorScalarMultiply) { + constexpr int kVectorSize = 29; + static int8_t input[kVectorSize]; + for (int i = 0; i < 29; ++i) { + input[i] = static_cast(i - 14); + } + const float scale = 0.1f; + std::vector output(kVectorSize, 0.0f); + VectorScalarMultiply(input, kVectorSize, scale, output.data()); + EXPECT_THAT(output, + ElementsAreArray(ArrayFloatNear( + {-1.4, -1.3, -1.2, -1.1, -1.0, -0.9, -0.8, -0.7, -0.6, -0.5, + -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5, + 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4}))); +} + TEST(uKernels, IsZeroTest) { constexpr int kVectorSize = 21; static float zeros[kVectorSize] = {0.0}; diff --git a/tensorflow/contrib/lite/kernels/internal/types.h b/tensorflow/contrib/lite/kernels/internal/types.h index fa2420713fea4faa3596251a95c2ed9606878b98..c44698b677a862bc41c947ea46fe204710b79668 100644 --- a/tensorflow/contrib/lite/kernels/internal/types.h +++ b/tensorflow/contrib/lite/kernels/internal/types.h @@ -23,7 +23,12 @@ limitations under the License. namespace tflite { enum class FusedActivationFunctionType : uint8 { kNone, kRelu6, kRelu1, kRelu }; -enum class PaddingType { kNone, kSame, kValid }; +enum class PaddingType : uint8 { kNone, kSame, kValid }; + +struct PaddingValues { + int8 width; + int8 height; +}; // This enumeration allows for non-default formats for the weights array // of a fully-connected operator, allowing the use of special optimized @@ -114,6 +119,8 @@ class RuntimeShape { // larger shapes are separately allocated. static constexpr int kMaxSmallSize = 4; + RuntimeShape& operator=(RuntimeShape const&) = delete; + RuntimeShape() : size_(0) {} explicit RuntimeShape(int dimensions_count) : size_(dimensions_count) { @@ -130,6 +137,20 @@ class RuntimeShape { BuildFrom(init_list); } + // Avoid using this constructor. We should be able to delete it when C++17 + // rolls out. + RuntimeShape(RuntimeShape const& other) : size_(other.DimensionsCount()) { + if (size_ > kMaxSmallSize) { + dims_pointer_ = new int32[size_]; + } + std::memcpy(DimsData(), other.DimsData(), sizeof(int32) * size_); + } + + bool operator==(const RuntimeShape& comp) const { + return this->size_ == comp.size_ && + std::memcmp(DimsData(), comp.DimsData(), size_ * sizeof(int32)) == 0; + } + ~RuntimeShape() { if (size_ > kMaxSmallSize) { delete[] dims_pointer_; @@ -186,6 +207,16 @@ class RuntimeShape { } } + // This will probably be factored out. Old code made substantial use of 4-D + // shapes, and so this function is used to extend smaller shapes. Note that + // (a) as Dims<4>-dependent code is eliminated, the reliance on this should be + // reduced, and (b) some kernels are stricly 4-D, but then the shapes of their + // inputs should already be 4-D, so this function should not be needed. + inline static RuntimeShape ExtendedShape(int new_shape_size, + const RuntimeShape& shape) { + return RuntimeShape(new_shape_size, shape, 1); + } + inline void BuildFrom(const std::initializer_list init_list) { BuildFrom>(init_list); } @@ -203,7 +234,25 @@ class RuntimeShape { return buffer_size; } + bool operator!=(const RuntimeShape& comp) const { return !((*this) == comp); } + private: + // For use only by ExtendFrom(), written to guarantee (return-value) copy + // elision in C++17. + // This creates a shape padded to the desired size with the specified value. + RuntimeShape(int new_shape_size, const RuntimeShape& shape, int pad_value) + : size_(0) { + TFLITE_CHECK_GE(new_shape_size, shape.DimensionsCount()); + TFLITE_CHECK_LE(new_shape_size, kMaxSmallSize); + Resize(new_shape_size); + const int size_increase = new_shape_size - shape.DimensionsCount(); + for (int i = 0; i < size_increase; ++i) { + SetDim(i, pad_value); + } + std::memcpy(DimsData() + size_increase, shape.DimsData(), + sizeof(int32) * shape.DimensionsCount()); + } + int32 size_; union { int32 dims_[kMaxSmallSize]; @@ -229,7 +278,9 @@ inline tflite::Dims<4> ToRuntimeDims(const tflite::RuntimeShape& array_shape) { // Gets next index to iterate through a multidimensional array. inline bool NextIndex(const int num_dims, const int* dims, int* current) { - TFLITE_DCHECK_GT(num_dims, 0); + if (num_dims == 0) { + return false; + } TFLITE_DCHECK(dims != nullptr); TFLITE_DCHECK(current != nullptr); int carry = 1; @@ -256,7 +307,9 @@ inline bool NextIndex(const int num_dims, const int* dims, int* current) { inline size_t ReducedOutputOffset(const int num_dims, const int* dims, const int* index, const int num_axis, const int* axis) { - TFLITE_DCHECK_GT(num_dims, 0); + if (num_dims == 0) { + return 0; + } TFLITE_DCHECK(dims != nullptr); TFLITE_DCHECK(index != nullptr); size_t offset = 0; @@ -359,6 +412,7 @@ inline int RequiredBufferSizeForDims(const Dims<4>& dims) { // arrays. inline int MatchingFlatSize(const RuntimeShape& shape, const RuntimeShape& check_shape_0) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); const int dims_count = shape.DimensionsCount(); for (int i = 0; i < dims_count; ++i) { TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); @@ -369,6 +423,7 @@ inline int MatchingFlatSize(const RuntimeShape& shape, inline int MatchingFlatSize(const RuntimeShape& shape, const RuntimeShape& check_shape_0, const RuntimeShape& check_shape_1) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); const int dims_count = shape.DimensionsCount(); for (int i = 0; i < dims_count; ++i) { TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); @@ -380,6 +435,7 @@ inline int MatchingFlatSize(const RuntimeShape& shape, const RuntimeShape& check_shape_0, const RuntimeShape& check_shape_1, const RuntimeShape& check_shape_2) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); const int dims_count = shape.DimensionsCount(); for (int i = 0; i < dims_count; ++i) { TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); @@ -392,6 +448,7 @@ inline int MatchingFlatSize(const RuntimeShape& shape, const RuntimeShape& check_shape_1, const RuntimeShape& check_shape_2, const RuntimeShape& check_shape_3) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); const int dims_count = shape.DimensionsCount(); for (int i = 0; i < dims_count; ++i) { TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); @@ -588,6 +645,82 @@ void ComputeStrides(Dims* dims) { } } +struct PoolParams { + FusedActivationFunctionType activation; + PaddingType padding_type; + PaddingValues padding_values; + int stride_height; + int stride_width; + int filter_height; + int filter_width; + // uint8, etc, activation params. + int32 quantized_activation_min; + int32 quantized_activation_max; + // float activation params. + float float_activation_min; + float float_activation_max; +}; + +enum class BroadcastableOpCategory : uint8 { + kNone, + kNonBroadcast, // Matching input shapes. + kFirstInputBroadcastsFast, // Fivefold nested loops. + kSecondInputBroadcastsFast, // Fivefold nested loops. + kGenericBroadcast, // Fall-back. +}; + +// For Add, Sub, Mul ops. +struct ArithmeticParams { + // Shape dependent / common to data / op types. + BroadcastableOpCategory broadcast_category; + // uint8 inference params. + int32 input1_offset; + int32 input2_offset; + int32 output_offset; + int32 output_multiplier; + int output_shift; + // Add / Sub, not Mul, uint8 inference params. + int left_shift; + int32 input1_multiplier; + int input1_shift; + int32 input2_multiplier; + int input2_shift; + // uint8, etc, activation params. + int32 quantized_activation_min; + int32 quantized_activation_max; + // float activation params. + float float_activation_min; + float float_activation_max; + + // Processed output dimensions. + // Let input "a" be the one that broadcasts in the faster-changing dimension. + // Then, after coalescing, for shapes {a0, a1, a2, a3, a4} and + // {b0, b1, b2, b3, b4}, + // broadcast_shape[4] = b0 = a0. + // broadcast_shape[3] = b1; a1 = 1. + // broadcast_shape[2] = b2 = a2. + // broadcast_shape[1] = a3; b3 = 1. + // broadcast_shape[0] = b4 = a4. + int broadcast_shape[5]; +}; + +template +inline void SetActivationParams(T min, T max, ArithmeticParams* params); + +template <> +inline void SetActivationParams(float min, float max, + ArithmeticParams* params) { + params->float_activation_min = min; + params->float_activation_max = max; +} + +template <> +inline void SetActivationParams(int32 min, int32 max, + ArithmeticParams* params) { + params->quantized_activation_min = min; + params->quantized_activation_max = max; +} + } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_TYPES_H_ diff --git a/tensorflow/contrib/lite/kernels/logical.cc b/tensorflow/contrib/lite/kernels/logical.cc new file mode 100644 index 0000000000000000000000000000000000000000..3dc39bf79a1c054c4d1c82b51a74a21051b58838 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/logical.cc @@ -0,0 +1,121 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace logical { +namespace { + +// Input/output tensor index. +constexpr int kInputTensor1 = 0; +constexpr int kInputTensor2 = 1; +constexpr int kOutputTensor = 0; + +// Op data for logical op. +struct OpData { + bool requires_broadcast; +}; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + auto* data = new OpData; + data->requires_broadcast = false; + return data; +} + +void Free(TfLiteContext* context, void* buffer) { + delete reinterpret_cast(buffer); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + // Reinterprete the opaque data provided by user. + OpData* data = reinterpret_cast(node->user_data); + + const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); + const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + + TF_LITE_ENSURE_EQ(context, input1->type, input2->type); + + const TfLiteType type = input1->type; + if (type != kTfLiteBool) { + context->ReportError(context, "Logical ops only support bool type."); + return kTfLiteError; + } + output->type = type; + + data->requires_broadcast = !HaveSameShapes(input1, input2); + + TfLiteIntArray* output_size = nullptr; + if (data->requires_broadcast) { + TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast( + context, input1, input2, &output_size)); + } else { + output_size = TfLiteIntArrayCopy(input1->dims); + } + + return context->ResizeTensor(context, output, output_size); +} + +TfLiteStatus LogicalImpl(TfLiteContext* context, TfLiteNode* node, + const std::function& func) { + OpData* data = reinterpret_cast(node->user_data); + + const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); + const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + + if (data->requires_broadcast) { + reference_ops::BroadcastLogical( + GetTensorData(input1), GetTensorDims(input1), + GetTensorData(input2), GetTensorDims(input2), + GetTensorData(output), GetTensorDims(output), func); + } else { + reference_ops::Logical(GetTensorData(input1), GetTensorDims(input1), + GetTensorData(input2), GetTensorDims(input2), + GetTensorData(output), GetTensorDims(output), + func); + } + + return kTfLiteOk; +} + +TfLiteStatus LogicalOrEval(TfLiteContext* context, TfLiteNode* node) { + const auto logical_or_func = std::logical_or(); + return LogicalImpl(context, node, logical_or_func); +} + +} // namespace +} // namespace logical + +TfLiteRegistration* Register_LOGICAL_OR() { + // Init, Free, Prepare, Eval are satisfying the Interface required by + // TfLiteRegistration. + static TfLiteRegistration r = {logical::Init, logical::Free, logical::Prepare, + logical::LogicalOrEval}; + return &r; +} + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/logical_test.cc b/tensorflow/contrib/lite/kernels/logical_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..382008245bf0b0e39218e16228b67ae389ac6add --- /dev/null +++ b/tensorflow/contrib/lite/kernels/logical_test.cc @@ -0,0 +1,87 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAre; + +class LogicalOpModel : public SingleOpModel { + public: + LogicalOpModel(std::initializer_list input1_shape, + std::initializer_list input2_shape, BuiltinOperator op) { + input1_ = AddInput(TensorType_BOOL); + input2_ = AddInput(TensorType_BOOL); + output_ = AddOutput(TensorType_BOOL); + ConfigureBuiltinOp(op); + BuildInterpreter({input1_shape, input2_shape}); + } + + int input1() { return input1_; } + int input2() { return input2_; } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input1_; + int input2_; + int output_; + + void ConfigureBuiltinOp(BuiltinOperator op) { + switch (op) { + case BuiltinOperator_LOGICAL_OR: { + SetBuiltinOp(op, BuiltinOptions_LogicalOrOptions, + CreateLogicalOrOptions(builder_).Union()); + break; + } + default: { FAIL() << "We shouldn't get here."; } + } + } +}; + +TEST(LogicalTest, LogicalOr) { + LogicalOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, BuiltinOperator_LOGICAL_OR); + model.PopulateTensor(model.input1(), {true, false, false, true}); + model.PopulateTensor(model.input2(), {true, false, true, false}); + model.Invoke(); + + EXPECT_THAT(model.GetOutput(), ElementsAre(true, false, true, true)); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4)); +} + +TEST(LogicalTest, BroadcastLogicalOr) { + LogicalOpModel model({1, 1, 1, 4}, {1, 1, 1, 1}, BuiltinOperator_LOGICAL_OR); + model.PopulateTensor(model.input1(), {true, false, false, true}); + model.PopulateTensor(model.input2(), {false}); + model.Invoke(); + + EXPECT_THAT(model.GetOutput(), ElementsAre(true, false, false, true)); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4)); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/lsh_projection.cc b/tensorflow/contrib/lite/kernels/lsh_projection.cc index 25d2dc2cdd699b4d9c8e83eb848fce0df3c59c15..69523b02cce0547fe87873e924deabb50cbeb4e5 100644 --- a/tensorflow/contrib/lite/kernels/lsh_projection.cc +++ b/tensorflow/contrib/lite/kernels/lsh_projection.cc @@ -50,7 +50,6 @@ limitations under the License. // Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] } // A flattened tensor represents projected bit vectors. -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/lstm.cc b/tensorflow/contrib/lite/kernels/lstm.cc index 3577ae6caa1e02ce2e5db2e8054ba9c2fccbe93e..ba251c451e549a09d265fc43fed7dc7eb6896d61 100644 --- a/tensorflow/contrib/lite/kernels/lstm.cc +++ b/tensorflow/contrib/lite/kernels/lstm.cc @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include @@ -97,7 +96,7 @@ constexpr int kCellStateTensor = 1; constexpr int kOutputTensor = 2; void* Init(TfLiteContext* context, const char* buffer, size_t length) { - auto* op_data = new OpData; + auto* op_data = new OpData(); op_data->kernel_type = kTfLiteLSTMFullKernel; context->AddTensors(context, /*tensors_to_add=*/7, &op_data->scratch_tensor_index); @@ -306,7 +305,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { const int n_output = recurrent_to_output_weights->dims->data[1]; // Check that input tensor dimensions matches with each other. - CheckInputTensorDimensions(context, node, n_input, n_output, n_cell); + TF_LITE_ENSURE_OK(context, CheckInputTensorDimensions(context, node, n_input, + n_output, n_cell)); // Get the pointer to output, activation_state and cell_state tensors. TfLiteTensor* output = GetOutput(context, node, kOutputTensor); @@ -846,7 +846,7 @@ enum OutputTensor { }; void* Init(TfLiteContext* context, const char* buffer, size_t length) { - auto* op_data = new OpData; + auto* op_data = new OpData(); op_data->kernel_type = kTfLiteLSTMBasicKernel; // `scratch_tensor_index` is unused in this kernel. op_data->scratch_tensor_index = -1; diff --git a/tensorflow/contrib/lite/kernels/lstm_test.cc b/tensorflow/contrib/lite/kernels/lstm_test.cc index 0b7c56133e3cbb3d85f75657b6141620a8019e61..0266f5fe57e6c60ea19ad5f8de05e879e7da9304 100644 --- a/tensorflow/contrib/lite/kernels/lstm_test.cc +++ b/tensorflow/contrib/lite/kernels/lstm_test.cc @@ -13,6 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ // Unit test for TFLite LSTM op. +// +// TODO(alanchiao): add unit test with invalid input dimensions for this and its +// variants. #include #include diff --git a/tensorflow/contrib/lite/kernels/mul.cc b/tensorflow/contrib/lite/kernels/mul.cc index 1f72f3a3c7af4f9e042c9b2ac09252fab5de1a4f..349f3e672611b76ba9eb0019bbd55a5881ed6535 100644 --- a/tensorflow/contrib/lite/kernels/mul.cc +++ b/tensorflow/contrib/lite/kernels/mul.cc @@ -100,29 +100,44 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { } template -void EvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLiteMulParams* params, const OpData* data, - const TfLiteTensor* input1, const TfLiteTensor* input2, - TfLiteTensor* output) { - float output_activation_min, output_activation_max; - CalculateActivationRange(params->activation, &output_activation_min, - &output_activation_max); -#define TF_LITE_MUL(type, opname) \ - type::opname(GetTensorData(input1), GetTensorDims(input1), \ - GetTensorData(input2), GetTensorDims(input2), \ - output_activation_min, output_activation_max, \ - GetTensorData(output), GetTensorDims(output)) - if (kernel_type == kReference) { - if (data->requires_broadcast) { - TF_LITE_MUL(reference_ops, BroadcastMul); +void EvalMul(TfLiteContext* context, TfLiteNode* node, TfLiteMulParams* params, + const OpData* data, const TfLiteTensor* input1, + const TfLiteTensor* input2, TfLiteTensor* output) { +#define TF_LITE_MUL(type, opname, data_type) \ + data_type output_activation_min, output_activation_max; \ + CalculateActivationRange(params->activation, &output_activation_min, \ + &output_activation_max); \ + type::opname(GetTensorData(input1), GetTensorDims(input1), \ + GetTensorData(input2), GetTensorDims(input2), \ + output_activation_min, output_activation_max, \ + GetTensorData(output), GetTensorDims(output)) + if (output->type == kTfLiteInt32) { + if (kernel_type == kReference) { + if (data->requires_broadcast) { + TF_LITE_MUL(reference_ops, BroadcastMul, int32_t); + } else { + TF_LITE_MUL(reference_ops, Mul, int32_t); + } } else { - TF_LITE_MUL(reference_ops, Mul); + if (data->requires_broadcast) { + TF_LITE_MUL(optimized_ops, BroadcastMul, int32_t); + } else { + TF_LITE_MUL(optimized_ops, Mul, int32_t); + } } - } else { - if (data->requires_broadcast) { - TF_LITE_MUL(optimized_ops, BroadcastMul); + } else if (output->type == kTfLiteFloat32) { + if (kernel_type == kReference) { + if (data->requires_broadcast) { + TF_LITE_MUL(reference_ops, BroadcastMul, float); + } else { + TF_LITE_MUL(reference_ops, Mul, float); + } } else { - TF_LITE_MUL(optimized_ops, Mul); + if (data->requires_broadcast) { + TF_LITE_MUL(optimized_ops, BroadcastMul, float); + } else { + TF_LITE_MUL(optimized_ops, Mul, float); + } } } #undef TF_LITE_MUL @@ -194,17 +209,17 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - if (output->type == kTfLiteFloat32) { - EvalFloat(context, node, params, data, input1, input2, output); + if (output->type == kTfLiteFloat32 || output->type == kTfLiteInt32) { + EvalMul(context, node, params, data, input1, input2, output); } else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt16) { TF_LITE_ENSURE_OK( context, EvalQuantized(context, node, params, data, input1, input2, output)); } else { - context->ReportError( - context, - "Mul only supports FLOAT32 and quantized UINT8 and INT16 now, got %d.", - output->type); + context->ReportError(context, + "Mul only supports FLOAT32, INT32 and quantized UINT8 " + "and INT16 now, got %d.", + output->type); return kTfLiteError; } diff --git a/tensorflow/contrib/lite/kernels/mul_test.cc b/tensorflow/contrib/lite/kernels/mul_test.cc index 43d56e50d2686ff2624f36a0c5d8e43279a572cc..2807550a6b07f3f9f1f1e3f72acc9882c76d166a 100644 --- a/tensorflow/contrib/lite/kernels/mul_test.cc +++ b/tensorflow/contrib/lite/kernels/mul_test.cc @@ -52,6 +52,13 @@ class FloatMulOpModel : public BaseMulOpModel { std::vector GetOutput() { return ExtractVector(output_); } }; +class IntegerMulOpModel : public BaseMulOpModel { + public: + using BaseMulOpModel::BaseMulOpModel; + + std::vector GetOutput() { return ExtractVector(output_); } +}; + // For quantized Mul, the error shouldn't exceed (2*step + step^2). // The param min=-1.0 & max=1.0 is used in the following tests. // The tolerance value is ~0.0157. @@ -133,6 +140,57 @@ TEST(FloatMulOpTest, WithBroadcast) { } } +TEST(IntegerMulOpTest, NoActivation) { + IntegerMulOpModel m({TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}}, + ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-20, 2, 7, 8}); + m.PopulateTensor(m.input2(), {1, 2, 3, 5}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({-20, 4, 21, 40})); +} + +TEST(IntegerMulOpTest, ActivationRELU_N1_TO_1) { + IntegerMulOpModel m({TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}}, + ActivationFunctionType_RELU_N1_TO_1); + m.PopulateTensor(m.input1(), {-20, 2, 7, 8}); + m.PopulateTensor(m.input2(), {1, 2, 3, 5}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1, 1, 1, 1})); +} + +TEST(IntegerMulOpTest, VariousInputShapes) { + std::vector> test_shapes = { + {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; + for (int i = 0; i < test_shapes.size(); ++i) { + IntegerMulOpModel m({TensorType_INT32, test_shapes[i]}, + {TensorType_INT32, test_shapes[i]}, + {TensorType_INT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-20, 2, 7, 8, 11, 20}); + m.PopulateTensor(m.input2(), {1, 2, 3, 5, 11, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({-20, 4, 21, 40, 121, 20})) + << "With shape number " << i; + } +} + +TEST(IntegerMulOpTest, WithBroadcast) { + std::vector> test_shapes = { + {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; + for (int i = 0; i < test_shapes.size(); ++i) { + IntegerMulOpModel m({TensorType_INT32, test_shapes[i]}, + {TensorType_INT32, {}}, // always a scalar + {TensorType_INT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-20, 2, 7, 8, 11, 20}); + m.PopulateTensor(m.input2(), {1}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({-20, 2, 7, 8, 11, 20}))) + << "With shape number " << i; + } +} + TEST(QuantizedMulOpTest, NoActivation) { QuantizedMulOpModel m({TensorType_UINT8, {1, 2, 2, 1}, -1.0, 1.0}, {TensorType_UINT8, {1, 2, 2, 1}, -1.0, 1.0}, diff --git a/tensorflow/contrib/lite/kernels/one_hot.cc b/tensorflow/contrib/lite/kernels/one_hot.cc new file mode 100644 index 0000000000000000000000000000000000000000..9ff3dca932d4284321b299cfc79571c43fce7155 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/one_hot.cc @@ -0,0 +1,199 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace one_hot { + +constexpr int kIndicesTensor = 0; +constexpr int kDepthTensor = 1; +constexpr int kOnValueTensor = 2; +constexpr int kOffValueTensor = 3; +constexpr int kOutputTensor = 0; + +// Convenience utility for destructuring a node into the appropriate tensors and +// data for the op. Note that this destructuring is quite cheap, so we can avoid +// allocating op-specific, persistent data on the heap. +struct OneHotContext { + OneHotContext(TfLiteContext* context, TfLiteNode* node) { + indices = GetInput(context, node, kIndicesTensor); + depth = GetInput(context, node, kDepthTensor); + on_value = GetInput(context, node, kOnValueTensor); + off_value = GetInput(context, node, kOffValueTensor); + output = GetOutput(context, node, kOutputTensor); + + const auto* params = + reinterpret_cast(node->builtin_data); + const int indices_dims = indices->dims->size; + axis = (params->axis == -1) ? indices_dims : params->axis; + output_dims = indices_dims + 1; + dtype = on_value->type; + } + + const TfLiteTensor* indices; + const TfLiteTensor* depth; + const TfLiteTensor* on_value; + const TfLiteTensor* off_value; + TfLiteTensor* output; + int axis; + int output_dims; + TfLiteType dtype; +}; + +template +void OneHotComputeImpl(const OneHotContext& op_context) { + // prefix_dim_size == # of elements before the axis + // depth == # of elements per axis + // suffix_dim_size == # of elements after the axis + int prefix_dim_size = 1; + for (int i = 0; i < op_context.axis; ++i) { + prefix_dim_size *= op_context.indices->dims->data[i]; + } + const int suffix_dim_size = NumElements(op_context.indices) / prefix_dim_size; + const int depth = *op_context.depth->data.i32; + + const T on_value = *GetTensorData(op_context.on_value); + const T off_value = *GetTensorData(op_context.off_value); + + // View the indices as a matrix of size: + // prefix_dim_size x suffix_dim_size + // View the output as a matrix of size: + // prefix_dim_size x depth x suffix_dim_size + // Then the output is: + // output(i, j, k) == (indices(i, k) == j) ? on : off + T* output = GetTensorData(op_context.output); + const TI* indices = GetTensorData(op_context.indices); + for (int i = 0; i < prefix_dim_size; ++i) { + for (int j = 0; j < depth; ++j) { + for (int k = 0; k < suffix_dim_size; ++k, ++output) { + *output = static_cast(indices[i * suffix_dim_size + k]) == j + ? on_value + : off_value; + } + } + } +} + +template +void OneHotCompute(const OneHotContext& op_context) { + if (op_context.indices->type == kTfLiteInt64) { + OneHotComputeImpl(op_context); + } else { + OneHotComputeImpl(op_context); + } +} + +TfLiteStatus ResizeOutputTensor(TfLiteContext* context, + const OneHotContext& op_context) { + TF_LITE_ENSURE(context, *op_context.depth->data.i32 >= 0); + TfLiteIntArray* output_size = TfLiteIntArrayCreate(op_context.output_dims); + for (int i = 0; i < op_context.output_dims; ++i) { + if (i < op_context.axis) { + output_size->data[i] = op_context.indices->dims->data[i]; + } else if (i == op_context.axis) { + output_size->data[i] = *op_context.depth->data.i32; + } else { + output_size->data[i] = op_context.indices->dims->data[i - 1]; + } + } + return context->ResizeTensor(context, op_context.output, output_size); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 4); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + OneHotContext op_context{context, node}; + switch (op_context.dtype) { + // TODO(b/111744875): Support uint8 and quantization. + case kTfLiteFloat32: + case kTfLiteInt16: + case kTfLiteInt32: + case kTfLiteInt64: + case kTfLiteBool: + op_context.output->type = op_context.dtype; + break; + default: + context->ReportError(context, "Unknown output data type: %d", + op_context.dtype); + return kTfLiteError; + } + + TF_LITE_ENSURE(context, op_context.indices->type == kTfLiteInt32 || + op_context.indices->type == kTfLiteInt64); + TF_LITE_ENSURE(context, op_context.axis >= 0 && + op_context.axis < op_context.output_dims); + TF_LITE_ENSURE_EQ(context, NumElements(op_context.depth), 1); + TF_LITE_ENSURE_EQ(context, NumElements(op_context.on_value), 1); + TF_LITE_ENSURE_EQ(context, NumElements(op_context.off_value), 1); + TF_LITE_ENSURE_EQ(context, op_context.on_value->type, op_context.dtype); + TF_LITE_ENSURE_EQ(context, op_context.off_value->type, op_context.dtype); + + if (!IsConstantTensor(op_context.depth)) { + SetTensorToDynamic(op_context.output); + return kTfLiteOk; + } + + return ResizeOutputTensor(context, op_context); +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + OneHotContext op_context{context, node}; + + if (IsDynamicTensor(op_context.output)) { + ResizeOutputTensor(context, op_context); + } + + switch (op_context.output->type) { + case kTfLiteFloat32: + OneHotCompute(op_context); + break; + case kTfLiteInt32: + OneHotCompute(op_context); + break; + case kTfLiteInt64: + OneHotCompute(op_context); + break; + case kTfLiteBool: + OneHotCompute(op_context); + break; + default: + return kTfLiteError; + } + + return kTfLiteOk; +} + +} // namespace one_hot + +TfLiteRegistration* Register_ONE_HOT() { + static TfLiteRegistration r = { + nullptr, + nullptr, + one_hot::Prepare, + one_hot::Eval, + }; + return &r; +} + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/one_hot_test.cc b/tensorflow/contrib/lite/kernels/one_hot_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..6b604ec7a7f86b333805d91a95cb5054f0257ae4 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/one_hot_test.cc @@ -0,0 +1,182 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include + +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +template +class OneHotOpModel : public SingleOpModel { + public: + OneHotOpModel(std::initializer_list input_shape, int depth_value, + TensorType dtype, int axis = -1, T on_value = 1, + T off_value = 0, TensorType indices_type = TensorType_INT32) { + indices_ = AddInput(indices_type); + int depth = AddInput(TensorType_INT32); + int on = AddInput(dtype); + int off = AddInput(dtype); + output_ = AddOutput(dtype); + SetBuiltinOp(BuiltinOperator_ONE_HOT, BuiltinOptions_OneHotOptions, + CreateOneHotOptions(builder_, axis).Union()); + BuildInterpreter({input_shape}); + + PopulateTensor(depth, {depth_value}); + PopulateTensor(on, {on_value}); + PopulateTensor(off, {off_value}); + } + + template + void SetIndices(std::initializer_list data) { + PopulateTensor(indices_, data); + } + + TfLiteStatus InvokeWithResult() { return interpreter_->Invoke(); } + + int32_t GetOutputSize() { return GetTensorSize(output_); } + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int indices_; + int output_; +}; + +TEST(OneHotOpTest, BasicFloat) { + const int depth = 3; + OneHotOpModel model({3}, depth, TensorType_FLOAT32); + model.SetIndices({0, 1, 2}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({3, 3})); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({1.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 1.f})); +} + +TEST(OneHotOpTest, BasicInt) { + const int depth = 3; + OneHotOpModel model({3}, depth, TensorType_INT32); + model.SetIndices({0, 1, 2}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({3, 3})); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0, 0, 0, 1, 0, 0, 0, 1})); +} + +TEST(OneHotOpTest, BasicBool) { + const int depth = 3; + OneHotOpModel model({3}, depth, TensorType_BOOL); + model.SetIndices({0, 1, 2}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({3, 3})); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({true, false, false, false, true, false, false, + false, true})); +} + +TEST(OneHotOpTest, SmallDepth) { + const int depth = 1; + OneHotOpModel model({3}, depth, TensorType_INT32); + model.SetIndices({0, 1, 2}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({3, 1})); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0, 0})); +} + +TEST(OneHotOpTest, BigDepth) { + const int depth = 4; + OneHotOpModel model({2}, depth, TensorType_INT32); + model.SetIndices({0, 1}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 4})); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0, 0, 0, 0, 1, 0, 0})); +} + +TEST(OneHotOpTest, OnOffValues) { + const int depth = 3; + const int axis = -1; + const int on = 5; + const int off = 0; + OneHotOpModel model({4}, depth, TensorType_INT32, axis, on, off); + model.SetIndices({0, 2, -1, 1}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({4, 3})); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0})); +} + +TEST(OneHotOpTest, ZeroAxis) { + const int depth = 3; + const int axis = 0; + const int on = 5; + const int off = 0; + OneHotOpModel model({4}, depth, TensorType_INT32, axis, on, off); + model.SetIndices({0, 2, -1, 1}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({3, 4})); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({5, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0})); +} + +TEST(OneHotOpTest, MultiDimensionalIndices) { + const int depth = 3; + const int axis = -1; + const float on = 2; + const float off = 0; + OneHotOpModel model({2, 2}, depth, TensorType_FLOAT32, axis, on, off); + model.SetIndices({0, 2, 1, -1}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 2, 3})); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({2, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0})); +} + +TEST(OneHotOpTest, Int64Indices) { + const int depth = 3; + const int axis = -1; + const int on = 1; + const int off = 0; + OneHotOpModel model({3}, depth, TensorType_INT32, axis, on, off, + TensorType_INT64); + std::initializer_list indices = {0, 1, 2}; + model.SetIndices(indices); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({3, 3})); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0, 0, 0, 1, 0, 0, 0, 1})); +} + +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/pack.cc b/tensorflow/contrib/lite/kernels/pack.cc new file mode 100644 index 0000000000000000000000000000000000000000..bb3416f6a6ca60250f137986e479e8f1085e2558 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/pack.cc @@ -0,0 +1,131 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace pack { +namespace { + +constexpr int kOutputTensor = 0; + +// Op data for pack op. +struct OpData { + int values_count; + int axis; +}; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + auto* data = new OpData; + data->axis = 0; + return data; +} + +void Free(TfLiteContext* context, void* buffer) { + delete reinterpret_cast(buffer); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + const OpData* data = reinterpret_cast(node->builtin_data); + + TF_LITE_ENSURE_EQ(context, NumInputs(node), data->values_count); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + const TfLiteTensor* input0 = GetInput(context, node, 0); + TF_LITE_ENSURE(context, NumDimensions(input0) < 4); + TF_LITE_ENSURE(context, NumDimensions(input0) >= data->axis); + // TODO(renjieliu): Support negative axis. + TF_LITE_ENSURE(context, data->axis >= 0); + if (input0->type != kTfLiteInt32 && input0->type != kTfLiteFloat32) { + context->ReportError(context, + "Currently pack only supports int32 and float32."); + return kTfLiteError; + } + // Make sure all inputs have the same shape and type. + for (int i = 1; i < data->values_count; ++i) { + const TfLiteTensor* input = GetInput(context, node, i); + TF_LITE_ENSURE(context, HaveSameShapes(input0, input)); + TF_LITE_ENSURE_EQ(context, input0->type, input->type); + } + + // Resize output. rank R will become rank R + 1 + const int dimension_size = NumDimensions(input0) + 1; + const TfLiteIntArray* input_shape = input0->dims; + TfLiteIntArray* output_shape = TfLiteIntArrayCreate(dimension_size); + int i = 0; + for (int index = 0; index < dimension_size; ++index) { + if (index == data->axis) { + output_shape->data[index] = data->values_count; + } else { + output_shape->data[index] = input_shape->data[i++]; + } + } + + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + TF_LITE_ENSURE_EQ(context, output->type, input0->type); + + return context->ResizeTensor(context, output, output_shape); +} + +template +void PackImpl(TfLiteContext* context, TfLiteNode* node, TfLiteTensor* output, + int values_count, int axis) { + VectorOfTensors all_inputs(*context, *node->inputs); + reference_ops::Pack(RemapDim(NumDimensions(output), axis), + all_inputs.data(), all_inputs.dims(), values_count, + GetTensorData(output), GetTensorDims(output)); +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + const OpData* data = reinterpret_cast(node->builtin_data); + + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + switch (output->type) { + case kTfLiteFloat32: { + PackImpl(context, node, output, data->values_count, data->axis); + break; + } + case kTfLiteInt32: { + PackImpl(context, node, output, data->values_count, data->axis); + break; + } + default: { + context->ReportError(context, + "Currently pack only supports int32 and float32."); + return kTfLiteError; + } + } + + return kTfLiteOk; +} + +} // namespace +} // namespace pack + +TfLiteRegistration* Register_PACK() { + static TfLiteRegistration r = {pack::Init, pack::Free, pack::Prepare, + pack::Eval}; + return &r; +} + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/pack_test.cc b/tensorflow/contrib/lite/kernels/pack_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..485a50ad3ac493fd02f619f7d7cbaf10d3a6aff0 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/pack_test.cc @@ -0,0 +1,120 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAre; +using ::testing::ElementsAreArray; + +template +class PackOpModel : public SingleOpModel { + public: + PackOpModel(const TensorData& input_template, int axis, int values_count) { + std::vector> all_input_shapes; + for (int i = 0; i < values_count; ++i) { + all_input_shapes.push_back(input_template.shape); + AddInput(input_template); + } + output_ = AddOutput({input_template.type, /*shape=*/{}, input_template.min, + input_template.max}); + SetBuiltinOp(BuiltinOperator_PACK, BuiltinOptions_PackOptions, + CreatePackOptions(builder_, values_count, axis).Union()); + BuildInterpreter(all_input_shapes); + } + + void SetInput(int index, std::initializer_list data) { + PopulateTensor(index, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int output_; +}; + +TEST(PackOpTest, FloatThreeInputs) { + PackOpModel model({TensorType_FLOAT32, {2}}, 0, 3); + model.SetInput(0, {1, 4}); + model.SetInput(1, {2, 5}); + model.SetInput(2, {3, 6}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 2)); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 4, 2, 5, 3, 6})); +} + +TEST(PackOpTest, FloatThreeInputsDifferentAxis) { + PackOpModel model({TensorType_FLOAT32, {2}}, 1, 3); + model.SetInput(0, {1, 4}); + model.SetInput(1, {2, 5}); + model.SetInput(2, {3, 6}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3)); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6})); +} + +TEST(PackOpTest, FloatMultilDimensions) { + PackOpModel model({TensorType_FLOAT32, {2, 3}}, 1, 2); + model.SetInput(0, {1, 2, 3, 4, 5, 6}); + model.SetInput(1, {7, 8, 9, 10, 11, 12}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 2, 3)); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12})); +} + +TEST(PackOpTest, IntThreeInputs) { + PackOpModel model({TensorType_INT32, {2}}, 0, 3); + model.SetInput(0, {1, 4}); + model.SetInput(1, {2, 5}); + model.SetInput(2, {3, 6}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 2)); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 4, 2, 5, 3, 6})); +} + +TEST(PackOpTest, IntThreeInputsDifferentAxis) { + PackOpModel model({TensorType_INT32, {2}}, 1, 3); + model.SetInput(0, {1, 4}); + model.SetInput(1, {2, 5}); + model.SetInput(2, {3, 6}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3)); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6})); +} + +TEST(PackOpTest, IntMultilDimensions) { + PackOpModel model({TensorType_INT32, {2, 3}}, 1, 2); + model.SetInput(0, {1, 2, 3, 4, 5, 6}); + model.SetInput(1, {7, 8, 9, 10, 11, 12}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 2, 3)); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12})); +} +} // namespace +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/kernels/pooling.cc b/tensorflow/contrib/lite/kernels/pooling.cc index 7240fe04ccdadfb7b9703c3f2775c4b3502bd1d9..29a5be068368365e67ad0653b775afe1e976f23a 100644 --- a/tensorflow/contrib/lite/kernels/pooling.cc +++ b/tensorflow/contrib/lite/kernels/pooling.cc @@ -12,7 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include @@ -126,13 +125,19 @@ void AverageEvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRange(params->activation, &activation_min, &activation_max); -#define TF_LITE_AVERAGE_POOL(type) \ - type::AveragePool(GetTensorData(input), GetTensorShape(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, \ - activation_min, activation_max, \ - GetTensorData(output), GetTensorShape(output)) +#define TF_LITE_AVERAGE_POOL(type) \ + tflite::PoolParams op_params; \ + op_params.stride_height = params->stride_height; \ + op_params.stride_width = params->stride_width; \ + op_params.filter_height = params->filter_height; \ + op_params.filter_width = params->filter_width; \ + op_params.padding_values.height = data->padding.height; \ + op_params.padding_values.width = data->padding.width; \ + op_params.float_activation_min = activation_min; \ + op_params.float_activation_max = activation_max; \ + type::AveragePool(op_params, GetTensorShape(input), \ + GetTensorData(input), GetTensorShape(output), \ + GetTensorData(output)) if (kernel_type == kReference) { TF_LITE_AVERAGE_POOL(reference_ops); } else { @@ -149,13 +154,19 @@ void AverageEvalQuantized(TfLiteContext* context, TfLiteNode* node, int32_t activation_max; CalculateActivationRangeUint8(params->activation, output, &activation_min, &activation_max); -#define TF_LITE_AVERAGE_POOL(type) \ - type::AveragePool(GetTensorData(input), GetTensorShape(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, \ - activation_min, activation_max, \ - GetTensorData(output), GetTensorShape(output)) +#define TF_LITE_AVERAGE_POOL(type) \ + tflite::PoolParams op_params; \ + op_params.stride_height = params->stride_height; \ + op_params.stride_width = params->stride_width; \ + op_params.filter_height = params->filter_height; \ + op_params.filter_width = params->filter_width; \ + op_params.padding_values.height = data->padding.height; \ + op_params.padding_values.width = data->padding.width; \ + op_params.quantized_activation_min = activation_min; \ + op_params.quantized_activation_max = activation_max; \ + type::AveragePool(op_params, GetTensorShape(input), \ + GetTensorData(input), GetTensorShape(output), \ + GetTensorData(output)) if (kernel_type == kReference) { TF_LITE_AVERAGE_POOL(reference_ops); } else { @@ -171,13 +182,18 @@ void MaxEvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRange(params->activation, &activation_min, &activation_max); -#define TF_LITE_MAX_POOL(type) \ - type::MaxPool(GetTensorData(input), GetTensorShape(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData(output), \ - GetTensorShape(output)) +#define TF_LITE_MAX_POOL(type) \ + tflite::PoolParams op_params; \ + op_params.stride_height = params->stride_height; \ + op_params.stride_width = params->stride_width; \ + op_params.filter_height = params->filter_height; \ + op_params.filter_width = params->filter_width; \ + op_params.padding_values.height = data->padding.height; \ + op_params.padding_values.width = data->padding.width; \ + op_params.float_activation_min = activation_min; \ + op_params.float_activation_max = activation_max; \ + type::MaxPool(op_params, GetTensorShape(input), GetTensorData(input), \ + GetTensorShape(output), GetTensorData(output)) if (kernel_type == kReference) { TF_LITE_MAX_POOL(reference_ops); } else { @@ -194,13 +210,19 @@ void MaxEvalQuantized(TfLiteContext* context, TfLiteNode* node, int32_t activation_max; CalculateActivationRangeUint8(params->activation, output, &activation_min, &activation_max); -#define TF_LITE_MAX_POOL(type) \ - type::MaxPool(GetTensorData(input), GetTensorShape(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData(output), \ - GetTensorShape(output)) +#define TF_LITE_MAX_POOL(type) \ + tflite::PoolParams op_params; \ + op_params.stride_height = params->stride_height; \ + op_params.stride_width = params->stride_width; \ + op_params.filter_height = params->filter_height; \ + op_params.filter_width = params->filter_width; \ + op_params.padding_values.height = data->padding.height; \ + op_params.padding_values.width = data->padding.width; \ + op_params.quantized_activation_min = activation_min; \ + op_params.quantized_activation_max = activation_max; \ + type::MaxPool(op_params, GetTensorShape(input), \ + GetTensorData(input), GetTensorShape(output), \ + GetTensorData(output)) if (kernel_type == kReference) { TF_LITE_MAX_POOL(reference_ops); } else { @@ -216,13 +238,18 @@ void L2EvalFloat(TfLiteContext* context, TfLiteNode* node, float activation_min, activation_max; CalculateActivationRange(params->activation, &activation_min, &activation_max); -#define TF_LITE_L2_POOL(type) \ - type::L2Pool(GetTensorData(input), GetTensorShape(input), \ - params->stride_width, params->stride_height, \ - data->padding.width, data->padding.height, \ - params->filter_width, params->filter_height, activation_min, \ - activation_max, GetTensorData(output), \ - GetTensorShape(output)) +#define TF_LITE_L2_POOL(type) \ + tflite::PoolParams op_params; \ + op_params.stride_height = params->stride_height; \ + op_params.stride_width = params->stride_width; \ + op_params.filter_height = params->filter_height; \ + op_params.filter_width = params->filter_width; \ + op_params.padding_values.height = data->padding.height; \ + op_params.padding_values.width = data->padding.width; \ + op_params.float_activation_min = activation_min; \ + op_params.float_activation_max = activation_max; \ + type::L2Pool(op_params, GetTensorShape(input), GetTensorData(input), \ + GetTensorShape(output), GetTensorData(output)) if (kernel_type == kReference) { TF_LITE_L2_POOL(reference_ops); } else { diff --git a/tensorflow/contrib/lite/kernels/pow_test.cc b/tensorflow/contrib/lite/kernels/pow_test.cc index 474d323bc3a1a0f224aa0575a5bbd35394aa2f53..74b3aef5bd39d8bdb6375f24bd00d793889deef8 100644 --- a/tensorflow/contrib/lite/kernels/pow_test.cc +++ b/tensorflow/contrib/lite/kernels/pow_test.cc @@ -50,22 +50,22 @@ class PowOpModel : public SingleOpModel { }; TEST(PowOpModel, Simple) { - PowOpModel model({TensorType_INT32, {1, 2, 2, 1}}, - {TensorType_INT32, {1, 2, 2, 1}}, - {TensorType_INT32, {}}); - model.PopulateTensor(model.input1(), {12, 2, 7, 8}); - model.PopulateTensor(model.input2(), {1, 2, 3, 1}); + PowOpModel model({TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {}}); + model.PopulateTensor(model.input1(), {12, 2, 7, 8}); + model.PopulateTensor(model.input2(), {1, 2, 3, 1}); model.Invoke(); EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1)); EXPECT_THAT(model.GetOutput(), ElementsAre(12, 4, 343, 8)); } TEST(PowOpModel, NegativeAndZeroValue) { - PowOpModel model({TensorType_INT32, {1, 2, 2, 1}}, - {TensorType_INT32, {1, 2, 2, 1}}, - {TensorType_INT32, {}}); - model.PopulateTensor(model.input1(), {0, 2, -7, 8}); - model.PopulateTensor(model.input2(), {1, 2, 3, 0}); + PowOpModel model({TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {}}); + model.PopulateTensor(model.input1(), {0, 2, -7, 8}); + model.PopulateTensor(model.input2(), {1, 2, 3, 0}); model.Invoke(); EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1)); EXPECT_THAT(model.GetOutput(), ElementsAre(0, 4, -343, 1)); @@ -98,10 +98,10 @@ TEST(PowOpModel, NegativeFloatTest) { } TEST(PowOpModel, BroadcastTest) { - PowOpModel model({TensorType_INT32, {1, 2, 2, 1}}, - {TensorType_INT32, {1}}, {TensorType_INT32, {}}); - model.PopulateTensor(model.input1(), {12, 2, 7, 8}); - model.PopulateTensor(model.input2(), {4}); + PowOpModel model({TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {1}}, {TensorType_INT32, {}}); + model.PopulateTensor(model.input1(), {12, 2, 7, 8}); + model.PopulateTensor(model.input2(), {4}); model.Invoke(); EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1)); EXPECT_THAT(model.GetOutput(), ElementsAre(20736, 16, 2401, 4096)); diff --git a/tensorflow/contrib/lite/kernels/reduce.cc b/tensorflow/contrib/lite/kernels/reduce.cc index 31c331a8c61ded203af9ff2ae127cb6f985e2932..e99f67c7258c555903069dff67a86a3703249c7c 100644 --- a/tensorflow/contrib/lite/kernels/reduce.cc +++ b/tensorflow/contrib/lite/kernels/reduce.cc @@ -78,6 +78,10 @@ TfLiteStatus ResizeOutputTensor(TfLiteContext* context, OpContext* op_context) { size_t num_axis = NumElements(op_context->axis); const TfLiteIntArray* input_dims = op_context->input->dims; int input_num_dims = NumDimensions(op_context->input); + if (input_num_dims == 0) { + return context->ResizeTensor(context, op_context->output, + TfLiteIntArrayCreate(0)); + } const int* axis = GetTensorData(op_context->axis); if (op_context->params->keep_dims) { TfLiteIntArray* output_dims = TfLiteIntArrayCreate(input_num_dims); @@ -315,6 +319,99 @@ TfLiteStatus EvalSum(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } +template +TfLiteStatus EvalProd(TfLiteContext* context, TfLiteNode* node) { + OpContext op_context(context, node); + int64_t num_axis = NumElements(op_context.axis); + TfLiteTensor* temp_index = GetTemporary(context, node, /*index=*/0); + TfLiteTensor* resolved_axis = GetTemporary(context, node, /*index=*/1); + // Resize the output tensor if the output tensor is dynamic. + if (IsDynamicTensor(op_context.output)) { + TF_LITE_ENSURE_OK(context, + ResizeTempAxis(context, &op_context, resolved_axis)); + TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); + } + +#define TF_LITE_PROD(kernel_type, data_type) \ + kernel_type::ReduceProd<>( \ + GetTensorData(op_context.input), \ + op_context.input->dims->data, op_context.input->dims->size, \ + GetTensorData(op_context.output), \ + op_context.output->dims->data, op_context.output->dims->size, \ + GetTensorData(op_context.axis), num_axis, \ + op_context.params->keep_dims, GetTensorData(temp_index), \ + GetTensorData(resolved_axis)) + + if (kernel_type == kReference) { + switch (op_context.input->type) { + case kTfLiteFloat32: + TF_LITE_ENSURE(context, TF_LITE_PROD(reference_ops, float)); + break; + case kTfLiteInt32: + TF_LITE_ENSURE(context, TF_LITE_PROD(reference_ops, int)); + break; + case kTfLiteInt64: + TF_LITE_ENSURE(context, TF_LITE_PROD(reference_ops, int64_t)); + break; + case kTfLiteUInt8: + // TODO(wangtz): uint8 reduce_prod is not yet supported. + default: + return kTfLiteError; + } + } +#undef TF_LITE_PROD + return kTfLiteOk; +} + +template +TfLiteStatus EvalMax(TfLiteContext* context, TfLiteNode* node) { + OpContext op_context(context, node); + int64_t num_axis = NumElements(op_context.axis); + TfLiteTensor* temp_index = GetTemporary(context, node, /*index=*/0); + TfLiteTensor* resolved_axis = GetTemporary(context, node, /*index=*/1); + // Resize the output tensor if the output tensor is dynamic. + if (IsDynamicTensor(op_context.output)) { + TF_LITE_ENSURE_OK(context, + ResizeTempAxis(context, &op_context, resolved_axis)); + TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); + } + +#define TF_LITE_MAX(kernel_type, data_type) \ + kernel_type::ReduceMax<>( \ + GetTensorData(op_context.input), \ + op_context.input->dims->data, op_context.input->dims->size, \ + GetTensorData(op_context.output), \ + op_context.output->dims->data, op_context.output->dims->size, \ + GetTensorData(op_context.axis), num_axis, \ + op_context.params->keep_dims, GetTensorData(temp_index), \ + GetTensorData(resolved_axis)) + + if (kernel_type == kReference) { + switch (op_context.input->type) { + case kTfLiteFloat32: + TF_LITE_ENSURE(context, TF_LITE_MAX(reference_ops, float)); + break; + case kTfLiteInt32: + TF_LITE_ENSURE(context, TF_LITE_MAX(reference_ops, int)); + break; + case kTfLiteInt64: + TF_LITE_ENSURE(context, TF_LITE_MAX(reference_ops, int64_t)); + break; + case kTfLiteUInt8: + TF_LITE_ENSURE_EQ(context, op_context.input->params.scale, + op_context.output->params.scale); + TF_LITE_ENSURE_EQ(context, op_context.input->params.zero_point, + op_context.output->params.zero_point); + TF_LITE_ENSURE(context, TF_LITE_MAX(reference_ops, uint8_t)); + break; + default: + return kTfLiteError; + } + } +#undef TF_LITE_MAX + return kTfLiteOk; +} + } // namespace reduce TfLiteRegistration* Register_MEAN_REF() { @@ -331,9 +428,27 @@ TfLiteRegistration* Register_SUM_REF() { return &r; } +TfLiteRegistration* Register_REDUCE_PROD_REF() { + static TfLiteRegistration r = {reduce::Init, reduce::Free, + reduce::PrepareSimple, + reduce::EvalProd}; + return &r; +} + +TfLiteRegistration* Register_REDUCE_MAX_REF() { + static TfLiteRegistration r = {reduce::Init, reduce::Free, + reduce::PrepareSimple, + reduce::EvalMax}; + return &r; +} + // TODO(kanlig): add optimized implementation of Mean. TfLiteRegistration* Register_MEAN() { return Register_MEAN_REF(); } TfLiteRegistration* Register_SUM() { return Register_SUM_REF(); } +TfLiteRegistration* Register_REDUCE_PROD() { + return Register_REDUCE_PROD_REF(); +} +TfLiteRegistration* Register_REDUCE_MAX() { return Register_REDUCE_MAX_REF(); } } // namespace builtin } // namespace ops diff --git a/tensorflow/contrib/lite/kernels/reduce_test.cc b/tensorflow/contrib/lite/kernels/reduce_test.cc index 9e946822c686f6f20505d60b6161239624c94696..5d432d34ef5118e7164d7f767dad6017aa640e51 100644 --- a/tensorflow/contrib/lite/kernels/reduce_test.cc +++ b/tensorflow/contrib/lite/kernels/reduce_test.cc @@ -22,13 +22,14 @@ namespace tflite { namespace { using ::testing::ElementsAreArray; +using ::testing::IsEmpty; class BaseOpModel : public SingleOpModel { public: - void SetAxis(std::initializer_list data) { PopulateTensor(axis_, data); } + void SetAxis(const std::vector& data) { PopulateTensor(axis_, data); } template - void SetInput(std::initializer_list data) { + void SetInput(std::vector data) { PopulateTensor(input_, data); } @@ -110,14 +111,72 @@ class SumOpDynamicModel : public BaseOpModel { } }; +// Model for the tests case where axis is a const tensor. +class ProdOpConstModel : public BaseOpModel { + public: + ProdOpConstModel(const TensorData& input, const TensorData& output, + std::initializer_list axis_shape, + std::initializer_list axis, bool keep_dims) { + input_ = AddInput(input); + axis_ = AddConstInput(TensorType_INT32, axis, axis_shape); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_REDUCE_PROD, BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder_, keep_dims).Union()); + BuildInterpreter({GetShape(input_)}); + } +}; + +// Model for the tests case where axis is a dynamic tensor. +class ProdOpDynamicModel : public BaseOpModel { + public: + ProdOpDynamicModel(const TensorData& input, const TensorData& output, + const TensorData& axis, bool keep_dims) { + input_ = AddInput(input); + axis_ = AddInput(axis); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_REDUCE_PROD, BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder_, keep_dims).Union()); + BuildInterpreter({GetShape(input_)}); + } +}; + +// Model for the tests case where axis is a const tensor. +class MaxOpConstModel : public BaseOpModel { + public: + MaxOpConstModel(const TensorData& input, const TensorData& output, + std::initializer_list axis_shape, + std::initializer_list axis, bool keep_dims) { + input_ = AddInput(input); + axis_ = AddConstInput(TensorType_INT32, axis, axis_shape); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_REDUCE_MAX, BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder_, keep_dims).Union()); + BuildInterpreter({GetShape(input_)}); + } +}; + +// Model for the tests case where axis is a dynamic tensor. +class MaxOpDynamicModel : public BaseOpModel { + public: + MaxOpDynamicModel(const TensorData& input, const TensorData& output, + const TensorData& axis, bool keep_dims) { + input_ = AddInput(input); + axis_ = AddInput(axis); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_REDUCE_MAX, BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder_, keep_dims).Union()); + BuildInterpreter({GetShape(input_)}); + } +}; + // for quantized Add, the error shouldn't exceed step float GetTolerance(int min, int max) { return (max - min) / 255.0; } // Tests for reduce_mean TEST(ConstFloatMeanOpTest, NotKeepDims) { - std::initializer_list data = { - 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, - 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; MeanOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}}, {4}, {1, 0, -3, -3}, false); m.SetInput(data); @@ -127,9 +186,9 @@ TEST(ConstFloatMeanOpTest, NotKeepDims) { } TEST(ConstFloatMeanOpTest, KeepDims) { - std::initializer_list data = { - 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, - 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; MeanOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}}, {2}, {0, 2}, true); m.SetInput(data); @@ -139,14 +198,24 @@ TEST(ConstFloatMeanOpTest, KeepDims) { ElementsAreArray(ArrayFloatNear({10.5, 12.5, 14.5}))); } +TEST(ConstFloatMeanOpTest, Scalar) { + std::vector data = {3.27}; + MeanOpConstModel m({TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}, {}, + {0}, true); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), IsEmpty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({3.27}))); +} + TEST(DynamicFloatMeanOpTest, NotKeepDims) { - std::initializer_list data = { - 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, - 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; MeanOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}}, {TensorType_INT32, {4}}, false); - std::initializer_list axis = {1, 0, -3, -3}; + std::vector axis = {1, 0, -3, -3}; m.SetAxis(axis); m.SetInput(data); m.Invoke(); @@ -155,13 +224,13 @@ TEST(DynamicFloatMeanOpTest, NotKeepDims) { } TEST(DynamicFloatMeanOpTest, KeepDims) { - std::initializer_list data = { - 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, - 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; MeanOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}}, {TensorType_INT32, {2}}, true); - std::initializer_list axis = {0, 2}; + std::vector axis = {0, 2}; m.SetAxis(axis); m.SetInput(data); m.Invoke(); @@ -171,10 +240,10 @@ TEST(DynamicFloatMeanOpTest, KeepDims) { } TEST(DynamicFloatMeanOpTest, Scale) { - std::initializer_list data = {9.527}; + std::vector data = {9.527}; MeanOpDynamicModel m({TensorType_FLOAT32, {1}}, {TensorType_FLOAT32, {1}}, {TensorType_INT32, {1}}, true); - std::initializer_list axis = {0}; + std::vector axis = {0}; m.SetAxis(axis); m.SetInput(data); m.Invoke(); @@ -185,7 +254,7 @@ TEST(DynamicFloatMeanOpTest, Scale) { TEST(ConstUint8MeanOpTest, NotKeepDims) { float kQuantizedTolerance = GetTolerance(-1.0, 1.0); - std::initializer_list data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; + std::vector data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; MeanOpConstModel m({TensorType_UINT8, {1, 3, 2}, -1.0, 1.0}, {TensorType_UINT8, {2}, -1.0, 1.0}, {1}, {1}, false); m.QuantizeAndPopulate(m.Input(), data); @@ -197,7 +266,7 @@ TEST(ConstUint8MeanOpTest, NotKeepDims) { TEST(ConstUint8MeanOpTest, KeepDims) { float kQuantizedTolerance = GetTolerance(-1.0, 1.0); - std::initializer_list data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; + std::vector data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; MeanOpConstModel m({TensorType_UINT8, {3, 2}, -1.0, 1.0}, {TensorType_UINT8, {3}, -1.0, 1.0}, {1}, {1}, true); m.QuantizeAndPopulate(m.Input(), data); @@ -210,11 +279,11 @@ TEST(ConstUint8MeanOpTest, KeepDims) { TEST(DynamicUint8MeanOpTest, NotKeepDims) { float kQuantizedTolerance = GetTolerance(-5.0, 2.0); - std::initializer_list data = {1.3, -4.8, -3.6, 0.24}; + std::vector data = {1.3, -4.8, -3.6, 0.24}; MeanOpDynamicModel m({TensorType_UINT8, {2, 2}, -5.0, 2.0}, {TensorType_UINT8, {2}, -5.0, 2.0}, {TensorType_INT32, {1}}, false); - std::initializer_list axis = {1}; + std::vector axis = {1}; m.SetAxis(axis); m.QuantizeAndPopulate(m.Input(), data); m.Invoke(); @@ -226,11 +295,11 @@ TEST(DynamicUint8MeanOpTest, NotKeepDims) { TEST(DynamicUint8MeanOpTest, KeepDims) { float kQuantizedTolerance = GetTolerance(-10.0, 12.0); - std::initializer_list data = {11.14, -0.14, 7.423, 0.879}; + std::vector data = {11.14, -0.14, 7.423, 0.879}; MeanOpDynamicModel m({TensorType_UINT8, {2, 2}, -10.0, 12.0}, {TensorType_UINT8, {2}, -10.0, 12.0}, {TensorType_INT32, {1}}, true); - std::initializer_list axis = {0}; + std::vector axis = {0}; m.SetAxis(axis); m.QuantizeAndPopulate(m.Input(), data); m.Invoke(); @@ -243,9 +312,9 @@ TEST(DynamicUint8MeanOpTest, KeepDims) { // Tests for reduce_sum TEST(ConstFloatSumOpTest, NotKeepDims) { - std::initializer_list data = { - 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, - 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; SumOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}}, {4}, {1, 0, -3, -3}, false); m.SetInput(data); @@ -256,9 +325,9 @@ TEST(ConstFloatSumOpTest, NotKeepDims) { } TEST(ConstFloatSumOpTest, KeepDims) { - std::initializer_list data = { - 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, - 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; SumOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}}, {2}, {0, 2}, true); m.SetInput(data); @@ -269,13 +338,13 @@ TEST(ConstFloatSumOpTest, KeepDims) { } TEST(DynamicFloatSumOpTest, NotKeepDims) { - std::initializer_list data = { - 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, - 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; SumOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}}, {TensorType_INT32, {4}}, false); - std::initializer_list axis = {1, 0, -3, -3}; + std::vector axis = {1, 0, -3, -3}; m.SetAxis(axis); m.SetInput(data); m.Invoke(); @@ -284,13 +353,23 @@ TEST(DynamicFloatSumOpTest, NotKeepDims) { ElementsAreArray(ArrayFloatNear({144, 156}))); } +TEST(ConstFloatSumOpTest, Scalar) { + std::vector data = {17.}; + SumOpConstModel m({TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}, {}, {0}, + false); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), IsEmpty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({17.}))); +} + TEST(DynamicFloatSumOpTest, KeepDims) { - std::initializer_list data = { - 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, - 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; SumOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}}, {TensorType_INT32, {2}}, true); - std::initializer_list axis = {0, 2}; + std::vector axis = {0, 2}; m.SetAxis(axis); m.SetInput(data); m.Invoke(); @@ -300,10 +379,10 @@ TEST(DynamicFloatSumOpTest, KeepDims) { } TEST(DynamicFloatSumOpTest, Scale) { - std::initializer_list data = {9.527}; + std::vector data = {9.527}; SumOpDynamicModel m({TensorType_FLOAT32, {1}}, {TensorType_FLOAT32, {1}}, {TensorType_INT32, {1}}, true); - std::initializer_list axis = {0}; + std::vector axis = {0}; m.SetAxis(axis); m.SetInput(data); m.Invoke(); @@ -313,7 +392,7 @@ TEST(DynamicFloatSumOpTest, Scale) { TEST(ConstUint8SumOpTest, NotKeepDims) { float kQuantizedTolerance = GetTolerance(-1.0, 1.0); - std::initializer_list data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; + std::vector data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; SumOpConstModel m({TensorType_UINT8, {1, 3, 2}, -1.0, 1.0}, {TensorType_UINT8, {2}, -1.0, 1.0}, {1}, {1}, false); m.QuantizeAndPopulate(m.Input(), data); @@ -326,7 +405,7 @@ TEST(ConstUint8SumOpTest, NotKeepDims) { TEST(ConstUint8SumOpTest, KeepDims) { float kQuantizedTolerance = GetTolerance(-1.0, 1.0); - std::initializer_list data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; + std::vector data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; SumOpConstModel m({TensorType_UINT8, {3, 2}, -1.0, 1.0}, {TensorType_UINT8, {3}, -1.0, 1.0}, {1}, {1}, true); m.QuantizeAndPopulate(m.Input(), data); @@ -339,11 +418,11 @@ TEST(ConstUint8SumOpTest, KeepDims) { TEST(DynamicUint8SumOpTest, NotKeepDims) { float kQuantizedTolerance = GetTolerance(-5.0, 2.0); - std::initializer_list data = {1.3, -4.8, -3.6, 0.24}; + std::vector data = {1.3, -4.8, -3.6, 0.24}; SumOpDynamicModel m({TensorType_UINT8, {2, 2}, -5.0, 2.0}, {TensorType_UINT8, {2}, -5.0, 2.0}, {TensorType_INT32, {1}}, false); - std::initializer_list axis = {1}; + std::vector axis = {1}; m.SetAxis(axis); m.QuantizeAndPopulate(m.Input(), data); m.Invoke(); @@ -355,11 +434,11 @@ TEST(DynamicUint8SumOpTest, NotKeepDims) { TEST(DynamicUint8SumOpTest, KeepDims) { float kQuantizedTolerance = GetTolerance(-10.0, 12.0); - std::initializer_list data = {11.14, -0.14, 7.423, 0.879}; + std::vector data = {11.14, -0.14, 7.423, 0.879}; SumOpDynamicModel m({TensorType_UINT8, {2, 2}, -10.0, 12.0}, {TensorType_UINT8, {2}, -10.0, 12.0}, {TensorType_INT32, {1}}, true); - std::initializer_list axis = {0}; + std::vector axis = {0}; m.SetAxis(axis); m.QuantizeAndPopulate(m.Input(), data); m.Invoke(); @@ -369,6 +448,223 @@ TEST(DynamicUint8SumOpTest, KeepDims) { ElementsAreArray(ArrayFloatNear({6.47059, 10.698}, kQuantizedTolerance))); } +// Tests for reduce_prod + +TEST(ConstFloatProdOpTest, NotKeepDims) { + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + ProdOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}}, + {4}, {1, 0, -3, -3}, false); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3.162341376e+11, 1.9619905536e+12}))); +} + +TEST(ConstFloatProdOpTest, KeepDims) { + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + ProdOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}}, + {2}, {0, 2}, true); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray( + ArrayFloatNear({7.74592e+06, 1.197504e+08, 6.6889152e+08}))); +} + +TEST(DynamicFloatProdOpTest, NotKeepDims) { + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + ProdOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}}, + {TensorType_FLOAT32, {2}}, {TensorType_INT32, {4}}, + false); + std::vector axis = {1, 0, -3, -3}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3.16234143225e+11, 1.9619905536e+12}))); +} + +TEST(DynamicFloatProdOpTest, KeepDims) { + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + ProdOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}}, + {TensorType_FLOAT32, {3}}, {TensorType_INT32, {2}}, + true); + std::vector axis = {0, 2}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray( + ArrayFloatNear({7.74592e+06, 1.197504e+08, 6.6889152e+08}))); +} + +TEST(DynamicFloatProdOpTest, Scale) { + std::vector data = {9.527}; + ProdOpDynamicModel m({TensorType_FLOAT32, {1}}, {TensorType_FLOAT32, {1}}, + {TensorType_INT32, {1}}, true); + std::vector axis = {0}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({9.527}))); +} + +// Tests for reduce_max + +TEST(ConstFloatMaxOpTest, NotKeepDims) { + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + MaxOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}}, + {4}, {1, 0, -3, -3}, false); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({23, 24}))); +} + +TEST(ConstFloatMaxOpTest, KeepDims) { + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + MaxOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}}, + {2}, {0, 2}, true); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({20, 22, 24}))); +} + +TEST(DynamicFloatMaxOpTest, NotKeepDims) { + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + MaxOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}}, + {TensorType_FLOAT32, {2}}, {TensorType_INT32, {4}}, + false); + std::vector axis = {1, 0, -3, -3}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({23, 24}))); +} + +TEST(DynamicFloatMaxOpTest, KeepDims) { + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + MaxOpDynamicModel m({TensorType_FLOAT32, {4, 3, 2}}, + {TensorType_FLOAT32, {3}}, {TensorType_INT32, {2}}, true); + std::vector axis = {0, 2}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({20, 22, 24}))); +} + +TEST(DynamicFloatMaxOpTest, Scale) { + std::vector data = {9.527}; + MaxOpDynamicModel m({TensorType_FLOAT32, {1}}, {TensorType_FLOAT32, {1}}, + {TensorType_INT32, {1}}, true); + std::vector axis = {0}; + m.SetAxis(axis); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({9.527}))); +} + +TEST(ConstUint8MaxOpTest, NotKeepDims) { + float kQuantizedTolerance = GetTolerance(-1.0, 1.0); + std::vector data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; + MaxOpConstModel m({TensorType_UINT8, {1, 3, 2}, -1.0, 1.0}, + {TensorType_UINT8, {2}, -1.0, 1.0}, {1}, {1}, false); + m.QuantizeAndPopulate(m.Input(), data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray( + ArrayFloatNear({0.501961, 0.603922}, kQuantizedTolerance))); +} + +TEST(ConstUint8MaxOpTest, KeepDims) { + float kQuantizedTolerance = GetTolerance(-1.0, 1.0); + std::vector data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; + MaxOpConstModel m({TensorType_UINT8, {3, 2}, -1.0, 1.0}, + {TensorType_UINT8, {3}, -1.0, 1.0}, {1}, {1}, true); + m.QuantizeAndPopulate(m.Input(), data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 1})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray( + ArrayFloatNear({0.4, 0.4, 0.603922}, kQuantizedTolerance))); +} + +TEST(DynamicUint8MaxOpTest, NotKeepDims) { + float kQuantizedTolerance = GetTolerance(-5.0, 2.0); + std::vector data = {1.3, -4.8, -3.6, 0.24}; + MaxOpDynamicModel m({TensorType_UINT8, {2, 2}, -5.0, 2.0}, + {TensorType_UINT8, {2}, -5.0, 2.0}, + {TensorType_INT32, {1}}, false); + std::vector axis = {1}; + m.SetAxis(axis); + m.QuantizeAndPopulate(m.Input(), data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray( + ArrayFloatNear({1.2902, 0.247059}, kQuantizedTolerance))); +} + +TEST(DynamicUint8MaxOpTest, KeepDims) { + float kQuantizedTolerance = GetTolerance(-10.0, 12.0); + std::vector data = {11.14, -0.14, 7.423, 0.879}; + MaxOpDynamicModel m({TensorType_UINT8, {2, 2}, -10.0, 12.0}, + {TensorType_UINT8, {2}, -10.0, 12.0}, + {TensorType_INT32, {1}}, true); + std::vector axis = {0}; + m.SetAxis(axis); + m.QuantizeAndPopulate(m.Input(), data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray( + ArrayFloatNear({11.1294, 0.862745}, kQuantizedTolerance))); +} + +TEST(DynamicUint8MaxOpTest, Scalar) { + float kQuantizedTolerance = GetTolerance(-10.0, 12.0); + std::vector data = {11.14}; + MaxOpDynamicModel m({TensorType_UINT8, {}, -10.0, 12.0}, + {TensorType_UINT8, {}, -10.0, 12.0}, + {TensorType_INT32, {1}}, true); + std::vector axis = {0}; + m.QuantizeAndPopulate(m.Input(), data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), IsEmpty()); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({11.1294}, kQuantizedTolerance))); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc index 0ca08cd8f38216549b4383ebaacbf4c54442cd97..e63272884141006f2a5613aa536c1bf4d4c4c53c 100644 --- a/tensorflow/contrib/lite/kernels/register.cc +++ b/tensorflow/contrib/lite/kernels/register.cc @@ -82,6 +82,7 @@ TfLiteRegistration* Register_PRELU(); TfLiteRegistration* Register_MAXIMUM(); TfLiteRegistration* Register_MINIMUM(); TfLiteRegistration* Register_ARG_MAX(); +TfLiteRegistration* Register_ARG_MIN(); TfLiteRegistration* Register_GREATER(); TfLiteRegistration* Register_GREATER_EQUAL(); TfLiteRegistration* Register_LESS(); @@ -90,6 +91,8 @@ TfLiteRegistration* Register_FLOOR(); TfLiteRegistration* Register_TILE(); TfLiteRegistration* Register_NEG(); TfLiteRegistration* Register_SUM(); +TfLiteRegistration* Register_REDUCE_PROD(); +TfLiteRegistration* Register_REDUCE_MAX(); TfLiteRegistration* Register_SELECT(); TfLiteRegistration* Register_SLICE(); TfLiteRegistration* Register_SIN(); @@ -102,6 +105,10 @@ TfLiteRegistration* Register_SQRT(); TfLiteRegistration* Register_RSQRT(); TfLiteRegistration* Register_SHAPE(); TfLiteRegistration* Register_POW(); +TfLiteRegistration* Register_FAKE_QUANT(); +TfLiteRegistration* Register_PACK(); +TfLiteRegistration* Register_ONE_HOT(); +TfLiteRegistration* Register_LOGICAL_OR(); BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_RELU, Register_RELU()); @@ -167,6 +174,7 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_MAXIMUM, Register_MAXIMUM()); AddBuiltin(BuiltinOperator_MINIMUM, Register_MINIMUM()); AddBuiltin(BuiltinOperator_ARG_MAX, Register_ARG_MAX()); + AddBuiltin(BuiltinOperator_ARG_MIN, Register_ARG_MIN()); AddBuiltin(BuiltinOperator_GREATER, Register_GREATER()); AddBuiltin(BuiltinOperator_GREATER_EQUAL, Register_GREATER_EQUAL()); AddBuiltin(BuiltinOperator_LESS, Register_LESS()); @@ -179,6 +187,8 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_TRANSPOSE_CONV, Register_TRANSPOSE_CONV()); AddBuiltin(BuiltinOperator_TILE, Register_TILE()); AddBuiltin(BuiltinOperator_SUM, Register_SUM()); + AddBuiltin(BuiltinOperator_REDUCE_PROD, Register_REDUCE_PROD()); + AddBuiltin(BuiltinOperator_REDUCE_MAX, Register_REDUCE_MAX()); AddBuiltin(BuiltinOperator_EXPAND_DIMS, Register_EXPAND_DIMS()); AddBuiltin(BuiltinOperator_SPARSE_TO_DENSE, Register_SPARSE_TO_DENSE()); AddBuiltin(BuiltinOperator_EQUAL, Register_EQUAL()); @@ -187,6 +197,10 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_RSQRT, Register_RSQRT()); AddBuiltin(BuiltinOperator_SHAPE, Register_SHAPE()); AddBuiltin(BuiltinOperator_POW, Register_POW()); + AddBuiltin(BuiltinOperator_FAKE_QUANT, Register_FAKE_QUANT(), 1, 2); + AddBuiltin(BuiltinOperator_PACK, Register_PACK()); + AddBuiltin(BuiltinOperator_ONE_HOT, Register_ONE_HOT()); + AddBuiltin(BuiltinOperator_LOGICAL_OR, Register_LOGICAL_OR()); // TODO(andrewharp, ahentz): Move these somewhere more appropriate so that // custom ops aren't always included by default. diff --git a/tensorflow/contrib/lite/kernels/reshape.cc b/tensorflow/contrib/lite/kernels/reshape.cc index 3287040695140e3e7921c9f517450b9416b050b6..49ba0571e2f214c0b2407240753fcec0661c71bf 100644 --- a/tensorflow/contrib/lite/kernels/reshape.cc +++ b/tensorflow/contrib/lite/kernels/reshape.cc @@ -25,16 +25,11 @@ namespace builtin { namespace reshape { constexpr int kInputTensor = 0; +constexpr int kShapeTensor = 1; constexpr int kOutputTensor = 0; -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - - // TODO(ahentz): we are often given a tensor with the shape but we only pay - // attention to what the shape specified in 'params'. - TF_LITE_ENSURE(context, NumInputs(node) == 1 || NumInputs(node) == 2); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - +TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node, + TfLiteIntArray* output_shape) { const TfLiteTensor* input = GetInput(context, node, kInputTensor); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); @@ -42,37 +37,84 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { // special -1 value, meaning it will be calculated automatically based on the // input. Here we calculate what that dimension should be so that the number // of output elements in the same as the number of input elements. - int num_input_elements = 1; - for (int i = 0; i < NumDimensions(input); ++i) { - num_input_elements *= SizeOfDimension(input, i); - } + int num_input_elements = NumElements(input); - TfLiteIntArray* output_size = TfLiteIntArrayCreate(params->num_dimensions); int num_output_elements = 1; int stretch_dim = -1; - for (int i = 0; i < params->num_dimensions; ++i) { - int value = params->shape[i]; + for (int i = 0; i < output_shape->size; ++i) { + int value = output_shape->data[i]; if (value == -1) { TF_LITE_ENSURE_EQ(context, stretch_dim, -1); stretch_dim = i; } else { num_output_elements *= value; - output_size->data[i] = value; } } if (stretch_dim != -1) { - output_size->data[stretch_dim] = num_input_elements / num_output_elements; - num_output_elements *= output_size->data[stretch_dim]; + output_shape->data[stretch_dim] = num_input_elements / num_output_elements; + num_output_elements *= output_shape->data[stretch_dim]; } TF_LITE_ENSURE_EQ(context, num_input_elements, num_output_elements); - return context->ResizeTensor(context, output, output_size); + return context->ResizeTensor(context, output, output_shape); +} + +TfLiteStatus ResizeOutputWithShapeTensor(TfLiteContext* context, + TfLiteNode* node) { + const TfLiteTensor* shape = GetInput(context, node, kShapeTensor); + + TfLiteIntArray* output_shape = TfLiteIntArrayCreate(shape->dims->data[0]); + for (int i = 0; i < output_shape->size; ++i) { + output_shape->data[i] = shape->data.i32[i]; + } + return ResizeOutput(context, node, output_shape); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + auto* params = reinterpret_cast(node->builtin_data); + + TF_LITE_ENSURE(context, NumInputs(node) == 1 || NumInputs(node) == 2); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + // Attempt to use shape tensor if it exists. + if (NumInputs(node) == 2) { + const TfLiteTensor* shape = GetInput(context, node, kShapeTensor); + // Check if the shape tensor is valid. + if (shape->dims->size == 1 && shape->type == kTfLiteInt32) { + // Set the output tensor as dynamic if the shape isn't constnat. + if (!IsConstantTensor(shape)) { + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + SetTensorToDynamic(output); + return kTfLiteOk; + } + // Shape is constant. Resize now. + return ResizeOutputWithShapeTensor(context, node); + } + } + // The function is returned above this line if the shape tensor is usable. + // Now fallback to the shape parameter in `TfLiteReshapeParams`. + int num_dimensions = params->num_dimensions; + if (num_dimensions == 1 && params->shape[0] == 0) { + // Legacy tflite models use a shape parameter of [0] to indicate scalars, + // so adjust accordingly. TODO(b/111614235): Allow zero-sized buffers during + // toco conversion. + num_dimensions = 0; + } + TfLiteIntArray* output_shape = TfLiteIntArrayCreate(num_dimensions); + for (int i = 0; i < num_dimensions; ++i) { + output_shape->data[i] = params->shape[i]; + } + return ResizeOutput(context, node, output_shape); } TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const TfLiteTensor* input = GetInput(context, node, kInputTensor); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + if (IsDynamicTensor(output)) { + TF_LITE_ENSURE_OK(context, ResizeOutputWithShapeTensor(context, node)); + } + memcpy(output->data.raw, input->data.raw, input->bytes); return kTfLiteOk; diff --git a/tensorflow/contrib/lite/kernels/reshape_test.cc b/tensorflow/contrib/lite/kernels/reshape_test.cc index aecbd0399f7454045e8189072f45b695b0525204..52d71350d3ba9a27bf9a8df7a194161c4fb7f87c 100644 --- a/tensorflow/contrib/lite/kernels/reshape_test.cc +++ b/tensorflow/contrib/lite/kernels/reshape_test.cc @@ -22,18 +22,27 @@ namespace tflite { namespace { using ::testing::ElementsAreArray; +using ::testing::IsEmpty; class ReshapeOpModel : public SingleOpModel { public: ReshapeOpModel(std::initializer_list input_shape, - std::initializer_list new_shape) { + std::initializer_list new_shape, + bool use_shape_input_tensor = false) { input_ = AddInput(TensorType_FLOAT32); output_ = AddOutput(TensorType_FLOAT32); + int shape_input_tensor = + use_shape_input_tensor ? AddInput(TensorType_INT32) : -1; SetBuiltinOp( BuiltinOperator_RESHAPE, BuiltinOptions_ReshapeOptions, CreateReshapeOptions(builder_, builder_.CreateVector(new_shape)) .Union()); - BuildInterpreter({input_shape}); + if (use_shape_input_tensor) { + BuildInterpreter({input_shape, GetShape(shape_input_tensor)}); + PopulateTensor(shape_input_tensor, new_shape); + } else { + BuildInterpreter({input_shape}); + } } void SetInput(std::initializer_list data) { @@ -71,6 +80,14 @@ TEST(ReshapeOpTest, SimpleTest) { EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 2, 2})); } +TEST(ReshapeOpTest, ShapeTensorInput) { + ReshapeOpModel m({1, 2, 4, 1}, {2, 2, 2}, /*use_shape_input_tensor=*/true); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6, 7, 8})); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 2, 2})); +} + TEST(ReshapeOpTest, WithStretchDimension) { ReshapeOpModel m({1, 2, 4, 1}, {2, 1, -1}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8}); @@ -79,6 +96,22 @@ TEST(ReshapeOpTest, WithStretchDimension) { EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 1, 4})); } +TEST(ReshapeOpTest, ScalarOutput) { + ReshapeOpModel m({1}, {}); + m.SetInput({3}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({3})); + EXPECT_THAT(m.GetOutputShape(), IsEmpty()); +} + +TEST(ReshapeOpTest, LegacyScalarOutput) { + ReshapeOpModel m({1}, {0}); + m.SetInput({3}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({3})); + EXPECT_THAT(m.GetOutputShape(), IsEmpty()); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/resize_bilinear_test.cc b/tensorflow/contrib/lite/kernels/resize_bilinear_test.cc index 10caffea03ebcec7862df1627541ac3d076b04e4..f4289105f7931ae572f219a61b5479287aff926a 100644 --- a/tensorflow/contrib/lite/kernels/resize_bilinear_test.cc +++ b/tensorflow/contrib/lite/kernels/resize_bilinear_test.cc @@ -247,7 +247,7 @@ TEST(ResizeBilinearOpTest, TwoDimensionalResizeWithTwoBatches8Bit) { 3, 6, // 9, 12, // 4, 10, // - 10, 16 // + 12, 16 // }); m.SetSize({3, 3}); m.Invoke(); @@ -256,8 +256,8 @@ TEST(ResizeBilinearOpTest, TwoDimensionalResizeWithTwoBatches8Bit) { 7, 9, 10, // 9, 11, 12, // 4, 8, 10, // - 8, 12, 14, // - 10, 13, 16, // + 9, 12, 14, // + 12, 14, 16, // }))); ResizeBilinearOpModel const_m({TensorType_UINT8, {2, 2, 2, 1}}, {3, 3}); @@ -265,7 +265,7 @@ TEST(ResizeBilinearOpTest, TwoDimensionalResizeWithTwoBatches8Bit) { 3, 6, // 9, 12, // 4, 10, // - 10, 16 // + 12, 16 // }); const_m.Invoke(); EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ @@ -273,35 +273,35 @@ TEST(ResizeBilinearOpTest, TwoDimensionalResizeWithTwoBatches8Bit) { 7, 9, 10, // 9, 11, 12, // 4, 8, 10, // - 8, 12, 14, // - 10, 13, 16, // + 9, 12, 14, // + 12, 14, 16, // }))); } TEST(ResizeBilinearOpTest, ThreeDimensionalResize8Bit) { ResizeBilinearOpModel m({TensorType_UINT8, {1, 2, 2, 2}}); m.SetInput({ - 3, 4, 6, 10, // - 9, 10, 12, 16, // + 3, 4, 6, 10, // + 10, 12, 14, 16, // }); m.SetSize({3, 3}); m.Invoke(); EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ - 3, 4, 5, 8, 6, 10, // - 7, 8, 9, 12, 10, 14, // - 9, 10, 11, 13, 12, 16, // + 3, 4, 5, 8, 6, 10, // + 7, 9, 10, 12, 11, 14, // + 10, 12, 12, 14, 14, 16, // }))); ResizeBilinearOpModel const_m({TensorType_UINT8, {1, 2, 2, 2}}, {3, 3}); const_m.SetInput({ - 3, 4, 6, 10, // - 9, 10, 12, 16, // + 3, 4, 6, 10, // + 10, 12, 14, 16, // }); const_m.Invoke(); EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ - 3, 4, 5, 8, 6, 10, // - 7, 8, 9, 12, 10, 14, // - 9, 10, 11, 13, 12, 16, // + 3, 4, 5, 8, 6, 10, // + 7, 9, 10, 12, 11, 14, // + 10, 12, 12, 14, 14, 16, // }))); } } // namespace diff --git a/tensorflow/contrib/lite/kernels/select.cc b/tensorflow/contrib/lite/kernels/select.cc index 9b6cee3cb55bf93b987fa8e59bdf9c591f5c0372..3cdb5db2090a3cb3eeb43c6e20a4fec09fe8a069 100644 --- a/tensorflow/contrib/lite/kernels/select.cc +++ b/tensorflow/contrib/lite/kernels/select.cc @@ -89,6 +89,9 @@ TfLiteStatus SelectEval(TfLiteContext* context, TfLiteNode* node) { case kTfLiteUInt8: \ TF_LITE_SELECT(uint8_t, op); \ break; \ + case kTfLiteInt16: \ + TF_LITE_SELECT(int16_t, op); \ + break; \ case kTfLiteInt32: \ TF_LITE_SELECT(int32_t, op); \ break; \ diff --git a/tensorflow/contrib/lite/kernels/select_test.cc b/tensorflow/contrib/lite/kernels/select_test.cc index 4664b9acb444747167f991944ddc120e9941ccd6..5b2e61cd29a7fd7c699fd81cb81e5f9a12c4b18f 100644 --- a/tensorflow/contrib/lite/kernels/select_test.cc +++ b/tensorflow/contrib/lite/kernels/select_test.cc @@ -96,6 +96,19 @@ TEST(SelectOpTest, SelectUInt8) { EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4})); } +TEST(SelectOpTest, SelectInt16) { + SelectOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, {1, 1, 1, 4}, + TensorType_INT16); + + model.PopulateTensor(model.input1(), {false, true, false, false}); + model.PopulateTensor(model.input2(), {1, 2, 3, 4}); + model.PopulateTensor(model.input3(), {5, 6, 7, 8}); + model.Invoke(); + + EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 2, 7, 8})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4})); +} + TEST(SelectOpTest, SelectInt32) { SelectOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_INT32); diff --git a/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc b/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc index c9269599e58f095ded4788e2ab064583ae0a708c..03079f1c3b4110da9193f91ed22940594152b10f 100644 --- a/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc +++ b/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc @@ -113,7 +113,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); } -#define TF_LITE_SPACE_TO_BATCH_ND(type, scalar) \ +#define TF_LITE_SPACE_TO_BATCH_ND(type, scalar, pad_value) \ type::SpaceToBatchND(GetTensorData(op_context.input), \ GetTensorDims(op_context.input), \ GetTensorData(op_context.block_shape), \ @@ -121,34 +121,36 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { GetTensorData(op_context.paddings), \ GetTensorDims(op_context.paddings), \ GetTensorData(op_context.output), \ - GetTensorDims(op_context.output)) + GetTensorDims(op_context.output), pad_value) switch (op_context.input->type) { // Already know in/out types are same. case kTfLiteFloat32: if (kernel_type == kReference) { - TF_LITE_SPACE_TO_BATCH_ND(reference_ops, float); + TF_LITE_SPACE_TO_BATCH_ND(reference_ops, float, 0); } else { - TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, float); + TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, float, 0); } break; case kTfLiteUInt8: if (kernel_type == kReference) { - TF_LITE_SPACE_TO_BATCH_ND(reference_ops, uint8_t); + TF_LITE_SPACE_TO_BATCH_ND(reference_ops, uint8_t, + op_context.output->params.zero_point); } else { - TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, uint8_t); + TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, uint8_t, + op_context.output->params.zero_point); } break; case kTfLiteInt32: if (kernel_type == kReference) { - TF_LITE_SPACE_TO_BATCH_ND(reference_ops, int32_t); + TF_LITE_SPACE_TO_BATCH_ND(reference_ops, int32_t, 0); } else { - TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, int32_t); + TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, int32_t, 0); } break; case kTfLiteInt64: if (kernel_type == kReference) { - TF_LITE_SPACE_TO_BATCH_ND(reference_ops, int64_t); + TF_LITE_SPACE_TO_BATCH_ND(reference_ops, int64_t, 0); } else { - TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, int64_t); + TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, int64_t, 0); } break; default: diff --git a/tensorflow/contrib/lite/kernels/space_to_batch_nd_test.cc b/tensorflow/contrib/lite/kernels/space_to_batch_nd_test.cc index 92a4a037d5873e608ee7bdbdfc5eaa5e9b62bc8c..5756573629a51917e39a312117a1fcd29c150dc0 100644 --- a/tensorflow/contrib/lite/kernels/space_to_batch_nd_test.cc +++ b/tensorflow/contrib/lite/kernels/space_to_batch_nd_test.cc @@ -23,6 +23,7 @@ namespace tflite { namespace { using ::testing::ElementsAreArray; +using ::testing::Matcher; class SpaceToBatchNDOpModel : public SingleOpModel { public: @@ -30,6 +31,10 @@ class SpaceToBatchNDOpModel : public SingleOpModel { PopulateTensor(input_, data); } + void SetQuantizedInput(std::initializer_list data) { + QuantizeAndPopulate(input_, data); + } + void SetBlockShape(std::initializer_list data) { PopulateTensor(block_shape_, data); } @@ -41,6 +46,11 @@ class SpaceToBatchNDOpModel : public SingleOpModel { std::vector GetOutput() { return ExtractVector(output_); } std::vector GetOutputShape() { return GetTensorShape(output_); } + std::vector GetDequantizedOutput() { + return Dequantize(ExtractVector(output_), + GetScale(output_), GetZeroPoint(output_)); + } + protected: int input_; int block_shape_; @@ -56,18 +66,19 @@ class SpaceToBatchNDOpModel : public SingleOpModel { // m.Invoke(); class SpaceToBatchNDOpConstModel : public SpaceToBatchNDOpModel { public: - SpaceToBatchNDOpConstModel(std::initializer_list input_shape, + SpaceToBatchNDOpConstModel(const TensorData& input, std::initializer_list block_shape, - std::initializer_list paddings) { - input_ = AddInput(TensorType_FLOAT32); + std::initializer_list paddings, + const TensorData& output) { + input_ = AddInput(input); block_shape_ = AddConstInput(TensorType_INT32, block_shape, {2}); paddings_ = AddConstInput(TensorType_INT32, paddings, {2, 2}); - output_ = AddOutput(TensorType_FLOAT32); + output_ = AddOutput(output); SetBuiltinOp(BuiltinOperator_SPACE_TO_BATCH_ND, BuiltinOptions_SpaceToBatchNDOptions, CreateSpaceToBatchNDOptions(builder_).Union()); - BuildInterpreter({input_shape}); + BuildInterpreter({input.shape}); } }; @@ -81,26 +92,30 @@ class SpaceToBatchNDOpConstModel : public SpaceToBatchNDOpModel { // m.Invoke(); class SpaceToBatchNDOpDynamicModel : public SpaceToBatchNDOpModel { public: - SpaceToBatchNDOpDynamicModel(std::initializer_list input_shape) { - input_ = AddInput(TensorType_FLOAT32); + SpaceToBatchNDOpDynamicModel(const TensorData& input, + const TensorData& output) { + input_ = AddInput(input); block_shape_ = AddInput(TensorType_INT32); paddings_ = AddInput(TensorType_INT32); - output_ = AddOutput(TensorType_FLOAT32); + output_ = AddOutput(output); SetBuiltinOp(BuiltinOperator_SPACE_TO_BATCH_ND, BuiltinOptions_SpaceToBatchNDOptions, CreateSpaceToBatchNDOptions(builder_).Union()); - BuildInterpreter({input_shape, {2}, {2, 2}}); + BuildInterpreter({input.shape, {2}, {2, 2}}); } }; TEST(SpaceToBatchNDOpTest, InvalidShapeTest) { - EXPECT_DEATH(SpaceToBatchNDOpConstModel({1, 3, 3, 1}, {2, 2}, {0, 0, 0, 0}), - "Cannot allocate tensors"); + EXPECT_DEATH( + SpaceToBatchNDOpConstModel({TensorType_FLOAT32, {1, 3, 3, 1}}, {2, 2}, + {0, 0, 0, 0}, {TensorType_FLOAT32}), + "Cannot allocate tensors"); } TEST(SpaceToBatchNDOpTest, SimpleConstTest) { - SpaceToBatchNDOpConstModel m({1, 4, 4, 1}, {2, 2}, {0, 0, 0, 0}); + SpaceToBatchNDOpConstModel m({TensorType_FLOAT32, {1, 4, 4, 1}}, {2, 2}, + {0, 0, 0, 0}, {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4, 2, 2, 1})); @@ -109,7 +124,8 @@ TEST(SpaceToBatchNDOpTest, SimpleConstTest) { } TEST(SpaceToBatchNDOpTest, SimpleDynamicTest) { - SpaceToBatchNDOpDynamicModel m({1, 4, 4, 1}); + SpaceToBatchNDOpDynamicModel m({TensorType_FLOAT32, {1, 4, 4, 1}}, + {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); m.SetBlockShape({2, 2}); m.SetPaddings({0, 0, 0, 0}); @@ -120,7 +136,8 @@ TEST(SpaceToBatchNDOpTest, SimpleDynamicTest) { } TEST(SpaceToBatchNDOpTest, MultipleInputBatchesConstTest) { - SpaceToBatchNDOpConstModel m({2, 2, 4, 1}, {2, 2}, {0, 0, 0, 0}); + SpaceToBatchNDOpConstModel m({TensorType_FLOAT32, {2, 2, 4, 1}}, {2, 2}, + {0, 0, 0, 0}, {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({8, 1, 2, 1})); @@ -129,7 +146,8 @@ TEST(SpaceToBatchNDOpTest, MultipleInputBatchesConstTest) { } TEST(SpaceToBatchNDOpTest, MultipleInputBatchesDynamicTest) { - SpaceToBatchNDOpDynamicModel m({2, 2, 4, 1}); + SpaceToBatchNDOpDynamicModel m({TensorType_FLOAT32, {2, 2, 4, 1}}, + {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); m.SetBlockShape({2, 2}); m.SetPaddings({0, 0, 0, 0}); @@ -140,7 +158,8 @@ TEST(SpaceToBatchNDOpTest, MultipleInputBatchesDynamicTest) { } TEST(SpaceToBatchNDOpTest, SimplePaddingConstTest) { - SpaceToBatchNDOpConstModel m({1, 5, 2, 1}, {3, 2}, {1, 0, 2, 0}); + SpaceToBatchNDOpConstModel m({TensorType_FLOAT32, {1, 5, 2, 1}}, {3, 2}, + {1, 0, 2, 0}, {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 2, 1})); @@ -151,7 +170,8 @@ TEST(SpaceToBatchNDOpTest, SimplePaddingConstTest) { } TEST(SpaceToBatchNDOpTest, SimplePaddingDynamicTest) { - SpaceToBatchNDOpDynamicModel m({1, 5, 2, 1}); + SpaceToBatchNDOpDynamicModel m({TensorType_FLOAT32, {1, 5, 2, 1}}, + {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); m.SetBlockShape({3, 2}); m.SetPaddings({1, 0, 2, 0}); @@ -164,7 +184,8 @@ TEST(SpaceToBatchNDOpTest, SimplePaddingDynamicTest) { } TEST(SpaceToBatchNDOpTest, ComplexPaddingConstTest) { - SpaceToBatchNDOpConstModel m({1, 4, 2, 1}, {3, 2}, {1, 1, 2, 4}); + SpaceToBatchNDOpConstModel m({TensorType_FLOAT32, {1, 4, 2, 1}}, {3, 2}, + {1, 1, 2, 4}, {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 4, 1})); @@ -176,7 +197,8 @@ TEST(SpaceToBatchNDOpTest, ComplexPaddingConstTest) { } TEST(SpaceToBatchNDOpTest, ComplexPaddingDynamicTest) { - SpaceToBatchNDOpDynamicModel m({1, 4, 2, 1}); + SpaceToBatchNDOpDynamicModel m({TensorType_FLOAT32, {1, 4, 2, 1}}, + {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8}); m.SetBlockShape({3, 2}); m.SetPaddings({1, 1, 2, 4}); @@ -189,6 +211,88 @@ TEST(SpaceToBatchNDOpTest, ComplexPaddingDynamicTest) { })); } +class QuantizedSpaceToBatchNDOpTest : public ::testing::Test { + protected: + std::vector> DequantizedArrayNear( + const std::vector& values, const float min, const float max) { + const float quantization_tolerance = (max - min) / 255.0; + return ArrayFloatNear(values, quantization_tolerance); + } +}; + +TEST_F(QuantizedSpaceToBatchNDOpTest, ZeroNotInQuantizationRange) { + // The test_util and actual quantization code currently ensure that the range + // must include zero, but if that ever changes, this test will catch it. + EXPECT_DEATH(SpaceToBatchNDOpConstModel m( + {TensorType_UINT8, {1, 2, 2, 1}, 1.0, 2.0}, {4, 2}, + {0, 0, 1, 1, 1, 1, 0, 0}, {TensorType_UINT8, {}, 1.0, 2.0}), + ".*Check failed: f_min <= 0.*"); +} + +TEST_F(QuantizedSpaceToBatchNDOpTest, SimplePaddingConstTest) { + SpaceToBatchNDOpConstModel m({TensorType_UINT8, {1, 5, 2, 1}, -1.0, 1.0}, + {3, 2}, {1, 0, 2, 0}, + {TensorType_UINT8, {}, -1.0, 1.0}); + m.SetQuantizedInput({-0.1, 0.2, -0.3, 0.4, -0.5, 0.6, -0.7, 0.8, -0.9, 0.1}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 2, 1})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(DequantizedArrayNear( + {0, 0, 0, -0.5, 0, 0, 0, 0.6, 0, -0.1, 0, -0.7, + 0, 0.2, 0, 0.8, 0, -0.3, 0, -0.9, 0, 0.4, 0, 0.1}, + -1.0, 1.0))); +} + +TEST_F(QuantizedSpaceToBatchNDOpTest, SimplePaddingDynamicTest) { + SpaceToBatchNDOpDynamicModel m({TensorType_UINT8, {1, 5, 2, 1}, -1.0, 1.0}, + {TensorType_UINT8, {}, -1.0, 1.0}); + m.SetQuantizedInput({-0.1, 0.2, -0.3, 0.4, -0.5, 0.6, -0.7, 0.8, -0.9, 0.1}); + m.SetBlockShape({3, 2}); + m.SetPaddings({1, 0, 2, 0}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 2, 1})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(DequantizedArrayNear( + {0, 0, 0, -0.5, 0, 0, 0, 0.6, 0, -0.1, 0, -0.7, + 0, 0.2, 0, 0.8, 0, -0.3, 0, -0.9, 0, 0.4, 0, 0.1}, + -1.0, 1.0))); +} + +TEST_F(QuantizedSpaceToBatchNDOpTest, ComplexPaddingConstTest) { + SpaceToBatchNDOpConstModel m({TensorType_UINT8, {1, 4, 2, 1}, -1.0, 1.0}, + {3, 2}, {1, 1, 2, 4}, + {TensorType_UINT8, {}, -1.0, 1.0}); + m.SetQuantizedInput({-0.1, 0.2, -0.3, 0.4, -0.5, 0.6, -0.7, 0.8}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 4, 1})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(DequantizedArrayNear( + { + 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, + 0, -0.1, 0, 0, 0, -0.7, 0, 0, 0, 0.2, 0, 0, 0, 0.8, 0, 0, + 0, -0.3, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, + }, + -1.0, 1.0))); +} + +TEST_F(QuantizedSpaceToBatchNDOpTest, ComplexPaddingDynamicTest) { + SpaceToBatchNDOpDynamicModel m({TensorType_UINT8, {1, 4, 2, 1}, -1.0, 1.0}, + {TensorType_UINT8, {}, -1.0, 1.0}); + m.SetQuantizedInput({-0.1, 0.2, -0.3, 0.4, -0.5, 0.6, -0.7, 0.8}); + m.SetBlockShape({3, 2}); + m.SetPaddings({1, 1, 2, 4}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 4, 1})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(DequantizedArrayNear( + { + 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, + 0, -0.1, 0, 0, 0, -0.7, 0, 0, 0, 0.2, 0, 0, 0, 0.8, 0, 0, + 0, -0.3, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, + }, + -1.0, 1.0))); +} + } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/sparse_to_dense.cc b/tensorflow/contrib/lite/kernels/sparse_to_dense.cc index 404c32ad9ca8b9f1e467b747708ccb451f2a5118..7be5e66c166cd752fc325f25d38e6522948e0f06 100644 --- a/tensorflow/contrib/lite/kernels/sparse_to_dense.cc +++ b/tensorflow/contrib/lite/kernels/sparse_to_dense.cc @@ -12,7 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/sub.cc b/tensorflow/contrib/lite/kernels/sub.cc index 1247525d416e8166a9e2e1d67c7907c00b0f6723..77a1f596898bb7fa99a7509a25229c627d762bdd 100644 --- a/tensorflow/contrib/lite/kernels/sub.cc +++ b/tensorflow/contrib/lite/kernels/sub.cc @@ -78,29 +78,47 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { } template -void EvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLiteSubParams* params, const OpData* data, - const TfLiteTensor* input1, const TfLiteTensor* input2, - TfLiteTensor* output) { - float output_activation_min, output_activation_max; - CalculateActivationRange(params->activation, &output_activation_min, - &output_activation_max); -#define TF_LITE_SUB(type, opname) \ - type::opname(GetTensorData(input1), GetTensorDims(input1), \ - GetTensorData(input2), GetTensorDims(input2), \ - output_activation_min, output_activation_max, \ - GetTensorData(output), GetTensorDims(output)) - if (kernel_type == kReference) { - if (data->requires_broadcast) { - TF_LITE_SUB(reference_ops, BroadcastSub); +void EvalSub(TfLiteContext* context, TfLiteNode* node, TfLiteSubParams* params, + const OpData* data, const TfLiteTensor* input1, + const TfLiteTensor* input2, TfLiteTensor* output) { +#define TF_LITE_SUB(type, opname, data_type) \ + data_type output_activation_min, output_activation_max; \ + CalculateActivationRange(params->activation, &output_activation_min, \ + &output_activation_max); \ + tflite::ArithmeticParams op_params; \ + SetActivationParams(output_activation_min, output_activation_max, \ + &op_params); \ + type::opname(op_params, GetTensorShape(input1), \ + GetTensorData(input1), GetTensorShape(input2), \ + GetTensorData(input2), GetTensorShape(output), \ + GetTensorData(output)) + if (output->type == kTfLiteInt32) { + if (kernel_type == kReference) { + if (data->requires_broadcast) { + TF_LITE_SUB(reference_ops, BroadcastSub4DSlow, int32_t); + } else { + TF_LITE_SUB(reference_ops, SubWithActivation, int32_t); + } } else { - TF_LITE_SUB(reference_ops, Sub); + if (data->requires_broadcast) { + TF_LITE_SUB(optimized_ops, BroadcastSub4DSlow, int32_t); + } else { + TF_LITE_SUB(optimized_ops, SubWithActivation, int32_t); + } } - } else { - if (data->requires_broadcast) { - TF_LITE_SUB(optimized_ops, BroadcastSub); + } else if (output->type == kTfLiteFloat32) { + if (kernel_type == kReference) { + if (data->requires_broadcast) { + TF_LITE_SUB(reference_ops, BroadcastSub4DSlow, float); + } else { + TF_LITE_SUB(reference_ops, SubWithActivation, float); + } } else { - TF_LITE_SUB(optimized_ops, Sub); + if (data->requires_broadcast) { + TF_LITE_SUB(optimized_ops, BroadcastSub4DSlow, float); + } else { + TF_LITE_SUB(optimized_ops, SubWithActivation, float); + } } } #undef TF_LITE_SUB @@ -128,36 +146,43 @@ void EvalQuantized(TfLiteContext* context, TfLiteNode* node, int input1_shift; QuantizeMultiplierSmallerThanOneExp(real_input1_multiplier, &input1_multiplier, &input1_shift); - input1_shift *= -1; int32 input2_multiplier; int input2_shift; QuantizeMultiplierSmallerThanOneExp(real_input2_multiplier, &input2_multiplier, &input2_shift); - input2_shift *= -1; int32 output_multiplier; int output_shift; QuantizeMultiplierSmallerThanOneExp(real_output_multiplier, &output_multiplier, &output_shift); - output_shift *= -1; int32 output_activation_min, output_activation_max; CalculateActivationRangeUint8(params->activation, output, &output_activation_min, &output_activation_max); -#define TF_LITE_SUB(type, opname) \ - type::opname(left_shift, GetTensorData(input1), \ - GetTensorDims(input1), input1_offset, input1_multiplier, \ - input1_shift, GetTensorData(input2), \ - GetTensorDims(input2), input2_offset, input2_multiplier, \ - input2_shift, output_offset, output_multiplier, output_shift, \ - output_activation_min, output_activation_max, \ - GetTensorData(output), GetTensorDims(output)); +#define TF_LITE_SUB(type, opname) \ + tflite::ArithmeticParams op_params; \ + op_params.left_shift = left_shift; \ + op_params.input1_offset = input1_offset; \ + op_params.input1_multiplier = input1_multiplier; \ + op_params.input1_shift = input1_shift; \ + op_params.input2_offset = input2_offset; \ + op_params.input2_multiplier = input2_multiplier; \ + op_params.input2_shift = input2_shift; \ + op_params.output_offset = output_offset; \ + op_params.output_multiplier = output_multiplier; \ + op_params.output_shift = output_shift; \ + SetActivationParams(output_activation_min, output_activation_max, \ + &op_params); \ + type::opname(op_params, GetTensorShape(input1), \ + GetTensorData(input1), GetTensorShape(input2), \ + GetTensorData(input2), GetTensorShape(output), \ + GetTensorData(output)) // The quantized version of Sub doesn't support activations, so we // always use BroadcastSub. if (kernel_type == kReference) { - TF_LITE_SUB(reference_ops, BroadcastSub); + TF_LITE_SUB(reference_ops, BroadcastSub4DSlow); } else { - TF_LITE_SUB(optimized_ops, BroadcastSub); + TF_LITE_SUB(optimized_ops, BroadcastSub4DSlow); } #undef TF_LITE_SUB } @@ -171,14 +196,15 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - if (output->type == kTfLiteFloat32) { - EvalFloat(context, node, params, data, input1, input2, output); + if (output->type == kTfLiteFloat32 || output->type == kTfLiteInt32) { + EvalSub(context, node, params, data, input1, input2, output); } else if (output->type == kTfLiteUInt8) { EvalQuantized(context, node, params, data, input1, input2, output); } else { context->ReportError( - context, "output type %d is not supported, requires float|uint8 types.", + context, + "output type %d is not supported, requires float|uint8|int32 types.", output->type); return kTfLiteError; } diff --git a/tensorflow/contrib/lite/kernels/sub_test.cc b/tensorflow/contrib/lite/kernels/sub_test.cc index ff07aeec49dbfcc0e1f65df3d674d5ec30f1b54c..5978c574d35492eda6b903fd83d95ecbd6b62148 100644 --- a/tensorflow/contrib/lite/kernels/sub_test.cc +++ b/tensorflow/contrib/lite/kernels/sub_test.cc @@ -52,6 +52,13 @@ class FloatSubOpModel : public BaseSubOpModel { std::vector GetOutput() { return ExtractVector(output_); } }; +class IntegerSubOpModel : public BaseSubOpModel { + public: + using BaseSubOpModel::BaseSubOpModel; + + std::vector GetOutput() { return ExtractVector(output_); } +}; + class QuantizedSubOpModel : public BaseSubOpModel { public: using BaseSubOpModel::BaseSubOpModel; @@ -125,6 +132,57 @@ TEST(FloatSubOpModel, WithBroadcast) { } } +TEST(IntegerSubOpModel, NoActivation) { + IntegerSubOpModel m({TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}}, + ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-20, 2, 7, 8}); + m.PopulateTensor(m.input2(), {1, 2, 3, 5}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({-21, 0, 4, 3})); +} + +TEST(IntegerSubOpModel, ActivationRELU_N1_TO_1) { + IntegerSubOpModel m({TensorType_INT32, {1, 2, 2, 1}}, + {TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}}, + ActivationFunctionType_RELU_N1_TO_1); + m.PopulateTensor(m.input1(), {-20, 2, 7, 8}); + m.PopulateTensor(m.input2(), {1, 2, 3, 5}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1, 0, 1, 1})); +} + +TEST(IntegerSubOpModel, VariousInputShapes) { + std::vector> test_shapes = { + {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; + for (int i = 0; i < test_shapes.size(); ++i) { + IntegerSubOpModel m({TensorType_INT32, test_shapes[i]}, + {TensorType_INT32, test_shapes[i]}, + {TensorType_INT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-20, 2, 7, 8, 11, 20}); + m.PopulateTensor(m.input2(), {1, 2, 3, 5, 11, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({-21, 0, 4, 3, 0, 19})) + << "With shape number " << i; + } +} + +TEST(IntegerSubOpModel, WithBroadcast) { + std::vector> test_shapes = { + {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}}; + for (int i = 0; i < test_shapes.size(); ++i) { + IntegerSubOpModel m({TensorType_INT32, test_shapes[i]}, + {TensorType_INT32, {}}, // always a scalar + {TensorType_INT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-20, 2, 7, 8, 11, 20}); + m.PopulateTensor(m.input2(), {1}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({-21, 1, 6, 7, 10, 19}))) + << "With shape number " << i; + } +} + TEST(QuantizedSubOpModel, QuantizedTestsNoActivation) { float kQuantizedTolerance = GetTolerance(-1.0, 1.0); std::vector> inputs1 = { diff --git a/tensorflow/contrib/lite/kernels/svdf.cc b/tensorflow/contrib/lite/kernels/svdf.cc index 22eebdd4ceb16aeabc5e799c708f7236b3e2be37..6d4912ce3aa40bf95dc1e26572b8a07fb6362744 100644 --- a/tensorflow/contrib/lite/kernels/svdf.cc +++ b/tensorflow/contrib/lite/kernels/svdf.cc @@ -16,7 +16,6 @@ limitations under the License. // SVDF op that compresses a fully connected op via low-rank matrix // factorization. See https://research.google.com/pubs/archive/43813.pdf for // details. -#include #include #include #include @@ -105,7 +104,7 @@ constexpr int kStateTensor = 0; constexpr int kOutputTensor = 1; void* Init(TfLiteContext* context, const char* buffer, size_t length) { - auto* op_data = new OpData; + auto* op_data = new OpData(); op_data->float_weights_time_initialized = false; context->AddTensors(context, /*tensors_to_add=*/4, &op_data->scratch_tensor_index); diff --git a/tensorflow/contrib/lite/kernels/transpose_conv.cc b/tensorflow/contrib/lite/kernels/transpose_conv.cc index 7182374a6f2ec39c670e02e6fda9b967ae0a5b43..a9baa5c6988877ccc2e007e5fefdc980d7a3a679 100644 --- a/tensorflow/contrib/lite/kernels/transpose_conv.cc +++ b/tensorflow/contrib/lite/kernels/transpose_conv.cc @@ -12,7 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include @@ -22,7 +21,6 @@ limitations under the License. #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/context.h" -#include "tensorflow/contrib/lite/kernels/eigen_support.h" #include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" #include "tensorflow/contrib/lite/kernels/internal/tensor.h" #include "tensorflow/contrib/lite/kernels/kernel_util.h" @@ -39,35 +37,9 @@ constexpr int kWeightsTensor = 1; constexpr int kDataInputTensor = 2; constexpr int kOutputTensor = 0; -const int kTensorNotAllocated = -1; - -struct OpData { - // IDs are the arbitrary identifiers used by TF Lite to identify and access - // memory buffers. - int im2col_id = kTensorNotAllocated; - - // im2col is the only temporary currently tracked, therefore always index 0. - // If more temporaries are added, they should be properly tracked. - int32_t im2col_index = 0; -}; - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - // This is a builtin op, so we don't use the contents in 'buffer', if any. - // Instead, we allocate a new object to use as scratch space for im2col, and - // to carry information from Prepare() to Eval(). - auto* data = new OpData; - eigen_support::IncrementUsageCounter(context); - return data; -} - -void Free(TfLiteContext* context, void* buffer) { - eigen_support::DecrementUsageCounter(context); - delete reinterpret_cast(buffer); -} - -TfLiteStatus ResizeOutputTensor(TfLiteContext* context, - const TfLiteTensor* output_shape, - TfLiteTensor* output) { +TfLiteStatus ResizeOutputShape(TfLiteContext* context, + const TfLiteTensor* output_shape, + TfLiteTensor* output) { // Currently only support int32 for output shape. if (output_shape->type != kTfLiteInt32) { context->ReportError(context, "Output shape is %d, not int32.", @@ -83,60 +55,15 @@ TfLiteStatus ResizeOutputTensor(TfLiteContext* context, return context->ResizeTensor(context, output, output_shape_array); } -// Allocate temporary im2col tensor. -static TfLiteStatus AllocateIm2colTensor(TfLiteContext* context, - TfLiteNode* node) { - OpData* data = reinterpret_cast(node->user_data); - if (data->im2col_id == kTensorNotAllocated) { - context->AddTensors(context, 1, &data->im2col_id); - } - - TfLiteIntArrayFree(node->temporaries); - node->temporaries = TfLiteIntArrayCreate(1); - node->temporaries->data[data->im2col_index] = data->im2col_id; - - return kTfLiteOk; -} - -TfLiteStatus ResizeIm2ColTensor(TfLiteContext* context, - const TfLiteTensor* output_shape, - const TfLiteTensor* weights, - const TfLiteTensor* input, - TfLiteTensor* im2col) { - if (output_shape->type != kTfLiteInt32) { - context->ReportError(context, "im2col shape is %d, not int32.", - output_shape->type); - return kTfLiteError; - } - TF_LITE_ENSURE_EQ(context, NumElements(output_shape), 4); - TfLiteIntArray* im2col_shape_array = TfLiteIntArrayCreate(4); - im2col_shape_array->data[0] = output_shape->data.i32[0]; - im2col_shape_array->data[1] = output_shape->data.i32[1]; - im2col_shape_array->data[2] = output_shape->data.i32[2]; - const int input_depth = SizeOfDimension(input, 3); - const int filter_width = SizeOfDimension(weights, 1); - const int filter_height = SizeOfDimension(weights, 2); - im2col_shape_array->data[3] = input_depth * filter_height * filter_width; - - im2col->type = input->type; - im2col->allocation_type = kTfLiteArenaRw; - return context->ResizeTensor(context, im2col, im2col_shape_array); -} - TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_EQ(context, NumInputs(node), 3); TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - TF_LITE_ENSURE_STATUS(AllocateIm2colTensor(context, node)); - const TfLiteTensor* output_shape = GetInput(context, node, kOutputShapeTensor); const TfLiteTensor* weights = GetInput(context, node, kWeightsTensor); const TfLiteTensor* input = GetInput(context, node, kDataInputTensor); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - OpData* user_data = reinterpret_cast(node->user_data); - TfLiteTensor* im2col = - &context->tensors[node->temporaries->data[user_data->im2col_index]]; TF_LITE_ENSURE_EQ(context, NumDimensions(output_shape), 1); TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4); @@ -153,15 +80,11 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_EQ(context, SizeOfDimension(input, 3), SizeOfDimension(weights, 3)); - if (IsConstantTensor(output_shape)) { - TF_LITE_ENSURE_STATUS(ResizeOutputTensor(context, output_shape, output)); - TF_LITE_ENSURE_STATUS( - ResizeIm2ColTensor(context, output_shape, weights, input, im2col)); - } else { - // Defer resizing until Eval(). + if (!IsConstantTensor(output_shape)) { SetTensorToDynamic(output); + return kTfLiteOk; } - return kTfLiteOk; + return ResizeOutputShape(context, output_shape, output); } TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { @@ -170,19 +93,13 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const TfLiteTensor* weights = GetInput(context, node, kWeightsTensor); const TfLiteTensor* input = GetInput(context, node, kDataInputTensor); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - OpData* user_data = reinterpret_cast(node->user_data); - TfLiteTensor* im2col = - &context->tensors[node->temporaries->data[user_data->im2col_index]]; + const auto* params = reinterpret_cast(node->builtin_data); if (IsDynamicTensor(output)) { TF_LITE_ENSURE_OK(context, - ResizeOutputTensor(context, output_shape, output)); - } - if (IsDynamicTensor(im2col)) { - TF_LITE_ENSURE_OK(context, ResizeIm2ColTensor(context, output_shape, - weights, input, im2col)); + ResizeOutputShape(context, output_shape, output)); } // Get height and width of the output image. @@ -201,12 +118,17 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // Currently only support float32. switch (input->type) { case kTfLiteFloat32: - optimized_ops::TransposeConv( + reference_ops::TransposeConv( GetTensorData(input), GetTensorDims(input), GetTensorData(weights), GetTensorDims(weights), stride_width, stride_height, padding_size.width, padding_size.height, GetTensorData(output), GetTensorDims(output), - GetTensorData(im2col), GetTensorDims(im2col)); + // Last two args specify im2col which reference_ops ignores. + // (Note this does not lead to a performance regression, as the + // previous optimized version was just a copy of the reference code.) + // TODO(b/110208176): Allocate im2col tensors and switch to + // optimized_ops. + GetTensorData(output), GetTensorDims(output)); break; default: context->ReportError(context, "Type %d, not currently supported.", @@ -219,8 +141,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace transpose_conv TfLiteRegistration* Register_TRANSPOSE_CONV() { - static TfLiteRegistration r = {transpose_conv::Init, transpose_conv::Free, - transpose_conv::Prepare, transpose_conv::Eval}; + static TfLiteRegistration r = {nullptr, nullptr, transpose_conv::Prepare, + transpose_conv::Eval}; return &r; } diff --git a/tensorflow/contrib/lite/kernels/transpose_conv_test.cc b/tensorflow/contrib/lite/kernels/transpose_conv_test.cc index c741df19dee09b140954d0c110800cbd849c2f11..55df8971806ed0baae9f5bcaebd24fb8065ec300 100644 --- a/tensorflow/contrib/lite/kernels/transpose_conv_test.cc +++ b/tensorflow/contrib/lite/kernels/transpose_conv_test.cc @@ -14,7 +14,6 @@ limitations under the License. ==============================================================================*/ #include #include -#include "absl/memory/memory.h" #include "tensorflow/contrib/lite/interpreter.h" #include "tensorflow/contrib/lite/kernels/register.h" #include "tensorflow/contrib/lite/kernels/test_util.h" @@ -25,49 +24,9 @@ namespace { using ::testing::ElementsAreArray; -class ConstTransposeConvOpModel : public SingleOpModel { - // Just to be extra confusing, transpose_conv has an _input_ named - // "output_shape". This input sets the shape of the output tensor of the op. - // In this version of the test class, "output_shape" is a constant that must - // be specified in the constructor. - public: - ConstTransposeConvOpModel(TfLiteRegistration* registration, - std::initializer_list input_shape, - std::initializer_list filter_shape, - std::initializer_list output_shape_data, - Padding padding, int stride_w, int stride_h) { - output_shape_ = AddConstInput(TensorType_INT32, output_shape_data, - {static_cast(output_shape_data.size())}); - filter_ = AddInput(TensorType_FLOAT32); - input_ = AddInput(TensorType_FLOAT32); - output_ = AddOutput(TensorType_FLOAT32); - SetBuiltinOp( - BuiltinOperator_TRANSPOSE_CONV, BuiltinOptions_TransposeConvOptions, - CreateTransposeConvOptions(builder_, padding, stride_w, stride_h) - .Union()); - resolver_ = absl::make_unique( - BuiltinOperator_TRANSPOSE_CONV, registration); - BuildInterpreter({{4}, filter_shape, input_shape}); - } - - int output_shape() { return output_shape_; } - int filter() { return filter_; } - int input() { return input_; } - - std::vector GetOutput() { return ExtractVector(output_); } - std::vector GetOutputShape() { return GetTensorShape(output_); } - - private: - int output_shape_; - int filter_; - int input_; - int output_; -}; - class TransposeConvOpModel : public SingleOpModel { public: - TransposeConvOpModel(TfLiteRegistration* registration, - std::initializer_list input_shape, + TransposeConvOpModel(std::initializer_list input_shape, std::initializer_list filter_shape, Padding padding, int stride_w, int stride_h) { output_shape_ = AddInput(TensorType_INT32); @@ -78,8 +37,6 @@ class TransposeConvOpModel : public SingleOpModel { BuiltinOperator_TRANSPOSE_CONV, BuiltinOptions_TransposeConvOptions, CreateTransposeConvOptions(builder_, padding, stride_w, stride_h) .Union()); - resolver_ = absl::make_unique( - BuiltinOperator_TRANSPOSE_CONV, registration); BuildInterpreter({{4}, filter_shape, input_shape}); } @@ -97,15 +54,6 @@ class TransposeConvOpModel : public SingleOpModel { int output_; }; -const auto kKernelMap = new std::map({}); - -class TransposeConvOpTest : public SingleOpTest { - protected: - const std::map& GetKernelMap() override { - return *kKernelMap; - } -}; - // Test case: // output = tf.nn.conv2d_backprop_input( // tf.constant([ 1, 4, 4, 1 ]), @@ -113,9 +61,8 @@ class TransposeConvOpTest : public SingleOpTest { // tf.constant(np.arange(1, 17), shape=[ 1, 4, 4, 1 ], dtype=tf.float32), // [1, 1, 1, 1 ], // "SAME") -TEST_P(TransposeConvOpTest, SimpleTest) { - TransposeConvOpModel m(GetRegistration(), {1, 4, 4, 1}, {1, 3, 3, 1}, - Padding_SAME, 1, 1); +TEST(TransposeConvOpModelTest, SimpleTest) { + TransposeConvOpModel m({1, 4, 4, 1}, {1, 3, 3, 1}, Padding_SAME, 1, 1); m.PopulateTensor(m.output_shape(), {1, 4, 4, 1}); m.PopulateTensor(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9}); m.PopulateTensor( @@ -128,21 +75,6 @@ TEST_P(TransposeConvOpTest, SimpleTest) { EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 4, 1})); } -// Test case: Same as above, but with a const "output_shape" -TEST_P(TransposeConvOpTest, ConstSimpleTest) { - ConstTransposeConvOpModel m(GetRegistration(), {1, 4, 4, 1}, {1, 4, 4, 1}, - {1, 3, 3, 1}, Padding_SAME, 1, 1); - m.PopulateTensor(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9}); - m.PopulateTensor( - m.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); - m.Invoke(); - - EXPECT_THAT(m.GetOutput(), - ElementsAreArray({29, 62, 83, 75, 99, 192, 237, 198, 207, 372, - 417, 330, 263, 446, 485, 365})); - EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 4, 1})); -} - // Test case: // filter = tf.constant(np.arange(1, 19), // shape=[ 3, 3, 1, 2 ], @@ -155,9 +87,8 @@ TEST_P(TransposeConvOpTest, ConstSimpleTest) { // "SAME") // And filter value is derived by: // filter = tf.reshape(tf.transpose(filter, perm=[3, 0, 1, 2]), shape=[18, 1]) -TEST_P(TransposeConvOpTest, TwoFiltersTest) { - TransposeConvOpModel m(GetRegistration(), {1, 4, 4, 2}, {1, 3, 3, 2}, - Padding_SAME, 1, 1); +TEST(TransposeConvOpModelTest, TwoFiltersTest) { + TransposeConvOpModel m({1, 4, 4, 2}, {1, 3, 3, 2}, Padding_SAME, 1, 1); m.PopulateTensor(m.output_shape(), {1, 4, 4, 1}); m.PopulateTensor(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18}); @@ -185,9 +116,8 @@ TEST_P(TransposeConvOpTest, TwoFiltersTest) { // "VALID") // And filter value is derived by: // filter = tf.reshape(tf.transpose(filter, perm=[3, 0, 1, 2]), shape=[1, 18]) -TEST_P(TransposeConvOpTest, PaddingValidTest) { - TransposeConvOpModel m(GetRegistration(), {1, 4, 4, 2}, {1, 3, 3, 2}, - Padding_VALID, 1, 1); +TEST(TransposeConvOpModelTest, PaddingValidTest) { + TransposeConvOpModel m({1, 4, 4, 2}, {1, 3, 3, 2}, Padding_VALID, 1, 1); m.PopulateTensor(m.output_shape(), {1, 6, 6, 1}); m.PopulateTensor(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18}); @@ -216,9 +146,8 @@ TEST_P(TransposeConvOpTest, PaddingValidTest) { // tf.constant(np.arange(1, 5), shape=[ 1, 2, 2, 1 ], dtype=tf.float32), // [1, 2, 2, 1 ], // "VALID") -TEST_P(TransposeConvOpTest, StrideValidTest) { - TransposeConvOpModel m(GetRegistration(), {1, 2, 2, 1}, {1, 3, 3, 1}, - Padding_VALID, 2, 2); +TEST(TransposeConvOpModelTest, StrideValidTest) { + TransposeConvOpModel m({1, 2, 2, 1}, {1, 3, 3, 1}, Padding_VALID, 2, 2); m.PopulateTensor(m.output_shape(), {1, 5, 5, 1}); m.PopulateTensor(m.filter(), {1, 2, 3, 4, 5, 6, 7, 8, 9}); m.PopulateTensor(m.input(), {1, 2, 3, 4}); @@ -241,9 +170,8 @@ TEST_P(TransposeConvOpTest, StrideValidTest) { // tf.constant(np.arange(1, 5), shape=[ 1, 2, 2, 1 ], dtype=tf.float32), // [1, 2, 2, 1 ], // "VALID") -TEST_P(TransposeConvOpTest, MultiChannelTest) { - TransposeConvOpModel m(GetRegistration(), {1, 2, 2, 1}, {2, 3, 3, 1}, - Padding_VALID, 2, 2); +TEST(TransposeConvOpModelTest, MultiChannelTest) { + TransposeConvOpModel m({1, 2, 2, 1}, {2, 3, 3, 1}, Padding_VALID, 2, 2); m.PopulateTensor(m.output_shape(), {1, 5, 5, 2}); m.PopulateTensor(m.filter(), {1, 3, 5, 7, 9, 11, 13, 15, 17, 2, 4, 6, 8, 10, 12, 14, 16, 18}); @@ -259,24 +187,6 @@ TEST_P(TransposeConvOpTest, MultiChannelTest) { EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 5, 5, 2})); } -// Test case: Same as above, but with a const "output_shape" -TEST_P(TransposeConvOpTest, ConstMultiChannelTest) { - ConstTransposeConvOpModel m(GetRegistration(), {1, 2, 2, 1}, {2, 3, 3, 1}, - {1, 5, 5, 2}, Padding_VALID, 2, 2); - m.PopulateTensor(m.filter(), {1, 3, 5, 7, 9, 11, 13, 15, 17, 2, 4, 6, - 8, 10, 12, 14, 16, 18}); - m.PopulateTensor(m.input(), {1, 2, 3, 4}); - m.Invoke(); - - EXPECT_THAT( - m.GetOutput(), - ElementsAreArray({1, 2, 3, 4, 7, 10, 6, 8, 10, 12, 7, 8, 9, - 10, 25, 28, 18, 20, 22, 24, 16, 20, 24, 28, 62, 72, - 42, 48, 54, 60, 21, 24, 27, 30, 61, 68, 36, 40, 44, - 48, 39, 42, 45, 48, 103, 110, 60, 64, 68, 72})); - EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 5, 5, 2})); -} - // Test case: // filter = tf.constant(np.random.randint(1, 10, size=9), // shape=[ 3, 3, 1, 1 ], @@ -289,9 +199,8 @@ TEST_P(TransposeConvOpTest, ConstMultiChannelTest) { // "SAME") // And filter value is derived by: // filter = tf.reshape(tf.transpose(filter, perm=[3, 0, 1, 2]), shape=[-1]) -TEST_P(TransposeConvOpTest, AccuracyTest) { - TransposeConvOpModel m(GetRegistration(), {1, 1, 2, 1}, {1, 3, 3, 1}, - Padding_SAME, 3, 3); +TEST(TransposeConvOpModelTest, AccuracyTest) { + TransposeConvOpModel m({1, 1, 2, 1}, {1, 3, 3, 1}, Padding_SAME, 3, 3); m.PopulateTensor(m.output_shape(), {1, 3, 4, 1}); m.PopulateTensor(m.filter(), {9, 5, 6, 9, 8, 5, 3, 1, 4}); m.PopulateTensor(m.input(), {323, 521}); @@ -303,10 +212,6 @@ TEST_P(TransposeConvOpTest, AccuracyTest) { EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 4, 1})); } -INSTANTIATE_TEST_CASE_P( - TransposeConvOpTest, TransposeConvOpTest, - ::testing::ValuesIn(SingleOpTest::GetKernelTags(*kKernelMap))); - } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc index 32daf2bb02d5f63391cc5ba45654acd4acfbfe56..0acd705950cb262bbb2625aa6143f88b429a6562 100644 --- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc +++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include @@ -274,7 +273,8 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { const int n_output = recurrent_to_output_weights->dims->data[1]; // Check that input tensor dimensions matches with each other. - CheckInputTensorDimensions(context, node, n_input, n_output, n_cell); + TF_LITE_ENSURE_OK(context, CheckInputTensorDimensions(context, node, n_input, + n_output, n_cell)); // Get the pointer to output, output_state and cell_state buffer tensors. TfLiteTensor* output = GetOutput(context, node, kOutputTensor); diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc index 164a0cbd08d6ce82a413f12ba6b1703087a30aba..0d6d29a171735a00a8dcc6cd0213a859b9f8094a 100644 --- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc +++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc @@ -12,7 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index c448fb71db204494042192d6a75ac4d600467e47..5814cddc5ba8d4099a449ea6e42fc031f6ef46b9 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -19,7 +19,6 @@ limitations under the License. #include #include #include -#include #include "tensorflow/contrib/lite/allocation.h" #include "tensorflow/contrib/lite/builtin_op_data.h" @@ -186,6 +185,8 @@ InterpreterBuilder::InterpreterBuilder(const ::tflite::Model* model, op_resolver_(op_resolver), error_reporter_(ValidateErrorReporter(error_reporter)) {} +InterpreterBuilder::~InterpreterBuilder() {} + TfLiteStatus InterpreterBuilder::BuildLocalIndexToRegistrationMapping() { TfLiteStatus status = kTfLiteOk; auto opcodes = model_->operator_codes(); @@ -204,8 +205,9 @@ TfLiteStatus InterpreterBuilder::BuildLocalIndexToRegistrationMapping() { } else if (builtin_code != BuiltinOperator_CUSTOM) { registration = op_resolver_.FindOp(builtin_code, version); if (registration == nullptr) { - error_reporter_->Report("Didn't find op for builtin opcode '%s'\n", - EnumNameBuiltinOperator(builtin_code)); + error_reporter_->Report( + "Didn't find op for builtin opcode '%s' version '%d'\n", + EnumNameBuiltinOperator(builtin_code), version); status = kTfLiteError; } } else if (!opcode->custom_code()) { @@ -613,6 +615,8 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, break; } case BuiltinOperator_MEAN: + case BuiltinOperator_REDUCE_MAX: + case BuiltinOperator_REDUCE_PROD: case BuiltinOperator_SUM: { auto* params = MallocPOD(); if (auto* schema_params = op->builtin_options_as_ReducerOptions()) { @@ -661,6 +665,15 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, *builtin_data = reinterpret_cast(params); break; } + case BuiltinOperator_ARG_MIN: { + auto* params = MallocPOD(); + if (const auto* schema_params = op->builtin_options_as_ArgMinOptions()) { + ConvertTensorType(schema_params->output_type(), ¶ms->output_type, + error_reporter); + } + *builtin_data = reinterpret_cast(params); + break; + } case BuiltinOperator_TRANSPOSE_CONV: { TfLiteTransposeConvParams* params = MallocPOD(); @@ -692,11 +705,39 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, *builtin_data = static_cast(params); break; } + case BuiltinOperator_PACK: { + TfLitePackParams* params = MallocPOD(); + if (auto* pack_params = op->builtin_options_as_PackOptions()) { + params->values_count = pack_params->values_count(); + params->axis = pack_params->axis(); + } + *builtin_data = reinterpret_cast(params); + break; + } case BuiltinOperator_DELEGATE: { // TODO(ycling): Revisit when supporting saving delegated models. error_reporter->Report("DELEGATE op shouldn't exist in model."); return kTfLiteError; } + case BuiltinOperator_FAKE_QUANT: { + auto* params = MallocPOD(); + if (auto* schema_params = op->builtin_options_as_FakeQuantOptions()) { + params->min = schema_params->min(); + params->max = schema_params->max(); + params->num_bits = schema_params->num_bits(); + params->narrow_range = schema_params->narrow_range(); + } + *builtin_data = static_cast(params); + break; + } + case BuiltinOperator_ONE_HOT: { + auto* params = MallocPOD(); + if (auto* schema_params = op->builtin_options_as_OneHotOptions()) { + params->axis = schema_params->axis(); + } + *builtin_data = static_cast(params); + break; + } // Below are the ops with no builtin_data strcture. case BuiltinOperator_BATCH_TO_SPACE_ND: @@ -739,6 +780,7 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, case BuiltinOperator_TOPK_V2: case BuiltinOperator_TRANSPOSE: case BuiltinOperator_POW: + case BuiltinOperator_LOGICAL_OR: break; } return kTfLiteOk; diff --git a/tensorflow/contrib/lite/model.h b/tensorflow/contrib/lite/model.h index 3946b490417104f620ecb55bb22d4ef99fd33bb7..8bc9ecd7ce9725c3d88985ccd92d48aed169fe31 100644 --- a/tensorflow/contrib/lite/model.h +++ b/tensorflow/contrib/lite/model.h @@ -156,6 +156,7 @@ class InterpreterBuilder { InterpreterBuilder(const ::tflite::Model* model, const OpResolver& op_resolver, ErrorReporter* error_reporter = DefaultErrorReporter()); + ~InterpreterBuilder(); InterpreterBuilder(const InterpreterBuilder&) = delete; InterpreterBuilder& operator=(const InterpreterBuilder&) = delete; TfLiteStatus operator()(std::unique_ptr* interpreter); diff --git a/tensorflow/contrib/lite/model_test.cc b/tensorflow/contrib/lite/model_test.cc index 15bae21a411c1241cf71ab4d3f0e0289eaac8ef3..df4f60d4ad4eb71f48eb3ad364f95f93b84f3d75 100644 --- a/tensorflow/contrib/lite/model_test.cc +++ b/tensorflow/contrib/lite/model_test.cc @@ -19,7 +19,6 @@ limitations under the License. #include #include #include -#include #include "tensorflow/contrib/lite/model.h" @@ -242,14 +241,6 @@ TEST(BasicFlatBufferModel, TestWithNullVerifier) { "tensorflow/contrib/lite/testdata/test_model.bin", nullptr)); } -struct TestErrorReporter : public ErrorReporter { - int Report(const char* format, va_list args) override { - calls++; - return 0; - } - int calls = 0; -}; - // This makes sure the ErrorReporter is marshalled from FlatBufferModel to // the Interpreter. TEST(BasicFlatBufferModel, TestCustomErrorReporter) { @@ -263,7 +254,7 @@ TEST(BasicFlatBufferModel, TestCustomErrorReporter) { TrivialResolver resolver; InterpreterBuilder(*model, resolver)(&interpreter); ASSERT_NE(interpreter->Invoke(), kTfLiteOk); - ASSERT_EQ(reporter.calls, 1); + ASSERT_EQ(reporter.num_calls(), 1); } // This makes sure the ErrorReporter is marshalled from FlatBufferModel to diff --git a/tensorflow/contrib/lite/models/smartreply/BUILD b/tensorflow/contrib/lite/models/smartreply/BUILD index 8b5fa240ac31d9ee61879c42aee3c5d449ae60db..9d88c396ba69948e3ae285c913a4499a1409b93a 100644 --- a/tensorflow/contrib/lite/models/smartreply/BUILD +++ b/tensorflow/contrib/lite/models/smartreply/BUILD @@ -47,6 +47,7 @@ cc_test( name = "extract_feature_op_test", size = "small", srcs = ["ops/extract_feature_test.cc"], + tags = ["no_oss"], deps = [ ":custom_ops", "//tensorflow/contrib/lite:framework", @@ -61,6 +62,7 @@ cc_test( name = "normalize_op_test", size = "small", srcs = ["ops/normalize_test.cc"], + tags = ["no_oss"], deps = [ ":custom_ops", "//tensorflow/contrib/lite:framework", @@ -75,6 +77,7 @@ cc_test( name = "predict_op_test", size = "small", srcs = ["ops/predict_test.cc"], + tags = ["no_oss"], deps = [ ":custom_ops", "//tensorflow/contrib/lite:framework", diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc index 905c0919cb690012c2feba2cca821aa43fb2ddff..1c06b29deb541fa73dd597c7f8e465c760f1720b 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/nnapi_delegate.cc @@ -548,6 +548,26 @@ TfLiteStatus AddOpsAndParams( add_squeeze_params(node.builtin_data); nn_op_type = ANEURALNETWORKS_SQUEEZE; break; + case tflite::BuiltinOperator_TRANSPOSE: + // The permutation input tensor value dictates the output dimensions. + // TODO(b/110888333): Support dynamically-sized tensors in delegates. + if ((node.inputs->size > 1) && + (interpreter->tensor(node.inputs->data[1])->allocation_type != + kTfLiteMmapRo)) { + logError("NNAPI does not yet support dynamic tensors."); + return kTfLiteError; + } + nnapi_version = 11; // require NNAPI 1.1 + nn_op_type = ANEURALNETWORKS_TRANSPOSE; + break; + case tflite::BuiltinOperator_L2_NORMALIZATION: + nn_op_type = ANEURALNETWORKS_L2_NORMALIZATION; + if (reinterpret_cast(node.builtin_data) + ->activation != kTfLiteActNone) { + FATAL( + "NNAPI does not support L2Normalization with fused activations"); + } + break; case tflite::BuiltinOperator_CONCAT_EMBEDDINGS: case tflite::BuiltinOperator_LSH_PROJECTION: case tflite::BuiltinOperator_HASHTABLE_LOOKUP: @@ -556,7 +576,6 @@ TfLiteStatus AddOpsAndParams( case tflite::BuiltinOperator_EMBEDDING_LOOKUP_SPARSE: case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM: case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM: - case tflite::BuiltinOperator_L2_NORMALIZATION: case tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION: case tflite::BuiltinOperator_PADV2: case tflite::BuiltinOperator_RESIZE_BILINEAR: @@ -567,7 +586,6 @@ TfLiteStatus AddOpsAndParams( case tflite::BuiltinOperator_SPACE_TO_BATCH_ND: case tflite::BuiltinOperator_BATCH_TO_SPACE_ND: case tflite::BuiltinOperator_TOPK_V2: - case tflite::BuiltinOperator_TRANSPOSE: case tflite::BuiltinOperator_SPLIT: case tflite::BuiltinOperator_STRIDED_SLICE: case tflite::BuiltinOperator_EXP: @@ -579,6 +597,7 @@ TfLiteStatus AddOpsAndParams( case tflite::BuiltinOperator_MAXIMUM: case tflite::BuiltinOperator_MINIMUM: case tflite::BuiltinOperator_ARG_MAX: + case tflite::BuiltinOperator_ARG_MIN: case tflite::BuiltinOperator_GREATER: case tflite::BuiltinOperator_GREATER_EQUAL: case tflite::BuiltinOperator_LESS: @@ -595,10 +614,16 @@ TfLiteStatus AddOpsAndParams( case tflite::BuiltinOperator_EQUAL: case tflite::BuiltinOperator_NOT_EQUAL: case tflite::BuiltinOperator_SUM: + case tflite::BuiltinOperator_REDUCE_MAX: + case tflite::BuiltinOperator_REDUCE_PROD: case tflite::BuiltinOperator_SQRT: case tflite::BuiltinOperator_RSQRT: case tflite::BuiltinOperator_SHAPE: case tflite::BuiltinOperator_POW: + case tflite::BuiltinOperator_FAKE_QUANT: + case tflite::BuiltinOperator_PACK: + case tflite::BuiltinOperator_LOGICAL_OR: + case tflite::BuiltinOperator_ONE_HOT: logError("Op code %d is currently not delegated to NNAPI", builtin); return kTfLiteError; break; diff --git a/tensorflow/contrib/lite/profiling/BUILD b/tensorflow/contrib/lite/profiling/BUILD index a162b87b8f98576ec7c3b2623d1d34f2baef6cce..1172722f7a70771af73eb07571349e431755471c 100644 --- a/tensorflow/contrib/lite/profiling/BUILD +++ b/tensorflow/contrib/lite/profiling/BUILD @@ -58,6 +58,7 @@ cc_test( name = "profile_summarizer_test", srcs = ["profile_summarizer_test.cc"], copts = common_copts, + tags = ["no_oss"], deps = [ ":profile_summarizer", "//tensorflow/contrib/lite:framework", diff --git a/tensorflow/contrib/lite/profiling/profile_summarizer.cc b/tensorflow/contrib/lite/profiling/profile_summarizer.cc index c37a0965884a803e82da536f73a8f32a28691651..720bd717b9e3b0c45cbdbaaad2b6900edacc3051 100644 --- a/tensorflow/contrib/lite/profiling/profile_summarizer.cc +++ b/tensorflow/contrib/lite/profiling/profile_summarizer.cc @@ -23,8 +23,6 @@ namespace tflite { namespace profiling { namespace { -using Detail = tensorflow::StatsCalculator::Detail; - struct OperatorDetails { std::string name; std::vector inputs; @@ -83,7 +81,7 @@ OperatorDetails GetOperatorDetails(const tflite::Interpreter& interpreter, OperatorDetails details; details.name = op_name; if (profiling_string) { - details.name += ":" + string(profiling_string); + details.name += ":" + std::string(profiling_string); } details.inputs = GetTensorNames(interpreter, inputs); details.outputs = GetTensorNames(interpreter, outputs); @@ -125,28 +123,17 @@ void ProfileSummarizer::ProcessProfiles( int64_t base_start_us = events[0]->begin_timestamp_us; int node_num = 0; int64_t curr_total_us = 0; - std::map details; for (auto event : events) { auto op_details = GetOperatorDetails(interpreter, event->event_metadata); auto node_name = ToString(op_details.outputs); - auto result = details.emplace(node_name, Detail()); - Detail* detail = &(result.first->second); - detail->start_us.UpdateStat(event->begin_timestamp_us - base_start_us); + int64_t start_us = event->begin_timestamp_us - base_start_us; int64_t node_exec_time = event->end_timestamp_us - event->begin_timestamp_us; - detail->rel_end_us.UpdateStat(node_exec_time); + stats_calculator_->AddNodeStats(node_name, op_details.name, node_num, + start_us, node_exec_time, 0 /*memory */); curr_total_us += node_exec_time; ++node_num; - - if (result.second) { - detail->name = node_name; - detail->type = op_details.name; - detail->run_order = node_num; - detail->times_called = 0; - } - ++detail->times_called; } - stats_calculator_->UpdateDetails(details); stats_calculator_->UpdateRunTotalUs(curr_total_us); } } // namespace profiling diff --git a/tensorflow/contrib/lite/profiling/time.cc b/tensorflow/contrib/lite/profiling/time.cc index 446660bb747cd6e3b694669b64ac1d95cf415fbe..875ddb02bcfc30f4c2ef543fe1c15bec467e5410 100644 --- a/tensorflow/contrib/lite/profiling/time.cc +++ b/tensorflow/contrib/lite/profiling/time.cc @@ -14,16 +14,34 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/profiling/time.h" +#if defined(_MSC_VER) +#include // NOLINT(build/c++11) +#else #include +#endif namespace tflite { namespace profiling { namespace time { + +#if defined(_MSC_VER) + +uint64_t NowMicros() { + return std::chrono::duration_cast( + std::chrono::system_clock::now().time_since_epoch()) + .count(); +} + +#else + uint64_t NowMicros() { struct timeval tv; gettimeofday(&tv, nullptr); return static_cast(tv.tv_sec) * 1000000 + tv.tv_usec; } + +#endif // defined(_MSC_VER) + } // namespace time } // namespace profiling } // namespace tflite diff --git a/tensorflow/contrib/lite/python/BUILD b/tensorflow/contrib/lite/python/BUILD index 27909a9458f6b09f96cb556a5254f01e54f46e05..860aff9e7e2de9616dea40f42a33bc1e4ee9f400 100644 --- a/tensorflow/contrib/lite/python/BUILD +++ b/tensorflow/contrib/lite/python/BUILD @@ -19,6 +19,8 @@ py_library( visibility = ["//visibility:public"], deps = [ "//tensorflow/contrib/lite/python/interpreter_wrapper:tensorflow_wrap_interpreter_wrapper", + "//tensorflow/python:util", + "//third_party/py/numpy", ], ) @@ -30,9 +32,10 @@ py_test( tags = ["no_oss"], deps = [ ":interpreter", - "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", - "//tensorflow/python:platform_test", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:platform", + "//third_party/py/numpy", ], ) @@ -69,7 +72,10 @@ py_test( srcs = ["lite_test.py"], data = [":interpreter_test_data"], srcs_version = "PY2AND3", - tags = ["no_windows"], + tags = [ + "no_oss", + "no_windows", + ], deps = [ ":lite", ], @@ -161,7 +167,10 @@ py_test( name = "convert_saved_model_test", srcs = ["convert_saved_model_test.py"], srcs_version = "PY2AND3", - tags = ["no_windows"], + tags = [ + "no_oss", + "no_windows", + ], visibility = ["//visibility:public"], deps = [ ":convert_saved_model", diff --git a/tensorflow/contrib/lite/python/convert.py b/tensorflow/contrib/lite/python/convert.py index 0ea2630f711727787332f207bdff6383aac8097c..ec49738fb5365a16c41cc6737198b5707508a3e2 100644 --- a/tensorflow/contrib/lite/python/convert.py +++ b/tensorflow/contrib/lite/python/convert.py @@ -115,6 +115,7 @@ def build_toco_convert_protos(input_tensors, inference_type=lite_constants.FLOAT, inference_input_type=None, input_format=lite_constants.TENSORFLOW_GRAPHDEF, + input_shapes=None, output_format=lite_constants.TFLITE, quantized_input_stats=None, default_ranges_stats=None, @@ -141,6 +142,8 @@ def build_toco_convert_protos(input_tensors, Must be `{FLOAT, QUANTIZED_UINT8}`. (default `inference_type`) input_format: Type of data to read Currently must be `{TENSORFLOW_GRAPHDEF}`. (default TENSORFLOW_GRAPHDEF) + input_shapes: Input array shape. It needs to be a list of the same length + as `input_tensors`, or None. (default None) output_format: Output file format. Currently must be `{TFLITE, GRAPHVIZ_DOT}`. (default TFLITE) quantized_input_stats: List of tuples of integers representing the mean and @@ -209,7 +212,11 @@ def build_toco_convert_protos(input_tensors, if inference_type == lite_constants.QUANTIZED_UINT8: input_array.mean_value, input_array.std_value = quantized_input_stats[idx] input_array.name = tensor_name(input_tensor) - input_array.shape.dims.extend(map(int, input_tensor.get_shape())) + if input_shapes is None: + shape = input_tensor.get_shape() + else: + shape = input_shapes[idx] + input_array.shape.dims.extend(map(int, shape)) for output_tensor in output_tensors: model.output_arrays.append(tensor_name(output_tensor)) diff --git a/tensorflow/contrib/lite/python/interpreter.py b/tensorflow/contrib/lite/python/interpreter.py index fd908234254185e0a0639618e936ca8ff58631da..3243bddac879b8eb0ca7a03d28b2f6094f905983 100644 --- a/tensorflow/contrib/lite/python/interpreter.py +++ b/tensorflow/contrib/lite/python/interpreter.py @@ -18,6 +18,7 @@ from __future__ import division from __future__ import print_function import sys +import numpy as np from tensorflow.python.util.lazy_loader import LazyLoader # Lazy load since some of the performance benchmark skylark rules @@ -56,9 +57,6 @@ class Interpreter(object): self._interpreter = ( _interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromBuffer( model_content)) - if not self._interpreter: - raise ValueError( - 'Failed to create model from {} bytes'.format(len(model_content))) elif not model_path and not model_path: raise ValueError('`model_path` or `model_content` must be specified.') else: @@ -66,8 +64,7 @@ class Interpreter(object): def allocate_tensors(self): self._ensure_safe() - if not self._interpreter.AllocateTensors(): - raise ValueError('Failed to allocate tensors') + return self._interpreter.AllocateTensors() def _safe_to_run(self): """Returns true if there exist no numpy array buffers. @@ -152,8 +149,7 @@ class Interpreter(object): Raises: ValueError: If the interpreter could not set the tensor. """ - if not self._interpreter.SetTensor(tensor_index, value): - raise ValueError('Failed to set tensor') + self._interpreter.SetTensor(tensor_index, value) def resize_tensor_input(self, input_index, tensor_size): """Resizes an input tensor. @@ -167,8 +163,10 @@ class Interpreter(object): ValueError: If the interpreter could not resize the input tensor. """ self._ensure_safe() - if not self._interpreter.ResizeInputTensor(input_index, tensor_size): - raise ValueError('Failed to resize input') + # `ResizeInputTensor` now only accepts int32 numpy array as `tensor_size + # parameter. + tensor_size = np.array(tensor_size, dtype=np.int32) + self._interpreter.ResizeInputTensor(input_index, tensor_size) def get_output_details(self): """Gets model output details. @@ -181,7 +179,9 @@ class Interpreter(object): ] def get_tensor(self, tensor_index): - """Gets the value of the input tensor. Note this makes a copy so prefer `tensor()`. + """Gets the value of the input tensor (get a copy). + + If you wish to avoid the copy, use `tensor()`. Args: tensor_index: Tensor index of tensor to get. This value can be gotten from @@ -208,7 +208,7 @@ class Interpreter(object): for i in range(10): input().fill(3.) interpreter.invoke() - print("inference %s" % output) + print("inference %s" % output()) Notice how this function avoids making a numpy array directly. This is because it is important to not hold actual numpy views to the data longer @@ -247,5 +247,7 @@ class Interpreter(object): ValueError: When the underlying interpreter fails raise ValueError. """ self._ensure_safe() - if not self._interpreter.Invoke(): - raise ValueError('Failed to invoke TFLite model') + self._interpreter.Invoke() + + def reset_all_variables_to_zero(self): + return self._interpreter.ResetVariableTensorsToZero() diff --git a/tensorflow/contrib/lite/python/interpreter_test.py b/tensorflow/contrib/lite/python/interpreter_test.py index 5f1fa26c3b7f76309a6f1f80aa3c1e4889781528..e77d52ca9950ec42300264bb56ebce253d4982b1 100644 --- a/tensorflow/contrib/lite/python/interpreter_test.py +++ b/tensorflow/contrib/lite/python/interpreter_test.py @@ -19,6 +19,7 @@ from __future__ import print_function import io import numpy as np +import six from tensorflow.contrib.lite.python import interpreter as interpreter_wrapper from tensorflow.python.framework import test_util @@ -82,7 +83,7 @@ class InterpreterTest(test_util.TensorFlowTestCase): test_input = np.array([[1, 2, 3, 4]], dtype=np.uint8) expected_output = np.array([[4, 3, 2, 1]], dtype=np.uint8) interpreter.resize_tensor_input(input_details[0]['index'], - np.array(test_input.shape, dtype=np.int32)) + test_input.shape) interpreter.allocate_tensors() interpreter.set_tensor(input_details[0]['index'], test_input) interpreter.invoke() @@ -91,6 +92,28 @@ class InterpreterTest(test_util.TensorFlowTestCase): self.assertTrue((expected_output == output_data).all()) +class InterpreterTestErrorPropagation(test_util.TensorFlowTestCase): + + def testInvalidModelContent(self): + with self.assertRaisesRegexp(ValueError, + 'Model provided has model identifier \''): + interpreter_wrapper.Interpreter(model_content=six.b('garbage')) + + def testInvalidModelFile(self): + with self.assertRaisesRegexp( + ValueError, 'Could not open \'totally_invalid_file_name\''): + interpreter_wrapper.Interpreter( + model_path='totally_invalid_file_name') + + def testInvokeBeforeReady(self): + interpreter = interpreter_wrapper.Interpreter( + model_path=resource_loader.get_path_to_datafile( + 'testdata/permute_float.tflite')) + with self.assertRaisesRegexp(RuntimeError, + 'Invoke called on model that is not ready'): + interpreter.invoke() + + class InterpreterTensorAccessorTest(test_util.TensorFlowTestCase): def setUp(self): diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD b/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD index 634c2a1e1f5005208b4eea5c853a43cccf4d244c..69ee95c320b72b68052c6f76f32c1493707f34b1 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/BUILD @@ -13,7 +13,6 @@ cc_library( deps = [ "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite/kernels:builtin_ops", - "//tensorflow/core:lib", "//third_party/py/numpy:headers", "//third_party/python_runtime:headers", "@com_google_absl//absl/memory", diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc index 5554d08fa08fdc6ddcb042d12f979164a144e337..9ab05f3068494a573ffa5b46f84be66a12d54e46 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc @@ -14,13 +14,13 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h" +#include #include #include "absl/memory/memory.h" #include "tensorflow/contrib/lite/interpreter.h" #include "tensorflow/contrib/lite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" -#include "tensorflow/core/platform/logging.h" // Disallow Numpy 1.7 deprecated symbols. #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION @@ -38,9 +38,58 @@ limitations under the License. #define CPP_TO_PYSTRING PyString_FromStringAndSize #endif +#define TFLITE_PY_CHECK(x) \ + if ((x) != kTfLiteOk) { \ + return error_reporter_->exception(); \ + } + +#define TFLITE_PY_TENSOR_BOUNDS_CHECK(i) \ + if (i >= interpreter_->tensors_size() || i < 0) { \ + PyErr_Format(PyExc_ValueError, \ + "Invalid tensor index %d exceeds max tensor index %lu", i, \ + interpreter_->tensors_size()); \ + return nullptr; \ + } + +#define TFLITE_PY_ENSURE_VALID_INTERPRETER() \ + if (!interpreter_) { \ + PyErr_SetString(PyExc_ValueError, "Interpreter was not initialized."); \ + return nullptr; \ + } + namespace tflite { namespace interpreter_wrapper { +class PythonErrorReporter : public tflite::ErrorReporter { + public: + PythonErrorReporter() {} + + // Report an error message + int Report(const char* format, va_list args) override { + char buf[1024]; + int formatted = vsnprintf(buf, sizeof(buf), format, args); + buffer_ << buf; + return formatted; + } + + // Set's a Python runtime exception with the last error. + PyObject* exception() { + std::string last_message = message(); + PyErr_SetString(PyExc_RuntimeError, last_message.c_str()); + return nullptr; + } + + // Gets the last error message and clears the buffer. + std::string message() { + std::string value = buffer_.str(); + buffer_.clear(); + return value; + } + + private: + std::stringstream buffer_; +}; + namespace { // Calls PyArray's initialization to initialize all the API pointers. Note that @@ -59,19 +108,8 @@ std::unique_ptr CreateInterpreter( ImportNumpy(); std::unique_ptr interpreter; - tflite::InterpreterBuilder(*model, resolver)(&interpreter); - if (interpreter) { - for (const int input_index : interpreter->inputs()) { - const TfLiteTensor* tensor = interpreter->tensor(input_index); - CHECK(tensor); - const TfLiteIntArray* dims = tensor->dims; - if (!dims) { - continue; - } - - std::vector input_dims(dims->data, dims->data + dims->size); - interpreter->ResizeInputTensor(input_index, input_dims); - } + if (tflite::InterpreterBuilder(*model, resolver)(&interpreter) != kTfLiteOk) { + return nullptr; } return interpreter; } @@ -95,10 +133,10 @@ int TfLiteTypeToPyArrayType(TfLiteType tf_lite_type) { case kTfLiteComplex64: return NPY_COMPLEX64; case kTfLiteNoType: - return -1; + return NPY_NOTYPE; + // Avoid default so compiler errors created when new types are made. } - LOG(ERROR) << "Unknown TfLiteType " << tf_lite_type; - return -1; + return NPY_NOTYPE; } TfLiteType TfLiteTypeFromPyArray(PyArrayObject* array) { @@ -122,8 +160,8 @@ TfLiteType TfLiteTypeFromPyArray(PyArrayObject* array) { return kTfLiteString; case NPY_COMPLEX64: return kTfLiteComplex64; + // Avoid default so compiler errors created when new types are made. } - LOG(ERROR) << "Unknown PyArray dtype " << pyarray_type; return kTfLiteNoType; } @@ -146,33 +184,54 @@ PyObject* PyTupleFromQuantizationParam(const TfLiteQuantizationParams& param) { } // namespace +InterpreterWrapper* InterpreterWrapper::CreateInterpreterWrapper( + std::unique_ptr model, + std::unique_ptr error_reporter, + std::string* error_msg) { + if (!model) { + *error_msg = error_reporter->message(); + return nullptr; + } + + auto resolver = absl::make_unique(); + auto interpreter = CreateInterpreter(model.get(), *resolver); + if (!interpreter) { + *error_msg = error_reporter->message(); + return nullptr; + } + + InterpreterWrapper* wrapper = + new InterpreterWrapper(std::move(model), std::move(error_reporter), + std::move(resolver), std::move(interpreter)); + return wrapper; +} + InterpreterWrapper::InterpreterWrapper( - std::unique_ptr model) + std::unique_ptr model, + std::unique_ptr error_reporter, + std::unique_ptr resolver, + std::unique_ptr interpreter) : model_(std::move(model)), - resolver_(absl::make_unique()), - interpreter_(CreateInterpreter(model_.get(), *resolver_)) {} + error_reporter_(std::move(error_reporter)), + resolver_(std::move(resolver)), + interpreter_(std::move(interpreter)) {} InterpreterWrapper::~InterpreterWrapper() {} -bool InterpreterWrapper::AllocateTensors() { - if (!interpreter_) { - LOG(ERROR) << "Cannot allocate tensors: invalid interpreter."; - return false; - } - - if (interpreter_->AllocateTensors() != kTfLiteOk) { - LOG(ERROR) << "Unable to allocate tensors."; - return false; - } - - return true; +PyObject* InterpreterWrapper::AllocateTensors() { + TFLITE_PY_ENSURE_VALID_INTERPRETER(); + TFLITE_PY_CHECK(interpreter_->AllocateTensors()); + Py_RETURN_NONE; } -bool InterpreterWrapper::Invoke() { - return interpreter_ ? (interpreter_->Invoke() == kTfLiteOk) : false; +PyObject* InterpreterWrapper::Invoke() { + TFLITE_PY_ENSURE_VALID_INTERPRETER(); + TFLITE_PY_CHECK(interpreter_->Invoke()); + Py_RETURN_NONE; } PyObject* InterpreterWrapper::InputIndices() const { + TFLITE_PY_ENSURE_VALID_INTERPRETER(); PyObject* np_array = PyArrayFromIntVector(interpreter_->inputs().data(), interpreter_->inputs().size()); @@ -186,35 +245,36 @@ PyObject* InterpreterWrapper::OutputIndices() const { return PyArray_Return(reinterpret_cast(np_array)); } -bool InterpreterWrapper::ResizeInputTensor(int i, PyObject* value) { - if (!interpreter_) { - LOG(ERROR) << "Invalid interpreter."; - return false; - } +PyObject* InterpreterWrapper::ResizeInputTensor(int i, PyObject* value) { + TFLITE_PY_ENSURE_VALID_INTERPRETER(); std::unique_ptr array_safe( PyArray_FromAny(value, nullptr, 0, 0, NPY_ARRAY_CARRAY, nullptr)); if (!array_safe) { - LOG(ERROR) << "Failed to convert value into readable tensor."; - return false; + PyErr_SetString(PyExc_ValueError, + "Failed to convert numpy value into readable tensor."); + return nullptr; } PyArrayObject* array = reinterpret_cast(array_safe.get()); if (PyArray_NDIM(array) != 1) { - LOG(ERROR) << "Expected 1-D defining input shape."; - return false; + PyErr_Format(PyExc_ValueError, "Shape should be 1D instead of %d.", + PyArray_NDIM(array)); + return nullptr; } if (PyArray_TYPE(array) != NPY_INT32) { - LOG(ERROR) << "Shape must be an int32 array"; - return false; + PyErr_Format(PyExc_ValueError, "Shape must be type int32 (was %d).", + PyArray_TYPE(array)); + return nullptr; } std::vector dims(PyArray_SHAPE(array)[0]); memcpy(dims.data(), PyArray_BYTES(array), dims.size() * sizeof(int)); - return (interpreter_->ResizeInputTensor(i, dims) == kTfLiteOk); + TFLITE_PY_CHECK(interpreter_->ResizeInputTensor(i, dims)); + Py_RETURN_NONE; } std::string InterpreterWrapper::TensorName(int i) const { @@ -227,21 +287,21 @@ std::string InterpreterWrapper::TensorName(int i) const { } PyObject* InterpreterWrapper::TensorType(int i) const { - if (!interpreter_ || i >= interpreter_->tensors_size() || i < 0) { - return nullptr; - } + TFLITE_PY_ENSURE_VALID_INTERPRETER(); + TFLITE_PY_TENSOR_BOUNDS_CHECK(i); const TfLiteTensor* tensor = interpreter_->tensor(i); - int typenum = TfLiteTypeToPyArrayType(tensor->type); - return PyArray_TypeObjectFromType(typenum); + int code = TfLiteTypeToPyArrayType(tensor->type); + if (code == -1) { + PyErr_Format(PyExc_ValueError, "Invalid tflite type code %d", code); + return nullptr; + } + return PyArray_TypeObjectFromType(code); } PyObject* InterpreterWrapper::TensorSize(int i) const { - if (!interpreter_ || i >= interpreter_->tensors_size() || i < 0) { - Py_INCREF(Py_None); - return Py_None; - } - + TFLITE_PY_ENSURE_VALID_INTERPRETER(); + TFLITE_PY_TENSOR_BOUNDS_CHECK(i); const TfLiteTensor* tensor = interpreter_->tensor(i); PyObject* np_array = PyArrayFromIntVector(tensor->dims->data, tensor->dims->size); @@ -250,100 +310,87 @@ PyObject* InterpreterWrapper::TensorSize(int i) const { } PyObject* InterpreterWrapper::TensorQuantization(int i) const { - if (!interpreter_ || i >= interpreter_->tensors_size() || i < 0) { - Py_INCREF(Py_None); - return Py_None; - } - + TFLITE_PY_ENSURE_VALID_INTERPRETER(); + TFLITE_PY_TENSOR_BOUNDS_CHECK(i); const TfLiteTensor* tensor = interpreter_->tensor(i); return PyTupleFromQuantizationParam(tensor->params); } -bool InterpreterWrapper::SetTensor(int i, PyObject* value) { - if (!interpreter_) { - LOG(ERROR) << "Invalid interpreter."; - return false; - } - - if (i >= interpreter_->tensors_size()) { - LOG(ERROR) << "Invalid tensor index: " << i << " exceeds max tensor index " - << interpreter_->tensors_size(); - return false; - } +PyObject* InterpreterWrapper::SetTensor(int i, PyObject* value) { + TFLITE_PY_ENSURE_VALID_INTERPRETER(); + TFLITE_PY_TENSOR_BOUNDS_CHECK(i); std::unique_ptr array_safe( PyArray_FromAny(value, nullptr, 0, 0, NPY_ARRAY_CARRAY, nullptr)); if (!array_safe) { - LOG(ERROR) << "Failed to convert value into readable tensor."; - return false; + PyErr_SetString(PyExc_ValueError, + "Failed to convert value into readable tensor."); + return nullptr; } PyArrayObject* array = reinterpret_cast(array_safe.get()); const TfLiteTensor* tensor = interpreter_->tensor(i); if (TfLiteTypeFromPyArray(array) != tensor->type) { - LOG(ERROR) << "Cannot set tensor:" - << " Got tensor of type " << TfLiteTypeFromPyArray(array) - << " but expected type " << tensor->type << " for input " << i; - return false; + PyErr_Format(PyExc_ValueError, + "Cannot set tensor:" + " Got tensor of type %d" + " but expected type %d for input %d ", + TfLiteTypeFromPyArray(array), tensor->type, i); + return nullptr; } if (PyArray_NDIM(array) != tensor->dims->size) { - LOG(ERROR) << "Cannot set tensor: Dimension mismatch"; - return false; + PyErr_SetString(PyExc_ValueError, "Cannot set tensor: Dimension mismatch"); + return nullptr; } for (int j = 0; j < PyArray_NDIM(array); j++) { if (tensor->dims->data[j] != PyArray_SHAPE(array)[j]) { - LOG(ERROR) << "Cannot set tensor: Dimension mismatch"; - return false; + PyErr_SetString(PyExc_ValueError, + "Cannot set tensor: Dimension mismatch"); + return nullptr; } } size_t size = PyArray_NBYTES(array); - DCHECK_EQ(size, tensor->bytes); + if (size != tensor->bytes) { + PyErr_Format(PyExc_ValueError, + "numpy array had %zu bytes but expected %zu bytes.", size, + tensor->bytes); + return nullptr; + } memcpy(tensor->data.raw, PyArray_DATA(array), size); - return true; + Py_RETURN_NONE; } namespace { -PyObject* CheckGetTensorArgs(Interpreter* interpreter, int tensor_index, +// Checks to see if a tensor access can succeed (returns nullptr on error). +// Otherwise returns Py_None. +PyObject* CheckGetTensorArgs(Interpreter* interpreter_, int tensor_index, TfLiteTensor** tensor, int* type_num) { - if (!interpreter) { - LOG(ERROR) << "Invalid interpreter."; - Py_INCREF(Py_None); - return Py_None; - } + TFLITE_PY_ENSURE_VALID_INTERPRETER(); + TFLITE_PY_TENSOR_BOUNDS_CHECK(tensor_index); - if (tensor_index >= interpreter->tensors_size() || tensor_index < 0) { - LOG(ERROR) << "Invalid tensor index: " << tensor_index - << " exceeds max tensor index " << interpreter->inputs().size(); - Py_INCREF(Py_None); - return Py_None; - } - - *tensor = interpreter->tensor(tensor_index); + *tensor = interpreter_->tensor(tensor_index); if ((*tensor)->bytes == 0) { - LOG(ERROR) << "Invalid tensor size"; - Py_INCREF(Py_None); - return Py_None; + PyErr_SetString(PyExc_ValueError, "Invalid tensor size."); + return nullptr; } *type_num = TfLiteTypeToPyArrayType((*tensor)->type); if (*type_num == -1) { - LOG(ERROR) << "Unknown tensor type " << (*tensor)->type; - Py_INCREF(Py_None); - return Py_None; + PyErr_SetString(PyExc_ValueError, "Unknown tensor type."); + return nullptr; } if (!(*tensor)->data.raw) { - LOG(ERROR) << "Tensor data is null."; - Py_INCREF(Py_None); - return Py_None; + PyErr_SetString(PyExc_ValueError, "Tensor data is null."); + return nullptr; } - return nullptr; + Py_RETURN_NONE; } } // namespace @@ -352,19 +399,20 @@ PyObject* InterpreterWrapper::GetTensor(int i) const { // Sanity check accessor TfLiteTensor* tensor = nullptr; int type_num = 0; - if (PyObject* pynone_or_nullptr = - CheckGetTensorArgs(interpreter_.get(), i, &tensor, &type_num)) { - return pynone_or_nullptr; - } + + PyObject* check_result = + CheckGetTensorArgs(interpreter_.get(), i, &tensor, &type_num); + if (check_result == nullptr) return check_result; + Py_XDECREF(check_result); + std::vector dims(tensor->dims->data, tensor->dims->data + tensor->dims->size); // Make a buffer copy but we must tell Numpy It owns that data or else // it will leak. void* data = malloc(tensor->bytes); if (!data) { - LOG(ERROR) << "Malloc to copy tensor failed."; - Py_INCREF(Py_None); - return Py_None; + PyErr_SetString(PyExc_ValueError, "Malloc to copy tensor failed."); + return nullptr; } memcpy(data, tensor->data.raw, tensor->bytes); PyObject* np_array = @@ -378,10 +426,11 @@ PyObject* InterpreterWrapper::tensor(PyObject* base_object, int i) { // Sanity check accessor TfLiteTensor* tensor = nullptr; int type_num = 0; - if (PyObject* pynone_or_nullptr = - CheckGetTensorArgs(interpreter_.get(), i, &tensor, &type_num)) { - return pynone_or_nullptr; - } + + PyObject* check_result = + CheckGetTensorArgs(interpreter_.get(), i, &tensor, &type_num); + if (check_result == nullptr) return check_result; + Py_XDECREF(check_result); std::vector dims(tensor->dims->data, tensor->dims->data + tensor->dims->size); @@ -394,22 +443,33 @@ PyObject* InterpreterWrapper::tensor(PyObject* base_object, int i) { } InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromFile( - const char* model_path) { + const char* model_path, std::string* error_msg) { + std::unique_ptr error_reporter(new PythonErrorReporter); std::unique_ptr model = - tflite::FlatBufferModel::BuildFromFile(model_path); - return model ? new InterpreterWrapper(std::move(model)) : nullptr; + tflite::FlatBufferModel::BuildFromFile(model_path, error_reporter.get()); + return CreateInterpreterWrapper(std::move(model), std::move(error_reporter), + error_msg); } InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromBuffer( - PyObject* data) { + PyObject* data, std::string* error_msg) { char * buf = nullptr; Py_ssize_t length; + std::unique_ptr error_reporter(new PythonErrorReporter); if (PY_TO_CPPSTRING(data, &buf, &length) == -1) { return nullptr; } std::unique_ptr model = - tflite::FlatBufferModel::BuildFromBuffer(buf, length); - return model ? new InterpreterWrapper(std::move(model)) : nullptr; + tflite::FlatBufferModel::BuildFromBuffer(buf, length, + error_reporter.get()); + return CreateInterpreterWrapper(std::move(model), std::move(error_reporter), + error_msg); +} + +PyObject* InterpreterWrapper::ResetVariableTensorsToZero() { + TFLITE_PY_ENSURE_VALID_INTERPRETER(); + TFLITE_PY_CHECK(interpreter_->ResetVariableTensorsToZero()); + Py_RETURN_NONE; } } // namespace interpreter_wrapper diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h index 681448be20cfc013a0c4d02a6aa549744b976077..3e03751da40064c64ab646d0b976a2ff5ca9c250 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h @@ -15,13 +15,13 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_PYTHON_INTERPRETER_WRAPPER_INTERPRETER_WRAPPER_H_ #define TENSORFLOW_CONTRIB_LITE_PYTHON_INTERPRETER_WRAPPER_INTERPRETER_WRAPPER_H_ +// Place `` before to avoid build failures in macOS. +#include #include #include #include -// Place `` before to avoid build failures in macOS. #include -#include // We forward declare TFLite classes here to avoid exposing them to SWIG. namespace tflite { @@ -36,41 +36,63 @@ class Interpreter; namespace interpreter_wrapper { +class PythonErrorReporter; + class InterpreterWrapper { public: // SWIG caller takes ownership of pointer. - static InterpreterWrapper* CreateWrapperCPPFromFile(const char* model_path); + static InterpreterWrapper* CreateWrapperCPPFromFile(const char* model_path, + std::string* error_msg); // SWIG caller takes ownership of pointer. - static InterpreterWrapper* CreateWrapperCPPFromBuffer(PyObject* data); + static InterpreterWrapper* CreateWrapperCPPFromBuffer(PyObject* data, + std::string* error_msg); ~InterpreterWrapper(); - bool AllocateTensors(); - bool Invoke(); + PyObject* AllocateTensors(); + PyObject* Invoke(); PyObject* InputIndices() const; PyObject* OutputIndices() const; - bool ResizeInputTensor(int i, PyObject* value); + PyObject* ResizeInputTensor(int i, PyObject* value); std::string TensorName(int i) const; PyObject* TensorType(int i) const; PyObject* TensorSize(int i) const; PyObject* TensorQuantization(int i) const; - bool SetTensor(int i, PyObject* value); + PyObject* SetTensor(int i, PyObject* value); PyObject* GetTensor(int i) const; + PyObject* ResetVariableTensorsToZero(); + // Returns a reference to tensor index i as a numpy array. The base_object // should be the interpreter object providing the memory. PyObject* tensor(PyObject* base_object, int i); private: - InterpreterWrapper(std::unique_ptr model); + // Helper function to construct an `InterpreterWrapper` object. + // It only returns InterpreterWrapper if it can construct an `Interpreter`. + // Otherwise it returns `nullptr`. + static InterpreterWrapper* CreateInterpreterWrapper( + std::unique_ptr model, + std::unique_ptr error_reporter, + std::string* error_msg); + + InterpreterWrapper( + std::unique_ptr model, + std::unique_ptr error_reporter, + std::unique_ptr resolver, + std::unique_ptr interpreter); // InterpreterWrapper is not copyable or assignable. We avoid the use of // InterpreterWrapper() = delete here for SWIG compatibility. InterpreterWrapper(); InterpreterWrapper(const InterpreterWrapper& rhs); + // The public functions which creates `InterpreterWrapper` should ensure all + // these member variables are initialized successfully. Otherwise it should + // report the error and return `nullptr`. const std::unique_ptr model_; + const std::unique_ptr error_reporter_; const std::unique_ptr resolver_; const std::unique_ptr interpreter_; }; diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.i b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.i index 7f51f9f00d1b2fe057052f7b7bd52bcb65231164..afb2092eacab1d8dcccf8c75cee1d8d5c34d7e75 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.i +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.i @@ -18,8 +18,51 @@ limitations under the License. %{ #define SWIG_FILE_WITH_INIT +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h" %} %include "tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h" + +namespace tflite { +namespace interpreter_wrapper { +%extend InterpreterWrapper { + + // Version of the constructor that handles producing Python exceptions + // that propagate strings. + static PyObject* CreateWrapperCPPFromFile(const char* model_path) { + std::string error; + if(tflite::interpreter_wrapper::InterpreterWrapper* ptr = + tflite::interpreter_wrapper::InterpreterWrapper + ::CreateWrapperCPPFromFile( + model_path, &error)) { + return SWIG_NewPointerObj( + ptr, SWIGTYPE_p_tflite__interpreter_wrapper__InterpreterWrapper, 1); + } else { + PyErr_SetString(PyExc_ValueError, error.c_str()); + return nullptr; + } + } + + // Version of the constructor that handles producing Python exceptions + // that propagate strings. + static PyObject* CreateWrapperCPPFromBuffer( + PyObject* data) { + std::string error; + if(tflite::interpreter_wrapper::InterpreterWrapper* ptr = + tflite::interpreter_wrapper::InterpreterWrapper + ::CreateWrapperCPPFromBuffer( + data, &error)) { + return SWIG_NewPointerObj( + ptr, SWIGTYPE_p_tflite__interpreter_wrapper__InterpreterWrapper, 1); + } else { + PyErr_SetString(PyExc_ValueError, error.c_str()); + return nullptr; + } + } +} + +} // namespace interpreter_wrapper +} // namespace tflite diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py index 29a1487c1f468055dde85ef6c2657a50f3d2f32b..2f9b9d469a27cc8910cb61c0da14769e5ff0baf0 100644 --- a/tensorflow/contrib/lite/python/lite.py +++ b/tensorflow/contrib/lite/python/lite.py @@ -40,24 +40,23 @@ from google.protobuf import text_format as _text_format from google.protobuf.message import DecodeError from tensorflow.contrib.lite.python import lite_constants as constants from tensorflow.contrib.lite.python.convert import build_toco_convert_protos # pylint: disable=unused-import -from tensorflow.contrib.lite.python.convert import tensor_name +from tensorflow.contrib.lite.python.convert import tensor_name as _tensor_name from tensorflow.contrib.lite.python.convert import toco_convert from tensorflow.contrib.lite.python.convert import toco_convert_protos # pylint: disable=unused-import -from tensorflow.contrib.lite.python.convert_saved_model import freeze_saved_model -from tensorflow.contrib.lite.python.convert_saved_model import get_tensors_from_tensor_names -from tensorflow.contrib.lite.python.convert_saved_model import set_tensor_shapes +from tensorflow.contrib.lite.python.convert_saved_model import freeze_saved_model as _freeze_saved_model +from tensorflow.contrib.lite.python.convert_saved_model import get_tensors_from_tensor_names as _get_tensors_from_tensor_names +from tensorflow.contrib.lite.python.convert_saved_model import set_tensor_shapes as _set_tensor_shapes from tensorflow.contrib.lite.python.interpreter import Interpreter # pylint: disable=unused-import from tensorflow.contrib.lite.python.op_hint import convert_op_hints_to_stubs # pylint: disable=unused-import from tensorflow.contrib.lite.python.op_hint import OpHint # pylint: disable=unused-import from tensorflow.core.framework import graph_pb2 as _graph_pb2 from tensorflow.python import keras as _keras from tensorflow.python.client import session as _session -from tensorflow.python.framework import graph_util as tf_graph_util -from tensorflow.python.framework.importer import import_graph_def -from tensorflow.python.ops.variables import global_variables_initializer -from tensorflow.python.saved_model import signature_constants -from tensorflow.python.saved_model import tag_constants -# from tensorflow.python.util.all_util import remove_undocumented +from tensorflow.python.framework import graph_util as _tf_graph_util +from tensorflow.python.framework.importer import import_graph_def as _import_graph_def +from tensorflow.python.ops.variables import global_variables_initializer as _global_variables_initializer +from tensorflow.python.saved_model import signature_constants as _signature_constants +from tensorflow.python.saved_model import tag_constants as _tag_constants class TocoConverter(object): @@ -196,7 +195,7 @@ class TocoConverter(object): input_arrays or output_arrays contains an invalid tensor name. """ with _session.Session() as sess: - sess.run(global_variables_initializer()) + sess.run(_global_variables_initializer()) # Read GraphDef from file. graph_def = _graph_pb2.GraphDef() @@ -218,12 +217,12 @@ class TocoConverter(object): raise ValueError( "Unable to parse input file '{}'.".format(graph_def_file)) sess.graph.as_default() - import_graph_def(graph_def, name="") + _import_graph_def(graph_def, name="") # Get input and output tensors. - input_tensors = get_tensors_from_tensor_names(sess.graph, input_arrays) - output_tensors = get_tensors_from_tensor_names(sess.graph, output_arrays) - set_tensor_shapes(input_tensors, input_shapes) + input_tensors = _get_tensors_from_tensor_names(sess.graph, input_arrays) + output_tensors = _get_tensors_from_tensor_names(sess.graph, output_arrays) + _set_tensor_shapes(input_tensors, input_shapes) # Check if graph is frozen. if not _is_frozen_graph(sess): @@ -261,12 +260,12 @@ class TocoConverter(object): TocoConverter class. """ if tag_set is None: - tag_set = set([tag_constants.SERVING]) + tag_set = set([_tag_constants.SERVING]) if signature_key is None: - signature_key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY + signature_key = _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY - result = freeze_saved_model(saved_model_dir, input_arrays, input_shapes, - output_arrays, tag_set, signature_key) + result = _freeze_saved_model(saved_model_dir, input_arrays, input_shapes, + output_arrays, tag_set, signature_key) return cls( graph_def=result[0], input_tensors=result[1], output_tensors=result[2]) @@ -299,15 +298,15 @@ class TocoConverter(object): # Get input and output tensors. if input_arrays: - input_tensors = get_tensors_from_tensor_names(sess.graph, input_arrays) + input_tensors = _get_tensors_from_tensor_names(sess.graph, input_arrays) else: input_tensors = keras_model.inputs if output_arrays: - output_tensors = get_tensors_from_tensor_names(sess.graph, output_arrays) + output_tensors = _get_tensors_from_tensor_names(sess.graph, output_arrays) else: output_tensors = keras_model.outputs - set_tensor_shapes(input_tensors, input_shapes) + _set_tensor_shapes(input_tensors, input_shapes) graph_def = _freeze_graph(sess, output_tensors) return cls(graph_def, input_tensors, output_tensors) @@ -328,12 +327,12 @@ class TocoConverter(object): for tensor in self._input_tensors: if not tensor.get_shape(): raise ValueError("Provide an input shape for input array '{0}'.".format( - tensor_name(tensor))) + _tensor_name(tensor))) shape = tensor.get_shape().as_list() if None in shape[1:]: raise ValueError( "None is only supported in the 1st dimension. Tensor '{0}' has " - "invalid shape '{1}'.".format(tensor_name(tensor), shape)) + "invalid shape '{1}'.".format(_tensor_name(tensor), shape)) elif shape[0] is None: self._set_batch_size(batch_size=1) @@ -343,7 +342,7 @@ class TocoConverter(object): quantized_stats = [] invalid_stats = [] for tensor in self._input_tensors: - name = tensor_name(tensor) + name = _tensor_name(tensor) if name in self.quantized_input_stats: quantized_stats.append(self.quantized_input_stats[name]) else: @@ -381,7 +380,7 @@ class TocoConverter(object): Returns: List of strings. """ - return [tensor_name(tensor) for tensor in self._input_tensors] + return [_tensor_name(tensor) for tensor in self._input_tensors] def _set_batch_size(self, batch_size): """Sets the first dimension of the input tensor to `batch_size`. @@ -428,11 +427,9 @@ def _freeze_graph(sess, output_tensors): Frozen GraphDef. """ if not _is_frozen_graph(sess): - sess.run(global_variables_initializer()) - output_arrays = [tensor_name(tensor) for tensor in output_tensors] - return tf_graph_util.convert_variables_to_constants(sess, sess.graph_def, - output_arrays) + sess.run(_global_variables_initializer()) + output_arrays = [_tensor_name(tensor) for tensor in output_tensors] + return _tf_graph_util.convert_variables_to_constants( + sess, sess.graph_def, output_arrays) else: return sess.graph_def - -# remove_undocumented(__name__) diff --git a/tensorflow/contrib/lite/python/tflite_convert.py b/tensorflow/contrib/lite/python/tflite_convert.py index 9bd1f4f76ee693414a8515a5bd2567001b53e2ea..d17482e60113da5bad3a76fa2ab634ae0ffb89fd 100644 --- a/tensorflow/contrib/lite/python/tflite_convert.py +++ b/tensorflow/contrib/lite/python/tflite_convert.py @@ -257,7 +257,7 @@ def run_main(_): parser.add_argument( "--input_arrays", type=str, - help="Names of the output arrays, comma-separated.") + help="Names of the input arrays, comma-separated.") parser.add_argument( "--input_shapes", type=str, diff --git a/tensorflow/contrib/lite/schema/BUILD b/tensorflow/contrib/lite/schema/BUILD index f095151cae835aa202ff4c9f43e175246f54f1cf..b616e449e6ddae6467a6b86269cd108c7eec0c26 100644 --- a/tensorflow/contrib/lite/schema/BUILD +++ b/tensorflow/contrib/lite/schema/BUILD @@ -30,7 +30,10 @@ py_test( size = "small", srcs = ["upgrade_schema_test.py"], srcs_version = "PY2AND3", - tags = ["no_pip"], + tags = [ + "no_oss", + "no_pip", + ], deps = [ ":upgrade_schema", "//tensorflow/python:client_testlib", @@ -64,6 +67,7 @@ cc_test( "schema_v3.fbs", ], tags = [ + "no_oss", "tflite_not_portable_android", "tflite_not_portable_ios", ], diff --git a/tensorflow/contrib/lite/schema/builtin_ops_header/BUILD b/tensorflow/contrib/lite/schema/builtin_ops_header/BUILD index 0148149a6adc141d67e82808f7e8c72ddb7e309a..4a627761daf45b0fddd7b99e8a9c3d0d0ed2ee5e 100644 --- a/tensorflow/contrib/lite/schema/builtin_ops_header/BUILD +++ b/tensorflow/contrib/lite/schema/builtin_ops_header/BUILD @@ -24,6 +24,7 @@ cc_binary( cc_test( name = "generator_test", srcs = ["generator_test.cc"], + tags = ["no_oss"], deps = [ ":generator", "@com_google_googletest//:gtest", @@ -36,6 +37,7 @@ cc_test( data = [ "//tensorflow/contrib/lite:builtin_ops.h", ], + tags = ["no_oss"], deps = [ ":generator", "@com_google_googletest//:gtest", diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index 15fb8bbdb8f100201750faf706eb45b697319dfb..8ed98ddaf40d1ca4d524407458d7b65d76c3ef2c 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -44,7 +44,7 @@ enum TensorType : byte { table QuantizationParameters { min:[float]; // For importing back into tensorflow. max:[float]; // For importing back into tensorflow. - scale:[float]; + scale:[float]; // For dequantizing the tensor's values. zero_point:[long]; } @@ -155,11 +155,18 @@ enum BuiltinOperator : byte { EQUAL = 71, NOT_EQUAL = 72, LOG = 73, - SUM=74, + SUM = 74, SQRT = 75, RSQRT = 76, SHAPE = 77, POW = 78, + ARG_MIN = 79, + FAKE_QUANT = 80, + REDUCE_PROD = 81, + REDUCE_MAX = 82, + PACK = 83, + LOGICAL_OR = 84, + ONE_HOT = 85, } // Options for the builtin operators. @@ -220,6 +227,11 @@ union BuiltinOptions { NotEqualOptions, ShapeOptions, PowOptions, + ArgMinOptions, + FakeQuantOptions, + PackOptions, + LogicalOrOptions, + OneHotOptions, } enum Padding : byte { SAME, VALID } @@ -469,6 +481,10 @@ table ArgMaxOptions { output_type : TensorType; } +table ArgMinOptions { + output_type : TensorType; +} + table GreaterOptions { } @@ -517,6 +533,28 @@ table ShapeOptions { table PowOptions { } +table FakeQuantOptions { + // Parameters supported by version 1: + min:float; + max:float; + num_bits:int; + + // Parameters supported by version 2: + narrow_range:bool; +} + +table PackOptions { + values_count:int; + axis:int; +} + +table LogicalOrOptions { +} + +table OneHotOptions { + axis:int; +} + // An OperatorCode can be an enum value (BuiltinOperator) if the operator is a // builtin, or a string if the operator is custom. table OperatorCode { diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h index fe0ff9a7a5ba0764475f4a7c14cd875b3cdb2aa8..4402f89b85de1df958fd32f57fae8ba9a0c6efee 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -157,6 +157,9 @@ struct TileOptionsT; struct ArgMaxOptions; struct ArgMaxOptionsT; +struct ArgMinOptions; +struct ArgMinOptionsT; + struct GreaterOptions; struct GreaterOptionsT; @@ -199,6 +202,18 @@ struct ShapeOptionsT; struct PowOptions; struct PowOptionsT; +struct FakeQuantOptions; +struct FakeQuantOptionsT; + +struct PackOptions; +struct PackOptionsT; + +struct LogicalOrOptions; +struct LogicalOrOptionsT; + +struct OneHotOptions; +struct OneHotOptionsT; + struct OperatorCode; struct OperatorCodeT; @@ -343,11 +358,18 @@ enum BuiltinOperator { BuiltinOperator_RSQRT = 76, BuiltinOperator_SHAPE = 77, BuiltinOperator_POW = 78, + BuiltinOperator_ARG_MIN = 79, + BuiltinOperator_FAKE_QUANT = 80, + BuiltinOperator_REDUCE_PROD = 81, + BuiltinOperator_REDUCE_MAX = 82, + BuiltinOperator_PACK = 83, + BuiltinOperator_LOGICAL_OR = 84, + BuiltinOperator_ONE_HOT = 85, BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_POW + BuiltinOperator_MAX = BuiltinOperator_ONE_HOT }; -inline BuiltinOperator (&EnumValuesBuiltinOperator())[78] { +inline BuiltinOperator (&EnumValuesBuiltinOperator())[85] { static BuiltinOperator values[] = { BuiltinOperator_ADD, BuiltinOperator_AVERAGE_POOL_2D, @@ -426,7 +448,14 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[78] { BuiltinOperator_SQRT, BuiltinOperator_RSQRT, BuiltinOperator_SHAPE, - BuiltinOperator_POW + BuiltinOperator_POW, + BuiltinOperator_ARG_MIN, + BuiltinOperator_FAKE_QUANT, + BuiltinOperator_REDUCE_PROD, + BuiltinOperator_REDUCE_MAX, + BuiltinOperator_PACK, + BuiltinOperator_LOGICAL_OR, + BuiltinOperator_ONE_HOT }; return values; } @@ -512,6 +541,13 @@ inline const char **EnumNamesBuiltinOperator() { "RSQRT", "SHAPE", "POW", + "ARG_MIN", + "FAKE_QUANT", + "REDUCE_PROD", + "REDUCE_MAX", + "PACK", + "LOGICAL_OR", + "ONE_HOT", nullptr }; return names; @@ -580,11 +616,16 @@ enum BuiltinOptions { BuiltinOptions_NotEqualOptions = 54, BuiltinOptions_ShapeOptions = 55, BuiltinOptions_PowOptions = 56, + BuiltinOptions_ArgMinOptions = 57, + BuiltinOptions_FakeQuantOptions = 58, + BuiltinOptions_PackOptions = 59, + BuiltinOptions_LogicalOrOptions = 60, + BuiltinOptions_OneHotOptions = 61, BuiltinOptions_MIN = BuiltinOptions_NONE, - BuiltinOptions_MAX = BuiltinOptions_PowOptions + BuiltinOptions_MAX = BuiltinOptions_OneHotOptions }; -inline BuiltinOptions (&EnumValuesBuiltinOptions())[57] { +inline BuiltinOptions (&EnumValuesBuiltinOptions())[62] { static BuiltinOptions values[] = { BuiltinOptions_NONE, BuiltinOptions_Conv2DOptions, @@ -642,7 +683,12 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[57] { BuiltinOptions_EqualOptions, BuiltinOptions_NotEqualOptions, BuiltinOptions_ShapeOptions, - BuiltinOptions_PowOptions + BuiltinOptions_PowOptions, + BuiltinOptions_ArgMinOptions, + BuiltinOptions_FakeQuantOptions, + BuiltinOptions_PackOptions, + BuiltinOptions_LogicalOrOptions, + BuiltinOptions_OneHotOptions }; return values; } @@ -706,6 +752,11 @@ inline const char **EnumNamesBuiltinOptions() { "NotEqualOptions", "ShapeOptions", "PowOptions", + "ArgMinOptions", + "FakeQuantOptions", + "PackOptions", + "LogicalOrOptions", + "OneHotOptions", nullptr }; return names; @@ -944,6 +995,26 @@ template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_PowOptions; }; +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ArgMinOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FakeQuantOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_PackOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_OneHotOptions; +}; + struct BuiltinOptionsUnion { BuiltinOptions type; void *value; @@ -1423,6 +1494,46 @@ struct BuiltinOptionsUnion { return type == BuiltinOptions_PowOptions ? reinterpret_cast(value) : nullptr; } + ArgMinOptionsT *AsArgMinOptions() { + return type == BuiltinOptions_ArgMinOptions ? + reinterpret_cast(value) : nullptr; + } + const ArgMinOptionsT *AsArgMinOptions() const { + return type == BuiltinOptions_ArgMinOptions ? + reinterpret_cast(value) : nullptr; + } + FakeQuantOptionsT *AsFakeQuantOptions() { + return type == BuiltinOptions_FakeQuantOptions ? + reinterpret_cast(value) : nullptr; + } + const FakeQuantOptionsT *AsFakeQuantOptions() const { + return type == BuiltinOptions_FakeQuantOptions ? + reinterpret_cast(value) : nullptr; + } + PackOptionsT *AsPackOptions() { + return type == BuiltinOptions_PackOptions ? + reinterpret_cast(value) : nullptr; + } + const PackOptionsT *AsPackOptions() const { + return type == BuiltinOptions_PackOptions ? + reinterpret_cast(value) : nullptr; + } + LogicalOrOptionsT *AsLogicalOrOptions() { + return type == BuiltinOptions_LogicalOrOptions ? + reinterpret_cast(value) : nullptr; + } + const LogicalOrOptionsT *AsLogicalOrOptions() const { + return type == BuiltinOptions_LogicalOrOptions ? + reinterpret_cast(value) : nullptr; + } + OneHotOptionsT *AsOneHotOptions() { + return type == BuiltinOptions_OneHotOptions ? + reinterpret_cast(value) : nullptr; + } + const OneHotOptionsT *AsOneHotOptions() const { + return type == BuiltinOptions_OneHotOptions ? + reinterpret_cast(value) : nullptr; + } }; bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type); @@ -4486,6 +4597,60 @@ inline flatbuffers::Offset CreateArgMaxOptions( flatbuffers::Offset CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +struct ArgMinOptionsT : public flatbuffers::NativeTable { + typedef ArgMinOptions TableType; + TensorType output_type; + ArgMinOptionsT() + : output_type(TensorType_FLOAT32) { + } +}; + +struct ArgMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ArgMinOptionsT NativeTableType; + enum { + VT_OUTPUT_TYPE = 4 + }; + TensorType output_type() const { + return static_cast(GetField(VT_OUTPUT_TYPE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_OUTPUT_TYPE) && + verifier.EndTable(); + } + ArgMinOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ArgMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ArgMinOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_output_type(TensorType output_type) { + fbb_.AddElement(ArgMinOptions::VT_OUTPUT_TYPE, static_cast(output_type), 0); + } + explicit ArgMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + ArgMinOptionsBuilder &operator=(const ArgMinOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateArgMinOptions( + flatbuffers::FlatBufferBuilder &_fbb, + TensorType output_type = TensorType_FLOAT32) { + ArgMinOptionsBuilder builder_(_fbb); + builder_.add_output_type(output_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + struct GreaterOptionsT : public flatbuffers::NativeTable { typedef GreaterOptions TableType; GreaterOptionsT() { @@ -5112,6 +5277,256 @@ inline flatbuffers::Offset CreatePowOptions( flatbuffers::Offset CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +struct FakeQuantOptionsT : public flatbuffers::NativeTable { + typedef FakeQuantOptions TableType; + float min; + float max; + int32_t num_bits; + bool narrow_range; + FakeQuantOptionsT() + : min(0.0f), + max(0.0f), + num_bits(0), + narrow_range(false) { + } +}; + +struct FakeQuantOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FakeQuantOptionsT NativeTableType; + enum { + VT_MIN = 4, + VT_MAX = 6, + VT_NUM_BITS = 8, + VT_NARROW_RANGE = 10 + }; + float min() const { + return GetField(VT_MIN, 0.0f); + } + float max() const { + return GetField(VT_MAX, 0.0f); + } + int32_t num_bits() const { + return GetField(VT_NUM_BITS, 0); + } + bool narrow_range() const { + return GetField(VT_NARROW_RANGE, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_MIN) && + VerifyField(verifier, VT_MAX) && + VerifyField(verifier, VT_NUM_BITS) && + VerifyField(verifier, VT_NARROW_RANGE) && + verifier.EndTable(); + } + FakeQuantOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FakeQuantOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FakeQuantOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_min(float min) { + fbb_.AddElement(FakeQuantOptions::VT_MIN, min, 0.0f); + } + void add_max(float max) { + fbb_.AddElement(FakeQuantOptions::VT_MAX, max, 0.0f); + } + void add_num_bits(int32_t num_bits) { + fbb_.AddElement(FakeQuantOptions::VT_NUM_BITS, num_bits, 0); + } + void add_narrow_range(bool narrow_range) { + fbb_.AddElement(FakeQuantOptions::VT_NARROW_RANGE, static_cast(narrow_range), 0); + } + explicit FakeQuantOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + FakeQuantOptionsBuilder &operator=(const FakeQuantOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFakeQuantOptions( + flatbuffers::FlatBufferBuilder &_fbb, + float min = 0.0f, + float max = 0.0f, + int32_t num_bits = 0, + bool narrow_range = false) { + FakeQuantOptionsBuilder builder_(_fbb); + builder_.add_num_bits(num_bits); + builder_.add_max(max); + builder_.add_min(min); + builder_.add_narrow_range(narrow_range); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PackOptionsT : public flatbuffers::NativeTable { + typedef PackOptions TableType; + int32_t values_count; + int32_t axis; + PackOptionsT() + : values_count(0), + axis(0) { + } +}; + +struct PackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PackOptionsT NativeTableType; + enum { + VT_VALUES_COUNT = 4, + VT_AXIS = 6 + }; + int32_t values_count() const { + return GetField(VT_VALUES_COUNT, 0); + } + int32_t axis() const { + return GetField(VT_AXIS, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_VALUES_COUNT) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } + PackOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PackOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_values_count(int32_t values_count) { + fbb_.AddElement(PackOptions::VT_VALUES_COUNT, values_count, 0); + } + void add_axis(int32_t axis) { + fbb_.AddElement(PackOptions::VT_AXIS, axis, 0); + } + explicit PackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + PackOptionsBuilder &operator=(const PackOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePackOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t values_count = 0, + int32_t axis = 0) { + PackOptionsBuilder builder_(_fbb); + builder_.add_axis(axis); + builder_.add_values_count(values_count); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogicalOrOptionsT : public flatbuffers::NativeTable { + typedef LogicalOrOptions TableType; + LogicalOrOptionsT() { + } +}; + +struct LogicalOrOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogicalOrOptionsT NativeTableType; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogicalOrOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogicalOrOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogicalOrOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + LogicalOrOptionsBuilder &operator=(const LogicalOrOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogicalOrOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LogicalOrOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct OneHotOptionsT : public flatbuffers::NativeTable { + typedef OneHotOptions TableType; + int32_t axis; + OneHotOptionsT() + : axis(0) { + } +}; + +struct OneHotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef OneHotOptionsT NativeTableType; + enum { + VT_AXIS = 4 + }; + int32_t axis() const { + return GetField(VT_AXIS, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } + OneHotOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct OneHotOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int32_t axis) { + fbb_.AddElement(OneHotOptions::VT_AXIS, axis, 0); + } + explicit OneHotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + OneHotOptionsBuilder &operator=(const OneHotOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateOneHotOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t axis = 0) { + OneHotOptionsBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +flatbuffers::Offset CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + struct OperatorCodeT : public flatbuffers::NativeTable { typedef OperatorCode TableType; BuiltinOperator builtin_code; @@ -5413,6 +5828,21 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const PowOptions *builtin_options_as_PowOptions() const { return builtin_options_type() == BuiltinOptions_PowOptions ? static_cast(builtin_options()) : nullptr; } + const ArgMinOptions *builtin_options_as_ArgMinOptions() const { + return builtin_options_type() == BuiltinOptions_ArgMinOptions ? static_cast(builtin_options()) : nullptr; + } + const FakeQuantOptions *builtin_options_as_FakeQuantOptions() const { + return builtin_options_type() == BuiltinOptions_FakeQuantOptions ? static_cast(builtin_options()) : nullptr; + } + const PackOptions *builtin_options_as_PackOptions() const { + return builtin_options_type() == BuiltinOptions_PackOptions ? static_cast(builtin_options()) : nullptr; + } + const LogicalOrOptions *builtin_options_as_LogicalOrOptions() const { + return builtin_options_type() == BuiltinOptions_LogicalOrOptions ? static_cast(builtin_options()) : nullptr; + } + const OneHotOptions *builtin_options_as_OneHotOptions() const { + return builtin_options_type() == BuiltinOptions_OneHotOptions ? static_cast(builtin_options()) : nullptr; + } const flatbuffers::Vector *custom_options() const { return GetPointer *>(VT_CUSTOM_OPTIONS); } @@ -5668,6 +6098,26 @@ template<> inline const PowOptions *Operator::builtin_options_as() c return builtin_options_as_PowOptions(); } +template<> inline const ArgMinOptions *Operator::builtin_options_as() const { + return builtin_options_as_ArgMinOptions(); +} + +template<> inline const FakeQuantOptions *Operator::builtin_options_as() const { + return builtin_options_as_FakeQuantOptions(); +} + +template<> inline const PackOptions *Operator::builtin_options_as() const { + return builtin_options_as_PackOptions(); +} + +template<> inline const LogicalOrOptions *Operator::builtin_options_as() const { + return builtin_options_as_LogicalOrOptions(); +} + +template<> inline const OneHotOptions *Operator::builtin_options_as() const { + return builtin_options_as_OneHotOptions(); +} + struct OperatorBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; @@ -7333,6 +7783,32 @@ inline flatbuffers::Offset CreateArgMaxOptions(flatbuffers::FlatB _output_type); } +inline ArgMinOptionsT *ArgMinOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new ArgMinOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void ArgMinOptions::UnPackTo(ArgMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = output_type(); _o->output_type = _e; }; +} + +inline flatbuffers::Offset ArgMinOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateArgMinOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ArgMinOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _output_type = _o->output_type; + return tflite::CreateArgMinOptions( + _fbb, + _output_type); +} + inline GreaterOptionsT *GreaterOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new GreaterOptionsT(); UnPackTo(_o, _resolver); @@ -7670,6 +8146,119 @@ inline flatbuffers::Offset CreatePowOptions(flatbuffers::FlatBufferB _fbb); } +inline FakeQuantOptionsT *FakeQuantOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new FakeQuantOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void FakeQuantOptions::UnPackTo(FakeQuantOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = min(); _o->min = _e; }; + { auto _e = max(); _o->max = _e; }; + { auto _e = num_bits(); _o->num_bits = _e; }; + { auto _e = narrow_range(); _o->narrow_range = _e; }; +} + +inline flatbuffers::Offset FakeQuantOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateFakeQuantOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FakeQuantOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _min = _o->min; + auto _max = _o->max; + auto _num_bits = _o->num_bits; + auto _narrow_range = _o->narrow_range; + return tflite::CreateFakeQuantOptions( + _fbb, + _min, + _max, + _num_bits, + _narrow_range); +} + +inline PackOptionsT *PackOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new PackOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void PackOptions::UnPackTo(PackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = values_count(); _o->values_count = _e; }; + { auto _e = axis(); _o->axis = _e; }; +} + +inline flatbuffers::Offset PackOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreatePackOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PackOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _values_count = _o->values_count; + auto _axis = _o->axis; + return tflite::CreatePackOptions( + _fbb, + _values_count, + _axis); +} + +inline LogicalOrOptionsT *LogicalOrOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new LogicalOrOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void LogicalOrOptions::UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LogicalOrOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLogicalOrOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalOrOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateLogicalOrOptions( + _fbb); +} + +inline OneHotOptionsT *OneHotOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new OneHotOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void OneHotOptions::UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = axis(); _o->axis = _e; }; +} + +inline flatbuffers::Offset OneHotOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateOneHotOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OneHotOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _axis = _o->axis; + return tflite::CreateOneHotOptions( + _fbb, + _axis); +} + inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new OperatorCodeT(); UnPackTo(_o, _resolver); @@ -8083,6 +8672,26 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob auto ptr = reinterpret_cast(obj); return verifier.VerifyTable(ptr); } + case BuiltinOptions_ArgMinOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_FakeQuantOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_PackOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } default: return false; } } @@ -8325,6 +8934,26 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c auto ptr = reinterpret_cast(obj); return ptr->UnPack(resolver); } + case BuiltinOptions_ArgMinOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_FakeQuantOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_PackOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } default: return nullptr; } } @@ -8555,6 +9184,26 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff auto ptr = reinterpret_cast(value); return CreatePowOptions(_fbb, ptr, _rehasher).Union(); } + case BuiltinOptions_ArgMinOptions: { + auto ptr = reinterpret_cast(value); + return CreateArgMinOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_FakeQuantOptions: { + auto ptr = reinterpret_cast(value); + return CreateFakeQuantOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_PackOptions: { + auto ptr = reinterpret_cast(value); + return CreatePackOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(value); + return CreateLogicalOrOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(value); + return CreateOneHotOptions(_fbb, ptr, _rehasher).Union(); + } default: return 0; } } @@ -8785,6 +9434,26 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL value = new PowOptionsT(*reinterpret_cast(u.value)); break; } + case BuiltinOptions_ArgMinOptions: { + value = new ArgMinOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_FakeQuantOptions: { + value = new FakeQuantOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_PackOptions: { + value = new PackOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LogicalOrOptions: { + value = new LogicalOrOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_OneHotOptions: { + value = new OneHotOptionsT(*reinterpret_cast(u.value)); + break; + } default: break; } @@ -9072,6 +9741,31 @@ inline void BuiltinOptionsUnion::Reset() { delete ptr; break; } + case BuiltinOptions_ArgMinOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_FakeQuantOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_PackOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } default: break; } value = nullptr; diff --git a/tensorflow/contrib/lite/simple_memory_arena.cc b/tensorflow/contrib/lite/simple_memory_arena.cc index 4eaf6f1bfe76efc1e6737d03d58be9bc87bb849d..cd0f1f7c17a50f6ce61fa2033e5d13580399f5cf 100644 --- a/tensorflow/contrib/lite/simple_memory_arena.cc +++ b/tensorflow/contrib/lite/simple_memory_arena.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/contrib/lite/simple_memory_arena.h" +#include #include #include #include @@ -34,7 +35,7 @@ namespace tflite { TfLiteStatus SimpleMemoryArena::Allocate(TfLiteContext* context, size_t alignment, size_t size, ArenaAlloc* new_alloc) { - TF_LITE_ENSURE(context, alignment < arena_alignment_); + TF_LITE_ENSURE(context, alignment <= arena_alignment_); if (size == 0) { new_alloc->offset = 0; diff --git a/tensorflow/contrib/lite/testdata/add.bin b/tensorflow/contrib/lite/testdata/add.bin new file mode 100644 index 0000000000000000000000000000000000000000..aef0fe3d82c9d92dc444076d3b46e05af1923f46 Binary files /dev/null and b/tensorflow/contrib/lite/testdata/add.bin differ diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD index 789bc695f8e9f8721edeb3b3a3f2af59b36adeed..a788d41ba7b370cd0e84c343202f1dca090180f3 100644 --- a/tensorflow/contrib/lite/testing/BUILD +++ b/tensorflow/contrib/lite/testing/BUILD @@ -140,6 +140,7 @@ cc_test( cc_library( name = "join", hdrs = ["join.h"], + deps = ["//tensorflow/contrib/lite:string"], ) cc_test( @@ -209,6 +210,10 @@ cc_library( cc_library( name = "util", hdrs = ["util.h"], + deps = [ + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:string", + ], ) cc_test( @@ -252,6 +257,7 @@ cc_test( srcs = ["tf_driver_test.cc"], data = ["//tensorflow/contrib/lite:testdata/multi_add.pb"], tags = [ + "no_oss", "tflite_not_portable", ], deps = [ @@ -268,6 +274,7 @@ cc_library( ":join", ":split", ":tf_driver", + "//tensorflow/contrib/lite:string", "//tensorflow/core:framework", ], ) @@ -277,6 +284,7 @@ cc_test( size = "small", srcs = ["generate_testspec_test.cc"], tags = [ + "no_oss", "tflite_not_portable", ], deps = [ @@ -333,7 +341,7 @@ tf_cc_test( ], tags = [ "no_cuda_on_cpu_tap", - "no_oss", + "no_oss", # needs test data "tflite_not_portable", ], deps = [ diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index 50237ed79232cff0be7ae8c5b125ac1ee7fdf520..6d03c0fd9ec980272b45d6a8072a98ee6564ca03 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -104,6 +104,8 @@ KNOWN_BUGS = { r"div.*int32": "72051395", # No support for SplitV r"split.*num_or_size_splits=\[2,2\]": "73377559", + # Scalar constants don't work. + r"constant.*shape=\[\]": "109811500", } @@ -229,6 +231,7 @@ _TF_TYPE_INFO = { tf.int32: (np.int32, "INT32"), tf.uint8: (np.uint8, "QUANTIZED_UINT8"), tf.int64: (np.int64, "INT64"), + tf.bool: (np.bool, "BOOL"), } @@ -242,7 +245,10 @@ def create_tensor_data(dtype, shape, min_value=-100, max_value=100): value = (max_value-min_value)*np.random.random_sample(shape)+min_value elif dtype in (tf.int32, tf.uint8, tf.int64): value = np.random.randint(min_value, max_value+1, shape) - return value.astype(dtype) + elif dtype == tf.bool: + value = np.random.choice([True, False], size=shape) + return np.dtype(dtype).type(value) if np.isscalar(value) else value.astype( + dtype) def create_scalar_data(dtype, min_value=-100, max_value=100): @@ -479,7 +485,7 @@ def make_zip_of_tests(zip_path, else report_lib.FAILED) report["toco_log"] = toco_log - if FLAGS.save_graphdefs: + if True or FLAGS.save_graphdefs: archive.writestr(label + ".pbtxt", text_format.MessageToString(graph_def), zipfile.ZIP_DEFLATED) @@ -678,6 +684,55 @@ def make_relu6_tests(zip_path): make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) +def make_prelu_tests(zip_path): + """Make a set of tests to do PReLU.""" + + test_parameters = [{ + # The canonical case for image processing is having a 4D `input` (NHWC) + # and `shared_axes`=[1, 2], so the alpha parameter is per channel. + "input_shape": [[1, 10, 10, 3], [3, 3, 3, 3]], + "shared_axes": [[1, 2], [1]], + }] + + def build_graph(parameters): + """Build the graph for the test case.""" + + input_tensor = tf.placeholder( + dtype=tf.float32, name="input", shape=parameters["input_shape"]) + prelu = tf.keras.layers.PReLU(shared_axes=parameters["shared_axes"]) + out = prelu(input_tensor) + return [input_tensor], [out] + + def build_inputs(parameters, sess, inputs, outputs): + """Build the inputs for the test case.""" + + input_shape = parameters["input_shape"] + input_values = create_tensor_data( + np.float32, input_shape, min_value=-10, max_value=10) + shared_axes = parameters["shared_axes"] + + alpha_shape = [] + for dim in range(1, len(input_shape)): + alpha_shape.append(1 if dim in shared_axes else input_shape[dim]) + + alpha_values = create_tensor_data(np.float32, alpha_shape) + + # There should be only 1 trainable variable tensor. + variables = tf.all_variables() + assert len(variables) == 1 + sess.run(variables[0].assign(alpha_values)) + + return [input_values], sess.run( + outputs, feed_dict=dict(zip(inputs, [input_values]))) + + make_zip_of_tests( + zip_path, + test_parameters, + build_graph, + build_inputs, + use_frozen_graph=True) + + # This function tests various TensorFLow functions that generates Const op, # including `tf.ones`, `tf.zeros` and random functions. def make_constant_tests(zip_path): @@ -685,21 +740,22 @@ def make_constant_tests(zip_path): test_parameters = [{ "dtype": [tf.float32, tf.int32], - "input_shape": [[1], [2], [1, 1, 1, 1], [2, 2, 2, 2]], + "input_shape": [[], [1], [2], [1, 1, 1, 1], [2, 2, 2, 2]], }] def build_graph(parameters): - # Since Toco & Tflite can't have a single constant op in the entire graph, - # this test adds a zero tensor with a constant op tensor. - input1 = tf.placeholder(dtype=parameters["dtype"], name="input1", - shape=parameters["input_shape"]) - out = tf.ones(parameters["input_shape"], dtype=parameters["dtype"]) + input1 - return [input1], [out] + dummy_input = tf.placeholder( + dtype=parameters["dtype"], + name="input1", + shape=parameters["input_shape"]) + out = tf.constant( + create_tensor_data(parameters["dtype"], parameters["input_shape"])) + return [dummy_input], [out] def build_inputs(parameters, sess, inputs, outputs): - input1 = np.zeros(parameters["input_shape"], - dtype=_TF_TYPE_INFO[parameters["dtype"]][0]) - return [input1], sess.run(outputs, feed_dict={inputs[0]: input1}) + dummy_input = np.zeros( + parameters["input_shape"], dtype=_TF_TYPE_INFO[parameters["dtype"]][0]) + return [dummy_input], sess.run(outputs, feed_dict={inputs[0]: dummy_input}) make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) @@ -723,6 +779,11 @@ def make_binary_op_tests(zip_path, binary_operator): "input_shape_1": [[1, 3, 4, 3]], "input_shape_2": [[3]], "activation": [True] + }, { + "dtype": [tf.float32], + "input_shape_1": [[]], + "input_shape_2": [[]], + "activation": [False] }] def build_graph(parameters): @@ -772,7 +833,7 @@ def make_reduce_tests(reduce_op): "input_dtype": [tf.float32, tf.int32, tf.int64], "input_shape": [[3, 2, 4]], "axis": [ - None, 0, 1, 2, [0, 1], [0, 2], [1, 2], [0, 1, 2], [1, 0], [2, 0], + 0, 1, 2, [0, 1], [0, 2], [1, 2], [0, 1, 2], [1, 0], [2, 0], [2, 1], [2, 1, 0], [2, 0, 1], -1, -2, -3, [1, -1], [0, -1], [-1, 0], [-1, -2, -3], [0, 0, 0], [2, 2, 0], [1, 0, -3, -3] ], @@ -782,13 +843,19 @@ def make_reduce_tests(reduce_op): "input_dtype": [tf.float32], "input_shape": [[1, 8, 8, 3]], "axis": [ - None, 0, 1, 2, 3, [1, 2], [0, 3], [1, 2, 3], [0, 1, 2, 3], + 0, 1, 2, 3, [1, 2], [0, 3], [1, 2, 3], [0, 1, 2, 3], [3, 2, 1, 0], [3, 1, 0, 2], [2, 0], [3, 0], [3, 1], [1, 0], -1, -2, -3, -4, [0, -2], [2, 3, -1, 0], [3, 1, 2, -3], [3, -4], [2, 2, 2], [2, 2, 3], [-3, -3, -4], [-3, 2, 1] ], "const_axis": [True, False], "keepdims": [True, False], + }, { + "input_dtype": [tf.float32], + "input_shape": [[], [1, 8, 8, 3], [3, 2, 4]], + "axis": [None], + "const_axis": [True], + "keepdims": [True, False], }] def build_graph(parameters): @@ -806,7 +873,7 @@ def make_reduce_tests(reduce_op): if isinstance(parameters["axis"], list): shape = [len(parameters["axis"])] else: - shape = [0] # shape for None or integers. + shape = [] # shape for None or integers. axis = tf.placeholder(dtype=tf.int32, name="axis", shape=shape) input_tensors = [input_tensor, axis] @@ -817,10 +884,11 @@ def make_reduce_tests(reduce_op): def build_inputs(parameters, sess, inputs, outputs): values = [ create_tensor_data(parameters["input_dtype"], - parameters["input_shape"])] + parameters["input_shape"], + min_value=-10, + max_value=10)] if not parameters["const_axis"]: - if parameters["axis"]: - values.append(np.array(parameters["axis"])) + values.append(np.array(parameters["axis"])) return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) @@ -830,22 +898,30 @@ def make_reduce_tests(reduce_op): def make_mean_tests(zip_path): """Make a set of tests to do mean.""" - return make_reduce_tests(tf.reduce_mean)(zip_path) def make_sum_tests(zip_path): """Make a set of tests to do sum.""" - return make_reduce_tests(tf.reduce_sum)(zip_path) +def make_reduce_prod_tests(zip_path): + """Make a set of tests to do prod.""" + return make_reduce_tests(tf.reduce_prod)(zip_path) + + +def make_reduce_max_tests(zip_path): + """Make a set of tests to do max.""" + return make_reduce_tests(tf.reduce_max)(zip_path) + + def make_exp_tests(zip_path): """Make a set of tests to do exp.""" test_parameters = [{ "input_dtype": [tf.float32], - "input_shape": [[3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], + "input_shape": [[], [3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], }] def build_graph(parameters): @@ -904,8 +980,8 @@ def make_maximum_tests(zip_path): test_parameters = [{ "input_dtype": [tf.float32], - "input_shape_1": [[3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], - "input_shape_2": [[3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], + "input_shape_1": [[], [3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], + "input_shape_2": [[], [3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], }] def build_graph(parameters): @@ -939,8 +1015,8 @@ def make_minimum_tests(zip_path): test_parameters = [{ "input_dtype": [tf.float32], - "input_shape_1": [[3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], - "input_shape_2": [[3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], + "input_shape_1": [[], [3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], + "input_shape_2": [[], [3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], }] def build_graph(parameters): @@ -1538,19 +1614,39 @@ def make_reshape_tests(zip_path): "dtype": [tf.float32, tf.int32], "input_shape": [[3, 4, 5, 7], [4, 105], [21, 5, 2, 2], [420]], "output_shape": [[15, 28], [420], [1, -1, 5, 7], [-1]], + "constant_shape": [True, False], + }, { + "dtype": [tf.float32], + "input_shape": [[1]], + "output_shape": [[]], + "constant_shape": [True, False], }] def build_graph(parameters): input_tensor = tf.placeholder(dtype=parameters["dtype"], name="input", shape=parameters["input_shape"]) - out = tf.reshape(input_tensor, shape=parameters["output_shape"]) - return [input_tensor], [out] + + # Get shape as either a placeholder or constants. + if parameters["constant_shape"]: + output_shape = parameters["output_shape"] + input_tensors = [input_tensor] + else: + # The shape of the shape tensor. + shape_tensor_shape = [len(parameters["output_shape"])] + output_shape = tf.placeholder( + dtype=tf.int32, name="output_shape", shape=shape_tensor_shape) + input_tensors = [input_tensor, output_shape] + out = tf.reshape(input_tensor, shape=output_shape) + return input_tensors, [out] def build_inputs(parameters, sess, inputs, outputs): - input_values = create_tensor_data(parameters["dtype"], - parameters["input_shape"]) - return [input_values], sess.run( - outputs, feed_dict=dict(zip(inputs, [input_values]))) + values = [ + create_tensor_data(parameters["dtype"], parameters["input_shape"]) + ] + if not parameters["constant_shape"]: + values.append(np.array(parameters["output_shape"])) + + return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) @@ -1581,6 +1677,65 @@ def make_shape_tests(zip_path): make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) +def make_one_hot_tests(zip_path): + """Make a set of tests to do one_hot.""" + + test_parameters = [{ + "indices_type": [tf.int32, tf.int64], + "indices_shape": [[3], [4, 4], [1, 5], [5, 1]], + "axis": [0, 1], + "dtype": [tf.int32, tf.int64, tf.float32], + "provide_optional_inputs": [True, False], + }] + + def build_graph(parameters): + indices = tf.placeholder( + dtype=parameters["indices_type"], + name="indices", + shape=parameters["indices_shape"]) + depth = tf.placeholder(dtype=tf.int32, name="depth", shape=()) + + if not parameters["provide_optional_inputs"]: + out = tf.one_hot(indices=indices, depth=depth) + return [indices, depth], [out] + + on_value = tf.placeholder( + dtype=parameters["dtype"], name="on_value", shape=()) + off_value = tf.placeholder( + dtype=parameters["dtype"], name="off_value", shape=()) + out = tf.one_hot( + indices=indices, + depth=depth, + on_value=on_value, + off_value=off_value, + axis=parameters["axis"], + dtype=parameters["dtype"]) + return [indices, depth, on_value, off_value], [out] + + def build_inputs(parameters, sess, inputs, outputs): + input_values = [ + create_tensor_data( + parameters["indices_type"], + shape=parameters["indices_shape"], + min_value=-1, + max_value=10), + create_tensor_data(tf.int32, shape=None, min_value=1, max_value=10), + ] + + if parameters["provide_optional_inputs"]: + input_values.append( + create_tensor_data( + parameters["dtype"], shape=None, min_value=1, max_value=10)) + input_values.append( + create_tensor_data( + parameters["dtype"], shape=None, min_value=-1, max_value=0)) + + return input_values, sess.run( + outputs, feed_dict=dict(zip(inputs, input_values))) + + make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) + + def make_resize_bilinear_tests(zip_path): """Make a set of tests to do resize_bilinear.""" @@ -2175,14 +2330,15 @@ def make_topk_tests(zip_path): make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) -def make_arg_max_tests(zip_path): +def make_arg_min_max_tests(zip_path): """Make a set of tests to do arg_max.""" test_parameters = [{ "input_dtype": [tf.float32, tf.int32], - "input_shape": [[1, 1, 1, 3], [2, 3, 4, 5], [2, 3, 3], [5, 5], [10]], + "input_shape": [[], [1, 1, 1, 3], [2, 3, 4, 5], [2, 3, 3], [5, 5], [10]], "output_type": [tf.int32, tf.int64], "axis_is_last_dim": [True, False], + "is_arg_max": [True], }] def build_graph(parameters): @@ -2195,7 +2351,10 @@ def make_arg_max_tests(zip_path): axis = len(parameters["input_shape"]) - 1 else: axis = random.randint(0, max(len(parameters["input_shape"]) - 2, 0)) - out = tf.arg_max(input_value, axis, output_type=parameters["output_type"]) + if parameters["is_arg_max"]: + out = tf.arg_max(input_value, axis, output_type=parameters["output_type"]) + else: + out = tf.arg_min(input_value, axis, output_type=parameters["output_type"]) return [input_value], [out] def build_inputs(parameters, sess, inputs, outputs): @@ -2212,7 +2371,8 @@ def make_equal_tests(zip_path): test_parameters = [{ "input_dtype": [tf.float32, tf.int32, tf.int64], - "input_shape_pair": [([1, 1, 1, 3], [1, 1, 1, 3]), + "input_shape_pair": [([], []), + ([1, 1, 1, 3], [1, 1, 1, 3]), ([2, 3, 4, 5], [2, 3, 4, 5]), ([2, 3, 3], [2, 3]), ([5, 5], [1]), ([10], [2, 4, 10])], }] @@ -2469,7 +2629,7 @@ def _make_elementwise_tests(op): """Actual function that generates examples.""" test_parameters = [{ "input_dtype": [tf.float32], - "input_shape": [[1], [1, 2], [5, 6, 7, 8], [3, 4, 5, 6]], + "input_shape": [[], [1], [1, 2], [5, 6, 7, 8], [3, 4, 5, 6]], }] def build_graph(parameters): @@ -2791,6 +2951,73 @@ def make_sparse_to_dense_tests(zip_path): make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) +def make_pack_tests(zip_path): + """Make a set of tests to do stack.""" + + test_parameters = [{ + "base_shape": [[3, 4, 3], [3, 4], [5]], + "num_tensors": [1, 2, 3, 4, 5, 6], + "axis": [0, 1, 2, 3], + "additional_shape": [1, 2, 3], + }] + + def get_shape(parameters): + """Return a tweaked version of 'base_shape'.""" + axis = parameters["axis"] + shape = parameters["base_shape"][:] + if axis < len(shape): + shape[axis] += parameters["additional_shape"] + return shape + + def build_graph(parameters): + all_tensors = [] + for n in range(0, parameters["num_tensors"]): + input_tensor = tf.placeholder( + dtype=tf.float32, name=("input%d" % n), shape=get_shape(parameters)) + all_tensors.append(input_tensor) + out = tf.stack(all_tensors, parameters["axis"]) + return all_tensors, [out] + + def build_inputs(parameters, sess, inputs, outputs): + all_values = [] + for _ in range(0, parameters["num_tensors"]): + input_values = create_tensor_data(np.float32, get_shape(parameters)) + all_values.append(input_values) + return all_values, sess.run( + outputs, feed_dict=dict(zip(inputs, all_values))) + + make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) + + +def make_logical_or_tests(zip_path): + """Make a set of tests to do logical_or.""" + + test_parameters = [{ + "input_shape_pair": [([], []), ([1, 1, 1, 3], [1, 1, 1, 3]), + ([2, 3, 4, 5], [2, 3, 4, 5]), ([2, 3, 3], [2, 3]), + ([5, 5], [1]), ([10], [2, 4, 10])], + }] + + def build_graph(parameters): + """Build the logical_or op testing graph.""" + input_value1 = tf.placeholder( + dtype=tf.bool, name="input1", shape=parameters["input_shape_pair"][0]) + input_value2 = tf.placeholder( + dtype=tf.bool, name="input2", shape=parameters["input_shape_pair"][1]) + out = tf.logical_or(input_value1, input_value2) + return [input_value1, input_value2], [out] + + def build_inputs(parameters, sess, inputs, outputs): + input_value1 = create_tensor_data(tf.bool, + parameters["input_shape_pair"][0]) + input_value2 = create_tensor_data(tf.bool, + parameters["input_shape_pair"][1]) + return [input_value1, input_value2], sess.run( + outputs, feed_dict=dict(zip(inputs, [input_value1, input_value2]))) + + make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) + + # Toco binary path provided by the generate rule. bin_path = None diff --git a/tensorflow/contrib/lite/testing/generate_testspec.cc b/tensorflow/contrib/lite/testing/generate_testspec.cc index c1092e4d25567f0374e3cd5a27bde32419d3db19..f29c188e6c2c55bdb13d257c70e23c2943abfa4a 100644 --- a/tensorflow/contrib/lite/testing/generate_testspec.cc +++ b/tensorflow/contrib/lite/testing/generate_testspec.cc @@ -13,6 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include + #include "tensorflow/contrib/lite/testing/generate_testspec.h" #include "tensorflow/contrib/lite/testing/join.h" #include "tensorflow/contrib/lite/testing/split.h" @@ -88,13 +90,13 @@ bool GenerateTestSpecFromTensorflowModel( TfDriver runner(input_layer, input_layer_type, input_layer_shape, output_layer); if (!runner.IsValid()) { - cerr << runner.GetErrorMessage() << endl; + std::cerr << runner.GetErrorMessage() << std::endl; return false; } runner.LoadModel(tensorflow_model_path); if (!runner.IsValid()) { - cerr << runner.GetErrorMessage() << endl; + std::cerr << runner.GetErrorMessage() << std::endl; return false; } @@ -118,14 +120,14 @@ bool GenerateTestSpecFromTensorflowModel( for (int j = 0; j < input_values.size(); j++) { runner.SetInput(j, input_values[j]); if (!runner.IsValid()) { - cerr << runner.GetErrorMessage() << endl; + std::cerr << runner.GetErrorMessage() << std::endl; return false; } } runner.Invoke(); if (!runner.IsValid()) { - cerr << runner.GetErrorMessage() << endl; + std::cerr << runner.GetErrorMessage() << std::endl; return false; } @@ -137,7 +139,7 @@ bool GenerateTestSpecFromTensorflowModel( for (int j = 0; j < output_layer.size(); j++) { stream << " output: \"" << runner.ReadOutput(j) << "\"\n"; if (!runner.IsValid()) { - cerr << runner.GetErrorMessage() << endl; + std::cerr << runner.GetErrorMessage() << std::endl; return false; } } diff --git a/tensorflow/contrib/lite/testing/generate_testspec.h b/tensorflow/contrib/lite/testing/generate_testspec.h index bfaf5e7ec89bbdd85b68a7dc45d7686e143e5d3d..b3d0db31c01a8cb1b8f34ff6dbb00c77de29b131 100644 --- a/tensorflow/contrib/lite/testing/generate_testspec.h +++ b/tensorflow/contrib/lite/testing/generate_testspec.h @@ -19,6 +19,8 @@ limitations under the License. #include #include +#include "tensorflow/contrib/lite/string.h" + namespace tflite { namespace testing { diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc index c4e20312d891be6f659845fe4fc66e085955b81b..106cbc1b8e1d289ec04721611294c6a4c79dabb4 100644 --- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc +++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc @@ -53,10 +53,6 @@ tensorflow::Env* env = tensorflow::Env::Default(); // Key is a substring of the test name and value is a bug number. // TODO(ahentz): make sure we clean this list up frequently. std::map kBrokenTests = { - {R"(^\/mul.*int32)", "68808744"}, - {R"(^\/div.*int32)", "68808744"}, - {R"(^\/sub.*int32)", "68808744"}, - // Pad and PadV2 only supports 4D tensors. {R"(^\/pad.*,input_shape=\[.,.\],paddings=\[\[.,.\],\[.,.\]\])", "70527055"}, @@ -97,11 +93,12 @@ std::map kBrokenTests = { {R"(^\/gather.*axis=1)", "76910444"}, // No support for arbitrary dimensions in ArgMax. - {R"(^\/arg_max.*axis_is_last_dim=False.*input_shape=\[.,.,.,.\])", + {R"(^\/arg_min_max.*axis_is_last_dim=False.*input_shape=\[.,.,.,.\])", + "77546240"}, + {R"(^\/arg_min_max.*axis_is_last_dim=False.*input_shape=\[.,.,.\])", "77546240"}, - {R"(^\/arg_max.*axis_is_last_dim=False.*input_shape=\[.,.,.\])", + {R"(^\/arg_min_max.*axis_is_last_dim=False.*input_shape=\[.,.\])", "77546240"}, - {R"(^\/arg_max.*axis_is_last_dim=False.*input_shape=\[.,.\])", "77546240"}, }; // Allows test data to be unzipped into a temporary directory and makes @@ -229,7 +226,8 @@ TEST_P(OpsTest, RunZipTests) { string message = test_driver.GetErrorMessage(); if (bug_number.empty()) { if (FLAGS_use_nnapi && FLAGS_ignore_unsupported_nnapi && !result) { - EXPECT_EQ(message, string("Failed to invoke interpreter")) << message; + EXPECT_EQ(message, string("Failed to invoke NNAPI interpreter")) + << message; } else { EXPECT_TRUE(result) << message; } diff --git a/tensorflow/contrib/lite/testing/join.h b/tensorflow/contrib/lite/testing/join.h index 1edee01cf97da3c53be1895e667b005551ac2991..4be19ad7569c3333b6647b91adbc6e77ff088f10 100644 --- a/tensorflow/contrib/lite/testing/join.h +++ b/tensorflow/contrib/lite/testing/join.h @@ -17,7 +17,8 @@ limitations under the License. #include #include -#include + +#include "tensorflow/contrib/lite/string.h" namespace tflite { namespace testing { diff --git a/tensorflow/contrib/lite/testing/test_runner.h b/tensorflow/contrib/lite/testing/test_runner.h index 96ab6be54e528334f9e4a8cc259e44f99878fefb..fac7d01aab4b1e4c251213041eb4b823cd7d66aa 100644 --- a/tensorflow/contrib/lite/testing/test_runner.h +++ b/tensorflow/contrib/lite/testing/test_runner.h @@ -90,7 +90,7 @@ class TestRunner { // Invalidate the test runner, preventing it from executing any further. void Invalidate(const string& error_message) { - cerr << error_message << std::endl; + std::cerr << error_message << std::endl; error_message_ = error_message; } bool IsValid() const { return error_message_.empty(); } diff --git a/tensorflow/contrib/lite/testing/tf_driver.cc b/tensorflow/contrib/lite/testing/tf_driver.cc index 3b27f6f3da92ce80c3830feb7c6af095e7c48e9c..ec435ca60d959a11a9392b6fbab99b0561f50942 100644 --- a/tensorflow/contrib/lite/testing/tf_driver.cc +++ b/tensorflow/contrib/lite/testing/tf_driver.cc @@ -28,8 +28,8 @@ namespace { tensorflow::Tensor CreateTensor(const tensorflow::DataType type, const std::vector& dim) { - tensorflow::TensorShape shape{gtl::ArraySlice{ - reinterpret_cast(dim.data()), dim.size()}}; + tensorflow::TensorShape shape{tensorflow::gtl::ArraySlice{ + reinterpret_cast(dim.data()), dim.size()}}; return {type, shape}; } @@ -179,7 +179,7 @@ void TfDriver::Invoke() { auto status = session_->Run({input_tensors_.begin(), input_tensors_.end()}, output_names_, {}, &output_tensors_); if (!status.ok()) { - Invalidate("Failed to invoke interpreter"); + Invalidate("Failed to run input data on graph"); } } diff --git a/tensorflow/contrib/lite/testing/tflite_diff_flags.h b/tensorflow/contrib/lite/testing/tflite_diff_flags.h index 7a57e8d3fba29cd106eb038992bb5ed12bb457ae..695c2a3de6c5d7c74a943134f0c97390710ef1e7 100644 --- a/tensorflow/contrib/lite/testing/tflite_diff_flags.h +++ b/tensorflow/contrib/lite/testing/tflite_diff_flags.h @@ -15,6 +15,8 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_FLAGS_H_ #define TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_FLAGS_H_ +#include + #include "tensorflow/contrib/lite/testing/split.h" #include "tensorflow/contrib/lite/testing/tflite_diff_util.h" #include "tensorflow/core/util/command_line_flags.h" diff --git a/tensorflow/contrib/lite/testing/util.h b/tensorflow/contrib/lite/testing/util.h index 6d20aec141c7c3a3e48af290edb169c6fd7254cf..8aa639157b8b68061f9ee8c3483959a79cb5794e 100644 --- a/tensorflow/contrib/lite/testing/util.h +++ b/tensorflow/contrib/lite/testing/util.h @@ -15,8 +15,39 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_TESTING_UTIL_H_ #define TENSORFLOW_CONTRIB_LITE_TESTING_UTIL_H_ +#include + +#include "tensorflow/contrib/lite/error_reporter.h" +#include "tensorflow/contrib/lite/string.h" + namespace tflite { +// An ErrorReporter that collects error message in a string, in addition +// to printing to stderr. +class TestErrorReporter : public ErrorReporter { + public: + int Report(const char* format, va_list args) override { + char buffer[1024]; + int size = vsnprintf(buffer, sizeof(buffer), format, args); + fprintf(stderr, "%s", buffer); + error_messages_ += buffer; + num_calls_++; + return size; + } + + void Reset() { + num_calls_ = 0; + error_messages_.clear(); + } + + int num_calls() const { return num_calls_; } + const string& error_messages() const { return error_messages_; } + + private: + int num_calls_ = 0; + string error_messages_; +}; + inline void LogToStderr() { #ifdef PLATFORM_GOOGLE FLAGS_logtostderr = true; diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD index 209dce56cbdfbbff5884aa9961bd29e9cf98f49d..c88079717ddc9bf39850762dffe711f0d2832d38 100644 --- a/tensorflow/contrib/lite/toco/BUILD +++ b/tensorflow/contrib/lite/toco/BUILD @@ -93,6 +93,7 @@ cc_library( ":runtime", ":toco_port", "//tensorflow/core:lib", + "@com_google_absl//absl/types:optional", ], ) @@ -176,7 +177,7 @@ cc_library( "graph_transformations/convert_reorder_axes.cc", "graph_transformations/convert_squeeze_to_reshape.cc", "graph_transformations/convert_trivial_addn_to_add.cc", - "graph_transformations/convert_trivial_stack_to_reshape.cc", + "graph_transformations/convert_trivial_pack_to_reshape.cc", "graph_transformations/convert_trivial_tile_to_concat.cc", "graph_transformations/convert_trivial_transpose_to_reshape.cc", "graph_transformations/create_im2col_arrays.cc", @@ -212,7 +213,7 @@ cc_library( "graph_transformations/quantization_util.h", "graph_transformations/quantize.cc", "graph_transformations/quantize_weights.cc", - "graph_transformations/read_fake_quant_min_max.cc", + "graph_transformations/read_array_minmax_and_narrow_range_from_fake_quant.cc", "graph_transformations/remove_final_dequantize_op.cc", "graph_transformations/remove_tensorflow_assert.cc", "graph_transformations/remove_tensorflow_identity.cc", @@ -236,19 +237,21 @@ cc_library( "graph_transformations/resolve_constant_fake_quant.cc", "graph_transformations/resolve_constant_fill.cc", "graph_transformations/resolve_constant_gather.cc", + "graph_transformations/resolve_constant_pack.cc", "graph_transformations/resolve_constant_random_uniform.cc", "graph_transformations/resolve_constant_range.cc", "graph_transformations/resolve_constant_reshape.cc", "graph_transformations/resolve_constant_shape_or_rank.cc", "graph_transformations/resolve_constant_slice.cc", - "graph_transformations/resolve_constant_stack.cc", "graph_transformations/resolve_constant_strided_slice.cc", "graph_transformations/resolve_constant_transpose.cc", "graph_transformations/resolve_constant_unary.cc", - "graph_transformations/resolve_mean_attributes.cc", + "graph_transformations/resolve_fake_quant_args_from_vars.cc", + "graph_transformations/resolve_gather_attributes.cc", "graph_transformations/resolve_multiply_by_zero.cc", "graph_transformations/resolve_pad_attributes.cc", "graph_transformations/resolve_padv2_attributes.cc", + "graph_transformations/resolve_reduce_attributes.cc", "graph_transformations/resolve_reorder_axes.cc", "graph_transformations/resolve_reshape_attributes.cc", "graph_transformations/resolve_slice_attributes.cc", @@ -336,6 +339,7 @@ cc_library( tf_cc_test( name = "import_tensorflow_test", srcs = ["import_tensorflow_test.cc"], + tags = ["no_oss"], deps = [ ":toco_tooling", "//tensorflow/core:framework", @@ -375,6 +379,7 @@ cc_library( tf_cc_test( name = "tooling_util_test", srcs = ["tooling_util_test.cc"], + tags = ["no_oss"], deps = [ ":model", ":tooling_util", @@ -409,6 +414,7 @@ tf_cc_test( data = [ "toco_port_test.cc", ], + tags = ["no_oss"], deps = [ ":toco_port", "@com_google_googletest//:gtest_main", diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc index 6be6b25f9318deb08bd427d5e3166909fae8f3ea..378212cb74b5a43607e93d6d00e15c296403a071 100644 --- a/tensorflow/contrib/lite/toco/export_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc @@ -215,6 +215,30 @@ void ConvertFloatTensorConst(const Model& model, const string& name, LegacyScalarPolicy::kAvoidLegacyScalars); } +void ConvertBoolTensorConst(const Model& model, const string& name, + GraphDef* tensorflow_graph) { + if (HasAlreadyExportedConst(name, *tensorflow_graph)) { + return; + } + CHECK(model.HasArray(name)); + const auto& array = model.GetArray(name); + tensorflow::NodeDef* const_op = tensorflow_graph->add_node(); + const_op->set_op("Const"); + const_op->set_name(name); + (*const_op->mutable_attr())["dtype"].set_type(DT_BOOL); + auto* tensor = (*const_op->mutable_attr())["value"].mutable_tensor(); + tensor->set_dtype(DT_BOOL); + const auto& data = array.GetBuffer().data; + for (auto index : data) { + tensor->add_bool_val(index); + } + const auto& array_shape = array.shape(); + auto* shape = tensor->mutable_tensor_shape(); + for (int i = 0; i < array_shape.dimensions_count(); i++) { + shape->add_dim()->set_size(array_shape.dims(i)); + } +} + void ConvertIntTensorConst(const Model& model, const string& name, GraphDef* tensorflow_graph) { if (HasAlreadyExportedConst(name, *tensorflow_graph)) { @@ -621,7 +645,8 @@ void ConvertAddOperator(const Model& model, const AddOperator& src_op, CHECK_EQ(src_op.inputs.size(), 2); *add_op->add_input() = src_op.inputs[0]; *add_op->add_input() = src_op.inputs[1]; - (*add_op->mutable_attr())["T"].set_type(DT_FLOAT); + (*add_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.outputs[0])); } void ConvertAddNOperator(const Model& model, const AddNOperator& src_op, @@ -633,7 +658,8 @@ void ConvertAddNOperator(const Model& model, const AddNOperator& src_op, *add_op->add_input() = input; } (*add_op->mutable_attr())["N"].set_i(src_op.inputs.size()); - (*add_op->mutable_attr())["T"].set_type(DT_FLOAT); + (*add_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.outputs[0])); } void ConvertMulOperator(const Model& model, const MulOperator& src_op, @@ -644,16 +670,18 @@ void ConvertMulOperator(const Model& model, const MulOperator& src_op, CHECK_EQ(src_op.inputs.size(), 2); *add_op->add_input() = src_op.inputs[0]; *add_op->add_input() = src_op.inputs[1]; - (*add_op->mutable_attr())["T"].set_type(DT_FLOAT); + (*add_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.outputs[0])); } -void ConvertReluOperator(const ReluOperator& src_op, +void ConvertReluOperator(const Model& model, const ReluOperator& src_op, GraphDef* tensorflow_graph) { tensorflow::NodeDef* relu_op = tensorflow_graph->add_node(); relu_op->set_op("Relu"); relu_op->set_name(src_op.outputs[0]); *relu_op->add_input() = src_op.inputs[0]; - (*relu_op->mutable_attr())["T"].set_type(DT_FLOAT); + (*relu_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.outputs[0])); } void ConvertRelu1Operator(const Relu1Operator& src_op, @@ -884,6 +912,9 @@ void ConvertFakeQuantOperator(const FakeQuantOperator& src_op, if (src_op.num_bits) { (*fakequant_op->mutable_attr())["num_bits"].set_i(src_op.num_bits); } + if (src_op.narrow_range) { + (*fakequant_op->mutable_attr())["narrow_range"].set_b(src_op.narrow_range); + } } void ConvertMaxPoolOperator(const MaxPoolOperator& src_op, @@ -1107,13 +1138,27 @@ void ConvertFloorOperator(const Model& model, const FloorOperator& src_op, void ConvertGatherOperator(const Model& model, const GatherOperator& src_op, GraphDef* tensorflow_graph) { tensorflow::NodeDef* gather_op = tensorflow_graph->add_node(); - gather_op->set_op("Gather"); + gather_op->set_op("GatherV2"); gather_op->set_name(src_op.outputs[0]); - CHECK_EQ(src_op.inputs.size(), 2); *gather_op->add_input() = src_op.inputs[0]; *gather_op->add_input() = src_op.inputs[1]; + if (!src_op.axis) { + // Dynamic axis. + CHECK_EQ(src_op.inputs.size(), 3); + *gather_op->add_input() = src_op.inputs[2]; + } else { + // Constant axis. + CHECK_EQ(src_op.inputs.size(), 2); + const string gather_axis = + AvailableArrayName(model, gather_op->name() + "/axis"); + CreateIntTensorConst(gather_axis, {src_op.axis.value()}, {}, + tensorflow_graph); + *gather_op->add_input() = gather_axis; + } + (*gather_op->mutable_attr())["Tindices"].set_type(DT_INT32); + (*gather_op->mutable_attr())["Taxis"].set_type(DT_INT32); const tensorflow::DataType params_type = GetTensorFlowDataType(model, src_op.inputs[0]); (*gather_op->mutable_attr())["Tparams"].set_type(params_type); @@ -1135,6 +1180,22 @@ void ConvertArgMaxOperator(const Model& model, const ArgMaxOperator& src_op, GetTensorFlowDataType(model, src_op.outputs[0])); } +void ConvertArgMinOperator(const Model& model, const ArgMinOperator& src_op, + GraphDef* tensorflow_graph) { + tensorflow::NodeDef* argmin_op = tensorflow_graph->add_node(); + argmin_op->set_op("ArgMin"); + argmin_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 2); + *argmin_op->add_input() = src_op.inputs[0]; + *argmin_op->add_input() = src_op.inputs[1]; + (*argmin_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.inputs[0])); + (*argmin_op->mutable_attr())["Tidx"].set_type( + GetTensorFlowDataType(model, src_op.inputs[1])); + (*argmin_op->mutable_attr())["output_type"].set_type( + GetTensorFlowDataType(model, src_op.outputs[0])); +} + void ConvertTransposeOperator(const Model& model, const TransposeOperator& src_op, GraphDef* tensorflow_graph) { @@ -1188,17 +1249,17 @@ void ConvertRangeOperator(const Model& model, const RangeOperator& src_op, GetTensorFlowDataType(src_op.dtype)); } -void ConvertStackOperator(const Model& model, const StackOperator& src_op, - GraphDef* tensorflow_graph) { - tensorflow::NodeDef* stack_op = tensorflow_graph->add_node(); - stack_op->set_op("Stack"); - stack_op->set_name(src_op.outputs[0]); +void ConvertPackOperator(const Model& model, const PackOperator& src_op, + GraphDef* tensorflow_graph) { + tensorflow::NodeDef* pack_op = tensorflow_graph->add_node(); + pack_op->set_op("Pack"); + pack_op->set_name(src_op.outputs[0]); for (const auto& input : src_op.inputs) { - *stack_op->add_input() = input; + *pack_op->add_input() = input; } - (*stack_op->mutable_attr())["elem_type"].set_type( - GetTensorFlowDataType(model, src_op.outputs[0])); - (*stack_op->mutable_attr())["axis"].set_i(src_op.axis); + (*pack_op->mutable_attr())["axis"].set_i(src_op.axis); + (*pack_op->mutable_attr())["N"].set_i(src_op.inputs.size()); + (*pack_op->mutable_attr())["T"].set_type(GetTensorFlowDataType(src_op.dtype)); } void ConvertFillOperator(const Model& model, const FillOperator& src_op, @@ -1255,6 +1316,20 @@ void ConvertResizeBilinearOperator(const Model& model, (*resize_op->mutable_attr())["align_corners"].set_b(src_op.align_corners); } +void ConvertOneHotOperator(const Model& model, const OneHotOperator& src_op, + GraphDef* tensorflow_graph) { + tensorflow::NodeDef* onehot_op = tensorflow_graph->add_node(); + onehot_op->set_op("OneHot"); + onehot_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 4); + for (const auto& input : src_op.inputs) { + *onehot_op->add_input() = input; + } + (*onehot_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.outputs[0])); + (*onehot_op->mutable_attr())["axis"].set_i(src_op.axis); +} + namespace { // TODO(aselle): Remove when available in absl absl::string_view FindLongestCommonPrefix(absl::string_view a, @@ -1604,10 +1679,11 @@ void ConvertSliceOperator(const Model& model, const SliceOperator& src_op, CreateSliceInput(src_op.inputs[2], src_op.size, tensorflow_graph); } -void ConvertMeanOperator(const Model& model, const MeanOperator& src_op, - GraphDef* tensorflow_graph) { +template +void ConvertReduceOperator(const Model& model, const T& src_op, + GraphDef* tensorflow_graph, const string& op_name) { tensorflow::NodeDef* new_op = tensorflow_graph->add_node(); - new_op->set_op("Mean"); + new_op->set_op(op_name); new_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); *new_op->add_input() = src_op.inputs[0]; @@ -1616,6 +1692,9 @@ void ConvertMeanOperator(const Model& model, const MeanOperator& src_op, const tensorflow::DataType params_type = GetTensorFlowDataType(model, src_op.inputs[0]); (*new_op->mutable_attr())["T"].set_type(params_type); + const tensorflow::DataType indices_type = + GetTensorFlowDataType(model, src_op.inputs[1]); + (*new_op->mutable_attr())["Tidx"].set_type(indices_type); if (src_op.keep_dims) { (*new_op->mutable_attr())["keep_dims"].set_b(true); @@ -1672,43 +1751,43 @@ void ConvertSubOperator(const Model& model, const SubOperator& src_op, void ConvertTensorFlowMinimumOperator(const Model& model, const TensorFlowMinimumOperator& src_op, GraphDef* tensorflow_graph) { - tensorflow::NodeDef* sub_op = tensorflow_graph->add_node(); - sub_op->set_op("Minimum"); - sub_op->set_name(src_op.outputs[0]); + tensorflow::NodeDef* min_op = tensorflow_graph->add_node(); + min_op->set_op("Minimum"); + min_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); - *sub_op->add_input() = src_op.inputs[0]; - *sub_op->add_input() = src_op.inputs[1]; + *min_op->add_input() = src_op.inputs[0]; + *min_op->add_input() = src_op.inputs[1]; const tensorflow::DataType data_type = GetTensorFlowDataType(model, src_op.inputs[0]); - (*sub_op->mutable_attr())["T"].set_type(data_type); + (*min_op->mutable_attr())["T"].set_type(data_type); } void ConvertTensorFlowMaximumOperator(const Model& model, const TensorFlowMaximumOperator& src_op, GraphDef* tensorflow_graph) { - tensorflow::NodeDef* sub_op = tensorflow_graph->add_node(); - sub_op->set_op("Maximum"); - sub_op->set_name(src_op.outputs[0]); + tensorflow::NodeDef* max_op = tensorflow_graph->add_node(); + max_op->set_op("Maximum"); + max_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); - *sub_op->add_input() = src_op.inputs[0]; - *sub_op->add_input() = src_op.inputs[1]; + *max_op->add_input() = src_op.inputs[0]; + *max_op->add_input() = src_op.inputs[1]; const tensorflow::DataType data_type = GetTensorFlowDataType(model, src_op.inputs[0]); - (*sub_op->mutable_attr())["T"].set_type(data_type); + (*max_op->mutable_attr())["T"].set_type(data_type); } void ConvertSelectOperator(const Model& model, const SelectOperator& src_op, GraphDef* tensorflow_graph) { - tensorflow::NodeDef* sub_op = tensorflow_graph->add_node(); - sub_op->set_op("Select"); - sub_op->set_name(src_op.outputs[0]); + tensorflow::NodeDef* select_op = tensorflow_graph->add_node(); + select_op->set_op("Select"); + select_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 3); - *sub_op->add_input() = src_op.inputs[0]; - *sub_op->add_input() = src_op.inputs[1]; - *sub_op->add_input() = src_op.inputs[2]; + *select_op->add_input() = src_op.inputs[0]; + *select_op->add_input() = src_op.inputs[1]; + *select_op->add_input() = src_op.inputs[2]; const tensorflow::DataType data_type = GetTensorFlowDataType(model, src_op.inputs[1]); - (*sub_op->mutable_attr())["T"].set_type(data_type); + (*select_op->mutable_attr())["T"].set_type(data_type); } void ConvertTileOperator(const Model& model, @@ -1731,11 +1810,14 @@ void ConvertTileOperator(const Model& model, void ConvertTopKV2Operator(const Model& model, const TopKV2Operator& src_op, GraphDef* tensorflow_graph) { tensorflow::NodeDef* topk_op = tensorflow_graph->add_node(); - topk_op->set_op("TOPKV2"); + topk_op->set_op("TopKV2"); topk_op->set_name(src_op.outputs[0]); CHECK_EQ(src_op.inputs.size(), 2); *topk_op->add_input() = src_op.inputs[0]; *topk_op->add_input() = src_op.inputs[1]; + const tensorflow::DataType data_type = + GetTensorFlowDataType(model, src_op.inputs[0]); + (*topk_op->mutable_attr())["T"].set_type(data_type); (*topk_op->mutable_attr())["sorted"].set_b(true); } @@ -1806,6 +1888,58 @@ void ConvertPowOperator(const Model& model, const PowOperator& src_op, (*pow_op->mutable_attr())["T"].set_type(data_type); } +void ConvertAnyOperator(const Model& model, const AnyOperator& src_op, + GraphDef* tensorflow_graph) { + tensorflow::NodeDef* any_op = tensorflow_graph->add_node(); + any_op->set_op("Any"); + any_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 2); + for (int i = 0; i < 2; ++i) { + *any_op->add_input() = src_op.inputs[i]; + } + const tensorflow::DataType data_type = + GetTensorFlowDataType(model, src_op.inputs[1]); + (*any_op->mutable_attr())["Tidx"].set_type(data_type); + (*any_op->mutable_attr())["keep_dims"].set_b(src_op.keep_dims); +} + +void ConvertLogicalAndOperator(const Model& model, + const LogicalAndOperator& src_op, + GraphDef* tensorflow_graph) { + tensorflow::NodeDef* logical_op = tensorflow_graph->add_node(); + logical_op->set_op("LogicalAnd"); + logical_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 2); + for (int i = 0; i < 2; ++i) { + *logical_op->add_input() = src_op.inputs[i]; + } +} + +void ConvertLogicalNotOperator(const Model& model, + const LogicalNotOperator& src_op, + GraphDef* tensorflow_graph) { + tensorflow::NodeDef* logical_op = tensorflow_graph->add_node(); + logical_op->set_op("LogicalNot"); + logical_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 1); + *logical_op->add_input() = src_op.inputs[0]; +} + +void ConvertLogicalOrOperator(const Model& model, + const LogicalOrOperator& src_op, + const char* op_name, GraphDef* tensorflow_graph) { + tensorflow::NodeDef* logical_or_op = tensorflow_graph->add_node(); + logical_or_op->set_op(op_name); + logical_or_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 2); + for (int i = 0; i < 2; ++i) { + *logical_or_op->add_input() = src_op.inputs[i]; + } + const tensorflow::DataType data_type = + GetTensorFlowDataType(model, src_op.inputs[0]); + (*logical_or_op->mutable_attr())["T"].set_type(data_type); +} + void ConvertOperator(const Model& model, const Operator& src_op, GraphDef* tensorflow_graph) { if (src_op.fused_activation_function != FusedActivationFunctionType::kNone) { @@ -1842,7 +1976,7 @@ void ConvertOperator(const Model& model, const Operator& src_op, ConvertMulOperator(model, static_cast(src_op), tensorflow_graph); } else if (src_op.type == OperatorType::kRelu) { - ConvertReluOperator(static_cast(src_op), + ConvertReluOperator(model, static_cast(src_op), tensorflow_graph); } else if (src_op.type == OperatorType::kRelu1) { ConvertRelu1Operator(static_cast(src_op), @@ -1942,8 +2076,24 @@ void ConvertOperator(const Model& model, const Operator& src_op, model, static_cast(src_op), tensorflow_graph); } else if (src_op.type == OperatorType::kMean) { - ConvertMeanOperator(model, static_cast(src_op), - tensorflow_graph); + ConvertReduceOperator(model, static_cast(src_op), + tensorflow_graph, "Mean"); + } else if (src_op.type == OperatorType::kSum) { + ConvertReduceOperator(model, + static_cast(src_op), + tensorflow_graph, "Sum"); + } else if (src_op.type == OperatorType::kReduceProd) { + ConvertReduceOperator(model, + static_cast(src_op), + tensorflow_graph, "Prod"); + } else if (src_op.type == OperatorType::kReduceMin) { + ConvertReduceOperator(model, + static_cast(src_op), + tensorflow_graph, "Min"); + } else if (src_op.type == OperatorType::kReduceMax) { + ConvertReduceOperator(model, + static_cast(src_op), + tensorflow_graph, "Max"); } else if (src_op.type == OperatorType::kSub) { ConvertSubOperator(model, static_cast(src_op), tensorflow_graph); @@ -1964,6 +2114,9 @@ void ConvertOperator(const Model& model, const Operator& src_op, } else if (src_op.type == OperatorType::kArgMax) { ConvertArgMaxOperator(model, static_cast(src_op), tensorflow_graph); + } else if (src_op.type == OperatorType::kArgMin) { + ConvertArgMinOperator(model, static_cast(src_op), + tensorflow_graph); } else if (src_op.type == OperatorType::kTopK_V2) { ConvertTopKV2Operator(model, static_cast(src_op), tensorflow_graph); @@ -1980,9 +2133,9 @@ void ConvertOperator(const Model& model, const Operator& src_op, } else if (src_op.type == OperatorType::kRange) { ConvertRangeOperator(model, static_cast(src_op), tensorflow_graph); - } else if (src_op.type == OperatorType::kStack) { - ConvertStackOperator(model, static_cast(src_op), - tensorflow_graph); + } else if (src_op.type == OperatorType::kPack) { + ConvertPackOperator(model, static_cast(src_op), + tensorflow_graph); } else if (src_op.type == OperatorType::kFill) { ConvertFillOperator(model, static_cast(src_op), tensorflow_graph); @@ -2023,6 +2176,24 @@ void ConvertOperator(const Model& model, const Operator& src_op, } else if (src_op.type == OperatorType::kPow) { ConvertPowOperator(model, static_cast(src_op), "Pow", tensorflow_graph); + } else if (src_op.type == OperatorType::kAny) { + ConvertAnyOperator(model, static_cast(src_op), + tensorflow_graph); + } else if (src_op.type == OperatorType::kLogicalAnd) { + ConvertLogicalAndOperator(model, + static_cast(src_op), + tensorflow_graph); + } else if (src_op.type == OperatorType::kLogicalNot) { + ConvertLogicalNotOperator(model, + static_cast(src_op), + tensorflow_graph); + } else if (src_op.type == OperatorType::kOneHot) { + ConvertOneHotOperator(model, static_cast(src_op), + tensorflow_graph); + } else if (src_op.type == OperatorType::kLogicalOr) { + ConvertLogicalOrOperator(model, + static_cast(src_op), + "LogicalOr", tensorflow_graph); } else { LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(src_op.type); } @@ -2101,6 +2272,9 @@ void ExportTensorFlowGraphDefImplementation(const Model& model, const auto& array = *array_pair.second; if (array.buffer) { switch (array.data_type) { + case ArrayDataType::kBool: + ConvertBoolTensorConst(model, array_name, tensorflow_graph); + break; case ArrayDataType::kFloat: ConvertFloatTensorConst(model, array_name, tensorflow_graph); break; diff --git a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md index 18b7848db86e553ec645fa87298420012b5f753f..4bf47aa3c4d1b682808ab8175c4d07d8a347067a 100644 --- a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md +++ b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md @@ -36,7 +36,7 @@ There are two approaches to running TOCO via command line. * `tflite_convert`: Starting from TensorFlow 1.9, the command-line tool `tflite_convert` will be installed as part of the Python package. All of the examples below use `tflite_convert` for simplicity. - * Example: `tflite --output_file=...` + * Example: `tflite_convert --output_file=...` * `bazel`: In order to run the latest version of TOCO, [clone the TensorFlow repository](https://www.tensorflow.org/install/install_sources#clone_the_tensorflow_repository) and use `bazel`. This is the recommended approach for converting models that diff --git a/tensorflow/contrib/lite/toco/graph_transformations/convert_expanddims_to_reshape.cc b/tensorflow/contrib/lite/toco/graph_transformations/convert_expanddims_to_reshape.cc index 56f48d47de4e86ece76ceef1d09a25f50957a8dc..310a88484c246b8035aa73b5e04ad677d575e4c4 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/convert_expanddims_to_reshape.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/convert_expanddims_to_reshape.cc @@ -40,11 +40,6 @@ bool ConvertExpandDimsToReshape::Run(Model* model, std::size_t op_index) { // Yield until input dims have been resolved. return false; } - if (input_array.shape().dimensions_count() == 0) { - // Input array cannot be 0-D. - // (Unsure if this is TF behavior, but was required to get a test to pass.) - return false; - } const auto& axis_array = model->GetArray(expand_op->inputs[1]); if (!axis_array.has_shape()) { diff --git a/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_stack_to_reshape.cc b/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_pack_to_reshape.cc similarity index 72% rename from tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_stack_to_reshape.cc rename to tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_pack_to_reshape.cc index 0615b5e6c6db910ee847188427b416fd812aa141..75113a2a8c7c446bd13de8b5c1a8d8ef3cf7fdd6 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_stack_to_reshape.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/convert_trivial_pack_to_reshape.cc @@ -25,19 +25,19 @@ limitations under the License. namespace toco { -bool ConvertTrivialStackToReshape::Run(Model* model, std::size_t op_index) { - auto stack_it = model->operators.begin() + op_index; - if (stack_it->get()->type != OperatorType::kStack) { +bool ConvertTrivialPackToReshape::Run(Model* model, std::size_t op_index) { + auto pack_it = model->operators.begin() + op_index; + if (pack_it->get()->type != OperatorType::kPack) { return false; } - auto* stack_op = static_cast(stack_it->get()); - if (stack_op->inputs.size() > 1) { + auto* pack_op = static_cast(pack_it->get()); + if (pack_op->inputs.size() > 1) { // Not trivial. return false; } - CHECK_EQ(stack_op->outputs.size(), 1); + CHECK_EQ(pack_op->outputs.size(), 1); - const auto& input_array = model->GetArray(stack_op->inputs[0]); + const auto& input_array = model->GetArray(pack_op->inputs[0]); if (!input_array.has_shape()) { // Yield until input dims have been resolved. return false; @@ -48,16 +48,16 @@ bool ConvertTrivialStackToReshape::Run(Model* model, std::size_t op_index) { return false; } - AddMessageF("Converting trivial %s to a reshape", LogName(*stack_op)); + AddMessageF("Converting trivial %s to a reshape", LogName(*pack_op)); // Note that we could convert to ExpandDims but toco prefers reshapes. auto* reshape_op = new TensorFlowReshapeOperator; - reshape_op->inputs = {stack_op->inputs[0]}; - reshape_op->outputs = stack_op->outputs; + reshape_op->inputs = {pack_op->inputs[0]}; + reshape_op->outputs = pack_op->outputs; // Create shape param. string shape_array_name = - AvailableArrayName(*model, stack_op->outputs[0] + "_shape"); + AvailableArrayName(*model, pack_op->outputs[0] + "_shape"); Array& shape_array = model->GetOrCreateArray(shape_array_name); *(shape_array.mutable_shape()->mutable_dims()) = { 1 + input_array.shape().dimensions_count()}; @@ -70,10 +70,10 @@ bool ConvertTrivialStackToReshape::Run(Model* model, std::size_t op_index) { } // Replace the operator in the graph. - const auto reshape_it = model->operators.emplace(stack_it, reshape_op); - stack_it = reshape_it + 1; - CHECK_EQ(stack_it->get(), stack_op); - model->operators.erase(stack_it); + const auto reshape_it = model->operators.emplace(pack_it, reshape_op); + pack_it = reshape_it + 1; + CHECK_EQ(pack_it->get(), pack_op); + model->operators.erase(pack_it); return true; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/dequantize.cc b/tensorflow/contrib/lite/toco/graph_transformations/dequantize.cc index 2c7ffe488477ef1a544dfe6f36a6e0d1ac40aa96..1688586733b0434c7fc98686a19f0ceb8092f33b 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/dequantize.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/dequantize.cc @@ -159,6 +159,7 @@ bool DequantizeArray(const string& array_name, new_array.GetOrCreateMinMax() = array->GetMinMax(); fakequant_op->minmax.reset(new MinMax); *fakequant_op->minmax = array->GetMinMax(); + fakequant_op->narrow_range = array->narrow_range; if (must_insert_fakequant_before) { for (const auto& op : model->operators) { for (string& output : op->outputs) { diff --git a/tensorflow/contrib/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc b/tensorflow/contrib/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc index 75642bbc37be6b3140e5b79a463ca70b5786d772..c13fc0de7502a9edc80dc399354708a5b1b96b02 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc @@ -181,7 +181,7 @@ bool EnsureUint8WeightsSafeForFastInt8Kernels::Run(Model* model, // future without worrying. static constexpr int kMinDistanceBetweenBadValues = 16; if (distance < kMinDistanceBetweenBadValues) { - if (allow_nudging_weights()) { + if (allow_nudging_weights() || has_default_ranges_flag()) { buffer_data[i] = 1; changed = true; continue; @@ -200,6 +200,15 @@ bool EnsureUint8WeightsSafeForFastInt8Kernels::Run(Model* model, } if (changed) { + if (has_default_ranges_flag()) { + std::cerr + << "Since the specified values of --default_ranges_min and " + "--default_ranges_max result in values incompatible with TFLite's " + "fast int8 kernels, " + "--allow_nudging_weights_to_use_fast_gemm_kernel " + "has been enabled. This may affect the accuracy of the model." + << std::endl; + } AddMessageF("Tweaked weights values for %s", LogName(op)); } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h index 8cd1298bcacd7b9c1379ccb4532885f686484278..8d9a4c4700e12ac1a187038a0a5efc1b033d4e57 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h +++ b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h @@ -116,7 +116,7 @@ DECLARE_GRAPH_TRANSFORMATION(ConvertExpandDimsToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertPureConvToDepthwise) DECLARE_GRAPH_TRANSFORMATION(ConvertSqueezeToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialAddNToAdd) -DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialStackToReshape) +DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialPackToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialTileToConcat) DECLARE_GRAPH_TRANSFORMATION(ConvertTrivialTransposeToReshape) DECLARE_GRAPH_TRANSFORMATION(ConvertReorderAxes) @@ -159,7 +159,7 @@ DECLARE_GRAPH_TRANSFORMATION(ResolveConstantBinaryOperator) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantUnaryOperator) DECLARE_GRAPH_TRANSFORMATION(CreateIm2colArrays) DECLARE_GRAPH_TRANSFORMATION(DropIm2colArrays) -DECLARE_GRAPH_TRANSFORMATION(ReadFakeQuantMinMax) +DECLARE_GRAPH_TRANSFORMATION(ReadArrayMinmaxAndNarrowRangeFromFakeQuant) DECLARE_GRAPH_TRANSFORMATION(ReorderElementwiseUnary) DECLARE_GRAPH_TRANSFORMATION(ReorderReshapeTranspose) DECLARE_GRAPH_TRANSFORMATION(ResolveReorderAxes) @@ -180,13 +180,13 @@ DECLARE_GRAPH_TRANSFORMATION(ResolvePadAttributes) DECLARE_GRAPH_TRANSFORMATION(ResolvePadV2Attributes) DECLARE_GRAPH_TRANSFORMATION(ResolveStridedSliceAttributes) DECLARE_GRAPH_TRANSFORMATION(ResolveSliceAttributes) -DECLARE_GRAPH_TRANSFORMATION(ResolveMeanAttributes) +DECLARE_GRAPH_TRANSFORMATION(ResolveReduceAttributes) DECLARE_GRAPH_TRANSFORMATION(ResolveTransposeAttributes) +DECLARE_GRAPH_TRANSFORMATION(ResolveConstantPack) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantRandomUniform) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantRange) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantShapeOrRank) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantSlice) -DECLARE_GRAPH_TRANSFORMATION(ResolveConstantStack) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantStridedSlice) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantFill) DECLARE_GRAPH_TRANSFORMATION(ResolveConstantGather) @@ -194,6 +194,8 @@ DECLARE_GRAPH_TRANSFORMATION(ResolveMultiplyByZero) DECLARE_GRAPH_TRANSFORMATION(Dequantize) DECLARE_GRAPH_TRANSFORMATION(UnpartitionEmbeddingLookup) DECLARE_GRAPH_TRANSFORMATION(ShuffleFCWeights) +DECLARE_GRAPH_TRANSFORMATION(ResolveFakeQuantArgsFromVars) +DECLARE_GRAPH_TRANSFORMATION(ResolveGatherAttributes) class PropagateDefaultMinMax : public GraphTransformation { public: @@ -260,8 +262,12 @@ class EnsureUint8WeightsSafeForFastInt8Kernels : public GraphTransformation { bool allow_nudging_weights() const { return allow_nudging_weights_; } void set_allow_nudging_weights(bool val) { allow_nudging_weights_ = val; } + bool has_default_ranges_flag() const { return has_default_ranges_flag_; } + void set_has_default_ranges_flag(bool val) { has_default_ranges_flag_ = val; } + private: bool allow_nudging_weights_ = false; + bool has_default_ranges_flag_ = false; }; #undef DECLARE_GRAPH_TRANSFORMATION diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_prelu.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_prelu.cc index 30be4ac0aa5e9f639bbf0630e142c2806faa3260..b90a156a0dcfcd77c3e2b47bb0d77e246f2fc625 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/identify_prelu.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_prelu.cc @@ -74,14 +74,30 @@ bool IdentifyPRelu::Run(Model* model, std::size_t op_index) { const auto* relu_neg_input_op = GetOpWithOutput(*model, mul_op->inputs[1]); if (relu_neg_input_op == nullptr || - relu_neg_input_op->type != OperatorType::kNeg || - relu_neg_input_op->fused_activation_function != - FusedActivationFunctionType::kRelu || relu_neg_input_op->inputs.size() != 1) { return false; } - if (relu_input_op->inputs[0] != relu_neg_input_op->inputs[0]) { + const Operator* final_input_op; + if (relu_neg_input_op->type == OperatorType::kNeg && + relu_neg_input_op->fused_activation_function == + FusedActivationFunctionType::kRelu) { + // This detects a Neg op with fused Relu activation function. + final_input_op = relu_neg_input_op; + } else { + // This detects a Neg op followed by a separated Relu op. + const auto* neg_input_op = + GetOpWithOutput(*model, relu_neg_input_op->inputs[0]); + if (neg_input_op == nullptr || neg_input_op->inputs.size() != 1 || + relu_neg_input_op->type != OperatorType::kRelu || + relu_neg_input_op->fused_activation_function != + FusedActivationFunctionType::kNone) { + return false; + } + final_input_op = neg_input_op; + } + + if (relu_input_op->inputs[0] != final_input_op->inputs[0]) { return false; } @@ -112,7 +128,6 @@ bool IdentifyPRelu::Run(Model* model, std::size_t op_index) { // intermediate tensors aren't used by other ops, those will be removed by // other graph transformation rules. model->operators.erase(FindOp(*model, add_op)); - return true; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/make_initial_dequantize_operator.cc b/tensorflow/contrib/lite/toco/graph_transformations/make_initial_dequantize_operator.cc index 45d9f73a1e6416b8f3fe3936c740da637961b7fc..f684de08abf72d05d4408bf6341fa5a3c2ed11cd 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/make_initial_dequantize_operator.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/make_initial_dequantize_operator.cc @@ -85,15 +85,8 @@ bool AddDequantizeOperatorToInput(const string& input_name, const Operator* op, dequantized_input_minmax = input_minmax; auto& input_qparams = input_array.GetOrCreateQuantizationParams(); input_array.data_type = input_array.final_data_type; - if (input_array.data_type == ArrayDataType::kUint8) { - GetQuantizationParamsFromMinMax(input_minmax, - &input_qparams); - } else if (input_array.data_type == ArrayDataType::kInt16) { - GetQuantizationParamsFromMinMax(input_minmax, - &input_qparams); - } else { - LOG(FATAL) << "unhandled data type"; - } + ChooseQuantizationParamsForArrayAndQuantizedDataType( + input_array, input_array.data_type, &input_qparams); transformation->AddMessageF( "Created %s" diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc index 00ab7cbaa90b399ca08bdfba82991fbd5d2c9f7e..f033ee013ee6f51d4e23083c467effae95a9a85d 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc @@ -62,6 +62,10 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { case OperatorType::kGreaterEqual: case OperatorType::kEqual: case OperatorType::kNotEqual: + case OperatorType::kAny: + case OperatorType::kLogicalAnd: + case OperatorType::kLogicalNot: + case OperatorType::kLogicalOr: // These operators unconditionally produce bool outputs SetDataTypeForAllOutputs(model, op, ArrayDataType::kBool); break; @@ -100,6 +104,13 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { model->GetArray(op->outputs[0]).data_type = argmax_op->output_data_type; break; } + case OperatorType::kArgMin: { + // Data type of the ArgMin op is specified. + CHECK_EQ(op->outputs.size(), 1); + auto* argmin_op = static_cast(op); + model->GetArray(op->outputs[0]).data_type = argmin_op->output_data_type; + break; + } case OperatorType::kRange: { auto* range_op = static_cast(op); // Output type of the Range op can be set via an attribute @@ -131,7 +142,8 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { CHECK_EQ(op->inputs.size(), 2); CHECK_EQ(op->outputs.size(), 2); CHECK(model->GetArray(op->inputs[1]).data_type == ArrayDataType::kInt32); - model->GetArray(op->outputs[0]).data_type = model->GetArray(op->inputs[0]).data_type; + model->GetArray(op->outputs[0]).data_type = + model->GetArray(op->inputs[0]).data_type; model->GetArray(op->outputs[1]).data_type = ArrayDataType ::kInt32; break; } @@ -144,8 +156,8 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { return false; } for (int i = 0; i < op->outputs.size(); ++i) { - auto output = op->outputs[i]; - auto data_type = unsupported_op->output_data_types[i]; + const string& output = op->outputs[i]; + const ArrayDataType data_type = unsupported_op->output_data_types[i]; model->GetArray(output).data_type = data_type; } break; @@ -183,6 +195,26 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { SetDataTypeForAllOutputs(model, op, data_type); break; } + case OperatorType::kPack: { + const ArrayDataType data_type = model->GetArray(op->inputs[0]).data_type; + for (const auto& input : op->inputs) { + CHECK(data_type == model->GetArray(input).data_type); + } + SetDataTypeForAllOutputs(model, op, data_type); + break; + } + case OperatorType::kOneHot: { + CHECK_EQ(op->inputs.size(), 4); + CHECK_EQ(op->outputs.size(), 1); + const ArrayDataType on_value_type = + model->GetArray(op->inputs[OneHotOperator::ON_VALUE_INPUT]).data_type; + const ArrayDataType off_value_type = + model->GetArray(op->inputs[OneHotOperator::OFF_VALUE_INPUT]) + .data_type; + CHECK(on_value_type == off_value_type); + model->GetArray(op->outputs[0]).data_type = on_value_type; + break; + } default: { // These operators produce outputs with the same type as their 1st input CHECK_GT(op->inputs.size(), 0); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc index 0f2592d05f6e01599735c5138c53ba7779ce805d..3ad6b0ec6f7a3c4a9a0ab3964c1198ee757ea4b5 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc @@ -30,15 +30,9 @@ namespace { bool ChangeArrayDataType(GraphTransformation* transformation, Array* array, ArrayDataType new_data_type, const MinMax* new_minmax) { - // The code below assumes kInt16, see - // GetQuantizationParamsFromMinMax - if (new_data_type != ArrayDataType::kInt16) { - return false; - } - - bool changed = false; // Ensure the array ends up in the new type (if it hasn't yet been quantized). - if ((array->final_data_type != new_data_type)) { + bool changed = false; + if (array->final_data_type != new_data_type) { array->final_data_type = new_data_type; changed = true; } @@ -72,12 +66,10 @@ bool ChangeArrayDataType(GraphTransformation* transformation, Array* array, "Rescaling min/max from %g,%g (%s) to %g,%g (%s)", array_minmax.min, array_minmax.max, ArrayDataTypeName(array->data_type), min, max, ArrayDataTypeName(new_data_type)); - array_minmax.min = min; array_minmax.max = max; - GetQuantizationParamsFromMinMax( - array_minmax, array->quantization_params.get()); - + ChooseQuantizationParamsForArrayAndQuantizedDataType( + *array, new_data_type, array->quantization_params.get()); // Directly change the type as the array was already quantized. array->data_type = new_data_type; changed = true; @@ -95,6 +87,7 @@ bool ChangeArrayDataType(GraphTransformation* transformation, Array* array, changed = true; } } + return changed; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc index 8eb0423283a267652e3d51361b8a0440f46d0c8b..3c9379fd878ea350064c6b0f562ae11e9a713365 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -437,6 +437,7 @@ void ProcessTensorFlowReshapeOperator(Model* model, product_non_wildcard_dims *= shape_data[i]; } } + const int input_flat_size = RequiredBufferSizeForShape(input_shape); if (has_wildcard) { CHECK_GE(input_flat_size, product_non_wildcard_dims) @@ -445,6 +446,12 @@ void ProcessTensorFlowReshapeOperator(Model* model, << op->outputs[0] << "\". Are your input shapes correct?"; shape_data[wildcard_index] = input_flat_size / product_non_wildcard_dims; } + + if (shape_data.size() == 1 && shape_data[0] == 0) { + // We have reshaped a scalar, so preserve as a scalar. + shape_data.clear(); + } + auto& output_shape = *output_array.mutable_shape(); *output_shape.mutable_dims() = shape_data; CHECK_EQ(input_flat_size, RequiredBufferSizeForShape(output_shape)) @@ -522,12 +529,14 @@ void ProcessAddNOperator(Model* model, Operator* op) { bool KeepDims(const Operator& op) { switch (op.type) { - case OperatorType::kMin: // Reduction Min + case OperatorType::kReduceMin: // Reduction Min return static_cast(op).keep_dims; - case OperatorType::kMax: // Reduction Max + case OperatorType::kReduceMax: // Reduction Max return static_cast(op).keep_dims; case OperatorType::kSum: return static_cast(op).keep_dims; + case OperatorType::kReduceProd: + return static_cast(op).keep_dims; case OperatorType::kMean: return static_cast(op).keep_dims; default: @@ -1034,17 +1043,28 @@ void ProcessGatherOperator(Model* model, GatherOperator* op) { return; } + // Yield until the axis has been resolved. + if (!op->axis) { + return; + } + int axis = op->axis.value(); + const auto& input_shape = input_array.shape(); const auto& indices_shape = indices_array.shape(); QCHECK_GE(input_shape.dimensions_count(), 1); op->input_rank = input_shape.dimensions_count(); + QCHECK_LT(axis, op->input_rank); - // Copy the input dimensions to the output except for dimension 0, + // Copy the input dimensions to the output except for the axis dimensions // where the dimension of indices_shape is used. - // TODO(mgubin): if axis != 0 this is not true, change when it's supported. auto output_dims = output_array.mutable_shape()->mutable_dims(); - output_dims->push_back(indices_shape.dims(0)); - for (int dim = 1; dim < input_shape.dimensions_count(); dim++) { + for (int dim = 0; dim < axis; ++dim) { + output_dims->push_back(input_shape.dims(dim)); + } + for (int dim = 0; dim < indices_shape.dimensions_count(); ++dim) { + output_dims->push_back(indices_shape.dims(dim)); + } + for (int dim = axis + 1; dim < input_shape.dimensions_count(); ++dim) { output_dims->push_back(input_shape.dims(dim)); } } @@ -1190,7 +1210,7 @@ void ProcessShapeOperator(Model* model, TensorFlowShapeOperator* op) { output_shape->ReplaceDims({input_array.shape().dimensions_count()}); } -void ProcessStackOperator(Model* model, StackOperator* op) { +void ProcessPackOperator(Model* model, PackOperator* op) { CHECK_GE(op->inputs.size(), 1); CHECK_EQ(op->outputs.size(), 1); auto& output_array = model->GetArray(op->outputs[0]); @@ -1199,7 +1219,7 @@ void ProcessStackOperator(Model* model, StackOperator* op) { return; } - std::unique_ptr stacked_shape; + std::unique_ptr packed_shape; for (const auto& input : op->inputs) { const auto& input_array = model->GetArray(input); if (!input_array.has_shape()) { @@ -1208,23 +1228,23 @@ void ProcessStackOperator(Model* model, StackOperator* op) { } Shape shape = input_array.shape(); - if (!stacked_shape) { - stacked_shape.reset(new Shape(shape)); + if (!packed_shape) { + packed_shape.reset(new Shape(shape)); } else { - CHECK(*stacked_shape == shape) << "All input arrays to Stack operators " - "must have the same shape. Input \"" - << input << "\" is different."; + CHECK(*packed_shape == shape) << "All input arrays to Pack operators " + "must have the same shape. Input \"" + << input << "\" is different."; } } int axis = op->axis; if (axis < 0) { // Handle negative axis - axis += stacked_shape->dims().size() + 1; + axis += packed_shape->dims().size() + 1; } - stacked_shape->mutable_dims()->insert( - stacked_shape->mutable_dims()->begin() + axis, op->inputs.size()); - output_array.copy_shape(*stacked_shape); + packed_shape->mutable_dims()->insert( + packed_shape->mutable_dims()->begin() + axis, op->inputs.size()); + output_array.copy_shape(*packed_shape); } void ProcessStridedSliceOperator(Model* model, StridedSliceOperator* op) { @@ -1404,7 +1424,8 @@ void ProcessTransposeOperator(Model* model, TransposeOperator* op) { } } -void ProcessArgMaxOperator(Model* model, ArgMaxOperator* op) { +template +void ProcessArgMinMaxOperator(Model* model, Op* op) { CHECK_EQ(op->inputs.size(), 2); const auto& input_array = model->GetArray(op->inputs[0]); // Yield until input dims have been resolved. @@ -1498,6 +1519,120 @@ void ProcessTileOperator(Model* model, TensorFlowTileOperator* op) { } } +void ProcessAnyOperator(Model* model, AnyOperator* op) { + CHECK_EQ(op->inputs.size(), 2); + CHECK_EQ(op->outputs.size(), 1); + + auto& output_array = model->GetArray(op->outputs[0]); + if (output_array.has_shape()) { + // We have already run. + return; + } + + const auto& input_array = model->GetArray(op->inputs[0]); + if (!input_array.has_shape()) { + // Yield until input dims have been resolved. + return; + } + const auto& input_shape = input_array.shape(); + + auto& reduction_indices_array = model->GetArray(op->inputs[1]); + if (!reduction_indices_array.has_shape()) { + // Yield until reduction indices shape been resolved. + return; + } + if (!reduction_indices_array.buffer) { + // Yield until the reduction indices are constant. + return; + } + CHECK(reduction_indices_array.data_type == ArrayDataType::kInt32) + << "Any reduction input must be int32"; + + int input_rank = input_shape.dimensions_count(); + std::set true_indices; + const auto& reduction_indices = + reduction_indices_array.GetBuffer().data; + for (int i = 0; i < reduction_indices.size(); ++i) { + const int32 reduction_index = reduction_indices[i]; + if (reduction_index < -input_rank || reduction_index >= input_rank) { + CHECK(false) << "Invalid reduction dimension " << reduction_index + << " for input with " << input_rank << " dimensions"; + } + int32 wrapped_index = reduction_index; + if (wrapped_index < 0) { + wrapped_index += input_rank; + } + true_indices.insert(wrapped_index); + } + + auto* mutable_dims = output_array.mutable_shape()->mutable_dims(); + mutable_dims->clear(); + for (int i = 0; i < input_rank; ++i) { + if (true_indices.count(i) > 0) { + if (op->keep_dims) { + mutable_dims->emplace_back(1); + } + } else { + mutable_dims->emplace_back(input_shape.dims(i)); + } + } +} + +void ProcessOneHotOperator(Model* model, OneHotOperator* op) { + CHECK_EQ(op->inputs.size(), 4); + CHECK_EQ(op->outputs.size(), 1); + auto& output_array = model->GetArray(op->outputs[0]); + if (output_array.has_shape()) { + // Shape already propagated + return; + } + + // Yield until indices dims have been resolved. + const auto& indices_array = + model->GetArray(op->inputs[OneHotOperator::INDICES_INPUT]); + if (!indices_array.has_shape()) { + return; + } + + // Yield until depth is constant and dims have been resolved. + if (!IsConstantParameterArray(*model, + op->inputs[OneHotOperator::DEPTH_INPUT])) { + return; + } + const auto& depth_array = + model->GetArray(op->inputs[OneHotOperator::DEPTH_INPUT]); + if (!depth_array.has_shape()) { + return; + } + + CHECK(depth_array.data_type == ArrayDataType::kInt32) + << "Depth array must be int32."; + CHECK_EQ(RequiredBufferSizeForShape(depth_array.shape()), 1) + << "Depth array must be scalar."; + + const int depth = depth_array.GetBuffer().data[0]; + CHECK_GE(depth, 0) << "Depth must be non-negative."; + + const int indices_dims = indices_array.shape().dimensions_count(); + const int output_dims = indices_dims + 1; + const int axis = op->axis == -1 ? indices_dims : op->axis; + CHECK_GE(axis, 0) << "Resolved axis must be non-negative."; + + auto* mutable_dims = output_array.mutable_shape()->mutable_dims(); + mutable_dims->resize(output_dims); + for (int i = 0; i < output_dims; ++i) { + int dim = 0; + if (i < axis) { + dim = indices_array.shape().dims(i); + } else if (i == axis) { + dim = depth; + } else { + dim = indices_array.shape().dims(i - 1); + } + (*mutable_dims)[i] = dim; + } +} + } // namespace bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { @@ -1536,6 +1671,9 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kFloor: case OperatorType::kExp: case OperatorType::kSin: + case OperatorType::kLogicalAnd: + case OperatorType::kLogicalNot: + case OperatorType::kLogicalOr: ProcessSimpleOperator(model, op, 0); break; case OperatorType::kGather: @@ -1604,9 +1742,10 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kL2Pool: ProcessL2PoolOperator(model, static_cast(op)); break; - case OperatorType::kMin: // Reduction Min - case OperatorType::kMax: // Reduction Max + case OperatorType::kReduceMin: // Reduction Min + case OperatorType::kReduceMax: // Reduction Max case OperatorType::kSum: + case OperatorType::kReduceProd: case OperatorType::kMean: ProcessTensorFlowReductionOperator(model, op); break; @@ -1655,8 +1794,8 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kShape: ProcessShapeOperator(model, static_cast(op)); break; - case OperatorType::kStack: - ProcessStackOperator(model, static_cast(op)); + case OperatorType::kPack: + ProcessPackOperator(model, static_cast(op)); break; case OperatorType::kReorderAxes: ProcessReorderAxesOperator(model, static_cast(op)); @@ -1696,10 +1835,26 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { static_cast(op)); break; case OperatorType::kArgMax: - ProcessArgMaxOperator(model, static_cast(op)); + ProcessArgMinMaxOperator( + model, static_cast(op)); break; - case OperatorType::kUnsupported: + case OperatorType::kArgMin: + ProcessArgMinMaxOperator( + model, static_cast(op)); break; + case OperatorType::kUnsupported: { + const auto* unsupported_op = + static_cast(op); + // Attribute can be not specified, ignore it. + if (unsupported_op->output_shapes.size() < op->outputs.size()) { + return false; + } + for (int i = 0; i < op->outputs.size(); ++i) { + const string& output = op->outputs[i]; + model->GetArray(output).copy_shape(unsupported_op->output_shapes.at(i)); + } + break; + } case OperatorType::kSvdf: ProcessSvdfOperator(model, static_cast(op)); break; @@ -1723,6 +1878,12 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kTile: ProcessTileOperator(model, static_cast(op)); break; + case OperatorType::kAny: + ProcessAnyOperator(model, static_cast(op)); + break; + case OperatorType::kOneHot: + ProcessOneHotOperator(model, static_cast(op)); + break; default: // Unimplemented, another graph transformation should drop it. LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(op->type); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/quantization_util.cc b/tensorflow/contrib/lite/toco/graph_transformations/quantization_util.cc index d74cad9a626b3a472e2740d6bdaaaf7aab5bd484..44733391f5a1d9ebf9a24f4f31b425a35354e1fc 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/quantization_util.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/quantization_util.cc @@ -74,46 +74,54 @@ ArrayDataType GetQuantizedDataType(const Array& array, } } -void GetQuantizationParams(ArrayDataType data_type, const MinMax& minmax, - QuantizationParams* quantization_params) { - switch (data_type) { +template +void ChooseQuantizationParamsForArrayAndQuantizedDataType( + const Array& array, QuantizationParams* quantization_params) { + *quantization_params = ::tflite::ChooseQuantizationParams>( + array.minmax->min, array.minmax->max, array.narrow_range); +} + +void ChooseQuantizationParamsForArrayAndQuantizedDataType( + const Array& array, ArrayDataType quantized_data_type, + QuantizationParams* quantization_params) { + switch (quantized_data_type) { case ArrayDataType::kInt8: - GetQuantizationParamsFromMinMax( - minmax, quantization_params); + ChooseQuantizationParamsForArrayAndQuantizedDataType< + ArrayDataType::kInt8>(array, quantization_params); break; case ArrayDataType::kUint8: - GetQuantizationParamsFromMinMax( - minmax, quantization_params); + ChooseQuantizationParamsForArrayAndQuantizedDataType< + ArrayDataType::kUint8>(array, quantization_params); break; case ArrayDataType::kInt16: - GetQuantizationParamsFromMinMax( - minmax, quantization_params); + ChooseQuantizationParamsForArrayAndQuantizedDataType< + ArrayDataType::kInt16>(array, quantization_params); break; case ArrayDataType::kUint16: - GetQuantizationParamsFromMinMax( - minmax, quantization_params); + ChooseQuantizationParamsForArrayAndQuantizedDataType< + ArrayDataType::kUint16>(array, quantization_params); break; case ArrayDataType::kInt32: - GetQuantizationParamsFromMinMax( - minmax, quantization_params); + ChooseQuantizationParamsForArrayAndQuantizedDataType< + ArrayDataType::kInt32>(array, quantization_params); break; case ArrayDataType::kUint32: - GetQuantizationParamsFromMinMax( - minmax, quantization_params); + ChooseQuantizationParamsForArrayAndQuantizedDataType< + ArrayDataType::kUint32>(array, quantization_params); break; case ArrayDataType::kInt64: - GetQuantizationParamsFromMinMax( - minmax, quantization_params); + ChooseQuantizationParamsForArrayAndQuantizedDataType< + ArrayDataType::kInt64>(array, quantization_params); break; case ArrayDataType::kUint64: - GetQuantizationParamsFromMinMax( - minmax, quantization_params); + ChooseQuantizationParamsForArrayAndQuantizedDataType< + ArrayDataType::kUint64>(array, quantization_params); break; case ArrayDataType::kFloat: case ArrayDataType::kNone: default: LOG(FATAL) << "Unhandled final quantization type " - << static_cast(data_type); + << static_cast(quantized_data_type); } } @@ -121,8 +129,8 @@ namespace { template std::unique_ptr QuantizeBuffer( - const GenericBuffer& buffer, - const QuantizationParams& quantization_params) { + const Array& array, const QuantizationParams& quantization_params) { + const GenericBuffer& buffer = *array.buffer; const auto inverse_scale = 1. / quantization_params.scale; CHECK(buffer.type == ArrayDataType::kFloat); const auto& float_buffer = @@ -140,8 +148,15 @@ std::unique_ptr QuantizeBuffer( } else { scaled_val = quantization_params.zero_point + inverse_scale * src_val; } - quantized_buffer->data[i] = - tflite::SafeCast>(std::round(scaled_val)); + auto integer_val = tflite::SafeCast>(std::round(scaled_val)); + // In addition to its effect on the choice of quantization params upstream + // of here, narrow_range also means nudge the min quantized value by +1, + // so e.g. uint8 values get constrained to [1, 255]. + if (integer_val == std::numeric_limits>::min() && + array.narrow_range) { + integer_val++; + } + quantized_buffer->data[i] = integer_val; } return std::unique_ptr(quantized_buffer); } @@ -155,7 +170,7 @@ void QuantizeArray(GraphTransformation* transformation, Model* model, CHECK(!array.quantization_params); array.GetOrCreateQuantizationParams() = quantization_params; if (array.buffer) { - array.buffer = QuantizeBuffer(*array.buffer, quantization_params); + array.buffer = QuantizeBuffer(array, quantization_params); } array.data_type = A; array.final_data_type = A; @@ -210,8 +225,8 @@ bool IsArrayQuantizedRangeSubset(GraphTransformation* transformation, } else { // Work around cases where we are asking for this prior to the Quantize // transformation having added the quantization_params. - GetQuantizationParams(quantized_data_type, *array.minmax, - &quantization_params); + ChooseQuantizationParamsForArrayAndQuantizedDataType( + array, quantized_data_type, &quantization_params); transformation->AddMessageF( "No quantization params - infering from data type %s with minmax " "%g,%g as zero_point=%g, scale=%g", diff --git a/tensorflow/contrib/lite/toco/graph_transformations/quantization_util.h b/tensorflow/contrib/lite/toco/graph_transformations/quantization_util.h index 79a2ce7e50887b4608b278471da0e5e63b5673e3..cf093c6f17b45839156dae0d06ca2fc7e5e2f3c6 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/quantization_util.h +++ b/tensorflow/contrib/lite/toco/graph_transformations/quantization_util.h @@ -38,21 +38,11 @@ bool GetQuantizedDataTypeNumericalRange(ArrayDataType data_type, ArrayDataType GetQuantizedDataType(const Array& array, ArrayDataType default_type); -// Returns the quantization params for the array with the given data type and -// minmax. -void GetQuantizationParams(ArrayDataType data_type, const MinMax& minmax, - QuantizationParams* quantization_params); - -// Returns the quantization params for the data type and minmax values. -template -void GetQuantizationParamsFromMinMax(const MinMax& minmax, - QuantizationParams* quantization_params) { - using Integer = DataType; - const double rmin = minmax.min; - const double rmax = minmax.max; - *quantization_params = - ::tflite::ChooseQuantizationParams(rmin, rmax); -} +// Chooses the quantization params for a given array and a given target +// quantized data type (which may not be the array's current data type). +void ChooseQuantizationParamsForArrayAndQuantizedDataType( + const Array& array, ArrayDataType quantized_data_type, + QuantizationParams* quantization_params); // Quantizes an array by setting its data type and (if constant) quantizing // all values in the array. diff --git a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc index 58885b4950733bfc9d394127e597a08232cd5663..f6ce3b3ecb2cc06708287804bf34aa152d668f8c 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc @@ -50,6 +50,7 @@ bool SupportsQuantization(const Operator& op) { type == OperatorType::kSqueeze || type == OperatorType::kPad || type == OperatorType::kPadV2 || type == OperatorType::kReshape || type == OperatorType::kTanh || type == OperatorType::kMul || + type == OperatorType::kBatchToSpaceND || type == OperatorType::kSpaceToBatchND || type == OperatorType::kSpaceToDepth || type == OperatorType::kStridedSlice || @@ -212,13 +213,15 @@ bool ChooseQuantizationForOperatorInput( if (op.type == OperatorType::kLstmCell) { if (input_index == LstmCellOperator::PREV_STATE_INPUT) { *quantized_data_type = ArrayDataType::kInt16; - GetQuantizationParams(*quantized_data_type, minmax, quantization_params); + ChooseQuantizationParamsForArrayAndQuantizedDataType( + array, *quantized_data_type, quantization_params); return true; } } *quantized_data_type = GetQuantizedDataType(array, ArrayDataType::kUint8); - GetQuantizationParams(*quantized_data_type, minmax, quantization_params); + ChooseQuantizationParamsForArrayAndQuantizedDataType( + array, *quantized_data_type, quantization_params); transformation->AddMessageF( "For input array %s with min=%g, max=%g, chose to quantize as %s (f=%s) " "with zero_point=%d, scale=%g", @@ -358,12 +361,14 @@ bool ChooseQuantizationForOperatorOutput( if (output_index == LstmCellOperator::STATE_OUTPUT || output_index == LstmCellOperator::ACTIV_TEMP) { *quantized_data_type = ArrayDataType::kInt16; - GetQuantizationParams(*quantized_data_type, minmax, quantization_params); + ChooseQuantizationParamsForArrayAndQuantizedDataType( + array, *quantized_data_type, quantization_params); return true; } } *quantized_data_type = GetQuantizedDataType(array, ArrayDataType::kUint8); - GetQuantizationParams(*quantized_data_type, minmax, quantization_params); + ChooseQuantizationParamsForArrayAndQuantizedDataType( + array, *quantized_data_type, quantization_params); transformation->AddMessageF( "For output array %s with min=%g, max=%g" ", chose to quantize as %s with zero_point=%d" diff --git a/tensorflow/contrib/lite/toco/graph_transformations/quantize_weights.cc b/tensorflow/contrib/lite/toco/graph_transformations/quantize_weights.cc index 88ea0945e7dd15ba325d34ea3fdbf34ff7d91381..7a8515f6d12f96d464ea0764907f9cc2a487d3e7 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/quantize_weights.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/quantize_weights.cc @@ -36,10 +36,8 @@ void GetQuantizationParamsFromArray(const Array& array, const std::vector& float_vals = array.GetBuffer().data; auto minmax = std::minmax_element(float_vals.begin(), float_vals.end()); - MinMax toco_minmax; - toco_minmax.min = *minmax.first; - toco_minmax.max = *minmax.second; - GetQuantizationParams(ArrayDataType::kUint8, toco_minmax, params); + *params = tflite::ChooseQuantizationParams( + *minmax.first, *minmax.second, array.narrow_range); } } // namespace diff --git a/tensorflow/contrib/lite/toco/graph_transformations/read_array_minmax_and_narrow_range_from_fake_quant.cc b/tensorflow/contrib/lite/toco/graph_transformations/read_array_minmax_and_narrow_range_from_fake_quant.cc new file mode 100644 index 0000000000000000000000000000000000000000..5b41c49bfaff245d599d26989e4ed3f9b0d582cf --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/read_array_minmax_and_narrow_range_from_fake_quant.cc @@ -0,0 +1,78 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include +#include +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +namespace { + +bool ApplyAttrsToArray(GraphTransformation* transformation, Model* model, + const FakeQuantOperator& fq_op, + const string& array_name) { + bool changed = false; + auto& annotated_array = model->GetArray(array_name); + if (!annotated_array.minmax) { + const MinMax& minmax = *fq_op.minmax; + annotated_array.GetOrCreateMinMax() = minmax; + transformation->AddMessageF( + "Read min/max annotation for array %s: min=%g, max=%g", array_name, + minmax.min, minmax.max); + changed = true; + } + if (fq_op.narrow_range && !annotated_array.narrow_range) { + annotated_array.narrow_range = true; + transformation->AddMessageF("Read narrow_range annotation for array %s", + array_name); + changed = true; + } + return changed; +} + +} // end namespace + +bool ReadArrayMinmaxAndNarrowRangeFromFakeQuant::Run(Model* model, + std::size_t op_index) { + const auto fakequant_it = model->operators.begin() + op_index; + auto* fakequant_base_op = fakequant_it->get(); + if (fakequant_base_op->type != OperatorType::kFakeQuant) { + return false; + } + auto* fq_op = static_cast(fakequant_base_op); + + if (!fq_op->minmax) { + // Need to be resolved first by ResolveFakeQuantArgsFromVars. + return false; + } + + // At this point, this FakeQuantOperator should have a MinMax + // attached to it, and should only have 1 input (it should not have + // 2nd and 3rd input arrays giving min and max anymore). + CHECK(fq_op->minmax); + CHECK_EQ(1, fq_op->inputs.size()); + + return ApplyAttrsToArray(this, model, *fq_op, fq_op->inputs[0]) || + ApplyAttrsToArray(this, model, *fq_op, fq_op->outputs[0]); +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/read_fake_quant_min_max.cc b/tensorflow/contrib/lite/toco/graph_transformations/read_fake_quant_min_max.cc deleted file mode 100644 index bdcca5b7caf61a62203debaa32c4d7a9b2eb43fa..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/lite/toco/graph_transformations/read_fake_quant_min_max.cc +++ /dev/null @@ -1,112 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include -#include -#include -#include -#include - -#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" -#include "tensorflow/contrib/lite/toco/model.h" -#include "tensorflow/contrib/lite/toco/tooling_util.h" -#include "tensorflow/core/platform/logging.h" - -namespace toco { - -namespace { - -bool ApplyMinMaxToArray(GraphTransformation* transformation, Model* model, - const MinMax& minmax, const string& array_name) { - auto& annotated_array = model->GetArray(array_name); - if (annotated_array.minmax) { - return false; - } - annotated_array.GetOrCreateMinMax() = minmax; - transformation->AddMessageF( - "Read min/max annotation for array %s: min=%g, max=%g", array_name, - minmax.min, minmax.max); - return true; -} - -} // end namespace - -bool ReadFakeQuantMinMax::Run(Model* model, std::size_t op_index) { - const auto fakequant_it = model->operators.begin() + op_index; - auto* fakequant_base_op = fakequant_it->get(); - if (fakequant_base_op->type != OperatorType::kFakeQuant) { - return false; - } - auto* fakequant_op = static_cast(fakequant_base_op); - - bool changed = false; - - if (!fakequant_op->minmax) { - CHECK_EQ(fakequant_op->inputs.size(), 3); - // We need to yield until the min and max parameters have been - // resolved to constant arrays. - for (int i = 1; i <= 2; i++) { - if (!IsConstantParameterArray(*model, fakequant_op->inputs[1])) { - return false; - } - } - - // Obtain the final min/max values - const auto& min_array = model->GetArray(fakequant_op->inputs[1]); - const auto& max_array = model->GetArray(fakequant_op->inputs[2]); - CHECK_EQ(RequiredBufferSizeForShape(min_array.shape()), 1); - CHECK_EQ(RequiredBufferSizeForShape(max_array.shape()), 1); - fakequant_op->minmax.reset(new MinMax); - MinMax& minmax = *fakequant_op->minmax; - minmax.min = min_array.GetBuffer().data[0]; - minmax.max = max_array.GetBuffer().data[0]; - // We always want [min, max] to contain 0. - if (minmax.min > 0 || minmax.max < 0) { - LOG(ERROR) << "For " << LogName(*fakequant_op) << " the MinMax range " - << "[" << minmax.min << ", " << minmax.max - << "] does not contain 0. " - << "Proceeding by tweaking it to contain 0, which will result " - "in poor accuracy."; - } - minmax.min = std::min(minmax.min, 0.); - minmax.max = std::max(minmax.max, 0.); - - // We won't use the input arrays that provided these min and max - // values, anymore. Delete them unless they are used by something - // else. - for (int i = 1; i <= 2; i++) { - if (CountOpsWithInput(*model, fakequant_op->inputs[i]) == 1) { - model->EraseArray(fakequant_op->inputs[i]); - } - } - fakequant_op->inputs.resize(1); - changed = true; - } - - // At this point, this FakeQuantOperator should have a MinMax - // attached to it, and should only have 1 input (it should not have - // 2nd and 3rd input arrays giving min and max anymore). - CHECK(fakequant_op->minmax); - CHECK_EQ(1, fakequant_op->inputs.size()); - - const MinMax& minmax = *fakequant_op->minmax; - - // Record the MinMax info on the input and output arrays - changed |= ApplyMinMaxToArray(this, model, minmax, fakequant_op->inputs[0]); - changed |= ApplyMinMaxToArray(this, model, minmax, fakequant_op->outputs[0]); - - return changed; -} - -} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc index 404f27e067402474484d3ee8e23595fb9f93a6c9..5295eeccecb05b05232922f4b5e4ef75a2b04672 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_reshape.cc @@ -59,6 +59,15 @@ bool IsReshapeTrivial(const Model& model, const Operator& op, if (CountOpsWithInput(model, op.outputs[0]) == 1) { const auto* next_op = GetOpWithInput(model, op.outputs[0]); if (next_op->type == OperatorType::kReshape) { + if (!IsDiscardableArray(model, next_op->outputs[0])) { + // If the |next_op| output is used as a model output we need to preserve + // its shape. + transformation->AddMessageF( + "%s cannot be merged into following reshape %s as it is " + "non-discardable and must keep the specified shape", + LogName(op), LogName(*next_op)); + return false; + } transformation->AddMessageF( "%s is trivial because its output is only consumed by another " "Reshape op %s", diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc index efb7bb218421dd045e3e8e2a38b9c70989f222e1..058f314b338aeeab94cb11fb8c1163427b559d3e 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc @@ -25,6 +25,37 @@ limitations under the License. namespace toco { +template +void GetBoundsForQuantizedDataType(double* min, double* max) { + using limits = std::numeric_limits>; + *min = limits::min(); + *max = limits::max(); +} + +void GetBoundsForQuantizedDataType(ArrayDataType quantized_data_type, + double* min, double* max) { + switch (quantized_data_type) { + case ArrayDataType::kUint8: + return GetBoundsForQuantizedDataType(min, max); + case ArrayDataType::kInt8: + return GetBoundsForQuantizedDataType(min, max); + case ArrayDataType::kUint16: + return GetBoundsForQuantizedDataType(min, max); + case ArrayDataType::kInt16: + return GetBoundsForQuantizedDataType(min, max); + case ArrayDataType::kUint32: + return GetBoundsForQuantizedDataType(min, max); + case ArrayDataType::kInt32: + return GetBoundsForQuantizedDataType(min, max); + case ArrayDataType::kUint64: + return GetBoundsForQuantizedDataType(min, max); + case ArrayDataType::kInt64: + return GetBoundsForQuantizedDataType(min, max); + default: + LOG(FATAL) << "unhandled quantized data type"; + } +} + bool ResolveConstantFakeQuant::Run(Model* model, std::size_t op_index) { const auto fakequant_it = model->operators.begin() + op_index; const auto* fakequant_base_op = fakequant_it->get(); @@ -76,14 +107,21 @@ bool ResolveConstantFakeQuant::Run(Model* model, std::size_t op_index) { const int size = input_buffer.data.size(); output_buffer.data.resize(size); QuantizationParams qparams; - GetQuantizationParamsFromMinMax(*fakequant_op->minmax, - &qparams); + ChooseQuantizationParamsForArrayAndQuantizedDataType( + output_array, quantized_data_type, &qparams); + double quantized_min, quantized_max; + GetBoundsForQuantizedDataType(quantized_data_type, &quantized_min, + &quantized_max); + if (fakequant_op->narrow_range) { + quantized_min++; + } + for (int i = 0; i < size; i++) { const double src_val = input_buffer.data[i]; const double unclamped_quantized_val = std::round(qparams.zero_point + src_val / qparams.scale); - const double quantized_val = - std::min(255., std::max(0., unclamped_quantized_val)); + const double quantized_val = std::min( + quantized_max, std::max(quantized_min, unclamped_quantized_val)); const double dst_val = qparams.scale * (quantized_val - qparams.zero_point); output_buffer.data[i] = dst_val; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_gather.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_gather.cc index debe298a5a93034bcb928d7384b5ec1fc7439e47..36d7dad0ce9de81ec132ef992538b6022916bfbd 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_gather.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_gather.cc @@ -69,7 +69,7 @@ bool ResolveConstantGather::Run(Model* model, std::size_t op_index) { } const auto* op = static_cast(base_op); - CHECK_EQ(op->inputs.size(), 2); + CHECK_GE(op->inputs.size(), 2); CHECK_EQ(op->outputs.size(), 1); auto& output_array = model->GetArray(op->outputs[0]); if (output_array.data_type == ArrayDataType::kNone) { @@ -81,10 +81,14 @@ bool ResolveConstantGather::Run(Model* model, std::size_t op_index) { return false; } - // Only handling axis=0 for now. - if (op->axis != 0) { + if (!op->axis) { + // Yield until axis has been set by ResolveGatherAttributes. + return false; + } + if (op->axis.value() != 0) { + // Only handling axis=0 for now. AddMessageF("%s has axis %d; only axis=0 is supported", LogName(*op), - op->axis); + op->axis.value()); return false; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_pack.cc similarity index 82% rename from tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc rename to tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_pack.cc index a4d5f1923a1dffdff1ef51eb5317fa5794a8bc27..e86616574d5a0f1345cde167d4ce0d41665d5a02 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_stack.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_pack.cc @@ -24,7 +24,7 @@ namespace toco { namespace { template -void Stack(Model* model, StackOperator const& op) { +void Pack(Model* model, PackOperator const& op) { auto& output_array = model->GetArray(op.outputs[0]); CHECK(output_array.data_type == Type); @@ -33,8 +33,8 @@ void Stack(Model* model, StackOperator const& op) { output_array.GetMutableBuffer().data; output_data.resize(RequiredBufferSizeForShape(output_array.shape())); - // Stack inputs into buffer - CHECK_EQ(op.axis, 0) << "Stacking only supported along first axis"; + // Pack inputs into buffer + CHECK_EQ(op.axis, 0) << "Packing only supported along first axis"; int dst_offset = 0; for (int i = 0; i < op.inputs.size(); i++) { // Append array data to output for each input array @@ -49,13 +49,13 @@ void Stack(Model* model, StackOperator const& op) { } // namespace -bool ResolveConstantStack::Run(Model* model, std::size_t op_index) { +bool ResolveConstantPack::Run(Model* model, std::size_t op_index) { auto it = model->operators.begin() + op_index; const auto* base_op = it->get(); - if (base_op->type != OperatorType::kStack) { + if (base_op->type != OperatorType::kPack) { return false; } - const auto* op = static_cast(base_op); + const auto* op = static_cast(base_op); CHECK_GE(op->inputs.size(), 1); CHECK_EQ(op->outputs.size(), 1); @@ -82,24 +82,24 @@ bool ResolveConstantStack::Run(Model* model, std::size_t op_index) { // Handle negative axis axis += model->GetArray(op->inputs[0]).shape().dims().size(); } - CHECK_EQ(axis, 0) << "Stacking only supported along 0th axis"; + CHECK_EQ(axis, 0) << "Packing only supported along 0th axis"; CHECK(!output_array.buffer); switch (output_array.data_type) { case ArrayDataType::kFloat: - Stack(model, *op); + Pack(model, *op); break; case ArrayDataType::kUint8: - Stack(model, *op); + Pack(model, *op); break; case ArrayDataType::kInt32: - Stack(model, *op); + Pack(model, *op); break; case ArrayDataType::kInt64: - Stack(model, *op); + Pack(model, *op); break; default: - LOG(FATAL) << "Unsupported data type given to Stack op with output \"" + LOG(FATAL) << "Unsupported data type given to Pack op with output \"" << op->outputs[0] << "\""; break; } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc index f89ef85fdb63ca4906c7f016e86bb1f9d8a7099a..fe3882c28df893080846b24ffa3cac7267f08ae2 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc @@ -57,8 +57,8 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { case OperatorType::kSqrt: case OperatorType::kSquare: case OperatorType::kSum: - case OperatorType::kMin: // Reduction Min - case OperatorType::kMax: // Reduction Max + case OperatorType::kReduceMin: // Reduction Min + case OperatorType::kReduceMax: // Reduction Max case OperatorType::kReshape: case OperatorType::kRelu6: case OperatorType::kRelu1: @@ -196,7 +196,7 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { } output_float_data[i] = sum; } - } else if (unary_op->type == OperatorType::kMin) { + } else if (unary_op->type == OperatorType::kReduceMin) { // At the moment only full reduction across all dimensions is supported. // TODO(starka): Output should not be padded. for (int i = 0; i < output_dims_count; i++) { @@ -207,7 +207,7 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { min = std::min(min, (*input_float_data)[i]); } output_float_data[0] = min; - } else if (unary_op->type == OperatorType::kMax) { + } else if (unary_op->type == OperatorType::kReduceMax) { // At the moment only full reduction across all dimensions is supported. // TODO(starka): Output should not be padded. for (int i = 0; i < output_dims_count; i++) { diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_fake_quant_args_from_vars.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_fake_quant_args_from_vars.cc new file mode 100644 index 0000000000000000000000000000000000000000..0dda1fd0b35fb0cdc3c605360df5126c52c05403 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_fake_quant_args_from_vars.cc @@ -0,0 +1,80 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include +#include +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +bool ResolveFakeQuantArgsFromVars::Run(Model* model, std::size_t op_index) { + const auto fakequant_it = model->operators.begin() + op_index; + auto* fakequant_base_op = fakequant_it->get(); + if (fakequant_base_op->type != OperatorType::kFakeQuant) { + return false; + } + auto* fakequant_op = static_cast(fakequant_base_op); + + if (fakequant_op->minmax) { + // Already resolved. + return false; + } + + CHECK_EQ(fakequant_op->inputs.size(), 3); + // We need to yield until the min and max parameters have been + // resolved to constant arrays. + for (int i = 1; i <= 2; i++) { + if (!IsConstantParameterArray(*model, fakequant_op->inputs[i])) { + return false; + } + } + + // Obtain the final min/max values + const auto& min_array = model->GetArray(fakequant_op->inputs[1]); + const auto& max_array = model->GetArray(fakequant_op->inputs[2]); + CHECK_EQ(RequiredBufferSizeForShape(min_array.shape()), 1); + CHECK_EQ(RequiredBufferSizeForShape(max_array.shape()), 1); + fakequant_op->minmax.reset(new MinMax); + MinMax& minmax = *fakequant_op->minmax; + minmax.min = min_array.GetBuffer().data[0]; + minmax.max = max_array.GetBuffer().data[0]; + // We always want [min, max] to contain 0. + if (minmax.min > 0 || minmax.max < 0) { + LOG(ERROR) << "For " << LogName(*fakequant_op) << " the MinMax range " + << "[" << minmax.min << ", " << minmax.max + << "] does not contain 0. " + << "Proceeding by tweaking it to contain 0, which will result " + "in poor accuracy."; + } + minmax.min = std::min(minmax.min, 0.); + minmax.max = std::max(minmax.max, 0.); + + // We won't use the input arrays that provided these min and max + // values, anymore. Delete them unless they are used by something + // else. + for (int i = 1; i <= 2; i++) { + DeleteArrayIfUsedOnce(fakequant_op->inputs[i], model); + } + fakequant_op->inputs.resize(1); + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_gather_attributes.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_gather_attributes.cc new file mode 100644 index 0000000000000000000000000000000000000000..ce825c91af428c866ca9f83b765399f209606af9 --- /dev/null +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_gather_attributes.cc @@ -0,0 +1,53 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include +#include + +#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" +#include "tensorflow/contrib/lite/toco/model.h" +#include "tensorflow/contrib/lite/toco/tooling_util.h" +#include "tensorflow/core/platform/logging.h" + +namespace toco { + +bool ResolveGatherAttributes::Run(Model* model, std::size_t op_index) { + auto* gather_op = model->operators[op_index].get(); + if (gather_op->type != OperatorType::kGather) return false; + auto* op = static_cast(gather_op); + + if (op->axis) { + // Attributes already resolved + return false; + } + if (op->inputs.size() != 3) return false; + if (!IsConstantParameterArray(*model, op->inputs[2])) return false; + + const auto& indices_array = model->GetArray(op->inputs[2]); + if (!indices_array.has_shape()) return false; + const auto& axis_data = indices_array.GetBuffer().data; + CHECK_EQ(axis_data.size(), 1) + << "Multidimensional gather not supported on " << LogName(*op); + op->axis = {axis_data[0]}; + + // Drop the axis array as we no longer need it. + DeleteArrayIfUsedOnce(op->inputs[2], model); + op->inputs.resize(2); + + return true; +} + +} // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_mean_attributes.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_reduce_attributes.cc similarity index 60% rename from tensorflow/contrib/lite/toco/graph_transformations/resolve_mean_attributes.cc rename to tensorflow/contrib/lite/toco/graph_transformations/resolve_reduce_attributes.cc index 013b50ac9ba8a51c23b19953d987b2fbf63fcea1..7d456af2fbc69352662b798cf1314f1653ef9f98 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_mean_attributes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_reduce_attributes.cc @@ -24,11 +24,8 @@ limitations under the License. namespace toco { -bool ResolveMeanAttributes::Run(Model* model, std::size_t op_index) { - auto* mean_op = model->operators[op_index].get(); - if (mean_op->type != OperatorType::kMean) return false; - auto* op = static_cast(mean_op); - +template +bool ResolveAttributes(Model* model, T* op) { if (!op->axis.empty()) { // Attributes already resolved return false; @@ -36,10 +33,28 @@ bool ResolveMeanAttributes::Run(Model* model, std::size_t op_index) { if (op->inputs.size() != 2) return false; if (!IsConstantParameterArray(*model, op->inputs[1])) return false; - const auto& indices_array = model->GetArray(op->inputs[1]); + const Array& indices_array = model->GetArray(op->inputs[1]); if (!indices_array.has_shape()) return false; op->axis = indices_array.GetBuffer().data; return true; } +bool ResolveReduceAttributes::Run(Model* model, std::size_t op_index) { + Operator* op = model->operators[op_index].get(); + switch (op->type) { + case OperatorType::kMean: + return ResolveAttributes(model, static_cast(op)); + case OperatorType::kSum: + return ResolveAttributes(model, static_cast(op)); + case OperatorType::kReduceProd: + return ResolveAttributes(model, static_cast(op)); + case OperatorType::kReduceMin: + return ResolveAttributes(model, static_cast(op)); + case OperatorType::kReduceMax: + return ResolveAttributes(model, static_cast(op)); + default: + return false; + } +} + } // namespace toco diff --git a/tensorflow/contrib/lite/toco/graph_transformations/tests/BUILD b/tensorflow/contrib/lite/toco/graph_transformations/tests/BUILD index 95e8433be2a332cfce5175f4f65ea0b83d5638c5..e163fc9ae1422504ef1b0a3c567c420f649f0827 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/tests/BUILD +++ b/tensorflow/contrib/lite/toco/graph_transformations/tests/BUILD @@ -10,6 +10,7 @@ load( tf_cc_test( name = "lstm_utils_test", srcs = ["lstm_utils_test.cc"], + tags = ["no_oss"], deps = [ "//tensorflow/contrib/lite/toco:graph_transformations", "//tensorflow/contrib/lite/toco:model", @@ -21,6 +22,7 @@ tf_cc_test( tf_cc_test( name = "quantize_weights_test", srcs = ["quantize_weights_test.cc"], + tags = ["no_oss"], deps = [ "//tensorflow/contrib/lite/toco:graph_transformations", "//tensorflow/contrib/lite/toco:model", @@ -33,6 +35,7 @@ tf_cc_test( tf_cc_test( name = "resolve_constant_concatenation_test", srcs = ["resolve_constant_concatenation_test.cc"], + tags = ["no_oss"], deps = [ "//tensorflow/contrib/lite/toco:graph_transformations", "//tensorflow/contrib/lite/toco:model", diff --git a/tensorflow/contrib/lite/toco/graph_transformations/unfuse_activation_functions.cc b/tensorflow/contrib/lite/toco/graph_transformations/unfuse_activation_functions.cc index 2c7046c8c77c94a89fc05a26d7d72b3661380475..69bad2fa89cb89cd74e3a4bca98da906a322a670 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/unfuse_activation_functions.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/unfuse_activation_functions.cc @@ -64,7 +64,14 @@ bool UnfuseActivationFunctions::Run(Model* model, std::size_t op_index) { const string& tmp_array_name = AvailableArrayName(*model, op->outputs[0] + "_unfused"); CHECK(!model->HasArray(tmp_array_name)); - model->GetOrCreateArray(tmp_array_name); + + const auto& output_array = model->GetArray(op->outputs[0]); + auto& tmp_array = model->GetOrCreateArray(tmp_array_name); + if (output_array.quantization_params) { + tmp_array.GetOrCreateQuantizationParams() = + output_array.GetQuantizationParams(); + } + ac_op->inputs = {tmp_array_name}; op->outputs = {tmp_array_name}; return true; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/unpartition_embedding_lookup.cc b/tensorflow/contrib/lite/toco/graph_transformations/unpartition_embedding_lookup.cc index cbea39bcc09ea6787c055d5aaca7f291c2b47a7f..dd9e26e68bd7e6d5cb751fdbf705b861c3f2f188 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/unpartition_embedding_lookup.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/unpartition_embedding_lookup.cc @@ -187,6 +187,7 @@ bool UnpartitionEmbeddingLookup::Run(Model* model, std::size_t op_index) { AvailableArrayName(*model, gather_ops[0]->inputs[0] + "_permuted/perm")); gather_params_permute_op->outputs.push_back( AvailableArrayName(*model, gather_ops[0]->inputs[0] + "_permuted")); + gather_params_permute_op->axis = {0}; op_it = model->operators.emplace(op_it, gather_params_permute_op) + 1; model->GetOrCreateArray(gather_params_permute_op->outputs[0]); const auto& partition_array = model->GetArray(gather_ops[0]->inputs[0]); @@ -212,6 +213,7 @@ bool UnpartitionEmbeddingLookup::Run(Model* model, std::size_t op_index) { mod_op->inputs[0]}; merged_gather_op->outputs = {stitch_op->outputs[0]}; merged_gather_op->input_rank = partition_array.shape().dimensions_count(); + merged_gather_op->axis = {0}; model->operators.emplace(op_it, merged_gather_op); AddMessageF( diff --git a/tensorflow/contrib/lite/toco/graph_transformations/unroll_batch_matmul.cc b/tensorflow/contrib/lite/toco/graph_transformations/unroll_batch_matmul.cc index da81ea2ff3b4ab0bee0550874a9c4ea1044a3579..5f0cece67a49de6d50fd08896d14d3f27df46b44 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/unroll_batch_matmul.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/unroll_batch_matmul.cc @@ -76,7 +76,7 @@ bool UnrollBatchMatMul::Run(Model* model, std::size_t op_index) { AddMessageF("Unrolling BatchMatMul %s %d times", LogName(*batch_op), batch_count); auto tail_it = batch_op_it; - std::vector stack_inputs; + std::vector pack_inputs; for (int batch = 0; batch < batch_count; ++batch) { std::string batch_name = std::string(batch_op->outputs[0]) + "_b" + std::to_string(batch); @@ -146,15 +146,15 @@ bool UnrollBatchMatMul::Run(Model* model, std::size_t op_index) { tail_it = model->operators.emplace(tail_it, matmul_op) + 1; // Add to stack. - stack_inputs.push_back(matmul_op->outputs[0]); + pack_inputs.push_back(matmul_op->outputs[0]); } - // The stack that will join all the individual matmul results together. - auto* stack_op = new StackOperator; - stack_op->inputs = stack_inputs; - stack_op->outputs = {batch_op->outputs[0]}; - stack_op->axis = 0; - model->operators.emplace(tail_it, stack_op); + // The pack that will join all the individual matmul results together. + auto* pack_op = new PackOperator; + pack_op->inputs = pack_inputs; + pack_op->outputs = {batch_op->outputs[0]}; + pack_op->axis = 0; + model->operators.emplace(tail_it, pack_op); // Remove the old batch matmul now that we've unrolled. batch_op_it = model->operators.begin(); diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index 5c32a39035f3c5396b09621bacaa58a7baa3ae9b..9a3db5c888cd091a28bb4feaf0bbffc8742f90b9 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -215,7 +215,7 @@ tensorflow::Status ImportFloatArray(const TensorProto& input_tensor, Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_FLOAT); const auto& input_shape = input_tensor.tensor_shape(); - CHECK_LE(input_shape.dim_size(), 4); + CHECK_LE(input_shape.dim_size(), 6); int input_flat_size; auto status = ImportShape(input_shape.dim(), &input_flat_size, output_array->mutable_shape()); @@ -253,7 +253,7 @@ tensorflow::Status ImportQuint8Array(const TensorProto& input_tensor, Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_QUINT8); const auto& input_shape = input_tensor.tensor_shape(); - CHECK_LE(input_shape.dim_size(), 4); + CHECK_LE(input_shape.dim_size(), 6); int input_flat_size; auto status = ImportShape(input_shape.dim(), &input_flat_size, output_array->mutable_shape()); @@ -290,7 +290,7 @@ tensorflow::Status ImportInt32Array(const TensorProto& input_tensor, Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_INT32); const auto& input_shape = input_tensor.tensor_shape(); - CHECK_LE(input_shape.dim_size(), 4); + CHECK_LE(input_shape.dim_size(), 6); int input_flat_size; auto status = ImportShape(input_shape.dim(), &input_flat_size, output_array->mutable_shape()); @@ -326,7 +326,7 @@ tensorflow::Status ImportInt64Array(const TensorProto& input_tensor, Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_INT64); const auto& input_shape = input_tensor.tensor_shape(); - CHECK_LE(input_shape.dim_size(), 4); + CHECK_LE(input_shape.dim_size(), 6); int input_flat_size; auto status = ImportShape(input_shape.dim(), &input_flat_size, output_array->mutable_shape()); @@ -363,7 +363,7 @@ tensorflow::Status ImportBoolArray(const TensorProto& input_tensor, Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_BOOL); const auto& input_shape = input_tensor.tensor_shape(); - CHECK_LE(input_shape.dim_size(), 4); + CHECK_LE(input_shape.dim_size(), 6); int input_flat_size; auto status = ImportShape(input_shape.dim(), &input_flat_size, output_array->mutable_shape()); @@ -409,7 +409,7 @@ tensorflow::Status ImportStringArray(const TensorProto& input_tensor, Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_STRING); const auto& input_shape = input_tensor.tensor_shape(); - CHECK_LE(input_shape.dim_size(), 4); + CHECK_LE(input_shape.dim_size(), 6); int input_flat_size; auto status = ImportShape(input_shape.dim(), &input_flat_size, output_array->mutable_shape()); @@ -755,6 +755,9 @@ tensorflow::Status ConvertFakeQuantWithMinMaxArgs( op->outputs.push_back(node.name()); // tf.fake_quant_with_min_max_args num_bits defaults to 8. op->num_bits = HasAttr(node, "num_bits") ? GetIntAttr(node, "num_bits") : 8; + if (HasAttr(node, "narrow_range")) { + op->narrow_range = GetBoolAttr(node, "narrow_range"); + } model->operators.emplace_back(op); return tensorflow::Status::OK(); } @@ -774,6 +777,9 @@ tensorflow::Status ConvertFakeQuantWithMinMaxVars( } op->outputs.push_back(node.name()); op->num_bits = HasAttr(node, "num_bits") ? GetIntAttr(node, "num_bits") : 8; + if (HasAttr(node, "narrow_range")) { + op->narrow_range = GetBoolAttr(node, "narrow_range"); + } model->operators.emplace_back(op); return tensorflow::Status::OK(); } @@ -799,22 +805,6 @@ tensorflow::Status ConvertSqueezeOperator( return tensorflow::Status::OK(); } -tensorflow::Status ConvertSumOperator( - const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, - Model* model) { - CHECK_EQ(node.op(), "Sum"); - TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); - auto* op = new TensorFlowSumOperator; - op->inputs.push_back(node.input(0)); - op->inputs.push_back(node.input(1)); - op->outputs.push_back(node.name()); - model->operators.emplace_back(op); - if (HasAttr(node, "keep_dims")) { - op->keep_dims = GetBoolAttr(node, "keep_dims"); - } - return tensorflow::Status::OK(); -} - tensorflow::Status ConvertSplitOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { @@ -1052,41 +1042,14 @@ tensorflow::Status ConvertSimpleOperator( return ConvertSimpleOperator(node, tf_import_flags, model); } -tensorflow::Status ConvertMaxOperator( - const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, - Model* model) { - CHECK_EQ(node.op(), "Max"); - TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); - auto* op = new TensorFlowMaxOperator; - op->inputs.push_back(node.input(0)); - op->inputs.push_back(node.input(1)); - op->outputs.push_back(node.name()); - model->operators.emplace_back(op); - if (HasAttr(node, "keep_dims")) { - op->keep_dims = GetBoolAttr(node, "keep_dims"); - } - return tensorflow::Status::OK(); -} - -tensorflow::Status ConvertMinOperator( - const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, - Model* model) { - CHECK_EQ(node.op(), "Min"); - TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); - auto* op = new TensorFlowMinOperator; - op->inputs.push_back(node.input(0)); - op->inputs.push_back(node.input(1)); - op->outputs.push_back(node.name()); - model->operators.emplace_back(op); - if (HasAttr(node, "keep_dims")) { - op->keep_dims = GetBoolAttr(node, "keep_dims"); - } - return tensorflow::Status::OK(); -} - tensorflow::Status ConvertUnsupportedOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { + // Names of special attributes in TF graph that are used by Toco. + static constexpr char kAttrOutputQuantized[] = "_output_quantized"; + static constexpr char kAttrOutputTypes[] = "_output_types"; + static constexpr char kAttrOutputShapes[] = "_output_shapes"; + LOG(INFO) << "Converting unsupported operation: " << node.op(); auto* op = new TensorFlowUnsupportedOperator; const int num_inputs = GetInputsCount(node, tf_import_flags); @@ -1097,11 +1060,11 @@ tensorflow::Status ConvertUnsupportedOperator( op->tensorflow_op = node.op(); node.SerializeToString(&op->tensorflow_node_def); model->operators.emplace_back(op); - if (HasAttr(node, "_output_quantized")) { - op->quantized = GetBoolAttr(node, "_output_quantized"); + if (HasAttr(node, kAttrOutputQuantized)) { + op->quantized = GetBoolAttr(node, kAttrOutputQuantized); } - if (HasAttr(node, "_output_types")) { - const auto& output_types = GetListAttr(node, "_output_types"); + if (HasAttr(node, kAttrOutputTypes)) { + const auto& output_types = GetListAttr(node, kAttrOutputTypes); for (int i = 0; i < output_types.type_size(); ++i) { op->output_data_types.push_back(ConvertDataType(output_types.type(i))); } @@ -1109,6 +1072,19 @@ tensorflow::Status ConvertUnsupportedOperator( const auto& output_type = GetDataTypeAttr(node, "Tout"); op->output_data_types.push_back(ConvertDataType(output_type)); } + if (HasAttr(node, kAttrOutputShapes)) { + const auto& output_shapes = GetListAttr(node, kAttrOutputShapes); + Shape output_shape; + for (int i = 0; i < output_shapes.shape_size(); ++i) { + const auto status = + ImportShape(output_shapes.shape(i).dim(), /*input_flat_size=*/nullptr, + &output_shape); + if (!status.ok()) { + return status; + } + op->output_shapes.push_back(output_shape); + } + } return tensorflow::Status::OK(); } @@ -1223,17 +1199,27 @@ tensorflow::Status ConvertGatherOperator( auto* op = new GatherOperator; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); - // TODO(ahentz): we currently ignore the third tensor in GatherV2 but we - // should read it an pass it on to the TF Lite Interpreter. + if (node.input_size() >= 3) { + // GatherV2 form where we are provided an axis. It may be either a constant + // or runtime defined value, so we just wire up the array and let + // ResolveGatherAttributes take care of it later on. + const auto axis_data_type = GetDataTypeAttr(node, "Taxis"); + CHECK(axis_data_type == DT_INT32 || axis_data_type == DT_INT64); + op->inputs.push_back(node.input(2)); + } else { + // Gather form that assumes axis=0. + op->axis = {0}; + } op->outputs.push_back(node.name()); model->operators.emplace_back(op); return tensorflow::Status::OK(); } -tensorflow::Status ConvertArgMaxOperator( +template +tensorflow::Status ConvertArgMinMaxOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { - CHECK_EQ(node.op(), "ArgMax"); + CHECK_EQ(node.op(), op_name); TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); const auto axis_data_type = HasAttr(node, "Tidx") ? GetDataTypeAttr(node, "Tidx") : DT_INT32; @@ -1242,7 +1228,7 @@ tensorflow::Status ConvertArgMaxOperator( : DT_INT64; CHECK(axis_data_type == DT_INT64 || axis_data_type == DT_INT32); CHECK(output_type == DT_INT64 || output_type == DT_INT32); - auto* op = new ArgMaxOperator; + auto* op = new Op; op->output_data_type = ConvertDataType(output_type); op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); @@ -1405,12 +1391,12 @@ tensorflow::Status ConvertBatchToSpaceNDOperator( return tensorflow::Status::OK(); } -tensorflow::Status ConvertMeanOperator( +template +tensorflow::Status ConvertReduceOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { - CHECK_EQ(node.op(), "Mean"); TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); - auto* op = new MeanOperator; + auto* op = new T; op->inputs.push_back(node.input(0)); op->inputs.push_back(node.input(1)); op->outputs.push_back(node.name()); @@ -1543,11 +1529,15 @@ tensorflow::Status ConvertRangeOperator( return tensorflow::Status::OK(); } -tensorflow::Status ConvertStackOperator( +// Note that it's easy to confuse/conflate "Stack" and "Pack" operators, but +// they aren't the same thing. tf.stack results in a "Pack" operator. "Stack" +// operators also exist, but involve manipulating the TF runtime stack, and are +// not directly related to tf.stack() usage. +tensorflow::Status ConvertPackOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { - CHECK((node.op() == "Stack") || (node.op() == "Pack")); - auto* op = new StackOperator; + CHECK_EQ(node.op(), "Pack"); + auto op = absl::make_unique(); const int num_inputs = GetInputsCount(node, tf_import_flags); QCHECK_GE(num_inputs, 1) << node.op() @@ -1557,10 +1547,11 @@ tensorflow::Status ConvertStackOperator( for (int i = 0; i < num_inputs; ++i) { op->inputs.push_back(node.input(i)); } - // Both "Stack" and "Pack" have the "axis" attribute. + op->values_count = HasAttr(node, "N") ? GetIntAttr(node, "N") : num_inputs; op->axis = HasAttr(node, "axis") ? GetIntAttr(node, "axis") : 0; + op->dtype = ConvertDataType(toco::GetDataTypeAttr(node, "T")); op->outputs.push_back(node.name()); - model->operators.emplace_back(op); + model->operators.emplace_back(std::move(op)); return tensorflow::Status::OK(); } @@ -1606,6 +1597,24 @@ tensorflow::Status ConvertShapeOperator( return tensorflow::Status::OK(); } +tensorflow::Status ConvertAnyOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { + CHECK_EQ(node.op(), "Any"); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); + const auto idx_type = + HasAttr(node, "Tidx") ? GetDataTypeAttr(node, "Tidx") : DT_INT32; + CHECK(idx_type == DT_INT32); + auto op = absl::make_unique(); + op->inputs.push_back(node.input(0)); + op->inputs.push_back(node.input(1)); + op->outputs.push_back(node.name()); + op->keep_dims = + HasAttr(node, "keep_dims") ? GetBoolAttr(node, "keep_dims") : false; + model->operators.push_back(std::move(op)); + return tensorflow::Status::OK(); +} + void StripCaretFromArrayNames(Model* model) { for (auto& op : model->operators) { for (auto& input : op->inputs) { @@ -1824,6 +1833,27 @@ tensorflow::Status ConvertSparseToDenseOperator( return tensorflow::Status::OK(); } +tensorflow::Status ConvertOneHotOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { + CHECK_EQ(node.op(), "OneHot"); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 4)); + + const auto dtype = GetDataTypeAttr(node, "T"); + // TODO(b/111744875): Support DT_UINT8 and quantization. + CHECK(dtype == DT_INT32 || dtype == DT_INT64 || dtype == DT_FLOAT || + dtype == DT_BOOL); + + auto op = absl::make_unique(); + op->axis = HasAttr(node, "axis") ? GetIntAttr(node, "axis") : -1; + for (const string& input : node.input()) { + op->inputs.push_back(input); + } + op->outputs.push_back(node.name()); + model->operators.emplace_back(op.release()); + return tensorflow::Status::OK(); +} + } // namespace namespace internal { @@ -1833,12 +1863,17 @@ using ConverterType = tensorflow::Status (*)( Model* model); using ConverterMapType = std::unordered_map; +constexpr char kArgMax[] = "ArgMax"; +constexpr char kArgMin[] = "ArgMin"; + ConverterMapType GetTensorFlowNodeConverterMap() { return std::unordered_map({ {"Add", ConvertSimpleOperator}, {"AddN", ConvertSimpleOperator}, {"All", ConvertSimpleOperator}, - {"ArgMax", ConvertArgMaxOperator}, + {"Any", ConvertAnyOperator}, + {"ArgMax", ConvertArgMinMaxOperator}, + {"ArgMin", ConvertArgMinMaxOperator}, {"Assert", ConvertSimpleOperator}, {"AvgPool", ConvertAvgPoolOperator}, {"BatchMatMul", ConvertBatchMatMulOperator}, @@ -1879,28 +1914,32 @@ ConverterMapType GetTensorFlowNodeConverterMap() { {"Less", ConvertSimpleOperator}, {"LessEqual", ConvertSimpleOperator}, {"Log", ConvertSimpleOperator}, - {"Log", ConvertSimpleOperator}, + {"LogicalAnd", ConvertSimpleOperator}, + {"LogicalOr", ConvertSimpleOperator}, + {"LogicalNot", ConvertSimpleOperator}, {"LogSoftmax", ConvertSimpleOperator}, {"MatMul", ConvertMatMulOperator}, - {"Max", ConvertMaxOperator}, + {"Max", ConvertReduceOperator}, {"MaxPool", ConvertMaxPoolOperator}, {"Maximum", ConvertSimpleOperator}, - {"Mean", ConvertMeanOperator}, + {"Mean", ConvertReduceOperator}, {"Merge", ConvertSimpleOperator}, - {"Min", ConvertMinOperator}, + {"Min", ConvertReduceOperator}, {"Minimum", ConvertSimpleOperator}, {"Mul", ConvertSimpleOperator}, {"Neg", ConvertSimpleOperator}, {"NextIteration", ConvertOperatorSpecialCasedAsRNNBackEdge}, {"NoOp", ConvertNoOpOperator}, {"NotEqual", ConvertSimpleOperator}, - {"Pack", ConvertStackOperator}, + {"OneHot", ConvertOneHotOperator}, + {"Pack", ConvertPackOperator}, {"Pad", ConvertSimpleOperator}, {"PadV2", ConvertSimpleOperator}, {"ParallelDynamicStitch", ConvertDynamicStitchOperator}, {"Placeholder", ConvertPlaceholderOperator}, {"PlaceholderWithDefault", ConvertIdentityOperator}, {"Pow", ConvertSimpleOperator}, + {"Prod", ConvertReduceOperator}, {"RandomUniform", ConvertRandomUniform}, {"Range", ConvertRangeOperator}, {"Rank", ConvertSimpleOperator}, @@ -1923,11 +1962,10 @@ ConverterMapType GetTensorFlowNodeConverterMap() { {"Sqrt", ConvertSimpleOperator}, {"Square", ConvertSimpleOperator}, {"Squeeze", ConvertSqueezeOperator}, - {"Stack", ConvertStackOperator}, {"StopGradient", ConvertIdentityOperator}, {"StridedSlice", ConvertStridedSliceOperator}, {"Sub", ConvertSimpleOperator}, - {"Sum", ConvertSumOperator}, + {"Sum", ConvertReduceOperator}, {"Svdf", ConvertSvdfOperator}, {"Switch", ConvertSwitchOperator}, {"Tanh", ConvertSimpleOperator}, diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index 3a1d243f87b20651aafe3b31cb14804e94dee72b..7d0dbfcc0550c043e868ceb4d131fbe9c2fdfd0d 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -23,6 +23,7 @@ limitations under the License. #include #include +#include "absl/types/optional.h" #include "tensorflow/contrib/lite/toco/model_flags.pb.h" #include "tensorflow/contrib/lite/toco/runtime/types.h" #include "tensorflow/contrib/lite/toco/toco_port.h" @@ -63,6 +64,7 @@ enum class OperatorType : uint8 { kMaxPool, kFakeQuant, kMul, + kOneHot, kRandomUniform, kRange, kRank, @@ -81,10 +83,11 @@ enum class OperatorType : uint8 { kResizeBilinear, kSin, kSpaceToBatchND, - kStack, + kPack, kBatchToSpaceND, kPad, kPadV2, + kReduceProd, // Reduction product kStridedSlice, kSlice, kSqueeze, @@ -106,10 +109,10 @@ enum class OperatorType : uint8 { kIdentity, kLess, kLessEqual, - kMax, // Reduction Max - kMaximum, // Element-wise Maximum - kMin, // Reduction Min - kMinimum, // Element-wise Minimum + kReduceMax, // Reduction Max + kMaximum, // Element-wise Maximum + kReduceMin, // Reduction Min + kMinimum, // Element-wise Minimum kMatMul, kMerge, kNeg, @@ -140,6 +143,11 @@ enum class OperatorType : uint8 { kEqual, kNotEqual, kPow, + kArgMin, + kAny, + kLogicalAnd, + kLogicalNot, + kLogicalOr, }; // Helper to deal with TensorFlow arrays using a different ordering of @@ -286,6 +294,46 @@ struct Buffer : GenericBuffer { std::vector> data; }; +class Shape { + public: + // For Shape, we stick to half-way encapsulation for now: + // we hide the raw dims_ member, but expose it raw by accessors + // because from some brainstorming, it's not at all easy to + // anticipate which flavor of more hermetic encapsulation would + // actually buy us future-proof-ness without being needlessly + // cumbersome. + Shape() {} + Shape(std::initializer_list dim_list) : dims_(dim_list) {} + + void ReplaceDims(std::initializer_list dim_list) { + dims_ = std::vector(dim_list); + } + + const std::vector& dims() const { return dims_; } + std::vector* mutable_dims() { return &dims_; } + const int dimensions_count() const { return dims_.size(); } + + // We still have that one convenience accessor to avoid + // the awkward double bracket issue: shape.dims()[i]. + int dims(int i) const { + // Always check for out-of-bounds accesses, even in optimized builds where + // standard assertions are disabled. Out-of-bounds access here is a common + // occurrence. + CHECK_GE(i, 0); + CHECK_GT(dims_.size(), i); + return dims_[i]; + } + + bool operator==(const Shape& comp) const { + return (this->dims_ == comp.dims()); + } + + bool operator!=(const Shape& comp) const { return !((*this) == comp); } + + private: + std::vector dims_; +}; + // Base class for all operator classes. struct Operator { // Non-default-constructible: only OperatorType-specific subclass @@ -790,6 +838,7 @@ struct FakeQuantOperator : Operator { FakeQuantOperator() : Operator(OperatorType::kFakeQuant) {} std::unique_ptr minmax; int num_bits = 8; + bool narrow_range = false; }; // Element-wise division operator. @@ -1154,10 +1203,12 @@ struct TensorFlowRsqrtOperator : Operator { // Inputs: this operator accepts any number >= 1 of inputs. // inputs[i]: the i-th array to merge. // -// TensorFlow equivalent: Stack or Pack -struct StackOperator : Operator { - StackOperator() : Operator(OperatorType::kStack) {} +// TensorFlow equivalent: Pack +struct PackOperator : Operator { + PackOperator() : Operator(OperatorType::kPack) {} + int values_count; int axis = 0; + ArrayDataType dtype = ArrayDataType::kNone; }; // Shape operator. Extracts the shape of the tensor. @@ -1227,6 +1278,19 @@ struct SubOperator : Operator { // TensorFlow equivalent: Sum struct TensorFlowSumOperator : Operator { TensorFlowSumOperator() : Operator(OperatorType::kSum) {} + std::vector axis; + bool keep_dims = false; +}; + +// Prod reduction: computes the product of all of entries across the axes. +// +// Inputs: +// inputs[0]: required: the input array +// +// TensorFlow equivalent: Prod +struct TensorFlowProdOperator : Operator { + TensorFlowProdOperator() : Operator(OperatorType::kReduceProd) {} + std::vector axis; bool keep_dims = false; }; @@ -1386,29 +1450,27 @@ struct TensorFlowNotEqualOperator : Operator { TensorFlowNotEqualOperator() : Operator(OperatorType::kNotEqual) {} }; -// Global max reduction: computes the max of all of entries in the input array. -// Thus the output is "0-dimensional": it consists of a single scalar value. +// Max reduction: computes the max of all of entries across the axes. // // Inputs: // inputs[0]: required: the input array // -// TensorFlow equivalent: Max --- except that we only support the special case -// of global reduction across all dimensions. +// TensorFlow equivalent: Max struct TensorFlowMaxOperator : Operator { - TensorFlowMaxOperator() : Operator(OperatorType::kMax) {} + TensorFlowMaxOperator() : Operator(OperatorType::kReduceMax) {} + std::vector axis; bool keep_dims = false; }; -// Global min reduction: computes the min of all of entries in the input array. -// Thus the output is "0-dimensional": it consists of a single scalar value. +// Min reduction: computes the min of all of entries across the axes. // // Inputs: // inputs[0]: required: the input array // -// TensorFlow equivalent: Min --- except that we only support the special case -// of global reduction across all dimensions. +// TensorFlow equivalent: Min struct TensorFlowMinOperator : Operator { - TensorFlowMinOperator() : Operator(OperatorType::kMin) {} + TensorFlowMinOperator() : Operator(OperatorType::kReduceMin) {} + std::vector axis; bool keep_dims = false; }; @@ -1449,6 +1511,8 @@ struct TensorFlowUnsupportedOperator : Operator { bool quantized = false; // Output data types std::vector output_data_types; + // Output shapes. + std::vector output_shapes; }; // Softmax activation function. @@ -1509,11 +1573,15 @@ struct FloorOperator : Operator { // Inputs: // inputs[0]: required: the params array // inputs[1]: required: the indices to gather +// inputs[2]: optional: axis // // TensorFlow equivalent: Gather struct GatherOperator : Operator { GatherOperator() : Operator(OperatorType::kGather) {} - int axis = 0; + // Axis is populated explicitly or implicitly from the axis input by + // ResolveGatherAttributes. An empty axis indicates that the axis has not yet + // be resolved. + absl::optional axis; int input_rank = 0; }; @@ -1528,6 +1596,17 @@ struct ArgMaxOperator : Operator { ArrayDataType output_data_type = ArrayDataType::kInt64; }; +// ArgMin operator. It returns the index of the minimum value along axis. +// +// Inputs: +// inputs[0]: required: the input tensor +// +// TensorFlow equivalent: ArgMin +struct ArgMinOperator : Operator { + ArgMinOperator() : Operator(OperatorType::kArgMin) {} + ArrayDataType output_data_type = ArrayDataType::kInt64; +}; + // ResizeBilinear operator. It resizes input images with bilinear interpolation. // It does not support align_corners at the moment. // @@ -1658,6 +1737,71 @@ struct PowOperator : Operator { PowOperator() : Operator(OperatorType::kPow) {} }; +// Any operator: +// +// Inputs: +// Inputs[0]: required: A boolean input tensor. +// Inputs[1]: required: reduction_indices. +// +// TensorFlow equivalent: tf.reduce_any. +struct AnyOperator : Operator { + AnyOperator() : Operator(OperatorType::kAny) {} + bool keep_dims = false; +}; + +// LogicalAnd operator: +// +// Inputs: +// Inputs[0]: required: A boolean tensor. +// Inputs[1]: required: A boolean tensor. +// +// TensorFlow equivalent: tf.logical_and. +struct LogicalAndOperator : Operator { + LogicalAndOperator() : Operator(OperatorType::kLogicalAnd) {} +}; + +// LogicalNot operator: +// +// Inputs: +// Inputs[0]: required: A boolean tensor. +// +// TensorFlow equivalent: tf.logical_not. +struct LogicalNotOperator : Operator { + LogicalNotOperator() : Operator(OperatorType::kLogicalNot) {} +}; + +// OneHot operator: +// +// Inputs: +// Inputs[0]: required: indices. +// Inputs[1]: required: depth. +// Inputs[2]: required: on_value. +// Inputs[3]: required: off_value. +// +// TensorFlow equivalent: OneHot. +struct OneHotOperator : Operator { + enum Inputs { + INDICES_INPUT = 0, + DEPTH_INPUT = 1, + ON_VALUE_INPUT = 2, + OFF_VALUE_INPUT = 3, + }; + + OneHotOperator() : Operator(OperatorType::kOneHot) {} + int axis = -1; +}; + +// LogicalOr operator: +// +// Inputs: +// Inputs[0]: required: A Bool tensor. +// Inputs[1]: required: A Bool tensor. +// +// TensorFlow equivalent: LogicalOr. +struct LogicalOrOperator : Operator { + LogicalOrOperator() : Operator(OperatorType::kLogicalOr) {} +}; + // Alloc's are used for transient arrays only. An Alloc specifies which interval // of the "transient_data" workspace buffer passed to inference functions, is to // be used for the transient array at hand. The 'start' and 'end' values are @@ -1671,46 +1815,6 @@ inline bool operator<(const Alloc& a, const Alloc& b) { return a.start < b.start; } -class Shape { - public: - // For Shape, we stick to half-way encapsulation for now: - // we hide the raw dims_ member, but expose it raw by accessors - // because from some brainstorming, it's not at all easy to - // anticipate which flavor of more hermetic encapsulation would - // actually buy us future-proof-ness without being needlessly - // cumbersome. - Shape() {} - Shape(std::initializer_list dim_list) : dims_(dim_list) {} - - void ReplaceDims(std::initializer_list dim_list) { - dims_ = std::vector(dim_list); - } - - const std::vector& dims() const { return dims_; } - std::vector* mutable_dims() { return &dims_; } - const int dimensions_count() const { return dims_.size(); } - - // We still have that one convenience accessor to avoid - // the awkward double bracket issue: shape.dims()[i]. - int dims(int i) const { - // Always check for out-of-bounds accesses, even in optimized builds where - // standard assertions are disabled. Out-of-bounds access here is a common - // occurrence. - CHECK_GE(i, 0); - CHECK_GT(dims_.size(), i); - return dims_[i]; - } - - bool operator==(const Shape& comp) const { - return (this->dims_ == comp.dims()); - } - - bool operator!=(const Shape& comp) const { return !((*this) == comp); } - - private: - std::vector dims_; -}; - // Array represents an array (either a constant parameter array or an // activations array) in a Model. struct Array { @@ -1842,6 +1946,40 @@ struct Array { // If this is non-null, then these quantization parameters are to be used // to assign a meaning as real numbers to the elements of this array. std::unique_ptr quantization_params; + // narrow_range is a detail of how toco handles FakeQuant operators with + // narrow_range, see + // https://www.tensorflow.org/api_docs/python/tf/fake_quant_with_min_max_vars + // + // For more context about what that is useful for, see the big comment in + // graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc + // + // The narrow_range flag applies only to quantized arrays, and changes + // their quantization in the following way when it is set to 'true': + // 1. The computation of {zero_point, scale} from {min, max} needs to be + // amended so that the real min value will get quantized to + // (min_quantized_value + 1) instead of just (min_quantized_value). + // E.g. for uint8 quantization, the real min value should get quantized to + // the uint8 value 1, not 0. + // 2. Quantized values should get clamped to the interval + // [min_quantized_value + 1, max_value]. Equivalently, the + // min_quantized_value should get nudged to (min_quantized_value + 1). + // The reason why 1. does not imply 2. is that real values may not belong to + // the stated [min, max] interval. Concretely, weights recorded at the last + // learning step may not fall in the [min, max] interval recorded over + // previous learning steps, as the values evolve across learning steps. + // + // Rationale why this is directly a field on Array: + // - This can't be just a field on FakeQuantOperator, because + // FakeQuantOperators are gone (DropFakeQuant) before we get to using that + // information (Quantize). We need a place to store that bit in the interim. + // - This can't be in QuantizationParams because we need to record this + // ahead of quantization, and QuantizationParams are only created during + // quantization. + // - This could be in MinMax, but that would be an abuse of what MinMax is + // about, and would break existing code that assumes that a MinMax is just + // a min and a max. Unlike MinMax which is agnostic as to the quantized + // data type, narrow_range refers to values in the quantized data type. + bool narrow_range = false; private: std::unique_ptr array_shape; diff --git a/tensorflow/contrib/lite/toco/model_cmdline_flags.cc b/tensorflow/contrib/lite/toco/model_cmdline_flags.cc index 06072d1fcb0612ed8193b3a0be1317923fe95bcc..d34da63e43eee3b48e575c33ddb6c89f7701865c 100644 --- a/tensorflow/contrib/lite/toco/model_cmdline_flags.cc +++ b/tensorflow/contrib/lite/toco/model_cmdline_flags.cc @@ -322,6 +322,10 @@ void ReadModelFlagsFromCommandLineFlags( for (int i = 0; i < input_shapes.size(); ++i) { auto* shape = model_flags->mutable_input_arrays(i)->mutable_shape(); shape->clear_dims(); + // Treat an empty input shape as a scalar. + if (input_shapes[i].empty()) { + continue; + } for (const auto& dim_str : absl::StrSplit(input_shapes[i], ',')) { int size; CHECK(absl::SimpleAtoi(dim_str, &size)) diff --git a/tensorflow/contrib/lite/toco/python/BUILD b/tensorflow/contrib/lite/toco/python/BUILD index 93fe756a55d378fa205ff88be5e18aff586e5dca..33c5b164622cee94d7ba16e7b1a3006dbacb9ca9 100644 --- a/tensorflow/contrib/lite/toco/python/BUILD +++ b/tensorflow/contrib/lite/toco/python/BUILD @@ -53,5 +53,8 @@ tf_py_test( data = [ ":toco_from_protos", ], - tags = ["no_pip"], + tags = [ + "no_oss", + "no_pip", + ], ) diff --git a/tensorflow/contrib/lite/toco/tensorflow_graph_matching/BUILD b/tensorflow/contrib/lite/toco/tensorflow_graph_matching/BUILD index 336e94de1ed3238d64f521cf1347acc8f0737de7..ea1fc2827ead7e7442bbf7f569e3ea88c3b0de57 100644 --- a/tensorflow/contrib/lite/toco/tensorflow_graph_matching/BUILD +++ b/tensorflow/contrib/lite/toco/tensorflow_graph_matching/BUILD @@ -60,6 +60,7 @@ cc_library( tf_cc_test( name = "resolve_svdf_test", srcs = ["resolve_svdf_test.cc"], + tags = ["no_oss"], deps = [ ":cluster", ":cluster_utils", diff --git a/tensorflow/contrib/lite/toco/tflite/BUILD b/tensorflow/contrib/lite/toco/tflite/BUILD index a02f90988b2863900b6a735fd69aa1975a762338..83e977d7b3b0a4d572faee3ba7e36690896ac8e8 100644 --- a/tensorflow/contrib/lite/toco/tflite/BUILD +++ b/tensorflow/contrib/lite/toco/tflite/BUILD @@ -37,6 +37,7 @@ tf_cc_test( srcs = [ "operator_test.cc", ], + tags = ["no_oss"], deps = [ ":operator", "//tensorflow/contrib/lite/toco:tooling_util", @@ -66,6 +67,7 @@ tf_cc_test( srcs = [ "types_test.cc", ], + tags = ["no_oss"], deps = [ ":types", "@com_google_googletest//:gtest_main", @@ -98,6 +100,7 @@ tf_cc_test( srcs = [ "export_test.cc", ], + tags = ["no_oss"], deps = [ ":export", "//tensorflow/contrib/lite/schema:schema_fbs", @@ -131,6 +134,7 @@ tf_cc_test( srcs = [ "import_test.cc", ], + tags = ["no_oss"], deps = [ ":import", "//tensorflow/contrib/lite:schema_fbs_version", diff --git a/tensorflow/contrib/lite/toco/tflite/export.cc b/tensorflow/contrib/lite/toco/tflite/export.cc index 19722468079a32b76f6952db6ca818da470a03ac..5ad307af14a0613188482ae17aed491dea06f984 100644 --- a/tensorflow/contrib/lite/toco/tflite/export.cc +++ b/tensorflow/contrib/lite/toco/tflite/export.cc @@ -336,17 +336,13 @@ void Export( auto op_codes = ExportOperatorCodes(model, ops_by_type, operators_map, &builder, &error_summary); - const string fake_quant_operation_name = "FAKE_QUANT"; - - if (error_summary.count(fake_quant_operation_name) != 0) { - LOG(ERROR) - << fake_quant_operation_name - << " operation was not converted. If running quantized make sure you " - "are passing --inference_type=QUANTIZED_UINT8 and values for " - "--std_values and --mean_values."; - // Remove the fake quant operation from the errors, since it shouldn't - // be provided a custom implementation. - error_summary.erase(fake_quant_operation_name); + for (const auto& op : model.operators) { + if (op->type == OperatorType::kFakeQuant) { + LOG(WARNING) << "FAKE_QUANT operation " << LogName(*op) + << " was not converted. If running quantized make sure you " + "are passing --inference_type=QUANTIZED_UINT8 and values " + "for --std_values and --mean_values."; + } } if (!allow_custom_ops && !error_summary.empty()) { // Remove ExpandDims and ReorderAxes from unimplemented list unless they diff --git a/tensorflow/contrib/lite/toco/tflite/export_test.cc b/tensorflow/contrib/lite/toco/tflite/export_test.cc index d1fdbcb8e9131e1d65fa32ca0395bbc17b2014e7..a95937ba0f4f66fedfab6c1528c8dc4e417297d0 100644 --- a/tensorflow/contrib/lite/toco/tflite/export_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/export_test.cc @@ -262,7 +262,7 @@ TEST_F(VersionedOpExportTest, Export) { EXPECT_EQ(1, (*operators)[1]->opcode_index()); } -// TODO(ahentz): tests for tensors, inputs, outpus, opcodes and operators. +// TODO(ahentz): tests for tensors, inputs, outputs, opcodes and operators. } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index 7e55ae92bd57447cc821b21b40ba289cb484a9ed..9380168f30522ad49f0cc6bc8d50539e45905e1e 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -282,25 +282,31 @@ class DepthToSpace : public CustomOperator { int GetVersion(const Operator& op) const override { return 1; } }; -class FakeQuant : public CustomOperator { +class FakeQuant + : public BuiltinOperator { public: - using CustomOperator::CustomOperator; - void WriteOptions(const TocoOperator& op, - flexbuffers::Builder* fbb) const override { - fbb->Float("min", op.minmax->min); - fbb->Float("max", op.minmax->max); - fbb->Int("num_bits", op.num_bits); + using BuiltinOperator::BuiltinOperator; + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreateFakeQuantOptions( + *builder, op.minmax->min, op.minmax->max, op.num_bits, op.narrow_range); } - void ReadOptions(const flexbuffers::Map& m, TocoOperator* op) const override { + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override { auto* minmax = new MinMax; - minmax->min = m["min"].AsFloat(); - minmax->max = m["max"].AsFloat(); + minmax->min = options.min(); + minmax->max = options.max(); op->minmax.reset(minmax); - const auto& num_bits = m["num_bits"]; - op->num_bits = num_bits.IsInt() ? num_bits.AsInt32() : 8; + op->num_bits = options.num_bits(); + op->narrow_range = options.narrow_range(); } - int GetVersion(const Operator& op) const override { return 1; } + int GetVersion(const Operator& op) const override { + const auto& fq_op = static_cast(op); + return fq_op.narrow_range ? 2 : 1; + } }; class FullyConnected @@ -364,12 +370,13 @@ class Gather : public BuiltinOperator WriteOptions( const TocoOperator& op, flatbuffers::FlatBufferBuilder* builder) const override { - return ::tflite::CreateGatherOptions(*builder, op.axis); + int axis = op.axis ? op.axis.value() : 0; + return ::tflite::CreateGatherOptions(*builder, axis); } void ReadOptions(const TfLiteOptions& options, TocoOperator* op) const override { - op->axis = options.axis(); + op->axis = {options.axis()}; } int GetVersion(const Operator& op) const override { return 1; } @@ -761,6 +768,44 @@ class Sum int GetVersion(const Operator& op) const override { return 1; } }; +class ReduceMax + : public BuiltinOperator { + public: + using BuiltinOperator::BuiltinOperator; + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreateReducerOptions(*builder, op.keep_dims); + } + + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override { + op->keep_dims = options.keep_dims(); + } + + int GetVersion(const Operator& op) const override { return 1; } +}; + +class ReduceProd + : public BuiltinOperator { + public: + using BuiltinOperator::BuiltinOperator; + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreateReducerOptions(*builder, op.keep_dims); + } + + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override { + op->keep_dims = options.keep_dims(); + } + + int GetVersion(const Operator& op) const override { return 1; } +}; + class ResizeBilinear : public BuiltinOperator { + public: + using BuiltinOperator::BuiltinOperator; + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreateArgMinOptions( + *builder, DataType::Serialize(op.output_data_type)); + } + + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override { + op->output_data_type = DataType::Deserialize(options.output_type()); + } + + int GetVersion(const Operator& op) const override { return 1; } +}; + class TransposeConv : public BuiltinOperator { + public: + using BuiltinOperator::BuiltinOperator; + + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreatePackOptions(*builder, op.values_count, op.axis); + } + + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override { + op->values_count = options.values_count(); + op->axis = options.axis(); + } + + int GetVersion(const Operator& op) const override { return 1; } +}; + class Shape : public BuiltinOperator { @@ -969,6 +1053,23 @@ class Shape int GetVersion(const Operator& op) const override { return 1; } }; +class OneHot : public BuiltinOperator { + public: + using BuiltinOperator::BuiltinOperator; + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreateOneHotOptions(*builder, op.axis); + } + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override { + op->axis = options.axis(); + } + + int GetVersion(const Operator& op) const override { return 1; } +}; + class TensorFlowUnsupported : public BaseOperator { public: using BaseOperator::BaseOperator; @@ -1158,6 +1259,10 @@ std::vector> BuildOperatorList() { ops.emplace_back( new Mean(::tflite::BuiltinOperator_MEAN, OperatorType::kMean)); ops.emplace_back(new Sum(::tflite::BuiltinOperator_SUM, OperatorType::kSum)); + ops.emplace_back(new ReduceProd(::tflite::BuiltinOperator_REDUCE_PROD, + OperatorType::kReduceProd)); + ops.emplace_back(new ReduceMax(::tflite::BuiltinOperator_REDUCE_MAX, + OperatorType::kReduceMax)); ops.emplace_back(new ResizeBilinear(::tflite::BuiltinOperator_RESIZE_BILINEAR, OperatorType::kResizeBilinear)); ops.emplace_back( @@ -1174,6 +1279,8 @@ std::vector> BuildOperatorList() { new Cast(::tflite::BuiltinOperator_CAST, OperatorType::kCast)); ops.emplace_back( new ArgMax(::tflite::BuiltinOperator_ARG_MAX, OperatorType::kArgMax)); + ops.emplace_back( + new ArgMin(::tflite::BuiltinOperator_ARG_MIN, OperatorType::kArgMin)); ops.emplace_back( new Tile(::tflite::BuiltinOperator_TILE, OperatorType::kTile)); ops.emplace_back(new ExpandDims(::tflite::BuiltinOperator_EXPAND_DIMS, @@ -1184,11 +1291,16 @@ std::vector> BuildOperatorList() { OperatorType::kSparseToDense)); ops.emplace_back( new Shape(::tflite::BuiltinOperator_SHAPE, OperatorType::kShape)); + ops.emplace_back(new FakeQuant(::tflite::BuiltinOperator_FAKE_QUANT, + OperatorType::kFakeQuant)); + ops.emplace_back( + new Pack(::tflite::BuiltinOperator_PACK, OperatorType::kPack)); + ops.emplace_back( + new OneHot(::tflite::BuiltinOperator_ONE_HOT, OperatorType::kOneHot)); // Custom Operators. ops.emplace_back( new DepthToSpace("DEPTH_TO_SPACE", OperatorType::kDepthToSpace)); - ops.emplace_back(new FakeQuant("FAKE_QUANT", OperatorType::kFakeQuant)); ops.emplace_back(new TensorFlowUnsupported("TENSORFLOW_UNSUPPORTED", OperatorType::kUnsupported)); @@ -1238,6 +1350,8 @@ std::vector> BuildOperatorList() { ops.emplace_back( new SimpleOperator("SLICE", OperatorType::kSlice)); ops.emplace_back(new SimpleOperator("POW", OperatorType::kPow)); + ops.emplace_back(new SimpleOperator( + "LOGICAL_OR", OperatorType::kLogicalOr)); // Element-wise operator ops.emplace_back(new SimpleOperator("SIN", OperatorType::kSin)); ops.emplace_back(new SimpleOperator("LOG", OperatorType::kLog)); diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc index 8b6808d3c78d8c51c1b33d09eb4082326100b028..384f7c118de82d9907e3791f880a5beee2a1a77a 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc @@ -127,6 +127,8 @@ TEST_F(OperatorTest, SimpleOperators) { CheckSimpleOperator("SQRT", OperatorType::kSqrt); CheckSimpleOperator("RSQRT", OperatorType::kRsqrt); CheckSimpleOperator("POW", OperatorType::kPow); + CheckSimpleOperator("LOGICAL_OR", + OperatorType::kLogicalOr); } TEST_F(OperatorTest, BuiltinAdd) { @@ -416,6 +418,13 @@ TEST_F(OperatorTest, BuiltinArgMax) { EXPECT_EQ(op.output_data_type, output_toco_op->output_data_type); } +TEST_F(OperatorTest, BuiltinArgMin) { + ArgMinOperator op; + auto output_toco_op = SerializeAndDeserialize( + GetOperator("ARG_MIN", OperatorType::kArgMin), op); + EXPECT_EQ(op.output_data_type, output_toco_op->output_data_type); +} + TEST_F(OperatorTest, BuiltinTransposeConv) { TransposeConvOperator op; op.stride_width = 123; @@ -445,6 +454,24 @@ TEST_F(OperatorTest, BuiltinSparseToDense) { EXPECT_EQ(op.validate_indices, output_toco_op->validate_indices); } +TEST_F(OperatorTest, BuiltinPack) { + PackOperator op; + op.values_count = 3; + op.axis = 1; + std::unique_ptr output_toco_op = + SerializeAndDeserialize(GetOperator("PACK", OperatorType::kPack), op); + EXPECT_EQ(op.values_count, output_toco_op->values_count); + EXPECT_EQ(op.axis, output_toco_op->axis); +} + +TEST_F(OperatorTest, BuiltinOneHot) { + OneHotOperator op; + op.axis = 2; + auto output_toco_op = SerializeAndDeserialize( + GetOperator("ONE_HOT", OperatorType::kOneHot), op); + EXPECT_EQ(op.axis, output_toco_op->axis); +} + TEST_F(OperatorTest, TensorFlowUnsupported) { TensorFlowUnsupportedOperator op; op.tensorflow_op = "MyCustomUnsupportedOp"; diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc index fc1636831b266b6aa426c564a0c1c7ca99bc0ff1..fcd3cbab07c06737f43d822e5b16f7c188f56b1a 100644 --- a/tensorflow/contrib/lite/toco/toco_tooling.cc +++ b/tensorflow/contrib/lite/toco/toco_tooling.cc @@ -55,7 +55,7 @@ void MakeGeneralGraphTransformationsSet( transformations->Add(new ConvertExpandDimsToReshape); transformations->Add(new ConvertSqueezeToReshape); transformations->Add(new ConvertTrivialAddNToAdd); - transformations->Add(new ConvertTrivialStackToReshape); + transformations->Add(new ConvertTrivialPackToReshape); transformations->Add(new ConvertTrivialTileToConcat); transformations->Add(new ConvertTrivialTransposeToReshape); transformations->Add(new ConvertReorderAxes); @@ -86,11 +86,11 @@ void MakeGeneralGraphTransformationsSet( transformations->Add(new ResolveConstantBinaryOperator); transformations->Add(new ResolveConstantFill); transformations->Add(new ResolveConstantGather); + transformations->Add(new ResolveConstantPack); transformations->Add(new ResolveConstantRandomUniform); transformations->Add(new ResolveConstantRange); transformations->Add(new ResolveConstantReshape); transformations->Add(new ResolveConstantSlice); - transformations->Add(new ResolveConstantStack); transformations->Add(new ResolveConstantStridedSlice); transformations->Add(new ResolveConstantTranspose); transformations->Add(new ResolveConstantUnaryOperator); @@ -105,17 +105,19 @@ void MakeGeneralGraphTransformationsSet( transformations->Add(new IdentifyRelu1); transformations->Add(new IdentifyPRelu); transformations->Add(new RemoveTrivialBinaryOperator); - transformations->Add(new ReadFakeQuantMinMax); + transformations->Add(new ResolveFakeQuantArgsFromVars); + transformations->Add(new ReadArrayMinmaxAndNarrowRangeFromFakeQuant); transformations->Add(new ResolveSpaceToBatchNDAttributes); transformations->Add(new ResolveBatchToSpaceNDAttributes); transformations->Add(new ResolvePadAttributes); transformations->Add(new ResolvePadV2Attributes); transformations->Add(new ResolveStridedSliceAttributes); transformations->Add(new ResolveSliceAttributes); - transformations->Add(new ResolveMeanAttributes); + transformations->Add(new ResolveReduceAttributes); transformations->Add(new ResolveConstantShapeOrRank); transformations->Add(new MakeInitialDequantizeOperator); transformations->Add(new UnpartitionEmbeddingLookup); + transformations->Add(new ResolveGatherAttributes); } bool SupportsQuantization(FileFormat format) { @@ -273,13 +275,16 @@ void Transform(const TocoFlags& toco_flags, Model* model) { transformations.Add(new toco::MergeLstmCellInputs); } } - if (toco_flags.quantize_weights()) { - transformations.Add(new QuantizeWeights); - } transformations.Add(new ResolveConstantConcatenation); RunGraphTransformations(model, "general graph transformations", transformations); + if (toco_flags.quantize_weights()) { + // Run the quantize weights transformation after batchnorms have been + // folded into the weights. + RunGraphTransformations(model, "quantize weights transformation", + {new QuantizeWeights}); + } if (quantize_output) { if (toco_flags.propagate_fake_quant_num_bits()) { RunGraphTransformations(model, @@ -304,8 +309,9 @@ void Transform(const TocoFlags& toco_flags, Model* model) { // HardcodeMinMax to move changes through the graph as we make changes. auto propagate_default_min_max = absl::make_unique(); - if (toco_flags.has_default_ranges_min() && - toco_flags.has_default_ranges_max()) { + bool has_default_ranges_flag = (toco_flags.has_default_ranges_min() && + toco_flags.has_default_ranges_max()); + if (has_default_ranges_flag) { propagate_default_min_max->DefineTypeRange( ArrayDataType::kUint8, toco_flags.default_ranges_min(), toco_flags.default_ranges_max()); @@ -330,6 +336,8 @@ void Transform(const TocoFlags& toco_flags, Model* model) { new EnsureUint8WeightsSafeForFastInt8Kernels; ensure_safe_for_int8_kernels->set_allow_nudging_weights( toco_flags.allow_nudging_weights_to_use_fast_gemm_kernel()); + ensure_safe_for_int8_kernels->set_has_default_ranges_flag( + has_default_ranges_flag); RunGraphTransformations(model, "quantization graph transformations", { new RemoveTrivialQuantizedActivationFunc, diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 01113506d0ebbf25c057ab0a50730a45eeef64a5..68155c73294d1cdd1a258aac98da9cd81fa4bbca 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -350,16 +350,17 @@ const char* OperatorTypeName(OperatorType type) { HANDLE_OPERATORTYPENAME_CASE(Less) HANDLE_OPERATORTYPENAME_CASE(LessEqual) HANDLE_OPERATORTYPENAME_CASE(MatMul) - HANDLE_OPERATORTYPENAME_CASE(Max) // Reduction Max - HANDLE_OPERATORTYPENAME_CASE(Maximum) // Element-wise Maximum + HANDLE_OPERATORTYPENAME_CASE(ReduceMax) // Reduction Max + HANDLE_OPERATORTYPENAME_CASE(Maximum) // Element-wise Maximum HANDLE_OPERATORTYPENAME_CASE(Merge) - HANDLE_OPERATORTYPENAME_CASE(Min) // Reduction Min - HANDLE_OPERATORTYPENAME_CASE(Minimum) // Element-wise Minimum + HANDLE_OPERATORTYPENAME_CASE(ReduceMin) // Reduction Min + HANDLE_OPERATORTYPENAME_CASE(Minimum) // Element-wise Minimum HANDLE_OPERATORTYPENAME_CASE(Neg) + HANDLE_OPERATORTYPENAME_CASE(OneHot) + HANDLE_OPERATORTYPENAME_CASE(Pack) HANDLE_OPERATORTYPENAME_CASE(Pad) HANDLE_OPERATORTYPENAME_CASE(PadV2) HANDLE_OPERATORTYPENAME_CASE(StridedSlice) - HANDLE_OPERATORTYPENAME_CASE(Stack) HANDLE_OPERATORTYPENAME_CASE(Range) HANDLE_OPERATORTYPENAME_CASE(Rank) HANDLE_OPERATORTYPENAME_CASE(Reshape) @@ -385,8 +386,10 @@ const char* OperatorTypeName(OperatorType type) { HANDLE_OPERATORTYPENAME_CASE(SpaceToBatchND) HANDLE_OPERATORTYPENAME_CASE(BatchToSpaceND) HANDLE_OPERATORTYPENAME_CASE(Mean) + HANDLE_OPERATORTYPENAME_CASE(ReduceProd) HANDLE_OPERATORTYPENAME_CASE(Svdf) HANDLE_OPERATORTYPENAME_CASE(ArgMax) + HANDLE_OPERATORTYPENAME_CASE(ArgMin) HANDLE_OPERATORTYPENAME_CASE(TopK_V2) HANDLE_OPERATORTYPENAME_CASE(Unsupported) HANDLE_OPERATORTYPENAME_CASE(Exp) @@ -397,6 +400,10 @@ const char* OperatorTypeName(OperatorType type) { HANDLE_OPERATORTYPENAME_CASE(Equal) HANDLE_OPERATORTYPENAME_CASE(NotEqual) HANDLE_OPERATORTYPENAME_CASE(Pow) + HANDLE_OPERATORTYPENAME_CASE(Any) + HANDLE_OPERATORTYPENAME_CASE(LogicalAnd) + HANDLE_OPERATORTYPENAME_CASE(LogicalNot) + HANDLE_OPERATORTYPENAME_CASE(LogicalOr) default: LOG(FATAL) << "Unhandled op type"; #undef HANDLE_OPERATORTYPENAME_CASE @@ -938,8 +945,12 @@ void CheckEachArray(const Model& model) { // shape. CHECK(array->has_shape()); // Constant buffer should has a valid shape. - for (int d : array->shape().dims()) { - CHECK_GE(d, 1); + bool is_scalar = + array->shape().dimensions_count() == 1 && array->shape().dims(0) == 0; + if (!is_scalar) { + for (int d : array->shape().dims()) { + CHECK_GE(d, 1); + } } // The shape flat-size should agree with the buffer length. CHECK_EQ(array->buffer->Length(), @@ -1265,8 +1276,13 @@ void InsertCopyOperator(Model* model, const string& source_array_name, auto* copy_op = new TensorFlowReshapeOperator; copy_op->inputs = { source_array_name, - CreateInt32Array(model, target_array_name + "_copy_shape", shape)}; + CreateInt32Array( + model, AvailableArrayName(*model, target_array_name + "_copy_shape"), + shape)}; copy_op->outputs = {target_array_name}; + if (target_array.has_shape()) { + copy_op->shape = target_array.shape().dims(); + } model->operators.emplace_back(copy_op); } @@ -1571,11 +1587,6 @@ void ResolveModelFlags(const ModelFlags& model_flags, Model* model) { model); } - for (const auto& input_array : model->flags.input_arrays()) { - if (input_array.has_shape()) { - CHECK(input_array.shape().dims_size()); - } - } model->flags.set_change_concat_input_ranges( model_flags.change_concat_input_ranges()); model->flags.set_allow_nonascii_arrays(model_flags.allow_nonascii_arrays()); @@ -1608,11 +1619,12 @@ void CheckIsReadyForQuantization(const Model& model) { << "Array " << input << ", which is an input to the " << HelpfulOperatorTypeName(*op) << " operator producing the output " << "array " << op->outputs[0] << ", is lacking min/max data, " - << "which is necessary for quantization. Either target a " - << "non-quantized output format, or change the input graph to " - << "contain min/max information, or pass --default_ranges_min= and " - << "--default_ranges_max= if you do not care about the accuracy of " - << "results."; + << "which is necessary for quantization. If accuracy matters, either " + << "target a non-quantized output format, or run quantized training " + << "with your model from a floating point checkpoint to change the " + << "input graph to contain min/max information. If you don't care " + << "about accuracy, you can pass --default_ranges_min= and " + << "--default_ranges_max= for easy experimentation."; } } } diff --git a/tensorflow/contrib/lite/toco/tooling_util_test.cc b/tensorflow/contrib/lite/toco/tooling_util_test.cc index 8609e5beddd200be4e5ebfe1fb2a79048e0e60ab..eb495646a2df0d0295eab54fcc5a5bf156a59d39 100644 --- a/tensorflow/contrib/lite/toco/tooling_util_test.cc +++ b/tensorflow/contrib/lite/toco/tooling_util_test.cc @@ -39,6 +39,8 @@ std::vector CreateShapePairs() { {Shape({256, 256, 3}), Shape({256, 256, 3}), Agreement::kBroadcast}, {Shape({256, 256, 3}), Shape({3}), Agreement::kBroadcast}, {Shape({8, 1, 6, 1}), Shape({7, 1, 5}), Agreement::kBroadcast}, + {Shape({}), Shape({3}), Agreement::kBroadcast}, + {Shape({}), Shape({3, 1}), Agreement::kBroadcast}, // These extend (and therefore broadcast). {Shape({3}), Shape({3}), Agreement::kExtend}, @@ -54,6 +56,7 @@ std::vector CreateShapePairs() { {Shape({15, 3, 5}), Shape({15, 1, 5}), Agreement::kBroadcastNotExtend}, {Shape({15, 3, 5}), Shape({3, 5}), Agreement::kBroadcastNotExtend}, {Shape({15, 3, 5}), Shape({3, 1}), Agreement::kBroadcastNotExtend}, + {Shape({3, 1}), Shape({}), Agreement::kBroadcastNotExtend}, // These do not broadcast (and therefore also do not extend). {Shape({3}), Shape({4}), Agreement::kNeither}, @@ -175,6 +178,20 @@ TEST(NumElementsTest, UnsignedInt64) { EXPECT_EQ(status.error_message(), kLargeTensorMessage); } +TEST(NumElementsTest, Scalar) { + tensorflow::Status status = tensorflow::Status::OK(); + + int32_t count; + status = NumElements(std::vector{}, &count); + EXPECT_TRUE(status.ok()); + EXPECT_EQ(count, 1); + + uint64_t countu64; + status = NumElements(std::vector{}, &countu64); + EXPECT_TRUE(status.ok()); + EXPECT_EQ(countu64, 1ULL); +} + TEST(FusedActivationTest, DefaultsToUnfused) { EXPECT_TRUE(OperatorSupportsFusedActivation(OperatorType::kAdd)); EXPECT_FALSE(OperatorSupportsFusedActivation(OperatorType::kNone)); diff --git a/tensorflow/contrib/lite/tools/BUILD b/tensorflow/contrib/lite/tools/BUILD index a3df37358fac4d688ce7c513ed951cdd7e6bca1a..0b268264031f4f1e86b2956a75bde173a945ddf4 100644 --- a/tensorflow/contrib/lite/tools/BUILD +++ b/tensorflow/contrib/lite/tools/BUILD @@ -14,6 +14,7 @@ py_binary( srcs = ["visualize.py"], data = [ "//tensorflow/contrib/lite/schema:schema.fbs", + "//tensorflow/python:platform", "@flatbuffers//:flatc", ], srcs_version = "PY2AND3", @@ -52,6 +53,7 @@ cc_test( "//tensorflow/contrib/lite:testdata/test_model_broken.bin", ], tags = [ + "no_oss", "tflite_not_portable_android", "tflite_not_portable_ios", ], @@ -78,6 +80,7 @@ cc_test( size = "small", srcs = ["verifier_test.cc"], tags = [ + "no_oss", "tflite_not_portable", ], deps = [ diff --git a/tensorflow/contrib/lite/tools/benchmark/BUILD b/tensorflow/contrib/lite/tools/benchmark/BUILD index 183a545295f690decec47f1c31aa473667408a3d..2cb07eb6ec9405a5fefec9cc49f3b1aaff663e4b 100644 --- a/tensorflow/contrib/lite/tools/benchmark/BUILD +++ b/tensorflow/contrib/lite/tools/benchmark/BUILD @@ -10,11 +10,16 @@ load("//tensorflow/contrib/lite:build_def.bzl", "tflite_copts") common_copts = ["-Wall"] + tflite_copts() +cc_library( + name = "logging", + hdrs = ["logging.h"], + copts = common_copts, +) + cc_binary( name = "benchmark_model", srcs = [ "benchmark_main.cc", - "logging.h", ], copts = common_copts, linkopts = tflite_linkopts() + select({ @@ -26,6 +31,26 @@ cc_binary( }), deps = [ ":benchmark_tflite_model_lib", + ":logging", + ], +) + +cc_test( + name = "benchmark_test", + srcs = ["benchmark_test.cc"], + args = [ + "--graph=$(location //tensorflow/contrib/lite:testdata/multi_add.bin)", + ], + data = ["//tensorflow/contrib/lite:testdata/multi_add.bin"], + tags = [ + "tflite_not_portable_android", + "tflite_not_portable_ios", + ], + deps = [ + ":benchmark_tflite_model_lib", + ":command_line_flags", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", ], ) @@ -58,6 +83,7 @@ cc_library( copts = common_copts, deps = [ ":benchmark_model_lib", + ":logging", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:string_util", "//tensorflow/contrib/lite/kernels:builtin_ops", @@ -70,23 +96,23 @@ cc_library( name = "benchmark_params", srcs = [ "benchmark_params.cc", - "logging.h", ], hdrs = ["benchmark_params.h"], copts = common_copts, + deps = [":logging"], ) cc_library( name = "benchmark_model_lib", srcs = [ "benchmark_model.cc", - "logging.h", ], hdrs = ["benchmark_model.h"], copts = common_copts, deps = [ ":benchmark_params", ":command_line_flags", + ":logging", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:string_util", "//tensorflow/contrib/lite/kernels:builtin_ops", diff --git a/tensorflow/contrib/lite/tools/benchmark/README.md b/tensorflow/contrib/lite/tools/benchmark/README.md index 93769305bde210b58f3b2cb668a9d8c1ad0ce396..f1e257ad104885a23cd7f17b9c21317c0881ccc0 100644 --- a/tensorflow/contrib/lite/tools/benchmark/README.md +++ b/tensorflow/contrib/lite/tools/benchmark/README.md @@ -115,7 +115,7 @@ E.g. for running the benchmark on big cores on Pixel 2 with a single thread one can use the following command: ``` -adb shell tasket f0 /data/local/tmp/benchmark_model \ +adb shell taskset f0 /data/local/tmp/benchmark_model \ --graph=/data/local/tmp/mobilenet_quant_v1_224.tflite \ --input_layer="input" \ --input_layer_shape="1,224,224,3" \ diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc index 08648bcfe26365d180d984fde8f8e04b22eb45dd..f86c0445b0525cd053c733b18bb7f1205d310d43 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc @@ -84,7 +84,7 @@ std::vector BenchmarkModel::GetFlags() { }; } -void BenchmarkModel::LogFlags() { +void BenchmarkModel::LogParams() { TFLITE_LOG(INFO) << "Num runs: [" << params_.Get("num_runs") << "]"; TFLITE_LOG(INFO) << "Inter-run delay (seconds): [" << params_.Get("run_delay") << "]"; @@ -98,10 +98,13 @@ void BenchmarkModel::LogFlags() { << "]"; } +void BenchmarkModel::PrepareInputsAndOutputs() {} + Stat BenchmarkModel::Run(int num_times, RunType run_type) { Stat run_stats; TFLITE_LOG(INFO) << "Running benchmark for " << num_times << " iterations "; for (int run = 0; run < num_times; run++) { + PrepareInputsAndOutputs(); listeners_.OnSingleRunStart(run_type); int64_t start_us = profiling::time::NowMicros(); RunImpl(); @@ -119,12 +122,18 @@ Stat BenchmarkModel::Run(int num_times, RunType run_type) { return run_stats; } +bool BenchmarkModel::ValidateParams() { return true; } + void BenchmarkModel::Run(int argc, char **argv) { if (!ParseFlags(argc, argv)) { return; } + Run(); +} - LogFlags(); +void BenchmarkModel::Run() { + ValidateParams(); + LogParams(); listeners_.OnBenchmarkStart(params_); int64_t initialization_start_us = profiling::time::NowMicros(); @@ -152,7 +161,7 @@ bool BenchmarkModel::ParseFlags(int argc, char **argv) { TFLITE_LOG(ERROR) << usage; return false; } - return ValidateFlags(); + return true; } } // namespace benchmark diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h index 942e21f67a7f864f16b7b1b85b2599d5c872b5c7..677a1ee68c247fb016c7ede4e1a614bacb7a0a15 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h @@ -137,19 +137,21 @@ class BenchmarkModel { BenchmarkModel(); BenchmarkModel(BenchmarkParams params) : params_(std::move(params)) {} virtual ~BenchmarkModel() {} - bool ParseFlags(int argc, char** argv); virtual void Init() = 0; void Run(int argc, char** argv); + virtual void Run(); void AddListener(BenchmarkListener* listener) { listeners_.AddListener(listener); } protected: - virtual void LogFlags(); - virtual bool ValidateFlags() { return true; } + virtual void LogParams(); + virtual bool ValidateParams(); + bool ParseFlags(int argc, char** argv); virtual std::vector GetFlags(); virtual uint64_t ComputeInputBytes() = 0; virtual tensorflow::Stat Run(int num_times, RunType run_type); + virtual void PrepareInputsAndOutputs(); virtual void RunImpl() = 0; BenchmarkParams params_; BenchmarkListeners listeners_; diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_params.h b/tensorflow/contrib/lite/tools/benchmark/benchmark_params.h index 33448dd1623577fdfda6316c588cc60ccbaa1994..c98f47bb0d89864dff54d7cdebe764e56e4cfda2 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_params.h +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_params.h @@ -31,6 +31,8 @@ class TypedBenchmarkParam; class BenchmarkParam { protected: enum class ParamType { TYPE_INT32, TYPE_FLOAT, TYPE_BOOL, TYPE_STRING }; + template + static ParamType GetValueType(); public: template @@ -49,8 +51,6 @@ class BenchmarkParam { private: static void AssertHasSameType(ParamType a, ParamType b); - template - static ParamType GetValueType(); const ParamType type_; }; diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_test.cc b/tensorflow/contrib/lite/tools/benchmark/benchmark_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..b697bb394db9b967dfaaff649517dcc23e85ccb0 --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_test.cc @@ -0,0 +1,74 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#include +#include +#include + +#include +#include +#include "tensorflow/contrib/lite/testing/util.h" +#include "tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h" +#include "tensorflow/contrib/lite/tools/benchmark/command_line_flags.h" + +namespace { +const std::string* g_model_path = nullptr; +} + +namespace tflite { +namespace benchmark { +namespace { + +BenchmarkParams CreateParams() { + BenchmarkParams params; + params.AddParam("num_runs", BenchmarkParam::Create(2)); + params.AddParam("run_delay", BenchmarkParam::Create(-1.0f)); + params.AddParam("num_threads", BenchmarkParam::Create(1)); + params.AddParam("benchmark_name", BenchmarkParam::Create("")); + params.AddParam("output_prefix", BenchmarkParam::Create("")); + params.AddParam("warmup_runs", BenchmarkParam::Create(1)); + params.AddParam("graph", BenchmarkParam::Create(*g_model_path)); + params.AddParam("input_layer", BenchmarkParam::Create("")); + params.AddParam("input_layer_shape", BenchmarkParam::Create("")); + params.AddParam("use_nnapi", BenchmarkParam::Create(false)); + return params; +} + +TEST(BenchmarkTest, DoesntCrash) { + ASSERT_THAT(g_model_path, testing::NotNull()); + + BenchmarkTfLiteModel benchmark(CreateParams()); + benchmark.Run(); +} + +} // namespace +} // namespace benchmark +} // namespace tflite + +int main(int argc, char** argv) { + std::string model_path; + std::vector flags = { + tflite::Flag::CreateFlag("graph", &model_path, "Path to model file.")}; + g_model_path = &model_path; + const bool parse_result = + tflite::Flags::Parse(&argc, const_cast(argv), flags); + if (!parse_result) { + std::cerr << tflite::Flags::Usage(argv[0], flags); + return 1; + } + + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc index 73affc26b034f415ae2a2101e0b558cdb94d8d5b..7f97f5d0cd6c412653f6d510406daf86b7baa3f7 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc @@ -198,8 +198,8 @@ std::vector BenchmarkTfLiteModel::GetFlags() { return flags; } -void BenchmarkTfLiteModel::LogFlags() { - BenchmarkModel::LogFlags(); +void BenchmarkTfLiteModel::LogParams() { + BenchmarkModel::LogParams(); TFLITE_LOG(INFO) << "Graph: [" << params_.Get("graph") << "]"; TFLITE_LOG(INFO) << "Input layers: [" << params_.Get("input_layer") << "]"; @@ -208,7 +208,7 @@ void BenchmarkTfLiteModel::LogFlags() { TFLITE_LOG(INFO) << "Use nnapi : [" << params_.Get("use_nnapi") << "]"; } -bool BenchmarkTfLiteModel::ValidateFlags() { +bool BenchmarkTfLiteModel::ValidateParams() { if (params_.Get("graph").empty()) { TFLITE_LOG(ERROR) << "Please specify the name of your TF Lite input file with --graph"; diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h index 50cc3f24b3bd2f31555eac69ff208fa2480449b9..9931dcbafe06cb9f8673462858244f6f2793b29d 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h @@ -54,8 +54,8 @@ class BenchmarkTfLiteModel : public BenchmarkModel { BenchmarkTfLiteModel(BenchmarkParams params); std::vector GetFlags() override; - void LogFlags() override; - bool ValidateFlags() override; + void LogParams() override; + bool ValidateParams() override; uint64_t ComputeInputBytes() override; void Init() override; void RunImpl() override; diff --git a/tensorflow/contrib/lite/tools/visualize.py b/tensorflow/contrib/lite/tools/visualize.py index f571dd59da0a3f4aff264b48fba3e41f75b50404..e07f899e4d8c249cb03d4251a722df0614007fed 100644 --- a/tensorflow/contrib/lite/tools/visualize.py +++ b/tensorflow/contrib/lite/tools/visualize.py @@ -28,11 +28,24 @@ import json import os import sys +from tensorflow.python.platform import resource_loader + # Schema to use for flatbuffers _SCHEMA = "third_party/tensorflow/contrib/lite/schema/schema.fbs" -# Where the binary will be once built in for the flatc converter -_BINARY = "third_party/flatbuffers/flatc" +# TODO(angerson): fix later when rules are simplified.. +_SCHEMA = resource_loader.get_path_to_datafile("../schema/schema.fbs") +_BINARY = resource_loader.get_path_to_datafile("../../../../flatbuffers/flatc") +# Account for different package positioning internal vs. external. +if not os.path.exists(_BINARY): + _BINARY = resource_loader.get_path_to_datafile( + "../../../../../flatbuffers/flatc") + +if not os.path.exists(_SCHEMA): + raise RuntimeError("Sorry, schema file cannot be found at %r" % _SCHEMA) +if not os.path.exists(_BINARY): + raise RuntimeError("Sorry, flatc is not available at %r" % _BINARY) + # A CSS description for making the visualizer _CSS = """ diff --git a/tensorflow/contrib/lite/util.h b/tensorflow/contrib/lite/util.h index 89d9b4f5cffa99e708f391fd8fe19208009b5e79..3c4801183bad834e5789c97a56416cdf4668f897 100644 --- a/tensorflow/contrib/lite/util.h +++ b/tensorflow/contrib/lite/util.h @@ -26,12 +26,17 @@ limitations under the License. namespace tflite { -// Converts a `std::vector` to a `TfLiteIntArray`. +// Converts a `std::vector` to a `TfLiteIntArray`. The caller takes ownership +// of the returned pointer. TfLiteIntArray* ConvertVectorToTfLiteIntArray(const std::vector& input); +// Converts an array (of the given size) to a `TfLiteIntArray`. The caller +// takes ownership of the returned pointer, and must make sure 'dims' has at +// least 'rank' elemnts. TfLiteIntArray* ConvertArrayToTfLiteIntArray(const int rank, const int* dims); // Checks whether a `TfLiteIntArray` and an int array have matching elements. +// The caller must guarantee that 'b' has at least 'b_size' elements. bool EqualArrayAndTfLiteIntArray(const TfLiteIntArray* a, const int b_size, const int* b); diff --git a/tensorflow/contrib/lookup/lookup_ops_test.py b/tensorflow/contrib/lookup/lookup_ops_test.py index 889accdd5aafae2931048ffdd26408cccb3c874e..8d510ede5827df3889307c0f38572bece84f102e 100644 --- a/tensorflow/contrib/lookup/lookup_ops_test.py +++ b/tensorflow/contrib/lookup/lookup_ops_test.py @@ -280,6 +280,21 @@ class HashTableOpTest(test.TestCase): table.init.run() self.assertAllEqual(3, table.size().eval()) + def testHashTableInt32String(self): + with self.test_session(): + default_val = "n/a" + keys = constant_op.constant([0, 1, 2], dtypes.int32) + values = constant_op.constant(["brain", "salad", "surgery"]) + table = lookup.HashTable( + lookup.KeyValueTensorInitializer(keys, values), default_val) + table.init.run() + + input_tensor = constant_op.constant([0, 1, -1]) + output = table.lookup(input_tensor) + + result = output.eval() + self.assertAllEqual([b"brain", b"salad", b"n/a"], result) + class MutableHashTableOpTest(test.TestCase): diff --git a/tensorflow/contrib/makefile/proto_text_cc_files.txt b/tensorflow/contrib/makefile/proto_text_cc_files.txt index 76428bc1d4e682e000998a6e28fc290e218c2341..7d26429f9c3b26bcd8819e92cbc15daed60ea9f4 100644 --- a/tensorflow/contrib/makefile/proto_text_cc_files.txt +++ b/tensorflow/contrib/makefile/proto_text_cc_files.txt @@ -35,6 +35,7 @@ tensorflow/core/lib/random/random.cc tensorflow/core/lib/random/distribution_sampler.cc tensorflow/core/lib/io/zlib_outputbuffer.cc tensorflow/core/lib/io/zlib_inputstream.cc +tensorflow/core/lib/io/zlib_compression_options.cc tensorflow/core/lib/io/two_level_iterator.cc tensorflow/core/lib/io/table_builder.cc tensorflow/core/lib/io/table.cc diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt index 6e7423f85e3b66e2f40b25c0b83d0fcaa54817a9..ecf2e120df98d82cca068e186f95e91e71ebc66d 100644 --- a/tensorflow/contrib/makefile/tf_op_files.txt +++ b/tensorflow/contrib/makefile/tf_op_files.txt @@ -229,6 +229,8 @@ tensorflow/core/kernels/cast_op_impl_int32.cc tensorflow/core/kernels/cast_op_impl_int64.cc tensorflow/core/kernels/cast_op_impl_int8.cc tensorflow/core/kernels/cast_op_impl_uint16.cc +tensorflow/core/kernels/cast_op_impl_uint32.cc +tensorflow/core/kernels/cast_op_impl_uint64.cc tensorflow/core/kernels/cast_op_impl_uint8.cc tensorflow/core/kernels/boosted_trees/prediction_ops.cc tensorflow/core/kernels/boosted_trees/resource_ops.cc diff --git a/tensorflow/contrib/metrics/BUILD b/tensorflow/contrib/metrics/BUILD index 66cb493e5c5bb9b8645e87dc7f5b274d916f64fc..21cd34f73ffbbf615a81c18b9d365bffa61397f4 100644 --- a/tensorflow/contrib/metrics/BUILD +++ b/tensorflow/contrib/metrics/BUILD @@ -31,6 +31,7 @@ py_library( "//tensorflow/python:check_ops", "//tensorflow/python:confusion_matrix", "//tensorflow/python:control_flow_ops", + "//tensorflow/python:distribute", "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:histogram_ops", "//tensorflow/python:init_ops", diff --git a/tensorflow/contrib/metrics/__init__.py b/tensorflow/contrib/metrics/__init__.py index 5effea3596bb83a08e0a8627e411684262aef5f7..88798d61b71388de63e492ba69284a72303d32ab 100644 --- a/tensorflow/contrib/metrics/__init__.py +++ b/tensorflow/contrib/metrics/__init__.py @@ -63,6 +63,7 @@ See the @{$python/contrib.metrics} guide. @@aggregate_metrics @@aggregate_metric_map @@confusion_matrix +@@f1_score @@set_difference @@set_intersection @@set_size diff --git a/tensorflow/contrib/metrics/python/metrics/classification.py b/tensorflow/contrib/metrics/python/metrics/classification.py index 26aba1cc51446e589856013d69526007fbe9d921..e5536122698a50852c4cb96f12ce52ab5d5f6e39 100644 --- a/tensorflow/contrib/metrics/python/metrics/classification.py +++ b/tensorflow/contrib/metrics/python/metrics/classification.py @@ -22,6 +22,9 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import metrics_impl +from tensorflow.python.ops import variable_scope +from tensorflow.python.training import distribute as distribute_lib # TODO(nsilberman): move into metrics/python/ops/ @@ -62,3 +65,121 @@ def accuracy(predictions, labels, weights=None, name=None): return math_ops.div(math_ops.reduce_sum(is_correct), math_ops.reduce_sum(num_values)) return math_ops.reduce_mean(is_correct) + + +def f1_score(labels, predictions, weights=None, num_thresholds=200, + metrics_collections=None, updates_collections=None, name=None): + """Computes the approximately best F1-score across different thresholds. + + The f1_score function applies a range of thresholds to the predictions to + convert them from [0, 1] to bool. Precision and recall are computed by + comparing them to the labels. The F1-Score is then defined as + 2 * precision * recall / (precision + recall). The best one across the + thresholds is returned. + + Disclaimer: In practice it may be desirable to choose the best threshold on + the validation set and evaluate the F1 score with this threshold on a + separate test set. Or it may be desirable to use a fixed threshold (e.g. 0.5). + + This function internally creates four local variables, `true_positives`, + `true_negatives`, `false_positives` and `false_negatives` that are used to + compute the pairs of recall and precision values for a linearly spaced set of + thresholds from which the best f1-score is derived. + + This value is ultimately returned as `f1-score`, an idempotent operation that + computes the F1-score (computed using the aforementioned variables). The + `num_thresholds` variable controls the degree of discretization with larger + numbers of thresholds more closely approximating the true best F1-score. + + For estimation of the metric over a stream of data, the function creates an + `update_op` operation that updates these variables and returns the F1-score. + + Example usage with a custom estimator: + def model_fn(features, labels, mode): + predictions = make_predictions(features) + loss = make_loss(predictions, labels) + train_op = tf.contrib.training.create_train_op( + total_loss=loss, + optimizer='Adam') + eval_metric_ops = {'f1': f1_score(labels, predictions)} + return tf.estimator.EstimatorSpec( + mode=mode, + predictions=predictions, + loss=loss, + train_op=train_op, + eval_metric_ops=eval_metric_ops, + export_outputs=export_outputs) + estimator = tf.estimator.Estimator(model_fn=model_fn) + + If `weights` is `None`, weights default to 1. Use weights of 0 to mask values. + + Args: + labels: A `Tensor` whose shape matches `predictions`. Will be cast to + `bool`. + predictions: A floating point `Tensor` of arbitrary shape and whose values + are in the range `[0, 1]`. + weights: Optional `Tensor` whose rank is either 0, or the same rank as + `labels`, and must be broadcastable to `labels` (i.e., all dimensions must + be either `1`, or the same as the corresponding `labels` dimension). + num_thresholds: The number of thresholds to use when discretizing the roc + curve. + metrics_collections: An optional list of collections that `f1_score` should + be added to. + updates_collections: An optional list of collections that `update_op` should + be added to. + name: An optional variable_scope name. + + Returns: + f1_score: A scalar `Tensor` representing the current best f1-score across + different thresholds. + update_op: An operation that increments the `true_positives`, + `true_negatives`, `false_positives` and `false_negatives` variables + appropriately and whose value matches the `f1_score`. + + Raises: + ValueError: If `predictions` and `labels` have mismatched shapes, or if + `weights` is not `None` and its shape doesn't match `predictions`, or if + either `metrics_collections` or `updates_collections` are not a list or + tuple. + """ + with variable_scope.variable_scope( + name, 'f1', (labels, predictions, weights)): + predictions, labels, weights = metrics_impl._remove_squeezable_dimensions( # pylint: disable=protected-access + predictions=predictions, labels=labels, weights=weights) + # To account for floating point imprecisions / avoid division by zero. + epsilon = 1e-7 + thresholds = [(i + 1) * 1.0 / (num_thresholds - 1) + for i in range(num_thresholds - 2)] + thresholds = [0.0 - epsilon] + thresholds + [1.0 + epsilon] + + # Confusion matrix. + values, update_ops = metrics_impl._confusion_matrix_at_thresholds( # pylint: disable=protected-access + labels, predictions, thresholds, weights, includes=('tp', 'fp', 'fn')) + + # Compute precision and recall at various thresholds. + def compute_best_f1_score(tp, fp, fn, name): + precision_at_t = math_ops.div(tp, epsilon + tp + fp, + name='precision_' + name) + recall_at_t = math_ops.div(tp, epsilon + tp + fn, name='recall_' + name) + # Compute F1 score. + f1_at_thresholds = ( + 2.0 * precision_at_t * recall_at_t / + (precision_at_t + recall_at_t + epsilon)) + return math_ops.reduce_max(f1_at_thresholds) + + def f1_across_towers(_, values): + best_f1 = compute_best_f1_score(tp=values['tp'], fp=values['fp'], + fn=values['fn'], name='value') + if metrics_collections: + ops.add_to_collections(metrics_collections, best_f1) + return best_f1 + + best_f1 = distribute_lib.get_tower_context().merge_call( + f1_across_towers, values) + + update_op = compute_best_f1_score(tp=update_ops['tp'], fp=update_ops['fp'], + fn=update_ops['fn'], name='update') + if updates_collections: + ops.add_to_collections(updates_collections, update_op) + + return best_f1, update_op diff --git a/tensorflow/contrib/metrics/python/metrics/classification_test.py b/tensorflow/contrib/metrics/python/metrics/classification_test.py index fa0f12d029620ad6427f715f035ff69f15c133e7..3d0b81c1bed02dae013141367fb052e16d31fe08 100644 --- a/tensorflow/contrib/metrics/python/metrics/classification_test.py +++ b/tensorflow/contrib/metrics/python/metrics/classification_test.py @@ -18,9 +18,16 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import numpy as np + from tensorflow.contrib.metrics.python.metrics import classification +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops +from tensorflow.python.ops import random_ops +from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -108,5 +115,200 @@ class ClassificationTest(test.TestCase): self.assertEqual(result, 0.5) +class F1ScoreTest(test.TestCase): + + def setUp(self): + super(F1ScoreTest, self).setUp() + np.random.seed(1) + + def testVars(self): + classification.f1_score( + predictions=array_ops.ones((10, 1)), + labels=array_ops.ones((10, 1)), + num_thresholds=3) + expected = {'f1/true_positives:0', 'f1/false_positives:0', + 'f1/false_negatives:0'} + self.assertEquals( + expected, set(v.name for v in variables.local_variables())) + self.assertEquals( + set(expected), set(v.name for v in variables.local_variables())) + self.assertEquals( + set(expected), + set(v.name for v in ops.get_collection(ops.GraphKeys.METRIC_VARIABLES))) + + def testMetricsCollection(self): + my_collection_name = '__metrics__' + f1, _ = classification.f1_score( + predictions=array_ops.ones((10, 1)), + labels=array_ops.ones((10, 1)), + num_thresholds=3, + metrics_collections=[my_collection_name]) + self.assertListEqual(ops.get_collection(my_collection_name), [f1]) + + def testUpdatesCollection(self): + my_collection_name = '__updates__' + _, f1_op = classification.f1_score( + predictions=array_ops.ones((10, 1)), + labels=array_ops.ones((10, 1)), + num_thresholds=3, + updates_collections=[my_collection_name]) + self.assertListEqual(ops.get_collection(my_collection_name), [f1_op]) + + def testValueTensorIsIdempotent(self): + predictions = random_ops.random_uniform( + (10, 3), maxval=1, dtype=dtypes.float32, seed=1) + labels = random_ops.random_uniform( + (10, 3), maxval=2, dtype=dtypes.int64, seed=2) + f1, f1_op = classification.f1_score(predictions, labels, num_thresholds=3) + + with self.test_session() as sess: + sess.run(variables.local_variables_initializer()) + + # Run several updates. + for _ in range(10): + sess.run([f1_op]) + + # Then verify idempotency. + initial_f1 = f1.eval() + for _ in range(10): + self.assertAllClose(initial_f1, f1.eval()) + + def testAllCorrect(self): + inputs = np.random.randint(0, 2, size=(100, 1)) + + with self.test_session() as sess: + predictions = constant_op.constant(inputs, dtype=dtypes.float32) + labels = constant_op.constant(inputs) + f1, f1_op = classification.f1_score(predictions, labels, num_thresholds=3) + + sess.run(variables.local_variables_initializer()) + sess.run([f1_op]) + + self.assertEqual(1, f1.eval()) + + def testSomeCorrect(self): + predictions = constant_op.constant( + [1, 0, 1, 0], shape=(1, 4), dtype=dtypes.float32) + labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4)) + f1, f1_op = classification.f1_score(predictions, labels, num_thresholds=1) + with self.test_session() as sess: + sess.run(variables.local_variables_initializer()) + sess.run([f1_op]) + # Threshold 0 will have around 0.5 precision and 1 recall yielding an F1 + # score of 2 * 0.5 * 1 / (1 + 0.5). + self.assertAlmostEqual(2 * 0.5 * 1 / (1 + 0.5), f1.eval()) + + def testAllIncorrect(self): + inputs = np.random.randint(0, 2, size=(10000, 1)) + + with self.test_session() as sess: + predictions = constant_op.constant(inputs, dtype=dtypes.float32) + labels = constant_op.constant(1 - inputs, dtype=dtypes.float32) + f1, f1_op = classification.f1_score(predictions, labels, num_thresholds=3) + + sess.run(variables.local_variables_initializer()) + sess.run([f1_op]) + + # Threshold 0 will have around 0.5 precision and 1 recall yielding an F1 + # score of 2 * 0.5 * 1 / (1 + 0.5). + self.assertAlmostEqual(2 * 0.5 * 1 / (1 + 0.5), f1.eval(), places=2) + + def testWeights1d(self): + with self.test_session() as sess: + predictions = constant_op.constant( + [[1, 0], [1, 0]], shape=(2, 2), dtype=dtypes.float32) + labels = constant_op.constant([[0, 1], [1, 0]], shape=(2, 2)) + weights = constant_op.constant( + [[0], [1]], shape=(2, 1), dtype=dtypes.float32) + f1, f1_op = classification.f1_score(predictions, labels, weights, + num_thresholds=3) + sess.run(variables.local_variables_initializer()) + sess.run([f1_op]) + + self.assertAlmostEqual(1.0, f1.eval(), places=5) + + def testWeights2d(self): + with self.test_session() as sess: + predictions = constant_op.constant( + [[1, 0], [1, 0]], shape=(2, 2), dtype=dtypes.float32) + labels = constant_op.constant([[0, 1], [1, 0]], shape=(2, 2)) + weights = constant_op.constant( + [[0, 0], [1, 1]], shape=(2, 2), dtype=dtypes.float32) + f1, f1_op = classification.f1_score(predictions, labels, weights, + num_thresholds=3) + sess.run(variables.local_variables_initializer()) + sess.run([f1_op]) + + self.assertAlmostEqual(1.0, f1.eval(), places=5) + + def testZeroLabelsPredictions(self): + with self.test_session() as sess: + predictions = array_ops.zeros([4], dtype=dtypes.float32) + labels = array_ops.zeros([4]) + f1, f1_op = classification.f1_score(predictions, labels, num_thresholds=3) + sess.run(variables.local_variables_initializer()) + sess.run([f1_op]) + + self.assertAlmostEqual(0.0, f1.eval(), places=5) + + def testWithMultipleUpdates(self): + num_samples = 1000 + batch_size = 10 + num_batches = int(num_samples / batch_size) + + # Create the labels and data. + labels = np.random.randint(0, 2, size=(num_samples, 1)) + noise = np.random.normal(0.0, scale=0.2, size=(num_samples, 1)) + predictions = 0.4 + 0.2 * labels + noise + predictions[predictions > 1] = 1 + predictions[predictions < 0] = 0 + thresholds = [-0.01, 0.5, 1.01] + + expected_max_f1 = -1.0 + for threshold in thresholds: + tp = 0 + fp = 0 + fn = 0 + tn = 0 + for i in range(num_samples): + if predictions[i] >= threshold: + if labels[i] == 1: + tp += 1 + else: + fp += 1 + else: + if labels[i] == 1: + fn += 1 + else: + tn += 1 + epsilon = 1e-7 + expected_prec = tp / (epsilon + tp + fp) + expected_rec = tp / (epsilon + tp + fn) + expected_f1 = (2 * expected_prec * expected_rec / + (epsilon + expected_prec + expected_rec)) + if expected_f1 > expected_max_f1: + expected_max_f1 = expected_f1 + + labels = labels.astype(np.float32) + predictions = predictions.astype(np.float32) + tf_predictions, tf_labels = (dataset_ops.Dataset + .from_tensor_slices((predictions, labels)) + .repeat() + .batch(batch_size) + .make_one_shot_iterator() + .get_next()) + f1, f1_op = classification.f1_score(tf_labels, tf_predictions, + num_thresholds=3) + + with self.test_session() as sess: + sess.run(variables.local_variables_initializer()) + for _ in range(num_batches): + sess.run([f1_op]) + # Since this is only approximate, we can't expect a 6 digits match. + # Although with higher number of samples/thresholds we should see the + # accuracy improving + self.assertAlmostEqual(expected_max_f1, f1.eval(), 2) + + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py index b14202ff9ec38016f926ee37c8acbd2bbb4c6ef5..a328670526089988c181a8e1146c911309640009 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py @@ -3715,6 +3715,7 @@ def count(values, name=None): """Computes the number of examples, or sum of `weights`. + This metric keeps track of the denominator in `tf.metrics.mean`. When evaluating some metric (e.g. mean) on one or more subsets of the data, this auxiliary metric is useful for keeping track of how many examples there are in each subset. @@ -3741,15 +3742,21 @@ def count(values, ValueError: If `weights` is not `None` and its shape doesn't match `values`, or if either `metrics_collections` or `updates_collections` are not a list or tuple. + RuntimeError: If eager execution is enabled. """ + if context.executing_eagerly(): + raise RuntimeError('tf.contrib.metrics.count is not supported when eager ' + 'execution is enabled.') with variable_scope.variable_scope(name, 'count', (values, weights)): + count_ = metrics_impl.metric_variable([], dtypes.float32, name='count') if weights is None: num_values = math_ops.to_float(array_ops.size(values)) else: - _, _, weights = metrics_impl._remove_squeezable_dimensions( # pylint: disable=protected-access + values = math_ops.to_float(values) + values, _, weights = metrics_impl._remove_squeezable_dimensions( # pylint: disable=protected-access predictions=values, labels=None, weights=weights) @@ -3758,15 +3765,14 @@ def count(values, num_values = math_ops.reduce_sum(weights) with ops.control_dependencies([values]): - update_op = state_ops.assign_add(count_, num_values) + update_count_op = state_ops.assign_add(count_, num_values) - if metrics_collections: - ops.add_to_collections(metrics_collections, count_) + count_ = metrics_impl._aggregate_variable(count_, metrics_collections) # pylint: disable=protected-access if updates_collections: - ops.add_to_collections(updates_collections, update_op) + ops.add_to_collections(updates_collections, update_count_op) - return count_, update_op + return count_, update_count_op def cohen_kappa(labels, diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py index a09fc4abd461323d67e914c70932688816fed764..401fedcbed8fef12308d563d108725a418dfef17 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py @@ -6854,6 +6854,11 @@ class CountTest(test.TestCase): array_ops.ones([4, 3]), updates_collections=[my_collection_name]) self.assertListEqual(ops.get_collection(my_collection_name), [update_op]) + def testReturnType(self): + c, op = metrics.count(array_ops.ones([4, 3])) + self.assertTrue(isinstance(c, ops.Tensor)) + self.assertTrue(isinstance(op, ops.Operation) or isinstance(op, ops.Tensor)) + def testBasic(self): with self.test_session() as sess: values_queue = data_flow_ops.FIFOQueue( diff --git a/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer.py b/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer.py index ef34f7bf7bf3eba047b50ce8abf883b0ed741a63..93050a3ae373603c516c7eb72c22f327f4a60a00 100644 --- a/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer.py +++ b/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer.py @@ -77,7 +77,7 @@ class LossScaleOptimizer(optimizer.Optimizer): If gradients clipping is applied, one can call `optimizer.compute_gradients()` and `optimizer.apply_gradients()` - seperately. + separately. Notice the following way of using LossScaleOptimizer is not intended. Always use `loss_scale_optimizer.compute_gradients()` to compute gradients instead of diff --git a/tensorflow/contrib/model_pruning/README.md b/tensorflow/contrib/model_pruning/README.md index 86f4fd6adf60d8fa54c13989bf4087e28f1e006f..9143d082bf08fefa7aa522455eb3af911e636ae0 100644 --- a/tensorflow/contrib/model_pruning/README.md +++ b/tensorflow/contrib/model_pruning/README.md @@ -66,10 +66,10 @@ is the sparsity_function_begin_step. In this equation, the sparsity_function_exponent is set to 3. ### Adding pruning ops to the training graph -The final step involves adding ops to the training graph that monitors the -distribution of the layer's weight magnitudes and determines the layer threshold -such masking all the weights below this threshold achieves the sparsity level -desired for the current training step. This can be achieved as follows: +The final step involves adding ops to the training graph that monitor the +distribution of the layer's weight magnitudes and determine the layer threshold, +such that masking all the weights below this threshold achieves the sparsity +level desired for the current training step. This can be achieved as follows: ```python tf.app.flags.DEFINE_string( @@ -79,7 +79,7 @@ tf.app.flags.DEFINE_string( with tf.graph.as_default(): # Create global step variable - global_step = tf.train.get_global_step() + global_step = tf.train.get_or_create_global_step() # Parse pruning hyperparameters pruning_hparams = pruning.get_pruning_hparams().parse(FLAGS.pruning_hparams) @@ -103,6 +103,7 @@ with tf.graph.as_default(): mon_sess.run(mask_update_op) ``` +Ensure that `global_step` is being [incremented](https://www.tensorflow.org/api_docs/python/tf/train/Optimizer#minimize), otherwise pruning will not work! ## Example: Pruning and training deep CNNs on the cifar10 dataset diff --git a/tensorflow/contrib/model_pruning/python/learning.py b/tensorflow/contrib/model_pruning/python/learning.py index 2b79c23cefe961b1c4056d41b5fcc0a0521efec6..26695237c27cc4fbe4e9fbaa2666d55836ed39b8 100644 --- a/tensorflow/contrib/model_pruning/python/learning.py +++ b/tensorflow/contrib/model_pruning/python/learning.py @@ -33,11 +33,14 @@ to support training of pruned models # Create the train_op train_op = slim.learning.create_train_op(total_loss, optimizer) - # Set up sparsity - sparsity = pruning.setup_gradual_sparsity(self.global_step) + # Parse pruning hyperparameters + pruning_hparams = pruning.get_pruning_hparams().parse(FLAGS.pruning_hparams) - # Create mask update op - mask_update_op = pruning.add_mask_update_ip(sparsity) + # Create a pruning object using the pruning_hparams + p = pruning.Pruning(pruning_hparams) + + # Add mask update ops to the graph + mask_update_op = p.conditional_mask_update_op() # Run training. learning.train(train_op, diff --git a/tensorflow/contrib/model_pruning/python/pruning.py b/tensorflow/contrib/model_pruning/python/pruning.py index 4b7af18b3316950afdb90c344ce777848c63e4c1..da9d398cbc06299a33ab400cc9b4d780531211db 100644 --- a/tensorflow/contrib/model_pruning/python/pruning.py +++ b/tensorflow/contrib/model_pruning/python/pruning.py @@ -518,11 +518,11 @@ class Pruning(object): summary.scalar('last_mask_update_step', self._last_update_step) masks = get_masks() thresholds = get_thresholds() - for index, mask in enumerate(masks): + for mask, threshold in zip(masks, thresholds): if not self._exists_in_do_not_prune_list(mask.name): - summary.scalar(mask.name + '/sparsity', nn_impl.zero_fraction(mask)) - summary.scalar(thresholds[index].op.name + '/threshold', - thresholds[index]) + summary.scalar(mask.op.name + '/sparsity', + nn_impl.zero_fraction(mask)) + summary.scalar(threshold.op.name + '/threshold', threshold) def print_hparams(self): logging.info(self._spec.to_json()) diff --git a/tensorflow/contrib/mpi_collectives/mpi_ops.py b/tensorflow/contrib/mpi_collectives/mpi_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..bd7096d9cee2d32bde5227a95038ae65cd8a6e18 --- /dev/null +++ b/tensorflow/contrib/mpi_collectives/mpi_ops.py @@ -0,0 +1,163 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= +"""Inter-process communication using MPI.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from tensorflow.python.framework import errors +from tensorflow.python.framework import load_library +from tensorflow.python.framework import ops +from tensorflow.python.platform import resource_loader +from tensorflow.python.platform import tf_logging as logging + + +def _load_library(name, op_list=None): + """Loads a .so file containing the specified operators. + + Args: + name: The name of the .so file to load. + op_list: A list of names of operators that the library should have. If None + then the .so file's contents will not be verified. + + Raises: + NameError if one of the required ops is missing. + """ + try: + filename = resource_loader.get_path_to_datafile(name) + library = load_library.load_op_library(filename) + for expected_op in (op_list or []): + for lib_op in library.OP_LIST.op: + if lib_op.name == expected_op: + break + else: + raise NameError('Could not find operator %s in dynamic library %s' % + (expected_op, name)) + return library + except errors.NotFoundError: + logging.warning('%s file could not be loaded.', name) + + +MPI_LIB = _load_library( + 'mpi_collectives.so', + ['MPISize', 'MPIRank', 'MPILocalRank', 'MPIAllgather', 'MPIAllreduce']) + + +def size(name=None): + """An op which returns the number of MPI processes. + + This is equivalent to running `MPI_Comm_size(MPI_COMM_WORLD, ...)` to get the + size of the global communicator. + + Returns: + An integer scalar containing the number of MPI processes. + """ + return MPI_LIB.mpi_size(name=name) + + +ops.NotDifferentiable('MPISize') + + +def rank(name=None): + """An op which returns the MPI rank of the calling process. + + This is equivalent to running `MPI_Comm_rank(MPI_COMM_WORLD, ...)` to get the + rank of the current process in the global communicator. + + Returns: + An integer scalar with the MPI rank of the calling process. + """ + return MPI_LIB.mpi_rank(name=name) + + +ops.NotDifferentiable('MPIRank') + + +def init(name=None): + """An op which initializes MPI on the device on which it is run. + + All future MPI ops must be run on the same device that the `init` op was run + on. + """ + return MPI_LIB.mpi_init(name=name) + + +ops.NotDifferentiable('MPIInit') + + +def local_rank(name=None): + """An op which returns the local MPI rank of the calling process, within the + node that it is running on. For example, if there are seven processes running + on a node, their local ranks will be zero through six, inclusive. + + This is equivalent to running `MPI_Comm_rank(...)` on a new communicator + which only includes processes on the same node. + + Returns: + An integer scalar with the local MPI rank of the calling process. + """ + return MPI_LIB.mpi_local_rank(name=name) + + +ops.NotDifferentiable('MPILocalRank') + + +def _allreduce(tensor, name=None): + """An op which sums an input tensor over all the MPI processes. + + The reduction operation is keyed by the name of the op. The tensor type and + shape must be the same on all MPI processes for a given name. The reduction + will not start until all processes are ready to send and receive the tensor. + + Returns: + A tensor of the same shape and type as `tensor`, summed across all + processes. + """ + return MPI_LIB.mpi_allreduce(tensor, name=name) + + +ops.NotDifferentiable('MPIAllreduce') + + +def allgather(tensor, name=None): + """An op which concatenates the input tensor with the same input tensor on + all other MPI processes. + + The concatenation is done on the first dimension, so the input tensors on the + different processes must have the same rank and shape, except for the first + dimension, which is allowed to be different. + + Returns: + A tensor of the same type as `tensor`, concatenated on dimension zero + across all processes. The shape is identical to the input shape, except for + the first dimension, which may be greater and is the sum of all first + dimensions of the tensors in different MPI processes. + """ + # Specify that first allgather is to collect the tensor gather sizes, + # indicated by passing in a scalar (0-D tensor) of value 0 + sizes_flag = tf.constant(0, dtype=tf.int64, name='size_flag_const') + my_size = tf.slice( + tf.shape(tensor, out_type=tf.int64), [0], [1], name='size_slice') + if name is None: + name = 'allgather' + sizing_name = '{}_sizing'.format(name) + sizes = MPI_LIB.mpi_allgather(my_size, sizes_flag, name=sizing_name) + return MPI_LIB.mpi_allgather(tensor, sizes, name=name) + + +ops.NotDifferentiable('MPIAllgather') diff --git a/tensorflow/contrib/mpi_collectives/ring.cc b/tensorflow/contrib/mpi_collectives/ring.cc new file mode 100644 index 0000000000000000000000000000000000000000..d93233eb210b80df10fd9c2c7975ce77112d18a2 --- /dev/null +++ b/tensorflow/contrib/mpi_collectives/ring.cc @@ -0,0 +1,80 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifdef TENSORFLOW_USE_MPI + +#define EIGEN_USE_THREADS + +#include "tensorflow/contrib/mpi_collectives/ring.h" + +namespace tensorflow { +namespace contrib { +namespace mpi { + +using CPUDevice = Eigen::ThreadPoolDevice; + +extern template MPI_Datatype MPIType(); +extern template MPI_Datatype MPIType(); +extern template MPI_Datatype MPIType(); +extern template DataType TensorFlowDataType(); +extern template DataType TensorFlowDataType(); +extern template DataType TensorFlowDataType(); + +// Generate all necessary specializations for RingAllreduce. +template Status RingAllreduce(OpKernelContext*, const Tensor*, + Tensor*, Tensor*); +template Status RingAllreduce(OpKernelContext*, + const Tensor*, Tensor*, + Tensor*); +template Status RingAllreduce(OpKernelContext*, const Tensor*, + Tensor*, Tensor*); + +// Generate all necessary specializations for RingAllgather. +template Status RingAllgather(OpKernelContext*, const Tensor*, + const std::vector&, + Tensor*); +template Status RingAllgather(OpKernelContext*, + const Tensor*, + const std::vector&, + Tensor*); +template Status RingAllgather(OpKernelContext*, const Tensor*, + const std::vector&, + Tensor*); + +// Copy data on a CPU using a straight-forward memcpy. +template <> +void CopyTensorData(void* dst, void* src, size_t size) { + std::memcpy(dst, src, size); +}; + +// Accumulate values on a CPU. +#define GENERATE_ACCUMULATE(type) \ + template <> \ + void AccumulateTensorData(type * dst, type * src, \ + size_t size) { \ + for (unsigned int i = 0; i < size; i++) { \ + dst[i] += src[i]; \ + } \ + }; +GENERATE_ACCUMULATE(int); +GENERATE_ACCUMULATE(long long); +GENERATE_ACCUMULATE(float); +#undef GENERATE_ACCUMULATE + +} // namespace mpi +} // namespace contrib +} // namespace tensorflow + +#endif // TENSORFLOW_USE_MPI diff --git a/tensorflow/contrib/mpi_collectives/ring.cu.cc b/tensorflow/contrib/mpi_collectives/ring.cu.cc new file mode 100644 index 0000000000000000000000000000000000000000..2f3eef366a9a3c10e59cd5298fc1626e1094dff8 --- /dev/null +++ b/tensorflow/contrib/mpi_collectives/ring.cu.cc @@ -0,0 +1,117 @@ +/* Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifdef TENSORFLOW_USE_MPI + +#if GOOGLE_CUDA + +#define EIGEN_USE_GPU + +#include "tensorflow/contrib/mpi_collectives/ring.h" + +namespace tensorflow { +namespace contrib { +namespace mpi { + +using CPUDevice = Eigen::ThreadPoolDevice; + +template <> +MPI_Datatype MPIType() { + return MPI_FLOAT; +}; +template <> +MPI_Datatype MPIType() { + return MPI_INT; +}; +template <> +MPI_Datatype MPIType() { + return MPI_LONG_LONG; +}; + +template <> +DataType TensorFlowDataType() { + return DT_FLOAT; +}; +template <> +DataType TensorFlowDataType() { + return DT_INT32; +}; +template <> +DataType TensorFlowDataType() { + return DT_INT64; +}; + +// Generate all necessary specializations for RingAllreduce. +template Status RingAllreduce(OpKernelContext*, const Tensor*, + Tensor*, Tensor*); +template Status RingAllreduce(OpKernelContext*, + const Tensor*, Tensor*, + Tensor*); +template Status RingAllreduce(OpKernelContext*, const Tensor*, + Tensor*, Tensor*); + +// Generate all necessary specializations for RingAllgather. +template Status RingAllgather(OpKernelContext*, const Tensor*, + const std::vector&, + Tensor*); +template Status RingAllgather(OpKernelContext*, + const Tensor*, + const std::vector&, + Tensor*); +template Status RingAllgather(OpKernelContext*, const Tensor*, + const std::vector&, + Tensor*); + +// Synchronously copy data on the GPU, using a different stream than the default +// and than TensorFlow to avoid synchronizing on operations unrelated to the +// allreduce. +template <> +void CopyTensorData(void* dst, void* src, size_t size) { + auto stream = CudaStreamForMPI(); + cudaMemcpyAsync(dst, src, size, cudaMemcpyDeviceToDevice, stream); + cudaStreamSynchronize(stream); +}; + +// Elementwise accumulation kernel for GPU. +template +__global__ void elemwise_accum(T* out, const T* in, const size_t N) { + for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < N; + i += blockDim.x * gridDim.x) { + out[i] += in[i]; + } +} + +// Synchronously accumulate tensors on the GPU, using a different stream than +// the default and than TensorFlow to avoid synchronizing on operations +// unrelated to the allreduce. +#define GENERATE_ACCUMULATE(type) \ + template <> \ + void AccumulateTensorData(type * dst, type * src, \ + size_t size) { \ + auto stream = CudaStreamForMPI(); \ + elemwise_accum<<<32, 256, 0, stream>>>(dst, src, size); \ + cudaStreamSynchronize(stream); \ + }; +GENERATE_ACCUMULATE(int); +GENERATE_ACCUMULATE(long long); +GENERATE_ACCUMULATE(float); +#undef GENERATE_ACCUMULATE + +} // namespace mpi +} // namespace contrib +} // namespace tensorflow +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_USE_MPI diff --git a/tensorflow/contrib/mpi_collectives/ring.h b/tensorflow/contrib/mpi_collectives/ring.h new file mode 100644 index 0000000000000000000000000000000000000000..cae57ce60eb09509af69f8ccab9eacedea361548 --- /dev/null +++ b/tensorflow/contrib/mpi_collectives/ring.h @@ -0,0 +1,327 @@ +/* Copyright 2016 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_CONTRIB_MPI_H_ +#define TENSORFLOW_CONTRIB_MPI_H_ + +#ifdef TENSORFLOW_USE_MPI + +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/shape_inference.h" + +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" +#include "tensorflow/core/framework/tensor_types.h" + +#if GOOGLE_CUDA +#include "cuda_runtime.h" +#endif + +// Needed to avoid header issues with C++-supporting MPI implementations +#define OMPI_SKIP_MPICXX +#include "third_party/mpi/mpi.h" + +#define TAG_TENSOR 12 + +namespace tensorflow { +namespace contrib { +namespace mpi { + +using CPUDevice = Eigen::ThreadPoolDevice; +using GPUDevice = Eigen::GpuDevice; + +// Convert from templated types to values we can pass to MPI. +template +MPI_Datatype MPIType(); + +// Convert from templated types to TensorFlow data types. +template +DataType TensorFlowDataType(); + +#define MPI_REQUIRES_OK(MPI_STATUS) \ + if ((MPI_STATUS) != MPI_SUCCESS) { \ + return errors::Unknown("MPI operation failed unexpectedly."); \ + } + +// Copy data from one tensor to another tensor. +// This uses a custom CUDA stream on GPU, which is necessary to overlay the +// backpropagation computations with the allreduce. +template +void CopyTensorData(void* destination, void* source, size_t size); + +// Add a tensor into another tensor, accumulating in place. +// This uses a custom CUDA stream on GPU, which is necessary to overlay the +// backpropagation computations with the allreduce. +template +void AccumulateTensorData(T* destination, T* source, size_t size); + +// We need to get the right stream for doing CUDA memory transfers and +// operations, which is possibly different from the standard TensorFlow stream. +#if GOOGLE_CUDA +cudaStream_t CudaStreamForMPI(); +#endif + +/* Perform a ring allreduce on the data. Allocate the necessary output tensor + * and store it in the output parameter. + * + * Assumes that all MPI processes are doing an allreduce of the same tensor, + * with the same dimensions. + * + * A ring allreduce is a bandwidth-optimal way to do an allreduce. To do the + * allreduce, the nodes involved are arranged in a ring: + * + * .--0--. + * / \ + * 3 1 + * \ / + * *--2--* + * + * Each node always sends to the next clockwise node in the ring, and receives + * from the previous one. + * + * The allreduce is done in two parts: a scatter-reduce and an allgather. In + * the scatter reduce, a reduction is done, so that each node ends up with a + * chunk of the final output tensor which has contributions from all other + * nodes. In the allgather, those chunks are distributed among all the nodes, + * so that all nodes have the entire output tensor. + * + * Both of these operations are done by dividing the input tensor into N + * evenly sized chunks (where N is the number of nodes in the ring). + * + * The scatter-reduce is done in N-1 steps. In the ith step, node j will send + * the (j - i)th chunk and receive the (j - i - 1)th chunk, adding it in to + * its existing data for that chunk. For example, in the first iteration with + * the ring depicted above, you will have the following transfers: + * + * Segment 0: Node 0 --> Node 1 + * Segment 1: Node 1 --> Node 2 + * Segment 2: Node 2 --> Node 3 + * Segment 3: Node 3 --> Node 0 + * + * In the second iteration, you'll have the following transfers: + * + * Segment 0: Node 1 --> Node 2 + * Segment 1: Node 2 --> Node 3 + * Segment 2: Node 3 --> Node 0 + * Segment 3: Node 0 --> Node 1 + * + * After this iteration, Node 2 has 3 of the four contributions to Segment 0. + * The last iteration has the following transfers: + * + * Segment 0: Node 2 --> Node 3 + * Segment 1: Node 3 --> Node 0 + * Segment 2: Node 0 --> Node 1 + * Segment 3: Node 1 --> Node 2 + * + * After this iteration, Node 3 has the fully accumulated Segment 0; Node 0 + * has the fully accumulated Segment 1; and so on. The scatter-reduce is + * complete. + * + * Next, the allgather distributes these fully accumululated chunks across all + * nodes. Communication proceeds in the same ring, once again in N-1 steps. At + * the ith step, node j will send chunk (j - i + 1) and receive chunk (j - i). + * For example, at the first iteration, the following transfers will occur: + * + * Segment 0: Node 3 --> Node 0 + * Segment 1: Node 0 --> Node 1 + * Segment 2: Node 1 --> Node 2 + * Segment 3: Node 2 --> Node 3 + * + * After the first iteration, Node 0 will have a fully accumulated Segment 0 + * (from Node 3) and Segment 1. In the next iteration, Node 0 will send its + * just-received Segment 0 onward to Node 1, and receive Segment 3 from Node 3. + * After this has continued for N - 1 iterations, all nodes will have a the + * fully accumulated tensor. + * + * Each node will do (N-1) sends for the scatter-reduce and (N-1) sends for the + * allgather. Each send will contain K / N bytes, if there are K bytes in the + * original tensor on every node. Thus, each node sends and receives 2K(N - 1)/N + * bytes of data, and the performance of the allreduce (assuming no latency in + * connections) is constrained by the slowest interconnect between the nodes. + * + */ +template +Status RingAllreduce(OpKernelContext* context, const Tensor* input, + Tensor* temp, Tensor* output) { + // Acquire MPI size and rank + int n, r; + MPI_REQUIRES_OK(MPI_Comm_size(MPI_COMM_WORLD, &n)); + MPI_REQUIRES_OK(MPI_Comm_rank(MPI_COMM_WORLD, &r)); + + T* buffer = (T*)output->tensor_data().data(); + + CopyTensorData((void*)buffer, (void*)input->tensor_data().data(), + output->tensor_data().size()); + + // Calculate segment sizes and segment ends + const size_t elements_to_reduce = input->NumElements(); + const size_t segment_size = elements_to_reduce / n; + std::vector segment_sizes(n, segment_size); + + const size_t residual = elements_to_reduce % n; + for (size_t i = 0; i < residual; ++i) { + segment_sizes[i]++; + } + + std::vector segment_starts(n); + segment_starts[0] = 0; + for (size_t i = 1; i < segment_starts.size(); ++i) { + segment_starts[i] = segment_starts[i - 1] + segment_sizes[i - 1]; + } + + assert(segment_starts[n - 1] + segment_sizes[n - 1] == elements_to_reduce); + + T* segment_recv = (T*)temp->tensor_data().data(); + + // Receive from your left neighbor with wrap-around + const size_t recv_from = ((r - 1) + n) % n; + + // Send to your right neighbor with wrap-around + const size_t send_to = (r + 1) % n; + + MPI_Status recv_status; + MPI_Request recv_req; + + // Now start ring. At every step, for every rank, we iterate through + // segments with wraparound and send and recv from our neighbors and reduce + // locally. At the i'th iteration, rank r, sends segment (r-i) and receives + // segment (r-i-1). + for (int i = 0; i < n - 1; i++) { + const size_t send_seg_id = ((r - i) + n) % n; + const size_t recv_seg_id = ((r - i - 1) + n) % n; + + T* segment_send = &(buffer[segment_starts[send_seg_id]]); + + MPI_REQUIRES_OK(MPI_Irecv(segment_recv, segment_sizes[recv_seg_id], + MPIType(), recv_from, TAG_TENSOR, + MPI_COMM_WORLD, &recv_req)); + + MPI_REQUIRES_OK(MPI_Send(segment_send, segment_sizes[send_seg_id], + MPIType(), send_to, TAG_TENSOR, + MPI_COMM_WORLD)); + + T* segment_update = &(buffer[segment_starts[recv_seg_id]]); + + // Wait for recv to complete before reduction + MPI_REQUIRES_OK(MPI_Wait(&recv_req, &recv_status)); + + const size_t recv_seg_size = segment_sizes[recv_seg_id]; + AccumulateTensorData(segment_update, segment_recv, + recv_seg_size); + } + + // Now start pipelined ring allgather. At every step, for every rank, we + // iterate through segments with wraparound and send and recv from our + // neighbors. At the i'th iteration, rank r, sends segment (r-i+1) and + // receives segment (r-i). + for (size_t i = 0; i < n - 1; ++i) { + const size_t send_seg_id = ((r - i + 1) + n) % n; + const size_t recv_seg_id = ((r - i) + n) % n; + + // Segment to send - at every iteration we send segment (r-i+1) + T* segment_send = &(buffer[segment_starts[send_seg_id]]); + + // Segment to recv - at every iteration we receive segment (r-i) + T* segment_recv = &(buffer[segment_starts[recv_seg_id]]); + + MPI_REQUIRES_OK(MPI_Sendrecv( + segment_send, segment_sizes[send_seg_id], MPIType(), send_to, + TAG_TENSOR, segment_recv, segment_sizes[recv_seg_id], MPIType(), + recv_from, TAG_TENSOR, MPI_COMM_WORLD, &recv_status)); + } + + return Status::OK(); +} + +// Perform a ring allgather on a Tensor. Other ranks may allgather with a +// tensor which differs in the first dimension only; all other dimensions must +// be the same. +// +// For more information on the ring allgather, read the documentation for the +// ring allreduce, which includes a ring allgather. +template +Status RingAllgather(OpKernelContext* context, const Tensor* input, + const std::vector& sizes, Tensor* output) { + // Acquire MPI size and rank + int n, r; + MPI_REQUIRES_OK(MPI_Comm_size(MPI_COMM_WORLD, &n)); + MPI_REQUIRES_OK(MPI_Comm_rank(MPI_COMM_WORLD, &r)); + + assert(sizes.size() == n); + assert(input->dim_size(0) == sizes[r]); + + // Compute number of elements in every "row". We can't compute number of + // elements in every chunks, because those chunks are variable length. + size_t elements_per_row = 1; + for (int i = 1; i < input->shape().dims(); i++) { + elements_per_row *= input->dim_size(i); + } + + // Copy data from input tensor to correct place in output tensor. + std::vector segment_starts(n); + segment_starts[0] = 0; + for (int i = 1; i < n; i++) { + segment_starts[i] = segment_starts[i - 1] + elements_per_row * sizes[i - 1]; + } + size_t offset = segment_starts[r]; + + // Copy data to the right offset for this rank. + T* buffer = (T*)output->tensor_data().data(); + CopyTensorData((void*)(buffer + offset), + (void*)input->tensor_data().data(), + elements_per_row * sizes[r] * sizeof(T)); + + // Receive from your left neighbor with wrap-around + const size_t recv_from = ((r - 1) + n) % n; + + // Send to your right neighbor with wrap-around + const size_t send_to = (r + 1) % n; + + // Perform a ring allgather. At every step, for every rank, we iterate + // through segments with wraparound and send and recv from our neighbors. + // At the i'th iteration, rank r, sends segment (r-i) and receives segment + // (r-1-i). + MPI_Status recv_status; + for (size_t i = 0; i < n - 1; ++i) { + const size_t send_seg_id = ((r - i) + n) % n; + const size_t recv_seg_id = ((r - i - 1) + n) % n; + + // Segment to send - at every iteration we send segment (r-i) + size_t offset_send = segment_starts[send_seg_id]; + size_t rows_send = sizes[send_seg_id]; + T* segment_send = &(buffer[offset_send]); + + // Segment to recv - at every iteration we receive segment (r-1-i) + size_t offset_recv = segment_starts[recv_seg_id]; + size_t rows_recv = sizes[recv_seg_id]; + T* segment_recv = &(buffer[offset_recv]); + + MPI_REQUIRES_OK(MPI_Sendrecv( + segment_send, elements_per_row * rows_send, MPIType(), send_to, + TAG_TENSOR, segment_recv, elements_per_row * rows_recv, MPIType(), + recv_from, TAG_TENSOR, MPI_COMM_WORLD, &recv_status)); + } + + return Status::OK(); +} + +} // namespace mpi +} // namespace contrib +} // namespace tensorflow + +#endif // TENSORFLOW_USE_MPI + +#undef TENSORFLOW_CONTRIB_MPI_H_ +#endif // TENSORFLOW_CONTRIB_MPI_H_ diff --git a/tensorflow/contrib/nccl/kernels/nccl_manager.cc b/tensorflow/contrib/nccl/kernels/nccl_manager.cc index b1cb89391ceaa70813be47cc1bba0c16f4f70e77..99fecf96517935bf3bde3636df83b4a9a4e1c779 100644 --- a/tensorflow/contrib/nccl/kernels/nccl_manager.cc +++ b/tensorflow/contrib/nccl/kernels/nccl_manager.cc @@ -445,7 +445,7 @@ void NcclManager::LoopKernelLaunches(NcclStream* nccl_stream) { se::Stream* comm_stream = nccl_stream->stream.get(); ScopedActivateExecutorContext scoped_context(nccl_stream->executor); const cudaStream_t* cu_stream = reinterpret_cast( - comm_stream->implementation()->CudaStreamMemberHack()); + comm_stream->implementation()->GpuStreamMemberHack()); while (true) { // Find collective to run. diff --git a/tensorflow/contrib/opt/python/training/addsign_test.py b/tensorflow/contrib/opt/python/training/addsign_test.py index 08d45ed73f3ae4b580d7078272e79fef22ef67c5..628a735e721d2f0c594dd59b5193499dfd7da02e 100644 --- a/tensorflow/contrib/opt/python/training/addsign_test.py +++ b/tensorflow/contrib/opt/python/training/addsign_test.py @@ -214,7 +214,7 @@ class AddSignTest(test.TestCase): # Run 7 steps of AddSign # first 4 steps with positive gradient # last 3 steps with negative gradient (sign(gm) should be -1) - for t in range(1, 4): + for t in range(1, 8): if t < 5: update.run() else: @@ -222,7 +222,7 @@ class AddSignTest(test.TestCase): var0_np, m0 = addsign_update_numpy( var0_np, - grads0_np, + grads0_np if t < 5 else -grads0_np, m0, learning_rate, alpha=alpha, @@ -232,7 +232,7 @@ class AddSignTest(test.TestCase): ) var1_np, m1 = addsign_update_numpy( var1_np, - grads1_np, + grads1_np if t < 5 else -grads1_np, m1, learning_rate, alpha=alpha, diff --git a/tensorflow/contrib/opt/python/training/ggt.py b/tensorflow/contrib/opt/python/training/ggt.py index 928c453517f825ed2d305ec498d07ac29c065f1a..cae952d8f50acbc3a176697fb3989db6c9ac3e9b 100644 --- a/tensorflow/contrib/opt/python/training/ggt.py +++ b/tensorflow/contrib/opt/python/training/ggt.py @@ -33,7 +33,7 @@ class GGTOptimizer(optimizer_v2.OptimizerV2): GGT has an advantage over sgd and adam on large models with poor conditioning, for example language models and CNNs, - see [ABCHSZZ 2018]([pdf](https://arxiv.org/pdf/1806.02958.pdf)). + see [[ABCHSZZ 2018]](https://arxiv.org/pdf/1806.02958.pdf). """ def __init__(self, diff --git a/tensorflow/contrib/opt/python/training/powersign_test.py b/tensorflow/contrib/opt/python/training/powersign_test.py index 5214082dd66f00eadadad71d50f7e00b178b8c10..0bcf5d230a8b7b5b778d233a79922dc34449f8dd 100644 --- a/tensorflow/contrib/opt/python/training/powersign_test.py +++ b/tensorflow/contrib/opt/python/training/powersign_test.py @@ -216,7 +216,7 @@ class PowerSignTest(test.TestCase): self.assertAllClose([1.0, 2.0], var0.eval()) self.assertAllClose([3.0, 4.0], var1.eval()) - # Run 3 steps of powersign + # Run 7 steps of powersign # first 4 steps with positive gradient # last 3 steps with negative gradient (sign(gm) should be -1) for t in range(1, 8): diff --git a/tensorflow/contrib/optimizer_v2/BUILD b/tensorflow/contrib/optimizer_v2/BUILD index 5225ecc14fef3cec9506eceb776805b74a87714e..3ba3ee29ec79687df522eb330665a2ce80061682 100644 --- a/tensorflow/contrib/optimizer_v2/BUILD +++ b/tensorflow/contrib/optimizer_v2/BUILD @@ -193,6 +193,7 @@ cuda_py_test( srcs = ["rmsprop_test.py"], additional_deps = [ ":training", + "@absl_py//absl/testing:parameterized", "//tensorflow/python:array_ops", "//tensorflow/python:embedding_ops", "//tensorflow/python:framework", diff --git a/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py b/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py index ec033c4a0163ba9ed39e55fa9e92dfdadc9a1b2f..a44bfd1bfd97e678fbf4c402ef5b1298dc518f75 100644 --- a/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py +++ b/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py @@ -38,12 +38,8 @@ class OptimizerTest(test.TestCase): @test_util.run_in_graph_and_eager_modes def testBasic(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): - # Note that we name the variables uniquely here since the variables don't - # seem to be getting deleted at the end of the loop. - var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype, - name='a_%d' % i) - var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype, - name='b_%d' % i) + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) def loss(): return 5 * var0 + 3 * var1 # pylint: disable=cell-var-from-loop # Note that for eager execution, minimize expects a function instead of a @@ -131,12 +127,8 @@ class OptimizerTest(test.TestCase): @test_util.run_in_graph_and_eager_modes def testNoGradients(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): - # Note that we name the variables uniquely here since the variables don't - # seem to be getting deleted at the end of the loop. - var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype, - name='a%d' % i) - var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype, - name='b%d' % i) + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) # pylint: disable=cell-var-from-loop def loss(): return 5 * var0 @@ -149,12 +141,8 @@ class OptimizerTest(test.TestCase): @test_util.run_in_graph_and_eager_modes def testNoGradientsForAnyVariables_Minimize(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): - # Note that we name the variables uniquely here since the variables don't - # seem to be getting deleted at the end of the loop. - var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype, - name='a_%d' % i) - var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype, - name='b_%d' % i) + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) def loss(): return constant_op.constant(5.0) sgd_op = gradient_descent.GradientDescentOptimizer(3.0) @@ -165,12 +153,8 @@ class OptimizerTest(test.TestCase): @test_util.run_in_graph_and_eager_modes def testNoGradientsForAnyVariables_ApplyGradients(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): - # Note that we name the variables uniquely here since the variables don't - # seem to be getting deleted at the end of the loop. - var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype, - name='a_%d' % i) - var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype, - name='b_%d' % i) + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) sgd_op = gradient_descent.GradientDescentOptimizer(3.0) with self.assertRaisesRegexp(ValueError, 'No gradients provided for any variable'): @@ -179,12 +163,8 @@ class OptimizerTest(test.TestCase): @test_util.run_in_graph_and_eager_modes def testGradientsAsVariables(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): - # Note that we name the variables uniquely here since the variables don't - # seem to be getting deleted at the end of the loop. - var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype, - name='a%d' % i) - var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype, - name='b%d' % i) + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) def loss(): return 5 * var0 + 3 * var1 # pylint: disable=cell-var-from-loop sgd_op = gradient_descent.GradientDescentOptimizer(3.0) diff --git a/tensorflow/contrib/optimizer_v2/rmsprop_test.py b/tensorflow/contrib/optimizer_v2/rmsprop_test.py index ed68f6afbf8bf9678649c1ce6fc59c3b91026dc0..dc23ef241a43900ed40f029f1b857820459e43d0 100644 --- a/tensorflow/contrib/optimizer_v2/rmsprop_test.py +++ b/tensorflow/contrib/optimizer_v2/rmsprop_test.py @@ -19,15 +19,16 @@ from __future__ import division from __future__ import print_function import copy -import itertools import math +from absl.testing import parameterized import numpy as np from tensorflow.contrib.optimizer_v2 import rmsprop from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util from tensorflow.python.ops import embedding_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops @@ -48,13 +49,8 @@ _TEST_PARAM_VALUES = [ [0.5, 0.95, 0.9, 1e-5, True, False], ] -_TESTPARAMS = [ - [data_type] + values - for data_type, values in itertools.product(_DATA_TYPES, _TEST_PARAM_VALUES) -] - -class RMSPropOptimizerTest(test.TestCase): +class RMSPropOptimizerTest(test.TestCase, parameterized.TestCase): def _rmsprop_update_numpy(self, var, g, mg, rms, mom, lr, decay, momentum, epsilon, centered): @@ -87,362 +83,366 @@ class RMSPropOptimizerTest(test.TestCase): var_t[gindex] = var[gindex] - mom_t[gindex] return var_t, mg_t, rms_t, mom_t - def testDense(self): - # TODO(yori): Use ParameterizedTest when available - for (dtype, learning_rate, decay, momentum, - epsilon, centered, use_resource) in _TESTPARAMS: - with self.test_session(use_gpu=True): - # Initialize variables for numpy implementation. - var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) - grads0_np = np.array([0.1, 0.2], dtype=dtype.as_numpy_dtype) - var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) - grads1_np = np.array([0.01, 0.2], dtype=dtype.as_numpy_dtype) - - if use_resource: - var0 = resource_variable_ops.ResourceVariable(var0_np) - var1 = resource_variable_ops.ResourceVariable(var1_np) - else: - var0 = variables.Variable(var0_np) - var1 = variables.Variable(var1_np) - grads0 = constant_op.constant(grads0_np) - grads1 = constant_op.constant(grads1_np) - opt = rmsprop.RMSPropOptimizer( - learning_rate=learning_rate, - decay=decay, - momentum=momentum, - epsilon=epsilon, - centered=centered) - - update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - - mg0 = opt.get_slot(var0, "mg") - self.assertEqual(mg0 is not None, centered) - mg1 = opt.get_slot(var1, "mg") - self.assertEqual(mg1 is not None, centered) - rms0 = opt.get_slot(var0, "rms") - self.assertTrue(rms0 is not None) - rms1 = opt.get_slot(var1, "rms") - self.assertTrue(rms1 is not None) - mom0 = opt.get_slot(var0, "momentum") - self.assertTrue(mom0 is not None) - mom1 = opt.get_slot(var1, "momentum") - self.assertTrue(mom1 is not None) - - mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) - rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) - mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - - # Fetch params to validate initial values - self.assertAllClose([1.0, 2.0], var0.eval()) - self.assertAllClose([3.0, 4.0], var1.eval()) - - # Run 4 steps of RMSProp - for _ in range(1, 5): - update.run() - - var0_np, mg0_np, rms0_np, mom0_np = self._rmsprop_update_numpy( - var0_np, grads0_np, mg0_np, rms0_np, mom0_np, learning_rate, - decay, momentum, epsilon, centered) - var1_np, mg1_np, rms1_np, mom1_np = self._rmsprop_update_numpy( - var1_np, grads1_np, mg1_np, rms1_np, mom1_np, learning_rate, - decay, momentum, epsilon, centered) - - # Validate updated params - if centered: - self.assertAllCloseAccordingToType(mg0_np, mg0.eval()) - self.assertAllCloseAccordingToType(mg1_np, mg1.eval()) - self.assertAllCloseAccordingToType(rms0_np, rms0.eval()) - self.assertAllCloseAccordingToType(rms1_np, rms1.eval()) - self.assertAllCloseAccordingToType(mom0_np, mom0.eval()) - self.assertAllCloseAccordingToType(mom1_np, mom1.eval()) - self.assertAllCloseAccordingToType(var0_np, var0.eval()) - self.assertAllCloseAccordingToType(var1_np, var1.eval()) - - def testMinimizeSparseResourceVariable(self): - for dtype in [dtypes.float32, dtypes.float64]: - with self.test_session(): - var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) - x = constant_op.constant([[4.0], [5.0]], dtype=dtype) - pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) - loss = pred * pred - sgd_op = rmsprop.RMSPropOptimizer( - learning_rate=1.0, - decay=0.0, - momentum=0.0, - epsilon=0.0, - centered=False).minimize(loss) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([[1.0, 2.0]], var0.eval()) - # Run 1 step of sgd - sgd_op.run() - # Validate updated params - self.assertAllCloseAccordingToType( - [[0., 1.]], var0.eval(), atol=0.01) - - def testMinimizeSparseResourceVariableCentered(self): - for dtype in [dtypes.float32, dtypes.float64]: - with self.test_session(): - var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) - x = constant_op.constant([[4.0], [5.0]], dtype=dtype) - pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) - loss = pred * pred - sgd_op = rmsprop.RMSPropOptimizer( - learning_rate=1.0, - decay=0.0, - momentum=0.0, - epsilon=1.0, - centered=True).minimize(loss) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([[1.0, 2.0]], var0.eval()) - # Run 1 step of sgd - sgd_op.run() - # Validate updated params - self.assertAllCloseAccordingToType( - [[-111, -138]], var0.eval(), atol=0.01) - - def testSparse(self): - # TODO(yori): Use ParameterizedTest when available - for (dtype, learning_rate, decay, - momentum, epsilon, centered, _) in _TESTPARAMS: - with self.test_session(use_gpu=True): - # Initialize variables for numpy implementation. - var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) - grads0_np = np.array([0.1], dtype=dtype.as_numpy_dtype) - var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) - grads1_np = np.array([0.01], dtype=dtype.as_numpy_dtype) - + @parameterized.named_parameters( + *test_util.generate_combinations_with_testcase_name( + dtype=_DATA_TYPES, param_value=_TEST_PARAM_VALUES)) + def testDense(self, dtype, param_value): + (learning_rate, decay, momentum, epsilon, centered, use_resource) = tuple( + param_value) + with self.test_session(use_gpu=True): + # Initialize variables for numpy implementation. + var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) + grads0_np = np.array([0.1, 0.2], dtype=dtype.as_numpy_dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) + grads1_np = np.array([0.01, 0.2], dtype=dtype.as_numpy_dtype) + + if use_resource: + var0 = resource_variable_ops.ResourceVariable(var0_np) + var1 = resource_variable_ops.ResourceVariable(var1_np) + else: var0 = variables.Variable(var0_np) var1 = variables.Variable(var1_np) - grads0_np_indices = np.array([0], dtype=np.int32) - grads0 = ops.IndexedSlices( - constant_op.constant(grads0_np), - constant_op.constant(grads0_np_indices), constant_op.constant([1])) - grads1_np_indices = np.array([1], dtype=np.int32) - grads1 = ops.IndexedSlices( - constant_op.constant(grads1_np), - constant_op.constant(grads1_np_indices), constant_op.constant([1])) - opt = rmsprop.RMSPropOptimizer( - learning_rate=learning_rate, - decay=decay, - momentum=momentum, - epsilon=epsilon, - centered=centered) - update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - - mg0 = opt.get_slot(var0, "mg") - self.assertEqual(mg0 is not None, centered) - mg1 = opt.get_slot(var1, "mg") - self.assertEqual(mg1 is not None, centered) - rms0 = opt.get_slot(var0, "rms") - self.assertTrue(rms0 is not None) - rms1 = opt.get_slot(var1, "rms") - self.assertTrue(rms1 is not None) - mom0 = opt.get_slot(var0, "momentum") - self.assertTrue(mom0 is not None) - mom1 = opt.get_slot(var1, "momentum") - self.assertTrue(mom1 is not None) - - mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) - rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) - mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - - # Fetch params to validate initial values - self.assertAllClose([1.0, 2.0], var0.eval()) - self.assertAllClose([3.0, 4.0], var1.eval()) - - # Run 4 steps of RMSProp - for _ in range(1, 5): - update.run() - - var0_np, mg0_np, rms0_np, mom0_np = self._sparse_rmsprop_update_numpy( - var0_np, grads0_np_indices, grads0_np, mg0_np, rms0_np, mom0_np, - learning_rate, decay, momentum, epsilon, centered) - var1_np, mg1_np, rms1_np, mom1_np = self._sparse_rmsprop_update_numpy( - var1_np, grads1_np_indices, grads1_np, mg1_np, rms1_np, mom1_np, - learning_rate, decay, momentum, epsilon, centered) - - # Validate updated params - if centered: - self.assertAllCloseAccordingToType(mg0_np, mg0.eval()) - self.assertAllCloseAccordingToType(mg1_np, mg1.eval()) - self.assertAllCloseAccordingToType(rms0_np, rms0.eval()) - self.assertAllCloseAccordingToType(rms1_np, rms1.eval()) - self.assertAllCloseAccordingToType(mom0_np, mom0.eval()) - self.assertAllCloseAccordingToType(mom1_np, mom1.eval()) - self.assertAllCloseAccordingToType(var0_np, var0.eval()) - self.assertAllCloseAccordingToType(var1_np, var1.eval()) - - def testWithoutMomentum(self): - for dtype in [dtypes.half, dtypes.float32]: - with self.test_session(use_gpu=True): - var0 = variables.Variable([1.0, 2.0], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - opt = rmsprop.RMSPropOptimizer( - learning_rate=2.0, decay=0.9, momentum=0.0, epsilon=1.0) - update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - - rms0 = opt.get_slot(var0, "rms") - self.assertTrue(rms0 is not None) - rms1 = opt.get_slot(var1, "rms") - self.assertTrue(rms1 is not None) - mom0 = opt.get_slot(var0, "momentum") - self.assertTrue(mom0 is not None) - mom1 = opt.get_slot(var1, "momentum") - self.assertTrue(mom1 is not None) - - # Fetch params to validate initial values - self.assertAllClose([1.0, 2.0], var0.eval()) - self.assertAllClose([3.0, 4.0], var1.eval()) - # Step 1: the rms accumulators where 1. So we should see a normal - # update: v -= grad * learning_rate - update.run() - # Check the root mean square accumulators. - self.assertAllCloseAccordingToType( - np.array([0.901, 0.901]), rms0.eval()) - self.assertAllCloseAccordingToType( - np.array([0.90001, 0.90001]), rms1.eval()) - # Check the parameters. - self.assertAllCloseAccordingToType( - np.array([ - 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)), - 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - ]), var0.eval()) - self.assertAllCloseAccordingToType( - np.array([ - 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)), - 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - ]), var1.eval()) - # Step 2: the root mean square accumulators contain the previous update. - update.run() - # Check the rms accumulators. - self.assertAllCloseAccordingToType( - np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]), rms0.eval()) - self.assertAllCloseAccordingToType( - np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]), rms1.eval()) - # Check the parameters. - self.assertAllCloseAccordingToType( - np.array([ - 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - - (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)), - 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - - (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)) - ]), var0.eval()) - self.assertAllCloseAccordingToType( - np.array([ - 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - - (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)), - 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - - (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)) - ]), var1.eval()) - - def testWithMomentum(self): - for dtype in [dtypes.half, dtypes.float32]: - with self.test_session(use_gpu=True): - var0 = variables.Variable([1.0, 2.0], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - - opt = rmsprop.RMSPropOptimizer( - learning_rate=2.0, decay=0.9, momentum=0.5, epsilon=1e-5) - update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - - rms0 = opt.get_slot(var0, "rms") - self.assertTrue(rms0 is not None) - rms1 = opt.get_slot(var1, "rms") - self.assertTrue(rms1 is not None) - mom0 = opt.get_slot(var0, "momentum") - self.assertTrue(mom0 is not None) - mom1 = opt.get_slot(var1, "momentum") - self.assertTrue(mom1 is not None) - - # Fetch params to validate initial values - self.assertAllClose([1.0, 2.0], var0.eval()) - self.assertAllClose([3.0, 4.0], var1.eval()) - # Step 1: rms = 1, mom = 0. So we should see a normal - # update: v -= grad * learning_rate + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + opt = rmsprop.RMSPropOptimizer( + learning_rate=learning_rate, + decay=decay, + momentum=momentum, + epsilon=epsilon, + centered=centered) + + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + variables.global_variables_initializer().run() + + mg0 = opt.get_slot(var0, "mg") + self.assertEqual(mg0 is not None, centered) + mg1 = opt.get_slot(var1, "mg") + self.assertEqual(mg1 is not None, centered) + rms0 = opt.get_slot(var0, "rms") + self.assertIsNotNone(rms0) + rms1 = opt.get_slot(var1, "rms") + self.assertIsNotNone(rms1) + mom0 = opt.get_slot(var0, "momentum") + self.assertIsNotNone(mom0) + mom1 = opt.get_slot(var1, "momentum") + self.assertIsNotNone(mom1) + + mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) + rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) + mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + # Run 4 steps of RMSProp + for _ in range(4): update.run() - # Check the root mean square accumulators. - self.assertAllCloseAccordingToType( - np.array([0.901, 0.901]), rms0.eval()) - self.assertAllCloseAccordingToType( - np.array([0.90001, 0.90001]), rms1.eval()) - # Check the momentum accumulators - self.assertAllCloseAccordingToType( - np.array([(0.1 * 2.0 / math.sqrt(0.901 + 1e-5)), - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5))]), mom0.eval()) - self.assertAllCloseAccordingToType( - np.array([(0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)), - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5))]), mom1.eval()) - - # Check that the parameters. - self.assertAllCloseAccordingToType( - np.array([ - 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)), - 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) - ]), var0.eval()) - self.assertAllCloseAccordingToType( - np.array([ - 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)), - 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) - ]), var1.eval()) - - # Step 2: the root mean square accumulators contain the previous update. + + var0_np, mg0_np, rms0_np, mom0_np = self._rmsprop_update_numpy( + var0_np, grads0_np, mg0_np, rms0_np, mom0_np, learning_rate, + decay, momentum, epsilon, centered) + var1_np, mg1_np, rms1_np, mom1_np = self._rmsprop_update_numpy( + var1_np, grads1_np, mg1_np, rms1_np, mom1_np, learning_rate, + decay, momentum, epsilon, centered) + + # Validate updated params + if centered: + self.assertAllCloseAccordingToType(mg0_np, mg0.eval()) + self.assertAllCloseAccordingToType(mg1_np, mg1.eval()) + self.assertAllCloseAccordingToType(rms0_np, rms0.eval()) + self.assertAllCloseAccordingToType(rms1_np, rms1.eval()) + self.assertAllCloseAccordingToType(mom0_np, mom0.eval()) + self.assertAllCloseAccordingToType(mom1_np, mom1.eval()) + self.assertAllCloseAccordingToType(var0_np, var0.eval()) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) + + @parameterized.parameters([dtypes.float32, dtypes.float64]) + def testMinimizeSparseResourceVariable(self, dtype): + with self.test_session(): + var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) + x = constant_op.constant([[4.0], [5.0]], dtype=dtype) + pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) + loss = pred * pred + sgd_op = rmsprop.RMSPropOptimizer( + learning_rate=1.0, + decay=0.0, + momentum=0.0, + epsilon=0.0, + centered=False).minimize(loss) + variables.global_variables_initializer().run() + # Fetch params to validate initial values + self.assertAllCloseAccordingToType([[1.0, 2.0]], var0.eval()) + # Run 1 step of sgd + sgd_op.run() + # Validate updated params + self.assertAllCloseAccordingToType( + [[0., 1.]], var0.eval(), atol=0.01) + + @parameterized.parameters([dtypes.float32, dtypes.float64]) + def testMinimizeSparseResourceVariableCentered(self, dtype): + with self.test_session(): + var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) + x = constant_op.constant([[4.0], [5.0]], dtype=dtype) + pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) + loss = pred * pred + sgd_op = rmsprop.RMSPropOptimizer( + learning_rate=1.0, + decay=0.0, + momentum=0.0, + epsilon=1.0, + centered=True).minimize(loss) + variables.global_variables_initializer().run() + # Fetch params to validate initial values + self.assertAllCloseAccordingToType([[1.0, 2.0]], var0.eval()) + # Run 1 step of sgd + sgd_op.run() + # Validate updated params + self.assertAllCloseAccordingToType( + [[-111, -138]], var0.eval(), atol=0.01) + + @parameterized.named_parameters( + *test_util.generate_combinations_with_testcase_name( + dtype=_DATA_TYPES, param_value=_TEST_PARAM_VALUES)) + def testSparse(self, dtype, param_value): + (learning_rate, decay, momentum, epsilon, centered, _) = tuple( + param_value) + with self.test_session(use_gpu=True): + # Initialize variables for numpy implementation. + var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) + grads0_np = np.array([0.1], dtype=dtype.as_numpy_dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) + grads1_np = np.array([0.01], dtype=dtype.as_numpy_dtype) + + var0 = variables.Variable(var0_np) + var1 = variables.Variable(var1_np) + grads0_np_indices = np.array([0], dtype=np.int32) + grads0 = ops.IndexedSlices( + constant_op.constant(grads0_np), + constant_op.constant(grads0_np_indices), constant_op.constant([1])) + grads1_np_indices = np.array([1], dtype=np.int32) + grads1 = ops.IndexedSlices( + constant_op.constant(grads1_np), + constant_op.constant(grads1_np_indices), constant_op.constant([1])) + opt = rmsprop.RMSPropOptimizer( + learning_rate=learning_rate, + decay=decay, + momentum=momentum, + epsilon=epsilon, + centered=centered) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + variables.global_variables_initializer().run() + + mg0 = opt.get_slot(var0, "mg") + self.assertEqual(mg0 is not None, centered) + mg1 = opt.get_slot(var1, "mg") + self.assertEqual(mg1 is not None, centered) + rms0 = opt.get_slot(var0, "rms") + self.assertIsNotNone(rms0) + rms1 = opt.get_slot(var1, "rms") + self.assertIsNotNone(rms1) + mom0 = opt.get_slot(var0, "momentum") + self.assertIsNotNone(mom0) + mom1 = opt.get_slot(var1, "momentum") + self.assertIsNotNone(mom1) + + mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) + rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) + mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + # Run 4 steps of RMSProp + for _ in range(4): update.run() - # Check the rms accumulators. - self.assertAllCloseAccordingToType( - np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]), rms0.eval()) - self.assertAllCloseAccordingToType( - np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]), rms1.eval()) - self.assertAllCloseAccordingToType( - np.array([ - 0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + - (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5)), - 0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + - (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5)) - ]), mom0.eval()) - self.assertAllCloseAccordingToType( - np.array([ - 0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + - (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5)), - 0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + - (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5)) - ]), mom1.eval()) - - # Check the parameters. - self.assertAllCloseAccordingToType( - np.array([ - 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) - - (0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + - (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5))), - 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) - - (0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + - (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5))) - ]), var0.eval()) - - self.assertAllCloseAccordingToType( - np.array([ - 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) - - (0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + - (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5))), - 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) - - (0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + - (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5))) - ]), var1.eval()) + + var0_np, mg0_np, rms0_np, mom0_np = self._sparse_rmsprop_update_numpy( + var0_np, grads0_np_indices, grads0_np, mg0_np, rms0_np, mom0_np, + learning_rate, decay, momentum, epsilon, centered) + var1_np, mg1_np, rms1_np, mom1_np = self._sparse_rmsprop_update_numpy( + var1_np, grads1_np_indices, grads1_np, mg1_np, rms1_np, mom1_np, + learning_rate, decay, momentum, epsilon, centered) + + # Validate updated params + if centered: + self.assertAllCloseAccordingToType(mg0_np, mg0.eval()) + self.assertAllCloseAccordingToType(mg1_np, mg1.eval()) + self.assertAllCloseAccordingToType(rms0_np, rms0.eval()) + self.assertAllCloseAccordingToType(rms1_np, rms1.eval()) + self.assertAllCloseAccordingToType(mom0_np, mom0.eval()) + self.assertAllCloseAccordingToType(mom1_np, mom1.eval()) + self.assertAllCloseAccordingToType(var0_np, var0.eval()) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) + + @parameterized.parameters(_DATA_TYPES) + def testWithoutMomentum(self, dtype): + with self.test_session(use_gpu=True): + var0 = variables.Variable([1.0, 2.0], dtype=dtype) + var1 = variables.Variable([3.0, 4.0], dtype=dtype) + grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) + grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) + opt = rmsprop.RMSPropOptimizer( + learning_rate=2.0, decay=0.9, momentum=0.0, epsilon=1.0) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + variables.global_variables_initializer().run() + + rms0 = opt.get_slot(var0, "rms") + self.assertIsNotNone(rms0) + rms1 = opt.get_slot(var1, "rms") + self.assertIsNotNone(rms1) + mom0 = opt.get_slot(var0, "momentum") + self.assertIsNotNone(mom0) + mom1 = opt.get_slot(var1, "momentum") + self.assertIsNotNone(mom1) + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + # Step 1: the rms accumulators where 1. So we should see a normal + # update: v -= grad * learning_rate + update.run() + # Check the root mean square accumulators. + self.assertAllCloseAccordingToType( + np.array([0.901, 0.901]), rms0.eval()) + self.assertAllCloseAccordingToType( + np.array([0.90001, 0.90001]), rms1.eval()) + # Check the parameters. + self.assertAllCloseAccordingToType( + np.array([ + 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)), + 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) + ]), var0.eval()) + self.assertAllCloseAccordingToType( + np.array([ + 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)), + 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) + ]), var1.eval()) + # Step 2: the root mean square accumulators contain the previous update. + update.run() + # Check the rms accumulators. + self.assertAllCloseAccordingToType( + np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]), rms0.eval()) + self.assertAllCloseAccordingToType( + np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]), rms1.eval()) + # Check the parameters. + self.assertAllCloseAccordingToType( + np.array([ + 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - + (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)), + 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - + (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)) + ]), var0.eval()) + self.assertAllCloseAccordingToType( + np.array([ + 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - + (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)), + 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - + (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)) + ]), var1.eval()) + + @parameterized.parameters(_DATA_TYPES) + def testWithMomentum(self, dtype): + with self.test_session(use_gpu=True): + var0 = variables.Variable([1.0, 2.0], dtype=dtype) + var1 = variables.Variable([3.0, 4.0], dtype=dtype) + grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) + grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) + + opt = rmsprop.RMSPropOptimizer( + learning_rate=2.0, decay=0.9, momentum=0.5, epsilon=1e-5) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + variables.global_variables_initializer().run() + + rms0 = opt.get_slot(var0, "rms") + self.assertIsNotNone(rms0) + rms1 = opt.get_slot(var1, "rms") + self.assertIsNotNone(rms1) + mom0 = opt.get_slot(var0, "momentum") + self.assertIsNotNone(mom0) + mom1 = opt.get_slot(var1, "momentum") + self.assertIsNotNone(mom1) + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + # Step 1: rms = 1, mom = 0. So we should see a normal + # update: v -= grad * learning_rate + update.run() + # Check the root mean square accumulators. + self.assertAllCloseAccordingToType( + np.array([0.901, 0.901]), rms0.eval()) + self.assertAllCloseAccordingToType( + np.array([0.90001, 0.90001]), rms1.eval()) + # Check the momentum accumulators + self.assertAllCloseAccordingToType( + np.array([(0.1 * 2.0 / math.sqrt(0.901 + 1e-5)), + (0.1 * 2.0 / math.sqrt(0.901 + 1e-5))]), mom0.eval()) + self.assertAllCloseAccordingToType( + np.array([(0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)), + (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5))]), mom1.eval()) + + # Check that the parameters. + self.assertAllCloseAccordingToType( + np.array([ + 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)), + 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + ]), var0.eval()) + self.assertAllCloseAccordingToType( + np.array([ + 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)), + 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + ]), var1.eval()) + + # Step 2: the root mean square accumulators contain the previous update. + update.run() + # Check the rms accumulators. + self.assertAllCloseAccordingToType( + np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]), rms0.eval()) + self.assertAllCloseAccordingToType( + np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]), rms1.eval()) + self.assertAllCloseAccordingToType( + np.array([ + 0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + + (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5)), + 0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + + (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5)) + ]), mom0.eval()) + self.assertAllCloseAccordingToType( + np.array([ + 0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + + (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5)), + 0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + + (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5)) + ]), mom1.eval()) + + # Check the parameters. + self.assertAllCloseAccordingToType( + np.array([ + 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) - + (0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + + (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5))), + 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) - + (0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + + (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5))) + ]), var0.eval()) + + self.assertAllCloseAccordingToType( + np.array([ + 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) - + (0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + + (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5))), + 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) - + (0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + + (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5))) + ]), var1.eval()) if __name__ == "__main__": diff --git a/tensorflow/contrib/proto/BUILD b/tensorflow/contrib/proto/BUILD index 3e9b1a0b8d8ec7c3c5fe5d1f2cf896dbb6c3de72..b27142cf4a6413eccb8489ea3eb775060ffd787b 100644 --- a/tensorflow/contrib/proto/BUILD +++ b/tensorflow/contrib/proto/BUILD @@ -16,17 +16,3 @@ py_library( "//tensorflow/contrib/proto/python/ops:encode_proto_op_py", ], ) - -py_library( - name = "proto_pip", - data = [ - "//tensorflow/contrib/proto/python/kernel_tests:test_messages", - ] + if_static( - [], - otherwise = ["//tensorflow/contrib/proto/python/kernel_tests:libtestexample.so"], - ), - deps = [ - ":proto", - "//tensorflow/contrib/proto/python/kernel_tests:py_test_deps", - ], -) diff --git a/tensorflow/contrib/proto/python/kernel_tests/BUILD b/tensorflow/contrib/proto/python/kernel_tests/BUILD index a380a131f86abc8dd921a123afdb964bf6c2466c..125c1cee292092e55bc17294a29f175c8cc3999c 100644 --- a/tensorflow/contrib/proto/python/kernel_tests/BUILD +++ b/tensorflow/contrib/proto/python/kernel_tests/BUILD @@ -4,47 +4,41 @@ licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) -# Much of the work in this BUILD file actually happens in the corresponding -# build_defs.bzl, which creates an individual testcase for each example .pbtxt -# file in this directory. -# -load(":build_defs.bzl", "decode_proto_test_suite") -load(":build_defs.bzl", "encode_proto_test_suite") - -# This expands to a tf_py_test for each test file. -# It defines the test_suite :decode_proto_op_tests. -decode_proto_test_suite( - name = "decode_proto_tests", - examples = glob(["*.pbtxt"]), -) - -# This expands to a tf_py_test for each test file. -# It defines the test_suite :encode_proto_op_tests. -encode_proto_test_suite( - name = "encode_proto_tests", - examples = glob(["*.pbtxt"]), -) - -# Below here are tests that are not tied to an example text proto. -filegroup( - name = "test_messages", - srcs = glob(["*.pbtxt"]), -) - load("//tensorflow:tensorflow.bzl", "tf_py_test") load("//tensorflow:tensorflow.bzl", "tf_cc_shared_object") load("//tensorflow/core:platform/default/build_config_root.bzl", "if_static") load("//tensorflow/core:platform/default/build_config.bzl", "tf_proto_library") tf_py_test( - name = "decode_proto_fail_test", + name = "decode_proto_op_test", size = "small", - srcs = ["decode_proto_fail_test.py"], + srcs = ["decode_proto_op_test.py"], additional_deps = [ + ":decode_proto_op_test_base", + ":py_test_deps", + "//tensorflow/contrib/proto:proto", + "//tensorflow/contrib/proto/python/ops:decode_proto_op_py", + ], + data = if_static( + [], + otherwise = [":libtestexample.so"], + ), + tags = [ + "no_pip", # TODO(b/78026780) + "no_windows", # TODO(b/78028010) + ], +) + +tf_py_test( + name = "encode_proto_op_test", + size = "small", + srcs = ["encode_proto_op_test.py"], + additional_deps = [ + ":encode_proto_op_test_base", ":py_test_deps", - "//third_party/py/numpy", "//tensorflow/contrib/proto:proto", "//tensorflow/contrib/proto/python/ops:decode_proto_op_py", + "//tensorflow/contrib/proto/python/ops:encode_proto_op_py", ], data = if_static( [], @@ -57,19 +51,41 @@ tf_py_test( ) py_library( - name = "test_case", - srcs = ["test_case.py"], - deps = ["//tensorflow/python:client_testlib"], + name = "proto_op_test_base", + testonly = 1, + srcs = ["proto_op_test_base.py"], + deps = [ + ":test_example_proto_py", + "//tensorflow/python:client_testlib", + ], +) + +py_library( + name = "decode_proto_op_test_base", + testonly = 1, + srcs = ["decode_proto_op_test_base.py"], + deps = [ + ":proto_op_test_base", + ":test_example_proto_py", + "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", + ], ) py_library( - name = "py_test_deps", + name = "encode_proto_op_test_base", + testonly = 1, + srcs = ["encode_proto_op_test_base.py"], deps = [ - ":test_case", + ":proto_op_test_base", ":test_example_proto_py", + "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) +py_library(name = "py_test_deps") + tf_proto_library( name = "test_example_proto", srcs = ["test_example.proto"], @@ -84,3 +100,30 @@ tf_cc_shared_object( ":test_example_proto_cc", ], ) + +py_library( + name = "descriptor_source_test_base", + testonly = 1, + srcs = ["descriptor_source_test_base.py"], + deps = [ + ":proto_op_test_base", + "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", + "@protobuf_archive//:protobuf_python", + ], +) + +tf_py_test( + name = "descriptor_source_test", + size = "small", + srcs = ["descriptor_source_test.py"], + additional_deps = [ + ":descriptor_source_test_base", + "//tensorflow/contrib/proto/python/ops:decode_proto_op_py", + "//tensorflow/contrib/proto/python/ops:encode_proto_op_py", + "//tensorflow/python:client_testlib", + ], + tags = [ + "no_pip", + ], +) diff --git a/tensorflow/contrib/proto/python/kernel_tests/build_defs.bzl b/tensorflow/contrib/proto/python/kernel_tests/build_defs.bzl deleted file mode 100644 index f425601691e21b36914f340d53ccadf9b4e3641f..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/proto/python/kernel_tests/build_defs.bzl +++ /dev/null @@ -1,89 +0,0 @@ -"""BUILD rules for generating file-driven proto test cases. - -The decode_proto_test_suite() and encode_proto_test_suite() rules take a list -of text protos and generates a tf_py_test() for each one. -""" - -load("//tensorflow:tensorflow.bzl", "tf_py_test") -load("//tensorflow:tensorflow.bzl", "register_extension_info") -load("//tensorflow/core:platform/default/build_config_root.bzl", "if_static") - -def _test_name(test, path): - return "%s_%s_test" % (test, path.split("/")[-1].split(".")[0]) - -def decode_proto_test_suite(name, examples): - """Build the decode_proto py_test for each test filename.""" - for test_filename in examples: - tf_py_test( - name = _test_name("decode_proto", test_filename), - srcs = ["decode_proto_op_test.py"], - size = "small", - data = [test_filename] + if_static( - [], - otherwise = [":libtestexample.so"], - ), - main = "decode_proto_op_test.py", - args = [ - "--message_text_file=\"%s/%s\"" % (native.package_name(), test_filename), - ], - additional_deps = [ - ":py_test_deps", - "//third_party/py/numpy", - "//tensorflow/contrib/proto:proto", - "//tensorflow/contrib/proto/python/ops:decode_proto_op_py", - ], - tags = [ - "no_pip", # TODO(b/78026780) - "no_windows", # TODO(b/78028010) - ], - ) - native.test_suite( - name = name, - tests = [":" + _test_name("decode_proto", test_filename) - for test_filename in examples], - ) - -def encode_proto_test_suite(name, examples): - """Build the encode_proto py_test for each test filename.""" - for test_filename in examples: - tf_py_test( - name = _test_name("encode_proto", test_filename), - srcs = ["encode_proto_op_test.py"], - size = "small", - data = [test_filename] + if_static( - [], - otherwise = [":libtestexample.so"], - ), - main = "encode_proto_op_test.py", - args = [ - "--message_text_file=\"%s/%s\"" % (native.package_name(), test_filename), - ], - additional_deps = [ - ":py_test_deps", - "//third_party/py/numpy", - "//tensorflow/contrib/proto:proto", - "//tensorflow/contrib/proto/python/ops:decode_proto_op_py", - "//tensorflow/contrib/proto/python/ops:encode_proto_op_py", - ], - tags = [ - "no_pip", # TODO(b/78026780) - "no_windows", # TODO(b/78028010) - ], - ) - native.test_suite( - name = name, - tests = [":" + _test_name("encode_proto", test_filename) - for test_filename in examples], - ) - -register_extension_info( - extension_name = "decode_proto_test_suite", - label_regex_map = { - "deps": "deps:decode_example_.*", - }) - -register_extension_info( - extension_name = "encode_proto_test_suite", - label_regex_map = { - "deps": "deps:encode_example_.*", - }) diff --git a/tensorflow/contrib/proto/python/kernel_tests/decode_proto_fail_test.py b/tensorflow/contrib/proto/python/kernel_tests/decode_proto_fail_test.py deleted file mode 100644 index 5298342ee79b08a50b13ce8715e891a332efb3bc..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/proto/python/kernel_tests/decode_proto_fail_test.py +++ /dev/null @@ -1,68 +0,0 @@ -# ============================================================================= -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================= - -# Python3 preparedness imports. -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - -from tensorflow.contrib.proto.python.kernel_tests import test_case -from tensorflow.contrib.proto.python.ops import decode_proto_op -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import errors -from tensorflow.python.platform import test - - -class DecodeProtoFailTest(test_case.ProtoOpTestCase): - """Test failure cases for DecodeToProto.""" - - def _TestCorruptProtobuf(self, sanitize): - """Test failure cases for DecodeToProto.""" - - # The goal here is to check the error reporting. - # Testing against a variety of corrupt protobufs is - # done by fuzzing. - corrupt_proto = 'This is not a binary protobuf' - - # Numpy silently truncates the strings if you don't specify dtype=object. - batch = np.array(corrupt_proto, dtype=object) - msg_type = 'tensorflow.contrib.proto.TestCase' - field_names = ['sizes'] - field_types = [dtypes.int32] - - with self.test_session() as sess: - ctensor, vtensor = decode_proto_op.decode_proto( - batch, - message_type=msg_type, - field_names=field_names, - output_types=field_types, - sanitize=sanitize) - with self.assertRaisesRegexp(errors.DataLossError, - 'Unable to parse binary protobuf' - '|Failed to consume entire buffer'): - _ = sess.run([ctensor] + vtensor) - - def testCorrupt(self): - self._TestCorruptProtobuf(sanitize=False) - - def testSanitizerCorrupt(self): - self._TestCorruptProtobuf(sanitize=True) - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/contrib/proto/python/kernel_tests/decode_proto_op_test.py b/tensorflow/contrib/proto/python/kernel_tests/decode_proto_op_test.py index d1c13c82bc264bc8bcc721eb68ee3916f32ef7a8..934035ec4c97e04846f493817d4b4ed65db94f14 100644 --- a/tensorflow/contrib/proto/python/kernel_tests/decode_proto_op_test.py +++ b/tensorflow/contrib/proto/python/kernel_tests/decode_proto_op_test.py @@ -13,287 +13,22 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================= -"""Table-driven test for decode_proto op. +"""Tests for decode_proto op.""" -This test is run once with each of the *.TestCase.pbtxt files -in the test directory. -""" # Python3 preparedness imports. from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numpy as np - -from google.protobuf import text_format - -from tensorflow.contrib.proto.python.kernel_tests import test_case -from tensorflow.contrib.proto.python.kernel_tests import test_example_pb2 +from tensorflow.contrib.proto.python.kernel_tests import decode_proto_op_test_base as test_base from tensorflow.contrib.proto.python.ops import decode_proto_op -from tensorflow.python.framework import dtypes -from tensorflow.python.platform import flags from tensorflow.python.platform import test -FLAGS = flags.FLAGS - -flags.DEFINE_string('message_text_file', None, - 'A file containing a text serialized TestCase protobuf.') - - -class DecodeProtoOpTest(test_case.ProtoOpTestCase): - - def _compareValues(self, fd, vs, evs): - """Compare lists/arrays of field values.""" - - if len(vs) != len(evs): - self.fail('Field %s decoded %d outputs, expected %d' % - (fd.name, len(vs), len(evs))) - for i, ev in enumerate(evs): - # Special case fuzzy match for float32. TensorFlow seems to mess with - # MAX_FLT slightly and the test doesn't work otherwise. - # TODO(nix): ask on TF list about why MAX_FLT doesn't pass through. - if fd.cpp_type == fd.CPPTYPE_FLOAT: - # Numpy isclose() is better than assertIsClose() which uses an absolute - # value comparison. - self.assertTrue( - np.isclose(vs[i], ev), 'expected %r, actual %r' % (ev, vs[i])) - elif fd.cpp_type == fd.CPPTYPE_STRING: - # In Python3 string tensor values will be represented as bytes, so we - # reencode the proto values to match that. - self.assertEqual(vs[i], ev.encode('ascii')) - else: - # Doubles and other types pass through unscathed. - self.assertEqual(vs[i], ev) - - def _compareRepeatedPrimitiveValue(self, batch_shape, sizes, fields, - field_dict): - """Compare protos of type RepeatedPrimitiveValue. - - Args: - batch_shape: the shape of the input tensor of serialized messages. - sizes: int matrix of repeat counts returned by decode_proto - fields: list of test_example_pb2.FieldSpec (types and expected values) - field_dict: map from field names to decoded numpy tensors of values - """ - - # Check that expected values match. - for field in fields: - values = field_dict[field.name] - self.assertEqual(dtypes.as_dtype(values.dtype), field.dtype) - - fd = field.expected.DESCRIPTOR.fields_by_name[field.name] - - # Values has the same shape as the input plus an extra - # dimension for repeats. - self.assertEqual(list(values.shape)[:-1], batch_shape) - - # Nested messages are represented as TF strings, requiring - # some special handling. - if field.name == 'message_value': - vs = [] - for buf in values.flat: - msg = test_example_pb2.PrimitiveValue() - msg.ParseFromString(buf) - vs.append(msg) - evs = getattr(field.expected, field.name) - if len(vs) != len(evs): - self.fail('Field %s decoded %d outputs, expected %d' % - (fd.name, len(vs), len(evs))) - for v, ev in zip(vs, evs): - self.assertEqual(v, ev) - continue - - # This can be a little confusing. For testing we are using - # RepeatedPrimitiveValue in two ways: it's the proto that we - # decode for testing, and it's used in the expected value as a - # union type. The two cases are slightly different: this is the - # second case. - # We may be fetching the uint64_value from the test proto, but - # in the expected proto we store it in the int64_value field - # because TensorFlow doesn't support unsigned int64. - tf_type_to_primitive_value_field = { - dtypes.float32: - 'float_value', - dtypes.float64: - 'double_value', - dtypes.int32: - 'int32_value', - dtypes.uint8: - 'uint8_value', - dtypes.int8: - 'int8_value', - dtypes.string: - 'string_value', - dtypes.int64: - 'int64_value', - dtypes.bool: - 'bool_value', - # Unhandled TensorFlow types: - # DT_INT16 DT_COMPLEX64 DT_QINT8 DT_QUINT8 DT_QINT32 - # DT_BFLOAT16 DT_QINT16 DT_QUINT16 DT_UINT16 - } - tf_field_name = tf_type_to_primitive_value_field.get(field.dtype) - if tf_field_name is None: - self.fail('Unhandled tensorflow type %d' % field.dtype) - - self._compareValues(fd, values.flat, - getattr(field.expected, tf_field_name)) - - def _runDecodeProtoTests(self, fields, case_sizes, batch_shape, batch, - message_type, message_format, sanitize, - force_disordered=False): - """Run decode tests on a batch of messages. - - Args: - fields: list of test_example_pb2.FieldSpec (types and expected values) - case_sizes: expected sizes array - batch_shape: the shape of the input tensor of serialized messages - batch: list of serialized messages - message_type: descriptor name for messages - message_format: format of messages, 'text' or 'binary' - sanitize: whether to sanitize binary protobuf inputs - force_disordered: whether to force fields encoded out of order. - """ - - if force_disordered: - # Exercise code path that handles out-of-order fields by prepending extra - # fields with tag numbers higher than any real field. Note that this won't - # work with sanitization because that forces reserialization using a - # trusted decoder and encoder. - assert not sanitize - extra_fields = test_example_pb2.ExtraFields() - extra_fields.string_value = 'IGNORE ME' - extra_fields.bool_value = False - extra_msg = extra_fields.SerializeToString() - batch = [extra_msg + msg for msg in batch] - - # Numpy silently truncates the strings if you don't specify dtype=object. - batch = np.array(batch, dtype=object) - batch = np.reshape(batch, batch_shape) - - field_names = [f.name for f in fields] - output_types = [f.dtype for f in fields] - - with self.test_session() as sess: - sizes, vtensor = decode_proto_op.decode_proto( - batch, - message_type=message_type, - field_names=field_names, - output_types=output_types, - message_format=message_format, - sanitize=sanitize) - - vlist = sess.run([sizes] + vtensor) - sizes = vlist[0] - # Values is a list of tensors, one for each field. - value_tensors = vlist[1:] - - # Check that the repeat sizes are correct. - self.assertTrue( - np.all(np.array(sizes.shape) == batch_shape + [len(field_names)])) - - # Check that the decoded sizes match the expected sizes. - self.assertEqual(len(sizes.flat), len(case_sizes)) - self.assertTrue( - np.all(sizes.flat == np.array( - case_sizes, dtype=np.int32))) - - field_dict = dict(zip(field_names, value_tensors)) - - self._compareRepeatedPrimitiveValue(batch_shape, sizes, fields, - field_dict) - - def testBinary(self): - with open(FLAGS.message_text_file, 'r') as fp: - case = text_format.Parse(fp.read(), test_example_pb2.TestCase()) - - batch = [primitive.SerializeToString() for primitive in case.primitive] - self._runDecodeProtoTests( - case.field, - case.sizes, - list(case.shape), - batch, - 'tensorflow.contrib.proto.RepeatedPrimitiveValue', - 'binary', - sanitize=False) - - def testBinaryDisordered(self): - with open(FLAGS.message_text_file, 'r') as fp: - case = text_format.Parse(fp.read(), test_example_pb2.TestCase()) - - batch = [primitive.SerializeToString() for primitive in case.primitive] - self._runDecodeProtoTests( - case.field, - case.sizes, - list(case.shape), - batch, - 'tensorflow.contrib.proto.RepeatedPrimitiveValue', - 'binary', - sanitize=False, - force_disordered=True) - - def testPacked(self): - with open(FLAGS.message_text_file, 'r') as fp: - case = text_format.Parse(fp.read(), test_example_pb2.TestCase()) - - # Now try with the packed serialization. - # We test the packed representations by loading the same test cases - # using PackedPrimitiveValue instead of RepeatedPrimitiveValue. - # To do this we rely on the text format being the same for packed and - # unpacked fields, and reparse the test message using the packed version - # of the proto. - packed_batch = [ - # Note: float_format='.17g' is necessary to ensure preservation of - # doubles and floats in text format. - text_format.Parse( - text_format.MessageToString( - primitive, float_format='.17g'), - test_example_pb2.PackedPrimitiveValue()).SerializeToString() - for primitive in case.primitive - ] - - self._runDecodeProtoTests( - case.field, - case.sizes, - list(case.shape), - packed_batch, - 'tensorflow.contrib.proto.PackedPrimitiveValue', - 'binary', - sanitize=False) - - def testText(self): - with open(FLAGS.message_text_file, 'r') as fp: - case = text_format.Parse(fp.read(), test_example_pb2.TestCase()) - - # Note: float_format='.17g' is necessary to ensure preservation of - # doubles and floats in text format. - text_batch = [ - text_format.MessageToString( - primitive, float_format='.17g') for primitive in case.primitive - ] - - self._runDecodeProtoTests( - case.field, - case.sizes, - list(case.shape), - text_batch, - 'tensorflow.contrib.proto.RepeatedPrimitiveValue', - 'text', - sanitize=False) - def testSanitizerGood(self): - with open(FLAGS.message_text_file, 'r') as fp: - case = text_format.Parse(fp.read(), test_example_pb2.TestCase()) +class DecodeProtoOpTest(test_base.DecodeProtoOpTestBase): - batch = [primitive.SerializeToString() for primitive in case.primitive] - self._runDecodeProtoTests( - case.field, - case.sizes, - list(case.shape), - batch, - 'tensorflow.contrib.proto.RepeatedPrimitiveValue', - 'binary', - sanitize=True) + def __init__(self, methodName='runTest'): # pylint: disable=invalid-name + super(DecodeProtoOpTest, self).__init__(decode_proto_op, methodName) if __name__ == '__main__': diff --git a/tensorflow/contrib/proto/python/kernel_tests/decode_proto_op_test_base.py b/tensorflow/contrib/proto/python/kernel_tests/decode_proto_op_test_base.py new file mode 100644 index 0000000000000000000000000000000000000000..e3570e38a3aac738b01b28eb4bfdf57e6abbc595 --- /dev/null +++ b/tensorflow/contrib/proto/python/kernel_tests/decode_proto_op_test_base.py @@ -0,0 +1,303 @@ +# ============================================================================= +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= +"""Tests for decode_proto op.""" + +# Python3 preparedness imports. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from absl.testing import parameterized +import numpy as np + + +from google.protobuf import text_format + +from tensorflow.contrib.proto.python.kernel_tests import proto_op_test_base as test_base +from tensorflow.contrib.proto.python.kernel_tests import test_example_pb2 +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors + + +class DecodeProtoOpTestBase(test_base.ProtoOpTestBase, parameterized.TestCase): + """Base class for testing proto decoding ops.""" + + def __init__(self, decode_module, methodName='runTest'): # pylint: disable=invalid-name + """DecodeProtoOpTestBase initializer. + + Args: + decode_module: a module containing the `decode_proto_op` method + methodName: the name of the test method (same as for test.TestCase) + """ + + super(DecodeProtoOpTestBase, self).__init__(methodName) + self._decode_module = decode_module + + def _compareValues(self, fd, vs, evs): + """Compare lists/arrays of field values.""" + + if len(vs) != len(evs): + self.fail('Field %s decoded %d outputs, expected %d' % + (fd.name, len(vs), len(evs))) + for i, ev in enumerate(evs): + # Special case fuzzy match for float32. TensorFlow seems to mess with + # MAX_FLT slightly and the test doesn't work otherwise. + # TODO(nix): ask on TF list about why MAX_FLT doesn't pass through. + if fd.cpp_type == fd.CPPTYPE_FLOAT: + # Numpy isclose() is better than assertIsClose() which uses an absolute + # value comparison. + self.assertTrue( + np.isclose(vs[i], ev), 'expected %r, actual %r' % (ev, vs[i])) + elif fd.cpp_type == fd.CPPTYPE_STRING: + # In Python3 string tensor values will be represented as bytes, so we + # reencode the proto values to match that. + self.assertEqual(vs[i], ev.encode('ascii')) + else: + # Doubles and other types pass through unscathed. + self.assertEqual(vs[i], ev) + + def _compareProtos(self, batch_shape, sizes, fields, field_dict): + """Compare protos of type TestValue. + + Args: + batch_shape: the shape of the input tensor of serialized messages. + sizes: int matrix of repeat counts returned by decode_proto + fields: list of test_example_pb2.FieldSpec (types and expected values) + field_dict: map from field names to decoded numpy tensors of values + """ + + # Check that expected values match. + for field in fields: + values = field_dict[field.name] + self.assertEqual(dtypes.as_dtype(values.dtype), field.dtype) + + fd = field.value.DESCRIPTOR.fields_by_name[field.name] + + # Values has the same shape as the input plus an extra + # dimension for repeats. + self.assertEqual(list(values.shape)[:-1], batch_shape) + + # Nested messages are represented as TF strings, requiring + # some special handling. + if field.name == 'message_value': + vs = [] + for buf in values.flat: + msg = test_example_pb2.PrimitiveValue() + msg.ParseFromString(buf) + vs.append(msg) + evs = getattr(field.value, field.name) + if len(vs) != len(evs): + self.fail('Field %s decoded %d outputs, expected %d' % + (fd.name, len(vs), len(evs))) + for v, ev in zip(vs, evs): + self.assertEqual(v, ev) + continue + + tf_type_to_primitive_value_field = { + dtypes.bool: + 'bool_value', + dtypes.float32: + 'float_value', + dtypes.float64: + 'double_value', + dtypes.int8: + 'int8_value', + dtypes.int32: + 'int32_value', + dtypes.int64: + 'int64_value', + dtypes.string: + 'string_value', + dtypes.uint8: + 'uint8_value', + dtypes.uint32: + 'uint32_value', + dtypes.uint64: + 'uint64_value', + } + tf_field_name = tf_type_to_primitive_value_field.get(field.dtype) + if tf_field_name is None: + self.fail('Unhandled tensorflow type %d' % field.dtype) + + self._compareValues(fd, values.flat, + getattr(field.value, tf_field_name)) + + def _runDecodeProtoTests(self, fields, case_sizes, batch_shape, batch, + message_type, message_format, sanitize, + force_disordered=False): + """Run decode tests on a batch of messages. + + Args: + fields: list of test_example_pb2.FieldSpec (types and expected values) + case_sizes: expected sizes array + batch_shape: the shape of the input tensor of serialized messages + batch: list of serialized messages + message_type: descriptor name for messages + message_format: format of messages, 'text' or 'binary' + sanitize: whether to sanitize binary protobuf inputs + force_disordered: whether to force fields encoded out of order. + """ + + if force_disordered: + # Exercise code path that handles out-of-order fields by prepending extra + # fields with tag numbers higher than any real field. Note that this won't + # work with sanitization because that forces reserialization using a + # trusted decoder and encoder. + assert not sanitize + extra_fields = test_example_pb2.ExtraFields() + extra_fields.string_value = 'IGNORE ME' + extra_fields.bool_value = False + extra_msg = extra_fields.SerializeToString() + batch = [extra_msg + msg for msg in batch] + + # Numpy silently truncates the strings if you don't specify dtype=object. + batch = np.array(batch, dtype=object) + batch = np.reshape(batch, batch_shape) + + field_names = [f.name for f in fields] + output_types = [f.dtype for f in fields] + + with self.test_session() as sess: + sizes, vtensor = self._decode_module.decode_proto( + batch, + message_type=message_type, + field_names=field_names, + output_types=output_types, + message_format=message_format, + sanitize=sanitize) + + vlist = sess.run([sizes] + vtensor) + sizes = vlist[0] + # Values is a list of tensors, one for each field. + value_tensors = vlist[1:] + + # Check that the repeat sizes are correct. + self.assertTrue( + np.all(np.array(sizes.shape) == batch_shape + [len(field_names)])) + + # Check that the decoded sizes match the expected sizes. + self.assertEqual(len(sizes.flat), len(case_sizes)) + self.assertTrue( + np.all(sizes.flat == np.array( + case_sizes, dtype=np.int32))) + + field_dict = dict(zip(field_names, value_tensors)) + + self._compareProtos(batch_shape, sizes, fields, field_dict) + + @parameterized.named_parameters(*test_base.ProtoOpTestBase.named_parameters()) + def testBinary(self, case): + batch = [value.SerializeToString() for value in case.values] + self._runDecodeProtoTests( + case.fields, + case.sizes, + list(case.shapes), + batch, + 'tensorflow.contrib.proto.TestValue', + 'binary', + sanitize=False) + + @parameterized.named_parameters(*test_base.ProtoOpTestBase.named_parameters()) + def testBinaryDisordered(self, case): + batch = [value.SerializeToString() for value in case.values] + self._runDecodeProtoTests( + case.fields, + case.sizes, + list(case.shapes), + batch, + 'tensorflow.contrib.proto.TestValue', + 'binary', + sanitize=False, + force_disordered=True) + + @parameterized.named_parameters(*test_base.ProtoOpTestBase.named_parameters()) + def testPacked(self, case): + # Now try with the packed serialization. + # + # We test the packed representations by loading the same test case using + # PackedTestValue instead of TestValue. To do this we rely on the text + # format being the same for packed and unpacked fields, and reparse the + # test message using the packed version of the proto. + packed_batch = [ + # Note: float_format='.17g' is necessary to ensure preservation of + # doubles and floats in text format. + text_format.Parse( + text_format.MessageToString( + value, float_format='.17g'), + test_example_pb2.PackedTestValue()).SerializeToString() + for value in case.values + ] + + self._runDecodeProtoTests( + case.fields, + case.sizes, + list(case.shapes), + packed_batch, + 'tensorflow.contrib.proto.PackedTestValue', + 'binary', + sanitize=False) + + @parameterized.named_parameters(*test_base.ProtoOpTestBase.named_parameters()) + def testText(self, case): + # Note: float_format='.17g' is necessary to ensure preservation of + # doubles and floats in text format. + text_batch = [ + text_format.MessageToString( + value, float_format='.17g') for value in case.values + ] + + self._runDecodeProtoTests( + case.fields, + case.sizes, + list(case.shapes), + text_batch, + 'tensorflow.contrib.proto.TestValue', + 'text', + sanitize=False) + + @parameterized.named_parameters(*test_base.ProtoOpTestBase.named_parameters()) + def testSanitizerGood(self, case): + batch = [value.SerializeToString() for value in case.values] + self._runDecodeProtoTests( + case.fields, + case.sizes, + list(case.shapes), + batch, + 'tensorflow.contrib.proto.TestValue', + 'binary', + sanitize=True) + + @parameterized.parameters((False), (True)) + def testCorruptProtobuf(self, sanitize): + corrupt_proto = 'This is not a binary protobuf' + + # Numpy silently truncates the strings if you don't specify dtype=object. + batch = np.array(corrupt_proto, dtype=object) + msg_type = 'tensorflow.contrib.proto.TestCase' + field_names = ['sizes'] + field_types = [dtypes.int32] + + with self.test_session() as sess: + ctensor, vtensor = self._decode_module.decode_proto( + batch, + message_type=msg_type, + field_names=field_names, + output_types=field_types, + sanitize=sanitize) + with self.assertRaisesRegexp(errors.DataLossError, + 'Unable to parse binary protobuf' + '|Failed to consume entire buffer'): + _ = sess.run([ctensor] + vtensor) diff --git a/tensorflow/contrib/proto/python/kernel_tests/defaut_values.TestCase.pbtxt b/tensorflow/contrib/proto/python/kernel_tests/defaut_values.TestCase.pbtxt deleted file mode 100644 index 4e316819077c7dbb28beefd4dc260568f26da680..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/proto/python/kernel_tests/defaut_values.TestCase.pbtxt +++ /dev/null @@ -1,94 +0,0 @@ -primitive { - # No fields specified, so we get all defaults -} -shape: 1 -sizes: 0 -field { - name: "double_default" - dtype: DT_DOUBLE - expected { double_value: 1.0 } -} -sizes: 0 -field { - name: "float_default" - dtype: DT_DOUBLE # Try casting the float field to double. - expected { double_value: 2.0 } -} -sizes: 0 -field { - name: "int64_default" - dtype: DT_INT64 - expected { int64_value: 3 } -} -sizes: 0 -field { - name: "uint64_default" - dtype: DT_INT64 - expected { int64_value: 4 } -} -sizes: 0 -field { - name: "int32_default" - dtype: DT_INT32 - expected { int32_value: 5 } -} -sizes: 0 -field { - name: "fixed64_default" - dtype: DT_INT64 - expected { int64_value: 6 } -} -sizes: 0 -field { - name: "fixed32_default" - dtype: DT_INT32 - expected { int32_value: 7 } -} -sizes: 0 -field { - name: "bool_default" - dtype: DT_BOOL - expected { bool_value: true } -} -sizes: 0 -field { - name: "string_default" - dtype: DT_STRING - expected { string_value: "a" } -} -sizes: 0 -field { - name: "bytes_default" - dtype: DT_STRING - expected { string_value: "a longer default string" } -} -sizes: 0 -field { - name: "uint32_default" - dtype: DT_INT32 - expected { int32_value: -1 } -} -sizes: 0 -field { - name: "sfixed32_default" - dtype: DT_INT32 - expected { int32_value: 10 } -} -sizes: 0 -field { - name: "sfixed64_default" - dtype: DT_INT64 - expected { int64_value: 11 } -} -sizes: 0 -field { - name: "sint32_default" - dtype: DT_INT32 - expected { int32_value: 12 } -} -sizes: 0 -field { - name: "sint64_default" - dtype: DT_INT64 - expected { int64_value: 13 } -} diff --git a/tensorflow/contrib/proto/python/kernel_tests/test_case.py b/tensorflow/contrib/proto/python/kernel_tests/descriptor_source_test.py similarity index 65% rename from tensorflow/contrib/proto/python/kernel_tests/test_case.py rename to tensorflow/contrib/proto/python/kernel_tests/descriptor_source_test.py index b95202c5df654cfc02339477b242b2c58575a4d5..32ca318f733ce11221539838dfdbcf710dca51a1 100644 --- a/tensorflow/contrib/proto/python/kernel_tests/test_case.py +++ b/tensorflow/contrib/proto/python/kernel_tests/descriptor_source_test.py @@ -13,23 +13,24 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================= -"""Test case base for testing proto operations.""" - +"""Tests for proto ops reading descriptors from other sources.""" # Python3 preparedness imports. from __future__ import absolute_import from __future__ import division from __future__ import print_function -import ctypes as ct -import os - +from tensorflow.contrib.proto.python.kernel_tests import descriptor_source_test_base as test_base +from tensorflow.contrib.proto.python.ops import decode_proto_op +from tensorflow.contrib.proto.python.ops import encode_proto_op from tensorflow.python.platform import test -class ProtoOpTestCase(test.TestCase): +class DescriptorSourceTest(test_base.DescriptorSourceTestBase): def __init__(self, methodName='runTest'): # pylint: disable=invalid-name - super(ProtoOpTestCase, self).__init__(methodName) - lib = os.path.join(os.path.dirname(__file__), 'libtestexample.so') - if os.path.isfile(lib): - ct.cdll.LoadLibrary(lib) + super(DescriptorSourceTest, self).__init__(decode_proto_op, encode_proto_op, + methodName) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/proto/python/kernel_tests/descriptor_source_test_base.py b/tensorflow/contrib/proto/python/kernel_tests/descriptor_source_test_base.py new file mode 100644 index 0000000000000000000000000000000000000000..9a1c04af324620fc893583ebb17cd99ea3ba166d --- /dev/null +++ b/tensorflow/contrib/proto/python/kernel_tests/descriptor_source_test_base.py @@ -0,0 +1,176 @@ +# ============================================================================= +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= +"""Tests for proto ops reading descriptors from other sources.""" +# Python3 preparedness imports. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +import numpy as np + +from google.protobuf.descriptor_pb2 import FieldDescriptorProto +from google.protobuf.descriptor_pb2 import FileDescriptorSet +from tensorflow.contrib.proto.python.kernel_tests import proto_op_test_base as test_base +from tensorflow.python.framework import dtypes +from tensorflow.python.platform import test + + +class DescriptorSourceTestBase(test.TestCase): + """Base class for testing descriptor sources.""" + + def __init__(self, decode_module, encode_module, methodName='runTest'): # pylint: disable=invalid-name + """DescriptorSourceTestBase initializer. + + Args: + decode_module: a module containing the `decode_proto_op` method + encode_module: a module containing the `encode_proto_op` method + methodName: the name of the test method (same as for test.TestCase) + """ + + super(DescriptorSourceTestBase, self).__init__(methodName) + self._decode_module = decode_module + self._encode_module = encode_module + + # NOTE: We generate the descriptor programmatically instead of via a compiler + # because of differences between different versions of the compiler. + # + # The generated descriptor should capture the subset of `test_example.proto` + # used in `test_base.simple_test_case()`. + def _createDescriptorFile(self): + set_proto = FileDescriptorSet() + + file_proto = set_proto.file.add( + name='types.proto', + package='tensorflow', + syntax='proto3') + enum_proto = file_proto.enum_type.add(name='DataType') + enum_proto.value.add(name='DT_DOUBLE', number=0) + enum_proto.value.add(name='DT_BOOL', number=1) + + file_proto = set_proto.file.add( + name='test_example.proto', + package='tensorflow.contrib.proto', + dependency=['types.proto']) + message_proto = file_proto.message_type.add(name='TestCase') + message_proto.field.add( + name='values', + number=1, + type=FieldDescriptorProto.TYPE_MESSAGE, + type_name='.tensorflow.contrib.proto.TestValue', + label=FieldDescriptorProto.LABEL_REPEATED) + message_proto.field.add( + name='shapes', + number=2, + type=FieldDescriptorProto.TYPE_INT32, + label=FieldDescriptorProto.LABEL_REPEATED) + message_proto.field.add( + name='sizes', + number=3, + type=FieldDescriptorProto.TYPE_INT32, + label=FieldDescriptorProto.LABEL_REPEATED) + message_proto.field.add( + name='fields', + number=4, + type=FieldDescriptorProto.TYPE_MESSAGE, + type_name='.tensorflow.contrib.proto.FieldSpec', + label=FieldDescriptorProto.LABEL_REPEATED) + + message_proto = file_proto.message_type.add( + name='TestValue') + message_proto.field.add( + name='double_value', + number=1, + type=FieldDescriptorProto.TYPE_DOUBLE, + label=FieldDescriptorProto.LABEL_REPEATED) + message_proto.field.add( + name='bool_value', + number=2, + type=FieldDescriptorProto.TYPE_BOOL, + label=FieldDescriptorProto.LABEL_REPEATED) + + message_proto = file_proto.message_type.add( + name='FieldSpec') + message_proto.field.add( + name='name', + number=1, + type=FieldDescriptorProto.TYPE_STRING, + label=FieldDescriptorProto.LABEL_OPTIONAL) + message_proto.field.add( + name='dtype', + number=2, + type=FieldDescriptorProto.TYPE_ENUM, + type_name='.tensorflow.DataType', + label=FieldDescriptorProto.LABEL_OPTIONAL) + message_proto.field.add( + name='value', + number=3, + type=FieldDescriptorProto.TYPE_MESSAGE, + type_name='.tensorflow.contrib.proto.TestValue', + label=FieldDescriptorProto.LABEL_OPTIONAL) + + fn = os.path.join(self.get_temp_dir(), 'descriptor.pb') + with open(fn, 'wb') as f: + f.write(set_proto.SerializeToString()) + return fn + + def _testRoundtrip(self, descriptor_source): + # Numpy silently truncates the strings if you don't specify dtype=object. + in_bufs = np.array( + [test_base.ProtoOpTestBase.simple_test_case().SerializeToString()], + dtype=object) + message_type = 'tensorflow.contrib.proto.TestCase' + field_names = ['values', 'shapes', 'sizes', 'fields'] + tensor_types = [dtypes.string, dtypes.int32, dtypes.int32, dtypes.string] + + with self.test_session() as sess: + sizes, field_tensors = self._decode_module.decode_proto( + in_bufs, + message_type=message_type, + field_names=field_names, + output_types=tensor_types, + descriptor_source=descriptor_source) + + out_tensors = self._encode_module.encode_proto( + sizes, + field_tensors, + message_type=message_type, + field_names=field_names, + descriptor_source=descriptor_source) + + out_bufs, = sess.run([out_tensors]) + + # Check that the re-encoded tensor has the same shape. + self.assertEqual(in_bufs.shape, out_bufs.shape) + + # Compare the input and output. + for in_buf, out_buf in zip(in_bufs.flat, out_bufs.flat): + # Check that the input and output serialized messages are identical. + # If we fail here, there is a difference in the serialized + # representation but the new serialization still parses. This could + # be harmless (a change in map ordering?) or it could be bad (e.g. + # loss of packing in the encoding). + self.assertEqual(in_buf, out_buf) + + def testWithFileDescriptorSet(self): + # First try parsing with a local proto db, which should fail. + with self.assertRaisesOpError('No descriptor found for message type'): + self._testRoundtrip('local://') + + # Now try parsing with a FileDescriptorSet which contains the test proto. + descriptor_file = self._createDescriptorFile() + self._testRoundtrip(descriptor_file) diff --git a/tensorflow/contrib/proto/python/kernel_tests/encode_proto_op_test.py b/tensorflow/contrib/proto/python/kernel_tests/encode_proto_op_test.py index 30e58e6336dc66830418c7cd2b3111a851d691b6..fc5cd25d43be1df2480630396c39f7a83e0eb57a 100644 --- a/tensorflow/contrib/proto/python/kernel_tests/encode_proto_op_test.py +++ b/tensorflow/contrib/proto/python/kernel_tests/encode_proto_op_test.py @@ -13,167 +13,24 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================= -"""Table-driven test for encode_proto op. +"""Tests for encode_proto op.""" -This test is run once with each of the *.TestCase.pbtxt files -in the test directory. - -It tests that encode_proto is a lossless inverse of decode_proto -(for the specified fields). -""" # Python3 readiness boilerplate from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numpy as np - -from google.protobuf import text_format - -from tensorflow.contrib.proto.python.kernel_tests import test_case -from tensorflow.contrib.proto.python.kernel_tests import test_example_pb2 +from tensorflow.contrib.proto.python.kernel_tests import encode_proto_op_test_base as test_base from tensorflow.contrib.proto.python.ops import decode_proto_op from tensorflow.contrib.proto.python.ops import encode_proto_op -from tensorflow.python.framework import dtypes -from tensorflow.python.ops import array_ops -from tensorflow.python.platform import flags from tensorflow.python.platform import test -FLAGS = flags.FLAGS - -flags.DEFINE_string('message_text_file', None, - 'A file containing a text serialized TestCase protobuf.') - - -class EncodeProtoOpTest(test_case.ProtoOpTestCase): - - def testBadInputs(self): - # Invalid field name - with self.test_session(): - with self.assertRaisesOpError('Unknown field: non_existent_field'): - encode_proto_op.encode_proto( - sizes=[[1]], - values=[np.array([[0.0]], dtype=np.int32)], - message_type='tensorflow.contrib.proto.RepeatedPrimitiveValue', - field_names=['non_existent_field']).eval() - - # Incorrect types. - with self.test_session(): - with self.assertRaisesOpError( - 'Incompatible type for field double_value.'): - encode_proto_op.encode_proto( - sizes=[[1]], - values=[np.array([[0.0]], dtype=np.int32)], - message_type='tensorflow.contrib.proto.RepeatedPrimitiveValue', - field_names=['double_value']).eval() - - # Incorrect shapes of sizes. - with self.test_session(): - with self.assertRaisesOpError( - r'sizes should be batch_size \+ \[len\(field_names\)\]'): - sizes = array_ops.placeholder(dtypes.int32) - values = array_ops.placeholder(dtypes.float64) - encode_proto_op.encode_proto( - sizes=sizes, - values=[values], - message_type='tensorflow.contrib.proto.RepeatedPrimitiveValue', - field_names=['double_value']).eval(feed_dict={ - sizes: [[[0, 0]]], - values: [[0.0]] - }) - - # Inconsistent shapes of values. - with self.test_session(): - with self.assertRaisesOpError( - 'Values must match up to the last dimension'): - sizes = array_ops.placeholder(dtypes.int32) - values1 = array_ops.placeholder(dtypes.float64) - values2 = array_ops.placeholder(dtypes.int32) - (encode_proto_op.encode_proto( - sizes=[[1, 1]], - values=[values1, values2], - message_type='tensorflow.contrib.proto.RepeatedPrimitiveValue', - field_names=['double_value', 'int32_value']).eval(feed_dict={ - values1: [[0.0]], - values2: [[0], [0]] - })) - - def _testRoundtrip(self, in_bufs, message_type, fields): - - field_names = [f.name for f in fields] - out_types = [f.dtype for f in fields] - - with self.test_session() as sess: - sizes, field_tensors = decode_proto_op.decode_proto( - in_bufs, - message_type=message_type, - field_names=field_names, - output_types=out_types) - - out_tensors = encode_proto_op.encode_proto( - sizes, - field_tensors, - message_type=message_type, - field_names=field_names) - - out_bufs, = sess.run([out_tensors]) - - # Check that the re-encoded tensor has the same shape. - self.assertEqual(in_bufs.shape, out_bufs.shape) - - # Compare the input and output. - for in_buf, out_buf in zip(in_bufs.flat, out_bufs.flat): - in_obj = test_example_pb2.RepeatedPrimitiveValue() - in_obj.ParseFromString(in_buf) - - out_obj = test_example_pb2.RepeatedPrimitiveValue() - out_obj.ParseFromString(out_buf) - - # Check that the deserialized objects are identical. - self.assertEqual(in_obj, out_obj) - - # Check that the input and output serialized messages are identical. - # If we fail here, there is a difference in the serialized - # representation but the new serialization still parses. This could - # be harmless (a change in map ordering?) or it could be bad (e.g. - # loss of packing in the encoding). - self.assertEqual(in_buf, out_buf) - - def testRoundtrip(self): - with open(FLAGS.message_text_file, 'r') as fp: - case = text_format.Parse(fp.read(), test_example_pb2.TestCase()) - - in_bufs = [primitive.SerializeToString() for primitive in case.primitive] - - # np.array silently truncates strings if you don't specify dtype=object. - in_bufs = np.reshape(np.array(in_bufs, dtype=object), list(case.shape)) - return self._testRoundtrip( - in_bufs, 'tensorflow.contrib.proto.RepeatedPrimitiveValue', case.field) - - def testRoundtripPacked(self): - with open(FLAGS.message_text_file, 'r') as fp: - case = text_format.Parse(fp.read(), test_example_pb2.TestCase()) - # Now try with the packed serialization. - # We test the packed representations by loading the same test cases - # using PackedPrimitiveValue instead of RepeatedPrimitiveValue. - # To do this we rely on the text format being the same for packed and - # unpacked fields, and reparse the test message using the packed version - # of the proto. - in_bufs = [ - # Note: float_format='.17g' is necessary to ensure preservation of - # doubles and floats in text format. - text_format.Parse( - text_format.MessageToString( - primitive, float_format='.17g'), - test_example_pb2.PackedPrimitiveValue()).SerializeToString() - for primitive in case.primitive - ] +class EncodeProtoOpTest(test_base.EncodeProtoOpTestBase): - # np.array silently truncates strings if you don't specify dtype=object. - in_bufs = np.reshape(np.array(in_bufs, dtype=object), list(case.shape)) - return self._testRoundtrip( - in_bufs, 'tensorflow.contrib.proto.PackedPrimitiveValue', case.field) + def __init__(self, methodName='runTest'): # pylint: disable=invalid-name + super(EncodeProtoOpTest, self).__init__(decode_proto_op, encode_proto_op, + methodName) if __name__ == '__main__': diff --git a/tensorflow/contrib/proto/python/kernel_tests/encode_proto_op_test_base.py b/tensorflow/contrib/proto/python/kernel_tests/encode_proto_op_test_base.py new file mode 100644 index 0000000000000000000000000000000000000000..07dfb924d3ede5bdb9b848c5eb0d3382ec053121 --- /dev/null +++ b/tensorflow/contrib/proto/python/kernel_tests/encode_proto_op_test_base.py @@ -0,0 +1,177 @@ +# ============================================================================= +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= +"""Table-driven test for encode_proto op. + +This test is run once with each of the *.TestCase.pbtxt files +in the test directory. + +It tests that encode_proto is a lossless inverse of decode_proto +(for the specified fields). +""" +# Python3 readiness boilerplate +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from absl.testing import parameterized +import numpy as np + +from google.protobuf import text_format + +from tensorflow.contrib.proto.python.kernel_tests import proto_op_test_base as test_base +from tensorflow.contrib.proto.python.kernel_tests import test_example_pb2 +from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops + + +class EncodeProtoOpTestBase(test_base.ProtoOpTestBase, parameterized.TestCase): + """Base class for testing proto encoding ops.""" + + def __init__(self, decode_module, encode_module, methodName='runTest'): # pylint: disable=invalid-name + """EncodeProtoOpTestBase initializer. + + Args: + decode_module: a module containing the `decode_proto_op` method + encode_module: a module containing the `encode_proto_op` method + methodName: the name of the test method (same as for test.TestCase) + """ + + super(EncodeProtoOpTestBase, self).__init__(methodName) + self._decode_module = decode_module + self._encode_module = encode_module + + def testBadInputs(self): + # Invalid field name + with self.test_session(): + with self.assertRaisesOpError('Unknown field: non_existent_field'): + self._encode_module.encode_proto( + sizes=[[1]], + values=[np.array([[0.0]], dtype=np.int32)], + message_type='tensorflow.contrib.proto.TestValue', + field_names=['non_existent_field']).eval() + + # Incorrect types. + with self.test_session(): + with self.assertRaisesOpError( + 'Incompatible type for field double_value.'): + self._encode_module.encode_proto( + sizes=[[1]], + values=[np.array([[0.0]], dtype=np.int32)], + message_type='tensorflow.contrib.proto.TestValue', + field_names=['double_value']).eval() + + # Incorrect shapes of sizes. + with self.test_session(): + with self.assertRaisesOpError( + r'sizes should be batch_size \+ \[len\(field_names\)\]'): + sizes = array_ops.placeholder(dtypes.int32) + values = array_ops.placeholder(dtypes.float64) + self._encode_module.encode_proto( + sizes=sizes, + values=[values], + message_type='tensorflow.contrib.proto.TestValue', + field_names=['double_value']).eval(feed_dict={ + sizes: [[[0, 0]]], + values: [[0.0]] + }) + + # Inconsistent shapes of values. + with self.test_session(): + with self.assertRaisesOpError( + 'Values must match up to the last dimension'): + sizes = array_ops.placeholder(dtypes.int32) + values1 = array_ops.placeholder(dtypes.float64) + values2 = array_ops.placeholder(dtypes.int32) + (self._encode_module.encode_proto( + sizes=[[1, 1]], + values=[values1, values2], + message_type='tensorflow.contrib.proto.TestValue', + field_names=['double_value', 'int32_value']).eval(feed_dict={ + values1: [[0.0]], + values2: [[0], [0]] + })) + + def _testRoundtrip(self, in_bufs, message_type, fields): + + field_names = [f.name for f in fields] + out_types = [f.dtype for f in fields] + + with self.test_session() as sess: + sizes, field_tensors = self._decode_module.decode_proto( + in_bufs, + message_type=message_type, + field_names=field_names, + output_types=out_types) + + out_tensors = self._encode_module.encode_proto( + sizes, + field_tensors, + message_type=message_type, + field_names=field_names) + + out_bufs, = sess.run([out_tensors]) + + # Check that the re-encoded tensor has the same shape. + self.assertEqual(in_bufs.shape, out_bufs.shape) + + # Compare the input and output. + for in_buf, out_buf in zip(in_bufs.flat, out_bufs.flat): + in_obj = test_example_pb2.TestValue() + in_obj.ParseFromString(in_buf) + + out_obj = test_example_pb2.TestValue() + out_obj.ParseFromString(out_buf) + + # Check that the deserialized objects are identical. + self.assertEqual(in_obj, out_obj) + + # Check that the input and output serialized messages are identical. + # If we fail here, there is a difference in the serialized + # representation but the new serialization still parses. This could + # be harmless (a change in map ordering?) or it could be bad (e.g. + # loss of packing in the encoding). + self.assertEqual(in_buf, out_buf) + + @parameterized.named_parameters(*test_base.ProtoOpTestBase.named_parameters()) + def testRoundtrip(self, case): + in_bufs = [value.SerializeToString() for value in case.values] + + # np.array silently truncates strings if you don't specify dtype=object. + in_bufs = np.reshape(np.array(in_bufs, dtype=object), list(case.shapes)) + return self._testRoundtrip( + in_bufs, 'tensorflow.contrib.proto.TestValue', case.fields) + + @parameterized.named_parameters(*test_base.ProtoOpTestBase.named_parameters()) + def testRoundtripPacked(self, case): + # Now try with the packed serialization. + # We test the packed representations by loading the same test cases using + # PackedTestValue instead of TestValue. To do this we rely on the text + # format being the same for packed and unpacked fields, and reparse the test + # message using the packed version of the proto. + in_bufs = [ + # Note: float_format='.17g' is necessary to ensure preservation of + # doubles and floats in text format. + text_format.Parse( + text_format.MessageToString( + value, float_format='.17g'), + test_example_pb2.PackedTestValue()).SerializeToString() + for value in case.values + ] + + # np.array silently truncates strings if you don't specify dtype=object. + in_bufs = np.reshape(np.array(in_bufs, dtype=object), list(case.shapes)) + return self._testRoundtrip( + in_bufs, 'tensorflow.contrib.proto.PackedTestValue', case.fields) diff --git a/tensorflow/contrib/proto/python/kernel_tests/minmax.TestCase.pbtxt b/tensorflow/contrib/proto/python/kernel_tests/minmax.TestCase.pbtxt deleted file mode 100644 index b170f89c0f00dd9dffd5785197bb3bfd1ca2cfee..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/proto/python/kernel_tests/minmax.TestCase.pbtxt +++ /dev/null @@ -1,161 +0,0 @@ -primitive { - double_value: -1.7976931348623158e+308 - double_value: 2.2250738585072014e-308 - double_value: 1.7976931348623158e+308 - float_value: -3.402823466e+38 - float_value: 1.175494351e-38 - float_value: 3.402823466e+38 - int64_value: -9223372036854775808 - int64_value: 9223372036854775807 - uint64_value: 0 - uint64_value: 18446744073709551615 - int32_value: -2147483648 - int32_value: 2147483647 - fixed64_value: 0 - fixed64_value: 18446744073709551615 - fixed32_value: 0 - fixed32_value: 4294967295 - bool_value: false - bool_value: true - string_value: "" - string_value: "I refer to the infinite." - uint32_value: 0 - uint32_value: 4294967295 - sfixed32_value: -2147483648 - sfixed32_value: 2147483647 - sfixed64_value: -9223372036854775808 - sfixed64_value: 9223372036854775807 - sint32_value: -2147483648 - sint32_value: 2147483647 - sint64_value: -9223372036854775808 - sint64_value: 9223372036854775807 -} -shape: 1 -sizes: 3 -sizes: 3 -sizes: 2 -sizes: 2 -sizes: 2 -sizes: 2 -sizes: 2 -sizes: 2 -sizes: 2 -sizes: 2 -sizes: 2 -sizes: 2 -sizes: 2 -sizes: 2 -field { - name: "double_value" - dtype: DT_DOUBLE - expected { - double_value: -1.7976931348623158e+308 - double_value: 2.2250738585072014e-308 - double_value: 1.7976931348623158e+308 - } -} -field { - name: "float_value" - dtype: DT_FLOAT - expected { - float_value: -3.402823466e+38 - float_value: 1.175494351e-38 - float_value: 3.402823466e+38 - } -} -field { - name: "int64_value" - dtype: DT_INT64 - expected { - int64_value: -9223372036854775808 - int64_value: 9223372036854775807 - } -} -field { - name: "uint64_value" - dtype: DT_INT64 - expected { - int64_value: 0 - int64_value: -1 - } -} -field { - name: "int32_value" - dtype: DT_INT32 - expected { - int32_value: -2147483648 - int32_value: 2147483647 - } -} -field { - name: "fixed64_value" - dtype: DT_INT64 - expected { - int64_value: 0 - int64_value: -1 # unsigned is 18446744073709551615 - } -} -field { - name: "fixed32_value" - dtype: DT_INT32 - expected { - int32_value: 0 - int32_value: -1 # unsigned is 4294967295 - } -} -field { - name: "bool_value" - dtype: DT_BOOL - expected { - bool_value: false - bool_value: true - } -} -field { - name: "string_value" - dtype: DT_STRING - expected { - string_value: "" - string_value: "I refer to the infinite." - } -} -field { - name: "uint32_value" - dtype: DT_INT32 - expected { - int32_value: 0 - int32_value: -1 # unsigned is 4294967295 - } -} -field { - name: "sfixed32_value" - dtype: DT_INT32 - expected { - int32_value: -2147483648 - int32_value: 2147483647 - } -} -field { - name: "sfixed64_value" - dtype: DT_INT64 - expected { - int64_value: -9223372036854775808 - int64_value: 9223372036854775807 - } -} -field { - name: "sint32_value" - dtype: DT_INT32 - expected { - int32_value: -2147483648 - int32_value: 2147483647 - } -} -field { - name: "sint64_value" - dtype: DT_INT64 - expected { - int64_value: -9223372036854775808 - int64_value: 9223372036854775807 - } -} diff --git a/tensorflow/contrib/proto/python/kernel_tests/nested.TestCase.pbtxt b/tensorflow/contrib/proto/python/kernel_tests/nested.TestCase.pbtxt deleted file mode 100644 index c664e52851b5bb3c439544537ce6402fc7cf3362..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/proto/python/kernel_tests/nested.TestCase.pbtxt +++ /dev/null @@ -1,16 +0,0 @@ -primitive { - message_value { - double_value: 23.5 - } -} -shape: 1 -sizes: 1 -field { - name: "message_value" - dtype: DT_STRING - expected { - message_value { - double_value: 23.5 - } - } -} diff --git a/tensorflow/contrib/proto/python/kernel_tests/optional.TestCase.pbtxt b/tensorflow/contrib/proto/python/kernel_tests/optional.TestCase.pbtxt deleted file mode 100644 index 125651d7eaa1901e4804712bb807322b02ed5bc6..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/proto/python/kernel_tests/optional.TestCase.pbtxt +++ /dev/null @@ -1,20 +0,0 @@ -primitive { - bool_value: true -} -shape: 1 -sizes: 1 -sizes: 0 -field { - name: "bool_value" - dtype: DT_BOOL - expected { - bool_value: true - } -} -field { - name: "double_value" - dtype: DT_DOUBLE - expected { - double_value: 0.0 - } -} diff --git a/tensorflow/contrib/proto/python/kernel_tests/promote_unsigned.TestCase.pbtxt b/tensorflow/contrib/proto/python/kernel_tests/promote_unsigned.TestCase.pbtxt deleted file mode 100644 index bc07efc8f3038c6c540855c97b2254575e517ef3..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/proto/python/kernel_tests/promote_unsigned.TestCase.pbtxt +++ /dev/null @@ -1,29 +0,0 @@ -primitive { - fixed32_value: 4294967295 - uint32_value: 4294967295 -} -shape: 1 -sizes: 1 -field { - name: "fixed32_value" - dtype: DT_INT64 - expected { - int64_value: 4294967295 - } -} -sizes: 1 -field { - name: "uint32_value" - dtype: DT_INT64 - expected { - int64_value: 4294967295 - } -} -sizes: 0 -field { - name: "uint32_default" - dtype: DT_INT64 - expected { - int64_value: 4294967295 # Comes from an explicitly-specified default - } -} diff --git a/tensorflow/contrib/proto/python/kernel_tests/proto_op_test_base.py b/tensorflow/contrib/proto/python/kernel_tests/proto_op_test_base.py new file mode 100644 index 0000000000000000000000000000000000000000..2950c7dfdc59a11ba7d2c07d8406bd4af26b5bd9 --- /dev/null +++ b/tensorflow/contrib/proto/python/kernel_tests/proto_op_test_base.py @@ -0,0 +1,419 @@ +# ============================================================================= +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= +"""Test case base for testing proto operations.""" + +# Python3 preparedness imports. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import ctypes as ct +import os + +from tensorflow.contrib.proto.python.kernel_tests import test_example_pb2 +from tensorflow.core.framework import types_pb2 +from tensorflow.python.platform import test + + +class ProtoOpTestBase(test.TestCase): + """Base class for testing proto decoding and encoding ops.""" + + def __init__(self, methodName="runTest"): # pylint: disable=invalid-name + super(ProtoOpTestBase, self).__init__(methodName) + lib = os.path.join(os.path.dirname(__file__), "libtestexample.so") + if os.path.isfile(lib): + ct.cdll.LoadLibrary(lib) + + @staticmethod + def named_parameters(): + return ( + ("defaults", ProtoOpTestBase.defaults_test_case()), + ("minmax", ProtoOpTestBase.minmax_test_case()), + ("nested", ProtoOpTestBase.nested_test_case()), + ("optional", ProtoOpTestBase.optional_test_case()), + ("promote", ProtoOpTestBase.promote_test_case()), + ("ragged", ProtoOpTestBase.ragged_test_case()), + ("shaped_batch", ProtoOpTestBase.shaped_batch_test_case()), + ("simple", ProtoOpTestBase.simple_test_case()), + ) + + @staticmethod + def defaults_test_case(): + test_case = test_example_pb2.TestCase() + test_case.values.add() # No fields specified, so we get all defaults. + test_case.shapes.append(1) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "double_value_with_default" + field.dtype = types_pb2.DT_DOUBLE + field.value.double_value.append(1.0) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "float_value_with_default" + field.dtype = types_pb2.DT_FLOAT + field.value.float_value.append(2.0) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "int64_value_with_default" + field.dtype = types_pb2.DT_INT64 + field.value.int64_value.append(3) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "sfixed64_value_with_default" + field.dtype = types_pb2.DT_INT64 + field.value.int64_value.append(11) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "sint64_value_with_default" + field.dtype = types_pb2.DT_INT64 + field.value.int64_value.append(13) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "uint64_value_with_default" + field.dtype = types_pb2.DT_UINT64 + field.value.uint64_value.append(4) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "fixed64_value_with_default" + field.dtype = types_pb2.DT_UINT64 + field.value.uint64_value.append(6) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "int32_value_with_default" + field.dtype = types_pb2.DT_INT32 + field.value.int32_value.append(5) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "sfixed32_value_with_default" + field.dtype = types_pb2.DT_INT32 + field.value.int32_value.append(10) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "sint32_value_with_default" + field.dtype = types_pb2.DT_INT32 + field.value.int32_value.append(12) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "uint32_value_with_default" + field.dtype = types_pb2.DT_UINT32 + field.value.uint32_value.append(9) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "fixed32_value_with_default" + field.dtype = types_pb2.DT_UINT32 + field.value.uint32_value.append(7) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "bool_value_with_default" + field.dtype = types_pb2.DT_BOOL + field.value.bool_value.append(True) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "string_value_with_default" + field.dtype = types_pb2.DT_STRING + field.value.string_value.append("a") + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "bytes_value_with_default" + field.dtype = types_pb2.DT_STRING + field.value.string_value.append("a longer default string") + return test_case + + @staticmethod + def minmax_test_case(): + test_case = test_example_pb2.TestCase() + value = test_case.values.add() + value.double_value.append(-1.7976931348623158e+308) + value.double_value.append(2.2250738585072014e-308) + value.double_value.append(1.7976931348623158e+308) + value.float_value.append(-3.402823466e+38) + value.float_value.append(1.175494351e-38) + value.float_value.append(3.402823466e+38) + value.int64_value.append(-9223372036854775808) + value.int64_value.append(9223372036854775807) + value.sfixed64_value.append(-9223372036854775808) + value.sfixed64_value.append(9223372036854775807) + value.sint64_value.append(-9223372036854775808) + value.sint64_value.append(9223372036854775807) + value.uint64_value.append(0) + value.uint64_value.append(18446744073709551615) + value.fixed64_value.append(0) + value.fixed64_value.append(18446744073709551615) + value.int32_value.append(-2147483648) + value.int32_value.append(2147483647) + value.sfixed32_value.append(-2147483648) + value.sfixed32_value.append(2147483647) + value.sint32_value.append(-2147483648) + value.sint32_value.append(2147483647) + value.uint32_value.append(0) + value.uint32_value.append(4294967295) + value.fixed32_value.append(0) + value.fixed32_value.append(4294967295) + value.bool_value.append(False) + value.bool_value.append(True) + value.string_value.append("") + value.string_value.append("I refer to the infinite.") + test_case.shapes.append(1) + test_case.sizes.append(3) + field = test_case.fields.add() + field.name = "double_value" + field.dtype = types_pb2.DT_DOUBLE + field.value.double_value.append(-1.7976931348623158e+308) + field.value.double_value.append(2.2250738585072014e-308) + field.value.double_value.append(1.7976931348623158e+308) + test_case.sizes.append(3) + field = test_case.fields.add() + field.name = "float_value" + field.dtype = types_pb2.DT_FLOAT + field.value.float_value.append(-3.402823466e+38) + field.value.float_value.append(1.175494351e-38) + field.value.float_value.append(3.402823466e+38) + test_case.sizes.append(2) + field = test_case.fields.add() + field.name = "int64_value" + field.dtype = types_pb2.DT_INT64 + field.value.int64_value.append(-9223372036854775808) + field.value.int64_value.append(9223372036854775807) + test_case.sizes.append(2) + field = test_case.fields.add() + field.name = "sfixed64_value" + field.dtype = types_pb2.DT_INT64 + field.value.int64_value.append(-9223372036854775808) + field.value.int64_value.append(9223372036854775807) + test_case.sizes.append(2) + field = test_case.fields.add() + field.name = "sint64_value" + field.dtype = types_pb2.DT_INT64 + field.value.int64_value.append(-9223372036854775808) + field.value.int64_value.append(9223372036854775807) + test_case.sizes.append(2) + field = test_case.fields.add() + field.name = "uint64_value" + field.dtype = types_pb2.DT_UINT64 + field.value.uint64_value.append(0) + field.value.uint64_value.append(18446744073709551615) + test_case.sizes.append(2) + field = test_case.fields.add() + field.name = "fixed64_value" + field.dtype = types_pb2.DT_UINT64 + field.value.uint64_value.append(0) + field.value.uint64_value.append(18446744073709551615) + test_case.sizes.append(2) + field = test_case.fields.add() + field.name = "int32_value" + field.dtype = types_pb2.DT_INT32 + field.value.int32_value.append(-2147483648) + field.value.int32_value.append(2147483647) + test_case.sizes.append(2) + field = test_case.fields.add() + field.name = "sfixed32_value" + field.dtype = types_pb2.DT_INT32 + field.value.int32_value.append(-2147483648) + field.value.int32_value.append(2147483647) + test_case.sizes.append(2) + field = test_case.fields.add() + field.name = "sint32_value" + field.dtype = types_pb2.DT_INT32 + field.value.int32_value.append(-2147483648) + field.value.int32_value.append(2147483647) + test_case.sizes.append(2) + field = test_case.fields.add() + field.name = "uint32_value" + field.dtype = types_pb2.DT_UINT32 + field.value.uint32_value.append(0) + field.value.uint32_value.append(4294967295) + test_case.sizes.append(2) + field = test_case.fields.add() + field.name = "fixed32_value" + field.dtype = types_pb2.DT_UINT32 + field.value.uint32_value.append(0) + field.value.uint32_value.append(4294967295) + test_case.sizes.append(2) + field = test_case.fields.add() + field.name = "bool_value" + field.dtype = types_pb2.DT_BOOL + field.value.bool_value.append(False) + field.value.bool_value.append(True) + test_case.sizes.append(2) + field = test_case.fields.add() + field.name = "string_value" + field.dtype = types_pb2.DT_STRING + field.value.string_value.append("") + field.value.string_value.append("I refer to the infinite.") + return test_case + + @staticmethod + def nested_test_case(): + test_case = test_example_pb2.TestCase() + value = test_case.values.add() + message_value = value.message_value.add() + message_value.double_value = 23.5 + test_case.shapes.append(1) + test_case.sizes.append(1) + field = test_case.fields.add() + field.name = "message_value" + field.dtype = types_pb2.DT_STRING + message_value = field.value.message_value.add() + message_value.double_value = 23.5 + return test_case + + @staticmethod + def optional_test_case(): + test_case = test_example_pb2.TestCase() + value = test_case.values.add() + value.bool_value.append(True) + test_case.shapes.append(1) + test_case.sizes.append(1) + field = test_case.fields.add() + field.name = "bool_value" + field.dtype = types_pb2.DT_BOOL + field.value.bool_value.append(True) + test_case.sizes.append(0) + field = test_case.fields.add() + field.name = "double_value" + field.dtype = types_pb2.DT_DOUBLE + field.value.double_value.append(0.0) + return test_case + + @staticmethod + def promote_test_case(): + test_case = test_example_pb2.TestCase() + value = test_case.values.add() + value.sint32_value.append(2147483647) + value.sfixed32_value.append(2147483647) + value.int32_value.append(2147483647) + value.fixed32_value.append(4294967295) + value.uint32_value.append(4294967295) + test_case.shapes.append(1) + test_case.sizes.append(1) + field = test_case.fields.add() + field.name = "sint32_value" + field.dtype = types_pb2.DT_INT64 + field.value.int64_value.append(2147483647) + test_case.sizes.append(1) + field = test_case.fields.add() + field.name = "sfixed32_value" + field.dtype = types_pb2.DT_INT64 + field.value.int64_value.append(2147483647) + test_case.sizes.append(1) + field = test_case.fields.add() + field.name = "int32_value" + field.dtype = types_pb2.DT_INT64 + field.value.int64_value.append(2147483647) + test_case.sizes.append(1) + field = test_case.fields.add() + field.name = "fixed32_value" + field.dtype = types_pb2.DT_UINT64 + field.value.uint64_value.append(4294967295) + test_case.sizes.append(1) + field = test_case.fields.add() + field.name = "uint32_value" + field.dtype = types_pb2.DT_UINT64 + field.value.uint64_value.append(4294967295) + return test_case + + @staticmethod + def ragged_test_case(): + test_case = test_example_pb2.TestCase() + value = test_case.values.add() + value.double_value.append(23.5) + value.double_value.append(123.0) + value.bool_value.append(True) + value = test_case.values.add() + value.double_value.append(3.1) + value.bool_value.append(False) + test_case.shapes.append(2) + test_case.sizes.append(2) + test_case.sizes.append(1) + test_case.sizes.append(1) + test_case.sizes.append(1) + field = test_case.fields.add() + field.name = "double_value" + field.dtype = types_pb2.DT_DOUBLE + field.value.double_value.append(23.5) + field.value.double_value.append(123.0) + field.value.double_value.append(3.1) + field.value.double_value.append(0.0) + field = test_case.fields.add() + field.name = "bool_value" + field.dtype = types_pb2.DT_BOOL + field.value.bool_value.append(True) + field.value.bool_value.append(False) + return test_case + + @staticmethod + def shaped_batch_test_case(): + test_case = test_example_pb2.TestCase() + value = test_case.values.add() + value.double_value.append(23.5) + value.bool_value.append(True) + value = test_case.values.add() + value.double_value.append(44.0) + value.bool_value.append(False) + value = test_case.values.add() + value.double_value.append(3.14159) + value.bool_value.append(True) + value = test_case.values.add() + value.double_value.append(1.414) + value.bool_value.append(True) + value = test_case.values.add() + value.double_value.append(-32.2) + value.bool_value.append(False) + value = test_case.values.add() + value.double_value.append(0.0001) + value.bool_value.append(True) + test_case.shapes.append(3) + test_case.shapes.append(2) + for _ in range(12): + test_case.sizes.append(1) + field = test_case.fields.add() + field.name = "double_value" + field.dtype = types_pb2.DT_DOUBLE + field.value.double_value.append(23.5) + field.value.double_value.append(44.0) + field.value.double_value.append(3.14159) + field.value.double_value.append(1.414) + field.value.double_value.append(-32.2) + field.value.double_value.append(0.0001) + field = test_case.fields.add() + field.name = "bool_value" + field.dtype = types_pb2.DT_BOOL + field.value.bool_value.append(True) + field.value.bool_value.append(False) + field.value.bool_value.append(True) + field.value.bool_value.append(True) + field.value.bool_value.append(False) + field.value.bool_value.append(True) + return test_case + + @staticmethod + def simple_test_case(): + test_case = test_example_pb2.TestCase() + value = test_case.values.add() + value.double_value.append(23.5) + value.bool_value.append(True) + test_case.shapes.append(1) + test_case.sizes.append(1) + field = test_case.fields.add() + field.name = "double_value" + field.dtype = types_pb2.DT_DOUBLE + field.value.double_value.append(23.5) + test_case.sizes.append(1) + field = test_case.fields.add() + field.name = "bool_value" + field.dtype = types_pb2.DT_BOOL + field.value.bool_value.append(True) + return test_case diff --git a/tensorflow/contrib/proto/python/kernel_tests/ragged.TestCase.pbtxt b/tensorflow/contrib/proto/python/kernel_tests/ragged.TestCase.pbtxt deleted file mode 100644 index 61c7ac53f72b0764a0d57241cbdcdd93fcbd9279..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/proto/python/kernel_tests/ragged.TestCase.pbtxt +++ /dev/null @@ -1,32 +0,0 @@ -primitive { - double_value: 23.5 - double_value: 123.0 - bool_value: true -} -primitive { - double_value: 3.1 - bool_value: false -} -shape: 2 -sizes: 2 -sizes: 1 -sizes: 1 -sizes: 1 -field { - name: "double_value" - dtype: DT_DOUBLE - expected { - double_value: 23.5 - double_value: 123.0 - double_value: 3.1 - double_value: 0.0 - } -} -field { - name: "bool_value" - dtype: DT_BOOL - expected { - bool_value: true - bool_value: false - } -} diff --git a/tensorflow/contrib/proto/python/kernel_tests/shaped_batch.TestCase.pbtxt b/tensorflow/contrib/proto/python/kernel_tests/shaped_batch.TestCase.pbtxt deleted file mode 100644 index f4828076d52dc5d03a887c4a445dbcf52414c361..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/proto/python/kernel_tests/shaped_batch.TestCase.pbtxt +++ /dev/null @@ -1,62 +0,0 @@ -primitive { - double_value: 23.5 - bool_value: true -} -primitive { - double_value: 44.0 - bool_value: false -} -primitive { - double_value: 3.14159 - bool_value: true -} -primitive { - double_value: 1.414 - bool_value: true -} -primitive { - double_value: -32.2 - bool_value: false -} -primitive { - double_value: 0.0001 - bool_value: true -} -shape: 3 -shape: 2 -sizes: 1 -sizes: 1 -sizes: 1 -sizes: 1 -sizes: 1 -sizes: 1 -sizes: 1 -sizes: 1 -sizes: 1 -sizes: 1 -sizes: 1 -sizes: 1 -field { - name: "double_value" - dtype: DT_DOUBLE - expected { - double_value: 23.5 - double_value: 44.0 - double_value: 3.14159 - double_value: 1.414 - double_value: -32.2 - double_value: 0.0001 - } -} -field { - name: "bool_value" - dtype: DT_BOOL - expected { - bool_value: true - bool_value: false - bool_value: true - bool_value: true - bool_value: false - bool_value: true - } -} diff --git a/tensorflow/contrib/proto/python/kernel_tests/simple.TestCase.pbtxt b/tensorflow/contrib/proto/python/kernel_tests/simple.TestCase.pbtxt deleted file mode 100644 index dc20ac147b0e772f05b4fc614f9f56513aceb1d5..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/proto/python/kernel_tests/simple.TestCase.pbtxt +++ /dev/null @@ -1,21 +0,0 @@ -primitive { - double_value: 23.5 - bool_value: true -} -shape: 1 -sizes: 1 -sizes: 1 -field { - name: "double_value" - dtype: DT_DOUBLE - expected { - double_value: 23.5 - } -} -field { - name: "bool_value" - dtype: DT_BOOL - expected { - bool_value: true - } -} diff --git a/tensorflow/contrib/proto/python/kernel_tests/test_example.proto b/tensorflow/contrib/proto/python/kernel_tests/test_example.proto index a2c88e372bf7c6b7f14c5bb55776b66c4c06bcd4..674d881220a1113631def47c5111e3ef401b99f3 100644 --- a/tensorflow/contrib/proto/python/kernel_tests/test_example.proto +++ b/tensorflow/contrib/proto/python/kernel_tests/test_example.proto @@ -1,6 +1,4 @@ // Test description and protos to work with it. -// -// Many of the protos in this file are for unit tests that haven't been written yet. syntax = "proto2"; @@ -8,54 +6,27 @@ import "tensorflow/core/framework/types.proto"; package tensorflow.contrib.proto; -// A TestCase holds a proto and a bunch of assertions -// about how it should decode. +// A TestCase holds a proto and assertions about how it should decode. message TestCase { - // A batch of primitives to be serialized and decoded. - repeated RepeatedPrimitiveValue primitive = 1; - // The shape of the batch. - repeated int32 shape = 2; + // Batches of primitive values. + repeated TestValue values = 1; + // The batch shapes. + repeated int32 shapes = 2; // Expected sizes for each field. repeated int32 sizes = 3; // Expected values for each field. - repeated FieldSpec field = 4; + repeated FieldSpec fields = 4; }; // FieldSpec describes the expected output for a single field. message FieldSpec { optional string name = 1; optional tensorflow.DataType dtype = 2; - optional RepeatedPrimitiveValue expected = 3; + optional TestValue value = 3; }; +// NOTE: This definition must be kept in sync with PackedTestValue. message TestValue { - optional PrimitiveValue primitive_value = 1; - optional EnumValue enum_value = 2; - optional MessageValue message_value = 3; - optional RepeatedMessageValue repeated_message_value = 4; - optional RepeatedPrimitiveValue repeated_primitive_value = 6; -} - -message PrimitiveValue { - optional double double_value = 1; - optional float float_value = 2; - optional int64 int64_value = 3; - optional uint64 uint64_value = 4; - optional int32 int32_value = 5; - optional fixed64 fixed64_value = 6; - optional fixed32 fixed32_value = 7; - optional bool bool_value = 8; - optional string string_value = 9; - optional bytes bytes_value = 12; - optional uint32 uint32_value = 13; - optional sfixed32 sfixed32_value = 15; - optional sfixed64 sfixed64_value = 16; - optional sint32 sint32_value = 17; - optional sint64 sint64_value = 18; -} - -// NOTE: This definition must be kept in sync with PackedPrimitiveValue. -message RepeatedPrimitiveValue { repeated double double_value = 1; repeated float float_value = 2; repeated int64 int64_value = 3; @@ -74,30 +45,31 @@ message RepeatedPrimitiveValue { repeated PrimitiveValue message_value = 19; // Optional fields with explicitly-specified defaults. - optional double double_default = 20 [default = 1.0]; - optional float float_default = 21 [default = 2.0]; - optional int64 int64_default = 22 [default = 3]; - optional uint64 uint64_default = 23 [default = 4]; - optional int32 int32_default = 24 [default = 5]; - optional fixed64 fixed64_default = 25 [default = 6]; - optional fixed32 fixed32_default = 26 [default = 7]; - optional bool bool_default = 27 [default = true]; - optional string string_default = 28 [default = "a"]; - optional bytes bytes_default = 29 [default = "a longer default string"]; - optional uint32 uint32_default = 30 [default = 4294967295]; - optional sfixed32 sfixed32_default = 31 [default = 10]; - optional sfixed64 sfixed64_default = 32 [default = 11]; - optional sint32 sint32_default = 33 [default = 12]; - optional sint64 sint64_default = 34 [default = 13]; + optional double double_value_with_default = 20 [default = 1.0]; + optional float float_value_with_default = 21 [default = 2.0]; + optional int64 int64_value_with_default = 22 [default = 3]; + optional uint64 uint64_value_with_default = 23 [default = 4]; + optional int32 int32_value_with_default = 24 [default = 5]; + optional fixed64 fixed64_value_with_default = 25 [default = 6]; + optional fixed32 fixed32_value_with_default = 26 [default = 7]; + optional bool bool_value_with_default = 27 [default = true]; + optional string string_value_with_default = 28 [default = "a"]; + optional bytes bytes_value_with_default = 29 + [default = "a longer default string"]; + optional uint32 uint32_value_with_default = 30 [default = 9]; + optional sfixed32 sfixed32_value_with_default = 31 [default = 10]; + optional sfixed64 sfixed64_value_with_default = 32 [default = 11]; + optional sint32 sint32_value_with_default = 33 [default = 12]; + optional sint64 sint64_value_with_default = 34 [default = 13]; } -// A PackedPrimitiveValue looks exactly the same as a RepeatedPrimitiveValue -// in the text format, but the binary serializion is different. -// We test the packed representations by loading the same test cases -// using this definition instead of RepeatedPrimitiveValue. -// NOTE: This definition must be kept in sync with RepeatedPrimitiveValue -// in every way except the packed=true declaration. -message PackedPrimitiveValue { +// A PackedTestValue looks exactly the same as a TestValue in the text format, +// but the binary serializion is different. We test the packed representations +// by loading the same test cases using this definition instead of TestValue. +// +// NOTE: This definition must be kept in sync with TestValue in every way except +// the packed=true declaration. +message PackedTestValue { repeated double double_value = 1 [packed = true]; repeated float float_value = 2 [packed = true]; repeated int64 int64_value = 3 [packed = true]; @@ -115,23 +87,53 @@ message PackedPrimitiveValue { repeated sint64 sint64_value = 18 [packed = true]; repeated PrimitiveValue message_value = 19; - optional double double_default = 20 [default = 1.0]; - optional float float_default = 21 [default = 2.0]; - optional int64 int64_default = 22 [default = 3]; - optional uint64 uint64_default = 23 [default = 4]; - optional int32 int32_default = 24 [default = 5]; - optional fixed64 fixed64_default = 25 [default = 6]; - optional fixed32 fixed32_default = 26 [default = 7]; - optional bool bool_default = 27 [default = true]; - optional string string_default = 28 [default = "a"]; - optional bytes bytes_default = 29 [default = "a longer default string"]; - optional uint32 uint32_default = 30 [default = 4294967295]; - optional sfixed32 sfixed32_default = 31 [default = 10]; - optional sfixed64 sfixed64_default = 32 [default = 11]; - optional sint32 sint32_default = 33 [default = 12]; - optional sint64 sint64_default = 34 [default = 13]; + optional double double_value_with_default = 20 [default = 1.0]; + optional float float_value_with_default = 21 [default = 2.0]; + optional int64 int64_value_with_default = 22 [default = 3]; + optional uint64 uint64_value_with_default = 23 [default = 4]; + optional int32 int32_value_with_default = 24 [default = 5]; + optional fixed64 fixed64_value_with_default = 25 [default = 6]; + optional fixed32 fixed32_value_with_default = 26 [default = 7]; + optional bool bool_value_with_default = 27 [default = true]; + optional string string_value_with_default = 28 [default = "a"]; + optional bytes bytes_value_with_default = 29 + [default = "a longer default string"]; + optional uint32 uint32_value_with_default = 30 [default = 9]; + optional sfixed32 sfixed32_value_with_default = 31 [default = 10]; + optional sfixed64 sfixed64_value_with_default = 32 [default = 11]; + optional sint32 sint32_value_with_default = 33 [default = 12]; + optional sint64 sint64_value_with_default = 34 [default = 13]; } +message PrimitiveValue { + optional double double_value = 1; + optional float float_value = 2; + optional int64 int64_value = 3; + optional uint64 uint64_value = 4; + optional int32 int32_value = 5; + optional fixed64 fixed64_value = 6; + optional fixed32 fixed32_value = 7; + optional bool bool_value = 8; + optional string string_value = 9; + optional bytes bytes_value = 12; + optional uint32 uint32_value = 13; + optional sfixed32 sfixed32_value = 15; + optional sfixed64 sfixed64_value = 16; + optional sint32 sint32_value = 17; + optional sint64 sint64_value = 18; +} + +// Message containing fields with field numbers higher than any field above. +// An instance of this message is prepended to each binary message in the test +// to exercise the code path that handles fields encoded out of order of field +// number. +message ExtraFields { + optional string string_value = 1776; + optional bool bool_value = 1777; +} + +// The messages below are for yet-to-be created tests. + message EnumValue { enum Color { RED = 0; @@ -171,12 +173,3 @@ message RepeatedMessageValue { repeated NestedMessageValue message_values = 11; } - -// Message containing fields with field numbers higher than any field above. An -// instance of this message is prepended to each binary message in the test to -// exercise the code path that handles fields encoded out of order of field -// number. -message ExtraFields { - optional string string_value = 1776; - optional bool bool_value = 1777; -} diff --git a/tensorflow/contrib/quantize/python/quantize.py b/tensorflow/contrib/quantize/python/quantize.py index 19e5bef1ea48ca4441cdef6b1a74e98e9cf6ddb9..4fc315d901a86ac235513aad6eb34d7f90f61801 100644 --- a/tensorflow/contrib/quantize/python/quantize.py +++ b/tensorflow/contrib/quantize/python/quantize.py @@ -278,6 +278,13 @@ def _FindLayersToQuantize(graph): ], ordered_inputs=False) + # batch_norms with forced updates have an Identity operation at the end. + # TODO(suharshs): Find a way to easily skip extra Identity operations. The + # current issue is that doing so can often match patterns across many layers + # incorrectly. + batch_norm_identity = graph_matcher.OpTypePattern( + 'Identity', inputs=[folded_bias_add_pattern]) + bias_add_pattern = graph_matcher.OpTypePattern( 'Add|BiasAdd', inputs=[layer_output_pattern, '*'], ordered_inputs=False) @@ -286,20 +293,22 @@ def _FindLayersToQuantize(graph): 'Add', inputs=[ graph_matcher.OneofPattern( - [bias_add_pattern, folded_bias_add_pattern]), '*' + [bias_add_pattern, folded_bias_add_pattern, batch_norm_identity]), + '*' ], ordered_inputs=False) # The input to the activation can come from bias add, fold bias add, the # bypasses. # TODO(suharshs): We should ideally skip Identity operations instead of - # treating them as an activation. + # treating them as activations. activation_pattern = graph_matcher.OpTypePattern( '|'.join(_ACTIVATION_TYPES) + '|Identity', inputs=[ graph_matcher.OneofPattern([ bias_add_pattern, folded_bias_add_pattern, + batch_norm_identity, bypass_pattern, ]) ]) diff --git a/tensorflow/contrib/quantize/python/quantize_graph.py b/tensorflow/contrib/quantize/python/quantize_graph.py index 11d052d7f491dc029d1bda9b47364d6e9c880a67..2944f964c7078814111c96890f18abe1607b68fc 100644 --- a/tensorflow/contrib/quantize/python/quantize_graph.py +++ b/tensorflow/contrib/quantize/python/quantize_graph.py @@ -191,6 +191,7 @@ def experimental_create_training_graph(input_graph=None, def experimental_create_eval_graph(input_graph=None, weight_bits=8, activation_bits=8, + quant_delay=None, scope=None): """Rewrites an eval input_graph in place for simulated quantization. @@ -209,6 +210,8 @@ def experimental_create_eval_graph(input_graph=None, default graph. weight_bits: Number of bits to use for quantizing weights. activation_bits: Number of bits to use for quantizing activations. + quant_delay: Number of steps after which weights and activations are + quantized during eval. scope: The scope to be transformed. If it's not None, only the ops which are in this scope will be transformed. @@ -221,4 +224,5 @@ def experimental_create_eval_graph(input_graph=None, is_training=False, weight_bits=weight_bits, activation_bits=activation_bits, + quant_delay=quant_delay, scope=scope) diff --git a/tensorflow/contrib/quantize/python/quantize_parameterized_test.py b/tensorflow/contrib/quantize/python/quantize_parameterized_test.py index 5e3af0a567536ef6fcfd86d82e94c0ba21077a85..31a2955ddb3b32f2b07c6125c8f83ffba335cc5f 100644 --- a/tensorflow/contrib/quantize/python/quantize_parameterized_test.py +++ b/tensorflow/contrib/quantize/python/quantize_parameterized_test.py @@ -654,8 +654,80 @@ class QuantizeTest(test_util.TensorFlowTestCase): graph_def_after = str(graph.as_graph_def()) self.assertEqual(graph_def_before, graph_def_after) - def _BatchNormParams(self, fused=False): - return {'center': True, 'scale': True, 'decay': 1.0 - 0.003, 'fused': fused} + def testBatchNormForcedUpdates(self): + parameter_list = [ + # (activation, activation_op_name, fused_batch_norm) + (nn_ops.relu6, 'Relu6', False), + (nn_ops.relu, 'Relu', False), + (array_ops.identity, 'Identity', False), + (nn_ops.relu6, 'Relu6', True), + (nn_ops.relu, 'Relu', True), + (array_ops.identity, 'Identity', True), + ] + for params in parameter_list: + self._TestBatchNormForcedUpdates(params[0], params[1], params[2], False) + self._TestBatchNormForcedUpdates(params[0], params[1], params[2], True) + + def _TestBatchNormForcedUpdates(self, activation, activation_op_name, + fused_batch_norm, use_resource): + """post_activation bypass quantization should happen with forced updates.""" + graph = ops.Graph() + with graph.as_default(): + variable_scope.get_variable_scope().set_use_resource(use_resource) + batch_size, height, width, depth = 5, 128, 128, 3 + input1 = array_ops.zeros((batch_size, height, width, depth)) + input2 = array_ops.zeros((batch_size, height / 2, width / 2, 32)) + # Setting updates_collections to None forces updates adding an extra + # identity operation following batch norms. + bn_params = self._BatchNormParams( + fused=fused_batch_norm, force_updates=True) + conv = conv2d( + input1, + 32, [5, 5], + stride=2, + padding='SAME', + weights_initializer=self._WeightInit(0.09), + activation_fn=activation, + normalizer_fn=batch_norm, + normalizer_params=bn_params, + scope='test/test') + bypass_tensor = math_ops.add(conv, input2, name='test/add') + # The output of the post_activation bypass will be another layer. + _ = conv2d( + bypass_tensor, + 32, [5, 5], + stride=2, + padding='SAME', + weights_initializer=self._WeightInit(0.09), + normalizer_fn=batch_norm, + normalizer_params=bn_params, + activation_fn=activation, + scope='test/unused') + + fold_batch_norms.FoldBatchNorms(graph, is_training=True) + quantize.Quantize(graph, is_training=True) + + # Ensure that the bypass node is preceded by and followed by a + # FakeQuantWithMinMaxVar operation, since the output of the Add isn't an + # activation. + self.assertTrue('FakeQuantWithMinMaxVars' in + [c.type for c in bypass_tensor.consumers()]) + self.assertTrue('FakeQuantWithMinMaxVars' in + [i.op.type for i in bypass_tensor.op.inputs]) + + with open('/tmp/bn_quant_test.pbtxt', 'w') as f: + f.write(str(graph.as_graph_def())) + + def _BatchNormParams(self, fused=False, force_updates=False): + params = { + 'center': True, + 'scale': True, + 'decay': 1.0 - 0.003, + 'fused': fused + } + if force_updates: + params['updates_collections'] = None + return params def _WeightInit(self, stddev): """Returns truncated normal variable initializer. diff --git a/tensorflow/contrib/recurrent/python/ops/recurrent.py b/tensorflow/contrib/recurrent/python/ops/recurrent.py index fa16b82ab62f27d034c3ca7584e7e1ca14be6f9b..4f289e0c85e2260a44a8ea2f3f1d6cacbc839f66 100644 --- a/tensorflow/contrib/recurrent/python/ops/recurrent.py +++ b/tensorflow/contrib/recurrent/python/ops/recurrent.py @@ -79,7 +79,7 @@ def _Index(struct, index): """ index = ops.convert_to_tensor(index) index.get_shape().assert_has_rank(0) - return nest.map_structure(lambda x: x[index], struct) + return nest.map_structure(lambda x: array_ops.gather(x, index), struct) def _Update(struct_acc, struct_x, t): diff --git a/tensorflow/contrib/rnn/BUILD b/tensorflow/contrib/rnn/BUILD index 4eb5c920b3517a8968ff730003e786ae2a9c9e26..2a84629080d20e38807a4be87e51646c3046ebf3 100644 --- a/tensorflow/contrib/rnn/BUILD +++ b/tensorflow/contrib/rnn/BUILD @@ -118,7 +118,6 @@ cuda_py_tests( "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:init_ops", "//tensorflow/python:math_ops", - "//tensorflow/python:random_ops", "//tensorflow/python:rnn", "//tensorflow/python:rnn_cell", "//tensorflow/python:variable_scope", diff --git a/tensorflow/contrib/rnn/__init__.py b/tensorflow/contrib/rnn/__init__.py index 07227bcb77d353200ee46763d51727ed9c0974a1..cb437f2a2f252fcb0763587b07fed19be5887282 100644 --- a/tensorflow/contrib/rnn/__init__.py +++ b/tensorflow/contrib/rnn/__init__.py @@ -59,6 +59,9 @@ See @{$python/contrib.rnn} guide. @@HighwayWrapper @@GLSTMCell @@SRUCell +@@IndRNNCell +@@IndyGRUCell +@@IndyLSTMCell @@AttentionCellWrapper diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py index 86f1e27abd53d011f37f06851dd6d0977853c8f4..85f0f8ced91e15cd0f9b3bc51f3a9e3aee12c978 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py @@ -18,7 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import functools import os import numpy as np @@ -35,7 +34,6 @@ from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import random_ops from tensorflow.python.ops import rnn from tensorflow.python.ops import rnn_cell_impl from tensorflow.python.ops import variable_scope @@ -117,6 +115,27 @@ class RNNCellTest(test.TestCase): }) self.assertEqual(res[0].shape, (1, 2)) + def testIndRNNCell(self): + with self.test_session() as sess: + with variable_scope.variable_scope( + "root", initializer=init_ops.constant_initializer(0.5)): + x = array_ops.zeros([1, 2]) + m = array_ops.zeros([1, 2]) + cell = contrib_rnn_cell.IndRNNCell(2) + g, _ = cell(x, m) + self.assertEqual([ + "root/ind_rnn_cell/%s_w:0" % rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + "root/ind_rnn_cell/%s_u:0" % rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + "root/ind_rnn_cell/%s:0" % rnn_cell_impl._BIAS_VARIABLE_NAME + ], [v.name for v in cell.trainable_variables]) + self.assertFalse(cell.non_trainable_variables) + sess.run([variables_lib.global_variables_initializer()]) + res = sess.run([g], { + x.name: np.array([[1., 1.]]), + m.name: np.array([[0.1, 0.1]]) + }) + self.assertEqual(res[0].shape, (1, 2)) + def testGRUCell(self): with self.test_session() as sess: with variable_scope.variable_scope( @@ -145,6 +164,34 @@ class RNNCellTest(test.TestCase): # Smoke test self.assertAllClose(res[0], [[0.156736, 0.156736]]) + def testIndyGRUCell(self): + with self.test_session() as sess: + with variable_scope.variable_scope( + "root", initializer=init_ops.constant_initializer(0.5)): + x = array_ops.zeros([1, 2]) + m = array_ops.zeros([1, 2]) + g, _ = contrib_rnn_cell.IndyGRUCell(2)(x, m) + sess.run([variables_lib.global_variables_initializer()]) + res = sess.run([g], { + x.name: np.array([[1., 1.]]), + m.name: np.array([[0.1, 0.1]]) + }) + # Smoke test + self.assertAllClose(res[0], [[0.185265, 0.17704]]) + with variable_scope.variable_scope( + "other", initializer=init_ops.constant_initializer(0.5)): + # Test IndyGRUCell with input_size != num_units. + x = array_ops.zeros([1, 3]) + m = array_ops.zeros([1, 2]) + g, _ = contrib_rnn_cell.IndyGRUCell(2)(x, m) + sess.run([variables_lib.global_variables_initializer()]) + res = sess.run([g], { + x.name: np.array([[1., 1., 1.]]), + m.name: np.array([[0.1, 0.1]]) + }) + # Smoke test + self.assertAllClose(res[0], [[0.155127, 0.157328]]) + def testSRUCell(self): with self.test_session() as sess: with variable_scope.variable_scope( @@ -345,6 +392,72 @@ class RNNCellTest(test.TestCase): self.assertAllClose(res[1], expected_mem0) self.assertAllClose(res[2], expected_mem1) + def testIndyLSTMCell(self): + for dtype in [dtypes.float16, dtypes.float32]: + np_dtype = dtype.as_numpy_dtype + with self.test_session(graph=ops.Graph()) as sess: + with variable_scope.variable_scope( + "root", initializer=init_ops.constant_initializer(0.5)): + x = array_ops.zeros([1, 2], dtype=dtype) + state_0 = (array_ops.zeros([1, 2], dtype=dtype),) * 2 + state_1 = (array_ops.zeros([1, 2], dtype=dtype),) * 2 + cell = rnn_cell_impl.MultiRNNCell( + [contrib_rnn_cell.IndyLSTMCell(2) for _ in range(2)]) + self.assertEqual(cell.dtype, None) + self.assertEqual("cell-0", cell._checkpoint_dependencies[0].name) + self.assertEqual("cell-1", cell._checkpoint_dependencies[1].name) + cell.get_config() # Should not throw an error + g, (out_state_0, out_state_1) = cell(x, (state_0, state_1)) + # Layer infers the input type. + self.assertEqual(cell.dtype, dtype.name) + expected_variable_names = [ + "root/multi_rnn_cell/cell_0/indy_lstm_cell/%s_w:0" % + rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + "root/multi_rnn_cell/cell_0/indy_lstm_cell/%s_u:0" % + rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + "root/multi_rnn_cell/cell_0/indy_lstm_cell/%s:0" % + rnn_cell_impl._BIAS_VARIABLE_NAME, + "root/multi_rnn_cell/cell_1/indy_lstm_cell/%s_w:0" % + rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + "root/multi_rnn_cell/cell_1/indy_lstm_cell/%s_u:0" % + rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + "root/multi_rnn_cell/cell_1/indy_lstm_cell/%s:0" % + rnn_cell_impl._BIAS_VARIABLE_NAME + ] + self.assertEqual(expected_variable_names, + [v.name for v in cell.trainable_variables]) + self.assertFalse(cell.non_trainable_variables) + sess.run([variables_lib.global_variables_initializer()]) + res = sess.run( + [g, out_state_0, out_state_1], { + x.name: np.array([[1., 1.]]), + state_0[0].name: 0.1 * np.ones([1, 2]), + state_0[1].name: 0.1 * np.ones([1, 2]), + state_1[0].name: 0.1 * np.ones([1, 2]), + state_1[1].name: 0.1 * np.ones([1, 2]), + }) + self.assertEqual(len(res), 3) + variables = variables_lib.global_variables() + self.assertEqual(expected_variable_names, [v.name for v in variables]) + # Only check the range of outputs as this is just a smoke test. + self.assertAllInRange(res[0], -1.0, 1.0) + self.assertAllInRange(res[1], -1.0, 1.0) + self.assertAllInRange(res[2], -1.0, 1.0) + with variable_scope.variable_scope( + "other", initializer=init_ops.constant_initializer(0.5)): + # Test IndyLSTMCell with input_size != num_units. + x = array_ops.zeros([1, 3], dtype=dtype) + state = (array_ops.zeros([1, 2], dtype=dtype),) * 2 + g, out_state = contrib_rnn_cell.IndyLSTMCell(2)(x, state) + sess.run([variables_lib.global_variables_initializer()]) + res = sess.run( + [g, out_state], { + x.name: np.array([[1., 1., 1.]], dtype=np_dtype), + state[0].name: 0.1 * np.ones([1, 2], dtype=np_dtype), + state[1].name: 0.1 * np.ones([1, 2], dtype=np_dtype), + }) + self.assertEqual(len(res), 2) + def testLSTMCell(self): with self.test_session() as sess: num_units = 8 @@ -935,50 +1048,6 @@ class DropoutWrapperTest(test.TestCase): self.assertAllClose(res0[1].h, res1[1].h) -class SlimRNNCellTest(test.TestCase): - - def testBasicRNNCell(self): - with self.test_session() as sess: - with variable_scope.variable_scope( - "root", initializer=init_ops.constant_initializer(0.5)): - x = array_ops.zeros([1, 2]) - m = array_ops.zeros([1, 2]) - my_cell = functools.partial(basic_rnn_cell, num_units=2) - # pylint: disable=protected-access - g, _ = rnn_cell_impl._SlimRNNCell(my_cell)(x, m) - # pylint: enable=protected-access - sess.run([variables_lib.global_variables_initializer()]) - res = sess.run([g], { - x.name: np.array([[1., 1.]]), - m.name: np.array([[0.1, 0.1]]) - }) - self.assertEqual(res[0].shape, (1, 2)) - - def testBasicRNNCellMatch(self): - batch_size = 32 - input_size = 100 - num_units = 10 - with self.test_session() as sess: - with variable_scope.variable_scope( - "root", initializer=init_ops.constant_initializer(0.5)): - inputs = random_ops.random_uniform((batch_size, input_size)) - _, initial_state = basic_rnn_cell(inputs, None, num_units) - rnn_cell = rnn_cell_impl.BasicRNNCell(num_units) - outputs, state = rnn_cell(inputs, initial_state) - variable_scope.get_variable_scope().reuse_variables() - my_cell = functools.partial(basic_rnn_cell, num_units=num_units) - # pylint: disable=protected-access - slim_cell = rnn_cell_impl._SlimRNNCell(my_cell) - # pylint: enable=protected-access - slim_outputs, slim_state = slim_cell(inputs, initial_state) - self.assertEqual(slim_outputs.get_shape(), outputs.get_shape()) - self.assertEqual(slim_state.get_shape(), state.get_shape()) - sess.run([variables_lib.global_variables_initializer()]) - res = sess.run([slim_outputs, slim_state, outputs, state]) - self.assertAllClose(res[0], res[2]) - self.assertAllClose(res[1], res[3]) - - def basic_rnn_cell(inputs, state, num_units, scope=None): if state is None: if inputs is not None: diff --git a/tensorflow/contrib/rnn/python/ops/rnn.py b/tensorflow/contrib/rnn/python/ops/rnn.py index 2f0caadda336b878e58e973e1c995cbec65d5732..0266b72dcb15e4aba01a9a31b4be75c5b84d44da 100644 --- a/tensorflow/contrib/rnn/python/ops/rnn.py +++ b/tensorflow/contrib/rnn/python/ops/rnn.py @@ -175,7 +175,7 @@ def stack_bidirectional_dynamic_rnn(cells_fw, Returns: A tuple (outputs, output_state_fw, output_state_bw) where: outputs: Output `Tensor` shaped: - `batch_size, max_time, layers_output]`. Where layers_output + `[batch_size, max_time, layers_output]`. Where layers_output are depth-concatenated forward and backward outputs. output_states_fw is the final states, one tensor per layer, of the forward rnn. diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py index b12e2cd5eddc3f8abdba62781692673a40e41d9b..1816b469ee5bf338453a82d18663f97f6565dc0c 100644 --- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py +++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py @@ -23,6 +23,7 @@ import math from tensorflow.contrib.compiler import jit from tensorflow.contrib.layers.python.layers import layers from tensorflow.contrib.rnn.python.ops import core_rnn_cell +from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import op_def_registry from tensorflow.python.framework import ops @@ -30,6 +31,7 @@ from tensorflow.python.framework import tensor_shape from tensorflow.python.layers import base as base_layer from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops +from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_impl # pylint: disable=unused-import @@ -3050,3 +3052,343 @@ class WeightNormLSTMCell(rnn_cell_impl.RNNCell): new_state = rnn_cell_impl.LSTMStateTuple(new_c, new_h) return new_h, new_state + + +class IndRNNCell(rnn_cell_impl.LayerRNNCell): + """Independently Recurrent Neural Network (IndRNN) cell + (cf. https://arxiv.org/abs/1803.04831). + + Args: + num_units: int, The number of units in the RNN cell. + activation: Nonlinearity to use. Default: `tanh`. + reuse: (optional) Python boolean describing whether to reuse variables + in an existing scope. If not `True`, and the existing scope already has + the given variables, an error is raised. + name: String, the name of the layer. Layers with the same name will + share weights, but to avoid mistakes we require reuse=True in such + cases. + dtype: Default dtype of the layer (default of `None` means use the type + of the first input). Required when `build` is called before `call`. + """ + + def __init__(self, + num_units, + activation=None, + reuse=None, + name=None, + dtype=None): + super(IndRNNCell, self).__init__(_reuse=reuse, name=name, dtype=dtype) + + # Inputs must be 2-dimensional. + self.input_spec = base_layer.InputSpec(ndim=2) + + self._num_units = num_units + self._activation = activation or math_ops.tanh + + @property + def state_size(self): + return self._num_units + + @property + def output_size(self): + return self._num_units + + def build(self, inputs_shape): + if inputs_shape[1].value is None: + raise ValueError( + "Expected inputs.shape[-1] to be known, saw shape: %s" % inputs_shape) + + input_depth = inputs_shape[1].value + # pylint: disable=protected-access + self._kernel_w = self.add_variable( + "%s_w" % rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + shape=[input_depth, self._num_units]) + self._kernel_u = self.add_variable( + "%s_u" % rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + shape=[1, self._num_units], + initializer=init_ops.random_uniform_initializer( + minval=-1, maxval=1, dtype=self.dtype)) + self._bias = self.add_variable( + rnn_cell_impl._BIAS_VARIABLE_NAME, + shape=[self._num_units], + initializer=init_ops.zeros_initializer(dtype=self.dtype)) + # pylint: enable=protected-access + + self.built = True + + def call(self, inputs, state): + """IndRNN: output = new_state = act(W * input + u * state + B).""" + + gate_inputs = math_ops.matmul(inputs, self._kernel_w) + ( + state * self._kernel_u) + gate_inputs = nn_ops.bias_add(gate_inputs, self._bias) + output = self._activation(gate_inputs) + return output, output + + +class IndyGRUCell(rnn_cell_impl.LayerRNNCell): + r"""Independently Gated Recurrent Unit cell. + + Based on IndRNNs (https://arxiv.org/abs/1803.04831) and similar to GRUCell, + yet with the \(U_r\), \(U_z\), and \(U\) matrices in equations 5, 6, and + 8 of http://arxiv.org/abs/1406.1078 respectively replaced by diagonal + matrices, i.e. a Hadamard product with a single vector: + + $$r_j = \sigma\left([\mathbf W_r\mathbf x]_j + + [\mathbf u_r\circ \mathbf h_{(t-1)}]_j\right)$$ + $$z_j = \sigma\left([\mathbf W_z\mathbf x]_j + + [\mathbf u_z\circ \mathbf h_{(t-1)}]_j\right)$$ + $$\tilde{h}^{(t)}_j = \phi\left([\mathbf W \mathbf x]_j + + [\mathbf u \circ \mathbf r \circ \mathbf h_{(t-1)}]_j\right)$$ + + where \(\circ\) denotes the Hadamard operator. This means that each IndyGRU + node sees only its own state, as opposed to seeing all states in the same + layer. + + TODO(gonnet): Write a paper describing this and add a reference here. + + Args: + num_units: int, The number of units in the GRU cell. + activation: Nonlinearity to use. Default: `tanh`. + reuse: (optional) Python boolean describing whether to reuse variables + in an existing scope. If not `True`, and the existing scope already has + the given variables, an error is raised. + kernel_initializer: (optional) The initializer to use for the weight + matrices applied to the input. + bias_initializer: (optional) The initializer to use for the bias. + name: String, the name of the layer. Layers with the same name will + share weights, but to avoid mistakes we require reuse=True in such + cases. + dtype: Default dtype of the layer (default of `None` means use the type + of the first input). Required when `build` is called before `call`. + """ + + def __init__(self, + num_units, + activation=None, + reuse=None, + kernel_initializer=None, + bias_initializer=None, + name=None, + dtype=None): + super(IndyGRUCell, self).__init__(_reuse=reuse, name=name, dtype=dtype) + + # Inputs must be 2-dimensional. + self.input_spec = base_layer.InputSpec(ndim=2) + + self._num_units = num_units + self._activation = activation or math_ops.tanh + self._kernel_initializer = kernel_initializer + self._bias_initializer = bias_initializer + + @property + def state_size(self): + return self._num_units + + @property + def output_size(self): + return self._num_units + + def build(self, inputs_shape): + if inputs_shape[1].value is None: + raise ValueError( + "Expected inputs.shape[-1] to be known, saw shape: %s" % inputs_shape) + + input_depth = inputs_shape[1].value + # pylint: disable=protected-access + self._gate_kernel_w = self.add_variable( + "gates/%s_w" % rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + shape=[input_depth, 2 * self._num_units], + initializer=self._kernel_initializer) + self._gate_kernel_u = self.add_variable( + "gates/%s_u" % rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + shape=[1, 2 * self._num_units], + initializer=init_ops.random_uniform_initializer( + minval=-1, maxval=1, dtype=self.dtype)) + self._gate_bias = self.add_variable( + "gates/%s" % rnn_cell_impl._BIAS_VARIABLE_NAME, + shape=[2 * self._num_units], + initializer=(self._bias_initializer + if self._bias_initializer is not None else + init_ops.constant_initializer(1.0, dtype=self.dtype))) + self._candidate_kernel_w = self.add_variable( + "candidate/%s" % rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + shape=[input_depth, self._num_units], + initializer=self._kernel_initializer) + self._candidate_kernel_u = self.add_variable( + "candidate/%s_u" % rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + shape=[1, self._num_units], + initializer=init_ops.random_uniform_initializer( + minval=-1, maxval=1, dtype=self.dtype)) + self._candidate_bias = self.add_variable( + "candidate/%s" % rnn_cell_impl._BIAS_VARIABLE_NAME, + shape=[self._num_units], + initializer=(self._bias_initializer + if self._bias_initializer is not None else + init_ops.zeros_initializer(dtype=self.dtype))) + # pylint: enable=protected-access + + self.built = True + + def call(self, inputs, state): + """Gated recurrent unit (GRU) with nunits cells.""" + + gate_inputs = math_ops.matmul(inputs, self._gate_kernel_w) + ( + gen_array_ops.tile(state, [1, 2]) * self._gate_kernel_u) + gate_inputs = nn_ops.bias_add(gate_inputs, self._gate_bias) + + value = math_ops.sigmoid(gate_inputs) + r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1) + + r_state = r * state + + candidate = math_ops.matmul(inputs, self._candidate_kernel_w) + ( + r_state * self._candidate_kernel_u) + candidate = nn_ops.bias_add(candidate, self._candidate_bias) + + c = self._activation(candidate) + new_h = u * state + (1 - u) * c + return new_h, new_h + + +class IndyLSTMCell(rnn_cell_impl.LayerRNNCell): + r"""Basic IndyLSTM recurrent network cell. + + Based on IndRNNs (https://arxiv.org/abs/1803.04831) and similar to + BasicLSTMCell, yet with the \(U_f\), \(U_i\), \(U_o\) and \(U_c\) + matrices in + https://en.wikipedia.org/wiki/Long_short-term_memory#LSTM_with_a_forget_gate + replaced by diagonal matrices, i.e. a Hadamard product with a single vector: + + $$f_t = \sigma_g\left(W_f x_t + u_f \circ h_{t-1} + b_f\right)$$ + $$i_t = \sigma_g\left(W_i x_t + u_i \circ h_{t-1} + b_i\right)$$ + $$o_t = \sigma_g\left(W_o x_t + u_o \circ h_{t-1} + b_o\right)$$ + $$c_t = f_t \circ c_{t-1} + + i_t \circ \sigma_c\left(W_c x_t + u_c \circ h_{t-1} + b_c\right)$$ + + where \(\circ\) denotes the Hadamard operator. This means that each IndyLSTM + node sees only its own state \(h\) and \(c\), as opposed to seeing all + states in the same layer. + + We add forget_bias (default: 1) to the biases of the forget gate in order to + reduce the scale of forgetting in the beginning of the training. + + It does not allow cell clipping, a projection layer, and does not + use peep-hole connections: it is the basic baseline. + + For advanced models, please use the full @{tf.nn.rnn_cell.LSTMCell} + that follows. + + TODO(gonnet): Write a paper describing this and add a reference here. + """ + + def __init__(self, + num_units, + forget_bias=1.0, + activation=None, + reuse=None, + kernel_initializer=None, + bias_initializer=None, + name=None, + dtype=None): + """Initialize the IndyLSTM cell. + + Args: + num_units: int, The number of units in the LSTM cell. + forget_bias: float, The bias added to forget gates (see above). + Must set to `0.0` manually when restoring from CudnnLSTM-trained + checkpoints. + activation: Activation function of the inner states. Default: `tanh`. + reuse: (optional) Python boolean describing whether to reuse variables + in an existing scope. If not `True`, and the existing scope already has + the given variables, an error is raised. + kernel_initializer: (optional) The initializer to use for the weight + matrix applied to the inputs. + bias_initializer: (optional) The initializer to use for the bias. + name: String, the name of the layer. Layers with the same name will + share weights, but to avoid mistakes we require reuse=True in such + cases. + dtype: Default dtype of the layer (default of `None` means use the type + of the first input). Required when `build` is called before `call`. + """ + super(IndyLSTMCell, self).__init__(_reuse=reuse, name=name, dtype=dtype) + + # Inputs must be 2-dimensional. + self.input_spec = base_layer.InputSpec(ndim=2) + + self._num_units = num_units + self._forget_bias = forget_bias + self._activation = activation or math_ops.tanh + self._kernel_initializer = kernel_initializer + self._bias_initializer = bias_initializer + + @property + def state_size(self): + return rnn_cell_impl.LSTMStateTuple(self._num_units, self._num_units) + + @property + def output_size(self): + return self._num_units + + def build(self, inputs_shape): + if inputs_shape[1].value is None: + raise ValueError( + "Expected inputs.shape[-1] to be known, saw shape: %s" % inputs_shape) + + input_depth = inputs_shape[1].value + # pylint: disable=protected-access + self._kernel_w = self.add_variable( + "%s_w" % rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + shape=[input_depth, 4 * self._num_units], + initializer=self._kernel_initializer) + self._kernel_u = self.add_variable( + "%s_u" % rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + shape=[1, 4 * self._num_units], + initializer=init_ops.random_uniform_initializer( + minval=-1, maxval=1, dtype=self.dtype)) + self._bias = self.add_variable( + rnn_cell_impl._BIAS_VARIABLE_NAME, + shape=[4 * self._num_units], + initializer=(self._bias_initializer + if self._bias_initializer is not None else + init_ops.zeros_initializer(dtype=self.dtype))) + # pylint: enable=protected-access + + self.built = True + + def call(self, inputs, state): + """Independent Long short-term memory cell (IndyLSTM). + + Args: + inputs: `2-D` tensor with shape `[batch_size, input_size]`. + state: An `LSTMStateTuple` of state tensors, each shaped + `[batch_size, num_units]`. + + Returns: + A pair containing the new hidden state, and the new state (a + `LSTMStateTuple`). + """ + sigmoid = math_ops.sigmoid + one = constant_op.constant(1, dtype=dtypes.int32) + c, h = state + + gate_inputs = math_ops.matmul(inputs, self._kernel_w) + gate_inputs += gen_array_ops.tile(h, [1, 4]) * self._kernel_u + gate_inputs = nn_ops.bias_add(gate_inputs, self._bias) + + # i = input_gate, j = new_input, f = forget_gate, o = output_gate + i, j, f, o = array_ops.split( + value=gate_inputs, num_or_size_splits=4, axis=one) + + forget_bias_tensor = constant_op.constant(self._forget_bias, dtype=f.dtype) + # Note that using `add` and `multiply` instead of `+` and `*` gives a + # performance improvement. So using those at the cost of readability. + add = math_ops.add + multiply = math_ops.multiply + new_c = add( + multiply(c, sigmoid(add(f, forget_bias_tensor))), + multiply(sigmoid(i), self._activation(j))) + new_h = multiply(self._activation(new_c), sigmoid(o)) + + new_state = rnn_cell_impl.LSTMStateTuple(new_c, new_h) + return new_h, new_state diff --git a/tensorflow/contrib/rpc/python/kernel_tests/BUILD b/tensorflow/contrib/rpc/python/kernel_tests/BUILD index 2311c15a68c46090cec0f97bd950296506b0817e..cb0b89ae55b96361428c7845d4d6aab72543feb7 100644 --- a/tensorflow/contrib/rpc/python/kernel_tests/BUILD +++ b/tensorflow/contrib/rpc/python/kernel_tests/BUILD @@ -1,5 +1,3 @@ -# TODO(b/76425722): Port everything in here to OS (currently excluded). - package(default_visibility = ["//visibility:public"]) licenses(["notice"]) # Apache 2.0 @@ -17,7 +15,6 @@ tf_proto_library( srcs = ["test_example.proto"], has_services = 1, cc_api_version = 2, - protodeps = ["//tensorflow/core:protos_all"], ) py_library( diff --git a/tensorflow/contrib/rpc/python/kernel_tests/rpc_op_test_base.py b/tensorflow/contrib/rpc/python/kernel_tests/rpc_op_test_base.py index 27273d16b1c09eba60e124e632b353b09ea2d063..1c23c28860dac6203ea4ec8e808f63d3e9e467e2 100644 --- a/tensorflow/contrib/rpc/python/kernel_tests/rpc_op_test_base.py +++ b/tensorflow/contrib/rpc/python/kernel_tests/rpc_op_test_base.py @@ -51,23 +51,23 @@ class RpcOpTestBase(object): def testScalarHostPortRpc(self): with self.test_session() as sess: request_tensors = ( - test_example_pb2.TestCase(shape=[1, 2, 3]).SerializeToString()) + test_example_pb2.TestCase(values=[1, 2, 3]).SerializeToString()) response_tensors = self.rpc( - method=self.get_method_name('IncrementTestShapes'), + method=self.get_method_name('Increment'), address=self._address, request=request_tensors) self.assertEqual(response_tensors.shape, ()) response_values = sess.run(response_tensors) response_message = test_example_pb2.TestCase() self.assertTrue(response_message.ParseFromString(response_values)) - self.assertAllEqual([2, 3, 4], response_message.shape) + self.assertAllEqual([2, 3, 4], response_message.values) def testScalarHostPortTryRpc(self): with self.test_session() as sess: request_tensors = ( - test_example_pb2.TestCase(shape=[1, 2, 3]).SerializeToString()) + test_example_pb2.TestCase(values=[1, 2, 3]).SerializeToString()) response_tensors, status_code, status_message = self.try_rpc( - method=self.get_method_name('IncrementTestShapes'), + method=self.get_method_name('Increment'), address=self._address, request=request_tensors) self.assertEqual(status_code.shape, ()) @@ -77,7 +77,7 @@ class RpcOpTestBase(object): sess.run((response_tensors, status_code, status_message))) response_message = test_example_pb2.TestCase() self.assertTrue(response_message.ParseFromString(response_values)) - self.assertAllEqual([2, 3, 4], response_message.shape) + self.assertAllEqual([2, 3, 4], response_message.values) # For the base Rpc op, don't expect to get error status back. self.assertEqual(errors.OK, status_code_values) self.assertEqual(b'', status_message_values) @@ -86,7 +86,7 @@ class RpcOpTestBase(object): with self.test_session() as sess: request_tensors = [] response_tensors = self.rpc( - method=self.get_method_name('IncrementTestShapes'), + method=self.get_method_name('Increment'), address=self._address, request=request_tensors) self.assertAllEqual(response_tensors.shape, [0]) @@ -95,7 +95,7 @@ class RpcOpTestBase(object): def testInvalidMethod(self): for method in [ - '/InvalidService.IncrementTestShapes', + '/InvalidService.Increment', self.get_method_name('InvalidMethodName') ]: with self.test_session() as sess: @@ -115,12 +115,12 @@ class RpcOpTestBase(object): with self.assertRaises(errors.UnavailableError): sess.run( self.rpc( - method=self.get_method_name('IncrementTestShapes'), + method=self.get_method_name('Increment'), address=address, request='')) _, status_code_value, status_message_value = sess.run( self.try_rpc( - method=self.get_method_name('IncrementTestShapes'), + method=self.get_method_name('Increment'), address=address, request='')) self.assertEqual(errors.UNAVAILABLE, status_code_value) @@ -182,10 +182,10 @@ class RpcOpTestBase(object): with self.test_session() as sess: request_tensors = [ test_example_pb2.TestCase( - shape=[i, i + 1, i + 2]).SerializeToString() for i in range(20) + values=[i, i + 1, i + 2]).SerializeToString() for i in range(20) ] response_tensors = self.rpc( - method=self.get_method_name('IncrementTestShapes'), + method=self.get_method_name('Increment'), address=self._address, request=request_tensors) self.assertEqual(response_tensors.shape, (20,)) @@ -194,17 +194,17 @@ class RpcOpTestBase(object): for i in range(20): response_message = test_example_pb2.TestCase() self.assertTrue(response_message.ParseFromString(response_values[i])) - self.assertAllEqual([i + 1, i + 2, i + 3], response_message.shape) + self.assertAllEqual([i + 1, i + 2, i + 3], response_message.values) def testVecHostPortManyParallelRpcs(self): with self.test_session() as sess: request_tensors = [ test_example_pb2.TestCase( - shape=[i, i + 1, i + 2]).SerializeToString() for i in range(20) + values=[i, i + 1, i + 2]).SerializeToString() for i in range(20) ] many_response_tensors = [ self.rpc( - method=self.get_method_name('IncrementTestShapes'), + method=self.get_method_name('Increment'), address=self._address, request=request_tensors) for _ in range(10) ] @@ -216,25 +216,25 @@ class RpcOpTestBase(object): for i in range(20): response_message = test_example_pb2.TestCase() self.assertTrue(response_message.ParseFromString(response_values[i])) - self.assertAllEqual([i + 1, i + 2, i + 3], response_message.shape) + self.assertAllEqual([i + 1, i + 2, i + 3], response_message.values) def testVecHostPortRpcUsingEncodeAndDecodeProto(self): with self.test_session() as sess: request_tensors = encode_proto_op.encode_proto( message_type='tensorflow.contrib.rpc.TestCase', - field_names=['shape'], + field_names=['values'], sizes=[[3]] * 20, values=[ [[i, i + 1, i + 2] for i in range(20)], ]) response_tensor_strings = self.rpc( - method=self.get_method_name('IncrementTestShapes'), + method=self.get_method_name('Increment'), address=self._address, request=request_tensors) _, (response_shape,) = decode_proto_op.decode_proto( bytes=response_tensor_strings, message_type='tensorflow.contrib.rpc.TestCase', - field_names=['shape'], + field_names=['values'], output_types=[dtypes.int32]) response_shape_values = sess.run(response_shape) self.assertAllEqual([[i + 1, i + 2, i + 3] @@ -285,9 +285,9 @@ class RpcOpTestBase(object): addresses = flatten([[ self._address, 'unix:/tmp/this_unix_socket_doesnt_exist_97820348!!@' ] for _ in range(10)]) - request = test_example_pb2.TestCase(shape=[0, 1, 2]).SerializeToString() + request = test_example_pb2.TestCase(values=[0, 1, 2]).SerializeToString() response_tensors, status_code, _ = self.try_rpc( - method=self.get_method_name('IncrementTestShapes'), + method=self.get_method_name('Increment'), address=addresses, request=request) response_tensors_values, status_code_values = sess.run((response_tensors, @@ -303,9 +303,9 @@ class RpcOpTestBase(object): flatten = lambda x: list(itertools.chain.from_iterable(x)) with self.test_session() as sess: methods = flatten( - [[self.get_method_name('IncrementTestShapes'), 'InvalidMethodName'] + [[self.get_method_name('Increment'), 'InvalidMethodName'] for _ in range(10)]) - request = test_example_pb2.TestCase(shape=[0, 1, 2]).SerializeToString() + request = test_example_pb2.TestCase(values=[0, 1, 2]).SerializeToString() response_tensors, status_code, _ = self.try_rpc( method=methods, address=self._address, request=request) response_tensors_values, status_code_values = sess.run((response_tensors, @@ -325,10 +325,10 @@ class RpcOpTestBase(object): ] for _ in range(10)]) requests = [ test_example_pb2.TestCase( - shape=[i, i + 1, i + 2]).SerializeToString() for i in range(20) + values=[i, i + 1, i + 2]).SerializeToString() for i in range(20) ] response_tensors, status_code, _ = self.try_rpc( - method=self.get_method_name('IncrementTestShapes'), + method=self.get_method_name('Increment'), address=addresses, request=requests) response_tensors_values, status_code_values = sess.run((response_tensors, @@ -343,4 +343,4 @@ class RpcOpTestBase(object): response_message = test_example_pb2.TestCase() self.assertTrue( response_message.ParseFromString(response_tensors_values[i])) - self.assertAllEqual([i + 1, i + 2, i + 3], response_message.shape) + self.assertAllEqual([i + 1, i + 2, i + 3], response_message.values) diff --git a/tensorflow/contrib/rpc/python/kernel_tests/rpc_op_test_servicer.py b/tensorflow/contrib/rpc/python/kernel_tests/rpc_op_test_servicer.py index 7cbd636cb16e3befc9ae27cb231696634e859a22..265254aa51c64ff5a76ad3a9f7e081c56dd639e7 100644 --- a/tensorflow/contrib/rpc/python/kernel_tests/rpc_op_test_servicer.py +++ b/tensorflow/contrib/rpc/python/kernel_tests/rpc_op_test_servicer.py @@ -30,8 +30,8 @@ from tensorflow.contrib.rpc.python.kernel_tests import test_example_pb2_grpc class RpcOpTestServicer(test_example_pb2_grpc.TestCaseServiceServicer): """Test servicer for RpcOp tests.""" - def IncrementTestShapes(self, request, context): - """Increment the entries in the shape attribute of request. + def Increment(self, request, context): + """Increment the entries in the `values` attribute of request. Args: request: input TestCase. @@ -40,8 +40,8 @@ class RpcOpTestServicer(test_example_pb2_grpc.TestCaseServiceServicer): Returns: output TestCase. """ - for i in range(len(request.shape)): - request.shape[i] += 1 + for i in range(len(request.values)): + request.values[i] += 1 return request def AlwaysFailWithInvalidArgument(self, request, context): diff --git a/tensorflow/contrib/rpc/python/kernel_tests/test_example.proto b/tensorflow/contrib/rpc/python/kernel_tests/test_example.proto index 96f4550f62bc17e713abe1f3843ec0964f57b046..8141466349afcebcd104153a9f28c8f382458098 100644 --- a/tensorflow/contrib/rpc/python/kernel_tests/test_example.proto +++ b/tensorflow/contrib/rpc/python/kernel_tests/test_example.proto @@ -1,29 +1,17 @@ // Test description and protos to work with it. -// -// Many of the protos in this file are for unit tests that haven't been written yet. syntax = "proto2"; -import "tensorflow/core/framework/types.proto"; - package tensorflow.contrib.rpc; -// A TestCase holds a proto and a bunch of assertions -// about how it should decode. +// A TestCase holds a sequence of values. message TestCase { - // A batch of primitives to be serialized and decoded. - repeated RepeatedPrimitiveValue primitive = 1; - // The shape of the batch. - repeated int32 shape = 2; - // Expected sizes for each field. - repeated int32 sizes = 3; - // Expected values for each field. - repeated FieldSpec field = 4; + repeated int32 values = 1; }; service TestCaseService { - // Copy input, and increment each entry in 'shape' by 1. - rpc IncrementTestShapes(TestCase) returns (TestCase) { + // Copy input, and increment each entry in 'values' by 1. + rpc Increment(TestCase) returns (TestCase) { } // Sleep forever. @@ -42,130 +30,3 @@ service TestCaseService { rpc SometimesFailWithInvalidArgument(TestCase) returns (TestCase) { } }; - -// FieldSpec describes the expected output for a single field. -message FieldSpec { - optional string name = 1; - optional tensorflow.DataType dtype = 2; - optional RepeatedPrimitiveValue expected = 3; -}; - -message TestValue { - optional PrimitiveValue primitive_value = 1; - optional EnumValue enum_value = 2; - optional MessageValue message_value = 3; - optional RepeatedMessageValue repeated_message_value = 4; - optional RepeatedPrimitiveValue repeated_primitive_value = 6; -} - -message PrimitiveValue { - optional double double_value = 1; - optional float float_value = 2; - optional int64 int64_value = 3; - optional uint64 uint64_value = 4; - optional int32 int32_value = 5; - optional fixed64 fixed64_value = 6; - optional fixed32 fixed32_value = 7; - optional bool bool_value = 8; - optional string string_value = 9; - optional bytes bytes_value = 12; - optional uint32 uint32_value = 13; - optional sfixed32 sfixed32_value = 15; - optional sfixed64 sfixed64_value = 16; - optional sint32 sint32_value = 17; - optional sint64 sint64_value = 18; -} - -// NOTE: This definition must be kept in sync with PackedPrimitiveValue. -message RepeatedPrimitiveValue { - repeated double double_value = 1; - repeated float float_value = 2; - repeated int64 int64_value = 3; - repeated uint64 uint64_value = 4; - repeated int32 int32_value = 5; - repeated fixed64 fixed64_value = 6; - repeated fixed32 fixed32_value = 7; - repeated bool bool_value = 8; - repeated string string_value = 9; - repeated bytes bytes_value = 12; - repeated uint32 uint32_value = 13; - repeated sfixed32 sfixed32_value = 15; - repeated sfixed64 sfixed64_value = 16; - repeated sint32 sint32_value = 17; - repeated sint64 sint64_value = 18; - repeated PrimitiveValue message_value = 19; -} - -// A PackedPrimitiveValue looks exactly the same as a RepeatedPrimitiveValue -// in the text format, but the binary serializion is different. -// We test the packed representations by loading the same test cases -// using this definition instead of RepeatedPrimitiveValue. -// NOTE: This definition must be kept in sync with RepeatedPrimitiveValue -// in every way except the packed=true declaration. -message PackedPrimitiveValue { - repeated double double_value = 1 [packed = true]; - repeated float float_value = 2 [packed = true]; - repeated int64 int64_value = 3 [packed = true]; - repeated uint64 uint64_value = 4 [packed = true]; - repeated int32 int32_value = 5 [packed = true]; - repeated fixed64 fixed64_value = 6 [packed = true]; - repeated fixed32 fixed32_value = 7 [packed = true]; - repeated bool bool_value = 8 [packed = true]; - repeated string string_value = 9; - repeated bytes bytes_value = 12; - repeated uint32 uint32_value = 13 [packed = true]; - repeated sfixed32 sfixed32_value = 15 [packed = true]; - repeated sfixed64 sfixed64_value = 16 [packed = true]; - repeated sint32 sint32_value = 17 [packed = true]; - repeated sint64 sint64_value = 18 [packed = true]; - repeated PrimitiveValue message_value = 19; -} - -message EnumValue { - enum Color { - RED = 0; - ORANGE = 1; - YELLOW = 2; - GREEN = 3; - BLUE = 4; - INDIGO = 5; - VIOLET = 6; - }; - optional Color enum_value = 14; - repeated Color repeated_enum_value = 15; -} - - -message InnerMessageValue { - optional float float_value = 2; - repeated bytes bytes_values = 8; -} - -message MiddleMessageValue { - repeated int32 int32_values = 5; - optional InnerMessageValue message_value = 11; - optional uint32 uint32_value = 13; -} - -message MessageValue { - optional double double_value = 1; - optional MiddleMessageValue message_value = 11; -} - -message RepeatedMessageValue { - message NestedMessageValue { - optional float float_value = 2; - repeated bytes bytes_values = 8; - } - - repeated NestedMessageValue message_values = 11; -} - -// Message containing fields with field numbers higher than any field above. An -// instance of this message is prepended to each binary message in the test to -// exercise the code path that handles fields encoded out of order of field -// number. -message ExtraFields { - optional string string_value = 1776; - optional bool bool_value = 1777; -} diff --git a/tensorflow/contrib/saved_model/BUILD b/tensorflow/contrib/saved_model/BUILD index 26fd4e2023806765ea4088f4c13a780ca7338bff..fbb50befdfb2ccbd97465c11f8219e604a0ebc18 100644 --- a/tensorflow/contrib/saved_model/BUILD +++ b/tensorflow/contrib/saved_model/BUILD @@ -93,3 +93,32 @@ py_test( "//tensorflow/python/saved_model:utils", ], ) + +py_library( + name = "keras_saved_model", + srcs = ["python/saved_model/keras_saved_model.py"], + srcs_version = "PY2AND3", + tags = ["no_windows"], + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/python:lib", + "//tensorflow/python:util", + "//tensorflow/python/keras:engine", + "//tensorflow/python/saved_model:constants", + ], +) + +py_test( + name = "keras_saved_model_test", + size = "small", + srcs = ["python/saved_model/keras_saved_model_test.py"], + srcs_version = "PY2AND3", + tags = ["no_windows"], + deps = [ + ":saved_model_py", + "//tensorflow/python:client_testlib", + "//tensorflow/python:training", + "//tensorflow/python/keras", + "//third_party/py/numpy", + ], +) diff --git a/tensorflow/contrib/saved_model/__init__.py b/tensorflow/contrib/saved_model/__init__.py index b4f27a055dad7a5b95112d561cc878609a558f8d..95e1a8967b2223fd3feb112af3cbe0c5991d2d03 100644 --- a/tensorflow/contrib/saved_model/__init__.py +++ b/tensorflow/contrib/saved_model/__init__.py @@ -24,11 +24,12 @@ from __future__ import division from __future__ import print_function # pylint: disable=unused-import,wildcard-import,line-too-long +from tensorflow.contrib.saved_model.python.saved_model.keras_saved_model import * from tensorflow.contrib.saved_model.python.saved_model.signature_def_utils import * # pylint: enable=unused-import,widcard-import,line-too-long from tensorflow.python.util.all_util import remove_undocumented -_allowed_symbols = ["get_signature_def_by_key"] +_allowed_symbols = ["get_signature_def_by_key", "load_model", "save_model"] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/saved_model/python/saved_model/__init__.py b/tensorflow/contrib/saved_model/python/saved_model/__init__.py index 7b91622b6127413ce122c4166a18255b65365d32..e3b76bb6f34846f02ccdf623d48ddd9c5909fdce 100644 --- a/tensorflow/contrib/saved_model/python/saved_model/__init__.py +++ b/tensorflow/contrib/saved_model/python/saved_model/__init__.py @@ -24,5 +24,6 @@ from __future__ import division from __future__ import print_function # pylint: disable=wildcard-import +from tensorflow.contrib.saved_model.python.saved_model import keras_saved_model from tensorflow.contrib.saved_model.python.saved_model import signature_def_utils # pylint: enable=wildcard-import diff --git a/tensorflow/contrib/saved_model/python/saved_model/keras_saved_model.py b/tensorflow/contrib/saved_model/python/saved_model/keras_saved_model.py new file mode 100644 index 0000000000000000000000000000000000000000..e2a969f053d3f1ded8aecd6411a62a198df48bb0 --- /dev/null +++ b/tensorflow/contrib/saved_model/python/saved_model/keras_saved_model.py @@ -0,0 +1,108 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# pylint: disable=protected-access +"""Utility functions to save/load keras Model to/from SavedModel.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.python.keras.models import model_from_json +from tensorflow.python.lib.io import file_io +from tensorflow.python.saved_model import constants +from tensorflow.python.util import compat + + +def save_model(model, saved_model_path): + """Save a `tf.keras.Model` into Tensorflow SavedModel format. + + `save_model` generates such files/folders under the `saved_model_path` folder: + 1) an asset folder containing the json string of the model's + configuration(topology). + 2) a checkpoint containing the model weights. + + Note that subclassed models can not be saved via this function, unless you + provide an implementation for get_config() and from_config(). + Also note that `tf.keras.optimizers.Optimizer` instances can not currently be + saved to checkpoints. Use optimizers from `tf.train`. + + Args: + model: A `tf.keras.Model` to be saved. + saved_model_path: a string specifying the path to the SavedModel directory. + + Raises: + NotImplementedError: If the passed in model is a subclassed model. + """ + if not model._is_graph_network: + raise NotImplementedError + + # save model configuration as a json string under assets folder. + model_json = model.to_json() + assets_destination_dir = os.path.join( + compat.as_bytes(saved_model_path), + compat.as_bytes(constants.ASSETS_DIRECTORY)) + + if not file_io.file_exists(assets_destination_dir): + file_io.recursive_create_dir(assets_destination_dir) + + model_json_filepath = os.path.join( + compat.as_bytes(assets_destination_dir), + compat.as_bytes(constants.SAVED_MODEL_FILENAME_JSON)) + file_io.write_string_to_file(model_json_filepath, model_json) + + # save model weights in checkpoint format. + checkpoint_destination_dir = os.path.join( + compat.as_bytes(saved_model_path), + compat.as_bytes(constants.VARIABLES_DIRECTORY)) + + if not file_io.file_exists(checkpoint_destination_dir): + file_io.recursive_create_dir(checkpoint_destination_dir) + + checkpoint_prefix = os.path.join( + compat.as_text(checkpoint_destination_dir), + compat.as_text(constants.VARIABLES_FILENAME)) + model.save_weights(checkpoint_prefix, save_format='tf', overwrite=True) + + +def load_model(saved_model_path): + """Load a keras.Model from SavedModel. + + load_model reinstantiates model state by: + 1) loading model topology from json (this will eventually come + from metagraph). + 2) loading model weights from checkpoint. + + Args: + saved_model_path: a string specifying the path to an existing SavedModel. + + Returns: + a keras.Model instance. + """ + # restore model topology from json string + model_json_filepath = os.path.join( + compat.as_bytes(saved_model_path), + compat.as_bytes(constants.ASSETS_DIRECTORY), + compat.as_bytes(constants.SAVED_MODEL_FILENAME_JSON)) + model_json = file_io.read_file_to_string(model_json_filepath) + model = model_from_json(model_json) + + # restore model weights + checkpoint_prefix = os.path.join( + compat.as_text(saved_model_path), + compat.as_text(constants.VARIABLES_DIRECTORY), + compat.as_text(constants.VARIABLES_FILENAME)) + model.load_weights(checkpoint_prefix) + return model diff --git a/tensorflow/contrib/saved_model/python/saved_model/keras_saved_model_test.py b/tensorflow/contrib/saved_model/python/saved_model/keras_saved_model_test.py new file mode 100644 index 0000000000000000000000000000000000000000..107ae1b07b777570e4124337595ceecd6e33cd0b --- /dev/null +++ b/tensorflow/contrib/saved_model/python/saved_model/keras_saved_model_test.py @@ -0,0 +1,201 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# pylint: disable=protected-access +"""Tests for saving/loading function for keras Model.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import shutil +import numpy as np + +from tensorflow.contrib.saved_model.python.saved_model import keras_saved_model +from tensorflow.python import keras +from tensorflow.python.framework import test_util +from tensorflow.python.keras.engine import training +from tensorflow.python.platform import test +from tensorflow.python.training import training as training_module + + +class TestModelSavingandLoading(test.TestCase): + + def test_saving_sequential_model(self): + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.RepeatVector(3)) + model.add(keras.layers.TimeDistributed(keras.layers.Dense(3))) + model.compile( + loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(lr=0.0001), + metrics=[keras.metrics.categorical_accuracy], + sample_weight_mode='temporal') + x = np.random.random((1, 3)) + y = np.random.random((1, 3, 3)) + model.train_on_batch(x, y) + + ref_y = model.predict(x) + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + + temp_saved_model = os.path.join(temp_dir, 'saved_model') + keras_saved_model.save_model(model, temp_saved_model) + + loaded_model = keras_saved_model.load_model(temp_saved_model) + y = loaded_model.predict(x) + self.assertAllClose(ref_y, y, atol=1e-05) + + @test_util.run_in_graph_and_eager_modes + def test_saving_sequential_model_without_compile(self): + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.RepeatVector(3)) + model.add(keras.layers.TimeDistributed(keras.layers.Dense(3))) + + x = np.random.random((1, 3)) + ref_y = model.predict(x) + + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + + temp_saved_model = os.path.join(temp_dir, 'saved_model') + keras_saved_model.save_model(model, temp_saved_model) + loaded_model = keras_saved_model.load_model(temp_saved_model) + + y = loaded_model.predict(x) + self.assertAllClose(ref_y, y, atol=1e-05) + + def test_saving_functional_model(self): + with self.test_session(): + inputs = keras.layers.Input(shape=(3,)) + x = keras.layers.Dense(2)(inputs) + output = keras.layers.Dense(3)(x) + + model = keras.models.Model(inputs, output) + model.compile( + loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(lr=0.0001), + metrics=[keras.metrics.categorical_accuracy]) + x = np.random.random((1, 3)) + y = np.random.random((1, 3)) + model.train_on_batch(x, y) + + ref_y = model.predict(x) + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + + temp_saved_model = os.path.join(temp_dir, 'saved_model') + keras_saved_model.save_model(model, temp_saved_model) + loaded_model = keras_saved_model.load_model(temp_saved_model) + + y = loaded_model.predict(x) + self.assertAllClose(ref_y, y, atol=1e-05) + + @test_util.run_in_graph_and_eager_modes + def test_saving_functional_model_without_compile(self): + with self.test_session(): + inputs = keras.layers.Input(shape=(3,)) + x = keras.layers.Dense(2)(inputs) + output = keras.layers.Dense(3)(x) + + model = keras.models.Model(inputs, output) + + x = np.random.random((1, 3)) + y = np.random.random((1, 3)) + + ref_y = model.predict(x) + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + + temp_saved_model = os.path.join(temp_dir, 'saved_model') + keras_saved_model.save_model(model, temp_saved_model) + loaded_model = keras_saved_model.load_model(temp_saved_model) + + y = loaded_model.predict(x) + self.assertAllClose(ref_y, y, atol=1e-05) + + @test_util.run_in_graph_and_eager_modes + def test_saving_with_tf_optimizer(self): + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_shape=(3,))) + model.add(keras.layers.Dense(3)) + model.compile( + loss='mse', + optimizer=training_module.RMSPropOptimizer(0.1), + metrics=['acc']) + + x = np.random.random((1, 3)) + y = np.random.random((1, 3)) + model.train_on_batch(x, y) + + ref_y = model.predict(x) + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + + temp_saved_model = os.path.join(temp_dir, 'saved_model') + keras_saved_model.save_model(model, temp_saved_model) + loaded_model = keras_saved_model.load_model(temp_saved_model) + loaded_model.compile( + loss='mse', + optimizer=training_module.RMSPropOptimizer(0.1), + metrics=['acc']) + y = loaded_model.predict(x) + self.assertAllClose(ref_y, y, atol=1e-05) + + # test that new updates are the same with both models + x = np.random.random((1, 3)) + y = np.random.random((1, 3)) + + ref_loss = model.train_on_batch(x, y) + loss = loaded_model.train_on_batch(x, y) + self.assertAllClose(ref_loss, loss, atol=1e-05) + + ref_y = model.predict(x) + y = loaded_model.predict(x) + self.assertAllClose(ref_y, y, atol=1e-05) + + # test saving/loading again + keras_saved_model.save_model(loaded_model, temp_saved_model) + loaded_model = keras_saved_model.load_model(temp_saved_model) + y = loaded_model.predict(x) + self.assertAllClose(ref_y, y, atol=1e-05) + + def test_saving_subclassed_model_raise_error(self): + # For now, saving subclassed model should raise an error. It should be + # avoided later with loading from SavedModel.pb. + + class SubclassedModel(training.Model): + + def __init__(self): + super(SubclassedModel, self).__init__() + self.layer1 = keras.layers.Dense(3) + self.layer2 = keras.layers.Dense(1) + + def call(self, inp): + return self.layer2(self.layer1(inp)) + + model = SubclassedModel() + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + temp_saved_model = os.path.join(temp_dir, 'saved_model') + with self.assertRaises(NotImplementedError): + keras_saved_model.save_model(model, temp_saved_model) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py index 178328619f087789df040489cd150ba018cc8d14..4073b390fc72cf0f84edd0d2ab56df5ffeb3e2e5 100644 --- a/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py +++ b/tensorflow/contrib/seq2seq/python/kernel_tests/beam_search_decoder_test.py @@ -132,6 +132,48 @@ class TestGatherTree(test.TestCase): def test_gather_tree_from_array_2d(self): self._test_gather_tree_from_array(depth_ndims=2) + def test_gather_tree_from_array_complex_trajectory(self): + # Max. time = 7, batch = 1, beam = 5. + array = np.expand_dims(np.array( + [[[25, 12, 114, 89, 97]], + [[9, 91, 64, 11, 162]], + [[34, 34, 34, 34, 34]], + [[2, 4, 2, 2, 4]], + [[2, 3, 6, 2, 2]], + [[2, 2, 2, 3, 2]], + [[2, 2, 2, 2, 2]]]), -1) + parent_ids = np.array( + [[[0, 0, 0, 0, 0]], + [[0, 0, 0, 0, 0]], + [[0, 1, 2, 3, 4]], + [[0, 0, 1, 2, 1]], + [[0, 1, 1, 2, 3]], + [[0, 1, 3, 1, 2]], + [[0, 1, 2, 3, 4]]]) + expected_array = np.expand_dims(np.array( + [[[25, 25, 25, 25, 25]], + [[9, 9, 91, 9, 9]], + [[34, 34, 34, 34, 34]], + [[2, 4, 2, 4, 4]], + [[2, 3, 6, 3, 6]], + [[2, 2, 2, 3, 2]], + [[2, 2, 2, 2, 2]]]), -1) + sequence_length = [[4, 6, 4, 7, 6]] + + array = ops.convert_to_tensor( + array, dtype=dtypes.float32) + parent_ids = ops.convert_to_tensor( + parent_ids, dtype=dtypes.int32) + expected_array = ops.convert_to_tensor( + expected_array, dtype=dtypes.float32) + + sorted_array = beam_search_decoder.gather_tree_from_array( + array, parent_ids, sequence_length) + + with self.test_session() as sess: + sorted_array, expected_array = sess.run([sorted_array, expected_array]) + self.assertAllEqual(expected_array, sorted_array) + class TestArrayShapeChecks(test.TestCase): diff --git a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py index c7fbeea3105ae4c9c9ec2fd131f3468018990028..f17dbb0fe3c13c3a43f043b82772949737dfb2de 100644 --- a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py +++ b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py @@ -145,24 +145,20 @@ def gather_tree_from_array(t, parent_ids, sequence_length): array_ops.expand_dims(math_ops.range(beam_width), 0), 0) beam_ids = array_ops.tile(beam_ids, [max_time, batch_size, 1]) - mask = array_ops.sequence_mask( - sequence_length, maxlen=max_time, dtype=dtypes.int32) - mask = array_ops.transpose(mask, perm=[2, 0, 1]) - - # Use beam_width + 1 to mark the end of beam. - masked_beam_ids = (beam_ids * mask) + (1 - mask) * (beam_width + 1) - max_sequence_lengths = math_ops.to_int32( math_ops.reduce_max(sequence_length, axis=1)) sorted_beam_ids = beam_search_ops.gather_tree( - step_ids=masked_beam_ids, + step_ids=beam_ids, parent_ids=parent_ids, max_sequence_lengths=max_sequence_lengths, end_token=beam_width + 1) # For out of range steps, simply copy the same beam. + in_bound_steps = array_ops.transpose( + array_ops.sequence_mask(sequence_length, maxlen=max_time), + perm=[2, 0, 1]) sorted_beam_ids = array_ops.where( - math_ops.cast(mask, dtypes.bool), x=sorted_beam_ids, y=beam_ids) + in_bound_steps, x=sorted_beam_ids, y=beam_ids) # Generate indices for gather_nd. time_ind = array_ops.tile(array_ops.reshape( diff --git a/tensorflow/contrib/signal/python/kernel_tests/mel_ops_test.py b/tensorflow/contrib/signal/python/kernel_tests/mel_ops_test.py index 345eb6cfaa67fd4cda6e7e3f01a1243bbf3c9fa1..f4348e80eac54933d67cdf7bd281d6a9c6c10381 100644 --- a/tensorflow/contrib/signal/python/kernel_tests/mel_ops_test.py +++ b/tensorflow/contrib/signal/python/kernel_tests/mel_ops_test.py @@ -53,7 +53,8 @@ def spectrogram_to_mel_matrix(num_mel_bins=20, num_spectrogram_bins=129, audio_sample_rate=8000, lower_edge_hertz=125.0, - upper_edge_hertz=3800.0): + upper_edge_hertz=3800.0, + unused_dtype=None): """Return a matrix that can post-multiply spectrogram rows to make mel. Copied from @@ -132,9 +133,9 @@ class LinearToMelTest(test.TestCase): # lower_edge_hertz, upper_edge_hertz) to test. configs = [ # Defaults. - (20, 129, 8000.0, 125.0, 3800.0), + (20, 129, 8000.0, 125.0, 3800.0, dtypes.float64), # Settings used by Tacotron (https://arxiv.org/abs/1703.10135). - (80, 1025, 24000.0, 80.0, 12000.0) + (80, 1025, 24000.0, 80.0, 12000.0, dtypes.float64) ] with self.test_session(use_gpu=True): for config in configs: @@ -143,7 +144,8 @@ class LinearToMelTest(test.TestCase): self.assertAllClose(mel_matrix_np, mel_matrix.eval(), atol=3e-6) def test_dtypes(self): - for dtype in (dtypes.float16, dtypes.float32, dtypes.float64): + # LinSpace is not supported for tf.float16. + for dtype in (dtypes.bfloat16, dtypes.float32, dtypes.float64): self.assertEqual(dtype, mel_ops.linear_to_mel_weight_matrix(dtype=dtype).dtype) @@ -167,7 +169,8 @@ class LinearToMelTest(test.TestCase): def test_constant_folding(self): """Mel functions should be constant foldable.""" - for dtype in (dtypes.float16, dtypes.float32, dtypes.float64): + # TODO(rjryan): tf.bloat16 cannot be constant folded by Grappler. + for dtype in (dtypes.float32, dtypes.float64): g = ops.Graph() with g.as_default(): mel_matrix = mel_ops.linear_to_mel_weight_matrix(dtype=dtype) diff --git a/tensorflow/contrib/signal/python/ops/mel_ops.py b/tensorflow/contrib/signal/python/ops/mel_ops.py index 1e84006116daa3f28c760037cb9eeafd53eaafb8..062d84aea183ab61501a8b07521adb1a1a17c63c 100644 --- a/tensorflow/contrib/signal/python/ops/mel_ops.py +++ b/tensorflow/contrib/signal/python/ops/mel_ops.py @@ -151,22 +151,21 @@ def linear_to_mel_weight_matrix(num_mel_bins=20, _validate_arguments(num_mel_bins, sample_rate, lower_edge_hertz, upper_edge_hertz, dtype) - # To preserve accuracy, we compute the matrix at float64 precision and then - # cast to `dtype` at the end. This function can be constant folded by graph - # optimization since there are no Tensor inputs. + # This function can be constant folded by graph optimization since there are + # no Tensor inputs. sample_rate = ops.convert_to_tensor( - sample_rate, dtypes.float64, name='sample_rate') + sample_rate, dtype, name='sample_rate') lower_edge_hertz = ops.convert_to_tensor( - lower_edge_hertz, dtypes.float64, name='lower_edge_hertz') + lower_edge_hertz, dtype, name='lower_edge_hertz') upper_edge_hertz = ops.convert_to_tensor( - upper_edge_hertz, dtypes.float64, name='upper_edge_hertz') - zero_float64 = ops.convert_to_tensor(0.0, dtypes.float64) + upper_edge_hertz, dtype, name='upper_edge_hertz') + zero = ops.convert_to_tensor(0.0, dtype) # HTK excludes the spectrogram DC bin. bands_to_zero = 1 nyquist_hertz = sample_rate / 2.0 linear_frequencies = math_ops.linspace( - zero_float64, nyquist_hertz, num_spectrogram_bins)[bands_to_zero:] + zero, nyquist_hertz, num_spectrogram_bins)[bands_to_zero:] spectrogram_bins_mel = array_ops.expand_dims( _hertz_to_mel(linear_frequencies), 1) @@ -193,11 +192,8 @@ def linear_to_mel_weight_matrix(num_mel_bins=20, # Intersect the line segments with each other and zero. mel_weights_matrix = math_ops.maximum( - zero_float64, math_ops.minimum(lower_slopes, upper_slopes)) + zero, math_ops.minimum(lower_slopes, upper_slopes)) # Re-add the zeroed lower bins we sliced out above. - mel_weights_matrix = array_ops.pad( - mel_weights_matrix, [[bands_to_zero, 0], [0, 0]]) - - # Cast to the desired type. - return math_ops.cast(mel_weights_matrix, dtype, name=name) + return array_ops.pad( + mel_weights_matrix, [[bands_to_zero, 0], [0, 0]], name=name) diff --git a/tensorflow/contrib/summary/summary_ops_test.py b/tensorflow/contrib/summary/summary_ops_test.py index 3e41e3d0b48ea06f9cb8c1862e27eacb5ebc4417..4d1807130c57039976dfa57c27bb0d4807e75212 100644 --- a/tensorflow/contrib/summary/summary_ops_test.py +++ b/tensorflow/contrib/summary/summary_ops_test.py @@ -20,6 +20,8 @@ import os import tempfile import time +import sqlite3 + import numpy as np import six @@ -275,6 +277,22 @@ class EagerFileTest(test_util.TensorFlowTestCase): class EagerDbTest(summary_test_util.SummaryDbTest): + def testDbURIOpen(self): + tmpdb_path = os.path.join(self.get_temp_dir(), 'tmpDbURITest.sqlite') + tmpdb_uri = six.moves.urllib_parse.urljoin("file:", tmpdb_path) + tmpdb_writer = summary_ops.create_db_writer( + tmpdb_uri, + "experimentA", + "run1", + "user1") + with summary_ops.always_record_summaries(): + with tmpdb_writer.as_default(): + summary_ops.scalar('t1', 2.0) + tmpdb = sqlite3.connect(tmpdb_path) + num = get_one(tmpdb, 'SELECT count(*) FROM Tags WHERE tag_name = "t1"') + self.assertEqual(num, 1) + tmpdb.close() + def testIntegerSummaries(self): step = training_util.create_global_step() writer = self.create_db_writer() diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD index 136856c0156c41046f9af61cdd6e3d5f8213309e..164f3e58e6c0b2486d270c457500c8dca0c7e7eb 100644 --- a/tensorflow/contrib/tensor_forest/BUILD +++ b/tensorflow/contrib/tensor_forest/BUILD @@ -223,7 +223,6 @@ tf_kernel_library( ":model_ops_lib", "//tensorflow/core:framework", "//tensorflow/core:lib", - "//tensorflow/core:lib_internal", ], alwayslink = 1, ) @@ -319,7 +318,6 @@ tf_kernel_library( ":stats_ops_lib", "//tensorflow/core:framework", "//tensorflow/core:lib", - "//tensorflow/core:lib_internal", ], alwayslink = 1, ) diff --git a/tensorflow/contrib/tensorboard/db/BUILD b/tensorflow/contrib/tensorboard/db/BUILD index 3f6b4cdc9ad10f5089f28af35a8be408918c7f90..6507546ee9f81108add181a9c83064c9860005e2 100644 --- a/tensorflow/contrib/tensorboard/db/BUILD +++ b/tensorflow/contrib/tensorboard/db/BUILD @@ -106,6 +106,7 @@ cc_library( "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", + "//tensorflow/core:png_internal", "//tensorflow/core:protos_all_cc", ], ) diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index adda0b758b172f5e80c165e4b28dbdbecef2ba16..46f3c36e3db51fde4c8732d4300a9d3eaddb452a 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -11,7 +11,6 @@ exports_files(["LICENSE"]) load( "//tensorflow:tensorflow.bzl", - "py_test", "tf_cc_test", "tf_copts", "tf_cuda_library", @@ -20,6 +19,7 @@ load( "tf_gen_op_libs", "tf_gen_op_wrapper_py", ) +load("//tensorflow:tensorflow.bzl", "cuda_py_tests") load("//tensorflow:tensorflow.bzl", "tf_cuda_cc_test") load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library") load("//tensorflow:tensorflow.bzl", "tf_py_wrap_cc") @@ -33,11 +33,13 @@ tf_cuda_cc_test( size = "small", srcs = ["tensorrt_test.cc"], tags = [ - "manual", - "notap", + "no_windows", + "nomac", ], deps = [ + "//tensorflow/core:gpu_init", "//tensorflow/core:lib", + "//tensorflow/core:stream_executor", "//tensorflow/core:test", "//tensorflow/core:test_main", ] + if_tensorrt([ @@ -83,6 +85,7 @@ cc_library( copts = tf_copts(), visibility = ["//visibility:public"], deps = [ + ":trt_allocator", ":trt_logging", ":trt_plugins", ":trt_resources", @@ -119,7 +122,6 @@ tf_cuda_library( tf_gen_op_wrapper_py( name = "trt_engine_op", - gen_locally = True, deps = [ ":trt_engine_op_op_lib", ":trt_logging", @@ -156,6 +158,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ + ":tf_trt_integration_test_base", ":trt_convert_py", ":trt_ops_py", "//tensorflow/python:errors", @@ -185,6 +188,9 @@ tf_py_wrap_cc( name = "wrap_conversion", srcs = ["trt_conversion.i"], copts = tf_copts(), + swig_includes = [ + "//tensorflow/python:platform/base.i", + ], deps = [ ":trt_conversion", ":trt_engine_op_kernel", @@ -195,17 +201,16 @@ tf_py_wrap_cc( tf_cuda_library( name = "trt_resources", srcs = [ - "resources/trt_allocator.cc", "resources/trt_int8_calibrator.cc", "resources/trt_resource_manager.cc", ], hdrs = [ - "resources/trt_allocator.h", "resources/trt_int8_calibrator.h", "resources/trt_resource_manager.h", "resources/trt_resources.h", ], deps = [ + ":trt_allocator", ":trt_logging", ":utils", "//tensorflow/core:framework_headers_lib", @@ -216,6 +221,34 @@ tf_cuda_library( ]), ) +tf_cuda_library( + name = "trt_allocator", + srcs = ["resources/trt_allocator.cc"], + hdrs = ["resources/trt_allocator.h"], + deps = [ + "//tensorflow/core:framework_headers_lib", + "//tensorflow/core:framework_lite", + "//tensorflow/core:lib_proto_parsing", + ] + if_tensorrt([ + "@local_config_tensorrt//:nv_infer", + ]), +) + +tf_cc_test( + name = "trt_allocator_test", + size = "small", + srcs = ["resources/trt_allocator_test.cc"], + tags = [ + "no_windows", + "nomac", + ], + deps = [ + ":trt_allocator", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + # Library for the node-level conversion portion of TensorRT operation creation tf_cuda_library( name = "trt_conversion", @@ -231,6 +264,7 @@ tf_cuda_library( ], deps = [ ":segment", + ":trt_allocator", ":trt_plugins", ":trt_logging", ":trt_resources", @@ -275,13 +309,21 @@ tf_cc_test( name = "segment_test", size = "small", srcs = ["segment/segment_test.cc"], + tags = [ + "no_windows", + "nomac", + ], deps = [ ":segment", - "//tensorflow/c:c_api", + "//tensorflow/cc:cc_ops", + "//tensorflow/cc:scope", + "//tensorflow/core:core_cpu", "//tensorflow/core:lib", + "//tensorflow/core:ops", "//tensorflow/core:protos_all_cc", "//tensorflow/core:test", "//tensorflow/core:test_main", + "//tensorflow/core:testlib", ], ) @@ -311,8 +353,9 @@ tf_cuda_cc_test( size = "small", srcs = ["plugin/trt_plugin_factory_test.cc"], tags = [ - "manual", - "notap", + "no_cuda_on_cpu_tap", + "no_windows", + "nomac", ], deps = [ ":trt_plugins", @@ -325,23 +368,48 @@ tf_cuda_cc_test( ]), ) -py_test( +py_library( + name = "tf_trt_integration_test_base", + srcs = ["test/tf_trt_integration_test_base.py"], + deps = [ + ":trt_convert_py", + ":trt_ops_py", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_test_lib", + ], +) + +cuda_py_tests( name = "tf_trt_integration_test", - srcs = ["test/tf_trt_integration_test.py"], - main = "test/tf_trt_integration_test.py", - srcs_version = "PY2AND3", - tags = [ - "manual", - "notap", + srcs = [ + "test/base_test.py", + # "test/batch_matmul_test.py", + # "test/biasadd_matmul_test.py", + # "test/binary_tensor_weight_broadcast_test.py", # Blocked by trt4 installation + # "test/concatenation_test.py", # Blocked by trt4 installation + "test/const_broadcast_test.py", + "test/multi_connection_neighbor_engine_test.py", + "test/neighboring_engine_test.py", + # "test/unary_test.py", # Blocked by trt4 installation + # "test/vgg_block_nchw_test.py", + # "test/vgg_block_test.py", + "test/memory_alignment_test.py", ], - deps = [ - ":init_py", + additional_deps = [ + ":tf_trt_integration_test_base", "//tensorflow/python:client_testlib", "//tensorflow/python:framework_test_lib", ], + tags = [ + "no_cuda_on_cpu_tap", + "no_windows", + "nomac", + ], ) cc_library( name = "utils", + srcs = ["convert/utils.cc"], hdrs = ["convert/utils.h"], + copts = tf_copts(), ) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 189944f29b5a0c24f544e0510a6fb19bd5727229..3383f6bc9b99879a1c661a0d49e42c6f3b878f66 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -86,27 +86,48 @@ bool IsTensorRTCandidate(const tensorflow::Node* node) { // TODO(jie): Segmentation shouldn't associated with op name. // Split it into a registration for each kernel. static const std::set candidate_ops = { - "Identity", - "Snapshot", - "Const", - "Conv2D", - "MaxPool", - "BiasAdd", - "Relu", - "Add", - "Mul", - "Sub", - "Rsqrt", - "Pad", - "Mean", - "AvgPool", - "ConcatV2", - "DepthwiseConv2dNative", - "FusedBatchNorm", - "FusedBatchNormV2", - // TODO(ben,jie): ... + "Identity", + "Snapshot", + "Const", + "Conv2D", + "MaxPool", + "BiasAdd", + "Relu", + "Add", + "Mul", + "Sub", + "Rsqrt", + "Pad", + "Mean", + "AvgPool", + "ConcatV2", + "DepthwiseConv2dNative", + "FusedBatchNorm", + "FusedBatchNormV2", + "Div", + "RealDiv", + "Rsqrt", + "Reciprocal", + "Exp", + "Log", + "Sqrt", + "Abs", + "Neg", +#if NV_TENSORRT_MAJOR > 3 + "MatMul", + "BatchMatMul", + "Softmax", + "Minimum", + "Maximum", + "TopKV2", + "Sum", + "Prod", + "Max", + "Min", +#endif + // TODO(ben,jie): ... }; - // LINT.ThenChange(//tensorflow/contrib/tensorrt/convert/convert_nodes.h) + // LINT.ThenChange(//tensorflow/contrib/tensorrt/convert/convert_nodes.cc) return (candidate_ops.count(node->type_string()) || PluginFactoryTensorRT::GetInstance()->IsPlugin(node->type_string())); } @@ -168,7 +189,7 @@ tensorflow::Status ConvertCalibGraphToInferGraph( "Can't get TRTCalibrator from resource manager!"); } cres->Unref(); - calib_rm->Cleanup(container_name); + TF_RETURN_IF_ERROR(calib_rm->Cleanup(container_name)); } } return tensorflow::Status::OK(); @@ -248,6 +269,7 @@ tensorflow::Status GetEngineInfo( const std::vector& reverse_topo_order, EngineInfo* info) { std::vector subgraph_node_ids; + std::set added_const_node_ids; // Used to prevent double insertion. std::set segment_devices; int input_port = 0; int output_port = 0; @@ -257,6 +279,7 @@ tensorflow::Status GetEngineInfo( // edge, thus there must not be any duplicates since source nodes of // input/output edges must be in different split of the graph. // TODO(aaroey): consider using node id and port instead. + // TODO(aaroey): using topo order instead of reverting reverse topo order. std::unordered_map created_edges; for (auto it = reverse_topo_order.rbegin(); it != reverse_topo_order.rend(); ++it) { @@ -275,19 +298,22 @@ tensorflow::Status GetEngineInfo( << " neither have requested device nor assigned device"; } } - int node_id = node->id(); - subgraph_node_ids.push_back(node_id); + const int node_id = node->id(); for (const auto edge : node->in_edges()) { auto input_node = edge->src(); - if (segment_nodes.count(input_node->name()) == 0) { + if (segment_nodes.count(input_node->name()) == 0 && + !edge->IsControlEdge() && !input_node->IsSource()) { // Add constant input node into the segment. We don't care if it has // other output edges going into other engines or TF nodes. Since we add // it only to the subsegment node list, not the subsegment itself, it // won't be removed from the graph. If it doesn't have any edges, TF // will prune it out. if (input_node->type_string() == "Const") { - subgraph_node_ids.push_back(input_node->id()); - } else if (!edge->IsControlEdge() && !input_node->IsSource()) { + if (added_const_node_ids.count(input_node->id()) == 0) { + added_const_node_ids.insert(input_node->id()); + subgraph_node_ids.push_back(input_node->id()); + } + } else { string s(input_node->name()); StrAppend(&s, ":", edge->src_output()); VLOG(1) << "Input edge = " << s; @@ -304,6 +330,9 @@ tensorflow::Status GetEngineInfo( } } } + // We need to add possible const input nodes before adding this node in + // order to keep the topological order. + subgraph_node_ids.push_back(node_id); for (const auto edge : node->out_edges()) { auto output_node = edge->dst(); if (segment_nodes.count(output_node->name()) == 0 && @@ -350,9 +379,9 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, nvinfer1::IGpuAllocator* alloc, int max_batch_size) { const auto& info = infos.at(pos); - std::vector out_shapes; - std::vector input_shapes; - std::vector shapes; + std::vector output_shape_protos; + std::vector input_shape_protos; + std::vector input_shapes; std::vector inputs; std::vector out_types; VLOG(1) << "Processing " << info.engine_name; @@ -365,11 +394,11 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, tensorflow::TensorShapeProto out_shape; // shape of the output node inside segment conn.inside_shape.AsProto(&out_shape); - if (out_shapes.size() <= conn.port_number) { - out_shapes.resize(conn.port_number + 1); + if (output_shape_protos.size() <= conn.port_number) { + output_shape_protos.resize(conn.port_number + 1); out_types.resize(conn.port_number + 1); } - out_shapes.at(conn.port_number) = out_shape; + output_shape_protos.at(conn.port_number) = out_shape; out_types.at(conn.port_number) = conn.connection_type; continue; } @@ -377,12 +406,12 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, // Set the shapes and data types of input edge. tensorflow::TensorShapeProto in_shape; conn.outside_shape.AsProto(&in_shape); - if (input_shapes.size() <= conn.port_number) { + if (input_shape_protos.size() <= conn.port_number) { + input_shape_protos.resize(conn.port_number + 1); input_shapes.resize(conn.port_number + 1); - shapes.resize(conn.port_number + 1); } - input_shapes.at(conn.port_number) = in_shape; - shapes.at(conn.port_number) = conn.outside_shape; + input_shape_protos.at(conn.port_number) = in_shape; + input_shapes.at(conn.port_number) = conn.outside_shape; string input_node = conn.outside_node_name; int input_port = conn.outside_port; @@ -410,6 +439,8 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, VLOG(1) << "Engine Input " << input_node << ":" << input_port << " -> " << info.engine_name << ":" << inputs.size(); // Skip duplicate inputs. + // TODO(aaroey): use std::find instead. GetEngineInfo already remove + // duplicate connections, so here we should never find any duplicate? bool new_input = true; for (const auto& inp : inputs) { if (inp.node == input_node && inp.index == input_port) { @@ -437,8 +468,8 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, TF_RETURN_IF_ERROR(ConvertGraphDefToEngine( info.segment_graph_def, info.precision_mode == INT8MODE ? FP32MODE : info.precision_mode, - max_batch_size, info.max_workspace_size_bytes, shapes, &trt_logger, - alloc, /*calibrator=*/nullptr, &engine, + max_batch_size, info.max_workspace_size_bytes, input_shapes, + &trt_logger, alloc, /*calibrator=*/nullptr, &engine, /*convert_successfully=*/nullptr)); TrtUniquePtrType engine_data(engine->serialize()); segment_string = @@ -486,8 +517,8 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, } tensorflow::NodeDef trt_node; tensorflow::Status status = - node_builder.Attr("input_shapes", input_shapes) - .Attr("output_shapes", out_shapes) + node_builder.Attr("input_shapes", input_shape_protos) + .Attr("output_shapes", output_shape_protos) .Attr("static_engine", info.engine_type == EngineInfo::EngineType::TRTStatic) .Attr("segment_funcdef_name", @@ -596,7 +627,9 @@ tensorflow::Status RegisterSegmentFunctionToFunctionLibrary( edge->src()->output_type(edge->src_output())); VLOG(1) << " input " << nout.node << ":" << nout.index << " dtype=" << tensorflow::DataTypeString(nout.data_type); - node_builder.Input({nout}); + // nvcc complains that Input() is + // ambiguous, so do not use Input({nout}). + node_builder.Input(nout); TF_RETURN_IF_ERROR(node_builder.Attr("T", node->output_type(0)) .Attr("index", i) .Finalize(&nd)); @@ -704,6 +737,7 @@ std::pair GetDeviceAndAllocator( } // Entry function from optimization pass. +// TODO(aaeory): parameter should use pointer type. tensorflow::Status ConvertAfterShapes(ConversionParams& params) { // Convert graphdef to graph. tensorflow::FunctionLibraryDefinition flib(tensorflow::OpRegistry::Global(), @@ -721,7 +755,8 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { segment_options.minimum_segment_size = params.minimum_segment_size; tensorflow::tensorrt::segment::SegmentNodesVector initial_segments; TF_RETURN_IF_ERROR(tensorrt::segment::SegmentGraph( - &graph, IsTensorRTCandidate, segment_options, &initial_segments)); + &graph, IsTensorRTCandidate, InputEdgeValidator(*params.graph_properties), + OutputEdgeValidator(), segment_options, &initial_segments)); if (initial_segments.size() > 1) { VLOG(0) << "MULTIPLE tensorrt candidate conversion: " << initial_segments.size(); @@ -801,7 +836,7 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { // The allocator is used to build the engine. The build and the built engine // will be destroyed after we get the serialized engine string, so it's fine // to use unique_ptr here. - std::unique_ptr alloc; + std::unique_ptr alloc; auto device_alloc = GetDeviceAndAllocator(params, engine); int cuda_device_id = 0; if (device_alloc.first >= 0) { diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 146b9c7344b0a9c2b3ec87b395e9b1096dbef06c..451d6fe698bbcf89570fdf54fb3d780a731e7d74 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" #include +#include #include #include #include @@ -49,15 +50,34 @@ limitations under the License. #if GOOGLE_TENSORRT #include "tensorrt/include/NvInfer.h" -// Check if the types are equal. Cast to int first so that failure log message -// would work! -#define CHECK_EQ_TYPE(val1, val2) CHECK_EQ((int)val1, (int)val2) +// Check if the types are equal. Cast to int first so that failure log message +// would work! +#define TFTRT_CHECK_EQ_TYPE(val1, val2) CHECK_EQ((int)val1, (int)val2) + +#define TFTRT_INTERNAL_ERROR_AT_NODE(node) \ + do { \ + return tensorflow::errors::Internal( \ + "TFTRT::", __FUNCTION__, "failed to add TRT layer, at: ", node); \ + } while (0) + +#define TFTRT_RETURN_ERROR_IF_FALSE(status, node) \ + do { \ + if (status == false) { \ + TFTRT_INTERNAL_ERROR_AT_NODE(node); \ + } \ + } while (0) + +#define TFTRT_RETURN_ERROR_IF_NULLPTR(ptr, node) \ + do { \ + if (ptr == nullptr) { \ + TFTRT_INTERNAL_ERROR_AT_NODE(node); \ + } \ + } while (0) namespace tensorflow { namespace tensorrt { namespace convert { using ::tensorflow::str_util::Split; - using ::tensorflow::strings::StrAppend; using ::tensorflow::strings::StrCat; @@ -75,13 +95,163 @@ inline tensorflow::Status ConvertDType(tensorflow::DataType tf_dtype, case tensorflow::DataType::DT_HALF: *trt_dtype = nvinfer1::DataType::kHALF; break; +#if NV_TENSORRT_MAJOR > 3 + case tensorflow::DataType::DT_INT32: + *trt_dtype = nvinfer1::DataType::kINT32; + break; +#endif default: return tensorflow::errors::InvalidArgument( - "Unsupported data type " + tensorflow::DataTypeString(tf_dtype)); + "Unsupported data type ", tensorflow::DataTypeString(tf_dtype)); } return tensorflow::Status::OK(); } +void GetInputProperties(const grappler::GraphProperties& graph_properties, + const Node* outside_node, const int out_port, + PartialTensorShape* shape, + tensorflow::DataType* dtype) { + if (graph_properties.HasOutputProperties(outside_node->name())) { + auto output_params = + graph_properties.GetOutputProperties(outside_node->name()); + auto out_shape = output_params.at(out_port); + *dtype = out_shape.dtype(); + *shape = out_shape.shape(); + } else { + VLOG(0) << "Unknown output shape" << outside_node->name(); + *dtype = outside_node->output_type(out_port); + } +} + +void GetOutputProperties(const grappler::GraphProperties& graph_properties, + const Node* outside_node, const int in_port, + PartialTensorShape* shape, + tensorflow::DataType* dtype) { + if (graph_properties.HasInputProperties(outside_node->name())) { + auto input_params = + graph_properties.GetInputProperties(outside_node->name()); + auto in_shape = input_params.at(in_port); + *dtype = in_shape.dtype(); + *shape = in_shape.shape(); + } else { + *dtype = outside_node->input_type(in_port); + } +} + +tensorflow::Status ValidateInputProperties(const PartialTensorShape& shape, + const tensorflow::DataType dtype, + nvinfer1::DataType* trt_dtype) { + // TODO(aaroey): some of these checks also apply to IsTensorRTCandidate(), so + // put them there instead. + TF_RETURN_IF_ERROR(ConvertDType(dtype, trt_dtype)); + if (shape.dims() < 0) { + return tensorflow::errors::InvalidArgument("Input tensor rank is unknown."); + } + if (shape.dims() > 9) { + return tensorflow::errors::OutOfRange( + "Input tensor rank is greater than 8."); + } + for (int d = 1; d < shape.dims(); ++d) { + if (shape.dim_size(d) < 0) { + return tensorflow::errors::InvalidArgument( + "Input tensor has a unknown non-batch dimemension at dim ", d); + } + } + return Status::OK(); +} + +// Return whether or not the broadcast is feasible; +bool TensorRTGetBroadcastShape(const nvinfer1::Dims& operand_l, + const bool operand_l_is_tensor, + const nvinfer1::Dims& operand_r, + const bool operand_r_is_tensor, + nvinfer1::Dims* operand_l_new_shape, + nvinfer1::Dims* operand_r_new_shape) { + // *************************************************************************** + // TensorRT Elementwise op supports broadcast but requires both tensor to be + // of Identical rank + // + // We consider case of: + // 1. operand_l to be a Tensor & operand_r to be a Const; + // 2. operand_l to be a Tensor & operand_r to be a Tensor; + // note: const op const (constant folding) should fallback to TensorFlow + // + // broadcast scheme: + // T: 1 3 5 (tensor would not have batch dimension) + // W: 1 1 3 1 (weight would have all explicit dimensions) + // i. fill in explicit dimensions + // -> T: -1 1 3 5 (we put a -1 for batch dimension) + // -> W: 1 1 3 1 + // ii. compare broadcast feasibility + // + // We cannot support the following since TensorRT does not allow manipulation + // on batch dimension, we cannot generate output with proper shape + // T: 3 5 1 + // W: 1 1 1 1 3 5 1 + // -> T: 1 1 1 -1 3 5 1 + // -> W: 1 1 1 1 3 5 1 + // *************************************************************************** + const int max_nb_dims = nvinfer1::Dims::MAX_DIMS + 1; + const size_t element_size = sizeof(operand_l.d[0]); + + // fill in dimensions + int l_s[max_nb_dims]; + std::fill(l_s, l_s + max_nb_dims, 1); + int l_d = operand_l_is_tensor ? operand_l.nbDims + 1 : operand_l.nbDims; + int r_s[max_nb_dims]; + std::fill(r_s, r_s + max_nb_dims, 1); + int r_d = operand_r_is_tensor ? operand_r.nbDims + 1 : operand_r.nbDims; + + int max_d = std::max(l_d, r_d); + std::memcpy(l_s + max_d - operand_l.nbDims, operand_l.d, + operand_l.nbDims * element_size); + std::memcpy(r_s + max_d - operand_r.nbDims, operand_r.d, + operand_r.nbDims * element_size); + + // set -1 for batch dimension, since batch size is not supposed to be + // broadcasted + if (operand_l_is_tensor) { + if (max_d != l_d) { // if broadcast beyond batch dimension, fail + return false; + } + l_s[0] = -1; + } + if (operand_r_is_tensor) { + if (max_d != r_d) { // if broadcast beyond batch dimension, fail + return false; + } + r_s[0] = -1; + } + + // compare broadcast feasibility + for (int i = max_d - 1; i >= 0; i--) { + if ((l_s[i] != r_s[i]) && (l_s[i] != 1) && (r_s[i] != 1)) { + return false; + } + } + + // output new TensorRT Dimension (stripping the batch dimension) + operand_l_new_shape->nbDims = max_d - 1; + std::memcpy(operand_l_new_shape->d, l_s + 1, (max_d - 1) * element_size); + operand_r_new_shape->nbDims = max_d - 1; + std::memcpy(operand_r_new_shape->d, r_s + 1, (max_d - 1) * element_size); + + return true; +} + +inline bool DimsEqual(const nvinfer1::Dims& dim_l, + const nvinfer1::Dims& dim_r) { + if (dim_l.nbDims != dim_r.nbDims) { + return false; + } + for (int i = 0; i < dim_l.nbDims; i++) { + if (dim_l.d[i] != dim_r.d[i]) { + return false; + } + } + return true; +} + inline nvinfer1::Dims GetTensorShape(const tensorflow::Tensor& tensor) { nvinfer1::Dims dims; dims.nbDims = tensor.dims(); @@ -91,7 +261,7 @@ inline nvinfer1::Dims GetTensorShape(const tensorflow::Tensor& tensor) { return dims; } -inline int64_t GetShapeSize(nvinfer1::Dims shape) { +inline int64_t GetShapeSize(const nvinfer1::Dims& shape) { // Returns total number of elements in shape int64_t count = 1; for (int d = 0; d < shape.nbDims; ++d) { @@ -104,7 +274,7 @@ static std::vector> CreateSamePadding( const nvinfer1::DimsHW& stride, const nvinfer1::DimsHW& kernel, const std::vector& input_dims) { std::vector> padding(input_dims.size()); - CHECK_EQ((size_t)stride.nbDims, input_dims.size()); // TODO(jie): N+C? NC+? + CHECK_EQ(stride.nbDims, input_dims.size()); // TODO(jie): N+C? NC+? for (size_t i = 0; i < input_dims.size(); ++i) { // Formula to calculate the padding @@ -134,6 +304,7 @@ string GetCommonNameScope(const string& op_name_a, const string& op_name_b) { return op_name_a.substr(0, last_scope_separator); } +// Class to convert TF weight to TRT weight. class TRT_ShapedWeights { public: TRT_ShapedWeights(tensorflow::DataType type, const void* values, @@ -145,12 +316,14 @@ class TRT_ShapedWeights { explicit TRT_ShapedWeights(tensorflow::DataType type) : shape_(), type_(type), values_(nullptr), empty_weight_flag_(true) {} + // TODO(aaroey): use rvalue reference. TRT_ShapedWeights(const TRT_ShapedWeights& rhs) : shape_(rhs.shape_), type_(rhs.type_), values_(rhs.values_), empty_weight_flag_(rhs.empty_weight_flag_) {} + // TODO(aaroey): use GetShapeSize() instead. int64_t count() const { int64_t c = 1; for (int i = 0; i < shape_.nbDims; i++) c *= shape_.d[i]; @@ -168,6 +341,7 @@ class TRT_ShapedWeights { const void* GetValues() const { return values_; } + // TODO(aaroey): get rid of this method. void SetValues(const void* values) { values_ = values; } size_t size_bytes() const { @@ -178,10 +352,12 @@ class TRT_ShapedWeights { // Default converter operator nvinfer1::Weights() const { return GetWeightsForTRT(); } + // TODO(aaroey): make these private. nvinfer1::Dims shape_; tensorflow::DataType type_; private: + // TODO(aaroey): this should not be const as it's always from TRTWeightStore. const void* values_; bool empty_weight_flag_; }; @@ -192,6 +368,7 @@ class TRT_TensorOrWeights { : tensor_(tensor), weights_(DT_FLOAT), variant_(TRT_NODE_TENSOR) {} explicit TRT_TensorOrWeights(const TRT_ShapedWeights& weights) : tensor_(nullptr), weights_(weights), variant_(TRT_NODE_WEIGHTS) {} + // TODO(aaroey): use rvalue reference. TRT_TensorOrWeights(const TRT_TensorOrWeights& rhs) : tensor_(rhs.tensor_), weights_(rhs.weights_), variant_(rhs.variant_) {} ~TRT_TensorOrWeights() {} @@ -200,19 +377,19 @@ class TRT_TensorOrWeights { bool is_weights() const { return variant_ == TRT_NODE_WEIGHTS; } nvinfer1::ITensor* tensor() { - CHECK_EQ(is_tensor(), true); + CHECK(is_tensor()); return tensor_; } const nvinfer1::ITensor* tensor() const { - CHECK_EQ(is_tensor(), true); + CHECK(is_tensor()); return tensor_; } TRT_ShapedWeights& weights() { - CHECK_EQ(is_weights(), true); + CHECK(is_weights()); return weights_; } const TRT_ShapedWeights& weights() const { - CHECK_EQ(is_weights(), true); + CHECK(is_weights()); return weights_; } nvinfer1::Dims shape() const { @@ -236,21 +413,25 @@ class TFAttrs { attrs_.insert({attr.first, &attr.second}); } } - bool count(string key) const { return attrs_.count(key); } - tensorflow::AttrValue const* at(string key) const { + + bool count(const string& key) const { return attrs_.count(key); } + + tensorflow::AttrValue const* at(const string& key) const { if (!attrs_.count(key)) { LOG(FATAL) << "Attribute not found: " << key; } return attrs_.at(key); } + template T get(const string& key) const; + template T get(const string& key, const T& default_value) const { return attrs_.count(key) ? this->get(key) : default_value; } - std::vector GetAllAttrKey() { + std::vector GetAllAttrKeys() const { std::vector attr_list; for (const auto& attr_item : attrs_) { attr_list.emplace_back(attr_item.first); @@ -285,15 +466,6 @@ std::vector TFAttrs::get>(const string& key) const { auto attr = this->at(key)->list().s(); return std::vector(attr.begin(), attr.end()); } -template <> -nvinfer1::Dims TFAttrs::get(const string& key) const { - auto values = this->get>(key); - nvinfer1::Dims dims; - dims.nbDims = values.size(); - std::copy(values.begin(), values.end(), dims.d); - // Note: No dimension type information is included - return dims; -} template <> nvinfer1::DataType TFAttrs::get(const string& key) const { @@ -319,10 +491,11 @@ bool TFAttrs::get(const string& key) const { } // TODO(jie): reorder4 & reorder2 should be merged? +// TODO(aaroey): fix the order of parameters. template -void Reorder4(nvinfer1::DimsNCHW shape, const T* idata, - nvinfer1::DimsNCHW istrides, T* odata, - nvinfer1::DimsNCHW ostrides) { +void Reorder4(const nvinfer1::DimsNCHW& shape, const T* idata, + const nvinfer1::DimsNCHW& istrides, T* odata, + const nvinfer1::DimsNCHW& ostrides) { for (int n = 0; n < shape.n(); ++n) { for (int c = 0; c < shape.c(); ++c) { for (int h = 0; h < shape.h(); ++h) { @@ -337,12 +510,13 @@ void Reorder4(nvinfer1::DimsNCHW shape, const T* idata, } template -void Reorder2(nvinfer1::DimsHW shape, const T* idata, nvinfer1::DimsHW istrides, - T* odata, nvinfer1::DimsHW ostrides) { +void Reorder2(const nvinfer1::DimsHW& shape, const T* idata, + const nvinfer1::DimsHW& istrides, T* odata, + const nvinfer1::DimsHW& ostrides) { for (int h = 0; h < shape.h(); ++h) { for (int w = 0; w < shape.w(); ++w) { odata[h * ostrides.h() + w * ostrides.w()] = - idata[h * ostrides.h() + w * ostrides.w()]; + idata[h * istrides.h() + w * istrides.w()]; } } } @@ -350,16 +524,17 @@ void Reorder2(nvinfer1::DimsHW shape, const T* idata, nvinfer1::DimsHW istrides, // TODO(jie): fallback to tensorflow!! void ReorderCKtoKC(const TRT_ShapedWeights& iweights, TRT_ShapedWeights* oweights) { - int c = iweights.shape_.d[0]; - int k = iweights.shape_.d[1]; + const int c = iweights.shape_.d[0]; + const int k = iweights.shape_.d[1]; oweights->shape_.d[0] = k; oweights->shape_.d[1] = c; - nvinfer1::DimsHW istrides = {1, k}; - nvinfer1::DimsHW ostrides = {c, 1}; + const nvinfer1::DimsHW istrides = {1, k}; + const nvinfer1::DimsHW ostrides = {c, 1}; switch (iweights.type_) { case tensorflow::DataType::DT_FLOAT: { Reorder2({k, c}, static_cast(iweights.GetValues()), istrides, + // TODO(aaroey): get rid of all the const_cast like this. static_cast(const_cast(oweights->GetValues())), ostrides); break; @@ -382,21 +557,24 @@ void ReorderRSCKToKCRS(const TRT_ShapedWeights& iweights, TRT_ShapedWeights* oweights, int num_groups) { CHECK_EQ(iweights.type_, oweights->type_); CHECK_EQ(iweights.size_bytes(), oweights->size_bytes()); - int r = iweights.shape_.d[0]; - int s = iweights.shape_.d[1]; - // TRT requires GKcRS, while TF depthwise has RSCK - // where c=1, C=G + // K indexes over output channels, C over input channels, and R and S over the + // height and width of the convolution + const int r = iweights.shape_.d[0]; + const int s = iweights.shape_.d[1]; + // TRT requires GKcRS, while TF depthwise has RSCK where c=1, C=G VLOG(2) << "num_groups: " << num_groups; - int c = iweights.shape_.d[2] / num_groups; + const int c = iweights.shape_.d[2] / num_groups; VLOG(2) << "c" << iweights.shape_.d[2] << " then " << c; - int k = iweights.shape_.d[3] * num_groups; + const int k = iweights.shape_.d[3] * num_groups; VLOG(2) << "k" << iweights.shape_.d[3] << " then " << k; + VLOG(2) << "r" << iweights.shape_.d[0] << " then " << r; + VLOG(2) << "s" << iweights.shape_.d[1] << " then " << s; oweights->shape_.d[0] = k / num_groups; oweights->shape_.d[1] = c * num_groups; oweights->shape_.d[2] = r; oweights->shape_.d[3] = s; - nvinfer1::DimsNCHW istrides = {1, k, s * k * c, c * k}; - nvinfer1::DimsNCHW ostrides = {c * r * s, r * s, s, 1}; + const nvinfer1::DimsNCHW istrides = {1, k, s * k * c, c * k}; + const nvinfer1::DimsNCHW ostrides = {c * r * s, r * s, s, 1}; switch (iweights.type_) { case tensorflow::DataType::DT_FLOAT: { Reorder4({k, c, r, s}, static_cast(iweights.GetValues()), @@ -428,11 +606,14 @@ using OpConverter = std::vector*)>; class Converter { + // TODO(aaroey): fix the order of members. std::unordered_map trt_tensors_; std::unordered_map op_registry_; OpConverter plugin_converter_; nvinfer1::INetworkDefinition* trt_network_; std::list> temp_bufs_; + // TODO(aaroey): inline the definition of TRTWeightStore here, and add APIs to + // operate the stored weights instead of operating it directly. TRTWeightStore* weight_store_; bool fp16_; void register_op_converters(); @@ -440,7 +621,7 @@ class Converter { std::vector* inputs) { for (auto const& input_name : node_def.input()) { /************************************************************************* - * TODO(jie) handle case 1) here + * TODO(jie): handle case 1) here. * Normalizes the inputs and extracts associated metadata: * 1) Inputs can contain a colon followed by a suffix of characters. * That suffix may be a single number (e.g. inputName:1) or several @@ -454,6 +635,7 @@ class Converter { if (input_name[0] == '^') continue; string name = input_name; auto first = name.find_first_of(':'); + // TODO(aaroey): why removing the colon but not the zero? A bug? if (first != string::npos && first + 2 == name.size() && name[first + 1] == '0') name.erase(first); @@ -462,12 +644,13 @@ class Converter { if (trt_tensors_.count(name)) { inputs->push_back(trt_tensors_.at(name)); } else { - string str("Node "); - StrAppend(&str, node_def.name(), " should have an input named '", name, + // TODO(aaroey): this should not happen, make it a CHECK. + // TODO(aaroey): use StrCat for pattern like this. + string msg("Node "); + StrAppend(&msg, node_def.name(), " should have an input named '", name, "' but it is not available"); - LOG(WARNING) << "input: " << name << " not available for node at " - << node_def.name(); - return tensorflow::errors::InvalidArgument(str); + LOG(ERROR) << msg; + return tensorflow::errors::InvalidArgument(msg); } } return tensorflow::Status::OK(); @@ -488,6 +671,7 @@ class Converter { weights.SetValues(weight_store_->store_.back().data()); return weights; } + // TODO(aaroey): fix all the namings. bool isFP16() { return fp16_; } TRT_ShapedWeights get_temp_weights_like(const TRT_ShapedWeights& weights) { return this->get_temp_weights(weights.type_, weights.shape_); @@ -496,9 +680,10 @@ class Converter { tensorflow::Status convert_node(const tensorflow::NodeDef& node_def) { std::vector inputs; TF_RETURN_IF_ERROR(this->get_inputs(node_def, &inputs)); - string op = node_def.op(); + const string& op = node_def.op(); std::vector outputs; if (PluginFactoryTensorRT::GetInstance()->IsPlugin(op)) { + // TODO(aaroey): plugin_converter_ is not set, fix it. TF_RETURN_IF_ERROR(plugin_converter_(*this, node_def, inputs, &outputs)); } else { if (!op_registry_.count(op)) { @@ -509,7 +694,7 @@ class Converter { TF_RETURN_IF_ERROR(op_converter(*this, node_def, inputs, &outputs)); } for (size_t i = 0; i < outputs.size(); ++i) { - TRT_TensorOrWeights output = outputs.at(i); + TRT_TensorOrWeights& output = outputs[i]; // TODO(jie): tf protobuf seems to be omitting the :0 suffix string output_name = node_def.name(); if (i != 0) output_name = StrCat(output_name, ":", i); @@ -527,26 +712,29 @@ class Converter { nvinfer1::INetworkDefinition* network() { return trt_network_; } - TRT_TensorOrWeights get_tensor(string name) { + TRT_TensorOrWeights get_tensor(const string& name) { if (!trt_tensors_.count(name)) { return TRT_TensorOrWeights(nullptr); } return trt_tensors_.at(name); } - bool insert_input_tensor(string name, nvinfer1::ITensor* tensor) { + bool insert_input_tensor(const string& name, nvinfer1::ITensor* tensor) { return trt_tensors_.insert({name, TRT_TensorOrWeights(tensor)}).second; } nvinfer1::ITensor* TransposeTensor(nvinfer1::ITensor* input_tensor, - std::vector order) { - auto dims = input_tensor->getDimensions(); + const std::vector& order) { + const auto dims = input_tensor->getDimensions(); // TODO(jie): change the return to status and properly exit if (order.size() - 1 != size_t(dims.nbDims)) LOG(ERROR) << "Dimension does not match, fail gracefully"; nvinfer1::IShuffleLayer* layer = this->network()->addShuffle(*input_tensor); + if (layer == nullptr) { + return nullptr; + } nvinfer1::Permutation permutation; for (int32_t i = 0; i < dims.nbDims; ++i) { permutation.order[i] = order[i + 1] - 1; @@ -577,13 +765,14 @@ TRT_ShapedWeights ConvertFP32ToFP16(Converter& ctx, } return weights; } + // **************************************************************************** // Constant folding functions // TODO(jie): once optimizer kicks in, we should have done constant folding // there. -//*****************************************************************************/ +// ***************************************************************************** struct LambdaFactory { - enum class OP_CATEGORY : int { RSQRT = 0, NEG, ADD, MUL, SUB }; + enum class OP_CATEGORY : int { RSQRT = 0, NEG, ADD, MUL, SUB, RECIP }; OP_CATEGORY op; template @@ -595,6 +784,8 @@ struct LambdaFactory { } case OP_CATEGORY::NEG: return [](T t) -> T { return -t; }; + case OP_CATEGORY::RECIP: + return [](T t) -> T { return 1.0 / t; }; default: VLOG(2) << "Not supported op for unary: " << static_cast(op); return nullptr; @@ -628,7 +819,6 @@ struct LambdaFactory { VLOG(2) << "LAMBDA VAL : " << val; return l + val; }; - // Return [val](T l)-> T {return l+val;}; case OP_CATEGORY::SUB: return [val](T l) -> T { VLOG(2) << "LAMBDA VAL : " << val; @@ -688,11 +878,13 @@ std::function LambdaFactory::unary() { } case OP_CATEGORY::NEG: return [](Eigen::half t) -> Eigen::half { return -t; }; + // TODO(aaroey): can we support RECIP? default: VLOG(2) << "Not supported op for unary: " << static_cast(op); return nullptr; } } + tensorflow::Status UnaryCompute(const TRT_ShapedWeights& iweights, TRT_ShapedWeights* oweights, LambdaFactory unary_op) { @@ -738,6 +930,7 @@ tensorflow::Status BinaryCompute(const TRT_ShapedWeights& iweights_l, if (iweights_l.count() != iweights_r.count()) { // We only supports broadcast of RankZero if (iweights_l.count() == 1) { + // TODO(aaroey): Remove loggings like this. VLOG(2) << "I bet it is not working!" << (*inp_l); std::transform(inp_r, inp_r + iweights_r.count(), oup, binary_op.broadcast_l(*inp_l)); @@ -790,117 +983,21 @@ tensorflow::Status BinaryCompute(const TRT_ShapedWeights& iweights_l, return tensorflow::Status::OK(); } -tensorflow::Status ConstantFoldUnary( - Converter& ctx, const tensorflow::NodeDef& node_def, - const std::vector& inputs, - std::vector* outputs) { - TRT_ShapedWeights weights_input = inputs.at(0).weights(); - - // Allocate output weights - TRT_ShapedWeights weights_output = ctx.get_temp_weights_like(weights_input); - - // FIXME assume type matches input weights - // Get trt type & shape - // Maybe this part has to be moved into the block of rsqrt later - // Check type consistency - CHECK_EQ(weights_input.type_, - TFAttrs(node_def).get("T")); - - LambdaFactory unary_op; - if (node_def.op() == "Rsqrt") { - // Compute rsqrt - unary_op.op = LambdaFactory::OP_CATEGORY::RSQRT; - auto ret = UnaryCompute(weights_input, &weights_output, unary_op); - // Pass the output - if (ret == tensorflow::Status::OK()) { - outputs->push_back(TRT_TensorOrWeights(weights_output)); - } - return ret; - } else { - return tensorflow::errors::Unimplemented("Binary op not supported: " + - node_def.op()); - } -} - -// TODO(jie,ben) broadcast is needed yet not implemented -// Let's get the simple stuff working first. Maybe we should fall back to TF -// approach for constant folding -tensorflow::Status ConstantFoldBinary( - Converter& ctx, const tensorflow::NodeDef& node_def, - const std::vector& inputs, - std::vector* outputs) { - TRT_ShapedWeights weights_input_l = inputs.at(0).weights(); - TRT_ShapedWeights weights_input_r = inputs.at(1).weights(); - - // Check type consistency - CHECK_EQ(weights_input_l.type_, weights_input_r.type_); - - if (weights_input_l.shape_.nbDims != weights_input_r.shape_.nbDims) - return tensorflow::errors::Unimplemented( - "Binary op implicit broadcast not supported: " + node_def.op()); - - // TODO(jie): constant fold should really fall back to TF. - int num_dims = weights_input_l.shape_.nbDims; - nvinfer1::Dims output_shape; - output_shape.nbDims = num_dims; - VLOG(2) << "nb_dims: " << num_dims - << ", the other: " << weights_input_r.shape_.nbDims; - for (int i = 0; i < num_dims; i++) { - if (weights_input_l.shape_.d[i] == weights_input_r.shape_.d[i]) { - output_shape.d[i] = weights_input_l.shape_.d[i]; - } else if (weights_input_l.shape_.d[i] == 1 || - weights_input_r.shape_.d[i] == 1) { - output_shape.d[i] = - std::max(weights_input_l.shape_.d[i], weights_input_r.shape_.d[i]); - } else { - return tensorflow::errors::Unimplemented( - "Binary op with incompatible shape at, " + node_def.op()); - } - VLOG(2) << "left: " << weights_input_l.shape_.d[i] - << "right: " << weights_input_r.shape_.d[i] - << "output: " << output_shape.d[i]; - } - - // FIXME assume type matches input weights - // Get trt type & shape - TFAttrs attrs(node_def); - // Maybe this part has to be moved into the block of rsqrt later - tensorflow::DataType dtype = attrs.get("T"); - - // Allocate output weights - TRT_ShapedWeights weights_output = ctx.get_temp_weights(dtype, output_shape); - - LambdaFactory binary_op; - if (node_def.op() == "Sub") { - binary_op.op = LambdaFactory::OP_CATEGORY::SUB; - } else if (node_def.op() == "Mul") { - binary_op.op = LambdaFactory::OP_CATEGORY::MUL; - } else if (node_def.op() == "Add") { - binary_op.op = LambdaFactory::OP_CATEGORY::ADD; - } else { - return tensorflow::errors::Unimplemented("Binary op not supported: " + - node_def.op()); - } - auto ret = BinaryCompute(weights_input_l, weights_input_r, &weights_output, - binary_op); - - // Pass the output - if (ret == tensorflow::Status::OK()) { - outputs->push_back(TRT_TensorOrWeights(weights_output)); - } - - return ret; -} - // TODO(jie): broadcast is needed yet not implemented. // Only implemented channel wise for the time being tensorflow::Status BinaryTensorOpWeight( Converter& ctx, const tensorflow::NodeDef& node_def, const nvinfer1::ITensor* tensor, TRT_ShapedWeights weights, - std::vector* outputs) { - // FIXME assume type matches input weights - // Get trt type & shape - // Maybe this part has to be moved into the block of rsqrt later + bool swapped_inputs, std::vector* outputs) { + // tensor is the left operand while weights is the right operand; + // when swapped_inputs set to true, those two are swapped. + // TODO(aaroey): use a set. + if (node_def.op() != "Sub" && node_def.op() != "Add" && + node_def.op() != "Mul" && node_def.op() != "Div" && + node_def.op() != "RealDiv") { + return tensorflow::errors::Unimplemented( + "op not supported: " + node_def.op() + ", at: " + node_def.name()); + } // Check type consistency nvinfer1::DataType ttype; @@ -910,6 +1007,12 @@ tensorflow::Status BinaryTensorOpWeight( auto dims_w = weights.shape_; auto dims_t = tensor->getDimensions(); + // TODO(jie): addScale checks for input tensor dimension + if (dims_t.nbDims != 3) { + return tensorflow::errors::InvalidArgument( + "addScale requires tensor with rank 3, " + node_def.name()); + } + // default to element-wise auto scale_mode = nvinfer1::ScaleMode::kELEMENTWISE; @@ -980,6 +1083,7 @@ tensorflow::Status BinaryTensorOpWeight( permutation[dims_t.nbDims] = 1; tensor = ctx.TransposeTensor(const_cast(tensor), permutation); + TFTRT_RETURN_ERROR_IF_NULLPTR(tensor, node_def.name()); } else { return tensorflow::errors::InvalidArgument( "Transpose cannot be applied, " + node_def.name()); @@ -997,11 +1101,35 @@ tensorflow::Status BinaryTensorOpWeight( // Maybe I should do a switch if (node_def.op() == "Sub") { - TRT_ShapedWeights neg_weights = ctx.get_temp_weights_like(weights); - LambdaFactory unary_op; - unary_op.op = LambdaFactory::OP_CATEGORY::NEG; - TF_RETURN_IF_ERROR(UnaryCompute(weights, &neg_weights, unary_op)); - shift_weights = neg_weights; + if (swapped_inputs) { + shift_weights = weights; + nvinfer1::IUnaryLayer* layer = + ctx.network()->addUnary(*const_cast(tensor), + nvinfer1::UnaryOperation::kNEG); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); + tensor = layer->getOutput(0); + } else { + TRT_ShapedWeights neg_weights = ctx.get_temp_weights_like(weights); + LambdaFactory unary_op; + unary_op.op = LambdaFactory::OP_CATEGORY::NEG; + TF_RETURN_IF_ERROR(UnaryCompute(weights, &neg_weights, unary_op)); + shift_weights = neg_weights; + } + } else if (node_def.op() == "Div" || node_def.op() == "RealDiv") { + if (swapped_inputs) { + scale_weights = weights; + nvinfer1::IUnaryLayer* layer = + ctx.network()->addUnary(*const_cast(tensor), + nvinfer1::UnaryOperation::kRECIP); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); + tensor = layer->getOutput(0); + } else { + TRT_ShapedWeights recip_weights = ctx.get_temp_weights_like(weights); + LambdaFactory unary_op; + unary_op.op = LambdaFactory::OP_CATEGORY::RECIP; + TF_RETURN_IF_ERROR(UnaryCompute(weights, &recip_weights, unary_op)); + scale_weights = recip_weights; + } } else if (node_def.op() == "Mul") { scale_weights = weights; } else if (node_def.op() == "Add") { @@ -1014,11 +1142,13 @@ tensorflow::Status BinaryTensorOpWeight( nvinfer1::IScaleLayer* layer = ctx.network()->addScale( *const_cast(tensor), scale_mode, shift_weights, scale_weights, power_weights); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); nvinfer1::ITensor* output_tensor = layer->getOutput(0); // transpose back dimension if (permutation_flag) { output_tensor = ctx.TransposeTensor(output_tensor, permutation); + TFTRT_RETURN_ERROR_IF_NULLPTR(output_tensor, node_def.name()); } // Pass the output @@ -1042,20 +1172,31 @@ tensorflow::Status ConvertConv2DHelper( if (data_format == "NHWC") { tensor = ctx.TransposeTensor(const_cast(tensor), {0, 3, 1, 2}); + TFTRT_RETURN_ERROR_IF_NULLPTR(tensor, node_def.name()); h_index = 1; w_index = 2; // TODO(jie): transpose it } // tensor after transpose (NCHW) - auto tensor_dim = tensor->getDimensions(); + const auto tensor_dim = tensor->getDimensions(); int num_groups = group; - if (num_groups == 0) // depthwise convolution - num_groups = tensor_dim.d[0]; + if (num_groups == 0) num_groups = tensor_dim.d[0]; // depthwise convolution VLOG(2) << "groups count: " << num_groups; TRT_ShapedWeights weights_rsck = inputs.at(1).weights(); + + VLOG(2) << "weight shape: " << weights_rsck.shape_.nbDims; + for (int i = 0; i < weights_rsck.shape_.nbDims; i++) { + VLOG(2) << weights_rsck.shape_.d[i]; + } + + if (weights_rsck.shape_.nbDims != 4) { + return tensorflow::errors::Internal( + "Conv2D expects kernel of dimension 4, at: " + node_def.name()); + } + if (ctx.isFP16()) { weights_rsck = ConvertFP32ToFP16(ctx, inputs.at(1).weights()); } @@ -1063,18 +1204,22 @@ tensorflow::Status ConvertConv2DHelper( TRT_ShapedWeights weights = ctx.get_temp_weights_like(weights_rsck); ReorderRSCKToKCRS(weights_rsck, &weights, num_groups); TRT_ShapedWeights biases(weights.type_); - int noutput = weights.shape_.d[0] * num_groups; + const int noutput = weights.shape_.d[0] * num_groups; nvinfer1::DimsHW kernel_size; kernel_size.h() = weights.shape_.d[2]; kernel_size.w() = weights.shape_.d[3]; + VLOG(2) << "RSCK: "; + for (int i = 0; i < 4; i++) { + VLOG(2) << " " << weights.shape_.d[i]; + } VLOG(2) << "kernel size: " << kernel_size.h() << ", " << kernel_size.w(); // TODO(jie): stride. (NHWC/NCHW) - auto tf_stride = attrs.get>("strides"); + const auto tf_stride = attrs.get>("strides"); VLOG(2) << "h_INDEX" << h_index << ", w_index " << w_index; VLOG(2) << "stride!!!: " << tf_stride[0] << tf_stride[1] << tf_stride[2] << tf_stride[3]; - nvinfer1::DimsHW stride(tf_stride[h_index], tf_stride[w_index]); + const nvinfer1::DimsHW stride(tf_stride[h_index], tf_stride[w_index]); std::vector> padding; // TODO(jie): padding. @@ -1102,6 +1247,7 @@ tensorflow::Status ConvertConv2DHelper( *const_cast(tensor), nvinfer1::DimsHW(padding[0].first, padding[1].first), nvinfer1::DimsHW(padding[0].second, padding[1].second)); + TFTRT_RETURN_ERROR_IF_NULLPTR(pad_layer, node_def.name()); padding = {{0, 0}, {0, 0}}; tensor = pad_layer->getOutput(0); auto dim_after = tensor->getDimensions(); @@ -1112,6 +1258,7 @@ tensorflow::Status ConvertConv2DHelper( nvinfer1::IConvolutionLayer* layer = ctx.network()->addConvolution(*const_cast(tensor), noutput, kernel_size, weights, biases); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); layer->setStride(stride); layer->setPadding({padding[0].first, padding[1].first}); @@ -1126,6 +1273,7 @@ tensorflow::Status ConvertConv2DHelper( if (data_format == "NHWC") { // TODO(jie): transpose it back! output_tensor = ctx.TransposeTensor(output_tensor, {0, 2, 3, 1}); + TFTRT_RETURN_ERROR_IF_NULLPTR(output_tensor, node_def.name()); } else { VLOG(2) << "NCHW !!!!"; } @@ -1147,35 +1295,91 @@ tensorflow::Status ConvertConv2DHelper( node_def.name()); } +// Helper function converts input into tensor with shape specified by dims. +bool PrepareTensorForShape(Converter& ctx, const TRT_TensorOrWeights& input, + const nvinfer1::Dims& dims, + const nvinfer1::ITensor** tensor) { + if (input.is_tensor()) { + if (DimsEqual(input.shape(), dims)) { + *tensor = input.tensor(); + } else { + nvinfer1::IShuffleLayer* layer = ctx.network()->addShuffle( + *const_cast(input.tensor())); + if (layer != nullptr) { + layer->setReshapeDimensions(dims); + *tensor = layer->getOutput(0); + } else { + return false; + } + } + } else { +#if NV_TENSORRT_MAJOR > 3 + nvinfer1::IConstantLayer* layer = + ctx.network()->addConstant(dims, input.weights()); + if (layer != nullptr) { + *tensor = layer->getOutput(0); + } else { + return false; + } +#else + return false; +#endif + } + return true; +} + tensorflow::Status BinaryTensorOpTensor( Converter& ctx, const tensorflow::NodeDef& node_def, - const nvinfer1::ITensor* tensor_l, const nvinfer1::ITensor* tensor_r, + const TRT_TensorOrWeights& operand_l, const TRT_TensorOrWeights& operand_r, std::vector* outputs) { static const std::unordered_map ops{ {"Add", nvinfer1::ElementWiseOperation::kSUM}, {"Mul", nvinfer1::ElementWiseOperation::kPROD}, {"Sub", nvinfer1::ElementWiseOperation::kSUB}, {"Div", nvinfer1::ElementWiseOperation::kDIV}, + {"RealDiv", nvinfer1::ElementWiseOperation::kDIV}, + {"Minimum", nvinfer1::ElementWiseOperation::kMIN}, + {"Maximum", nvinfer1::ElementWiseOperation::kMAX}, }; - // FIXME assume type matches input weights + const nvinfer1::ITensor* tensor_l; + const nvinfer1::ITensor* tensor_r; + + nvinfer1::Dims dim_l; + nvinfer1::Dims dim_r; + + if (!TensorRTGetBroadcastShape(operand_l.shape(), operand_l.is_tensor(), + operand_r.shape(), operand_r.is_tensor(), + &dim_l, &dim_r)) { + return tensorflow::errors::InvalidArgument( + "Binary op broadcast scheme not supported by TensorRT op: " + + node_def.op() + ", at: " + node_def.name()); + } + + TFTRT_RETURN_ERROR_IF_FALSE( + PrepareTensorForShape(ctx, operand_l, dim_l, &tensor_l), node_def.name()); + TFTRT_RETURN_ERROR_IF_FALSE( + PrepareTensorForShape(ctx, operand_r, dim_r, &tensor_r), node_def.name()); + // get trt type & shape TFAttrs attrs(node_def); // maybe this part has to be moved into the block of rsqrt later nvinfer1::DataType dtype = attrs.get("T"); // check type consistency - CHECK_EQ_TYPE(tensor_l->getType(), dtype); - CHECK_EQ_TYPE(tensor_r->getType(), dtype); + TFTRT_CHECK_EQ_TYPE(tensor_l->getType(), dtype); + TFTRT_CHECK_EQ_TYPE(tensor_r->getType(), dtype); auto op_pair = ops.find(node_def.op()); - if (op_pair == ops.end()) + if (op_pair == ops.end()) { return tensorflow::errors::Unimplemented( - "binary op: " + node_def.op() + - " not supported at: " + node_def.name()); + "binary op: ", node_def.op(), " not supported at: ", node_def.name()); + } nvinfer1::IElementWiseLayer* layer = ctx.network()->addElementWise( + // TODO(aaroey): will tensor_l/tensor_r get modified? *const_cast(tensor_l), *const_cast(tensor_r), op_pair->second); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); nvinfer1::ITensor* output_tensor = layer->getOutput(0); @@ -1202,7 +1406,7 @@ tensorflow::Status ConvertPlugin(Converter& ctx, // passing attributes // TODO(jie): support more general attribute TFAttrs attrs(node_def); - auto attr_key_vector = attrs.GetAllAttrKey(); + auto attr_key_vector = attrs.GetAllAttrKeys(); for (auto attr_key : attr_key_vector) { // TODO(jie): support only list of float for toy example here. auto data = attrs.get>(attr_key); @@ -1223,29 +1427,6 @@ tensorflow::Status ConvertPlugin(Converter& ctx, return tensorflow::Status::OK(); } -tensorflow::Status ConvertPlaceholder( - Converter& ctx, const tensorflow::NodeDef& node_def, - const std::vector& inputs, - std::vector* outputs) { - VLOG(2) << "Placeholder should have been replace already"; - return tensorflow::errors::Unimplemented("cannot convert Placeholder op"); - // OK this make sense since we are supposed to replace it with input - TFAttrs attrs(node_def); - nvinfer1::DataType dtype = attrs.get("dtype"); - nvinfer1::Dims dims = attrs.get("shape"); - - dims.nbDims--; - for (int i = 0; i < dims.nbDims; i++) dims.d[i] = dims.d[i + 1]; - - nvinfer1::ITensor* output = - ctx.network()->addInput(node_def.name().c_str(), dtype, dims); - if (!output) { - return tensorflow::errors::InvalidArgument("Failed to create Input layer"); - } - outputs->push_back(TRT_TensorOrWeights(output)); - return tensorflow::Status::OK(); -} - tensorflow::Status ConvertConv2D(Converter& ctx, const tensorflow::NodeDef& node_def, const std::vector& inputs, @@ -1271,65 +1452,64 @@ tensorflow::Status ConvertPool(Converter& ctx, int h_index = 2; int w_index = 3; - auto data_format = attrs.get("data_format"); + const auto data_format = attrs.get("data_format"); if (data_format == "NHWC") { h_index = 1; w_index = 2; tensor = ctx.TransposeTensor(const_cast(tensor), {0, 3, 1, 2}); - } else { - VLOG(2) << "NCHW !!!!"; + TFTRT_RETURN_ERROR_IF_NULLPTR(tensor, node_def.name()); } + nvinfer1::PoolingType type; - // TODO(jie): support other pooling type - if (node_def.op() == "MaxPool") + if (node_def.op() == "MaxPool") { type = nvinfer1::PoolingType::kMAX; - else if (node_def.op() == "AvgPool") + } else if (node_def.op() == "AvgPool") { type = nvinfer1::PoolingType::kAVERAGE; - else - return tensorflow::errors::Unimplemented("Only supports Max pool"); + } else { + return tensorflow::errors::Unimplemented("Unsupported pool type: ", + node_def.op()); + } - // TODO(jie): NCHW - auto tf_stride = attrs.get>("strides"); - nvinfer1::DimsHW stride(tf_stride[h_index], tf_stride[w_index]); + const auto tf_stride = attrs.get>("strides"); + const nvinfer1::DimsHW stride(tf_stride[h_index], tf_stride[w_index]); - auto tf_kernel = attrs.get>("ksize"); - nvinfer1::DimsHW ksize(tf_kernel[h_index], tf_kernel[w_index]); + const auto tf_kernel = attrs.get>("ksize"); + const nvinfer1::DimsHW ksize(tf_kernel[h_index], tf_kernel[w_index]); auto tensor_dim = tensor->getDimensions(); std::vector> padding; - // TODO(jie): padding. - if (attrs.get("padding") == "SAME") { + const string padding_type = attrs.get("padding"); + if (padding_type == "SAME") { // This is NCHW tensor with no batch dimension. // 1 -> h // 2 -> w padding = CreateSamePadding( stride, ksize, {static_cast(tensor_dim.d[1]), static_cast(tensor_dim.d[2])}); - } else if (attrs.get("padding") == "VALID") { - // No padding for valid padding here - VLOG(2) << "No padding added for VALID padding in pool" << node_def.name(); + } else if (padding_type == "VALID") { padding = {{0, 0}, {0, 0}}; } else { - return tensorflow::errors::Unimplemented( - "Current MaxPool cannot support padding other than SAME"); + return tensorflow::errors::Unimplemented("Unsupported padding type: ", + padding_type); } if (padding[0].first != padding[0].second || padding[1].first != padding[1].second) { - // TODO(jie): handle asymmetric padding VLOG(2) << "Padding!!!: " << padding[0].first << padding[0].second << padding[1].first << padding[1].second; auto pad_layer = ctx.network()->addPadding( *const_cast(tensor), nvinfer1::DimsHW(padding[0].first, padding[1].first), nvinfer1::DimsHW(padding[0].second, padding[1].second)); + TFTRT_RETURN_ERROR_IF_NULLPTR(pad_layer, node_def.name()); padding = {{0, 0}, {0, 0}}; tensor = pad_layer->getOutput(0); } nvinfer1::IPoolingLayer* layer = ctx.network()->addPooling( *const_cast(tensor), type, ksize); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); layer->setStride(stride); layer->setPadding({padding[0].first, padding[1].first}); @@ -1337,10 +1517,8 @@ tensorflow::Status ConvertPool(Converter& ctx, nvinfer1::ITensor* output_tensor = layer->getOutput(0); if (data_format == "NHWC") { - // TODO(jie): transpose it back! output_tensor = ctx.TransposeTensor(output_tensor, {0, 2, 3, 1}); - } else { - VLOG(2) << "NCHW !!!!"; + TFTRT_RETURN_ERROR_IF_NULLPTR(output_tensor, node_def.name()); } outputs->push_back(TRT_TensorOrWeights(output_tensor)); return tensorflow::Status::OK(); @@ -1353,6 +1531,7 @@ tensorflow::Status ConvertActivation( const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); nvinfer1::IActivationLayer* layer = ctx.network()->addActivation( *const_cast(tensor), nvinfer1::ActivationType::kRELU); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); nvinfer1::ITensor* output_tensor = layer->getOutput(0); outputs->push_back(TRT_TensorOrWeights(output_tensor)); return tensorflow::Status::OK(); @@ -1363,40 +1542,61 @@ tensorflow::Status ConvertScale(Converter& ctx, const std::vector& inputs, std::vector* outputs) { if (inputs.size() != 2 || !inputs.at(0).is_tensor() || - !inputs.at(1).is_weights()) + !inputs.at(1).is_weights()) { return tensorflow::errors::Unimplemented( - "Only supports tensor op weight for now, at " + node_def.name()); - // Implement tensor binaryOp weight [channel wise] for now; - const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); + "ConvertScale only supports tensorweight: ", node_def.name()); + } + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); TRT_ShapedWeights weights = inputs.at(1).weights(); if (ctx.isFP16()) { weights = ConvertFP32ToFP16(ctx, inputs.at(1).weights()); } TRT_ShapedWeights empty_weights(weights.type_); - TFAttrs attrs(node_def); - // Transpose NHWC - auto data_format = attrs.get("data_format"); + const auto data_format = attrs.get("data_format"); + int channel_index; + const auto dims = tensor->getDimensions(); if (data_format == "NHWC") { - tensor = ctx.TransposeTensor(const_cast(tensor), - {0, 3, 1, 2}); - // TODO(jie): transpose it + // 1). NHWC is really N+C + channel_index = dims.nbDims - 1; // batch dimension is implicit here! } else { - VLOG(2) << "NCHW !!!!"; + // 2). NCHW is really N+CHW + channel_index = dims.nbDims - 3; // batch dimension is implicit here! } - auto dims = tensor->getDimensions(); - VLOG(2) << "tensor dimensions: " << dims.nbDims; - for (int i = 0; i < dims.nbDims; i++) { - VLOG(2) << "i: " << dims.d[i]; + nvinfer1::Permutation permutation; + for (int32_t i = 0; i < dims.nbDims; ++i) { + permutation.order[i] = i; } - dims = weights.shape_; - VLOG(2) << "tensor dimensions: " << dims.nbDims; - for (int i = 0; i < dims.nbDims; i++) { - VLOG(2) << "i: " << dims.d[i]; + + if (channel_index >= 0) { + permutation.order[0] = channel_index; + permutation.order[channel_index] = 0; + } else { + return tensorflow::errors::Unimplemented( + "TFTRT::BiasAdd cannot apply on batch dimension, at ", node_def.name()); + } + + // TensorRT addScale requires input to be of rank 3, we need to apply + // transpose as well as reshape + if (channel_index != 0 || dims.nbDims != 3) { + nvinfer1::IShuffleLayer* shuffle_layer = + ctx.network()->addShuffle(*const_cast(tensor)); + TFTRT_RETURN_ERROR_IF_NULLPTR(shuffle_layer, node_def.name()); + nvinfer1::Dims reshape_dims; + reshape_dims.nbDims = 3; + reshape_dims.d[0] = 0; // 0 copy from the input + reshape_dims.d[1] = dims.nbDims >= 2 ? 0 : 1; // 0 copy from the input + reshape_dims.d[2] = dims.nbDims >= 3 ? -1 : 1; // -1 infer from the rest + if (channel_index != 0) { + // maybe we do not need this check. concerned about TRT optimization + shuffle_layer->setFirstTranspose(permutation); + } + shuffle_layer->setReshapeDimensions(reshape_dims); + tensor = shuffle_layer->getOutput(0); } nvinfer1::ScaleMode mode = nvinfer1::ScaleMode::kCHANNEL; @@ -1407,14 +1607,26 @@ tensorflow::Status ConvertScale(Converter& ctx, nvinfer1::IScaleLayer* layer = ctx.network()->addScale(*const_cast(tensor), mode, weights, empty_weights, empty_weights); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); nvinfer1::ITensor* output_tensor = layer->getOutput(0); - if (data_format == "NHWC") { - // TODO(jie): transpose it back! - output_tensor = ctx.TransposeTensor(output_tensor, {0, 2, 3, 1}); - } else { - VLOG(2) << "NCHW !!!!"; + + // restore transpose & reshape + if (channel_index != 0 || dims.nbDims != 3) { + nvinfer1::IShuffleLayer* shuffle_layer = ctx.network()->addShuffle( + *const_cast(output_tensor)); + TFTRT_RETURN_ERROR_IF_NULLPTR(shuffle_layer, node_def.name()); + nvinfer1::Dims reshape_dims = dims; + int tmp = reshape_dims.d[channel_index]; + reshape_dims.d[channel_index] = reshape_dims.d[0]; + reshape_dims.d[0] = tmp; + shuffle_layer->setReshapeDimensions(reshape_dims); + if (channel_index != 0) { + shuffle_layer->setSecondTranspose(permutation); + } + output_tensor = shuffle_layer->getOutput(0); } + outputs->push_back(TRT_TensorOrWeights(output_tensor)); return tensorflow::Status::OK(); } @@ -1431,11 +1643,13 @@ tensorflow::Status ConvertConst(Converter& ctx, // Create shaped weights as output tensorflow::Tensor tensor; - if (!tensor.FromProto(weights_tensor)) - return tensorflow::errors::Internal("Cannot parse weight tensor proto: " + + if (!tensor.FromProto(weights_tensor)) { + return tensorflow::errors::Internal("Cannot parse weight tensor proto: ", node_def.name()); + } TRT_ShapedWeights weights(dtype); + // TODO(aaroey): we should choose the array using dtype and shape. if (!weights_tensor.float_val().empty()) { VLOG(2) << "SCALAR!!!" << node_def.name(); nvinfer1::Dims scalar_shape; @@ -1443,22 +1657,16 @@ tensorflow::Status ConvertConst(Converter& ctx, VLOG(2) << "dimensions: " << tensor.dims(); VLOG(2) << "size: " << weights_tensor.float_val_size(); scalar_shape = GetTensorShape(tensor); + VLOG(2) << "details: "; for (int i = 0; i < scalar_shape.nbDims; i++) VLOG(2) << scalar_shape.d[i]; - if (GetShapeSize(scalar_shape) != weights_tensor.float_val_size()) { - if (weights_tensor.float_val_size() == 1 || - scalar_shape.d[0] == weights_tensor.float_val_size()) { - scalar_shape.nbDims = 1; - // no dimension provided. flatten it - scalar_shape.d[0] = weights_tensor.float_val_size(); - scalar_shape.type[0] = nvinfer1::DimensionType::kSPATIAL; - } else { - LOG(WARNING) << "Broadcast on weights only supports kCHANNEL and" - << " kUNIFORM, at: " << node_def.name(); - string err_str("Broadcast method is not supported for '"); - StrAppend(&err_str, node_def.name(), "' of type ", node_def.op()); - return tensorflow::errors::InvalidArgument(err_str); - } + if (GetShapeSize(scalar_shape) != weights_tensor.float_val_size() && + weights_tensor.float_val_size() != 1) { + LOG(ERROR) << "Broadcast on weights only supports kCHANNEL and" + << " kUNIFORM, at: " << node_def.name(); + string err_str("Broadcast method is not supported for '"); + StrAppend(&err_str, node_def.name(), "' of type ", node_def.op()); + return tensorflow::errors::InvalidArgument(err_str); } } else { VLOG(2) << "Dimensions: " << tensor.dims(); @@ -1468,39 +1676,42 @@ tensorflow::Status ConvertConst(Converter& ctx, scalar_shape.type[0] = nvinfer1::DimensionType::kSPATIAL; for (int i = 1; i < nvinfer1::Dims::MAX_DIMS; i++) { scalar_shape.d[i] = 0; - scalar_shape.type[i] = nvinfer1::DimensionType::kSPATIAL; } } + // TODO(aaroey): use GetShapeSize(). size_t len_data = tensorflow::DataTypeSize(dtype); for (int i = 0; i < scalar_shape.nbDims; i++) len_data *= scalar_shape.d[i]; ctx.weight_store()->store_.push_back(std::vector(len_data)); void* dst = static_cast(&(ctx.weight_store()->store_.back()[0])); - std::vector tensor_data( - weights_tensor.float_val().begin(), - weights_tensor.float_val() - .end()); // make a local copy first to flatten - memcpy(dst, tensor_data.data(), len_data); // store into weight store + if (weights_tensor.float_val_size() == 1) { + std::fill_n((float*)dst, GetShapeSize(scalar_shape), + *weights_tensor.float_val().begin()); + } else { + // TODO(aaroey): get rid of this copy as RepeatedField is always + // contiguous make a local copy first to flatten doesn't have to be + // contiguous + std::vector tensor_data(weights_tensor.float_val().begin(), + weights_tensor.float_val().end()); + memcpy(dst, tensor_data.data(), len_data); // store into weight store + } + VLOG(2) << "create shape details: "; + for (int i = 0; i < scalar_shape.nbDims; i++) VLOG(2) << scalar_shape.d[i]; weights = TRT_ShapedWeights(dtype, dst, scalar_shape); } else if (!weights_tensor.int_val().empty()) { + // TODO(aaroey): this is very similar to the above code for float, merge + // them. VLOG(2) << "int!!!" << node_def.name(); nvinfer1::Dims scalar_shape; if (tensor.dims() > 0) { VLOG(2) << "dimensions: " << tensor.dims(); scalar_shape = GetTensorShape(tensor); - if (GetShapeSize(scalar_shape) != weights_tensor.int_val_size()) { - if (weights_tensor.int_val_size() == 1 || - scalar_shape.d[0] == weights_tensor.int_val_size()) { - scalar_shape.nbDims = 1; - // no dimension provided. flatten it - scalar_shape.d[0] = weights_tensor.int_val_size(); - scalar_shape.type[0] = nvinfer1::DimensionType::kSPATIAL; - } else { - LOG(WARNING) << "Broadcast on weights only supports kCHANNEL and" - << " kUNIFORM, at: " << node_def.name(); - string err_str("Broadcast method is not supported for '"); - StrAppend(&err_str, node_def.name(), "' of type ", node_def.op()); - return tensorflow::errors::InvalidArgument(err_str); - } + if (GetShapeSize(scalar_shape) != weights_tensor.int_val_size() && + weights_tensor.int_val_size() != 1) { + LOG(WARNING) << "Broadcast on weights only supports kCHANNEL and" + << " kUNIFORM, at: " << node_def.name(); + string err_str("Broadcast method is not supported for '"); + StrAppend(&err_str, node_def.name(), "' of type ", node_def.op()); + return tensorflow::errors::InvalidArgument(err_str); } } else { VLOG(2) << "dimensions: " << tensor.dims(); @@ -1513,23 +1724,30 @@ tensorflow::Status ConvertConst(Converter& ctx, scalar_shape.type[i] = nvinfer1::DimensionType::kSPATIAL; } } - // we should not have converted //if (ctx.isFP16()) { + // we should not have converted size_t len_data = tensorflow::DataTypeSize(dtype); for (int i = 0; i < scalar_shape.nbDims; i++) len_data *= scalar_shape.d[i]; size_t len_tensor = weights_tensor.int_val_size() * sizeof(int32); len_data = std::max(len_data, len_tensor); ctx.weight_store()->store_.push_back(std::vector(len_data)); void* dst = static_cast(&(ctx.weight_store()->store_.back()[0])); - std::vector tensor_data( - weights_tensor.int_val().begin(), - weights_tensor.int_val().end()); // make a local copy first to flatten - // doesn't have to be contigous - memcpy(dst, tensor_data.data(), len_tensor); // store into weight store + if (weights_tensor.int_val_size() == 1) { + std::fill_n((int*)dst, GetShapeSize(scalar_shape), + *weights_tensor.int_val().begin()); + } else { + // TODO(aaroey): get rid of this copy as RepeatedField is always + // contiguous make a local copy first to flatten doesn't have to be + // contiguous + std::vector tensor_data(weights_tensor.int_val().begin(), + weights_tensor.int_val().end()); + memcpy(dst, tensor_data.data(), len_tensor); // store into weight store + } weights = TRT_ShapedWeights(dtype, dst, scalar_shape); } else if (!weights_tensor.tensor_content().empty()) { - // obsolete method. - // After optimization path, we do not see weights in this format. - // fp16 conversion technically should be needed here. + // obsolete method. + // After optimization path, we do not see weights in this format. + // TODO(aaroey): why? + // fp16 conversion technically should be needed here. VLOG(2) << "TENSOR!!!" << node_def.name(); const auto& content = weights_tensor.tensor_content(); @@ -1543,8 +1761,8 @@ tensorflow::Status ConvertConst(Converter& ctx, content, static_cast(const_cast(weights.GetValues()))); } } else { - return tensorflow::errors::Unimplemented( - "Not supported constant type, at " + node_def.name()); + return tensorflow::errors::Unimplemented("Not supported constant type, at ", + node_def.name()); } // Pass the output outputs->push_back(TRT_TensorOrWeights(weights)); @@ -1563,96 +1781,144 @@ tensorflow::Status ConvertBinary(Converter& ctx, const tensorflow::NodeDef& node_def, const std::vector& inputs, std::vector* outputs) { - if (inputs.size() != 2) + if (inputs.size() != 2) { return tensorflow::errors::FailedPrecondition( - "Binary ops require two tensor input, at " + node_def.name()); - - if (inputs.at(0).is_weights() && inputs.at(1).is_weights()) - return ConstantFoldBinary(ctx, node_def, inputs, outputs); - - if (inputs.at(0).is_tensor() && inputs.at(1).is_weights()) - return BinaryTensorOpWeight(ctx, node_def, inputs.at(0).tensor(), - inputs.at(1).weights(), outputs); + "Binary ops require two tensor input, at ", node_def.name()); + } - if (inputs.at(0).is_weights() && inputs.at(1).is_tensor()) - return BinaryTensorOpWeight(ctx, node_def, inputs.at(1).tensor(), - inputs.at(0).weights(), outputs); + // Constant folding should have been done by TensorFlow - if (inputs.at(0).is_tensor() && inputs.at(1).is_tensor()) - return BinaryTensorOpTensor(ctx, node_def, inputs.at(0).tensor(), - inputs.at(1).tensor(), outputs); + if (inputs.at(0).is_weights() && inputs.at(1).is_weights()) { + return tensorflow::errors::Unimplemented( + "Constant folding is falled back to TensorFlow, binary op received " + "both input as constant at: ", + node_def.name()); + } - return tensorflow::errors::Unknown("Binary op input error, at " + - node_def.name()); + // Try to convert into Scale layer first (for better performance) + // Since scale layer supports restricted broadcast policy and op types, we + // allow failure and try to handle it through Elementwise op + // (BinaryTensorOpTensor) + Status status = tensorflow::Status::OK(); + if (inputs.at(0).is_tensor() && inputs.at(1).is_weights()) { + status = BinaryTensorOpWeight(ctx, node_def, inputs.at(0).tensor(), + inputs.at(1).weights(), false, outputs); + } else if (inputs.at(0).is_weights() && inputs.at(1).is_tensor()) { + status = BinaryTensorOpWeight(ctx, node_def, inputs.at(1).tensor(), + inputs.at(0).weights(), true, outputs); +#if NV_TENSORRT_MAJOR == 3 + } else { +#else + } + if ((inputs.at(0).is_tensor() && inputs.at(1).is_tensor()) || !status.ok()) { +#endif + status = BinaryTensorOpTensor(ctx, node_def, inputs.at(0), inputs.at(1), + outputs); + } + return status; } tensorflow::Status ConvertUnary(Converter& ctx, const tensorflow::NodeDef& node_def, const std::vector& inputs, std::vector* outputs) { - if (inputs.size() != 1) + static const std::unordered_map ops{ + {"Neg", nvinfer1::UnaryOperation::kNEG}, + {"Exp", nvinfer1::UnaryOperation::kEXP}, + {"Log", nvinfer1::UnaryOperation::kLOG}, + {"Sqrt", nvinfer1::UnaryOperation::kSQRT}, + {"Abs", nvinfer1::UnaryOperation::kABS}, + {"Reciprocal", nvinfer1::UnaryOperation::kRECIP}, + }; + + if (inputs.size() != 1) { return tensorflow::errors::FailedPrecondition( - "Unary ops require single tensor input, at " + node_def.name()); + "Unary ops require single tensor input, at ", node_def.name()); + } - if (inputs.at(0).is_weights()) - return ConstantFoldUnary(ctx, node_def, inputs, outputs); - else if (inputs.at(0).is_tensor()) +#if NV_TENSORRT_MAJOR == 3 + if (inputs.at(0).is_weights()) { return tensorflow::errors::Unimplemented( - "Unary op for tensor not supported, at " + node_def.name()); + "Constant folding for unary op is not supported", node_def.name()); + } +#endif - return tensorflow::errors::Unknown("Binary op input error, at " + - node_def.name()); + // TODO(jie): check type + const nvinfer1::ITensor* tensor; + TFTRT_RETURN_ERROR_IF_FALSE( + PrepareTensorForShape(ctx, inputs.at(0), inputs.at(0).shape(), &tensor), + node_def.name()); + + nvinfer1::IUnaryLayer* layer; + if (node_def.op() == "Rsqrt") { + layer = ctx.network()->addUnary(*const_cast(tensor), + nvinfer1::UnaryOperation::kSQRT); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); + tensor = layer->getOutput(0); + layer = ctx.network()->addUnary(*const_cast(tensor), + nvinfer1::UnaryOperation::kRECIP); + } else if (ops.count(node_def.op()) != 0) { + layer = ctx.network()->addUnary(*const_cast(tensor), + ops.at(node_def.op())); + } else { + return tensorflow::errors::InvalidArgument( + "Binary op: ", node_def.op(), " not supported, at ", node_def.name()); + } + + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); } -tensorflow::Status ConvertReduce(Converter& ctx, - const tensorflow::NodeDef& node_def, - const std::vector& inputs, - std::vector* outputs) { +#if NV_TENSORRT_MAJOR == 3 +tensorflow::Status ConvertReducePool( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { if (inputs.size() != 2 || !inputs.at(0).is_tensor() || - !inputs.at(1).is_weights()) + !inputs.at(1).is_weights()) { return tensorflow::errors::InvalidArgument( - "Input expects tensor and weights, at" + node_def.name()); + "Input expects tensor and weights, at", node_def.name()); + } // Implement tensor binaryOp weight [channel wise] for now; const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); - auto dims = tensor->getDimensions(); + const auto dims = tensor->getDimensions(); // Restore implicit batch dimension - int nb_dims = dims.nbDims + 1; + const int nb_dims = dims.nbDims + 1; TRT_ShapedWeights index_list = inputs.at(1).weights(); - TFAttrs attrs(node_def); - // TODO(jie): handle data type. - // Index type here is done through TF type, so I can leverage their - // EnumToDataType for my cast auto index_type = attrs.get("Tidx"); // Only expect to handle INT32 as attributes for now - if (index_type != tensorflow::DataType::DT_INT32) + if (index_type != tensorflow::DataType::DT_INT32) { return tensorflow::errors::Unimplemented("Tidx supports only DT_INT32"); - auto index_list_data = + } + const auto index_list_data = static_cast(const_cast(index_list.GetValues())); - // Hack warning: have to fall back to pool layer since reduce is not in public - // TRT yet. - if (nb_dims != 4) + if (nb_dims != 4) { return tensorflow::errors::InvalidArgument( - "TRT only support reduce on 4 dimensional tensors, at" + + "TRT only support reduce on 4 dimensional tensors, at", node_def.name()); - if (index_list.count() > 2) + } + if (index_list.count() > 2) { return tensorflow::errors::InvalidArgument( - "TRT cannot support reduce on more than 2 dimensions, at" + + "TRT cannot support reduce on more than 2 dimensions, at", node_def.name()); + } std::set idx_set; // We cannot operate on Channel. permutation flag used to transpose tensor int permuted_index = -1; for (int i = 0; i < index_list.count(); i++) { - if (index_list_data[i] == 0) - return tensorflow::errors::InvalidArgument("TRT cannot reduce at 0, at" + + if (index_list_data[i] == 0) { + return tensorflow::errors::InvalidArgument("TRT cannot reduce at 0, at", node_def.name()); + } if (index_list_data[i] == 1) permuted_index = 1; - idx_set.emplace(index_list_data[i]); } @@ -1673,6 +1939,7 @@ tensorflow::Status ConvertReduce(Converter& ctx, // Apply permutation before extracting dimension for pool_kernel tensor = ctx.TransposeTensor(const_cast(tensor), permutation_order); + TFTRT_RETURN_ERROR_IF_NULLPTR(tensor, node_def.name()); } // Apply permutation before extracting dimension for pool_kernel @@ -1685,34 +1952,104 @@ tensorflow::Status ConvertReduce(Converter& ctx, nvinfer1::IPoolingLayer* layer = ctx.network()->addPooling(*const_cast(tensor), nvinfer1::PoolingType::kAVERAGE, pool_kernel); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); output_tensor = layer->getOutput(0); } else { - return tensorflow::errors::Unimplemented( - "Op not supported " + node_def.op() + " , at " + node_def.name()); + return tensorflow::errors::Unimplemented("Op not supported ", node_def.op(), + " , at ", node_def.name()); } if (permuted_index != -1) { // Apply permutation before extracting dimension for pool_kernel output_tensor = ctx.TransposeTensor( const_cast(output_tensor), permutation_order); + TFTRT_RETURN_ERROR_IF_NULLPTR(output_tensor, node_def.name()); } outputs->push_back(TRT_TensorOrWeights(output_tensor)); return tensorflow::Status::OK(); } +#elif NV_TENSORRT_MAJOR > 3 +tensorflow::Status ConvertReduce(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + if (inputs.size() != 2 || !inputs.at(0).is_tensor() || + !inputs.at(1).is_weights()) { + return tensorflow::errors::InvalidArgument( + "Input expects tensor and weights, at", node_def.name()); + } + + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); + TRT_ShapedWeights index_list = inputs.at(1).weights(); + + TFAttrs attrs(node_def); + auto index_type = attrs.get("Tidx"); + + // Only expect to handle INT32 as attributes for now + if (index_type != tensorflow::DataType::DT_INT32) { + return tensorflow::errors::Unimplemented("Tidx supports only DT_INT32"); + } + + const auto keep_dims = attrs.get("keep_dims"); + auto index_list_data = + static_cast(const_cast(index_list.GetValues())); + + int axes = 0; + if (index_list.count() == 0) { + return tensorflow::errors::InvalidArgument( + "TRT cannot support reduce on all (batch) dimensions, at", + node_def.name()); + } else { + for (int i = 0; i < index_list.count(); i++) { + if (index_list_data[i] == 0) { + return tensorflow::errors::InvalidArgument( + "TRT cannot reduce at batch dimension, at", node_def.name()); + } + axes |= (1 << (index_list_data[i] - 1)); + } + } + + nvinfer1::ReduceOperation reduce_operation; + if (node_def.op() == "Sum") { + reduce_operation = nvinfer1::ReduceOperation::kSUM; + } else if (node_def.op() == "Prod") { + reduce_operation = nvinfer1::ReduceOperation::kPROD; + } else if (node_def.op() == "Max") { + reduce_operation = nvinfer1::ReduceOperation::kMAX; + } else if (node_def.op() == "Min") { + reduce_operation = nvinfer1::ReduceOperation::kMIN; + } else if (node_def.op() == "Mean") { + reduce_operation = nvinfer1::ReduceOperation::kAVG; + } else { + return tensorflow::errors::Unimplemented("Op not supported ", node_def.op(), + " , at ", node_def.name()); + } + + nvinfer1::ILayer* layer = + ctx.network()->addReduce(*const_cast(tensor), + reduce_operation, axes, keep_dims); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); + + outputs->push_back(TRT_TensorOrWeights(layer->getOutput(0))); + return tensorflow::Status::OK(); +} +#endif tensorflow::Status ConvertPad(Converter& ctx, const tensorflow::NodeDef& node_def, const std::vector& inputs, std::vector* outputs) { + // TODO(aaroey): make a routine for this check and reuse it. if (inputs.size() != 2 || !inputs.at(0).is_tensor() || - !inputs.at(1).is_weights()) + !inputs.at(1).is_weights()) { return tensorflow::errors::InvalidArgument( - "Input expects tensor and weights, at" + node_def.name()); + "Input expects tensor and weights, at", node_def.name()); + } // Implement tensor binaryOp weight [channel wise] for now; const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); - auto dims = tensor->getDimensions(); + const auto dims = tensor->getDimensions(); // Restore implicit batch dimension - int nb_dims = dims.nbDims + 1; + const int nb_dims = dims.nbDims + 1; TRT_ShapedWeights pads = inputs.at(1).weights(); @@ -1722,21 +2059,24 @@ tensorflow::Status ConvertPad(Converter& ctx, auto padding_type = attrs.get("Tpaddings"); // TODO(jie): handle data type conversion for TRT? - if (pads.shape_.d[0] != nb_dims || pads.shape_.d[1] != 2) + if (pads.shape_.d[0] != nb_dims || pads.shape_.d[1] != 2) { return tensorflow::errors::InvalidArgument( - "Pad only supports explicit padding on 4 dimensional tensor, at " + + "Pad only supports explicit padding on 4 dimensional tensor, at ", node_def.name()); + } // Only expect to handle INT32 as attributes for now - if (padding_type != tensorflow::DataType::DT_INT32) + if (padding_type != tensorflow::DataType::DT_INT32) { return tensorflow::errors::Unimplemented( "Tpaddings supports only DT_INT32"); + } auto pad_data = static_cast(const_cast(pads.GetValues())); std::vector pad_index; for (int i = 0; i < nb_dims; i++) { - if (pad_data[2 * i] != 0 || pad_data[2 * i + 1] != 0) + if (pad_data[2 * i] != 0 || pad_data[2 * i + 1] != 0) { pad_index.push_back(i); + } } // No padding at all, we should exit @@ -1746,20 +2086,23 @@ tensorflow::Status ConvertPad(Converter& ctx, } // Only supports padding on less than 2 axis GIE-2579 - if (pad_index.size() > 2) + if (pad_index.size() > 2) { return tensorflow::errors::InvalidArgument( "Padding layer does not support padding on > 2"); + } // Padding on batch dimension is not supported - if (pad_index[0] == 0) + if (pad_index[0] == 0) { return tensorflow::errors::InvalidArgument( "Padding layer does not support padding on batch dimension"); + } // Not doing the legit thing here. ignoring padding on dim 1 and 3; // TODO(jie): implement pad as uff parser - if (pad_index.size() == 2 && pad_index[0] == 0 && pad_index[1] == 3) + if (pad_index.size() == 2 && pad_index[0] == 0 && pad_index[1] == 3) { return tensorflow::errors::Unimplemented( "Padding layer does not support padding on dimension 1 and 3 yet"); + } bool legit_pad = true; nvinfer1::DimsHW pre_padding(0, 0); @@ -1770,6 +2113,7 @@ tensorflow::Status ConvertPad(Converter& ctx, legit_pad = false; tensor = ctx.TransposeTensor(const_cast(tensor), {0, 3, 2, 1}); + TFTRT_RETURN_ERROR_IF_NULLPTR(tensor, node_def.name()); permuted_pad_index[0] = 3; } @@ -1786,11 +2130,14 @@ tensorflow::Status ConvertPad(Converter& ctx, nvinfer1::IPaddingLayer* layer = ctx.network()->addPadding( *const_cast(tensor), pre_padding, post_padding); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); nvinfer1::ITensor* output_tensor = layer->getOutput(0); - if (!legit_pad) + if (!legit_pad) { output_tensor = ctx.TransposeTensor( const_cast(output_tensor), {0, 3, 2, 1}); + TFTRT_RETURN_ERROR_IF_NULLPTR(output_tensor, node_def.name()); + } outputs->push_back(TRT_TensorOrWeights(output_tensor)); return tensorflow::Status::OK(); @@ -1803,9 +2150,10 @@ tensorflow::Status ConvertConcat(Converter& ctx, // not including the last input (axis) here int input_size = static_cast(inputs.size()) - 1; - if (!inputs.at(0).is_tensor()) + if (!inputs.at(0).is_tensor()) { return tensorflow::errors::InvalidArgument( - "Concat in TRT support only Tensor input, at " + node_def.name()); + "Concat in TRT support only Tensor input, at ", node_def.name()); + } // We are retrieving the axis TRT_ShapedWeights axis = inputs.at(input_size).weights(); @@ -1816,8 +2164,8 @@ tensorflow::Status ConvertConcat(Converter& ctx, // TODO(jie): handle data type // Only expect to handle INT32 as index attributes for now if (index_type != tensorflow::DataType::DT_INT32) - return tensorflow::errors::Unimplemented( - "Tidx supports only DT_INT32, at " + node_def.name()); + return tensorflow::errors::Unimplemented("Tidx supports only DT_INT32, at ", + node_def.name()); int index = *(static_cast(const_cast(axis.GetValues()))); @@ -1825,23 +2173,29 @@ tensorflow::Status ConvertConcat(Converter& ctx, auto dim = inputs.at(0).tensor()->getDimensions(); // dimension check - if (index > dim.nbDims + 1) + if (index > dim.nbDims + 1) { return tensorflow::errors::InvalidArgument( - "Concatenate on axis out of dimension range, at " + node_def.name()); - - if (index == 0) + "Concatenate on axis out of dimension range, at ", node_def.name()); + } + if (index == 0) { return tensorflow::errors::InvalidArgument( - "Concatenate on batch dimension not supported, at " + node_def.name()); + "Concatenate on batch dimension not supported, at ", node_def.name()); + } + if (index < 0) { + index = dim.nbDims + index + 1; + } +#if NV_TENSORRT_MAJOR == 3 // incase we need permutation; std::vector permutation_order(dim.nbDims + 1); for (int i = 0; i < dim.nbDims + 1; i++) permutation_order[i] = i; if (index != 1) { - permutation_order[1] = index - 1; - permutation_order[index - 1] = 1; + permutation_order[1] = index; + permutation_order[index] = 1; } +#endif std::vector inputs_vec; // Shap chack (all input tensor should have same shape) @@ -1849,24 +2203,28 @@ tensorflow::Status ConvertConcat(Converter& ctx, for (int i = 0; i < input_size; i++) { auto tensor_i = inputs.at(i).tensor(); auto dim_i = tensor_i->getDimensions(); - if (dim_i.nbDims != dim.nbDims) + if (dim_i.nbDims != dim.nbDims) { return tensorflow::errors::InvalidArgument( - "Concatenate receives inputs with inconsistent dimensions, at " + + "Concatenate receives inputs with inconsistent dimensions, at ", node_def.name()); - + } for (int j = 0; j < dim.nbDims; j++) { // check dimension consistency on non-concatenate axis - if (j != index - 1 && dim_i.d[j] != dim.d[j]) + if (j != index - 1 && dim_i.d[j] != dim.d[j]) { return tensorflow::errors::InvalidArgument( - "Concatenate receives inputs with inconsistent shape, at" + + "Concatenate receives inputs with inconsistent shape, at", node_def.name()); + } } - // TRT does concatenation only on channel! - if (index != 1) +#if NV_TENSORRT_MAJOR == 3 + // TRT3 does concatenation only on channel! + if (index != 1) { tensor_i = ctx.TransposeTensor(const_cast(tensor_i), permutation_order); - + TFTRT_RETURN_ERROR_IF_NULLPTR(tensor_i, node_def.name()); + } +#endif inputs_vec.push_back(tensor_i); } @@ -1874,11 +2232,18 @@ tensorflow::Status ConvertConcat(Converter& ctx, nvinfer1::IConcatenationLayer* layer = ctx.network()->addConcatenation( const_cast(inputs_vec.data()), inputs_vec.size()); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); +#if NV_TENSORRT_MAJOR > 3 + layer->setAxis(index - 1); +#endif nvinfer1::ITensor* output_tensor = layer->getOutput(0); +#if NV_TENSORRT_MAJOR == 3 if (index != 1) { output_tensor = ctx.TransposeTensor(output_tensor, permutation_order); + TFTRT_RETURN_ERROR_IF_NULLPTR(output_tensor, node_def.name()); } +#endif outputs->push_back(TRT_TensorOrWeights(output_tensor)); return tensorflow::Status::OK(); } @@ -1997,112 +2362,243 @@ tensorflow::Status ConvertFusedBatchNorm( combined_offset_weights.GetWeightsForTRT(), combined_scale_weights.GetWeightsForTRT(), dummy_power_weights.GetWeightsForTRT()); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); nvinfer1::ITensor* output_tensor = layer->getOutput(0); outputs->push_back(TRT_TensorOrWeights(output_tensor)); return tensorflow::Status::OK(); } -tensorflow::Status ConvertMatMul(Converter& ctx, - const tensorflow::NodeDef& node_def, - const std::vector& inputs, - std::vector* outputs) { - const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); - - // TODO(jie): transpose! - TFAttrs attrs(node_def); +#if NV_TENSORRT_MAJOR > 3 +tensorflow::Status ConvertMatMulHelper( + Converter& ctx, TRT_TensorOrWeights tensor_input, + TRT_ShapedWeights weights_raw, bool transpose_weight, string node_name, + std::vector* outputs) { + nvinfer1::ITensor* output_tensor; + if (!tensor_input.is_tensor()) { + return tensorflow::errors::InvalidArgument("Input 0 expects tensor"); + } + const nvinfer1::ITensor* tensor = tensor_input.tensor(); - TRT_ShapedWeights weights_ck = inputs.at(1).weights(); - TRT_ShapedWeights weights = ctx.get_temp_weights_like(weights_ck); - ReorderCKtoKC(weights_ck, &weights); + TRT_ShapedWeights weights(weights_raw.type_); + if (transpose_weight) { + weights = weights_raw; + } else { + TRT_ShapedWeights weights_ck = weights_raw; + weights = ctx.get_temp_weights_like(weights_ck); + ReorderCKtoKC(weights_raw, &weights); + } TRT_ShapedWeights biases(weights.type_); int noutput = weights.shape_.d[0]; + auto input_dim = tensor->getDimensions(); + while (input_dim.nbDims != 3) { + input_dim.d[input_dim.nbDims++] = 1; + } + TFTRT_RETURN_ERROR_IF_FALSE( + PrepareTensorForShape(ctx, tensor_input, input_dim, &tensor), node_name); + nvinfer1::IFullyConnectedLayer* layer = ctx.network()->addFullyConnected( *const_cast(tensor), noutput, weights, biases); - - nvinfer1::ITensor* output_tensor = layer->getOutput(0); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_name); + output_tensor = layer->getOutput(0); + + const nvinfer1::ITensor* temp_tensor; + auto output_dim = output_tensor->getDimensions(); + output_dim.nbDims = 1; + TFTRT_RETURN_ERROR_IF_FALSE( + PrepareTensorForShape(ctx, TRT_TensorOrWeights(output_tensor), output_dim, + &temp_tensor), + node_name); + output_tensor = const_cast(temp_tensor); outputs->push_back(TRT_TensorOrWeights(output_tensor)); return tensorflow::Status::OK(); } -tensorflow::Status ConvertReshape( +// inputs are both two dimensional (tensorflow::ops::MatMul) +tensorflow::Status ConvertMatMul(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + if (!inputs.at(0).is_tensor()) { + return tensorflow::errors::InvalidArgument("Input 0 expects tensor, at" + + node_def.name()); + } + + TFAttrs attrs(node_def); + // TODO(jie): INT32 should be converted? + tensorflow::DataType tf_dtype = attrs.get("T"); + if (tf_dtype != tensorflow::DataType::DT_FLOAT && + tf_dtype != tensorflow::DataType::DT_HALF) { + return tensorflow::errors::Unimplemented( + "data type is not supported, for node " + node_def.name() + " got " + + tensorflow::DataTypeString(tf_dtype)); + } + bool transpose_a = attrs.get("transpose_a"); + bool transpose_b = attrs.get("transpose_b"); + + // FullyConnected: + if (transpose_a) { + return tensorflow::errors::Internal( + "Transpose_a is not supported for TensorRT FullyConnected (op: " + + node_def.op() + "), at: " + node_def.name()); + } + if (inputs.at(1).is_tensor()) { + return tensorflow::errors::Internal( + "Operand 1 must be constant for TensorRT FullyConnected (op: " + + node_def.op() + "), at: " + node_def.name()); + } + return ConvertMatMulHelper(ctx, inputs.at(0), inputs.at(1).weights(), + transpose_b, node_def.name(), outputs); +} + +tensorflow::Status ConvertBatchMatMul( Converter& ctx, const tensorflow::NodeDef& node_def, const std::vector& inputs, std::vector* outputs) { - if (inputs.size() != 2 || !inputs.at(0).is_tensor() || - !inputs.at(1).is_weights()) - return tensorflow::errors::InvalidArgument( - "Input expects tensor and weights, at" + node_def.name()); + TFAttrs attrs(node_def); - // implement tensor binaryOp weight [channel wise] for now; - const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); - auto dims = tensor->getDimensions(); - // restore implicit batch dimension + // TODO(jie): INT32 should be converted? + tensorflow::DataType tf_dtype = attrs.get("T"); + if (tf_dtype != tensorflow::DataType::DT_FLOAT && + tf_dtype != tensorflow::DataType::DT_HALF) { + return tensorflow::errors::Unimplemented( + "data type is not supported, for node " + node_def.name() + " got " + + tensorflow::DataTypeString(tf_dtype)); + } - TRT_ShapedWeights shape = inputs.at(1).weights(); + bool transpose_a = attrs.get("adj_x"); + bool transpose_b = attrs.get("adj_y"); - TFAttrs attrs(node_def); + auto dims = inputs.at(0).shape(); + if (dims.nbDims == 1) { // NC * CK is only supported through fully connected + if (transpose_a == false && inputs.at(0).is_tensor() && + inputs.at(1).is_weights()) { + return ConvertMatMulHelper(ctx, inputs.at(0), inputs.at(1).weights(), + transpose_b, node_def.name(), outputs); + } else { + return tensorflow::errors::InvalidArgument( + "Invalid configuration for MatMul, at: " + node_def.name()); + } + } - auto padding_type = attrs.get("Tshape"); + const nvinfer1::ITensor* tensor_l; + const nvinfer1::ITensor* tensor_r; + auto dims_l = inputs.at(0).shape(); + auto dims_r = inputs.at(1).shape(); + if (inputs.at(0).is_weights()) { + if (inputs.at(0).shape().d[0] != 1) { + return tensorflow::errors::InvalidArgument( + "Input 0 as weight assumes broadcast across batch for MatMul, at: " + + node_def.name()); + } else { + for (int i = 0; i < dims_l.nbDims - 1; i++) { + dims_l.d[i] = dims_l.d[i + 1]; + } + dims_l.nbDims--; + } + } + if (inputs.at(1).is_weights()) { + if (inputs.at(1).shape().d[0] != 1) { + return tensorflow::errors::InvalidArgument( + "Input 1 as weight assumes broadcast across batch for MatMul, at: " + + node_def.name()); + } else { + for (int i = 0; i < dims_r.nbDims - 1; i++) { + dims_r.d[i] = dims_r.d[i + 1]; + } + dims_r.nbDims--; + } + } - if (shape.shape_.nbDims != 1) - return tensorflow::errors::InvalidArgument( - "reshape new shape is not 1 dimensional, at " + node_def.name()); + TFTRT_RETURN_ERROR_IF_FALSE( + PrepareTensorForShape(ctx, inputs.at(0), dims_l, &tensor_l), + node_def.name()); + TFTRT_RETURN_ERROR_IF_FALSE( + PrepareTensorForShape(ctx, inputs.at(1), dims_r, &tensor_r), + node_def.name()); - // Only expect to handle INT32 as attributes for now - if (padding_type != tensorflow::DataType::DT_INT32) - return tensorflow::errors::Unimplemented( - "reshape new shape supports only DT_INT32, at " + node_def.name()); + nvinfer1::IMatrixMultiplyLayer* layer = ctx.network()->addMatrixMultiply( + *const_cast(tensor_l), transpose_a, + *const_cast(tensor_r), transpose_b); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} +#endif - auto shape_data = static_cast(const_cast(shape.GetValues())); +#if NV_TENSORRT_MAJOR > 3 +tensorflow::Status ConvertSoftmax( + Converter& ctx, const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); - if (shape_data[0] != -1) + int nbDims = tensor->getDimensions().nbDims; + if (nbDims == 0) { return tensorflow::errors::InvalidArgument( - "reshape new shape first dimension is not -1, at " + node_def.name()); + "TensorRT Softmax cannot apply on batch dimension, at" + + node_def.name()); + } + nvinfer1::ISoftMaxLayer* layer = + ctx.network()->addSoftMax(*const_cast(tensor)); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); + // Tensorflow SoftMax assumes applying softmax on the last dimension. + layer->setAxes(1 << (nbDims - 1)); - auto shape_num_dims = shape.shape_.d[0]; - VLOG(2) << "shape dimensions: " << shape_num_dims; - int volume_w = 1; - for (int i = 1; i < shape.shape_.d[0]; i++) volume_w *= shape_data[i]; + nvinfer1::ITensor* output_tensor = layer->getOutput(0); + outputs->push_back(TRT_TensorOrWeights(output_tensor)); + return tensorflow::Status::OK(); +} +#endif - int volume_t = 1; - for (int i = 0; i < dims.nbDims; i++) volume_t *= dims.d[i]; +#if NV_TENSORRT_MAJOR > 3 +tensorflow::Status ConvertTopK(Converter& ctx, + const tensorflow::NodeDef& node_def, + const std::vector& inputs, + std::vector* outputs) { + const nvinfer1::ITensor* tensor = inputs.at(0).tensor(); - VLOG(2) << "volume: " << volume_t << " volume weights: " << volume_w; - if (volume_w != volume_t) + int nbDims = tensor->getDimensions().nbDims; + if (nbDims == 0) { return tensorflow::errors::InvalidArgument( - "volume does not agree between tensor and new shape, at " + - node_def.name()); + "TensorRT TopK cannot apply on batch dimension, at" + node_def.name()); + } - nvinfer1::IShuffleLayer* layer = - ctx.network()->addShuffle(*const_cast(tensor)); + TRT_ShapedWeights k_w = inputs.at(1).weights(); + int k = *(static_cast(const_cast(k_w.GetValues()))); - nvinfer1::Dims reshape_dims; - VLOG(2) << "new dimension: " << shape_num_dims - 1; - reshape_dims.nbDims = shape_num_dims - 1; - for (int32_t i = 0; i < reshape_dims.nbDims; ++i) { - reshape_dims.d[i] = shape_data[i + 1]; + nvinfer1::TopKOperation op; + uint32_t reducedAxes = 0; + if (node_def.op() == "TopKV2") { + op = nvinfer1::TopKOperation::kMAX; + reducedAxes |= 1 << (nbDims - 1); + } else { + return tensorflow::errors::Unimplemented( + "Operation: " + node_def.op() + + " not implemented, at: " + node_def.name()); } - layer->setReshapeDimensions(reshape_dims); - VLOG(2) << "new dimension: " << shape_num_dims - 1; - nvinfer1::ITensor* output_tensor = layer->getOutput(0); - auto dims_output = output_tensor->getDimensions(); - VLOG(2) << "output tensor dimension:" << dims_output.nbDims; - outputs->push_back(TRT_TensorOrWeights(output_tensor)); + nvinfer1::ITopKLayer* layer = ctx.network()->addTopK( + *const_cast(tensor), op, k, reducedAxes); + TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name()); + + nvinfer1::ITensor* output_value_tensor = layer->getOutput(0); + nvinfer1::ITensor* output_indices_tensor = layer->getOutput(1); + outputs->push_back(TRT_TensorOrWeights(output_value_tensor)); + outputs->push_back(TRT_TensorOrWeights(output_indices_tensor)); return tensorflow::Status::OK(); } +#endif void Converter::register_op_converters() { // vgg_16 slim implementation - op_registry_["Placeholder"] = ConvertPlaceholder; op_registry_["Conv2D"] = ConvertConv2D; op_registry_["DepthwiseConv2dNative"] = ConvertConv2DDepthwise; op_registry_["Relu"] = ConvertActivation; op_registry_["MaxPool"] = ConvertPool; op_registry_["AvgPool"] = ConvertPool; - // This could be really handled as ConvertBinary op_registry_["BiasAdd"] = ConvertScale; op_registry_["Const"] = ConvertConst; // TODO(ben,jie): this is a temp hack. @@ -2113,17 +2609,39 @@ void Converter::register_op_converters() { op_registry_["Add"] = ConvertBinary; op_registry_["Mul"] = ConvertBinary; op_registry_["Sub"] = ConvertBinary; - op_registry_["Rsqrt"] = ConvertUnary; - op_registry_["Mean"] = ConvertReduce; op_registry_["Pad"] = ConvertPad; - // TODO(ben,jie): Add more ops op_registry_["ConcatV2"] = ConvertConcat; - op_registry_["MatMul"] = ConvertMatMul; - op_registry_["Reshape"] = ConvertReshape; op_registry_["FusedBatchNorm"] = ConvertFusedBatchNorm; op_registry_["FusedBatchNormV2"] = ConvertFusedBatchNorm; + op_registry_["Div"] = ConvertBinary; + op_registry_["RealDiv"] = ConvertBinary; + + op_registry_["Rsqrt"] = ConvertUnary; + op_registry_["Reciprocal"] = ConvertUnary; + op_registry_["Exp"] = ConvertUnary; + op_registry_["Log"] = ConvertUnary; + op_registry_["Sqrt"] = ConvertUnary; + op_registry_["Abs"] = ConvertUnary; + op_registry_["Neg"] = ConvertUnary; +#if NV_TENSORRT_MAJOR == 3 + op_registry_["Mean"] = ConvertReducePool; +#endif +#if NV_TENSORRT_MAJOR > 3 + op_registry_["Sum"] = ConvertReduce; + op_registry_["Prod"] = ConvertReduce; + op_registry_["Max"] = ConvertReduce; + op_registry_["Min"] = ConvertReduce; + op_registry_["Mean"] = ConvertReduce; + op_registry_["Maximum"] = ConvertBinary; + op_registry_["Minimum"] = ConvertBinary; + op_registry_["Softmax"] = ConvertSoftmax; + op_registry_["MatMul"] = ConvertMatMul; + op_registry_["BatchMatMul"] = ConvertBatchMatMul; + op_registry_["TopKV2"] = ConvertTopK; +#endif + plugin_converter_ = ConvertPlugin; } @@ -2177,25 +2695,22 @@ tensorflow::Status ConvertGraphDefToEngine( (node_def.op() == "Placeholder")) { nvinfer1::DimsCHW input_dim_pseudo_chw; for (int i = 0; i < 8; i++) input_dim_pseudo_chw.d[i] = 0; - nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT); - auto type_status = - ConvertDType(node_def.attr().at("dtype").type(), &dtype); - if (type_status != tensorflow::Status::OK()) { - LOG(WARNING) << "Type conversion failed for " << node_name; - return type_status; - } int32 slot_number = -1; - if (!tensorflow::strings::safe_strto32(node_name.c_str() + 8, - &slot_number)) { - LOG(ERROR) << "Failed to parse slot number from " << node_name - << " +8= " << node_name.c_str() + 8; + if (!tensorflow::strings::safe_strto32( + node_name.c_str() + strlen(kInputPHName), &slot_number)) { + return tensorflow::errors::InvalidArgument( + "Failed to parse slot number from ", node_name); } + nvinfer1::DataType dtype; auto shape = input_shapes.at(slot_number); - if (shape.dims() > 8) { - LOG(ERROR) << "Tensor rank is greater than 8 for " << node_name - << " at input slot " << slot_number; - return tensorflow::errors::OutOfRange( - "Input tensor rank is greater than 8"); + auto status = ValidateInputProperties( + shape, node_def.attr().at("dtype").type(), &dtype); + if (!status.ok()) { + const string error_message = + StrCat("Validation failed for ", node_name, " and input slot ", + slot_number, ": ", status.error_message()); + LOG(WARNING) << error_message; + return Status(status.code(), error_message); } if (VLOG_IS_ON(1)) { string dim_str("dims="); @@ -2226,10 +2741,10 @@ tensorflow::Status ConvertGraphDefToEngine( } else if (tensorflow::str_util::StartsWith(node_name, kOutputPHName) && (node_def.op() == "Identity")) { int32 slot_number = -1; - if (!tensorflow::strings::safe_strto32(node_name.c_str() + 9, - &slot_number)) { - LOG(ERROR) << "Failed to parse slot number from " << node_name - << " +9=" << node_name.c_str() + 9; + if (!tensorflow::strings::safe_strto32( + node_name.c_str() + strlen(kOutputPHName), &slot_number)) { + return tensorflow::errors::InvalidArgument( + "Failed to parse slot number from ", node_name); } if (output_tensors.size() <= slot_number) { output_tensors.resize(slot_number + 1); @@ -2288,38 +2803,20 @@ tensorflow::Status ConvertSegmentToGraphDef( "Cannot find node with id ", connection.outside_id, " in the graph."); } // Updates the shape and data types of input/output connections. - tensorflow::DataType input_type = tensorflow::DT_FLOAT; + tensorflow::DataType dtype; tensorflow::PartialTensorShape partial_shape; if (connection.is_input_edge) { - if (graph_properties.HasOutputProperties(connection.outside_node_name)) { - auto output_params = - graph_properties.GetOutputProperties(connection.outside_node_name); - auto out_shape = output_params.at(connection.outside_port); - input_type = out_shape.dtype(); - std::vector dims; - partial_shape = out_shape.shape(); - connection.outside_shape = partial_shape; - } else { - VLOG(0) << "Unknown output shape" << outside_node->name(); - input_type = graph->FindNodeId(connection.outside_id) - ->output_type(connection.outside_port); - } - connection.connection_type = input_type; - - } else { // output edge - if (graph_properties.HasInputProperties(connection.outside_node_name)) { - auto input_params = - graph_properties.GetInputProperties(connection.outside_node_name); - auto in_shape = input_params.at(connection.outside_port); - input_type = in_shape.dtype(); - partial_shape = in_shape.shape(); - connection.inside_shape = partial_shape; - } else { - input_type = graph->FindNodeId(connection.inside_id) - ->output_type(connection.outside_port); - } - connection.connection_type = input_type; + GetInputProperties(graph_properties, + graph->FindNodeId(connection.outside_id), + connection.outside_port, &partial_shape, &dtype); + + } else { + GetOutputProperties(graph_properties, + graph->FindNodeId(connection.outside_id), + connection.outside_port, &partial_shape, &dtype); } + connection.outside_shape = partial_shape; + connection.connection_type = dtype; // Add dummy input/output nodes to the segment graphdef. if (connection.is_input_edge) { @@ -2335,7 +2832,7 @@ tensorflow::Status ConvertSegmentToGraphDef( auto seg_node = segment_def->add_node(); tensorflow::NodeDefBuilder builder(node_name, "Placeholder"); auto status = builder.Attr("shape", partial_shape) - .Attr("dtype", input_type) + .Attr("dtype", dtype) .Finalize(seg_node); VLOG(1) << "Constructing input " << node_name << " for the edge " << connection.outside_node_name << ":" << connection.outside_port @@ -2353,7 +2850,7 @@ tensorflow::Status ConvertSegmentToGraphDef( marker_nodes.insert(node_name); auto seg_node = segment_def->add_node(); tensorflow::NodeDefBuilder builder(node_name, "Identity"); - auto status = builder.Input(connection.inside_node_name, 0, input_type) + auto status = builder.Input(connection.inside_node_name, 0, dtype) .Finalize(seg_node); VLOG(1) << "Constructing output " << node_name << " for the edge " << connection.inside_node_name << ":" << connection.inside_port @@ -2391,6 +2888,38 @@ tensorflow::Status ConvertSegmentToGraphDef( return tensorflow::Status::OK(); } +bool InputEdgeValidator::operator()(const tensorflow::Edge* in_edge) const { + if (in_edge->IsControlEdge()) return true; + PartialTensorShape shape; + tensorflow::DataType dtype; + GetInputProperties(graph_properties_, in_edge->src(), in_edge->src_output(), + &shape, &dtype); + nvinfer1::DataType trt_dtype; + Status status = ValidateInputProperties(shape, dtype, &trt_dtype); + if (!status.ok()) { + VLOG(2) << "--> Need to remove input node " << in_edge->dst()->name() + << ": " << status; + return false; + } + if (shape.dims() < 3 && in_edge->src()->type_string() != "Const") { + VLOG(2) << "--> Need to remove input node " << in_edge->dst()->name() + << " which has an input at port " << in_edge->dst_input() + << " with #dim<3 and is not a const: " << shape; + return false; + } + return true; +} + +bool OutputEdgeValidator::operator()(const tensorflow::Edge* out_edge) const { + if (out_edge->IsControlEdge()) return true; + if (out_edge->src()->type_string() == "Const") { + VLOG(2) << "--> Need to remove output node " << out_edge->src()->name() + << " which is a Const."; + return false; + } + return true; +} + } // namespace convert } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index 1a4c0e755d1cd1e88ac26c39996eb3a750421a0a..6ae60ec352587feb8b26d6fcc69c907a5f145760 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -23,6 +23,7 @@ limitations under the License. #include #include "tensorflow/contrib/tensorrt/convert/utils.h" +#include "tensorflow/contrib/tensorrt/log/trt_logger.h" #include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" #include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" #include "tensorflow/core/framework/graph.pb.h" @@ -104,6 +105,8 @@ struct EngineInfo { // topological order. // - segment_def: the output GraphDef, whose non-input/output nodedefs will be // sorted in topological order. +// +// TODO(aaroey): add tests to validate these properties. tensorflow::Status ConvertSegmentToGraphDef( const tensorflow::Graph* graph, const tensorflow::grappler::GraphProperties& graph_properties, @@ -128,6 +131,30 @@ tensorflow::Status ConvertGraphDefToEngine( TrtUniquePtrType* engine, bool* convert_successfully); +// Helper class for the segmenter to determine whether an input edge to the TRT +// segment is valid. +class InputEdgeValidator { + public: + InputEdgeValidator(const grappler::GraphProperties& graph_properties) + : graph_properties_(graph_properties) {} + + // Return true if the specified edge is eligible to be an input edge of the + // TRT segment. + bool operator()(const tensorflow::Edge* in_edge) const; + + private: + const grappler::GraphProperties& graph_properties_; +}; + +// Helper class for the segmenter to determine whether an output edge from the +// TRT segment is valid. +class OutputEdgeValidator { + public: + // Return true if the specified edge is eligible to be an output edge of the + // TRT segment. + bool operator()(const tensorflow::Edge* out_edge) const; +}; + } // namespace convert } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc index ec9dbfa13bfd0a158dcf41cf1fdb7128a2adf641..044c736c03e0dcad0d27d6b9ad9d244816596536 100644 --- a/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc +++ b/tensorflow/contrib/tensorrt/convert/trt_optimization_pass.cc @@ -17,6 +17,7 @@ limitations under the License. #include "tensorflow/core/grappler/clusters/cluster.h" #include "tensorflow/core/grappler/grappler_item.h" #include "tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h" +#include "tensorflow/core/lib/strings/numbers.h" #include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" @@ -232,8 +233,25 @@ tensorflow::Status TRTOptimizationPass::Optimize( tensorflow::grappler::GraphProperties static_graph_properties(item); TF_RETURN_IF_ERROR(static_graph_properties.InferStatically(true)); tensorflow::tensorrt::convert::ConversionParams cp; + + std::vector nodes_to_preserve; + for (const auto& n : item.NodesToPreserve()) { + auto tokens = str_util::Split(n, ":"); + string s = tokens.at(0); + for (int i = 1; i < tokens.size() - 1; ++i) { + StrAppend(&s, ":", tokens.at(i)); + } + int dumm_port = -1; + // If the last token is not an integer, it must be part of the name. + // Otherwise it is port number. + if (tokens.size() > 1 && + !strings::safe_strto32(tokens.back(), &dumm_port)) { + StrAppend(&s, ":", tokens.back()); + } + nodes_to_preserve.push_back(s); + } cp.input_graph_def = &item.graph; - cp.output_names = &item.fetch; + cp.output_names = &nodes_to_preserve; cp.max_batch_size = maximum_batch_size_; cp.max_workspace_size_bytes = maximum_workspace_size_; cp.output_graph_def = optimized_graph; diff --git a/tensorflow/contrib/tensorrt/convert/utils.cc b/tensorflow/contrib/tensorrt/convert/utils.cc new file mode 100644 index 0000000000000000000000000000000000000000..17857cf4d002b663f38248cc0ff989915ec864b4 --- /dev/null +++ b/tensorflow/contrib/tensorrt/convert/utils.cc @@ -0,0 +1,35 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/convert/utils.h" + +namespace tensorflow { +namespace tensorrt { + +bool IsGoogleTensorRTEnabled() { + // TODO(laigd): consider also checking if tensorrt shared libraries are + // accessible. We can then direct users to this function to make sure they can + // safely write code that uses tensorrt conditionally. E.g. if it does not + // check for for tensorrt, and user mistakenly uses tensorrt, they will just + // crash and burn. +#if GOOGLE_CUDA && GOOGLE_TENSORRT + return true; +#else + return false; +#endif +} + +} // namespace tensorrt +} // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/convert/utils.h b/tensorflow/contrib/tensorrt/convert/utils.h index f601c06701fdbf983b708cf5f5c7d22634bb810b..8b5f4d614a9c1f849f0aec9df42100bb4126b439 100644 --- a/tensorflow/contrib/tensorrt/convert/utils.h +++ b/tensorflow/contrib/tensorrt/convert/utils.h @@ -31,6 +31,8 @@ struct TrtDestroyer { template using TrtUniquePtrType = std::unique_ptr>; +bool IsGoogleTensorRTEnabled(); + } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD index a89cf3ab8bfaecc74fc5890ccb7e7a7147278182..69058c5826822c519a69d50860c06b8ab3ec6578 100644 --- a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD @@ -112,7 +112,9 @@ cuda_py_test( ], tags = [ "manual", + "no_windows", "noguitar", + "nomac", "notap", ], ) diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc index 988b35f74f3989481f59c52c6320623a26704327..2de79737501a11d9760f9a7d3953cf132e512145 100644 --- a/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/inc_op_kernel.cu.cc @@ -65,7 +65,7 @@ class IncPluginTRT : public OpKernel { reinterpret_cast(context->op_device_context() ->stream() ->implementation() - ->CudaStreamMemberHack())); + ->GpuStreamMemberHack())); IncrementKernel(input_tensor.flat().data(), inc_, output_tensor->flat().data(), input_shape.num_elements(), *stream); diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index 8a17eb02f1af7c8f148c9cd4e14cc3876b6e13e3..6699b71d285f1f4fa8cc9bb66679c65e71d16dcc 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -15,9 +15,11 @@ limitations under the License. #include "tensorflow/contrib/tensorrt/kernels/trt_engine_op.h" #include + #include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" #include "tensorflow/contrib/tensorrt/convert/utils.h" #include "tensorflow/contrib/tensorrt/log/trt_logger.h" +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" #include "tensorflow/contrib/tensorrt/resources/trt_resources.h" #include "tensorflow/core/framework/graph_to_functiondef.h" @@ -43,11 +45,11 @@ using ::tensorflow::strings::StrCat; // Helps simultaneous execution of native and TRT engines. class AsyncHelper : public tensorflow::core::RefCounted { public: - AsyncHelper(tensorflow::AsyncOpKernel::DoneCallback done) { done_ = done; } + AsyncHelper(AsyncOpKernel::DoneCallback done) { done_ = done; } ~AsyncHelper() override { done_(); } private: - tensorflow::AsyncOpKernel::DoneCallback done_; + AsyncOpKernel::DoneCallback done_; }; #define TYPECASE(dt, X, Y) \ @@ -150,7 +152,7 @@ TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) } } -void TRTEngineOp::ExecuteNativeSegment(tensorflow::OpKernelContext* ctx, +void TRTEngineOp::ExecuteNativeSegment(OpKernelContext* ctx, AsyncHelper* helper) { if (!calibration_mode_) { VLOG(1) << "Executing native engine"; @@ -191,7 +193,7 @@ void TRTEngineOp::ExecuteNativeSegment(tensorflow::OpKernelContext* ctx, }); } -void TRTEngineOp::ExecuteCalibration(tensorflow::OpKernelContext* ctx, +void TRTEngineOp::ExecuteCalibration(OpKernelContext* ctx, AsyncHelper* helper) { helper->Ref(); tensorflow::core::ScopedUnref sc(helper); @@ -230,13 +232,13 @@ void TRTEngineOp::ExecuteCalibration(tensorflow::OpKernelContext* ctx, reinterpret_cast(ctx->op_device_context() ->stream() ->implementation() - ->CudaStreamMemberHack())); + ->GpuStreamMemberHack())); calib_res->calibrator_->setBatch(input_data, *stream); VLOG(2) << "Passed calibration data"; ExecuteNativeSegment(ctx, helper); } -int TRTEngineOp::GetEngineBatch(tensorflow::OpKernelContext* ctx) { +int TRTEngineOp::GetEngineBatch(OpKernelContext* ctx) { int num_batch = ctx->input(0).shape().dim_size(0); int smallest_engine = 0; for (const auto i : cached_engine_batches_) { @@ -252,21 +254,20 @@ int TRTEngineOp::GetEngineBatch(tensorflow::OpKernelContext* ctx) { cached_engine_batches_.push_back(num_batch); VLOG(1) << "Running with batch size " << num_batch; } else { - string s("Engine buffer is full. buffer limit= "); - StrAppend(&s, max_cached_engines_, ", current entries= "); - for (auto i : cached_engine_batches_) StrAppend(&s, i, ", "); - StrAppend(&s, "Requested batch= ", num_batch); - LOG(ERROR) << s; - ctx->SetStatus(tensorflow::errors::ResourceExhausted( - "Requested batch size is not available and engine cache is full")); + string msg = + StrCat("Engine buffer is full. buffer limit=", max_cached_engines_, + ", current entries="); + for (auto i : cached_engine_batches_) StrAppend(&msg, i, ","); + StrAppend(&msg, "Requested batch=", num_batch); + LOG(WARNING) << msg; return -1; } } return smallest_engine; } -void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, - tensorflow::AsyncOpKernel::DoneCallback done) { +void TRTEngineOp::ComputeAsync(OpKernelContext* ctx, + AsyncOpKernel::DoneCallback done) { auto helper = new AsyncHelper(done); tensorflow::core::ScopedUnref sc(helper); if (calibration_mode_) { @@ -274,32 +275,52 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, return; } const int smallest_engine = GetEngineBatch(ctx); - if (smallest_engine < 0) return; // GetEngineBatch already set the status. + if (smallest_engine < 0) { + LOG(WARNING) << "Failed to get engine batch, running native segment"; + ExecuteNativeSegment(ctx, helper); + return; + } const int num_batch = ctx->input(0).shape().dim_size(0); auto& engine_ctx_pair = GetEngine(smallest_engine, ctx); auto& trt_engine_ptr = engine_ctx_pair.first; if (!trt_engine_ptr) { LOG(WARNING) << "Engine retrieval for batch size " << num_batch - << " failed Running native segment"; + << " failed. Running native segment"; + ExecuteNativeSegment(ctx, helper); + return; + } + const bool retry = ExecuteTrtEngine(ctx, num_batch, trt_engine_ptr.get(), + engine_ctx_pair.second.get()); + if (retry) { + LOG(WARNING) << "Failed to execute engine, retrying with native segment"; ExecuteNativeSegment(ctx, helper); return; } +} +bool TRTEngineOp::ExecuteTrtEngine( + OpKernelContext* ctx, const int num_batch, + nvinfer1::ICudaEngine* trt_engine_ptr, + nvinfer1::IExecutionContext* trt_execution_context_ptr) { + const bool kRetry = true; const int num_binding = ctx->num_inputs() + ctx->num_outputs(); std::vector buffers(num_binding); for (int i = 0; i < ctx->num_inputs(); i++) { - const string inp_name = StrCat(kInputPHName, i); + const string input_name = StrCat(kInputPHName, i); const size_t binding_index = - trt_engine_ptr->getBindingIndex(inp_name.c_str()); + trt_engine_ptr->getBindingIndex(input_name.c_str()); + if (binding_index == -1) { + LOG(ERROR) << "Input node not found, at " << input_name; + return kRetry; + } const Tensor& input_tensor = ctx->input(i); const TensorShape& input_shape = input_tensor.shape(); if (num_batch != input_shape.dim_size(0)) { - LOG(ERROR) << "input data inconsistent batch size"; - ctx->SetStatus(tensorflow::errors::FailedPrecondition( - "Different batch sizes between input tensors")); - return; + LOG(ERROR) << "Input data has inconsistent batch size: " << num_batch + << " vs " << input_shape.dim_size(0); + return kRetry; } auto dtype = trt_engine_ptr->getBindingDataType(binding_index); switch (dtype) { @@ -308,19 +329,18 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, break; case nvinfer1::DataType::kHALF: LOG(ERROR) << "FP16 inputs are not supported yet!"; - ctx->SetStatus(tensorflow::errors::InvalidArgument( - "FP16 inputs are not supported!")); - return; + return kRetry; case nvinfer1::DataType::kINT8: LOG(ERROR) << "INT8 inputs are not supported yet!"; - ctx->SetStatus(tensorflow::errors::InvalidArgument( - "INT8 inputs are not supported!")); - return; + return kRetry; +#if NV_TENSORRT_MAJOR > 3 + case nvinfer1::DataType::kINT32: + buffers[binding_index] = (void*)(input_tensor.flat().data()); + break; +#endif default: LOG(ERROR) << "Unknown TRT data type: " << int(dtype); - ctx->SetStatus(tensorflow::errors::InvalidArgument( - "Unknown output TRT data type! ", static_cast(dtype))); - return; + return kRetry; } } @@ -337,20 +357,23 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, std::vector trt_shape(dims.nbDims + 1); trt_shape[0] = num_batch; for (int j = 0; j < dims.nbDims; j++) trt_shape[j + 1] = dims.d[j]; - OP_REQUIRES_OK( - ctx, TensorShapeUtils::MakeShape(trt_shape.data(), trt_shape.size(), - &output_shape)); + auto status = TensorShapeUtils::MakeShape( + trt_shape.data(), trt_shape.size(), &output_shape); + if (!status.ok()) { + LOG(ERROR) << "Failed to get output shape: " << status; + return kRetry; + } } else { - LOG(ERROR) << "output node not found, at " << output_name; - ctx->SetStatus(tensorflow::errors::Internal("output ", output_name, - " couldn't be found!")); - return; + LOG(ERROR) << "Output node not found, at " << output_name; + return kRetry; } auto status = ctx->allocate_output(i, output_shape, &output_tensor); if (!status.ok()) { LOG(ERROR) << "Allocating output failed with " << status; ctx->SetStatus(status); - return; + // Do not retry since we cannot allocate the same output twice. + // TODO(aaroey): ideally we should retry, fix this. + return !kRetry; } auto dtype = trt_engine_ptr->getBindingDataType(binding_index); switch (dtype) { @@ -359,39 +382,38 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, reinterpret_cast(output_tensor->flat().data()); break; case nvinfer1::DataType::kHALF: - LOG(ERROR) << "half size is not supported yet!"; - ctx->SetStatus(tensorflow::errors::InvalidArgument( - "Half outputs are not supported!")); - return; + LOG(WARNING) << "half size is not supported yet!"; + return kRetry; case nvinfer1::DataType::kINT8: - LOG(ERROR) << "int8 is not supported yet!"; - ctx->SetStatus(tensorflow::errors::InvalidArgument( - "INT8 outputs are not supported!")); - return; + LOG(WARNING) << "int8 is not supported yet!"; + return kRetry; +#if NV_TENSORRT_MAJOR > 3 + case nvinfer1::DataType::kINT32: + buffers[binding_index] = + reinterpret_cast(output_tensor->flat().data()); + break; +#endif default: - LOG(ERROR) << "Unknown TRT data type: " << static_cast(dtype); - ctx->SetStatus(tensorflow::errors::InvalidArgument( - "Unsupported output data type! ", static_cast(dtype))); - return; + LOG(WARNING) << "Unknown TRT data type: " << static_cast(dtype); + return kRetry; } } - // copied from cuda_kernel_helper since it seems only valid in *.cu.cc files + // Copied from cuda_kernel_helper since it seems only valid in *.cu.cc files const cudaStream_t* stream = CHECK_NOTNULL( reinterpret_cast(ctx->op_device_context() ->stream() ->implementation() - ->CudaStreamMemberHack())); + ->GpuStreamMemberHack())); // TODO(jie): trt enqueue does not return error - auto& trt_execution_context_ptr = engine_ctx_pair.second; auto ret = trt_execution_context_ptr->enqueue(num_batch, &buffers[0], *stream, nullptr); if (!ret) { - LOG(ERROR) << "Failed to enqueue batch for TRT engine: " << name(); - ctx->SetStatus(tensorflow::errors::Internal( - "Failed to enqueue batch for TRT engine: ", name())); + LOG(WARNING) << "Failed to enqueue batch for TRT engine: " << name(); + return kRetry; } - // sync should be done by TF. + // Synchronization will be done by TF. + return !kRetry; } TRTEngineOp::~TRTEngineOp() { @@ -411,8 +433,6 @@ nvinfer1::IGpuAllocator* TRTEngineOp::GetAllocator(OpKernelContext* ctx) { if (!alloc) { LOG(ERROR) << "Can't find device allocator for gpu device " << device->name(); - ctx->SetStatus(tensorflow::errors::Internal( - "Can't get device allocator for device ", device->name())); return nullptr; } allocator_.reset(new TRTDeviceAllocator(alloc)); @@ -439,14 +459,14 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, #if NV_TENSORRT_MAJOR > 3 auto allocator = GetAllocator(ctx); if (allocator == nullptr) { - // GetAllocator already set the Status. return null_pair; } infer->setGpuAllocator(allocator); #endif TrtUniquePtrType static_engine( infer->deserializeCudaEngine(serialized_segment_.c_str(), - serialized_segment_.size(), nullptr)); + serialized_segment_.size(), + PluginFactoryTensorRT::GetInstance())); auto raw_static_engine = static_engine.get(); const auto max_batch_size = raw_static_engine->getMaxBatchSize(); engine_map_[max_batch_size] = { @@ -455,7 +475,9 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, raw_static_engine->createExecutionContext())}; // Runtime is safe to delete after engine creation serialized_segment_.clear(); - if (max_batch_size < batch_size) return null_pair; + if (max_batch_size < batch_size) { + return null_pair; + } return engine_map_.at(max_batch_size); } // static_engine_ @@ -467,7 +489,6 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, #if NV_TENSORRT_MAJOR > 3 allocator = GetAllocator(ctx); if (allocator == nullptr) { - // GetAllocator already set the Status. return null_pair; } #endif @@ -491,9 +512,8 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, // retry in the future. engine_map_[batch_size] = {nullptr, nullptr}; } - LOG(ERROR) << "Engine creation for batch size " << batch_size - << " failed " << status; - ctx->SetStatus(tensorflow::errors::Internal("Engine creation failed!")); + LOG(WARNING) << "Engine creation for batch size " << batch_size + << " failed " << status; return null_pair; } VLOG(1) << "Conversion is done"; @@ -505,7 +525,7 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, } tensorflow::Status TRTEngineOp::AllocateCalibrationResources( - tensorflow::OpKernelContext* ctx, TRTCalibrationResource** cr) { + OpKernelContext* ctx, TRTCalibrationResource** cr) { auto cres = new TRTCalibrationResource(); *cr = cres; // Get the allocator. diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h index 6fe318be6a6bc9f01ce3b52e0430f2090b53002b..59b744e6d35d603795c0e87c89c0a8d56c26b3cb 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h @@ -60,6 +60,12 @@ class TRTEngineOp : public AsyncOpKernel { // Execute replaced native segment as function Op. void ExecuteNativeSegment(OpKernelContext* ctx, AsyncHelper* helper); + // Execute the tensorrt engine. Returns whether we need to retry by running + // the native segment. + bool ExecuteTrtEngine(OpKernelContext* ctx, const int num_batch, + nvinfer1::ICudaEngine* trt_engine_ptr, + nvinfer1::IExecutionContext* trt_execution_context_ptr); + // Allocate necessary resources for calibration Status AllocateCalibrationResources(OpKernelContext* ctx, TRTCalibrationResource** cr); @@ -81,7 +87,7 @@ class TRTEngineOp : public AsyncOpKernel { std::vector output_nodes_; // keep device allocator for TRT. - std::unique_ptr allocator_; + std::unique_ptr allocator_; // serialized protobuf segment or trt engine depending on static_engine_ flag. string serialized_segment_; diff --git a/tensorflow/contrib/tensorrt/ops/trt_engine_op.cc b/tensorflow/contrib/tensorrt/ops/trt_engine_op.cc index 383635f428812984915a8c46ad3b92cc7b28a5f7..e0c7b6272379a20e3dacb6cd7c3b39de735d844d 100644 --- a/tensorflow/contrib/tensorrt/ops/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/ops/trt_engine_op.cc @@ -42,8 +42,14 @@ REGISTER_OP("TRTEngineOp") .Attr("precision_mode: {'FP32', 'FP16', 'INT8', 'INT8CALIB'}") .Attr("calibration_data: string = ''") .Input("in_tensor: InT") - .Output("out_tensor: OutT") - .SetShapeFn(shape_inference::TRTEngineOpShapeInference); + .Output("out_tensor: OutT"); +// TODO(jie): TF requires concrete output shape for concrete input shapes. +// This is tricky for batch dimension, since we cannot ensure which input +// would carry the correct batch dimension (for the current stage of the +// implementation, we do require all input tensor to carry the same batch +// size, but this could change in the future). Hence we disable shape +// inference function as a workaround. +// .SetShapeFn(shape_inference::TRTEngineOpShapeInference); } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc index 2bc591484dcaf5b35c39f3d0523dd89dcd152e6a..cccc91226265ed139fb8db0b71c40b868f729562 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc @@ -65,9 +65,6 @@ bool PluginFactoryTensorRT::RegisterPlugin( void PluginFactoryTensorRT::DestroyPlugins() { tensorflow::mutex_lock lock(instance_m_); - for (auto& owned_plugin_ptr : owned_plugins_) { - owned_plugin_ptr.release(); - } owned_plugins_.clear(); } diff --git a/tensorflow/contrib/tensorrt/python/__init__.py b/tensorflow/contrib/tensorrt/python/__init__.py index 0b2321b5fc7bcbd53c01d1c97cafcfcb229a83ef..fe4fa166a10d914d028938925266683e62861421 100644 --- a/tensorflow/contrib/tensorrt/python/__init__.py +++ b/tensorflow/contrib/tensorrt/python/__init__.py @@ -22,4 +22,5 @@ from __future__ import print_function from tensorflow.contrib.tensorrt.python.ops import trt_engine_op from tensorflow.contrib.tensorrt.python.trt_convert import calib_graph_to_infer_graph from tensorflow.contrib.tensorrt.python.trt_convert import create_inference_graph +from tensorflow.contrib.tensorrt.python.trt_convert import is_tensorrt_enabled # pylint: enable=unused-import,line-too-long diff --git a/tensorflow/contrib/tensorrt/python/trt_convert.py b/tensorflow/contrib/tensorrt/python/trt_convert.py index 79f512dbcf6bd4d84b98cf69630778734566391c..2b67931661397cee0de9faa66b58a608c69ecdc5 100644 --- a/tensorflow/contrib/tensorrt/python/trt_convert.py +++ b/tensorflow/contrib/tensorrt/python/trt_convert.py @@ -23,6 +23,7 @@ import six as _six from tensorflow.contrib.tensorrt.wrap_conversion import calib_convert from tensorflow.contrib.tensorrt.wrap_conversion import get_linked_tensorrt_version from tensorflow.contrib.tensorrt.wrap_conversion import get_loaded_tensorrt_version +from tensorflow.contrib.tensorrt.wrap_conversion import is_tensorrt_enabled from tensorflow.contrib.tensorrt.wrap_conversion import trt_convert from tensorflow.core.framework import graph_pb2 from tensorflow.core.protobuf import rewriter_config_pb2 diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc index 9f115990c3a3e6e92093e5f0d82b985af1b25482..d8f97bfbbc7adb10a5dda6fbc2f7a660f6cd7742 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc @@ -19,12 +19,42 @@ limitations under the License. #if GOOGLE_CUDA #if GOOGLE_TENSORRT +#include "cuda/include/cuda_runtime_api.h" +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA + +namespace tensorflow { +namespace tensorrt { + +// std::align is not supported, so this method mimic its behavior. +void* Align(size_t alignment, size_t size, void*& ptr, size_t& space) { + QCHECK_GT(alignment, 0) << "alignment must be greater than 0."; + QCHECK_EQ(0, alignment & (alignment - 1)) << "Alignment must be power of 2."; + QCHECK_GT(size, 0) << "size must be greater than 0."; + QCHECK(ptr) << "ptr must not be nullptr."; + QCHECK_GT(space, 0) << "space must be greater than 0."; + const uintptr_t ptr_val = reinterpret_cast(ptr); + QCHECK_GE(ptr_val + space, ptr_val) << "Provided space overflows."; + if (size > space) return nullptr; + const uintptr_t aligned_ptr_val = ((ptr_val + alignment - 1) & -alignment); + if (aligned_ptr_val > ptr_val + space - size) return nullptr; + ptr = reinterpret_cast(aligned_ptr_val); + const uintptr_t diff = aligned_ptr_val - ptr_val; + space -= diff; + return ptr; +} + +} // namespace tensorrt +} // namespace tensorflow + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT #if NV_TENSORRT_MAJOR > 2 -#include "cuda/include/cuda_runtime_api.h" namespace tensorflow { namespace tensorrt { + void* TRTCudaAllocator::allocate(uint64_t size, uint64_t alignment, uint32_t flags) { assert((alignment & (alignment - 1)) == 0); // zero or a power of 2. @@ -37,10 +67,23 @@ void TRTCudaAllocator::free(void* memory) { cudaFree(memory); } void* TRTDeviceAllocator::allocate(uint64_t size, uint64_t alignment, uint32_t flags) { + // WAR for allocator alignment requirement. Certain cuda API calls require GPU + // memory with alignemtn to cudaDeviceProp::textureAlignment. + // See issue #20856 + alignment = 512; assert((alignment & (alignment - 1)) == 0); // zero or a power of 2. - void* mem = allocator_->AllocateRaw(alignment, size); - VLOG(2) << "Allocated " << size << " bytes with alignment " << alignment - << " @ " << mem; + size_t total_size = size + alignment; + void* mem = allocator_->AllocateRaw(alignment, total_size); + if (!mem) return nullptr; + + void* alloc_mem = mem; + QCHECK(Align(alignment, size, mem, total_size)); + if (mem != alloc_mem) { + QCHECK(mem_map_.insert({mem, alloc_mem}).second); + } + VLOG(2) << "Allocated " << total_size << " bytes memory @" << alloc_mem + << "; aligned to " << size << " bytes @" << mem << " with alignment " + << alignment; return mem; } @@ -51,12 +94,20 @@ TRTDeviceAllocator::TRTDeviceAllocator(tensorflow::Allocator* allocator) void TRTDeviceAllocator::free(void* memory) { VLOG(2) << "Deallocating @ " << memory; - allocator_->DeallocateRaw(memory); + // allocated memory adjusted for alignment, restore the original pointer + if (memory) { + auto alloc_mem = mem_map_.find(memory); + if (alloc_mem != mem_map_.end()) { + memory = alloc_mem->second; + mem_map_.erase(alloc_mem->first); + } + allocator_->DeallocateRaw(memory); + } } } // namespace tensorrt } // namespace tensorflow #endif -#endif -#endif +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.h b/tensorflow/contrib/tensorrt/resources/trt_allocator.h index c5d2cec730f4ae97e4c6bcc19897fd9f321122a7..6f944920835b475fc7d12167dbcefa0111b6fb19 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator.h +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.h @@ -16,13 +16,25 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_ALLOCATOR_H_ #define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_ALLOCATOR_H_ -#include "tensorflow/contrib/tensorrt/log/trt_logger.h" +#include + #include "tensorflow/core/framework/allocator.h" #if GOOGLE_CUDA #if GOOGLE_TENSORRT #include "tensorrt/include/NvInfer.h" +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA + +namespace tensorflow { +namespace tensorrt { +// std::align is not supported, so this function mimic its behavior. +void* Align(size_t alignment, size_t size, void*& ptr, size_t& space); +} // namespace tensorrt +} // namespace tensorflow +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT #if NV_TENSORRT_MAJOR == 3 // Define interface here temporarily until TRT 4.0 is released namespace nvinfer1 { @@ -37,7 +49,14 @@ class IGpuAllocator { namespace tensorflow { namespace tensorrt { -class TRTCudaAllocator : public nvinfer1::IGpuAllocator { +class TRTBaseAllocator : public nvinfer1::IGpuAllocator { + // Base allocator class so we can have a virtual destructor; + public: + // python wrapper seems to be not happy with an pure virtual destructor; + virtual ~TRTBaseAllocator() = default; +}; + +class TRTCudaAllocator : public TRTBaseAllocator { // Allocator implementation that is using cuda allocator instead of device // allocator in case we can't get device allocator from TF. public: @@ -47,10 +66,13 @@ class TRTCudaAllocator : public nvinfer1::IGpuAllocator { void free(void* memory) override; }; -class TRTDeviceAllocator : public nvinfer1::IGpuAllocator { +class TRTDeviceAllocator : public TRTBaseAllocator { // Allocator implementation wrapping TF device allocators. public: TRTDeviceAllocator(tensorflow::Allocator* allocator); + + // TODO(aaroey): base class doesn't have a virtual destructor, work with + // Nvidia to fix it. virtual ~TRTDeviceAllocator() { VLOG(1) << "Destroying allocator attached to " << allocator_->Name(); } @@ -59,6 +81,9 @@ class TRTDeviceAllocator : public nvinfer1::IGpuAllocator { private: tensorflow::Allocator* allocator_; + + // supporting alignment from allocation request requires a map to free; + std::unordered_map mem_map_; }; } // namespace tensorrt diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc b/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..f515ed03f245f11ad461bac07970c5001a56aaad --- /dev/null +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc @@ -0,0 +1,79 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" + +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { +namespace tensorrt { + +bool RunTest(const size_t alignment, const size_t size, + const intptr_t orig_ptr_val, const size_t orig_space) { + void* const orig_ptr = reinterpret_cast(orig_ptr_val); + void* ptr = orig_ptr; + size_t space = orig_space; + void* result = Align(alignment, size, ptr, space); + if (result == nullptr) { + EXPECT_EQ(orig_ptr, ptr); + EXPECT_EQ(orig_space, space); + return false; + } else { + EXPECT_EQ(result, ptr); + const intptr_t ptr_val = reinterpret_cast(ptr); + EXPECT_EQ(0, ptr_val % alignment); + EXPECT_GE(ptr_val, orig_ptr_val); + EXPECT_GE(space, size); + EXPECT_LE(space, orig_space); + EXPECT_EQ(ptr_val + space, orig_ptr_val + orig_space); + return true; + } +} + +TEST(TRTAllocatorTest, Align) { + for (const size_t space : + {1, 2, 3, 4, 7, 8, 9, 10, 16, 32, 511, 512, 513, 700, 12345}) { + for (size_t alignment = 1; alignment <= space * 4; alignment *= 2) { + for (const intptr_t ptr_val : + {1ul, alignment == 1 ? 1ul : alignment - 1, alignment, alignment + 1, + alignment + (alignment / 2)}) { + if (ptr_val % alignment == 0) { + for (const size_t size : + {1ul, space == 1 ? 1ul : space - 1, space, space + 1}) { + EXPECT_EQ(space >= size, RunTest(alignment, size, ptr_val, space)); + } + } else { + EXPECT_FALSE(RunTest(alignment, space, ptr_val, space)); + const size_t diff = alignment - ptr_val % alignment; + if (space > diff) { + EXPECT_TRUE( + RunTest(alignment, space - diff, ptr_val + diff, space - diff)); + for (const size_t size : + {1ul, space - diff > 1 ? space - diff - 1 : 1ul, space - diff, + space - diff + 1, space - 1}) { + EXPECT_EQ(space - diff >= size, + RunTest(alignment, size, ptr_val, space)); + } + } else { + EXPECT_FALSE(RunTest(alignment, 1, ptr_val, space)); + } + } + } + } + } +} + +} // namespace tensorrt +} // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/resources/trt_resources.h b/tensorflow/contrib/tensorrt/resources/trt_resources.h index b7d5ffd6748ba34c6c4ddbfbfbb44edb6bf2aca8..d7d56cb95e033ea55bd3aa385a707e7a7cfc557b 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_resources.h +++ b/tensorflow/contrib/tensorrt/resources/trt_resources.h @@ -64,7 +64,7 @@ class TRTCalibrationResource : public tensorflow::ResourceBase { std::unique_ptr calibrator_; TrtUniquePtrType builder_; TrtUniquePtrType engine_; - std::unique_ptr allocator_; + std::unique_ptr allocator_; tensorflow::tensorrt::Logger logger_; // TODO(sami): Use threadpool threads! std::unique_ptr thr_; diff --git a/tensorflow/contrib/tensorrt/segment/segment.cc b/tensorflow/contrib/tensorrt/segment/segment.cc index cc42913ecadc3e15fbb4a4a322f125579f075da2..008fffc95430b1c423788a4e958e06e700cac233 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.cc +++ b/tensorflow/contrib/tensorrt/segment/segment.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/contrib/tensorrt/segment/segment.h" +#include #include #include #include @@ -32,6 +33,7 @@ namespace tensorflow { namespace tensorrt { namespace segment { using ::tensorflow::strings::StrAppend; + // A simple graph representation to mirror tensorflow::Graph. This structure // helps saving memory since segmenter modifies the graph in place, preventing // the need to create a copy of the graph. It is composed of edges and nodes. @@ -215,7 +217,7 @@ namespace { bool CheckCycles(const std::unique_ptr& g, const SimpleNode* src, const std::vector& start) { - // copied from TF ReverseDFS. + // Copied from TF ReverseDFS, which only works for tensorflow::Graph. struct Work { SimpleNode* node; bool leave; // Are we entering or leaving n? @@ -269,6 +271,24 @@ bool CanContractEdge(const SimpleEdge* edge, // 1. Get all nodes incoming to 'dst', excluding 'src' // 2. Reverse DFS from those nodes // 3. If reverse DFS reaches 'src' then we have a cycle + // + // TODO(aaroey): there are several problems with the current approach: + // 1. src->dst->src, this is not detected but it should be; + // 2. src->dst->...(any node sequence that doesn't contain src)...->dst, this + // is detected but it should not be. + // + // Note that it's fine that dst connects back to src indirectly (i.e. through + // a path with length > 1 that consists of intermedia nodes other than src). + // While loops is one example. + // + // The goal is to make sure that the trt subgraph: + // 1. has no loops (i.e. is a DAG), and + // 2. if there is a path in the subgraph from X to Y (X and Y are both nodes + // in the subgraph), then all paths from X to Y are in the subgraph. + // + // To achieve this goal, the correct way seems to be: + // 1. remove any direct edge from src->dst; + // 2. detect if src can reach dst, if so they cannot be merged. std::vector dfs_start_nodes; for (SimpleNode* node : dst->in_nodes()) { if (node != src) { @@ -276,8 +296,8 @@ bool CanContractEdge(const SimpleEdge* edge, } } - bool is_cycle = CheckCycles(graph, src, dfs_start_nodes); - return !is_cycle; + const bool has_cycle = CheckCycles(graph, src, dfs_start_nodes); + return !has_cycle; } } // namespace @@ -342,22 +362,20 @@ void ContractEdge(SimpleEdge* edge, SimpleGraph* graph, } tensorflow::Status SegmentGraph( - const tensorflow::GraphDef& gdef, - const std::function& candidate_fn, - const SegmentOptions& options, SegmentNodesVector* segments) { - // Create a Graph representation of the GraphDef. - tensorflow::FunctionLibraryDefinition flib(tensorflow::OpRegistry::Global(), - gdef.library()); - tensorflow::Graph graph(flib); - TF_RETURN_IF_ERROR(tensorflow::ConvertGraphDefToGraph( - tensorflow::GraphConstructorOptions(), gdef, &graph)); - return SegmentGraph(&graph, candidate_fn, options, segments); -} - -tensorflow::Status SegmentGraph( - tensorflow::Graph* tf_graph, + const tensorflow::Graph* tf_graph, const std::function& candidate_fn, + const std::function& input_candidate_fn, + const std::function& output_candidate_fn, const SegmentOptions& options, SegmentNodesVector* segments) { + // Steps: + // 1. run the segmentation algorithm to find all the segments, which uses + // candidate_fn to determine the candidates segment nodes; + // 2. for each segments, remove the nodes that are inputs/outputs of the + // segment but are not eligible, using input/output_candidate_fn to + // determine the eligibilities; + // 3. convert the segment into expected return format and return the result. + + // --------------------------------- Step 1 --------------------------------- auto graph = std::unique_ptr(new SimpleGraph(tf_graph)); // Use a union-find to collect the nodes that belong to the same // segment. A node value of nullptr indicates that the node is not a candidate @@ -372,14 +390,19 @@ tensorflow::Status SegmentGraph( node_segments.emplace_back(node); } - // The segmentation algorithm below visits nodes in reverse - // topological order and attempts to merge nodes along output - // edges. That means that subgraphs grow from the output-side of the - // network towards the inputs. In general this is not guaranteed to - // produce a globally optimal segmentation. In the future if we have - // a measure of how beneficial it is to include a given node in a - // TRT subgraph then we can revisit this algorithm to take advantage - // of that information. + // The segmentation algorithm below visits nodes in reverse topological order + // and attempts to merge nodes along output edges. That means that subgraphs + // grow from the output-side of the network towards the inputs. + // + // In general this is not guaranteed to produce a globally optimal + // segmentation. For exaample, consider graph with node {A, B, C, D} and edges + // {A->B, A->C, B->D, C->D), where A, B, D are trt compatible but C is not, so + // in theory we can choose to contract either A, B or B, D but not both, but + // here it always choose to contract B, D. + // + // In the future if we have a measure of how beneficial it is to include a + // given node in a TRT subgraph then we can revisit this algorithm to take + // advantage of that information. std::vector tforder; tensorflow::GetPostOrder(*tf_graph, &tforder); // use postorder implementation from tensorflow and construct mirror in @@ -392,13 +415,11 @@ tensorflow::Status SegmentGraph( for (const SimpleNode* node : order) { // All output nodes of 'node' have been visited... VLOG(2) << "Trying node " << node->name() << " id=" << node->id(); - // 'node' must be a TRT candidate... if (node_segments[node->id()].Value() == nullptr) { VLOG(2) << "... not a TRT candidate"; continue; } - // Contract output edges to combine 'node' with output // nodes. Iterate since combining two nodes may unblock other // combining. @@ -416,7 +437,6 @@ tensorflow::Status SegmentGraph( VLOG(2) << "... ... not a TRT candidate"; continue; } - if (CanContractEdge(out_edge, graph)) { VLOG(2) << "... ... can contract"; contract_edges.insert(out_edge); @@ -424,11 +444,9 @@ tensorflow::Status SegmentGraph( VLOG(2) << "... ... cannot contract, would form cycle"; } } - if (contract_edges.empty()) { break; } - // Contract edges and collect the adjacent nodes into the same // segment/subgraph. while (!contract_edges.empty()) { @@ -457,11 +475,22 @@ tensorflow::Status SegmentGraph( // Collect the segments/subgraphs. Each subgraph is represented by a // set of the names of the nodes in that subgraph. - std::unordered_map> sg_map; + + // A map from the segment identifier (currently the name of the root node of + // the segment tree) to the segment nodes set. + std::unordered_map> sg_map; + + // A map from the segment identifier (currently the name of the root node of + // the segment tree) to the device names that the nodes in the segment are + // assigned to. + // + // TODO(aaroey): nodes assigned to different devices should not be merged, + // fix this. std::unordered_map> device_maps; + for (auto& u : node_segments) { if ((u.Value() != nullptr) && (u.ParentValue() != nullptr)) { - sg_map[u.ParentValue()->name()].insert(u.Value()->name()); + sg_map[u.ParentValue()->name()].insert(u.Value()->tf_node()); auto tf_node = u.Value()->tf_node(); // has_assigned_device_name() is expected to return true // when called from optimization pass. However, since graph @@ -482,25 +511,104 @@ tensorflow::Status SegmentGraph( } } + // --------------------------------- Step 2 --------------------------------- + // Remove ineligible input/output nodes. + for (auto& itr : sg_map) { + std::set& segment_nodes = itr.second; + VLOG(1) << "Segment original size: " << segment_nodes.size(); + while (true) { + std::deque in_nodes_que, out_nodes_que; + // Find an input node that is not eligible and add it to the queue. + // Nodes that has no incoming edges should not be treated as "input", + // as there are really no inputs to them. Similar for output nodes. + for (auto node : segment_nodes) { + bool added = false; + for (const tensorflow::Edge* edge : node->in_edges()) { + if (!edge->IsControlEdge() && !edge->src()->IsSource() && + !segment_nodes.count(edge->src())) { // 'node' is an input node. + if (!input_candidate_fn(edge)) { + in_nodes_que.push_back(node); + added = true; + break; + } + } + } + if (added) continue; // Only adding the node once to either queue. + for (const tensorflow::Edge* edge : node->out_edges()) { + if (!edge->dst()->IsSink() && !edge->IsControlEdge() && + !segment_nodes.count(edge->dst())) { // 'node' is an output node. + if (!output_candidate_fn(edge)) { + out_nodes_que.push_back(node); + break; + } + } + } + } + if (in_nodes_que.empty() && out_nodes_que.empty()) { + // No more ineligible input/output nodes. + break; + } + // Now for each ineligible node, remove all of its inputs or outputs from + // the subgraph. + // + // It can be proven that, if the original subgraph: + // 1. is a DAG, and + // 2. all paths between two nodes in the subgraph are all inside the + // subgraph + // then after doing this operation the resulting subgraph will keep the + // same properties 1 and 2. + // + // For simplicity we use heuristics: for input nodes remove all its + // input, for output nodes remove all its output. In this way, for common + // cases the number of removed nodes should be minimum. + auto remove_nodes = [&segment_nodes]( + bool is_input_nodes, + std::deque* que) { + // Run a BFS on the queue to find all the input/output nodes. + std::set visited; + while (!que->empty()) { + auto node = que->front(); + que->pop_front(); + if (!visited.insert(node).second) continue; + segment_nodes.erase(node); + for (auto in : + is_input_nodes ? node->in_nodes() : node->out_nodes()) { + if (segment_nodes.count(in)) { + que->push_back(in); + VLOG(2) << "Need to remove node " << in->name() + << " because one of its " + << (is_input_nodes ? "output" : "input") + << " nodes in the graph was removed: " << node->name(); + } + } + } + }; + remove_nodes(true, &in_nodes_que); + remove_nodes(false, &out_nodes_que); + } + VLOG(1) << "Segment new size: " << segment_nodes.size(); + } + + // --------------------------------- Step 3 --------------------------------- // Convert the segments into the expected return format for (const auto& itr : sg_map) { - const auto& segment_node_names = itr.second; + const std::set& segment_nodes = itr.second; if (VLOG_IS_ON(1)) { string s; - for (const auto& name : segment_node_names) { - s += " " + name; - } - VLOG(1) << "Segment " << segments->size() << ":" << s; + for (auto node : segment_nodes) s += " " + node->name(); + VLOG(1) << "Segment " << segments->size() << ": " << s; } // Don't use small segments. - if (static_cast(segment_node_names.size()) < - options.minimum_segment_size) { + if (static_cast(segment_nodes.size()) < options.minimum_segment_size) { VLOG(1) << "Segment " << segments->size() << " has only " - << segment_node_names.size() << " nodes, dropping"; + << segment_nodes.size() << " nodes, dropping"; continue; } + // TODO(sami): Make segmenter placement aware once trtscopes are in place + std::set segment_node_names; + for (auto node : itr.second) segment_node_names.insert(node->name()); const auto& dev_itr = device_maps.find(itr.first); if (dev_itr == device_maps.end() || dev_itr->second.empty()) { VLOG(1) << "No device assigned to segment " << segments->size(); diff --git a/tensorflow/contrib/tensorrt/segment/segment.h b/tensorflow/contrib/tensorrt/segment/segment.h index 81b4bfe49fe375d19f4c7811459f38e25d2edea8..8c44eb782aa37052680d0e06023f29dc65e327c6 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.h +++ b/tensorflow/contrib/tensorrt/segment/segment.h @@ -40,22 +40,6 @@ struct SegmentOptions { std::set exclude_node_list; }; -// Get the subgraphs of a graph that can be handled by TensorRT. -// -// @param gdef The GraphDef describing the network -// @param candidate_fn A function that returns true for a NodeDef if -// that node can be handled by TensorRT. -// @param segments Returns the TensorRT segments/subgraphs. Each entry -// in the vector describes a subgraph by giving a set of the names of -// all the NodeDefs in that subgraph. -// @return the status. -// -// TODO(aaroey): remove this method. -tensorflow::Status SegmentGraph( - const tensorflow::GraphDef& gdef, - const std::function& candidate_fn, - const SegmentOptions& options, SegmentNodesVector* segments); - // Get the subgraphs of a graph that can be handled by TensorRT. // // @param graph tensorflow::Graph of the network @@ -66,8 +50,10 @@ tensorflow::Status SegmentGraph( // all the NodeDefs in that subgraph. // @return the status. tensorflow::Status SegmentGraph( - tensorflow::Graph* tf_graph, + const tensorflow::Graph* tf_graph, const std::function& candidate_fn, + const std::function& input_candidate_fn, + const std::function& output_candidate_fn, const SegmentOptions& options, SegmentNodesVector* segments); } // namespace segment diff --git a/tensorflow/contrib/tensorrt/segment/segment_test.cc b/tensorflow/contrib/tensorrt/segment/segment_test.cc index f5b2d258d70d5577a9d68f2d9f6d6e678ede97ce..432e7b1c047cb3b22d47f7432b6aad639a3a3b2d 100644 --- a/tensorflow/contrib/tensorrt/segment/segment_test.cc +++ b/tensorflow/contrib/tensorrt/segment/segment_test.cc @@ -14,350 +14,245 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/tensorrt/segment/segment.h" -#include "tensorflow/c/c_api.h" -#include "tensorflow/core/framework/graph.pb.h" + +#include "tensorflow/cc/framework/scope.h" +#include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/graph/testlib.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/test.h" #include "tensorflow/core/platform/types.h" +#include "tensorflow/core/public/session.h" namespace tensorflow { namespace tensorrt { namespace segment { namespace test { +namespace ops = ::tensorflow::ops; class SegmentTest : public ::testing::Test { - public: - bool GetGraphDef(TF_Graph* graph, tensorflow::GraphDef* graph_def); - - TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s, const char* name); - TF_Operation* Add(TF_Operation* l, TF_Operation* r, TF_Graph* graph, - TF_Status* s, const char* name); - - std::function MakeCandidateFn( - const std::set& node_names); - protected: - void PlaceholderHelper(TF_Graph* graph, TF_Status* s, const char* name, - TF_Operation** op); - void AddHelper(TF_Operation* l, TF_Operation* r, TF_Graph* graph, - TF_Status* s, const char* name, TF_Operation** op, bool check); - - SegmentOptions default_options_; -}; - -bool SegmentTest::GetGraphDef(TF_Graph* graph, - tensorflow::GraphDef* graph_def) { - TF_Status* s = TF_NewStatus(); - TF_Buffer* buffer = TF_NewBuffer(); - TF_GraphToGraphDef(graph, buffer, s); - bool ret = TF_GetCode(s) == TF_OK; - EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - if (ret) ret = graph_def->ParseFromArray(buffer->data, buffer->length); - TF_DeleteBuffer(buffer); - TF_DeleteStatus(s); - return ret; -} + std::function MakeCandidateFn( + const std::set& node_names) { + return [node_names](const tensorflow::Node* node) -> bool { + return node_names.find(node->name()) != node_names.end(); + }; + } -std::function SegmentTest::MakeCandidateFn( - const std::set& node_names) { - return [node_names](const tensorflow::Node* node) -> bool { - return node_names.find(node->name()) != node_names.end(); - }; -} + std::function MakeInputEdgeCandidateFn( + const std::set& node_names) { + return [node_names](const tensorflow::Edge* in_edge) -> bool { + return node_names.find(in_edge->dst()->name()) != node_names.end(); + }; + } -void SegmentTest::PlaceholderHelper(TF_Graph* graph, TF_Status* s, - const char* name, TF_Operation** op) { - TF_OperationDescription* desc = TF_NewOperation(graph, "Placeholder", name); - TF_SetAttrType(desc, "dtype", TF_INT32); - *op = TF_FinishOperation(desc, s); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - ASSERT_NE(*op, nullptr); -} + std::function MakeOutputEdgeCandidateFn( + const std::set& node_names) { + return [node_names](const tensorflow::Edge* out_edge) -> bool { + return node_names.find(out_edge->src()->name()) != node_names.end(); + }; + } -TF_Operation* SegmentTest::Placeholder(TF_Graph* graph, TF_Status* s, - const char* name) { - TF_Operation* op; - PlaceholderHelper(graph, s, name, &op); - return op; -} + void RunTest(const tensorflow::Graph* graph, + const std::set& candidates, + const std::set& input_candidates, + const std::set& output_candidates, + const std::vector>& expected_segments) { + SegmentNodesVector segments; + TF_EXPECT_OK(SegmentGraph(graph, MakeCandidateFn(candidates), + MakeInputEdgeCandidateFn(input_candidates), + MakeOutputEdgeCandidateFn(output_candidates), + default_options_, &segments)); + ValidateSegment(segments, expected_segments); + } -void SegmentTest::AddHelper(TF_Operation* l, TF_Operation* r, TF_Graph* graph, - TF_Status* s, const char* name, TF_Operation** op, - bool check) { - TF_OperationDescription* desc = TF_NewOperation(graph, "AddN", name); - TF_Output add_inputs[2] = {{l, 0}, {r, 0}}; - TF_AddInputList(desc, add_inputs, 2); - *op = TF_FinishOperation(desc, s); - if (check) { - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - ASSERT_NE(*op, nullptr); + void ValidateSegment(const SegmentNodesVector& segments, + const std::vector>& expected_segments) { + EXPECT_EQ(expected_segments.size(), segments.size()); + for (int i = 0; i < segments.size(); ++i) { + const auto& segment_node_names = segments[i].first; + const auto& expected = expected_segments[i]; + for (const auto& name : expected) { + EXPECT_TRUE(segment_node_names.count(name)) + << "Segment " << i << " is missing expected node: " << name; + } + if (segment_node_names.size() == expected.size()) continue; + for (const auto& name : segment_node_names) { + EXPECT_TRUE(expected.count(name)) + << "Unexpected node found in segment " << i << ": " << name; + } + } } -} -TF_Operation* SegmentTest::Add(TF_Operation* l, TF_Operation* r, - TF_Graph* graph, TF_Status* s, - const char* name) { - TF_Operation* op; - AddHelper(l, r, graph, s, name, &op, true); - return op; + SegmentOptions default_options_; +}; + +std::set operator-(const std::set& lhs, const string& rhs) { + std::set result = lhs; + CHECK(result.erase(rhs)); + return result; } TEST_F(SegmentTest, Empty) { - TF_Graph* graph = TF_NewGraph(); - - GraphDef graph_def; - ASSERT_TRUE(GetGraphDef(graph, &graph_def)); - - SegmentNodesVector segments; - ASSERT_EQ( - SegmentGraph(graph_def, MakeCandidateFn({}), default_options_, &segments), - tensorflow::Status::OK()); - + Scope s = Scope::NewRootScope(); + tensorflow::Graph g(OpRegistry::Global()); + TF_EXPECT_OK(s.ToGraph(&g)); // Expect no segments/subgraphs. - EXPECT_TRUE(segments.empty()); - TF_DeleteGraph(graph); + RunTest(&g, {}, {}, {}, {}); } TEST_F(SegmentTest, Simple) { - TF_Status* s = TF_NewStatus(); - TF_Graph* graph = TF_NewGraph(); - // feed - // // || + // // \\ // add0 add1 - // | | / + // | \ / // | add2 - // | / || + // | / \\ // add3 add4 - // | / + // \ / // - // - TF_Operation* feed = Placeholder(graph, s, "feed"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - EXPECT_EQ(string("feed"), string(TF_OperationName(feed))); - - TF_Operation* add0 = Add(feed, feed, graph, s, "add0"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add1 = Add(feed, feed, graph, s, "add1"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add2 = Add(add0, add1, graph, s, "add2"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add3 = Add(add0, add2, graph, s, "add3"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - EXPECT_EQ(string("add3"), string(TF_OperationName(add3))); - TF_Operation* add4 = Add(add2, add2, graph, s, "add4"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - EXPECT_EQ(string("add4"), string(TF_OperationName(add4))); - - GraphDef graph_def; - ASSERT_TRUE(GetGraphDef(graph, &graph_def)); - - SegmentNodesVector segments; - ASSERT_EQ( - SegmentGraph(graph_def, - MakeCandidateFn({"add0", "add1", "add2", "add3", "add4"}), - default_options_, &segments), - tensorflow::Status::OK()); - - // Expect all Add operations to be collapsed into a single segment - ASSERT_EQ(segments.size(), 1); - std::vector expected{"add0", "add1", "add2", "add3", "add4"}; - for (const auto& ex : expected) { - EXPECT_TRUE(segments[0].first.find(ex) != segments[0].first.end()) - << "Missing expected node " << ex; - } - TF_DeleteGraph(graph); - TF_DeleteStatus(s); + Scope s = Scope::NewRootScope(); + auto feed = ops::Placeholder(s.WithOpName("feed"), DT_FLOAT); + auto add0 = ops::Add(s.WithOpName("add0"), feed, feed); + auto add1 = ops::Add(s.WithOpName("add1"), feed, feed); + auto add2 = ops::Add(s.WithOpName("add2"), add0, add1); + auto add3 = ops::Add(s.WithOpName("add3"), add0, add2); + auto add4 = ops::Add(s.WithOpName("add4"), add2, add2); + tensorflow::Graph g(OpRegistry::Global()); + TF_EXPECT_OK(s.ToGraph(&g)); + + // All Add operations are candidates, and we expect all of them to be + // collapsed into a single segment + const std::set all_adds = {"add0", "add1", "add2", "add3", "add4"}; + RunTest(&g, all_adds, all_adds, all_adds, {all_adds}); + + // Make add1 not a candidate, and we expect all other Add operations to be + // collapsed into a single segment + auto without_add1 = all_adds - "add1"; + RunTest(&g, without_add1, without_add1, without_add1, {without_add1}); + + // Make add1 not a candidate and add2 not an input candidate, and we expect + // add0 and add2 are removed from the segment. + auto without_add2 = all_adds - "add2"; + RunTest(&g, without_add1, without_add2, without_add1, {{"add3", "add4"}}); + + // Making add2 not an input candidate itself won't affect anything. + RunTest(&g, all_adds, without_add2, all_adds, {all_adds}); + + // Making add1 not an input candidate. + RunTest(&g, all_adds, without_add1, all_adds, {without_add1}); + + // Making add3 not an output candidate doesn't affect anything, since it's + // output is sink. + auto without_add3 = all_adds - "add3"; + RunTest(&g, all_adds, all_adds, without_add3, {all_adds}); } TEST_F(SegmentTest, AvoidCycle) { - TF_Status* s = TF_NewStatus(); - TF_Graph* graph = TF_NewGraph(); - - // add2 is not a TRT candidate so add0/add3 cannot be formed as a - // subgraph - // // feed - // // || + // // \\ // add0 add1 - // | | / + // | \ / // | add2 - // | / || + // | / \\ // add3 add4 - // | / + // \ / // - // - TF_Operation* feed = Placeholder(graph, s, "feed"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - EXPECT_EQ(string("feed"), string(TF_OperationName(feed))); - - TF_Operation* add0 = Add(feed, feed, graph, s, "add0"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add1 = Add(feed, feed, graph, s, "add1"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add2 = Add(add0, add1, graph, s, "add2"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add3 = Add(add0, add2, graph, s, "add3"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - EXPECT_EQ(string("add3"), string(TF_OperationName(add3))); - TF_Operation* add4 = Add(add2, add2, graph, s, "add4"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - EXPECT_EQ(string("add4"), string(TF_OperationName(add4))); - - GraphDef graph_def; - ASSERT_TRUE(GetGraphDef(graph, &graph_def)); - - SegmentNodesVector segments; - ASSERT_EQ( - SegmentGraph(graph_def, MakeCandidateFn({"add0", "add1", "add3", "add4"}), - default_options_, &segments), - tensorflow::Status::OK()); - - // Expect no subgraphs - EXPECT_EQ(segments.size(), 0); - TF_DeleteGraph(graph); - TF_DeleteStatus(s); + Scope s = Scope::NewRootScope(); + auto feed = ops::Placeholder(s.WithOpName("feed"), DT_FLOAT); + auto add0 = ops::Add(s.WithOpName("add0"), feed, feed); + auto add1 = ops::Add(s.WithOpName("add1"), feed, feed); + auto add2 = ops::Add(s.WithOpName("add2"), add0, add1); + auto add3 = ops::Add(s.WithOpName("add3"), add0, add2); + auto add4 = ops::Add(s.WithOpName("add4"), add2, add2); + tensorflow::Graph g(OpRegistry::Global()); + TF_EXPECT_OK(s.ToGraph(&g)); + + // add2 is not a TRT candidate so there should be no segments generated. + const std::set without_add2 = {"add0", "add1", "add3", "add4"}; + RunTest(&g, without_add2, without_add2, without_add2, {}); } TEST_F(SegmentTest, Multiple) { - TF_Status* s = TF_NewStatus(); - TF_Graph* graph = TF_NewGraph(); - - // add5 is not a TRT candidate so two subgraphs should be formed - // - // feed - // // || || - // add0 add1 add7 - // | | / / || - // | add2-----add5 add8 - // | / | | | | - // add3 add4 add6 - // | | / - // - // - TF_Operation* feed = Placeholder(graph, s, "feed"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - EXPECT_EQ(string("feed"), string(TF_OperationName(feed))); - - TF_Operation* add0 = Add(feed, feed, graph, s, "add0"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add1 = Add(feed, feed, graph, s, "add1"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add7 = Add(feed, feed, graph, s, "add7"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add2 = Add(add0, add1, graph, s, "add2"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add5 = Add(add2, add7, graph, s, "add5"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add8 = Add(add7, add7, graph, s, "add8"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add3 = Add(add0, add2, graph, s, "add3"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - EXPECT_EQ(string("add3"), string(TF_OperationName(add3))); - TF_Operation* add4 = Add(add2, add5, graph, s, "add4"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - EXPECT_EQ(string("add4"), string(TF_OperationName(add4))); - TF_Operation* add6 = Add(add5, add8, graph, s, "add6"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - EXPECT_EQ(string("add6"), string(TF_OperationName(add6))); - - GraphDef graph_def; - ASSERT_TRUE(GetGraphDef(graph, &graph_def)); - - SegmentNodesVector segments; - ASSERT_EQ(SegmentGraph(graph_def, - MakeCandidateFn({"add0", "add1", "add2", "add3", - "add4", "add6", "add7", "add8"}), - default_options_, &segments), - tensorflow::Status::OK()); - - // Expect two subgraphs - EXPECT_EQ(segments.size(), 2); - - std::vector expected0{"add6", "add8"}; - for (const auto& ex : expected0) { - EXPECT_TRUE(segments[0].first.find(ex) != segments[0].first.end()) - << "Missing expected node " << ex; - } - - std::vector expected1{"add0", "add1", "add2", "add3"}; - for (const auto& ex : expected1) { - EXPECT_TRUE(segments[1].first.find(ex) != segments[1].first.end()) - << "Missing expected node " << ex; - } - TF_DeleteGraph(graph); - TF_DeleteStatus(s); + // feed + // // || \\ + // add0 add1 add7 + // | \ / / \\ + // | add2 / \\ + // | || \ | || + // | || add5 add8 + // | / \ / \ / + // add3 add4 add6 + // \ | / + // + Scope s = Scope::NewRootScope(); + auto feed = ops::Placeholder(s.WithOpName("feed"), DT_FLOAT); + auto add0 = ops::Add(s.WithOpName("add0"), feed, feed); + auto add1 = ops::Add(s.WithOpName("add1"), feed, feed); + auto add7 = ops::Add(s.WithOpName("add7"), feed, feed); + auto add2 = ops::Add(s.WithOpName("add2"), add0, add1); + auto add5 = ops::Add(s.WithOpName("add5"), add2, add7); + auto add8 = ops::Add(s.WithOpName("add8"), add7, add7); + auto add3 = ops::Add(s.WithOpName("add3"), add0, add2); + auto add4 = ops::Add(s.WithOpName("add4"), add2, add5); + auto add6 = ops::Add(s.WithOpName("add6"), add5, add8); + tensorflow::Graph g(OpRegistry::Global()); + TF_EXPECT_OK(s.ToGraph(&g)); + + const std::set all_adds = {"add0", "add1", "add2", "add3", "add4", + "add5", "add6", "add7", "add8"}; + // Make add5 not a TRT candidate, and we expect two segments. + auto without_add5 = all_adds - "add5"; + RunTest(&g, without_add5, without_add5, without_add5, + {{"add6", "add8"}, {"add0", "add1", "add2", "add3"}}); + + // Make add8 not a candidate and add6 not an input candidate, then all direct + // and indirect inputs of add6 will be removed from the segment. + auto without_add8 = all_adds - "add8"; + auto without_add6 = all_adds - "add6"; + RunTest(&g, without_add8, without_add6, all_adds, {{"add3", "add4"}}); + + // Make add3 not a candidate and add0 not an output candidate, then all + // direct and indirect outputs of add0 will be removed from the segment. + auto without_add3 = all_adds - "add3"; + auto without_add0 = all_adds - "add0"; + RunTest(&g, without_add3, all_adds, without_add0, {{"add1", "add7", "add8"}}); } TEST_F(SegmentTest, BigIfElse) { - TF_Status* s = TF_NewStatus(); - TF_Graph* graph = TF_NewGraph(); - - // add2 is not a TRT candidate - // // feed // || // add0 - // // || + // // \\ // add1 add4 // || || // add2 add5 // || || // add3 add6 - // || // + // \\ // // add7 // || // - // - TF_Operation* feed = Placeholder(graph, s, "feed"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - EXPECT_EQ(string("feed"), string(TF_OperationName(feed))); - - TF_Operation* add0 = Add(feed, feed, graph, s, "add0"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add1 = Add(add0, add0, graph, s, "add1"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add2 = Add(add1, add1, graph, s, "add2"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add3 = Add(add2, add2, graph, s, "add3"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add4 = Add(add0, add0, graph, s, "add4"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add5 = Add(add4, add4, graph, s, "add5"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add6 = Add(add5, add5, graph, s, "add6"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - TF_Operation* add7 = Add(add3, add6, graph, s, "add7"); - ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); - EXPECT_EQ(string("add7"), string(TF_OperationName(add7))); - - GraphDef graph_def; - ASSERT_TRUE(GetGraphDef(graph, &graph_def)); - - SegmentNodesVector segments; - ASSERT_EQ(SegmentGraph(graph_def, - MakeCandidateFn({"add0", "add1", "add3", "add4", - "add5", "add6", "add7"}), - default_options_, &segments), - tensorflow::Status::OK()); - - // Expect 2 subgraphs - EXPECT_EQ(segments.size(), 2); - - std::vector expected0{"add3", "add4", "add5", "add6", "add7"}; - for (const auto& ex : expected0) { - EXPECT_TRUE(segments[0].first.find(ex) != segments[0].first.end()) - << "Missing expected node " << ex; - } - - std::vector expected1{"add0", "add1"}; - for (const auto& ex : expected1) { - EXPECT_TRUE(segments[1].first.find(ex) != segments[1].first.end()) - << "Missing expected node " << ex; - } - TF_DeleteGraph(graph); - TF_DeleteStatus(s); + Scope s = Scope::NewRootScope(); + auto feed = ops::Placeholder(s.WithOpName("feed"), DT_FLOAT); + auto add0 = ops::Add(s.WithOpName("add0"), feed, feed); + auto add1 = ops::Add(s.WithOpName("add1"), add0, add0); + auto add2 = ops::Add(s.WithOpName("add2"), add1, add1); + auto add3 = ops::Add(s.WithOpName("add3"), add2, add2); + auto add4 = ops::Add(s.WithOpName("add4"), add0, add0); + auto add5 = ops::Add(s.WithOpName("add5"), add4, add4); + auto add6 = ops::Add(s.WithOpName("add6"), add5, add5); + auto add7 = ops::Add(s.WithOpName("add7"), add3, add6); + tensorflow::Graph g(OpRegistry::Global()); + TF_EXPECT_OK(s.ToGraph(&g)); + + // Make add2 not a TRT candidate, and we expect 2 segments. + const std::set all_adds = {"add0", "add1", "add2", "add3", + "add4", "add5", "add6", "add7"}; + RunTest(&g, all_adds - "add2", all_adds, all_adds, + {{"add3", "add4", "add5", "add6", "add7"}, {"add0", "add1"}}); } } // namespace test diff --git a/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc index 227ac120dde8c986379c687987cd1bd822d559f7..f30dba59ad55317d7ad7730e4dc66c9aba4e6a6b 100644 --- a/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc +++ b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc @@ -28,36 +28,50 @@ limitations under the License. namespace tensorflow { namespace shape_inference { -tensorflow::Status TRTEngineOpShapeInference(InferenceContext* context) { - std::vector shapes; - for (int i = 0; i < context->num_outputs(); ++i) { - context->set_output(i, context->UnknownShape()); +tensorflow::Status TRTEngineOpShapeInference(InferenceContext* c) { + for (int i = 0; i < c->num_outputs(); ++i) { + c->set_output(i, c->UnknownShape()); } - auto status = context->GetAttr("input_shapes", &shapes); - // it is ok to not to have shapes - if (!status.ok()) return Status::OK(); - if ((int)shapes.size() != context->num_inputs()) return Status::OK(); - bool different_input = false; - for (int i = 0; i < context->num_inputs(); ++i) { - if (shapes.at(i) != context->input_tensor(i)->shape()) - different_input = true; + + // Check the sanity of the input shapes. + std::vector input_shapes; + TF_RETURN_IF_ERROR(c->GetAttr("input_shapes", &input_shapes)); + if (input_shapes.size() != c->num_inputs()) { + return tensorflow::errors::InvalidArgument( + "The actual number of inputs doesn't match the number of input " + "shapes set in the attr: ", + c->num_inputs(), " vs ", input_shapes.size()); + } + bool input_match = true; + for (int i = 0; i < c->num_inputs(); ++i) { + ShapeHandle handle; + TF_RETURN_IF_ERROR( + c->MakeShapeFromTensorShape(input_shapes.at(i), &handle)); + ShapeHandle merged; + if (!c->Merge(c->input(i), handle, &merged).ok()) { + // Input shape doesn't match what was set in attr, fine. + input_match = false; + } } - if (different_input) return Status::OK(); - shapes.resize(0); - status = context->GetAttr("output_shapes", &shapes); - if (!status.ok()) return Status::OK(); - if ((int)shapes.size() != context->num_outputs()) return Status::OK(); - std::vector shape_handles(shapes.size()); - for (size_t i = 0; i < shapes.size(); ++i) { - status = - context->MakeShapeFromTensorShape(shapes.at(i), &shape_handles.at(i)); - if (!status.ok()) return Status::OK(); + + // Check the sanity of the output shapes. + std::vector output_shapes; + TF_RETURN_IF_ERROR(c->GetAttr("output_shapes", &output_shapes)); + if (output_shapes.size() != c->num_outputs()) { + return tensorflow::errors::InvalidArgument( + "The actual number of outputs doesn't match the number of output " + "shapes set in the attr: ", + c->num_outputs(), " vs ", output_shapes.size()); } - for (int i = 0; i < context->num_outputs(); ++i) { - context->set_output(i, shape_handles.at(i)); + for (size_t i = 0; i < output_shapes.size(); ++i) { + ShapeHandle handle; + TF_RETURN_IF_ERROR( + c->MakeShapeFromTensorShape(output_shapes.at(i), &handle)); + if (input_match) c->set_output(i, handle); } return Status::OK(); } + } // namespace shape_inference } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/tensorrt_test.cc b/tensorflow/contrib/tensorrt/tensorrt_test.cc index 3712a9a6fe349d949ef2666652b9d750538d5535..769982c6456f76663e50fe3ec59651127e3720ac 100644 --- a/tensorflow/contrib/tensorrt/tensorrt_test.cc +++ b/tensorflow/contrib/tensorrt/tensorrt_test.cc @@ -13,7 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include "tensorflow/core/common_runtime/gpu/gpu_init.h" #include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/stream_executor.h" #include "tensorflow/core/platform/test.h" #if GOOGLE_CUDA @@ -130,6 +132,13 @@ void Execute(nvinfer1::IExecutionContext* context, const float* input, } TEST(TensorrtTest, BasicFunctions) { + // Handle the case where the test is run on machine with no gpu available. + if (CHECK_NOTNULL(GPUMachineManager())->VisibleDeviceCount() <= 0) { + LOG(WARNING) << "No gpu device available, probably not being run on a gpu " + "machine. Skipping..."; + return; + } + // Create the network model. nvinfer1::IHostMemory* model = CreateNetwork(); // Use the model to create an engine and then an execution context. diff --git a/tensorflow/contrib/tensorrt/test/base_test.py b/tensorflow/contrib/tensorrt/test/base_test.py new file mode 100644 index 0000000000000000000000000000000000000000..edd30ad7a95dd3c7f74634699660caad30c0b645 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/base_test.py @@ -0,0 +1,126 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Basic tests for TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import nn +from tensorflow.python.ops import nn_ops +from tensorflow.python.platform import test + + +class SimpleSingleEngineGraphDefTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Create a graph containing single segment.""" + # TODO(aaroey): test graph with different dtypes. + dtype = dtypes.float32 + input_name = "input" + input_dims = [100, 24, 24, 2] + g = ops.Graph() + with g.as_default(): + inp = array_ops.placeholder( + dtype=dtype, shape=[None] + input_dims[1:], name=input_name) + with g.device("/GPU:0"): + conv_filter = constant_op.constant( + [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], + name="weights", + dtype=dtype) + conv = nn.conv2d( + input=inp, + filter=conv_filter, + strides=[1, 2, 2, 1], + padding="SAME", + name="conv") + bias = constant_op.constant( + [4., 1.5, 2., 3., 5., 7.], name="bias", dtype=dtype) + added = nn.bias_add(conv, bias, name="bias_add") + relu = nn.relu(added, "relu") + identity = array_ops.identity(relu, "identity") + pool = nn_ops.max_pool( + identity, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") + array_ops.squeeze(pool, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=1, + expected_output_dims=(100, 6, 6, 6), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + + +class SimpleMultiEngineGraphDefTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Create a graph containing multiple segment.""" + # TODO(aaroey): test graph with different dtypes. + dtype = dtypes.float32 + input_name = "input" + input_dims = [100, 24, 24, 2] + g = ops.Graph() + with g.as_default(): + inp = array_ops.placeholder( + dtype=dtype, shape=[None] + input_dims[1:], name=input_name) + with g.device("/GPU:0"): + conv_filter = constant_op.constant( + [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], + name="weights", + dtype=dtype) + conv = nn.conv2d( + input=inp, + filter=conv_filter, + strides=[1, 2, 2, 1], + padding="SAME", + name="conv") + c1 = constant_op.constant( + np.random.randn(input_dims[0], 12, 12, 6), dtype=dtype) + p = conv * c1 + c2 = constant_op.constant( + np.random.randn(input_dims[0], 12, 12, 6), dtype=dtype) + q = conv / c2 + + edge = self.trt_incompatible_op(q) + edge /= edge + r = edge + edge + + p -= edge + q *= edge + s = p + q + s -= r + array_ops.squeeze(s, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=2, + expected_output_dims=(100, 12, 12, 6), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + + +# TODO(aaroey): add a large complex graph to test. + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/batch_matmul_test.py b/tensorflow/contrib/tensorrt/test/batch_matmul_test.py new file mode 100644 index 0000000000000000000000000000000000000000..730b6843fb9885b8ba0db2ad199b95d9d3219774 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/batch_matmul_test.py @@ -0,0 +1,76 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import test + + +class BatchMatMulTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Testing conversion of BatchMatMul in TF-TRT conversion.""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [12, 5, 8, 12] + w1_name = "matmul_w1" + w1_dims = [12, 5, 12, 7] + w2_name = "matmul_w2" + w2_dims = [12, 12, 7] + g = ops.Graph() + with g.as_default(): + inp = array_ops.placeholder( + dtype=dtype, shape=[None] + input_dims[1:], name=input_name) + w1 = array_ops.placeholder(dtype=dtype, shape=w1_dims, name=w1_name) + w2 = array_ops.placeholder(dtype=dtype, shape=w2_dims, name=w2_name) + with g.device("/GPU:0"): + b = constant_op.constant(np.random.randn(12, 5, 12, 7), dtype=dtype) + c = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtype) + d = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtype) + x1 = math_ops.matmul(inp, b) + x1 = x1 + c + x2 = math_ops.matmul(inp, w1) + x2 = x2 * d + e = gen_array_ops.reshape(inp, [12, 40, 12]) + x3 = math_ops.matmul(e, w2) + f = constant_op.constant(np.random.randn(40, 1), dtype=dtype) + x3 = x3 + f + x3 = gen_array_ops.reshape(x3, [12, 5, 8, 7]) + out = x1 + x2 + x3 + array_ops.squeeze(out, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name, w1_name, w2_name], + input_dims=[input_dims, w1_dims, w2_dims], + num_expected_engines=1, + expected_output_dims=(12, 5, 8, 7), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py b/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py new file mode 100644 index 0000000000000000000000000000000000000000..0c03a10b640c8b243318bb4327d2ac5aac803be7 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py @@ -0,0 +1,112 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn +from tensorflow.python.platform import test + + +class BiasaddMatMulTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Testing conversion of BiasAdd MatMul in TF-TRT conversion.""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [48, 12] + g = ops.Graph() + with g.as_default(): + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) + + b = constant_op.constant(np.random.randn(12, 4), dtype=dtype) + x1 = math_ops.matmul(x, b) + b = constant_op.constant(np.random.randn(1, 4), dtype=dtype) + x1 = x1 + b + + b = constant_op.constant(np.random.randn(48, 4), dtype=dtype) + x2 = math_ops.matmul(x, b, transpose_a=True) + x2 = gen_array_ops.reshape(x2, [48, 1]) + + b = constant_op.constant(np.random.randn(4, 12), dtype=dtype) + x3 = math_ops.matmul(x, b, transpose_b=True) + + b = constant_op.constant(np.random.randn(16, 48), dtype=dtype) + x4 = math_ops.matmul(x, b, transpose_b=True, transpose_a=True) + x4 = gen_array_ops.reshape(x4, [48, 4]) + + x5 = gen_array_ops.reshape(x, [4, 144]) + b = constant_op.constant(np.random.randn(144, 48), dtype=dtype) + x5 = math_ops.matmul(x5, b) + b = constant_op.constant(np.random.randn(48), dtype=dtype) + x5 = nn.bias_add(x5, b) + x5 = gen_array_ops.reshape(x5, [48, 4]) + + x6 = gen_array_ops.reshape(x, [4, 12, 12]) + b = constant_op.constant(np.random.randn(12), dtype=dtype) + x6 = nn.bias_add(x6, b, data_format="NHWC") + x6 = gen_array_ops.reshape(x6, [48, -1]) + + x7 = gen_array_ops.reshape(x, [4, 12, 3, 4]) + b = constant_op.constant(np.random.randn(4), dtype=dtype) + x7 = nn.bias_add(x7, b, data_format="NHWC") + x7 = gen_array_ops.reshape(x7, [48, -1]) + + x8 = gen_array_ops.reshape(x, [4, 12, 3, 2, 2]) + b = constant_op.constant(np.random.randn(2), dtype=dtype) + x8 = nn.bias_add(x8, b, data_format="NHWC") + x8 = gen_array_ops.reshape(x8, [48, -1]) + + x9 = gen_array_ops.reshape(x, [4, 12, 3, 2, 2]) + b = constant_op.constant(np.random.randn(3), dtype=dtype) + x9 = nn.bias_add(x9, b, data_format="NCHW") + x9 = gen_array_ops.reshape(x9, [48, -1]) + + x10 = gen_array_ops.reshape(x, [4, 12, 3, 4]) + b = constant_op.constant(np.random.randn(12), dtype=dtype) + x10 = nn.bias_add(x10, b, data_format="NCHW") + x10 = gen_array_ops.reshape(x10, [48, -1]) + + x11 = gen_array_ops.reshape(x, [4, 12, 12]) + b = constant_op.constant(np.random.randn(4), dtype=dtype) + x11 = nn.bias_add(x11, b, data_format="NCHW") + x11 = gen_array_ops.reshape(x11, [48, -1]) + + out = array_ops.concat( + [x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11], axis=-1) + out = array_ops.squeeze(out, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=7, + expected_output_dims=(48, 89), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py b/tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py new file mode 100644 index 0000000000000000000000000000000000000000..dd673463a5930df4d0e4c1c7410b3f5eb88d664c --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py @@ -0,0 +1,119 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import test + + +class BinaryTensorWeightBroadcastTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Tests for scale & elementwise layers in TF-TRT.""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [10, 24, 24, 20] + g = ops.Graph() + with g.as_default(): + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) + # scale + a = constant_op.constant(np.random.randn(1), dtype=dtype) + f = x + a + x = math_ops.sigmoid(f) + # scale + a = constant_op.constant(np.random.randn(1), dtype=dtype) + f = a + x + x = math_ops.sigmoid(f) + # scale + a = constant_op.constant(np.random.randn(24, 1, 1), dtype=dtype) + f = x + a + x = math_ops.sigmoid(f) + # scale + a = constant_op.constant(np.random.randn(24, 1, 1), dtype=dtype) + f = a + x + x = math_ops.sigmoid(f) + # scale + a = constant_op.constant(np.random.randn(24, 24, 20), dtype=dtype) + f = a + x + x = math_ops.sigmoid(f) + # scale + a = constant_op.constant(np.random.randn(24, 24, 20), dtype=dtype) + f = x + a + x = math_ops.sigmoid(f) + # elementwise + a = constant_op.constant(np.random.randn(20), dtype=dtype) + f = x + a + x = math_ops.sigmoid(f) + # elementwise + a = constant_op.constant(np.random.randn(20), dtype=dtype) + f = a + x + x = math_ops.sigmoid(f) + # elementwise + a = constant_op.constant(np.random.randn(1, 24, 1, 1), dtype=dtype) + f = a + x + x = math_ops.sigmoid(f) + # elementwise + a = constant_op.constant(np.random.randn(1, 24, 1, 1), dtype=dtype) + f = x + a + x = math_ops.sigmoid(f) + # elementwise + a = constant_op.constant(np.random.randn(1, 24, 24, 1), dtype=dtype) + f = a + x + x = math_ops.sigmoid(f) + # elementwise + a = constant_op.constant(np.random.randn(1, 24, 24, 1), dtype=dtype) + f = x + a + x = math_ops.sigmoid(f) + # elementwise + a = constant_op.constant(np.random.randn(1, 24, 24, 20), dtype=dtype) + f = a + x + x = math_ops.sigmoid(f) + # elementwise + a = constant_op.constant(np.random.randn(1, 24, 24, 20), dtype=dtype) + f = x + a + x = math_ops.sigmoid(f) + # elementwise + a = constant_op.constant(np.random.randn(24, 20), dtype=dtype) + f = a + x + x = math_ops.sigmoid(f) + # elementwise + a = constant_op.constant(np.random.randn(24, 20), dtype=dtype) + f = x + a + x = math_ops.sigmoid(f) + gen_array_ops.reshape(x, [5, -1], name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=16, + expected_output_dims=(5, 23040), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/concatenation_test.py b/tensorflow/contrib/tensorrt/test/concatenation_test.py new file mode 100644 index 0000000000000000000000000000000000000000..8c51c45b0a2c6f370415b9c8ac99a63dd37be900 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/concatenation_test.py @@ -0,0 +1,83 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_array_ops +from tensorflow.python.ops import gen_math_ops +from tensorflow.python.platform import test + + +class ConcatenationTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Testing Concatenation in TF-TRT conversion.""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [2, 3, 3, 1] + g = ops.Graph() + with g.as_default(): + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) + # scale + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype) + r1 = x / a + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype) + r2 = a / x + a = constant_op.constant(np.random.randn(1, 3, 1), dtype=dtype) + r3 = a + x + a = constant_op.constant(np.random.randn(1, 3, 1), dtype=dtype) + r4 = x * a + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype) + r5 = x - a + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype) + r6 = a - x + a = constant_op.constant(np.random.randn(3, 1), dtype=dtype) + r7 = x - a + a = constant_op.constant(np.random.randn(3, 1), dtype=dtype) + r8 = a - x + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype) + r9 = gen_math_ops.maximum(x, a) + a = constant_op.constant(np.random.randn(3, 1), dtype=dtype) + r10 = gen_math_ops.minimum(a, x) + a = constant_op.constant(np.random.randn(3), dtype=dtype) + r11 = x * a + a = constant_op.constant(np.random.randn(1), dtype=dtype) + r12 = a * x + concat1 = array_ops.concat([r1, r2, r3, r4, r5, r6], axis=-1) + concat2 = array_ops.concat([r7, r8, r9, r10, r11, r12], axis=3) + x = array_ops.concat([concat1, concat2], axis=-1) + gen_array_ops.reshape(x, [2, -1], name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=1, + expected_output_dims=(2, 126), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/const_broadcast_test.py b/tensorflow/contrib/tensorrt/test/const_broadcast_test.py new file mode 100644 index 0000000000000000000000000000000000000000..97b29bf05ddc3a0396472d0500ff53ceca7c5d4b --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/const_broadcast_test.py @@ -0,0 +1,68 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import nn +from tensorflow.python.platform import test + + +class ConstBroadcastTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Test for Constant broadcasting in TF-TRT.""" + dtype = dtypes.float32 + input_name = 'input' + input_dims = [5, 12, 12, 2] + g = ops.Graph() + with g.as_default(): + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) + filt1 = constant_op.constant( + 0.3, shape=(3, 3, 2, 1), dtype=dtype, name='filt1') + y1 = nn.conv2d(x, filt1, strides=[1, 1, 1, 1], padding='SAME', name='y1') + z1 = nn.relu(y1, name='z1') + filt2 = constant_op.constant( + np.random.randn(9), shape=(3, 3, 1, 1), dtype=dtype, name='filt2') + y2 = nn.conv2d(z1, filt2, strides=[1, 1, 1, 1], padding='SAME', name='y2') + z2 = nn.relu(y2, name='z') + filt3 = constant_op.constant( + np.random.randn(3, 3, 1, 1), + shape=(3, 3, 1, 1), + dtype=dtype, + name='filt3') + y3 = nn.conv2d(z2, filt3, strides=[1, 1, 1, 1], padding='SAME', name='y3') + nn.relu(y3, name='output') + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=1, + expected_output_dims=(5, 12, 12, 1), + allclose_atol=1.e-02, + allclose_rtol=1.e-02) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/tensorrt/test/memory_alignment_test.py b/tensorflow/contrib/tensorrt/test/memory_alignment_test.py new file mode 100644 index 0000000000000000000000000000000000000000..3dd95c6f62fe504cb23e01fdb8b9785cee080de4 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/memory_alignment_test.py @@ -0,0 +1,72 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import nn +from tensorflow.python.platform import test + + +class MemoryAlignmentTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Testing conversion of BatchMatMul in TF-TRT conversion.""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [2, 15, 15, 3] + g = ops.Graph() + with g.as_default(): + inp = array_ops.placeholder( + dtype=dtype, shape=[None] + input_dims[1:], name=input_name) + with g.device("/GPU:0"): + e1 = constant_op.constant( + np.random.randn(1, 1, 3, 5), name="kernel_1", dtype=dtype) + e2 = constant_op.constant( + np.random.randn(1, 1, 5, 10), name="kernel_2", dtype=dtype) + conv = nn.conv2d( + input=inp, + filter=e1, + strides=[1, 1, 1, 1], + padding="VALID", + name="conv") + out = nn.conv2d( + input=conv, + filter=e2, + strides=[1, 1, 1, 1], + padding="VALID", + name="conv_2") + array_ops.squeeze(out, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=1, + expected_output_dims=(2, 15, 15, 10), + allclose_atol=1.e-02, + allclose_rtol=1.e-02) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py b/tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py new file mode 100644 index 0000000000000000000000000000000000000000..734ccf6345777d543138daba2b720c9dc03f3295 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py @@ -0,0 +1,87 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_math_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn +from tensorflow.python.platform import test + + +class MultiConnectionNeighborEngineTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Test for multi connection neighboring nodes wiring tests in TF-TRT.""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [2, 3, 7, 5] + g = ops.Graph() + with g.as_default(): + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) + e = constant_op.constant( + np.random.normal(.05, .005, [3, 2, 3, 4]), + name="weights", + dtype=dtype) + conv = nn.conv2d( + input=x, + filter=e, + data_format="NCHW", + strides=[1, 1, 1, 1], + padding="VALID", + name="conv") + b = constant_op.constant( + np.random.normal(2.0, 1.0, [1, 4, 1, 1]), name="bias", dtype=dtype) + t = conv + b + + b = constant_op.constant( + np.random.normal(5.0, 1.0, [1, 4, 1, 1]), name="bias", dtype=dtype) + q = conv - b + edge = math_ops.sigmoid(q) + + b = constant_op.constant( + np.random.normal(5.0, 1.0, [1, 4, 1, 1]), name="bias", dtype=dtype) + d = b + conv + edge3 = math_ops.sigmoid(d) + + edge1 = gen_math_ops.tan(conv) + t = t - edge1 + q = q + edge + t = t + q + t = t + d + t = t - edge3 + array_ops.squeeze(t, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=2, + expected_output_dims=(2, 4, 5, 4), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/neighboring_engine_test.py b/tensorflow/contrib/tensorrt/test/neighboring_engine_test.py new file mode 100644 index 0000000000000000000000000000000000000000..50265c0845005748d75bf8afc49df11a528c9169 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/neighboring_engine_test.py @@ -0,0 +1,69 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_math_ops +from tensorflow.python.ops import nn +from tensorflow.python.platform import test + + +class NeighboringEngineTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Neighboring node wiring tests in TF-TRT conversion.""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [2, 3, 7, 5] + g = ops.Graph() + with g.as_default(): + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) + e = constant_op.constant( + np.random.normal(.3, 0.05, [3, 2, 3, 4]), name="weights", dtype=dtype) + conv = nn.conv2d( + input=x, + filter=e, + data_format="NCHW", + strides=[1, 1, 1, 1], + padding="VALID", + name="conv") + b = constant_op.constant( + np.random.normal(1.0, 1.0, [1, 4, 1, 1]), name="bias", dtype=dtype) + t = conv * b + e = gen_math_ops.tan(conv) + t = t - e + array_ops.squeeze(t, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=2, + expected_output_dims=(2, 4, 5, 4), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py deleted file mode 100644 index d9c41f90d0ab111b48c37aeaae5f0ce3177646c2..0000000000000000000000000000000000000000 --- a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py +++ /dev/null @@ -1,347 +0,0 @@ -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Script to test TF-TensorRT integration.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from collections import namedtuple -import itertools -import warnings -import numpy as np -import six - -from tensorflow.contrib import tensorrt as trt -from tensorflow.core.protobuf import config_pb2 -from tensorflow.core.protobuf import rewriter_config_pb2 -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import importer -from tensorflow.python.framework import ops -from tensorflow.python.framework import test_util -from tensorflow.python.ops import array_ops -from tensorflow.python.ops import math_ops -from tensorflow.python.ops import nn -from tensorflow.python.ops import nn_ops -from tensorflow.python.platform import test - -INPUT_NAME = "input" -OUTPUT_NAME = "output" -INPUT_DIMS = [100, 24, 24, 2] -MODE_FP32 = "FP32" -MODE_FP16 = "FP16" -MODE_INT8 = "INT8" - -if six.PY2: - to_bytes = lambda s: s - to_string = lambda s: s -else: - to_bytes = lambda s: s.encode("utf-8", errors="surrogateescape") - to_string = lambda s: s.decode("utf-8") - - -# TODO(aaroey): test graph with different dtypes. -def GetSingleEngineGraphDef(dtype=dtypes.float32): - """Create a graph containing single segment.""" - g = ops.Graph() - with g.as_default(): - inp = array_ops.placeholder( - dtype=dtype, shape=[None] + INPUT_DIMS[1:], name=INPUT_NAME) - with g.device("/GPU:0"): - conv_filter = constant_op.constant( - [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], - name="weights", - dtype=dtype) - conv = nn.conv2d( - input=inp, - filter=conv_filter, - strides=[1, 2, 2, 1], - padding="SAME", - name="conv") - bias = constant_op.constant( - [4., 1.5, 2., 3., 5., 7.], name="bias", dtype=dtype) - added = nn.bias_add(conv, bias, name="bias_add") - relu = nn.relu(added, "relu") - identity = array_ops.identity(relu, "identity") - pool = nn_ops.max_pool( - identity, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") - array_ops.squeeze(pool, name=OUTPUT_NAME) - return g.as_graph_def() - - -# TODO(aaroey): test graph with different dtypes. -def GetMultiEngineGraphDef(dtype=dtypes.float32): - """Create a graph containing multiple segment.""" - g = ops.Graph() - with g.as_default(): - inp = array_ops.placeholder( - dtype=dtype, shape=[None] + INPUT_DIMS[1:], name=INPUT_NAME) - with g.device("/GPU:0"): - conv_filter = constant_op.constant( - [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], - name="weights", - dtype=dtype) - conv = nn.conv2d( - input=inp, - filter=conv_filter, - strides=[1, 2, 2, 1], - padding="SAME", - name="conv") - c1 = constant_op.constant( - np.random.randn(INPUT_DIMS[0], 12, 12, 6), dtype=dtype) - p = conv * c1 - c2 = constant_op.constant( - np.random.randn(INPUT_DIMS[0], 12, 12, 6), dtype=dtype) - q = conv / c2 - - edge = math_ops.sin(q) - edge /= edge - r = edge + edge - - p -= edge - q *= edge - s = p + q - s -= r - array_ops.squeeze(s, name=OUTPUT_NAME) - return g.as_graph_def() - - -TestGraph = namedtuple("TestGraph", - ["gdef", "num_expected_engines", "expected_output_dims"]) - -TEST_GRAPHS = { - "SingleEngineGraph": - TestGraph( - gdef=GetSingleEngineGraphDef(), - num_expected_engines=1, - expected_output_dims=(100, 6, 6, 6)), - "MultiEngineGraph": - TestGraph( - gdef=GetMultiEngineGraphDef(), - num_expected_engines=2, - expected_output_dims=(100, 12, 12, 6)), - # TODO(aaroey): add a large complex graph to test. -} - - -class TfTrtIntegrationTest(test_util.TensorFlowTestCase): - """Class to test Tensorflow-TensorRT integration.""" - - def setUp(self): - """Setup method.""" - super(TfTrtIntegrationTest, self).setUp() - warnings.simplefilter("always") - self._input = np.random.random_sample(INPUT_DIMS) - - def _GetConfigProto(self, - use_optimizer, - precision_mode=None, - is_dynamic_op=None): - if use_optimizer: - rewriter_cfg = rewriter_config_pb2.RewriterConfig() - rewriter_cfg.optimizers.extend(["constfold", "layout"]) - custom_op = rewriter_cfg.custom_optimizers.add() - custom_op.name = "TensorRTOptimizer" - custom_op.parameter_map["minimum_segment_size"].i = 3 - custom_op.parameter_map["max_batch_size"].i = self._input.shape[0] - custom_op.parameter_map["is_dynamic_op"].b = is_dynamic_op - custom_op.parameter_map["max_workspace_size_bytes"].i = 1 << 25 - custom_op.parameter_map["precision_mode"].s = to_bytes(precision_mode) - graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_cfg) - else: - graph_options = config_pb2.GraphOptions() - - gpu_options = config_pb2.GPUOptions() - if trt.trt_convert.get_linked_tensorrt_version()[0] == 3: - gpu_options.per_process_gpu_memory_fraction = 0.50 - - config = config_pb2.ConfigProto( - gpu_options=gpu_options, graph_options=graph_options) - return config - - def _RunGraph(self, graph_key, gdef, input_data, config, num_runs=2): - """Run given graphdef multiple times.""" - g = ops.Graph() - with g.as_default(): - inp, out = importer.import_graph_def( - graph_def=gdef, return_elements=[INPUT_NAME, OUTPUT_NAME], name="") - inp = inp.outputs[0] - out = out.outputs[0] - with self.test_session( - graph=g, config=config, use_gpu=True, force_gpu=True) as sess: - val = None - # Defaults to 2 runs to verify result across multiple runs is same. - for _ in range(num_runs): - new_val = sess.run(out, {inp: input_data}) - self.assertEquals(TEST_GRAPHS[graph_key].expected_output_dims, - new_val.shape) - if val is not None: - self.assertAllEqual(new_val, val) - val = new_val - return val - - # Use real data that is representative of the inference dataset - # for calibration. For this test script it is random data. - def _RunCalibration(self, graph_key, gdef, input_data, config): - """Run calibration on given graph.""" - return self._RunGraph(graph_key, gdef, input_data, config, 30) - - def _GetTrtGraph(self, gdef, precision_mode, is_dynamic_op): - """Return trt converted graph.""" - return trt.create_inference_graph( - input_graph_def=gdef, - outputs=[OUTPUT_NAME], - max_batch_size=self._input.shape[0], - max_workspace_size_bytes=1 << 25, - precision_mode=precision_mode, - minimum_segment_size=2, - is_dynamic_op=is_dynamic_op) - - def _VerifyGraphDef(self, - graph_key, - gdef, - precision_mode=None, - is_calibrated=None, - dynamic_engine=None): - num_engines = 0 - for n in gdef.node: - if n.op == "TRTEngineOp": - num_engines += 1 - self.assertNotEqual("", n.attr["serialized_segment"].s) - self.assertNotEqual("", n.attr["segment_funcdef_name"].s) - self.assertEquals(n.attr["precision_mode"].s, precision_mode) - self.assertEquals(n.attr["static_engine"].b, not dynamic_engine) - if precision_mode == MODE_INT8 and is_calibrated: - self.assertNotEqual("", n.attr["calibration_data"].s) - else: - self.assertEquals("", n.attr["calibration_data"].s) - if precision_mode is None: - self.assertEquals(num_engines, 0) - else: - self.assertEquals(num_engines, - TEST_GRAPHS[graph_key].num_expected_engines) - - def _RunTest(self, graph_key, use_optimizer, precision_mode, - dynamic_infer_engine, dynamic_calib_engine): - assert precision_mode in [MODE_FP32, MODE_FP16, MODE_INT8] - input_gdef = TEST_GRAPHS[graph_key].gdef - self._VerifyGraphDef(graph_key, input_gdef) - - # Get reference result without running trt. - config_no_trt = self._GetConfigProto(False) - print("Running original graph w/o trt, config:\n%s" % str(config_no_trt)) - ref_result = self._RunGraph(graph_key, input_gdef, self._input, - config_no_trt) - - # Run calibration if necessary. - if precision_mode == MODE_INT8: - - calib_config = self._GetConfigProto(use_optimizer, precision_mode, - dynamic_calib_engine) - print("Running calibration graph, config:\n%s" % str(calib_config)) - if use_optimizer: - self.assertTrue(False) - # TODO(aaroey): uncomment this and get infer_gdef when this mode is - # supported. - # result = self._RunCalibration(graph_key, input_gdef, self._input, - # calib_config) - else: - calib_gdef = self._GetTrtGraph(input_gdef, precision_mode, - dynamic_calib_engine) - self._VerifyGraphDef(graph_key, calib_gdef, precision_mode, False, - dynamic_calib_engine) - result = self._RunCalibration(graph_key, calib_gdef, self._input, - calib_config) - infer_gdef = trt.calib_graph_to_infer_graph(calib_gdef) - self._VerifyGraphDef(graph_key, infer_gdef, precision_mode, True, - dynamic_calib_engine) - self.assertAllClose(ref_result, result, rtol=1.e-03) - else: - infer_gdef = input_gdef - - # Run inference. - infer_config = self._GetConfigProto(use_optimizer, precision_mode, - dynamic_infer_engine) - print("Running final inference graph, config:\n%s" % str(infer_config)) - if use_optimizer: - result = self._RunGraph(graph_key, infer_gdef, self._input, infer_config) - else: - trt_infer_gdef = self._GetTrtGraph(infer_gdef, precision_mode, - dynamic_infer_engine) - self._VerifyGraphDef(graph_key, trt_infer_gdef, precision_mode, True, - dynamic_infer_engine) - result = self._RunGraph(graph_key, trt_infer_gdef, self._input, - infer_config) - self.assertAllClose(ref_result, result, rtol=1.e-03) - - def testIdempotence(self): - # Test that applying tensorrt optimizer or offline conversion tools multiple - # times to the same graph will result in same graph. - # TODO(aaroey): implement this. - pass - - -def GetTests(): - - def _GetTest(g, u, p, i, c): - - def _Test(self): - print("Running test with parameters: graph_key=%s, use_optimizer=%s, " - "precision_mode=%s, dynamic_infer_engine=%s, " - "dynamic_calib_engine=%s" % (g, u, p, i, c)) - self._RunTest(g, u, p, i, c) - - return _Test - - use_optimizer_options = [False, True] - precision_mode_options = [MODE_FP32, MODE_FP16, MODE_INT8] - dynamic_infer_engine_options = [False, True] - dynamic_calib_engine_options = [False, True] - for (graph_key, use_optimizer, precision_mode, - dynamic_infer_engine, dynamic_calib_engine) in itertools.product( - TEST_GRAPHS, use_optimizer_options, precision_mode_options, - dynamic_infer_engine_options, dynamic_calib_engine_options): - if precision_mode == MODE_INT8: - if not dynamic_calib_engine and dynamic_infer_engine: - # TODO(aaroey): test this case, the conversion from static calibration - # engine to dynamic inference engine should be a noop. - continue - if use_optimizer: - # TODO(aaroey): if use_optimizer is True we need to get the inference - # graphdef using custom python wrapper class, which is not currently - # supported yet. - continue - if not dynamic_calib_engine: - # TODO(aaroey): construction of static calibration engine is not - # supported yet. - continue - if dynamic_calib_engine and not dynamic_infer_engine: - # TODO(aaroey): construction of static inference engine using dynamic - # calibration engine is not supported yet. - continue - else: # In non int8 mode. - if dynamic_calib_engine: - # dynamic_calib_engine doesn't affect non-int8 modes, so just let - # related tests run once on dynamic_calib_engine=False. - continue - yield _GetTest(graph_key, use_optimizer, precision_mode, - dynamic_infer_engine, dynamic_calib_engine) - - -if __name__ == "__main__": - for index, t in enumerate(GetTests()): - setattr(TfTrtIntegrationTest, "testTfTRT_" + str(index), t) - test.main() diff --git a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py new file mode 100644 index 0000000000000000000000000000000000000000..bb7f5a77f011ee5c4fe748c246ac632a7bb19aff --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py @@ -0,0 +1,329 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utilities to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from collections import namedtuple +import itertools +import warnings +import numpy as np +import six + +from tensorflow.contrib.tensorrt.python import trt_convert +# pylint: disable=unused-import +from tensorflow.contrib.tensorrt.python.ops import trt_engine_op +# pylint: enable=unused-import +from tensorflow.core.protobuf import config_pb2 +from tensorflow.core.protobuf import rewriter_config_pb2 +from tensorflow.python.framework import importer +from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import tf_logging as logging + +TfTrtIntegrationTestParams = namedtuple("TfTrtIntegrationTestParams", [ + "gdef", "input_names", "input_dims", "num_expected_engines", + "expected_output_dims", "allclose_atol", "allclose_rtol" +]) + +PRECISION_MODES = ["FP32", "FP16", "INT8"] + + +def _IsQuantizationMode(mode): + return mode == "INT8" + + +class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): + """Class to test Tensorflow-TensorRT integration.""" + + @property + def output_name(self): + return "output" + + @property + def trt_incompatible_op(self): + return math_ops.sin + + @property + def precision_modes(self): + return ["FP32", "FP16", "INT8"] + + def _ToBytes(self, s): + if six.PY2: + return s + else: + return s.encode("utf-8") + + def _ToString(self, s): + if six.PY2: + return s + else: + return s.decode("utf-8") + + def setUp(self): + """Setup method.""" + super(TfTrtIntegrationTestBase, self).setUp() + warnings.simplefilter("always") + + def GetParams(self): + """Return a TfTrtIntegrationTestParams for test, implemented by subclass.""" + raise NotImplementedError() + + def _GetConfigProto(self, + params, + use_optimizer, + precision_mode=None, + is_dynamic_op=None): + """Get config proto based on specific settings.""" + if use_optimizer: + rewriter_cfg = rewriter_config_pb2.RewriterConfig() + rewriter_cfg.optimizers.extend(["constfold", "layout"]) + custom_op = rewriter_cfg.custom_optimizers.add() + custom_op.name = "TensorRTOptimizer" + custom_op.parameter_map["minimum_segment_size"].i = 3 + custom_op.parameter_map["max_batch_size"].i = max( + [dims[0] for dims in params.input_dims]) + custom_op.parameter_map["is_dynamic_op"].b = is_dynamic_op + custom_op.parameter_map["max_workspace_size_bytes"].i = 1 << 25 + custom_op.parameter_map["precision_mode"].s = self._ToBytes( + precision_mode) + graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_cfg) + else: + graph_options = config_pb2.GraphOptions() + + gpu_options = config_pb2.GPUOptions() + gpu_options.allow_growth = True + if trt_convert.get_linked_tensorrt_version()[0] == 3: + gpu_options.per_process_gpu_memory_fraction = 0.50 + + config = config_pb2.ConfigProto( + gpu_options=gpu_options, graph_options=graph_options) + return config + + def _RunGraph(self, params, gdef, input_data, config, num_runs=2): + """Run given graphdef multiple times.""" + assert len(params.input_names) == len(input_data) + g = ops.Graph() + with g.as_default(): + io_ops = importer.import_graph_def( + graph_def=gdef, + return_elements=params.input_names + [self.output_name], + name="") + inp = [i.outputs[0] for i in io_ops[:-1]] + assert len(inp) == len(input_data) + out = io_ops[-1].outputs[0] + with self.test_session( + graph=g, config=config, use_gpu=True, force_gpu=True) as sess: + val = None + # Defaults to 2 runs to verify result across multiple runs is same. + for _ in range(num_runs): + new_val = sess.run(out, + {inp[i]: input_data[i] for i in range(len(inp))}) + self.assertEqual(params.expected_output_dims, new_val.shape) + if val is not None: + self.assertAllEqual(val, new_val) + val = new_val + return val + + # Use real data that is representative of the inference dataset + # for calibration. For this test script it is random data. + def _RunCalibration(self, params, gdef, input_data, config): + """Run calibration on given graph.""" + return self._RunGraph(params, gdef, input_data, config, 30) + + def _GetTrtGraphDef(self, params, gdef, precision_mode, is_dynamic_op): + """Return trt converted graphdef.""" + return trt_convert.create_inference_graph( + input_graph_def=gdef, + outputs=[self.output_name], + max_batch_size=max([dims[0] for dims in params.input_dims]), + max_workspace_size_bytes=1 << 25, + precision_mode=precision_mode, + minimum_segment_size=2, + is_dynamic_op=is_dynamic_op) + + def _VerifyGraphDef(self, + params, + gdef, + precision_mode=None, + is_calibrated=None, + dynamic_engine=None): + num_engines = 0 + for n in gdef.node: + # TODO(jie): we should have coverage for failed conversion (TF fallback). + # where the conversion will fail and we shouldn't count this engine as the + # converted engines. + if n.op == "TRTEngineOp": + num_engines += 1 + self.assertNotEqual(self._ToBytes(""), n.attr["serialized_segment"].s) + self.assertNotEqual(self._ToBytes(""), n.attr["segment_funcdef_name"].s) + self.assertEqual( + self._ToBytes(precision_mode), n.attr["precision_mode"].s) + self.assertEqual(not dynamic_engine, n.attr["static_engine"].b) + if _IsQuantizationMode(precision_mode) and is_calibrated: + self.assertNotEqual(self._ToBytes(""), n.attr["calibration_data"].s) + else: + self.assertEqual(self._ToBytes(""), n.attr["calibration_data"].s) + if precision_mode is None: # This means gdef is the original GraphDef. + self.assertEqual(0, num_engines) + else: + self.assertEqual(num_engines, params.num_expected_engines) + + def RunTest(self, params, use_optimizer, precision_mode, + dynamic_infer_engine, dynamic_calib_engine): + assert precision_mode in PRECISION_MODES + input_data = [np.random.random_sample(dims) for dims in params.input_dims] + input_gdef = params.gdef + self._VerifyGraphDef(params, input_gdef) + + # Get reference result without running trt. + config_no_trt = self._GetConfigProto(params, False) + logging.info("Running original graph w/o trt, config:\n%s", + str(config_no_trt)) + ref_result = self._RunGraph(params, input_gdef, input_data, config_no_trt) + + # Run calibration if necessary. + if _IsQuantizationMode(precision_mode): + + calib_config = self._GetConfigProto(params, use_optimizer, precision_mode, + dynamic_calib_engine) + logging.info("Running calibration graph, config:\n%s", str(calib_config)) + if use_optimizer: + self.assertTrue(False) + # TODO(aaroey): uncomment this and get infer_gdef when this mode is + # supported. + # result = self._RunCalibration(params, input_gdef, input_data, + # calib_config) + else: + calib_gdef = self._GetTrtGraphDef(params, input_gdef, precision_mode, + dynamic_calib_engine) + self._VerifyGraphDef(params, calib_gdef, precision_mode, False, + dynamic_calib_engine) + result = self._RunCalibration(params, calib_gdef, input_data, + calib_config) + infer_gdef = trt_convert.calib_graph_to_infer_graph(calib_gdef) + self._VerifyGraphDef(params, infer_gdef, precision_mode, True, + dynamic_calib_engine) + + self.assertAllClose( + ref_result, + result, + atol=params.allclose_atol, + rtol=params.allclose_rtol) + else: + infer_gdef = input_gdef + + # Run inference. + infer_config = self._GetConfigProto(params, use_optimizer, precision_mode, + dynamic_infer_engine) + logging.info("Running final inference graph, config:\n%s", + str(infer_config)) + if use_optimizer: + result = self._RunGraph(params, infer_gdef, input_data, infer_config) + else: + trt_infer_gdef = self._GetTrtGraphDef(params, infer_gdef, precision_mode, + dynamic_infer_engine) + self._VerifyGraphDef(params, trt_infer_gdef, precision_mode, True, + dynamic_infer_engine) + result = self._RunGraph(params, trt_infer_gdef, input_data, infer_config) + + self.assertAllClose( + ref_result, + result, + atol=params.allclose_atol, + rtol=params.allclose_rtol) + + def testIdempotence(self): + # Test that applying tensorrt optimizer or offline conversion tools multiple + # times to the same graph will result in same graph. + # + # TODO(aaroey): currently the conversion is not deterministic, this is + # mainly because during tensorflow::ConvertGraphDefToGraph(), the graph uses + # EdgeSet which use a map keyed by Edge*, so the order of input/output edges + # of a node is nondeterministic, thus the order for segmenter to contract + # edges is nondeterministic. Need to evaluate whether we should fix this. + pass + + +def _AddTests(test_class): + """Adds test methods to TfTrtIntegrationTestBase.""" + + def _GetTest(use_optimizer, precision_mode, dynamic_infer_engine, + dynamic_calib_engine): + """Gets a single test method based on the parameters.""" + + def _Test(self): + params = self.GetParams() + logging.info( + "Running test with parameters: use_optimizer=%s, precision_mode=%s, " + "dynamic_infer_engine=%s, dynamic_calib_engine=%s", use_optimizer, + precision_mode, dynamic_infer_engine, dynamic_calib_engine) + self.RunTest(params, use_optimizer, precision_mode, dynamic_infer_engine, + dynamic_calib_engine) + + return _Test + + use_optimizer_options = [False, True] + dynamic_infer_engine_options = [False, True] + dynamic_calib_engine_options = [False, True] + for (use_optimizer, precision_mode, + dynamic_infer_engine, dynamic_calib_engine) in itertools.product( + use_optimizer_options, PRECISION_MODES, dynamic_infer_engine_options, + dynamic_calib_engine_options): + if _IsQuantizationMode(precision_mode): + if not dynamic_calib_engine and dynamic_infer_engine: + # TODO(aaroey): test this case, the conversion from static calibration + # engine to dynamic inference engine should be a noop. + continue + if use_optimizer: + # TODO(aaroey): if use_optimizer is True we need to get the inference + # graphdef using custom python wrapper class, which is not currently + # supported yet. + continue + if not dynamic_calib_engine: + # TODO(aaroey): construction of static calibration engine is not + # supported yet. + continue + if dynamic_calib_engine and not dynamic_infer_engine: + # TODO(aaroey): construction of static inference engine using dynamic + # calibration engine is not supported yet. + continue + else: # In non int8 mode. + if dynamic_calib_engine: + # dynamic_calib_engine doesn't affect non-int8 modes, so just let + # related tests run once on dynamic_calib_engine=False. + continue + + conversion = "OptimizerConversion" if use_optimizer else "ToolConversion" + infer_engine_type = ("DynamicInferEngine" + if dynamic_infer_engine else "StaticInferEngine") + calib_engine_type = "" + if precision_mode == "INT8": + calib_engine_type = ("DynamicCalibEngine" + if dynamic_calib_engine else "StaticCalibEngine") + test_name = "%s_%s_%s%s" % (conversion, precision_mode, infer_engine_type, + ("_" + calib_engine_type) + if len(calib_engine_type) else "") + setattr( + test_class, "testTfTRT_" + test_name, + _GetTest(use_optimizer, precision_mode, dynamic_infer_engine, + dynamic_calib_engine)) + + +if trt_convert.is_tensorrt_enabled(): + _AddTests(TfTrtIntegrationTestBase) diff --git a/tensorflow/contrib/tensorrt/test/unary_test.py b/tensorflow/contrib/tensorrt/test/unary_test.py new file mode 100644 index 0000000000000000000000000000000000000000..b9e977cf67b4e94282c10313477276b04ea828aa --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/unary_test.py @@ -0,0 +1,110 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_array_ops +from tensorflow.python.ops import gen_math_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import test + + +class UnaryTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Test for unary operations in TF-TRT.""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [12, 5, 8, 1, 1, 12] + input2_name = "input_2" + input2_dims = [12, 5, 8, 1, 12, 1, 1] + g = ops.Graph() + with g.as_default(): + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) + q = math_ops.abs(x) + q = q + 1.0 + q = gen_math_ops.exp(q) + q = gen_math_ops.log(q) + q = array_ops.squeeze(q, axis=-2) + q = math_ops.abs(q) + q = q + 2.2 + q = gen_math_ops.sqrt(q) + q = gen_math_ops.rsqrt(q) + q = math_ops.negative(q) + q = array_ops.squeeze(q, axis=3) + q = math_ops.abs(q) + q = q + 3.0 + a = gen_math_ops.reciprocal(q) + + x = constant_op.constant(np.random.randn(5, 8, 12), dtype=dtype) + q = math_ops.abs(x) + q = q + 2.0 + q = gen_math_ops.exp(q) + q = gen_math_ops.log(q) + q = math_ops.abs(q) + q = q + 2.1 + q = gen_math_ops.sqrt(q) + q = gen_math_ops.rsqrt(q) + q = math_ops.negative(q) + q = math_ops.abs(q) + q = q + 4.0 + b = gen_math_ops.reciprocal(q) + + # TODO(jie): this one will break, broadcasting on batch. + x = array_ops.placeholder( + dtype=dtype, shape=input2_dims, name=input2_name) + q = math_ops.abs(x) + q = q + 5.0 + q = gen_math_ops.exp(q) + q = array_ops.squeeze(q, axis=[-1, -2, 3]) + q = gen_math_ops.log(q) + q = math_ops.abs(q) + q = q + 5.1 + q = gen_array_ops.reshape(q, [12, 5, 1, 1, 8, 1, 12]) + q = array_ops.squeeze(q, axis=[5, 2, 3]) + q = gen_math_ops.sqrt(q) + q = math_ops.abs(q) + q = q + 5.2 + q = gen_math_ops.rsqrt(q) + q = math_ops.negative(q) + q = math_ops.abs(q) + q = q + 5.3 + c = gen_math_ops.reciprocal(q) + + q = a * b + q = q / c + array_ops.squeeze(q, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name, input2_name], + input_dims=[input_dims, input2_dims], + num_expected_engines=5, + expected_output_dims=(12, 5, 8, 12), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py b/tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py new file mode 100644 index 0000000000000000000000000000000000000000..2b134c3bce2b36e4530f8f8e58cce8d07c9bb13b --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py @@ -0,0 +1,82 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import nn +from tensorflow.python.ops import nn_impl +from tensorflow.python.ops import nn_ops +from tensorflow.python.platform import test + + +class VGGBlockNCHWTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Single vgg layer in NCHW unit tests in TF-TRT.""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [5, 2, 8, 8] + g = ops.Graph() + with g.as_default(): + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) + x, _, _ = nn_impl.fused_batch_norm( + x, + np.random.randn(2).astype(np.float32), + np.random.randn(2).astype(np.float32), + mean=np.random.randn(2).astype(np.float32), + variance=np.random.randn(2).astype(np.float32), + data_format="NCHW", + is_training=False) + e = constant_op.constant( + np.random.randn(1, 1, 2, 6), name="weights", dtype=dtype) + conv = nn.conv2d( + input=x, + filter=e, + data_format="NCHW", + strides=[1, 1, 2, 2], + padding="SAME", + name="conv") + b = constant_op.constant(np.random.randn(6), name="bias", dtype=dtype) + t = nn.bias_add(conv, b, data_format="NCHW", name="biasAdd") + relu = nn.relu(t, "relu") + idty = array_ops.identity(relu, "ID") + v = nn_ops.max_pool( + idty, [1, 1, 2, 2], [1, 1, 2, 2], + "VALID", + data_format="NCHW", + name="max_pool") + array_ops.squeeze(v, name="output") + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=1, + expected_output_dims=(5, 6, 2, 2), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/vgg_block_test.py b/tensorflow/contrib/tensorrt/test/vgg_block_test.py new file mode 100644 index 0000000000000000000000000000000000000000..bec2f23eff3b1799d70519462f42c326d17924c1 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/vgg_block_test.py @@ -0,0 +1,73 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import nn +from tensorflow.python.ops import nn_impl +from tensorflow.python.ops import nn_ops +from tensorflow.python.platform import test + + +class VGGBlockTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Single vgg layer test in TF-TRT conversion.""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [5, 8, 8, 2] + g = ops.Graph() + with g.as_default(): + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) + x, _, _ = nn_impl.fused_batch_norm( + x, + np.random.randn(2).astype(np.float32), + np.random.randn(2).astype(np.float32), + mean=np.random.randn(2).astype(np.float32), + variance=np.random.randn(2).astype(np.float32), + is_training=False) + e = constant_op.constant( + np.random.randn(1, 1, 2, 6), name="weights", dtype=dtype) + conv = nn.conv2d( + input=x, filter=e, strides=[1, 2, 2, 1], padding="SAME", name="conv") + b = constant_op.constant(np.random.randn(6), name="bias", dtype=dtype) + t = nn.bias_add(conv, b, name="biasAdd") + relu = nn.relu(t, "relu") + idty = array_ops.identity(relu, "ID") + v = nn_ops.max_pool( + idty, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") + array_ops.squeeze(v, name="output") + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=1, + expected_output_dims=(5, 2, 2, 6), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/trt_conversion.i b/tensorflow/contrib/tensorrt/trt_conversion.i index d6628cd1eb69e46b188de613dee803a2e0dd07d4..422740fdf6ec381dc6f6c01e736ce8b3398586ce 100644 --- a/tensorflow/contrib/tensorrt/trt_conversion.i +++ b/tensorflow/contrib/tensorrt/trt_conversion.i @@ -100,6 +100,7 @@ _LIST_OUTPUT_TYPEMAP(int, PyLong_FromLong); #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/util/stat_summarizer.h" #include "tensorflow/contrib/tensorrt/convert/convert_graph.h" +#include "tensorflow/contrib/tensorrt/convert/utils.h" %} %ignoreall @@ -108,6 +109,7 @@ _LIST_OUTPUT_TYPEMAP(int, PyLong_FromLong); %unignore calib_convert; %unignore get_linked_tensorrt_version; %unignore get_loaded_tensorrt_version; +%unignore is_tensorrt_enabled; %{ @@ -140,7 +142,7 @@ std::pair trt_convert( return std::pair{out_status, ""}; } - if(precision_mode < 0 || precision_mode > 2){ + if (precision_mode < 0 || precision_mode > 2) { out_status = "InvalidArgument;Invalid precision_mode"; return std::pair{out_status, ""}; } @@ -232,7 +234,8 @@ version_struct get_linked_tensorrt_version() { #endif // GOOGLE_CUDA && GOOGLE_TENSORRT return s; } -version_struct get_loaded_tensorrt_version(){ + +version_struct get_loaded_tensorrt_version() { // Return the version from the loaded library. version_struct s; #if GOOGLE_CUDA && GOOGLE_TENSORRT @@ -244,6 +247,10 @@ version_struct get_loaded_tensorrt_version(){ return s; } +bool is_tensorrt_enabled() { + return tensorflow::tensorrt::IsGoogleTensorRTEnabled(); +} + %} std::pair calib_convert(string graph_def_string, bool is_dyn_op); @@ -258,5 +265,6 @@ std::pair trt_convert(string graph_def_string, std::vector cached_engine_batches); version_struct get_linked_tensorrt_version(); version_struct get_loaded_tensorrt_version(); +bool is_tensorrt_enabled(); %unignoreall diff --git a/tensorflow/contrib/timeseries/examples/multivariate.py b/tensorflow/contrib/timeseries/examples/multivariate.py index ed799542fd50cd150f13533c5f33bd67ed09fff6..e81cb18ad7b928a6fd2a748ea6b258c49cf722ae 100644 --- a/tensorflow/contrib/timeseries/examples/multivariate.py +++ b/tensorflow/contrib/timeseries/examples/multivariate.py @@ -80,8 +80,8 @@ def multivariate_train_and_sample( session=session, steps=1)) next_sample = numpy.random.multivariate_normal( # Squeeze out the batch and series length dimensions (both 1). - mean=numpy.squeeze(current_prediction["mean"], axis=[0, 1]), - cov=numpy.squeeze(current_prediction["covariance"], axis=[0, 1])) + mean=numpy.squeeze(current_prediction["mean"], axis=(0, 1)), + cov=numpy.squeeze(current_prediction["covariance"], axis=(0, 1))) # Update model state so that future predictions are conditional on the # value we just sampled. filtering_features = { diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD index ec9a7861e7f7ef48344f9b60bda40173c2b31f6e..7020989d6895fd6322db45cda6f7dd99d417d937 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/BUILD +++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD @@ -157,6 +157,7 @@ py_library( py_test( name = "head_test", + size = "large", srcs = [ "head_test.py", ], diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD index c08f088be78d1cb1caa18a805844541b3d573fad..5a7825f29a29585af87c113b2475fb9a1d795d75 100644 --- a/tensorflow/contrib/tpu/BUILD +++ b/tensorflow/contrib/tpu/BUILD @@ -15,8 +15,8 @@ package( default_visibility = [ "//cloud/vmm/testing/tests/tpu:__subpackages__", "//learning/brain:__subpackages__", + "//learning/deepmind:__subpackages__", "//tensorflow:__subpackages__", - "//third_party/cloud_tpu:__subpackages__", ], ) @@ -37,6 +37,7 @@ cc_library( py_library( name = "tpu_estimator", srcs = [ + "python/tpu/error_handling.py", "python/tpu/tpu_config.py", "python/tpu/tpu_context.py", "python/tpu/tpu_estimator.py", @@ -160,13 +161,56 @@ py_library( ], ) +py_library( + name = "keras_support", + srcs = [ + "python/tpu/keras_support.py", + ], + srcs_version = "PY2AND3", + visibility = [ + "//cloud/vmm/testing/tests/tpu:__subpackages__", + "//learning/brain:__subpackages__", + # TODO(b/111651964): Clean special visibility for keras_support. + # + # Note: If you are an end user, please do not add your project to this + # visibility. This feature is experimental, and will be made public + # when ready. + "//third_party/cloud_tpu/models/keras:__subpackages__", + "//tensorflow:__subpackages__", + ], + deps = [ + ":tpu_lib", + ":tpu_py", + "//tensorflow/contrib/cluster_resolver:tpu_cluster_resolver_py", + "//tensorflow/contrib/distribute/python:tpu_strategy", + "//tensorflow/contrib/framework:framework_py", + "//tensorflow/contrib/tpu/proto:compilation_result_proto_py", + "//tensorflow/core:protos_all_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:linalg_ops", + "//tensorflow/python:math_ops", + "//tensorflow/python:platform", + "//tensorflow/python:random_ops", + "//tensorflow/python:session", + "//tensorflow/python:tensor_spec", + "//tensorflow/python:variable_scope", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/estimator:model_fn", + "//tensorflow/python/keras:backend", + "//tensorflow/python/keras:engine", + "//tensorflow/python/keras:layers", + "//third_party/py/numpy", + ], +) + py_library( name = "tpu_lib", srcs = [ "python/tpu/__init__.py", "python/tpu/bfloat16.py", "python/tpu/device_assignment.py", - "python/tpu/keras_support.py", "python/tpu/session_support.py", "python/tpu/topology.py", "python/tpu/tpu.py", diff --git a/tensorflow/contrib/tpu/__init__.py b/tensorflow/contrib/tpu/__init__.py index dc9066855990f372c28dc481959117daa4c2da97..d5484e9032fb874e9f608ec398be4cd03b2aaf32 100644 --- a/tensorflow/contrib/tpu/__init__.py +++ b/tensorflow/contrib/tpu/__init__.py @@ -42,9 +42,11 @@ @@TPUEstimator @@TPUEstimatorSpec +@@export_estimator_savedmodel @@RunConfig @@InputPipelineConfig @@TPUConfig +@@bfloat16_scope """ from __future__ import absolute_import diff --git a/tensorflow/contrib/tpu/profiler/tpu_profiler.proto b/tensorflow/contrib/tpu/profiler/tpu_profiler.proto index f0fca63db0bca80cdaa27e491b2a03ae2246c007..da4a95e0450a9d0c20593ca60b69f3ad467d455d 100644 --- a/tensorflow/contrib/tpu/profiler/tpu_profiler.proto +++ b/tensorflow/contrib/tpu/profiler/tpu_profiler.proto @@ -11,6 +11,9 @@ service TPUProfiler { // Starts a profiling session, blocks until it completes, and returns data. rpc Profile(ProfileRequest) returns (ProfileResponse) { } + // Collects profiling data and returns user-friendly metrics. + rpc Monitor(MonitorRequest) returns (MonitorResponse) { + } } message ProfileOptions { @@ -104,3 +107,26 @@ message ProfileResponse { // next-field: 8 } + +message MonitorRequest { + // Duration for which to profile between each update. + uint64 duration_ms = 1; + + // Indicates the level at which we want to monitor. Currently, two levels are + // supported: + // Level 1: An ultra lightweight mode that captures only some utilization + // metrics. + // Level 2: More verbose than level 1. Collects utilization metrics, device + // information, step time information, etc. Do not use this option if the TPU + // host is being very heavily used. + int32 monitoring_level = 2; + + // next-field: 3 +} + +message MonitorResponse { + // Properly formatted string data that can be directly returned back to user. + string data = 1; + + // next-field: 2 +} diff --git a/tensorflow/contrib/tpu/proto/BUILD b/tensorflow/contrib/tpu/proto/BUILD index 26016f47dfb36990fd73267c70619878ac3450e5..598b73b438cb239187a911b2d1425b434c889d8d 100644 --- a/tensorflow/contrib/tpu/proto/BUILD +++ b/tensorflow/contrib/tpu/proto/BUILD @@ -15,6 +15,16 @@ tf_proto_library( "tpu_embedding_config.proto", ], cc_api_version = 2, + protodeps = [":optimization_parameters_proto"], + visibility = ["//visibility:public"], +) + +tf_proto_library( + name = "optimization_parameters_proto", + srcs = [ + "optimization_parameters.proto", + ], + cc_api_version = 2, visibility = ["//visibility:public"], ) diff --git a/tensorflow/contrib/tpu/proto/optimization_parameters.proto b/tensorflow/contrib/tpu/proto/optimization_parameters.proto new file mode 100644 index 0000000000000000000000000000000000000000..2cc17d6d928370afbb0e3b1e89252f7a687c27d3 --- /dev/null +++ b/tensorflow/contrib/tpu/proto/optimization_parameters.proto @@ -0,0 +1,164 @@ +syntax = "proto3"; + +package tensorflow.tpu; + +import "google/protobuf/wrappers.proto"; + +message ClippingLimits { + google.protobuf.FloatValue lower = 1; // -inf if not set + google.protobuf.FloatValue upper = 2; // +inf if not set +} + +// Get the learning rate from a source that can change +// dynamically. +message DynamicLearningRate { +} + +// Source of learning rate to use. +message LearningRate { + oneof learning_rate { + float constant = 1; + DynamicLearningRate dynamic = 2; + } +} + +message AdagradParameters { + float initial_accumulator = 1; +} + +message StochasticGradientDescentParameters { +} + +message FtrlParameters { + float l1 = 1; + float l2 = 2; + float lr_power = 3; + float initial_accum = 4; + float initial_linear = 5; +} + +// The Adam optimizer does not implement hyper-parameter update; use the dynamic +// learning rate feature instead, setting the learning rate to: +// user learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t) +// Here, t is the current timestep. +// https://github.com/tensorflow/tensorflow/blob/ab51450c817674c8ff08a7ae4f8ac50cdc4bed8b/tensorflow/python/training/adam.py#L54 +message AdamParameters { + float beta1 = 3; + float beta2 = 4; + float epsilon = 5; + float initial_m = 6; + float initial_v = 7; +} + +message MomentumParameters { + float momentum = 1; + bool use_nesterov = 2; + float initial_accum = 3; +} + +message RmsPropParameters { + float rho = 1; + float momentum = 2; + float epsilon = 3; + float initial_ms = 4; + float initial_mom = 5; +} + +message CenteredRmsPropParameters { + float rho = 1; + float momentum = 2; + float epsilon = 3; + float initial_ms = 4; + float initial_mom = 5; + float initial_mg = 6; +} + +message MdlAdagradLightParameters { + float l2 = 1; + float lr_power = 2; + float min_servable_mdl_benefit = 3; + float mdl_mix_in_margin = 4; + float mdl_benefit_rampup_coeff = 5; + float mdl_min_weight = 6; + float benefit_revisit_scale = 7; + float max_event_benefit = 8; + float max_total_benefit = 9; + float mdl_hard_limit = 10; + bool hard_limit_min_benefit = 11; + bool mdl_regularize = 12; + float initial_accumulator = 13; + float initial_weight = 14; + float initial_benefit = 15; +} + +message AdadeltaParameters { + float rho = 1; + float epsilon = 2; + float initial_accumulator = 3; + float initial_update = 4; +} + +message ProximalAdagradParameters { + float l1 = 1; + float l2 = 2; + float initial_accumulator = 3; +} + +message OptimizationParameters { + // Learning rate used for updating the embedding layer parameters. + LearningRate learning_rate = 13; + reserved 1; // Old learning rate tag. + + // Limits to which to clip the weight values after the backward pass; not + // present means no limits are applied. + ClippingLimits clipping_limits = 2; + + // Limits to which to clip the backward pass gradient before using it for + // updates; not present means no limits are applied. + ClippingLimits gradient_clipping_limits = 7; + + // Whether to use gradient accumulation (do two passes over the input + // gradients: one to accumulate them into a temporary array and another to + // apply them using the actual optimization algorithm). + bool use_gradient_accumulation = 15; + + // Optimization algorithm parameters; which field is selected determines which + // algorithm to use. + oneof parameters { + AdagradParameters adagrad = 3; + StochasticGradientDescentParameters stochastic_gradient_descent = 4; + FtrlParameters ftrl = 5; + AdamParameters adam = 6; + MomentumParameters momentum = 8; + RmsPropParameters rms_prop = 9; + CenteredRmsPropParameters centered_rms_prop = 10; + MdlAdagradLightParameters mdl_adagrad_light = 11; + AdadeltaParameters adadelta = 12; + ProximalAdagradParameters proximal_adagrad = 14; + } +} + +// Specification of an optimization algorithm's state variables (both the main +// value vector and any extra accumulators, etc.). +message StateVariableSpecification { + // Parameter name for the state variable. + string name = 1; + + // A normal state variable that should be saved and restored in checkpoints + // and used as an input or output to non-debug TensorFlow ops. + message UserDefined { + } + + // A state variable that should be filled with a constant and normally hidden + // from users (used for intermediate gradients being accumulated, for + // example). + message FillWithConstant { + double initial_value = 1; + } + + // Usage type of this state variable. + oneof usage { + UserDefined user_defined = 2; + FillWithConstant fill_with_constant = 3; + } +} diff --git a/tensorflow/contrib/tpu/proto/tpu_embedding_config.proto b/tensorflow/contrib/tpu/proto/tpu_embedding_config.proto index b0ec968d3a401f1b80ed1bf6fd7a83a69c068fe2..3476cc89534efb7fe05640935d1387d02737f240 100644 --- a/tensorflow/contrib/tpu/proto/tpu_embedding_config.proto +++ b/tensorflow/contrib/tpu/proto/tpu_embedding_config.proto @@ -2,6 +2,8 @@ syntax = "proto3"; package tensorflow.tpu; +import "tensorflow/contrib/tpu/proto/optimization_parameters.proto"; + // The TPUEmbeddingConfiguration contains specification of TPU Embedding lookups // and gradient updates separate from the TF Graph. message TPUEmbeddingConfiguration { @@ -30,15 +32,6 @@ message TPUEmbeddingConfiguration { // The number of training examples per TensorNode. int32 batch_size = 4; - message GradientDescentOptimizer { - float learning_rate = 1; - } - - message AdagradOptimizer { - float learning_rate = 1; - float initial_accumulator = 2; - } - // Each Embedding message TPUEmbeddingTable { // Name of the embedding table. This will be used to name Variables in the @@ -66,10 +59,7 @@ message TPUEmbeddingConfiguration { // separately to the convolutional or recurrent network. int32 num_features = 5; - oneof optimizer { - GradientDescentOptimizer gradient_descent = 6; - AdagradOptimizer adagrad = 7; - } + OptimizationParameters optimization_parameters = 6; } repeated TPUEmbeddingTable table_config = 5; diff --git a/tensorflow/contrib/tpu/python/tpu/error_handling.py b/tensorflow/contrib/tpu/python/tpu/error_handling.py new file mode 100644 index 0000000000000000000000000000000000000000..52e1ea42370d653d1de7c12eee4b456ec7ce921c --- /dev/null +++ b/tensorflow/contrib/tpu/python/tpu/error_handling.py @@ -0,0 +1,132 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# =================================================================== +"""ErrorRendezvous handler for collecting errors from multiple threads.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import contextlib +import sys +import threading +import time + +import six + +from tensorflow.python.framework import errors +from tensorflow.python.platform import tf_logging as logging + +_UNINTERESTING_ERRORS = (errors.CancelledError,) + + +class ErrorRendezvous(object): + """Resolve errors from multiple threads during TPU execution. + + TPU errors can occur on the infeed or outfeed threads as well as the main + training thread. + + Depending on which thread "wins" and receives the session error first, we may + end up showing users a confusing and non-actionable error message (session + cancelled) instead of a root cause (e.g. a bad filename). + + The rendezvous object provides a location to capture these errors until all + threads terminate. At that point we can choose the most informative error + to report. + """ + + def __init__(self, num_sources): + # string -> (message, traceback) + self._errors = {} + self._num_sources = num_sources + self._session_cancel_timer = None + + def record_error(self, source, exc_info, session=None): + """Report an exception from the given source. + + If a session is passed, a timer will be registered to close it after a few + seconds. This is necessary to ensure the main training loop does not hang + if an infeed/oufeed error occurs. We sleep a few seconds to allow a more + interesting error from another thread to propagate. + + Args: + source: string, source of the error + exc_info: Output from `sys.exc_info` (type, value, traceback) + session: Session to close after delay. + """ + _, value, _ = exc_info + self._errors[source] = exc_info + logging.info('Error recorded from %s: %s', source, value) + + if session is not None and self._session_cancel_timer is None: + + def _cancel_session(): + time.sleep(5) + try: + session.close() + except: # pylint: disable=bare-except + pass + + self._session_cancel_timer = threading.Thread(target=_cancel_session,) + self._session_cancel_timer.daemon = True + self._session_cancel_timer.start() + + def record_done(self, source): + """Mark execution source `source` as done. + + If an error was originally reported from `source` it is left intact. + + Args: + source: `str`, source being recorded + """ + logging.info('%s marked as finished', source) + if source not in self._errors: + self._errors[source] = None + + @contextlib.contextmanager + def catch_errors(self, source, session=None): + """Context manager to report any errors within a block.""" + try: + yield + except Exception: # pylint: disable=broad-except + self.record_error(source, sys.exc_info(), session) + + def raise_errors(self, timeout_sec=0): + """Wait for up to `timeout` seconds for all error sources to finish. + + Preferentially raise "interesting" errors (errors not in the + _UNINTERESTING_ERRORS) set. + + Args: + timeout_sec: Seconds to wait for other error sources. + """ + for _ in range(timeout_sec): + if len(self._errors) == self._num_sources: + break + time.sleep(1) + + kept_errors = [(k, v) for (k, v) in self._errors.items() if v is not None] + + # First check for any interesting errors, then fall back on the session + # cancelled errors etc. + for k, (typ, value, traceback) in kept_errors: + if isinstance(value, _UNINTERESTING_ERRORS): + continue + else: + logging.warn('Reraising captured error') + six.reraise(typ, value, traceback) + + for k, (typ, value, traceback) in kept_errors: + logging.warn('Reraising captured error') + six.reraise(typ, value, traceback) diff --git a/tensorflow/contrib/tpu/python/tpu/keras_support.py b/tensorflow/contrib/tpu/python/tpu/keras_support.py index 754154438235f4c5e9e8db996acc8d843ab18431..81798ee42313cb9e2232a4796f56d4d16068b82f 100644 --- a/tensorflow/contrib/tpu/python/tpu/keras_support.py +++ b/tensorflow/contrib/tpu/python/tpu/keras_support.py @@ -45,6 +45,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import abc import collections import contextlib import re @@ -59,11 +60,15 @@ from tensorflow.contrib.framework.python.framework import experimental from tensorflow.contrib.tpu.proto import compilation_result_pb2 as tpu_compilation_result from tensorflow.contrib.tpu.python.ops import tpu_ops from tensorflow.contrib.tpu.python.tpu import tpu +from tensorflow.contrib.tpu.python.tpu import tpu_function from tensorflow.contrib.tpu.python.tpu import tpu_optimizer from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session as tf_session +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_spec from tensorflow.python.keras import backend as K from tensorflow.python.keras import models @@ -71,7 +76,9 @@ from tensorflow.python.keras import optimizers as keras_optimizers from tensorflow.python.keras.engine import base_layer from tensorflow.python.keras.layers import embeddings from tensorflow.python.ops import array_ops +from tensorflow.python.ops import gen_linalg_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_ops from tensorflow.python.ops import variable_scope from tensorflow.python.platform import tf_logging as logging @@ -99,6 +106,45 @@ class TPUEmbedding(embeddings.Embedding): return math_ops.tensordot(inputs, self.embeddings, 1) +class KerasCrossShardOptimizer(keras_optimizers.Optimizer): + """An optimizer that averages gradients across TPU shards.""" + + def __init__(self, opt, name='KerasCrossShardOptimizer'): + """Construct a new cross-shard optimizer. + + Args: + opt: An existing `Optimizer` to encapsulate. + name: Optional name prefix for the operations created when applying + gradients. Defaults to "KerasCrossShardOptimizer". + + Raises: + ValueError: If reduction is not a valid cross-shard reduction. + """ + super(KerasCrossShardOptimizer, self).__init__() + self._name = name + self._opt = opt + + def get_updates(self, loss, params): + logging.info('Get updates: %s', loss) + self._opt.get_gradients = self.get_gradients + return self._opt.get_updates(loss, params) + + def get_gradients(self, loss, params): + num_shards = tpu_function.get_tpu_context().number_of_shards + grads = super(KerasCrossShardOptimizer, self).get_gradients(loss, params) + return [tpu_ops.cross_replica_sum(grad) / num_shards for grad in grads] + + def set_weights(self, weights): + self._opt.set_weights() + + def get_weights(self): + return self._opt.get_weights() + + @property + def lr(self): + return self._opt.lr + + class TPUModelOp( collections.namedtuple('TPUModelOp', [ 'compile_op', 'execute_op', 'infeed_tensors', 'infeed_op', 'outfeed_op' @@ -113,8 +159,13 @@ def _valid_name(tensor_name): def _replicated_optimizer(opt): """Wrap the optimizer `opt` with CrossShardOptimizer if applicable.""" - return keras_optimizers.TFOptimizer( - optimizer=tpu_optimizer.CrossShardOptimizer(opt.optimizer)) + if tpu_function.get_tpu_context().number_of_shards == 1: + return opt + + if isinstance(opt, keras_optimizers.TFOptimizer): + return tpu_optimizer.CrossShardOptimizer(opt.optimizer) + else: + return KerasCrossShardOptimizer(opt) class TPURewriteContext(object): @@ -154,7 +205,6 @@ class TPURewriteContext(object): caller_obj = caller_frame.f_locals.get('self') if (caller_obj is not None and isinstance(caller_obj, base_layer.Layer) and name is not None): - logging.info('Intercepted name_scope: %s', caller_obj) return variable_scope.variable_scope( name, default_name, values, reuse=variable_scope.AUTO_REUSE) @@ -163,8 +213,51 @@ class TPURewriteContext(object): self._default_placeholder = array_ops.placeholder self._default_name_scope = ops.name_scope self._default_make_variable = base_layer.make_variable + self._default_random_normal = random_ops.random_normal + self._default_qr = gen_linalg_ops.qr array_ops.placeholder = _placeholder + + # Replace random_ops.random_normal with a dummy function because + # `random_normal` isn't yet implemented on the TPU. Because these + # initialized values are overwritten by the CPU values, this is okay. + def random_normal(shape, + mean=0.0, + stddev=1.0, + dtype=dtypes.float32, + seed=None, + name=None): + del mean + del stddev + del seed + return array_ops.zeros(shape, dtype=dtype, name=name) + + random_ops.random_normal = random_normal + + # Replace gen_linalg_ops.qr because QR decomposition is not yet implemented. + # TODO(saeta): Remove qr override once we confirm the qr implementation is + # ok. + # pylint: disable=redefined-builtin + def qr(input, full_matrices=False, name=None): + """Dummy implementation of qr decomposition.""" + del full_matrices # TODO(saeta): Properly handle the full matrix case. + input_shape = input.shape + if len(input_shape) < 2: + raise ValueError('Invalid shape passed to qr: %s' % input_shape) + p = min(input_shape[-1], input_shape[-2]) + if len(input_shape) == 2: + q = array_ops.zeros((p, p), name=name) + r = array_ops.zeros(input_shape, name=name) + return (r, q) + elif len(input_shape) == 3: + n = input_shape[0] + q = array_ops.zeros((n, p, p), name=name) + r = array_ops.zeros(input_shape, name=name) + return (r, q) + else: + raise ValueError('Invalid shape passed to qr: %s' % input_shape) + gen_linalg_ops.qr = qr + ops.name_scope = _name_scope base_layer.make_variable = variable_scope.get_variable logging.info('Overriding default placeholder.') @@ -174,6 +267,334 @@ class TPURewriteContext(object): array_ops.placeholder = self._default_placeholder ops.name_scope = self._default_name_scope base_layer.make_variable = self._default_make_variable + random_ops.random_normal = self._default_random_normal + gen_linalg_ops.qr = self._default_qr + + +class SizedInfeed(collections.namedtuple('SizedInfeed', + ['sharded_infeed_tensors', + 'infeed_ops'])): + """Represents an instantiation of the infeed ops for a concrete input shape. + + sharded_infeed_tensors: A data structure of Tensors used to represent the + placeholder tensors that must be fed when using feed_dicts. + + infeed_ops: the set of ops that will be run to drive infeed for a single step. + """ + pass + + +class TPUInfeedInstance(object): + """TPUInfeedInstance represents the logic to manage feeding in a single step. + + See the comments on the `TPUInfeedManager` for a description for how infeed + is managed. + """ + + @abc.abstractmethod + def make_input_specs(self, input_tensors): + """Constructs the infeed_specs for the given Infeed instance. + + Args: + input_tensors: The inputs to the model. + + Returns: + A list of + """ + pass + + def make_feed_dict(self, tpu_model_op): + """Constructs a feed_dict for this instance, given the tpu_model_op. + + Args: + tpu_model_op: A `TPUModelOp` representing the TPU Model for this + instance's input spec. + + Returns: + A dictionary to use as the feed_dict of a `session.run` call. + """ + pass + + +class TPUInfeedManager(object): + """TPUInfeedManager manages the data infeeding of data to a TPU computation. + + Because there are multiple data sources (e.g. in-memory NumPy arrays, + `tf.data.Dataset`s), we abstract the different logic behind a single + interface: the `TPUInfeedManager`. + + (1) A `TPUFunction` is called with a set of inputs. Based on the inputs, + `TPUFunction` retrieves the corresponding `TPUInfeedManager` (or constructs a + new one if required). + + (2) The `TPUFunction` calls `make_infeed_instance` on the `TPUInfeedManager` + which returns a `TPUInfeedInstance`. + + (3) The `TPUFunction` checks in the shape cache for a pre-compiled instance of + the model based on the returned `input_specs` from `TPUInfeedInstance`. + + (4) [Optional.] If the model has not already been instantiated for the given + input spec, the `TPUFunction` compiles the model for the input spec (using the + `TPUInfeedManager`). + + (5) The `TPUInfeedInstance` constructs the session.run's feed_dict given the + compiled model instance corresponding to its shape. + """ + + @abc.abstractmethod + def make_infeed_instance(self, inputs): + """Given a single step's input, construct a `TPUInfeedInstance`. + + Args: + inputs: The inputs to a given step. + + Returns: + A subclass of `TPUInfeedInstance`. + """ + pass + + @abc.abstractmethod + def build_infeed_from_input_specs(self, input_specs, execution_mode): + """For a given input specification (size, type), construct the infeed ops. + + This is called only once for a given input specification and builds the + graph ops. It does not have a pointer to the actual infeed data. + + Args: + input_specs: TODO(saeta): Document me! + execution_mode: TODO(saeta): Document me! + + Returns: + A `SizedInfeed` instance. + """ + pass + + +class TPUNumpyInfeedManager(TPUInfeedManager): + """TPU Infeed manager for Numpy inputs.""" + + class NumpyInfeedInstance(TPUInfeedInstance): + """Infeed instance for Numpy inputs.""" + + def __init__(self, sharded_inputs): + self._sharded_inputs = sharded_inputs + + def make_input_specs(self, input_tensors): + # Compute an input specification (used to generate infeed enqueue and + # dequeue operations). We use the shape from our input array and the + # dtype from our model. A user may pass in a float64 for a float32 + # input: for model compatibility we still must generate a float32 infeed. + input_specs = [] + # We use the shape and dtype from the first shard to compute the input + # metadata (`input_specs`); all replicas have the same type and shape. + for tensor, ary in zip(input_tensors, self._sharded_inputs[0]): + input_specs.append( + tensor_spec.TensorSpec(ary.shape, tensor.dtype, + _valid_name(tensor.name))) + + return input_specs + + def make_feed_dict(self, tpu_model_op): + infeed_dict = {} + for infeed_tensors, inputs in zip(tpu_model_op.infeed_tensors, + self._sharded_inputs): + for tensor, value in zip(infeed_tensors, inputs): + infeed_dict[tensor] = value + return infeed_dict + + def __init__(self, distribution_strategy): + self._strategy = distribution_strategy + + def _split_tensors(self, inputs): + """Split input data across shards. + + Each input is sliced along the batch axis. + + Args: + inputs: List of Numpy arrays to run on the TPU. + + Returns: + List of lists containing the input to feed to each TPU shard. + """ + if self._strategy.num_towers == 1: + return [inputs] + + batch_size = inputs[0].shape[0] + assert batch_size % self._strategy.num_towers == 0, ( + 'batch_size must be divisible by strategy.num_towers (%s vs %s)' % + (batch_size, self._strategy.num_towers)) + shard_size = batch_size // self._strategy.num_towers + input_list = [] + for index in range(self._strategy.num_towers): + shard_inputs = [ + x[index * shard_size:(index + 1) * shard_size] for x in inputs + ] + input_list.append(shard_inputs) + return input_list + + def make_infeed_instance(self, inputs): + sharded_inputs = self._split_tensors(inputs) + return self.NumpyInfeedInstance(sharded_inputs) + + def build_infeed_from_input_specs(self, input_specs, execution_mode): + infeed_op = [] + shard_infeed_tensors = [] + + for shard_id in range(self._strategy.num_towers): + with ops.device('/device:CPU:0'): + infeed_tensors = [] + with ops.device('/device:TPU:%d' % shard_id): + for spec in input_specs: + # Construct placeholders for each of the inputs. + infeed_tensors.append( + array_ops.placeholder( + dtype=spec.dtype, + shape=spec.shape, + name='infeed-enqueue-%s-%d' % (spec.name, shard_id))) + shard_infeed_tensors.append(infeed_tensors) + + infeed_op.append( + tpu_ops.infeed_enqueue_tuple( + infeed_tensors, [spec.shape for spec in input_specs], + name='infeed-enqueue-%s-%d' % (execution_mode, shard_id), + device_ordinal=shard_id)) + return SizedInfeed(infeed_ops=infeed_op, + sharded_infeed_tensors=shard_infeed_tensors) + + +class TPUDatasetInfeedManager(TPUInfeedManager): + """Manages infeed for a `tf.data.Dataset` into a TPU computation. + """ + + class DatasetInfeedInstance(TPUInfeedInstance): + """An instance of the TPU infeed.""" + + def __init__(self, input_specs): + self._input_specs = input_specs + + def make_input_specs(self, input_tensors): + # TODO(saeta): Do error checking here! + return self._input_specs + + def make_feed_dict(self, tpu_model_op): + # TODO(saeta): Verify tpu_model_op is as expected! + return {} + + def __init__(self, dataset, distribution_strategy, tpu_session): + """Constructs a TPUDatasetInfeedManager. + + Must be called within a `KerasTPUModel.tpu_session` context! + + Args: + dataset: A `tf.data.Dataset` to infeed. + distribution_strategy: The `TPUDistributionStrategy` used to configure the + Keras TPU model. + tpu_session: The `tf.Session` object used for running the TPU model. + """ + self._verify_dataset_shape(dataset) + self._dataset = dataset + self._strategy = distribution_strategy + dummy_x_shape = dataset.output_shapes[0].as_list() + dummy_x_shape[0] *= distribution_strategy.num_towers + dummy_y_shape = dataset.output_shapes[1].as_list() + dummy_y_shape[0] *= distribution_strategy.num_towers + self._iterator = dataset.make_initializable_iterator() + tpu_session.run(self._iterator.initializer) + + self._get_next_ops = [] + ctrl_deps = [] + for i in range(distribution_strategy.num_towers): + with ops.control_dependencies(ctrl_deps): # Ensure deterministic + # TODO(saeta): Ensure correct placement! + get_next_op = self._iterator.get_next() + self._get_next_ops.append(get_next_op) + ctrl_deps.extend(get_next_op) + + # Use dummy numpy inputs for the rest of Keras' shape checking. We + # intercept them when building the model. + self._dummy_x = np.zeros(dummy_x_shape, + dtype=dataset.output_types[0].as_numpy_dtype) + self._dummy_y = np.zeros(dummy_y_shape, + dtype=dataset.output_types[1].as_numpy_dtype) + + input_specs = [] + if isinstance(self._iterator.output_shapes, tuple): + assert isinstance(self._iterator.output_types, tuple) + assert len(self._iterator.output_shapes) == len( + self._iterator.output_types) + for i in range(len(self._iterator.output_shapes)): + spec = tensor_spec.TensorSpec(self._iterator.output_shapes[i], + self._iterator.output_types[i]) + input_specs.append(spec) + elif isinstance(self._iterator.output_shapes, tensor_shape.TensorShape): + spec = tensor_spec.TensorSpec(self._iterator.output_shapes, + self._iterator.output_types) + input_specs.append(spec) + + self._infeed_instance = self.DatasetInfeedInstance(input_specs) + + def _verify_dataset_shape(self, dataset): + """Verifies a dataset is of an appropriate shape for TPUs.""" + if not isinstance(dataset, dataset_ops.Dataset): + raise ValueError('The function passed as the `x` parameter did not ' + 'return a `tf.data.Dataset`.') + if not isinstance(dataset.output_classes, tuple): + raise ValueError('The dataset must return a tuple of tf.Tensors, ' + 'instead it returns: %s' % dataset.output_classes) + if len(dataset.output_classes) != 2: + raise ValueError( + 'The dataset must return a 2-element tuple, got ' + '%s output classes instead.' % (dataset.output_classes,)) + for i, cls in enumerate(dataset.output_classes): + if cls != ops.Tensor: + raise ValueError('The dataset returned a non-Tensor type (%s) at ' + 'index %d.' % (cls, i)) + for i, shape in enumerate(dataset.output_shapes): + if not shape: + raise ValueError('The dataset returns a scalar tensor in ' + 'tuple index %d. Did you forget to batch? ' + '(Output shapes: %s).' % (i, + dataset.output_shapes)) + for j, dim in enumerate(shape): + if dim.value is None: + if j == 0: + hint = (' Hint: did you use `ds.batch(BATCH_SIZE, ' + 'drop_remainder=True)`?') + else: + hint = '' + raise ValueError( + 'The Keras-TPU integration for `tf.data` ' + 'currently requires static shapes. The provided ' + 'dataset only has a partially defined shape. ' + '(Dimension %d of output tensor %d is not statically known ' + 'for output shapes: %s.%s)' % (i, j, dataset.output_shapes, hint)) + + @property + def dummy_x(self): + return self._dummy_x + + @property + def dummy_y(self): + return self._dummy_y + + def make_infeed_instance(self, inputs): + # TODO(saeta): Verify inputs is as expected. + return self._infeed_instance + + def build_infeed_from_input_specs(self, input_specs, execution_mode): + shard_infeed_tensors = self._get_next_ops + assert len(shard_infeed_tensors) == self._strategy.num_towers + infeed_ops = [] + for shard_id in range(self._strategy.num_towers): + with ops.device('/device:CPU:0'): + infeed_ops.append( + tpu_ops.infeed_enqueue_tuple( + shard_infeed_tensors[shard_id], + [spec.shape for spec in input_specs], + name='infeed-enqueue-%s-%d' % (execution_mode, shard_id), + device_ordinal=shard_id)) + return SizedInfeed(infeed_ops=infeed_ops, + sharded_infeed_tensors=shard_infeed_tensors) class TPUFunction(object): @@ -195,7 +616,13 @@ class TPUFunction(object): self._compilation_cache = {} self._cloned_model = None - def _specialize_model(self, input_specs): + # Copy optimizer configuration. This is done prior to `_specialize_model` + # as the configuration may require evaluating variables in the CPU session. + self._optimizer_config = None + if not isinstance(self.model.optimizer, keras_optimizers.TFOptimizer): + self._optimizer_config = self.model.optimizer.get_config() + + def _specialize_model(self, input_specs, infeed_manager): """Specialize `self.model` (a Keras model) for the given input shapes.""" # Re-create our input and output layers inside our subgraph. They will be # attached to the true computation when we clone our model in `tpu_fn`. @@ -221,8 +648,8 @@ class TPUFunction(object): name='infeed-%s' % self.execution_mode) assert len(infeed_tensors) == len(infeed_layers), ( - 'Infeed inputs did not match model: %s vs %s', (infeed_layers, - infeed_tensors)) + 'Infeed inputs did not match model: %s vs %s' % (infeed_layers, + infeed_tensors)) tpu_targets = [] tpu_input_map = {} @@ -236,11 +663,23 @@ class TPUFunction(object): # Clone our CPU model, running within the TPU device context. with TPURewriteContext(tpu_input_map): - self._cloned_model = models.clone_model(self.model) + # TODO(power): Replicate variables. + with ops.device('/device:TPU:0'): + self._cloned_model = models.clone_model(self.model) + + # Create a copy of the optimizer for this graph. + if isinstance(self.model.optimizer, keras_optimizers.TFOptimizer): + cloned_optimizer = keras_optimizers.TFOptimizer( + self.model.optimizer.optimizer) + else: + logging.info('Cloning %s %s', self.model.optimizer.__class__.__name__, + self._optimizer_config) + cloned_optimizer = self.model.optimizer.__class__.from_config( + self._optimizer_config) if is_training or is_test: self._cloned_model.compile( - optimizer=_replicated_optimizer(self.model.optimizer), + optimizer=_replicated_optimizer(cloned_optimizer), loss=self.model.loss, loss_weights=self.model.loss_weights, metrics=self.model.metrics, @@ -299,37 +738,24 @@ class TPUFunction(object): # Generate CPU side operations to enqueue features/labels and dequeue # outputs from the model call. - infeed_op = [] + sized_infeed = infeed_manager.build_infeed_from_input_specs( + input_specs, self.execution_mode) + # Build output ops. outfeed_op = [] - shard_infeed_tensors = [] - for shard_id in range(self._strategy.num_towers): - with ops.device('/device:TPU:%d' % shard_id): - infeed_tensors = [] - for spec in input_specs: - infeed_tensors.append( - array_ops.placeholder( - dtype=spec.dtype, - shape=spec.shape, - name='infeed-enqueue-%s-%d' % (spec.name, shard_id))) - shard_infeed_tensors.append(infeed_tensors) - - infeed_op.append( - tpu_ops.infeed_enqueue_tuple( - infeed_tensors, [spec.shape for spec in input_specs], - name='infeed-enqueue-%s-%d' % (self.execution_mode, shard_id))) - + with ops.device('/device:CPU:0'): outfeed_op.extend( tpu_ops.outfeed_dequeue_tuple( dtypes=[spec.dtype for spec in self._outfeed_spec], shapes=[spec.shape for spec in self._outfeed_spec], - name='outfeed-dequeue-%s-%d' % (self.execution_mode, shard_id))) + name='outfeed-dequeue-%s-%d' % (self.execution_mode, shard_id), + device_ordinal=shard_id)) return TPUModelOp( compile_op, execute_op, - infeed_tensors=shard_infeed_tensors, - infeed_op=infeed_op, + infeed_tensors=sized_infeed.sharded_infeed_tensors, + infeed_op=sized_infeed.infeed_ops, outfeed_op=outfeed_op) def _test_model_compiles(self, tpu_model_ops): @@ -348,37 +774,17 @@ class TPUFunction(object): logging.info('Finished compiling. Time elapsed: %s secs', end_time - start_time) - def _split_tensors(self, inputs): - """Split input data across shards. - - Each input is sliced along the batch axis. - - Args: - inputs: List of Numpy arrays to run on the TPU. - - Returns: - List of lists containing the input to feed to each TPU shard. - """ - if self._strategy.num_towers == 1: - return [inputs] - - batch_size = inputs[0].shape[0] - assert batch_size % self._strategy.num_towers == 0, ( - 'batch_size must be divisible by strategy.num_towers (%s vs %s)' % - (batch_size, self._strategy.num_towers) - ) - shard_size = batch_size // self._strategy.num_towers - input_list = [] - for index in range(self._strategy.num_towers): - shard_inputs = [ - x[index * shard_size:(index + 1) * shard_size] for x in inputs - ] - input_list.append(shard_inputs) - return input_list - def __call__(self, inputs): assert isinstance(inputs, list) + infeed_manager = None + for x, mgr in self.model._numpy_to_infeed_manager_list: + if inputs[0] is x: + infeed_manager = mgr + break + if infeed_manager is None: + infeed_manager = TPUNumpyInfeedManager(self.model._strategy) + # Strip sample weight from inputs if (self.execution_mode == model_fn_lib.ModeKeys.TRAIN or self.execution_mode == model_fn_lib.ModeKeys.EVAL): @@ -387,21 +793,9 @@ class TPUFunction(object): else: input_tensors = self.model._feed_inputs - shard_inputs = self._split_tensors(inputs) + infeed_instance = infeed_manager.make_infeed_instance(inputs) del inputs # To avoid accident usage. - - # Compute an input specification (used to generate infeed enqueue and - # dequeue operations). We use the shape from our input array and the - # dtype from our model. A user may pass in a float64 for a float32 - # input: for model compatibility we still must generate a float32 infeed. - input_specs = [] - - # We use the shape and dtype from the first shard to compute the input - # metadata (`input_specs`); all replicas have the same type and shape. - for tensor, ary in zip(input_tensors, shard_inputs[0]): - input_specs.append( - tensor_spec.TensorSpec(ary.shape, tensor.dtype, - _valid_name(tensor.name))) + input_specs = infeed_instance.make_input_specs(input_tensors) # XLA requires every operation in the graph has a fixed shape. To # handle varying batch sizes we recompile a new sub-graph for each @@ -412,7 +806,8 @@ class TPUFunction(object): with self.model.tpu_session(): logging.info('New input shapes; (re-)compiling: mode=%s, %s', self.execution_mode, input_specs) - new_tpu_model_ops = self._specialize_model(input_specs) + new_tpu_model_ops = self._specialize_model(input_specs, + infeed_manager) self._compilation_cache[shape_key] = new_tpu_model_ops self._test_model_compiles(new_tpu_model_ops) @@ -420,11 +815,7 @@ class TPUFunction(object): self.model._initialize_weights(self._cloned_model) tpu_model_ops = self._compilation_cache[shape_key] - infeed_dict = {} - for infeed_tensors, inputs in zip(tpu_model_ops.infeed_tensors, - shard_inputs): - for tensor, value in zip(infeed_tensors, inputs): - infeed_dict[tensor] = value + infeed_dict = infeed_instance.make_feed_dict(tpu_model_ops) with self.model.tpu_session() as session: _, _, outfeed_outputs = session.run([ @@ -438,9 +829,8 @@ class TPUFunction(object): outputs_per_replica = len(self._outfeed_spec) for i in range(self._strategy.num_towers): - output_group = outfeed_outputs[ - i * outputs_per_replica:(i+1) * outputs_per_replica - ] + output_group = outfeed_outputs[i * outputs_per_replica:(i + 1) * + outputs_per_replica] for j in range(outputs_per_replica): outputs[j].append(output_group[j]) @@ -459,6 +849,11 @@ class KerasTPUModel(models.Model): name=cpu_model.name, ) + # Create a mapping from numpy arrays to infeed managers. + # Note: uses a list of tuples instead of a map because numpy arrays are + # not hashable. + self._numpy_to_infeed_manager_list = [] + self.predict_function = None self.test_function = None self.train_function = None @@ -470,14 +865,16 @@ class KerasTPUModel(models.Model): self._tpu_weights_initialized = False self._graph = ops.Graph() - cluster_resolver = tpu_cluster_resolver.TPUClusterResolver( + self._cluster_resolver = tpu_cluster_resolver.TPUClusterResolver( tpu_name_or_address) - cluster_spec = cluster_resolver.cluster_spec() + master = self._cluster_resolver.master() + cluster_spec = self._cluster_resolver.cluster_spec() self._session = tf_session.Session( graph=self._graph, - target=cluster_resolver.master(), + target=master, config=config_pb2.ConfigProto(isolate_session_state=True)) + # TODO(saeta): Confirm the lines below work in ClusterSpec propagation env. if cluster_spec: self._session.cluster_def.CopyFrom(cluster_spec.as_cluster_def()) @@ -529,10 +926,91 @@ class KerasTPUModel(models.Model): sample_weight_mode, weighted_metrics, target_tensors, **kwargs) - # Keras optimizers are not compatible with TPU rewrite - if not isinstance(self.optimizer, keras_optimizers.TFOptimizer): + def fit(self, + x=None, + y=None, + batch_size=None, + epochs=1, + verbose=1, + callbacks=None, + validation_split=0., + validation_data=None, + shuffle=True, + class_weight=None, + sample_weight=None, + initial_epoch=0, + steps_per_epoch=None, + validation_steps=None, + **kwargs): + assert not self._numpy_to_infeed_manager_list # Ensure empty. + + infeed_managers = [] # Managers to clean up at the end of the fit call. + if isinstance(x, dataset_ops.Dataset): + # TODO(b/111413240): Support taking a tf.data.Dataset directly. + raise ValueError( + 'Taking a Dataset directly is not yet supported. Please ' + 'wrap your dataset construction code in a function and ' + 'pass that to fit instead. For examples, see: ' + 'https://github.com/tensorflow/tpu/tree/master/models/experimental' + '/keras') + if callable(x): + with self.tpu_session() as sess: + dataset = x() + if steps_per_epoch is None: + raise ValueError('When using tf.data as input to a model, you ' + 'should specify the steps_per_epoch argument.') + if y is not None: + raise ValueError('When using tf.data as input to a model, y must be ' + 'None') + infeed_manager = TPUDatasetInfeedManager(dataset, self._strategy, sess) + # Use dummy numpy inputs for the rest of Keras' shape checking. We + # intercept them when building the model. + x = infeed_manager.dummy_x + y = infeed_manager.dummy_y + infeed_managers.append((x, infeed_manager)) + + if isinstance(validation_data, dataset_ops.Dataset): + # TODO(b/111413240): Support taking a tf.data.Dataset directly. raise ValueError( - 'Optimizer must be a TFOptimizer, got: %s' % self.optimizer) + 'Taking a Dataset directly is not yet supported. Please ' + 'wrap your dataset construction code in a function and ' + 'pass that to fit instead. For examples, see: ' + 'https://github.com/tensorflow/tpu/tree/master/models/experimental' + '/keras') + if callable(validation_data): + with self.tpu_session() as sess: + dataset = validation_data() + if validation_steps is None: + raise ValueError('When using tf.data as validation for a model, you ' + 'should specify the validation_steps argument.') + infeed_manager = TPUDatasetInfeedManager(dataset, self._strategy, sess) + # Use dummy numpy inputs for the rest of Keras' shape checking. We + # intercept them when building the model. + val_x = infeed_manager.dummy_x + val_y = infeed_manager.dummy_y + infeed_managers.append((val_x, infeed_manager)) + validation_data = (val_x, val_y) + + self._numpy_to_infeed_manager_list = infeed_managers + try: + return super(KerasTPUModel, self).fit( + x, + y, + batch_size, + epochs, + verbose, + callbacks, + validation_split, + validation_data, + shuffle, + class_weight, + sample_weight, + initial_epoch, + steps_per_epoch, + validation_steps, + **kwargs) + finally: + self._numpy_to_infeed_manager_list = [] def _make_train_function(self): if not self.train_function: @@ -615,10 +1093,10 @@ class KerasTPUModel(models.Model): K.set_session(default_session) def shutdown(self): - logging.info('Shutting down TPU session.') - with self.tpu_session() as session: - session.run(tpu.shutdown_system()) - + # TODO(b/111364423): Actually shut down the system. + logging.info('Skipping shutting down TPU system.') + # with self.tpu_session() as session: + # session.run(tpu.shutdown_system()) self._session.close() @@ -652,7 +1130,7 @@ Output shape: %(output_shape)s 'layer': layer, 'input_shape': layer.input_shape, 'output_shape': layer.output_shape - }) + }) @experimental @@ -687,6 +1165,10 @@ def tpu_model(model, tpu_name_or_address=None, strategy=None): Returns: A new `KerasTPUModel` instance. """ + # Force initialization of the CPU model. + model.get_weights() + model.reset_states() + _validate_shapes(model) # TODO(xiejw): Validate TPU model. TPUModel only? # TODO(xiejw): Validate replicas. Full or 1. Shall we allow subset? diff --git a/tensorflow/contrib/tpu/python/tpu/tpu.py b/tensorflow/contrib/tpu/python/tpu/tpu.py index 6a64893d9abcd64360554ab00502cdf360b820b6..92c1eaba710d888d461dad39766bb9189ad1ab78 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu.py @@ -151,6 +151,41 @@ class TPUReplicateContext(control_flow_ops.XLAControlFlowContext): self._name = name self._unsupported_ops = [] self._pivot = pivot + self._replicated_vars = {} + + def get_replicated_var_handle(self, var): + """Returns a variable handle for replicated TPU variable 'var'. + + This is an method used by an experimental replicated variable + implementation and is not intended as a public API. + + Args: + var: The replicated TPU variable. + + Returns: + The handle of the TPU replicated input node. + """ + handle = self._replicated_vars.get(var) + if handle is not None: + return handle + + # Builds a TPUReplicatedInput node for the variable, if one does not already + # exist. The TPUReplicatedInput node must belong to the enclosing + # control-flow scope of the TPUReplicateContext. + # TODO(phawkins): consider changing the contract of the TPU encapsulation + # so the TPUReplicatedInput nodes go inside the TPUReplicateContext scope + # instead. + + # pylint: disable=protected-access + graph = ops.get_default_graph() + saved_context = graph._get_control_flow_context() + graph._set_control_flow_context(self.outer_context) + handle = tpu_ops.tpu_replicated_input( + [v.handle for v in var._vars], name=var.name + "/handle") + graph._set_control_flow_context(saved_context) + # pylint: enable=protected-access + self._replicated_vars[var] = handle + return handle def report_unsupported_operations(self): if self._unsupported_ops: @@ -279,7 +314,9 @@ class TPUReplicateContext(control_flow_ops.XLAControlFlowContext): # Capture the device function stack at the time of first entry # since that is the stack that will be used outside_compilation. graph = ops.get_default_graph() - self._outer_device_function_stack = list(graph._device_function_stack) # pylint: disable=protected-access + # pylint: disable=protected-access + self._outer_device_function_stack = graph._device_function_stack.copy() + # pylint: enable=protected-access super(TPUReplicateContext, self).Enter() def HostComputeCore(self): @@ -598,23 +635,14 @@ def split_compile_and_replicate(computation, with tpu_function.tpu_shard_context( num_replicas), ops.control_dependencies([metadata]): - # For backward compatibility reasons, we tag replicated inputs with the - # _tpu_replicated_input attribute. This does nothing and exists only for - # backward compatibility. - # TODO(phawkins): delete the attr_scope after 6/28/2018. - # pylint: disable=protected-access - with graph._attr_scope({ - "_tpu_replicated_input": attr_value_pb2.AttrValue(b=True) - }): - # Add identity ops so even unused inputs are "consumed" by the - # computation. This is to avoid orphaned TPUReplicatedInput nodes. - # TODO(phawkins): consider instead pruning unused TPUReplicatedInput - # and eliding trivial TPUReplicatedInput/TPUReplicatedOutput pairs. - computation_inputs = [ - array_ops.identity(x, name="replicated_input_{}".format(i)) - for i, x in enumerate(computation_inputs) - ] - # pylint: enable=protected-access + # Add identity ops so even unused inputs are "consumed" by the + # computation. This is to avoid orphaned TPUReplicatedInput nodes. + # TODO(phawkins): consider instead pruning unused TPUReplicatedInput + # and eliding trivial TPUReplicatedInput/TPUReplicatedOutput pairs. + computation_inputs = [ + array_ops.identity(x, name="replicated_input_{}".format(i)) + for i, x in enumerate(computation_inputs) + ] # If there is an infeed queue, adds the dequeued values to the # computation's inputs. diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_config.py b/tensorflow/contrib/tpu/python/tpu/tpu_config.py index 6d7331e3c79ade9c12c15de79f550cf3973c4e6c..9e010922dcf565e78944bd77d49f7d3fa07f2cc4 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_config.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_config.py @@ -23,8 +23,6 @@ import collections import json import os -import numpy as np - from tensorflow.contrib.tpu.python.tpu import util as util_lib from tensorflow.core.protobuf import config_pb2 from tensorflow.python.estimator import run_config as run_config_lib @@ -43,6 +41,7 @@ class InputPipelineConfig(object): PER_SHARD_V1 = 1 PER_HOST_V1 = 2 PER_HOST_V2 = 3 + BROADCAST = 4 # TODO(b/72511246) Provide a simplified api to configure model parallelism. @@ -50,7 +49,7 @@ class TPUConfig( collections.namedtuple('TPUConfig', [ 'iterations_per_loop', 'num_shards', - 'computation_shape', + 'num_cores_per_replica', 'per_host_input_for_training', 'tpu_job_name', 'initial_infeed_sleep_secs', @@ -67,22 +66,22 @@ class TPUConfig( case, this number equals the total number of TPU cores. For model-parallelism, the total number of TPU cores equals product(computation_shape) * num_shards. - computation_shape: Defaults to `None`, which disables model parallelism. A - list of size 3 which describes the shape of a model replica's block of - cores. This is required by model-parallelism which enables partitioning - the model to multiple cores. For example, [2, 2, 1] means the model is - partitioned across 4 cores which span two cores in both x and y - coordinates. Please refer to @{tf.contrib.tpu.Topology} for the - geometry of a TPU mesh. + num_cores_per_replica: Defaults to `None`, which disables model parallelism. + An integer which describes the number of TPU cores per model replica. This + is required by model-parallelism which enables partitioning + the model to multiple cores. Currently num_cores_per_replica must be + 1, 2, 4, or 8. per_host_input_for_training: If `True`, `PER_HOST_V1`, or `PER_HOST_V2`, - `input_fn` is invoked per-host rather than per-core. With per-host input - pipeline configuration, `input_fn` is invoked once on each host. With the - per-core input pipeline configuration, it is invoked once for each core. + `input_fn` is invoked once on each host. With the per-core input pipeline + configuration, it is invoked once for each core. With a global batch size `train_batch_size` in `TPUEstimator` constructor, the batch size for each shard is `train_batch_size` // #hosts in the `True` or `PER_HOST_V1` mode. In `PER_HOST_V2` mode, it is - `train_batch_size` // #cores. With the per-core input pipeline - configuration, the shard batch size is also `train_batch_size` // #cores. + `train_batch_size` // #cores. In `BROADCAST` mode, `input_fn` is only + invoked once on host 0 and the tensors are broadcasted to all other + replicas. The batch size equals to train_batch_size`. With the per-core + input pipeline configuration, the shard batch size is also + `train_batch_size` // #cores. Note: per_host_input_for_training==PER_SHARD_V1 only supports mode.TRAIN. tpu_job_name: The name of the TPU job. Typically, this name is auto-inferred within TPUEstimator, however when using ClusterSpec propagation in more @@ -99,7 +98,7 @@ class TPUConfig( def __new__(cls, iterations_per_loop=2, num_shards=None, - computation_shape=None, + num_cores_per_replica=None, per_host_input_for_training=True, tpu_job_name=None, initial_infeed_sleep_secs=None): @@ -112,19 +111,12 @@ class TPUConfig( if num_shards is not None: util_lib.check_positive_integer(num_shards, 'TPUConfig num_shards') - # Check computation_shape - if computation_shape is not None and len(computation_shape) != 3: - raise ValueError( - 'computation_shape must be a list with length 3 or None; got {}'. - format(str(computation_shape))) - - if computation_shape is not None: - computation_shape_array = np.asarray(computation_shape, dtype=np.int32) - # This prevents any computation being replicated across multiple hosts, so - # that each host feeds the same number of computations. - if any(computation_shape_array < 1) or any(computation_shape_array > 2): - raise ValueError('computation_shape elements can only be 1 or 2; got ' - 'computation_shape={}'.format(computation_shape)) + # Parse computation_shape + if num_cores_per_replica is not None: + if num_cores_per_replica not in [1, 2, 4, 8]: + raise ValueError( + 'num_cores_per_replica must be 1, 2, 4, or 8; got {}'.format( + str(num_cores_per_replica))) # per_host_input_for_training may be True, False, or integer in [1..3]. # Map legacy values (True, False) to numeric values. @@ -144,7 +136,7 @@ class TPUConfig( cls, iterations_per_loop=iterations_per_loop, num_shards=num_shards, - computation_shape=computation_shape, + num_cores_per_replica=num_cores_per_replica, per_host_input_for_training=per_host_input_for_training, tpu_job_name=tpu_job_name, initial_infeed_sleep_secs=initial_infeed_sleep_secs) @@ -214,6 +206,12 @@ class RunConfig(run_config_lib.RunConfig): self._session_config.cluster_def.CopyFrom( self._cluster_spec.as_cluster_def()) + def _maybe_overwrite_session_config_for_distributed_training(self): + # Overrides the parent class session_config overwrite for between-graph. TPU + # runs with in-graph, which should not have device filter. Doing nothing + # ("pass") basically disables it. + pass + @property def evaluation_master(self): return self._evaluation_master diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_config_test.py b/tensorflow/contrib/tpu/python/tpu/tpu_config_test.py index 37ef3dbe1e66efe18b13ab9153ee346c08b9774a..2326fe97a807e6708a9cdc24fea889b998025a45 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_config_test.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_config_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import json from tensorflow.contrib.tpu.python.tpu import tpu_config as tpu_config_lib +from tensorflow.core.protobuf import config_pb2 from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.platform import test @@ -33,6 +34,46 @@ def _set_tf_config_env_variable(tf_config): class TPURunConfigTest(test.TestCase): + def test_no_session_config_set_in_local_case(self): + run_config = tpu_config_lib.RunConfig() + self.assertIsNone(run_config.session_config) + + def test_no_session_config_overwrite_in_local_case(self): + session_config = config_pb2.ConfigProto(allow_soft_placement=True) + run_config = tpu_config_lib.RunConfig(session_config=session_config) + self.assertEqual(session_config, run_config.session_config) + + def test_no_session_config_set_with_cluster_spec(self): + tf_config = { + 'cluster': { + run_config_lib.TaskType.CHIEF: ['host3:3'], + run_config_lib.TaskType.WORKER: ['host3:4'] + }, + 'task': { + 'type': run_config_lib.TaskType.CHIEF, + 'index': 0 + } + } + with _set_tf_config_env_variable(tf_config): + run_config = tpu_config_lib.RunConfig() + self.assertIsNone(run_config.session_config) + + def test_no_session_config_overwrite_with_cluster_spec(self): + tf_config = { + 'cluster': { + run_config_lib.TaskType.CHIEF: ['host3:3'], + run_config_lib.TaskType.WORKER: ['host3:4'] + }, + 'task': { + 'type': run_config_lib.TaskType.CHIEF, + 'index': 0 + } + } + with _set_tf_config_env_variable(tf_config): + session_config = config_pb2.ConfigProto(allow_soft_placement=True) + run_config = tpu_config_lib.RunConfig(session_config=session_config) + self.assertEqual(session_config, run_config.session_config) + def test_fail_with_invalid_num_shards(self): with self.assertRaisesRegexp(ValueError, 'must be positive'): tpu_config_lib.RunConfig( @@ -43,15 +84,11 @@ class TPURunConfigTest(test.TestCase): tpu_config_lib.RunConfig( tpu_config=tpu_config_lib.TPUConfig(iterations_per_loop=0)) - def test_fail_with_invalid_computation_shape(self): - with self.assertRaisesRegexp(ValueError, - 'computation_shape must be a list with length' - ' 3 or None'): - tpu_config_lib.TPUConfig(computation_shape=[2, 1]) - - with self.assertRaisesRegexp(ValueError, - 'computation_shape elements can only be'): - tpu_config_lib.TPUConfig(computation_shape=[1, 3, 1]) + def test_fail_with_invalid_num_cores_per_replica(self): + with self.assertRaisesRegexp( + ValueError, 'num_cores_per_replica must be 1, 2, 4, or 8;' + ' got 7'): + tpu_config_lib.TPUConfig(num_cores_per_replica=7) class TPURunConfigMasterTest(test.TestCase): diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_context.py b/tensorflow/contrib/tpu/python/tpu/tpu_context.py index aec59f3885ca7a2046c24ce5b94917ad6c3693e7..a9cf54f77d8192b51af094e71707a958594874f6 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_context.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_context.py @@ -21,8 +21,6 @@ from __future__ import print_function from contextlib import contextmanager import copy -import numpy as np - from tensorflow.contrib.tpu.python.tpu import device_assignment as tpu_device_assignment from tensorflow.contrib.tpu.python.tpu import tpu_config from tensorflow.contrib.tpu.python.tpu import tpu_system_metadata as tpu_system_metadata_lib @@ -33,15 +31,26 @@ from tensorflow.python.platform import tf_logging as logging _DEFAULT_JOB_NAME = 'tpu_worker' _DEFAULT_COORDINATOR_JOB_NAME = 'coordinator' _LOCAL_MASTERS = ('', 'local') +_NUM_CORES_TO_COMPUTATION_SHAPE = { + 1: [1, 1, 1], + 2: [1, 1, 2], + 4: [1, 2, 2], + 8: [2, 2, 2] +} class TPUContext(object): """The context of current input_fn invocation.""" - def __init__(self, internal_ctx, input_device=None, invocation_index=None): + def __init__(self, + internal_ctx, + input_device=None, + invocation_index=None, + call_from_input_fn=True): self._internal_ctx = internal_ctx self._input_device = input_device self._invocation_index = invocation_index + self._call_from_input_fn = call_from_input_fn def current_input_fn_deployment(self): """The configuration of the current input_fn invocation. @@ -69,11 +78,21 @@ class TPUContext(object): total invocation count is equal to the number of hosts in the system and num replicas consumed by current invocation is equal to number of cores per host. + + Raises: + RuntimeError: If this method must not be called from input_fn. """ + if not self._call_from_input_fn: + raise RuntimeError('This TPUContext instance must not be called from' + ' model_fn.') + if self._internal_ctx.is_input_sharded_per_core(): total_invocation_count = (self._internal_ctx.num_hosts * self._internal_ctx.num_of_replicas_per_host) replicas_consumed = 1 + elif self._internal_ctx.is_input_broadcast_with_iterators(): + total_invocation_count = 1 + replicas_consumed = self._internal_ctx.num_replicas else: total_invocation_count = self._internal_ctx.num_hosts replicas_consumed = self._internal_ctx.num_of_replicas_per_host @@ -105,6 +124,14 @@ class TPUContext(object): 'num_of_replicas_per_host is not supported for model_parallelism') return self._internal_ctx.num_of_replicas_per_host + @property + def device_assignment(self): + """Returns device_assignment object.""" + if self._call_from_input_fn: + raise RuntimeError('This TPUContext instance must not be called from' + ' input_fn.') + return self._internal_ctx.device_assignment + def device_for_replica(self, replica_id): """Returns the tuple of (CPU device and device ordinal) for replica. @@ -119,24 +146,7 @@ class TPUContext(object): # Note that: For the non-model parallelism, the mapping could be # a random permutation. The order should not matter in most cases # as far as model is replicated to all cores in the system. - - # If the precise replica_id to device mapping is required, please - # set the computation_shape as [1,1,1] in TPUConfig to enable - # the model parallelism. - if self._internal_ctx.model_parallelism_enabled: - return RuntimeError( - 'device_for_replica is not yet implemented for model parallelism. ' - 'b/79689078.') - - master = self._internal_ctx.master_job - job_device = '' if master is None else ('/job:%s' % master) - - num_of_replicas_per_host = self._internal_ctx.num_of_replicas_per_host - host_id = replica_id / num_of_replicas_per_host - ordinal_id = replica_id % num_of_replicas_per_host - - host_device = '%s/task:%d/device:CPU:0' % (job_device, host_id) - return (host_device, ordinal_id) + return self._internal_ctx.device_for_replica(replica_id) class _InternalTPUContext(object): @@ -175,9 +185,14 @@ class _InternalTPUContext(object): self._eval_on_tpu = eval_on_tpu self._model_parallelism_enabled = ( - use_tpu and config.tpu_config.computation_shape) + use_tpu and config.tpu_config.num_cores_per_replica) self._mode = None - + num_cores_per_replica = config.tpu_config.num_cores_per_replica + if num_cores_per_replica: + self._computation_shape = _NUM_CORES_TO_COMPUTATION_SHAPE[ + num_cores_per_replica] + else: + self._computation_shape = None self._lazy_tpu_system_metadata_dict = {} # key by master address self._lazy_device_assignment_dict = {} # key by master address self._lazy_validation_dict = {} # key by ModeKeys @@ -238,11 +253,12 @@ class _InternalTPUContext(object): device_assignment = tpu_device_assignment.device_assignment( tpu_system_metadata.topology, - computation_shape=self._config.tpu_config.computation_shape, + computation_shape=self._computation_shape, num_replicas=self.num_replicas) - logging.info('computation_shape: %s', - str(self._config.tpu_config.computation_shape)) + logging.info('num_cores_per_replica: %s', + str(self._config.tpu_config.num_cores_per_replica)) + logging.info('computation_shape: %s', str(self._computation_shape)) logging.info('num_replicas: %d', self.num_replicas) logging.info('device_assignment.topology.device_coordinates: %s', str(device_assignment.topology.device_coordinates)) @@ -283,23 +299,20 @@ class _InternalTPUContext(object): num_cores_in_system = self.num_cores if self.model_parallelism_enabled: - computation_shape_array = np.asarray( - self._config.tpu_config.computation_shape, dtype=np.int32) - num_cores_per_replica = np.prod(computation_shape_array) + num_cores_per_replica = self._config.tpu_config.num_cores_per_replica if num_cores_per_replica > num_cores_in_system: raise ValueError( 'The num of cores required by the model parallelism, specified by ' - 'TPUConfig.computation_shape, is larger than the total num of ' - 'TPU cores in the system. computation_shape: {}, num cores ' - 'in the system: {}'.format( - self._config.tpu_config.computation_shape, - num_cores_in_system)) + 'TPUConfig.num_cores_per_replica, is larger than the total num of ' + 'TPU cores in the system. num_cores_per_replica: {}, num cores ' + 'in the system: {}'.format(num_cores_per_replica, + num_cores_in_system)) if num_cores_in_system % num_cores_per_replica != 0: raise RuntimeError( 'The num of cores in the system ({}) is not divisible by the num ' 'of cores ({}) required by the model parallelism, specified by ' - 'TPUConfig.computation_shape. This should never happen!'.format( + 'TPUConfig.num_cores_per_replica. This should never happen!'.format( num_cores_in_system, num_cores_per_replica)) return num_cores_in_system // num_cores_per_replica @@ -327,6 +340,11 @@ class _InternalTPUContext(object): return (self._config.tpu_config.per_host_input_for_training is tpu_config.InputPipelineConfig.PER_HOST_V2) + def is_input_broadcast_with_iterators(self): + """Return true if input_fn should be run in the full_replicae config.""" + return (self._config.tpu_config.per_host_input_for_training is + tpu_config.InputPipelineConfig.BROADCAST) + def is_running_on_cpu(self, is_export_mode=False): """Determines whether the input_fn and model_fn should be invoked on CPU. @@ -391,7 +409,7 @@ class _InternalTPUContext(object): """Returns the shard batch size for `input_fn`.""" global_batch_size = self.global_batch_size - if self.is_running_on_cpu(): + if (self.is_running_on_cpu() or self.is_input_broadcast_with_iterators()): return global_batch_size # On TPU @@ -406,7 +424,7 @@ class _InternalTPUContext(object): """Returns the shard batch size for `model_fn`.""" global_batch_size = self.global_batch_size - if self.is_running_on_cpu(): + if (self.is_running_on_cpu() or self.is_input_broadcast_with_iterators()): return global_batch_size # On TPU. always sharded per shard. @@ -463,17 +481,23 @@ class _InternalTPUContext(object): master = self.master_job - def _placement_function(_sentinal=None, core_id=None, host_id=None): # pylint: disable=invalid-name + def _placement_function(_sentinal=None, replica_id=None, host_id=None): # pylint: disable=invalid-name + """Return the host device given replica_id or host_id.""" assert _sentinal is None - if core_id is not None and host_id is not None: + if replica_id is not None and host_id is not None: raise RuntimeError( - 'core_id and host_id can have only one non-None value.') + 'replica_id and host_id can have only one non-None value.') if master is None: return '/replica:0/task:0/device:CPU:0' else: - if core_id is not None: - host_id = core_id / self.num_of_cores_per_host + if replica_id is not None: + if self.model_parallelism_enabled: + return self.device_assignment.host_device( + replica=replica_id, job=master) + else: + host_id = replica_id / self.num_of_cores_per_host + return '/job:%s/task:%d/device:CPU:0' % (master, host_id) return _placement_function @@ -546,7 +570,7 @@ class _InternalTPUContext(object): 'be ({}), got ({}). For non-model-parallelism, num_replicas should ' 'be the total num of TPU cores in the system. For ' 'model-parallelism, the total number of TPU cores should be ' - 'product(computation_shape) * num_replicas. Please set it ' + 'num_cores_per_replica * num_replicas. Please set it ' 'accordingly or leave it as `None`'.format( self._get_master_address(), num_replicas, user_provided_num_replicas)) @@ -554,7 +578,8 @@ class _InternalTPUContext(object): raise ValueError(message) if mode == model_fn_lib.ModeKeys.TRAIN: - if self._train_batch_size % num_replicas != 0: + if (self._train_batch_size % num_replicas != 0 and + not self.is_input_broadcast_with_iterators()): raise ValueError( 'train batch size {} must be divisible by number of replicas {}' .format(self._train_batch_size, num_replicas)) @@ -564,11 +589,12 @@ class _InternalTPUContext(object): raise ValueError( 'eval_batch_size in TPUEstimator constructor cannot be `None`' 'if .evaluate is running on TPU.') - if self._eval_batch_size % num_replicas != 0: + if (self._eval_batch_size % num_replicas != 0 and + not self.is_input_broadcast_with_iterators()): raise ValueError( 'eval batch size {} must be divisible by number of replicas {}' .format(self._eval_batch_size, num_replicas)) - if num_hosts > 1: + if num_hosts > 1 and not self.is_input_broadcast_with_iterators(): raise ValueError( 'TPUEstimator.evaluate should be running on single TPU worker. ' 'got {}.'.format(num_hosts)) @@ -578,11 +604,12 @@ class _InternalTPUContext(object): raise ValueError( 'predict_batch_size in TPUEstimator constructor should not be ' '`None` if .predict is running on TPU.') - if self._predict_batch_size % num_replicas != 0: + if (self._predict_batch_size % num_replicas != 0 and + not self.is_input_broadcast_with_iterators()): raise ValueError( 'predict batch size {} must be divisible by number of replicas {}' .format(self._predict_batch_size, num_replicas)) - if num_hosts > 1: + if num_hosts > 1 and not self.is_input_broadcast_with_iterators(): raise ValueError( 'TPUEstimator.predict should be running on single TPU worker. ' 'got {}.'.format(num_hosts)) @@ -590,6 +617,33 @@ class _InternalTPUContext(object): # Record the state "validated" into lazy dictionary. self._lazy_validation_dict[mode] = True + def device_for_replica(self, replica_id): + """Returns the tuple of (CPU device and device ordinal) for replica. + + This should be used for full replicate for non-model-parallelism. + + Args: + replica_id: Int, the replica index. + + Returns: + A tuple of device spec for CPU device and int device ordinal. + """ + master = self.master_job + + if self.model_parallelism_enabled: + return (self.device_assignment.host_device( + replica=replica_id, job=master), + self.device_assignment.tpu_ordinal(replica=replica_id)) + + job_device = '' if master is None else ('/job:%s' % master) + + num_of_replicas_per_host = self.num_of_replicas_per_host + host_id = replica_id / num_of_replicas_per_host + ordinal_id = replica_id % num_of_replicas_per_host + + host_device = '%s/task:%d/device:CPU:0' % (job_device, host_id) + return (host_device, ordinal_id) + class _OneCoreTPUContext(_InternalTPUContext): """Special _InternalTPUContext for one core usage.""" @@ -625,7 +679,7 @@ def _get_tpu_context(config, train_batch_size, eval_batch_size, """Returns an instance of `_InternalTPUContext`.""" if (config.tpu_config.num_shards == 1 and - config.tpu_config.computation_shape is None): + config.tpu_config.num_cores_per_replica is None): logging.warning( 'Setting TPUConfig.num_shards==1 is an unsupported behavior. ' 'Please fix as soon as possible (leaving num_shards as None.') diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 49cd318b8956369f49d77d3cb1b030e171fa07aa..ee9ad525ee34ff114808a4dc7a49702b19c78543 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -22,9 +22,9 @@ import collections import copy import os import signal +import sys import threading import time -import traceback import numpy as np import six @@ -32,6 +32,7 @@ from six.moves import queue as Queue # pylint: disable=redefined-builtin from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib.tpu.python.ops import tpu_ops +from tensorflow.contrib.tpu.python.tpu import error_handling from tensorflow.contrib.tpu.python.tpu import session_support from tensorflow.contrib.tpu.python.tpu import tpu from tensorflow.contrib.tpu.python.tpu import tpu_config @@ -257,7 +258,10 @@ class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=prote eval_metrics=None, export_outputs=None, scaffold_fn=None, - host_call=None): + host_call=None, + training_hooks=None, + evaluation_hooks=None, + prediction_hooks=None): """Creates a validated `TPUEstimatorSpec` instance.""" host_calls = {} if eval_metrics is not None: @@ -265,6 +269,17 @@ class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=prote if host_call is not None: host_calls['host_call'] = host_call _OutfeedHostCall.validate(host_calls) + + training_hooks = list(training_hooks or []) + evaluation_hooks = list(evaluation_hooks or []) + prediction_hooks = list(prediction_hooks or []) + + for hook in training_hooks + evaluation_hooks + prediction_hooks: + if not isinstance(hook, session_run_hook.SessionRunHook): + raise TypeError( + 'All hooks must be SessionRunHook instances, given: {}'.format( + hook)) + return super(TPUEstimatorSpec, cls).__new__( cls, mode=mode, @@ -274,7 +289,10 @@ class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=prote eval_metrics=eval_metrics, export_outputs=export_outputs, scaffold_fn=scaffold_fn, - host_call=host_call) + host_call=host_call, + training_hooks=training_hooks, + evaluation_hooks=evaluation_hooks, + prediction_hooks=prediction_hooks) def as_estimator_spec(self): """Creates an equivalent `EstimatorSpec` used by CPU train/eval.""" @@ -290,6 +308,7 @@ class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=prote hooks = None if self.host_call is not None: hooks = [_OutfeedHostCallHook(host_call_ret['host_call'])] + hooks = list(hooks or []) scaffold = self.scaffold_fn() if self.scaffold_fn else None return model_fn_lib.EstimatorSpec( mode=self.mode, @@ -299,9 +318,9 @@ class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=prote eval_metric_ops=eval_metric_ops, export_outputs=self.export_outputs, scaffold=scaffold, - training_hooks=hooks, - evaluation_hooks=hooks, - prediction_hooks=hooks) + training_hooks=self.training_hooks + hooks, + evaluation_hooks=self.evaluation_hooks + hooks, + prediction_hooks=self.prediction_hooks + hooks) class _OpQueueContext(object): @@ -365,17 +384,17 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): ctx, enqueue_ops, dequeue_ops, - run_infeed_loop_on_coordinator=True): + run_infeed_loop_on_coordinator=True, + rendezvous=None): self._master_job = ctx.master_job self._enqueue_ops = enqueue_ops self._dequeue_ops = dequeue_ops + self._rendezvous = rendezvous self._run_infeed_loop_on_coordinator = run_infeed_loop_on_coordinator self._initial_infeed_sleep_secs = ( ctx.config.tpu_config.initial_infeed_sleep_secs) - self._session_cancel_timer = None - self._feed_error = None self._finished = False @@ -392,62 +411,6 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): for op in summary_writer_init_ops: self._finalize_ops.append(contrib_summary.flush(writer=op.inputs[0])) - def _log_error(self, session, error): - """Log an infeed or outfeed error. - - This logs a short error message immediately, and schedules a timer to - emit the full stack trace and error message after a short period of time. - If the main session has terminated by the time the timer triggers, we - assume the real source of the error was from the main session and avoid - emitting a stack trace for the infeed. - - Args: - session: `tf.Session`, session to be terminated error: exception that - triggered logging. - error: the Exception to log. - """ - logging.warning( - '\n\n' - 'Error occurred during infeed/outfeed. This may be due to a compile ' - 'error in the main session. Waiting for a short time for the main ' - 'session to come back.\n\n%s', error) - - self._feed_error = traceback.format_exc() - - # If we've already encountered a feed error, don't schedule another - # cancellation op. - if self._session_cancel_timer: - return - - def _cancel_session(): - """Close the session to avoid the main thread from hanging. - - If input pipeline triggers any error, the infeed thread dies but the main - thread for TPU computation waits for the infeed enqueue forever. Close the - Session to cancel the main thread Session.run execution. - - We sleep for a few seconds before closing to give some time for the TPU - compilation error, if any, propagating, from TPU to CPU host. Compilation - errors should be reported by the main thread so that the program can be - interrupted and users can take action. Due to a race condition, the - infeed thread might see an error first. Closing the session here - immediately would result in a session cancellation exception in the main - thread, instead of the expected compile error. User code that depends on - having the proper exception type will therefore be confused. - """ - time.sleep(5) - - # If the main session is still running, the infeed/outfeed errors are - # legitimate, and should be logged. - if not self._finished and self._feed_error: - logging.error('Feed error: %s', self._feed_error) - logging.error('Closing session. A RuntimeError should follow.') - session.close() - - self._session_cancel_timer = threading.Thread(target=_cancel_session) - self._session_cancel_timer.daemon = True - self._session_cancel_timer.start() - def _run_infeed(self, queue_ctx, session): logging.info('Starting infeed thread controller.') if self._initial_infeed_sleep_secs: @@ -456,7 +419,7 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): time.sleep(self._initial_infeed_sleep_secs) logging.info('%s thread starting after sleep', self._name) - try: + with self._rendezvous.catch_errors(source='infeed', session=session): if self._run_infeed_loop_on_coordinator: for count, steps in enumerate(queue_ctx.read_iteration_counts()): for i in xrange(steps): @@ -466,19 +429,15 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): for _ in queue_ctx.read_iteration_counts(): session.run(self._enqueue_ops) logging.info('Infeed thread finished, shutting down.') - except Exception as e: # pylint: disable=broad-except - self._log_error(session, e) def _run_outfeed(self, queue_ctx, session): logging.info('Starting outfeed thread controller.') - try: + with self._rendezvous.catch_errors(source='outfeed', session=session): for count, steps in enumerate(queue_ctx.read_iteration_counts()): for i in xrange(steps): logging.debug('Outfeed dequeue for iteration (%d, %d)', count, i) session.run(self._dequeue_ops) logging.info('Outfeed thread finished, shutting down.') - except Exception as e: # pylint: disable=broad-except - self._log_error(session, e) def _create_infeed_controller(self, name, target, args): return _OpQueueContext(name=name, target=target, args=args) @@ -497,11 +456,6 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): def before_run(self, run_context): self._feed_error = None - # Wait for the cancellation timer to complete before continuing. - if self._session_cancel_timer: - self._session_cancel_timer.join() - self._session_cancel_timer = None - iterations = run_context.session.run(self._iterations_per_loop_var) logging.info('Enqueue next (%d) batch(es) of data to infeed.', iterations) @@ -512,16 +466,14 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): self._outfeed_controller.send_next_batch_signal(iterations) def end(self, session): - if self._session_cancel_timer: - logging.warning('Feed error occurred; waiting for message.') - self._session_cancel_timer.join() - self._finished = True logging.info('Stop infeed thread controller') self._infeed_controller.join() + self._rendezvous.record_done('infeed') logging.info('Stop output thread controller') self._outfeed_controller.join() + self._rendezvous.record_done('outfeed') logging.info('Shutdown TPU system.') session.run(self._finalize_ops) @@ -529,9 +481,10 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): class TPUInfeedOutfeedSessionHookForPrediction(TPUInfeedOutfeedSessionHook): - def __init__(self, ctx, enqueue_ops, dequeue_ops): + def __init__(self, ctx, enqueue_ops, dequeue_ops, rendezvous=None): super(TPUInfeedOutfeedSessionHookForPrediction, self).__init__( - ctx, enqueue_ops, dequeue_ops, run_infeed_loop_on_coordinator=False) + ctx, enqueue_ops, dequeue_ops, run_infeed_loop_on_coordinator=False, + rendezvous=rendezvous) def _create_infeed_controller(self, name, target, args): return _OpSignalOnceQueueContext(name=name, target=target, args=args) @@ -701,8 +654,6 @@ def generate_per_core_enqueue_ops_fn_for_host( infeed_queue = tpu_feed.InfeedQueue( number_of_tuple_elements=len(per_host_sharded_inputs[0])) captured_infeed_queue.capture(infeed_queue) - infeed_queue.set_configuration_from_sharded_input_tensors( - per_host_sharded_inputs) per_host_enqueue_ops = infeed_queue.generate_enqueue_ops( per_host_sharded_inputs, tpu_ordinal_function=tpu_ordinal_function_impl) @@ -837,8 +788,6 @@ def generate_per_host_v2_enqueue_ops_fn_for_host( infeed_queue = tpu_feed.InfeedQueue( number_of_tuple_elements=len(per_host_sharded_inputs[0])) captured_infeed_queue.capture(infeed_queue) - infeed_queue.set_configuration_from_sharded_input_tensors( - per_host_sharded_inputs) per_host_enqueue_ops = infeed_queue.generate_enqueue_ops( per_host_sharded_inputs, tpu_ordinal_function=tpu_ordinal_function_impl) @@ -847,6 +796,84 @@ def generate_per_host_v2_enqueue_ops_fn_for_host( return enqueue_ops_fn, captured_infeed_queue, hooks, is_dataset +def generate_broadcast_enqueue_ops_fn(ctx, input_fn, inputs_structure_recorder, + num_hosts): + """Generates infeed enqueue ops for one input_fn on all the hosts.""" + captured_infeed_queue = _CapturedObject() + hooks = [] + device_0 = ctx.tpu_host_placement_function(host_id=0) + with ops.device(device_0): + user_context = tpu_context.TPUContext( + internal_ctx=ctx, input_device=device_0, invocation_index=0) + inputs = _Inputs.from_input_fn(input_fn(user_context)) + + is_dataset = inputs.is_dataset + if ctx.mode == model_fn_lib.ModeKeys.PREDICT: + if not is_dataset: + raise TypeError( + 'For mode PREDICT, `input_fn` must return `Dataset` instead of ' + '`features` and `labels`.') + + inputs = _InputsWithStoppingSignals( + dataset=inputs.dataset, + batch_size=ctx.batch_size_for_input_fn, + add_padding=True) + + if is_dataset: + hooks.append(inputs.dataset_initializer_hook()) + num_replicas_per_host = ctx.num_of_replicas_per_host + + def tpu_ordinal_function_impl(replica_id): + if ctx.device_assignment: + return ctx.device_assignment.tpu_ordinal(replica=replica_id) + else: + return replica_id % num_replicas_per_host + + def device_function_impl(replica_id): + return ctx.tpu_host_placement_function(replica_id=replica_id) + + def enqueue_ops_fn(): + """Generates enqueue ops for all the hosts.""" + broadcasted_inputs = [] + flattened_inputs = None # Cache result from input_fn. + signals = None + for host_id in xrange(num_hosts): + with ops.device(ctx.tpu_host_placement_function(host_id=host_id)): + for _ in xrange(ctx.num_of_replicas_per_host): + # Note: input_fn is only called once at host 0 for the first replica. + # The features and labels returned from that invocation are + # broadcasted to other replicas(including the replicas on other + # hosts). + if flattened_inputs is None: + features, labels = inputs.features_and_labels() # Calls get_next() + signals = inputs.signals() + + inputs_structure_recorder.validate_and_record_structure( + features, labels, signals) + flattened_inputs = ( + inputs_structure_recorder.flatten_features_and_labels( + features, labels, signals)) + broadcasted_inputs.append(flattened_inputs) + + infeed_queue = tpu_feed.InfeedQueue( + number_of_tuple_elements=len(broadcasted_inputs[0])) + captured_infeed_queue.capture(infeed_queue) + enqueue_ops = infeed_queue.generate_enqueue_ops( + broadcasted_inputs, + tpu_ordinal_function=tpu_ordinal_function_impl, + placement_function=device_function_impl) + + if signals is None: + return enqueue_ops + else: + return { + 'ops': enqueue_ops, + 'signals': signals, + } + + return enqueue_ops_fn, captured_infeed_queue, hooks, is_dataset + + class _InputPipeline(object): """`_InputPipeline` handles invoking `input_fn` and piping to infeed queue. @@ -1079,6 +1106,24 @@ class _InputPipeline(object): # Infeed_queue_getter must be called after enqueue_ops_fn is called. infeed_queues.append(captured_infeed_queue.get()) + elif self._ctx.is_input_broadcast_with_iterators(): + # Only calls input_fn in host 0. + host_device = tpu_host_placement_fn(host_id=0) + enqueue_ops_fn, captured_infeed_queue, hooks, is_dataset = ( + generate_broadcast_enqueue_ops_fn(self._ctx, self._input_fn, + self._inputs_structure_recorder, + num_hosts)) + all_hooks.extend(hooks) + if is_dataset: + run_infeed_loop_on_coordinator = False + wrap_fn = ( + _wrap_computation_in_while_loop + if self._ctx.mode != model_fn_lib.ModeKeys.PREDICT else + _wrap_computation_in_while_loop_with_stopping_signals) + enqueue_ops.append(wrap_fn(device=host_device, op_fn=enqueue_ops_fn)) + else: + enqueue_ops.append(enqueue_ops_fn()) + infeed_queues.append(captured_infeed_queue.get()) else: for host_id in range(num_hosts): host_device = tpu_host_placement_fn(host_id=host_id) @@ -1193,6 +1238,7 @@ class _ModelFnWrapper(object): host_call = _OutfeedHostCall(self._ctx) captured_scaffold_fn = _CapturedObject() + captured_training_hooks = _CapturedObject() def train_step(loss): """Training step function for use inside a while loop.""" @@ -1209,6 +1255,8 @@ class _ModelFnWrapper(object): else: captured_scaffold_fn.capture(None) + captured_training_hooks.capture(estimator_spec.training_hooks) + # We must run train_op to update the variables prior to running the # outfeed. with ops.control_dependencies([train_op]): @@ -1220,7 +1268,8 @@ class _ModelFnWrapper(object): with ops.control_dependencies(host_call_outfeed_ops): return array_ops.identity(loss) - return train_step, host_call, captured_scaffold_fn + return (train_step, host_call, captured_scaffold_fn, + captured_training_hooks) def convert_to_single_tpu_eval_step(self, dequeue_fn): """Converts user provided model_fn` as a single eval step on TPU. @@ -1250,6 +1299,7 @@ class _ModelFnWrapper(object): """ host_calls = _OutfeedHostCall(self._ctx) captured_scaffold_fn = _CapturedObject() + captured_eval_hooks = _CapturedObject() def eval_step(total_loss): """Evaluation step function for use inside a while loop.""" @@ -1264,8 +1314,11 @@ class _ModelFnWrapper(object): loss = tpu_estimator_spec.loss captured_scaffold_fn.capture(tpu_estimator_spec.scaffold_fn) + captured_eval_hooks.capture(tpu_estimator_spec.evaluation_hooks) + to_record = {} - to_record['eval_metrics'] = tpu_estimator_spec.eval_metrics + if tpu_estimator_spec.eval_metrics: + to_record['eval_metrics'] = tpu_estimator_spec.eval_metrics if tpu_estimator_spec.host_call is not None: # We assume that evaluate won't update global step, so we don't wrap # this host_call. @@ -1275,7 +1328,7 @@ class _ModelFnWrapper(object): with ops.control_dependencies(host_calls.create_enqueue_op()): return math_ops.add(total_loss, loss) - return eval_step, host_calls, captured_scaffold_fn + return eval_step, host_calls, captured_scaffold_fn, captured_eval_hooks def convert_to_single_tpu_predict_step(self, dequeue_fn): """Converts user provided model_fn` as a single predict step on TPU. @@ -1290,6 +1343,7 @@ class _ModelFnWrapper(object): """ host_calls = _OutfeedHostCall(self._ctx) captured_scaffold_fn = _CapturedObject() + captured_predict_hooks = _CapturedObject() def predict_step(unused_scalar_stopping_signal): """Evaluation step function for use inside a while loop.""" @@ -1310,6 +1364,7 @@ class _ModelFnWrapper(object): self._verify_tpu_spec_predictions(tpu_estimator_spec.predictions) captured_scaffold_fn.capture(tpu_estimator_spec.scaffold_fn) + captured_predict_hooks.capture(tpu_estimator_spec.prediction_hooks) to_record = {} identity_fn = lambda **kwargs: kwargs to_record['predictions'] = [identity_fn, tpu_estimator_spec.predictions] @@ -1321,7 +1376,8 @@ class _ModelFnWrapper(object): with ops.control_dependencies(host_calls.create_enqueue_op()): return _StopSignals.as_scalar_stopping_signal(stopping_signals) - return predict_step, host_calls, captured_scaffold_fn + return (predict_step, host_calls, captured_scaffold_fn, + captured_predict_hooks) def _verify_tpu_spec_predictions(self, predictions): """Validates TPUEstimatorSpec.predictions dict.""" @@ -1422,6 +1478,11 @@ class _ModelFnWrapper(object): running_on_cpu = self._ctx.is_running_on_cpu(is_export_mode) _add_item_to_params(params, _USE_TPU_KEY, not running_on_cpu) + if not running_on_cpu: + user_context = tpu_context.TPUContext( + internal_ctx=self._ctx, call_from_input_fn=False) + _add_item_to_params(params, _CTX_KEY, user_context) + estimator_spec = self._model_fn(features=features, **kwargs) if (running_on_cpu and isinstance(estimator_spec, model_fn_lib._TPUEstimatorSpec)): # pylint: disable=protected-access @@ -1438,11 +1499,9 @@ class _ModelFnWrapper(object): err_msg = '{} returned by EstimatorSpec is not supported in TPUEstimator.' if estimator_spec.training_chief_hooks: - raise ValueError(err_msg.format('training_chief_hooks')) - if estimator_spec.training_hooks: - raise ValueError(err_msg.format('training_hooks')) - if estimator_spec.evaluation_hooks: - raise ValueError(err_msg.format('evaluation_hooks')) + raise ValueError( + err_msg.format('training_chief_hooks') + 'If you want' + + ' to pass training hooks, please pass via training_hooks.') if estimator_spec.scaffold: logging.warning('EstimatorSpec.Scaffold is ignored by TPU train/eval. ' @@ -1563,7 +1622,7 @@ class _OutfeedHostCall(object): RuntimeError: If outfeed tensor is scalar. """ if not self._names: - return [] + return {} ret = {} # For each i, dequeue_ops[i] is a list containing the tensors from all @@ -1582,11 +1641,13 @@ class _OutfeedHostCall(object): # Outfeed ops execute on each replica's first logical core. Note: we must # constraint it such that we have at most one outfeed dequeue and enqueue # per replica. - tpu_device_placement_fn = self._ctx.tpu_device_placement_function for i in xrange(self._ctx.num_replicas): - with ops.device(tpu_device_placement_fn(i)): + host_device, ordinal_id = self._ctx.device_for_replica(i) + with ops.device(host_device): outfeed_tensors = tpu_ops.outfeed_dequeue_tuple( - dtypes=tensor_dtypes, shapes=tensor_shapes) + dtypes=tensor_dtypes, + shapes=tensor_shapes, + device_ordinal=ordinal_id) for j, item in enumerate(outfeed_tensors): dequeue_ops[j].append(item) @@ -1601,7 +1662,7 @@ class _OutfeedHostCall(object): # place all ops on tpu host if possible. # # TODO(jhseu): Evaluate whether this is right for summaries. - with ops.device(self._ctx.tpu_host_placement_function(core_id=0)): + with ops.device(self._ctx.tpu_host_placement_function(replica_id=0)): for name in self._names: dequeue_ops = dequeue_ops_by_name[name] for i, item in enumerate(dequeue_ops): @@ -1710,6 +1771,9 @@ class InstallSignalHandlerHook(session_run_hook.SessionRunHook): class TPUEstimator(estimator_lib.Estimator): """Estimator with TPU support. + TPUEstimator also supports training on CPU and GPU. You don't need to define + a separate `tf.estimator.Estimator`. + TPUEstimator handles many of the details of running on TPU devices, such as replicating inputs and models for each core, and returning to host periodically to run hooks. @@ -1747,7 +1811,8 @@ class TPUEstimator(estimator_lib.Estimator): Current limitations: -------------------- - 1. TPU evaluation only works on a single host (one TPU worker). + 1. TPU evaluation only works on a single host (one TPU worker) except + BROADCAST mode. 2. `input_fn` for evaluation should **NOT** raise an end-of-input exception (`OutOfRangeError` or `StopIteration`). And all evaluation steps and all @@ -1918,10 +1983,9 @@ class TPUEstimator(estimator_lib.Estimator): """Constructs an `TPUEstimator` instance. Args: - model_fn: Model function as required by `Estimator`. For training, the - returned `EstimatorSpec` cannot have hooks as it is not supported in - `TPUEstimator`. Instead, the user can pass the training hooks as - an argument to `TPUEstimator.train()`. + model_fn: Model function as required by `Estimator` which returns + EstimatorSpec or TPUEstimatorSpec. `training_hooks`, 'evaluation_hooks', + and `prediction_hooks` must not capure any TPU Tensor inside the model_fn. model_dir: Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model. If `None`, the model_dir in @@ -1986,7 +2050,7 @@ class TPUEstimator(estimator_lib.Estimator): if (config.tpu_config.per_host_input_for_training is tpu_config.InputPipelineConfig.PER_SHARD_V1 and - config.tpu_config.computation_shape): + config.tpu_config.num_cores_per_replica): raise ValueError( 'Model parallelism only supports per host input for training. ' 'Please adjust TPURunconfig.per_host_input_for_training.') @@ -2033,6 +2097,7 @@ class TPUEstimator(estimator_lib.Estimator): self._export_to_tpu = export_to_tpu self._is_input_fn_invoked = None + self._rendezvous = {} def _add_meta_graph_for_mode(self, builder, @@ -2276,6 +2341,65 @@ class TPUEstimator(estimator_lib.Estimator): """ pass + def train(self, + input_fn, + hooks=None, + steps=None, + max_steps=None, + saving_listeners=None): + rendezvous = error_handling.ErrorRendezvous(num_sources=3) + self._rendezvous[model_fn_lib.ModeKeys.TRAIN] = rendezvous + try: + return super(TPUEstimator, self).train( + input_fn=input_fn, hooks=hooks, steps=steps, max_steps=max_steps, + saving_listeners=saving_listeners + ) + except Exception: # pylint: disable=broad-except + rendezvous.record_error('training_loop', sys.exc_info()) + finally: + rendezvous.record_done('training_loop') + rendezvous.raise_errors() + + def evaluate(self, input_fn, steps=None, hooks=None, checkpoint_path=None, + name=None): + rendezvous = error_handling.ErrorRendezvous(num_sources=3) + self._rendezvous[model_fn_lib.ModeKeys.EVAL] = rendezvous + try: + return super(TPUEstimator, self).evaluate( + input_fn, steps=steps, hooks=hooks, checkpoint_path=checkpoint_path, + name=name + ) + except Exception: # pylint: disable=broad-except + rendezvous.record_error('evaluation_loop', sys.exc_info()) + finally: + rendezvous.record_done('evaluation_loop') + rendezvous.raise_errors() + + def predict(self, + input_fn, + predict_keys=None, + hooks=None, + checkpoint_path=None, + yield_single_examples=True): + rendezvous = error_handling.ErrorRendezvous(num_sources=3) + self._rendezvous[model_fn_lib.ModeKeys.PREDICT] = rendezvous + try: + for result in super(TPUEstimator, self).predict( + input_fn=input_fn, + predict_keys=predict_keys, + hooks=hooks, + checkpoint_path=checkpoint_path, + yield_single_examples=yield_single_examples): + yield result + except Exception: # pylint: disable=broad-except + rendezvous.record_error('prediction_loop', sys.exc_info()) + finally: + rendezvous.record_done('prediction_loop') + rendezvous.raise_errors() + + rendezvous.record_done('prediction_loop') + rendezvous.raise_errors() + def _augment_model_fn(self, model_fn, batch_axis): """Returns a new model_fn, which wraps the TPU support.""" @@ -2298,10 +2422,20 @@ class TPUEstimator(estimator_lib.Estimator): # Clear the bit. self._is_input_fn_invoked = None + # examples_hook is added to training_hooks for both CPU and TPU + # execution. + examples_hook = ExamplesPerSecondHook( + ctx.global_batch_size, + output_dir=self.model_dir, + every_n_steps=self._log_every_n_steps) + if ctx.is_running_on_cpu(is_export_mode=is_export_mode): logging.info('Running %s on CPU', mode) - return model_fn_wrapper.call_without_tpu( + estimator_spec = model_fn_wrapper.call_without_tpu( features, labels, is_export_mode=is_export_mode) + estimator_spec = estimator_spec._replace( + training_hooks=estimator_spec.training_hooks + (examples_hook,)) + return estimator_spec assert labels is None, '`labels` passed to `model_fn` must be `None`.' # TPUEstimator._call_input_fn passes `input_fn` as features to here. @@ -2320,7 +2454,7 @@ class TPUEstimator(estimator_lib.Estimator): graph.add_to_collection(_TPU_ENQUEUE_OPS, enqueue_op) if mode == model_fn_lib.ModeKeys.TRAIN: - loss, host_call, scaffold = ( + loss, host_call, scaffold, training_hooks = ( _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn)) host_ops = host_call.create_tpu_hostcall() if host_ops is None: @@ -2360,7 +2494,9 @@ class TPUEstimator(estimator_lib.Estimator): enqueue_ops, host_ops, run_infeed_loop_on_coordinator=( - run_infeed_loop_on_coordinator)), + run_infeed_loop_on_coordinator), + rendezvous=self._rendezvous[mode], + ), InstallSignalHandlerHook(), training.LoggingTensorHook( { @@ -2369,14 +2505,13 @@ class TPUEstimator(estimator_lib.Estimator): }, every_n_iter=logging_hook_frequency) ]) - examples_hook = ExamplesPerSecondHook( - ctx.global_batch_size, - output_dir=self.model_dir, - every_n_steps=self._log_every_n_steps) examples_hook._set_steps_per_run( # pylint: disable=protected-access self._config.tpu_config.iterations_per_loop) hooks.append(examples_hook) + if training_hooks: + hooks.extend(training_hooks) + chief_hooks = [] if (self._config.save_checkpoints_secs or self._config.save_checkpoints_steps): @@ -2388,6 +2523,7 @@ class TPUEstimator(estimator_lib.Estimator): checkpoint_hook._set_steps_per_run( # pylint: disable=protected-access self._config.tpu_config.iterations_per_loop) chief_hooks.append(checkpoint_hook) + summary.scalar(model_fn_lib.LOSS_METRIC_KEY, loss) with ops.control_dependencies([loss]): update_ops = _sync_variables_ops() @@ -2407,7 +2543,7 @@ class TPUEstimator(estimator_lib.Estimator): scaffold=scaffold) if mode == model_fn_lib.ModeKeys.EVAL: - total_loss, host_calls, scaffold = _eval_on_tpu_system( + total_loss, host_calls, scaffold, eval_hooks = _eval_on_tpu_system( ctx, model_fn_wrapper, dequeue_fn) iterations_per_loop_var = _create_or_get_iterations_per_loop() mean_loss = math_ops.div(total_loss, @@ -2432,7 +2568,8 @@ class TPUEstimator(estimator_lib.Estimator): host_call_ret = host_calls.create_tpu_hostcall() eval_metric_ops = {} eval_update_ops = [] - for k, v in host_call_ret['eval_metrics'].items(): + + for k, v in host_call_ret.get('eval_metrics', {}).items(): eval_metric_ops[k] = (v[0], dummy_update_op) eval_update_ops.append(v[1]) @@ -2446,9 +2583,13 @@ class TPUEstimator(estimator_lib.Estimator): enqueue_ops, eval_update_ops + host_ops, run_infeed_loop_on_coordinator=( - run_infeed_loop_on_coordinator)), + run_infeed_loop_on_coordinator), + rendezvous=self._rendezvous[mode]), ] + input_hooks + if eval_hooks: + hooks.extend(eval_hooks) + return model_fn_lib.EstimatorSpec( mode, loss=mean_loss, @@ -2459,8 +2600,9 @@ class TPUEstimator(estimator_lib.Estimator): # Predict assert mode == model_fn_lib.ModeKeys.PREDICT - dummy_predict_op, host_calls, scaffold = _predict_on_tpu_system( - ctx, model_fn_wrapper, dequeue_fn) + (dummy_predict_op, host_calls, + scaffold, prediction_hooks) = _predict_on_tpu_system( + ctx, model_fn_wrapper, dequeue_fn) with ops.control_dependencies([dummy_predict_op]): internal_ops_to_run = _sync_variables_ops() with ops.control_dependencies(internal_ops_to_run): @@ -2512,10 +2654,13 @@ class TPUEstimator(estimator_lib.Estimator): hooks = [ _StoppingPredictHook(scalar_stopping_signal), - TPUInfeedOutfeedSessionHookForPrediction(ctx, enqueue_ops, - host_ops), + TPUInfeedOutfeedSessionHookForPrediction( + ctx, enqueue_ops, host_ops, rendezvous=self._rendezvous[mode]), ] + input_hooks + if prediction_hooks: + hooks.extend(prediction_hooks) + return model_fn_lib.EstimatorSpec( mode, prediction_hooks=hooks, @@ -2599,8 +2744,8 @@ def _eval_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): """Executes `model_fn_wrapper` multiple times on all TPU shards.""" iterations_per_loop_var = _create_or_get_iterations_per_loop() - single_tpu_eval_step, host_calls, captured_scaffold_fn = ( - model_fn_wrapper.convert_to_single_tpu_eval_step(dequeue_fn)) + (single_tpu_eval_step, host_calls, captured_scaffold_fn, captured_eval_hooks + ) = model_fn_wrapper.convert_to_single_tpu_eval_step(dequeue_fn) def multi_tpu_eval_steps_on_single_shard(): return training_loop.repeat( @@ -2615,15 +2760,16 @@ def _eval_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): device_assignment=ctx.device_assignment) scaffold = _get_scaffold(captured_scaffold_fn) - return loss, host_calls, scaffold + return loss, host_calls, scaffold, captured_eval_hooks.get() def _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): """Executes `model_fn_wrapper` multiple times on all TPU shards.""" iterations_per_loop_var = _create_or_get_iterations_per_loop() - single_tpu_train_step, host_call, captured_scaffold_fn = ( - model_fn_wrapper.convert_to_single_tpu_train_step(dequeue_fn)) + (single_tpu_train_step, host_call, captured_scaffold_fn, + captured_training_hooks) = ( + model_fn_wrapper.convert_to_single_tpu_train_step(dequeue_fn)) def multi_tpu_train_steps_on_single_shard(): return training_loop.repeat( @@ -2638,15 +2784,16 @@ def _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): device_assignment=ctx.device_assignment) scaffold = _get_scaffold(captured_scaffold_fn) - return loss, host_call, scaffold + return loss, host_call, scaffold, captured_training_hooks.get() def _predict_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): """Executes `model_fn_wrapper` multiple times on all TPU shards.""" num_cores = ctx.num_cores - single_tpu_predict_step, host_calls, captured_scaffold_fn = ( - model_fn_wrapper.convert_to_single_tpu_predict_step(dequeue_fn)) + (single_tpu_predict_step, host_calls, captured_scaffold_fn, + captured_predict_hooks + ) = model_fn_wrapper.convert_to_single_tpu_predict_step(dequeue_fn) def multi_tpu_predict_steps_on_single_shard(): @@ -2666,7 +2813,7 @@ def _predict_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): outputs_from_all_shards=False) scaffold = _get_scaffold(captured_scaffold_fn) - return dummy_predict_op, host_calls, scaffold + return dummy_predict_op, host_calls, scaffold, captured_predict_hooks.get() def _wrap_computation_in_while_loop(device, op_fn): @@ -3163,3 +3310,47 @@ def _add_item_to_params(params, key, value): else: # Now params is Python dict. params[key] = value + + +def export_estimator_savedmodel(estimator, + export_dir_base, + serving_input_receiver_fn, + assets_extra=None, + as_text=False, + checkpoint_path=None, + strip_default_attrs=False): + """Export `Estimator` trained model for TPU inference. + + Args: + estimator: `Estimator` with which model has been trained. + export_dir_base: A string containing a directory in which to create + timestamped subdirectories containing exported SavedModels. + serving_input_receiver_fn: A function that takes no argument and + returns a `ServingInputReceiver` or `TensorServingInputReceiver`. + assets_extra: A dict specifying how to populate the assets.extra directory + within the exported SavedModel, or `None` if no extra assets are needed. + as_text: whether to write the SavedModel proto in text format. + checkpoint_path: The checkpoint path to export. If `None` (the default), + the most recent checkpoint found within the model directory is chosen. + strip_default_attrs: Boolean. If `True`, default-valued attributes will be + removed from the NodeDefs. + + Returns: + The string path to the exported directory. + """ + # `TPUEstimator` requires `tpu_config.RunConfig`, so we cannot use + # `estimator.config`. + config = tpu_config.RunConfig(model_dir=estimator.model_dir) + est = TPUEstimator( + estimator._model_fn, # pylint: disable=protected-access + config=config, + params=estimator.params, + use_tpu=True, + train_batch_size=2048, # Does not matter. + eval_batch_size=2048, # Does not matter. + ) + return est.export_savedmodel(export_dir_base, serving_input_receiver_fn, + assets_extra, + as_text, + checkpoint_path, + strip_default_attrs) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_feed.py b/tensorflow/contrib/tpu/python/tpu/tpu_feed.py index 604e6600c81a4136a1f10e79a725a887a96f4d86..a44b4f4622afabced9cb1b801acedb0e7b1e5d12 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_feed.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_feed.py @@ -461,7 +461,10 @@ class InfeedQueue(object): name=full_name, device_ordinal=tpu_ordinal) - def generate_enqueue_ops(self, sharded_inputs, tpu_ordinal_function=None): + def generate_enqueue_ops(self, + sharded_inputs, + tpu_ordinal_function=None, + placement_function=None): """Generates the host-side Ops to enqueue the shards of a tuple. sharded_inputs is a list, one for each shard, of lists of @@ -483,6 +486,9 @@ class InfeedQueue(object): shard index as input and returns the ordinal of the TPU device the shard's infeed should be placed on. tpu_ordinal_function must be set if the inputs are placed on CPU devices. + placement_function: if not None, a function that takes the shard index as + input and returns the host device where the enqueue op should be placed + on. Returns: A list of host-side Ops, one for each shard, that when executed together @@ -508,8 +514,12 @@ class InfeedQueue(object): tpu_ordinal_function = lambda index: -1 name_prefix = "%s/enqueue" % self._name return [ - self._generate_enqueue_op(shard, name_prefix, index, - tpu_ordinal=tpu_ordinal_function(index)) + self._generate_enqueue_op( + shard, + name_prefix, + index, + tpu_ordinal=tpu_ordinal_function(index), + device=placement_function(index) if placement_function else None) for (shard, index) in zip(sharded_inputs, xrange(self.number_of_shards)) ] diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py b/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py index 15f99d7eebddd46f9f6902b68f01e42359a72cbe..53d33f40777a1c6d93f19c30b2ef5902d63ad2fd 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_optimizer.py @@ -23,6 +23,7 @@ import collections from tensorflow.contrib.tpu.python.ops import tpu_ops from tensorflow.contrib.tpu.python.tpu import tpu_function +from tensorflow.python.framework import ops from tensorflow.python.ops.losses import losses from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import optimizer @@ -153,8 +154,9 @@ class CrossShardOptimizer(optimizer.Optimizer): if grad is None: summed_grads_and_vars.append((grad, var)) else: - summed_grads_and_vars.append((tpu_ops.cross_replica_sum( - grad, self._group_assignment), var)) + with ops.colocate_with(grad): + summed_grads_and_vars.append((tpu_ops.cross_replica_sum( + grad, self._group_assignment), var)) return self._opt.apply_gradients(summed_grads_and_vars, global_step, name) def get_slot(self, *args, **kwargs): diff --git a/tensorflow/contrib/training/python/training/sgdr_learning_rate_decay.py b/tensorflow/contrib/training/python/training/sgdr_learning_rate_decay.py new file mode 100644 index 0000000000000000000000000000000000000000..ed0f398e30a7f3c0b1b9378f8fc5d5bfbea1536a --- /dev/null +++ b/tensorflow/contrib/training/python/training/sgdr_learning_rate_decay.py @@ -0,0 +1,187 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""SGDR learning rate decay function.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math + +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import ops +from tensorflow.python.ops import math_ops, control_flow_ops + + +def sgdr_decay(learning_rate, global_step, initial_period_steps, + t_mul=2.0, m_mul=1.0, name=None): + """Implements Stochastic Gradient Descent with Warm Restarts (SGDR). + + As described in "SGDR: Stochastic Gradient Descent + with Warm Restarts" by Ilya Loshchilov & Frank Hutter, Proceedings of + ICLR'2017, available at https://arxiv.org/pdf/1608.03983.pdf + + The learning rate decreases according to cosine annealing: + + ```python + learning_rate * 0.5 * (1 + cos(x_val * pi)) # for x_val defined in [0, 1] + ``` + + Thus, at the beginning (when the restart index i = 0), + the learning rate decreases for `initial_period_steps` steps from the initial + learning rate `learning_rate` (when `x_val=0`, we get `cos(0)=1`) to + 0 (when `x_val=1`, we get `cos(pi)=-1`). + + The decrease within the i-th period takes `t_i` steps, + where `t_0` = `initial_period_steps` is the user-defined number of batch + iterations (not epochs as in the paper) to be performed before the first + restart is launched. + + Then, we perform the first restart (i=1) by setting the learning rate to + `learning_rate*(m_mul^i)`, where `m_mul in [0,1]` (set to 1 by default). + The i-th restart runs for `t_i=t_0*(t_mul^i)` steps, i.e., every new + restart runs `t_mul` times longer than the previous one. + + Importantly, when one has no access to a validation set, SGDR suggests + to report the best expected / recommended solution in the following way: + When we are within our initial run (i=0), every new solution represents + SGDR's recommended solution. Instead, when i>0, the recommended solution is + the one obtained at the end of each restart. + + Note that the minimum learning rate is set to 0 for simplicity, + you can adjust the code to deal with any positive minimum learning rate + as defined in the paper. + + `initial_period_steps` is the duration of the first period measured in terms + of number of minibatch updates. If one wants to use epochs, one should compute + the number of updates required for an epoch. + + For example, assume the following parameters and intention: + Minibatch size: 100 + Training dataset size: 10000 + If the user wants the first decay period to span across 5 epochs, then + `initial_period_steps` = 5 * 10000/100 = 500 + + Train for 10000 batch iterations with the initial learning rate set to + 0.1, then restart to run 2 times longer, i.e, for 20000 batch iterations + and with the initial learning rate 0.05, then restart again and again, + doubling the runtime of each new period and with two times smaller + initial learning rate. + + To accomplish the above, one would write: + + ```python + ... + global_step = tf.Variable(0, trainable=False) + starter_learning_rate = 0.1 + learning_rate = sgdr_decay(starter_learning_rate, global_step, + initial_period_steps=10000, t_mul=2, m_mul=0.5) + # Passing global_step to minimize() will increment it at each step. + learning_step = ( + tf.train.GradientDescentOptimizer(learning_rate) + .minimize(...my loss..., global_step=global_step) + ) + + # Step | 0 | 1000 | 5000 | 9000 | 9999 | 10000 | 11000 | + # LR | 0.1 | 0.097 | 0.05 | 0.002 | 0.00 | 0.05 | 0.0496 | + + # Step | 20000 | 29000 | 29999 | 30000 | + # LR | 0.025 | 0.0003 | 0.00 | 0.025 | + ``` + + Args: + learning_rate: A scalar `float32` or `float64` `Tensor` or a + Python number. The initial learning rate. + global_step: A scalar `int32` or `int64` `Tensor` or a Python number. + Global step to use for the decay computation. Must not be negative. + initial_period_steps: Duration of the first period measured as the number + of minibatch updates, if one wants to use epochs, one should compute + the number of updates required for an epoch. + t_mul: A scalar `float32` or `float64` `Tensor` or a Python number. + Must be positive. + Used to derive the number of iterations in the i-th period: + `initial_period_steps * (t_mul^i)`. Defaults to 2.0. + m_mul: A scalar `float32` or `float64` `Tensor` or a Python number. + Must be positive. + Used to derive the initial learning rate of the i-th period: + `learning_rate * (m_mul^i)`. Defaults to 1.0 + + Returns: + A scalar `Tensor` of the same type as `learning_rate`. + The learning rate for a provided global_step. + Raises: + ValueError: if `global_step` is not supplied. + """ + + if global_step is None: + raise ValueError("global_step is required for sgdr_decay.") + with ops.name_scope(name, "SGDRDecay", + [learning_rate, global_step, + initial_period_steps, t_mul, m_mul]) as name: + learning_rate = ops.convert_to_tensor(learning_rate, + name="initial_learning_rate") + dtype = learning_rate.dtype + global_step = math_ops.cast(global_step, dtype) + t_0 = math_ops.cast(initial_period_steps, dtype) + t_mul = math_ops.cast(t_mul, dtype) + m_mul = math_ops.cast(m_mul, dtype) + + c_one = math_ops.cast(constant_op.constant(1.0), dtype) + c_half = math_ops.cast(constant_op.constant(0.5), dtype) + c_pi = math_ops.cast(constant_op.constant(math.pi), dtype) + + # Find normalized value of the current step + x_val = math_ops.div(global_step, t_0) + + def compute_step(x_val, geometric=False): + if geometric: + # Consider geometric series where t_mul != 1 + # 1 + t_mul + t_mul^2 ... = (1 - t_mul^i_restart) / (1 - t_mul) + + # First find how many restarts were performed for a given x_val + # Find maximal integer i_restart value for which this equation holds + # x_val >= (1 - t_mul^i_restart) / (1 - t_mul) + # x_val * (1 - t_mul) <= (1 - t_mul^i_restart) + # t_mul^i_restart <= (1 - x_val * (1 - t_mul)) + + # tensorflow allows only log with base e + # i_restart <= log(1 - x_val * (1 - t_mul) / log(t_mul) + # Find how many restarts were performed + + i_restart = math_ops.floor( + math_ops.log(c_one - x_val * (c_one - t_mul)) / math_ops.log(t_mul)) + # Compute the sum of all restarts before the current one + sum_r = (c_one - t_mul ** i_restart) / (c_one - t_mul) + # Compute our position within the current restart + x_val = (x_val - sum_r) / t_mul ** i_restart + + else: + # Find how many restarts were performed + i_restart = math_ops.floor(x_val) + # Compute our position within the current restart + x_val = x_val - i_restart + return i_restart, x_val + + i_restart, x_val = control_flow_ops.cond( + math_ops.equal(t_mul, c_one), + lambda: compute_step(x_val, geometric=False), + lambda: compute_step(x_val, geometric=True)) + + # If m_mul < 1, then the initial learning rate of every new restart will be + # smaller, i.e., by a factor of m_mul ** i_restart at i_restart-th restart + m_fac = learning_rate * (m_mul ** i_restart) + + return math_ops.multiply(c_half * m_fac, + (math_ops.cos(x_val * c_pi) + c_one), name=name) diff --git a/tensorflow/contrib/training/python/training/sgdr_learning_rate_decay_test.py b/tensorflow/contrib/training/python/training/sgdr_learning_rate_decay_test.py new file mode 100644 index 0000000000000000000000000000000000000000..4a46e9a49ef203384e36698f81d6cbe3a3881ef8 --- /dev/null +++ b/tensorflow/contrib/training/python/training/sgdr_learning_rate_decay_test.py @@ -0,0 +1,145 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Functional test for sgdr learning rate decay.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math + +from sgdr_learning_rate_decay import sgdr_decay +from tensorflow.python.platform import googletest +from tensorflow.python.framework import test_util +from tensorflow.python.framework import dtypes +from tensorflow import placeholder + + +class SGDRDecayTest(test_util.TensorFlowTestCase): + """Unit tests for SGDR learning rate decay.""" + + def get_original_values(self, lr, t_e, mult_factor, iter_per_epoch, epochs): + """Get an array with learning rate values from the consecutive steps using + the original implementation + (https://github.com/loshchil/SGDR/blob/master/SGDR_WRNs.py).""" + t0 = math.pi / 2.0 + tt = 0 + te_next = t_e + + lr_values = [] + sh_lr = lr + for epoch in range(epochs): + for _ in range(iter_per_epoch): + # In the original approach training function is executed here + lr_values.append(sh_lr) + dt = 2.0 * math.pi / float(2.0 * t_e) + tt = tt + float(dt) / iter_per_epoch + if tt >= math.pi: + tt = tt - math.pi + cur_t = t0 + tt + new_lr = lr * (1.0 + math.sin(cur_t)) / 2.0 # lr_min = 0, lr_max = lr + sh_lr = new_lr + if (epoch + 1) == te_next: # time to restart + sh_lr = lr + tt = 0 # by setting to 0 we set lr to lr_max, see above + t_e = t_e * mult_factor # change the period of restarts + te_next = te_next + t_e # note the next restart's epoch + + return lr_values + + def get_sgdr_values(self, lr, initial_period_steps, t_mul, iters): + """Get an array with learning rate values from the consecutive steps + using current tensorflow implementation.""" + with self.test_session(): + step = placeholder(dtypes.int32) + + decay = sgdr_decay(lr, step, initial_period_steps, t_mul) + lr_values = [] + for i in range(iters): + lr_values.append(decay.eval(feed_dict={step: i})) + + return lr_values + + def testCompareToOriginal(self): + """Compare values generated by tensorflow implementation to the values + generated by the original implementation + (https://github.com/loshchil/SGDR/blob/master/SGDR_WRNs.py).""" + with self.test_session(): + lr = 10.0 + init_steps = 2 + t_mul = 3 + iters = 10 + epochs = 50 + + org_lr = self.get_original_values(lr, init_steps, t_mul, iters, epochs) + sgdr_lr = self.get_sgdr_values(lr, init_steps*iters, t_mul, iters*epochs) + + for org, sgdr in zip(org_lr, sgdr_lr): + self.assertAllClose(org, sgdr) + + def testMDecay(self): + """Test m_mul argument. Check values for learning rate at the beginning + of the first, second, third and fourth period. """ + with self.test_session(): + step = placeholder(dtypes.int32) + + lr = 0.1 + t_e = 10 + t_mul = 3 + m_mul = 0.9 + + decay = sgdr_decay(lr, step, t_e, t_mul, m_mul) + + test_step = 0 + self.assertAllClose(decay.eval(feed_dict={step: test_step}), + lr) + + test_step = t_e + self.assertAllClose(decay.eval(feed_dict={step: test_step}), + lr * m_mul) + + test_step = t_e + t_e*t_mul + self.assertAllClose(decay.eval(feed_dict={step: test_step}), + lr * m_mul**2) + + test_step = t_e + t_e*t_mul + t_e * (t_mul**2) + self.assertAllClose(decay.eval(feed_dict={step: test_step}), + lr * (m_mul**3)) + + def testCos(self): + """Check learning rate values at the beginning, in the middle + and at the end of the period.""" + with self.test_session(): + step = placeholder(dtypes.int32) + lr = 0.2 + t_e = 1000 + t_mul = 1 + + decay = sgdr_decay(lr, step, t_e, t_mul) + + test_step = 0 + self.assertAllClose(decay.eval(feed_dict={step: test_step}), lr) + + test_step = t_e//2 + self.assertAllClose(decay.eval(feed_dict={step: test_step}), lr/2) + + test_step = t_e + self.assertAllClose(decay.eval(feed_dict={step: test_step}), lr) + + test_step = t_e*3//2 + self.assertAllClose(decay.eval(feed_dict={step: test_step}), lr/2) + +if __name__ == "__main__": + googletest.main() diff --git a/tensorflow/contrib/verbs/rdma_mgr.cc b/tensorflow/contrib/verbs/rdma_mgr.cc index 9cb3d1fbbfdbc6d85a7a9799bd82438f0bf70c4f..3cb5e61facf860f2740935f66bf548096296280f 100644 --- a/tensorflow/contrib/verbs/rdma_mgr.cc +++ b/tensorflow/contrib/verbs/rdma_mgr.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/core/common_runtime/bfc_allocator.h" #include "tensorflow/core/common_runtime/gpu/gpu_process_state.h" #include "tensorflow/core/common_runtime/gpu/gpu_util.h" +#include "tensorflow/core/common_runtime/pool_allocator.h" #include "tensorflow/core/common_runtime/process_state.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.h" #include "tensorflow/core/distributed_runtime/session_mgr.h" @@ -255,28 +256,25 @@ void MRDeleter(ibv_mr* mr) { } } -// TODO(byronyi): remove this class duplicated from the one in -// common/runtime/gpu/pool_allocator.h when it is available in common_runtime -class BasicCPUAllocator : public SubAllocator { - public: - ~BasicCPUAllocator() override {} - - void* Alloc(size_t alignment, size_t num_bytes) override { - return port::AlignedMalloc(num_bytes, alignment); - } - void Free(void* ptr, size_t) override { port::AlignedFree(ptr); } -}; - // TODO(byronyi): remove this class and its registration when the default -// cpu_allocator() returns visitable allocator +// cpu_allocator() returns visitable allocator, or cpu_allocator() is no +// longer in use. class BFCRdmaAllocator : public BFCAllocator { public: BFCRdmaAllocator() - : BFCAllocator(new BasicCPUAllocator(), 1LL << 36, true, "cpu_rdma_bfc") { + : BFCAllocator(new BasicCPUAllocator(port::kNUMANoAffinity), 1LL << 36, + true, "cpu_rdma_bfc") {} +}; +class BFCRdmaAllocatorFactory : public AllocatorFactory { + public: + Allocator* CreateAllocator() { return new BFCRdmaAllocator; } + + SubAllocator* CreateSubAllocator(int numa_node) { + return new BasicCPUAllocator(numa_node); } }; -REGISTER_MEM_ALLOCATOR("BFCRdmaAllocator", 101, BFCRdmaAllocator); +REGISTER_MEM_ALLOCATOR("BFCRdmaAllocator", 101, BFCRdmaAllocatorFactory); void RdmaMgr::InitAllocators() { RdmaMemoryMgr::Singleton().pd_ = rdma_adapter_->pd_; @@ -284,8 +282,8 @@ void RdmaMgr::InitAllocators() { Allocator* allocators[] = { #if GOOGLE_CUDA GPUProcessState::singleton()->GetCUDAHostAllocator(0), - ProcessState::singleton()->GetCPUAllocator(0), #endif // GOOGLE_CUDA + ProcessState::singleton()->GetCPUAllocator(0), cpu_allocator(), }; diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 97880219b80d663e9ee4eb8f0373786b23284b54..35a112e8340ccee1f27fb1cd44227a37bff5bacd 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -150,7 +150,6 @@ load( "//third_party/mkl:build_defs.bzl", "if_mkl", ) -load("@io_bazel_rules_closure//closure:defs.bzl", "closure_proto_library") exports_files(["ops/ops.pbtxt"]) @@ -334,6 +333,7 @@ filegroup( "platform/init_main.h", "platform/mem.h", "platform/mutex.h", + "platform/numa.h", "platform/thread_annotations.h", ], visibility = ["//visibility:private"], @@ -662,6 +662,7 @@ cc_library( "lib/random/random_distributions.h", "lib/random/simple_philox.h", "lib/strings/numbers.h", + "lib/strings/proto_serialization.h", "lib/strings/str_util.h", "lib/strings/strcat.h", "lib/strings/stringprintf.h", @@ -846,6 +847,7 @@ tf_cuda_library( "util/sparse/sparse_tensor.h", "util/stat_summarizer.h", "util/stat_summarizer_options.h", + "util/status_util.h", "util/stream_executor_util.h", "util/strided_slice_op.h", "util/tensor_format.h", @@ -882,6 +884,16 @@ cc_library( copts = tf_copts(), ) +tf_cc_test( + name = "stats_calculator_test", + srcs = ["util/stats_calculator_test.cc"], + deps = [ + ":stats_calculator_portable", + ":test", + ":test_main", + ], +) + cc_library( name = "overflow", hdrs = ["util/overflow.h"], @@ -1644,6 +1656,7 @@ cc_library( copts = tf_copts(android_optimization_level_override = None) + [ "-DSUPPORT_SELECTIVE_REGISTRATION", ], + linkopts = if_android(["-lz"]), tags = [ "manual", "notap", @@ -1667,6 +1680,7 @@ cc_library( copts = tf_copts(android_optimization_level_override = None) + tf_opts_nortti_if_android() + [ "-DSUPPORT_SELECTIVE_REGISTRATION", ], + linkopts = if_android(["-lz"]), tags = [ "manual", "notap", @@ -1923,7 +1937,6 @@ tf_proto_library_cc( srcs = ["protobuf/master_service.proto"], has_services = 1, cc_api_version = 2, - cc_grpc_version = 1, cc_stubby_versions = ["2"], protodeps = [":master_proto"], visibility = [ @@ -1953,8 +1966,10 @@ LIB_INTERNAL_PRIVATE_HEADERS = ["framework/resource_handle.h"] + glob( "**/*test*", "lib/gif/**/*", "lib/jpeg/**/*", + "lib/png/**/*", "platform/gif.h", "platform/jpeg.h", + "platform/png.h", "platform/**/cuda.h", "platform/**/stream_executor.h", ], @@ -2049,6 +2064,7 @@ cc_library( "lib/hash/crc32c_accelerate.cc", "lib/gif/**/*", "lib/jpeg/**/*", + "lib/png/**/*", "platform/**/env_time.cc", "platform/**/cuda_libdevice_path.cc", "platform/**/device_tracer.cc", @@ -2144,6 +2160,39 @@ cc_library( ], ) +cc_library( + name = "png_internal", + srcs = ["lib/png/png_io.cc"], + hdrs = [ + "lib/bfloat16/bfloat16.h", + "lib/core/casts.h", + "lib/core/stringpiece.h", + "lib/png/png_io.h", + "platform/byte_order.h", + "platform/cpu_info.h", + "platform/default/integral_types.h", + "platform/default/logging.h", + "platform/logging.h", + "platform/macros.h", + "platform/platform.h", + "platform/png.h", + "platform/types.h", + ], + copts = tf_copts(), + linkopts = select({ + "//tensorflow:freebsd": [], + "//tensorflow:windows": [], + "//tensorflow:windows_msvc": [], + "//conditions:default": ["-ldl"], + }), + deps = [ + ":lib", + ":lib_internal", + "//tensorflow/core/platform/default/build_config:png", + "@zlib_archive//:zlib", + ], +) + cc_library( name = "tflite_portable_logging", srcs = [], @@ -2429,6 +2478,7 @@ tf_cuda_library( "framework/resource_handle.cc", "util/memmapped_file_system.*", "util/memmapped_file_system_writer.*", + "util/stats_calculator.*", "util/version_info.cc", ], ) + select({ @@ -2455,6 +2505,7 @@ tf_cuda_library( ":protos_all_proto_text", ":error_codes_proto_text", ":protos_all_cc", + ":stats_calculator_portable", ":version_lib", "//tensorflow/core/platform/default/build_config:platformlib", "//tensorflow/core/kernels:bounds_check", @@ -2873,6 +2924,14 @@ tf_cuda_library( ] + tf_additional_device_tracer_deps(), ) +cc_library( + name = "session_ref", + srcs = ["common_runtime/session_ref.cc"], + hdrs = ["common_runtime/session_ref.h"], + copts = tf_copts(), + deps = [":core_cpu_base"], +) + cc_library( name = "gpu_id", hdrs = [ @@ -3188,6 +3247,7 @@ tf_cc_tests( ":test", ":test_main", "//third_party/eigen3", + "@zlib_archive//:zlib", ], ) @@ -3237,6 +3297,28 @@ tf_cc_test( ], ) +tf_cc_test( + name = "platform_numa_test", + size = "small", + srcs = ["platform/numa_test.cc"], + tags = [ + # This test will not pass unless it has access to all NUMA nodes + # on the executing machine. + "manual", + "notap", + ], + deps = [ + ":framework", + ":lib", + ":lib_internal", + ":lib_test_internal", + ":protos_all_cc", + ":test", + ":test_main", + "//third_party/eigen3", + ], +) + tf_cc_test( name = "platform_setround_test", size = "small", @@ -3601,6 +3683,7 @@ tf_cc_test_mkl( deps = [ ":core", ":core_cpu", + ":core_cpu_internal", ":framework", ":framework_internal", ":test", @@ -3674,7 +3757,6 @@ tf_cc_tests_gpu( "common_runtime/gpu/gpu_bfc_allocator_test.cc", "common_runtime/gpu/gpu_device_test.cc", "common_runtime/gpu/gpu_id_manager_test.cc", - "common_runtime/gpu/gpu_event_mgr_test.cc", "common_runtime/gpu/pool_allocator_test.cc", ], linkstatic = tf_kernel_tests_linkstatic(), @@ -3698,6 +3780,23 @@ tf_cc_tests_gpu( ], ) +tf_cc_test_gpu( + name = "gpu_event_mgr_test", + srcs = ["common_runtime/gpu/gpu_event_mgr_test.cc"], + linkstatic = tf_kernel_tests_linkstatic(), + tags = tf_cuda_tests_tags(), + deps = [ + ":framework", + ":framework_internal", + ":lib", + ":lib_internal", + ":protos_all_cc", + ":test", + ":test_main", + ":testlib", + ], +) + tf_cuda_cc_test( name = "gpu_device_unified_memory_test", size = "small", diff --git a/tensorflow/core/api_def/base_api/api_def_DrawBoundingBoxes.pbtxt b/tensorflow/core/api_def/base_api/api_def_DrawBoundingBoxes.pbtxt index 6c3ae09f5d6e448a34032dd3dec2280290584d13..35c916e26922705554035b268035dac6ef3ceeb7 100644 --- a/tensorflow/core/api_def/base_api/api_def_DrawBoundingBoxes.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_DrawBoundingBoxes.pbtxt @@ -30,7 +30,7 @@ height of the underlying image. For example, if an image is 100 x 200 pixels (height x width) and the bounding box is `[0.1, 0.2, 0.5, 0.9]`, the upper-left and bottom-right coordinates of -the bounding box will be `(40, 10)` to `(100, 50)` (in (x,y) coordinates). +the bounding box will be `(40, 10)` to `(180, 50)` (in (x,y) coordinates). Parts of the bounding box may fall outside the image. END diff --git a/tensorflow/core/api_def/base_api/api_def_IteratorFromStringHandleV2.pbtxt b/tensorflow/core/api_def/base_api/api_def_IteratorFromStringHandleV2.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..9d464b2aea7904ab87c6864ef1007b4c8634a434 --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_IteratorFromStringHandleV2.pbtxt @@ -0,0 +1,4 @@ +op { + graph_op_name: "IteratorFromStringHandleV2" + visibility: HIDDEN +} diff --git a/tensorflow/core/api_def/base_api/api_def_IteratorV2.pbtxt b/tensorflow/core/api_def/base_api/api_def_IteratorV2.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..becc7290162e9efb929380b2fe4388021c78249a --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_IteratorV2.pbtxt @@ -0,0 +1,4 @@ +op { + graph_op_name: "IteratorV2" + visibility: HIDDEN +} diff --git a/tensorflow/core/api_def/base_api/api_def_NonMaxSuppressionV4.pbtxt b/tensorflow/core/api_def/base_api/api_def_NonMaxSuppressionV4.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..75df90f570b84730da0378ba2532215b4811d073 --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_NonMaxSuppressionV4.pbtxt @@ -0,0 +1,78 @@ +op { + graph_op_name: "NonMaxSuppressionV4" + in_arg { + name: "boxes" + description: <

javac -cp libtensorflow-1.9.0-rc0.jar HelloTF.java
+
javac -cp libtensorflow-1.9.0.jar HelloTF.java
### Running @@ -241,11 +241,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.9.0-rc0.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.9.0.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.9.0-rc0.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.9.0.jar;. -Djava.library.path=jni HelloTF
If the program prints Hello from version, you've successfully installed TensorFlow for Java and are ready to use the API. If the program diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index f21c073a1bb9cbef0e0aef1deaf178051ce2e4b5..3a9a01c57ec7e5906109502cd933910b0f6e20d3 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -1,38 +1,38 @@ -# Installing TensorFlow on Ubuntu +# Install TensorFlow on Ubuntu This guide explains how to install TensorFlow on Ubuntu Linux. While these -instructions may work on other Linux variants, they are tested and supported with -the following system requirements: - -* 64-bit desktops or laptops -* Ubuntu 16.04 or higher +instructions may work on other Linux variants, they are tested and supported +with the following system requirements: +* 64-bit desktops or laptops +* Ubuntu 16.04 or higher ## Choose which TensorFlow to install The following TensorFlow variants are available for installation: -* __TensorFlow with CPU support only__. If your system does not have a - NVIDIA® GPU, you must install this version. This version of TensorFlow is - usually easier to install, so even if you have an NVIDIA GPU, we recommend - installing this version first. -* __TensorFlow with GPU support__. TensorFlow programs usually run much faster on - a GPU instead of a CPU. If you run performance-critical applications and your - system has an NVIDIA® GPU that meets the prerequisites, you should install - this version. See [TensorFlow GPU support](#NVIDIARequirements) for details. - +* __TensorFlow with CPU support only__. If your system does not have a + NVIDIA® GPU, you must install this version. This version of TensorFlow + is usually easier to install, so even if you have an NVIDIA GPU, we + recommend installing this version first. +* __TensorFlow with GPU support__. TensorFlow programs usually run much faster + on a GPU instead of a CPU. If you run performance-critical applications and + your system has an NVIDIA® GPU that meets the prerequisites, you should + install this version. See [TensorFlow GPU support](#NVIDIARequirements) for + details. ## How to install TensorFlow There are a few options to install TensorFlow on your machine: -* [Use pip in a virtual environment](#InstallingVirtualenv) *(recommended)* -* [Use pip in your system environment](#InstallingNativePip) -* [Configure a Docker container](#InstallingDocker) -* [Use pip in Anaconda](#InstallingAnaconda) -* [Install TensorFlow from source](/install/install_sources) +* [Use pip in a virtual environment](#InstallingVirtualenv) *(recommended)* +* [Use pip in your system environment](#InstallingNativePip) +* [Configure a Docker container](#InstallingDocker) +* [Use pip in Anaconda](#InstallingAnaconda) +* [Install TensorFlow from source](/install/install_sources) + ### Use `pip` in a virtual environment Key Point: Using a virtual environment is the recommended install method. @@ -41,8 +41,8 @@ The [Virtualenv](https://virtualenv.pypa.io/en/stable/) tool creates virtual Python environments that are isolated from other Python development on the same machine. In this scenario, you install TensorFlow and its dependencies within a virtual environment that is available when *activated*. Virtualenv provides a -reliable way to install and run TensorFlow while avoiding conflicts with the rest -of the system. +reliable way to install and run TensorFlow while avoiding conflicts with the +rest of the system. ##### 1. Install Python, `pip`, and `virtualenv`. @@ -62,10 +62,10 @@ To install these packages on Ubuntu: We *recommend* using `pip` version 8.1 or higher. If using a release before -version 8.1, upgrade `pip`: +version 8.1, upgrade `pip`: 
-  sudo pip install -U pip
+  pip install --upgrade pip
If not using Ubuntu and [setuptools](https://pypi.org/project/setuptools/) is @@ -102,7 +102,7 @@ When the Virtualenv is activated, the shell prompt displays as `(venv) $`. Within the active virtual environment, upgrade `pip`: 
-(venv)$ pip install -U pip
+(venv)$ pip install --upgrade pip
You can install other Python packages within the virtual environment without @@ -112,15 +112,15 @@ affecting packages outside the `virtualenv`. Choose one of the available TensorFlow packages for installation: -* `tensorflow` —Current release for CPU -* `tensorflow-gpu` —Current release with GPU support -* `tf-nightly` —Nightly build for CPU -* `tf-nightly-gpu` —Nightly build with GPU support +* `tensorflow` —Current release for CPU +* `tensorflow-gpu` —Current release with GPU support +* `tf-nightly` —Nightly build for CPU +* `tf-nightly-gpu` —Nightly build with GPU support Within an active Virtualenv environment, use `pip` to install the package: 
-  pip install -U tensorflow
+  pip install --upgrade tensorflow
Use `pip list` to show the packages installed in the virtual environment. @@ -160,14 +160,14 @@ To uninstall TensorFlow, remove the Virtualenv directory you created in step 2: rm -r ~/tensorflow/venv - + ### Use `pip` in your system environment Use `pip` to install the TensorFlow package directly on your system without using a container or virtual environment for isolation. This method is -recommended for system administrators that want a TensorFlow installation that is -available to everyone on a multi-user system. +recommended for system administrators that want a TensorFlow installation that +is available to everyone on a multi-user system. Since a system install is not isolated, it could interfere with other Python-based installations. But if you understand `pip` and your Python @@ -195,10 +195,10 @@ To install these packages on Ubuntu: We *recommend* using `pip` version 8.1 or higher. If using a release before -version 8.1, upgrade `pip`: +version 8.1, upgrade `pip`: 
-  sudo pip install -U pip
+  pip install --upgrade pip
If not using Ubuntu and [setuptools](https://pypi.org/project/setuptools/) is @@ -212,16 +212,16 @@ installed, use `easy_install` to install `pip`: Choose one of the available TensorFlow packages for installation: -* `tensorflow` —Current release for CPU -* `tensorflow-gpu` —Current release with GPU support -* `tf-nightly` —Nightly build for CPU -* `tf-nightly-gpu` —Nightly build with GPU support +* `tensorflow` —Current release for CPU +* `tensorflow-gpu` —Current release with GPU support +* `tf-nightly` —Nightly build for CPU +* `tf-nightly-gpu` —Nightly build with GPU support And use `pip` to install the package for Python 2 or 3: 
-  sudo pip install -U tensorflow   # Python 2.7
-  sudo pip3 install -U tensorflow  # Python 3.n
+  pip install --upgrade --user tensorflow   # Python 2.7
+  pip3 install --upgrade --user tensorflow  # Python 3.n
Use `pip list` to show the packages installed on the system. @@ -239,8 +239,8 @@ If the above steps failed, try installing the TensorFlow binary using the remote URL of the `pip` package: 
-  sudo pip install --upgrade remote-pkg-URL   # Python 2.7
-  sudo pip3 install --upgrade remote-pkg-URL  # Python 3.n
+  pip install --user --upgrade remote-pkg-URL   # Python 2.7
+  pip3 install --user --upgrade remote-pkg-URL  # Python 3.n
The remote-pkg-URL depends on the operating system, Python version, @@ -255,42 +255,41 @@ encounter problems. To uninstall TensorFlow on your system, use one of following commands: 
-  sudo pip uninstall tensorflow   # for Python 2.7
-  sudo pip3 uninstall tensorflow  # for Python 3.n
+  pip uninstall tensorflow   # for Python 2.7
+  pip3 uninstall tensorflow  # for Python 3.n
+ ### Configure a Docker container -Docker completely isolates the TensorFlow installation -from pre-existing packages on your machine. The Docker container contains -TensorFlow and all its dependencies. Note that the Docker image can be quite -large (hundreds of MBs). You might choose the Docker installation if you are -incorporating TensorFlow into a larger application architecture that already -uses Docker. +Docker completely isolates the TensorFlow installation from pre-existing +packages on your machine. The Docker container contains TensorFlow and all its +dependencies. Note that the Docker image can be quite large (hundreds of MBs). +You might choose the Docker installation if you are incorporating TensorFlow +into a larger application architecture that already uses Docker. Take the following steps to install TensorFlow through Docker: - 1. Install Docker on your machine as described in the - [Docker documentation](http://docs.docker.com/engine/installation/). - 2. Optionally, create a Linux group called docker to allow - launching containers without sudo as described in the - [Docker documentation](https://docs.docker.com/engine/installation/linux/linux-postinstall/). - (If you don't do this step, you'll have to use sudo each time - you invoke Docker.) - 3. To install a version of TensorFlow that supports GPUs, you must first - install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker), which - is stored in github. - 4. Launch a Docker container that contains one of the - [TensorFlow binary images](https://hub.docker.com/r/tensorflow/tensorflow/tags/). +1. Install Docker on your machine as described in the + [Docker documentation](http://docs.docker.com/engine/installation/). +2. Optionally, create a Linux group called docker to allow + launching containers without sudo as described in the + [Docker documentation](https://docs.docker.com/engine/installation/linux/linux-postinstall/). + (If you don't do this step, you'll have to use sudo each time you invoke + Docker.) +3. To install a version of TensorFlow that supports GPUs, you must first + install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker), which is + stored in github. +4. Launch a Docker container that contains one of the + [TensorFlow binary images](https://hub.docker.com/r/tensorflow/tensorflow/tags/). The remainder of this section explains how to launch a Docker container. - #### CPU-only -To launch a Docker container with CPU-only support (that is, without -GPU support), enter a command of the following format: +To launch a Docker container with CPU-only support (that is, without GPU +support), enter a command of the following format: 
 $ docker run -it -p hostPort:containerPort TensorFlowCPUImage
@@ -298,29 +297,31 @@ $ docker run -it -p hostPort:containerPort TensorFlowCPUImage
 
 where:
 
-  * -p hostPort:containerPort is optional.
-    If you plan to run TensorFlow programs from the shell, omit this option.
-    If you plan to run TensorFlow programs as Jupyter notebooks, set both
-    hostPort and containerPort
-    to 8888.  If you'd like to run TensorBoard inside the container,
-    add a second `-p` flag, setting both hostPort and containerPort
-    to 6006.
-  * TensorFlowCPUImage is required. It identifies the Docker
+*   -p hostPort:containerPort is optional. If you plan to run
+    TensorFlow programs from the shell, omit this option. If you plan to run
+    TensorFlow programs as Jupyter notebooks, set both hostPort
+    and containerPort to 8888. If you'd like to run
+    TensorBoard inside the container, add a second `-p` flag, setting both
+    hostPort and containerPort to 6006.
+*   TensorFlowCPUImage is required. It identifies the Docker
     container. Specify one of the following values:
-    * tensorflow/tensorflow, which is the TensorFlow CPU binary image.
-    * tensorflow/tensorflow:latest-devel, which is the latest
-      TensorFlow CPU Binary image plus source code.
-    * tensorflow/tensorflow:version, which is the
-      specified version (for example, 1.1.0rc1) of TensorFlow CPU binary image.
-    * tensorflow/tensorflow:version-devel, which is
-      the specified version (for example, 1.1.0rc1) of the TensorFlow GPU
-      binary image plus source code.
+
+    *   tensorflow/tensorflow, which is the TensorFlow CPU binary
+        image.
+    *   tensorflow/tensorflow:latest-devel, which is the latest
+        TensorFlow CPU Binary image plus source code.
+    *   tensorflow/tensorflow:version, which is the specified
+        version (for example, 1.1.0rc1) of TensorFlow CPU binary image.
+    *   tensorflow/tensorflow:version-devel, which is the
+        specified version (for example, 1.1.0rc1) of the TensorFlow GPU binary
+        image plus source code.
 
     TensorFlow images are available at
     [dockerhub](https://hub.docker.com/r/tensorflow/tensorflow/).
 
-For example, the following command launches the latest TensorFlow CPU binary image
-in a Docker container from which you can run TensorFlow programs in a shell:
+For example, the following command launches the latest TensorFlow CPU binary
+image in a Docker container from which you can run TensorFlow programs in a
+shell:
 
 
 $ docker run -it tensorflow/tensorflow bash
@@ -336,10 +337,11 @@ $ docker run -it -p 8888:8888 tensorflow/tensorflow
 
 Docker will download the TensorFlow binary image the first time you launch it.
 
-
 #### GPU support
 
-To launch a Docker container with NVidia GPU support, enter a command of the following format (this [does not require any local CUDA installation](https://github.com/nvidia/nvidia-docker/wiki/CUDA#requirements)):
+To launch a Docker container with NVidia GPU support, enter a command of the
+following format (this
+[does not require any local CUDA installation](https://github.com/nvidia/nvidia-docker/wiki/CUDA#requirements)):
 
 
 $ nvidia-docker run -it -p hostPort:containerPort TensorFlowGPUImage
@@ -347,34 +349,34 @@ $ nvidia-docker run -it -p hostPort:containerPort TensorFlowGPUImage-p hostPort:containerPort is optional. If you plan
-    to run TensorFlow programs from the shell, omit this option. If you plan
-    to run TensorFlow programs as Jupyter notebooks, set both
-    hostPort and containerPort to `8888`.
-  * TensorFlowGPUImage specifies the Docker container. You must
-    specify one of the following values:
-    * tensorflow/tensorflow:latest-gpu, which is the latest
-      TensorFlow GPU binary image.
-    * tensorflow/tensorflow:latest-devel-gpu, which is
-      the latest TensorFlow GPU Binary image plus source code.
-    * tensorflow/tensorflow:version-gpu, which is the
-      specified version (for example, 0.12.1) of the TensorFlow GPU
-      binary image.
-    * tensorflow/tensorflow:version-devel-gpu, which is
-      the specified version (for example, 0.12.1) of the TensorFlow GPU
-      binary image plus source code.
-
-We recommend installing one of the `latest` versions. For example, the
-following command launches the latest TensorFlow GPU binary image in a
-Docker container from which you can run TensorFlow programs in a shell:
+*   -p hostPort:containerPort is optional. If you plan to run
+    TensorFlow programs from the shell, omit this option. If you plan to run
+    TensorFlow programs as Jupyter notebooks, set both hostPort
+    and containerPort to `8888`.
+*   TensorFlowGPUImage specifies the Docker container. You must specify
+    one of the following values:
+    *   tensorflow/tensorflow:latest-gpu, which is the latest
+        TensorFlow GPU binary image.
+    *   tensorflow/tensorflow:latest-devel-gpu, which is the latest
+        TensorFlow GPU Binary image plus source code.
+    *   tensorflow/tensorflow:version-gpu, which is the
+        specified version (for example, 0.12.1) of the TensorFlow GPU binary
+        image.
+    *   tensorflow/tensorflow:version-devel-gpu, which is the
+        specified version (for example, 0.12.1) of the TensorFlow GPU binary
+        image plus source code.
+
+We recommend installing one of the `latest` versions. For example, the following
+command launches the latest TensorFlow GPU binary image in a Docker container
+from which you can run TensorFlow programs in a shell:
 
 
 $ nvidia-docker run -it tensorflow/tensorflow:latest-gpu bash
 

 
-The following command also launches the latest TensorFlow GPU binary image
-in a Docker container. In this Docker container, you can run TensorFlow
-programs in a Jupyter notebook:
+The following command also launches the latest TensorFlow GPU binary image in a
+Docker container. In this Docker container, you can run TensorFlow programs in a
+Jupyter notebook:
 
 
 $ nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:latest-gpu
@@ -390,14 +392,12 @@ Docker will download the TensorFlow binary image the first time you launch it.
 For more details see the
 [TensorFlow docker readme](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/docker).
 
-
 #### Next Steps
 
-You should now
-[validate your installation](#ValidateYourInstallation).
-
+You should now [validate your installation](#ValidateYourInstallation).
 
 
+
 ### Use `pip` in Anaconda
 
 Anaconda provides the `conda` utility to create a virtual environment. However,
@@ -410,61 +410,59 @@ not tested on new TensorFlow releases.
 
 Take the following steps to install TensorFlow in an Anaconda environment:
 
-  1. Follow the instructions on the
-     [Anaconda download site](https://www.continuum.io/downloads)
-     to download and install Anaconda.
+1.  Follow the instructions on the
+    [Anaconda download site](https://www.continuum.io/downloads) to download and
+    install Anaconda.
 
-  2. Create a conda environment named tensorflow to run a version
-     of Python by invoking the following command:
+2.  Create a conda environment named tensorflow to run a version of
+    Python by invoking the following command:
 
      
$ conda create -n tensorflow pip python=2.7 # or python=3.3, etc.

 
-  3. Activate the conda environment by issuing the following command:
+3.  Activate the conda environment by issuing the following command:
 
      
$ source activate tensorflow
      (tensorflow)$  # Your prompt should change 

 
-  4. Issue a command of the following format to install
-     TensorFlow inside your conda environment:
+4.  Issue a command of the following format to install TensorFlow inside your
+    conda environment:
 
      
(tensorflow)$ pip install --ignore-installed --upgrade tfBinaryURL

 
-     where tfBinaryURL is the
-     [URL of the TensorFlow Python package](#the_url_of_the_tensorflow_python_package).
-     For example, the following command installs the CPU-only version of
-     TensorFlow for Python 3.4:
+    where tfBinaryURL is the
+    [URL of the TensorFlow Python package](#the_url_of_the_tensorflow_python_package).
+    For example, the following command installs the CPU-only version of
+    TensorFlow for Python 3.4:
 
      
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl

+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp34-cp34m-linux_x86_64.whl

 
 
+
 ## Validate your installation
 
 To validate your TensorFlow installation, do the following:
 
-  1. Ensure that your environment is prepared to run TensorFlow programs.
-  2. Run a short TensorFlow program.
-
+1.  Ensure that your environment is prepared to run TensorFlow programs.
+2.  Run a short TensorFlow program.
 
 ### Prepare your environment
 
-If you installed on native pip, Virtualenv, or Anaconda, then
-do the following:
+If you installed on native pip, Virtualenv, or Anaconda, then do the following:
 
-  1. Start a terminal.
-  2. If you installed with Virtualenv or Anaconda, activate your container.
-  3. If you installed TensorFlow source code, navigate to any
-     directory *except* one containing TensorFlow source code.
+1.  Start a terminal.
+2.  If you installed with Virtualenv or Anaconda, activate your container.
+3.  If you installed TensorFlow source code, navigate to any directory *except*
+    one containing TensorFlow source code.
 
-If you installed through Docker, start a Docker container
-from which you can run bash. For example:
+If you installed through Docker, start a Docker container from which you can run
+bash. For example:
 
 
 $ docker run -it tensorflow/tensorflow bash
 

 
-
 ### Run a short TensorFlow program
 
 Invoke python from your shell as follows:
@@ -486,94 +484,71 @@ TensorFlow programs:
 
 
Hello, TensorFlow!

 
-If the system outputs an error message instead of a greeting, see [Common
-installation problems](#common_installation_problems).
+If the system outputs an error message instead of a greeting, see
+[Common installation problems](#common_installation_problems).
 
 To learn more, see the [TensorFlow tutorials](../tutorials/).
 
 
-## TensorFlow GPU support
-
-To install TensorFlow with GPU support, configure the following NVIDIA® software
-on your system:
-
-* [CUDA Toolkit 9.0](http://nvidia.com/cuda). For details, see
-  [NVIDIA's documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/).
-  Append the relevant CUDA pathnames to the `LD_LIBRARY_PATH` environmental
-  variable as described in the NVIDIA documentation.
-* [cuDNN SDK v7](http://developer.nvidia.com/cudnn). For details, see
-  [NVIDIA's documentation](http://docs.nvidia.com/deeplearning/sdk/cudnn-install/).
-  Create the `CUDA_HOME` environment variable as described in the NVIDIA
-  documentation.
-* A GPU card with CUDA Compute Capability 3.0 or higher for building TensorFlow
-  from source. To use the TensorFlow binaries, version 3.5 or higher is required.
-  See the [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a
-  list of supported GPU cards.
-* [GPU drivers](http://nvidia.com/drivers) that support your version of the CUDA
-  Toolkit.
-* The `libcupti-dev` library is the NVIDIA CUDA Profile Tools Interface. This
-  library provides advanced profiling support. To install this library,
-  use the following command for CUDA Toolkit >= 8.0:
-
-
-  sudo apt-get install cuda-command-line-tools
-

-
-Add this path to the `LD_LIBRARY_PATH` environmental variable:
-
-
-  export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:+${LD_LIBRARY_PATH}:}/usr/local/cuda/extras/CUPTI/lib64
-

-
-* *OPTIONAL*:  For optimized performance during inference, install
-  *NVIDIA TensorRT 3.0*. To install the minimal amount of TensorRT
-  runtime components required to use with the pre-built `tensorflow-gpu` package:
 
-
-  wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvinfer-runtime-trt-repo-ubuntu1404-3.0.4-ga-cuda9.0_1.0-1_amd64.deb
-  sudo dpkg -i nvinfer-runtime-trt-repo-ubuntu1404-3.0.4-ga-cuda9.0_1.0-1_amd64.deb
-  sudo apt-get update
-  sudo apt-get install -y --allow-downgrades libnvinfer-dev libcudnn7-dev=7.0.5.15-1+cuda9.0 libcudnn7=7.0.5.15-1+cuda9.0
-

-
-Note: For compatibility with the pre-built `tensorflow-gpu` package, use the
-Ubuntu *14.04* package of TensorRT (shown above). Use this even when installing
-on an Ubuntu 16.04 system.
-
-To build the TensorFlow-TensorRT integration module from source instead of using
-the pre-built binaries, see the
-[module documentation](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/tensorrt#using-tensorrt-in-tensorflow).
-For detailed TensorRT installation instructions, see
-[NVIDIA's TensorRT documentation](http://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html).
-
-To avoid cuDNN version conflicts during later system upgrades, hold the cuDNN
-version at 7.0.5:
-
-
-  sudo apt-mark hold libcudnn7 libcudnn7-dev
-

-
-To allow upgrades, remove the this hold:
-
-
-  sudo apt-mark unhold libcudnn7 libcudnn7-dev
-

-
-If you have an earlier version of the preceding packages, upgrade to the
-specified versions. If upgrading is not possible, you can still run TensorFlow
-with GPU support by @{$install_sources}.
+## TensorFlow GPU support
 
+Note: Due to the number of libraries required, using [Docker](#InstallingDocker)
+is recommended over installing directly on the host system.
+
+The following NVIDIA® hardware must be installed on your system:
+
+*   GPU card with CUDA Compute Capability 3.5 or higher. See
+    [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a list of
+    supported GPU cards.
+
+The following NVIDIA® software must be installed on your system:
+
+*   [GPU drivers](http://nvidia.com/driver). CUDA 9.0 requires 384.x or higher.
+*   [CUDA Toolkit 9.0](http://nvidia.com/cuda).
+*   [cuDNN SDK](http://developer.nvidia.com/cudnn) (>= 7.0). Version 7.1 is
+    recommended.
+*   [CUPTI](http://docs.nvidia.com/cuda/cupti/) ships with the CUDA Toolkit, but
+    you also need to append its path to the `LD_LIBRARY_PATH` environment
+    variable: `export
+    LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64`
+*   *OPTIONAL*: [NCCL 2.2](https://developer.nvidia.com/nccl) to use TensorFlow
+    with multiple GPUs.
+*   *OPTIONAL*:
+    [TensorRT](http://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html)
+    which can improve latency and throughput for inference for some models.
+
+To use a GPU with CUDA Compute Capability 3.0, or different versions of the
+preceding NVIDIA libraries see
+@{$install_sources$installing TensorFlow from Sources}. If using Ubuntu 16.04
+and possibly other Debian based linux distros, `apt-get` can be used with the
+NVIDIA repository to simplify installation.
+
+```bash
+# Adds NVIDIA package repository.
+sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
+wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
+wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
+sudo dpkg -i cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
+sudo dpkg -i nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
+sudo apt-get update
+# Includes optional NCCL 2.x.
+sudo apt-get install cuda9.0 cuda-cublas-9-0 cuda-cufft-9-0 cuda-curand-9-0 \
+  cuda-cusolver-9-0 cuda-cusparse-9-0 libcudnn7=7.1.4.18-1+cuda9.0 \
+   libnccl2=2.2.13-1+cuda9.0 cuda-command-line-tools-9-0
+# Optionally install TensorRT runtime, must be done after above cuda install.
+sudo apt-get update
+sudo apt-get install libnvinfer4=4.1.2-1+cuda9.0
+```
 
 ## Common installation problems
 
 We are relying on Stack Overflow to document TensorFlow installation problems
-and their remedies.  The following table contains links to Stack Overflow
-answers for some common installation problems.
-If you encounter an error message or other
-installation problem not listed in the following table, search for it
-on Stack Overflow.  If Stack Overflow doesn't show the error message,
-ask a new question about it on Stack Overflow and specify
-the `tensorflow` tag.
+and their remedies. The following table contains links to Stack Overflow answers
+for some common installation problems. If you encounter an error message or
+other installation problem not listed in the following table, search for it on
+Stack Overflow. If Stack Overflow doesn't show the error message, ask a new
+question about it on Stack Overflow and specify the `tensorflow` tag.
 
 
@@ -657,74 +632,67 @@ the `tensorflow` tag.
 
 

 
 Link to GitHub or Stack Overflow Error Message 

 
-
 
+
 ## The URL of the TensorFlow Python package
 
 A few installation mechanisms require the URL of the TensorFlow Python package.
 The value you specify depends on three factors:
 
-  * operating system
-  * Python version
-  * CPU only vs. GPU support
+*   operating system
+*   Python version
+*   CPU only vs. GPU support
 
 This section documents the relevant values for Linux installations.
 
-
 ### Python 2.7
 
 CPU only:
 
 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp27-none-linux_x86_64.whl
 

 
-
 GPU support:
 
 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp27-none-linux_x86_64.whl
 

 
 Note that GPU support requires the NVIDIA hardware and software described in
 [NVIDIA requirements to run TensorFlow with GPU support](#NVIDIARequirements).
 
-
 ### Python 3.4
 
 CPU only:
 
 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp34-cp34m-linux_x86_64.whl
 

 
-
 GPU support:
 
 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp34-cp34m-linux_x86_64.whl
 

 
 Note that GPU support requires the NVIDIA hardware and software described in
 [NVIDIA requirements to run TensorFlow with GPU support](#NVIDIARequirements).
 
-
 ### Python 3.5
 
 CPU only:
 
 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp35-cp35m-linux_x86_64.whl
 

 
-
 GPU support:
 
 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp35-cp35m-linux_x86_64.whl
 

 
-
 Note that GPU support requires the NVIDIA hardware and software described in
 [NVIDIA requirements to run TensorFlow with GPU support](#NVIDIARequirements).
 
@@ -733,16 +701,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp36-cp36m-linux_x86_64.whl
 

 
-
 GPU support:
 
 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp36-cp36m-linux_x86_64.whl
 

 
-
 Note that GPU support requires the NVIDIA hardware and software described in
 [NVIDIA requirements to run TensorFlow with GPU support](#NVIDIARequirements).
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index c6f0c17924c95e11d22b08c8976d9044c365dce2..1a7b2b815d101e1dca87a2dd987441a0b51f636a 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -1,4 +1,4 @@
-# Installing TensorFlow on macOS
+# Install TensorFlow on macOS
 
 This guide explains how to install TensorFlow on macOS. Although these
 instructions might also work on other macOS variants, we have only
@@ -119,7 +119,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      TensorFlow in the active Virtualenv is as follows:
 
      
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl

+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py3-none-any.whl

 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -242,7 +242,7 @@ take the following steps:
      issue the following command:
 
      
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl 

+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py3-none-any.whl 

 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -350,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl

+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py2-none-any.whl
@@ -517,7 +517,7 @@ The value you specify depends on your Python version. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py2-none-any.whl
@@ -525,5 +525,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-a 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_raspbian.md b/tensorflow/docs_src/install/install_raspbian.md index 46c4944ca7448df2c993ee44d5099494b759dea8..58a5285c78be9bc187ae4679c79213ae40df2f30 100644 --- a/tensorflow/docs_src/install/install_raspbian.md +++ b/tensorflow/docs_src/install/install_raspbian.md @@ -1,4 +1,4 @@ -# Installing TensorFlow on Raspbian +# Install TensorFlow on Raspbian This guide explains how to install TensorFlow on a Raspberry Pi running Raspbian. Although these instructions might also work on other Pi variants, we diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index fc1f6d05bdc26785090e1fc2c6f47826660090ac..31dcad64d43bc9cef46839db050b88944f3375fb 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -1,28 +1,27 @@ -# Installing TensorFlow from Sources +# Install TensorFlow from Sources -This guide explains how to build TensorFlow sources into a TensorFlow -binary and how to install that TensorFlow binary. Note that we provide -well-tested, pre-built TensorFlow binaries for Ubuntu, macOS, and Windows -systems. In addition, there are pre-built TensorFlow -[docker images](https://hub.docker.com/r/tensorflow/tensorflow/). -So, don't build a TensorFlow binary yourself unless you are very -comfortable building complex packages from source and dealing with -the inevitable aftermath should things not go exactly as documented. +This guide explains how to build TensorFlow sources into a TensorFlow binary and +how to install that TensorFlow binary. Note that we provide well-tested, +pre-built TensorFlow binaries for Ubuntu, macOS, and Windows systems. In +addition, there are pre-built TensorFlow +[docker images](https://hub.docker.com/r/tensorflow/tensorflow/). So, don't +build a TensorFlow binary yourself unless you are very comfortable building +complex packages from source and dealing with the inevitable aftermath should +things not go exactly as documented. -If the last paragraph didn't scare you off, welcome. This guide explains -how to build TensorFlow on 64-bit desktops and laptops running either of -the following operating systems: +If the last paragraph didn't scare you off, welcome. This guide explains how to +build TensorFlow on 64-bit desktops and laptops running either of the following +operating systems: * Ubuntu * macOS X -Note: Some users have successfully built and installed TensorFlow from -sources on non-supported systems. Please remember that we do not fix -issues stemming from these attempts. +Note: Some users have successfully built and installed TensorFlow from sources +on non-supported systems. Please remember that we do not fix issues stemming +from these attempts. -We **do not support** building TensorFlow on Windows. That said, if you'd -like to try to build TensorFlow on Windows anyway, use either of the -following: +We **do not support** building TensorFlow on Windows. That said, if you'd like +to try to build TensorFlow on Windows anyway, use either of the following: * [Bazel on Windows](https://bazel.build/versions/master/docs/windows.html) * [TensorFlow CMake build](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/cmake) @@ -32,38 +31,33 @@ instructions. Older CPUs may not be able to execute these binaries. ## Determine which TensorFlow to install -You must choose one of the following types of TensorFlow to build and -install: - -* **TensorFlow with CPU support only**. If your system does not have a - NVIDIA® GPU, build and install this version. Note that this version of - TensorFlow is typically easier to build and install, so even if you - have an NVIDIA GPU, we recommend building and installing this version - first. -* **TensorFlow with GPU support**. TensorFlow programs typically run - significantly faster on a GPU than on a CPU. Therefore, if your system - has a NVIDIA GPU and you need to run performance-critical applications, - you should ultimately build and install this version. - Beyond the NVIDIA GPU itself, your system must also fulfill the NVIDIA - software requirements described in one of the following documents: +You must choose one of the following types of TensorFlow to build and install: - * @{$install_linux#NVIDIARequirements$Installing TensorFlow on Ubuntu} - * @{$install_mac#NVIDIARequirements$Installing TensorFlow on macOS} +* **TensorFlow with CPU support only**. If your system does not have a NVIDIA® + GPU, build and install this version. Note that this version of TensorFlow is + typically easier to build and install, so even if you have an NVIDIA GPU, we + recommend building and installing this version first. +* **TensorFlow with GPU support**. TensorFlow programs typically run + significantly faster on a GPU than on a CPU. Therefore, if your system has a + NVIDIA GPU and you need to run performance-critical applications, you should + ultimately build and install this version. Beyond the NVIDIA GPU itself, + your system must also fulfill the NVIDIA software requirements described in + one of the following documents: + * @ {$install_linux#NVIDIARequirements$Installing TensorFlow on Ubuntu} + * @ {$install_mac#NVIDIARequirements$Installing TensorFlow on macOS} ## Clone the TensorFlow repository -Start the process of building TensorFlow by cloning a TensorFlow -repository. +Start the process of building TensorFlow by cloning a TensorFlow repository. To clone **the latest** TensorFlow repository, issue the following command: 
$ git clone https://github.com/tensorflow/tensorflow
-The preceding git clone command creates a subdirectory -named `tensorflow`. After cloning, you may optionally build a -**specific branch** (such as a release branch) by invoking the -following commands: +The preceding git clone command creates a subdirectory named +`tensorflow`. After cloning, you may optionally build a **specific branch** +(such as a release branch) by invoking the following commands: 
 $ cd tensorflow
@@ -75,38 +69,34 @@ issue the following command:
 
 
$ git checkout r1.0

 
-Next, you must prepare your environment for
-[Linux](#PrepareLinux)
-or
+Next, you must prepare your environment for [Linux](#PrepareLinux) or
 [macOS](#PrepareMac)
 
-
 
-## Prepare environment for Linux
 
-Before building TensorFlow on Linux, install the following build
-tools on your system:
+## Prepare environment for Linux
 
-  * bazel
-  * TensorFlow Python dependencies
-  * optionally, NVIDIA packages to support TensorFlow for GPU.
+Before building TensorFlow on Linux, install the following build tools on your
+system:
 
+*   bazel
+*   TensorFlow Python dependencies
+*   optionally, NVIDIA packages to support TensorFlow for GPU.
 
 ### Install Bazel
 
 If bazel is not installed on your system, install it now by following
 [these directions](https://bazel.build/versions/master/docs/install.html).
 
-
 ### Install TensorFlow Python dependencies
 
 To install TensorFlow, you must install the following packages:
 
-  * `numpy`, which is a numerical processing package that TensorFlow requires.
-  * `dev`, which enables adding extensions to Python.
-  * `pip`, which enables you to install and manage certain Python packages.
-  * `wheel`, which enables you to manage Python compressed packages in
-    the wheel (.whl) format.
+*   `numpy`, which is a numerical processing package that TensorFlow requires.
+*   `dev`, which enables adding extensions to Python.
+*   `pip`, which enables you to install and manage certain Python packages.
+*   `wheel`, which enables you to manage Python compressed packages in the wheel
+    (.whl) format.
 
 To install these packages for Python 2.7, issue the following command:
 
@@ -120,68 +110,70 @@ To install these packages for Python 3.n, issue the following command:
 $ sudo apt-get install python3-numpy python3-dev python3-pip python3-wheel
- ### Optional: install TensorFlow for GPU prerequisites If you are building TensorFlow without GPU support, skip this section. -The following NVIDIA hardware must be installed on your system: - - * GPU card with CUDA Compute Capability 3.0 or higher. See - [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) - for a list of supported GPU cards. - -The following NVIDIA software must be installed on your system: - - * [CUDA Toolkit](http://nvidia.com/cuda) (>= 8.0). We recommend version 9.0. - For details, see - [NVIDIA's documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/). - Ensure that you append the relevant CUDA pathnames to the - `LD_LIBRARY_PATH` environment variable as described in the - NVIDIA documentation. - * [GPU drivers](http://nvidia.com/driver) supporting your version of the CUDA - Toolkit. - * [cuDNN SDK](http://developer.nvidia.com/cudnn) (>= 6.0). We recommend version 7.0. For details, see - [NVIDIA's documentation](http://docs.nvidia.com/deeplearning/sdk/cudnn-install/). - * [CUPTI](http://docs.nvidia.com/cuda/cupti/) ships with the CUDA Toolkit, but - you also need to append its path to the `LD_LIBRARY_PATH` environment - variable: +The following NVIDIA® hardware must be installed on your system: - 
 $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64
+* GPU card with CUDA Compute Capability 3.5 or higher. See + [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a list of + supported GPU cards. + +The following NVIDIA® software must be installed on your system: + +* [GPU drivers](http://nvidia.com/driver). CUDA 9.0 requires 384.x or higher. +* [CUDA Toolkit](http://nvidia.com/cuda) (>= 8.0). We recommend version 9.0. +* [cuDNN SDK](http://developer.nvidia.com/cudnn) (>= 6.0). We recommend + version 7.1.x. +* [CUPTI](http://docs.nvidia.com/cuda/cupti/) ships with the CUDA Toolkit, but + you also need to append its path to the `LD_LIBRARY_PATH` environment + variable: `export + LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64` +* *OPTIONAL*: [NCCL 2.2](https://developer.nvidia.com/nccl) to use TensorFlow + with multiple GPUs. +* *OPTIONAL*: + [TensorRT](http://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html) + which can improve latency and throughput for inference for some models. + +While it is possible to install the NVIDIA libraries via `apt-get` from the +NVIDIA repository, the libraries and headers are installed in locations that +make it difficult to configure and debug build issues. Downloading and +installing the libraries manually or using docker +([latest-devel-gpu](https://hub.docker.com/r/tensorflow/tensorflow/tags/)) is +recommended. ### Next After preparing the environment, you must now [configure the installation](#ConfigureInstallation). - + ## Prepare environment for macOS Before building TensorFlow, you must install the following on your system: - * bazel - * TensorFlow Python dependencies. - * optionally, NVIDIA packages to support TensorFlow for GPU. - +* bazel +* TensorFlow Python dependencies. +* optionally, NVIDIA packages to support TensorFlow for GPU. ### Install bazel If bazel is not installed on your system, install it now by following [these directions](https://bazel.build/versions/master/docs/install.html#mac-os-x). - ### Install python dependencies To build TensorFlow, you must install the following packages: - * six - * numpy, which is a numerical processing package that TensorFlow requires. - * wheel, which enables you to manage Python compressed packages - in the wheel (.whl) format. +* six +* numpy, which is a numerical processing package that TensorFlow requires. +* wheel, which enables you to manage Python compressed packages in the wheel + (.whl) format. -You may install the python dependencies using pip. If you don't have pip -on your machine, we recommend using homebrew to install Python and pip as +You may install the python dependencies using pip. If you don't have pip on your +machine, we recommend using homebrew to install Python and pip as [documented here](http://docs.python-guide.org/en/latest/starting/install/osx/). If you follow these instructions, you will not need to disable SIP. @@ -192,22 +184,23 @@ After installing pip, invoke the following commands: Note: These are just the minimum requirements to _build_ tensorflow. Installing the pip package will download additional packages required to _run_ it. If you plan on executing tasks directly with `bazel` , without the pip installation, -you may need to install additional python packages. For example, you should -`pip install mock enum34` before running TensorFlow's tests with bazel. +you may need to install additional python packages. For example, you should `pip +install mock enum34` before running TensorFlow's tests with bazel. + ## Configure the installation -The root of the source tree contains a bash script named -configure. This script asks you to identify the pathname of all -relevant TensorFlow dependencies and specify other build configuration options -such as compiler flags. You must run this script *prior* to -creating the pip package and installing TensorFlow. +The root of the source tree contains a bash script named configure. +This script asks you to identify the pathname of all relevant TensorFlow +dependencies and specify other build configuration options such as compiler +flags. You must run this script *prior* to creating the pip package and +installing TensorFlow. -If you wish to build TensorFlow with GPU, `configure` will ask -you to specify the version numbers of CUDA and cuDNN. If several -versions of CUDA or cuDNN are installed on your system, explicitly select -the desired version instead of relying on the default. +If you wish to build TensorFlow with GPU, `configure` will ask you to specify +the version numbers of CUDA and cuDNN. If several versions of CUDA or cuDNN are +installed on your system, explicitly select the desired version instead of +relying on the default. One of the questions that `configure` will ask is as follows: @@ -215,73 +208,117 @@ One of the questions that `configure` will ask is as follows: Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native] -This question refers to a later phase in which you'll use bazel to [build the -pip package](#build-the-pip-package) or the [C/Java libraries](#BuildCorJava). -We recommend accepting the default (`-march=native`), which will optimize the -generated code for your local machine's CPU type. However, if you are building -TensorFlow on one CPU type but will run TensorFlow on a different CPU type, then -consider specifying a more specific optimization -flag as described in [the gcc -documentation](https://gcc.gnu.org/onlinedocs/gcc-4.5.3/gcc/i386-and-x86_002d64-Options.html). +This question refers to a later phase in which you'll use bazel to +[build the pip package](#build-the-pip-package) or the +[C/Java libraries](#BuildCorJava). We recommend accepting the default +(`-march=native`), which will optimize the generated code for your local +machine's CPU type. However, if you are building TensorFlow on one CPU type but +will run TensorFlow on a different CPU type, then consider specifying a more +specific optimization flag as described in +[the gcc documentation](https://gcc.gnu.org/onlinedocs/gcc-4.5.3/gcc/i386-and-x86_002d64-Options.html). -Here is an example execution of the `configure` script. Note that your -own input will likely differ from our sample input: +Here is an example execution of the `configure` script. Note that your own input +will likely differ from our sample input: 
 $ cd tensorflow  # cd to the top-level directory created
 $ ./configure
+You have bazel 0.15.0 installed.
 Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python2.7
+
+
 Found possible Python library paths:
   /usr/local/lib/python2.7/dist-packages
   /usr/lib/python2.7/dist-packages
 Please input the desired Python library path to use.  Default is [/usr/lib/python2.7/dist-packages]
 
-Using python library path: /usr/local/lib/python2.7/dist-packages
-Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:
-Do you wish to use jemalloc as the malloc implementation? [Y/n]
-jemalloc enabled
-Do you wish to build TensorFlow with Google Cloud Platform support? [y/N]
-No Google Cloud Platform support will be enabled for TensorFlow
-Do you wish to build TensorFlow with Hadoop File System support? [y/N]
-No Hadoop File System support will be enabled for TensorFlow
-Do you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N]
-No XLA support will be enabled for TensorFlow
-Do you wish to build TensorFlow with VERBS support? [y/N]
-No VERBS support will be enabled for TensorFlow
-Do you wish to build TensorFlow with OpenCL support? [y/N]
-No OpenCL support will be enabled for TensorFlow
-Do you wish to build TensorFlow with CUDA support? [y/N] Y
-CUDA support will be enabled for TensorFlow
-Do you want to use clang as CUDA compiler? [y/N]
-nvcc will be used as CUDA compiler
+Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]:
+jemalloc as malloc support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]:
+Google Cloud Platform support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with Hadoop File System support? [Y/n]:
+Hadoop File System support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with Amazon AWS Platform support? [Y/n]:
+Amazon AWS Platform support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with Apache Kafka Platform support? [Y/n]:
+Apache Kafka Platform support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with XLA JIT support? [y/N]:
+No XLA JIT support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with GDR support? [y/N]:
+No GDR support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with VERBS support? [y/N]:
+No VERBS support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]:
+No OpenCL SYCL support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with CUDA support? [y/N]: Y
+CUDA support will be enabled for TensorFlow.
+
 Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 9.0]: 9.0
+
+
 Please specify the location where CUDA 9.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
-Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
-Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 7
+
+
+Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 7.0
+
+
 Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
-Please specify a list of comma-separated CUDA compute capabilities you want to build with.
+
+
+Do you wish to build TensorFlow with TensorRT support? [y/N]:
+No TensorRT support will be enabled for TensorFlow.
+
+Please specify the NCCL version you want to use. If NCLL 2.2 is not installed, then you can use version 1.3 that can be fetched automatically but it may have worse performance with multiple GPUs. [Default is 2.2]: 1.3
+
+
+Please specify a list of comma-separated Cuda compute capabilities you want to build with.
 You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
-Please note that each additional compute capability significantly increases your build time and binary size.
-[Default is: "3.5,5.2"]: 3.0
-Do you wish to build TensorFlow with MPI support? [y/N]
-MPI support will not be enabled for TensorFlow
+Please note that each additional compute capability significantly increases your
+build time and binary size. [Default is: 3.5,7.0] 6.1
+
+
+Do you want to use clang as CUDA compiler? [y/N]:
+nvcc will be used as CUDA compiler.
+
+Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
+
+
+Do you wish to build TensorFlow with MPI support? [y/N]:
+No MPI support will be enabled for TensorFlow.
+
+Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:
+
+
+Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]:
+Not configuring the WORKSPACE for Android builds.
+
+Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
+    --config=mkl            # Build with MKL support.
+    --config=monolithic     # Config for mostly static monolithic build.
 Configuration finished
-If you told `configure` to build for GPU support, then `configure` -will create a canonical set of symbolic links to the CUDA libraries -on your system. Therefore, every time you change the CUDA library paths, -you must rerun the `configure` script before re-invoking -the bazel build command. +If you told `configure` to build for GPU support, then `configure` will create a +canonical set of symbolic links to the CUDA libraries on your system. Therefore, +every time you change the CUDA library paths, you must rerun the `configure` +script before re-invoking the bazel build command. Note the following: - * Although it is possible to build both CUDA and non-CUDA configs - under the same source tree, we recommend running `bazel clean` when - switching between these two configurations in the same source tree. - * If you don't run the `configure` script *before* running the - `bazel build` command, the `bazel build` command will fail. - +* Although it is possible to build both CUDA and non-CUDA configs under the + same source tree, we recommend running `bazel clean` when switching between + these two configurations in the same source tree. +* If you don't run the `configure` script *before* running the `bazel build` + command, the `bazel build` command will fail. ## Build the pip package @@ -297,7 +334,8 @@ To build a pip package for TensorFlow with CPU-only support: $ bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package -To build a pip package for TensorFlow with CPU-only support for the Intel® MKL-DNN: +To build a pip package for TensorFlow with CPU-only support for the Intel® +MKL-DNN: 
 $ bazel build --config=mkl --config=opt //tensorflow/tools/pip_package:build_pip_package
@@ -311,37 +349,35 @@ To build a pip package for TensorFlow with GPU support:
 $ bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
-**NOTE on gcc 5 or later:** the binary pip packages available on the -TensorFlow website are built with gcc 4, which uses the older ABI. To -make your build compatible with the older ABI, you need to add -`--cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0"` to your `bazel build` command. -ABI compatibility allows custom ops built against the TensorFlow pip package -to continue to work against your built package. +**NOTE on gcc 5 or later:** the binary pip packages available on the TensorFlow +website are built with gcc 4, which uses the older ABI. To make your build +compatible with the older ABI, you need to add +`--cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0"` to your `bazel build` command. ABI +compatibility allows custom ops built against the TensorFlow pip package to +continue to work against your built package. -Tip: By default, building TensorFlow from sources consumes -a lot of RAM. If RAM is an issue on your system, you may limit RAM usage -by specifying --local_resources 2048,.5,1.0 while -invoking `bazel`. +Tip: By default, building TensorFlow from sources consumes a lot of RAM. +If RAM is an issue on your system, you may limit RAM usage by specifying +--local_resources 2048,.5,1.0 while invoking `bazel`. -The bazel build command builds a script named -`build_pip_package`. Running this script as follows will build -a `.whl` file within the `/tmp/tensorflow_pkg` directory: +The bazel build command builds a script named `build_pip_package`. +Running this script as follows will build a `.whl` file within the +`/tmp/tensorflow_pkg` directory: 
 $ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
- ## Install the pip package -Invoke `pip install` to install that pip package. -The filename of the `.whl` file depends on your platform. -For example, the following command will install the pip package +Invoke `pip install` to install that pip package. The filename of the `.whl` +file depends on your platform. For example, the following command will install +the pip package -for TensorFlow 1.9.0rc0 on Linux: +for TensorFlow 1.9.0 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc0-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0-py2-none-any.whl
## Validate your installation @@ -374,26 +410,29 @@ TensorFlow programs: To learn more, see the [TensorFlow tutorials](../tutorials/). -If the system outputs an error message instead of a greeting, see [Common -installation problems](#common_installation_problems). +If the system outputs an error message instead of a greeting, see +[Common installation problems](#common_installation_problems). ## Common build and installation problems The build and installation problems you encounter typically depend on the -operating system. See the "Common installation problems" section -of one of the following guides: - - * @{$install_linux#common_installation_problems$Installing TensorFlow on Linux} - * @{$install_mac#common_installation_problems$Installing TensorFlow on Mac OS} - * @{$install_windows#common_installation_problems$Installing TensorFlow on Windows} - -Beyond the errors documented in those two guides, the following table -notes additional errors specific to building TensorFlow. Note that we -are relying on Stack Overflow as the repository for build and installation -problems. If you encounter an error message not listed in the preceding -two guides or in the following table, search for it on Stack Overflow. If -Stack Overflow doesn't show the error message, ask a new question on -Stack Overflow and specify the `tensorflow` tag. +operating system. See the "Common installation problems" section of one of the +following guides: + +* @ + {$install_linux#common_installation_problems$Installing TensorFlow on Linux} +* @ + {$install_mac#common_installation_problems$Installing TensorFlow on Mac OS} +* @ + {$install_windows#common_installation_problems$Installing TensorFlow on Windows} + +Beyond the errors documented in those two guides, the following table notes +additional errors specific to building TensorFlow. Note that we are relying on +Stack Overflow as the repository for build and installation problems. If you +encounter an error message not listed in the preceding two guides or in the +following table, search for it on Stack Overflow. If Stack Overflow doesn't show +the error message, ask a new question on Stack Overflow and specify the +`tensorflow` tag. @@ -440,6 +479,7 @@ Stack Overflow and specify the `tensorflow` tag.
Stack Overflow Link Error Message
## Tested source configurations + **Linux** @@ -508,6 +548,7 @@ Stack Overflow and specify the `tensorflow` tag.
Version:CPU/GPU:Python Version:Compiler:Build Tools:cuDNN:CUDA:
+ ## Build the C or Java libraries The instructions above are tailored to building the TensorFlow Python packages. @@ -516,10 +557,12 @@ If you're interested in building the libraries for the TensorFlow C API, do the following: 1. Follow the steps up to [Configure the installation](#ConfigureInstallation) -2. Build the C libraries following instructions in the [README](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/lib_package/README.md). +2. Build the C libraries following instructions in the + [README](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/lib_package/README.md). -If you're interested inv building the libraries for the TensorFlow Java API, -do the following: +If you're interested inv building the libraries for the TensorFlow Java API, do +the following: 1. Follow the steps up to [Configure the installation](#ConfigureInstallation) -2. Build the Java library following instructions in the [README](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/lib_package/README.md). +2. Build the Java library following instructions in the + [README](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/lib_package/README.md). diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md index 7b7b17ce81407bbbff837a00bb43162b4b2d44f3..e9061bf3c1467e38c77a28989a5377171c4d577c 100644 --- a/tensorflow/docs_src/install/install_windows.md +++ b/tensorflow/docs_src/install/install_windows.md @@ -1,4 +1,4 @@ -# Installing TensorFlow on Windows +# Install TensorFlow on Windows This guide explains how to install TensorFlow on Windows. Although these instructions might also work on other Windows variants, we have only diff --git a/tensorflow/docs_src/install/migration.md b/tensorflow/docs_src/install/migration.md index d6c31f96bd624f03f0b868a030383851c4e48ef7..19315ace2d76b63da0370cb811729934c801cf11 100644 --- a/tensorflow/docs_src/install/migration.md +++ b/tensorflow/docs_src/install/migration.md @@ -1,5 +1,4 @@ - -# Transitioning to TensorFlow 1.0 +# Transition to TensorFlow 1.0 The APIs in TensorFlow 1.0 have changed in ways that are not all backwards diff --git a/tensorflow/docs_src/javascript/index.md b/tensorflow/docs_src/javascript/index.md deleted file mode 100644 index ad63eeb255d870064567a0de8a28815ce2ae0172..0000000000000000000000000000000000000000 --- a/tensorflow/docs_src/javascript/index.md +++ /dev/null @@ -1,5 +0,0 @@ -# JavaScript - -You may develop TensorFlow programs in JavaScript, training and deploying -models right in your browser. For details, see -[js.tensorflow.org](https://js.tensorflow.org). diff --git a/tensorflow/docs_src/javascript/leftnav_files b/tensorflow/docs_src/javascript/leftnav_files deleted file mode 100644 index fc0ab8a5435943f6442969ec5787305b98c7908b..0000000000000000000000000000000000000000 --- a/tensorflow/docs_src/javascript/leftnav_files +++ /dev/null @@ -1 +0,0 @@ -index.md diff --git a/tensorflow/docs_src/mobile/README.md b/tensorflow/docs_src/mobile/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ecf42672654ab4a8d2ea8c9bb4752ed65d6c8a9a --- /dev/null +++ b/tensorflow/docs_src/mobile/README.md @@ -0,0 +1,3 @@ +# TF Lite subsite + +This subsite directory lives in [tensorflow/contrib/lite/g3doc](../../contrib/lite/g3doc/). diff --git a/tensorflow/docs_src/mobile/index.md b/tensorflow/docs_src/mobile/index.md deleted file mode 100644 index 419ae7094a180fb166eb5b00cc382773b95b91f4..0000000000000000000000000000000000000000 --- a/tensorflow/docs_src/mobile/index.md +++ /dev/null @@ -1,36 +0,0 @@ -# Overview - -TensorFlow was designed to be a good deep learning solution for mobile -platforms. Currently we have two solutions for deploying machine learning -applications on mobile and embedded devices: -@{$mobile/mobile_intro$TensorFlow for Mobile} and @{$mobile/tflite$TensorFlow Lite}. - -## TensorFlow Lite versus TensorFlow Mobile - -Here are a few of the differences between the two: - -- TensorFlow Lite is an evolution of TensorFlow Mobile. In most cases, apps - developed with TensorFlow Lite will have a smaller binary size, fewer - dependencies, and better performance. - -- TensorFlow Lite is in developer preview, so not all use cases are covered yet. - We expect you to use TensorFlow Mobile to cover production cases. - -- TensorFlow Lite supports only a limited set of operators, so not all models - will work on it by default. TensorFlow for Mobile has a fuller set of - supported functionality. - -TensorFlow Lite provides better performance and a small binary size on mobile -platforms as well as the ability to leverage hardware acceleration if available -on their platforms. In addition, it has many fewer dependencies so it can be -built and hosted on simpler, more constrained device scenarios. TensorFlow Lite -also allows targeting accelerators through the [Neural Networks -API](https://developer.android.com/ndk/guides/neuralnetworks/index.html). - -TensorFlow Lite currently has coverage for a limited set of operators. While -TensorFlow for Mobile supports only a constrained set of ops by default, in -principle if you use an arbitrary operator in TensorFlow, it can be customized -to build that kernel. Thus use cases which are not currently supported by -TensorFlow Lite should continue to use TensorFlow for Mobile. As TensorFlow Lite -evolves, it will gain additional operators, and the decision will be easier to -make. diff --git a/tensorflow/docs_src/mobile/leftnav_files b/tensorflow/docs_src/mobile/leftnav_files deleted file mode 100644 index 97340ef7e1af64634f8590b5d21a344b5181cb73..0000000000000000000000000000000000000000 --- a/tensorflow/docs_src/mobile/leftnav_files +++ /dev/null @@ -1,15 +0,0 @@ -index.md -### TensorFlow Lite -tflite/index.md -tflite/devguide.md -tflite/demo_android.md -tflite/demo_ios.md -tflite/performance.md ->>> -### TensorFlow Mobile -mobile_intro.md -android_build.md -ios_build.md -linking_libs.md -prepare_models.md -optimizing.md diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md index cb0f5ca9242098d06aa0a9898e4a3774fab527b8..dafacbe37974f80c85131509824956ea1c5c8426 100644 --- a/tensorflow/docs_src/performance/performance_guide.md +++ b/tensorflow/docs_src/performance/performance_guide.md @@ -464,7 +464,7 @@ equal to the number of physical cores rather than logical cores. config = tf.ConfigProto() config.intra_op_parallelism_threads = 44 config.inter_op_parallelism_threads = 44 - tf.session(config=config) + tf.Session(config=config) ``` diff --git a/tensorflow/docs_src/performance/xla/broadcasting.md b/tensorflow/docs_src/performance/xla/broadcasting.md index eaa709c2f84245341044b93060f932a22fbe54c7..7018ded53f8bc078a43b6af54a9ba13796374458 100644 --- a/tensorflow/docs_src/performance/xla/broadcasting.md +++ b/tensorflow/docs_src/performance/xla/broadcasting.md @@ -99,7 +99,7 @@ dimensions 1 and 2 of the cuboid. This type of broadcast is used in the binary ops in `XlaBuilder`, if the `broadcast_dimensions` argument is given. For example, see -[XlaBuilder::Add](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.cc). +[XlaBuilder::Add](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.cc). In the XLA source code, this type of broadcasting is sometimes called "InDim" broadcasting. diff --git a/tensorflow/docs_src/performance/xla/developing_new_backend.md b/tensorflow/docs_src/performance/xla/developing_new_backend.md index 74ea15bb2bac2014257f0b1719820f7ee313b66b..840f6983c2837771acbd79b221efcb5537ae4d7d 100644 --- a/tensorflow/docs_src/performance/xla/developing_new_backend.md +++ b/tensorflow/docs_src/performance/xla/developing_new_backend.md @@ -44,7 +44,7 @@ It is possible to model a new implementation on the existing [`xla::CPUCompiler`] (https://www.tensorflow.org/code/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc) and [`xla::GPUCompiler`] -(https://www.tensorflow.org/code/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc) +(https://www.tensorflow.org/code/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc) classes, since these already emit LLVM IR. Depending on the nature of the hardware, it is possible that many of the LLVM IR generation aspects will have to be changed, but a lot of code can be shared with the existing backends. diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md index 4c4f3f39348f59aa018d19d4a7368f09bcef89ed..5f7482f90f1c9312f23aa299a5592ade830cb984 100644 --- a/tensorflow/docs_src/performance/xla/operation_semantics.md +++ b/tensorflow/docs_src/performance/xla/operation_semantics.md @@ -1,7 +1,7 @@ # Operation Semantics The following describes the semantics of operations defined in the -[`XlaBuilder`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h) +[`XlaBuilder`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h) interface. Typically, these operations map one-to-one to operations defined in the RPC interface in [`xla_data.proto`](https://www.tensorflow.org/code/tensorflow/compiler/xla/xla_data.proto). @@ -16,7 +16,7 @@ and familiar names; for example a *vector* is a 1-dimensional array and a ## BatchNormGrad See also -[`XlaBuilder::BatchNormGrad`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h) +[`XlaBuilder::BatchNormGrad`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h) and [the original batch normalization paper](https://arxiv.org/abs/1502.03167) for a detailed description of the algorithm. @@ -80,7 +80,7 @@ The output type is a tuple of three handles: ## BatchNormInference See also -[`XlaBuilder::BatchNormInference`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h) +[`XlaBuilder::BatchNormInference`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h) and [the original batch normalization paper](https://arxiv.org/abs/1502.03167) for a detailed description of the algorithm. @@ -115,7 +115,7 @@ The output is an n-dimensional, normalized array with the same shape as input ## BatchNormTraining See also -[`XlaBuilder::BatchNormTraining`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h) +[`XlaBuilder::BatchNormTraining`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h) and [`the original batch normalization paper`](https://arxiv.org/abs/1502.03167) for a detailed description of the algorithm. @@ -167,7 +167,7 @@ spatial dimensions using the formulas above. ## BitcastConvertType See also -[`XlaBuilder::BitcastConvertType`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::BitcastConvertType`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Similar to a `tf.bitcast` in TensorFlow, performs an element-wise bitcast operation from a data shape to a target shape. The dimensions must match, and @@ -189,7 +189,7 @@ and destination element types must not be tuples. ## Broadcast See also -[`XlaBuilder::Broadcast`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Broadcast`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Adds dimensions to an array by duplicating the data in the array. @@ -217,7 +217,7 @@ For example, if `operand` is a scalar `f32` with value `2.0f`, and ## Call See also -[`XlaBuilder::Call`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Call`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Invokes a computation with the given arguments. @@ -236,7 +236,7 @@ The arity and types of the `args` must match the parameters of the ## Clamp See also -[`XlaBuilder::Clamp`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Clamp`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Clamps an operand to within the range between a minimum and maximum value. @@ -269,7 +269,7 @@ Clamp(min, operand, max) = s32[3]{0, 5, 6}; ## Collapse See also -[`XlaBuilder::Collapse`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h) +[`XlaBuilder::Collapse`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h) and the @{tf.reshape} operation. Collapses dimensions of an array into one dimension. @@ -332,7 +332,7 @@ then v12 == f32[8x3] {{10, 11, 12}, ## Concatenate See also -[`XlaBuilder::ConcatInDim`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::ConcatInDim`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Concatenate composes an array from multiple array operands. The array is of the same rank as each of the input array operands (which must be of the same rank as @@ -388,7 +388,7 @@ Diagram: ## Conditional See also -[`XlaBuilder::Conditional`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Conditional`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Conditional(pred, true_operand, true_computation, false_operand, false_computation)` @@ -416,7 +416,7 @@ executed depending on the value of `pred`. ## Conv (convolution) See also -[`XlaBuilder::Conv`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Conv`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). As ConvWithGeneralPadding, but the padding is specified in a short-hand way as either SAME or VALID. SAME padding pads the input (`lhs`) with zeroes so that @@ -426,7 +426,7 @@ account. VALID padding simply means no padding. ## ConvWithGeneralPadding (convolution) See also -[`XlaBuilder::ConvWithGeneralPadding`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::ConvWithGeneralPadding`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Computes a convolution of the kind used in neural networks. Here, a convolution can be thought of as a n-dimensional window moving across a n-dimensional base @@ -538,7 +538,7 @@ for (b, oz, oy, ox) { // output coordinates ## ConvertElementType See also -[`XlaBuilder::ConvertElementType`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::ConvertElementType`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Similar to an element-wise `static_cast` in C++, performs an element-wise conversion operation from a data shape to a target shape. The dimensions must @@ -572,7 +572,7 @@ then b == f32[3]{0.0, 1.0, 2.0} ## CrossReplicaSum See also -[`XlaBuilder::CrossReplicaSum`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::CrossReplicaSum`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Computes a sum across replicas. @@ -607,7 +607,7 @@ than another. ## CustomCall See also -[`XlaBuilder::CustomCall`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::CustomCall`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Call a user-provided function within a computation. @@ -668,7 +668,7 @@ idempotent. ## Dot See also -[`XlaBuilder::Dot`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Dot`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Dot(lhs, rhs)` @@ -697,7 +697,7 @@ multiplications or matrix/matrix multiplications. ## DotGeneral See also -[`XlaBuilder::DotGeneral`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::DotGeneral`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `DotGeneral(lhs, rhs, dimension_numbers)` @@ -784,15 +784,13 @@ non-contracting/non-batch dimension. ## DynamicSlice See also -[`XlaBuilder::DynamicSlice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::DynamicSlice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). DynamicSlice extracts a sub-array from the input array at dynamic `start_indices`. The size of the slice in each dimension is passed in `size_indices`, which specify the end point of exclusive slice intervals in each dimension: [start, start + size). The shape of `start_indices` must be rank == 1, with dimension size equal to the rank of `operand`. -Note: handling of out-of-bounds slice indices (generated by incorrect runtime -calculation of 'start_indices') is currently implementation-defined. `DynamicSlice(operand, start_indices, size_indices)` @@ -812,6 +810,17 @@ calculation of 'start_indices') is currently implementation-defined. : : : dimension to avoid wrapping modulo : : : : dimension size. : +The effective slice indices are computed by applying the following +transformation for each index `i` in `[1, N)` before performing the slice: + +``` +start_indices[i] = clamp(start_indices[i], 0, operand.dimension_size[i] - size_indices[i]) +``` + +This ensures that the extracted slice is always in-bounds with respect to the +operand array. If the slice is in-bounds before the transformation is applied, +the transformation has no effect. + 1-dimensional example: ``` @@ -839,7 +848,7 @@ DynamicSlice(b, s, {2, 2}) produces: ## DynamicUpdateSlice See also -[`XlaBuilder::DynamicUpdateSlice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::DynamicUpdateSlice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). DynamicUpdateSlice generates a result which is the value of the input array `operand`, with a slice `update` overwritten at `start_indices`. @@ -847,8 +856,6 @@ The shape of `update` determines the shape of the sub-array of the result which is updated. The shape of `start_indices` must be rank == 1, with dimension size equal to the rank of `operand`. -Note: handling of out-of-bounds slice indices (generated by incorrect runtime -calculation of 'start_indices') is currently implementation-defined. `DynamicUpdateSlice(operand, update, start_indices)` @@ -866,6 +873,17 @@ calculation of 'start_indices') is currently implementation-defined. : : : dimension. Value must be greater than or equal : : : : to zero. : +The effective slice indices are computed by applying the following +transformation for each index `i` in `[1, N)` before performing the slice: + +``` +start_indices[i] = clamp(start_indices[i], 0, operand.dimension_size[i] - update.dimension_size[i]) +``` + +This ensures that the updated slice is always in-bounds with respect to the +operand array. If the slice is in-bounds before the transformation is applied, +the transformation has no effect. + 1-dimensional example: ``` @@ -902,7 +920,7 @@ DynamicUpdateSlice(b, u, s) produces: ## Element-wise binary arithmetic operations See also -[`XlaBuilder::Add`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Add`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). A set of element-wise binary arithmetic operations is supported. @@ -947,7 +965,7 @@ shapes of both operands. The semantics are described in detail on the ## Element-wise comparison operations See also -[`XlaBuilder::Eq`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Eq`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). A set of standard element-wise binary comparison operations is supported. Note that standard IEEE 754 floating-point comparison semantics apply when comparing @@ -1033,7 +1051,7 @@ potentially different runtime offset) of an input tensor into an output tensor. ### General Semantics See also -[`XlaBuilder::Gather`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Gather`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). For a more intuitive description, see the "Informal Description" section below. `gather(operand, gather_indices, output_window_dims, elided_window_dims, window_bounds, gather_dims_to_operand_dims)` @@ -1236,7 +1254,7 @@ concatenation of all these rows. ## GetTupleElement See also -[`XlaBuilder::GetTupleElement`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::GetTupleElement`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Indexes into a tuple with a compile-time-constant value. @@ -1257,7 +1275,7 @@ See also @{tf.tuple}. ## Infeed See also -[`XlaBuilder::Infeed`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Infeed`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Infeed(shape)` @@ -1293,17 +1311,30 @@ Infeed of the device. > which case the compiler will provide information about how the Infeed > operations are serialized in the compiled program. +## Iota + + `Iota()` + +Builds a constant literal on device rather than a potentially large host +transfer. Creates a rank 1 tensor of values starting at zero and incrementing +by one. + +Arguments | Type | Semantics +------------------ | --------------- | --------------------------- +`type` | `PrimitiveType` | type U +`size` | `int64` | The number of elements in the tensor. + ## Map See also -[`XlaBuilder::Map`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Map`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Map(operands..., computation)` | Arguments | Type | Semantics | | ----------------- | ---------------------- | ------------------------------ | | `operands` | sequence of N `XlaOp`s | N arrays of types T_0..T_{N-1} | -| `computation` | `XlaComputation` | computation of type `T_0, T_1, | +| `computation` | `XlaComputation` | computation of type `T_0, T_1, | : : : ..., T_{N + M -1} -> S` with N : : : : parameters of type T and M of : : : : arbitrary type : @@ -1325,7 +1356,7 @@ input arrays to produce the output array. ## Pad See also -[`XlaBuilder::Pad`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Pad`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Pad(operand, padding_value, padding_config)` @@ -1364,7 +1395,7 @@ are all 0. The figure below shows examples of different `edge_padding` and ## Recv See also -[`XlaBuilder::Recv`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Recv`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Recv(shape, channel_handle)` @@ -1398,7 +1429,7 @@ complete and returns the received data. ## Reduce See also -[`XlaBuilder::Reduce`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Reduce`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Applies a reduction function to an array. @@ -1515,7 +1546,7 @@ Reducing the 3D array over all its dimensions produces the scalar `84`. ## ReducePrecision See also -[`XlaBuilder::ReducePrecision`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::ReducePrecision`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Models the effect of converting floating-point values to a lower-precision format (such as IEEE-FP16) and back to the original format. The number of @@ -1546,7 +1577,7 @@ portion of the conversion is then simply a no-op. ## ReduceWindow See also -[`XlaBuilder::ReduceWindow`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::ReduceWindow`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Applies a reduction function to all elements in each window of the input multi-dimensional array, producing an output multi-dimensional array with the @@ -1629,7 +1660,7 @@ context of [`Reduce`](#reduce) for more details. ## Reshape See also -[`XlaBuilder::Reshape`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h) +[`XlaBuilder::Reshape`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h) and the [`Collapse`](#collapse) operation. Reshapes the dimensions of an array into a new configuration. @@ -1710,7 +1741,7 @@ Reshape(5, {}, {1,1}) == f32[1x1] {{5}}; ## Rev (reverse) See also -[`XlaBuilder::Rev`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Rev`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Rev(operand, dimensions)` @@ -1732,7 +1763,7 @@ the two window dimensions during the gradient computation in neural networks. ## RngNormal See also -[`XlaBuilder::RngNormal`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::RngNormal`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Constructs an output of a given shape with random numbers generated following the $$N(\mu, \sigma)$$ normal distribution. The parameters `mu` and `sigma`, and @@ -1752,7 +1783,7 @@ be scalar valued. ## RngUniform See also -[`XlaBuilder::RngUniform`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::RngUniform`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Constructs an output of a given shape with random numbers generated following the uniform distribution over the interval $$[a,b)$$. The parameters and output @@ -1773,7 +1804,7 @@ is implementation-defined. ## Select See also -[`XlaBuilder::Select`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Select`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Constructs an output array from elements of two input arrays, based on the values of a predicate array. @@ -1824,7 +1855,7 @@ the same shape!) then `pred` has to be a scalar of type `PRED`. ## SelectAndScatter See also -[`XlaBuilder::SelectAndScatter`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::SelectAndScatter`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). This operation can be considered as a composite operation that first computes `ReduceWindow` on the `operand` array to select an element from each window, and @@ -1904,7 +1935,7 @@ context of [`Reduce`](#reduce) for more details. ## Send See also -[`XlaBuilder::Send`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Send`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Send(operand, channel_handle)` @@ -1959,7 +1990,7 @@ computations. For example, below schedules lead to deadlocks. ## Slice See also -[`XlaBuilder::Slice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Slice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Slicing extracts a sub-array from the input array. The sub-array is of the same rank as the input and contains the values inside a bounding box within the input @@ -2008,37 +2039,44 @@ Slice(b, {2, 1}, {4, 3}) produces: ## Sort See also -[`XlaBuilder::Sort`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Sort`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). There are two versions of the Sort instruction: a single-operand and a two-operand version. `Sort(operand)` -Arguments | Type | Semantics ---------- | ------- | -------------------- -`operand` | `XlaOp` | The operand to sort. +Arguments | Type | Semantics +----------- | ------- | -------------------- +`operand` | `XlaOp` | The operand to sort. +`dimension` | `int64` | The dimension along which to sort. -Sorts the elements in the operand in ascending order. The operand must be rank-1. -If the operand's elements have floating point type, and the operand contains -NaN elements, the order of elements in the output is implementation-defined. +Sorts the elements in the operand in ascending order along the provided +dimension. For example, for a rank-2 (matrix) operand, a `dimension` value of 0 +will sort each column independently, and a `dimension` value of 1 will sort each +row independently. If the operand's elements have floating point type, and the +operand contains NaN elements, the order of elements in the output is +implementation-defined. `Sort(key, value)` Sorts both the key and the value operands. The keys are sorted as in the single-operand version. The values are sorted according to the order of their corresponding keys. For example, if the inputs are `keys = [3, 1]` and -`values = [42, 50]`, then the output of the sort is the tuple `{[1, 3], [50, 42]}`. +`values = [42, 50]`, then the output of the sort is the tuple +`{[1, 3], [50, 42]}`. + The sort is not guaranteed to be stable, that is, if the keys array contains duplicates, the order of their corresponding values may not be preserved. -Arguments | Type | Semantics ---------- | ------- | ------------------- -`keys` | `XlaOp` | The sort keys. -`values` | `XlaOp` | The values to sort. +Arguments | Type | Semantics +----------- | ------- | ------------------- +`keys` | `XlaOp` | The sort keys. +`values` | `XlaOp` | The values to sort. +`dimension` | `int64` | The dimension along which to sort. -The `keys` and `values` operand must both be rank-1, and must have the same -dimensions, but may have different element types. +The `keys` and `values` must have the same dimensions, but may have different +element types. ## Transpose @@ -2061,7 +2099,7 @@ This is the same as Reshape(operand, permutation, ## Tuple See also -[`XlaBuilder::Tuple`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Tuple`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). A tuple containing a variable number of data handles, each of which has its own shape. @@ -2080,7 +2118,7 @@ Tuples can be deconstructed (accessed) via the [`GetTupleElement`] ## While See also -[`XlaBuilder::While`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::While`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `While(condition, body, init)` diff --git a/tensorflow/docs_src/tutorials/_index.yaml b/tensorflow/docs_src/tutorials/_index.yaml index 6fc8155669bb8672eef3ed4a62af00516648c90e..953411468978846724b52bae73537e80694a78ee 100644 --- a/tensorflow/docs_src/tutorials/_index.yaml +++ b/tensorflow/docs_src/tutorials/_index.yaml @@ -2,6 +2,7 @@ project_path: /_project.yaml book_path: /_book.yaml description: landing_page: + custom_css_path: /site-assets/css/style.css show_side_navs: True rows: - description: > @@ -14,57 +15,6 @@ landing_page:

items: - custom_html: > -

Learn and use ML

@@ -170,15 +120,16 @@ landing_page:

Estimators can train large models on multiple machines in a - production environment. Read the - Estimators guide for details. + production environment. TensorFlow provides a collection of + pre-made Estimators to implement common ML algorithms. See the + Estimators guide.

    -
  1. Build a Convolutional Neural Network using Estimators
    2. +
  2. Build a linear model with Estimators
    3. +
  3. Wide and deep learning with Estimators
    4. +
  4. Boosted trees
  5. How to build a simple text classifier with TF-Hub
    6. -
  6. Classifying Higgs boson processes
    7. -
  7. Wide and deep learning using Estimators
    8. -
  8. Large-scale linear models
    9. +
  9. Build a Convolutional Neural Network using Estimators
diff --git a/tensorflow/docs_src/tutorials/_toc.yaml b/tensorflow/docs_src/tutorials/_toc.yaml index d46d570a93c7da03ab12e960e65d46d5db793cbd..d33869af6ee7fffe39874f690b154b92034675a2 100644 --- a/tensorflow/docs_src/tutorials/_toc.yaml +++ b/tensorflow/docs_src/tutorials/_toc.yaml @@ -24,7 +24,7 @@ toc: - title: Overview path: /tutorials/eager/ - title: Eager execution - path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/eager_intro.ipynb + path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/eager_basics.ipynb status: external - title: Automatic differentiation path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/automatic_differentiation.ipynb @@ -37,15 +37,29 @@ toc: status: external - title: "Custom training: walkthrough" path: /tutorials/eager/custom_training_walkthrough - - title: Neural machine translation + - title: Translation with attention path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb status: external -- title: Images +- title: ML at production scale style: accordion section: + - title: Linear model with Estimators + path: /tutorials/estimators/linear + - title: Wide and deep learning + path: https://github.com/tensorflow/models/tree/master/official/wide_deep + status: external + - title: Boosted trees + path: https://github.com/tensorflow/models/tree/master/official/boosted_trees + status: external + - title: Text classifier with TF-Hub + path: /hub/tutorials/text_classification_with_tf_hub - title: Build a CNN using Estimators - path: /tutorials/images/layers + path: /tutorials/estimators/cnn + +- title: Images + style: accordion + section: - title: Image recognition path: /tutorials/images/image_recognition - title: Image retraining @@ -69,10 +83,6 @@ toc: - title: Data representation style: accordion section: - - title: Linear models - path: /tutorials/representation/wide - - title: Wide and deep learning - path: /tutorials/representation/wide_and_deep - title: Vector representations of words path: /tutorials/representation/word2vec - title: Kernel methods diff --git a/tensorflow/docs_src/tutorials/eager/custom_training_walkthrough.md b/tensorflow/docs_src/tutorials/eager/custom_training_walkthrough.md index b45fbefac01c575515798af4692318ea1e905607..b564a27ecfd1b06c6b977302ba463bb763a6fb38 100644 --- a/tensorflow/docs_src/tutorials/eager/custom_training_walkthrough.md +++ b/tensorflow/docs_src/tutorials/eager/custom_training_walkthrough.md @@ -1,3 +1,3 @@ # Custom training: walkthrough -[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/eager.ipynb) +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/tutorials/eager/custom_training_walkthrough.ipynb) diff --git a/tensorflow/docs_src/tutorials/eager/index.md b/tensorflow/docs_src/tutorials/eager/index.md index 5445e0c3439392d4eeb8a6b3e9d229407b5b014e..a13b39609435256ded88072ce40c929a1494aad0 100644 --- a/tensorflow/docs_src/tutorials/eager/index.md +++ b/tensorflow/docs_src/tutorials/eager/index.md @@ -5,7 +5,7 @@ operations. Write custom layers, forward passes, and training loops with auto differentiation. Start with these notebooks, then read the [eager execution guide](../../guide/eager). -1. [Eager execution](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/eager_intro.ipynb){:.external} +1. [Eager execution](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/eager_basics.ipynb){:.external} 2. [Automatic differentiation and gradient tape](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/automatic_differentiation.ipynb){:.external} 3. [Custom training: basics](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/custom_training.ipynb){:.external} 4. [Custom layers](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb){:.external} diff --git a/tensorflow/docs_src/tutorials/images/layers.md b/tensorflow/docs_src/tutorials/estimators/cnn.md similarity index 100% rename from tensorflow/docs_src/tutorials/images/layers.md rename to tensorflow/docs_src/tutorials/estimators/cnn.md diff --git a/tensorflow/docs_src/tutorials/estimators/linear.md b/tensorflow/docs_src/tutorials/estimators/linear.md new file mode 100644 index 0000000000000000000000000000000000000000..067a33ac036ec54826c6e88d0c9dc11b07e95976 --- /dev/null +++ b/tensorflow/docs_src/tutorials/estimators/linear.md @@ -0,0 +1,3 @@ +# Build a linear model with Estimators + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/tutorials/estimators/linear.ipynb) diff --git a/tensorflow/docs_src/tutorials/images/deep_cnn.md b/tensorflow/docs_src/tutorials/images/deep_cnn.md index 1590f15eb91a0f20a91af3d899c3e08428f6c997..27963575f5a02eb8a91b490fdfcc33d35749963c 100644 --- a/tensorflow/docs_src/tutorials/images/deep_cnn.md +++ b/tensorflow/docs_src/tutorials/images/deep_cnn.md @@ -80,21 +80,21 @@ for details. It consists of 1,068,298 learnable parameters and requires about ## Code Organization The code for this tutorial resides in -[`models/tutorials/image/cifar10/`](https://www.tensorflow.org/code/tensorflow_models/tutorials/image/cifar10/). +[`models/tutorials/image/cifar10/`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/). File | Purpose --- | --- -[`cifar10_input.py`](https://www.tensorflow.org/code/tensorflow_models/tutorials/image/cifar10/cifar10_input.py) | Reads the native CIFAR-10 binary file format. -[`cifar10.py`](https://www.tensorflow.org/code/tensorflow_models/tutorials/image/cifar10/cifar10.py) | Builds the CIFAR-10 model. -[`cifar10_train.py`](https://www.tensorflow.org/code/tensorflow_models/tutorials/image/cifar10/cifar10_train.py) | Trains a CIFAR-10 model on a CPU or GPU. -[`cifar10_multi_gpu_train.py`](https://www.tensorflow.org/code/tensorflow_models/tutorials/image/cifar10/cifar10_multi_gpu_train.py) | Trains a CIFAR-10 model on multiple GPUs. -[`cifar10_eval.py`](https://www.tensorflow.org/code/tensorflow_models/tutorials/image/cifar10/cifar10_eval.py) | Evaluates the predictive performance of a CIFAR-10 model. +[`cifar10_input.py`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10_input.py) | Reads the native CIFAR-10 binary file format. +[`cifar10.py`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10.py) | Builds the CIFAR-10 model. +[`cifar10_train.py`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10_train.py) | Trains a CIFAR-10 model on a CPU or GPU. +[`cifar10_multi_gpu_train.py`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10_multi_gpu_train.py) | Trains a CIFAR-10 model on multiple GPUs. +[`cifar10_eval.py`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10_eval.py) | Evaluates the predictive performance of a CIFAR-10 model. ## CIFAR-10 Model The CIFAR-10 network is largely contained in -[`cifar10.py`](https://www.tensorflow.org/code/tensorflow_models/tutorials/image/cifar10/cifar10.py). +[`cifar10.py`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10.py). The complete training graph contains roughly 765 operations. We find that we can make the code most reusable by constructing the graph with the following modules: diff --git a/tensorflow/docs_src/tutorials/images/image_recognition.md b/tensorflow/docs_src/tutorials/images/image_recognition.md index 432d470d0cd281f688b28761d6d6a49f4d3e1efe..d545de73df57a7bc775a83cc1fc41ffa185874c5 100644 --- a/tensorflow/docs_src/tutorials/images/image_recognition.md +++ b/tensorflow/docs_src/tutorials/images/image_recognition.md @@ -449,7 +449,7 @@ covering them. To find out more about implementing convolutional neural networks, you can jump to the TensorFlow @{$deep_cnn$deep convolutional networks tutorial}, -or start a bit more gently with our @{$layers$MNIST starter tutorial}. +or start a bit more gently with our [Estimator MNIST tutorial](../estimators/cnn.md). Finally, if you want to get up to speed on research in this area, you can read the recent work of all the papers referenced in this tutorial. diff --git a/tensorflow/docs_src/tutorials/keras/basic_classification.md b/tensorflow/docs_src/tutorials/keras/basic_classification.md index 91bbd85b2442522ef34eba236bf5bab2fc8654a7..e028af99b936a92cf359a7b4e561f7bcf3c4bffc 100644 --- a/tensorflow/docs_src/tutorials/keras/basic_classification.md +++ b/tensorflow/docs_src/tutorials/keras/basic_classification.md @@ -1,3 +1,3 @@ # Basic Classification -[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/basic_classification.ipynb) +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/tutorials/keras/basic_classification.ipynb) diff --git a/tensorflow/docs_src/tutorials/keras/basic_regression.md b/tensorflow/docs_src/tutorials/keras/basic_regression.md index a535f22f5a41e7cb34cb8424b60d10d4ad43940e..8721b7aca19e3f37b6989bb1b280ac3b4fdffc8e 100644 --- a/tensorflow/docs_src/tutorials/keras/basic_regression.md +++ b/tensorflow/docs_src/tutorials/keras/basic_regression.md @@ -1,3 +1,3 @@ # Basic Regression -[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/basic_regression.ipynb) +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/tutorials/keras/basic_regression.ipynb) diff --git a/tensorflow/docs_src/tutorials/keras/basic_text_classification.md b/tensorflow/docs_src/tutorials/keras/basic_text_classification.md index 7c5d4f78968f94e4d5685a2dffe75ab649431e38..c2a16bdd204c303cd166f283229cb9eaf73540b0 100644 --- a/tensorflow/docs_src/tutorials/keras/basic_text_classification.md +++ b/tensorflow/docs_src/tutorials/keras/basic_text_classification.md @@ -1,3 +1,3 @@ # Basic Text Classification -[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/basic_text_classification.ipynb) +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/tutorials/keras/basic_text_classification.ipynb) diff --git a/tensorflow/docs_src/tutorials/keras/overfit_and_underfit.md b/tensorflow/docs_src/tutorials/keras/overfit_and_underfit.md index e5b5ae7b5a70f476c25cc7bb76572bf6433c289f..f07f3addd82235181cc6c4c5d32d44da2c72107f 100644 --- a/tensorflow/docs_src/tutorials/keras/overfit_and_underfit.md +++ b/tensorflow/docs_src/tutorials/keras/overfit_and_underfit.md @@ -1,3 +1,3 @@ # Overfitting and Underfitting -[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/overfit_and_underfit.ipynb) +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/tutorials/keras/overfit_and_underfit.ipynb) diff --git a/tensorflow/docs_src/tutorials/keras/save_and_restore_models.md b/tensorflow/docs_src/tutorials/keras/save_and_restore_models.md index 44b377294562cf5a0c8139e88d0c7226506b32ba..a799b379a004d545b12d7c1d37b78ee3baeee1fc 100644 --- a/tensorflow/docs_src/tutorials/keras/save_and_restore_models.md +++ b/tensorflow/docs_src/tutorials/keras/save_and_restore_models.md @@ -1,3 +1,3 @@ # Save and restore Models -[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/save_and_restore_models.ipynb) +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/tutorials/keras/save_and_restore_models.ipynb) diff --git a/tensorflow/docs_src/tutorials/representation/linear.md b/tensorflow/docs_src/tutorials/representation/linear.md index 3f247ade266d2675eac4d0f59a4744daa61f27ea..1b418cf065a141dc46833bb0d3c2048658efc388 100644 --- a/tensorflow/docs_src/tutorials/representation/linear.md +++ b/tensorflow/docs_src/tutorials/representation/linear.md @@ -11,8 +11,9 @@ those tools. It explains: deep learning to get the advantages of both. Read this overview to decide whether the Estimator's linear model tools might -be useful to you. Then do the @{$wide$Linear Models tutorial} to -give it a try. This overview uses code samples from the tutorial, but the +be useful to you. Then work through the +[Estimator wide and deep learning tutorial](https://github.com/tensorflow/models/tree/master/official/wide_deep) +to give it a try. This overview uses code samples from the tutorial, but the tutorial walks through the code in greater detail. To understand this overview it will help to have some familiarity @@ -176,7 +177,7 @@ the name of a `FeatureColumn`. Each key's value is a tensor containing the values of that feature for all data instances. See @{$premade_estimators#input_fn} for a more comprehensive look at input functions, and `input_fn` in the -[linear models tutorial code](https://github.com/tensorflow/models/tree/master/official/wide_deep/wide_deep.py) +[wide and deep learning tutorial](https://github.com/tensorflow/models/tree/master/official/wide_deep) for an example implementation of an input function. The input function is passed to the `train()` and `evaluate()` calls that @@ -234,4 +235,5 @@ e = tf.estimator.DNNLinearCombinedClassifier( dnn_feature_columns=deep_columns, dnn_hidden_units=[100, 50]) ``` -For more information, see the @{$wide_and_deep$Wide and Deep Learning tutorial}. +For more information, see the +[wide and deep learning tutorial](https://github.com/tensorflow/models/tree/master/official/wide_deep). diff --git a/tensorflow/docs_src/tutorials/representation/wide.md b/tensorflow/docs_src/tutorials/representation/wide.md deleted file mode 100644 index 27ce75a30dd2acd5925702611042270e767b0c73..0000000000000000000000000000000000000000 --- a/tensorflow/docs_src/tutorials/representation/wide.md +++ /dev/null @@ -1,461 +0,0 @@ -# TensorFlow Linear Model Tutorial - -In this tutorial, we will use the tf.estimator API in TensorFlow to solve a -binary classification problem: Given census data about a person such as age, -education, marital status, and occupation (the features), we will try to predict -whether or not the person earns more than 50,000 dollars a year (the target -label). We will train a **logistic regression** model, and given an individual's -information our model will output a number between 0 and 1, which can be -interpreted as the probability that the individual has an annual income of over -50,000 dollars. - -## Setup - -To try the code for this tutorial: - -1. @{$install$Install TensorFlow} if you haven't already. - -2. Download [the tutorial code](https://github.com/tensorflow/models/tree/master/official/wide_deep/). - -3. Execute the data download script we provide to you: - - $ python data_download.py - -4. Execute the tutorial code with the following command to train the linear -model described in this tutorial: - - $ python wide_deep.py --model_type=wide - -Read on to find out how this code builds its linear model. - -## Reading The Census Data - -The dataset we'll be using is the -[Census Income Dataset](https://archive.ics.uci.edu/ml/datasets/Census+Income). -We have provided -[data_download.py](https://github.com/tensorflow/models/tree/master/official/wide_deep/data_download.py) -which downloads the code and performs some additional cleanup. - -Since the task is a binary classification problem, we'll construct a label -column named "label" whose value is 1 if the income is over 50K, and 0 -otherwise. For reference, see `input_fn` in -[wide_deep.py](https://github.com/tensorflow/models/tree/master/official/wide_deep/wide_deep.py). - -Next, let's take a look at the dataframe and see which columns we can use to -predict the target label. The columns can be grouped into two types—categorical -and continuous columns: - -* A column is called **categorical** if its value can only be one of the - categories in a finite set. For example, the relationship status of a person - (wife, husband, unmarried, etc.) or the education level (high school, - college, etc.) are categorical columns. -* A column is called **continuous** if its value can be any numerical value in - a continuous range. For example, the capital gain of a person (e.g. $14,084) - is a continuous column. - -Here's a list of columns available in the Census Income dataset: - -| Column Name | Type | Description | -| -------------- | ----------- | --------------------------------- | -| age | Continuous | The age of the individual | -| workclass | Categorical | The type of employer the | -: : : individual has (government, : -: : : military, private, etc.). : -| fnlwgt | Continuous | The number of people the census | -: : : takers believe that observation : -: : : represents (sample weight). Final : -: : : weight will not be used. : -| education | Categorical | The highest level of education | -: : : achieved for that individual. : -| education_num | Continuous | The highest level of education in | -: : : numerical form. : -| marital_status | Categorical | Marital status of the individual. | -| occupation | Categorical | The occupation of the individual. | -| relationship | Categorical | Wife, Own-child, Husband, | -: : : Not-in-family, Other-relative, : -: : : Unmarried. : -| race | Categorical | Amer-Indian-Eskimo, Asian-Pac- | -: : : Islander, Black, White, Other. : -| gender | Categorical | Female, Male. | -| capital_gain | Continuous | Capital gains recorded. | -| capital_loss | Continuous | Capital Losses recorded. | -| hours_per_week | Continuous | Hours worked per week. | -| native_country | Categorical | Country of origin of the | -: : : individual. : -| income_bracket | Categorical | ">50K" or "<=50K", meaning | -: : : whether the person makes more : -: : : than $50,000 annually. : - -## Converting Data into Tensors - -When building a tf.estimator model, the input data is specified by means of an -Input Builder function. This builder function will not be called until it is -later passed to tf.estimator.Estimator methods such as `train` and `evaluate`. -The purpose of this function is to construct the input data, which is -represented in the form of @{tf.Tensor}s or @{tf.SparseTensor}s. -In more detail, the input builder function returns the following as a pair: - -1. `features`: A dict from feature column names to `Tensors` or - `SparseTensors`. -2. `labels`: A `Tensor` containing the label column. - -The keys of the `features` will be used to construct columns in the next -section. Because we want to call the `train` and `evaluate` methods with -different data, we define a method that returns an input function based on the -given data. Note that the returned input function will be called while -constructing the TensorFlow graph, not while running the graph. What it is -returning is a representation of the input data as the fundamental unit of -TensorFlow computations, a `Tensor` (or `SparseTensor`). - -Each continuous column in the train or test data will be converted into a -`Tensor`, which in general is a good format to represent dense data. For -categorical data, we must represent the data as a `SparseTensor`. This data -format is good for representing sparse data. Our `input_fn` uses the `tf.data` -API, which makes it easy to apply transformations to our dataset: - -```python -def input_fn(data_file, num_epochs, shuffle, batch_size): - """Generate an input function for the Estimator.""" - assert tf.gfile.Exists(data_file), ( - '%s not found. Please make sure you have either run data_download.py or ' - 'set both arguments --train_data and --test_data.' % data_file) - - def parse_csv(value): - print('Parsing', data_file) - columns = tf.decode_csv(value, record_defaults=_CSV_COLUMN_DEFAULTS) - features = dict(zip(_CSV_COLUMNS, columns)) - labels = features.pop('income_bracket') - return features, tf.equal(labels, '>50K') - - # Extract lines from input files using the Dataset API. - dataset = tf.data.TextLineDataset(data_file) - - if shuffle: - dataset = dataset.shuffle(buffer_size=_SHUFFLE_BUFFER) - - dataset = dataset.map(parse_csv, num_parallel_calls=5) - - # We call repeat after shuffling, rather than before, to prevent separate - # epochs from blending together. - dataset = dataset.repeat(num_epochs) - dataset = dataset.batch(batch_size) - - iterator = dataset.make_one_shot_iterator() - features, labels = iterator.get_next() - return features, labels -``` - -## Selecting and Engineering Features for the Model - -Selecting and crafting the right set of feature columns is key to learning an -effective model. A **feature column** can be either one of the raw columns in -the original dataframe (let's call them **base feature columns**), or any new -columns created based on some transformations defined over one or multiple base -columns (let's call them **derived feature columns**). Basically, "feature -column" is an abstract concept of any raw or derived variable that can be used -to predict the target label. - -### Base Categorical Feature Columns - -To define a feature column for a categorical feature, we can create a -`CategoricalColumn` using the tf.feature_column API. If you know the set of all -possible feature values of a column and there are only a few of them, you can -use `categorical_column_with_vocabulary_list`. Each key in the list will get -assigned an auto-incremental ID starting from 0. For example, for the -`relationship` column we can assign the feature string "Husband" to an integer -ID of 0 and "Not-in-family" to 1, etc., by doing: - -```python -relationship = tf.feature_column.categorical_column_with_vocabulary_list( - 'relationship', [ - 'Husband', 'Not-in-family', 'Wife', 'Own-child', 'Unmarried', - 'Other-relative']) -``` - -What if we don't know the set of possible values in advance? Not a problem. We -can use `categorical_column_with_hash_bucket` instead: - -```python -occupation = tf.feature_column.categorical_column_with_hash_bucket( - 'occupation', hash_bucket_size=1000) -``` - -What will happen is that each possible value in the feature column `occupation` -will be hashed to an integer ID as we encounter them in training. See an example -illustration below: - -ID | Feature ---- | ------------- -... | -9 | `"Machine-op-inspct"` -... | -103 | `"Farming-fishing"` -... | -375 | `"Protective-serv"` -... | - -No matter which way we choose to define a `SparseColumn`, each feature string -will be mapped into an integer ID by looking up a fixed mapping or by hashing. -Note that hashing collisions are possible, but may not significantly impact the -model quality. Under the hood, the `LinearModel` class is responsible for -managing the mapping and creating `tf.Variable` to store the model parameters -(also known as model weights) for each feature ID. The model parameters will be -learned through the model training process we'll go through later. - -We'll do the similar trick to define the other categorical features: - -```python -education = tf.feature_column.categorical_column_with_vocabulary_list( - 'education', [ - 'Bachelors', 'HS-grad', '11th', 'Masters', '9th', 'Some-college', - 'Assoc-acdm', 'Assoc-voc', '7th-8th', 'Doctorate', 'Prof-school', - '5th-6th', '10th', '1st-4th', 'Preschool', '12th']) - -marital_status = tf.feature_column.categorical_column_with_vocabulary_list( - 'marital_status', [ - 'Married-civ-spouse', 'Divorced', 'Married-spouse-absent', - 'Never-married', 'Separated', 'Married-AF-spouse', 'Widowed']) - -relationship = tf.feature_column.categorical_column_with_vocabulary_list( - 'relationship', [ - 'Husband', 'Not-in-family', 'Wife', 'Own-child', 'Unmarried', - 'Other-relative']) - -workclass = tf.feature_column.categorical_column_with_vocabulary_list( - 'workclass', [ - 'Self-emp-not-inc', 'Private', 'State-gov', 'Federal-gov', - 'Local-gov', '?', 'Self-emp-inc', 'Without-pay', 'Never-worked']) - -# To show an example of hashing: -occupation = tf.feature_column.categorical_column_with_hash_bucket( - 'occupation', hash_bucket_size=1000) -``` - -### Base Continuous Feature Columns - -Similarly, we can define a `NumericColumn` for each continuous feature column -that we want to use in the model: - -```python -age = tf.feature_column.numeric_column('age') -education_num = tf.feature_column.numeric_column('education_num') -capital_gain = tf.feature_column.numeric_column('capital_gain') -capital_loss = tf.feature_column.numeric_column('capital_loss') -hours_per_week = tf.feature_column.numeric_column('hours_per_week') -``` - -### Making Continuous Features Categorical through Bucketization - -Sometimes the relationship between a continuous feature and the label is not -linear. As a hypothetical example, a person's income may grow with age in the -early stage of one's career, then the growth may slow at some point, and finally -the income decreases after retirement. In this scenario, using the raw `age` as -a real-valued feature column might not be a good choice because the model can -only learn one of the three cases: - -1. Income always increases at some rate as age grows (positive correlation), -1. Income always decreases at some rate as age grows (negative correlation), or -1. Income stays the same no matter at what age (no correlation) - -If we want to learn the fine-grained correlation between income and each age -group separately, we can leverage **bucketization**. Bucketization is a process -of dividing the entire range of a continuous feature into a set of consecutive -bins/buckets, and then converting the original numerical feature into a bucket -ID (as a categorical feature) depending on which bucket that value falls into. -So, we can define a `bucketized_column` over `age` as: - -```python -age_buckets = tf.feature_column.bucketized_column( - age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65]) -``` - -where the `boundaries` is a list of bucket boundaries. In this case, there are -10 boundaries, resulting in 11 age group buckets (from age 17 and below, 18-24, -25-29, ..., to 65 and over). - -### Intersecting Multiple Columns with CrossedColumn - -Using each base feature column separately may not be enough to explain the data. -For example, the correlation between education and the label (earning > 50,000 -dollars) may be different for different occupations. Therefore, if we only learn -a single model weight for `education="Bachelors"` and `education="Masters"`, we -won't be able to capture every single education-occupation combination (e.g. -distinguishing between `education="Bachelors" AND occupation="Exec-managerial"` -and `education="Bachelors" AND occupation="Craft-repair"`). To learn the -differences between different feature combinations, we can add **crossed feature -columns** to the model. - -```python -education_x_occupation = tf.feature_column.crossed_column( - ['education', 'occupation'], hash_bucket_size=1000) -``` - -We can also create a `CrossedColumn` over more than two columns. Each -constituent column can be either a base feature column that is categorical -(`SparseColumn`), a bucketized real-valued feature column (`BucketizedColumn`), -or even another `CrossColumn`. Here's an example: - -```python -age_buckets_x_education_x_occupation = tf.feature_column.crossed_column( - [age_buckets, 'education', 'occupation'], hash_bucket_size=1000) -``` - -## Defining The Logistic Regression Model - -After processing the input data and defining all the feature columns, we're now -ready to put them all together and build a Logistic Regression model. In the -previous section we've seen several types of base and derived feature columns, -including: - -* `CategoricalColumn` -* `NumericColumn` -* `BucketizedColumn` -* `CrossedColumn` - -All of these are subclasses of the abstract `FeatureColumn` class, and can be -added to the `feature_columns` field of a model: - -```python -base_columns = [ - education, marital_status, relationship, workclass, occupation, - age_buckets, -] -crossed_columns = [ - tf.feature_column.crossed_column( - ['education', 'occupation'], hash_bucket_size=1000), - tf.feature_column.crossed_column( - [age_buckets, 'education', 'occupation'], hash_bucket_size=1000), -] - -model_dir = tempfile.mkdtemp() -model = tf.estimator.LinearClassifier( - model_dir=model_dir, feature_columns=base_columns + crossed_columns) -``` - -The model also automatically learns a bias term, which controls the prediction -one would make without observing any features (see the section "How Logistic -Regression Works" for more explanations). The learned model files will be stored -in `model_dir`. - -## Training and Evaluating Our Model - -After adding all the features to the model, now let's look at how to actually -train the model. Training a model is just a single command using the -tf.estimator API: - -```python -model.train(input_fn=lambda: input_fn(train_data, num_epochs, True, batch_size)) -``` - -After the model is trained, we can evaluate how good our model is at predicting -the labels of the holdout data: - -```python -results = model.evaluate(input_fn=lambda: input_fn( - test_data, 1, False, batch_size)) -for key in sorted(results): - print('%s: %s' % (key, results[key])) -``` - -The first line of the final output should be something like -`accuracy: 0.83557522`, which means the accuracy is 83.6%. Feel free to try more -features and transformations and see if you can do even better! - -After the model is evaluated, we can use the model to predict whether an individual has an annual income of over -50,000 dollars given an individual's information input. -```python - pred_iter = model.predict(input_fn=lambda: input_fn(FLAGS.test_data, 1, False, 1)) - for pred in pred_iter: - print(pred['classes']) -``` - -The model prediction output would be like `[b'1']` or `[b'0']` which means whether corresponding individual has an annual income of over 50,000 dollars or not. - -If you'd like to see a working end-to-end example, you can download our -[example code](https://github.com/tensorflow/models/tree/master/official/wide_deep/wide_deep.py) -and set the `model_type` flag to `wide`. - -## Adding Regularization to Prevent Overfitting - -Regularization is a technique used to avoid **overfitting**. Overfitting happens -when your model does well on the data it is trained on, but worse on test data -that the model has not seen before, such as live traffic. Overfitting generally -occurs when a model is excessively complex, such as having too many parameters -relative to the number of observed training data. Regularization allows for you -to control your model's complexity and makes the model more generalizable to -unseen data. - -In the Linear Model library, you can add L1 and L2 regularizations to the model -as: - -``` -model = tf.estimator.LinearClassifier( - model_dir=model_dir, feature_columns=base_columns + crossed_columns, - optimizer=tf.train.FtrlOptimizer( - learning_rate=0.1, - l1_regularization_strength=1.0, - l2_regularization_strength=1.0)) -``` - -One important difference between L1 and L2 regularization is that L1 -regularization tends to make model weights stay at zero, creating sparser -models, whereas L2 regularization also tries to make the model weights closer to -zero but not necessarily zero. Therefore, if you increase the strength of L1 -regularization, you will have a smaller model size because many of the model -weights will be zero. This is often desirable when the feature space is very -large but sparse, and when there are resource constraints that prevent you from -serving a model that is too large. - -In practice, you should try various combinations of L1, L2 regularization -strengths and find the best parameters that best control overfitting and give -you a desirable model size. - -## How Logistic Regression Works - -Finally, let's take a minute to talk about what the Logistic Regression model -actually looks like in case you're not already familiar with it. We'll denote -the label as \\(Y\\), and the set of observed features as a feature vector -\\(\mathbf{x}=[x_1, x_2, ..., x_d]\\). We define \\(Y=1\\) if an individual -earned > 50,000 dollars and \\(Y=0\\) otherwise. In Logistic Regression, the -probability of the label being positive (\\(Y=1\\)) given the features -\\(\mathbf{x}\\) is given as: - -$$ P(Y=1|\mathbf{x}) = \frac{1}{1+\exp(-(\mathbf{w}^T\mathbf{x}+b))}$$ - -where \\(\mathbf{w}=[w_1, w_2, ..., w_d]\\) are the model weights for the -features \\(\mathbf{x}=[x_1, x_2, ..., x_d]\\). \\(b\\) is a constant that is -often called the **bias** of the model. The equation consists of two parts—A -linear model and a logistic function: - -* **Linear Model**: First, we can see that \\(\mathbf{w}^T\mathbf{x}+b = b + - w_1x_1 + ... +w_dx_d\\) is a linear model where the output is a linear - function of the input features \\(\mathbf{x}\\). The bias \\(b\\) is the - prediction one would make without observing any features. The model weight - \\(w_i\\) reflects how the feature \\(x_i\\) is correlated with the positive - label. If \\(x_i\\) is positively correlated with the positive label, the - weight \\(w_i\\) increases, and the probability \\(P(Y=1|\mathbf{x})\\) will - be closer to 1. On the other hand, if \\(x_i\\) is negatively correlated - with the positive label, then the weight \\(w_i\\) decreases and the - probability \\(P(Y=1|\mathbf{x})\\) will be closer to 0. - -* **Logistic Function**: Second, we can see that there's a logistic function - (also known as the sigmoid function) \\(S(t) = 1/(1+\exp(-t))\\) being - applied to the linear model. The logistic function is used to convert the - output of the linear model \\(\mathbf{w}^T\mathbf{x}+b\\) from any real - number into the range of \\([0, 1]\\), which can be interpreted as a - probability. - -Model training is an optimization problem: The goal is to find a set of model -weights (i.e. model parameters) to minimize a **loss function** defined over the -training data, such as logistic loss for Logistic Regression models. The loss -function measures the discrepancy between the ground-truth label and the model's -prediction. If the prediction is very close to the ground-truth label, the loss -value will be low; if the prediction is very far from the label, then the loss -value would be high. - -## Learn Deeper - -If you're interested in learning more, check out our -@{$wide_and_deep$Wide & Deep Learning Tutorial} where we'll show you how to -combine the strengths of linear models and deep neural networks by jointly -training them using the tf.estimator API. diff --git a/tensorflow/docs_src/tutorials/representation/wide_and_deep.md b/tensorflow/docs_src/tutorials/representation/wide_and_deep.md deleted file mode 100644 index 44677a810bc5c253c198d81fae2be723c4f8ae4e..0000000000000000000000000000000000000000 --- a/tensorflow/docs_src/tutorials/representation/wide_and_deep.md +++ /dev/null @@ -1,243 +0,0 @@ -# TensorFlow Wide & Deep Learning Tutorial - -In the previous @{$wide$TensorFlow Linear Model Tutorial}, we trained a logistic -regression model to predict the probability that the individual has an annual -income of over 50,000 dollars using the -[Census Income Dataset](https://archive.ics.uci.edu/ml/datasets/Census+Income). -TensorFlow is great for training deep neural networks too, and you might be -thinking which one you should choose—well, why not both? Would it be possible to -combine the strengths of both in one model? - -In this tutorial, we'll introduce how to use the tf.estimator API to jointly -train a wide linear model and a deep feed-forward neural network. This approach -combines the strengths of memorization and generalization. It's useful for -generic large-scale regression and classification problems with sparse input -features (e.g., categorical features with a large number of possible feature -values). If you're interested in learning more about how Wide & Deep Learning -works, please check out our [research paper](https://arxiv.org/abs/1606.07792). - -![Wide & Deep Spectrum of Models](https://www.tensorflow.org/images/wide_n_deep.svg "Wide & Deep") - -The figure above shows a comparison of a wide model (logistic regression with -sparse features and transformations), a deep model (feed-forward neural network -with an embedding layer and several hidden layers), and a Wide & Deep model -(joint training of both). At a high level, there are only 3 steps to configure a -wide, deep, or Wide & Deep model using the tf.estimator API: - -1. Select features for the wide part: Choose the sparse base columns and - crossed columns you want to use. -1. Select features for the deep part: Choose the continuous columns, the - embedding dimension for each categorical column, and the hidden layer sizes. -1. Put them all together in a Wide & Deep model - (`DNNLinearCombinedClassifier`). - -And that's it! Let's go through a simple example. - -## Setup - -To try the code for this tutorial: - -1. @{$install$Install TensorFlow} if you haven't already. - -2. Download [the tutorial code](https://github.com/tensorflow/models/tree/master/official/wide_deep/). - -3. Execute the data download script we provide to you: - - $ python data_download.py - -4. Execute the tutorial code with the following command to train the wide and -deep model described in this tutorial: - - $ python wide_deep.py - -Read on to find out how this code builds its model. - - -## Define Base Feature Columns - -First, let's define the base categorical and continuous feature columns that -we'll use. These base columns will be the building blocks used by both the wide -part and the deep part of the model. - -```python -import tensorflow as tf - -# Continuous columns -age = tf.feature_column.numeric_column('age') -education_num = tf.feature_column.numeric_column('education_num') -capital_gain = tf.feature_column.numeric_column('capital_gain') -capital_loss = tf.feature_column.numeric_column('capital_loss') -hours_per_week = tf.feature_column.numeric_column('hours_per_week') - -education = tf.feature_column.categorical_column_with_vocabulary_list( - 'education', [ - 'Bachelors', 'HS-grad', '11th', 'Masters', '9th', 'Some-college', - 'Assoc-acdm', 'Assoc-voc', '7th-8th', 'Doctorate', 'Prof-school', - '5th-6th', '10th', '1st-4th', 'Preschool', '12th']) - -marital_status = tf.feature_column.categorical_column_with_vocabulary_list( - 'marital_status', [ - 'Married-civ-spouse', 'Divorced', 'Married-spouse-absent', - 'Never-married', 'Separated', 'Married-AF-spouse', 'Widowed']) - -relationship = tf.feature_column.categorical_column_with_vocabulary_list( - 'relationship', [ - 'Husband', 'Not-in-family', 'Wife', 'Own-child', 'Unmarried', - 'Other-relative']) - -workclass = tf.feature_column.categorical_column_with_vocabulary_list( - 'workclass', [ - 'Self-emp-not-inc', 'Private', 'State-gov', 'Federal-gov', - 'Local-gov', '?', 'Self-emp-inc', 'Without-pay', 'Never-worked']) - -# To show an example of hashing: -occupation = tf.feature_column.categorical_column_with_hash_bucket( - 'occupation', hash_bucket_size=1000) - -# Transformations. -age_buckets = tf.feature_column.bucketized_column( - age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65]) -``` - -## The Wide Model: Linear Model with Crossed Feature Columns - -The wide model is a linear model with a wide set of sparse and crossed feature -columns: - -```python -base_columns = [ - education, marital_status, relationship, workclass, occupation, - age_buckets, -] - -crossed_columns = [ - tf.feature_column.crossed_column( - ['education', 'occupation'], hash_bucket_size=1000), - tf.feature_column.crossed_column( - [age_buckets, 'education', 'occupation'], hash_bucket_size=1000), -] -``` - -You can also see the @{$wide$TensorFlow Linear Model Tutorial} for more details. - -Wide models with crossed feature columns can memorize sparse interactions -between features effectively. That being said, one limitation of crossed feature -columns is that they do not generalize to feature combinations that have not -appeared in the training data. Let's add a deep model with embeddings to fix -that. - -## The Deep Model: Neural Network with Embeddings - -The deep model is a feed-forward neural network, as shown in the previous -figure. Each of the sparse, high-dimensional categorical features are first -converted into a low-dimensional and dense real-valued vector, often referred to -as an embedding vector. These low-dimensional dense embedding vectors are -concatenated with the continuous features, and then fed into the hidden layers -of a neural network in the forward pass. The embedding values are initialized -randomly, and are trained along with all other model parameters to minimize the -training loss. If you're interested in learning more about embeddings, check out -the TensorFlow tutorial on @{$word2vec$Vector Representations of Words} or -[Word embedding](https://en.wikipedia.org/wiki/Word_embedding) on Wikipedia. - -Another way to represent categorical columns to feed into a neural network is -via a one-hot or multi-hot representation. This is often appropriate for -categorical columns with only a few possible values. As an example of a one-hot -representation, for the relationship column, `"Husband"` can be represented as -[1, 0, 0, 0, 0, 0], and `"Not-in-family"` as [0, 1, 0, 0, 0, 0], etc. This is a -fixed representation, whereas embeddings are more flexible and calculated at -training time. - -We'll configure the embeddings for the categorical columns using -`embedding_column`, and concatenate them with the continuous columns. -We also use `indicator_column` to create multi-hot representations of some -categorical columns. - -```python -deep_columns = [ - age, - education_num, - capital_gain, - capital_loss, - hours_per_week, - tf.feature_column.indicator_column(workclass), - tf.feature_column.indicator_column(education), - tf.feature_column.indicator_column(marital_status), - tf.feature_column.indicator_column(relationship), - # To show an example of embedding - tf.feature_column.embedding_column(occupation, dimension=8), -] -``` - -The higher the `dimension` of the embedding is, the more degrees of freedom the -model will have to learn the representations of the features. For simplicity, we -set the dimension to 8 for all feature columns here. Empirically, a more -informed decision for the number of dimensions is to start with a value on the -order of \\(\log_2(n)\\) or \\(k\sqrt[4]n\\), where \\(n\\) is the number of -unique features in a feature column and \\(k\\) is a small constant (usually -smaller than 10). - -Through dense embeddings, deep models can generalize better and make predictions -on feature pairs that were previously unseen in the training data. However, it -is difficult to learn effective low-dimensional representations for feature -columns when the underlying interaction matrix between two feature columns is -sparse and high-rank. In such cases, the interaction between most feature pairs -should be zero except a few, but dense embeddings will lead to nonzero -predictions for all feature pairs, and thus can over-generalize. On the other -hand, linear models with crossed features can memorize these “exception rules” -effectively with fewer model parameters. - -Now, let's see how to jointly train wide and deep models and allow them to -complement each other’s strengths and weaknesses. - -## Combining Wide and Deep Models into One - -The wide models and deep models are combined by summing up their final output -log odds as the prediction, then feeding the prediction to a logistic loss -function. All the graph definition and variable allocations have already been -handled for you under the hood, so you simply need to create a -`DNNLinearCombinedClassifier`: - -```python -model = tf.estimator.DNNLinearCombinedClassifier( - model_dir='/tmp/census_model', - linear_feature_columns=base_columns + crossed_columns, - dnn_feature_columns=deep_columns, - dnn_hidden_units=[100, 50]) -``` - -## Training and Evaluating The Model - -Before we train the model, let's read in the Census dataset as we did in the -@{$wide$TensorFlow Linear Model tutorial}. See `data_download.py` as well as -`input_fn` within -[`wide_deep.py`](https://github.com/tensorflow/models/tree/master/official/wide_deep/wide_deep.py). - -After reading in the data, you can train and evaluate the model: - -```python -# Train and evaluate the model every `FLAGS.epochs_per_eval` epochs. -for n in range(FLAGS.train_epochs // FLAGS.epochs_per_eval): - model.train(input_fn=lambda: input_fn( - FLAGS.train_data, FLAGS.epochs_per_eval, True, FLAGS.batch_size)) - - results = model.evaluate(input_fn=lambda: input_fn( - FLAGS.test_data, 1, False, FLAGS.batch_size)) - - # Display evaluation metrics - print('Results at epoch', (n + 1) * FLAGS.epochs_per_eval) - print('-' * 30) - - for key in sorted(results): - print('%s: %s' % (key, results[key])) -``` - -The final output accuracy should be somewhere around 85.5%. If you'd like to -see a working end-to-end example, you can download our -[example code](https://github.com/tensorflow/models/tree/master/official/wide_deep/wide_deep.py). - -Note that this tutorial is just a quick example on a small dataset to get you -familiar with the API. Wide & Deep Learning will be even more powerful if you -try it on a large dataset with many sparse feature columns that have a large -number of possible feature values. Again, feel free to take a look at our -[research paper](https://arxiv.org/abs/1606.07792) for more ideas about how to -apply Wide & Deep Learning in real-world large-scale machine learning problems. diff --git a/tensorflow/docs_src/tutorials/representation/word2vec.md b/tensorflow/docs_src/tutorials/representation/word2vec.md index 3fe7352bd2383177ca200a0265dee41dba430144..0a1c41c84a3971cb6237e37ccaaa884e53de2aae 100644 --- a/tensorflow/docs_src/tutorials/representation/word2vec.md +++ b/tensorflow/docs_src/tutorials/representation/word2vec.md @@ -23,7 +23,7 @@ straight in, feel free to look at the minimalistic implementation in This basic example contains the code needed to download some data, train on it a bit and visualize the result. Once you get comfortable with reading and running the basic version, you can graduate to -[models/tutorials/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec.py) +[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec.py) which is a more serious implementation that showcases some more advanced TensorFlow principles about how to efficiently use threads to move data into a text model, how to checkpoint during training, etc. @@ -341,7 +341,7 @@ t-SNE. Et voila! As expected, words that are similar end up clustering nearby each other. For a more heavyweight implementation of word2vec that showcases more of the advanced features of TensorFlow, see the implementation in -[models/tutorials/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec.py). +[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec.py). ## Evaluating Embeddings: Analogical Reasoning @@ -357,7 +357,7 @@ Download the dataset for this task from To see how we do this evaluation, have a look at the `build_eval_graph()` and `eval()` functions in -[models/tutorials/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec.py). +[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec.py). The choice of hyperparameters can strongly influence the accuracy on this task. To achieve state-of-the-art performance on this task requires training over a @@ -385,13 +385,13 @@ your model is seriously bottlenecked on input data, you may want to implement a custom data reader for your problem, as described in @{$new_data_formats$New Data Formats}. For the case of Skip-Gram modeling, we've actually already done this for you as an example in -[models/tutorials/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec.py). +[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec.py). If your model is no longer I/O bound but you want still more performance, you can take things further by writing your own TensorFlow Ops, as described in @{$adding_an_op$Adding a New Op}. Again we've provided an example of this for the Skip-Gram case -[models/tutorials/embedding/word2vec_optimized.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec_optimized.py). +[models/tutorials/embedding/word2vec_optimized.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec_optimized.py). Feel free to benchmark these against each other to measure performance improvements at each stage. diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowObjectDetectionAPIModel.java b/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowObjectDetectionAPIModel.java index 614d3c7dd7766bb6eb7cd83deb85064d9522cbe5..9739e580185b316b3cc509e815ac05a28a267b29 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowObjectDetectionAPIModel.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowObjectDetectionAPIModel.java @@ -137,7 +137,7 @@ public class TensorFlowObjectDetectionAPIModel implements Classifier { Trace.beginSection("recognizeImage"); Trace.beginSection("preprocessBitmap"); - // Preprocess the image data from 0-255 int to normalized float based + // Preprocess the image data to extract R, G and B bytes from int of form 0x00RRGGBB // on the provided parameters. bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight()); diff --git a/tensorflow/examples/saved_model/saved_model_half_plus_two.py b/tensorflow/examples/saved_model/saved_model_half_plus_two.py index 0d6f1ef655bcaba43c0d68e1e924bcb4b29967af..2d1e0c6f6de88ae116fe1951ca24505d41743fa9 100644 --- a/tensorflow/examples/saved_model/saved_model_half_plus_two.py +++ b/tensorflow/examples/saved_model/saved_model_half_plus_two.py @@ -33,6 +33,13 @@ where `a`, `b` and `c` are variables with `a=0.5` and `b=2` and `c=3`. Output from this program is typically used to exercise SavedModel load and execution code. + +To create a CPU model: + bazel run -c opt saved_half_plus_two -- --device=cpu + +To create GPU model: + bazel run --config=cuda -c opt saved_half_plus_two -- \ + --device=gpu """ from __future__ import absolute_import @@ -105,42 +112,52 @@ def _build_classification_signature(input_tensor, scores_tensor): def _generate_saved_model_for_half_plus_two(export_dir, as_text=False, - use_main_op=False): + use_main_op=False, + device_type="cpu"): """Generates SavedModel for half plus two. Args: export_dir: The directory to which the SavedModel should be written. as_text: Writes the SavedModel protocol buffer in text format to disk. use_main_op: Whether to supply a main op during SavedModel build time. + device_name: Device to force ops to run on. """ builder = tf.saved_model.builder.SavedModelBuilder(export_dir) - with tf.Session(graph=tf.Graph()) as sess: - # Set up the model parameters as variables to exercise variable loading - # functionality upon restore. - a = tf.Variable(0.5, name="a") - b = tf.Variable(2.0, name="b") - c = tf.Variable(3.0, name="c") - - # Create a placeholder for serialized tensorflow.Example messages to be fed. - serialized_tf_example = tf.placeholder(tf.string, name="tf_example") - - # Parse the tensorflow.Example looking for a feature named "x" with a single - # floating point value. - feature_configs = { - "x": tf.FixedLenFeature( - [1], dtype=tf.float32), - "x2": tf.FixedLenFeature( - [1], dtype=tf.float32, default_value=[0.0]) - } - tf_example = tf.parse_example(serialized_tf_example, feature_configs) - # Use tf.identity() to assign name - x = tf.identity(tf_example["x"], name="x") - y = tf.add(tf.multiply(a, x), b, name="y") - y2 = tf.add(tf.multiply(a, x), c, name="y2") - - x2 = tf.identity(tf_example["x2"], name="x2") - y3 = tf.add(tf.multiply(a, x2), c, name="y3") + device_name = "/cpu:0" + if device_type == "gpu": + device_name = "/gpu:0" + + with tf.Session( + graph=tf.Graph(), + config=tf.ConfigProto(log_device_placement=True)) as sess: + with tf.device(device_name): + # Set up the model parameters as variables to exercise variable loading + # functionality upon restore. + a = tf.Variable(0.5, name="a") + b = tf.Variable(2.0, name="b") + c = tf.Variable(3.0, name="c") + + # Create a placeholder for serialized tensorflow.Example messages to be + # fed. + serialized_tf_example = tf.placeholder(tf.string, name="tf_example") + + # Parse the tensorflow.Example looking for a feature named "x" with a + # single floating point value. + feature_configs = { + "x": tf.FixedLenFeature([1], dtype=tf.float32), + "x2": tf.FixedLenFeature([1], dtype=tf.float32, default_value=[0.0]) + } + # parse_example only works on CPU + with tf.device("/cpu:0"): + tf_example = tf.parse_example(serialized_tf_example, feature_configs) + # Use tf.identity() to assign name + x = tf.identity(tf_example["x"], name="x") + y = tf.add(tf.multiply(a, x), b, name="y") + y2 = tf.add(tf.multiply(a, x), c, name="y2") + + x2 = tf.identity(tf_example["x2"], name="x2") + y3 = tf.add(tf.multiply(a, x2), c, name="y3") # Create an assets file that can be saved and restored as part of the # SavedModel. @@ -185,20 +202,7 @@ def _generate_saved_model_for_half_plus_two(export_dir, } # Initialize all variables and then save the SavedModel. sess.run(tf.global_variables_initializer()) - signature_def_map = { - "regress_x_to_y": - _build_regression_signature(serialized_tf_example, y), - "regress_x_to_y2": - _build_regression_signature(serialized_tf_example, y2), - "regress_x2_to_y3": - _build_regression_signature(x2, y3), - "classify_x_to_y": - _build_classification_signature(serialized_tf_example, y), - "classify_x2_to_y3": - _build_classification_signature(x2, y3), - tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: - predict_signature_def - } + if use_main_op: builder.add_meta_graph_and_variables( sess, [tf.saved_model.tag_constants.SERVING], @@ -212,19 +216,30 @@ def _generate_saved_model_for_half_plus_two(export_dir, signature_def_map=signature_def_map, assets_collection=tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS), legacy_init_op=tf.group(assign_filename_op)) - builder.save(as_text) + builder.save(as_text) def main(_): - _generate_saved_model_for_half_plus_two(FLAGS.output_dir) - print("SavedModel generated at: %s" % FLAGS.output_dir) + _generate_saved_model_for_half_plus_two( + FLAGS.output_dir, device_type=FLAGS.device) + print("SavedModel generated for %(device)s at: %(dir)s" % { + "device": FLAGS.device, + "dir": FLAGS.output_dir + }) - _generate_saved_model_for_half_plus_two(FLAGS.output_dir_pbtxt, as_text=True) - print("SavedModel generated at: %s" % FLAGS.output_dir_pbtxt) + _generate_saved_model_for_half_plus_two( + FLAGS.output_dir_pbtxt, as_text=True, device_type=FLAGS.device) + print("SavedModel generated for %(device)s at: %(dir)s" % { + "device": FLAGS.device, + "dir": FLAGS.output_dir_pbtxt + }) _generate_saved_model_for_half_plus_two( - FLAGS.output_dir_main_op, use_main_op=True) - print("SavedModel generated at: %s" % FLAGS.output_dir_main_op) + FLAGS.output_dir_main_op, use_main_op=True, device_type=FLAGS.device) + print("SavedModel generated for %(device)s at: %(dir)s " % { + "device": FLAGS.device, + "dir": FLAGS.output_dir_main_op + }) if __name__ == "__main__": @@ -244,5 +259,10 @@ if __name__ == "__main__": type=str, default="/tmp/saved_model_half_plus_two_main_op", help="Directory where to output the SavedModel with a main op.") + parser.add_argument( + "--device", + type=str, + default="cpu", + help="Force model to run on 'cpu' or 'gpu'") FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/speech_commands/BUILD b/tensorflow/examples/speech_commands/BUILD index 13bca34a86b0c2fba7e5e8e3527d13587feacaae..7a44e2ee4fdf690ce576f720bb371785f88779b4 100644 --- a/tensorflow/examples/speech_commands/BUILD +++ b/tensorflow/examples/speech_commands/BUILD @@ -56,6 +56,7 @@ tf_py_test( srcs = ["input_data_test.py"], additional_deps = [ ":input_data", + ":models", "//tensorflow/python:client_testlib", ], ) diff --git a/tensorflow/examples/speech_commands/freeze.py b/tensorflow/examples/speech_commands/freeze.py index c8671d9c41169c07ce3134a49bf81a4ac29a8c60..89e790d4e4436cdc49af0fb2ae53dea8485ae9c5 100644 --- a/tensorflow/examples/speech_commands/freeze.py +++ b/tensorflow/examples/speech_commands/freeze.py @@ -54,7 +54,7 @@ FLAGS = None def create_inference_graph(wanted_words, sample_rate, clip_duration_ms, clip_stride_ms, window_size_ms, window_stride_ms, - dct_coefficient_count, model_architecture): + feature_bin_count, model_architecture, preprocess): """Creates an audio model with the nodes needed for inference. Uses the supplied arguments to create a model, and inserts the input and @@ -67,14 +67,19 @@ def create_inference_graph(wanted_words, sample_rate, clip_duration_ms, clip_stride_ms: How often to run recognition. Useful for models with cache. window_size_ms: Time slice duration to estimate frequencies from. window_stride_ms: How far apart time slices should be. - dct_coefficient_count: Number of frequency bands to analyze. + feature_bin_count: Number of frequency bands to analyze. model_architecture: Name of the kind of model to generate. + preprocess: How the spectrogram is processed to produce features, for + example 'mfcc' or 'average'. + + Raises: + Exception: If the preprocessing mode isn't recognized. """ words_list = input_data.prepare_words_list(wanted_words.split(',')) model_settings = models.prepare_model_settings( len(words_list), sample_rate, clip_duration_ms, window_size_ms, - window_stride_ms, dct_coefficient_count) + window_stride_ms, feature_bin_count, preprocess) runtime_settings = {'clip_stride_ms': clip_stride_ms} wav_data_placeholder = tf.placeholder(tf.string, [], name='wav_data') @@ -88,15 +93,25 @@ def create_inference_graph(wanted_words, sample_rate, clip_duration_ms, window_size=model_settings['window_size_samples'], stride=model_settings['window_stride_samples'], magnitude_squared=True) - fingerprint_input = contrib_audio.mfcc( - spectrogram, - decoded_sample_data.sample_rate, - dct_coefficient_count=dct_coefficient_count) - fingerprint_frequency_size = model_settings['dct_coefficient_count'] - fingerprint_time_size = model_settings['spectrogram_length'] - reshaped_input = tf.reshape(fingerprint_input, [ - -1, fingerprint_time_size * fingerprint_frequency_size - ]) + + if preprocess == 'average': + fingerprint_input = tf.nn.pool( + tf.expand_dims(spectrogram, -1), + window_shape=[1, model_settings['average_window_width']], + strides=[1, model_settings['average_window_width']], + pooling_type='AVG', + padding='SAME') + elif preprocess == 'mfcc': + fingerprint_input = contrib_audio.mfcc( + spectrogram, + sample_rate, + dct_coefficient_count=model_settings['fingerprint_width']) + else: + raise Exception('Unknown preprocess mode "%s" (should be "mfcc" or' + ' "average")' % (preprocess)) + + fingerprint_size = model_settings['fingerprint_size'] + reshaped_input = tf.reshape(fingerprint_input, [-1, fingerprint_size]) logits = models.create_model( reshaped_input, model_settings, model_architecture, is_training=False, @@ -110,10 +125,12 @@ def main(_): # Create the model and load its weights. sess = tf.InteractiveSession() - create_inference_graph(FLAGS.wanted_words, FLAGS.sample_rate, - FLAGS.clip_duration_ms, FLAGS.clip_stride_ms, - FLAGS.window_size_ms, FLAGS.window_stride_ms, - FLAGS.dct_coefficient_count, FLAGS.model_architecture) + create_inference_graph( + FLAGS.wanted_words, FLAGS.sample_rate, FLAGS.clip_duration_ms, + FLAGS.clip_stride_ms, FLAGS.window_size_ms, FLAGS.window_stride_ms, + FLAGS.feature_bin_count, FLAGS.model_architecture, FLAGS.preprocess) + if FLAGS.quantize: + tf.contrib.quantize.create_eval_graph() models.load_variables_from_checkpoint(sess, FLAGS.start_checkpoint) # Turn all the variables into inline constants inside the graph and save it. @@ -155,10 +172,11 @@ if __name__ == '__main__': default=10.0, help='How long the stride is between spectrogram timeslices',) parser.add_argument( - '--dct_coefficient_count', + '--feature_bin_count', type=int, default=40, - help='How many bins to use for the MFCC fingerprint',) + help='How many bins to use for the MFCC fingerprint', + ) parser.add_argument( '--start_checkpoint', type=str, @@ -176,5 +194,15 @@ if __name__ == '__main__': help='Words to use (others will be added to an unknown label)',) parser.add_argument( '--output_file', type=str, help='Where to save the frozen graph.') + parser.add_argument( + '--quantize', + type=bool, + default=False, + help='Whether to train the model for eight-bit deployment') + parser.add_argument( + '--preprocess', + type=str, + default='mfcc', + help='Spectrogram processing mode. Can be "mfcc" or "average"') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/speech_commands/freeze_test.py b/tensorflow/examples/speech_commands/freeze_test.py index 97c6eac675f696d89d069258edf6eec901cfad0b..c8de6c2152909cd6dfca9acc895c25b0ae8e09ca 100644 --- a/tensorflow/examples/speech_commands/freeze_test.py +++ b/tensorflow/examples/speech_commands/freeze_test.py @@ -24,14 +24,62 @@ from tensorflow.python.platform import test class FreezeTest(test.TestCase): - def testCreateInferenceGraph(self): + def testCreateInferenceGraphWithMfcc(self): with self.test_session() as sess: - freeze.create_inference_graph('a,b,c,d', 16000, 1000.0, 30.0, 30.0, 10.0, - 40, 'conv') + freeze.create_inference_graph( + wanted_words='a,b,c,d', + sample_rate=16000, + clip_duration_ms=1000.0, + clip_stride_ms=30.0, + window_size_ms=30.0, + window_stride_ms=10.0, + feature_bin_count=40, + model_architecture='conv', + preprocess='mfcc') self.assertIsNotNone(sess.graph.get_tensor_by_name('wav_data:0')) self.assertIsNotNone( sess.graph.get_tensor_by_name('decoded_sample_data:0')) self.assertIsNotNone(sess.graph.get_tensor_by_name('labels_softmax:0')) + ops = [node.op for node in sess.graph_def.node] + self.assertEqual(1, ops.count('Mfcc')) + + def testCreateInferenceGraphWithoutMfcc(self): + with self.test_session() as sess: + freeze.create_inference_graph( + wanted_words='a,b,c,d', + sample_rate=16000, + clip_duration_ms=1000.0, + clip_stride_ms=30.0, + window_size_ms=30.0, + window_stride_ms=10.0, + feature_bin_count=40, + model_architecture='conv', + preprocess='average') + self.assertIsNotNone(sess.graph.get_tensor_by_name('wav_data:0')) + self.assertIsNotNone( + sess.graph.get_tensor_by_name('decoded_sample_data:0')) + self.assertIsNotNone(sess.graph.get_tensor_by_name('labels_softmax:0')) + ops = [node.op for node in sess.graph_def.node] + self.assertEqual(0, ops.count('Mfcc')) + + def testFeatureBinCount(self): + with self.test_session() as sess: + freeze.create_inference_graph( + wanted_words='a,b,c,d', + sample_rate=16000, + clip_duration_ms=1000.0, + clip_stride_ms=30.0, + window_size_ms=30.0, + window_stride_ms=10.0, + feature_bin_count=80, + model_architecture='conv', + preprocess='average') + self.assertIsNotNone(sess.graph.get_tensor_by_name('wav_data:0')) + self.assertIsNotNone( + sess.graph.get_tensor_by_name('decoded_sample_data:0')) + self.assertIsNotNone(sess.graph.get_tensor_by_name('labels_softmax:0')) + ops = [node.op for node in sess.graph_def.node] + self.assertEqual(0, ops.count('Mfcc')) if __name__ == '__main__': diff --git a/tensorflow/examples/speech_commands/generate_streaming_test_wav.py b/tensorflow/examples/speech_commands/generate_streaming_test_wav.py index 053206ae2f144ce05efa7eb490626aef01a6bc49..9858906927737cd520a9fd02f04437d01e0f6d31 100644 --- a/tensorflow/examples/speech_commands/generate_streaming_test_wav.py +++ b/tensorflow/examples/speech_commands/generate_streaming_test_wav.py @@ -87,11 +87,12 @@ def main(_): words_list = input_data.prepare_words_list(FLAGS.wanted_words.split(',')) model_settings = models.prepare_model_settings( len(words_list), FLAGS.sample_rate, FLAGS.clip_duration_ms, - FLAGS.window_size_ms, FLAGS.window_stride_ms, FLAGS.dct_coefficient_count) + FLAGS.window_size_ms, FLAGS.window_stride_ms, FLAGS.feature_bin_count, + 'mfcc') audio_processor = input_data.AudioProcessor( '', FLAGS.data_dir, FLAGS.silence_percentage, 10, FLAGS.wanted_words.split(','), FLAGS.validation_percentage, - FLAGS.testing_percentage, model_settings) + FLAGS.testing_percentage, model_settings, FLAGS.data_dir) output_audio_sample_count = FLAGS.sample_rate * FLAGS.test_duration_seconds output_audio = np.zeros((output_audio_sample_count,), dtype=np.float32) @@ -242,10 +243,11 @@ if __name__ == '__main__': default=10.0, help='How long the stride is between spectrogram timeslices',) parser.add_argument( - '--dct_coefficient_count', + '--feature_bin_count', type=int, default=40, - help='How many bins to use for the MFCC fingerprint',) + help='How many bins to use for the MFCC fingerprint', + ) parser.add_argument( '--wanted_words', type=str, diff --git a/tensorflow/examples/speech_commands/input_data.py b/tensorflow/examples/speech_commands/input_data.py index 63dd18457fea42acb09058b9ddd4623d72d1fd04..30f2cfa9fef7d0b5800c7e557bde4702dbafaf26 100644 --- a/tensorflow/examples/speech_commands/input_data.py +++ b/tensorflow/examples/speech_commands/input_data.py @@ -153,14 +153,14 @@ class AudioProcessor(object): def __init__(self, data_url, data_dir, silence_percentage, unknown_percentage, wanted_words, validation_percentage, testing_percentage, - model_settings): + model_settings, summaries_dir): self.data_dir = data_dir self.maybe_download_and_extract_dataset(data_url, data_dir) self.prepare_data_index(silence_percentage, unknown_percentage, wanted_words, validation_percentage, testing_percentage) self.prepare_background_data() - self.prepare_processing_graph(model_settings) + self.prepare_processing_graph(model_settings, summaries_dir) def maybe_download_and_extract_dataset(self, data_url, dest_directory): """Download and extract data set tar file. @@ -325,7 +325,7 @@ class AudioProcessor(object): if not self.background_data: raise Exception('No background wav files were found in ' + search_path) - def prepare_processing_graph(self, model_settings): + def prepare_processing_graph(self, model_settings, summaries_dir): """Builds a TensorFlow graph to apply the input distortions. Creates a graph that loads a WAVE file, decodes it, scales the volume, @@ -341,48 +341,88 @@ class AudioProcessor(object): - time_shift_offset_placeholder_: How much to move the clip in time. - background_data_placeholder_: PCM sample data for background noise. - background_volume_placeholder_: Loudness of mixed-in background. - - mfcc_: Output 2D fingerprint of processed audio. + - output_: Output 2D fingerprint of processed audio. Args: model_settings: Information about the current model being trained. + summaries_dir: Path to save training summary information to. + + Raises: + ValueError: If the preprocessing mode isn't recognized. """ - desired_samples = model_settings['desired_samples'] - self.wav_filename_placeholder_ = tf.placeholder(tf.string, []) - wav_loader = io_ops.read_file(self.wav_filename_placeholder_) - wav_decoder = contrib_audio.decode_wav( - wav_loader, desired_channels=1, desired_samples=desired_samples) - # Allow the audio sample's volume to be adjusted. - self.foreground_volume_placeholder_ = tf.placeholder(tf.float32, []) - scaled_foreground = tf.multiply(wav_decoder.audio, - self.foreground_volume_placeholder_) - # Shift the sample's start position, and pad any gaps with zeros. - self.time_shift_padding_placeholder_ = tf.placeholder(tf.int32, [2, 2]) - self.time_shift_offset_placeholder_ = tf.placeholder(tf.int32, [2]) - padded_foreground = tf.pad( - scaled_foreground, - self.time_shift_padding_placeholder_, - mode='CONSTANT') - sliced_foreground = tf.slice(padded_foreground, - self.time_shift_offset_placeholder_, - [desired_samples, -1]) - # Mix in background noise. - self.background_data_placeholder_ = tf.placeholder(tf.float32, - [desired_samples, 1]) - self.background_volume_placeholder_ = tf.placeholder(tf.float32, []) - background_mul = tf.multiply(self.background_data_placeholder_, - self.background_volume_placeholder_) - background_add = tf.add(background_mul, sliced_foreground) - background_clamp = tf.clip_by_value(background_add, -1.0, 1.0) - # Run the spectrogram and MFCC ops to get a 2D 'fingerprint' of the audio. - spectrogram = contrib_audio.audio_spectrogram( - background_clamp, - window_size=model_settings['window_size_samples'], - stride=model_settings['window_stride_samples'], - magnitude_squared=True) - self.mfcc_ = contrib_audio.mfcc( - spectrogram, - wav_decoder.sample_rate, - dct_coefficient_count=model_settings['dct_coefficient_count']) + with tf.get_default_graph().name_scope('data'): + desired_samples = model_settings['desired_samples'] + self.wav_filename_placeholder_ = tf.placeholder( + tf.string, [], name='wav_filename') + wav_loader = io_ops.read_file(self.wav_filename_placeholder_) + wav_decoder = contrib_audio.decode_wav( + wav_loader, desired_channels=1, desired_samples=desired_samples) + # Allow the audio sample's volume to be adjusted. + self.foreground_volume_placeholder_ = tf.placeholder( + tf.float32, [], name='foreground_volume') + scaled_foreground = tf.multiply(wav_decoder.audio, + self.foreground_volume_placeholder_) + # Shift the sample's start position, and pad any gaps with zeros. + self.time_shift_padding_placeholder_ = tf.placeholder( + tf.int32, [2, 2], name='time_shift_padding') + self.time_shift_offset_placeholder_ = tf.placeholder( + tf.int32, [2], name='time_shift_offset') + padded_foreground = tf.pad( + scaled_foreground, + self.time_shift_padding_placeholder_, + mode='CONSTANT') + sliced_foreground = tf.slice(padded_foreground, + self.time_shift_offset_placeholder_, + [desired_samples, -1]) + # Mix in background noise. + self.background_data_placeholder_ = tf.placeholder( + tf.float32, [desired_samples, 1], name='background_data') + self.background_volume_placeholder_ = tf.placeholder( + tf.float32, [], name='background_volume') + background_mul = tf.multiply(self.background_data_placeholder_, + self.background_volume_placeholder_) + background_add = tf.add(background_mul, sliced_foreground) + background_clamp = tf.clip_by_value(background_add, -1.0, 1.0) + # Run the spectrogram and MFCC ops to get a 2D 'fingerprint' of the audio. + spectrogram = contrib_audio.audio_spectrogram( + background_clamp, + window_size=model_settings['window_size_samples'], + stride=model_settings['window_stride_samples'], + magnitude_squared=True) + tf.summary.image( + 'spectrogram', tf.expand_dims(spectrogram, -1), max_outputs=1) + # The number of buckets in each FFT row in the spectrogram will depend on + # how many input samples there are in each window. This can be quite + # large, with a 160 sample window producing 127 buckets for example. We + # don't need this level of detail for classification, so we often want to + # shrink them down to produce a smaller result. That's what this section + # implements. One method is to use average pooling to merge adjacent + # buckets, but a more sophisticated approach is to apply the MFCC + # algorithm to shrink the representation. + if model_settings['preprocess'] == 'average': + self.output_ = tf.nn.pool( + tf.expand_dims(spectrogram, -1), + window_shape=[1, model_settings['average_window_width']], + strides=[1, model_settings['average_window_width']], + pooling_type='AVG', + padding='SAME') + tf.summary.image('shrunk_spectrogram', self.output_, max_outputs=1) + elif model_settings['preprocess'] == 'mfcc': + self.output_ = contrib_audio.mfcc( + spectrogram, + wav_decoder.sample_rate, + dct_coefficient_count=model_settings['fingerprint_width']) + tf.summary.image( + 'mfcc', tf.expand_dims(self.output_, -1), max_outputs=1) + else: + raise ValueError('Unknown preprocess mode "%s" (should be "mfcc" or' + ' "average")' % (model_settings['preprocess'])) + + # Merge all the summaries and write them out to /tmp/retrain_logs (by + # default) + self.merged_summaries_ = tf.summary.merge_all(scope='data') + self.summary_writer_ = tf.summary.FileWriter(summaries_dir + '/data', + tf.get_default_graph()) def set_size(self, mode): """Calculates the number of samples in the dataset partition. @@ -418,6 +458,9 @@ class AudioProcessor(object): Returns: List of sample data for the transformed samples, and list of label indexes + + Raises: + ValueError: If background samples are too short. """ # Pick one of the partitions to choose samples from. candidates = self.data_index[mode] @@ -460,6 +503,11 @@ class AudioProcessor(object): if use_background or sample['label'] == SILENCE_LABEL: background_index = np.random.randint(len(self.background_data)) background_samples = self.background_data[background_index] + if len(background_samples) <= model_settings['desired_samples']: + raise ValueError( + 'Background sample is too short! Need more than %d' + ' samples but only %d were found' % + (model_settings['desired_samples'], len(background_samples))) background_offset = np.random.randint( 0, len(background_samples) - model_settings['desired_samples']) background_clipped = background_samples[background_offset:( @@ -482,7 +530,10 @@ class AudioProcessor(object): else: input_dict[self.foreground_volume_placeholder_] = 1 # Run the graph to produce the output audio. - data[i - offset, :] = sess.run(self.mfcc_, feed_dict=input_dict).flatten() + summary, data_tensor = sess.run( + [self.merged_summaries_, self.output_], feed_dict=input_dict) + self.summary_writer_.add_summary(summary) + data[i - offset, :] = data_tensor.flatten() label_index = self.word_to_index[sample['label']] labels[i - offset] = label_index return data, labels diff --git a/tensorflow/examples/speech_commands/input_data_test.py b/tensorflow/examples/speech_commands/input_data_test.py index 13f294d39dbf89367496d2a16f466f8e2195d900..2e551be9a208221dc8b788e4d795e68bde21c9e5 100644 --- a/tensorflow/examples/speech_commands/input_data_test.py +++ b/tensorflow/examples/speech_commands/input_data_test.py @@ -25,6 +25,7 @@ import tensorflow as tf from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio from tensorflow.examples.speech_commands import input_data +from tensorflow.examples.speech_commands import models from tensorflow.python.platform import test @@ -32,7 +33,7 @@ class InputDataTest(test.TestCase): def _getWavData(self): with self.test_session() as sess: - sample_data = tf.zeros([1000, 2]) + sample_data = tf.zeros([32000, 2]) wav_encoder = contrib_audio.encode_wav(sample_data, 16000) wav_data = sess.run(wav_encoder) return wav_data @@ -57,9 +58,31 @@ class InputDataTest(test.TestCase): "label_count": 4, "window_size_samples": 100, "window_stride_samples": 100, - "dct_coefficient_count": 40, + "fingerprint_width": 40, + "preprocess": "mfcc", } + def _runGetDataTest(self, preprocess, window_length_ms): + tmp_dir = self.get_temp_dir() + wav_dir = os.path.join(tmp_dir, "wavs") + os.mkdir(wav_dir) + self._saveWavFolders(wav_dir, ["a", "b", "c"], 100) + background_dir = os.path.join(wav_dir, "_background_noise_") + os.mkdir(background_dir) + wav_data = self._getWavData() + for i in range(10): + file_path = os.path.join(background_dir, "background_audio_%d.wav" % i) + self._saveTestWavFile(file_path, wav_data) + model_settings = models.prepare_model_settings( + 4, 16000, 1000, window_length_ms, 20, 40, preprocess) + with self.test_session() as sess: + audio_processor = input_data.AudioProcessor( + "", wav_dir, 10, 10, ["a", "b"], 10, 10, model_settings, tmp_dir) + result_data, result_labels = audio_processor.get_data( + 10, 0, model_settings, 0.3, 0.1, 100, "training", sess) + self.assertEqual(10, len(result_data)) + self.assertEqual(10, len(result_labels)) + def testPrepareWordsList(self): words_list = ["a", "b"] self.assertGreater( @@ -76,8 +99,9 @@ class InputDataTest(test.TestCase): def testPrepareDataIndex(self): tmp_dir = self.get_temp_dir() self._saveWavFolders(tmp_dir, ["a", "b", "c"], 100) - audio_processor = input_data.AudioProcessor("", tmp_dir, 10, 10, ["a", "b"], - 10, 10, self._model_settings()) + audio_processor = input_data.AudioProcessor("", tmp_dir, 10, 10, + ["a", "b"], 10, 10, + self._model_settings(), tmp_dir) self.assertLess(0, audio_processor.set_size("training")) self.assertTrue("training" in audio_processor.data_index) self.assertTrue("validation" in audio_processor.data_index) @@ -90,7 +114,7 @@ class InputDataTest(test.TestCase): self._saveWavFolders(tmp_dir, ["a", "b", "c"], 0) with self.assertRaises(Exception) as e: _ = input_data.AudioProcessor("", tmp_dir, 10, 10, ["a", "b"], 10, 10, - self._model_settings()) + self._model_settings(), tmp_dir) self.assertTrue("No .wavs found" in str(e.exception)) def testPrepareDataIndexMissing(self): @@ -98,7 +122,7 @@ class InputDataTest(test.TestCase): self._saveWavFolders(tmp_dir, ["a", "b", "c"], 100) with self.assertRaises(Exception) as e: _ = input_data.AudioProcessor("", tmp_dir, 10, 10, ["a", "b", "d"], 10, - 10, self._model_settings()) + 10, self._model_settings(), tmp_dir) self.assertTrue("Expected to find" in str(e.exception)) def testPrepareBackgroundData(self): @@ -110,8 +134,9 @@ class InputDataTest(test.TestCase): file_path = os.path.join(background_dir, "background_audio_%d.wav" % i) self._saveTestWavFile(file_path, wav_data) self._saveWavFolders(tmp_dir, ["a", "b", "c"], 100) - audio_processor = input_data.AudioProcessor("", tmp_dir, 10, 10, ["a", "b"], - 10, 10, self._model_settings()) + audio_processor = input_data.AudioProcessor("", tmp_dir, 10, 10, + ["a", "b"], 10, 10, + self._model_settings(), tmp_dir) self.assertEqual(10, len(audio_processor.background_data)) def testLoadWavFile(self): @@ -148,44 +173,27 @@ class InputDataTest(test.TestCase): "label_count": 4, "window_size_samples": 100, "window_stride_samples": 100, - "dct_coefficient_count": 40, + "fingerprint_width": 40, + "preprocess": "mfcc", } audio_processor = input_data.AudioProcessor("", wav_dir, 10, 10, ["a", "b"], - 10, 10, model_settings) + 10, 10, model_settings, tmp_dir) self.assertIsNotNone(audio_processor.wav_filename_placeholder_) self.assertIsNotNone(audio_processor.foreground_volume_placeholder_) self.assertIsNotNone(audio_processor.time_shift_padding_placeholder_) self.assertIsNotNone(audio_processor.time_shift_offset_placeholder_) self.assertIsNotNone(audio_processor.background_data_placeholder_) self.assertIsNotNone(audio_processor.background_volume_placeholder_) - self.assertIsNotNone(audio_processor.mfcc_) + self.assertIsNotNone(audio_processor.output_) - def testGetData(self): - tmp_dir = self.get_temp_dir() - wav_dir = os.path.join(tmp_dir, "wavs") - os.mkdir(wav_dir) - self._saveWavFolders(wav_dir, ["a", "b", "c"], 100) - background_dir = os.path.join(wav_dir, "_background_noise_") - os.mkdir(background_dir) - wav_data = self._getWavData() - for i in range(10): - file_path = os.path.join(background_dir, "background_audio_%d.wav" % i) - self._saveTestWavFile(file_path, wav_data) - model_settings = { - "desired_samples": 160, - "fingerprint_size": 40, - "label_count": 4, - "window_size_samples": 100, - "window_stride_samples": 100, - "dct_coefficient_count": 40, - } - audio_processor = input_data.AudioProcessor("", wav_dir, 10, 10, ["a", "b"], - 10, 10, model_settings) - with self.test_session() as sess: - result_data, result_labels = audio_processor.get_data( - 10, 0, model_settings, 0.3, 0.1, 100, "training", sess) - self.assertEqual(10, len(result_data)) - self.assertEqual(10, len(result_labels)) + def testGetDataAverage(self): + self._runGetDataTest("average", 10) + + def testGetDataAverageLongWindow(self): + self._runGetDataTest("average", 30) + + def testGetDataMfcc(self): + self._runGetDataTest("mfcc", 30) def testGetUnprocessedData(self): tmp_dir = self.get_temp_dir() @@ -198,10 +206,11 @@ class InputDataTest(test.TestCase): "label_count": 4, "window_size_samples": 100, "window_stride_samples": 100, - "dct_coefficient_count": 40, + "fingerprint_width": 40, + "preprocess": "mfcc", } audio_processor = input_data.AudioProcessor("", wav_dir, 10, 10, ["a", "b"], - 10, 10, model_settings) + 10, 10, model_settings, tmp_dir) result_data, result_labels = audio_processor.get_unprocessed_data( 10, model_settings, "training") self.assertEqual(10, len(result_data)) diff --git a/tensorflow/examples/speech_commands/models.py b/tensorflow/examples/speech_commands/models.py index ab611f414a8afa1f08b955918071b04ae0ef88db..4d1454be0d733ccf6ea41f822030f139135fb895 100644 --- a/tensorflow/examples/speech_commands/models.py +++ b/tensorflow/examples/speech_commands/models.py @@ -24,9 +24,21 @@ import math import tensorflow as tf +def _next_power_of_two(x): + """Calculates the smallest enclosing power of two for an input. + + Args: + x: Positive float or integer number. + + Returns: + Next largest power of two integer. + """ + return 1 if x == 0 else 2**(int(x) - 1).bit_length() + + def prepare_model_settings(label_count, sample_rate, clip_duration_ms, - window_size_ms, window_stride_ms, - dct_coefficient_count): + window_size_ms, window_stride_ms, feature_bin_count, + preprocess): """Calculates common settings needed for all models. Args: @@ -35,10 +47,14 @@ def prepare_model_settings(label_count, sample_rate, clip_duration_ms, clip_duration_ms: Length of each audio clip to be analyzed. window_size_ms: Duration of frequency analysis window. window_stride_ms: How far to move in time between frequency windows. - dct_coefficient_count: Number of frequency bins to use for analysis. + feature_bin_count: Number of frequency bins to use for analysis. + preprocess: How the spectrogram is processed to produce features. Returns: Dictionary containing common settings. + + Raises: + ValueError: If the preprocessing mode isn't recognized. """ desired_samples = int(sample_rate * clip_duration_ms / 1000) window_size_samples = int(sample_rate * window_size_ms / 1000) @@ -48,16 +64,28 @@ def prepare_model_settings(label_count, sample_rate, clip_duration_ms, spectrogram_length = 0 else: spectrogram_length = 1 + int(length_minus_window / window_stride_samples) - fingerprint_size = dct_coefficient_count * spectrogram_length + if preprocess == 'average': + fft_bin_count = 1 + (_next_power_of_two(window_size_samples) / 2) + average_window_width = int(math.floor(fft_bin_count / feature_bin_count)) + fingerprint_width = int(math.ceil(fft_bin_count / average_window_width)) + elif preprocess == 'mfcc': + average_window_width = -1 + fingerprint_width = feature_bin_count + else: + raise ValueError('Unknown preprocess mode "%s" (should be "mfcc" or' + ' "average")' % (preprocess)) + fingerprint_size = fingerprint_width * spectrogram_length return { 'desired_samples': desired_samples, 'window_size_samples': window_size_samples, 'window_stride_samples': window_stride_samples, 'spectrogram_length': spectrogram_length, - 'dct_coefficient_count': dct_coefficient_count, + 'fingerprint_width': fingerprint_width, 'fingerprint_size': fingerprint_size, 'label_count': label_count, 'sample_rate': sample_rate, + 'preprocess': preprocess, + 'average_window_width': average_window_width, } @@ -106,10 +134,14 @@ def create_model(fingerprint_input, model_settings, model_architecture, elif model_architecture == 'low_latency_svdf': return create_low_latency_svdf_model(fingerprint_input, model_settings, is_training, runtime_settings) + elif model_architecture == 'tiny_conv': + return create_tiny_conv_model(fingerprint_input, model_settings, + is_training) else: raise Exception('model_architecture argument "' + model_architecture + '" not recognized, should be one of "single_fc", "conv",' + - ' "low_latency_conv, or "low_latency_svdf"') + ' "low_latency_conv, "low_latency_svdf",' + + ' or "tiny_conv"') def load_variables_from_checkpoint(sess, start_checkpoint): @@ -152,9 +184,12 @@ def create_single_fc_model(fingerprint_input, model_settings, is_training): dropout_prob = tf.placeholder(tf.float32, name='dropout_prob') fingerprint_size = model_settings['fingerprint_size'] label_count = model_settings['label_count'] - weights = tf.Variable( - tf.truncated_normal([fingerprint_size, label_count], stddev=0.001)) - bias = tf.Variable(tf.zeros([label_count])) + weights = tf.get_variable( + name='weights', + initializer=tf.truncated_normal_initializer(stddev=0.001), + shape=[fingerprint_size, label_count]) + bias = tf.get_variable( + name='bias', initializer=tf.zeros_initializer, shape=[label_count]) logits = tf.matmul(fingerprint_input, weights) + bias if is_training: return logits, dropout_prob @@ -212,18 +247,21 @@ def create_conv_model(fingerprint_input, model_settings, is_training): """ if is_training: dropout_prob = tf.placeholder(tf.float32, name='dropout_prob') - input_frequency_size = model_settings['dct_coefficient_count'] + input_frequency_size = model_settings['fingerprint_width'] input_time_size = model_settings['spectrogram_length'] fingerprint_4d = tf.reshape(fingerprint_input, [-1, input_time_size, input_frequency_size, 1]) first_filter_width = 8 first_filter_height = 20 first_filter_count = 64 - first_weights = tf.Variable( - tf.truncated_normal( - [first_filter_height, first_filter_width, 1, first_filter_count], - stddev=0.01)) - first_bias = tf.Variable(tf.zeros([first_filter_count])) + first_weights = tf.get_variable( + name='first_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[first_filter_height, first_filter_width, 1, first_filter_count]) + first_bias = tf.get_variable( + name='first_bias', + initializer=tf.zeros_initializer, + shape=[first_filter_count]) first_conv = tf.nn.conv2d(fingerprint_4d, first_weights, [1, 1, 1, 1], 'SAME') + first_bias first_relu = tf.nn.relu(first_conv) @@ -235,14 +273,17 @@ def create_conv_model(fingerprint_input, model_settings, is_training): second_filter_width = 4 second_filter_height = 10 second_filter_count = 64 - second_weights = tf.Variable( - tf.truncated_normal( - [ - second_filter_height, second_filter_width, first_filter_count, - second_filter_count - ], - stddev=0.01)) - second_bias = tf.Variable(tf.zeros([second_filter_count])) + second_weights = tf.get_variable( + name='second_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[ + second_filter_height, second_filter_width, first_filter_count, + second_filter_count + ]) + second_bias = tf.get_variable( + name='second_bias', + initializer=tf.zeros_initializer, + shape=[second_filter_count]) second_conv = tf.nn.conv2d(max_pool, second_weights, [1, 1, 1, 1], 'SAME') + second_bias second_relu = tf.nn.relu(second_conv) @@ -259,10 +300,14 @@ def create_conv_model(fingerprint_input, model_settings, is_training): flattened_second_conv = tf.reshape(second_dropout, [-1, second_conv_element_count]) label_count = model_settings['label_count'] - final_fc_weights = tf.Variable( - tf.truncated_normal( - [second_conv_element_count, label_count], stddev=0.01)) - final_fc_bias = tf.Variable(tf.zeros([label_count])) + final_fc_weights = tf.get_variable( + name='final_fc_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[second_conv_element_count, label_count]) + final_fc_bias = tf.get_variable( + name='final_fc_bias', + initializer=tf.zeros_initializer, + shape=[label_count]) final_fc = tf.matmul(flattened_second_conv, final_fc_weights) + final_fc_bias if is_training: return final_fc, dropout_prob @@ -318,7 +363,7 @@ def create_low_latency_conv_model(fingerprint_input, model_settings, """ if is_training: dropout_prob = tf.placeholder(tf.float32, name='dropout_prob') - input_frequency_size = model_settings['dct_coefficient_count'] + input_frequency_size = model_settings['fingerprint_width'] input_time_size = model_settings['spectrogram_length'] fingerprint_4d = tf.reshape(fingerprint_input, [-1, input_time_size, input_frequency_size, 1]) @@ -327,11 +372,14 @@ def create_low_latency_conv_model(fingerprint_input, model_settings, first_filter_count = 186 first_filter_stride_x = 1 first_filter_stride_y = 1 - first_weights = tf.Variable( - tf.truncated_normal( - [first_filter_height, first_filter_width, 1, first_filter_count], - stddev=0.01)) - first_bias = tf.Variable(tf.zeros([first_filter_count])) + first_weights = tf.get_variable( + name='first_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[first_filter_height, first_filter_width, 1, first_filter_count]) + first_bias = tf.get_variable( + name='first_bias', + initializer=tf.zeros_initializer, + shape=[first_filter_count]) first_conv = tf.nn.conv2d(fingerprint_4d, first_weights, [ 1, first_filter_stride_y, first_filter_stride_x, 1 ], 'VALID') + first_bias @@ -351,30 +399,42 @@ def create_low_latency_conv_model(fingerprint_input, model_settings, flattened_first_conv = tf.reshape(first_dropout, [-1, first_conv_element_count]) first_fc_output_channels = 128 - first_fc_weights = tf.Variable( - tf.truncated_normal( - [first_conv_element_count, first_fc_output_channels], stddev=0.01)) - first_fc_bias = tf.Variable(tf.zeros([first_fc_output_channels])) + first_fc_weights = tf.get_variable( + name='first_fc_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[first_conv_element_count, first_fc_output_channels]) + first_fc_bias = tf.get_variable( + name='first_fc_bias', + initializer=tf.zeros_initializer, + shape=[first_fc_output_channels]) first_fc = tf.matmul(flattened_first_conv, first_fc_weights) + first_fc_bias if is_training: second_fc_input = tf.nn.dropout(first_fc, dropout_prob) else: second_fc_input = first_fc second_fc_output_channels = 128 - second_fc_weights = tf.Variable( - tf.truncated_normal( - [first_fc_output_channels, second_fc_output_channels], stddev=0.01)) - second_fc_bias = tf.Variable(tf.zeros([second_fc_output_channels])) + second_fc_weights = tf.get_variable( + name='second_fc_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[first_fc_output_channels, second_fc_output_channels]) + second_fc_bias = tf.get_variable( + name='second_fc_bias', + initializer=tf.zeros_initializer, + shape=[second_fc_output_channels]) second_fc = tf.matmul(second_fc_input, second_fc_weights) + second_fc_bias if is_training: final_fc_input = tf.nn.dropout(second_fc, dropout_prob) else: final_fc_input = second_fc label_count = model_settings['label_count'] - final_fc_weights = tf.Variable( - tf.truncated_normal( - [second_fc_output_channels, label_count], stddev=0.01)) - final_fc_bias = tf.Variable(tf.zeros([label_count])) + final_fc_weights = tf.get_variable( + name='final_fc_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[second_fc_output_channels, label_count]) + final_fc_bias = tf.get_variable( + name='final_fc_bias', + initializer=tf.zeros_initializer, + shape=[label_count]) final_fc = tf.matmul(final_fc_input, final_fc_weights) + final_fc_bias if is_training: return final_fc, dropout_prob @@ -422,7 +482,7 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings, Args: fingerprint_input: TensorFlow node that will output audio feature vectors. The node is expected to produce a 2D Tensor of shape: - [batch, model_settings['dct_coefficient_count'] * + [batch, model_settings['fingerprint_width'] * model_settings['spectrogram_length']] with the features corresponding to the same time slot arranged contiguously, and the oldest slot at index [:, 0], and newest at [:, -1]. @@ -440,7 +500,7 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings, if is_training: dropout_prob = tf.placeholder(tf.float32, name='dropout_prob') - input_frequency_size = model_settings['dct_coefficient_count'] + input_frequency_size = model_settings['fingerprint_width'] input_time_size = model_settings['spectrogram_length'] # Validation. @@ -462,8 +522,11 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings, num_filters = rank * num_units # Create the runtime memory: [num_filters, batch, input_time_size] batch = 1 - memory = tf.Variable(tf.zeros([num_filters, batch, input_time_size]), - trainable=False, name='runtime-memory') + memory = tf.get_variable( + initializer=tf.zeros_initializer, + shape=[num_filters, batch, input_time_size], + trainable=False, + name='runtime-memory') # Determine the number of new frames in the input, such that we only operate # on those. For training we do not use the memory, and thus use all frames # provided in the input. @@ -483,8 +546,10 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings, new_fingerprint_input = tf.expand_dims(new_fingerprint_input, 2) # Create the frequency filters. - weights_frequency = tf.Variable( - tf.truncated_normal([input_frequency_size, num_filters], stddev=0.01)) + weights_frequency = tf.get_variable( + name='weights_frequency', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[input_frequency_size, num_filters]) # Expand to add input channels dimensions. # weights_frequency: [input_frequency_size, 1, num_filters] weights_frequency = tf.expand_dims(weights_frequency, 1) @@ -506,8 +571,10 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings, activations_time = new_memory # Create the time filters. - weights_time = tf.Variable( - tf.truncated_normal([num_filters, input_time_size], stddev=0.01)) + weights_time = tf.get_variable( + name='weights_time', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[num_filters, input_time_size]) # Apply the time filter on the outputs of the feature filters. # weights_time: [num_filters, input_time_size, 1] # outputs: [num_filters, batch, 1] @@ -524,7 +591,8 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings, units_output = tf.transpose(units_output) # Appy bias. - bias = tf.Variable(tf.zeros([num_units])) + bias = tf.get_variable( + name='bias', initializer=tf.zeros_initializer, shape=[num_units]) first_bias = tf.nn.bias_add(units_output, bias) # Relu. @@ -536,31 +604,135 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings, first_dropout = first_relu first_fc_output_channels = 256 - first_fc_weights = tf.Variable( - tf.truncated_normal([num_units, first_fc_output_channels], stddev=0.01)) - first_fc_bias = tf.Variable(tf.zeros([first_fc_output_channels])) + first_fc_weights = tf.get_variable( + name='first_fc_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[num_units, first_fc_output_channels]) + first_fc_bias = tf.get_variable( + name='first_fc_bias', + initializer=tf.zeros_initializer, + shape=[first_fc_output_channels]) first_fc = tf.matmul(first_dropout, first_fc_weights) + first_fc_bias if is_training: second_fc_input = tf.nn.dropout(first_fc, dropout_prob) else: second_fc_input = first_fc second_fc_output_channels = 256 - second_fc_weights = tf.Variable( - tf.truncated_normal( - [first_fc_output_channels, second_fc_output_channels], stddev=0.01)) - second_fc_bias = tf.Variable(tf.zeros([second_fc_output_channels])) + second_fc_weights = tf.get_variable( + name='second_fc_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[first_fc_output_channels, second_fc_output_channels]) + second_fc_bias = tf.get_variable( + name='second_fc_bias', + initializer=tf.zeros_initializer, + shape=[second_fc_output_channels]) second_fc = tf.matmul(second_fc_input, second_fc_weights) + second_fc_bias if is_training: final_fc_input = tf.nn.dropout(second_fc, dropout_prob) else: final_fc_input = second_fc label_count = model_settings['label_count'] - final_fc_weights = tf.Variable( - tf.truncated_normal( - [second_fc_output_channels, label_count], stddev=0.01)) - final_fc_bias = tf.Variable(tf.zeros([label_count])) + final_fc_weights = tf.get_variable( + name='final_fc_weights', + initializer=tf.truncated_normal(stddev=0.01), + shape=[second_fc_output_channels, label_count]) + final_fc_bias = tf.get_variable( + name='final_fc_bias', + initializer=tf.zeros_initializer, + shape=[label_count]) final_fc = tf.matmul(final_fc_input, final_fc_weights) + final_fc_bias if is_training: return final_fc, dropout_prob else: return final_fc + + +def create_tiny_conv_model(fingerprint_input, model_settings, is_training): + """Builds a convolutional model aimed at microcontrollers. + + Devices like DSPs and microcontrollers can have very small amounts of + memory and limited processing power. This model is designed to use less + than 20KB of working RAM, and fit within 32KB of read-only (flash) memory. + + Here's the layout of the graph: + + (fingerprint_input) + v + [Conv2D]<-(weights) + v + [BiasAdd]<-(bias) + v + [Relu] + v + [MatMul]<-(weights) + v + [BiasAdd]<-(bias) + v + + This doesn't produce particularly accurate results, but it's designed to be + used as the first stage of a pipeline, running on a low-energy piece of + hardware that can always be on, and then wake higher-power chips when a + possible utterance has been found, so that more accurate analysis can be done. + + During training, a dropout node is introduced after the relu, controlled by a + placeholder. + + Args: + fingerprint_input: TensorFlow node that will output audio feature vectors. + model_settings: Dictionary of information about the model. + is_training: Whether the model is going to be used for training. + + Returns: + TensorFlow node outputting logits results, and optionally a dropout + placeholder. + """ + if is_training: + dropout_prob = tf.placeholder(tf.float32, name='dropout_prob') + input_frequency_size = model_settings['fingerprint_width'] + input_time_size = model_settings['spectrogram_length'] + fingerprint_4d = tf.reshape(fingerprint_input, + [-1, input_time_size, input_frequency_size, 1]) + first_filter_width = 8 + first_filter_height = 10 + first_filter_count = 8 + first_weights = tf.get_variable( + name='first_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[first_filter_height, first_filter_width, 1, first_filter_count]) + first_bias = tf.get_variable( + name='first_bias', + initializer=tf.zeros_initializer, + shape=[first_filter_count]) + first_conv_stride_x = 2 + first_conv_stride_y = 2 + first_conv = tf.nn.conv2d(fingerprint_4d, first_weights, + [1, first_conv_stride_y, first_conv_stride_x, 1], + 'SAME') + first_bias + first_relu = tf.nn.relu(first_conv) + if is_training: + first_dropout = tf.nn.dropout(first_relu, dropout_prob) + else: + first_dropout = first_relu + first_dropout_shape = first_dropout.get_shape() + first_dropout_output_width = first_dropout_shape[2] + first_dropout_output_height = first_dropout_shape[1] + first_dropout_element_count = int( + first_dropout_output_width * first_dropout_output_height * + first_filter_count) + flattened_first_dropout = tf.reshape(first_dropout, + [-1, first_dropout_element_count]) + label_count = model_settings['label_count'] + final_fc_weights = tf.get_variable( + name='final_fc_weights', + initializer=tf.truncated_normal_initializer(stddev=0.01), + shape=[first_dropout_element_count, label_count]) + final_fc_bias = tf.get_variable( + name='final_fc_bias', + initializer=tf.zeros_initializer, + shape=[label_count]) + final_fc = ( + tf.matmul(flattened_first_dropout, final_fc_weights) + final_fc_bias) + if is_training: + return final_fc, dropout_prob + else: + return final_fc diff --git a/tensorflow/examples/speech_commands/models_test.py b/tensorflow/examples/speech_commands/models_test.py index 80c795367fa01f214d78d3fa7df7864b6b243b97..0c373967ed8fb9cddcc82972e0fc8bba186add2e 100644 --- a/tensorflow/examples/speech_commands/models_test.py +++ b/tensorflow/examples/speech_commands/models_test.py @@ -26,12 +26,29 @@ from tensorflow.python.platform import test class ModelsTest(test.TestCase): + def _modelSettings(self): + return models.prepare_model_settings( + label_count=10, + sample_rate=16000, + clip_duration_ms=1000, + window_size_ms=20, + window_stride_ms=10, + feature_bin_count=40, + preprocess="mfcc") + def testPrepareModelSettings(self): self.assertIsNotNone( - models.prepare_model_settings(10, 16000, 1000, 20, 10, 40)) + models.prepare_model_settings( + label_count=10, + sample_rate=16000, + clip_duration_ms=1000, + window_size_ms=20, + window_stride_ms=10, + feature_bin_count=40, + preprocess="mfcc")) def testCreateModelConvTraining(self): - model_settings = models.prepare_model_settings(10, 16000, 1000, 20, 10, 40) + model_settings = self._modelSettings() with self.test_session() as sess: fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]]) logits, dropout_prob = models.create_model(fingerprint_input, @@ -42,7 +59,7 @@ class ModelsTest(test.TestCase): self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name)) def testCreateModelConvInference(self): - model_settings = models.prepare_model_settings(10, 16000, 1000, 20, 10, 40) + model_settings = self._modelSettings() with self.test_session() as sess: fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]]) logits = models.create_model(fingerprint_input, model_settings, "conv", @@ -51,7 +68,7 @@ class ModelsTest(test.TestCase): self.assertIsNotNone(sess.graph.get_tensor_by_name(logits.name)) def testCreateModelLowLatencyConvTraining(self): - model_settings = models.prepare_model_settings(10, 16000, 1000, 20, 10, 40) + model_settings = self._modelSettings() with self.test_session() as sess: fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]]) logits, dropout_prob = models.create_model( @@ -62,7 +79,7 @@ class ModelsTest(test.TestCase): self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name)) def testCreateModelFullyConnectedTraining(self): - model_settings = models.prepare_model_settings(10, 16000, 1000, 20, 10, 40) + model_settings = self._modelSettings() with self.test_session() as sess: fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]]) logits, dropout_prob = models.create_model( @@ -73,7 +90,7 @@ class ModelsTest(test.TestCase): self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name)) def testCreateModelBadArchitecture(self): - model_settings = models.prepare_model_settings(10, 16000, 1000, 20, 10, 40) + model_settings = self._modelSettings() with self.test_session(): fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]]) with self.assertRaises(Exception) as e: @@ -81,6 +98,17 @@ class ModelsTest(test.TestCase): "bad_architecture", True) self.assertTrue("not recognized" in str(e.exception)) + def testCreateModelTinyConvTraining(self): + model_settings = self._modelSettings() + with self.test_session() as sess: + fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]]) + logits, dropout_prob = models.create_model( + fingerprint_input, model_settings, "tiny_conv", True) + self.assertIsNotNone(logits) + self.assertIsNotNone(dropout_prob) + self.assertIsNotNone(sess.graph.get_tensor_by_name(logits.name)) + self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name)) + if __name__ == "__main__": test.main() diff --git a/tensorflow/examples/speech_commands/train.py b/tensorflow/examples/speech_commands/train.py index fc28eb0631dc5e1947c2a31a6acdb02ed8d28f3a..eca34f8812b76a60168c97a745f5890bf3ee0269 100644 --- a/tensorflow/examples/speech_commands/train.py +++ b/tensorflow/examples/speech_commands/train.py @@ -98,12 +98,12 @@ def main(_): model_settings = models.prepare_model_settings( len(input_data.prepare_words_list(FLAGS.wanted_words.split(','))), FLAGS.sample_rate, FLAGS.clip_duration_ms, FLAGS.window_size_ms, - FLAGS.window_stride_ms, FLAGS.dct_coefficient_count) + FLAGS.window_stride_ms, FLAGS.feature_bin_count, FLAGS.preprocess) audio_processor = input_data.AudioProcessor( - FLAGS.data_url, FLAGS.data_dir, FLAGS.silence_percentage, - FLAGS.unknown_percentage, + FLAGS.data_url, FLAGS.data_dir, + FLAGS.silence_percentage, FLAGS.unknown_percentage, FLAGS.wanted_words.split(','), FLAGS.validation_percentage, - FLAGS.testing_percentage, model_settings) + FLAGS.testing_percentage, model_settings, FLAGS.summaries_dir) fingerprint_size = model_settings['fingerprint_size'] label_count = model_settings['label_count'] time_shift_samples = int((FLAGS.time_shift_ms * FLAGS.sample_rate) / 1000) @@ -122,8 +122,25 @@ def main(_): 'lists, but are %d and %d long instead' % (len(training_steps_list), len(learning_rates_list))) - fingerprint_input = tf.placeholder( + input_placeholder = tf.placeholder( tf.float32, [None, fingerprint_size], name='fingerprint_input') + if FLAGS.quantize: + # TODO(petewarden): These values have been derived from the observed ranges + # of spectrogram and MFCC inputs. If the preprocessing pipeline changes, + # they may need to be updated. + if FLAGS.preprocess == 'average': + fingerprint_min = 0.0 + fingerprint_max = 2048.0 + elif FLAGS.preprocess == 'mfcc': + fingerprint_min = -247.0 + fingerprint_max = 30.0 + else: + raise Exception('Unknown preprocess mode "%s" (should be "mfcc" or' + ' "average")' % (FLAGS.preprocess)) + fingerprint_input = tf.fake_quant_with_min_max_args( + input_placeholder, fingerprint_min, fingerprint_max) + else: + fingerprint_input = input_placeholder logits, dropout_prob = models.create_model( fingerprint_input, @@ -146,7 +163,8 @@ def main(_): with tf.name_scope('cross_entropy'): cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy( labels=ground_truth_input, logits=logits) - tf.summary.scalar('cross_entropy', cross_entropy_mean) + if FLAGS.quantize: + tf.contrib.quantize.create_training_graph(quant_delay=0) with tf.name_scope('train'), tf.control_dependencies(control_dependencies): learning_rate_input = tf.placeholder( tf.float32, [], name='learning_rate_input') @@ -157,7 +175,9 @@ def main(_): confusion_matrix = tf.confusion_matrix( ground_truth_input, predicted_indices, num_classes=label_count) evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - tf.summary.scalar('accuracy', evaluation_step) + with tf.get_default_graph().name_scope('eval'): + tf.summary.scalar('cross_entropy', cross_entropy_mean) + tf.summary.scalar('accuracy', evaluation_step) global_step = tf.train.get_or_create_global_step() increment_global_step = tf.assign(global_step, global_step + 1) @@ -165,7 +185,7 @@ def main(_): saver = tf.train.Saver(tf.global_variables()) # Merge all the summaries and write them out to /tmp/retrain_logs (by default) - merged_summaries = tf.summary.merge_all() + merged_summaries = tf.summary.merge_all(scope='eval') train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train', sess.graph) validation_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/validation') @@ -207,8 +227,11 @@ def main(_): # Run the graph with this batch of training data. train_summary, train_accuracy, cross_entropy_value, _, _ = sess.run( [ - merged_summaries, evaluation_step, cross_entropy_mean, train_step, - increment_global_step + merged_summaries, + evaluation_step, + cross_entropy_mean, + train_step, + increment_global_step, ], feed_dict={ fingerprint_input: train_fingerprints, @@ -364,10 +387,11 @@ if __name__ == '__main__': default=10.0, help='How far to move in time between spectogram timeslices.',) parser.add_argument( - '--dct_coefficient_count', + '--feature_bin_count', type=int, default=40, - help='How many bins to use for the MFCC fingerprint',) + help='How many bins to use for the MFCC fingerprint', + ) parser.add_argument( '--how_many_training_steps', type=str, @@ -423,6 +447,16 @@ if __name__ == '__main__': type=bool, default=False, help='Whether to check for invalid numbers during processing') + parser.add_argument( + '--quantize', + type=bool, + default=False, + help='Whether to train the model for eight-bit deployment') + parser.add_argument( + '--preprocess', + type=str, + default='mfcc', + help='Spectrogram processing mode. Can be "mfcc" or "average"') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/tutorials/mnist/mnist_deep.py b/tensorflow/examples/tutorials/mnist/mnist_deep.py index 1e0294db27bc675870afceca77a2cdcd4b3f5ad3..5d8d8d84fe26c0a3ec69791885f3c7ce5e0fba15 100644 --- a/tensorflow/examples/tutorials/mnist/mnist_deep.py +++ b/tensorflow/examples/tutorials/mnist/mnist_deep.py @@ -34,6 +34,8 @@ from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf +import numpy + FLAGS = None @@ -164,8 +166,15 @@ def main(_): print('step %d, training accuracy %g' % (i, train_accuracy)) train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) - print('test accuracy %g' % accuracy.eval(feed_dict={ - x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) + # compute in batches to avoid OOM on GPUs + accuracy_l = [] + for _ in range(20): + batch = mnist.test.next_batch(500, shuffle=False) + accuracy_l.append(accuracy.eval(feed_dict={x: batch[0], + y_: batch[1], + keep_prob: 1.0})) + print('test accuracy %g' % numpy.mean(accuracy_l)) + if __name__ == '__main__': parser = argparse.ArgumentParser() diff --git a/tensorflow/go/README.md b/tensorflow/go/README.md index e251356ec8e97311affaf752c0a515be97013fa8..288a32530a7ed2f4d773912591907395c82db34e 100644 --- a/tensorflow/go/README.md +++ b/tensorflow/go/README.md @@ -46,7 +46,7 @@ from source. ```sh cd ${GOPATH}/src/github.com/tensorflow/tensorflow ./configure - bazel build --config opt //tensorflow:libtensorflow.so + bazel build -c opt //tensorflow:libtensorflow.so ``` This can take a while (tens of minutes, more if also building for GPU). diff --git a/tensorflow/go/graph.go b/tensorflow/go/graph.go index 08943a527cbdc072b12b066240c213be45ffd54c..32a77550ee2fa5606b402600aa6429950d8e72a5 100644 --- a/tensorflow/go/graph.go +++ b/tensorflow/go/graph.go @@ -177,7 +177,14 @@ type OpSpec struct { // being added. ControlDependencies []*Operation - // Other possible fields: Device, ColocateWith. + // The device on which the operation should be executed. + // If omitted, an appropriate device will automatically be selected. + // + // For example, if set of "/device:GPU:0", then the operation will + // execute on GPU #0. + Device string + + // Other possible fields: ColocateWith. } // AddOperation adds an operation to g. @@ -225,6 +232,11 @@ func (g *Graph) AddOperation(args OpSpec) (*Operation, error) { return nil, fmt.Errorf("%v (memory will be leaked)", err) } } + if len(args.Device) > 0 { + cdevice := C.CString(args.Device) + C.TF_SetDevice(cdesc, cdevice) + C.free(unsafe.Pointer(cdevice)) + } c := C.TF_FinishOperation(cdesc, status.c) if err := status.Err(); err != nil { return nil, err diff --git a/tensorflow/go/op/scope.go b/tensorflow/go/op/scope.go index 13de4294dc2ebdfff9bb68d277c09239d0bc8593..ac39808d838f4737b81b170d3f540d10ed38fe42 100644 --- a/tensorflow/go/op/scope.go +++ b/tensorflow/go/op/scope.go @@ -37,6 +37,7 @@ type Scope struct { namemap map[string]int namespace string controlDependencies []*tf.Operation + device string err *scopeErr } @@ -82,6 +83,7 @@ func (s *Scope) AddOperation(args tf.OpSpec) *tf.Operation { args.Name = s.namespace + "/" + args.Name } args.ControlDependencies = append(args.ControlDependencies, s.controlDependencies...) + args.Device = s.device op, err := s.graph.AddOperation(args) if err != nil { s.UpdateErr(args.Type, err) @@ -98,10 +100,12 @@ func (s *Scope) SubScope(namespace string) *Scope { namespace = s.namespace + "/" + namespace } return &Scope{ - graph: s.graph, - namemap: make(map[string]int), - namespace: namespace, - err: s.err, + graph: s.graph, + namemap: make(map[string]int), + namespace: namespace, + controlDependencies: s.controlDependencies, + device: s.device, + err: s.err, } } @@ -123,6 +127,25 @@ func (s *Scope) WithControlDependencies(ops ...*tf.Operation) *Scope { namemap: s.namemap, namespace: s.namespace, controlDependencies: deps, + device: s.device, + err: s.err, + } +} + +// WithDevice returns a new Scope which will cause all operations added to the +// graph to execute on devices that match the provided device specification. +// +// For example, WithDevice("/device:GPU:0") will cause operations added to +// the graph to execute on GPU #0. +// +// An empty string removes any device restrictions. +func (s *Scope) WithDevice(device string) *Scope { + return &Scope{ + graph: s.graph, + namemap: s.namemap, + namespace: s.namespace, + controlDependencies: s.controlDependencies, + device: device, err: s.err, } } diff --git a/tensorflow/go/op/scope_test.go b/tensorflow/go/op/scope_test.go index b58a61de98b0f5b04959e1eca35c6b6c4d77e42b..be7b0ad8926aadac47218b7625036d7e12b9554b 100644 --- a/tensorflow/go/op/scope_test.go +++ b/tensorflow/go/op/scope_test.go @@ -112,6 +112,21 @@ func TestControlDependencies(t *testing.T) { } } +func TestDevice(t *testing.T) { + s := NewScope() + matrix := Const(s, [][]float32{{3.0}}) + s = s.WithDevice("/device:GPU:0") + square := MatMul(s.SubScope("square"), matrix, matrix) + s = s.WithDevice("") + cube := MatMul(s.SubScope("cube"), square, matrix) + if got, want := square.Op.Device(), "/device:GPU:0"; got != want { + t.Errorf("Got %q, want %q", got, want) + } + if got, want := cube.Op.Device(), ""; got != want { + t.Errorf("Got %q, want %q", got, want) + } +} + func TestScopeFinalize(t *testing.T) { var ( root = NewScope() diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index d20e88e95b02b6c4f12fbaec3a9576ffc0266180..1e765d1cd70a61964495dfe02ba893bb843dc174 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -327,12 +327,12 @@ func FakeQuantWithMinMaxArgs(scope *Scope, inputs tf.Output, optional ...FakeQua return op.Output(0) } -// Scatter `updates` into a new (initially zero) tensor according to `indices`. +// Scatter `updates` into a new tensor according to `indices`. // -// Creates a new tensor by applying sparse `updates` to individual -// values or slices within a zero tensor of the given `shape` according to -// indices. This operator is the inverse of the @{tf.gather_nd} operator which -// extracts values or slices from a given tensor. +// Creates a new tensor by applying sparse `updates` to individual values or +// slices within a tensor (initially zero for numeric, empty for string) of +// the given `shape` according to indices. This operator is the inverse of the +// @{tf.gather_nd} operator which extracts values or slices from a given tensor. // // **WARNING**: The order in which updates are applied is nondeterministic, so the // output will be nondeterministic if `indices` contains duplicates. @@ -430,7 +430,8 @@ type QuantizeAndDequantizeV2Attr func(optionalAttr) // QuantizeAndDequantizeV2SignedInput sets the optional signed_input attribute to value. // -// value: If the quantization is signed or unsigned. +// value: Whether the quantization is signed or unsigned. (actually this parameter should +// have been called `signed_output`) // If not specified, defaults to true func QuantizeAndDequantizeV2SignedInput(value bool) QuantizeAndDequantizeV2Attr { return func(m optionalAttr) { @@ -450,7 +451,7 @@ func QuantizeAndDequantizeV2NumBits(value int64) QuantizeAndDequantizeV2Attr { // QuantizeAndDequantizeV2RangeGiven sets the optional range_given attribute to value. // -// value: If the range is given or should be computed from the tensor. +// value: Whether the range is given or should be determined from the `input` tensor. // If not specified, defaults to false func QuantizeAndDequantizeV2RangeGiven(value bool) QuantizeAndDequantizeV2Attr { return func(m optionalAttr) { @@ -461,61 +462,64 @@ func QuantizeAndDequantizeV2RangeGiven(value bool) QuantizeAndDequantizeV2Attr { // Quantizes then dequantizes a tensor. // // This op simulates the precision loss from the quantized forward pass by: +// // 1. Quantizing the tensor to fixed point numbers, which should match the target // quantization method when it is used in inference. // 2. Dequantizing it back to floating point numbers for the following ops, most // likely matmul. // -// There are different ways to quantize. This version does not use the full range -// of the output type, choosing to elide the lowest possible value for symmetry -// (e.g., output range is -127 to 127, not -128 to 127 for signed 8 bit -// quantization), so that 0.0 maps to 0. -// -// To perform this op, we first find the range of values in our tensor. The range -// we use is always centered on 0, so we find m such that -// -// 1. m = max(abs(input_min), abs(input_max)) if range_given is true, -// 2. m = max(abs(min_elem(input)), abs(max_elem(input))) otherwise. -// -// Our input tensor range is then [-m, m]. +// There are different ways to quantize. This version uses only scaling, so 0.0 +// maps to 0. // -// Next, we choose our fixed-point quantization buckets, [min_fixed, max_fixed]. -// If signed_input is true, this is +// From the specified 'num_bits' in the quantized output type, it determines +// minimum and maximum representable quantized values. // -// [min_fixed, max_fixed ] = -// [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1]. +// e.g. // -// Otherwise, if signed_input is false, the fixed-point range is +// * [-128, 127] for signed, num_bits = 8, or +// * [0, 255] for unsigned, num_bits = 8. // -// [min_fixed, max_fixed] = [0, (1 << num_bits) - 1]. +// If range_given == False, the initial input_min, input_max will be determined +// automatically as the minimum and maximum values in the input tensor, otherwise +// the specified values of input_min, input_max are used. // -// From this we compute our scaling factor, s: +// Note: If the input_min, input_max are specified, they do not need to equal the +// actual minimum and maximum values in the tensor. e.g. in some cases it may be +// beneficial to specify these values such that the low probability extremes of the +// input distribution are clipped. // -// s = (max_fixed - min_fixed) / (2 * m). +// This op determines the maximum scale_factor that would map the initial +// [input_min, input_max] range to a range that lies within the representable +// quantized range. // -// Now we can quantize and dequantize the elements of our tensor. An element e -// is transformed into e': +// It determines the scale from one of input_min and input_max, then updates the +// other one to maximize the respresentable range. // -// e' = (e * s).round_to_nearest() / s. +// e.g. // -// Note that we have a different number of buckets in the signed vs. unsigned -// cases. For example, if num_bits == 8, we get 254 buckets in the signed case -// vs. 255 in the unsigned case. +// * if the output is signed, num_bits = 8, [input_min, input_max] = [-10.0, +// 5.0]: it would use a scale_factor of -128 / -10.0 = 12.8 In this case, it +// would update input_max to be 127 / 12.8 = 9.921875 +// * if the output is signed, num_bits = 8, [input_min, input_max] = [-10.0, +// 10.0]: it would use a scale_factor of 127 / 10.0 = 12.7 In this case, it +// would update input_min to be 128.0 / 12.7 = -10.07874 +// * if the output is unsigned, input_min is forced to be 0, and only the +// specified input_max is used. // -// For example, suppose num_bits = 8 and m = 1. Then +// After determining the scale_factor and updating the input range, it applies the +// following to each value in the 'input' tensor. // -// [min_fixed, max_fixed] = [-127, 127], and -// s = (127 + 127) / 2 = 127. +// output = round(clamp(value, input_min, input_max) * scale_factor) / scale_factor. // -// Given the vector {-1, -0.5, 0, 0.3}, this is quantized to -// {-127, -63, 0, 38}, and dequantized to {-1, -63.0/127, 0, 38.0/127}. // // Arguments: // input: Tensor to quantize and then dequantize. -// input_min: If range_given, this is the min of the range, otherwise this input -// will be ignored. -// input_max: If range_given, this is the max of the range, otherwise this input -// will be ignored. +// input_min: If `range_given == True`, this specifies the minimum input value that needs to +// be represented, otherwise it is determined from the min value of the `input` +// tensor. +// input_max: If `range_given == True`, this specifies the maximum input value that needs to +// be represented, otherwise it is determined from the max value of the `input` +// tensor. func QuantizeAndDequantizeV2(scope *Scope, input tf.Output, input_min tf.Output, input_max tf.Output, optional ...QuantizeAndDequantizeV2Attr) (output tf.Output) { if scope.Err() != nil { return @@ -2249,7 +2253,7 @@ func CheckNumerics(scope *Scope, tensor tf.Output, message string) (output tf.Ou // (K-1)-dimensional tensor of indices into `params`, where each element defines a // slice of `params`: // -// output[i_0, ..., i_{K-2}] = params[indices[i0, ..., i_{K-2}]] +// output[\\(i_0, ..., i_{K-2}\\)] = params[indices[\\(i_0, ..., i_{K-2}\\)]] // // Whereas in @{tf.gather} `indices` defines slices into the first // dimension of `params`, in `tf.gather_nd`, `indices` defines slices into the @@ -3015,6 +3019,45 @@ func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.O return op.Output(0) } +// Broadcast an array for a compatible shape. +// +// Broadcasting is the process of making arrays to have compatible shapes +// for arithmetic operations. Two shapes are compatible if for each +// dimension pair they are either equal or one of them is one. When trying +// to broadcast a Tensor to a shape, it starts with the trailing dimensions, +// and works its way forward. +// +// For example, +// ``` +// >>> x = tf.constant([1, 2, 3]) +// >>> y = tf.broadcast_to(x, [3, 3]) +// >>> sess.run(y) +// array([[1, 2, 3], +// [1, 2, 3], +// [1, 2, 3]], dtype=int32) +// ``` +// In the above example, the input Tensor with the shape of `[1, 3]` +// is broadcasted to output Tensor with shape of `[3, 3]`. +// +// Arguments: +// input: A Tensor to broadcast. +// shape: An 1-D `int` Tensor. The shape of the desired output. +// +// Returns A Tensor. +func BroadcastTo(scope *Scope, input tf.Output, shape tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BroadcastTo", + Input: []tf.Input{ + input, shape, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Converts a flat index or array of flat indices into a tuple of // // coordinate arrays. @@ -3069,6 +3112,152 @@ func InplaceSub(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Outpu return op.Output(0) } +// Updates specified rows with values in `v`. +// +// Computes `x[i, :] = v; return x`. +// +// Arguments: +// x: A tensor of type `T`. +// i: A vector. Indices into the left-most dimension of `x`. +// v: A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size. +// +// Returns A `Tensor` of type T. An alias of `x`. The content of `y` is undefined if there are duplicates in `i`. +func InplaceUpdate(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "InplaceUpdate", + Input: []tf.Input{ + x, i, v, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Makes a copy of `x`. +// +// Arguments: +// x: The source tensor of type `T`. +// +// Returns y: A `Tensor` of type `T`. A copy of `x`. Guaranteed that `y` +// is not an alias of `x`. +func DeepCopy(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DeepCopy", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// PackAttr is an optional argument to Pack. +type PackAttr func(optionalAttr) + +// PackAxis sets the optional axis attribute to value. +// +// value: Dimension along which to pack. Negative values wrap around, so the +// valid range is `[-(R+1), R+1)`. +// If not specified, defaults to 0 +func PackAxis(value int64) PackAttr { + return func(m optionalAttr) { + m["axis"] = value + } +} + +// Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor. +// +// Packs the `N` tensors in `values` into a tensor with rank one higher than each +// tensor in `values`, by packing them along the `axis` dimension. +// Given a list of tensors of shape `(A, B, C)`; +// +// if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`. +// if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`. +// Etc. +// +// For example: +// +// ``` +// # 'x' is [1, 4] +// # 'y' is [2, 5] +// # 'z' is [3, 6] +// pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim. +// pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]] +// ``` +// +// This is the opposite of `unpack`. +// +// Arguments: +// values: Must be of same shape and type. +// +// Returns The packed tensor. +func Pack(scope *Scope, values []tf.Output, optional ...PackAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Pack", + Input: []tf.Input{ + tf.OutputList(values), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Concatenates a list of `N` tensors along the first dimension. +// +// The input tensors are all required to have size 1 in the first dimension. +// +// For example: +// +// ``` +// # 'x' is [[1, 4]] +// # 'y' is [[2, 5]] +// # 'z' is [[3, 6]] +// parallel_concat([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim. +// ``` +// +// The difference between concat and parallel_concat is that concat requires all +// of the inputs be computed before the operation will begin but doesn't require +// that the input shapes be known during graph construction. Parallel concat +// will copy pieces of the input into the output as they become available, in +// some situations this can provide a performance benefit. +// +// Arguments: +// values: Tensors to be concatenated. All must have size 1 in the first dimension +// and same shape. +// shape: the final shape of the result; should be equal to the shapes of any input +// but with the number of input values in the first dimension. +// +// Returns The concatenated tensor. +func ParallelConcat(scope *Scope, values []tf.Output, shape tf.Shape) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"shape": shape} + opspec := tf.OpSpec{ + Type: "ParallelConcat", + Input: []tf.Input{ + tf.OutputList(values), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes the mean along sparse segments of a tensor. // // Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of @@ -3121,6 +3310,57 @@ func StackPopV2(scope *Scope, handle tf.Output, elem_type tf.DataType) (elem tf. return op.Output(0) } +// Computes the sum along sparse segments of a tensor. +// +// Like `SparseSegmentSum`, but allows missing ids in `segment_ids`. If an id is +// misisng, the `output` tensor at that position will be zeroed. +// +// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of +// segments. +// +// For example: +// +// ```python +// c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]) +// +// tf.sparse_segment_sum_with_num_segments( +// c, tf.constant([0, 1]), tf.constant([0, 0]), num_segments=3) +// # => [[0 0 0 0] +// # [0 0 0 0] +// # [0 0 0 0]] +// +// tf.sparse_segment_sum_with_num_segments(c, +// tf.constant([0, 1]), +// tf.constant([0, 2], +// num_segments=4)) +// # => [[ 1 2 3 4] +// # [ 0 0 0 0] +// # [-1 -2 -3 -4] +// # [ 0 0 0 0]] +// ``` +// +// Arguments: +// +// indices: A 1-D tensor. Has same rank as `segment_ids`. +// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. +// num_segments: Should equal the number of distinct segment IDs. +// +// Returns Has same shape as data, except for dimension 0 which +// has size `num_segments`. +func SparseSegmentSumWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSegmentSumWithNumSegments", + Input: []tf.Input{ + data, indices, segment_ids, num_segments, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // PreventGradientAttr is an optional argument to PreventGradient. type PreventGradientAttr func(optionalAttr) @@ -3309,7 +3549,7 @@ func Relu6(scope *Scope, features tf.Output) (activations tf.Output) { // segments. // // Computes a tensor such that -// `(output[i] = sum_{j...} data[j...]` where the sum is over tuples `j...` such +// \\(output[i] = sum_{j...} data[j...]\\) where the sum is over tuples `j...` such // that `segment_ids[j...] == i`. Unlike `SegmentSum`, `segment_ids` // need not be sorted and need not cover all values in the full // range of valid values. @@ -3678,11 +3918,13 @@ func Atan2(scope *Scope, y tf.Output, x tf.Output) (z tf.Output) { // // window_size: A scalar representing the number of elements in the // sliding window. -// stride: A scalar representing the steps moving the sliding window -// forward in one iteration. It must be in `[1, window_size)`. +// window_shift: A scalar representing the steps moving the sliding window +// forward in one iteration. It must be positive. +// window_stride: A scalar representing the stride of the input elements of the sliding window. +// It must be positive. // // -func SlideDataset(scope *Scope, input_dataset tf.Output, window_size tf.Output, stride tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { +func SlideDataset(scope *Scope, input_dataset tf.Output, window_size tf.Output, window_shift tf.Output, window_stride tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { if scope.Err() != nil { return } @@ -3690,7 +3932,7 @@ func SlideDataset(scope *Scope, input_dataset tf.Output, window_size tf.Output, opspec := tf.OpSpec{ Type: "SlideDataset", Input: []tf.Input{ - input_dataset, window_size, stride, + input_dataset, window_size, window_shift, window_stride, }, Attrs: attrs, } @@ -4635,28 +4877,168 @@ func Rsqrt(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// MatrixInverseAttr is an optional argument to MatrixInverse. -type MatrixInverseAttr func(optionalAttr) +// AudioSpectrogramAttr is an optional argument to AudioSpectrogram. +type AudioSpectrogramAttr func(optionalAttr) -// MatrixInverseAdjoint sets the optional adjoint attribute to value. +// AudioSpectrogramMagnitudeSquared sets the optional magnitude_squared attribute to value. +// +// value: Whether to return the squared magnitude or just the +// magnitude. Using squared magnitude can avoid extra calculations. // If not specified, defaults to false -func MatrixInverseAdjoint(value bool) MatrixInverseAttr { +func AudioSpectrogramMagnitudeSquared(value bool) AudioSpectrogramAttr { return func(m optionalAttr) { - m["adjoint"] = value + m["magnitude_squared"] = value } } -// Computes the inverse of one or more square invertible matrices or their -// -// adjoints (conjugate transposes). +// Produces a visualization of audio data over time. // -// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions -// form square matrices. The output is a tensor of the same shape as the input -// containing the inverse for all input submatrices `[..., :, :]`. +// Spectrograms are a standard way of representing audio information as a series of +// slices of frequency information, one slice for each window of time. By joining +// these together into a sequence, they form a distinctive fingerprint of the sound +// over time. // -// The op uses LU decomposition with partial pivoting to compute the inverses. +// This op expects to receive audio data as an input, stored as floats in the range +// -1 to 1, together with a window width in samples, and a stride specifying how +// far to move the window between slices. From this it generates a three +// dimensional output. The lowest dimension has an amplitude value for each +// frequency during that time slice. The next dimension is time, with successive +// frequency slices. The final dimension is for the channels in the input, so a +// stereo audio input would have two here for example. // -// If a matrix is not invertible there is no guarantee what the op does. It +// This means the layout when converted and saved as an image is rotated 90 degrees +// clockwise from a typical spectrogram. Time is descending down the Y axis, and +// the frequency decreases from left to right. +// +// Each value in the result represents the square root of the sum of the real and +// imaginary parts of an FFT on the current window of samples. In this way, the +// lowest dimension represents the power of each frequency in the current window, +// and adjacent windows are concatenated in the next dimension. +// +// To get a more intuitive and visual look at what this operation does, you can run +// tensorflow/examples/wav_to_spectrogram to read in an audio file and save out the +// resulting spectrogram as a PNG image. +// +// Arguments: +// input: Float representation of audio data. +// window_size: How wide the input window is in samples. For the highest efficiency +// this should be a power of two, but other values are accepted. +// stride: How widely apart the center of adjacent sample windows should be. +// +// Returns 3D representation of the audio frequencies as an image. +func AudioSpectrogram(scope *Scope, input tf.Output, window_size int64, stride int64, optional ...AudioSpectrogramAttr) (spectrogram tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"window_size": window_size, "stride": stride} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AudioSpectrogram", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// CTCBeamSearchDecoderAttr is an optional argument to CTCBeamSearchDecoder. +type CTCBeamSearchDecoderAttr func(optionalAttr) + +// CTCBeamSearchDecoderMergeRepeated sets the optional merge_repeated attribute to value. +// +// value: If true, merge repeated classes in output. +// If not specified, defaults to true +func CTCBeamSearchDecoderMergeRepeated(value bool) CTCBeamSearchDecoderAttr { + return func(m optionalAttr) { + m["merge_repeated"] = value + } +} + +// Performs beam search decoding on the logits given in input. +// +// A note about the attribute merge_repeated: For the beam search decoder, +// this means that if consecutive entries in a beam are the same, only +// the first of these is emitted. That is, when the top path is "A B B B B", +// "A B" is returned if merge_repeated = True but "A B B B B" is +// returned if merge_repeated = False. +// +// Arguments: +// inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. +// sequence_length: A vector containing sequence lengths, size `(batch)`. +// beam_width: A scalar >= 0 (beam search beam width). +// top_paths: A scalar >= 0, <= beam_width (controls output size). +// +// Returns A list (length: top_paths) of indices matrices. Matrix j, +// size `(total_decoded_outputs[j] x 2)`, has indices of a +// `SparseTensor`. The rows store: [batch, time].A list (length: top_paths) of values vectors. Vector j, +// size `(length total_decoded_outputs[j])`, has the values of a +// `SparseTensor`. The vector stores the decoded classes for beam j.A list (length: top_paths) of shape vector. Vector j, +// size `(2)`, stores the shape of the decoded `SparseTensor[j]`. +// Its values are: `[batch_size, max_decoded_length[j]]`.A matrix, shaped: `(batch_size x top_paths)`. The +// sequence log-probabilities. +func CTCBeamSearchDecoder(scope *Scope, inputs tf.Output, sequence_length tf.Output, beam_width int64, top_paths int64, optional ...CTCBeamSearchDecoderAttr) (decoded_indices []tf.Output, decoded_values []tf.Output, decoded_shape []tf.Output, log_probability tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"beam_width": beam_width, "top_paths": top_paths} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CTCBeamSearchDecoder", + Input: []tf.Input{ + inputs, sequence_length, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + if scope.Err() != nil { + return + } + var idx int + var err error + if decoded_indices, idx, err = makeOutputList(op, idx, "decoded_indices"); err != nil { + scope.UpdateErr("CTCBeamSearchDecoder", err) + return + } + if decoded_values, idx, err = makeOutputList(op, idx, "decoded_values"); err != nil { + scope.UpdateErr("CTCBeamSearchDecoder", err) + return + } + if decoded_shape, idx, err = makeOutputList(op, idx, "decoded_shape"); err != nil { + scope.UpdateErr("CTCBeamSearchDecoder", err) + return + } + log_probability = op.Output(idx) + return decoded_indices, decoded_values, decoded_shape, log_probability +} + +// MatrixInverseAttr is an optional argument to MatrixInverse. +type MatrixInverseAttr func(optionalAttr) + +// MatrixInverseAdjoint sets the optional adjoint attribute to value. +// If not specified, defaults to false +func MatrixInverseAdjoint(value bool) MatrixInverseAttr { + return func(m optionalAttr) { + m["adjoint"] = value + } +} + +// Computes the inverse of one or more square invertible matrices or their +// +// adjoints (conjugate transposes). +// +// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +// form square matrices. The output is a tensor of the same shape as the input +// containing the inverse for all input submatrices `[..., :, :]`. +// +// The op uses LU decomposition with partial pivoting to compute the inverses. +// +// If a matrix is not invertible there is no guarantee what the op does. It // may detect the condition and raise an exception or it may simply return a // garbage result. // @@ -4705,6 +5087,21 @@ func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return op.Output(0) } +// Computes the derivative of a Gamma random sample w.r.t. `alpha`. +func RandomGammaGrad(scope *Scope, alpha tf.Output, sample tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RandomGammaGrad", + Input: []tf.Input{ + alpha, sample, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes square of x element-wise. // // I.e., \\(y = x * x = x^2\\). @@ -4968,12 +5365,26 @@ func IsBoostedTreesEnsembleInitialized(scope *Scope, tree_ensemble_handle tf.Out return op.Output(0) } +// CastAttr is an optional argument to Cast. +type CastAttr func(optionalAttr) + +// CastTruncate sets the optional Truncate attribute to value. +// If not specified, defaults to false +func CastTruncate(value bool) CastAttr { + return func(m optionalAttr) { + m["Truncate"] = value + } +} + // Cast x of type SrcT to y of DstT. -func Cast(scope *Scope, x tf.Output, DstT tf.DataType) (y tf.Output) { +func Cast(scope *Scope, x tf.Output, DstT tf.DataType, optional ...CastAttr) (y tf.Output) { if scope.Err() != nil { return } attrs := map[string]interface{}{"DstT": DstT} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ Type: "Cast", Input: []tf.Input{ @@ -5453,7 +5864,7 @@ func LessEqual(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { // // For each batch `i` and class `j` we have // -// softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j])) +// $$softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))$$ // // Arguments: // logits: 2-D with shape `[batch_size, num_classes]`. @@ -5758,146 +6169,6 @@ func Dilation2DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, ou return op.Output(0) } -// CTCBeamSearchDecoderAttr is an optional argument to CTCBeamSearchDecoder. -type CTCBeamSearchDecoderAttr func(optionalAttr) - -// CTCBeamSearchDecoderMergeRepeated sets the optional merge_repeated attribute to value. -// -// value: If true, merge repeated classes in output. -// If not specified, defaults to true -func CTCBeamSearchDecoderMergeRepeated(value bool) CTCBeamSearchDecoderAttr { - return func(m optionalAttr) { - m["merge_repeated"] = value - } -} - -// Performs beam search decoding on the logits given in input. -// -// A note about the attribute merge_repeated: For the beam search decoder, -// this means that if consecutive entries in a beam are the same, only -// the first of these is emitted. That is, when the top path is "A B B B B", -// "A B" is returned if merge_repeated = True but "A B B B B" is -// returned if merge_repeated = False. -// -// Arguments: -// inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. -// sequence_length: A vector containing sequence lengths, size `(batch)`. -// beam_width: A scalar >= 0 (beam search beam width). -// top_paths: A scalar >= 0, <= beam_width (controls output size). -// -// Returns A list (length: top_paths) of indices matrices. Matrix j, -// size `(total_decoded_outputs[j] x 2)`, has indices of a -// `SparseTensor`. The rows store: [batch, time].A list (length: top_paths) of values vectors. Vector j, -// size `(length total_decoded_outputs[j])`, has the values of a -// `SparseTensor`. The vector stores the decoded classes for beam j.A list (length: top_paths) of shape vector. Vector j, -// size `(2)`, stores the shape of the decoded `SparseTensor[j]`. -// Its values are: `[batch_size, max_decoded_length[j]]`.A matrix, shaped: `(batch_size x top_paths)`. The -// sequence log-probabilities. -func CTCBeamSearchDecoder(scope *Scope, inputs tf.Output, sequence_length tf.Output, beam_width int64, top_paths int64, optional ...CTCBeamSearchDecoderAttr) (decoded_indices []tf.Output, decoded_values []tf.Output, decoded_shape []tf.Output, log_probability tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"beam_width": beam_width, "top_paths": top_paths} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "CTCBeamSearchDecoder", - Input: []tf.Input{ - inputs, sequence_length, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - if scope.Err() != nil { - return - } - var idx int - var err error - if decoded_indices, idx, err = makeOutputList(op, idx, "decoded_indices"); err != nil { - scope.UpdateErr("CTCBeamSearchDecoder", err) - return - } - if decoded_values, idx, err = makeOutputList(op, idx, "decoded_values"); err != nil { - scope.UpdateErr("CTCBeamSearchDecoder", err) - return - } - if decoded_shape, idx, err = makeOutputList(op, idx, "decoded_shape"); err != nil { - scope.UpdateErr("CTCBeamSearchDecoder", err) - return - } - log_probability = op.Output(idx) - return decoded_indices, decoded_values, decoded_shape, log_probability -} - -// AudioSpectrogramAttr is an optional argument to AudioSpectrogram. -type AudioSpectrogramAttr func(optionalAttr) - -// AudioSpectrogramMagnitudeSquared sets the optional magnitude_squared attribute to value. -// -// value: Whether to return the squared magnitude or just the -// magnitude. Using squared magnitude can avoid extra calculations. -// If not specified, defaults to false -func AudioSpectrogramMagnitudeSquared(value bool) AudioSpectrogramAttr { - return func(m optionalAttr) { - m["magnitude_squared"] = value - } -} - -// Produces a visualization of audio data over time. -// -// Spectrograms are a standard way of representing audio information as a series of -// slices of frequency information, one slice for each window of time. By joining -// these together into a sequence, they form a distinctive fingerprint of the sound -// over time. -// -// This op expects to receive audio data as an input, stored as floats in the range -// -1 to 1, together with a window width in samples, and a stride specifying how -// far to move the window between slices. From this it generates a three -// dimensional output. The lowest dimension has an amplitude value for each -// frequency during that time slice. The next dimension is time, with successive -// frequency slices. The final dimension is for the channels in the input, so a -// stereo audio input would have two here for example. -// -// This means the layout when converted and saved as an image is rotated 90 degrees -// clockwise from a typical spectrogram. Time is descending down the Y axis, and -// the frequency decreases from left to right. -// -// Each value in the result represents the square root of the sum of the real and -// imaginary parts of an FFT on the current window of samples. In this way, the -// lowest dimension represents the power of each frequency in the current window, -// and adjacent windows are concatenated in the next dimension. -// -// To get a more intuitive and visual look at what this operation does, you can run -// tensorflow/examples/wav_to_spectrogram to read in an audio file and save out the -// resulting spectrogram as a PNG image. -// -// Arguments: -// input: Float representation of audio data. -// window_size: How wide the input window is in samples. For the highest efficiency -// this should be a power of two, but other values are accepted. -// stride: How widely apart the center of adjacent sample windows should be. -// -// Returns 3D representation of the audio frequencies as an image. -func AudioSpectrogram(scope *Scope, input tf.Output, window_size int64, stride int64, optional ...AudioSpectrogramAttr) (spectrogram tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"window_size": window_size, "stride": stride} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "AudioSpectrogram", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Compute the polygamma function \\(\psi^{(n)}(x)\\). // // The polygamma function is defined as: @@ -6071,53 +6342,6 @@ func MutexV2(scope *Scope, optional ...MutexV2Attr) (resource tf.Output) { return op.Output(0) } -// AvgPool3DAttr is an optional argument to AvgPool3D. -type AvgPool3DAttr func(optionalAttr) - -// AvgPool3DDataFormat sets the optional data_format attribute to value. -// -// value: The data format of the input and output data. With the -// default format "NDHWC", the data is stored in the order of: -// [batch, in_depth, in_height, in_width, in_channels]. -// Alternatively, the format could be "NCDHW", the data storage order is: -// [batch, in_channels, in_depth, in_height, in_width]. -// If not specified, defaults to "NDHWC" -func AvgPool3DDataFormat(value string) AvgPool3DAttr { - return func(m optionalAttr) { - m["data_format"] = value - } -} - -// Performs 3D average pooling on the input. -// -// Arguments: -// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over. -// ksize: 1-D tensor of length 5. The size of the window for each dimension of -// the input tensor. Must have `ksize[0] = ksize[4] = 1`. -// strides: 1-D tensor of length 5. The stride of the sliding window for each -// dimension of `input`. Must have `strides[0] = strides[4] = 1`. -// padding: The type of padding algorithm to use. -// -// Returns The average pooled output tensor. -func AvgPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "AvgPool3D", - Input: []tf.Input{ - input, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Returns element-wise remainder of division. This emulates C semantics in that // // the result here is consistent with a truncating divide. E.g. @@ -6678,8 +6902,9 @@ type CropAndResizeAttr func(optionalAttr) // CropAndResizeMethod sets the optional method attribute to value. // -// value: A string specifying the interpolation method. Only 'bilinear' is -// supported for now. +// value: A string specifying the sampling method for resizing. It can be either +// `"bilinear"` or `"nearest"` and default to `"bilinear"`. Currently two sampling +// methods are supported: Bilinear and Nearest Neighbor. // If not specified, defaults to "bilinear" func CropAndResizeMethod(value string) CropAndResizeAttr { return func(m optionalAttr) { @@ -6697,19 +6922,23 @@ func CropAndResizeExtrapolationValue(value float32) CropAndResizeAttr { } } -// Extracts crops from the input image tensor and bilinearly resizes them (possibly +// Extracts crops from the input image tensor and resizes them. // -// with aspect ratio change) to a common output size specified by `crop_size`. This -// is more general than the `crop_to_bounding_box` op which extracts a fixed size -// slice from the input image and does not allow resizing or aspect ratio change. +// Extracts crops from the input image tensor and resizes them using bilinear +// sampling or nearest neighbor sampling (possibly with aspect ratio change) to a +// common output size specified by `crop_size`. This is more general than the +// `crop_to_bounding_box` op which extracts a fixed size slice from the input image +// and does not allow resizing or aspect ratio change. // // Returns a tensor with `crops` from the input `image` at positions defined at the // bounding box locations in `boxes`. The cropped boxes are all resized (with -// bilinear interpolation) to a fixed `size = [crop_height, crop_width]`. The -// result is a 4-D tensor `[num_boxes, crop_height, crop_width, depth]`. The -// resizing is corner aligned. In particular, if `boxes = [[0, 0, 1, 1]]`, the -// method will give identical results to using `tf.image.resize_bilinear()` -// with `align_corners=True`. +// bilinear or nearest neighbor interpolation) to a fixed +// `size = [crop_height, crop_width]`. The result is a 4-D tensor +// `[num_boxes, crop_height, crop_width, depth]`. The resizing is corner aligned. +// In particular, if `boxes = [[0, 0, 1, 1]]`, the method will give identical +// results to using `tf.image.resize_bilinear()` or +// `tf.image.resize_nearest_neighbor()`(depends on the `method` argument) with +// `align_corners=True`. // // Arguments: // image: A 4-D tensor of shape `[batch, image_height, image_width, depth]`. @@ -7092,6 +7321,26 @@ func Min(scope *Scope, input tf.Output, axis tf.Output, optional ...MinAttr) (ou return op.Output(0) } +// Computes the Bessel i1e function of `x` element-wise. +// +// Exponentially scaled modified Bessel function of order 0 defined as +// `bessel_i1e(x) = exp(-abs(x)) bessel_i1(x)`. +// +// This function is faster and numerically stabler than `bessel_i1(x)`. +func BesselI1e(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BesselI1e", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Transforms a Tensor into a serialized TensorProto proto. // // Arguments: @@ -8114,27 +8363,6 @@ func CollectiveBcastSend(scope *Scope, input tf.Output, group_size int64, group_ return op.Output(0) } -// Makes a copy of `x`. -// -// Arguments: -// x: The source tensor of type `T`. -// -// Returns y: A `Tensor` of type `T`. A copy of `x`. Guaranteed that `y` -// is not an alias of `x`. -func DeepCopy(scope *Scope, x tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "DeepCopy", - Input: []tf.Input{ - x, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Split a `SparseTensor` into `num_split` tensors along one dimension. // // If the `shape[split_dim]` is not an integer multiple of `num_split`. Slices @@ -8308,6 +8536,21 @@ func DataFormatVecPermute(scope *Scope, x tf.Output, optional ...DataFormatVecPe return op.Output(0) } +// Computes the gradient of `igamma(a, x)` wrt `a`. +func IgammaGradA(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "IgammaGradA", + Input: []tf.Input{ + a, x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Converts each string in the input Tensor to its hash mod by a number of buckets. // // The hash function is deterministic on the content of the string within the @@ -8972,6 +9215,85 @@ func ResourceScatterDiv(scope *Scope, resource tf.Output, indices tf.Output, upd return scope.AddOperation(opspec) } +// ResourceScatterNdAddAttr is an optional argument to ResourceScatterNdAdd. +type ResourceScatterNdAddAttr func(optionalAttr) + +// ResourceScatterNdAddUseLocking sets the optional use_locking attribute to value. +// +// value: An optional bool. Defaults to True. If True, the assignment will +// be protected by a lock; otherwise the behavior is undefined, +// but may exhibit less contention. +// If not specified, defaults to true +func ResourceScatterNdAddUseLocking(value bool) ResourceScatterNdAddAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Adds sparse `updates` to individual values or slices within a given +// +// variable according to `indices`. +// +// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. +// +// `indices` must be integer tensor, containing indices into `ref`. +// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. +// +// The innermost dimension of `indices` (with length `K`) corresponds to +// indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th +// dimension of `ref`. +// +// `updates` is `Tensor` of rank `Q-1+P-K` with shape: +// +// ``` +// [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. +// ``` +// +// For example, say we want to update 4 scattered elements to a rank-1 tensor to +// 8 elements. In Python, that update would look like this: +// +// ```python +// ref = tfe.Variable([1, 2, 3, 4, 5, 6, 7, 8]) +// indices = tf.constant([[4], [3], [1] ,[7]]) +// updates = tf.constant([9, 10, 11, 12]) +// update = tf.scatter_nd_add(ref, indices, updates) +// with tf.Session() as sess: +// print sess.run(update) +// ``` +// +// The resulting update to ref would look like this: +// +// [1, 12, 3, 14, 14, 6, 7, 20] +// +// See @{tf.scatter_nd} for more details about how to make updates to +// slices. +// +// Arguments: +// ref: A resource handle. Must be from a VarHandleOp. +// indices: A Tensor. Must be one of the following types: int32, int64. +// A tensor of indices into ref. +// updates: A Tensor. Must have the same type as ref. A tensor of +// values to add to ref. +// +// Returns the created operation. +func ResourceScatterNdAdd(scope *Scope, ref tf.Output, indices tf.Output, updates tf.Output, optional ...ResourceScatterNdAddAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceScatterNdAdd", + Input: []tf.Input{ + ref, indices, updates, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + // Mutually reduces multiple tensors of identical type and shape. func CollectiveReduce(scope *Scope, input tf.Output, group_size int64, group_key int64, instance_key int64, merge_op string, final_op string, subdiv_offsets []int64) (data tf.Output) { if scope.Err() != nil { @@ -8992,28 +9314,90 @@ func CollectiveReduce(scope *Scope, input tf.Output, group_size int64, group_key // StatelessRandomNormalAttr is an optional argument to StatelessRandomNormal. type StatelessRandomNormalAttr func(optionalAttr) -// StatelessRandomNormalDtype sets the optional dtype attribute to value. +// StatelessRandomNormalDtype sets the optional dtype attribute to value. +// +// value: The type of the output. +// If not specified, defaults to DT_FLOAT +func StatelessRandomNormalDtype(value tf.DataType) StatelessRandomNormalAttr { + return func(m optionalAttr) { + m["dtype"] = value + } +} + +// Outputs deterministic pseudorandom values from a normal distribution. +// +// The generated values will have mean 0 and standard deviation 1. +// +// The outputs are a deterministic function of `shape` and `seed`. +// +// Arguments: +// shape: The shape of the output tensor. +// seed: 2 seeds (shape [2]). +// +// Returns Random values with specified shape. +func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomNormalAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "StatelessRandomNormal", + Input: []tf.Input{ + shape, seed, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// StringSplitV2Attr is an optional argument to StringSplitV2. +type StringSplitV2Attr func(optionalAttr) + +// StringSplitV2Maxsplit sets the optional maxsplit attribute to value. // -// value: The type of the output. -// If not specified, defaults to DT_FLOAT -func StatelessRandomNormalDtype(value tf.DataType) StatelessRandomNormalAttr { +// value: An `int`. If `maxsplit > 0`, limit of the split of the result. +// If not specified, defaults to -1 +func StringSplitV2Maxsplit(value int64) StringSplitV2Attr { return func(m optionalAttr) { - m["dtype"] = value + m["maxsplit"] = value } } -// Outputs deterministic pseudorandom values from a normal distribution. +// Split elements of `source` based on `sep` into a `SparseTensor`. // -// The generated values will have mean 0 and standard deviation 1. +// Let N be the size of source (typically N will be the batch size). Split each +// element of `source` based on `sep` and return a `SparseTensor` +// containing the split tokens. Empty tokens are ignored. // -// The outputs are a deterministic function of `shape` and `seed`. +// For example, N = 2, source[0] is 'hello world' and source[1] is 'a b c', +// then the output will be +// ``` +// st.indices = [0, 0; +// 0, 1; +// 1, 0; +// 1, 1; +// 1, 2] +// st.shape = [2, 3] +// st.values = ['hello', 'world', 'a', 'b', 'c'] +// ``` // -// Arguments: -// shape: The shape of the output tensor. -// seed: 2 seeds (shape [2]). +// If `sep` is given, consecutive delimiters are not grouped together and are +// deemed to delimit empty strings. For example, source of `"1<>2<><>3"` and +// sep of `"<>"` returns `["1", "2", "", "3"]`. If `sep` is None or an empty +// string, consecutive whitespace are regarded as a single separator, and the +// result will contain no empty strings at the startor end if the string has +// leading or trailing whitespace. // -// Returns Random values with specified shape. -func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, optional ...StatelessRandomNormalAttr) (output tf.Output) { +// Note that the above mentioned behavior matches python's str.split. +// +// Arguments: +// input: `1-D` string `Tensor`, the strings to split. +// sep: `0-D` string `Tensor`, the delimiter character. +func StringSplitV2(scope *Scope, input tf.Output, sep tf.Output, optional ...StringSplitV2Attr) (indices tf.Output, values tf.Output, shape tf.Output) { if scope.Err() != nil { return } @@ -9022,14 +9406,14 @@ func StatelessRandomNormal(scope *Scope, shape tf.Output, seed tf.Output, option a(attrs) } opspec := tf.OpSpec{ - Type: "StatelessRandomNormal", + Type: "StringSplitV2", Input: []tf.Input{ - shape, seed, + input, sep, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } // MaxPoolAttr is an optional argument to MaxPool. @@ -9116,9 +9500,11 @@ func SparseMatMulBIsSparse(value bool) SparseMatMulAttr { // Multiply matrix "a" by matrix "b". // // The inputs must be two-dimensional matrices and the inner dimension of "a" must -// match the outer dimension of "b". This op is optimized for the case where at -// least one of "a" or "b" is sparse. The breakeven for using this versus a dense -// matrix multiply on one platform was 30% zero values in the sparse matrix. +// match the outer dimension of "b". Both "a" and "b" must be `Tensor`s not +// `SparseTensor`s. This op is optimized for the case where at least one of "a" or +// "b" is sparse, in the sense that they have a large proportion of zero values. +// The breakeven for using this versus a dense matrix multiply on one platform was +// 30% zero values in the sparse matrix. // // The gradient computation of this operation will only take advantage of sparsity // in the input gradient when that gradient comes from a Relu. @@ -9749,6 +10135,51 @@ func AvgPoolGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksize return op.Output(0) } +// Greedily selects a subset of bounding boxes in descending order of score, +// +// pruning away boxes that have high overlaps +// with previously selected boxes. Bounding boxes with score less than +// `score_threshold` are removed. N-by-n overlap values are supplied as square matrix, +// which allows for defining a custom overlap criterium (eg. intersection over union, +// intersection over area, etc.). +// +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// +// selected_indices = tf.image.non_max_suppression_with_overlaps( +// overlaps, scores, max_output_size, overlap_threshold, score_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// +// Arguments: +// overlaps: A 2-D float tensor of shape `[num_boxes, num_boxes]` representing +// the n-by-n box overlap values. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// overlap_threshold: A 0-D float tensor representing the threshold for deciding whether +// boxes overlap too. +// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove +// boxes based on score. +// +// Returns A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`. +func NonMaxSuppressionWithOverlaps(scope *Scope, overlaps tf.Output, scores tf.Output, max_output_size tf.Output, overlap_threshold tf.Output, score_threshold tf.Output) (selected_indices tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NonMaxSuppressionWithOverlaps", + Input: []tf.Input{ + overlaps, scores, max_output_size, overlap_threshold, score_threshold, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // StageClearAttr is an optional argument to StageClear. type StageClearAttr func(optionalAttr) @@ -10041,6 +10472,57 @@ func Atan(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } +// ResourceApplyAdaMaxAttr is an optional argument to ResourceApplyAdaMax. +type ResourceApplyAdaMaxAttr func(optionalAttr) + +// ResourceApplyAdaMaxUseLocking sets the optional use_locking attribute to value. +// +// value: If `True`, updating of the var, m, and v tensors will be protected +// by a lock; otherwise the behavior is undefined, but may exhibit less +// contention. +// If not specified, defaults to false +func ResourceApplyAdaMaxUseLocking(value bool) ResourceApplyAdaMaxAttr { + return func(m optionalAttr) { + m["use_locking"] = value + } +} + +// Update '*var' according to the AdaMax algorithm. +// +// m_t <- beta1 * m_{t-1} + (1 - beta1) * g +// v_t <- max(beta2 * v_{t-1}, abs(g)) +// variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon) +// +// Arguments: +// var_: Should be from a Variable(). +// m: Should be from a Variable(). +// v: Should be from a Variable(). +// beta1_power: Must be a scalar. +// lr: Scaling factor. Must be a scalar. +// beta1: Momentum factor. Must be a scalar. +// beta2: Momentum factor. Must be a scalar. +// epsilon: Ridge term. Must be a scalar. +// grad: The gradient. +// +// Returns the created operation. +func ResourceApplyAdaMax(scope *Scope, var_ tf.Output, m tf.Output, v tf.Output, beta1_power tf.Output, lr tf.Output, beta1 tf.Output, beta2 tf.Output, epsilon tf.Output, grad tf.Output, optional ...ResourceApplyAdaMaxAttr) (o *tf.Operation) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "ResourceApplyAdaMax", + Input: []tf.Input{ + var_, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, + }, + Attrs: attrs, + } + return scope.AddOperation(opspec) +} + // Encode audio data using the WAV file format. // // This operation will generate a string suitable to be saved out to create a .wav @@ -10649,6 +11131,120 @@ func ResourceApplyPowerSign(scope *Scope, var_ tf.Output, m tf.Output, lr tf.Out return scope.AddOperation(opspec) } +// CudnnRNNBackpropV2Attr is an optional argument to CudnnRNNBackpropV2. +type CudnnRNNBackpropV2Attr func(optionalAttr) + +// CudnnRNNBackpropV2RnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNBackpropV2RnnMode(value string) CudnnRNNBackpropV2Attr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNBackpropV2InputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNBackpropV2InputMode(value string) CudnnRNNBackpropV2Attr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNBackpropV2Direction sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNBackpropV2Direction(value string) CudnnRNNBackpropV2Attr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNBackpropV2Dropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropV2Dropout(value float32) CudnnRNNBackpropV2Attr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNBackpropV2Seed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropV2Seed(value int64) CudnnRNNBackpropV2Attr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNBackpropV2Seed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNBackpropV2Seed2(value int64) CudnnRNNBackpropV2Attr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// Backprop step of CudnnRNN. +// +// Compute the backprop of both data and weights in a RNN. Takes an extra +// "host_reserved" inupt than CudnnRNNBackprop, which is used to determine RNN +// cudnnRNNAlgo_t and cudnnMathType_t. +// +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicates whether there is a linear projection between the input and +// the actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. Should be +// "unidirectional" or "bidirectional". +// dropout: Dropout probability. When set to 0., dropout is disabled. +// seed: The 1st part of a seed to initialize dropout. +// seed2: The 2nd part of a seed to initialize dropout. +// input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. +// input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, +// num_units]. +// input_c: For LSTM, a 3-D tensor with the shape of +// [num_layer * dir, batch, num_units]. For other models, it is ignored. +// params: A 1-D tensor that contains the weights and biases in an opaque layout. +// The size must be created through CudnnRNNParamsSize, and initialized +// separately. Note that they might not be compatible across different +// generations. So it is a good idea to save and restore +// output: A 3-D tensor with the shape of [seq_length, batch_size, +// dir * num_units]. +// output_h: The same shape has input_h. +// output_c: The same shape as input_c for LSTM. An empty tensor for other models. +// output_backprop: A 3-D tensor with the same shape as output in the forward pass. +// output_h_backprop: A 3-D tensor with the same shape as output_h in the forward +// pass. +// output_c_backprop: A 3-D tensor with the same shape as output_c in the forward +// pass. +// reserve_space: The same reserve_space produced in the forward operation. +// host_reserved: The same host_reserved produced in the forward operation. +// input_backprop: The backprop to input in the forward pass. Has the same shape +// as input. +// input_h_backprop: The backprop to input_h in the forward pass. Has the same +// shape as input_h. +// input_c_backprop: The backprop to input_c in the forward pass. Has the same +// shape as input_c. +// params_backprop: The backprop to the params buffer in the forward pass. Has the +// same shape as params. +func CudnnRNNBackpropV2(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, output tf.Output, output_h tf.Output, output_c tf.Output, output_backprop tf.Output, output_h_backprop tf.Output, output_c_backprop tf.Output, reserve_space tf.Output, host_reserved tf.Output, optional ...CudnnRNNBackpropV2Attr) (input_backprop tf.Output, input_h_backprop tf.Output, input_c_backprop tf.Output, params_backprop tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNNBackpropV2", + Input: []tf.Input{ + input, input_h, input_c, params, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space, host_reserved, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3) +} + // Locks a mutex resource. The output is the lock. So long as the lock tensor // // is alive, any other request to use `MutexLock` with this mutex will wait. @@ -10836,6 +11432,34 @@ func BatchDataset(scope *Scope, input_dataset tf.Output, batch_size tf.Output, o return op.Output(0) } +// Check if the input matches the regex pattern. +// +// The input is a string tensor of any shape. The pattern is a scalar +// string tensor which is applied to every element of the input tensor. +// The boolean values (True or False) of the output tensor indicate +// if the input matches the regex pattern provided. +// +// The pattern follows the re2 syntax (https://github.com/google/re2/wiki/Syntax) +// +// Arguments: +// input: A string tensor of the text to be processed. +// pattern: A 1-D string tensor of the regular expression to match the input. +// +// Returns A bool tensor with the same shape as `input`. +func RegexFullMatch(scope *Scope, input tf.Output, pattern tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RegexFullMatch", + Input: []tf.Input{ + input, pattern, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Says whether the targets are in the top `K` predictions. // // This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the @@ -11328,7 +11952,7 @@ func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistorted // SampleDistortedBoundingBoxAreaRange sets the optional area_range attribute to value. // // value: The cropped area of the image must contain a fraction of the -// supplied image within in this range. +// supplied image within this range. // If not specified, defaults to func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr { return func(m optionalAttr) { @@ -12100,6 +12724,7 @@ func RFFT2D(scope *Scope, input tf.Output, fft_length tf.Output) (output tf.Outp // [0, 0, 2, 2, 0, 0] // [0, 0, 0, 0, 0, 0]] // ``` +// func Pad(scope *Scope, input tf.Output, paddings tf.Output) (output tf.Output) { if scope.Err() != nil { return @@ -13418,9 +14043,11 @@ func ReduceJoinSeparator(value string) ReduceJoinAttr { // Joins a string Tensor across the given dimensions. // // Computes the string join across dimensions in the given string Tensor of shape -// `[d_0, d_1, ..., d_n-1]`. Returns a new Tensor created by joining the input +// `[\\(d_0, d_1, ..., d_{n-1}\\)]`. Returns a new Tensor created by joining the input // strings with the given separator (default: empty string). Negative indices are -// counted backwards from the end, with `-1` being equivalent to `n - 1`. +// counted backwards from the end, with `-1` being equivalent to `n - 1`. If +// indices are not specified, joins across all dimensions beginning from `n - 1` +// through `0`. // // For example: // @@ -13433,9 +14060,10 @@ func ReduceJoinSeparator(value string) ReduceJoinAttr { // tf.reduce_join(a, 0, keep_dims=True) ==> [["ac", "bd"]] // tf.reduce_join(a, 1, keep_dims=True) ==> [["ab"], ["cd"]] // tf.reduce_join(a, 0, separator=".") ==> ["a.c", "b.d"] -// tf.reduce_join(a, [0, 1]) ==> ["acbd"] -// tf.reduce_join(a, [1, 0]) ==> ["abcd"] -// tf.reduce_join(a, []) ==> ["abcd"] +// tf.reduce_join(a, [0, 1]) ==> "acbd" +// tf.reduce_join(a, [1, 0]) ==> "abcd" +// tf.reduce_join(a, []) ==> [["a", "b"], ["c", "d"]] +// tf.reduce_join(a) = tf.reduce_join(a, [1, 0]) ==> "abcd" // ``` // // Arguments: @@ -13867,10 +14495,87 @@ func Minimum(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { return } opspec := tf.OpSpec{ - Type: "Minimum", + Type: "Minimum", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// MfccAttr is an optional argument to Mfcc. +type MfccAttr func(optionalAttr) + +// MfccUpperFrequencyLimit sets the optional upper_frequency_limit attribute to value. +// +// value: The highest frequency to use when calculating the +// ceptstrum. +// If not specified, defaults to 4000 +func MfccUpperFrequencyLimit(value float32) MfccAttr { + return func(m optionalAttr) { + m["upper_frequency_limit"] = value + } +} + +// MfccLowerFrequencyLimit sets the optional lower_frequency_limit attribute to value. +// +// value: The lowest frequency to use when calculating the +// ceptstrum. +// If not specified, defaults to 20 +func MfccLowerFrequencyLimit(value float32) MfccAttr { + return func(m optionalAttr) { + m["lower_frequency_limit"] = value + } +} + +// MfccFilterbankChannelCount sets the optional filterbank_channel_count attribute to value. +// +// value: Resolution of the Mel bank used internally. +// If not specified, defaults to 40 +func MfccFilterbankChannelCount(value int64) MfccAttr { + return func(m optionalAttr) { + m["filterbank_channel_count"] = value + } +} + +// MfccDctCoefficientCount sets the optional dct_coefficient_count attribute to value. +// +// value: How many output channels to produce per time slice. +// If not specified, defaults to 13 +func MfccDctCoefficientCount(value int64) MfccAttr { + return func(m optionalAttr) { + m["dct_coefficient_count"] = value + } +} + +// Transforms a spectrogram into a form that's useful for speech recognition. +// +// Mel Frequency Cepstral Coefficients are a way of representing audio data that's +// been effective as an input feature for machine learning. They are created by +// taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the +// higher frequencies that are less significant to the human ear. They have a long +// history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrum +// is a good resource to learn more. +// +// Arguments: +// spectrogram: Typically produced by the Spectrogram op, with magnitude_squared +// set to true. +// sample_rate: How many samples per second the source audio used. +func Mfcc(scope *Scope, spectrogram tf.Output, sample_rate tf.Output, optional ...MfccAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Mfcc", Input: []tf.Input{ - x, y, + spectrogram, sample_rate, }, + Attrs: attrs, } op := scope.AddOperation(opspec) return op.Output(0) @@ -14294,65 +14999,6 @@ func TensorArraySplitV2(scope *Scope, handle tf.Output, value tf.Output, lengths return op.Output(0) } -// PackAttr is an optional argument to Pack. -type PackAttr func(optionalAttr) - -// PackAxis sets the optional axis attribute to value. -// -// value: Dimension along which to pack. Negative values wrap around, so the -// valid range is `[-(R+1), R+1)`. -// If not specified, defaults to 0 -func PackAxis(value int64) PackAttr { - return func(m optionalAttr) { - m["axis"] = value - } -} - -// Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor. -// -// Packs the `N` tensors in `values` into a tensor with rank one higher than each -// tensor in `values`, by packing them along the `axis` dimension. -// Given a list of tensors of shape `(A, B, C)`; -// -// if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`. -// if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`. -// Etc. -// -// For example: -// -// ``` -// # 'x' is [1, 4] -// # 'y' is [2, 5] -// # 'z' is [3, 6] -// pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim. -// pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]] -// ``` -// -// This is the opposite of `unpack`. -// -// Arguments: -// values: Must be of same shape and type. -// -// Returns The packed tensor. -func Pack(scope *Scope, values []tf.Output, optional ...PackAttr) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "Pack", - Input: []tf.Input{ - tf.OutputList(values), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Reorders a SparseTensor into the canonical, row-major ordering. // // Note that by convention, all sparse ops preserve the canonical ordering along @@ -14507,27 +15153,27 @@ func CudnnRNNBackpropSeed2(value int64) CudnnRNNBackpropAttr { // // rnn_mode: Indicates the type of the RNN model. // input_mode: Indicate whether there is a linear projection between the input and -// The actual computation before the first layer. 'skip_input' is only allowed +// the actual computation before the first layer. 'skip_input' is only allowed // when input_size == num_units; 'auto_select' implies 'skip_input' when // input_size == num_units; otherwise, it implies 'linear_input'. -// direction: Indicates whether a bidirectional model will be used. -// dir = (direction == bidirectional) ? 2 : 1 -// dropout: dropout probability. When set to 0., dropout is disabled. -// seed: the 1st part of a seed to initialize dropout. -// seed2: the 2nd part of a seed to initialize dropout. -// input: a 3-D tensor with the shape of [seq_length, batch_size, input_size]. -// input_h: a 3-D tensor with the shape of [num_layer * dir, batch_size, +// direction: Indicates whether a bidirectional model will be used. Should be +// "unidirectional" or "bidirectional". +// dropout: Dropout probability. When set to 0., dropout is disabled. +// seed: The 1st part of a seed to initialize dropout. +// seed2: The 2nd part of a seed to initialize dropout. +// input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. +// input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, // num_units]. // input_c: For LSTM, a 3-D tensor with the shape of // [num_layer * dir, batch, num_units]. For other models, it is ignored. -// params: a 1-D tensor that contains the weights and biases in an opaque layout. +// params: A 1-D tensor that contains the weights and biases in an opaque layout. // The size must be created through CudnnRNNParamsSize, and initialized // separately. Note that they might not be compatible across different // generations. So it is a good idea to save and restore -// output: a 3-D tensor with the shape of [seq_length, batch_size, +// output: A 3-D tensor with the shape of [seq_length, batch_size, // dir * num_units]. -// output_h: the same shape has input_h. -// output_c: the same shape as input_c for LSTM. An empty tensor for other models. +// output_h: The same shape has input_h. +// output_c: The same shape as input_c for LSTM. An empty tensor for other models. // output_backprop: A 3-D tensor with the same shape as output in the forward pass. // output_h_backprop: A 3-D tensor with the same shape as output_h in the forward // pass. @@ -15012,30 +15658,6 @@ func Sigmoid(scope *Scope, x tf.Output) (y tf.Output) { return op.Output(0) } -// Updates specified rows with values in `v`. -// -// Computes `x[i, :] = v; return x`. -// -// Arguments: -// x: A tensor of type `T`. -// i: A vector. Indices into the left-most dimension of `x`. -// v: A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size. -// -// Returns A `Tensor` of type T. An alias of `x`. The content of `y` is undefined if there are duplicates in `i`. -func InplaceUpdate(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "InplaceUpdate", - Input: []tf.Input{ - x, i, v, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // FusedBatchNormAttr is an optional argument to FusedBatchNorm. type FusedBatchNormAttr func(optionalAttr) @@ -15512,6 +16134,30 @@ func OrderedMapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataTyp return key, values } +// Calculates the prior from the training data (the bias) and fills in the first node with the logits' prior. Returns a boolean indicating whether to continue centering. +// +// Arguments: +// tree_ensemble_handle: Handle to the tree ensemble. +// mean_gradients: A tensor with shape=[logits_dimension] with mean of gradients for a first node. +// mean_hessians: A tensor with shape=[logits_dimension] mean of hessians for a first node. +// l1: l1 regularization factor on leaf weights, per instance based. +// l2: l2 regularization factor on leaf weights, per instance based. +// +// Returns Bool, whether to continue bias centering. +func BoostedTreesCenterBias(scope *Scope, tree_ensemble_handle tf.Output, mean_gradients tf.Output, mean_hessians tf.Output, l1 tf.Output, l2 tf.Output) (continue_centering tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BoostedTreesCenterBias", + Input: []tf.Input{ + tree_ensemble_handle, mean_gradients, mean_hessians, l1, l2, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // SerializeManySparseAttr is an optional argument to SerializeManySparse. type SerializeManySparseAttr func(optionalAttr) @@ -17080,6 +17726,7 @@ func QuantizeV2RoundMode(value string) QuantizeV2Attr { // out[i] = (in[i] - min_range) * range(T) / (max_range - min_range) // if T == qint8, out[i] -= (range(T) + 1) / 2.0 // ``` +// // here `range(T) = numeric_limits::max() - numeric_limits::min()` // // *MIN_COMBINED Mode Example* @@ -17123,6 +17770,7 @@ func QuantizeV2RoundMode(value string) QuantizeV2Attr { // // We first find the range of values in our tensor. The // range we use is always centered on 0, so we find m such that +// // ```c++ // m = max(abs(input_min), abs(input_max)) // ``` @@ -17131,6 +17779,7 @@ func QuantizeV2RoundMode(value string) QuantizeV2Attr { // // Next, we choose our fixed-point quantization buckets, `[min_fixed, max_fixed]`. // If T is signed, this is +// // ``` // num_bits = sizeof(T) * 8 // [min_fixed, max_fixed] = @@ -17138,16 +17787,19 @@ func QuantizeV2RoundMode(value string) QuantizeV2Attr { // ``` // // Otherwise, if T is unsigned, the fixed-point range is +// // ``` // [min_fixed, max_fixed] = [0, (1 << num_bits) - 1] // ``` // // From this we compute our scaling factor, s: +// // ```c++ // s = (max_fixed - min_fixed) / (2 * m) // ``` // // Now we can quantize the elements of our tensor: +// // ```c++ // result = round(input * s) // ``` @@ -17244,6 +17896,31 @@ func QuantizedReluX(scope *Scope, features tf.Output, max_value tf.Output, min_f return op.Output(0), op.Output(1), op.Output(2) } +// Creates a dataset that batches `batch_size` elements from `input_dataset`. +// +// Arguments: +// +// batch_size: A scalar representing the number of elements to accumulate in a batch. +// drop_remainder: A scalar representing whether the last batch should be dropped in case its size +// is smaller than desired. +// +// +func BatchDatasetV2(scope *Scope, input_dataset tf.Output, batch_size tf.Output, drop_remainder tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "BatchDatasetV2", + Input: []tf.Input{ + input_dataset, batch_size, drop_remainder, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // QuantizedConv2DAttr is an optional argument to QuantizedConv2D. type QuantizedConv2DAttr func(optionalAttr) @@ -17883,6 +18560,34 @@ func MutableHashTableOfTensorsV2(scope *Scope, key_dtype tf.DataType, value_dtyp return op.Output(0) } +// The gradient operator for the SparseSlice op. +// +// This op takes in the upstream gradient w.r.t. non-empty values of +// the sliced `SparseTensor`, and outputs the gradients w.r.t. +// the non-empty values of input `SparseTensor`. +// +// Arguments: +// backprop_val_grad: 1-D. The gradient with respect to +// the non-empty values of the sliced `SparseTensor`. +// input_indices: 2-D. The `indices` of the input `SparseTensor`. +// input_start: 1-D. tensor represents the start of the slice. +// output_indices: 2-D. The `indices` of the sliced `SparseTensor`. +// +// Returns 1-D. The gradient with respect to the non-empty values of input `SparseTensor`. +func SparseSliceGrad(scope *Scope, backprop_val_grad tf.Output, input_indices tf.Output, input_start tf.Output, output_indices tf.Output) (val_grad tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SparseSliceGrad", + Input: []tf.Input{ + backprop_val_grad, input_indices, input_start, output_indices, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes the gradient of the sigmoid of `x` wrt its input. // // Specifically, `grad = dy * y * (1 - y)`, where `y = sigmoid(x)`, and @@ -17927,6 +18632,31 @@ func HSVToRGB(scope *Scope, images tf.Output) (output tf.Output) { return op.Output(0) } +// Creates a dataset by applying optimizations to `input_dataset`. +// +// Creates a dataset by applying optimizations to `input_dataset`. +// +// Arguments: +// input_dataset: A variant tensor representing the input dataset. +// optimizations: A `tf.string` vector `tf.Tensor` identifying optimizations to use. +// +// +func OptimizeDataset(scope *Scope, input_dataset tf.Output, optimizations tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "OptimizeDataset", + Input: []tf.Input{ + input_dataset, optimizations, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Retrieves the tree ensemble resource stamp token, number of trees and growing statistics. // // Arguments: @@ -18101,6 +18831,26 @@ func AssignVariableOp(scope *Scope, resource tf.Output, value tf.Output) (o *tf. return scope.AddOperation(opspec) } +// Strip leading and trailing whitespaces from the Tensor. +// +// Arguments: +// input: A string `Tensor` of any shape. +// +// Returns A string `Tensor` of the same shape as the input. +func StringStrip(scope *Scope, input tf.Output) (output tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "StringStrip", + Input: []tf.Input{ + input, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Returns a tensor of ones with the same shape and type as x. // // Arguments: @@ -18155,6 +18905,10 @@ func SparseFillEmptyRowsGrad(scope *Scope, reverse_index_map tf.Output, grad_val // // if < 0, `scale * features` otherwise. // +// To be used together with +// `initializer = tf.variance_scaling_initializer(factor=1.0, mode='FAN_IN')`. +// For correct dropout, use `tf.contrib.nn.alpha_dropout`. +// // See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) func Selu(scope *Scope, features tf.Output) (activations tf.Output) { if scope.Err() != nil { @@ -18837,7 +19591,7 @@ func MatrixTriangularSolveLower(value bool) MatrixTriangularSolveAttr { // adjoint. // // @compatibility(numpy) -// Equivalent to np.linalg.triangular_solve +// Equivalent to scipy.linalg.solve_triangular // @end_compatibility // If not specified, defaults to false func MatrixTriangularSolveAdjoint(value bool) MatrixTriangularSolveAttr { @@ -19613,9 +20367,9 @@ func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyReso // ``` // // Arguments: -// start: First entry in the range. -// stop: Last entry in the range. -// num: Number of values to generate. +// start: 0-D tensor. First entry in the range. +// stop: 0-D tensor. Last entry in the range. +// num: 0-D tensor. Number of values to generate. // // Returns 1-D. The generated values. func LinSpace(scope *Scope, start tf.Output, stop tf.Output, num tf.Output) (output tf.Output) { @@ -20395,115 +21149,38 @@ func QuantizedMul(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x Input: []tf.Input{ x, y, min_x, max_x, min_y, max_y, }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// QuantizedAddAttr is an optional argument to QuantizedAdd. -type QuantizedAddAttr func(optionalAttr) - -// QuantizedAddToutput sets the optional Toutput attribute to value. -// If not specified, defaults to DT_QINT32 -func QuantizedAddToutput(value tf.DataType) QuantizedAddAttr { - return func(m optionalAttr) { - m["Toutput"] = value - } -} - -// Returns x + y element-wise, working on quantized buffers. -// -// Arguments: -// -// -// min_x: The float value that the lowest quantized `x` value represents. -// max_x: The float value that the highest quantized `x` value represents. -// min_y: The float value that the lowest quantized `y` value represents. -// max_y: The float value that the highest quantized `y` value represents. -// -// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. -// -// *NOTE*: `QuantizedAdd` supports limited forms of broadcasting. More about -// broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) -func QuantizedAdd(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x tf.Output, min_y tf.Output, max_y tf.Output, optional ...QuantizedAddAttr) (z tf.Output, min_z tf.Output, max_z tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "QuantizedAdd", - Input: []tf.Input{ - x, y, min_x, max_x, min_y, max_y, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0), op.Output(1), op.Output(2) -} - -// MfccAttr is an optional argument to Mfcc. -type MfccAttr func(optionalAttr) - -// MfccUpperFrequencyLimit sets the optional upper_frequency_limit attribute to value. -// -// value: The highest frequency to use when calculating the -// ceptstrum. -// If not specified, defaults to 4000 -func MfccUpperFrequencyLimit(value float32) MfccAttr { - return func(m optionalAttr) { - m["upper_frequency_limit"] = value + Attrs: attrs, } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2) } -// MfccLowerFrequencyLimit sets the optional lower_frequency_limit attribute to value. -// -// value: The lowest frequency to use when calculating the -// ceptstrum. -// If not specified, defaults to 20 -func MfccLowerFrequencyLimit(value float32) MfccAttr { - return func(m optionalAttr) { - m["lower_frequency_limit"] = value - } -} +// QuantizedAddAttr is an optional argument to QuantizedAdd. +type QuantizedAddAttr func(optionalAttr) -// MfccFilterbankChannelCount sets the optional filterbank_channel_count attribute to value. -// -// value: Resolution of the Mel bank used internally. -// If not specified, defaults to 40 -func MfccFilterbankChannelCount(value int64) MfccAttr { +// QuantizedAddToutput sets the optional Toutput attribute to value. +// If not specified, defaults to DT_QINT32 +func QuantizedAddToutput(value tf.DataType) QuantizedAddAttr { return func(m optionalAttr) { - m["filterbank_channel_count"] = value + m["Toutput"] = value } } -// MfccDctCoefficientCount sets the optional dct_coefficient_count attribute to value. +// Returns x + y element-wise, working on quantized buffers. // -// value: How many output channels to produce per time slice. -// If not specified, defaults to 13 -func MfccDctCoefficientCount(value int64) MfccAttr { - return func(m optionalAttr) { - m["dct_coefficient_count"] = value - } -} - -// Transforms a spectrogram into a form that's useful for speech recognition. +// Arguments: // -// Mel Frequency Cepstral Coefficients are a way of representing audio data that's -// been effective as an input feature for machine learning. They are created by -// taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the -// higher frequencies that are less significant to the human ear. They have a long -// history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrum -// is a good resource to learn more. // -// Arguments: -// spectrogram: Typically produced by the Spectrogram op, with magnitude_squared -// set to true. -// sample_rate: How many samples per second the source audio used. -func Mfcc(scope *Scope, spectrogram tf.Output, sample_rate tf.Output, optional ...MfccAttr) (output tf.Output) { +// min_x: The float value that the lowest quantized `x` value represents. +// max_x: The float value that the highest quantized `x` value represents. +// min_y: The float value that the lowest quantized `y` value represents. +// max_y: The float value that the highest quantized `y` value represents. +// +// Returns The float value that the lowest quantized output value represents.The float value that the highest quantized output value represents. +// +// *NOTE*: `QuantizedAdd` supports limited forms of broadcasting. More about +// broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) +func QuantizedAdd(scope *Scope, x tf.Output, y tf.Output, min_x tf.Output, max_x tf.Output, min_y tf.Output, max_y tf.Output, optional ...QuantizedAddAttr) (z tf.Output, min_z tf.Output, max_z tf.Output) { if scope.Err() != nil { return } @@ -20512,14 +21189,14 @@ func Mfcc(scope *Scope, spectrogram tf.Output, sample_rate tf.Output, optional . a(attrs) } opspec := tf.OpSpec{ - Type: "Mfcc", + Type: "QuantizedAdd", Input: []tf.Input{ - spectrogram, sample_rate, + x, y, min_x, max_x, min_y, max_y, }, Attrs: attrs, } op := scope.AddOperation(opspec) - return op.Output(0) + return op.Output(0), op.Output(1), op.Output(2) } // Given a quantized tensor described by (input, input_min, input_max), outputs a @@ -20873,6 +21550,37 @@ func LookupTableInsertV2(scope *Scope, table_handle tf.Output, keys tf.Output, v return scope.AddOperation(opspec) } +// Creates a dataset that batches and pads `batch_size` elements from the input. +// +// Arguments: +// +// batch_size: A scalar representing the number of elements to accumulate in a +// batch. +// padded_shapes: A list of int64 tensors representing the desired padded shapes +// of the corresponding output components. These shapes may be partially +// specified, using `-1` to indicate that a particular dimension should be +// padded to the maximum size of all batch elements. +// padding_values: A list of scalars containing the padding value to use for +// each of the outputs. +// drop_remainder: A scalar representing whether the last batch should be dropped in case its size +// is smaller than desired. +// +func PaddedBatchDatasetV2(scope *Scope, input_dataset tf.Output, batch_size tf.Output, padded_shapes []tf.Output, padding_values []tf.Output, drop_remainder tf.Output, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "PaddedBatchDatasetV2", + Input: []tf.Input{ + input_dataset, batch_size, tf.OutputList(padded_shapes), tf.OutputList(padding_values), drop_remainder, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Returns element-wise smallest integer in not less than x. func Ceil(scope *Scope, x tf.Output) (y tf.Output) { if scope.Err() != nil { @@ -21744,7 +22452,7 @@ func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr { // generated sequentially as '*tag*/image/0', '*tag*/image/1', etc. // // The `bad_color` argument is the color to use in the generated images for -// non-finite input values. It is a `unit8` 1-D tensor of length `channels`. +// non-finite input values. It is a `uint8` 1-D tensor of length `channels`. // Each element must be in the range `[0, 255]` (It represents the value of a // pixel in the output image). Non-finite values in the input tensor are // replaced by this tensor in the output image. The default value is the color @@ -22202,7 +22910,7 @@ func TensorListSetItem(scope *Scope, input_handle tf.Output, index tf.Output, it // Computes the matrix exponential of one or more square matrices: // -// exp(A) = \sum_{n=0}^\infty A^n/n! +// \\(exp(A) = \sum_{n=0}^\infty A^n/n!\\) // // The exponential is computed using a combination of the scaling and squaring // method and the Pade approximation. Details can be founds in: @@ -22582,6 +23290,28 @@ func MatrixSolve(scope *Scope, matrix tf.Output, rhs tf.Output, optional ...Matr return op.Output(0) } +// Returns a serialized GraphDef representing `input_dataset`. +// +// Returns a graph representation for `input_dataset`. +// +// Arguments: +// input_dataset: A variant tensor representing the dataset to return the graph representation for. +// +// Returns The graph representation of the dataset (as serialized GraphDef). +func DatasetToGraph(scope *Scope, input_dataset tf.Output) (graph tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DatasetToGraph", + Input: []tf.Input{ + input_dataset, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // SvdAttr is an optional argument to Svd. type SvdAttr func(optionalAttr) @@ -23605,10 +24335,10 @@ func ResourceApplyAdamUseNesterov(value bool) ResourceApplyAdamAttr { // Update '*var' according to the Adam algorithm. // -// lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t) -// m_t <- beta1 * m_{t-1} + (1 - beta1) * g_t -// v_t <- beta2 * v_{t-1} + (1 - beta2) * g_t * g_t -// variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon) +// $$lr_t := \text{learning_rate} * \sqrt{(1 - beta_2^t) / (1 - beta_1^t)}$$ +// $$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$ +// $$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$ +// $$variable := variable - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$ // // Arguments: // var_: Should be from a Variable(). @@ -24072,7 +24802,7 @@ func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistort // SampleDistortedBoundingBoxV2AreaRange sets the optional area_range attribute to value. // // value: The cropped area of the image must contain a fraction of the -// supplied image within in this range. +// supplied image within this range. // If not specified, defaults to func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr { return func(m optionalAttr) { @@ -24581,10 +25311,57 @@ func NonMaxSuppressionV2(scope *Scope, boxes tf.Output, scores tf.Output, max_ou return op.Output(0) } +// Greedily selects a subset of bounding boxes in descending order of score, +// +// pruning away boxes that have high intersection-over-union (IOU) overlap +// with previously selected boxes. Bounding boxes with score less than +// `score_threshold` are removed. Bounding boxes are supplied as +// [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any +// diagonal pair of box corners and the coordinates can be provided as normalized +// (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm +// is agnostic to where the origin is in the coordinate system and more +// generally is invariant to orthogonal transformations and translations +// of the coordinate system; thus translating or reflections of the coordinate +// system result in the same boxes being selected by the algorithm. +// The output of this operation is a set of integers indexing into the input +// collection of bounding boxes representing the selected boxes. The bounding +// box coordinates corresponding to the selected indices can then be obtained +// using the `tf.gather operation`. For example: +// selected_indices = tf.image.non_max_suppression_v2( +// boxes, scores, max_output_size, iou_threshold, score_threshold) +// selected_boxes = tf.gather(boxes, selected_indices) +// +// Arguments: +// boxes: A 2-D float tensor of shape `[num_boxes, 4]`. +// scores: A 1-D float tensor of shape `[num_boxes]` representing a single +// score corresponding to each box (each row of boxes). +// max_output_size: A scalar integer tensor representing the maximum number of +// boxes to be selected by non max suppression. +// iou_threshold: A 0-D float tensor representing the threshold for deciding whether +// boxes overlap too much with respect to IOU. +// score_threshold: A 0-D float tensor representing the threshold for deciding when to remove +// boxes based on score. +// +// Returns A 1-D integer tensor of shape `[M]` representing the selected +// indices from the boxes tensor, where `M <= max_output_size`. +func NonMaxSuppressionV3(scope *Scope, boxes tf.Output, scores tf.Output, max_output_size tf.Output, iou_threshold tf.Output, score_threshold tf.Output) (selected_indices tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "NonMaxSuppressionV3", + Input: []tf.Input{ + boxes, scores, max_output_size, iou_threshold, score_threshold, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes the matrix logarithm of one or more square matrices: // // -// log(exp(A)) = A +// \\(log(exp(A)) = A\\) // // This op is only defined for complex matrices. If A is positive-definite and // real, then casting to a complex matrix, taking the logarithm and casting back @@ -24621,6 +25398,31 @@ func MatrixLogarithm(scope *Scope, input tf.Output) (output tf.Output) { return op.Output(0) } +// This op is used as a placeholder in If branch functions. It doesn't provide a +// valid output when run, so must either be removed (e.g. replaced with a +// function input) or guaranteed not to be used (e.g. if mirroring an +// intermediate output needed for the gradient computation of the other branch). +// +// Arguments: +// dtype: The type of the output. +// shape: The purported shape of the output. This is only used for shape inference; +// the output will not necessarily have this shape. Can be a partial shape. +// +// Returns \"Fake\" output value. This should not be consumed by another op. +func FakeParam(scope *Scope, dtype tf.DataType, shape tf.Shape) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"dtype": dtype, "shape": shape} + opspec := tf.OpSpec{ + Type: "FakeParam", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // EncodeProtoAttr is an optional argument to EncodeProto. type EncodeProtoAttr func(optionalAttr) @@ -24962,6 +25764,23 @@ func ReaderResetV2(scope *Scope, reader_handle tf.Output) (o *tf.Operation) { return scope.AddOperation(opspec) } +// A dataset that splits the elements of its input into multiple elements. +func UnbatchDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "UnbatchDataset", + Input: []tf.Input{ + input_dataset, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // RpcAttr is an optional argument to Rpc. type RpcAttr func(optionalAttr) @@ -25214,6 +26033,36 @@ func ConcatenateDataset(scope *Scope, input_dataset tf.Output, another_dataset t return op.Output(0) } +// Debugging/model interpretability outputs for each example. +// +// It traverses all the trees and computes debug metrics for individual examples, +// such as getting split feature ids and logits after each split along the decision +// path used to compute directional feature contributions. +// +// Arguments: +// +// bucketized_features: A list of rank 1 Tensors containing bucket id for each +// feature. +// logits_dimension: scalar, dimension of the logits, to be used for constructing the protos in +// examples_debug_outputs_serialized. +// +// Returns Output rank 1 Tensor containing a proto serialized as a string for each example. +func BoostedTreesExampleDebugOutputs(scope *Scope, tree_ensemble_handle tf.Output, bucketized_features []tf.Output, logits_dimension int64) (examples_debug_outputs_serialized tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"logits_dimension": logits_dimension} + opspec := tf.OpSpec{ + Type: "BoostedTreesExampleDebugOutputs", + Input: []tf.Input{ + tree_ensemble_handle, tf.OutputList(bucketized_features), + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Adds a value to the current value of a variable. // // Any ReadVariableOp with a control dependency on this op is guaranteed to @@ -25802,57 +26651,6 @@ func CacheDataset(scope *Scope, input_dataset tf.Output, filename tf.Output, out return op.Output(0) } -// Computes the sum along sparse segments of a tensor. -// -// Like `SparseSegmentSum`, but allows missing ids in `segment_ids`. If an id is -// misisng, the `output` tensor at that position will be zeroed. -// -// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of -// segments. -// -// For example: -// -// ```python -// c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]) -// -// tf.sparse_segment_sum_with_num_segments( -// c, tf.constant([0, 1]), tf.constant([0, 0]), num_segments=3) -// # => [[0 0 0 0] -// # [0 0 0 0] -// # [0 0 0 0]] -// -// tf.sparse_segment_sum_with_num_segments(c, -// tf.constant([0, 1]), -// tf.constant([0, 2], -// num_segments=4)) -// # => [[ 1 2 3 4] -// # [ 0 0 0 0] -// # [-1 -2 -3 -4] -// # [ 0 0 0 0]] -// ``` -// -// Arguments: -// -// indices: A 1-D tensor. Has same rank as `segment_ids`. -// segment_ids: A 1-D tensor. Values should be sorted and can be repeated. -// num_segments: Should equal the number of distinct segment IDs. -// -// Returns Has same shape as data, except for dimension 0 which -// has size `num_segments`. -func SparseSegmentSumWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "SparseSegmentSumWithNumSegments", - Input: []tf.Input{ - data, indices, segment_ids, num_segments, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Creates a dataset that executes a SQL query and emits rows of the result set. // // Arguments: @@ -25964,6 +26762,26 @@ func TFRecordDataset(scope *Scope, filenames tf.Output, compression_type tf.Outp return op.Output(0) } +// A container for an iterator resource. +// +// Returns A handle to the iterator that can be passed to a "MakeIterator" or +// "IteratorGetNext" op. In contrast to Iterator, AnonymousIterator prevents +// resource sharing by name, and does not keep a reference to the resource +// container. +func AnonymousIterator(scope *Scope, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "AnonymousIterator", + + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // BatchToSpace for 4-D tensors of type T. // // This is a legacy version of the more general BatchToSpaceND. @@ -26467,6 +27285,95 @@ func Cross(scope *Scope, a tf.Output, b tf.Output) (product tf.Output) { return op.Output(0) } +// Writes the given dataset to the given file using the TFRecord format. +// +// Arguments: +// input_dataset: A variant tensor representing the dataset to write. +// filename: A scalar string tensor representing the filename to use. +// compression_type: A scalar string tensor containing either (i) the empty string (no +// compression), (ii) "ZLIB", or (iii) "GZIP". +// +// Returns the created operation. +func DatasetToTFRecord(scope *Scope, input_dataset tf.Output, filename tf.Output, compression_type tf.Output) (o *tf.Operation) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "DatasetToTFRecord", + Input: []tf.Input{ + input_dataset, filename, compression_type, + }, + } + return scope.AddOperation(opspec) +} + +// AvgPool3DAttr is an optional argument to AvgPool3D. +type AvgPool3DAttr func(optionalAttr) + +// AvgPool3DDataFormat sets the optional data_format attribute to value. +// +// value: The data format of the input and output data. With the +// default format "NDHWC", the data is stored in the order of: +// [batch, in_depth, in_height, in_width, in_channels]. +// Alternatively, the format could be "NCDHW", the data storage order is: +// [batch, in_channels, in_depth, in_height, in_width]. +// If not specified, defaults to "NDHWC" +func AvgPool3DDataFormat(value string) AvgPool3DAttr { + return func(m optionalAttr) { + m["data_format"] = value + } +} + +// Performs 3D average pooling on the input. +// +// Arguments: +// input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over. +// ksize: 1-D tensor of length 5. The size of the window for each dimension of +// the input tensor. Must have `ksize[0] = ksize[4] = 1`. +// strides: 1-D tensor of length 5. The stride of the sliding window for each +// dimension of `input`. Must have `strides[0] = strides[4] = 1`. +// padding: The type of padding algorithm to use. +// +// Returns The average pooled output tensor. +func AvgPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...AvgPool3DAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "AvgPool3D", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// A placeholder for input pipeline graph optimizations. +// +// A placeholder for input pipeline graph optimizations. +// +// Arguments: +// input_dataset: A variant tensor representing the input dataset. +func SinkDataset(scope *Scope, input_dataset tf.Output) (handle tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "SinkDataset", + Input: []tf.Input{ + input_dataset, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Performs a padding as a preprocess during a convolution. // // Similar to FusedResizeAndPadConv2d, this op allows for an optimized @@ -27022,6 +27929,26 @@ func QueueEnqueueV2(scope *Scope, handle tf.Output, components []tf.Output, opti return scope.AddOperation(opspec) } +// Computes the Bessel i0e function of `x` element-wise. +// +// Exponentially scaled modified Bessel function of order 0 defined as +// `bessel_i0e(x) = exp(-abs(x)) bessel_i0(x)`. +// +// This function is faster and numerically stabler than `bessel_i0(x)`. +func BesselI0e(scope *Scope, x tf.Output) (y tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BesselI0e", + Input: []tf.Input{ + x, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // QueueDequeueManyV2Attr is an optional argument to QueueDequeueManyV2. type QueueDequeueManyV2Attr func(optionalAttr) @@ -27132,6 +28059,29 @@ func EncodeBase64(scope *Scope, input tf.Output, optional ...EncodeBase64Attr) ( return op.Output(0) } +// A dataset that creates window datasets from the input dataset. +// +// Arguments: +// +// window_size: A scalar representing the number of elements to accumulate in a window. +// +// +func WindowDataset(scope *Scope, input_dataset tf.Output, window_size tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes} + opspec := tf.OpSpec{ + Type: "WindowDataset", + Input: []tf.Input{ + input_dataset, window_size, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Deprecated. Use TensorArrayCloseV3 // // DEPRECATED at GraphDef version 26: Use TensorArrayCloseV3 @@ -27504,30 +28454,30 @@ func CudnnRNNIsTraining(value bool) CudnnRNNAttr { // // rnn_mode: Indicates the type of the RNN model. // input_mode: Indicate whether there is a linear projection between the input and -// The actual computation before the first layer. 'skip_input' is only allowed +// the actual computation before the first layer. 'skip_input' is only allowed // when input_size == num_units; 'auto_select' implies 'skip_input' when // input_size == num_units; otherwise, it implies 'linear_input'. -// direction: Indicates whether a bidirectional model will be used. -// dir = (direction == bidirectional) ? 2 : 1 -// dropout: dropout probability. When set to 0., dropout is disabled. -// seed: the 1st part of a seed to initialize dropout. -// seed2: the 2nd part of a seed to initialize dropout. -// input: a 3-D tensor with the shape of [seq_length, batch_size, input_size]. -// input_h: a 3-D tensor with the shape of [num_layer * dir, batch_size, +// direction: Indicates whether a bidirectional model will be used. Should be +// "unidirectional" or "bidirectional". +// dropout: Dropout probability. When set to 0., dropout is disabled. +// seed: The 1st part of a seed to initialize dropout. +// seed2: The 2nd part of a seed to initialize dropout. +// input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. +// input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, // num_units]. // input_c: For LSTM, a 3-D tensor with the shape of // [num_layer * dir, batch, num_units]. For other models, it is ignored. -// params: a 1-D tensor that contains the weights and biases in an opaque layout. +// params: A 1-D tensor that contains the weights and biases in an opaque layout. // The size must be created through CudnnRNNParamsSize, and initialized // separately. Note that they might not be compatible across different // generations. So it is a good idea to save and restore -// output: a 3-D tensor with the shape of [seq_length, batch_size, +// output: A 3-D tensor with the shape of [seq_length, batch_size, // dir * num_units]. -// output_h: the same shape has input_h. -// output_c: the same shape as input_c for LSTM. An empty tensor for other models. +// output_h: The same shape has input_h. +// output_c: The same shape as input_c for LSTM. An empty tensor for other models. // is_training: Indicates whether this operation is used for inferenece or // training. -// reserve_space: an opaque tensor that can be used in backprop calculation. It +// reserve_space: An opaque tensor that can be used in backprop calculation. It // is only produced if is_training is false. func CudnnRNN(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, optional ...CudnnRNNAttr) (output tf.Output, output_h tf.Output, output_c tf.Output, reserve_space tf.Output) { if scope.Err() != nil { @@ -27548,6 +28498,37 @@ func CudnnRNN(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Outpu return op.Output(0), op.Output(1), op.Output(2), op.Output(3) } +// Creates a TensorArray for storing multiple gradients of values in the given handle. +// +// Similar to TensorArrayGradV3. However it creates an accumulator with an +// expanded shape compared to the input TensorArray whose gradient is being +// computed. This enables multiple gradients for the same TensorArray to be +// calculated using the same accumulator. +// +// Arguments: +// handle: The handle to the forward TensorArray. +// flow_in: A float scalar that enforces proper chaining of operations. +// shape_to_prepend: An int32 vector representing a shape. Elements in the gradient accumulator will +// have shape which is this shape_to_prepend value concatenated with shape of the +// elements in the TensorArray corresponding to the input handle. +// source: The gradient source string, used to decide which gradient TensorArray +// to return. +func TensorArrayGradWithShape(scope *Scope, handle tf.Output, flow_in tf.Output, shape_to_prepend tf.Output, source string) (grad_handle tf.Output, flow_out tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"source": source} + opspec := tf.OpSpec{ + Type: "TensorArrayGradWithShape", + Input: []tf.Input{ + handle, flow_in, shape_to_prepend, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1) +} + // Compare values of `input` to `threshold` and pack resulting bits into a `uint8`. // // Each comparison returns a boolean `true` (if `input_value > threshold`) @@ -27938,7 +28919,7 @@ func RandomShuffleQueueV2(scope *Scope, component_types []tf.DataType, optional // // For example, if an image is 100 x 200 pixels (height x width) and the bounding // box is `[0.1, 0.2, 0.5, 0.9]`, the upper-left and bottom-right coordinates of -// the bounding box will be `(40, 10)` to `(100, 50)` (in (x,y) coordinates). +// the bounding box will be `(40, 10)` to `(180, 50)` (in (x,y) coordinates). // // Parts of the bounding box may fall outside the image. // @@ -28279,7 +29260,7 @@ func BoostedTreesCreateEnsemble(scope *Scope, tree_ensemble_handle tf.Output, st // `input` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. // // `indices` must be integer tensor, containing indices into `input`. -// It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. +// It must be shape \\([d_0, ..., d_{Q-2}, K]\\) where `0 < K <= P`. // // The innermost dimension of `indices` (with length `K`) corresponds to // indices into elements (if `K = P`) or `(P-K)`-dimensional slices @@ -28287,9 +29268,7 @@ func BoostedTreesCreateEnsemble(scope *Scope, tree_ensemble_handle tf.Output, st // // `updates` is `Tensor` of rank `Q-1+P-K` with shape: // -// ``` -// [d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]]. -// ``` +// $$[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].$$ // // For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 // elements. In Python, that addition would look like this: @@ -29050,6 +30029,119 @@ func OrderedMapSize(scope *Scope, dtypes []tf.DataType, optional ...OrderedMapSi return op.Output(0) } +// CudnnRNNV2Attr is an optional argument to CudnnRNNV2. +type CudnnRNNV2Attr func(optionalAttr) + +// CudnnRNNV2RnnMode sets the optional rnn_mode attribute to value. +// If not specified, defaults to "lstm" +func CudnnRNNV2RnnMode(value string) CudnnRNNV2Attr { + return func(m optionalAttr) { + m["rnn_mode"] = value + } +} + +// CudnnRNNV2InputMode sets the optional input_mode attribute to value. +// If not specified, defaults to "linear_input" +func CudnnRNNV2InputMode(value string) CudnnRNNV2Attr { + return func(m optionalAttr) { + m["input_mode"] = value + } +} + +// CudnnRNNV2Direction sets the optional direction attribute to value. +// If not specified, defaults to "unidirectional" +func CudnnRNNV2Direction(value string) CudnnRNNV2Attr { + return func(m optionalAttr) { + m["direction"] = value + } +} + +// CudnnRNNV2Dropout sets the optional dropout attribute to value. +// If not specified, defaults to 0 +func CudnnRNNV2Dropout(value float32) CudnnRNNV2Attr { + return func(m optionalAttr) { + m["dropout"] = value + } +} + +// CudnnRNNV2Seed sets the optional seed attribute to value. +// If not specified, defaults to 0 +func CudnnRNNV2Seed(value int64) CudnnRNNV2Attr { + return func(m optionalAttr) { + m["seed"] = value + } +} + +// CudnnRNNV2Seed2 sets the optional seed2 attribute to value. +// If not specified, defaults to 0 +func CudnnRNNV2Seed2(value int64) CudnnRNNV2Attr { + return func(m optionalAttr) { + m["seed2"] = value + } +} + +// CudnnRNNV2IsTraining sets the optional is_training attribute to value. +// If not specified, defaults to true +func CudnnRNNV2IsTraining(value bool) CudnnRNNV2Attr { + return func(m optionalAttr) { + m["is_training"] = value + } +} + +// A RNN backed by cuDNN. +// +// Computes the RNN from the input and initial states, with respect to the params +// buffer. Produces one extra output "host_reserved" than CudnnRNN. +// +// rnn_mode: Indicates the type of the RNN model. +// input_mode: Indicates whether there is a linear projection between the input and +// the actual computation before the first layer. 'skip_input' is only allowed +// when input_size == num_units; 'auto_select' implies 'skip_input' when +// input_size == num_units; otherwise, it implies 'linear_input'. +// direction: Indicates whether a bidirectional model will be used. Should be +// "unidirectional" or "bidirectional". +// dropout: Dropout probability. When set to 0., dropout is disabled. +// seed: The 1st part of a seed to initialize dropout. +// seed2: The 2nd part of a seed to initialize dropout. +// input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. +// input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, +// num_units]. +// input_c: For LSTM, a 3-D tensor with the shape of +// [num_layer * dir, batch, num_units]. For other models, it is ignored. +// params: A 1-D tensor that contains the weights and biases in an opaque layout. +// The size must be created through CudnnRNNParamsSize, and initialized +// separately. Note that they might not be compatible across different +// generations. So it is a good idea to save and restore +// output: A 3-D tensor with the shape of [seq_length, batch_size, +// dir * num_units]. +// output_h: The same shape has input_h. +// output_c: The same shape as input_c for LSTM. An empty tensor for other models. +// is_training: Indicates whether this operation is used for inferenece or +// training. +// reserve_space: An opaque tensor that can be used in backprop calculation. It +// is only produced if is_training is true. +// host_reserved: An opaque tensor that can be used in backprop calculation. It is +// only produced if is_training is true. It is output on host memory rather than +// device memory. +func CudnnRNNV2(scope *Scope, input tf.Output, input_h tf.Output, input_c tf.Output, params tf.Output, optional ...CudnnRNNV2Attr) (output tf.Output, output_h tf.Output, output_c tf.Output, reserve_space tf.Output, host_reserved tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "CudnnRNNV2", + Input: []tf.Input{ + input, input_h, input_c, params, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4) +} + // ShapeNAttr is an optional argument to ShapeN. type ShapeNAttr func(optionalAttr) @@ -30668,45 +31760,3 @@ func DecodeWav(scope *Scope, contents tf.Output, optional ...DecodeWavAttr) (aud op := scope.AddOperation(opspec) return op.Output(0), op.Output(1) } - -// Concatenates a list of `N` tensors along the first dimension. -// -// The input tensors are all required to have size 1 in the first dimension. -// -// For example: -// -// ``` -// # 'x' is [[1, 4]] -// # 'y' is [[2, 5]] -// # 'z' is [[3, 6]] -// parallel_concat([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim. -// ``` -// -// The difference between concat and parallel_concat is that concat requires all -// of the inputs be computed before the operation will begin but doesn't require -// that the input shapes be known during graph construction. Parallel concat -// will copy pieces of the input into the output as they become available, in -// some situations this can provide a performance benefit. -// -// Arguments: -// values: Tensors to be concatenated. All must have size 1 in the first dimension -// and same shape. -// shape: the final shape of the result; should be equal to the shapes of any input -// but with the number of input values in the first dimension. -// -// Returns The concatenated tensor. -func ParallelConcat(scope *Scope, values []tf.Output, shape tf.Shape) (output tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"shape": shape} - opspec := tf.OpSpec{ - Type: "ParallelConcat", - Input: []tf.Input{ - tf.OutputList(values), - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} diff --git a/tensorflow/go/operation.go b/tensorflow/go/operation.go index 25ec71870315917351d68db6a16d25fe037d543b..d6a37e0a8633f936fda7ec9612c6c097c9029c31 100644 --- a/tensorflow/go/operation.go +++ b/tensorflow/go/operation.go @@ -45,6 +45,12 @@ func (op *Operation) NumOutputs() int { return int(C.TF_OperationNumOutputs(op.c)) } +// Device returns a specification of the device on which this operation +// will be executed, or the empty string if there is no such specification. +func (op *Operation) Device() string { + return C.GoString(C.TF_OperationDevice(op.c)) +} + // OutputListSize returns the size of the list of Outputs that is produced by a // named output of op. // diff --git a/tensorflow/go/operation_test.go b/tensorflow/go/operation_test.go index 06b65bdfb7eb814a2bead191374029cc0fdf025e..4af9e33ad0aea5d269d876f154f96cbc99243cad 100644 --- a/tensorflow/go/operation_test.go +++ b/tensorflow/go/operation_test.go @@ -228,6 +228,29 @@ func TestOperationConsumers(t *testing.T) { } } +func TestOperationDevice(t *testing.T) { + graph := NewGraph() + v, err := NewTensor(float32(1.0)) + if err != nil { + t.Fatal(err) + } + op, err := graph.AddOperation(OpSpec{ + Type: "Const", + Name: "Const", + Attrs: map[string]interface{}{ + "dtype": v.DataType(), + "value": v, + }, + Device: "/device:GPU:0", + }) + if err != nil { + t.Fatal(err) + } + if got, want := op.Device(), "/device:GPU:0"; got != want { + t.Errorf("Got %q, want %q", got, want) + } +} + func forceGC() { var mem runtime.MemStats runtime.ReadMemStats(&mem) diff --git a/tensorflow/java/maven/hadoop/pom.xml b/tensorflow/java/maven/hadoop/pom.xml index 0642be06fa148933902ab450c5cf2f771e268828..2c2c4106cb9ada6a4f3b8217792f8ad6fd248871 100644 --- a/tensorflow/java/maven/hadoop/pom.xml +++ b/tensorflow/java/maven/hadoop/pom.xml @@ -1,12 +1,30 @@ - - + 4.0.0 - TensorFlow TFRecord InputFormat/OutputFormat for Apache Hadoop + org.tensorflow hadoop jar + 1.10.0-rc0 + tensorflow-hadoop + https://www.tensorflow.org + TensorFlow TFRecord InputFormat/OutputFormat for Apache Hadoop + + + UTF-8 + 1.6 + 1.6 + 2.6.0 + 3.3.1 + 4.11 + + + + + Apache License Version 2.0 + http://www.apache.org/licenses/LICENSE-2.0.txt + + https://github.com/tensorflow/ecosystem.git @@ -14,11 +32,161 @@ scm:git:https://github.com/tensorflow/ecosystem.git - https://github.com/tensorflow/ecosystem/ - - org.tensorflow - parentpom - 1.9.0-rc0 - ../ - - \ No newline at end of file + + + + + org.apache.maven.plugins + maven-gpg-plugin + 1.5 + + + sign-artifacts + verify + + sign + + + + + + + + + org.apache.maven.plugins + maven-source-plugin + 2.2.1 + + + attach-sources + + jar-no-fork + + + + + + org.apache.maven.plugins + maven-javadoc-plugin + 2.9.1 + + + attach-javadocs + + jar + + + + + + + + + + org.tensorflow + proto + ${project.version} + + + org.apache.hadoop + hadoop-common + ${hadoop.version} + + + com.google.protobuf + protobuf-java + + + + + org.apache.hadoop + hadoop-mapreduce-client-core + ${hadoop.version} + + + com.google.protobuf + protobuf-java + + + + + com.google.protobuf + protobuf-java + ${protobuf.version} + + + junit + junit + ${junit.version} + test + + + org.apache.hadoop + hadoop-mapreduce-client-jobclient + ${hadoop.version} + test-jar + true + test + + + com.google.protobuf + protobuf-java + + + + + + + + + ossrh + + + + ossrh + https://oss.sonatype.org/content/repositories/snapshots + + + ossrh + https://oss.sonatype.org/service/local/staging/deploy/maven2/ + + + + + + org.apache.maven.plugins + maven-gpg-plugin + + + + + + bintray + + + + bintray + https://api.bintray.com/maven/google/tensorflow/tensorflow/;publish=0 + + + + + + org.apache.maven.plugins + maven-gpg-plugin + + + + + + + + + TensorFlowers + TensorFlow + http://www.tensorflow.org + + + diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml index a7fa9ea5cc78f9d83cfb105f09837e958c60d5b4..5d4e04ecd3b884dccbcad0bd7d89dc4b5dece593 100644 --- a/tensorflow/java/maven/libtensorflow/pom.xml +++ b/tensorflow/java/maven/libtensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0-rc1 + 1.10.0-rc0 ../ libtensorflow diff --git a/tensorflow/java/maven/libtensorflow_jni/pom.xml b/tensorflow/java/maven/libtensorflow_jni/pom.xml index 83aae29f1ea0f893c40597a1be6f77668d8206e9..e107904f7da861f3c499cec555bebd7671344dd9 100644 --- a/tensorflow/java/maven/libtensorflow_jni/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0-rc1 + 1.10.0-rc0 ../ libtensorflow_jni diff --git a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml index 50bd8ee5f9e6d268976540ca8180380447bc8f18..b3c525233f50f86244ae629b8e58b0eb9d7fe465 100644 --- a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0-rc1 + 1.10.0-rc0 ../ libtensorflow_jni_gpu diff --git a/tensorflow/java/maven/pom.xml b/tensorflow/java/maven/pom.xml index b4746794ea9e417bb0bb9253ca356976a48eb1e8..a2943a317239da50e27c28514e587c2eb3e9dd3f 100644 --- a/tensorflow/java/maven/pom.xml +++ b/tensorflow/java/maven/pom.xml @@ -6,7 +6,7 @@ 4.0.0 org.tensorflow parentpom - 1.9.0-rc1 + 1.10.0-rc0 pom https://www.tensorflow.org diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml index 618a2a124c77240b0a2b65f33577a6330929ae83..7080d81b7d2ee1969f01282a97d6000eb0f8e7b5 100644 --- a/tensorflow/java/maven/proto/pom.xml +++ b/tensorflow/java/maven/proto/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0-rc1 + 1.10.0-rc0 ../ proto diff --git a/tensorflow/java/maven/run_inside_container.sh b/tensorflow/java/maven/run_inside_container.sh index 2e771064e4a9a9ca4c50f5ecf8cae91cf8b5ce6c..2240d6b7b9957b480cf2053ecb65487fa64bbf08 100644 --- a/tensorflow/java/maven/run_inside_container.sh +++ b/tensorflow/java/maven/run_inside_container.sh @@ -203,7 +203,10 @@ download_tf_ecosystem() { cd "${ECOSYSTEM_DIR}" git clone "${TF_ECOSYSTEM_URL}" cd ecosystem - git checkout r${TF_VERSION} + # TF_VERSION is a semver string (..[-suffix]) + # but the branch is just (r.). + RELEASE_BRANCH=$(echo "${TF_VERSION}" | sed -e 's/\([0-9]\+\.[0-9]\+\)\.[0-9]\+.*/\1/') + git checkout r${RELEASE_BRANCH} # Copy the TensorFlow Hadoop source cp -r "${ECOSYSTEM_DIR}/ecosystem/hadoop/src" "${HADOOP_DIR}" diff --git a/tensorflow/java/maven/spark-connector/pom.xml b/tensorflow/java/maven/spark-connector/pom.xml index 19c752d08be1deec40042bc1aa8fd1159b2f2be9..003d09a0b718874a320cdf9157ad69d0a095332b 100644 --- a/tensorflow/java/maven/spark-connector/pom.xml +++ b/tensorflow/java/maven/spark-connector/pom.xml @@ -1,12 +1,23 @@ - - + + 4.0.0 - TensorFlow TFRecord connector for Apache Spark DataFrames - spark-connector + org.tensorflow + spark-connector_2.11 jar + 1.10.0-rc0 + spark-tensorflow-connector + https://www.tensorflow.org + TensorFlow TFRecord connector for Apache Spark DataFrames + + + + The Apache Software License, Version 2.0 + http://www.apache.org/licenses/LICENSE-2.0.txt + repo + + https://github.com/tensorflow/ecosystem.git @@ -14,11 +25,325 @@ scm:git:https://github.com/tensorflow/ecosystem.git - https://github.com/tensorflow/ecosystem/ - - org.tensorflow - parentpom - 1.9.0-rc0 - ../ - - \ No newline at end of file + + UTF-8 + 3.2.2 + 2.11 + 1.0 + 2.2.6 + 3.0 + 1.8 + 2.3.0 + 2.7.3 + 4.11 + + + + + + + true + net.alchim31.maven + scala-maven-plugin + ${scala.maven.version} + + + compile + + add-source + compile + + + + -Xms256m + -Xmx512m + + + -g:vars + -deprecation + -feature + -unchecked + -Xfatal-warnings + -language:implicitConversions + -language:existentials + + + + + test + + add-source + testCompile + + + + attach-javadocs + + doc-jar + + + + + incremental + true + ${scala.binary.version} + false + + + + true + org.scalatest + scalatest-maven-plugin + ${scalatest.maven.version} + + + scalaTest + test + + test + + + + + + + maven-shade-plugin + 3.1.0 + + + package + + shade + + + true + + + com.google.protobuf:protobuf-java + org.tensorflow:hadoop + org.tensorflow:proto + + + + + + com.google.protobuf:protobuf-java + + **/*.java + + + + + + com.google.protobuf + + org.tensorflow.spark.shaded.com.google.protobuf + + + + + + + + + + org.apache.maven.plugins + maven-gpg-plugin + 1.5 + + + sign-artifacts + verify + + sign + + + + + + + + + net.alchim31.maven + scala-maven-plugin + + + org.apache.maven.plugins + maven-shade-plugin + + + org.scalatest + scalatest-maven-plugin + + + org.apache.maven.plugins + maven-compiler-plugin + ${maven.compiler.version} + + ${java.version} + ${java.version} + + + + org.apache.maven.plugins + maven-source-plugin + 2.2.1 + + + attach-sources + + jar-no-fork + + + + + + org.apache.maven.plugins + maven-javadoc-plugin + 2.9.1 + + + attach-javadocs + + jar + + + + + + + + + + test + + true + + !NEVERSETME + + + + + + net.alchim31.maven + scala-maven-plugin + + + + + + + org.scalatest + scalatest_${scala.binary.version} + ${scala.test.version} + test + + + + + + org.scalatest + scalatest_${scala.binary.version} + test + + + + + + + ossrh + + + + ossrh + https://oss.sonatype.org/content/repositories/snapshots + + + ossrh + https://oss.sonatype.org/service/local/staging/deploy/maven2/ + + + + + + org.apache.maven.plugins + maven-gpg-plugin + + + + + + bintray + + + + bintray + https://api.bintray.com/maven/google/tensorflow/tensorflow/;publish=0 + + + + + + org.apache.maven.plugins + maven-gpg-plugin + + + + + + + + + TensorFlowers + TensorFlow + http://www.tensorflow.org + + + + + + org.tensorflow + hadoop + ${project.version} + + + org.apache.spark + spark-core_${scala.binary.version} + ${spark.version} + provided + + + org.apache.spark + spark-sql_${scala.binary.version} + ${spark.version} + provided + + + org.apache.spark + spark-mllib_${scala.binary.version} + ${spark.version} + provided + + + org.apache.hadoop + hadoop-yarn-api + ${yarn.api.version} + provided + + + org.apache.spark + spark-mllib_${scala.binary.version} + ${spark.version} + test-jar + test + + + junit + junit + ${junit.version} + test + + + diff --git a/tensorflow/java/maven/tensorflow/pom.xml b/tensorflow/java/maven/tensorflow/pom.xml index 157c4b8e82d6b8062ce8c9c98432cfe97a20d190..b9affbf6997d5526d4691c0244cf1c751ea7d1a7 100644 --- a/tensorflow/java/maven/tensorflow/pom.xml +++ b/tensorflow/java/maven/tensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0-rc1 + 1.10.0-rc0 ../ tensorflow diff --git a/tensorflow/java/src/gen/cc/java_defs.h b/tensorflow/java/src/gen/cc/java_defs.h index f5f54bf4d31af159624c668f1abb106f68944737..d9d6f8adc8ac9e58dbfe3609171803b55e76e42d 100644 --- a/tensorflow/java/src/gen/cc/java_defs.h +++ b/tensorflow/java/src/gen/cc/java_defs.h @@ -16,9 +16,9 @@ limitations under the License. #ifndef TENSORFLOW_JAVA_SRC_GEN_CC_JAVA_DEFS_H_ #define TENSORFLOW_JAVA_SRC_GEN_CC_JAVA_DEFS_H_ -#include #include #include +#include #include namespace tensorflow { diff --git a/tensorflow/java/src/gen/cc/op_generator.h b/tensorflow/java/src/gen/cc/op_generator.h index 759d800ecfb5bec10b7bf8454baf5fc4c389e990..05decd6b54944f18205cce4d2341d7009ce7d806 100644 --- a/tensorflow/java/src/gen/cc/op_generator.h +++ b/tensorflow/java/src/gen/cc/op_generator.h @@ -19,10 +19,10 @@ limitations under the License. #include #include -#include "tensorflow/core/framework/op_def.pb.h" #include "tensorflow/core/framework/api_def.pb.h" #include "tensorflow/core/framework/op_def.pb.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/platform/env.h" #include "tensorflow/java/src/gen/cc/op_specs.h" namespace tensorflow { diff --git a/tensorflow/java/src/gen/cc/op_specs.cc b/tensorflow/java/src/gen/cc/op_specs.cc index 63e99fbb04fd6ba34f2bbd2bc3fe7644a31ddf7f..941ab2699cb887375987f14200664b9bfaf6815a 100644 --- a/tensorflow/java/src/gen/cc/op_specs.cc +++ b/tensorflow/java/src/gen/cc/op_specs.cc @@ -14,9 +14,9 @@ limitations under the License. ==============================================================================*/ #include -#include #include #include +#include #include "re2/re2.h" #include "tensorflow/core/framework/op.h" @@ -50,7 +50,7 @@ class TypeResolver { // For example, if the argument's datatype is DT_STRING, this method will // return "java.lang.String", so the argument can become "Operand" // in the Ops API - Type TypeOf(const OpDef_ArgDef& arg_def, bool *iterable_out); + Type TypeOf(const OpDef_ArgDef& arg_def, bool* iterable_out); // Returns types of an input attribute // @@ -62,7 +62,7 @@ class TypeResolver { // , so the attribute can be used as a "Float" object // in the Ops API and casted to a "float" when passing through the JNI layer. std::pair TypesOf(const OpDef_AttrDef& attr_def, - bool *iterable_out); + bool* iterable_out); // Returns true if the type of this attribute has already been resolved bool IsAttributeVisited(const string& attr_name) { @@ -89,8 +89,7 @@ class TypeResolver { } }; -Type TypeResolver::TypeOf(const OpDef_ArgDef& arg_def, - bool* iterable_out) { +Type TypeResolver::TypeOf(const OpDef_ArgDef& arg_def, bool* iterable_out) { *iterable_out = false; if (!arg_def.number_attr().empty()) { // when number_attr is set, argument has to be a list of tensors @@ -154,13 +153,13 @@ Type TypeResolver::TypeOf(const OpDef_ArgDef& arg_def, } else { LOG(FATAL) << "Cannot resolve data type of argument \"" << arg_def.name() - << "\" in operation \"" << op_def_.name() << "\""; + << "\" in operation \"" << op_def_.name() << "\""; } return type; } std::pair TypeResolver::TypesOf(const OpDef_AttrDef& attr_def, - bool* iterable_out) { + bool* iterable_out) { std::pair types = MakeTypePair(Type::Wildcard()); *iterable_out = false; StringPiece attr_type = attr_def.type(); @@ -185,7 +184,7 @@ std::pair TypeResolver::TypesOf(const OpDef_AttrDef& attr_def, } else if (attr_type == "tensor") { types = MakeTypePair(Type::Class("Tensor", "org.tensorflow") - .add_parameter(Type::Wildcard())); + .add_parameter(Type::Wildcard())); } else if (attr_type == "type") { Type type = *iterable_out ? Type::Wildcard() : NextGeneric(); @@ -196,7 +195,7 @@ std::pair TypeResolver::TypesOf(const OpDef_AttrDef& attr_def, } else { LOG(FATAL) << "Cannot resolve data type for attribute \"" << attr_type - << "\" in operation \"" << op_def_.name() << "\""; + << "\" in operation \"" << op_def_.name() << "\""; } visited_attrs_.insert(std::make_pair(attr_def.name(), types.first)); return types; @@ -219,47 +218,43 @@ string SnakeToCamelCase(const string& str, bool upper = false) { return result; } -bool FindAndCut(re2::StringPiece* input, const RE2& expr, - re2::StringPiece* before_match, re2::StringPiece* ret_match = nullptr) { - re2::StringPiece match; - if (!expr.Match(*input, 0, input->size(), RE2::UNANCHORED, &match, 1)) { - return false; - } - before_match->set(input->data(), match.begin() - input->begin()); - input->remove_prefix(match.end() - before_match->begin()); - if (ret_match != nullptr) { - *ret_match = match; - } +bool FindAndCut(string* input, const RE2& expr, string* before_match, + string* ret_match = nullptr) { + string match; + if (!RE2::PartialMatch(*input, expr, &match)) return false; + *before_match = input->substr(0, input->find(match)); + *input = input->substr(before_match->size() + match.size()); + if (ret_match != nullptr) *ret_match = match; return true; } -string ParseDocumentation(re2::StringPiece input) { +string ParseDocumentation(const string& inp) { std::stringstream javadoc_text; // TODO(karllessard) This is a very minimalist utility method for converting // markdown syntax, as found in ops descriptions, to Javadoc/html tags. Check // for alternatives to increase the level of support for markups. std::vector markups_subexpr; - markups_subexpr.push_back("\n+\\*\\s+"); // lists - markups_subexpr.push_back("\n{2,}"); // paragraphs + markups_subexpr.push_back("\n+\\*\\s+"); // lists + markups_subexpr.push_back("\n{2,}"); // paragraphs markups_subexpr.push_back("`{3,}\\s*[^\\s\n]*\\s*\n"); // code blocks - markups_subexpr.push_back("`+"); // inlined code and code blocks + markups_subexpr.push_back("`+"); // inlined code and code blocks markups_subexpr.push_back("\\*{1,2}\\b"); // text emphasis - markups_subexpr.push_back("\\["); // hyperlinks - const RE2 markup_expr(str_util::Join(markups_subexpr, "|")); + markups_subexpr.push_back("\\["); // hyperlinks + const RE2 markup_expr("(" + str_util::Join(markups_subexpr, "|") + ")"); bool in_list = false; + string input = inp; while (true) { - re2::StringPiece text; - re2::StringPiece markup; + string text, markup; if (!FindAndCut(&input, markup_expr, &text, &markup)) { javadoc_text << input; break; // end of loop } javadoc_text << text; - if (markup.starts_with("\n")) { + if (str_util::StartsWith(markup, "\n")) { javadoc_text << "\n"; - if (markup.contains("*")) { + if (str_util::StrContains(markup, "*")) { // new list item javadoc_text << (in_list ? "\n" : "
    \n") << "
  • \n"; in_list = true; @@ -267,18 +262,18 @@ string ParseDocumentation(re2::StringPiece input) { // end of list javadoc_text << "
    • \n
\n"; in_list = false; - } else if (!input.starts_with("```")) { + } else if (!str_util::StartsWith(input, "```")) { // new paragraph (not required if a 
 block follows)
         javadoc_text << "
\n";
       }
-    } else if (markup.starts_with("```")) {
+    } else if (str_util::StartsWith(markup, "```")) {
       // code blocks
-      if (FindAndCut(&input, "```\\s*\n*", &text)) {
+      if (FindAndCut(&input, "(```\\s*\n*)", &text)) {
         javadoc_text << "
{@code\n" << text << "}
\n";
       } else {
         javadoc_text << markup;
       }
-    } else if (markup.starts_with("`")) {
+    } else if (str_util::StartsWith("(" + markup + ")", "`")) {
       // inlined code
       if (FindAndCut(&input, markup, &text)) {
         javadoc_text << "{@code " << text << "}";
@@ -287,26 +282,28 @@ string ParseDocumentation(re2::StringPiece input) {
       }
     } else if (markup == "**") {
       // text emphasis (strong)
-      if (FindAndCut(&input, "\\b\\*{2}", &text)) {
+      if (FindAndCut(&input, "(\\b\\*{2})", &text)) {
         javadoc_text << "" << ParseDocumentation(text) << "";
       } else {
         javadoc_text << markup;
       }
     } else if (markup == "*") {
       // text emphasis (normal)
-      if (FindAndCut(&input, "\\b\\*{1}", &text)) {
+      if (FindAndCut(&input, "(\\b\\*{1})", &text)) {
         javadoc_text << "" << ParseDocumentation(text) << "";
       } else {
         javadoc_text << markup;
       }
-    } else if (markup.starts_with("[")) {
+    } else if (str_util::StartsWith(markup, "[")) {
       // hyperlinks
       string label;
       string link;
-      if (RE2::Consume(&input, "([^\\[]+)\\]\\((http.+)\\)", &label, &link)) {
+      if (RE2::PartialMatch(input, "([^\\[]+)\\]\\((http.+)\\)", &label,
+                            &link) &&
+          str_util::StartsWith(input, label + link)) {
+        input = input.substr(label.size() + link.size());
         javadoc_text << ""
-            << ParseDocumentation(label)
-            << "";
+                     << ParseDocumentation(label) << "";
       } else {
         javadoc_text << markup;
       }
@@ -319,57 +316,56 @@ string ParseDocumentation(re2::StringPiece input) {
 }
 
 ArgumentSpec CreateInput(const OpDef_ArgDef& input_def,
-    const ApiDef::Arg& input_api_def, TypeResolver* type_resolver) {
+                         const ApiDef::Arg& input_api_def,
+                         TypeResolver* type_resolver) {
   bool iterable = false;
   Type type = type_resolver->TypeOf(input_def, &iterable);
-  Type var_type = Type::Interface("Operand", "org.tensorflow")
-    .add_parameter(type);
+  Type var_type =
+      Type::Interface("Operand", "org.tensorflow").add_parameter(type);
   if (iterable) {
     var_type = Type::IterableOf(var_type);
   }
-  return ArgumentSpec(input_api_def.name(),
+  return ArgumentSpec(
+      input_api_def.name(),
       Variable::Create(SnakeToCamelCase(input_api_def.rename_to()), var_type),
-      type,
-      ParseDocumentation(input_api_def.description()),
-      iterable);
+      type, ParseDocumentation(input_api_def.description()), iterable);
 }
 
 AttributeSpec CreateAttribute(const OpDef_AttrDef& attr_def,
-    const ApiDef::Attr& attr_api_def, TypeResolver* type_resolver) {
+                              const ApiDef::Attr& attr_api_def,
+                              TypeResolver* type_resolver) {
   bool iterable = false;
   std::pair types = type_resolver->TypesOf(attr_def, &iterable);
-  Type var_type = types.first.kind() == Type::GENERIC ?
-      Type::Class("Class").add_parameter(types.first) : types.first;
+  Type var_type = types.first.kind() == Type::GENERIC
+                      ? Type::Class("Class").add_parameter(types.first)
+                      : types.first;
   if (iterable) {
     var_type = Type::ListOf(var_type);
   }
-  return AttributeSpec(attr_api_def.name(),
+  return AttributeSpec(
+      attr_api_def.name(),
       Variable::Create(SnakeToCamelCase(attr_api_def.rename_to()), var_type),
-      types.first,
-      types.second,
-      ParseDocumentation(attr_api_def.description()),
-      iterable,
-      attr_api_def.has_default_value());
+      types.first, types.second, ParseDocumentation(attr_api_def.description()),
+      iterable, attr_api_def.has_default_value());
 }
 
 ArgumentSpec CreateOutput(const OpDef_ArgDef& output_def,
-    const ApiDef::Arg& output_api, TypeResolver* type_resolver) {
+                          const ApiDef::Arg& output_api,
+                          TypeResolver* type_resolver) {
   bool iterable = false;
   Type type = type_resolver->TypeOf(output_def, &iterable);
-  Type var_type = Type::Class("Output", "org.tensorflow")
-    .add_parameter(type);
+  Type var_type = Type::Class("Output", "org.tensorflow").add_parameter(type);
   if (iterable) {
     var_type = Type::ListOf(var_type);
   }
-  return ArgumentSpec(output_api.name(),
+  return ArgumentSpec(
+      output_api.name(),
       Variable::Create(SnakeToCamelCase(output_api.rename_to()), var_type),
-      type,
-      ParseDocumentation(output_api.description()),
-      iterable);
+      type, ParseDocumentation(output_api.description()), iterable);
 }
 
 EndpointSpec CreateEndpoint(const OpDef& op_def, const ApiDef& api_def,
-    const ApiDef_Endpoint& endpoint_def) {
+                            const ApiDef_Endpoint& endpoint_def) {
   std::vector name_tokens = str_util::Split(endpoint_def.name(), ".");
   string package;
   string name;
@@ -377,27 +373,25 @@ EndpointSpec CreateEndpoint(const OpDef& op_def, const ApiDef& api_def,
     package = name_tokens.at(0);
     name = name_tokens.at(1);
   } else {
-    package = kDefaultEndpointPackage;
+    package = "core";  // generate unclassified ops in the 'core' package
     name = name_tokens.at(0);
   }
-  return EndpointSpec(package,
-      name,
-      Javadoc::Create(ParseDocumentation(api_def.summary()))
-          .details(ParseDocumentation(api_def.description())));
+  return EndpointSpec(package, name,
+                      Javadoc::Create(ParseDocumentation(api_def.summary()))
+                          .details(ParseDocumentation(api_def.description())));
 }
 
 }  // namespace
 
 OpSpec OpSpec::Create(const OpDef& op_def, const ApiDef& api_def) {
-  OpSpec op(api_def.graph_op_name(),
-      api_def.visibility() == ApiDef::HIDDEN,
-      op_def.deprecation().explanation());
+  OpSpec op(api_def.graph_op_name(), api_def.visibility() == ApiDef::HIDDEN,
+            op_def.deprecation().explanation());
   TypeResolver type_resolver(op_def);
   for (const string& next_input_name : api_def.arg_order()) {
     for (int i = 0; i < op_def.input_arg().size(); ++i) {
       if (op_def.input_arg(i).name() == next_input_name) {
         op.inputs_.push_back(CreateInput(op_def.input_arg(i), api_def.in_arg(i),
-            &type_resolver));
+                                         &type_resolver));
         break;
       }
     }
@@ -406,8 +400,8 @@ OpSpec OpSpec::Create(const OpDef& op_def, const ApiDef& api_def) {
     // do not parse attributes already visited, they have probably been inferred
     // before as an input argument type
     if (!type_resolver.IsAttributeVisited(op_def.attr(i).name())) {
-      AttributeSpec attr = CreateAttribute(op_def.attr(i), api_def.attr(i),
-          &type_resolver);
+      AttributeSpec attr =
+          CreateAttribute(op_def.attr(i), api_def.attr(i), &type_resolver);
       // attributes with a default value are optional
       if (attr.has_default_value() && attr.type().kind() != Type::GENERIC) {
         op.optional_attributes_.push_back(attr);
@@ -417,8 +411,8 @@ OpSpec OpSpec::Create(const OpDef& op_def, const ApiDef& api_def) {
     }
   }
   for (int i = 0; i < op_def.output_arg().size(); ++i) {
-    op.outputs_.push_back(CreateOutput(op_def.output_arg(i), api_def.out_arg(i),
-        &type_resolver));
+    op.outputs_.push_back(
+        CreateOutput(op_def.output_arg(i), api_def.out_arg(i), &type_resolver));
   }
   for (const auto& endpoint_def : api_def.endpoint()) {
     op.endpoints_.push_back(CreateEndpoint(op_def, api_def, endpoint_def));
diff --git a/tensorflow/java/src/gen/cc/op_specs.h b/tensorflow/java/src/gen/cc/op_specs.h
index 3b53c730df23c6f81f968f09b9d145a8efa1030a..30ecb8ce53d15372606981639183d3ba0e4466a4 100644
--- a/tensorflow/java/src/gen/cc/op_specs.h
+++ b/tensorflow/java/src/gen/cc/op_specs.h
@@ -19,9 +19,9 @@ limitations under the License.
 #include 
 #include 
 
-#include "tensorflow/core/framework/op_def.pb.h"
 #include "tensorflow/core/framework/api_def.pb.h"
 #include "tensorflow/core/framework/attr_value.pb.h"
+#include "tensorflow/core/framework/op_def.pb.h"
 #include "tensorflow/java/src/gen/cc/java_defs.h"
 
 namespace tensorflow {
@@ -38,9 +38,8 @@ class EndpointSpec {
   // javadoc: the endpoint class documentation
   // TODO(annarev): hardcode depcreated to false until deprecated is possible
   EndpointSpec(const string& package, const string& name,
-      const Javadoc& javadoc)
-    : package_(package), name_(name), javadoc_(javadoc),
-      deprecated_(false) {}
+               const Javadoc& javadoc)
+      : package_(package), name_(name), javadoc_(javadoc), deprecated_(false) {}
 
   const string& package() const { return package_; }
   const string& name() const { return name_; }
@@ -63,10 +62,13 @@ class ArgumentSpec {
   // type: the tensor type of this argument
   // description: a description of this argument, in javadoc
   // iterable: true if this argument is a list
-  ArgumentSpec(const string& op_def_name, const Variable& var,
-      const Type& type, const string& description, bool iterable)
-    : op_def_name_(op_def_name), var_(var), type_(type),
-      description_(description), iterable_(iterable) {}
+  ArgumentSpec(const string& op_def_name, const Variable& var, const Type& type,
+               const string& description, bool iterable)
+      : op_def_name_(op_def_name),
+        var_(var),
+        type_(type),
+        description_(description),
+        iterable_(iterable) {}
 
   const string& op_def_name() const { return op_def_name_; }
   const Variable& var() const { return var_; }
@@ -94,11 +96,16 @@ class AttributeSpec {
   // iterable: true if this attribute is a list
   // has_default_value: true if this attribute has a default value if not set
   AttributeSpec(const string& op_def_name, const Variable& var,
-      const Type& type, const Type& jni_type, const string& description,
-      bool iterable, bool has_default_value)
-    : op_def_name_(op_def_name), var_(var), type_(type),
-      description_(description), iterable_(iterable),
-      jni_type_(jni_type), has_default_value_(has_default_value) {}
+                const Type& type, const Type& jni_type,
+                const string& description, bool iterable,
+                bool has_default_value)
+      : op_def_name_(op_def_name),
+        var_(var),
+        type_(type),
+        description_(description),
+        iterable_(iterable),
+        jni_type_(jni_type),
+        has_default_value_(has_default_value) {}
 
   const string& op_def_name() const { return op_def_name_; }
   const Variable& var() const { return var_; }
@@ -147,9 +154,10 @@ class OpSpec {
   // hidden: true if this op should not be visible through the Graph Ops API
   // deprecation_explanation: message to show if all endpoints are deprecated
   explicit OpSpec(const string& graph_op_name, bool hidden,
-      const string& deprecation_explanation)
-    : graph_op_name_(graph_op_name), hidden_(hidden),
-      deprecation_explanation_(deprecation_explanation) {}
+                  const string& deprecation_explanation)
+      : graph_op_name_(graph_op_name),
+        hidden_(hidden),
+        deprecation_explanation_(deprecation_explanation) {}
 
   const string graph_op_name_;
   const bool hidden_;
diff --git a/tensorflow/java/src/main/java/org/tensorflow/Input.java b/tensorflow/java/src/main/java/org/tensorflow/Input.java
new file mode 100644
index 0000000000000000000000000000000000000000..13bc463e7d6a991858332a353681b24fff417547
--- /dev/null
+++ b/tensorflow/java/src/main/java/org/tensorflow/Input.java
@@ -0,0 +1,48 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package org.tensorflow;
+
+/**
+ * Interface implemented by operands of a TensorFlow operation.
+ *
+ * 
Example usage:
+ *
+ * 
{@code
+ * // The "decodeJpeg" operation can be used as input to the "cast" operation
+ * Input decodeJpeg = ops.image().decodeJpeg(...);
+ * ops.math().cast(decodeJpeg, DataType.FLOAT);
+ *
+ * // The output "y" of the "unique" operation can be used as input to the "cast" operation
+ * Output y = ops.array().unique(...).y();
+ * ops.math().cast(y, DataType.FLOAT);
+ *
+ * // The "split" operation can be used as input list to the "concat" operation
+ * Iterable split = ops.array().split(...);
+ * ops.array().concat(0, split);
+ * }

+ */
+public interface Input {
+
+  /**
+   * Returns the symbolic handle of a tensor.
+   *
+   * 
Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is
+   * used to obtain a symbolic handle that represents the computation of the input.
+   *
+   * @see OperationBuilder#addInput(Output)
+   */
+  Output asOutput();
+}
diff --git a/tensorflow/java/src/main/java/org/tensorflow/SavedModelBundle.java b/tensorflow/java/src/main/java/org/tensorflow/SavedModelBundle.java
index c8b9126f033685c0320dfd2d8594061510bdd1e5..49594e6b47b9295d164a1823386b0981776e66f4 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/SavedModelBundle.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/SavedModelBundle.java
@@ -25,18 +25,86 @@ package org.tensorflow;
  * protocol buffer).
  */
 public class SavedModelBundle implements AutoCloseable {
+  /** Options for loading a SavedModel. */
+  public static final class Loader {
+    /** Load a SavedModelBundle with the configured options. */
+    public SavedModelBundle load() {
+      return SavedModelBundle.load(exportDir, tags, configProto, runOptions);
+    }
+
+    /**
+     * Sets options to use when executing model initialization operations.
+     *
+     * @param options Serialized RunOptions
+     *     protocol buffer.
+     */
+    public Loader withRunOptions(byte[] options) {
+      this.runOptions = options;
+      return this;
+    }
+
+    /**
+     * Set configuration of the Session object created when loading the model.
+     *
+     * @param configProto Serialized ConfigProto
+     *     protocol buffer.
+     */
+    public Loader withConfigProto(byte[] configProto) {
+      this.configProto = configProto;
+      return this;
+    }
+
+    /**
+     * Sets the set of tags that identify the specific graph in the saved model to load.
+     *
+     * @param tags the tags identifying the specific MetaGraphDef to load.
+     */
+    public Loader withTags(String... tags) {
+      this.tags = tags;
+      return this;
+    }
+
+    private Loader(String exportDir) {
+      this.exportDir = exportDir;
+    }
+
+    private String exportDir = null;
+    private String[] tags = null;
+    private byte[] configProto = null;
+    private byte[] runOptions = null;
+  }
 
   /**
    * Load a saved model from an export directory. The model that is being loaded should be created
    * using the Saved Model
    * API.
    *
+   * 
This method is a shorthand for:
+   *
+   * 
{@code
+   * SavedModelBundler.loader().withTags(tags).load();
+   * }

+   *
    * @param exportDir the directory path containing a saved model.
    * @param tags the tags identifying the specific metagraphdef to load.
    * @return a bundle containing the graph and associated session.
    */
   public static SavedModelBundle load(String exportDir, String... tags) {
-    return load(exportDir, tags, null);
+    return loader(exportDir).withTags(tags).load();
+  }
+
+  /**
+   * Load a saved model.
+   *
+   * 
Returns a Loader object that can set configuration options before actually
+   * loading the model,
+   *
+   * @param exportDir the directory path containing a saved model.
+   */
+  public static Loader loader(String exportDir) {
+    return new Loader(exportDir);
   }
 
   /**
@@ -95,7 +163,8 @@ public class SavedModelBundle implements AutoCloseable {
     return new SavedModelBundle(graph, session, metaGraphDef);
   }
 
-  private static native SavedModelBundle load(String exportDir, String[] tags, byte[] runOptions);
+  private static native SavedModelBundle load(
+      String exportDir, String[] tags, byte[] config, byte[] runOptions);
 
   static {
     TensorFlow.init();
diff --git a/tensorflow/java/src/main/native/saved_model_bundle_jni.cc b/tensorflow/java/src/main/native/saved_model_bundle_jni.cc
index de6382a79c484bac1c8c6746562199c4abdc52de..68999fb2da8b9bd6e2df1f76abfa4f0d86952a0c 100644
--- a/tensorflow/java/src/main/native/saved_model_bundle_jni.cc
+++ b/tensorflow/java/src/main/native/saved_model_bundle_jni.cc
@@ -22,12 +22,25 @@ limitations under the License.
 
 JNIEXPORT jobject JNICALL Java_org_tensorflow_SavedModelBundle_load(
     JNIEnv* env, jclass clazz, jstring export_dir, jobjectArray tags,
-    jbyteArray run_options) {
+    jbyteArray config, jbyteArray run_options) {
   TF_Status* status = TF_NewStatus();
   jobject bundle = nullptr;
 
   // allocate parameters for TF_LoadSessionFromSavedModel
   TF_SessionOptions* opts = TF_NewSessionOptions();
+  if (config != nullptr) {
+    size_t sz = env->GetArrayLength(config);
+    if (sz > 0) {
+      jbyte* config_data = env->GetByteArrayElements(config, nullptr);
+      TF_SetConfig(opts, static_cast(config_data), sz, status);
+      env->ReleaseByteArrayElements(config, config_data, JNI_ABORT);
+      if (!throwExceptionIfNotOK(env, status)) {
+        TF_DeleteSessionOptions(opts);
+        TF_DeleteStatus(status);
+        return nullptr;
+      }
+    }
+  }
   TF_Buffer* crun_options = nullptr;
   if (run_options != nullptr) {
     size_t sz = env->GetArrayLength(run_options);
diff --git a/tensorflow/java/src/main/native/saved_model_bundle_jni.h b/tensorflow/java/src/main/native/saved_model_bundle_jni.h
index 6cce6a81bd195842d4c2bb86fddbfbb21e0c8f5b..a4b05d0409797e8aa712d22f247fedc2ffbefdf1 100644
--- a/tensorflow/java/src/main/native/saved_model_bundle_jni.h
+++ b/tensorflow/java/src/main/native/saved_model_bundle_jni.h
@@ -26,10 +26,10 @@ extern "C" {
  * Class:     org_tensorflow_SavedModelBundle
  * Method:    load
  * Signature:
- * (Ljava/lang/String;[Ljava/lang/String;[B)Lorg/tensorflow/SavedModelBundle;
+ * (Ljava/lang/String;[Ljava/lang/String;[B;[B)Lorg/tensorflow/SavedModelBundle;
  */
 JNIEXPORT jobject JNICALL Java_org_tensorflow_SavedModelBundle_load(
-    JNIEnv *, jclass, jstring, jobjectArray, jbyteArray);
+    JNIEnv *, jclass, jstring, jobjectArray, jbyteArray, jbyteArray);
 
 #ifdef __cplusplus
 }  // extern "C"
diff --git a/tensorflow/java/src/main/native/session_jni.cc b/tensorflow/java/src/main/native/session_jni.cc
index cb54daf13795c24e11566845892da6b5c4896cf5..8b1152578555c0d9b5b4b383460116050c89c3d5 100644
--- a/tensorflow/java/src/main/native/session_jni.cc
+++ b/tensorflow/java/src/main/native/session_jni.cc
@@ -86,20 +86,22 @@ JNIEXPORT jlong JNICALL Java_org_tensorflow_Session_allocate2(
   TF_Graph* graph = reinterpret_cast(graph_handle);
   TF_Status* status = TF_NewStatus();
   TF_SessionOptions* opts = TF_NewSessionOptions();
-  const char* ctarget = nullptr;
   jbyte* cconfig = nullptr;
-  if (target != nullptr) {
-    ctarget = env->GetStringUTFChars(target, nullptr);
-  }
   if (config != nullptr) {
     cconfig = env->GetByteArrayElements(config, nullptr);
     TF_SetConfig(opts, cconfig,
                  static_cast(env->GetArrayLength(config)), status);
     if (!throwExceptionIfNotOK(env, status)) {
       env->ReleaseByteArrayElements(config, cconfig, JNI_ABORT);
+      TF_DeleteSessionOptions(opts);
+      TF_DeleteStatus(status);
       return 0;
     }
   }
+  const char* ctarget = nullptr;
+  if (target != nullptr) {
+    ctarget = env->GetStringUTFChars(target, nullptr);
+  }
   TF_Session* session = TF_NewSession(graph, opts, status);
   if (config != nullptr) {
     env->ReleaseByteArrayElements(config, cconfig, JNI_ABORT);
diff --git a/tensorflow/java/src/test/java/org/tensorflow/SavedModelBundleTest.java b/tensorflow/java/src/test/java/org/tensorflow/SavedModelBundleTest.java
index 7922f3329c7d7276edd139d6e3cc741c9c01cf2a..7d936867a785483442203098166664daf7a77b49 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/SavedModelBundleTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/SavedModelBundleTest.java
@@ -47,7 +47,61 @@ public class SavedModelBundleTest {
       fail("not expected");
     } catch (org.tensorflow.TensorFlowException e) {
       // expected exception
-      assertTrue(e.getMessage().contains("SavedModel not found"));
+      assertTrue(e.getMessage().contains("Could not find SavedModel"));
     }
   }
+
+  @Test
+  public void loader() {
+    try (SavedModelBundle bundle = SavedModelBundle.loader(SAVED_MODEL_PATH)
+        .withTags("serve")
+        .withConfigProto(sillyConfigProto())
+        .withRunOptions(sillyRunOptions())
+        .load()) {
+      assertNotNull(bundle.session());
+      assertNotNull(bundle.graph());
+      assertNotNull(bundle.metaGraphDef());
+    }
+  }
+
+  private static byte[] sillyRunOptions() {
+    // Ideally this would use the generated Java sources for protocol buffers
+    // and end up with something like the snippet below. However, generating
+    // the Java files for the .proto files in tensorflow/core:protos_all is
+    // a bit cumbersome in bazel until the proto_library rule is setup.
+    //
+    // See https://github.com/bazelbuild/bazel/issues/52#issuecomment-194341866
+    // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251515362
+    // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251692558
+    //
+    // For this test, for now, the use of specific bytes suffices.
+    return new byte[] {0x08, 0x03};
+    /*
+    return org.tensorflow.framework.RunOptions.newBuilder()
+        .setTraceLevel(RunOptions.TraceLevel.FULL_TRACE)
+        .build()
+        .toByteArray();
+    */
+  }
+
+  public static byte[] sillyConfigProto() {
+    // Ideally this would use the generated Java sources for protocol buffers
+    // and end up with something like the snippet below. However, generating
+    // the Java files for the .proto files in tensorflow/core:protos_all is
+    // a bit cumbersome in bazel until the proto_library rule is setup.
+    //
+    // See https://github.com/bazelbuild/bazel/issues/52#issuecomment-194341866
+    // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251515362
+    // https://github.com/bazelbuild/rules_go/pull/121#issuecomment-251692558
+    //
+    // For this test, for now, the use of specific bytes suffices.
+    return new byte[] {0x10, 0x01, 0x28, 0x01};
+    /*
+    return org.tensorflow.framework.ConfigProto.newBuilder()
+        .setInterOpParallelismThreads(1)
+        .setIntraOpParallelismThreads(1)
+        .build()
+        .toByteArray();
+     */
+  }
 }
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index ebfcfff4a5263ec8af31b461d274a8a6f9b6ec34..d35731d3cd49611b10dde881fa126f0f6fd674e2 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -73,7 +73,7 @@ py_library(
     visibility = [
         "//tensorflow:__pkg__",
         "//tensorflow/python/tools:__pkg__",
-        "//tensorflow/tools/api/generator:__pkg__",
+        "//tensorflow/python/tools/api/generator:__pkg__",
     ],
     deps = [
         ":array_ops",
@@ -96,6 +96,7 @@ py_library(
         ":image_ops",
         ":initializers_ns",
         ":io_ops",
+        ":kernels",
         ":layers",
         ":lib",
         ":list_ops",
@@ -705,7 +706,9 @@ py_library(
         "framework/error_interpolation.py",
     ],
     srcs_version = "PY2AND3",
-    deps = [],
+    deps = [
+        ":util",
+    ],
 )
 
 py_library(
@@ -743,8 +746,8 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":framework",
+        ":framework_ops",
         ":function",
-        ":op_def_registry",
         ":tensor_shape",
         ":versions",
         "//tensorflow/core:protos_all_py",
@@ -760,8 +763,10 @@ py_test(
     deps = [
         ":array_ops",
         ":client_testlib",
+        ":constant_op",
         ":dtypes",
         ":framework_ops",
+        ":function",
         ":function_def_to_graph",
         ":graph_to_function_def",
         ":math_ops",
@@ -785,6 +790,19 @@ py_library(
     ],
 )
 
+py_library(
+    name = "kernels",
+    srcs = [
+        "framework/kernels.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":pywrap_tensorflow",
+        ":util",
+        "//tensorflow/core:protos_all_py",
+    ],
+)
+
 py_library(
     name = "op_def_library",
     srcs = ["framework/op_def_library.py"],
@@ -822,6 +840,7 @@ py_library(
         ":platform",
         ":registry",
         ":tensor_shape",
+        ":traceable_stack",
         ":util",
         ":versions",
         "//tensorflow/core:protos_all_py",
@@ -887,6 +906,17 @@ py_library(
     ],
 )
 
+# This target is maintained separately from :util to provide separate visibility
+# for legacy users who were granted visibility when the functions were private
+# members of ops.Graph.
+py_library(
+    name = "tf_stack",
+    srcs = ["util/tf_stack.py"],
+    srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
+    deps = [],
+)
+
 py_library(
     name = "tensor_shape",
     srcs = ["framework/tensor_shape.py"],
@@ -921,6 +951,16 @@ py_library(
     ],
 )
 
+py_library(
+    name = "traceable_stack",
+    srcs = ["framework/traceable_stack.py"],
+    srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
+    deps = [
+        ":util",
+    ],
+)
+
 py_library(
     name = "versions",
     srcs = ["framework/versions.py"],
@@ -1018,7 +1058,9 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":client_testlib",
+        ":constant_op",
         ":error_interpolation",
+        ":traceable_stack",
     ],
 )
 
@@ -1207,6 +1249,21 @@ py_test(
     ],
 )
 
+py_test(
+    name = "framework_traceable_stack_test",
+    size = "small",
+    srcs = ["framework/traceable_stack_test.py"],
+    main = "framework/traceable_stack_test.py",
+    srcs_version = "PY2AND3",
+    deps = [
+        ":framework_test_lib",
+        ":platform_test",
+        ":test_ops",
+        ":traceable_stack",
+        ":util",
+    ],
+)
+
 tf_gen_op_wrapper_py(
     name = "test_ops",
     out = "framework/test_ops.py",
@@ -1439,6 +1496,20 @@ py_test(
     ],
 )
 
+py_test(
+    name = "framework_kernels_test",
+    size = "small",
+    srcs = ["framework/kernels_test.py"],
+    main = "framework/kernels_test.py",
+    srcs_version = "PY2AND3",
+    deps = [
+        ":framework_test_lib",
+        ":kernels",
+        ":platform_test",
+        ":test_ops",
+    ],
+)
+
 tf_gen_op_wrapper_private_py(
     name = "array_ops_gen",
     visibility = [
@@ -2096,8 +2167,8 @@ py_library(
         ":linalg_ops_gen",
         ":linalg_ops_impl",
         ":math_ops",
-        ":nn_ops",
         ":random_ops",
+        ":util",
         "//third_party/py/numpy",
     ],
 )
@@ -3005,6 +3076,20 @@ cuda_py_test(
     shard_count = 5,
 )
 
+cuda_py_test(
+    name = "init_ops_test",
+    size = "small",
+    srcs = ["ops/init_ops_test.py"],
+    additional_deps = [
+        ":client_testlib",
+        ":init_ops",
+        ":framework_ops",
+        ":resource_variable_ops",
+        "//third_party/py/numpy",
+        "//tensorflow/python/eager:context",
+    ],
+)
+
 cuda_py_test(
     name = "math_grad_test",
     size = "small",
@@ -3297,6 +3382,9 @@ py_library(
         ],
     ),
     srcs_version = "PY2AND3",
+    visibility = visibility + [
+        "//tensorflow:__pkg__",
+    ],
     deps = [
         "//third_party/py/numpy",
         "@org_python_pypi_backports_weakref",
@@ -3319,6 +3407,7 @@ py_test(
         ":math_ops",
         ":util",
         "//third_party/py/numpy",
+        "@absl_py//absl/testing:parameterized",
     ],
 )
 
@@ -3569,6 +3658,7 @@ tf_cuda_library(
         "//tensorflow/core:graph",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:session_ref",
         "//third_party/py/numpy:headers",
         "//third_party/python_runtime:headers",
     ],
@@ -4079,6 +4169,7 @@ cuda_py_test(
         ":math_ops",
         "//tensorflow/core:protos_all_py",
     ],
+    tags = ["no_windows_gpu"],
 )
 
 py_test(
diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py
index e037925961f2bfc8b8906fa81c2d7908ea590a62..f8e20e1b8934782c4d65bd75ec8ab53c86723851 100644
--- a/tensorflow/python/client/session.py
+++ b/tensorflow/python/client/session.py
@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
 import functools
 import re
 import threading
@@ -243,7 +244,7 @@ class _FetchMapper(object):
     elif isinstance(fetch, (list, tuple)):
       # NOTE(touts): This is also the code path for namedtuples.
       return _ListFetchMapper(fetch)
-    elif isinstance(fetch, dict):
+    elif isinstance(fetch, collections.Mapping):
       return _DictFetchMapper(fetch)
     else:
       # Look for a handler in the registered expansions.
@@ -540,10 +541,11 @@ class _DeviceAttributes(object):
         (in bytes).
   """
 
-  def __init__(self, name, device_type, memory_limit_bytes):
+  def __init__(self, name, device_type, memory_limit_bytes, incarnation):
     self._name = device.canonical_name(name)
     self._device_type = device_type
     self._memory_limit_bytes = memory_limit_bytes
+    self._incarnation = incarnation
 
   @property
   def name(self):
@@ -557,11 +559,16 @@ class _DeviceAttributes(object):
   def memory_limit_bytes(self):
     return self._memory_limit_bytes
 
+  @property
+  def incarnation(self):
+    return self._incarnation
+
   def __repr__(self):
-    return '_DeviceAttributes(%s, %s, %d)' % (
+    return '_DeviceAttributes(%s, %s, %d, %d)' % (
         self.name,
         self.device_type,
         self.memory_limit_bytes,
+        self.incarnation,
     )
 
 
@@ -623,7 +630,7 @@ class BaseSession(SessionInterface):
     opts = tf_session.TF_NewSessionOptions(target=self._target, config=config)
     try:
       # pylint: disable=protected-access
-      self._session = tf_session.TF_NewSession(self._graph._c_graph, opts)
+      self._session = tf_session.TF_NewSessionRef(self._graph._c_graph, opts)
       # pylint: enable=protected-access
     finally:
       tf_session.TF_DeleteSessionOptions(opts)
@@ -658,7 +665,9 @@ class BaseSession(SessionInterface):
       name = tf_session.TF_DeviceListName(raw_device_list, i)
       device_type = tf_session.TF_DeviceListType(raw_device_list, i)
       memory = tf_session.TF_DeviceListMemoryBytes(raw_device_list, i)
-      device_list.append(_DeviceAttributes(name, device_type, memory))
+      incarnation = tf_session.TF_DeviceListIncarnation(raw_device_list, i)
+      device_list.append(
+          _DeviceAttributes(name, device_type, memory, incarnation))
     tf_session.TF_DeleteDeviceList(raw_device_list)
     return device_list
 
@@ -1226,8 +1235,12 @@ class BaseSession(SessionInterface):
 
       return _fetch_handler_run
 
-  # Captures the name of a node in an error status.
-  _NODEDEF_NAME_RE = re.compile(r'\[\[Node: ([^ ]*?) =')
+  # Captures the name of a node in an error status. The regex below matches
+  # both the old and the new formats:
+  # Old format: [[Node:  = ...]]
+  # New format: [[{{node }} = ...]]
+  _NODEDEF_NAME_RE = re.compile(
+      r'\[\[(Node: )?(\{\{node )?([^\} ]*)(\}\})?\s*=')
 
   def _do_run(self, handle, target_list, fetch_list, feed_dict, options,
               run_metadata):
@@ -1282,7 +1295,7 @@ class BaseSession(SessionInterface):
       node_def = None
       op = None
       if m is not None:
-        node_name = m.group(1)
+        node_name = m.group(3)
         try:
           op = self._graph.get_operation_by_name(node_name)
           node_def = op.node_def
diff --git a/tensorflow/python/client/session_list_devices_test.py b/tensorflow/python/client/session_list_devices_test.py
index c5d82c213ac890ac4c968eba506695c3a2ce93c4..dd381c689fde31531668d83441b5ee92bd1ab9ec 100644
--- a/tensorflow/python/client/session_list_devices_test.py
+++ b/tensorflow/python/client/session_list_devices_test.py
@@ -37,6 +37,8 @@ class SessionListDevicesTest(test_util.TensorFlowTestCase):
       devices = sess.list_devices()
       self.assertTrue('/job:localhost/replica:0/task:0/device:CPU:0' in set(
           [d.name for d in devices]), devices)
+      # All valid device incarnations must be non-zero.
+      self.assertTrue(all(d.incarnation != 0 for d in devices))
 
   def testInvalidDeviceNumber(self):
     opts = tf_session.TF_NewSessionOptions()
@@ -54,6 +56,8 @@ class SessionListDevicesTest(test_util.TensorFlowTestCase):
       devices = sess.list_devices()
       self.assertTrue('/job:local/replica:0/task:0/device:CPU:0' in set(
           [d.name for d in devices]), devices)
+      # All valid device incarnations must be non-zero.
+      self.assertTrue(all(d.incarnation != 0 for d in devices))
 
   def testListDevicesClusterSpecPropagation(self):
     server1 = server_lib.Server.create_local_server()
@@ -67,11 +71,13 @@ class SessionListDevicesTest(test_util.TensorFlowTestCase):
     config = config_pb2.ConfigProto(cluster_def=cluster_def)
     with session.Session(server1.target, config=config) as sess:
       devices = sess.list_devices()
-      device_names = set([d.name for d in devices])
+      device_names = set(d.name for d in devices)
       self.assertTrue(
           '/job:worker/replica:0/task:0/device:CPU:0' in device_names)
       self.assertTrue(
           '/job:worker/replica:0/task:1/device:CPU:0' in device_names)
+      # All valid device incarnations must be non-zero.
+      self.assertTrue(all(d.incarnation != 0 for d in devices))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py
index b72e029d1ccb688f5992f6cc8695969be5e5e2e3..052be683856beb41ab572e808c260817b05ef5ae 100644
--- a/tensorflow/python/client/session_test.py
+++ b/tensorflow/python/client/session_test.py
@@ -35,6 +35,7 @@ from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.client import session
 from tensorflow.python.framework import common_shapes
 from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import device as framework_device_lib
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import function
@@ -104,18 +105,20 @@ class SessionTest(test_util.TensorFlowTestCase):
           copy_val)
 
   def testManyCPUs(self):
-    # TODO(keveman): Implement ListDevices and test for the number of
-    # devices returned by ListDevices.
     with session.Session(
         config=config_pb2.ConfigProto(device_count={
-            'CPU': 2
-        })):
+            'CPU': 2, 'GPU': 0
+        })) as sess:
       inp = constant_op.constant(10.0, name='W1')
       self.assertAllEqual(inp.eval(), 10.0)
 
+      devices = sess.list_devices()
+      self.assertEqual(2, len(devices))
+      for device in devices:
+        self.assertEqual('CPU', framework_device_lib.DeviceSpec.from_string(
+            device.name).device_type)
+
   def testPerSessionThreads(self):
-    # TODO(keveman): Implement ListDevices and test for the number of
-    # devices returned by ListDevices.
     with session.Session(
         config=config_pb2.ConfigProto(use_per_session_threads=True)):
       inp = constant_op.constant(10.0, name='W1')
@@ -1868,19 +1871,21 @@ class SessionTest(test_util.TensorFlowTestCase):
 
   def testDeviceAttributes(self):
     attrs = session._DeviceAttributes(
-        '/job:worker/replica:0/task:3/device:CPU:2', 'TYPE', 1337)
+        '/job:worker/replica:0/task:3/device:CPU:2', 'TYPE', 1337, 1000000)
     self.assertEqual(1337, attrs.memory_limit_bytes)
     self.assertEqual('/job:worker/replica:0/task:3/device:CPU:2', attrs.name)
     self.assertEqual('TYPE', attrs.device_type)
+    self.assertEqual(1000000, attrs.incarnation)
     str_repr = '%s' % attrs
     self.assertTrue(str_repr.startswith('_DeviceAttributes'), str_repr)
 
   def testDeviceAttributesCanonicalization(self):
     attrs = session._DeviceAttributes('/job:worker/replica:0/task:3/cpu:1',
-                                      'TYPE', 1337)
+                                      'TYPE', 1337, 1000000)
     self.assertEqual(1337, attrs.memory_limit_bytes)
     self.assertEqual('/job:worker/replica:0/task:3/device:CPU:1', attrs.name)
     self.assertEqual('TYPE', attrs.device_type)
+    self.assertEqual(1000000, attrs.incarnation)
     str_repr = '%s' % attrs
     self.assertTrue(str_repr.startswith('_DeviceAttributes'), str_repr)
 
diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i
index 985cb904360ac293461936bf67fb1b1de2c77b4a..39a2922ac0e54367f454c36921a029a9a7d7e82e 100644
--- a/tensorflow/python/client/tf_session.i
+++ b/tensorflow/python/client/tf_session.i
@@ -138,6 +138,11 @@ tensorflow::ImportNumpy();
   $result = PyLong_FromLongLong($1);
 }
 
+// Convert TF_DeviceListIncarnation uint64_t output to Python integer
+%typemap(out) uint64_t {
+  $result = PyLong_FromUnsignedLongLong($1);
+}
+
 // We use TF_OperationGetControlInputs_wrapper instead of
 // TF_OperationGetControlInputs
 %ignore TF_OperationGetControlInputs;
@@ -772,6 +777,7 @@ def TF_Reset(target, containers=None, config=None):
   $1 = &types_local;
 }
 
+%unignore TF_NewSessionRef;
 %unignore SetRequireShapeInferenceFns;
 %unignore TF_TryEvaluateConstant_wrapper;
 %noexception TF_TryEvaluateConstant_wrapper;
diff --git a/tensorflow/python/client/tf_session_helper.cc b/tensorflow/python/client/tf_session_helper.cc
index b6481e7e29e4057f08e1c78b310bf5581afc5411..bcd4af291282bbefda3db0309bb9f0a913f186ce 100644
--- a/tensorflow/python/client/tf_session_helper.cc
+++ b/tensorflow/python/client/tf_session_helper.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "tensorflow/c/c_api.h"
 #include "tensorflow/c/c_api_internal.h"
 #include "tensorflow/c/tf_status_helper.h"
+#include "tensorflow/core/common_runtime/session_ref.h"
 #include "tensorflow/core/framework/allocator.h"
 #include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/attr_value_util.h"
@@ -42,6 +43,19 @@ static const char* kFeedDictErrorMsg =
     "feed_dict must be a dictionary mapping strings to NumPy arrays.";
 }  // end namespace
 
+TF_Session* TF_NewSessionRef(TF_Graph* graph, const TF_SessionOptions* opts,
+                             TF_Status* status) {
+  TF_Session* tf_session = TF_NewSession(graph, opts, status);
+  if (tf_session == nullptr) {
+    return nullptr;
+  }
+
+  Session* session = reinterpret_cast(tf_session->session);
+  SessionRef* session_ref = new SessionRef(session);
+  tf_session->session = session_ref;
+  return tf_session;
+}
+
 void TF_Run_wrapper_helper(TF_DeprecatedSession* session, const char* handle,
                            const TF_Buffer* run_options, PyObject* feed_dict,
                            const NameVector& output_names,
diff --git a/tensorflow/python/client/tf_session_helper.h b/tensorflow/python/client/tf_session_helper.h
index cfd27c2bee990ab4e2829652a532761e674ed8e0..dab7e71aac5a7f4cbf9f8825ad6dd5d3f556bd43 100644
--- a/tensorflow/python/client/tf_session_helper.h
+++ b/tensorflow/python/client/tf_session_helper.h
@@ -40,6 +40,9 @@ typedef tensorflow::gtl::InlinedVector PyObjectVector;
 // A TF_TensorVector is a vector of borrowed pointers to TF_Tensors.
 typedef gtl::InlinedVector TF_TensorVector;
 
+TF_Session* TF_NewSessionRef(TF_Graph* graph, const TF_SessionOptions* opts,
+                             TF_Status* status);
+
 // Run the graph associated with the session starting with the
 // supplied inputs[].  Regardless of success or failure, inputs[] are
 // stolen by the implementation (i.e. the implementation will
diff --git a/tensorflow/python/compat/BUILD b/tensorflow/python/compat/BUILD
index 58ceafca0638a90c2e66ddea0e4bbb1547455f48..e0a1c8e0571879e9661cdb0714cc6a794b7ea455 100644
--- a/tensorflow/python/compat/BUILD
+++ b/tensorflow/python/compat/BUILD
@@ -9,6 +9,7 @@ py_library(
     srcs = ["compat.py"],
     srcs_version = "PY2AND3",
     visibility = ["//tensorflow:internal"],
+    deps = ["//tensorflow/python:util"],
 )
 
 tf_py_test(
diff --git a/tensorflow/python/compat/compat.py b/tensorflow/python/compat/compat.py
index 68a6421c2c56c9f007cbd8aee3111c4abfde691c..247ea7349d7b0edc1b7ff8371b6df656aea75ed0 100644
--- a/tensorflow/python/compat/compat.py
+++ b/tensorflow/python/compat/compat.py
@@ -24,13 +24,17 @@ from __future__ import print_function
 
 import datetime
 from tensorflow.python.util import tf_contextlib
+from tensorflow.python.util.tf_export import tf_export
 
 _FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 8, 1)
 
 
+@tf_export("compat.forward_compatible")
 def forward_compatible(year, month, day):
   """Return true if the forward compatibility window has expired.
 
+  See @{$guide/version_compat#backward_and_partial_forward_compatibility}.
+
   Forward-compatibility refers to scenarios where the producer of a TensorFlow
   model (a GraphDef or SavedModel) is compiled against a version of the
   TensorFlow library newer than what the consumer was compiled against. The
@@ -82,10 +86,13 @@ def forward_compatible(year, month, day):
   return _FORWARD_COMPATIBILITY_HORIZON > datetime.date(year, month, day)
 
 
+@tf_export("compat.forward_compatibility_horizon")
 @tf_contextlib.contextmanager
 def forward_compatibility_horizon(year, month, day):
   """Context manager for testing forward compatibility of generated graphs.
 
+  See @{$guide/version_compat#backward_and_partial_forward_compatibility}.
+
   To ensure forward compatibility of generated graphs (see `forward_compatible`)
   with older binaries, new features can be gated with:
 
diff --git a/tensorflow/python/data/kernel_tests/BUILD b/tensorflow/python/data/kernel_tests/BUILD
index 3bde62fa1d8a71c0d6f2bbfbff29bb842a9248f0..b66b87ce6c4fe231261c42f0e4ee5e322be814c6 100644
--- a/tensorflow/python/data/kernel_tests/BUILD
+++ b/tensorflow/python/data/kernel_tests/BUILD
@@ -318,7 +318,7 @@ tf_py_test(
     ],
 )
 
-tf_py_test(
+cuda_py_test(
     name = "iterator_ops_test",
     size = "small",
     srcs = ["iterator_ops_test.py"],
@@ -349,6 +349,7 @@ tf_py_test(
         "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:tensor_shape",
         "//tensorflow/python:training",
+        "//tensorflow/python/compat:compat",
     ],
     grpc_enabled = True,
 )
diff --git a/tensorflow/python/data/kernel_tests/iterator_ops_test.py b/tensorflow/python/data/kernel_tests/iterator_ops_test.py
index 820c167b6bb9dc3b1c25d9c6156cef17ad20eb1b..b434fa7334398674a442f2ee5aa21de41b290cc4 100644
--- a/tensorflow/python/data/kernel_tests/iterator_ops_test.py
+++ b/tensorflow/python/data/kernel_tests/iterator_ops_test.py
@@ -25,6 +25,7 @@ import numpy as np
 from tensorflow.core.protobuf import cluster_pb2
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.client import session
+from tensorflow.python.compat import compat as forward_compat
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.data.ops import iterator_ops
 from tensorflow.python.data.ops import readers
@@ -415,6 +416,69 @@ class IteratorTest(test.TestCase):
         sess.run(
             next_element, feed_dict={handle_placeholder: iterator_4_handle})
 
+  def testIteratorStringHandleFuture(self):
+    with forward_compat.forward_compatibility_horizon(2018, 8, 4):
+      dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3])
+      dataset_4 = dataset_ops.Dataset.from_tensor_slices([10, 20, 30, 40])
+
+      iterator_3 = dataset_3.make_one_shot_iterator()
+      iterator_4 = dataset_4.make_one_shot_iterator()
+
+      handle_placeholder = array_ops.placeholder(dtypes.string, shape=[])
+      feedable_iterator = iterator_ops.Iterator.from_string_handle(
+          handle_placeholder, dataset_3.output_types, dataset_3.output_shapes)
+      next_element = feedable_iterator.get_next()
+
+      self.assertEqual(dataset_3.output_types, feedable_iterator.output_types)
+      self.assertEqual(dataset_4.output_types, feedable_iterator.output_types)
+      self.assertEqual([], feedable_iterator.output_shapes)
+
+      with self.test_session() as sess:
+        iterator_3_handle = sess.run(iterator_3.string_handle())
+        iterator_4_handle = sess.run(iterator_4.string_handle())
+
+        self.assertEqual(
+            10,
+            sess.run(
+                next_element,
+                feed_dict={handle_placeholder: iterator_4_handle}))
+        self.assertEqual(
+            1,
+            sess.run(
+                next_element,
+                feed_dict={handle_placeholder: iterator_3_handle}))
+        self.assertEqual(
+            20,
+            sess.run(
+                next_element,
+                feed_dict={handle_placeholder: iterator_4_handle}))
+        self.assertEqual(
+            2,
+            sess.run(
+                next_element,
+                feed_dict={handle_placeholder: iterator_3_handle}))
+        self.assertEqual(
+            30,
+            sess.run(
+                next_element,
+                feed_dict={handle_placeholder: iterator_4_handle}))
+        self.assertEqual(
+            3,
+            sess.run(
+                next_element,
+                feed_dict={handle_placeholder: iterator_3_handle}))
+        self.assertEqual(
+            40,
+            sess.run(
+                next_element,
+                feed_dict={handle_placeholder: iterator_4_handle}))
+        with self.assertRaises(errors.OutOfRangeError):
+          sess.run(
+              next_element, feed_dict={handle_placeholder: iterator_3_handle})
+        with self.assertRaises(errors.OutOfRangeError):
+          sess.run(
+              next_element, feed_dict={handle_placeholder: iterator_4_handle})
+
   def testIteratorStringHandleReuseTensorObject(self):
     dataset = dataset_ops.Dataset.from_tensor_slices([1, 2, 3])
     one_shot_iterator = dataset.make_one_shot_iterator()
diff --git a/tensorflow/python/data/kernel_tests/map_dataset_op_test.py b/tensorflow/python/data/kernel_tests/map_dataset_op_test.py
index 0ecd821e9e473522b0cf4bd7bbceb071ecf5bb9e..637bde9ae4eb839e2b983ceec082f868f3ed2728 100644
--- a/tensorflow/python/data/kernel_tests/map_dataset_op_test.py
+++ b/tensorflow/python/data/kernel_tests/map_dataset_op_test.py
@@ -666,6 +666,13 @@ class MapDatasetTest(test.TestCase):
         "currently support nested datasets as outputs."):
       _ = dataset.map(dataset_ops.Dataset.from_tensor_slices)
 
+  def testReturnValueError(self):
+    dataset = dataset_ops.Dataset.from_tensors([1.0, 2.0, 3.0])
+    with self.assertRaisesRegexp(
+        TypeError, r"Unsupported return value from function passed to "
+        r"Dataset.map\(\): None."):
+      _ = dataset.map(lambda x: None)
+
 
 class MapDatasetBenchmark(test.Benchmark):
 
diff --git a/tensorflow/python/data/ops/BUILD b/tensorflow/python/data/ops/BUILD
index fa2e86eab18b0b97ea01a96e309b0ea82d91b267..f15eb6310f6176338155c4c0b370f59db7cfa210 100644
--- a/tensorflow/python/data/ops/BUILD
+++ b/tensorflow/python/data/ops/BUILD
@@ -40,6 +40,7 @@ py_library(
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:tensor_shape",
+        "//tensorflow/python/compat",
         "//tensorflow/python/data/util:convert",
     ],
 )
@@ -54,6 +55,7 @@ py_library(
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:resource_variable_ops",
         "//tensorflow/python:tensor_shape",
+        "//tensorflow/python/compat",
         "//tensorflow/python/data/util:nest",
         "//tensorflow/python/data/util:sparse",
         "//tensorflow/python/eager:context",
diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py
index d2a8c0f3137aa25d2e5327cd4e61c04298656e4d..88de4b588cc3369e9d67a03c600e68186bb267ad 100644
--- a/tensorflow/python/data/ops/dataset_ops.py
+++ b/tensorflow/python/data/ops/dataset_ops.py
@@ -24,6 +24,7 @@ import warnings
 import numpy as np
 import six
 
+from tensorflow.python.compat import compat
 from tensorflow.python.data.ops import iterator_ops
 from tensorflow.python.data.util import nest
 from tensorflow.python.data.util import random_seed
@@ -107,8 +108,12 @@ class Dataset(object):
           "execution is enabled.")
     if shared_name is None:
       shared_name = ""
-    iterator_resource = gen_dataset_ops.iterator(
-        container="", shared_name=shared_name, **flat_structure(self))
+    if compat.forward_compatible(2018, 8, 3):
+      iterator_resource = gen_dataset_ops.iterator_v2(
+          container="", shared_name=shared_name, **flat_structure(self))
+    else:
+      iterator_resource = gen_dataset_ops.iterator(
+          container="", shared_name=shared_name, **flat_structure(self))
     with ops.colocate_with(iterator_resource):
       initializer = gen_dataset_ops.make_iterator(self._as_variant_tensor(),
                                                   iterator_resource)
@@ -1425,7 +1430,11 @@ class StructuredFunctionWrapper(object):
           flat_shapes.append(component)
           flat_types.append(component)
         else:
-          t = ops.convert_to_tensor(t)
+          try:
+            t = ops.convert_to_tensor(t)
+          except (ValueError, TypeError):
+            raise TypeError("Unsupported return value from function passed to "
+                            "%s: %s." % (transformation_name, t))
           flat_ret.append(t)
           flat_classes.append(ops.Tensor)
           flat_shapes.append(t.get_shape())
diff --git a/tensorflow/python/data/ops/iterator_ops.py b/tensorflow/python/data/ops/iterator_ops.py
index b6dba4e3ca3874b8e9bc3b7ea92fb91fe41759d8..494df178dfa48eaa6c864cb1c3d89f9a0cb9af43 100644
--- a/tensorflow/python/data/ops/iterator_ops.py
+++ b/tensorflow/python/data/ops/iterator_ops.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 import threading
 import warnings
 
+from tensorflow.python.compat import compat
 from tensorflow.python.data.util import nest
 from tensorflow.python.data.util import sparse
 from tensorflow.python.eager import context
@@ -56,6 +57,13 @@ GET_NEXT_CALL_WARNING_MESSAGE = (
 GLOBAL_ITERATORS = "iterators"
 
 
+def _device_stack_is_empty():
+  # pylint: disable=protected-access
+  device_stack = ops.get_default_graph()._device_functions_outer_to_inner
+  # pylint: enable=protected-access
+  return not bool(device_stack)
+
+
 @tf_export("data.Iterator")
 class Iterator(object):
   """Represents the state of iterating through a `Dataset`."""
@@ -172,13 +180,32 @@ class Iterator(object):
     nest.assert_same_structure(output_types, output_shapes)
     if shared_name is None:
       shared_name = ""
-    iterator_resource = gen_dataset_ops.iterator(
-        container="",
-        shared_name=shared_name,
-        output_types=nest.flatten(
-            sparse.as_dense_types(output_types, output_classes)),
-        output_shapes=nest.flatten(
-            sparse.as_dense_shapes(output_shapes, output_classes)))
+    if compat.forward_compatible(2018, 8, 3):
+      if _device_stack_is_empty():
+        with ops.device("/cpu:0"):
+          iterator_resource = gen_dataset_ops.iterator_v2(
+              container="",
+              shared_name=shared_name,
+              output_types=nest.flatten(
+                  sparse.as_dense_types(output_types, output_classes)),
+              output_shapes=nest.flatten(
+                  sparse.as_dense_shapes(output_shapes, output_classes)))
+      else:
+        iterator_resource = gen_dataset_ops.iterator_v2(
+            container="",
+            shared_name=shared_name,
+            output_types=nest.flatten(
+                sparse.as_dense_types(output_types, output_classes)),
+            output_shapes=nest.flatten(
+                sparse.as_dense_shapes(output_shapes, output_classes)))
+    else:
+      iterator_resource = gen_dataset_ops.iterator(
+          container="",
+          shared_name=shared_name,
+          output_types=nest.flatten(
+              sparse.as_dense_types(output_types, output_classes)),
+          output_shapes=nest.flatten(
+              sparse.as_dense_shapes(output_shapes, output_classes)))
     return Iterator(iterator_resource, None, output_types, output_shapes,
                     output_classes)
 
@@ -242,12 +269,29 @@ class Iterator(object):
       output_classes = nest.map_structure(lambda _: ops.Tensor, output_types)
     nest.assert_same_structure(output_types, output_shapes)
     string_handle = ops.convert_to_tensor(string_handle, dtype=dtypes.string)
-    iterator_resource = gen_dataset_ops.iterator_from_string_handle(
-        string_handle,
-        output_types=nest.flatten(
-            sparse.as_dense_types(output_types, output_classes)),
-        output_shapes=nest.flatten(
-            sparse.as_dense_shapes(output_shapes, output_classes)))
+    if compat.forward_compatible(2018, 8, 3):
+      if _device_stack_is_empty():
+        with ops.device("/cpu:0"):
+          iterator_resource = gen_dataset_ops.iterator_from_string_handle_v2(
+              string_handle,
+              output_types=nest.flatten(
+                  sparse.as_dense_types(output_types, output_classes)),
+              output_shapes=nest.flatten(
+                  sparse.as_dense_shapes(output_shapes, output_classes)))
+      else:
+        iterator_resource = gen_dataset_ops.iterator_from_string_handle_v2(
+            string_handle,
+            output_types=nest.flatten(
+                sparse.as_dense_types(output_types, output_classes)),
+            output_shapes=nest.flatten(
+                sparse.as_dense_shapes(output_shapes, output_classes)))
+    else:
+      iterator_resource = gen_dataset_ops.iterator_from_string_handle(
+          string_handle,
+          output_types=nest.flatten(
+              sparse.as_dense_types(output_types, output_classes)),
+          output_shapes=nest.flatten(
+              sparse.as_dense_shapes(output_shapes, output_classes)))
     return Iterator(iterator_resource, None, output_types, output_shapes,
                     output_classes)
 
@@ -462,7 +506,8 @@ class EagerIterator(object):
           "tf.data.Dataset.make_initializable_iterator or "
           "tf.data.Dataset.make_one_shot_iterator for graph construction".
           format(type(self)))
-    with ops.device("/device:CPU:0"):
+    self._device = context.context().device_name
+    with ops.device("/cpu:0"):
       ds_variant = dataset._as_variant_tensor()  # pylint: disable=protected-access
       self._output_classes = dataset.output_classes
       self._output_types = dataset.output_types
@@ -471,14 +516,14 @@ class EagerIterator(object):
           sparse.as_dense_types(self._output_types, self._output_classes))
       self._flat_output_shapes = nest.flatten(
           sparse.as_dense_shapes(self._output_shapes, self._output_classes))
-      self._resource = gen_dataset_ops.anonymous_iterator(
-          output_types=self._flat_output_types,
-          output_shapes=self._flat_output_shapes)
-      gen_dataset_ops.make_iterator(ds_variant, self._resource)
-      # Delete the resource when this object is deleted
-      self._resource_deleter = resource_variable_ops.EagerResourceDeleter(
-          handle=self._resource, handle_device="/device:CPU:0")
-    self._device = context.context().device_name
+      with ops.colocate_with(ds_variant):
+        self._resource = gen_dataset_ops.anonymous_iterator(
+            output_types=self._flat_output_types,
+            output_shapes=self._flat_output_shapes)
+        gen_dataset_ops.make_iterator(ds_variant, self._resource)
+        # Delete the resource when this object is deleted
+        self._resource_deleter = resource_variable_ops.EagerResourceDeleter(
+            handle=self._resource, handle_device=self._device)
 
   def __iter__(self):
     return self
diff --git a/tensorflow/python/data/util/nest.py b/tensorflow/python/data/util/nest.py
index 32e08021dc80d11baaead68ea062b6dab7a8dfdd..1b596bdfc0e7cc18be8ffbb96a5e3a797d7cf619 100644
--- a/tensorflow/python/data/util/nest.py
+++ b/tensorflow/python/data/util/nest.py
@@ -13,7 +13,6 @@
 # limitations under the License.
 # ==============================================================================
 
-# TODO(shivaniagrawal): Merge with core nest
 """## Functions for working with arbitrarily nested sequences of elements.
 
 NOTE(mrry): This fork of the `tensorflow.python.util.nest` module
diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index c025dc8aa58a500ace3e28ba4528abd4f4c38ba7..27b8ebd362eea4468d20c65ee39e1b55e8dcd17d 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -404,6 +404,7 @@ py_library(
     deps = [
         ":debug_errors",
         ":debug_fibonacci",
+        ":debug_keras",
         ":debug_mnist",
         ":debug_tflearn_iris",
     ],
@@ -802,6 +803,7 @@ cuda_py_test(
         "//tensorflow/python:platform_test",
         "//tensorflow/python:variables",
     ],
+    tags = ["no_windows_gpu"],
 )
 
 py_test(
diff --git a/tensorflow/python/debug/examples/examples_test.sh b/tensorflow/python/debug/examples/examples_test.sh
index 2d35b2d8bb10d17decfa404afd5004d3409c06e5..f7d597c8c065ced5efe95031a83877a92d7ccae1 100755
--- a/tensorflow/python/debug/examples/examples_test.sh
+++ b/tensorflow/python/debug/examples/examples_test.sh
@@ -99,7 +99,7 @@ if [[ -d "${CUSTOM_DUMP_ROOT}" ]]; then
 fi
 
 # Test debugging of tf.keras.
-cat << EOF | "${DEBUG_KERAS_BIN}" --debug --ui_type=readline
+cat << EOF | ${DEBUG_KERAS_BIN} --debug --ui_type=readline
 run -f has_inf_or_nan
 EOF
 
diff --git a/tensorflow/python/distribute/BUILD b/tensorflow/python/distribute/BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..2bd0b4320afb500afad30e3d5cb0000711e1b664
--- /dev/null
+++ b/tensorflow/python/distribute/BUILD
@@ -0,0 +1,43 @@
+package(
+    default_visibility = ["//tensorflow:internal"],
+)
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+load("//tensorflow:tensorflow.bzl", "py_test")
+
+py_library(
+    name = "distribute_coordinator",
+    srcs = [
+        "distribute_coordinator.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:training",
+    ],
+)
+
+py_test(
+    name = "distribute_coordinator_test",
+    size = "small",
+    srcs = ["distribute_coordinator_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        ":distribute_coordinator",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:distributed_framework_test_lib",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:session",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python:variables",
+    ],
+)
diff --git a/tensorflow/python/distribute/distribute_coordinator.py b/tensorflow/python/distribute/distribute_coordinator.py
new file mode 100644
index 0000000000000000000000000000000000000000..04c50dbafc0c1497a86c0e4b7e64661a21af51b4
--- /dev/null
+++ b/tensorflow/python/distribute/distribute_coordinator.py
@@ -0,0 +1,361 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""A unified and split coordinator for distributed TensorFlow."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+import json
+import os
+import threading
+
+from tensorflow.core.protobuf import cluster_pb2
+from tensorflow.python.training import server_lib
+
+
+class _TaskType(object):
+  PS = "ps"
+  WORKER = "worker"
+  CHIEF = "chief"
+  EVALUATOR = "evaluator"
+
+
+_coordinator_context = threading.local()
+
+
+def get_current_coordinator_context():
+  """Returns the current coordinator context."""
+  try:
+    return _coordinator_context.current
+  except AttributeError:
+    return None
+
+
+class _Barrier(object):
+  """A reusable barrier class for worker synchronization."""
+
+  def __init__(self, num_participants):
+    """Initializes the barrier object.
+
+    Args:
+      num_participants: an integer which is the expected number of calls of
+        `wait` pass to through this barrier.
+    """
+    self._num_participants = num_participants
+    self._counter = 0
+    self._flag = False
+    self._local_sense = threading.local()
+    self._lock = threading.Lock()
+    self._condition = threading.Condition()
+
+  def wait(self):
+    """Waits until all other callers reach the same wait call."""
+    if not hasattr(self._local_sense, "value"):
+      self._local_sense.value = False
+    self._local_sense.value = not self._flag
+    with self._lock:
+      self._counter += 1
+      if self._counter == self._num_participants:
+        self._counter = 0
+        self._flag = self._local_sense.value
+    with self._condition:
+      while self._flag != self._local_sense.value:
+        self._condition.wait()
+      self._condition.notify_all()
+
+
+def _get_num_workers(cluster_spec):
+  """Gets number of workers including chief."""
+  if not cluster_spec:
+    return 0
+  return len(cluster_spec.as_dict().get(_TaskType.WORKER, [])) + len(
+      cluster_spec.as_dict().get(_TaskType.CHIEF, []))
+
+
+class _CoordinatorContext(object):
+  """The coordinator context class.
+
+  This context object provides configuration information for each task. One
+  context manager with a coordinator context object will be created per
+  invocation to the `worker_fn` where `get_current_coordinator_context` can be
+  called to access the coordinator context object.
+  """
+
+  def __init__(self,
+               cluster_spec,
+               task_type,
+               task_id,
+               between_graph=False,
+               rpc_layer="grpc",
+               worker_barrier=None):
+    """Initialize the coordinator context object.
+
+    Args:
+      cluster_spec: a ClusterSpec object. It can be empty or None in the local
+        training case.
+      task_type: a string indicating the role of the corresponding task, such as
+        "worker" or "ps". It can be None if it is local training or
+        `between_graph` is False.
+      task_id: an integer indicating id of the corresponding task. It can be
+        None if it is local training or `between_graph` is False.
+      between_graph: whether it is between-graph replication or not.
+      rpc_layer: optional string specifying the RPC protocol for communication
+        with worker masters. If None or empty, hosts in the `cluster_spec` will
+        be used directly.
+      worker_barrier: optional, the barrier object for worker synchronization.
+
+    Raises:
+      ValueError: if task_type or task_id is Node or empty and it is distributed
+        between-graph replicated training.
+    """
+    if cluster_spec and between_graph:
+      if not task_type or task_id is None:
+        raise ValueError("`task_type` and `task_id` must be set in the "
+                         "distributed between-graph replicated training.")
+      if task_type not in cluster_spec.jobs:
+        raise ValueError("`task_type` %r not found in the `cluster_spec` %r" %
+                         (task_type, cluster_spec))
+    self._cluster_spec = cluster_spec
+    self._task_type = task_type
+    self._task_id = task_id
+    self._worker_barrier = worker_barrier
+    self._rpc_layer = rpc_layer
+    self._master_target = self._get_master_target()
+    self._num_workers = _get_num_workers(cluster_spec)
+    self._is_chief_node = self._is_chief()
+
+  def __enter__(self):
+    old_context = get_current_coordinator_context()
+    if old_context:
+      raise ValueError(
+          "You cannot run distribute coordinator in a `worker_fn`.")
+    _coordinator_context.current = self
+
+  def __exit__(self, unused_exception_type, unused_exception_value,
+               unused_traceback):
+    _coordinator_context.current = None
+
+  def _get_master_target(self):
+    """Return the master target for a task."""
+    # If cluster_spec is None or empty, we use local master.
+    if not self._cluster_spec:
+      return "local"
+
+    # If task_type is None, then it is in-graph replicated training. In this
+    # case we use the chief or first worker's master target.
+    if not self._task_type:
+      if _TaskType.CHIEF in self._cluster_spec.jobs:
+        assert not self.between_graph
+        task_type = _TaskType.CHIEF
+        task_id = 0
+      else:
+        assert _TaskType.WORKER in self._cluster_spec.jobs
+        task_type = _TaskType.WORKER
+        task_id = 0
+    else:
+      task_type = self._task_type
+      task_id = self._task_id
+
+    prefix = ""
+    if self._rpc_layer:
+      prefix = self._rpc_layer + "://"
+    return prefix + self._cluster_spec.job_tasks(task_type)[task_id or 0]
+
+  def _is_chief(self):
+    """Return whether the task is the chief worker."""
+    if (not self._cluster_spec or self._task_type in [_TaskType.CHIEF, None]):
+      return True
+
+    # If not local and chief not in the cluster_spec, use the first worker as
+    # chief.
+    if (_TaskType.CHIEF not in self._cluster_spec.jobs and
+        self._task_type == _TaskType.WORKER and self._task_id == 0):
+      return True
+    return False
+
+  def wait_for_other_workers(self):
+    """Waits for other workers to reach the same call to this method.
+
+    Raises:
+      ValueError: if `worker_barrier` is not passed to the __init__ method.
+    """
+    if not self._worker_barrier:
+      raise ValueError(
+          "`worker_barrier is not set in the coordinator context.`")
+    self._worker_barrier.wait()
+
+  @property
+  def distributed_mode(self):
+    """Whether it is distributed training or not."""
+    return bool(self._cluster_spec)
+
+  @property
+  def cluster_spec(self):
+    """Returns a copy of the cluster_spec object."""
+    return copy.deepcopy(self._cluster_spec)
+
+  @property
+  def task_type(self):
+    """Returns the role of the corresponing task."""
+    return self._task_type
+
+  @property
+  def task_id(self):
+    """Returns the id or index of the corresponing task."""
+    return self._task_id
+
+  @property
+  def master_target(self):
+    """Returns the session master for the corresponding task to connect to."""
+    return self._master_target
+
+  @property
+  def is_chief(self):
+    """Returns whether the task is a chief node."""
+    return self._is_chief_node
+
+  @property
+  def num_workers(self):
+    """Returns number of workers in the cluster, including chief."""
+    return self._num_workers
+
+
+def _run(worker_fn, cluster_spec, task_type, task_id, between_graph, rpc_layer,
+         worker_barrier):
+  with _CoordinatorContext(cluster_spec, task_type, task_id, between_graph,
+                           rpc_layer, worker_barrier):
+    worker_fn()
+
+
+def run_distribute_coordinator(worker_fn,
+                               cluster_spec=None,
+                               between_graph=False,
+                               rpc_layer=None):
+  """Run the coordinator for distributed TensorFlow.
+
+  This function runs a unified and split coordinator for distributed TensorFlow.
+  Given a `cluster_spec` specifying server addresses and their roles in a
+  cluster, this coordinator will figure out how to set them up, give the
+  underlying function the right targets for master sessions and coordinate their
+  training.
+
+  In addition to be the distribute coordinator, this is also the source of
+  configurations for each job in the distributed training. As there are multiple
+  ways to configure a distributed TensorFlow cluster, its context object
+  provides these configurations so that users or higher-level APIs don't have to
+  figure out the configuration for each job by themselves.
+
+  In the between-graph replicated training, this coordinator will create
+  multiple threads and each calls the `worker_fn` which is supposed to create
+  its own graph and connect to one worker master given by its coordinator
+  context. In the in-graph replicated training, it has only one thread calling
+  this `worker_fn`.
+
+  The `worker_fn` defines the training logic and is called under a its own
+  coordinator context which can be accessed to via
+  `get_current_coordinator_context`. A coordinator context provides access to
+  configurations for each task, e.g. the task_type, task_id, master target and
+  so on. Since `worker_fn` will be called in a thread and possibly multiple
+  times, caller should be careful when it accesses global data. For example, it
+  is unsafe to define flags in a `worker_fn` or to define different environment
+  variables for different `worker_fn`s.
+
+  The `worker_fn` for the between-graph replication is defined as if there are
+  only one worker corresponding to the `worker_fn` and possibly ps jobs. It
+  assigns variables to parameter servers and all other operations to that
+  worker. In the in-graph replication case, the `worker_fn` has to define
+  operations for all worker jobs. Using a distribution strategy can simplify the
+  `worker_fn` by not having to worry about the replication and device assignment
+  of variables and operations.
+
+  This method is intended to be invoked by high-level APIs so that users don't
+  have to explictly call it to run this coordinator. For those who don't use
+  high-level APIs, to change a program to use this coordinator, wrap everything
+  in a the program after global data definitions such as commandline flag
+  definition into the `worker_fn` and get task-specific configurations from
+  the coordinator context.
+
+  The `cluster_spec` can be either passed by the argument or parsed from the
+  "TF_CONFIG" envrionment variable. Example of a TF_CONFIG:
+  ```
+    cluster = {'chief': ['host0:2222'],
+               'ps': ['host1:2222', 'host2:2222'],
+               'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
+    os.environ['TF_CONFIG'] = json.dumps({'cluster': cluster})
+  ```
+
+  If `cluster_spec` is not given in any format, it becomes local training and
+  this coordinator will connect to a local session.
+
+  For evaluation, if "evaluator" exist in the cluster_spec, a separate thread
+  will be created with its `task_type` set to "evaluator". If "evaluator" is not
+  set in the cluster_spec, it entirely depends on the `worker_fn` for how to do
+  evaluation.
+
+  Args:
+    worker_fn: the function to be called and given the access to a coordinator
+      context object.
+    cluster_spec: a dict, ClusterDef or ClusterSpec specifying servers and roles
+      in a cluster. If not set or empty, fall back to local training.
+    between_graph: a boolean. It is only useful when `cluster_spec` is set and
+      not empty. If true, it will use between-graph replicated training;
+      otherwise it will use in-graph replicated training.
+    rpc_layer: optional string, the protocol for RPC, e.g. "grpc".
+
+  Raises:
+    ValueError: if `cluster_spec` is supplied but not a dict or a ClusterDef or
+      a ClusterSpec.
+  """
+  if not cluster_spec:
+    tf_config = json.loads(os.environ.get("TF_CONFIG", "{}"))
+    cluster_spec = tf_config.get("cluster", {})
+
+  if cluster_spec:
+    if isinstance(cluster_spec, (dict, cluster_pb2.ClusterDef)):
+      cluster_spec = server_lib.ClusterSpec(cluster_spec)
+    elif not isinstance(cluster_spec, server_lib.ClusterSpec):
+      raise ValueError(
+          "`cluster_spec' should be dict or a `tf.train.ClusterSpec` or a "
+          "`tf.train.ClusterDef` object")
+    # TODO(yuefengz): validate cluster_spec.
+
+  threads = []
+  if cluster_spec and _TaskType.EVALUATOR in cluster_spec.jobs:
+    t = threading.Thread(
+        target=_run,
+        args=(worker_fn, cluster_spec, _TaskType.EVALUATOR, 0, between_graph,
+              rpc_layer, None))
+    t.start()
+    threads.append(t)
+
+  if cluster_spec and between_graph:
+    worker_barrier = _Barrier(_get_num_workers(cluster_spec))
+    for task_type in [_TaskType.CHIEF, _TaskType.WORKER]:
+      for task_id in range(len(cluster_spec.as_dict().get(task_type, []))):
+        t = threading.Thread(
+            target=_run,
+            args=(worker_fn, cluster_spec, task_type, task_id, between_graph,
+                  rpc_layer, worker_barrier))
+        t.start()
+        threads.append(t)
+  else:
+    # Local or in-graph replicated training.
+    _run(worker_fn, cluster_spec, None, None, between_graph, rpc_layer, None)
+
+  # TODO(yuefengz): wrapper threads into thread coordinator?
+  for t in threads:
+    t.join()
diff --git a/tensorflow/python/distribute/distribute_coordinator_test.py b/tensorflow/python/distribute/distribute_coordinator_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..82fd823352c03b941a32c8d50510a8d142b466a2
--- /dev/null
+++ b/tensorflow/python/distribute/distribute_coordinator_test.py
@@ -0,0 +1,291 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for distribute coordinator."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import contextlib
+import copy
+import threading
+import six
+
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.python.client import session
+from tensorflow.python.distribute import distribute_coordinator
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import test
+
+CHIEF = distribute_coordinator._TaskType.CHIEF
+WORKER = distribute_coordinator._TaskType.WORKER
+PS = distribute_coordinator._TaskType.PS
+EVALUATOR = distribute_coordinator._TaskType.EVALUATOR
+
+NUM_WORKERS = 3
+NUM_PS = 2
+
+
+def _bytes_to_str(maybe_bytes):
+  if isinstance(maybe_bytes, six.string_types):
+    return maybe_bytes
+  else:
+    return str(maybe_bytes, "utf-8")
+
+
+class DistributeCoordinatorTest(test.TestCase):
+
+  @classmethod
+  def setUpClass(cls):
+    # We have to create a global in-process cluster because once an in-process
+    # tensorflow server is created, there is no way to terminate it. Please see
+    # multi_worker_test_base.py for more details.
+    cls._workers, cls._ps = test_util.create_local_cluster(
+        NUM_WORKERS, num_ps=NUM_PS)
+    cls._cluster_spec = {
+        WORKER: [_bytes_to_str(w.target) for w in cls._workers],
+        PS: [_bytes_to_str(ps.target) for ps in cls._ps]
+    }
+
+  def setUp(self):
+    self._result_correct = 0
+    self._lock = threading.Lock()
+    self._task_context = {}
+
+  @contextlib.contextmanager
+  def _test_session(self, target):
+    config = config_pb2.ConfigProto(allow_soft_placement=True)
+    config.graph_options.optimizer_options.opt_level = -1
+    with session.Session(graph=None, config=config, target=target) as sess:
+      yield sess
+
+  def _in_graph_worker_fn(self):
+    context = distribute_coordinator.get_current_coordinator_context()
+    self.assertTrue(context is not None)
+    with self._test_session(target=context.master_target) as sess:
+      xs = []
+      expected = 0.0
+      for i in range(context.num_workers):
+        with ops.device("/job:worker/task:%d" % i):
+          x = variable_scope.get_variable("x_%d" % i, initializer=10.0)
+          x_add = x.assign_add(float(i))
+          xs.append(x_add)
+          expected += i + 10.0
+
+      with ops.device("/job:worker/task:0"):
+        result = math_ops.add_n(xs)
+
+      variables.global_variables_initializer().run()
+      result_value = sess.run(result)
+    self.assertEqual(result_value, expected)
+    if result_value == expected:
+      self._result_correct += 1
+
+  def testInGraph(self):
+    """Test it runs in-graph replicated training correctly."""
+    distribute_coordinator.run_distribute_coordinator(
+        self._in_graph_worker_fn,
+        cluster_spec=self._cluster_spec,
+        between_graph=False)
+    self.assertEqual(self._result_correct, 1)
+
+  def _between_graph_worker_fn(self):
+    context = distribute_coordinator.get_current_coordinator_context()
+    self.assertTrue(context is not None)
+    with self._test_session(target=context.master_target) as sess:
+      with ops.device("/job:ps/task:0"):
+        # TODO(yuefengz): investigate why not using resource variable will make
+        # the test flaky.
+        x = variable_scope.get_variable(
+            "x", initializer=10.0, use_resource=True)
+      with ops.device("/job:ps/task:1"):
+        y = variable_scope.get_variable(
+            "y", initializer=20.0, use_resource=True)
+
+      x_add = x.assign_add(2.0)
+      y_sub = y.assign_sub(2.0)
+      train_op = control_flow_ops.group([x_add, y_sub])
+
+      if context.is_chief:
+        variables.global_variables_initializer().run()
+
+      # Synchronize workers after initializaton.
+      context.wait_for_other_workers()
+
+      sess.run(train_op)
+
+      # Synchronize workers after one step to make sure they all have finished
+      # training.
+      context.wait_for_other_workers()
+
+      x_val, y_val = sess.run([x, y])
+
+      self.assertEqual(x_val, 16.0)
+      self.assertEqual(y_val, 14.0)
+      if x_val == 16.0 and y_val == 14.0:
+        with self._lock:
+          self._result_correct += 1
+
+  def testBetweenGraph(self):
+    """Test it runs between-graph replicated training correctly."""
+    distribute_coordinator.run_distribute_coordinator(
+        self._between_graph_worker_fn,
+        cluster_spec=self._cluster_spec,
+        between_graph=True)
+
+    # Each finished worker will increment self._result_correct.
+    self.assertEqual(self._result_correct, NUM_WORKERS)
+
+  def _dump_task_context(self):
+    """Dumps the propoerties of each coordinator context.
+
+    It dumps the context properties to a dict mapping from task_type to a list
+    of tuples of master_target, num_workers, is_chief and distribute_mode, where
+    the list is indexed by the task_id.
+    """
+    context = distribute_coordinator.get_current_coordinator_context()
+    self.assertTrue(context is not None)
+    task_type = str(context.task_type)
+    task_id = context.task_id or 0
+    with self._lock:
+      if task_type not in self._task_context:
+        self._task_context[task_type] = []
+      while len(self._task_context[task_type]) <= task_id:
+        self._task_context[task_type].append(None)
+      self._task_context[task_type][task_id] = (context.master_target,
+                                                context.num_workers,
+                                                context.is_chief,
+                                                context.distributed_mode)
+
+  def testBetweenGraphContext(self):
+    # Dumps the task contexts to the self._task_context dict.
+    distribute_coordinator.run_distribute_coordinator(
+        self._dump_task_context,
+        cluster_spec=self._cluster_spec,
+        between_graph=True)
+
+    # There is only one type of task and there three such tasks.
+    self.assertEqual(len(self._task_context), 1)
+    self.assertTrue(WORKER in self._task_context)
+    self.assertEqual(len(self._task_context[WORKER]), NUM_WORKERS)
+
+    # Check whether each task has the right master_target, num_workers, is_chief
+    # and distributed_mode.
+    self.assertEqual(
+        self._task_context[WORKER][0],
+        (_bytes_to_str(self._workers[0].target), NUM_WORKERS, True, True))
+    self.assertEqual(
+        self._task_context[WORKER][1],
+        (_bytes_to_str(self._workers[1].target), NUM_WORKERS, False, True))
+    self.assertEqual(
+        self._task_context[WORKER][2],
+        (_bytes_to_str(self._workers[2].target), NUM_WORKERS, False, True))
+
+  def testInGraphContext(self):
+    # Dumps the task contexts to the self._task_context dict.
+    distribute_coordinator.run_distribute_coordinator(
+        self._dump_task_context,
+        cluster_spec=self._cluster_spec,
+        between_graph=False)
+
+    # There is only a "None" task in the dumped task context.
+    self.assertEqual(len(self._task_context), 1)
+    self.assertTrue("None" in self._task_context)
+    self.assertEqual(len(self._task_context["None"]), 1)
+
+    # Check whether each task has the right master_target, num_workers, is_chief
+    # and distributed_mode.
+    self.assertEqual(
+        self._task_context["None"][0],
+        (_bytes_to_str(self._workers[0].target), NUM_WORKERS, True, True))
+
+  def testLocalContext(self):
+    # Dumps the task contexts to the self._task_context dict.
+    distribute_coordinator.run_distribute_coordinator(
+        self._dump_task_context, cluster_spec=None, between_graph=True)
+
+    # There is only a "None" task.
+    self.assertEqual(len(self._task_context), 1)
+    self.assertTrue("None" in self._task_context)
+    self.assertEqual(len(self._task_context["None"]), 1)
+
+    # Check whether each task has the right master_target, num_workers, is_chief
+    # and distributed_mode.
+    self.assertEqual(self._task_context["None"][0], ("local", 0, True, False))
+
+  def testBetweenGraphContextWithChief(self):
+    # Adds a chief node, so there are NUM_WORKERS + 1 workers in total.
+    cluster_spec = copy.deepcopy(self._cluster_spec)
+    cluster_spec[CHIEF] = ["fake_chief"]
+
+    # Dumps the task contexts to the self._task_context dict.
+    distribute_coordinator.run_distribute_coordinator(
+        self._dump_task_context,
+        cluster_spec=cluster_spec,
+        between_graph=True,
+        rpc_layer="grpc")
+
+    # There are one CHIEF and three workers.
+    self.assertEqual(len(self._task_context), 2)
+    self.assertTrue(CHIEF in self._task_context)
+    self.assertTrue(WORKER in self._task_context)
+    self.assertEqual(len(self._task_context[CHIEF]), 1)
+    self.assertEqual(len(self._task_context[WORKER]), NUM_WORKERS)
+
+    # Check whether each task has the right master_target, num_workers, is_chief
+    # and distributed_mode.
+    self.assertEqual(self._task_context[CHIEF][0],
+                     ("grpc://fake_chief", 4, True, True))
+    self.assertEqual(self._task_context[WORKER][0],
+                     ("grpc://" + _bytes_to_str(self._workers[0].target),
+                      NUM_WORKERS + 1, False, True))
+    self.assertEqual(self._task_context[WORKER][1],
+                     ("grpc://" + _bytes_to_str(self._workers[1].target),
+                      NUM_WORKERS + 1, False, True))
+    self.assertEqual(self._task_context[WORKER][2],
+                     ("grpc://" + _bytes_to_str(self._workers[2].target),
+                      NUM_WORKERS + 1, False, True))
+
+  def testInGraphContextWithEval(self):
+    # Adds a EVALUATOR job.
+    cluster_spec = copy.deepcopy(self._cluster_spec)
+    cluster_spec[EVALUATOR] = ["fake_evaluator"]
+
+    # Dumps the task contexts to the self._task_context dict.
+    distribute_coordinator.run_distribute_coordinator(
+        self._dump_task_context, cluster_spec=cluster_spec, between_graph=False)
+
+    # There are one "None" task and one EVALUATOR task.
+    self.assertEqual(len(self._task_context), 2)
+    self.assertTrue("None" in self._task_context)
+    self.assertTrue(EVALUATOR in self._task_context)
+    self.assertEqual(len(self._task_context["None"]), 1)
+    self.assertEqual(len(self._task_context[EVALUATOR]), 1)
+
+    # Check whether each task has the right master_target, num_workers, is_chief
+    # and distributed_mode.
+    self.assertEqual(self._task_context["None"][0],
+                     (_bytes_to_str(self._workers[0].target), 3, True, True))
+    self.assertEqual(self._task_context[EVALUATOR][0],
+                     ("fake_evaluator", 3, False, True))
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/python/eager/BUILD b/tensorflow/python/eager/BUILD
index 6ede8e4f4d9c549faae3223d400d25b7712bbc74..32a8452f620fb54a658fe0a60cb3213ddcb1c61d 100644
--- a/tensorflow/python/eager/BUILD
+++ b/tensorflow/python/eager/BUILD
@@ -322,6 +322,7 @@ cuda_py_test(
         "//tensorflow/python:math_ops",
         "//tensorflow/python:pywrap_tensorflow",
         "//tensorflow/python:random_ops",
+        "//tensorflow/python/keras",
     ],
 )
 
@@ -404,6 +405,7 @@ cuda_py_test(
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_test_lib",
+        "@six_archive//:six",
     ],
     tags = [
         "optonly",  # The test is too slow in non-opt mode
diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py
index 3e3c82e56a8c957839e420550bfb073d400b4a77..c59ad09bf1f0fbae093ce360ce3d0f544d933d6e 100644
--- a/tensorflow/python/eager/backprop.py
+++ b/tensorflow/python/eager/backprop.py
@@ -599,15 +599,18 @@ def _fast_fill(value, shape, dtype):
 
 
 def _zeros(shape, dtype):
-  """Wraps array_ops.zeros to cache last zero for a given shape and dtype."""
-  device = context.context().device_name
+  """Helper to return (possibly cached) zero tensors in eager mode."""
   if dtype == dtypes.variant:
     # TODO(apassos): need to save enough information about variant tensors to do
     # a zeros
     return None
-  # pylint: disable=protected-access
-  cache_key = shape, dtype, device, context.context()._eager_context.mode
-  # pylint: enable=protected-access
+
+  ctx = context.context()
+  if not ctx.executing_eagerly():
+    return array_ops.zeros(shape, dtype)
+
+  device = ctx.device_name
+  cache_key = shape, dtype, device
   cached = _zeros_cache.get(cache_key)
   if cached is None:
     cached = _fast_fill(0, shape, dtype)
@@ -616,6 +619,9 @@ def _zeros(shape, dtype):
 
 
 def _ones(shape, dtype):
+  if not context.context().executing_eagerly():
+    return array_ops.ones(shape, dtype)
+
   if shape == ():  # pylint: disable=g-explicit-bool-comparison
     return constant_op.constant(1, dtype=dtype)
   return _fast_fill(1, shape, dtype)
@@ -643,10 +649,10 @@ class GradientTape(object):
   Operations are recorded if they are executed within this context manager and
   at least one of their inputs is being "watched".
 
-  Trainable variables (created by `tf.contrib.eager.Variable` or
-  @{tf.get_variable}, trainable=True is default in both cases) are automatically
-  watched. Tensors can be manually watched by invoking the `watch` method on
-  this context manager.
+  Trainable variables (created by `tf.Variable` or @{tf.get_variable},
+  trainable=True is default in both cases) are automatically watched. Tensors
+  can be manually watched by invoking the `watch` method on this context
+  manager.
 
   For example, consider the function `y = x * x`. The gradient at `x = 3.0` can
   be computed as:
@@ -713,10 +719,15 @@ class GradientTape(object):
     if self._recording:
       self._pop_tape()
 
-  def _push_tape(self):
+  def _push_tape(self, existing_tape=False):
     if self._recording:
       raise ValueError("Tape is already recording.")
-    self._tape = tape.push_new_tape(persistent=self._persistent)
+    if existing_tape:
+      if self._tape is None:
+        raise ValueError("There is no existing tape.")
+      tape.push_tape(self._tape)
+    else:
+      self._tape = tape.push_new_tape(persistent=self._persistent)
     self._recording = True
 
   def _pop_tape(self):
@@ -764,7 +775,7 @@ class GradientTape(object):
     try:
       yield
     finally:
-      self._push_tape()
+      self._push_tape(existing_tape=True)
 
   def reset(self):
     """Clears all information stored in this tape.
diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py
index ebbd3cd98e892fddb556fc95a4292e05d16fc167..3d3f54b9c468fa6e47838a2d440c0330651402da 100644
--- a/tensorflow/python/eager/backprop_test.py
+++ b/tensorflow/python/eager/backprop_test.py
@@ -96,6 +96,19 @@ class BackpropTest(test.TestCase):
     self.assertAllEqual(grads_and_vars[0][0], 1.0)
     self.assertAllEqual(id(grads_and_vars[0][1]), id(x))
 
+  def testGradientInsideLoop(self):
+    with ops.Graph().as_default():
+      v = resource_variable_ops.ResourceVariable(1.0)
+
+      def body(_):
+        _ = v + 1.0  # This reads the variable inside the loop context
+        with backprop.GradientTape() as t:
+          result = v * 2
+        self.assertTrue(t.gradient(result, v) is not None)
+        return 1.0
+
+      control_flow_ops.while_loop(lambda i: False, body, [1.0])
+
   def testWhereGradient(self):
     # Note: where is special because only some of its arguments are of
     # differentiable dtypes.
@@ -223,11 +236,23 @@ class BackpropTest(test.TestCase):
 
   def testTapeStopRecording(self):
     with backprop.GradientTape() as t:
-      x = constant_op.constant(1.0)
+      x = resource_variable_ops.ResourceVariable(1.0)
       with t.stop_recording():
         y = x * x
     self.assertEqual(t.gradient(y, x), None)
 
+  def testTapeStopStartRecording(self):
+    with backprop.GradientTape(persistent=True) as t:
+      x = resource_variable_ops.ResourceVariable(1.0)
+      x2 = x * 2  # This should be differentiated through.
+      with t.stop_recording():
+        y = x2 * x2
+      z = x2 * x2
+    self.assertEqual(t.gradient(y, x2), None)
+
+    # If the x*2 was not differentiated through, this would be 2.0, not 4.0
+    self.assertEqual(t.gradient(z, x2).numpy(), 4.0)
+
   def testTapeReset(self):
     with backprop.GradientTape() as t:
       v = resource_variable_ops.ResourceVariable(1.0)
@@ -900,32 +925,23 @@ class BackpropTest(test.TestCase):
         'did you forget to return a value from fn?'):
       val_and_grads_fn(x, y)
 
-  def testZerosCacheDoesntLeakAcrossModes(self):
-    with ops.Graph().as_default():
-      t = random_ops.random_normal(shape=[100, 2])
-      x = random_ops.random_normal(shape=[100, 4])
-      dy = random_ops.random_normal(shape=[100, 4])
-      with backprop.GradientTape() as gradient_tape:
-        gradient_tape.watch(x)
-        x1, _ = array_ops.split(x, num_or_size_splits=2, axis=1)
-        y1 = x1 ** 2.
-        y = array_ops.concat([y1, t], axis=1)
-
-      dx = gradient_tape.gradient(y, x, output_gradients=dy)
-      with self.test_session() as sess:
-        sess.run(variables.global_variables_initializer())
-        sess.run(dx)
-
-    t = random_ops.random_normal(shape=[100, 2])
-    x = random_ops.random_normal(shape=[100, 4])
-    dy = random_ops.random_normal(shape=[100, 4])
-    with backprop.GradientTape() as gradient_tape:
-      gradient_tape.watch(x)
-      x1, _ = array_ops.split(x, num_or_size_splits=2, axis=1)
-      y1 = x1 ** 2.
-      y = array_ops.concat([y1, t], axis=1)
-
-    dx = gradient_tape.gradient(y, x, output_gradients=dy)
+  def testZerosCacheDoesntLeakAcrossGraphs(self):
+    with context.graph_mode():
+      def get_grad():
+        with ops.Graph().as_default(), self.test_session():
+          t = constant_op.constant(1, dtype=dtypes.float32, shape=(10, 4))
+          x = constant_op.constant(2, dtype=dtypes.float32, shape=(10, 4))
+          with backprop.GradientTape() as gt:
+            tape.watch(x)
+            x1, _ = array_ops.split(x, num_or_size_splits=2, axis=1)
+            y1 = x1**2
+            y = array_ops.concat([y1, t], axis=1)
+          return self.evaluate(gt.gradient(y, x))
+
+      grad1 = get_grad()
+      grad2 = get_grad()
+
+      self.assertAllEqual(grad1, grad2)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/python/eager/benchmarks_test.py b/tensorflow/python/eager/benchmarks_test.py
index 3aad4a114a710280b5046666256b6b43dc0d5523..afc4bf006679cbcd50ec36b1883a1b38c993bebb 100644
--- a/tensorflow/python/eager/benchmarks_test.py
+++ b/tensorflow/python/eager/benchmarks_test.py
@@ -31,6 +31,7 @@ import numpy as np
 import six
 from six.moves import xrange  # pylint: disable=redefined-builtin
 
+from tensorflow.python import keras
 from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.eager import backprop  # pylint: disable=unused-import
 from tensorflow.python.eager import context
@@ -70,6 +71,25 @@ def c_tfe_py_fastpath_execute(a,
     six.raise_from(core._status_to_exception(e.code, message), None)
 
 
+class SubclassedKerasModel(keras.Model):
+
+  def __init__(self):
+    super(SubclassedKerasModel, self).__init__()
+    self.layer = keras.layers.Dense(
+        10, kernel_initializer="ones", bias_initializer="zeros")
+
+  def call(self, x):
+    return self.layer(x)
+
+
+def make_keras_model():
+  x = keras.Input(shape=(10,))
+  y = keras.layers.Dense(
+      10, kernel_initializer="ones", bias_initializer="zeros")(
+          x)
+  return keras.Model(inputs=x, outputs=y)
+
+
 class MicroBenchmarks(test.Benchmark):
 
   def __init__(self):
@@ -115,6 +135,7 @@ class MicroBenchmarks(test.Benchmark):
 
     def func():
       ops.EagerTensor(value, context=handle, device=device, dtype=dtype)
+
     self._run(func, 30000)
 
   def benchmark_create_float_tensor_from_list_CPU(self):
@@ -211,8 +232,8 @@ class MicroBenchmarks(test.Benchmark):
     inputs = [m]
 
     def f():
-      pywrap_tensorflow.TFE_Py_Execute(
-          ctx_handle, None, "Identity", inputs, attrs, 1)
+      pywrap_tensorflow.TFE_Py_Execute(ctx_handle, None, "Identity", inputs,
+                                       attrs, 1)
 
     self._run(f, 30000)
 
@@ -234,14 +255,13 @@ class MicroBenchmarks(test.Benchmark):
     def f():
       with backprop.GradientTape():
         pass
+
     self._run(f, 30000)
 
   def benchmark_tf_gradient_function_no_op(self):
     with context.device(CPU):
       m = gen_array_ops.identity(self._m_2)
-      self._run(
-          lambda: backprop.gradients_function(lambda x: x, [0])(m),
-          30000)
+      self._run(lambda: backprop.gradients_function(lambda x: x, [0])(m), 30000)
 
   def _benchmark_np_matmul(self, m, transpose_b, num_iters):
     a = m.cpu().numpy()
@@ -255,6 +275,7 @@ class MicroBenchmarks(test.Benchmark):
     self._run(func, num_iters, execution_mode=execution_mode)
 
   def _benchmark_gen_math_ops_matmul(self, m, transpose_b, num_iters):
+
     def func():
       gen_math_ops.mat_mul(m, m, transpose_b=transpose_b)
 
@@ -276,9 +297,10 @@ class MicroBenchmarks(test.Benchmark):
     device = context.context().device_name
     attrs = ("transpose_a", False, "transpose_b", transpose_b, "T",
              m.dtype.as_datatype_enum)
+
     def func():
-      pywrap_tensorflow.TFE_Py_Execute(ctx_handle, device, "MatMul",
-                                       inputs, attrs, 1)
+      pywrap_tensorflow.TFE_Py_Execute(ctx_handle, device, "MatMul", inputs,
+                                       attrs, 1)
 
     self._run(func, num_iters)
 
@@ -542,6 +564,30 @@ class MicroBenchmarks(test.Benchmark):
       self._benchmark_read_variable_with_tape(
           m, num_iters=self._num_iters_2_by_2)
 
+  def benchmark_keras_model_subclassed(self):
+    model = SubclassedKerasModel()
+    data = random_ops.random_uniform((10, 10))
+
+    func = lambda: model(data)
+    # First call is more expensive (creates variables etc.), discount that.
+    func()
+
+    # The whole point of this test is to contrast subclassing with
+    # the functional style of keras model building, so validate that
+    # the models are equivalent.
+    assert np.equal(func(), make_keras_model()(data)).all()
+
+    self._run(func, 30000)
+
+  def benchmark_keras_model_functional(self):
+    model = make_keras_model()
+    data = random_ops.random_uniform((10, 10))
+    func = lambda: model(data)
+    # Symmetry with benchmark_keras_model_subclassed
+    func()
+    assert np.equal(func(), SubclassedKerasModel()(data)).all()
+    self._run(func, 30000)
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/eager/context.py b/tensorflow/python/eager/context.py
index 85b9491903de2ea6ffe1c5ac7ef76efdfda2818b..495a674526fa231a3a8595d5d84ac8f6660b207f 100644
--- a/tensorflow/python/eager/context.py
+++ b/tensorflow/python/eager/context.py
@@ -177,6 +177,11 @@ class Context(object):
         - tf.contrib.eager.SYNC: executes each operation synchronously.
         - tf.contrib.eager.ASYNC: executes each operation asynchronously. These
           operations may return "non-ready" handles.
+      server_def: (Optional.) A tensorflow::ServerDef proto.
+        Enables execution on remote devices. GrpcServers need to be started by
+        creating an identical server_def to this, and setting the appropriate
+        task_indexes, so that the servers can communicate. It will then be
+        possible to execute operations on remote devices.
 
     Raises:
      ValueError: If execution_mode is not valid.
diff --git a/tensorflow/python/eager/core_test.py b/tensorflow/python/eager/core_test.py
index 3fabe7060e980423268eb6f52ab4043cc4a4847c..cc765725a48631f0c50662dedf7fe7af7b30f9a3 100644
--- a/tensorflow/python/eager/core_test.py
+++ b/tensorflow/python/eager/core_test.py
@@ -610,6 +610,14 @@ class TFETest(test_util.TensorFlowTestCase):
       self.assertEquals(typ, dtypes.float32)
       self.assertIsInstance(t, ops.EagerTensor)
 
+  def testConvertMixedEagerTensorsWithVariables(self):
+    var = resource_variable_ops.ResourceVariable(1.0)
+    types, tensors = execute_lib.convert_to_mixed_eager_tensors(
+        ['foo', var], context.context())
+    self.assertAllEqual([dtypes.string, dtypes.float32], types)
+    for t in tensors:
+      self.assertIsInstance(t, ops.EagerTensor)
+
 
 class SendRecvTest(test_util.TensorFlowTestCase):
 
diff --git a/tensorflow/python/eager/execute.py b/tensorflow/python/eager/execute.py
index 2ff5b8d8f489731c14d8abb81652a17026ed4935..f9b8d2cb5db9aedcd834afcde00dac3afa4008bb 100644
--- a/tensorflow/python/eager/execute.py
+++ b/tensorflow/python/eager/execute.py
@@ -198,11 +198,7 @@ def args_to_matching_eager(l, ctx, default_dtype=None):
 
 
 def convert_to_mixed_eager_tensors(values, ctx):
-  v = [
-      t if isinstance(t, ops.EagerTensor) else ops.EagerTensor(
-          t, context=ctx._handle, device=ctx.device_name)  # pylint: disable=protected-access
-      for t in values
-  ]
+  v = [ops.internal_convert_to_tensor(t, ctx=ctx) for t in values]
   types = [t._datatype_enum() for t in v]  # pylint: disable=protected-access
   return types, v
 
diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py
index 7a7e8cd219858e74cb30f22c194fe86d1a4b5e83..99129c25374b11f78d4d414485c2051440cb1897 100644
--- a/tensorflow/python/eager/function.py
+++ b/tensorflow/python/eager/function.py
@@ -21,6 +21,7 @@ from __future__ import print_function
 
 import collections
 import functools
+import threading
 
 import numpy as np
 
@@ -92,10 +93,11 @@ def capture_value(tensor_map, value, dtype, name):
 class CapturingGraph(ops.Graph):
   """Graph used when constructing eager functions."""
 
-  def __init__(self, captures):
+  def __init__(self):
     super(CapturingGraph, self).__init__()
     self._building_function = True
-    self.captures = captures
+    # Maps external tensor id -> internal tensor (e.g. input placeholder).
+    self.captures = {}
     # Map from resource tensor name to last op (in program order) which uses
     # this tensor. Used to enforce that execution order matches program order
     # for resource tensors.
@@ -137,7 +139,7 @@ class CapturingGraph(ops.Graph):
       inputs[i] = self.capture(inp)
     return super(CapturingGraph, self).create_op(
         op_type, inputs, dtypes, input_types, name, attrs, op_def,
-        compute_shapes, compute_device)
+        compute_device=compute_device)
 
 
 # pylint: disable=invalid-name
@@ -469,37 +471,39 @@ class GraphModeFunction(object):
 
   def _construct_backprop_function(self):
     """Constructs the backprop function object for this function."""
-    with self._graph.as_default(), context.graph_mode():
-      c_known_ops = set()
-      c_captured_tensors = set()
-
-      existing_op_len = len(self._graph.get_operations())
-      filtered_outputs = [x for x in self._python_returns if x is not None]
+    filtered_outputs = [x for x in self._python_returns if x is not None]
+    backwards_graph = CapturingGraph()
+    backwards_graph._graph_key = self._graph._graph_key  # pylint: disable=protected-access
+    for collection in self._graph.collections:
+      backwards_graph.get_collection_ref(
+          collection)[:] = self._graph.get_collection(collection)
+    backwards_graph.seed = self._graph.seed
+    with backwards_graph.as_default():
       self._out_grad_placeholders = [
           graph_placeholder(x.dtype, x.shape) for x in filtered_outputs]
-      in_gradients = gradients_impl.gradients(
+      in_gradients = gradients_impl._GradientsHelper(  # pylint: disable=protected-access
           filtered_outputs,
           self._input_placeholders,
-          grad_ys=self._out_grad_placeholders)
-      for op in self._graph.get_operations()[existing_op_len:]:
-        if op.type in ["Variable", "VariableV2", "VarHandleOp"]:
-          raise ValueError("defun cannot capture variables created without "
-                           "using tf.get_variable. Op: %s" % op)
-        c_known_ops.add(op)
-        for i in op.inputs:
-          if i.op not in c_known_ops:
-            c_captured_tensors.add(i)
+          grad_ys=self._out_grad_placeholders,
+          src_graph=self._graph)
 
     backward_outputs = tuple(
         grad for grad in _flatten(in_gradients) if grad is not None)
     output_shapes = tuple(grad.shape for grad in backward_outputs)
 
-    captures = list(sorted(c_captured_tensors, key=lambda x: x.name))
+    captures = backwards_graph.captures
+    ids = list(sorted(captures.keys()))
+    if ids:
+      extra_inputs, extra_placeholders = zip(*[captures[x] for x in ids])
+    else:
+      extra_inputs = []
+      extra_placeholders = []
+
     forward_name = _forward_name(self._func_name)
     self._forward_fdef = _EagerDefinedFunction(
         forward_name, self._graph, self._ops, self._input_placeholders,
-        filtered_outputs + captures, self._attrs)
-    all_inputs = self._out_grad_placeholders + captures
+        filtered_outputs + list(extra_inputs), self._attrs)
+    all_inputs = self._out_grad_placeholders + list(extra_placeholders)
     # Excluding input ops from the body as we do not intend to execute these
     # operations when the function is executed.
     all_ignored_ops = frozenset(x.op for x in all_inputs)
@@ -507,11 +511,12 @@ class GraphModeFunction(object):
     # means rerunning the function-defining code will always define the same
     # function, which is useful if we serialize this etc.
     function_def_ops = tuple(x
-                             for x in sorted(c_known_ops, key=lambda x: x.name)
+                             for x in sorted(backwards_graph.get_operations(),
+                                             key=lambda x: x.name)
                              if x not in all_ignored_ops)
     bname = _backward_name(self._func_name)
     self._backward_function = GraphModeFunction(
-        bname, all_inputs, [], self._graph, function_def_ops,
+        bname, all_inputs, [], backwards_graph, function_def_ops,
         backward_outputs, in_gradients, output_shapes, attrs=self._attrs)
 
   def _backprop_call(self, args):
@@ -656,55 +661,58 @@ def _deterministic_dict_values(kwds):
 def _trace_and_define_function(name, func, compiled, args, kwds):
   """Defines and returns graph-mode version of func."""
   graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
-  with context.graph_mode():
-    captures = {}
-    tmp_graph = CapturingGraph(captures)
-    # Inherit the graph key, since this is used for matching variables in
-    # optimizers.
-    tmp_graph._graph_key = graph_key  # pylint: disable=protected-access
-    # Copy the graph collections to ensure summaries and other things work. This
-    # lets the function access (but not mutate) collections of the containing
-    # graph, such as the global step and the summary writer collections.
-    curr_graph = ops.get_default_graph()
-    for collection in curr_graph.collections:
-      tmp_graph.get_collection_ref(collection)[:] = curr_graph.get_collection(
-          collection)
-    with tmp_graph.as_default(), AutomaticControlDependencies() as a:
-      func_args = _get_defun_inputs(args)
-      func_kwds = _get_defun_inputs(kwds)
-
-      def convert(x):
-        if x is None:
-          return None
-        x = ops.convert_to_tensor_or_indexed_slices(x)
-        x = a.mark_as_return(x)
-        return x
-
-      this_tape = tape.push_new_tape()
-      try:
-        func_outputs = func(*func_args, **func_kwds)
-        func_outputs = nest.map_structure(convert, func_outputs)
-      finally:
-        tape.pop_tape(this_tape)
-      variables = this_tape.watched_variables()
-
-      # Returning a closed-over tensor as an output does not trigger a
-      # call to convert_to_tensor, so we manually capture all such tensors.
-      outputs_list = _flatten(func_outputs)
-      func_def_outputs = [
-          tmp_graph.capture(x) for x in outputs_list
-          if x is not None
-      ]
-
-      ids = list(sorted(captures.keys()))
-      if ids:
-        extra_inputs, extra_placeholders = zip(* [captures[x] for x in ids])
-      else:
-        extra_inputs = []
-        extra_placeholders = []
-      output_shapes = tuple(
-          x.shape if isinstance(x, ops.Tensor) else None
-          for x in func_def_outputs)
+  tmp_graph = CapturingGraph()
+  # Inherit the graph key, since this is used for matching variables in
+  # optimizers.
+  tmp_graph._graph_key = graph_key  # pylint: disable=protected-access
+  # Copy the graph collections to ensure summaries and other things work. This
+  # lets the function access (but not mutate) collections of the containing
+  # graph, such as the global step and the summary writer collections.
+  curr_graph = ops.get_default_graph()
+  for collection in curr_graph.collections:
+    tmp_graph.get_collection_ref(collection)[:] = curr_graph.get_collection(
+        collection)
+  if context.executing_eagerly():
+    tmp_graph.seed = context.global_seed()
+  else:
+    tmp_graph.seed = curr_graph.seed
+  with tmp_graph.as_default(), AutomaticControlDependencies() as a:
+    func_args = _get_defun_inputs(args)
+    func_kwds = _get_defun_inputs(kwds)
+
+    def convert(x):
+      if x is None:
+        return None
+      x = ops.convert_to_tensor_or_indexed_slices(x)
+      x = a.mark_as_return(x)
+      return x
+
+    this_tape = tape.push_new_tape()
+    try:
+      func_outputs = func(*func_args, **func_kwds)
+      func_outputs = nest.map_structure(convert, func_outputs)
+    finally:
+      tape.pop_tape(this_tape)
+    variables = this_tape.watched_variables()
+
+    # Returning a closed-over tensor as an output does not trigger a
+    # call to convert_to_tensor, so we manually capture all such tensors.
+    outputs_list = _flatten(func_outputs)
+    func_def_outputs = [
+        tmp_graph.capture(x) for x in outputs_list
+        if x is not None
+    ]
+
+    captures = tmp_graph.captures
+    ids = list(sorted(captures.keys()))
+    if ids:
+      extra_inputs, extra_placeholders = zip(* [captures[x] for x in ids])
+    else:
+      extra_inputs = []
+      extra_placeholders = []
+    output_shapes = tuple(
+        x.shape if isinstance(x, ops.Tensor) else None
+        for x in func_def_outputs)
 
   func_kwds_values = _deterministic_dict_values(func_kwds)
   flat_inputs = [
@@ -770,6 +778,11 @@ class _PolymorphicFunction(object):
 
   See the documentation for `defun` for more information on the semantics of
   defined functions.
+
+  _PolymorphicFunction class is thread-compatible meaning that minimal
+  usage of defuns (defining and calling) is thread-safe, but if users call other
+  methods or invoke the base `python_function` themselves, external
+  synchronization is necessary.
   """
 
   def __init__(self, python_function, name, compiled=False):
@@ -787,6 +800,8 @@ class _PolymorphicFunction(object):
     self._arguments_to_functions = {}
     self._variables = []
 
+    self._lock = threading.Lock()
+
   def __get__(self, instance, owner):
     """Makes it possible to defun instance methods."""
     del owner
@@ -825,15 +840,16 @@ class _PolymorphicFunction(object):
     # signature so we don't improperly capture tensors such as variables.
     signature += tuple([context.executing_eagerly() or ops.get_default_graph()])
 
-    if signature not in self._arguments_to_functions:
-      graph_function = _trace_and_define_function(
-          self._name, self._python_function, self._compiled, args, kwds)
-      self._arguments_to_functions[signature] = graph_function
-      self._variables.extend(
-          [v for v in graph_function.variables if v not in self._variables])
-      return graph_function, inputs
-    else:
-      return self._arguments_to_functions[signature], inputs
+    with self._lock:
+      if signature not in self._arguments_to_functions:
+        graph_function = _trace_and_define_function(
+            self._name, self._python_function, self._compiled, args, kwds)
+        self._arguments_to_functions[signature] = graph_function
+        self._variables.extend(
+            [v for v in graph_function.variables if v not in self._variables])
+        return graph_function, inputs
+      else:
+        return self._arguments_to_functions[signature], inputs
 
   def __call__(self, *args, **kwds):
     """Calls a graph function specialized for this input signature."""
@@ -1065,7 +1081,7 @@ def defun(func=None, compiled=False):
   tf.enable_eager_execution()
 
   def fn():
-    x = tf.contrib.eager.Variable(0.0)
+    x = tf.Variable(0.0)
     x.assign_add(1.0)
     return x.read_value()
 
@@ -1082,19 +1098,18 @@ def defun(func=None, compiled=False):
   ```
 
   Finally, because each input signature is bound to a unique graph, if your
-  Python function constructs `tf.contrib.eager.Variable` objects, then each
-  graph constructed for that Python function will reference a unique set of
-  variables. To circumvent this problem, we recommend against compiling Python
-  functions that create `tf.contrib.eager.Variable` objects. Instead, Python
-  functions should either lexically close over `tf.contrib.eager.Variable`
-  objects or accept them as arguments, preferably encapsulated in an
-  object-oriented container. If you must create variables inside your Python
-  function and you want each graph generated for it to reference the same set of
-  variables, add logic to your Python function that ensures that variables are
-  only created the first time it is called and are reused for every subsequent
-  invocation; note that this is precisely what @{tf.keras.layers.Layer} objects
-  do, so we recommend using them to represent variable-bearing computations
-  whenever possible.
+  Python function constructs `tf.Variable` objects, then each graph constructed
+  for that Python function will reference a unique set of variables. To
+  circumvent this problem, we recommend against compiling Python functions that
+  create `tf.Variable` objects. Instead, Python functions should either
+  lexically close over `tf.Variable` objects or accept them as arguments,
+  preferably encapsulated in an object-oriented container. If you must create
+  variables inside your Python function and you want each graph generated for it
+  to reference the same set of variables, add logic to your Python function that
+  ensures that variables are only created the first time it is called and are
+  reused for every subsequent invocation; note that this is precisely what
+  @{tf.keras.layers.Layer} objects do, so we recommend using them to represent
+  variable-bearing computations whenever possible.
 
   Args:
     func: function to be compiled. If `func` is None, returns a
@@ -1296,7 +1311,7 @@ class AutomaticControlDependencies(object):
     # Ensures the merge always runs
     ops_which_must_run.add(new_merge[0].op)
     if inp in last_op_using_resource_tensor:
-      # Ensures the switch exectutes after the previous op using the resource.
+      # Ensures the switch executes after the previous op using the resource.
       switch_op._add_control_input(last_op_using_resource_tensor[inp])  # pylint: disable=protected-access
     # Ensure the next op outside the cond happens after the merge.
     last_op_using_resource_tensor[inp] = new_merge[0].op
diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py
index 1de25811b4ee2cbee03229e9351baf41517c6bf9..2e86563a7d0835424d77b5df31e7600cebd52a77 100644
--- a/tensorflow/python/eager/function_test.py
+++ b/tensorflow/python/eager/function_test.py
@@ -27,8 +27,10 @@ from tensorflow.python.eager import function
 from tensorflow.python.eager import tape
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
 from tensorflow.python.framework import function as tf_function
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import random_seed
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import test_util
 from tensorflow.python.layers import convolutional
@@ -38,11 +40,14 @@ from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
-from tensorflow.python.training import gradient_descent
+from tensorflow.python.training import adam
+from tensorflow.python.training import momentum
+from tensorflow.python.training import training_ops
 from tensorflow.python.util import compat
 
 
@@ -134,6 +139,18 @@ class FunctionTest(test.TestCase):
     out = sq_op(t)
     self.assertAllEqual(out, math_ops.matmul(t, t).numpy())
 
+  def disabled_testRandomSeed(self):
+
+    @function.defun
+    def f():
+      return random_ops.random_normal(())
+
+    random_seed.set_random_seed(1)
+    x = f()
+    self.assertNotEqual(x, f())
+    random_seed.set_random_seed(1)
+    self.assertAllEqual(f(), x)
+
   def testNestedInputsDefunOpGraphMode(self):
     matmul = function.defun(math_ops.matmul)
 
@@ -196,6 +213,19 @@ class FunctionTest(test.TestCase):
     self.assertEqual(fn_op.output_shapes, None)
     self.assertAllEqual(fn_op(x, x), None)
 
+  @test_util.run_in_graph_and_eager_modes()
+  def testDefunCondGradient(self):
+
+    @function.defun
+    def f(x):
+      return control_flow_ops.cond(x > 0.5, lambda: 2 * x, lambda: 3 * x)
+
+    with backprop.GradientTape() as t:
+      x = constant_op.constant(1.0)
+      t.watch(x)
+      y = f(x)
+    self.assertAllEqual(self.evaluate(t.gradient(y, x)), 2.0)
+
   def testDefunCapturedInt32(self):
     x = constant_op.constant(1, dtype=dtypes.int32)
 
@@ -545,10 +575,8 @@ class FunctionTest(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes
   def testFunctionWithResourcesOnDifferentDevices(self):
-    # TODO(akshayka): Remove the `skipTest` once we can whitelist ops as
-    # safe to be invoked with resources on different devices.
-    self.skipTest('The Placer disallows ops with resource inputs '
-                  'on different devices.')
+    if not context.context().num_gpus():
+      self.skipTest('No GPUs found.')
 
     with ops.device('/cpu:0'):
       v_cpu = resource_variable_ops.ResourceVariable([0.0, 1.0, 2.0])
@@ -567,6 +595,44 @@ class FunctionTest(test.TestCase):
     expected = self.evaluate(sum_gather())
     self.assertAllEqual(expected, self.evaluate(defined()))
 
+  @test_util.run_in_graph_and_eager_modes
+  def testOpInFunctionWithConflictingResourceInputs(self):
+    if not context.context().num_gpus():
+      self.skipTest('No GPUs found.')
+
+    with ops.device('/cpu:0'):
+      v_cpu = resource_variable_ops.ResourceVariable(
+          [0.0, 1.0, 2.0], name='cpu')
+      v_also_cpu = resource_variable_ops.ResourceVariable(
+          [0.0, 1.0, 2.0], name='also_cpu')
+
+    with ops.device('/gpu:0'):
+      v_gpu = resource_variable_ops.ResourceVariable(
+          [0.0, 1.0, 2.0], name='gpu')
+
+    @function.defun
+    def resource_apply_adam():
+      training_ops.resource_apply_adam(
+          v_cpu.handle,
+          v_gpu.handle,
+          v_also_cpu.handle,
+          1.0,  # beta1_power
+          1.0,  # beta2_power
+          1.0,  # learning_rate
+          1.0,  # beta1
+          1.0,  # beta2
+          1.0,  # epsilon,
+          [1.0, 1.0, 1.0],  # grad
+          False)  # use_locking
+      return None
+
+    with self.assertRaisesRegexp(
+        errors.InvalidArgumentError, 'Could not colocate node with its '
+        'resource and reference inputs.*'):
+      if not context.executing_eagerly():
+        self.evaluate(variables.global_variables_initializer())
+      self.evaluate(resource_apply_adam())
+
   def testFunctionHandlesInputsOnDifferentDevices(self):
     if not context.context().num_gpus():
       self.skipTest('No GPUs found')
@@ -1102,7 +1168,7 @@ class AutomaticControlDependenciesTest(test.TestCase):
     def loss(v):
       return v**2
 
-    optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)
+    optimizer = momentum.MomentumOptimizer(learning_rate=1.0, momentum=1.0)
 
     @function.defun
     def train():
@@ -1114,12 +1180,29 @@ class AutomaticControlDependenciesTest(test.TestCase):
     value = train()
     self.assertEqual(value.numpy(), -1.0)
 
+  # TODO(b/111663004): This should work when the outer context is graph
+  # building.
+  def testOptimizerNonSlotVarsInDefunNoError(self):
+    def loss(v):
+      return v**2
+
+    optimizer = adam.AdamOptimizer(learning_rate=1.0)
+
+    @function.defun
+    def train():
+      v = resource_variable_ops.ResourceVariable(1.0)
+      grad = backprop.implicit_grad(loss)(v)
+      optimizer.apply_gradients(grad)
+      return v.read_value()
+
+    train()
+
   def testOptimizerInDefunWithCapturedVariable(self):
     v = resource_variable_ops.ResourceVariable(1.0)
     def loss():
       return v**2
 
-    optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)
+    optimizer = momentum.MomentumOptimizer(learning_rate=1.0, momentum=1.0)
 
     @function.defun
     def train():
diff --git a/tensorflow/python/eager/graph_callable.py b/tensorflow/python/eager/graph_callable.py
index 848adf4fd3b2c93e7b5afb3ec2911857663c29bb..2dc5060984498a4c033ed89537abbb94ae8503bf 100644
--- a/tensorflow/python/eager/graph_callable.py
+++ b/tensorflow/python/eager/graph_callable.py
@@ -118,7 +118,7 @@ class _VariableCapturingScope(object):
         initializer=None,
         regularizer=None,
         reuse=None,
-        trainable=True,
+        trainable=None,
         collections=None,
         caching_device=None,  # pylint: disable=redefined-outer-name
         partitioner=None,
@@ -156,7 +156,7 @@ class _VariableCapturingScope(object):
         initializer=None,
         regularizer=None,
         reuse=None,
-        trainable=True,
+        trainable=None,
         collections=None,
         caching_device=None,  # pylint: disable=redefined-outer-name
         partitioner=None,
@@ -280,8 +280,7 @@ def _graph_callable_internal(func, shape_and_dtypes):
     # This graph will store both the initialization and the call version of the
     # wrapped function. It will later be used by the backprop code to build the
     # backprop graph, if necessary.
-    captures = {}
-    tmp_graph = function.CapturingGraph(captures)
+    tmp_graph = function.CapturingGraph()
     # Inherit the graph key from the original graph to ensure optimizers don't
     # misbehave.
     tmp_graph._container = container  # pylint: disable=protected-access
@@ -331,6 +330,7 @@ def _graph_callable_internal(func, shape_and_dtypes):
 
   sorted_variables = sorted(variable_captures.variables.values(),
                             key=lambda x: x.name)
+  captures = tmp_graph.captures
   ids = list(sorted(captures.keys()))
   if ids:
     extra_inputs, extra_placeholders = zip(*[captures[x] for x in ids])
diff --git a/tensorflow/python/eager/memory_test.py b/tensorflow/python/eager/memory_test.py
index 74c6cbdd319a3a0476adbff08fc6e70fee65df5c..a1a59d511fdd4b831ea853b1f1cb3212322a3b84 100644
--- a/tensorflow/python/eager/memory_test.py
+++ b/tensorflow/python/eager/memory_test.py
@@ -24,6 +24,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import six
+
 from tensorflow.python import keras
 from tensorflow.python.eager import backprop
 from tensorflow.python.eager import context
@@ -63,7 +65,7 @@ class MemoryTest(test.TestCase):
 
       initial = memory_profiler.memory_usage(-1)[0]
 
-      for _ in xrange(num_iters):
+      for _ in six.moves.range(num_iters):
         f()
 
       increase = memory_profiler.memory_usage(-1)[0] - initial
diff --git a/tensorflow/python/eager/ops_test.py b/tensorflow/python/eager/ops_test.py
index fc76ede4c502ae8b554c925a921e419bf003c40c..17a090d5262f790c92dfa1a92d47f9b5ac6c07d9 100644
--- a/tensorflow/python/eager/ops_test.py
+++ b/tensorflow/python/eager/ops_test.py
@@ -370,6 +370,10 @@ class OpsTest(test_util.TensorFlowTestCase):
     with self.assertRaises(TypeError):
       float(x)
 
+  def testRange(self):
+    x = constant_op.constant(2)
+    self.assertEqual([0, 1], list(range(x)))
+
   def testFormatString(self):
     x = constant_op.constant(3.1415)
     self.assertEqual('3.14', '{:.2f}'.format(x))
diff --git a/tensorflow/python/eager/pywrap_tensor.cc b/tensorflow/python/eager/pywrap_tensor.cc
index ea604647faede0e5b86a17938d0a7c8a7621dec1..15d2ccf9d2b533ced7fd0d104d7a3be3c2ad4dd3 100644
--- a/tensorflow/python/eager/pywrap_tensor.cc
+++ b/tensorflow/python/eager/pywrap_tensor.cc
@@ -154,6 +154,7 @@ TFE_TensorHandle* EagerCast(TFE_Context* ctx, TFE_TensorHandle* handle,
   if (TF_GetCode(out_status) != TF_OK) RETURN_ERROR
   TFE_OpSetAttrType(op, "SrcT", src_type_enum);
   TFE_OpSetAttrType(op, "DstT", dst_type_enum);
+  TFE_OpSetAttrBool(op, "Truncate", false);
   TFE_TensorHandle* output = nullptr;
   int num_outputs = 1;
   TFE_Execute(op, &output, &num_outputs, out_status);
@@ -620,10 +621,6 @@ static PyType_Slot EagerTensor_Type_slots[] = {
     {Py_tp_init, reinterpret_cast(EagerTensor_init)},
     {0, nullptr},
 };
-
-PyType_Spec EagerTensor_Type_spec = {"EagerTensor", sizeof(EagerTensor), 0,
-                                     Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HEAPTYPE,
-                                     EagerTensor_Type_slots};
 #else
 // TODO(agarwal): support active_trace.
 static PyTypeObject _EagerTensorType = {
@@ -754,6 +751,34 @@ PyObject* TFE_Py_InitEagerTensor(PyObject* base_class) {
 #if PY_MAJOR_VERSION >= 3
   PyObject* bases = PyTuple_New(1);
   PyTuple_SET_ITEM(bases, 0, base_class);
+
+  tensorflow::Safe_PyObjectPtr base_class_module(
+      PyObject_GetAttrString(base_class, "__module__"));
+  const char* module = nullptr;
+  if (PyErr_Occurred()) {
+    PyErr_Clear();
+    module = "__builtin__";
+  } else {
+    module = PyBytes_AsString(base_class_module.get());
+    if (module == nullptr) {
+      PyErr_Clear();
+      module = PyUnicode_AsUTF8(base_class_module.get());
+      if (module == nullptr) {
+        PyErr_Clear();
+        module = "__builtin__";
+      }
+    }
+  }
+
+  // NOTE: The c_str from this string needs to outlast the function, hence is
+  // static.
+  static tensorflow::string fully_qualified_name =
+      tensorflow::strings::StrCat(module, ".EagerTensor");
+
+  static PyType_Spec EagerTensor_Type_spec = {
+      fully_qualified_name.c_str(), sizeof(EagerTensor), 0,
+      Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HEAPTYPE, EagerTensor_Type_slots};
+
   EagerTensorType = reinterpret_cast(
       PyType_FromSpecWithBases(&EagerTensor_Type_spec, bases));
   if (PyErr_Occurred()) {
diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc
index 57b4dab51cc766042dfa895b197b3e3de037269d..0eabea321c120e1eb483b7161e88f964340db76d 100644
--- a/tensorflow/python/eager/pywrap_tfe_src.cc
+++ b/tensorflow/python/eager/pywrap_tfe_src.cc
@@ -845,11 +845,9 @@ int64_t get_uid() {
 PyObject* TFE_Py_UID() { return PyLong_FromLongLong(get_uid()); }
 
 void TFE_DeleteContextCapsule(PyObject* context) {
-  TF_Status* status = TF_NewStatus();
   TFE_Context* ctx =
       reinterpret_cast(PyCapsule_GetPointer(context, nullptr));
-  TFE_DeleteContext(ctx, status);
-  TF_DeleteStatus(status);
+  TFE_DeleteContext(ctx);
 }
 
 static tensorflow::int64 MakeInt(PyObject* integer) {
@@ -1173,14 +1171,14 @@ static tensorflow::eager::TapeTensor TapeTensorFromTensor(PyObject* tensor) {
   if (EagerTensor_CheckExact(tensor)) {
     TFE_TensorHandle* t = EagerTensor_Handle(tensor);
     tensorflow::int64 id = EagerTensor_id(tensor);
-    const tensorflow::Tensor* tensor = nullptr;
-    const tensorflow::Status status = t->handle->Tensor(&tensor);
+    tensorflow::TensorShape tensor_shape;
+    const tensorflow::Status status = t->handle->Shape(&tensor_shape);
+
     if (MaybeRaiseExceptionFromStatus(status, nullptr)) {
       return tensorflow::eager::TapeTensor{id, t->handle->dtype,
                                            tensorflow::TensorShape({})};
     } else {
-      return tensorflow::eager::TapeTensor{id, t->handle->dtype,
-                                           tensor->shape()};
+      return tensorflow::eager::TapeTensor{id, t->handle->dtype, tensor_shape};
     }
   }
   tensorflow::int64 id = FastTensorId(tensor);
@@ -1898,14 +1896,39 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs,
 
 void MaybeWatchVariable(PyObject* input) {
   DCHECK(CheckResourceVariable(input));
-  DCHECK(PyObject_HasAttrString(input, "trainable"));
+  DCHECK(PyObject_HasAttrString(input, "_trainable"));
 
   tensorflow::Safe_PyObjectPtr trainable(
-      PyObject_GetAttrString(input, "trainable"));
+      PyObject_GetAttrString(input, "_trainable"));
   if (trainable.get() == Py_False) return;
   TFE_Py_TapeSetWatchVariable(input);
 }
 
+bool CastTensor(const FastPathOpExecInfo& op_exec_info,
+                const TF_DataType& desired_dtype,
+                tensorflow::Safe_TFE_TensorHandlePtr* handle,
+                TF_Status* status) {
+  TF_DataType input_dtype = TFE_TensorHandleDataType(handle->get());
+  TF_DataType output_dtype = input_dtype;
+
+  if (desired_dtype >= 0 && desired_dtype != input_dtype) {
+    *handle = tensorflow::make_safe(
+        tensorflow::EagerCast(op_exec_info.ctx, handle->get(), input_dtype,
+                              static_cast(desired_dtype), status));
+    if (!status->status.ok()) return false;
+    output_dtype = desired_dtype;
+  }
+
+  if (output_dtype != TF_INT32) {
+    // Note that this is a shallow copy and will share the underlying buffer
+    // if copying to the same device.
+    *handle = tensorflow::make_safe(TFE_TensorHandleCopyToDevice(
+        handle->get(), op_exec_info.ctx, op_exec_info.device_name, status));
+    if (!status->status.ok()) return false;
+  }
+  return true;
+}
+
 bool ReadVariableOp(const FastPathOpExecInfo& parent_op_exec_info,
                     PyObject* input, tensorflow::Safe_PyObjectPtr* output,
                     TF_Status* status) {
@@ -1938,9 +1961,31 @@ bool ReadVariableOp(const FastPathOpExecInfo& parent_op_exec_info,
   TFE_Execute(op, &output_handle, &num_retvals, status);
   if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) return false;
 
-  // Always create the py object (and correctly DECREF it) from the returned
-  // value, else the data will leak.
-  output->reset(EagerTensorFromHandle(output_handle));
+  if (!PyObject_HasAttrString(input, "_read_dtype")) {
+    // Always create the py object (and correctly DECREF it) from the returned
+    // value, else the data will leak.
+    output->reset(EagerTensorFromHandle(output_handle));
+  } else {
+    // This is a _MixedPrecisionVariable which potentially does casting when
+    // being read.
+    tensorflow::Safe_PyObjectPtr read_dtype(
+        PyObject_GetAttrString(input, "_read_dtype"));
+    int desired_dtype = -1;
+    if (!ParseTypeValue("_read_dtype", read_dtype.get(), status,
+                        &desired_dtype)) {
+      return false;
+    }
+
+    auto safe_output_handle = tensorflow::make_safe(output_handle);
+    // Retires output_handle in the future.
+    output_handle = nullptr;
+    if (!CastTensor(parent_op_exec_info,
+                    static_cast(desired_dtype),
+                    &safe_output_handle, status)) {
+      return false;
+    }
+    output->reset(EagerTensorFromHandle(safe_output_handle.release()));
+  }
 
   // TODO(nareshmodi): Should we run post exec callbacks here?
   if (parent_op_exec_info.run_gradient_callback) {
@@ -2010,27 +2055,13 @@ bool ConvertToTensor(
     }
   }
 
-  TF_DataType handle_dtype = TFE_TensorHandleDataType(handle.get());
-  if (desired_dtype >= 0 && desired_dtype != handle_dtype) {
-    handle = tensorflow::make_safe(
-        tensorflow::EagerCast(op_exec_info.ctx, handle.get(), handle_dtype,
-                              static_cast(desired_dtype), status));
-    if (!status->status.ok()) return false;
-
-    handle_dtype = TFE_TensorHandleDataType(handle.get());
-  }
-
-  if (handle_dtype != TF_INT32) {
-    // Note that this is a shallow copy and will share the underlying buffer
-    // if copying to the same device.
-    handle = tensorflow::make_safe(TFE_TensorHandleCopyToDevice(
-        handle.get(), op_exec_info.ctx, op_exec_info.device_name, status));
-    if (!status->status.ok()) return false;
+  if (!CastTensor(op_exec_info, static_cast(desired_dtype),
+                  &handle, status)) {
+    return false;
   }
-
+  TF_DataType output_dtype = TFE_TensorHandleDataType(handle.get());
   output_handle->reset(EagerTensorFromHandle(handle.release()));
-
-  dtype_setter(handle_dtype);
+  dtype_setter(output_dtype);
 
   return true;
 }
diff --git a/tensorflow/python/eager/pywrap_tfe_test.py b/tensorflow/python/eager/pywrap_tfe_test.py
index faaae40b3f1ef02984a7a75c23ae4acae65ac335..fd8ab695b8fbb732bb853cd4affadf98d4861cc2 100644
--- a/tensorflow/python/eager/pywrap_tfe_test.py
+++ b/tensorflow/python/eager/pywrap_tfe_test.py
@@ -23,6 +23,7 @@ from tensorflow.python.eager import backprop
 from tensorflow.python.eager import context
 from tensorflow.python.eager import test
 from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
@@ -69,6 +70,25 @@ class Tests(test.TestCase):
 
     self.assertAllEqual(x, y)
 
+  @test_util.assert_no_new_tensors
+  @test_util.assert_no_garbage_created
+  def testFastpathExecute_MixedPrecisionVariableMatMulCorrectResponse(self):
+    ctx = context.context()
+    a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
+    a_2_by_2_fp16 = math_ops.cast(a_2_by_2, dtype=dtypes.float16)
+    m = resource_variable_ops.ResourceVariable(a_2_by_2)
+    m = resource_variable_ops._MixedPrecisionVariable(
+        m, read_dtype=dtypes.float16)
+    x = pywrap_tensorflow.TFE_Py_FastPathExecute(
+        ctx._handle, ctx.device_name, "MatMul", None, None, m, m, "transpose_a",
+        False, "transpose_b", False)
+    y = pywrap_tensorflow.TFE_Py_FastPathExecute(
+        ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2_fp16,
+        a_2_by_2_fp16, "transpose_a", False, "transpose_b", False)
+
+    self.assertEqual(x.dtype, dtypes.float16)
+    self.assertAllEqual(x, y)
+
   @test_util.assert_no_new_tensors
   @test_util.assert_no_garbage_created
   def testFastpathExecute_TapeWrite(self):
@@ -98,6 +118,29 @@ class Tests(test.TestCase):
     self.assertAllEqual(dz_dy.numpy(),
                         constant_op.constant(4.0, shape=[2, 2]).numpy())
 
+  @test_util.assert_no_new_tensors
+  @test_util.assert_no_garbage_created
+  def testFastpathExecute_MixedPrecisionVariableTapeWrite(self):
+    ctx = context.context()
+    with backprop.GradientTape(persistent=True) as tape:
+      a_2_by_2 = constant_op.constant(
+          [[1.0, 2.0], [3.0, 4.0]], dtype=dtypes.float32)
+      a_2_by_2_fp16 = math_ops.cast(a_2_by_2, dtype=dtypes.float16)
+      m1 = resource_variable_ops.ResourceVariable(a_2_by_2)
+      m2 = resource_variable_ops._MixedPrecisionVariable(
+          m1, read_dtype=dtypes.float16)
+      tape.watch(m2)
+      z = pywrap_tensorflow.TFE_Py_FastPathExecute(
+          ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2_fp16, m2,
+          "transpose_a", False, "transpose_b", False)
+    dz_dy = tape.gradient(z, [m2])[0]
+    self.assertEqual(dz_dy.dtype, dtypes.float16)
+
+    expected_grads = math_ops.matmul(
+        array_ops.transpose(a_2_by_2_fp16),
+        constant_op.constant(1., shape=[2, 2], dtype=dtypes.float16)).numpy()
+    self.assertAllEqual(dz_dy.numpy(), expected_grads)
+
   # Tests homogeneous list op
   @test_util.assert_no_new_tensors
   @test_util.assert_no_garbage_created
diff --git a/tensorflow/python/eager/tensor_test.py b/tensorflow/python/eager/tensor_test.py
index 626a4eb1eee9bda6c910c9dfa9cfff27b04444c1..871136e2c893ff92bc13caa9405b0a8f3fd1385d 100644
--- a/tensorflow/python/eager/tensor_test.py
+++ b/tensorflow/python/eager/tensor_test.py
@@ -278,7 +278,7 @@ class TFETensorUtilTest(test_util.TensorFlowTestCase):
 
     with self.assertRaisesRegexp(
         TypeError,
-        r"tensors argument must be a list or a tuple. Got \"EagerTensor\""):
+        r"tensors argument must be a list or a tuple. Got.*EagerTensor"):
       pywrap_tensorflow.TFE_Py_TensorShapeSlice(t1, -2)
 
   def testNegativeSliceDim(self):
diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD
index 8ee38d35cc152e6c281e83d7fd49540ddaee2a7e..817c8e6848dcab5e04770e33dda803ed2a4a0c9a 100644
--- a/tensorflow/python/estimator/BUILD
+++ b/tensorflow/python/estimator/BUILD
@@ -40,9 +40,9 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":gc",
+        ":metric_keys",
+        ":util",
         "//tensorflow:tensorflow_py_no_contrib",
-        "//tensorflow/python/estimator:metric_keys",
-        "//tensorflow/python/estimator:util",
     ],
 )
 
@@ -171,6 +171,7 @@ py_test(
     name = "baseline_test",
     size = "medium",
     srcs = ["canned/baseline_test.py"],
+    shard_count = 4,
     srcs_version = "PY2AND3",
     tags = [
         "no_pip",
@@ -207,6 +208,7 @@ py_test(
     name = "boosted_trees_test",
     size = "medium",
     srcs = ["canned/boosted_trees_test.py"],
+    shard_count = 2,
     srcs_version = "PY2AND3",
     tags = [
         "optonly",
@@ -676,6 +678,7 @@ py_test(
     name = "keras_test",
     size = "large",
     srcs = ["keras_test.py"],
+    shard_count = 4,
     srcs_version = "PY2AND3",
     tags = [
         "no_windows",
@@ -683,9 +686,9 @@ py_test(
     ],
     deps = [
         ":keras",
+        ":numpy_io",
+        ":run_config",
         "//tensorflow:tensorflow_py_no_contrib",
-        "//tensorflow/python/estimator:numpy_io",
-        "//tensorflow/python/estimator:run_config",
         "//third_party/py/numpy",
     ],
 )
@@ -706,6 +709,14 @@ py_library(
     visibility = ["//visibility:public"],
 )
 
+py_library(
+    name = "expect_h5py_installed",
+    # This is a dummy rule used as a numpy dependency in open-source.
+    # We expect h5py to already be installed on the system, e.g. via
+    # `pip install h5py'
+    visibility = ["//visibility:public"],
+)
+
 py_library(
     name = "expect_six_installed",
     # This is a dummy rule used as a numpy dependency in open-source.
diff --git a/tensorflow/python/estimator/api/BUILD b/tensorflow/python/estimator/api/BUILD
index aa5a29e6dd148c39ebb098cb99cb1907d9c5a9d9..a75fa7d0aee56c4fd4faccfaf2fa07c399cedcc9 100644
--- a/tensorflow/python/estimator/api/BUILD
+++ b/tensorflow/python/estimator/api/BUILD
@@ -6,13 +6,14 @@ package(
 
 licenses(["notice"])  # Apache 2.0
 
-load("//tensorflow/tools/api/generator:api_gen.bzl", "gen_api_init_files")
-load("//tensorflow/tools/api/generator:api_gen.bzl", "ESTIMATOR_API_INIT_FILES")
+load("//tensorflow/python/tools/api/generator:api_gen.bzl", "gen_api_init_files")
+load("//tensorflow/python/tools/api/generator:api_gen.bzl", "ESTIMATOR_API_INIT_FILES")
 
 gen_api_init_files(
     name = "estimator_python_api_gen",
     api_name = "estimator",
     output_files = ESTIMATOR_API_INIT_FILES,
+    output_package = "tensorflow.python.estimator.api",
     package = "tensorflow.python.estimator",
     package_dep = "//tensorflow/python/estimator:estimator_py",
 )
diff --git a/tensorflow/python/estimator/canned/baseline_test.py b/tensorflow/python/estimator/canned/baseline_test.py
index 7bf2e62da9c4598c28ad38825aac2031c9d51905..e46a3a156dfd546b733067299906857fbd705736 100644
--- a/tensorflow/python/estimator/canned/baseline_test.py
+++ b/tensorflow/python/estimator/canned/baseline_test.py
@@ -154,6 +154,8 @@ class BaselineRegressorEvaluationTest(test.TestCase):
     self.assertDictEqual({
         metric_keys.MetricKeys.LOSS: 9.,
         metric_keys.MetricKeys.LOSS_MEAN: 9.,
+        metric_keys.MetricKeys.PREDICTION_MEAN: 13.,
+        metric_keys.MetricKeys.LABEL_MEAN: 10.,
         ops.GraphKeys.GLOBAL_STEP: 100
     }, eval_metrics)
 
@@ -176,6 +178,8 @@ class BaselineRegressorEvaluationTest(test.TestCase):
     self.assertDictEqual({
         metric_keys.MetricKeys.LOSS: 18.,
         metric_keys.MetricKeys.LOSS_MEAN: 9.,
+        metric_keys.MetricKeys.PREDICTION_MEAN: 13.,
+        metric_keys.MetricKeys.LABEL_MEAN: 10.,
         ops.GraphKeys.GLOBAL_STEP: 100
     }, eval_metrics)
 
@@ -204,6 +208,8 @@ class BaselineRegressorEvaluationTest(test.TestCase):
     self.assertDictEqual({
         metric_keys.MetricKeys.LOSS: 27.,
         metric_keys.MetricKeys.LOSS_MEAN: 9.,
+        metric_keys.MetricKeys.PREDICTION_MEAN: 13.,
+        metric_keys.MetricKeys.LABEL_MEAN: 10.,
         ops.GraphKeys.GLOBAL_STEP: 100
     }, eval_metrics)
 
@@ -229,7 +235,9 @@ class BaselineRegressorEvaluationTest(test.TestCase):
 
     self.assertItemsEqual(
         (metric_keys.MetricKeys.LOSS, metric_keys.MetricKeys.LOSS_MEAN,
-         ops.GraphKeys.GLOBAL_STEP), eval_metrics.keys())
+         metric_keys.MetricKeys.PREDICTION_MEAN,
+         metric_keys.MetricKeys.LABEL_MEAN, ops.GraphKeys.GLOBAL_STEP),
+        eval_metrics.keys())
 
     # Logit is bias which is [46, 58]
     self.assertAlmostEqual(0, eval_metrics[metric_keys.MetricKeys.LOSS])
diff --git a/tensorflow/python/estimator/canned/boosted_trees.py b/tensorflow/python/estimator/canned/boosted_trees.py
index a22e9745c1929a29394add8ade835b2aa5fbd13b..8b423f76de8e3b685e0426ef48fdd9f195925ff1 100644
--- a/tensorflow/python/estimator/canned/boosted_trees.py
+++ b/tensorflow/python/estimator/canned/boosted_trees.py
@@ -17,6 +17,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import abc
 import collections
 import functools
 
@@ -45,7 +46,7 @@ from tensorflow.python.util.tf_export import estimator_export
 # TODO(nponomareva): Reveal pruning params here.
 _TreeHParams = collections.namedtuple('TreeHParams', [
     'n_trees', 'max_depth', 'learning_rate', 'l1', 'l2', 'tree_complexity',
-    'min_node_weight', 'center_bias'
+    'min_node_weight', 'center_bias', 'pruning_mode'
 ])
 
 _HOLD_FOR_MULTI_CLASS_SUPPORT = object()
@@ -384,6 +385,260 @@ class _StopAtAttemptsHook(session_run_hook.SessionRunHook):
       run_context.request_stop()
 
 
+def _get_max_splits(tree_hparams):
+  """Calculates the max possible number of splits based on tree params."""
+  # maximum number of splits possible in the whole tree =2^(D-1)-1
+  max_splits = (1 << tree_hparams.max_depth) - 1
+  return max_splits
+
+
+class _EnsembleGrower(object):
+  """Abstract base class for different types of ensemble growers.
+
+  Use it to receive training ops for growing and centering bias, depending
+  on the implementation (for example, in memory or accumulator-based
+  distributed):
+    grower = ...create subclass grower(tree_ensemble, tree_hparams)
+    grow_op = grower.grow_tree(stats_summaries_list, feature_ids_list,
+                               last_layer_nodes_range)
+    training_ops.append(grow_op)
+  """
+
+  def __init__(self, tree_ensemble, tree_hparams):
+    """Initializes a grower object.
+
+    Args:
+      tree_ensemble: A TreeEnsemble variable.
+      tree_hparams: TODO. collections.namedtuple for hyper parameters.
+    Raises:
+      ValueError: when pruning mode is invalid or pruning is used and no tree
+      complexity is set.
+    """
+    self._tree_ensemble = tree_ensemble
+    self._tree_hparams = tree_hparams
+    # pylint: disable=protected-access
+    self._pruning_mode_parsed = boosted_trees_ops.PruningMode.from_str(
+        tree_hparams.pruning_mode)
+
+    if (self._pruning_mode_parsed != boosted_trees_ops.PruningMode.NO_PRUNING
+        and tree_hparams.tree_complexity <= 0):
+      raise ValueError('For pruning, tree_complexity must be positive.')
+    # pylint: enable=protected-access
+
+  @abc.abstractmethod
+  def center_bias(self, center_bias_var, gradients, hessians):
+    """Centers bias, if ready, based on statistics.
+
+    Args:
+      center_bias_var: A variable that will be updated when bias centering
+        finished.
+      gradients: A rank 2 tensor of gradients.
+      hessians: A rank 2 tensor of hessians.
+
+    Returns:
+      An operation for centering bias.
+    """
+
+  @abc.abstractmethod
+  def grow_tree(self, stats_summaries_list, feature_ids_list,
+                last_layer_nodes_range):
+    """Grows a tree, if ready, based on provided statistics.
+
+    Args:
+      stats_summaries_list: List of stats summary tensors, representing sums of
+        gradients and hessians for each feature bucket.
+      feature_ids_list: a list of lists of feature ids for each bucket size.
+      last_layer_nodes_range: A tensor representing ids of the nodes in the
+        current layer, to be split.
+
+    Returns:
+      An op for growing a tree.
+    """
+
+  #  ============= Helper methods ===========
+
+  def _center_bias_fn(self, center_bias_var, mean_gradients, mean_hessians):
+    """Updates the ensembles and cache (if needed) with logits prior."""
+    continue_centering = boosted_trees_ops.center_bias(
+        self._tree_ensemble.resource_handle,
+        mean_gradients=mean_gradients,
+        mean_hessians=mean_hessians,
+        l1=self._tree_hparams.l1,
+        l2=self._tree_hparams.l2)
+    return center_bias_var.assign(continue_centering)
+
+  def _grow_tree_from_stats_summaries(self, stats_summaries_list,
+                                      feature_ids_list, last_layer_nodes_range):
+    """Updates ensemble based on the best gains from stats summaries."""
+    node_ids_per_feature = []
+    gains_list = []
+    thresholds_list = []
+    left_node_contribs_list = []
+    right_node_contribs_list = []
+    all_feature_ids = []
+    assert len(stats_summaries_list) == len(feature_ids_list)
+
+    max_splits = _get_max_splits(self._tree_hparams)
+
+    for i, feature_ids in enumerate(feature_ids_list):
+      (numeric_node_ids_per_feature, numeric_gains_list,
+       numeric_thresholds_list, numeric_left_node_contribs_list,
+       numeric_right_node_contribs_list) = (
+           boosted_trees_ops.calculate_best_gains_per_feature(
+               node_id_range=last_layer_nodes_range,
+               stats_summary_list=stats_summaries_list[i],
+               l1=self._tree_hparams.l1,
+               l2=self._tree_hparams.l2,
+               tree_complexity=self._tree_hparams.tree_complexity,
+               min_node_weight=self._tree_hparams.min_node_weight,
+               max_splits=max_splits))
+
+      all_feature_ids += feature_ids
+      node_ids_per_feature += numeric_node_ids_per_feature
+      gains_list += numeric_gains_list
+      thresholds_list += numeric_thresholds_list
+      left_node_contribs_list += numeric_left_node_contribs_list
+      right_node_contribs_list += numeric_right_node_contribs_list
+
+    grow_op = boosted_trees_ops.update_ensemble(
+        # Confirm if local_tree_ensemble or tree_ensemble should be used.
+        self._tree_ensemble.resource_handle,
+        feature_ids=all_feature_ids,
+        node_ids=node_ids_per_feature,
+        gains=gains_list,
+        thresholds=thresholds_list,
+        left_node_contribs=left_node_contribs_list,
+        right_node_contribs=right_node_contribs_list,
+        learning_rate=self._tree_hparams.learning_rate,
+        max_depth=self._tree_hparams.max_depth,
+        pruning_mode=self._pruning_mode_parsed)
+    return grow_op
+
+
+class _InMemoryEnsembleGrower(_EnsembleGrower):
+  """A base class for ensemble growers."""
+
+  def __init__(self, tree_ensemble, tree_hparams):
+
+    super(_InMemoryEnsembleGrower, self).__init__(
+        tree_ensemble=tree_ensemble, tree_hparams=tree_hparams)
+
+  def center_bias(self, center_bias_var, gradients, hessians):
+    # For in memory, we already have a full batch of gradients and hessians,
+    # so just take a mean and proceed with centering.
+    mean_gradients = array_ops.expand_dims(
+        math_ops.reduce_mean(gradients, 0), 0)
+    mean_heassians = array_ops.expand_dims(math_ops.reduce_mean(hessians, 0), 0)
+    return self._center_bias_fn(center_bias_var, mean_gradients, mean_heassians)
+
+  def grow_tree(self, stats_summaries_list, feature_ids_list,
+                last_layer_nodes_range):
+    # For in memory, we already have full data in one batch, so we can grow the
+    # tree immediately.
+    return self._grow_tree_from_stats_summaries(
+        stats_summaries_list, feature_ids_list, last_layer_nodes_range)
+
+
+class _AccumulatorEnsembleGrower(_EnsembleGrower):
+  """A base class for ensemble growers."""
+
+  def __init__(self, tree_ensemble, tree_hparams, stamp_token,
+               n_batches_per_layer, bucket_size_list, is_chief):
+    super(_AccumulatorEnsembleGrower, self).__init__(
+        tree_ensemble=tree_ensemble, tree_hparams=tree_hparams)
+    self._stamp_token = stamp_token
+    self._n_batches_per_layer = n_batches_per_layer
+    self._bucket_size_list = bucket_size_list
+    self._is_chief = is_chief
+
+  def center_bias(self, center_bias_var, gradients, hessians):
+    # For not in memory situation, we need to accumulate enough of batches first
+    # before proceeding with centering bias.
+
+    # Create an accumulator.
+    bias_dependencies = []
+    bias_accumulator = data_flow_ops.ConditionalAccumulator(
+        dtype=dtypes.float32,
+        # The stats consist of grads and hessians means only.
+        # TODO(nponomareva): this will change for a multiclass
+        shape=[2, 1],
+        shared_name='bias_accumulator')
+
+    grads_and_hess = array_ops.stack([gradients, hessians], axis=0)
+    grads_and_hess = math_ops.reduce_mean(grads_and_hess, axis=1)
+
+    apply_grad = bias_accumulator.apply_grad(grads_and_hess, self._stamp_token)
+    bias_dependencies.append(apply_grad)
+
+    # Center bias if enough batches were processed.
+    with ops.control_dependencies(bias_dependencies):
+      if not self._is_chief:
+        return control_flow_ops.no_op()
+
+      def center_bias_from_accumulator():
+        accumulated = array_ops.unstack(bias_accumulator.take_grad(1), axis=0)
+        return self._center_bias_fn(center_bias_var,
+                                    array_ops.expand_dims(accumulated[0], 0),
+                                    array_ops.expand_dims(accumulated[1], 0))
+
+      center_bias_op = control_flow_ops.cond(
+          math_ops.greater_equal(bias_accumulator.num_accumulated(),
+                                 self._n_batches_per_layer),
+          center_bias_from_accumulator,
+          control_flow_ops.no_op,
+          name='wait_until_n_batches_for_bias_accumulated')
+      return center_bias_op
+
+  def grow_tree(self, stats_summaries_list, feature_ids_list,
+                last_layer_nodes_range):
+    # For not in memory situation, we need to accumulate enough of batches first
+    # before proceeding with building a tree layer.
+    max_splits = _get_max_splits(self._tree_hparams)
+
+    # Prepare accumulators.
+    accumulators = []
+    dependencies = []
+    for i, feature_ids in enumerate(feature_ids_list):
+      stats_summaries = stats_summaries_list[i]
+      accumulator = data_flow_ops.ConditionalAccumulator(
+          dtype=dtypes.float32,
+          # The stats consist of grads and hessians (the last dimension).
+          shape=[len(feature_ids), max_splits, self._bucket_size_list[i], 2],
+          shared_name='numeric_stats_summary_accumulator_' + str(i))
+      accumulators.append(accumulator)
+
+      apply_grad = accumulator.apply_grad(
+          array_ops.stack(stats_summaries, axis=0), self._stamp_token)
+      dependencies.append(apply_grad)
+
+    # Grow the tree if enough batches is accumulated.
+    with ops.control_dependencies(dependencies):
+      if not self._is_chief:
+        return control_flow_ops.no_op()
+
+      min_accumulated = math_ops.reduce_min(
+          array_ops.stack([acc.num_accumulated() for acc in accumulators]))
+
+      def grow_tree_from_accumulated_summaries_fn():
+        """Updates tree with the best layer from accumulated summaries."""
+        # Take out the accumulated summaries from the accumulator and grow.
+        stats_summaries_list = []
+        stats_summaries_list = [
+            array_ops.unstack(accumulator.take_grad(1), axis=0)
+            for accumulator in accumulators
+        ]
+        grow_op = self._grow_tree_from_stats_summaries(
+            stats_summaries_list, feature_ids_list, last_layer_nodes_range)
+        return grow_op
+
+      grow_model = control_flow_ops.cond(
+          math_ops.greater_equal(min_accumulated, self._n_batches_per_layer),
+          grow_tree_from_accumulated_summaries_fn,
+          control_flow_ops.no_op,
+          name='wait_until_n_batches_accumulated')
+      return grow_model
+
+
 def _bt_model_fn(
     features,
     labels,
@@ -431,6 +686,7 @@ def _bt_model_fn(
   is_single_machine = (config.num_worker_replicas <= 1)
   sorted_feature_columns = sorted(feature_columns, key=lambda tc: tc.name)
   center_bias = tree_hparams.center_bias
+
   if train_in_memory:
     assert n_batches_per_layer == 1, (
         'When train_in_memory is enabled, input_fn should return the entire '
@@ -441,11 +697,6 @@ def _bt_model_fn(
       raise ValueError('train_in_memory is supported only for '
                        'non-distributed training.')
   worker_device = control_flow_ops.no_op().device
-  # maximum number of splits possible in the whole tree =2^(D-1)-1
-  # TODO(youngheek): perhaps storage could be optimized by storing stats with
-  # the dimension max_splits_per_layer, instead of max_splits (for the entire
-  # tree).
-  max_splits = (1 << tree_hparams.max_depth) - 1
   train_op = []
   with ops.name_scope(name) as name:
     # Prepare.
@@ -543,6 +794,11 @@ def _bt_model_fn(
         hessians = gradients_impl.gradients(
             gradients, logits, name='Hessians')[0]
 
+      # TODO(youngheek): perhaps storage could be optimized by storing stats
+      # with the dimension max_splits_per_layer, instead of max_splits (for the
+      # entire tree).
+      max_splits = _get_max_splits(tree_hparams)
+
       stats_summaries_list = []
       for i, feature_ids in enumerate(feature_ids_list):
         num_buckets = bucket_size_list[i]
@@ -559,169 +815,28 @@ def _bt_model_fn(
         ]
         stats_summaries_list.append(summaries)
 
-      # ========= Helper methods for both in and not in memory. ==============
-      def grow_tree_from_stats_summaries(stats_summaries_list,
-                                         feature_ids_list):
-        """Updates ensemble based on the best gains from stats summaries."""
-        node_ids_per_feature = []
-        gains_list = []
-        thresholds_list = []
-        left_node_contribs_list = []
-        right_node_contribs_list = []
-        all_feature_ids = []
-
-        assert len(stats_summaries_list) == len(feature_ids_list)
-
-        for i, feature_ids in enumerate(feature_ids_list):
-          (numeric_node_ids_per_feature, numeric_gains_list,
-           numeric_thresholds_list, numeric_left_node_contribs_list,
-           numeric_right_node_contribs_list) = (
-               boosted_trees_ops.calculate_best_gains_per_feature(
-                   node_id_range=last_layer_nodes_range,
-                   stats_summary_list=stats_summaries_list[i],
-                   l1=tree_hparams.l1,
-                   l2=tree_hparams.l2,
-                   tree_complexity=tree_hparams.tree_complexity,
-                   min_node_weight=tree_hparams.min_node_weight,
-                   max_splits=max_splits))
-
-          all_feature_ids += feature_ids
-          node_ids_per_feature += numeric_node_ids_per_feature
-          gains_list += numeric_gains_list
-          thresholds_list += numeric_thresholds_list
-          left_node_contribs_list += numeric_left_node_contribs_list
-          right_node_contribs_list += numeric_right_node_contribs_list
-
-        grow_op = boosted_trees_ops.update_ensemble(
-            # Confirm if local_tree_ensemble or tree_ensemble should be used.
-            tree_ensemble.resource_handle,
-            feature_ids=all_feature_ids,
-            node_ids=node_ids_per_feature,
-            gains=gains_list,
-            thresholds=thresholds_list,
-            left_node_contribs=left_node_contribs_list,
-            right_node_contribs=right_node_contribs_list,
-            learning_rate=tree_hparams.learning_rate,
-            max_depth=tree_hparams.max_depth,
-            pruning_mode=boosted_trees_ops.PruningMode.NO_PRUNING)
-        return grow_op
-
-      def _center_bias_fn(mean_gradients, mean_hessians):
-        """Updates the ensembles and cache (if needed) with logits prior."""
-        continue_centering = boosted_trees_ops.center_bias(
-            tree_ensemble.resource_handle,
-            mean_gradients=mean_gradients,
-            mean_hessians=mean_hessians,
-            l1=tree_hparams.l1,
-            l2=tree_hparams.l2
-        )
-        return center_bias_var.assign(continue_centering)
-
-      # ========= End of helper methods. ==============
-
       if train_in_memory and is_single_machine:
-        train_op.append(distribute_lib.increment_var(global_step))
-
-        mean_gradients = array_ops.expand_dims(
-            math_ops.reduce_mean(gradients, 0), 0)
-        mean_heassians = array_ops.expand_dims(
-            math_ops.reduce_mean(hessians, 0), 0)
-
-        train_op.append(
-            control_flow_ops.cond(
-                center_bias_var,
-                lambda: _center_bias_fn(mean_gradients, mean_heassians),
-                functools.partial(grow_tree_from_stats_summaries,
-                                  stats_summaries_list, feature_ids_list)))
+        grower = _InMemoryEnsembleGrower(tree_ensemble, tree_hparams)
       else:
-
-        def center_bias_not_in_mem():
-          """Accumulates the data and updates the logits bias, when ready."""
-          bias_dependencies = []
-
-          bias_accumulator = data_flow_ops.ConditionalAccumulator(
-              dtype=dtypes.float32,
-              # The stats consist of grads and hessians means only.
-              # TODO(nponomareva): this will change for a multiclass
-              shape=[2, 1],
-              shared_name='bias_accumulator')
-
-          grads_and_hess = array_ops.stack([gradients, hessians], axis=0)
-          grads_and_hess = math_ops.reduce_mean(grads_and_hess, axis=1)
-
-          apply_grad = bias_accumulator.apply_grad(grads_and_hess, stamp_token)
-          bias_dependencies.append(apply_grad)
-
-          def center_bias_from_accumulator():
-            accumulated = array_ops.unstack(
-                bias_accumulator.take_grad(1), axis=0)
-            return _center_bias_fn(
-                array_ops.expand_dims(accumulated[0], 0),
-                array_ops.expand_dims(accumulated[1], 0))
-
-          with ops.control_dependencies(bias_dependencies):
-            if config.is_chief:
-              center_bias_op = control_flow_ops.cond(
-                  math_ops.greater_equal(bias_accumulator.num_accumulated(),
-                                         n_batches_per_layer),
-                  center_bias_from_accumulator,
-                  control_flow_ops.no_op,
-                  name='wait_until_n_batches_for_bias_accumulated')
-
-              return center_bias_op
-
-        def grow_not_in_mem():
-          """Accumulates the data and grows a layer when ready."""
-
-          accumulators = []
-          dependencies = []
-          for i, feature_ids in enumerate(feature_ids_list):
-            stats_summaries = stats_summaries_list[i]
-            accumulator = data_flow_ops.ConditionalAccumulator(
-                dtype=dtypes.float32,
-                # The stats consist of grads and hessians (the last dimension).
-                shape=[len(feature_ids), max_splits, bucket_size_list[i], 2],
-                shared_name='numeric_stats_summary_accumulator_' + str(i))
-            accumulators.append(accumulator)
-
-            apply_grad = accumulator.apply_grad(
-                array_ops.stack(stats_summaries, axis=0), stamp_token)
-            dependencies.append(apply_grad)
-
-          def grow_tree_from_accumulated_summaries_fn():
-            """Updates tree with the best layer from accumulated summaries."""
-            # Take out the accumulated summaries from the accumulator and grow.
-            stats_summaries_list = []
-
-            stats_summaries_list = [
-                array_ops.unstack(accumulator.take_grad(1), axis=0)
-                for accumulator in accumulators
-            ]
-
-            grow_op = grow_tree_from_stats_summaries(stats_summaries_list,
-                                                     feature_ids_list)
-            return grow_op
-
-          with ops.control_dependencies(dependencies):
-            if config.is_chief:
-              min_accumulated = math_ops.reduce_min(
-                  array_ops.stack(
-                      [acc.num_accumulated() for acc in accumulators]))
-
-              grow_model = control_flow_ops.cond(
-                  math_ops.greater_equal(min_accumulated, n_batches_per_layer),
-                  grow_tree_from_accumulated_summaries_fn,
-                  control_flow_ops.no_op,
-                  name='wait_until_n_batches_accumulated')
-
-              return grow_model
-
-        update_model = control_flow_ops.cond(
-            center_bias_var, center_bias_not_in_mem, grow_not_in_mem)
-        train_op.append(update_model)
-        with ops.control_dependencies([update_model]):
-          increment_global = distribute_lib.increment_var(global_step)
-          train_op.append(increment_global)
+        grower = _AccumulatorEnsembleGrower(tree_ensemble, tree_hparams,
+                                            stamp_token, n_batches_per_layer,
+                                            bucket_size_list, config.is_chief)
+
+      update_model = control_flow_ops.cond(
+          center_bias_var,
+          functools.partial(
+              grower.center_bias,
+              center_bias_var,
+              gradients,
+              hessians,
+          ),
+          functools.partial(grower.grow_tree, stats_summaries_list,
+                            feature_ids_list, last_layer_nodes_range))
+      train_op.append(update_model)
+
+      with ops.control_dependencies([update_model]):
+        increment_global = distribute_lib.increment_var(global_step)
+        train_op.append(increment_global)
 
       return control_flow_ops.group(train_op, name='train_op')
 
@@ -822,7 +937,8 @@ class BoostedTreesClassifier(estimator.Estimator):
                tree_complexity=0.,
                min_node_weight=0.,
                config=None,
-               center_bias=False):
+               center_bias=False,
+               pruning_mode='none'):
     """Initializes a `BoostedTreesClassifier` instance.
 
     Example:
@@ -896,7 +1012,11 @@ class BoostedTreesClassifier(estimator.Estimator):
         regression problems, the first node will return the mean of the labels.
         For binary classification problems, it will return a logit for a prior
         probability of label 1.
-
+      pruning_mode: one of 'none', 'pre', 'post' to indicate no pruning, pre-
+        pruning (do not split a node if not enough gain is observed) and post
+        pruning (build the tree up to a max depth and then prune branches with
+        negative gain). For pre and post pruning, you MUST provide
+        tree_complexity >0.
 
     Raises:
       ValueError: when wrong arguments are given or unsupported functionalities
@@ -909,9 +1029,9 @@ class BoostedTreesClassifier(estimator.Estimator):
         n_classes, weight_column, label_vocabulary=label_vocabulary)
 
     # HParams for the model.
-    tree_hparams = _TreeHParams(n_trees, max_depth, learning_rate,
-                                l1_regularization, l2_regularization,
-                                tree_complexity, min_node_weight, center_bias)
+    tree_hparams = _TreeHParams(
+        n_trees, max_depth, learning_rate, l1_regularization, l2_regularization,
+        tree_complexity, min_node_weight, center_bias, pruning_mode)
 
     def _model_fn(features, labels, mode, config):
       return _bt_model_fn(  # pylint: disable=protected-access
@@ -955,7 +1075,8 @@ class BoostedTreesRegressor(estimator.Estimator):
                tree_complexity=0.,
                min_node_weight=0.,
                config=None,
-               center_bias=False):
+               center_bias=False,
+               pruning_mode='none'):
     """Initializes a `BoostedTreesRegressor` instance.
 
     Example:
@@ -1022,6 +1143,11 @@ class BoostedTreesRegressor(estimator.Estimator):
         regression problems, the first node will return the mean of the labels.
         For binary classification problems, it will return a logit for a prior
         probability of label 1.
+      pruning_mode: one of 'none', 'pre', 'post' to indicate no pruning, pre-
+        pruning (do not split a node if not enough gain is observed) and post
+        pruning (build the tree up to a max depth and then prune branches with
+        negative gain). For pre and post pruning, you MUST provide
+        tree_complexity >0.
 
     Raises:
       ValueError: when wrong arguments are given or unsupported functionalities
@@ -1033,9 +1159,9 @@ class BoostedTreesRegressor(estimator.Estimator):
     head = _create_regression_head(label_dimension, weight_column)
 
     # HParams for the model.
-    tree_hparams = _TreeHParams(n_trees, max_depth, learning_rate,
-                                l1_regularization, l2_regularization,
-                                tree_complexity, min_node_weight, center_bias)
+    tree_hparams = _TreeHParams(
+        n_trees, max_depth, learning_rate, l1_regularization, l2_regularization,
+        tree_complexity, min_node_weight, center_bias, pruning_mode)
 
     def _model_fn(features, labels, mode, config):
       return _bt_model_fn(  # pylint: disable=protected-access
diff --git a/tensorflow/python/estimator/canned/boosted_trees_test.py b/tensorflow/python/estimator/canned/boosted_trees_test.py
index f807641057990971407f69ff0ba4d3513302e452..ec597e468615e0bb16b832e82d4049ef5961c4c3 100644
--- a/tensorflow/python/estimator/canned/boosted_trees_test.py
+++ b/tensorflow/python/estimator/canned/boosted_trees_test.py
@@ -1508,7 +1508,8 @@ class ModelFnTests(test_util.TensorFlowTestCase):
         l2=0.01,
         tree_complexity=0.,
         min_node_weight=0.,
-        center_bias=center_bias)
+        center_bias=center_bias,
+        pruning_mode='none')
 
     estimator_spec = boosted_trees._bt_model_fn(  # pylint:disable=protected-access
         features=features,
diff --git a/tensorflow/python/estimator/canned/dnn_testing_utils.py b/tensorflow/python/estimator/canned/dnn_testing_utils.py
index ba1782125905fd14ec9b89a29c891062824028f3..de226ed0ef28e6a026e5df6ce128e178254a8c93 100644
--- a/tensorflow/python/estimator/canned/dnn_testing_utils.py
+++ b/tensorflow/python/estimator/canned/dnn_testing_utils.py
@@ -1271,6 +1271,8 @@ class BaseDNNRegressorEvaluateTest(object):
     self.assertAllClose({
         metric_keys.MetricKeys.LOSS: expected_loss,
         metric_keys.MetricKeys.LOSS_MEAN: expected_loss,
+        metric_keys.MetricKeys.PREDICTION_MEAN: -2.08,
+        metric_keys.MetricKeys.LABEL_MEAN: 1.0,
         ops.GraphKeys.GLOBAL_STEP: global_step
     }, dnn_regressor.evaluate(input_fn=_input_fn, steps=1))
 
@@ -1301,6 +1303,8 @@ class BaseDNNRegressorEvaluateTest(object):
     self.assertAllClose({
         metric_keys.MetricKeys.LOSS: expected_loss,
         metric_keys.MetricKeys.LOSS_MEAN: expected_loss / label_dimension,
+        metric_keys.MetricKeys.PREDICTION_MEAN: 0.39 / 3.0,
+        metric_keys.MetricKeys.LABEL_MEAN: 0.5 / 3.0,
         ops.GraphKeys.GLOBAL_STEP: global_step
     }, dnn_regressor.evaluate(input_fn=_input_fn, steps=1))
 
diff --git a/tensorflow/python/estimator/canned/head.py b/tensorflow/python/estimator/canned/head.py
index b74ef1015cc564c20370e17e94e3a09d460c4f85..da9a64c2bc9f6b6797ef6cc115f36a73616b2e1e 100644
--- a/tensorflow/python/estimator/canned/head.py
+++ b/tensorflow/python/estimator/canned/head.py
@@ -1398,15 +1398,21 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head):
         weights=weights,
         processed_labels=labels)
 
-  def _eval_metric_ops(self, weights, unreduced_loss, regularization_loss):
+  def _eval_metric_ops(self, predicted_value, labels, weights, unreduced_loss,
+                       regularization_loss):
     """Returns the Eval metric ops."""
     keys = metric_keys.MetricKeys
     # Estimator already adds a metric for loss.
     eval_metric_ops = {
         _summary_key(self._name, keys.LOSS_MEAN):
-            metrics_lib.mean(
-                values=unreduced_loss,
-                weights=weights)
+            metrics_lib.mean(values=unreduced_loss, weights=weights),
+        _summary_key(self._name, keys.PREDICTION_MEAN):
+            _predictions_mean(
+                predictions=predicted_value,
+                weights=weights,
+                name=keys.PREDICTION_MEAN),
+        _summary_key(self._name, keys.LABEL_MEAN):
+            metrics_lib.mean(values=labels, weights=weights)
     }
     if regularization_loss is not None:
       regularization_loss_key = _summary_key(
@@ -1489,13 +1495,13 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head):
             predictions=predictions,
             loss=regularized_training_loss,
             eval_metrics=_create_eval_metrics_tuple(
-                self._eval_metric_ops,
-                {
+                self._eval_metric_ops, {
+                    'predicted_value': predicted_value,
+                    'labels': labels,
                     'weights': weights,
                     'unreduced_loss': unreduced_loss,
                     'regularization_loss': regularization_loss,
-                }
-            ))
+                }))
 
       # Train.
       if optimizer is not None:
diff --git a/tensorflow/python/estimator/canned/head_test.py b/tensorflow/python/estimator/canned/head_test.py
index 08ce5ca8e833fdd88f9c45b668f0914fcc70acd0..bd2e0ae943fb4da2acc09b120db59cf08e4ed9e6 100644
--- a/tensorflow/python/estimator/canned/head_test.py
+++ b/tensorflow/python/estimator/canned/head_test.py
@@ -3103,8 +3103,10 @@ class RegressionHead(test.TestCase):
     self.assertItemsEqual((prediction_key,), spec.predictions.keys())
     self.assertEqual(dtypes.float32, spec.predictions[prediction_key].dtype)
     self.assertEqual(dtypes.float32, spec.loss.dtype)
-    self.assertItemsEqual(
-        (metric_keys.MetricKeys.LOSS_MEAN,), spec.eval_metric_ops.keys())
+    self.assertItemsEqual((metric_keys.MetricKeys.LOSS_MEAN,
+                           metric_keys.MetricKeys.PREDICTION_MEAN,
+                           metric_keys.MetricKeys.LABEL_MEAN),
+                          spec.eval_metric_ops.keys())
     self.assertIsNone(spec.train_op)
     self.assertIsNone(spec.export_outputs)
     _assert_no_hooks(self, spec)
@@ -3140,6 +3142,9 @@ class RegressionHead(test.TestCase):
 
     expected_metric_keys = [
         '{}/some_regression_head'.format(metric_keys.MetricKeys.LOSS_MEAN),
+        '{}/some_regression_head'.format(
+            metric_keys.MetricKeys.PREDICTION_MEAN),
+        '{}/some_regression_head'.format(metric_keys.MetricKeys.LABEL_MEAN),
     ]
     self.assertItemsEqual(expected_metric_keys, spec.eval_metric_ops.keys())
 
@@ -3170,6 +3175,8 @@ class RegressionHead(test.TestCase):
     expected_metrics = {
         keys.LOSS_MEAN: expected_unregularized_loss,
         keys.LOSS_REGULARIZATION: expected_regularization_loss,
+        keys.PREDICTION_MEAN: (45 + 41) / 2.0,
+        keys.LABEL_MEAN: (43 + 44) / 2.0,
     }
 
     # Assert predictions, loss, and metrics.
@@ -3471,8 +3478,10 @@ class RegressionHead(test.TestCase):
     self.assertItemsEqual((prediction_key,), spec.predictions.keys())
     self.assertEqual(dtypes.float32, spec.predictions[prediction_key].dtype)
     self.assertEqual(dtypes.float32, spec.loss.dtype)
-    self.assertItemsEqual(
-        (metric_keys.MetricKeys.LOSS_MEAN,), spec.eval_metric_ops.keys())
+    self.assertItemsEqual((metric_keys.MetricKeys.LOSS_MEAN,
+                           metric_keys.MetricKeys.PREDICTION_MEAN,
+                           metric_keys.MetricKeys.LABEL_MEAN),
+                          spec.eval_metric_ops.keys())
     self.assertIsNone(spec.train_op)
     self.assertIsNone(spec.export_outputs)
     _assert_no_hooks(self, spec)
@@ -3700,8 +3709,10 @@ class RegressionHead(test.TestCase):
     self.assertItemsEqual((prediction_key,), spec.predictions.keys())
     self.assertEqual(dtypes.float32, spec.predictions[prediction_key].dtype)
     self.assertEqual(dtypes.float32, spec.loss.dtype)
-    self.assertItemsEqual(
-        (metric_keys.MetricKeys.LOSS_MEAN,), spec.eval_metric_ops.keys())
+    self.assertItemsEqual((metric_keys.MetricKeys.LOSS_MEAN,
+                           metric_keys.MetricKeys.PREDICTION_MEAN,
+                           metric_keys.MetricKeys.LABEL_MEAN),
+                          spec.eval_metric_ops.keys())
     self.assertIsNone(spec.train_op)
     self.assertIsNone(spec.export_outputs)
     _assert_no_hooks(self, spec)
@@ -3832,7 +3843,13 @@ class RegressionHead(test.TestCase):
     # losses = [1*(35-45)^2, .1*(42-41)^2, 1.5*(45-44)^2] = [100, .1, 1.5]
     # loss = sum(losses) = 100+.1+1.5 = 101.6
     # loss_mean = loss/(1+.1+1.5) = 101.6/2.6 = 39.076923
-    expected_metrics = {metric_keys.MetricKeys.LOSS_MEAN: 39.076923}
+    expected_metrics = {
+        metric_keys.MetricKeys.LOSS_MEAN:
+            39.076923,
+        metric_keys.MetricKeys.PREDICTION_MEAN:
+            (45 + 41 * 0.1 + 44 * 1.5) / 2.6,
+        metric_keys.MetricKeys.LABEL_MEAN: (35 + 42 * 0.1 + 45 * 1.5) / 2.6,
+    }
 
     # Assert spec contains expected tensors.
     self.assertEqual(dtypes.float32, spec.loss.dtype)
diff --git a/tensorflow/python/estimator/canned/linear_testing_utils.py b/tensorflow/python/estimator/canned/linear_testing_utils.py
index 9e9c2f7c4b0a79718da43769d983f49adbe537ca..c3934c7a801033d587465f0926301f30d4257fc7 100644
--- a/tensorflow/python/estimator/canned/linear_testing_utils.py
+++ b/tensorflow/python/estimator/canned/linear_testing_utils.py
@@ -261,6 +261,8 @@ class BaseLinearRegressorEvaluationTest(object):
     self.assertDictEqual({
         metric_keys.MetricKeys.LOSS: 9.,
         metric_keys.MetricKeys.LOSS_MEAN: 9.,
+        metric_keys.MetricKeys.PREDICTION_MEAN: 13.,
+        metric_keys.MetricKeys.LABEL_MEAN: 10.,
         ops.GraphKeys.GLOBAL_STEP: 100
     }, eval_metrics)
 
@@ -286,6 +288,8 @@ class BaseLinearRegressorEvaluationTest(object):
     self.assertDictEqual({
         metric_keys.MetricKeys.LOSS: 18.,
         metric_keys.MetricKeys.LOSS_MEAN: 9.,
+        metric_keys.MetricKeys.PREDICTION_MEAN: 13.,
+        metric_keys.MetricKeys.LABEL_MEAN: 10.,
         ops.GraphKeys.GLOBAL_STEP: 100
     }, eval_metrics)
 
@@ -316,6 +320,8 @@ class BaseLinearRegressorEvaluationTest(object):
     self.assertDictEqual({
         metric_keys.MetricKeys.LOSS: 27.,
         metric_keys.MetricKeys.LOSS_MEAN: 9.,
+        metric_keys.MetricKeys.PREDICTION_MEAN: 13.,
+        metric_keys.MetricKeys.LABEL_MEAN: 10.,
         ops.GraphKeys.GLOBAL_STEP: 100
     }, eval_metrics)
 
@@ -346,7 +352,9 @@ class BaseLinearRegressorEvaluationTest(object):
 
     self.assertItemsEqual(
         (metric_keys.MetricKeys.LOSS, metric_keys.MetricKeys.LOSS_MEAN,
-         ops.GraphKeys.GLOBAL_STEP), eval_metrics.keys())
+         metric_keys.MetricKeys.PREDICTION_MEAN,
+         metric_keys.MetricKeys.LABEL_MEAN, ops.GraphKeys.GLOBAL_STEP),
+        eval_metrics.keys())
 
     # Logit is
     #   [2., 4., 5.] * [1.0, 2.0] + [7.0, 8.0] = [39, 50] + [7.0, 8.0]
@@ -383,7 +391,9 @@ class BaseLinearRegressorEvaluationTest(object):
     eval_metrics = est.evaluate(input_fn=input_fn, steps=1)
     self.assertItemsEqual(
         (metric_keys.MetricKeys.LOSS, metric_keys.MetricKeys.LOSS_MEAN,
-         ops.GraphKeys.GLOBAL_STEP), eval_metrics.keys())
+         metric_keys.MetricKeys.PREDICTION_MEAN,
+         metric_keys.MetricKeys.LABEL_MEAN, ops.GraphKeys.GLOBAL_STEP),
+        eval_metrics.keys())
 
     # Logit is [(20. * 10.0 + 4 * 2.0 + 5.0), (40. * 10.0 + 8 * 2.0 + 5.0)] =
     # [213.0, 421.0], while label is [213., 421.]. Loss = 0.
diff --git a/tensorflow/python/estimator/canned/metric_keys.py b/tensorflow/python/estimator/canned/metric_keys.py
index 4f7c849ba4b058492c55dd27e0bf79f8d540ece9..9d49240fea4579fffe25172092080560ccd1d35d 100644
--- a/tensorflow/python/estimator/canned/metric_keys.py
+++ b/tensorflow/python/estimator/canned/metric_keys.py
@@ -47,3 +47,8 @@ class MetricKeys(object):
   PROBABILITY_MEAN_AT_CLASS = 'probability_mean/class%d'
   AUC_AT_CLASS = 'auc/class%d'
   AUC_PR_AT_CLASS = 'auc_precision_recall/class%d'
+
+  # The following require a class name applied.
+  PROBABILITY_MEAN_AT_NAME = 'probability_mean/%s'
+  AUC_AT_NAME = 'auc/%s'
+  AUC_PR_AT_NAME = 'auc_precision_recall/%s'
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 350a95eea1f1112ea270156855409d7a1b264bfb..2dceee6a7ed697bf9a52dfa42a326fd7db1d4d01 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -29,8 +29,6 @@ import six
 
 from google.protobuf import message
 from tensorflow.core.framework import summary_pb2
-from tensorflow.core.protobuf import config_pb2
-from tensorflow.core.protobuf import rewriter_config_pb2
 from tensorflow.python.client import session as tf_session
 from tensorflow.python.eager import context
 from tensorflow.python.estimator import model_fn as model_fn_lib
@@ -181,49 +179,16 @@ class Estimator(object):
     """
     Estimator._assert_members_are_not_overridden(self)
 
-    if config is None:
-      self._config = run_config.RunConfig()
-      logging.info('Using default config.')
-    else:
-      if not isinstance(config, run_config.RunConfig):
-        raise ValueError(
-            'config must be an instance of RunConfig, but provided %s.' %
-            config)
-      self._config = config
+    config = maybe_overwrite_model_dir_and_session_config(config, model_dir)
+    self._config = config
 
     # The distribute field contains an instance of DistributionStrategy.
     self._distribution = self._config.train_distribute
-
     # Model directory.
-    model_dir = compat_internal.path_to_str(model_dir)
-    if (model_dir is not None) and (self._config.model_dir is not None):
-      if model_dir != self._config.model_dir:
-        # TODO(alanyee): remove this suppression after it is no longer needed
-        # pylint: disable=g-doc-exception
-        raise ValueError(
-            "model_dir are set both in constructor and RunConfig, but with "
-            "different values. In constructor: '{}', in RunConfig: "
-            "'{}' ".format(model_dir, self._config.model_dir))
-        # pylint: enable=g-doc-exception
-
-    self._model_dir = model_dir or self._config.model_dir
-    if self._model_dir is None:
-      self._model_dir = tempfile.mkdtemp()
-      logging.warning('Using temporary folder as model directory: %s',
-                      self._model_dir)
-    if self._config.model_dir is None:
-      self._config = self._config.replace(model_dir=self._model_dir)
+    self._model_dir = self._config.model_dir
+    self._session_config = self._config.session_config
     logging.info('Using config: %s', str(vars(self._config)))
 
-    if self._config.session_config is None:
-      rewrite_opts = rewriter_config_pb2.RewriterConfig(
-          meta_optimizer_iterations=rewriter_config_pb2.RewriterConfig.ONE)
-      graph_opts = config_pb2.GraphOptions(rewrite_options=rewrite_opts)
-      self._session_config = config_pb2.ConfigProto(
-          allow_soft_placement=True, graph_options=graph_opts)
-    else:
-      self._session_config = self._config.session_config
-
     self._device_fn = (
         self._config.device_fn or _get_replica_device_setter(self._config))
 
@@ -573,12 +538,19 @@ class Estimator(object):
 
   def _assert_members_are_not_overridden(self):
     """Asserts members of `Estimator` are not overridden."""
+    # TPUEstimator is special cased (owned by TF).
+    if self.__class__.__name__ == 'TPUEstimator':
+      return
+
     allowed_overrides = set([
-        '_call_input_fn', '_create_global_step',
+        '_call_input_fn', '_call_model_fn',
         '_convert_train_steps_to_hooks', '_convert_eval_steps_to_hooks',
-        '_tf_api_names', '_estimator_api_names', '_estimator_api_constants',
+        '_create_global_step', '_create_and_assert_global_step',
+        '_tf_api_names', '_tf_api_names_v1', '_estimator_api_names',
+        '_estimator_api_names_v1', '_estimator_api_constants',
+        '_estimator_api_constants_v1',
         '_validate_features_in_predict_input',
-        '_call_model_fn', '_add_meta_graph_for_mode'
+        '_add_meta_graph_for_mode'
     ])
     estimator_members = set([m for m in Estimator.__dict__.keys()
                              if not m.startswith('__')])
@@ -905,9 +877,10 @@ class Estimator(object):
 
       with tf_session.Session(config=self._session_config) as session:
 
-        local_init_op = (
-            estimator_spec.scaffold.local_init_op or
-            monitored_session.Scaffold.default_local_init_op())
+        if estimator_spec.scaffold.local_init_op is not None:
+          local_init_op = estimator_spec.scaffold.local_init_op
+        else:
+          local_init_op = monitored_session.Scaffold.default_local_init_op()
 
         # This saver will be used both for restoring variables now,
         # and in saving out the metagraph below. This ensures that any
@@ -1159,13 +1132,19 @@ class Estimator(object):
     with ops.Graph().as_default() as g, g.device(self._device_fn):
       random_seed.set_random_seed(self._config.tf_random_seed)
       global_step_tensor = self._create_and_assert_global_step(g)
-      training_util._get_or_create_global_step_read()  # pylint: disable=protected-access
+
+      # Skip creating a read variable if _create_and_assert_global_step
+      # returns None (e.g. tf.contrib.estimator.SavedModelEstimator).
+      if global_step_tensor is not None:
+        training_util._get_or_create_global_step_read(g)  # pylint: disable=protected-access
+
       features, labels, input_hooks = (
           self._get_features_and_labels_from_input_fn(
               input_fn, model_fn_lib.ModeKeys.TRAIN))
       worker_hooks.extend(input_hooks)
       estimator_spec = self._call_model_fn(
           features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
+      global_step_tensor = training_util.get_global_step(g)
       return self._train_with_estimator_spec(estimator_spec, worker_hooks,
                                              hooks, global_step_tensor,
                                              saving_listeners)
@@ -1452,13 +1431,13 @@ class Estimator(object):
   def _evaluate_build_graph(self, input_fn, hooks=None, checkpoint_path=None):
     """Builds the graph and related hooks to run evaluation."""
     random_seed.set_random_seed(self._config.tf_random_seed)
-    global_step_tensor = self._create_and_assert_global_step(
-        ops.get_default_graph())
+    self._create_and_assert_global_step(ops.get_default_graph())
     features, labels, input_hooks = (
         self._get_features_and_labels_from_input_fn(input_fn,
                                                     model_fn_lib.ModeKeys.EVAL))
     estimator_spec = self._call_model_fn(
         features, labels, model_fn_lib.ModeKeys.EVAL, self.config)
+    global_step_tensor = training_util.get_global_step(ops.get_default_graph())
 
     # Call to warm_start has to be after model_fn is called.
     self._maybe_warm_start(checkpoint_path)
@@ -1484,7 +1463,21 @@ class Estimator(object):
     all_hooks.extend(hooks)
     all_hooks.extend(list(estimator_spec.evaluation_hooks or []))
 
-    return estimator_spec.scaffold, update_op, eval_dict, all_hooks
+    # New local variables have been added, so update the estimator spec's
+    # local init op if it was defined.
+    scaffold = estimator_spec.scaffold
+    if estimator_spec.scaffold and estimator_spec.scaffold.local_init_op:
+      # Ensure that eval step has been created before updating local init op.
+      evaluation._get_or_create_eval_step()  # pylint: disable=protected-access
+
+      scaffold = monitored_session.Scaffold(
+          local_init_op=control_flow_ops.group(
+              estimator_spec.scaffold.local_init_op,
+              monitored_session.Scaffold.default_local_init_op()),
+          copy_from_scaffold=scaffold
+      )
+
+    return scaffold, update_op, eval_dict, all_hooks
 
   def _evaluate_run(self, checkpoint_path, scaffold, update_op, eval_dict,
                     all_hooks, output_dir):
@@ -1520,6 +1513,48 @@ class Estimator(object):
       warm_starting_util.warm_start(*self._warm_start_settings)
 
 
+def maybe_overwrite_model_dir_and_session_config(config, model_dir):
+  """Overwrite estimator config by `model_dir` and `session_config` if needed.
+
+  Args:
+    config: Original estimator config.
+    model_dir: Estimator model checkpoint directory.
+
+  Returns:
+    Overwritten estimator config.
+
+  Raises:
+    ValueError: Model directory inconsistent between `model_dir` and `config`.
+  """
+
+  if config is None:
+    config = run_config.RunConfig()
+    logging.info('Using default config.')
+  if not isinstance(config, run_config.RunConfig):
+    raise ValueError(
+        'config must be an instance of `RunConfig`, but provided %s.' % config)
+
+  if config.session_config is None:
+    session_config = run_config.get_default_session_config()
+    config = run_config.RunConfig.replace(config, session_config=session_config)
+
+  model_dir = compat_internal.path_to_str(model_dir)
+  if model_dir is not None:
+    if (getattr(config, 'model_dir', None) is not None and
+        config.model_dir != model_dir):
+      raise ValueError(
+          "`model_dir` are set both in constructor and `RunConfig`, but with "
+          "different values. In constructor: '{}', in `RunConfig`: "
+          "'{}' ".format(model_dir, config.model_dir))
+    config = run_config.RunConfig.replace(config, model_dir=model_dir)
+  elif getattr(config, 'model_dir', None) is None:
+    model_dir = tempfile.mkdtemp()
+    logging.warning('Using temporary folder as model directory: %s', model_dir)
+    config = run_config.RunConfig.replace(config, model_dir=model_dir)
+
+  return config
+
+
 def create_per_tower_ready_op(scaffold):
   """Create a Scaffold.ready_op inside a tower."""
   if scaffold.ready_op:
@@ -1915,6 +1950,19 @@ class WarmStartSettings(
     )
 
 
+def _get_saved_model_ckpt(saved_model_dir):
+  """Return path to variables checkpoint in a SavedModel directory."""
+  if not gfile.Exists(
+      os.path.join(compat.as_bytes(saved_model_dir),
+                   compat.as_bytes('variables/variables.index'))):
+    raise ValueError('Directory provided has an invalid SavedModel format: %s'
+                     % saved_model_dir)
+  return os.path.join(
+      compat.as_bytes(saved_model_dir),
+      compat.as_bytes('{}/{}'.format(constants.VARIABLES_DIRECTORY,
+                                     constants.VARIABLES_FILENAME)))
+
+
 def _get_default_warm_start_settings(warm_start_from):
   """Returns default WarmStartSettings.
 
@@ -1938,10 +1986,8 @@ def _get_default_warm_start_settings(warm_start_from):
     if gfile.Exists(os.path.join(compat.as_bytes(warm_start_from),
                                  compat.as_bytes('variables/variables.index'))):
       logging.info('Warm-starting from a SavedModel')
-      return WarmStartSettings(ckpt_to_initialize_from=os.path.join(
-          compat.as_bytes(warm_start_from),
-          compat.as_bytes('{}/{}'.format(constants.VARIABLES_DIRECTORY,
-                                         constants.VARIABLES_FILENAME))))
+      return WarmStartSettings(
+          ckpt_to_initialize_from=_get_saved_model_ckpt(warm_start_from))
     return WarmStartSettings(ckpt_to_initialize_from=warm_start_from)
   elif isinstance(warm_start_from, WarmStartSettings):
     return warm_start_from
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index 2a0e4e761755e272a316ce2d326b0c0a51ecbaba..16d741bec81e42d28a3e04eef47cd34072474f03 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -28,6 +28,7 @@ import six
 
 from google.protobuf import text_format
 
+from tensorflow.core.protobuf import rewriter_config_pb2
 from tensorflow.python.client import session
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.estimator import estimator
@@ -174,7 +175,7 @@ class EstimatorInheritanceConstraintTest(test.TestCase):
 class EstimatorConstructorTest(test.TestCase):
 
   def test_config_must_be_a_run_config(self):
-    with self.assertRaisesRegexp(ValueError, 'an instance of RunConfig'):
+    with self.assertRaisesRegexp(ValueError, 'an instance of `RunConfig`'):
       estimator.Estimator(model_fn=None, config='NotARunConfig')
 
   def test_model_fn_must_be_provided(self):
@@ -203,6 +204,10 @@ class EstimatorConstructorTest(test.TestCase):
 
     est = estimator.Estimator(model_fn=model_fn)
     self.assertTrue(isinstance(est.config, run_config.RunConfig))
+    self.assertTrue(est._session_config.allow_soft_placement)
+    rewrite_options = est._session_config.graph_options.rewrite_options
+    self.assertEqual(rewrite_options.meta_optimizer_iterations,
+                     rewriter_config_pb2.RewriterConfig.ONE)
 
   def test_default_model_dir(self):
 
@@ -267,7 +272,7 @@ class EstimatorConstructorTest(test.TestCase):
 
     with self.assertRaisesRegexp(
         ValueError,
-        'model_dir are set both in constructor and RunConfig, but '
+        '`model_dir` are set both in constructor and `RunConfig`, but '
         'with different values'):
       estimator.Estimator(
           model_fn=model_fn, config=FakeConfig(), model_dir=_ANOTHER_TMP_DIR)
@@ -2304,6 +2309,43 @@ class EstimatorExportTest(test.TestCase):
     with self.assertRaisesRegexp(ValueError, err_regex):
       est._export_all_saved_models(export_dir_base, input_receiver_fn_map)
 
+  def test_export_all_saved_models_metric_operation(self):
+    """Ensures metrics ops.Operations can be expoerted (b/109740581)."""
+
+    def _model_fn(features, labels, mode):
+      del features, labels  # Unused
+      metrics = {'metrics': (constant_op.constant([0]),
+                             control_flow_ops.no_op())}
+      return model_fn_lib.EstimatorSpec(
+          mode,
+          predictions=constant_op.constant(10.),
+          loss=constant_op.constant(1.),
+          train_op=state_ops.assign_add(training.get_global_step(), 1),
+          eval_metric_ops=metrics)
+
+    tmpdir = tempfile.mkdtemp()
+    est = estimator.Estimator(model_fn=_model_fn)
+    est.train(input_fn=dummy_input_fn, steps=1)
+
+    # Perform the export.
+    export_dir_base = os.path.join(
+        compat.as_bytes(tmpdir), compat.as_bytes('metric_operation_export'))
+
+    input_receiver_fn_map = {
+        model_fn_lib.ModeKeys.EVAL: _get_supervised_input_receiver_fn()}
+
+    export_dir = est._export_all_saved_models(
+        export_dir_base, input_receiver_fn_map)
+
+    # Restore, to validate that the export was well-formed.
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        meta_graph = loader.load(sess, [tag_constants.EVAL], export_dir)
+        sig_outputs = meta_graph.signature_def[
+            model_fn_lib.ModeKeys.EVAL].outputs
+        self.assertEqual(
+            sig_outputs['metrics/update_op'].name, 'metric_op_wrapper:0')
+
   def test_export_savedmodel_with_saveables_proto_roundtrip(self):
     tmpdir = tempfile.mkdtemp()
     est = estimator.Estimator(
diff --git a/tensorflow/python/estimator/export/export_output.py b/tensorflow/python/estimator/export/export_output.py
index 6c26d299851eaea74f1e564d0fac217f238d76a2..20382a58d8d6fa5be938ee08fcf1487043868301 100644
--- a/tensorflow/python/estimator/export/export_output.py
+++ b/tensorflow/python/estimator/export/export_output.py
@@ -23,6 +23,7 @@ import abc
 import six
 
 
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.saved_model import signature_def_utils
@@ -338,8 +339,16 @@ class _SupervisedOutput(ExportOutput):
         raise ValueError(
             '{} update_op must be a Tensor or Operation; got {}.'.format(
                 key, metric_op))
+
+      # We must wrap any ops in a Tensor before export, as the SignatureDef
+      # proto expects tensors only. See b/109740581
+      metric_op_tensor = metric_op
+      if isinstance(metric_op, ops.Operation):
+        with ops.control_dependencies([metric_op]):
+          metric_op_tensor = constant_op.constant([], name='metric_op_wrapper')
+
       outputs[val_name] = metric_val
-      outputs[op_name] = metric_op
+      outputs[op_name] = metric_op_tensor
 
     return outputs
 
diff --git a/tensorflow/python/estimator/export/export_output_test.py b/tensorflow/python/estimator/export/export_output_test.py
index b21ba91b0fbb7e14df5eb74dbabace57d3596cc9..d94c764fd7c353a5eeb13c5272b7fe0c4ebdfe07 100644
--- a/tensorflow/python/estimator/export/export_output_test.py
+++ b/tensorflow/python/estimator/export/export_output_test.py
@@ -24,8 +24,10 @@ from tensorflow.core.protobuf import meta_graph_pb2
 from tensorflow.python.estimator.export import export_output as export_output_lib
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.platform import test
 from tensorflow.python.saved_model import signature_constants
 
@@ -335,5 +337,18 @@ class SupervisedOutputTest(test.TestCase):
     self.assertTrue("predictions/output1" in sig_def.outputs)
     self.assertTrue("features" in sig_def.inputs)
 
+  def test_metric_op_is_operation(self):
+    """Tests that ops.Operation is wrapped by a tensor for metric_ops."""
+    loss = {"my_loss": constant_op.constant([0])}
+    predictions = {u"output1": constant_op.constant(["foo"])}
+    metrics = {"metrics": (constant_op.constant([0]), control_flow_ops.no_op())}
+
+    outputter = MockSupervisedOutput(loss, predictions, metrics)
+    self.assertEqual(outputter.metrics["metrics/value"], metrics["metrics"][0])
+    self.assertEqual(
+        outputter.metrics["metrics/update_op"].name, "metric_op_wrapper:0")
+    self.assertTrue(
+        isinstance(outputter.metrics["metrics/update_op"], ops.Tensor))
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/estimator/keras.py b/tensorflow/python/estimator/keras.py
index cb37f99704a8d01af6149bd3c8030b653981d0e2..079560c4959e65dc4e47c668a4b669a882f71f61 100644
--- a/tensorflow/python/estimator/keras.py
+++ b/tensorflow/python/estimator/keras.py
@@ -21,11 +21,11 @@ from __future__ import print_function
 
 import os
 import re
+
 from tensorflow.python.client import session
 from tensorflow.python.estimator import estimator as estimator_lib
 from tensorflow.python.estimator import export as export_lib
 from tensorflow.python.estimator import model_fn as model_fn_lib
-from tensorflow.python.estimator import run_config as run_config_lib
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import random_seed
 from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib
@@ -39,7 +39,7 @@ from tensorflow.python.keras.utils.generic_utils import CustomObjectScope
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import metrics as metrics_module
-from tensorflow.python.ops import variables as variables_module
+from tensorflow.python.platform import gfile
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.saved_model import signature_constants
 from tensorflow.python.training import distribute as distribute_lib
@@ -71,16 +71,22 @@ def _convert_tensor(x):
   return x
 
 
-def _any_variable_initialized():
-  """Check if any variable has been initialized in the Keras model.
+def _any_weight_initialized(keras_model):
+  """Check if any weights has been initialized in the Keras model.
+
+  Args:
+    keras_model: An instance of compiled keras model.
 
   Returns:
-    boolean, True if at least one variable has been initialized, else False.
+    boolean, True if at least one weight has been initialized, else False.
+    Currently keras initialize all weights at get_session().
   """
-  variables = variables_module.global_variables()
-  for v in variables:
-    if getattr(v, '_keras_initialized', False):
-      return True
+  if keras_model is None:
+    return False
+  for layer in keras_model.layers:
+    for weight in layer.weights:
+      if hasattr(weight, '_keras_initialized'):
+        return True
   return False
 
 
@@ -175,7 +181,7 @@ def _in_place_subclassed_model_reset(model):
   # Replace layers on the model with fresh layers
   layers_to_names = {value: key for key, value in attributes_cache.items()}
   original_layers = model._layers[:]
-  model._layers = []
+  model._layers = data_structures.NoDependency([])
   for layer in original_layers:  # We preserve layer order.
     config = layer.get_config()
     # This will not work for nested subclassed models used as layers.
@@ -223,7 +229,8 @@ def _in_place_subclassed_model_reset(model):
       ]
       for name in attributes_to_cache:
         attributes_cache[name] = getattr(model, name)
-  model._original_attributes_cache = attributes_cache
+  model._original_attributes_cache = data_structures.NoDependency(
+      attributes_cache)
   # Reset built state
   model.built = False
   model.inputs = None
@@ -421,29 +428,34 @@ def _create_keras_model_fn(keras_model, custom_objects=None):
   return model_fn
 
 
-def _save_first_checkpoint(keras_model, estimator, custom_objects,
-                           keras_weights):
+def _save_first_checkpoint(keras_model, custom_objects, config):
   """Save first checkpoint for the keras Estimator.
 
   Args:
     keras_model: an instance of compiled keras model.
-    estimator: keras estimator.
     custom_objects: Dictionary for custom objects.
-    keras_weights: A flat list of Numpy arrays for weights of given keras_model.
+    config: Estimator config.
 
   Returns:
-    The model_fn for a keras Estimator.
+    The path where keras model checkpoint is saved.
   """
+  # save checkpoint into subdirectory to allow warm start
+  keras_model_dir = os.path.join(config.model_dir, 'keras')
   # Load weights and save to checkpoint if there is no checkpoint
-  latest_path = saver_lib.latest_checkpoint(estimator.model_dir)
+  latest_path = saver_lib.latest_checkpoint(keras_model_dir)
   if not latest_path:
+    keras_weights = None
+    if _any_weight_initialized(keras_model):
+      keras_weights = keras_model.get_weights()
+    if not gfile.IsDirectory(keras_model_dir):
+      gfile.MakeDirs(keras_model_dir)
     with ops.Graph().as_default():
-      random_seed.set_random_seed(estimator.config.tf_random_seed)
+      random_seed.set_random_seed(config.tf_random_seed)
       training_util.create_global_step()
       model = _clone_and_build_model(model_fn_lib.ModeKeys.TRAIN, keras_model,
                                      custom_objects)
       # save to checkpoint
-      with session.Session(config=estimator._session_config) as sess:
+      with session.Session(config=config.session_config) as sess:
         if keras_weights:
           model.set_weights(keras_weights)
         # Make update ops and initialize all variables.
@@ -453,7 +465,9 @@ def _save_first_checkpoint(keras_model, estimator, custom_objects,
           K._initialize_variables(sess)
           # pylint: enable=protected-access
         saver = saver_lib.Saver()
-        saver.save(sess, os.path.join(estimator.model_dir, 'keras_model.ckpt'))
+        latest_path = os.path.join(keras_model_dir, 'keras_model.ckpt')
+        saver.save(sess, latest_path)
+  return latest_path
 
 
 def model_to_estimator(keras_model=None,
@@ -473,9 +487,9 @@ def model_to_estimator(keras_model=None,
       format, which can be generated with the `save()` method of a Keras model.
       This argument is mutually exclusive with `keras_model`.
     custom_objects: Dictionary for custom objects.
-    model_dir: Directory to save Estimator model parameters, graph, summary
+    model_dir: Directory to save `Estimator` model parameters, graph, summary
       files for TensorBoard, etc.
-    config: Configuration object.
+    config: `RunConfig` to config `Estimator`.
 
   Returns:
     An Estimator from given keras model.
@@ -512,45 +526,40 @@ def model_to_estimator(keras_model=None,
         'Please compile the model with `model.compile()` '
         'before calling `model_to_estimator()`.')
 
-  if isinstance(config, dict):
-    config = run_config_lib.RunConfig(**config)
+  config = estimator_lib.maybe_overwrite_model_dir_and_session_config(config,
+                                                                      model_dir)
 
   keras_model_fn = _create_keras_model_fn(keras_model, custom_objects)
-  estimator = estimator_lib.Estimator(
-      keras_model_fn, model_dir=model_dir, config=config)
-
-  # Check if we need to call get_weights:
-  if _any_variable_initialized():
-    keras_weights = keras_model.get_weights()
+  if _any_weight_initialized(keras_model):
     # Warn if config passed to estimator tries to update GPUOptions. If a
     # session has already been created, the GPUOptions passed to the first
     # session sticks.
-    if estimator._session_config.HasField('gpu_options'):
+    if config.session_config.HasField('gpu_options'):
       logging.warning(
           'The Keras backend session has already been set. '
           'The _session_config passed to model_to_estimator will not be used.')
   else:
     # Pass the config into keras backend's default session.
-    sess = session.Session(config=estimator._session_config)
+    sess = session.Session(config=config.session_config)
     K.set_session(sess)
-    keras_weights = None
 
+  warm_start_path = None
   if keras_model._is_graph_network:
-    # TODO(yifeif): move checkpoint initialization to scaffold.init_fn
-    _save_first_checkpoint(keras_model,
-                           estimator,
-                           custom_objects,
-                           keras_weights)
+    warm_start_path = _save_first_checkpoint(keras_model, custom_objects,
+                                             config)
   elif keras_model.built:
-    logging.warning('You are creating an Estimator from a Keras model '
-                    'manually subclassed from `Model`, that was '
-                    'already called on some inputs (and thus already had '
-                    'weights). We are currently unable to preserve '
-                    'the model\'s state (its weights) '
-                    'as part of the estimator '
-                    'in this case. Be warned that the estimator '
-                    'has been created using '
-                    'a freshly initialized version of your model.\n'
-                    'Note that this doesn\'t affect the state of the '
-                    'model instance you passed as `keras_model` argument.')
+    logging.warning('You are creating an Estimator from a Keras model manually '
+                    'subclassed from `Model`, that was already called on some '
+                    'inputs (and thus already had weights). We are currently '
+                    'unable to preserve the model\'s state (its weights) as '
+                    'part of the estimator in this case. Be warned that the '
+                    'estimator has been created using a freshly initialized '
+                    'version of your model.\n'
+                    'Note that this doesn\'t affect the state of the model '
+                    'instance you passed as `keras_model` argument.')
+
+  estimator = estimator_lib.Estimator(keras_model_fn,
+                                      config=config,
+                                      warm_start_from=warm_start_path)
+
   return estimator
diff --git a/tensorflow/python/estimator/keras_test.py b/tensorflow/python/estimator/keras_test.py
index 5e094ae92bcf88a48d7afe3fb88bbced4971b587..332e38572622d230616dd55fc7a7f1bcf62dbe96 100644
--- a/tensorflow/python/estimator/keras_test.py
+++ b/tensorflow/python/estimator/keras_test.py
@@ -32,13 +32,14 @@ from tensorflow.python.estimator.inputs import numpy_io
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
 from tensorflow.python.keras import testing_utils
-from tensorflow.python.keras.applications import mobilenet
 from tensorflow.python.keras.optimizers import SGD
+from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops.parsing_ops import gen_parsing_ops
 from tensorflow.python.platform import gfile
 from tensorflow.python.platform import test
 from tensorflow.python.summary.writer import writer_cache
 from tensorflow.python.training import rmsprop
+from tensorflow.python.training import session_run_hook
 
 
 try:
@@ -51,6 +52,8 @@ _TRAIN_SIZE = 200
 _INPUT_SIZE = (10,)
 _NUM_CLASS = 2
 
+_TMP_DIR = '/tmp'
+
 
 def simple_sequential_model():
   model = keras.models.Sequential()
@@ -60,9 +63,9 @@ def simple_sequential_model():
   return model
 
 
-def simple_functional_model():
+def simple_functional_model(activation='relu'):
   a = keras.layers.Input(shape=_INPUT_SIZE)
-  b = keras.layers.Dense(16, activation='relu')(a)
+  b = keras.layers.Dense(16, activation=activation)(a)
   b = keras.layers.Dropout(0.1)(b)
   b = keras.layers.Dense(_NUM_CLASS, activation='softmax')(b)
   model = keras.models.Model(inputs=[a], outputs=[b])
@@ -168,6 +171,12 @@ def multi_inputs_multi_outputs_model():
   return model
 
 
+class MyHook(session_run_hook.SessionRunHook):
+
+  def begin(self):
+    _ = variable_scope.get_variable('temp', [1])
+
+
 class TestKerasEstimator(test_util.TensorFlowTestCase):
 
   def setUp(self):
@@ -204,6 +213,55 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
       writer_cache.FileWriterCache.clear()
       gfile.DeleteRecursively(self._config.model_dir)
 
+  # see b/109935364
+  @test_util.run_in_graph_and_eager_modes
+  def test_train_with_hooks(self):
+    for model_type in ['sequential', 'functional']:
+      keras_model, (_, _), (
+          _, _), train_input_fn, eval_input_fn = get_resource_for_simple_model(
+              model_type=model_type, is_evaluate=True)
+      keras_model.compile(
+          loss='categorical_crossentropy',
+          optimizer=rmsprop.RMSPropOptimizer(1e-3),
+          metrics=['mse', keras.metrics.categorical_accuracy])
+
+      my_hook = MyHook()
+      with self.test_session():
+        est_keras = keras_lib.model_to_estimator(
+            keras_model=keras_model, config=self._config)
+        before_eval_results = est_keras.evaluate(
+            input_fn=eval_input_fn, steps=1)
+        est_keras.train(input_fn=train_input_fn, hooks=[my_hook],
+                        steps=_TRAIN_SIZE / 16)
+        after_eval_results = est_keras.evaluate(input_fn=eval_input_fn, steps=1)
+        self.assertLess(after_eval_results['loss'], before_eval_results['loss'])
+
+      writer_cache.FileWriterCache.clear()
+      gfile.DeleteRecursively(self._config.model_dir)
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_train_with_model_fit_and_hooks(self):
+    keras_model, (x_train, y_train), _, \
+      train_input_fn, eval_input_fn = get_resource_for_simple_model(
+          model_type='sequential', is_evaluate=True)
+
+    keras_model.compile(
+        loss='categorical_crossentropy',
+        optimizer=rmsprop.RMSPropOptimizer(1e-3),
+        metrics=['mse', keras.metrics.categorical_accuracy])
+    my_hook = MyHook()
+    with self.test_session():
+      keras_model.fit(x_train, y_train, epochs=1)
+
+      keras_est = keras_lib.model_to_estimator(
+          keras_model=keras_model, config=self._config)
+      before_eval_results = keras_est.evaluate(input_fn=eval_input_fn)
+      keras_est.train(input_fn=train_input_fn, hooks=[my_hook],
+                      steps=_TRAIN_SIZE / 16)
+      after_eval_results = keras_est.evaluate(input_fn=eval_input_fn, steps=1)
+      self.assertLess(after_eval_results['loss'], before_eval_results['loss'])
+
+  @test_util.run_in_graph_and_eager_modes
   def test_train_with_tf_optimizer(self):
     for model_type in ['sequential', 'functional']:
       keras_model, (_, _), (
@@ -217,11 +275,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
       with self.test_session():
         est_keras = keras_lib.model_to_estimator(
             keras_model=keras_model,
-            # Also use dict config argument to get test coverage for that line.
-            config={
-                'tf_random_seed': _RANDOM_SEED,
-                'model_dir': self._base_dir,
-            })
+            config=self._config)
         before_eval_results = est_keras.evaluate(
             input_fn=eval_input_fn, steps=1)
         est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16)
@@ -231,6 +285,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
       writer_cache.FileWriterCache.clear()
       gfile.DeleteRecursively(self._config.model_dir)
 
+  @test_util.run_in_graph_and_eager_modes
   def test_train_with_subclassed_model(self):
     keras_model, (_, _), (
         _, _), train_input_fn, eval_input_fn = get_resource_for_simple_model(
@@ -472,23 +527,43 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
         est_keras.train(input_fn=invald_output_name_input_fn, steps=100)
 
   def test_custom_objects(self):
-    keras_mobile = mobilenet.MobileNet(weights=None)
-    keras_mobile.compile(loss='categorical_crossentropy', optimizer='adam')
+
+    def relu6(x):
+      return keras.backend.relu(x, max_value=6)
+
+    keras_model = simple_functional_model(activation=relu6)
+    keras_model.compile(loss='categorical_crossentropy', optimizer='adam')
     custom_objects = {
-        'relu6': mobilenet.relu6,
-        'DepthwiseConv2D': mobilenet.DepthwiseConv2D
+        'relu6': relu6
     }
+
+    (x_train, y_train), _ = testing_utils.get_test_data(
+        train_samples=_TRAIN_SIZE,
+        test_samples=50,
+        input_shape=(10,),
+        num_classes=2)
+    y_train = keras.utils.to_categorical(y_train, 2)
+    input_name = keras_model.input_names[0]
+    output_name = keras_model.output_names[0]
+    train_input_fn = numpy_io.numpy_input_fn(
+        x=randomize_io_type(x_train, input_name),
+        y=randomize_io_type(y_train, output_name),
+        shuffle=False,
+        num_epochs=None,
+        batch_size=16)
     with self.assertRaisesRegexp(ValueError, 'relu6'):
       with self.test_session():
-        keras_lib.model_to_estimator(
-            keras_model=keras_mobile,
+        est = keras_lib.model_to_estimator(
+            keras_model=keras_model,
             model_dir=tempfile.mkdtemp(dir=self._base_dir))
+        est.train(input_fn=train_input_fn, steps=1)
 
     with self.test_session():
-      keras_lib.model_to_estimator(
-          keras_model=keras_mobile,
+      est = keras_lib.model_to_estimator(
+          keras_model=keras_model,
           model_dir=tempfile.mkdtemp(dir=self._base_dir),
           custom_objects=custom_objects)
+      est.train(input_fn=train_input_fn, steps=1)
 
   def test_tf_config(self):
     keras_model, (_, _), (_, _), _, _ = get_resource_for_simple_model()
@@ -525,12 +600,73 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
       gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.3)
       sess_config = config_pb2.ConfigProto(gpu_options=gpu_options)
       self._config._session_config = sess_config
-      keras_lib.model_to_estimator(
-          keras_model=keras_model, config=self._config)
-      self.assertEqual(
-          keras.backend.get_session()
-          ._config.gpu_options.per_process_gpu_memory_fraction,
-          gpu_options.per_process_gpu_memory_fraction)
+      with self.test_session():
+        keras_lib.model_to_estimator(
+            keras_model=keras_model, config=self._config)
+        self.assertEqual(
+            keras.backend.get_session()
+            ._config.gpu_options.per_process_gpu_memory_fraction,
+            gpu_options.per_process_gpu_memory_fraction)
+
+  def test_with_empty_config(self):
+    keras_model, _, _, _, _ = get_resource_for_simple_model(
+        model_type='sequential', is_evaluate=True)
+    keras_model.compile(
+        loss='categorical_crossentropy',
+        optimizer='rmsprop',
+        metrics=['mse', keras.metrics.categorical_accuracy])
+
+    with self.test_session():
+      est_keras = keras_lib.model_to_estimator(
+          keras_model=keras_model, model_dir=self._base_dir,
+          config=run_config_lib.RunConfig())
+      self.assertEqual(run_config_lib.get_default_session_config(),
+                       est_keras._session_config)
+      self.assertEqual(est_keras._session_config,
+                       est_keras._config.session_config)
+      self.assertEqual(self._base_dir, est_keras._config.model_dir)
+      self.assertEqual(self._base_dir, est_keras._model_dir)
+
+    with self.test_session():
+      est_keras = keras_lib.model_to_estimator(
+          keras_model=keras_model, model_dir=self._base_dir,
+          config=None)
+      self.assertEqual(run_config_lib.get_default_session_config(),
+                       est_keras._session_config)
+      self.assertEqual(est_keras._session_config,
+                       est_keras._config.session_config)
+      self.assertEqual(self._base_dir, est_keras._config.model_dir)
+      self.assertEqual(self._base_dir, est_keras._model_dir)
+
+  def test_with_empty_config_and_empty_model_dir(self):
+    keras_model, _, _, _, _ = get_resource_for_simple_model(
+        model_type='sequential', is_evaluate=True)
+    keras_model.compile(
+        loss='categorical_crossentropy',
+        optimizer='rmsprop',
+        metrics=['mse', keras.metrics.categorical_accuracy])
+
+    with self.test_session():
+      with test.mock.patch.object(tempfile, 'mkdtemp', return_value=_TMP_DIR):
+        est_keras = keras_lib.model_to_estimator(
+            keras_model=keras_model,
+            config=run_config_lib.RunConfig())
+        self.assertEqual(est_keras._model_dir, _TMP_DIR)
+
+  def test_with_conflicting_model_dir_and_config(self):
+    keras_model, _, _, _, _ = get_resource_for_simple_model(
+        model_type='sequential', is_evaluate=True)
+    keras_model.compile(
+        loss='categorical_crossentropy',
+        optimizer='rmsprop',
+        metrics=['mse', keras.metrics.categorical_accuracy])
+
+    with self.test_session():
+      with self.assertRaisesRegexp(ValueError, '`model_dir` are set both in '
+                                   'constructor and `RunConfig`'):
+        keras_lib.model_to_estimator(
+            keras_model=keras_model, model_dir=self._base_dir,
+            config=run_config_lib.RunConfig(model_dir=_TMP_DIR))
 
   def test_pretrained_weights(self):
     keras_model, (_, _), (_, _), _, _ = get_resource_for_simple_model()
diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py
index a9fd8f8e1a4259fece1a5996343970900c853ce0..9db9ccd01d3fe996c2b047d65cc2f35747b005e1 100644
--- a/tensorflow/python/estimator/model_fn.py
+++ b/tensorflow/python/estimator/model_fn.py
@@ -380,15 +380,12 @@ def _maybe_add_default_serving_output(export_outputs):
   return export_outputs
 
 
-class _TPUEstimatorSpec(collections.namedtuple('TPUEstimatorSpec', [
-    'mode',
-    'predictions',
-    'loss',
-    'train_op',
-    'eval_metrics',
-    'export_outputs',
-    'scaffold_fn',
-    'host_call'])):
+class _TPUEstimatorSpec(
+    collections.namedtuple('TPUEstimatorSpec', [
+        'mode', 'predictions', 'loss', 'train_op', 'eval_metrics',
+        'export_outputs', 'scaffold_fn', 'host_call', 'training_hooks',
+        'evaluation_hooks', 'prediction_hooks'
+    ])):
   """Ops and objects returned from a `model_fn` and passed to `TPUEstimator`.
 
   This is a simplified implementation of `tf.contrib.tpu.EstimatorSpec`. See
@@ -404,17 +401,24 @@ class _TPUEstimatorSpec(collections.namedtuple('TPUEstimatorSpec', [
               eval_metrics=None,
               export_outputs=None,
               scaffold_fn=None,
-              host_call=None):
+              host_call=None,
+              training_hooks=None,
+              evaluation_hooks=None,
+              prediction_hooks=None):
     """Creates a `_TPUEstimatorSpec` instance."""
-    return super(_TPUEstimatorSpec, cls).__new__(cls,
-                                                 mode=mode,
-                                                 predictions=predictions,
-                                                 loss=loss,
-                                                 train_op=train_op,
-                                                 eval_metrics=eval_metrics,
-                                                 export_outputs=export_outputs,
-                                                 scaffold_fn=scaffold_fn,
-                                                 host_call=host_call)
+    return super(_TPUEstimatorSpec, cls).__new__(
+        cls,
+        mode=mode,
+        predictions=predictions,
+        loss=loss,
+        train_op=train_op,
+        eval_metrics=eval_metrics,
+        export_outputs=export_outputs,
+        scaffold_fn=scaffold_fn,
+        host_call=host_call,
+        training_hooks=training_hooks,
+        evaluation_hooks=evaluation_hooks,
+        prediction_hooks=prediction_hooks)
 
   def as_estimator_spec(self):
     """Creates an equivalent `EstimatorSpec` used by CPU train/eval."""
@@ -423,12 +427,16 @@ class _TPUEstimatorSpec(collections.namedtuple('TPUEstimatorSpec', [
     else:
       metric_fn, tensors = self.eval_metrics
       eval_metric_ops = metric_fn(**tensors)
-    return EstimatorSpec(mode=self.mode,
-                         predictions=self.predictions,
-                         loss=self.loss,
-                         train_op=self.train_op,
-                         eval_metric_ops=eval_metric_ops,
-                         export_outputs=self.export_outputs)
+    return EstimatorSpec(
+        mode=self.mode,
+        predictions=self.predictions,
+        loss=self.loss,
+        train_op=self.train_op,
+        eval_metric_ops=eval_metric_ops,
+        export_outputs=self.export_outputs,
+        training_hooks=self.training_hooks,
+        evaluation_hooks=self.evaluation_hooks,
+        prediction_hooks=self.prediction_hooks)
 
 
 def _check_is_tensor_or_operation(x, name):
diff --git a/tensorflow/python/estimator/run_config.py b/tensorflow/python/estimator/run_config.py
index 3d60c63b68968c98a00364948bd3de0581daadd4..6c1de166a48cdfd476b62e9b1689d5d6b7c02dc3 100644
--- a/tensorflow/python/estimator/run_config.py
+++ b/tensorflow/python/estimator/run_config.py
@@ -48,7 +48,8 @@ _DEFAULT_REPLACEABLE_LIST = [
     'keep_checkpoint_every_n_hours',
     'log_step_count_steps',
     'train_distribute',
-    'device_fn'
+    'device_fn',
+    'protocol'
 ]
 
 _SAVE_CKPT_ERR = (
@@ -288,6 +289,21 @@ def _validate_properties(run_config):
             message='device_fn must be callable with exactly'
                     ' one argument "op".')
 
+  _validate('protocol',
+            lambda protocol: protocol in (None, "grpc", "grpc+verbs"),
+            message='protocol should be grpc or grpc+verbs')
+
+
+def get_default_session_config():
+  """Returns tf.ConfigProto instance."""
+
+  rewrite_opts = rewriter_config_pb2.RewriterConfig(
+      meta_optimizer_iterations=rewriter_config_pb2.RewriterConfig.ONE)
+  graph_opts = config_pb2.GraphOptions(rewrite_options=rewrite_opts)
+
+  return config_pb2.ConfigProto(allow_soft_placement=True,
+                                graph_options=graph_opts)
+
 
 class TaskType(object):
   MASTER = 'master'
@@ -312,7 +328,8 @@ class RunConfig(object):
                keep_checkpoint_every_n_hours=10000,
                log_step_count_steps=100,
                train_distribute=None,
-               device_fn=None):
+               device_fn=None,
+               protocol=None):
     """Constructs a RunConfig.
 
     All distributed training related properties `cluster_spec`, `is_chief`,
@@ -436,7 +453,7 @@ class RunConfig(object):
         the feature.
       log_step_count_steps: The frequency, in number of global steps, that the
         global step/sec and the loss will be logged during training.
-      train_distribute: an optional instance of
+      train_distribute: An optional instance of
         `tf.contrib.distribute.DistributionStrategy`. If specified,
         then Estimator will distribute the user's model during training,
         according to the policy specified by that strategy.
@@ -444,6 +461,8 @@ class RunConfig(object):
         `Operation` and returns the device string. If `None`, defaults to
         the device function returned by `tf.train.replica_device_setter`
         with round-robin strategy.
+      protocol: An optional argument which specifies the protocol used when
+        starting server. None means default to grpc.
 
     Raises:
       ValueError: If both `save_checkpoints_steps` and `save_checkpoints_secs`
@@ -481,18 +500,28 @@ class RunConfig(object):
         keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours,
         log_step_count_steps=log_step_count_steps,
         train_distribute=train_distribute,
-        device_fn=device_fn)
+        device_fn=device_fn,
+        protocol=protocol)
 
     self._init_distributed_setting_from_environment_var(tf_config)
 
-    # Get session_config only for distributed mode (cluster_spec is present).
+    self._maybe_overwrite_session_config_for_distributed_training()
+
+  def _maybe_overwrite_session_config_for_distributed_training(self):
+    """Overwrites the session_config for distributed training.
+
+    The default overwrite is optimized for between-graph training. Subclass
+    should override this method if necessary.
+    """
+    # Get session_config only for between-graph distributed mode (cluster_spec
+    # is present).
     if not self._session_config and self._cluster_spec:
       RunConfig._replace(
           self,
           allowed_properties_list=_DEFAULT_REPLACEABLE_LIST,
-          session_config=self._get_default_session_config())
+          session_config=self._get_default_session_config_distributed())
 
-  def _get_default_session_config(self):
+  def _get_default_session_config_distributed(self):
     """Returns None or tf.ConfigProto instance with default device_filters set.
 
     Device filters are set such that chief/master and worker communicates with
@@ -745,6 +774,11 @@ class RunConfig(object):
     """
     return self._train_distribute
 
+  @property
+  def protocol(self):
+    """Returns the optional protocol value."""
+    return self._protocol
+
   def replace(self, **kwargs):
     """Returns a new instance of `RunConfig` replacing specified properties.
 
@@ -760,7 +794,8 @@ class RunConfig(object):
       - `keep_checkpoint_every_n_hours`,
       - `log_step_count_steps`,
       - `train_distribute`,
-      - `device_fn`.
+      - `device_fn`,
+      - `protocol`.
 
     In addition, either `save_checkpoints_steps` or `save_checkpoints_secs`
     can be set (should not be both).
diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py
index 57301010920be90c63e00594d686df3a09466c91..a01b2300ddbe8bf131f70de435a4d7509849bae9 100644
--- a/tensorflow/python/estimator/training.py
+++ b/tensorflow/python/estimator/training.py
@@ -312,10 +312,10 @@ def train_and_evaluate(estimator, train_spec, eval_spec):
   #       hidden_units=[1024, 512, 256])
 
   # Input pipeline for train and evaluate.
-  def train_input_fn: # returns x, y
+  def train_input_fn(): # returns x, y
     # please shuffle the data.
     pass
-  def eval_input_fn_eval: # returns x, y
+  def eval_input_fn(): # returns x, y
     pass
 
   train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=1000)
@@ -732,7 +732,8 @@ class _TrainingExecutor(object):
         job_name=config.task_type,
         task_index=config.task_id,
         config=session_config,
-        start=False)
+        start=False,
+        protocol=config.protocol)
     server.start()
     return server
 
diff --git a/tensorflow/python/estimator/training_test.py b/tensorflow/python/estimator/training_test.py
index 6bee7cbe83a5e9b623ea16ebe48cce93e27534e2..dc106c7d3baf561a203341a2063c1a9b86fa2b5b 100644
--- a/tensorflow/python/estimator/training_test.py
+++ b/tensorflow/python/estimator/training_test.py
@@ -472,6 +472,7 @@ class _TrainingExecutorTrainingTest(object):
         job_name=mock_est.config.task_type,
         task_index=mock_est.config.task_id,
         config=test.mock.ANY,
+        protocol=None,
         start=False)
 
     self.assertTrue(mock_server_instance.start.called)
@@ -502,6 +503,7 @@ class _TrainingExecutorTrainingTest(object):
         job_name=mock_est.config.task_type,
         task_index=mock_est.config.task_id,
         config=test.mock.ANY,
+        protocol=None,
         start=False)
 
     self.assertTrue(mock_server_instance.start.called)
@@ -729,6 +731,7 @@ class TrainingExecutorRunMasterTest(test.TestCase):
         job_name=mock_est.config.task_type,
         task_index=mock_est.config.task_id,
         config=test.mock.ANY,
+        protocol=None,
         start=False)
 
     self.assertTrue(mock_server_instance.start.called)
@@ -1481,6 +1484,7 @@ class TrainingExecutorRunPsTest(test.TestCase):
         job_name=mock_est.config.task_type,
         task_index=mock_est.config.task_id,
         config=test.mock.ANY,
+        protocol=None,
         start=False)
 
     self.assertTrue(mock_server_instance.start.called)
diff --git a/tensorflow/python/feature_column/BUILD b/tensorflow/python/feature_column/BUILD
index 295d4ca094cc8cb85c0f1f7fd47c20b910c270df..80707030e6eb3c423a1b8ae38624ddad3e87fb04 100644
--- a/tensorflow/python/feature_column/BUILD
+++ b/tensorflow/python/feature_column/BUILD
@@ -48,6 +48,39 @@ py_library(
     ],
 )
 
+py_library(
+    name = "feature_column_v2",
+    srcs = ["feature_column_v2.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:check_ops",
+        "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:embedding_ops",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:init_ops",
+        "//tensorflow/python:lookup_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:parsing_ops",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:resource_variable_ops",
+        "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:sparse_tensor",
+        "//tensorflow/python:string_ops",
+        "//tensorflow/python:template",
+        "//tensorflow/python:tensor_shape",
+        "//tensorflow/python:training",
+        "//tensorflow/python:util",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python:variables",
+        "//tensorflow/python/keras",
+        "//third_party/py/numpy",
+        "@six_archive//:six",
+    ],
+)
+
 filegroup(
     name = "vocabulary_testdata",
     srcs = [
@@ -92,3 +125,38 @@ py_test(
         "//tensorflow/python/estimator:numpy_io",
     ],
 )
+
+py_test(
+    name = "feature_column_v2_test",
+    srcs = ["feature_column_v2_test.py"],
+    data = [":vocabulary_testdata"],
+    srcs_version = "PY2AND3",
+    tags = [
+        "no_cuda_on_cpu_tap",
+        "no_pip",
+    ],
+    deps = [
+        ":feature_column_py",
+        ":feature_column_v2",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:lookup_ops",
+        "//tensorflow/python:parsing_ops",
+        "//tensorflow/python:partitioned_variables",
+        "//tensorflow/python:session",
+        "//tensorflow/python:sparse_tensor",
+        "//tensorflow/python:training",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python:variables",
+        "//tensorflow/python/eager:backprop",
+        "//tensorflow/python/eager:context",
+        "//tensorflow/python/estimator:numpy_io",
+        "//third_party/py/numpy",
+    ],
+)
diff --git a/tensorflow/python/feature_column/feature_column_v2.py b/tensorflow/python/feature_column/feature_column_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4dd23f58de60bacae68f9b67ed30c5d4ae49b15
--- /dev/null
+++ b/tensorflow/python/feature_column/feature_column_v2.py
@@ -0,0 +1,3600 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""This API defines FeatureColumn abstraction.
+
+FeatureColumns provide a high level abstraction for ingesting and representing
+features. FeatureColumns are also the primary way of encoding features for
+canned @{tf.estimator.Estimator}s.
+
+When using FeatureColumns with `Estimators`, the type of feature column you
+should choose depends on (1) the feature type and (2) the model type.
+
+1. Feature type:
+
+  * Continuous features can be represented by `numeric_column`.
+  * Categorical features can be represented by any `categorical_column_with_*`
+  column:
+    - `categorical_column_with_vocabulary_list`
+    - `categorical_column_with_vocabulary_file`
+    - `categorical_column_with_hash_bucket`
+    - `categorical_column_with_identity`
+    - `weighted_categorical_column`
+
+2. Model type:
+
+  * Deep neural network models (`DNNClassifier`, `DNNRegressor`).
+
+    Continuous features can be directly fed into deep neural network models.
+
+      age_column = numeric_column("age")
+
+    To feed sparse features into DNN models, wrap the column with
+    `embedding_column` or `indicator_column`. `indicator_column` is recommended
+    for features with only a few possible values. For features with many
+    possible values, to reduce the size of your model, `embedding_column` is
+    recommended.
+
+      embedded_dept_column = embedding_column(
+          categorical_column_with_vocabulary_list(
+              "department", ["math", "philosophy", ...]), dimension=10)
+
+  * Wide (aka linear) models (`LinearClassifier`, `LinearRegressor`).
+
+    Sparse features can be fed directly into linear models. They behave like an
+    indicator column but with an efficient implementation.
+
+      dept_column = categorical_column_with_vocabulary_list("department",
+          ["math", "philosophy", "english"])
+
+    It is recommended that continuous features be bucketized before being
+    fed into linear models.
+
+      bucketized_age_column = bucketized_column(
+          source_column=age_column,
+          boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
+
+    Sparse features can be crossed (also known as conjuncted or combined) in
+    order to form non-linearities, and then fed into linear models.
+
+      cross_dept_age_column = crossed_column(
+          columns=["department", bucketized_age_column],
+          hash_bucket_size=1000)
+
+Example of building canned `Estimator`s using FeatureColumns:
+
+  ```python
+  # Define features and transformations
+  deep_feature_columns = [age_column, embedded_dept_column]
+  wide_feature_columns = [dept_column, bucketized_age_column,
+      cross_dept_age_column]
+
+  # Build deep model
+  estimator = DNNClassifier(
+      feature_columns=deep_feature_columns,
+      hidden_units=[500, 250, 50])
+  estimator.train(...)
+
+  # Or build a wide model
+  estimator = LinearClassifier(
+      feature_columns=wide_feature_columns)
+  estimator.train(...)
+
+  # Or build a wide and deep model!
+  estimator = DNNLinearCombinedClassifier(
+      linear_feature_columns=wide_feature_columns,
+      dnn_feature_columns=deep_feature_columns,
+      dnn_hidden_units=[500, 250, 50])
+  estimator.train(...)
+  ```
+
+
+FeatureColumns can also be transformed into a generic input layer for
+custom models using `input_layer`.
+
+Example of building model using FeatureColumns, this can be used in a
+`model_fn` which is given to the {tf.estimator.Estimator}:
+
+  ```python
+  # Building model via layers
+
+  deep_feature_columns = [age_column, embedded_dept_column]
+  columns_to_tensor = parse_feature_columns_from_examples(
+      serialized=my_data,
+      feature_columns=deep_feature_columns)
+  first_layer = input_layer(
+      features=columns_to_tensor,
+      feature_columns=deep_feature_columns)
+  second_layer = fully_connected(first_layer, ...)
+  ```
+
+NOTE: Functions prefixed with "_" indicate experimental or private parts of
+the API subject to change, and should not be relied upon!
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import abc
+import collections
+import math
+
+import numpy as np
+import six
+
+
+from tensorflow.python.eager import context
+from tensorflow.python.feature_column import feature_column as fc_old
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.keras.engine import training
+from tensorflow.python.layers import base
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import embedding_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import lookup_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import parsing_ops
+from tensorflow.python.ops import resource_variable_ops
+from tensorflow.python.ops import sparse_ops
+from tensorflow.python.ops import string_ops
+from tensorflow.python.ops import template
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import gfile
+from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import checkpoint_utils
+from tensorflow.python.util import nest
+
+
+def _internal_input_layer(features,
+                          feature_columns,
+                          weight_collections=None,
+                          trainable=True,
+                          cols_to_vars=None,
+                          scope=None):
+  """See input_layer. `scope` is a name or variable scope to use."""
+
+  feature_columns = fc_old._normalize_feature_columns(feature_columns)  # pylint: disable=protected-access
+  for column in feature_columns:
+    if not isinstance(column, fc_old._DenseColumn):  # pylint: disable=protected-access
+      raise ValueError(
+          'Items of feature_columns must be a _DenseColumn. '
+          'You can wrap a categorical column with an '
+          'embedding_column or indicator_column. Given: {}'.format(column))
+  weight_collections = list(weight_collections or [])
+  if ops.GraphKeys.GLOBAL_VARIABLES not in weight_collections:
+    weight_collections.append(ops.GraphKeys.GLOBAL_VARIABLES)
+  if ops.GraphKeys.MODEL_VARIABLES not in weight_collections:
+    weight_collections.append(ops.GraphKeys.MODEL_VARIABLES)
+
+  # a non-None `scope` can allow for variable reuse, when, e.g., this function
+  # is wrapped by a `make_template`.
+  with variable_scope.variable_scope(
+      scope, default_name='input_layer', values=features.values()):
+    builder = fc_old._LazyBuilder(features)  # pylint: disable=protected-access
+    output_tensors = []
+    ordered_columns = []
+    for column in sorted(feature_columns, key=lambda x: x.name):
+      ordered_columns.append(column)
+      with variable_scope.variable_scope(
+          None, default_name=column._var_scope_name):  # pylint: disable=protected-access
+        tensor = column._get_dense_tensor(  # pylint: disable=protected-access
+            builder,
+            weight_collections=weight_collections,
+            trainable=trainable)
+        num_elements = column._variable_shape.num_elements()  # pylint: disable=protected-access
+        batch_size = array_ops.shape(tensor)[0]
+        output_tensors.append(
+            array_ops.reshape(tensor, shape=(batch_size, num_elements)))
+        if cols_to_vars is not None:
+          # Retrieve any variables created (some _DenseColumn's don't create
+          # variables, in which case an empty list is returned).
+          cols_to_vars[column] = ops.get_collection(
+              ops.GraphKeys.GLOBAL_VARIABLES,
+              scope=variable_scope.get_variable_scope().name)
+    _verify_static_batch_size_equality(output_tensors, ordered_columns)
+    return array_ops.concat(output_tensors, 1)
+
+
+def input_layer(features,
+                feature_columns,
+                weight_collections=None,
+                trainable=True,
+                cols_to_vars=None):
+  """Returns a dense `Tensor` as input layer based on given `feature_columns`.
+
+  Generally a single example in training data is described with FeatureColumns.
+  At the first layer of the model, this column oriented data should be converted
+  to a single `Tensor`.
+
+  Example:
+
+  ```python
+  price = numeric_column('price')
+  keywords_embedded = embedding_column(
+      categorical_column_with_hash_bucket("keywords", 10K), dimensions=16)
+  columns = [price, keywords_embedded, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  dense_tensor = input_layer(features, columns)
+  for units in [128, 64, 32]:
+    dense_tensor = tf.layers.dense(dense_tensor, units, tf.nn.relu)
+  prediction = tf.layers.dense(dense_tensor, 1)
+  ```
+
+  Args:
+    features: A mapping from key to tensors. `_FeatureColumn`s look up via these
+      keys. For example `numeric_column('price')` will look at 'price' key in
+      this dict. Values can be a `SparseTensor` or a `Tensor` depends on
+      corresponding `_FeatureColumn`.
+    feature_columns: An iterable containing the FeatureColumns to use as inputs
+      to your model. All items should be instances of classes derived from
+      `_DenseColumn` such as `numeric_column`, `embedding_column`,
+      `bucketized_column`, `indicator_column`. If you have categorical features,
+      you can wrap them with an `embedding_column` or `indicator_column`.
+    weight_collections: A list of collection names to which the Variable will be
+      added. Note that variables will also be added to collections
+      `tf.GraphKeys.GLOBAL_VARIABLES` and `ops.GraphKeys.MODEL_VARIABLES`.
+    trainable: If `True` also add the variable to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    cols_to_vars: If not `None`, must be a dictionary that will be filled with a
+      mapping from `_FeatureColumn` to list of `Variable`s.  For example, after
+      the call, we might have cols_to_vars =
+      {_EmbeddingColumn(
+        categorical_column=_HashedCategoricalColumn(
+          key='sparse_feature', hash_bucket_size=5, dtype=tf.string),
+        dimension=10): [],
+        'bias': [],
+        _NumericColumn(
+          key='numeric_feature2', shape=(2,)):
+        []}
+      If a column creates no variables, its value will be an empty list. Note
+      that cols_to_vars will also contain a string key 'bias' that maps to a
+      list of Variables.
+
+  Returns:
+    A `Tensor` which represents predictions/logits of a linear model. Its shape
+    is (batch_size, units) and its dtype is `float32`.
+
+  Raises:
+    ValueError: if an item in `feature_columns` is neither a `_DenseColumn`
+      nor `_CategoricalColumn`.
+  """
+  with variable_scope.variable_scope(None, 'linear_model') as vs:
+    model_name = _strip_leading_slashes(vs.name)
+  linear_model_layer = _LinearModel(
+      feature_columns=feature_columns,
+      units=units,
+      sparse_combiner=sparse_combiner,
+      weight_collections=weight_collections,
+      trainable=trainable,
+      name=model_name)
+  retval = linear_model_layer(features)  # pylint: disable=not-callable
+  if cols_to_vars is not None:
+    cols_to_vars.update(linear_model_layer.cols_to_vars())
+  return retval
+
+
+def _add_to_collections(var, weight_collections):
+  """Adds a var to the list of weight_collections provided.
+
+  Handles the case for partitioned and non-partitioned variables.
+
+  Args:
+    var: A variable or Partitioned Variable.
+    weight_collections: List of collections to add variable to.
+  """
+  for weight_collection in weight_collections:
+    # The layer self.add_variable call already adds it to GLOBAL_VARIABLES.
+    if weight_collection == ops.GraphKeys.GLOBAL_VARIABLES:
+      continue
+    # TODO(rohanj): Explore adding a _get_variable_list method on `Variable`
+    # so that we don't have to do this check.
+    if isinstance(var, variables.PartitionedVariable):
+      for constituent_var in list(var):
+        ops.add_to_collection(weight_collection, constituent_var)
+    else:
+      ops.add_to_collection(weight_collection, var)
+
+
+class _FCLinearWrapper(base.Layer):
+  """Wraps a _FeatureColumn in a layer for use in a linear model.
+
+  See `linear_model` above.
+  """
+
+  def __init__(self,
+               feature_column,
+               units=1,
+               sparse_combiner='sum',
+               weight_collections=None,
+               trainable=True,
+               name=None,
+               **kwargs):
+    super(_FCLinearWrapper, self).__init__(
+        trainable=trainable, name=name, **kwargs)
+    self._feature_column = feature_column
+    self._units = units
+    self._sparse_combiner = sparse_combiner
+    self._weight_collections = weight_collections
+
+  def build(self, _):
+    if isinstance(self._feature_column, fc_old._CategoricalColumn):  # pylint: disable=protected-access
+      weight = self.add_variable(
+          name='weights',
+          shape=(self._feature_column._num_buckets, self._units),  # pylint: disable=protected-access
+          initializer=init_ops.zeros_initializer(),
+          trainable=self.trainable)
+    else:
+      num_elements = self._feature_column._variable_shape.num_elements()  # pylint: disable=protected-access
+      weight = self.add_variable(
+          name='weights',
+          shape=[num_elements, self._units],
+          initializer=init_ops.zeros_initializer(),
+          trainable=self.trainable)
+    _add_to_collections(weight, self._weight_collections)
+    self._weight_var = weight
+    self.built = True
+
+  def call(self, builder):
+    weighted_sum = fc_old._create_weighted_sum(  # pylint: disable=protected-access
+        column=self._feature_column,
+        builder=builder,
+        units=self._units,
+        sparse_combiner=self._sparse_combiner,
+        weight_collections=self._weight_collections,
+        trainable=self.trainable,
+        weight_var=self._weight_var)
+    return weighted_sum
+
+
+class _BiasLayer(base.Layer):
+  """A layer for the bias term.
+  """
+
+  def __init__(self,
+               units=1,
+               trainable=True,
+               weight_collections=None,
+               name=None,
+               **kwargs):
+    super(_BiasLayer, self).__init__(trainable=trainable, name=name, **kwargs)
+    self._units = units
+    self._weight_collections = weight_collections
+
+  def build(self, _):
+    self._bias_variable = self.add_variable(
+        'bias_weights',
+        shape=[self._units],
+        initializer=init_ops.zeros_initializer(),
+        trainable=self.trainable)
+    _add_to_collections(self._bias_variable, self._weight_collections)
+    self.built = True
+
+  def call(self, _):
+    return self._bias_variable
+
+
+def _get_expanded_variable_list(variable):
+  if (isinstance(variable, variables.Variable) or
+      resource_variable_ops.is_resource_variable(variable)):
+    return [variable]  # Single variable case.
+  else:  # Must be a PartitionedVariable, so convert into a list.
+    return list(variable)
+
+
+def _strip_leading_slashes(name):
+  return name.rsplit('/', 1)[-1]
+
+
+class _LinearModel(training.Model):
+  """Creates a linear model using feature columns.
+
+  See `linear_model` for details.
+  """
+
+  def __init__(self,
+               feature_columns,
+               units=1,
+               sparse_combiner='sum',
+               weight_collections=None,
+               trainable=True,
+               name=None,
+               **kwargs):
+    super(_LinearModel, self).__init__(name=name, **kwargs)
+    self._feature_columns = fc_old._normalize_feature_columns(  # pylint: disable=protected-access
+        feature_columns)
+    self._weight_collections = list(weight_collections or [])
+    if ops.GraphKeys.GLOBAL_VARIABLES not in self._weight_collections:
+      self._weight_collections.append(ops.GraphKeys.GLOBAL_VARIABLES)
+    if ops.GraphKeys.MODEL_VARIABLES not in self._weight_collections:
+      self._weight_collections.append(ops.GraphKeys.MODEL_VARIABLES)
+
+    column_layers = {}
+    for column in sorted(self._feature_columns, key=lambda x: x.name):
+      with variable_scope.variable_scope(
+          None, default_name=column._var_scope_name) as vs:  # pylint: disable=protected-access
+        # Having the fully expressed variable scope name ends up doubly
+        # expressing the outer scope (scope with which this method was called)
+        # in the name of the variable that would get created.
+        column_name = _strip_leading_slashes(vs.name)
+      column_layer = _FCLinearWrapper(column, units, sparse_combiner,
+                                      self._weight_collections, trainable,
+                                      column_name, **kwargs)
+      column_layers[column_name] = column_layer
+    self._column_layers = self._add_layers(column_layers)
+    self._bias_layer = _BiasLayer(
+        units=units,
+        trainable=trainable,
+        weight_collections=self._weight_collections,
+        name='bias_layer',
+        **kwargs)
+    self._cols_to_vars = {}
+
+  def cols_to_vars(self):
+    """Returns a dict mapping _FeatureColumns to variables.
+
+    See `linear_model` for more information.
+    This is not populated till `call` is called i.e. layer is built.
+    """
+    return self._cols_to_vars
+
+  def call(self, features):
+    with variable_scope.variable_scope(self.name):
+      for column in self._feature_columns:
+        if not isinstance(
+            column,
+            (
+                fc_old._DenseColumn,  # pylint: disable=protected-access
+                fc_old._CategoricalColumn)):  # pylint: disable=protected-access
+          raise ValueError(
+              'Items of feature_columns must be either a '
+              '_DenseColumn or _CategoricalColumn. Given: {}'.format(column))
+      weighted_sums = []
+      ordered_columns = []
+      builder = fc_old._LazyBuilder(features)  # pylint: disable=protected-access
+      for layer in sorted(self._column_layers.values(), key=lambda x: x.name):
+        column = layer._feature_column  # pylint: disable=protected-access
+        ordered_columns.append(column)
+        weighted_sum = layer(builder)
+        weighted_sums.append(weighted_sum)
+        self._cols_to_vars[column] = ops.get_collection(
+            ops.GraphKeys.GLOBAL_VARIABLES, scope=layer.scope_name)
+
+      _verify_static_batch_size_equality(weighted_sums, ordered_columns)
+      predictions_no_bias = math_ops.add_n(
+          weighted_sums, name='weighted_sum_no_bias')
+      predictions = nn_ops.bias_add(
+          predictions_no_bias,
+          self._bias_layer(  # pylint: disable=not-callable
+              builder,
+              scope=variable_scope.get_variable_scope()),  # pylint: disable=not-callable
+          name='weighted_sum')
+      bias = self._bias_layer.variables[0]
+      self._cols_to_vars['bias'] = _get_expanded_variable_list(bias)
+    return predictions
+
+  def _add_layers(self, layers):
+    # "Magic" required for keras.Model classes to track all the variables in
+    # a list of layers.Layer objects.
+    # TODO(ashankar): Figure out API so user code doesn't have to do this.
+    for name, layer in layers.items():
+      setattr(self, 'layer-%s' % name, layer)
+    return layers
+
+
+def _transform_features(features, feature_columns, state_manager):
+  """Returns transformed features based on features columns passed in.
+
+  Please note that most probably you would not need to use this function. Please
+  check `input_layer` and `linear_model` to see whether they will
+  satisfy your use case or not.
+
+  Example:
+
+  ```python
+  # Define features and transformations
+  crosses_a_x_b = crossed_column(
+      columns=["sparse_feature_a", "sparse_feature_b"], hash_bucket_size=10000)
+  price_buckets = bucketized_column(
+      source_column=numeric_column("price"), boundaries=[...])
+
+  columns = [crosses_a_x_b, price_buckets]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  transformed = transform_features(features=features, feature_columns=columns)
+
+  assertCountEqual(columns, transformed.keys())
+  ```
+
+  Args:
+    features: A mapping from key to tensors. `FeatureColumn`s look up via these
+      keys. For example `numeric_column('price')` will look at 'price' key in
+      this dict. Values can be a `SparseTensor` or a `Tensor` depends on
+      corresponding `FeatureColumn`.
+    feature_columns: An iterable containing all the `FeatureColumn`s.
+    state_manager: A StateManager object that holds the FeatureColumn state.
+
+  Returns:
+    A `dict` mapping `FeatureColumn` to `Tensor` and `SparseTensor` values.
+  """
+  feature_columns = _normalize_feature_columns(feature_columns)
+  outputs = {}
+  with ops.name_scope(
+      None, default_name='transform_features', values=features.values()):
+    transformation_cache = FeatureTransformationCache(features)
+    for column in sorted(feature_columns, key=lambda x: x.name):
+      with ops.name_scope(None, default_name=column.name):
+        outputs[column] = transformation_cache.get(column, state_manager)
+  return outputs
+
+
+def make_parse_example_spec(feature_columns):
+  """Creates parsing spec dictionary from input feature_columns.
+
+  The returned dictionary can be used as arg 'features' in `tf.parse_example`.
+
+  Typical usage example:
+
+  ```python
+  # Define features and transformations
+  feature_a = categorical_column_with_vocabulary_file(...)
+  feature_b = numeric_column(...)
+  feature_c_bucketized = bucketized_column(numeric_column("feature_c"), ...)
+  feature_a_x_feature_c = crossed_column(
+      columns=["feature_a", feature_c_bucketized], ...)
+
+  feature_columns = set(
+      [feature_b, feature_c_bucketized, feature_a_x_feature_c])
+  features = tf.parse_example(
+      serialized=serialized_examples,
+      features=make_parse_example_spec(feature_columns))
+  ```
+
+  For the above example, make_parse_example_spec would return the dict:
+
+  ```python
+  {
+      "feature_a": parsing_ops.VarLenFeature(tf.string),
+      "feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
+      "feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)
+  }
+  ```
+
+  Args:
+    feature_columns: An iterable containing all feature columns. All items
+      should be instances of classes derived from `FeatureColumn`.
+
+  Returns:
+    A dict mapping each feature key to a `FixedLenFeature` or `VarLenFeature`
+    value.
+
+  Raises:
+    ValueError: If any of the given `feature_columns` is not a `FeatureColumn`
+      instance.
+  """
+  result = {}
+  for column in feature_columns:
+    if not isinstance(column, FeatureColumn):
+      raise ValueError('All feature_columns must be FeatureColumn instances. '
+                       'Given: {}'.format(column))
+    config = column.parse_example_spec
+    for key, value in six.iteritems(config):
+      if key in result and value != result[key]:
+        raise ValueError(
+            'feature_columns contain different parse_spec for key '
+            '{}. Given {} and {}'.format(key, value, result[key]))
+    result.update(config)
+  return result
+
+
+def embedding_column(
+    categorical_column, dimension, combiner='mean', initializer=None,
+    ckpt_to_load_from=None, tensor_name_in_ckpt=None, max_norm=None,
+    trainable=True):
+  """`_DenseColumn` that converts from sparse, categorical input.
+
+  Use this when your inputs are sparse, but you want to convert them to a dense
+  representation (e.g., to feed to a DNN).
+
+  Inputs must be a `_CategoricalColumn` created by any of the
+  `categorical_column_*` function. Here is an example of using
+  `embedding_column` with `DNNClassifier`:
+
+  ```python
+  video_id = categorical_column_with_identity(
+      key='video_id', num_buckets=1000000, default_value=0)
+  columns = [embedding_column(video_id, 9),...]
+
+  estimator = tf.estimator.DNNClassifier(feature_columns=columns, ...)
+
+  label_column = ...
+  def input_fn():
+    features = tf.parse_example(
+        ..., features=make_parse_example_spec(columns + [label_column]))
+    labels = features.pop(label_column.name)
+    return features, labels
+
+  estimator.train(input_fn=input_fn, steps=100)
+  ```
+
+  Here is an example using `embedding_column` with model_fn:
+
+  ```python
+  def model_fn(features, ...):
+    video_id = categorical_column_with_identity(
+        key='video_id', num_buckets=1000000, default_value=0)
+    columns = [embedding_column(video_id, 9),...]
+    dense_tensor = input_layer(features, columns)
+    # Form DNN layers, calculate loss, and return EstimatorSpec.
+    ...
+  ```
+
+  Args:
+    categorical_column: A `_CategoricalColumn` created by a
+      `categorical_column_with_*` function. This column produces the sparse IDs
+      that are inputs to the embedding lookup.
+    dimension: An integer specifying dimension of the embedding, must be > 0.
+    combiner: A string specifying how to reduce if there are multiple entries
+      in a single row. Currently 'mean', 'sqrtn' and 'sum' are supported, with
+      'mean' the default. 'sqrtn' often achieves good accuracy, in particular
+      with bag-of-words columns. Each of this can be thought as example level
+      normalizations on the column. For more information, see
+      `tf.embedding_lookup_sparse`.
+    initializer: A variable initializer function to be used in embedding
+      variable initialization. If not specified, defaults to
+      `tf.truncated_normal_initializer` with mean `0.0` and standard deviation
+      `1/sqrt(dimension)`.
+    ckpt_to_load_from: String representing checkpoint name/pattern from which to
+      restore column weights. Required if `tensor_name_in_ckpt` is not `None`.
+    tensor_name_in_ckpt: Name of the `Tensor` in `ckpt_to_load_from` from
+      which to restore the column weights. Required if `ckpt_to_load_from` is
+      not `None`.
+    max_norm: If not `None`, embedding values are l2-normalized to this value.
+    trainable: Whether or not the embedding is trainable. Default is True.
+
+  Returns:
+    `_DenseColumn` that converts from sparse input.
+
+  Raises:
+    ValueError: if `dimension` not > 0.
+    ValueError: if exactly one of `ckpt_to_load_from` and `tensor_name_in_ckpt`
+      is specified.
+    ValueError: if `initializer` is specified and is not callable.
+    RuntimeError: If eager execution is enabled.
+  """
+  if (dimension is None) or (dimension < 1):
+    raise ValueError('Invalid dimension {}.'.format(dimension))
+  if (ckpt_to_load_from is None) != (tensor_name_in_ckpt is None):
+    raise ValueError('Must specify both `ckpt_to_load_from` and '
+                     '`tensor_name_in_ckpt` or none of them.')
+
+  if (initializer is not None) and (not callable(initializer)):
+    raise ValueError('initializer must be callable if specified. '
+                     'Embedding of column_name: {}'.format(
+                         categorical_column.name))
+  if initializer is None:
+    initializer = init_ops.truncated_normal_initializer(
+        mean=0.0, stddev=1 / math.sqrt(dimension))
+
+  return EmbeddingColumn(
+      categorical_column=categorical_column,
+      dimension=dimension,
+      combiner=combiner,
+      initializer=initializer,
+      ckpt_to_load_from=ckpt_to_load_from,
+      tensor_name_in_ckpt=tensor_name_in_ckpt,
+      max_norm=max_norm,
+      trainable=trainable)
+
+
+def shared_embedding_columns(
+    categorical_columns, dimension, combiner='mean', initializer=None,
+    shared_embedding_collection_name=None, ckpt_to_load_from=None,
+    tensor_name_in_ckpt=None, max_norm=None, trainable=True):
+  """List of dense columns that convert from sparse, categorical input.
+
+  This is similar to `embedding_column`, except that it produces a list of
+  embedding columns that share the same embedding weights.
+
+  Use this when your inputs are sparse and of the same type (e.g. watched and
+  impression video IDs that share the same vocabulary), and you want to convert
+  them to a dense representation (e.g., to feed to a DNN).
+
+  Inputs must be a list of categorical columns created by any of the
+  `categorical_column_*` function. They must all be of the same type and have
+  the same arguments except `key`. E.g. they can be
+  categorical_column_with_vocabulary_file with the same vocabulary_file. Some or
+  all columns could also be weighted_categorical_column.
+
+  Here is an example embedding of two features for a DNNClassifier model:
+
+  ```python
+  watched_video_id = categorical_column_with_vocabulary_file(
+      'watched_video_id', video_vocabulary_file, video_vocabulary_size)
+  impression_video_id = categorical_column_with_vocabulary_file(
+      'impression_video_id', video_vocabulary_file, video_vocabulary_size)
+  columns = shared_embedding_columns(
+      [watched_video_id, impression_video_id], dimension=10)
+
+  estimator = tf.estimator.DNNClassifier(feature_columns=columns, ...)
+
+  label_column = ...
+  def input_fn():
+    features = tf.parse_example(
+        ..., features=make_parse_example_spec(columns + [label_column]))
+    labels = features.pop(label_column.name)
+    return features, labels
+
+  estimator.train(input_fn=input_fn, steps=100)
+  ```
+
+  Here is an example using `shared_embedding_columns` with model_fn:
+
+  ```python
+  def model_fn(features, ...):
+    watched_video_id = categorical_column_with_vocabulary_file(
+        'watched_video_id', video_vocabulary_file, video_vocabulary_size)
+    impression_video_id = categorical_column_with_vocabulary_file(
+        'impression_video_id', video_vocabulary_file, video_vocabulary_size)
+    columns = shared_embedding_columns(
+        [watched_video_id, impression_video_id], dimension=10)
+    dense_tensor = input_layer(features, columns)
+    # Form DNN layers, calculate loss, and return EstimatorSpec.
+    ...
+  ```
+
+  Args:
+    categorical_columns: List of categorical columns created by a
+      `categorical_column_with_*` function. These columns produce the sparse IDs
+      that are inputs to the embedding lookup. All columns must be of the same
+      type and have the same arguments except `key`. E.g. they can be
+      categorical_column_with_vocabulary_file with the same vocabulary_file.
+      Some or all columns could also be weighted_categorical_column.
+    dimension: An integer specifying dimension of the embedding, must be > 0.
+    combiner: A string specifying how to reduce if there are multiple entries
+      in a single row. Currently 'mean', 'sqrtn' and 'sum' are supported, with
+      'mean' the default. 'sqrtn' often achieves good accuracy, in particular
+      with bag-of-words columns. Each of this can be thought as example level
+      normalizations on the column. For more information, see
+      `tf.embedding_lookup_sparse`.
+    initializer: A variable initializer function to be used in embedding
+      variable initialization. If not specified, defaults to
+      `tf.truncated_normal_initializer` with mean `0.0` and standard deviation
+      `1/sqrt(dimension)`.
+    shared_embedding_collection_name: Optional collective name of these columns.
+      If not given, a reasonable name will be chosen based on the names of
+      `categorical_columns`.
+    ckpt_to_load_from: String representing checkpoint name/pattern from which to
+      restore column weights. Required if `tensor_name_in_ckpt` is not `None`.
+    tensor_name_in_ckpt: Name of the `Tensor` in `ckpt_to_load_from` from
+      which to restore the column weights. Required if `ckpt_to_load_from` is
+      not `None`.
+    max_norm: If not `None`, each embedding is clipped if its l2-norm is
+      larger than this value, before combining.
+    trainable: Whether or not the embedding is trainable. Default is True.
+
+  Returns:
+    A list of dense columns that converts from sparse input. The order of
+    results follows the ordering of `categorical_columns`.
+
+  Raises:
+    ValueError: if `dimension` not > 0.
+    ValueError: if any of the given `categorical_columns` is of different type
+      or has different arguments than the others.
+    ValueError: if exactly one of `ckpt_to_load_from` and `tensor_name_in_ckpt`
+      is specified.
+    ValueError: if `initializer` is specified and is not callable.
+    RuntimeError: if eager execution is enabled.
+  """
+  if context.executing_eagerly():
+    raise RuntimeError('shared_embedding_columns are not supported when eager '
+                       'execution is enabled.')
+
+  if (dimension is None) or (dimension < 1):
+    raise ValueError('Invalid dimension {}.'.format(dimension))
+  if (ckpt_to_load_from is None) != (tensor_name_in_ckpt is None):
+    raise ValueError('Must specify both `ckpt_to_load_from` and '
+                     '`tensor_name_in_ckpt` or none of them.')
+
+  if (initializer is not None) and (not callable(initializer)):
+    raise ValueError('initializer must be callable if specified.')
+  if initializer is None:
+    initializer = init_ops.truncated_normal_initializer(
+        mean=0.0, stddev=1. / math.sqrt(dimension))
+
+  # Sort the columns so the default collection name is deterministic even if the
+  # user passes columns from an unsorted collection, such as dict.values().
+  sorted_columns = sorted(categorical_columns, key=lambda x: x.name)
+
+  c0 = sorted_columns[0]
+  num_buckets = c0.num_buckets
+  if not isinstance(c0, CategoricalColumn):
+    raise ValueError(
+        'All categorical_columns must be subclasses of CategoricalColumn. '
+        'Given: {}, of type: {}'.format(c0, type(c0)))
+  if isinstance(c0, WeightedCategoricalColumn):
+    c0 = c0.categorical_column
+  for c in sorted_columns[1:]:
+    if isinstance(c, WeightedCategoricalColumn):
+      c = c.categorical_column
+    if not isinstance(c, type(c0)):
+      raise ValueError(
+          'To use shared_embedding_column, all categorical_columns must have '
+          'the same type, or be weighted_categorical_column of the same type. '
+          'Given column: {} of type: {} does not match given column: {} of '
+          'type: {}'.format(c0, type(c0), c, type(c)))
+    if num_buckets != c.num_buckets:
+      raise ValueError(
+          'To use shared_embedding_column, all categorical_columns must have '
+          'the same number of buckets. Given column: {} with buckets: {} does  '
+          'not match column: {} with buckets: {}'.format(
+              c0, num_buckets, c, c.num_buckets))
+
+  if not shared_embedding_collection_name:
+    shared_embedding_collection_name = '_'.join(c.name for c in sorted_columns)
+    shared_embedding_collection_name += '_shared_embedding'
+
+  result = []
+  for column in categorical_columns:
+    result.append(
+        SharedEmbeddingColumn(
+            categorical_column=column,
+            initializer=initializer,
+            dimension=dimension,
+            combiner=combiner,
+            shared_embedding_collection_name=shared_embedding_collection_name,
+            ckpt_to_load_from=ckpt_to_load_from,
+            tensor_name_in_ckpt=tensor_name_in_ckpt,
+            max_norm=max_norm,
+            trainable=trainable))
+
+  return result
+
+
+def numeric_column(key,
+                   shape=(1,),
+                   default_value=None,
+                   dtype=dtypes.float32,
+                   normalizer_fn=None):
+  """Represents real valued or numerical features.
+
+  Example:
+
+  ```python
+  price = numeric_column('price')
+  columns = [price, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  dense_tensor = input_layer(features, columns)
+
+  # or
+  bucketized_price = bucketized_column(price, boundaries=[...])
+  columns = [bucketized_price, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  linear_prediction = linear_model(features, columns)
+  ```
+
+  Args:
+    key: A unique string identifying the input feature. It is used as the
+      column name and the dictionary key for feature parsing configs, feature
+      `Tensor` objects, and feature columns.
+    shape: An iterable of integers specifies the shape of the `Tensor`. An
+      integer can be given which means a single dimension `Tensor` with given
+      width. The `Tensor` representing the column will have the shape of
+      [batch_size] + `shape`.
+    default_value: A single value compatible with `dtype` or an iterable of
+      values compatible with `dtype` which the column takes on during
+      `tf.Example` parsing if data is missing. A default value of `None` will
+      cause `tf.parse_example` to fail if an example does not contain this
+      column. If a single value is provided, the same value will be applied as
+      the default value for every item. If an iterable of values is provided,
+      the shape of the `default_value` should be equal to the given `shape`.
+    dtype: defines the type of values. Default value is `tf.float32`. Must be a
+      non-quantized, real integer or floating point type.
+    normalizer_fn: If not `None`, a function that can be used to normalize the
+      value of the tensor after `default_value` is applied for parsing.
+      Normalizer function takes the input `Tensor` as its argument, and returns
+      the output `Tensor`. (e.g. lambda x: (x - 3.0) / 4.2). Please note that
+      even though the most common use case of this function is normalization, it
+      can be used for any kind of Tensorflow transformations.
+
+  Returns:
+    A `NumericColumn`.
+
+  Raises:
+    TypeError: if any dimension in shape is not an int
+    ValueError: if any dimension in shape is not a positive integer
+    TypeError: if `default_value` is an iterable but not compatible with `shape`
+    TypeError: if `default_value` is not compatible with `dtype`.
+    ValueError: if `dtype` is not convertible to `tf.float32`.
+  """
+  shape = _check_shape(shape, key)
+  if not (dtype.is_integer or dtype.is_floating):
+    raise ValueError('dtype must be convertible to float. '
+                     'dtype: {}, key: {}'.format(dtype, key))
+  default_value = _check_default_value(shape, default_value, dtype, key)
+
+  if normalizer_fn is not None and not callable(normalizer_fn):
+    raise TypeError(
+        'normalizer_fn must be a callable. Given: {}'.format(normalizer_fn))
+
+  _assert_key_is_string(key)
+  return NumericColumn(
+      key,
+      shape=shape,
+      default_value=default_value,
+      dtype=dtype,
+      normalizer_fn=normalizer_fn)
+
+
+def bucketized_column(source_column, boundaries):
+  """Represents discretized dense input.
+
+  Buckets include the left boundary, and exclude the right boundary. Namely,
+  `boundaries=[0., 1., 2.]` generates buckets `(-inf, 0.)`, `[0., 1.)`,
+  `[1., 2.)`, and `[2., +inf)`.
+
+  For example, if the inputs are
+
+  ```python
+  boundaries = [0, 10, 100]
+  input tensor = [[-5, 10000]
+                  [150,   10]
+                  [5,    100]]
+  ```
+
+  then the output will be
+
+  ```python
+  output = [[0, 3]
+            [3, 2]
+            [1, 3]]
+  ```
+
+  Example:
+
+  ```python
+  price = numeric_column('price')
+  bucketized_price = bucketized_column(price, boundaries=[...])
+  columns = [bucketized_price, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  linear_prediction = linear_model(features, columns)
+
+  # or
+  columns = [bucketized_price, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  dense_tensor = input_layer(features, columns)
+  ```
+
+  `bucketized_column` can also be crossed with another categorical column using
+  `crossed_column`:
+
+  ```python
+  price = numeric_column('price')
+  # bucketized_column converts numerical feature to a categorical one.
+  bucketized_price = bucketized_column(price, boundaries=[...])
+  # 'keywords' is a string feature.
+  price_x_keywords = crossed_column([bucketized_price, 'keywords'], 50K)
+  columns = [price_x_keywords, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  linear_prediction = linear_model(features, columns)
+  ```
+
+  Args:
+    source_column: A one-dimensional dense column which is generated with
+      `numeric_column`.
+    boundaries: A sorted list or tuple of floats specifying the boundaries.
+
+  Returns:
+    A `BucketizedColumn`.
+
+  Raises:
+    ValueError: If `source_column` is not a numeric column, or if it is not
+      one-dimensional.
+    ValueError: If `boundaries` is not a sorted list or tuple.
+  """
+  if not isinstance(source_column, NumericColumn):
+    raise ValueError(
+        'source_column must be a column generated with numeric_column(). '
+        'Given: {}'.format(source_column))
+  if len(source_column.shape) > 1:
+    raise ValueError(
+        'source_column must be one-dimensional column. '
+        'Given: {}'.format(source_column))
+  if (not boundaries or
+      not (isinstance(boundaries, list) or isinstance(boundaries, tuple))):
+    raise ValueError('boundaries must be a sorted list.')
+  for i in range(len(boundaries) - 1):
+    if boundaries[i] >= boundaries[i + 1]:
+      raise ValueError('boundaries must be a sorted list.')
+  return BucketizedColumn(source_column, tuple(boundaries))
+
+
+def _assert_string_or_int(dtype, prefix):
+  if (dtype != dtypes.string) and (not dtype.is_integer):
+    raise ValueError(
+        '{} dtype must be string or integer. dtype: {}.'.format(prefix, dtype))
+
+
+def _assert_key_is_string(key):
+  if not isinstance(key, six.string_types):
+    raise ValueError(
+        'key must be a string. Got: type {}. Given key: {}.'.format(
+            type(key), key))
+
+
+def categorical_column_with_hash_bucket(key,
+                                        hash_bucket_size,
+                                        dtype=dtypes.string):
+  """Represents sparse feature where ids are set by hashing.
+
+  Use this when your sparse features are in string or integer format, and you
+  want to distribute your inputs into a finite number of buckets by hashing.
+  output_id = Hash(input_feature_string) % bucket_size for string type input.
+  For int type input, the value is converted to its string representation first
+  and then hashed by the same formula.
+
+  For input dictionary `features`, `features[key]` is either `Tensor` or
+  `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int
+  and `''` for string, which will be dropped by this feature column.
+
+  Example:
+
+  ```python
+  keywords = categorical_column_with_hash_bucket("keywords", 10K)
+  columns = [keywords, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  linear_prediction = linear_model(features, columns)
+
+  # or
+  keywords_embedded = embedding_column(keywords, 16)
+  columns = [keywords_embedded, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  dense_tensor = input_layer(features, columns)
+  ```
+
+  Args:
+    key: A unique string identifying the input feature. It is used as the
+      column name and the dictionary key for feature parsing configs, feature
+      `Tensor` objects, and feature columns.
+    hash_bucket_size: An int > 1. The number of buckets.
+    dtype: The type of features. Only string and integer types are supported.
+
+  Returns:
+    A `HashedCategoricalColumn`.
+
+  Raises:
+    ValueError: `hash_bucket_size` is not greater than 1.
+    ValueError: `dtype` is neither string nor integer.
+  """
+  if hash_bucket_size is None:
+    raise ValueError('hash_bucket_size must be set. ' 'key: {}'.format(key))
+
+  if hash_bucket_size < 1:
+    raise ValueError('hash_bucket_size must be at least 1. '
+                     'hash_bucket_size: {}, key: {}'.format(
+                         hash_bucket_size, key))
+
+  _assert_key_is_string(key)
+  _assert_string_or_int(dtype, prefix='column_name: {}'.format(key))
+
+  return HashedCategoricalColumn(key, hash_bucket_size, dtype)
+
+
+def categorical_column_with_vocabulary_file(key,
+                                            vocabulary_file,
+                                            vocabulary_size=None,
+                                            num_oov_buckets=0,
+                                            default_value=None,
+                                            dtype=dtypes.string):
+  """A `CategoricalColumn` with a vocabulary file.
+
+  Use this when your inputs are in string or integer format, and you have a
+  vocabulary file that maps each value to an integer ID. By default,
+  out-of-vocabulary values are ignored. Use either (but not both) of
+  `num_oov_buckets` and `default_value` to specify how to include
+  out-of-vocabulary values.
+
+  For input dictionary `features`, `features[key]` is either `Tensor` or
+  `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int
+  and `''` for string, which will be dropped by this feature column.
+
+  Example with `num_oov_buckets`:
+  File '/us/states.txt' contains 50 lines, each with a 2-character U.S. state
+  abbreviation. All inputs with values in that file are assigned an ID 0-49,
+  corresponding to its line number. All other values are hashed and assigned an
+  ID 50-54.
+
+  ```python
+  states = categorical_column_with_vocabulary_file(
+      key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
+      num_oov_buckets=5)
+  columns = [states, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  linear_prediction = linear_model(features, columns)
+  ```
+
+  Example with `default_value`:
+  File '/us/states.txt' contains 51 lines - the first line is 'XX', and the
+  other 50 each have a 2-character U.S. state abbreviation. Both a literal 'XX'
+  in input, and other values missing from the file, will be assigned ID 0. All
+  others are assigned the corresponding line number 1-50.
+
+  ```python
+  states = categorical_column_with_vocabulary_file(
+      key='states', vocabulary_file='/us/states.txt', vocabulary_size=51,
+      default_value=0)
+  columns = [states, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  linear_prediction, _, _ = linear_model(features, columns)
+  ```
+
+  And to make an embedding with either:
+
+  ```python
+  columns = [embedding_column(states, 3),...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  dense_tensor = input_layer(features, columns)
+  ```
+
+  Args:
+    key: A unique string identifying the input feature. It is used as the
+      column name and the dictionary key for feature parsing configs, feature
+      `Tensor` objects, and feature columns.
+    vocabulary_file: The vocabulary file name.
+    vocabulary_size: Number of the elements in the vocabulary. This must be no
+      greater than length of `vocabulary_file`, if less than length, later
+      values are ignored. If None, it is set to the length of `vocabulary_file`.
+    num_oov_buckets: Non-negative integer, the number of out-of-vocabulary
+      buckets. All out-of-vocabulary inputs will be assigned IDs in the range
+      `[vocabulary_size, vocabulary_size+num_oov_buckets)` based on a hash of
+      the input value. A positive `num_oov_buckets` can not be specified with
+      `default_value`.
+    default_value: The integer ID value to return for out-of-vocabulary feature
+      values, defaults to `-1`. This can not be specified with a positive
+      `num_oov_buckets`.
+    dtype: The type of features. Only string and integer types are supported.
+
+  Returns:
+    A `CategoricalColumn` with a vocabulary file.
+
+  Raises:
+    ValueError: `vocabulary_file` is missing or cannot be opened.
+    ValueError: `vocabulary_size` is missing or < 1.
+    ValueError: `num_oov_buckets` is a negative integer.
+    ValueError: `num_oov_buckets` and `default_value` are both specified.
+    ValueError: `dtype` is neither string nor integer.
+  """
+  if not vocabulary_file:
+    raise ValueError('Missing vocabulary_file in {}.'.format(key))
+
+  if vocabulary_size is None:
+    if not gfile.Exists(vocabulary_file):
+      raise ValueError('vocabulary_file in {} does not exist.'.format(key))
+
+    with gfile.GFile(vocabulary_file) as f:
+      vocabulary_size = sum(1 for _ in f)
+    logging.info(
+        'vocabulary_size = %d in %s is inferred from the number of elements '
+        'in the vocabulary_file %s.', vocabulary_size, key, vocabulary_file)
+
+  # `vocabulary_size` isn't required for lookup, but it is for `_num_buckets`.
+  if vocabulary_size < 1:
+    raise ValueError('Invalid vocabulary_size in {}.'.format(key))
+  if num_oov_buckets:
+    if default_value is not None:
+      raise ValueError(
+          'Can\'t specify both num_oov_buckets and default_value in {}.'.format(
+              key))
+    if num_oov_buckets < 0:
+      raise ValueError('Invalid num_oov_buckets {} in {}.'.format(
+          num_oov_buckets, key))
+  _assert_string_or_int(dtype, prefix='column_name: {}'.format(key))
+  _assert_key_is_string(key)
+  return VocabularyFileCategoricalColumn(
+      key=key,
+      vocabulary_file=vocabulary_file,
+      vocabulary_size=vocabulary_size,
+      num_oov_buckets=0 if num_oov_buckets is None else num_oov_buckets,
+      default_value=-1 if default_value is None else default_value,
+      dtype=dtype)
+
+
+def categorical_column_with_vocabulary_list(
+    key, vocabulary_list, dtype=None, default_value=-1, num_oov_buckets=0):
+  """A `_CategoricalColumn` with in-memory vocabulary.
+
+  Use this when your inputs are in string or integer format, and you have an
+  in-memory vocabulary mapping each value to an integer ID. By default,
+  out-of-vocabulary values are ignored. Use either (but not both) of
+  `num_oov_buckets` and `default_value` to specify how to include
+  out-of-vocabulary values.
+
+  For input dictionary `features`, `features[key]` is either `Tensor` or
+  `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int
+  and `''` for string, which will be dropped by this feature column.
+
+  Example with `num_oov_buckets`:
+  In the following example, each input in `vocabulary_list` is assigned an ID
+  0-3 corresponding to its index (e.g., input 'B' produces output 2). All other
+  inputs are hashed and assigned an ID 4-5.
+
+  ```python
+  colors = categorical_column_with_vocabulary_list(
+      key='colors', vocabulary_list=('R', 'G', 'B', 'Y'),
+      num_oov_buckets=2)
+  columns = [colors, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  linear_prediction, _, _ = linear_model(features, columns)
+  ```
+
+  Example with `default_value`:
+  In the following example, each input in `vocabulary_list` is assigned an ID
+  0-4 corresponding to its index (e.g., input 'B' produces output 3). All other
+  inputs are assigned `default_value` 0.
+
+
+  ```python
+  colors = categorical_column_with_vocabulary_list(
+      key='colors', vocabulary_list=('X', 'R', 'G', 'B', 'Y'), default_value=0)
+  columns = [colors, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  linear_prediction, _, _ = linear_model(features, columns)
+  ```
+
+  And to make an embedding with either:
+
+  ```python
+  columns = [embedding_column(colors, 3),...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  dense_tensor = input_layer(features, columns)
+  ```
+
+  Args:
+    key: A unique string identifying the input feature. It is used as the
+      column name and the dictionary key for feature parsing configs, feature
+      `Tensor` objects, and feature columns.
+    vocabulary_list: An ordered iterable defining the vocabulary. Each feature
+      is mapped to the index of its value (if present) in `vocabulary_list`.
+      Must be castable to `dtype`.
+    dtype: The type of features. Only string and integer types are supported.
+      If `None`, it will be inferred from `vocabulary_list`.
+    default_value: The integer ID value to return for out-of-vocabulary feature
+      values, defaults to `-1`. This can not be specified with a positive
+      `num_oov_buckets`.
+    num_oov_buckets: Non-negative integer, the number of out-of-vocabulary
+      buckets. All out-of-vocabulary inputs will be assigned IDs in the range
+      `[len(vocabulary_list), len(vocabulary_list)+num_oov_buckets)` based on a
+      hash of the input value. A positive `num_oov_buckets` can not be specified
+      with `default_value`.
+
+  Returns:
+    A `CategoricalColumn` with in-memory vocabulary.
+
+  Raises:
+    ValueError: if `vocabulary_list` is empty, or contains duplicate keys.
+    ValueError: `num_oov_buckets` is a negative integer.
+    ValueError: `num_oov_buckets` and `default_value` are both specified.
+    ValueError: if `dtype` is not integer or string.
+  """
+  if (vocabulary_list is None) or (len(vocabulary_list) < 1):
+    raise ValueError(
+        'vocabulary_list {} must be non-empty, column_name: {}'.format(
+            vocabulary_list, key))
+  if len(set(vocabulary_list)) != len(vocabulary_list):
+    raise ValueError(
+        'Duplicate keys in vocabulary_list {}, column_name: {}'.format(
+            vocabulary_list, key))
+  vocabulary_dtype = dtypes.as_dtype(np.array(vocabulary_list).dtype)
+  if num_oov_buckets:
+    if default_value != -1:
+      raise ValueError(
+          'Can\'t specify both num_oov_buckets and default_value in {}.'.format(
+              key))
+    if num_oov_buckets < 0:
+      raise ValueError('Invalid num_oov_buckets {} in {}.'.format(
+          num_oov_buckets, key))
+  _assert_string_or_int(
+      vocabulary_dtype, prefix='column_name: {} vocabulary'.format(key))
+  if dtype is None:
+    dtype = vocabulary_dtype
+  elif dtype.is_integer != vocabulary_dtype.is_integer:
+    raise ValueError(
+        'dtype {} and vocabulary dtype {} do not match, column_name: {}'.format(
+            dtype, vocabulary_dtype, key))
+  _assert_string_or_int(dtype, prefix='column_name: {}'.format(key))
+  _assert_key_is_string(key)
+
+  return VocabularyListCategoricalColumn(
+      key=key,
+      vocabulary_list=tuple(vocabulary_list),
+      dtype=dtype,
+      default_value=default_value,
+      num_oov_buckets=num_oov_buckets)
+
+
+def categorical_column_with_identity(key, num_buckets, default_value=None):
+  """A `CategoricalColumn` that returns identity values.
+
+  Use this when your inputs are integers in the range `[0, num_buckets)`, and
+  you want to use the input value itself as the categorical ID. Values outside
+  this range will result in `default_value` if specified, otherwise it will
+  fail.
+
+  Typically, this is used for contiguous ranges of integer indexes, but
+  it doesn't have to be. This might be inefficient, however, if many of IDs
+  are unused. Consider `categorical_column_with_hash_bucket` in that case.
+
+  For input dictionary `features`, `features[key]` is either `Tensor` or
+  `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int
+  and `''` for string, which will be dropped by this feature column.
+
+  In the following examples, each input in the range `[0, 1000000)` is assigned
+  the same value. All other inputs are assigned `default_value` 0. Note that a
+  literal 0 in inputs will result in the same default ID.
+
+  Linear model:
+
+  ```python
+  video_id = categorical_column_with_identity(
+      key='video_id', num_buckets=1000000, default_value=0)
+  columns = [video_id, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  linear_prediction, _, _ = linear_model(features, columns)
+  ```
+
+  Embedding for a DNN model:
+
+  ```python
+  columns = [embedding_column(video_id, 9),...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  dense_tensor = input_layer(features, columns)
+  ```
+
+  Args:
+    key: A unique string identifying the input feature. It is used as the
+      column name and the dictionary key for feature parsing configs, feature
+      `Tensor` objects, and feature columns.
+    num_buckets: Range of inputs and outputs is `[0, num_buckets)`.
+    default_value: If `None`, this column's graph operations will fail for
+      out-of-range inputs. Otherwise, this value must be in the range
+      `[0, num_buckets)`, and will replace inputs in that range.
+
+  Returns:
+    A `CategoricalColumn` that returns identity values.
+
+  Raises:
+    ValueError: if `num_buckets` is less than one.
+    ValueError: if `default_value` is not in range `[0, num_buckets)`.
+  """
+  if num_buckets < 1:
+    raise ValueError(
+        'num_buckets {} < 1, column_name {}'.format(num_buckets, key))
+  if (default_value is not None) and (
+      (default_value < 0) or (default_value >= num_buckets)):
+    raise ValueError(
+        'default_value {} not in range [0, {}), column_name {}'.format(
+            default_value, num_buckets, key))
+  _assert_key_is_string(key)
+  return IdentityCategoricalColumn(
+      key=key, number_buckets=num_buckets, default_value=default_value)
+
+
+def indicator_column(categorical_column):
+  """Represents multi-hot representation of given categorical column.
+
+  - For DNN model, `indicator_column` can be used to wrap any
+    `categorical_column_*` (e.g., to feed to DNN). Consider to Use
+    `embedding_column` if the number of buckets/unique(values) are large.
+
+  - For Wide (aka linear) model, `indicator_column` is the internal
+    representation for categorical column when passing categorical column
+    directly (as any element in feature_columns) to `linear_model`. See
+    `linear_model` for details.
+
+  ```python
+  name = indicator_column(categorical_column_with_vocabulary_list(
+      'name', ['bob', 'george', 'wanda'])
+  columns = [name, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  dense_tensor = input_layer(features, columns)
+
+  dense_tensor == [[1, 0, 0]]  # If "name" bytes_list is ["bob"]
+  dense_tensor == [[1, 0, 1]]  # If "name" bytes_list is ["bob", "wanda"]
+  dense_tensor == [[2, 0, 0]]  # If "name" bytes_list is ["bob", "bob"]
+  ```
+
+  Args:
+    categorical_column: A `CategoricalColumn` which is created by
+      `categorical_column_with_*` or `crossed_column` functions.
+
+  Returns:
+    An `IndicatorColumn`.
+  """
+  return IndicatorColumn(categorical_column)
+
+
+def weighted_categorical_column(
+    categorical_column, weight_feature_key, dtype=dtypes.float32):
+  """Applies weight values to a `_CategoricalColumn`.
+
+  Use this when each of your sparse inputs has both an ID and a value. For
+  example, if you're representing text documents as a collection of word
+  frequencies, you can provide 2 parallel sparse input features ('terms' and
+  'frequencies' below).
+
+  Example:
+
+  Input `tf.Example` objects:
+
+  ```proto
+  [
+    features {
+      feature {
+        key: "terms"
+        value {bytes_list {value: "very" value: "model"}}
+      }
+      feature {
+        key: "frequencies"
+        value {float_list {value: 0.3 value: 0.1}}
+      }
+    },
+    features {
+      feature {
+        key: "terms"
+        value {bytes_list {value: "when" value: "course" value: "human"}}
+      }
+      feature {
+        key: "frequencies"
+        value {float_list {value: 0.4 value: 0.1 value: 0.2}}
+      }
+    }
+  ]
+  ```
+
+  ```python
+  categorical_column = categorical_column_with_hash_bucket(
+      column_name='terms', hash_bucket_size=1000)
+  weighted_column = weighted_categorical_column(
+      categorical_column=categorical_column, weight_feature_key='frequencies')
+  columns = [weighted_column, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  linear_prediction, _, _ = linear_model(features, columns)
+  ```
+
+  This assumes the input dictionary contains a `SparseTensor` for key
+  'terms', and a `SparseTensor` for key 'frequencies'. These 2 tensors must have
+  the same indices and dense shape.
+
+  Args:
+    categorical_column: A `_CategoricalColumn` created by
+      `categorical_column_with_*` functions.
+    weight_feature_key: String key for weight values.
+    dtype: Type of weights, such as `tf.float32`. Only float and integer weights
+      are supported.
+
+  Returns:
+    A `CategoricalColumn` composed of two sparse features: one represents id,
+    the other represents weight (value) of the id feature in that example.
+
+  Raises:
+    ValueError: if `dtype` is not convertible to float.
+  """
+  if (dtype is None) or not (dtype.is_integer or dtype.is_floating):
+    raise ValueError('dtype {} is not convertible to float.'.format(dtype))
+  return WeightedCategoricalColumn(
+      categorical_column=categorical_column,
+      weight_feature_key=weight_feature_key,
+      dtype=dtype)
+
+
+def crossed_column(keys, hash_bucket_size, hash_key=None):
+  """Returns a column for performing crosses of categorical features.
+
+  Crossed features will be hashed according to `hash_bucket_size`. Conceptually,
+  the transformation can be thought of as:
+    Hash(cartesian product of features) % `hash_bucket_size`
+
+  For example, if the input features are:
+
+  * SparseTensor referred by first key:
+
+    ```python
+    shape = [2, 2]
+    {
+        [0, 0]: "a"
+        [1, 0]: "b"
+        [1, 1]: "c"
+    }
+    ```
+
+  * SparseTensor referred by second key:
+
+    ```python
+    shape = [2, 1]
+    {
+        [0, 0]: "d"
+        [1, 0]: "e"
+    }
+    ```
+
+  then crossed feature will look like:
+
+  ```python
+   shape = [2, 2]
+  {
+      [0, 0]: Hash64("d", Hash64("a")) % hash_bucket_size
+      [1, 0]: Hash64("e", Hash64("b")) % hash_bucket_size
+      [1, 1]: Hash64("e", Hash64("c")) % hash_bucket_size
+  }
+  ```
+
+  Here is an example to create a linear model with crosses of string features:
+
+  ```python
+  keywords_x_doc_terms = crossed_column(['keywords', 'doc_terms'], 50K)
+  columns = [keywords_x_doc_terms, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  linear_prediction = linear_model(features, columns)
+  ```
+
+  You could also use vocabulary lookup before crossing:
+
+  ```python
+  keywords = categorical_column_with_vocabulary_file(
+      'keywords', '/path/to/vocabulary/file', vocabulary_size=1K)
+  keywords_x_doc_terms = crossed_column([keywords, 'doc_terms'], 50K)
+  columns = [keywords_x_doc_terms, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  linear_prediction = linear_model(features, columns)
+  ```
+
+  If an input feature is of numeric type, you can use
+  `categorical_column_with_identity`, or `bucketized_column`, as in the example:
+
+  ```python
+  # vertical_id is an integer categorical feature.
+  vertical_id = categorical_column_with_identity('vertical_id', 10K)
+  price = numeric_column('price')
+  # bucketized_column converts numerical feature to a categorical one.
+  bucketized_price = bucketized_column(price, boundaries=[...])
+  vertical_id_x_price = crossed_column([vertical_id, bucketized_price], 50K)
+  columns = [vertical_id_x_price, ...]
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  linear_prediction = linear_model(features, columns)
+  ```
+
+  To use crossed column in DNN model, you need to add it in an embedding column
+  as in this example:
+
+  ```python
+  vertical_id_x_price = crossed_column([vertical_id, bucketized_price], 50K)
+  vertical_id_x_price_embedded = embedding_column(vertical_id_x_price, 10)
+  dense_tensor = input_layer(features, [vertical_id_x_price_embedded, ...])
+  ```
+
+  Args:
+    keys: An iterable identifying the features to be crossed. Each element can
+      be either:
+      * string: Will use the corresponding feature which must be of string type.
+      * `CategoricalColumn`: Will use the transformed tensor produced by this
+        column. Does not support hashed categorical column.
+    hash_bucket_size: An int > 1. The number of buckets.
+    hash_key: Specify the hash_key that will be used by the `FingerprintCat64`
+      function to combine the crosses fingerprints on SparseCrossOp (optional).
+
+  Returns:
+    A `CrossedColumn`.
+
+  Raises:
+    ValueError: If `len(keys) < 2`.
+    ValueError: If any of the keys is neither a string nor `CategoricalColumn`.
+    ValueError: If any of the keys is `HashedCategoricalColumn`.
+    ValueError: If `hash_bucket_size < 1`.
+  """
+  if not hash_bucket_size or hash_bucket_size < 1:
+    raise ValueError('hash_bucket_size must be > 1. '
+                     'hash_bucket_size: {}'.format(hash_bucket_size))
+  if not keys or len(keys) < 2:
+    raise ValueError(
+        'keys must be a list with length > 1. Given: {}'.format(keys))
+  for key in keys:
+    if (not isinstance(key, six.string_types) and
+        not isinstance(key, CategoricalColumn)):
+      raise ValueError(
+          'Unsupported key type. All keys must be either string, or '
+          'categorical column except HashedCategoricalColumn. '
+          'Given: {}'.format(key))
+    if isinstance(key, HashedCategoricalColumn):
+      raise ValueError(
+          'categorical_column_with_hash_bucket is not supported for crossing. '
+          'Hashing before crossing will increase probability of collision. '
+          'Instead, use the feature name as a string. Given: {}'.format(key))
+  return CrossedColumn(
+      keys=tuple(keys), hash_bucket_size=hash_bucket_size, hash_key=hash_key)
+
+
+class StateManager(object):
+  """Manages the state associated with FeatureColumns.
+
+  Some `FeatureColumn`s create variables or resources to assist their
+  computation. The `StateManager` is responsible for creating and storing these
+  objects since `FeatureColumn`s are supposed to be stateless configuration
+  only.
+  """
+
+  def get_variable(self,
+                   feature_column,
+                   name,
+                   shape,
+                   dtype=None,
+                   initializer=None):
+    """Creates a new variable or returns an existing one.
+
+    Args:
+      feature_column: A `FeatureColumn` object this variable corresponds to.
+      name: variable name.
+      shape: variable shape.
+      dtype: The type of the variable. Defaults to `self.dtype` or `float32`.
+      initializer: initializer instance (callable).
+
+    Returns:
+      The variable.
+    """
+    raise NotImplementedError('StateManager.get_variable')
+
+  def get_resource(self, feature_column, name, resource_creator):
+    """Creates a new resource or returns an existing one.
+
+    Resources can be things such as tables etc.
+
+    Args:
+      feature_column: A `FeatureColumn` object this variable corresponds to.
+      name: Name of the resource.
+      resource_creator: A callable that can create the resource.
+
+    Returns:
+      The resource.
+    """
+    raise NotImplementedError('StateManager.get_resource')
+
+
+class FeatureColumn(object):
+  """Represents a feature column abstraction.
+
+  WARNING: Do not subclass this layer unless you know what you are doing:
+  the API is subject to future changes.
+
+  To distinguish between the concept of a feature family and a specific binary
+  feature within a family, we refer to a feature family like "country" as a
+  feature column. For example, we can have a feature in a `tf.Example` format:
+    {key: "country",  value: [ "US" ]}
+  In this example the value of feature is "US" and "country" refers to the
+  column of the feature.
+
+  This class is an abstract class. Users should not create instances of this.
+  """
+  __metaclass__ = abc.ABCMeta
+
+  @abc.abstractproperty
+  def name(self):
+    """Returns string. Used for naming."""
+    pass
+
+  @abc.abstractmethod
+  def transform_feature(self, transformation_cache, state_manager):
+    """Returns intermediate representation (usually a `Tensor`).
+
+    Uses `transformation_cache` to create an intermediate representation
+    (usually a `Tensor`) that other feature columns can use.
+
+    Example usage of `transformation_cache`:
+    Let's say a Feature column depends on raw feature ('raw') and another
+    `FeatureColumn` (input_fc). To access corresponding `Tensor`s,
+    transformation_cache will be used as follows:
+
+    ```python
+    raw_tensor = transformation_cache.get('raw', state_manager)
+    fc_tensor = transformation_cache.get(input_fc, state_manager)
+    ```
+
+    Args:
+      transformation_cache: A `FeatureTransformationCache` object to access
+        features.
+      state_manager: A `StateManager` to create / access resources such as
+        lookup tables.
+
+    Returns:
+      Transformed feature `Tensor`.
+    """
+    pass
+
+  @abc.abstractproperty
+  def parse_example_spec(self):
+    """Returns a `tf.Example` parsing spec as dict.
+
+    It is used for get_parsing_spec for `tf.parse_example`. Returned spec is a
+    dict from keys ('string') to `VarLenFeature`, `FixedLenFeature`, and other
+    supported objects. Please check documentation of @{tf.parse_example} for all
+    supported spec objects.
+
+    Let's say a Feature column depends on raw feature ('raw') and another
+    `FeatureColumn` (input_fc). One possible implementation of
+    parse_example_spec is as follows:
+
+    ```python
+    spec = {'raw': tf.FixedLenFeature(...)}
+    spec.update(input_fc.parse_example_spec)
+    return spec
+    ```
+    """
+    pass
+
+  def create_state(self, state_manager):
+    """Uses the `state_manager` to create state for the FeatureColumn.
+
+    Args:
+      state_manager: A `StateManager` to create / access resources such as
+        lookup tables and variables.
+    """
+    pass
+
+
+class DenseColumn(FeatureColumn):
+  """Represents a column which can be represented as `Tensor`.
+
+  Some examples of this type are: numeric_column, embedding_column,
+  indicator_column.
+  """
+
+  __metaclass__ = abc.ABCMeta
+
+  @abc.abstractproperty
+  def variable_shape(self):
+    """`TensorShape` of `get_dense_tensor`, without batch dimension."""
+    pass
+
+  @abc.abstractmethod
+  def get_dense_tensor(self, transformation_cache, state_manager):
+    """Returns a `Tensor`.
+
+    The output of this function will be used by model-builder-functions. For
+    example the pseudo code of `input_layer` will be like:
+
+    ```python
+    def input_layer(features, feature_columns, ...):
+      outputs = [fc.get_dense_tensor(...) for fc in feature_columns]
+      return tf.concat(outputs)
+    ```
+
+    Args:
+      transformation_cache: A `FeatureTransformationCache` object to access
+        features.
+      state_manager: A `StateManager` to create / access resources such as
+        lookup tables.
+
+    Returns:
+      `Tensor` of shape [batch_size] + `variable_shape`.
+    """
+    pass
+
+
+def _create_weighted_sum(column,
+                         transformation_cache,
+                         state_manager,
+                         units,
+                         sparse_combiner,
+                         weight_collections,
+                         trainable,
+                         weight_var=None):
+  """Creates a weighted sum for a dense/categorical column for linear_model."""
+  if isinstance(column, CategoricalColumn):
+    return _create_categorical_column_weighted_sum(
+        column=column,
+        transformation_cache=transformation_cache,
+        state_manager=state_manager,
+        units=units,
+        sparse_combiner=sparse_combiner,
+        weight_collections=weight_collections,
+        trainable=trainable,
+        weight_var=weight_var)
+  else:
+    return _create_dense_column_weighted_sum(
+        column=column,
+        transformation_cache=transformation_cache,
+        state_manager=state_manager,
+        units=units,
+        weight_collections=weight_collections,
+        trainable=trainable,
+        weight_var=weight_var)
+
+
+def _create_dense_column_weighted_sum(column,
+                                      transformation_cache,
+                                      state_manager,
+                                      units,
+                                      weight_collections,
+                                      trainable,
+                                      weight_var=None):
+  """Create a weighted sum of a dense column for linear_model."""
+  tensor = column.get_dense_tensor(transformation_cache, state_manager)
+  num_elements = column.variable_shape.num_elements()
+  batch_size = array_ops.shape(tensor)[0]
+  tensor = array_ops.reshape(tensor, shape=(batch_size, num_elements))
+  if weight_var is not None:
+    weight = weight_var
+  else:
+    weight = variable_scope.get_variable(
+        name='weights',
+        shape=[num_elements, units],
+        initializer=init_ops.zeros_initializer(),
+        trainable=trainable,
+        collections=weight_collections)
+  return math_ops.matmul(tensor, weight, name='weighted_sum')
+
+
+class CategoricalColumn(FeatureColumn):
+  """Represents a categorical feature.
+
+  A categorical feature typically handled with a @{tf.SparseTensor} of IDs.
+  """
+  __metaclass__ = abc.ABCMeta
+
+  IdWeightPair = collections.namedtuple(  # pylint: disable=invalid-name
+      'IdWeightPair', ('id_tensor', 'weight_tensor'))
+
+  @abc.abstractproperty
+  def num_buckets(self):
+    """Returns number of buckets in this sparse feature."""
+    pass
+
+  @abc.abstractmethod
+  def get_sparse_tensors(self, transformation_cache, state_manager):
+    """Returns an IdWeightPair.
+
+    `IdWeightPair` is a pair of `SparseTensor`s which represents ids and
+    weights.
+
+    `IdWeightPair.id_tensor` is typically a `batch_size` x `num_buckets`
+    `SparseTensor` of `int64`. `IdWeightPair.weight_tensor` is either a
+    `SparseTensor` of `float` or `None` to indicate all weights should be
+    taken to be 1. If specified, `weight_tensor` must have exactly the same
+    shape and indices as `sp_ids`. Expected `SparseTensor` is same as parsing
+    output of a `VarLenFeature` which is a ragged matrix.
+
+    Args:
+      transformation_cache: A `FeatureTransformationCache` object to access
+        features.
+      state_manager: A `StateManager` to create / access resources such as
+        lookup tables.
+    """
+    pass
+
+
+def _create_categorical_column_weighted_sum(column,
+                                            transformation_cache,
+                                            state_manager,
+                                            units,
+                                            sparse_combiner,
+                                            weight_collections,
+                                            trainable,
+                                            weight_var=None):
+  # pylint: disable=g-doc-return-or-yield,g-doc-args
+  """Create a weighted sum of a categorical column for linear_model.
+
+  Note to maintainer: As implementation details, the weighted sum is
+  implemented via embedding_lookup_sparse toward efficiency. Mathematically,
+  they are the same.
+
+  To be specific, conceptually, categorical column can be treated as multi-hot
+  vector. Say:
+
+  ```python
+    x = [0 0 1]  # categorical column input
+    w = [a b c]  # weights
+  ```
+  The weighted sum is `c` in this case, which is same as `w[2]`.
+
+  Another example is
+
+  ```python
+    x = [0 1 1]  # categorical column input
+    w = [a b c]  # weights
+  ```
+  The weighted sum is `b + c` in this case, which is same as `w[2] + w[3]`.
+
+  For both cases, we can implement weighted sum via embedding_lookup with
+  sparse_combiner = "sum".
+  """
+
+  sparse_tensors = column.get_sparse_tensors(transformation_cache,
+                                             state_manager)
+  id_tensor = sparse_ops.sparse_reshape(sparse_tensors.id_tensor, [
+      array_ops.shape(sparse_tensors.id_tensor)[0], -1
+  ])
+  weight_tensor = sparse_tensors.weight_tensor
+  if weight_tensor is not None:
+    weight_tensor = sparse_ops.sparse_reshape(
+        weight_tensor, [array_ops.shape(weight_tensor)[0], -1])
+
+  if weight_var is not None:
+    weight = weight_var
+  else:
+    weight = variable_scope.get_variable(
+        name='weights',
+        shape=(column.num_buckets, units),
+        initializer=init_ops.zeros_initializer(),
+        trainable=trainable,
+        collections=weight_collections)
+  return _safe_embedding_lookup_sparse(
+      weight,
+      id_tensor,
+      sparse_weights=weight_tensor,
+      combiner=sparse_combiner,
+      name='weighted_sum')
+
+
+class SequenceDenseColumn(FeatureColumn):
+  """Represents dense sequence data."""
+
+  __metaclass__ = abc.ABCMeta
+
+  TensorSequenceLengthPair = collections.namedtuple(  # pylint: disable=invalid-name
+      'TensorSequenceLengthPair', ('dense_tensor', 'sequence_length'))
+
+  @abc.abstractmethod
+  def get_sequence_dense_tensor(self, transformation_cache, state_manager):
+    """Returns a `TensorSequenceLengthPair`.
+
+    Args:
+      transformation_cache: A `FeatureTransformationCache` object to access
+        features.
+      state_manager: A `StateManager` to create / access resources such as
+        lookup tables.
+    """
+    pass
+
+
+class FeatureTransformationCache(object):
+  """Handles caching of transformations while building the model.
+
+  `FeatureColumn` specifies how to digest an input column to the network. Some
+  feature columns require data transformations. This class caches those
+  transformations.
+
+  Some features may be used in more than one place. For example, one can use a
+  bucketized feature by itself and a cross with it. In that case we
+  should create only one bucketization op instead of creating ops for each
+  feature column separately. To handle re-use of transformed columns,
+  `FeatureTransformationCache` caches all previously transformed columns.
+
+  Example:
+  We're trying to use the following `FeatureColumn`s:
+
+  ```python
+  bucketized_age = fc.bucketized_column(fc.numeric_column("age"), ...)
+  keywords = fc.categorical_column_with_hash_buckets("keywords", ...)
+  age_X_keywords = fc.crossed_column([bucketized_age, "keywords"])
+  ... = linear_model(features,
+                          [bucketized_age, keywords, age_X_keywords]
+  ```
+
+  If we transform each column independently, then we'll get duplication of
+  bucketization (one for cross, one for bucketization itself).
+  The `FeatureTransformationCache` eliminates this duplication.
+  """
+
+  def __init__(self, features):
+    """Creates a `FeatureTransformationCache`.
+
+    Args:
+      features: A mapping from feature column to objects that are `Tensor` or
+        `SparseTensor`, or can be converted to same via
+        `sparse_tensor.convert_to_tensor_or_sparse_tensor`. A `string` key
+        signifies a base feature (not-transformed). A `FeatureColumn` key
+        means that this `Tensor` is the output of an existing `FeatureColumn`
+        which can be reused.
+    """
+    self._features = features.copy()
+    self._feature_tensors = {}
+
+  def get(self, key, state_manager):
+    """Returns a `Tensor` for the given key.
+
+    A `str` key is used to access a base feature (not-transformed). When a
+    `FeatureColumn` is passed, the transformed feature is returned if it
+    already exists, otherwise the given `FeatureColumn` is asked to provide its
+    transformed output, which is then cached.
+
+    Args:
+      key: a `str` or a `FeatureColumn`.
+      state_manager: A StateManager object that holds the FeatureColumn state.
+
+    Returns:
+      The transformed `Tensor` corresponding to the `key`.
+
+    Raises:
+      ValueError: if key is not found or a transformed `Tensor` cannot be
+        computed.
+    """
+    if key in self._feature_tensors:
+      # FeatureColumn is already transformed or converted.
+      return self._feature_tensors[key]
+
+    if key in self._features:
+      feature_tensor = self._get_raw_feature_as_tensor(key)
+      self._feature_tensors[key] = feature_tensor
+      return feature_tensor
+
+    if isinstance(key, six.string_types):
+      raise ValueError('Feature {} is not in features dictionary.'.format(key))
+
+    if not isinstance(key, FeatureColumn):
+      raise TypeError('"key" must be either a "str" or "FeatureColumn". '
+                      'Provided: {}'.format(key))
+
+    column = key
+    logging.debug('Transforming feature_column %s.', column)
+    transformed = column.transform_feature(self, state_manager)
+    if transformed is None:
+      raise ValueError('Column {} is not supported.'.format(column.name))
+    self._feature_tensors[column] = transformed
+    return transformed
+
+  def _get_raw_feature_as_tensor(self, key):
+    """Gets the raw_feature (keyed by `key`) as `tensor`.
+
+    The raw feature is converted to (sparse) tensor and maybe expand dim.
+
+    For both `Tensor` and `SparseTensor`, the rank will be expanded (to 2) if
+    the rank is 1. This supports dynamic rank also. For rank 0 raw feature, will
+    error out as it is not supported.
+
+    Args:
+      key: A `str` key to access the raw feature.
+
+    Returns:
+      A `Tensor` or `SparseTensor`.
+
+    Raises:
+      ValueError: if the raw feature has rank 0.
+    """
+    raw_feature = self._features[key]
+    feature_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
+        raw_feature)
+
+    def expand_dims(input_tensor):
+      # Input_tensor must have rank 1.
+      if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
+        return sparse_ops.sparse_reshape(
+            input_tensor, [array_ops.shape(input_tensor)[0], -1])
+      else:
+        return array_ops.expand_dims(input_tensor, -1)
+
+    rank = feature_tensor.get_shape().ndims
+    if rank is not None:
+      if rank == 0:
+        raise ValueError(
+            'Feature (key: {}) cannot have rank 0. Give: {}'.format(
+                key, feature_tensor))
+      return feature_tensor if rank != 1 else expand_dims(feature_tensor)
+
+    # Handle dynamic rank.
+    with ops.control_dependencies([
+        check_ops.assert_positive(
+            array_ops.rank(feature_tensor),
+            message='Feature (key: {}) cannot have rank 0. Given: {}'.format(
+                key, feature_tensor))]):
+      return control_flow_ops.cond(
+          math_ops.equal(1, array_ops.rank(feature_tensor)),
+          lambda: expand_dims(feature_tensor),
+          lambda: feature_tensor)
+
+
+# TODO(ptucker): Move to third_party/tensorflow/python/ops/sparse_ops.py
+def _shape_offsets(shape):
+  """Returns moving offset for each dimension given shape."""
+  offsets = []
+  for dim in reversed(shape):
+    if offsets:
+      offsets.append(dim * offsets[-1])
+    else:
+      offsets.append(dim)
+  offsets.reverse()
+  return offsets
+
+
+# TODO(ptucker): Move to third_party/tensorflow/python/ops/sparse_ops.py
+def _to_sparse_input_and_drop_ignore_values(input_tensor, ignore_value=None):
+  """Converts a `Tensor` to a `SparseTensor`, dropping ignore_value cells.
+
+  If `input_tensor` is already a `SparseTensor`, just return it.
+
+  Args:
+    input_tensor: A string or integer `Tensor`.
+    ignore_value: Entries in `dense_tensor` equal to this value will be
+      absent from the resulting `SparseTensor`. If `None`, default value of
+      `dense_tensor`'s dtype will be used ('' for `str`, -1 for `int`).
+
+  Returns:
+    A `SparseTensor` with the same shape as `input_tensor`.
+
+  Raises:
+    ValueError: when `input_tensor`'s rank is `None`.
+  """
+  input_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
+      input_tensor)
+  if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
+    return input_tensor
+  with ops.name_scope(None, 'to_sparse_input', (input_tensor, ignore_value,)):
+    if ignore_value is None:
+      if input_tensor.dtype == dtypes.string:
+        # Exception due to TF strings are converted to numpy objects by default.
+        ignore_value = ''
+      elif input_tensor.dtype.is_integer:
+        ignore_value = -1  # -1 has a special meaning of missing feature
+      else:
+        # NOTE: `as_numpy_dtype` is a property, so with the parentheses this is
+        # constructing a new numpy object of the given type, which yields the
+        # default value for that type.
+        ignore_value = input_tensor.dtype.as_numpy_dtype()
+    ignore_value = math_ops.cast(
+        ignore_value, input_tensor.dtype, name='ignore_value')
+    indices = array_ops.where(
+        math_ops.not_equal(input_tensor, ignore_value), name='indices')
+    return sparse_tensor_lib.SparseTensor(
+        indices=indices,
+        values=array_ops.gather_nd(input_tensor, indices, name='values'),
+        dense_shape=array_ops.shape(
+            input_tensor, out_type=dtypes.int64, name='dense_shape'))
+
+
+def _normalize_feature_columns(feature_columns):
+  """Normalizes the `feature_columns` input.
+
+  This method converts the `feature_columns` to list type as best as it can. In
+  addition, verifies the type and other parts of feature_columns, required by
+  downstream library.
+
+  Args:
+    feature_columns: The raw feature columns, usually passed by users.
+
+  Returns:
+    The normalized feature column list.
+
+  Raises:
+    ValueError: for any invalid inputs, such as empty, duplicated names, etc.
+  """
+  if isinstance(feature_columns, FeatureColumn):
+    feature_columns = [feature_columns]
+
+  if isinstance(feature_columns, collections.Iterator):
+    feature_columns = list(feature_columns)
+
+  if isinstance(feature_columns, dict):
+    raise ValueError('Expected feature_columns to be iterable, found dict.')
+
+  for column in feature_columns:
+    if not isinstance(column, FeatureColumn):
+      raise ValueError('Items of feature_columns must be a FeatureColumn. '
+                       'Given (type {}): {}.'.format(type(column), column))
+  if not feature_columns:
+    raise ValueError('feature_columns must not be empty.')
+  name_to_column = dict()
+  for column in feature_columns:
+    if column.name in name_to_column:
+      raise ValueError('Duplicate feature column name found for columns: {} '
+                       'and {}. This usually means that these columns refer to '
+                       'same base feature. Either one must be discarded or a '
+                       'duplicated but renamed item must be inserted in '
+                       'features dict.'.format(column,
+                                               name_to_column[column.name]))
+    name_to_column[column.name] = column
+
+  return feature_columns
+
+
+class NumericColumn(
+    DenseColumn,
+    collections.namedtuple(
+        'NumericColumn',
+        ('key', 'shape', 'default_value', 'dtype', 'normalizer_fn'))):
+  """see `numeric_column`."""
+
+  @property
+  def name(self):
+    """See `FeatureColumn` base class."""
+    return self.key
+
+  @property
+  def parse_example_spec(self):
+    """See `FeatureColumn` base class."""
+    return {
+        self.key:
+            parsing_ops.FixedLenFeature(self.shape, self.dtype,
+                                        self.default_value)
+    }
+
+  def transform_feature(self, transformation_cache, state_manager):
+    """See `FeatureColumn` base class.
+
+    In this case, we apply the `normalizer_fn` to the input tensor.
+
+    Args:
+      transformation_cache: A `FeatureTransformationCache` object to access
+        features.
+      state_manager: A `StateManager` to create / access resources such as
+        lookup tables.
+
+    Returns:
+      Normalized input tensor.
+    Raises:
+      ValueError: If a SparseTensor is passed in.
+    """
+    input_tensor = transformation_cache.get(self.key, state_manager)
+    if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
+      raise ValueError(
+          'The corresponding Tensor of numerical column must be a Tensor. '
+          'SparseTensor is not supported. key: {}'.format(self.key))
+    if self.normalizer_fn is not None:
+      input_tensor = self.normalizer_fn(input_tensor)
+    return math_ops.to_float(input_tensor)
+
+  @property
+  def variable_shape(self):
+    """See `DenseColumn` base class."""
+    return tensor_shape.TensorShape(self.shape)
+
+  def get_dense_tensor(self, transformation_cache, state_manager):
+    """Returns dense `Tensor` representing numeric feature.
+
+    Args:
+      transformation_cache: A `FeatureTransformationCache` object to access
+        features.
+      state_manager: A `StateManager` to create / access resources such as
+        lookup tables.
+
+    Returns:
+      Dense `Tensor` created within `transform_feature`.
+    """
+    # Feature has been already transformed. Return the intermediate
+    # representation created by _transform_feature.
+    return transformation_cache.get(self, state_manager)
+
+
+class BucketizedColumn(DenseColumn, CategoricalColumn,
+                       collections.namedtuple('BucketizedColumn',
+                                              ('source_column', 'boundaries'))):
+  """See `bucketized_column`."""
+
+  @property
+  def name(self):
+    """See `FeatureColumn` base class."""
+    return '{}_bucketized'.format(self.source_column.name)
+
+  @property
+  def parse_example_spec(self):
+    """See `FeatureColumn` base class."""
+    return self.source_column.parse_example_spec
+
+  def transform_feature(self, transformation_cache, state_manager):
+    """Returns bucketized categorical `source_column` tensor."""
+    source_tensor = transformation_cache.get(self.source_column, state_manager)
+    return math_ops._bucketize(  # pylint: disable=protected-access
+        source_tensor,
+        boundaries=self.boundaries)
+
+  @property
+  def variable_shape(self):
+    """See `DenseColumn` base class."""
+    return tensor_shape.TensorShape(
+        tuple(self.source_column.shape) + (len(self.boundaries) + 1,))
+
+  def get_dense_tensor(self, transformation_cache, state_manager):
+    """Returns one hot encoded dense `Tensor`."""
+    input_tensor = transformation_cache.get(self, state_manager)
+    return array_ops.one_hot(
+        indices=math_ops.to_int64(input_tensor),
+        depth=len(self.boundaries) + 1,
+        on_value=1.,
+        off_value=0.)
+
+  @property
+  def num_buckets(self):
+    """See `CategoricalColumn` base class."""
+    # By construction, source_column is always one-dimensional.
+    return (len(self.boundaries) + 1) * self.source_column.shape[0]
+
+  def get_sparse_tensors(self, transformation_cache, state_manager):
+    """Converts dense inputs to SparseTensor so downstream code can use it."""
+    input_tensor = transformation_cache.get(self, state_manager)
+    batch_size = array_ops.shape(input_tensor)[0]
+    # By construction, source_column is always one-dimensional.
+    source_dimension = self.source_column.shape[0]
+
+    i1 = array_ops.reshape(
+        array_ops.tile(
+            array_ops.expand_dims(math_ops.range(0, batch_size), 1),
+            [1, source_dimension]),
+        (-1,))
+    i2 = array_ops.tile(math_ops.range(0, source_dimension), [batch_size])
+    # Flatten the bucket indices and unique them across dimensions
+    # E.g. 2nd dimension indices will range from k to 2*k-1 with k buckets
+    bucket_indices = (
+        array_ops.reshape(input_tensor, (-1,)) +
+        (len(self.boundaries) + 1) * i2)
+
+    indices = math_ops.to_int64(array_ops.transpose(array_ops.stack((i1, i2))))
+    dense_shape = math_ops.to_int64(array_ops.stack(
+        [batch_size, source_dimension]))
+    sparse_tensor = sparse_tensor_lib.SparseTensor(
+        indices=indices,
+        values=bucket_indices,
+        dense_shape=dense_shape)
+    return CategoricalColumn.IdWeightPair(sparse_tensor, None)
+
+
+class EmbeddingColumn(
+    DenseColumn, SequenceDenseColumn,
+    collections.namedtuple(
+        'EmbeddingColumn',
+        ('categorical_column', 'dimension', 'combiner', 'initializer',
+         'ckpt_to_load_from', 'tensor_name_in_ckpt', 'max_norm', 'trainable'))):
+  """See `embedding_column`."""
+
+  @property
+  def name(self):
+    """See `FeatureColumn` base class."""
+    return '{}_embedding'.format(self.categorical_column.name)
+
+  @property
+  def parse_example_spec(self):
+    """See `FeatureColumn` base class."""
+    return self.categorical_column.parse_example_spec
+
+  def transform_feature(self, transformation_cache, state_manager):
+    """Transforms underlying `categorical_column`."""
+    return transformation_cache.get(self.categorical_column, state_manager)
+
+  @property
+  def variable_shape(self):
+    """See `DenseColumn` base class."""
+    return tensor_shape.vector(self.dimension)
+
+  def _get_dense_tensor_internal(self, transformation_cache, state_manager):
+    """Private method that follows the signature of _get_dense_tensor."""
+    # Get sparse IDs and weights.
+    sparse_tensors = self.categorical_column.get_sparse_tensors(
+        transformation_cache, state_manager)
+    sparse_ids = sparse_tensors.id_tensor
+    sparse_weights = sparse_tensors.weight_tensor
+
+    embedding_shape = (self.categorical_column.num_buckets, self.dimension)
+    embedding_weights = state_manager.get_variable(
+        self,
+        name='embedding_weights',
+        shape=embedding_shape,
+        dtype=dtypes.float32,
+        initializer=self.initializer)
+
+    if self.ckpt_to_load_from is not None:
+      to_restore = embedding_weights
+      if isinstance(to_restore, variables.PartitionedVariable):
+        to_restore = to_restore._get_variable_list()  # pylint: disable=protected-access
+      checkpoint_utils.init_from_checkpoint(self.ckpt_to_load_from, {
+          self.tensor_name_in_ckpt: to_restore
+      })
+
+    # Return embedding lookup result.
+    return _safe_embedding_lookup_sparse(
+        embedding_weights=embedding_weights,
+        sparse_ids=sparse_ids,
+        sparse_weights=sparse_weights,
+        combiner=self.combiner,
+        name='%s_weights' % self.name,
+        max_norm=self.max_norm)
+
+  def get_dense_tensor(self, transformation_cache, state_manager):
+    """Returns tensor after doing the embedding lookup.
+
+    Args:
+      transformation_cache: A `FeatureTransformationCache` object to access
+        features.
+      state_manager: A `StateManager` to create / access resources such as
+        lookup tables.
+
+    Returns:
+      Embedding lookup tensor.
+
+    Raises:
+      ValueError: `categorical_column` is SequenceCategoricalColumn.
+    """
+    if isinstance(self.categorical_column, SequenceCategoricalColumn):
+      raise ValueError(
+          'In embedding_column: {}. '
+          'categorical_column must not be of type SequenceCategoricalColumn. '
+          'Suggested fix A: If you wish to use input_layer, use a '
+          'non-sequence categorical_column_with_*. '
+          'Suggested fix B: If you wish to create sequence input, use '
+          'sequence_input_layer instead of input_layer. '
+          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
+                                       self.categorical_column))
+    return self._get_dense_tensor_internal(transformation_cache, state_manager)
+
+  def get_sequence_dense_tensor(self, transformation_cache, state_manager):
+    """See `SequenceDenseColumn` base class."""
+    if not isinstance(self.categorical_column, SequenceCategoricalColumn):
+      raise ValueError(
+          'In embedding_column: {}. '
+          'categorical_column must be of type SequenceCategoricalColumn '
+          'to use sequence_input_layer. '
+          'Suggested fix: Use one of sequence_categorical_column_with_*. '
+          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
+                                       self.categorical_column))
+    dense_tensor = self._get_dense_tensor_internal(  # pylint: disable=protected-access
+        transformation_cache, state_manager)
+    sparse_tensors = self.categorical_column.get_sparse_tensors(
+        transformation_cache, state_manager)
+    sequence_length = _sequence_length_from_sparse_tensor(
+        sparse_tensors.id_tensor)
+    return SequenceDenseColumn.TensorSequenceLengthPair(
+        dense_tensor=dense_tensor, sequence_length=sequence_length)
+
+
+def _get_graph_for_variable(var):
+  if isinstance(var, variables.PartitionedVariable):
+    return list(var)[0].graph
+  else:
+    return var.graph
+
+
+class SharedEmbeddingColumn(
+    DenseColumn, SequenceDenseColumn,
+    collections.namedtuple(
+        'SharedEmbeddingColumn',
+        ('categorical_column', 'dimension', 'combiner', 'initializer',
+         'shared_embedding_collection_name', 'ckpt_to_load_from',
+         'tensor_name_in_ckpt', 'max_norm', 'trainable'))):
+  """See `embedding_column`."""
+
+  @property
+  def name(self):
+    """See `FeatureColumn` base class."""
+    return '{}_shared_embedding'.format(self.categorical_column.name)
+
+  @property
+  def shared_collection_name(self):
+    """Returns the shared name of this column.
+
+    A group of columns share an embedding. Each one of those columns would have
+    the same `shared_collection_name` by which they could be collectively
+    referred to.
+    """
+    return self.shared_embedding_collection_name
+
+  @property
+  def parse_example_spec(self):
+    """See `FeatureColumn` base class."""
+    return self.categorical_column.parse_example_spec
+
+  def transform_feature(self, transformation_cache, state_manager):
+    """See `FeatureColumn` base class."""
+    return transformation_cache.get(self.categorical_column, state_manager)
+
+  @property
+  def variable_shape(self):
+    """See `DenseColumn` base class."""
+    return tensor_shape.vector(self.dimension)
+
+  def _get_dense_tensor_internal(self, transformation_cache, state_manager):
+    """Private method that follows the signature of _get_dense_tensor."""
+    # This method is called from a variable_scope with name _var_scope_name,
+    # which is shared among all shared embeddings. Open a name_scope here, so
+    # that the ops for different columns have distinct names.
+    with ops.name_scope(None, default_name=self.name):
+      # Get sparse IDs and weights.
+      sparse_tensors = self.categorical_column.get_sparse_tensors(
+          transformation_cache, state_manager)
+      sparse_ids = sparse_tensors.id_tensor
+      sparse_weights = sparse_tensors.weight_tensor
+
+      embedding_shape = (self.categorical_column.num_buckets, self.dimension)
+      embedding_weights = state_manager.get_variable(
+          self,
+          name='embedding_weights',
+          shape=embedding_shape,
+          dtype=dtypes.float32,
+          initializer=self.initializer)
+
+      if self.ckpt_to_load_from is not None:
+        to_restore = embedding_weights
+        if isinstance(to_restore, variables.PartitionedVariable):
+          to_restore = to_restore._get_variable_list()  # pylint: disable=protected-access
+        checkpoint_utils.init_from_checkpoint(self.ckpt_to_load_from, {
+            self.tensor_name_in_ckpt: to_restore
+        })
+
+      # Return embedding lookup result.
+      return _safe_embedding_lookup_sparse(
+          embedding_weights=embedding_weights,
+          sparse_ids=sparse_ids,
+          sparse_weights=sparse_weights,
+          combiner=self.combiner,
+          name='%s_weights' % self.name,
+          max_norm=self.max_norm)
+
+  def get_dense_tensor(self, transformation_cache, state_manager):
+    """Returns the embedding lookup result."""
+    if isinstance(self.categorical_column, SequenceCategoricalColumn):
+      raise ValueError(
+          'In embedding_column: {}. '
+          'categorical_column must not be of type SequenceCategoricalColumn. '
+          'Suggested fix A: If you wish to use input_layer, use a '
+          'non-sequence categorical_column_with_*. '
+          'Suggested fix B: If you wish to create sequence input, use '
+          'sequence_input_layer instead of input_layer. '
+          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
+                                       self.categorical_column))
+    return self._get_dense_tensor_internal(transformation_cache, state_manager)
+
+  def get_sequence_dense_tensor(self, transformation_cache, state_manager):
+    """See `SequenceDenseColumn` base class."""
+    if not isinstance(self.categorical_column, SequenceCategoricalColumn):
+      raise ValueError(
+          'In embedding_column: {}. '
+          'categorical_column must be of type SequenceCategoricalColumn '
+          'to use sequence_input_layer. '
+          'Suggested fix: Use one of sequence_categorical_column_with_*. '
+          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
+                                       self.categorical_column))
+    dense_tensor = self.get_dense_tensor_internal(transformation_cache,
+                                                  state_manager)
+    sparse_tensors = self.categorical_column.get_sparse_tensors(
+        transformation_cache, state_manager)
+    sequence_length = _sequence_length_from_sparse_tensor(
+        sparse_tensors.id_tensor)
+    return SequenceDenseColumn.TensorSequenceLengthPair(
+        dense_tensor=dense_tensor, sequence_length=sequence_length)
+
+
+def _create_tuple(shape, value):
+  """Returns a tuple with given shape and filled with value."""
+  if shape:
+    return tuple([_create_tuple(shape[1:], value) for _ in range(shape[0])])
+  return value
+
+
+def _as_tuple(value):
+  if not nest.is_sequence(value):
+    return value
+  return tuple([_as_tuple(v) for v in value])
+
+
+def _check_shape(shape, key):
+  """Returns shape if it's valid, raises error otherwise."""
+  assert shape is not None
+  if not nest.is_sequence(shape):
+    shape = [shape]
+  shape = tuple(shape)
+  for dimension in shape:
+    if not isinstance(dimension, int):
+      raise TypeError('shape dimensions must be integer. '
+                      'shape: {}, key: {}'.format(shape, key))
+    if dimension < 1:
+      raise ValueError('shape dimensions must be greater than 0. '
+                       'shape: {}, key: {}'.format(shape, key))
+  return shape
+
+
+def _is_shape_and_default_value_compatible(default_value, shape):
+  """Verifies compatibility of shape and default_value."""
+  # Invalid condition:
+  #  * if default_value is not a scalar and shape is empty
+  #  * or if default_value is an iterable and shape is not empty
+  if nest.is_sequence(default_value) != bool(shape):
+    return False
+  if not shape:
+    return True
+  if len(default_value) != shape[0]:
+    return False
+  for i in range(shape[0]):
+    if not _is_shape_and_default_value_compatible(default_value[i], shape[1:]):
+      return False
+  return True
+
+
+def _check_default_value(shape, default_value, dtype, key):
+  """Returns default value as tuple if it's valid, otherwise raises errors.
+
+  This function verifies that `default_value` is compatible with both `shape`
+  and `dtype`. If it is not compatible, it raises an error. If it is compatible,
+  it casts default_value to a tuple and returns it. `key` is used only
+  for error message.
+
+  Args:
+    shape: An iterable of integers specifies the shape of the `Tensor`.
+    default_value: If a single value is provided, the same value will be applied
+      as the default value for every item. If an iterable of values is
+      provided, the shape of the `default_value` should be equal to the given
+      `shape`.
+    dtype: defines the type of values. Default value is `tf.float32`. Must be a
+      non-quantized, real integer or floating point type.
+    key: Column name, used only for error messages.
+
+  Returns:
+    A tuple which will be used as default value.
+
+  Raises:
+    TypeError: if `default_value` is an iterable but not compatible with `shape`
+    TypeError: if `default_value` is not compatible with `dtype`.
+    ValueError: if `dtype` is not convertible to `tf.float32`.
+  """
+  if default_value is None:
+    return None
+
+  if isinstance(default_value, int):
+    return _create_tuple(shape, default_value)
+
+  if isinstance(default_value, float) and dtype.is_floating:
+    return _create_tuple(shape, default_value)
+
+  if callable(getattr(default_value, 'tolist', None)):  # Handles numpy arrays
+    default_value = default_value.tolist()
+
+  if nest.is_sequence(default_value):
+    if not _is_shape_and_default_value_compatible(default_value, shape):
+      raise ValueError(
+          'The shape of default_value must be equal to given shape. '
+          'default_value: {}, shape: {}, key: {}'.format(
+              default_value, shape, key))
+    # Check if the values in the list are all integers or are convertible to
+    # floats.
+    is_list_all_int = all(
+        isinstance(v, int) for v in nest.flatten(default_value))
+    is_list_has_float = any(
+        isinstance(v, float) for v in nest.flatten(default_value))
+    if is_list_all_int:
+      return _as_tuple(default_value)
+    if is_list_has_float and dtype.is_floating:
+      return _as_tuple(default_value)
+  raise TypeError('default_value must be compatible with dtype. '
+                  'default_value: {}, dtype: {}, key: {}'.format(
+                      default_value, dtype, key))
+
+
+class HashedCategoricalColumn(
+    CategoricalColumn,
+    collections.namedtuple('HashedCategoricalColumn',
+                           ('key', 'hash_bucket_size', 'dtype'))):
+  """see `categorical_column_with_hash_bucket`."""
+
+  @property
+  def name(self):
+    """See `FeatureColumn` base class."""
+    return self.key
+
+  @property
+  def parse_example_spec(self):
+    """See `FeatureColumn` base class."""
+    return {self.key: parsing_ops.VarLenFeature(self.dtype)}
+
+  def transform_feature(self, transformation_cache, state_manager):
+    """Hashes the values in the feature_column."""
+    input_tensor = _to_sparse_input_and_drop_ignore_values(
+        transformation_cache.get(self.key, state_manager))
+    if not isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
+      raise ValueError('SparseColumn input must be a SparseTensor.')
+
+    _assert_string_or_int(
+        input_tensor.dtype,
+        prefix='column_name: {} input_tensor'.format(self.key))
+
+    if self.dtype.is_integer != input_tensor.dtype.is_integer:
+      raise ValueError(
+          'Column dtype and SparseTensors dtype must be compatible. '
+          'key: {}, column dtype: {}, tensor dtype: {}'.format(
+              self.key, self.dtype, input_tensor.dtype))
+
+    if self.dtype == dtypes.string:
+      sparse_values = input_tensor.values
+    else:
+      sparse_values = string_ops.as_string(input_tensor.values)
+
+    sparse_id_values = string_ops.string_to_hash_bucket_fast(
+        sparse_values, self.hash_bucket_size, name='lookup')
+    return sparse_tensor_lib.SparseTensor(
+        input_tensor.indices, sparse_id_values, input_tensor.dense_shape)
+
+  @property
+  def num_buckets(self):
+    """Returns number of buckets in this sparse feature."""
+    return self.hash_bucket_size
+
+  def get_sparse_tensors(self, transformation_cache, state_manager):
+    """See `CategoricalColumn` base class."""
+    return CategoricalColumn.IdWeightPair(
+        transformation_cache.get(self, state_manager), None)
+
+
+class VocabularyFileCategoricalColumn(
+    CategoricalColumn,
+    collections.namedtuple('VocabularyFileCategoricalColumn',
+                           ('key', 'vocabulary_file', 'vocabulary_size',
+                            'num_oov_buckets', 'dtype', 'default_value'))):
+  """See `categorical_column_with_vocabulary_file`."""
+
+  @property
+  def name(self):
+    """See `FeatureColumn` base class."""
+    return self.key
+
+  @property
+  def parse_example_spec(self):
+    """See `FeatureColumn` base class."""
+    return {self.key: parsing_ops.VarLenFeature(self.dtype)}
+
+  def transform_feature(self, transformation_cache, state_manager):
+    """Creates a lookup table for the vocabulary."""
+    input_tensor = _to_sparse_input_and_drop_ignore_values(
+        transformation_cache.get(self.key, state_manager))
+
+    if self.dtype.is_integer != input_tensor.dtype.is_integer:
+      raise ValueError(
+          'Column dtype and SparseTensors dtype must be compatible. '
+          'key: {}, column dtype: {}, tensor dtype: {}'.format(
+              self.key, self.dtype, input_tensor.dtype))
+
+    _assert_string_or_int(
+        input_tensor.dtype,
+        prefix='column_name: {} input_tensor'.format(self.key))
+
+    key_dtype = self.dtype
+    if input_tensor.dtype.is_integer:
+      # `index_table_from_file` requires 64-bit integer keys.
+      key_dtype = dtypes.int64
+      input_tensor = math_ops.to_int64(input_tensor)
+
+    # TODO(rohanj): Use state manager to manage the index table creation.
+    return lookup_ops.index_table_from_file(
+        vocabulary_file=self.vocabulary_file,
+        num_oov_buckets=self.num_oov_buckets,
+        vocab_size=self.vocabulary_size,
+        default_value=self.default_value,
+        key_dtype=key_dtype,
+        name='{}_lookup'.format(self.key)).lookup(input_tensor)
+
+  @property
+  def num_buckets(self):
+    """Returns number of buckets in this sparse feature."""
+    return self.vocabulary_size + self.num_oov_buckets
+
+  def get_sparse_tensors(self, transformation_cache, state_manager):
+    """See `CategoricalColumn` base class."""
+    return CategoricalColumn.IdWeightPair(
+        transformation_cache.get(self, state_manager), None)
+
+
+class VocabularyListCategoricalColumn(
+    CategoricalColumn,
+    collections.namedtuple(
+        'VocabularyListCategoricalColumn',
+        ('key', 'vocabulary_list', 'dtype', 'default_value', 'num_oov_buckets'))
+):
+  """See `categorical_column_with_vocabulary_list`."""
+
+  @property
+  def name(self):
+    """See `FeatureColumn` base class."""
+    return self.key
+
+  @property
+  def parse_example_spec(self):
+    """See `FeatureColumn` base class."""
+    return {self.key: parsing_ops.VarLenFeature(self.dtype)}
+
+  def transform_feature(self, transformation_cache, state_manager):
+    """Creates a lookup table for the vocabulary list."""
+    input_tensor = _to_sparse_input_and_drop_ignore_values(
+        transformation_cache.get(self.key, state_manager))
+
+    if self.dtype.is_integer != input_tensor.dtype.is_integer:
+      raise ValueError(
+          'Column dtype and SparseTensors dtype must be compatible. '
+          'key: {}, column dtype: {}, tensor dtype: {}'.format(
+              self.key, self.dtype, input_tensor.dtype))
+
+    _assert_string_or_int(
+        input_tensor.dtype,
+        prefix='column_name: {} input_tensor'.format(self.key))
+
+    key_dtype = self.dtype
+    if input_tensor.dtype.is_integer:
+      # `index_table_from_tensor` requires 64-bit integer keys.
+      key_dtype = dtypes.int64
+      input_tensor = math_ops.to_int64(input_tensor)
+
+    # TODO(rohanj): Use state manager to manage the index table creation.
+    return lookup_ops.index_table_from_tensor(
+        vocabulary_list=tuple(self.vocabulary_list),
+        default_value=self.default_value,
+        num_oov_buckets=self.num_oov_buckets,
+        dtype=key_dtype,
+        name='{}_lookup'.format(self.key)).lookup(input_tensor)
+
+  @property
+  def num_buckets(self):
+    """Returns number of buckets in this sparse feature."""
+    return len(self.vocabulary_list) + self.num_oov_buckets
+
+  def get_sparse_tensors(self, transformation_cache, state_manager):
+    """See `CategoricalColumn` base class."""
+    return CategoricalColumn.IdWeightPair(
+        transformation_cache.get(self, state_manager), None)
+
+
+class IdentityCategoricalColumn(
+    CategoricalColumn,
+    collections.namedtuple('IdentityCategoricalColumn',
+                           ('key', 'number_buckets', 'default_value'))):
+
+  """See `categorical_column_with_identity`."""
+
+  @property
+  def name(self):
+    """See `FeatureColumn` base class."""
+    return self.key
+
+  @property
+  def parse_example_spec(self):
+    """See `FeatureColumn` base class."""
+    return {self.key: parsing_ops.VarLenFeature(dtypes.int64)}
+
+  def transform_feature(self, transformation_cache, state_manager):
+    """Returns a SparseTensor with identity values."""
+    input_tensor = _to_sparse_input_and_drop_ignore_values(
+        transformation_cache.get(self.key, state_manager))
+
+    if not input_tensor.dtype.is_integer:
+      raise ValueError(
+          'Invalid input, not integer. key: {} dtype: {}'.format(
+              self.key, input_tensor.dtype))
+
+    values = math_ops.to_int64(input_tensor.values, name='values')
+    num_buckets = math_ops.to_int64(self.num_buckets, name='num_buckets')
+    zero = math_ops.to_int64(0, name='zero')
+    if self.default_value is None:
+      # Fail if values are out-of-range.
+      assert_less = check_ops.assert_less(
+          values, num_buckets, data=(values, num_buckets),
+          name='assert_less_than_num_buckets')
+      assert_greater = check_ops.assert_greater_equal(
+          values, zero, data=(values,),
+          name='assert_greater_or_equal_0')
+      with ops.control_dependencies((assert_less, assert_greater)):
+        values = array_ops.identity(values)
+    else:
+      # Assign default for out-of-range values.
+      values = array_ops.where(
+          math_ops.logical_or(
+              values < zero, values >= num_buckets, name='out_of_range'),
+          array_ops.fill(
+              dims=array_ops.shape(values),
+              value=math_ops.to_int64(self.default_value),
+              name='default_values'),
+          values)
+
+    return sparse_tensor_lib.SparseTensor(
+        indices=input_tensor.indices,
+        values=values,
+        dense_shape=input_tensor.dense_shape)
+
+  @property
+  def num_buckets(self):
+    """Returns number of buckets in this sparse feature."""
+    return self.number_buckets
+
+  def get_sparse_tensors(self, transformation_cache, state_manager):
+    """See `CategoricalColumn` base class."""
+    return CategoricalColumn.IdWeightPair(
+        transformation_cache.get(self, state_manager), None)
+
+
+class WeightedCategoricalColumn(
+    CategoricalColumn,
+    collections.namedtuple(
+        'WeightedCategoricalColumn',
+        ('categorical_column', 'weight_feature_key', 'dtype'))):
+  """See `weighted_categorical_column`."""
+
+  @property
+  def name(self):
+    """See `FeatureColumn` base class."""
+    return '{}_weighted_by_{}'.format(
+        self.categorical_column.name, self.weight_feature_key)
+
+  @property
+  def parse_example_spec(self):
+    """See `FeatureColumn` base class."""
+    config = self.categorical_column.parse_example_spec
+    if self.weight_feature_key in config:
+      raise ValueError('Parse config {} already exists for {}.'.format(
+          config[self.weight_feature_key], self.weight_feature_key))
+    config[self.weight_feature_key] = parsing_ops.VarLenFeature(self.dtype)
+    return config
+
+  @property
+  def num_buckets(self):
+    """See `DenseColumn` base class."""
+    return self.categorical_column.num_buckets
+
+  def transform_feature(self, transformation_cache, state_manager):
+    """Applies weights to tensor generated from `categorical_column`'."""
+    weight_tensor = transformation_cache.get(self.weight_feature_key,
+                                             state_manager)
+    if weight_tensor is None:
+      raise ValueError('Missing weights {}.'.format(self.weight_feature_key))
+    weight_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
+        weight_tensor)
+    if self.dtype != weight_tensor.dtype.base_dtype:
+      raise ValueError('Bad dtype, expected {}, but got {}.'.format(
+          self.dtype, weight_tensor.dtype))
+    if not isinstance(weight_tensor, sparse_tensor_lib.SparseTensor):
+      # The weight tensor can be a regular Tensor. In this case, sparsify it.
+      weight_tensor = _to_sparse_input_and_drop_ignore_values(
+          weight_tensor, ignore_value=0.0)
+    if not weight_tensor.dtype.is_floating:
+      weight_tensor = math_ops.to_float(weight_tensor)
+    return (transformation_cache.get(self.categorical_column, state_manager),
+            weight_tensor)
+
+  def get_sparse_tensors(self, transformation_cache, state_manager):
+    """See `CategoricalColumn` base class."""
+    tensors = transformation_cache.get(self, state_manager)
+    return CategoricalColumn.IdWeightPair(tensors[0], tensors[1])
+
+
+class CrossedColumn(
+    CategoricalColumn,
+    collections.namedtuple('CrossedColumn',
+                           ('keys', 'hash_bucket_size', 'hash_key'))):
+  """See `crossed_column`."""
+
+  @property
+  def name(self):
+    """See `FeatureColumn` base class."""
+    feature_names = []
+    for key in _collect_leaf_level_keys(self):
+      if isinstance(key, FeatureColumn):
+        feature_names.append(key.name)
+      else:  # key must be a string
+        feature_names.append(key)
+    return '_X_'.join(sorted(feature_names))
+
+  @property
+  def parse_example_spec(self):
+    """See `FeatureColumn` base class."""
+    config = {}
+    for key in self.keys:
+      if isinstance(key, FeatureColumn):
+        config.update(key.parse_example_spec)
+      else:  # key must be a string
+        config.update({key: parsing_ops.VarLenFeature(dtypes.string)})
+    return config
+
+  def transform_feature(self, transformation_cache, state_manager):
+    """Generates a hashed sparse cross from the input tensors."""
+    feature_tensors = []
+    for key in _collect_leaf_level_keys(self):
+      if isinstance(key, six.string_types):
+        feature_tensors.append(transformation_cache.get(key, state_manager))
+      elif isinstance(key, CategoricalColumn):
+        ids_and_weights = key.get_sparse_tensors(transformation_cache,
+                                                 state_manager)
+        if ids_and_weights.weight_tensor is not None:
+          raise ValueError(
+              'crossed_column does not support weight_tensor, but the given '
+              'column populates weight_tensor. '
+              'Given column: {}'.format(key.name))
+        feature_tensors.append(ids_and_weights.id_tensor)
+      else:
+        raise ValueError('Unsupported column type. Given: {}'.format(key))
+    return sparse_ops.sparse_cross_hashed(
+        inputs=feature_tensors,
+        num_buckets=self.hash_bucket_size,
+        hash_key=self.hash_key)
+
+  @property
+  def num_buckets(self):
+    """Returns number of buckets in this sparse feature."""
+    return self.hash_bucket_size
+
+  def get_sparse_tensors(self, transformation_cache, state_manager):
+    """See `CategoricalColumn` base class."""
+    return CategoricalColumn.IdWeightPair(
+        transformation_cache.get(self, state_manager), None)
+
+
+def _collect_leaf_level_keys(cross):
+  """Collects base keys by expanding all nested crosses.
+
+  Args:
+    cross: A `CrossedColumn`.
+
+  Returns:
+    A list of strings or `CategoricalColumn` instances.
+  """
+  leaf_level_keys = []
+  for k in cross.keys:
+    if isinstance(k, CrossedColumn):
+      leaf_level_keys.extend(_collect_leaf_level_keys(k))
+    else:
+      leaf_level_keys.append(k)
+  return leaf_level_keys
+
+
+# TODO(zakaria): Move this to embedding_ops and make it public.
+def _safe_embedding_lookup_sparse(embedding_weights,
+                                  sparse_ids,
+                                  sparse_weights=None,
+                                  combiner='mean',
+                                  default_id=None,
+                                  name=None,
+                                  partition_strategy='div',
+                                  max_norm=None):
+  """Lookup embedding results, accounting for invalid IDs and empty features.
+
+  The partitioned embedding in `embedding_weights` must all be the same shape
+  except for the first dimension. The first dimension is allowed to vary as the
+  vocabulary size is not necessarily a multiple of `P`.  `embedding_weights`
+  may be a `PartitionedVariable` as returned by using `tf.get_variable()` with a
+  partitioner.
+
+  Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
+  with non-positive weight. For an entry with no features, the embedding vector
+  for `default_id` is returned, or the 0-vector if `default_id` is not supplied.
+
+  The ids and weights may be multi-dimensional. Embeddings are always aggregated
+  along the last dimension.
+
+  Args:
+    embedding_weights:  A list of `P` float `Tensor`s or values representing
+        partitioned embedding `Tensor`s.  Alternatively, a `PartitionedVariable`
+        created by partitioning along dimension 0.  The total unpartitioned
+        shape should be `[e_0, e_1, ..., e_m]`, where `e_0` represents the
+        vocab size and `e_1, ..., e_m` are the embedding dimensions.
+    sparse_ids: `SparseTensor` of shape `[d_0, d_1, ..., d_n]` containing the
+        ids. `d_0` is typically batch size.
+    sparse_weights: `SparseTensor` of same shape as `sparse_ids`, containing
+        float weights corresponding to `sparse_ids`, or `None` if all weights
+        are be assumed to be 1.0.
+    combiner: A string specifying how to combine embedding results for each
+        entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean"
+        the default.
+    default_id: The id to use for an entry with no features.
+    name: A name for this operation (optional).
+    partition_strategy: A string specifying the partitioning strategy.
+        Currently `"div"` and `"mod"` are supported. Default is `"div"`.
+    max_norm: If not `None`, all embeddings are l2-normalized to max_norm before
+        combining.
+
+
+  Returns:
+    Dense `Tensor` of shape `[d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]`.
+
+  Raises:
+    ValueError: if `embedding_weights` is empty.
+  """
+  if embedding_weights is None:
+    raise ValueError('Missing embedding_weights %s.' % embedding_weights)
+  if isinstance(embedding_weights, variables.PartitionedVariable):
+    embedding_weights = list(embedding_weights)  # get underlying Variables.
+  if not isinstance(embedding_weights, list):
+    embedding_weights = [embedding_weights]
+  if len(embedding_weights) < 1:
+    raise ValueError('Missing embedding_weights %s.' % embedding_weights)
+
+  dtype = sparse_weights.dtype if sparse_weights is not None else None
+  embedding_weights = [
+      ops.convert_to_tensor(w, dtype=dtype) for w in embedding_weights
+  ]
+
+  with ops.name_scope(name, 'embedding_lookup',
+                      embedding_weights + [sparse_ids,
+                                           sparse_weights]) as scope:
+    # Reshape higher-rank sparse ids and weights to linear segment ids.
+    original_shape = sparse_ids.dense_shape
+    original_rank_dim = sparse_ids.dense_shape.get_shape()[0]
+    original_rank = (
+        array_ops.size(original_shape)
+        if original_rank_dim.value is None
+        else original_rank_dim.value)
+    sparse_ids = sparse_ops.sparse_reshape(sparse_ids, [
+        math_ops.reduce_prod(
+            array_ops.slice(original_shape, [0], [original_rank - 1])),
+        array_ops.gather(original_shape, original_rank - 1)])
+    if sparse_weights is not None:
+      sparse_weights = sparse_tensor_lib.SparseTensor(
+          sparse_ids.indices,
+          sparse_weights.values, sparse_ids.dense_shape)
+
+    # Prune invalid ids and weights.
+    sparse_ids, sparse_weights = _prune_invalid_ids(sparse_ids, sparse_weights)
+    if combiner != 'sum':
+      sparse_ids, sparse_weights = _prune_invalid_weights(
+          sparse_ids, sparse_weights)
+
+    # Fill in dummy values for empty features, if necessary.
+    sparse_ids, is_row_empty = sparse_ops.sparse_fill_empty_rows(sparse_ids,
+                                                                 default_id or
+                                                                 0)
+    if sparse_weights is not None:
+      sparse_weights, _ = sparse_ops.sparse_fill_empty_rows(sparse_weights, 1.0)
+
+    result = embedding_ops.embedding_lookup_sparse(
+        embedding_weights,
+        sparse_ids,
+        sparse_weights,
+        combiner=combiner,
+        partition_strategy=partition_strategy,
+        name=None if default_id is None else scope,
+        max_norm=max_norm)
+
+    if default_id is None:
+      # Broadcast is_row_empty to the same shape as embedding_lookup_result,
+      # for use in Select.
+      is_row_empty = array_ops.tile(
+          array_ops.reshape(is_row_empty, [-1, 1]),
+          array_ops.stack([1, array_ops.shape(result)[1]]))
+
+      result = array_ops.where(is_row_empty,
+                               array_ops.zeros_like(result),
+                               result,
+                               name=scope)
+
+    # Reshape back from linear ids back into higher-dimensional dense result.
+    final_result = array_ops.reshape(
+        result,
+        array_ops.concat([
+            array_ops.slice(
+                math_ops.cast(original_shape, dtypes.int32), [0],
+                [original_rank - 1]),
+            array_ops.slice(array_ops.shape(result), [1], [-1])
+        ], 0))
+    final_result.set_shape(tensor_shape.unknown_shape(
+        (original_rank_dim - 1).value).concatenate(result.get_shape()[1:]))
+    return final_result
+
+
+def _prune_invalid_ids(sparse_ids, sparse_weights):
+  """Prune invalid IDs (< 0) from the input ids and weights."""
+  is_id_valid = math_ops.greater_equal(sparse_ids.values, 0)
+  if sparse_weights is not None:
+    is_id_valid = math_ops.logical_and(
+        is_id_valid,
+        array_ops.ones_like(sparse_weights.values, dtype=dtypes.bool))
+  sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_id_valid)
+  if sparse_weights is not None:
+    sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_id_valid)
+  return sparse_ids, sparse_weights
+
+
+def _prune_invalid_weights(sparse_ids, sparse_weights):
+  """Prune invalid weights (< 0) from the input ids and weights."""
+  if sparse_weights is not None:
+    is_weights_valid = math_ops.greater(sparse_weights.values, 0)
+    sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_weights_valid)
+    sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_weights_valid)
+  return sparse_ids, sparse_weights
+
+
+class IndicatorColumn(DenseColumn, SequenceDenseColumn,
+                      collections.namedtuple('IndicatorColumn',
+                                             ('categorical_column'))):
+  """Represents a one-hot column for use in deep networks.
+
+  Args:
+    categorical_column: A `CategoricalColumn` which is created by
+      `categorical_column_with_*` function.
+  """
+
+  @property
+  def name(self):
+    """See `FeatureColumn` base class."""
+    return '{}_indicator'.format(self.categorical_column.name)
+
+  def transform_feature(self, transformation_cache, state_manager):
+    """Returns dense `Tensor` representing feature.
+
+    Args:
+      transformation_cache: A `FeatureTransformationCache` object to access
+        features.
+      state_manager: A `StateManager` to create / access resources such as
+        lookup tables.
+
+    Returns:
+      Transformed feature `Tensor`.
+
+    Raises:
+      ValueError: if input rank is not known at graph building time.
+    """
+    id_weight_pair = self.categorical_column.get_sparse_tensors(
+        transformation_cache, state_manager)
+    id_tensor = id_weight_pair.id_tensor
+    weight_tensor = id_weight_pair.weight_tensor
+
+    # If the underlying column is weighted, return the input as a dense tensor.
+    if weight_tensor is not None:
+      weighted_column = sparse_ops.sparse_merge(
+          sp_ids=id_tensor,
+          sp_values=weight_tensor,
+          vocab_size=int(self.variable_shape[-1]))
+      # Remove (?, -1) index
+      weighted_column = sparse_ops.sparse_slice(weighted_column, [0, 0],
+                                                weighted_column.dense_shape)
+      return sparse_ops.sparse_tensor_to_dense(weighted_column)
+
+    dense_id_tensor = sparse_ops.sparse_tensor_to_dense(
+        id_tensor, default_value=-1)
+
+    # One hot must be float for tf.concat reasons since all other inputs to
+    # input_layer are float32.
+    one_hot_id_tensor = array_ops.one_hot(
+        dense_id_tensor,
+        depth=self.variable_shape[-1],
+        on_value=1.0,
+        off_value=0.0)
+
+    # Reduce to get a multi-hot per example.
+    return math_ops.reduce_sum(one_hot_id_tensor, axis=[-2])
+
+  @property
+  def parse_example_spec(self):
+    """See `FeatureColumn` base class."""
+    return self.categorical_column.parse_example_spec
+
+  @property
+  def variable_shape(self):
+    """Returns a `TensorShape` representing the shape of the dense `Tensor`."""
+    return tensor_shape.TensorShape([1, self.categorical_column.num_buckets])
+
+  def get_dense_tensor(self, transformation_cache, state_manager):
+    """Returns dense `Tensor` representing feature.
+
+    Args:
+      transformation_cache: A `FeatureTransformationCache` object to access
+        features.
+      state_manager: A `StateManager` to create / access resources such as
+        lookup tables.
+
+    Returns:
+      Dense `Tensor` created within `transform_feature`.
+
+    Raises:
+      ValueError: If `categorical_column` is a `SequenceCategoricalColumn`.
+    """
+    if isinstance(self.categorical_column, SequenceCategoricalColumn):
+      raise ValueError(
+          'In indicator_column: {}. '
+          'categorical_column must not be of type SequenceCategoricalColumn. '
+          'Suggested fix A: If you wish to use input_layer, use a '
+          'non-sequence categorical_column_with_*. '
+          'Suggested fix B: If you wish to create sequence input, use '
+          'sequence_input_layer instead of input_layer. '
+          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
+                                       self.categorical_column))
+    # Feature has been already transformed. Return the intermediate
+    # representation created by transform_feature.
+    return transformation_cache.get(self, state_manager)
+
+  def get_sequence_dense_tensor(self, transformation_cache, state_manager):
+    """See `SequenceDenseColumn` base class."""
+    if not isinstance(self.categorical_column, SequenceCategoricalColumn):
+      raise ValueError(
+          'In indicator_column: {}. '
+          'categorical_column must be of type SequenceCategoricalColumn '
+          'to use sequence_input_layer. '
+          'Suggested fix: Use one of sequence_categorical_column_with_*. '
+          'Given (type {}): {}'.format(self.name, type(self.categorical_column),
+                                       self.categorical_column))
+    # Feature has been already transformed. Return the intermediate
+    # representation created by transform_feature.
+    dense_tensor = transformation_cache.get(self, state_manager)
+    sparse_tensors = self.categorical_column.get_sparse_tensors(
+        transformation_cache, state_manager)
+    sequence_length = _sequence_length_from_sparse_tensor(
+        sparse_tensors.id_tensor)
+    return SequenceDenseColumn.TensorSequenceLengthPair(
+        dense_tensor=dense_tensor, sequence_length=sequence_length)
+
+
+def _verify_static_batch_size_equality(tensors, columns):
+  # bath_size is a tf.Dimension object.
+  expected_batch_size = None
+  for i in range(0, len(tensors)):
+    if tensors[i].shape[0].value is not None:
+      if expected_batch_size is None:
+        bath_size_column_index = i
+        expected_batch_size = tensors[i].shape[0]
+      elif not expected_batch_size.is_compatible_with(tensors[i].shape[0]):
+        raise ValueError(
+            'Batch size (first dimension) of each feature must be same. '
+            'Batch size of columns ({}, {}): ({}, {})'.format(
+                columns[bath_size_column_index].name, columns[i].name,
+                expected_batch_size, tensors[i].shape[0]))
+
+
+def _sequence_length_from_sparse_tensor(sp_tensor, num_elements=1):
+  """Returns a [batch_size] Tensor with per-example sequence length."""
+  with ops.name_scope(None, 'sequence_length') as name_scope:
+    row_ids = sp_tensor.indices[:, 0]
+    column_ids = sp_tensor.indices[:, 1]
+    column_ids += array_ops.ones_like(column_ids)
+    seq_length = math_ops.to_int64(
+        math_ops.segment_max(column_ids, segment_ids=row_ids) / num_elements)
+    # If the last n rows do not have ids, seq_length will have shape
+    # [batch_size - n]. Pad the remaining values with zeros.
+    n_pad = array_ops.shape(sp_tensor)[:1] - array_ops.shape(seq_length)[:1]
+    padding = array_ops.zeros(n_pad, dtype=seq_length.dtype)
+    return array_ops.concat([seq_length, padding], axis=0, name=name_scope)
+
+
+class SequenceCategoricalColumn(FeatureColumn,
+                                collections.namedtuple(
+                                    'SequenceCategoricalColumn',
+                                    ('categorical_column'))):
+  """Represents sequences of categorical data."""
+
+  @property
+  def name(self):
+    """See `FeatureColumn` base class."""
+    return self.categorical_column.name
+
+  @property
+  def parse_example_spec(self):
+    """See `FeatureColumn` base class."""
+    return self.categorical_column.parse_example_spec
+
+  def transform_feature(self, transformation_cache, state_manager):
+    """See `FeatureColumn` base class."""
+    return self.categorical_column.transform_feature(transformation_cache,
+                                                     state_manager)
+
+  @property
+  def num_buckets(self):
+    """Returns number of buckets in this sparse feature."""
+    return self.categorical_column.num_buckets
+
+  def get_sequence_sparse_tensors(self, transformation_cache, state_manager):
+    """Returns an IdWeightPair.
+
+    `IdWeightPair` is a pair of `SparseTensor`s which represents ids and
+    weights.
+
+    `IdWeightPair.id_tensor` is typically a `batch_size` x `num_buckets`
+    `SparseTensor` of `int64`. `IdWeightPair.weight_tensor` is either a
+    `SparseTensor` of `float` or `None` to indicate all weights should be
+    taken to be 1. If specified, `weight_tensor` must have exactly the same
+    shape and indices as `sp_ids`. Expected `SparseTensor` is same as parsing
+    output of a `VarLenFeature` which is a ragged matrix.
+
+    Args:
+      transformation_cache: A `FeatureTransformationCache` object to access
+        features.
+      state_manager: A `StateManager` to create / access resources such as
+        lookup tables.
+    """
+    sparse_tensors = self.categorical_column.get_sparse_tensors(
+        transformation_cache, state_manager)
+    id_tensor = sparse_tensors.id_tensor
+    weight_tensor = sparse_tensors.weight_tensor
+    # Expands final dimension, so that embeddings are not combined during
+    # embedding lookup.
+    check_id_rank = check_ops.assert_equal(
+        array_ops.rank(id_tensor), 2,
+        data=[
+            'Column {} expected ID tensor of rank 2. '.format(self.name),
+            'id_tensor shape: ', array_ops.shape(id_tensor)])
+    with ops.control_dependencies([check_id_rank]):
+      id_tensor = sparse_ops.sparse_reshape(
+          id_tensor,
+          shape=array_ops.concat([id_tensor.dense_shape, [1]], axis=0))
+    if weight_tensor is not None:
+      check_weight_rank = check_ops.assert_equal(
+          array_ops.rank(weight_tensor), 2,
+          data=[
+              'Column {} expected weight tensor of rank 2.'.format(self.name),
+              'weight_tensor shape:', array_ops.shape(weight_tensor)])
+      with ops.control_dependencies([check_weight_rank]):
+        weight_tensor = sparse_ops.sparse_reshape(
+            weight_tensor,
+            shape=array_ops.concat([weight_tensor.dense_shape, [1]], axis=0))
+    return CategoricalColumn.IdWeightPair(id_tensor, weight_tensor)
diff --git a/tensorflow/python/feature_column/feature_column_v2_test.py b/tensorflow/python/feature_column/feature_column_v2_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..80a9d5d40e275fce664ef52e5d5413930432d683
--- /dev/null
+++ b/tensorflow/python/feature_column/feature_column_v2_test.py
@@ -0,0 +1,6583 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for feature_column."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import copy
+
+import numpy as np
+
+from tensorflow.core.example import example_pb2
+from tensorflow.core.example import feature_pb2
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.core.protobuf import rewriter_config_pb2
+from tensorflow.python.client import session
+from tensorflow.python.eager import backprop
+from tensorflow.python.eager import context
+from tensorflow.python.estimator.inputs import numpy_io
+from tensorflow.python.feature_column import feature_column as fc_old
+from tensorflow.python.feature_column import feature_column_v2 as fc
+from tensorflow.python.feature_column.feature_column_v2 import FeatureColumn
+from tensorflow.python.feature_column.feature_column_v2 import FeatureTransformationCache
+from tensorflow.python.feature_column.feature_column_v2 import InputLayer
+from tensorflow.python.feature_column.feature_column_v2 import StateManager
+from tensorflow.python.feature_column.feature_column_v2 import _LinearModel
+from tensorflow.python.feature_column.feature_column_v2 import _transform_features
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import lookup_ops
+from tensorflow.python.ops import parsing_ops
+from tensorflow.python.ops import partitioned_variables
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables as variables_lib
+from tensorflow.python.platform import test
+from tensorflow.python.training import coordinator
+from tensorflow.python.training import queue_runner_impl
+
+
+def _initialized_session(config=None):
+  sess = session.Session(config=config)
+  sess.run(variables_lib.global_variables_initializer())
+  sess.run(lookup_ops.tables_initializer())
+  return sess
+
+
+class LazyColumnTest(test.TestCase):
+
+  def test_transformations_called_once(self):
+
+    class TransformCounter(FeatureColumn):
+
+      def __init__(self):
+        self.num_transform = 0
+
+      @property
+      def name(self):
+        return 'TransformCounter'
+
+      def transform_feature(self, transformation_cache, state_manager):
+        self.num_transform += 1  # Count transform calls.
+        return transformation_cache.get('a', state_manager)
+
+      @property
+      def parse_example_spec(self):
+        pass
+
+    transformation_cache = FeatureTransformationCache(
+        features={'a': [[2], [3.]]})
+    column = TransformCounter()
+    self.assertEqual(0, column.num_transform)
+    transformation_cache.get(column, None)
+    self.assertEqual(1, column.num_transform)
+    transformation_cache.get(column, None)
+    self.assertEqual(1, column.num_transform)
+
+  def test_returns_transform_output(self):
+
+    class Transformer(FeatureColumn):
+
+      @property
+      def name(self):
+        return 'Transformer'
+
+      def transform_feature(self, transformation_cache, state_manager):
+        return 'Output'
+
+      @property
+      def parse_example_spec(self):
+        pass
+
+    transformation_cache = FeatureTransformationCache(
+        features={'a': [[2], [3.]]})
+    column = Transformer()
+    self.assertEqual('Output', transformation_cache.get(column, None))
+    self.assertEqual('Output', transformation_cache.get(column, None))
+
+  def test_does_not_pollute_given_features_dict(self):
+
+    class Transformer(FeatureColumn):
+
+      @property
+      def name(self):
+        return 'Transformer'
+
+      def transform_feature(self, transformation_cache, state_manager):
+        return 'Output'
+
+      @property
+      def parse_example_spec(self):
+        pass
+
+    features = {'a': [[2], [3.]]}
+    transformation_cache = FeatureTransformationCache(features=features)
+    transformation_cache.get(Transformer(), None)
+    self.assertEqual(['a'], list(features.keys()))
+
+  def test_error_if_feature_is_not_found(self):
+    transformation_cache = FeatureTransformationCache(
+        features={'a': [[2], [3.]]})
+    with self.assertRaisesRegexp(ValueError,
+                                 'bbb is not in features dictionary'):
+      transformation_cache.get('bbb', None)
+    with self.assertRaisesRegexp(ValueError,
+                                 'bbb is not in features dictionary'):
+      transformation_cache.get(u'bbb', None)
+
+  def test_not_supported_feature_column(self):
+
+    class NotAProperColumn(FeatureColumn):
+
+      @property
+      def name(self):
+        return 'NotAProperColumn'
+
+      def transform_feature(self, transformation_cache, state_manager):
+        # It should return not None.
+        pass
+
+      @property
+      def parse_example_spec(self):
+        pass
+
+    transformation_cache = FeatureTransformationCache(
+        features={'a': [[2], [3.]]})
+    with self.assertRaisesRegexp(ValueError,
+                                 'NotAProperColumn is not supported'):
+      transformation_cache.get(NotAProperColumn(), None)
+
+  def test_key_should_be_string_or_feature_colum(self):
+
+    class NotAFeatureColumn(object):
+      pass
+
+    transformation_cache = FeatureTransformationCache(
+        features={'a': [[2], [3.]]})
+    with self.assertRaisesRegexp(
+        TypeError, '"key" must be either a "str" or "FeatureColumn".'):
+      transformation_cache.get(NotAFeatureColumn(), None)
+
+
+class NumericColumnTest(test.TestCase):
+
+  def test_defaults(self):
+    a = fc.numeric_column('aaa')
+    self.assertEqual('aaa', a.key)
+    self.assertEqual('aaa', a.name)
+    self.assertEqual((1,), a.shape)
+    self.assertIsNone(a.default_value)
+    self.assertEqual(dtypes.float32, a.dtype)
+    self.assertIsNone(a.normalizer_fn)
+
+  def test_key_should_be_string(self):
+    with self.assertRaisesRegexp(ValueError, 'key must be a string.'):
+      fc.numeric_column(key=('aaa',))
+
+  def test_shape_saved_as_tuple(self):
+    a = fc.numeric_column('aaa', shape=[1, 2], default_value=[[3, 2.]])
+    self.assertEqual((1, 2), a.shape)
+
+  def test_default_value_saved_as_tuple(self):
+    a = fc.numeric_column('aaa', default_value=4.)
+    self.assertEqual((4.,), a.default_value)
+    a = fc.numeric_column('aaa', shape=[1, 2], default_value=[[3, 2.]])
+    self.assertEqual(((3., 2.),), a.default_value)
+
+  def test_shape_and_default_value_compatibility(self):
+    fc.numeric_column('aaa', shape=[2], default_value=[1, 2.])
+    with self.assertRaisesRegexp(ValueError, 'The shape of default_value'):
+      fc.numeric_column('aaa', shape=[2], default_value=[1, 2, 3.])
+    fc.numeric_column(
+        'aaa', shape=[3, 2], default_value=[[2, 3], [1, 2], [2, 3.]])
+    with self.assertRaisesRegexp(ValueError, 'The shape of default_value'):
+      fc.numeric_column(
+          'aaa', shape=[3, 1], default_value=[[2, 3], [1, 2], [2, 3.]])
+    with self.assertRaisesRegexp(ValueError, 'The shape of default_value'):
+      fc.numeric_column(
+          'aaa', shape=[3, 3], default_value=[[2, 3], [1, 2], [2, 3.]])
+
+  def test_default_value_type_check(self):
+    fc.numeric_column(
+        'aaa', shape=[2], default_value=[1, 2.], dtype=dtypes.float32)
+    fc.numeric_column(
+        'aaa', shape=[2], default_value=[1, 2], dtype=dtypes.int32)
+    with self.assertRaisesRegexp(TypeError, 'must be compatible with dtype'):
+      fc.numeric_column(
+          'aaa', shape=[2], default_value=[1, 2.], dtype=dtypes.int32)
+    with self.assertRaisesRegexp(TypeError,
+                                 'default_value must be compatible with dtype'):
+      fc.numeric_column('aaa', default_value=['string'])
+
+  def test_shape_must_be_positive_integer(self):
+    with self.assertRaisesRegexp(TypeError, 'shape dimensions must be integer'):
+      fc.numeric_column(
+          'aaa', shape=[
+              1.0,
+          ])
+
+    with self.assertRaisesRegexp(ValueError,
+                                 'shape dimensions must be greater than 0'):
+      fc.numeric_column(
+          'aaa', shape=[
+              0,
+          ])
+
+  def test_dtype_is_convertible_to_float(self):
+    with self.assertRaisesRegexp(ValueError,
+                                 'dtype must be convertible to float'):
+      fc.numeric_column('aaa', dtype=dtypes.string)
+
+  def test_scalar_default_value_fills_the_shape(self):
+    a = fc.numeric_column('aaa', shape=[2, 3], default_value=2.)
+    self.assertEqual(((2., 2., 2.), (2., 2., 2.)), a.default_value)
+
+  def test_parse_spec(self):
+    a = fc.numeric_column('aaa', shape=[2, 3], dtype=dtypes.int32)
+    self.assertEqual({
+        'aaa': parsing_ops.FixedLenFeature((2, 3), dtype=dtypes.int32)
+    }, a.parse_example_spec)
+
+  def test_parse_example_no_default_value(self):
+    price = fc.numeric_column('price', shape=[2])
+    data = example_pb2.Example(features=feature_pb2.Features(
+        feature={
+            'price':
+                feature_pb2.Feature(float_list=feature_pb2.FloatList(
+                    value=[20., 110.]))
+        }))
+    features = parsing_ops.parse_example(
+        serialized=[data.SerializeToString()],
+        features=fc.make_parse_example_spec([price]))
+    self.assertIn('price', features)
+    with self.test_session():
+      self.assertAllEqual([[20., 110.]], features['price'].eval())
+
+  def test_parse_example_with_default_value(self):
+    price = fc.numeric_column('price', shape=[2], default_value=11.)
+    data = example_pb2.Example(features=feature_pb2.Features(
+        feature={
+            'price':
+                feature_pb2.Feature(float_list=feature_pb2.FloatList(
+                    value=[20., 110.]))
+        }))
+    no_data = example_pb2.Example(features=feature_pb2.Features(
+        feature={
+            'something_else':
+                feature_pb2.Feature(float_list=feature_pb2.FloatList(
+                    value=[20., 110.]))
+        }))
+    features = parsing_ops.parse_example(
+        serialized=[data.SerializeToString(),
+                    no_data.SerializeToString()],
+        features=fc.make_parse_example_spec([price]))
+    self.assertIn('price', features)
+    with self.test_session():
+      self.assertAllEqual([[20., 110.], [11., 11.]], features['price'].eval())
+
+  def test_normalizer_fn_must_be_callable(self):
+    with self.assertRaisesRegexp(TypeError, 'must be a callable'):
+      fc.numeric_column('price', normalizer_fn='NotACallable')
+
+  def test_normalizer_fn_transform_feature(self):
+
+    def _increment_two(input_tensor):
+      return input_tensor + 2.
+
+    price = fc.numeric_column('price', shape=[2], normalizer_fn=_increment_two)
+    output = _transform_features({'price': [[1., 2.], [5., 6.]]}, [price], None)
+    with self.test_session():
+      self.assertAllEqual([[3., 4.], [7., 8.]], output[price].eval())
+
+  def test_get_dense_tensor(self):
+
+    def _increment_two(input_tensor):
+      return input_tensor + 2.
+
+    price = fc.numeric_column('price', shape=[2], normalizer_fn=_increment_two)
+    transformation_cache = FeatureTransformationCache({
+        'price': [[1., 2.], [5., 6.]]
+    })
+    self.assertEqual(
+        transformation_cache.get(price, None),
+        price.get_dense_tensor(transformation_cache, None))
+
+  def test_sparse_tensor_not_supported(self):
+    price = fc.numeric_column('price')
+    transformation_cache = FeatureTransformationCache({
+        'price':
+            sparse_tensor.SparseTensor(
+                indices=[[0, 0]], values=[0.3], dense_shape=[1, 1])
+    })
+    with self.assertRaisesRegexp(ValueError, 'must be a Tensor'):
+      price.transform_feature(transformation_cache, None)
+
+  def test_deep_copy(self):
+    a = fc.numeric_column('aaa', shape=[1, 2], default_value=[[3., 2.]])
+    a_copy = copy.deepcopy(a)
+    self.assertEqual(a_copy.name, 'aaa')
+    self.assertEqual(a_copy.shape, (1, 2))
+    self.assertEqual(a_copy.default_value, ((3., 2.),))
+
+  def test_numpy_default_value(self):
+    a = fc.numeric_column(
+        'aaa', shape=[1, 2], default_value=np.array([[3., 2.]]))
+    self.assertEqual(a.default_value, ((3., 2.),))
+
+  def test_linear_model(self):
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default():
+      features = {'price': [[1.], [5.]]}
+      predictions = fc.linear_model(features, [price])
+      bias = get_linear_model_bias()
+      price_var = get_linear_model_column_var(price)
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias.eval())
+        self.assertAllClose([[0.]], price_var.eval())
+        self.assertAllClose([[0.], [0.]], predictions.eval())
+        sess.run(price_var.assign([[10.]]))
+        self.assertAllClose([[10.], [50.]], predictions.eval())
+
+  def test_keras_linear_model(self):
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default():
+      features = {'price': [[1.], [5.]]}
+      predictions = get_keras_linear_model_predictions(features, [price])
+      bias = get_linear_model_bias()
+      price_var = get_linear_model_column_var(price)
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias.eval())
+        self.assertAllClose([[0.]], price_var.eval())
+        self.assertAllClose([[0.], [0.]], predictions.eval())
+        sess.run(price_var.assign([[10.]]))
+        self.assertAllClose([[10.], [50.]], predictions.eval())
+
+
+class BucketizedColumnTest(test.TestCase):
+
+  def test_invalid_source_column_type(self):
+    a = fc.categorical_column_with_hash_bucket('aaa', hash_bucket_size=10)
+    with self.assertRaisesRegexp(
+        ValueError,
+        'source_column must be a column generated with numeric_column'):
+      fc.bucketized_column(a, boundaries=[0, 1])
+
+  def test_invalid_source_column_shape(self):
+    a = fc.numeric_column('aaa', shape=[2, 3])
+    with self.assertRaisesRegexp(
+        ValueError, 'source_column must be one-dimensional column'):
+      fc.bucketized_column(a, boundaries=[0, 1])
+
+  def test_invalid_boundaries(self):
+    a = fc.numeric_column('aaa')
+    with self.assertRaisesRegexp(
+        ValueError, 'boundaries must be a sorted list'):
+      fc.bucketized_column(a, boundaries=None)
+    with self.assertRaisesRegexp(
+        ValueError, 'boundaries must be a sorted list'):
+      fc.bucketized_column(a, boundaries=1.)
+    with self.assertRaisesRegexp(
+        ValueError, 'boundaries must be a sorted list'):
+      fc.bucketized_column(a, boundaries=[1, 0])
+    with self.assertRaisesRegexp(
+        ValueError, 'boundaries must be a sorted list'):
+      fc.bucketized_column(a, boundaries=[1, 1])
+
+  def test_name(self):
+    a = fc.numeric_column('aaa', dtype=dtypes.int32)
+    b = fc.bucketized_column(a, boundaries=[0, 1])
+    self.assertEqual('aaa_bucketized', b.name)
+
+  def test_parse_spec(self):
+    a = fc.numeric_column('aaa', shape=[2], dtype=dtypes.int32)
+    b = fc.bucketized_column(a, boundaries=[0, 1])
+    self.assertEqual({
+        'aaa': parsing_ops.FixedLenFeature((2,), dtype=dtypes.int32)
+    }, b.parse_example_spec)
+
+  def test_variable_shape(self):
+    a = fc.numeric_column('aaa', shape=[2], dtype=dtypes.int32)
+    b = fc.bucketized_column(a, boundaries=[0, 1])
+    # Column 'aaa` has shape [2] times three buckets -> variable_shape=[2, 3].
+    self.assertAllEqual((2, 3), b.variable_shape)
+
+  def test_num_buckets(self):
+    a = fc.numeric_column('aaa', shape=[2], dtype=dtypes.int32)
+    b = fc.bucketized_column(a, boundaries=[0, 1])
+    # Column 'aaa` has shape [2] times three buckets -> num_buckets=6.
+    self.assertEqual(6, b.num_buckets)
+
+  def test_parse_example(self):
+    price = fc.numeric_column('price', shape=[2])
+    bucketized_price = fc.bucketized_column(price, boundaries=[0, 50])
+    data = example_pb2.Example(features=feature_pb2.Features(
+        feature={
+            'price':
+                feature_pb2.Feature(float_list=feature_pb2.FloatList(
+                    value=[20., 110.]))
+        }))
+    features = parsing_ops.parse_example(
+        serialized=[data.SerializeToString()],
+        features=fc.make_parse_example_spec([bucketized_price]))
+    self.assertIn('price', features)
+    with self.test_session():
+      self.assertAllEqual([[20., 110.]], features['price'].eval())
+
+  def test_transform_feature(self):
+    price = fc.numeric_column('price', shape=[2])
+    bucketized_price = fc.bucketized_column(price, boundaries=[0, 2, 4, 6])
+    with ops.Graph().as_default():
+      transformed_tensor = _transform_features({
+          'price': [[-1., 1.], [5., 6.]]
+      }, [bucketized_price], None)
+      with _initialized_session():
+        self.assertAllEqual([[0, 1], [3, 4]],
+                            transformed_tensor[bucketized_price].eval())
+
+  def test_get_dense_tensor_one_input_value(self):
+    """Tests _get_dense_tensor() for input with shape=[1]."""
+    price = fc.numeric_column('price', shape=[1])
+    bucketized_price = fc.bucketized_column(price, boundaries=[0, 2, 4, 6])
+    with ops.Graph().as_default():
+      transformation_cache = FeatureTransformationCache({
+          'price': [[-1.], [1.], [5.], [6.]]
+      })
+      with _initialized_session():
+        bucketized_price_tensor = bucketized_price.get_dense_tensor(
+            transformation_cache, None)
+        self.assertAllClose(
+            # One-hot tensor.
+            [[[1., 0., 0., 0., 0.]],
+             [[0., 1., 0., 0., 0.]],
+             [[0., 0., 0., 1., 0.]],
+             [[0., 0., 0., 0., 1.]]],
+            bucketized_price_tensor.eval())
+
+  def test_get_dense_tensor_two_input_values(self):
+    """Tests _get_dense_tensor() for input with shape=[2]."""
+    price = fc.numeric_column('price', shape=[2])
+    bucketized_price = fc.bucketized_column(price, boundaries=[0, 2, 4, 6])
+    with ops.Graph().as_default():
+      transformation_cache = FeatureTransformationCache({
+          'price': [[-1., 1.], [5., 6.]]
+      })
+      with _initialized_session():
+        bucketized_price_tensor = bucketized_price.get_dense_tensor(
+            transformation_cache, None)
+        self.assertAllClose(
+            # One-hot tensor.
+            [[[1., 0., 0., 0., 0.], [0., 1., 0., 0., 0.]],
+             [[0., 0., 0., 1., 0.], [0., 0., 0., 0., 1.]]],
+            bucketized_price_tensor.eval())
+
+  def test_get_sparse_tensors_one_input_value(self):
+    """Tests _get_sparse_tensors() for input with shape=[1]."""
+    price = fc.numeric_column('price', shape=[1])
+    bucketized_price = fc.bucketized_column(price, boundaries=[0, 2, 4, 6])
+    with ops.Graph().as_default():
+      transformation_cache = FeatureTransformationCache({
+          'price': [[-1.], [1.], [5.], [6.]]
+      })
+      with _initialized_session() as sess:
+        id_weight_pair = bucketized_price.get_sparse_tensors(
+            transformation_cache, None)
+        self.assertIsNone(id_weight_pair.weight_tensor)
+        id_tensor_value = sess.run(id_weight_pair.id_tensor)
+        self.assertAllEqual(
+            [[0, 0], [1, 0], [2, 0], [3, 0]], id_tensor_value.indices)
+        self.assertAllEqual([0, 1, 3, 4], id_tensor_value.values)
+        self.assertAllEqual([4, 1], id_tensor_value.dense_shape)
+
+  def test_get_sparse_tensors_two_input_values(self):
+    """Tests _get_sparse_tensors() for input with shape=[2]."""
+    price = fc.numeric_column('price', shape=[2])
+    bucketized_price = fc.bucketized_column(price, boundaries=[0, 2, 4, 6])
+    with ops.Graph().as_default():
+      transformation_cache = FeatureTransformationCache({
+          'price': [[-1., 1.], [5., 6.]]
+      })
+      with _initialized_session() as sess:
+        id_weight_pair = bucketized_price.get_sparse_tensors(
+            transformation_cache, None)
+        self.assertIsNone(id_weight_pair.weight_tensor)
+        id_tensor_value = sess.run(id_weight_pair.id_tensor)
+        self.assertAllEqual(
+            [[0, 0], [0, 1], [1, 0], [1, 1]], id_tensor_value.indices)
+        # Values 0-4 correspond to the first column of the input price.
+        # Values 5-9 correspond to the second column of the input price.
+        self.assertAllEqual([0, 6, 3, 9], id_tensor_value.values)
+        self.assertAllEqual([2, 2], id_tensor_value.dense_shape)
+
+  def test_sparse_tensor_input_not_supported(self):
+    price = fc.numeric_column('price')
+    bucketized_price = fc.bucketized_column(price, boundaries=[0, 1])
+    transformation_cache = FeatureTransformationCache({
+        'price':
+            sparse_tensor.SparseTensor(
+                indices=[[0, 0]], values=[0.3], dense_shape=[1, 1])
+    })
+    with self.assertRaisesRegexp(ValueError, 'must be a Tensor'):
+      bucketized_price.transform_feature(transformation_cache, None)
+
+  def test_deep_copy(self):
+    a = fc.numeric_column('aaa', shape=[2])
+    a_bucketized = fc.bucketized_column(a, boundaries=[0, 1])
+    a_bucketized_copy = copy.deepcopy(a_bucketized)
+    self.assertEqual(a_bucketized_copy.name, 'aaa_bucketized')
+    self.assertAllEqual(a_bucketized_copy.variable_shape, (2, 3))
+    self.assertEqual(a_bucketized_copy.boundaries, (0, 1))
+
+  def test_linear_model_one_input_value(self):
+    """Tests linear_model() for input with shape=[1]."""
+    price = fc_old.numeric_column('price', shape=[1])
+    bucketized_price = fc_old.bucketized_column(price, boundaries=[0, 2, 4, 6])
+    with ops.Graph().as_default():
+      features = {'price': [[-1.], [1.], [5.], [6.]]}
+      predictions = fc.linear_model(features, [bucketized_price])
+      bias = get_linear_model_bias()
+      bucketized_price_var = get_linear_model_column_var(bucketized_price)
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias.eval())
+        # One weight variable per bucket, all initialized to zero.
+        self.assertAllClose(
+            [[0.], [0.], [0.], [0.], [0.]], bucketized_price_var.eval())
+        self.assertAllClose([[0.], [0.], [0.], [0.]], predictions.eval())
+        sess.run(bucketized_price_var.assign(
+            [[10.], [20.], [30.], [40.], [50.]]))
+        # price -1. is in the 0th bucket, whose weight is 10.
+        # price 1. is in the 1st bucket, whose weight is 20.
+        # price 5. is in the 3rd bucket, whose weight is 40.
+        # price 6. is in the 4th bucket, whose weight is 50.
+        self.assertAllClose([[10.], [20.], [40.], [50.]], predictions.eval())
+        sess.run(bias.assign([1.]))
+        self.assertAllClose([[11.], [21.], [41.], [51.]], predictions.eval())
+
+  def test_linear_model_two_input_values(self):
+    """Tests linear_model() for input with shape=[2]."""
+    price = fc_old.numeric_column('price', shape=[2])
+    bucketized_price = fc_old.bucketized_column(price, boundaries=[0, 2, 4, 6])
+    with ops.Graph().as_default():
+      features = {'price': [[-1., 1.], [5., 6.]]}
+      predictions = fc.linear_model(features, [bucketized_price])
+      bias = get_linear_model_bias()
+      bucketized_price_var = get_linear_model_column_var(bucketized_price)
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias.eval())
+        # One weight per bucket per input column, all initialized to zero.
+        self.assertAllClose(
+            [[0.], [0.], [0.], [0.], [0.], [0.], [0.], [0.], [0.], [0.]],
+            bucketized_price_var.eval())
+        self.assertAllClose([[0.], [0.]], predictions.eval())
+        sess.run(bucketized_price_var.assign(
+            [[10.], [20.], [30.], [40.], [50.],
+             [60.], [70.], [80.], [90.], [100.]]))
+        # 1st example:
+        #   price -1. is in the 0th bucket, whose weight is 10.
+        #   price 1. is in the 6th bucket, whose weight is 70.
+        # 2nd example:
+        #   price 5. is in the 3rd bucket, whose weight is 40.
+        #   price 6. is in the 9th bucket, whose weight is 100.
+        self.assertAllClose([[80.], [140.]], predictions.eval())
+        sess.run(bias.assign([1.]))
+        self.assertAllClose([[81.], [141.]], predictions.eval())
+
+  def test_keras_linear_model_one_input_value(self):
+    """Tests _LinearModel for input with shape=[1]."""
+    price = fc_old.numeric_column('price', shape=[1])
+    bucketized_price = fc_old.bucketized_column(price, boundaries=[0, 2, 4, 6])
+    with ops.Graph().as_default():
+      features = {'price': [[-1.], [1.], [5.], [6.]]}
+      predictions = get_keras_linear_model_predictions(features,
+                                                       [bucketized_price])
+      bias = get_linear_model_bias()
+      bucketized_price_var = get_linear_model_column_var(bucketized_price)
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias.eval())
+        # One weight variable per bucket, all initialized to zero.
+        self.assertAllClose([[0.], [0.], [0.], [0.], [0.]],
+                            bucketized_price_var.eval())
+        self.assertAllClose([[0.], [0.], [0.], [0.]], predictions.eval())
+        sess.run(
+            bucketized_price_var.assign([[10.], [20.], [30.], [40.], [50.]]))
+        # price -1. is in the 0th bucket, whose weight is 10.
+        # price 1. is in the 1st bucket, whose weight is 20.
+        # price 5. is in the 3rd bucket, whose weight is 40.
+        # price 6. is in the 4th bucket, whose weight is 50.
+        self.assertAllClose([[10.], [20.], [40.], [50.]], predictions.eval())
+        sess.run(bias.assign([1.]))
+        self.assertAllClose([[11.], [21.], [41.], [51.]], predictions.eval())
+
+  def test_keras_linear_model_two_input_values(self):
+    """Tests _LinearModel for input with shape=[2]."""
+    price = fc_old.numeric_column('price', shape=[2])
+    bucketized_price = fc_old.bucketized_column(price, boundaries=[0, 2, 4, 6])
+    with ops.Graph().as_default():
+      features = {'price': [[-1., 1.], [5., 6.]]}
+      predictions = get_keras_linear_model_predictions(features,
+                                                       [bucketized_price])
+      bias = get_linear_model_bias()
+      bucketized_price_var = get_linear_model_column_var(bucketized_price)
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias.eval())
+        # One weight per bucket per input column, all initialized to zero.
+        self.assertAllClose(
+            [[0.], [0.], [0.], [0.], [0.], [0.], [0.], [0.], [0.], [0.]],
+            bucketized_price_var.eval())
+        self.assertAllClose([[0.], [0.]], predictions.eval())
+        sess.run(
+            bucketized_price_var.assign([[10.], [20.], [30.], [40.], [50.],
+                                         [60.], [70.], [80.], [90.], [100.]]))
+        # 1st example:
+        #   price -1. is in the 0th bucket, whose weight is 10.
+        #   price 1. is in the 6th bucket, whose weight is 70.
+        # 2nd example:
+        #   price 5. is in the 3rd bucket, whose weight is 40.
+        #   price 6. is in the 9th bucket, whose weight is 100.
+        self.assertAllClose([[80.], [140.]], predictions.eval())
+        sess.run(bias.assign([1.]))
+        self.assertAllClose([[81.], [141.]], predictions.eval())
+
+
+class HashedCategoricalColumnTest(test.TestCase):
+
+  def test_defaults(self):
+    a = fc.categorical_column_with_hash_bucket('aaa', 10)
+    self.assertEqual('aaa', a.name)
+    self.assertEqual('aaa', a.key)
+    self.assertEqual(10, a.hash_bucket_size)
+    self.assertEqual(dtypes.string, a.dtype)
+
+  def test_key_should_be_string(self):
+    with self.assertRaisesRegexp(ValueError, 'key must be a string.'):
+      fc.categorical_column_with_hash_bucket(('key',), 10)
+
+  def test_bucket_size_should_be_given(self):
+    with self.assertRaisesRegexp(ValueError, 'hash_bucket_size must be set.'):
+      fc.categorical_column_with_hash_bucket('aaa', None)
+
+  def test_bucket_size_should_be_positive(self):
+    with self.assertRaisesRegexp(ValueError,
+                                 'hash_bucket_size must be at least 1'):
+      fc.categorical_column_with_hash_bucket('aaa', 0)
+
+  def test_dtype_should_be_string_or_integer(self):
+    fc.categorical_column_with_hash_bucket('aaa', 10, dtype=dtypes.string)
+    fc.categorical_column_with_hash_bucket('aaa', 10, dtype=dtypes.int32)
+    with self.assertRaisesRegexp(ValueError, 'dtype must be string or integer'):
+      fc.categorical_column_with_hash_bucket('aaa', 10, dtype=dtypes.float32)
+
+  def test_deep_copy(self):
+    original = fc.categorical_column_with_hash_bucket('aaa', 10)
+    for column in (original, copy.deepcopy(original)):
+      self.assertEqual('aaa', column.name)
+      self.assertEqual(10, column.hash_bucket_size)
+      self.assertEqual(10, column.num_buckets)
+      self.assertEqual(dtypes.string, column.dtype)
+
+  def test_parse_spec_string(self):
+    a = fc.categorical_column_with_hash_bucket('aaa', 10)
+    self.assertEqual({
+        'aaa': parsing_ops.VarLenFeature(dtypes.string)
+    }, a.parse_example_spec)
+
+  def test_parse_spec_int(self):
+    a = fc.categorical_column_with_hash_bucket('aaa', 10, dtype=dtypes.int32)
+    self.assertEqual({
+        'aaa': parsing_ops.VarLenFeature(dtypes.int32)
+    }, a.parse_example_spec)
+
+  def test_parse_example(self):
+    a = fc.categorical_column_with_hash_bucket('aaa', 10)
+    data = example_pb2.Example(features=feature_pb2.Features(
+        feature={
+            'aaa':
+                feature_pb2.Feature(bytes_list=feature_pb2.BytesList(
+                    value=[b'omar', b'stringer']))
+        }))
+    features = parsing_ops.parse_example(
+        serialized=[data.SerializeToString()],
+        features=fc.make_parse_example_spec([a]))
+    self.assertIn('aaa', features)
+    with self.test_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=[[0, 0], [0, 1]],
+              values=np.array([b'omar', b'stringer'], dtype=np.object_),
+              dense_shape=[1, 2]),
+          features['aaa'].eval())
+
+  def test_strings_should_be_hashed(self):
+    hashed_sparse = fc.categorical_column_with_hash_bucket('wire', 10)
+    wire_tensor = sparse_tensor.SparseTensor(
+        values=['omar', 'stringer', 'marlo'],
+        indices=[[0, 0], [1, 0], [1, 1]],
+        dense_shape=[2, 2])
+    outputs = _transform_features({'wire': wire_tensor}, [hashed_sparse], None)
+    output = outputs[hashed_sparse]
+    # Check exact hashed output. If hashing changes this test will break.
+    expected_values = [6, 4, 1]
+    with self.test_session():
+      self.assertEqual(dtypes.int64, output.values.dtype)
+      self.assertAllEqual(expected_values, output.values.eval())
+      self.assertAllEqual(wire_tensor.indices.eval(), output.indices.eval())
+      self.assertAllEqual(wire_tensor.dense_shape.eval(),
+                          output.dense_shape.eval())
+
+  def test_tensor_dtype_should_be_string_or_integer(self):
+    string_fc = fc.categorical_column_with_hash_bucket(
+        'a_string', 10, dtype=dtypes.string)
+    int_fc = fc.categorical_column_with_hash_bucket(
+        'a_int', 10, dtype=dtypes.int32)
+    float_fc = fc.categorical_column_with_hash_bucket(
+        'a_float', 10, dtype=dtypes.string)
+    int_tensor = sparse_tensor.SparseTensor(
+        values=[101],
+        indices=[[0, 0]],
+        dense_shape=[1, 1])
+    string_tensor = sparse_tensor.SparseTensor(
+        values=['101'],
+        indices=[[0, 0]],
+        dense_shape=[1, 1])
+    float_tensor = sparse_tensor.SparseTensor(
+        values=[101.],
+        indices=[[0, 0]],
+        dense_shape=[1, 1])
+    transformation_cache = FeatureTransformationCache({
+        'a_int': int_tensor,
+        'a_string': string_tensor,
+        'a_float': float_tensor
+    })
+    transformation_cache.get(string_fc, None)
+    transformation_cache.get(int_fc, None)
+    with self.assertRaisesRegexp(ValueError, 'dtype must be string or integer'):
+      transformation_cache.get(float_fc, None)
+
+  def test_dtype_should_match_with_tensor(self):
+    hashed_sparse = fc.categorical_column_with_hash_bucket(
+        'wire', 10, dtype=dtypes.int64)
+    wire_tensor = sparse_tensor.SparseTensor(
+        values=['omar'], indices=[[0, 0]], dense_shape=[1, 1])
+    transformation_cache = FeatureTransformationCache({'wire': wire_tensor})
+    with self.assertRaisesRegexp(ValueError, 'dtype must be compatible'):
+      transformation_cache.get(hashed_sparse, None)
+
+  def test_ints_should_be_hashed(self):
+    hashed_sparse = fc.categorical_column_with_hash_bucket(
+        'wire', 10, dtype=dtypes.int64)
+    wire_tensor = sparse_tensor.SparseTensor(
+        values=[101, 201, 301],
+        indices=[[0, 0], [1, 0], [1, 1]],
+        dense_shape=[2, 2])
+    transformation_cache = FeatureTransformationCache({'wire': wire_tensor})
+    output = transformation_cache.get(hashed_sparse, None)
+    # Check exact hashed output. If hashing changes this test will break.
+    expected_values = [3, 7, 5]
+    with self.test_session():
+      self.assertAllEqual(expected_values, output.values.eval())
+
+  def test_int32_64_is_compatible(self):
+    hashed_sparse = fc.categorical_column_with_hash_bucket(
+        'wire', 10, dtype=dtypes.int64)
+    wire_tensor = sparse_tensor.SparseTensor(
+        values=constant_op.constant([101, 201, 301], dtype=dtypes.int32),
+        indices=[[0, 0], [1, 0], [1, 1]],
+        dense_shape=[2, 2])
+    transformation_cache = FeatureTransformationCache({'wire': wire_tensor})
+    output = transformation_cache.get(hashed_sparse, None)
+    # Check exact hashed output. If hashing changes this test will break.
+    expected_values = [3, 7, 5]
+    with self.test_session():
+      self.assertAllEqual(expected_values, output.values.eval())
+
+  def test_get_sparse_tensors(self):
+    hashed_sparse = fc.categorical_column_with_hash_bucket('wire', 10)
+    transformation_cache = FeatureTransformationCache({
+        'wire':
+            sparse_tensor.SparseTensor(
+                values=['omar', 'stringer', 'marlo'],
+                indices=[[0, 0], [1, 0], [1, 1]],
+                dense_shape=[2, 2])
+    })
+    id_weight_pair = hashed_sparse.get_sparse_tensors(transformation_cache,
+                                                      None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    self.assertEqual(
+        transformation_cache.get(hashed_sparse, None), id_weight_pair.id_tensor)
+
+  def DISABLED_test_get_sparse_tensors_weight_collections(self):
+    column = fc.categorical_column_with_hash_bucket('aaa', 10)
+    inputs = sparse_tensor.SparseTensor(
+        values=['omar', 'stringer', 'marlo'],
+        indices=[[0, 0], [1, 0], [1, 1]],
+        dense_shape=[2, 2])
+    column._get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }),
+        weight_collections=('my_weights',))
+
+    self.assertItemsEqual(
+        [], ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES))
+    self.assertItemsEqual([], ops.get_collection('my_weights'))
+
+  def test_get_sparse_tensors_dense_input(self):
+    hashed_sparse = fc.categorical_column_with_hash_bucket('wire', 10)
+    transformation_cache = FeatureTransformationCache({
+        'wire': (('omar', ''), ('stringer', 'marlo'))
+    })
+    id_weight_pair = hashed_sparse.get_sparse_tensors(transformation_cache,
+                                                      None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    self.assertEqual(
+        transformation_cache.get(hashed_sparse, None), id_weight_pair.id_tensor)
+
+  def test_linear_model(self):
+    wire_column = fc_old.categorical_column_with_hash_bucket('wire', 4)
+    self.assertEqual(4, wire_column._num_buckets)
+    with ops.Graph().as_default():
+      predictions = fc.linear_model({
+          wire_column.name: sparse_tensor.SparseTensorValue(
+              indices=((0, 0), (1, 0), (1, 1)),
+              values=('marlo', 'skywalker', 'omar'),
+              dense_shape=(2, 2))
+      }, (wire_column,))
+      bias = get_linear_model_bias()
+      wire_var = get_linear_model_column_var(wire_column)
+      with _initialized_session():
+        self.assertAllClose((0.,), bias.eval())
+        self.assertAllClose(((0.,), (0.,), (0.,), (0.,)), wire_var.eval())
+        self.assertAllClose(((0.,), (0.,)), predictions.eval())
+        wire_var.assign(((1.,), (2.,), (3.,), (4.,))).eval()
+        # 'marlo' -> 3: wire_var[3] = 4
+        # 'skywalker' -> 2, 'omar' -> 2: wire_var[2] + wire_var[2] = 3+3 = 6
+        self.assertAllClose(((4.,), (6.,)), predictions.eval())
+
+  def test_keras_linear_model(self):
+    wire_column = fc_old.categorical_column_with_hash_bucket('wire', 4)
+    self.assertEqual(4, wire_column._num_buckets)
+    with ops.Graph().as_default():
+      predictions = get_keras_linear_model_predictions({
+          wire_column.name:
+              sparse_tensor.SparseTensorValue(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=('marlo', 'skywalker', 'omar'),
+                  dense_shape=(2, 2))
+      }, (wire_column,))
+      bias = get_linear_model_bias()
+      wire_var = get_linear_model_column_var(wire_column)
+      with _initialized_session():
+        self.assertAllClose((0.,), bias.eval())
+        self.assertAllClose(((0.,), (0.,), (0.,), (0.,)), wire_var.eval())
+        self.assertAllClose(((0.,), (0.,)), predictions.eval())
+        wire_var.assign(((1.,), (2.,), (3.,), (4.,))).eval()
+        # 'marlo' -> 3: wire_var[3] = 4
+        # 'skywalker' -> 2, 'omar' -> 2: wire_var[2] + wire_var[2] = 3+3 = 6
+        self.assertAllClose(((4.,), (6.,)), predictions.eval())
+
+
+class CrossedColumnTest(test.TestCase):
+
+  def test_keys_empty(self):
+    with self.assertRaisesRegexp(
+        ValueError, 'keys must be a list with length > 1'):
+      fc.crossed_column([], 10)
+
+  def test_keys_length_one(self):
+    with self.assertRaisesRegexp(
+        ValueError, 'keys must be a list with length > 1'):
+      fc.crossed_column(['a'], 10)
+
+  def test_key_type_unsupported(self):
+    with self.assertRaisesRegexp(ValueError, 'Unsupported key type'):
+      fc.crossed_column(['a', fc.numeric_column('c')], 10)
+
+    with self.assertRaisesRegexp(
+        ValueError, 'categorical_column_with_hash_bucket is not supported'):
+      fc.crossed_column(
+          ['a', fc.categorical_column_with_hash_bucket('c', 10)], 10)
+
+  def test_hash_bucket_size_negative(self):
+    with self.assertRaisesRegexp(
+        ValueError, 'hash_bucket_size must be > 1'):
+      fc.crossed_column(['a', 'c'], -1)
+
+  def test_hash_bucket_size_zero(self):
+    with self.assertRaisesRegexp(
+        ValueError, 'hash_bucket_size must be > 1'):
+      fc.crossed_column(['a', 'c'], 0)
+
+  def test_hash_bucket_size_none(self):
+    with self.assertRaisesRegexp(
+        ValueError, 'hash_bucket_size must be > 1'):
+      fc.crossed_column(['a', 'c'], None)
+
+  def test_name(self):
+    a = fc.numeric_column('a', dtype=dtypes.int32)
+    b = fc.bucketized_column(a, boundaries=[0, 1])
+    crossed1 = fc.crossed_column(['d1', 'd2'], 10)
+
+    crossed2 = fc.crossed_column([b, 'c', crossed1], 10)
+    self.assertEqual('a_bucketized_X_c_X_d1_X_d2', crossed2.name)
+
+  def test_name_ordered_alphabetically(self):
+    """Tests that the name does not depend on the order of given columns."""
+    a = fc.numeric_column('a', dtype=dtypes.int32)
+    b = fc.bucketized_column(a, boundaries=[0, 1])
+    crossed1 = fc.crossed_column(['d1', 'd2'], 10)
+
+    crossed2 = fc.crossed_column([crossed1, 'c', b], 10)
+    self.assertEqual('a_bucketized_X_c_X_d1_X_d2', crossed2.name)
+
+  def test_name_leaf_keys_ordered_alphabetically(self):
+    """Tests that the name does not depend on the order of given columns."""
+    a = fc.numeric_column('a', dtype=dtypes.int32)
+    b = fc.bucketized_column(a, boundaries=[0, 1])
+    crossed1 = fc.crossed_column(['d2', 'c'], 10)
+
+    crossed2 = fc.crossed_column([crossed1, 'd1', b], 10)
+    self.assertEqual('a_bucketized_X_c_X_d1_X_d2', crossed2.name)
+
+  def test_parse_spec(self):
+    a = fc.numeric_column('a', shape=[2], dtype=dtypes.int32)
+    b = fc.bucketized_column(a, boundaries=[0, 1])
+    crossed = fc.crossed_column([b, 'c'], 10)
+    self.assertEqual({
+        'a': parsing_ops.FixedLenFeature((2,), dtype=dtypes.int32),
+        'c': parsing_ops.VarLenFeature(dtypes.string),
+    }, crossed.parse_example_spec)
+
+  def test_num_buckets(self):
+    a = fc.numeric_column('a', shape=[2], dtype=dtypes.int32)
+    b = fc.bucketized_column(a, boundaries=[0, 1])
+    crossed = fc.crossed_column([b, 'c'], 15)
+    self.assertEqual(15, crossed.num_buckets)
+
+  def test_deep_copy(self):
+    a = fc.numeric_column('a', dtype=dtypes.int32)
+    b = fc.bucketized_column(a, boundaries=[0, 1])
+    crossed1 = fc.crossed_column(['d1', 'd2'], 10)
+    crossed2 = fc.crossed_column([b, 'c', crossed1], 15, hash_key=5)
+    crossed2_copy = copy.deepcopy(crossed2)
+    self.assertEqual('a_bucketized_X_c_X_d1_X_d2', crossed2_copy.name,)
+    self.assertEqual(15, crossed2_copy.hash_bucket_size)
+    self.assertEqual(5, crossed2_copy.hash_key)
+
+  def test_parse_example(self):
+    price = fc.numeric_column('price', shape=[2])
+    bucketized_price = fc.bucketized_column(price, boundaries=[0, 50])
+    price_cross_wire = fc.crossed_column([bucketized_price, 'wire'], 10)
+    data = example_pb2.Example(features=feature_pb2.Features(
+        feature={
+            'price':
+                feature_pb2.Feature(float_list=feature_pb2.FloatList(
+                    value=[20., 110.])),
+            'wire':
+                feature_pb2.Feature(bytes_list=feature_pb2.BytesList(
+                    value=[b'omar', b'stringer'])),
+        }))
+    features = parsing_ops.parse_example(
+        serialized=[data.SerializeToString()],
+        features=fc.make_parse_example_spec([price_cross_wire]))
+    self.assertIn('price', features)
+    self.assertIn('wire', features)
+    with self.test_session():
+      self.assertAllEqual([[20., 110.]], features['price'].eval())
+      wire_sparse = features['wire']
+      self.assertAllEqual([[0, 0], [0, 1]], wire_sparse.indices.eval())
+      # Use byte constants to pass the open-source test.
+      self.assertAllEqual([b'omar', b'stringer'], wire_sparse.values.eval())
+      self.assertAllEqual([1, 2], wire_sparse.dense_shape.eval())
+
+  def test_transform_feature(self):
+    price = fc.numeric_column('price', shape=[2])
+    bucketized_price = fc.bucketized_column(price, boundaries=[0, 50])
+    hash_bucket_size = 10
+    price_cross_wire = fc.crossed_column(
+        [bucketized_price, 'wire'], hash_bucket_size)
+    features = {
+        'price': constant_op.constant([[1., 2.], [5., 6.]]),
+        'wire': sparse_tensor.SparseTensor(
+            values=['omar', 'stringer', 'marlo'],
+            indices=[[0, 0], [1, 0], [1, 1]],
+            dense_shape=[2, 2]),
+    }
+    outputs = _transform_features(features, [price_cross_wire], None)
+    output = outputs[price_cross_wire]
+    with self.test_session() as sess:
+      output_val = sess.run(output)
+      self.assertAllEqual(
+          [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2], [1, 3]], output_val.indices)
+      for val in output_val.values:
+        self.assertIn(val, list(range(hash_bucket_size)))
+      self.assertAllEqual([2, 4], output_val.dense_shape)
+
+  def test_get_sparse_tensors(self):
+    a = fc.numeric_column('a', dtype=dtypes.int32, shape=(2,))
+    b = fc.bucketized_column(a, boundaries=(0, 1))
+    crossed1 = fc.crossed_column(['d1', 'd2'], 10)
+    crossed2 = fc.crossed_column([b, 'c', crossed1], 15, hash_key=5)
+    with ops.Graph().as_default():
+      transformation_cache = FeatureTransformationCache({
+          'a':
+              constant_op.constant(((-1., .5), (.5, 1.))),
+          'c':
+              sparse_tensor.SparseTensor(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=['cA', 'cB', 'cC'],
+                  dense_shape=(2, 2)),
+          'd1':
+              sparse_tensor.SparseTensor(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=['d1A', 'd1B', 'd1C'],
+                  dense_shape=(2, 2)),
+          'd2':
+              sparse_tensor.SparseTensor(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=['d2A', 'd2B', 'd2C'],
+                  dense_shape=(2, 2)),
+      })
+      id_weight_pair = crossed2.get_sparse_tensors(transformation_cache, None)
+      with _initialized_session():
+        id_tensor_eval = id_weight_pair.id_tensor.eval()
+        self.assertAllEqual(
+            ((0, 0), (0, 1), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5),
+             (1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (1, 11), (1, 12), (1, 13),
+             (1, 14), (1, 15)),
+            id_tensor_eval.indices)
+        # Check exact hashed output. If hashing changes this test will break.
+        # All values are within [0, hash_bucket_size).
+        expected_values = (
+            6, 14, 0, 13, 8, 8, 10, 12, 2, 0, 1, 9, 8, 12, 2, 0, 10, 11)
+        self.assertAllEqual(expected_values, id_tensor_eval.values)
+        self.assertAllEqual((2, 16), id_tensor_eval.dense_shape)
+
+  def test_get_sparse_tensors_simple(self):
+    """Same as test_get_sparse_tensors, but with simpler values."""
+    a = fc.numeric_column('a', dtype=dtypes.int32, shape=(2,))
+    b = fc.bucketized_column(a, boundaries=(0, 1))
+    crossed = fc.crossed_column([b, 'c'], hash_bucket_size=5, hash_key=5)
+    with ops.Graph().as_default():
+      transformation_cache = FeatureTransformationCache({
+          'a':
+              constant_op.constant(((-1., .5), (.5, 1.))),
+          'c':
+              sparse_tensor.SparseTensor(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=['cA', 'cB', 'cC'],
+                  dense_shape=(2, 2)),
+      })
+      id_weight_pair = crossed.get_sparse_tensors(transformation_cache, None)
+      with _initialized_session():
+        id_tensor_eval = id_weight_pair.id_tensor.eval()
+        self.assertAllEqual(
+            ((0, 0), (0, 1), (1, 0), (1, 1), (1, 2), (1, 3)),
+            id_tensor_eval.indices)
+        # Check exact hashed output. If hashing changes this test will break.
+        # All values are within [0, hash_bucket_size).
+        expected_values = (1, 0, 1, 3, 4, 2)
+        self.assertAllEqual(expected_values, id_tensor_eval.values)
+        self.assertAllEqual((2, 4), id_tensor_eval.dense_shape)
+
+  def test_linear_model(self):
+    """Tests linear_model.
+
+    Uses data from test_get_sparse_tesnsors_simple.
+    """
+    a = fc_old.numeric_column('a', dtype=dtypes.int32, shape=(2,))
+    b = fc_old.bucketized_column(a, boundaries=(0, 1))
+    crossed = fc_old.crossed_column([b, 'c'], hash_bucket_size=5, hash_key=5)
+    with ops.Graph().as_default():
+      predictions = fc.linear_model({
+          'a': constant_op.constant(((-1., .5), (.5, 1.))),
+          'c': sparse_tensor.SparseTensor(
+              indices=((0, 0), (1, 0), (1, 1)),
+              values=['cA', 'cB', 'cC'],
+              dense_shape=(2, 2)),
+      }, (crossed,))
+      bias = get_linear_model_bias()
+      crossed_var = get_linear_model_column_var(crossed)
+      with _initialized_session() as sess:
+        self.assertAllClose((0.,), bias.eval())
+        self.assertAllClose(
+            ((0.,), (0.,), (0.,), (0.,), (0.,)), crossed_var.eval())
+        self.assertAllClose(((0.,), (0.,)), predictions.eval())
+        sess.run(crossed_var.assign(((1.,), (2.,), (3.,), (4.,), (5.,))))
+        # Expected ids after cross = (1, 0, 1, 3, 4, 2)
+        self.assertAllClose(((3.,), (14.,)), predictions.eval())
+        sess.run(bias.assign((.1,)))
+        self.assertAllClose(((3.1,), (14.1,)), predictions.eval())
+
+  def test_linear_model_with_weights(self):
+
+    class _TestColumnWithWeights(fc_old._CategoricalColumn):
+      """Produces sparse IDs and sparse weights."""
+
+      @property
+      def name(self):
+        return 'test_column'
+
+      @property
+      def _parse_example_spec(self):
+        return {
+            self.name: parsing_ops.VarLenFeature(dtypes.int32),
+            '{}_weights'.format(self.name): parsing_ops.VarLenFeature(
+                dtypes.float32),
+            }
+
+      @property
+      def _num_buckets(self):
+        return 5
+
+      def _transform_feature(self, inputs):
+        return (inputs.get(self.name),
+                inputs.get('{}_weights'.format(self.name)))
+
+      def _get_sparse_tensors(self, inputs, weight_collections=None,
+                              trainable=None):
+        """Populates both id_tensor and weight_tensor."""
+        ids_and_weights = inputs.get(self)
+        return fc_old._CategoricalColumn.IdWeightPair(
+            id_tensor=ids_and_weights[0], weight_tensor=ids_and_weights[1])
+
+    t = _TestColumnWithWeights()
+    crossed = fc_old.crossed_column([t, 'c'], hash_bucket_size=5, hash_key=5)
+    with ops.Graph().as_default():
+      with self.assertRaisesRegexp(
+          ValueError,
+          'crossed_column does not support weight_tensor.*{}'.format(t.name)):
+        fc.linear_model({
+            t.name: sparse_tensor.SparseTensor(
+                indices=((0, 0), (1, 0), (1, 1)),
+                values=[0, 1, 2],
+                dense_shape=(2, 2)),
+            '{}_weights'.format(t.name): sparse_tensor.SparseTensor(
+                indices=((0, 0), (1, 0), (1, 1)),
+                values=[1., 10., 2.],
+                dense_shape=(2, 2)),
+            'c': sparse_tensor.SparseTensor(
+                indices=((0, 0), (1, 0), (1, 1)),
+                values=['cA', 'cB', 'cC'],
+                dense_shape=(2, 2)),
+        }, (crossed,))
+
+  def test_keras_linear_model(self):
+    """Tests _LinearModel.
+
+    Uses data from test_get_sparse_tesnsors_simple.
+    """
+    a = fc_old.numeric_column('a', dtype=dtypes.int32, shape=(2,))
+    b = fc_old.bucketized_column(a, boundaries=(0, 1))
+    crossed = fc_old.crossed_column([b, 'c'], hash_bucket_size=5, hash_key=5)
+    with ops.Graph().as_default():
+      predictions = get_keras_linear_model_predictions({
+          'a':
+              constant_op.constant(((-1., .5), (.5, 1.))),
+          'c':
+              sparse_tensor.SparseTensor(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=['cA', 'cB', 'cC'],
+                  dense_shape=(2, 2)),
+      }, (crossed,))
+      bias = get_linear_model_bias()
+      crossed_var = get_linear_model_column_var(crossed)
+      with _initialized_session() as sess:
+        self.assertAllClose((0.,), bias.eval())
+        self.assertAllClose(((0.,), (0.,), (0.,), (0.,), (0.,)),
+                            crossed_var.eval())
+        self.assertAllClose(((0.,), (0.,)), predictions.eval())
+        sess.run(crossed_var.assign(((1.,), (2.,), (3.,), (4.,), (5.,))))
+        # Expected ids after cross = (1, 0, 1, 3, 4, 2)
+        self.assertAllClose(((3.,), (14.,)), predictions.eval())
+        sess.run(bias.assign((.1,)))
+        self.assertAllClose(((3.1,), (14.1,)), predictions.eval())
+
+  def test_keras_linear_model_with_weights(self):
+
+    class _TestColumnWithWeights(fc_old._CategoricalColumn):
+      """Produces sparse IDs and sparse weights."""
+
+      @property
+      def name(self):
+        return 'test_column'
+
+      @property
+      def _parse_example_spec(self):
+        return {
+            self.name:
+                parsing_ops.VarLenFeature(dtypes.int32),
+            '{}_weights'.format(self.name):
+                parsing_ops.VarLenFeature(dtypes.float32),
+        }
+
+      @property
+      def _num_buckets(self):
+        return 5
+
+      def _transform_feature(self, inputs):
+        return (inputs.get(self.name),
+                inputs.get('{}_weights'.format(self.name)))
+
+      def _get_sparse_tensors(self,
+                              inputs,
+                              weight_collections=None,
+                              trainable=None):
+        """Populates both id_tensor and weight_tensor."""
+        ids_and_weights = inputs.get(self)
+        return fc_old._CategoricalColumn.IdWeightPair(
+            id_tensor=ids_and_weights[0], weight_tensor=ids_and_weights[1])
+
+    t = _TestColumnWithWeights()
+    crossed = fc_old.crossed_column([t, 'c'], hash_bucket_size=5, hash_key=5)
+    with ops.Graph().as_default():
+      with self.assertRaisesRegexp(
+          ValueError,
+          'crossed_column does not support weight_tensor.*{}'.format(t.name)):
+        get_keras_linear_model_predictions({
+            t.name:
+                sparse_tensor.SparseTensor(
+                    indices=((0, 0), (1, 0), (1, 1)),
+                    values=[0, 1, 2],
+                    dense_shape=(2, 2)),
+            '{}_weights'.format(t.name):
+                sparse_tensor.SparseTensor(
+                    indices=((0, 0), (1, 0), (1, 1)),
+                    values=[1., 10., 2.],
+                    dense_shape=(2, 2)),
+            'c':
+                sparse_tensor.SparseTensor(
+                    indices=((0, 0), (1, 0), (1, 1)),
+                    values=['cA', 'cB', 'cC'],
+                    dense_shape=(2, 2)),
+        }, (crossed,))
+
+
+def get_linear_model_bias(name='linear_model'):
+  with variable_scope.variable_scope(name, reuse=True):
+    return variable_scope.get_variable('bias_weights')
+
+
+def get_linear_model_column_var(column, name='linear_model'):
+  return ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES,
+                            name + '/' + column.name)[0]
+
+
+def get_keras_linear_model_predictions(features,
+                                       feature_columns,
+                                       units=1,
+                                       sparse_combiner='sum',
+                                       weight_collections=None,
+                                       trainable=True,
+                                       cols_to_vars=None):
+  keras_linear_model = _LinearModel(
+      feature_columns,
+      units,
+      sparse_combiner,
+      weight_collections,
+      trainable,
+      name='linear_model')
+  retval = keras_linear_model(features)  # pylint: disable=not-callable
+  if cols_to_vars is not None:
+    cols_to_vars.update(keras_linear_model.cols_to_vars())
+  return retval
+
+
+class LinearModelTest(test.TestCase):
+
+  def test_raises_if_empty_feature_columns(self):
+    with self.assertRaisesRegexp(ValueError,
+                                 'feature_columns must not be empty'):
+      fc.linear_model(features={}, feature_columns=[])
+
+  def test_should_be_feature_column(self):
+    with self.assertRaisesRegexp(ValueError, 'must be a _FeatureColumn'):
+      fc.linear_model(features={'a': [[0]]}, feature_columns='NotSupported')
+
+  def test_should_be_dense_or_categorical_column(self):
+
+    class NotSupportedColumn(fc_old._FeatureColumn):
+
+      @property
+      def name(self):
+        return 'NotSupportedColumn'
+
+      def _transform_feature(self, cache):
+        pass
+
+      @property
+      def _parse_example_spec(self):
+        pass
+
+    with self.assertRaisesRegexp(
+        ValueError, 'must be either a _DenseColumn or _CategoricalColumn'):
+      fc.linear_model(
+          features={'a': [[0]]}, feature_columns=[NotSupportedColumn()])
+
+  def test_does_not_support_dict_columns(self):
+    with self.assertRaisesRegexp(
+        ValueError, 'Expected feature_columns to be iterable, found dict.'):
+      fc.linear_model(
+          features={'a': [[0]]},
+          feature_columns={'a': fc_old.numeric_column('a')})
+
+  def test_raises_if_duplicate_name(self):
+    with self.assertRaisesRegexp(
+        ValueError, 'Duplicate feature column name found for columns'):
+      fc.linear_model(
+          features={'a': [[0]]},
+          feature_columns=[
+              fc_old.numeric_column('a'),
+              fc_old.numeric_column('a')
+          ])
+
+  def test_dense_bias(self):
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default():
+      features = {'price': [[1.], [5.]]}
+      predictions = fc.linear_model(features, [price])
+      bias = get_linear_model_bias()
+      price_var = get_linear_model_column_var(price)
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias.eval())
+        sess.run(price_var.assign([[10.]]))
+        sess.run(bias.assign([5.]))
+        self.assertAllClose([[15.], [55.]], predictions.eval())
+
+  def test_sparse_bias(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default():
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar', 'stringer', 'marlo'],  # hashed to = [2, 0, 3]
+          indices=[[0, 0], [1, 0], [1, 1]],
+          dense_shape=[2, 2])
+      features = {'wire_cast': wire_tensor}
+      predictions = fc.linear_model(features, [wire_cast])
+      bias = get_linear_model_bias()
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias.eval())
+        self.assertAllClose([[0.], [0.], [0.], [0.]], wire_cast_var.eval())
+        sess.run(wire_cast_var.assign([[10.], [100.], [1000.], [10000.]]))
+        sess.run(bias.assign([5.]))
+        self.assertAllClose([[1005.], [10015.]], predictions.eval())
+
+  def test_dense_and_sparse_bias(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default():
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar', 'stringer', 'marlo'],  # hashed to = [2, 0, 3]
+          indices=[[0, 0], [1, 0], [1, 1]],
+          dense_shape=[2, 2])
+      features = {'wire_cast': wire_tensor, 'price': [[1.], [5.]]}
+      predictions = fc.linear_model(features, [wire_cast, price])
+      bias = get_linear_model_bias()
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      price_var = get_linear_model_column_var(price)
+      with _initialized_session() as sess:
+        sess.run(wire_cast_var.assign([[10.], [100.], [1000.], [10000.]]))
+        sess.run(bias.assign([5.]))
+        sess.run(price_var.assign([[10.]]))
+        self.assertAllClose([[1015.], [10065.]], predictions.eval())
+
+  def test_dense_and_sparse_column(self):
+    """When the column is both dense and sparse, uses sparse tensors."""
+
+    class _DenseAndSparseColumn(fc_old._DenseColumn, fc_old._CategoricalColumn):
+
+      @property
+      def name(self):
+        return 'dense_and_sparse_column'
+
+      @property
+      def _parse_example_spec(self):
+        return {self.name: parsing_ops.VarLenFeature(self.dtype)}
+
+      def _transform_feature(self, inputs):
+        return inputs.get(self.name)
+
+      @property
+      def _variable_shape(self):
+        raise ValueError('Should not use this method.')
+
+      def _get_dense_tensor(self, inputs, weight_collections=None,
+                            trainable=None):
+        raise ValueError('Should not use this method.')
+
+      @property
+      def _num_buckets(self):
+        return 4
+
+      def _get_sparse_tensors(self, inputs, weight_collections=None,
+                              trainable=None):
+        sp_tensor = sparse_tensor.SparseTensor(
+            indices=[[0, 0], [1, 0], [1, 1]],
+            values=[2, 0, 3],
+            dense_shape=[2, 2])
+        return fc_old._CategoricalColumn.IdWeightPair(sp_tensor, None)
+
+    dense_and_sparse_column = _DenseAndSparseColumn()
+    with ops.Graph().as_default():
+      sp_tensor = sparse_tensor.SparseTensor(
+          values=['omar', 'stringer', 'marlo'],
+          indices=[[0, 0], [1, 0], [1, 1]],
+          dense_shape=[2, 2])
+      features = {dense_and_sparse_column.name: sp_tensor}
+      predictions = fc.linear_model(features, [dense_and_sparse_column])
+      bias = get_linear_model_bias()
+      dense_and_sparse_column_var = get_linear_model_column_var(
+          dense_and_sparse_column)
+      with _initialized_session() as sess:
+        sess.run(dense_and_sparse_column_var.assign(
+            [[10.], [100.], [1000.], [10000.]]))
+        sess.run(bias.assign([5.]))
+        self.assertAllClose([[1005.], [10015.]], predictions.eval())
+
+  def test_dense_multi_output(self):
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default():
+      features = {'price': [[1.], [5.]]}
+      predictions = fc.linear_model(features, [price], units=3)
+      bias = get_linear_model_bias()
+      price_var = get_linear_model_column_var(price)
+      with _initialized_session() as sess:
+        self.assertAllClose(np.zeros((3,)), bias.eval())
+        self.assertAllClose(np.zeros((1, 3)), price_var.eval())
+        sess.run(price_var.assign([[10., 100., 1000.]]))
+        sess.run(bias.assign([5., 6., 7.]))
+        self.assertAllClose([[15., 106., 1007.], [55., 506., 5007.]],
+                            predictions.eval())
+
+  def test_sparse_multi_output(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default():
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar', 'stringer', 'marlo'],  # hashed to = [2, 0, 3]
+          indices=[[0, 0], [1, 0], [1, 1]],
+          dense_shape=[2, 2])
+      features = {'wire_cast': wire_tensor}
+      predictions = fc.linear_model(features, [wire_cast], units=3)
+      bias = get_linear_model_bias()
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      with _initialized_session() as sess:
+        self.assertAllClose(np.zeros((3,)), bias.eval())
+        self.assertAllClose(np.zeros((4, 3)), wire_cast_var.eval())
+        sess.run(
+            wire_cast_var.assign([[10., 11., 12.], [100., 110., 120.], [
+                1000., 1100., 1200.
+            ], [10000., 11000., 12000.]]))
+        sess.run(bias.assign([5., 6., 7.]))
+        self.assertAllClose([[1005., 1106., 1207.], [10015., 11017., 12019.]],
+                            predictions.eval())
+
+  def test_dense_multi_dimension(self):
+    price = fc_old.numeric_column('price', shape=2)
+    with ops.Graph().as_default():
+      features = {'price': [[1., 2.], [5., 6.]]}
+      predictions = fc.linear_model(features, [price])
+      price_var = get_linear_model_column_var(price)
+      with _initialized_session() as sess:
+        self.assertAllClose([[0.], [0.]], price_var.eval())
+        sess.run(price_var.assign([[10.], [100.]]))
+        self.assertAllClose([[210.], [650.]], predictions.eval())
+
+  def test_sparse_multi_rank(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default():
+      wire_tensor = array_ops.sparse_placeholder(dtypes.string)
+      wire_value = sparse_tensor.SparseTensorValue(
+          values=['omar', 'stringer', 'marlo', 'omar'],  # hashed = [2, 0, 3, 2]
+          indices=[[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 0, 1]],
+          dense_shape=[2, 2, 2])
+      features = {'wire_cast': wire_tensor}
+      predictions = fc.linear_model(features, [wire_cast])
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      with _initialized_session() as sess:
+        self.assertAllClose(np.zeros((4, 1)), wire_cast_var.eval())
+        self.assertAllClose(
+            np.zeros((2, 1)),
+            predictions.eval(feed_dict={wire_tensor: wire_value}))
+        sess.run(wire_cast_var.assign([[10.], [100.], [1000.], [10000.]]))
+        self.assertAllClose(
+            [[1010.], [11000.]],
+            predictions.eval(feed_dict={wire_tensor: wire_value}))
+
+  def test_sparse_combiner(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default():
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar', 'stringer', 'marlo'],  # hashed to = [2, 0, 3]
+          indices=[[0, 0], [1, 0], [1, 1]],
+          dense_shape=[2, 2])
+      features = {'wire_cast': wire_tensor}
+      predictions = fc.linear_model(
+          features, [wire_cast], sparse_combiner='mean')
+      bias = get_linear_model_bias()
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      with _initialized_session() as sess:
+        sess.run(wire_cast_var.assign([[10.], [100.], [1000.], [10000.]]))
+        sess.run(bias.assign([5.]))
+        self.assertAllClose([[1005.], [5010.]], predictions.eval())
+
+  def test_sparse_combiner_with_negative_weights(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    wire_cast_weights = fc_old.weighted_categorical_column(wire_cast, 'weights')
+
+    with ops.Graph().as_default():
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar', 'stringer', 'marlo'],  # hashed to = [2, 0, 3]
+          indices=[[0, 0], [1, 0], [1, 1]],
+          dense_shape=[2, 2])
+      features = {
+          'wire_cast': wire_tensor,
+          'weights': constant_op.constant([[1., 1., -1.0]])
+      }
+      predictions = fc.linear_model(
+          features, [wire_cast_weights], sparse_combiner='sum')
+      bias = get_linear_model_bias()
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      with _initialized_session() as sess:
+        sess.run(wire_cast_var.assign([[10.], [100.], [1000.], [10000.]]))
+        sess.run(bias.assign([5.]))
+        self.assertAllClose([[1005.], [-9985.]], predictions.eval())
+
+  def test_dense_multi_dimension_multi_output(self):
+    price = fc_old.numeric_column('price', shape=2)
+    with ops.Graph().as_default():
+      features = {'price': [[1., 2.], [5., 6.]]}
+      predictions = fc.linear_model(features, [price], units=3)
+      bias = get_linear_model_bias()
+      price_var = get_linear_model_column_var(price)
+      with _initialized_session() as sess:
+        self.assertAllClose(np.zeros((3,)), bias.eval())
+        self.assertAllClose(np.zeros((2, 3)), price_var.eval())
+        sess.run(price_var.assign([[1., 2., 3.], [10., 100., 1000.]]))
+        sess.run(bias.assign([2., 3., 4.]))
+        self.assertAllClose([[23., 205., 2007.], [67., 613., 6019.]],
+                            predictions.eval())
+
+  def test_raises_if_shape_mismatch(self):
+    price = fc_old.numeric_column('price', shape=2)
+    with ops.Graph().as_default():
+      features = {'price': [[1.], [5.]]}
+      with self.assertRaisesRegexp(
+          Exception,
+          r'Cannot reshape a tensor with 2 elements to shape \[2,2\]'):
+        fc.linear_model(features, [price])
+
+  def test_dense_reshaping(self):
+    price = fc_old.numeric_column('price', shape=[1, 2])
+    with ops.Graph().as_default():
+      features = {'price': [[[1., 2.]], [[5., 6.]]]}
+      predictions = fc.linear_model(features, [price])
+      bias = get_linear_model_bias()
+      price_var = get_linear_model_column_var(price)
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias.eval())
+        self.assertAllClose([[0.], [0.]], price_var.eval())
+        self.assertAllClose([[0.], [0.]], predictions.eval())
+        sess.run(price_var.assign([[10.], [100.]]))
+        self.assertAllClose([[210.], [650.]], predictions.eval())
+
+  def test_dense_multi_column(self):
+    price1 = fc_old.numeric_column('price1', shape=2)
+    price2 = fc_old.numeric_column('price2')
+    with ops.Graph().as_default():
+      features = {
+          'price1': [[1., 2.], [5., 6.]],
+          'price2': [[3.], [4.]]
+      }
+      predictions = fc.linear_model(features, [price1, price2])
+      bias = get_linear_model_bias()
+      price1_var = get_linear_model_column_var(price1)
+      price2_var = get_linear_model_column_var(price2)
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias.eval())
+        self.assertAllClose([[0.], [0.]], price1_var.eval())
+        self.assertAllClose([[0.]], price2_var.eval())
+        self.assertAllClose([[0.], [0.]], predictions.eval())
+        sess.run(price1_var.assign([[10.], [100.]]))
+        sess.run(price2_var.assign([[1000.]]))
+        sess.run(bias.assign([7.]))
+        self.assertAllClose([[3217.], [4657.]], predictions.eval())
+
+  def test_fills_cols_to_vars(self):
+    price1 = fc_old.numeric_column('price1', shape=2)
+    price2 = fc_old.numeric_column('price2')
+    with ops.Graph().as_default():
+      features = {'price1': [[1., 2.], [5., 6.]], 'price2': [[3.], [4.]]}
+      cols_to_vars = {}
+      fc.linear_model(features, [price1, price2], cols_to_vars=cols_to_vars)
+      bias = get_linear_model_bias()
+      price1_var = get_linear_model_column_var(price1)
+      price2_var = get_linear_model_column_var(price2)
+      self.assertAllEqual(cols_to_vars['bias'], [bias])
+      self.assertAllEqual(cols_to_vars[price1], [price1_var])
+      self.assertAllEqual(cols_to_vars[price2], [price2_var])
+
+  def test_fills_cols_to_vars_partitioned_variables(self):
+    price1 = fc_old.numeric_column('price1', shape=2)
+    price2 = fc_old.numeric_column('price2', shape=3)
+    with ops.Graph().as_default():
+      features = {
+          'price1': [[1., 2.], [6., 7.]],
+          'price2': [[3., 4., 5.], [8., 9., 10.]]
+      }
+      cols_to_vars = {}
+      with variable_scope.variable_scope(
+          'linear',
+          partitioner=partitioned_variables.fixed_size_partitioner(2, axis=0)):
+        fc.linear_model(features, [price1, price2], cols_to_vars=cols_to_vars)
+      with _initialized_session():
+        self.assertEqual([0.], cols_to_vars['bias'][0].eval())
+        # Partitioning shards the [2, 1] price1 var into 2 [1, 1] Variables.
+        self.assertAllEqual([[0.]], cols_to_vars[price1][0].eval())
+        self.assertAllEqual([[0.]], cols_to_vars[price1][1].eval())
+        # Partitioning shards the [3, 1] price2 var into a [2, 1] Variable and
+        # a [1, 1] Variable.
+        self.assertAllEqual([[0.], [0.]], cols_to_vars[price2][0].eval())
+        self.assertAllEqual([[0.]], cols_to_vars[price2][1].eval())
+
+  def test_dense_collection(self):
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default() as g:
+      features = {'price': [[1.], [5.]]}
+      fc.linear_model(features, [price], weight_collections=['my-vars'])
+      my_vars = g.get_collection('my-vars')
+      bias = get_linear_model_bias()
+      price_var = get_linear_model_column_var(price)
+      self.assertIn(bias, my_vars)
+      self.assertIn(price_var, my_vars)
+
+  def test_sparse_collection(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default() as g:
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar'], indices=[[0, 0]], dense_shape=[1, 1])
+      features = {'wire_cast': wire_tensor}
+      fc.linear_model(
+          features, [wire_cast], weight_collections=['my-vars'])
+      my_vars = g.get_collection('my-vars')
+      bias = get_linear_model_bias()
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      self.assertIn(bias, my_vars)
+      self.assertIn(wire_cast_var, my_vars)
+
+  def test_dense_trainable_default(self):
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default() as g:
+      features = {'price': [[1.], [5.]]}
+      fc.linear_model(features, [price])
+      bias = get_linear_model_bias()
+      price_var = get_linear_model_column_var(price)
+      trainable_vars = g.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
+      self.assertIn(bias, trainable_vars)
+      self.assertIn(price_var, trainable_vars)
+
+  def test_sparse_trainable_default(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default() as g:
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar'], indices=[[0, 0]], dense_shape=[1, 1])
+      features = {'wire_cast': wire_tensor}
+      fc.linear_model(features, [wire_cast])
+      trainable_vars = g.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
+      bias = get_linear_model_bias()
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      self.assertIn(bias, trainable_vars)
+      self.assertIn(wire_cast_var, trainable_vars)
+
+  def test_dense_trainable_false(self):
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default() as g:
+      features = {'price': [[1.], [5.]]}
+      fc.linear_model(features, [price], trainable=False)
+      trainable_vars = g.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
+      self.assertEqual([], trainable_vars)
+
+  def test_sparse_trainable_false(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default() as g:
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar'], indices=[[0, 0]], dense_shape=[1, 1])
+      features = {'wire_cast': wire_tensor}
+      fc.linear_model(features, [wire_cast], trainable=False)
+      trainable_vars = g.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
+      self.assertEqual([], trainable_vars)
+
+  def test_column_order(self):
+    price_a = fc_old.numeric_column('price_a')
+    price_b = fc_old.numeric_column('price_b')
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default() as g:
+      features = {
+          'price_a': [[1.]],
+          'price_b': [[3.]],
+          'wire_cast':
+              sparse_tensor.SparseTensor(
+                  values=['omar'], indices=[[0, 0]], dense_shape=[1, 1])
+      }
+      fc.linear_model(
+          features, [price_a, wire_cast, price_b],
+          weight_collections=['my-vars'])
+      my_vars = g.get_collection('my-vars')
+      self.assertIn('price_a', my_vars[0].name)
+      self.assertIn('price_b', my_vars[1].name)
+      self.assertIn('wire_cast', my_vars[2].name)
+
+    with ops.Graph().as_default() as g:
+      features = {
+          'price_a': [[1.]],
+          'price_b': [[3.]],
+          'wire_cast':
+              sparse_tensor.SparseTensor(
+                  values=['omar'], indices=[[0, 0]], dense_shape=[1, 1])
+      }
+      fc.linear_model(
+          features, [wire_cast, price_b, price_a],
+          weight_collections=['my-vars'])
+      my_vars = g.get_collection('my-vars')
+      self.assertIn('price_a', my_vars[0].name)
+      self.assertIn('price_b', my_vars[1].name)
+      self.assertIn('wire_cast', my_vars[2].name)
+
+  def test_static_batch_size_mismatch(self):
+    price1 = fc_old.numeric_column('price1')
+    price2 = fc_old.numeric_column('price2')
+    with ops.Graph().as_default():
+      features = {
+          'price1': [[1.], [5.], [7.]],  # batchsize = 3
+          'price2': [[3.], [4.]]  # batchsize = 2
+      }
+    with self.assertRaisesRegexp(
+        ValueError,
+        'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+      fc.linear_model(features, [price1, price2])
+
+  def test_subset_of_static_batch_size_mismatch(self):
+    price1 = fc_old.numeric_column('price1')
+    price2 = fc_old.numeric_column('price2')
+    price3 = fc_old.numeric_column('price3')
+    with ops.Graph().as_default():
+      features = {
+          'price1': array_ops.placeholder(dtype=dtypes.int64),  # batchsize = 3
+          'price2': [[3.], [4.]],  # batchsize = 2
+          'price3': [[3.], [4.], [5.]]  # batchsize = 3
+      }
+      with self.assertRaisesRegexp(
+          ValueError,
+          'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+        fc.linear_model(features, [price1, price2, price3])
+
+  def test_runtime_batch_size_mismatch(self):
+    price1 = fc_old.numeric_column('price1')
+    price2 = fc_old.numeric_column('price2')
+    with ops.Graph().as_default():
+      features = {
+          'price1': array_ops.placeholder(dtype=dtypes.int64),  # batchsize = 3
+          'price2': [[3.], [4.]]  # batchsize = 2
+      }
+      predictions = fc.linear_model(features, [price1, price2])
+      with _initialized_session() as sess:
+        with self.assertRaisesRegexp(errors.OpError,
+                                     'must have the same size and shape'):
+          sess.run(
+              predictions, feed_dict={features['price1']: [[1.], [5.], [7.]]})
+
+  def test_runtime_batch_size_matches(self):
+    price1 = fc_old.numeric_column('price1')
+    price2 = fc_old.numeric_column('price2')
+    with ops.Graph().as_default():
+      features = {
+          'price1': array_ops.placeholder(dtype=dtypes.int64),  # batchsize = 2
+          'price2': array_ops.placeholder(dtype=dtypes.int64),  # batchsize = 2
+      }
+      predictions = fc.linear_model(features, [price1, price2])
+      with _initialized_session() as sess:
+        sess.run(
+            predictions,
+            feed_dict={
+                features['price1']: [[1.], [5.]],
+                features['price2']: [[1.], [5.]],
+            })
+
+  def test_with_numpy_input_fn(self):
+    price = fc_old.numeric_column('price')
+    price_buckets = fc_old.bucketized_column(
+        price, boundaries=[
+            0.,
+            10.,
+            100.,
+        ])
+    body_style = fc_old.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+
+    input_fn = numpy_io.numpy_input_fn(
+        x={
+            'price': np.array([-1., 2., 13., 104.]),
+            'body-style': np.array(['sedan', 'hardtop', 'wagon', 'sedan']),
+        },
+        batch_size=2,
+        shuffle=False)
+    features = input_fn()
+    net = fc.linear_model(features, [price_buckets, body_style])
+    # self.assertEqual(1 + 3 + 5, net.shape[1])
+    with _initialized_session() as sess:
+      coord = coordinator.Coordinator()
+      threads = queue_runner_impl.start_queue_runners(sess, coord=coord)
+
+      bias = get_linear_model_bias()
+      price_buckets_var = get_linear_model_column_var(price_buckets)
+      body_style_var = get_linear_model_column_var(body_style)
+
+      sess.run(price_buckets_var.assign([[10.], [100.], [1000.], [10000.]]))
+      sess.run(body_style_var.assign([[-10.], [-100.], [-1000.]]))
+      sess.run(bias.assign([5.]))
+
+      self.assertAllClose([[10 - 1000 + 5.], [100 - 10 + 5.]], sess.run(net))
+
+      coord.request_stop()
+      coord.join(threads)
+
+  def test_with_1d_sparse_tensor(self):
+    price = fc_old.numeric_column('price')
+    price_buckets = fc_old.bucketized_column(
+        price, boundaries=[
+            0.,
+            10.,
+            100.,
+        ])
+    body_style = fc_old.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+
+    # Provides 1-dim tensor and dense tensor.
+    features = {
+        'price': constant_op.constant([-1., 12.,]),
+        'body-style': sparse_tensor.SparseTensor(
+            indices=((0,), (1,)),
+            values=('sedan', 'hardtop'),
+            dense_shape=(2,)),
+    }
+    self.assertEqual(1, features['price'].shape.ndims)
+    self.assertEqual(1, features['body-style'].dense_shape.get_shape()[0])
+
+    net = fc.linear_model(features, [price_buckets, body_style])
+    with _initialized_session() as sess:
+      bias = get_linear_model_bias()
+      price_buckets_var = get_linear_model_column_var(price_buckets)
+      body_style_var = get_linear_model_column_var(body_style)
+
+      sess.run(price_buckets_var.assign([[10.], [100.], [1000.], [10000.]]))
+      sess.run(body_style_var.assign([[-10.], [-100.], [-1000.]]))
+      sess.run(bias.assign([5.]))
+
+      self.assertAllClose([[10 - 1000 + 5.], [1000 - 10 + 5.]], sess.run(net))
+
+  def test_with_1d_unknown_shape_sparse_tensor(self):
+    price = fc_old.numeric_column('price')
+    price_buckets = fc_old.bucketized_column(
+        price, boundaries=[
+            0.,
+            10.,
+            100.,
+        ])
+    body_style = fc_old.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+    country = fc_old.categorical_column_with_vocabulary_list(
+        'country', vocabulary_list=['US', 'JP', 'CA'])
+
+    # Provides 1-dim tensor and dense tensor.
+    features = {
+        'price': array_ops.placeholder(dtypes.float32),
+        'body-style': array_ops.sparse_placeholder(dtypes.string),
+        'country': array_ops.placeholder(dtypes.string),
+    }
+    self.assertIsNone(features['price'].shape.ndims)
+    self.assertIsNone(features['body-style'].get_shape().ndims)
+
+    price_data = np.array([-1., 12.])
+    body_style_data = sparse_tensor.SparseTensorValue(
+        indices=((0,), (1,)),
+        values=('sedan', 'hardtop'),
+        dense_shape=(2,))
+    country_data = np.array(['US', 'CA'])
+
+    net = fc.linear_model(features, [price_buckets, body_style, country])
+    bias = get_linear_model_bias()
+    price_buckets_var = get_linear_model_column_var(price_buckets)
+    body_style_var = get_linear_model_column_var(body_style)
+    with _initialized_session() as sess:
+      sess.run(price_buckets_var.assign([[10.], [100.], [1000.], [10000.]]))
+      sess.run(body_style_var.assign([[-10.], [-100.], [-1000.]]))
+      sess.run(bias.assign([5.]))
+
+      self.assertAllClose([[10 - 1000 + 5.], [1000 - 10 + 5.]],
+                          sess.run(
+                              net,
+                              feed_dict={
+                                  features['price']: price_data,
+                                  features['body-style']: body_style_data,
+                                  features['country']: country_data
+                              }))
+
+  def test_with_rank_0_feature(self):
+    price = fc_old.numeric_column('price')
+    features = {
+        'price': constant_op.constant(0),
+    }
+    self.assertEqual(0, features['price'].shape.ndims)
+
+    # Static rank 0 should fail
+    with self.assertRaisesRegexp(ValueError, 'Feature .* cannot have rank 0'):
+      fc.linear_model(features, [price])
+
+    # Dynamic rank 0 should fail
+    features = {
+        'price': array_ops.placeholder(dtypes.float32),
+    }
+    net = fc.linear_model(features, [price])
+    self.assertEqual(1, net.shape[1])
+    with _initialized_session() as sess:
+      with self.assertRaisesOpError('Feature .* cannot have rank 0'):
+        sess.run(net, feed_dict={features['price']: np.array(1)})
+
+  def test_multiple_linear_models(self):
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default():
+      features1 = {'price': [[1.], [5.]]}
+      features2 = {'price': [[2.], [10.]]}
+      predictions1 = fc.linear_model(features1, [price])
+      predictions2 = fc.linear_model(features2, [price])
+      bias1 = get_linear_model_bias(name='linear_model')
+      bias2 = get_linear_model_bias(name='linear_model_1')
+      price_var1 = get_linear_model_column_var(price, name='linear_model')
+      price_var2 = get_linear_model_column_var(price, name='linear_model_1')
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias1.eval())
+        sess.run(price_var1.assign([[10.]]))
+        sess.run(bias1.assign([5.]))
+        self.assertAllClose([[15.], [55.]], predictions1.eval())
+        self.assertAllClose([0.], bias2.eval())
+        sess.run(price_var2.assign([[10.]]))
+        sess.run(bias2.assign([5.]))
+        self.assertAllClose([[25.], [105.]], predictions2.eval())
+
+
+class _LinearModelTest(test.TestCase):
+
+  def test_raises_if_empty_feature_columns(self):
+    with self.assertRaisesRegexp(ValueError,
+                                 'feature_columns must not be empty'):
+      get_keras_linear_model_predictions(features={}, feature_columns=[])
+
+  def test_should_be_feature_column(self):
+    with self.assertRaisesRegexp(ValueError, 'must be a _FeatureColumn'):
+      get_keras_linear_model_predictions(
+          features={'a': [[0]]}, feature_columns='NotSupported')
+
+  def test_should_be_dense_or_categorical_column(self):
+
+    class NotSupportedColumn(fc_old._FeatureColumn):
+
+      @property
+      def name(self):
+        return 'NotSupportedColumn'
+
+      def _transform_feature(self, cache):
+        pass
+
+      @property
+      def _parse_example_spec(self):
+        pass
+
+    with self.assertRaisesRegexp(
+        ValueError, 'must be either a _DenseColumn or _CategoricalColumn'):
+      get_keras_linear_model_predictions(
+          features={'a': [[0]]}, feature_columns=[NotSupportedColumn()])
+
+  def test_does_not_support_dict_columns(self):
+    with self.assertRaisesRegexp(
+        ValueError, 'Expected feature_columns to be iterable, found dict.'):
+      fc.linear_model(
+          features={'a': [[0]]},
+          feature_columns={'a': fc_old.numeric_column('a')})
+
+  def test_raises_if_duplicate_name(self):
+    with self.assertRaisesRegexp(
+        ValueError, 'Duplicate feature column name found for columns'):
+      get_keras_linear_model_predictions(
+          features={'a': [[0]]},
+          feature_columns=[
+              fc_old.numeric_column('a'),
+              fc_old.numeric_column('a')
+          ])
+
+  def test_dense_bias(self):
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default():
+      features = {'price': [[1.], [5.]]}
+      predictions = get_keras_linear_model_predictions(features, [price])
+      bias = get_linear_model_bias()
+      price_var = get_linear_model_column_var(price)
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias.eval())
+        sess.run(price_var.assign([[10.]]))
+        sess.run(bias.assign([5.]))
+        self.assertAllClose([[15.], [55.]], predictions.eval())
+
+  def test_sparse_bias(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default():
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar', 'stringer', 'marlo'],  # hashed to = [2, 0, 3]
+          indices=[[0, 0], [1, 0], [1, 1]],
+          dense_shape=[2, 2])
+      features = {'wire_cast': wire_tensor}
+      predictions = get_keras_linear_model_predictions(features, [wire_cast])
+      bias = get_linear_model_bias()
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias.eval())
+        self.assertAllClose([[0.], [0.], [0.], [0.]], wire_cast_var.eval())
+        sess.run(wire_cast_var.assign([[10.], [100.], [1000.], [10000.]]))
+        sess.run(bias.assign([5.]))
+        self.assertAllClose([[1005.], [10015.]], predictions.eval())
+
+  def test_dense_and_sparse_bias(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default():
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar', 'stringer', 'marlo'],  # hashed to = [2, 0, 3]
+          indices=[[0, 0], [1, 0], [1, 1]],
+          dense_shape=[2, 2])
+      features = {'wire_cast': wire_tensor, 'price': [[1.], [5.]]}
+      predictions = get_keras_linear_model_predictions(features,
+                                                       [wire_cast, price])
+      bias = get_linear_model_bias()
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      price_var = get_linear_model_column_var(price)
+      with _initialized_session() as sess:
+        sess.run(wire_cast_var.assign([[10.], [100.], [1000.], [10000.]]))
+        sess.run(bias.assign([5.]))
+        sess.run(price_var.assign([[10.]]))
+        self.assertAllClose([[1015.], [10065.]], predictions.eval())
+
+  def test_dense_and_sparse_column(self):
+    """When the column is both dense and sparse, uses sparse tensors."""
+
+    class _DenseAndSparseColumn(fc_old._DenseColumn, fc_old._CategoricalColumn):
+
+      @property
+      def name(self):
+        return 'dense_and_sparse_column'
+
+      @property
+      def _parse_example_spec(self):
+        return {self.name: parsing_ops.VarLenFeature(self.dtype)}
+
+      def _transform_feature(self, inputs):
+        return inputs.get(self.name)
+
+      @property
+      def _variable_shape(self):
+        raise ValueError('Should not use this method.')
+
+      def _get_dense_tensor(self,
+                            inputs,
+                            weight_collections=None,
+                            trainable=None):
+        raise ValueError('Should not use this method.')
+
+      @property
+      def _num_buckets(self):
+        return 4
+
+      def _get_sparse_tensors(self,
+                              inputs,
+                              weight_collections=None,
+                              trainable=None):
+        sp_tensor = sparse_tensor.SparseTensor(
+            indices=[[0, 0], [1, 0], [1, 1]],
+            values=[2, 0, 3],
+            dense_shape=[2, 2])
+        return fc_old._CategoricalColumn.IdWeightPair(sp_tensor, None)
+
+    dense_and_sparse_column = _DenseAndSparseColumn()
+    with ops.Graph().as_default():
+      sp_tensor = sparse_tensor.SparseTensor(
+          values=['omar', 'stringer', 'marlo'],
+          indices=[[0, 0], [1, 0], [1, 1]],
+          dense_shape=[2, 2])
+      features = {dense_and_sparse_column.name: sp_tensor}
+      predictions = get_keras_linear_model_predictions(
+          features, [dense_and_sparse_column])
+      bias = get_linear_model_bias()
+      dense_and_sparse_column_var = get_linear_model_column_var(
+          dense_and_sparse_column)
+      with _initialized_session() as sess:
+        sess.run(
+            dense_and_sparse_column_var.assign([[10.], [100.], [1000.],
+                                                [10000.]]))
+        sess.run(bias.assign([5.]))
+        self.assertAllClose([[1005.], [10015.]], predictions.eval())
+
+  def test_dense_multi_output(self):
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default():
+      features = {'price': [[1.], [5.]]}
+      predictions = get_keras_linear_model_predictions(
+          features, [price], units=3)
+      bias = get_linear_model_bias()
+      price_var = get_linear_model_column_var(price)
+      with _initialized_session() as sess:
+        self.assertAllClose(np.zeros((3,)), bias.eval())
+        self.assertAllClose(np.zeros((1, 3)), price_var.eval())
+        sess.run(price_var.assign([[10., 100., 1000.]]))
+        sess.run(bias.assign([5., 6., 7.]))
+        self.assertAllClose([[15., 106., 1007.], [55., 506., 5007.]],
+                            predictions.eval())
+
+  def test_sparse_multi_output(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default():
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar', 'stringer', 'marlo'],  # hashed to = [2, 0, 3]
+          indices=[[0, 0], [1, 0], [1, 1]],
+          dense_shape=[2, 2])
+      features = {'wire_cast': wire_tensor}
+      predictions = get_keras_linear_model_predictions(
+          features, [wire_cast], units=3)
+      bias = get_linear_model_bias()
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      with _initialized_session() as sess:
+        self.assertAllClose(np.zeros((3,)), bias.eval())
+        self.assertAllClose(np.zeros((4, 3)), wire_cast_var.eval())
+        sess.run(
+            wire_cast_var.assign([[10., 11., 12.], [100., 110., 120.],
+                                  [1000., 1100.,
+                                   1200.], [10000., 11000., 12000.]]))
+        sess.run(bias.assign([5., 6., 7.]))
+        self.assertAllClose([[1005., 1106., 1207.], [10015., 11017., 12019.]],
+                            predictions.eval())
+
+  def test_dense_multi_dimension(self):
+    price = fc_old.numeric_column('price', shape=2)
+    with ops.Graph().as_default():
+      features = {'price': [[1., 2.], [5., 6.]]}
+      predictions = get_keras_linear_model_predictions(features, [price])
+      price_var = get_linear_model_column_var(price)
+      with _initialized_session() as sess:
+        self.assertAllClose([[0.], [0.]], price_var.eval())
+        sess.run(price_var.assign([[10.], [100.]]))
+        self.assertAllClose([[210.], [650.]], predictions.eval())
+
+  def test_sparse_multi_rank(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default():
+      wire_tensor = array_ops.sparse_placeholder(dtypes.string)
+      wire_value = sparse_tensor.SparseTensorValue(
+          values=['omar', 'stringer', 'marlo', 'omar'],  # hashed = [2, 0, 3, 2]
+          indices=[[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 0, 1]],
+          dense_shape=[2, 2, 2])
+      features = {'wire_cast': wire_tensor}
+      predictions = get_keras_linear_model_predictions(features, [wire_cast])
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      with _initialized_session() as sess:
+        self.assertAllClose(np.zeros((4, 1)), wire_cast_var.eval())
+        self.assertAllClose(
+            np.zeros((2, 1)),
+            predictions.eval(feed_dict={wire_tensor: wire_value}))
+        sess.run(wire_cast_var.assign([[10.], [100.], [1000.], [10000.]]))
+        self.assertAllClose(
+            [[1010.], [11000.]],
+            predictions.eval(feed_dict={wire_tensor: wire_value}))
+
+  def test_sparse_combiner(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default():
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar', 'stringer', 'marlo'],  # hashed to = [2, 0, 3]
+          indices=[[0, 0], [1, 0], [1, 1]],
+          dense_shape=[2, 2])
+      features = {'wire_cast': wire_tensor}
+      predictions = get_keras_linear_model_predictions(
+          features, [wire_cast], sparse_combiner='mean')
+      bias = get_linear_model_bias()
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      with _initialized_session() as sess:
+        sess.run(wire_cast_var.assign([[10.], [100.], [1000.], [10000.]]))
+        sess.run(bias.assign([5.]))
+        self.assertAllClose([[1005.], [5010.]], predictions.eval())
+
+  def test_dense_multi_dimension_multi_output(self):
+    price = fc_old.numeric_column('price', shape=2)
+    with ops.Graph().as_default():
+      features = {'price': [[1., 2.], [5., 6.]]}
+      predictions = get_keras_linear_model_predictions(
+          features, [price], units=3)
+      bias = get_linear_model_bias()
+      price_var = get_linear_model_column_var(price)
+      with _initialized_session() as sess:
+        self.assertAllClose(np.zeros((3,)), bias.eval())
+        self.assertAllClose(np.zeros((2, 3)), price_var.eval())
+        sess.run(price_var.assign([[1., 2., 3.], [10., 100., 1000.]]))
+        sess.run(bias.assign([2., 3., 4.]))
+        self.assertAllClose([[23., 205., 2007.], [67., 613., 6019.]],
+                            predictions.eval())
+
+  def test_raises_if_shape_mismatch(self):
+    price = fc_old.numeric_column('price', shape=2)
+    with ops.Graph().as_default():
+      features = {'price': [[1.], [5.]]}
+      with self.assertRaisesRegexp(
+          Exception,
+          r'Cannot reshape a tensor with 2 elements to shape \[2,2\]'):
+        get_keras_linear_model_predictions(features, [price])
+
+  def test_dense_reshaping(self):
+    price = fc_old.numeric_column('price', shape=[1, 2])
+    with ops.Graph().as_default():
+      features = {'price': [[[1., 2.]], [[5., 6.]]]}
+      predictions = get_keras_linear_model_predictions(features, [price])
+      bias = get_linear_model_bias()
+      price_var = get_linear_model_column_var(price)
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias.eval())
+        self.assertAllClose([[0.], [0.]], price_var.eval())
+        self.assertAllClose([[0.], [0.]], predictions.eval())
+        sess.run(price_var.assign([[10.], [100.]]))
+        self.assertAllClose([[210.], [650.]], predictions.eval())
+
+  def test_dense_multi_column(self):
+    price1 = fc_old.numeric_column('price1', shape=2)
+    price2 = fc_old.numeric_column('price2')
+    with ops.Graph().as_default():
+      features = {'price1': [[1., 2.], [5., 6.]], 'price2': [[3.], [4.]]}
+      predictions = get_keras_linear_model_predictions(features,
+                                                       [price1, price2])
+      bias = get_linear_model_bias()
+      price1_var = get_linear_model_column_var(price1)
+      price2_var = get_linear_model_column_var(price2)
+      with _initialized_session() as sess:
+        self.assertAllClose([0.], bias.eval())
+        self.assertAllClose([[0.], [0.]], price1_var.eval())
+        self.assertAllClose([[0.]], price2_var.eval())
+        self.assertAllClose([[0.], [0.]], predictions.eval())
+        sess.run(price1_var.assign([[10.], [100.]]))
+        sess.run(price2_var.assign([[1000.]]))
+        sess.run(bias.assign([7.]))
+        self.assertAllClose([[3217.], [4657.]], predictions.eval())
+
+  def test_fills_cols_to_vars(self):
+    price1 = fc_old.numeric_column('price1', shape=2)
+    price2 = fc_old.numeric_column('price2')
+    with ops.Graph().as_default():
+      features = {'price1': [[1., 2.], [5., 6.]], 'price2': [[3.], [4.]]}
+      cols_to_vars = {}
+      get_keras_linear_model_predictions(
+          features, [price1, price2], cols_to_vars=cols_to_vars)
+      bias = get_linear_model_bias()
+      price1_var = get_linear_model_column_var(price1)
+      price2_var = get_linear_model_column_var(price2)
+      self.assertAllEqual(cols_to_vars['bias'], [bias])
+      self.assertAllEqual(cols_to_vars[price1], [price1_var])
+      self.assertAllEqual(cols_to_vars[price2], [price2_var])
+
+  def test_fills_cols_to_vars_partitioned_variables(self):
+    price1 = fc_old.numeric_column('price1', shape=2)
+    price2 = fc_old.numeric_column('price2', shape=3)
+    with ops.Graph().as_default():
+      features = {
+          'price1': [[1., 2.], [6., 7.]],
+          'price2': [[3., 4., 5.], [8., 9., 10.]]
+      }
+      cols_to_vars = {}
+      with variable_scope.variable_scope(
+          'linear',
+          partitioner=partitioned_variables.fixed_size_partitioner(2, axis=0)):
+        get_keras_linear_model_predictions(
+            features, [price1, price2], cols_to_vars=cols_to_vars)
+      with _initialized_session():
+        self.assertEqual([0.], cols_to_vars['bias'][0].eval())
+        # Partitioning shards the [2, 1] price1 var into 2 [1, 1] Variables.
+        self.assertAllEqual([[0.]], cols_to_vars[price1][0].eval())
+        self.assertAllEqual([[0.]], cols_to_vars[price1][1].eval())
+        # Partitioning shards the [3, 1] price2 var into a [2, 1] Variable and
+        # a [1, 1] Variable.
+        self.assertAllEqual([[0.], [0.]], cols_to_vars[price2][0].eval())
+        self.assertAllEqual([[0.]], cols_to_vars[price2][1].eval())
+
+  def test_dense_collection(self):
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default() as g:
+      features = {'price': [[1.], [5.]]}
+      get_keras_linear_model_predictions(
+          features, [price], weight_collections=['my-vars'])
+      my_vars = g.get_collection('my-vars')
+      bias = get_linear_model_bias()
+      price_var = get_linear_model_column_var(price)
+      self.assertIn(bias, my_vars)
+      self.assertIn(price_var, my_vars)
+
+  def test_sparse_collection(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default() as g:
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar'], indices=[[0, 0]], dense_shape=[1, 1])
+      features = {'wire_cast': wire_tensor}
+      get_keras_linear_model_predictions(
+          features, [wire_cast], weight_collections=['my-vars'])
+      my_vars = g.get_collection('my-vars')
+      bias = get_linear_model_bias()
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      self.assertIn(bias, my_vars)
+      self.assertIn(wire_cast_var, my_vars)
+
+  def test_dense_trainable_default(self):
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default() as g:
+      features = {'price': [[1.], [5.]]}
+      get_keras_linear_model_predictions(features, [price])
+      bias = get_linear_model_bias()
+      price_var = get_linear_model_column_var(price)
+      trainable_vars = g.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
+      self.assertIn(bias, trainable_vars)
+      self.assertIn(price_var, trainable_vars)
+
+  def test_sparse_trainable_default(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default() as g:
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar'], indices=[[0, 0]], dense_shape=[1, 1])
+      features = {'wire_cast': wire_tensor}
+      get_keras_linear_model_predictions(features, [wire_cast])
+      trainable_vars = g.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
+      bias = get_linear_model_bias()
+      wire_cast_var = get_linear_model_column_var(wire_cast)
+      self.assertIn(bias, trainable_vars)
+      self.assertIn(wire_cast_var, trainable_vars)
+
+  def test_dense_trainable_false(self):
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default() as g:
+      features = {'price': [[1.], [5.]]}
+      get_keras_linear_model_predictions(features, [price], trainable=False)
+      trainable_vars = g.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
+      self.assertEqual([], trainable_vars)
+
+  def test_sparse_trainable_false(self):
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default() as g:
+      wire_tensor = sparse_tensor.SparseTensor(
+          values=['omar'], indices=[[0, 0]], dense_shape=[1, 1])
+      features = {'wire_cast': wire_tensor}
+      get_keras_linear_model_predictions(features, [wire_cast], trainable=False)
+      trainable_vars = g.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
+      self.assertEqual([], trainable_vars)
+
+  def test_column_order(self):
+    price_a = fc_old.numeric_column('price_a')
+    price_b = fc_old.numeric_column('price_b')
+    wire_cast = fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+    with ops.Graph().as_default() as g:
+      features = {
+          'price_a': [[1.]],
+          'price_b': [[3.]],
+          'wire_cast':
+              sparse_tensor.SparseTensor(
+                  values=['omar'], indices=[[0, 0]], dense_shape=[1, 1])
+      }
+      get_keras_linear_model_predictions(
+          features, [price_a, wire_cast, price_b],
+          weight_collections=['my-vars'])
+      my_vars = g.get_collection('my-vars')
+      self.assertIn('price_a', my_vars[0].name)
+      self.assertIn('price_b', my_vars[1].name)
+      self.assertIn('wire_cast', my_vars[2].name)
+
+    with ops.Graph().as_default() as g:
+      features = {
+          'price_a': [[1.]],
+          'price_b': [[3.]],
+          'wire_cast':
+              sparse_tensor.SparseTensor(
+                  values=['omar'], indices=[[0, 0]], dense_shape=[1, 1])
+      }
+      get_keras_linear_model_predictions(
+          features, [wire_cast, price_b, price_a],
+          weight_collections=['my-vars'])
+      my_vars = g.get_collection('my-vars')
+      self.assertIn('price_a', my_vars[0].name)
+      self.assertIn('price_b', my_vars[1].name)
+      self.assertIn('wire_cast', my_vars[2].name)
+
+  def test_static_batch_size_mismatch(self):
+    price1 = fc_old.numeric_column('price1')
+    price2 = fc_old.numeric_column('price2')
+    with ops.Graph().as_default():
+      features = {
+          'price1': [[1.], [5.], [7.]],  # batchsize = 3
+          'price2': [[3.], [4.]]  # batchsize = 2
+      }
+    with self.assertRaisesRegexp(
+        ValueError,
+        'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+      get_keras_linear_model_predictions(features, [price1, price2])
+
+  def test_subset_of_static_batch_size_mismatch(self):
+    price1 = fc_old.numeric_column('price1')
+    price2 = fc_old.numeric_column('price2')
+    price3 = fc_old.numeric_column('price3')
+    with ops.Graph().as_default():
+      features = {
+          'price1': array_ops.placeholder(dtype=dtypes.int64),  # batchsize = 3
+          'price2': [[3.], [4.]],  # batchsize = 2
+          'price3': [[3.], [4.], [5.]]  # batchsize = 3
+      }
+      with self.assertRaisesRegexp(
+          ValueError,
+          'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+        get_keras_linear_model_predictions(features, [price1, price2, price3])
+
+  def test_runtime_batch_size_mismatch(self):
+    price1 = fc_old.numeric_column('price1')
+    price2 = fc_old.numeric_column('price2')
+    with ops.Graph().as_default():
+      features = {
+          'price1': array_ops.placeholder(dtype=dtypes.int64),  # batchsize = 3
+          'price2': [[3.], [4.]]  # batchsize = 2
+      }
+      predictions = get_keras_linear_model_predictions(features,
+                                                       [price1, price2])
+      with _initialized_session() as sess:
+        with self.assertRaisesRegexp(errors.OpError,
+                                     'must have the same size and shape'):
+          sess.run(
+              predictions, feed_dict={features['price1']: [[1.], [5.], [7.]]})
+
+  def test_runtime_batch_size_matches(self):
+    price1 = fc_old.numeric_column('price1')
+    price2 = fc_old.numeric_column('price2')
+    with ops.Graph().as_default():
+      features = {
+          'price1': array_ops.placeholder(dtype=dtypes.int64),  # batchsize = 2
+          'price2': array_ops.placeholder(dtype=dtypes.int64),  # batchsize = 2
+      }
+      predictions = get_keras_linear_model_predictions(features,
+                                                       [price1, price2])
+      with _initialized_session() as sess:
+        sess.run(
+            predictions,
+            feed_dict={
+                features['price1']: [[1.], [5.]],
+                features['price2']: [[1.], [5.]],
+            })
+
+  def test_with_numpy_input_fn(self):
+    price = fc_old.numeric_column('price')
+    price_buckets = fc_old.bucketized_column(
+        price, boundaries=[
+            0.,
+            10.,
+            100.,
+        ])
+    body_style = fc_old.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+
+    input_fn = numpy_io.numpy_input_fn(
+        x={
+            'price': np.array([-1., 2., 13., 104.]),
+            'body-style': np.array(['sedan', 'hardtop', 'wagon', 'sedan']),
+        },
+        batch_size=2,
+        shuffle=False)
+    features = input_fn()
+    net = get_keras_linear_model_predictions(features,
+                                             [price_buckets, body_style])
+    # self.assertEqual(1 + 3 + 5, net.shape[1])
+    with _initialized_session() as sess:
+      coord = coordinator.Coordinator()
+      threads = queue_runner_impl.start_queue_runners(sess, coord=coord)
+
+      bias = get_linear_model_bias()
+      price_buckets_var = get_linear_model_column_var(price_buckets)
+      body_style_var = get_linear_model_column_var(body_style)
+
+      sess.run(price_buckets_var.assign([[10.], [100.], [1000.], [10000.]]))
+      sess.run(body_style_var.assign([[-10.], [-100.], [-1000.]]))
+      sess.run(bias.assign([5.]))
+
+      self.assertAllClose([[10 - 1000 + 5.], [100 - 10 + 5.]], sess.run(net))
+
+      coord.request_stop()
+      coord.join(threads)
+
+  def test_with_1d_sparse_tensor(self):
+    price = fc_old.numeric_column('price')
+    price_buckets = fc_old.bucketized_column(
+        price, boundaries=[
+            0.,
+            10.,
+            100.,
+        ])
+    body_style = fc_old.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+
+    # Provides 1-dim tensor and dense tensor.
+    features = {
+        'price':
+            constant_op.constant([
+                -1.,
+                12.,
+            ]),
+        'body-style':
+            sparse_tensor.SparseTensor(
+                indices=((0,), (1,)),
+                values=('sedan', 'hardtop'),
+                dense_shape=(2,)),
+    }
+    self.assertEqual(1, features['price'].shape.ndims)
+    self.assertEqual(1, features['body-style'].dense_shape.get_shape()[0])
+
+    net = get_keras_linear_model_predictions(features,
+                                             [price_buckets, body_style])
+    with _initialized_session() as sess:
+      bias = get_linear_model_bias()
+      price_buckets_var = get_linear_model_column_var(price_buckets)
+      body_style_var = get_linear_model_column_var(body_style)
+
+      sess.run(price_buckets_var.assign([[10.], [100.], [1000.], [10000.]]))
+      sess.run(body_style_var.assign([[-10.], [-100.], [-1000.]]))
+      sess.run(bias.assign([5.]))
+
+      self.assertAllClose([[10 - 1000 + 5.], [1000 - 10 + 5.]], sess.run(net))
+
+  def test_with_1d_unknown_shape_sparse_tensor(self):
+    price = fc_old.numeric_column('price')
+    price_buckets = fc_old.bucketized_column(
+        price, boundaries=[
+            0.,
+            10.,
+            100.,
+        ])
+    body_style = fc_old.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+    country = fc_old.categorical_column_with_vocabulary_list(
+        'country', vocabulary_list=['US', 'JP', 'CA'])
+
+    # Provides 1-dim tensor and dense tensor.
+    features = {
+        'price': array_ops.placeholder(dtypes.float32),
+        'body-style': array_ops.sparse_placeholder(dtypes.string),
+        'country': array_ops.placeholder(dtypes.string),
+    }
+    self.assertIsNone(features['price'].shape.ndims)
+    self.assertIsNone(features['body-style'].get_shape().ndims)
+
+    price_data = np.array([-1., 12.])
+    body_style_data = sparse_tensor.SparseTensorValue(
+        indices=((0,), (1,)), values=('sedan', 'hardtop'), dense_shape=(2,))
+    country_data = np.array(['US', 'CA'])
+
+    net = get_keras_linear_model_predictions(
+        features, [price_buckets, body_style, country])
+    bias = get_linear_model_bias()
+    price_buckets_var = get_linear_model_column_var(price_buckets)
+    body_style_var = get_linear_model_column_var(body_style)
+    with _initialized_session() as sess:
+      sess.run(price_buckets_var.assign([[10.], [100.], [1000.], [10000.]]))
+      sess.run(body_style_var.assign([[-10.], [-100.], [-1000.]]))
+      sess.run(bias.assign([5.]))
+
+      self.assertAllClose([[10 - 1000 + 5.], [1000 - 10 + 5.]],
+                          sess.run(
+                              net,
+                              feed_dict={
+                                  features['price']: price_data,
+                                  features['body-style']: body_style_data,
+                                  features['country']: country_data
+                              }))
+
+  def test_with_rank_0_feature(self):
+    price = fc_old.numeric_column('price')
+    features = {
+        'price': constant_op.constant(0),
+    }
+    self.assertEqual(0, features['price'].shape.ndims)
+
+    # Static rank 0 should fail
+    with self.assertRaisesRegexp(ValueError, 'Feature .* cannot have rank 0'):
+      get_keras_linear_model_predictions(features, [price])
+
+    # Dynamic rank 0 should fail
+    features = {
+        'price': array_ops.placeholder(dtypes.float32),
+    }
+    net = get_keras_linear_model_predictions(features, [price])
+    self.assertEqual(1, net.shape[1])
+    with _initialized_session() as sess:
+      with self.assertRaisesOpError('Feature .* cannot have rank 0'):
+        sess.run(net, feed_dict={features['price']: np.array(1)})
+
+
+class InputLayerTest(test.TestCase):
+
+  @test_util.run_in_graph_and_eager_modes()
+  def test_retrieving_input(self):
+    features = {'a': [0.]}
+    input_layer = InputLayer(fc_old.numeric_column('a'))
+    inputs = self.evaluate(input_layer(features))
+    self.assertAllClose([[0.]], inputs)
+
+  def test_reuses_variables(self):
+    with context.eager_mode():
+      sparse_input = sparse_tensor.SparseTensor(
+          indices=((0, 0), (1, 0), (2, 0)),
+          values=(0, 1, 2),
+          dense_shape=(3, 3))
+
+      # Create feature columns (categorical and embedding).
+      categorical_column = fc_old.categorical_column_with_identity(
+          key='a', num_buckets=3)
+      embedding_dimension = 2
+      def _embedding_column_initializer(shape, dtype, partition_info):
+        del shape  # unused
+        del dtype  # unused
+        del partition_info  # unused
+        embedding_values = (
+            (1, 0),  # id 0
+            (0, 1),  # id 1
+            (1, 1))  # id 2
+        return embedding_values
+
+      embedding_column = fc_old.embedding_column(
+          categorical_column,
+          dimension=embedding_dimension,
+          initializer=_embedding_column_initializer)
+
+      input_layer = InputLayer([embedding_column])
+      features = {'a': sparse_input}
+
+      inputs = input_layer(features)
+      variables = input_layer.variables
+
+      # Sanity check: test that the inputs are correct.
+      self.assertAllEqual([[1, 0], [0, 1], [1, 1]], inputs)
+
+      # Check that only one variable was created.
+      self.assertEqual(1, len(variables))
+
+      # Check that invoking input_layer on the same features does not create
+      # additional variables
+      _ = input_layer(features)
+      self.assertEqual(1, len(variables))
+      self.assertEqual(variables[0], input_layer.variables[0])
+
+  def test_feature_column_input_layer_gradient(self):
+    with context.eager_mode():
+      sparse_input = sparse_tensor.SparseTensor(
+          indices=((0, 0), (1, 0), (2, 0)),
+          values=(0, 1, 2),
+          dense_shape=(3, 3))
+
+      # Create feature columns (categorical and embedding).
+      categorical_column = fc_old.categorical_column_with_identity(
+          key='a', num_buckets=3)
+      embedding_dimension = 2
+
+      def _embedding_column_initializer(shape, dtype, partition_info):
+        del shape  # unused
+        del dtype  # unused
+        del partition_info  # unused
+        embedding_values = (
+            (1, 0),  # id 0
+            (0, 1),  # id 1
+            (1, 1))  # id 2
+        return embedding_values
+
+      embedding_column = fc_old.embedding_column(
+          categorical_column,
+          dimension=embedding_dimension,
+          initializer=_embedding_column_initializer)
+
+      input_layer = InputLayer([embedding_column])
+      features = {'a': sparse_input}
+
+      def scale_matrix():
+        matrix = input_layer(features)
+        return 2 * matrix
+
+      # Sanity check: Verify that scale_matrix returns the correct output.
+      self.assertAllEqual([[2, 0], [0, 2], [2, 2]], scale_matrix())
+
+      # Check that the returned gradient is correct.
+      grad_function = backprop.implicit_grad(scale_matrix)
+      grads_and_vars = grad_function()
+      indexed_slice = grads_and_vars[0][0]
+      gradient = grads_and_vars[0][0].values
+
+      self.assertAllEqual([0, 1, 2], indexed_slice.indices)
+      self.assertAllEqual([[2, 2], [2, 2], [2, 2]], gradient)
+
+
+class FunctionalInputLayerTest(test.TestCase):
+
+  def test_raises_if_empty_feature_columns(self):
+    with self.assertRaisesRegexp(ValueError,
+                                 'feature_columns must not be empty'):
+      fc.input_layer(features={}, feature_columns=[])
+
+  def test_should_be_dense_column(self):
+    with self.assertRaisesRegexp(ValueError, 'must be a _DenseColumn'):
+      fc.input_layer(
+          features={'a': [[0]]},
+          feature_columns=[
+              fc_old.categorical_column_with_hash_bucket('wire_cast', 4)
+          ])
+
+  def test_does_not_support_dict_columns(self):
+    with self.assertRaisesRegexp(
+        ValueError, 'Expected feature_columns to be iterable, found dict.'):
+      fc.input_layer(
+          features={'a': [[0]]},
+          feature_columns={'a': fc_old.numeric_column('a')})
+
+  def test_bare_column(self):
+    with ops.Graph().as_default():
+      features = features = {'a': [0.]}
+      net = fc.input_layer(features, fc_old.numeric_column('a'))
+      with _initialized_session():
+        self.assertAllClose([[0.]], net.eval())
+
+  def test_column_generator(self):
+    with ops.Graph().as_default():
+      features = features = {'a': [0.], 'b': [1.]}
+      columns = (fc_old.numeric_column(key) for key in features)
+      net = fc.input_layer(features, columns)
+      with _initialized_session():
+        self.assertAllClose([[0., 1.]], net.eval())
+
+  def test_raises_if_duplicate_name(self):
+    with self.assertRaisesRegexp(
+        ValueError, 'Duplicate feature column name found for columns'):
+      fc.input_layer(
+          features={'a': [[0]]},
+          feature_columns=[
+              fc_old.numeric_column('a'),
+              fc_old.numeric_column('a')
+          ])
+
+  def test_one_column(self):
+    price = fc_old.numeric_column('price')
+    with ops.Graph().as_default():
+      features = {'price': [[1.], [5.]]}
+      net = fc.input_layer(features, [price])
+      with _initialized_session():
+        self.assertAllClose([[1.], [5.]], net.eval())
+
+  def test_multi_dimension(self):
+    price = fc_old.numeric_column('price', shape=2)
+    with ops.Graph().as_default():
+      features = {'price': [[1., 2.], [5., 6.]]}
+      net = fc.input_layer(features, [price])
+      with _initialized_session():
+        self.assertAllClose([[1., 2.], [5., 6.]], net.eval())
+
+  def test_raises_if_shape_mismatch(self):
+    price = fc_old.numeric_column('price', shape=2)
+    with ops.Graph().as_default():
+      features = {'price': [[1.], [5.]]}
+      with self.assertRaisesRegexp(
+          Exception,
+          r'Cannot reshape a tensor with 2 elements to shape \[2,2\]'):
+        fc.input_layer(features, [price])
+
+  def test_reshaping(self):
+    price = fc_old.numeric_column('price', shape=[1, 2])
+    with ops.Graph().as_default():
+      features = {'price': [[[1., 2.]], [[5., 6.]]]}
+      net = fc.input_layer(features, [price])
+      with _initialized_session():
+        self.assertAllClose([[1., 2.], [5., 6.]], net.eval())
+
+  def test_multi_column(self):
+    price1 = fc_old.numeric_column('price1', shape=2)
+    price2 = fc_old.numeric_column('price2')
+    with ops.Graph().as_default():
+      features = {
+          'price1': [[1., 2.], [5., 6.]],
+          'price2': [[3.], [4.]]
+      }
+      net = fc.input_layer(features, [price1, price2])
+      with _initialized_session():
+        self.assertAllClose([[1., 2., 3.], [5., 6., 4.]], net.eval())
+
+  def test_fills_cols_to_vars(self):
+    # Provide three _DenseColumn's to input_layer: a _NumericColumn, a
+    # _BucketizedColumn, and an _EmbeddingColumn.  Only the _EmbeddingColumn
+    # creates a Variable.
+    price1 = fc_old.numeric_column('price1')
+    dense_feature = fc_old.numeric_column('dense_feature')
+    dense_feature_bucketized = fc_old.bucketized_column(
+        dense_feature, boundaries=[0.])
+    some_sparse_column = fc_old.categorical_column_with_hash_bucket(
+        'sparse_feature', hash_bucket_size=5)
+    some_embedding_column = fc_old.embedding_column(
+        some_sparse_column, dimension=10)
+    with ops.Graph().as_default():
+      features = {
+          'price1': [[3.], [4.]],
+          'dense_feature': [[-1.], [4.]],
+          'sparse_feature': [['a'], ['x']],
+      }
+      cols_to_vars = {}
+      all_cols = [price1, dense_feature_bucketized, some_embedding_column]
+      fc.input_layer(features, all_cols, cols_to_vars=cols_to_vars)
+      self.assertItemsEqual(list(cols_to_vars.keys()), all_cols)
+      self.assertEqual(0, len(cols_to_vars[price1]))
+      self.assertEqual(0, len(cols_to_vars[dense_feature_bucketized]))
+      self.assertEqual(1, len(cols_to_vars[some_embedding_column]))
+      self.assertIsInstance(cols_to_vars[some_embedding_column][0],
+                            variables_lib.Variable)
+      self.assertAllEqual(cols_to_vars[some_embedding_column][0].shape, [5, 10])
+
+  def test_fills_cols_to_vars_partitioned_variables(self):
+    price1 = fc_old.numeric_column('price1')
+    dense_feature = fc_old.numeric_column('dense_feature')
+    dense_feature_bucketized = fc_old.bucketized_column(
+        dense_feature, boundaries=[0.])
+    some_sparse_column = fc_old.categorical_column_with_hash_bucket(
+        'sparse_feature', hash_bucket_size=5)
+    some_embedding_column = fc_old.embedding_column(
+        some_sparse_column, dimension=10)
+    with ops.Graph().as_default():
+      features = {
+          'price1': [[3.], [4.]],
+          'dense_feature': [[-1.], [4.]],
+          'sparse_feature': [['a'], ['x']],
+      }
+      cols_to_vars = {}
+      all_cols = [price1, dense_feature_bucketized, some_embedding_column]
+      with variable_scope.variable_scope(
+          'input_from_feature_columns',
+          partitioner=partitioned_variables.fixed_size_partitioner(3, axis=0)):
+        fc.input_layer(features, all_cols, cols_to_vars=cols_to_vars)
+      self.assertItemsEqual(list(cols_to_vars.keys()), all_cols)
+      self.assertEqual(0, len(cols_to_vars[price1]))
+      self.assertEqual(0, len(cols_to_vars[dense_feature_bucketized]))
+      self.assertEqual(3, len(cols_to_vars[some_embedding_column]))
+      self.assertAllEqual(cols_to_vars[some_embedding_column][0].shape, [2, 10])
+      self.assertAllEqual(cols_to_vars[some_embedding_column][1].shape, [2, 10])
+      self.assertAllEqual(cols_to_vars[some_embedding_column][2].shape, [1, 10])
+
+  def test_column_order(self):
+    price_a = fc_old.numeric_column('price_a')
+    price_b = fc_old.numeric_column('price_b')
+    with ops.Graph().as_default():
+      features = {
+          'price_a': [[1.]],
+          'price_b': [[3.]],
+      }
+      net1 = fc.input_layer(features, [price_a, price_b])
+      net2 = fc.input_layer(features, [price_b, price_a])
+      with _initialized_session():
+        self.assertAllClose([[1., 3.]], net1.eval())
+        self.assertAllClose([[1., 3.]], net2.eval())
+
+  def test_fails_for_categorical_column(self):
+    animal = fc_old.categorical_column_with_identity('animal', num_buckets=4)
+    with ops.Graph().as_default():
+      features = {
+          'animal':
+              sparse_tensor.SparseTensor(
+                  indices=[[0, 0], [0, 1]], values=[1, 2], dense_shape=[1, 2])
+      }
+      with self.assertRaisesRegexp(Exception, 'must be a _DenseColumn'):
+        fc.input_layer(features, [animal])
+
+  def test_static_batch_size_mismatch(self):
+    price1 = fc_old.numeric_column('price1')
+    price2 = fc_old.numeric_column('price2')
+    with ops.Graph().as_default():
+      features = {
+          'price1': [[1.], [5.], [7.]],  # batchsize = 3
+          'price2': [[3.], [4.]]  # batchsize = 2
+      }
+      with self.assertRaisesRegexp(
+          ValueError,
+          'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+        fc.input_layer(features, [price1, price2])
+
+  def test_subset_of_static_batch_size_mismatch(self):
+    price1 = fc_old.numeric_column('price1')
+    price2 = fc_old.numeric_column('price2')
+    price3 = fc_old.numeric_column('price3')
+    with ops.Graph().as_default():
+      features = {
+          'price1': array_ops.placeholder(dtype=dtypes.int64),  # batchsize = 3
+          'price2': [[3.], [4.]],  # batchsize = 2
+          'price3': [[3.], [4.], [5.]]  # batchsize = 3
+      }
+      with self.assertRaisesRegexp(
+          ValueError,
+          'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+        fc.input_layer(features, [price1, price2, price3])
+
+  def test_runtime_batch_size_mismatch(self):
+    price1 = fc_old.numeric_column('price1')
+    price2 = fc_old.numeric_column('price2')
+    with ops.Graph().as_default():
+      features = {
+          'price1': array_ops.placeholder(dtype=dtypes.int64),  # batchsize = 3
+          'price2': [[3.], [4.]]  # batchsize = 2
+      }
+      net = fc.input_layer(features, [price1, price2])
+      with _initialized_session() as sess:
+        with self.assertRaisesRegexp(errors.OpError,
+                                     'Dimensions of inputs should match'):
+          sess.run(net, feed_dict={features['price1']: [[1.], [5.], [7.]]})
+
+  def test_runtime_batch_size_matches(self):
+    price1 = fc_old.numeric_column('price1')
+    price2 = fc_old.numeric_column('price2')
+    with ops.Graph().as_default():
+      features = {
+          'price1': array_ops.placeholder(dtype=dtypes.int64),  # batchsize = 2
+          'price2': array_ops.placeholder(dtype=dtypes.int64),  # batchsize = 2
+      }
+      net = fc.input_layer(features, [price1, price2])
+      with _initialized_session() as sess:
+        sess.run(
+            net,
+            feed_dict={
+                features['price1']: [[1.], [5.]],
+                features['price2']: [[1.], [5.]],
+            })
+
+  def test_multiple_layers_with_same_embedding_column(self):
+    some_sparse_column = fc_old.categorical_column_with_hash_bucket(
+        'sparse_feature', hash_bucket_size=5)
+    some_embedding_column = fc_old.embedding_column(
+        some_sparse_column, dimension=10)
+
+    with ops.Graph().as_default():
+      features = {
+          'sparse_feature': [['a'], ['x']],
+      }
+      all_cols = [some_embedding_column]
+      fc.input_layer(features, all_cols)
+      fc.input_layer(features, all_cols)
+      # Make sure that 2 variables get created in this case.
+      self.assertEqual(2, len(
+          ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)))
+      expected_var_names = [
+          'input_layer/sparse_feature_embedding/embedding_weights:0',
+          'input_layer_1/sparse_feature_embedding/embedding_weights:0'
+      ]
+      self.assertItemsEqual(
+          expected_var_names,
+          [v.name for v in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)])
+
+  def test_multiple_layers_with_same_shared_embedding_column(self):
+    categorical_column_a = fc_old.categorical_column_with_identity(
+        key='aaa', num_buckets=3)
+    categorical_column_b = fc_old.categorical_column_with_identity(
+        key='bbb', num_buckets=3)
+    embedding_dimension = 2
+    embedding_column_b, embedding_column_a = fc_old.shared_embedding_columns(
+        [categorical_column_b, categorical_column_a],
+        dimension=embedding_dimension)
+
+    with ops.Graph().as_default():
+      features = {
+          'aaa':
+              sparse_tensor.SparseTensor(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=(0, 1, 0),
+                  dense_shape=(2, 2)),
+          'bbb':
+              sparse_tensor.SparseTensor(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=(1, 2, 1),
+                  dense_shape=(2, 2)),
+      }
+      all_cols = [embedding_column_a, embedding_column_b]
+      fc.input_layer(features, all_cols)
+      fc.input_layer(features, all_cols)
+      # Make sure that only 1 variable gets created in this case.
+      self.assertEqual(1, len(
+          ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)))
+      self.assertItemsEqual(
+          ['input_layer/aaa_bbb_shared_embedding/embedding_weights:0'],
+          [v.name for v in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)])
+
+  def test_multiple_layers_with_same_shared_embedding_column_diff_graphs(self):
+    categorical_column_a = fc_old.categorical_column_with_identity(
+        key='aaa', num_buckets=3)
+    categorical_column_b = fc_old.categorical_column_with_identity(
+        key='bbb', num_buckets=3)
+    embedding_dimension = 2
+    embedding_column_b, embedding_column_a = fc_old.shared_embedding_columns(
+        [categorical_column_b, categorical_column_a],
+        dimension=embedding_dimension)
+    all_cols = [embedding_column_a, embedding_column_b]
+
+    with ops.Graph().as_default():
+      features = {
+          'aaa':
+              sparse_tensor.SparseTensor(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=(0, 1, 0),
+                  dense_shape=(2, 2)),
+          'bbb':
+              sparse_tensor.SparseTensor(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=(1, 2, 1),
+                  dense_shape=(2, 2)),
+      }
+      fc.input_layer(features, all_cols)
+      # Make sure that only 1 variable gets created in this case.
+      self.assertEqual(1, len(
+          ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)))
+
+    with ops.Graph().as_default():
+      features1 = {
+          'aaa':
+              sparse_tensor.SparseTensor(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=(0, 1, 0),
+                  dense_shape=(2, 2)),
+          'bbb':
+              sparse_tensor.SparseTensor(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=(1, 2, 1),
+                  dense_shape=(2, 2)),
+      }
+
+      fc.input_layer(features1, all_cols)
+      # Make sure that only 1 variable gets created in this case.
+      self.assertEqual(1, len(
+          ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)))
+      self.assertItemsEqual(
+          ['input_layer/aaa_bbb_shared_embedding/embedding_weights:0'],
+          [v.name for v in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)])
+
+  def test_with_numpy_input_fn(self):
+    embedding_values = (
+        (1., 2., 3., 4., 5.),  # id 0
+        (6., 7., 8., 9., 10.),  # id 1
+        (11., 12., 13., 14., 15.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      del shape, dtype, partition_info
+      return embedding_values
+
+    # price has 1 dimension in input_layer
+    price = fc_old.numeric_column('price')
+    body_style = fc_old.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+    # one_hot_body_style has 3 dims in input_layer.
+    one_hot_body_style = fc_old.indicator_column(body_style)
+    # embedded_body_style has 5 dims in input_layer.
+    embedded_body_style = fc_old.embedding_column(
+        body_style, dimension=5, initializer=_initializer)
+
+    input_fn = numpy_io.numpy_input_fn(
+        x={
+            'price': np.array([11., 12., 13., 14.]),
+            'body-style': np.array(['sedan', 'hardtop', 'wagon', 'sedan']),
+        },
+        batch_size=2,
+        shuffle=False)
+    features = input_fn()
+    net = fc.input_layer(features,
+                         [price, one_hot_body_style, embedded_body_style])
+    self.assertEqual(1 + 3 + 5, net.shape[1])
+    with _initialized_session() as sess:
+      coord = coordinator.Coordinator()
+      threads = queue_runner_impl.start_queue_runners(sess, coord=coord)
+
+      # Each row is formed by concatenating `embedded_body_style`,
+      # `one_hot_body_style`, and `price` in order.
+      self.assertAllEqual(
+          [[11., 12., 13., 14., 15., 0., 0., 1., 11.],
+           [1., 2., 3., 4., 5., 1., 0., 0., 12]],
+          sess.run(net))
+
+      coord.request_stop()
+      coord.join(threads)
+
+  def test_with_1d_sparse_tensor(self):
+    embedding_values = (
+        (1., 2., 3., 4., 5.),  # id 0
+        (6., 7., 8., 9., 10.),  # id 1
+        (11., 12., 13., 14., 15.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      del shape, dtype, partition_info
+      return embedding_values
+
+    # price has 1 dimension in input_layer
+    price = fc_old.numeric_column('price')
+
+    # one_hot_body_style has 3 dims in input_layer.
+    body_style = fc_old.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+    one_hot_body_style = fc_old.indicator_column(body_style)
+
+    # embedded_body_style has 5 dims in input_layer.
+    country = fc_old.categorical_column_with_vocabulary_list(
+        'country', vocabulary_list=['US', 'JP', 'CA'])
+    embedded_country = fc_old.embedding_column(
+        country, dimension=5, initializer=_initializer)
+
+    # Provides 1-dim tensor and dense tensor.
+    features = {
+        'price': constant_op.constant([11., 12.,]),
+        'body-style': sparse_tensor.SparseTensor(
+            indices=((0,), (1,)),
+            values=('sedan', 'hardtop'),
+            dense_shape=(2,)),
+        # This is dense tensor for the categorical_column.
+        'country': constant_op.constant(['CA', 'US']),
+    }
+    self.assertEqual(1, features['price'].shape.ndims)
+    self.assertEqual(1, features['body-style'].dense_shape.get_shape()[0])
+    self.assertEqual(1, features['country'].shape.ndims)
+
+    net = fc.input_layer(features,
+                         [price, one_hot_body_style, embedded_country])
+    self.assertEqual(1 + 3 + 5, net.shape[1])
+    with _initialized_session() as sess:
+
+      # Each row is formed by concatenating `embedded_body_style`,
+      # `one_hot_body_style`, and `price` in order.
+      self.assertAllEqual(
+          [[0., 0., 1., 11., 12., 13., 14., 15., 11.],
+           [1., 0., 0., 1., 2., 3., 4., 5., 12.]],
+          sess.run(net))
+
+  def test_with_1d_unknown_shape_sparse_tensor(self):
+    embedding_values = (
+        (1., 2.),  # id 0
+        (6., 7.),  # id 1
+        (11., 12.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      del shape, dtype, partition_info
+      return embedding_values
+
+    # price has 1 dimension in input_layer
+    price = fc_old.numeric_column('price')
+
+    # one_hot_body_style has 3 dims in input_layer.
+    body_style = fc_old.categorical_column_with_vocabulary_list(
+        'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
+    one_hot_body_style = fc_old.indicator_column(body_style)
+
+    # embedded_body_style has 5 dims in input_layer.
+    country = fc_old.categorical_column_with_vocabulary_list(
+        'country', vocabulary_list=['US', 'JP', 'CA'])
+    embedded_country = fc_old.embedding_column(
+        country, dimension=2, initializer=_initializer)
+
+    # Provides 1-dim tensor and dense tensor.
+    features = {
+        'price': array_ops.placeholder(dtypes.float32),
+        'body-style': array_ops.sparse_placeholder(dtypes.string),
+        # This is dense tensor for the categorical_column.
+        'country': array_ops.placeholder(dtypes.string),
+    }
+    self.assertIsNone(features['price'].shape.ndims)
+    self.assertIsNone(features['body-style'].get_shape().ndims)
+    self.assertIsNone(features['country'].shape.ndims)
+
+    price_data = np.array([11., 12.])
+    body_style_data = sparse_tensor.SparseTensorValue(
+        indices=((0,), (1,)),
+        values=('sedan', 'hardtop'),
+        dense_shape=(2,))
+    country_data = np.array([['US'], ['CA']])
+
+    net = fc.input_layer(features,
+                         [price, one_hot_body_style, embedded_country])
+    self.assertEqual(1 + 3 + 2, net.shape[1])
+    with _initialized_session() as sess:
+
+      # Each row is formed by concatenating `embedded_body_style`,
+      # `one_hot_body_style`, and `price` in order.
+      self.assertAllEqual(
+          [[0., 0., 1., 1., 2., 11.], [1., 0., 0., 11., 12., 12.]],
+          sess.run(
+              net,
+              feed_dict={
+                  features['price']: price_data,
+                  features['body-style']: body_style_data,
+                  features['country']: country_data
+              }))
+
+  def test_with_rank_0_feature(self):
+    # price has 1 dimension in input_layer
+    price = fc_old.numeric_column('price')
+    features = {
+        'price': constant_op.constant(0),
+    }
+    self.assertEqual(0, features['price'].shape.ndims)
+
+    # Static rank 0 should fail
+    with self.assertRaisesRegexp(ValueError, 'Feature .* cannot have rank 0'):
+      fc.input_layer(features, [price])
+
+    # Dynamic rank 0 should fail
+    features = {
+        'price': array_ops.placeholder(dtypes.float32),
+    }
+    net = fc.input_layer(features, [price])
+    self.assertEqual(1, net.shape[1])
+    with _initialized_session() as sess:
+      with self.assertRaisesOpError('Feature .* cannot have rank 0'):
+        sess.run(net, feed_dict={features['price']: np.array(1)})
+
+
+class MakeParseExampleSpecTest(test.TestCase):
+
+  class _TestFeatureColumn(FeatureColumn,
+                           collections.namedtuple('_TestFeatureColumn',
+                                                  ('parse_spec'))):
+
+    @property
+    def name(self):
+      return "_TestFeatureColumn"
+
+    def transform_feature(self, transformation_cache, state_manager):
+      pass
+
+    @property
+    def parse_example_spec(self):
+      return self.parse_spec
+
+  def test_no_feature_columns(self):
+    actual = fc.make_parse_example_spec([])
+    self.assertDictEqual({}, actual)
+
+  def test_invalid_type(self):
+    key1 = 'key1'
+    parse_spec1 = parsing_ops.FixedLenFeature(
+        shape=(2,), dtype=dtypes.float32, default_value=0.)
+    with self.assertRaisesRegexp(
+        ValueError,
+        'All feature_columns must be FeatureColumn instances.*invalid_column'):
+      fc.make_parse_example_spec(
+          (self._TestFeatureColumn({key1: parse_spec1}), 'invalid_column'))
+
+  def test_one_feature_column(self):
+    key1 = 'key1'
+    parse_spec1 = parsing_ops.FixedLenFeature(
+        shape=(2,), dtype=dtypes.float32, default_value=0.)
+    actual = fc.make_parse_example_spec(
+        (self._TestFeatureColumn({key1: parse_spec1}),))
+    self.assertDictEqual({key1: parse_spec1}, actual)
+
+  def test_two_feature_columns(self):
+    key1 = 'key1'
+    parse_spec1 = parsing_ops.FixedLenFeature(
+        shape=(2,), dtype=dtypes.float32, default_value=0.)
+    key2 = 'key2'
+    parse_spec2 = parsing_ops.VarLenFeature(dtype=dtypes.string)
+    actual = fc.make_parse_example_spec(
+        (self._TestFeatureColumn({key1: parse_spec1}),
+         self._TestFeatureColumn({key2: parse_spec2})))
+    self.assertDictEqual({key1: parse_spec1, key2: parse_spec2}, actual)
+
+  def test_equal_keys_different_parse_spec(self):
+    key1 = 'key1'
+    parse_spec1 = parsing_ops.FixedLenFeature(
+        shape=(2,), dtype=dtypes.float32, default_value=0.)
+    parse_spec2 = parsing_ops.VarLenFeature(dtype=dtypes.string)
+    with self.assertRaisesRegexp(
+        ValueError,
+        'feature_columns contain different parse_spec for key key1'):
+      fc.make_parse_example_spec(
+          (self._TestFeatureColumn({key1: parse_spec1}),
+           self._TestFeatureColumn({key1: parse_spec2})))
+
+  def test_equal_keys_equal_parse_spec(self):
+    key1 = 'key1'
+    parse_spec1 = parsing_ops.FixedLenFeature(
+        shape=(2,), dtype=dtypes.float32, default_value=0.)
+    actual = fc.make_parse_example_spec(
+        (self._TestFeatureColumn({key1: parse_spec1}),
+         self._TestFeatureColumn({key1: parse_spec1})))
+    self.assertDictEqual({key1: parse_spec1}, actual)
+
+  def test_multiple_features_dict(self):
+    """parse_spc for one column is a dict with length > 1."""
+    key1 = 'key1'
+    parse_spec1 = parsing_ops.FixedLenFeature(
+        shape=(2,), dtype=dtypes.float32, default_value=0.)
+    key2 = 'key2'
+    parse_spec2 = parsing_ops.VarLenFeature(dtype=dtypes.string)
+    key3 = 'key3'
+    parse_spec3 = parsing_ops.VarLenFeature(dtype=dtypes.int32)
+    actual = fc.make_parse_example_spec(
+        (self._TestFeatureColumn({key1: parse_spec1}),
+         self._TestFeatureColumn({key2: parse_spec2, key3: parse_spec3})))
+    self.assertDictEqual(
+        {key1: parse_spec1, key2: parse_spec2, key3: parse_spec3}, actual)
+
+
+def _assert_sparse_tensor_value(test_case, expected, actual):
+  test_case.assertEqual(np.int64, np.array(actual.indices).dtype)
+  test_case.assertAllEqual(expected.indices, actual.indices)
+
+  test_case.assertEqual(
+      np.array(expected.values).dtype, np.array(actual.values).dtype)
+  test_case.assertAllEqual(expected.values, actual.values)
+
+  test_case.assertEqual(np.int64, np.array(actual.dense_shape).dtype)
+  test_case.assertAllEqual(expected.dense_shape, actual.dense_shape)
+
+
+class VocabularyFileCategoricalColumnTest(test.TestCase):
+
+  def setUp(self):
+    super(VocabularyFileCategoricalColumnTest, self).setUp()
+
+    # Contains ints, Golden State Warriors jersey numbers: 30, 35, 11, 23, 22
+    self._warriors_vocabulary_file_name = test.test_src_dir_path(
+        'python/feature_column/testdata/warriors_vocabulary.txt')
+    self._warriors_vocabulary_size = 5
+
+    # Contains strings, character names from 'The Wire': omar, stringer, marlo
+    self._wire_vocabulary_file_name = test.test_src_dir_path(
+        'python/feature_column/testdata/wire_vocabulary.txt')
+    self._wire_vocabulary_size = 3
+
+  def test_defaults(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa', vocabulary_file='path_to_file', vocabulary_size=3)
+    self.assertEqual('aaa', column.name)
+    self.assertEqual('aaa', column.key)
+    self.assertEqual(3, column.num_buckets)
+    self.assertEqual({
+        'aaa': parsing_ops.VarLenFeature(dtypes.string)
+    }, column.parse_example_spec)
+
+  def test_key_should_be_string(self):
+    with self.assertRaisesRegexp(ValueError, 'key must be a string.'):
+      fc.categorical_column_with_vocabulary_file(
+          key=('aaa',), vocabulary_file='path_to_file', vocabulary_size=3)
+
+  def test_all_constructor_args(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa', vocabulary_file='path_to_file', vocabulary_size=3,
+        num_oov_buckets=4, dtype=dtypes.int32)
+    self.assertEqual(7, column.num_buckets)
+    self.assertEqual({
+        'aaa': parsing_ops.VarLenFeature(dtypes.int32)
+    }, column.parse_example_spec)
+
+  def test_deep_copy(self):
+    original = fc.categorical_column_with_vocabulary_file(
+        key='aaa', vocabulary_file='path_to_file', vocabulary_size=3,
+        num_oov_buckets=4, dtype=dtypes.int32)
+    for column in (original, copy.deepcopy(original)):
+      self.assertEqual('aaa', column.name)
+      self.assertEqual(7, column.num_buckets)
+      self.assertEqual({
+          'aaa': parsing_ops.VarLenFeature(dtypes.int32)
+      }, column.parse_example_spec)
+
+  def test_vocabulary_file_none(self):
+    with self.assertRaisesRegexp(ValueError, 'Missing vocabulary_file'):
+      fc.categorical_column_with_vocabulary_file(
+          key='aaa', vocabulary_file=None, vocabulary_size=3)
+
+  def test_vocabulary_file_empty_string(self):
+    with self.assertRaisesRegexp(ValueError, 'Missing vocabulary_file'):
+      fc.categorical_column_with_vocabulary_file(
+          key='aaa', vocabulary_file='', vocabulary_size=3)
+
+  def test_invalid_vocabulary_file(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa', vocabulary_file='file_does_not_exist', vocabulary_size=10)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('marlo', 'skywalker', 'omar'),
+        dense_shape=(2, 2))
+    column.get_sparse_tensors(FeatureTransformationCache({'aaa': inputs}), None)
+    with self.assertRaisesRegexp(errors.OpError, 'file_does_not_exist'):
+      with self.test_session():
+        lookup_ops.tables_initializer().run()
+
+  def test_invalid_vocabulary_size(self):
+    with self.assertRaisesRegexp(ValueError, 'Invalid vocabulary_size'):
+      fc.categorical_column_with_vocabulary_file(
+          key='aaa', vocabulary_file=self._wire_vocabulary_file_name,
+          vocabulary_size=-1)
+    with self.assertRaisesRegexp(ValueError, 'Invalid vocabulary_size'):
+      fc.categorical_column_with_vocabulary_file(
+          key='aaa', vocabulary_file=self._wire_vocabulary_file_name,
+          vocabulary_size=0)
+
+  def test_too_large_vocabulary_size(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa',
+        vocabulary_file=self._wire_vocabulary_file_name,
+        vocabulary_size=self._wire_vocabulary_size + 1)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('marlo', 'skywalker', 'omar'),
+        dense_shape=(2, 2))
+    column.get_sparse_tensors(FeatureTransformationCache({'aaa': inputs}), None)
+    with self.assertRaisesRegexp(errors.OpError, 'Invalid vocab_size'):
+      with self.test_session():
+        lookup_ops.tables_initializer().run()
+
+  def test_invalid_num_oov_buckets(self):
+    with self.assertRaisesRegexp(ValueError, 'Invalid num_oov_buckets'):
+      fc.categorical_column_with_vocabulary_file(
+          key='aaa', vocabulary_file='path', vocabulary_size=3,
+          num_oov_buckets=-1)
+
+  def test_invalid_dtype(self):
+    with self.assertRaisesRegexp(ValueError, 'dtype must be string or integer'):
+      fc.categorical_column_with_vocabulary_file(
+          key='aaa', vocabulary_file='path', vocabulary_size=3,
+          dtype=dtypes.float64)
+
+  def test_invalid_buckets_and_default_value(self):
+    with self.assertRaisesRegexp(
+        ValueError, 'both num_oov_buckets and default_value'):
+      fc.categorical_column_with_vocabulary_file(
+          key='aaa',
+          vocabulary_file=self._wire_vocabulary_file_name,
+          vocabulary_size=self._wire_vocabulary_size,
+          num_oov_buckets=100,
+          default_value=2)
+
+  def test_invalid_input_dtype_int32(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa',
+        vocabulary_file=self._wire_vocabulary_file_name,
+        vocabulary_size=self._wire_vocabulary_size,
+        dtype=dtypes.string)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(12, 24, 36),
+        dense_shape=(2, 2))
+    with self.assertRaisesRegexp(ValueError, 'dtype must be compatible'):
+      column.get_sparse_tensors(
+          FeatureTransformationCache({
+              'aaa': inputs
+          }), None)
+
+  def test_invalid_input_dtype_string(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa',
+        vocabulary_file=self._warriors_vocabulary_file_name,
+        vocabulary_size=self._warriors_vocabulary_size,
+        dtype=dtypes.int32)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('omar', 'stringer', 'marlo'),
+        dense_shape=(2, 2))
+    with self.assertRaisesRegexp(ValueError, 'dtype must be compatible'):
+      column.get_sparse_tensors(
+          FeatureTransformationCache({
+              'aaa': inputs
+          }), None)
+
+  def test_parse_example(self):
+    a = fc.categorical_column_with_vocabulary_file(
+        key='aaa', vocabulary_file='path_to_file', vocabulary_size=3)
+    data = example_pb2.Example(features=feature_pb2.Features(
+        feature={
+            'aaa':
+                feature_pb2.Feature(bytes_list=feature_pb2.BytesList(
+                    value=[b'omar', b'stringer']))
+        }))
+    features = parsing_ops.parse_example(
+        serialized=[data.SerializeToString()],
+        features=fc.make_parse_example_spec([a]))
+    self.assertIn('aaa', features)
+    with self.test_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=[[0, 0], [0, 1]],
+              values=np.array([b'omar', b'stringer'], dtype=np.object_),
+              dense_shape=[1, 2]),
+          features['aaa'].eval())
+
+  def test_get_sparse_tensors(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa',
+        vocabulary_file=self._wire_vocabulary_file_name,
+        vocabulary_size=self._wire_vocabulary_size)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('marlo', 'skywalker', 'omar'),
+        dense_shape=(2, 2))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((2, -1, 0), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
+  def test_get_sparse_tensors_none_vocabulary_size(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa', vocabulary_file=self._wire_vocabulary_file_name)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('marlo', 'skywalker', 'omar'),
+        dense_shape=(2, 2))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(self,
+                                  sparse_tensor.SparseTensorValue(
+                                      indices=inputs.indices,
+                                      values=np.array(
+                                          (2, -1, 0), dtype=np.int64),
+                                      dense_shape=inputs.dense_shape),
+                                  id_weight_pair.id_tensor.eval())
+
+  def test_transform_feature(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa',
+        vocabulary_file=self._wire_vocabulary_file_name,
+        vocabulary_size=self._wire_vocabulary_size)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('marlo', 'skywalker', 'omar'),
+        dense_shape=(2, 2))
+    id_tensor = _transform_features({'aaa': inputs}, [column], None)[column]
+    with _initialized_session():
+      _assert_sparse_tensor_value(self,
+                                  sparse_tensor.SparseTensorValue(
+                                      indices=inputs.indices,
+                                      values=np.array(
+                                          (2, -1, 0), dtype=np.int64),
+                                      dense_shape=inputs.dense_shape),
+                                  id_tensor.eval())
+
+  def DISABLED_test_get_sparse_tensors_weight_collections(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa',
+        vocabulary_file=self._wire_vocabulary_file_name,
+        vocabulary_size=self._wire_vocabulary_size)
+    inputs = sparse_tensor.SparseTensor(
+        values=['omar', 'stringer', 'marlo'],
+        indices=[[0, 0], [1, 0], [1, 1]],
+        dense_shape=[2, 2])
+    column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }),
+        weight_collections=('my_weights',))
+
+    self.assertItemsEqual(
+        [], ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES))
+    self.assertItemsEqual([], ops.get_collection('my_weights'))
+
+  def test_get_sparse_tensors_dense_input(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa',
+        vocabulary_file=self._wire_vocabulary_file_name,
+        vocabulary_size=self._wire_vocabulary_size)
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': (('marlo', ''), ('skywalker', 'omar'))
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=((0, 0), (1, 0), (1, 1)),
+              values=np.array((2, -1, 0), dtype=np.int64),
+              dense_shape=(2, 2)),
+          id_weight_pair.id_tensor.eval())
+
+  def test_get_sparse_tensors_default_value_in_vocabulary(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa',
+        vocabulary_file=self._wire_vocabulary_file_name,
+        vocabulary_size=self._wire_vocabulary_size,
+        default_value=2)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('marlo', 'skywalker', 'omar'),
+        dense_shape=(2, 2))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((2, 2, 0), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
+  def test_get_sparse_tensors_with_oov_buckets(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa',
+        vocabulary_file=self._wire_vocabulary_file_name,
+        vocabulary_size=self._wire_vocabulary_size,
+        num_oov_buckets=100)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1), (1, 2)),
+        values=('marlo', 'skywalker', 'omar', 'heisenberg'),
+        dense_shape=(2, 3))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((2, 33, 0, 62), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
+  def test_get_sparse_tensors_small_vocabulary_size(self):
+    # 'marlo' is the last entry in our vocabulary file, so be setting
+    # `vocabulary_size` to 1 less than number of entries in file, we take
+    # 'marlo' out of the vocabulary.
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa',
+        vocabulary_file=self._wire_vocabulary_file_name,
+        vocabulary_size=self._wire_vocabulary_size - 1)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('marlo', 'skywalker', 'omar'),
+        dense_shape=(2, 2))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((-1, -1, 0), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
+  def test_get_sparse_tensors_int32(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa',
+        vocabulary_file=self._warriors_vocabulary_file_name,
+        vocabulary_size=self._warriors_vocabulary_size,
+        dtype=dtypes.int32)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1), (2, 2)),
+        values=(11, 100, 30, 22),
+        dense_shape=(3, 3))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((2, -1, 0, 4), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
+  def test_get_sparse_tensors_int32_dense_input(self):
+    default_value = -100
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa',
+        vocabulary_file=self._warriors_vocabulary_file_name,
+        vocabulary_size=self._warriors_vocabulary_size,
+        dtype=dtypes.int32,
+        default_value=default_value)
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': ((11, -1, -1), (100, 30, -1), (-1, -1, 22))
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=((0, 0), (1, 0), (1, 1), (2, 2)),
+              values=np.array((2, default_value, 0, 4), dtype=np.int64),
+              dense_shape=(3, 3)),
+          id_weight_pair.id_tensor.eval())
+
+  def test_get_sparse_tensors_int32_with_oov_buckets(self):
+    column = fc.categorical_column_with_vocabulary_file(
+        key='aaa',
+        vocabulary_file=self._warriors_vocabulary_file_name,
+        vocabulary_size=self._warriors_vocabulary_size,
+        dtype=dtypes.int32,
+        num_oov_buckets=100)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1), (2, 2)),
+        values=(11, 100, 30, 22),
+        dense_shape=(3, 3))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((2, 60, 0, 4), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
+  def test_linear_model(self):
+    wire_column = fc_old.categorical_column_with_vocabulary_file(
+        key='wire',
+        vocabulary_file=self._wire_vocabulary_file_name,
+        vocabulary_size=self._wire_vocabulary_size,
+        num_oov_buckets=1)
+    self.assertEqual(4, wire_column._num_buckets)
+    with ops.Graph().as_default():
+      predictions = fc.linear_model({
+          wire_column.name: sparse_tensor.SparseTensorValue(
+              indices=((0, 0), (1, 0), (1, 1)),
+              values=('marlo', 'skywalker', 'omar'),
+              dense_shape=(2, 2))
+      }, (wire_column,))
+      bias = get_linear_model_bias()
+      wire_var = get_linear_model_column_var(wire_column)
+      with _initialized_session():
+        self.assertAllClose((0.,), bias.eval())
+        self.assertAllClose(((0.,), (0.,), (0.,), (0.,)), wire_var.eval())
+        self.assertAllClose(((0.,), (0.,)), predictions.eval())
+        wire_var.assign(((1.,), (2.,), (3.,), (4.,))).eval()
+        # 'marlo' -> 2: wire_var[2] = 3
+        # 'skywalker' -> 3, 'omar' -> 0: wire_var[3] + wire_var[0] = 4+1 = 5
+        self.assertAllClose(((3.,), (5.,)), predictions.eval())
+
+  def test_keras_linear_model(self):
+    wire_column = fc_old.categorical_column_with_vocabulary_file(
+        key='wire',
+        vocabulary_file=self._wire_vocabulary_file_name,
+        vocabulary_size=self._wire_vocabulary_size,
+        num_oov_buckets=1)
+    self.assertEqual(4, wire_column._num_buckets)
+    with ops.Graph().as_default():
+      predictions = get_keras_linear_model_predictions({
+          wire_column.name:
+              sparse_tensor.SparseTensorValue(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=('marlo', 'skywalker', 'omar'),
+                  dense_shape=(2, 2))
+      }, (wire_column,))
+      bias = get_linear_model_bias()
+      wire_var = get_linear_model_column_var(wire_column)
+      with _initialized_session():
+        self.assertAllClose((0.,), bias.eval())
+        self.assertAllClose(((0.,), (0.,), (0.,), (0.,)), wire_var.eval())
+        self.assertAllClose(((0.,), (0.,)), predictions.eval())
+        wire_var.assign(((1.,), (2.,), (3.,), (4.,))).eval()
+        # 'marlo' -> 2: wire_var[2] = 3
+        # 'skywalker' -> 3, 'omar' -> 0: wire_var[3] + wire_var[0] = 4+1 = 5
+        self.assertAllClose(((3.,), (5.,)), predictions.eval())
+
+
+class VocabularyListCategoricalColumnTest(test.TestCase):
+
+  def test_defaults_string(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
+    self.assertEqual('aaa', column.name)
+    self.assertEqual('aaa', column.key)
+    self.assertEqual(3, column.num_buckets)
+    self.assertEqual({
+        'aaa': parsing_ops.VarLenFeature(dtypes.string)
+    }, column.parse_example_spec)
+
+  def test_key_should_be_string(self):
+    with self.assertRaisesRegexp(ValueError, 'key must be a string.'):
+      fc.categorical_column_with_vocabulary_list(
+          key=('aaa',), vocabulary_list=('omar', 'stringer', 'marlo'))
+
+  def test_defaults_int(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa', vocabulary_list=(12, 24, 36))
+    self.assertEqual('aaa', column.name)
+    self.assertEqual('aaa', column.key)
+    self.assertEqual(3, column.num_buckets)
+    self.assertEqual({
+        'aaa': parsing_ops.VarLenFeature(dtypes.int64)
+    }, column.parse_example_spec)
+
+  def test_all_constructor_args(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa', vocabulary_list=(12, 24, 36), dtype=dtypes.int32,
+        default_value=-99)
+    self.assertEqual(3, column.num_buckets)
+    self.assertEqual({
+        'aaa': parsing_ops.VarLenFeature(dtypes.int32)
+    }, column.parse_example_spec)
+
+  def test_deep_copy(self):
+    original = fc.categorical_column_with_vocabulary_list(
+        key='aaa', vocabulary_list=(12, 24, 36), dtype=dtypes.int32)
+    for column in (original, copy.deepcopy(original)):
+      self.assertEqual('aaa', column.name)
+      self.assertEqual(3, column.num_buckets)
+      self.assertEqual({
+          'aaa': parsing_ops.VarLenFeature(dtypes.int32)
+      }, column.parse_example_spec)
+
+  def test_invalid_dtype(self):
+    with self.assertRaisesRegexp(ValueError, 'dtype must be string or integer'):
+      fc.categorical_column_with_vocabulary_list(
+          key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'),
+          dtype=dtypes.float32)
+
+  def test_invalid_mapping_dtype(self):
+    with self.assertRaisesRegexp(
+        ValueError, r'vocabulary dtype must be string or integer'):
+      fc.categorical_column_with_vocabulary_list(
+          key='aaa', vocabulary_list=(12., 24., 36.))
+
+  def test_mismatched_int_dtype(self):
+    with self.assertRaisesRegexp(
+        ValueError, r'dtype.*and vocabulary dtype.*do not match'):
+      fc.categorical_column_with_vocabulary_list(
+          key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'),
+          dtype=dtypes.int32)
+
+  def test_mismatched_string_dtype(self):
+    with self.assertRaisesRegexp(
+        ValueError, r'dtype.*and vocabulary dtype.*do not match'):
+      fc.categorical_column_with_vocabulary_list(
+          key='aaa', vocabulary_list=(12, 24, 36), dtype=dtypes.string)
+
+  def test_none_mapping(self):
+    with self.assertRaisesRegexp(
+        ValueError, r'vocabulary_list.*must be non-empty'):
+      fc.categorical_column_with_vocabulary_list(
+          key='aaa', vocabulary_list=None)
+
+  def test_empty_mapping(self):
+    with self.assertRaisesRegexp(
+        ValueError, r'vocabulary_list.*must be non-empty'):
+      fc.categorical_column_with_vocabulary_list(
+          key='aaa', vocabulary_list=tuple([]))
+
+  def test_duplicate_mapping(self):
+    with self.assertRaisesRegexp(ValueError, 'Duplicate keys'):
+      fc.categorical_column_with_vocabulary_list(
+          key='aaa', vocabulary_list=(12, 24, 12))
+
+  def test_invalid_num_oov_buckets(self):
+    with self.assertRaisesRegexp(ValueError, 'Invalid num_oov_buckets'):
+      fc.categorical_column_with_vocabulary_list(
+          key='aaa', vocabulary_list=(12, 24, 36),
+          num_oov_buckets=-1)
+
+  def test_invalid_buckets_and_default_value(self):
+    with self.assertRaisesRegexp(
+        ValueError, 'both num_oov_buckets and default_value'):
+      fc.categorical_column_with_vocabulary_list(
+          key='aaa',
+          vocabulary_list=(12, 24, 36),
+          num_oov_buckets=100,
+          default_value=2)
+
+  def test_invalid_input_dtype_int32(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=('omar', 'stringer', 'marlo'))
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(12, 24, 36),
+        dense_shape=(2, 2))
+    with self.assertRaisesRegexp(ValueError, 'dtype must be compatible'):
+      column.get_sparse_tensors(
+          FeatureTransformationCache({
+              'aaa': inputs
+          }), None)
+
+  def test_invalid_input_dtype_string(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=(12, 24, 36))
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('omar', 'stringer', 'marlo'),
+        dense_shape=(2, 2))
+    with self.assertRaisesRegexp(ValueError, 'dtype must be compatible'):
+      column.get_sparse_tensors(
+          FeatureTransformationCache({
+              'aaa': inputs
+          }), None)
+
+  def test_parse_example_string(self):
+    a = fc.categorical_column_with_vocabulary_list(
+        key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
+    data = example_pb2.Example(features=feature_pb2.Features(
+        feature={
+            'aaa':
+                feature_pb2.Feature(bytes_list=feature_pb2.BytesList(
+                    value=[b'omar', b'stringer']))
+        }))
+    features = parsing_ops.parse_example(
+        serialized=[data.SerializeToString()],
+        features=fc.make_parse_example_spec([a]))
+    self.assertIn('aaa', features)
+    with self.test_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=[[0, 0], [0, 1]],
+              values=np.array([b'omar', b'stringer'], dtype=np.object_),
+              dense_shape=[1, 2]),
+          features['aaa'].eval())
+
+  def test_parse_example_int(self):
+    a = fc.categorical_column_with_vocabulary_list(
+        key='aaa', vocabulary_list=(11, 21, 31))
+    data = example_pb2.Example(features=feature_pb2.Features(
+        feature={
+            'aaa':
+                feature_pb2.Feature(int64_list=feature_pb2.Int64List(
+                    value=[11, 21]))
+        }))
+    features = parsing_ops.parse_example(
+        serialized=[data.SerializeToString()],
+        features=fc.make_parse_example_spec([a]))
+    self.assertIn('aaa', features)
+    with self.test_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=[[0, 0], [0, 1]],
+              values=[11, 21],
+              dense_shape=[1, 2]),
+          features['aaa'].eval())
+
+  def test_get_sparse_tensors(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=('omar', 'stringer', 'marlo'))
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('marlo', 'skywalker', 'omar'),
+        dense_shape=(2, 2))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((2, -1, 0), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
+  def test_transform_feature(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=('omar', 'stringer', 'marlo'))
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('marlo', 'skywalker', 'omar'),
+        dense_shape=(2, 2))
+    id_tensor = _transform_features({'aaa': inputs}, [column], None)[column]
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((2, -1, 0), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_tensor.eval())
+
+  def DISABLED_test_get_sparse_tensors_weight_collections(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=('omar', 'stringer', 'marlo'))
+    inputs = sparse_tensor.SparseTensor(
+        values=['omar', 'stringer', 'marlo'],
+        indices=[[0, 0], [1, 0], [1, 1]],
+        dense_shape=[2, 2])
+    column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }),
+        weight_collections=('my_weights',))
+
+    self.assertItemsEqual(
+        [], ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES))
+    self.assertItemsEqual([], ops.get_collection('my_weights'))
+
+  def test_get_sparse_tensors_dense_input(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=('omar', 'stringer', 'marlo'))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': (('marlo', ''), ('skywalker', 'omar'))
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=((0, 0), (1, 0), (1, 1)),
+              values=np.array((2, -1, 0), dtype=np.int64),
+              dense_shape=(2, 2)),
+          id_weight_pair.id_tensor.eval())
+
+  def test_get_sparse_tensors_default_value_in_vocabulary(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=('omar', 'stringer', 'marlo'),
+        default_value=2)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('marlo', 'skywalker', 'omar'),
+        dense_shape=(2, 2))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((2, 2, 0), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
+  def test_get_sparse_tensors_with_oov_buckets(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=('omar', 'stringer', 'marlo'),
+        num_oov_buckets=100)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1), (1, 2)),
+        values=('marlo', 'skywalker', 'omar', 'heisenberg'),
+        dense_shape=(2, 3))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((2, 33, 0, 62), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
+  def test_get_sparse_tensors_int32(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=np.array((30, 35, 11, 23, 22), dtype=np.int32),
+        dtype=dtypes.int32)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1), (2, 2)),
+        values=np.array((11, 100, 30, 22), dtype=np.int32),
+        dense_shape=(3, 3))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((2, -1, 0, 4), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
+  def test_get_sparse_tensors_int32_dense_input(self):
+    default_value = -100
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=np.array((30, 35, 11, 23, 22), dtype=np.int32),
+        dtype=dtypes.int32,
+        default_value=default_value)
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa':
+                np.array(
+                    ((11, -1, -1), (100, 30, -1), (-1, -1, 22)), dtype=np.int32)
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=((0, 0), (1, 0), (1, 1), (2, 2)),
+              values=np.array((2, default_value, 0, 4), dtype=np.int64),
+              dense_shape=(3, 3)),
+          id_weight_pair.id_tensor.eval())
+
+  def test_get_sparse_tensors_int32_with_oov_buckets(self):
+    column = fc.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=np.array((30, 35, 11, 23, 22), dtype=np.int32),
+        dtype=dtypes.int32,
+        num_oov_buckets=100)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1), (2, 2)),
+        values=(11, 100, 30, 22),
+        dense_shape=(3, 3))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((2, 60, 0, 4), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
+  def test_linear_model(self):
+    wire_column = fc_old.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=('omar', 'stringer', 'marlo'),
+        num_oov_buckets=1)
+    self.assertEqual(4, wire_column._num_buckets)
+    with ops.Graph().as_default():
+      predictions = fc.linear_model({
+          wire_column.name: sparse_tensor.SparseTensorValue(
+              indices=((0, 0), (1, 0), (1, 1)),
+              values=('marlo', 'skywalker', 'omar'),
+              dense_shape=(2, 2))
+      }, (wire_column,))
+      bias = get_linear_model_bias()
+      wire_var = get_linear_model_column_var(wire_column)
+      with _initialized_session():
+        self.assertAllClose((0.,), bias.eval())
+        self.assertAllClose(((0.,), (0.,), (0.,), (0.,)), wire_var.eval())
+        self.assertAllClose(((0.,), (0.,)), predictions.eval())
+        wire_var.assign(((1.,), (2.,), (3.,), (4.,))).eval()
+        # 'marlo' -> 2: wire_var[2] = 3
+        # 'skywalker' -> 3, 'omar' -> 0: wire_var[3] + wire_var[0] = 4+1 = 5
+        self.assertAllClose(((3.,), (5.,)), predictions.eval())
+
+  def test_keras_linear_model(self):
+    wire_column = fc_old.categorical_column_with_vocabulary_list(
+        key='aaa',
+        vocabulary_list=('omar', 'stringer', 'marlo'),
+        num_oov_buckets=1)
+    self.assertEqual(4, wire_column._num_buckets)
+    with ops.Graph().as_default():
+      predictions = get_keras_linear_model_predictions({
+          wire_column.name:
+              sparse_tensor.SparseTensorValue(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=('marlo', 'skywalker', 'omar'),
+                  dense_shape=(2, 2))
+      }, (wire_column,))
+      bias = get_linear_model_bias()
+      wire_var = get_linear_model_column_var(wire_column)
+      with _initialized_session():
+        self.assertAllClose((0.,), bias.eval())
+        self.assertAllClose(((0.,), (0.,), (0.,), (0.,)), wire_var.eval())
+        self.assertAllClose(((0.,), (0.,)), predictions.eval())
+        wire_var.assign(((1.,), (2.,), (3.,), (4.,))).eval()
+        # 'marlo' -> 2: wire_var[2] = 3
+        # 'skywalker' -> 3, 'omar' -> 0: wire_var[3] + wire_var[0] = 4+1 = 5
+        self.assertAllClose(((3.,), (5.,)), predictions.eval())
+
+
+class IdentityCategoricalColumnTest(test.TestCase):
+
+  def test_constructor(self):
+    column = fc.categorical_column_with_identity(key='aaa', num_buckets=3)
+    self.assertEqual('aaa', column.name)
+    self.assertEqual('aaa', column.key)
+    self.assertEqual(3, column.num_buckets)
+    self.assertEqual({
+        'aaa': parsing_ops.VarLenFeature(dtypes.int64)
+    }, column.parse_example_spec)
+
+  def test_key_should_be_string(self):
+    with self.assertRaisesRegexp(ValueError, 'key must be a string.'):
+      fc.categorical_column_with_identity(key=('aaa',), num_buckets=3)
+
+  def test_deep_copy(self):
+    original = fc.categorical_column_with_identity(key='aaa', num_buckets=3)
+    for column in (original, copy.deepcopy(original)):
+      self.assertEqual('aaa', column.name)
+      self.assertEqual(3, column.num_buckets)
+      self.assertEqual({
+          'aaa': parsing_ops.VarLenFeature(dtypes.int64)
+      }, column.parse_example_spec)
+
+  def test_invalid_num_buckets_zero(self):
+    with self.assertRaisesRegexp(ValueError, 'num_buckets 0 < 1'):
+      fc.categorical_column_with_identity(key='aaa', num_buckets=0)
+
+  def test_invalid_num_buckets_negative(self):
+    with self.assertRaisesRegexp(ValueError, 'num_buckets -1 < 1'):
+      fc.categorical_column_with_identity(key='aaa', num_buckets=-1)
+
+  def test_invalid_default_value_too_small(self):
+    with self.assertRaisesRegexp(ValueError, 'default_value -1 not in range'):
+      fc.categorical_column_with_identity(
+          key='aaa', num_buckets=3, default_value=-1)
+
+  def test_invalid_default_value_too_big(self):
+    with self.assertRaisesRegexp(ValueError, 'default_value 3 not in range'):
+      fc.categorical_column_with_identity(
+          key='aaa', num_buckets=3, default_value=3)
+
+  def test_invalid_input_dtype(self):
+    column = fc.categorical_column_with_identity(key='aaa', num_buckets=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('omar', 'stringer', 'marlo'),
+        dense_shape=(2, 2))
+    with self.assertRaisesRegexp(ValueError, 'Invalid input, not integer'):
+      column.get_sparse_tensors(
+          FeatureTransformationCache({
+              'aaa': inputs
+          }), None)
+
+  def test_parse_example(self):
+    a = fc.categorical_column_with_identity(key='aaa', num_buckets=30)
+    data = example_pb2.Example(features=feature_pb2.Features(
+        feature={
+            'aaa':
+                feature_pb2.Feature(int64_list=feature_pb2.Int64List(
+                    value=[11, 21]))
+        }))
+    features = parsing_ops.parse_example(
+        serialized=[data.SerializeToString()],
+        features=fc.make_parse_example_spec([a]))
+    self.assertIn('aaa', features)
+    with self.test_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=[[0, 0], [0, 1]],
+              values=np.array([11, 21], dtype=np.int64),
+              dense_shape=[1, 2]),
+          features['aaa'].eval())
+
+  def test_get_sparse_tensors(self):
+    column = fc.categorical_column_with_identity(key='aaa', num_buckets=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(0, 1, 0),
+        dense_shape=(2, 2))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((0, 1, 0), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
+  def test_transform_feature(self):
+    column = fc.categorical_column_with_identity(key='aaa', num_buckets=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(0, 1, 0),
+        dense_shape=(2, 2))
+    id_tensor = _transform_features({'aaa': inputs}, [column], None)[column]
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((0, 1, 0), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_tensor.eval())
+
+  def DISABLED_test_get_sparse_tensors_weight_collections(self):
+    column = fc.categorical_column_with_identity(key='aaa', num_buckets=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(0, 1, 0),
+        dense_shape=(2, 2))
+    column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }),
+        weight_collections=('my_weights',))
+
+    self.assertItemsEqual(
+        [], ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES))
+    self.assertItemsEqual([], ops.get_collection('my_weights'))
+
+  def test_get_sparse_tensors_dense_input(self):
+    column = fc.categorical_column_with_identity(key='aaa', num_buckets=3)
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': ((0, -1), (1, 0))
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=((0, 0), (1, 0), (1, 1)),
+              values=np.array((0, 1, 0), dtype=np.int64),
+              dense_shape=(2, 2)),
+          id_weight_pair.id_tensor.eval())
+
+  def test_get_sparse_tensors_with_inputs_too_small(self):
+    column = fc.categorical_column_with_identity(key='aaa', num_buckets=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(1, -1, 0),
+        dense_shape=(2, 2))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      with self.assertRaisesRegexp(
+          errors.OpError, 'assert_greater_or_equal_0'):
+        id_weight_pair.id_tensor.eval()
+
+  def test_get_sparse_tensors_with_inputs_too_big(self):
+    column = fc.categorical_column_with_identity(key='aaa', num_buckets=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(1, 99, 0),
+        dense_shape=(2, 2))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      with self.assertRaisesRegexp(
+          errors.OpError, 'assert_less_than_num_buckets'):
+        id_weight_pair.id_tensor.eval()
+
+  def test_get_sparse_tensors_with_default_value(self):
+    column = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=4, default_value=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(1, -1, 99),
+        dense_shape=(2, 2))
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array((1, 3, 3), dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_weight_pair.id_tensor.eval())
+
+  def test_get_sparse_tensors_with_default_value_and_placeholder_inputs(self):
+    column = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=4, default_value=3)
+    input_indices = array_ops.placeholder(dtype=dtypes.int64)
+    input_values = array_ops.placeholder(dtype=dtypes.int32)
+    input_shape = array_ops.placeholder(dtype=dtypes.int64)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=input_indices,
+        values=input_values,
+        dense_shape=input_shape)
+    id_weight_pair = column.get_sparse_tensors(
+        FeatureTransformationCache({
+            'aaa': inputs
+        }), None)
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=np.array(((0, 0), (1, 0), (1, 1)), dtype=np.int64),
+              values=np.array((1, 3, 3), dtype=np.int64),
+              dense_shape=np.array((2, 2), dtype=np.int64)),
+          id_weight_pair.id_tensor.eval(feed_dict={
+              input_indices: ((0, 0), (1, 0), (1, 1)),
+              input_values: (1, -1, 99),
+              input_shape: (2, 2),
+          }))
+
+  def test_linear_model(self):
+    column = fc_old.categorical_column_with_identity(key='aaa', num_buckets=3)
+    self.assertEqual(3, column.num_buckets)
+    with ops.Graph().as_default():
+      predictions = fc.linear_model({
+          column.name: sparse_tensor.SparseTensorValue(
+              indices=((0, 0), (1, 0), (1, 1)),
+              values=(0, 2, 1),
+              dense_shape=(2, 2))
+      }, (column,))
+      bias = get_linear_model_bias()
+      weight_var = get_linear_model_column_var(column)
+      with _initialized_session():
+        self.assertAllClose((0.,), bias.eval())
+        self.assertAllClose(((0.,), (0.,), (0.,)), weight_var.eval())
+        self.assertAllClose(((0.,), (0.,)), predictions.eval())
+        weight_var.assign(((1.,), (2.,), (3.,))).eval()
+        # weight_var[0] = 1
+        # weight_var[2] + weight_var[1] = 3+2 = 5
+        self.assertAllClose(((1.,), (5.,)), predictions.eval())
+
+  def test_keras_linear_model(self):
+    column = fc_old.categorical_column_with_identity(key='aaa', num_buckets=3)
+    self.assertEqual(3, column.num_buckets)
+    with ops.Graph().as_default():
+      predictions = get_keras_linear_model_predictions({
+          column.name:
+              sparse_tensor.SparseTensorValue(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=(0, 2, 1),
+                  dense_shape=(2, 2))
+      }, (column,))
+      bias = get_linear_model_bias()
+      weight_var = get_linear_model_column_var(column)
+      with _initialized_session():
+        self.assertAllClose((0.,), bias.eval())
+        self.assertAllClose(((0.,), (0.,), (0.,)), weight_var.eval())
+        self.assertAllClose(((0.,), (0.,)), predictions.eval())
+        weight_var.assign(((1.,), (2.,), (3.,))).eval()
+        # weight_var[0] = 1
+        # weight_var[2] + weight_var[1] = 3+2 = 5
+        self.assertAllClose(((1.,), (5.,)), predictions.eval())
+
+
+class TransformFeaturesTest(test.TestCase):
+
+  # All transform tests are distributed in column test.
+  # Here we only test multi column case and naming
+  def transform_multi_column(self):
+    bucketized_price = fc.bucketized_column(
+        fc.numeric_column('price'), boundaries=[0, 2, 4, 6])
+    hashed_sparse = fc.categorical_column_with_hash_bucket('wire', 10)
+    with ops.Graph().as_default():
+      features = {
+          'price': [[-1.], [5.]],
+          'wire':
+              sparse_tensor.SparseTensor(
+                  values=['omar', 'stringer', 'marlo'],
+                  indices=[[0, 0], [1, 0], [1, 1]],
+                  dense_shape=[2, 2])
+      }
+      transformed = _transform_features(features,
+                                        [bucketized_price, hashed_sparse], None)
+      with _initialized_session():
+        self.assertIn(bucketized_price.name, transformed[bucketized_price].name)
+        self.assertAllEqual([[0], [3]], transformed[bucketized_price].eval())
+        self.assertIn(hashed_sparse.name, transformed[hashed_sparse].name)
+        self.assertAllEqual([6, 4, 1], transformed[hashed_sparse].values.eval())
+
+  def test_column_order(self):
+    """When the column is both dense and sparse, uses sparse tensors."""
+
+    class _LoggerColumn(FeatureColumn):
+
+      def __init__(self, name):
+        self._name = name
+
+      @property
+      def name(self):
+        return self._name
+
+      def transform_feature(self, transformation_cache, state_manager):
+        self.call_order = call_logger['count']
+        call_logger['count'] += 1
+        return 'Anything'
+
+      @property
+      def parse_example_spec(self):
+        pass
+
+    with ops.Graph().as_default():
+      column1 = _LoggerColumn('1')
+      column2 = _LoggerColumn('2')
+      call_logger = {'count': 0}
+      _transform_features({}, [column1, column2], None)
+      self.assertEqual(0, column1.call_order)
+      self.assertEqual(1, column2.call_order)
+
+      call_logger = {'count': 0}
+      _transform_features({}, [column2, column1], None)
+      self.assertEqual(0, column1.call_order)
+      self.assertEqual(1, column2.call_order)
+
+
+class IndicatorColumnTest(test.TestCase):
+
+  def test_indicator_column(self):
+    a = fc.categorical_column_with_hash_bucket('a', 4)
+    indicator_a = fc.indicator_column(a)
+    self.assertEqual(indicator_a.categorical_column.name, 'a')
+    self.assertEqual(indicator_a.name, 'a_indicator')
+    self.assertEqual(indicator_a.variable_shape, [1, 4])
+
+    b = fc.categorical_column_with_hash_bucket('b', hash_bucket_size=100)
+    indicator_b = fc.indicator_column(b)
+    self.assertEqual(indicator_b.categorical_column.name, 'b')
+    self.assertEqual(indicator_b.name, 'b_indicator')
+    self.assertEqual(indicator_b.variable_shape, [1, 100])
+
+  def test_1D_shape_succeeds(self):
+    animal = fc.indicator_column(
+        fc.categorical_column_with_hash_bucket('animal', 4))
+    transformation_cache = FeatureTransformationCache({
+        'animal': ['fox', 'fox']
+    })
+    output = transformation_cache.get(animal, None)
+    with self.test_session():
+      self.assertAllEqual([[0., 0., 1., 0.], [0., 0., 1., 0.]], output.eval())
+
+  def test_2D_shape_succeeds(self):
+    # TODO(ispir/cassandrax): Swith to categorical_column_with_keys when ready.
+    animal = fc.indicator_column(
+        fc.categorical_column_with_hash_bucket('animal', 4))
+    transformation_cache = FeatureTransformationCache({
+        'animal':
+            sparse_tensor.SparseTensor(
+                indices=[[0, 0], [1, 0]],
+                values=['fox', 'fox'],
+                dense_shape=[2, 1])
+    })
+    output = transformation_cache.get(animal, None)
+    with self.test_session():
+      self.assertAllEqual([[0., 0., 1., 0.], [0., 0., 1., 0.]], output.eval())
+
+  def test_multi_hot(self):
+    animal = fc.indicator_column(
+        fc.categorical_column_with_identity('animal', num_buckets=4))
+
+    transformation_cache = FeatureTransformationCache({
+        'animal':
+            sparse_tensor.SparseTensor(
+                indices=[[0, 0], [0, 1]], values=[1, 1], dense_shape=[1, 2])
+    })
+    output = transformation_cache.get(animal, None)
+    with self.test_session():
+      self.assertAllEqual([[0., 2., 0., 0.]], output.eval())
+
+  def test_multi_hot2(self):
+    animal = fc.indicator_column(
+        fc.categorical_column_with_identity('animal', num_buckets=4))
+    transformation_cache = FeatureTransformationCache({
+        'animal':
+            sparse_tensor.SparseTensor(
+                indices=[[0, 0], [0, 1]], values=[1, 2], dense_shape=[1, 2])
+    })
+    output = transformation_cache.get(animal, None)
+    with self.test_session():
+      self.assertAllEqual([[0., 1., 1., 0.]], output.eval())
+
+  def test_deep_copy(self):
+    a = fc.categorical_column_with_hash_bucket('a', 4)
+    column = fc.indicator_column(a)
+    column_copy = copy.deepcopy(column)
+    self.assertEqual(column_copy.categorical_column.name, 'a')
+    self.assertEqual(column.name, 'a_indicator')
+    self.assertEqual(column.variable_shape, [1, 4])
+
+  def test_parse_example(self):
+    a = fc.categorical_column_with_vocabulary_list(
+        key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
+    a_indicator = fc.indicator_column(a)
+    data = example_pb2.Example(features=feature_pb2.Features(
+        feature={
+            'aaa':
+                feature_pb2.Feature(bytes_list=feature_pb2.BytesList(
+                    value=[b'omar', b'stringer']))
+        }))
+    features = parsing_ops.parse_example(
+        serialized=[data.SerializeToString()],
+        features=fc.make_parse_example_spec([a_indicator]))
+    self.assertIn('aaa', features)
+    with self.test_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=[[0, 0], [0, 1]],
+              values=np.array([b'omar', b'stringer'], dtype=np.object_),
+              dense_shape=[1, 2]),
+          features['aaa'].eval())
+
+  def test_transform(self):
+    a = fc.categorical_column_with_vocabulary_list(
+        key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
+    a_indicator = fc.indicator_column(a)
+    features = {
+        'aaa': sparse_tensor.SparseTensorValue(
+            indices=((0, 0), (1, 0), (1, 1)),
+            values=('marlo', 'skywalker', 'omar'),
+            dense_shape=(2, 2))
+    }
+    indicator_tensor = _transform_features(features, [a_indicator],
+                                           None)[a_indicator]
+    with _initialized_session():
+      self.assertAllEqual([[0, 0, 1], [1, 0, 0]], indicator_tensor.eval())
+
+  def test_transform_with_weighted_column(self):
+    # Github issue 12557
+    ids = fc.categorical_column_with_vocabulary_list(
+        key='ids', vocabulary_list=('a', 'b', 'c'))
+    weights = fc.weighted_categorical_column(ids, 'weights')
+    indicator = fc.indicator_column(weights)
+    features = {
+        'ids': constant_op.constant([['c', 'b', 'a']]),
+        'weights': constant_op.constant([[2., 4., 6.]])
+    }
+    indicator_tensor = _transform_features(features, [indicator],
+                                           None)[indicator]
+    with _initialized_session():
+      self.assertAllEqual([[6., 4., 2.]], indicator_tensor.eval())
+
+  def test_transform_with_missing_value_in_weighted_column(self):
+    # Github issue 12583
+    ids = fc.categorical_column_with_vocabulary_list(
+        key='ids', vocabulary_list=('a', 'b', 'c'))
+    weights = fc.weighted_categorical_column(ids, 'weights')
+    indicator = fc.indicator_column(weights)
+    features = {
+        'ids': constant_op.constant([['c', 'b', 'unknown']]),
+        'weights': constant_op.constant([[2., 4., 6.]])
+    }
+    indicator_tensor = _transform_features(features, [indicator],
+                                           None)[indicator]
+    with _initialized_session():
+      self.assertAllEqual([[0., 4., 2.]], indicator_tensor.eval())
+
+  def test_transform_with_missing_value_in_categorical_column(self):
+    # Github issue 12583
+    ids = fc.categorical_column_with_vocabulary_list(
+        key='ids', vocabulary_list=('a', 'b', 'c'))
+    indicator = fc.indicator_column(ids)
+    features = {
+        'ids': constant_op.constant([['c', 'b', 'unknown']]),
+    }
+    indicator_tensor = _transform_features(features, [indicator],
+                                           None)[indicator]
+    with _initialized_session():
+      self.assertAllEqual([[0., 1., 1.]], indicator_tensor.eval())
+
+  def test_linear_model(self):
+    animal = fc_old.indicator_column(
+        fc_old.categorical_column_with_identity('animal', num_buckets=4))
+    with ops.Graph().as_default():
+      features = {
+          'animal':
+              sparse_tensor.SparseTensor(
+                  indices=[[0, 0], [0, 1]], values=[1, 2], dense_shape=[1, 2])
+      }
+
+      predictions = fc.linear_model(features, [animal])
+      weight_var = get_linear_model_column_var(animal)
+      with _initialized_session():
+        # All should be zero-initialized.
+        self.assertAllClose([[0.], [0.], [0.], [0.]], weight_var.eval())
+        self.assertAllClose([[0.]], predictions.eval())
+        weight_var.assign([[1.], [2.], [3.], [4.]]).eval()
+        self.assertAllClose([[2. + 3.]], predictions.eval())
+
+  def test_keras_linear_model(self):
+    animal = fc_old.indicator_column(
+        fc_old.categorical_column_with_identity('animal', num_buckets=4))
+    with ops.Graph().as_default():
+      features = {
+          'animal':
+              sparse_tensor.SparseTensor(
+                  indices=[[0, 0], [0, 1]], values=[1, 2], dense_shape=[1, 2])
+      }
+
+      predictions = get_keras_linear_model_predictions(features, [animal])
+      weight_var = get_linear_model_column_var(animal)
+      with _initialized_session():
+        # All should be zero-initialized.
+        self.assertAllClose([[0.], [0.], [0.], [0.]], weight_var.eval())
+        self.assertAllClose([[0.]], predictions.eval())
+        weight_var.assign([[1.], [2.], [3.], [4.]]).eval()
+        self.assertAllClose([[2. + 3.]], predictions.eval())
+
+  def test_input_layer(self):
+    animal = fc_old.indicator_column(
+        fc_old.categorical_column_with_identity('animal', num_buckets=4))
+    with ops.Graph().as_default():
+      features = {
+          'animal':
+              sparse_tensor.SparseTensor(
+                  indices=[[0, 0], [0, 1]], values=[1, 2], dense_shape=[1, 2])
+      }
+      net = fc.input_layer(features, [animal])
+      with _initialized_session():
+        self.assertAllClose([[0., 1., 1., 0.]], net.eval())
+
+
+class _TestStateManager(StateManager):
+
+  def __init__(self, trainable=True):
+    # Dict of feature_column to a dict of variables.
+    self._all_variables = {}
+    self._trainable = trainable
+
+  def get_variable(self,
+                   feature_column,
+                   name,
+                   shape,
+                   dtype=None,
+                   initializer=None):
+    if feature_column not in self._all_variables:
+      self._all_variables[feature_column] = {}
+    var_dict = self._all_variables[feature_column]
+    if name in var_dict:
+      return var_dict[name]
+    else:
+      var = variable_scope.get_variable(
+          name=name,
+          shape=shape,
+          initializer=initializer,
+          trainable=self._trainable)
+      var_dict[name] = var
+      return var
+
+
+class EmbeddingColumnTest(test.TestCase):
+
+  def test_defaults(self):
+    categorical_column = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=3)
+    embedding_dimension = 2
+    embedding_column = fc.embedding_column(
+        categorical_column, dimension=embedding_dimension)
+    self.assertIs(categorical_column, embedding_column.categorical_column)
+    self.assertEqual(embedding_dimension, embedding_column.dimension)
+    self.assertEqual('mean', embedding_column.combiner)
+    self.assertIsNone(embedding_column.ckpt_to_load_from)
+    self.assertIsNone(embedding_column.tensor_name_in_ckpt)
+    self.assertIsNone(embedding_column.max_norm)
+    self.assertTrue(embedding_column.trainable)
+    self.assertEqual('aaa_embedding', embedding_column.name)
+    self.assertEqual((embedding_dimension,), embedding_column.variable_shape)
+    self.assertEqual({
+        'aaa': parsing_ops.VarLenFeature(dtypes.int64)
+    }, embedding_column.parse_example_spec)
+
+  def test_all_constructor_args(self):
+    categorical_column = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=3)
+    embedding_dimension = 2
+    embedding_column = fc.embedding_column(
+        categorical_column, dimension=embedding_dimension,
+        combiner='my_combiner', initializer=lambda: 'my_initializer',
+        ckpt_to_load_from='my_ckpt', tensor_name_in_ckpt='my_ckpt_tensor',
+        max_norm=42., trainable=False)
+    self.assertIs(categorical_column, embedding_column.categorical_column)
+    self.assertEqual(embedding_dimension, embedding_column.dimension)
+    self.assertEqual('my_combiner', embedding_column.combiner)
+    self.assertEqual('my_ckpt', embedding_column.ckpt_to_load_from)
+    self.assertEqual('my_ckpt_tensor', embedding_column.tensor_name_in_ckpt)
+    self.assertEqual(42., embedding_column.max_norm)
+    self.assertFalse(embedding_column.trainable)
+    self.assertEqual('aaa_embedding', embedding_column.name)
+    self.assertEqual((embedding_dimension,), embedding_column.variable_shape)
+    self.assertEqual({
+        'aaa': parsing_ops.VarLenFeature(dtypes.int64)
+    }, embedding_column.parse_example_spec)
+
+  def test_deep_copy(self):
+    categorical_column = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=3)
+    embedding_dimension = 2
+    original = fc.embedding_column(
+        categorical_column, dimension=embedding_dimension,
+        combiner='my_combiner', initializer=lambda: 'my_initializer',
+        ckpt_to_load_from='my_ckpt', tensor_name_in_ckpt='my_ckpt_tensor',
+        max_norm=42., trainable=False)
+    for embedding_column in (original, copy.deepcopy(original)):
+      self.assertEqual('aaa', embedding_column.categorical_column.name)
+      self.assertEqual(3, embedding_column.categorical_column.num_buckets)
+      self.assertEqual({
+          'aaa': parsing_ops.VarLenFeature(dtypes.int64)
+      }, embedding_column.categorical_column.parse_example_spec)
+
+      self.assertEqual(embedding_dimension, embedding_column.dimension)
+      self.assertEqual('my_combiner', embedding_column.combiner)
+      self.assertEqual('my_ckpt', embedding_column.ckpt_to_load_from)
+      self.assertEqual('my_ckpt_tensor', embedding_column.tensor_name_in_ckpt)
+      self.assertEqual(42., embedding_column.max_norm)
+      self.assertFalse(embedding_column.trainable)
+      self.assertEqual('aaa_embedding', embedding_column.name)
+      self.assertEqual((embedding_dimension,), embedding_column.variable_shape)
+      self.assertEqual({
+          'aaa': parsing_ops.VarLenFeature(dtypes.int64)
+      }, embedding_column.parse_example_spec)
+
+  def test_invalid_initializer(self):
+    categorical_column = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=3)
+    with self.assertRaisesRegexp(ValueError, 'initializer must be callable'):
+      fc.embedding_column(categorical_column, dimension=2, initializer='not_fn')
+
+  def test_parse_example(self):
+    a = fc.categorical_column_with_vocabulary_list(
+        key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
+    a_embedded = fc.embedding_column(a, dimension=2)
+    data = example_pb2.Example(features=feature_pb2.Features(
+        feature={
+            'aaa':
+                feature_pb2.Feature(bytes_list=feature_pb2.BytesList(
+                    value=[b'omar', b'stringer']))
+        }))
+    features = parsing_ops.parse_example(
+        serialized=[data.SerializeToString()],
+        features=fc.make_parse_example_spec([a_embedded]))
+    self.assertIn('aaa', features)
+    with self.test_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=[[0, 0], [0, 1]],
+              values=np.array([b'omar', b'stringer'], dtype=np.object_),
+              dense_shape=[1, 2]),
+          features['aaa'].eval())
+
+  def test_transform_feature(self):
+    a = fc.categorical_column_with_identity(key='aaa', num_buckets=3)
+    a_embedded = fc.embedding_column(a, dimension=2)
+    features = {
+        'aaa': sparse_tensor.SparseTensor(
+            indices=((0, 0), (1, 0), (1, 1)),
+            values=(0, 1, 0),
+            dense_shape=(2, 2))
+    }
+    outputs = _transform_features(features, [a, a_embedded], None)
+    output_a = outputs[a]
+    output_embedded = outputs[a_embedded]
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self, output_a.eval(), output_embedded.eval())
+
+  def test_get_dense_tensor(self):
+    # Inputs.
+    vocabulary_size = 3
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        # example 2, ids []
+        # example 3, ids [1]
+        indices=((0, 0), (1, 0), (1, 4), (3, 0)),
+        values=(2, 0, 1, 1),
+        dense_shape=(4, 5))
+
+    # Embedding variable.
+    embedding_dimension = 2
+    embedding_values = (
+        (1., 2.),  # id 0
+        (3., 5.),  # id 1
+        (7., 11.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return embedding_values
+
+    # Expected lookup result, using combiner='mean'.
+    expected_lookups = (
+        # example 0, ids [2], embedding = [7, 11]
+        (7., 11.),
+        # example 1, ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
+        (2., 3.5),
+        # example 2, ids [], embedding = [0, 0]
+        (0., 0.),
+        # example 3, ids [1], embedding = [3, 5]
+        (3., 5.),
+    )
+
+    # Build columns.
+    categorical_column = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column = fc.embedding_column(
+        categorical_column, dimension=embedding_dimension,
+        initializer=_initializer)
+    state_manager = _TestStateManager()
+
+    # Provide sparse input and get dense result.
+    embedding_lookup = embedding_column.get_dense_tensor(
+        FeatureTransformationCache({
+            'aaa': sparse_input
+        }), state_manager)
+
+    # Assert expected embedding variable and lookups.
+    global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+    self.assertItemsEqual(('embedding_weights:0',),
+                          tuple([v.name for v in global_vars]))
+    with _initialized_session():
+      self.assertAllEqual(embedding_values, global_vars[0].eval())
+      self.assertAllEqual(expected_lookups, embedding_lookup.eval())
+
+  def test_get_dense_tensor_3d(self):
+    # Inputs.
+    vocabulary_size = 4
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        # example 2, ids []
+        # example 3, ids [1]
+        indices=((0, 0, 0), (1, 1, 0), (1, 1, 4), (3, 0, 0), (3, 1, 2)),
+        values=(2, 0, 1, 1, 2),
+        dense_shape=(4, 2, 5))
+
+    # Embedding variable.
+    embedding_dimension = 3
+    embedding_values = (
+        (1., 2., 4.),   # id 0
+        (3., 5., 1.),   # id 1
+        (7., 11., 2.),  # id 2
+        (2., 7., 12.)   # id 3
+    )
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return embedding_values
+
+    # Expected lookup result, using combiner='mean'.
+    expected_lookups = (
+        # example 0, ids [[2], []], embedding = [[7, 11, 2], [0, 0, 0]]
+        ((7., 11., 2.), (0., 0., 0.)),
+        # example 1, ids [[], [0, 1]], embedding
+        # = mean([[], [1, 2, 4] + [3, 5, 1]]) = [[0, 0, 0], [2, 3.5, 2.5]]
+        ((0., 0., 0.), (2., 3.5, 2.5)),
+        # example 2, ids [[], []], embedding = [[0, 0, 0], [0, 0, 0]]
+        ((0., 0., 0.), (0., 0., 0.)),
+        # example 3, ids [[1], [2]], embedding = [[3, 5, 1], [7, 11, 2]]
+        ((3., 5., 1.), (7., 11., 2.)),
+    )
+
+    # Build columns.
+    categorical_column = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column = fc.embedding_column(
+        categorical_column, dimension=embedding_dimension,
+        initializer=_initializer)
+    state_manager = _TestStateManager()
+
+    # Provide sparse input and get dense result.
+    embedding_lookup = embedding_column.get_dense_tensor(
+        FeatureTransformationCache({
+            'aaa': sparse_input
+        }), state_manager)
+
+    # Assert expected embedding variable and lookups.
+    global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+    self.assertItemsEqual(('embedding_weights:0',),
+                          tuple([v.name for v in global_vars]))
+    with _initialized_session():
+      self.assertAllEqual(embedding_values, global_vars[0].eval())
+      self.assertAllEqual(expected_lookups, embedding_lookup.eval())
+
+  def DISABLED_test_get_dense_tensor_weight_collections(self):
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        # example 2, ids []
+        # example 3, ids [1]
+        indices=((0, 0), (1, 0), (1, 4), (3, 0)),
+        values=(2, 0, 1, 1),
+        dense_shape=(4, 5))
+
+    # Build columns.
+    categorical_column = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=3)
+    embedding_column = fc.embedding_column(categorical_column, dimension=2)
+
+    # Provide sparse input and get dense result.
+    embedding_column.get_dense_tensor(
+        FeatureTransformationCache({
+            'aaa': sparse_input
+        }),
+        weight_collections=('my_vars',))
+
+    # Assert expected embedding variable and lookups.
+    global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+    self.assertItemsEqual(('embedding_weights:0',),
+                          tuple([v.name for v in global_vars]))
+    my_vars = ops.get_collection('my_vars')
+    self.assertItemsEqual(
+        ('embedding_weights:0',), tuple([v.name for v in my_vars]))
+
+  def test_get_dense_tensor_placeholder_inputs(self):
+    # Inputs.
+    vocabulary_size = 3
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        # example 2, ids []
+        # example 3, ids [1]
+        indices=((0, 0), (1, 0), (1, 4), (3, 0)),
+        values=(2, 0, 1, 1),
+        dense_shape=(4, 5))
+
+    # Embedding variable.
+    embedding_dimension = 2
+    embedding_values = (
+        (1., 2.),  # id 0
+        (3., 5.),  # id 1
+        (7., 11.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return embedding_values
+
+    # Expected lookup result, using combiner='mean'.
+    expected_lookups = (
+        # example 0, ids [2], embedding = [7, 11]
+        (7., 11.),
+        # example 1, ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
+        (2., 3.5),
+        # example 2, ids [], embedding = [0, 0]
+        (0., 0.),
+        # example 3, ids [1], embedding = [3, 5]
+        (3., 5.),
+    )
+
+    # Build columns.
+    categorical_column = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column = fc.embedding_column(
+        categorical_column, dimension=embedding_dimension,
+        initializer=_initializer)
+    state_manager = _TestStateManager()
+
+    # Provide sparse input and get dense result.
+    input_indices = array_ops.placeholder(dtype=dtypes.int64)
+    input_values = array_ops.placeholder(dtype=dtypes.int64)
+    input_shape = array_ops.placeholder(dtype=dtypes.int64)
+    embedding_lookup = embedding_column.get_dense_tensor(
+        FeatureTransformationCache({
+            'aaa':
+                sparse_tensor.SparseTensorValue(
+                    indices=input_indices,
+                    values=input_values,
+                    dense_shape=input_shape)
+        }), state_manager)
+
+    # Assert expected embedding variable and lookups.
+    global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+    self.assertItemsEqual(
+        ('embedding_weights:0',), tuple([v.name for v in global_vars]))
+    with _initialized_session():
+      self.assertAllEqual(embedding_values, global_vars[0].eval())
+      self.assertAllEqual(expected_lookups, embedding_lookup.eval(
+          feed_dict={
+              input_indices: sparse_input.indices,
+              input_values: sparse_input.values,
+              input_shape: sparse_input.dense_shape,
+          }))
+
+  def test_get_dense_tensor_restore_from_ckpt(self):
+    # Inputs.
+    vocabulary_size = 3
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        # example 2, ids []
+        # example 3, ids [1]
+        indices=((0, 0), (1, 0), (1, 4), (3, 0)),
+        values=(2, 0, 1, 1),
+        dense_shape=(4, 5))
+
+    # Embedding variable. The checkpoint file contains _embedding_values.
+    embedding_dimension = 2
+    embedding_values = (
+        (1., 2.),  # id 0
+        (3., 5.),  # id 1
+        (7., 11.)  # id 2
+    )
+    ckpt_path = test.test_src_dir_path(
+        'python/feature_column/testdata/embedding.ckpt')
+    ckpt_tensor = 'my_embedding'
+
+    # Expected lookup result, using combiner='mean'.
+    expected_lookups = (
+        # example 0, ids [2], embedding = [7, 11]
+        (7., 11.),
+        # example 1, ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
+        (2., 3.5),
+        # example 2, ids [], embedding = [0, 0]
+        (0., 0.),
+        # example 3, ids [1], embedding = [3, 5]
+        (3., 5.),
+    )
+
+    # Build columns.
+    categorical_column = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column = fc.embedding_column(
+        categorical_column, dimension=embedding_dimension,
+        ckpt_to_load_from=ckpt_path,
+        tensor_name_in_ckpt=ckpt_tensor)
+    state_manager = _TestStateManager()
+
+    # Provide sparse input and get dense result.
+    embedding_lookup = embedding_column.get_dense_tensor(
+        FeatureTransformationCache({
+            'aaa': sparse_input
+        }), state_manager)
+
+    # Assert expected embedding variable and lookups.
+    global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+    self.assertItemsEqual(
+        ('embedding_weights:0',), tuple([v.name for v in global_vars]))
+    with _initialized_session():
+      self.assertAllEqual(embedding_values, global_vars[0].eval())
+      self.assertAllEqual(expected_lookups, embedding_lookup.eval())
+
+  def test_linear_model(self):
+    # Inputs.
+    batch_size = 4
+    vocabulary_size = 3
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        # example 2, ids []
+        # example 3, ids [1]
+        indices=((0, 0), (1, 0), (1, 4), (3, 0)),
+        values=(2, 0, 1, 1),
+        dense_shape=(batch_size, 5))
+
+    # Embedding variable.
+    embedding_dimension = 2
+    embedding_shape = (vocabulary_size, embedding_dimension)
+    zeros_embedding_values = np.zeros(embedding_shape)
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual(embedding_shape, shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return zeros_embedding_values
+
+    # Build columns.
+    categorical_column = fc_old.categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column = fc_old.embedding_column(
+        categorical_column,
+        dimension=embedding_dimension,
+        initializer=_initializer)
+
+    with ops.Graph().as_default():
+      predictions = fc.linear_model({
+          categorical_column.name: sparse_input
+      }, (embedding_column,))
+      expected_var_names = (
+          'linear_model/bias_weights:0',
+          'linear_model/aaa_embedding/weights:0',
+          'linear_model/aaa_embedding/embedding_weights:0',
+      )
+      self.assertItemsEqual(
+          expected_var_names,
+          [v.name for v in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)])
+      trainable_vars = {
+          v.name: v for v in ops.get_collection(
+              ops.GraphKeys.TRAINABLE_VARIABLES)
+      }
+      self.assertItemsEqual(expected_var_names, trainable_vars.keys())
+      bias = trainable_vars['linear_model/bias_weights:0']
+      embedding_weights = trainable_vars[
+          'linear_model/aaa_embedding/embedding_weights:0']
+      linear_weights = trainable_vars[
+          'linear_model/aaa_embedding/weights:0']
+      with _initialized_session():
+        # Predictions with all zero weights.
+        self.assertAllClose(np.zeros((1,)), bias.eval())
+        self.assertAllClose(zeros_embedding_values, embedding_weights.eval())
+        self.assertAllClose(
+            np.zeros((embedding_dimension, 1)), linear_weights.eval())
+        self.assertAllClose(np.zeros((batch_size, 1)), predictions.eval())
+
+        # Predictions with all non-zero weights.
+        embedding_weights.assign((
+            (1., 2.),  # id 0
+            (3., 5.),  # id 1
+            (7., 11.)  # id 2
+        )).eval()
+        linear_weights.assign(((4.,), (6.,))).eval()
+        # example 0, ids [2], embedding[0] = [7, 11]
+        # example 1, ids [0, 1], embedding[1] = mean([1, 2] + [3, 5]) = [2, 3.5]
+        # example 2, ids [], embedding[2] = [0, 0]
+        # example 3, ids [1], embedding[3] = [3, 5]
+        # sum(embeddings * linear_weights)
+        # = [4*7 + 6*11, 4*2 + 6*3.5, 4*0 + 6*0, 4*3 + 6*5] = [94, 29, 0, 42]
+        self.assertAllClose(((94.,), (29.,), (0.,), (42.,)), predictions.eval())
+
+  def test_keras_linear_model(self):
+    # Inputs.
+    batch_size = 4
+    vocabulary_size = 3
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        # example 2, ids []
+        # example 3, ids [1]
+        indices=((0, 0), (1, 0), (1, 4), (3, 0)),
+        values=(2, 0, 1, 1),
+        dense_shape=(batch_size, 5))
+
+    # Embedding variable.
+    embedding_dimension = 2
+    embedding_shape = (vocabulary_size, embedding_dimension)
+    zeros_embedding_values = np.zeros(embedding_shape)
+
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual(embedding_shape, shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return zeros_embedding_values
+
+    # Build columns.
+    categorical_column = fc_old.categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column = fc_old.embedding_column(
+        categorical_column,
+        dimension=embedding_dimension,
+        initializer=_initializer)
+
+    with ops.Graph().as_default():
+      predictions = get_keras_linear_model_predictions({
+          categorical_column.name: sparse_input
+      }, (embedding_column,))
+      expected_var_names = (
+          'linear_model/bias_weights:0',
+          'linear_model/aaa_embedding/weights:0',
+          'linear_model/aaa_embedding/embedding_weights:0',
+      )
+      self.assertItemsEqual(
+          expected_var_names,
+          [v.name for v in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)])
+      trainable_vars = {
+          v.name: v
+          for v in ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
+      }
+      self.assertItemsEqual(expected_var_names, trainable_vars.keys())
+      bias = trainable_vars['linear_model/bias_weights:0']
+      embedding_weights = trainable_vars[
+          'linear_model/aaa_embedding/embedding_weights:0']
+      linear_weights = trainable_vars['linear_model/aaa_embedding/weights:0']
+      with _initialized_session():
+        # Predictions with all zero weights.
+        self.assertAllClose(np.zeros((1,)), bias.eval())
+        self.assertAllClose(zeros_embedding_values, embedding_weights.eval())
+        self.assertAllClose(
+            np.zeros((embedding_dimension, 1)), linear_weights.eval())
+        self.assertAllClose(np.zeros((batch_size, 1)), predictions.eval())
+
+        # Predictions with all non-zero weights.
+        embedding_weights.assign((
+            (1., 2.),  # id 0
+            (3., 5.),  # id 1
+            (7., 11.)  # id 2
+        )).eval()
+        linear_weights.assign(((4.,), (6.,))).eval()
+        # example 0, ids [2], embedding[0] = [7, 11]
+        # example 1, ids [0, 1], embedding[1] = mean([1, 2] + [3, 5]) = [2, 3.5]
+        # example 2, ids [], embedding[2] = [0, 0]
+        # example 3, ids [1], embedding[3] = [3, 5]
+        # sum(embeddings * linear_weights)
+        # = [4*7 + 6*11, 4*2 + 6*3.5, 4*0 + 6*0, 4*3 + 6*5] = [94, 29, 0, 42]
+        self.assertAllClose(((94.,), (29.,), (0.,), (42.,)), predictions.eval())
+
+  def test_input_layer(self):
+    # Inputs.
+    vocabulary_size = 3
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        # example 2, ids []
+        # example 3, ids [1]
+        indices=((0, 0), (1, 0), (1, 4), (3, 0)),
+        values=(2, 0, 1, 1),
+        dense_shape=(4, 5))
+
+    # Embedding variable.
+    embedding_dimension = 2
+    embedding_values = (
+        (1., 2.),  # id 0
+        (3., 5.),  # id 1
+        (7., 11.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return embedding_values
+
+    # Expected lookup result, using combiner='mean'.
+    expected_lookups = (
+        # example 0, ids [2], embedding = [7, 11]
+        (7., 11.),
+        # example 1, ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
+        (2., 3.5),
+        # example 2, ids [], embedding = [0, 0]
+        (0., 0.),
+        # example 3, ids [1], embedding = [3, 5]
+        (3., 5.),
+    )
+
+    # Build columns.
+    categorical_column = fc_old.categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column = fc_old.embedding_column(
+        categorical_column,
+        dimension=embedding_dimension,
+        initializer=_initializer)
+
+    # Provide sparse input and get dense result.
+    input_layer = fc.input_layer({'aaa': sparse_input}, (embedding_column,))
+
+    # Assert expected embedding variable and lookups.
+    global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+    self.assertItemsEqual(
+        ('input_layer/aaa_embedding/embedding_weights:0',),
+        tuple([v.name for v in global_vars]))
+    trainable_vars = ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
+    self.assertItemsEqual(
+        ('input_layer/aaa_embedding/embedding_weights:0',),
+        tuple([v.name for v in trainable_vars]))
+    with _initialized_session():
+      self.assertAllEqual(embedding_values, trainable_vars[0].eval())
+      self.assertAllEqual(expected_lookups, input_layer.eval())
+
+  def test_input_layer_not_trainable(self):
+    # Inputs.
+    vocabulary_size = 3
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        # example 2, ids []
+        # example 3, ids [1]
+        indices=((0, 0), (1, 0), (1, 4), (3, 0)),
+        values=(2, 0, 1, 1),
+        dense_shape=(4, 5))
+
+    # Embedding variable.
+    embedding_dimension = 2
+    embedding_values = (
+        (1., 2.),  # id 0
+        (3., 5.),  # id 1
+        (7., 11.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return embedding_values
+
+    # Expected lookup result, using combiner='mean'.
+    expected_lookups = (
+        # example 0, ids [2], embedding = [7, 11]
+        (7., 11.),
+        # example 1, ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
+        (2., 3.5),
+        # example 2, ids [], embedding = [0, 0]
+        (0., 0.),
+        # example 3, ids [1], embedding = [3, 5]
+        (3., 5.),
+    )
+
+    # Build columns.
+    categorical_column = fc_old.categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column = fc_old.embedding_column(
+        categorical_column,
+        dimension=embedding_dimension,
+        initializer=_initializer,
+        trainable=False)
+
+    # Provide sparse input and get dense result.
+    input_layer = fc.input_layer({'aaa': sparse_input}, (embedding_column,))
+
+    # Assert expected embedding variable and lookups.
+    global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+    self.assertItemsEqual(
+        ('input_layer/aaa_embedding/embedding_weights:0',),
+        tuple([v.name for v in global_vars]))
+    self.assertItemsEqual(
+        [], ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES))
+    with _initialized_session():
+      self.assertAllEqual(embedding_values, global_vars[0].eval())
+      self.assertAllEqual(expected_lookups, input_layer.eval())
+
+
+class _TestSharedEmbeddingStateManager(StateManager):
+  """Manages the state for shared embedding columns.
+
+  This can handle multiple groups of shared embedding columns.
+  """
+
+  def __init__(self, trainable=True):
+    # Dict of shared_embedding_collection_name to a dict of variables.
+    self._all_variables = {}
+    self._trainable = trainable
+
+  def get_variable(self,
+                   feature_column,
+                   name,
+                   shape,
+                   dtype=None,
+                   initializer=None):
+    if not isinstance(feature_column, fc.SharedEmbeddingColumn):
+      raise ValueError(
+          'SharedEmbeddingStateManager can only handle SharedEmbeddingColumns. '
+          'Given type: {} '.format(type(feature_column)))
+
+    collection_name = feature_column.shared_collection_name
+    if collection_name not in self._all_variables:
+      self._all_variables[collection_name] = {}
+    var_dict = self._all_variables[collection_name]
+    if name in var_dict:
+      return var_dict[name]
+    else:
+      var = variable_scope.get_variable(
+          name=name,
+          shape=shape,
+          initializer=initializer,
+          trainable=self._trainable)
+      var_dict[name] = var
+      return var
+
+
+class SharedEmbeddingColumnTest(test.TestCase):
+
+  def test_defaults(self):
+    categorical_column_a = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=3)
+    categorical_column_b = fc.categorical_column_with_identity(
+        key='bbb', num_buckets=3)
+    embedding_dimension = 2
+    embedding_column_b, embedding_column_a = fc.shared_embedding_columns(
+        [categorical_column_b, categorical_column_a],
+        dimension=embedding_dimension)
+    self.assertIs(categorical_column_a, embedding_column_a.categorical_column)
+    self.assertIs(categorical_column_b, embedding_column_b.categorical_column)
+    self.assertEqual(embedding_dimension, embedding_column_a.dimension)
+    self.assertEqual(embedding_dimension, embedding_column_b.dimension)
+    self.assertEqual('mean', embedding_column_a.combiner)
+    self.assertEqual('mean', embedding_column_b.combiner)
+    self.assertIsNone(embedding_column_a.ckpt_to_load_from)
+    self.assertIsNone(embedding_column_b.ckpt_to_load_from)
+    self.assertEqual('aaa_bbb_shared_embedding',
+                     embedding_column_a.shared_collection_name)
+    self.assertEqual('aaa_bbb_shared_embedding',
+                     embedding_column_b.shared_collection_name)
+    self.assertIsNone(embedding_column_a.tensor_name_in_ckpt)
+    self.assertIsNone(embedding_column_b.tensor_name_in_ckpt)
+    self.assertIsNone(embedding_column_a.max_norm)
+    self.assertIsNone(embedding_column_b.max_norm)
+    self.assertTrue(embedding_column_a.trainable)
+    self.assertTrue(embedding_column_b.trainable)
+    self.assertEqual('aaa_shared_embedding', embedding_column_a.name)
+    self.assertEqual('bbb_shared_embedding', embedding_column_b.name)
+    self.assertEqual((embedding_dimension,), embedding_column_a.variable_shape)
+    self.assertEqual((embedding_dimension,), embedding_column_b.variable_shape)
+    self.assertEqual({
+        'aaa': parsing_ops.VarLenFeature(dtypes.int64)
+    }, embedding_column_a.parse_example_spec)
+    self.assertEqual({
+        'bbb': parsing_ops.VarLenFeature(dtypes.int64)
+    }, embedding_column_b.parse_example_spec)
+
+  def test_all_constructor_args(self):
+    categorical_column_a = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=3)
+    categorical_column_b = fc.categorical_column_with_identity(
+        key='bbb', num_buckets=3)
+    embedding_dimension = 2
+    embedding_column_a, embedding_column_b = fc.shared_embedding_columns(
+        [categorical_column_a, categorical_column_b],
+        dimension=embedding_dimension,
+        combiner='my_combiner',
+        initializer=lambda: 'my_initializer',
+        shared_embedding_collection_name='shared_embedding_collection_name',
+        ckpt_to_load_from='my_ckpt',
+        tensor_name_in_ckpt='my_ckpt_tensor',
+        max_norm=42.,
+        trainable=False)
+    self.assertIs(categorical_column_a, embedding_column_a.categorical_column)
+    self.assertIs(categorical_column_b, embedding_column_b.categorical_column)
+    self.assertEqual(embedding_dimension, embedding_column_a.dimension)
+    self.assertEqual(embedding_dimension, embedding_column_b.dimension)
+    self.assertEqual('my_combiner', embedding_column_a.combiner)
+    self.assertEqual('my_combiner', embedding_column_b.combiner)
+    self.assertEqual('shared_embedding_collection_name',
+                     embedding_column_a.shared_collection_name)
+    self.assertEqual('shared_embedding_collection_name',
+                     embedding_column_b.shared_collection_name)
+    self.assertEqual('my_ckpt', embedding_column_a.ckpt_to_load_from)
+    self.assertEqual('my_ckpt', embedding_column_b.ckpt_to_load_from)
+    self.assertEqual('my_ckpt_tensor', embedding_column_a.tensor_name_in_ckpt)
+    self.assertEqual('my_ckpt_tensor', embedding_column_b.tensor_name_in_ckpt)
+    self.assertEqual(42., embedding_column_a.max_norm)
+    self.assertEqual(42., embedding_column_b.max_norm)
+    self.assertFalse(embedding_column_a.trainable)
+    self.assertFalse(embedding_column_b.trainable)
+    self.assertEqual('aaa_shared_embedding', embedding_column_a.name)
+    self.assertEqual('bbb_shared_embedding', embedding_column_b.name)
+    self.assertEqual((embedding_dimension,), embedding_column_a.variable_shape)
+    self.assertEqual((embedding_dimension,), embedding_column_b.variable_shape)
+    self.assertEqual({
+        'aaa': parsing_ops.VarLenFeature(dtypes.int64)
+    }, embedding_column_a.parse_example_spec)
+    self.assertEqual({
+        'bbb': parsing_ops.VarLenFeature(dtypes.int64)
+    }, embedding_column_b.parse_example_spec)
+
+  def test_deep_copy(self):
+    categorical_column_a = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=3)
+    categorical_column_b = fc.categorical_column_with_identity(
+        key='bbb', num_buckets=3)
+    embedding_dimension = 2
+    original_a, _ = fc.shared_embedding_columns(
+        [categorical_column_a, categorical_column_b],
+        dimension=embedding_dimension,
+        combiner='my_combiner',
+        initializer=lambda: 'my_initializer',
+        shared_embedding_collection_name='shared_embedding_collection_name',
+        ckpt_to_load_from='my_ckpt',
+        tensor_name_in_ckpt='my_ckpt_tensor',
+        max_norm=42., trainable=False)
+    for embedding_column_a in (original_a, copy.deepcopy(original_a)):
+      self.assertEqual('aaa', embedding_column_a.categorical_column.name)
+      self.assertEqual(3, embedding_column_a.categorical_column.num_buckets)
+      self.assertEqual({
+          'aaa': parsing_ops.VarLenFeature(dtypes.int64)
+      }, embedding_column_a.categorical_column.parse_example_spec)
+
+      self.assertEqual(embedding_dimension, embedding_column_a.dimension)
+      self.assertEqual('my_combiner', embedding_column_a.combiner)
+      self.assertEqual('shared_embedding_collection_name',
+                       embedding_column_a.shared_collection_name)
+      self.assertEqual('my_ckpt', embedding_column_a.ckpt_to_load_from)
+      self.assertEqual('my_ckpt_tensor', embedding_column_a.tensor_name_in_ckpt)
+      self.assertEqual(42., embedding_column_a.max_norm)
+      self.assertFalse(embedding_column_a.trainable)
+      self.assertEqual('aaa_shared_embedding', embedding_column_a.name)
+      self.assertEqual((embedding_dimension,),
+                       embedding_column_a.variable_shape)
+      self.assertEqual({
+          'aaa': parsing_ops.VarLenFeature(dtypes.int64)
+      }, embedding_column_a.parse_example_spec)
+
+  def test_invalid_initializer(self):
+    categorical_column_a = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=3)
+    categorical_column_b = fc.categorical_column_with_identity(
+        key='bbb', num_buckets=3)
+    with self.assertRaisesRegexp(ValueError, 'initializer must be callable'):
+      fc.shared_embedding_columns(
+          [categorical_column_a, categorical_column_b], dimension=2,
+          initializer='not_fn')
+
+  def test_incompatible_column_type(self):
+    categorical_column_a = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=3)
+    categorical_column_b = fc.categorical_column_with_identity(
+        key='bbb', num_buckets=3)
+    categorical_column_c = fc.categorical_column_with_hash_bucket(
+        key='ccc', hash_bucket_size=3)
+    with self.assertRaisesRegexp(
+        ValueError, 'all categorical_columns must have the same type.*'
+        'IdentityCategoricalColumn.*HashedCategoricalColumn'):
+      fc.shared_embedding_columns(
+          [categorical_column_a, categorical_column_b, categorical_column_c],
+          dimension=2)
+
+  def test_weighted_categorical_column_ok(self):
+    categorical_column_a = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=3)
+    weighted_categorical_column_a = fc.weighted_categorical_column(
+        categorical_column_a, weight_feature_key='aaa_weights')
+    categorical_column_b = fc.categorical_column_with_identity(
+        key='bbb', num_buckets=3)
+    weighted_categorical_column_b = fc.weighted_categorical_column(
+        categorical_column_b, weight_feature_key='bbb_weights')
+    fc.shared_embedding_columns(
+        [weighted_categorical_column_a, categorical_column_b], dimension=2)
+    fc.shared_embedding_columns(
+        [categorical_column_a, weighted_categorical_column_b], dimension=2)
+    fc.shared_embedding_columns(
+        [weighted_categorical_column_a, weighted_categorical_column_b],
+        dimension=2)
+
+  def test_parse_example(self):
+    a = fc.categorical_column_with_vocabulary_list(
+        key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
+    b = fc.categorical_column_with_vocabulary_list(
+        key='bbb', vocabulary_list=('omar', 'stringer', 'marlo'))
+    a_embedded, b_embedded = fc.shared_embedding_columns(
+        [a, b], dimension=2)
+    data = example_pb2.Example(features=feature_pb2.Features(
+        feature={
+            'aaa':
+                feature_pb2.Feature(bytes_list=feature_pb2.BytesList(
+                    value=[b'omar', b'stringer'])),
+            'bbb':
+                feature_pb2.Feature(bytes_list=feature_pb2.BytesList(
+                    value=[b'stringer', b'marlo'])),
+        }))
+    features = parsing_ops.parse_example(
+        serialized=[data.SerializeToString()],
+        features=fc.make_parse_example_spec([a_embedded, b_embedded]))
+    self.assertIn('aaa', features)
+    self.assertIn('bbb', features)
+    with self.test_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=[[0, 0], [0, 1]],
+              values=np.array([b'omar', b'stringer'], dtype=np.object_),
+              dense_shape=[1, 2]),
+          features['aaa'].eval())
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=[[0, 0], [0, 1]],
+              values=np.array([b'stringer', b'marlo'], dtype=np.object_),
+              dense_shape=[1, 2]),
+          features['bbb'].eval())
+
+  def test_transform_feature(self):
+    a = fc.categorical_column_with_identity(key='aaa', num_buckets=3)
+    b = fc.categorical_column_with_identity(key='bbb', num_buckets=3)
+    a_embedded, b_embedded = fc.shared_embedding_columns(
+        [a, b], dimension=2)
+    features = {
+        'aaa': sparse_tensor.SparseTensor(
+            indices=((0, 0), (1, 0), (1, 1)),
+            values=(0, 1, 0),
+            dense_shape=(2, 2)),
+        'bbb': sparse_tensor.SparseTensor(
+            indices=((0, 0), (1, 0), (1, 1)),
+            values=(1, 2, 1),
+            dense_shape=(2, 2)),
+    }
+    outputs = _transform_features(features, [a, a_embedded, b, b_embedded],
+                                  None)
+    output_a = outputs[a]
+    output_a_embedded = outputs[a_embedded]
+    output_b = outputs[b]
+    output_b_embedded = outputs[b_embedded]
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self, output_a.eval(), output_a_embedded.eval())
+      _assert_sparse_tensor_value(
+          self, output_b.eval(), output_b_embedded.eval())
+
+  def test_get_dense_tensor(self):
+    # Inputs.
+    vocabulary_size = 3
+    # -1 values are ignored.
+    input_a = np.array(
+        [[2, -1, -1],  # example 0, ids [2]
+         [0, 1, -1]])  # example 1, ids [0, 1]
+    input_b = np.array(
+        [[0, -1, -1],  # example 0, ids [0]
+         [-1, -1, -1]])  # example 1, ids []
+    input_features = {
+        'aaa': input_a,
+        'bbb': input_b
+    }
+
+    # Embedding variable.
+    embedding_dimension = 2
+    embedding_values = (
+        (1., 2.),  # id 0
+        (3., 5.),  # id 1
+        (7., 11.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return embedding_values
+
+    # Expected lookup result, using combiner='mean'.
+    expected_lookups_a = (
+        # example 0:
+        (7., 11.),  # ids [2], embedding = [7, 11]
+        # example 1:
+        (2., 3.5),  # ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
+    )
+    expected_lookups_b = (
+        # example 0:
+        (1., 2.),  # ids [0], embedding = [1, 2]
+        # example 1:
+        (0., 0.),  # ids [], embedding = [0, 0]
+    )
+
+    # Build columns.
+    categorical_column_a = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    categorical_column_b = fc.categorical_column_with_identity(
+        key='bbb', num_buckets=vocabulary_size)
+    embedding_column_a, embedding_column_b = fc.shared_embedding_columns(
+        [categorical_column_a, categorical_column_b],
+        dimension=embedding_dimension, initializer=_initializer)
+    state_manager = _TestSharedEmbeddingStateManager()
+
+    # Provide sparse input and get dense result.
+    embedding_lookup_a = embedding_column_a.get_dense_tensor(
+        FeatureTransformationCache(input_features), state_manager)
+    embedding_lookup_b = embedding_column_b.get_dense_tensor(
+        FeatureTransformationCache(input_features), state_manager)
+
+    # Assert expected embedding variable and lookups.
+    global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+    self.assertItemsEqual(('embedding_weights:0',),
+                          tuple([v.name for v in global_vars]))
+    embedding_var = global_vars[0]
+    with _initialized_session():
+      self.assertAllEqual(embedding_values, embedding_var.eval())
+      self.assertAllEqual(expected_lookups_a, embedding_lookup_a.eval())
+      self.assertAllEqual(expected_lookups_b, embedding_lookup_b.eval())
+
+  def DISABLED_test_get_dense_tensor_weight_collections(self):
+    # Inputs.
+    vocabulary_size = 3
+    # -1 values are ignored.
+    input_a = np.array([
+        [2, -1, -1],  # example 0, ids [2]
+        [0, 1, -1]
+    ])  # example 1, ids [0, 1]
+    input_b = np.array([
+        [0, -1, -1],  # example 0, ids [0]
+        [-1, -1, -1]
+    ])  # example 1, ids []
+    input_features = {'aaa': input_a, 'bbb': input_b}
+
+    # Embedding variable.
+    embedding_dimension = 2
+    embedding_values = (
+        (1., 2.),  # id 0
+        (3., 5.),  # id 1
+        (7., 11.)  # id 2
+    )
+
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return embedding_values
+
+    # Build columns.
+    categorical_column_a = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    categorical_column_b = fc.categorical_column_with_identity(
+        key='bbb', num_buckets=vocabulary_size)
+    embedding_column_a, embedding_column_b = fc.shared_embedding_columns(
+        [categorical_column_a, categorical_column_b],
+        dimension=embedding_dimension,
+        initializer=_initializer)
+
+    fc.input_layer(
+        input_features, [embedding_column_a, embedding_column_b],
+        weight_collections=('my_vars',))
+
+    # Assert expected embedding variable and lookups.
+    global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+    self.assertItemsEqual(
+        ('input_layer/aaa_bbb_shared_embedding/embedding_weights:0',),
+        tuple(v.name for v in global_vars))
+    my_vars = ops.get_collection('my_vars')
+    self.assertItemsEqual(
+        ('input_layer/aaa_bbb_shared_embedding/embedding_weights:0',),
+        tuple(v.name for v in my_vars))
+
+  def test_get_dense_tensor_placeholder_inputs(self):
+    # Inputs.
+    vocabulary_size = 3
+    # -1 values are ignored.
+    input_a = np.array(
+        [[2, -1, -1],  # example 0, ids [2]
+         [0, 1, -1]])  # example 1, ids [0, 1]
+    input_b = np.array(
+        [[0, -1, -1],  # example 0, ids [0]
+         [-1, -1, -1]])  # example 1, ids []
+    # Specify shape, because dense input must have rank specified.
+    input_a_placeholder = array_ops.placeholder(
+        dtype=dtypes.int64, shape=[None, 3])
+    input_b_placeholder = array_ops.placeholder(
+        dtype=dtypes.int64, shape=[None, 3])
+    input_features = {
+        'aaa': input_a_placeholder,
+        'bbb': input_b_placeholder,
+    }
+    feed_dict = {
+        input_a_placeholder: input_a,
+        input_b_placeholder: input_b,
+    }
+
+    # Embedding variable.
+    embedding_dimension = 2
+    embedding_values = (
+        (1., 2.),  # id 0
+        (3., 5.),  # id 1
+        (7., 11.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return embedding_values
+
+    # Build columns.
+    categorical_column_a = fc.categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    categorical_column_b = fc.categorical_column_with_identity(
+        key='bbb', num_buckets=vocabulary_size)
+    embedding_column_a, embedding_column_b = fc.shared_embedding_columns(
+        [categorical_column_a, categorical_column_b],
+        dimension=embedding_dimension, initializer=_initializer)
+    state_manager = _TestSharedEmbeddingStateManager()
+
+    # Provide sparse input and get dense result.
+    embedding_lookup_a = embedding_column_a.get_dense_tensor(
+        FeatureTransformationCache(input_features), state_manager)
+    embedding_lookup_b = embedding_column_b.get_dense_tensor(
+        FeatureTransformationCache(input_features), state_manager)
+
+    with _initialized_session() as sess:
+      sess.run([embedding_lookup_a, embedding_lookup_b], feed_dict=feed_dict)
+
+  def test_linear_model(self):
+    # Inputs.
+    batch_size = 2
+    vocabulary_size = 3
+    # -1 values are ignored.
+    input_a = np.array(
+        [[2, -1, -1],  # example 0, ids [2]
+         [0, 1, -1]])  # example 1, ids [0, 1]
+    input_b = np.array(
+        [[0, -1, -1],  # example 0, ids [0]
+         [-1, -1, -1]])  # example 1, ids []
+
+    # Embedding variable.
+    embedding_dimension = 2
+    embedding_shape = (vocabulary_size, embedding_dimension)
+    zeros_embedding_values = np.zeros(embedding_shape)
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual(embedding_shape, shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return zeros_embedding_values
+
+    # Build columns.
+    categorical_column_a = fc_old.categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    categorical_column_b = fc_old.categorical_column_with_identity(
+        key='bbb', num_buckets=vocabulary_size)
+    embedding_column_a, embedding_column_b = fc_old.shared_embedding_columns(
+        [categorical_column_a, categorical_column_b],
+        dimension=embedding_dimension,
+        initializer=_initializer)
+
+    with ops.Graph().as_default():
+      predictions = fc.linear_model({
+          categorical_column_a.name: input_a,
+          categorical_column_b.name: input_b,
+      }, (embedding_column_a, embedding_column_b))
+      # Linear weights do not follow the column name. But this is a rare use
+      # case, and fixing it would add too much complexity to the code.
+      expected_var_names = (
+          'linear_model/bias_weights:0',
+          'linear_model/aaa_bbb_shared_embedding/weights:0',
+          'linear_model/aaa_bbb_shared_embedding/embedding_weights:0',
+          'linear_model/aaa_bbb_shared_embedding_1/weights:0',
+      )
+      self.assertItemsEqual(
+          expected_var_names,
+          [v.name for v in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)])
+      trainable_vars = {
+          v.name: v for v in ops.get_collection(
+              ops.GraphKeys.TRAINABLE_VARIABLES)
+      }
+      self.assertItemsEqual(expected_var_names, trainable_vars.keys())
+      bias = trainable_vars['linear_model/bias_weights:0']
+      embedding_weights = trainable_vars[
+          'linear_model/aaa_bbb_shared_embedding/embedding_weights:0']
+      linear_weights_a = trainable_vars[
+          'linear_model/aaa_bbb_shared_embedding/weights:0']
+      linear_weights_b = trainable_vars[
+          'linear_model/aaa_bbb_shared_embedding_1/weights:0']
+      with _initialized_session():
+        # Predictions with all zero weights.
+        self.assertAllClose(np.zeros((1,)), bias.eval())
+        self.assertAllClose(zeros_embedding_values, embedding_weights.eval())
+        self.assertAllClose(
+            np.zeros((embedding_dimension, 1)), linear_weights_a.eval())
+        self.assertAllClose(
+            np.zeros((embedding_dimension, 1)), linear_weights_b.eval())
+        self.assertAllClose(np.zeros((batch_size, 1)), predictions.eval())
+
+        # Predictions with all non-zero weights.
+        embedding_weights.assign((
+            (1., 2.),  # id 0
+            (3., 5.),  # id 1
+            (7., 11.)  # id 2
+        )).eval()
+        linear_weights_a.assign(((4.,), (6.,))).eval()
+        # example 0, ids [2], embedding[0] = [7, 11]
+        # example 1, ids [0, 1], embedding[1] = mean([1, 2] + [3, 5]) = [2, 3.5]
+        # sum(embeddings * linear_weights)
+        # = [4*7 + 6*11, 4*2 + 6*3.5] = [94, 29]
+        linear_weights_b.assign(((3.,), (5.,))).eval()
+        # example 0, ids [0], embedding[0] = [1, 2]
+        # example 1, ids [], embedding[1] = 0, 0]
+        # sum(embeddings * linear_weights)
+        # = [3*1 + 5*2, 3*0 +5*0] = [13, 0]
+        self.assertAllClose([[94. + 13.], [29.]], predictions.eval())
+
+  def test_keras_linear_model(self):
+    # Inputs.
+    batch_size = 2
+    vocabulary_size = 3
+    # -1 values are ignored.
+    input_a = np.array([
+        [2, -1, -1],  # example 0, ids [2]
+        [0, 1, -1]
+    ])  # example 1, ids [0, 1]
+    input_b = np.array([
+        [0, -1, -1],  # example 0, ids [0]
+        [-1, -1, -1]
+    ])  # example 1, ids []
+
+    # Embedding variable.
+    embedding_dimension = 2
+    embedding_shape = (vocabulary_size, embedding_dimension)
+    zeros_embedding_values = np.zeros(embedding_shape)
+
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual(embedding_shape, shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return zeros_embedding_values
+
+    # Build columns.
+    categorical_column_a = fc_old.categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    categorical_column_b = fc_old.categorical_column_with_identity(
+        key='bbb', num_buckets=vocabulary_size)
+    embedding_column_a, embedding_column_b = fc_old.shared_embedding_columns(
+        [categorical_column_a, categorical_column_b],
+        dimension=embedding_dimension,
+        initializer=_initializer)
+
+    with ops.Graph().as_default():
+      predictions = get_keras_linear_model_predictions({
+          categorical_column_a.name: input_a,
+          categorical_column_b.name: input_b,
+      }, (embedding_column_a, embedding_column_b))
+      # Linear weights do not follow the column name. But this is a rare use
+      # case, and fixing it would add too much complexity to the code.
+      expected_var_names = (
+          'linear_model/bias_weights:0',
+          'linear_model/aaa_bbb_shared_embedding/weights:0',
+          'linear_model/aaa_bbb_shared_embedding/embedding_weights:0',
+          'linear_model/aaa_bbb_shared_embedding_1/weights:0',
+      )
+      self.assertItemsEqual(
+          expected_var_names,
+          [v.name for v in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)])
+      trainable_vars = {
+          v.name: v
+          for v in ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
+      }
+      self.assertItemsEqual(expected_var_names, trainable_vars.keys())
+      bias = trainable_vars['linear_model/bias_weights:0']
+      embedding_weights = trainable_vars[
+          'linear_model/aaa_bbb_shared_embedding/embedding_weights:0']
+      linear_weights_a = trainable_vars[
+          'linear_model/aaa_bbb_shared_embedding/weights:0']
+      linear_weights_b = trainable_vars[
+          'linear_model/aaa_bbb_shared_embedding_1/weights:0']
+      with _initialized_session():
+        # Predictions with all zero weights.
+        self.assertAllClose(np.zeros((1,)), bias.eval())
+        self.assertAllClose(zeros_embedding_values, embedding_weights.eval())
+        self.assertAllClose(
+            np.zeros((embedding_dimension, 1)), linear_weights_a.eval())
+        self.assertAllClose(
+            np.zeros((embedding_dimension, 1)), linear_weights_b.eval())
+        self.assertAllClose(np.zeros((batch_size, 1)), predictions.eval())
+
+        # Predictions with all non-zero weights.
+        embedding_weights.assign((
+            (1., 2.),  # id 0
+            (3., 5.),  # id 1
+            (7., 11.)  # id 2
+        )).eval()
+        linear_weights_a.assign(((4.,), (6.,))).eval()
+        # example 0, ids [2], embedding[0] = [7, 11]
+        # example 1, ids [0, 1], embedding[1] = mean([1, 2] + [3, 5]) = [2, 3.5]
+        # sum(embeddings * linear_weights)
+        # = [4*7 + 6*11, 4*2 + 6*3.5] = [94, 29]
+        linear_weights_b.assign(((3.,), (5.,))).eval()
+        # example 0, ids [0], embedding[0] = [1, 2]
+        # example 1, ids [], embedding[1] = 0, 0]
+        # sum(embeddings * linear_weights)
+        # = [3*1 + 5*2, 3*0 +5*0] = [13, 0]
+        self.assertAllClose([[94. + 13.], [29.]], predictions.eval())
+
+  def _test_input_layer(self, trainable=True):
+    # Inputs.
+    vocabulary_size = 3
+    sparse_input_a = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        indices=((0, 0), (1, 0), (1, 4)),
+        values=(2, 0, 1),
+        dense_shape=(2, 5))
+    sparse_input_b = sparse_tensor.SparseTensorValue(
+        # example 0, ids [0]
+        # example 1, ids []
+        indices=((0, 0),),
+        values=(0,),
+        dense_shape=(2, 5))
+
+    # Embedding variable.
+    embedding_dimension = 2
+    embedding_values = (
+        (1., 2.),  # id 0
+        (3., 5.),  # id 1
+        (7., 11.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return embedding_values
+
+    # Expected lookup result, using combiner='mean'.
+    expected_lookups = (
+        # example 0:
+        # A ids [2], embedding = [7, 11]
+        # B ids [0], embedding = [1, 2]
+        (7., 11., 1., 2.),
+        # example 1:
+        # A ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
+        # B ids [], embedding = [0, 0]
+        (2., 3.5, 0., 0.),
+    )
+
+    # Build columns.
+    categorical_column_a = fc_old.categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    categorical_column_b = fc_old.categorical_column_with_identity(
+        key='bbb', num_buckets=vocabulary_size)
+    embedding_column_a, embedding_column_b = fc_old.shared_embedding_columns(
+        [categorical_column_a, categorical_column_b],
+        dimension=embedding_dimension,
+        initializer=_initializer,
+        trainable=trainable)
+
+    # Provide sparse input and get dense result.
+    input_layer = fc.input_layer(
+        features={'aaa': sparse_input_a, 'bbb': sparse_input_b},
+        feature_columns=(embedding_column_b, embedding_column_a))
+
+    # Assert expected embedding variable and lookups.
+    global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+    self.assertItemsEqual(
+        ['input_layer/aaa_bbb_shared_embedding/embedding_weights:0'],
+        tuple([v.name for v in global_vars]))
+    trainable_vars = ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
+    if trainable:
+      self.assertItemsEqual(
+          ['input_layer/aaa_bbb_shared_embedding/embedding_weights:0'],
+          tuple([v.name for v in trainable_vars]))
+    else:
+      self.assertItemsEqual([], tuple([v.name for v in trainable_vars]))
+    shared_embedding_vars = global_vars
+    with _initialized_session():
+      self.assertAllEqual(embedding_values, shared_embedding_vars[0].eval())
+      self.assertAllEqual(expected_lookups, input_layer.eval())
+
+  def test_input_layer(self):
+    self._test_input_layer()
+
+  def test_input_layer_no_trainable(self):
+    self._test_input_layer(trainable=False)
+
+
+class WeightedCategoricalColumnTest(test.TestCase):
+
+  def test_defaults(self):
+    column = fc.weighted_categorical_column(
+        categorical_column=fc.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    self.assertEqual('ids_weighted_by_values', column.name)
+    self.assertEqual(3, column.num_buckets)
+    self.assertEqual({
+        'ids': parsing_ops.VarLenFeature(dtypes.int64),
+        'values': parsing_ops.VarLenFeature(dtypes.float32)
+    }, column.parse_example_spec)
+
+  def test_deep_copy(self):
+    """Tests deepcopy of categorical_column_with_hash_bucket."""
+    original = fc.weighted_categorical_column(
+        categorical_column=fc.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    for column in (original, copy.deepcopy(original)):
+      self.assertEqual('ids_weighted_by_values', column.name)
+      self.assertEqual(3, column.num_buckets)
+      self.assertEqual({
+          'ids': parsing_ops.VarLenFeature(dtypes.int64),
+          'values': parsing_ops.VarLenFeature(dtypes.float32)
+      }, column.parse_example_spec)
+
+  def test_invalid_dtype_none(self):
+    with self.assertRaisesRegexp(ValueError, 'is not convertible to float'):
+      fc.weighted_categorical_column(
+          categorical_column=fc.categorical_column_with_identity(
+              key='ids', num_buckets=3),
+          weight_feature_key='values',
+          dtype=None)
+
+  def test_invalid_dtype_string(self):
+    with self.assertRaisesRegexp(ValueError, 'is not convertible to float'):
+      fc.weighted_categorical_column(
+          categorical_column=fc.categorical_column_with_identity(
+              key='ids', num_buckets=3),
+          weight_feature_key='values',
+          dtype=dtypes.string)
+
+  def test_invalid_input_dtype(self):
+    column = fc.weighted_categorical_column(
+        categorical_column=fc.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    strings = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('omar', 'stringer', 'marlo'),
+        dense_shape=(2, 2))
+    with self.assertRaisesRegexp(ValueError, 'Bad dtype'):
+      _transform_features({'ids': strings, 'values': strings}, (column,), None)
+
+  def test_column_name_collision(self):
+    with self.assertRaisesRegexp(ValueError, r'Parse config.*already exists'):
+      fc.weighted_categorical_column(
+          categorical_column=fc.categorical_column_with_identity(
+              key='aaa', num_buckets=3),
+          weight_feature_key='aaa').parse_example_spec()
+
+  def test_missing_weights(self):
+    column = fc.weighted_categorical_column(
+        categorical_column=fc.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=('omar', 'stringer', 'marlo'),
+        dense_shape=(2, 2))
+    with self.assertRaisesRegexp(
+        ValueError, 'values is not in features dictionary'):
+      _transform_features({'ids': inputs}, (column,), None)
+
+  def test_parse_example(self):
+    a = fc.categorical_column_with_vocabulary_list(
+        key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
+    a_weighted = fc.weighted_categorical_column(a, weight_feature_key='weights')
+    data = example_pb2.Example(features=feature_pb2.Features(
+        feature={
+            'aaa':
+                feature_pb2.Feature(bytes_list=feature_pb2.BytesList(
+                    value=[b'omar', b'stringer'])),
+            'weights':
+                feature_pb2.Feature(float_list=feature_pb2.FloatList(
+                    value=[1., 10.]))
+        }))
+    features = parsing_ops.parse_example(
+        serialized=[data.SerializeToString()],
+        features=fc.make_parse_example_spec([a_weighted]))
+    self.assertIn('aaa', features)
+    self.assertIn('weights', features)
+    with self.test_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=[[0, 0], [0, 1]],
+              values=np.array([b'omar', b'stringer'], dtype=np.object_),
+              dense_shape=[1, 2]),
+          features['aaa'].eval())
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=[[0, 0], [0, 1]],
+              values=np.array([1., 10.], dtype=np.float32),
+              dense_shape=[1, 2]),
+          features['weights'].eval())
+
+  def test_transform_features(self):
+    column = fc.weighted_categorical_column(
+        categorical_column=fc.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(0, 1, 0),
+        dense_shape=(2, 2))
+    weights = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(0.5, 1.0, 0.1),
+        dense_shape=(2, 2))
+    id_tensor, weight_tensor = _transform_features({
+        'ids': inputs,
+        'values': weights,
+    }, (column,), None)[column]
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array(inputs.values, dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_tensor.eval())
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=weights.indices,
+              values=np.array(weights.values, dtype=np.float32),
+              dense_shape=weights.dense_shape),
+          weight_tensor.eval())
+
+  def test_transform_features_dense_input(self):
+    column = fc.weighted_categorical_column(
+        categorical_column=fc.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    weights = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(0.5, 1.0, 0.1),
+        dense_shape=(2, 2))
+    id_tensor, weight_tensor = _transform_features({
+        'ids': ((0, -1), (1, 0)),
+        'values': weights,
+    }, (column,), None)[column]
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=((0, 0), (1, 0), (1, 1)),
+              values=np.array((0, 1, 0), dtype=np.int64),
+              dense_shape=(2, 2)),
+          id_tensor.eval())
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=weights.indices,
+              values=np.array(weights.values, dtype=np.float32),
+              dense_shape=weights.dense_shape),
+          weight_tensor.eval())
+
+  def test_transform_features_dense_weights(self):
+    column = fc.weighted_categorical_column(
+        categorical_column=fc.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(2, 1, 0),
+        dense_shape=(2, 2))
+    id_tensor, weight_tensor = _transform_features({
+        'ids': inputs,
+        'values': ((.5, 0.), (1., .1)),
+    }, (column,), None)[column]
+    with _initialized_session():
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=inputs.indices,
+              values=np.array(inputs.values, dtype=np.int64),
+              dense_shape=inputs.dense_shape),
+          id_tensor.eval())
+      _assert_sparse_tensor_value(
+          self,
+          sparse_tensor.SparseTensorValue(
+              indices=((0, 0), (1, 0), (1, 1)),
+              values=np.array((.5, 1., .1), dtype=np.float32),
+              dense_shape=(2, 2)),
+          weight_tensor.eval())
+
+  def test_keras_linear_model(self):
+    column = fc_old.weighted_categorical_column(
+        categorical_column=fc_old.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    with ops.Graph().as_default():
+      predictions = get_keras_linear_model_predictions({
+          'ids':
+              sparse_tensor.SparseTensorValue(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=(0, 2, 1),
+                  dense_shape=(2, 2)),
+          'values':
+              sparse_tensor.SparseTensorValue(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=(.5, 1., .1),
+                  dense_shape=(2, 2))
+      }, (column,))
+      bias = get_linear_model_bias()
+      weight_var = get_linear_model_column_var(column)
+      with _initialized_session():
+        self.assertAllClose((0.,), bias.eval())
+        self.assertAllClose(((0.,), (0.,), (0.,)), weight_var.eval())
+        self.assertAllClose(((0.,), (0.,)), predictions.eval())
+        weight_var.assign(((1.,), (2.,), (3.,))).eval()
+        # weight_var[0] * weights[0, 0] = 1 * .5 = .5
+        # weight_var[2] * weights[1, 0] + weight_var[1] * weights[1, 1]
+        # = 3*1 + 2*.1 = 3+.2 = 3.2
+        self.assertAllClose(((.5,), (3.2,)), predictions.eval())
+
+  def test_keras_linear_model_mismatched_shape(self):
+    column = fc_old.weighted_categorical_column(
+        categorical_column=fc_old.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    with ops.Graph().as_default():
+      with self.assertRaisesRegexp(ValueError,
+                                   r'Dimensions.*are not compatible'):
+        get_keras_linear_model_predictions({
+            'ids':
+                sparse_tensor.SparseTensorValue(
+                    indices=((0, 0), (1, 0), (1, 1)),
+                    values=(0, 2, 1),
+                    dense_shape=(2, 2)),
+            'values':
+                sparse_tensor.SparseTensorValue(
+                    indices=((0, 0), (0, 1), (1, 0), (1, 1)),
+                    values=(.5, 11., 1., .1),
+                    dense_shape=(2, 2))
+        }, (column,))
+
+  def test_keras_linear_model_mismatched_dense_values(self):
+    column = fc_old.weighted_categorical_column(
+        categorical_column=fc_old.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    with ops.Graph().as_default():
+      predictions = get_keras_linear_model_predictions(
+          {
+              'ids':
+                  sparse_tensor.SparseTensorValue(
+                      indices=((0, 0), (1, 0), (1, 1)),
+                      values=(0, 2, 1),
+                      dense_shape=(2, 2)),
+              'values': ((.5,), (1.,))
+          }, (column,),
+          sparse_combiner='mean')
+      # Disabling the constant folding optimizer here since it changes the
+      # error message differently on CPU and GPU.
+      config = config_pb2.ConfigProto()
+      config.graph_options.rewrite_options.constant_folding = (
+          rewriter_config_pb2.RewriterConfig.OFF)
+      with _initialized_session(config):
+        with self.assertRaisesRegexp(errors.OpError, 'Incompatible shapes'):
+          predictions.eval()
+
+  def test_keras_linear_model_mismatched_dense_shape(self):
+    column = fc_old.weighted_categorical_column(
+        categorical_column=fc_old.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    with ops.Graph().as_default():
+      predictions = get_keras_linear_model_predictions({
+          'ids':
+              sparse_tensor.SparseTensorValue(
+                  indices=((0, 0), (1, 0), (1, 1)),
+                  values=(0, 2, 1),
+                  dense_shape=(2, 2)),
+          'values': ((.5,), (1.,), (.1,))
+      }, (column,))
+      bias = get_linear_model_bias()
+      weight_var = get_linear_model_column_var(column)
+      with _initialized_session():
+        self.assertAllClose((0.,), bias.eval())
+        self.assertAllClose(((0.,), (0.,), (0.,)), weight_var.eval())
+        self.assertAllClose(((0.,), (0.,)), predictions.eval())
+        weight_var.assign(((1.,), (2.,), (3.,))).eval()
+        # weight_var[0] * weights[0, 0] = 1 * .5 = .5
+        # weight_var[2] * weights[1, 0] + weight_var[1] * weights[1, 1]
+        # = 3*1 + 2*.1 = 3+.2 = 3.2
+        self.assertAllClose(((.5,), (3.2,)), predictions.eval())
+
+  def test_linear_model(self):
+    column = fc_old.weighted_categorical_column(
+        categorical_column=fc_old.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    with ops.Graph().as_default():
+      predictions = fc.linear_model({
+          'ids': sparse_tensor.SparseTensorValue(
+              indices=((0, 0), (1, 0), (1, 1)),
+              values=(0, 2, 1),
+              dense_shape=(2, 2)),
+          'values': sparse_tensor.SparseTensorValue(
+              indices=((0, 0), (1, 0), (1, 1)),
+              values=(.5, 1., .1),
+              dense_shape=(2, 2))
+      }, (column,))
+      bias = get_linear_model_bias()
+      weight_var = get_linear_model_column_var(column)
+      with _initialized_session():
+        self.assertAllClose((0.,), bias.eval())
+        self.assertAllClose(((0.,), (0.,), (0.,)), weight_var.eval())
+        self.assertAllClose(((0.,), (0.,)), predictions.eval())
+        weight_var.assign(((1.,), (2.,), (3.,))).eval()
+        # weight_var[0] * weights[0, 0] = 1 * .5 = .5
+        # weight_var[2] * weights[1, 0] + weight_var[1] * weights[1, 1]
+        # = 3*1 + 2*.1 = 3+.2 = 3.2
+        self.assertAllClose(((.5,), (3.2,)), predictions.eval())
+
+  def test_linear_model_mismatched_shape(self):
+    column = fc_old.weighted_categorical_column(
+        categorical_column=fc_old.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    with ops.Graph().as_default():
+      with self.assertRaisesRegexp(
+          ValueError, r'Dimensions.*are not compatible'):
+        fc.linear_model({
+            'ids': sparse_tensor.SparseTensorValue(
+                indices=((0, 0), (1, 0), (1, 1)),
+                values=(0, 2, 1),
+                dense_shape=(2, 2)),
+            'values': sparse_tensor.SparseTensorValue(
+                indices=((0, 0), (0, 1), (1, 0), (1, 1)),
+                values=(.5, 11., 1., .1),
+                dense_shape=(2, 2))
+        }, (column,))
+
+  def test_linear_model_mismatched_dense_values(self):
+    column = fc_old.weighted_categorical_column(
+        categorical_column=fc_old.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    with ops.Graph().as_default():
+      predictions = fc.linear_model(
+          {
+              'ids':
+                  sparse_tensor.SparseTensorValue(
+                      indices=((0, 0), (1, 0), (1, 1)),
+                      values=(0, 2, 1),
+                      dense_shape=(2, 2)),
+              'values': ((.5,), (1.,))
+          }, (column,),
+          sparse_combiner='mean')
+      # Disabling the constant folding optimizer here since it changes the
+      # error message differently on CPU and GPU.
+      config = config_pb2.ConfigProto()
+      config.graph_options.rewrite_options.constant_folding = (
+          rewriter_config_pb2.RewriterConfig.OFF)
+      with _initialized_session(config):
+        with self.assertRaisesRegexp(errors.OpError, 'Incompatible shapes'):
+          predictions.eval()
+
+  def test_linear_model_mismatched_dense_shape(self):
+    column = fc_old.weighted_categorical_column(
+        categorical_column=fc_old.categorical_column_with_identity(
+            key='ids', num_buckets=3),
+        weight_feature_key='values')
+    with ops.Graph().as_default():
+      predictions = fc.linear_model({
+          'ids': sparse_tensor.SparseTensorValue(
+              indices=((0, 0), (1, 0), (1, 1)),
+              values=(0, 2, 1),
+              dense_shape=(2, 2)),
+          'values': ((.5,), (1.,), (.1,))
+      }, (column,))
+      bias = get_linear_model_bias()
+      weight_var = get_linear_model_column_var(column)
+      with _initialized_session():
+        self.assertAllClose((0.,), bias.eval())
+        self.assertAllClose(((0.,), (0.,), (0.,)), weight_var.eval())
+        self.assertAllClose(((0.,), (0.,)), predictions.eval())
+        weight_var.assign(((1.,), (2.,), (3.,))).eval()
+        # weight_var[0] * weights[0, 0] = 1 * .5 = .5
+        # weight_var[2] * weights[1, 0] + weight_var[1] * weights[1, 1]
+        # = 3*1 + 2*.1 = 3+.2 = 3.2
+        self.assertAllClose(((.5,), (3.2,)), predictions.eval())
+
+  # TODO(ptucker): Add test with embedding of weighted categorical.
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/python/framework/common_shapes.py b/tensorflow/python/framework/common_shapes.py
index 3c5aebbce8af117aa1e216f1ef07ded181c997ea..40788e24c486c4357042672e3697063a4c7fb381 100644
--- a/tensorflow/python/framework/common_shapes.py
+++ b/tensorflow/python/framework/common_shapes.py
@@ -28,6 +28,18 @@ from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import tensor_util
 
 
+def has_fully_defined_shape(tensor):
+  """Returns true if tensor has a fully defined shape."""
+  return isinstance(tensor, ops.EagerTensor) or tensor.shape.is_fully_defined()
+
+
+def rank(tensor):
+  """Return a rank if it is a tensor, else return None."""
+  if isinstance(tensor, ops.Tensor):
+    return tensor._rank()  # pylint: disable=protected-access
+  return None
+
+
 def scalar_shape(unused_op):
   """Shape function for ops that output a scalar value."""
   return [tensor_shape.scalar()]
diff --git a/tensorflow/python/framework/error_interpolation.py b/tensorflow/python/framework/error_interpolation.py
index 9ccae761471e24ddb1d4d6acd89ebcc9650d1320..7719d0301987e6e0f0d98e52cf4a5332e523f63e 100644
--- a/tensorflow/python/framework/error_interpolation.py
+++ b/tensorflow/python/framework/error_interpolation.py
@@ -24,11 +24,15 @@ from __future__ import print_function
 
 import collections
 import itertools
+import os
 import re
 import string
 
 import six
 
+from tensorflow.python.util import tf_stack
+
+
 _NAME_REGEX = r"[A-Za-z0-9.][A-Za-z0-9_.\-/]*?"
 _FORMAT_REGEX = r"[A-Za-z0-9_.\-/${}:]+"
 _TAG_REGEX = r"\^\^({name}):({name}):({fmt})\^\^".format(
@@ -38,6 +42,11 @@ _INTERPOLATION_PATTERN = re.compile(_INTERPOLATION_REGEX)
 
 _ParseTag = collections.namedtuple("_ParseTag", ["type", "name", "format"])
 
+_BAD_FILE_SUBSTRINGS = [
+    os.path.join("tensorflow", "python"),
+    "
+    file: Replaced with the filename in which the node was defined.
+    line: Replaced by the line number at which the node was defined.
+    colocations: Replaced by a multi-line message describing the file and
+        line numbers at which this node was colocated with other nodes.
+
   Args:
     message: String to parse
 
@@ -72,9 +87,186 @@ def _parse_message(message):
   return seps, tags
 
 
-# TODO(jtkeeling): Modify to actually interpolate format strings rather than
-# echoing them.
-def interpolate(error_message):
+def _compute_device_summary_from_list(device_assignment_list, prefix=""):
+  """Return a summary of an op's device function stack.
+
+  Args:
+    device_assignment_list: The op._device_assignments list.
+    prefix:  An optional string prefix used before each line of the multi-
+        line string returned by this function.
+
+  Returns:
+    A multi-line string similar to:
+        Device assignments active during op creation:
+          with tf.device(/cpu:0): 
+          with tf.device(some_func): 
+    The first line will have no padding to its left by default.  Subsequent
+    lines will have two spaces of left-padding.  Use the prefix argument
+    to increase indentation.
+  """
+  if not device_assignment_list:
+    message = "No device assignments were active during op creation."
+    return prefix + message
+
+  str_list = []
+  str_list.append("%sDevice assignments active during op creation:" % prefix)
+
+  for traceable_obj in device_assignment_list:
+    location_summary = "<{file}:{line}>".format(file=traceable_obj.filename,
+                                                line=traceable_obj.lineno)
+    subs = {
+        "prefix": prefix,
+        "indent": "  ",
+        "dev_name": traceable_obj.obj,
+        "loc": location_summary,
+    }
+    str_list.append(
+        "{prefix}{indent}with tf.device({dev_name}): {loc}".format(**subs))
+
+  return "\n".join(str_list)
+
+
+def _compute_device_assignment_summary_from_op(op, prefix=""):
+  if not op:
+    return ""
+  # pylint: disable=protected-access
+  return _compute_device_summary_from_list(op._device_assignments, prefix)
+  # pylint: enable=protected-access
+
+
+def _compute_colocation_summary_from_dict(colocation_dict, prefix=""):
+  """Return a summary of an op's colocation stack.
+
+  Args:
+    colocation_dict: The op._colocation_dict.
+    prefix:  An optional string prefix used before each line of the multi-
+        line string returned by this function.
+
+  Returns:
+    A multi-line string similar to:
+        Node-device colocations active during op creation:
+          with tf.colocate_with(test_node_1): 
+          with tf.colocate_with(test_node_2): 
+    The first line will have no padding to its left by default.  Subsequent
+    lines will have two spaces of left-padding.  Use the prefix argument
+    to increase indentation.
+  """
+  if not colocation_dict:
+    message = "No node-device colocations were active during op creation."
+    return prefix + message
+
+  str_list = []
+  str_list.append("%sNode-device colocations active during op creation:"
+                  % prefix)
+
+  for name, location in colocation_dict.items():
+    location_summary = "<{file}:{line}>".format(file=location.filename,
+                                                line=location.lineno)
+    subs = {
+        "prefix": prefix,
+        "indent": "  ",
+        "name": name,
+        "loc": location_summary,
+    }
+    str_list.append(
+        "{prefix}{indent}with tf.colocate_with({name}): {loc}".format(**subs))
+
+  return "\n".join(str_list)
+
+
+def _compute_colocation_summary_from_op(op, prefix=""):
+  """Fetch colocation file, line, and nesting and return a summary string."""
+  if not op:
+    return ""
+  # pylint: disable=protected-access
+  return _compute_colocation_summary_from_dict(op._colocation_dict, prefix)
+  # pylint: enable=protected-access
+
+
+def _find_index_of_defining_frame_for_op(op):
+  """Return index in op._traceback with first 'useful' frame.
+
+  This method reads through the stack stored in op._traceback looking for the
+  innermost frame which (hopefully) belongs to the caller.  It accomplishes this
+  by rejecting frames whose filename appears to come from TensorFlow (see
+  error_interpolation._BAD_FILE_SUBSTRINGS for the list of rejected substrings).
+
+  Args:
+    op: the Operation object for which we would like to find the defining
+        location.
+
+  Returns:
+    Integer index into op._traceback where the first non-TF file was found
+    (innermost to outermost), or 0 (for the outermost stack frame) if all files
+    came from TensorFlow.
+  """
+  # pylint: disable=protected-access
+  # Index 0 of tf_traceback is the outermost frame.
+  tf_traceback = tf_stack.convert_stack(op._traceback)
+  size = len(tf_traceback)
+  # pylint: enable=protected-access
+  filenames = [frame[tf_stack.TB_FILENAME] for frame in tf_traceback]
+  # We process the filenames from the innermost frame to outermost.
+  for idx, filename in enumerate(reversed(filenames)):
+    contains_bad_substrings = [ss in filename for ss in _BAD_FILE_SUBSTRINGS]
+    if not any(contains_bad_substrings):
+      return size - idx - 1
+  return 0
+
+
+def _get_defining_frame_from_op(op):
+  """Find and return stack frame where op was defined."""
+  frame = None
+  if op:
+    # pylint: disable=protected-access
+    frame_index = _find_index_of_defining_frame_for_op(op)
+    frame = op._traceback[frame_index]
+    # pylint: enable=protected-access
+  return frame
+
+
+def _compute_field_dict(op):
+  """Return a dictionary mapping interpolation tokens to values.
+
+  Args:
+    op: op.Operation object having a _traceback member.
+
+  Returns:
+    A dictionary mapping string tokens to string values.  The keys are shown
+    below along with example values.
+    {
+      "file": "tool_utils.py",
+      "line": "124",
+      "colocations":
+          '''Node-device colocations active during op creation:
+               with tf.colocate_with(test_node_1): 
+               with tf.colocate_with(test_node_2): '''
+    }
+    If op is None or lacks a _traceback field, the returned values will be
+    "".
+  """
+  default_value = ""
+  field_dict = {
+      "file": default_value,
+      "line": default_value,
+      "colocations": default_value,
+      "devices": default_value,
+  }
+  frame = _get_defining_frame_from_op(op)
+  if frame:
+    field_dict["file"] = frame[tf_stack.TB_FILENAME]
+    field_dict["line"] = frame[tf_stack.TB_LINENO]
+  colocation_summary = _compute_colocation_summary_from_op(op)
+  if colocation_summary:
+    field_dict["colocations"] = colocation_summary
+  device_summary = _compute_device_assignment_summary_from_op(op)
+  if device_summary:
+    field_dict["devices"] = device_summary
+
+  return field_dict
+
+
+def interpolate(error_message, graph):
   """Interpolates an error message.
 
   The error message can contain tags of the form ^^type:name:format^^ which will
@@ -82,11 +274,28 @@ def interpolate(error_message):
 
   Args:
     error_message: A string to interpolate.
+    graph: ops.Graph object containing all nodes referenced in the error
+        message.
 
   Returns:
     The string with tags of the form ^^type:name:format^^ interpolated.
   """
   seps, tags = _parse_message(error_message)
-  subs = [string.Template(tag.format).safe_substitute({}) for tag in tags]
+
+  node_name_to_substitution_dict = {}
+  for name in [t.name for t in tags]:
+    if name in node_name_to_substitution_dict:
+      continue
+    try:
+      op = graph.get_operation_by_name(name)
+    except KeyError:
+      op = None
+
+    node_name_to_substitution_dict[name] = _compute_field_dict(op)
+
+  subs = [
+      string.Template(tag.format).safe_substitute(
+          node_name_to_substitution_dict[tag.name]) for tag in tags
+  ]
   return "".join(
       itertools.chain(*six.moves.zip_longest(seps, subs, fillvalue="")))
diff --git a/tensorflow/python/framework/error_interpolation_test.py b/tensorflow/python/framework/error_interpolation_test.py
index ad448deb622cb6a3d24e502d7238d3f614d5af4d..fbf182879b17f4008712f861cfbf013b45c2380b 100644
--- a/tensorflow/python/framework/error_interpolation_test.py
+++ b/tensorflow/python/framework/error_interpolation_test.py
@@ -18,31 +18,277 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
+
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import error_interpolation
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import traceable_stack
 from tensorflow.python.platform import test
+from tensorflow.python.util import tf_stack
+
+
+def _make_frame_with_filename(op, idx, filename):
+  """Return a copy of an existing stack frame with a new filename."""
+  stack_frame = list(op._traceback[idx])
+  stack_frame[tf_stack.TB_FILENAME] = filename
+  return tuple(stack_frame)
+
+
+def _modify_op_stack_with_filenames(op, num_user_frames, user_filename,
+                                    num_inner_tf_frames):
+  """Replace op._traceback with a new traceback using special filenames."""
+  tf_filename = "%d" + error_interpolation._BAD_FILE_SUBSTRINGS[0]
+  user_filename = os.path.join("%d", "my_favorite_file.py")
+
+  num_requested_frames = num_user_frames + num_inner_tf_frames
+  num_actual_frames = len(op._traceback)
+  num_outer_frames = num_actual_frames - num_requested_frames
+  assert num_requested_frames <= num_actual_frames, "Too few real frames."
+
+  # The op's traceback has outermost frame at index 0.
+  stack = []
+  for idx in range(0, num_outer_frames):
+    stack.append(op._traceback[idx])
+  for idx in range(len(stack), len(stack)+num_user_frames):
+    stack.append(_make_frame_with_filename(op, idx, user_filename % idx))
+  for idx in range(len(stack), len(stack)+num_inner_tf_frames):
+    stack.append(_make_frame_with_filename(op, idx, tf_filename % idx))
+  op._traceback = stack
+
+
+class ComputeDeviceSummaryFromOpTest(test.TestCase):
+
+  def testCorrectFormatWithActiveDeviceAssignments(self):
+    assignments = []
+    assignments.append(
+        traceable_stack.TraceableObject("/cpu:0",
+                                        filename="hope.py",
+                                        lineno=24))
+    assignments.append(
+        traceable_stack.TraceableObject("/gpu:2",
+                                        filename="please.py",
+                                        lineno=42))
+
+    summary = error_interpolation._compute_device_summary_from_list(
+        assignments, prefix="  ")
+
+    self.assertIn("tf.device(/cpu:0)", summary)
+    self.assertIn("", summary)
+    self.assertIn("tf.device(/gpu:2)", summary)
+    self.assertIn("", summary)
+
+  def testCorrectFormatWhenNoColocationsWereActive(self):
+    device_assignment_list = []
+    summary = error_interpolation._compute_device_summary_from_list(
+        device_assignment_list, prefix="  ")
+    self.assertIn("No device assignments", summary)
+
+
+class ComputeColocationSummaryFromOpTest(test.TestCase):
+
+  def testCorrectFormatWithActiveColocations(self):
+    t_obj_1 = traceable_stack.TraceableObject(None,
+                                              filename="test_1.py",
+                                              lineno=27)
+    t_obj_2 = traceable_stack.TraceableObject(None,
+                                              filename="test_2.py",
+                                              lineno=38)
+    colocation_dict = {
+        "test_node_1": t_obj_1,
+        "test_node_2": t_obj_2,
+    }
+    summary = error_interpolation._compute_colocation_summary_from_dict(
+        colocation_dict, prefix="  ")
+    self.assertIn("colocate_with(test_node_1)", summary)
+    self.assertIn("", summary)
+    self.assertIn("colocate_with(test_node_2)", summary)
+    self.assertIn("", summary)
+
+  def testCorrectFormatWhenNoColocationsWereActive(self):
+    colocation_dict = {}
+    summary = error_interpolation._compute_colocation_summary_from_dict(
+        colocation_dict, prefix="  ")
+    self.assertIn("No node-device colocations", summary)
+
+
+class InterpolateFilenamesAndLineNumbersTest(test.TestCase):
+
+  def setUp(self):
+    ops.reset_default_graph()
+    # Add nodes to the graph for retrieval by name later.
+    constant_op.constant(1, name="One")
+    constant_op.constant(2, name="Two")
+    three = constant_op.constant(3, name="Three")
+    self.graph = three.graph
+
+    # Change the list of bad file substrings so that constant_op.py is chosen
+    # as the defining stack frame for constant_op.constant ops.
+    self.old_bad_strings = error_interpolation._BAD_FILE_SUBSTRINGS
+    error_interpolation._BAD_FILE_SUBSTRINGS = [
+        "%sops.py" % os.sep,
+        "%sutil" % os.sep,
+    ]
+
+  def tearDown(self):
+    error_interpolation._BAD_FILE_SUBSTRINGS = self.old_bad_strings
 
+  def testFindIndexOfDefiningFrameForOp(self):
+    local_op = constant_op.constant(42).op
+    user_filename = "hope.py"
+    _modify_op_stack_with_filenames(local_op,
+                                    num_user_frames=3,
+                                    user_filename=user_filename,
+                                    num_inner_tf_frames=5)
+    idx = error_interpolation._find_index_of_defining_frame_for_op(local_op)
+    # Expected frame is 6th from the end because there are 5 inner frames witih
+    # TF filenames.
+    expected_frame = len(local_op._traceback) - 6
+    self.assertEqual(expected_frame, idx)
 
-class InterpolateTest(test.TestCase):
+  def testFindIndexOfDefiningFrameForOpReturnsZeroOnError(self):
+    local_op = constant_op.constant(43).op
+    # Truncate stack to known length.
+    local_op._traceback = local_op._traceback[:7]
+    # Ensure all frames look like TF frames.
+    _modify_op_stack_with_filenames(local_op,
+                                    num_user_frames=0,
+                                    user_filename="user_file.py",
+                                    num_inner_tf_frames=7)
+    idx = error_interpolation._find_index_of_defining_frame_for_op(local_op)
+    self.assertEqual(0, idx)
 
   def testNothingToDo(self):
     normal_string = "This is just a normal string"
-    interpolated_string = error_interpolation.interpolate(normal_string)
+    interpolated_string = error_interpolation.interpolate(normal_string,
+                                                          self.graph)
     self.assertEqual(interpolated_string, normal_string)
 
   def testOneTag(self):
-    one_tag_string = "^^node:Foo:${file}^^"
-    interpolated_string = error_interpolation.interpolate(one_tag_string)
-    self.assertEqual(interpolated_string, "${file}")
+    one_tag_string = "^^node:Two:${file}^^"
+    interpolated_string = error_interpolation.interpolate(one_tag_string,
+                                                          self.graph)
+    self.assertTrue(interpolated_string.endswith("constant_op.py"),
+                    "interpolated_string '%s' did not end with constant_op.py"
+                    % interpolated_string)
+
+  def testOneTagWithAFakeNameResultsInPlaceholders(self):
+    one_tag_string = "^^node:MinusOne:${file}^^"
+    interpolated_string = error_interpolation.interpolate(one_tag_string,
+                                                          self.graph)
+    self.assertEqual(interpolated_string, "")
 
   def testTwoTagsNoSeps(self):
-    two_tags_no_seps = "^^node:Foo:${file}^^^^node:Bar:${line}^^"
-    interpolated_string = error_interpolation.interpolate(two_tags_no_seps)
-    self.assertEqual(interpolated_string, "${file}${line}")
+    two_tags_no_seps = "^^node:One:${file}^^^^node:Three:${line}^^"
+    interpolated_string = error_interpolation.interpolate(two_tags_no_seps,
+                                                          self.graph)
+    self.assertRegexpMatches(interpolated_string, "constant_op.py[0-9]+")
 
   def testTwoTagsWithSeps(self):
-    two_tags_with_seps = "123^^node:Foo:${file}^^456^^node:Bar:${line}^^789"
-    interpolated_string = error_interpolation.interpolate(two_tags_with_seps)
-    self.assertEqual(interpolated_string, "123${file}456${line}789")
+    two_tags_with_seps = ";;;^^node:Two:${file}^^,,,^^node:Three:${line}^^;;;"
+    interpolated_string = error_interpolation.interpolate(two_tags_with_seps,
+                                                          self.graph)
+    expected_regex = "^;;;.*constant_op.py,,,[0-9]*;;;$"
+    self.assertRegexpMatches(interpolated_string, expected_regex)
+
+
+class InterpolateDeviceSummaryTest(test.TestCase):
+
+  def _fancy_device_function(self, unused_op):
+    return "/cpu:*"
+
+  def setUp(self):
+    ops.reset_default_graph()
+    self.zero = constant_op.constant([0.0], name="zero")
+    with ops.device("/cpu"):
+      self.one = constant_op.constant([1.0], name="one")
+      with ops.device("/cpu:0"):
+        self.two = constant_op.constant([2.0], name="two")
+    with ops.device(self._fancy_device_function):
+      self.three = constant_op.constant(3.0, name="three")
+
+    self.graph = self.three.graph
+
+  def testNodeZeroHasNoDeviceSummaryInfo(self):
+    message = "^^node:zero:${devices}^^"
+    result = error_interpolation.interpolate(message, self.graph)
+    self.assertIn("No device assignments were active", result)
+
+  def testNodeOneHasExactlyOneInterpolatedDevice(self):
+    message = "^^node:one:${devices}^^"
+    result = error_interpolation.interpolate(message, self.graph)
+    num_devices = result.count("tf.device")
+    self.assertEqual(1, num_devices)
+    self.assertIn("tf.device(/cpu)", result)
+
+  def testNodeTwoHasTwoInterpolatedDevice(self):
+    message = "^^node:two:${devices}^^"
+    result = error_interpolation.interpolate(message, self.graph)
+    num_devices = result.count("tf.device")
+    self.assertEqual(2, num_devices)
+    self.assertIn("tf.device(/cpu)", result)
+    self.assertIn("tf.device(/cpu:0)", result)
+
+  def testNodeThreeHasFancyFunctionDisplayNameForInterpolatedDevice(self):
+    message = "^^node:three:${devices}^^"
+    result = error_interpolation.interpolate(message, self.graph)
+    num_devices = result.count("tf.device")
+    self.assertEqual(1, num_devices)
+    name_re = r"_fancy_device_function<.*error_interpolation_test.py, [0-9]+>"
+    expected_re = r"with tf.device\(.*%s\)" % name_re
+    self.assertRegexpMatches(result, expected_re)
+
+
+class InterpolateColocationSummaryTest(test.TestCase):
+
+  def setUp(self):
+    ops.reset_default_graph()
+    # Add nodes to the graph for retrieval by name later.
+    node_one = constant_op.constant(1, name="One")
+    node_two = constant_op.constant(2, name="Two")
+
+    # node_three has one colocation group, obviously.
+    with ops.colocate_with(node_one):
+      node_three = constant_op.constant(3, name="Three_with_one")
+
+    # node_four has one colocation group even though three is (transitively)
+    # colocated with one.
+    with ops.colocate_with(node_three):
+      constant_op.constant(4, name="Four_with_three")
+
+    # node_five has two colocation groups because one and two are not colocated.
+    with ops.colocate_with(node_two):
+      with ops.colocate_with(node_one):
+        constant_op.constant(5, name="Five_with_one_with_two")
+
+    self.graph = node_three.graph
+
+  def testNodeThreeHasColocationInterpolation(self):
+    message = "^^node:Three_with_one:${colocations}^^"
+    result = error_interpolation.interpolate(message, self.graph)
+    self.assertIn("colocate_with(One)", result)
+
+  def testNodeFourHasColocationInterpolationForNodeThreeOnly(self):
+    message = "^^node:Four_with_three:${colocations}^^"
+    result = error_interpolation.interpolate(message, self.graph)
+    self.assertIn("colocate_with(Three_with_one)", result)
+    self.assertNotIn(
+        "One", result,
+        "Node One should not appear in Four_with_three's summary:\n%s"
+        % result)
+
+  def testNodeFiveHasColocationInterpolationForNodeOneAndTwo(self):
+    message = "^^node:Five_with_one_with_two:${colocations}^^"
+    result = error_interpolation.interpolate(message, self.graph)
+    self.assertIn("colocate_with(One)", result)
+    self.assertIn("colocate_with(Two)", result)
+
+  def testColocationInterpolationForNodeLackingColocation(self):
+    message = "^^node:One:${colocations}^^"
+    result = error_interpolation.interpolate(message, self.graph)
+    self.assertIn("No node-device colocations", result)
+    self.assertNotIn("One", result)
+    self.assertNotIn("Two", result)
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/python/framework/fast_tensor_util.pyx b/tensorflow/python/framework/fast_tensor_util.pyx
index 17d112a1ece9ae3d121b894d9b246d24b46d84e2..2e3e15f53a919bac669b56e4a8f27c1808da345a 100644
--- a/tensorflow/python/framework/fast_tensor_util.pyx
+++ b/tensorflow/python/framework/fast_tensor_util.pyx
@@ -6,6 +6,13 @@ cimport numpy as np
 
 from tensorflow.python.util import compat
 
+def AppendBFloat16ArrayToTensorProto(
+    tensor_proto, np.ndarray[np.uint16_t, ndim=1] nparray):
+  cdef long i, n
+  n = nparray.size
+  for i in range(n):
+    tensor_proto.half_val.append(nparray[i])
+
 
 def AppendFloat16ArrayToTensorProto(
     # For numpy, npy_half is a typedef for npy_uint16,
diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py
index 6525607faea62a461ee38fa0393ac29b809bb9b6..c76743d2c629b8f3fb6961602a1575209967339a 100644
--- a/tensorflow/python/framework/function.py
+++ b/tensorflow/python/framework/function.py
@@ -38,8 +38,8 @@ from tensorflow.python.ops import cond_v2_impl
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.util import compat
+from tensorflow.python.util import function_utils
 from tensorflow.python.util import tf_contextlib
-from tensorflow.python.util import tf_decorator
 from tensorflow.python.util import tf_inspect
 
 # This is to avoid a circular dependency with cond_v2_impl.
@@ -255,9 +255,12 @@ class _DefinedFunction(object):
     # Constructed only when C API is enabled, lazily
     self._c_func = None
     self._sub_functions = dict()  # Constructed with _definition or _c_func
-    device_stack = ops.get_default_graph()._device_function_stack  # pylint: disable=protected-access
+    # pylint: disable=protected-access
+    device_funcs = ops.get_default_graph()._device_functions_outer_to_inner
+    # pylint: enable=protected-access
+
     # Get the innermost device if possbile.
-    self._caller_device = device_stack[-1] if device_stack else None
+    self._caller_device = device_funcs[-1] if device_funcs else None
 
     # Cached OpDef for this function. When C API is enabled, this is
     # the only part of FunctionDef that we cache in Python. When C API
@@ -354,7 +357,7 @@ class _DefinedFunction(object):
     if self._func_name:
       base_func_name = self._func_name
     else:
-      base_func_name = _get_func_name(self._func)
+      base_func_name = function_utils.get_func_name(self._func)
       if self._grad_func:
         base_func_name += ("_%s" % self._grad_func.name)
     kwargs_attr = _parse_kwargs_as_attrs(base_func_name, **self._extra_kwargs)
@@ -841,7 +844,7 @@ def func_graph_from_py_func(func, arg_names, arg_types, name=None,
     ValueError: if func returns None.
   """
   if not name:
-    name = _get_func_name(func)
+    name = function_utils.get_func_name(func)
   func_graph = _FuncGraph(name, capture_by_value)
 
   with func_graph.as_default(), ops.device(device):
@@ -1139,19 +1142,6 @@ def _parse_kwargs_as_attrs(func_name, **kwargs):
   return attrs
 
 
-def _get_func_name(func):
-  _, func = tf_decorator.unwrap(func)
-  if callable(func):
-    if tf_inspect.isfunction(func):
-      return func.__name__
-    elif tf_inspect.ismethod(func):
-      return "%s.%s" % (func.__self__.__name__, func.__name__)
-    else:  # Probably a class instance with __call__
-      return type(func)
-  else:
-    raise ValueError("Argument must be callable")
-
-
 def get_extra_vars():
   """Returns the captured variables by the function.
 
diff --git a/tensorflow/python/framework/function_def_to_graph.py b/tensorflow/python/framework/function_def_to_graph.py
index 46c9c4c14adc7d4adeb11b45210cb296acb55086..1b09506662d26a4d0be8e7d77f7f0fea61d6835b 100644
--- a/tensorflow/python/framework/function_def_to_graph.py
+++ b/tensorflow/python/framework/function_def_to_graph.py
@@ -25,7 +25,7 @@ from tensorflow.core.framework import types_pb2
 from tensorflow.core.framework import versions_pb2
 from tensorflow.python.framework import function
 from tensorflow.python.framework import importer
-from tensorflow.python.framework import op_def_registry
+from tensorflow.python.framework import ops
 from tensorflow.python.framework import versions
 from tensorflow.python.ops import cond_v2_impl
 
@@ -114,6 +114,10 @@ def function_def_to_graph_def(fdef, input_shapes=None):
           producer=versions.GRAPH_DEF_VERSION,
           min_consumer=versions.GRAPH_DEF_VERSION_MIN_CONSUMER))
 
+  # Copy *all* functions from outer graph to `graph_def` so that both direct
+  # and indirect references are safely handled.
+  ops.get_default_graph()._copy_functions_to_graph_def(graph_def, 0)  # pylint: disable=protected-access
+
   if input_shapes and len(input_shapes) != len(fdef.signature.input_arg):
     raise ValueError("Length of input_shapes must match the number of " +
                      "input_args. len(input_shapes): {} len(input_arg): {}".
@@ -142,24 +146,18 @@ def function_def_to_graph_def(fdef, input_shapes=None):
     nested_to_flat_tensor_name[arg_def.name] = "{}:0".format(arg_def.name)
 
   for node_def in fdef.node_def:
-    op_def = op_def_registry.get_registered_ops().get(node_def.op)
-    if not op_def:
-      # TODO(b/80470245): Support functions which refer other functions.
-      raise NotImplementedError(
-          "No op registered for {},".format(node_def.op) +
-          " it may be a function. function_def_to_graph_def " +
-          "currently does not support converting functions with " +
-          "references to other graph functions.")
+    op_def = ops.get_default_graph()._get_op_def(node_def.op)  # pylint: disable=protected-access
 
     for attr in op_def.attr:
-      if attr.type in ("func", "list(func)"):
-        # TODO(b/80470245): Support functions which refer other functions.
-        raise NotImplementedError("Unsupported attr {} ".format(attr.name) +
-                                  " with type {}".format(attr.type) +
-                                  " in op {}. ".format(op_def.name) +
-                                  "function_def_to_graph_def currently does " +
-                                  "not support converting functions with " +
-                                  "references to other graph functions.")
+      if attr.type == "func":
+        fname = node_def.attr[attr.name].func.name
+        if not ops.get_default_graph()._is_function(fname):  # pylint: disable=protected-access
+          raise ValueError("%s function not found." % fname)
+      elif attr.type == "list(func)":
+        for fn in node_def.attr[attr.name].list.func:
+          fname = fn.name
+          if not ops.get_default_graph()._is_function(fname):  # pylint: disable=protected-access
+            raise ValueError("%s function not found." % fname)
 
     # Iterate over output_args in op_def to build the map.
     # Index of the output tensor in the flattened list of *all* output
diff --git a/tensorflow/python/framework/function_def_to_graph_test.py b/tensorflow/python/framework/function_def_to_graph_test.py
index 0f4e6ef54fb02cc6ba52c9de2ccabea982fd2323..cd2a16ed5a83f8ffcd8288967815b7ad2761c52f 100644
--- a/tensorflow/python/framework/function_def_to_graph_test.py
+++ b/tensorflow/python/framework/function_def_to_graph_test.py
@@ -18,7 +18,9 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import function
 from tensorflow.python.framework import function_def_to_graph
 from tensorflow.python.framework import graph_to_function_def
 from tensorflow.python.framework import ops
@@ -79,7 +81,6 @@ class FunctionDefToGraphTest(test.TestCase):
 
     g = function_def_to_graph.function_def_to_graph(
         fdef, input_shapes=[None, tensor_shape.matrix(5, 7)])
-    print(g.as_graph_def())
     self.assertIsNone(g.inputs[0].shape.dims)
     self.assertSequenceEqual(g.inputs[1].shape.dims, [5, 7])
     self.assertSequenceEqual(g.outputs[0].shape.dims, [5, 7])
@@ -179,6 +180,37 @@ class FunctionDefToGraphDefTest(test.TestCase):
     self.assertEqual(g.node[0].attr["shape"].shape.unknown_rank, False)
     self.assertFalse("shape" in g.node[2].attr)
 
+  def testFunctionCallsFromFunction(self):
+    x = constant_op.constant(5.0)
+    y = constant_op.constant(10.0)
+
+    @function.Defun()
+    def fn():
+
+      @function.Defun()
+      def inner_fn():
+        return x + y
+
+      return inner_fn()
+
+    # Instantiate the function in this graph so that
+    # `function_def_to_graph` can find it.
+    fn()
+
+    def fn2():
+      return 2 * fn()
+
+    fdef = function._DefinedFunction(fn2, [], []).definition
+    func_graph = function_def_to_graph.function_def_to_graph(fdef)
+    with func_graph.as_default():
+      x_ph, y_ph = func_graph.inputs
+      with self.test_session(graph=func_graph) as sess:
+        self.assertEqual(
+            sess.run(func_graph.outputs[0], feed_dict={
+                x_ph: 5.0,
+                y_ph: 10.0
+            }), 30.0)
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/framework/function_test.py b/tensorflow/python/framework/function_test.py
index 15e41ba91f9ae121d3d4ea48e3e71eace7cd9a3e..1707f929b89203e1890ee96fd153ace2063b449c 100644
--- a/tensorflow/python/framework/function_test.py
+++ b/tensorflow/python/framework/function_test.py
@@ -537,19 +537,25 @@ class FunctionTest(test.TestCase):
   def testResourceVarAsImplicitInput(self):
     g = ops.Graph()
     with g.as_default(), ops.device("cpu:0"):
+      expected_type = dtypes.float32
+      expected_shape = tensor_shape.TensorShape((4, 4))
       v = variable_scope.get_variable(
-          "var", (4, 4), dtypes.float32, use_resource=True)
+          "var", expected_shape, expected_type, use_resource=True)
 
       @function.Defun()
       def Foo():
-        return array_ops.identity(v)
+        captured = array_ops.identity(v)
+        self.assertEqual(expected_type, captured.dtype)
+        self.assertEqual(expected_shape, captured.shape)
+        return captured, array_ops.shape(captured)
 
-      y = v.value()
-      z = Foo()
+      expected_val = v.value()
+      actual_val, actual_shape = Foo()
 
     with self.test_session(graph=g):
       v.initializer.run()
-      self.assertAllEqual(y.eval(), z.eval())
+      self.assertAllEqual(expected_val.eval(), actual_val.eval())
+      self.assertAllEqual(expected_shape, actual_shape.eval())
 
   def testDefineErrors(self):
     with ops.Graph().as_default():
diff --git a/tensorflow/python/framework/importer.py b/tensorflow/python/framework/importer.py
index 699d2b70d176db7718a6e480f9f7b08a65ae6a8e..687bfebd4306596233df8db6a639e65df2f85980 100644
--- a/tensorflow/python/framework/importer.py
+++ b/tensorflow/python/framework/importer.py
@@ -205,7 +205,7 @@ def _PopulateTFImportGraphDefOptions(options, prefix, input_map,
   for input_src, input_dst in input_map.items():
     input_src = compat.as_str(input_src)
     if input_src.startswith('^'):
-      src_name = compat.as_bytes(input_src[1:])
+      src_name = compat.as_str(input_src[1:])
       dst_op = input_dst._as_tf_output().oper  # pylint: disable=protected-access
       c_api.TF_ImportGraphDefOptionsRemapControlDependency(
           options, src_name, dst_op)
diff --git a/tensorflow/python/framework/importer_test.py b/tensorflow/python/framework/importer_test.py
index c5a54470d27b5949fd642b057feda7f3f1a4347f..7182c28666e8ab81962ed12f6490bc67144aa75d 100644
--- a/tensorflow/python/framework/importer_test.py
+++ b/tensorflow/python/framework/importer_test.py
@@ -30,6 +30,7 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import function
 from tensorflow.python.framework import importer
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import test_ops  # pylint: disable=unused-import
 from tensorflow.python.framework import versions
 from tensorflow.python.ops import array_ops
@@ -419,6 +420,46 @@ class ImportGraphDefTest(test.TestCase):
       with self.test_session() as sess:
         self.assertEqual(sess.run(imported_r), 10)
 
+  def testImportWhileLoopInCond(self):
+    # Produce GraphDef containing while loop.
+    graph = ops.Graph()
+    with graph.as_default():
+      r = control_flow_ops.while_loop(lambda i: i < 10, lambda i: i + 1, [0])
+    graph_def = graph.as_graph_def()
+
+    # Import the GraphDef inside a cond and make sure it runs.
+    with ops.Graph().as_default():
+
+      def ImportFn():
+        return importer.import_graph_def(graph_def, return_elements=[r.name])[0]
+
+      pred = array_ops.placeholder(dtypes.bool)
+      out = control_flow_ops.cond(pred, ImportFn,
+                                  lambda: constant_op.constant(1))
+      with self.test_session() as sess:
+        self.assertEqual(sess.run(out, {pred: True}), 10)
+        self.assertEqual(sess.run(out, {pred: False}), 1)
+
+  def testImportWhileLoopInWhileLoop(self):
+    self.skipTest("b/111757448")
+    # Produce GraphDef containing while loop.
+    graph = ops.Graph()
+    with graph.as_default():
+      r = control_flow_ops.while_loop(lambda i: i < 10, lambda i: i + 1, [0])
+    graph_def = graph.as_graph_def()
+
+    # Import the GraphDef inside another loop and make sure it runs.
+    with ops.Graph().as_default():
+
+      def ImportFn(_):
+        return importer.import_graph_def(graph_def, return_elements=[r.name])[0]
+
+      out = control_flow_ops.while_loop(
+          lambda i: i < 2, ImportFn, [0],
+          shape_invariants=[tensor_shape.TensorShape(None)])
+      with self.test_session() as sess:
+        self.assertEqual(sess.run(out), 10)
+
   def testTypeMismatchInGraphDef(self):
     # TODO(skyewm): improve error message
     error_msg = ("Input 0 of node import/B was passed int32 from import/A:0 "
diff --git a/tensorflow/python/framework/kernels.py b/tensorflow/python/framework/kernels.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7641f3442e4c5a6508a3463c700ade97ce202a9
--- /dev/null
+++ b/tensorflow/python/framework/kernels.py
@@ -0,0 +1,46 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functions for querying registered kernels."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.core.framework import kernel_def_pb2
+from tensorflow.python import pywrap_tensorflow as c_api
+from tensorflow.python.util import compat
+
+
+def get_all_registered_kernels():
+  """Returns a KernelList proto of all registered kernels.
+  """
+  buf = c_api.TF_GetAllRegisteredKernels()
+  data = c_api.TF_GetBuffer(buf)
+  kernel_list = kernel_def_pb2.KernelList()
+  kernel_list.ParseFromString(compat.as_bytes(data))
+  return kernel_list
+
+
+def get_registered_kernels_for_op(name):
+  """Returns a KernelList proto of registered kernels for a given op.
+
+  Args:
+    name: A string representing the name of the op whose kernels to retrieve.
+  """
+  buf = c_api.TF_GetRegisteredKernelsForOp(name)
+  data = c_api.TF_GetBuffer(buf)
+  kernel_list = kernel_def_pb2.KernelList()
+  kernel_list.ParseFromString(compat.as_bytes(data))
+  return kernel_list
diff --git a/tensorflow/python/framework/kernels_test.py b/tensorflow/python/framework/kernels_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..c53500be73a05b2d9b379fd61e899a091b7db9b1
--- /dev/null
+++ b/tensorflow/python/framework/kernels_test.py
@@ -0,0 +1,41 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for querying registered kernels."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import kernels
+from tensorflow.python.framework import test_util
+from tensorflow.python.platform import googletest
+
+
+class GetAllRegisteredKernelsTest(test_util.TensorFlowTestCase):
+
+  def testFindsAtLeastOneKernel(self):
+    kernel_list = kernels.get_all_registered_kernels()
+    self.assertGreater(len(kernel_list.kernel), 0)
+
+
+class GetRegisteredKernelsForOp(test_util.TensorFlowTestCase):
+
+  def testFindsAtLeastOneKernel(self):
+    kernel_list = kernels.get_registered_kernels_for_op("KernelLabel")
+    self.assertGreater(len(kernel_list.kernel), 0)
+    self.assertEqual(kernel_list.kernel[0].op, "KernelLabel")
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/python/framework/meta_graph.py b/tensorflow/python/framework/meta_graph.py
index 923e76fc9c8f231cc9a43bc05280dac1ea458d3c..33631282bd03a15daddb334e6f40e6b52f84c750 100644
--- a/tensorflow/python/framework/meta_graph.py
+++ b/tensorflow/python/framework/meta_graph.py
@@ -696,6 +696,67 @@ def import_scoped_meta_graph(meta_graph_or_file,
   Raises:
     ValueError: If the graph_def contains unbound inputs.
   """
+  return import_scoped_meta_graph_with_return_elements(
+      meta_graph_or_file, clear_devices, graph, import_scope, input_map,
+      unbound_inputs_col_name, restore_collections_predicate)[0]
+
+
+def import_scoped_meta_graph_with_return_elements(
+    meta_graph_or_file,
+    clear_devices=False,
+    graph=None,
+    import_scope=None,
+    input_map=None,
+    unbound_inputs_col_name="unbound_inputs",
+    restore_collections_predicate=(lambda key: True),
+    return_elements=None):
+  """Imports graph from `MetaGraphDef` and returns vars and return elements.
+
+  This function takes a `MetaGraphDef` protocol buffer as input. If
+  the argument is a file containing a `MetaGraphDef` protocol buffer ,
+  it constructs a protocol buffer from the file content. The function
+  then adds all the nodes from the `graph_def` field to the
+  current graph, recreates the desired collections, and returns a dictionary of
+  all the Variables imported into the name scope.
+
+  In combination with `export_scoped_meta_graph()`, this function can be used to
+
+  * Serialize a graph along with other Python objects such as `QueueRunner`,
+    `Variable` into a `MetaGraphDef`.
+
+  * Restart training from a saved graph and checkpoints.
+
+  * Run inference from a saved graph and checkpoints.
+
+  Args:
+    meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including
+      the path) containing a `MetaGraphDef`.
+    clear_devices: Boolean which controls whether to clear device information
+      from graph_def. Default false.
+    graph: The `Graph` to import into. If `None`, use the default graph.
+    import_scope: Optional `string`. Name scope into which to import the
+      subgraph. If `None`, the graph is imported to the root name scope.
+    input_map: A dictionary mapping input names (as strings) in `graph_def` to
+      `Tensor` objects. The values of the named input tensors in the imported
+      graph will be re-mapped to the respective `Tensor` values.
+    unbound_inputs_col_name: Collection name for looking up unbound inputs.
+    restore_collections_predicate: a predicate on collection names. A collection
+      named c (i.e whose key is c) will be restored iff
+      1) `restore_collections_predicate(c)` is True, and
+      2) `c != unbound_inputs_col_name`.
+    return_elements:  A list of strings containing operation names in the
+      `MetaGraphDef` that will be returned as `Operation` objects; and/or
+      tensor names in `MetaGraphDef` that will be returned as `Tensor` objects.
+
+  Returns:
+    A tuple of (
+      dictionary of all the `Variables` imported into the name scope,
+      list of `Operation` or `Tensor` objects from the `return_elements` list).
+
+  Raises:
+    ValueError: If the graph_def contains unbound inputs.
+
+  """
   if context.executing_eagerly():
     raise ValueError("Exporting/importing meta graphs is not supported when "
                      "eager execution is enabled.")
@@ -737,11 +798,12 @@ def import_scoped_meta_graph(meta_graph_or_file,
     scope_to_prepend_to_names = graph.unique_name(
         import_scope or "", mark_as_used=False)
 
-    importer.import_graph_def(
+    imported_return_elements = importer.import_graph_def(
         input_graph_def,
         name=(import_scope or scope_to_prepend_to_names),
         input_map=input_map,
-        producer_op_list=producer_op_list)
+        producer_op_list=producer_op_list,
+        return_elements=return_elements)
 
     # Restores all the other collections.
     variable_objects = {}
@@ -806,7 +868,7 @@ def import_scoped_meta_graph(meta_graph_or_file,
     for v in variables:
       var_list[ops.strip_name_scope(v.name, scope_to_prepend_to_names)] = v
 
-  return var_list
+  return var_list, imported_return_elements
 
 
 def export_scoped_meta_graph(filename=None,
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index cf0b1e36fb3f02c85873a0da81dc056d2fbd5f6a..c25e29b0f46d2050098aedb3f82e0c1029f435a7 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -20,7 +20,6 @@ from __future__ import print_function
 
 import collections
 import copy
-import linecache
 import os
 import re
 import sys
@@ -49,14 +48,17 @@ from tensorflow.python.framework import errors
 from tensorflow.python.framework import op_def_registry
 from tensorflow.python.framework import registry
 from tensorflow.python.framework import tensor_shape
+from tensorflow.python.framework import traceable_stack
 from tensorflow.python.framework import versions
 from tensorflow.python.ops import control_flow_util
 from tensorflow.python.platform import app
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import compat
 from tensorflow.python.util import decorator_utils
+from tensorflow.python.util import function_utils
 from tensorflow.python.util import lock_util
 from tensorflow.python.util import tf_contextlib
+from tensorflow.python.util import tf_stack
 from tensorflow.python.util.deprecation import deprecated_args
 from tensorflow.python.util.tf_export import tf_export
 
@@ -72,6 +74,31 @@ def tensor_id(tensor):
   return tensor._id  # pylint: disable=protected-access
 
 
+class _UserDeviceSpec(object):
+  """Store user-specified device and provide computation of merged device."""
+
+  def __init__(self, device_name_or_function):
+    self._device_name_or_function = device_name_or_function
+
+    self.display_name = str(self._device_name_or_function)
+    if callable(self._device_name_or_function):
+      dev_func = self._device_name_or_function
+      func_name = function_utils.get_func_name(dev_func)
+      func_code = function_utils.get_func_code(dev_func)
+      if func_code:
+        fname = func_code.co_filename
+        lineno = func_code.co_firstlineno
+      else:
+        fname = "unknown"
+        lineno = -1
+      self.display_name = "%s<%s, %d>" % (func_name, fname, lineno)
+
+    self.function = self._device_name_or_function
+    if not (self._device_name_or_function is None or
+            callable(self._device_name_or_function)):
+      self.function = pydev.merge_device(self._device_name_or_function)
+
+
 class _NullContextmanager(object):
 
   def __enter__(self):
@@ -706,9 +733,9 @@ class _EagerTensorBase(Tensor):
     """
     if self.dtype == dtypes.resource:
       raise ValueError("Resource handles are not convertible to numpy.")
-    return self.cpu()._numpy()  # pylint: disable=protected-access
+    return self._cpu_nograd()._numpy()  # pylint: disable=protected-access
 
-  # __int__ and  __float__ may copy the tensor to CPU and
+  # __int__, __float__ and __index__ may copy the tensor to CPU and
   # only work for scalars; values are cast as per numpy.
   def __int__(self):
     return int(self.numpy())
@@ -716,6 +743,9 @@ class _EagerTensorBase(Tensor):
   def __float__(self):
     return float(self.numpy())
 
+  def __index__(self):
+    return int(self.numpy())
+
   def __array__(self, dtype=None):
     return np.array(self.numpy(), dtype=dtype)
 
@@ -780,8 +810,8 @@ class _EagerTensorBase(Tensor):
   def _override_operator(name, func):
     setattr(_EagerTensorBase, name, func)
 
-  def _copy(self, ctx=None, device_name=None):
-    """Copies tensor to dest device."""
+  def _copy_nograd(self, ctx=None, device_name=None):
+    """Copies tensor to dest device, but doesn't record the operation."""
     # pylint: disable=protected-access
     # Creates a new tensor on the dest device.
     if ctx is None:
@@ -793,7 +823,11 @@ class _EagerTensorBase(Tensor):
       new_tensor = self._copy_to_device(context=ctx._handle, device=device_name)
     except core._NotOkStatusException as e:
       six.raise_from(core._status_to_exception(e.code, e.message), None)
+    return new_tensor
 
+  def _copy(self, ctx=None, device_name=None):
+    """Copies tensor to dest device."""
+    new_tensor = self._copy_nograd(ctx, device_name)
     # Record the copy on tape and define backprop copy as well.
     if context.executing_eagerly():
       self_device = self.device
@@ -824,6 +858,16 @@ class _EagerTensorBase(Tensor):
     """Returns the number of Tensor dimensions."""
     return self.shape.ndims
 
+  def _cpu_nograd(self):
+    """A copy of this Tensor with contents backed by host memory.
+
+    The copy cannot be differentiated through.
+
+    Returns:
+      A CPU-memory backed Tensor object with the same contents as this Tensor.
+    """
+    return self._copy_nograd(context.context(), "CPU:0")
+
   def cpu(self):
     """A copy of this Tensor with contents backed by host memory."""
     return self._copy(context.context(), "CPU:0")
@@ -1697,10 +1741,19 @@ class Operation(object):
     # This will be set by self.inputs.
     self._inputs_val = None
 
-    self._id_value = self._graph._next_id()  # pylint: disable=protected-access
+    # pylint: disable=protected-access
+    self._id_value = self._graph._next_id()
     self._original_op = original_op
-    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access
-    self._control_flow_context = self.graph._get_control_flow_context()  # pylint: disable=protected-access
+    self._traceback = tf_stack.extract_stack()
+
+    # List of _UserDevSpecs holding code location of device context manager
+    # invocations and the users original argument to them.
+    self._device_code_locations = None
+    # Dict mapping op name to file and line information for op colocation
+    # context managers.
+    self._colocation_code_locations = None
+    self._control_flow_context = self.graph._get_control_flow_context()
+    # pylint: enable=protected-access
 
     # Initialize self._c_op.
     if c_op:
@@ -1838,6 +1891,72 @@ class Operation(object):
     """
     return c_api.TF_OperationDevice(self._c_op)
 
+  @property
+  def _device_assignments(self):
+    """Code locations for device context managers active at op creation.
+
+    This property will return a list of traceable_stack.TraceableObject
+    instances where .obj is a string representing the assigned device
+    (or information about the function that would be applied to this op
+    to compute the desired device) and the filename and lineno members
+    record the location of the relevant device context manager.
+
+    For example, suppose file_a contained these lines:
+
+      file_a.py:
+        15: with tf.device('/gpu:0'):
+        16:   node_b = tf.constant(4, name='NODE_B')
+
+    Then a TraceableObject t_obj representing the device context manager
+    would have these member values:
+
+      t_obj.obj -> '/gpu:0'
+      t_obj.filename = 'file_a.py'
+      t_obj.lineno = 15
+
+    and node_b.op._device_assignments would return the list [t_obj].
+
+    Returns:
+      [str: traceable_stack.TraceableObject, ...] as per this method's
+      description, above.
+    """
+    return self._device_code_locations or []
+
+  @property
+  def _colocation_dict(self):
+    """Code locations for colocation context managers active at op creation.
+
+    This property will return a dictionary for which the keys are nodes with
+    which this Operation is colocated, and for which the values are
+    traceable_stack.TraceableObject instances.  The TraceableObject instances
+    record the location of the relevant colocation context manager but have the
+    "obj" field set to None to prevent leaking private data.
+
+    For example, suppose file_a contained these lines:
+
+      file_a.py:
+        14: node_a = tf.constant(3, name='NODE_A')
+        15: with tf.colocate_with(node_a):
+        16:   node_b = tf.constant(4, name='NODE_B')
+
+    Then a TraceableObject t_obj representing the colocation context manager
+    would have these member values:
+
+      t_obj.obj -> None
+      t_obj.filename = 'file_a.py'
+      t_obj.lineno = 15
+
+    and node_b.op._colocation_dict would return the dictionary
+
+      { 'NODE_A': t_obj }
+
+    Returns:
+      {str: traceable_stack.TraceableObject} as per this method's description,
+      above.
+    """
+    locations_dict = self._colocation_code_locations or {}
+    return locations_dict.copy()
+
   @property
   def _output_types(self):
     """List this operation's output types.
@@ -2140,7 +2259,7 @@ class Operation(object):
   @property
   def traceback(self):
     """Returns the call stack from when this operation was constructed."""
-    return self._graph._convert_stack(self._traceback)  # pylint: disable=protected-access
+    return tf_stack.convert_stack(self._traceback)
 
   @property
   def traceback_with_start_lines(self):
@@ -2149,9 +2268,8 @@ class Operation(object):
     Returns:
       A list of 5-tuples (filename, lineno, name, code, func_start_lineno).
     """
-    return self._graph._convert_stack(  # pylint: disable=protected-access
-        self._traceback,
-        include_func_start_lineno=True)
+    return tf_stack.convert_stack(self._traceback,
+                                  include_func_start_lineno=True)
 
   def _set_attr(self, attr_name, attr_value):
     """Private method used to set an attribute in the node_def."""
@@ -2603,7 +2721,6 @@ def _name_from_scope_name(name):
 _MUTATION_LOCK_GROUP = 0
 _SESSION_RUN_LOCK_GROUP = 1
 
-
 @tf_export("Graph")
 class Graph(object):
   """A TensorFlow computation, represented as a dataflow graph.
@@ -2679,7 +2796,7 @@ class Graph(object):
     # Functions that will be applied to choose a device if none is specified.
     # After switch_to_thread_local(), self._thread_local._device_function_stack
     # is used instead.
-    self._graph_device_function_stack = []
+    self._graph_device_function_stack = traceable_stack.TraceableStack()
     # Default original_op applied to new ops.
     self._default_original_op = None
     # Current control flow context. It could be either CondContext or
@@ -2712,7 +2829,7 @@ class Graph(object):
     self._building_function = False
     # Stack of colocate_with ops. After switch_to_thread_local(),
     # self._thread_local._colocation_stack is used instead.
-    self._graph_colocation_stack = []
+    self._graph_colocation_stack = traceable_stack.TraceableStack()
     # Set of tensors that are dangerous to feed!
     self._unfeedable_tensors = set()
     # Set of operations that are dangerous to fetch!
@@ -2752,36 +2869,6 @@ class Graph(object):
     """Temporary hack; can be overridden to force C API usage."""
     return _USE_C_API
 
-  def _convert_stack(self, stack, include_func_start_lineno=False):
-    """Converts a stack extracted using _extract_stack() to a traceback stack.
-
-    Args:
-      stack: A list of n 5-tuples,
-        (filename, lineno, name, frame_globals, func_start_lineno).
-      include_func_start_lineno: True if function start line number should be
-        included as the 5th entry in return tuples.
-
-    Returns:
-      A list of n 4-tuples or 5-tuples
-      (filename, lineno, name, code, [optional: func_start_lineno]), where the
-      code tuple element is calculated from the corresponding elements of the
-      input tuple.
-    """
-    ret = []
-    for (filename, lineno, name, frame_globals, func_start_lineno,
-         unused_frame_info) in stack:
-      linecache.checkcache(filename)
-      line = linecache.getline(filename, lineno, frame_globals)
-      if line:
-        line = line.strip()
-      else:
-        line = None
-      if include_func_start_lineno:
-        ret.append((filename, lineno, name, line, func_start_lineno))
-      else:
-        ret.append((filename, lineno, name, line))
-    return ret
-
   # Note: this method is private because the API of tf.Graph() is public and
   # frozen, and this functionality is still not ready for public visibility.
   @tf_contextlib.contextmanager
@@ -2789,63 +2876,23 @@ class Graph(object):
     # This step makes a copy of the existing stack, and it also initializes
     # self._thread_local._variable_creator_stack if it doesn't exist yet.
     old = list(self._variable_creator_stack)
-    self._thread_local._variable_creator_stack.append(creator)
+    self._thread_local._variable_creator_stack.append(creator)  # pylint: disable=protected-access
     try:
       yield
     finally:
-      self._thread_local._variable_creator_stack = old
+      self._thread_local._variable_creator_stack = old  # pylint: disable=protected-access
 
   # Note: this method is private because the API of tf.Graph() is public and
   # frozen, and this functionality is still not ready for public visibility.
   @property
   def _variable_creator_stack(self):
     if not hasattr(self._thread_local, "_variable_creator_stack"):
-      self._thread_local._variable_creator_stack = []
-    return list(self._thread_local._variable_creator_stack)
+      self._thread_local._variable_creator_stack = []  # pylint: disable=protected-access
+    return list(self._thread_local._variable_creator_stack)  # pylint: disable=protected-access
 
   @_variable_creator_stack.setter
   def _variable_creator_stack(self, variable_creator_stack):
-    self._thread_local._variable_creator_stack = variable_creator_stack
-
-  def _extract_stack(self):
-    """A lightweight, extensible re-implementation of traceback.extract_stack.
-
-    NOTE(mrry): traceback.extract_stack eagerly retrieves the line of code for
-      each stack frame using linecache, which results in an abundance of stat()
-      calls. This implementation does not retrieve the code, and any consumer
-      should apply _convert_stack to the result to obtain a traceback that can
-      be formatted etc. using traceback methods.
-
-    Derived classes can implement _extract_frame_info() to add extra information
-    to the traceback.
-
-    Returns:
-      A list of 6-tuples
-      (filename, lineno, name, frame_globals, func_start_lineno, custom_info)
-      corresponding to the call stack of the current thread.
-    """
-    try:
-      raise ZeroDivisionError
-    except ZeroDivisionError:
-      f = sys.exc_info()[2].tb_frame.f_back
-    ret = []
-    while f is not None:
-      lineno = f.f_lineno
-      co = f.f_code
-      filename = co.co_filename
-      name = co.co_name
-      frame_globals = f.f_globals
-      func_start_lineno = co.co_firstlineno
-      frame_info = self._extract_frame_info(f)
-      ret.append((filename, lineno, name, frame_globals, func_start_lineno,
-                  frame_info))
-      f = f.f_back
-    ret.reverse()
-    return ret
-
-  def _extract_frame_info(self, frame):  # pylint: disable=unused-argument
-    """Extracts custom information from a frame in an op traceback."""
-    return None
+    self._thread_local._variable_creator_stack = variable_creator_stack  # pylint: disable=protected-access
 
   def _check_not_finalized(self):
     """Check if the graph is finalized.
@@ -3287,7 +3334,7 @@ class Graph(object):
 
     if self._colocation_stack:
       all_colocation_groups = []
-      for colocation_op in self._colocation_stack:
+      for colocation_op in self._colocation_stack.peek_objs():
         all_colocation_groups.extend(colocation_op.colocation_groups())
         if colocation_op.device:
           # Make this device match the device of the colocated op, to provide
@@ -3306,6 +3353,7 @@ class Graph(object):
       # pylint: disable=protected-access
       op._set_attr("_class", attr_value_pb2.AttrValue(
           list=attr_value_pb2.AttrValue.ListValue(s=all_colocation_groups)))
+      op._colocation_code_locations = self._snapshot_colocation_stack_metadata()
       # pylint: enable=protected-access
 
     # Sets "container" attribute if
@@ -3615,9 +3663,13 @@ class Graph(object):
     This method should be used if you want to create multiple graphs
     in the same process. For convenience, a global default graph is
     provided, and all ops will be added to this graph if you do not
-    create a new graph explicitly. Use this method with the `with` keyword
-    to specify that ops created within the scope of a block should be
-    added to this graph.
+    create a new graph explicitly.
+
+    Use this method with the `with` keyword to specify that ops created within
+    the scope of a block should be added to this graph. In this case, once
+    the scope of the `with` is exited, the previous default graph is set again
+    as default. There is a stack, so it's ok to have multiple nested levels
+    of `as_default` calls.
 
     The default graph is a property of the current thread. If you
     create a new thread, and wish to use the default graph in that
@@ -3788,8 +3840,8 @@ class Graph(object):
       Nothing.
     """
     old_original_op = self._default_original_op
+    self._default_original_op = op
     try:
-      self._default_original_op = op
       yield
     finally:
       self._default_original_op = old_original_op
@@ -3906,15 +3958,15 @@ class Graph(object):
         # op name regex, which constrains the initial character.
         if not _VALID_OP_NAME_REGEX.match(name):
           raise ValueError("'%s' is not a valid scope name" % name)
+    old_stack = self._name_stack
+    if not name:  # Both for name=None and name="" we re-set to empty scope.
+      new_stack = None
+    elif name[-1] == "/":
+      new_stack = _name_from_scope_name(name)
+    else:
+      new_stack = self.unique_name(name)
+    self._name_stack = new_stack
     try:
-      old_stack = self._name_stack
-      if not name:  # Both for name=None and name="" we re-set to empty scope.
-        new_stack = None
-      elif name[-1] == "/":
-        new_stack = _name_from_scope_name(name)
-      else:
-        new_stack = self.unique_name(name)
-      self._name_stack = new_stack
       yield "" if new_stack is None else new_stack + "/"
     finally:
       self._name_stack = old_stack
@@ -3995,8 +4047,8 @@ class Graph(object):
                                   ignore_existing=False):
     with self.colocate_with(op, ignore_existing):
       if gradient_uid is not None and self._control_flow_context is not None:
+        self._control_flow_context.EnterGradientColocation(op, gradient_uid)
         try:
-          self._control_flow_context.EnterGradientColocation(op, gradient_uid)
           yield
         finally:
           self._control_flow_context.ExitGradientColocation(op, gradient_uid)
@@ -4038,7 +4090,6 @@ class Graph(object):
     Yields:
       A context manager that specifies the op with which to colocate
       newly created ops.
-
     """
     if op is None and not ignore_existing:
       raise ValueError("Trying to reset colocation (op is None) but "
@@ -4056,14 +4107,17 @@ class Graph(object):
     # In the future, a caller may specify that device_functions win
     # over colocation, in which case we can add support.
     device_fn_tmp = self._device_function_stack
-    self._device_function_stack = []
+    self._device_function_stack = traceable_stack.TraceableStack()
 
     if ignore_existing:
       current_stack = self._colocation_stack
-      self._colocation_stack = []
+      self._colocation_stack = traceable_stack.TraceableStack()
 
     if op is not None:
-      self._colocation_stack.append(op)
+      # offset refers to the stack frame used for storing code location.
+      # We use 4, the sum of 1 to use our caller's stack frame and 3
+      # to jump over layers of context managers above us.
+      self._colocation_stack.push_obj(op, offset=4)
 
     try:
       yield
@@ -4071,12 +4125,19 @@ class Graph(object):
       # Restore device function stack
       self._device_function_stack = device_fn_tmp
       if op is not None:
-        self._colocation_stack.pop()
+        self._colocation_stack.pop_obj()
 
       # Reset the colocation stack if requested.
       if ignore_existing:
         self._colocation_stack = current_stack
 
+  def _add_device_to_stack(self, device_name_or_function, offset=0):
+    """Add device to stack manually, separate from a context manager."""
+    total_offset = 1 + offset
+    spec = _UserDeviceSpec(device_name_or_function)
+    self._device_function_stack.push_obj(spec, offset=total_offset)
+    return spec
+
   @tf_contextlib.contextmanager
   def device(self, device_name_or_function):
     # pylint: disable=line-too-long
@@ -4134,31 +4195,26 @@ class Graph(object):
     Yields:
       A context manager that specifies the default device to use for newly
       created ops.
-
     """
-    # pylint: enable=line-too-long
-    if (device_name_or_function is not None and
-        not callable(device_name_or_function)):
-      device_function = pydev.merge_device(device_name_or_function)
-    else:
-      device_function = device_name_or_function
-
+    self._add_device_to_stack(device_name_or_function, offset=2)
     try:
-      self._device_function_stack.append(device_function)
       yield
     finally:
-      self._device_function_stack.pop()
+      self._device_function_stack.pop_obj()
 
   def _apply_device_functions(self, op):
     """Applies the current device function stack to the given operation."""
-    # Apply any device functions in reverse order, so that the most recently
+    # Apply any device functions in LIFO order, so that the most recently
     # pushed function has the first chance to apply a device to the op.
     # We apply here because the result can depend on the Operation's
     # signature, which is computed in the Operation constructor.
-    for device_function in reversed(self._device_function_stack):
-      if device_function is None:
+    # pylint: disable=protected-access
+    for device_spec in self._device_function_stack.peek_objs():
+      if device_spec.function is None:
         break
-      op._set_device(device_function(op))  # pylint: disable=protected-access
+      op._set_device(device_spec.function(op))
+    op._device_code_locations = self._snapshot_device_function_stack_metadata()
+    # pylint: enable=protected-access
 
   # pylint: disable=g-doc-return-or-yield
   @tf_contextlib.contextmanager
@@ -4207,8 +4263,8 @@ class Graph(object):
         yields the container name.
     """
     original_container = self._container
+    self._container = container_name
     try:
-      self._container = container_name
       yield self._container
     finally:
       self._container = original_container
@@ -4682,35 +4738,74 @@ class Graph(object):
     if self._stack_state_is_thread_local:
       # This may be called from a thread where device_function_stack doesn't yet
       # exist.
+      # pylint: disable=protected-access
       if not hasattr(self._thread_local, "_device_function_stack"):
-        self._thread_local._device_function_stack = (
-            self._graph_device_function_stack[:])
+        stack_copy_for_this_thread = self._graph_device_function_stack.copy()
+        self._thread_local._device_function_stack = stack_copy_for_this_thread
       return self._thread_local._device_function_stack
+      # pylint: enable=protected-access
     else:
       return self._graph_device_function_stack
 
+  @property
+  def _device_functions_outer_to_inner(self):
+    user_device_specs = self._device_function_stack.peek_objs()
+    device_functions = [spec.function for spec in user_device_specs]
+    device_functions_outer_to_inner = list(reversed(device_functions))
+    return device_functions_outer_to_inner
+
+  def _snapshot_device_function_stack_metadata(self):
+    """Return device function stack as a list of TraceableObjects.
+
+    Returns:
+      [traceable_stack.TraceableObject, ...] where each TraceableObject's .obj
+      member is a displayable name for the user's argument to Graph.device, and
+      the filename and lineno members point to the code location where
+      Graph.device was called directly or indirectly by the user.
+    """
+    traceable_objects = self._device_function_stack.peek_traceable_objs()
+    snapshot = []
+    for obj in traceable_objects:
+      obj_copy = obj.copy_metadata()
+      obj_copy.obj = obj.obj.display_name
+      snapshot.append(obj_copy)
+    return snapshot
+
   @_device_function_stack.setter
   def _device_function_stack(self, device_function_stack):
     if self._stack_state_is_thread_local:
+      # pylint: disable=protected-access
       self._thread_local._device_function_stack = device_function_stack
+      # pylint: enable=protected-access
     else:
       self._graph_device_function_stack = device_function_stack
 
   @property
   def _colocation_stack(self):
+    """Return thread-local copy of colocation stack."""
     if self._stack_state_is_thread_local:
       # This may be called from a thread where colocation_stack doesn't yet
       # exist.
+      # pylint: disable=protected-access
       if not hasattr(self._thread_local, "_colocation_stack"):
-        self._thread_local._colocation_stack = self._graph_colocation_stack[:]
+        stack_copy_for_this_thread = self._graph_colocation_stack.copy()
+        self._thread_local._colocation_stack = stack_copy_for_this_thread
       return self._thread_local._colocation_stack
+      # pylint: enable=protected-access
     else:
       return self._graph_colocation_stack
 
+  def _snapshot_colocation_stack_metadata(self):
+    """Return colocation stack metadata as a dictionary."""
+    traceable_objects = self._colocation_stack.peek_traceable_objs()
+    return {obj.obj.name: obj.copy_metadata() for obj in traceable_objects}
+
   @_colocation_stack.setter
   def _colocation_stack(self, colocation_stack):
     if self._stack_state_is_thread_local:
+      # pylint: disable=protected-access
       self._thread_local._colocation_stack = colocation_stack
+      # pylint: enable=protected-access
     else:
       self._graph_colocation_stack = colocation_stack
 
@@ -4879,8 +4974,8 @@ class _DefaultStack(threading.local):
   @tf_contextlib.contextmanager
   def get_controller(self, default):
     """A context manager for manipulating a default stack."""
+    self.stack.append(default)
     try:
-      self.stack.append(default)
       yield default
     finally:
       # stack may be empty if reset() was called
@@ -5068,13 +5163,15 @@ class _DefaultGraphStack(_DefaultStack):  # pylint: disable=protected-access
 
   @tf_contextlib.contextmanager
   def get_controller(self, default):
+    context.context().context_switches.push(
+        default.building_function, default.as_default)
     try:
-      context.context().context_switches.push(
-          default.building_function, default.as_default)
       with super(_DefaultGraphStack, self).get_controller(
           default) as g, context.graph_mode():
         yield g
     finally:
+      # If an exception is raised here it may be hiding a related exception in
+      # the try-block (just above).
       context.context().context_switches.pop()
 
 
@@ -5110,6 +5207,9 @@ def init_scope():
         `init_scope` will simply install a fresh graph as the default one.
 
     (3) The gradient tape is paused while the scope is active.
+
+  Raises:
+    RuntimeError: if graph state is incompatible with this initialization.
   """
   # pylint: enable=g-doc-return-or-yield,line-too-long
 
@@ -5122,10 +5222,10 @@ def init_scope():
     # the name scope of the current context.
     default_graph = get_default_graph()
     scope = default_graph.get_name_scope()
-    if scope and scope[-1] != '/':
+    if scope and scope[-1] != "/":
       # Names that end with trailing slashes are treated by `name_scope` as
       # absolute.
-      scope = scope + '/'
+      scope = scope + "/"
     inner_device_stack = default_graph._device_function_stack  # pylint: disable=protected-access
 
     outer_context = None
@@ -5170,6 +5270,8 @@ def init_scope():
           outer_graph._device_function_stack = inner_device_stack  # pylint: disable=protected-access
         yield
     finally:
+      # If an exception is raised here it may be hiding a related exception in
+      # try-block (just above).
       if outer_graph is not None:
         outer_graph._device_function_stack = outer_device_stack  # pylint: disable=protected-access
 
@@ -5237,7 +5339,10 @@ def enable_eager_execution(config=None,
      to this function.
   """
   return enable_eager_execution_internal(
-      config, device_policy, execution_mode, None)
+      config=config,
+      device_policy=device_policy,
+      execution_mode=execution_mode,
+      server_def=None)
 
 
 def enable_eager_execution_internal(config=None,
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index 150100d771bb41d3693d39dc6fa19baa40da4c04..48328a7f58da60d273f01afbb9a970a66c23c612 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import gc
+import os
 import threading
 import weakref
 
@@ -2542,6 +2543,56 @@ class StatisticsTest(test_util.TensorFlowTestCase):
     self.assertEqual(3, flops_total.value)
 
 
+class DeviceStackTest(test_util.TensorFlowTestCase):
+
+  def testBasicDeviceAssignmentMetadata(self):
+
+    def device_func(unused_op):
+      return "/cpu:*"
+
+    const_zero = constant_op.constant([0.0], name="zero")
+    with ops.device("/cpu"):
+      const_one = constant_op.constant([1.0], name="one")
+      with ops.device("/cpu:0"):
+        const_two = constant_op.constant([2.0], name="two")
+    with ops.device(device_func):
+      const_three = constant_op.constant(3.0, name="three")
+
+    self.assertEqual(0, len(const_zero.op._device_assignments))
+
+    one_list = const_one.op._device_assignments
+    self.assertEqual(1, len(one_list))
+    self.assertEqual("/cpu", one_list[0].obj)
+    self.assertEqual("ops_test.py", os.path.basename(one_list[0].filename))
+
+    two_list = const_two.op._device_assignments
+    self.assertEqual(2, len(two_list))
+    devices = [t.obj for t in two_list]
+    self.assertEqual(set(["/cpu", "/cpu:0"]), set(devices))
+
+    three_list = const_three.op._device_assignments
+    self.assertEqual(1, len(three_list))
+    func_description = three_list[0].obj
+    expected_regex = r"device_func<.*ops_test.py, [0-9]+"
+    self.assertRegexpMatches(func_description, expected_regex)
+
+  def testDeviceAssignmentMetadataForGraphDeviceAndTfDeviceFunctions(self):
+
+    with ops.device("/cpu"):
+      const_one = constant_op.constant([1.0], name="one")
+    with ops.get_default_graph().device("/cpu"):
+      const_two = constant_op.constant([2.0], name="two")
+
+    one_metadata = const_one.op._device_assignments[0]
+    two_metadata = const_two.op._device_assignments[0]
+
+    # Verify both types of device assignment return the right stack info.
+    self.assertRegexpMatches("ops_test.py",
+                             os.path.basename(one_metadata.filename))
+    self.assertEqual(one_metadata.filename, two_metadata.filename)
+    self.assertEqual(one_metadata.lineno + 2, two_metadata.lineno)
+
+
 class ColocationGroupTest(test_util.TensorFlowTestCase):
 
   def testBasic(self):
@@ -2554,6 +2605,18 @@ class ColocationGroupTest(test_util.TensorFlowTestCase):
     with self.assertRaises(ValueError):
       c.op.get_attr("_class")
 
+  def testBasicColocationMetadata(self):
+    const_two = constant_op.constant([2.0], name="two")
+    with ops.colocate_with(const_two.op):
+      const_three = constant_op.constant(3.0, name="three")
+    locations_dict = const_three.op._colocation_dict
+    self.assertIn("two", locations_dict)
+    metadata = locations_dict["two"]
+    self.assertIsNone(metadata.obj)
+    # Check that this test's filename is recorded as the file containing the
+    # colocation statement.
+    self.assertEqual("ops_test.py", os.path.basename(metadata.filename))
+
   def testColocationDeviceInteraction(self):
     with ops.device("/cpu:0"):
       with ops.device("/device:GPU:0"):
diff --git a/tensorflow/python/framework/python_op_gen.cc b/tensorflow/python/framework/python_op_gen.cc
index ec3748b40ec53814f036ca3463c1840d31bc1140..76d4c2017cac46761c53cabddd4a6506e519f136 100644
--- a/tensorflow/python/framework/python_op_gen.cc
+++ b/tensorflow/python/framework/python_op_gen.cc
@@ -943,6 +943,7 @@ from tensorflow.python.framework import common_shapes as _common_shapes
 from tensorflow.python.framework import op_def_registry as _op_def_registry
 from tensorflow.python.framework import ops as _ops
 from tensorflow.python.framework import op_def_library as _op_def_library
+from tensorflow.python.util.deprecation import deprecated_endpoints
 from tensorflow.python.util.tf_export import tf_export
 
 )");
diff --git a/tensorflow/python/framework/python_op_gen_internal.cc b/tensorflow/python/framework/python_op_gen_internal.cc
index 940bffb906db753f3699b6a8d2401741bc50a517..031b4a384ea23033afc5d5e1b3318ee02037842c 100644
--- a/tensorflow/python/framework/python_op_gen_internal.cc
+++ b/tensorflow/python/framework/python_op_gen_internal.cc
@@ -588,10 +588,12 @@ void GenPythonOp::AddExport() {
     return;
   }
 
+  // Add @tf_export decorator.
   strings::StrAppend(&result_, "@tf_export(");
 
   // Add all endpoint names to tf_export.
   bool first_endpoint = true;
+  std::vector deprecated_endpoints;
   for (const auto& endpoint : api_def_.endpoint()) {
     if (!first_endpoint) {
       strings::StrAppend(&result_, ", ");
@@ -601,9 +603,32 @@ void GenPythonOp::AddExport() {
     string endpoint_name;
     python_op_gen_internal::GenerateLowerCaseOpName(endpoint.name(),
                                                     &endpoint_name);
+    if (endpoint.deprecated()) {
+      deprecated_endpoints.push_back(endpoint_name);
+    }
     strings::StrAppend(&result_, "'", endpoint_name, "'");
   }
   strings::StrAppend(&result_, ")\n");
+
+  // If all endpoints are deprecated, add @deprecated decorator.
+  if (!api_def_.deprecation_message().empty()) {
+    const string instructions = api_def_.deprecation_message();
+    strings::StrAppend(&result_, "@deprecated(None, '", instructions, "')\n");
+  }
+  // Add @deprecated_endpoints decorator.
+  if (!deprecated_endpoints.empty()) {
+    strings::StrAppend(&result_, "@deprecated_endpoints(");
+    bool first_endpoint = true;
+    for (auto& endpoint_name : deprecated_endpoints) {
+      if (first_endpoint) {
+        first_endpoint = false;
+      } else {
+        strings::StrAppend(&result_, ", ");
+      }
+      strings::StrAppend(&result_, "'", endpoint_name, "'");
+    }
+    strings::StrAppend(&result_, ")\n");
+  }
 }
 
 void GenPythonOp::AddDefLine(const string& function_name,
diff --git a/tensorflow/python/framework/subscribe.py b/tensorflow/python/framework/subscribe.py
index 7797d991da7c1c3a429bbf9e60772f0a1952c723..cee73989743ed123b2c9a6ab4e3021dc5d44a98f 100644
--- a/tensorflow/python/framework/subscribe.py
+++ b/tensorflow/python/framework/subscribe.py
@@ -47,7 +47,7 @@ def _recursive_apply(tensors, apply_fn):
   tensors_type = type(tensors)
   if tensors_type is ops.Tensor:
     return apply_fn(tensors)
-  elif tensors_type is variables.Variable:
+  elif isinstance(tensors, variables.Variable):
     return apply_fn(tensors.value())
   elif isinstance(tensors, (list, tuple)):
     tensors = [_recursive_apply(t, apply_fn) for t in tensors]
diff --git a/tensorflow/python/framework/tensor_util.py b/tensorflow/python/framework/tensor_util.py
index ca63efbc84dab20850845841e9e212a681b6bb06..9a0f34fad2ea789175786ec89cc1156061218610 100644
--- a/tensorflow/python/framework/tensor_util.py
+++ b/tensorflow/python/framework/tensor_util.py
@@ -67,10 +67,16 @@ def SlowAppendBFloat16ArrayToTensorProto(tensor_proto, proto_values):
       [ExtractBitsFromBFloat16(x) for x in proto_values])
 
 
+def FastAppendBFloat16ArrayToTensorProto(tensor_proto, proto_values):
+  fast_tensor_util.AppendBFloat16ArrayToTensorProto(
+      tensor_proto, np.asarray(
+          proto_values, dtype=dtypes.bfloat16.as_numpy_dtype).view(np.uint16))
+
+
 if _FAST_TENSOR_UTIL_AVAILABLE:
   _NP_TO_APPEND_FN = {
       dtypes.bfloat16.as_numpy_dtype:
-          SlowAppendBFloat16ArrayToTensorProto,
+          FastAppendBFloat16ArrayToTensorProto,
       np.float16:
           _MediumAppendFloat16ArrayToTensorProto,
       np.float32:
@@ -935,8 +941,10 @@ def constant_value_as_shape(tensor):  # pylint: disable=invalid-name
 def is_tensor(x):  # pylint: disable=invalid-name
   """Check whether `x` is of tensor type.
 
-  Check whether an object is a tensor. Equivalent to
-  `isinstance(x, [tf.Tensor, tf.SparseTensor, tf.Variable])`.
+  Check whether an object is a tensor. This check is equivalent to calling
+  `isinstance(x, [tf.Tensor, tf.SparseTensor, tf.Variable])` and also checks
+  if all the component variables of a MirroredVariable or a TowerLocalVariable
+  are tensors.
 
   Args:
     x: A python object to check.
@@ -944,4 +952,5 @@ def is_tensor(x):  # pylint: disable=invalid-name
   Returns:
     `True` if `x` is a tensor, `False` if not.
   """
-  return isinstance(x, ops._TensorLike) or ops.is_dense_tensor_like(x)  # pylint: disable=protected-access
+  return (isinstance(x, ops._TensorLike) or ops.is_dense_tensor_like(x) or  # pylint: disable=protected-access
+          (hasattr(x, "is_tensor_like") and x.is_tensor_like))
diff --git a/tensorflow/python/framework/tensor_util_test.py b/tensorflow/python/framework/tensor_util_test.py
index d6edc1364369e1b4d06093879571cdb4e9ffe409..395cf43b3f189e7ed61ab4bcf479d24de801f3ef 100644
--- a/tensorflow/python/framework/tensor_util_test.py
+++ b/tensorflow/python/framework/tensor_util_test.py
@@ -50,13 +50,13 @@ class TensorUtilTest(test.TestCase):
   def testFloatN(self):
     t = tensor_util.make_tensor_proto([10.0, 20.0, 30.0])
     if sys.byteorder == "big":
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_FLOAT
         tensor_shape { dim { size: 3 } }
         tensor_content: "A \000\000A\240\000\000A\360\000\000"
         """, t)
     else:
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_FLOAT
         tensor_shape { dim { size: 3 } }
         tensor_content: "\000\000 A\000\000\240A\000\000\360A"
@@ -68,13 +68,13 @@ class TensorUtilTest(test.TestCase):
   def testFloatTyped(self):
     t = tensor_util.make_tensor_proto([10.0, 20.0, 30.0], dtype=dtypes.float32)
     if sys.byteorder == "big":
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_FLOAT
         tensor_shape { dim { size: 3 } }
         tensor_content: "A \000\000A\240\000\000A\360\000\000"
         """, t)
     else:
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_FLOAT
         tensor_shape { dim { size: 3 } }
         tensor_content: "\000\000 A\000\000\240A\000\000\360A"
@@ -86,13 +86,13 @@ class TensorUtilTest(test.TestCase):
   def testFloatTypeCoerce(self):
     t = tensor_util.make_tensor_proto([10, 20, 30], dtype=dtypes.float32)
     if sys.byteorder == "big":
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_FLOAT
         tensor_shape { dim { size: 3 } }
         tensor_content: "A \000\000A\240\000\000A\360\000\000"
         """, t)
     else:
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_FLOAT
         tensor_shape { dim { size: 3 } }
         tensor_content: "\000\000 A\000\000\240A\000\000\360A"
@@ -105,13 +105,13 @@ class TensorUtilTest(test.TestCase):
     arr = np.asarray([10, 20, 30], dtype="int")
     t = tensor_util.make_tensor_proto(arr, dtype=dtypes.float32)
     if sys.byteorder == "big":
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_FLOAT
         tensor_shape { dim { size: 3 } }
         tensor_content: "A \000\000A\240\000\000A\360\000\000"
         """, t)
     else:
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_FLOAT
         tensor_shape { dim { size: 3 } }
         tensor_content: "\000\000 A\000\000\240A\000\000\360A"
@@ -123,13 +123,13 @@ class TensorUtilTest(test.TestCase):
   def testFloatSizes(self):
     t = tensor_util.make_tensor_proto([10.0, 20.0, 30.0], shape=[1, 3])
     if sys.byteorder == "big":
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_FLOAT
         tensor_shape { dim { size: 1 } dim { size: 3 } }
         tensor_content: "A \000\000A\240\000\000A\360\000\000"
         """, t)
     else:
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_FLOAT
         tensor_shape { dim { size: 1 } dim { size: 3 } }
         tensor_content: "\000\000 A\000\000\240A\000\000\360A"
@@ -141,13 +141,13 @@ class TensorUtilTest(test.TestCase):
   def testFloatSizes2(self):
     t = tensor_util.make_tensor_proto([10.0, 20.0, 30.0], shape=[3, 1])
     if sys.byteorder == "big":
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_FLOAT
         tensor_shape { dim { size: 3 } dim { size: 1 } }
         tensor_content: "A \000\000A\240\000\000A\360\000\000"
         """, t)
     else:
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_FLOAT
         tensor_shape { dim { size: 3 } dim { size: 1 } }
         tensor_content: "\000\000 A\000\000\240A\000\000\360A"
@@ -169,13 +169,13 @@ class TensorUtilTest(test.TestCase):
     t = tensor_util.make_tensor_proto(
         np.array([[10.0, 20.0, 30.0]], dtype=np.float64))
     if sys.byteorder == "big":
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_DOUBLE
         tensor_shape { dim { size: 1 } dim { size: 3 } }
         tensor_content: "@$\000\000\000\000\000\000@4\000\000\000\000\000\000@>\000\000\000\000\000\000"
         """, t)
     else:
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_DOUBLE
         tensor_shape { dim { size: 1 } dim { size: 3 } }
         tensor_content: "\000\000\000\000\000\000$@\000\000\000\000\000\0004@\000\000\000\000\000\000>@"
@@ -206,13 +206,13 @@ class TensorUtilTest(test.TestCase):
     self.assertEquals(np.float32, a.dtype)
     self.assertAllClose(np.array([5.0, 20.0, 30.0], dtype=np.float32), a)
     if sys.byteorder == "big":
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_FLOAT
         tensor_shape { dim { size: 3 } }
         tensor_content: "A \000\000A\240\000\000A\360\000\000"
         """, t)
     else:
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_FLOAT
         tensor_shape { dim { size: 3 } }
         tensor_content: "\000\000 A\000\000\240A\000\000\360A"
@@ -299,16 +299,16 @@ class TensorUtilTest(test.TestCase):
   def testIntNDefaultType(self):
     t = tensor_util.make_tensor_proto([10, 20, 30, 40], shape=[2, 2])
     if sys.byteorder == "big":
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_INT32
         tensor_shape { dim { size: 2 } dim { size: 2 } }
-        tensor_content: "\000\000\000\\n\000\000\000\024\000\000\000\036\000\000\000("
+        tensor_content: "\000\000\000\n\000\000\000\024\000\000\000\036\000\000\000("
         """, t)
     else:
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_INT32
         tensor_shape { dim { size: 2 } dim { size: 2 } }
-        tensor_content: "\\n\000\000\000\024\000\000\000\036\000\000\000(\000\000\000"
+        tensor_content: "\n\000\000\000\024\000\000\000\036\000\000\000(\000\000\000"
         """, t)
     a = tensor_util.MakeNdarray(t)
     self.assertEquals(np.int32, a.dtype)
@@ -380,16 +380,16 @@ class TensorUtilTest(test.TestCase):
     t = tensor_util.make_tensor_proto(
         [10, 20, 30], shape=[1, 3], dtype=dtypes.int64)
     if sys.byteorder == "big":
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_INT64
         tensor_shape { dim { size: 1 } dim { size: 3 } }
-        tensor_content: "\000\000\000\000\000\000\000\\n\000\000\000\000\000\000\000\024\000\000\000\000\000\000\000\036"
+        tensor_content: "\000\000\000\000\000\000\000\n\000\000\000\000\000\000\000\024\000\000\000\000\000\000\000\036"
         """, t)
     else:
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_INT64
         tensor_shape { dim { size: 1 } dim { size: 3 } }
-        tensor_content: "\\n\000\000\000\000\000\000\000\024\000\000\000\000\000\000\000\036\000\000\000\000\000\000\000"
+        tensor_content: "\n\000\000\000\000\000\000\000\024\000\000\000\000\000\000\000\036\000\000\000\000\000\000\000"
         """, t)
     a = tensor_util.MakeNdarray(t)
     self.assertEquals(np.int64, a.dtype)
@@ -398,16 +398,16 @@ class TensorUtilTest(test.TestCase):
   def testLongNpArray(self):
     t = tensor_util.make_tensor_proto(np.array([10, 20, 30]))
     if sys.byteorder == "big":
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_INT64
         tensor_shape { dim { size: 3 } }
-        tensor_content: "\000\000\000\000\000\000\000\\n\000\000\000\000\000\000\000\024\000\000\000\000\000\000\000\036"
+        tensor_content: "\000\000\000\000\000\000\000\n\000\000\000\000\000\000\000\024\000\000\000\000\000\000\000\036"
         """, t)
     else:
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_INT64
         tensor_shape { dim { size: 3 } }
-        tensor_content: "\\n\000\000\000\000\000\000\000\024\000\000\000\000\000\000\000\036\000\000\000\000\000\000\000"
+        tensor_content: "\n\000\000\000\000\000\000\000\024\000\000\000\000\000\000\000\036\000\000\000\000\000\000\000"
         """, t)
     a = tensor_util.MakeNdarray(t)
     self.assertEquals(np.int64, a.dtype)
@@ -419,13 +419,13 @@ class TensorUtilTest(test.TestCase):
 
     t = tensor_util.make_tensor_proto(data, dtype=dtypes.qint32)
     if sys.byteorder == "big":
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_QINT32
         tensor_shape { dim { size: 3 } }
         tensor_content: "\000\000\000\025\000\000\000\026\000\000\000\027"
         """, t)
     else:
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_QINT32
         tensor_shape { dim { size: 3 } }
         tensor_content: "\025\000\000\000\026\000\000\000\027\000\000\000"
@@ -435,7 +435,7 @@ class TensorUtilTest(test.TestCase):
     self.assertAllEqual(np.array(data, dtype=a.dtype), a)
 
     t = tensor_util.make_tensor_proto(data, dtype=dtypes.quint8)
-    self.assertProtoEquals("""
+    self.assertProtoEquals(r"""
       dtype: DT_QUINT8
       tensor_shape { dim { size: 3 } }
       tensor_content: "\025\026\027"
@@ -445,7 +445,7 @@ class TensorUtilTest(test.TestCase):
     self.assertAllEqual(np.array(data, dtype=a.dtype), a)
 
     t = tensor_util.make_tensor_proto(data, dtype=dtypes.qint8)
-    self.assertProtoEquals("""
+    self.assertProtoEquals(r"""
       dtype: DT_QINT8
       tensor_shape { dim { size: 3 } }
       tensor_content: "\025\026\027"
@@ -456,13 +456,13 @@ class TensorUtilTest(test.TestCase):
 
     t = tensor_util.make_tensor_proto(data, dtype=dtypes.quint16)
     if sys.byteorder == "big":
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_QUINT16
         tensor_shape { dim { size: 3 } }
         tensor_content: "\000\025\000\026\000\027"
         """, t)
     else:
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_QUINT16
         tensor_shape { dim { size: 3 } }
         tensor_content: "\025\000\026\000\027\000"
@@ -473,13 +473,13 @@ class TensorUtilTest(test.TestCase):
 
     t = tensor_util.make_tensor_proto(data, dtype=dtypes.qint16)
     if sys.byteorder == "big":
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_QINT16
         tensor_shape { dim { size: 3 } }
         tensor_content: "\000\025\000\026\000\027"
         """, t)
     else:
-      self.assertProtoEquals("""
+      self.assertProtoEquals(r"""
         dtype: DT_QINT16
         tensor_shape { dim { size: 3 } }
         tensor_content: "\025\000\026\000\027\000"
diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index 2bc2a189fa8e825613ca834e2c06ea916074d455..fc47b1cca51c977a9398cf1c8a7c09cb0a088037 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -19,6 +19,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
+from collections import OrderedDict
 import contextlib
 import gc
 import itertools
@@ -571,6 +573,78 @@ def assert_no_garbage_created(f):
   return decorator
 
 
+def _combine_named_parameters(**kwargs):
+  """Generate combinations based on its keyword arguments.
+
+  Two sets of returned combinations can be concatenated using +.  Their product
+  can be computed using `times()`.
+
+  Args:
+    **kwargs: keyword arguments of form `option=[possibilities, ...]`
+         or `option=the_only_possibility`.
+
+  Returns:
+    a list of dictionaries for each combination. Keys in the dictionaries are
+    the keyword argument names.  Each key has one value - one of the
+    corresponding keyword argument values.
+  """
+  if not kwargs:
+    return [OrderedDict()]
+
+  sort_by_key = lambda k: k[0][0]
+  kwargs = OrderedDict(sorted(kwargs.items(), key=sort_by_key))
+  first = list(kwargs.items())[0]
+
+  rest = dict(list(kwargs.items())[1:])
+  rest_combined = _combine_named_parameters(**rest)
+
+  key = first[0]
+  values = first[1]
+  if not isinstance(values, list):
+    values = [values]
+
+  combinations = [
+      OrderedDict(sorted(list(combined.items()) + [(key, v)], key=sort_by_key))
+      for v in values
+      for combined in rest_combined
+  ]
+  return combinations
+
+
+def generate_combinations_with_testcase_name(**kwargs):
+  """Generate combinations based on its keyword arguments using combine().
+
+  This function calls combine() and appends a testcase name to the list of
+  dictionaries returned. The 'testcase_name' key is a required for named
+  parameterized tests.
+
+  Args:
+    **kwargs: keyword arguments of form `option=[possibilities, ...]`
+         or `option=the_only_possibility`.
+
+  Returns:
+    a list of dictionaries for each combination. Keys in the dictionaries are
+    the keyword argument names.  Each key has one value - one of the
+    corresponding keyword argument values.
+  """
+  combinations = _combine_named_parameters(**kwargs)
+  named_combinations = []
+  for combination in combinations:
+    assert isinstance(combination, OrderedDict)
+    name = "".join([
+        "_{}_{}".format(
+            "".join(filter(str.isalnum, key)),
+            "".join(filter(str.isalnum, str(value))))
+        for key, value in combination.items()
+    ])
+    named_combinations.append(
+        OrderedDict(
+            list(combination.items()) + [("testcase_name",
+                                          "_test{}".format(name))]))
+
+  return named_combinations
+
+
 def run_all_in_graph_and_eager_modes(cls):
   """Execute all test methods in the given class with and without eager."""
   base_decorator = run_in_graph_and_eager_modes
@@ -1227,8 +1301,8 @@ class TensorFlowTestCase(googletest.TestCase):
       a = a._asdict()
     if hasattr(b, "_asdict"):
       b = b._asdict()
-    a_is_dict = isinstance(a, dict)
-    if a_is_dict != isinstance(b, dict):
+    a_is_dict = isinstance(a, collections.Mapping)
+    if a_is_dict != isinstance(b, collections.Mapping):
       raise ValueError("Can't compare dict to non-dict, a%s vs b%s. %s" %
                        (path_str, path_str, msg))
     if a_is_dict:
diff --git a/tensorflow/python/framework/test_util_test.py b/tensorflow/python/framework/test_util_test.py
index 122c14c8473f133f6a3bed1e6297394eaa1b845c..f983cbef0415de98fd6cae717f12fbcde3fe78bd 100644
--- a/tensorflow/python/framework/test_util_test.py
+++ b/tensorflow/python/framework/test_util_test.py
@@ -73,7 +73,7 @@ class TestUtilTest(test_util.TensorFlowTestCase):
     test_util.assert_equal_graph_def(def_57, def_75)
     # Compare two unequal graphs
     with self.assertRaisesRegexp(AssertionError,
-                                 r"^Found unexpected node 'seven"):
+                                 r"^Found unexpected node '{{node seven}}"):
       test_util.assert_equal_graph_def(def_57, def_empty)
 
   def testIsGoogleCudaEnabled(self):
diff --git a/tensorflow/python/framework/traceable_stack.py b/tensorflow/python/framework/traceable_stack.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f4d28237ffba80e5aa604b880fccf00482a9ca5
--- /dev/null
+++ b/tensorflow/python/framework/traceable_stack.py
@@ -0,0 +1,132 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""A simple stack that associates filename and line numbers with each object."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.util import tf_stack
+
+
+class TraceableObject(object):
+  """Wrap an object together with its the code definition location."""
+
+  # Return codes for the set_filename_and_line_from_caller() method.
+  SUCCESS, HEURISTIC_USED, FAILURE = (0, 1, 2)
+
+  def __init__(self, obj, filename=None, lineno=None):
+    self.obj = obj
+    self.filename = filename
+    self.lineno = lineno
+
+  def set_filename_and_line_from_caller(self, offset=0):
+    """Set filename and line using the caller's stack frame.
+
+    If the requested stack information is not available, a heuristic may
+    be applied and self.HEURISTIC USED will be returned.  If the heuristic
+    fails then no change will be made to the filename and lineno members
+    (None by default) and self.FAILURE will be returned.
+
+    Args:
+      offset: Integer.  If 0, the caller's stack frame is used.  If 1,
+          the caller's caller's stack frame is used.  Larger values are
+          permissible but if out-of-range (larger than the number of stack
+          frames available) the outermost stack frame will be used.
+
+    Returns:
+      TraceableObject.SUCCESS if appropriate stack information was found,
+      TraceableObject.HEURISTIC_USED if the offset was larger than the stack,
+      and TraceableObject.FAILURE if the stack was empty.
+    """
+    # Offset is defined in "Args" as relative to the caller.  We are one frame
+    # beyond the caller.
+    local_offset = offset + 1
+
+    frame_records = tf_stack.extract_stack()
+    if not frame_records:
+      return self.FAILURE
+    if len(frame_records) >= local_offset:
+      # Negative indexing is one-indexed instead of zero-indexed.
+      negative_offset = -(local_offset + 1)
+      self.filename, self.lineno = frame_records[negative_offset][:2]
+      return self.SUCCESS
+    else:
+      # If the offset is too large then we use the largest offset possible,
+      # meaning we use the outermost stack frame at index 0.
+      self.filename, self.lineno = frame_records[0][:2]
+      return self.HEURISTIC_USED
+
+  def copy_metadata(self):
+    """Return a TraceableObject like this one, but without the object."""
+    return self.__class__(None, filename=self.filename, lineno=self.lineno)
+
+
+class TraceableStack(object):
+  """A stack of TraceableObjects."""
+
+  def __init__(self, existing_stack=None):
+    """Constructor.
+
+    Args:
+      existing_stack: [TraceableObject, ...] If provided, this object will
+        set its new stack to a SHALLOW COPY of existing_stack.
+    """
+    self._stack = existing_stack[:] if existing_stack else []
+
+  def push_obj(self, obj, offset=0):
+    """Add object to the stack and record its filename and line information.
+
+    Args:
+      obj: An object to store on the stack.
+      offset: Integer.  If 0, the caller's stack frame is used.  If 1,
+          the caller's caller's stack frame is used.
+
+    Returns:
+      TraceableObject.SUCCESS if appropriate stack information was found,
+      TraceableObject.HEURISTIC_USED if the stack was smaller than expected,
+      and TraceableObject.FAILURE if the stack was empty.
+    """
+    traceable_obj = TraceableObject(obj)
+    self._stack.append(traceable_obj)
+    # Offset is defined in "Args" as relative to the caller.  We are 1 frame
+    # beyond the caller and need to compensate.
+    return traceable_obj.set_filename_and_line_from_caller(offset + 1)
+
+  def pop_obj(self):
+    """Remove last-inserted object and return it, without filename/line info."""
+    return self._stack.pop().obj
+
+  def peek_objs(self):
+    """Return list of stored objects ordered newest to oldest."""
+    return [t_obj.obj for t_obj in reversed(self._stack)]
+
+  def peek_traceable_objs(self):
+    """Return list of stored TraceableObjects ordered newest to oldest."""
+    return list(reversed(self._stack))
+
+  def __len__(self):
+    """Return number of items on the stack, and used for truth-value testing."""
+    return len(self._stack)
+
+  def copy(self):
+    """Return a copy of self referencing the same objects but in a new list.
+
+    This method is implemented to support thread-local stacks.
+
+    Returns:
+      TraceableStack with a new list that holds existing objects.
+    """
+    return TraceableStack(self._stack)
diff --git a/tensorflow/python/framework/traceable_stack_test.py b/tensorflow/python/framework/traceable_stack_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e7876f6318da368a373ca554e674a21b0d869c3
--- /dev/null
+++ b/tensorflow/python/framework/traceable_stack_test.py
@@ -0,0 +1,133 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for tensorflow.python.framework.traceable_stack."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import test_util
+from tensorflow.python.framework import traceable_stack
+from tensorflow.python.platform import googletest
+from tensorflow.python.util import tf_inspect as inspect
+
+_LOCAL_OBJECT = lambda x: x
+_THIS_FILENAME = inspect.getsourcefile(_LOCAL_OBJECT)
+
+
+class TraceableObjectTest(test_util.TensorFlowTestCase):
+
+  def testSetFilenameAndLineFromCallerUsesCallersStack(self):
+    t_obj = traceable_stack.TraceableObject(17)
+
+    # Do not separate placeholder from the set_filename_and_line_from_caller()
+    # call one line below it as it is used to calculate the latter's line
+    # number.
+    placeholder = lambda x: x
+    result = t_obj.set_filename_and_line_from_caller()
+
+    expected_lineno = inspect.getsourcelines(placeholder)[1] + 1
+    self.assertEqual(expected_lineno, t_obj.lineno)
+    self.assertEqual(_THIS_FILENAME, t_obj.filename)
+    self.assertEqual(t_obj.SUCCESS, result)
+
+  def testSetFilenameAndLineFromCallerRespectsOffset(self):
+
+    def call_set_filename_and_line_from_caller(t_obj):
+      # We expect to retrieve the line number from _our_ caller.
+      return t_obj.set_filename_and_line_from_caller(offset=1)
+
+    t_obj = traceable_stack.TraceableObject(None)
+    # Do not separate placeholder from the
+    # call_set_filename_and_line_from_caller() call one line below it as it is
+    # used to calculate the latter's line number.
+    placeholder = lambda x: x
+    result = call_set_filename_and_line_from_caller(t_obj)
+
+    expected_lineno = inspect.getsourcelines(placeholder)[1] + 1
+    self.assertEqual(expected_lineno, t_obj.lineno)
+    self.assertEqual(t_obj.SUCCESS, result)
+
+  def testSetFilenameAndLineFromCallerHandlesRidiculousOffset(self):
+    t_obj = traceable_stack.TraceableObject('The quick brown fox.')
+    # This line shouldn't die.
+    result = t_obj.set_filename_and_line_from_caller(offset=300)
+
+    # We expect a heuristic to be used because we are not currently 300 frames
+    # down on the stack.  The filename and lineno of the outermost frame are not
+    # predictable -- in some environments the filename is this test file, but in
+    # other environments it is not (e.g. due to a test runner calling this
+    # file).  Therefore we only test that the called function knows it applied a
+    # heuristic for the ridiculous stack offset.
+    self.assertEqual(t_obj.HEURISTIC_USED, result)
+
+
+class TraceableStackTest(test_util.TensorFlowTestCase):
+
+  def testPushPeekPopObj(self):
+    t_stack = traceable_stack.TraceableStack()
+    t_stack.push_obj(42.0)
+    t_stack.push_obj('hope')
+
+    expected_lifo_peek = ['hope', 42.0]
+    self.assertEqual(expected_lifo_peek, t_stack.peek_objs())
+
+    self.assertEqual('hope', t_stack.pop_obj())
+    self.assertEqual(42.0, t_stack.pop_obj())
+
+  def testPushPopPreserveLifoOrdering(self):
+    t_stack = traceable_stack.TraceableStack()
+    t_stack.push_obj(0)
+    t_stack.push_obj(1)
+    t_stack.push_obj(2)
+    t_stack.push_obj(3)
+
+    obj_3 = t_stack.pop_obj()
+    obj_2 = t_stack.pop_obj()
+    obj_1 = t_stack.pop_obj()
+    obj_0 = t_stack.pop_obj()
+
+    self.assertEqual(3, obj_3)
+    self.assertEqual(2, obj_2)
+    self.assertEqual(1, obj_1)
+    self.assertEqual(0, obj_0)
+
+  def testPushObjSetsFilenameAndLineInfoForCaller(self):
+    t_stack = traceable_stack.TraceableStack()
+
+    # We expect that the line number recorded for the 1-object will come from
+    # the call to t_stack.push_obj(1).  Do not separate the next two lines!
+    placeholder_1 = lambda x: x
+    t_stack.push_obj(1)
+
+    # We expect that the line number recorded for the 2-object will come from
+    # the call to call_push_obj() and _not_ the call to t_stack.push_obj().
+    def call_push_obj(obj):
+      t_stack.push_obj(obj, offset=1)
+
+    # Do not separate the next two lines!
+    placeholder_2 = lambda x: x
+    call_push_obj(2)
+
+    expected_lineno_1 = inspect.getsourcelines(placeholder_1)[1] + 1
+    expected_lineno_2 = inspect.getsourcelines(placeholder_2)[1] + 1
+
+    t_obj_2, t_obj_1 = t_stack.peek_traceable_objs()
+    self.assertEqual(expected_lineno_2, t_obj_2.lineno)
+    self.assertEqual(expected_lineno_1, t_obj_1.lineno)
+
+
+if __name__ == '__main__':
+  googletest.main()
diff --git a/tensorflow/python/grappler/layout_optimizer_test.py b/tensorflow/python/grappler/layout_optimizer_test.py
index 7d07c77c797668c858014cc31cf713050627d72f..8cc971c61d5964d0fad1bfa843c3ef8d3407599f 100644
--- a/tensorflow/python/grappler/layout_optimizer_test.py
+++ b/tensorflow/python/grappler/layout_optimizer_test.py
@@ -1340,7 +1340,7 @@ class LayoutOptimizerTest(test.TestCase):
       expected_num_transposes = 2
       self.assertEqual(expected_num_transposes, num_transposes)
       self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)
-      self.assertAllEqual(output_val_ref, output_val)
+      self.assertAllClose(output_val_ref, output_val, atol=1e-3)
 
   def testLoop(self):
     if test.is_gpu_available(cuda_only=True):
diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD
index 8b6b28bc776fa500a93d0a3fb3bf91081ba86967..df409d2aa5c5911a2de4253445a1f8b7e5a184df 100755
--- a/tensorflow/python/keras/BUILD
+++ b/tensorflow/python/keras/BUILD
@@ -451,6 +451,7 @@ cuda_py_test(
         "//tensorflow/python:client_testlib",
     ],
     shard_count = 2,
+    tags = ["no_windows_gpu"],
 )
 
 py_test(
@@ -703,6 +704,17 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "training_gpu_test",
+    size = "small",
+    srcs = ["engine/training_gpu_test.py"],
+    additional_deps = [
+        ":keras",
+        "//third_party/py/numpy",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
 py_test(
     name = "imagenet_utils_test",
     size = "small",
@@ -790,6 +802,19 @@ py_test(
     ],
 )
 
+py_test(
+    name = "training_utils_test",
+    size = "medium",
+    srcs = ["engine/training_utils_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["notsan"],
+    deps = [
+        ":keras",
+        "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
+    ],
+)
+
 py_test(
     name = "model_subclassing_test",
     size = "medium",
diff --git a/tensorflow/python/keras/activations.py b/tensorflow/python/keras/activations.py
index f608dea430f0573503713f0cbc60f8921e6df51e..99645de736fc9e3f34c3ea29171cde0f91d8345a 100644
--- a/tensorflow/python/keras/activations.py
+++ b/tensorflow/python/keras/activations.py
@@ -128,20 +128,26 @@ def softsign(x):
 
 
 @tf_export('keras.activations.relu')
-def relu(x, alpha=0., max_value=None):
+def relu(x, alpha=0., max_value=None, threshold=0):
   """Rectified Linear Unit.
 
+  With default values, it returns element-wise `max(x, 0)`.
+
+  Otherwise, it follows:
+  `f(x) = max_value` for `x >= max_value`,
+  `f(x) = x` for `threshold <= x < max_value`,
+  `f(x) = alpha * (x - threshold)` otherwise.
+
   Arguments:
-      x: Input tensor.
-      alpha: Slope of the negative part. Defaults to zero.
-      max_value: Maximum value for the output.
+      x: A tensor or variable.
+      alpha: A scalar, slope of negative section (default=`0.`).
+      max_value: float. Saturation threshold.
+      threshold: float. Threshold value for thresholded activation.
 
   Returns:
-      The (leaky) rectified linear unit activation: `x` if `x > 0`,
-        `alpha * x` if `x < 0`. If `max_value` is defined, the result
-        is truncated to this value.
+      A tensor.
   """
-  return K.relu(x, alpha=alpha, max_value=max_value)
+  return K.relu(x, alpha=alpha, max_value=max_value, threshold=threshold)
 
 
 @tf_export('keras.activations.tanh')
diff --git a/tensorflow/python/keras/applications/mobilenet.py b/tensorflow/python/keras/applications/mobilenet.py
index e56c695a288026d12de6bc0bdb65706c71eefe14..7285e0396376f7af2ca397911bbf502633dba0bf 100644
--- a/tensorflow/python/keras/applications/mobilenet.py
+++ b/tensorflow/python/keras/applications/mobilenet.py
@@ -72,13 +72,9 @@ from __future__ import print_function
 import os
 
 from tensorflow.python.keras import backend as K
-from tensorflow.python.keras import constraints
-from tensorflow.python.keras import initializers
-from tensorflow.python.keras import regularizers
 from tensorflow.python.keras.applications import imagenet_utils
 from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape
 from tensorflow.python.keras.applications.imagenet_utils import decode_predictions
-from tensorflow.python.keras.engine.base_layer import InputSpec
 from tensorflow.python.keras.layers import Activation
 from tensorflow.python.keras.layers import BatchNormalization
 from tensorflow.python.keras.layers import Conv2D
@@ -87,10 +83,10 @@ from tensorflow.python.keras.layers import Dropout
 from tensorflow.python.keras.layers import GlobalAveragePooling2D
 from tensorflow.python.keras.layers import GlobalMaxPooling2D
 from tensorflow.python.keras.layers import Input
+from tensorflow.python.keras.layers import ReLU
 from tensorflow.python.keras.layers import Reshape
 from tensorflow.python.keras.layers import ZeroPadding2D
 from tensorflow.python.keras.models import Model
-from tensorflow.python.keras.utils import conv_utils
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.keras.utils.data_utils import get_file
 from tensorflow.python.platform import tf_logging as logging
@@ -100,10 +96,6 @@ from tensorflow.python.util.tf_export import tf_export
 BASE_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.6/'
 
 
-def relu6(x):
-  return K.relu(x, max_value=6)
-
-
 @tf_export('keras.applications.mobilenet.preprocess_input')
 def preprocess_input(x):
   """Preprocesses a numpy array encoding a batch of images.
@@ -130,12 +122,6 @@ def MobileNet(input_shape=None,
               classes=1000):
   """Instantiates the MobileNet architecture.
 
-  To load a MobileNet model via `load_model`, import the custom
-  objects `relu6` and pass them to the `custom_objects` parameter.
-  E.g.
-  model = load_model('mobilenet.h5', custom_objects={
-                     'relu6': mobilenet.relu6})
-
   Arguments:
       input_shape: optional shape tuple, only to be specified
           if `include_top` is False (otherwise the input shape
@@ -412,7 +398,7 @@ def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)):
       strides=strides,
       name='conv1')(x)
   x = BatchNormalization(axis=channel_axis, name='conv1_bn')(x)
-  return Activation(relu6, name='conv1_relu')(x)
+  return ReLU(6, name='conv1_relu')(x)
 
 
 def _depthwise_conv_block(inputs,
@@ -479,7 +465,7 @@ def _depthwise_conv_block(inputs,
       use_bias=False,
       name='conv_dw_%d' % block_id)(x)
   x = BatchNormalization(axis=channel_axis, name='conv_dw_%d_bn' % block_id)(x)
-  x = Activation(relu6, name='conv_dw_%d_relu' % block_id)(x)
+  x = ReLU(6, name='conv_dw_%d_relu' % block_id)(x)
 
   x = Conv2D(
       pointwise_conv_filters, (1, 1),
@@ -489,4 +475,4 @@ def _depthwise_conv_block(inputs,
       name='conv_pw_%d' % block_id)(
           x)
   x = BatchNormalization(axis=channel_axis, name='conv_pw_%d_bn' % block_id)(x)
-  return Activation(relu6, name='conv_pw_%d_relu' % block_id)(x)
+  return ReLU(6, name='conv_pw_%d_relu' % block_id)(x)
diff --git a/tensorflow/python/keras/backend.py b/tensorflow/python/keras/backend.py
index 824513dce07fc31edc6f8eca512efd99a1a258cc..38794f1612d7509cb9e75631679712dbb6729c89 100644
--- a/tensorflow/python/keras/backend.py
+++ b/tensorflow/python/keras/backend.py
@@ -963,13 +963,14 @@ def zeros(shape, dtype=None, name=None):
              [ 0.,  0.,  0.,  0.]], dtype=float32)
   ```
   """
-  if dtype is None:
-    dtype = floatx()
-  tf_dtype = dtypes_module.as_dtype(dtype)
-  v = array_ops.zeros(shape=shape, dtype=tf_dtype, name=name)
-  if py_all(v.get_shape().as_list()):
-    return variable(v, dtype=dtype, name=name)
-  return v
+  with ops.init_scope():
+    if dtype is None:
+      dtype = floatx()
+    tf_dtype = dtypes_module.as_dtype(dtype)
+    v = array_ops.zeros(shape=shape, dtype=tf_dtype, name=name)
+    if py_all(v.get_shape().as_list()):
+      return variable(v, dtype=dtype, name=name)
+    return v
 
 
 @tf_export('keras.backend.ones')
@@ -996,13 +997,14 @@ def ones(shape, dtype=None, name=None):
              [ 1.,  1.,  1.,  1.]], dtype=float32)
   ```
   """
-  if dtype is None:
-    dtype = floatx()
-  tf_dtype = dtypes_module.as_dtype(dtype)
-  v = array_ops.ones(shape=shape, dtype=tf_dtype, name=name)
-  if py_all(v.get_shape().as_list()):
-    return variable(v, dtype=dtype, name=name)
-  return v
+  with ops.init_scope():
+    if dtype is None:
+      dtype = floatx()
+    tf_dtype = dtypes_module.as_dtype(dtype)
+    v = array_ops.ones(shape=shape, dtype=tf_dtype, name=name)
+    if py_all(v.get_shape().as_list()):
+      return variable(v, dtype=dtype, name=name)
+    return v
 
 
 @tf_export('keras.backend.eye')
@@ -3370,26 +3372,48 @@ def in_test_phase(x, alt, training=None):
 
 
 @tf_export('keras.backend.relu')
-def relu(x, alpha=0., max_value=None):
+def relu(x, alpha=0., max_value=None, threshold=0):
   """Rectified linear unit.
 
   With default values, it returns element-wise `max(x, 0)`.
 
+  Otherwise, it follows:
+  `f(x) = max_value` for `x >= max_value`,
+  `f(x) = x` for `threshold <= x < max_value`,
+  `f(x) = alpha * (x - threshold)` otherwise.
+
   Arguments:
       x: A tensor or variable.
       alpha: A scalar, slope of negative section (default=`0.`).
-      max_value: Saturation threshold.
+      max_value: float. Saturation threshold.
+      threshold: float. Threshold value for thresholded activation.
 
   Returns:
       A tensor.
   """
+  clip_max = max_value is not None
+
   if alpha != 0.:
-    negative_part = nn.relu(-x)
-  x = nn.relu(x)
-  if max_value is not None:
+    if threshold != 0:
+      negative_part = nn.relu(-x + threshold)
+    else:
+      negative_part = nn.relu(-x)
+
+  if threshold != 0:
+    # computes x for x > threshold else 0
+    x = x * math_ops.cast(math_ops.greater(x, threshold), floatx())
+  elif max_value == 6:
+    # if no threshold, then can use nn.relu6 native TF op for performance
+    x = nn.relu6(x)
+    clip_max = False
+  else:
+    x = nn.relu(x)
+
+  if clip_max:
     max_value = _to_tensor(max_value, x.dtype.base_dtype)
     zero = _to_tensor(0., x.dtype.base_dtype)
     x = clip_ops.clip_by_value(x, zero, max_value)
+
   if alpha != 0.:
     alpha = _to_tensor(alpha, x.dtype.base_dtype)
     x -= alpha * negative_part
@@ -3456,7 +3480,7 @@ def softsign(x):
 
 
 @tf_export('keras.backend.categorical_crossentropy')
-def categorical_crossentropy(target, output, from_logits=False):
+def categorical_crossentropy(target, output, from_logits=False, axis=-1):
   """Categorical crossentropy between an output tensor and a target tensor.
 
   Arguments:
@@ -3466,28 +3490,33 @@ def categorical_crossentropy(target, output, from_logits=False):
           case `output` is expected to be the logits).
       from_logits: Boolean, whether `output` is the
           result of a softmax, or is a tensor of logits.
+      axis: Int specifying the channels axis. `axis=-1` corresponds to data
+          format `channels_last', and `axis=1` corresponds to data format
+          `channels_first`.
 
   Returns:
       Output tensor.
+
+  Raises:
+      ValueError: if `axis` is neither -1 nor one of the axes of `output`.
   """
+  rank = len(output.get_shape())
+  axis = axis % rank
   # Note: nn.softmax_cross_entropy_with_logits_v2
   # expects logits, Keras expects probabilities.
   if not from_logits:
     # scale preds so that the class probas of each sample sum to 1
-    output = output / math_ops.reduce_sum(  # pylint: disable=g-no-augmented-assignment
-        output, len(output.get_shape()) - 1, True)
+    output = output / math_ops.reduce_sum(output, axis, True)
     # manual computation of crossentropy
     epsilon_ = _to_tensor(epsilon(), output.dtype.base_dtype)
     output = clip_ops.clip_by_value(output, epsilon_, 1. - epsilon_)
-    return -math_ops.reduce_sum(
-        target * math_ops.log(output),
-        axis=len(output.get_shape()) - 1)
+    return -math_ops.reduce_sum(target * math_ops.log(output), axis)
   else:
     return nn.softmax_cross_entropy_with_logits_v2(labels=target, logits=output)
 
 
 @tf_export('keras.backend.sparse_categorical_crossentropy')
-def sparse_categorical_crossentropy(target, output, from_logits=False):
+def sparse_categorical_crossentropy(target, output, from_logits=False, axis=-1):
   """Categorical crossentropy with integer targets.
 
   Arguments:
@@ -3497,10 +3526,22 @@ def sparse_categorical_crossentropy(target, output, from_logits=False):
           case `output` is expected to be the logits).
       from_logits: Boolean, whether `output` is the
           result of a softmax, or is a tensor of logits.
+      axis: Int specifying the channels axis. `axis=-1` corresponds to data
+          format `channels_last', and `axis=1` corresponds to data format
+          `channels_first`.
 
   Returns:
       Output tensor.
+
+  Raises:
+      ValueError: if `axis` is neither -1 nor one of the axes of `output`.
   """
+  rank = len(output.get_shape())
+  axis = axis % rank
+  if axis != rank - 1:
+    permutation = list(range(axis)) + list(range(axis + 1, rank)) + [axis]
+    output = array_ops.transpose(output, perm=permutation)
+
   # Note: nn.sparse_softmax_cross_entropy_with_logits
   # expects logits, Keras expects probabilities.
   if not from_logits:
diff --git a/tensorflow/python/keras/backend_test.py b/tensorflow/python/keras/backend_test.py
index 36478ea089a871667908d70e33422aef8444a3e4..40e79100613daef6731cfc71d5aad0cb0b6cc275 100644
--- a/tensorflow/python/keras/backend_test.py
+++ b/tensorflow/python/keras/backend_test.py
@@ -23,6 +23,7 @@ import scipy.sparse
 
 from tensorflow.python import keras
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
@@ -490,6 +491,66 @@ class BackendLinearAlgebraTest(test.TestCase):
                                        input_shape_a=(4, 7),
                                        input_shape_b=(4, 7))
 
+  def test_relu(self):
+    x = ops.convert_to_tensor([[-4, 0], [2, 7]], 'float32')
+    with self.test_session():
+      # standard relu
+      relu_op = keras.backend.relu(x)
+      self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 7]])
+
+      # alpha
+      relu_op = keras.backend.relu(x, alpha=0.5)
+      self.assertAllClose(keras.backend.eval(relu_op), [[-2, 0], [2, 7]])
+
+      # max_value < some elements
+      relu_op = keras.backend.relu(x, max_value=5)
+      self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 5]])
+
+      # nn.relu6 used
+      relu_op = keras.backend.relu(x, max_value=6)
+      self.assertTrue('Relu6' in relu_op.name)  # uses tf.nn.relu6
+      self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 6]])
+
+      # max value > 6
+      relu_op = keras.backend.relu(x, max_value=10)
+      self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 7]])
+
+      # max value is float
+      relu_op = keras.backend.relu(x, max_value=4.3)
+      self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 4.3]])
+
+      # max value == 0
+      relu_op = keras.backend.relu(x, max_value=0)
+      self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [0, 0]])
+
+      # alpha and max_value
+      relu_op = keras.backend.relu(x, alpha=0.25, max_value=3)
+      self.assertAllClose(keras.backend.eval(relu_op), [[-1, 0], [2, 3]])
+
+      # threshold
+      relu_op = keras.backend.relu(x, threshold=3)
+      self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [0, 7]])
+
+      # threshold is float
+      relu_op = keras.backend.relu(x, threshold=1.5)
+      self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 7]])
+
+      # threshold is negative
+      relu_op = keras.backend.relu(x, threshold=-5)
+      self.assertAllClose(keras.backend.eval(relu_op), [[-4, 0], [2, 7]])
+
+      # threshold and max_value
+      relu_op = keras.backend.relu(x, threshold=3, max_value=5)
+      self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [0, 5]])
+
+      # threshold and alpha
+      relu_op = keras.backend.relu(x, alpha=0.25, threshold=4)
+      self.assertAllClose(keras.backend.eval(relu_op), [[-2, -1], [-0.5, 7]])
+
+      # threshold, alpha, and max_value
+      relu_op = keras.backend.relu(x, alpha=0.25, threshold=4, max_value=5)
+      self.assertAllClose(keras.backend.eval(relu_op), [[-2, -1], [-0.5, 5]])
+
 
 class BackendShapeOpsTest(test.TestCase):
 
diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py
index 3ae06d7ab870f7125a123de51fab95d543efe56c..070d41147d4e8ab8ca6d2620431321cf77a6aaea 100644
--- a/tensorflow/python/keras/callbacks.py
+++ b/tensorflow/python/keras/callbacks.py
@@ -31,11 +31,18 @@ import time
 import numpy as np
 import six
 
+from tensorflow.python.eager import context
+from tensorflow.python.framework import dtypes
 from tensorflow.python.keras import backend as K
+from tensorflow.python.keras.engine.training_utils import standardize_input_data
 from tensorflow.python.keras.utils.generic_utils import Progbar
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import summary_ops_v2
+from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.summary import summary as tf_summary
+from tensorflow.python.training import saver
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -697,7 +704,9 @@ class TensorBoard(Callback):
       write_images: whether to write model weights to visualize as
           image in TensorBoard.
       embeddings_freq: frequency (in epochs) at which selected embedding
-          layers will be saved.
+          layers will be saved. If set to 0, embeddings won't be computed.
+          Data to be visualized in TensorBoard's Embedding tab must be passed
+          as `embeddings_data`.
       embeddings_layer_names: a list of names of layers to keep eye on. If
           None or empty list all the embedding layer will be watched.
       embeddings_metadata: a dictionary which maps layer name to a file name
@@ -705,6 +714,19 @@ class TensorBoard(Callback):
           [details](https://www.tensorflow.org/how_tos/embedding_viz/#metadata_optional)
           about metadata files format. In case if the same metadata file is
           used for all embedding layers, string can be passed.
+      embeddings_data: data to be embedded at layers specified in
+          `embeddings_layer_names`. Numpy array (if the model has a single
+          input) or list of Numpy arrays (if the model has multiple inputs).
+          Learn [more about embeddings](https://www.tensorflow.org/programmers_guide/embedding)
+
+  Raises:
+      ValueError: If histogram_freq is set and no validation data is provided.
+
+  @compatbility(eager)
+  Using `Tensorboard` callback will work while eager execution is enabled,
+  however outputting histogram summaries of weights and gradients is not
+  supported, and thus `histogram_freq` will be ignored.
+  @end_compatibility
   """
 
   # pylint: enable=line-too-long
@@ -715,24 +737,43 @@ class TensorBoard(Callback):
                batch_size=32,
                write_graph=True,
                write_grads=False,
-               write_images=False):
+               write_images=False,
+               embeddings_freq=0,
+               embeddings_layer_names=None,
+               embeddings_metadata=None,
+               embeddings_data=None):
     super(TensorBoard, self).__init__()
     self.log_dir = log_dir
     self.histogram_freq = histogram_freq
+    if self.histogram_freq and context.executing_eagerly():
+      logging.warning(
+          UserWarning('Weight and gradient histograms not supported for eager'
+                      'execution, setting `histogram_freq` to `0`.'))
+      self.histogram_freq = 0
     self.merged = None
     self.write_graph = write_graph
     self.write_grads = write_grads
     self.write_images = write_images
     self.batch_size = batch_size
     self._current_batch = 0
-    # abstracted writer class to be able to stub for testing
-    self._writer_class = tf_summary.FileWriter
-
-  def set_model(self, model):
-    """Sets Keras model and creates summary ops."""
+    self._total_batches_seen = 0
+    self.embeddings_freq = embeddings_freq
+    self.embeddings_layer_names = embeddings_layer_names
+    self.embeddings_metadata = embeddings_metadata
+    self.embeddings_data = embeddings_data
+
+  def _init_writer(self):
+    """Sets file writer."""
+    if context.executing_eagerly():
+      self.writer = summary_ops_v2.create_file_writer(self.log_dir)
+    elif self.write_graph:
+      self.writer = tf_summary.FileWriter(self.log_dir, K.get_session().graph)
+    else:
+      self.writer = tf_summary.FileWriter(self.log_dir)
 
-    self.model = model
-    self.sess = K.get_session()
+  def _make_histogram_ops(self, model):
+    """Defines histogram ops when histogram_freq > 0."""
+    # only make histogram summary op if it hasn't already been made
     if self.histogram_freq and self.merged is None:
       for layer in self.model.layers:
         for weight in layer.weights:
@@ -772,35 +813,162 @@ class TensorBoard(Callback):
             def is_indexed_slices(grad):
               return type(grad).__name__ == 'IndexedSlices'
 
-            grads = [grad.values if is_indexed_slices(grad) else grad
-                     for grad in grads]
+            grads = [
+                grad.values if is_indexed_slices(grad) else grad
+                for grad in grads
+            ]
             tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads)
 
         if hasattr(layer, 'output'):
-          tf_summary.histogram('{}_out'.format(layer.name), layer.output)
-    self.merged = tf_summary.merge_all()
+          if isinstance(layer.output, list):
+            for i, output in enumerate(layer.output):
+              tf_summary.histogram('{}_out_{}'.format(layer.name, i), output)
+          else:
+            tf_summary.histogram('{}_out'.format(layer.name), layer.output)
 
-    if self.write_graph:
-      self.writer = self._writer_class(self.log_dir, self.sess.graph)
-    else:
-      self.writer = self._writer_class(self.log_dir)
+  def set_model(self, model):
+    """Sets Keras model and creates summary ops."""
+
+    self.model = model
+    self._init_writer()
+    # histogram summaries only enabled in graph mode
+    if not context.executing_eagerly():
+      self._make_histogram_ops(model)
+      self.merged = tf_summary.merge_all()
+
+    # If both embedding_freq and embeddings_data are available, we will
+    # visualize embeddings.
+    if self.embeddings_freq and self.embeddings_data is not None:
+      self.embeddings_data = standardize_input_data(self.embeddings_data,
+                                                    model.input_names)
+
+      # If embedding_layer_names are not provided, get all of the embedding
+      # layers from the model.
+      embeddings_layer_names = self.embeddings_layer_names
+      if not embeddings_layer_names:
+        embeddings_layer_names = [
+            layer.name
+            for layer in self.model.layers
+            if type(layer).__name__ == 'Embedding'
+        ]
+
+      self.assign_embeddings = []
+      embeddings_vars = {}
+
+      self.batch_id = batch_id = array_ops.placeholder(dtypes.int32)
+      self.step = step = array_ops.placeholder(dtypes.int32)
+
+      for layer in self.model.layers:
+        if layer.name in embeddings_layer_names:
+          embedding_input = self.model.get_layer(layer.name).output
+          embedding_size = np.prod(embedding_input.shape[1:])
+          embedding_input = array_ops.reshape(embedding_input,
+                                              (step, int(embedding_size)))
+          shape = (self.embeddings_data[0].shape[0], int(embedding_size))
+          embedding = variables.Variable(
+              array_ops.zeros(shape), name=layer.name + '_embedding')
+          embeddings_vars[layer.name] = embedding
+          batch = state_ops.assign(embedding[batch_id:batch_id + step],
+                                   embedding_input)
+          self.assign_embeddings.append(batch)
+
+      self.saver = saver.Saver(list(embeddings_vars.values()))
+
+      # Create embeddings_metadata dictionary
+      if isinstance(self.embeddings_metadata, str):
+        embeddings_metadata = {
+            layer_name: self.embeddings_metadata
+            for layer_name in embeddings_vars.keys()
+        }
+      else:
+        # If embedding_metadata is already a dictionary
+        embeddings_metadata = self.embeddings_metadata
+
+      try:
+        from tensorboard.plugins import projector
+      except ImportError:
+        raise ImportError('Failed to import TensorBoard. Please make sure that '
+                          'TensorBoard integration is complete."')
+
+      # TODO(psv): Add integration tests to test embedding visualization
+      # with TensorBoard callback. We are unable to write a unit test for this
+      # because TensorBoard dependency assumes TensorFlow package is installed.
+      config = projector.ProjectorConfig()
+      for layer_name, tensor in embeddings_vars.items():
+        embedding = config.embeddings.add()
+        embedding.tensor_name = tensor.name
+
+        if (embeddings_metadata is not None and
+            layer_name in embeddings_metadata):
+          embedding.metadata_path = embeddings_metadata[layer_name]
+
+      projector.visualize_embeddings(self.writer, config)
 
   def _fetch_callback(self, summary):
     self.writer.add_summary(
-        summary, self._epoch + self._current_batch / self._batches_per_epoch)
-    self._current_batch += 1
+        summary,
+        self._epoch + self._current_val_batch / self._validation_batches)
+    self._current_val_batch += 1
+
+  def _write_custom_summaries(self, step, logs=None):
+    """Writes metrics out as custom scalar summaries.
+
+    Arguments:
+        step: the global step to use for Tensorboard.
+        logs: dict. Keys are scalar summary names, values are
+            NumPy scalars.
+
+    """
+    logs = logs or {}
+    if context.executing_eagerly():
+      # use v2 summary ops
+      with self.writer.as_default(), summary_ops_v2.always_record_summaries():
+        for name, value in logs.items():
+          summary_ops_v2.scalar(name, value.item(), step=step)
+    else:
+      # use FileWriter from v1 summary
+      for name, value in logs.items():
+        summary = tf_summary.Summary()
+        summary_value = summary.value.add()
+        summary_value.simple_value = value.item()
+        summary_value.tag = name
+        self.writer.add_summary(summary, step)
+    self.writer.flush()
+
+  def on_train_begin(self, logs=None):
+    """Checks if histogram summaries can be run."""
+    # will never be set when in eager
+    if self.histogram_freq:
+      if 'validation_steps' in self.params:
+        self._validation_batches = self.params['validation_steps']
+      elif self.validation_data:
+        self._validation_batches = math.ceil(
+            self.validation_data[0].shape[0] / self.batch_size)
+      else:
+        raise ValueError('If printing histograms, validation data must be '
+                         'provided.')
+      if self._validation_batches == 0:
+        raise ValueError(
+            'If printing histograms, validation data must have length > 0.')
+
+  def on_batch_end(self, batch, logs=None):
+    """Writes scalar summaries for metrics on every training batch."""
+    # Don't output batch_size and batch number as Tensorboard summaries
+    logs = logs or {}
+    batch_logs = {('batch_' + k): v
+                  for k, v in logs.items()
+                  if k not in ['batch', 'size']}
+    self._write_custom_summaries(self._total_batches_seen, batch_logs)
+    self._total_batches_seen += 1
 
   def on_epoch_begin(self, epoch, logs=None):
     """Add histogram op to Model test_function callbacks, reset batch count."""
 
-    if not self.validation_data and self.histogram_freq:
-      raise ValueError('If printing histograms, validation_data must be '
-                       'provided, and cannot be a generator.')
+    # check if histogram summary should be run for this epoch
     if self.histogram_freq and epoch % self.histogram_freq == 0:
       self._epoch = epoch
-      self._current_batch = 0
-      self._batches_per_epoch = math.ceil(
-          self.validation_data[0].shape[0] / self.batch_size)
+      self._current_val_batch = 0
+      # add the histogram summary op if it should run this epoch
       if self.merged not in self.model.test_function.fetches:
         self.model.test_function.fetches.append(self.merged)
         self.model.test_function.fetch_callbacks[
@@ -809,23 +977,59 @@ class TensorBoard(Callback):
   def on_epoch_end(self, epoch, logs=None):
     """Checks if summary ops should run next epoch, logs scalar summaries."""
 
-    logs = logs or {}
+    # don't output batch_size and
+    # batch number as Tensorboard summaries
+    logs = {('epoch_' + k): v
+            for k, v in logs.items()
+            if k not in ['batch', 'size']}
+    self._write_custom_summaries(epoch, logs)
 
-    if self.histogram_freq and self.histogram_freq > 1:
+    # pop the histogram summary op after each epoch
+    if self.histogram_freq:
       if self.merged in self.model.test_function.fetches:
         self.model.test_function.fetches.remove(self.merged)
       if self.merged in self.model.test_function.fetch_callbacks:
         self.model.test_function.fetch_callbacks.pop(self.merged)
 
-    for name, value in logs.items():
-      if name in ['batch', 'size']:
-        continue
-      summary = tf_summary.Summary()
-      summary_value = summary.value.add()
-      summary_value.simple_value = value.item()
-      summary_value.tag = name
-      self.writer.add_summary(summary, epoch)
-    self.writer.flush()
+    if self.embeddings_data is None and self.embeddings_freq:
+      raise ValueError('To visualize embeddings, embeddings_data must '
+                       'be provided.')
+
+    if self.embeddings_freq and self.embeddings_data is not None:
+      if epoch % self.embeddings_freq == 0:
+        # We need a second forward-pass here because we're passing
+        # the `embeddings_data` explicitly. This design allows to pass
+        # arbitrary data as `embeddings_data` and results from the fact
+        # that we need to know the size of the `tf.Variable`s which
+        # hold the embeddings in `set_model`. At this point, however,
+        # the `validation_data` is not yet set.
+
+        embeddings_data = self.embeddings_data
+        n_samples = embeddings_data[0].shape[0]
+        i = 0
+        while i < n_samples:
+          step = min(self.batch_size, n_samples - i)
+          batch = slice(i, i + step)
+
+          if isinstance(self.model.input, list):
+            feed_dict = {
+                model_input: embeddings_data[idx][batch]
+                for idx, model_input in enumerate(self.model.input)
+            }
+          else:
+            feed_dict = {self.model.input: embeddings_data[0][batch]}
+
+          feed_dict.update({self.batch_id: i, self.step: step})
+
+          if self.model.uses_learning_phase:
+            feed_dict[K.learning_phase()] = False
+
+          self.sess.run(self.assign_embeddings, feed_dict=feed_dict)
+          self.saver.save(self.sess,
+                          os.path.join(self.log_dir, 'keras_embedding.ckpt'),
+                          epoch)
+
+          i += self.batch_size
 
   def on_train_end(self, logs=None):
     self.writer.close()
diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py
index d56f2f5bfc7d7045a4c1d2bde764fe1143764922..d38a753263aef4b99d8344125893c797885b9559 100644
--- a/tensorflow/python/keras/callbacks_test.py
+++ b/tensorflow/python/keras/callbacks_test.py
@@ -22,6 +22,7 @@ import csv
 import os
 import re
 import shutil
+import tempfile
 import threading
 import unittest
 
@@ -29,10 +30,12 @@ import numpy as np
 
 from tensorflow.core.framework import summary_pb2
 from tensorflow.python import keras
+from tensorflow.python.framework import test_util
 from tensorflow.python.keras import testing_utils
 from tensorflow.python.platform import test
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.summary.writer import writer_cache
+from tensorflow.python.training import adam
 
 try:
   import h5py  # pylint:disable=g-import-not-at-top
@@ -63,7 +66,7 @@ class KerasCallbacksTest(test.TestCase):
       np.random.seed(1337)
 
       temp_dir = self.get_temp_dir()
-      self.addCleanup(shutil.rmtree, temp_dir)
+      self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
 
       filepath = os.path.join(temp_dir, 'checkpoint.h5')
       (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
@@ -479,7 +482,7 @@ class KerasCallbacksTest(test.TestCase):
     with self.test_session():
       np.random.seed(1337)
       temp_dir = self.get_temp_dir()
-      self.addCleanup(shutil.rmtree, temp_dir)
+      self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
       filepath = os.path.join(temp_dir, 'log.tsv')
 
       sep = '\t'
@@ -557,7 +560,7 @@ class KerasCallbacksTest(test.TestCase):
     # does not result in invalid CSVs.
     np.random.seed(1337)
     tmpdir = self.get_temp_dir()
-    self.addCleanup(shutil.rmtree, tmpdir)
+    self.addCleanup(shutil.rmtree, tmpdir, ignore_errors=True)
 
     with self.test_session():
       fp = os.path.join(tmpdir, 'test.csv')
@@ -649,7 +652,7 @@ class KerasCallbacksTest(test.TestCase):
     np.random.seed(1337)
 
     temp_dir = self.get_temp_dir()
-    self.addCleanup(shutil.rmtree, temp_dir)
+    self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
 
     (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
         train_samples=TRAIN_SAMPLES,
@@ -747,7 +750,7 @@ class KerasCallbacksTest(test.TestCase):
   def test_TensorBoard_histogram_freq_must_have_validation_data(self):
     np.random.seed(1337)
     tmpdir = self.get_temp_dir()
-    self.addCleanup(shutil.rmtree, tmpdir)
+    self.addCleanup(shutil.rmtree, tmpdir, ignore_errors=True)
 
     with self.test_session():
       filepath = os.path.join(tmpdir, 'logs')
@@ -813,28 +816,13 @@ class KerasCallbacksTest(test.TestCase):
       for cb in cbs:
         cb.on_train_end()
 
-      # fit generator with validation data generator should raise ValueError if
-      # histogram_freq > 0
-      cbs = callbacks_factory(histogram_freq=1)
-      with self.assertRaises(ValueError):
-        model.fit_generator(
-            data_generator(True),
-            len(x_train),
-            epochs=2,
-            validation_data=data_generator(False),
-            validation_steps=1,
-            callbacks=cbs)
-
-      for cb in cbs:
-        cb.on_train_end()
-
       # Make sure file writer cache is clear to avoid failures during cleanup.
       writer_cache.FileWriterCache.clear()
 
   def test_TensorBoard_multi_input_output(self):
     np.random.seed(1337)
     tmpdir = self.get_temp_dir()
-    self.addCleanup(shutil.rmtree, tmpdir)
+    self.addCleanup(shutil.rmtree, tmpdir, ignore_errors=True)
 
     with self.test_session():
       filepath = os.path.join(tmpdir, 'logs')
@@ -932,9 +920,12 @@ class KerasCallbacksTest(test.TestCase):
       def close(self):
         pass
 
+    def _init_writer(obj):
+      obj.writer = FileWriterStub(obj.log_dir)
+
     np.random.seed(1337)
     tmpdir = self.get_temp_dir()
-    self.addCleanup(shutil.rmtree, tmpdir)
+    self.addCleanup(shutil.rmtree, tmpdir, ignore_errors=True)
     (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
         train_samples=TRAIN_SAMPLES,
         test_samples=TEST_SAMPLES,
@@ -955,13 +946,13 @@ class KerasCallbacksTest(test.TestCase):
           loss='categorical_crossentropy',
           optimizer='sgd',
           metrics=['accuracy'])
+      keras.callbacks.TensorBoard._init_writer = _init_writer
       tsb = keras.callbacks.TensorBoard(
           log_dir=tmpdir,
           histogram_freq=1,
           write_images=True,
           write_grads=True,
           batch_size=5)
-      tsb._writer_class = FileWriterStub
       cbks = [tsb]
 
       # fit with validation data
@@ -976,6 +967,56 @@ class KerasCallbacksTest(test.TestCase):
 
       self.assertAllEqual(tsb.writer.steps_seen, [0, 0.5, 1, 1.5, 2, 2.5])
 
+  def test_Tensorboard_histogram_summaries_with_generator(self):
+    np.random.seed(1337)
+    tmpdir = self.get_temp_dir()
+    self.addCleanup(shutil.rmtree, tmpdir, ignore_errors=True)
+
+    def generator():
+      x = np.random.randn(10, 100).astype(np.float32)
+      y = np.random.randn(10, 10).astype(np.float32)
+      while True:
+        yield x, y
+
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(keras.layers.Dense(10, input_dim=100, activation='relu'))
+      model.add(keras.layers.Dense(10, activation='softmax'))
+      model.compile(
+          loss='categorical_crossentropy',
+          optimizer='sgd',
+          metrics=['accuracy'])
+      tsb = keras.callbacks.TensorBoard(
+          log_dir=tmpdir,
+          histogram_freq=1,
+          write_images=True,
+          write_grads=True,
+          batch_size=5)
+      cbks = [tsb]
+
+      # fit with validation generator
+      model.fit_generator(
+          generator(),
+          steps_per_epoch=2,
+          epochs=2,
+          validation_data=generator(),
+          validation_steps=2,
+          callbacks=cbks,
+          verbose=0)
+
+      with self.assertRaises(ValueError):
+        # fit with validation generator but no
+        # validation_steps
+        model.fit_generator(
+            generator(),
+            steps_per_epoch=2,
+            epochs=2,
+            validation_data=generator(),
+            callbacks=cbks,
+            verbose=0)
+
+      self.assertTrue(os.path.exists(tmpdir))
+
   @unittest.skipIf(
       os.name == 'nt',
       'use_multiprocessing=True does not work on windows properly.')
@@ -1026,7 +1067,7 @@ class KerasCallbacksTest(test.TestCase):
   def test_TensorBoard_with_ReduceLROnPlateau(self):
     with self.test_session():
       temp_dir = self.get_temp_dir()
-      self.addCleanup(shutil.rmtree, temp_dir)
+      self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
 
       (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
           train_samples=TRAIN_SAMPLES,
@@ -1061,6 +1102,112 @@ class KerasCallbacksTest(test.TestCase):
 
       assert os.path.exists(temp_dir)
 
+  def test_Tensorboard_batch_logging(self):
+
+    class FileWriterStub(object):
+
+      def __init__(self, logdir, graph=None):
+        self.logdir = logdir
+        self.graph = graph
+        self.batches_logged = []
+        self.summary_values = []
+        self.summary_tags = []
+
+      def add_summary(self, summary, step):
+        self.summary_values.append(summary.value[0].simple_value)
+        self.summary_tags.append(summary.value[0].tag)
+        self.batches_logged.append(step)
+
+      def flush(self):
+        pass
+
+      def close(self):
+        pass
+
+    temp_dir = self.get_temp_dir()
+    self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
+
+    tb_cbk = keras.callbacks.TensorBoard(temp_dir)
+    tb_cbk.writer = FileWriterStub(temp_dir)
+
+    for batch in range(5):
+      tb_cbk.on_batch_end(batch, {'acc': np.float32(batch)})
+    self.assertEqual(tb_cbk.writer.batches_logged, [0, 1, 2, 3, 4])
+    self.assertEqual(tb_cbk.writer.summary_values, [0., 1., 2., 3., 4.])
+    self.assertEqual(tb_cbk.writer.summary_tags, ['batch_acc'] * 5)
+
+  def test_Tensorboard_epoch_and_batch_logging(self):
+
+    class FileWriterStub(object):
+
+      def __init__(self, logdir, graph=None):
+        self.logdir = logdir
+        self.graph = graph
+
+      def add_summary(self, summary, step):
+        if 'batch_' in summary.value[0].tag:
+          self.batch_summary = (step, summary)
+        elif 'epoch_' in summary.value[0].tag:
+          self.epoch_summary = (step, summary)
+
+      def flush(self):
+        pass
+
+      def close(self):
+        pass
+
+    temp_dir = self.get_temp_dir()
+    self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
+
+    tb_cbk = keras.callbacks.TensorBoard(temp_dir)
+    tb_cbk.writer = FileWriterStub(temp_dir)
+
+    tb_cbk.on_batch_end(0, {'acc': np.float32(5.0)})
+    tb_cbk.on_epoch_end(0, {'acc': np.float32(10.0)})
+    batch_step, batch_summary = tb_cbk.writer.batch_summary
+    self.assertEqual(batch_step, 0)
+    self.assertEqual(batch_summary.value[0].simple_value, 5.0)
+    epoch_step, epoch_summary = tb_cbk.writer.epoch_summary
+    self.assertEqual(epoch_step, 0)
+    self.assertEqual(epoch_summary.value[0].simple_value, 10.0)
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_Tensorboard_eager(self):
+    with self.test_session():
+      temp_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
+      self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
+
+      (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
+          train_samples=TRAIN_SAMPLES,
+          test_samples=TEST_SAMPLES,
+          input_shape=(INPUT_DIM,),
+          num_classes=NUM_CLASSES)
+      y_test = keras.utils.to_categorical(y_test)
+      y_train = keras.utils.to_categorical(y_train)
+
+      model = keras.models.Sequential()
+      model.add(
+          keras.layers.Dense(
+              NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu'))
+      model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax'))
+      model.compile(
+          loss='binary_crossentropy',
+          optimizer=adam.AdamOptimizer(0.01),
+          metrics=['accuracy'])
+
+      cbks = [keras.callbacks.TensorBoard(log_dir=temp_dir)]
+
+      model.fit(
+          x_train,
+          y_train,
+          batch_size=BATCH_SIZE,
+          validation_data=(x_test, y_test),
+          callbacks=cbks,
+          epochs=2,
+          verbose=0)
+
+      self.assertTrue(os.path.exists(temp_dir))
+
   def test_RemoteMonitorWithJsonPayload(self):
     if requests is None:
       self.skipTest('`requests` required to run this test')
diff --git a/tensorflow/python/keras/engine/base_layer.py b/tensorflow/python/keras/engine/base_layer.py
index 361778570bc7e87bc0642a2c52d43762c6828eb4..e1214f810334e3dea6fa7047e139f15871ba164a 100644
--- a/tensorflow/python/keras/engine/base_layer.py
+++ b/tensorflow/python/keras/engine/base_layer.py
@@ -26,6 +26,7 @@ import numpy as np
 from six.moves import zip  # pylint: disable=redefined-builtin
 
 from tensorflow.python.eager import context
+from tensorflow.python.eager import function as eager_function
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
@@ -174,6 +175,12 @@ class Layer(checkpointable.CheckpointableBase):
 
     self.supports_masking = False
 
+    call_argspec = tf_inspect.getargspec(self.call)
+    if 'training' in call_argspec.args:
+      self._expects_training_arg = True
+    else:
+      self._expects_training_arg = False
+
     # Manage input shape information if passed.
     if 'input_shape' in kwargs or 'batch_input_shape' in kwargs:
       # In this case we will later create an input layer
@@ -460,14 +467,18 @@ class Layer(checkpointable.CheckpointableBase):
     """Alias for `add_weight`."""
     return self.add_weight(*args, **kwargs)
 
-  def add_weight(self, name, shape,
+  def add_weight(self,
+                 name,
+                 shape,
                  dtype=None,
                  initializer=None,
                  regularizer=None,
-                 trainable=True,
+                 trainable=None,
                  constraint=None,
                  partitioner=None,
                  use_resource=None,
+                 synchronization=vs.VariableSynchronization.AUTO,
+                 aggregation=vs.VariableAggregation.NONE,
                  getter=None):
     """Adds a new variable to the layer, or gets an existing one; returns it.
 
@@ -482,10 +493,20 @@ class Layer(checkpointable.CheckpointableBase):
         or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
         Note, if the current variable scope is marked as non-trainable
         then this parameter is ignored and any added variables are also
-        marked as non-trainable.
+        marked as non-trainable. `trainable` defaults to `True` unless
+        `synchronization` is set to `ON_READ`.
       constraint: constraint instance (callable).
       partitioner: Partitioner to be passed to the `Checkpointable` API.
       use_resource: Whether to use `ResourceVariable`.
+      synchronization: Indicates when a distributed a variable will be
+        aggregated. Accepted values are constants defined in the class
+        @{tf.VariableSynchronization}. By default the synchronization is set to
+        `AUTO` and the current `DistributionStrategy` chooses
+        when to synchronize. If `synchronization` is set to `ON_READ`,
+        `trainable` must not be set to `True`.
+      aggregation: Indicates how a distributed variable will be aggregated.
+        Accepted values are constants defined in the class
+        @{tf.VariableAggregation}.
       getter: Variable getter argument to be passed to the `Checkpointable` API.
 
     Returns:
@@ -496,7 +517,8 @@ class Layer(checkpointable.CheckpointableBase):
     Raises:
       RuntimeError: If called with partioned variable regularization and
         eager execution is enabled.
-      ValueError: When giving unsupported dtype and no initializer.
+      ValueError: When giving unsupported dtype and no initializer or when
+        trainable has been set to True with synchronization set as `ON_READ`.
     """
     if dtype is None:
       dtype = self.dtype or backend.floatx()
@@ -505,6 +527,19 @@ class Layer(checkpointable.CheckpointableBase):
     regularizer = regularizers.get(regularizer)
     constraint = constraints.get(constraint)
 
+    if synchronization == vs.VariableSynchronization.ON_READ:
+      if trainable:
+        raise ValueError(
+            'Synchronization value can be set to '
+            'VariableSynchronization.ON_READ only for non-trainable variables. '
+            'You have specified trainable=True and '
+            'synchronization=VariableSynchronization.ON_READ.')
+      else:
+        # Set trainable to be false when variable is to be synced on read.
+        trainable = False
+    elif trainable is None:
+      trainable = True
+
     # Initialize variable when no initializer provided
     if initializer is None:
       # If dtype is DT_FLOAT, provide a uniform unit scaling initializer
@@ -532,7 +567,9 @@ class Layer(checkpointable.CheckpointableBase):
         constraint=constraint,
         trainable=trainable and self.trainable,
         partitioner=partitioner,
-        use_resource=use_resource)
+        use_resource=use_resource,
+        synchronization=synchronization,
+        aggregation=aggregation)
 
     if regularizer is not None:
       # TODO(fchollet): in the future, this should be handled at the
@@ -655,8 +692,8 @@ class Layer(checkpointable.CheckpointableBase):
 
     # Handle Keras mask propagation from previous layer to current layer.
     previous_mask = None
-    if (not hasattr(self, '_compute_previous_mask') or
-        self._compute_previous_mask):
+    if build_graph and (not hasattr(self, '_compute_previous_mask') or
+                        self._compute_previous_mask):
       previous_mask = collect_previous_mask(inputs)
       if not hasattr(self, '_call_fn_args'):
         self._call_fn_args = self._no_dependency(
@@ -693,9 +730,18 @@ class Layer(checkpointable.CheckpointableBase):
             self._dtype = input_list[0].dtype.base_dtype.name
           except AttributeError:
             pass
-        if all(hasattr(x, 'get_shape') for x in input_list):
-          input_shapes = nest.map_structure(lambda x: x.get_shape(), inputs)
-        self.build(input_shapes)
+
+        if all(hasattr(x, 'shape') for x in input_list):
+          input_shapes = nest.map_structure(lambda x: x.shape, inputs)
+
+        if (not hasattr(self, '_is_graph_network') or
+            self.__class__.__name__ == 'Sequential'):
+          # Only if self is a layer or an instance of a sequential model do we
+          # need to build it.
+          self.build(input_shapes)
+        # We must set self.built since user defined build functions are not
+        # constrained to set self.built.
+        self.built = True
 
       # Check input assumptions set after layer building, e.g. input shape.
       if build_graph or in_deferred_mode:
@@ -711,7 +757,7 @@ class Layer(checkpointable.CheckpointableBase):
         # Deferred mode behavior: use `compute_output_shape` to
         # infer the number of outputs of the layer and their shapes.
         if input_shapes is None:
-          input_shapes = nest.map_structure(lambda x: x.get_shape(), inputs)
+          input_shapes = nest.map_structure(lambda x: x.shape, inputs)
 
         output_shapes = self.compute_output_shape(input_shapes)
         output_shapes = nest.flatten(output_shapes)
@@ -731,8 +777,6 @@ class Layer(checkpointable.CheckpointableBase):
       if in_deferred_mode or build_graph and have_all_keras_metadata(inputs):
         inputs, outputs = self._set_connectivity_metadata_(
             inputs, outputs, args, kwargs)
-
-      self.built = True
       if context.executing_eagerly():
         return outputs
 
@@ -749,17 +793,8 @@ class Layer(checkpointable.CheckpointableBase):
     if hasattr(self, '_initial_weights') and self._initial_weights is not None:
       self.set_weights(self._initial_weights)
       del self._initial_weights
-    self._post_build_cleanup()
     return outputs
 
-  def _post_build_cleanup(self):
-    """Hooks to run after all sub-Layers are built."""
-    # Note that in addition to Layer.__call__, this method is called by Model
-    # after building a graph network (which skips __call__). It should be called
-    # when possible if self.built may have switched from False to True, and is
-    # idempotent.
-    pass  # No-op for Layers which don't override this method.
-
   def apply(self, inputs, *args, **kwargs):
     """Apply the layer on a input.
 
@@ -933,6 +968,39 @@ class Layer(checkpointable.CheckpointableBase):
     Returns:
         An input shape tuple.
     """
+    if context.executing_eagerly():
+      # In this case we build the model first in order to do shape inference.
+      # This is acceptable because the framework only calls
+      # `compute_output_shape` on shape values that the layer would later be
+      # built for. It would however cause issues in case a user attempts to
+      # use `compute_output_shape` manually (these users will have to
+      # implement `compute_output_shape` themselves).
+      self.build(input_shape)
+
+      with context.graph_mode():
+        graph = eager_function.CapturingGraph()
+        with graph.as_default():
+          if isinstance(input_shape, list):
+            inputs = [generate_placeholders_from_shape(shape)
+                      for shape in input_shape]
+          else:
+            inputs = generate_placeholders_from_shape(input_shape)
+
+          try:
+            if self._expects_training_arg:
+              outputs = self(inputs, training=False)
+            else:
+              outputs = self(inputs)
+          except TypeError:
+            raise NotImplementedError('We could not automatically infer '
+                                      'the static shape of the layer\'s output.'
+                                      ' Please implement the '
+                                      '`compute_output_shape` method on your '
+                                      'layer (%s).' % self.__class__.__name__)
+      if isinstance(outputs, list):
+        return [output.shape for output in outputs]
+      else:
+        return outputs.shape
     raise NotImplementedError
 
   def compute_mask(self, inputs, mask=None):  # pylint: disable=unused-argument
@@ -1295,7 +1363,7 @@ class Layer(checkpointable.CheckpointableBase):
                          ', but the layer isn\'t built. '
                          'You can build it manually via: `' + self.name +
                          '.build(batch_input_shape)`.')
-    weight_shapes = [w.get_shape().as_list() for w in self.weights]
+    weight_shapes = [w.shape.as_list() for w in self.weights]
     return int(sum([np.prod(w) for w in weight_shapes]))
 
   @property
@@ -1378,7 +1446,7 @@ class Layer(checkpointable.CheckpointableBase):
       if (spec.ndim is not None or
           spec.min_ndim is not None or
           spec.max_ndim is not None):
-        if x.get_shape().ndims is None:
+        if x.shape.ndims is None:
           raise ValueError('Input ' + str(input_index) + ' of layer ' +
                            self.name + ' is incompatible with the layer: '
                            'its rank is undefined, but the layer requires a '
@@ -1386,29 +1454,29 @@ class Layer(checkpointable.CheckpointableBase):
 
       # Check ndim.
       if spec.ndim is not None:
-        ndim = x.get_shape().ndims
+        ndim = x.shape.ndims
         if ndim != spec.ndim:
           raise ValueError('Input ' + str(input_index) + ' of layer ' +
                            self.name + ' is incompatible with the layer: '
                            'expected ndim=' + str(spec.ndim) + ', found ndim=' +
                            str(ndim) + '. Full shape received: ' +
-                           str(x.get_shape().as_list()))
+                           str(x.shape.as_list()))
       if spec.max_ndim is not None:
-        ndim = x.get_shape().ndims
+        ndim = x.shape.ndims
         if ndim is not None and ndim > spec.max_ndim:
           raise ValueError('Input ' + str(input_index) + ' of layer ' +
                            self.name + ' is incompatible with the layer: '
                            'expected max_ndim=' + str(spec.max_ndim) +
                            ', found ndim=' + str(ndim))
       if spec.min_ndim is not None:
-        ndim = x.get_shape().ndims
+        ndim = x.shape.ndims
         if ndim is not None and ndim < spec.min_ndim:
           raise ValueError('Input ' + str(input_index) + ' of layer ' +
                            self.name + ' is incompatible with the layer: '
                            ': expected min_ndim=' + str(spec.min_ndim) +
                            ', found ndim=' + str(ndim) +
                            '. Full shape received: ' +
-                           str(x.get_shape().as_list()))
+                           str(x.shape.as_list()))
       # Check dtype.
       if spec.dtype is not None:
         if x.dtype != spec.dtype:
@@ -1418,7 +1486,7 @@ class Layer(checkpointable.CheckpointableBase):
                            ', found dtype=' + str(x.dtype))
       # Check specific shape axes.
       if spec.axes:
-        shape = x.get_shape().as_list()
+        shape = x.shape.as_list()
         if shape is not None:
           for axis, value in spec.axes.items():
             if hasattr(value, 'value'):
@@ -1431,7 +1499,7 @@ class Layer(checkpointable.CheckpointableBase):
                   ' but received input with shape ' + str(shape))
       # Check shape.
       if spec.shape is not None:
-        shape = x.get_shape().as_list()
+        shape = x.shape.as_list()
         if shape is not None:
           for spec_dim, dim in zip(spec.shape, shape):
             if spec_dim is not None and dim is not None:
@@ -1706,12 +1774,12 @@ class DeferredTensor(object):
 
   def __str__(self):
     return "DeferredTensor('%s', shape=%s, dtype=%s)" % (self.name,
-                                                         self.get_shape(),
+                                                         self.shape,
                                                          self.dtype.name)
 
   def __repr__(self):
     return "" % (self.name,
-                                                        self.get_shape(),
+                                                        self.shape,
                                                         self.dtype.name)
 
 
@@ -1806,11 +1874,13 @@ def make_variable(name,
                   dtype=dtypes.float32,
                   initializer=None,
                   partition_info=None,
-                  trainable=True,
+                  trainable=None,
                   caching_device=None,
                   validate_shape=True,
                   constraint=None,
                   use_resource=None,
+                  synchronization=vs.VariableSynchronization.AUTO,
+                  aggregation=vs.VariableAggregation.NONE,
                   partitioner=None):  # pylint: disable=unused-argument
   """Temporary util to create a variable (relies on `variable_scope.variable`).
 
@@ -1836,11 +1906,21 @@ def make_variable(name,
       or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
       Note, if the current variable scope is marked as non-trainable
       then this parameter is ignored and any added variables are also
-      marked as non-trainable.
+      marked as non-trainable. `trainable` defaults to `True` unless
+      `synchronization` is set to `ON_READ`.
     caching_device: Passed to `vs.variable`.
     validate_shape: Passed to `vs.variable`.
     constraint: Constraint instance (callable).
     use_resource: Whether to use a `ResourceVariable`.
+    synchronization: Indicates when a distributed a variable will be
+      aggregated. Accepted values are constants defined in the class
+      @{tf.VariableSynchronization}. By default the synchronization is set to
+      `AUTO` and the current `DistributionStrategy` chooses
+      when to synchronize. If `synchronization` is set to `ON_READ`,
+      `trainable` must not be set to `True`.
+    aggregation: Indicates how a distributed variable will be aggregated.
+      Accepted values are constants defined in the class
+      @{tf.VariableAggregation}.
     partitioner: Not handled at this time.
 
   Returns:
@@ -1872,5 +1952,22 @@ def make_variable(name,
       dtype=variable_dtype,
       validate_shape=validate_shape,
       constraint=constraint,
-      use_resource=use_resource)
+      use_resource=use_resource,
+      synchronization=synchronization,
+      aggregation=aggregation)
   return v
+
+
+def generate_dummy_data_from_shape(shape):
+  if isinstance(shape, tensor_shape.TensorShape):
+    shape = shape.as_list()
+
+  # Replace Nones in input shape with dummy `1` value
+  shape = [x.value if isinstance(x, tensor_shape.Dimension) else x
+           for x in shape]
+  shape = [1 if x is None else x for x in shape]
+  return array_ops.ones(shape, dtype=backend.floatx())
+
+
+def generate_placeholders_from_shape(shape):
+  return array_ops.placeholder(shape=shape, dtype=backend.floatx())
diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py
index a4d96de74fc90e31d52f9a67e845a84f9ceb5034..20a29dbf2035ea9ebb934782612879bd687086af 100644
--- a/tensorflow/python/keras/engine/network.py
+++ b/tensorflow/python/keras/engine/network.py
@@ -20,7 +20,6 @@ from __future__ import division
 from __future__ import print_function
 
 import copy
-import functools
 import json
 import os
 import weakref
@@ -30,6 +29,7 @@ from six.moves import zip  # pylint: disable=redefined-builtin
 
 from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.eager import context
+from tensorflow.python.framework import errors
 from tensorflow.python.framework import errors_impl
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
@@ -144,10 +144,6 @@ class Network(base_layer.Layer):
 
     self._checkpointable_saver = checkpointable_utils.CheckpointableSaver(
         weakref.ref(self))
-    # A zero-argument function which should be called and set back to None as
-    # soon as the network is built (only applicable to subclassed Models). Runs
-    # restore operations when graph building.
-    self._in_progress_restore_finalizer = None
 
   @checkpointable.no_automatic_dependency_tracking
   def _init_graph_network(self, inputs, outputs, name=None):
@@ -318,8 +314,8 @@ class Network(base_layer.Layer):
     else:
       self._expects_training_arg = False
     self._call_convention = self._determine_call_convention(call_argspec)
-    self.outputs = None
-    self.inputs = None
+    self.outputs = []
+    self.inputs = []
     self.built = False
 
   def _determine_call_convention(self, call_argspec):
@@ -739,6 +735,86 @@ class Network(base_layer.Layer):
       return specs[0]
     return specs
 
+  def build(self, input_shape):
+    """Builds the model based on input shapes received.
+
+    This is to be used for subclassed models, which do not know at instantiation
+    time what their inputs look like.
+
+    Args:
+     input_shape: Single tuple, TensorShape, or list of shapes, where shapes
+         are tuples, integers, or TensorShapes.
+
+    Raises:
+      ValueError:
+        1. In case of invalid user-provided data (not of type tuple,
+           list, or TensorShape).
+        2. If the model requires call arguments that are agnostic
+           to the input shapes (positional or kwarg in call signature).
+        3. If not all layers were properly built.
+        4. If float type inputs are not supported within the layers.
+
+      In each of these cases, the user should build their model by calling it
+      on real tensor data.
+    """
+    if self._is_graph_network:
+      self.built = True
+      return
+
+    # If subclass network
+    if input_shape is None:
+      raise ValueError('Input shape must be defined when calling build on a '
+                       'model subclass network.')
+    valid_types = (tuple, list, tensor_shape.TensorShape)
+    if not isinstance(input_shape, valid_types):
+      raise ValueError('Specified input shape is not one of the valid types. '
+                       'Please specify a batch input shape of type tuple or '
+                       'list of input shapes. User provided '
+                       'input type: {}'.format(type(input_shape)))
+
+    if input_shape and not self.inputs:
+      if isinstance(input_shape, list):
+        # List of input shapes
+        x = [base_layer.generate_dummy_data_from_shape(shape)
+             for shape in input_shape]
+      else:
+        x = base_layer.generate_dummy_data_from_shape(input_shape)
+
+      kwargs = {}
+      num_call_args = len(tf_inspect.getargspec(self.call).args)
+      if self._expects_training_arg and num_call_args == 3:
+        # Has call signature of call(self, input, training)
+        kwargs['training'] = False
+      elif num_call_args > 2:
+        # Has invalid call signature of call(self, input, *args, **kwargs)
+        raise ValueError('Currently, you cannot build your model if it has '
+                         'positional or keyword arguments that are not '
+                         'inputs to the model, but are required for its '
+                         '`call` method. Instead, in order to instantiate '
+                         'and build your model, `call` your model on real '
+                         'tensor data with all expected call arguments.')
+
+      try:
+        self.call(x, **kwargs)
+      except (errors.InvalidArgumentError, TypeError):
+        raise ValueError('You cannot build your model by calling `build` '
+                         'if your layers do not support float type inputs. '
+                         'Instead, in order to instantiate and build your '
+                         'model, `call` your model on real tensor data (of '
+                         'the correct dtype).')
+
+    if self._layers:
+      self._track_layers(self._layers)
+    if self.layers:
+      for layer in self.layers:
+        if not layer.built:
+          raise ValueError('Layer: {} was not built in your model. Calling '
+                           '`build` manually on a subclassed model is only '
+                           'allowed for models with a static topology. '
+                           'In this case, you can build your model by '
+                           'calling it on real tensor data.'.format(layer))
+    self.built = True
+
   def call(self, inputs, training=None, mask=None):
     """Calls the model on new inputs.
 
@@ -779,6 +855,8 @@ class Network(base_layer.Layer):
 
   def compute_output_shape(self, input_shape):
     if not self._is_graph_network:
+      if context.executing_eagerly():
+        return super(Network, self).compute_output_shape(input_shape)
       raise NotImplementedError
 
     if isinstance(input_shape, list):
@@ -889,7 +967,7 @@ class Network(base_layer.Layer):
         mask: List of masks (tensors or None).
 
     Returns:
-        Three lists: output_tensors, output_masks, output_shapes
+        Two lists: output_tensors, output_masks
     """
     # Note: masking support is relevant mainly for Keras.
     # It cannot be factored out without having the fully reimplement the network
@@ -956,7 +1034,6 @@ class Network(base_layer.Layer):
               else:
                 output_masks = [None for _ in output_tensors]
               computed_tensors = [computed_tensor]
-              computed_masks = [computed_mask]
             else:
               computed_tensors = [x[0] for x in computed_data]
               computed_masks = [x[1] for x in computed_data]
@@ -1423,13 +1500,9 @@ class Network(base_layer.Layer):
             'load_weights).')
       if not context.executing_eagerly():
         session = backend.get_session()
-        finalizer = functools.partial(status.run_restore_ops, session=session)
-        if self.built:
-          finalizer()
-        else:
-          # Hold on to this status object until the network is built (for
-          # subclassed Models). Then we'll run restore ops if necessary.
-          self._in_progress_restore_finalizer = finalizer
+        # Restore existing variables (if any) immediately, and set up a
+        # streaming restore for any variables created in the future.
+        checkpointable_utils.streaming_restore(status=status, session=session)
       return status
     if h5py is None:
       raise ImportError(
@@ -1447,14 +1520,6 @@ class Network(base_layer.Layer):
       else:
         saving.load_weights_from_hdf5_group(f, self.layers)
 
-  def _post_build_cleanup(self):
-    super(Network, self)._post_build_cleanup()
-    if self._in_progress_restore_finalizer is not None:
-      # Runs queued restore operations left over from load_weights when graph
-      # building.
-      self._in_progress_restore_finalizer()
-      self._in_progress_restore_finalizer = None
-
   def _updated_config(self):
     """Util shared between different serialization methods.
 
diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py
index 030328f2a66f0ec406ac271aecfbf2dbebf22f5f..e029e614e04be299f22714fac0fd8b2cd93c0e92 100644
--- a/tensorflow/python/keras/engine/saving_test.py
+++ b/tensorflow/python/keras/engine/saving_test.py
@@ -722,18 +722,23 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase):
         self.assertEqual(len(graph.get_operations()), op_count)
 
   def _weight_loading_test_template(self, make_model_fn):
-    with self.test_session() as session:
+    with self.test_session():
       model = make_model_fn()
+      model.compile(
+          loss='mse',
+          optimizer=training_module.RMSPropOptimizer(0.1),
+          metrics=['acc'])
       temp_dir = self.get_temp_dir()
       prefix = os.path.join(temp_dir, 'ckpt')
+      train_x = np.random.random((3, 2))
+      train_y = np.random.random((3,))
+      x = constant_op.constant(train_x, dtype=dtypes.float32)
 
-      x = constant_op.constant(np.random.random((3, 2)), dtype=dtypes.float32)
-      executing_eagerly = context.executing_eagerly()
-      ref_y_tensor = model(x)
-      if not executing_eagerly:
-        session.run([v.initializer for v in model.variables])
-      ref_y = self.evaluate(ref_y_tensor)
+      model.train_on_batch(train_x, train_y)
       model.save_weights(prefix, save_format='tf')
+      ref_y_before_train = model.predict(train_x)
+      model.train_on_batch(train_x, train_y)
+      ref_y_after_train = model.predict(train_x)
       for v in model.variables:
         self.evaluate(
             v.assign(random_ops.random_normal(shape=array_ops.shape(v))))
@@ -741,16 +746,27 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase):
       self.addCleanup(shutil.rmtree, temp_dir)
 
       model.load_weights(prefix)
-      y = self.evaluate(model(x))
-      self.assertAllClose(ref_y, y)
+      self.assertAllClose(ref_y_before_train, self.evaluate(model(x)))
 
       # Test restore-on-create if this is a subclassed Model (graph Networks
       # will have already created their variables).
       load_model = make_model_fn()
       load_model.load_weights(prefix)
-      restore_on_create_y_tensor = load_model(x)
-      restore_on_create_y = self.evaluate(restore_on_create_y_tensor)
-      self.assertAllClose(ref_y, restore_on_create_y)
+      self.assertAllClose(
+          ref_y_before_train,
+          self.evaluate(load_model(x)))
+      load_model = make_model_fn()
+      load_model.load_weights(prefix)
+      # We need to run some of the restore ops for predict(), but not all
+      # variables have been created yet (optimizer slot variables). Tests
+      # incremental restore.
+      load_model.predict(train_x)
+      load_model.compile(
+          loss='mse',
+          optimizer=training_module.RMSPropOptimizer(0.1),
+          metrics=['acc'])
+      load_model.train_on_batch(train_x, train_y)
+      self.assertAllClose(ref_y_after_train, self.evaluate(load_model(x)))
 
   @test_util.run_in_graph_and_eager_modes
   def test_weight_loading_graph_model(self):
@@ -858,5 +874,6 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase):
         SubclassedModel, SubclassedModelRestore,
         _restore_init_fn)
 
+
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/python/keras/engine/sequential.py b/tensorflow/python/keras/engine/sequential.py
index 371504a503168e7443895bb22a57126b274da226..41cdfda660e69f41e4f3d15e2e61ac8f45654436 100644
--- a/tensorflow/python/keras/engine/sequential.py
+++ b/tensorflow/python/keras/engine/sequential.py
@@ -213,13 +213,31 @@ class Sequential(Model):
       self.outputs = [self.layers[-1].output]
       self.build()
 
-  @checkpointable.no_automatic_dependency_tracking
   def build(self, input_shape=None):
-    if input_shape and not self.inputs:
-      batch_shape = tuple(input_shape)
+    self._set_inputs_and_outputs(input_shape=input_shape)
+
+  def symbolic_set_inputs(self, inputs):
+    self._set_inputs_and_outputs(tensor=inputs)
+
+  @checkpointable.no_automatic_dependency_tracking
+  def _set_inputs_and_outputs(self, input_shape=None, tensor=None):
+    """Set model's input and output specs based on the input received.
+
+    If `tensor` is provided, `input_shape` is not required.
+
+    Args:
+      input_shape: Optional shape of input.
+      tensor: Optional existing tensor to wrap into the `Input` layer.
+    """
+    if not self.inputs:
       dtype = K.floatx()
-      x = Input(
-          batch_shape=batch_shape, dtype=dtype, name=self.name + '_input')
+      if tensor is not None:
+        batch_shape = (None,) + tuple(tensor.get_shape().as_list()[1:])
+        x = Input(dtype=dtype, name=self.name + '_input', tensor=tensor)
+      elif input_shape is not None:
+        batch_shape = tuple(input_shape)
+        x = Input(
+            batch_shape=batch_shape, dtype=dtype, name=self.name + '_input')
       self.inputs = [x]
       for layer in self._layers:
         x = layer(x)
diff --git a/tensorflow/python/keras/engine/sequential_test.py b/tensorflow/python/keras/engine/sequential_test.py
index 0f54e29cee38bd12d691b03ae98d3e578b7ff907..4f4adca33344dddc6e9c92cda94fff7289b35302 100644
--- a/tensorflow/python/keras/engine/sequential_test.py
+++ b/tensorflow/python/keras/engine/sequential_test.py
@@ -22,7 +22,6 @@ import numpy as np
 
 from tensorflow.python import keras
 from tensorflow.python.data.ops import dataset_ops
-from tensorflow.python.eager import context
 from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import test
@@ -104,9 +103,6 @@ class TestSequential(test.TestCase):
 
   @tf_test_util.run_in_graph_and_eager_modes
   def test_sequential_deferred_build_with_dataset_iterators(self):
-    if not context.executing_eagerly():
-      # TODO(psv/fchollet): Add support for this use case in graph mode.
-      return
     num_hidden = 5
     input_dim = 3
     num_classes = 2
@@ -136,6 +132,48 @@ class TestSequential(test.TestCase):
                      [None, num_classes])
     self.assertEqual(len(model.weights), 2 * 2)
 
+  def test_training_and_eval_methods_on_symbolic_tensors(self):
+    with self.test_session():
+
+      def create_model():
+        model = keras.Sequential()
+        model.add(keras.layers.Dense(10, activation='relu'))
+        model.add(keras.layers.Dense(4, activation='softmax'))
+
+        model.compile(
+            optimizer=rmsprop.RMSPropOptimizer(1e-3),
+            loss='categorical_crossentropy',
+            metrics=['accuracy'])
+        return model
+
+      inputs = keras.backend.zeros(shape=(10, 3))
+      targets = keras.backend.zeros(shape=(10, 4))
+
+      model = create_model()
+      model.fit(inputs, targets, epochs=10, steps_per_epoch=30)
+
+      model = create_model()
+      model.evaluate(inputs, targets, steps=2, verbose=0)
+
+      model = create_model()
+      model.predict(inputs, steps=2)
+
+      model = create_model()
+      model.train_on_batch(inputs, targets)
+
+      model = create_model()
+      model.test_on_batch(inputs, targets)
+
+      model = create_model()
+      model.fit(
+          inputs,
+          targets,
+          epochs=1,
+          steps_per_epoch=2,
+          verbose=0,
+          validation_data=(inputs, targets),
+          validation_steps=2)
+
   @tf_test_util.run_in_graph_and_eager_modes
   def test_invalid_use_cases(self):
     # Added objects must be layer instances
diff --git a/tensorflow/python/keras/engine/topology_test.py b/tensorflow/python/keras/engine/topology_test.py
index 3eb69bd7f3d42f5cd8d6cc6d2d32cc9eb808d9a4..34f74db6ef17b2ec72c6cfe0da9a8efb28f25c38 100644
--- a/tensorflow/python/keras/engine/topology_test.py
+++ b/tensorflow/python/keras/engine/topology_test.py
@@ -110,7 +110,6 @@ class TopologyConstructionTest(test.TestCase):
     layer = keras.layers.BatchNormalization()
     _ = layer.apply(x1)
 
-    print('BN updates', layer._updates)
     self.assertEqual(len(layer.updates), 2)
     self.assertEqual(len(layer.get_updates_for(x1)), 2)
     self.assertEqual(len(layer.get_updates_for(None)), 0)
@@ -960,9 +959,6 @@ class DeferredModeTest(test.TestCase):
       def call(self, inputs):
         return inputs[0] + inputs[1]
 
-      def compute_output_shape(self, input_shape):
-        return input_shape[0]
-
     c = AddLayer()([a, input_b])  # pylint: disable=not-callable
     c = keras.layers.Dense(2)(c)
 
@@ -978,6 +974,101 @@ class DeferredModeTest(test.TestCase):
       self.assertEqual(outputs[1].shape.as_list(), [10, 2])
 
 
+class DefaultShapeInferenceBehaviorTest(test.TestCase):
+
+  def _testShapeInference(self, model, input_shape, expected_output_shape):
+    input_value = np.random.random(input_shape)
+    output_value = model.predict(input_value)
+    self.assertEqual(output_value.shape, expected_output_shape)
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testSingleInputCase(self):
+
+    class LayerWithOneInput(keras.layers.Layer):
+
+      def build(self, input_shape):
+        self.w = array_ops.ones(shape=(3, 4))
+
+      def call(self, inputs):
+        return keras.backend.dot(inputs, self.w)
+
+    inputs = input_layer_lib.Input(shape=(3,))
+    layer = LayerWithOneInput()
+
+    if context.executing_eagerly():
+      self.assertEqual(
+          layer.compute_output_shape((None, 3)).as_list(), [None, 4])
+      # As a side-effect, compute_output_shape builds the layer.
+      self.assertTrue(layer.built)
+      # We can still query the layer's compute_output_shape with compatible
+      # input shapes.
+      self.assertEqual(
+          layer.compute_output_shape((6, 3)).as_list(), [6, 4])
+
+    outputs = layer(inputs)
+    model = keras.Model(inputs, outputs)
+    self._testShapeInference(model, (2, 3), (2, 4))
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testMultiInputOutputCase(self):
+
+    class MultiInputOutputLayer(keras.layers.Layer):
+
+      def build(self, input_shape):
+        self.w = array_ops.ones(shape=(3, 4))
+
+      def call(self, inputs):
+        a = keras.backend.dot(inputs[0], self.w)
+        b = a + inputs[1]
+        return [a, b]
+
+    input_a = input_layer_lib.Input(shape=(3,))
+    input_b = input_layer_lib.Input(shape=(4,))
+    output_a, output_b = MultiInputOutputLayer()([input_a, input_b])
+    model = keras.Model([input_a, input_b], [output_a, output_b])
+    output_a_val, output_b_val = model.predict(
+        [np.random.random((2, 3)), np.random.random((2, 4))])
+    self.assertEqual(output_a_val.shape, (2, 4))
+    self.assertEqual(output_b_val.shape, (2, 4))
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testTrainingArgument(self):
+
+    class LayerWithTrainingArg(keras.layers.Layer):
+
+      def build(self, input_shape):
+        self.w = array_ops.ones(shape=(3, 4))
+
+      def call(self, inputs, training):
+        return keras.backend.dot(inputs, self.w)
+
+    inputs = input_layer_lib.Input(shape=(3,))
+    outputs = LayerWithTrainingArg()(inputs, training=False)
+    model = keras.Model(inputs, outputs)
+    self._testShapeInference(model, (2, 3), (2, 4))
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testUnsupportedSignature(self):
+
+    class LayerWithAdditionalArg(keras.layers.Layer):
+
+      def build(self, input_shape):
+        self.w = array_ops.ones(shape=(3, 4))
+
+      def call(self, inputs, some_arg):
+        return keras.backend.dot(inputs, self.w) + some_arg
+
+    inputs = input_layer_lib.Input(shape=(3,))
+    if context.executing_eagerly():
+      with self.assertRaises(NotImplementedError):
+        outputs = LayerWithAdditionalArg()(inputs, some_arg=0)
+    else:
+      # Works with graph mode because the graph of ops is built together with
+      # the graph of layers.
+      outputs = LayerWithAdditionalArg()(inputs, some_arg=0)
+      _ = keras.Model(inputs, outputs)
+
+
 class GraphUtilsTest(test.TestCase):
 
   def testGetReachableFromInputs(self):
diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py
index 8e632651fa7553fbc7ce31aa42e9963b606d20f9..0fe14e99e068339c5ae9a11fe0fbbba3f205a043 100644
--- a/tensorflow/python/keras/engine/training.py
+++ b/tensorflow/python/keras/engine/training.py
@@ -24,13 +24,11 @@ import numpy as np
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.data.ops import iterator_ops
 from tensorflow.python.eager import context
-from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras import backend as K
 from tensorflow.python.keras import losses
-from tensorflow.python.keras import metrics as metrics_module
 from tensorflow.python.keras import optimizers
 from tensorflow.python.keras.engine import base_layer
 from tensorflow.python.keras.engine import training_arrays
@@ -39,7 +37,6 @@ from tensorflow.python.keras.engine import training_generator
 from tensorflow.python.keras.engine import training_utils
 from tensorflow.python.keras.engine.network import Network
 from tensorflow.python.keras.utils.generic_utils import slice_arrays
-from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import optimizer as tf_optimizer_module
 from tensorflow.python.training.checkpointable import base as checkpointable
@@ -217,10 +214,9 @@ class Model(Network):
       for name in self.output_names:
         if name not in loss:
           logging.warning(
-              'Output "' + name + '" missing from loss dictionary. '
-              'We assume this was done on purpose, '
-              'and we will not be expecting '
-              'any data to be passed to "' + name + '" during training.')
+              'Output "' + name + '" missing from loss dictionary. We assume '
+              'this was done on purpose. The fit and evaluate APIs will not be '
+              'expecting any data to be passed to "' + name + '".')
         loss_functions.append(losses.get(loss.get(name)))
     elif isinstance(loss, list):
       if len(loss) != len(self.outputs):
@@ -373,21 +369,14 @@ class Model(Network):
               'sample_weight_mode dictionary: "' + name + '". '
               'Only expected the following keys: ' + str(self.output_names))
       for i, name in enumerate(self.output_names):
-        if i in skip_target_weighing_indices:
-          weight = None
-          sample_weight_modes.append(None)
-        else:
-          if name not in sample_weight_mode:
-            raise ValueError(
-                'Output "' + name + '" missing from sample_weight_modes '
-                'dictionary')
-          if sample_weight_mode.get(name) == 'temporal':
-            weight = K.placeholder(ndim=2, name=name + '_sample_weights')
-            sample_weight_modes.append('temporal')
-          else:
-            weight = K.placeholder(ndim=1, name=name + 'sample_weights')
-            sample_weight_modes.append(None)
+        if (i not in skip_target_weighing_indices and
+            name not in sample_weight_mode):
+          raise ValueError('Output "' + name +
+                           '" missing from sample_weight_modes dictionary')
+        weight, mode = training_utils.get_output_sample_weight_and_mode(
+            skip_target_weighing_indices, sample_weight_mode.get(name), name, i)
         sample_weights.append(weight)
+        sample_weight_modes.append(mode)
     elif isinstance(sample_weight_mode, list):
       if len(sample_weight_mode) != len(self.outputs):
         raise ValueError('When passing a list as sample_weight_mode, '
@@ -395,36 +384,17 @@ class Model(Network):
                          'The model has ' + str(len(self.outputs)) +
                          ' outputs, but you passed '
                          'sample_weight_mode=' + str(sample_weight_mode))
-      for i in range(len(self.output_names)):
-        if i in skip_target_weighing_indices:
-          weight = None
-          sample_weight_modes.append(None)
-        else:
-          mode = sample_weight_mode[i]
-          name = self.output_names[i]
-          if mode == 'temporal':
-            weight = K.placeholder(ndim=2, name=name + '_sample_weights')
-            sample_weight_modes.append('temporal')
-          else:
-            weight = K.placeholder(ndim=1, name=name + '_sample_weights')
-            sample_weight_modes.append(None)
+      for i, name in enumerate(self.output_names):
+        weight, mode = training_utils.get_output_sample_weight_and_mode(
+            skip_target_weighing_indices, sample_weight_mode[i], name, i)
         sample_weights.append(weight)
+        sample_weight_modes.append(mode)
     else:
       for i, name in enumerate(self.output_names):
-        if i in skip_target_weighing_indices:
-          sample_weight_modes.append(None)
-          sample_weights.append(None)
-        else:
-          if sample_weight_mode == 'temporal':
-            sample_weights.append(array_ops.placeholder_with_default(
-                constant_op.constant([[1.]], dtype=K.floatx()),
-                shape=[None, None], name=name + '_sample_weights'))
-            sample_weight_modes.append('temporal')
-          else:
-            sample_weights.append(array_ops.placeholder_with_default(
-                constant_op.constant([1.], dtype=K.floatx()),
-                shape=[None], name=name + '_sample_weights'))
-            sample_weight_modes.append(None)
+        weight, mode = training_utils.get_output_sample_weight_and_mode(
+            skip_target_weighing_indices, sample_weight_mode, name, i)
+        sample_weights.append(weight)
+        sample_weight_modes.append(mode)
     self.sample_weight_modes = sample_weight_modes
     self._feed_sample_weight_modes = []
     for i in range(len(self.outputs)):
@@ -487,43 +457,21 @@ class Model(Network):
         weights = sample_weights[i]
         output_metrics = nested_metrics[i]
         output_weighted_metrics = nested_weighted_metrics[i]
+        output_shape = self.outputs[i].get_shape().as_list()
+        loss_fn = self.loss_functions[i]
 
-        def handle_metrics(metrics, weights=None):
+        def handle_metrics(metrics, output_shape, loss_fn, weights=None):
+          """Invokes metric functions for the output."""
 
           for metric in metrics:
-            if metric in ('accuracy', 'acc', 'crossentropy', 'ce'):
-              # custom handling of accuracy/crossentropy
-              # (because of class mode duality)
-              output_shape = self.outputs[i].get_shape().as_list()
-              if (output_shape[-1] == 1 or
-                  self.loss_functions[i] == losses.binary_crossentropy):
-                # case: binary accuracy/crossentropy
-                if metric in ('accuracy', 'acc'):
-                  metric_fn = metrics_module.binary_accuracy
-                elif metric in ('crossentropy', 'ce'):
-                  metric_fn = metrics_module.binary_crossentropy
-              elif self.loss_functions[
-                  i] == losses.sparse_categorical_crossentropy:
-                # case: categorical accuracy/crossentropy with sparse targets
-                if metric in ('accuracy', 'acc'):
-                  metric_fn = metrics_module.sparse_categorical_accuracy
-                elif metric in ('crossentropy', 'ce'):
-                  metric_fn = metrics_module.sparse_categorical_crossentropy
-              else:
-                # case: categorical accuracy/crossentropy
-                if metric in ('accuracy', 'acc'):
-                  metric_fn = metrics_module.categorical_accuracy
-                elif metric in ('crossentropy', 'ce'):
-                  metric_fn = metrics_module.categorical_crossentropy
-              weighted_metric_fn = training_utils.weighted_masked_objective(
-                  metric_fn)
-            else:
-              metric_fn = metrics_module.get(metric)
-              weighted_metric_fn = training_utils.weighted_masked_objective(
-                  metric_fn)
-            metric_name = training_utils.get_base_metric_name(
+            metric_fn = training_utils.get_metric_function(
+                metric, output_shape=output_shape, loss_fn=loss_fn)
+            metric_name = training_utils.get_metric_name(
                 metric, weighted=weights is not None)
+
             with K.name_scope(metric_name):
+              weighted_metric_fn = training_utils.weighted_masked_objective(
+                  metric_fn)
               metric_result = weighted_metric_fn(
                   y_true, y_pred, weights=weights, mask=masks[i])
 
@@ -537,8 +485,9 @@ class Model(Network):
               self.stateful_metric_functions.append(metric_fn)
               self.metrics_updates += metric_fn.updates
 
-        handle_metrics(output_metrics)
-        handle_metrics(output_weighted_metrics, weights=weights)
+        handle_metrics(output_metrics, output_shape, loss_fn)
+        handle_metrics(
+            output_weighted_metrics, output_shape, loss_fn, weights=weights)
 
     # Prepare gradient updates and state updates.
     self.total_loss = total_loss
@@ -599,7 +548,7 @@ class Model(Network):
         # Unconditional updates
         updates += self.get_updates_for(None)
         # Conditional updates relevant to this model
-        updates += self.get_updates_for(self._feed_inputs)
+        updates += self.get_updates_for(self.inputs)
         # Stateful metrics updates
         updates += self.metrics_updates
         # Gets loss and metrics. Updates weights at each call.
@@ -608,7 +557,6 @@ class Model(Network):
             updates=updates,
             name='train_function',
             **self._function_kwargs)
-    self._post_build_cleanup()
 
   def _make_test_function(self):
     if not hasattr(self, 'test_function'):
@@ -626,7 +574,6 @@ class Model(Network):
           updates=self.state_updates + self.metrics_updates,
           name='test_function',
           **self._function_kwargs)
-    self._post_build_cleanup()
 
   def _make_predict_function(self):
     if not hasattr(self, 'predict_function'):
@@ -645,7 +592,6 @@ class Model(Network):
           updates=self.state_updates,
           name='predict_function',
           **kwargs)
-    self._post_build_cleanup()
 
   def _get_iterator_get_next_tensors(self, iterator):
     get_next_op = self._iterator_get_next.get(iterator, None)
@@ -897,7 +843,11 @@ class Model(Network):
         for output_shape, loss_fn in zip(self._feed_output_shapes,
                                          self._feed_loss_fns):
           if loss_fn is losses.sparse_categorical_crossentropy:
-            feed_output_shapes.append(output_shape[:-1] + (1,))
+            if K.image_data_format() == 'channels_first':
+              feed_output_shapes.append(
+                  (output_shape[0], 1) + output_shape[2:])
+            else:
+              feed_output_shapes.append(output_shape[:-1] + (1,))
           elif (not hasattr(loss_fn, '__name__') or
                 getattr(losses, loss_fn.__name__, None) is None):
             # If `loss_fn` is not a function (e.g. callable class)
@@ -988,10 +938,14 @@ class Model(Network):
         inputs = inputs[0]
 
       if tensor_util.is_tensor(inputs):
-        input_shape = (None,) + tuple(inputs.get_shape().as_list()[1:])
+        if context.executing_eagerly():
+          input_shape = (None,) + tuple(inputs.get_shape().as_list()[1:])
+          self.build(input_shape=input_shape)
+        else:
+          self.symbolic_set_inputs(inputs)
       else:
         input_shape = (None,) + inputs.shape[1:]
-      self.build(input_shape=input_shape)
+        self.build(input_shape=input_shape)
     elif context.executing_eagerly():
       self._eager_set_inputs(inputs)
     else:
diff --git a/tensorflow/python/keras/engine/training_arrays.py b/tensorflow/python/keras/engine/training_arrays.py
index e82f5c03320094348213ac3d22cc13709c6af08c..6572e2c344abd0234fe6b5d2437428bf2999d37f 100644
--- a/tensorflow/python/keras/engine/training_arrays.py
+++ b/tensorflow/python/keras/engine/training_arrays.py
@@ -50,7 +50,6 @@ def fit_loop(model,
              val_targets=None,
              val_sample_weights=None,
              shuffle=True,
-             callback_metrics=None,
              initial_epoch=0,
              steps_per_epoch=None,
              validation_steps=None):
@@ -69,8 +68,6 @@ def fit_loop(model,
       val_targets: List of target arrays.
       val_sample_weights: Optional list of sample weight arrays.
       shuffle: Whether to shuffle the data at the beginning of each epoch
-      callback_metrics: List of strings, the display names of the metrics
-          passed to the callbacks. They should be the
           concatenation of list the display names of the outputs of
            `f` and the list of display names of the outputs of `f_val`.
       initial_epoch: Epoch at which to start training
@@ -121,15 +118,11 @@ def fit_loop(model,
 
   out_labels = model.metrics_names
   if do_validation:
-    callback_metrics = copy.copy(out_labels) + [
-        'val_' + n for n in out_labels
-    ]
-    if callbacks is not None and any(
-        [isinstance(callback, cbks.TensorBoard) for callback in callbacks]):
-      # need to create the test_function before start of the first epoch
-      # because TensorBoard callback on_epoch_begin adds summary to the
-      # list of fetches of the test_function
-      model._make_test_function()
+    callback_metrics = copy.copy(out_labels) + ['val_' + n for n in out_labels]
+    # need to create the test_function before start of the first epoch
+    # because TensorBoard callback on_epoch_begin adds summary to the
+    # list of fetches of the test_function
+    model._make_test_function()
   else:
     callback_metrics = copy.copy(out_labels)
 
@@ -162,7 +155,7 @@ def fit_loop(model,
 
   callbacks.set_model(callback_model)
 
-  callbacks.set_params({
+  callback_params = {
       'batch_size': batch_size,
       'epochs': epochs,
       'steps': steps_per_epoch,
@@ -170,11 +163,17 @@ def fit_loop(model,
       'verbose': verbose,
       'do_validation': do_validation,
       'metrics': callback_metrics or [],
-  })
-  callbacks.on_train_begin()
-  callback_model.stop_training = False
+  }
+  if validation_steps:
+    callback_params.update({'validation_steps': validation_steps})
+  callbacks.set_params(callback_params)
+
   for cbk in callbacks:
     cbk.validation_data = val_ins
+  # validation_data must be set before on_train_begin() is called
+  # so that TensorboardCallback can validate its input
+  callbacks.on_train_begin()
+  callback_model.stop_training = False
 
   # To prevent a slowdown, we find beforehand the arrays that need conversion.
   feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights
@@ -193,9 +192,7 @@ def fit_loop(model,
     if steps_per_epoch is not None:
       # Step-wise fit loop.
       for step_index in range(steps_per_epoch):
-        batch_logs = {}
-        batch_logs['batch'] = step_index
-        batch_logs['size'] = 1
+        batch_logs = {'batch': step_index, 'size': 1}
         callbacks.on_batch_begin(step_index, batch_logs)
         try:
           outs = f(ins)
@@ -384,7 +381,9 @@ def predict_loop(model, inputs, batch_size=32, verbose=0, steps=None):
     return outs
 
 
-def test_loop(model, inputs, targets,
+def test_loop(model,
+              inputs,
+              targets,
               sample_weights=None,
               batch_size=None,
               verbose=0,
@@ -481,8 +480,7 @@ def test_loop(model, inputs, targets,
 
       if isinstance(batch_outs, list):
         if batch_index == 0:
-          for batch_out in enumerate(batch_outs):
-            outs.append(0.)
+          outs.extend([0.] * len(batch_outs))
         for i, batch_out in enumerate(batch_outs):
           if i in stateful_metric_indices:
             outs[i] = batch_out
diff --git a/tensorflow/python/keras/engine/training_eager.py b/tensorflow/python/keras/engine/training_eager.py
index e8838cd3bca7b3afba80504f9e705943474423c5..0b25b827ad4381daee4a2992cd83406de8d22708 100644
--- a/tensorflow/python/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/engine/training_eager.py
@@ -30,36 +30,11 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras import backend
 from tensorflow.python.keras import callbacks as cbks
-from tensorflow.python.keras import losses
-from tensorflow.python.keras import metrics as metrics_module
 from tensorflow.python.keras.engine import training_utils
 from tensorflow.python.keras.utils import generic_utils
-from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import tf_logging as logging
 
 
-def _get_metrics_info(metric, internal_output_shapes=None, loss_func=None):
-  if metric == 'accuracy' or metric == 'acc':
-    # custom handling of accuracy
-    # (because of class mode duality)
-    output_shape = internal_output_shapes
-    if output_shape[-1] == 1 or loss_func == losses.binary_crossentropy:
-      # case: binary accuracy
-      acc_fn = metrics_module.binary_accuracy
-    elif loss_func == losses.sparse_categorical_crossentropy:
-      # case: categorical accuracy with sparse targets
-      acc_fn = metrics_module.sparse_categorical_accuracy
-    else:
-      acc_fn = metrics_module.categorical_accuracy
-
-    metric_name = 'acc'
-    return metric_name, acc_fn
-  else:
-    metric_fn = metrics_module.get(metric)
-    metric_name = metric_fn.__name__
-    return metric_name, metric_fn
-
-
 def _eager_loss_fn(outputs, targets, loss_fn, output_name):
   with backend.name_scope(output_name + '_loss'):
     loss = loss_fn(targets, outputs)
@@ -75,9 +50,8 @@ def _eager_metrics_fn(model, outputs, targets):
       targets: The predictions or targets of the given model.
 
   Returns:
-      Returns the metric names and metric results for each output of the model.
+      Returns the metric results for each output of the model.
   """
-  metric_names = []
   metric_results = []
   if not isinstance(outputs, list):
     outputs = [outputs]
@@ -88,18 +62,15 @@ def _eager_metrics_fn(model, outputs, targets):
   for i in range(len(model.outputs)):
     output_metrics = model.nested_metrics[i]
     for nested_output_metric in output_metrics:
-      metric_name, metric_fn = _get_metrics_info(
+      metric_fn = training_utils.get_metric_function(
           nested_output_metric, backend.int_shape(model.outputs[i]),
           model.loss_functions[i])
-
-      if len(model.output_names) > 1:
-        metric_name = model.output_names[i] + '_' + metric_name
-        if metric_name not in model.metrics_names:
-          model.metrics_names.append(metric_name)
+      # weighted metrics are not supported in eager mode
+      metric_name = training_utils.get_metric_name(
+          nested_output_metric, weighted=False)
 
       with backend.name_scope(metric_name):
         metric_result = metric_fn(targets[i], outputs[i])
-        metric_names.append(metric_name)
         metric_results.append(backend.mean(metric_result))
 
   return metric_results
@@ -194,7 +165,8 @@ def iterator_fit_loop(model,
                       callbacks=None,
                       callback_metrics=None,
                       validation_steps=None,
-                      do_validation=False):
+                      do_validation=False,
+                      batch_size=None):
   """Fit function for eager execution when input is given as dataset iterator.
 
   Updates the given epoch logs.
@@ -224,16 +196,23 @@ def iterator_fit_loop(model,
       validation_steps: Number of steps to run validation for (only if doing
         validation from data tensors). Ignored with default value of `None`.
       do_validation: Boolean value indicating whether we should do validation.
+      batch_size: int, val_inputs and val_targets will be evaled batch by
+        batch with size batch_size if they are array.
 
   Raises:
       ValueError: In case of mismatch between given number of inputs and
         expectations of the model.
   """
   assert isinstance(inputs, iterator_ops.EagerIterator)
+
+  # make sure either x,y or x,y,sample_weights is provided
+  if (not isinstance(inputs.output_shapes, (list, tuple)) or
+      len(inputs.output_shapes) not in (2, 3)):
+    raise ValueError('Please provide either inputs and targets'
+                     'or inputs, targets, and sample_weights')
+
   for step_index in range(steps_per_epoch):
-    batch_logs = {}
-    batch_logs['batch'] = step_index
-    batch_logs['size'] = 1
+    batch_logs = {'batch': step_index, 'size': 1}
     callbacks.on_batch_begin(step_index, batch_logs)
 
     # Get data from the iterator.
@@ -247,19 +226,21 @@ def iterator_fit_loop(model,
           'batches (in this case, %d batches).' % steps_per_epoch * epochs)
       break
 
-    if not isinstance(next_element, (list, tuple)) or len(next_element) != 2:
-      raise ValueError('Please provide data as a list or tuple of 2 elements '
-                       ' - input and target pair. Received %s' % next_element)
-    x, y = next_element
+    if len(inputs.output_shapes) == 2:
+      x, y = next_element
+      sample_weights = None
+    else:
+      x, y, sample_weights = next_element
 
     # Validate and standardize data.
     x, y, sample_weights = model._standardize_user_data(
-        x, y, class_weight=class_weight)
+        x, y, sample_weight=sample_weights, class_weight=class_weight)
     x = training_utils.cast_if_floating_dtype(x)
     y = training_utils.cast_if_floating_dtype(y)
     if sample_weights:
       sample_weights = [
-          ops.convert_to_tensor(val, dtype=backend.floatx())
+          training_utils.cast_if_floating_dtype(
+              ops.convert_to_tensor(val, dtype=backend.floatx()))
           if val is not None else None for val in sample_weights
       ]
 
@@ -307,122 +288,8 @@ def iterator_fit_loop(model,
             val_targets,
             sample_weights=val_sample_weights,
             steps=validation_steps,
-            verbose=0)
-        if not isinstance(val_outs, list):
-          val_outs = [val_outs]
-        # Same labels assumed.
-        for l, o in zip(out_labels, val_outs):
-          epoch_logs['val_' + l] = o
-
-
-def batch_fit_loop(model,
-                   inputs,
-                   targets,
-                   epoch_logs,
-                   index_array,
-                   out_labels,
-                   callback_model,
-                   batch_size,
-                   sample_weights=None,
-                   val_inputs=None,
-                   val_targets=None,
-                   val_sample_weights=None,
-                   callbacks=None,
-                   shuffle=True,
-                   num_train_samples=None,
-                   do_validation=False):
-  """Fit function for eager execution when input is given as arrays or tensors.
-
-  Updates the given epoch logs.
-
-  Arguments:
-      model: Instance of the `Model`.
-      inputs: List of input arrays.
-      targets: List of target arrays.
-      epoch_logs: Dictionary of logs from every epoch.
-      index_array: Index array generated from number of training samples.
-      out_labels: Output labels generated from model metric names.
-      callback_model: Instance of `Model` to callback.
-      batch_size: Integer batch size or None if unknown.
-      sample_weights: Optional list of sample weight arrays.
-      val_inputs: Input data for validation.
-      val_targets: Target data for validation.
-      val_sample_weights: Sample weight data for validation.
-      callbacks: List of callbacks to be called during training.
-      shuffle: Whether to shuffle the data at the beginning of each epoch.
-      num_train_samples: Integer number of training samples.
-      do_validation: Boolean value indicating whether we should do validation.
-  """
-  # TODO(psv): Create a dataset iterator instead of manually creating batches
-  # here and in batch_test_loop, batch_predict_loop.
-  if shuffle == 'batch':
-    index_array = model._batch_shuffle(index_array, batch_size)
-  elif shuffle:
-    np.random.shuffle(index_array)
-
-  batches = generic_utils.make_batches(num_train_samples, batch_size)
-
-  for batch_index, (batch_start, batch_end) in enumerate(batches):
-    batch_ids = index_array[batch_start:batch_end]
-    inputs_batch = slice_arrays(inputs, batch_ids, contiguous=not shuffle)
-    targets_batch = slice_arrays(targets, batch_ids, contiguous=not shuffle)
-    if sample_weights:
-      sample_weights_batch = slice_arrays(
-          sample_weights, batch_ids, contiguous=not shuffle)
-    else:
-      sample_weights_batch = None
-    batch_logs = {}
-    batch_logs['batch'] = batch_index
-    batch_logs['size'] = len(batch_ids)
-
-    callbacks.on_batch_begin(batch_index, batch_logs)
-
-    inputs_batch = [
-        ops.convert_to_tensor(val, dtype=backend.floatx())
-        for val in inputs_batch
-    ]
-    targets_batch = [
-        ops.convert_to_tensor(val, dtype=backend.floatx())
-        for val in targets_batch
-    ]
-    if sample_weights:
-      sample_weights_batch = [
-          ops.convert_to_tensor(val, dtype=backend.floatx())
-          if val is not None else None for val in sample_weights_batch
-      ]
-
-    outs, loss, loss_metrics = _process_single_batch(
-        model,
-        inputs_batch,
-        targets_batch,
-        sample_weights=sample_weights_batch,
-        training=True)
-
-    if not isinstance(outs, list):
-      outs = [outs]
-
-    for l, o in zip(out_labels, outs):
-      batch_logs[l] = o
-    # Required for eager execution
-    metrics_results = _eager_metrics_fn(model, outs, targets_batch)
-    batch_logs['loss'] = tensor_util.constant_value(backend.mean(loss))
-
-    for k, v in zip(model.metrics_names,
-                    [backend.mean(loss)] + loss_metrics + metrics_results):
-      batch_logs[k] = tensor_util.constant_value(v)
-    callbacks.on_batch_end(batch_index, batch_logs)
-    if callback_model.stop_training:
-      break
-
-    if batch_index == len(batches) - 1:  # Last batch.
-      if do_validation:
-        val_outs = test_loop(
-            model,
-            val_inputs,
-            val_targets,
-            sample_weights=val_sample_weights,
-            batch_size=batch_size,
-            verbose=0)
+            verbose=0,
+            batch_size=batch_size)
         if not isinstance(val_outs, list):
           val_outs = [val_outs]
         # Same labels assumed.
@@ -451,6 +318,11 @@ def iterator_test_loop(model, inputs, steps, verbose=0):
         expectations of the model.
   """
   assert isinstance(inputs, iterator_ops.EagerIterator)
+  # make sure either x,y or x,y,sample_weights is provided
+  if (not isinstance(inputs.output_shapes, (list, tuple)) or
+      len(inputs.output_shapes) < 2 or len(inputs.output_shapes) > 3):
+    raise ValueError('Please provide either inputs and targets'
+                     'or inputs, targets, and sample_weights')
   outs = []
   num_samples = 0
   if verbose == 1:
@@ -466,10 +338,11 @@ def iterator_test_loop(model, inputs, steps, verbose=0):
           '(in this case, %d batches).', steps)
       break
 
-    if not isinstance(next_element, (list, tuple)) or len(next_element) != 2:
-      raise ValueError('Please provide data as a list or tuple of 2 elements '
-                       ' - input and target pair. Received %s' % next_element)
-    x, y = next_element
+    if len(inputs.output_shapes) == 2:
+      x, y = next_element
+      sample_weights = None
+    else:
+      x, y, sample_weights = next_element
 
     # Validate and standardize data.
     x, y, sample_weights = model._standardize_user_data(x, y)
@@ -512,94 +385,6 @@ def iterator_test_loop(model, inputs, steps, verbose=0):
   return outs
 
 
-def batch_test_loop(model,
-                    inputs,
-                    targets,
-                    batch_size,
-                    sample_weights=None,
-                    verbose=0):
-  """Test function for eager execution when input is given as arrays or tensors.
-
-  Arguments:
-      model: Model instance that is being evaluated in Eager mode.
-      inputs: List of input arrays.
-      targets: List of target arrays.
-      batch_size: Integer batch size.
-      sample_weights: Optional list of sample weight arrays.
-      verbose: Verbosity mode.
-
-  Returns:
-      Scalar loss (if the model has a single output and no metrics)
-      or list of scalars (if the model has multiple outputs
-      and/or metrics). The attribute `model.metrics_names` will give you
-      the display labels for the scalar outputs.
-  """
-  outs = []
-  feed_data = inputs + targets
-  if sample_weights:
-    feed_data += sample_weights
-  num_samples = training_utils.check_num_samples(
-      feed_data, batch_size=batch_size)
-  if verbose == 1:
-    progbar = generic_utils.Progbar(target=num_samples)
-  batches = generic_utils.make_batches(num_samples, batch_size)
-  index_array = np.arange(num_samples)
-  for batch_index, (batch_start, batch_end) in enumerate(batches):
-    batch_ids = index_array[batch_start:batch_end]
-    inputs_batch = slice_arrays(inputs, batch_ids)
-    targets_batch = slice_arrays(targets, batch_ids)
-    if sample_weights:
-      sample_weights_batch = slice_arrays(sample_weights, batch_ids)
-    else:
-      sample_weights_batch = None
-
-    inputs_batch = [
-        ops.convert_to_tensor(val, dtype=backend.floatx())
-        for val in inputs_batch
-    ]
-    targets_batch = [
-        ops.convert_to_tensor(val, dtype=backend.floatx())
-        for val in targets_batch
-    ]
-    if sample_weights:
-      sample_weights_batch = [
-          ops.convert_to_tensor(val, dtype=backend.floatx())
-          if val is not None else None for val in sample_weights_batch
-      ]
-
-    loss_outs, loss, loss_metrics = _model_loss(
-        model,
-        inputs_batch,
-        targets_batch,
-        sample_weights=sample_weights_batch,
-        training=False)
-    metrics_results = _eager_metrics_fn(model, loss_outs, targets_batch)
-    batch_outs = []
-    for _, v in zip(model.metrics_names,
-                    [backend.mean(loss)] + loss_metrics + metrics_results):
-      batch_outs.append(tensor_util.constant_value(v))
-
-    if isinstance(batch_outs, list):
-      if batch_index == 0:
-        for _ in enumerate(batch_outs):
-          outs.append(0.)
-      for i, batch_out in enumerate(batch_outs):
-        outs[i] += batch_out * len(batch_ids)
-    else:
-      if batch_index == 0:
-        outs.append(0.)
-      outs[0] += batch_outs * len(batch_ids)
-
-    if verbose == 1:
-      progbar.update(batch_end)
-
-  for i in range(len(outs)):
-    outs[i] /= num_samples
-  if len(outs) == 1:
-    return outs[0]
-  return outs
-
-
 def iterator_predict_loop(model, inputs, steps, verbose=0):
   """Predict function for eager execution when input is dataset iterator.
 
@@ -619,6 +404,12 @@ def iterator_predict_loop(model, inputs, steps, verbose=0):
         expectations of the model.
   """
   assert isinstance(inputs, iterator_ops.EagerIterator)
+  if not isinstance(inputs.output_shapes,
+                    (list, tuple)) or len(inputs.output_shapes) > 2:
+    raise ValueError(
+        'Please provide data as a list or tuple of 1 or 2 elements '
+        ' - input or input and target pair. Received %s. We do not use the '
+        '`target` value here.' % inputs.output_shapes)
   outs = []
   if verbose == 1:
     progbar = generic_utils.Progbar(target=steps)
@@ -634,12 +425,8 @@ def iterator_predict_loop(model, inputs, steps, verbose=0):
           'batches (in this case, %d batches).', steps)
       break
 
-    if not isinstance(next_element, (list, tuple)) or len(next_element) != 2:
-      raise ValueError(
-          'Please provide data as a list or tuple of 2 elements '
-          ' - input and target pair. Received %s. We do not use the '
-          '`target` value here.' % next_element)
-    x, _ = next_element
+    # expects a tuple, where first element of tuple represents inputs
+    x = next_element[0]
 
     # Validate and standardize data.
     x, _, _ = model._standardize_user_data(x)
@@ -670,99 +457,6 @@ def iterator_predict_loop(model, inputs, steps, verbose=0):
   return outs
 
 
-def batch_predict_loop(model, inputs, batch_size, verbose=0):
-  """Predict function for eager execution when input is arrays or tensors.
-
-  Arguments:
-      model: Instance of `Model`.
-      inputs: List of input arrays.
-      batch_size: Integer batch size.
-      verbose: Verbosity mode.
-
-  Returns:
-      Array of predictions (if the model has a single output)
-      or list of arrays of predictions (if the model has multiple outputs).
-  """
-  outs = []
-  num_samples = training_utils.check_num_samples(inputs, batch_size)
-  if verbose == 1:
-    progbar = generic_utils.Progbar(target=num_samples)
-  batches = generic_utils.make_batches(num_samples, batch_size)
-  index_array = np.arange(num_samples)
-  for batch_index, (batch_start, batch_end) in enumerate(batches):
-    batch_ids = index_array[batch_start:batch_end]
-    inputs_batch = slice_arrays(inputs, batch_ids)
-
-    inputs_batch = [
-        ops.convert_to_tensor(val, dtype=backend.floatx())
-        for val in inputs_batch
-    ]
-
-    if len(inputs_batch) == 1:
-      if model._expects_training_arg:
-        batch_outs = model.call(inputs_batch[0], training=False)
-      else:
-        batch_outs = model.call(inputs_batch[0])
-    else:
-      if model._expects_training_arg:
-        batch_outs = model.call(inputs_batch, training=False)
-      else:
-        batch_outs = model.call(inputs_batch)
-
-    if not isinstance(batch_outs, list):
-      batch_outs = [batch_outs]
-    if batch_index == 0:
-      # Pre-allocate the results arrays.
-      for batch_out in batch_outs:
-        dims = batch_out.shape[1:].dims
-        dims_list = [d.value for d in dims]
-        shape = (num_samples,) + tuple(dims_list)
-        outs.append(np.zeros(shape, dtype=batch_out.dtype.as_numpy_dtype))
-    for i, batch_out in enumerate(batch_outs):
-      outs[i][batch_start:batch_end] = batch_out
-    if verbose == 1:
-      progbar.update(batch_end)
-
-  if len(outs) == 1:
-    return outs[0]
-  return outs
-
-
-def slice_arrays(arrays, indices, contiguous=True):
-  """Slices batches out of provided arrays (workaround for eager tensors).
-
-  Unfortunately eager tensors don't have the same slicing behavior as
-  Numpy arrays (they follow the same slicing behavior as symbolic TF tensors),
-  hence we cannot use `generic_utils.slice_arrays` directly
-  and we have to implement this workaround based on `concat`. This has a
-  performance cost.
-
-  Arguments:
-    arrays: Single array or list of arrays.
-    indices: List of indices in the array that should be included in the output
-      batch.
-    contiguous: Boolean flag indicating whether the indices are contiguous.
-
-  Returns:
-    Slice of data (either single array or list of arrays).
-  """
-  if any(tensor_util.is_tensor(x) for x in arrays):
-    converted_to_list = False
-    if not isinstance(arrays, list):
-      converted_to_list = True
-      arrays = [arrays]
-    if not contiguous:
-      entries = [[x[i:i + 1] for i in indices] for x in arrays]
-      slices = [array_ops.concat(x, axis=0) for x in entries]
-    else:
-      slices = [x[indices[0]:indices[-1] + 1] for x in arrays]
-    if converted_to_list:
-      slices = slices[0]
-    return slices
-  else:
-    return generic_utils.slice_arrays(arrays, indices)
-
-
 def _process_single_batch(model,
                           inputs,
                           targets,
@@ -895,7 +589,6 @@ def fit_loop(model,
              verbose=1,
              callbacks=None,
              shuffle=True,
-             callback_metrics=None,
              initial_epoch=0,
              steps_per_epoch=None,
              validation_steps=None):
@@ -917,10 +610,6 @@ def fit_loop(model,
       verbose: Verbosity mode, 0, 1 or 2
       callbacks: List of callbacks to be called during training
       shuffle: Whether to shuffle the data at the beginning of each epoch
-      callback_metrics: List of strings, the display names of the metrics
-          passed to the callbacks. They should be the
-          concatenation of list the display names of the outputs of
-           `f` and the list of display names of the outputs of `f_val`.
       initial_epoch: Epoch at which to start training
           (useful for resuming a previous training run)
       steps_per_epoch: Total number of steps (batches of samples)
@@ -935,19 +624,25 @@ def fit_loop(model,
   Raises:
     ValueError: In case of invalid argument values.
   """
+  # Convert training inputs to an EagerIterator
+  inputs, steps_per_epoch = training_utils.convert_to_iterator(
+      x=inputs,
+      y=targets,
+      sample_weights=sample_weights,
+      batch_size=batch_size,
+      steps_per_epoch=steps_per_epoch,
+      epochs=epochs,
+      shuffle=shuffle)
   # Required for eager execution
   with backend.learning_phase_scope(1):
     do_validation = False
     if val_inputs:
       do_validation = True
-      if (steps_per_epoch is None and verbose and inputs and
-          hasattr(inputs[0], 'shape') and hasattr(val_inputs[0], 'shape')):
-        print('Train on %d samples, validate on %d samples' %
-              (inputs[0].shape[0], val_inputs[0].shape[0]))
 
     num_train_samples = None
     out_labels = None
-    if steps_per_epoch is None or model._is_compiled:
+    callback_metrics = None
+    if model._is_compiled:
       out_labels = model.metrics_names
       if do_validation:
         callback_metrics = copy.copy(out_labels) + [
@@ -956,28 +651,10 @@ def fit_loop(model,
       else:
         callback_metrics = copy.copy(out_labels)
 
-    if steps_per_epoch is None:
-      if sample_weights:
-        feed_data = inputs + targets + sample_weights
-      else:
-        feed_data = inputs + targets
-      num_train_samples = training_utils.check_num_samples(
-          feed_data,
-          batch_size=batch_size,
-          steps=steps_per_epoch,
-          steps_name='steps_per_epoch')
-
-      if num_train_samples is not None:
-        index_array = np.arange(num_train_samples)
-
     model.history = cbks.History()
     callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history]
     if verbose:
-      if steps_per_epoch is not None:
-        count_mode = 'steps'
-      else:
-        count_mode = 'samples'
-      callbacks += [cbks.ProgbarLogger(count_mode)]
+      callbacks += [cbks.ProgbarLogger('steps')]
     callbacks = cbks.CallbackList(callbacks)
 
     # it's possible to callback a different model than self
@@ -989,7 +666,7 @@ def fit_loop(model,
 
     callbacks.set_model(callback_model)
 
-    callbacks.set_params({
+    callback_params = {
         'batch_size': batch_size,
         'epochs': epochs,
         'steps': steps_per_epoch,
@@ -997,9 +674,11 @@ def fit_loop(model,
         'verbose': verbose,
         'do_validation': do_validation,
         'metrics': callback_metrics or [],
-    })
-    callbacks.on_train_begin()
-    callback_model.stop_training = False
+    }
+    if validation_steps:
+      callback_params.update({'validation_steps': validation_steps})
+    callbacks.set_params(callback_params)
+
     for cbk in callbacks:
       if not val_inputs:
         cbk.validation_data = []
@@ -1009,47 +688,32 @@ def fit_loop(model,
         cbk.validation_data = val_inputs + val_targets + val_sample_weights
       else:
         cbk.validation_data = val_inputs + val_targets
+    # validation_data must be set before on_train_begin() is called
+    # so that TensorboardCallback can validate its input
+    callbacks.on_train_begin()
+    callback_model.stop_training = False
 
     for epoch in range(initial_epoch, epochs):
       callbacks.on_epoch_begin(epoch)
       epoch_logs = {}
-
-      if steps_per_epoch is not None:
-        iterator_fit_loop(
-            model,
-            inputs,
-            class_weight,
-            steps_per_epoch=steps_per_epoch,
-            callback_model=callback_model,
-            out_labels=out_labels,
-            epoch_logs=epoch_logs,
-            val_inputs=val_inputs,
-            val_targets=val_targets,
-            val_sample_weights=val_sample_weights,
-            epochs=epochs,
-            verbose=verbose,
-            callbacks=callbacks,
-            callback_metrics=callback_metrics,
-            validation_steps=validation_steps,
-            do_validation=do_validation)
-      else:
-        batch_fit_loop(
-            model,
-            inputs,
-            targets,
-            epoch_logs=epoch_logs,
-            index_array=index_array,
-            out_labels=out_labels,
-            callback_model=callback_model,
-            batch_size=batch_size,
-            sample_weights=sample_weights,
-            val_inputs=val_inputs,
-            val_targets=val_targets,
-            val_sample_weights=val_sample_weights,
-            callbacks=callbacks,
-            shuffle=shuffle,
-            num_train_samples=num_train_samples,
-            do_validation=do_validation)
+      iterator_fit_loop(
+          model,
+          inputs,
+          class_weight,
+          steps_per_epoch=steps_per_epoch,
+          callback_model=callback_model,
+          out_labels=out_labels,
+          epoch_logs=epoch_logs,
+          val_inputs=val_inputs,
+          val_targets=val_targets,
+          val_sample_weights=val_sample_weights,
+          epochs=epochs,
+          verbose=verbose,
+          callbacks=callbacks,
+          callback_metrics=callback_metrics,
+          validation_steps=validation_steps,
+          do_validation=do_validation,
+          batch_size=batch_size)
       callbacks.on_epoch_end(epoch, epoch_logs)
       if callback_model.stop_training:
         break
@@ -1081,17 +745,14 @@ def test_loop(model, inputs, targets,
       and/or metrics). The attribute `model.metrics_names` will give you
       the display labels for the scalar outputs.
   """
+  inputs, steps = training_utils.convert_to_iterator(
+      x=inputs,
+      y=targets,
+      sample_weights=sample_weights,
+      batch_size=batch_size,
+      steps_per_epoch=steps)
   with backend.learning_phase_scope(0):
-    if steps is not None:
-      return iterator_test_loop(model, inputs, steps, verbose=verbose)
-    else:
-      return batch_test_loop(
-          model,
-          inputs,
-          targets,
-          batch_size=batch_size,
-          sample_weights=sample_weights,
-          verbose=verbose)
+    return iterator_test_loop(model, inputs, steps, verbose=verbose)
 
 
 def predict_loop(model, inputs,
@@ -1115,8 +776,6 @@ def predict_loop(model, inputs,
       (if the model has multiple outputs).
   """
   with backend.learning_phase_scope(0):
-    if steps is not None:
-      return iterator_predict_loop(model, inputs, steps, verbose=verbose)
-    else:
-      return batch_predict_loop(
-          model, inputs, batch_size=batch_size, verbose=verbose)
+    inputs, steps = training_utils.convert_to_iterator(
+        x=inputs, batch_size=batch_size, steps_per_epoch=steps)
+    return iterator_predict_loop(model, inputs, steps, verbose=verbose)
diff --git a/tensorflow/python/keras/engine/training_eager_test.py b/tensorflow/python/keras/engine/training_eager_test.py
index bdb30351290644e2f7e8135c047ef6732054a08a..b0f57f0770e81a39b7edad5324a120bdbbfc4a28 100644
--- a/tensorflow/python/keras/engine/training_eager_test.py
+++ b/tensorflow/python/keras/engine/training_eager_test.py
@@ -31,284 +31,6 @@ from tensorflow.python.training.rmsprop import RMSPropOptimizer
 
 class TrainingTest(test.TestCase):
 
-  def test_fit_on_arrays(self):
-    a = keras.layers.Input(shape=(3,), name='input_a')
-    b = keras.layers.Input(shape=(3,), name='input_b')
-
-    dense = keras.layers.Dense(4, name='dense')
-    c = dense(a)
-    d = dense(b)
-    e = keras.layers.Dropout(0.5, name='dropout')(c)
-
-    model = keras.models.Model([a, b], [d, e])
-
-    optimizer = RMSPropOptimizer(learning_rate=0.001)
-    loss = 'mse'
-    loss_weights = [1., 0.5]
-    metrics = ['mae']
-    model.compile(optimizer, loss, metrics=metrics, loss_weights=loss_weights)
-
-    input_a_np = np.random.random((10, 3))
-    input_b_np = np.random.random((10, 3))
-
-    output_d_np = np.random.random((10, 4))
-    output_e_np = np.random.random((10, 4))
-
-    # Test fit at different verbosity
-    model.fit(
-        [input_a_np, input_b_np], [output_d_np, output_e_np],
-        epochs=1,
-        batch_size=5,
-        verbose=0)
-    model.fit(
-        [input_a_np, input_b_np], [output_d_np, output_e_np],
-        epochs=1,
-        batch_size=5,
-        verbose=1)
-    model.fit(
-        [input_a_np, input_b_np], [output_d_np, output_e_np],
-        epochs=2,
-        batch_size=5,
-        verbose=2)
-
-    # Test with validation data
-    model.fit(
-        [input_a_np, input_b_np], [output_d_np, output_e_np],
-        validation_data=([input_a_np, input_b_np], [output_d_np,
-                                                    output_e_np]),
-        epochs=1,
-        batch_size=5,
-        verbose=0)
-    model.fit(
-        [input_a_np, input_b_np], [output_d_np, output_e_np],
-        validation_data=([input_a_np, input_b_np], [output_d_np,
-                                                    output_e_np]),
-        epochs=2,
-        batch_size=5,
-        verbose=1)
-    model.fit(
-        [input_a_np, input_b_np], [output_d_np, output_e_np],
-        validation_data=([input_a_np, input_b_np], [output_d_np,
-                                                    output_e_np]),
-        epochs=2,
-        batch_size=5,
-        verbose=2)
-    model.train_on_batch([input_a_np, input_b_np], [output_d_np, output_e_np])
-
-    # Test with validation split
-    model.fit(
-        [input_a_np, input_b_np], [output_d_np, output_e_np],
-        epochs=2,
-        batch_size=5,
-        verbose=0,
-        validation_split=0.2)
-
-    # Test with dictionary inputs
-    model.fit(
-        {
-            'input_a': input_a_np,
-            'input_b': input_b_np
-        }, {'dense': output_d_np,
-            'dropout': output_e_np},
-        epochs=1,
-        batch_size=5,
-        verbose=0)
-    model.fit(
-        {
-            'input_a': input_a_np,
-            'input_b': input_b_np
-        }, {'dense': output_d_np,
-            'dropout': output_e_np},
-        epochs=1,
-        batch_size=5,
-        verbose=1)
-    model.fit(
-        {
-            'input_a': input_a_np,
-            'input_b': input_b_np
-        }, {'dense': output_d_np,
-            'dropout': output_e_np},
-        validation_data=({'input_a': input_a_np,
-                          'input_b': input_b_np
-                         },
-                         {
-                             'dense': output_d_np,
-                             'dropout': output_e_np
-                         }),
-        epochs=1,
-        batch_size=5,
-        verbose=0)
-    model.train_on_batch({
-        'input_a': input_a_np,
-        'input_b': input_b_np
-    }, {'dense': output_d_np,
-        'dropout': output_e_np})
-    # Test with lists for loss, metrics
-    loss = ['mae', 'mse']
-    metrics = ['acc', 'mae']
-    model.compile(optimizer, loss, metrics=metrics)
-    model.fit(
-        [input_a_np, input_b_np], [output_d_np, output_e_np],
-        epochs=1,
-        batch_size=5,
-        verbose=0)
-
-    # Test with dictionaries for loss, metrics, loss weights
-    loss = {'dense': 'mse', 'dropout': 'mae'}
-    loss_weights = {'dense': 1., 'dropout': 0.5}
-    metrics = {'dense': 'mse', 'dropout': 'mae'}
-    model.compile(optimizer, loss, metrics=metrics, loss_weights=loss_weights)
-    model.fit(
-        [input_a_np, input_b_np], [output_d_np, output_e_np],
-        epochs=1,
-        batch_size=5,
-        verbose=0)
-
-    # Invalid use cases
-    with self.assertRaises(AttributeError):
-      model.fit(
-          [input_a_np, input_b_np], [output_d_np, output_e_np],
-          epochs=1,
-          validation_data=([input_a_np, input_b_np], 0, 0),
-          verbose=0)
-    with self.assertRaises(ValueError):
-      model.train_on_batch({'input_a': input_a_np},
-                           [output_d_np, output_e_np])
-    with self.assertRaises(ValueError):
-      model.train_on_batch([input_a_np], [output_d_np, output_e_np])
-    with self.assertRaises(AttributeError):
-      model.train_on_batch(1, [output_d_np, output_e_np])
-    with self.assertRaises(ValueError):
-      model.train_on_batch(input_a_np, [output_d_np, output_e_np])
-    with self.assertRaises(ValueError):
-      bad_input = np.random.random((11, 3))
-      model.train_on_batch([bad_input, input_b_np],
-                           [output_d_np, output_e_np])
-    with self.assertRaises(ValueError):
-      bad_target = np.random.random((11, 4))
-      model.train_on_batch([input_a_np, input_b_np],
-                           [bad_target, output_e_np])
-
-    # Build single-input model
-    x = keras.layers.Input(shape=(3,), name='input_a')
-    y = keras.layers.Dense(4)(x)
-    model = keras.models.Model(x, y)
-    model.compile(optimizer=RMSPropOptimizer(learning_rate=0.001), loss='mse')
-    # This will work
-    model.fit([input_a_np], output_d_np, epochs=1)
-    with self.assertRaises(ValueError):
-      model.fit([input_a_np, input_a_np], output_d_np, epochs=1)
-
-  def test_evaluate_predict_on_arrays(self):
-    a = keras.layers.Input(shape=(3,), name='input_a')
-    b = keras.layers.Input(shape=(3,), name='input_b')
-
-    dense = keras.layers.Dense(4, name='dense')
-    c = dense(a)
-    d = dense(b)
-    e = keras.layers.Dropout(0.5, name='dropout')(c)
-
-    model = keras.models.Model([a, b], [d, e])
-
-    optimizer = RMSPropOptimizer(learning_rate=0.001)
-    loss = 'mse'
-    loss_weights = [1., 0.5]
-    metrics = ['acc', 'mae']
-    model.compile(
-        optimizer,
-        loss,
-        metrics=metrics,
-        loss_weights=loss_weights,
-        sample_weight_mode=None)
-
-    input_a_np = np.random.random((10, 3))
-    input_b_np = np.random.random((10, 3))
-
-    output_d_np = np.random.random((10, 4))
-    output_e_np = np.random.random((10, 4))
-
-    # Test evaluate at different verbosity
-    out = model.evaluate(
-        [input_a_np, input_b_np], [output_d_np, output_e_np],
-        batch_size=5,
-        verbose=0)
-    self.assertEqual(len(out), 7)
-    out = model.evaluate(
-        [input_a_np, input_b_np], [output_d_np, output_e_np],
-        batch_size=5,
-        verbose=1)
-    self.assertEqual(len(out), 7)
-    out = model.evaluate(
-        [input_a_np, input_b_np], [output_d_np, output_e_np],
-        batch_size=5,
-        verbose=2)
-    self.assertEqual(len(out), 7)
-    out = model.test_on_batch([input_a_np, input_b_np],
-                              [output_d_np, output_e_np])
-    self.assertEqual(len(out), 7)
-
-    # Test evaluate with dictionary inputs
-    model.evaluate(
-        {
-            'input_a': input_a_np,
-            'input_b': input_b_np
-        }, {'dense': output_d_np,
-            'dropout': output_e_np},
-        batch_size=5,
-        verbose=0)
-    model.evaluate(
-        {
-            'input_a': input_a_np,
-            'input_b': input_b_np
-        }, {'dense': output_d_np,
-            'dropout': output_e_np},
-        batch_size=5,
-        verbose=1)
-
-    # Test predict
-    out = model.predict([input_a_np, input_b_np], batch_size=5)
-    self.assertEqual(len(out), 2)
-    out = model.predict({'input_a': input_a_np, 'input_b': input_b_np})
-    self.assertEqual(len(out), 2)
-    out = model.predict_on_batch({
-        'input_a': input_a_np,
-        'input_b': input_b_np
-    })
-    self.assertEqual(len(out), 2)
-
-  def test_invalid_loss_or_metrics(self):
-    num_classes = 5
-    train_samples = 1000
-    test_samples = 1000
-    input_dim = 5
-
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(10, input_shape=(input_dim,)))
-    model.add(keras.layers.Activation('relu'))
-    model.add(keras.layers.Dense(num_classes))
-    model.add(keras.layers.Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer=RMSPropOptimizer(learning_rate=0.001))
-    np.random.seed(1337)
-
-    (x_train, y_train), (_, _) = testing_utils.get_test_data(
-        train_samples=train_samples,
-        test_samples=test_samples,
-        input_shape=(input_dim,),
-        num_classes=num_classes)
-
-    with self.assertRaises(ValueError):
-      model.fit(x_train, np.concatenate([y_train, y_train], axis=-1))
-
-    with self.assertRaises(TypeError):
-      model.compile(loss='categorical_crossentropy',
-                    optimizer=RMSPropOptimizer(learning_rate=0.001),
-                    metrics=set(0))
-
-    with self.assertRaises(ValueError):
-      model.compile(loss=None,
-                    optimizer='rms')
-
   def test_model_methods_with_eager_tensors_multi_io(self):
     a = keras.layers.Input(shape=(3,), name='input_a')
     b = keras.layers.Input(shape=(3,), name='input_b')
diff --git a/tensorflow/python/keras/engine/training_generator.py b/tensorflow/python/keras/engine/training_generator.py
index d81b384f0e1810614bd98e3861b4324f0f8a4dca..432cf2bddd052b40dd80dc530c9c6ce23d57d57b 100644
--- a/tensorflow/python/keras/engine/training_generator.py
+++ b/tensorflow/python/keras/engine/training_generator.py
@@ -96,14 +96,25 @@ def fit_generator(model,
   else:
     callback_model = model
   callbacks.set_model(callback_model)
-  callbacks.set_params({
+
+  callback_params = {
       'epochs': epochs,
       'steps': steps_per_epoch,
       'verbose': verbose,
       'do_validation': do_validation,
       'metrics': callback_metrics,
-  })
-  callbacks.on_train_begin()
+  }
+  if do_validation:
+    # need to create the test_function before start of the first epoch
+    # because TensorBoard callback on_epoch_begin adds summary to the
+    # list of fetches of the test_function
+    model._make_test_function()
+    # determine the number of validation batches given a generator
+    if validation_steps:
+      callback_params.update({'validation_steps': validation_steps})
+    elif isinstance(validation_data, Sequence):
+      callback_params.update({'validation_steps': len(validation_data)})
+  callbacks.set_params(callback_params)
 
   enqueuer = None
   val_enqueuer = None
@@ -149,6 +160,9 @@ def fit_generator(model,
         output_generator = generator
 
     callback_model.stop_training = False
+    # validation_data must be set before on_train_begin() is called
+    # so that TensorboardCallback can validate its input
+    callbacks.on_train_begin()
     # Construct epoch logs.
     epoch_logs = {}
     while epoch < epochs:
diff --git a/tensorflow/python/keras/engine/training_gpu_test.py b/tensorflow/python/keras/engine/training_gpu_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..5825ce814fd84bf59637f6079e7402d752e2b77b
--- /dev/null
+++ b/tensorflow/python/keras/engine/training_gpu_test.py
@@ -0,0 +1,125 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for training routines."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python import keras
+from tensorflow.python.framework import test_util
+from tensorflow.python.keras import backend as K
+from tensorflow.python.keras.layers.convolutional import Conv2D
+from tensorflow.python.platform import test
+from tensorflow.python.training import rmsprop
+
+
+class TrainingGPUTest(test.TestCase):
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_model_with_crossentropy_losses_channels_first(self):
+    """Tests use of all crossentropy losses with `channels_first`.
+
+    Tests `sparse_categorical_crossentropy`, `categorical_crossentropy`,
+    and `binary_crossentropy`.
+    Verifies that evaluate gives the same result with either `channels_first`
+    or `channels_last` image_data_format.
+    """
+    def prepare_simple_model(input_tensor, loss_name, target):
+      axis = 1 if K.image_data_format() == 'channels_first' else -1
+      loss = None
+      num_channels = None
+      activation = None
+      if loss_name == 'sparse_categorical_crossentropy':
+        loss = lambda y_true, y_pred: K.sparse_categorical_crossentropy(  # pylint: disable=g-long-lambda
+            y_true, y_pred, axis=axis)
+        num_channels = np.amax(target) + 1
+        activation = 'softmax'
+      elif loss_name == 'categorical_crossentropy':
+        loss = lambda y_true, y_pred: K.categorical_crossentropy(  # pylint: disable=g-long-lambda
+            y_true, y_pred, axis=axis)
+        num_channels = target.shape[axis]
+        activation = 'softmax'
+      elif loss_name == 'binary_crossentropy':
+        loss = lambda y_true, y_pred: K.binary_crossentropy(y_true, y_pred)  # pylint: disable=unnecessary-lambda
+        num_channels = target.shape[axis]
+        activation = 'sigmoid'
+      predictions = Conv2D(num_channels,
+                           1,
+                           activation=activation,
+                           kernel_initializer='ones',
+                           bias_initializer='ones')(input_tensor)
+      simple_model = keras.models.Model(inputs=input_tensor,
+                                        outputs=predictions)
+      simple_model.compile(optimizer=rmsprop.RMSPropOptimizer(1e-3), loss=loss)
+      return simple_model
+
+    if test.is_gpu_available(cuda_only=True):
+      with self.test_session(use_gpu=True):
+        losses_to_test = ['sparse_categorical_crossentropy',
+                          'categorical_crossentropy', 'binary_crossentropy']
+
+        data_channels_first = np.array([[[[8., 7.1, 0.], [4.5, 2.6, 0.55],
+                                          [0.9, 4.2, 11.2]]]], dtype=np.float32)
+        # Labels for testing 4-class sparse_categorical_crossentropy, 4-class
+        # categorical_crossentropy, and 2-class binary_crossentropy:
+        labels_channels_first = [np.array([[[[0, 1, 3], [2, 1, 0], [2, 2, 1]]]], dtype=np.float32),  # pylint: disable=line-too-long
+                                 np.array([[[[0, 1, 0], [0, 1, 0], [0, 0, 0]],
+                                            [[1, 0, 0], [0, 0, 1], [0, 1, 0]],
+                                            [[0, 0, 0], [1, 0, 0], [0, 0, 1]],
+                                            [[0, 0, 1], [0, 0, 0], [1, 0, 0]]]], dtype=np.float32),  # pylint: disable=line-too-long
+                                 np.array([[[[0, 1, 0], [0, 1, 0], [0, 0, 1]],
+                                            [[1, 0, 1], [1, 0, 1], [1, 1, 0]]]], dtype=np.float32)]  # pylint: disable=line-too-long
+        # Compute one loss for each loss function in the list `losses_to_test`:
+        loss_channels_last = [0., 0., 0.]
+        loss_channels_first = [0., 0., 0.]
+
+        old_data_format = K.image_data_format()
+
+        # Evaluate a simple network with channels last, with all three loss
+        # functions:
+        K.set_image_data_format('channels_last')
+        data = np.moveaxis(data_channels_first, 1, -1)
+        for index, loss_function in enumerate(losses_to_test):
+          labels = np.moveaxis(labels_channels_first[index], 1, -1)
+          inputs = keras.Input(shape=(3, 3, 1))
+          model = prepare_simple_model(inputs, loss_function, labels)
+          loss_channels_last[index] = model.evaluate(x=data, y=labels,
+                                                     batch_size=1, verbose=0)
+
+        # Evaluate the same network with channels first, with all three loss
+        # functions:
+        K.set_image_data_format('channels_first')
+        data = data_channels_first
+        for index, loss_function in enumerate(losses_to_test):
+          labels = labels_channels_first[index]
+          inputs = keras.Input(shape=(1, 3, 3))
+          model = prepare_simple_model(inputs, loss_function, labels)
+          loss_channels_first[index] = model.evaluate(x=data, y=labels,
+                                                      batch_size=1, verbose=0)
+
+        K.set_image_data_format(old_data_format)
+
+        np.testing.assert_allclose(loss_channels_first,
+                                   loss_channels_last,
+                                   err_msg='{}{}'.format(
+                                       'Computed different losses for ',
+                                       'channels_first and channels_last'))
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/python/keras/engine/training_test.py b/tensorflow/python/keras/engine/training_test.py
index d9e548f01f86fd96c3abd7b3cdaf5106653393fd..be9b0a21d79b6867a1cd590ec0d3fab0ff597899 100644
--- a/tensorflow/python/keras/engine/training_test.py
+++ b/tensorflow/python/keras/engine/training_test.py
@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import logging
 import os
 import unittest
 
@@ -25,6 +26,7 @@ import numpy as np
 
 from tensorflow.python import keras
 from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.eager import context
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import test_util as tf_test_util
@@ -44,6 +46,7 @@ except ImportError:
 
 class TrainingTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes
   def test_fit_on_arrays(self):
     with self.test_session():
       a = keras.layers.Input(shape=(3,), name='input_a')
@@ -56,7 +59,7 @@ class TrainingTest(test.TestCase):
 
       model = keras.models.Model([a, b], [d, e])
 
-      optimizer = 'rmsprop'
+      optimizer = RMSPropOptimizer(learning_rate=0.001)
       loss = 'mse'
       loss_weights = [1., 0.5]
       metrics = ['mae']
@@ -223,7 +226,7 @@ class TrainingTest(test.TestCase):
       x = keras.layers.Input(shape=(3,), name='input_a')
       y = keras.layers.Dense(4)(x)
       model = keras.models.Model(x, y)
-      model.compile(optimizer='rmsprop', loss='mse')
+      model.compile(optimizer, loss='mse')
       # This will work
       model.fit([input_a_np], output_d_np, epochs=1)
       with self.assertRaises(ValueError):
@@ -239,6 +242,7 @@ class TrainingTest(test.TestCase):
                 batch_size=5,
                 verbose=2)
 
+  @tf_test_util.run_in_graph_and_eager_modes
   def test_evaluate_predict_on_arrays(self):
     with self.test_session():
       a = keras.layers.Input(shape=(3,), name='input_a')
@@ -251,7 +255,7 @@ class TrainingTest(test.TestCase):
 
       model = keras.models.Model([a, b], [d, e])
 
-      optimizer = 'rmsprop'
+      optimizer = RMSPropOptimizer(learning_rate=0.001)
       loss = 'mse'
       loss_weights = [1., 0.5]
       metrics = ['mae']
@@ -321,6 +325,7 @@ class TrainingTest(test.TestCase):
       })
       self.assertEqual(len(out), 2)
 
+  @tf_test_util.run_in_graph_and_eager_modes
   def test_invalid_loss_or_metrics(self):
     num_classes = 5
     train_samples = 1000
@@ -333,27 +338,29 @@ class TrainingTest(test.TestCase):
       model.add(keras.layers.Activation('relu'))
       model.add(keras.layers.Dense(num_classes))
       model.add(keras.layers.Activation('softmax'))
-      model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
+      optimizer = RMSPropOptimizer(learning_rate=0.001)
+      model.compile(optimizer, loss='categorical_crossentropy')
       np.random.seed(1337)
       (x_train, y_train), (_, _) = testing_utils.get_test_data(
           train_samples=train_samples,
           test_samples=test_samples,
           input_shape=(input_dim,),
           num_classes=num_classes)
-      with self.assertRaises(ValueError):
-        model.fit(x_train, y_train)
 
       with self.assertRaises(ValueError):
         model.fit(x_train, np.concatenate([y_train, y_train], axis=-1))
 
       with self.assertRaises(TypeError):
-        model.compile(loss='categorical_crossentropy',
-                      optimizer='rmsprop',
-                      metrics=set(0))
+        model.compile(
+            optimizer, loss='categorical_crossentropy', metrics=set(0))
 
-      with self.assertRaises(ValueError):
-        model.compile(loss=None,
-                      optimizer='rmsprop')
+      if not context.executing_eagerly():
+        # TODO(psv): Investigate these use cases in eager mode.
+        with self.assertRaises(ValueError):
+          model.fit(x_train, y_train)
+
+        with self.assertRaises(ValueError):
+          model.compile(optimizer, loss=None)
 
   def test_training_on_sparse_data_with_dense_placeholders(self):
     if scipy_sparse is None:
@@ -415,6 +422,28 @@ class TrainingTest(test.TestCase):
       x2 = model.predict(val_a)
       self.assertAllClose(x1, x2, atol=1e-7)
 
+  def test_compile_warning_for_loss_missing_output(self):
+    with self.test_session():
+      inp = keras.layers.Input(shape=(16,), name='input_a')
+      out_1 = keras.layers.Dense(8, name='dense_1')(inp)
+      out_2 = keras.layers.Dense(3, activation='softmax', name='dense_2')(out_1)
+      model = keras.models.Model(inputs=[inp], outputs=[out_1, out_2])
+
+      with test.mock.patch.object(logging, 'warning') as mock_log:
+        model.compile(
+            loss={
+                'dense_2': 'categorical_crossentropy',
+            },
+            optimizer='rmsprop',
+            metrics={
+                'dense_2': 'categorical_accuracy',
+                'dense_1': 'categorical_accuracy',
+            })
+        msg = ('Output "dense_1" missing from loss dictionary. We assume this '
+               'was done on purpose. The fit and evaluate APIs will not be '
+               'expecting any data to be passed to "dense_1".')
+        self.assertRegexpMatches(str(mock_log.call_args), msg)
+
 
 class LossWeightingTest(test.TestCase):
 
@@ -708,6 +737,54 @@ class LossWeightingTest(test.TestCase):
         model.fit(x_np, [y_np, y_np], epochs=1,
                   sample_weight={'1': bad_w_np})
 
+  def test_default_sample_weight(self):
+    """Verifies that fit works without having to set sample_weight."""
+
+    num_classes = 5
+    input_dim = 5
+    timesteps = 3
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(
+          keras.layers.TimeDistributed(
+              keras.layers.Dense(num_classes),
+              input_shape=(timesteps, input_dim)))
+
+      x = np.random.random((10, timesteps, input_dim))
+      y = np.random.random((10, timesteps, num_classes))
+
+      # sample_weight_mode is a list and mode value is None
+      model.compile(loss='mse', optimizer='rmsprop', sample_weight_mode=[None])
+      model.fit(x, y, epochs=1, batch_size=10)
+
+      # sample_weight_mode is a list and mode value is `temporal`
+      model.compile(
+          loss='mse', optimizer='rmsprop', sample_weight_mode=['temporal'])
+      model.fit(x, y, epochs=1, batch_size=10)
+
+      # sample_weight_mode is a dict and mode value is None
+      model.compile(
+          loss='mse',
+          optimizer='rmsprop',
+          sample_weight_mode={'time_distributed': None})
+      model.fit(x, y, epochs=1, batch_size=10)
+
+      # sample_weight_mode is a dict and mode value is `temporal`
+      model.compile(
+          loss='mse',
+          optimizer='rmsprop',
+          sample_weight_mode={'time_distributed': 'temporal'})
+      model.fit(x, y, epochs=1, batch_size=10)
+
+      # sample_weight_mode is a not a list/dict and mode value is None
+      model.compile(loss='mse', optimizer='rmsprop', sample_weight_mode=None)
+      model.fit(x, y, epochs=1, batch_size=10)
+
+      # sample_weight_mode is a not a list/dict and mode value is `temporal`
+      model.compile(
+          loss='mse', optimizer='rmsprop', sample_weight_mode='temporal')
+      model.fit(x, y, epochs=1, batch_size=10)
+
 
 class LossMaskingTest(test.TestCase):
 
@@ -744,6 +821,22 @@ class LossMaskingTest(test.TestCase):
               keras.backend.variable(weights), keras.backend.variable(mask)))
 
 
+class LearningPhaseTest(test.TestCase):
+
+  def test_empty_model_no_learning_phase(self):
+    with self.test_session():
+      model = keras.models.Sequential()
+      self.assertFalse(model.uses_learning_phase)
+
+  def test_dropout_has_learning_phase(self):
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(keras.layers.Dense(2, input_dim=3))
+      model.add(keras.layers.Dropout(0.5))
+      model.add(keras.layers.Dense(2))
+      self.assertTrue(model.uses_learning_phase)
+
+
 class TestDynamicTrainability(test.TestCase):
 
   def test_trainable_warning(self):
diff --git a/tensorflow/python/keras/engine/training_utils.py b/tensorflow/python/keras/engine/training_utils.py
index 728a2b493b9f076cc2942766d2677c1f24fb3c15..f2cd9c89dafe4553cdd6e6137a62c254ad54f25c 100644
--- a/tensorflow/python/keras/engine/training_utils.py
+++ b/tensorflow/python/keras/engine/training_utils.py
@@ -19,18 +19,151 @@ from __future__ import division
 from __future__ import print_function
 
 import copy
+import math
 
 import numpy as np
 
+from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.data.ops import iterator_ops
 from tensorflow.python.eager import context
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras import backend as K
 from tensorflow.python.keras import losses
 from tensorflow.python.keras import metrics as metrics_module
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 
 
+def _map_nested(data, func):
+  """Maps each nested element using func."""
+  if isinstance(data, list):
+    return [_map_nested(nested_data, func) for nested_data in data]
+  elif isinstance(data, tuple):
+    return tuple(_map_nested(nested_data, func) for nested_data in data)
+  elif isinstance(data, dict):
+    return {
+        k: _map_nested(nested_data, func) for k, nested_data in data.items()
+    }
+  else:
+    return func(data)
+
+
+def _nested_all(data, cond_func):
+  """Checks if all elements in a nested structure satisfy cond_func."""
+  if isinstance(data, (tuple, list)):
+    return all([_nested_all(nested_data, cond_func) for nested_data in data])
+  elif isinstance(data, dict):
+    return all(
+        [_nested_all(nested_data, cond_func) for nested_data in data.values()])
+  else:
+    return cond_func(data)
+
+
+def _nested_any(data, cond_func):
+  """Checks if any nested_elements in a nested structure satisfy cond_func."""
+  if isinstance(data, (tuple, list)):
+    return any([_nested_any(nested_data, cond_func) for nested_data in data])
+  elif isinstance(data, dict):
+    return any(
+        [_nested_any(nested_data, cond_func) for nested_data in data.values()])
+  else:
+    return cond_func(data)
+
+
+def _convert_lists_to_tuples(data):
+  """Converts all lists to tuples, since Datasets expect tuples."""
+  if isinstance(data, (tuple, list)):
+    return tuple(_convert_lists_to_tuples(nested_data) for nested_data in data)
+  elif isinstance(data, dict):
+    return {
+        k: _convert_lists_to_tuples(nested_data)
+        for k, nested_data in data.items()
+    }
+  else:
+    return data
+
+
+def _get_batch_axis_size(data):
+  """Returns batch axis shape for nested data."""
+  if isinstance(data, (tuple, list)):
+    return _get_batch_axis_size(data[0])
+  elif isinstance(data, dict):
+    return _get_batch_axis_size(list(data.values()))
+  else:
+    return int(data.shape[0])
+
+
+def convert_to_iterator(x=None,
+                        y=None,
+                        sample_weights=None,
+                        batch_size=None,
+                        steps_per_epoch=None,
+                        epochs=1,
+                        shuffle=False):
+  """Converts NumPy arrays or EagerTensors to an EagerIterator.
+
+  Combines all provided data into a single EagerIterator.
+
+  Arguments:
+      x: NumPy array or EagerTensor,  or list of Numpy arrays or EagerTensors
+        representing inputs to a model.
+      y: Optional. NumPy array or EagerTensor, or list of Numpy arrays or
+        EagerTensors representing targets of a model.
+      sample_weights: Optional NumPy array or EagerTensor representing sample
+        weights.
+      batch_size: Used to batch data and calculate how many steps EagerIterator
+        should take per epoch.
+      steps_per_epoch: If provided, how many steps EagerIterator should take per
+        epoch.
+      epochs: Epochs to repeat iterator for.
+      shuffle: Whether to shuffle data after each epoch.
+
+  Raises:
+      ValueError: if steps_per_epoch cannot be calculated from the data
+      provided.
+
+  Returns:
+      (Iterator, steps_per_epoch).
+
+  """
+  if isinstance(x, iterator_ops.EagerIterator):
+    return x, steps_per_epoch
+
+  if not _nested_any(sample_weights, lambda x: x is None):
+    data = (x, y, sample_weights)
+  elif not _nested_any(y, lambda x: x is None):
+    data = (x, y)
+  else:
+    # always wrap in a tuple, so we know y, sample_weights weren't set
+    # even when x has multiple elements
+    data = (x,)
+
+  data = _convert_lists_to_tuples(data)
+  if steps_per_epoch is None and batch_size is not None:
+    num_samples = _get_batch_axis_size(data)
+    steps_per_epoch = int(math.ceil(num_samples / batch_size))
+
+  if steps_per_epoch is None:
+    raise ValueError('Could not determine steps_per_epoch.'
+                     'Please provide either batch_size or'
+                     'steps_per_epoch.')
+
+  # TODO(omalleyt) for NumPy arrays in graph mode
+  # placeholder ops should be used
+  # this is only ideal for eager mode
+  dataset = dataset_ops.Dataset.from_tensor_slices(data)
+
+  if batch_size is not None:
+    dataset = dataset.batch(batch_size)
+  if shuffle:
+    dataset = dataset.shuffle(buffer_size=10000)
+  dataset = dataset.repeat(epochs)
+  iterator = dataset.make_one_shot_iterator()
+
+  return iterator, steps_per_epoch
+
+
 def check_num_samples(ins,
                       batch_size=None,
                       steps=None,
@@ -128,8 +261,8 @@ def standardize_input_data(data,
     except KeyError as e:
       raise ValueError('No data provided for "' + e.args[0] + '". Need data '
                        'for each key in: ' + str(names))
-  elif isinstance(data, list):
-    if isinstance(data[0], list):
+  elif isinstance(data, (list, tuple)):
+    if isinstance(data[0], (list, tuple)):
       data = [np.asarray(d) for d in data]
     elif len(names) == 1 and isinstance(data[0], (float, int)):
       data = [np.asarray(data)]
@@ -482,6 +615,9 @@ def standardize_weights(y,
   Raises:
       ValueError: In case of invalid user-provided arguments.
   """
+  # Iterator may return sample_weight as 1-tuple
+  if isinstance(sample_weight, tuple):
+    sample_weight = sample_weight[0]
   if sample_weight_mode is not None:
     if sample_weight_mode != 'temporal':
       raise ValueError('"sample_weight_mode '
@@ -566,17 +702,16 @@ def populate_metric_names(model):
   for i in range(len(model.outputs)):
     metrics = model.nested_metrics[i]
     for metric in metrics:
-      base_metric_name = get_base_metric_name(metric)
+      base_metric_name = get_metric_name(metric)
       add_metric_name(model, base_metric_name, i)
 
 
-def get_base_metric_name(metric, weighted=False):
-  """Returns the metric name given the metric function.
+def get_metric_name(metric, weighted=False):
+  """Returns the metric name corresponding to the given metric input.
 
   Arguments:
       metric: Metric function name or reference.
-      weighted: Boolean indicating if the metric for which we are adding
-          names is weighted.
+      weighted: Boolean indicating if the given metric is weighted.
 
   Returns:
       a metric name.
@@ -600,6 +735,36 @@ def get_base_metric_name(metric, weighted=False):
   return metric_name
 
 
+def get_metric_function(metric, output_shape=None, loss_fn=None):
+  """Returns the metric function corresponding to the given metric input.
+
+  Arguments:
+      metric: Metric function name or reference.
+      output_shape: The shape of the output that this metric
+          will be calculated for.
+      loss_fn: The loss function used.
+
+  Returns:
+      The metric function.
+  """
+  if metric in ['accuracy', 'acc']:
+    if output_shape[-1] == 1 or loss_fn == losses.binary_crossentropy:
+      return metrics_module.binary_accuracy  # case: binary accuracy
+    elif loss_fn == losses.sparse_categorical_crossentropy:
+      # case: categorical accuracy with sparse targets
+      return metrics_module.sparse_categorical_accuracy
+    return metrics_module.categorical_accuracy  # case: categorical accuracy
+  elif metric in ['crossentropy', 'ce']:
+    if output_shape[-1] == 1 or loss_fn == losses.binary_crossentropy:
+      return metrics_module.binary_crossentropy  # case: binary cross-entropy
+    elif loss_fn == losses.sparse_categorical_crossentropy:
+      # case: categorical cross-entropy with sparse targets
+      return metrics_module.sparse_categorical_crossentropy
+    # case: categorical cross-entropy
+    return metrics_module.categorical_crossentropy
+  return metrics_module.get(metric)
+
+
 def add_metric_name(model, metric_name, index):
   """Makes the metric name unique and adds it to the model's metric name list.
 
@@ -722,3 +887,25 @@ def cast_if_floating_dtype(x):
         for val in x
     ]
   return math_ops.cast(x, dtype=K.floatx()) if x.dtype.is_floating else x
+
+
+def get_output_sample_weight_and_mode(skip_target_weighing_indices,
+                                      sample_weight_mode, output_name,
+                                      output_index):
+  """Returns the sample weight and weight mode for a single output."""
+  if output_index in skip_target_weighing_indices:
+    return None, None
+
+  if sample_weight_mode == 'temporal':
+    default_value = [[1.]]
+    shape = [None, None]
+    mode = 'temporal'
+  else:
+    default_value = [1.]
+    shape = [None]
+    mode = None
+  weight = array_ops.placeholder_with_default(
+      constant_op.constant(default_value, dtype=K.floatx()),
+      shape=shape,
+      name=output_name + '_sample_weights')
+  return weight, mode
diff --git a/tensorflow/python/keras/engine/training_utils_test.py b/tensorflow/python/keras/engine/training_utils_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..297a1ae494f8c55265a98a60490a8b0d240b3969
--- /dev/null
+++ b/tensorflow/python/keras/engine/training_utils_test.py
@@ -0,0 +1,150 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for training utility functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.keras.engine import training_utils
+from tensorflow.python.platform import test
+
+
+class TrainingUtilTest(test.TestCase):
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_convert_to_iterator_single_numpy(self):
+    batch_size = 2
+    a = np.ones([10, 10])
+    iterator, steps_per_epoch = training_utils.convert_to_iterator(
+        x=a, batch_size=batch_size)
+    self.assertEquals(steps_per_epoch, 5)
+
+    expected_batch = a[:batch_size, :]
+    actual_batch, = iterator.get_next()
+    self.assertAllEqual(expected_batch, actual_batch)
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_convert_to_iterator_single_tensor(self):
+    batch_size = 2
+    a = ops.convert_to_tensor(np.ones([10, 10]))
+    iterator, steps_per_epoch = training_utils.convert_to_iterator(
+        x=a, batch_size=batch_size)
+    self.assertEquals(steps_per_epoch, 5)
+
+    expected_batch = a[:batch_size, :]
+    actual_batch, = iterator.get_next()
+    self.assertAllEqual(expected_batch, actual_batch)
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_convert_to_iterator_y(self):
+    batch_size = 2
+    a = np.ones([10, 100])
+    b = np.ones([10, 10])
+    iterator, steps_per_epoch = training_utils.convert_to_iterator(
+        x=a, y=b, batch_size=batch_size)
+    self.assertEquals(steps_per_epoch, 5)
+
+    expected_x = a[:batch_size, :]
+    expected_y = b[:batch_size, :]
+    actual_x, actual_y = iterator.get_next()
+    self.assertAllEqual(expected_x, actual_x)
+    self.assertAllEqual(expected_y, actual_y)
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_convert_to_iterator_sample_weights(self):
+    batch_size = 2
+    a = ops.convert_to_tensor(np.ones([10, 100]))
+    b = ops.convert_to_tensor(np.ones([10, 10]))
+    sw = ops.convert_to_tensor(np.ones([10]))
+    iterator, steps_per_epoch = training_utils.convert_to_iterator(
+        x=a, y=b, sample_weights=sw, batch_size=batch_size)
+    self.assertEquals(steps_per_epoch, 5)
+
+    expected_x = a[:batch_size, :]
+    expected_y = b[:batch_size, :]
+    expected_sw = sw[:batch_size]
+    actual_x, actual_y, actual_sw = iterator.get_next()
+    self.assertAllEqual(expected_x, actual_x)
+    self.assertAllEqual(expected_y, actual_y)
+    self.assertAllEqual(expected_sw, actual_sw)
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_convert_to_iterator_nested(self):
+    batch_size = 2
+    x = {'1': np.ones([10, 100]), '2': [np.zeros([10, 10]), np.ones([10, 20])]}
+    iterator, steps_per_epoch = training_utils.convert_to_iterator(
+        x=x, batch_size=batch_size)
+    self.assertEquals(steps_per_epoch, 5)
+
+    expected_x1 = x['1'][:batch_size, :]
+    expected_x2_0 = x['2'][0][:batch_size, :]
+    expected_x2_1 = x['2'][1][:batch_size, :]
+
+    actual_x, = iterator.get_next()
+    actual_x1 = actual_x['1'][:batch_size, :]
+    actual_x2_0 = actual_x['2'][0][:batch_size, :]
+    actual_x2_1 = actual_x['2'][1][:batch_size, :]
+
+    self.assertAllEqual(expected_x1, actual_x1)
+    self.assertAllEqual(expected_x2_0, actual_x2_0)
+    self.assertAllEqual(expected_x2_1, actual_x2_1)
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_convert_to_iterator_epochs(self):
+    batch_size = 2
+    a = np.ones([10, 10])
+    iterator, steps_per_epoch = training_utils.convert_to_iterator(
+        x=a, batch_size=batch_size, epochs=2)
+    self.assertEquals(steps_per_epoch, 5)
+
+    expected_batch = a[:batch_size, :]
+    # loop through one whole epoch
+    for _ in range(6):
+      actual_batch, = iterator.get_next()
+    self.assertAllEqual(expected_batch, actual_batch)
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_convert_to_iterator_insufficient_info(self):
+    # with batch_size and steps_per_epoch not set
+    with self.assertRaises(ValueError):
+      a = np.ones([10, 10])
+      _ = training_utils.convert_to_iterator(x=a)
+
+  def test_nested_all(self):
+    nested_data = {'a': True, 'b': [True, True, (False, True)]}
+    all_true = training_utils._nested_all(nested_data, lambda x: x)
+    self.assertEquals(all_true, False)
+
+    nested_data = {'a': True, 'b': [True, True, (True, True)]}
+    all_true = training_utils._nested_all(nested_data, lambda x: x)
+    self.assertEquals(all_true, True)
+
+  def test_nested_any(self):
+    nested_data = [False, {'a': False, 'b': (False, True)}]
+    any_true = training_utils._nested_any(nested_data, lambda x: x)
+    self.assertEquals(any_true, True)
+
+    nested_data = [False, {'a': False, 'b': (False, False)}]
+    any_true = training_utils._nested_any(nested_data, lambda x: x)
+    self.assertEquals(any_true, False)
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/python/keras/initializers.py b/tensorflow/python/keras/initializers.py
index b9b2e9ad598fabe8cbfbbcbd57d4d71ddf630df7..b9d856efa8f20500595a2f2a49447c724b9a563e 100644
--- a/tensorflow/python/keras/initializers.py
+++ b/tensorflow/python/keras/initializers.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Keras initializer classes (soon to be replaced with core TF initializers).
+"""Keras initializer serialization / deserialization.
 """
 from __future__ import absolute_import
 from __future__ import division
@@ -22,150 +22,27 @@ import six
 
 from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
 from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
+
+# These imports are brought in so that keras.initializers.deserialize
+# has them available in module_objects.
 from tensorflow.python.ops.init_ops import Constant
+from tensorflow.python.ops.init_ops import glorot_normal_initializer
+from tensorflow.python.ops.init_ops import glorot_uniform_initializer
+from tensorflow.python.ops.init_ops import he_normal  # pylint: disable=unused-import
+from tensorflow.python.ops.init_ops import he_uniform  # pylint: disable=unused-import
 from tensorflow.python.ops.init_ops import Identity
 from tensorflow.python.ops.init_ops import Initializer  # pylint: disable=unused-import
+from tensorflow.python.ops.init_ops import lecun_normal  # pylint: disable=unused-import
+from tensorflow.python.ops.init_ops import lecun_uniform  # pylint: disable=unused-import
 from tensorflow.python.ops.init_ops import Ones
 from tensorflow.python.ops.init_ops import Orthogonal
 from tensorflow.python.ops.init_ops import RandomNormal
 from tensorflow.python.ops.init_ops import RandomUniform
 from tensorflow.python.ops.init_ops import TruncatedNormal
-from tensorflow.python.ops.init_ops import VarianceScaling
+from tensorflow.python.ops.init_ops import VarianceScaling  # pylint: disable=unused-import
 from tensorflow.python.ops.init_ops import Zeros
-from tensorflow.python.util.tf_export import tf_export
-
-
-@tf_export('keras.initializers.lecun_normal')
-def lecun_normal(seed=None):
-  """LeCun normal initializer.
-
-  It draws samples from a truncated normal distribution centered on 0
-  with `stddev = sqrt(1 / fan_in)`
-  where `fan_in` is the number of input units in the weight tensor.
-
-  Arguments:
-      seed: A Python integer. Used to seed the random generator.
-
-  Returns:
-      An initializer.
-
-  References:
-      - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
-      - [Efficient
-      Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
-  """
-  return VarianceScaling(
-      scale=1., mode='fan_in', distribution='normal', seed=seed)
-
-
-@tf_export('keras.initializers.lecun_uniform')
-def lecun_uniform(seed=None):
-  """LeCun uniform initializer.
-
-  It draws samples from a uniform distribution within [-limit, limit]
-  where `limit` is `sqrt(3 / fan_in)`
-  where `fan_in` is the number of input units in the weight tensor.
-
-  Arguments:
-      seed: A Python integer. Used to seed the random generator.
-
-  Returns:
-      An initializer.
-
-  References:
-      LeCun 98, Efficient Backprop,
-      http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf
-  """
-  return VarianceScaling(
-      scale=1., mode='fan_in', distribution='uniform', seed=seed)
-
-
-@tf_export('keras.initializers.glorot_normal')
-def glorot_normal(seed=None):
-  """Glorot normal initializer, also called Xavier normal initializer.
-
-  It draws samples from a truncated normal distribution centered on 0
-  with `stddev = sqrt(2 / (fan_in + fan_out))`
-  where `fan_in` is the number of input units in the weight tensor
-  and `fan_out` is the number of output units in the weight tensor.
-
-  Arguments:
-      seed: A Python integer. Used to seed the random generator.
-
-  Returns:
-      An initializer.
 
-  References:
-      Glorot & Bengio, AISTATS 2010
-      http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf
-  """
-  return VarianceScaling(
-      scale=1., mode='fan_avg', distribution='normal', seed=seed)
-
-
-@tf_export('keras.initializers.glorot_uniform')
-def glorot_uniform(seed=None):
-  """Glorot uniform initializer, also called Xavier uniform initializer.
-
-  It draws samples from a uniform distribution within [-limit, limit]
-  where `limit` is `sqrt(6 / (fan_in + fan_out))`
-  where `fan_in` is the number of input units in the weight tensor
-  and `fan_out` is the number of output units in the weight tensor.
-
-  Arguments:
-      seed: A Python integer. Used to seed the random generator.
-
-  Returns:
-      An initializer.
-
-  References:
-      Glorot & Bengio, AISTATS 2010
-      http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf
-  """
-  return VarianceScaling(
-      scale=1., mode='fan_avg', distribution='uniform', seed=seed)
-
-
-@tf_export('keras.initializers.he_normal')
-def he_normal(seed=None):
-  """He normal initializer.
-
-  It draws samples from a truncated normal distribution centered on 0
-  with `stddev = sqrt(2 / fan_in)`
-  where `fan_in` is the number of input units in the weight tensor.
-
-  Arguments:
-      seed: A Python integer. Used to seed the random generator.
-
-  Returns:
-      An initializer.
-
-  References:
-      He et al., http://arxiv.org/abs/1502.01852
-  """
-  return VarianceScaling(
-      scale=2., mode='fan_in', distribution='normal', seed=seed)
-
-
-@tf_export('keras.initializers.he_uniform')
-def he_uniform(seed=None):
-  """He uniform variance scaling initializer.
-
-  It draws samples from a uniform distribution within [-limit, limit]
-  where `limit` is `sqrt(6 / fan_in)`
-  where `fan_in` is the number of input units in the weight tensor.
-
-  Arguments:
-      seed: A Python integer. Used to seed the random generator.
-
-  Returns:
-      An initializer.
-
-  References:
-      He et al., http://arxiv.org/abs/1502.01852
-  """
-  return VarianceScaling(
-      scale=2., mode='fan_in', distribution='uniform', seed=seed)
+from tensorflow.python.util.tf_export import tf_export
 
 
 # Compatibility aliases
@@ -179,6 +56,8 @@ normal = random_normal = RandomNormal
 truncated_normal = TruncatedNormal
 identity = Identity
 orthogonal = Orthogonal
+glorot_normal = glorot_normal_initializer
+glorot_uniform = glorot_uniform_initializer
 
 # pylint: enable=invalid-name
 
diff --git a/tensorflow/python/keras/initializers_test.py b/tensorflow/python/keras/initializers_test.py
index c519e194bdc21692025f259533b8b75e2dc48c09..51725e03f201db092a515456b280e7eca2927ac3 100644
--- a/tensorflow/python/keras/initializers_test.py
+++ b/tensorflow/python/keras/initializers_test.py
@@ -31,16 +31,6 @@ class KerasInitializersTest(test.TestCase):
               target_max=None, target_min=None):
     variable = keras.backend.variable(init(shape))
     output = keras.backend.get_value(variable)
-    lim = 3e-2
-    if target_std is not None:
-      self.assertGreater(lim, abs(output.std() - target_std))
-    if target_mean is not None:
-      self.assertGreater(lim, abs(output.mean() - target_mean))
-    if target_max is not None:
-      self.assertGreater(lim, abs(output.max() - target_max))
-    if target_min is not None:
-      self.assertGreater(lim, abs(output.min() - target_min))
-
     # Test serialization (assumes deterministic behavior).
     config = init.get_config()
     reconstructed_init = init.__class__.from_config(config)
diff --git a/tensorflow/python/keras/layers/advanced_activations.py b/tensorflow/python/keras/layers/advanced_activations.py
index eba10da6f3ce1367f4cb0180d16efdc5913fcddc..61ab69c16f14b8d734a306ab3ad18c73eaf160ca 100644
--- a/tensorflow/python/keras/layers/advanced_activations.py
+++ b/tensorflow/python/keras/layers/advanced_activations.py
@@ -284,6 +284,13 @@ class Softmax(Layer):
 class ReLU(Layer):
   """Rectified Linear Unit activation function.
 
+  With default values, it returns element-wise `max(x, 0)`.
+
+  Otherwise, it follows:
+  `f(x) = max_value` for `x >= max_value`,
+  `f(x) = x` for `threshold <= x < max_value`,
+  `f(x) = negative_slope * (x - threshold)` otherwise.
+
   Input shape:
       Arbitrary. Use the keyword argument `input_shape`
       (tuple of integers, does not include the samples axis)
@@ -294,21 +301,39 @@ class ReLU(Layer):
 
   Arguments:
       max_value: float >= 0. Maximum activation value.
+      negative_slope: float >= 0. Negative slope coefficient.
+      threshold: float. Threshold value for thresholded activation.
   """
 
-  def __init__(self, max_value=None, **kwargs):
+  def __init__(self, max_value=None, negative_slope=0, threshold=0, **kwargs):
     super(ReLU, self).__init__(**kwargs)
-    self.support_masking = True
-    self.max_value = K.cast_to_floatx(max_value)
-    if self.max_value < 0.:
+    if max_value is not None and max_value < 0.:
       raise ValueError('max_value of Relu layer '
                        'cannot be negative value: ' + str(max_value))
+    if negative_slope < 0.:
+      raise ValueError('negative_slope of Relu layer '
+                       'cannot be negative value: ' + str(negative_slope))
+
+    self.support_masking = True
+    self.max_value = K.cast_to_floatx(max_value)
+    self.negative_slope = K.cast_to_floatx(negative_slope)
+    self.threshold = K.cast_to_floatx(threshold)
 
   def call(self, inputs):
-    return activations.relu(inputs, max_value=self.max_value)
+    # alpha is used for leaky relu slope in activations instead of
+    # negative_slope.
+    return activations.relu(
+        inputs,
+        alpha=self.negative_slope,
+        max_value=self.max_value,
+        threshold=self.threshold)
 
   def get_config(self):
-    config = {'max_value': self.max_value}
+    config = {
+        'max_value': self.max_value,
+        'negative_slope': self.negative_slope,
+        'threshold': self.threshold
+    }
     base_config = super(ReLU, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
 
diff --git a/tensorflow/python/keras/layers/advanced_activations_test.py b/tensorflow/python/keras/layers/advanced_activations_test.py
index 9e1f15b1bc508d8be0a2c0190d07eb1c2bed95c4..53c1baa2bbd4367eb09d5bc792de9f20baa981ef 100644
--- a/tensorflow/python/keras/layers/advanced_activations_test.py
+++ b/tensorflow/python/keras/layers/advanced_activations_test.py
@@ -75,6 +75,14 @@ class AdvancedActivationsTest(test.TestCase):
         testing_utils.layer_test(keras.layers.ReLU,
                                  kwargs={'max_value': -10},
                                  input_shape=(2, 3, 4))
+    with self.assertRaisesRegexp(
+        ValueError,
+        'negative_slope of Relu layer cannot be negative value: -2'):
+      with self.test_session():
+        testing_utils.layer_test(
+            keras.layers.ReLU,
+            kwargs={'negative_slope': -2},
+            input_shape=(2, 3, 4))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/python/keras/layers/convolutional_recurrent.py b/tensorflow/python/keras/layers/convolutional_recurrent.py
index 84d794cada86b15755c28592d4c8093a4d3ef87e..e61dd3043d96e69f76cb5bb041de304f5c1c2642 100644
--- a/tensorflow/python/keras/layers/convolutional_recurrent.py
+++ b/tensorflow/python/keras/layers/convolutional_recurrent.py
@@ -788,7 +788,7 @@ class ConvLSTM2D(ConvRNN2D):
 
   Arguments:
     filters: Integer, the dimensionality of the output space
-        (i.e. the number output of filters in the convolution).
+        (i.e. the number of output filters in the convolution).
     kernel_size: An integer or tuple/list of n integers, specifying the
         dimensions of the convolution window.
     strides: An integer or tuple/list of n integers,
diff --git a/tensorflow/python/keras/layers/core.py b/tensorflow/python/keras/layers/core.py
index 2bf6229ccba808360e73a333bdec3dac624d81ce..f28cade474e450174f95c9a8e06e26b04e95eb69 100644
--- a/tensorflow/python/keras/layers/core.py
+++ b/tensorflow/python/keras/layers/core.py
@@ -26,6 +26,7 @@ import warnings
 import numpy as np
 
 from tensorflow.python.eager import context
+from tensorflow.python.framework import common_shapes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.keras import activations
@@ -929,13 +930,13 @@ class Dense(Layer):
 
   def call(self, inputs):
     inputs = ops.convert_to_tensor(inputs, dtype=self.dtype)
-    shape = inputs.get_shape().as_list()
-    if len(shape) > 2:
+    rank = common_shapes.rank(inputs)
+    if rank > 2:
       # Broadcasting is required for the inputs.
-      outputs = standard_ops.tensordot(inputs, self.kernel, [[len(shape) - 1],
-                                                             [0]])
+      outputs = standard_ops.tensordot(inputs, self.kernel, [[rank - 1], [0]])
       # Reshape the output back to the original ndim of the input.
       if not context.executing_eagerly():
+        shape = inputs.get_shape().as_list()
         output_shape = shape[:-1] + [self.units]
         outputs.set_shape(output_shape)
     else:
diff --git a/tensorflow/python/keras/layers/cudnn_recurrent_test.py b/tensorflow/python/keras/layers/cudnn_recurrent_test.py
index 8fd970239f205031954c728474abdf10ea80e99e..2ed0aa8f2684009251e61c92a1ac167f1ba2f0af 100644
--- a/tensorflow/python/keras/layers/cudnn_recurrent_test.py
+++ b/tensorflow/python/keras/layers/cudnn_recurrent_test.py
@@ -220,7 +220,7 @@ class CuDNNTest(test.TestCase, parameterized.TestCase):
         self.assertNotEqual(out4.max(), out5.max())
 
   @parameterized.named_parameters(
-      *testing_utils.generate_combinations_with_testcase_name(
+      *test_util.generate_combinations_with_testcase_name(
           rnn_type=['LSTM', 'GRU'], to_cudnn=[True, False],
           bidirectional=[True, False], implementation=[1, 2],
           model_nest_level=[1, 2], model_type=['seq', 'func']))
@@ -301,7 +301,7 @@ class CuDNNTest(test.TestCase, parameterized.TestCase):
     os.remove(fname)
 
   @parameterized.named_parameters(
-      *testing_utils.generate_combinations_with_testcase_name(
+      *test_util.generate_combinations_with_testcase_name(
           rnn_type=['LSTM', 'GRU'], to_cudnn=[True, False]))
   def test_load_weights_between_noncudnn_rnn_time_distributed(self, rnn_type,
                                                               to_cudnn):
diff --git a/tensorflow/python/keras/layers/embeddings.py b/tensorflow/python/keras/layers/embeddings.py
index 910fff720f6312041a25922cf5c63dfa8f83ec76..629a9ec9a10c8afd4d98174a9183a2e9b08269ea 100644
--- a/tensorflow/python/keras/layers/embeddings.py
+++ b/tensorflow/python/keras/layers/embeddings.py
@@ -112,6 +112,7 @@ class Embedding(Layer):
     self.activity_regularizer = regularizers.get(activity_regularizer)
     self.embeddings_constraint = constraints.get(embeddings_constraint)
     self.mask_zero = mask_zero
+    self.supports_masking = mask_zero
     self.input_length = input_length
 
   @tf_utils.shape_type_conversion
@@ -127,8 +128,8 @@ class Embedding(Layer):
   def compute_mask(self, inputs, mask=None):
     if not self.mask_zero:
       return None
-    else:
-      return math_ops.not_equal(inputs, 0)
+
+    return math_ops.not_equal(inputs, 0)
 
   @tf_utils.shape_type_conversion
   def compute_output_shape(self, input_shape):
diff --git a/tensorflow/python/keras/layers/normalization.py b/tensorflow/python/keras/layers/normalization.py
index 8b894ca6b1c256210bb9ded33ae36da2fc4c001a..a7835bc0a2ad1865c2d98b5f539a6643f2272b81 100644
--- a/tensorflow/python/keras/layers/normalization.py
+++ b/tensorflow/python/keras/layers/normalization.py
@@ -181,12 +181,6 @@ class BatchNormalization(Layer):
       self.renorm_clipping = renorm_clipping
       self.renorm_momentum = renorm_momentum
 
-  def _add_tower_local_variable(self, *args, **kwargs):
-    tower_context = distribute_lib.get_tower_context()
-    with tower_context.tower_local_var_scope(
-        variable_scope.VariableAggregation.MEAN):
-      return self.add_weight(*args, **kwargs)
-
   def build(self, input_shape):
     input_shape = tensor_shape.TensorShape(input_shape)
     if not input_shape.ndims:
@@ -314,19 +308,23 @@ class BatchNormalization(Layer):
         self._scope.set_partitioner(None)
       else:
         partitioner = None
-      self.moving_mean = self._add_tower_local_variable(
+      self.moving_mean = self.add_weight(
           name='moving_mean',
           shape=param_shape,
           dtype=param_dtype,
           initializer=self.moving_mean_initializer,
-          trainable=False)
+          synchronization=variable_scope.VariableSynchronization.ON_READ,
+          trainable=False,
+          aggregation=variable_scope.VariableAggregation.MEAN)
 
-      self.moving_variance = self._add_tower_local_variable(
+      self.moving_variance = self.add_weight(
           name='moving_variance',
           shape=param_shape,
           dtype=param_dtype,
           initializer=self.moving_variance_initializer,
-          trainable=False)
+          synchronization=variable_scope.VariableSynchronization.ON_READ,
+          trainable=False,
+          aggregation=variable_scope.VariableAggregation.MEAN)
 
       if self.renorm:
         # Create variables to maintain the moving mean and standard deviation.
@@ -337,12 +335,14 @@ class BatchNormalization(Layer):
         # stack to be cleared. The nested ones use a `lambda` to set the desired
         # device and ignore any devices that may be set by the custom getter.
         def _renorm_variable(name, shape):
-          var = self._add_tower_local_variable(
+          var = self.add_weight(
               name=name,
               shape=shape,
               dtype=param_dtype,
               initializer=init_ops.zeros_initializer(),
-              trainable=False)
+              synchronization=variable_scope.VariableSynchronization.ON_READ,
+              trainable=False,
+              aggregation=variable_scope.VariableAggregation.MEAN)
           return var
 
         with distribute_lib.get_distribution_strategy().colocate_vars_with(
@@ -370,7 +370,7 @@ class BatchNormalization(Layer):
         decay = ops.convert_to_tensor(1.0 - momentum, name='decay')
         if decay.dtype != variable.dtype.base_dtype:
           decay = math_ops.cast(decay, variable.dtype.base_dtype)
-        update_delta = (variable - value) * decay
+        update_delta = (variable - math_ops.cast(value, variable.dtype)) * decay
         return state_ops.assign_sub(variable, update_delta, name=scope)
 
   def _fused_batch_norm(self, inputs, training):
@@ -619,6 +619,10 @@ class BatchNormalization(Layer):
     else:
       mean, variance = self.moving_mean, self.moving_variance
 
+    mean = math_ops.cast(mean, inputs.dtype)
+    variance = math_ops.cast(variance, inputs.dtype)
+    if offset is not None:
+      offset = math_ops.cast(offset, inputs.dtype)
     outputs = nn.batch_normalization(inputs,
                                      _broadcast(mean),
                                      _broadcast(variance),
diff --git a/tensorflow/python/keras/layers/normalization_test.py b/tensorflow/python/keras/layers/normalization_test.py
index b22f3bd1529812f6b5f63efe5cf6b6133db97f07..a97b4cac469f596112481e1b3b3f93b17ea20074 100644
--- a/tensorflow/python/keras/layers/normalization_test.py
+++ b/tensorflow/python/keras/layers/normalization_test.py
@@ -95,6 +95,24 @@ class NormalizationLayersTest(test.TestCase):
       np.testing.assert_allclose(out.mean(), 0.0, atol=1e-1)
       np.testing.assert_allclose(out.std(), 1.0, atol=1e-1)
 
+  def test_batchnorm_mixed_precision(self):
+    with self.test_session():
+      model = keras.models.Sequential()
+      norm = keras.layers.BatchNormalization(input_shape=(10,), momentum=0.8)
+      model.add(norm)
+      model.compile(loss='mse', optimizer='sgd')
+
+      # centered on 5.0, variance 10.0
+      x = np.random.normal(
+          loc=5.0, scale=10.0, size=(1000, 10)).astype(np.float16)
+      model.fit(x, x, epochs=4, verbose=0)
+      out = model.predict(x)
+      out -= keras.backend.eval(norm.beta)
+      out /= keras.backend.eval(norm.gamma)
+
+      np.testing.assert_allclose(out.mean(), 0.0, atol=1e-1)
+      np.testing.assert_allclose(out.std(), 1.0, atol=1e-1)
+
   def test_batchnorm_convnet(self):
     if test.is_gpu_available(cuda_only=True):
       with self.test_session(use_gpu=True):
diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py
index 32d25c5a650d3b66d944eee945cafa2d6f54d405..534c0eca0898c14d4a99e4bcada64229293cae61 100644
--- a/tensorflow/python/keras/layers/recurrent.py
+++ b/tensorflow/python/keras/layers/recurrent.py
@@ -37,6 +37,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training.checkpointable import base as checkpointable
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -235,7 +236,8 @@ class RNN(Layer):
   """Base class for recurrent layers.
 
   Arguments:
-      cell: A RNN cell instance. A RNN cell is a class that has:
+      cell: A RNN cell instance or a list of RNN cell instances.
+          A RNN cell is a class that has:
           - a `call(input_at_t, states_at_t)` method, returning
               `(output_at_t, states_at_t_plus_1)`. The call method of the
               cell can also take the optional argument `constants`, see
@@ -248,9 +250,9 @@ class RNN(Layer):
               (one size per state). In this case, the first entry
               (`state_size[0]`) should be the same as
               the size of the cell output.
-          It is also possible for `cell` to be a list of RNN cell instances,
-          in which cases the cells get stacked on after the other in the RNN,
-          implementing an efficient stacked RNN.
+          In the case that `cell` is a list of RNN cell instances, the cells
+          will be stacked on after the other in the RNN, implementing an
+          efficient stacked RNN.
       return_sequences: Boolean. Whether to return the last output
           in the output sequence, or the full sequence.
       return_state: Boolean. Whether to return the last state
@@ -402,6 +404,8 @@ class RNN(Layer):
                        'one integer per RNN state).')
     super(RNN, self).__init__(**kwargs)
     self.cell = cell
+    if isinstance(cell, checkpointable.CheckpointableBase):
+      self._track_checkpointable(self.cell, name='cell')
     self.return_sequences = return_sequences
     self.return_state = return_state
     self.go_backwards = go_backwards
diff --git a/tensorflow/python/keras/layers/recurrent_test.py b/tensorflow/python/keras/layers/recurrent_test.py
index 802374d2d28d792c1e32bf5095b928f569144b49..fefb92826b33b65a14ba667207995b6e4194c202 100644
--- a/tensorflow/python/keras/layers/recurrent_test.py
+++ b/tensorflow/python/keras/layers/recurrent_test.py
@@ -28,6 +28,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.platform import test
+from tensorflow.python.training.checkpointable import util as checkpointable_util
 
 
 class RNNTest(test.TestCase):
@@ -556,5 +557,22 @@ class RNNTest(test.TestCase):
         [tuple(o.as_list()) for o in output_shape],
         expected_output_shape)
 
+  def test_checkpointable_dependencies(self):
+    rnn = keras.layers.SimpleRNN
+    with self.test_session():
+      x = np.random.random((2, 2, 2))
+      y = np.random.random((2, 2))
+      model = keras.models.Sequential()
+      model.add(rnn(2))
+      model.compile(optimizer='rmsprop', loss='mse')
+      model.fit(x, y, epochs=1, batch_size=1)
+
+      # check whether the model variables are present in the
+      # checkpointable list of objects
+      checkpointed_objects = set(checkpointable_util.list_objects(model))
+      for v in model.variables:
+        self.assertIn(v, checkpointed_objects)
+
+
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/python/keras/layers/wrappers.py b/tensorflow/python/keras/layers/wrappers.py
index e61acf8e771eb8de1c466ffa5e1c4c7f543f77ef..f0c1e76156f2c01d6fceea6d2a6b4c8b6d79ba69 100644
--- a/tensorflow/python/keras/layers/wrappers.py
+++ b/tensorflow/python/keras/layers/wrappers.py
@@ -47,7 +47,6 @@ class Wrapper(Layer):
   def __init__(self, layer, **kwargs):
     assert isinstance(layer, Layer)
     self.layer = layer
-    self._track_checkpointable(layer, name='layer')
     # Tracks mapping of Wrapper inputs to inner layer inputs. Useful when
     # the inner layer has update ops that depend on its inputs (as opposed
     # to the inputs to the Wrapper layer).
@@ -168,6 +167,39 @@ class TimeDistributed(Wrapper):
           '`Layer` instance. You passed: {input}'.format(input=layer))
     super(TimeDistributed, self).__init__(layer, **kwargs)
     self.supports_masking = True
+    self._track_checkpointable(layer, name='layer')
+
+  def _get_shape_tuple(self, init_tuple, tensor, start_idx, int_shape=None):
+    """Finds non-specific dimensions in the static shapes.
+
+    The static shapes are replaced with the corresponding dynamic shapes of the
+    tensor.
+
+    Arguments:
+        init_tuple: a tuple, the first part of the output shape
+        tensor: the tensor from which to get the (static and dynamic) shapes
+            as the last part of the output shape
+        start_idx: int, which indicate the first dimension to take from
+            the static shape of the tensor
+        int_shape: an alternative static shape to take as the last part
+            of the output shape
+    Returns:
+        The new int_shape with the first part from init_tuple
+        and the last part from either `int_shape` (if provided)
+        or `tensor.shape`, where every `None` is replaced by
+        the corresponding dimension from `tf.shape(tensor)`.
+    """
+    # replace all None in int_shape by K.shape
+    if int_shape is None:
+      int_shape = K.int_shape(tensor)[start_idx:]
+    if not any(not s for s in int_shape):
+      return init_tuple + tuple(int_shape)
+    shape = K.shape(tensor)
+    int_shape = list(int_shape)
+    for i, s in enumerate(int_shape):
+      if not s:
+        int_shape[i] = shape[start_idx + i]
+    return init_tuple + tuple(int_shape)
 
   def build(self, input_shape):
     input_shape = tensor_shape.TensorShape(input_shape).as_list()
@@ -224,18 +256,24 @@ class TimeDistributed(Wrapper):
       input_length = input_shape[1]
       if not input_length:
         input_length = array_ops.shape(inputs)[1]
+      inner_input_shape = self._get_shape_tuple((-1,), inputs, 2)
       # Shape: (num_samples * timesteps, ...). And track the
       # transformation in self._input_map.
       input_uid = generic_utils.object_list_uid(inputs)
-      inputs = array_ops.reshape(inputs, (-1,) + input_shape[2:])
+      inputs = array_ops.reshape(inputs, inner_input_shape)
       self._input_map[input_uid] = inputs
       # (num_samples * timesteps, ...)
+      if generic_utils.has_arg(self.layer.call, 'mask') and mask is not None:
+        inner_mask_shape = self._get_shape_tuple((-1,), mask, 2)
+        kwargs['mask'] = K.reshape(mask, inner_mask_shape)
       y = self.layer.call(inputs, **kwargs)
       if hasattr(y, '_uses_learning_phase'):
         uses_learning_phase = y._uses_learning_phase
       # Shape: (num_samples, timesteps, ...)
       output_shape = self.compute_output_shape(input_shape).as_list()
-      y = array_ops.reshape(y, (-1, input_length) + tuple(output_shape[2:]))
+      output_shape = self._get_shape_tuple(
+          (-1, input_length), y, 1, output_shape[2:])
+      y = array_ops.reshape(y, output_shape)
 
     # Apply activity regularizer if any:
     if (hasattr(self.layer, 'activity_regularizer') and
@@ -247,6 +285,80 @@ class TimeDistributed(Wrapper):
       y._uses_learning_phase = True
     return y
 
+  def compute_mask(self, inputs, mask=None):
+    """Computes an output mask tensor for Embedding layer.
+
+    This is based on the inputs, mask, and the inner layer.
+    If batch size is specified:
+    Simply return the input `mask`. (An rnn-based implementation with
+    more than one rnn inputs is required but not supported in tf.keras yet.)
+    Otherwise we call `compute_mask` of the inner layer at each time step.
+    If the output mask at each time step is not `None`:
+    (E.g., inner layer is Masking or RNN)
+    Concatenate all of them and return the concatenation.
+    If the output mask at each time step is `None` and the input mask is not
+    `None`:(E.g., inner layer is Dense)
+    Reduce the input_mask to 2 dimensions and return it.
+    Otherwise (both the output mask and the input mask are `None`):
+    (E.g., `mask` is not used at all)
+    Return `None`.
+
+    Arguments:
+      inputs: Tensor with shape [batch size, timesteps, ...] indicating the
+          input to TimeDistributed. If static shape information is available for
+          "batch size", `mask` is returned unmodified.
+      mask: Either None (indicating no masking) or a Tensor indicating the
+          input mask for TimeDistributed. The shape can be static or dynamic.
+
+    Returns:
+      Either None (no masking), or a [batch size, timesteps, ...] Tensor with
+      an output mask for the TimeDistributed layer with the shape beyond the
+      second dimension being the value of the input mask shape(if the computed
+      output mask is none), an output mask with the shape beyond the first
+      dimension being the value of the mask shape(if mask is not None) or
+      output mask with the shape beyond the first dimension being the
+      value of the computed output shape.
+
+    """
+    # cases need to call the layer.compute_mask when input_mask is None:
+    # Masking layer and Embedding layer with mask_zero
+    input_shape = K.int_shape(inputs)
+    if input_shape[0]:
+      # batch size matters, we currently do not handle mask explicitly
+      return mask
+    inner_mask = mask
+    if inner_mask is not None:
+      inner_mask_shape = self._get_shape_tuple((-1,), mask, 2)
+      inner_mask = K.reshape(inner_mask, inner_mask_shape)
+    input_uid = generic_utils.object_list_uid(inputs)
+    inner_inputs = self._input_map[input_uid]
+    output_mask = self.layer.compute_mask(inner_inputs, inner_mask)
+    if output_mask is None:
+      if mask is None:
+        return None
+      # input_mask is not None, and output_mask is None:
+      # we should return a not-None mask
+      output_mask = mask
+      for _ in range(2, len(K.int_shape(mask))):
+        output_mask = K.any(output_mask, axis=-1)
+    else:
+      # output_mask is not None. We need to reshape it
+      input_length = input_shape[1]
+      if not input_length:
+        input_length = K.shape(inputs)[1]
+      output_mask_int_shape = K.int_shape(output_mask)
+      if output_mask_int_shape is None:
+        # if the output_mask does not have a static shape,
+        # its shape must be the same as mask's
+        if mask is not None:
+          output_mask_int_shape = K.int_shape(mask)
+        else:
+          output_mask_int_shape = K.compute_output_shape(input_shape)[:-1]
+      output_mask_shape = self._get_shape_tuple(
+          (-1, input_length), output_mask, 1, output_mask_int_shape[1:])
+      output_mask = K.reshape(output_mask, output_mask_shape)
+    return output_mask
+
 
 @tf_export('keras.layers.Bidirectional')
 class Bidirectional(Wrapper):
@@ -305,6 +417,8 @@ class Bidirectional(Wrapper):
     self._num_constants = None
     super(Bidirectional, self).__init__(layer, **kwargs)
     self.input_spec = layer.input_spec
+    self._track_checkpointable(self.forward_layer, name='forward_layer')
+    self._track_checkpointable(self.backward_layer, name='backward_layer')
 
   @property
   def trainable(self):
@@ -414,7 +528,8 @@ class Bidirectional(Wrapper):
     else:
       return super(Bidirectional, self).__call__(inputs, **kwargs)
 
-  def call(self, inputs,
+  def call(self,
+           inputs,
            training=None,
            mask=None,
            initial_state=None,
diff --git a/tensorflow/python/keras/layers/wrappers_test.py b/tensorflow/python/keras/layers/wrappers_test.py
index c8f0d216e6f7a3bb715286bd6e7975a5dc1ac1cc..0cd774ef0fa70ede62d496db981817b58666bcfc 100644
--- a/tensorflow/python/keras/layers/wrappers_test.py
+++ b/tensorflow/python/keras/layers/wrappers_test.py
@@ -87,6 +87,8 @@ class TimeDistributedTest(test.TestCase):
     # test config
     model.get_config()
 
+    # check whether the model variables are present in the
+    # checkpointable list of objects
     checkpointed_objects = set(checkpointable_util.list_objects(model))
     for v in model.variables:
       self.assertIn(v, checkpointed_objects)
@@ -190,8 +192,8 @@ class TimeDistributedTest(test.TestCase):
     x = keras.layers.Input(shape=(3, 2))
     layer = keras.layers.TimeDistributed(keras.layers.BatchNormalization())
     _ = layer(x)
-    assert len(layer.updates) == 2
-    assert len(layer.trainable_weights) == 2
+    self.assertEquals(len(layer.updates), 2)
+    self.assertEquals(len(layer.trainable_weights), 2)
     layer.trainable = False
     assert not layer.updates
     assert not layer.trainable_weights
@@ -199,6 +201,62 @@ class TimeDistributedTest(test.TestCase):
     assert len(layer.updates) == 2
     assert len(layer.trainable_weights) == 2
 
+  def test_TimeDistributed_with_masked_embedding_and_unspecified_shape(self):
+    with self.test_session():
+      # test with unspecified shape and Embeddings with mask_zero
+      model = keras.models.Sequential()
+      model.add(keras.layers.TimeDistributed(
+          keras.layers.Embedding(5, 6, mask_zero=True),
+          input_shape=(None, None)))  # N by t_1 by t_2 by 6
+      model.add(keras.layers.TimeDistributed(
+          keras.layers.SimpleRNN(7, return_sequences=True)))
+      model.add(keras.layers.TimeDistributed(
+          keras.layers.SimpleRNN(8, return_sequences=False)))
+      model.add(keras.layers.SimpleRNN(1, return_sequences=False))
+      model.compile(optimizer='rmsprop', loss='mse')
+      model_input = np.random.randint(low=1, high=5, size=(10, 3, 4),
+                                      dtype='int32')
+      for i in range(4):
+        model_input[i, i:, i:] = 0
+      model.fit(model_input,
+                np.random.random((10, 1)), epochs=1, batch_size=10)
+      mask_outputs = [model.layers[0].compute_mask(model.input)]
+      for layer in model.layers[1:]:
+        mask_outputs.append(layer.compute_mask(layer.input, mask_outputs[-1]))
+      func = keras.backend.function([model.input], mask_outputs[:-1])
+      mask_outputs_val = func([model_input])
+      ref_mask_val_0 = model_input > 0         # embedding layer
+      ref_mask_val_1 = ref_mask_val_0          # first RNN layer
+      ref_mask_val_2 = np.any(ref_mask_val_1, axis=-1)     # second RNN layer
+      ref_mask_val = [ref_mask_val_0, ref_mask_val_1, ref_mask_val_2]
+      for i in range(3):
+        self.assertAllEqual(mask_outputs_val[i], ref_mask_val[i])
+      self.assertIs(mask_outputs[-1], None)  # final layer
+
+  def test_TimeDistributed_with_masking_layer(self):
+    with self.test_session():
+      # test with Masking layer
+      model = keras.models.Sequential()
+      model.add(keras.layers.TimeDistributed(keras.layers.Masking(
+          mask_value=0.,), input_shape=(None, 4)))
+      model.add(keras.layers.TimeDistributed(keras.layers.Dense(5)))
+      model.compile(optimizer='rmsprop', loss='mse')
+      model_input = np.random.randint(low=1, high=5, size=(10, 3, 4))
+      for i in range(4):
+        model_input[i, i:, :] = 0.
+      model.compile(optimizer='rmsprop', loss='mse')
+      model.fit(model_input,
+                np.random.random((10, 3, 5)), epochs=1, batch_size=6)
+      mask_outputs = [model.layers[0].compute_mask(model.input)]
+      mask_outputs += [model.layers[1].compute_mask(model.layers[1].input,
+                                                    mask_outputs[-1])]
+      func = keras.backend.function([model.input], mask_outputs)
+      mask_outputs_val = func([model_input])
+      self.assertEqual((mask_outputs_val[0]).all(),
+                       model_input.all())
+      self.assertEqual((mask_outputs_val[1]).all(),
+                       model_input.all())
+
 
 class BidirectionalTest(test.TestCase):
 
@@ -222,6 +280,12 @@ class BidirectionalTest(test.TestCase):
         model.compile(optimizer=RMSPropOptimizer(0.01), loss='mse')
         model.fit(x, y, epochs=1, batch_size=1)
 
+        # check whether the model variables are present in the
+        # checkpointable list of objects
+        checkpointed_objects = set(checkpointable_util.list_objects(model))
+        for v in model.variables:
+          self.assertIn(v, checkpointed_objects)
+
         # test compute output shape
         ref_shape = model.layers[-1].output.get_shape()
         shape = model.layers[-1].compute_output_shape(
diff --git a/tensorflow/python/keras/metrics.py b/tensorflow/python/keras/metrics.py
index e03d7dfe93585efd06f4701a8d20f61fc314d564..7d8b1fec45cc53fa0a5fc0da269772fbf16653ce 100644
--- a/tensorflow/python/keras/metrics.py
+++ b/tensorflow/python/keras/metrics.py
@@ -19,9 +19,18 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from abc import ABCMeta
+from abc import abstractmethod
+
+import types
 import six
 
+from tensorflow.python.eager import context
+from tensorflow.python.eager import function
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
 from tensorflow.python.keras import backend as K
+from tensorflow.python.keras.engine.base_layer import Layer
 from tensorflow.python.keras.losses import binary_crossentropy
 from tensorflow.python.keras.losses import categorical_crossentropy
 from tensorflow.python.keras.losses import cosine_proximity
@@ -37,14 +46,471 @@ from tensorflow.python.keras.losses import sparse_categorical_crossentropy
 from tensorflow.python.keras.losses import squared_hinge
 from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
 from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import confusion_matrix
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
+from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import variable_scope as vs
+from tensorflow.python.ops import weights_broadcast_ops
+from tensorflow.python.training import distribute as distribute_lib
+from tensorflow.python.util import tf_decorator
 from tensorflow.python.util.tf_export import tf_export
 
 
+def check_is_tensor_or_operation(x, name):
+  """Raises type error if the given input is not a tensor or operation."""
+  if not (isinstance(x, ops.Tensor) or isinstance(x, ops.Operation)):
+    raise TypeError('{0} must be a Tensor or Operation, given: {1}'.format(
+        name, x))
+
+
+def update_state_wrapper(update_state_fn):
+  """Decorator to wrap metric `update_state()` with `defun()`, `add_update()`.
+
+  Args:
+    update_state_fn: function that accumulates metric statistics.
+
+  Returns:
+    If eager execution is enabled, returns None.
+    If graph execution is enabled, returns an update op. This op should be
+      executed to update the metric state with the given inputs.
+  """
+
+  def decorated(metric_obj, *args, **kwargs):
+    """Decorated function with `defun()` and `add_update()`."""
+
+    # Converting update_state_fn() into a graph function, so that
+    # we can return a single op that performs all of the variable updates.
+    # Assigning to a different method name to avoid reference cycle.
+    defuned_update_state_fn = function.defun(update_state_fn)
+    update_op = defuned_update_state_fn(*args, **kwargs)
+    if update_op is not None:  # update_op will be None in eager execution.
+      metric_obj.add_update(update_op, inputs=True)
+      check_is_tensor_or_operation(
+          update_op, 'Metric {0}\'s update'.format(metric_obj.name))
+    return update_op
+
+  return tf_decorator.make_decorator(update_state_fn, decorated)
+
+
+def result_wrapper(result_fn):
+  """Decorator to wrap metric `result()` function in `merge_call()`.
+
+  Result computation is an idempotent operation that simply calculates the
+  metric value using the state variables.
+
+  If metric state variables are distributed across towers/devices and
+  `result()` is requested from the context of one device - This function wraps
+  `result()` in a distribution strategy `merge_call()`. With this,
+  the metric state variables will be aggregated across devices.
+
+  Args:
+    result_fn: function that computes the metric result.
+
+  Returns:
+    The metric result tensor.
+  """
+
+  def decorated(metric_obj, *args):
+    """Decorated function with merge_call."""
+    tower_context = distribute_lib.get_tower_context()
+    if tower_context is None:  # if in cross tower context already
+      result_t = result_fn(*args)
+    else:
+      # TODO(psv): Test distribution of metrics using different distribution
+      # strategies.
+
+      # Creating a wrapper for merge_fn. merge_call invokes the given merge_fn
+      # with distribution object as the first parameter. We create a wrapper
+      # here so that the result function need not have that parameter.
+      def merge_fn_wrapper(distribution, merge_fn, *args):
+        # We will get `PerDevice` merge function. Taking the first one as all
+        # are identical copies of the function that we had passed below.
+        return distribution.unwrap(merge_fn)[0](*args)
+
+      # Wrapping result in merge_call. merge_call is used when we want to leave
+      # tower mode and compute a value in cross tower mode.
+      result_t = tower_context.merge_call(merge_fn_wrapper, result_fn, *args)
+    check_is_tensor_or_operation(result_t,
+                                 'Metric {0}\'s result'.format(metric_obj.name))
+    return result_t
+
+  return tf_decorator.make_decorator(result_fn, decorated)
+
+
+def _safe_div(numerator, denominator):
+  """Divides two tensors element-wise, returning 0 if the denominator is <= 0.
+
+  Args:
+    numerator: A `Tensor`.
+    denominator: A `Tensor`, with dtype matching `numerator`.
+
+  Returns:
+    0 if `denominator` <= 0, else `numerator` / `denominator`
+  """
+  t = math_ops.truediv(numerator, denominator)
+  zero = array_ops.zeros_like(t, dtype=denominator.dtype)
+  condition = math_ops.greater(denominator, zero)
+  zero = math_ops.cast(zero, t.dtype)
+  return array_ops.where(condition, t, zero)
+
+
+def _squeeze_or_expand_dimensions(y_pred, y_true, sample_weight):
+  """Squeeze or expand last dimension if needed.
+
+  1. Squeezes last dim of `y_pred` or `y_true` if their rank differs by 1
+  (using `confusion_matrix.remove_squeezable_dimensions`).
+  2. Squeezes or expands last dim of `sample_weight` if its rank differs by 1
+  from the new rank of `y_pred`.
+  If `sample_weight` is scalar, it is kept scalar.
+
+  This will use static shape if available. Otherwise, it will add graph
+  operations, which could result in a performance hit.
+
+  Args:
+    y_pred: Predicted values, a `Tensor` of arbitrary dimensions.
+    y_true: Optional label `Tensor` whose dimensions match `y_pred`.
+    sample_weight: Optional weight scalar or `Tensor` whose dimensions match
+      `y_pred`.
+
+  Returns:
+    Tuple of `y_pred`, `y_true` and `sample_weight`. Each of them possibly has
+    the last dimension squeezed,
+    `sample_weight` could be extended by one dimension.
+  """
+  if y_true is not None:
+    # squeeze last dim of `y_pred` or `y_true` if their rank differs by 1
+    y_true, y_pred = confusion_matrix.remove_squeezable_dimensions(
+        y_true, y_pred)
+    y_pred.get_shape().assert_is_compatible_with(y_true.get_shape())
+
+  if sample_weight is None:
+    return y_pred, y_true, None
+
+  sample_weight = ops.convert_to_tensor(sample_weight)
+  weights_shape = sample_weight.get_shape()
+  weights_rank = weights_shape.ndims
+  if weights_rank == 0:  # If weights is scalar, do nothing.
+    return y_pred, y_true, sample_weight
+
+  y_pred_shape = y_pred.get_shape()
+  y_pred_rank = y_pred_shape.ndims
+  if (y_pred_rank is not None) and (weights_rank is not None):
+    # Use static rank.
+    if weights_rank - y_pred_rank == 1:
+      sample_weight = array_ops.squeeze(sample_weight, [-1])
+    elif y_pred_rank - weights_rank == 1:
+      sample_weight = array_ops.expand_dims(sample_weight, [-1])
+    return y_pred, y_true, sample_weight
+
+  # Use dynamic rank.
+  weights_rank_tensor = array_ops.rank(sample_weight)
+  rank_diff = weights_rank_tensor - array_ops.rank(y_pred)
+  maybe_squeeze_weights = lambda: array_ops.squeeze(sample_weight, [-1])
+
+  def _maybe_expand_weights():
+    return control_flow_ops.cond(
+        math_ops.equal(rank_diff,
+                       -1), lambda: array_ops.expand_dims(sample_weight, [-1]),
+        lambda: sample_weight)
+
+  def _maybe_adjust_weights():
+    return control_flow_ops.cond(
+        math_ops.equal(rank_diff, 1), maybe_squeeze_weights,
+        _maybe_expand_weights)
+
+  # squeeze or expand last dim of `sample_weight` if its rank differs by 1
+  # from the new rank of `y_pred`.
+  sample_weight = control_flow_ops.cond(
+      math_ops.equal(weights_rank_tensor, 0), lambda: sample_weight,
+      _maybe_adjust_weights)
+  return y_pred, y_true, sample_weight
+
+
+class Metric(Layer):
+  """Encapsulates metric logic and state.
+
+  Usage with eager execution:
+
+  ```python
+  m = SomeMetric(...)
+  for input in ...:
+    m.update_state(input)
+  print('Final result: ', m.result().numpy())
+  ```
+
+  Usage with graph execution:
+
+  ```python
+  m = SomeMetric(...)
+  init_op = tf.global_variables_initializer()  # Initialize variables
+  with tf.Session() as sess:
+    sess.run(init_op)
+    for input in ...:
+      update_op = m.update_state(input)
+      sess.run(update_op)
+    print('Final result: ', sess.run(m.result()))
+  ```
+
+  To be implemented by subclasses:
+  * `__init__()`: All state variables should be created in this method by
+    calling `self.add_weight()` like: `self.var = self.add_weight(...)`
+  * `update_state()`: Has all updates to the state variables like:
+    self.var.assign_add(...).
+  * `result()`: Computes and returns a value for the metric
+    from the state variables.
+
+  Example subclass implementation:
+
+  ```
+  class BinaryTruePositives(Metric):
+    def __init__(self, name='binary-true-positives', dtype=None):
+      super(BinaryTruePositives, self).__init__(name=name, dtype=dtype)
+      self.true_positives = self.add_weight(
+          'true_positives', initializer=init_ops.zeros_initializer)
+
+    def update_state(self, y_true, y_pred, sample_weight=None):
+      y_true = math_ops.cast(y_true, dtypes.bool)
+      y_pred = math_ops.cast(y_pred, dtypes.bool)
+      y_pred, y_true, sample_weight = _squeeze_or_expand_dimensions(
+          y_pred, y_true, sample_weight)
+
+      values = math_ops.logical_and(
+          math_ops.equal(y_true, True), math_ops.equal(y_pred, True))
+      values = math_ops.cast(values, self._dtype)
+      if sample_weight is not None:
+        sample_weight = math_ops.cast(sample_weight, self._dtype)
+        values = math_ops.multiply(values, sample_weight)
+      state_ops.assign_add(self.true_positives, math_ops.reduce_sum(values))
+
+    def result(self):
+      return array_ops.identity(self.true_positives)
+  ```
+  """
+  __metaclass__ = ABCMeta
+
+  def __init__(self, name=None, dtype=None):
+    super(Metric, self).__init__(name=name, dtype=dtype)
+    self.stateful = True  # All metric layers are stateful.
+    self.built = True
+    self._dtype = K.floatx() if dtype is None else dtypes.as_dtype(dtype).name
+
+  def __new__(cls, *args, **kwargs):
+    obj = super(Metric, cls).__new__(cls, *args, **kwargs)
+    obj.update_state = types.MethodType(
+        update_state_wrapper(obj.update_state), obj)
+    obj.result = types.MethodType(result_wrapper(obj.result), obj)
+    return obj
+
+  def __call__(self, *args, **kwargs):
+    """Accumulates statistics and then computes metric result value.
+
+    Args:
+      *args:
+      **kwargs: A mini-batch of inputs to the Metric,
+        passed on to `update_state()`.
+
+    Returns:
+      The metric value tensor.
+    """
+    update_op = self.update_state(*args, **kwargs)  # pylint: disable=not-callable
+    with ops.control_dependencies([update_op]):
+      return self.result()  # pylint: disable=not-callable
+
+  def reset_states(self):
+    """Resets all of the metric state variables.
+
+    This function is called between epochs/steps,
+    when a metric is evaluated during training.
+    """
+    for v in self.variables:
+      K.set_value(v, 0)
+
+  @abstractmethod
+  def update_state(self, *args, **kwargs):
+    """Accumulates statistics for the metric.
+
+    Note: This function is executed as a graph function in graph mode.
+    This means:
+      a) Operations on the same resource are executed in textual order.
+         This should make it easier to do things like add the updated
+         value of a variable to another, for example.
+      b) You don't need to worry about collecting the update ops to execute.
+         All update ops added to the graph by this function will be executed.
+      As a result, code should generally work the same way with graph or
+      eager execution.
+    and adds the update op to the metric layer.
+
+    Args:
+      *args:
+      **kwargs: A mini-batch of inputs to the Metric.
+    """
+    NotImplementedError('Must be implemented in subclasses.')
+
+  @abstractmethod
+  def result(self):
+    """Computes and returns the metric value tensor.
+
+    Result computation is an idempotent operation that simply calculates the
+    metric value using the state variables.
+    """
+    NotImplementedError('Must be implemented in subclasses.')
+
+  ### For use by subclasses ###
+  def add_weight(self,
+                 name,
+                 shape=(),
+                 aggregation=vs.VariableAggregation.SUM,
+                 synchronization=vs.VariableSynchronization.ON_READ,
+                 initializer=None):
+    """Adds state variable. Only for use by subclasses."""
+    return super(Metric, self).add_weight(
+        name=name,
+        shape=shape,
+        dtype=self._dtype,
+        trainable=False,
+        initializer=initializer,
+        synchronization=synchronization,
+        aggregation=aggregation)
+
+  ### End: For use by subclasses ###
+
+
+class Mean(Metric):
+  """Computes the (weighted) mean of the given values.
+
+  This metric creates two variables, `total` and `count` that are used to
+  compute the average of `values`. This average is ultimately returned as `mean`
+  which is an idempotent operation that simply divides `total` by `count`.
+
+  If `sample_weight` is `None`, weights default to 1.
+  Use `sample_weight` of 0 to mask values.
+  """
+
+  def __init__(self, name='mean', dtype=None):
+    """Creates a `Mean` instance.
+
+    Args:
+      name: (Optional) string name of the metric instance.
+      dtype: (Optional) data type of the metric result.
+    """
+    super(Mean, self).__init__(name=name, dtype=dtype)
+    # Create new state variables
+    self.total = self.add_weight(
+        'total', initializer=init_ops.zeros_initializer)
+    self.count = self.add_weight(
+        'count', initializer=init_ops.zeros_initializer)
+
+  def update_state(self, values, sample_weight=None):
+    """Accumulates statistics for computing the mean.
+
+    For example, if `values` is [1, 3, 5, 7] then the mean is 4. If
+    the `sample_weight` is specified as [1, 1, 0, 0] then the mean would be 2.
+
+    Args:
+      values: Per-example value.
+      sample_weight: Optional weighting of each example. Defaults to 1.
+    """
+    values = math_ops.cast(values, self._dtype)
+    if sample_weight is None:
+      num_values = math_ops.cast(array_ops.size(values), self._dtype)
+    else:
+      sample_weight = math_ops.cast(sample_weight, self._dtype)
+
+      # Update dimensions of weights to match with values.
+      values, _, sample_weight = _squeeze_or_expand_dimensions(
+          values, None, sample_weight)
+      sample_weight = weights_broadcast_ops.broadcast_weights(
+          sample_weight, values)
+      num_values = math_ops.reduce_sum(sample_weight)
+      values = math_ops.multiply(values, sample_weight)
+    values = math_ops.reduce_sum(values)
+
+    # Update state variables
+    state_ops.assign_add(self.total, values)
+    state_ops.assign_add(self.count, num_values)
+
+  def result(self):
+    return _safe_div(self.total, self.count)
+
+
+class MeanMetricWrapper(Mean):
+  """Wraps a stateless metric function with the Mean metric."""
+
+  def __init__(self, fn, name=None, dtype=None, **kwargs):
+    """Creates a `MeanMetricWrapper` instance.
+
+    Args:
+      fn: The metric function to wrap, with signature
+        `fn(y_true, y_pred, **kwargs)`.
+      name: (Optional) string name of the metric instance.
+      dtype: (Optional) data type of the metric result.
+      **kwargs: The keyword arguments that are passed on to `fn`.
+    """
+    super(MeanMetricWrapper, self).__init__(name=name, dtype=dtype)
+    self._fn = fn
+    self._fn_kwargs = kwargs
+
+  def update_state(self, y_true, y_pred, sample_weight=None):
+    """Accumulates metric statistics.
+
+    `y_true` and `y_pred` should have the same shape.
+
+    Args:
+      y_true: The ground truth values.
+      y_pred: The predicted values.
+      sample_weight: Optional weighting of each example. Defaults to 1. Can be
+        a `Tensor` whose rank is either 0, or the same rank as `y_true`,
+        and must be broadcastable to `y_true`.
+    """
+    y_true = math_ops.cast(y_true, self._dtype)
+    y_pred = math_ops.cast(y_pred, self._dtype)
+    y_pred, y_true, sample_weight = _squeeze_or_expand_dimensions(
+        y_pred, y_true, sample_weight)
+
+    matches = self._fn(y_true, y_pred, **self._fn_kwargs)
+    super(MeanMetricWrapper, self).update_state(
+        matches, sample_weight=sample_weight)
+
+  def get_config(self):
+    config = self._fn_kwargs
+    base_config = super(MeanMetricWrapper, self).get_config()
+    return dict(list(base_config.items()) + list(config.items()))
+
+
+class BinaryAccuracy(MeanMetricWrapper):
+  """Calculates how often predictions matches labels.
+
+  This metric creates two local variables, `total` and `count` that are used to
+  compute the frequency with which `y_pred` matches `y_true`. This frequency is
+  ultimately returned as `binary accuracy`: an idempotent operation that simply
+  divides `total` by `count`.
+
+  If `sample_weight` is `None`, weights default to 1.
+  Use `sample_weight` of 0 to mask values.
+  """
+
+  def __init__(self, name='binary-accuracy', dtype=None, threshold=0.5):
+    """Creates a `BinaryAccuracy` instance.
+
+    Args:
+      name: (Optional) string name of the metric instance.
+      dtype: (Optional) data type of the metric result.
+      threshold: (Optional) Float representing the threshold for deciding
+      whether prediction values are 1 or 0.
+    """
+    super(BinaryAccuracy, self).__init__(
+        binary_accuracy, name, dtype=dtype, threshold=threshold)
+
+
 @tf_export('keras.metrics.binary_accuracy')
-def binary_accuracy(y_true, y_pred):
-  return K.mean(math_ops.equal(y_true, math_ops.round(y_pred)), axis=-1)
+def binary_accuracy(y_true, y_pred, threshold=0.5):
+  threshold = math_ops.cast(threshold, y_pred.dtype)
+  y_pred = math_ops.cast(y_pred > threshold, y_pred.dtype)
+  return K.mean(math_ops.equal(y_true, y_pred), axis=-1)
 
 
 @tf_export('keras.metrics.categorical_accuracy')
diff --git a/tensorflow/python/keras/metrics_test.py b/tensorflow/python/keras/metrics_test.py
index 15e793f5fcf0b416978095da370fbdaabd1490a6..d5833797080c5a40ddd4e7f905a2641d80f66425 100644
--- a/tensorflow/python/keras/metrics_test.py
+++ b/tensorflow/python/keras/metrics_test.py
@@ -18,67 +18,72 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
 import numpy as np
 
-from tensorflow.python import keras
+from tensorflow.python.eager import context
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import test_util
+from tensorflow.python.keras import backend as K
+from tensorflow.python.keras import layers
+from tensorflow.python.keras import metrics
+from tensorflow.python.keras.engine.training import Model
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
+from tensorflow.python.training.checkpointable import util as checkpointable_utils
 
 
 class KerasMetricsTest(test.TestCase):
 
   def test_metrics(self):
     with self.test_session():
-      y_a = keras.backend.variable(np.random.random((6, 7)))
-      y_b = keras.backend.variable(np.random.random((6, 7)))
-      for metric in [keras.metrics.binary_accuracy,
-                     keras.metrics.categorical_accuracy]:
+      y_a = K.variable(np.random.random((6, 7)))
+      y_b = K.variable(np.random.random((6, 7)))
+      for metric in [metrics.binary_accuracy, metrics.categorical_accuracy]:
         output = metric(y_a, y_b)
-        self.assertEqual(keras.backend.eval(output).shape, (6,))
+        self.assertEqual(K.eval(output).shape, (6,))
 
   def test_sparse_categorical_accuracy(self):
     with self.test_session():
-      metric = keras.metrics.sparse_categorical_accuracy
-      y_a = keras.backend.variable(np.random.randint(0, 7, (6,)))
-      y_b = keras.backend.variable(np.random.random((6, 7)))
-      self.assertEqual(keras.backend.eval(metric(y_a, y_b)).shape, (6,))
+      metric = metrics.sparse_categorical_accuracy
+      y_a = K.variable(np.random.randint(0, 7, (6,)))
+      y_b = K.variable(np.random.random((6, 7)))
+      self.assertEqual(K.eval(metric(y_a, y_b)).shape, (6,))
 
   def test_sparse_top_k_categorical_accuracy(self):
     with self.test_session():
-      y_pred = keras.backend.variable(np.array([[0.3, 0.2, 0.1],
-                                                [0.1, 0.2, 0.7]]))
-      y_true = keras.backend.variable(np.array([[1], [0]]))
-      result = keras.backend.eval(
-          keras.metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=3))
+      y_pred = K.variable(np.array([[0.3, 0.2, 0.1], [0.1, 0.2, 0.7]]))
+      y_true = K.variable(np.array([[1], [0]]))
+      result = K.eval(
+          metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=3))
       self.assertEqual(result, 1)
-      result = keras.backend.eval(
-          keras.metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=2))
+      result = K.eval(
+          metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=2))
       self.assertEqual(result, 0.5)
-      result = keras.backend.eval(
-          keras.metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=1))
+      result = K.eval(
+          metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=1))
       self.assertEqual(result, 0.)
 
   def test_top_k_categorical_accuracy(self):
     with self.test_session():
-      y_pred = keras.backend.variable(np.array([[0.3, 0.2, 0.1],
-                                                [0.1, 0.2, 0.7]]))
-      y_true = keras.backend.variable(np.array([[0, 1, 0], [1, 0, 0]]))
-      result = keras.backend.eval(
-          keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=3))
+      y_pred = K.variable(np.array([[0.3, 0.2, 0.1], [0.1, 0.2, 0.7]]))
+      y_true = K.variable(np.array([[0, 1, 0], [1, 0, 0]]))
+      result = K.eval(metrics.top_k_categorical_accuracy(y_true, y_pred, k=3))
       self.assertEqual(result, 1)
-      result = keras.backend.eval(
-          keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=2))
+      result = K.eval(metrics.top_k_categorical_accuracy(y_true, y_pred, k=2))
       self.assertEqual(result, 0.5)
-      result = keras.backend.eval(
-          keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=1))
+      result = K.eval(metrics.top_k_categorical_accuracy(y_true, y_pred, k=1))
       self.assertEqual(result, 0.)
 
   def test_stateful_metrics(self):
     with self.test_session():
       np.random.seed(1334)
 
-      class BinaryTruePositives(keras.layers.Layer):
+      class BinaryTruePositives(layers.Layer):
         """Stateful Metric to count the total true positives over all batches.
 
         Assumes predictions and targets of shape `(samples, 1)`.
@@ -91,11 +96,11 @@ class KerasMetricsTest(test.TestCase):
 
         def __init__(self, name='true_positives', **kwargs):
           super(BinaryTruePositives, self).__init__(name=name, **kwargs)
-          self.true_positives = keras.backend.variable(value=0, dtype='int32')
+          self.true_positives = K.variable(value=0, dtype='int32')
           self.stateful = True
 
         def reset_states(self):
-          keras.backend.set_value(self.true_positives, 0)
+          K.set_value(self.true_positives, 0)
 
         def __call__(self, y_true, y_pred):
           """Computes the number of true positives in a batch.
@@ -120,14 +125,14 @@ class KerasMetricsTest(test.TestCase):
           return current_true_pos + true_pos
 
       metric_fn = BinaryTruePositives()
-      config = keras.metrics.serialize(metric_fn)
-      metric_fn = keras.metrics.deserialize(
+      config = metrics.serialize(metric_fn)
+      metric_fn = metrics.deserialize(
           config, custom_objects={'BinaryTruePositives': BinaryTruePositives})
 
       # Test on simple model
-      inputs = keras.Input(shape=(2,))
-      outputs = keras.layers.Dense(1, activation='sigmoid')(inputs)
-      model = keras.Model(inputs, outputs)
+      inputs = layers.Input(shape=(2,))
+      outputs = layers.Dense(1, activation='sigmoid')(inputs)
+      model = Model(inputs, outputs)
       model.compile(optimizer='sgd',
                     loss='binary_crossentropy',
                     metrics=['acc', metric_fn])
@@ -184,6 +189,214 @@ class KerasMetricsTest(test.TestCase):
       self.assertAllClose(
           val_outs[2], history.history['val_true_positives'][-1], atol=1e-5)
 
+  @test_util.run_in_graph_and_eager_modes
+  def test_mean(self):
+    m = metrics.Mean(name='my_mean')
+
+    # check config
+    self.assertEqual(m.name, 'my_mean')
+    self.assertTrue(m.stateful)
+    self.assertEqual(m.dtype, dtypes.float32)
+    self.assertEqual(len(m.variables), 2)
+    self.evaluate(variables.global_variables_initializer())
+
+    # check initial state
+    self.assertEqual(self.evaluate(m.total), 0)
+    self.assertEqual(self.evaluate(m.count), 0)
+
+    # check __call__()
+    self.assertEqual(self.evaluate(m(100)), 100)
+    self.assertEqual(self.evaluate(m.total), 100)
+    self.assertEqual(self.evaluate(m.count), 1)
+
+    # check update_state() and result() + state accumulation + tensor input
+    update_op = m.update_state(ops.convert_n_to_tensor([1, 5]))
+    self.evaluate(update_op)
+    self.assertAlmostEqual(self.evaluate(m.result()), 106 / 3, 2)
+    self.assertEqual(self.evaluate(m.total), 106)  # 100 + 1 + 5
+    self.assertEqual(self.evaluate(m.count), 3)
+
+    # check reset_states()
+    m.reset_states()
+    self.assertEqual(self.evaluate(m.total), 0)
+    self.assertEqual(self.evaluate(m.count), 0)
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_mean_with_sample_weight(self):
+    m = metrics.Mean(dtype=dtypes.float64)
+    self.assertEqual(m.dtype, dtypes.float64)
+    self.evaluate(variables.global_variables_initializer())
+
+    # check scalar weight
+    result_t = m(100, sample_weight=0.5)
+    self.assertEqual(self.evaluate(result_t), 50 / 0.5)
+    self.assertEqual(self.evaluate(m.total), 50)
+    self.assertEqual(self.evaluate(m.count), 0.5)
+
+    # check weights not scalar and weights rank matches values rank
+    result_t = m([1, 5], sample_weight=[1, 0.2])
+    result = self.evaluate(result_t)
+    self.assertAlmostEqual(result, 52 / 1.7, 2)
+    self.assertAlmostEqual(self.evaluate(m.total), 52, 2)  # 50 + 1 + 5 * 0.2
+    self.assertAlmostEqual(self.evaluate(m.count), 1.7, 2)  # 0.5 + 1.2
+
+    # check weights broadcast
+    result_t = m([1, 2], sample_weight=0.5)
+    self.assertAlmostEqual(self.evaluate(result_t), 53.5 / 2.7, 2)
+    self.assertAlmostEqual(self.evaluate(m.total), 53.5, 2)  # 52 + 0.5 + 1
+    self.assertAlmostEqual(self.evaluate(m.count), 2.7, 2)  # 1.7 + 0.5 + 0.5
+
+    # check weights squeeze
+    result_t = m([1, 5], sample_weight=[[1], [0.2]])
+    self.assertAlmostEqual(self.evaluate(result_t), 55.5 / 3.9, 2)
+    self.assertAlmostEqual(self.evaluate(m.total), 55.5, 2)  # 53.5 + 1 + 1
+    self.assertAlmostEqual(self.evaluate(m.count), 3.9, 2)  # 2.7 + 1.2
+
+    # check weights expand
+    result_t = m([[1], [5]], sample_weight=[1, 0.2])
+    self.assertAlmostEqual(self.evaluate(result_t), 57.5 / 5.1, 2)
+    self.assertAlmostEqual(self.evaluate(m.total), 57.5, 2)  # 55.5 + 1 + 1
+    self.assertAlmostEqual(self.evaluate(m.count), 5.1, 2)  # 3.9 + 1.2
+
+  def test_mean_graph_with_placeholder(self):
+    with context.graph_mode(), self.test_session() as sess:
+      m = metrics.Mean()
+      v = array_ops.placeholder(dtypes.float32)
+      w = array_ops.placeholder(dtypes.float32)
+      sess.run(variables.global_variables_initializer())
+
+      # check __call__()
+      result_t = m(v, sample_weight=w)
+      result = sess.run(result_t, feed_dict=({v: 100, w: 0.5}))
+      self.assertEqual(sess.run(m.total), 50)
+      self.assertEqual(sess.run(m.count), 0.5)
+      self.assertEqual(result, 50 / 0.5)
+
+      # check update_state() and result()
+      result = sess.run(result_t, feed_dict=({v: [1, 5], w: [1, 0.2]}))
+      self.assertAlmostEqual(sess.run(m.total), 52, 2)  # 50 + 1 + 5 * 0.2
+      self.assertAlmostEqual(sess.run(m.count), 1.7, 2)  # 0.5 + 1.2
+      self.assertAlmostEqual(result, 52 / 1.7, 2)
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_save_restore(self):
+    checkpoint_directory = self.get_temp_dir()
+    checkpoint_prefix = os.path.join(checkpoint_directory, 'ckpt')
+    m = metrics.Mean()
+    checkpoint = checkpointable_utils.Checkpoint(mean=m)
+    self.evaluate(variables.global_variables_initializer())
+
+    # update state
+    self.evaluate(m(100.))
+    self.evaluate(m(200.))
+
+    # save checkpoint and then add an update
+    save_path = checkpoint.save(checkpoint_prefix)
+    self.evaluate(m(1000.))
+
+    # restore to the same checkpoint mean object
+    checkpoint.restore(save_path).assert_consumed().run_restore_ops()
+    self.evaluate(m(300.))
+    self.assertEqual(200., self.evaluate(m.result()))
+
+    # restore to a different checkpoint mean object
+    restore_mean = metrics.Mean()
+    restore_checkpoint = checkpointable_utils.Checkpoint(mean=restore_mean)
+    status = restore_checkpoint.restore(save_path)
+    restore_update = restore_mean(300.)
+    status.assert_consumed().run_restore_ops()
+    self.evaluate(restore_update)
+    self.assertEqual(200., self.evaluate(restore_mean.result()))
+    self.assertEqual(3, self.evaluate(restore_mean.count))
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_binary_accuracy(self):
+    acc_obj = metrics.BinaryAccuracy(name='my acc')
+
+    # check config
+    self.assertEqual(acc_obj.name, 'my acc')
+    self.assertTrue(acc_obj.stateful)
+    self.assertEqual(len(acc_obj.variables), 2)
+    self.assertEqual(acc_obj.dtype, dtypes.float32)
+    self.evaluate(variables.global_variables_initializer())
+
+    # verify that correct value is returned
+    update_op = acc_obj.update_state([[1], [0]], [[1], [0]])
+    self.evaluate(update_op)
+    result = self.evaluate(acc_obj.result())
+    self.assertEqual(result, 1)  # 2/2
+
+    # check y_pred squeeze
+    update_op = acc_obj.update_state([[1], [1]], [[[1]], [[0]]])
+    self.evaluate(update_op)
+    result = self.evaluate(acc_obj.result())
+    self.assertAlmostEqual(result, 0.75, 2)  # 3/4
+
+    # check y_true squeeze
+    result_t = acc_obj([[[1]], [[1]]], [[1], [0]])
+    result = self.evaluate(result_t)
+    self.assertAlmostEqual(result, 0.67, 2)  # 4/6
+
+    # check with sample_weight
+    result_t = acc_obj([[1], [1]], [[1], [0]], [[0.5], [0.2]])
+    result = self.evaluate(result_t)
+    self.assertAlmostEqual(result, 0.67, 2)  # 4.5/6.7
+
+    # check incompatible shapes
+    with self.assertRaisesRegexp(ValueError,
+                                 r'Shapes \(1,\) and \(2,\) are incompatible'):
+      acc_obj.update_state([1, 1], [1])
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_binary_accuracy_threshold(self):
+    acc_obj = metrics.BinaryAccuracy(threshold=0.7)
+    self.evaluate(variables.global_variables_initializer())
+    result_t = acc_obj([[1], [1], [0], [0]], [[0.9], [0.6], [0.4], [0.8]])
+    result = self.evaluate(result_t)
+    self.assertAlmostEqual(result, 0.5, 2)
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_invalid_result(self):
+
+    class InvalidResult(metrics.Metric):
+
+      def __init__(self, name='invalid-result', dtype=dtypes.float64):
+        super(InvalidResult, self).__init__(name=name, dtype=dtype)
+
+      def update_state(self, *args, **kwargs):
+        pass
+
+      def result(self):
+        return 1
+
+    invalid_result_obj = InvalidResult()
+    with self.assertRaisesRegexp(
+        TypeError,
+        'Metric invalid-result\'s result must be a Tensor or Operation, given:'
+    ):
+      invalid_result_obj.result()
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_invalid_update(self):
+
+    class InvalidUpdate(metrics.Metric):
+
+      def __init__(self, name='invalid-update', dtype=dtypes.float64):
+        super(InvalidUpdate, self).__init__(name=name, dtype=dtype)
+
+      def update_state(self, *args, **kwargs):
+        return [1]
+
+      def result(self):
+        pass
+
+    invalid_update_obj = InvalidUpdate()
+    with self.assertRaisesRegexp(
+        TypeError,
+        'Metric invalid-update\'s update must be a Tensor or Operation, given:'
+    ):
+      invalid_update_obj.update_state()
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/python/keras/model_subclassing_test.py b/tensorflow/python/keras/model_subclassing_test.py
index 3ac4852eff6910a9861ae959f990978cea33d595..3a153573f8d5b23d40ffcac6721550507100d16b 100644
--- a/tensorflow/python/keras/model_subclassing_test.py
+++ b/tensorflow/python/keras/model_subclassing_test.py
@@ -29,6 +29,8 @@ from tensorflow.python.eager import context
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import embedding_ops
+from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.platform import test
 from tensorflow.python.training.checkpointable import data_structures
@@ -65,6 +67,22 @@ class SimpleTestModel(keras.Model):
     return self.dense2(x)
 
 
+class SimpleConvTestModel(keras.Model):
+
+  def __init__(self, num_classes=10):
+    super(SimpleConvTestModel, self).__init__(name='test_model')
+    self.num_classes = num_classes
+
+    self.conv1 = keras.layers.Conv2D(32, (3, 3), activation='relu')
+    self.flatten = keras.layers.Flatten()
+    self.dense1 = keras.layers.Dense(num_classes, activation='softmax')
+
+  def call(self, x):
+    x = self.conv1(x)
+    x = self.flatten(x)
+    return self.dense1(x)
+
+
 class MultiIOTestModel(keras.Model):
 
   def __init__(self, use_bn=False, use_dp=False, num_classes=(2, 3)):
@@ -162,9 +180,6 @@ def get_nested_model_3(input_dim, num_classes):
       x = self.dense2(x)
       return self.bn(x)
 
-    def compute_output_shape(self, input_shape):
-      return tensor_shape.TensorShape((input_shape[0], 5))
-
   test_model = Inner()
   x = test_model(x)
   outputs = keras.layers.Dense(num_classes)(x)
@@ -173,6 +188,213 @@ def get_nested_model_3(input_dim, num_classes):
 
 class ModelSubclassingTest(test.TestCase):
 
+  @test_util.run_in_graph_and_eager_modes
+  def test_invalid_input_shape_build(self):
+    num_classes = 2
+    input_dim = 50
+
+    model = SimpleTestModel(num_classes=num_classes,
+                            use_dp=True,
+                            use_bn=True)
+
+    self.assertFalse(model.built, 'Model should not have been built')
+    self.assertFalse(model.weights, ('Model should have no weights since it '
+                                     'has not been built.'))
+    with self.assertRaisesRegexp(
+        ValueError, 'input shape is not one of the valid types'):
+      model.build(input_shape=tensor_shape.Dimension(input_dim))
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_embed_dtype_with_subclass_build(self):
+    class Embedding(keras.layers.Layer):
+      """An Embedding layer."""
+
+      def __init__(self, vocab_size, embedding_dim, **kwargs):
+        super(Embedding, self).__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.embedding_dim = embedding_dim
+
+      def build(self, _):
+        self.embedding = self.add_variable(
+            'embedding_kernel',
+            shape=[self.vocab_size, self.embedding_dim],
+            dtype=np.float32,
+            initializer=init_ops.random_uniform_initializer(-0.1, 0.1),
+            trainable=True)
+
+      def call(self, x):
+        return embedding_ops.embedding_lookup(self.embedding, x)
+
+    class EmbedModel(keras.Model):
+
+      def __init__(self, vocab_size, embed_size):
+        super(EmbedModel, self).__init__()
+        self.embed1 = Embedding(vocab_size, embed_size)
+
+      def call(self, inputs):
+        return self.embed1(inputs)
+
+    model = EmbedModel(100, 20)
+    self.assertFalse(model.built, 'Model should not have been built')
+    self.assertFalse(model.weights, ('Model should have no weights since it '
+                                     'has not been built.'))
+    with self.assertRaisesRegexp(
+        ValueError, 'if your layers do not support float type inputs'):
+      model.build(input_shape=(35, 20))
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_single_time_step_rnn_build(self):
+    dim = 4
+    timesteps = 1
+    batch_input_shape = (None, timesteps, dim)
+    units = 3
+
+    class SimpleRNNModel(keras.Model):
+
+      def __init__(self):
+        super(SimpleRNNModel, self).__init__()
+        self.lstm = keras.layers.LSTM(units)
+
+      def call(self, inputs):
+        return self.lstm(inputs)
+
+    model = SimpleRNNModel()
+    self.assertFalse(model.built, 'Model should not have been built')
+    self.assertFalse(model.weights, ('Model should have no weights since it '
+                                     'has not been built.'))
+    model.build(batch_input_shape)
+    self.assertTrue(model.weights, ('Model should have weights now that it '
+                                    'has been properly built.'))
+    self.assertTrue(model.built, 'Model should be built after calling `build`.')
+    model(array_ops.ones((32, timesteps, dim)))
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_single_io_subclass_build(self):
+    num_classes = 2
+    input_dim = 50
+    batch_size = None
+
+    model = SimpleTestModel(num_classes=num_classes,
+                            use_dp=True,
+                            use_bn=True)
+
+    self.assertFalse(model.built, 'Model should not have been built')
+    self.assertFalse(model.weights, ('Model should have no weights since it '
+                                     'has not been built.'))
+    model.build(input_shape=(batch_size, input_dim))
+    self.assertTrue(model.weights, ('Model should have weights now that it '
+                                    'has been properly built.'))
+    self.assertTrue(model.built, 'Model should be built after calling `build`.')
+    model(array_ops.ones((32, input_dim)))
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_single_io_dimension_subclass_build(self):
+    num_classes = 2
+    input_dim = tensor_shape.Dimension(50)
+    batch_size = tensor_shape.Dimension(None)
+
+    model = SimpleTestModel(num_classes=num_classes,
+                            use_dp=True,
+                            use_bn=True)
+
+    self.assertFalse(model.built, 'Model should not have been built')
+    self.assertFalse(model.weights, ('Model should have no weights since it '
+                                     'has not been built.'))
+    model.build(input_shape=(batch_size, input_dim))
+    self.assertTrue(model.weights, ('Model should have weights now that it '
+                                    'has been properly built.'))
+    self.assertTrue(model.built, 'Model should be built after calling `build`.')
+    model(array_ops.ones((32, input_dim)))
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_multidim_io_subclass_build(self):
+    num_classes = 10
+    # Input size, e.g. image
+    batch_size = 32
+    input_shape = (32, 32, 3)
+
+    model = SimpleConvTestModel(num_classes)
+    self.assertFalse(model.built, 'Model should not have been built')
+    self.assertFalse(model.weights, ('Model should have no weights since it '
+                                     'has not been built.'))
+    batch_input_shape = (batch_size,) + input_shape
+    model.build(input_shape=batch_input_shape)
+    self.assertTrue(model.weights, ('Model should have weights now that it '
+                                    'has been properly built.'))
+    self.assertTrue(model.built, 'Model should be built after calling `build`.')
+
+    model(array_ops.ones(batch_input_shape))
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_tensorshape_io_subclass_build(self):
+    num_classes = 10
+    # Input size, e.g. image
+    batch_size = None
+    input_shape = (32, 32, 3)
+
+    model = SimpleConvTestModel(num_classes)
+    self.assertFalse(model.built, 'Model should not have been built')
+    self.assertFalse(model.weights, ('Model should have no weights since it '
+                                     'has not been built.'))
+    model.build(
+        input_shape=tensor_shape.TensorShape((batch_size,) + input_shape))
+    self.assertTrue(model.weights, ('Model should have weights now that it '
+                                    'has been properly built.'))
+    self.assertTrue(model.built, 'Model should be built after calling `build`.')
+
+    model(array_ops.ones((32,) + input_shape))
+
+  def test_subclass_save_model(self):
+    num_classes = 10
+    # Input size, e.g. image
+    batch_size = None
+    input_shape = (32, 32, 3)
+
+    model = SimpleConvTestModel(num_classes)
+    self.assertFalse(model.built, 'Model should not have been built')
+    self.assertFalse(model.weights, ('Model should have no weights since it '
+                                     'has not been built.'))
+    model.build(
+        input_shape=tensor_shape.TensorShape((batch_size,) + input_shape))
+    self.assertTrue(model.weights, ('Model should have weights now that it '
+                                    'has been properly built.'))
+    self.assertTrue(model.built, 'Model should be built after calling `build`.')
+    weights = model.get_weights()
+
+    tf_format_name = os.path.join(self.get_temp_dir(), 'ckpt')
+    model.save_weights(tf_format_name)
+    if h5py is not None:
+      hdf5_format_name = os.path.join(self.get_temp_dir(), 'weights.h5')
+      model.save_weights(hdf5_format_name)
+
+    model = SimpleConvTestModel(num_classes)
+    model.build(
+        input_shape=tensor_shape.TensorShape((batch_size,) + input_shape))
+    if h5py is not None:
+      model.load_weights(hdf5_format_name)
+      self.assertAllClose(weights, model.get_weights())
+    model.load_weights(tf_format_name)
+    self.assertAllClose(weights, model.get_weights())
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_multi_io_subclass_build(self):
+    batch_size = None
+    num_samples = 1000
+    input_dim = 50
+    model = MultiIOTestModel()
+    self.assertFalse(model.built, 'Model should not have been built')
+    self.assertFalse(model.weights, ('Model should have no weights since it '
+                                     'has not been built.'))
+    batch_input_shape = tensor_shape.TensorShape((batch_size, input_dim))
+    model.build(
+        input_shape=[batch_input_shape, batch_input_shape])
+    self.assertTrue(model.weights, ('Model should have weights now that it '
+                                    'has been properly built.'))
+    self.assertTrue(model.built, 'Model should be built after calling `build`.')
+    x1 = array_ops.ones((num_samples, input_dim))
+    x2 = array_ops.ones((num_samples, input_dim))
+    model([x1, x2])
+
   @test_util.run_in_graph_and_eager_modes
   def test_single_io_workflow_with_np_arrays(self):
     num_classes = 2
@@ -750,6 +972,16 @@ class CustomCallModel(keras.Model):
       return combined
 
 
+class TrainingNoDefaultModel(keras.Model):
+
+  def __init__(self):
+    super(TrainingNoDefaultModel, self).__init__()
+    self.dense1 = keras.layers.Dense(1)
+
+  def call(self, x, training):
+    return self.dense1(x)
+
+
 class CustomCallSignatureTests(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes
@@ -766,6 +998,32 @@ class CustomCallSignatureTests(test.TestCase):
     output = model(first, second=second, training=False)
     self.assertAllClose(expected_output, self.evaluate(output))
 
+  @test_util.run_in_graph_and_eager_modes
+  def test_training_args_call_build(self):
+    input_dim = 2
+
+    model = TrainingNoDefaultModel()
+    self.assertFalse(model.built, 'Model should not have been built')
+    self.assertFalse(model.weights, ('Model should have no weights since it '
+                                     'has not been built.'))
+    model.build((None, input_dim))
+    self.assertTrue(model.weights, ('Model should have weights now that it '
+                                    'has been properly built.'))
+    self.assertTrue(model.built, 'Model should be built after calling `build`.')
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_custom_call_kwargs_and_build(self):
+    first_input_shape = (2, 3)
+    second_input_shape = (2, 5)
+
+    model = CustomCallModel()
+    self.assertFalse(model.built, 'Model should not have been built')
+    self.assertFalse(model.weights, ('Model should have no weights since it '
+                                     'has not been built.'))
+    with self.assertRaisesRegexp(
+        ValueError, 'cannot build your model if it has positional'):
+      model.build(input_shape=[first_input_shape, second_input_shape])
+
   @test_util.run_in_graph_and_eager_modes
   def test_inputs_in_signature(self):
 
@@ -829,14 +1087,9 @@ class CustomCallSignatureTests(test.TestCase):
 
   def test_training_no_default(self):
 
-    class TrainingNoDefault(keras.Model):
-
-      def call(self, x, training):
-        return x
-
     with context.graph_mode():
-      model = TrainingNoDefault()
-      arg = array_ops.ones([])
+      model = TrainingNoDefaultModel()
+      arg = array_ops.ones([1, 1])
       model(arg, True)
       six.assertCountEqual(self, [arg], model.inputs)
 
diff --git a/tensorflow/python/keras/models_test.py b/tensorflow/python/keras/models_test.py
index ad3819e6e730b48e294b340d39fddeb6d7f2d6bf..1525104ac92e4c8fc9d52d28a187083d4fc91a4a 100644
--- a/tensorflow/python/keras/models_test.py
+++ b/tensorflow/python/keras/models_test.py
@@ -37,6 +37,7 @@ class TestModelCloning(test.TestCase):
 
       model = keras.models.Sequential()
       model.add(keras.layers.Dense(4, input_shape=(4,)))
+      model.add(keras.layers.BatchNormalization())
       model.add(keras.layers.Dropout(0.5))
       model.add(keras.layers.Dense(4))
 
@@ -46,6 +47,8 @@ class TestModelCloning(test.TestCase):
     with self.test_session():
       # With placeholder creation
       new_model = keras.models.clone_model(model)
+      # update ops from batch norm needs to be included
+      self.assertEquals(len(new_model.get_updates_for(new_model.inputs)), 2)
       new_model.compile('rmsprop', 'mse')
       new_model.train_on_batch(val_a, val_out)
 
@@ -53,6 +56,7 @@ class TestModelCloning(test.TestCase):
       input_a = keras.Input(shape=(4,))
       new_model = keras.models.clone_model(
           model, input_tensors=input_a)
+      self.assertEquals(len(new_model.get_updates_for(new_model.inputs)), 2)
       new_model.compile('rmsprop', 'mse')
       new_model.train_on_batch(val_a, val_out)
 
@@ -60,6 +64,7 @@ class TestModelCloning(test.TestCase):
       input_a = keras.backend.variable(val_a)
       new_model = keras.models.clone_model(
           model, input_tensors=input_a)
+      self.assertEquals(len(new_model.get_updates_for(new_model.inputs)), 2)
       new_model.compile('rmsprop', 'mse')
       new_model.train_on_batch(None, val_out)
 
@@ -76,6 +81,7 @@ class TestModelCloning(test.TestCase):
 
       x_a = dense_1(input_a)
       x_a = keras.layers.Dropout(0.5)(x_a)
+      x_a = keras.layers.BatchNormalization()(x_a)
       x_b = dense_1(input_b)
       x_a = dense_2(x_a)
       outputs = keras.layers.add([x_a, x_b])
@@ -87,6 +93,7 @@ class TestModelCloning(test.TestCase):
     with self.test_session():
       # With placeholder creation
       new_model = keras.models.clone_model(model)
+      self.assertEquals(len(new_model.get_updates_for(new_model.inputs)), 2)
       new_model.compile('rmsprop', 'mse')
       new_model.train_on_batch([val_a, val_b], val_out)
 
@@ -95,6 +102,7 @@ class TestModelCloning(test.TestCase):
       input_b = keras.Input(shape=(4,), name='b')
       new_model = keras.models.clone_model(
           model, input_tensors=[input_a, input_b])
+      self.assertEquals(len(new_model.get_updates_for(new_model.inputs)), 2)
       new_model.compile('rmsprop', 'mse')
       new_model.train_on_batch([val_a, val_b], val_out)
 
@@ -103,6 +111,7 @@ class TestModelCloning(test.TestCase):
       input_b = keras.backend.variable(val_b)
       new_model = keras.models.clone_model(
           model, input_tensors=[input_a, input_b])
+      self.assertEquals(len(new_model.get_updates_for(new_model.inputs)), 2)
       new_model.compile('rmsprop', 'mse')
       new_model.train_on_batch(None, val_out)
 
diff --git a/tensorflow/python/keras/testing_utils.py b/tensorflow/python/keras/testing_utils.py
index 17aba7d86c236d9bb30d3a3376b3aac40b69e77d..6e8ee06ff53691c3f439114bd6c4745c03cf9a10 100644
--- a/tensorflow/python/keras/testing_utils.py
+++ b/tensorflow/python/keras/testing_utils.py
@@ -18,7 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from collections import OrderedDict
 import numpy as np
 
 from tensorflow.python import keras
@@ -185,75 +184,3 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None,
   # for further checks in the caller function
   return actual_output
 
-
-def _combine_named_parameters(**kwargs):
-  """Generate combinations based on its keyword arguments.
-
-  Two sets of returned combinations can be concatenated using +.  Their product
-  can be computed using `times()`.
-
-  Args:
-    **kwargs: keyword arguments of form `option=[possibilities, ...]`
-         or `option=the_only_possibility`.
-
-  Returns:
-    a list of dictionaries for each combination. Keys in the dictionaries are
-    the keyword argument names.  Each key has one value - one of the
-    corresponding keyword argument values.
-  """
-  if not kwargs:
-    return [OrderedDict()]
-
-  sort_by_key = lambda k: k[0][0]
-  kwargs = OrderedDict(sorted(kwargs.items(), key=sort_by_key))
-  first = list(kwargs.items())[0]
-
-  rest = dict(list(kwargs.items())[1:])
-  rest_combined = _combine_named_parameters(**rest)
-
-  key = first[0]
-  values = first[1]
-  if not isinstance(values, list):
-    values = [values]
-
-  combinations = [
-      OrderedDict(sorted(list(combined.items()) + [(key, v)], key=sort_by_key))
-      for v in values
-      for combined in rest_combined
-  ]
-  return combinations
-
-
-def generate_combinations_with_testcase_name(**kwargs):
-  """Generate combinations based on its keyword arguments using combine().
-
-  This function calls combine() and appends a testcase name to the list of
-  dictionaries returned. The 'testcase_name' key is a required for named
-  parameterized tests.
-
-  Args:
-    **kwargs: keyword arguments of form `option=[possibilities, ...]`
-         or `option=the_only_possibility`.
-
-  Returns:
-    a list of dictionaries for each combination. Keys in the dictionaries are
-    the keyword argument names.  Each key has one value - one of the
-    corresponding keyword argument values.
-  """
-  combinations = _combine_named_parameters(**kwargs)
-  named_combinations = []
-  for combination in combinations:
-    assert isinstance(combination, OrderedDict)
-    name = ''.join([
-        '_{}_{}'.format(
-            ''.join(filter(str.isalnum, key)),
-            ''.join(filter(str.isalnum, str(value))))
-        for key, value in combination.items()
-    ])
-    named_combinations.append(
-        OrderedDict(
-            list(combination.items()) + [('testcase_name',
-                                          '_test{}'.format(name))]))
-
-  return named_combinations
-
diff --git a/tensorflow/python/keras/utils/np_utils.py b/tensorflow/python/keras/utils/np_utils.py
index 9d9c72b162700cb3bca2cf83d56db30f8df1deb9..c24e87308bee20e4ed978514699d4beb2ee4fbb9 100644
--- a/tensorflow/python/keras/utils/np_utils.py
+++ b/tensorflow/python/keras/utils/np_utils.py
@@ -33,7 +33,8 @@ def to_categorical(y, num_classes=None):
       num_classes: total number of classes.
 
   Returns:
-      A binary matrix representation of the input.
+      A binary matrix representation of the input. The classes axis is placed
+      last.
   """
   y = np.array(y, dtype='int')
   input_shape = y.shape
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 6bfd1936e38da0b03bb6a9baba7d899957283349..adf97569ab4446fdc23b7dc3c0e7d92a9a5b20ae 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -1525,6 +1525,7 @@ cuda_py_test(
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
     ],
+    tags = ["no_windows_gpu"],
 )
 
 cuda_py_test(
@@ -2057,6 +2058,7 @@ cuda_py_test(
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:math_ops",
     ],
+    tags = ["no_windows_gpu"],
 )
 
 tf_py_test(
@@ -3092,7 +3094,7 @@ tf_py_test(
 
 tf_py_test(
     name = "cond_v2_test",
-    size = "small",
+    size = "medium",
     srcs = ["cond_v2_test.py"],
     additional_deps = [
         "//tensorflow/python:array_ops",
@@ -3107,4 +3109,5 @@ tf_py_test(
         "//tensorflow/python:training",
     ],
     grpc_enabled = True,
+    tags = ["no_gpu"],  # TODO(b/111656070)
 )
diff --git a/tensorflow/python/kernel_tests/argmax_op_test.py b/tensorflow/python/kernel_tests/argmax_op_test.py
index ce0676990221fb441b99043083647f9d65722db8..1202c463e80d21b7cf88e5596cfc64eaa38ef8ba 100644
--- a/tensorflow/python/kernel_tests/argmax_op_test.py
+++ b/tensorflow/python/kernel_tests/argmax_op_test.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 import numpy as np
 
 from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 
@@ -115,6 +116,12 @@ class ArgMaxTest(test.TestCase):
         ans = op([1]).eval()
         self.assertAllEqual(ans, 0)
 
+  def testOutputEmpty(self):
+    with self.test_session():
+      for op in math_ops.argmin, math_ops.argmax:
+        ret = op(array_ops.zeros(shape=[1, 0, 2]), axis=-1).eval()
+        self.assertEqual(ret.shape, (1, 0))
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/kernel_tests/bitcast_op_test.py b/tensorflow/python/kernel_tests/bitcast_op_test.py
index a535468b058d289d5cc6611ff542d89615793834..a2c6b54273f7f617ee78253e6184befd8f81e4ac 100644
--- a/tensorflow/python/kernel_tests/bitcast_op_test.py
+++ b/tensorflow/python/kernel_tests/bitcast_op_test.py
@@ -76,12 +76,18 @@ class BitcastTest(test.TestCase):
     datatype = dtypes.int8
     array_ops.bitcast(x, datatype, None)
 
-  def testQuantizeType(self):
+  def testQuantizedType(self):
     shape = [3, 4]
     x = np.zeros(shape, np.uint16)
     datatype = dtypes.quint16
     self._testBitcast(x, datatype, shape)
 
+  def testUnsignedType(self):
+    shape = [3, 4]
+    x = np.zeros(shape, np.int64)
+    datatype = dtypes.uint64
+    self._testBitcast(x, datatype, shape)
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/kernel_tests/cond_v2_test.py b/tensorflow/python/kernel_tests/cond_v2_test.py
index 759db5d5f43a144150918446e6ce206b3095904f..97ce245fc835a90a83026802353646f9dc8720e5 100644
--- a/tensorflow/python/kernel_tests/cond_v2_test.py
+++ b/tensorflow/python/kernel_tests/cond_v2_test.py
@@ -22,6 +22,7 @@ from __future__ import print_function
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import function
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import cond_v2
@@ -35,10 +36,12 @@ from tensorflow.python.training import saver
 from tensorflow.python.util import compat
 
 
-class NewCondTest(test.TestCase):
+class CondV2Test(test.TestCase):
 
-  def _testCond(self, true_fn, false_fn, train_vals):
-    with self.test_session() as sess:
+  def _testCond(self, true_fn, false_fn, train_vals, feed_dict=None):
+    if not feed_dict:
+      feed_dict = {}
+    with self.test_session(graph=ops.get_default_graph()) as sess:
       pred = array_ops.placeholder(dtypes.bool, name="pred")
 
       expected = control_flow_ops.cond(pred, true_fn, false_fn, name="expected")
@@ -47,13 +50,17 @@ class NewCondTest(test.TestCase):
       expected_grad = gradients_impl.gradients(expected, train_vals)
       actual_grad = gradients_impl.gradients(actual, train_vals)
 
+      sess_run_args = {pred: True}
+      sess_run_args.update(feed_dict)
       expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run(
-          (expected, actual, expected_grad, actual_grad), {pred: True})
+          (expected, actual, expected_grad, actual_grad), sess_run_args)
       self.assertEqual(expected_val, actual_val)
       self.assertEqual(expected_grad_val, actual_grad_val)
 
+      sess_run_args = {pred: False}
+      sess_run_args.update(feed_dict)
       expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run(
-          (expected, actual, expected_grad, actual_grad), {pred: False})
+          (expected, actual, expected_grad, actual_grad), sess_run_args)
       self.assertEqual(expected_val, actual_val)
       self.assertEqual(expected_grad_val, actual_grad_val)
 
@@ -131,6 +138,349 @@ class NewCondTest(test.TestCase):
         self.assertIn("foo_cond_1_true", ops.get_default_graph()._functions)
         self.assertIn("foo_cond_1_false", ops.get_default_graph()._functions)
 
+  def testDefunInCond(self):
+    x = constant_op.constant(1.0, name="x")
+    y = constant_op.constant(2.0, name="y")
+
+    def true_fn():
+
+      @function.Defun()
+      def fn():
+        return x * y * 2.0
+
+      return fn()
+
+    def false_fn():
+      return 2.0
+
+    self._testCond(true_fn, false_fn, [x])
+    self._testCond(true_fn, false_fn, [x, y])
+    self._testCond(true_fn, false_fn, [y])
+
+  def testNestedDefunInCond(self):
+    x = constant_op.constant(1.0, name="x")
+    y = constant_op.constant(2.0, name="y")
+
+    def true_fn():
+      return 2.0
+
+    def false_fn():
+
+      @function.Defun()
+      def fn():
+
+        @function.Defun()
+        def nested_fn():
+          return x * y * 2.0
+
+        return nested_fn()
+
+      return fn()
+
+    self._testCond(true_fn, false_fn, [x])
+    self._testCond(true_fn, false_fn, [x, y])
+    self._testCond(true_fn, false_fn, [y])
+
+  def testDoubleNestedDefunInCond(self):
+    x = constant_op.constant(1.0, name="x")
+    y = constant_op.constant(2.0, name="y")
+
+    def true_fn():
+
+      @function.Defun()
+      def fn():
+
+        @function.Defun()
+        def nested_fn():
+
+          @function.Defun()
+          def nested_nested_fn():
+            return x * y * 2.0
+
+          return nested_nested_fn()
+
+        return nested_fn()
+
+      return fn()
+
+    def false_fn():
+      return 2.0
+
+    self._testCond(true_fn, false_fn, [x])
+    self._testCond(true_fn, false_fn, [x, y])
+    self._testCond(true_fn, false_fn, [y])
+
+  def testNestedCond(self):
+
+    def run_test(pred_value):
+
+      def build_graph():
+        pred = array_ops.placeholder(dtypes.bool, name="pred")
+        x = constant_op.constant(1.0, name="x")
+        y = constant_op.constant(2.0, name="y")
+
+        def true_fn():
+          return 2.0
+
+        def false_fn():
+
+          def false_true_fn():
+            return x * y * 2.0
+
+          def false_false_fn():
+            return x * 5.0
+
+          return _cond(pred, false_true_fn, false_false_fn, "inside_false_fn")
+
+        return x, y, pred, true_fn, false_fn
+
+      with ops.Graph().as_default():
+        x, y, pred, true_fn, false_fn = build_graph()
+        self._testCond(true_fn, false_fn, [x, y], {pred: pred_value})
+        self._testCond(true_fn, false_fn, [x], {pred: pred_value})
+        self._testCond(true_fn, false_fn, [y], {pred: pred_value})
+
+    run_test(True)
+    run_test(False)
+
+  def testDoubleNestedCond(self):
+
+    def run_test(pred1_value, pred2_value):
+
+      def build_graph():
+        pred1 = array_ops.placeholder(dtypes.bool, name="pred1")
+        pred2 = array_ops.placeholder(dtypes.bool, name="pred2")
+        x = constant_op.constant(1.0, name="x")
+        y = constant_op.constant(2.0, name="y")
+
+        def true_fn():
+          return 2.0
+
+        def false_fn():
+
+          def false_true_fn():
+
+            def false_true_true_fn():
+              return x * y * 2.0
+
+            def false_true_false_fn():
+              return x * 10.0
+
+            return _cond(
+                pred1,
+                false_true_true_fn,
+                false_true_false_fn,
+                name="inside_false_true_fn")
+
+          def false_false_fn():
+            return x * 5.0
+
+          return _cond(
+              pred2, false_true_fn, false_false_fn, name="inside_false_fn")
+
+        return x, y, pred1, pred2, true_fn, false_fn
+
+      with ops.Graph().as_default():
+        x, y, pred1, pred2, true_fn, false_fn = build_graph()
+        self._testCond(true_fn, false_fn, [x, y], {
+            pred1: pred1_value,
+            pred2: pred2_value
+        })
+        x, y, pred1, pred2, true_fn, false_fn = build_graph()
+        self._testCond(true_fn, false_fn, [x], {
+            pred1: pred1_value,
+            pred2: pred2_value
+        })
+        x, y, pred1, pred2, true_fn, false_fn = build_graph()
+        self._testCond(true_fn, false_fn, [y], {
+            pred1: pred1_value,
+            pred2: pred2_value
+        })
+
+    run_test(True, True)
+    run_test(True, False)
+    run_test(False, False)
+    run_test(False, True)
+
+  def testGradientFromInsideDefun(self):
+
+    def build_graph():
+      pred_outer = array_ops.placeholder(dtypes.bool, name="pred_outer")
+      pred_inner = array_ops.placeholder(dtypes.bool, name="pred_inner")
+      x = constant_op.constant(1.0, name="x")
+      y = constant_op.constant(2.0, name="y")
+
+      def true_fn():
+        return 2.0
+
+      def false_fn():
+
+        def inner_true_fn():
+          return x * y * 2.0
+
+        def inner_false_fn():
+          return x * 5.0
+
+        return cond_v2.cond_v2(
+            pred_inner, inner_true_fn, inner_false_fn, name="inner_cond")
+
+      cond_outer = cond_v2.cond_v2(
+          pred_outer, true_fn, false_fn, name="outer_cond")
+
+      # Compute grads inside a Defun.
+      @function.Defun()
+      def nesting_fn():
+        return gradients_impl.gradients(cond_outer, [x, y])
+
+      grads = nesting_fn()
+
+      return grads, pred_outer, pred_inner
+
+    with ops.Graph().as_default():
+      grads, pred_outer, pred_inner = build_graph()
+      with self.test_session(graph=ops.get_default_graph()) as sess:
+        self.assertSequenceEqual(
+            sess.run(grads, {
+                pred_outer: True,
+                pred_inner: True
+            }), [0., 0.])
+        self.assertSequenceEqual(
+            sess.run(grads, {
+                pred_outer: True,
+                pred_inner: False
+            }), [0., 0.])
+        self.assertSequenceEqual(
+            sess.run(grads, {
+                pred_outer: False,
+                pred_inner: True
+            }), [4., 2.])
+        self.assertSequenceEqual(
+            sess.run(grads, {
+                pred_outer: False,
+                pred_inner: False
+            }), [5., 0.])
+
+  def testGradientFromInsideNestedDefun(self):
+
+    def build_graph():
+      pred_outer = array_ops.placeholder(dtypes.bool, name="pred_outer")
+      pred_inner = array_ops.placeholder(dtypes.bool, name="pred_inner")
+      x = constant_op.constant(1.0, name="x")
+      y = constant_op.constant(2.0, name="y")
+
+      def true_fn():
+        return 2.0
+
+      def false_fn():
+
+        def inner_true_fn():
+          return x * y * 2.0
+
+        def inner_false_fn():
+          return x * 5.0
+
+        return cond_v2.cond_v2(
+            pred_inner, inner_true_fn, inner_false_fn, name="inner_cond")
+
+      cond_outer = cond_v2.cond_v2(
+          pred_outer, true_fn, false_fn, name="outer_cond")
+
+      # Compute grads inside a Defun.
+      @function.Defun()
+      def nesting_fn():
+
+        @function.Defun()
+        def inner_nesting_fn():
+          return gradients_impl.gradients(cond_outer, [x, y])
+
+        return inner_nesting_fn()
+
+      grads = nesting_fn()
+
+      return grads, pred_outer, pred_inner
+
+    with ops.Graph().as_default():
+      grads, pred_outer, pred_inner = build_graph()
+      with self.test_session(graph=ops.get_default_graph()) as sess:
+        self.assertSequenceEqual(
+            sess.run(grads, {
+                pred_outer: True,
+                pred_inner: True
+            }), [0., 0.])
+        self.assertSequenceEqual(
+            sess.run(grads, {
+                pred_outer: True,
+                pred_inner: False
+            }), [0., 0.])
+        self.assertSequenceEqual(
+            sess.run(grads, {
+                pred_outer: False,
+                pred_inner: True
+            }), [4., 2.])
+        self.assertSequenceEqual(
+            sess.run(grads, {
+                pred_outer: False,
+                pred_inner: False
+            }), [5., 0.])
+
+  def testBuildCondAndGradientInsideDefun(self):
+
+    def build_graph():
+      pred_outer = array_ops.placeholder(dtypes.bool, name="pred_outer")
+      pred_inner = array_ops.placeholder(dtypes.bool, name="pred_inner")
+      x = constant_op.constant(1.0, name="x")
+      y = constant_op.constant(2.0, name="y")
+
+      # Build cond and its gradient inside a Defun.
+      @function.Defun()
+      def fn():
+
+        def true_fn():
+          return 2.0
+
+        def false_fn():
+
+          def inner_true_fn():
+            return x * y * 2.0
+
+          def inner_false_fn():
+            return x * 5.0
+
+          return cond_v2.cond_v2(
+              pred_inner, inner_true_fn, inner_false_fn, name="inner_cond")
+
+        cond_outer = cond_v2.cond_v2(
+            pred_outer, true_fn, false_fn, name="outer_cond")
+        return gradients_impl.gradients(cond_outer, [x, y])
+
+      grads = fn()
+
+      return grads, pred_outer, pred_inner
+
+    with ops.Graph().as_default():
+      grads, pred_outer, pred_inner = build_graph()
+      with self.test_session(graph=ops.get_default_graph()) as sess:
+        self.assertSequenceEqual(
+            sess.run(grads, {
+                pred_outer: True,
+                pred_inner: True
+            }), [0., 0.])
+        self.assertSequenceEqual(
+            sess.run(grads, {
+                pred_outer: True,
+                pred_inner: False
+            }), [0., 0.])
+        self.assertSequenceEqual(
+            sess.run(grads, {
+                pred_outer: False,
+                pred_inner: True
+            }), [4., 2.])
+        self.assertSequenceEqual(
+            sess.run(grads, {
+                pred_outer: False,
+                pred_inner: False
+            }), [5., 0.])
+
   def testSecondDerivative(self):
     with self.test_session() as sess:
       pred = array_ops.placeholder(dtypes.bool, name="pred")
@@ -532,5 +882,17 @@ class CondV2ColocationGroupAndDeviceTest(test.TestCase):
         self.assertTrue(len(run_metadata.partition_graphs) >= 2)
 
 
+def _cond(pred, true_fn, false_fn, name):
+  if _is_old_cond():
+    return control_flow_ops.cond(pred, true_fn, false_fn, name=name)
+  else:
+    return cond_v2.cond_v2(pred, true_fn, false_fn, name=name)
+
+
+def _is_old_cond():
+  return isinstance(ops.get_default_graph()._get_control_flow_context(),
+                    control_flow_ops.CondContext)
+
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
index 68873df97ea2a632d98de4936a20a1f81bce93e9..b567b71424263d83ed9467313151240091a36eb1 100644
--- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
+++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
@@ -734,11 +734,11 @@ class ControlFlowTest(test.TestCase):
 
       def body_fn(i):
         with ops.control_dependencies([increment]):
-          return i + i
+          return i + 1
 
-      result = control_flow_ops.while_loop(cond=lambda i: i < 1,
+      result = control_flow_ops.while_loop(cond=lambda i: i < 2,
                                            body=body_fn, loop_vars=[1])
-      result.eval()
+      self.assertAllEqual(result.eval(), 2)
       self.assertAllEqual(v.eval(), 1.0)
 
   def testWhileExternalControlDependenciesNoInput(self):
diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py
index 474d06b8f3a4276c65711d74ba0d1db6fb06cbf9..00de94f0041294c7f6b183c4caf5f92bfe1c25dd 100644
--- a/tensorflow/python/kernel_tests/conv_ops_test.py
+++ b/tensorflow/python/kernel_tests/conv_ops_test.py
@@ -1706,7 +1706,7 @@ class SeparableConv2DTest(test.TestCase):
   def testSeparableConv2D(self):
     self._testSeparableConv2D("NHWC")
 
-  def testSeparableConv2DNCHW(self):
+  def disabledtestSeparableConv2DNCHW(self):
     if not test.is_gpu_available():
       return
     self._testSeparableConv2D("NCHW")
diff --git a/tensorflow/python/kernel_tests/decode_jpeg_op_test.py b/tensorflow/python/kernel_tests/decode_jpeg_op_test.py
index 510daf79dc4252c3e2943e2ba23c1012370bf456..66b3e0f22fd2ab07311895da5df5448ee4e6e6f0 100644
--- a/tensorflow/python/kernel_tests/decode_jpeg_op_test.py
+++ b/tensorflow/python/kernel_tests/decode_jpeg_op_test.py
@@ -110,7 +110,8 @@ class DecodeJpegBenchmark(test.Benchmark):
       start_time = time.time()
       for _ in xrange(num_iters):
         sess.run(r)
-    return time.time() - start_time
+      end_time = time.time()
+    return end_time - start_time
 
   def benchmarkDecodeJpegSmall(self):
     """Evaluate single DecodeImageOp for small size image."""
diff --git a/tensorflow/python/kernel_tests/depthwise_conv_op_test.py b/tensorflow/python/kernel_tests/depthwise_conv_op_test.py
index 7134e02c348b47048cff5b0c205d1dd613c31a81..58845552db5e22dd4e5e9a6de09de023c58be512 100644
--- a/tensorflow/python/kernel_tests/depthwise_conv_op_test.py
+++ b/tensorflow/python/kernel_tests/depthwise_conv_op_test.py
@@ -90,7 +90,7 @@ def CheckGradConfigsToTest():
 class DepthwiseConv2DTest(test.TestCase):
 
   # This is testing that depthwise_conv2d and depthwise_conv2d_native
-  # produce the same results.  It also tests that NCHW and NWHC
+  # produce the same results.  It also tests that NCHW and NHWC
   # formats agree, by comparing the depthwise_conv2d_native with
   # 'NCHW' format (with transposition) matches the 'NHWC' format using
   # the higher level interface.
@@ -142,7 +142,7 @@ class DepthwiseConv2DTest(test.TestCase):
       native_t1 = t1
       strides = [1, stride, stride, 1]
       if data_format == "NCHW":
-        # Transpose from NWHC input to NCHW
+        # Transpose from NHWC input to NCHW
         # Ex. [4, 5, 5, 48] to [4, 48, 5, 5]
         native_t1 = array_ops.transpose(t1, [0, 3, 1, 2])
         strides = [1, 1, stride, stride]
@@ -368,7 +368,7 @@ class DepthwiseConv2DTest(test.TestCase):
       native_input = input_tensor
       strides = [1, stride, stride, 1]
       if data_format == "NCHW":
-        # Transpose from NWHC input to NCHW
+        # Transpose from NHWC input to NCHW
         # Ex. [4, 5, 5, 48] to [4, 48, 5, 5]
         native_input = array_ops.transpose(input_tensor, [0, 3, 1, 2])
         input_shape = [
diff --git a/tensorflow/python/kernel_tests/distributions/util_test.py b/tensorflow/python/kernel_tests/distributions/util_test.py
index 9d38ffcb4a963efb71153f59d6269ba84a5d1379..61faa8466edcf404dc48fc0596c47cb3c2094f13 100644
--- a/tensorflow/python/kernel_tests/distributions/util_test.py
+++ b/tensorflow/python/kernel_tests/distributions/util_test.py
@@ -311,8 +311,10 @@ class EmbedCheckCategoricalEventShapeTest(test.TestCase):
   @test_util.run_in_graph_and_eager_modes
   def testUnsupportedDtype(self):
     with self.test_session():
+      param = ops.convert_to_tensor(
+          np.ones([2**11 + 1]).astype(dtypes.qint16.as_numpy_dtype),
+          dtype=dtypes.qint16)
       with self.assertRaises(TypeError):
-        param = array_ops.ones([int(2**11+1)], dtype=dtypes.qint16)
         du.embed_check_categorical_event_shape(param)
 
 
diff --git a/tensorflow/python/kernel_tests/functional_ops_test.py b/tensorflow/python/kernel_tests/functional_ops_test.py
index 5272a3631fa6a49ea913694b382f4331b46c8a29..24800d2b7a7aec9e43419d65c73a5a7ec3e64e24 100644
--- a/tensorflow/python/kernel_tests/functional_ops_test.py
+++ b/tensorflow/python/kernel_tests/functional_ops_test.py
@@ -1097,10 +1097,8 @@ class PartitionedCallTest(test.TestCase):
     self.assertEqual(value, 2.0)
 
   def testFunctionWithResourcesOnDifferentDevices(self):
-    # TODO(akshayka): Remove the `skipTest` once we can whitelist ops as
-    # safe to be invoked with resources on different devices.
-    self.skipTest("The Placer disallows ops with resource inputs "
-                  "on different devices.")
+    if not test_util.is_gpu_available():
+      self.skipTest("No GPUs available.")
 
     with ops.device("/cpu:0"):
       v_cpu_zero = resource_variable_ops.ResourceVariable(
diff --git a/tensorflow/python/kernel_tests/gather_nd_op_test.py b/tensorflow/python/kernel_tests/gather_nd_op_test.py
index 58e2a8ac2a3b827647b1b1176f4b69e6a88b76c6..c0b419e1d13405d04c34fb642cec15760ddcf50f 100644
--- a/tensorflow/python/kernel_tests/gather_nd_op_test.py
+++ b/tensorflow/python/kernel_tests/gather_nd_op_test.py
@@ -203,8 +203,7 @@ class GatherNdTest(test.TestCase):
       indices = [[[0], [7]]]  # Make this one higher rank
       gather_nd = array_ops.gather_nd(params, indices)
       with self.assertRaisesOpError(
-          r"flat indices\[1, :\] = \[7\] does not index into param "
-          r"\(shape: \[3\]\)"):
+          r"indices\[0,1\] = \[7\] does not index into param shape \[3\]"):
         gather_nd.eval()
 
   def _disabledTestBadIndicesGPU(self):
@@ -217,8 +216,7 @@ class GatherNdTest(test.TestCase):
       indices = [[[0], [7]]]  # Make this one higher rank
       gather_nd = array_ops.gather_nd(params, indices)
       with self.assertRaisesOpError(
-          r"flat indices\[1, :\] = \[7\] does not index into param "
-          r"\(shape: \[3\]\)"):
+          r"indices\[0,1\] = \[7\] does not index into param shape \[3\]"):
         gather_nd.eval()
 
   def testBadIndicesWithSlicesCPU(self):
@@ -227,8 +225,7 @@ class GatherNdTest(test.TestCase):
       indices = [[[0], [0], [1]]]  # Make this one higher rank
       gather_nd = array_ops.gather_nd(params, indices)
       with self.assertRaisesOpError(
-          r"flat indices\[2, :\] = \[1\] does not index into param "
-          r"\(shape: \[1,3\]\)"):
+          r"indices\[0,2\] = \[1\] does not index into param shape \[1,3\]"):
         gather_nd.eval()
 
   def _disabledTestBadIndicesWithSlicesGPU(self):
@@ -241,8 +238,7 @@ class GatherNdTest(test.TestCase):
       indices = [[[0], [0], [1]]]  # Make this one higher rank
       gather_nd = array_ops.gather_nd(params, indices)
       with self.assertRaisesOpError(
-          r"flat indices\[2, :\] = \[1\] does not index into param "
-          r"\(shape: \[1,3\]\)"):
+          r"indices\[0,2\] = \[1\] does not index into param shape \[1,3\]"):
         gather_nd.eval()
 
   def testGradientsRank2Elements(self):
diff --git a/tensorflow/python/kernel_tests/init_ops_test.py b/tensorflow/python/kernel_tests/init_ops_test.py
index 927ca012ae6fc876364734c6f9bafd62ccc87467..f6097ad48984a1bb62708185ebf9782b72036e6a 100644
--- a/tensorflow/python/kernel_tests/init_ops_test.py
+++ b/tensorflow/python/kernel_tests/init_ops_test.py
@@ -830,7 +830,7 @@ class ConvolutionOrthogonal1dInitializerTest(test.TestCase):
     tol = 1e-3
     gain = 3.14
     # Check orthogonality/isometry by computing the ratio between
-    # the 2-norms of the inputs and ouputs.
+    # the 2-norms of the inputs and outputs.
     for kernel_size in [[1], [2], [3], [4], [5], [6]]:
       convolution = convolutional.conv1d
       inputs = random_ops.random_normal(shape, dtype=dtype)
@@ -925,7 +925,7 @@ class ConvolutionOrthogonal2dInitializerTest(test.TestCase):
     tol = 1e-3
     gain = 3.14
     # Check orthogonality/isometry by computing the ratio between
-    # the 2-norms of the inputs and ouputs.
+    # the 2-norms of the inputs and outputs.
     for kernel_size in [[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]]:
       convolution = convolutional.conv2d
       inputs = random_ops.random_normal(shape, dtype=dtype)
@@ -1050,7 +1050,7 @@ class ConvolutionOrthogonal3dInitializerTest(test.TestCase):
     tol = 1e-3
     gain = 3.14
     # Check orthogonality/isometry by computing the ratio between
-    # the 2-norms of the inputs and ouputs.
+    # the 2-norms of the inputs and outputs.
     for kernel_size in [[1, 1, 1], [2, 2, 2], [3, 3, 3]]:
       convolution = convolutional.conv3d
       inputs = random_ops.random_normal(shape, dtype=dtype)
diff --git a/tensorflow/python/kernel_tests/linalg/BUILD b/tensorflow/python/kernel_tests/linalg/BUILD
index 69d3aa401751f56ea338a5ac4b24d65e68dbddeb..f4ec3e3996a17405b65d240534d2f2d47973d418 100644
--- a/tensorflow/python/kernel_tests/linalg/BUILD
+++ b/tensorflow/python/kernel_tests/linalg/BUILD
@@ -197,7 +197,7 @@ cuda_py_test(
 
 cuda_py_test(
     name = "linear_operator_low_rank_update_test",
-    size = "medium",
+    size = "large",
     srcs = ["linear_operator_low_rank_update_test.py"],
     additional_deps = [
         "//tensorflow/python/ops/linalg",
@@ -234,3 +234,21 @@ cuda_py_test(
         "optonly",
     ],
 )
+
+cuda_py_test(
+    name = "linear_operator_zeros_test",
+    size = "medium",
+    srcs = ["linear_operator_zeros_test.py"],
+    additional_deps = [
+        "//tensorflow/python/ops/linalg",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:linalg_ops",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:random_ops",
+    ],
+    shard_count = 5,
+    tags = ["optonly"],  # Test is flaky without optimization.
+)
diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py
index 34b35a4ffb878c63f851f2b31491e7bfa4057417..0e38dbd48d2252be4b3f0455ad69994ac5814126 100644
--- a/tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py
+++ b/tensorflow/python/kernel_tests/linalg/linear_operator_low_rank_update_test.py
@@ -48,12 +48,6 @@ class BaseLinearOperatorLowRankUpdatetest(object):
   # If False, A = L + UDU^H or A = L + UU^H, depending on _use_diag_update
   _use_v = None
 
-  @property
-  def _dtypes_to_test(self):
-    # TODO(langmore) Test complex types once cholesky works with them.
-    # See comment in LinearOperatorLowRankUpdate.__init__.
-    return [dtypes.float32, dtypes.float64]
-
   @property
   def _operator_build_infos(self):
     build_info = linear_operator_test_util.OperatorBuildInfo
@@ -68,6 +62,15 @@ class BaseLinearOperatorLowRankUpdatetest(object):
         build_info((3, 4, 4)),
         build_info((2, 1, 4, 4))]
 
+  def _gen_positive_diag(self, dtype, diag_shape):
+    if dtype.is_complex:
+      diag = linear_operator_test_util.random_uniform(
+          diag_shape, minval=1e-4, maxval=1., dtype=dtypes.float32)
+      return math_ops.cast(diag, dtype=dtype)
+
+    return linear_operator_test_util.random_uniform(
+        diag_shape, minval=1e-4, maxval=1., dtype=dtype)
+
   def _operator_and_matrix(self, build_info, dtype, use_placeholder):
     # Recall A = L + UDV^H
     shape = list(build_info.shape)
@@ -78,8 +81,7 @@ class BaseLinearOperatorLowRankUpdatetest(object):
 
     # base_operator L will be a symmetric positive definite diagonal linear
     # operator, with condition number as high as 1e4.
-    base_diag = linear_operator_test_util.random_uniform(
-        diag_shape, minval=1e-4, maxval=1., dtype=dtype)
+    base_diag = self._gen_positive_diag(dtype, diag_shape)
     lin_op_base_diag = base_diag
 
     # U
@@ -94,8 +96,7 @@ class BaseLinearOperatorLowRankUpdatetest(object):
 
     # D
     if self._is_diag_update_positive:
-      diag_update = linear_operator_test_util.random_uniform(
-          diag_update_shape, minval=1e-4, maxval=1., dtype=dtype)
+      diag_update = self._gen_positive_diag(dtype, diag_update_shape)
     else:
       diag_update = linear_operator_test_util.random_normal(
           diag_update_shape, stddev=1e-4, dtype=dtype)
@@ -110,7 +111,9 @@ class BaseLinearOperatorLowRankUpdatetest(object):
           diag_update, shape=None)
 
     base_operator = linalg.LinearOperatorDiag(
-        lin_op_base_diag, is_positive_definite=True)
+        lin_op_base_diag,
+        is_positive_definite=True,
+        is_self_adjoint=True)
 
     operator = linalg.LinearOperatorLowRankUpdate(
         base_operator,
@@ -169,6 +172,7 @@ class LinearOperatorLowRankUpdatetestWithDiagUseCholesky(
     self._rtol[dtypes.float32] = 1e-5
     self._atol[dtypes.float64] = 1e-10
     self._rtol[dtypes.float64] = 1e-10
+    self._rtol[dtypes.complex64] = 1e-4
 
 
 class LinearOperatorLowRankUpdatetestWithDiagCannotUseCholesky(
@@ -188,6 +192,7 @@ class LinearOperatorLowRankUpdatetestWithDiagCannotUseCholesky(
     self._rtol[dtypes.float32] = 1e-4
     self._atol[dtypes.float64] = 1e-9
     self._rtol[dtypes.float64] = 1e-9
+    self._rtol[dtypes.complex64] = 1e-4
 
 
 class LinearOperatorLowRankUpdatetestNoDiagUseCholesky(
@@ -206,6 +211,7 @@ class LinearOperatorLowRankUpdatetestNoDiagUseCholesky(
     self._rtol[dtypes.float32] = 1e-5
     self._atol[dtypes.float64] = 1e-10
     self._rtol[dtypes.float64] = 1e-10
+    self._rtol[dtypes.complex64] = 1e-4
 
 
 class LinearOperatorLowRankUpdatetestNoDiagCannotUseCholesky(
@@ -225,6 +231,7 @@ class LinearOperatorLowRankUpdatetestNoDiagCannotUseCholesky(
     self._rtol[dtypes.float32] = 1e-4
     self._atol[dtypes.float64] = 1e-9
     self._rtol[dtypes.float64] = 1e-9
+    self._rtol[dtypes.complex64] = 1e-4
 
 
 class LinearOperatorLowRankUpdatetestWithDiagNotSquare(
diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_lower_triangular_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_lower_triangular_test.py
index 167c6cacd1a5bbbaa70a7fdd236ddd70ea8cd4e8..b389e0cbdf72f2cd43751bd75e5b103b313df4b7 100644
--- a/tensorflow/python/kernel_tests/linalg/linear_operator_lower_triangular_test.py
+++ b/tensorflow/python/kernel_tests/linalg/linear_operator_lower_triangular_test.py
@@ -17,7 +17,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import random_seed
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops.linalg import linalg as linalg_lib
@@ -32,12 +31,6 @@ class LinearOperatorLowerTriangularTest(
     linear_operator_test_util.SquareLinearOperatorDerivedClassTest):
   """Most tests done in the base class LinearOperatorDerivedClassTest."""
 
-  @property
-  def _dtypes_to_test(self):
-    # TODO(langmore) Test complex types once supported by
-    # matrix_triangular_solve.
-    return [dtypes.float32, dtypes.float64]
-
   def _operator_and_matrix(self, build_info, dtype, use_placeholder):
     shape = list(build_info.shape)
     # Upper triangle will be nonzero, but ignored.
diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_zeros_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_zeros_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f60b55e0ad416ea1f09996f633e09a7dc2c3741
--- /dev/null
+++ b/tensorflow/python/kernel_tests/linalg/linear_operator_zeros_test.py
@@ -0,0 +1,192 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import random_seed
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops.linalg import linalg as linalg_lib
+from tensorflow.python.ops.linalg import linear_operator_test_util
+from tensorflow.python.platform import test
+
+
+random_seed.set_random_seed(23)
+rng = np.random.RandomState(2016)
+
+
+class LinearOperatorZerosTest(
+    linear_operator_test_util.SquareLinearOperatorDerivedClassTest):
+  """Most tests done in the base class LinearOperatorDerivedClassTest."""
+
+  @property
+  def _tests_to_skip(self):
+    return ["log_abs_det", "solve", "solve_with_broadcast"]
+
+  @property
+  def _operator_build_infos(self):
+    build_info = linear_operator_test_util.OperatorBuildInfo
+    return [
+        build_info((1, 1)),
+        build_info((1, 3, 3)),
+        build_info((3, 4, 4)),
+        build_info((2, 1, 4, 4))]
+
+  def _operator_and_matrix(self, build_info, dtype, use_placeholder):
+    del use_placeholder
+    shape = list(build_info.shape)
+    assert shape[-1] == shape[-2]
+
+    batch_shape = shape[:-2]
+    num_rows = shape[-1]
+
+    operator = linalg_lib.LinearOperatorZeros(
+        num_rows, batch_shape=batch_shape, dtype=dtype)
+    matrix = array_ops.zeros(shape=shape, dtype=dtype)
+
+    return operator, matrix
+
+  def test_assert_positive_definite(self):
+    operator = linalg_lib.LinearOperatorZeros(num_rows=2)
+    with self.assertRaisesOpError("non-positive definite"):
+      operator.assert_positive_definite()
+
+  def test_assert_non_singular(self):
+    with self.assertRaisesOpError("non-invertible"):
+      operator = linalg_lib.LinearOperatorZeros(num_rows=2)
+      operator.assert_non_singular()
+
+  def test_assert_self_adjoint(self):
+    with self.test_session():
+      operator = linalg_lib.LinearOperatorZeros(num_rows=2)
+      operator.assert_self_adjoint().run()  # Should not fail
+
+  def test_non_scalar_num_rows_raises_static(self):
+    with self.assertRaisesRegexp(ValueError, "must be a 0-D Tensor"):
+      linalg_lib.LinearOperatorZeros(num_rows=[2])
+    with self.assertRaisesRegexp(ValueError, "must be a 0-D Tensor"):
+      linalg_lib.LinearOperatorZeros(num_rows=2, num_columns=[2])
+
+  def test_non_integer_num_rows_raises_static(self):
+    with self.assertRaisesRegexp(TypeError, "must be integer"):
+      linalg_lib.LinearOperatorZeros(num_rows=2.)
+    with self.assertRaisesRegexp(TypeError, "must be integer"):
+      linalg_lib.LinearOperatorZeros(num_rows=2, num_columns=2.)
+
+  def test_negative_num_rows_raises_static(self):
+    with self.assertRaisesRegexp(ValueError, "must be non-negative"):
+      linalg_lib.LinearOperatorZeros(num_rows=-2)
+    with self.assertRaisesRegexp(ValueError, "must be non-negative"):
+      linalg_lib.LinearOperatorZeros(num_rows=2, num_columns=-2)
+
+  def test_non_1d_batch_shape_raises_static(self):
+    with self.assertRaisesRegexp(ValueError, "must be a 1-D"):
+      linalg_lib.LinearOperatorZeros(num_rows=2, batch_shape=2)
+
+  def test_non_integer_batch_shape_raises_static(self):
+    with self.assertRaisesRegexp(TypeError, "must be integer"):
+      linalg_lib.LinearOperatorZeros(num_rows=2, batch_shape=[2.])
+
+  def test_negative_batch_shape_raises_static(self):
+    with self.assertRaisesRegexp(ValueError, "must be non-negative"):
+      linalg_lib.LinearOperatorZeros(num_rows=2, batch_shape=[-2])
+
+  def test_non_scalar_num_rows_raises_dynamic(self):
+    with self.test_session():
+      num_rows = array_ops.placeholder(dtypes.int32)
+      operator = linalg_lib.LinearOperatorZeros(
+          num_rows, assert_proper_shapes=True)
+      with self.assertRaisesOpError("must be a 0-D Tensor"):
+        operator.to_dense().eval(feed_dict={num_rows: [2]})
+
+  def test_negative_num_rows_raises_dynamic(self):
+    with self.test_session():
+      n = array_ops.placeholder(dtypes.int32)
+      operator = linalg_lib.LinearOperatorZeros(
+          num_rows=n, assert_proper_shapes=True)
+      with self.assertRaisesOpError("must be non-negative"):
+        operator.to_dense().eval(feed_dict={n: -2})
+
+      operator = linalg_lib.LinearOperatorZeros(
+          num_rows=2, num_columns=n, assert_proper_shapes=True)
+      with self.assertRaisesOpError("must be non-negative"):
+        operator.to_dense().eval(feed_dict={n: -2})
+
+  def test_non_1d_batch_shape_raises_dynamic(self):
+    with self.test_session():
+      batch_shape = array_ops.placeholder(dtypes.int32)
+      operator = linalg_lib.LinearOperatorZeros(
+          num_rows=2, batch_shape=batch_shape, assert_proper_shapes=True)
+      with self.assertRaisesOpError("must be a 1-D"):
+        operator.to_dense().eval(feed_dict={batch_shape: 2})
+
+  def test_negative_batch_shape_raises_dynamic(self):
+    with self.test_session():
+      batch_shape = array_ops.placeholder(dtypes.int32)
+      operator = linalg_lib.LinearOperatorZeros(
+          num_rows=2, batch_shape=batch_shape, assert_proper_shapes=True)
+      with self.assertRaisesOpError("must be non-negative"):
+        operator.to_dense().eval(feed_dict={batch_shape: [-2]})
+
+  def test_wrong_matrix_dimensions_raises_static(self):
+    operator = linalg_lib.LinearOperatorZeros(num_rows=2)
+    x = rng.randn(3, 3).astype(np.float32)
+    with self.assertRaisesRegexp(ValueError, "Dimensions.*not compatible"):
+      operator.matmul(x)
+
+  def test_wrong_matrix_dimensions_raises_dynamic(self):
+    num_rows = array_ops.placeholder(dtypes.int32)
+    x = array_ops.placeholder(dtypes.float32)
+
+    with self.test_session():
+      operator = linalg_lib.LinearOperatorZeros(
+          num_rows, assert_proper_shapes=True)
+      y = operator.matmul(x)
+      with self.assertRaisesOpError("Incompatible.*dimensions"):
+        y.eval(feed_dict={num_rows: 2, x: rng.rand(3, 3)})
+
+  def test_is_x_flags(self):
+    # The is_x flags are by default all True.
+    operator = linalg_lib.LinearOperatorZeros(num_rows=2)
+    self.assertFalse(operator.is_positive_definite)
+    self.assertFalse(operator.is_non_singular)
+    self.assertTrue(operator.is_self_adjoint)
+
+
+class LinearOperatorZerosNotSquareTest(
+    linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest):
+
+  def _operator_and_matrix(self, build_info, dtype, use_placeholder):
+    del use_placeholder
+    shape = list(build_info.shape)
+
+    batch_shape = shape[:-2]
+    num_rows = shape[-2]
+    num_columns = shape[-1]
+
+    operator = linalg_lib.LinearOperatorZeros(
+        num_rows, num_columns, is_square=False, is_self_adjoint=False,
+        batch_shape=batch_shape, dtype=dtype)
+    matrix = array_ops.zeros(shape=shape, dtype=dtype)
+
+    return operator, matrix
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py b/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py
index d8ce9fffbd2bc0d18033339a02e0ad84f8f4c952..3cbbd48c8cb26d5cdb457c9599bfc9131000d174 100644
--- a/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py
+++ b/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py
@@ -82,7 +82,7 @@ def CheckGradConfigsToTest():
 class DepthwiseConv2DTest(test.TestCase):
 
   # This is testing that depthwise_conv2d and depthwise_conv2d_native
-  # produce the same results.  It also tests that NCHW and NWHC
+  # produce the same results.  It also tests that NCHW and NHWC
   # formats agree, by comparing the depthwise_conv2d_native with
   # 'NCHW' format (with transposition) matches the 'NHWC' format using
   # the higher level interface.
@@ -123,7 +123,7 @@ class DepthwiseConv2DTest(test.TestCase):
       native_t1 = t1
       strides = [1, stride, stride, 1]
       if data_format == "NCHW":
-        # Transpose from NWHC input to NCHW
+        # Transpose from NHWC input to NCHW
         # Ex. [4, 5, 5, 48] to [4, 48, 5, 5]
         native_t1 = array_ops.transpose(t1, [0, 3, 1, 2])
         strides = [1, 1, stride, stride]
diff --git a/tensorflow/python/kernel_tests/random/random_ops_test.py b/tensorflow/python/kernel_tests/random/random_ops_test.py
index e4b5c3832a2252aedc8820a650b022cd30b7f285..0ef6a95cfc994ecdfb734f133984fbad774d8691 100644
--- a/tensorflow/python/kernel_tests/random/random_ops_test.py
+++ b/tensorflow/python/kernel_tests/random/random_ops_test.py
@@ -24,13 +24,42 @@ from six.moves import xrange  # pylint: disable=redefined-builtin
 from tensorflow.python.eager import context
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import random_seed
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 
 
-class RandomNormalTest(test.TestCase):
+class RandomOpTestCommon(test.TestCase):
+
+  # Checks that executing the same rng_func multiple times rarely produces the
+  # same result.
+  def _testSingleSessionNotConstant(self,
+                                    rng_func,
+                                    num,
+                                    dtype,
+                                    min_or_mean,
+                                    max_or_stddev,
+                                    use_gpu,
+                                    op_seed=None,
+                                    graph_seed=None):
+    with self.test_session(use_gpu=use_gpu, graph=ops.Graph()) as sess:
+      if graph_seed is not None:
+        random_seed.set_random_seed(graph_seed)
+      x = rng_func([num], min_or_mean, max_or_stddev, dtype=dtype, seed=op_seed)
+
+      y = sess.run(x)
+      z = sess.run(x)
+      w = sess.run(x)
+
+      # We use exact equality here. If the random-number generator is producing
+      # the same output, all three outputs will be bitwise identical.
+      self.assertTrue((not np.array_equal(y, z)) or
+                      (not np.array_equal(z, w)) or (not np.array_equal(y, w)))
+
+
+class RandomNormalTest(RandomOpTestCommon):
 
   def _Sampler(self, num, mu, sigma, dtype, use_gpu, seed=None):
 
@@ -90,6 +119,36 @@ class RandomNormalTest(test.TestCase):
         diff = rnd2 - rnd1
         self.assertTrue(np.linalg.norm(diff.eval()) > 0.1)
 
+  def testSingleSessionNotConstant(self):
+    for use_gpu in [False, True]:
+      for dt in dtypes.float16, dtypes.float32, dtypes.float64:
+        self._testSingleSessionNotConstant(
+            random_ops.random_normal, 100, dt, 0.0, 1.0, use_gpu=use_gpu)
+
+  def testSingleSessionOpSeedNotConstant(self):
+    for use_gpu in [False, True]:
+      for dt in dtypes.float16, dtypes.float32, dtypes.float64:
+        self._testSingleSessionNotConstant(
+            random_ops.random_normal,
+            100,
+            dt,
+            0.0,
+            1.0,
+            use_gpu=use_gpu,
+            op_seed=1345)
+
+  def testSingleSessionGraphSeedNotConstant(self):
+    for use_gpu in [False, True]:
+      for dt in dtypes.float16, dtypes.float32, dtypes.float64:
+        self._testSingleSessionNotConstant(
+            random_ops.random_normal,
+            100,
+            dt,
+            0.0,
+            1.0,
+            use_gpu=use_gpu,
+            graph_seed=965)
+
 
 class TruncatedNormalTest(test.TestCase):
 
@@ -187,7 +246,7 @@ class TruncatedNormalTest(test.TestCase):
       self.assertAllEqual(rnd1, rnd2)
 
 
-class RandomUniformTest(test.TestCase):
+class RandomUniformTest(RandomOpTestCommon):
 
   def _Sampler(self, num, minv, maxv, dtype, use_gpu, seed=None):
 
@@ -291,6 +350,39 @@ class RandomUniformTest(test.TestCase):
         diff = (rnd2 - rnd1).eval()
         self.assertTrue(np.linalg.norm(diff) > 0.1)
 
+  def testSingleSessionNotConstant(self):
+    for use_gpu in [False, True]:
+      for dt in (dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32,
+                 dtypes.int64):
+        self._testSingleSessionNotConstant(
+            random_ops.random_uniform, 100, dt, 0, 17, use_gpu=use_gpu)
+
+  def testSingleSessionOpSeedNotConstant(self):
+    for use_gpu in [False, True]:
+      for dt in (dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32,
+                 dtypes.int64):
+        self._testSingleSessionNotConstant(
+            random_ops.random_uniform,
+            100,
+            dt,
+            10,
+            20,
+            use_gpu=use_gpu,
+            op_seed=1345)
+
+  def testSingleSessionGraphSeedNotConstant(self):
+    for use_gpu in [False, True]:
+      for dt in (dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32,
+                 dtypes.int64):
+        self._testSingleSessionNotConstant(
+            random_ops.random_uniform,
+            100,
+            dt,
+            20,
+            200,
+            use_gpu=use_gpu,
+            graph_seed=965)
+
 
 class RandomShapeTest(test.TestCase):
 
diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py
index 0fb0b8895cbc847639999ad1bd23e7fb04c86034..c739cd2c0d7454364d3f513823d44d979d273cf2 100644
--- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py
+++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py
@@ -246,6 +246,15 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase):
     read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)
     self.assertEqual(self.evaluate(read), [[2]])
 
+  def testUseResource(self):
+    v = variables.Variable(1.0, use_resource=True)
+    self.assertTrue(isinstance(v, resource_variable_ops.ResourceVariable))
+
+  def testEagerNoUseResource(self):
+    with context.eager_mode():
+      v = variables.Variable(1.0)
+      self.assertTrue(isinstance(v, resource_variable_ops.ResourceVariable))
+
   @test_util.run_in_graph_and_eager_modes
   def testScatterMin(self):
     with ops.device("cpu:0"):
@@ -852,5 +861,62 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase):
       state_ops.scatter_update(v, [0, 1], [0, 1, 2])
 
 
+class _MixedPrecisionVariableTest(test_util.TensorFlowTestCase):
+
+  @test_util.run_in_graph_and_eager_modes()
+  def test_dense_var_to_tensor_read_dtype_same_as_var_dtype(self):
+    # read_dtype is same as dtype
+    v = resource_variable_ops.ResourceVariable(1.0, dtype=dtypes.float32)
+    v = resource_variable_ops._MixedPrecisionVariable(v, dtypes.float32)
+    if not context.executing_eagerly():
+      v.initializer.run()
+
+    # dtype is not read_dtype, return NotImplemented
+    self.assertEqual(
+        NotImplemented, v._dense_var_to_tensor(dtype=dtypes.float16))
+    self.assertEqual(NotImplemented,
+                     v._dense_var_to_tensor(dtype=dtypes.float16, as_ref=True))
+
+    # as_ref is False
+    t = v._dense_var_to_tensor(as_ref=False)
+    self.assertTrue(isinstance(t, ops.Tensor))
+    self.assertEqual(t.dtype, dtypes.float32)
+    self.assertEqual(self.evaluate(t), 1.0)
+
+    t = v._dense_var_to_tensor(dtype=dtypes.float32, as_ref=False)
+    self.assertTrue(isinstance(t, ops.Tensor))
+    self.assertEqual(t.dtype, dtypes.float32)
+    self.assertEqual(self.evaluate(t), 1.0)
+
+    # as_ref is True
+    self.assertEqual(NotImplemented, v._dense_var_to_tensor(as_ref=True))
+    self.assertEqual(NotImplemented,
+                     v._dense_var_to_tensor(dtype=dtypes.float32, as_ref=True))
+
+  @test_util.run_in_graph_and_eager_modes()
+  def test_dense_var_to_tensor_read_dtype_different_from_var_dtype(self):
+    # read_dtype is different from dtype
+    v = resource_variable_ops.ResourceVariable(1.0, dtype=dtypes.float32)
+    v = resource_variable_ops._MixedPrecisionVariable(v, dtypes.float16)
+    if not context.executing_eagerly():
+      v.initializer.run()
+
+    # as_ref is False
+    t = v._dense_var_to_tensor(as_ref=False)
+    self.assertTrue(isinstance(t, ops.Tensor))
+    self.assertEqual(t.dtype, dtypes.float16)
+    self.assertEqual(self.evaluate(t), 1.0)
+
+    t = v._dense_var_to_tensor(dtype=dtypes.float16, as_ref=False)
+    self.assertTrue(isinstance(t, ops.Tensor))
+    self.assertEqual(t.dtype, dtypes.float16)
+    self.assertEqual(self.evaluate(t), 1.0)
+
+    # as_ref is True
+    self.assertEqual(NotImplemented, v._dense_var_to_tensor(as_ref=True))
+    self.assertEqual(NotImplemented,
+                     v._dense_var_to_tensor(dtype=dtypes.float16, as_ref=True))
+
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py
index 957baf8c6089a6a033f54762fef290399d80cd09..acee180a6c3e55643052b439d95a65b073288ac6 100644
--- a/tensorflow/python/kernel_tests/rnn_test.py
+++ b/tensorflow/python/kernel_tests/rnn_test.py
@@ -268,6 +268,12 @@ class RNNTest(test.TestCase):
     self._assert_cell_builds(rnn_cell_impl.GRUCell, f64, 5, 7, 3)
     self._assert_cell_builds(rnn_cell_impl.LSTMCell, f32, 5, 7, 3)
     self._assert_cell_builds(rnn_cell_impl.LSTMCell, f64, 5, 7, 3)
+    self._assert_cell_builds(contrib_rnn.IndRNNCell, f32, 5, 7, 3)
+    self._assert_cell_builds(contrib_rnn.IndRNNCell, f64, 5, 7, 3)
+    self._assert_cell_builds(contrib_rnn.IndyGRUCell, f32, 5, 7, 3)
+    self._assert_cell_builds(contrib_rnn.IndyGRUCell, f64, 5, 7, 3)
+    self._assert_cell_builds(contrib_rnn.IndyLSTMCell, f32, 5, 7, 3)
+    self._assert_cell_builds(contrib_rnn.IndyLSTMCell, f64, 5, 7, 3)
 
 
 ######### Benchmarking RNN code
diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py
index f9b9c77bbf7e2a8afdbfbd0929a68856b8aae51c..f2f30234696be7f6c8c98d041bc415ccf5cb4ecf 100644
--- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py
+++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py
@@ -268,12 +268,12 @@ class StatefulScatterNdTest(test.TestCase):
         # Test some out of range errors.
         indices = np.array([[-1], [0], [5]])
         with self.assertRaisesOpError(
-            r"Invalid indices: \[0,0\] = \[-1\] does not index into \[6\]"):
+            r"indices\[0\] = \[-1\] does not index into shape \[6\]"):
           op(ref, indices, updates).eval()
 
         indices = np.array([[2], [0], [6]])
         with self.assertRaisesOpError(
-            r"Invalid indices: \[2,0\] = \[6\] does not index into \[6\]"):
+            r"indices\[2\] = \[6\] does not index into shape \[6\]"):
           op(ref, indices, updates).eval()
 
   def testRank3ValidShape(self):
@@ -369,6 +369,29 @@ class ScatterNdTest(test.TestCase):
     del input_  # input_ is not used in scatter_nd
     return array_ops.scatter_nd(indices, updates, shape)
 
+  @test_util.run_in_graph_and_eager_modes
+  def testBool(self):
+    indices = constant_op.constant(
+        [[4], [3], [1], [7]], dtype=dtypes.int32)
+    updates = constant_op.constant(
+        [False, True, False, True], dtype=dtypes.bool)
+    expected = np.array(
+        [False, False, False, True, False, False, False, True])
+    scatter = self.scatter_nd(indices, updates, shape=(8,))
+    result = self.evaluate(scatter)
+    self.assertAllEqual(expected, result)
+
+    # Same indice is updated twice by same value.
+    indices = constant_op.constant(
+        [[4], [3], [3], [7]], dtype=dtypes.int32)
+    updates = constant_op.constant(
+        [False, True, True, True], dtype=dtypes.bool)
+    expected = np.array([
+        False, False, False, True, False, False, False, True])
+    scatter = self.scatter_nd(indices, updates, shape=(8,))
+    result = self.evaluate(scatter)
+    self.assertAllEqual(expected, result)
+
   @test_util.run_in_graph_and_eager_modes
   def testInvalidShape(self):
     # TODO(apassos) figure out how to unify these errors
diff --git a/tensorflow/python/kernel_tests/variable_scope_test.py b/tensorflow/python/kernel_tests/variable_scope_test.py
index 054c6f9dd79156bc4b4f3179528fe56235fdf369..ae2a0ab29abed2902c0095f7b0886c1afa704af4 100644
--- a/tensorflow/python/kernel_tests/variable_scope_test.py
+++ b/tensorflow/python/kernel_tests/variable_scope_test.py
@@ -1054,7 +1054,7 @@ class VariableScopeTest(test.TestCase):
           "testGetCollection_foo/testGetCollection_a:0"
       ])
 
-  def testGetTrainableVariables(self):
+  def testGetTrainableVariablesWithGetVariable(self):
     with self.test_session():
       _ = variable_scope.get_variable("testGetTrainableVariables_a", [])
       with variable_scope.variable_scope(
@@ -1062,10 +1062,72 @@ class VariableScopeTest(test.TestCase):
         _ = variable_scope.get_variable("testGetTrainableVariables_b", [])
         _ = variable_scope.get_variable(
             "testGetTrainableVariables_c", [], trainable=False)
+
+        # sync `ON_READ` sets trainable=False
+        _ = variable_scope.get_variable(
+            "testGetTrainableVariables_d", [],
+            synchronization=variable_scope.VariableSynchronization.ON_READ)
         self.assertEqual(
             [v.name for v in scope.trainable_variables()],
-            ["testGetTrainableVariables_foo/"
-             "testGetTrainableVariables_b:0"])
+            ["testGetTrainableVariables_foo/testGetTrainableVariables_b:0"])
+
+        # All other sync values sets trainable=True
+        _ = variable_scope.get_variable(
+            "testGetTrainableVariables_e", [],
+            synchronization=variable_scope.VariableSynchronization.ON_WRITE)
+        self.assertEqual([v.name for v in scope.trainable_variables()], [
+            "testGetTrainableVariables_foo/testGetTrainableVariables_b:0",
+            "testGetTrainableVariables_foo/testGetTrainableVariables_e:0"
+        ])
+
+      with self.assertRaisesRegexp(
+          ValueError, "Synchronization value can be set to "
+          "VariableSynchronization.ON_READ only for non-trainable variables. "
+          "You have specified trainable=True and "
+          "synchronization=VariableSynchronization.ON_READ."):
+        _ = variable_scope.get_variable(
+            "testGetTrainableVariables_e", [],
+            synchronization=variable_scope.VariableSynchronization.ON_READ,
+            trainable=True)
+
+  def testGetTrainableVariablesWithVariable(self):
+    with self.test_session():
+      _ = variable_scope.variable(1.0, name="testGetTrainableVariables_a")
+      with variable_scope.variable_scope(
+          "testGetTrainableVariables_foo") as scope:
+        _ = variable_scope.variable(1.0, name="testGetTrainableVariables_b")
+        _ = variable_scope.variable(
+            1.0, name="testGetTrainableVariables_c", trainable=False)
+
+        # sync `ON_READ` sets trainable=False
+        _ = variable_scope.variable(
+            1.0,
+            name="testGetTrainableVariables_d",
+            synchronization=variable_scope.VariableSynchronization.ON_READ)
+        self.assertEqual(
+            [v.name for v in scope.trainable_variables()],
+            ["testGetTrainableVariables_foo/testGetTrainableVariables_b:0"])
+
+        # All other sync values sets trainable=True
+        _ = variable_scope.variable(
+            1.0,
+            name="testGetTrainableVariables_e",
+            synchronization=variable_scope.VariableSynchronization.ON_WRITE)
+        self.assertEqual([v.name for v in scope.trainable_variables()], [
+            "testGetTrainableVariables_foo/testGetTrainableVariables_b:0",
+            "testGetTrainableVariables_foo/testGetTrainableVariables_e:0"
+        ])
+
+      with self.assertRaisesRegexp(
+          ValueError, "Synchronization value can be set to "
+          "VariableSynchronization.ON_READ only for non-trainable variables. "
+          "You have specified trainable=True and "
+          "synchronization=VariableSynchronization.ON_READ."):
+        _ = variable_scope.variable(
+            1.0,
+            name="testGetTrainableVariables_e",
+            synchronization=variable_scope.VariableSynchronization.ON_READ,
+            trainable=True)
 
   def testGetGlobalVariables(self):
     with self.test_session():
diff --git a/tensorflow/python/kernel_tests/variables_test.py b/tensorflow/python/kernel_tests/variables_test.py
index 62d596da91682c396c04efbc64cf063c8e29e7cc..2b9c62ad6f15aea65bd8d504b2f5e713ee38fc83 100644
--- a/tensorflow/python/kernel_tests/variables_test.py
+++ b/tensorflow/python/kernel_tests/variables_test.py
@@ -642,6 +642,8 @@ class PartitionedVariableTest(test.TestCase):
       iterated_partitions = list(partitioned_variable)
       self.assertEqual(2, num_partitions)
       self.assertEqual([v0, v1], iterated_partitions)
+      self.assertEqual([2], partitioned_variable.get_shape())
+      self.assertEqual([2], partitioned_variable.shape)
       self.assertEqual([2], concatenated.get_shape())
       self.assertEqual([2], concatenated.shape)
 
diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index b8969a41aba1f8ee84233ce7ac398193183d292f..cf13b526175c232d0bc7389bd7c2dc9b23f75353 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -152,10 +152,17 @@ class Layer(base_layer.Layer):
             scope, default_name=self._base_name) as captured_scope:
           self._scope = captured_scope
 
-  def add_weight(self, name, shape, dtype=None,
-                 initializer=None, regularizer=None,
-                 trainable=True, constraint=None,
+  def add_weight(self,
+                 name,
+                 shape,
+                 dtype=None,
+                 initializer=None,
+                 regularizer=None,
+                 trainable=None,
+                 constraint=None,
                  use_resource=None,
+                 synchronization=vs.VariableSynchronization.AUTO,
+                 aggregation=vs.VariableAggregation.NONE,
                  partitioner=None):
     """Adds a new variable to the layer, or gets an existing one; returns it.
 
@@ -170,9 +177,19 @@ class Layer(base_layer.Layer):
         or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
         Note, if the current variable scope is marked as non-trainable
         then this parameter is ignored and any added variables are also
-        marked as non-trainable.
+        marked as non-trainable. `trainable` defaults to `True` unless
+        `synchronization` is set to `ON_READ`.
       constraint: constraint instance (callable).
       use_resource: Whether to use `ResourceVariable`.
+      synchronization: Indicates when a distributed a variable will be
+        aggregated. Accepted values are constants defined in the class
+        @{tf.VariableSynchronization}. By default the synchronization is set to
+        `AUTO` and the current `DistributionStrategy` chooses
+        when to synchronize. If `synchronization` is set to `ON_READ`,
+        `trainable` must not be set to `True`.
+      aggregation: Indicates how a distributed variable will be aggregated.
+        Accepted values are constants defined in the class
+        @{tf.VariableAggregation}.
       partitioner: (optional) partitioner instance (callable).  If
         provided, when the requested variable is created it will be split
         into multiple partitions according to `partitioner`.  In this case,
@@ -190,7 +207,21 @@ class Layer(base_layer.Layer):
     Raises:
       RuntimeError: If called with partioned variable regularization and
         eager execution is enabled.
+      ValueError: When trainable has been set to True with synchronization
+        set as `ON_READ`.
     """
+    if synchronization == vs.VariableSynchronization.ON_READ:
+      if trainable:
+        raise ValueError(
+            'Synchronization value can be set to '
+            'VariableSynchronization.ON_READ only for non-trainable variables. '
+            'You have specified trainable=True and '
+            'synchronization=VariableSynchronization.ON_READ.')
+      else:
+        # Set trainable to be false when variable is to be synced on read.
+        trainable = False
+    elif trainable is None:
+      trainable = True
 
     def _should_add_regularizer(variable, existing_variable_set):
       if isinstance(variable, tf_variables.PartitionedVariable):
@@ -240,6 +271,8 @@ class Layer(base_layer.Layer):
             constraint=constraint,
             partitioner=partitioner,
             use_resource=use_resource,
+            synchronization=synchronization,
+            aggregation=aggregation,
             getter=vs.get_variable)
 
         if regularizer:
diff --git a/tensorflow/python/layers/base_test.py b/tensorflow/python/layers/base_test.py
index 298e96e711cbf8a0f625f95d737d1e7a83f4431d..d2443db6651cdab2aaf5fb2b9d678080b48bb254 100644
--- a/tensorflow/python/layers/base_test.py
+++ b/tensorflow/python/layers/base_test.py
@@ -90,12 +90,34 @@ class BaseLayerTest(test.TestCase):
 
       # regularizers only supported in GRAPH mode.
       regularizer = lambda x: math_ops.reduce_sum(x) * 1e-3
-      variable = layer.add_variable(
+      _ = layer.add_variable(
           'reg_var', [2, 2],
           initializer=init_ops.zeros_initializer(),
           regularizer=regularizer)
       self.assertEqual(len(layer.losses), 1)
 
+    # Test that sync `ON_READ` variables are defaulted to be non-trainable.
+    variable_3 = layer.add_variable(
+        'sync_on_read_var', [2, 2],
+        initializer=init_ops.zeros_initializer(),
+        synchronization=variable_scope.VariableSynchronization.ON_READ,
+        aggregation=variable_scope.VariableAggregation.SUM)
+    self.assertEqual(layer.non_trainable_variables, [variable_2, variable_3])
+
+  def testInvalidTrainableSynchronizationCombination(self):
+    layer = base_layers.Layer(name='my_layer')
+
+    with self.assertRaisesRegexp(
+        ValueError, 'Synchronization value can be set to '
+        'VariableSynchronization.ON_READ only for non-trainable variables. '
+        'You have specified trainable=True and '
+        'synchronization=VariableSynchronization.ON_READ.'):
+      _ = layer.add_variable(
+          'v', [2, 2],
+          initializer=init_ops.zeros_initializer(),
+          synchronization=variable_scope.VariableSynchronization.ON_READ,
+          trainable=True)
+
   def testReusePartitionedVaraiblesAndRegularizers(self):
     regularizer = lambda x: math_ops.reduce_sum(x) * 1e-3
     partitioner = partitioned_variables.fixed_size_partitioner(3)
@@ -104,7 +126,7 @@ class BaseLayerTest(test.TestCase):
                                          partitioner=partitioner,
                                          reuse=reuse):
         layer = base_layers.Layer(name='my_layer')
-        variable = layer.add_variable(
+        _ = layer.add_variable(
             'reg_part_var', [4, 4],
             initializer=init_ops.zeros_initializer(),
             regularizer=regularizer)
diff --git a/tensorflow/python/lib/core/numpy.h b/tensorflow/python/lib/core/numpy.h
index d4621d61ee98b9eb4b19213145059d242c88f40c..0098d938a086621a9fd98fa69b48aa78b5341171 100644
--- a/tensorflow/python/lib/core/numpy.h
+++ b/tensorflow/python/lib/core/numpy.h
@@ -30,9 +30,10 @@ limitations under the License.
 #endif
 
 // Place `` before  to avoid build failure in macOS.
-#include 
 #include 
 
+#include 
+
 #include "numpy/arrayobject.h"
 #include "numpy/ufuncobject.h"
 
diff --git a/tensorflow/python/lib/core/py_util.cc b/tensorflow/python/lib/core/py_util.cc
index 6b6c82015fd2b73e410d64306ecbd613ccf1967c..2ee898ea1d3efcb8e93e0c244842280f2e52aaf6 100644
--- a/tensorflow/python/lib/core/py_util.cc
+++ b/tensorflow/python/lib/core/py_util.cc
@@ -16,9 +16,10 @@ limitations under the License.
 #include "tensorflow/python/lib/core/py_util.h"
 
 // Place `` before  to avoid build failure in macOS.
-#include 
 #include 
 
+#include 
+
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 
diff --git a/tensorflow/python/ops/array_grad.py b/tensorflow/python/ops/array_grad.py
index fe459a96b98733f8a706b0c3b84000c5a74894ad..a2b5f77f915404b124b14c5d34996cd85cb2b468 100644
--- a/tensorflow/python/ops/array_grad.py
+++ b/tensorflow/python/ops/array_grad.py
@@ -790,7 +790,7 @@ def _ExtractImagePatchesGrad(op, grad):
 
   sp_mat = sparse_tensor.SparseTensor(
       array_ops.constant(idx, dtype=ops.dtypes.int64),
-      array_ops.ones((len(idx),), dtype=ops.dtypes.float32), sp_shape)
+      array_ops.ones((len(idx),), dtype=grad.dtype), sp_shape)
 
   jac = sparse_ops.sparse_tensor_dense_matmul(sp_mat, grad_flat)
 
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 361667ec49aba9705787c3c7ac096add36afb40b..ec6488ea6321508677c88dfe077acb0160400cfe 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -636,10 +636,10 @@ def strided_slice(input_,
   `foo[:4, tf.newaxis, :2]` would produce a shape `(4, 1, 2)` tensor.
 
   If the ith bit of `shrink_axis_mask` is set, it implies that the ith
-  specification shrinks the dimensionality by 1. `begin[i]`, `end[i]` and
-  `strides[i]` must imply a slice of size 1 in the dimension. For example in
-  Python one might do `foo[:, 3, :]` which would result in
-  `shrink_axis_mask` equal to 2.
+  specification shrinks the dimensionality by 1, taking on the value at index
+  `begin[i]`. `end[i]` and `strides[i]` are ignored in this case. For example in
+  Python one might do `foo[:, 3, :]` which would result in `shrink_axis_mask`
+  equal to 2.
 
 
   NOTE: `begin` and `end` are zero-indexed.
diff --git a/tensorflow/python/ops/boosted_trees_ops.py b/tensorflow/python/ops/boosted_trees_ops.py
index 868a4f6b84df2c0d1b8b55a254f16f1be5ee1f1d..f7cbfe0312fe5e7d8d75580af9f362236fd5b79d 100644
--- a/tensorflow/python/ops/boosted_trees_ops.py
+++ b/tensorflow/python/ops/boosted_trees_ops.py
@@ -37,8 +37,19 @@ from tensorflow.python.training import saver
 
 
 class PruningMode(object):
+  """Class for working with Pruning modes."""
   NO_PRUNING, PRE_PRUNING, POST_PRUNING = range(0, 3)
 
+  _map = {'none': NO_PRUNING, 'pre': PRE_PRUNING, 'post': POST_PRUNING}
+
+  @classmethod
+  def from_str(cls, mode):
+    if mode in cls._map:
+      return cls._map[mode]
+    else:
+      raise ValueError('pruning_mode mode must be one of: {}'.format(', '.join(
+          sorted(cls._map))))
+
 
 class _TreeEnsembleSavable(saver.BaseSaverBuilder.SaveableObject):
   """SaveableObject implementation for TreeEnsemble."""
diff --git a/tensorflow/python/ops/cond_v2_impl.py b/tensorflow/python/ops/cond_v2_impl.py
index d310f83dca97889157eb078b11a3ca51caae2fc2..44c5c050c08240dd13766b71e8e708c9b0317399 100644
--- a/tensorflow/python/ops/cond_v2_impl.py
+++ b/tensorflow/python/ops/cond_v2_impl.py
@@ -58,12 +58,14 @@ def cond_v2(pred, true_fn, false_fn, name="cond"):
 
   with ops.name_scope(name) as scope:
     # Identify if there is a caller device, & get the innermost if possible.
-    device_stack = ops.get_default_graph()._device_function_stack
-    caller_device = device_stack[-1] if device_stack else None
+    # pylint: disable=protected-access
+    device_funcs = ops.get_default_graph()._device_functions_outer_to_inner
+    caller_device = device_funcs[-1] if device_funcs else None
 
     caller_colocation_stack = ops.get_default_graph()._colocation_stack
     caller_container = ops.get_default_graph()._container
     caller_collection_ref = ops.get_default_graph()._collections
+    # pylint: enable=protected-access
 
     func_name_prefix = scope.replace("/", "_")
 
@@ -106,7 +108,7 @@ def cond_v2(pred, true_fn, false_fn, name="cond"):
     false_graph.outputs.extend(extra_false_outputs)
 
     # Create the If op.
-    tensors = gen_functional_ops._if(
+    tensors = gen_functional_ops._if(  # pylint: disable=protected-access
         pred, cond_inputs, [t.dtype for t in true_graph.outputs],
         _create_new_tf_function(true_graph),
         _create_new_tf_function(false_graph),
@@ -125,8 +127,10 @@ def cond_v2(pred, true_fn, false_fn, name="cond"):
     # TODO(b/110167197) this approach requires cond_v2 to have at least 1 output
     if_op = tensors[0].op
     if not control_flow_util.IsInXLAContext(if_op):
+      # pylint: disable=protected-access
       if_op._set_attr("_lower_using_switch_merge",
                       attr_value_pb2.AttrValue(b=True))
+      # pylint: enable=protected-access
 
     return tensors[:num_cond_outputs]
 
@@ -135,6 +139,10 @@ def cond_v2(pred, true_fn, false_fn, name="cond"):
 def _IfGrad(op, *grads):  # pylint: disable=invalid-name
   """The gradient of an If op produced by cond_v2."""
   true_graph, false_graph = _get_func_graphs(op)
+  # Note: op.graph != ops.get_default_graph() when we are computing the gradient
+  # of a nested cond.
+  assert true_graph._outer_graph == op.graph
+  assert false_graph._outer_graph == op.graph
 
   # Create grad functions that compute the gradient of the true/false forward
   # graphs. These functions will capture tensors from the forward pass
@@ -147,15 +155,16 @@ def _IfGrad(op, *grads):  # pylint: disable=invalid-name
   assert ([t.dtype for t in true_grad_graph.outputs] ==
           [t.dtype for t in false_grad_graph.outputs])
 
-  # Match up the captured grad function inputs with outputs of 'op' and other
-  # external tensors.
-  true_grad_inputs = _get_grad_inputs(op, true_graph, true_grad_graph)
-  false_grad_inputs = _get_grad_inputs(op, false_graph, false_grad_graph)
+  # Resolve references to forward graph tensors in grad graphs and ensure
+  # they are in-scope, i.e., belong to one of outer graphs of the grad graph.
+  true_grad_extra_inputs = _resolve_grad_inputs(true_graph, true_grad_graph)
+  false_grad_extra_inputs = _resolve_grad_inputs(false_graph, false_grad_graph)
 
   # Make the inputs to true_grad_graph and false_grad_graph match. Note that
   # this modifies true_grad_graph and false_grad_graph.
   grad_inputs = _make_inputs_match(true_grad_graph, false_grad_graph,
-                                   true_grad_inputs, false_grad_inputs)
+                                   true_grad_extra_inputs,
+                                   false_grad_extra_inputs)
 
   # Add all intermediate tensors as function outputs so they're available for
   # higher-order gradient computations.
@@ -199,11 +208,20 @@ def _get_func_graphs(if_op):
     input_shapes = [t.shape for t in extra_inputs]
     func_name = if_op.get_attr(branch_name).name
     fdef = if_op.graph._get_function(func_name).definition
-    func_graph = _function_def_to_graph.function_def_to_graph(
-        fdef, input_shapes)
+    # `if_op.graph` may not be the same as `ops.get_default_graph()` e.g.
+    # in the case of nested if ops or when the gradient is being computed
+    # from inside a Defun. We build the `func_graph` with `if_op.graph` as its
+    # `outer_graph`. This resembles how the `_FuncGraph` was built in the
+    # forward pass. We need this so that we can resolve references to tensors
+    # in `func_graph` from its gradient graph in `_resolve_grad_inputs`.
+    with if_op.graph.as_default():
+      func_graph = _function_def_to_graph.function_def_to_graph(
+          fdef, input_shapes)
     func_graph.extra_inputs = extra_inputs
     func_graph.extra_args = func_graph.inputs
     func_graph._captured = dict(zip(extra_inputs, func_graph.inputs))
+    # Set the if op so that the gradient code can use it.
+    func_graph._if = if_op
     return func_graph
 
   return (_get_func_graph_for_branch("then_branch"),
@@ -240,7 +258,7 @@ def _grad_fn(func_graph, grads):
   # Build the gradient graph. Note that this builds the gradient computation of
   # func_graph in the current graph, which requires capturing tensors from
   # func_graph. The captured func_graph tensors are resolved to external tensors
-  # in _get_grad_inputs.
+  # in _resolve_grad_inputs.
   result = _gradients_impl._GradientsHelper(
       ys, func_graph.inputs, grad_ys=grad_ys,
       src_graph=func_graph)
@@ -261,43 +279,49 @@ def _create_grad_func(func_graph, grads, name):
                                            [], [], name)
 
 
-def _get_grad_inputs(if_op, cond_graph, grad_graph):
-  """Returns the tensors we should pass to grad_graph.
+def _resolve_grad_inputs(cond_graph, grad_graph):
+  """Returns the tensors to pass as `extra_inputs` to `grad_graph`.
 
-  This method handles tensors captured from cond_graph in grad_graph. It
-  converts these to suitable input tensors from the outer graph.
+  The `grad_graph` may have external references to
+  1. Its outer graph containing the input gradients. These references are kept
+     as is.
+  2. Tensors in the forward pass graph. These tensors may not be "live"
+     when the gradient is being computed. We replace such references by their
+     corresponding tensor in the least common ancestor graph of `grad_graph` and
+     `cond_graph`. Since we export intermediate tensors for all branch
+     functions, this is always possible.
 
   Args:
-    if_op: Operation. The forward-pass If op that uses cond_graph.
     cond_graph: function._FuncGraph. The forward-pass function.
     grad_graph: function._FuncGraph. The gradients function.
 
   Returns:
     A list of inputs tensors to be passed to grad_graph.
   """
-  inputs = []
-
-  # Maps placeholders in cond_graph -> input tensor in outer graph.
-  forward_input_map = {v: k for k, v in cond_graph._captured.items()}
+  new_extra_inputs = []
 
   for t in grad_graph.extra_inputs:
-    if t.graph == ops.get_default_graph():
-      # t is in the outer graph (e.g. one of the input gradients).
-      inputs.append(t)
-    elif t in forward_input_map:
-      # t is an input placeholder in cond_graph. Get the corresponding input
-      # tensor in the outer graph.
-      assert t.graph == cond_graph
-      assert forward_input_map[t].graph == ops.get_default_graph()
-      inputs.append(forward_input_map[t])
-    else:
-      # t is an intermediate value in cond_graph. Get the corresponding output
-      # of 'if_op' (note that all intermediate values are outputs).
-      assert t.graph == cond_graph
-      output_idx = cond_graph.outputs.index(t)
-      inputs.append(if_op.outputs[output_idx])
-
-  return inputs
+    if t.graph != grad_graph._outer_graph:
+      # `t` is a tensor in `cond_graph` or one of its ancestors. We bubble this
+      # tensor to the least common ancestor of the `cond_graph` and
+      # `grad_graph` so that it is "in-scope" for `grad_graph`.
+      # TODO(srbs): `_is_ancestor` calls may be expensive. Compute the least
+      # common ancestor once and re-use.
+      assert _is_ancestor(cond_graph, t.graph)
+      while not _is_ancestor(grad_graph, t.graph):
+        assert isinstance(t.graph, _function._FuncGraph)
+        if t in t.graph.extra_args:
+          # TODO(srbs): Consider building a map of extra_args -> extra_inputs.
+          # instead of searching for `t` twice.
+          t = t.graph.extra_inputs[t.graph.extra_args.index(t)]
+        else:
+          # Note: All intermediate tensors are output by the If op.
+          # TODO(srbs): .index() calls may be expensive. Optimize.
+          t = t.graph._if.outputs[t.graph.outputs.index(t)]
+      assert _is_ancestor(grad_graph, t.graph)
+    new_extra_inputs.append(t)
+
+  return new_extra_inputs
 
 
 def _create_new_tf_function(func_graph):
@@ -326,7 +350,8 @@ def _create_new_tf_function(func_graph):
   # a new TF_Function that we add to the graph.
   fdef = _function.function_def_from_tf_function(c_func)
   defined_func = _function._from_definition(fdef)
-  defined_func.add_to_graph(ops.get_default_graph())
+  defined_func._sub_functions = func_graph._functions
+  defined_func.add_to_graph(func_graph._outer_graph)
 
   return func_graph.name
 
@@ -389,7 +414,8 @@ def _pad_params(true_graph, false_graph, true_params, false_params):
   return new_true_params, new_false_inputs
 
 
-def _make_inputs_match(true_graph, false_graph, true_inputs, false_inputs):
+def _make_inputs_match(true_graph, false_graph, true_extra_inputs,
+                       false_extra_inputs):
   """Modifies true_graph and false_graph so they have the same input signature.
 
   This method reorders and/or adds parameters to true_graph and false_graph so
@@ -400,9 +426,9 @@ def _make_inputs_match(true_graph, false_graph, true_inputs, false_inputs):
   Args:
     true_graph: function._FuncGraph
     false_graph: function._FuncGraph
-    true_inputs: a list of Tensors in the outer graph. The inputs for
+    true_extra_inputs: a list of Tensors in the outer graph. The inputs for
       true_graph.
-    false_inputs: a list of Tensors in the outer graph. The inputs for
+    false_extra_inputs: a list of Tensors in the outer graph. The inputs for
       false_graph.
 
   Returns:
@@ -411,12 +437,12 @@ def _make_inputs_match(true_graph, false_graph, true_inputs, false_inputs):
     false_inputs.
   """
   shared_inputs, true_only_inputs, false_only_inputs = _separate_unique_inputs(
-      true_inputs, false_inputs)
+      true_extra_inputs, false_extra_inputs)
 
   new_inputs = shared_inputs + true_only_inputs + false_only_inputs
 
-  true_input_to_param = dict(zip(true_inputs, true_graph.inputs))
-  false_input_to_param = dict(zip(false_inputs, false_graph.inputs))
+  true_input_to_param = dict(zip(true_extra_inputs, true_graph.inputs))
+  false_input_to_param = dict(zip(false_extra_inputs, false_graph.inputs))
 
   true_graph.inputs = (
       [true_input_to_param[t] for t in shared_inputs] +
@@ -432,6 +458,9 @@ def _make_inputs_match(true_graph, false_graph, true_inputs, false_inputs):
   true_graph.extra_inputs = new_inputs
   false_graph.extra_inputs = new_inputs
 
+  true_graph.extra_args = true_graph.inputs
+  false_graph.extra_args = false_graph.inputs
+
   true_graph._captured = dict(zip(new_inputs, true_graph.inputs))
   false_graph._captured = dict(zip(new_inputs, false_graph.inputs))
 
@@ -454,14 +483,30 @@ def _create_dummy_params(func_graph, template_tensors):
 
 
 def _get_grad_fn_name(func_graph):
-  """Returns a unique name to use for the grad function of `func_graph`."""
+  """Returns a unique name to use for the grad function of `func_graph`.
+
+  Ensures this name is unique in the entire hierarchy.
+
+  Args:
+    func_graph: The _FuncGraph.
+
+  Returns:
+    A string, the name to use for the gradient function.
+  """
   name = "%s_grad" % func_graph.name
 
   base_name = name
   counter = 1
-  if ops.get_default_graph()._is_function(name):
-    name = "%s_%s" % (base_name, counter)
-    counter += 1
+  has_conflict = True
+  while has_conflict:
+    curr_graph = func_graph._outer_graph
+    has_conflict = curr_graph._is_function(name)
+    while not has_conflict and isinstance(curr_graph, _function._FuncGraph):
+      curr_graph = curr_graph._outer_graph
+      has_conflict = curr_graph._is_function(name)
+    if has_conflict:
+      name = "%s_%s" % (base_name, counter)
+      counter += 1
 
   return name
 
@@ -477,3 +522,11 @@ def _check_same_outputs(true_graph, false_graph):
         "arguments, got:\n"
         "  true_fn: %s\n"
         "  false_fn: %s" % (true_output_types, false_output_types))
+
+
+def _is_ancestor(graph, maybe_ancestor):
+  if maybe_ancestor == graph:
+    return True
+  if isinstance(graph, _function._FuncGraph):
+    return _is_ancestor(graph._outer_graph, maybe_ancestor)
+  return False
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 04545cceb7e166d227a46974ba3602e3cfd36512..aeac61c005ab5dae0a3e467ca89ee9026e26eec0 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -1817,15 +1817,34 @@ class CondContext(ControlFlowContext):
   def _AddOpInternal(self, op):
     """Add `op` to the current context."""
     if not op.inputs:
-      # Remove any external control dependency on this op
+      # If we're in a while loop, remove any control inputs from outside the
+      # loop.
       self._RemoveExternalControlEdges(op)
-      # pylint: disable=protected-access
-      op._add_control_input(self._pivot.op)
-      # pylint: enable=protected-access
+
+      if not any(util.OpInContext(input_op, self)
+                 for input_op in op.control_inputs):
+        # pylint: disable=protected-access
+        op._add_control_input(self._pivot.op)
+        # pylint: enable=protected-access
     else:
+      # Make each input to 'op' available in this CondContext. If an input is
+      # already part of this context there's nothing to do, but if it's
+      # external, AddValue() will handle adding the appropriate Switch node and
+      # other bookkeeping.
       for index in range(len(op.inputs)):
         x = op.inputs[index]
-        real_x = self.AddValue(x)
+        if op.type == "Merge" and x.op.type == "NextIteration":
+          # Edge case: if we're importing a while loop inside this CondContext,
+          # AddValue() will not correctly handle the NextIteration inputs to
+          # Merge node. The problem is that the NextIteration should also be
+          # part of this context, but if we're importing it won't have been
+          # processed and added to the context yet, so AddValue() will try to
+          # add a Switch which results in an invalid graph. Instead, we use the
+          # NextIteration input as-is here, and it will eventually be added to
+          # the context via AddOp().
+          real_x = x
+        else:
+          real_x = self.AddValue(x)
         if real_x != x:
           # pylint: disable=protected-access
           op._update_input(index, real_x)
@@ -3146,7 +3165,7 @@ def while_loop(cond,
   happen is that the thread updating `x` can never get ahead of the
   counter thread because the thread incrementing `x` depends on the value
   of the counter.
-  
+
   ```python
   import tensorflow as tf
 
diff --git a/tensorflow/python/ops/control_flow_util.py b/tensorflow/python/ops/control_flow_util.py
index 7a18986c5b03446d2b0e0a2ecd161dccdc3d70e1..72c074ed1af208da274edd52572961ecaa613b34 100644
--- a/tensorflow/python/ops/control_flow_util.py
+++ b/tensorflow/python/ops/control_flow_util.py
@@ -214,6 +214,14 @@ def IsContainingContext(ctxt, maybe_containing_ctxt):
   return True
 
 
+def OpInContext(op, ctxt):
+  return IsContainingContext(op._get_control_flow_context(), ctxt)  # pylint: disable=protected-access
+
+
+def TensorInContext(tensor, ctxt):
+  return OpInContext(tensor.op, ctxt)
+
+
 def CheckInputFromValidContext(op, input_op):
   """Returns whether `input_op` can be used from `op`s context.
 
diff --git a/tensorflow/python/ops/conv2d_benchmark.py b/tensorflow/python/ops/conv2d_benchmark.py
index aacdaa7ad019d8aae2d0b533cde8412ab0f0fa22..28111c273059bca3c4cc643b4aa826f9be402308 100644
--- a/tensorflow/python/ops/conv2d_benchmark.py
+++ b/tensorflow/python/ops/conv2d_benchmark.py
@@ -175,7 +175,8 @@ class Conv2DBenchmark(test.Benchmark):
 
     data_types = [dtypes.float32, dtypes.float16]
     data_formats = ["NHWC", "NCHW"]
-    in_channels = list(range(3, 16))
+    in_channels = list(range(1, 10)) + list(range(10, 20, 2)) + list(
+        range(20, 33, 4))
     out_channels = [4, 16, 32]
     hw_strides = [[2, 2]]
     paddings = ["VALID", "SAME"]
diff --git a/tensorflow/python/ops/functional_ops.py b/tensorflow/python/ops/functional_ops.py
index 53ae6d843fecf3be93ab35a73e890da3962c5aea..4ecc74675ae673bcc30f18dde75a396ff673bfaa 100644
--- a/tensorflow/python/ops/functional_ops.py
+++ b/tensorflow/python/ops/functional_ops.py
@@ -775,7 +775,7 @@ def While(input_, cond, body, name=None, hostmem=None):
       a string, non-empty means True and empty means False. If the
       tensor is not a scalar, non-emptiness means True and False
       otherwise.
-    body: . A funcion takes a list of tensors and returns another
+    body: . A function takes a list of tensors and returns another
       list tensors. Both lists have the same types as specified
       by T.
     name: A name for the operation (optional).
diff --git a/tensorflow/python/ops/histogram_ops_test.py b/tensorflow/python/ops/histogram_ops_test.py
index a226ac81bb536934cd191872ffc1aca84925abc0..2e57ae8a2dd5dcc0398955f44d3c46e3097522b1 100644
--- a/tensorflow/python/ops/histogram_ops_test.py
+++ b/tensorflow/python/ops/histogram_ops_test.py
@@ -84,6 +84,23 @@ class HistogramFixedWidthTest(test.TestCase):
   def setUp(self):
     self.rng = np.random.RandomState(0)
 
+  def test_with_invalid_value_range(self):
+    values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
+    with self.assertRaisesRegexp(
+        ValueError, "Shape must be rank 1 but is rank 0"):
+      histogram_ops.histogram_fixed_width(values, 1.0)
+    with self.assertRaisesRegexp(ValueError, "Dimension must be 2 but is 3"):
+      histogram_ops.histogram_fixed_width(values, [1.0, 2.0, 3.0])
+
+  def test_with_invalid_nbins(self):
+    values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
+    with self.assertRaisesRegexp(
+        ValueError, "Shape must be rank 0 but is rank 1"):
+      histogram_ops.histogram_fixed_width(values, [1.0, 5.0], nbins=[1, 2])
+    with self.assertRaisesRegexp(
+        ValueError, "Requires nbins > 0"):
+      histogram_ops.histogram_fixed_width(values, [1.0, 5.0], nbins=-5)
+
   def test_empty_input_gives_all_zero_counts(self):
     # Bins will be:
     #   (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py
index a2eae452ae551eb1792e5b21477d31c55d64fd79..855a4d0c33c9785378ad2da6d174486e90a70fc2 100644
--- a/tensorflow/python/ops/image_ops_impl.py
+++ b/tensorflow/python/ops/image_ops_impl.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python.compat import compat
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
@@ -55,6 +56,7 @@ ops.NotDifferentiable('SampleDistortedBoundingBoxV2')
 ops.NotDifferentiable('ExtractGlimpse')
 ops.NotDifferentiable('NonMaxSuppression')
 ops.NotDifferentiable('NonMaxSuppressionV2')
+ops.NotDifferentiable('NonMaxSuppressionWithOverlaps')
 
 
 # pylint: disable=invalid-name
@@ -1752,6 +1754,22 @@ def is_jpeg(contents, name=None):
     return math_ops.equal(substr, b'\xff\xd8\xff', name=name)
 
 
+def _is_png(contents, name=None):
+  r"""Convenience function to check if the 'contents' encodes a PNG image.
+
+  Args:
+    contents: 0-D `string`. The encoded image bytes.
+    name: A name for the operation (optional)
+
+  Returns:
+     A scalar boolean tensor indicating if 'contents' may be a PNG image.
+     is_png is susceptible to false positives.
+  """
+  with ops.name_scope(name, 'is_png'):
+    substr = string_ops.substr(contents, 0, 3)
+    return math_ops.equal(substr, b'\211PN', name=name)
+
+
 @tf_export('image.decode_image')
 def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None):
   """Convenience function for `decode_bmp`, `decode_gif`, `decode_jpeg`,
@@ -1829,8 +1847,8 @@ def decode_image(contents, channels=None, dtype=dtypes.uint8, name=None):
 
     def check_png():
       """Checks if an image is PNG."""
-      is_png = math_ops.equal(substr, b'\211PN', name='is_png')
-      return control_flow_ops.cond(is_png, _png, check_gif, name='cond_png')
+      return control_flow_ops.cond(
+          _is_png(contents), _png, check_gif, name='cond_png')
 
     def _jpeg():
       """Decodes a jpeg image."""
@@ -2093,6 +2111,108 @@ def non_max_suppression(boxes,
                                                 iou_threshold, score_threshold)
 
 
+@tf_export('image.non_max_suppression_padded')
+def non_max_suppression_padded(boxes,
+                               scores,
+                               max_output_size,
+                               iou_threshold=0.5,
+                               score_threshold=float('-inf'),
+                               pad_to_max_output_size=False,
+                               name=None):
+  """Greedily selects a subset of bounding boxes in descending order of score.
+
+  Performs algorithmically equivalent operation to tf.image.non_max_suppression,
+  with the addition of an optional parameter which zero-pads the output to
+  be of size `max_output_size`.
+  The output of this operation is a tuple containing the set of integers
+  indexing into the input collection of bounding boxes representing the selected
+  boxes and the number of valid indices in the index set.  The bounding box
+  coordinates corresponding to the selected indices can then be obtained using
+  the `tf.slice` and `tf.gather` operations.  For example:
+    selected_indices_padded, num_valid = tf.image.non_max_suppression_padded(
+        boxes, scores, max_output_size, iou_threshold,
+        score_threshold, pad_to_max_output_size=True)
+    selected_indices = tf.slice(
+        selected_indices_padded, tf.constant([0]), num_valid)
+    selected_boxes = tf.gather(boxes, selected_indices)
+
+  Args:
+    boxes: A 2-D float `Tensor` of shape `[num_boxes, 4]`.
+    scores: A 1-D float `Tensor` of shape `[num_boxes]` representing a single
+      score corresponding to each box (each row of boxes).
+    max_output_size: A scalar integer `Tensor` representing the maximum number
+      of boxes to be selected by non max suppression.
+    iou_threshold: A float representing the threshold for deciding whether boxes
+      overlap too much with respect to IOU.
+    score_threshold: A float representing the threshold for deciding when to
+      remove boxes based on score.
+    pad_to_max_output_size: bool.  If True, size of `selected_indices` output
+      is padded to `max_output_size`.
+    name: A name for the operation (optional).
+
+  Returns:
+    selected_indices: A 1-D integer `Tensor` of shape `[M]` representing the
+      selected indices from the boxes tensor, where `M <= max_output_size`.
+    valid_outputs: A scalar integer `Tensor` denoting how many elements in
+    `selected_indices` are valid.  Valid elements occur first, then padding.
+  """
+  with ops.name_scope(name, 'non_max_suppression_padded'):
+    iou_threshold = ops.convert_to_tensor(iou_threshold, name='iou_threshold')
+    score_threshold = ops.convert_to_tensor(
+        score_threshold, name='score_threshold')
+    if compat.forward_compatible(2018, 8, 7) or pad_to_max_output_size:
+      return gen_image_ops.non_max_suppression_v4(
+          boxes, scores, max_output_size, iou_threshold, score_threshold,
+          pad_to_max_output_size)
+    else:
+      return gen_image_ops.non_max_suppression_v3(
+          boxes, scores, max_output_size, iou_threshold, score_threshold)
+
+
+@tf_export('image.non_max_suppression_overlaps')
+def non_max_suppression_with_overlaps(overlaps,
+                                      scores,
+                                      max_output_size,
+                                      overlap_threshold=0.5,
+                                      score_threshold=float('-inf'),
+                                      name=None):
+  """Greedily selects a subset of bounding boxes in descending order of score.
+
+  Prunes away boxes that have high overlap with previously selected boxes.
+  N-by-n overlap values are supplied as square matrix.
+  The output of this operation is a set of integers indexing into the input
+  collection of bounding boxes representing the selected boxes.  The bounding
+  box coordinates corresponding to the selected indices can then be obtained
+  using the `tf.gather operation`.  For example:
+    selected_indices = tf.image.non_max_suppression_overlaps(
+        overlaps, scores, max_output_size, iou_threshold)
+    selected_boxes = tf.gather(boxes, selected_indices)
+
+  Args:
+    overlaps: A 2-D float `Tensor` of shape `[num_boxes, num_boxes]`.
+    scores: A 1-D float `Tensor` of shape `[num_boxes]` representing a single
+      score corresponding to each box (each row of boxes).
+    max_output_size: A scalar integer `Tensor` representing the maximum number
+      of boxes to be selected by non max suppression.
+    overlap_threshold: A float representing the threshold for deciding whether
+      boxes overlap too much with respect to the provided overlap values.
+    score_threshold: A float representing the threshold for deciding when to
+      remove boxes based on score.
+    name: A name for the operation (optional).
+
+  Returns:
+    selected_indices: A 1-D integer `Tensor` of shape `[M]` representing the
+      selected indices from the overlaps tensor, where `M <= max_output_size`.
+  """
+  with ops.name_scope(name, 'non_max_suppression_overlaps'):
+    overlap_threshold = ops.convert_to_tensor(
+        overlap_threshold, name='overlap_threshold')
+    # pylint: disable=protected-access
+    return gen_image_ops._non_max_suppression_v3(
+        overlaps, scores, max_output_size, overlap_threshold, score_threshold)
+    # pylint: enable=protected-access
+
+
 _rgb_to_yiq_kernel = [[0.299, 0.59590059,
                        0.2115], [0.587, -0.27455667, -0.52273617],
                       [0.114, -0.32134392, 0.31119955]]
diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py
index 5bfc5ce2a7a1913b097ee67d1b18d684b5ebcaa5..c315722b6ba12d45d023820b09bb7c1de7c2268a 100644
--- a/tensorflow/python/ops/init_ops.py
+++ b/tensorflow/python/ops/init_ops.py
@@ -1136,7 +1136,8 @@ convolutional_orthogonal_3d = ConvolutionOrthogonal3D
 # pylint: enable=invalid-name
 
 
-@tf_export("glorot_uniform_initializer")
+@tf_export("glorot_uniform_initializer", "keras.initializers.glorot_uniform",
+           "initializers.glorot_uniform")
 def glorot_uniform_initializer(seed=None, dtype=dtypes.float32):
   """The Glorot uniform initializer, also called Xavier uniform initializer.
 
@@ -1160,7 +1161,8 @@ def glorot_uniform_initializer(seed=None, dtype=dtypes.float32):
       scale=1.0, mode="fan_avg", distribution="uniform", seed=seed, dtype=dtype)
 
 
-@tf_export("glorot_normal_initializer")
+@tf_export("glorot_normal_initializer", "keras.initializers.glorot_normal",
+           "initializers.glorot_normal")
 def glorot_normal_initializer(seed=None, dtype=dtypes.float32):
   """The Glorot normal initializer, also called Xavier normal initializer.
 
@@ -1181,7 +1183,98 @@ def glorot_normal_initializer(seed=None, dtype=dtypes.float32):
     An initializer.
   """
   return variance_scaling_initializer(
-      scale=1.0, mode="fan_avg", distribution="normal", seed=seed, dtype=dtype)
+      scale=1.0,
+      mode="fan_avg",
+      distribution="truncated_normal",
+      seed=seed,
+      dtype=dtype)
+
+
+@tf_export("keras.initializers.lecun_normal", "initializers.lecun_normal")
+def lecun_normal(seed=None):
+  """LeCun normal initializer.
+
+  It draws samples from a truncated normal distribution centered on 0
+  with `stddev = sqrt(1 / fan_in)`
+  where `fan_in` is the number of input units in the weight tensor.
+
+  Arguments:
+      seed: A Python integer. Used to seed the random generator.
+
+  Returns:
+      An initializer.
+
+  References:
+      - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
+      - [Efficient
+      Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
+  """
+  return VarianceScaling(
+      scale=1., mode="fan_in", distribution="truncated_normal", seed=seed)
+
+
+@tf_export("keras.initializers.lecun_uniform", "initializers.lecun_uniform")
+def lecun_uniform(seed=None):
+  """LeCun uniform initializer.
+
+  It draws samples from a uniform distribution within [-limit, limit]
+  where `limit` is `sqrt(3 / fan_in)`
+  where `fan_in` is the number of input units in the weight tensor.
+
+  Arguments:
+      seed: A Python integer. Used to seed the random generator.
+
+  Returns:
+      An initializer.
+
+  References:
+      LeCun 98, Efficient Backprop,
+      http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf
+  """
+  return VarianceScaling(
+      scale=1., mode="fan_in", distribution="uniform", seed=seed)
+
+
+@tf_export("keras.initializers.he_normal", "initializers.he_normal")
+def he_normal(seed=None):
+  """He normal initializer.
+
+  It draws samples from a truncated normal distribution centered on 0
+  with `stddev = sqrt(2 / fan_in)`
+  where `fan_in` is the number of input units in the weight tensor.
+
+  Arguments:
+      seed: A Python integer. Used to seed the random generator.
+
+  Returns:
+      An initializer.
+
+  References:
+      He et al., http://arxiv.org/abs/1502.01852
+  """
+  return VarianceScaling(
+      scale=2., mode="fan_in", distribution="truncated_normal", seed=seed)
+
+
+@tf_export("keras.initializers.he_uniform", "initializers.he_uniform")
+def he_uniform(seed=None):
+  """He uniform variance scaling initializer.
+
+  It draws samples from a uniform distribution within [-limit, limit]
+  where `limit` is `sqrt(6 / fan_in)`
+  where `fan_in` is the number of input units in the weight tensor.
+
+  Arguments:
+      seed: A Python integer. Used to seed the random generator.
+
+  Returns:
+      An initializer.
+
+  References:
+      He et al., http://arxiv.org/abs/1502.01852
+  """
+  return VarianceScaling(
+      scale=2., mode="fan_in", distribution="uniform", seed=seed)
 
 
 # Utility functions.
diff --git a/tensorflow/python/ops/init_ops_test.py b/tensorflow/python/ops/init_ops_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6fffa907951e5c09a4c1d59e2bdc7f28d86806b
--- /dev/null
+++ b/tensorflow/python/ops/init_ops_test.py
@@ -0,0 +1,196 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for initializers in init_ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.eager import context
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import resource_variable_ops
+from tensorflow.python.platform import test
+
+
+class InitializersTest(test.TestCase):
+
+  def _runner(self,
+              init,
+              shape,
+              target_mean=None,
+              target_std=None,
+              target_max=None,
+              target_min=None):
+    variable = resource_variable_ops.ResourceVariable(init(shape))
+    if context.executing_eagerly():
+      output = variable.numpy()
+    else:
+      sess = ops.get_default_session()
+      sess.run(variable.initializer)
+      output = sess.run(variable)
+    lim = 3e-2
+    if target_std is not None:
+      self.assertGreater(lim, abs(output.std() - target_std))
+    if target_mean is not None:
+      self.assertGreater(lim, abs(output.mean() - target_mean))
+    if target_max is not None:
+      self.assertGreater(lim, abs(output.max() - target_max))
+    if target_min is not None:
+      self.assertGreater(lim, abs(output.min() - target_min))
+
+  def test_uniform(self):
+    tensor_shape = (9, 6, 7)
+    with self.test_session():
+      self._runner(
+          init_ops.RandomUniform(minval=-1, maxval=1, seed=124),
+          tensor_shape,
+          target_mean=0.,
+          target_max=1,
+          target_min=-1)
+
+  def test_normal(self):
+    tensor_shape = (8, 12, 99)
+    with self.test_session():
+      self._runner(
+          init_ops.RandomNormal(mean=0, stddev=1, seed=153),
+          tensor_shape,
+          target_mean=0.,
+          target_std=1)
+
+  def test_truncated_normal(self):
+    tensor_shape = (12, 99, 7)
+    with self.test_session():
+      self._runner(
+          init_ops.TruncatedNormal(mean=0, stddev=1, seed=126),
+          tensor_shape,
+          target_mean=0.,
+          target_max=2,
+          target_min=-2)
+
+  def test_constant(self):
+    tensor_shape = (5, 6, 4)
+    with self.test_session():
+      self._runner(
+          init_ops.Constant(2),
+          tensor_shape,
+          target_mean=2,
+          target_max=2,
+          target_min=2)
+
+  def test_lecun_uniform(self):
+    tensor_shape = (5, 6, 4, 2)
+    with self.test_session():
+      fan_in, _ = init_ops._compute_fans(tensor_shape)
+      std = np.sqrt(1. / fan_in)
+      self._runner(
+          init_ops.lecun_uniform(seed=123),
+          tensor_shape,
+          target_mean=0.,
+          target_std=std)
+
+  def test_glorot_uniform_initializer(self):
+    tensor_shape = (5, 6, 4, 2)
+    with self.test_session():
+      fan_in, fan_out = init_ops._compute_fans(tensor_shape)
+      std = np.sqrt(2. / (fan_in + fan_out))
+      self._runner(
+          init_ops.glorot_uniform_initializer(seed=123),
+          tensor_shape,
+          target_mean=0.,
+          target_std=std)
+
+  def test_he_uniform(self):
+    tensor_shape = (5, 6, 4, 2)
+    with self.test_session():
+      fan_in, _ = init_ops._compute_fans(tensor_shape)
+      std = np.sqrt(2. / fan_in)
+      self._runner(
+          init_ops.he_uniform(seed=123),
+          tensor_shape,
+          target_mean=0.,
+          target_std=std)
+
+  def test_lecun_normal(self):
+    tensor_shape = (5, 6, 4, 2)
+    with self.test_session():
+      fan_in, _ = init_ops._compute_fans(tensor_shape)
+      std = np.sqrt(1. / fan_in)
+      self._runner(
+          init_ops.lecun_normal(seed=123),
+          tensor_shape,
+          target_mean=0.,
+          target_std=std)
+
+  def test_glorot_normal_initializer(self):
+    tensor_shape = (5, 6, 4, 2)
+    with self.test_session():
+      fan_in, fan_out = init_ops._compute_fans(tensor_shape)
+      std = np.sqrt(2. / (fan_in + fan_out))
+      self._runner(
+          init_ops.glorot_normal_initializer(seed=123),
+          tensor_shape,
+          target_mean=0.,
+          target_std=std)
+
+  def test_he_normal(self):
+    tensor_shape = (5, 6, 4, 2)
+    with self.test_session():
+      fan_in, _ = init_ops._compute_fans(tensor_shape)
+      std = np.sqrt(2. / fan_in)
+      self._runner(
+          init_ops.he_normal(seed=123),
+          tensor_shape,
+          target_mean=0.,
+          target_std=std)
+
+  def test_Orthogonal(self):
+    tensor_shape = (20, 20)
+    with self.test_session():
+      self._runner(init_ops.Orthogonal(seed=123), tensor_shape, target_mean=0.)
+
+  def test_Identity(self):
+    with self.test_session():
+      tensor_shape = (3, 4, 5)
+      with self.assertRaises(ValueError):
+        self._runner(
+            init_ops.Identity(),
+            tensor_shape,
+            target_mean=1. / tensor_shape[0],
+            target_max=1.)
+
+      tensor_shape = (3, 3)
+      self._runner(
+          init_ops.Identity(),
+          tensor_shape,
+          target_mean=1. / tensor_shape[0],
+          target_max=1.)
+
+  def test_Zeros(self):
+    tensor_shape = (4, 5)
+    with self.test_session():
+      self._runner(
+          init_ops.Zeros(), tensor_shape, target_mean=0., target_max=0.)
+
+  def test_Ones(self):
+    tensor_shape = (4, 5)
+    with self.test_session():
+      self._runner(init_ops.Ones(), tensor_shape, target_mean=1., target_max=1.)
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/python/ops/linalg/linalg.py b/tensorflow/python/ops/linalg/linalg.py
index a7ba0bbe9cbc4be9daea79cc97eaac4c21523c04..c29b5033bb137e8376e1c19985755b4fc72e8834 100644
--- a/tensorflow/python/ops/linalg/linalg.py
+++ b/tensorflow/python/ops/linalg/linalg.py
@@ -31,6 +31,7 @@ from tensorflow.python.ops.linalg.linear_operator_identity import *
 from tensorflow.python.ops.linalg.linear_operator_kronecker import *
 from tensorflow.python.ops.linalg.linear_operator_low_rank_update import *
 from tensorflow.python.ops.linalg.linear_operator_lower_triangular import *
+from tensorflow.python.ops.linalg.linear_operator_zeros import *
 # pylint: enable=wildcard-import
 
 # Seal API.
diff --git a/tensorflow/python/ops/linalg/linear_operator_diag.py b/tensorflow/python/ops/linalg/linear_operator_diag.py
index 5beaea65a5171ad7e92042a2afa81c0507e51d0e..ed53decc00dc90df5c6c97d9fd9d5cb124ddf660 100644
--- a/tensorflow/python/ops/linalg/linear_operator_diag.py
+++ b/tensorflow/python/ops/linalg/linear_operator_diag.py
@@ -231,8 +231,11 @@ class LinearOperatorDiag(linear_operator.LinearOperator):
     return math_ops.reduce_prod(self._diag, reduction_indices=[-1])
 
   def _log_abs_determinant(self):
-    return math_ops.reduce_sum(
+    log_det = math_ops.reduce_sum(
         math_ops.log(math_ops.abs(self._diag)), reduction_indices=[-1])
+    if self.dtype.is_complex:
+      log_det = math_ops.cast(log_det, dtype=self.dtype)
+    return log_det
 
   def _solve(self, rhs, adjoint=False, adjoint_arg=False):
     diag_term = math_ops.conj(self._diag) if adjoint else self._diag
diff --git a/tensorflow/python/ops/linalg/linear_operator_low_rank_update.py b/tensorflow/python/ops/linalg/linear_operator_low_rank_update.py
index 08e5896e1034fb1782beacfb18fef16da083bded..2b2bf80f276a62d20aae717ac9fa08f9769f455e 100644
--- a/tensorflow/python/ops/linalg/linear_operator_low_rank_update.py
+++ b/tensorflow/python/ops/linalg/linear_operator_low_rank_update.py
@@ -18,16 +18,15 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import linalg_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.linalg import linear_operator
 from tensorflow.python.ops.linalg import linear_operator_diag
 from tensorflow.python.ops.linalg import linear_operator_identity
 from tensorflow.python.ops.linalg import linear_operator_util
+from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util.tf_export import tf_export
 
 __all__ = [
@@ -153,8 +152,7 @@ class LinearOperatorLowRankUpdate(linear_operator.LinearOperator):
     `is_X` matrix property hints, which will trigger the appropriate code path.
 
     Args:
-      base_operator:  Shape `[B1,...,Bb, M, N]` real `float16`, `float32` or
-        `float64` `LinearOperator`.  This is `L` above.
+      base_operator:  Shape `[B1,...,Bb, M, N]`.
       u:  Shape `[B1,...,Bb, M, K]` `Tensor` of same `dtype` as `base_operator`.
         This is `U` above.
       diag_update:  Optional shape `[B1,...,Bb, K]` `Tensor` with same `dtype`
@@ -183,23 +181,12 @@ class LinearOperatorLowRankUpdate(linear_operator.LinearOperator):
     Raises:
       ValueError:  If `is_X` flags are set in an inconsistent way.
     """
-    # TODO(langmore) support complex types.
-    # Complex types are not allowed due to tf.cholesky() requiring float.
-    # If complex dtypes are allowed, we update the following
-    # 1. is_diag_update_positive should still imply that `diag > 0`, but we need
-    #    to remind the user that this implies diag is real.  This is needed
-    #    because if diag has non-zero imaginary part, it will not be
-    #    self-adjoint positive definite.
     dtype = base_operator.dtype
-    allowed_dtypes = [
-        dtypes.float16,
-        dtypes.float32,
-        dtypes.float64,
-    ]
-    if dtype not in allowed_dtypes:
-      raise TypeError(
-          "Argument matrix must have dtype in %s.  Found: %s"
-          % (allowed_dtypes, dtype))
+
+    if diag_update is not None:
+      if is_diag_update_positive and dtype.is_complex:
+        logging.warn("Note: setting is_diag_update_positive with a complex "
+                     "dtype means that diagonal is real and positive.")
 
     if diag_update is None:
       if is_diag_update_positive is False:
@@ -271,8 +258,6 @@ class LinearOperatorLowRankUpdate(linear_operator.LinearOperator):
       self._set_diag_operators(diag_update, is_diag_update_positive)
       self._is_diag_update_positive = is_diag_update_positive
 
-      check_ops.assert_same_float_dtype((base_operator, self.u, self.v,
-                                         self._diag_update))
       self._check_shapes()
 
       # Pre-compute the so-called "capacitance" matrix
@@ -407,6 +392,8 @@ class LinearOperatorLowRankUpdate(linear_operator.LinearOperator):
     else:
       det_c = linalg_ops.matrix_determinant(self._capacitance)
       log_abs_det_c = math_ops.log(math_ops.abs(det_c))
+      if self.dtype.is_complex:
+        log_abs_det_c = math_ops.cast(log_abs_det_c, dtype=self.dtype)
 
     return log_abs_det_c + log_abs_det_d + log_abs_det_l
 
diff --git a/tensorflow/python/ops/linalg/linear_operator_lower_triangular.py b/tensorflow/python/ops/linalg/linear_operator_lower_triangular.py
index fb1eb2fedba5b47ce38f9635527b91e18d894a8f..ca6d3f54051d7bf0ff748804d3cd314b144c2f88 100644
--- a/tensorflow/python/ops/linalg/linear_operator_lower_triangular.py
+++ b/tensorflow/python/ops/linalg/linear_operator_lower_triangular.py
@@ -119,8 +119,7 @@ class LinearOperatorLowerTriangular(linear_operator.LinearOperator):
     Args:
       tril:  Shape `[B1,...,Bb, N, N]` with `b >= 0`, `N >= 0`.
         The lower triangular part of `tril` defines this operator.  The strictly
-        upper triangle is ignored.  Allowed dtypes: `float16`, `float32`,
-        `float64`.
+        upper triangle is ignored.
       is_non_singular:  Expect that this operator is non-singular.
         This operator is non-singular if and only if its diagonal elements are
         all non-zero.
@@ -137,7 +136,6 @@ class LinearOperatorLowerTriangular(linear_operator.LinearOperator):
       name: A name for this `LinearOperator`.
 
     Raises:
-      TypeError:  If `diag.dtype` is not an allowed type.
       ValueError:  If `is_square` is `False`.
     """
 
@@ -163,12 +161,12 @@ class LinearOperatorLowerTriangular(linear_operator.LinearOperator):
 
   def _check_tril(self, tril):
     """Static check of the `tril` argument."""
-    # TODO(langmore) Add complex types once matrix_triangular_solve works for
-    # them.
     allowed_dtypes = [
         dtypes.float16,
         dtypes.float32,
         dtypes.float64,
+        dtypes.complex64,
+        dtypes.complex128,
     ]
     dtype = tril.dtype
     if dtype not in allowed_dtypes:
diff --git a/tensorflow/python/ops/linalg/linear_operator_zeros.py b/tensorflow/python/ops/linalg/linear_operator_zeros.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8a79c065b32f452cfbb49c6bbd485556cc79445
--- /dev/null
+++ b/tensorflow/python/ops/linalg/linear_operator_zeros.py
@@ -0,0 +1,452 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""`LinearOperator` acting like a zero matrix."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops.linalg import linalg_impl as linalg
+from tensorflow.python.ops.linalg import linear_operator
+from tensorflow.python.ops.linalg import linear_operator_util
+from tensorflow.python.util.tf_export import tf_export
+
+__all__ = [
+    "LinearOperatorZeros",
+]
+
+
+@tf_export("linalg.LinearOperatorZeros")
+class LinearOperatorZeros(linear_operator.LinearOperator):
+  """`LinearOperator` acting like a [batch] zero matrix.
+
+  This operator acts like a [batch] zero matrix `A` with shape
+  `[B1,...,Bb, N, M]` for some `b >= 0`.  The first `b` indices index a
+  batch member.  For every batch index `(i1,...,ib)`, `A[i1,...,ib, : :]` is
+  an `N x M` matrix.  This matrix `A` is not materialized, but for
+  purposes of broadcasting this shape will be relevant.
+
+  `LinearOperatorZeros` is initialized with `num_rows`, and optionally
+  `num_columns, `batch_shape`, and `dtype` arguments.  If `num_columns` is
+  `None`, then this operator will be initialized as a square matrix. If
+  `batch_shape` is `None`, this operator efficiently passes through all
+  arguments.  If `batch_shape` is provided, broadcasting may occur, which will
+  require making copies.
+
+  ```python
+  # Create a 2 x 2 zero matrix.
+  operator = LinearOperatorZero(num_rows=2, dtype=tf.float32)
+
+  operator.to_dense()
+  ==> [[0., 0.]
+       [0., 0.]]
+
+  operator.shape
+  ==> [2, 2]
+
+  operator.determinant()
+  ==> 0.
+
+  x = ... Shape [2, 4] Tensor
+  operator.matmul(x)
+  ==> Shape [2, 4] Tensor, same as x.
+
+  # Create a 2-batch of 2x2 zero matrices
+  operator = LinearOperatorZeros(num_rows=2, batch_shape=[2])
+  operator.to_dense()
+  ==> [[[0., 0.]
+        [0., 0.]],
+       [[0., 0.]
+        [0., 0.]]]
+
+  # Here, even though the operator has a batch shape, the input is the same as
+  # the output, so x can be passed through without a copy.  The operator is able
+  # to detect that no broadcast is necessary because both x and the operator
+  # have statically defined shape.
+  x = ... Shape [2, 2, 3]
+  operator.matmul(x)
+  ==> Shape [2, 2, 3] Tensor, same as tf.zeros_like(x)
+
+  # Here the operator and x have different batch_shape, and are broadcast.
+  # This requires a copy, since the output is different size than the input.
+  x = ... Shape [1, 2, 3]
+  operator.matmul(x)
+  ==> Shape [2, 2, 3] Tensor, equal to tf.zeros_like([x, x])
+  ```
+
+  ### Shape compatibility
+
+  This operator acts on [batch] matrix with compatible shape.
+  `x` is a batch matrix with compatible shape for `matmul` and `solve` if
+
+  ```
+  operator.shape = [B1,...,Bb] + [N, M],  with b >= 0
+  x.shape =   [C1,...,Cc] + [M, R],
+  and [C1,...,Cc] broadcasts with [B1,...,Bb] to [D1,...,Dd]
+  ```
+
+  #### Matrix property hints
+
+  This `LinearOperator` is initialized with boolean flags of the form `is_X`,
+  for `X = non_singular, self_adjoint, positive_definite, square`.
+  These have the following meaning:
+
+  * If `is_X == True`, callers should expect the operator to have the
+    property `X`.  This is a promise that should be fulfilled, but is *not* a
+    runtime assert.  For example, finite floating point precision may result
+    in these promises being violated.
+  * If `is_X == False`, callers should expect the operator to not have `X`.
+  * If `is_X == None` (the default), callers should have no expectation either
+    way.
+  """
+
+  def __init__(self,
+               num_rows,
+               num_columns=None,
+               batch_shape=None,
+               dtype=None,
+               is_non_singular=False,
+               is_self_adjoint=True,
+               is_positive_definite=False,
+               is_square=True,
+               assert_proper_shapes=False,
+               name="LinearOperatorZeros"):
+    r"""Initialize a `LinearOperatorZeros`.
+
+    The `LinearOperatorZeros` is initialized with arguments defining `dtype`
+    and shape.
+
+    This operator is able to broadcast the leading (batch) dimensions, which
+    sometimes requires copying data.  If `batch_shape` is `None`, the operator
+    can take arguments of any batch shape without copying.  See examples.
+
+    Args:
+      num_rows:  Scalar non-negative integer `Tensor`.  Number of rows in the
+        corresponding zero matrix.
+      num_columns:  Scalar non-negative integer `Tensor`.  Number of columns in
+        the corresponding zero matrix. If `None`, defaults to the value of
+        `num_rows`.
+      batch_shape:  Optional `1-D` integer `Tensor`.  The shape of the leading
+        dimensions.  If `None`, this operator has no leading dimensions.
+      dtype:  Data type of the matrix that this operator represents.
+      is_non_singular:  Expect that this operator is non-singular.
+      is_self_adjoint:  Expect that this operator is equal to its hermitian
+        transpose.
+      is_positive_definite:  Expect that this operator is positive definite,
+        meaning the quadratic form `x^H A x` has positive real part for all
+        nonzero `x`.  Note that we do not require the operator to be
+        self-adjoint to be positive-definite.  See:
+        https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices
+      is_square:  Expect that this operator acts like square [batch] matrices.
+      assert_proper_shapes:  Python `bool`.  If `False`, only perform static
+        checks that initialization and method arguments have proper shape.
+        If `True`, and static checks are inconclusive, add asserts to the graph.
+      name: A name for this `LinearOperator`
+
+    Raises:
+      ValueError:  If `num_rows` is determined statically to be non-scalar, or
+        negative.
+      ValueError:  If `num_columns` is determined statically to be non-scalar,
+        or negative.
+      ValueError:  If `batch_shape` is determined statically to not be 1-D, or
+        negative.
+      ValueError:  If any of the following is not `True`:
+        `{is_self_adjoint, is_non_singular, is_positive_definite}`.
+    """
+    dtype = dtype or dtypes.float32
+    self._assert_proper_shapes = assert_proper_shapes
+
+    with ops.name_scope(name):
+      dtype = dtypes.as_dtype(dtype)
+      if not is_self_adjoint and is_square:
+        raise ValueError("A zero operator is always self adjoint.")
+      if is_non_singular:
+        raise ValueError("A zero operator is always singular.")
+      if is_positive_definite:
+        raise ValueError("A zero operator is always not positive-definite.")
+
+      super(LinearOperatorZeros, self).__init__(
+          dtype=dtype,
+          is_non_singular=is_non_singular,
+          is_self_adjoint=is_self_adjoint,
+          is_positive_definite=is_positive_definite,
+          is_square=is_square,
+          name=name)
+
+      self._num_rows = linear_operator_util.shape_tensor(
+          num_rows, name="num_rows")
+      self._num_rows_static = tensor_util.constant_value(self._num_rows)
+
+      if num_columns is None:
+        num_columns = num_rows
+
+      self._num_columns = linear_operator_util.shape_tensor(
+          num_columns, name="num_columns")
+      self._num_columns_static = tensor_util.constant_value(self._num_columns)
+
+      self._check_domain_range_possibly_add_asserts()
+
+      if (self._num_rows_static is not None and
+          self._num_columns_static is not None):
+        if is_square and self._num_rows_static != self._num_columns_static:
+          raise ValueError(
+              "LinearOperatorZeros initialized as is_square=True, but got "
+              "num_rows({}) != num_columns({})".format(
+                  self._num_rows_static,
+                  self._num_columns_static))
+
+      if batch_shape is None:
+        self._batch_shape_arg = None
+      else:
+        self._batch_shape_arg = linear_operator_util.shape_tensor(
+            batch_shape, name="batch_shape_arg")
+        self._batch_shape_static = tensor_util.constant_value(
+            self._batch_shape_arg)
+        self._check_batch_shape_possibly_add_asserts()
+
+  def _shape(self):
+    matrix_shape = tensor_shape.TensorShape((self._num_rows_static,
+                                             self._num_columns_static))
+    if self._batch_shape_arg is None:
+      return matrix_shape
+
+    batch_shape = tensor_shape.TensorShape(self._batch_shape_static)
+    return batch_shape.concatenate(matrix_shape)
+
+  def _shape_tensor(self):
+    matrix_shape = array_ops.stack((self._num_rows, self._num_columns), axis=0)
+    if self._batch_shape_arg is None:
+      return matrix_shape
+
+    return array_ops.concat((self._batch_shape_arg, matrix_shape), 0)
+
+  def _assert_non_singular(self):
+    raise errors.InvalidArgumentError(
+        node_def=None, op=None, message="Zero operators are always "
+        "non-invertible.")
+
+  def _assert_positive_definite(self):
+    raise errors.InvalidArgumentError(
+        node_def=None, op=None, message="Zero operators are always "
+        "non-positive definite.")
+
+  def _assert_self_adjoint(self):
+    return control_flow_ops.no_op("assert_self_adjoint")
+
+  def _possibly_broadcast_batch_shape(self, x):
+    """Return 'x', possibly after broadcasting the leading dimensions."""
+    # If we have no batch shape, our batch shape broadcasts with everything!
+    if self._batch_shape_arg is None:
+      return x
+
+    # Static attempt:
+    #   If we determine that no broadcast is necessary, pass x through
+    #   If we need a broadcast, add to an array of zeros.
+    #
+    # special_shape is the shape that, when broadcast with x's shape, will give
+    # the correct broadcast_shape.  Note that
+    #   We have already verified the second to last dimension of self.shape
+    #   matches x's shape in assert_compatible_matrix_dimensions.
+    #   Also, the final dimension of 'x' can have any shape.
+    #   Therefore, the final two dimensions of special_shape are 1's.
+    special_shape = self.batch_shape.concatenate([1, 1])
+    bshape = array_ops.broadcast_static_shape(x.get_shape(), special_shape)
+    if special_shape.is_fully_defined():
+      # bshape.is_fully_defined iff special_shape.is_fully_defined.
+      if bshape == x.get_shape():
+        return x
+      # Use the built in broadcasting of addition.
+      zeros = array_ops.zeros(shape=special_shape, dtype=self.dtype)
+      return x + zeros
+
+    # Dynamic broadcast:
+    #   Always add to an array of zeros, rather than using a "cond", since a
+    #   cond would require copying data from GPU --> CPU.
+    special_shape = array_ops.concat((self.batch_shape_tensor(), [1, 1]), 0)
+    zeros = array_ops.zeros(shape=special_shape, dtype=self.dtype)
+    return x + zeros
+
+  def _matmul(self, x, adjoint=False, adjoint_arg=False):
+    if self._assert_proper_shapes:
+      x = linalg.adjoint(x) if adjoint_arg else x
+      aps = linear_operator_util.assert_compatible_matrix_dimensions(self, x)
+      x = control_flow_ops.with_dependencies([aps], x)
+    if self.is_square:
+      # Note that adjoint has no effect since this matrix is self-adjoint.
+      if adjoint_arg:
+        output_shape = array_ops.concat([
+            array_ops.shape(x)[:-2],
+            [array_ops.shape(x)[-1], array_ops.shape(x)[-2]]], axis=0)
+      else:
+        output_shape = array_ops.shape(x)
+
+      return self._possibly_broadcast_batch_shape(
+          array_ops.zeros(shape=output_shape, dtype=x.dtype))
+
+    x_shape = array_ops.shape(x)
+    n = self._num_columns if adjoint else self._num_rows
+    m = x_shape[-2] if adjoint_arg else x_shape[-1]
+
+    output_shape = array_ops.concat([x_shape[:-2], [n, m]], axis=0)
+
+    zeros = array_ops.zeros(shape=output_shape, dtype=x.dtype)
+    return self._possibly_broadcast_batch_shape(zeros)
+
+  def _determinant(self):
+    if self.batch_shape.is_fully_defined():
+      return array_ops.zeros(shape=self.batch_shape, dtype=self.dtype)
+    else:
+      return array_ops.zeros(shape=self.batch_shape_tensor(), dtype=self.dtype)
+
+  def _trace(self):
+    # Get Tensor of all zeros of same shape as self.batch_shape.
+    if self.batch_shape.is_fully_defined():
+      return array_ops.zeros(shape=self.batch_shape, dtype=self.dtype)
+    else:
+      return array_ops.zeros(shape=self.batch_shape_tensor(), dtype=self.dtype)
+
+  def _diag_part(self):
+    return self._zeros_diag()
+
+  def add_to_tensor(self, mat, name="add_to_tensor"):
+    """Add matrix represented by this operator to `mat`.  Equiv to `I + mat`.
+
+    Args:
+      mat:  `Tensor` with same `dtype` and shape broadcastable to `self`.
+      name:  A name to give this `Op`.
+
+    Returns:
+      A `Tensor` with broadcast shape and same `dtype` as `self`.
+    """
+    return self._possibly_broadcast_batch_shape(mat)
+
+  def _check_domain_range_possibly_add_asserts(self):
+    """Static check of init arg `num_rows`, possibly add asserts."""
+    # Possibly add asserts.
+    if self._assert_proper_shapes:
+      self._num_rows = control_flow_ops.with_dependencies([
+          check_ops.assert_rank(
+              self._num_rows,
+              0,
+              message="Argument num_rows must be a 0-D Tensor."),
+          check_ops.assert_non_negative(
+              self._num_rows,
+              message="Argument num_rows must be non-negative."),
+      ], self._num_rows)
+      self._num_columns = control_flow_ops.with_dependencies([
+          check_ops.assert_rank(
+              self._num_columns,
+              0,
+              message="Argument num_columns must be a 0-D Tensor."),
+          check_ops.assert_non_negative(
+              self._num_columns,
+              message="Argument num_columns must be non-negative."),
+      ], self._num_columns)
+
+    # Static checks.
+    if not self._num_rows.dtype.is_integer:
+      raise TypeError("Argument num_rows must be integer type.  Found:"
+                      " %s" % self._num_rows)
+
+    if not self._num_columns.dtype.is_integer:
+      raise TypeError("Argument num_columns must be integer type.  Found:"
+                      " %s" % self._num_columns)
+
+    num_rows_static = self._num_rows_static
+    num_columns_static = self._num_columns_static
+
+    if num_rows_static is not None:
+      if num_rows_static.ndim != 0:
+        raise ValueError("Argument num_rows must be a 0-D Tensor.  Found:"
+                         " %s" % num_rows_static)
+
+      if num_rows_static < 0:
+        raise ValueError("Argument num_rows must be non-negative.  Found:"
+                         " %s" % num_rows_static)
+    if num_columns_static is not None:
+      if num_columns_static.ndim != 0:
+        raise ValueError("Argument num_columns must be a 0-D Tensor.  Found:"
+                         " %s" % num_columns_static)
+
+      if num_columns_static < 0:
+        raise ValueError("Argument num_columns must be non-negative.  Found:"
+                         " %s" % num_columns_static)
+
+  def _check_batch_shape_possibly_add_asserts(self):
+    """Static check of init arg `batch_shape`, possibly add asserts."""
+    if self._batch_shape_arg is None:
+      return
+
+    # Possibly add asserts
+    if self._assert_proper_shapes:
+      self._batch_shape_arg = control_flow_ops.with_dependencies([
+          check_ops.assert_rank(
+              self._batch_shape_arg,
+              1,
+              message="Argument batch_shape must be a 1-D Tensor."),
+          check_ops.assert_non_negative(
+              self._batch_shape_arg,
+              message="Argument batch_shape must be non-negative."),
+      ], self._batch_shape_arg)
+
+    # Static checks
+    if not self._batch_shape_arg.dtype.is_integer:
+      raise TypeError("Argument batch_shape must be integer type.  Found:"
+                      " %s" % self._batch_shape_arg)
+
+    if self._batch_shape_static is None:
+      return  # Cannot do any other static checks.
+
+    if self._batch_shape_static.ndim != 1:
+      raise ValueError("Argument batch_shape must be a 1-D Tensor.  Found:"
+                       " %s" % self._batch_shape_static)
+
+    if np.any(self._batch_shape_static < 0):
+      raise ValueError("Argument batch_shape must be non-negative.  Found:"
+                       "%s" % self._batch_shape_static)
+
+  def _min_matrix_dim(self):
+    """Minimum of domain/range dimension, if statically available, else None."""
+    domain_dim = self.domain_dimension.value
+    range_dim = self.range_dimension.value
+    if domain_dim is None or range_dim is None:
+      return None
+    return min(domain_dim, range_dim)
+
+  def _min_matrix_dim_tensor(self):
+    """Minimum of domain/range dimension, as a tensor."""
+    return math_ops.reduce_min(self.shape_tensor()[-2:])
+
+  def _zeros_diag(self):
+    """Returns the diagonal of this operator as all zeros."""
+    if self.shape.is_fully_defined():
+      d_shape = self.batch_shape.concatenate([self._min_matrix_dim()])
+    else:
+      d_shape = array_ops.concat(
+          [self.batch_shape_tensor(),
+           [self._min_matrix_dim_tensor()]], axis=0)
+
+    return array_ops.zeros(shape=d_shape, dtype=self.dtype)
diff --git a/tensorflow/python/ops/linalg_ops.py b/tensorflow/python/ops/linalg_ops.py
index a0dfa543f9b3aee15f11b073dc683b1d2d14388f..f4a93560bee558512f33214148ddec22590b9dd6 100644
--- a/tensorflow/python/ops/linalg_ops.py
+++ b/tensorflow/python/ops/linalg_ops.py
@@ -401,7 +401,7 @@ def svd(tensor, full_matrices=False, compute_uv=True, name=None):
   import tensorflow as tf
   import numpy as np
   s, u, v = tf.linalg.svd(a)
-  tf_a_approx = tf.matmul(u, tf.matmul(tf.linalg.diag(s), v, adjoint_v=True))
+  tf_a_approx = tf.matmul(u, tf.matmul(tf.linalg.diag(s), v, adjoint_b=True))
   u, s, v_adj = np.linalg.svd(a, full_matrices=False)
   np_a_approx = np.dot(u, np.dot(np.diag(s), v_adj))
   # tf_a_approx and np_a_approx should be numerically close.
diff --git a/tensorflow/python/ops/logging_ops.py b/tensorflow/python/ops/logging_ops.py
index 8276047cb678f3d340701718156f8a1cfd6831cb..df41933f8a864be3ada72dbf101420c886dfb36b 100644
--- a/tensorflow/python/ops/logging_ops.py
+++ b/tensorflow/python/ops/logging_ops.py
@@ -35,9 +35,12 @@ from tensorflow.python.util.tf_export import tf_export
 
 
 # Assert and Print are special symbols in python, so we must
-# have an upper-case version of them.  For users with Python 3 or Python 2.7
-# with `from __future__ import print_function`, we also allow lowercase.
-@tf_export("Print", "print")
+# have an upper-case version of them.
+#
+# For users with Python 3 or Python 2.7
+# with `from __future__ import print_function`, we could also allow lowercase.
+# See https://github.com/tensorflow/tensorflow/issues/18053
+@tf_export("Print")
 def Print(input_, data, message=None, first_n=None, summarize=None,
           name=None):
   """Prints a list of tensors.
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index cdb6dc8f22919420ff44e217578315d17cb93d8c..fbe6b62302cb7e0ab9dc4aadd2f58a48800eb2a6 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -37,11 +37,11 @@ from tensorflow.python.ops import gen_math_ops
 from tensorflow.python.ops import gen_nn_ops
 from tensorflow.python.ops import gen_sparse_ops
 from tensorflow.python.ops import gen_spectral_ops
-from tensorflow.python.platform import tf_logging as logging
 # go/tf-wildcard-import
 # pylint: disable=wildcard-import
 from tensorflow.python.ops.gen_math_ops import *
 # pylint: enable=wildcard-import
+from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import compat
 from tensorflow.python.util import deprecation
 from tensorflow.python.util import nest
@@ -628,16 +628,17 @@ def cast(x, dtype, name=None):
   ```
 
   The operation supports data types (for `x` and `dtype`) of
-  `uint8`, `int8`, `uint16`, `int16`, `int32`, `int64`, `float16`, `float32`,
-  `float64`, `complex64`, `complex128`, `bfloat16`. In case of casting from
-  complex types (`complex64`, `complex128`) to real types, only the real part
-  of `x` is returned. In case of casting from real types to complex types
-  (`complex64`, `complex128`), the imaginary part of the returned value is set
-  to `0`. The handling of complex types here matches the behavior of numpy.
+  `uint8`, `uint16`, `uint32`, `uint64`, `int8`, `int16`, `int32`, `int64`,
+  `float16`, `float32`, `float64`, `complex64`, `complex128`, `bfloat16`.
+  In case of casting from complex types (`complex64`, `complex128`) to real
+  types, only the real part of `x` is returned. In case of casting from real
+  types to complex types (`complex64`, `complex128`), the imaginary part of the
+  returned value is set to `0`. The handling of complex types here matches the
+  behavior of numpy.
 
   Args:
     x: A `Tensor` or `SparseTensor` of numeric type. It could be
-      `uint8`, `int8`, `uint16`, `int16`, `int32`, `int64`,
+      `uint8`, `uint16`, `uint32`, `uint64`, `int8`, `int16`, `int32`, `int64`,
       `float16`, `float32`, `float64`, `complex64`, `complex128`, `bfloat16`.
     dtype: The destination type. The list of supported dtypes is the same
       as `x`.
@@ -651,6 +652,9 @@ def cast(x, dtype, name=None):
     TypeError: If `x` cannot be cast to the `dtype`.
   """
   base_type = dtypes.as_dtype(dtype).base_dtype
+  if isinstance(x,
+                (ops.Tensor, _resource_variable_type)) and base_type == x.dtype:
+    return x
   with ops.name_scope(name, "Cast", [x]) as name:
     if isinstance(x, sparse_tensor.SparseTensor):
       values_cast = cast(x.values, base_type, name=name)
@@ -1222,8 +1226,9 @@ def _ReductionDims(x, axis, reduction_indices):
     return axis
   else:
     # Fast path: avoid creating Rank and Range ops if ndims is known.
-    if isinstance(x, ops.Tensor) and x._rank() is not None:  # pylint: disable=protected-access
-      return constant_op.constant(np.arange(x._rank()), dtype=dtypes.int32)  # pylint: disable=protected-access
+    rank = common_shapes.rank(x)
+    if rank is not None:
+      return constant_op.constant(np.arange(rank), dtype=dtypes.int32)
     if (isinstance(x, sparse_tensor.SparseTensor) and
         x.dense_shape.get_shape().is_fully_defined()):
       rank = x.dense_shape.get_shape()[0].value  # sparse.dense_shape is 1-D.
@@ -1234,8 +1239,8 @@ def _ReductionDims(x, axis, reduction_indices):
 
 
 def _may_reduce_to_scalar(keepdims, axis, reduction_indices, output):
-  """Set a reduction's output's shape to be a scalar if we are certain."""
-  if (not output.shape.is_fully_defined()) and (not keepdims) and (
+  """Set a reduction's output shape to be a scalar if we are certain."""
+  if not common_shapes.has_fully_defined_shape(output) and (not keepdims) and (
       axis is None) and (reduction_indices is None):
     output.set_shape(())
   return output
diff --git a/tensorflow/python/ops/metrics_impl.py b/tensorflow/python/ops/metrics_impl.py
index bfd225b0d837783fc854835f862fb4a12550fffc..3aedeb6acd94d1fcef1aa3cff768c5b53cf9fdaf 100644
--- a/tensorflow/python/ops/metrics_impl.py
+++ b/tensorflow/python/ops/metrics_impl.py
@@ -73,16 +73,16 @@ def metric_variable(shape, dtype, validate_shape=True, name=None):
     A (non-trainable) variable initialized to zero, or if inside a
     `DistributionStrategy` scope a tower-local variable container.
   """
-  with distribute_lib.get_tower_context().tower_local_var_scope(
-      variable_scope.VariableAggregation.SUM):
-    # Note that "tower local" implies trainable=False.
-    return variable_scope.variable(
-        lambda: array_ops.zeros(shape, dtype),
-        collections=[
-            ops.GraphKeys.LOCAL_VARIABLES, ops.GraphKeys.METRIC_VARIABLES
-        ],
-        validate_shape=validate_shape,
-        name=name)
+  # Note that synchronization "ON_READ" implies trainable=False.
+  return variable_scope.variable(
+      lambda: array_ops.zeros(shape, dtype),
+      collections=[
+          ops.GraphKeys.LOCAL_VARIABLES, ops.GraphKeys.METRIC_VARIABLES
+      ],
+      validate_shape=validate_shape,
+      synchronization=variable_scope.VariableSynchronization.ON_READ,
+      aggregation=variable_scope.VariableAggregation.SUM,
+      name=name)
 
 
 def _remove_squeezable_dimensions(predictions, labels, weights):
diff --git a/tensorflow/python/ops/parallel_for/BUILD b/tensorflow/python/ops/parallel_for/BUILD
index 065c2caedc9d334543512941f3513e45360b460f..6c804a50e70c8873c827e9fdc5a5cc27f95a2a1b 100644
--- a/tensorflow/python/ops/parallel_for/BUILD
+++ b/tensorflow/python/ops/parallel_for/BUILD
@@ -125,5 +125,4 @@ cuda_py_test(
         "//tensorflow/python:random_ops",
         "//tensorflow/python/ops/losses",
     ],
-    tags = ["no_gpu"],  # TODO(b/80127739): test is flaky
 )
diff --git a/tensorflow/python/ops/parallel_for/__init__.py b/tensorflow/python/ops/parallel_for/__init__.py
index b49d865968b0bab02380cb934431f4933590570e..dd8bc6d487f625c9ab442c91da417dce00074a2a 100644
--- a/tensorflow/python/ops/parallel_for/__init__.py
+++ b/tensorflow/python/ops/parallel_for/__init__.py
@@ -23,13 +23,3 @@ from tensorflow.python.ops.parallel_for.control_flow_ops import for_loop
 from tensorflow.python.ops.parallel_for.control_flow_ops import pfor
 from tensorflow.python.ops.parallel_for.gradients import batch_jacobian
 from tensorflow.python.ops.parallel_for.gradients import jacobian
-from tensorflow.python.util.all_util import remove_undocumented
-
-_allowed_symbols = [
-    'pfor',
-    'for_loop',
-    'jacobian',
-    'batch_jacobian',
-]
-
-remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/python/ops/parallel_for/gradients_test.py b/tensorflow/python/ops/parallel_for/gradients_test.py
index 310a2154f71c29702de1d43d8fc4af931b3217eb..3a6d9149ad80e5087c8ecc755e6b81b67d4a5ed2 100644
--- a/tensorflow/python/ops/parallel_for/gradients_test.py
+++ b/tensorflow/python/ops/parallel_for/gradients_test.py
@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import functools
+import os
 import time
 
 import numpy as np
@@ -444,6 +445,10 @@ class GradientsTest(test.TestCase):
     self.run_and_assert_equal(pfor_outputs, while_outputs)
 
   def test_mnist_per_eg_grad(self):
+    # It looks like CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED
+    # configuration of Winograd can cause low precision output resulting in
+    # tests failing. So we disable that here.
+    os.environ["TF_ENABLE_WINOGRAD_NONFUSED"] = "0"
     data_format = ("channels_first"
                    if test.is_gpu_available() else "channels_last")
     # Note that we we are setting training=False here so that dropout produces
@@ -451,8 +456,13 @@ class GradientsTest(test.TestCase):
     pfor_outputs, while_outputs = create_mnist_per_eg_grad(
         4, data_format, training=False)
     self.run_and_assert_equal(pfor_outputs, while_outputs, rtol=1e-3)
+    os.environ.pop("TF_ENABLE_WINOGRAD_NONFUSED", None)
 
   def test_mnist_per_eg_jacobian(self):
+    # It looks like CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED
+    # configuration of Winograd can cause low precision output resulting in
+    # tests failing. So we disable that here.
+    os.environ["TF_ENABLE_WINOGRAD_NONFUSED"] = "0"
     data_format = ("channels_first"
                    if test.is_gpu_available() else "channels_last")
     # Note that we we are setting training=False here so that dropout produces
@@ -460,6 +470,7 @@ class GradientsTest(test.TestCase):
     pfor_outputs, while_outputs = create_mnist_per_eg_jacobian(
         2, data_format, training=False)
     self.run_and_assert_equal(pfor_outputs, while_outputs, rtol=1e-3)
+    os.environ.pop("TF_ENABLE_WINOGRAD_NONFUSED", None)
 
   def test_fc_jacobian(self):
     jacobians, per_eg_jacobians_pfor, per_eg_jacobians_while = (
diff --git a/tensorflow/python/ops/parallel_for/pfor.py b/tensorflow/python/ops/parallel_for/pfor.py
index ec4ef0f1ab58750502d76a8de120cc3c5ea16c99..77ec3bc0d40ecba11c1624af1ad4be0578b5e4f7 100644
--- a/tensorflow/python/ops/parallel_for/pfor.py
+++ b/tensorflow/python/ops/parallel_for/pfor.py
@@ -592,7 +592,7 @@ class WhileOp(object):
       inputs = args[:num_enters]
       output_tas = args[num_enters:]
       # TODO(agarwal): see which outputs have consumers and only populate the
-      # TensorArrays corresonding to those. Or do those paths get trimmed out
+      # TensorArrays corresponding to those. Or do those paths get trimmed out
       # from inside the while_loop body?
       assert len(inputs) >= len(output_tas)
       assert len(inputs) == len(inputs_stacked)
diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py
index 15cafbbde50335de0dc0cd8849425c07b4ac81d3..8b259b6b6b3fc7198c496a2ab3c70aa8ea1fe8c6 100644
--- a/tensorflow/python/ops/resource_variable_ops.py
+++ b/tensorflow/python/ops/resource_variable_ops.py
@@ -181,7 +181,8 @@ def shape_safe_assign_variable_handle(handle, shape, value, name=None):
                                                       name=name)
 
 
-class ResourceVariable(variables.Variable):
+# TODO(apassos) make this be variables.Variable
+class ResourceVariable(variables.RefVariable):
   """Variable based on resource handles.
 
   See the @{$variables$Variables How To} for a high level overview.
@@ -195,15 +196,16 @@ class ResourceVariable(variables.Variable):
   the variable are fixed. The value can be changed using one of the assign
   methods.
 
-  Just like any `Tensor`, variables created with `ResourceVariable()` can be
-  used as inputs for other Ops in the graph. Additionally, all the operators
-  overloaded for the `Tensor` class are carried over to variables, so you can
-  also add nodes to the graph by just doing arithmetic on variables.
+  Just like any `Tensor`, variables created with
+  `tf.Variable(use_resource=True)` can be used as inputs for other Ops in the
+  graph. Additionally, all the operators overloaded for the `Tensor` class are
+  carried over to variables, so you can also add nodes to the graph by just
+  doing arithmetic on variables.
 
-  Unlike tf.Variable, a tf.ResourceVariable has well-defined semantics. Each
+  Unlike ref-based variable, a ResourceVariable has well-defined semantics. Each
   usage of a ResourceVariable in a TensorFlow graph adds a read_value operation
-  to the graph. The Tensors returned by a read_value operation are guaranteed
-  to see all modifications to the value of the variable which happen in any
+  to the graph. The Tensors returned by a read_value operation are guaranteed to
+  see all modifications to the value of the variable which happen in any
   operation on which the read_value depends on (either directly, indirectly, or
   via a control dependency) and guaranteed to not see any modification to the
   value of the variable from operations that depend on the read_value operation.
@@ -217,7 +219,7 @@ class ResourceVariable(variables.Variable):
   can cause tf.Variable and tf.ResourceVariable to behave differently:
 
   ```python
-  a = tf.ResourceVariable(1.0)
+  a = tf.Variable(1.0, use_resource=True)
   a.initializer.run()
 
   assign = a.assign(2.0)
@@ -741,8 +743,14 @@ class ResourceVariable(variables.Variable):
   def _read_variable_op(self):
     if self.trainable:
       tape.watch_variable(self)
-    return gen_resource_variable_ops.read_variable_op(self._handle,
-                                                      self._dtype)
+    result = gen_resource_variable_ops.read_variable_op(self._handle,
+                                                        self._dtype)
+    if not context.executing_eagerly():
+      # Note that if a control flow context is active the input of the read op
+      # might not actually be the handle. This line bypasses it.
+      tape.record_operation(
+          "ReadVariableOp", [result], [self._handle], lambda x: [x])
+    return result
 
   def read_value(self):
     """Constructs an op which reads the value of this variable.
@@ -867,6 +875,19 @@ class ResourceVariable(variables.Variable):
 
   __array_priority__ = 100
 
+  def is_initialized(self, name=None):
+    """Checks whether a resource variable has been initialized.
+
+    Outputs boolean scalar indicating whether the tensor has been initialized.
+
+    Args:
+      name: A name for the operation (optional).
+
+    Returns:
+      A `Tensor` of type `bool`.
+    """
+    return gen_resource_variable_ops.var_is_initialized_op(self.handle, name)
+
   def assign_sub(self, delta, use_locking=None, name=None, read_value=True):
     """Subtracts a value from this variable.
 
@@ -1091,6 +1112,113 @@ class _UnreadVariable(ResourceVariable):
 ops.register_tensor_conversion_function(_UnreadVariable, _dense_var_to_tensor)
 ops.register_dense_tensor_like_type(_UnreadVariable)
 
+
+class _MixedPrecisionVariable(ResourceVariable):
+  """Represents a variable that can return in desired dtype when read.
+
+  In mixed precision training, it is usually desirable to use different dtypes
+  for variables and computation. This class will be used to wrap created
+  ResourceVariable when mixed precision training is enabled. It allows layers to
+  perform computation in a different dtype than their variable dtypes, in order
+  to achieve higher performance without causing quality loss.
+  """
+
+  def __init__(self, var, read_dtype):
+    """Creates a MixedPrecisionVariable.
+
+    Args:
+      var: A ResourceVariable instance.
+      read_dtype: A tf.DType, the returned dtype when read, default to None.
+        Casting is performed if read_dtype is not None and differs from
+        var.dtype.
+    Returns:
+      An MixedPrecisionVariable instance.
+    Raises:
+      ValueError: if var is not a ResourceVariable instance, or read_dtype is
+        not a tf.DType instance.
+    """
+    # pylint: disable=super-init-not-called
+    # We do not call super init on purpose.
+    if not isinstance(var, ResourceVariable):
+      raise ValueError("InvalidArgument: var must be a ResourceVariable type.")
+    if not isinstance(read_dtype, dtypes.DType):
+      raise ValueError("InvalidArgument: read_dtype must be a tf.DType type.")
+
+    self._var = var
+    self._trainable = var.trainable
+    self._save_slice_info = None
+    self._graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
+    self._in_graph_mode = var._in_graph_mode  # pylint: disable=protected-access
+    self._handle = var.handle
+    self._shape = var.shape
+    self._initial_value = None
+    if isinstance(self.handle, ops.EagerTensor):
+      self._handle_name = ""
+    else:
+      self._handle_name = self.handle.name
+    self._unique_id = var._unique_id  # pylint: disable=protected-access
+    self._dtype = var.dtype
+    self._constraint = None
+    self._cached_value = None
+    self._is_initialized_op = var._is_initialized_op  # pylint: disable=protected-access
+    self._initializer_op = var._initializer_op  # pylint: disable=protected-access
+    # This needs to be set before read_value() is called.
+    self._read_dtype = read_dtype
+    if context.executing_eagerly():
+      self._graph_element = None
+    else:
+      self._graph_element = self.read_value()
+    self._handle_deleter = (
+        var._handle_deleter if not self._in_graph_mode  # pylint: disable=protected-access
+        else None)
+    # pylint: enable=super-init-not-called
+
+  @property
+  def name(self):
+    return self._var.name
+
+  def value(self):
+    return self._read_variable_op()
+
+  def read_value(self):
+    return self._read_variable_op()
+
+  def _read_variable_op(self):
+    with ops.colocate_with(self._handle):
+      res = gen_resource_variable_ops.read_variable_op(self._handle,
+                                                       self._dtype)
+      if self._read_dtype != self._dtype:
+        return math_ops.cast(res, self._read_dtype)
+      else:
+        return res
+
+  def set_shape(self, shape):
+    self._shape = shape
+    self._cached_shape_as_list = None
+
+  @property
+  def op(self):
+    """The op for this variable."""
+    return self._var.op
+
+  @property
+  def read_dtype(self):
+    """The dtype of the returned tensor when reading the var."""
+    return self._read_dtype
+
+  def _dense_var_to_tensor(self, dtype=None, name=None, as_ref=False):
+    del name
+    dtype = dtype or self.read_dtype
+    if dtype != self.read_dtype or as_ref:
+      return NotImplemented
+    else:
+      res = self.value()
+    return res
+
+  def _should_act_as_resource_variable(self):
+    """To pass resource_variable_ops.is_resource_variable check."""
+    pass
+
 # Register a conversion function which reads the value of the variable,
 # allowing instances of the class to be used as tensors.
 
diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py
index deba133fb9910f28c7f902f334174734c3c742f7..7b6ab20975114da5599aabdd0c6e664191f5fe48 100644
--- a/tensorflow/python/ops/rnn.py
+++ b/tensorflow/python/ops/rnn.py
@@ -417,24 +417,30 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None,
 
     # Backward direction
     if not time_major:
-      time_dim = 1
-      batch_dim = 0
+      time_axis = 1
+      batch_axis = 0
     else:
-      time_dim = 0
-      batch_dim = 1
+      time_axis = 0
+      batch_axis = 1
 
-    def _reverse(input_, seq_lengths, seq_dim, batch_dim):
+    def _reverse(input_, seq_lengths, seq_axis, batch_axis):
       if seq_lengths is not None:
         return array_ops.reverse_sequence(
             input=input_, seq_lengths=seq_lengths,
-            seq_dim=seq_dim, batch_dim=batch_dim)
+            seq_axis=seq_axis, batch_axis=batch_axis)
       else:
-        return array_ops.reverse(input_, axis=[seq_dim])
+        return array_ops.reverse(input_, axis=[seq_axis])
 
     with vs.variable_scope("bw") as bw_scope:
-      inputs_reverse = _reverse(
-          inputs, seq_lengths=sequence_length,
-          seq_dim=time_dim, batch_dim=batch_dim)
+
+      def _map_reverse(inp):
+        return _reverse(
+            inp,
+            seq_lengths=sequence_length,
+            seq_axis=time_axis,
+            batch_axis=batch_axis)
+
+      inputs_reverse = nest.map_structure(_map_reverse, inputs)
       tmp, output_state_bw = dynamic_rnn(
           cell=cell_bw, inputs=inputs_reverse, sequence_length=sequence_length,
           initial_state=initial_state_bw, dtype=dtype,
@@ -443,7 +449,7 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None,
 
   output_bw = _reverse(
       tmp, seq_lengths=sequence_length,
-      seq_dim=time_dim, batch_dim=batch_dim)
+      seq_axis=time_axis, batch_axis=batch_axis)
 
   outputs = (output_fw, output_bw)
   output_states = (output_state_fw, output_state_bw)
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index 82a044a0d4c8710f5ade0aa460f4354a0dd35deb..42806ba6ec486b88085ddc063c82a6873a1b23c8 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -47,7 +47,6 @@ from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.ops import variables as tf_variables
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training.checkpointable import base as checkpointable
-from tensorflow.python.training.checkpointable import tracking as checkpointable_tracking
 from tensorflow.python.util import nest
 from tensorflow.python.util.tf_export import tf_export
 
@@ -55,16 +54,6 @@ from tensorflow.python.util.tf_export import tf_export
 _BIAS_VARIABLE_NAME = "bias"
 _WEIGHTS_VARIABLE_NAME = "kernel"
 
-
-# TODO(jblespiau): Remove this function when we are sure there are no longer
-# any usage (even if protected, it is being used). Prefer assert_like_rnncell.
-def _like_rnncell(cell):
-  """Checks that a given object is an RNNCell by using duck typing."""
-  conditions = [hasattr(cell, "output_size"), hasattr(cell, "state_size"),
-                hasattr(cell, "zero_state"), callable(cell)]
-  return all(conditions)
-
-
 # This can be used with self.assertRaisesRegexp for assert_like_rnncell.
 ASSERT_LIKE_RNNCELL_ERROR_REGEXP = "is not an RNNCell"
 
@@ -1272,6 +1261,11 @@ class MultiRNNCell(RNNCell):
       raise TypeError(
           "cells must be a list or tuple, but saw: %s." % cells)
 
+    if len(set([id(cell) for cell in cells])) < len(cells):
+      logging.log_first_n(logging.WARN,
+                          "At least two cells provided to MultiRNNCell "
+                          "are the same object and will share weights.", 1)
+
     self._cells = cells
     for cell_number, cell in enumerate(self._cells):
       # Add Checkpointable dependencies on these cells so their variables get
@@ -1330,48 +1324,3 @@ class MultiRNNCell(RNNCell):
                   array_ops.concat(new_states, 1))
 
     return cur_inp, new_states
-
-
-class _SlimRNNCell(RNNCell, checkpointable_tracking.NotCheckpointable):
-  """A simple wrapper for slim.rnn_cells."""
-
-  def __init__(self, cell_fn):
-    """Create a SlimRNNCell from a cell_fn.
-
-    Args:
-      cell_fn: a function which takes (inputs, state, scope) and produces the
-        outputs and the new_state. Additionally when called with inputs=None and
-        state=None it should return (initial_outputs, initial_state).
-
-    Raises:
-      TypeError: if cell_fn is not callable
-      ValueError: if cell_fn cannot produce a valid initial state.
-    """
-    if not callable(cell_fn):
-      raise TypeError("cell_fn %s needs to be callable", cell_fn)
-    self._cell_fn = cell_fn
-    self._cell_name = cell_fn.func.__name__
-    init_output, init_state = self._cell_fn(None, None)
-    output_shape = init_output.get_shape()
-    state_shape = init_state.get_shape()
-    self._output_size = output_shape.with_rank(2)[1].value
-    self._state_size = state_shape.with_rank(2)[1].value
-    if self._output_size is None:
-      raise ValueError("Initial output created by %s has invalid shape %s" %
-                       (self._cell_name, output_shape))
-    if self._state_size is None:
-      raise ValueError("Initial state created by %s has invalid shape %s" %
-                       (self._cell_name, state_shape))
-
-  @property
-  def state_size(self):
-    return self._state_size
-
-  @property
-  def output_size(self):
-    return self._output_size
-
-  def __call__(self, inputs, state, scope=None):
-    scope = scope or self._cell_name
-    output, state = self._cell_fn(inputs, state, scope=scope)
-    return output, state
diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py
index 1e3f662ff34f67d2b5f226427c8a03d82b9f2a7c..af103d3cc7649128824132c5520b561425819369 100644
--- a/tensorflow/python/ops/script_ops.py
+++ b/tensorflow/python/ops/script_ops.py
@@ -130,7 +130,7 @@ class FuncRegistry(object):
   def __init__(self):
     self._lock = threading.Lock()
     self._unique_id = 0  # GUARDED_BY(self._lock)
-    # Only store weakrefs to the funtions. The strong reference is stored in
+    # Only store weakrefs to the functions. The strong reference is stored in
     # the graph.
     self._funcs = weakref.WeakValueDictionary()
 
diff --git a/tensorflow/python/ops/state_ops.py b/tensorflow/python/ops/state_ops.py
index 8cb6a0537e928effbcf4c475bcc4e974182da2a7..2c93cf72c75ba27145e06abe69bcbef9418b39e0 100644
--- a/tensorflow/python/ops/state_ops.py
+++ b/tensorflow/python/ops/state_ops.py
@@ -19,7 +19,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.python.eager import context
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import gen_resource_variable_ops
@@ -124,9 +123,7 @@ def is_variable_initialized(ref, name=None):
   if ref.dtype._is_ref_dtype:
     return gen_state_ops.is_variable_initialized(ref=ref, name=name)
   # Handle resource variables.
-  if context.executing_eagerly() or ref.op.type == "VarHandleOp":
-    return gen_resource_variable_ops.var_is_initialized_op(ref.handle,
-                                                           name=name)
+  return ref.is_initialized(name=name)
 
 
 @tf_export("assign_sub")
diff --git a/tensorflow/python/ops/tensor_array_ops.py b/tensorflow/python/ops/tensor_array_ops.py
index cc92da4fd7afd49d0dd80bd859d7393f2761303f..f86dfb35276f608c5cb323fe5deceb58733be007 100644
--- a/tensorflow/python/ops/tensor_array_ops.py
+++ b/tensorflow/python/ops/tensor_array_ops.py
@@ -554,7 +554,7 @@ class _EagerTensorArray(object):
       self._tensor_array.extend([None for _ in range(index - size + 1)])
 
     if not isinstance(value, ops.EagerTensor):
-      value = constant_op.constant(value)
+      value = ops.convert_to_tensor(value)
 
     if self._infer_shape:
       if self._element_shape is None:
@@ -633,8 +633,8 @@ class _EagerTensorArray(object):
   def split(self, value, lengths, name=None):
     """See TensorArray."""
     # error checking to match graph-mode errors
-    value = constant_op.constant(value)
-    lengths = constant_op.constant(lengths)
+    value = ops.convert_to_tensor(value)
+    lengths = ops.convert_to_tensor(lengths)
     sum_lengths = math_ops.reduce_sum(lengths)
     if lengths.shape.ndims != 1:
       raise errors_impl.InvalidArgumentError(
diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py
index 1e06bf07d5aaa88a4a30760450cffc32a20f4ca5..aca44bcd449d05db5885768391262284e61bf07b 100644
--- a/tensorflow/python/ops/variable_scope.py
+++ b/tensorflow/python/ops/variable_scope.py
@@ -191,36 +191,9 @@ class _ReuseMode(enum.Enum):
   # REUSE_TRUE = 3
 
 
-@tf_export("VariableSynchronization")
-class VariableSynchronization(enum.Enum):
-  """Indicates when a distributed variable will be synced."""
-
-  # Indicates that the synchronization will be determined by the current
-  # `DistributionStrategy` (eg. With `MirroredStrategy` this would be
-  # `ON_WRITE`).
-  AUTO = 0
-
-  # Indicates that there will only be one copy of the variable, so there is no
-  # need to sync.
-  NONE = 1
-
-  # Indicates that the variable will be aggregated across devices
-  # every time it is updated.
-  ON_WRITE = 2
-
-  # Indicates that the variable will be aggregated across devices
-  # when it is read (eg. when checkpointing or when evaluating an op that uses
-  # the variable).
-  ON_READ = 3
-
-
-@tf_export("VariableAggregation")
-class VariableAggregation(enum.Enum):
-  """Indicates how a distributed variable will be aggregated."""
-  NONE = 0
-  SUM = 1
-  MEAN = 2
-
+# TODO(apassos) remove these forwarding symbols.
+VariableSynchronization = variables.VariableSynchronization  # pylint: disable=invalid-name
+VariableAggregation = variables.VariableAggregation  # pylint: disable=invalid-name
 
 AUTO_REUSE = _ReuseMode.AUTO_REUSE
 tf_export("AUTO_REUSE").export_constant(__name__, "AUTO_REUSE")
@@ -255,7 +228,7 @@ class _VariableStore(object):
                    initializer=None,
                    regularizer=None,
                    reuse=None,
-                   trainable=True,
+                   trainable=None,
                    collections=None,
                    caching_device=None,
                    partitioner=None,
@@ -300,6 +273,8 @@ class _VariableStore(object):
         forced to be False.
       trainable: If `True` also add the variable to the graph collection
         `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+        `trainable` defaults to `True` unless `synchronization` is
+        set to `ON_READ`.
       collections: List of graph collections keys to add the `Variable` to.
         Defaults to `[GraphKeys.GLOBAL_VARIABLES]` (see `tf.Variable`).
       caching_device: Optional device string or function describing where the
@@ -341,7 +316,8 @@ class _VariableStore(object):
         aggregated. Accepted values are constants defined in the class
         @{tf.VariableSynchronization}. By default the synchronization is set to
         `AUTO` and the current `DistributionStrategy` chooses
-        when to synchronize.
+        when to synchronize. If `synchronization` is set to `ON_READ`,
+        `trainable` must not be set to `True`.
       aggregation: Indicates how a distributed variable will be aggregated.
         Accepted values are constants defined in the class
         @{tf.VariableAggregation}.
@@ -404,7 +380,7 @@ class _VariableStore(object):
         initializer=None,
         regularizer=None,
         reuse=None,
-        trainable=True,
+        trainable=None,
         collections=None,
         caching_device=None,
         partitioner=None,
@@ -477,6 +453,10 @@ class _VariableStore(object):
           synchronization=synchronization,
           aggregation=aggregation)
 
+    # Set trainable value based on synchronization value.
+    trainable = _get_trainable_value(
+        synchronization=synchronization, trainable=trainable)
+
     if custom_getter is not None:
       # Handle backwards compatibility with getter arguments that were added
       # to the API after users started writing custom getters.
@@ -519,11 +499,20 @@ class _VariableStore(object):
           synchronization=synchronization,
           aggregation=aggregation)
 
-  def _get_partitioned_variable(
-      self, name, partitioner, shape=None, dtype=dtypes.float32,
-      initializer=None, regularizer=None, reuse=None,
-      trainable=True, collections=None, caching_device=None,
-      validate_shape=True, use_resource=None, constraint=None):
+  def _get_partitioned_variable(self,
+                                name,
+                                partitioner,
+                                shape=None,
+                                dtype=dtypes.float32,
+                                initializer=None,
+                                regularizer=None,
+                                reuse=None,
+                                trainable=None,
+                                collections=None,
+                                caching_device=None,
+                                validate_shape=True,
+                                use_resource=None,
+                                constraint=None):
     """Gets or creates a sharded variable list with these parameters.
 
     The `partitioner` must be a callable that accepts a fully defined
@@ -773,7 +762,7 @@ class _VariableStore(object):
                            regularizer=None,
                            partition_info=None,
                            reuse=None,
-                           trainable=True,
+                           trainable=None,
                            collections=None,
                            caching_device=None,
                            validate_shape=True,
@@ -1136,7 +1125,7 @@ class VariableScope(object):
                    initializer=None,
                    regularizer=None,
                    reuse=None,
-                   trainable=True,
+                   trainable=None,
                    collections=None,
                    caching_device=None,
                    partitioner=None,
@@ -1207,7 +1196,7 @@ class VariableScope(object):
                                 dtype=None,
                                 initializer=None,
                                 regularizer=None,
-                                trainable=True,
+                                trainable=None,
                                 collections=None,
                                 caching_device=None,
                                 partitioner=None,
@@ -1422,7 +1411,7 @@ def get_variable(name,
                  dtype=None,
                  initializer=None,
                  regularizer=None,
-                 trainable=True,
+                 trainable=None,
                  collections=None,
                  caching_device=None,
                  partitioner=None,
@@ -2334,43 +2323,64 @@ def _compute_slice_dim_and_shape(full_shape, slicing):
   return slice_dim, slice_shape
 
 
+def _get_trainable_value(synchronization, trainable):
+  """Computes the trainable value based on the given arguments."""
+  if synchronization == VariableSynchronization.ON_READ:
+    if trainable:
+      raise ValueError(
+          "Synchronization value can be set to "
+          "VariableSynchronization.ON_READ only for non-trainable variables. "
+          "You have specified trainable=True and "
+          "synchronization=VariableSynchronization.ON_READ.")
+    else:
+      # Set trainable to be false when variable is to be synced on read.
+      trainable = False
+  elif trainable is None:
+    trainable = True
+  return trainable
+
+
 def default_variable_creator(next_creator=None, **kwargs):
   """Default variable creator."""
   assert next_creator is None
   initial_value = kwargs.get("initial_value", None)
-  trainable = kwargs.get("trainable", True)
+  trainable = kwargs.get("trainable", None)
   collections = kwargs.get("collections", None)
   validate_shape = kwargs.get("validate_shape", True)
   caching_device = kwargs.get("caching_device", None)
   name = kwargs.get("name", None)
+  variable_def = kwargs.get("variable_def", None)
   dtype = kwargs.get("dtype", None)
+  expected_shape = kwargs.get("expected_shape", None)
+  import_scope = kwargs.get("import_scope", None)
   constraint = kwargs.get("constraint", None)
   use_resource = kwargs.get("use_resource", None)
 
-  # Enforce `ON_READ` variables to be not trainable.
+  # Set trainable value based on synchronization value.
   synchronization = kwargs.get("synchronization", VariableSynchronization.AUTO)
-  if synchronization == VariableSynchronization.ON_READ:
-    trainable = False
+  trainable = _get_trainable_value(
+      synchronization=synchronization, trainable=trainable)
 
   if use_resource is None:
     use_resource = get_variable_scope().use_resource
-  if use_resource or (use_resource is None and context.executing_eagerly()):
+  use_resource = use_resource or context.executing_eagerly()
+  if use_resource:
     return resource_variable_ops.ResourceVariable(
         initial_value=initial_value, trainable=trainable,
         collections=collections, validate_shape=validate_shape,
         caching_device=caching_device, name=name, dtype=dtype,
-        constraint=constraint)
-  elif not use_resource and context.executing_eagerly():
-    raise RuntimeError(
-        "VariableScope should use resource variable when eager execution is"
-        " enabled, but use_resource is False."
-    )
+        constraint=constraint, variable_def=variable_def,
+        import_scope=import_scope)
   else:
-    return variables.Variable(
+    return variables.RefVariable(
         initial_value=initial_value, trainable=trainable,
         collections=collections, validate_shape=validate_shape,
         caching_device=caching_device, name=name, dtype=dtype,
-        constraint=constraint)
+        constraint=constraint, variable_def=variable_def,
+        expected_shape=expected_shape, import_scope=import_scope)
+
+
+variables.default_variable_creator = default_variable_creator
 
 
 def _make_getter(captured_getter, captured_previous):
@@ -2378,36 +2388,8 @@ def _make_getter(captured_getter, captured_previous):
   return lambda **kwargs: captured_getter(captured_previous, **kwargs)
 
 
-def variable(initial_value=None,
-             trainable=True,
-             collections=None,
-             validate_shape=True,
-             caching_device=None,
-             name=None,
-             dtype=None,
-             constraint=None,
-             use_resource=None,
-             synchronization=VariableSynchronization.AUTO,
-             aggregation=VariableAggregation.NONE):
-  previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
-  for getter in ops.get_default_graph()._variable_creator_stack:  # pylint: disable=protected-access
-    previous_getter = _make_getter(getter, previous_getter)
-
-  # Reset `aggregation` that is explicitly set as `None` to the enum None value.
-  if aggregation is None:
-    aggregation = VariableAggregation.NONE
-  return previous_getter(
-      initial_value=initial_value,
-      trainable=trainable,
-      collections=collections,
-      validate_shape=validate_shape,
-      caching_device=caching_device,
-      name=name,
-      dtype=dtype,
-      constraint=constraint,
-      use_resource=use_resource,
-      synchronization=synchronization,
-      aggregation=aggregation)
+# TODO(apassos) remove forwarding symbol
+variable = variables.Variable
 
 
 @tf_contextlib.contextmanager
@@ -2441,6 +2423,8 @@ def variable_creator_scope(variable_creator):
       trainable: If `True`, the default, also adds the variable to the graph
         collection `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as
         the default list of variables to use by the `Optimizer` classes.
+        `trainable` defaults to `True` unless `synchronization` is
+        set to `ON_READ`.
       collections: List of graph collections keys. The new variable is added to
         these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
       validate_shape: If `False`, allows the variable to be initialized with a
@@ -2463,7 +2447,8 @@ def variable_creator_scope(variable_creator):
         aggregated. Accepted values are constants defined in the class
         @{tf.VariableSynchronization}. By default the synchronization is set to
         `AUTO` and the current `DistributionStrategy` chooses
-        when to synchronize.
+        when to synchronize. If `synchronization` is set to `ON_READ`,
+        `trainable` must not be set to `True`.
       aggregation: Indicates how a distributed variable will be aggregated.
         Accepted values are constants defined in the class
         @{tf.VariableAggregation}.
diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py
index 9a09cdaa52425713cf18362dd8726fe7207c604f..fc00ce68aeaf49ea88b1a40ee40ecebe69bb0eee 100644
--- a/tensorflow/python/ops/variables.py
+++ b/tensorflow/python/ops/variables.py
@@ -17,6 +17,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import enum  # pylint: disable=g-bad-import-order
+
+import six
+
 from tensorflow.core.framework import attr_value_pb2
 from tensorflow.core.framework import variable_pb2
 from tensorflow.python.eager import context
@@ -36,8 +40,101 @@ from tensorflow.python.util.deprecation import deprecated
 from tensorflow.python.util.tf_export import tf_export
 
 
+def default_variable_creator(_, **kwds):
+  del kwds
+  raise NotImplementedError("variable_scope needs to be imported")
+
+
+def _make_getter(captured_getter, captured_previous):
+  """To avoid capturing loop variables."""
+  def getter(**kwargs):
+    return captured_getter(captured_previous, **kwargs)
+  return getter
+
+
+@tf_export("VariableSynchronization")
+class VariableSynchronization(enum.Enum):
+  """Indicates when a distributed variable will be synced."""
+
+  # Indicates that the synchronization will be determined by the current
+  # `DistributionStrategy` (eg. With `MirroredStrategy` this would be
+  # `ON_WRITE`).
+  AUTO = 0
+
+  # Indicates that there will only be one copy of the variable, so there is no
+  # need to sync.
+  NONE = 1
+
+  # Indicates that the variable will be aggregated across devices
+  # every time it is updated.
+  ON_WRITE = 2
+
+  # Indicates that the variable will be aggregated across devices
+  # when it is read (eg. when checkpointing or when evaluating an op that uses
+  # the variable).
+  ON_READ = 3
+
+
+@tf_export("VariableAggregation")
+class VariableAggregation(enum.Enum):
+  """Indicates how a distributed variable will be aggregated."""
+  NONE = 0
+  SUM = 1
+  MEAN = 2
+
+
+class VariableMetaclass(type):
+  """Metaclass to allow construction of tf.Variable to be overridden."""
+
+  def _variable_call(cls,
+                     initial_value=None,
+                     trainable=None,
+                     collections=None,
+                     validate_shape=True,
+                     caching_device=None,
+                     name=None,
+                     variable_def=None,
+                     dtype=None,
+                     expected_shape=None,
+                     import_scope=None,
+                     constraint=None,
+                     use_resource=None,
+                     synchronization=VariableSynchronization.AUTO,
+                     aggregation=VariableAggregation.NONE):
+    """Call on Variable class. Useful to force the signature."""
+    previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
+    for getter in ops.get_default_graph()._variable_creator_stack:  # pylint: disable=protected-access
+      previous_getter = _make_getter(getter, previous_getter)
+
+    # Reset `aggregation` that is explicitly set as `None` to the enum NONE.
+    if aggregation is None:
+      aggregation = VariableAggregation.NONE
+    return previous_getter(
+        initial_value=initial_value,
+        trainable=trainable,
+        collections=collections,
+        validate_shape=validate_shape,
+        caching_device=caching_device,
+        name=name,
+        variable_def=variable_def,
+        dtype=dtype,
+        expected_shape=expected_shape,
+        import_scope=import_scope,
+        constraint=constraint,
+        use_resource=use_resource,
+        synchronization=synchronization,
+        aggregation=aggregation)
+
+  def __call__(cls, *args, **kwargs):
+    if cls is Variable:
+      return cls._variable_call(*args, **kwargs)
+    else:
+      return super(VariableMetaclass, cls).__call__(*args, **kwargs)
+
+
 @tf_export("Variable")
-class Variable(checkpointable.CheckpointableBase):
+class Variable(six.with_metaclass(VariableMetaclass,
+                                  checkpointable.CheckpointableBase)):
   """See the @{$variables$Variables How To} for a high level overview.
 
   A variable maintains state in the graph across calls to `run()`. You add a
@@ -49,112 +146,663 @@ class Variable(checkpointable.CheckpointableBase):
   the variable are fixed. The value can be changed using one of the assign
   methods.
 
-  If you want to change the shape of a variable later you have to use an
-  `assign` Op with `validate_shape=False`.
+  If you want to change the shape of a variable later you have to use an
+  `assign` Op with `validate_shape=False`.
+
+  Just like any `Tensor`, variables created with `Variable()` can be used as
+  inputs for other Ops in the graph. Additionally, all the operators
+  overloaded for the `Tensor` class are carried over to variables, so you can
+  also add nodes to the graph by just doing arithmetic on variables.
+
+  ```python
+  import tensorflow as tf
+
+  # Create a variable.
+  w = tf.Variable(, name=)
+
+  # Use the variable in the graph like any Tensor.
+  y = tf.matmul(w, ...another variable or tensor...)
+
+  # The overloaded operators are available too.
+  z = tf.sigmoid(w + y)
+
+  # Assign a new value to the variable with `assign()` or a related method.
+  w.assign(w + 1.0)
+  w.assign_add(1.0)
+  ```
+
+  When you launch the graph, variables have to be explicitly initialized before
+  you can run Ops that use their value. You can initialize a variable by
+  running its *initializer op*, restoring the variable from a save file, or
+  simply running an `assign` Op that assigns a value to the variable. In fact,
+  the variable *initializer op* is just an `assign` Op that assigns the
+  variable's initial value to the variable itself.
+
+  ```python
+  # Launch the graph in a session.
+  with tf.Session() as sess:
+      # Run the variable initializer.
+      sess.run(w.initializer)
+      # ...you now can run ops that use the value of 'w'...
+  ```
+
+  The most common initialization pattern is to use the convenience function
+  `global_variables_initializer()` to add an Op to the graph that initializes
+  all the variables. You then run that Op after launching the graph.
+
+  ```python
+  # Add an Op to initialize global variables.
+  init_op = tf.global_variables_initializer()
+
+  # Launch the graph in a session.
+  with tf.Session() as sess:
+      # Run the Op that initializes global variables.
+      sess.run(init_op)
+      # ...you can now run any Op that uses variable values...
+  ```
+
+  If you need to create a variable with an initial value dependent on another
+  variable, use the other variable's `initialized_value()`. This ensures that
+  variables are initialized in the right order.
+
+  All variables are automatically collected in the graph where they are
+  created. By default, the constructor adds the new variable to the graph
+  collection `GraphKeys.GLOBAL_VARIABLES`. The convenience function
+  `global_variables()` returns the contents of that collection.
+
+  When building a machine learning model it is often convenient to distinguish
+  between variables holding the trainable model parameters and other variables
+  such as a `global step` variable used to count training steps. To make this
+  easier, the variable constructor supports a `trainable=` parameter. If
+  `True`, the new variable is also added to the graph collection
+  `GraphKeys.TRAINABLE_VARIABLES`. The convenience function
+  `trainable_variables()` returns the contents of this collection. The
+  various `Optimizer` classes use this collection as the default list of
+  variables to optimize.
+
+  WARNING: tf.Variable objects by default have a non-intuitive memory model. A
+  Variable is represented internally as a mutable Tensor which can
+  non-deterministically alias other Tensors in a graph. The set of operations
+  which consume a Variable and can lead to aliasing is undetermined and can
+  change across TensorFlow versions. Avoid writing code which relies on the
+  value of a Variable either changing or not changing as other operations
+  happen. For example, using Variable objects or simple functions thereof as
+  predicates in a `tf.cond` is dangerous and error-prone:
+
+  ```
+  v = tf.Variable(True)
+  tf.cond(v, lambda: v.assign(False), my_false_fn)  # Note: this is broken.
+  ```
+
+  Here replacing adding `use_resource=True` when constructing the variable will
+  fix any nondeterminism issues:
+  ```
+  v = tf.Variable(True, use_resource=True)
+  tf.cond(v, lambda: v.assign(False), my_false_fn)
+  ```
+
+  To use the replacement for variables which does
+  not have these issues:
+
+  * Add `use_resource=True` when constructing `tf.Variable`;
+  * Call `tf.get_variable_scope().set_use_resource(True)` inside a
+    `tf.variable_scope` before the `tf.get_variable()` call.
+  """
+
+  def __init__(self,
+               initial_value=None,
+               trainable=True,
+               collections=None,
+               validate_shape=True,
+               caching_device=None,
+               name=None,
+               variable_def=None,
+               dtype=None,
+               expected_shape=None,
+               import_scope=None,
+               constraint=None,
+               use_resource=None,
+               synchronization=VariableSynchronization.AUTO,
+               aggregation=VariableAggregation.NONE):
+    """Creates a new variable with value `initial_value`.
+
+    The new variable is added to the graph collections listed in `collections`,
+    which defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
+
+    If `trainable` is `True` the variable is also added to the graph collection
+    `GraphKeys.TRAINABLE_VARIABLES`.
+
+    This constructor creates both a `variable` Op and an `assign` Op to set the
+    variable to its initial value.
+
+    Args:
+      initial_value: A `Tensor`, or Python object convertible to a `Tensor`,
+        which is the initial value for the Variable. The initial value must have
+        a shape specified unless `validate_shape` is set to False. Can also be a
+        callable with no argument that returns the initial value when called. In
+        that case, `dtype` must be specified. (Note that initializer functions
+        from init_ops.py must first be bound to a shape before being used here.)
+      trainable: If `True`, the default, also adds the variable to the graph
+        collection `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as
+        the default list of variables to use by the `Optimizer` classes.
+      collections: List of graph collections keys. The new variable is added to
+        these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
+      validate_shape: If `False`, allows the variable to be initialized with a
+        value of unknown shape. If `True`, the default, the shape of
+        `initial_value` must be known.
+      caching_device: Optional device string describing where the Variable
+        should be cached for reading.  Defaults to the Variable's device.
+        If not `None`, caches on another device.  Typical use is to cache
+        on the device where the Ops using the Variable reside, to deduplicate
+        copying through `Switch` and other conditional statements.
+      name: Optional name for the variable. Defaults to `'Variable'` and gets
+        uniquified automatically.
+      variable_def: `VariableDef` protocol buffer. If not `None`, recreates
+        the Variable object with its contents, referencing the variable's nodes
+        in the graph, which must already exist. The graph is not changed.
+        `variable_def` and the other arguments are mutually exclusive.
+      dtype: If set, initial_value will be converted to the given type.
+        If `None`, either the datatype will be kept (if `initial_value` is
+        a Tensor), or `convert_to_tensor` will decide.
+      expected_shape: A TensorShape. If set, initial_value is expected
+        to have this shape.
+      import_scope: Optional `string`. Name scope to add to the
+        `Variable.` Only used when initializing from protocol buffer.
+      constraint: An optional projection function to be applied to the variable
+        after being updated by an `Optimizer` (e.g. used to implement norm
+        constraints or value constraints for layer weights). The function must
+        take as input the unprojected Tensor representing the value of the
+        variable and return the Tensor for the projected value
+        (which must have the same shape). Constraints are not safe to
+        use when doing asynchronous distributed training.
+      use_resource: if True, a ResourceVariable is created; otherwise an
+       old-style ref-based variable is created. When eager execution is enabled
+       a resource variable is always created.
+      synchronization: Indicates when a distributed a variable will be
+        aggregated. Accepted values are constants defined in the class
+        @{tf.VariableSynchronization}. By default the synchronization is set to
+        `AUTO` and the current `DistributionStrategy` chooses
+        when to synchronize. If `synchronization` is set to `ON_READ`,
+        `trainable` must not be set to `True`.
+      aggregation: Indicates how a distributed variable will be aggregated.
+        Accepted values are constants defined in the class
+        @{tf.VariableAggregation}.
+
+    Raises:
+      ValueError: If both `variable_def` and initial_value are specified.
+      ValueError: If the initial value is not specified, or does not have a
+        shape and `validate_shape` is `True`.
+      RuntimeError: If eager execution is enabled.
+    """
+    raise NotImplementedError
+
+  def __repr__(self):
+    raise NotImplementedError
+
+  def value(self):
+    """Returns the last snapshot of this variable.
+
+    You usually do not need to call this method as all ops that need the value
+    of the variable call it automatically through a `convert_to_tensor()` call.
+
+    Returns a `Tensor` which holds the value of the variable.  You can not
+    assign a new value to this tensor as it is not a reference to the variable.
+
+    To avoid copies, if the consumer of the returned value is on the same device
+    as the variable, this actually returns the live value of the variable, not
+    a copy.  Updates to the variable are seen by the consumer.  If the consumer
+    is on a different device it will get a copy of the variable.
+
+    Returns:
+      A `Tensor` containing the value of the variable.
+    """
+    raise NotImplementedError
+
+  def read_value(self):
+    """Returns the value of this variable, read in the current context.
+
+    Can be different from value() if it's on another device, with control
+    dependencies, etc.
+
+    Returns:
+      A `Tensor` containing the value of the variable.
+    """
+    raise NotImplementedError
+
+  def set_shape(self, shape):
+    """Overrides the shape for this variable.
+
+    Args:
+      shape: the `TensorShape` representing the overridden shape.
+    """
+    raise NotImplementedError
+
+  @property
+  def trainable(self):
+    raise NotImplementedError
+
+  def eval(self, session=None):
+    """In a session, computes and returns the value of this variable.
+
+    This is not a graph construction method, it does not add ops to the graph.
+
+    This convenience method requires a session where the graph
+    containing this variable has been launched. If no session is
+    passed, the default session is used.  See @{tf.Session} for more
+    information on launching a graph and on sessions.
+
+    ```python
+    v = tf.Variable([1, 2])
+    init = tf.global_variables_initializer()
+
+    with tf.Session() as sess:
+        sess.run(init)
+        # Usage passing the session explicitly.
+        print(v.eval(sess))
+        # Usage with the default session.  The 'with' block
+        # above makes 'sess' the default session.
+        print(v.eval())
+    ```
+
+    Args:
+      session: The session to use to evaluate this variable. If
+        none, the default session is used.
+
+    Returns:
+      A numpy `ndarray` with a copy of the value of this variable.
+    """
+    raise NotImplementedError
+
+  def initialized_value(self):
+    """Returns the value of the initialized variable.
+
+    You should use this instead of the variable itself to initialize another
+    variable with a value that depends on the value of this variable.
+
+    ```python
+    # Initialize 'v' with a random tensor.
+    v = tf.Variable(tf.truncated_normal([10, 40]))
+    # Use `initialized_value` to guarantee that `v` has been
+    # initialized before its value is used to initialize `w`.
+    # The random values are picked only once.
+    w = tf.Variable(v.initialized_value() * 2.0)
+    ```
+
+    Returns:
+      A `Tensor` holding the value of this variable after its initializer
+      has run.
+    """
+    raise NotImplementedError
+
+  @property
+  def initial_value(self):
+    """Returns the Tensor used as the initial value for the variable.
+
+    Note that this is different from `initialized_value()` which runs
+    the op that initializes the variable before returning its value.
+    This method returns the tensor that is used by the op that initializes
+    the variable.
+
+    Returns:
+      A `Tensor`.
+    """
+    raise NotImplementedError
+
+  @property
+  def constraint(self):
+    """Returns the constraint function associated with this variable.
+
+    Returns:
+      The constraint function that was passed to the variable constructor.
+      Can be `None` if no constraint was passed.
+    """
+    raise NotImplementedError
+
+  def assign(self, value, use_locking=False):
+    """Assigns a new value to the variable.
+
+    This is essentially a shortcut for `assign(self, value)`.
+
+    Args:
+      value: A `Tensor`. The new value for this variable.
+      use_locking: If `True`, use locking during the assignment.
+
+    Returns:
+      A `Tensor` that will hold the new value of this variable after
+      the assignment has completed.
+    """
+    raise NotImplementedError
+
+  def assign_add(self, delta, use_locking=False):
+    """Adds a value to this variable.
+
+     This is essentially a shortcut for `assign_add(self, delta)`.
+
+    Args:
+      delta: A `Tensor`. The value to add to this variable.
+      use_locking: If `True`, use locking during the operation.
+
+    Returns:
+      A `Tensor` that will hold the new value of this variable after
+      the addition has completed.
+    """
+    raise NotImplementedError
+
+  def assign_sub(self, delta, use_locking=False):
+    """Subtracts a value from this variable.
+
+    This is essentially a shortcut for `assign_sub(self, delta)`.
+
+    Args:
+      delta: A `Tensor`. The value to subtract from this variable.
+      use_locking: If `True`, use locking during the operation.
+
+    Returns:
+      A `Tensor` that will hold the new value of this variable after
+      the subtraction has completed.
+    """
+    raise NotImplementedError
+
+  def scatter_sub(self, sparse_delta, use_locking=False):
+    """Subtracts `IndexedSlices` from this variable.
+
+    This is essentially a shortcut for `scatter_sub(self, sparse_delta.indices,
+    sparse_delta.values)`.
+
+    Args:
+      sparse_delta: `IndexedSlices` to be subtracted from this variable.
+      use_locking: If `True`, use locking during the operation.
+
+    Returns:
+      A `Tensor` that will hold the new value of this variable after
+      the scattered subtraction has completed.
+
+    Raises:
+      ValueError: if `sparse_delta` is not an `IndexedSlices`.
+    """
+    raise NotImplementedError
+
+  def count_up_to(self, limit):
+    """Increments this variable until it reaches `limit`.
+
+    When that Op is run it tries to increment the variable by `1`. If
+    incrementing the variable would bring it above `limit` then the Op raises
+    the exception `OutOfRangeError`.
+
+    If no error is raised, the Op outputs the value of the variable before
+    the increment.
+
+    This is essentially a shortcut for `count_up_to(self, limit)`.
+
+    Args:
+      limit: value at which incrementing the variable raises an error.
+
+    Returns:
+      A `Tensor` that will hold the variable value before the increment. If no
+      other Op modifies this variable, the values produced will all be
+      distinct.
+    """
+    raise NotImplementedError
+
+  def load(self, value, session=None):
+    """Load new value into this variable.
+
+    Writes new value to variable's memory. Doesn't add ops to the graph.
+
+    This convenience method requires a session where the graph
+    containing this variable has been launched. If no session is
+    passed, the default session is used.  See @{tf.Session} for more
+    information on launching a graph and on sessions.
+
+    ```python
+    v = tf.Variable([1, 2])
+    init = tf.global_variables_initializer()
+
+    with tf.Session() as sess:
+        sess.run(init)
+        # Usage passing the session explicitly.
+        v.load([2, 3], sess)
+        print(v.eval(sess)) # prints [2 3]
+        # Usage with the default session.  The 'with' block
+        # above makes 'sess' the default session.
+        v.load([3, 4], sess)
+        print(v.eval()) # prints [3 4]
+    ```
+
+    Args:
+        value: New variable value
+        session: The session to use to evaluate this variable. If
+          none, the default session is used.
+
+    Raises:
+        ValueError: Session is not passed and no default session
+    """
+    raise NotImplementedError
+
+  # Conversion to tensor.
+  @staticmethod
+  def _TensorConversionFunction(v, dtype=None, name=None, as_ref=False):  # pylint: disable=invalid-name
+    """Utility function for converting a Variable to a Tensor."""
+    _ = name
+    if dtype and not dtype.is_compatible_with(v.dtype):
+      raise ValueError(
+          "Incompatible type conversion requested to type '%s' for variable "
+          "of type '%s'" % (dtype.name, v.dtype.name))
+    if as_ref:
+      return v._ref()  # pylint: disable=protected-access
+    else:
+      return v.value()
+
+  @staticmethod
+  def _OverloadAllOperators():  # pylint: disable=invalid-name
+    """Register overloads for all operators."""
+    for operator in ops.Tensor.OVERLOADABLE_OPERATORS:
+      Variable._OverloadOperator(operator)
+    # For slicing, bind getitem differently than a tensor (use SliceHelperVar
+    # instead)
+    # pylint: disable=protected-access
+    setattr(Variable, "__getitem__", array_ops._SliceHelperVar)
+
+  @staticmethod
+  def _OverloadOperator(operator):  # pylint: disable=invalid-name
+    """Defer an operator overload to `ops.Tensor`.
+
+    We pull the operator out of ops.Tensor dynamically to avoid ordering issues.
+
+    Args:
+      operator: string. The operator name.
+    """
+
+    def _run_op(a, *args):
+      # pylint: disable=protected-access
+      return getattr(ops.Tensor, operator)(a._AsTensor(), *args)
+    # Propagate __doc__ to wrapper
+    try:
+      _run_op.__doc__ = getattr(ops.Tensor, operator).__doc__
+    except AttributeError:
+      pass
+
+    setattr(Variable, operator, _run_op)
+
+  # NOTE(mrry): This enables the Variable's overloaded "right" binary
+  # operators to run when the left operand is an ndarray, because it
+  # accords the Variable class higher priority than an ndarray, or a
+  # numpy matrix.
+  # TODO(mrry): Convert this to using numpy's __numpy_ufunc__
+  # mechanism, which allows more control over how Variables interact
+  # with ndarrays.
+  __array_priority__ = 100
+
+  @property
+  def name(self):
+    """The name of this variable."""
+    raise NotImplementedError
+
+  @property
+  def initializer(self):
+    """The initializer operation for this variable."""
+    raise NotImplementedError
+
+  @property
+  def device(self):
+    """The device of this variable."""
+    raise NotImplementedError
+
+  @property
+  def dtype(self):
+    """The `DType` of this variable."""
+    raise NotImplementedError
+
+  @property
+  def op(self):
+    """The `Operation` of this variable."""
+    raise NotImplementedError
+
+  @property
+  def graph(self):
+    """The `Graph` of this variable."""
+    raise NotImplementedError
+
+  @property
+  def shape(self):
+    """The `TensorShape` of this variable.
+
+    Returns:
+      A `TensorShape`.
+    """
+    raise NotImplementedError
+
+  def get_shape(self):
+    """Alias of Variable.shape."""
+    raise NotImplementedError
+
+  def to_proto(self, export_scope=None):
+    """Converts a `Variable` to a `VariableDef` protocol buffer.
+
+    Args:
+      export_scope: Optional `string`. Name scope to remove.
 
-  Just like any `Tensor`, variables created with `Variable()` can be used as
-  inputs for other Ops in the graph. Additionally, all the operators
-  overloaded for the `Tensor` class are carried over to variables, so you can
-  also add nodes to the graph by just doing arithmetic on variables.
+    Returns:
+      A `VariableDef` protocol buffer, or `None` if the `Variable` is not
+      in the specified name scope.
+    """
+    raise NotImplementedError
 
-  ```python
-  import tensorflow as tf
+  @staticmethod
+  def from_proto(variable_def, import_scope=None):
+    """Returns a `Variable` object created from `variable_def`."""
+    return RefVariable(variable_def=variable_def,
+                       import_scope=import_scope)
 
-  # Create a variable.
-  w = tf.Variable(, name=)
+  class SaveSliceInfo(object):
+    """Information on how to save this Variable as a slice.
 
-  # Use the variable in the graph like any Tensor.
-  y = tf.matmul(w, ...another variable or tensor...)
+    Provides internal support for saving variables as slices of a larger
+    variable.  This API is not public and is subject to change.
 
-  # The overloaded operators are available too.
-  z = tf.sigmoid(w + y)
+    Available properties:
 
-  # Assign a new value to the variable with `assign()` or a related method.
-  w.assign(w + 1.0)
-  w.assign_add(1.0)
-  ```
+    * full_name
+    * full_shape
+    * var_offset
+    * var_shape
+    """
 
-  When you launch the graph, variables have to be explicitly initialized before
-  you can run Ops that use their value. You can initialize a variable by
-  running its *initializer op*, restoring the variable from a save file, or
-  simply running an `assign` Op that assigns a value to the variable. In fact,
-  the variable *initializer op* is just an `assign` Op that assigns the
-  variable's initial value to the variable itself.
+    def __init__(self,
+                 full_name=None,
+                 full_shape=None,
+                 var_offset=None,
+                 var_shape=None,
+                 save_slice_info_def=None,
+                 import_scope=None):
+      """Create a `SaveSliceInfo`.
 
-  ```python
-  # Launch the graph in a session.
-  with tf.Session() as sess:
-      # Run the variable initializer.
-      sess.run(w.initializer)
-      # ...you now can run ops that use the value of 'w'...
-  ```
+      Args:
+        full_name: Name of the full variable of which this `Variable` is a
+            slice.
+        full_shape: Shape of the full variable, as a list of int.
+        var_offset: Offset of this `Variable` into the full variable, as a
+            list of int.
+        var_shape: Shape of this `Variable`, as a list of int.
+        save_slice_info_def: `SaveSliceInfoDef` protocol buffer. If not `None`,
+          recreates the SaveSliceInfo object its contents.
+          `save_slice_info_def` and other arguments are mutually
+          exclusive.
+        import_scope: Optional `string`. Name scope to add. Only used
+          when initializing from protocol buffer.
+      """
+      if save_slice_info_def:
+        assert isinstance(save_slice_info_def, variable_pb2.SaveSliceInfoDef)
+        self.full_name = ops.prepend_name_scope(
+            save_slice_info_def.full_name, import_scope=import_scope)
+        self.full_shape = [i for i in save_slice_info_def.full_shape]
+        self.var_offset = [i for i in save_slice_info_def.var_offset]
+        self.var_shape = [i for i in save_slice_info_def.var_shape]
+      else:
+        self.full_name = full_name
+        self.full_shape = full_shape
+        self.var_offset = var_offset
+        self.var_shape = var_shape
 
-  The most common initialization pattern is to use the convenience function
-  `global_variables_initializer()` to add an Op to the graph that initializes
-  all the variables. You then run that Op after launching the graph.
+    @property
+    def spec(self):
+      """Computes the spec string used for saving."""
+      full_shape_str = " ".join(["%d" % d for d in self.full_shape]) + " "
+      sl_spec = ":".join([
+          "%d,%d" % (o, s) for o, s in zip(self.var_offset, self.var_shape)
+      ])
+      return full_shape_str + sl_spec
 
-  ```python
-  # Add an Op to initialize global variables.
-  init_op = tf.global_variables_initializer()
+    def to_proto(self, export_scope=None):
+      """Returns a SaveSliceInfoDef() proto.
 
-  # Launch the graph in a session.
-  with tf.Session() as sess:
-      # Run the Op that initializes global variables.
-      sess.run(init_op)
-      # ...you can now run any Op that uses variable values...
-  ```
+      Args:
+        export_scope: Optional `string`. Name scope to remove.
 
-  If you need to create a variable with an initial value dependent on another
-  variable, use the other variable's `initialized_value()`. This ensures that
-  variables are initialized in the right order.
+      Returns:
+        A `SaveSliceInfoDef` protocol buffer, or None if the `Variable` is not
+        in the specified name scope.
+      """
+      if (export_scope is None or
+          self.full_name.startswith(export_scope)):
+        save_slice_info_def = variable_pb2.SaveSliceInfoDef()
+        save_slice_info_def.full_name = ops.strip_name_scope(
+            self.full_name, export_scope)
+        for i in self.full_shape:
+          save_slice_info_def.full_shape.append(i)
+        for i in self.var_offset:
+          save_slice_info_def.var_offset.append(i)
+        for i in self.var_shape:
+          save_slice_info_def.var_shape.append(i)
+        return save_slice_info_def
+      else:
+        return None
 
-  All variables are automatically collected in the graph where they are
-  created. By default, the constructor adds the new variable to the graph
-  collection `GraphKeys.GLOBAL_VARIABLES`. The convenience function
-  `global_variables()` returns the contents of that collection.
+  def __iadd__(self, other):
+    raise NotImplementedError
 
-  When building a machine learning model it is often convenient to distinguish
-  between variables holding the trainable model parameters and other variables
-  such as a `global step` variable used to count training steps. To make this
-  easier, the variable constructor supports a `trainable=` parameter. If
-  `True`, the new variable is also added to the graph collection
-  `GraphKeys.TRAINABLE_VARIABLES`. The convenience function
-  `trainable_variables()` returns the contents of this collection. The
-  various `Optimizer` classes use this collection as the default list of
-  variables to optimize.
+  def __isub__(self, other):
+    raise NotImplementedError
 
-  WARNING: tf.Variable objects have a non-intuitive memory model. A Variable is
-  represented internally as a mutable Tensor which can non-deterministically
-  alias other Tensors in a graph. The set of operations which consume a Variable
-  and can lead to aliasing is undetermined and can change across TensorFlow
-  versions. Avoid writing code which relies on the value of a Variable either
-  changing or not changing as other operations happen. For example, using
-  Variable objects or simple functions thereof as predicates in a `tf.cond` is
-  dangerous and error-prone:
+  def __imul__(self, other):
+    raise NotImplementedError
 
-  ```
-  v = tf.Variable(True)
-  tf.cond(v, lambda: v.assign(False), my_false_fn)  # Note: this is broken.
-  ```
+  def __idiv__(self, other):
+    raise NotImplementedError
 
-  Here replacing tf.Variable with tf.contrib.eager.Variable will fix any
-  nondeterminism issues.
+  def __itruediv__(self, other):
+    raise NotImplementedError
 
-  To use the replacement for variables which does
-  not have these issues:
+  def __irealdiv__(self, other):
+    raise NotImplementedError
 
-  * Replace `tf.Variable` with `tf.contrib.eager.Variable`;
-  * Call `tf.get_variable_scope().set_use_resource(True)` inside a
-    `tf.variable_scope` before the `tf.get_variable()` call.
+  def __ipow__(self, other):
+    raise NotImplementedError
 
-  @compatibility(eager)
-  `tf.Variable` is not compatible with eager execution.  Use
-  `tf.contrib.eager.Variable` instead which is compatible with both eager
-  execution and graph construction.  See [the TensorFlow Eager Execution
-  guide](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/g3doc/guide.md#variables-and-optimizers)
-  for details on how variables work in eager execution.
-  @end_compatibility
-  """
+
+# TODO(apassos): do not repeat all comments here
+class RefVariable(Variable):
+  """Ref-based implementation of variables."""
 
   def __init__(self,
                initial_value=None,
@@ -225,19 +873,7 @@ class Variable(checkpointable.CheckpointableBase):
       ValueError: If the initial value is not specified, or does not have a
         shape and `validate_shape` is `True`.
       RuntimeError: If eager execution is enabled.
-
-    @compatibility(eager)
-    `tf.Variable` is not compatible with eager execution.  Use
-    `tfe.Variable` instead which is compatible with both eager execution
-    and graph construction.  See [the TensorFlow Eager Execution
-    guide](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/g3doc/guide.md#variables-and-optimizers)
-    for details on how variables work in eager execution.
-    @end_compatibility
     """
-    if context.executing_eagerly():
-      raise RuntimeError(
-          "tf.Variable not supported when eager execution is enabled. "
-          "Please use tf.contrib.eager.Variable instead")
     self._in_graph_mode = True
     if variable_def:
       # If variable_def is provided, recreates the variable from its fields.
@@ -348,8 +984,7 @@ class Variable(checkpointable.CheckpointableBase):
       # Ensure that we weren't lifted into the eager context.
       if context.executing_eagerly():
         raise RuntimeError(
-            "tf.Variable not supported when eager execution is enabled. "
-            "Please use tf.contrib.eager.Variable instead")
+            "RefVariable not supported when eager execution is enabled. ")
       with ops.name_scope(name, "Variable", [] if init_from_fn else
                           [initial_value]) as name:
 
@@ -1068,12 +1703,6 @@ class Variable(checkpointable.CheckpointableBase):
     else:
       return None
 
-  @staticmethod
-  def from_proto(variable_def, import_scope=None):
-    """Returns a `Variable` object created from `variable_def`."""
-    return Variable(variable_def=variable_def,
-                    import_scope=import_scope)
-
   def __iadd__(self, other):
     logging.log_first_n(
         logging.WARN,
@@ -1130,90 +1759,6 @@ class Variable(checkpointable.CheckpointableBase):
         " if you want a new python Tensor object.", 1)
     return self ** other
 
-  class SaveSliceInfo(object):
-    """Information on how to save this Variable as a slice.
-
-    Provides internal support for saving variables as slices of a larger
-    variable.  This API is not public and is subject to change.
-
-    Available properties:
-
-    * full_name
-    * full_shape
-    * var_offset
-    * var_shape
-    """
-
-    def __init__(self,
-                 full_name=None,
-                 full_shape=None,
-                 var_offset=None,
-                 var_shape=None,
-                 save_slice_info_def=None,
-                 import_scope=None):
-      """Create a `SaveSliceInfo`.
-
-      Args:
-        full_name: Name of the full variable of which this `Variable` is a
-            slice.
-        full_shape: Shape of the full variable, as a list of int.
-        var_offset: Offset of this `Variable` into the full variable, as a
-            list of int.
-        var_shape: Shape of this `Variable`, as a list of int.
-        save_slice_info_def: `SaveSliceInfoDef` protocol buffer. If not `None`,
-          recreates the SaveSliceInfo object its contents.
-          `save_slice_info_def` and other arguments are mutually
-          exclusive.
-        import_scope: Optional `string`. Name scope to add. Only used
-          when initializing from protocol buffer.
-      """
-      if save_slice_info_def:
-        assert isinstance(save_slice_info_def, variable_pb2.SaveSliceInfoDef)
-        self.full_name = ops.prepend_name_scope(
-            save_slice_info_def.full_name, import_scope=import_scope)
-        self.full_shape = [i for i in save_slice_info_def.full_shape]
-        self.var_offset = [i for i in save_slice_info_def.var_offset]
-        self.var_shape = [i for i in save_slice_info_def.var_shape]
-      else:
-        self.full_name = full_name
-        self.full_shape = full_shape
-        self.var_offset = var_offset
-        self.var_shape = var_shape
-
-    @property
-    def spec(self):
-      """Computes the spec string used for saving."""
-      full_shape_str = " ".join(["%d" % d for d in self.full_shape]) + " "
-      sl_spec = ":".join([
-          "%d,%d" % (o, s) for o, s in zip(self.var_offset, self.var_shape)
-      ])
-      return full_shape_str + sl_spec
-
-    def to_proto(self, export_scope=None):
-      """Returns a SaveSliceInfoDef() proto.
-
-      Args:
-        export_scope: Optional `string`. Name scope to remove.
-
-      Returns:
-        A `SaveSliceInfoDef` protocol buffer, or None if the `Variable` is not
-        in the specified name scope.
-      """
-      if (export_scope is None or
-          self.full_name.startswith(export_scope)):
-        save_slice_info_def = variable_pb2.SaveSliceInfoDef()
-        save_slice_info_def.full_name = ops.strip_name_scope(
-            self.full_name, export_scope)
-        for i in self.full_shape:
-          save_slice_info_def.full_shape.append(i)
-        for i in self.var_offset:
-          save_slice_info_def.var_offset.append(i)
-        for i in self.var_shape:
-          save_slice_info_def.var_shape.append(i)
-        return save_slice_info_def
-      else:
-        return None
-
   def _set_save_slice_info(self, save_slice_info):
     """Sets the slice info for this `Variable`.
 
@@ -1230,7 +1775,7 @@ class PartitionedVariable(object):
   """A container for partitioned `Variable` objects.
 
   @compatibility(eager) `tf.PartitionedVariable` is not compatible with
-  eager execution.  Use `tfe.Variable` instead which is compatible
+  eager execution.  Use `tf.Variable` instead which is compatible
   with both eager execution and graph construction.  See [the
   TensorFlow Eager Execution
   guide](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/g3doc/guide.md#variables-and-optimizers)
@@ -1404,6 +1949,10 @@ class PartitionedVariable(object):
   def dtype(self):
     return self._dtype
 
+  @property
+  def shape(self):
+    return self.get_shape()
+
   def get_shape(self):
     return self._shape
 
diff --git a/tensorflow/python/platform/benchmark.py b/tensorflow/python/platform/benchmark.py
index eba2baaf6f836c872c8315e558c51733fc013ec2..fa17b17d104221990ed7847b725c4b741cb4aca7 100644
--- a/tensorflow/python/platform/benchmark.py
+++ b/tensorflow/python/platform/benchmark.py
@@ -66,11 +66,11 @@ def _global_report_benchmark(
     if not isinstance(extras, dict):
       raise TypeError("extras must be a dict")
 
-    logging.info("Benchmark [%s] iters: %d, wall_time: %g, cpu_time: %g,"
-                 "throughput: %g %s", name, iters if iters is not None else -1,
-                 wall_time if wall_time is not None else -1, cpu_time if
-                 cpu_time is not None else -1, throughput if
-                 throughput is not None else -1, str(extras) if extras else "")
+  logging.info("Benchmark [%s] iters: %d, wall_time: %g, cpu_time: %g,"
+               "throughput: %g %s", name, iters if iters is not None else -1,
+               wall_time if wall_time is not None else -1, cpu_time if
+               cpu_time is not None else -1, throughput if
+               throughput is not None else -1, str(extras) if extras else "")
 
   entries = test_log_pb2.BenchmarkEntries()
   entry = entries.entry.add()
diff --git a/tensorflow/python/platform/gfile.py b/tensorflow/python/platform/gfile.py
index fd697d70bf200f1f661b410a9636d7b60e87f430..45de047894dddc8a82eb50bb2a38cd6d4ffcabcb 100644
--- a/tensorflow/python/platform/gfile.py
+++ b/tensorflow/python/platform/gfile.py
@@ -38,7 +38,14 @@ from tensorflow.python.util.tf_export import tf_export
 
 @tf_export('gfile.GFile', 'gfile.Open')
 class GFile(_FileIO):
-  """File I/O wrappers without thread locking."""
+  """File I/O wrappers without thread locking.
+
+  Note, that this  is somewhat like builtin Python  file I/O, but
+  there are  semantic differences to  make it more  efficient for
+  some backing filesystems.  For example, a write  mode file will
+  not  be opened  until the  first  write call  (to minimize  RPC
+  invocations in network filesystems).
+  """
 
   def __init__(self, name, mode='r'):
     super(GFile, self).__init__(name=name, mode=mode)
@@ -46,7 +53,14 @@ class GFile(_FileIO):
 
 @tf_export('gfile.FastGFile')
 class FastGFile(_FileIO):
-  """File I/O wrappers without thread locking."""
+  """File I/O wrappers without thread locking.
+
+  Note, that this  is somewhat like builtin Python  file I/O, but
+  there are  semantic differences to  make it more  efficient for
+  some backing filesystems.  For example, a write  mode file will
+  not  be opened  until the  first  write call  (to minimize  RPC
+  invocations in network filesystems).
+  """
 
   def __init__(self, name, mode='r'):
     super(FastGFile, self).__init__(name=name, mode=mode)
diff --git a/tensorflow/python/platform/self_check.py b/tensorflow/python/platform/self_check.py
index 966a094e55e09d51c2d5edd36eb3ca29e71935f8..844ae999186f6eed89b113469782840f08502a85 100644
--- a/tensorflow/python/platform/self_check.py
+++ b/tensorflow/python/platform/self_check.py
@@ -78,7 +78,7 @@ def preload_check():
               "Could not find %r. TensorFlow requires that this DLL be "
               "installed in a directory that is named in your %%PATH%% "
               "environment variable. Download and install CUDA %s from "
-              "this URL: https://developer.nvidia.com/cuda-toolkit"
+              "this URL: https://developer.nvidia.com/cuda-90-download-archive"
               % (build_info.cudart_dll_name, build_info.cuda_version_number))
 
       if hasattr(build_info, "cudnn_dll_name") and hasattr(
diff --git a/tensorflow/python/profiler/model_analyzer_test.py b/tensorflow/python/profiler/model_analyzer_test.py
index f9891f3b1e2e94f61329babd1409e3efacc7f5b3..c0e16ca536e5ff2b3fdbd17088f3b1eebe0b50ec 100644
--- a/tensorflow/python/profiler/model_analyzer_test.py
+++ b/tensorflow/python/profiler/model_analyzer_test.py
@@ -106,7 +106,7 @@ class PrintModelAnalysisTest(test.TestCase):
               # Make sure time is profiled.
               gap = 1 if test.is_gpu_available() else 2
               for i in range(3, 6, gap):
-                mat = re.search('(.*)[um]s/(.*)[um]s', metrics[i])
+                mat = re.search('(.*)(?:us|ms|sec)/(.*)(?:us|ms|sec)', metrics[i])
                 self.assertGreater(float(mat.group(1)), 0.0)
                 self.assertGreater(float(mat.group(2)), 0.0)
               # Make sure device is profiled.
diff --git a/tensorflow/python/profiler/profile_context.py b/tensorflow/python/profiler/profile_context.py
index 18eb66ef988c9f49eb04264545d417d8a986e16e..fa4260a7120d72eacff32a7b4960b34545eb32e5 100644
--- a/tensorflow/python/profiler/profile_context.py
+++ b/tensorflow/python/profiler/profile_context.py
@@ -88,16 +88,19 @@ def _profiled_run(self,
       to_profiles = self.profile_context._profile_candidates()
       for to_prof in to_profiles:
         cmd, opts, _ = to_prof
+        saved_views = self.profile_context._views.setdefault(cmd, {})
         if self.profile_context._debug:
           sys.stderr.write('debug: profiling %s step: %d\n' % (cmd, step))
         if cmd == 'graph':
-          self.profile_context.profiler.profile_graph(opts)
+          saved_views[step] = self.profile_context.profiler.profile_graph(opts)
         elif cmd == 'scope':
-          self.profile_context.profiler.profile_name_scope(opts)
+          saved_views[step] = self.profile_context.profiler.profile_name_scope(
+              opts)
         elif cmd == 'op':
-          self.profile_context.profiler.profile_operations(opts)
+          saved_views[step] = self.profile_context.profiler.profile_operations(
+              opts)
         elif cmd == 'code':
-          self.profile_context.profiler.profile_python(opts)
+          saved_views[step] = self.profile_context.profiler.profile_python(opts)
         else:
           raise ValueError('Unknown cmd: %s\n' % cmd)
       return ret
@@ -185,8 +188,30 @@ class ProfileContext(object):
     self._traced_steps = 0
     self._auto_profiles = []
     self._profiler = None
+    self._views = {}
     self._lock = threading.Lock()
 
+  def get_profiles(self, cmd):
+    """Returns profiling results for each step at which `cmd` was run.
+
+    Args:
+      cmd: string, profiling command used in an `add_auto_profiling` call.
+
+    Returns:
+      dict[int: (MultiGraphNodeProto | GraphNodeProto)]. Keys are steps at which
+      the profiling command was run. Values are the outputs of profiling.
+      For "code" and "op" commands this will be a `MultiGraphNodeProto`, for
+      "scope" and "graph" commands this will be a `GraphNodeProto.
+
+    Raises:
+      ValueError: if `cmd` was never run (either because no session.run call was
+      made or because there was no `add_auto_profiling` call with the specified
+      `cmd`.
+    """
+    if cmd not in self._views:
+      raise ValueError('No autoprofiler for command: {}, was run'.format(cmd))
+    return self._views[cmd]
+
   def add_auto_profiling(self, cmd, options, profile_steps):
     """Traces and profiles at some session run steps.
 
diff --git a/tensorflow/python/profiler/profile_context_test.py b/tensorflow/python/profiler/profile_context_test.py
index a623beee23ebf98cf96bd0f334f813db5ae04040..107ad443c32e20ab69f3c2fb71c652d97a9c0cc6 100644
--- a/tensorflow/python/profiler/profile_context_test.py
+++ b/tensorflow/python/profiler/profile_context_test.py
@@ -61,6 +61,8 @@ class ProfilerContextTest(test.TestCase):
               profile_str = f.read()
             gfile.Remove(outfile)
 
+      self.assertEqual(set([15, 50, 100]), set(pctx.get_profiles("op").keys()))
+
     with lib.ProfilerFromFile(
         os.path.join(test.get_temp_dir(), "profile_100")) as profiler:
       profiler.profile_operations(options=opts)
diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py
index e58be804c2738dbad0e2f90c21d6eff3832a8148..8c985a7c2fa2b515c2daed1349996dd30f6d7ce1 100644
--- a/tensorflow/python/saved_model/builder_impl.py
+++ b/tensorflow/python/saved_model/builder_impl.py
@@ -34,6 +34,7 @@ from tensorflow.python.platform import tf_logging
 from tensorflow.python.saved_model import constants
 from tensorflow.python.training import saver as tf_saver
 from tensorflow.python.util import compat
+from tensorflow.python.util.deprecation import deprecated_args
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -133,39 +134,32 @@ class SavedModelBuilder(object):
     tf_logging.info("Assets written to: %s",
                     compat.as_text(assets_destination_dir))
 
-  def _maybe_add_legacy_init_op(self, legacy_init_op=None):
-    """Add legacy init op to the SavedModel.
+  def _maybe_add_main_op(self, main_op):
+    """Adds main op to the SavedModel.
 
     Args:
-      legacy_init_op: Optional legacy init op to support backward compatibility.
+      main_op: Main op to run as part of graph initialization. If None, no
+        main op will be added to the graph.
 
     Raises:
-      TypeError if legacy init op is not of type `Operation`.
-      AssertionError if the graph already contains one or more legacy init ops.
+      TypeError: if main op is provided but is not of type `Operation`.
+      ValueError: if the Graph already contains an init op.
     """
-    if legacy_init_op is not None:
-      if not isinstance(legacy_init_op, ops.Operation):
-        raise TypeError("legacy_init_op needs to be an Operation: %r" %
-                        legacy_init_op)
-      if ops.get_collection(constants.LEGACY_INIT_OP_KEY):
-        raise AssertionError(
-            "graph already contains one or more legacy init ops under the "
-            "collection {}.".format(constants.LEGACY_INIT_OP_KEY))
-      ops.add_to_collection(constants.LEGACY_INIT_OP_KEY, legacy_init_op)
-
-  def _add_main_op(self, main_op):
-    """Add main op to the SavedModel.
+    if main_op is None:
+      return
 
-    Args:
-      main_op: Main op to run as part of graph initialization.
+    if not isinstance(main_op, ops.Operation):
+      raise TypeError("main_op needs to be an Operation: %r" % main_op)
 
-    Raises:
-      TypeError if main op is not of type `Operation`.
-    """
-    if main_op is not None:
-      if not isinstance(main_op, ops.Operation):
-        raise TypeError("main_op needs to be an Operation: %r" % main_op)
-      ops.add_to_collection(constants.MAIN_OP_KEY, main_op)
+    # Validate that no other init ops have been added to this graph already.
+    # We check main_op and legacy_init_op for thoroughness and explicitness.
+    for init_op_key in (constants.MAIN_OP_KEY, constants.LEGACY_INIT_OP_KEY):
+      if ops.get_collection(init_op_key):
+        raise ValueError(
+            "Graph already contains one or more main ops under the "
+            "collection {}.".format(init_op_key))
+
+    ops.add_to_collection(constants.MAIN_OP_KEY, main_op)
 
   def _add_train_op(self, train_op):
     """Add train op to the SavedModel.
@@ -257,16 +251,12 @@ class SavedModelBuilder(object):
           self._validate_tensor_info(outputs[outputs_key])
 
   def _add_collections(
-      self, assets_collection, legacy_init_op, main_op, train_op):
+      self, assets_collection, main_op, train_op):
     """Add asset and op collections to be saved."""
     # Save asset files and write them to disk, if any.
     self._save_and_write_assets(assets_collection)
 
-    if main_op is None:
-      # Add legacy init op to the SavedModel.
-      self._maybe_add_legacy_init_op(legacy_init_op)
-    else:
-      self._add_main_op(main_op)
+    self._maybe_add_main_op(main_op)
 
     self._add_train_op(train_op)
 
@@ -282,6 +272,9 @@ class SavedModelBuilder(object):
           allow_empty=True)
     return saver
 
+  @deprecated_args(None,
+                   "Pass your op to the equivalent parameter main_op instead.",
+                   "legacy_init_op")
   def add_meta_graph(self,
                      tags,
                      signature_def_map=None,
@@ -306,7 +299,7 @@ class SavedModelBuilder(object):
           that this collection should be a subset of the assets saved as part of
           the first meta graph in the SavedModel.
       legacy_init_op: Legacy support for op or group of ops to execute after the
-          restore op upon a load.
+          restore op upon a load. Deprecated; please use main_op instead.
       clear_devices: Set to true if the device info on the default graph should
           be cleared.
       main_op: Op or group of ops to execute when the graph is loaded. Note
@@ -333,8 +326,12 @@ class SavedModelBuilder(object):
     # properly populated.
     self._validate_signature_def_map(signature_def_map)
 
+    # legacy_init_op is deprecated, and going away in TF 2.0.
+    # Re-mapping to main_op, as treatment is identical regardless.
+    main_op = main_op or legacy_init_op
+
     # Add assets and ops
-    self._add_collections(assets_collection, legacy_init_op, main_op, None)
+    self._add_collections(assets_collection, main_op, None)
 
     saver = self._maybe_create_saver(saver)
 
@@ -351,6 +348,9 @@ class SavedModelBuilder(object):
     # Tag the meta graph def and add it to the SavedModel.
     self._tag_and_add_meta_graph(meta_graph_def, tags, signature_def_map)
 
+  @deprecated_args(None,
+                   "Pass your op to the equivalent parameter main_op instead.",
+                   "legacy_init_op")
   def add_meta_graph_and_variables(self,
                                    sess,
                                    tags,
@@ -378,7 +378,7 @@ class SavedModelBuilder(object):
         def.
       assets_collection: Assets collection to be saved with SavedModel.
       legacy_init_op: Legacy support for op or group of ops to execute after the
-          restore op upon a load.
+          restore op upon a load. Deprecated; please use main_op instead.
       clear_devices: Set to true if the device info on the default graph should
           be cleared.
       main_op: Op or group of ops to execute when the graph is loaded. Note
@@ -402,8 +402,12 @@ class SavedModelBuilder(object):
     # properly populated.
     self._validate_signature_def_map(signature_def_map)
 
+    # legacy_init_op is deprecated, and going away in TF 2.0.
+    # Re-mapping to main_op, as treatment is identical regardless.
+    main_op = main_op or legacy_init_op
+
     # Add assets and ops
-    self._add_collections(assets_collection, legacy_init_op, main_op, None)
+    self._add_collections(assets_collection, main_op, None)
 
     # Create the variables sub-directory, if it does not exist.
     variables_dir = os.path.join(
diff --git a/tensorflow/python/saved_model/constants.py b/tensorflow/python/saved_model/constants.py
index 61c6ffbd0d11ef48c6dfb8d14a4328df7f7c5df5..cb251f08bb56fd5496ea4f3aaedfd2822ae1565c 100644
--- a/tensorflow/python/saved_model/constants.py
+++ b/tensorflow/python/saved_model/constants.py
@@ -60,6 +60,10 @@ SAVED_MODEL_FILENAME_PBTXT = "saved_model.pbtxt"
 tf_export("saved_model.constants.SAVED_MODEL_FILENAME_PBTXT").export_constant(
     __name__, "SAVED_MODEL_FILENAME_PBTXT")
 
+# File name for json format of SavedModel.
+# Not exported while keras_saved_model is in contrib.
+SAVED_MODEL_FILENAME_JSON = "saved_model.json"
+
 # Subdirectory name containing the variables/checkpoint files.
 VARIABLES_DIRECTORY = "variables"
 tf_export("saved_model.constants.VARIABLES_DIRECTORY").export_constant(
@@ -69,5 +73,3 @@ tf_export("saved_model.constants.VARIABLES_DIRECTORY").export_constant(
 VARIABLES_FILENAME = "variables"
 tf_export("saved_model.constants.VARIABLES_FILENAME").export_constant(
     __name__, "VARIABLES_FILENAME")
-
-
diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py
index e5f649fdabb5cc2600a6fdd0e5ed9950d6bb23c2..16077f52fab72e7700df7e67782a549bbde21751 100644
--- a/tensorflow/python/saved_model/loader_impl.py
+++ b/tensorflow/python/saved_model/loader_impl.py
@@ -116,11 +116,14 @@ def _get_asset_tensors(export_dir, meta_graph_def_to_load, import_scope=None):
   return asset_tensor_dict
 
 
-def _get_main_op_tensor(meta_graph_def_to_load):
+def _get_main_op_tensor(
+    meta_graph_def_to_load, init_op_key=constants.MAIN_OP_KEY):
   """Gets the main op tensor, if one exists.
 
   Args:
     meta_graph_def_to_load: The meta graph def from the SavedModel to be loaded.
+    init_op_key: name of collection to check; should be one of MAIN_OP_KEY
+      or the deprecated LEGACY_INIT_OP_KEY
 
   Returns:
     The main op tensor, if it exists and `None` otherwise.
@@ -131,38 +134,15 @@ def _get_main_op_tensor(meta_graph_def_to_load):
   """
   collection_def = meta_graph_def_to_load.collection_def
   main_op_tensor = None
-  if constants.MAIN_OP_KEY in collection_def:
-    main_ops = collection_def[constants.MAIN_OP_KEY].node_list.value
+  if init_op_key in collection_def:
+    main_ops = collection_def[init_op_key].node_list.value
     if len(main_ops) != 1:
-      raise RuntimeError("Expected exactly one SavedModel main op.")
-    main_op_tensor = ops.get_collection(constants.MAIN_OP_KEY)[0]
+      raise RuntimeError("Expected exactly one SavedModel main op. "
+                         "Found: {}".format(main_ops))
+    main_op_tensor = ops.get_collection(init_op_key)[0]
   return main_op_tensor
 
 
-def _get_legacy_init_op_tensor(meta_graph_def_to_load):
-  """Gets the legacy init op tensor, if one exists.
-
-  Args:
-    meta_graph_def_to_load: The meta graph def from the SavedModel to be loaded.
-
-  Returns:
-    The legacy init op tensor, if it exists and `None` otherwise.
-
-  Raises:
-    RuntimeError: If the collection def corresponding to the legacy init op key
-        has other than exactly one tensor.
-  """
-  collection_def = meta_graph_def_to_load.collection_def
-  legacy_init_op_tensor = None
-  if constants.LEGACY_INIT_OP_KEY in collection_def:
-    legacy_init_ops = collection_def[
-        constants.LEGACY_INIT_OP_KEY].node_list.value
-    if len(legacy_init_ops) != 1:
-      raise RuntimeError("Expected exactly one legacy serving init op.")
-    legacy_init_op_tensor = ops.get_collection(constants.LEGACY_INIT_OP_KEY)[0]
-  return legacy_init_op_tensor
-
-
 @tf_export("saved_model.loader.maybe_saved_model_directory")
 def maybe_saved_model_directory(export_dir):
   """Checks whether the provided export directory could contain a SavedModel.
@@ -284,12 +264,15 @@ class SavedModelLoader(object):
       **saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph.
 
     Returns:
-      Saver defined by the MetaGraph, which can be used to restore the variable
-      values.
+      A tuple of
+        * Saver defined by the MetaGraph, which can be used to restore the
+          variable values.
+        * List of `Operation`/`Tensor` objects returned from
+          `tf.import_graph_def` (may be `None`).
     """
     meta_graph_def = self.get_meta_graph_def_from_tags(tags)
     with graph.as_default():
-      return tf_saver.import_meta_graph(
+      return tf_saver._import_meta_graph_with_return_elements(  # pylint: disable=protected-access
           meta_graph_def, import_scope=import_scope, **saver_kwargs)
 
   def restore_variables(self, sess, saver, import_scope=None):
@@ -340,8 +323,8 @@ class SavedModelLoader(object):
           self._export_dir, meta_graph_def, import_scope=import_scope)
 
       main_op_tensor = (
-          _get_main_op_tensor(meta_graph_def) or
-          (_get_legacy_init_op_tensor(meta_graph_def)))
+          _get_main_op_tensor(meta_graph_def, constants.MAIN_OP_KEY) or
+          _get_main_op_tensor(meta_graph_def, constants.LEGACY_INIT_OP_KEY))
       if main_op_tensor is not None:
         sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary)
 
@@ -361,8 +344,8 @@ class SavedModelLoader(object):
       `MetagraphDef` proto of the graph that was loaded.
     """
     with sess.graph.as_default():
-      saver = self.load_graph(sess.graph, tags, import_scope,
-                              **saver_kwargs)
+      saver, _ = self.load_graph(sess.graph, tags, import_scope,
+                                 **saver_kwargs)
       self.restore_variables(sess, saver, import_scope)
       self.run_init_ops(sess, tags, import_scope)
     return self.get_meta_graph_def_from_tags(tags)
diff --git a/tensorflow/python/saved_model/loader_test.py b/tensorflow/python/saved_model/loader_test.py
index ce18859f6b9e4c141c4b27f3643c8d4004eb56f6..9a0b276a4b20390ad6bae012e4d61e39c57ac4fc 100644
--- a/tensorflow/python/saved_model/loader_test.py
+++ b/tensorflow/python/saved_model/loader_test.py
@@ -111,7 +111,8 @@ class SavedModelLoaderTest(test.TestCase):
   def test_load_with_import_scope(self):
     loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP)
     with self.test_session(graph=ops.Graph()) as sess:
-      saver = loader.load_graph(sess.graph, ["foo_graph"], import_scope="baz")
+      saver, _ = loader.load_graph(
+          sess.graph, ["foo_graph"], import_scope="baz")
 
       # The default saver should not work when the import scope is set.
       with self.assertRaises(errors.NotFoundError):
@@ -149,7 +150,7 @@ class SavedModelLoaderTest(test.TestCase):
   def test_run_init_op(self):
     loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP)
     graph = ops.Graph()
-    saver = loader.load_graph(graph, ["foo_graph"])
+    saver, _ = loader.load_graph(graph, ["foo_graph"])
     with self.test_session(graph=graph) as sess:
       loader.restore_variables(sess, saver)
       self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval())
@@ -203,7 +204,7 @@ class SavedModelLoaderTest(test.TestCase):
 
     loader = loader_impl.SavedModelLoader(path)
     with self.test_session(graph=ops.Graph()) as sess:
-      saver = loader.load_graph(sess.graph, ["foo_graph"])
+      saver, _ = loader.load_graph(sess.graph, ["foo_graph"])
       self.assertFalse(variables._all_saveable_objects())
       self.assertIsNotNone(saver)
 
@@ -212,6 +213,18 @@ class SavedModelLoaderTest(test.TestCase):
       self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval())
       self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval())
 
+  def test_load_saved_model_graph_with_return_elements(self):
+    """Ensure that the correct elements are returned."""
+    loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL)
+    graph = ops.Graph()
+    _, ret = loader.load_graph(graph, ["foo_graph"],
+                               return_elements=["y:0", "x:0"])
+
+    self.assertEqual(graph.get_tensor_by_name("y:0"), ret[0])
+    self.assertEqual(graph.get_tensor_by_name("x:0"), ret[1])
+
+    with self.assertRaisesRegexp(ValueError, "not found in graph"):
+      loader.load_graph(graph, ["foo_graph"], return_elements=["z:0"])
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/saved_model/saved_model_test.py b/tensorflow/python/saved_model/saved_model_test.py
index fb4732aca21d4661aaea21a472475690687a42be..00b669fc97950d25a6e29f728c649f4dba482162 100644
--- a/tensorflow/python/saved_model/saved_model_test.py
+++ b/tensorflow/python/saved_model/saved_model_test.py
@@ -846,9 +846,19 @@ class SavedModelTest(test.TestCase):
   def testLegacyInitOpWithNonEmptyCollection(self):
     export_dir = self._get_export_dir(
         "test_legacy_init_op_with_non_empty_collection")
+    self._testInitOpsWithNonEmptyCollection(
+        export_dir, constants.LEGACY_INIT_OP_KEY)
+
+  def testMainOpWithNonEmptyCollection(self):
+    export_dir = self._get_export_dir(
+        "test_main_op_with_non_empty_collection")
+    self._testInitOpsWithNonEmptyCollection(export_dir, constants.MAIN_OP_KEY)
+
+  def _testInitOpsWithNonEmptyCollection(self, export_dir, key):
     builder = saved_model_builder.SavedModelBuilder(export_dir)
 
-    with self.test_session(graph=ops.Graph()) as sess:
+    g = ops.Graph()
+    with self.test_session(graph=g) as sess:
       # Initialize variable `v1` to 1.
       v1 = variables.Variable(1, name="v1")
       ops.add_to_collection("v", v1)
@@ -857,19 +867,21 @@ class SavedModelTest(test.TestCase):
       v2 = variables.Variable(42, name="v2", trainable=False, collections=[])
       ops.add_to_collection("v", v2)
 
-      # Set up an assignment op to be run as part of the legacy_init_op.
+      # Set up an assignment op to be run as part of the init op.
       assign_v2 = state_ops.assign(v2, v1)
-      legacy_init_op = control_flow_ops.group(assign_v2, name="legacy_init_op")
+      init_op = control_flow_ops.group(assign_v2, name="init_op")
 
       sess.run(variables.global_variables_initializer())
 
-      ops.add_to_collection(constants.LEGACY_INIT_OP_KEY,
-                            control_flow_ops.no_op())
-      # AssertionError should be raised since the LEGACY_INIT_OP_KEY collection
+      ops.add_to_collection(key, control_flow_ops.no_op())
+      # ValueError should be raised since the LEGACY_INIT_OP_KEY collection
       # is not empty and we don't support multiple init ops.
-      with self.assertRaises(AssertionError):
+      with self.assertRaisesRegexp(ValueError, "Graph already contains"):
         builder.add_meta_graph_and_variables(
-            sess, ["foo"], legacy_init_op=legacy_init_op)
+            sess, ["foo"], legacy_init_op=init_op)
+      # We shouldn't be able to add as MAIN_OP, either.
+      with self.assertRaisesRegexp(ValueError, "Graph already contains"):
+        builder.add_meta_graph_and_variables(sess, ["foo"], main_op=init_op)
 
   def testTrainOp(self):
     export_dir = self._get_export_dir("test_train_op")
diff --git a/tensorflow/python/summary/writer/writer.py b/tensorflow/python/summary/writer/writer.py
index aca084fc9168e710316e4c988594cff69e54ebab..60e96ee947506d5b020ad1ed580a5da0c4e6bdec 100644
--- a/tensorflow/python/summary/writer/writer.py
+++ b/tensorflow/python/summary/writer/writer.py
@@ -325,7 +325,7 @@ class FileWriter(SummaryToEventTransformer):
     ```
 
     The `session` argument to the constructor makes the returned `FileWriter` a
-    a compatibility layer over new graph-based summaries (`tf.contrib.summary`).
+    compatibility layer over new graph-based summaries (`tf.contrib.summary`).
     Crucially, this means the underlying writer resource and events file will
     be shared with any other `FileWriter` using the same `session` and `logdir`,
     and with any `tf.contrib.summary.SummaryWriter` in this session using the
diff --git a/tensorflow/tools/api/generator/BUILD b/tensorflow/python/tools/api/generator/BUILD
similarity index 71%
rename from tensorflow/tools/api/generator/BUILD
rename to tensorflow/python/tools/api/generator/BUILD
index 8c760e6f52598a5e7399c9250adf99283572d3a4..223d1281ba42afdcb72c84c249471d2dff13722d 100644
--- a/tensorflow/tools/api/generator/BUILD
+++ b/tensorflow/python/tools/api/generator/BUILD
@@ -3,8 +3,9 @@
 
 licenses(["notice"])  # Apache 2.0
 
-load("//tensorflow/tools/api/generator:api_gen.bzl", "ESTIMATOR_API_INIT_FILES")
-load("//tensorflow/tools/api/generator:api_gen.bzl", "TENSORFLOW_API_INIT_FILES")
+load("//tensorflow:tensorflow.bzl", "py_test")
+load("//tensorflow/python/tools/api/generator:api_gen.bzl", "ESTIMATOR_API_INIT_FILES")
+load("//tensorflow/python/tools/api/generator:api_gen.bzl", "TENSORFLOW_API_INIT_FILES")
 
 exports_files(
     [
@@ -13,6 +14,18 @@ exports_files(
     ],
 )
 
+py_binary(
+    name = "create_python_api",
+    srcs = ["//tensorflow/python/tools/api/generator:create_python_api.py"],
+    main = "//tensorflow/python/tools/api/generator:create_python_api.py",
+    srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
+    deps = [
+        "//tensorflow/python:no_contrib",
+        "//tensorflow/python/tools/api/generator:doc_srcs",
+    ],
+)
+
 py_library(
     name = "doc_srcs",
     srcs = ["doc_srcs.py"],
diff --git a/tensorflow/tools/api/generator/api_gen.bzl b/tensorflow/python/tools/api/generator/api_gen.bzl
similarity index 69%
rename from tensorflow/tools/api/generator/api_gen.bzl
rename to tensorflow/python/tools/api/generator/api_gen.bzl
index d746b5d3e4f7745d78563eac65ccdf822511a7ef..00e1c4e1996e417343d03e74403ce022975c6f35 100644
--- a/tensorflow/tools/api/generator/api_gen.bzl
+++ b/tensorflow/python/tools/api/generator/api_gen.bzl
@@ -102,36 +102,41 @@ ESTIMATOR_API_INIT_FILES = [
     # END GENERATED ESTIMATOR FILES
 ]
 
-# Creates a genrule that generates a directory structure with __init__.py
-# files that import all exported modules (i.e. modules with tf_export
-# decorators).
-#
-# Args:
-#   name: name of genrule to create.
-#   output_files: List of __init__.py files that should be generated.
-#     This list should include file name for every module exported using
-#     tf_export. For e.g. if an op is decorated with
-#     @tf_export('module1.module2', 'module3'). Then, output_files should
-#     include module1/module2/__init__.py and module3/__init__.py.
-#   root_init_template: Python init file that should be used as template for
-#     root __init__.py file. "# API IMPORTS PLACEHOLDER" comment inside this
-#     template will be replaced with root imports collected by this genrule.
-#   srcs: genrule sources. If passing root_init_template, the template file
-#     must be included in sources.
-#   api_name: Name of the project that you want to generate API files for
-#     (e.g. "tensorflow" or "estimator").
-#   package: Python package containing the @tf_export decorators you want to
-#     process
-#   package_dep: Python library target containing your package.
-
 def gen_api_init_files(
         name,
         output_files = TENSORFLOW_API_INIT_FILES,
         root_init_template = None,
         srcs = [],
         api_name = "tensorflow",
+        api_version = 2,
         package = "tensorflow.python",
-        package_dep = "//tensorflow/python:no_contrib"):
+        package_dep = "//tensorflow/python:no_contrib",
+        output_package = "tensorflow"):
+    """Creates API directory structure and __init__.py files.
+
+    Creates a genrule that generates a directory structure with __init__.py
+    files that import all exported modules (i.e. modules with tf_export
+    decorators).
+
+    Args:
+      name: name of genrule to create.
+      output_files: List of __init__.py files that should be generated.
+        This list should include file name for every module exported using
+        tf_export. For e.g. if an op is decorated with
+        @tf_export('module1.module2', 'module3'). Then, output_files should
+        include module1/module2/__init__.py and module3/__init__.py.
+      root_init_template: Python init file that should be used as template for
+        root __init__.py file. "# API IMPORTS PLACEHOLDER" comment inside this
+        template will be replaced with root imports collected by this genrule.
+      srcs: genrule sources. If passing root_init_template, the template file
+        must be included in sources.
+      api_name: Name of the project that you want to generate API files for
+        (e.g. "tensorflow" or "estimator").
+      api_version: TensorFlow API version to generate. Must be either 1 or 2.
+      package: Python package containing the @tf_export decorators you want to
+        process
+      package_dep: Python library target containing your package.
+    """
     root_init_template_flag = ""
     if root_init_template:
       root_init_template_flag = "--root_init_template=$(location " + root_init_template + ")"
@@ -139,13 +144,14 @@ def gen_api_init_files(
     api_gen_binary_target = "create_" + package + "_api"
     native.py_binary(
         name = "create_" + package + "_api",
-        srcs = ["//tensorflow/tools/api/generator:create_python_api.py"],
-        main = "//tensorflow/tools/api/generator:create_python_api.py",
+        srcs = ["//tensorflow/python/tools/api/generator:create_python_api.py"],
+        main = "//tensorflow/python/tools/api/generator:create_python_api.py",
         srcs_version = "PY2AND3",
         visibility = ["//visibility:public"],
         deps = [
             package_dep,
-            "//tensorflow/tools/api/generator:doc_srcs",
+            "//tensorflow/python:util",
+            "//tensorflow/python/tools/api/generator:doc_srcs",
         ],
     )
 
@@ -154,7 +160,9 @@ def gen_api_init_files(
         outs = output_files,
         cmd = (
             "$(location :" + api_gen_binary_target + ") " +
-            root_init_template_flag + " --apidir=$(@D) --apiname=" + api_name + " --package=" + package + " $(OUTS)"),
+            root_init_template_flag + " --apidir=$(@D) --apiname=" +
+            api_name + " --apiversion=" + str(api_version) + " --package=" + package +
+            " --output_package=" + output_package + " $(OUTS)"),
         srcs = srcs,
         tools = [":" + api_gen_binary_target ],
         visibility = ["//tensorflow:__pkg__"],
diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/python/tools/api/generator/create_python_api.py
similarity index 89%
rename from tensorflow/tools/api/generator/create_python_api.py
rename to tensorflow/python/tools/api/generator/create_python_api.py
index 48d7dcd09eb38f53031afde70fe2e1a9b660ad1a..863c922216fa275fa8a9dda04a212a32a57551c0 100644
--- a/tensorflow/tools/api/generator/create_python_api.py
+++ b/tensorflow/python/tools/api/generator/create_python_api.py
@@ -24,11 +24,12 @@ import importlib
 import os
 import sys
 
+from tensorflow.python.tools.api.generator import doc_srcs
 from tensorflow.python.util import tf_decorator
 from tensorflow.python.util import tf_export
-from tensorflow.tools.api.generator import doc_srcs
 
 API_ATTRS = tf_export.API_ATTRS
+API_ATTRS_V1 = tf_export.API_ATTRS_V1
 
 _DEFAULT_PACKAGE = 'tensorflow.python'
 _GENFILES_DIR_SUFFIX = 'genfiles/'
@@ -38,14 +39,14 @@ _SYMBOLS_TO_SKIP_EXPLICITLY = {
     'tensorflow.python.platform.flags.FLAGS'
 }
 _GENERATED_FILE_HEADER = """# This file is MACHINE GENERATED! Do not edit.
-# Generated by: tensorflow/tools/api/generator/create_python_api.py script.
+# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
 \"\"\"%s
 \"\"\"
 
 from __future__ import print_function
 
 """
-_GENERATED_FILE_FOOTER = "\n\ndel print_function\n"
+_GENERATED_FILE_FOOTER = '\n\ndel print_function\n'
 
 
 class SymbolExposedTwiceError(Exception):
@@ -159,13 +160,16 @@ __all__.remove('print_function')
     return module_text_map
 
 
-def get_api_init_text(package, api_name):
+def get_api_init_text(package, output_package, api_name, api_version):
   """Get a map from destination module to __init__.py code for that module.
 
   Args:
     package: Base python package containing python with target tf_export
       decorators.
+    output_package: Base output python package where generated API will
+      be added.
     api_name: API you want to generate (e.g. `tensorflow` or `estimator`).
+    api_version: API version you want to generate (`v1` or `v2`).
 
   Returns:
     A dictionary where
@@ -173,6 +177,12 @@ def get_api_init_text(package, api_name):
       value: (string) text that should be in __init__.py files for
         corresponding modules.
   """
+  if api_version == 1:
+    names_attr = API_ATTRS_V1[api_name].names
+    constants_attr = API_ATTRS_V1[api_name].constants
+  else:
+    names_attr = API_ATTRS[api_name].names
+    constants_attr = API_ATTRS[api_name].constants
   module_code_builder = _ModuleInitCodeBuilder()
 
   # Traverse over everything imported above. Specifically,
@@ -193,7 +203,7 @@ def get_api_init_text(package, api_name):
       attr = getattr(module, module_contents_name)
 
       # If attr is _tf_api_constants attribute, then add the constants.
-      if module_contents_name == API_ATTRS[api_name].constants:
+      if module_contents_name == constants_attr:
         for exports, value in attr:
           for export in exports:
             names = export.split('.')
@@ -205,9 +215,8 @@ def get_api_init_text(package, api_name):
       _, attr = tf_decorator.unwrap(attr)
       # If attr is a symbol with _tf_api_names attribute, then
       # add import for it.
-      if (hasattr(attr, '__dict__') and
-          API_ATTRS[api_name].names in attr.__dict__):
-        for export in getattr(attr, API_ATTRS[api_name].names):  # pylint: disable=protected-access
+      if (hasattr(attr, '__dict__') and names_attr in attr.__dict__):
+        for export in getattr(attr, names_attr):  # pylint: disable=protected-access
           names = export.split('.')
           dest_module = '.'.join(names[:-1])
           module_code_builder.add_import(
@@ -218,7 +227,6 @@ def get_api_init_text(package, api_name):
   # For e.g. if we import 'foo.bar.Value'. Then, we also
   # import 'bar' in 'foo'.
   imported_modules = set(module_code_builder.module_imports.keys())
-  import_from = '.'
   for module in imported_modules:
     if not module:
       continue
@@ -229,6 +237,9 @@ def get_api_init_text(package, api_name):
       if submodule_index > 0:
         parent_module += ('.' + module_split[submodule_index-1] if parent_module
                           else module_split[submodule_index-1])
+      import_from = output_package
+      if submodule_index > 0:
+        import_from += '.' + '.'.join(module_split[:submodule_index])
       module_code_builder.add_import(
           -1, parent_module, import_from,
           module_split[submodule_index], module_split[submodule_index])
@@ -294,7 +305,8 @@ def get_module_docstring(module_name, package, api_name):
 
 
 def create_api_files(
-    output_files, package, root_init_template, output_dir, api_name):
+    output_files, package, root_init_template, output_dir, output_package,
+    api_name, api_version):
   """Creates __init__.py files for the Python API.
 
   Args:
@@ -306,7 +318,9 @@ def create_api_files(
       "#API IMPORTS PLACEHOLDER" comment in the template file will be replaced
       with imports.
     output_dir: output API root directory.
+    output_package: Base output package where generated API will be added.
     api_name: API you want to generate (e.g. `tensorflow` or `estimator`).
+    api_version: API version to generate (`v1` or `v2`).
 
   Raises:
     ValueError: if an output file is not under api/ directory,
@@ -323,7 +337,8 @@ def create_api_files(
       os.makedirs(os.path.dirname(file_path))
     open(file_path, 'a').close()
 
-  module_text_map = get_api_init_text(package, api_name)
+  module_text_map = get_api_init_text(
+      package, output_package, api_name, api_version)
 
   # Add imports to output files.
   missing_output_files = []
@@ -381,6 +396,13 @@ def main():
       '--apiname', required=True, type=str,
       choices=API_ATTRS.keys(),
       help='The API you want to generate.')
+  parser.add_argument(
+      '--apiversion', default=2, type=int,
+      choices=[1, 2],
+      help='The API version you want to generate.')
+  parser.add_argument(
+      '--output_package', default='tensorflow', type=str,
+      help='Root output package.')
 
   args = parser.parse_args()
 
@@ -395,7 +417,8 @@ def main():
   # Populate `sys.modules` with modules containing tf_export().
   importlib.import_module(args.package)
   create_api_files(outputs, args.package, args.root_init_template,
-                   args.apidir, args.apiname)
+                   args.apidir, args.output_package, args.apiname,
+                   args.apiversion)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/tools/api/generator/create_python_api_test.py b/tensorflow/python/tools/api/generator/create_python_api_test.py
similarity index 90%
rename from tensorflow/tools/api/generator/create_python_api_test.py
rename to tensorflow/python/tools/api/generator/create_python_api_test.py
index 651ec9d040302a4343ae6e0053cf6a4b37a971d4..a565a49d967d3b850058f5370272cfedb43791f4 100644
--- a/tensorflow/tools/api/generator/create_python_api_test.py
+++ b/tensorflow/python/tools/api/generator/create_python_api_test.py
@@ -22,8 +22,8 @@ import imp
 import sys
 
 from tensorflow.python.platform import test
+from tensorflow.python.tools.api.generator import create_python_api
 from tensorflow.python.util.tf_export import tf_export
-from tensorflow.tools.api.generator import create_python_api
 
 
 @tf_export('test_op', 'test_op1')
@@ -58,7 +58,8 @@ class CreatePythonApiTest(test.TestCase):
   def testFunctionImportIsAdded(self):
     imports = create_python_api.get_api_init_text(
         package=create_python_api._DEFAULT_PACKAGE,
-        api_name='tensorflow')
+        output_package='tensorflow',
+        api_name='tensorflow', api_version=1)
     expected_import = (
         'from tensorflow.python.test_module '
         'import test_op as test_op1')
@@ -75,7 +76,8 @@ class CreatePythonApiTest(test.TestCase):
   def testClassImportIsAdded(self):
     imports = create_python_api.get_api_init_text(
         package=create_python_api._DEFAULT_PACKAGE,
-        api_name='tensorflow')
+        output_package='tensorflow',
+        api_name='tensorflow', api_version=2)
     expected_import = ('from tensorflow.python.test_module '
                        'import TestClass')
     self.assertTrue(
@@ -85,7 +87,8 @@ class CreatePythonApiTest(test.TestCase):
   def testConstantIsAdded(self):
     imports = create_python_api.get_api_init_text(
         package=create_python_api._DEFAULT_PACKAGE,
-        api_name='tensorflow')
+        output_package='tensorflow',
+        api_name='tensorflow', api_version=1)
     expected = ('from tensorflow.python.test_module '
                 'import _TEST_CONSTANT')
     self.assertTrue(expected in str(imports),
diff --git a/tensorflow/tools/api/generator/doc_srcs.py b/tensorflow/python/tools/api/generator/doc_srcs.py
similarity index 100%
rename from tensorflow/tools/api/generator/doc_srcs.py
rename to tensorflow/python/tools/api/generator/doc_srcs.py
diff --git a/tensorflow/tools/api/generator/doc_srcs_test.py b/tensorflow/python/tools/api/generator/doc_srcs_test.py
similarity index 95%
rename from tensorflow/tools/api/generator/doc_srcs_test.py
rename to tensorflow/python/tools/api/generator/doc_srcs_test.py
index dbff904abe6251ad180140c4c7c404f051b17d55..481d9874a4bcdcdadcdcb16b5b5c1b10b765dc48 100644
--- a/tensorflow/tools/api/generator/doc_srcs_test.py
+++ b/tensorflow/python/tools/api/generator/doc_srcs_test.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # =============================================================================
-"""Tests for tensorflow.tools.api.generator.doc_srcs."""
+"""Tests for tensorflow.python.tools.api.generator.doc_srcs."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -23,7 +23,7 @@ import importlib
 import sys
 
 from tensorflow.python.platform import test
-from tensorflow.tools.api.generator import doc_srcs
+from tensorflow.python.tools.api.generator import doc_srcs
 
 
 FLAGS = None
diff --git a/tensorflow/python/tools/freeze_graph.py b/tensorflow/python/tools/freeze_graph.py
index e9f1def48c462dcd8a5acf0e3d29d562cd1b3d58..4349699a94c707bdb8b5164efbdccc123894dbdf 100644
--- a/tensorflow/python/tools/freeze_graph.py
+++ b/tensorflow/python/tools/freeze_graph.py
@@ -38,6 +38,7 @@ from __future__ import division
 from __future__ import print_function
 
 import argparse
+import re
 import sys
 
 from google.protobuf import text_format
@@ -116,16 +117,43 @@ def freeze_graph_with_def_protos(input_graph_def,
       var_list = {}
       reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint)
       var_to_shape_map = reader.get_variable_to_shape_map()
+
+      # List of all partition variables. Because the condition is heuristic
+      # based, the list could include false positives.
+      all_parition_variable_names = [
+          tensor.name.split(":")[0]
+          for op in sess.graph.get_operations()
+          for tensor in op.values()
+          if re.search(r"/part_\d+/", tensor.name)
+      ]
+      has_partition_var = False
+
       for key in var_to_shape_map:
         try:
           tensor = sess.graph.get_tensor_by_name(key + ":0")
+          if any(key in name for name in all_parition_variable_names):
+            has_partition_var = True
         except KeyError:
           # This tensor doesn't exist in the graph (for example it's
           # 'global_step' or a similar housekeeping element) so skip it.
           continue
         var_list[key] = tensor
-      saver = saver_lib.Saver(
-          var_list=var_list, write_version=checkpoint_version)
+
+      try:
+        saver = saver_lib.Saver(
+            var_list=var_list, write_version=checkpoint_version)
+      except TypeError as e:
+        # `var_list` is required to be a map of variable names to Variable
+        # tensors. Partition variables are Identity tensors that cannot be
+        # handled by Saver.
+        if has_partition_var:
+          print("Models containing partition variables cannot be converted "
+                "from checkpoint files. Please pass in a SavedModel using "
+                "the flag --input_saved_model_dir.")
+          return -1
+        else:
+          raise e
+
       saver.restore(sess, input_checkpoint)
       if initializer_nodes:
         sess.run(initializer_nodes.replace(" ", "").split(","))
diff --git a/tensorflow/python/tools/freeze_graph_test.py b/tensorflow/python/tools/freeze_graph_test.py
index 91f0061ebccaebbdbb09f283d9d52d813459f493..e38945fabccfb6a49643cb9d49cff385631e628f 100644
--- a/tensorflow/python/tools/freeze_graph_test.py
+++ b/tensorflow/python/tools/freeze_graph_test.py
@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import os
+import re
 
 from tensorflow.core.example import example_pb2
 from tensorflow.core.framework import graph_pb2
@@ -31,7 +32,10 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn
 from tensorflow.python.ops import parsing_ops
+from tensorflow.python.ops import partitioned_variables
+from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 from tensorflow.python.saved_model import builder as saved_model_builder
@@ -262,6 +266,69 @@ class FreezeGraphTest(test_util.TensorFlowTestCase):
         output = sess.run(output_node, feed_dict={input_node: [example]})
         self.assertNear(feature_value, output, 0.00001)
 
+  def testSinglePartitionedVariable(self):
+    """Ensures partitioned variables fail cleanly with freeze graph."""
+    checkpoint_prefix = os.path.join(self.get_temp_dir(), "saved_checkpoint")
+    checkpoint_state_name = "checkpoint_state"
+    input_graph_name = "input_graph.pb"
+    output_graph_name = "output_graph.pb"
+
+    # Create a graph with partition variables. When weights are partitioned into
+    # a single partition, the weights variable is followed by a identity ->
+    # identity (an additional identity node).
+    partitioner = partitioned_variables.fixed_size_partitioner(1)
+    with ops.Graph().as_default():
+      with variable_scope.variable_scope("part", partitioner=partitioner):
+        batch_size, height, width, depth = 5, 128, 128, 3
+        input1 = array_ops.zeros(
+            (batch_size, height, width, depth), name="input1")
+        input2 = array_ops.zeros(
+            (batch_size, height, width, depth), name="input2")
+
+        num_nodes = depth
+        filter1 = variable_scope.get_variable("filter", [num_nodes, num_nodes])
+        filter2 = array_ops.reshape(filter1, [1, 1, num_nodes, num_nodes])
+        conv = nn.conv2d(
+            input=input1, filter=filter2, strides=[1, 1, 1, 1], padding="SAME")
+        node = math_ops.add(conv, input2, name="test/add")
+        node = nn.relu6(node, name="test/relu6")
+
+      # Save graph and checkpoints.
+      sess = session.Session()
+      sess.run(variables.global_variables_initializer())
+
+      saver = saver_lib.Saver()
+      checkpoint_path = saver.save(
+          sess,
+          checkpoint_prefix,
+          global_step=0,
+          latest_filename=checkpoint_state_name)
+      graph_io.write_graph(sess.graph, self.get_temp_dir(), input_graph_name)
+
+      # Ensure this graph has partition variables.
+      self.assertTrue([
+          tensor.name.split(":")[0]
+          for op in sess.graph.get_operations()
+          for tensor in op.values()
+          if re.search(r"/part_\d+/", tensor.name)
+      ])
+
+    # Test freezing graph doesn't make it crash.
+    output_node_names = "save/restore_all"
+    output_graph_path = os.path.join(self.get_temp_dir(), output_graph_name)
+
+    return_value = freeze_graph.freeze_graph_with_def_protos(
+        input_graph_def=sess.graph_def,
+        input_saver_def=None,
+        input_checkpoint=checkpoint_path,
+        output_node_names=output_node_names,
+        restore_op_name="save/restore_all",  # default value
+        filename_tensor_name="save/Const:0",  # default value
+        output_graph=output_graph_path,
+        clear_devices=False,
+        initializer_nodes="")
+    self.assertTrue(return_value, -1)
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/training/adam.py b/tensorflow/python/training/adam.py
index b65c88e972454da14dc5161a19cd26280d51d28f..bcbe5907d6370e0c0a268c2ea6a2f10bdf30683e 100644
--- a/tensorflow/python/training/adam.py
+++ b/tensorflow/python/training/adam.py
@@ -109,12 +109,13 @@ class AdamOptimizer(optimizer.Optimizer):
     self._updated_lr = None
 
   def _get_beta_accumulators(self):
-    if context.executing_eagerly():
-      graph = None
-    else:
-      graph = ops.get_default_graph()
-    return (self._get_non_slot_variable("beta1_power", graph=graph),
-            self._get_non_slot_variable("beta2_power", graph=graph))
+    with ops.init_scope():
+      if context.executing_eagerly():
+        graph = None
+      else:
+        graph = ops.get_default_graph()
+      return (self._get_non_slot_variable("beta1_power", graph=graph),
+              self._get_non_slot_variable("beta2_power", graph=graph))
 
   def _create_slots(self, var_list):
     # Create the beta1 and beta2 accumulators on the same device as the first
diff --git a/tensorflow/python/training/adam_test.py b/tensorflow/python/training/adam_test.py
index ccdc7e384da2ae792a681298c7076fc582d362df..8f844276540bf5b5cc9a61a3cb072b3cfa9cfa7a 100644
--- a/tensorflow/python/training/adam_test.py
+++ b/tensorflow/python/training/adam_test.py
@@ -315,6 +315,12 @@ class AdamOptimizerTest(test.TestCase):
 
   def testTwoSessions(self):
     optimizer = adam.AdamOptimizer()
+
+    with context.eager_mode():
+      var0 = variables.Variable(np.array([1.0, 2.0]), name="v0")
+      grads0 = constant_op.constant(np.array([0.1, 0.1]))
+      optimizer.apply_gradients([(grads0, var0)])
+
     g = ops.Graph()
     with g.as_default():
       with session.Session():
diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py
index 5b372e82b3f637b78db4388b58b8d04a838fbe60..a052081630f34fe28e4d650e1752cd723fa65731 100644
--- a/tensorflow/python/training/checkpoint_utils.py
+++ b/tensorflow/python/training/checkpoint_utils.py
@@ -24,11 +24,11 @@ from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import io_ops
 from tensorflow.python.ops import resource_variable_ops
-from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import gfile
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import distribute as distribute_lib
 from tensorflow.python.training import saver
 from tensorflow.python.util.tf_export import tf_export
 
@@ -179,6 +179,16 @@ def init_from_checkpoint(ckpt_dir_or_file, assignment_map):
     tf.errors.OpError: If missing checkpoints or tensors in checkpoints.
     ValueError: If missing variables in current graph.
   """
+  if distribute_lib.get_cross_tower_context():
+    _init_from_checkpoint(None, ckpt_dir_or_file, assignment_map)
+  else:
+    distribute_lib.get_tower_context().merge_call(
+        _init_from_checkpoint, ckpt_dir_or_file, assignment_map)
+
+
+def _init_from_checkpoint(_, ckpt_dir_or_file, assignment_map):
+  """See `init_from_checkpoint` for documentation."""
+
   ckpt_file = _get_checkpoint_filename(ckpt_dir_or_file)
   reader = load_checkpoint(ckpt_dir_or_file)
   variable_map = reader.get_variable_to_shape_map()
@@ -187,10 +197,9 @@ def init_from_checkpoint(ckpt_dir_or_file, assignment_map):
     var = None
     # Check if this is Variable object or list of Variable objects (in case of
     # partitioned variables).
-    is_var = lambda x: isinstance(x, variables.Variable)
-    if is_var(current_var_or_name) or (
+    if _is_variable(current_var_or_name) or (
         isinstance(current_var_or_name, list)
-        and all(is_var(v) for v in current_var_or_name)):
+        and all(_is_variable(v) for v in current_var_or_name)):
       var = current_var_or_name
     else:
       store_vars = vs._get_default_variable_store()._vars  # pylint:disable=protected-access
@@ -205,7 +214,7 @@ def init_from_checkpoint(ckpt_dir_or_file, assignment_map):
         raise ValueError("Tensor %s is not found in %s checkpoint %s" % (
             tensor_name_in_ckpt, ckpt_dir_or_file, variable_map
         ))
-      if is_var(var):
+      if _is_variable(var):
         # Additional at-call-time checks.
         if not var.get_shape().is_compatible_with(
             variable_map[tensor_name_in_ckpt]):
@@ -297,13 +306,21 @@ def _set_checkpoint_initializer(variable,
   with ops.device(variable.device), ops.device("/cpu:0"):
     restore_op = io_ops.restore_v2(
         ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0]
-    if isinstance(variable, resource_variable_ops.ResourceVariable):
-      init_op = variable.assign(restore_op, read_value=False)
-    else:
-      init_op = state_ops.assign(variable, restore_op)
-    variable._initializer_op = init_op  # pylint:disable=protected-access
+
+    names_to_saveables = saver.BaseSaverBuilder.OpListToDict([variable])
+    saveable_objects = []
+    for name, op in names_to_saveables.items():
+      for s in saver.BaseSaverBuilder.SaveableObjectsForOp(op, name):
+        saveable_objects.append(s)
+
+    assert len(saveable_objects) == 1  # Should be only one variable.
+    init_op = saveable_objects[0].restore([restore_op], restored_shapes=None)
+
+    # pylint:disable=protected-access
+    variable._initializer_op = init_op
     restore_op.set_shape(variable.shape)
-    variable._initial_value = restore_op  # pylint:disable=protected-access
+    variable._initial_value = restore_op
+    # pylint:enable=protected-access
 
 
 def _set_variable_or_list_initializer(variable_or_list, ckpt_file,
@@ -337,6 +354,11 @@ def _set_variable_or_list_initializer(variable_or_list, ckpt_file,
     _set_checkpoint_initializer(variable_or_list, ckpt_file, tensor_name, "")
 
 
+def _is_variable(x):
+  return (isinstance(x, variables.Variable) or
+          resource_variable_ops.is_resource_variable(x))
+
+
 def _collect_partitioned_variable(name, all_vars):
   """Returns list of `tf.Variable` that comprise the partitioned variable."""
   if name + "/part_0" in all_vars:
diff --git a/tensorflow/python/training/checkpoint_utils_test.py b/tensorflow/python/training/checkpoint_utils_test.py
index 4e08a1c859fbaac75e7cd09ad498d9fea14c6338..1c1f126ce94614ae02d701a4a15c8956e3dd4f2f 100644
--- a/tensorflow/python/training/checkpoint_utils_test.py
+++ b/tensorflow/python/training/checkpoint_utils_test.py
@@ -386,7 +386,9 @@ class CheckpointsTest(test.TestCase):
         op for op in g.get_operations()
         if (op.name.startswith("init_from_checkpoint/") and
             not op.name.startswith("init_from_checkpoint/checkpoint_initializer"
-                                  ) and op.type != "AssignVariableOp")
+                                  ) and
+            op.type != "AssignVariableOp" and
+            op.type != "Identity")
     ]
     self.assertEqual(ops_in_init_from_checkpoint_scope, [])
 
diff --git a/tensorflow/python/training/checkpointable/base.py b/tensorflow/python/training/checkpointable/base.py
index ee35b01328436911fd7926b25b14433377ec4188..66837ee52fd6d1b6b2bb98b82a0b2f293879c7e0 100644
--- a/tensorflow/python/training/checkpointable/base.py
+++ b/tensorflow/python/training/checkpointable/base.py
@@ -144,7 +144,7 @@ class _CheckpointPosition(object):
         # process deferred restorations for it and its dependencies.
         restore_ops = checkpointable._restore_from_checkpoint_position(self)  # pylint: disable=protected-access
         if restore_ops:
-          self._checkpoint.restore_ops.extend(restore_ops)
+          self._checkpoint.new_restore_ops(restore_ops)
 
   def bind_object(self, checkpointable):
     """Set a checkpoint<->object correspondence and process slot variables.
@@ -501,12 +501,6 @@ class CheckpointableBase(object):
       ValueError: If the variable name is not unique.
     """
     self._maybe_initialize_checkpointable()
-    if overwrite and self._lookup_dependency(name) is not None:
-      raise ValueError(
-          ("A variable named '%s' already exists in this Checkpointable, but "
-           "Checkpointable._add_variable called to create another with "
-           "that name. Variable names must be unique within a Checkpointable "
-           "object.") % (name,))
     with ops.init_scope():
       if context.executing_eagerly():
         # If this is a variable with a single Tensor stored in the checkpoint,
diff --git a/tensorflow/python/training/checkpointable/base_test.py b/tensorflow/python/training/checkpointable/base_test.py
index 950e9c5b535a8314e1068b772f48a14b572df691..fd935ac559ed7cd607145e7b2433a00c1f8431ea 100644
--- a/tensorflow/python/training/checkpointable/base_test.py
+++ b/tensorflow/python/training/checkpointable/base_test.py
@@ -16,8 +16,11 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import variable_scope
 from tensorflow.python.platform import test
 from tensorflow.python.training.checkpointable import base
+from tensorflow.python.training.checkpointable import util
 
 
 class InterfaceTests(test.TestCase):
@@ -37,5 +40,22 @@ class InterfaceTests(test.TestCase):
     self.assertIs(duplicate_name_dep, current_dependency)
     self.assertEqual("leaf", current_name)
 
+  def testAddVariableOverwrite(self):
+    root = base.CheckpointableBase()
+    a = root._add_variable_with_custom_getter(
+        name="v", shape=[], getter=variable_scope.get_variable)
+    self.assertEqual([root, a], util.list_objects(root))
+    with ops.Graph().as_default():
+      b = root._add_variable_with_custom_getter(
+          name="v", shape=[], overwrite=True,
+          getter=variable_scope.get_variable)
+      self.assertEqual([root, b], util.list_objects(root))
+    with ops.Graph().as_default():
+      with self.assertRaisesRegexp(
+          ValueError, "already declared as a dependency"):
+        root._add_variable_with_custom_getter(
+            name="v", shape=[], overwrite=False,
+            getter=variable_scope.get_variable)
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/training/checkpointable/data_structures.py b/tensorflow/python/training/checkpointable/data_structures.py
index 019d43f09c10a4975a9b483593af30b5bbe06089..507cda87349cda25012a0170f230637d0a9758bc 100644
--- a/tensorflow/python/training/checkpointable/data_structures.py
+++ b/tensorflow/python/training/checkpointable/data_structures.py
@@ -57,6 +57,8 @@ def _wrap_or_unwrap(value):
     return value.value
   if isinstance(value, base.CheckpointableBase):
     return value  # Skip conversion for already checkpointable objects.
+  elif isinstance(value, dict):
+    return _DictWrapper(value)
   elif isinstance(value, list):
     return _ListWrapper(value)
   else:
@@ -438,12 +440,15 @@ class Mapping(CheckpointableDataStructure, collections.Mapping):
   def __init__(self, *args, **kwargs):
     """Construct a new sequence. Arguments are passed to `dict()`."""
     super(Mapping, self).__init__()
-    self._storage = dict(*args, **kwargs)
+    self._storage = self._make_storage(*args, **kwargs)
     self._storage.update(
         {key: self._track_value(
             value, name=self._name_element(key))
          for key, value in self._storage.items()})
 
+  def _make_storage(self, *args, **kwargs):
+    return dict(*args, **kwargs)
+
   def _name_element(self, key):
     if not isinstance(key, six.string_types):
       raise TypeError(
@@ -476,3 +481,185 @@ class Mapping(CheckpointableDataStructure, collections.Mapping):
 
   def __iter__(self):
     return iter(self._storage)
+
+
+# Unlike _ListWrapper, having _DictWrapper inherit from dict and pass isinstance
+# checks seems infeasible. CPython will not call Python methods/properties on
+# dictionary subclasses when running e.g. {}.update(dict_subclass), and instead
+# collects elements directly from dict_subclass's C structs. So subclassing dict
+# implies that the storage has to be "self" (i.e. the C structs for the object
+# must be updated correctly), but we also need that storage to be the wrapped
+# dictionary to avoid synchronization bugs (un-tracked external modifications
+# should still show up when the dict is accessed through the wrapper). Monkey
+# patching all of the "wrapped" dict's methods instead of creating a wrapper
+# object is an option, but not a very attractive one (replacing methods without
+# creating reference cycles is difficult, and then dicts would need to be
+# special cased everywhere as being checkpointable).
+class _DictWrapper(Mapping, collections.MutableMapping):
+  """Wraps built-in dicts to support restore-on-create for variables.
+
+  _DictWrapper is to Mapping as _ListWrapper is to List. Unlike Mapping,
+  _DictWrapper allows non-string keys and values and arbitrary mutations (delete
+  keys, reassign values). Like _ListWrapper, these mutations mean that
+  _DictWrapper will raise an exception on save.
+  """
+
+  def __new__(cls, *args):
+    if len(args) == 1 and isinstance(args[0], dict):
+      return super(_DictWrapper, cls).__new__(cls)
+    else:
+      # Allow construction from a sequence, e.g. for nest.pack_sequence_as. In
+      # this case there's nothing to wrap, so we make a normal dictionary. Also
+      # allows constructing empty instances of the _DictWrapper type, as Session
+      # is wont to do (and again there's nothing to wrap, so a normal dictionary
+      # makes more sense).
+      return dict(*args)
+
+  def __init__(self, wrapped_dict):
+    self._non_string_key = False
+    self._non_append_mutation = False
+    self._external_modification = False
+    super(_DictWrapper, self).__init__(wrapped_dict)
+    self._update_snapshot()
+
+  def _make_storage(self, wrapped_dict):
+    """Re-use the wrapped dict for storage (to force them to be in sync)."""
+    return wrapped_dict
+
+  @property
+  def _checkpoint_dependencies(self):
+    """Check that the object is saveable before listing its dependencies."""
+    self._check_external_modification()
+    if self._non_string_key:
+      raise ValueError(
+          "Unable to save the object %s (a dictionary wrapper constructed "
+          "automatically on attribute assignment). The wrapped dictionary "
+          "contains a non-string key which maps to a checkpointable object or "
+          "mutable data structure.\n\nIf you don't need this dictionary "
+          "checkpointed, wrap it in a tf.contrib.checkpoint.NoDependency "
+          "object; it will be automatically un-wrapped and subsequently "
+          "ignored." % (self,))
+    if self._non_append_mutation:
+      raise ValueError(
+          "Unable to save the object %s (a dictionary wrapper constructed "
+          "automatically on attribute assignment). A key mapping to a "
+          "checkpointable object was overwritten or deleted, which would "
+          "cause problems for restoration.\n\nIf you don't need this "
+          "dictionary checkpointed, wrap it in a "
+          "tf.contrib.checkpoint.NoDependency object; it will be automatically "
+          "un-wrapped and subsequently ignored." % (self,))
+    if self._external_modification:
+      raise ValueError(
+          "Unable to save the object %s (a dictionary wrapper constructed "
+          "automatically on attribute assignment). The wrapped dictionary was "
+          "modified outside the wrapper (its final value was %s, its value "
+          "when a checkpoint dependency was added was %s), which breaks "
+          "restoration on object creation.\n\nIf you don't need this "
+          "dictionary checkpointed, wrap it in a "
+          "tf.contrib.checkpoint.NoDependency object; it will be automatically "
+          "un-wrapped and subsequently ignored." % (
+              self, self, self._last_wrapped_dict_snapshot))
+    assert not self._dirty  # Any reason for dirtiness should have an exception.
+    return super(_DictWrapper, self)._checkpoint_dependencies
+
+  @property
+  def _dirty(self):
+    """Check if there has already been a mutation which prevents saving."""
+    return (self._external_modification
+            or self._non_append_mutation
+            or self._non_string_key)
+
+  def _check_external_modification(self):
+    """Checks for any changes to the wrapped dict not through the wrapper."""
+    if self._dirty:
+      return
+    if self != self._last_wrapped_dict_snapshot:
+      self._external_modification = True
+      self._last_wrapped_dict_snapshot = None
+
+  def _update_snapshot(self):
+    """Acknowledges tracked changes to the wrapped dict."""
+    if self._dirty:
+      return
+    self._last_wrapped_dict_snapshot = dict(self)
+
+  def _track_value(self, value, name):
+    """Allows storage of non-checkpointable objects."""
+    if isinstance(name, six.string_types):
+      string_key = True
+    else:
+      name = "-non_string_key"
+      string_key = False
+    try:
+      no_dependency = isinstance(value, NoDependency)
+      value = super(_DictWrapper, self)._track_value(value=value, name=name)
+      if not (string_key or no_dependency):
+        # A non-string key maps to a checkpointable value. This data structure
+        # is not saveable.
+        self._non_string_key = True
+      return value
+    except ValueError:
+      # Even if this value isn't checkpointable, we need to make sure
+      # NoDependency objects get unwrapped.
+      return sticky_attribute_assignment(
+          checkpointable=self, value=value, name=name)
+
+  def _name_element(self, key):
+    """Don't throw errors for non-string keys."""
+    if isinstance(key, six.string_types):
+      return super(_DictWrapper, self)._name_element(key)
+    else:
+      return key
+
+  def __setitem__(self, key, value):
+    """Allow any modifications, but possibly mark the wrapper as unsaveable."""
+    self._check_external_modification()
+    no_dep = isinstance(value, NoDependency)
+    if isinstance(key, six.string_types):
+      existing_dependency = self._lookup_dependency(key)
+      value = self._track_value(value, name=key)
+    else:
+      value = _wrap_or_unwrap(value)
+      existing_dependency = None
+      if not no_dep and isinstance(value, base.CheckpointableBase):
+        # Non-string keys are OK as long as we have no reason to add a
+        # dependency on the value (either because the value is not
+        # checkpointable, or because it was wrapped in a NoDependency object).
+        self._non_string_key = True
+    current_value = self._storage.setdefault(key, value)
+    if current_value is not value:
+      if ((not no_dep and isinstance(value, base.CheckpointableBase))
+          # We don't want to just check that the existing object is
+          # checkpointable, since it may have been wrapped in a NoDependency
+          # object.
+          or existing_dependency is not None):
+        # A checkpointable object was replaced under the same key; this means
+        # that restoring would be error-prone, so we'll throw an exception on
+        # save.
+        self._non_append_mutation = True
+      self._storage[key] = value
+
+    self._update_snapshot()
+
+  def __delitem__(self, key):
+    self._check_external_modification()
+    existing_value = self[key]
+    if isinstance(existing_value, base.CheckpointableBase):
+      # Deleting tracked checkpointable values means restoring is problematic,
+      # so we'll throw an exception on save.
+      self._non_append_mutation = True
+    del self._storage[key]
+    self._update_snapshot()
+
+  def __repr__(self):
+    return "DictWrapper(%s)" % (repr(self._storage),)
+
+  def __hash__(self):
+    raise TypeError("unhashable type: 'DictWrapper'")
+
+  def __eq__(self, other):
+    return self._storage == getattr(other, "_storage", other)
+
+  def update(self, *args, **kwargs):
+    for key, value in dict(*args, **kwargs).items():
+      self[key] = value
diff --git a/tensorflow/python/training/checkpointable/data_structures_test.py b/tensorflow/python/training/checkpointable/data_structures_test.py
index ec8c9da8090c968e8931f96949f5b982dd94f215..472b7c32b48ac02d6719a59a6020e97ff9c46cc2 100644
--- a/tensorflow/python/training/checkpointable/data_structures_test.py
+++ b/tensorflow/python/training/checkpointable/data_structures_test.py
@@ -19,6 +19,7 @@ from __future__ import print_function
 import os
 
 import numpy
+import six
 
 from tensorflow.python.eager import context
 from tensorflow.python.eager import test
@@ -32,6 +33,7 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.training.checkpointable import data_structures
 from tensorflow.python.training.checkpointable import tracking
+from tensorflow.python.training.checkpointable import util
 
 
 class HasList(training.Model):
@@ -72,11 +74,14 @@ class ListTests(test.TestCase):
     model = HasList()
     output = model(array_ops.ones([32, 2]))
     self.assertAllEqual([32, 12], output.shape)
-    self.assertEqual(2, len(model.layers))
-    self.assertIs(model.layer_list, model.layers[0])
-    self.assertEqual(10, len(model.layers[0].layers))
+    self.assertEqual(11, len(model.layers))
+    self.assertEqual(10, len(model.layer_list.layers))
+    six.assertCountEqual(
+        self,
+        model.layers,
+        model.layer_list.layers + model.layers_with_updates)
     for index in range(10):
-      self.assertEqual(3 + index, model.layers[0].layers[index].units)
+      self.assertEqual(3 + index, model.layer_list.layers[index].units)
     self.assertEqual(2, len(model._checkpoint_dependencies))
     self.assertIs(model.layer_list, model._checkpoint_dependencies[0].ref)
     self.assertIs(model.layers_with_updates,
@@ -123,9 +128,11 @@ class ListTests(test.TestCase):
         self.l2 = []
 
     model = HasEqualContainers()
-    model.l1.append(HasEqualContainers())
-    model.l2.append(HasEqualContainers())
-    self.assertEqual([model.l1, model.l2], model.layers)
+    first_layer = HasEqualContainers()
+    model.l1.append(first_layer)
+    second_layer = HasEqualContainers()
+    model.l2.append(second_layer)
+    self.assertEqual([first_layer, second_layer], model.layers)
 
   def testNotCheckpointable(self):
     class NotCheckpointable(object):
@@ -260,9 +267,8 @@ class MappingTests(test.TestCase):
     model = HasMapping()
     output = model(array_ops.ones([32, 2]))
     self.assertAllEqual([32, 7], output.shape)
-    self.assertEqual(1, len(model.layers))
-    self.assertIs(model.layer_dict, model.layers[0])
-    self.assertEqual(3, len(model.layers[0].layers))
+    self.assertEqual(5, len(model.layers))
+    six.assertCountEqual(self, model.layers, model.layer_dict.layers)
     self.assertEqual(1, len(model._checkpoint_dependencies))
     self.assertIs(model.layer_dict, model._checkpoint_dependencies[0].ref)
     self.evaluate([v.initializer for v in model.variables])
@@ -298,6 +304,124 @@ class MappingTests(test.TestCase):
                         data_structures.Mapping()])
     self.assertEqual(2, len(has_mappings))
     self.assertNotIn(data_structures.Mapping(), has_mappings)
+    # In contrast to Mapping, dict wrappers are not hashable
+    a = tracking.Checkpointable()
+    a.d = {}
+    self.assertEqual({}, a.d)
+    self.assertFalse({} != a.d)  # pylint: disable=g-explicit-bool-comparison
+    self.assertNotEqual({1: 2}, a.d)
+    with self.assertRaisesRegexp(TypeError, "unhashable"):
+      set([a.d])
+
+  def testDictWrapperBadKeys(self):
+    a = tracking.Checkpointable()
+    a.d = {}
+    a.d[1] = data_structures.List()
+    model = training.Model()
+    model.sub = a
+    save_path = os.path.join(self.get_temp_dir(), "ckpt")
+    with self.assertRaisesRegexp(ValueError, "non-string key"):
+      model.save_weights(save_path)
+
+  def testDictWrapperNoDependency(self):
+    a = tracking.Checkpointable()
+    a.d = data_structures.NoDependency({})
+    a.d[1] = [3]
+    self.assertEqual([a], util.list_objects(a))
+    model = training.Model()
+    model.sub = a
+    save_path = os.path.join(self.get_temp_dir(), "ckpt")
+    model.save_weights(save_path)
+    model.load_weights(save_path)
+
+  def testNonStringKeyNotCheckpointableValue(self):
+    a = tracking.Checkpointable()
+    a.d = {}
+    a.d["a"] = [3]
+    a.d[1] = data_structures.NoDependency([3])
+    self.assertEqual([a, a.d, a.d["a"]], util.list_objects(a))
+    model = training.Model()
+    model.sub = a
+    save_path = os.path.join(self.get_temp_dir(), "ckpt")
+    model.save_weights(save_path)
+    model.load_weights(save_path)
+
+  def testNonAppendNotCheckpointable(self):
+    # Non-append mutations (deleting or overwriting values) are OK when the
+    # values aren't tracked.
+    a = tracking.Checkpointable()
+    a.d = {}
+    a.d["a"] = [3]
+    a.d[1] = 3
+    a.d[1] = 2
+    self.assertEqual(2, a.d[1])
+    del a.d[1]
+    a.d[2] = data_structures.NoDependency(tracking.Checkpointable())
+    second = tracking.Checkpointable()
+    a.d[2] = data_structures.NoDependency(second)
+    self.assertIs(second, a.d[2])
+    self.assertEqual([a, a.d, a.d["a"]], util.list_objects(a))
+    model = training.Model()
+    model.sub = a
+    save_path = os.path.join(self.get_temp_dir(), "ckpt")
+    model.save_weights(save_path)
+    model.load_weights(save_path)
+
+  def testDelNoSave(self):
+    model = training.Model()
+    model.d = {}
+    model.d["a"] = []
+    del model.d["a"]
+    save_path = os.path.join(self.get_temp_dir(), "ckpt")
+    with self.assertRaisesRegexp(ValueError, "overwritten or deleted"):
+      model.save_weights(save_path)
+
+  def testPopNoSave(self):
+    model = training.Model()
+    model.d = {}
+    model.d["a"] = []
+    model.d.pop("a")
+    save_path = os.path.join(self.get_temp_dir(), "ckpt")
+    with self.assertRaisesRegexp(ValueError, "overwritten or deleted"):
+      model.save_weights(save_path)
+
+  def testExternalModificationNoSave(self):
+    model = training.Model()
+    external_reference = {}
+    model.d = external_reference
+    external_reference["a"] = []
+    save_path = os.path.join(self.get_temp_dir(), "ckpt")
+    with self.assertRaisesRegexp(ValueError, "modified outside the wrapper"):
+      model.save_weights(save_path)
+
+  def testOverwriteNoSave(self):
+    model = training.Model()
+    model.d = {}
+    model.d["a"] = {}
+    model.d["a"] = {}
+    save_path = os.path.join(self.get_temp_dir(), "ckpt")
+    with self.assertRaisesRegexp(ValueError, "overwritten or deleted"):
+      model.save_weights(save_path)
+
+  def testIter(self):
+    model = training.Model()
+    model.d = {1: 3}
+    model.d[1] = 3
+    self.assertEqual([1], list(model.d))
+    new_dict = {}
+    # This update() is super tricky. If the dict wrapper subclasses dict,
+    # CPython will access its storage directly instead of calling any
+    # methods/properties on the object. So the options are either not to
+    # subclass dict (in which case update will call normal iter methods, but the
+    # object won't pass isinstance checks) or to subclass dict and keep that
+    # storage updated (no shadowing all its methods like _ListWrapper).
+    new_dict.update(model.d)
+    self.assertEqual({1: 3}, new_dict)
+
+  def testConstructableFromSequence(self):
+    result = data_structures._DictWrapper([(1, 2), (3, 4)])
+    self.assertIsInstance(result, dict)
+    self.assertEqual({1: 2, 3: 4}, result)
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/training/checkpointable/layer_utils.py b/tensorflow/python/training/checkpointable/layer_utils.py
index 978fcb2252cd4481b8286bdf3afd58b30ce6d665..d65b631fe9b855af20329b35cd3f725004a89822 100644
--- a/tensorflow/python/training/checkpointable/layer_utils.py
+++ b/tensorflow/python/training/checkpointable/layer_utils.py
@@ -32,10 +32,15 @@ def is_layer(obj):
 
 def filter_empty_layer_containers(layer_list):
   """Filter out empty Layer-like containers."""
-  return [layer for layer in layer_list
-          # Filter out only empty Checkpointable data structures. Empty Networks
-          # will still show up in Model.layers.
-          if is_layer(layer) or getattr(layer, "layers", True)]
+  filtered = []
+  for obj in layer_list:
+    if is_layer(obj):
+      filtered.append(obj)
+    else:
+      # Checkpointable data structures will not show up in ".layers" lists, but
+      # the layers they contain will.
+      filtered.extend(obj.layers)
+  return filtered
 
 
 def gather_trainable_weights(trainable, sub_layers, extra_variables):
diff --git a/tensorflow/python/training/checkpointable/tracking_test.py b/tensorflow/python/training/checkpointable/tracking_test.py
index 96da0d6e4720b44815de137c0efdd74645bae0fc..f8d17cd417e4e81fd1e37d21a0a7de1d8ef8d3c4 100644
--- a/tensorflow/python/training/checkpointable/tracking_test.py
+++ b/tensorflow/python/training/checkpointable/tracking_test.py
@@ -19,6 +19,7 @@ from __future__ import print_function
 import os
 
 import numpy
+import six
 
 from tensorflow.python.framework import test_util
 from tensorflow.python.keras.engine import training
@@ -143,6 +144,29 @@ class InterfaceTests(test.TestCase):
     with self.assertRaisesRegexp(ValueError, "A list element was replaced"):
       checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
 
+  @test_util.run_in_graph_and_eager_modes
+  def testDictionariesBasic(self):
+    a = training.Model()
+    b = training.Model()
+    a.attribute = {"b": b}
+    c = training.Model()
+    a.attribute["c"] = []
+    a.attribute["c"].append(c)
+    a_deps = util.list_objects(a)
+    self.assertIn(b, a_deps)
+    self.assertIn(c, a_deps)
+    self.assertIs(b, a.attribute["b"])
+    six.assertCountEqual(
+        self,
+        ["b", "c"],
+        [dep.name for dep in a.attribute._checkpoint_dependencies])
+    self.assertEqual([b, c], a.layers)
+    self.assertEqual([b, c], a.attribute.layers)
+    self.assertEqual([c], a.attribute["c"].layers)
+    checkpoint = util.Checkpoint(a=a)
+    save_path = checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
+    checkpoint.restore(save_path).assert_consumed()
+
   @test_util.run_in_graph_and_eager_modes
   def testNoDepList(self):
     a = training.Model()
@@ -159,12 +183,13 @@ class InterfaceTests(test.TestCase):
   @test_util.run_in_graph_and_eager_modes
   def testAssertions(self):
     a = tracking.Checkpointable()
-    a.l = [numpy.zeros([2, 2])]
-    self.assertAllEqual([numpy.zeros([2, 2])], a.l)
-    self.assertAllClose([numpy.zeros([2, 2])], a.l)
-    nest.map_structure(self.assertAllClose, a.l, [numpy.zeros([2, 2])])
-    a.tensors = [array_ops.ones([2, 2]), array_ops.zeros([3, 3])]
-    self.assertAllClose([numpy.ones([2, 2]), numpy.zeros([3, 3])],
+    a.l = {"k": [numpy.zeros([2, 2])]}
+    self.assertAllEqual(nest.flatten({"k": [numpy.zeros([2, 2])]}),
+                        nest.flatten(a.l))
+    self.assertAllClose({"k": [numpy.zeros([2, 2])]}, a.l)
+    nest.map_structure(self.assertAllClose, a.l, {"k": [numpy.zeros([2, 2])]})
+    a.tensors = {"k": [array_ops.ones([2, 2]), array_ops.zeros([3, 3])]}
+    self.assertAllClose({"k": [numpy.ones([2, 2]), numpy.zeros([3, 3])]},
                         self.evaluate(a.tensors))
 
 if __name__ == "__main__":
diff --git a/tensorflow/python/training/checkpointable/util.py b/tensorflow/python/training/checkpointable/util.py
index 6ae5765b133cc72b67f3d9864d0f67abf33f0648..664b2348c0e44303ea8e297c462383da3e8cf3db 100644
--- a/tensorflow/python/training/checkpointable/util.py
+++ b/tensorflow/python/training/checkpointable/util.py
@@ -101,6 +101,7 @@ class _CheckpointRestoreCoordinator(object):
     # this checkpoint.
     self.restore_ops = []
     self.restore_ops_by_name = {}
+    self.new_restore_ops_callback = None
     # A mapping from optimizer proto ids to lists of slot variables to be
     # restored when the optimizer is tracked. Only includes slot variables whose
     # regular variables have already been created, and only for optimizer
@@ -121,6 +122,11 @@ class _CheckpointRestoreCoordinator(object):
                     slot_variable_id=slot_reference.slot_variable_node_id,
                     slot_name=slot_reference.slot_name))
 
+  def new_restore_ops(self, new_ops):
+    self.restore_ops.extend(new_ops)
+    if self.new_restore_ops_callback:
+      self.new_restore_ops_callback(new_ops)  # pylint: disable=not-callable
+
 
 class _NameBasedRestoreCoordinator(object):
   """Keeps the status of a name-based checkpoint restore."""
@@ -361,24 +367,42 @@ class _ObjectIdentityWeakKeyDictionary(_ObjectIdentityDictionary):
         yield unwrapped
 
 
-class _ObjectIdentityWeakSet(collections.MutableSet):
-  """Like weakref.WeakSet, but compares objects with "is"."""
+class _ObjectIdentitySet(collections.MutableSet):
+  """Like the built-in set, but compares objects with "is"."""
 
-  def __init__(self):
-    self._storage = set()
+  def __init__(self, *args):
+    self._storage = set([self._wrap_key(obj) for obj in list(*args)])
+
+  def _wrap_key(self, key):
+    return _ObjectIdentityWrapper(key)
 
   def __contains__(self, key):
-    return _WeakObjectIdentityWrapper(key) in self._storage
+    return self._wrap_key(key) in self._storage
 
   def discard(self, key):
-    self._storage.discard(_WeakObjectIdentityWrapper(key))
+    self._storage.discard(self._wrap_key(key))
 
   def add(self, key):
-    self._storage.add(_WeakObjectIdentityWrapper(key))
+    self._storage.add(self._wrap_key(key))
+
+  def __len__(self):
+    return len(self._storage)
+
+  def __iter__(self):
+    keys = list(self._storage)
+    for key in keys:
+      yield key.unwrapped
+
+
+class _ObjectIdentityWeakSet(_ObjectIdentitySet):
+  """Like weakref.WeakSet, but compares objects with "is"."""
+
+  def _wrap_key(self, key):
+    return _WeakObjectIdentityWrapper(key)
 
   def __len__(self):
     # Iterate, discarding old weak refs
-    return len(list(self))
+    return len([_ for _ in self])
 
   def __iter__(self):
     keys = list(self._storage)
@@ -747,7 +771,7 @@ def capture_dependencies(template):
           initial_value=initializer,
           name=name,
           **inner_kwargs)
-    if name.startswith(name_prefix):
+    if name is not None and name.startswith(name_prefix):
       scope_stripped_name = name[len(name_prefix) + 1:]
       if not checkpointable_parent:
         return template._add_variable_with_custom_getter(  # pylint: disable=protected-access
@@ -803,6 +827,31 @@ class _LoadStatus(object):
     pass
 
 
+def streaming_restore(status, session=None):
+  """When graph building, runs restore ops as soon as they come in.
+
+  Args:
+    status: A _LoadStatus objects from an object-based saver's
+      restore(). Streaming restore from name-based checkpoints is not currently
+      supported.
+    session: A session to run new restore ops in.
+  """
+  if context.executing_eagerly():
+    # Streaming restore is the default/only behavior when executing eagerly.
+    return
+  if session is None:
+    session = ops.get_default_session()
+  if isinstance(status, NameBasedSaverStatus):
+    raise NotImplementedError(
+        "Streaming restore not supported from name-based checkpoints. File a "
+        "feature request if this limitation bothers you.")
+  status.run_restore_ops(session=session)
+  # pylint: disable=protected-access
+  status._checkpoint.new_restore_ops_callback = (
+      lambda ops: session.run(ops, feed_dict=status._feed_dict))
+  # pylint: enable=protected-access
+
+
 class CheckpointLoadStatus(_LoadStatus):
   """Checks the status of checkpoint loading and manages restore ops.
 
@@ -857,8 +906,8 @@ class CheckpointLoadStatus(_LoadStatus):
     for checkpointable_object in list_objects(self._root_checkpointable):
       self._checkpoint.all_python_objects.add(checkpointable_object)
     unused_python_objects = (
-        set(self._checkpoint.all_python_objects)
-        - set(self._checkpoint.object_by_proto_id.values()))
+        _ObjectIdentitySet(self._checkpoint.all_python_objects)
+        - _ObjectIdentitySet(self._checkpoint.object_by_proto_id.values()))
     if unused_python_objects:
       raise AssertionError(
           ("Some Python objects were not bound to checkpointed values, likely "
@@ -974,11 +1023,13 @@ _DEPRECATED_RESTORE_INSTRUCTIONS = (
     "one this message is coming from) and use that checkpoint in the future.")
 
 
-@deprecation.deprecated(
-    date=None, instructions=_DEPRECATED_RESTORE_INSTRUCTIONS)
 class NameBasedSaverStatus(_LoadStatus):
   """Status for loading a name-based training checkpoint."""
 
+  # Ideally this deprecation decorator would be on the class, but that
+  # interferes with isinstance checks.
+  @deprecation.deprecated(
+      date=None, instructions=_DEPRECATED_RESTORE_INSTRUCTIONS)
   def __init__(self, checkpoint, root_checkpointable):
     self._checkpoint = checkpoint
     self._root_checkpointable = root_checkpointable
diff --git a/tensorflow/python/training/distribute.py b/tensorflow/python/training/distribute.py
index d33fd7376a7244535f7a0f393dd6125b125b8018..c719045c7f8cf3ba7b1a9c0bdb1f610ba8091464 100644
--- a/tensorflow/python/training/distribute.py
+++ b/tensorflow/python/training/distribute.py
@@ -614,48 +614,6 @@ class DistributionStrategy(object):
     # Note: should support "colocate_with" argument.
     raise NotImplementedError("must be implemented in descendants")
 
-  def tower_local_var_scope(self, aggregation):
-    """Inside this scope, new variables will not be mirrored.
-
-    There will still be one component variable per tower, but there is
-    no requirement that they stay in sync. Instead, when saving them
-    or calling `read_var()`, we use the value that results when
-    calling `reduce()` on all the towers' variables.
-
-    Note: tower-local implies not trainable. Instead, it is expected
-    that each tower will directly update (using `assign_add()` or
-    whatever) its local variable instance but only the aggregated
-    value (accessible using `read_var()`) will be exported from the
-    model. When it is acceptable to only aggregate on export, we
-    greatly reduce communication overhead by using tower-local
-    variables.
-
-    Note: All component variables will be initialized to the same
-    value, using the initialization expression from the first tower.
-    The values will match even if the initialization expression uses
-    random numbers.
-
-    Args:
-      aggregation: Indicates how a variable will be aggregated. Accepted values
-        are @{tf.VariableAggregation.SUM}, @{tf.VariableAggregation.MEAN}.
-
-    Returns:
-      A context manager.
-    """
-    # TODO(psv): Remove this after adding support for synchronization and
-    # aggregation parameters in get_variable() and mirrored strategy.
-    def create_tower_local_variable(next_creator, *args, **kwargs):
-      _require_distribution_strategy_scope(self)
-      kwargs["use_resource"] = True
-
-      # Set synchronization to be ON_READ for tower local variables.
-      kwargs["synchronization"] = variable_scope.VariableSynchronization.ON_READ
-      kwargs["aggregation"] = aggregation
-      return next_creator(*args, **kwargs)
-
-    _require_distribution_strategy_scope(self)
-    return variable_scope.variable_creator_scope(create_tower_local_variable)
-
   def read_var(self, v):
     """Reads the value of a variable.
 
@@ -1103,10 +1061,6 @@ class TowerContext(object):
     finally:
       _pop_per_thread_mode()
 
-  def tower_local_var_scope(self, aggregation):
-    """Alias for distribution_strategy.tower_local_var_scope()."""
-    return self._distribution_strategy.tower_local_var_scope(aggregation)
-
   @property
   def is_single_tower(self):
     """Returns whether there is a single tower or multiple."""
@@ -1158,16 +1112,6 @@ class _DefaultDistributionStrategy(DistributionStrategy):
     return _CurrentDistributionContext(
         self, variable_scope.variable_creator_scope(creator))
 
-  def tower_local_var_scope(self, aggregation):
-    """Does not set to resource variables."""
-    def create_tower_local_variable(next_creator, *args, **kwargs):
-      _require_distribution_strategy_scope(self)
-      kwargs["trainable"] = False
-      return next_creator(*args, **kwargs)
-
-    _require_distribution_strategy_scope(self)
-    return variable_scope.variable_creator_scope(create_tower_local_variable)
-
   def colocate_vars_with(self, colocate_with_variable):
     """Does not require `self.scope`."""
     _require_distribution_strategy_scope(self)
diff --git a/tensorflow/python/training/learning_rate_decay.py b/tensorflow/python/training/learning_rate_decay.py
index 51190264e81ad177c56a6864b616aee52d954c43..fd195a7965ab7512728e4e9e9e0c51a00b6ad79d 100644
--- a/tensorflow/python/training/learning_rate_decay.py
+++ b/tensorflow/python/training/learning_rate_decay.py
@@ -356,7 +356,15 @@ def natural_exp_decay(learning_rate,
   The function returns the decayed learning rate.  It is computed as:
 
   ```python
-  decayed_learning_rate = learning_rate * exp(-decay_rate * global_step)
+  decayed_learning_rate = learning_rate * exp(-decay_rate * global_step /
+  decay_step)
+  ```
+
+  or, if `staircase` is `True`, as:
+
+  ```python
+  decayed_learning_rate = learning_rate * exp(-decay_rate * floor(global_step /
+  decay_step))
   ```
 
   Example: decay exponentially with a base of 0.96:
@@ -365,8 +373,10 @@ def natural_exp_decay(learning_rate,
   ...
   global_step = tf.Variable(0, trainable=False)
   learning_rate = 0.1
+  decay_steps = 5
   k = 0.5
-  learning_rate = tf.train.exponential_time_decay(learning_rate, global_step, k)
+  learning_rate = tf.train.natural_exp_decay(learning_rate, global_step,
+                                             decay_steps, k)
 
   # Passing global_step to minimize() will increment it at each step.
   learning_step = (
diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py
index 971ed5c8b5ed3bd78b0d467e5c3fa4b7a72c96a1..f75db080595c6f348fe7e9302041bf19f72a301f 100644
--- a/tensorflow/python/training/optimizer.py
+++ b/tensorflow/python/training/optimizer.py
@@ -77,9 +77,10 @@ def _deduplicate_indexed_slices(values, indices):
 
 
 def _var_key(var):
-  if context.executing_eagerly():
-    return var._unique_id  # pylint: disable=protected-access
-  return (var.op.graph, var.op.name)
+  # TODO(ashankar): Consolidate handling for eager and graph
+  if hasattr(var, "op"):
+    return (var.op.graph, var.op.name)
+  return var._unique_id  # pylint: disable=protected-access
 
 
 class _OptimizableVariable(object):
diff --git a/tensorflow/python/training/quantize_training.i b/tensorflow/python/training/quantize_training.i
index fb5e47efa0259d02df3ccf2e9b1430e027f8fcfb..54d6789616473382cf87abe4f701092bbd4e272f 100644
--- a/tensorflow/python/training/quantize_training.i
+++ b/tensorflow/python/training/quantize_training.i
@@ -73,6 +73,8 @@ def do_quantize_training_on_graphdef(input_graph, num_bits):
 
 do_quantize_training_on_graphdef._tf_api_names = [
     'train.do_quantize_training_on_graphdef']
+do_quantize_training_on_graphdef._tf_api_names_v1 = [
+    'train.do_quantize_training_on_graphdef']
 %}
 
 %unignoreall
diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py
index 1ee975fbe48e8ba724d8f40040b122c5c02aa352..c80cdf03be43f2af8b0247109dc52af3e95c8318 100644
--- a/tensorflow/python/training/saver.py
+++ b/tensorflow/python/training/saver.py
@@ -126,8 +126,10 @@ class BaseSaverBuilder(object):
           def f():
             with ops.device(v.device):
               x = v.read_value()
-            with ops.device("/device:CPU:0"):
-              return array_ops.identity(x)
+              # To allow variables placed on non-CPU devices to be checkpointed,
+              # we copy them to CPU on the same machine first.
+              with ops.device("/device:CPU:0"):
+                return array_ops.identity(x)
           return f
 
         self.handle_op = var.handle
@@ -1923,6 +1925,14 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False,
   execution is enabled.
   @end_compatibility
   """  # pylint: disable=g-doc-exception
+  return _import_meta_graph_with_return_elements(
+      meta_graph_or_file, clear_devices, import_scope, **kwargs)[0]
+
+
+def _import_meta_graph_with_return_elements(
+    meta_graph_or_file, clear_devices=False, import_scope=None,
+    return_elements=None, **kwargs):
+  """Import MetaGraph, and return both a saver and returned elements."""
   if context.executing_eagerly():
     raise RuntimeError("Exporting/importing meta graphs is not supported when "
                        "eager execution is enabled. No graph exists when eager "
@@ -1932,12 +1942,22 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False,
   else:
     meta_graph_def = meta_graph_or_file
 
-  imported_vars = meta_graph.import_scoped_meta_graph(
-      meta_graph_def,
-      clear_devices=clear_devices,
-      import_scope=import_scope,
-      **kwargs)
+  imported_vars, imported_return_elements = (
+      meta_graph.import_scoped_meta_graph_with_return_elements(
+          meta_graph_def,
+          clear_devices=clear_devices,
+          import_scope=import_scope,
+          return_elements=return_elements,
+          **kwargs))
+
+  saver = _create_saver_from_imported_meta_graph(
+      meta_graph_def, import_scope, imported_vars)
+  return saver, imported_return_elements
+
 
+def _create_saver_from_imported_meta_graph(
+    meta_graph_def, import_scope, imported_vars):
+  """Return a saver for restoring variable values to an imported MetaGraph."""
   if meta_graph_def.HasField("saver_def"):
     # Infer the scope that is prepended by `import_scoped_meta_graph`.
     scope = import_scope
diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py
index ae9c244aaf372dcbcf365cf3e6a21ae77d9ae7d0..204e81dda0f5a252fca874f82f0078b536624946 100644
--- a/tensorflow/python/training/saver_test.py
+++ b/tensorflow/python/training/saver_test.py
@@ -174,6 +174,24 @@ class SaverTest(test.TestCase):
   def testResourceBasic(self):
     self.basicSaveRestore(resource_variable_ops.ResourceVariable)
 
+  def testResourceColocation(self):
+    partitioner = partitioned_variables.fixed_size_partitioner(num_shards=2)
+    with ops_lib.device("/job:ps/device:GPU:0"):
+      v = variable_scope.get_variable("v0",
+                                      shape=[10, 2],
+                                      partitioner=partitioner,
+                                      use_resource=True)
+    saver_module.Saver({"v0": v}).build()
+    save_op = None
+    for op in ops_lib.get_default_graph().get_operations():
+      if op.type == "SaveV2":
+        save_op = op
+        break
+    assert save_op is not None
+    for save_inp in save_op.inputs[3:]:
+      # Input to SaveV2 op is placed on CPU of the same device as the Variable.
+      self.assertEqual("/job:ps/device:CPU:0", save_inp.device)
+
   def testResourceVariableReadOpsAddedDeterministically(self):
     graph_defs = []
     num_graphs = 10
@@ -768,6 +786,37 @@ class SaverTest(test.TestCase):
       self.assertEqual(20.0, v1.eval())
       save.save(sess, save_path)
 
+  # Test restoring large tensors (triggers a thread pool)
+  def testRestoreLargeTensors(self):
+    save_dir = self.get_temp_dir()
+    def _model():
+      small_v = [variable_scope.get_variable(
+          "small%d" % i, shape=[10, 2], use_resource=True) for i in range(5)]
+      large_v = [variable_scope.get_variable(
+          "large%d" % i, shape=[32000, 1000], use_resource=True)
+                 for i in range(3)]
+      return small_v + large_v
+
+    save_graph = ops_lib.Graph()
+    with save_graph.as_default(), self.test_session(graph=save_graph) as sess:
+      orig_vars = _model()
+      sess.run(variables.global_variables_initializer())
+      save = saver_module.Saver(max_to_keep=1)
+      variables.global_variables_initializer().run()
+      save.save(sess, save_dir)
+      orig_vals = sess.run(orig_vars)
+
+    restore_graph = ops_lib.Graph()
+    with restore_graph.as_default(), self.test_session(
+        graph=restore_graph) as sess:
+      restored_vars = _model()
+      save = saver_module.Saver(max_to_keep=1)
+      save.restore(sess, save_dir)
+      restored_vals = sess.run(restored_vars)
+
+    for orig, restored in zip(orig_vals, restored_vals):
+      self.assertAllEqual(orig, restored)
+
 
 class SaveRestoreShardedTest(test.TestCase):
 
diff --git a/tensorflow/python/training/server_lib.py b/tensorflow/python/training/server_lib.py
index 2f421d1cc0a0190670082fabf4e25470c6a1723b..58cf5277fe5fc17d74a9c670b8e608b469806337 100644
--- a/tensorflow/python/training/server_lib.py
+++ b/tensorflow/python/training/server_lib.py
@@ -42,8 +42,8 @@ def _make_server_def(server_or_cluster_def, job_name, task_index, protocol,
       Defaults to the value in `server_or_cluster_def`, if specified. Otherwise
       defaults to 0 if the server's job has only one task.
     protocol: (Optional.) Specifies the protocol to be used by the server.
-      Acceptable values include `"grpc"`. Defaults to the value in
-      `server_or_cluster_def`, if specified. Otherwise defaults to `"grpc"`.
+      Acceptable values include `"grpc", "grpc+verbs"`. Defaults to the value
+      in `server_or_cluster_def`, if specified. Otherwise defaults to `"grpc"`.
     config: (Options.) A `tf.ConfigProto` that specifies default configuration
       options for all sessions that run on this server.
 
@@ -129,8 +129,9 @@ class Server(object):
         job. Defaults to the value in `server_or_cluster_def`, if specified.
         Otherwise defaults to 0 if the server's job has only one task.
       protocol: (Optional.) Specifies the protocol to be used by the server.
-        Acceptable values include `"grpc"`. Defaults to the value in
-        `server_or_cluster_def`, if specified. Otherwise defaults to `"grpc"`.
+        Acceptable values include `"grpc", "grpc+verbs"`. Defaults to the
+        value in `server_or_cluster_def`, if specified. Otherwise defaults to
+        `"grpc"`.
       config: (Options.) A `tf.ConfigProto` that specifies default
         configuration options for all sessions that run on this server.
       start: (Optional.) Boolean, indicating whether to start the server
diff --git a/tensorflow/python/training/warm_starting_util.py b/tensorflow/python/training/warm_starting_util.py
index ec740abdd15ae2904f79246429deaa5fc831dad5..b1a7cfab8315586c9122bb6be41db65c7fb76aa1 100644
--- a/tensorflow/python/training/warm_starting_util.py
+++ b/tensorflow/python/training/warm_starting_util.py
@@ -22,7 +22,6 @@ import collections
 import six
 
 from tensorflow.python.framework import ops
-from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables as variables_lib
@@ -83,11 +82,6 @@ class VocabInfo(
     )
 
 
-def _is_variable(x):
-  return (isinstance(x, variables_lib.Variable) or
-          isinstance(x, resource_variable_ops.ResourceVariable))
-
-
 def _infer_var_name(var):
   """Returns name of the `var`.
 
@@ -126,9 +120,10 @@ def _warm_start_var(var, prev_ckpt, prev_tensor_name=None):
     prev_tensor_name: Name of the tensor to lookup in provided `prev_ckpt`. If
       None, we lookup tensor with same name as given `var`.
   """
-  if _is_variable(var):
+  if checkpoint_utils._is_variable(var):  # pylint: disable=protected-access
     current_var_name = _infer_var_name([var])
-  elif isinstance(var, list) and all(_is_variable(v) for v in var):
+  elif (isinstance(var, list) and
+        all(checkpoint_utils._is_variable(v) for v in var)):  # pylint: disable=protected-access
     current_var_name = _infer_var_name(var)
   elif isinstance(var, variables_lib.PartitionedVariable):
     current_var_name = _infer_var_name([var])
@@ -193,9 +188,10 @@ def _warm_start_var_with_vocab(var,
           prev_vocab_path):
     raise ValueError("Invalid args: Must provide all of [current_vocab_path, "
                      "current_vocab_size, prev_ckpt, prev_vocab_path}.")
-  if _is_variable(var):
+  if checkpoint_utils._is_variable(var):
     var = [var]
-  elif isinstance(var, list) and all(_is_variable(v) for v in var):
+  elif (isinstance(var, list) and
+        all(checkpoint_utils._is_variable(v) for v in var)):
     var = var
   elif isinstance(var, variables_lib.PartitionedVariable):
     var = var._get_variable_list()
@@ -271,7 +267,7 @@ def _get_grouped_variables(vars_to_warm_start):
       for v in vars_to_warm_start:
         list_of_vars += ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES,
                                            scope=v)
-    elif all([_is_variable(v) for v in vars_to_warm_start]):
+    elif all([checkpoint_utils._is_variable(v) for v in vars_to_warm_start]):  # pylint: disable=protected-access
       list_of_vars = vars_to_warm_start
     else:
       raise ValueError("If `vars_to_warm_start` is a list, it must be all "
diff --git a/tensorflow/python/util/deprecation.py b/tensorflow/python/util/deprecation.py
index 376be39978fb11463ae8a870492a359c89a9f2ce..9e2202eaf8268bc70e54577f19d42c974a80e0e4 100644
--- a/tensorflow/python/util/deprecation.py
+++ b/tensorflow/python/util/deprecation.py
@@ -37,6 +37,11 @@ _PRINT_DEPRECATION_WARNINGS = True
 _PRINTED_WARNING = {}
 
 
+class DeprecatedNamesAlreadySet(Exception):
+  """Raised when setting deprecated names multiple times for the same symbol."""
+  pass
+
+
 def _add_deprecated_function_notice_to_docstring(doc, date, instructions):
   """Adds a deprecation notice to a docstring for deprecated functions."""
   main_text = ['THIS FUNCTION IS DEPRECATED. It will be removed %s.' %
@@ -87,6 +92,27 @@ def _call_location(outer=False):
     return '%s:%d' % (entry[1], entry[2])
 
 
+def _wrap_decorator(wrapped_function):
+  """Indicate that one function wraps another.
+
+  This decorator wraps a function using `tf_decorator.make_decorator`
+  so that doc generation scripts can pick up original function
+  signature.
+  It would be better to use @functools.wrap decorator, but it would
+  not update function signature to match wrapped function in Python 2.
+
+  Args:
+    wrapped_function: The function that decorated function wraps.
+
+  Returns:
+    Function that accepts wrapper function as an argument and returns
+    `TFDecorator` instance.
+  """
+  def wrapper(wrapper_func):
+    return tf_decorator.make_decorator(wrapped_function, wrapper_func)
+  return wrapper
+
+
 def deprecated_alias(deprecated_name, name, func_or_class, warn_once=True):
   """Deprecate a symbol in favor of a new name with identical semantics.
 
@@ -144,7 +170,7 @@ def deprecated_alias(deprecated_name, name, func_or_class, warn_once=True):
   if tf_inspect.isclass(func_or_class):
 
     # Make a new class with __init__ wrapped in a warning.
-    class NewClass(func_or_class):  # pylint: disable=missing-docstring
+    class _NewClass(func_or_class):  # pylint: disable=missing-docstring
       __doc__ = decorator_utils.add_notice_to_docstring(
           func_or_class.__doc__, 'Please use %s instead.' % name,
           'DEPRECATED CLASS',
@@ -153,27 +179,28 @@ def deprecated_alias(deprecated_name, name, func_or_class, warn_once=True):
       __name__ = func_or_class.__name__
       __module__ = _call_location(outer=True)
 
+      @_wrap_decorator(func_or_class.__init__)
       def __init__(self, *args, **kwargs):
-        if hasattr(NewClass.__init__, '__func__'):
+        if hasattr(_NewClass.__init__, '__func__'):
           # Python 2
-          NewClass.__init__.__func__.__doc__ = func_or_class.__init__.__doc__
+          _NewClass.__init__.__func__.__doc__ = func_or_class.__init__.__doc__
         else:
           # Python 3
-          NewClass.__init__.__doc__ = func_or_class.__init__.__doc__
+          _NewClass.__init__.__doc__ = func_or_class.__init__.__doc__
 
         if _PRINT_DEPRECATION_WARNINGS:
           # We're making the alias as we speak. The original may have other
           # aliases, so we cannot use it to check for whether it's already been
           # warned about.
-          if NewClass.__init__ not in _PRINTED_WARNING:
+          if _NewClass.__init__ not in _PRINTED_WARNING:
             if warn_once:
-              _PRINTED_WARNING[NewClass.__init__] = True
+              _PRINTED_WARNING[_NewClass.__init__] = True
             logging.warning(
                 'From %s: The name %s is deprecated. Please use %s instead.\n',
                 _call_location(), deprecated_name, name)
-        super(NewClass, self).__init__(*args, **kwargs)
+        super(_NewClass, self).__init__(*args, **kwargs)
 
-    return NewClass
+    return _NewClass
   else:
     decorator_utils.validate_callable(func_or_class, 'deprecated')
 
@@ -197,6 +224,35 @@ def deprecated_alias(deprecated_name, name, func_or_class, warn_once=True):
             func_or_class.__doc__, None, 'Please use %s instead.' % name))
 
 
+def deprecated_endpoints(*args):
+  """Decorator for marking endpoints deprecated.
+
+  This decorator does not print deprecation messages.
+  TODO(annarev): eventually start printing deprecation warnings when
+  @deprecation_endpoints decorator is added.
+
+  Args:
+    *args: Deprecated endpoint names.
+
+  Returns:
+    A function that takes symbol as an argument and adds
+    _tf_deprecated_api_names to that symbol.
+    _tf_deprecated_api_names would be set to a list of deprecated
+    endpoint names for the symbol.
+  """
+  def deprecated_wrapper(func):
+    # pylint: disable=protected-access
+    if '_tf_deprecated_api_names' in func.__dict__:
+      raise DeprecatedNamesAlreadySet(
+          'Cannot set deprecated names for %s to %s. '
+          'Deprecated names are already set to %s.' % (
+              func.__name__, str(args), str(func._tf_deprecated_api_names)))
+    func._tf_deprecated_api_names = args
+    # pylint: disable=protected-access
+    return func
+  return deprecated_wrapper
+
+
 def deprecated(date, instructions, warn_once=True):
   """Decorator for marking functions or methods deprecated.
 
diff --git a/tensorflow/python/util/deprecation_test.py b/tensorflow/python/util/deprecation_test.py
index bdd0bc48d29319914e184ea4331a5e9d4a1c3328..90c73a0a58d129af44cc051874acda37d5c78394 100644
--- a/tensorflow/python/util/deprecation_test.py
+++ b/tensorflow/python/util/deprecation_test.py
@@ -22,6 +22,7 @@ from __future__ import print_function
 from tensorflow.python.platform import test
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import deprecation
+from tensorflow.python.util import tf_inspect
 
 
 class DeprecatedAliasTest(test.TestCase):
@@ -73,6 +74,11 @@ class DeprecatedAliasTest(test.TestCase):
     self.assertEqual(["test", "deprecated", "deprecated again"],
                      MyClass.init_args)
 
+    # Check __init__ signature matches for doc generation.
+    self.assertEqual(
+        tf_inspect.getfullargspec(MyClass.__init__),
+        tf_inspect.getfullargspec(deprecated_cls.__init__))
+
 
 class DeprecationTest(test.TestCase):
 
@@ -929,5 +935,27 @@ class DeprecationArgumentsTest(test.TestCase):
     self.assertEqual(new_docs, new_docs_ref)
 
 
+class DeprecatedEndpointsTest(test.TestCase):
+
+  def testSingleDeprecatedEndpoint(self):
+    @deprecation.deprecated_endpoints("foo1")
+    def foo():
+      pass
+    self.assertEqual(("foo1",), foo._tf_deprecated_api_names)
+
+  def testMultipleDeprecatedEndpoint(self):
+    @deprecation.deprecated_endpoints("foo1", "foo2")
+    def foo():
+      pass
+    self.assertEqual(("foo1", "foo2"), foo._tf_deprecated_api_names)
+
+  def testCannotSetDeprecatedEndpointsTwice(self):
+    with self.assertRaises(deprecation.DeprecatedNamesAlreadySet):
+      @deprecation.deprecated_endpoints("foo1")
+      @deprecation.deprecated_endpoints("foo2")
+      def foo():  # pylint: disable=unused-variable
+        pass
+
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/util/function_utils.py b/tensorflow/python/util/function_utils.py
index 7bbbde3cd288a7373c1ac845977a4d92d2a1b7c0..4e9b07e20ac7ef176316d3532958c84754628e56 100644
--- a/tensorflow/python/util/function_utils.py
+++ b/tensorflow/python/util/function_utils.py
@@ -20,6 +20,8 @@ from __future__ import print_function
 
 import functools
 
+import six
+
 from tensorflow.python.util import tf_decorator
 from tensorflow.python.util import tf_inspect
 
@@ -55,3 +57,36 @@ def fn_args(fn):
     if _is_bounded_method(fn):
       args.remove('self')
   return tuple(args)
+
+
+def get_func_name(func):
+  """Returns name of passed callable."""
+  _, func = tf_decorator.unwrap(func)
+  if callable(func):
+    if tf_inspect.isfunction(func):
+      return func.__name__
+    elif tf_inspect.ismethod(func):
+      return '%s.%s' % (six.get_method_self(func).__class__.__name__,
+                        six.get_method_function(func).__name__)
+    else:  # Probably a class instance with __call__
+      return str(type(func))
+  else:
+    raise ValueError('Argument must be callable')
+
+
+def get_func_code(func):
+  """Returns func_code of passed callable, or None if not available."""
+  _, func = tf_decorator.unwrap(func)
+  if callable(func):
+    if tf_inspect.isfunction(func) or tf_inspect.ismethod(func):
+      return six.get_function_code(func)
+    # Since the object is not a function or method, but is a callable, we will
+    # try to access the __call__method as a function.  This works with callable
+    # classes but fails with functool.partial objects despite their __call__
+    # attribute.
+    try:
+      return six.get_function_code(func.__call__)
+    except AttributeError:
+      return None
+  else:
+    raise ValueError('Argument must be callable')
diff --git a/tensorflow/python/util/function_utils_test.py b/tensorflow/python/util/function_utils_test.py
index e78cf6a5b02af317b08ff3a833f7b73b062f106e..1588328c262982e5b71446e499c8d0217c28c0a5 100644
--- a/tensorflow/python/util/function_utils_test.py
+++ b/tensorflow/python/util/function_utils_test.py
@@ -24,6 +24,16 @@ from tensorflow.python.platform import test
 from tensorflow.python.util import function_utils
 
 
+def silly_example_function():
+  pass
+
+
+class SillyCallableClass(object):
+
+  def __call__(self):
+    pass
+
+
 class FnArgsTest(test.TestCase):
 
   def test_simple_function(self):
@@ -124,5 +134,73 @@ class FnArgsTest(test.TestCase):
     self.assertEqual(3, double_wrapped_fn(3))
     self.assertEqual(3, double_wrapped_fn(a=3))
 
+
+class GetFuncNameTest(test.TestCase):
+
+  def testWithSimpleFunction(self):
+    self.assertEqual(
+        'silly_example_function',
+        function_utils.get_func_name(silly_example_function))
+
+  def testWithClassMethod(self):
+    self.assertEqual(
+        'GetFuncNameTest.testWithClassMethod',
+        function_utils.get_func_name(self.testWithClassMethod))
+
+  def testWithCallableClass(self):
+    callable_instance = SillyCallableClass()
+    self.assertRegexpMatches(
+        function_utils.get_func_name(callable_instance),
+        '<.*SillyCallableClass.*>')
+
+  def testWithFunctoolsPartial(self):
+    partial = functools.partial(silly_example_function)
+    self.assertRegexpMatches(
+        function_utils.get_func_name(partial),
+        '<.*functools.partial.*>')
+
+  def testWithLambda(self):
+    anon_fn = lambda x: x
+    self.assertEqual('', function_utils.get_func_name(anon_fn))
+
+  def testRaisesWithNonCallableObject(self):
+    with self.assertRaises(ValueError):
+      function_utils.get_func_name(None)
+
+
+class GetFuncCodeTest(test.TestCase):
+
+  def testWithSimpleFunction(self):
+    code = function_utils.get_func_code(silly_example_function)
+    self.assertIsNotNone(code)
+    self.assertRegexpMatches(code.co_filename, 'function_utils_test.py')
+
+  def testWithClassMethod(self):
+    code = function_utils.get_func_code(self.testWithClassMethod)
+    self.assertIsNotNone(code)
+    self.assertRegexpMatches(code.co_filename, 'function_utils_test.py')
+
+  def testWithCallableClass(self):
+    callable_instance = SillyCallableClass()
+    code = function_utils.get_func_code(callable_instance)
+    self.assertIsNotNone(code)
+    self.assertRegexpMatches(code.co_filename, 'function_utils_test.py')
+
+  def testWithLambda(self):
+    anon_fn = lambda x: x
+    code = function_utils.get_func_code(anon_fn)
+    self.assertIsNotNone(code)
+    self.assertRegexpMatches(code.co_filename, 'function_utils_test.py')
+
+  def testWithFunctoolsPartial(self):
+    partial = functools.partial(silly_example_function)
+    code = function_utils.get_func_code(partial)
+    self.assertIsNone(code)
+
+  def testRaisesWithNonCallableObject(self):
+    with self.assertRaises(ValueError):
+      function_utils.get_func_code(None)
+
+
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/python/util/nest.py b/tensorflow/python/util/nest.py
index d63f59a8c8e836d3f8ad3686da0b0b3f010a9225..5aac559b9b017aa236dcb6c20538d90daef903be 100644
--- a/tensorflow/python/util/nest.py
+++ b/tensorflow/python/util/nest.py
@@ -73,7 +73,7 @@ def _sequence_like(instance, args):
   Returns:
     `args` with the type of `instance`.
   """
-  if isinstance(instance, dict):
+  if isinstance(instance, (dict, _collections.Mapping)):
     # Pack dictionaries in a deterministic order by sorting the keys.
     # Notice this means that we ignore the original order of `OrderedDict`
     # instances. This is intentional, to avoid potential bugs caused by mixing
@@ -89,7 +89,7 @@ def _sequence_like(instance, args):
 
 
 def _yield_value(iterable):
-  if isinstance(iterable, dict):
+  if isinstance(iterable, (dict, _collections.Mapping)):
     # Iterate through dictionaries in a deterministic order by sorting the
     # keys. Notice this means that we ignore the original order of `OrderedDict`
     # instances. This is intentional, to avoid potential bugs caused by mixing
@@ -215,7 +215,7 @@ def flatten_dict_items(dictionary):
     ValueError: If any key and value have not the same structure, or if keys are
       not unique.
   """
-  if not isinstance(dictionary, dict):
+  if not isinstance(dictionary, (dict, _collections.Mapping)):
     raise TypeError("input must be a dictionary")
   flat_dictionary = {}
   for i, v in _six.iteritems(dictionary):
@@ -455,7 +455,7 @@ def assert_shallow_structure(shallow_tree, input_tree, check_types=True):
           "structure has length %s, while shallow structure has length %s."
           % (len(input_tree), len(shallow_tree)))
 
-    if check_types and isinstance(shallow_tree, dict):
+    if check_types and isinstance(shallow_tree, (dict, _collections.Mapping)):
       if set(input_tree) != set(shallow_tree):
         raise ValueError(
             "The two structures don't have the same keys. Input "
@@ -716,7 +716,7 @@ def yield_flat_paths(nest):
 
   # The _maybe_add_final_path_element function is used below in order to avoid
   # adding trailing slashes when the sub-element recursed into is a leaf.
-  if isinstance(nest, dict):
+  if isinstance(nest, (dict, _collections.Mapping)):
     for key in _sorted(nest):
       value = nest[key]
       for sub_path in yield_flat_paths(value):
@@ -760,3 +760,4 @@ def flatten_with_joined_string_paths(structure, separator="/"):
 
 
 _pywrap_tensorflow.RegisterSequenceClass(_collections.Sequence)
+_pywrap_tensorflow.RegisterMappingClass(_collections.Mapping)
diff --git a/tensorflow/python/util/nest_test.py b/tensorflow/python/util/nest_test.py
index 2f12b25354a905b2aafa870c28f1e9c0b693e888..26c6ea4b012e1d0577b63144145625d9b03bc54b 100644
--- a/tensorflow/python/util/nest_test.py
+++ b/tensorflow/python/util/nest_test.py
@@ -21,6 +21,7 @@ from __future__ import print_function
 import collections
 import time
 
+from absl.testing import parameterized
 import numpy as np
 from six.moves import xrange  # pylint: disable=redefined-builtin
 
@@ -33,7 +34,22 @@ from tensorflow.python.platform import test
 from tensorflow.python.util import nest
 
 
-class NestTest(test.TestCase):
+class _CustomMapping(collections.Mapping):
+
+  def __init__(self, *args, **kwargs):
+    self._wrapped = dict(*args, **kwargs)
+
+  def __getitem__(self, key):
+    return self._wrapped[key]
+
+  def __iter__(self):
+    return iter(self._wrapped)
+
+  def __len__(self):
+    return len(self._wrapped)
+
+
+class NestTest(parameterized.TestCase, test.TestCase):
 
   PointXY = collections.namedtuple("Point", ["x", "y"])  # pylint: disable=invalid-name
 
@@ -72,26 +88,32 @@ class NestTest(test.TestCase):
     with self.assertRaises(ValueError):
       nest.pack_sequence_as([5, 6, [7, 8]], ["a", "b", "c"])
 
+  @parameterized.parameters({"mapping_type": collections.OrderedDict},
+                            {"mapping_type": _CustomMapping})
   @test_util.assert_no_new_pyobjects_executing_eagerly
-  def testFlattenDictOrder(self):
+  def testFlattenDictOrder(self, mapping_type):
     """`flatten` orders dicts by key, including OrderedDicts."""
-    ordered = collections.OrderedDict([("d", 3), ("b", 1), ("a", 0), ("c", 2)])
+    ordered = mapping_type([("d", 3), ("b", 1), ("a", 0), ("c", 2)])
     plain = {"d": 3, "b": 1, "a": 0, "c": 2}
     ordered_flat = nest.flatten(ordered)
     plain_flat = nest.flatten(plain)
     self.assertEqual([0, 1, 2, 3], ordered_flat)
     self.assertEqual([0, 1, 2, 3], plain_flat)
 
-  def testPackDictOrder(self):
+  @parameterized.parameters({"mapping_type": collections.OrderedDict},
+                            {"mapping_type": _CustomMapping})
+  def testPackDictOrder(self, mapping_type):
     """Packing orders dicts by key, including OrderedDicts."""
-    ordered = collections.OrderedDict([("d", 0), ("b", 0), ("a", 0), ("c", 0)])
+    custom = mapping_type([("d", 0), ("b", 0), ("a", 0), ("c", 0)])
     plain = {"d": 0, "b": 0, "a": 0, "c": 0}
     seq = [0, 1, 2, 3]
-    ordered_reconstruction = nest.pack_sequence_as(ordered, seq)
+    custom_reconstruction = nest.pack_sequence_as(custom, seq)
     plain_reconstruction = nest.pack_sequence_as(plain, seq)
+    self.assertIsInstance(custom_reconstruction, mapping_type)
+    self.assertIsInstance(plain_reconstruction, dict)
     self.assertEqual(
-        collections.OrderedDict([("d", 3), ("b", 1), ("a", 0), ("c", 2)]),
-        ordered_reconstruction)
+        mapping_type([("d", 3), ("b", 1), ("a", 0), ("c", 2)]),
+        custom_reconstruction)
     self.assertEqual({"d": 3, "b": 1, "a": 0, "c": 2}, plain_reconstruction)
 
   Abc = collections.namedtuple("A", ("b", "c"))  # pylint: disable=invalid-name
@@ -101,8 +123,10 @@ class NestTest(test.TestCase):
     # A nice messy mix of tuples, lists, dicts, and `OrderedDict`s.
     mess = [
         "z",
-        NestTest.Abc(3, 4),
-        {
+        NestTest.Abc(3, 4), {
+            "d": _CustomMapping({
+                41: 4
+            }),
             "c": [
                 1,
                 collections.OrderedDict([
@@ -111,17 +135,19 @@ class NestTest(test.TestCase):
                 ]),
             ],
             "b": 5
-        },
-        17
+        }, 17
     ]
 
     flattened = nest.flatten(mess)
-    self.assertEqual(flattened, ["z", 3, 4, 5, 1, 2, 3, 17])
+    self.assertEqual(flattened, ["z", 3, 4, 5, 1, 2, 3, 4, 17])
 
     structure_of_mess = [
         14,
         NestTest.Abc("a", True),
         {
+            "d": _CustomMapping({
+                41: 42
+            }),
             "c": [
                 0,
                 collections.OrderedDict([
@@ -142,6 +168,10 @@ class NestTest(test.TestCase):
     self.assertIsInstance(unflattened_ordered_dict, collections.OrderedDict)
     self.assertEqual(list(unflattened_ordered_dict.keys()), ["b", "a"])
 
+    unflattened_custom_mapping = unflattened[2]["d"]
+    self.assertIsInstance(unflattened_custom_mapping, _CustomMapping)
+    self.assertEqual(list(unflattened_custom_mapping.keys()), [41])
+
   def testFlatten_numpyIsNotFlattened(self):
     structure = np.array([1, 2, 3])
     flattened = nest.flatten(structure)
@@ -179,19 +209,23 @@ class NestTest(test.TestCase):
     self.assertFalse(nest.is_sequence(math_ops.tanh(ones)))
     self.assertFalse(nest.is_sequence(np.ones((4, 5))))
 
-  def testFlattenDictItems(self):
-    dictionary = {(4, 5, (6, 8)): ("a", "b", ("c", "d"))}
+  @parameterized.parameters({"mapping_type": _CustomMapping},
+                            {"mapping_type": dict})
+  def testFlattenDictItems(self, mapping_type):
+    dictionary = mapping_type({(4, 5, (6, 8)): ("a", "b", ("c", "d"))})
     flat = {4: "a", 5: "b", 6: "c", 8: "d"}
     self.assertEqual(nest.flatten_dict_items(dictionary), flat)
 
     with self.assertRaises(TypeError):
       nest.flatten_dict_items(4)
 
-    bad_dictionary = {(4, 5, (4, 8)): ("a", "b", ("c", "d"))}
+    bad_dictionary = mapping_type({(4, 5, (4, 8)): ("a", "b", ("c", "d"))})
     with self.assertRaisesRegexp(ValueError, "not unique"):
       nest.flatten_dict_items(bad_dictionary)
 
-    another_bad_dictionary = {(4, 5, (6, 8)): ("a", "b", ("c", ("d", "e")))}
+    another_bad_dictionary = mapping_type({
+        (4, 5, (6, 8)): ("a", "b", ("c", ("d", "e")))
+    })
     with self.assertRaisesRegexp(
         ValueError, "Key had [0-9]* elements, but value had [0-9]* elements"):
       nest.flatten_dict_items(another_bad_dictionary)
diff --git a/tensorflow/python/util/py_checkpoint_reader.i b/tensorflow/python/util/py_checkpoint_reader.i
index 8004898cbcbce7ce593ce35efdc6493e052468bd..1c73f7f06f1937a8db0bd858421c2e884892e25b 100644
--- a/tensorflow/python/util/py_checkpoint_reader.i
+++ b/tensorflow/python/util/py_checkpoint_reader.i
@@ -166,6 +166,7 @@ def NewCheckpointReader(filepattern):
     return CheckpointReader(compat.as_bytes(filepattern), status)
 
 NewCheckpointReader._tf_api_names = ['train.NewCheckpointReader']
+NewCheckpointReader._tf_api_names_v1 = ['train.NewCheckpointReader']
 %}
 
 %include "tensorflow/c/checkpoint_reader.h"
diff --git a/tensorflow/python/util/stat_summarizer.i b/tensorflow/python/util/stat_summarizer.i
index 73fa85494b72d920d00577c826b76c3381d963a4..a5a7984d914f24964c377149f8125ceb3126c009 100644
--- a/tensorflow/python/util/stat_summarizer.i
+++ b/tensorflow/python/util/stat_summarizer.i
@@ -27,8 +27,8 @@ limitations under the License.
 
 %ignoreall
 
-%unignore _NewStatSummarizer;
-%unignore _DeleteStatSummarizer;
+%unignore NewStatSummarizer;
+%unignore DeleteStatSummarizer;
 %unignore tensorflow;
 %unignore tensorflow::StatSummarizer;
 %unignore tensorflow::StatSummarizer::StatSummarizer;
@@ -43,20 +43,20 @@ limitations under the License.
 
 // TODO(ashankar): Remove the unused argument from the API.
 %{
-tensorflow::StatSummarizer* _NewStatSummarizer(
+tensorflow::StatSummarizer* NewStatSummarizer(
       const string& unused) {
   return new tensorflow::StatSummarizer(tensorflow::StatSummarizerOptions());
 }
 %}
 
 %{
-void _DeleteStatSummarizer(tensorflow::StatSummarizer* ss) {
+void DeleteStatSummarizer(tensorflow::StatSummarizer* ss) {
   delete ss;
 }
 %}
 
-tensorflow::StatSummarizer* _NewStatSummarizer(const string& unused);
-void _DeleteStatSummarizer(tensorflow::StatSummarizer* ss);
+tensorflow::StatSummarizer* NewStatSummarizer(const string& unused);
+void DeleteStatSummarizer(tensorflow::StatSummarizer* ss);
 
 %extend tensorflow::StatSummarizer {
   void ProcessStepStatsStr(const string& step_stats_str) {
@@ -76,16 +76,3 @@ void _DeleteStatSummarizer(tensorflow::StatSummarizer* ss);
 %include "tensorflow/core/util/stat_summarizer_options.h"
 %include "tensorflow/core/util/stat_summarizer.h"
 %unignoreall
-
-%insert("python") %{
-
-# Wrapping NewStatSummarizer and DeletStatSummarizer because
-# SWIG-generated functions are built-in functions and do not support
-# setting _tf_api_names attribute.
-
-def NewStatSummarizer(unused):
-  return _NewStatSummarizer(unused)
-
-def DeleteStatSummarizer(stat_summarizer):
-  _DeleteStatSummarizer(stat_summarizer)
-%}
diff --git a/tensorflow/python/util/tf_export.py b/tensorflow/python/util/tf_export.py
index e154ffb68a4f0ccdebf5320cad7d3da056117197..274f32c21f77483464a12a1beb25043a208b4b35 100644
--- a/tensorflow/python/util/tf_export.py
+++ b/tensorflow/python/util/tf_export.py
@@ -63,12 +63,63 @@ API_ATTRS = {
         '_estimator_api_constants')
 }
 
+API_ATTRS_V1 = {
+    TENSORFLOW_API_NAME: _Attributes(
+        '_tf_api_names_v1',
+        '_tf_api_constants_v1'),
+    ESTIMATOR_API_NAME: _Attributes(
+        '_estimator_api_names_v1',
+        '_estimator_api_constants_v1')
+}
+
 
 class SymbolAlreadyExposedError(Exception):
   """Raised when adding API names to symbol that already has API names."""
   pass
 
 
+def get_canonical_name_for_symbol(symbol, api_name=TENSORFLOW_API_NAME):
+  """Get canonical name for the API symbol.
+
+  Canonical name is the first non-deprecated endpoint name.
+
+  Args:
+    symbol: API function or class.
+    api_name: API name (tensorflow or estimator).
+
+  Returns:
+    Canonical name for the API symbol (for e.g. initializers.zeros) if
+    canonical name could be determined. Otherwise, returns None.
+  """
+  if not hasattr(symbol, '__dict__'):
+    return None
+  api_names_attr = API_ATTRS[api_name].names
+  _, undecorated_symbol = tf_decorator.unwrap(symbol)
+  if api_names_attr not in undecorated_symbol.__dict__:
+    return None
+  api_names = getattr(undecorated_symbol, api_names_attr)
+  # TODO(annarev): may be add a separate deprecated attribute
+  # for estimator names.
+  deprecated_api_names = undecorated_symbol.__dict__.get(
+      '_tf_deprecated_api_names', [])
+  return get_canonical_name(api_names, deprecated_api_names)
+
+
+def get_canonical_name(api_names, deprecated_api_names):
+  """Get first non-deprecated endpoint name.
+
+  Args:
+    api_names: API names iterable.
+    deprecated_api_names: Deprecated API names iterable.
+  Returns:
+    Canonical name if there is at least one non-deprecated endpoint.
+    Otherwise returns None.
+  """
+  return next(
+      (name for name in api_names if name not in deprecated_api_names),
+      None)
+
+
 class api_export(object):  # pylint: disable=invalid-name
   """Provides ways to export symbols to the TensorFlow API."""
 
@@ -78,13 +129,16 @@ class api_export(object):  # pylint: disable=invalid-name
     Args:
       *args: API names in dot delimited format.
       **kwargs: Optional keyed arguments.
-          overrides: List of symbols that this is overriding
+        v1: Names for the TensorFlow V1 API. If not set, we will use V2 API
+          names both for TensorFlow V1 and V2 APIs.
+        overrides: List of symbols that this is overriding
           (those overrided api exports will be removed). Note: passing overrides
           has no effect on exporting a constant.
-          api_name: Name of the API you want to generate (e.g. `tensorflow` or
+        api_name: Name of the API you want to generate (e.g. `tensorflow` or
           `estimator`). Default is `tensorflow`.
     """
     self._names = args
+    self._names_v1 = kwargs.get('v1', args)
     self._api_name = kwargs.get('api_name', TENSORFLOW_API_NAME)
     self._overrides = kwargs.get('overrides', [])
 
@@ -102,24 +156,27 @@ class api_export(object):  # pylint: disable=invalid-name
         and kwarg `allow_multiple_exports` not set.
     """
     api_names_attr = API_ATTRS[self._api_name].names
-
+    api_names_attr_v1 = API_ATTRS_V1[self._api_name].names
     # Undecorate overridden names
     for f in self._overrides:
       _, undecorated_f = tf_decorator.unwrap(f)
       delattr(undecorated_f, api_names_attr)
+      delattr(undecorated_f, api_names_attr_v1)
 
     _, undecorated_func = tf_decorator.unwrap(func)
+    self.set_attr(undecorated_func, api_names_attr, self._names)
+    self.set_attr(undecorated_func, api_names_attr_v1, self._names_v1)
+    return func
 
+  def set_attr(self, func, api_names_attr, names):
     # Check for an existing api. We check if attribute name is in
     # __dict__ instead of using hasattr to verify that subclasses have
     # their own _tf_api_names as opposed to just inheriting it.
-    if api_names_attr in undecorated_func.__dict__:
+    if api_names_attr in func.__dict__:
       raise SymbolAlreadyExposedError(
           'Symbol %s is already exposed as %s.' %
-          (undecorated_func.__name__, getattr(
-              undecorated_func, api_names_attr)))  # pylint: disable=protected-access
-    setattr(undecorated_func, api_names_attr, self._names)
-    return func
+          (func.__name__, getattr(func, api_names_attr)))  # pylint: disable=protected-access
+    setattr(func, api_names_attr, names)
 
   def export_constant(self, module_name, name):
     """Store export information for constants/string literals.
@@ -140,12 +197,20 @@ class api_export(object):  # pylint: disable=invalid-name
       name: (string) Current constant name.
     """
     module = sys.modules[module_name]
-    if not hasattr(module, API_ATTRS[self._api_name].constants):
-      setattr(module, API_ATTRS[self._api_name].constants, [])
+    api_constants_attr = API_ATTRS[self._api_name].constants
+    api_constants_attr_v1 = API_ATTRS_V1[self._api_name].constants
+
+    if not hasattr(module, api_constants_attr):
+      setattr(module, api_constants_attr, [])
     # pylint: disable=protected-access
-    getattr(module, API_ATTRS[self._api_name].constants).append(
+    getattr(module, api_constants_attr).append(
         (self._names, name))
 
+    if not hasattr(module, api_constants_attr_v1):
+      setattr(module, api_constants_attr_v1, [])
+    getattr(module, api_constants_attr_v1).append(
+        (self._names_v1, name))
+
 
 tf_export = functools.partial(api_export, api_name=TENSORFLOW_API_NAME)
 estimator_export = functools.partial(tf_export, api_name=ESTIMATOR_API_NAME)
diff --git a/tensorflow/python/util/tf_export_test.py b/tensorflow/python/util/tf_export_test.py
index b9e26ecb33383f5aa936a6bc92acea6d91eb996e..4ae1dc55e06b434aeb4a95e2ca9aa68e4eef56de 100644
--- a/tensorflow/python/util/tf_export_test.py
+++ b/tensorflow/python/util/tf_export_test.py
@@ -60,6 +60,8 @@ class ValidateExportTest(test.TestCase):
     for symbol in [_test_function, _test_function, TestClassA, TestClassB]:
       if hasattr(symbol, '_tf_api_names'):
         del symbol._tf_api_names
+      if hasattr(symbol, '_tf_api_names_v1'):
+        del symbol._tf_api_names_v1
 
   def _CreateMockModule(self, name):
     mock_module = self.MockModule(name)
diff --git a/tensorflow/python/util/tf_inspect.py b/tensorflow/python/util/tf_inspect.py
index fbd65617670b15bfc69506bab1e83369081502af..ec20998bdd68444e830d78689465f104177e7fec 100644
--- a/tensorflow/python/util/tf_inspect.py
+++ b/tensorflow/python/util/tf_inspect.py
@@ -300,6 +300,16 @@ def getsource(object):  # pylint: disable=redefined-builtin
   return _inspect.getsource(tf_decorator.unwrap(object)[1])
 
 
+def getsourcefile(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.getsourcefile."""
+  return _inspect.getsourcefile(tf_decorator.unwrap(object)[1])
+
+
+def getsourcelines(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.getsourcelines."""
+  return _inspect.getsourcelines(tf_decorator.unwrap(object)[1])
+
+
 def isbuiltin(object):  # pylint: disable=redefined-builtin
   """TFDecorator-aware replacement for inspect.isbuiltin."""
   return _inspect.isbuiltin(tf_decorator.unwrap(object)[1])
diff --git a/tensorflow/python/util/tf_inspect_test.py b/tensorflow/python/util/tf_inspect_test.py
index beaf350de1e469a7675a4b55ff341419262b79b2..2f6021c7d8e64f2474334ff38f203d0f5fc93f81 100644
--- a/tensorflow/python/util/tf_inspect_test.py
+++ b/tensorflow/python/util/tf_inspect_test.py
@@ -326,6 +326,18 @@ def test_decorated_function_with_defaults(a, b=2, c='Hello'):
     self.assertEqual(
         expected, tf_inspect.getsource(test_decorated_function_with_defaults))
 
+  def testGetSourceFile(self):
+    self.assertEqual(
+        __file__,
+        tf_inspect.getsourcefile(test_decorated_function_with_defaults))
+
+  def testGetSourceLines(self):
+    expected = inspect.getsourcelines(
+        test_decorated_function_with_defaults.decorated_target)
+    self.assertEqual(
+        expected,
+        tf_inspect.getsourcelines(test_decorated_function_with_defaults))
+
   def testIsBuiltin(self):
     self.assertEqual(
         tf_inspect.isbuiltin(TestDecoratedClass),
diff --git a/tensorflow/python/util/tf_stack.py b/tensorflow/python/util/tf_stack.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe4f4a63eb52d4b9549f42ddeb00f7d95f15d5d2
--- /dev/null
+++ b/tensorflow/python/util/tf_stack.py
@@ -0,0 +1,103 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functions used to extract and analyze stacks.  Faster than Python libs."""
+# pylint: disable=g-bad-name
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import linecache
+import sys
+
+# Names for indices into TF traceback tuples.
+TB_FILENAME = 0
+TB_LINENO = 1
+TB_FUNCNAME = 2
+TB_CODEDICT = 3  # Dictionary of Python interpreter state.
+
+
+def extract_stack(extract_frame_info_fn=None):
+  """A lightweight, extensible re-implementation of traceback.extract_stack.
+
+  NOTE(mrry): traceback.extract_stack eagerly retrieves the line of code for
+      each stack frame using linecache, which results in an abundance of stat()
+      calls. This implementation does not retrieve the code, and any consumer
+      should apply _convert_stack to the result to obtain a traceback that can
+      be formatted etc. using traceback methods.
+
+  Args:
+    extract_frame_info_fn: Optional callable fn(stack_frame) applied to each
+        stack frame.  This callable's return value is stored as the sixth (last)
+        element of the returned tuples.  If not provided, the returned tuples
+        will have None as their sixth value.
+
+  Returns:
+    A list of 6-tuples
+        (filename, lineno, name, frame_globals, func_start_lineno, custom_info)
+    corresponding to the call stack of the current thread.  The returned tuples
+    have the innermost stack frame at the end, unlike the Python inspect
+    module's stack() function.
+  """
+  default_fn = lambda f: None
+  extract_frame_info_fn = extract_frame_info_fn or default_fn
+  try:
+    raise ZeroDivisionError
+  except ZeroDivisionError:
+    f = sys.exc_info()[2].tb_frame.f_back
+  ret = []
+  while f is not None:
+    lineno = f.f_lineno
+    co = f.f_code
+    filename = co.co_filename
+    name = co.co_name
+    frame_globals = f.f_globals
+    func_start_lineno = co.co_firstlineno
+    frame_info = extract_frame_info_fn(f)
+    ret.append((filename, lineno, name, frame_globals, func_start_lineno,
+                frame_info))
+    f = f.f_back
+  ret.reverse()
+  return ret
+
+
+def convert_stack(stack, include_func_start_lineno=False):
+  """Converts a stack extracted using extract_stack() to a traceback stack.
+
+  Args:
+    stack: A list of n 5-tuples,
+      (filename, lineno, name, frame_globals, func_start_lineno).
+    include_func_start_lineno: True if function start line number should be
+      included as the 5th entry in return tuples.
+
+  Returns:
+    A list of n 4-tuples or 5-tuples
+    (filename, lineno, name, code, [optional: func_start_lineno]), where the
+    code tuple element is calculated from the corresponding elements of the
+    input tuple.
+  """
+  ret = []
+  for (filename, lineno, name, frame_globals, func_start_lineno,
+       unused_frame_info) in stack:
+    linecache.checkcache(filename)
+    line = linecache.getline(filename, lineno, frame_globals)
+    if line:
+      line = line.strip()
+    else:
+      line = None
+    if include_func_start_lineno:
+      ret.append((filename, lineno, name, line, func_start_lineno))
+    else:
+      ret.append((filename, lineno, name, line))
+  return ret
diff --git a/tensorflow/python/util/util.cc b/tensorflow/python/util/util.cc
index 366f8a0deb533c3ee258ea618136d44a28160f8f..ad85a44f8d3c634f943f1ca0f0a96c2c3202e704 100644
--- a/tensorflow/python/util/util.cc
+++ b/tensorflow/python/util/util.cc
@@ -31,6 +31,8 @@ namespace {
 
 // Type object for collections.Sequence. This is set by RegisterSequenceClass.
 PyObject* CollectionsSequenceType = nullptr;
+// Type object for collections.Mapping, set by RegisterMappingClass.
+PyObject* CollectionsMappingType = nullptr;
 PyTypeObject* SparseTensorValueType = nullptr;
 
 const int kMaxItemsInCache = 1024;
@@ -45,6 +47,23 @@ bool IsString(PyObject* o) {
          PyUnicode_Check(o);
 }
 
+// Work around a writable-strings warning with Python 2's PyMapping_Keys macro,
+// and while we're at it give them consistent behavior by making sure the
+// returned value is a list.
+//
+// As with PyMapping_Keys, returns a new reference.
+PyObject* MappingKeys(PyObject* o) {
+#if PY_MAJOR_VERSION >= 3
+  return PyMapping_Keys(o);
+#else
+  static char key_method_name[] = "keys";
+  Safe_PyObjectPtr raw_result(PyObject_CallMethod(o, key_method_name, nullptr));
+  return PySequence_Fast(
+      raw_result.get(),
+      "The '.keys()' method of a custom mapping returned a non-sequence.");
+#endif
+}
+
 // Equivalent to Python's 'o.__class__.__name__'
 // Note that '__class__' attribute is set only in new-style classes.
 // A lot of tensorflow code uses __class__ without checks, so it seems like
@@ -85,6 +104,119 @@ string PyObjectToString(PyObject* o) {
   }
 }
 
+class CachedTypeCheck {
+ public:
+  explicit CachedTypeCheck(std::function ternary_predicate)
+      : ternary_predicate_(std::move(ternary_predicate)) {}
+
+  ~CachedTypeCheck() {
+    mutex_lock l(type_to_sequence_map_mu_);
+    for (const auto& pair : type_to_sequence_map_) {
+      Py_DECREF(pair.first);
+    }
+  }
+
+  // Caches successful executions of the one-argument (PyObject*) callable
+  // "ternary_predicate" based on the type of "o". -1 from the callable
+  // indicates an unsuccessful check (not cached), 0 indicates that "o"'s type
+  // does not match the predicate, and 1 indicates that it does. Used to avoid
+  // calling back into Python for expensive isinstance checks.
+  int CachedLookup(PyObject* o) {
+    // Try not to return to Python - see if the type has already been seen
+    // before.
+
+    auto* type = Py_TYPE(o);
+
+    {
+      mutex_lock l(type_to_sequence_map_mu_);
+      auto it = type_to_sequence_map_.find(type);
+      if (it != type_to_sequence_map_.end()) {
+        return it->second;
+      }
+    }
+
+    int check_result = ternary_predicate_(o);
+
+    if (check_result == -1) {
+      return -1;  // Type check error, not cached.
+    }
+
+    // NOTE: This is never decref'd as long as the object lives, which is likely
+    // forever, but we don't want the type to get deleted as long as it is in
+    // the map. This should not be too much of a leak, as there should only be a
+    // relatively small number of types in the map, and an even smaller number
+    // that are eligible for decref. As a precaution, we limit the size of the
+    // map to 1024.
+    {
+      mutex_lock l(type_to_sequence_map_mu_);
+      if (type_to_sequence_map_.size() < kMaxItemsInCache) {
+        Py_INCREF(type);
+        type_to_sequence_map_.insert({type, check_result});
+      }
+    }
+
+    return check_result;
+  }
+
+ private:
+  std::function ternary_predicate_;
+  mutex type_to_sequence_map_mu_;
+  std::unordered_map type_to_sequence_map_
+      GUARDED_BY(type_to_sequence_map_mu_);
+};
+
+// Returns 1 if `o` is considered a mapping for the purposes of Flatten().
+// Returns 0 otherwise.
+// Returns -1 if an error occurred.
+int IsMappingHelper(PyObject* o) {
+  static auto* const check_cache = new CachedTypeCheck([](PyObject* to_check) {
+    return PyObject_IsInstance(to_check, CollectionsMappingType);
+  });
+  if (PyDict_Check(o)) return true;
+  if (TF_PREDICT_FALSE(CollectionsMappingType == nullptr)) {
+    PyErr_SetString(
+        PyExc_RuntimeError,
+        tensorflow::strings::StrCat(
+            "collections.Mapping type has not been set. "
+            "Please call RegisterMappingClass before using this module")
+            .c_str());
+    return -1;
+  }
+  return check_cache->CachedLookup(o);
+}
+
+// Returns 1 if `o` is considered a sequence for the purposes of Flatten().
+// Returns 0 otherwise.
+// Returns -1 if an error occurred.
+int IsSequenceHelper(PyObject* o) {
+  static auto* const check_cache = new CachedTypeCheck([](PyObject* to_check) {
+    int is_instance = PyObject_IsInstance(to_check, CollectionsSequenceType);
+
+    // Don't cache a failed is_instance check.
+    if (is_instance == -1) return -1;
+
+    return static_cast(is_instance != 0 && !IsString(to_check));
+  });
+  // We treat dicts and other mappings as special cases of sequences.
+  if (IsMappingHelper(o)) return true;
+  if (PySet_Check(o) && !WarnedThatSetIsNotSequence) {
+    LOG(WARNING) << "Sets are not currently considered sequences, "
+                    "but this may change in the future, "
+                    "so consider avoiding using them.";
+    WarnedThatSetIsNotSequence = true;
+  }
+  if (TF_PREDICT_FALSE(CollectionsSequenceType == nullptr)) {
+    PyErr_SetString(
+        PyExc_RuntimeError,
+        tensorflow::strings::StrCat(
+            "collections.Sequence type has not been set. "
+            "Please call RegisterSequenceClass before using this module")
+            .c_str());
+    return -1;
+  }
+  return check_cache->CachedLookup(o);
+}
+
 // Implements the same idea as tensorflow.util.nest._yield_value
 // During construction we check if the iterable is a dictionary.
 // If so, we construct a sequence from its sorted keys that will be used
@@ -96,7 +228,12 @@ string PyObjectToString(PyObject* o) {
 // 'iterable' must not be modified while ValIterator is used.
 class ValIterator {
  public:
-  explicit ValIterator(PyObject* iterable) : dict_(nullptr), index_(0) {
+  explicit ValIterator(PyObject* iterable)
+      : dict_(nullptr),
+        mapping_(nullptr),
+        last_mapping_element_(nullptr),
+        seq_(nullptr),
+        index_(0) {
     if (PyDict_Check(iterable)) {
       dict_ = iterable;
       // PyDict_Keys returns a list, which can be used with
@@ -108,6 +245,10 @@ class ValIterator {
       // bugs caused by mixing ordered and plain dicts (e.g., flattening
       // a dict but using a corresponding `OrderedDict` to pack it back).
       PyList_Sort(seq_);
+    } else if (IsMappingHelper(iterable)) {
+      mapping_ = iterable;
+      seq_ = MappingKeys(iterable);
+      PyList_Sort(seq_);
     } else {
       seq_ = PySequence_Fast(iterable, "");
     }
@@ -122,7 +263,9 @@ class ValIterator {
     PyObject* element = nullptr;
     if (index_ < size_) {
       // Both PySequence_Fast_GET_ITEM and PyDict_GetItem return borrowed
-      // references.
+      // references. For general mappings, ValIterator keeps a reference to the
+      // last retrieved element (and decrefs it before producing the next
+      // element) to abstract away the borrowed/new difference.
       element = PySequence_Fast_GET_ITEM(seq_, index_);
       ++index_;
       if (dict_ != nullptr) {
@@ -132,85 +275,32 @@ class ValIterator {
                           "Dictionary was modified during iteration over it");
           return nullptr;
         }
+      } else if (mapping_ != nullptr) {
+        element = PyObject_GetItem(mapping_, element);
+        if (element == nullptr) {
+          PyErr_SetString(PyExc_RuntimeError,
+                          "Mapping was modified during iteration over it");
+          return nullptr;
+        }
+        last_mapping_element_.reset(element);
       }
     }
     return element;
   }
 
  private:
-  PyObject* seq_;
+  // Special casing for things that pass PyDict_Check (faster, no Python calls)
   PyObject* dict_;
+
+  // General mappings which have custom Python logic
+  PyObject* mapping_;
+  Safe_PyObjectPtr last_mapping_element_;
+
+  PyObject* seq_;
   Py_ssize_t size_;
   Py_ssize_t index_;
 };
 
-mutex g_type_to_sequence_map(LINKER_INITIALIZED);
-std::unordered_map* IsTypeSequenceMap() {
-  static auto* const m = new std::unordered_map;
-  return m;
-}
-
-// Returns 1 if `o` is considered a sequence for the purposes of Flatten().
-// Returns 0 otherwise.
-// Returns -1 if an error occurred.
-int IsSequenceHelper(PyObject* o) {
-  if (PyDict_Check(o)) return true;
-  if (PySet_Check(o) && !WarnedThatSetIsNotSequence) {
-    LOG(WARNING) << "Sets are not currently considered sequences, "
-                    "but this may change in the future, "
-                    "so consider avoiding using them.";
-    WarnedThatSetIsNotSequence = true;
-  }
-  if (TF_PREDICT_FALSE(CollectionsSequenceType == nullptr)) {
-    PyErr_SetString(
-        PyExc_RuntimeError,
-        tensorflow::strings::StrCat(
-            "collections.Sequence type has not been set. "
-            "Please call RegisterSequenceClass before using this module")
-            .c_str());
-    return -1;
-  }
-
-  // Try not to return to Python - see if the type has already been seen
-  // before.
-
-  auto* type_to_sequence_map = IsTypeSequenceMap();
-  auto* type = Py_TYPE(o);
-
-  {
-    mutex_lock l(g_type_to_sequence_map);
-    auto it = type_to_sequence_map->find(type);
-    if (it != type_to_sequence_map->end()) {
-      return it->second;
-    }
-  }
-
-  // NOTE: We explicitly release the g_type_to_sequence_map mutex,
-  // because PyObject_IsInstance() may release the GIL, allowing another thread
-  // concurrent entry to this function.
-  int is_instance = PyObject_IsInstance(o, CollectionsSequenceType);
-
-  // Don't cache a failed is_instance check.
-  if (is_instance == -1) return -1;
-
-  bool is_sequence = static_cast(is_instance != 0 && !IsString(o));
-
-  // NOTE: This is never decref'd, but we don't want the type to get deleted
-  // as long as it is in the map. This should not be too much of a
-  // leak, as there should only be a relatively small number of types in the
-  // map, and an even smaller number that are eligible for decref. As a
-  // precaution, we limit the size of the map to 1024.
-  {
-    mutex_lock l(g_type_to_sequence_map);
-    if (type_to_sequence_map->size() < kMaxItemsInCache) {
-      Py_INCREF(type);
-      type_to_sequence_map->insert({type, is_sequence});
-    }
-  }
-
-  return is_sequence;
-}
-
 bool IsSparseTensorValueType(PyObject* o) {
   if (TF_PREDICT_FALSE(SparseTensorValueType == nullptr)) {
     return false;
@@ -226,21 +316,35 @@ int IsSequenceForDataHelper(PyObject* o) {
 
 bool GetNextValuesForDict(PyObject* nested,
                           std::vector* next_values) {
-  std::vector result;
-
-  PyObject* keys = PyDict_Keys(nested);
-  if (PyList_Sort(keys) == -1) return false;
-  Py_ssize_t size = PyList_Size(keys);
+  Safe_PyObjectPtr keys(PyDict_Keys(nested));
+  if (PyList_Sort(keys.get()) == -1) return false;
+  Py_ssize_t size = PyList_Size(keys.get());
   for (Py_ssize_t i = 0; i < size; ++i) {
     // We know that key and item will not be deleted because nested owns
     // a reference to them and callers of flatten must not modify nested
     // while the method is running.
-    PyObject* key = PyList_GET_ITEM(keys, i);
+    PyObject* key = PyList_GET_ITEM(keys.get(), i);
     PyObject* item = PyDict_GetItem(nested, key);
     Py_INCREF(item);
     next_values->emplace_back(item);
   }
-  Py_DECREF(keys);
+  return true;
+}
+
+bool GetNextValuesForMapping(PyObject* nested,
+                             std::vector* next_values) {
+  Safe_PyObjectPtr keys(MappingKeys(nested));
+  if (keys.get() == nullptr) {
+    return false;
+  }
+  if (PyList_Sort(keys.get()) == -1) return false;
+  Py_ssize_t size = PyList_Size(keys.get());
+  for (Py_ssize_t i = 0; i < size; ++i) {
+    PyObject* key = PyList_GET_ITEM(keys.get(), i);
+    // Unlike PyDict_GetItem, PyObject_GetItem returns a new reference.
+    PyObject* item = PyObject_GetItem(nested, key);
+    next_values->emplace_back(item);
+  }
   return true;
 }
 
@@ -265,6 +369,9 @@ bool GetNextValues(PyObject* nested,
   if (PyDict_Check(nested)) {
     // if nested is dictionary, sort it by key and recurse on each value
     return GetNextValuesForDict(nested, next_values);
+  } else if (IsMappingHelper(nested)) {
+    // same treatment as dictionaries, but for custom mapping types
+    return GetNextValuesForMapping(nested, next_values);
   }
   // iterate and recurse
   return GetNextValuesForIterable(nested, next_values);
@@ -276,6 +383,9 @@ bool GetNextValuesForData(PyObject* nested,
   if (PyDict_Check(nested)) {
     // if nested is dictionary, sort it by key and recurse on each value
     return GetNextValuesForDict(nested, next_values);
+  } else if (IsMappingHelper(nested)) {
+    // same treatment as dictionaries, but for custom mapping types
+    return GetNextValuesForMapping(nested, next_values);
   } else if (IsSparseTensorValueType(nested)) {
     // if nested is a SparseTensorValue, just return itself as a single item
     Py_INCREF(nested);
@@ -320,8 +430,8 @@ bool FlattenHelper(
 // 'dict1' and 'dict2' are assumed to be Python dictionaries.
 void SetDifferentKeysError(PyObject* dict1, PyObject* dict2, string* error_msg,
                            bool* is_type_error) {
-  PyObject* k1 = PyDict_Keys(dict1);
-  PyObject* k2 = PyDict_Keys(dict2);
+  PyObject* k1 = MappingKeys(dict1);
+  PyObject* k2 = MappingKeys(dict2);
   *is_type_error = false;
   *error_msg = tensorflow::strings::StrCat(
       "The two dictionaries don't have the same set of keys. "
@@ -396,9 +506,12 @@ bool AssertSameStructureHelper(PyObject* o1, PyObject* o2, bool check_types,
       }
     } else if (type1 != type2
                /* If both sequences are list types, don't complain. This allows
-               one to be a list subclass (e.g. _ListWrapper used for automatic
-               dependency tracking.) */
-               && !(PyList_Check(o1) && PyList_Check(o2))) {
+                  one to be a list subclass (e.g. _ListWrapper used for
+                  automatic dependency tracking.) */
+               && !(PyList_Check(o1) && PyList_Check(o2))
+               /* Two mapping types will also compare equal, making _DictWrapper
+                  and dict compare equal. */
+               && !(IsMappingHelper(o1) && IsMappingHelper(o2))) {
       *is_type_error = true;
       *error_msg = tensorflow::strings::StrCat(
           "The two namedtuples don't have the same sequence type. "
@@ -423,6 +536,24 @@ bool AssertSameStructureHelper(PyObject* o1, PyObject* o2, bool check_types,
           return true;
         }
       }
+    } else if (IsMappingHelper(o1)) {
+      // Fallback for custom mapping types. Instead of using PyDict methods
+      // which stay in C, we call iter(o1).
+      if (PyMapping_Size(o1) != PyMapping_Size(o2)) {
+        SetDifferentKeysError(o1, o2, error_msg, is_type_error);
+        return true;
+      }
+
+      Safe_PyObjectPtr iter(PyObject_GetIter(o1));
+      PyObject* key;
+      while ((key = PyIter_Next(iter.get())) != nullptr) {
+        if (!PyMapping_HasKey(o2, key)) {
+          SetDifferentKeysError(o1, o2, error_msg, is_type_error);
+          Py_DECREF(key);
+          return true;
+        }
+        Py_DECREF(key);
+      }
     }
   }
 
@@ -470,6 +601,19 @@ void RegisterSequenceClass(PyObject* sequence_class) {
   CollectionsSequenceType = sequence_class;
 }
 
+void RegisterMappingClass(PyObject* mapping_class) {
+  if (!PyType_Check(mapping_class)) {
+    PyErr_SetString(
+        PyExc_TypeError,
+        tensorflow::strings::StrCat(
+            "Expecting a class definition for `collections.Mapping`. Got ",
+            Py_TYPE(mapping_class)->tp_name)
+            .c_str());
+    return;
+  }
+  CollectionsMappingType = mapping_class;
+}
+
 void RegisterSparseTensorValueClass(PyObject* sparse_tensor_value_class) {
   if (!PyType_Check(sparse_tensor_value_class)) {
     PyErr_SetString(
diff --git a/tensorflow/python/util/util.h b/tensorflow/python/util/util.h
index 70efc10c9abe7c57da61311bb2eb7ae362a48e3d..41dcc969f88f8bb81b70b734cbf41fe10135a9c0 100644
--- a/tensorflow/python/util/util.h
+++ b/tensorflow/python/util/util.h
@@ -118,7 +118,9 @@ PyObject* Flatten(PyObject* nested);
 // the type from the module. This approach also requires some trigger from
 // Python so that we know that Python interpreter had been initialzied.
 void RegisterSequenceClass(PyObject* sequence_class);
-// Similar to the above function, except for the
+// Like RegisterSequenceClass, but for collections.Mapping.
+void RegisterMappingClass(PyObject* mapping_class);
+// Similar to the above functions, except for the
 // sparse_tensor.SparseTensorValue class.
 void RegisterSparseTensorValueClass(PyObject* sparse_tensor_value_class);
 
diff --git a/tensorflow/python/util/util.i b/tensorflow/python/util/util.i
index 9f3b11b982bb0d52f903b09975cc7029fa8cb013..6ad148429541a6855a4f1da5a5c89d6479f53f39 100644
--- a/tensorflow/python/util/util.i
+++ b/tensorflow/python/util/util.i
@@ -31,6 +31,9 @@ limitations under the License.
 %unignore tensorflow::swig::RegisterSequenceClass;
 %noexception tensorflow::swig::RegisterSequenceClass;
 
+%unignore tensorflow::swig::RegisterMappingClass;
+%noexception tensorflow::swig::RegisterMappingClass;
+
 %unignore tensorflow::swig::RegisterSparseTensorValueClass;
 %noexception tensorflow::swig::RegisterSparseTensorValueClass;
 
diff --git a/tensorflow/security/advisory/tfsa-2018-001.md b/tensorflow/security/advisory/tfsa-2018-001.md
index bb97543a21988b4370ddac912102add6a10e2b35..1966789c8467539ef7f19e281b3a4acfbaace6ae 100644
--- a/tensorflow/security/advisory/tfsa-2018-001.md
+++ b/tensorflow/security/advisory/tfsa-2018-001.md
@@ -22,7 +22,7 @@ TensorFlow 1.3.0, 1.3.1, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0
 ### Mitigation
 
 We have patched the vulnerability in GitHub commit
-[49f73c55](https://github.com/tensorflow/tensorflow/commit/49f73c55d56edffebde4bca4a407ad69c1cae4333c55).
+[49f73c55](https://github.com/tensorflow/tensorflow/commit/49f73c55d56edffebde4bca4a407ad69c1cae433).
 If users are running TensorFlow in production or on untrusted data, they are
 encouraged to apply this patch.
 
diff --git a/tensorflow/security/index.md b/tensorflow/security/index.md
index ea39e17ab2bb417bba1ebe4a589833309fc2c626..0f176151c2c4527d60c0cb451d33c9206a50bd81 100644
--- a/tensorflow/security/index.md
+++ b/tensorflow/security/index.md
@@ -4,7 +4,7 @@ We regularly publish security advisories about using TensorFlow.
 
 *Note*: In conjunction with these security advisories, we strongly encourage
 TensorFlow users to read and understand TensorFlow's security model as outlined
-in (https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md)[SECURITY.md].
+in [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md).
 
 | Advisory Number | Type               | Versions affected | Reported by           | Additional Information      |
 |-----------------|--------------------|:-----------------:|-----------------------|-----------------------------|
diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc
index 84916385a89b6e2bafb8a3c0a8f435ec9626e816..1c3940e92ce506e3fd73f0896995320588965cab 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.cc
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc
@@ -322,6 +322,7 @@ port::Status GetLoadedCudnnVersion(CudnnVersion* version) {
 CudnnSupport::CudnnSupport(CUDAExecutor* parent) : parent_(parent) {}
 
 port::Status CudnnSupport::Init() {
+  ScopedActivateExecutorContext context(parent_);
   cudnnHandle_t cudnn_handle = nullptr;
   auto status = cudnnCreate(&cudnn_handle);
   if (status == CUDNN_STATUS_SUCCESS) {
@@ -791,6 +792,11 @@ class CudnnActivationDescriptor {
     double relu_ceiling = 0.0;
     cudnnActivationMode_t mode;
     switch (activation_mode) {
+#if CUDNN_VERSION >= 7100
+      case dnn::ActivationMode::kNone:
+        mode = CUDNN_ACTIVATION_IDENTITY;
+        break;
+#endif
       case dnn::ActivationMode::kRelu6:
         relu_ceiling = 6.0;
         mode = CUDNN_ACTIVATION_CLIPPED_RELU;
@@ -2480,10 +2486,11 @@ port::Status CudnnSupport::DoFusedConvolveImpl(
     DeviceMemory* output_data, ScratchAllocator* scratch_allocator,
     const dnn::AlgorithmConfig& algorithm_config,
     dnn::ProfileResult* output_profile_result) {
-  if (activation_mode != dnn::ActivationMode::kRelu) {
+  if (activation_mode != dnn::ActivationMode::kRelu &&
+      activation_mode != dnn::ActivationMode::kNone) {
     return port::Status(port::error::INVALID_ARGUMENT,
                         "cudnnConvolutionBiasActivationForward() only supports "
-                        "Relu activation.");
+                        "Relu or None activation.");
   }
 
   CudnnTensorDescriptor conv_input_nd(
@@ -3603,7 +3610,7 @@ bool CudnnSupport::DoPoolForward(
     const dnn::BatchDescriptor& input_dimensions,
     const DeviceMemory& input_data,
     const dnn::BatchDescriptor& output_dimensions,
-    DeviceMemory* output_data) {
+    DeviceMemory* output_data, ScratchAllocator* workspace_allocator) {
   // Alpha is the scaling factor for input.
   double alpha = 1.0;
   // Beta is the scaling factor for output.
@@ -3628,7 +3635,7 @@ bool CudnnSupport::DoPoolForward(
     const dnn::BatchDescriptor& input_dimensions,
     const DeviceMemory& input_data,
     const dnn::BatchDescriptor& output_dimensions,
-    DeviceMemory* output_data) {
+    DeviceMemory* output_data, ScratchAllocator* workspace_allocator) {
   // Alpha is the scaling factor for input.
   float alpha = 1.0;
   // Beta is the scaling factor for output.
@@ -3653,7 +3660,8 @@ bool CudnnSupport::DoPoolForward(
     const dnn::BatchDescriptor& input_dimensions,
     const DeviceMemory& input_data,
     const dnn::BatchDescriptor& output_dimensions,
-    DeviceMemory* output_data) {
+    DeviceMemory* output_data,
+    ScratchAllocator* workspace_allocator) {
   // Alpha is the scaling factor for input.
   float alpha = 1.0;
   // Beta is the scaling factor for output.
@@ -3679,7 +3687,8 @@ bool CudnnSupport::DoPoolBackward(
     const dnn::BatchDescriptor& output_dimensions,
     const DeviceMemory& output_data,
     const DeviceMemory& input_diff_data,
-    DeviceMemory* output_diff_data) {
+    DeviceMemory* output_diff_data,
+    ScratchAllocator* workspace_allocator) {
   // Alpha is the scaling factor for input.
   double alpha = 1.0;
   // Beta is the scaling factor for output.
@@ -3708,7 +3717,8 @@ bool CudnnSupport::DoPoolBackward(
     const dnn::BatchDescriptor& output_dimensions,
     const DeviceMemory& output_data,
     const DeviceMemory& input_diff_data,
-    DeviceMemory* output_diff_data) {
+    DeviceMemory* output_diff_data,
+    ScratchAllocator* workspace_allocator) {
   // Alpha is the scaling factor for input.
   float alpha = 1.0;
   // Beta is the scaling factor for output.
@@ -3737,7 +3747,8 @@ bool CudnnSupport::DoPoolBackward(
     const dnn::BatchDescriptor& output_dimensions,
     const DeviceMemory& output_data,
     const DeviceMemory& input_diff_data,
-    DeviceMemory* output_diff_data) {
+    DeviceMemory* output_diff_data,
+    ScratchAllocator* workspace_allocator) {
   // Alpha is the scaling factor for input.
   float alpha = 1.0;
   // Beta is the scaling factor for output.
@@ -3806,7 +3817,8 @@ bool CudnnSupport::DoNormalizeBackwardWithDimensions(
     const dnn::BatchDescriptor& dimensions, const DeviceMemory& raw_data,
     const DeviceMemory& normalized_data,
     const DeviceMemory& normalized_variable_gradient,
-    DeviceMemory* raw_variable_gradient) {
+    DeviceMemory* raw_variable_gradient,
+    ScratchAllocator* workspace_allocator) {
   // Check for unsupported modes.
   if (normalize_descriptor.wrap_around()) {
     LOG(ERROR) << "CUDA LRN does not support cudnn-around mode";
diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.h b/tensorflow/stream_executor/cuda/cuda_dnn.h
index c924d41cb5239d704e658f0b5452e04087caeba2..9d88f971bb17510099978a03b673f39576c32587 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.h
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.h
@@ -515,21 +515,24 @@ class CudnnSupport : public dnn::DnnSupport {
                      const dnn::BatchDescriptor& input_dimensions,
                      const DeviceMemory& input_data,
                      const dnn::BatchDescriptor& output_dimensions,
-                     DeviceMemory* output_data) override;
+                     DeviceMemory* output_data,
+                     ScratchAllocator* workspace_allocator) override;
 
   bool DoPoolForward(Stream* stream,
                      const dnn::PoolingDescriptor& pooling_dimensions,
                      const dnn::BatchDescriptor& input_dimensions,
                      const DeviceMemory& input_data,
                      const dnn::BatchDescriptor& output_dimensions,
-                     DeviceMemory* output_data) override;
+                     DeviceMemory* output_data,
+                     ScratchAllocator* workspace_allocator) override;
 
   bool DoPoolForward(Stream* stream,
                      const dnn::PoolingDescriptor& pooling_dimensions,
                      const dnn::BatchDescriptor& input_dimensions,
                      const DeviceMemory& input_data,
                      const dnn::BatchDescriptor& output_dimensions,
-                     DeviceMemory* output_data) override;
+                     DeviceMemory* output_data,
+                     ScratchAllocator* workspace_allocator) override;
 
   bool DoPoolBackward(Stream* stream,
                       const dnn::PoolingDescriptor& pooling_dimensions,
@@ -538,7 +541,8 @@ class CudnnSupport : public dnn::DnnSupport {
                       const dnn::BatchDescriptor& output_dimensions,
                       const DeviceMemory& output_data,
                       const DeviceMemory& input_diff_data,
-                      DeviceMemory* output_diff_data) override;
+                      DeviceMemory* output_diff_data,
+                      ScratchAllocator* workspace_allocator) override;
 
   bool DoPoolBackward(Stream* stream,
                       const dnn::PoolingDescriptor& pooling_dimensions,
@@ -547,7 +551,8 @@ class CudnnSupport : public dnn::DnnSupport {
                       const dnn::BatchDescriptor& output_dimensions,
                       const DeviceMemory& output_data,
                       const DeviceMemory& input_diff_data,
-                      DeviceMemory* output_diff_data) override;
+                      DeviceMemory* output_diff_data,
+                      ScratchAllocator* workspace_allocator) override;
 
   bool DoPoolBackward(Stream* stream,
                       const dnn::PoolingDescriptor& pooling_dimensions,
@@ -556,7 +561,8 @@ class CudnnSupport : public dnn::DnnSupport {
                       const dnn::BatchDescriptor& output_dimensions,
                       const DeviceMemory& output_data,
                       const DeviceMemory& input_diff_data,
-                      DeviceMemory* output_diff_data) override;
+                      DeviceMemory* output_diff_data,
+                      ScratchAllocator* workspace_allocator) override;
 
   bool DoNormalize(Stream* stream,
                    const dnn::NormalizeDescriptor& normalize_descriptor,
@@ -575,7 +581,8 @@ class CudnnSupport : public dnn::DnnSupport {
       const DeviceMemory& raw_data,
       const DeviceMemory& normalized_data,
       const DeviceMemory& normalized_variable_gradient,
-      DeviceMemory* raw_variable_gradient) override;
+      DeviceMemory* raw_variable_gradient,
+      ScratchAllocator* workspace_allocator) override;
 
   bool DoDepthConcatenate(
       Stream* stream, port::ArraySlice input_dimensions,
diff --git a/tensorflow/stream_executor/cuda/cuda_driver.cc b/tensorflow/stream_executor/cuda/cuda_driver.cc
index d508f6594a9f9ac3c924b0b952620b6a4ac727ea..dbece3adf938da95d550f32da14cd5f67ff802c2 100644
--- a/tensorflow/stream_executor/cuda/cuda_driver.cc
+++ b/tensorflow/stream_executor/cuda/cuda_driver.cc
@@ -102,117 +102,16 @@ class CreatedContexts {
 /* static */ int64 CreatedContexts::next_id_ = 1;  // 0 means "no context"
 
 // Formats CUresult to output prettified values into a log stream.
-// Error summaries taken from:
-// http://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__TYPES.html#group__CUDA__TYPES_1gc6c391505e117393cc2558fff6bfc2e9
-//
-// TODO(leary) switch to cuGetErrorName when updated cuda.h is available.
 string ToString(CUresult result) {
-#define OSTREAM_CUDA_ERROR(__name) \
-  case CUDA_ERROR_##__name:        \
-    return "CUDA_ERROR_" #__name;
-
-///////////////
-// NOTE: here we specify return code values outside of the enum explicitly
-// because our in-tree cuda.h is from the CUDA 5.5 SDK, but CUDA 6.0+ driver
-// libraries are deployed in the fleet these error codes are backwards
-// compatible, but if we see a "new" one, we want to be able to identify it in
-// the logs.
-//
-// Once we get a cuda.h that has cuGetErrorName (TODO is above) we can
-// eliminate this function and just rely on the driver to provide us these
-// strings.
-//
-// NOTE: "Must reboot all context" below is shorthand for, "must
-// destroy/recreate the offending context and any allocation which come from
-// it if you are to continue using CUDA."
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wswitch"
-  switch (result) {
-    OSTREAM_CUDA_ERROR(INVALID_VALUE)
-    OSTREAM_CUDA_ERROR(OUT_OF_MEMORY)
-    OSTREAM_CUDA_ERROR(NOT_INITIALIZED)
-    OSTREAM_CUDA_ERROR(DEINITIALIZED)
-    OSTREAM_CUDA_ERROR(NO_DEVICE)
-    OSTREAM_CUDA_ERROR(INVALID_DEVICE)
-    OSTREAM_CUDA_ERROR(INVALID_IMAGE)
-    OSTREAM_CUDA_ERROR(INVALID_CONTEXT)
-    OSTREAM_CUDA_ERROR(INVALID_HANDLE)
-    OSTREAM_CUDA_ERROR(NOT_FOUND)
-    OSTREAM_CUDA_ERROR(NOT_READY)
-    OSTREAM_CUDA_ERROR(NO_BINARY_FOR_GPU)
-
-    // Encountered an uncorrectable ECC error during execution.
-    OSTREAM_CUDA_ERROR(ECC_UNCORRECTABLE)
-
-    // Load/store on an invalid address. Must reboot all context.
-    case 700:
-      return "CUDA_ERROR_ILLEGAL_ADDRESS";
-    // Passed too many / wrong arguments, too many threads for register count.
-    case 701:
-      return "CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES";
-    // Kernel took too long to execute.
-    case 702:
-      return "CUDA_ERROR_LAUNCH_TIMEOUT";
-    // Kernel launch uses an incompatible texturing mode.
-    case 703:
-      return "CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING";
-    // Trying to re-enable peer access that already has it enabled.
-    case 704:
-      return "CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED";
-    // Trying to disable peer access that has not yet been enabled.
-    case 705:
-      return "CUDA_ERROR_PEER_ACCESS_NOT_ENABLED";
-    // Primary context for the specified device has already been initialized.
-    case 708:
-      return "CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE";
-    // Context current to calling thread has been destroyed or is a primary
-    // context that has not yet been initialized.
-    case 709:
-      return "CUDA_ERROR_CONTEXT_IS_DESTROYED";
-    // Device-side assert triggered during kernel execution. Must reboot all
-    // context.
-    case 710:
-      return "CUDA_ERROR_ASSERT";
-    // Hardware resources to enable peer access have been exhausted.
-    case 711:
-      return "CUDA_ERROR_TOO_MANY_PEERS";
-    // Memory range has already been registered.
-    case 712:
-      return "CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED";
-    // Pointer does not correspond to any currently registered memory region.
-    case 713:
-      return "CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED";
-    // Due to stack corruption or exceeding stack size limit. Must reboot all
-    // context.
-    case 714:
-      return "CUDA_ERROR_HARDWARE_STACK_ERROR";
-    case 715:
-      return "CUDA_ERROR_ILLEGAL_INSTRUCTION";
-    // Load/store on an unaligned memory address. Must reboot all context.
-    case 716:
-      return "CUDA_ERROR_MISALIGNED_ADDRESS";
-    // Device instruction with specific address space given address not
-    // belonging to allowed address space. Must reboot all context.
-    case 717:
-      return "CUDA_ERROR_INVALID_ADDRESS_SPACE";
-    // Device program counter wrapped its address space. Must reboot all
-    // context.
-    case 718:
-      return "CUDA_ERROR_INVALID_PC";
-    // Exception on device while executing a kernel; e.g. deref invalid device
-    // pointer, accessing OOB shared memory. Must reboot all context.
-    case 719:
-      return "CUDA_ERROR_LAUNCH_FAILED";
-
-      OSTREAM_CUDA_ERROR(CONTEXT_ALREADY_IN_USE)
-      OSTREAM_CUDA_ERROR(PEER_ACCESS_UNSUPPORTED)
-      OSTREAM_CUDA_ERROR(NOT_PERMITTED)
-      OSTREAM_CUDA_ERROR(NOT_SUPPORTED)
-      OSTREAM_CUDA_ERROR(UNKNOWN)  // Unknown internal error to CUDA.
-    default:
-      return port::StrCat("CUresult(", static_cast(result), ")");
+  const char *error_name;
+  if (cuGetErrorName(result, &error_name)) {
+    return port::StrCat("UNKNOWN ERROR (", static_cast(result), ")");
+  }
+  const char *error_string;
+  if (cuGetErrorString(result, &error_string)) {
+    return error_name;
   }
-#pragma GCC diagnostic pop
+  return port::StrCat(error_name, ": ", error_string);
 }
 
 // Returns the current context and checks that it is in the set of CUDA contexts
diff --git a/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc b/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc
index f11022ef1dfd4a1a08d035f5328724d93ac808be..73f05b94db8f022825ccc72c2222a78634423ddf 100644
--- a/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc
+++ b/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc
@@ -206,6 +206,48 @@ static string GetBinaryDir(bool strip_exe) {
   return exe_path;
 }
 
+bool CUDAExecutor::LoadModuleFromCuBin(const char *cubin, CUmodule *module) {
+  uint64_t module_refcount;
+  std::tie(*module, module_refcount) = gpu_binary_to_module_[cubin];
+
+  if (*module == nullptr) {
+    auto load_status = CUDADriver::LoadCubin(context_, cubin, module);
+    if (!load_status.ok()) {
+      LOG(ERROR) << "failed to load CUBIN: " << load_status;
+      return false;
+    }
+    module_refcount = 1;
+    VLOG(3) << "Loaded CUBIN " << static_cast(cubin)
+            << " as module " << *module;
+  } else {
+    ++module_refcount;
+    VLOG(3) << "CUBIN " << static_cast(cubin)
+            << " is already loaded as module " << *module;
+  }
+  gpu_binary_to_module_[cubin] = {*module, module_refcount};
+  return true;
+}
+
+bool CUDAExecutor::LoadModuleFromPtx(const char *ptx, CUmodule *module) {
+  uint64_t module_refcount;
+  std::tie(*module, module_refcount) = gpu_binary_to_module_[ptx];
+
+  if (*module == nullptr) {
+    if (!CUDADriver::LoadPtx(context_, ptx, module)) {
+      return false;
+    }
+    VLOG(3) << "Loaded PTX " << static_cast(ptx) << " as module "
+            << *module;
+    module_refcount = 1;
+  } else {
+    ++module_refcount;
+    VLOG(3) << "PTX " << static_cast(ptx)
+            << " is already loaded as module " << module;
+  }
+  gpu_binary_to_module_[ptx] = {*module, module_refcount};
+  return true;
+}
+
 bool CUDAExecutor::GetKernel(const MultiKernelLoaderSpec &spec,
                              KernelBase *kernel) {
   CUDAKernel *cuda_kernel = AsCUDAKernel(kernel);
@@ -215,28 +257,13 @@ bool CUDAExecutor::GetKernel(const MultiKernelLoaderSpec &spec,
   VLOG(3) << "GetKernel on kernel " << kernel << " : " << kernel->name();
 
   if (spec.has_cuda_cubin_in_memory()) {
+    mutex_lock lock{in_memory_modules_mu_};
     kernelname = &spec.cuda_cubin_in_memory().kernelname();
     const char *cubin = spec.cuda_cubin_in_memory().bytes();
-    mutex_lock lock{in_memory_modules_mu_};
-    uint64_t module_refcount;
-    std::tie(module, module_refcount) = gpu_binary_to_module_[cubin];
-
-    if (module == nullptr) {
-      auto load_status = CUDADriver::LoadCubin(context_, cubin, &module);
-      if (!load_status.ok()) {
-        LOG(ERROR) << "failed to load CUBIN: " << load_status;
-        return false;
-      }
-      module_refcount = 1;
-      VLOG(3) << "Loaded CUBIN " << static_cast(cubin)
-              << " as module " << module;
-    } else {
-      ++module_refcount;
-      VLOG(3) << "CUBIN " << static_cast(cubin)
-              << " is already loaded as module " << module;
+    if (!LoadModuleFromCuBin(cubin, &module)) {
+      return false;
     }
     kernel_to_gpu_binary_[kernel] = cubin;
-    gpu_binary_to_module_[cubin] = {module, module_refcount};
   } else if (spec.has_cuda_ptx_in_memory()) {
     kernelname = &spec.cuda_ptx_in_memory().kernelname();
 
@@ -254,24 +281,10 @@ bool CUDAExecutor::GetKernel(const MultiKernelLoaderSpec &spec,
     }
 
     mutex_lock lock{in_memory_modules_mu_};
-    uint64_t module_refcount;
-    std::tie(module, module_refcount) = gpu_binary_to_module_[ptx];
-
-    if (module == nullptr) {
-      if (!CUDADriver::LoadPtx(context_, ptx, &module)) {
-        LOG(ERROR) << "failed to load PTX for kernel " << *kernelname;
-        return false;
-      }
-      VLOG(3) << "Loaded PTX " << static_cast(ptx)
-              << " as module " << module;
-      module_refcount = 1;
-    } else {
-      ++module_refcount;
-      VLOG(3) << "PTX " << static_cast(ptx)
-              << " is already loaded as module " << module;
+    if (!LoadModuleFromPtx(ptx, &module)) {
+      return false;
     }
     kernel_to_gpu_binary_[kernel] = ptx;
-    gpu_binary_to_module_[ptx] = {module, module_refcount};
   } else {
     LOG(WARNING) << "no method of loading CUDA kernel provided";
     return false;
@@ -295,6 +308,23 @@ bool CUDAExecutor::GetKernel(const MultiKernelLoaderSpec &spec,
   return true;
 }
 
+bool CUDAExecutor::UnloadGpuBinary(const void *gpu_binary) {
+  auto module_it = gpu_binary_to_module_.find(gpu_binary);
+  if (gpu_binary_to_module_.end() == module_it) {
+    VLOG(3) << "No loaded CUDA module for " << gpu_binary;
+    return false;
+  }
+  auto &module = module_it->second.first;
+  auto &refcount = module_it->second.second;
+  VLOG(3) << "Found CUDA module " << module << " with refcount " << refcount;
+  if (--refcount == 0) {
+    VLOG(3) << "Unloading CUDA module " << module;
+    CUDADriver::UnloadModule(context_, module);
+    gpu_binary_to_module_.erase(module_it);
+  }
+  return true;
+}
+
 void CUDAExecutor::UnloadKernel(const KernelBase *kernel) {
   VLOG(3) << "Unloading kernel " << kernel << " : " << kernel->name();
 
@@ -307,25 +337,52 @@ void CUDAExecutor::UnloadKernel(const KernelBase *kernel) {
   }
   VLOG(3) << "Kernel " << kernel << " : " << kernel->name()
           << " has loaded GPU code " << gpu_binary_it->second;
-  auto module_it = gpu_binary_to_module_.find(gpu_binary_it->second);
-  if (gpu_binary_to_module_.end() == module_it) {
-    VLOG(3) << "Kernel " << kernel << " : " << kernel->name()
-            << " has no loaded CUDA module.";
-    return;  // This kernel never loaded any modules
-  }
-  auto &module = module_it->second.first;
-  auto &refcount = module_it->second.second;
-  VLOG(3) << "Kernel " << kernel << " : " << kernel->name()
-          << " has loaded GPU code " << gpu_binary_it->second
-          << " into CUDA module " << module << " with refcount " << refcount;
-  if (--refcount == 0) {
-    VLOG(3) << "Unloading CUDA module " << module;
-    CUDADriver::UnloadModule(context_, module);
-    gpu_binary_to_module_.erase(module_it);
-  }
+  UnloadGpuBinary(gpu_binary_it->second);
   kernel_to_gpu_binary_.erase(gpu_binary_it);
 }
 
+bool CUDAExecutor::LoadModule(const MultiModuleLoaderSpec &spec,
+                              ModuleHandle *module_handle) {
+  // In CUDAExecutor we store the pointer to the GPU binary (PTX or CUBIN) as
+  // ModuleHandle::id().
+  CUmodule cu_module;
+  if (spec.has_cuda_cubin_in_memory()) {
+    mutex_lock lock{in_memory_modules_mu_};
+    if (!LoadModuleFromCuBin(
+            reinterpret_cast(spec.cuda_cubin_in_memory().data()),
+            &cu_module)) {
+      return false;
+    }
+    *module_handle = ModuleHandle(const_cast(
+        static_cast(spec.cuda_cubin_in_memory().data())));
+    return true;
+  } else if (spec.has_cuda_ptx_in_memory()) {
+    if (cc_major_ == 0 && cc_minor_ == 0) {
+      return false;
+    }
+
+    if (!spec.cuda_ptx_in_memory()) {
+      return false;
+    }
+
+    mutex_lock lock{in_memory_modules_mu_};
+    if (!LoadModuleFromPtx(spec.cuda_ptx_in_memory(), &cu_module)) {
+      return false;
+    }
+    *module_handle = ModuleHandle(const_cast(
+        static_cast(spec.cuda_ptx_in_memory())));
+    return true;
+  }
+  LOG(WARNING) << "no method of loading CUDA module provided";
+  return false;
+}
+
+bool CUDAExecutor::UnloadModule(ModuleHandle module_handle) {
+  const char *gpu_binary = reinterpret_cast(module_handle.id());
+  mutex_lock lock{in_memory_modules_mu_};
+  return UnloadGpuBinary(gpu_binary);
+}
+
 bool CUDAExecutor::GetKernelMetadata(CUDAKernel *cuda_kernel,
                                      KernelMetadata *kernel_metadata) {
   int value;
@@ -783,16 +840,26 @@ bool CUDAExecutor::DeviceMemoryUsage(int64 *free, int64 *total) const {
   return CUDADriver::GetDeviceMemoryInfo(context_, free, total);
 }
 
-bool CUDAExecutor::GetSymbol(const string& symbol_name, void **mem,
+bool CUDAExecutor::GetSymbol(const string &symbol_name,
+                             ModuleHandle module_handle, void **mem,
                              size_t *bytes) {
+  auto lookup_in_module = [&](CUmodule module) {
+    CHECK(module != nullptr);
+    return CUDADriver::GetModuleSymbol(context_, module, symbol_name.c_str(),
+                                       reinterpret_cast(mem),
+                                       bytes);
+  };
+
   {  // give limited scope to mutex_lock
     mutex_lock lock{in_memory_modules_mu_};
+    if (static_cast(module_handle)) {
+      auto it = gpu_binary_to_module_.find(module_handle.id());
+      CHECK(it != gpu_binary_to_module_.end());
+      return lookup_in_module(it->second.first);
+    }
+
     for (auto &it : gpu_binary_to_module_) {
-      CUmodule module = it.second.first;
-      CHECK(module != nullptr);
-      if (CUDADriver::GetModuleSymbol(context_, module, symbol_name.c_str(),
-                                      reinterpret_cast(mem),
-                                      bytes)) {
+      if (lookup_in_module(it.second.first)) {
         return true;
       }
     }
@@ -844,7 +911,7 @@ CUDAExecutor::GetTimerImplementation() {
   return std::unique_ptr(new CUDATimer(this));
 }
 
-void *CUDAExecutor::CudaContextHack() { return context_; }
+void *CUDAExecutor::GpuContextHack() { return context_; }
 
 CudaContext* CUDAExecutor::cuda_context() { return context_; }
 
diff --git a/tensorflow/stream_executor/cuda/cuda_gpu_executor.h b/tensorflow/stream_executor/cuda/cuda_gpu_executor.h
index 773cbfb8a17a416d18ae599bf4f72e1550538dee..8a954d5461c60749019c87971cee22089bbd22e5 100644
--- a/tensorflow/stream_executor/cuda/cuda_gpu_executor.h
+++ b/tensorflow/stream_executor/cuda/cuda_gpu_executor.h
@@ -62,6 +62,9 @@ class CUDAExecutor : public internal::StreamExecutorInterface {
   bool GetKernel(const MultiKernelLoaderSpec &spec,
                  KernelBase *kernel) override;
   void UnloadKernel(const KernelBase *kernel) override;
+  bool LoadModule(const MultiModuleLoaderSpec &spec,
+                  ModuleHandle *module_handle) override;
+  bool UnloadModule(ModuleHandle module_handle) override;
 
   bool Launch(Stream *stream, const ThreadDim &thread_dims,
               const BlockDim &block_dims, const KernelBase &k,
@@ -175,7 +178,8 @@ class CUDAExecutor : public internal::StreamExecutorInterface {
 
   // Search for the symbol and returns a device pointer and size.
   // Returns false if symbol does not exist.
-  bool GetSymbol(const string& symbol_name, void **mem, size_t *bytes) override;
+  bool GetSymbol(const string &symbol_name, ModuleHandle module_handle,
+                 void **mem, size_t *bytes) override;
 
   DeviceDescription *PopulateDeviceDescription() const override;
 
@@ -210,7 +214,7 @@ class CUDAExecutor : public internal::StreamExecutorInterface {
 
   std::unique_ptr GetTimerImplementation() override;
 
-  void *CudaContextHack() override;
+  void *GpuContextHack() override;
 
   CudaContext* cuda_context();
 
@@ -239,6 +243,16 @@ class CUDAExecutor : public internal::StreamExecutorInterface {
   void VlogOccupancyInfo(const KernelBase &kernel, const ThreadDim &thread_dims,
                          const BlockDim &block_dims);
 
+  bool LoadModuleFromCuBin(const char *cubin, CUmodule *module)
+      EXCLUSIVE_LOCKS_REQUIRED(in_memory_modules_mu_);
+
+  // Loads the PTX text `ptx` as a CUDA module.  `ptx` must be null terminated.
+  bool LoadModuleFromPtx(const char *ptx, CUmodule *module)
+      EXCLUSIVE_LOCKS_REQUIRED(in_memory_modules_mu_);
+
+  bool UnloadGpuBinary(const void *gpu_binary)
+      EXCLUSIVE_LOCKS_REQUIRED(in_memory_modules_mu_);
+
   // Guards the in-memory-module mapping.
   mutex in_memory_modules_mu_;
 
diff --git a/tensorflow/stream_executor/cuda/cuda_stream.h b/tensorflow/stream_executor/cuda/cuda_stream.h
index 02edff643117fc2e3c6e6f74d2932f3f4c00c66d..bb8bda4755344d859668425f89614cc87d7e2d3e 100644
--- a/tensorflow/stream_executor/cuda/cuda_stream.h
+++ b/tensorflow/stream_executor/cuda/cuda_stream.h
@@ -40,8 +40,8 @@ class CUDAStream : public internal::StreamInterface {
   // Note: teardown is handled by a parent's call to DeallocateStream.
   ~CUDAStream() override {}
 
-  void *CudaStreamHack() override { return cuda_stream_; }
-  void **CudaStreamMemberHack() override {
+  void *GpuStreamHack() override { return cuda_stream_; }
+  void **GpuStreamMemberHack() override {
     return reinterpret_cast(&cuda_stream_);
   }
 
diff --git a/tensorflow/stream_executor/dnn.cc b/tensorflow/stream_executor/dnn.cc
index 82aa8ceb3298a30a4c117882dc96c504d9d10226..2a30f922bca4d1dc7d8a9d4ee6e26f7bdf41251c 100644
--- a/tensorflow/stream_executor/dnn.cc
+++ b/tensorflow/stream_executor/dnn.cc
@@ -117,6 +117,8 @@ string FilterLayoutString(FilterLayout layout) {
   switch (layout) {
     case FilterLayout::kOutputInputYX:
       return "OutputInputYX";
+    case FilterLayout::kOutputYXInput:
+      return "OutputYXInput";
     case FilterLayout::kOutputInputYX4:
       return "OutputInputYX4";
     case FilterLayout::kInputYXOutput:
diff --git a/tensorflow/stream_executor/dnn.h b/tensorflow/stream_executor/dnn.h
index 9eca5abe1ae7265ebca0a1ea653823816deaa8f5..a7449c2df423bd2ffd0759e305a8fb02f2ac8cab 100644
--- a/tensorflow/stream_executor/dnn.h
+++ b/tensorflow/stream_executor/dnn.h
@@ -1552,14 +1552,16 @@ class DnnSupport {
                              const dnn::BatchDescriptor& input_dimensions,
                              const DeviceMemory& input_data,
                              const dnn::BatchDescriptor& output_dimensions,
-                             DeviceMemory* output_data) = 0;
+                             DeviceMemory* output_data,
+                             ScratchAllocator* workspace_allocator) = 0;
 
   virtual bool DoPoolForward(Stream* stream,
                              const dnn::PoolingDescriptor& pooling_dimensions,
                              const dnn::BatchDescriptor& input_dimensions,
                              const DeviceMemory& input_data,
                              const dnn::BatchDescriptor& output_dimensions,
-                             DeviceMemory* output_data) {
+                             DeviceMemory* output_data,
+                             ScratchAllocator* workspace_allocator) {
     LOG(FATAL) << "DoPoolForward not implemented for double.";
     return false;
   }
@@ -1569,7 +1571,8 @@ class DnnSupport {
                              const dnn::BatchDescriptor& input_dimensions,
                              const DeviceMemory& input_data,
                              const dnn::BatchDescriptor& output_dimensions,
-                             DeviceMemory* output_data) {
+                             DeviceMemory* output_data,
+                             ScratchAllocator* workspace_allocator) {
     LOG(FATAL) << "DoPoolForward not implemented for float16.";
     return false;
   }
@@ -1582,7 +1585,8 @@ class DnnSupport {
                               const dnn::BatchDescriptor& output_dimensions,
                               const DeviceMemory& output_data,
                               const DeviceMemory& input_diff_data,
-                              DeviceMemory* output_diff_data) {
+                              DeviceMemory* output_diff_data,
+                              ScratchAllocator* workspace_allocator) {
     LOG(FATAL) << "DoPoolBackward not implemented.";
     return false;
   }
@@ -1594,7 +1598,8 @@ class DnnSupport {
                               const dnn::BatchDescriptor& output_dimensions,
                               const DeviceMemory& output_data,
                               const DeviceMemory& input_diff_data,
-                              DeviceMemory* output_diff_data) {
+                              DeviceMemory* output_diff_data,
+                              ScratchAllocator* workspace_allocator) {
     LOG(FATAL) << "DoPoolBackward not implemented.";
     return false;
   }
@@ -1606,7 +1611,8 @@ class DnnSupport {
                               const dnn::BatchDescriptor& output_dimensions,
                               const DeviceMemory& output_data,
                               const DeviceMemory& input_diff_data,
-                              DeviceMemory* output_diff_data) {
+                              DeviceMemory* output_diff_data,
+                              ScratchAllocator* workspace_allocator) {
     LOG(FATAL) << "DoPoolBackward not implemented.";
     return false;
   }
@@ -1653,7 +1659,8 @@ class DnnSupport {
       const DeviceMemory& raw_data,
       const DeviceMemory& normalized_data,
       const DeviceMemory& normalized_variable_gradient,
-      DeviceMemory* raw_variable_gradient) {
+      DeviceMemory* raw_variable_gradient,
+      ScratchAllocator* workspace_allocator) {
     return false;
   }
 
diff --git a/tensorflow/stream_executor/event.cc b/tensorflow/stream_executor/event.cc
index 50a6edd80bd39004e32f09bcde36fbc8a8b59ad9..52efe771bc3c43e65b4539f811196e2d8785eb77 100644
--- a/tensorflow/stream_executor/event.cc
+++ b/tensorflow/stream_executor/event.cc
@@ -15,9 +15,9 @@ limitations under the License.
 
 #include "tensorflow/stream_executor/event.h"
 
+#include "tensorflow/stream_executor/stream.h"
 #include "tensorflow/stream_executor/stream_executor_internal.h"
 #include "tensorflow/stream_executor/stream_executor_pimpl.h"
-#include "tensorflow/stream_executor/stream.h"
 
 namespace stream_executor {
 
@@ -27,9 +27,12 @@ Event::Event(StreamExecutor* stream_exec)
           stream_exec_->implementation()->CreateEventImplementation()) {}
 
 Event::~Event() {
-  auto status = stream_exec_->DeallocateEvent(this);
-  if (!status.ok()) {
-    LOG(ERROR) << status.error_message();
+  // Deal with nullptr implementation_, as this event may have been std::moved.
+  if (stream_exec_ && implementation_) {
+    auto status = stream_exec_->DeallocateEvent(this);
+    if (!status.ok()) {
+      LOG(ERROR) << status.error_message();
+    }
   }
 }
 
diff --git a/tensorflow/stream_executor/event.h b/tensorflow/stream_executor/event.h
index 1f37262c78d82f72f8818f35db273e87a47bdc1c..9cc87a7c129962820ed0c84d02faada4ba460d51 100644
--- a/tensorflow/stream_executor/event.h
+++ b/tensorflow/stream_executor/event.h
@@ -61,6 +61,9 @@ class Event {
   // Returns a pointer to the underlying platform-specific implementation.
   internal::EventInterface* implementation() { return implementation_.get(); }
 
+  Event(Event&&) = default;
+  Event& operator=(Event&&) = default;
+
  private:
   friend class Stream;
 
diff --git a/tensorflow/stream_executor/host/host_gpu_executor.cc b/tensorflow/stream_executor/host/host_gpu_executor.cc
index c8a629733006e17b7642a59afb8e0cb468f2c538..8adf739b170c42e5aeda5ccf3ea469f2c3cea07c 100644
--- a/tensorflow/stream_executor/host/host_gpu_executor.cc
+++ b/tensorflow/stream_executor/host/host_gpu_executor.cc
@@ -26,8 +26,6 @@ limitations under the License.
 #include "tensorflow/stream_executor/lib/statusor.h"
 #include "tensorflow/stream_executor/plugin_registry.h"
 
-bool FLAGS_stream_executor_cpu_real_clock_rate = false;
-
 namespace stream_executor {
 namespace host {
 
@@ -190,11 +188,8 @@ DeviceDescription *HostExecutor::PopulateDeviceDescription() const {
   // doesn't result in thrashing or other badness? 4GiB chosen arbitrarily.
   builder.set_device_memory_size(static_cast(4) * 1024 * 1024 * 1024);
 
-  float cycle_counter_frequency = 1e9;
-  if (FLAGS_stream_executor_cpu_real_clock_rate) {
-    cycle_counter_frequency = static_cast(
-        tensorflow::profile_utils::CpuUtils::GetCycleCounterFrequency());
-  }
+  float cycle_counter_frequency = static_cast(
+      tensorflow::profile_utils::CpuUtils::GetCycleCounterFrequency());
   builder.set_clock_rate_ghz(cycle_counter_frequency / 1e9);
 
   auto built = builder.Build();
diff --git a/tensorflow/stream_executor/host/host_gpu_executor.h b/tensorflow/stream_executor/host/host_gpu_executor.h
index e82f57569f35eb286ecc81caec30a77f148bd675..858396ef96ebd53ada010a3b6befbdc6532df26f 100644
--- a/tensorflow/stream_executor/host/host_gpu_executor.h
+++ b/tensorflow/stream_executor/host/host_gpu_executor.h
@@ -202,7 +202,7 @@ class HostExecutor : public internal::StreamExecutorInterface {
     return std::unique_ptr(new HostTimer());
   }
 
-  void *CudaContextHack() override { return nullptr; }
+  void *GpuContextHack() override { return nullptr; }
 
  private:
   const PluginConfig plugin_config_;
diff --git a/tensorflow/stream_executor/host/host_stream.h b/tensorflow/stream_executor/host/host_stream.h
index 5d7b8a378268c3226a61fa43e738f209e84b30e9..be88f074cf6ece7bf925bf4dea546bb8aa2b4661 100644
--- a/tensorflow/stream_executor/host/host_stream.h
+++ b/tensorflow/stream_executor/host/host_stream.h
@@ -34,8 +34,8 @@ class HostStream : public internal::StreamInterface {
 
   bool EnqueueTask(std::function task);
 
-  void *CudaStreamHack() override { return nullptr; }
-  void **CudaStreamMemberHack() override { return nullptr; }
+  void *GpuStreamHack() override { return nullptr; }
+  void **GpuStreamMemberHack() override { return nullptr; }
 
   void BlockUntilDone();
 
diff --git a/tensorflow/stream_executor/module_spec.h b/tensorflow/stream_executor/module_spec.h
new file mode 100644
index 0000000000000000000000000000000000000000..75bdfed2d70364da4b191804d1e0973ee2658b70
--- /dev/null
+++ b/tensorflow/stream_executor/module_spec.h
@@ -0,0 +1,66 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_STREAM_EXECUTOR_MODULE_SPEC_H_
+#define TENSORFLOW_STREAM_EXECUTOR_MODULE_SPEC_H_
+
+#include "tensorflow/stream_executor/lib/array_slice.h"
+#include "tensorflow/stream_executor/lib/stringpiece.h"
+#include "tensorflow/stream_executor/platform/logging.h"
+#include "tensorflow/stream_executor/platform/port.h"
+
+namespace stream_executor {
+
+// Describes how to load a module on a target platform.
+//
+// The exact meaning of a "module" may differ from platform to platform but
+// loosely speaking a module a collection of kernels and global variables.  It
+// corresponds to CUmodule when running on CUDA.
+class MultiModuleLoaderSpec {
+ public:
+  bool has_cuda_cubin_in_memory() const { return has_cuda_cubin_in_memory_; }
+  port::ArraySlice cuda_cubin_in_memory() const {
+    CHECK(has_cuda_cubin_in_memory());
+    return {cuda_cubin_in_memory_.data(), cuda_cubin_in_memory_.size()};
+  }
+
+  bool has_cuda_ptx_in_memory() const { return has_cuda_ptx_in_memory_; }
+  const char* cuda_ptx_in_memory() const {
+    CHECK(has_cuda_ptx_in_memory());
+    return cuda_ptx_in_memory_;
+  }
+
+  void AddCudaCubinInMemory(port::ArraySlice cubin_bytes) {
+    CHECK(!cubin_bytes.empty());
+    has_cuda_cubin_in_memory_ = true;
+    cuda_cubin_in_memory_ = cubin_bytes;
+  }
+
+  void AddCudaPtxInMemory(const char* ptx) {
+    has_cuda_ptx_in_memory_ = true;
+    // The CUDA driver does not like getting an empty string as PTX.
+    cuda_ptx_in_memory_ = *ptx ? ptx : nullptr;
+  }
+
+ private:
+  port::ArraySlice cuda_cubin_in_memory_;
+  bool has_cuda_cubin_in_memory_ = false;
+  const char* cuda_ptx_in_memory_;
+  bool has_cuda_ptx_in_memory_ = false;
+};
+
+}  // namespace stream_executor
+
+#endif  // TENSORFLOW_STREAM_EXECUTOR_MODULE_SPEC_H_
diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc
index 0cd0790a72b49bb259b9c72268535b5d74531cf5..b0c061fd74b817eb06b370a1e8495f4a3a96a34b 100644
--- a/tensorflow/stream_executor/stream.cc
+++ b/tensorflow/stream_executor/stream.cc
@@ -267,6 +267,12 @@ Stream::Stream(StreamExecutor *parent,
 Stream::~Stream() {
   VLOG_CALL();
 
+  // Ensure the stream is completed.
+  auto status = BlockHostUntilDone();
+  if (!status.ok()) {
+    LOG(WARNING) << "Error blocking host until done in stream destructor: "
+                 << status;
+  }
   temporary_memory_manager_.ForceDeallocateAll();
 
   if (allocated_) {
@@ -1377,15 +1383,16 @@ Stream &Stream::ThenPoolForward(
     const dnn::BatchDescriptor &input_dimensions,
     const DeviceMemory &input_data,
     const dnn::BatchDescriptor &output_dimensions,
-    DeviceMemory *output_data) {
+    DeviceMemory *output_data, ScratchAllocator *workspace_allocator) {
   VLOG_CALL(PARAM(pooling_dimensions), PARAM(input_dimensions),
-            PARAM(input_data), PARAM(output_dimensions), PARAM(output_data));
+            PARAM(input_data), PARAM(output_dimensions), PARAM(output_data),
+            PARAM(workspace_allocator));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
       CheckError(dnn->DoPoolForward(this, pooling_dimensions, input_dimensions,
-                                    input_data, output_dimensions,
-                                    output_data));
+                                    input_data, output_dimensions, output_data,
+                                    workspace_allocator));
     } else {
       SetError();
       LOG(WARNING)
@@ -1401,15 +1408,16 @@ Stream &Stream::ThenPoolForward(
     const dnn::BatchDescriptor &input_dimensions,
     const DeviceMemory &input_data,
     const dnn::BatchDescriptor &output_dimensions,
-    DeviceMemory *output_data) {
+    DeviceMemory *output_data, ScratchAllocator *workspace_allocator) {
   VLOG_CALL(PARAM(pooling_dimensions), PARAM(input_dimensions),
-            PARAM(input_data), PARAM(output_dimensions), PARAM(output_data));
+            PARAM(input_data), PARAM(output_dimensions), PARAM(output_data),
+            PARAM(workspace_allocator));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
       CheckError(dnn->DoPoolForward(this, pooling_dimensions, input_dimensions,
-                                    input_data, output_dimensions,
-                                    output_data));
+                                    input_data, output_dimensions, output_data,
+                                    workspace_allocator));
     } else {
       SetErrorAndLogNoDnnSupport();
     }
@@ -1422,15 +1430,17 @@ Stream &Stream::ThenPoolForward(
     const dnn::BatchDescriptor &input_dimensions,
     const DeviceMemory &input_data,
     const dnn::BatchDescriptor &output_dimensions,
-    DeviceMemory *output_data) {
+    DeviceMemory *output_data,
+    ScratchAllocator *workspace_allocator) {
   VLOG_CALL(PARAM(pooling_dimensions), PARAM(input_dimensions),
-            PARAM(input_data), PARAM(output_dimensions), PARAM(output_data));
+            PARAM(input_data), PARAM(output_dimensions), PARAM(output_data),
+            PARAM(workspace_allocator));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
       CheckError(dnn->DoPoolForward(this, pooling_dimensions, input_dimensions,
-                                    input_data, output_dimensions,
-                                    output_data));
+                                    input_data, output_dimensions, output_data,
+                                    workspace_allocator));
     } else {
       SetErrorAndLogNoDnnSupport();
     }
@@ -1445,16 +1455,19 @@ Stream &Stream::ThenPoolBackward(
     const dnn::BatchDescriptor &output_dimensions,
     const DeviceMemory &output_data,
     const DeviceMemory &input_diff_data,
-    DeviceMemory *output_diff_data) {
+    DeviceMemory *output_diff_data,
+    ScratchAllocator *workspace_allocator) {
   VLOG_CALL(PARAM(pooling_dimensions), PARAM(input_dimensions),
             PARAM(input_data), PARAM(output_dimensions), PARAM(output_data),
-            PARAM(input_diff_data), PARAM(output_diff_data));
+            PARAM(input_diff_data), PARAM(output_diff_data),
+            PARAM(workspace_allocator));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
       CheckError(dnn->DoPoolBackward(this, pooling_dimensions, input_dimensions,
                                      input_data, output_dimensions, output_data,
-                                     input_diff_data, output_diff_data));
+                                     input_diff_data, output_diff_data,
+                                     workspace_allocator));
     } else {
       SetError();
       LOG(WARNING)
@@ -1472,16 +1485,19 @@ Stream &Stream::ThenPoolBackward(
     const dnn::BatchDescriptor &output_dimensions,
     const DeviceMemory &output_data,
     const DeviceMemory &input_diff_data,
-    DeviceMemory *output_diff_data) {
+    DeviceMemory *output_diff_data,
+    ScratchAllocator *workspace_allocator) {
   VLOG_CALL(PARAM(pooling_dimensions), PARAM(input_dimensions),
             PARAM(input_data), PARAM(output_dimensions), PARAM(output_data),
-            PARAM(input_diff_data), PARAM(output_diff_data));
+            PARAM(input_diff_data), PARAM(output_diff_data),
+            PARAM(workspace_allocator));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
       CheckError(dnn->DoPoolBackward(this, pooling_dimensions, input_dimensions,
                                      input_data, output_dimensions, output_data,
-                                     input_diff_data, output_diff_data));
+                                     input_diff_data, output_diff_data,
+                                     workspace_allocator));
     } else {
       SetErrorAndLogNoDnnSupport();
     }
@@ -1496,16 +1512,19 @@ Stream &Stream::ThenPoolBackward(
     const dnn::BatchDescriptor &output_dimensions,
     const DeviceMemory &output_data,
     const DeviceMemory &input_diff_data,
-    DeviceMemory *output_diff_data) {
+    DeviceMemory *output_diff_data,
+    ScratchAllocator *workspace_allocator) {
   VLOG_CALL(PARAM(pooling_dimensions), PARAM(input_dimensions),
             PARAM(input_data), PARAM(output_dimensions), PARAM(output_data),
-            PARAM(input_diff_data), PARAM(output_diff_data));
+            PARAM(input_diff_data), PARAM(output_diff_data),
+            PARAM(workspace_allocator));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
       CheckError(dnn->DoPoolBackward(this, pooling_dimensions, input_dimensions,
                                      input_data, output_dimensions, output_data,
-                                     input_diff_data, output_diff_data));
+                                     input_diff_data, output_diff_data,
+                                     workspace_allocator));
     } else {
       SetErrorAndLogNoDnnSupport();
     }
@@ -1552,16 +1571,18 @@ Stream &Stream::ThenNormalizeBackwardWithDimensions(
     const dnn::BatchDescriptor &dimensions, const DeviceMemory &raw_data,
     const DeviceMemory &normalized_data,
     const DeviceMemory &normalized_variable_gradient,
-    DeviceMemory *raw_variable_gradient) {
+    DeviceMemory *raw_variable_gradient,
+    ScratchAllocator *workspace_allocator) {
   VLOG_CALL(PARAM(normalize_descriptor), PARAM(dimensions), PARAM(raw_data),
             PARAM(normalized_data), PARAM(normalized_variable_gradient),
-            PARAM(raw_variable_gradient));
+            PARAM(raw_variable_gradient), PARAM(workspace_allocator));
 
   if (ok()) {
     if (dnn::DnnSupport *dnn = parent_->AsDnn()) {
       CheckError(dnn->DoNormalizeBackwardWithDimensions(
           this, normalize_descriptor, dimensions, raw_data, normalized_data,
-          normalized_variable_gradient, raw_variable_gradient));
+          normalized_variable_gradient, raw_variable_gradient,
+          workspace_allocator));
     } else {
       SetErrorAndLogNoDnnSupport();
     }
@@ -1920,7 +1941,14 @@ void Stream::ReturnSubStream(Stream *sub_stream) {
   mutex_lock lock(mu_);
   for (auto &stream : sub_streams_) {
     if (stream.first.get() == sub_stream) {
-      stream.second = true;
+      // Streams have a monotonic state machine; if a stream
+      // encounters an error, it will remain in an error state
+      // forever. Only allow re-use of ok streams.
+      //
+      // TODO(toddw): Improve this mechanism, if necessary, to drop
+      // failed streams completely.
+      const bool ready_to_reuse = sub_stream->ok();
+      stream.second = ready_to_reuse;
       return;
     }
   }
@@ -5228,24 +5256,11 @@ port::Status Stream::BlockHostUntilDone() {
     return status;
   }
 
-  port::Status first_error;
-  {
-    // Wait until all active sub-streams have done their tasks.
-    mutex_lock lock(mu_);
-    for (auto &stream : sub_streams_) {
-      if (!stream.second) {
-        first_error.Update(stream.first->BlockHostUntilDone());
-        // Set this sub-stream as available.
-        stream.second = true;
-      }
-    }
-  }
-
   temporary_memory_manager_.DeallocateFinalizedTemporaries();
 
-  first_error.Update(parent_->BlockHostUntilDone(this));
-  CheckError(first_error.ok());
-  return first_error;
+  port::Status error = parent_->BlockHostUntilDone(this);
+  CheckError(error.ok());
+  return error;
 }
 
 }  // namespace stream_executor
diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h
index e8885e1eb682d9ee67c6b7594f96c0911c7c1fa2..706442a6662429edbe65ea94b933777694e9b2be 100644
--- a/tensorflow/stream_executor/stream.h
+++ b/tensorflow/stream_executor/stream.h
@@ -125,7 +125,7 @@ class Stream {
   Stream *GetOrCreateSubStream() LOCKS_EXCLUDED(mu_);
 
   // Return the sub-stream back to the host stream so that it can be reused
-  // later.
+  // later. Sub-streams that are !ok() will not be reused.
   void ReturnSubStream(Stream *sub_stream) LOCKS_EXCLUDED(mu_);
 
   // Allocate temporary memories. The stream will deallocate them when blocked
@@ -629,19 +629,22 @@ class Stream {
                           const dnn::BatchDescriptor &input_dimensions,
                           const DeviceMemory &input_data,
                           const dnn::BatchDescriptor &output_dimensions,
-                          DeviceMemory *output_data);
+                          DeviceMemory *output_data,
+                          ScratchAllocator *workspace_allocator = nullptr);
 
   Stream &ThenPoolForward(const dnn::PoolingDescriptor &pooling_dimensions,
                           const dnn::BatchDescriptor &input_dimensions,
                           const DeviceMemory &input_data,
                           const dnn::BatchDescriptor &output_dimensions,
-                          DeviceMemory *output_data);
+                          DeviceMemory *output_data,
+                          ScratchAllocator *workspace_allocator = nullptr);
 
   Stream &ThenPoolForward(const dnn::PoolingDescriptor &pooling_dimensions,
                           const dnn::BatchDescriptor &input_dimensions,
                           const DeviceMemory &input_data,
                           const dnn::BatchDescriptor &output_dimensions,
-                          DeviceMemory *output_data);
+                          DeviceMemory *output_data,
+                          ScratchAllocator *workspace_allocator = nullptr);
 
   Stream &ThenPoolBackward(const dnn::PoolingDescriptor &pooling_dimensions,
                            const dnn::BatchDescriptor &input_dimensions,
@@ -649,7 +652,8 @@ class Stream {
                            const dnn::BatchDescriptor &output_dimensions,
                            const DeviceMemory &output_data,
                            const DeviceMemory &input_diff_data,
-                           DeviceMemory *output_diff_data);
+                           DeviceMemory *output_diff_data,
+                           ScratchAllocator *workspace_allocator = nullptr);
 
   Stream &ThenPoolBackward(const dnn::PoolingDescriptor &pooling_dimensions,
                            const dnn::BatchDescriptor &input_dimensions,
@@ -657,7 +661,8 @@ class Stream {
                            const dnn::BatchDescriptor &output_dimensions,
                            const DeviceMemory &output_data,
                            const DeviceMemory &input_diff_data,
-                           DeviceMemory *output_diff_data);
+                           DeviceMemory *output_diff_data,
+                           ScratchAllocator *workspace_allocator = nullptr);
 
   Stream &ThenPoolBackward(const dnn::PoolingDescriptor &pooling_dimensions,
                            const dnn::BatchDescriptor &input_dimensions,
@@ -665,7 +670,8 @@ class Stream {
                            const dnn::BatchDescriptor &output_dimensions,
                            const DeviceMemory &output_data,
                            const DeviceMemory &input_diff_data,
-                           DeviceMemory *output_diff_data);
+                           DeviceMemory *output_diff_data,
+                           ScratchAllocator *workspace_allocator = nullptr);
 
   Stream &ThenNormalize(const dnn::NormalizeDescriptor &normalize_descriptor,
                         const DeviceMemory &input_data,
@@ -684,7 +690,8 @@ class Stream {
       const DeviceMemory &raw_data,
       const DeviceMemory &normalized_data,
       const DeviceMemory &normalized_variable_gradient,
-      DeviceMemory *raw_variable_gradient);
+      DeviceMemory *raw_variable_gradient,
+      ScratchAllocator *workspace_allocator = nullptr);
 
   Stream &ThenActivate(dnn::ActivationMode activation_mode,
                        const dnn::BatchDescriptor &dimensions,
diff --git a/tensorflow/stream_executor/stream_executor_internal.h b/tensorflow/stream_executor/stream_executor_internal.h
index 9c989b971dcee6dd99aa155cd2230ba849d204fe..f34b1fc083adec40d57bf65cb49a4e7901ee1864 100644
--- a/tensorflow/stream_executor/stream_executor_internal.h
+++ b/tensorflow/stream_executor/stream_executor_internal.h
@@ -36,20 +36,38 @@ limitations under the License.
 #include "tensorflow/stream_executor/kernel_cache_config.h"
 #include "tensorflow/stream_executor/kernel_spec.h"
 #include "tensorflow/stream_executor/launch_dim.h"
+#include "tensorflow/stream_executor/lib/inlined_vector.h"
 #include "tensorflow/stream_executor/lib/status.h"
 #include "tensorflow/stream_executor/lib/statusor.h"
+#include "tensorflow/stream_executor/module_spec.h"
 #include "tensorflow/stream_executor/platform.h"
 #include "tensorflow/stream_executor/platform/port.h"
 #include "tensorflow/stream_executor/plugin_registry.h"
 #include "tensorflow/stream_executor/shared_memory_config.h"
 #include "tensorflow/stream_executor/trace_listener.h"
-#include "tensorflow/stream_executor/lib/inlined_vector.h"
 
 namespace stream_executor {
 
 class Stream;
 class Timer;
 
+// An opaque handle to a loaded module.
+//
+// An instance of this is returned from StreamExecutor::GetModule.
+class ModuleHandle {
+ public:
+  /*implicit*/ ModuleHandle(void *id = nullptr) : id_(id) {}
+
+  // A ModuleHandle with id() == nullptr is an invalid module handle, akin to a
+  // null pointer.
+  void *id() const { return id_; }
+
+  explicit operator bool() const { return id() != nullptr; }
+
+ private:
+  void *id_;
+};
+
 namespace internal {
 
 // Platform-dependent interface class for the generic Events interface, in
@@ -100,19 +118,20 @@ class StreamInterface {
   // Default destructor for the abstract interface.
   virtual ~StreamInterface() {}
 
-  // Returns the CUDA stream associated with this platform's stream
+  // Returns the GPU stream associated with this platform's stream
   // implementation.
   //
-  // WARNING: checks that the underlying platform is, in fact, CUDA, causing a
-  // fatal error if it is not. This hack is made available solely for use from
-  // distbelief code, which temporarily has strong ties to CUDA as a platform.
-  virtual void *CudaStreamHack() { return nullptr; }
-
-  // See the above comment on CudaStreamHack -- this further breaks abstraction
-  // for Eigen within distbelief, which has strong ties to CUDA as a platform,
-  // and a historical attachment to a programming model which takes a
+  // WARNING: checks that the underlying platform is, in fact, CUDA or ROCm,
+  // causing a fatal error if it is not. This hack is made available solely for
+  // use from distbelief code, which temporarily has strong ties to CUDA or
+  // ROCm as a platform.
+  virtual void *GpuStreamHack() { return nullptr; }
+
+  // See the above comment on GpuStreamHack -- this further breaks abstraction
+  // for Eigen within distbelief, which has strong ties to CUDA or ROCm as a
+  // platform, and a historical attachment to a programming model which takes a
   // stream-slot rather than a stream-value.
-  virtual void **CudaStreamMemberHack() { return nullptr; }
+  virtual void **GpuStreamMemberHack() { return nullptr; }
 
  private:
   SE_DISALLOW_COPY_AND_ASSIGN(StreamInterface);
@@ -163,6 +182,11 @@ class StreamExecutorInterface {
                          KernelBase *kernel) {
     return false;
   }
+  virtual bool LoadModule(const MultiModuleLoaderSpec &spec,
+                          ModuleHandle *module_handle) {
+    return false;
+  }
+  virtual bool UnloadModule(ModuleHandle module_handle) { return false; }
   virtual bool Launch(Stream *stream, const ThreadDim &thread_dims,
                       const BlockDim &block_dims, const KernelBase &k,
                       const KernelArgsArrayBase &args) {
@@ -246,7 +270,12 @@ class StreamExecutorInterface {
   // null, however, both of them cannot be null at the same time. To use
   // constant memory in CUDA, GetSymbol has to be used. Returns true if symbol
   // is found.
-  virtual bool GetSymbol(const string& symbol_name, void **mem, size_t *bytes) {
+  //
+  // If ModuleHandle is set then we search for `symbol_name` only within the
+  // module corresponding to `module_handle`.  Otherwise all loaded modules are
+  // searched.
+  virtual bool GetSymbol(const string &symbol_name, ModuleHandle module_handle,
+                         void **mem, size_t *bytes) {
     return false;
   }
 
@@ -324,13 +353,14 @@ class StreamExecutorInterface {
   virtual std::unique_ptr GetStreamImplementation() = 0;
   virtual std::unique_ptr GetTimerImplementation() = 0;
 
-  // Returns the CUDA context associated with this StreamExecutor platform
-  // implementation.
+  // Returns the CUDA or ROCm context associated with this StreamExecutor
+  // platform implementation.
   //
-  // WARNING: checks that the underlying platform is, in fact, CUDA, causing a
-  // fatal error if it is not. This hack is made available solely for use from
-  // distbelief code, which temporarily has strong ties to CUDA as a platform.
-  virtual void *CudaContextHack() { return nullptr; }
+  // WARNING: checks that the underlying platform is, in fact, CUDA or ROCm,
+  // causing a fatal error if it is not. This hack is made available solely for
+  // use from distbelief code, which temporarily has strong ties to CUDA or ROCm
+  // as a platform.
+  virtual void *GpuContextHack() { return nullptr; }
 
  private:
   SE_DISALLOW_COPY_AND_ASSIGN(StreamExecutorInterface);
diff --git a/tensorflow/stream_executor/stream_executor_pimpl.cc b/tensorflow/stream_executor/stream_executor_pimpl.cc
index 000795ff0048dddb0eb4a08956e6de6f5e336f28..2e0137a485e77ef6bd62d07e334cbdc41132ce96 100644
--- a/tensorflow/stream_executor/stream_executor_pimpl.cc
+++ b/tensorflow/stream_executor/stream_executor_pimpl.cc
@@ -220,6 +220,15 @@ void StreamExecutor::UnloadKernel(const KernelBase *kernel) {
   implementation_->UnloadKernel(kernel);
 }
 
+bool StreamExecutor::LoadModule(const MultiModuleLoaderSpec &spec,
+                                ModuleHandle *module_handle) {
+  return implementation_->LoadModule(spec, module_handle);
+}
+
+bool StreamExecutor::UnloadModule(ModuleHandle module_handle) {
+  return implementation_->UnloadModule(module_handle);
+}
+
 void StreamExecutor::Deallocate(DeviceMemoryBase *mem) {
   VLOG(1) << "Called StreamExecutor::Deallocate(mem=" << mem->opaque()
           << ") mem->size()=" << mem->size() << StackTraceIfVLOG10();
@@ -459,9 +468,34 @@ void *StreamExecutor::Allocate(uint64 size) {
   return buf;
 }
 
-bool StreamExecutor::GetSymbol(const string &symbol_name, void **mem,
+port::StatusOr StreamExecutor::GetUntypedSymbol(
+    const string &symbol_name, ModuleHandle module_handle) {
+  // If failed to get the symbol, opaque/bytes are unchanged. Initialize them to
+  // be nullptr/0 for consistency with DeviceMemory semantics.
+  void *opaque = nullptr;
+  size_t bytes = 0;
+  if (GetSymbol(symbol_name, module_handle, &opaque, &bytes)) {
+    return DeviceMemoryBase(opaque, bytes);
+  }
+
+  if (static_cast(module_handle)) {
+    return port::Status(
+        port::error::NOT_FOUND,
+        port::StrCat("Check if module containing symbol ", symbol_name,
+                     " is loaded (module_handle = ",
+                     reinterpret_cast(module_handle.id()), ")"));
+  } else {
+    return port::Status(
+        port::error::NOT_FOUND,
+        port::StrCat("Check if kernel using the symbol is loaded: ",
+                     symbol_name));
+  }
+}
+
+bool StreamExecutor::GetSymbol(const string &symbol_name,
+                               ModuleHandle module_handle, void **mem,
                                size_t *bytes) {
-  return implementation_->GetSymbol(symbol_name, mem, bytes);
+  return implementation_->GetSymbol(symbol_name, module_handle, mem, bytes);
 }
 
 void *StreamExecutor::UnifiedMemoryAllocate(uint64 bytes) {
diff --git a/tensorflow/stream_executor/stream_executor_pimpl.h b/tensorflow/stream_executor/stream_executor_pimpl.h
index ad80a1ba259ce0c6e2785373cc986b8bf34f6460..47b3a2b030ca68a079a1f9de238a2ed58f18b7e8 100644
--- a/tensorflow/stream_executor/stream_executor_pimpl.h
+++ b/tensorflow/stream_executor/stream_executor_pimpl.h
@@ -106,6 +106,16 @@ class StreamExecutor {
   // Releases any state associated with the previously loaded kernel.
   void UnloadKernel(const KernelBase *kernel);
 
+  // Loads a module for the platform this StreamExecutor is acting upon.
+  //
+  // `spec` describes the module to be loaded.  On success writes the handle for
+  // the loaded module to `module_handle` and returns true.  Else returns false.
+  bool LoadModule(const MultiModuleLoaderSpec &spec,
+                  ModuleHandle *module_handle);
+
+  // Unloads the module with handle `module_handle`.
+  bool UnloadModule(ModuleHandle module_handle);
+
   // Synchronously allocates an array on the device of type T with element_count
   // elements.
   template 
@@ -169,8 +179,16 @@ class StreamExecutor {
   // type of symbol and T match.
   // - Note: symbol_name should include its namespace as well. For example,
   //         pass "nms0::symbol" if referring to nms0::symbol.
+  //
+  // If `module_handle` is set then searches only within the module
+  // corresponding to `module_handle`.
   template 
-  port::StatusOr> GetSymbol(const string &symbol_name);
+  port::StatusOr> GetSymbol(const string &symbol_name,
+                                            ModuleHandle module_handle = {});
+
+  // An untyped version of GetSymbol.
+  port::StatusOr GetUntypedSymbol(
+      const string &symbol_name, ModuleHandle module_handle = {});
 
   // Deallocate the DeviceMemory previously allocated via this interface.
   // Deallocation of a nullptr-representative value is permitted.
@@ -507,7 +525,8 @@ class StreamExecutor {
 
   // Finds and retrieves device memory for the symbol on the underlying
   // platform.
-  bool GetSymbol(const string& symbol_name, void **mem, size_t *bytes);
+  bool GetSymbol(const string &symbol_name, ModuleHandle module_handle,
+                 void **mem, size_t *bytes);
 
   // Entrains a memcpy operation onto stream, with a host destination location
   // host_dst and a device memory source, with target size size.
@@ -678,6 +697,41 @@ class StreamExecutor {
   SE_DISALLOW_COPY_AND_ASSIGN(StreamExecutor);
 };
 
+// A wrapper around ModuleHandle that uses RAII to manage its lifetime.
+class ScopedModuleHandle {
+ public:
+  explicit ScopedModuleHandle(StreamExecutor *executor,
+                              ModuleHandle module_handle)
+      : executor_(executor), module_handle_(module_handle) {}
+
+  ScopedModuleHandle(ScopedModuleHandle &&other) {
+    executor_ = other.executor_;
+    module_handle_ = other.module_handle_;
+    other.executor_ = nullptr;
+    other.module_handle_ = ModuleHandle();
+  }
+
+  ScopedModuleHandle &operator=(ScopedModuleHandle &&other) {
+    executor_ = other.executor_;
+    module_handle_ = other.module_handle_;
+    other.executor_ = nullptr;
+    other.module_handle_ = ModuleHandle();
+    return *this;
+  }
+
+  ~ScopedModuleHandle() {
+    if (static_cast(module_handle_)) {
+      CHECK(executor_->UnloadModule(module_handle_));
+    }
+  }
+
+ private:
+  StreamExecutor *executor_;
+  ModuleHandle module_handle_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(ScopedModuleHandle);
+};
+
 ////////////
 // Inlines
 
@@ -690,19 +744,13 @@ inline DeviceMemory StreamExecutor::AllocateArray(uint64 element_count) {
 
 template 
 inline port::StatusOr> StreamExecutor::GetSymbol(
-    const string &symbol_name) {
-  // If failed to get the symbol, opaque/bytes are unchanged. Initialize them to
-  // be nullptr/0 for consistency with DeviceMemory semantics.
-  void *opaque = nullptr;
-  size_t bytes = 0;
-  if (GetSymbol(symbol_name, &opaque, &bytes)) {
-    CHECK_EQ(bytes % sizeof(T), 0);
-    return DeviceMemory::MakeFromByteSize(opaque, bytes);
+    const string &symbol_name, ModuleHandle module_handle) {
+  port::StatusOr untyped_symbol =
+      GetUntypedSymbol(symbol_name, module_handle);
+  if (!untyped_symbol.ok()) {
+    return untyped_symbol.status();
   }
-  return port::Status(
-      port::error::NOT_FOUND,
-      port::StrCat("Check if kernel using the symbol is loaded: ",
-                   symbol_name));
+  return DeviceMemory(untyped_symbol.ValueOrDie());
 }
 
 template 
diff --git a/tensorflow/stream_executor/stream_test.cc b/tensorflow/stream_executor/stream_test.cc
new file mode 100644
index 0000000000000000000000000000000000000000..47dd67583497463897d0a740d81c9012a0ff9452
--- /dev/null
+++ b/tensorflow/stream_executor/stream_test.cc
@@ -0,0 +1,139 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/stream_executor/stream_executor.h"
+
+#include "tensorflow/core/platform/test.h"
+
+namespace stream_executor {
+namespace {
+
+class StreamTest : public ::testing::Test {
+ protected:
+  std::unique_ptr NewStreamExecutor() {
+    Platform* platform =
+        MultiPlatformManager::PlatformWithName("Host").ConsumeValueOrDie();
+    StreamExecutorConfig config(/*ordinal=*/0);
+    return platform->GetUncachedExecutor(config).ConsumeValueOrDie();
+  }
+};
+
+TEST_F(StreamTest, NoInitNotOk) {
+  std::unique_ptr executor = NewStreamExecutor();
+  Stream stream(executor.get());
+  EXPECT_FALSE(stream.ok());
+}
+
+TEST_F(StreamTest, InitOk) {
+  std::unique_ptr executor = NewStreamExecutor();
+  Stream stream(executor.get());
+  stream.Init();
+  EXPECT_TRUE(stream.ok());
+}
+
+TEST_F(StreamTest, OneSubStream) {
+  std::unique_ptr executor = NewStreamExecutor();
+  Stream stream(executor.get());
+  stream.Init();
+  EXPECT_TRUE(stream.ok());
+
+  // Get and return a sub-stream. Sub-streams are always initialized.
+  Stream* sub_stream1 = stream.GetOrCreateSubStream();
+  EXPECT_TRUE(sub_stream1->ok());
+  stream.ReturnSubStream(sub_stream1);
+
+  // Get and return another sub-stream.
+  Stream* sub_stream2 = stream.GetOrCreateSubStream();
+  EXPECT_TRUE(sub_stream2->ok());
+  stream.ReturnSubStream(sub_stream1);
+
+  // The underlying sub-streams should be the same, since sub_stream1
+  // was returned before we tried to get sub_stream2.
+  EXPECT_EQ(sub_stream1, sub_stream2);
+}
+
+TEST_F(StreamTest, TwoSubStreams) {
+  std::unique_ptr executor = NewStreamExecutor();
+  Stream stream(executor.get());
+  stream.Init();
+  EXPECT_TRUE(stream.ok());
+
+  // Get two sub-streams.
+  Stream* sub_stream1 = stream.GetOrCreateSubStream();
+  EXPECT_TRUE(sub_stream1->ok());
+  Stream* sub_stream2 = stream.GetOrCreateSubStream();
+  EXPECT_TRUE(sub_stream2->ok());
+
+  // The underlying sub-streams should be different, since neither
+  // sub-stream has been returned.
+  EXPECT_NE(sub_stream1, sub_stream2);
+
+  // Return sub_stream1 and get sub_stream3, which should be the same.
+  stream.ReturnSubStream(sub_stream1);
+  Stream* sub_stream3 = stream.GetOrCreateSubStream();
+  EXPECT_TRUE(sub_stream3->ok());
+  EXPECT_EQ(sub_stream1, sub_stream3);
+  EXPECT_NE(sub_stream2, sub_stream3);
+
+  // Return sub_stream2 and get sub_stream4, which should be the same.
+  stream.ReturnSubStream(sub_stream2);
+  Stream* sub_stream4 = stream.GetOrCreateSubStream();
+  EXPECT_TRUE(sub_stream4->ok());
+  EXPECT_EQ(sub_stream2, sub_stream4);
+  EXPECT_NE(sub_stream3, sub_stream4);
+}
+
+TEST_F(StreamTest, FailedSubStreamNotReused) {
+  std::unique_ptr executor = NewStreamExecutor();
+  Stream stream(executor.get());
+  stream.Init();
+  EXPECT_TRUE(stream.ok());
+
+  // Get a sub-stream.
+  Stream* sub_stream1 = stream.GetOrCreateSubStream();
+  EXPECT_TRUE(sub_stream1->ok());
+
+  // Force an error on the stream; here we call a method that requires
+  // DNN support, which we know the Host platform doesn't support.
+  sub_stream1->ThenDepthConcatenate({}, {}, nullptr);
+  EXPECT_FALSE(sub_stream1->ok());
+
+  // Return sub_stream1 and get sub_stream2.
+  stream.ReturnSubStream(sub_stream1);
+  Stream* sub_stream2 = stream.GetOrCreateSubStream();
+  EXPECT_TRUE(sub_stream2->ok());
+
+  // The underlying streams should be different. They would have been
+  // the same, but since we forced an error on sub_stream1, it will
+  // not be re-used. Sadly we can't just check:
+  //   EXPECT_NE(sub_stream1, sub_stream2);
+  //
+  // The above should hold logically, but it may fail if the new
+  // stream instance allocated for sub_stream2 happens to reside in
+  // the same memory address as sub_stream1.
+  //
+  // The check that sub_stream2->ok() serves as a good-enough check.
+
+  // Return sub_stream2 and get sub_stream3. The previous error on
+  // sub_stream1 has no effect on these streams, and they are the
+  // same.
+  stream.ReturnSubStream(sub_stream2);
+  Stream* sub_stream3 = stream.GetOrCreateSubStream();
+  EXPECT_TRUE(sub_stream3->ok());
+  EXPECT_EQ(sub_stream2, sub_stream3);
+}
+
+}  // namespace
+}  // namespace stream_executor
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index e4632c48112d40fb96b4c2b510da93678b11efc4..58282ec1c71cb04accea1bafa728808c2f60d315 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -9,6 +9,7 @@ load(
     "tf_additional_grpc_deps_py",
     "tf_additional_xla_deps_py",
     "if_static",
+    "if_dynamic_kernels",
 )
 load(
     "@local_config_tensorrt//:build_defs.bzl",
@@ -24,7 +25,10 @@ load(
     "if_mkl",
     "if_mkl_lnx_x64"
 )
-
+load(
+    "//third_party/mkl_dnn:build_defs.bzl",
+    "if_mkl_open_source_only",
+)
 def register_extension_info(**kwargs):
     pass
 
@@ -134,6 +138,14 @@ def if_not_mobile(a):
       "//conditions:default": a,
   })
 
+# Config setting selector used when building for products
+# which requires restricted licenses to be avoided.
+def if_not_lgpl_restricted(a):
+  _ = (a,)
+  return select({
+      "//conditions:default": [],
+  })
+
 def if_not_windows(a):
   return select({
       clean_dep("//tensorflow:windows"): [],
@@ -180,9 +192,13 @@ def get_win_copts(is_external=False):
         "/DEIGEN_AVOID_STL_ARRAY",
         "/Iexternal/gemmlowp",
         "/wd4018",  # -Wno-sign-compare
-        "/U_HAS_EXCEPTIONS",
-        "/D_HAS_EXCEPTIONS=1",
-        "/EHsc",  # -fno-exceptions
+        # Bazel's CROSSTOOL currently pass /EHsc to enable exception by
+        # default. We can't pass /EHs-c- to disable exception, otherwise
+        # we will get a waterfall of flag conflict warnings. Wait for
+        # Bazel to fix this.
+        # "/D_HAS_EXCEPTIONS=0",
+        # "/EHs-c-",
+        "/wd4577",
         "/DNOGDI",
     ]
     if is_external:
@@ -214,6 +230,7 @@ def tf_copts(android_optimization_level_override="-O2", is_external=False):
       + if_cuda(["-DGOOGLE_CUDA=1"])
       + if_tensorrt(["-DGOOGLE_TENSORRT=1"])
       + if_mkl(["-DINTEL_MKL=1", "-DEIGEN_USE_VML"])
+      + if_mkl_open_source_only(["-DDO_NOT_USE_ML"])
       + if_mkl_lnx_x64(["-fopenmp"])
       + if_android_arm(["-mfpu=neon"])
       + if_linux_x86_64(["-msse3"])
@@ -228,6 +245,7 @@ def tf_copts(android_optimization_level_override="-O2", is_external=False):
             clean_dep("//tensorflow:windows"): get_win_copts(is_external),
             clean_dep("//tensorflow:windows_msvc"): get_win_copts(is_external),
             clean_dep("//tensorflow:ios"): ["-std=c++11"],
+            clean_dep("//tensorflow:no_lgpl_deps"): ["-D__TENSORFLOW_NO_LGPL_DEPS__", "-pthread"],
             "//conditions:default": ["-pthread"]
       }))
 
@@ -301,18 +319,36 @@ def tf_binary_additional_srcs():
           clean_dep("//tensorflow:libtensorflow_framework.so"),
       ])
 
+
+# Helper functions to add kernel dependencies to tf binaries when using dynamic
+# kernel linking.
+def tf_binary_dynamic_kernel_dsos(kernels):
+  return if_dynamic_kernels(
+      extra_deps=["libtfkernel_%s.so" % clean_dep(k) for k in kernels],
+      otherwise=[])
+
+# Helper functions to add kernel dependencies to tf binaries when using static
+# kernel linking.
+def tf_binary_dynamic_kernel_deps(kernels):
+  return if_dynamic_kernels(
+      extra_deps=[],
+      otherwise=kernels)
+
 def tf_cc_shared_object(
     name,
     srcs=[],
     deps=[],
+    data=[],
     linkopts=[],
     framework_so=tf_binary_additional_srcs(),
+    kernels=[],
     **kwargs):
   native.cc_binary(
       name=name,
       srcs=srcs + framework_so,
-      deps=deps,
+      deps=deps + tf_binary_dynamic_kernel_deps(kernels),
       linkshared = 1,
+      data = data + tf_binary_dynamic_kernel_dsos(kernels),
       linkopts=linkopts + _rpath_linkopts(name) + select({
           clean_dep("//tensorflow:darwin"): [
               "-Wl,-install_name,@rpath/" + name.split("/")[-1],
@@ -336,18 +372,21 @@ register_extension_info(
 def tf_cc_binary(name,
                  srcs=[],
                  deps=[],
+                 data=[],
                  linkopts=[],
                  copts=tf_copts(),
+                 kernels=[],
                  **kwargs):
   native.cc_binary(
       name=name,
       copts=copts,
       srcs=srcs + tf_binary_additional_srcs(),
-      deps=deps + if_mkl(
+      deps=deps + tf_binary_dynamic_kernel_deps(kernels) + if_mkl(
           [
               "//third_party/mkl:intel_binary_blob",
           ],
       ),
+      data=data +  tf_binary_dynamic_kernel_dsos(kernels),
       linkopts=linkopts + _rpath_linkopts(name),
       **kwargs)
 
@@ -532,9 +571,6 @@ def tf_gen_op_wrappers_cc(name,
 #     is invalid to specify both "hidden" and "op_whitelist".
 #   cc_linkopts: Optional linkopts to be added to tf_cc_binary that contains the
 #     specified ops.
-#   gen_locally: if True, the genrule to generate the Python library will be run
-#     without sandboxing. This would help when the genrule depends on symlinks
-#     which may not be supported in the sandbox.
 def tf_gen_op_wrapper_py(name,
                          out=None,
                          hidden=None,
@@ -545,8 +581,7 @@ def tf_gen_op_wrapper_py(name,
                          generated_target_name=None,
                          op_whitelist=[],
                          cc_linkopts=[],
-                         api_def_srcs=[],
-                         gen_locally=False):
+                         api_def_srcs=[]):
   if (hidden or hidden_file) and op_whitelist:
     fail('Cannot pass specify both hidden and op_whitelist.')
 
@@ -601,7 +636,6 @@ def tf_gen_op_wrapper_py(name,
         outs=[out],
         srcs=api_def_srcs + [hidden_file],
         tools=[tool_name] + tf_binary_additional_srcs(),
-        local = (1 if gen_locally else 0),
         cmd=("$(location " + tool_name + ") " + api_def_args_str +
              " @$(location " + hidden_file + ") " +
              ("1" if require_shape_functions else "0") + " > $@"))
@@ -611,7 +645,6 @@ def tf_gen_op_wrapper_py(name,
         outs=[out],
         srcs=api_def_srcs,
         tools=[tool_name] + tf_binary_additional_srcs(),
-        local = (1 if gen_locally else 0),
         cmd=("$(location " + tool_name + ") " + api_def_args_str + " " +
              op_list_arg + " " +
              ("1" if require_shape_functions else "0") + " " +
@@ -641,11 +674,13 @@ def tf_gen_op_wrapper_py(name,
 def tf_cc_test(name,
                srcs,
                deps,
+               data=[],
                linkstatic=0,
                extra_copts=[],
                suffix="",
                linkopts=[],
                nocopts=None,
+               kernels=[],
                **kwargs):
   native.cc_test(
       name="%s%s" % (name, suffix),
@@ -665,11 +700,12 @@ def tf_cc_test(name,
             "-lm"
         ],
       }) + linkopts + _rpath_linkopts(name),
-      deps=deps + if_mkl(
+      deps=deps + tf_binary_dynamic_kernel_deps(kernels) + if_mkl(
           [
               "//third_party/mkl:intel_binary_blob",
           ],
       ),
+      data=data + tf_binary_dynamic_kernel_dsos(kernels),
       # Nested select() statements seem not to be supported when passed to
       # linkstatic, and we already have a cuda select() passed in to this
       # function.
@@ -770,6 +806,7 @@ def tf_cuda_only_cc_test(name,
                     size="medium",
                     linkstatic=0,
                     args=[],
+                    kernels=[],
                     linkopts=[]):
   native.cc_test(
       name="%s%s" % (name, "_gpu"),
@@ -777,8 +814,8 @@ def tf_cuda_only_cc_test(name,
       size=size,
       args=args,
       copts= _cuda_copts() + tf_copts(),
-      data=data,
-      deps=deps + if_cuda([
+      data=data + tf_binary_dynamic_kernel_dsos(kernels),
+      deps=deps + tf_binary_dynamic_kernel_deps(kernels) + if_cuda([
           clean_dep("//tensorflow/core:cuda"),
           clean_dep("//tensorflow/core:gpu_lib")]),
       linkopts=if_not_windows(["-lpthread", "-lm"]) + linkopts + _rpath_linkopts(name),
@@ -821,10 +858,15 @@ def tf_cc_tests(srcs,
 def tf_cc_test_mkl(srcs,
                    deps,
                    name="",
+                   data=[],
                    linkstatic=0,
                    tags=[],
                    size="medium",
+                   kernels=[],
                    args=None):
+  # -fno-exceptions in nocopts breaks compilation if header modules are enabled.
+  disable_header_modules = ["-use_header_modules"]
+
   for src in srcs:
     native.cc_test(
       name=src_to_test_name(src),
@@ -841,15 +883,17 @@ def tf_cc_test_mkl(srcs,
             "-lm"
         ],
       }) + _rpath_linkopts(src_to_test_name(src)),
-      deps=deps + if_mkl(
+      deps=deps + tf_binary_dynamic_kernel_deps(kernels) + if_mkl(
           [
               "//third_party/mkl:intel_binary_blob",
           ],
       ),
+      data=data + tf_binary_dynamic_kernel_dsos(kernels),
       linkstatic=linkstatic,
       tags=tags,
       size=size,
       args=args,
+      features=disable_header_modules,
       nocopts="-fno-exceptions")
 
 
@@ -884,12 +928,13 @@ def tf_cuda_cc_tests(srcs,
 def tf_java_test(name,
                  srcs=[],
                  deps=[],
+                 kernels=[],
                  *args,
                  **kwargs):
   native.java_test(
       name=name,
       srcs=srcs,
-      deps=deps + tf_binary_additional_srcs(),
+      deps=deps + tf_binary_additional_srcs() + tf_binary_dynamic_kernel_dsos(kernels) + tf_binary_dynamic_kernel_deps(kernels),
       *args,
       **kwargs)
 
@@ -984,16 +1029,17 @@ register_extension_info(
     label_regex_for_dep = "{extension_name}",
 )
 
-def tf_kernel_library(name,
-                      prefix=None,
-                      srcs=None,
-                      gpu_srcs=None,
-                      hdrs=None,
-                      deps=None,
-                      alwayslink=1,
-                      copts=None,
-                      is_external=False,
-                      **kwargs):
+def tf_kernel_library(
+        name,
+        prefix = None,
+        srcs = None,
+        gpu_srcs = None,
+        hdrs = None,
+        deps = None,
+        alwayslink = 1,
+        copts = None,
+        is_external = False,
+        **kwargs):
   """A rule to build a TensorFlow OpKernel.
 
   May either specify srcs/hdrs or prefix.  Similar to tf_cuda_library,
@@ -1023,6 +1069,7 @@ def tf_kernel_library(name,
     deps = []
   if not copts:
     copts = []
+  textual_hdrs = []
   copts = copts + tf_copts(is_external=is_external)
   if prefix:
     if native.glob([prefix + "*.cu.cc"], exclude=["*test*"]):
@@ -1033,8 +1080,13 @@ def tf_kernel_library(name,
     srcs = srcs + native.glob(
         [prefix + "*.cc"], exclude=[prefix + "*test*", prefix + "*.cu.cc"])
     hdrs = hdrs + native.glob(
-        [prefix + "*.h"], exclude=[prefix + "*test*", prefix + "*.cu.h"])
-
+            [prefix + "*.h"],
+            exclude = [prefix + "*test*", prefix + "*.cu.h", prefix + "*impl.h"],
+        )
+    textual_hdrs = native.glob(
+            [prefix + "*impl.h"],
+            exclude = [prefix + "*test*", prefix + "*.cu.h"],
+        )
   cuda_deps = [clean_dep("//tensorflow/core:gpu_lib")]
   if gpu_srcs:
     for gpu_src in gpu_srcs:
@@ -1048,6 +1100,7 @@ def tf_kernel_library(name,
       name=name,
       srcs=srcs,
       hdrs=hdrs,
+      textual_hdrs = textual_hdrs,
       copts=copts,
       cuda_deps=cuda_deps,
       linkstatic=1,  # Needed since alwayslink is broken in bazel b/27630669
@@ -1055,6 +1108,15 @@ def tf_kernel_library(name,
       deps=deps,
       **kwargs)
 
+  # TODO(gunan): CUDA dependency not clear here. Fix it.
+  tf_cc_shared_object(
+      name="libtfkernel_%s.so" % name,
+      srcs=srcs + hdrs,
+      copts=copts,
+      deps=deps,
+      tags=["manual", "notap"])
+
+
 register_extension_info(
     extension_name = "tf_kernel_library",
     label_regex_for_dep = "{extension_name}(_gpu)?",
@@ -1081,6 +1143,9 @@ def tf_mkl_kernel_library(name,
     hdrs = hdrs + native.glob(
         [prefix + "*.h"])
 
+  # -fno-exceptions in nocopts breaks compilation if header modules are enabled.
+  disable_header_modules = ["-use_header_modules"]
+
   native.cc_library(
       name=name,
       srcs=if_mkl(srcs),
@@ -1088,7 +1153,8 @@ def tf_mkl_kernel_library(name,
       deps=deps,
       alwayslink=alwayslink,
       copts=copts,
-      nocopts=nocopts
+      nocopts=nocopts,
+      features = disable_header_modules
   )
 
 register_extension_info(
@@ -1327,7 +1393,7 @@ def tf_custom_op_library(name, srcs=[], gpu_srcs=[], deps=[], linkopts=[]):
       name=name,
       srcs=srcs,
       deps=deps + if_cuda(cuda_deps),
-      data=[name + "_check_deps"],
+      data=if_static([name + "_check_deps"]),
       copts=tf_copts(is_external=True),
       features = ["windows_export_all_symbols"],
       linkopts=linkopts + select({
@@ -1423,7 +1489,7 @@ def tf_py_wrap_cc(name,
       srcs=srcs,
       swig_includes=swig_includes,
       deps=deps + extra_deps,
-      toolchain_deps=["//tools/defaults:crosstool"],
+      toolchain_deps=["@bazel_tools//tools/cpp:current_cc_toolchain"],
       module_name=module_name,
       py_module_name=name)
   vscriptname=name+"_versionscript"
diff --git a/tensorflow/tools/api/golden/tensorflow.-config-proto.-experimental.pbtxt b/tensorflow/tools/api/golden/tensorflow.-config-proto.-experimental.pbtxt
index 9e09a8d48ec7a501cb25a30163b5dae84b7c8655..eb41deee13de99d6e9534c32141096edc018ed1c 100644
--- a/tensorflow/tools/api/golden/tensorflow.-config-proto.-experimental.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-config-proto.-experimental.pbtxt
@@ -8,5 +8,17 @@ tf_proto {
       label: LABEL_OPTIONAL
       type: TYPE_STRING
     }
+    field {
+      name: "client_handles_error_formatting"
+      number: 2
+      label: LABEL_OPTIONAL
+      type: TYPE_BOOL
+    }
+    field {
+      name: "executor_type"
+      number: 3
+      label: LABEL_OPTIONAL
+      type: TYPE_STRING
+    }
   }
 }
diff --git a/tensorflow/tools/api/golden/tensorflow.-config-proto.pbtxt b/tensorflow/tools/api/golden/tensorflow.-config-proto.pbtxt
index 4af4ed70ef0698e996905bcb3b2222380b8694d8..e565b903d22c3921743becbdd34f33a8850e84d5 100644
--- a/tensorflow/tools/api/golden/tensorflow.-config-proto.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-config-proto.pbtxt
@@ -131,6 +131,18 @@ tf_proto {
         label: LABEL_OPTIONAL
         type: TYPE_STRING
       }
+      field {
+        name: "client_handles_error_formatting"
+        number: 2
+        label: LABEL_OPTIONAL
+        type: TYPE_BOOL
+      }
+      field {
+        name: "executor_type"
+        number: 3
+        label: LABEL_OPTIONAL
+        type: TYPE_STRING
+      }
     }
   }
 }
diff --git a/tensorflow/tools/api/golden/tensorflow.-variable-scope.pbtxt b/tensorflow/tools/api/golden/tensorflow.-variable-scope.pbtxt
index ec1f72453fdb540463503a626d75d481907a3676..c13eb7b8bb9474f3534582c8af8c3ee4b6c7e076 100644
--- a/tensorflow/tools/api/golden/tensorflow.-variable-scope.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-variable-scope.pbtxt
@@ -56,7 +56,7 @@ tf_class {
   }
   member_method {
     name: "get_variable"
-    argspec: "args=[\'self\', \'var_store\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'reuse\', \'trainable\', \'collections\', \'caching_device\', \'partitioner\', \'validate_shape\', \'use_resource\', \'custom_getter\', \'constraint\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
+    argspec: "args=[\'self\', \'var_store\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'reuse\', \'trainable\', \'collections\', \'caching_device\', \'partitioner\', \'validate_shape\', \'use_resource\', \'custom_getter\', \'constraint\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
   }
   member_method {
     name: "global_variables"
diff --git a/tensorflow/tools/api/golden/tensorflow.-variable.pbtxt b/tensorflow/tools/api/golden/tensorflow.-variable.pbtxt
index 23b552cc38488bdc15d7deed20f563379dba24c3..e841c4ad8904ae1ae49881b47648b901a4abf778 100644
--- a/tensorflow/tools/api/golden/tensorflow.-variable.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-variable.pbtxt
@@ -49,7 +49,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'initial_value\', \'trainable\', \'collections\', \'validate_shape\', \'caching_device\', \'name\', \'variable_def\', \'dtype\', \'expected_shape\', \'import_scope\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'initial_value\', \'trainable\', \'collections\', \'validate_shape\', \'caching_device\', \'name\', \'variable_def\', \'dtype\', \'expected_shape\', \'import_scope\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
   }
   member_method {
     name: "assign"
diff --git a/tensorflow/tools/api/golden/tensorflow.compat.pbtxt b/tensorflow/tools/api/golden/tensorflow.compat.pbtxt
index bab480ff9b105546790aadb72f3eb88a795ebbff..f1d760603e981a0b9a72fdc379dc81932ac71d67 100644
--- a/tensorflow/tools/api/golden/tensorflow.compat.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.compat.pbtxt
@@ -32,6 +32,14 @@ tf_module {
     name: "as_text"
     argspec: "args=[\'bytes_or_text\', \'encoding\'], varargs=None, keywords=None, defaults=[\'utf-8\'], "
   }
+  member_method {
+    name: "forward_compatibility_horizon"
+    argspec: "args=[\'year\', \'month\', \'day\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "forward_compatible"
+    argspec: "args=[\'year\', \'month\', \'day\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "path_to_str"
     argspec: "args=[\'path\'], varargs=None, keywords=None, defaults=None"
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-classifier.pbtxt
index 9dbb5d16a4e903a755c86bd0a6241180d1999f4d..c23b04b4ef85a290f055d35d0c7f0d4d8a18a2de 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-classifier.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-classifier.pbtxt
@@ -21,7 +21,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'feature_columns\', \'n_batches_per_layer\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'n_trees\', \'max_depth\', \'learning_rate\', \'l1_regularization\', \'l2_regularization\', \'tree_complexity\', \'min_node_weight\', \'config\', \'center_bias\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'None\', \'100\', \'6\', \'0.1\', \'0.0\', \'0.0\', \'0.0\', \'0.0\', \'None\', \'False\'], "
+    argspec: "args=[\'self\', \'feature_columns\', \'n_batches_per_layer\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'n_trees\', \'max_depth\', \'learning_rate\', \'l1_regularization\', \'l2_regularization\', \'tree_complexity\', \'min_node_weight\', \'config\', \'center_bias\', \'pruning_mode\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'None\', \'100\', \'6\', \'0.1\', \'0.0\', \'0.0\', \'0.0\', \'0.0\', \'None\', \'False\', \'none\'], "
   }
   member_method {
     name: "eval_dir"
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-regressor.pbtxt
index 34a30c2874b90285706c9df6bec8cbbdc3451fe4..6878d28fffabc895433f97415ee71cfe8f6232c1 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-regressor.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-regressor.pbtxt
@@ -21,7 +21,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'feature_columns\', \'n_batches_per_layer\', \'model_dir\', \'label_dimension\', \'weight_column\', \'n_trees\', \'max_depth\', \'learning_rate\', \'l1_regularization\', \'l2_regularization\', \'tree_complexity\', \'min_node_weight\', \'config\', \'center_bias\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'100\', \'6\', \'0.1\', \'0.0\', \'0.0\', \'0.0\', \'0.0\', \'None\', \'False\'], "
+    argspec: "args=[\'self\', \'feature_columns\', \'n_batches_per_layer\', \'model_dir\', \'label_dimension\', \'weight_column\', \'n_trees\', \'max_depth\', \'learning_rate\', \'l1_regularization\', \'l2_regularization\', \'tree_complexity\', \'min_node_weight\', \'config\', \'center_bias\', \'pruning_mode\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'100\', \'6\', \'0.1\', \'0.0\', \'0.0\', \'0.0\', \'0.0\', \'None\', \'False\', \'none\'], "
   }
   member_method {
     name: "eval_dir"
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-run-config.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-run-config.pbtxt
index c8da55d8021b7659446d0771a089b7b605d86c4f..5aa4b3d4fb269785841e74c51f2121ce64377691 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.-run-config.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.-run-config.pbtxt
@@ -50,6 +50,10 @@ tf_class {
     name: "num_worker_replicas"
     mtype: ""
   }
+  member {
+    name: "protocol"
+    mtype: ""
+  }
   member {
     name: "save_checkpoints_secs"
     mtype: ""
@@ -88,7 +92,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'model_dir\', \'tf_random_seed\', \'save_summary_steps\', \'save_checkpoints_steps\', \'save_checkpoints_secs\', \'session_config\', \'keep_checkpoint_max\', \'keep_checkpoint_every_n_hours\', \'log_step_count_steps\', \'train_distribute\', \'device_fn\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'100\', \'\', \'\', \'None\', \'5\', \'10000\', \'100\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'model_dir\', \'tf_random_seed\', \'save_summary_steps\', \'save_checkpoints_steps\', \'save_checkpoints_secs\', \'session_config\', \'keep_checkpoint_max\', \'keep_checkpoint_every_n_hours\', \'log_step_count_steps\', \'train_distribute\', \'device_fn\', \'protocol\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'100\', \'\', \'\', \'None\', \'5\', \'10000\', \'100\', \'None\', \'None\', \'None\'], "
   }
   member_method {
     name: "replace"
diff --git a/tensorflow/tools/api/golden/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/tensorflow.image.pbtxt
index e89b4dbffdfe85f471fb1dd1b976cc701d526c64..5c46dc5ee7dc04f57591d4883ec8eb034a34d2d0 100644
--- a/tensorflow/tools/api/golden/tensorflow.image.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.image.pbtxt
@@ -120,6 +120,14 @@ tf_module {
     name: "non_max_suppression"
     argspec: "args=[\'boxes\', \'scores\', \'max_output_size\', \'iou_threshold\', \'score_threshold\', \'name\'], varargs=None, keywords=None, defaults=[\'0.5\', \'-inf\', \'None\'], "
   }
+  member_method {
+    name: "non_max_suppression_overlaps"
+    argspec: "args=[\'overlaps\', \'scores\', \'max_output_size\', \'overlap_threshold\', \'score_threshold\', \'name\'], varargs=None, keywords=None, defaults=[\'0.5\', \'-inf\', \'None\'], "
+  }
+  member_method {
+    name: "non_max_suppression_padded"
+    argspec: "args=[\'boxes\', \'scores\', \'max_output_size\', \'iou_threshold\', \'score_threshold\', \'pad_to_max_output_size\', \'name\'], varargs=None, keywords=None, defaults=[\'0.5\', \'-inf\', \'False\', \'None\'], "
+  }
   member_method {
     name: "pad_to_bounding_box"
     argspec: "args=[\'image\', \'offset_height\', \'offset_width\', \'target_height\', \'target_width\'], varargs=None, keywords=None, defaults=None"
diff --git a/tensorflow/tools/api/golden/tensorflow.initializers.pbtxt b/tensorflow/tools/api/golden/tensorflow.initializers.pbtxt
index eaf0036cacfadce335a84bcf61f47f9d360be7e2..bc0426f2f145763552dcb46fb6c2efc7c42b974f 100644
--- a/tensorflow/tools/api/golden/tensorflow.initializers.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.initializers.pbtxt
@@ -44,6 +44,30 @@ tf_module {
     name: "global_variables"
     argspec: "args=[], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "glorot_normal"
+    argspec: "args=[\'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \"\"], "
+  }
+  member_method {
+    name: "glorot_uniform"
+    argspec: "args=[\'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \"\"], "
+  }
+  member_method {
+    name: "he_normal"
+    argspec: "args=[\'seed\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "he_uniform"
+    argspec: "args=[\'seed\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "lecun_normal"
+    argspec: "args=[\'seed\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "lecun_uniform"
+    argspec: "args=[\'seed\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "local_variables"
     argspec: "args=[], varargs=None, keywords=None, defaults=None"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
index 11cdd6f0b5e48f5835385fdd4e3e5144fb7d5166..40e82b18b68f9e8353dcb04f76ebb36446d3ab3f 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
@@ -119,7 +119,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
index 4afad3e4df308d412a1c18dea3b4e99aa1d2c84f..65cfad77d1f3cdf682b6681fbebc950e6c1ca8a8 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
@@ -124,7 +124,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
@@ -266,6 +266,10 @@ tf_class {
     name: "summary"
     argspec: "args=[\'self\', \'line_length\', \'positions\', \'print_fn\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
   }
+  member_method {
+    name: "symbolic_set_inputs"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "test_on_batch"
     argspec: "args=[\'self\', \'x\', \'y\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.activations.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.activations.pbtxt
index 2cd83baf65cf4114e58f52cdc40de7e4b6df7554..2e9de9ebb21021ab82ed4409243e13db49d7327c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.activations.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.activations.pbtxt
@@ -22,7 +22,7 @@ tf_module {
   }
   member_method {
     name: "relu"
-    argspec: "args=[\'x\', \'alpha\', \'max_value\'], varargs=None, keywords=None, defaults=[\'0.0\', \'None\'], "
+    argspec: "args=[\'x\', \'alpha\', \'max_value\', \'threshold\'], varargs=None, keywords=None, defaults=[\'0.0\', \'None\', \'0\'], "
   }
   member_method {
     name: "selu"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.backend.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.backend.pbtxt
index c6149e8aa7e3650e628e37b0e00a54348012475b..126ce8db6a73e2c486dbf34512812e630b3e9a32 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.backend.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.backend.pbtxt
@@ -70,7 +70,7 @@ tf_module {
   }
   member_method {
     name: "categorical_crossentropy"
-    argspec: "args=[\'target\', \'output\', \'from_logits\'], varargs=None, keywords=None, defaults=[\'False\'], "
+    argspec: "args=[\'target\', \'output\', \'from_logits\', \'axis\'], varargs=None, keywords=None, defaults=[\'False\', \'-1\'], "
   }
   member_method {
     name: "clear_session"
@@ -366,7 +366,7 @@ tf_module {
   }
   member_method {
     name: "relu"
-    argspec: "args=[\'x\', \'alpha\', \'max_value\'], varargs=None, keywords=None, defaults=[\'0.0\', \'None\'], "
+    argspec: "args=[\'x\', \'alpha\', \'max_value\', \'threshold\'], varargs=None, keywords=None, defaults=[\'0.0\', \'None\', \'0\'], "
   }
   member_method {
     name: "repeat"
@@ -462,7 +462,7 @@ tf_module {
   }
   member_method {
     name: "sparse_categorical_crossentropy"
-    argspec: "args=[\'target\', \'output\', \'from_logits\'], varargs=None, keywords=None, defaults=[\'False\'], "
+    argspec: "args=[\'target\', \'output\', \'from_logits\', \'axis\'], varargs=None, keywords=None, defaults=[\'False\', \'-1\'], "
   }
   member_method {
     name: "spatial_2d_padding"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-tensor-board.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-tensor-board.pbtxt
index 2f52464315d8c1b526792c92f5cf8e83ce3ce087..e58ba18c1c0d06df3a53d93ae18f5bf0931df329 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-tensor-board.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-tensor-board.pbtxt
@@ -5,7 +5,7 @@ tf_class {
   is_instance: ""
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'log_dir\', \'histogram_freq\', \'batch_size\', \'write_graph\', \'write_grads\', \'write_images\'], varargs=None, keywords=None, defaults=[\'./logs\', \'0\', \'32\', \'True\', \'False\', \'False\'], "
+    argspec: "args=[\'self\', \'log_dir\', \'histogram_freq\', \'batch_size\', \'write_graph\', \'write_grads\', \'write_images\', \'embeddings_freq\', \'embeddings_layer_names\', \'embeddings_metadata\', \'embeddings_data\'], varargs=None, keywords=None, defaults=[\'./logs\', \'0\', \'32\', \'True\', \'False\', \'False\', \'0\', \'None\', \'None\', \'None\'], "
   }
   member_method {
     name: "on_batch_begin"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.initializers.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.initializers.pbtxt
index 14a667870d3118e48bfac03eee9accb3d48a72ce..8645e5430295dff0a5b7c715b03860fb7734e7f1 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.initializers.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.initializers.pbtxt
@@ -90,11 +90,11 @@ tf_module {
   }
   member_method {
     name: "glorot_normal"
-    argspec: "args=[\'seed\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \"\"], "
   }
   member_method {
     name: "glorot_uniform"
-    argspec: "args=[\'seed\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \"\"], "
   }
   member_method {
     name: "he_normal"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt
index 2bf973debb175d27bb80e627d7ccbb41b567020d..86e328888e596852caf9ad1020dfdedb71864969 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt
index 03f20e72c2a325cec000cf4a5cfc0f1bbf255c8f..b0ed54578109c6ae8d5bc2c9f5c978b562a9cc84 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt
index 4b46b8d15afb0a2f636962b762e1808312c2f7c3..42f98ed03d426d60cabeb0b533311d41eb378285 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt
index d8a1c76fd07634ef413152020a397897f2d5b97c..000898a4be928e4e64b4072ef3170b6fbc930bdf 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt
index 622926bc4b8b2430ee1ab936665acb5744155e0d..380b49f99ce6e62770a9516ba81db99f194c5b37 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt
index 82100d8e09c8e95730993527293d2b72ce69f1d4..82db5e6137639e516f6df6f0e130e73be516c9b8 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt
index 408061077cdeab2f8fd08c7e972744e5ee383f52..b6ff688ec36f8c47b2ac9694fb84350818be25c5 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt
index a3c80311043eeb95b06855f662a5e3d344803ba3..b41290f8b067397bf6678d9e98ac53f28a05a3fc 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt
index e2dfaca29f86bd9d91d524ec337afad81e7f2da3..88a033e61f42e2fb02b08968ff001ea21195972a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt
index 4f068d2066a450bab77becc85a33662b78ad03e2..c1b9b96044ed2e057b8e86dda59ee7f7166cfd43 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt
index b8c261a74364e9bb6bf8f6c7463993fbff5e9552..f59f7727a3eaeb4fa5631cb1b42901ea6d39b06b 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt
index 4ccd6cace650e2efd1583c75f6639c8598bb8f20..7d3744ed92636a972bae2f9b62a6b2da8f91d106 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt
index 2790e5fd850c24bd3e94cd15a6e079e1c9f79868..3fd4ccdab2573964c2f3192d503e9fb15f442dc5 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt
@@ -107,7 +107,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt
index b1326bd0e6054b2a3fd36e7ad42cd3d4a0cad8dc..ba21b50be41f3adc735b3350bdf9dbeae3c2e358 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt
index e3ac3dbf28da731e14640d5f464547d62391a28f..46f9fa2bbbbe3cfff3aade33c5ebdec92bc70ef0 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt
@@ -188,7 +188,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt
index 1117a695a395f495d988464bbf59d4b8e01877e6..c3ad326589d2822bc5dd381d78216b25f5fb6f95 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt
index b9de1421428dcf61b988df343a22996cfb8fecef..fd9eb43066be580a7df57aeb717b59569c9bba61 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt
@@ -100,7 +100,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt
index deb535e06e06008a17b80c8e13d8f01ad1535059..40d61688f29a81e873a26c8a5eb823d679320ed6 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt
index 9a9a223fbad11cafd8620110d80b27d5382dd29c..b8c227d7257311578e41abe0a384ed93e6a2866c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt
@@ -100,7 +100,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt
index 1c59b0bdf624b09a7454f2d51698951a790f393a..095d35e5749d0113956b04f971e6a8ca1fa277b8 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt
index 30cf5489f4fcd4af3d0bd957fc9c576c57ee2bbd..8f999611982bbfe3c613ef26d93782e299275f19 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt
index 0ec69508d5a1992b46d1a7c65255cfb5408ab439..96d522a016aedba01032a1c05a69511cb03d19af 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt
@@ -100,7 +100,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt
index 4cd8928403c98abad85bc1349a29148c73003c9d..de2824dab4526d90eebf9cef16710cadf82f4850 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt
index 4b4912496deac2a79a5b0ea3d1ca0f8fa625301a..1d563241d8f0d93bcd19a319eb8383f4bcdf4388 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt
@@ -100,7 +100,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt
index d0ad9cf56702e585e31a79de0f93d9efd48ed484..c87e52c53796f0743365a9d8780decf237bba070 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt
index 98cff95a7fe9d4e58cf883502df08c58c651cd76..dccf5523e3870b6c1ce0de70c648ab47968a105f 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt
index 2357498b46376ef13de102944b69931a9e7d3584..7ac4116d922eea51e5a7e7fe3d02ad919300c459 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt
index 3324cbff304c5106360f3f3d3d608a528fa5fc31..024f72705de1e76866a8132246884dffb0c4e72a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-g-r-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-g-r-u.pbtxt
index 6c81823654b78a936cded4a1d5a6f54e02dc7fc9..4e0233331bd47e86e8a4df2f84b5392517fbf884 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-g-r-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-g-r-u.pbtxt
@@ -108,7 +108,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt
index 487e04fd0790cb39ef6aee8d0498b3aae6726084..32d46ce8f3deff6077eaf5a1a8cf7ba64478d9f4 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cu-d-n-n-l-s-t-m.pbtxt
@@ -108,7 +108,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt
index 137e7cced4e8113dd6a54a837e08cfd5af35c94d..858486c725c3be5ecae2a02d0d3134ebeb113ce1 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-depthwise-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-depthwise-conv2-d.pbtxt
index 7161665d2550c1cc3aff1c28f9d7676276b62303..f65d7509262bfeb148588e069c08961058a3fa74 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-depthwise-conv2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-depthwise-conv2-d.pbtxt
@@ -100,7 +100,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt
index 24affa248121bcb1e1a947417a95ad4f5ba55ab2..2e71ef503d54927edbb3e1ef6c701ac845883e46 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt
index 7ba19a42695da37b4ad43cdde2c0d4978fd0a1eb..42533bcd21b28a0acf183db195a6b5c1848a5d91 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt
index 503aa9162c3a78e9bb42ce16af98451441adbbb7..b5df16941792a29d72f2ee709993b007d342d2d0 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt
index 1737e590a29c5777b5eca2b4cb23081aa8ece738..0ea17919a9bb13ffdedd60ce618bca23dd52712f 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt
index 021d024dc2150a75532ea7597d85f36efd2a3cf2..a33248bc005a73d0be679cd62150d6019b475305 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt
index 65387008bf3f78e404d8d8bbd7bb8cd3789bf256..4ba21a25cda83122fbced7fed76d4b1ae28cb4c8 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt
index 4f791acf0585c95d6c0f1d5ea48e607f9a05188d..a7a570418e0a78873237c1c8cefe36a212e4c9af 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt
@@ -171,7 +171,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt
index abc30e54e0630a2d7b4de6074445e155e0ac2782..763bc231136908d469b7f942aec94f6248d2e2d4 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt
index 20791bb448d17788ea4aebe4900169a70a9703d6..3c50a3d7f28809b2b810b52951207e48f9f50e34 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt
index 449a91d8735c59f563360307cdb35c5a30344d82..ac78bdafada8c157efd4ab8746be15726eb0bc24 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt
index bb361e129728ddd42c21144937efbc617d98ba30..275282d9d2b1753cf0189b605f921bb039ef5f3c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt
index e564bf3216104a902fb6cfbe65b1e2b6dafc2524..0e31e6058bd6036a5fb4422335917718f4f82851 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt
index 4cb9cc3ec84d679b78465e43caa5a257466d5676..aacd0b1791dda5babb6eef5d87a1335c8d519b08 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt
index 5ed52b88ae3e2dd25b560206db404952034a04cd..c23654866341818aeb804cfb71dae052049e3f25 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt
index f4559d29d75ef7cd8fcbdeac0a1a2c9e633246bc..6b9c0290aac35d80c7f87acfc44479c57623a645 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt
index 64e2d061e26997365c461113d3ea15140fef64dd..0d7b2211e6cd35ca331b4a1068f237e7ca07f70c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt
index 3372ad645388beb54f7ed9e3715449facba07f87..d080ad6aedbd5183da890cd63f5f18453d5d476a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt
index 08a6860bcd7d9a260e44af87c51796a9cc2af379..fcb0a109da208ff5bd20447ddced9816a42af311 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt
index 22c9eab64fde41e1199ecbb1b8b03939653ecd00..1d0e22abd0d8732182881c43ee79400642cef24b 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt
index 74c405ba9b1b465f89c4fef43020181a1a7f3d31..653c9f547bc888a8fec87137f7d495141d4f8599 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt
index 39f6f981931296eb6d31eb6580f93b479ff64ce6..cdbaf82cf6746e878619647439d2256f6e2c4aa3 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt
index 7b25e80b6b7653c5e76bf176b54110b1aabaf5ea..230c5e903438b0a75edf80f0f5c8706987c66a78 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
index 3619b8bfc44373ba6b8e306b020ac63d4b498573..511456e740837455818ff3f9be270daed03f334f 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt
index 8ef3d71dd82efc79e333770d4a7a7c8aee1a4202..4a3492ebd652e5ab8f0faf8a1583480abc80fba7 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt
@@ -171,7 +171,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt
index ecbaa9ce2c76bf3d2964a6c79c96c4d67cc3b80e..5d05cf689fb399d6630f68b09fd123d2d968786b 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt
index 9b90db1e5e56d1e5749669bba8dba1cdbd45bb55..7efa29be77c075a29784d8cd3ebfcd871bc9aa0c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt
@@ -97,7 +97,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt
index 3c60eaab7f1df15331004685676d74943d5d538f..0ca8e0b52c4a81c4ff3b756aa6c24b47a664f999 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt
index 3dac1ff342ac1b7f984e9af5a6028ef71da701df..f754fa1da85692c28f31a76bbfa987b3c4c30731 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt
index 7f1b5db4d34f706f2107ef43ab9c5acf67dac9f6..c9516b8f07d0b6a818bf99d45499d161c2a5cffd 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt
index b3e31000f3bca0821377d70b1d88a20aa8f8e4ef..850ecff9743b5f5048bb81c5a15b0a4be6b4d0ce 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt
index bbd9d1b0dc075bb9241f240b423933db20b38b75..7c69e31f9af9bbd221882d160fa4206997ec3b08 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt
index fe72beea802d12b996948b00436b274ee7e83177..fba42642d7c701688c2bd274cf97e077e7ff571c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt
index e9bf57b2b0e60376a28c0abfc16fba393df3e73c..9c277411ea5ce26df9c033ada773ad2e45292cb1 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt
index 0eecc58a2b6a2846a2c92502cc23bd328f8b5193..7c2f6ccc8a98017aba014ab6a7896e0a4bf40324 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt
index 96785a7d8559611a19b7f36216dbf0f8a3e39e61..802178dba63d66cca1629bcb7bef0f578c9a6659 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt
index 42c46cccb37b1ab7ece7760e6858b2180ea833b9..e870dfe9ade75da367f87a4b54d38ba4274bab2e 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt
index ac816f68d492cbfc5503c057a869e3e981de9190..c1337ce0cbac2d1e0e011f5309bfb2722960d3b2 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-minimum.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-minimum.pbtxt
index 56e32e9d3690a92c3f6e41bf2b5164c6bf62f443..ed27a62765d5670802d4593b3e648e3f65eaf926 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-minimum.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-minimum.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt
index 9ae99563e9a1b3b0700116ed88c13f94fafe1658..b9f05cb3e56f89cb02e1a74c3ec0d362ea27f2bf 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt
index 815f3bc2d142069adb4e418a4dc6ef82d683373f..336d9f76fb1e6215b763b5064cd6be68d4d0d5a0 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt
index e704992b4a18f6bdbd9474af2ee59ea81534d80a..46282217e01e8a137d9fc564f0e3544602d93de4 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt
index b3a58fa11eda61baa5c932bcc04fdca7459a215f..42cd7e87eebdd969f002d8bcd0dca101168c58e0 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt
@@ -102,7 +102,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt
index f3a96ab895dc9dbf8e2362dbcbfdccdf6af749ec..4d3de58bd188e301ef516ac5eeae6cf0709d66da 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt
@@ -82,7 +82,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'max_value\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'max_value\', \'negative_slope\', \'threshold\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'0\', \'0\'], "
   }
   member_method {
     name: "add_loss"
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt
index 78f464583b4e8083f4cdd1a8c6b9f377645cd562..9f094a877a3a47ff89a022db563803f5f391ff2a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt
index 222344fd0497afe9a32d1d05ec37aa160479d88a..2f519a24385ac4e147798ed3e96101cff23e19aa 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt
index 55fddf576cac6afabe984cd51e2ddbf112a55d25..6b93116ba02c2b7e9c5bdf79ddfa1f93050062a4 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt
@@ -100,7 +100,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt
index 96314ce49849a50ccc6b968b50c98ddae74c6c70..fd17115e2733d561bff1d53d62d32458b03dc65b 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt
@@ -100,7 +100,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt
index 88bdf9956603c590940e3ef857765586df7e91d7..4b37a94478857ac8550ea0c4f464058c68770047 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt
@@ -100,7 +100,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt
index 6eeea7a8d1312ada423206378b4c6ee079ffdd73..5bdadca74aeb963adef4999b7e758add1aec4681 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt
@@ -100,7 +100,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
index 3050d46249003716eb0778104b729ee9cb52b34f..9dfda96fc81572d70d76ba767b69ee2e41f017ee 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt
index dda4c9358ba5faa084ad2e6cf75ff83b6a7b2b20..7b7684ccd27a1d4c3fabf56c2669f77095f501ef 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt
@@ -159,7 +159,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt
index cc6275158b67e94c3c39802cc7c0f9e169c8b144..3b15407fca2cf65f7fa31f29b84db52b5c5d1a7a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt
index 5eb7e750477b17571ef861305806894dd2b9ac38..6d04415267c9ce21268b9d86a5b078d8f92db93f 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt
index 500cb8c14ead3eeff28d11b72e2300cc471756d2..04950654d55f30bf095167d176b5b2717e72f2cd 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt
index 1113a7634fa98b499175d90ae7da2d3fb9fb1a13..c424e6dcc869f977100e77fdb543983c3ab7e63c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
index c4b9f93561de6a5d8ecc19bbae17831466b51fe6..1160d2840f5ddd2937db53406af9d4d2132a6515 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
@@ -102,7 +102,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-subtract.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-subtract.pbtxt
index 35ad87ad5d91f1cc5d413b0adc8e9e5d1403726a..740a03367bd69edf797d3ea8616fdde72f6726b7 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-subtract.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-subtract.pbtxt
@@ -99,7 +99,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt
index 282c98d79a6e1da46e4d7ea2e5c7228754792f09..a08c583adb4175ff5ee77869c80c6c0204018166 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt
index acab93706b29fedc1bf7b48da2f5b6636dea48e5..c1294fed0fcfca9c8607bf3e5d41efd240fd4d45 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt
@@ -103,7 +103,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt
index a5ec228a074721775d4ec0369345b5439d84e186..dc401d3ed0fee5b6fb4bb5563941c3461eb592f4 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt
index d8d8e0bfe95a6cf2ef61cdb344b963df3f21aabb..4b5165ae9793f900fb474affe52b9abaeb64adbd 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt
index 97d6dc06fb2e883b20540e4496efa5b39a538263..789af15fea8c0d41dd3f0c00e7be3afd6afafecf 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt
index ea9bb41b9979de9049397892372f37aafc719a68..0536a7cee7e6dd5878f532854753cebeaa043c21 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt
@@ -102,7 +102,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt
index e6d1d2e089b01c4eb212d01c456f6fa6b850f7de..8915353ec334f28c4ed058b20a506ff102ca1f61 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt
index f62017305f26519181b1ef86bdd0946d44d16b88..6efb5ef15a133877666decfd1f2b40fad4463469 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt
index 07a1fde5bdc35535ca5d8443a97cb85adc54b14a..4c33c5d0bf800239e2bff4cc874e594b515a8071 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt
@@ -98,7 +98,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt
index a97a9b57587070ec4841b627920ac91737a67997..73b577da373b1381a7e8d5841d6e002452a21f9e 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt
@@ -22,7 +22,7 @@ tf_module {
   }
   member_method {
     name: "binary_accuracy"
-    argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+    argspec: "args=[\'y_true\', \'y_pred\', \'threshold\'], varargs=None, keywords=None, defaults=[\'0.5\'], "
   }
   member_method {
     name: "binary_crossentropy"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
index 62aa929d32b57518abbe924c036062eb7ccd3acf..85f7c2bfedb936d3b21624448cf8875775de918b 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
@@ -119,7 +119,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
index 93ecbbce9b17b9ca6157e65bbabd6c36008c3992..6a83129f7df46a63c8fa1080a6a35dc3f558c549 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
@@ -124,7 +124,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
@@ -266,6 +266,10 @@ tf_class {
     name: "summary"
     argspec: "args=[\'self\', \'line_length\', \'positions\', \'print_fn\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
   }
+  member_method {
+    name: "symbolic_set_inputs"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "test_on_batch"
     argspec: "args=[\'self\', \'x\', \'y\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt
index 11067058d5852669e1672bf3eb8b7c680d0e5dc9..c82e67526b21696a7d56517dc2cb6998882dc7a5 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt
@@ -109,7 +109,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt
index 3259e706d7f7ea4d0348c1ee586c50f5a2c82b39..1d031cb5f8461145127b0f13d77e6b8774f5a0b3 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt
@@ -109,7 +109,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt
index e561f2f415018840420232a97f0ece3f3c60d0d7..a8dda6655df1d06ca77b74f0a992c8fd7e7a357d 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt
@@ -109,7 +109,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt
index 3124a35c7852a97e79a3cfe575017484f2f5731f..97f65ed89436bd0b4027bb0cbeb80b6f1419269c 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt
@@ -108,7 +108,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt
index b5ec61255ace78c1fa13370727eb5f5084522f4a..ccd9578f0d62bd70ea252ddeac587d59c926b018 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt
@@ -109,7 +109,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt
index b2c89ae66f53299289508eef174b5c44a6be2606..9cbb58d721bb49bde562a57728a9ee46968e611e 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt
@@ -110,7 +110,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt
index 9e4f4969dc6e1b6a39cf1d25c5e5e6175fa87c7c..c75ea3911e17bc879d140068ef54521effd2824e 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt
@@ -109,7 +109,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt
index 9850e6d7659d311c93dabad73d35f2fcd028dd52..5dc834e5141e58d255357e02d7446a06e6e2aa45 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt
@@ -110,7 +110,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt
index be113826cc2b9589e1f8bbde896fbcbe183d4d1b..96ab209874ac14d6acf2e8115e7f04fc35c4b2bd 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt
@@ -109,7 +109,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt
index 0d951bf6336ac7b65be57535c1065e5f87a77a0b..7e9656b3525c1d53940b869607616ff414a466cf 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt
@@ -108,7 +108,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt
index f1beeed9ef0cb54318249e42b1279680ea117ba8..e9a2269a6e8de1f9a12f1b54d2e6dced3d4f8902 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt
@@ -108,7 +108,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt
index b75a012811ff10f055382ea1315eaba506c24ed8..7d2eaaab2a8cb9159214a16ba65473d0b6870ac4 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt
@@ -108,7 +108,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt
index 80e0fb228b034727854ab1a4df97e25c6bc2cd97..8bc3eb26e9ca0bf0f129db336b7ca23466fd036f 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt
@@ -106,7 +106,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt
index 50ff484d733633e20e9923dbbf1344af7b51ba9a..6a0dcce56ac0184ffe995662fd62b89e16257a29 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt
@@ -109,7 +109,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt
index cea809744cd07cc6ed0d1655f217cb5821e503e4..b6c84edf2a2f86240369b4053cd7351d0b59442d 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt
@@ -109,7 +109,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt
index ab9e89554c81decf5ee7e42dc963da9ab35e65c7..062a02fa590537b9efbf540a874eeaa6d36697f3 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt
@@ -109,7 +109,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt
index 4362568445e892d6127759c925d47426d49d9927..eaad0fb23ef7501c8c5b7acee6a9677665b7057f 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt
@@ -110,7 +110,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt
index 3cad824cd3b197b91a749347c860ff926610c081..ece28a8ce962d8fafb3f7a397a814b903e915d48 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt
@@ -110,7 +110,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-zeros.__metaclass__.pbtxt b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-zeros.__metaclass__.pbtxt
new file mode 100644
index 0000000000000000000000000000000000000000..49ff85728ffab559ec706691356ce071aab89083
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-zeros.__metaclass__.pbtxt
@@ -0,0 +1,14 @@
+path: "tensorflow.linalg.LinearOperatorZeros.__metaclass__"
+tf_class {
+  is_instance: ""
+  member_method {
+    name: "__init__"
+  }
+  member_method {
+    name: "mro"
+  }
+  member_method {
+    name: "register"
+    argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-zeros.pbtxt b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-zeros.pbtxt
new file mode 100644
index 0000000000000000000000000000000000000000..a1b0e06b4753488bc9fcbe9aeb0d260092745f9c
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-zeros.pbtxt
@@ -0,0 +1,130 @@
+path: "tensorflow.linalg.LinearOperatorZeros"
+tf_class {
+  is_instance: ""
+  is_instance: ""
+  is_instance: ""
+  member {
+    name: "batch_shape"
+    mtype: ""
+  }
+  member {
+    name: "domain_dimension"
+    mtype: ""
+  }
+  member {
+    name: "dtype"
+    mtype: ""
+  }
+  member {
+    name: "graph_parents"
+    mtype: ""
+  }
+  member {
+    name: "is_non_singular"
+    mtype: ""
+  }
+  member {
+    name: "is_positive_definite"
+    mtype: ""
+  }
+  member {
+    name: "is_self_adjoint"
+    mtype: ""
+  }
+  member {
+    name: "is_square"
+    mtype: ""
+  }
+  member {
+    name: "name"
+    mtype: ""
+  }
+  member {
+    name: "range_dimension"
+    mtype: ""
+  }
+  member {
+    name: "shape"
+    mtype: ""
+  }
+  member {
+    name: "tensor_rank"
+    mtype: ""
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'num_rows\', \'num_columns\', \'batch_shape\', \'dtype\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'assert_proper_shapes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'False\', \'True\', \'False\', \'True\', \'False\', \'LinearOperatorZeros\'], "
+  }
+  member_method {
+    name: "add_to_tensor"
+    argspec: "args=[\'self\', \'mat\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], "
+  }
+  member_method {
+    name: "assert_non_singular"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], "
+  }
+  member_method {
+    name: "assert_positive_definite"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], "
+  }
+  member_method {
+    name: "assert_self_adjoint"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], "
+  }
+  member_method {
+    name: "batch_shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], "
+  }
+  member_method {
+    name: "determinant"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], "
+  }
+  member_method {
+    name: "diag_part"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], "
+  }
+  member_method {
+    name: "domain_dimension_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], "
+  }
+  member_method {
+    name: "log_abs_determinant"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], "
+  }
+  member_method {
+    name: "matmul"
+    argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], "
+  }
+  member_method {
+    name: "matvec"
+    argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], "
+  }
+  member_method {
+    name: "range_dimension_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], "
+  }
+  member_method {
+    name: "shape_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], "
+  }
+  member_method {
+    name: "solve"
+    argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], "
+  }
+  member_method {
+    name: "solvevec"
+    argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], "
+  }
+  member_method {
+    name: "tensor_rank_tensor"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], "
+  }
+  member_method {
+    name: "to_dense"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], "
+  }
+  member_method {
+    name: "trace"
+    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], "
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt b/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt
index 3b5845f99a474ed976b91dab4f80ac2f231e7fc1..d979116887a739d2d372687fac0e5ea3b39a4b69 100644
--- a/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt
@@ -52,6 +52,10 @@ tf_module {
     name: "LinearOperatorScaledIdentity"
     mtype: ""
   }
+  member {
+    name: "LinearOperatorZeros"
+    mtype: ""
+  }
   member_method {
     name: "adjoint"
     argspec: "args=[\'matrix\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
index a8d9e120cb4aa965c1d85df59de1fbabc196bf54..c74773000aa31b0c51677b49eed6e83cc1f073ae 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
@@ -117,7 +117,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
index c039890e1f4c1d57e7b795f1f09cff71921f6554..d251f548069b430de0fe9af83b6e9c641ea9237c 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
@@ -117,7 +117,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
index 62c393de34475a8806015bed187572f79cf2a196..8a63b4918008674041c9c216a5e5547ed7152fce 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
@@ -116,7 +116,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
index f121ba7939acb14681aa6b04b333668dded37aad..db1aae275792dad94c4cf823d0d30f934e397601 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
@@ -120,7 +120,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
index 4583dc32b2e98d4a9912378fe0e3d841882772fd..d76eab7eb874c981ac111cf6f96f28363f5e4375 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
@@ -117,7 +117,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
index 5016b6ac3010e2e184674db4837173c57c44b97e..944db6ac937acb0d6a134aa2f17dfaa0d3d618ff 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
@@ -117,7 +117,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
index 59623fc983a63c2966882aa5113423c0a9e23b72..72b40cc9f7a720888a1399a60aa216013e0b9918 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
@@ -116,7 +116,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
index e2ab5aaee9456ffbe42894f2384d7bc9c7ad6a6f..a5c2b4aefd6a1b96cbe63271ca27de06616f1deb 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
@@ -115,7 +115,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
index bd2a6d61f8578a2a3c8d94d3a8d5eb49679df2f7..61d5f04b22a4b4e3801643958b73a35403b79139 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
@@ -116,7 +116,7 @@ tf_class {
   }
   member_method {
     name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\'], "
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
   }
   member_method {
     name: "apply"
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index bf2533e1b5d992d818faefa8e5a53aa8f553fa0e..5eb42b4db3c95a3bd139596665384a116f467b6c 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -258,7 +258,7 @@ tf_module {
   }
   member {
     name: "Variable"
-    mtype: ""
+    mtype: ""
   }
   member {
     name: "VariableAggregation"
@@ -1174,7 +1174,7 @@ tf_module {
   }
   member_method {
     name: "get_variable"
-    argspec: "args=[\'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'collections\', \'caching_device\', \'partitioner\', \'validate_shape\', \'use_resource\', \'custom_getter\', \'constraint\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
+    argspec: "args=[\'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'collections\', \'caching_device\', \'partitioner\', \'validate_shape\', \'use_resource\', \'custom_getter\', \'constraint\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
   }
   member_method {
     name: "get_variable_scope"
@@ -1560,10 +1560,6 @@ tf_module {
     name: "pow"
     argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
-  member_method {
-    name: "print"
-    argspec: "args=[\'input_\', \'data\', \'message\', \'first_n\', \'summarize\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
-  }
   member_method {
     name: "py_func"
     argspec: "args=[\'func\', \'inp\', \'Tout\', \'stateful\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], "
diff --git a/tensorflow/tools/api/lib/python_object_to_proto_visitor.py b/tensorflow/tools/api/lib/python_object_to_proto_visitor.py
index 1cf330e70247260cd9e50b18903bdfecad6260e4..3a48cf683c908021a6a87849601227283a8e2034 100644
--- a/tensorflow/tools/api/lib/python_object_to_proto_visitor.py
+++ b/tensorflow/tools/api/lib/python_object_to_proto_visitor.py
@@ -88,6 +88,9 @@ def _SanitizedMRO(obj):
   """
   return_list = []
   for cls in tf_inspect.getmro(obj):
+    if cls.__name__ == '_NewClass':
+      # Ignore class created by @deprecated_alias decorator.
+      continue
     str_repr = str(cls)
     return_list.append(str_repr)
     if 'tensorflow' not in str_repr:
diff --git a/tensorflow/tools/api/tests/api_compatibility_test.py b/tensorflow/tools/api/tests/api_compatibility_test.py
index 90375a794f64a9edd2bab2671f5870ae02e84e3c..d1b34fb242cd6303b61315b64ec60e6fc503aca2 100644
--- a/tensorflow/tools/api/tests/api_compatibility_test.py
+++ b/tensorflow/tools/api/tests/api_compatibility_test.py
@@ -34,6 +34,13 @@ import sys
 import unittest
 
 import tensorflow as tf
+# pylint: disable=g-import-not-at-top
+try:
+  from tensorflow.compat import v1 as tf_v1
+  # We import compat.v1 as tf_v1 instead.
+  del tf.compat.v1
+except ImportError:
+  tf_v1 = None
 
 from google.protobuf import message
 from google.protobuf import text_format
@@ -46,6 +53,7 @@ from tensorflow.tools.api.lib import api_objects_pb2
 from tensorflow.tools.api.lib import python_object_to_proto_visitor
 from tensorflow.tools.common import public_api
 from tensorflow.tools.common import traverse
+# pylint: enable=g-import-not-at-top
 
 
 # FLAGS defined at the bottom:
@@ -215,25 +223,19 @@ class ApiCompatibilityTest(test.TestCase):
     visitor.do_not_descend_map['tf'].append('contrib')
     traverse.traverse(tf, visitor)
 
-  @unittest.skipUnless(
-      sys.version_info.major == 2,
-      'API compabitility test goldens are generated using python2.')
-  def testAPIBackwardsCompatibility(self):
-    # Extract all API stuff.
+  def checkBackwardsCompatibility(self, root, golden_file_pattern):
+     # Extract all API stuff.
     visitor = python_object_to_proto_visitor.PythonObjectToProtoVisitor()
 
     public_api_visitor = public_api.PublicAPIVisitor(visitor)
     public_api_visitor.do_not_descend_map['tf'].append('contrib')
     public_api_visitor.do_not_descend_map['tf.GPUOptions'] = ['Experimental']
-    traverse.traverse(tf, public_api_visitor)
+    traverse.traverse(root, public_api_visitor)
 
     proto_dict = visitor.GetProtos()
 
     # Read all golden files.
-    expression = os.path.join(
-        resource_loader.get_root_dir_with_all_resources(),
-        _KeyToFilePath('*'))
-    golden_file_list = file_io.get_matching_files(expression)
+    golden_file_list = file_io.get_matching_files(golden_file_pattern)
 
     def _ReadFileToProto(filename):
       """Read a filename, create a protobuf from its contents."""
@@ -254,6 +256,26 @@ class ApiCompatibilityTest(test.TestCase):
         verbose=FLAGS.verbose_diffs,
         update_goldens=FLAGS.update_goldens)
 
+  @unittest.skipUnless(
+      sys.version_info.major == 2,
+      'API compabitility test goldens are generated using python2.')
+  def testAPIBackwardsCompatibility(self):
+    golden_file_pattern = os.path.join(
+        resource_loader.get_root_dir_with_all_resources(),
+        _KeyToFilePath('*'))
+    self.checkBackwardsCompatibility(tf, golden_file_pattern)
+
+  @unittest.skipUnless(
+      sys.version_info.major == 2,
+      'API compabitility test goldens are generated using python2.')
+  def testAPIBackwardsCompatibilityV1(self):
+    if not tf_v1:
+      return
+    golden_file_pattern = os.path.join(
+        resource_loader.get_root_dir_with_all_resources(),
+        _KeyToFilePath('*'))
+    self.checkBackwardsCompatibility(tf_v1, golden_file_pattern)
+
 
 if __name__ == '__main__':
   parser = argparse.ArgumentParser()
diff --git a/tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le b/tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le
index e879c34bbdadd7b90973fda0f7c3fdb71a385856..ada2c63880972b3fb9cf525becdf8aae2c248e5f 100644
--- a/tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le
+++ b/tensorflow/tools/ci_build/Dockerfile.cpu.ppc64le
@@ -7,7 +7,7 @@ COPY install/*.sh /install/
 RUN /install/install_bootstrap_deb_packages.sh
 RUN add-apt-repository -y ppa:openjdk-r/ppa
 RUN /install/install_deb_packages.sh
-RUN apt-get update && apt-get install -y libopenblas-dev
+RUN /install/install_openblas_ppc64le.sh
 RUN /install/install_hdf5_ppc64le.sh
 RUN /install/install_pip_packages.sh
 RUN /install/install_bazel_from_source.sh
diff --git a/tensorflow/tools/ci_build/Dockerfile.gpu b/tensorflow/tools/ci_build/Dockerfile.gpu
index 7591ecc04efa887ec1d35ba92881386f5a25241d..383f9545c9fc47c6f2c0213a2c07af48085461a3 100644
--- a/tensorflow/tools/ci_build/Dockerfile.gpu
+++ b/tensorflow/tools/ci_build/Dockerfile.gpu
@@ -14,6 +14,7 @@ RUN /install/install_bootstrap_deb_packages.sh
 RUN add-apt-repository -y ppa:openjdk-r/ppa && \
     add-apt-repository -y ppa:george-edison55/cmake-3.x
 RUN /install/install_deb_packages.sh
+
 RUN /install/install_pip_packages.sh
 RUN /install/install_bazel.sh
 RUN /install/install_golang.sh
@@ -22,6 +23,11 @@ RUN /install/install_golang.sh
 COPY install/.bazelrc /etc/bazel.bazelrc
 ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
 
+# Link NCCL libray and header where the build script expects them.
+RUN mkdir /usr/local/cuda-9.0/lib &&  \
+    ln -s /usr/lib/x86_64-linux-gnu/libnccl.so.2 /usr/local/cuda/lib/libnccl.so.2 && \
+    ln -s /usr/include/nccl.h /usr/local/cuda/include/nccl.h
+
 # Configure the build for our CUDA configuration.
 ENV TF_NEED_CUDA 1
 ENV TF_CUDA_COMPUTE_CAPABILITIES 3.0
diff --git a/tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le b/tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le
index 89671387472a15c112a09fa2fa7a9798446d135b..a404f129abe143c107e15ea560c6e11691b7f07b 100644
--- a/tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le
+++ b/tensorflow/tools/ci_build/Dockerfile.gpu.ppc64le
@@ -13,7 +13,7 @@ ARG DEBIAN_FRONTEND=noninteractive
 RUN /install/install_bootstrap_deb_packages.sh
 RUN add-apt-repository -y ppa:openjdk-r/ppa
 RUN /install/install_deb_packages.sh
-RUN apt-get update && apt-get install -y libopenblas-dev
+RUN /install/install_openblas_ppc64le.sh 
 RUN /install/install_hdf5_ppc64le.sh
 RUN /install/install_pip_packages.sh
 RUN /install/install_bazel_from_source.sh
diff --git a/tensorflow/tools/ci_build/ci_build.sh b/tensorflow/tools/ci_build/ci_build.sh
index f6a50d3d4c4f948e37ff841a880b373f1034fd76..77265e0f50bb2c17c9fac76c710ba8bb8559bd7e 100755
--- a/tensorflow/tools/ci_build/ci_build.sh
+++ b/tensorflow/tools/ci_build/ci_build.sh
@@ -115,6 +115,7 @@ DOCKER_IMG_NAME=$(echo "${DOCKER_IMG_NAME}" | tr '[:upper:]' '[:lower:]')
 
 # Print arguments.
 echo "WORKSPACE: ${WORKSPACE}"
+echo "CI_DOCKER_BUILD_EXTRA_PARAMS: ${CI_DOCKER_BUILD_EXTRA_PARAMS[*]}"
 echo "CI_DOCKER_EXTRA_PARAMS: ${CI_DOCKER_EXTRA_PARAMS[*]}"
 echo "COMMAND: ${COMMAND[*]}"
 echo "CI_COMMAND_PREFIX: ${CI_COMMAND_PREFIX[*]}"
@@ -126,7 +127,7 @@ echo ""
 
 # Build the docker container.
 echo "Building container (${DOCKER_IMG_NAME})..."
-docker build -t ${DOCKER_IMG_NAME} \
+docker build -t ${DOCKER_IMG_NAME} ${CI_DOCKER_BUILD_EXTRA_PARAMS[@]} \
     -f "${DOCKERFILE_PATH}" "${DOCKER_CONTEXT_PATH}"
 
 # Check docker build status
diff --git a/tensorflow/tools/ci_build/ci_parameterized_build.sh b/tensorflow/tools/ci_build/ci_parameterized_build.sh
index 08e2c3edd2d22fbb7b9912c9ce7ec561dc5a7113..5115be8c6d0c9cf1f5319256c20bc1f7ab01bad5 100755
--- a/tensorflow/tools/ci_build/ci_parameterized_build.sh
+++ b/tensorflow/tools/ci_build/ci_parameterized_build.sh
@@ -150,36 +150,7 @@ BAZEL_TARGET="//tensorflow/... -//tensorflow/compiler/..."
 if [[ -n "$TF_SKIP_CONTRIB_TESTS" ]]; then
   BAZEL_TARGET="$BAZEL_TARGET -//tensorflow/contrib/..."
 else
-  BAZEL_TARGET="${BAZEL_TARGET} -//tensorflow/contrib/lite/..."
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite:context_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite:framework"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite:interpreter_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite:model_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/toco:toco"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite:simple_memory_arena_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite:string_util_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:activations_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:add_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:basic_rnn_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:concatenation_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:conv_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:depthwise_conv_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:embedding_lookup_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:embedding_lookup_sparse_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:fully_connected_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:hashtable_lookup_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:local_response_norm_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:lsh_projection_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:lstm_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:l2norm_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:mul_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:pooling_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:reshape_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:resize_bilinear_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:skip_gram_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:softmax_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:space_to_depth_test"
-  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:svdf_test"
+  BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/..."
 fi
 
 TUT_TEST_DATA_DIR="/tmp/tf_tutorial_test_data"
diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh
index db37edf8097844646236aace5e3517a8080d70cb..866fe95d2b4b358b63b14b8744eb631a58e18b49 100755
--- a/tensorflow/tools/ci_build/ci_sanity.sh
+++ b/tensorflow/tools/ci_build/ci_sanity.sh
@@ -354,7 +354,7 @@ do_external_licenses_check(){
 
   # Whitelist
   echo ${EXTRA_LICENSE_FILE}
-  grep -e "@bazel_tools//src" -e "@bazel_tools//tools/" -e "@com_google_absl//" -e "//external" -e "@local" -e "@com_github_googlecloudplatform_google_cloud_cpp//" -v ${EXTRA_LICENSES_FILE} > temp.txt
+  grep -e "@bazel_tools//src" -e "@bazel_tools//tools/" -e "@com_google_absl//" -e "//external" -e "@local" -e "@com_github_googlecloudplatform_google_cloud_cpp//" -e "@embedded_jdk//" -v ${EXTRA_LICENSES_FILE} > temp.txt
   mv temp.txt ${EXTRA_LICENSES_FILE}
 
 
diff --git a/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh b/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh
index d0816c92b7308a1079579e605ee9af491a0533fb..75da9bb8356db08c7b9570db673a30ae850e129e 100755
--- a/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh
+++ b/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh
@@ -35,6 +35,30 @@ elif [[ ${BASH_VER_MAJOR} -eq 4 ]] && [[ ${BASH_VER_MINOR} -lt 2 ]]; then
   exit 1
 fi
 
+function is_absolute {
+  [[ "$1" = /* ]] || [[ "$1" =~ ^[a-zA-Z]:[/\\].* ]]
+}
+
+RUNFILES_MANIFEST_FILE="${TEST_SRCDIR}/MANIFEST"
+function rlocation() {
+  if is_absolute "$1" ; then
+    # If the file path is already fully specified, simply return it.
+    echo "$1"
+  elif [[ -e "$TEST_SRCDIR/$1" ]]; then
+    # If the file exists in the $TEST_SRCDIR then just use it.
+    echo "$TEST_SRCDIR/$1"
+  elif [[ -e "$RUNFILES_MANIFEST_FILE" ]]; then
+    # If a runfiles manifest file exists then use it.
+    echo "$(grep "^$1 " "$RUNFILES_MANIFEST_FILE" | sed 's/[^ ]* //')"
+  fi
+}
+
+TEST_BINARY="$(rlocation $TEST_WORKSPACE/${1#./})"
+shift
+
+# Make sure /var/lock exists, this may not be true under MSYS
+mkdir -p /var/lock
+
 TF_GPU_COUNT=${TF_GPU_COUNT:-8}
 
 for i in `seq 0 $((TF_GPU_COUNT-1))`; do
@@ -45,8 +69,8 @@ for i in `seq 0 $((TF_GPU_COUNT-1))`; do
       # This export only works within the brackets, so it is isolated to one
       # single command.
       export CUDA_VISIBLE_DEVICES=$i
-      echo "Running test $* on GPU $CUDA_VISIBLE_DEVICES"
-      $@
+      echo "Running test $TEST_BINARY $* on GPU $CUDA_VISIBLE_DEVICES"
+      "$TEST_BINARY" $@
     )
     return_code=$?
     flock -u "$lock_fd"
diff --git a/tensorflow/tools/ci_build/install/install_bazel.sh b/tensorflow/tools/ci_build/install/install_bazel.sh
index 3e27a94cf2bf3110ac181d6ef5a57366be17255f..e284401b8aa469ebcbed856cd09dd597be242d7a 100755
--- a/tensorflow/tools/ci_build/install/install_bazel.sh
+++ b/tensorflow/tools/ci_build/install/install_bazel.sh
@@ -15,7 +15,7 @@
 # ==============================================================================
 
 # Select bazel version.
-BAZEL_VERSION="0.11.0"
+BAZEL_VERSION="0.15.0"
 
 set +e
 local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}')
diff --git a/tensorflow/tools/ci_build/install/install_bazel_from_source.sh b/tensorflow/tools/ci_build/install/install_bazel_from_source.sh
index ddad00c5f01a78164903702b03c816c427aeb0b8..87be81577d0efb395a12afc85109f10ad4178c27 100755
--- a/tensorflow/tools/ci_build/install/install_bazel_from_source.sh
+++ b/tensorflow/tools/ci_build/install/install_bazel_from_source.sh
@@ -18,7 +18,7 @@
 # It will compile bazel from source and install it in /usr/local/bin
 
 # Select bazel version.
-BAZEL_VERSION="0.11.0"
+BAZEL_VERSION="0.15.0"
 
 set +e
 local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}')
diff --git a/tensorflow/tools/ci_build/install/install_openblas_ppc64le.sh b/tensorflow/tools/ci_build/install/install_openblas_ppc64le.sh
new file mode 100755
index 0000000000000000000000000000000000000000..107cc61ff5aba222dfd49ae8935b7234df4da169
--- /dev/null
+++ b/tensorflow/tools/ci_build/install/install_openblas_ppc64le.sh
@@ -0,0 +1,29 @@
+#!/usr/bin/env bash
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+OPENBLAS_SRC_PATH=/tmp/openblas_src/
+POWER="POWER8"
+USE_OPENMP="USE_OPENMP=1"
+OPENBLAS_INSTALL_PATH="/usr"
+apt-get update
+apt-get install -y gfortran gfortran-5
+rm -rf ${OPENBLAS_SRC_PATH}
+git clone -b release-0.3.0 https://github.com/xianyi/OpenBLAS ${OPENBLAS_SRC_PATH}
+cd ${OPENBLAS_SRC_PATH}
+# Pick up fix for OpenBLAS issue 1571
+git cherry-pick -X theirs 961d25e9c7e4a1758adb1dbeaa15187de69dd052
+make TARGET=${POWER} ${USE_OPENMP} FC=gfortran
+make PREFIX=${OPENBLAS_INSTALL_PATH} install
diff --git a/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh b/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
index 2b68de3c5b9bbb0c09ddead7466049827fac4147..f6fa9251d43074e119ea0eacb721727cec953c0c 100755
--- a/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
+++ b/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh
@@ -34,35 +34,4 @@ yes "" | $PYTHON_BIN_PATH configure.py
 bazel test --test_tag_filters=-no_oss,-oss_serial,-gpu,-benchmark-test -k \
     --jobs=${N_JOBS} --test_timeout 300,450,1200,3600 --config=opt \
     --test_size_filters=small,medium --test_output=errors -- \
-    //tensorflow/contrib/... \
-    -//tensorflow/contrib/lite/... \
-    //tensorflow/contrib/lite:context_test \
-    //tensorflow/contrib/lite:framework \
-    //tensorflow/contrib/lite:interpreter_test \
-    //tensorflow/contrib/lite:model_test \
-    //tensorflow/contrib/lite/toco:toco \
-    //tensorflow/contrib/lite:simple_memory_arena_test \
-    //tensorflow/contrib/lite:string_util_test \
-    //tensorflow/contrib/lite/kernels:activations_test \
-    //tensorflow/contrib/lite/kernels:add_test \
-    //tensorflow/contrib/lite/kernels:basic_rnn_test \
-    //tensorflow/contrib/lite/kernels:concatenation_test \
-    //tensorflow/contrib/lite/kernels:conv_test \
-    //tensorflow/contrib/lite/kernels:depthwise_conv_test \
-    //tensorflow/contrib/lite/kernels:embedding_lookup_test \
-    //tensorflow/contrib/lite/kernels:embedding_lookup_sparse_test \
-    //tensorflow/contrib/lite/kernels:fully_connected_test \
-    //tensorflow/contrib/lite/testing:generated_zip_tests \
-    //tensorflow/contrib/lite/kernels:hashtable_lookup_test \
-    //tensorflow/contrib/lite/kernels:local_response_norm_test \
-    //tensorflow/contrib/lite/kernels:lsh_projection_test \
-    //tensorflow/contrib/lite/kernels:lstm_test \
-    //tensorflow/contrib/lite/kernels:l2norm_test \
-    //tensorflow/contrib/lite/kernels:mul_test \
-    //tensorflow/contrib/lite/kernels:pooling_test \
-    //tensorflow/contrib/lite/kernels:reshape_test \
-    //tensorflow/contrib/lite/kernels:resize_bilinear_test \
-    //tensorflow/contrib/lite/kernels:skip_gram_test \
-    //tensorflow/contrib/lite/kernels:softmax_test \
-    //tensorflow/contrib/lite/kernels:space_to_depth_test \
-    //tensorflow/contrib/lite/kernels:svdf_test
+    //tensorflow/contrib/...
diff --git a/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh b/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh
index ad22ebe4eb304fe6b6f8613f43f2c7c001111503..a1d91a61237eb606337a7f95c1824662697ca69f 100755
--- a/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh
+++ b/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh
@@ -34,12 +34,17 @@ echo "TF_DOCKER_BUILD_DEVEL_BRANCH=${TF_DOCKER_BUILD_DEVEL_BRANCH}"
 echo "TF_DOCKER_BUILD_IMAGE_NAME=${TF_DOCKER_BUILD_IMAGE_NAME}"
 echo "TF_DOCKER_BUILD_VERSION=${TF_DOCKER_BUILD_VERSION}"
 
+# Build containers for AVX
+# Include the instructions for sandybridge and later, but tune for ivybridge
+TF_BAZEL_BUILD_OPTIONS="--config=mkl --copt=-march=sandybridge --copt=-mtune=ivybridge --copt=-O3 --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0"
+
 # build the python 2 container and whl
 TF_DOCKER_BUILD_TYPE="MKL" \
   TF_DOCKER_BUILD_IS_DEVEL="YES" \
   TF_DOCKER_BUILD_DEVEL_BRANCH="${TF_DOCKER_BUILD_DEVEL_BRANCH}" \
   TF_DOCKER_BUILD_IMAGE_NAME="${TF_DOCKER_BUILD_IMAGE_NAME}" \
   TF_DOCKER_BUILD_VERSION="${TF_DOCKER_BUILD_VERSION}" \
+  TF_BAZEL_BUILD_OPTIONS="${TF_BAZEL_BUILD_OPTIONS}" \
   ${WORKSPACE}/tensorflow/tools/docker/parameterized_docker_build.sh 
 
 # build the python 3 container and whl
@@ -49,5 +54,29 @@ TF_DOCKER_BUILD_TYPE="MKL" \
   TF_DOCKER_BUILD_IMAGE_NAME="${TF_DOCKER_BUILD_IMAGE_NAME}" \
   TF_DOCKER_BUILD_VERSION="${TF_DOCKER_BUILD_VERSION}" \
   TF_DOCKER_BUILD_PYTHON_VERSION="PYTHON3" \
+  TF_BAZEL_BUILD_OPTIONS="${TF_BAZEL_BUILD_OPTIONS}" \
+  ${WORKSPACE}/tensorflow/tools/docker/parameterized_docker_build.sh
+
+# Build containers for AVX2
+# Include the instructions for haswell and later, but tune for broadwell
+TF_BAZEL_BUILD_OPTIONS="--config=mkl --copt=-march=haswell --copt=-mtune=broadwell --copt=-O3 --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0"
+
+# build the python 2 container and whl
+TF_DOCKER_BUILD_TYPE="MKL" \
+  TF_DOCKER_BUILD_IS_DEVEL="YES" \
+  TF_DOCKER_BUILD_DEVEL_BRANCH="${TF_DOCKER_BUILD_DEVEL_BRANCH}" \
+  TF_DOCKER_BUILD_IMAGE_NAME="${TF_DOCKER_BUILD_IMAGE_NAME}" \
+  TF_DOCKER_BUILD_VERSION="${TF_DOCKER_BUILD_VERSION}-avx2" \
+  TF_BAZEL_BUILD_OPTIONS="${TF_BAZEL_BUILD_OPTIONS}" \
   ${WORKSPACE}/tensorflow/tools/docker/parameterized_docker_build.sh 
 
+# build the python 3 container and whl
+TF_DOCKER_BUILD_TYPE="MKL" \
+  TF_DOCKER_BUILD_IS_DEVEL="YES" \
+  TF_DOCKER_BUILD_DEVEL_BRANCH="${TF_DOCKER_BUILD_DEVEL_BRANCH}" \
+  TF_DOCKER_BUILD_IMAGE_NAME="${TF_DOCKER_BUILD_IMAGE_NAME}" \
+  TF_DOCKER_BUILD_VERSION="${TF_DOCKER_BUILD_VERSION}-avx2" \
+  TF_DOCKER_BUILD_PYTHON_VERSION="PYTHON3" \
+  TF_BAZEL_BUILD_OPTIONS="${TF_BAZEL_BUILD_OPTIONS}" \
+  ${WORKSPACE}/tensorflow/tools/docker/parameterized_docker_build.sh
+
diff --git a/tensorflow/tools/ci_build/update_version.py b/tensorflow/tools/ci_build/update_version.py
index 642dde36a7caae35df764d5d7513df972e1e5615..30c318a58fae4c84033ea5e906f3ec88818c4b65 100755
--- a/tensorflow/tools/ci_build/update_version.py
+++ b/tensorflow/tools/ci_build/update_version.py
@@ -248,16 +248,6 @@ def update_md_files(old_version, new_version):
     replace_string_in_line(r"%s<\/version>" % old_version,
                            "%s" % new_version, filepath)
 
-  # Update any links to colab notebooks.
-  def colab_url(version):
-    version_string = "%s.%s.%s" % (version.major, version.minor, version.patch)
-    prefix = "https://colab.research.google.com/github/tensorflow/models/blob/r"
-    return prefix + version_string + "/"
-
-  replace_string_in_line(
-      colab_url(old_version), colab_url(new_version),
-      "%s/docs_src/get_started/eager.md" % TF_SRC_DIR)
-
 
 def major_minor_change(old_version, new_version):
   """Check if a major or minor change occurred."""
diff --git a/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh b/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
index e10483e7fdc55926d678b157cffbd98b5d57def6..0482cf619a831ebb87e76cd18efbdac83a0d2f11 100644
--- a/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
+++ b/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
@@ -23,10 +23,6 @@ function run_configure_for_gpu_build {
   # Enable CUDA support
   export TF_NEED_CUDA=1
 
-  # TODO(pcloudy): Remove this after TensorFlow uses its own CRSOOTOOL
-  # for GPU build on Windows
-  export USE_MSVC_WRAPPER=1
-
   yes "" | ./configure
 }
 
@@ -37,10 +33,10 @@ function set_remote_cache_options {
   echo "build --tls_enabled=true" >> "${TMP_BAZELRC}"
   echo "build --remote_timeout=3600" >> "${TMP_BAZELRC}"
   echo "build --auth_enabled=true" >> "${TMP_BAZELRC}"
-  echo "build --spawn_strategy=remote" >> "${TMP_BAZELRC}"
-  echo "build --strategy=Javac=remote" >> "${TMP_BAZELRC}"
-  echo "build --strategy=Closure=remote" >> "${TMP_BAZELRC}"
-  echo "build --genrule_strategy=remote" >> "${TMP_BAZELRC}"
+  echo "build --spawn_strategy=standalone" >> "${TMP_BAZELRC}"
+  echo "build --strategy=Javac=standalone" >> "${TMP_BAZELRC}"
+  echo "build --strategy=Closure=standalone" >> "${TMP_BAZELRC}"
+  echo "build --genrule_strategy=standalone" >> "${TMP_BAZELRC}"
   echo "build --google_credentials=$GOOGLE_CLOUD_CREDENTIAL" >> "${TMP_BAZELRC}"
 }
 
diff --git a/tensorflow/tools/ci_build/windows/bazel/common_env.sh b/tensorflow/tools/ci_build/windows/bazel/common_env.sh
index 8a237e4e28376771742ba93b795950d368660196..333a89d3f5e43edeb440c2a0ac69bd50a1663732 100644
--- a/tensorflow/tools/ci_build/windows/bazel/common_env.sh
+++ b/tensorflow/tools/ci_build/windows/bazel/common_env.sh
@@ -26,7 +26,8 @@
 # * Bazel windows executable copied as "bazel.exe" and included in PATH.
 
 # Use a temporary directory with a short name.
-export TMPDIR="C:/tmp"
+export TMPDIR=${TMPDIR:-"C:/tmp"}
+export TMPDIR=$(cygpath -m "$TMPDIR")
 mkdir -p "$TMPDIR"
 
 # Set bash path
@@ -54,10 +55,10 @@ export PATH="/c/${PYTHON_BASE_PATH}/Scripts:$PATH"
 export TF_CUDA_VERSION=${TF_CUDA_VERSION:-9.0}
 export TF_CUDNN_VERSION=${TF_CUDNN_VERSION:-7.0}
 export TF_CUDA_COMPUTE_CAPABILITIES=${TF_CUDA_COMPUTE_CAPABILITIES:-3.7}
-export CUDA_INSTALL_PATH=${CUDA_INSTALL_PATH:-"C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v${TF_CUDA_VERSION}"}
+export CUDA_TOOLKIT_PATH=${CUDA_TOOLKIT_PATH:-"C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v${TF_CUDA_VERSION}"}
 export CUDNN_INSTALL_PATH=${CUDNN_INSTALL_PATH:-"C:/tools/cuda"}
 
 # Add Cuda and Cudnn dll directories into PATH
-export PATH="$(cygpath -u "${CUDA_INSTALL_PATH}")/bin:$PATH"
-export PATH="$(cygpath -u "${CUDA_INSTALL_PATH}")/extras/CUPTI/libx64:$PATH"
+export PATH="$(cygpath -u "${CUDA_TOOLKIT_PATH}")/bin:$PATH"
+export PATH="$(cygpath -u "${CUDA_TOOLKIT_PATH}")/extras/CUPTI/libx64:$PATH"
 export PATH="$(cygpath -u "${CUDNN_INSTALL_PATH}")/bin:$PATH"
diff --git a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh
index ed7340146789078bf12fc3bbfba46fb0f740ba54..47e0e5dd59af76c733e7f2271294ad0e5c7e6b26 100644
--- a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh
+++ b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh
@@ -53,30 +53,39 @@ function cleanup {
 }
 trap cleanup EXIT
 
-skip_test=0
-release_build=0
+PY_TEST_DIR="py_test_dir"
 
+SKIP_TEST=0
+RELEASE_BUILD=0
+TEST_TARGET="//${PY_TEST_DIR}/tensorflow/python/... \
+    //${PY_TEST_DIR}/tensorflow/contrib/... "
+
+# --skip_test            Skip running tests
+# --enable_remote_cache  Add options to enable remote cache for build and test
+# --release_build        Build for release, compilation time will be longer to
+#                        ensure performance
+# --test_core_only       Use tensorflow/python/... as test target
+# --test_contrib_only    Use tensorflow/contrib/... as test target
 for ARG in "$@"; do
-  if [[ "$ARG" == --skip_test ]]; then
-    skip_test=1
-  elif [[ "$ARG" == --enable_remote_cache ]]; then
-    set_remote_cache_options
-  elif [[ "$ARG" == --release_build ]]; then
-    release_build=1
-  fi
+  case "$ARG" in
+    --skip_test) SKIP_TEST=1 ;;
+    --enable_remote_cache) set_remote_cache_options ;;
+    --release_build) RELEASE_BUILD=1 ;;
+    --test_core_only) TEST_TARGET="//${PY_TEST_DIR}/tensorflow/python/..." ;;
+    --test_contrib_only) TEST_TARGET="//${PY_TEST_DIR}/tensorflow/contrib/..." ;;
+    *)
+  esac
 done
 
-if [[ "$release_build" != 1 ]]; then
-  # --define=override_eigen_strong_inline=true speeds up the compiling of conv_grad_ops_3d.cc and conv_ops_3d.cc
+if [[ "$RELEASE_BUILD" == 1 ]]; then
+  # Overriding eigen strong inline speeds up the compiling of conv_grad_ops_3d.cc and conv_ops_3d.cc
   # by 20 minutes. See https://github.com/tensorflow/tensorflow/issues/10521
-  # Because this hurts the performance of TF, we don't enable it in release build.
-  echo "build --define=override_eigen_strong_inline=true" >> "${TMP_BAZELRC}"
+  # Because this hurts the performance of TF, we don't override it in release build.
+  export TF_OVERRIDE_EIGEN_STRONG_INLINE=0
+else
+  export TF_OVERRIDE_EIGEN_STRONG_INLINE=1
 fi
 
-# The host and target platforms are the same in Windows build. So we don't have
-# to distinct them. This helps avoid building the same targets twice.
-echo "build --distinct_host_configuration=false" >> "${TMP_BAZELRC}"
-
 # Enable short object file path to avoid long path issue on Windows.
 echo "startup --output_user_root=${TMPDIR}" >> "${TMP_BAZELRC}"
 
@@ -88,12 +97,11 @@ run_configure_for_cpu_build
 
 bazel build --announce_rc --config=opt tensorflow/tools/pip_package:build_pip_package || exit $?
 
-if [[ "$skip_test" == 1 ]]; then
+if [[ "$SKIP_TEST" == 1 ]]; then
   exit 0
 fi
 
 # Create a python test directory to avoid package name conflict
-PY_TEST_DIR="py_test_dir"
 create_python_test_dir "${PY_TEST_DIR}"
 
 ./bazel-bin/tensorflow/tools/pip_package/build_pip_package "$PWD/${PY_TEST_DIR}"
@@ -111,7 +119,7 @@ bazel test --announce_rc --config=opt -k --test_output=errors \
   --define=no_tensorflow_py_deps=true --test_lang_filters=py \
   --test_tag_filters=-no_pip,-no_windows,-no_oss \
   --build_tag_filters=-no_pip,-no_windows,-no_oss --build_tests_only \
+  --test_size_filters=small,medium \
   --jobs="${N_JOBS}" --test_timeout="300,450,1200,3600" \
   --flaky_test_attempts=3 \
-  //${PY_TEST_DIR}/tensorflow/python/... \
-  //${PY_TEST_DIR}/tensorflow/contrib/...
+  ${TEST_TARGET}
diff --git a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh
index fe3bce428fb2feb053cb1b8c097f707dd2762a20..e3eee110808ce6cf1905a44b9108ad6de49f10cb 100644
--- a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh
+++ b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh
@@ -53,30 +53,39 @@ function cleanup {
 }
 trap cleanup EXIT
 
-skip_test=0
-release_build=0
+PY_TEST_DIR="py_test_dir"
 
+SKIP_TEST=0
+RELEASE_BUILD=0
+TEST_TARGET="//${PY_TEST_DIR}/tensorflow/python/... \
+  //${PY_TEST_DIR}/tensorflow/contrib/... "
+
+# --skip_test            Skip running tests
+# --enable_remote_cache  Add options to enable remote cache for build and test
+# --release_build        Build for release, compilation time will be longer to
+#                        ensure performance
+# --test_core_only       Use tensorflow/python/... as test target
+# --test_contrib_only    Use tensorflow/contrib/... as test target
 for ARG in "$@"; do
-  if [[ "$ARG" == --skip_test ]]; then
-    skip_test=1
-  elif [[ "$ARG" == --enable_remote_cache ]]; then
-    set_remote_cache_options
-  elif [[ "$ARG" == --release_build ]]; then
-    release_build=1
-  fi
+  case "$ARG" in
+    --skip_test) SKIP_TEST=1 ;;
+    --enable_remote_cache) set_remote_cache_options ;;
+    --release_build) RELEASE_BUILD=1 ;;
+    --test_core_only) TEST_TARGET="//${PY_TEST_DIR}/tensorflow/python/..." ;;
+    --test_contrib_only) TEST_TARGET="//${PY_TEST_DIR}/tensorflow/contrib/..." ;;
+    *)
+  esac
 done
 
-if [[ "$release_build" != 1 ]]; then
-  # --define=override_eigen_strong_inline=true speeds up the compiling of conv_grad_ops_3d.cc and conv_ops_3d.cc
+if [[ "$RELEASE_BUILD" == 1 ]]; then
+  # Overriding eigen strong inline speeds up the compiling of conv_grad_ops_3d.cc and conv_ops_3d.cc
   # by 20 minutes. See https://github.com/tensorflow/tensorflow/issues/10521
-  # Because this hurts the performance of TF, we don't enable it in release build.
-  echo "build --define=override_eigen_strong_inline=true" >> "${TMP_BAZELRC}"
+  # Because this hurts the performance of TF, we don't override it in release build.
+  export TF_OVERRIDE_EIGEN_STRONG_INLINE=0
+else
+  export TF_OVERRIDE_EIGEN_STRONG_INLINE=1
 fi
 
-# The host and target platforms are the same in Windows build. So we don't have
-# to distinct them. This helps avoid building the same targets twice.
-echo "build --distinct_host_configuration=false" >> "${TMP_BAZELRC}"
-
 # Enable short object file path to avoid long path issue on Windows.
 echo "startup --output_user_root=${TMPDIR}" >> "${TMP_BAZELRC}"
 
@@ -91,12 +100,11 @@ run_configure_for_gpu_build
 
 bazel build --announce_rc --config=opt tensorflow/tools/pip_package:build_pip_package || exit $?
 
-if [[ "$skip_test" == 1 ]]; then
+if [[ "$SKIP_TEST" == 1 ]]; then
   exit 0
 fi
 
 # Create a python test directory to avoid package name conflict
-PY_TEST_DIR="py_test_dir"
 create_python_test_dir "${PY_TEST_DIR}"
 
 ./bazel-bin/tensorflow/tools/pip_package/build_pip_package "$PWD/${PY_TEST_DIR}"
@@ -105,14 +113,18 @@ create_python_test_dir "${PY_TEST_DIR}"
 PIP_NAME=$(ls ${PY_TEST_DIR}/tensorflow-*.whl)
 reinstall_tensorflow_pip ${PIP_NAME}
 
+TF_GPU_COUNT=${TF_GPU_COUNT:-8}
+
 # Define no_tensorflow_py_deps=true so that every py_test has no deps anymore,
 # which will result testing system installed tensorflow
 # GPU tests are very flaky when running concurrently, so set local_test_jobs=1
 bazel test --announce_rc --config=opt -k --test_output=errors \
+  --test_env=TF_GPU_COUNT \
+  --run_under=//tensorflow/tools/ci_build/gpu_build:parallel_gpu_execute \
   --define=no_tensorflow_py_deps=true --test_lang_filters=py \
   --test_tag_filters=-no_pip,-no_windows,-no_windows_gpu,-no_gpu,-no_pip_gpu,-no_oss \
   --build_tag_filters=-no_pip,-no_windows,-no_windows_gpu,-no_gpu,-no_pip_gpu,-no_oss --build_tests_only \
-  --local_test_jobs=1 --test_timeout="300,450,1200,3600" \
+  --test_size_filters=small,medium \
+  --local_test_jobs=$TF_GPU_COUNT --test_timeout="300,450,1200,3600" \
   --flaky_test_attempts=3 \
-  //${PY_TEST_DIR}/tensorflow/python/... \
-  //${PY_TEST_DIR}/tensorflow/contrib/...
+  ${TEST_TARGET}
diff --git a/tensorflow/tools/compatibility/BUILD b/tensorflow/tools/compatibility/BUILD
index b7bfb29aae4fcaa55e01ba924f72cf79d2b09ad1..55792c51fe87f0ded92730c13409169f6c67d035 100644
--- a/tensorflow/tools/compatibility/BUILD
+++ b/tensorflow/tools/compatibility/BUILD
@@ -8,10 +8,17 @@ load(
     "tf_cc_test",  # @unused
 )
 
+py_library(
+    name = "ast_edits",
+    srcs = ["ast_edits.py"],
+    srcs_version = "PY2AND3",
+)
+
 py_binary(
     name = "tf_upgrade",
     srcs = ["tf_upgrade.py"],
     srcs_version = "PY2AND3",
+    deps = [":ast_edits"],
 )
 
 py_test(
@@ -26,6 +33,28 @@ py_test(
     ],
 )
 
+py_binary(
+    name = "tf_upgrade_v2",
+    srcs = [
+        "renames_v2.py",
+        "tf_upgrade_v2.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [":ast_edits"],
+)
+
+py_test(
+    name = "tf_upgrade_v2_test",
+    srcs = ["tf_upgrade_v2_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":tf_upgrade_v2",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_test_lib",
+        "@six_archive//:six",
+    ],
+)
+
 # Keep for reference, this test will succeed in 0.11 but fail in 1.0
 # py_test(
 #     name = "test_file_v0_11",
@@ -62,9 +91,37 @@ py_test(
     ],
 )
 
+genrule(
+    name = "generate_upgraded_file_v2",
+    testonly = 1,
+    srcs = ["testdata/test_file_v1_10.py"],
+    outs = [
+        "test_file_v2_0.py",
+        "report_v2.txt",
+    ],
+    cmd = ("$(location :tf_upgrade_v2)" +
+           " --infile $(location testdata/test_file_v1_10.py)" +
+           " --outfile $(location test_file_v2_0.py)" +
+           " --reportfile $(location report_v2.txt)"),
+    tools = [":tf_upgrade_v2"],
+)
+
+py_test(
+    name = "test_file_v2_0",
+    size = "small",
+    srcs = ["test_file_v2_0.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow:tensorflow_py",
+    ],
+)
+
 exports_files(
     [
+        "ast_edits.py",
         "tf_upgrade.py",
+        "renames_v2.py",
         "testdata/test_file_v0_11.py",
+        "testdata/test_file_v1_10.py",
     ],
 )
diff --git a/tensorflow/tools/compatibility/ast_edits.py b/tensorflow/tools/compatibility/ast_edits.py
new file mode 100644
index 0000000000000000000000000000000000000000..23cc4a21a9e6f81c8dc5016bc2cb6a2f151c7924
--- /dev/null
+++ b/tensorflow/tools/compatibility/ast_edits.py
@@ -0,0 +1,502 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Upgrader for Python scripts according to an API change specification."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import ast
+import collections
+import os
+import shutil
+import sys
+import tempfile
+import traceback
+
+
+class APIChangeSpec(object):
+  """This class defines the transformations that need to happen.
+
+  This class must provide the following fields:
+
+  * `function_keyword_renames`: maps function names to a map of old -> new
+    argument names
+  * `function_renames`: maps function names to new function names
+  * `change_to_function`: a set of function names that have changed (for
+    notifications)
+  * `function_reorders`: maps functions whose argument order has changed to the
+    list of arguments in the new order
+  * `function_handle`: maps function names to custom handlers for the function
+
+  For an example, see `TFAPIChangeSpec`.
+  """
+
+
+class _FileEditTuple(
+    collections.namedtuple("_FileEditTuple",
+                           ["comment", "line", "start", "old", "new"])):
+  """Each edit that is recorded by a _FileEditRecorder.
+
+  Fields:
+    comment: A description of the edit and why it was made.
+    line: The line number in the file where the edit occurs (1-indexed).
+    start: The line number in the file where the edit occurs (0-indexed).
+    old: text string to remove (this must match what was in file).
+    new: text string to add in place of `old`.
+  """
+
+  __slots__ = ()
+
+
+class _FileEditRecorder(object):
+  """Record changes that need to be done to the file."""
+
+  def __init__(self, filename):
+    # all edits are lists of chars
+    self._filename = filename
+
+    self._line_to_edit = collections.defaultdict(list)
+    self._errors = []
+
+  def process(self, text):
+    """Process a list of strings, each corresponding to the recorded changes.
+
+    Args:
+      text: A list of lines of text (assumed to contain newlines)
+    Returns:
+      A tuple of the modified text and a textual description of what is done.
+    Raises:
+      ValueError: if substitution source location does not have expected text.
+    """
+
+    change_report = ""
+
+    # Iterate of each line
+    for line, edits in self._line_to_edit.items():
+      offset = 0
+      # sort by column so that edits are processed in order in order to make
+      # indexing adjustments cumulative for changes that change the string
+      # length
+      edits.sort(key=lambda x: x.start)
+
+      # Extract each line to a list of characters, because mutable lists
+      # are editable, unlike immutable strings.
+      char_array = list(text[line - 1])
+
+      # Record a description of the change
+      change_report += "%r Line %d\n" % (self._filename, line)
+      change_report += "-" * 80 + "\n\n"
+      for e in edits:
+        change_report += "%s\n" % e.comment
+      change_report += "\n    Old: %s" % (text[line - 1])
+
+      # Make underscore buffers for underlining where in the line the edit was
+      change_list = [" "] * len(text[line - 1])
+      change_list_new = [" "] * len(text[line - 1])
+
+      # Iterate for each edit
+      for e in edits:
+        # Create effective start, end by accounting for change in length due
+        # to previous edits
+        start_eff = e.start + offset
+        end_eff = start_eff + len(e.old)
+
+        # Make sure the edit is changing what it should be changing
+        old_actual = "".join(char_array[start_eff:end_eff])
+        if old_actual != e.old:
+          raise ValueError("Expected text %r but got %r" %
+                           ("".join(e.old), "".join(old_actual)))
+        # Make the edit
+        char_array[start_eff:end_eff] = list(e.new)
+
+        # Create the underline highlighting of the before and after
+        change_list[e.start:e.start + len(e.old)] = "~" * len(e.old)
+        change_list_new[start_eff:end_eff] = "~" * len(e.new)
+
+        # Keep track of how to generate effective ranges
+        offset += len(e.new) - len(e.old)
+
+      # Finish the report comment
+      change_report += "         %s\n" % "".join(change_list)
+      text[line - 1] = "".join(char_array)
+      change_report += "    New: %s" % (text[line - 1])
+      change_report += "         %s\n\n" % "".join(change_list_new)
+    return "".join(text), change_report, self._errors
+
+  def add(self, comment, line, start, old, new, error=None):
+    """Add a new change that is needed.
+
+    Args:
+      comment: A description of what was changed
+      line: Line number (1 indexed)
+      start: Column offset (0 indexed)
+      old: old text
+      new: new text
+      error: this "edit" is something that cannot be fixed automatically
+    Returns:
+      None
+    """
+
+    self._line_to_edit[line].append(
+        _FileEditTuple(comment, line, start, old, new))
+    if error:
+      self._errors.append("%s:%d: %s" % (self._filename, line, error))
+
+
+class _ASTCallVisitor(ast.NodeVisitor):
+  """AST Visitor that processes function calls.
+
+  Updates function calls from old API version to new API version using a given
+  change spec.
+  """
+
+  def __init__(self, filename, lines, api_change_spec):
+    self._filename = filename
+    self._file_edit = _FileEditRecorder(filename)
+    self._lines = lines
+    self._api_change_spec = api_change_spec
+
+  def process(self, lines):
+    return self._file_edit.process(lines)
+
+  def generic_visit(self, node):
+    ast.NodeVisitor.generic_visit(self, node)
+
+  def _rename_functions(self, node, full_name):
+    function_renames = self._api_change_spec.function_renames
+    try:
+      new_name = function_renames[full_name]
+      self._file_edit.add("Renamed function %r to %r" % (full_name, new_name),
+                          node.lineno, node.col_offset, full_name, new_name)
+    except KeyError:
+      pass
+
+  def _get_attribute_full_path(self, node):
+    """Traverse an attribute to generate a full name e.g. tf.foo.bar.
+
+    Args:
+      node: A Node of type Attribute.
+
+    Returns:
+      a '.'-delimited full-name or None if the tree was not a simple form.
+      i.e. `foo()+b).bar` returns None, while `a.b.c` would return "a.b.c".
+    """
+    curr = node
+    items = []
+    while not isinstance(curr, ast.Name):
+      if not isinstance(curr, ast.Attribute):
+        return None
+      items.append(curr.attr)
+      curr = curr.value
+    items.append(curr.id)
+    return ".".join(reversed(items))
+
+  def _find_true_position(self, node):
+    """Return correct line number and column offset for a given node.
+
+    This is necessary mainly because ListComp's location reporting reports
+    the next token after the list comprehension list opening.
+
+    Args:
+      node: Node for which we wish to know the lineno and col_offset
+    """
+    import re
+    find_open = re.compile("^\s*(\\[).*$")
+    find_string_chars = re.compile("['\"]")
+
+    if isinstance(node, ast.ListComp):
+      # Strangely, ast.ListComp returns the col_offset of the first token
+      # after the '[' token which appears to be a bug. Workaround by
+      # explicitly finding the real start of the list comprehension.
+      line = node.lineno
+      col = node.col_offset
+      # loop over lines
+      while 1:
+        # Reverse the text to and regular expression search for whitespace
+        text = self._lines[line - 1]
+        reversed_preceding_text = text[:col][::-1]
+        # First find if a [ can be found with only whitespace between it and
+        # col.
+        m = find_open.match(reversed_preceding_text)
+        if m:
+          new_col_offset = col - m.start(1) - 1
+          return line, new_col_offset
+        else:
+          if (reversed_preceding_text == "" or
+              reversed_preceding_text.isspace()):
+            line = line - 1
+            prev_line = self._lines[line - 1]
+            # TODO(aselle):
+            # this is poor comment detection, but it is good enough for
+            # cases where the comment does not contain string literal starting/
+            # ending characters. If ast gave us start and end locations of the
+            # ast nodes rather than just start, we could use string literal
+            # node ranges to filter out spurious #'s that appear in string
+            # literals.
+            comment_start = prev_line.find("#")
+            if comment_start == -1:
+              col = len(prev_line) - 1
+            elif find_string_chars.search(prev_line[comment_start:]) is None:
+              col = comment_start
+            else:
+              return None, None
+          else:
+            return None, None
+    # Most other nodes return proper locations (with notably does not), but
+    # it is not possible to use that in an argument.
+    return node.lineno, node.col_offset
+
+  def visit_Call(self, node):  # pylint: disable=invalid-name
+    """Handle visiting a call node in the AST.
+
+    Args:
+      node: Current Node
+    """
+
+    # Find a simple attribute name path e.g. "tf.foo.bar"
+    full_name = self._get_attribute_full_path(node.func)
+
+    # Make sure the func is marked as being part of a call
+    node.func.is_function_for_call = True
+
+    if full_name:
+      # Call special handlers
+      function_handles = self._api_change_spec.function_handle
+      if full_name in function_handles:
+        function_handles[full_name](self._file_edit, node)
+
+      # Examine any non-keyword argument and make it into a keyword argument
+      # if reordering required.
+      function_reorders = self._api_change_spec.function_reorders
+      function_keyword_renames = (
+          self._api_change_spec.function_keyword_renames)
+
+      if full_name in function_reorders:
+        reordered = function_reorders[full_name]
+        for idx, arg in enumerate(node.args):
+          lineno, col_offset = self._find_true_position(arg)
+          if lineno is None or col_offset is None:
+            self._file_edit.add(
+                "Failed to add keyword %r to reordered function %r" %
+                (reordered[idx], full_name),
+                arg.lineno,
+                arg.col_offset,
+                "",
+                "",
+                error="A necessary keyword argument failed to be inserted.")
+          else:
+            keyword_arg = reordered[idx]
+            if (full_name in function_keyword_renames and
+                keyword_arg in function_keyword_renames[full_name]):
+              keyword_arg = function_keyword_renames[full_name][keyword_arg]
+            self._file_edit.add("Added keyword %r to reordered function %r" %
+                                (reordered[idx], full_name), lineno, col_offset,
+                                "", keyword_arg + "=")
+
+      # Examine each keyword argument and convert it to the final renamed form
+      renamed_keywords = ({} if full_name not in function_keyword_renames else
+                          function_keyword_renames[full_name])
+      for keyword in node.keywords:
+        argkey = keyword.arg
+        argval = keyword.value
+
+        if argkey in renamed_keywords:
+          argval_lineno, argval_col_offset = self._find_true_position(argval)
+          if argval_lineno is not None and argval_col_offset is not None:
+            # TODO(aselle): We should scan backward to find the start of the
+            # keyword key. Unfortunately ast does not give you the location of
+            # keyword keys, so we are forced to infer it from the keyword arg
+            # value.
+            key_start = argval_col_offset - len(argkey) - 1
+            key_end = key_start + len(argkey) + 1
+            if (self._lines[argval_lineno - 1][key_start:key_end] == argkey +
+                "="):
+              self._file_edit.add("Renamed keyword argument from %r to %r" %
+                                  (argkey,
+                                   renamed_keywords[argkey]), argval_lineno,
+                                  argval_col_offset - len(argkey) - 1,
+                                  argkey + "=", renamed_keywords[argkey] + "=")
+              continue
+          self._file_edit.add(
+              "Failed to rename keyword argument from %r to %r" %
+              (argkey, renamed_keywords[argkey]),
+              argval.lineno,
+              argval.col_offset - len(argkey) - 1,
+              "",
+              "",
+              error="Failed to find keyword lexographically. Fix manually.")
+
+    ast.NodeVisitor.generic_visit(self, node)
+
+  def visit_Attribute(self, node):  # pylint: disable=invalid-name
+    """Handle bare Attributes i.e. [tf.foo, tf.bar].
+
+    Args:
+      node: Node that is of type ast.Attribute
+    """
+    full_name = self._get_attribute_full_path(node)
+    if full_name:
+      self._rename_functions(node, full_name)
+    if full_name in self._api_change_spec.change_to_function:
+      if not hasattr(node, "is_function_for_call"):
+        new_text = full_name + "()"
+        self._file_edit.add("Changed %r to %r" % (full_name, new_text),
+                            node.lineno, node.col_offset, full_name, new_text)
+
+    ast.NodeVisitor.generic_visit(self, node)
+
+
+class ASTCodeUpgrader(object):
+  """Handles upgrading a set of Python files using a given API change spec."""
+
+  def __init__(self, api_change_spec):
+    if not isinstance(api_change_spec, APIChangeSpec):
+      raise TypeError("Must pass APIChangeSpec to ASTCodeUpgrader, got %s" %
+                      type(api_change_spec))
+    self._api_change_spec = api_change_spec
+
+  def process_file(self, in_filename, out_filename):
+    """Process the given python file for incompatible changes.
+
+    Args:
+      in_filename: filename to parse
+      out_filename: output file to write to
+    Returns:
+      A tuple representing number of files processed, log of actions, errors
+    """
+
+    # Write to a temporary file, just in case we are doing an implace modify.
+    with open(in_filename, "r") as in_file, \
+        tempfile.NamedTemporaryFile("w", delete=False) as temp_file:
+      ret = self.process_opened_file(in_filename, in_file, out_filename,
+                                     temp_file)
+
+    shutil.move(temp_file.name, out_filename)
+    return ret
+
+  # Broad exceptions are required here because ast throws whatever it wants.
+  # pylint: disable=broad-except
+  def process_opened_file(self, in_filename, in_file, out_filename, out_file):
+    """Process the given python file for incompatible changes.
+
+    This function is split out to facilitate StringIO testing from
+    tf_upgrade_test.py.
+
+    Args:
+      in_filename: filename to parse
+      in_file: opened file (or StringIO)
+      out_filename: output file to write to
+      out_file: opened file (or StringIO)
+    Returns:
+      A tuple representing number of files processed, log of actions, errors
+    """
+    process_errors = []
+    text = "-" * 80 + "\n"
+    text += "Processing file %r\n outputting to %r\n" % (in_filename,
+                                                         out_filename)
+    text += "-" * 80 + "\n\n"
+
+    parsed_ast = None
+    lines = in_file.readlines()
+    try:
+      parsed_ast = ast.parse("".join(lines))
+    except Exception:
+      text += "Failed to parse %r\n\n" % in_filename
+      text += traceback.format_exc()
+    if parsed_ast:
+      visitor = _ASTCallVisitor(in_filename, lines, self._api_change_spec)
+      visitor.visit(parsed_ast)
+      out_text, new_text, process_errors = visitor.process(lines)
+      text += new_text
+      if out_file:
+        out_file.write(out_text)
+    text += "\n"
+    return 1, text, process_errors
+
+  # pylint: enable=broad-except
+
+  def process_tree(self, root_directory, output_root_directory,
+                   copy_other_files):
+    """Processes upgrades on an entire tree of python files in place.
+
+    Note that only Python files. If you have custom code in other languages,
+    you will need to manually upgrade those.
+
+    Args:
+      root_directory: Directory to walk and process.
+      output_root_directory: Directory to use as base.
+      copy_other_files: Copy files that are not touched by this converter.
+
+    Returns:
+      A tuple of files processed, the report string ofr all files, and errors
+    """
+
+    # make sure output directory doesn't exist
+    if output_root_directory and os.path.exists(output_root_directory):
+      print("Output directory %r must not already exist." %
+            (output_root_directory))
+      sys.exit(1)
+
+    # make sure output directory does not overlap with root_directory
+    norm_root = os.path.split(os.path.normpath(root_directory))
+    norm_output = os.path.split(os.path.normpath(output_root_directory))
+    if norm_root == norm_output:
+      print("Output directory %r same as input directory %r" %
+            (root_directory, output_root_directory))
+      sys.exit(1)
+
+    # Collect list of files to process (we do this to correctly handle if the
+    # user puts the output directory in some sub directory of the input dir)
+    files_to_process = []
+    files_to_copy = []
+    for dir_name, _, file_list in os.walk(root_directory):
+      py_files = [f for f in file_list if f.endswith(".py")]
+      copy_files = [f for f in file_list if not f.endswith(".py")]
+      for filename in py_files:
+        fullpath = os.path.join(dir_name, filename)
+        fullpath_output = os.path.join(output_root_directory,
+                                       os.path.relpath(fullpath,
+                                                       root_directory))
+        files_to_process.append((fullpath, fullpath_output))
+      if copy_other_files:
+        for filename in copy_files:
+          fullpath = os.path.join(dir_name, filename)
+          fullpath_output = os.path.join(output_root_directory,
+                                         os.path.relpath(
+                                             fullpath, root_directory))
+          files_to_copy.append((fullpath, fullpath_output))
+
+    file_count = 0
+    tree_errors = []
+    report = ""
+    report += ("=" * 80) + "\n"
+    report += "Input tree: %r\n" % root_directory
+    report += ("=" * 80) + "\n"
+
+    for input_path, output_path in files_to_process:
+      output_directory = os.path.dirname(output_path)
+      if not os.path.isdir(output_directory):
+        os.makedirs(output_directory)
+      file_count += 1
+      _, l_report, l_errors = self.process_file(input_path, output_path)
+      tree_errors += l_errors
+      report += l_report
+    for input_path, output_path in files_to_copy:
+      output_directory = os.path.dirname(output_path)
+      if not os.path.isdir(output_directory):
+        os.makedirs(output_directory)
+      shutil.copy(input_path, output_path)
+    return file_count, report, tree_errors
diff --git a/tensorflow/tools/compatibility/renames_v2.py b/tensorflow/tools/compatibility/renames_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..216aa41b60eb566db37244b72cbeef024546607f
--- /dev/null
+++ b/tensorflow/tools/compatibility/renames_v2.py
@@ -0,0 +1,134 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# pylint: disable=line-too-long
+"""List of renames to apply when converting from TF 1.0 to TF 2.0.
+
+THIS FILE IS AUTOGENERATED: To update, please run:
+  bazel build tensorflow/tools/compatibility/update:generate_v2_renames_map
+  bazel-bin/tensorflow/tools/compatibility/update/generate_v2_renames_map
+This file should be updated whenever endpoints are deprecated.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+renames = {
+    'tf.acos': 'tf.math.acos',
+    'tf.acosh': 'tf.math.acosh',
+    'tf.add': 'tf.math.add',
+    'tf.as_string': 'tf.dtypes.as_string',
+    'tf.asin': 'tf.math.asin',
+    'tf.asinh': 'tf.math.asinh',
+    'tf.atan': 'tf.math.atan',
+    'tf.atan2': 'tf.math.atan2',
+    'tf.atanh': 'tf.math.atanh',
+    'tf.batch_to_space_nd': 'tf.manip.batch_to_space_nd',
+    'tf.betainc': 'tf.math.betainc',
+    'tf.ceil': 'tf.math.ceil',
+    'tf.check_numerics': 'tf.debugging.check_numerics',
+    'tf.cholesky': 'tf.linalg.cholesky',
+    'tf.cos': 'tf.math.cos',
+    'tf.cosh': 'tf.math.cosh',
+    'tf.cross': 'tf.linalg.cross',
+    'tf.decode_base64': 'tf.io.decode_base64',
+    'tf.decode_compressed': 'tf.io.decode_compressed',
+    'tf.decode_json_example': 'tf.io.decode_json_example',
+    'tf.decode_raw': 'tf.io.decode_raw',
+    'tf.dequantize': 'tf.quantization.dequantize',
+    'tf.diag': 'tf.linalg.tensor_diag',
+    'tf.diag_part': 'tf.linalg.tensor_diag_part',
+    'tf.digamma': 'tf.math.digamma',
+    'tf.encode_base64': 'tf.io.encode_base64',
+    'tf.equal': 'tf.math.equal',
+    'tf.erfc': 'tf.math.erfc',
+    'tf.exp': 'tf.math.exp',
+    'tf.expm1': 'tf.math.expm1',
+    'tf.extract_image_patches': 'tf.image.extract_image_patches',
+    'tf.fake_quant_with_min_max_args': 'tf.quantization.fake_quant_with_min_max_args',
+    'tf.fake_quant_with_min_max_args_gradient': 'tf.quantization.fake_quant_with_min_max_args_gradient',
+    'tf.fake_quant_with_min_max_vars': 'tf.quantization.fake_quant_with_min_max_vars',
+    'tf.fake_quant_with_min_max_vars_gradient': 'tf.quantization.fake_quant_with_min_max_vars_gradient',
+    'tf.fake_quant_with_min_max_vars_per_channel': 'tf.quantization.fake_quant_with_min_max_vars_per_channel',
+    'tf.fake_quant_with_min_max_vars_per_channel_gradient': 'tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient',
+    'tf.fft': 'tf.spectral.fft',
+    'tf.floor': 'tf.math.floor',
+    'tf.gather_nd': 'tf.manip.gather_nd',
+    'tf.greater': 'tf.math.greater',
+    'tf.greater_equal': 'tf.math.greater_equal',
+    'tf.ifft': 'tf.spectral.ifft',
+    'tf.igamma': 'tf.math.igamma',
+    'tf.igammac': 'tf.math.igammac',
+    'tf.invert_permutation': 'tf.math.invert_permutation',
+    'tf.is_finite': 'tf.debugging.is_finite',
+    'tf.is_inf': 'tf.debugging.is_inf',
+    'tf.is_nan': 'tf.debugging.is_nan',
+    'tf.less': 'tf.math.less',
+    'tf.less_equal': 'tf.math.less_equal',
+    'tf.lgamma': 'tf.math.lgamma',
+    'tf.log': 'tf.math.log',
+    'tf.log1p': 'tf.math.log1p',
+    'tf.logical_and': 'tf.math.logical_and',
+    'tf.logical_not': 'tf.math.logical_not',
+    'tf.logical_or': 'tf.math.logical_or',
+    'tf.matching_files': 'tf.io.matching_files',
+    'tf.matrix_band_part': 'tf.linalg.band_part',
+    'tf.matrix_determinant': 'tf.linalg.det',
+    'tf.matrix_diag': 'tf.linalg.diag',
+    'tf.matrix_diag_part': 'tf.linalg.diag_part',
+    'tf.matrix_inverse': 'tf.linalg.inv',
+    'tf.matrix_set_diag': 'tf.linalg.set_diag',
+    'tf.matrix_solve': 'tf.linalg.solve',
+    'tf.matrix_triangular_solve': 'tf.linalg.triangular_solve',
+    'tf.maximum': 'tf.math.maximum',
+    'tf.minimum': 'tf.math.minimum',
+    'tf.not_equal': 'tf.math.not_equal',
+    'tf.parse_tensor': 'tf.io.parse_tensor',
+    'tf.polygamma': 'tf.math.polygamma',
+    'tf.qr': 'tf.linalg.qr',
+    'tf.quantized_concat': 'tf.quantization.quantized_concat',
+    'tf.read_file': 'tf.io.read_file',
+    'tf.reciprocal': 'tf.math.reciprocal',
+    'tf.regex_replace': 'tf.strings.regex_replace',
+    'tf.reshape': 'tf.manip.reshape',
+    'tf.reverse': 'tf.manip.reverse',
+    'tf.reverse_v2': 'tf.manip.reverse',
+    'tf.rint': 'tf.math.rint',
+    'tf.rsqrt': 'tf.math.rsqrt',
+    'tf.scatter_nd': 'tf.manip.scatter_nd',
+    'tf.segment_max': 'tf.math.segment_max',
+    'tf.segment_mean': 'tf.math.segment_mean',
+    'tf.segment_min': 'tf.math.segment_min',
+    'tf.segment_prod': 'tf.math.segment_prod',
+    'tf.segment_sum': 'tf.math.segment_sum',
+    'tf.sin': 'tf.math.sin',
+    'tf.sinh': 'tf.math.sinh',
+    'tf.space_to_batch_nd': 'tf.manip.space_to_batch_nd',
+    'tf.squared_difference': 'tf.math.squared_difference',
+    'tf.string_join': 'tf.strings.join',
+    'tf.string_strip': 'tf.strings.strip',
+    'tf.string_to_hash_bucket': 'tf.strings.to_hash_bucket',
+    'tf.string_to_hash_bucket_fast': 'tf.strings.to_hash_bucket_fast',
+    'tf.string_to_hash_bucket_strong': 'tf.strings.to_hash_bucket_strong',
+    'tf.string_to_number': 'tf.strings.to_number',
+    'tf.substr': 'tf.strings.substr',
+    'tf.tan': 'tf.math.tan',
+    'tf.tile': 'tf.manip.tile',
+    'tf.unsorted_segment_max': 'tf.math.unsorted_segment_max',
+    'tf.unsorted_segment_min': 'tf.math.unsorted_segment_min',
+    'tf.unsorted_segment_prod': 'tf.math.unsorted_segment_prod',
+    'tf.unsorted_segment_sum': 'tf.math.unsorted_segment_sum',
+    'tf.write_file': 'tf.io.write_file',
+    'tf.zeta': 'tf.math.zeta'
+}
diff --git a/tensorflow/contrib/autograph/utils/type_hints.py b/tensorflow/tools/compatibility/testdata/test_file_v1_10.py
similarity index 50%
rename from tensorflow/contrib/autograph/utils/type_hints.py
rename to tensorflow/tools/compatibility/testdata/test_file_v1_10.py
index aeb9e545610460afbe364dfcfc7a54b9aede29fe..a49035a1a09bb6b6ea33a375766c9c414f871df1 100644
--- a/tensorflow/contrib/autograph/utils/type_hints.py
+++ b/tensorflow/tools/compatibility/testdata/test_file_v1_10.py
@@ -1,4 +1,4 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,30 +12,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""No-op utilities that provide static type hints.
-
-These are used when the data type is not known at creation, for instance in the
-case of empty lists.
-"""
+"""Tests for tf upgrader."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
+import tensorflow as tf
+from tensorflow.python.framework import test_util
+from tensorflow.python.platform import test as test_lib
 
 
-def set_element_type(entity, dtype, shape=None):
-  """Indicates that the entity is expected hold items of specified type.
+class TestUpgrade(test_util.TensorFlowTestCase):
+  """Test various APIs that have been changed in 2.0."""
 
-  This function is a no-op. Its presence merely marks the data type of its
-  argument. The staged TensorFlow ops will reflect and assert this data type.
+  def testRenames(self):
+    with self.test_session():
+      self.assertAllClose(1.04719755, tf.acos(0.5).eval())
+      self.assertAllClose(0.5, tf.rsqrt(4.0).eval())
 
-  Args:
-    entity: A Tensor or TensorArray.
-    dtype: TensorFlow dtype value to assert for entity.
-    shape: Optional shape to assert for entity.
-  Returns:
-    The value of entity, unchanged.
-  """
-  del dtype
-  del shape
-  return entity
+if __name__ == "__main__":
+  test_lib.main()
diff --git a/tensorflow/tools/compatibility/tf_upgrade.py b/tensorflow/tools/compatibility/tf_upgrade.py
index 1f8833582af4c922115e637117e775e619439786..96705b1a4c27e72ba1d50f16dad10c35705b1782 100644
--- a/tensorflow/tools/compatibility/tf_upgrade.py
+++ b/tensorflow/tools/compatibility/tf_upgrade.py
@@ -19,491 +19,11 @@ from __future__ import division
 from __future__ import print_function
 
 import argparse
-import ast
-import collections
-import os
-import shutil
-import sys
-import tempfile
-import traceback
 
+from tensorflow.tools.compatibility import ast_edits
 
-class APIChangeSpec(object):
-  """This class defines the transformations that need to happen.
 
-  This class must provide the following fields:
-
-  * `function_keyword_renames`: maps function names to a map of old -> new
-    argument names
-  * `function_renames`: maps function names to new function names
-  * `change_to_function`: a set of function names that have changed (for
-    notifications)
-  * `function_reorders`: maps functions whose argument order has changed to the
-    list of arguments in the new order
-  * `function_handle`: maps function names to custom handlers for the function
-
-  For an example, see `TFAPIChangeSpec`.
-  """
-
-
-class _FileEditTuple(
-    collections.namedtuple("_FileEditTuple",
-                           ["comment", "line", "start", "old", "new"])):
-  """Each edit that is recorded by a _FileEditRecorder.
-
-  Fields:
-    comment: A description of the edit and why it was made.
-    line: The line number in the file where the edit occurs (1-indexed).
-    start: The line number in the file where the edit occurs (0-indexed).
-    old: text string to remove (this must match what was in file).
-    new: text string to add in place of `old`.
-  """
-
-  __slots__ = ()
-
-
-class _FileEditRecorder(object):
-  """Record changes that need to be done to the file."""
-
-  def __init__(self, filename):
-    # all edits are lists of chars
-    self._filename = filename
-
-    self._line_to_edit = collections.defaultdict(list)
-    self._errors = []
-
-  def process(self, text):
-    """Process a list of strings, each corresponding to the recorded changes.
-
-    Args:
-      text: A list of lines of text (assumed to contain newlines)
-    Returns:
-      A tuple of the modified text and a textual description of what is done.
-    Raises:
-      ValueError: if substitution source location does not have expected text.
-    """
-
-    change_report = ""
-
-    # Iterate of each line
-    for line, edits in self._line_to_edit.items():
-      offset = 0
-      # sort by column so that edits are processed in order in order to make
-      # indexing adjustments cumulative for changes that change the string
-      # length
-      edits.sort(key=lambda x: x.start)
-
-      # Extract each line to a list of characters, because mutable lists
-      # are editable, unlike immutable strings.
-      char_array = list(text[line - 1])
-
-      # Record a description of the change
-      change_report += "%r Line %d\n" % (self._filename, line)
-      change_report += "-" * 80 + "\n\n"
-      for e in edits:
-        change_report += "%s\n" % e.comment
-      change_report += "\n    Old: %s" % (text[line - 1])
-
-      # Make underscore buffers for underlining where in the line the edit was
-      change_list = [" "] * len(text[line - 1])
-      change_list_new = [" "] * len(text[line - 1])
-
-      # Iterate for each edit
-      for e in edits:
-        # Create effective start, end by accounting for change in length due
-        # to previous edits
-        start_eff = e.start + offset
-        end_eff = start_eff + len(e.old)
-
-        # Make sure the edit is changing what it should be changing
-        old_actual = "".join(char_array[start_eff:end_eff])
-        if old_actual != e.old:
-          raise ValueError("Expected text %r but got %r" %
-                           ("".join(e.old), "".join(old_actual)))
-        # Make the edit
-        char_array[start_eff:end_eff] = list(e.new)
-
-        # Create the underline highlighting of the before and after
-        change_list[e.start:e.start + len(e.old)] = "~" * len(e.old)
-        change_list_new[start_eff:end_eff] = "~" * len(e.new)
-
-        # Keep track of how to generate effective ranges
-        offset += len(e.new) - len(e.old)
-
-      # Finish the report comment
-      change_report += "         %s\n" % "".join(change_list)
-      text[line - 1] = "".join(char_array)
-      change_report += "    New: %s" % (text[line - 1])
-      change_report += "         %s\n\n" % "".join(change_list_new)
-    return "".join(text), change_report, self._errors
-
-  def add(self, comment, line, start, old, new, error=None):
-    """Add a new change that is needed.
-
-    Args:
-      comment: A description of what was changed
-      line: Line number (1 indexed)
-      start: Column offset (0 indexed)
-      old: old text
-      new: new text
-      error: this "edit" is something that cannot be fixed automatically
-    Returns:
-      None
-    """
-
-    self._line_to_edit[line].append(
-        _FileEditTuple(comment, line, start, old, new))
-    if error:
-      self._errors.append("%s:%d: %s" % (self._filename, line, error))
-
-
-class _ASTCallVisitor(ast.NodeVisitor):
-  """AST Visitor that processes function calls.
-
-  Updates function calls from old API version to new API version using a given
-  change spec.
-  """
-
-  def __init__(self, filename, lines, api_change_spec):
-    self._filename = filename
-    self._file_edit = _FileEditRecorder(filename)
-    self._lines = lines
-    self._api_change_spec = api_change_spec
-
-  def process(self, lines):
-    return self._file_edit.process(lines)
-
-  def generic_visit(self, node):
-    ast.NodeVisitor.generic_visit(self, node)
-
-  def _rename_functions(self, node, full_name):
-    function_renames = self._api_change_spec.function_renames
-    try:
-      new_name = function_renames[full_name]
-      self._file_edit.add("Renamed function %r to %r" % (full_name, new_name),
-                          node.lineno, node.col_offset, full_name, new_name)
-    except KeyError:
-      pass
-
-  def _get_attribute_full_path(self, node):
-    """Traverse an attribute to generate a full name e.g. tf.foo.bar.
-
-    Args:
-      node: A Node of type Attribute.
-
-    Returns:
-      a '.'-delimited full-name or None if the tree was not a simple form.
-      i.e. `foo()+b).bar` returns None, while `a.b.c` would return "a.b.c".
-    """
-    curr = node
-    items = []
-    while not isinstance(curr, ast.Name):
-      if not isinstance(curr, ast.Attribute):
-        return None
-      items.append(curr.attr)
-      curr = curr.value
-    items.append(curr.id)
-    return ".".join(reversed(items))
-
-  def _find_true_position(self, node):
-    """Return correct line number and column offset for a given node.
-
-    This is necessary mainly because ListComp's location reporting reports
-    the next token after the list comprehension list opening.
-
-    Args:
-      node: Node for which we wish to know the lineno and col_offset
-    """
-    import re
-    find_open = re.compile("^\s*(\\[).*$")
-    find_string_chars = re.compile("['\"]")
-
-    if isinstance(node, ast.ListComp):
-      # Strangely, ast.ListComp returns the col_offset of the first token
-      # after the '[' token which appears to be a bug. Workaround by
-      # explicitly finding the real start of the list comprehension.
-      line = node.lineno
-      col = node.col_offset
-      # loop over lines
-      while 1:
-        # Reverse the text to and regular expression search for whitespace
-        text = self._lines[line - 1]
-        reversed_preceding_text = text[:col][::-1]
-        # First find if a [ can be found with only whitespace between it and
-        # col.
-        m = find_open.match(reversed_preceding_text)
-        if m:
-          new_col_offset = col - m.start(1) - 1
-          return line, new_col_offset
-        else:
-          if (reversed_preceding_text == "" or
-              reversed_preceding_text.isspace()):
-            line = line - 1
-            prev_line = self._lines[line - 1]
-            # TODO(aselle):
-            # this is poor comment detection, but it is good enough for
-            # cases where the comment does not contain string literal starting/
-            # ending characters. If ast gave us start and end locations of the
-            # ast nodes rather than just start, we could use string literal
-            # node ranges to filter out spurious #'s that appear in string
-            # literals.
-            comment_start = prev_line.find("#")
-            if comment_start == -1:
-              col = len(prev_line) - 1
-            elif find_string_chars.search(prev_line[comment_start:]) is None:
-              col = comment_start
-            else:
-              return None, None
-          else:
-            return None, None
-    # Most other nodes return proper locations (with notably does not), but
-    # it is not possible to use that in an argument.
-    return node.lineno, node.col_offset
-
-  def visit_Call(self, node):  # pylint: disable=invalid-name
-    """Handle visiting a call node in the AST.
-
-    Args:
-      node: Current Node
-    """
-
-    # Find a simple attribute name path e.g. "tf.foo.bar"
-    full_name = self._get_attribute_full_path(node.func)
-
-    # Make sure the func is marked as being part of a call
-    node.func.is_function_for_call = True
-
-    if full_name:
-      # Call special handlers
-      function_handles = self._api_change_spec.function_handle
-      if full_name in function_handles:
-        function_handles[full_name](self._file_edit, node)
-
-      # Examine any non-keyword argument and make it into a keyword argument
-      # if reordering required.
-      function_reorders = self._api_change_spec.function_reorders
-      function_keyword_renames = (
-          self._api_change_spec.function_keyword_renames)
-
-      if full_name in function_reorders:
-        reordered = function_reorders[full_name]
-        for idx, arg in enumerate(node.args):
-          lineno, col_offset = self._find_true_position(arg)
-          if lineno is None or col_offset is None:
-            self._file_edit.add(
-                "Failed to add keyword %r to reordered function %r" %
-                (reordered[idx], full_name),
-                arg.lineno,
-                arg.col_offset,
-                "",
-                "",
-                error="A necessary keyword argument failed to be inserted.")
-          else:
-            keyword_arg = reordered[idx]
-            if (full_name in function_keyword_renames and
-                keyword_arg in function_keyword_renames[full_name]):
-              keyword_arg = function_keyword_renames[full_name][keyword_arg]
-            self._file_edit.add("Added keyword %r to reordered function %r" %
-                                (reordered[idx], full_name), lineno, col_offset,
-                                "", keyword_arg + "=")
-
-      # Examine each keyword argument and convert it to the final renamed form
-      renamed_keywords = ({} if full_name not in function_keyword_renames else
-                          function_keyword_renames[full_name])
-      for keyword in node.keywords:
-        argkey = keyword.arg
-        argval = keyword.value
-
-        if argkey in renamed_keywords:
-          argval_lineno, argval_col_offset = self._find_true_position(argval)
-          if argval_lineno is not None and argval_col_offset is not None:
-            # TODO(aselle): We should scan backward to find the start of the
-            # keyword key. Unfortunately ast does not give you the location of
-            # keyword keys, so we are forced to infer it from the keyword arg
-            # value.
-            key_start = argval_col_offset - len(argkey) - 1
-            key_end = key_start + len(argkey) + 1
-            if (self._lines[argval_lineno - 1][key_start:key_end] == argkey +
-                "="):
-              self._file_edit.add("Renamed keyword argument from %r to %r" %
-                                  (argkey,
-                                   renamed_keywords[argkey]), argval_lineno,
-                                  argval_col_offset - len(argkey) - 1,
-                                  argkey + "=", renamed_keywords[argkey] + "=")
-              continue
-          self._file_edit.add(
-              "Failed to rename keyword argument from %r to %r" %
-              (argkey, renamed_keywords[argkey]),
-              argval.lineno,
-              argval.col_offset - len(argkey) - 1,
-              "",
-              "",
-              error="Failed to find keyword lexographically. Fix manually.")
-
-    ast.NodeVisitor.generic_visit(self, node)
-
-  def visit_Attribute(self, node):  # pylint: disable=invalid-name
-    """Handle bare Attributes i.e. [tf.foo, tf.bar].
-
-    Args:
-      node: Node that is of type ast.Attribute
-    """
-    full_name = self._get_attribute_full_path(node)
-    if full_name:
-      self._rename_functions(node, full_name)
-    if full_name in self._api_change_spec.change_to_function:
-      if not hasattr(node, "is_function_for_call"):
-        new_text = full_name + "()"
-        self._file_edit.add("Changed %r to %r" % (full_name, new_text),
-                            node.lineno, node.col_offset, full_name, new_text)
-
-    ast.NodeVisitor.generic_visit(self, node)
-
-
-class ASTCodeUpgrader(object):
-  """Handles upgrading a set of Python files using a given API change spec."""
-
-  def __init__(self, api_change_spec):
-    if not isinstance(api_change_spec, APIChangeSpec):
-      raise TypeError("Must pass APIChangeSpec to ASTCodeUpgrader, got %s" %
-                      type(api_change_spec))
-    self._api_change_spec = api_change_spec
-
-  def process_file(self, in_filename, out_filename):
-    """Process the given python file for incompatible changes.
-
-    Args:
-      in_filename: filename to parse
-      out_filename: output file to write to
-    Returns:
-      A tuple representing number of files processed, log of actions, errors
-    """
-
-    # Write to a temporary file, just in case we are doing an implace modify.
-    with open(in_filename, "r") as in_file, \
-        tempfile.NamedTemporaryFile("w", delete=False) as temp_file:
-      ret = self.process_opened_file(in_filename, in_file, out_filename,
-                                     temp_file)
-
-    shutil.move(temp_file.name, out_filename)
-    return ret
-
-  # Broad exceptions are required here because ast throws whatever it wants.
-  # pylint: disable=broad-except
-  def process_opened_file(self, in_filename, in_file, out_filename, out_file):
-    """Process the given python file for incompatible changes.
-
-    This function is split out to facilitate StringIO testing from
-    tf_upgrade_test.py.
-
-    Args:
-      in_filename: filename to parse
-      in_file: opened file (or StringIO)
-      out_filename: output file to write to
-      out_file: opened file (or StringIO)
-    Returns:
-      A tuple representing number of files processed, log of actions, errors
-    """
-    process_errors = []
-    text = "-" * 80 + "\n"
-    text += "Processing file %r\n outputting to %r\n" % (in_filename,
-                                                         out_filename)
-    text += "-" * 80 + "\n\n"
-
-    parsed_ast = None
-    lines = in_file.readlines()
-    try:
-      parsed_ast = ast.parse("".join(lines))
-    except Exception:
-      text += "Failed to parse %r\n\n" % in_filename
-      text += traceback.format_exc()
-    if parsed_ast:
-      visitor = _ASTCallVisitor(in_filename, lines, self._api_change_spec)
-      visitor.visit(parsed_ast)
-      out_text, new_text, process_errors = visitor.process(lines)
-      text += new_text
-      if out_file:
-        out_file.write(out_text)
-    text += "\n"
-    return 1, text, process_errors
-
-  # pylint: enable=broad-except
-
-  def process_tree(self, root_directory, output_root_directory,
-                   copy_other_files):
-    """Processes upgrades on an entire tree of python files in place.
-
-    Note that only Python files. If you have custom code in other languages,
-    you will need to manually upgrade those.
-
-    Args:
-      root_directory: Directory to walk and process.
-      output_root_directory: Directory to use as base.
-      copy_other_files: Copy files that are not touched by this converter.
-
-    Returns:
-      A tuple of files processed, the report string ofr all files, and errors
-    """
-
-    # make sure output directory doesn't exist
-    if output_root_directory and os.path.exists(output_root_directory):
-      print("Output directory %r must not already exist." %
-            (output_root_directory))
-      sys.exit(1)
-
-    # make sure output directory does not overlap with root_directory
-    norm_root = os.path.split(os.path.normpath(root_directory))
-    norm_output = os.path.split(os.path.normpath(output_root_directory))
-    if norm_root == norm_output:
-      print("Output directory %r same as input directory %r" %
-            (root_directory, output_root_directory))
-      sys.exit(1)
-
-    # Collect list of files to process (we do this to correctly handle if the
-    # user puts the output directory in some sub directory of the input dir)
-    files_to_process = []
-    files_to_copy = []
-    for dir_name, _, file_list in os.walk(root_directory):
-      py_files = [f for f in file_list if f.endswith(".py")]
-      copy_files = [f for f in file_list if not f.endswith(".py")]
-      for filename in py_files:
-        fullpath = os.path.join(dir_name, filename)
-        fullpath_output = os.path.join(output_root_directory,
-                                       os.path.relpath(fullpath,
-                                                       root_directory))
-        files_to_process.append((fullpath, fullpath_output))
-      if copy_other_files:
-        for filename in copy_files:
-          fullpath = os.path.join(dir_name, filename)
-          fullpath_output = os.path.join(output_root_directory,
-                                         os.path.relpath(
-                                             fullpath, root_directory))
-          files_to_copy.append((fullpath, fullpath_output))
-
-    file_count = 0
-    tree_errors = []
-    report = ""
-    report += ("=" * 80) + "\n"
-    report += "Input tree: %r\n" % root_directory
-    report += ("=" * 80) + "\n"
-
-    for input_path, output_path in files_to_process:
-      output_directory = os.path.dirname(output_path)
-      if not os.path.isdir(output_directory):
-        os.makedirs(output_directory)
-      file_count += 1
-      _, l_report, l_errors = self.process_file(input_path, output_path)
-      tree_errors += l_errors
-      report += l_report
-    for input_path, output_path in files_to_copy:
-      output_directory = os.path.dirname(output_path)
-      if not os.path.isdir(output_directory):
-        os.makedirs(output_directory)
-      shutil.copy(input_path, output_path)
-    return file_count, report, tree_errors
-
-
-class TFAPIChangeSpec(APIChangeSpec):
+class TFAPIChangeSpec(ast_edits.APIChangeSpec):
   """List of maps that describe what changed in the API."""
 
   def __init__(self):
@@ -718,7 +238,7 @@ Simple usage:
       default="report.txt")
   args = parser.parse_args()
 
-  upgrade = ASTCodeUpgrader(TFAPIChangeSpec())
+  upgrade = ast_edits.ASTCodeUpgrader(TFAPIChangeSpec())
   report_text = None
   report_filename = args.report_filename
   files_processed = 0
diff --git a/tensorflow/tools/compatibility/tf_upgrade_test.py b/tensorflow/tools/compatibility/tf_upgrade_test.py
index 3d02eacba6e7a91e6d3c88e8297306de9782f4bf..66325ea2ad36265c6c3779b414774abab8213a84 100644
--- a/tensorflow/tools/compatibility/tf_upgrade_test.py
+++ b/tensorflow/tools/compatibility/tf_upgrade_test.py
@@ -22,6 +22,7 @@ import tempfile
 import six
 from tensorflow.python.framework import test_util
 from tensorflow.python.platform import test as test_lib
+from tensorflow.tools.compatibility import ast_edits
 from tensorflow.tools.compatibility import tf_upgrade
 
 
@@ -36,7 +37,7 @@ class TestUpgrade(test_util.TensorFlowTestCase):
   def _upgrade(self, old_file_text):
     in_file = six.StringIO(old_file_text)
     out_file = six.StringIO()
-    upgrader = tf_upgrade.ASTCodeUpgrader(tf_upgrade.TFAPIChangeSpec())
+    upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade.TFAPIChangeSpec())
     count, report, errors = (
         upgrader.process_opened_file("test.py", in_file,
                                      "test_out.py", out_file))
@@ -139,7 +140,7 @@ class TestUpgradeFiles(test_util.TensorFlowTestCase):
     upgraded = "tf.multiply(a, b)\n"
     temp_file.write(original)
     temp_file.close()
-    upgrader = tf_upgrade.ASTCodeUpgrader(tf_upgrade.TFAPIChangeSpec())
+    upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade.TFAPIChangeSpec())
     upgrader.process_file(temp_file.name, temp_file.name)
     self.assertAllEqual(open(temp_file.name).read(), upgraded)
     os.unlink(temp_file.name)
diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2.py b/tensorflow/tools/compatibility/tf_upgrade_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..9702430a1219c33e6d68875e1366ee7ebb2ce308
--- /dev/null
+++ b/tensorflow/tools/compatibility/tf_upgrade_v2.py
@@ -0,0 +1,115 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Upgrader for Python scripts from 1.* TensorFlow to 2.0 TensorFlow."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+
+from tensorflow.tools.compatibility import ast_edits
+from tensorflow.tools.compatibility import renames_v2
+
+
+class TFAPIChangeSpec(ast_edits.APIChangeSpec):
+  """List of maps that describe what changed in the API."""
+
+  def __init__(self):
+    # Maps from a function name to a dictionary that describes how to
+    # map from an old argument keyword to the new argument keyword.
+    self.function_keyword_renames = {}
+
+    # Mapping from function to the new name of the function
+    self.function_renames = renames_v2.renames
+
+    # Variables that should be changed to functions.
+    self.change_to_function = {}
+
+    # Functions that were reordered should be changed to the new keyword args
+    # for safety, if positional arguments are used. If you have reversed the
+    # positional arguments yourself, this could do the wrong thing.
+    self.function_reorders = {}
+
+    # Specially handled functions.
+    self.function_handle = {}
+
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser(
+      formatter_class=argparse.RawDescriptionHelpFormatter,
+      description="""Convert a TensorFlow Python file to 2.0
+
+Simple usage:
+  tf_convert_v2.py --infile foo.py --outfile bar.py
+  tf_convert_v2.py --intree ~/code/old --outtree ~/code/new
+""")
+  parser.add_argument(
+      "--infile",
+      dest="input_file",
+      help="If converting a single file, the name of the file "
+      "to convert")
+  parser.add_argument(
+      "--outfile",
+      dest="output_file",
+      help="If converting a single file, the output filename.")
+  parser.add_argument(
+      "--intree",
+      dest="input_tree",
+      help="If converting a whole tree of files, the directory "
+      "to read from (relative or absolute).")
+  parser.add_argument(
+      "--outtree",
+      dest="output_tree",
+      help="If converting a whole tree of files, the output "
+      "directory (relative or absolute).")
+  parser.add_argument(
+      "--copyotherfiles",
+      dest="copy_other_files",
+      help=("If converting a whole tree of files, whether to "
+            "copy the other files."),
+      type=bool,
+      default=False)
+  parser.add_argument(
+      "--reportfile",
+      dest="report_filename",
+      help=("The name of the file where the report log is "
+            "stored."
+            "(default: %(default)s)"),
+      default="report.txt")
+  args = parser.parse_args()
+
+  upgrade = ast_edits.ASTCodeUpgrader(TFAPIChangeSpec())
+  report_text = None
+  report_filename = args.report_filename
+  files_processed = 0
+  if args.input_file:
+    files_processed, report_text, errors = upgrade.process_file(
+        args.input_file, args.output_file)
+    files_processed = 1
+  elif args.input_tree:
+    files_processed, report_text, errors = upgrade.process_tree(
+        args.input_tree, args.output_tree, args.copy_other_files)
+  else:
+    parser.print_help()
+  if report_text:
+    open(report_filename, "w").write(report_text)
+    print("TensorFlow 2.0 Upgrade Script")
+    print("-----------------------------")
+    print("Converted %d files\n" % files_processed)
+    print("Detected %d errors that require attention" % len(errors))
+    print("-" * 80)
+    print("\n".join(errors))
+    print("\nMake sure to read the detailed log %r\n" % report_filename)
diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..57ac04de0667b83b66853b7cee7b4a34bc9f2f2f
--- /dev/null
+++ b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py
@@ -0,0 +1,83 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for tf 2.0 upgrader."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import os
+import tempfile
+import six
+from tensorflow.python.framework import test_util
+from tensorflow.python.platform import test as test_lib
+from tensorflow.tools.compatibility import ast_edits
+from tensorflow.tools.compatibility import tf_upgrade_v2
+
+
+class TestUpgrade(test_util.TensorFlowTestCase):
+  """Test various APIs that have been changed in 2.0.
+
+  We also test whether a converted file is executable. test_file_v1_10.py
+  aims to exhaustively test that API changes are convertible and actually
+  work when run with current TensorFlow.
+  """
+
+  def _upgrade(self, old_file_text):
+    in_file = six.StringIO(old_file_text)
+    out_file = six.StringIO()
+    upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade_v2.TFAPIChangeSpec())
+    count, report, errors = (
+        upgrader.process_opened_file("test.py", in_file,
+                                     "test_out.py", out_file))
+    return count, report, errors, out_file.getvalue()
+
+  def testParseError(self):
+    _, report, unused_errors, unused_new_text = self._upgrade(
+        "import tensorflow as tf\na + \n")
+    self.assertTrue(report.find("Failed to parse") != -1)
+
+  def testReport(self):
+    text = "tf.acos(a)\n"
+    _, report, unused_errors, unused_new_text = self._upgrade(text)
+    # This is not a complete test, but it is a sanity test that a report
+    # is generating information.
+    self.assertTrue(report.find("Renamed function `tf.acos` to `tf.math.acos`"))
+
+  def testRename(self):
+    text = "tf.acos(a)\n"
+    _, unused_report, unused_errors, new_text = self._upgrade(text)
+    self.assertEqual(new_text, "tf.math.acos(a)\n")
+    text = "tf.rsqrt(tf.log(3.8))\n"
+    _, unused_report, unused_errors, new_text = self._upgrade(text)
+    self.assertEqual(new_text, "tf.math.rsqrt(tf.math.log(3.8))\n")
+
+
+class TestUpgradeFiles(test_util.TensorFlowTestCase):
+
+  def testInplace(self):
+    """Check to make sure we don't have a file system race."""
+    temp_file = tempfile.NamedTemporaryFile("w", delete=False)
+    original = "tf.acos(a, b)\n"
+    upgraded = "tf.math.acos(a, b)\n"
+    temp_file.write(original)
+    temp_file.close()
+    upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade_v2.TFAPIChangeSpec())
+    upgrader.process_file(temp_file.name, temp_file.name)
+    self.assertAllEqual(open(temp_file.name).read(), upgraded)
+    os.unlink(temp_file.name)
+
+
+if __name__ == "__main__":
+  test_lib.main()
diff --git a/tensorflow/tools/compatibility/update/BUILD b/tensorflow/tools/compatibility/update/BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..feb37c902ec3359e6221937f4334ab2504394fa3
--- /dev/null
+++ b/tensorflow/tools/compatibility/update/BUILD
@@ -0,0 +1,15 @@
+licenses(["notice"])  # Apache 2.0
+
+package(default_visibility = ["//visibility:private"])
+
+py_binary(
+    name = "generate_v2_renames_map",
+    srcs = ["generate_v2_renames_map.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow:tensorflow_py",
+        "//tensorflow/python:lib",
+        "//tensorflow/tools/common:public_api",
+        "//tensorflow/tools/common:traverse",
+    ],
+)
diff --git a/tensorflow/tools/compatibility/update/generate_v2_renames_map.py b/tensorflow/tools/compatibility/update/generate_v2_renames_map.py
new file mode 100644
index 0000000000000000000000000000000000000000..567eceb0b6595ceac624fe8211f22885a6490d85
--- /dev/null
+++ b/tensorflow/tools/compatibility/update/generate_v2_renames_map.py
@@ -0,0 +1,103 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# pylint: disable=line-too-long
+"""Script for updating tensorflow/tools/compatibility/renames_v2.py.
+
+To update renames_v2.py, run:
+  bazel build tensorflow/tools/compatibility/update:generate_v2_renames_map
+  bazel-bin/tensorflow/tools/compatibility/update/generate_v2_renames_map
+"""
+# pylint: enable=line-too-long
+
+import tensorflow as tf
+
+from tensorflow.python.lib.io import file_io
+from tensorflow.python.util import tf_decorator
+from tensorflow.python.util import tf_export
+from tensorflow.tools.common import public_api
+from tensorflow.tools.common import traverse
+
+
+_OUTPUT_FILE_PATH = 'third_party/tensorflow/tools/compatibility/renames_v2.py'
+_FILE_HEADER = """# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# pylint: disable=line-too-long
+\"\"\"List of renames to apply when converting from TF 1.0 to TF 2.0.
+
+THIS FILE IS AUTOGENERATED: To update, please run:
+  bazel build tensorflow/tools/compatibility/update:generate_v2_renames_map
+  bazel-bin/tensorflow/tools/compatibility/update/generate_v2_renames_map
+This file should be updated whenever endpoints are deprecated.
+\"\"\"
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+"""
+
+
+def update_renames_v2(output_file_path):
+  """Writes a Python dictionary mapping deprecated to canonical API names.
+
+  Args:
+    output_file_path: File path to write output to. Any existing contents
+      would be replaced.
+  """
+  # Set of rename lines to write to output file in the form:
+  #   'tf.deprecated_name': 'tf.canonical_name'
+  rename_line_set = set()
+  # _tf_api_names attribute name
+  tensorflow_api_attr = tf_export.API_ATTRS[tf_export.TENSORFLOW_API_NAME].names
+
+  def visit(unused_path, unused_parent, children):
+    """Visitor that collects rename strings to add to rename_line_set."""
+    for child in children:
+      _, attr = tf_decorator.unwrap(child[1])
+      if not hasattr(attr, '__dict__'):
+        continue
+      api_names = attr.__dict__.get(tensorflow_api_attr, [])
+      deprecated_api_names = attr.__dict__.get('_tf_deprecated_api_names', [])
+      canonical_name = tf_export.get_canonical_name(
+          api_names, deprecated_api_names)
+      for name in deprecated_api_names:
+        rename_line_set.add('    \'tf.%s\': \'tf.%s\'' % (name, canonical_name))
+
+  visitor = public_api.PublicAPIVisitor(visit)
+  visitor.do_not_descend_map['tf'].append('contrib')
+  traverse.traverse(tf, visitor)
+
+  renames_file_text = '%srenames = {\n%s\n}\n' % (
+      _FILE_HEADER, ',\n'.join(sorted(rename_line_set)))
+  file_io.write_string_to_file(output_file_path, renames_file_text)
+
+
+def main(unused_argv):
+  update_renames_v2(_OUTPUT_FILE_PATH)
+
+
+if __name__ == '__main__':
+  tf.app.run(main=main)
diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel
index 57a491255ea968b08e6e9cbaf9dd0178e8d2c3bf..f7fe4119dabd5423a14d64176cb0f5debd830c8b 100644
--- a/tensorflow/tools/docker/Dockerfile.devel
+++ b/tensorflow/tools/docker/Dockerfile.devel
@@ -63,7 +63,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc
 RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \
     >>/etc/bazel.bazelrc
 # Install the most recent bazel release.
-ENV BAZEL_VERSION 0.11.0
+ENV BAZEL_VERSION 0.15.0
 WORKDIR /
 RUN mkdir /bazel && \
     cd /bazel && \
diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu
index 204b5b4dba1b607fb709b7f45d145ceafc33f3e7..340f96df483411ab8f5714a76e00fd8b5f5c6435 100644
--- a/tensorflow/tools/docker/Dockerfile.devel-gpu
+++ b/tensorflow/tools/docker/Dockerfile.devel-gpu
@@ -15,6 +15,8 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
         git \
         libcudnn7=7.1.4.18-1+cuda9.0 \
         libcudnn7-dev=7.1.4.18-1+cuda9.0 \
+        libnccl2=2.2.13-1+cuda9.0 \
+        libnccl-dev=2.2.13-1+cuda9.0 \
         libcurl3-dev \
         libfreetype6-dev \
         libhdf5-serial-dev \
@@ -33,6 +35,11 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     find /usr/local/cuda-9.0/lib64/ -type f -name 'lib*_static.a' -not -name 'libcudart_static.a' -delete && \
     rm /usr/lib/x86_64-linux-gnu/libcudnn_static_v7.a
 
+# Link NCCL libray and header where the build script expects them.
+RUN mkdir /usr/local/cuda-9.0/lib &&  \
+    ln -s /usr/lib/x86_64-linux-gnu/libnccl.so.2 /usr/local/cuda/lib/libnccl.so.2 && \
+    ln -s /usr/include/nccl.h /usr/local/cuda/include/nccl.h
+
 RUN curl -fSsL -O https://bootstrap.pypa.io/get-pip.py && \
     python get-pip.py && \
     rm get-pip.py
@@ -72,7 +79,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc
 RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \
     >>/etc/bazel.bazelrc
 # Install the most recent bazel release.
-ENV BAZEL_VERSION 0.11.0
+ENV BAZEL_VERSION 0.15.0
 WORKDIR /
 RUN mkdir /bazel && \
     cd /bazel && \
@@ -91,10 +98,13 @@ RUN git clone --branch=r1.9 --depth=1 https://github.com/tensorflow/tensorflow.g
 ENV CI_BUILD_PYTHON python
 ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
 ENV TF_NEED_CUDA 1
-ENV TF_CUDA_COMPUTE_CAPABILITIES=3.0,3.5,5.2,6.0,6.1
+ENV TF_CUDA_COMPUTE_CAPABILITIES=3.5,5.2,6.0,6.1,7.0
 ENV TF_CUDA_VERSION=9.0
 ENV TF_CUDNN_VERSION=7
 
+# NCCL 2.x
+ENV TF_NCCL_VERSION=2
+
 RUN ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1 && \
     LD_LIBRARY_PATH=/usr/local/cuda/lib64/stubs:${LD_LIBRARY_PATH} \
     tensorflow/tools/ci_build/builds/configured GPU \
diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7 b/tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7
new file mode 100644
index 0000000000000000000000000000000000000000..30bc2d28069758f20e99d84b159b63a164aece1d
--- /dev/null
+++ b/tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7
@@ -0,0 +1,115 @@
+FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
+
+LABEL maintainer="Gunhan Gulsoy "
+
+# It is possible to override these for releases.
+ARG TF_BRANCH=master
+ARG BAZEL_VERSION=0.15.0
+ARG TF_AVAILABLE_CPUS=32
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential \
+        curl \
+        git \
+        golang \
+        libcurl3-dev \
+        libfreetype6-dev \
+        libpng12-dev \
+        libzmq3-dev \
+        pkg-config \
+        python-dev \
+        python-pip \
+        rsync \
+        software-properties-common \
+        unzip \
+        zip \
+        zlib1g-dev \
+        openjdk-8-jdk \
+        openjdk-8-jre-headless \
+        wget \
+        && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN pip --no-cache-dir install --upgrade \
+        pip setuptools
+
+RUN pip --no-cache-dir install \
+        ipykernel \
+        jupyter \
+        matplotlib \
+        numpy \
+        scipy \
+        sklearn \
+        pandas \
+        wheel \
+        && \
+    python -m ipykernel.kernelspec
+
+# Set up our notebook config.
+COPY jupyter_notebook_config.py /root/.jupyter/
+
+# Jupyter has issues with being run directly:
+#   https://github.com/ipython/ipython/issues/7062
+# We just add a little wrapper script.
+COPY run_jupyter.sh /
+
+# Set up Bazel.
+
+# Running bazel inside a `docker build` command causes trouble, cf:
+#   https://github.com/bazelbuild/bazel/issues/134
+# The easiest solution is to set up a bazelrc file forcing --batch.
+RUN echo "startup --batch" >>/etc/bazel.bazelrc
+# Similarly, we need to workaround sandboxing issues:
+#   https://github.com/bazelbuild/bazel/issues/418
+RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \
+    >>/etc/bazel.bazelrc
+WORKDIR /
+RUN mkdir /bazel && \
+    cd /bazel && \
+    wget --quiet https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
+    wget --quiet https://raw.githubusercontent.com/bazelbuild/bazel/master/LICENSE && \
+    chmod +x bazel-*.sh && \
+    ./bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \
+    rm -f /bazel/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh
+
+# Download and build TensorFlow.
+WORKDIR /
+RUN git clone https://github.com/tensorflow/tensorflow.git && \
+    cd tensorflow && \
+    git checkout ${TF_BRANCH}
+WORKDIR /tensorflow
+
+# Configure the build for our CUDA configuration.
+ENV CI_BUILD_PYTHON=python \
+    LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:${LD_LIBRARY_PATH} \
+    CUDNN_INSTALL_PATH=/usr/lib/x86_64-linux-gnu \
+    PYTHON_BIN_PATH=/usr/bin/python \
+    PYTHON_LIB_PATH=/usr/local/lib/python2.7/dist-packages \
+    TF_NEED_CUDA=1 \
+    TF_CUDA_VERSION=9.0 \
+    TF_CUDA_COMPUTE_CAPABILITIES=3.0,3.5,5.2,6.0,6.1,7.0 \
+    TF_CUDNN_VERSION=7
+RUN ./configure
+
+# Build and Install TensorFlow.
+RUN ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1 && \
+    LD_LIBRARY_PATH=/usr/local/cuda/lib64/stubs:${LD_LIBRARY_PATH} \
+    bazel build -c opt \
+                --config=cuda \
+                --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" \
+                --jobs=${TF_AVAILABLE_CPUS} \
+                tensorflow/tools/pip_package:build_pip_package && \
+    mkdir /pip_pkg && \
+    bazel-bin/tensorflow/tools/pip_package/build_pip_package /pip_pkg && \
+    pip --no-cache-dir install --upgrade /pip_pkg/tensorflow-*.whl && \
+    rm -rf /pip_pkg && \
+    rm -rf /root/.cache
+# Clean up pip wheel and Bazel cache when done.
+
+WORKDIR /root
+
+# TensorBoard
+EXPOSE 6006
+# IPython
+EXPOSE 8888
diff --git a/tensorflow/tools/docker/Dockerfile.devel-mkl b/tensorflow/tools/docker/Dockerfile.devel-mkl
index 6dca0e393fa8d61ec819a5f9b5a2e5ffd3c7be92..c85641b38301e90a3dfbc3e67bc0e6deabbd68db 100755
--- a/tensorflow/tools/docker/Dockerfile.devel-mkl
+++ b/tensorflow/tools/docker/Dockerfile.devel-mkl
@@ -73,7 +73,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc
 RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \
     >>/etc/bazel.bazelrc
 # Install the most recent bazel release.
-ENV BAZEL_VERSION 0.11.0
+ENV BAZEL_VERSION 0.14.1
 WORKDIR /
 RUN mkdir /bazel && \
     cd /bazel && \
diff --git a/tensorflow/tools/docker/Dockerfile.gpu b/tensorflow/tools/docker/Dockerfile.gpu
index 9197651ff4326e9b40264183a94b82e936746010..28d4371da32ede5f6003ff3fadb11ef14fb87bcf 100644
--- a/tensorflow/tools/docker/Dockerfile.gpu
+++ b/tensorflow/tools/docker/Dockerfile.gpu
@@ -13,6 +13,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
         cuda-cusparse-9-0 \
         curl \
         libcudnn7=7.1.4.18-1+cuda9.0 \
+        libnccl2=2.2.13-1+cuda9.0 \
         libfreetype6-dev \
         libhdf5-serial-dev \
         libpng12-dev \
diff --git a/tensorflow/tools/docker/notebooks/1_hello_tensorflow.ipynb b/tensorflow/tools/docker/notebooks/1_hello_tensorflow.ipynb
index 0633b03259a06363d0d069eb479971f8b87f983e..8fa871ef7729a9194de282b84cdd9539c80f8555 100644
--- a/tensorflow/tools/docker/notebooks/1_hello_tensorflow.ipynb
+++ b/tensorflow/tools/docker/notebooks/1_hello_tensorflow.ipynb
@@ -665,7 +665,7 @@
       "source": [
         "## What's next?\n",
         "\n",
-        "This has been a gentle introduction to TensorFlow, focused on what TensorFlow is and the very basics of doing anything in TensorFlow. If you'd like more, the next tutorial in the series is Getting Started with TensorFlow, also available in the [notebooks directory](..)."
+        "This has been a gentle introduction to TensorFlow, focused on what TensorFlow is and the very basics of doing anything in TensorFlow. If you'd like more, the next tutorial in the series is Getting Started with TensorFlow, also available in the [notebooks directory](../notebooks)."
       ]
     }
   ],
diff --git a/tensorflow/tools/docs/doc_generator_visitor.py b/tensorflow/tools/docs/doc_generator_visitor.py
index 259a4694fdcc0048a25d9facf2d45eaa86d6daaa..c090dbd8da8dd9d39d9a90ae21eb305168c0c27d 100644
--- a/tensorflow/tools/docs/doc_generator_visitor.py
+++ b/tensorflow/tools/docs/doc_generator_visitor.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 import six
 
+from tensorflow.python.util import tf_export
 from tensorflow.python.util import tf_inspect
 
 
@@ -201,7 +202,6 @@ class DocGeneratorVisitor(object):
             raw_duplicates[master_name] = [master_name, full_name]
         else:
           reverse_index[object_id] = full_name
-
     # Decide on master names, rewire duplicates and make a duplicate_of map
     # mapping all non-master duplicates to the master name. The master symbol
     # does not have an entry in this map.
@@ -211,10 +211,15 @@ class DocGeneratorVisitor(object):
     duplicates = {}
     for names in raw_duplicates.values():
       names = sorted(names)
-
-      # Choose the lexicographically first name with the minimum number of
-      # submodules. This will prefer highest level namespace for any symbol.
-      master_name = min(names, key=lambda name: name.count('.'))
+      master_name = (
+          tf_export.get_canonical_name_for_symbol(self._index[names[0]])
+          if names else None)
+      if master_name:
+        master_name = 'tf.%s' % master_name
+      else:
+        # Choose the lexicographically first name with the minimum number of
+        # submodules. This will prefer highest level namespace for any symbol.
+        master_name = min(names, key=lambda name: name.count('.'))
 
       duplicates[master_name] = names
       for name in names:
diff --git a/tensorflow/tools/docs/generate.py b/tensorflow/tools/docs/generate.py
index fc93085e3e0316cf274f4d9b325d6af0ea3a2f83..f96887e4c70b0580fd8a799c8f1d602491a66ef2 100644
--- a/tensorflow/tools/docs/generate.py
+++ b/tensorflow/tools/docs/generate.py
@@ -31,6 +31,11 @@ if __name__ == '__main__':
   doc_generator = generate_lib.DocGenerator()
   doc_generator.add_output_dir_argument()
   doc_generator.add_src_dir_argument()
+  doc_generator.argument_parser.add_argument(
+      '--site_api_path',
+      type=str, default='api_docs/python',
+      help='The path from the site-root to api_docs'
+           'directory for this project')
 
   # This doc generator works on the TensorFlow codebase. Since this script lives
   # at tensorflow/tools/docs, and all code is defined somewhere inside
diff --git a/tensorflow/tools/docs/generate_lib.py b/tensorflow/tools/docs/generate_lib.py
index e7634cd5dcf19d5f21b0bd42b282dfe928659a52..4f70a6936490dab833dd32c30598f2e6f493feaa 100644
--- a/tensorflow/tools/docs/generate_lib.py
+++ b/tensorflow/tools/docs/generate_lib.py
@@ -55,7 +55,8 @@ def write_docs(output_dir,
                parser_config,
                yaml_toc,
                root_title='TensorFlow',
-               search_hints=True):
+               search_hints=True,
+               site_api_path=None):
   """Write previously extracted docs to disk.
 
   Write a docs page for each symbol included in the indices of parser_config to
@@ -73,6 +74,8 @@ def write_docs(output_dir,
     root_title: The title name for the root level index.md.
     search_hints: (bool) include meta-data search hints at the top of each
       output file.
+    site_api_path: Used to write the api-duplicates _redirects.yaml file. if
+      None (the default) the file is not generated.
 
   Raises:
     ValueError: if `output_dir` is not an absolute path
@@ -92,6 +95,9 @@ def write_docs(output_dir,
   #  - symbol name(string):pathname (string)
   symbol_to_file = {}
 
+  # Collect redirects for an api _redirects.yaml file.
+  redirects = ['redirects:\n']
+
   # Parse and write Markdown pages, resolving cross-links (@{symbol}).
   for full_name, py_object in six.iteritems(parser_config.index):
     parser_config.reference_resolver.current_doc_full_name = full_name
@@ -150,6 +156,25 @@ def write_docs(output_dir,
       raise OSError(
           'Cannot write documentation for %s to %s' % (full_name, directory))
 
+    if site_api_path:
+      duplicates = parser_config.duplicates.get(full_name, [])
+      if not duplicates:
+        continue
+
+      duplicates = [item for item in duplicates if item != full_name]
+      template = ('- from: /{}\n'
+                  '  to: /{}\n')
+      for dup in duplicates:
+        from_path = os.path.join(site_api_path, dup.replace('.', '/'))
+        to_path = os.path.join(site_api_path, full_name.replace('.', '/'))
+        redirects.append(
+            template.format(from_path, to_path))
+
+  if site_api_path:
+    api_redirects_path = os.path.join(output_dir, '_redirects.yaml')
+    with open(api_redirects_path, 'w') as redirect_file:
+      redirect_file.write(''.join(redirects))
+
   if yaml_toc:
     # Generate table of contents
 
@@ -608,7 +633,8 @@ class DocGenerator(object):
         parser_config,
         yaml_toc=self.yaml_toc,
         root_title=root_title,
-        search_hints=getattr(flags, 'search_hints', True))
+        search_hints=getattr(flags, 'search_hints', True),
+        site_api_path=getattr(flags, 'site_api_path', None))
 
     # Replace all the @{} references in files under `FLAGS.src_dir`
     replace_refs(flags.src_dir, flags.output_dir, reference_resolver, '*.md')
diff --git a/tensorflow/tools/docs/generate_lib_test.py b/tensorflow/tools/docs/generate_lib_test.py
index 7a6f9fd9f799db5a14015d77e5297955c76a51cd..de18b1325454ce4c1c02bb943f7443c3e1876d5f 100644
--- a/tensorflow/tools/docs/generate_lib_test.py
+++ b/tensorflow/tools/docs/generate_lib_test.py
@@ -107,7 +107,18 @@ class GenerateTest(googletest.TestCase):
 
     output_dir = googletest.GetTempDir()
 
-    generate_lib.write_docs(output_dir, parser_config, yaml_toc=True)
+    generate_lib.write_docs(output_dir, parser_config, yaml_toc=True,
+                            site_api_path='api_docs/python')
+
+    # Check redirects
+    redirects_file = os.path.join(output_dir, '_redirects.yaml')
+    self.assertTrue(os.path.exists(redirects_file))
+    with open(redirects_file) as f:
+      redirects = f.read()
+    self.assertEqual(redirects.split(), [
+        'redirects:', '-', 'from:', '/api_docs/python/tf/test_function', 'to:',
+        '/api_docs/python/tf/TestModule/test_function'
+    ])
 
     # Make sure that the right files are written to disk.
     self.assertTrue(os.path.exists(os.path.join(output_dir, 'index.md')))
diff --git a/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc b/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc
index f1d361e07d8f00aa37a4e063a7d17bf85de74fde..156636ab8215d9abdc9e0ed461df550f1c7ed09c 100644
--- a/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc
+++ b/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc
@@ -159,7 +159,7 @@ Status FuseScaleOffsetToConvWeights(const std::vector& scale_values,
   NodeDef bias_add_node;
   bias_add_node.set_op("BiasAdd");
   bias_add_node.set_name(conv_output_name);
-  if (!conv_node.attr().count("data_format")) {
+  if (conv_node.attr().count("data_format") > 0) {
     CopyNodeAttr(conv_node, "data_format", "data_format", &bias_add_node);
   }
   CopyNodeAttr(conv_node, "T", "T", &bias_add_node);
diff --git a/tensorflow/tools/graph_transforms/transform_utils.cc b/tensorflow/tools/graph_transforms/transform_utils.cc
index af17fd75bc1ccac61538c17658d59ee2efd6254a..cb084e49b7c797acd85d77c65ce2c69fd05be4ce 100644
--- a/tensorflow/tools/graph_transforms/transform_utils.cc
+++ b/tensorflow/tools/graph_transforms/transform_utils.cc
@@ -247,9 +247,16 @@ Status SortByExecutionOrder(const GraphDef& input_graph_def,
     }
   }
 
-  if (processed < input_graph_def.node_size()) {
-    return errors::InvalidArgument(input_graph_def.node_size() - processed,
-                                   " nodes in a cycle");
+  if (processed < num_nodes) {
+    LOG(WARNING) << "IN " << __func__ << (num_nodes - processed)
+                 << " NODES IN A CYCLE";
+    for (int64 i = 0; i < num_nodes; i++) {
+      if (pending_count[i] != 0) {
+        LOG(WARNING) << "PENDING: " << SummarizeNodeDef(input_graph_def.node(i))
+                     << "WITH PENDING COUNT = " << pending_count[i];
+      }
+    }
+    return errors::InvalidArgument(num_nodes - processed, " nodes in a cycle");
   }
   return Status::OK();
 }
diff --git a/tensorflow/tools/lib_package/BUILD b/tensorflow/tools/lib_package/BUILD
index 173f418dc8d998bc51d208a04c8671bacf364cdc..44d8a37a8f5b9172bdcf5a571be9a4ca73a63819 100644
--- a/tensorflow/tools/lib_package/BUILD
+++ b/tensorflow/tools/lib_package/BUILD
@@ -143,6 +143,7 @@ genrule(
         "@zlib_archive//:zlib.h",
     ] + if_mkl([
         "//third_party/mkl:LICENSE",
+        "//third_party/mkl_dnn:LICENSE",
     ]),
     outs = ["include/tensorflow/c/LICENSE"],
     cmd = "$(location :concat_licenses.sh) $(SRCS) >$@",
@@ -182,6 +183,7 @@ genrule(
         "@zlib_archive//:zlib.h",
     ] + if_mkl([
         "//third_party/mkl:LICENSE",
+        "//third_party/mkl_dnn:LICENSE",
     ]),
     outs = ["include/tensorflow/jni/LICENSE"],
     cmd = "$(location :concat_licenses.sh) $(SRCS) >$@",
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index c9d53f46c3cff9eceb6eb03a872d05e8afd06047..ab39ed8d696625bf82ee64cee643de26fe7e32a6 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -11,7 +11,7 @@ load(
 )
 load("//third_party/mkl:build_defs.bzl", "if_mkl")
 load("//tensorflow:tensorflow.bzl", "if_cuda")
-load("@local_config_tensorrt//:build_defs.bzl", "if_tensorrt")
+load("@local_config_syslibs//:build_defs.bzl", "if_not_system_lib")
 load("//tensorflow/core:platform/default/build_config_root.bzl", "tf_additional_license_deps")
 
 # This returns a list of headers of all public header libraries (e.g.,
@@ -78,7 +78,7 @@ COMMON_PIP_DEPS = [
     "//tensorflow/contrib/labeled_tensor:labeled_tensor_pip",
     "//tensorflow/contrib/nn:nn_py",
     "//tensorflow/contrib/predictor:predictor_pip",
-    "//tensorflow/contrib/proto:proto_pip",
+    "//tensorflow/contrib/proto:proto",
     "//tensorflow/contrib/receptive_field:receptive_field_pip",
     "//tensorflow/contrib/rpc:rpc_pip",
     "//tensorflow/contrib/session_bundle:session_bundle_pip",
@@ -104,6 +104,7 @@ COMMON_PIP_DEPS = [
     "//tensorflow/python/kernel_tests/testdata:self_adjoint_eig_op_test_files",
     "//tensorflow/python/saved_model:saved_model",
     "//tensorflow/python/tools:tools_pip",
+    "//tensorflow/python/tools/api/generator:create_python_api",
     "//tensorflow/python:test_ops",
     "//tensorflow/tools/dist_test/server:grpc_tensorflow_server",
 ]
@@ -144,7 +145,6 @@ filegroup(
         "@gast_archive//:PKG-INFO",
         "@gemmlowp//:LICENSE",
         "@gif_archive//:COPYING",
-        "@grpc//:LICENSE",
         "@highwayhash//:LICENSE",
         "@jemalloc//:COPYING",
         "@jpeg//:LICENSE.md",
@@ -153,8 +153,6 @@ filegroup(
         "@lmdb//:LICENSE",
         "@local_config_nccl//:LICENSE",
         "@local_config_sycl//sycl:LICENSE.text",
-        "@grpc//third_party/nanopb:LICENSE.txt",
-        "@grpc//third_party/address_sorting:LICENSE",
         "@nasm//:LICENSE",
         "@nsync//:LICENSE",
         "@pcre//:LICENCE",
@@ -168,7 +166,15 @@ filegroup(
         "@org_python_pypi_backports_weakref//:LICENSE",
     ] + if_mkl([
         "//third_party/mkl:LICENSE",
-    ]) + tf_additional_license_deps(),
+        "//third_party/mkl_dnn:LICENSE",
+    ]) + if_not_system_lib(
+        "grpc",
+        [
+            "@grpc//:LICENSE",
+            "@grpc//third_party/nanopb:LICENSE.txt",
+            "@grpc//third_party/address_sorting:LICENSE",
+        ],
+    ) + tf_additional_license_deps(),
 )
 
 sh_binary(
@@ -183,9 +189,7 @@ sh_binary(
             "//tensorflow/contrib/lite/python:tflite_convert",
             "//tensorflow/contrib/lite/toco/python:toco_from_protos",
         ],
-    }) + if_mkl(["//third_party/mkl:intel_binary_blob"]) + if_tensorrt([
-        "//tensorflow/contrib/tensorrt:init_py",
-    ]),
+    }) + if_mkl(["//third_party/mkl:intel_binary_blob"]),
 )
 
 # A genrule for generating a marker file for the pip package on Windows
diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh
index 9e41514cfa1a70d649eab6fd23a599db4afae2a8..ca40f2eaa81128b5091899702f82f69aa7984a07 100755
--- a/tensorflow/tools/pip_package/build_pip_package.sh
+++ b/tensorflow/tools/pip_package/build_pip_package.sh
@@ -17,8 +17,12 @@
 
 set -e
 
+function is_absolute {
+  [[ "$1" = /* ]] || [[ "$1" =~ ^[a-zA-Z]:[/\\].* ]]
+}
+
 function real_path() {
-  [[ $1 = /* ]] && echo "$1" || echo "$PWD/${1#./}"
+  is_absolute "$1" && echo "$1" || echo "$PWD/${1#./}"
 }
 
 function cp_external() {
@@ -27,7 +31,7 @@ function cp_external() {
 
   pushd .
   cd "$src_dir"
-  for f in `find . ! -type d ! -name '*.py' ! -name '*local_config_cuda*' ! -name '*local_config_tensorrt*' ! -name '*org_tensorflow*'`; do
+  for f in `find . ! -type d ! -name '*.py' ! -path '*local_config_cuda*' ! -path '*local_config_tensorrt*' ! -path '*local_config_syslibs*' ! -path '*org_tensorflow*'`; do
     mkdir -p "${dest_dir}/$(dirname ${f})"
     cp "${f}" "${dest_dir}/$(dirname ${f})/"
   done
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index c630ca04b885d35da6550d4e5f3e6912b5fd7a00..1f4c3d47bfe532d12635df0566ed3e6cef5e6a33 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -45,7 +45,7 @@ DOCLINES = __doc__.split('\n')
 # This version string is semver compatible, but incompatible with pip.
 # For pip, we will remove all '-' characters from this string, and use the
 # result for pip.
-_VERSION = '1.9.0-rc0'
+_VERSION = '1.9.0'
 
 REQUIRED_PACKAGES = [
     'absl-py >= 0.1.6',
@@ -55,7 +55,7 @@ REQUIRED_PACKAGES = [
     'six >= 1.10.0',
     'protobuf >= 3.6.0',
     'setuptools <= 39.1.0',
-    'tensorboard >= 1.8.0, < 1.9.0',
+    'tensorboard >= 1.10.0, < 1.11.0',
     'termcolor >= 1.1.0',
 ]
 
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 4982cc26db3d33d4a126a6b4dd22430a2ca37eb5..45b1abeb10de2e22b5c5760e9e248242ad02fbab 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -8,6 +8,7 @@ load("//third_party/git:git_configure.bzl", "git_configure")
 load("//third_party/py:python_configure.bzl", "python_configure")
 
 load("//third_party/sycl:sycl_configure.bzl", "sycl_configure")
+load("//third_party/systemlibs:syslibs_configure.bzl", "syslibs_configure")
 load("//third_party/toolchains/clang6:repo.bzl", "clang6_configure")
 load("//third_party/toolchains/cpus/arm:arm_compiler_configure.bzl", "arm_compiler_configure")
 load("//third_party:repo.bzl", "tf_http_archive")
@@ -35,6 +36,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   nccl_configure(name="local_config_nccl")
   git_configure(name="local_config_git")
   sycl_configure(name="local_config_sycl")
+  syslibs_configure(name="local_config_syslibs")
   python_configure(name="local_config_python")
 
   # For windows bazel build
@@ -142,13 +144,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "ortools_archive",
       urls = [
-          "https://mirror.bazel.build/github.com/google/or-tools/archive/253f7955c6a1fd805408fba2e42ac6d45b312d15.tar.gz",
-          # Please uncomment me, when the next upgrade happens. Then
-          # remove the whitelist entry in third_party/repo.bzl.
-          # "https://github.com/google/or-tools/archive/253f7955c6a1fd805408fba2e42ac6d45b312d15.tar.gz",
+          "https://mirror.bazel.build/github.com/google/or-tools/archive/v6.7.2.tar.gz",
+          "https://github.com/google/or-tools/archive/v6.7.2.tar.gz",
       ],
-      sha256 = "932075525642b04ac6f1b50589f1df5cd72ec2f448b721fd32234cf183f0e755",
-      strip_prefix = "or-tools-253f7955c6a1fd805408fba2e42ac6d45b312d15/src",
+      sha256 = "d025a95f78b5fc5eaa4da5f395f23d11c23cf7dbd5069f1f627f002de87b86b9",
+      strip_prefix = "or-tools-6.7.2/src",
       build_file = clean_dep("//third_party:ortools.BUILD"),
   )
 
@@ -161,16 +161,17 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       ],
       sha256 = "2f945446b71336e7f5a2bcace1abcf0b23fbba368266c6a1be33de3de3b3c912",
       strip_prefix = "re2-2018-04-01",
+      system_build_file = clean_dep("//third_party/systemlibs:re2.BUILD"),
   )
 
   tf_http_archive(
       name = "com_github_googlecloudplatform_google_cloud_cpp",
       urls = [
-          "https://mirror.bazel.build/github.com/GoogleCloudPlatform/google-cloud-cpp/archive/53f822805e77ea7715f5b52c592a162c515c7219.tar.gz",
-          "https://github.com/GoogleCloudPlatform/google-cloud-cpp/archive/53f822805e77ea7715f5b52c592a162c515c7219.tar.gz",
+          "https://mirror.bazel.build/github.com/GoogleCloudPlatform/google-cloud-cpp/archive/f875700a023bdd706333cde45aee8758b272c357.tar.gz",
+          "https://github.com/GoogleCloudPlatform/google-cloud-cpp/archive/f875700a023bdd706333cde45aee8758b272c357.tar.gz",
       ],
-      sha256 = "06853bfca77ef4aec09db5ab48c548f68ef2e18f17404cbce61f8d9b820f951b",
-      strip_prefix = "google-cloud-cpp-53f822805e77ea7715f5b52c592a162c515c7219",
+      sha256 = "a34f3c50b237686dc870b13baaa6a5836ce3473f2f2a02717299f0ff318372db",
+      strip_prefix = "google-cloud-cpp-f875700a023bdd706333cde45aee8758b272c357",
   )
 
   tf_http_archive(
@@ -219,13 +220,14 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "nasm",
       urls = [
-          "https://mirror.bazel.build/www.nasm.us/pub/nasm/releasebuilds/2.12.02/nasm-2.12.02.tar.bz2",
-          "http://pkgs.fedoraproject.org/repo/pkgs/nasm/nasm-2.12.02.tar.bz2/d15843c3fb7db39af80571ee27ec6fad/nasm-2.12.02.tar.bz2",
-          "http://www.nasm.us/pub/nasm/releasebuilds/2.12.02/nasm-2.12.02.tar.bz2",
+          "https://mirror.bazel.build/www.nasm.us/pub/nasm/releasebuilds/2.13.03/nasm-2.13.03.tar.bz2",
+          "http://pkgs.fedoraproject.org/repo/pkgs/nasm/nasm-2.13.03.tar.bz2/sha512/d7a6b4cee8dfd603d8d4c976e5287b5cc542fa0b466ff989b743276a6e28114e64289bf02a7819eca63142a5278aa6eed57773007e5f589e15768e6456a8919d/nasm-2.13.03.tar.bz2",
+          "http://www.nasm.us/pub/nasm/releasebuilds/2.13.03/nasm-2.13.03.tar.bz2",
       ],
-      sha256 = "00b0891c678c065446ca59bcee64719d0096d54d6886e6e472aeee2e170ae324",
-      strip_prefix = "nasm-2.12.02",
+      sha256 = "63ec86477ad3f0f6292325fd89e1d93aea2e2fd490070863f17d48f7cd387011",
+      strip_prefix = "nasm-2.13.03",
       build_file = clean_dep("//third_party:nasm.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:nasm.BUILD"),
   )
 
   tf_http_archive(
@@ -237,6 +239,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       sha256 = "1a17020f859cb12711175a67eab5c71fc1904e04b587046218e36106e07eabde",
       strip_prefix = "libjpeg-turbo-1.5.3",
       build_file = clean_dep("//third_party/jpeg:jpeg.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:jpeg.BUILD"),
   )
 
   tf_http_archive(
@@ -249,17 +252,19 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       strip_prefix = "libpng-1.6.34",
       build_file = clean_dep("//third_party:png.BUILD"),
       patch_file = clean_dep("//third_party:png_fix_rpi.patch"),
+      system_build_file = clean_dep("//third_party/systemlibs:png.BUILD"),
   )
 
   tf_http_archive(
       name = "org_sqlite",
       urls = [
-          "https://mirror.bazel.build/www.sqlite.org/2018/sqlite-amalgamation-3230100.zip",
-          "https://www.sqlite.org/2018/sqlite-amalgamation-3230100.zip",
+          "https://mirror.bazel.build/www.sqlite.org/2018/sqlite-amalgamation-3240000.zip",
+          "https://www.sqlite.org/2018/sqlite-amalgamation-3240000.zip",
       ],
-      sha256 = "4239a1f69e5721d07d9a374eb84d594225229e54be4ee628da2995f4315d8dfc",
-      strip_prefix = "sqlite-amalgamation-3230100",
+      sha256 = "ad68c1216c3a474cf360c7581a4001e952515b3649342100f2d7ca7c8e313da6",
+      strip_prefix = "sqlite-amalgamation-3240000",
       build_file = clean_dep("//third_party:sqlite.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:sqlite.BUILD"),
   )
 
   tf_http_archive(
@@ -271,6 +276,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       sha256 = "34a7377ba834397db019e8eb122e551a49c98f49df75ec3fcc92b9a794a4f6d1",
       strip_prefix = "giflib-5.1.4",
       build_file = clean_dep("//third_party:gif.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:gif.BUILD"),
   )
 
   tf_http_archive(
@@ -282,6 +288,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       sha256 = "105f8d68616f8248e24bf0e9372ef04d3cc10104f1980f54d57b2ce73a5ad56a",
       strip_prefix = "six-1.10.0",
       build_file = clean_dep("//third_party:six.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:six.BUILD"),
   )
 
   tf_http_archive(
@@ -293,6 +300,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       sha256 = "ff6d2e2962d834acb125cc4dcc80c54a8c17c253f4cc9d9c43b5102a560bb75d",
       strip_prefix = "astor-0.6.2",
       build_file = clean_dep("//third_party:astor.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:astor.BUILD"),
   )
 
   tf_http_archive(
@@ -315,6 +323,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       sha256 = "1d6d69ce66211143803fbc56652b41d73b4a400a2891d7bf7a1cdf4c02de613b",
       strip_prefix = "termcolor-1.1.0",
       build_file = clean_dep("//third_party:termcolor.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:termcolor.BUILD"),
   )
 
   tf_http_archive(
@@ -405,11 +414,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "com_github_gflags_gflags",
       urls = [
-          "https://mirror.bazel.build/github.com/gflags/gflags/archive/f8a0efe03aa69b3336d8e228b37d4ccb17324b88.tar.gz",
-          "https://github.com/gflags/gflags/archive/f8a0efe03aa69b3336d8e228b37d4ccb17324b88.tar.gz",
+          "https://mirror.bazel.build/github.com/gflags/gflags/archive/v2.2.1.tar.gz",
+          "https://github.com/gflags/gflags/archive/v2.2.1.tar.gz",
       ],
-      sha256 = "4d222fab8f1ede4709cdff417d15a1336f862d7334a81abf76d09c15ecf9acd1",
-      strip_prefix = "gflags-f8a0efe03aa69b3336d8e228b37d4ccb17324b88",
+      sha256 = "ae27cdbcd6a2f935baa78e4f21f675649271634c092b1be01469440495609d0e",
+      strip_prefix = "gflags-2.2.1",
   )
 
   tf_http_archive(
@@ -421,6 +430,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       ],
       strip_prefix = "pcre-8.42",
       build_file = clean_dep("//third_party:pcre.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:pcre.BUILD"),
   )
 
   tf_http_archive(
@@ -433,6 +443,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       ],
       strip_prefix = "swig-3.0.8",
       build_file = clean_dep("//third_party:swig.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:swig.BUILD"),
   )
 
   tf_http_archive(
@@ -444,6 +455,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       ],
       strip_prefix = "curl-7.60.0",
       build_file = clean_dep("//third_party:curl.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:curl.BUILD"),
   )
 
   tf_http_archive(
@@ -454,9 +466,9 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       ],
       sha256 = "50db9cf2221354485eb7c3bd55a4c27190caef7048a2a1a15fbe60a498f98b44",
       strip_prefix = "grpc-1.13.0",
+      system_build_file = clean_dep("//third_party/systemlibs:grpc.BUILD"),
   )
 
-
   tf_http_archive(
       name = "linenoise",
       sha256 = "7f51f45887a3d31b4ce4fa5965210a5e64637ceac12720cfce7954d6a2e812f7",
@@ -473,11 +485,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "llvm",
       urls = [
-          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/d5d94ca3a7f8526c2e4e5f663f9dc79ae5d39d93.tar.gz",
-	  "https://github.com/llvm-mirror/llvm/archive/d5d94ca3a7f8526c2e4e5f663f9dc79ae5d39d93.tar.gz",
+          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/7b3bfc8151f3a6bcd9642c49c1f86f66cc43a428.tar.gz",
+	  "https://github.com/llvm-mirror/llvm/archive/7b3bfc8151f3a6bcd9642c49c1f86f66cc43a428.tar.gz",
       ],
-      sha256 = "280fdc888e2eb88a3a8cc4e7d3034fffc87f98e3e686be31f8c719c6e5b67d2d",
-      strip_prefix = "llvm-d5d94ca3a7f8526c2e4e5f663f9dc79ae5d39d93",
+      sha256 = "c6cbb21acd46e3e00faa8c379595ecffb99ef77622da17f29371db2bfad1d3d3",
+      strip_prefix = "llvm-7b3bfc8151f3a6bcd9642c49c1f86f66cc43a428",
       build_file = clean_dep("//third_party/llvm:llvm.autogenerated.BUILD"),
   )
 
@@ -490,6 +502,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       sha256 = "f3927859882eb608868c8c31586bb7eb84562a40a6bf5cc3e13b6b564641ea28",
       strip_prefix = "lmdb-LMDB_0.9.22/libraries/liblmdb",
       build_file = clean_dep("//third_party:lmdb.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:lmdb.BUILD"),
   )
 
   tf_http_archive(
@@ -501,6 +514,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       sha256 = "c49deac9e0933bcb7044f08516861a2d560988540b23de2ac1ad443b219afdb6",
       strip_prefix = "jsoncpp-1.8.4",
       build_file = clean_dep("//third_party:jsoncpp.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:jsoncpp.BUILD"),
   )
 
   tf_http_archive(
@@ -522,6 +536,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       sha256 = "c3e5e9fdd5004dcb542feda5ee4f0ff0744628baf8ed2dd5d66f8ca1197cb1a1",
       strip_prefix = "zlib-1.2.11",
       build_file = clean_dep("//third_party:zlib.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:zlib.BUILD"),
   )
 
   tf_http_archive(
@@ -543,6 +558,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       sha256 = "3dfa02e873ff51a11ee02b9ca391807f0c8ea0529a4924afa645fbf97163f9d4",
       strip_prefix = "snappy-1.1.7",
       build_file = clean_dep("//third_party:snappy.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:snappy.BUILD"),
   )
 
   tf_http_archive(
@@ -613,6 +629,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       sha256 = "3c8f25c02e806c3ce0ab5fb7da1817f89fc9732709024e2a81b6b82f7cc792a8",
       strip_prefix = "jemalloc-4.4.0",
       build_file = clean_dep("//third_party:jemalloc.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:jemalloc.BUILD"),
   )
 
   java_import_external(
@@ -683,24 +700,25 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
 
   tf_http_archive(
       name = "cython",
-      sha256 = "6dcd30b5ceb887b2b965ee7ceb82ea3acb5f0642fe2206c7636b45acea4798e5",
+      sha256 = "bccc9aa050ea02595b2440188813b936eaf345e85fb9692790cecfe095cf91aa",
       urls = [
-          "https://mirror.bazel.build/github.com/cython/cython/archive/3732784c45cfb040a5b0936951d196f83a12ea17.tar.gz",
-          "https://github.com/cython/cython/archive/3732784c45cfb040a5b0936951d196f83a12ea17.tar.gz",
+          "https://mirror.bazel.build/github.com/cython/cython/archive/0.28.4.tar.gz",
+          "https://github.com/cython/cython/archive/0.28.4.tar.gz",
       ],
-      strip_prefix = "cython-3732784c45cfb040a5b0936951d196f83a12ea17",
+      strip_prefix = "cython-0.28.4",
       build_file = clean_dep("//third_party:cython.BUILD"),
       delete = ["BUILD.bazel"],
+      system_build_file = clean_dep("//third_party/systemlibs:cython.BUILD"),
   )
 
   tf_http_archive(
       name = "bazel_toolchains",
       urls = [
-          "https://mirror.bazel.build/github.com/bazelbuild/bazel-toolchains/archive/44200e0c026d86c53470d107b3697a3e46469c43.tar.gz",
-          "https://github.com/bazelbuild/bazel-toolchains/archive/44200e0c026d86c53470d107b3697a3e46469c43.tar.gz",
+          "https://mirror.bazel.build/github.com/bazelbuild/bazel-toolchains/archive/37acf1841ab1475c98a152cb9e446460c8ae29e1.tar.gz",
+          "https://github.com/bazelbuild/bazel-toolchains/archive/37acf1841ab1475c98a152cb9e446460c8ae29e1.tar.gz",
       ],
-      strip_prefix = "bazel-toolchains-44200e0c026d86c53470d107b3697a3e46469c43",
-      sha256 = "699b55a6916c687f4b7dc092dbbf5f64672cde0dc965f79717735ec4e5416556",
+      strip_prefix = "bazel-toolchains-37acf1841ab1475c98a152cb9e446460c8ae29e1",
+      sha256 = "3b604699685c5c65dd3f6f17425570a4b2f00ddba2f750db15acc72e55bb098b",
   )
 
   tf_http_archive(
@@ -723,6 +741,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
           "https://github.com/google/flatbuffers/archive/v1.9.0.tar.gz",
       ],
       build_file = clean_dep("//third_party/flatbuffers:flatbuffers.BUILD"),
+      system_build_file = clean_dep("//third_party/systemlibs:flatbuffers.BUILD"),
   )
 
   native.new_http_archive(
diff --git a/third_party/clang_toolchain/download_clang.bzl b/third_party/clang_toolchain/download_clang.bzl
index a014a806a69ecf9d7e43c51daf3672fc5750e706..ab57b9dfa00094bc2eee727ee98009ce41870379 100644
--- a/third_party/clang_toolchain/download_clang.bzl
+++ b/third_party/clang_toolchain/download_clang.bzl
@@ -35,18 +35,18 @@ def download_clang(repo_ctx, out_folder):
 
   # Latest CLANG_REVISION and CLANG_SUB_REVISION of the Chromiums's release
   # can be found in https://chromium.googlesource.com/chromium/src/tools/clang/+/master/scripts/update.py
-  CLANG_REVISION = '335091'
+  CLANG_REVISION = '336424'
   CLANG_SUB_REVISION = 1
 
   package_version = '%s-%s' % (CLANG_REVISION, CLANG_SUB_REVISION)
 
   checksums = {
       'Linux_x64':
-          '17002b75293fccfdd175eacdc9ee47d97b58d7e98fef343384fbbef1b68ce99f',
+          '2ea97e047470da648f5d078af008bce6891287592382cee3d53a1187d996da94',
       'Mac':
-          '9351e46d28315daaa06a1eb55bd0370ed4aaeb693a2a3e82e48d2737d7723468',
+          'c6e28909cce63ee35e0d51284d9f0f6e8838f7fb8b7a0dc9536c2ea900552df0',
       'Win':
-          'e78a1e469224d6f6751b4df4374bf58893ac03900ec924e4c8264888ba4aeb1e',
+          '1299fda7c4378bfb81337f7e5f351c8a1f953f51e0744e2170454b8d722f3db7',
   }
 
   platform_folder = _get_platform_folder(repo_ctx.os.name)
diff --git a/third_party/codegen.BUILD b/third_party/codegen.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..df436c81635a71421a67fa8d8c84eb8dfcc97d7b
--- /dev/null
+++ b/third_party/codegen.BUILD
@@ -0,0 +1,16 @@
+# -*- mode: python; -*-
+#
+# Description:
+#   Extension to ast that allow ast -> python code generation.
+
+package(default_visibility = ["//visibility:public"])
+
+licenses(["notice"])  # New BSD
+
+exports_files(["LICENSE"])
+
+py_library(
+    name = "com_github_andreif_codegen",
+    srcs = glob(["codegen.py"]),
+    srcs_version = "PY2AND3",
+)
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/Core b/third_party/eigen3/unsupported/Eigen/CXX11/Core
deleted file mode 100644
index 1b3690716c03ca635755d920cd3be598cb920c6a..0000000000000000000000000000000000000000
--- a/third_party/eigen3/unsupported/Eigen/CXX11/Core
+++ /dev/null
@@ -1,46 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2013 Christian Seiler 
-// Copyright (C) 2014 Benoit Steiner 
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_CORE_MODULE
-#define EIGEN_CXX11_CORE_MODULE
-
-#include 
-
-#include 
-
-/** \defgroup CXX11_Core_Module C++11 Core Module
-  *
-  * This module provides common core features for all modules that
-  * explicitly depend on C++11. Currently, this is only the Tensor
-  * module. Note that at this stage, you should not need to include
-  * this module directly.
-  *
-  * It also provides a limited fallback for compilers that don't support
-  * CXX11 yet, such as nvcc.
-  *
-  * \code
-  * #include 
-  * \endcode
-  */
-
-// Only a subset of cxx11 is allowed at Google, so we default to emulate the
-// cxx11 functionality that we need.
-#include "src/Core/util/FixedSizeVector.h"
-#if 1
-#include 
-#include "src/Core/util/EmulateCXX11Meta.h"
-#else
-#include "src/Core/util/CXX11Workarounds.h"
-#include "src/Core/util/CXX11Meta.h"
-#endif
-#include 
-
-#endif // EIGEN_CXX11_CORE_MODULE
-
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/NeuralNetworks b/third_party/eigen3/unsupported/Eigen/CXX11/NeuralNetworks
deleted file mode 100644
index 7741b68d8a73dfc738f73e4630b5e2020de50756..0000000000000000000000000000000000000000
--- a/third_party/eigen3/unsupported/Eigen/CXX11/NeuralNetworks
+++ /dev/null
@@ -1,35 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner 
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_NEURAL_NETWORKS_MODULE
-#define EIGEN_CXX11_NEURAL_NETWORKS_MODULE
-
-#include "unsupported/Eigen/CXX11/Tensor"
-
-/** \defgroup CXX11_NeuralNetworks_Module Neural Networks Module
-  *
-  * This module provides an efficient implementation of the common primitives
-  * used by neural networks.
-  * The primitives are  built on top of the tensor library.
-  *
-  * \code
-  * #include 
-  * \endcode
-  */
-
-#include "unsupported/Eigen/CXX11/src/NeuralNetworks/Activations.h"
-#include "unsupported/Eigen/CXX11/src/NeuralNetworks/Attention.h"
-#include "unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h"
-#include "unsupported/Eigen/CXX11/src/NeuralNetworks/SoftMax.h"
-#include "unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h"
-#include "unsupported/Eigen/CXX11/src/NeuralNetworks/CuboidConvolution.h"
-#include "unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h"
-#include "unsupported/Eigen/CXX11/src/NeuralNetworks/SpatialConvolutions.h"
-
-#endif  // EIGEN_CXX11_NEURAL_NETWORKS_MODULE
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/FixedPointTypes.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/FixedPointTypes.h
index 6b625abc3e569ffcd50aa978b3f715024d36cb0b..5ab36649187a41507f1201804090a801d7f639f9 100644
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/FixedPointTypes.h
+++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/FixedPointTypes.h
@@ -7,8 +7,8 @@
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 
-#ifndef EIGEN_CXX11_FIXED_POINT_TYPES_H
-#define EIGEN_CXX11_FIXED_POINT_TYPES_H
+#ifndef CXX11_SRC_FIXEDPOINT_FIXEDPOINTTYPES_H_
+#define CXX11_SRC_FIXEDPOINT_FIXEDPOINTTYPES_H_
 
 #include 
 #include 
@@ -339,4 +339,4 @@ EIGEN_STRONG_INLINE std::ostream& operator<<(std::ostream& os, QInt32 a) {
 
 }  // namespace Eigen
 
-#endif  // EIGEN_CXX11_FIXED_POINT_TYPES_H
+#endif  // CXX11_SRC_FIXEDPOINT_FIXEDPOINTTYPES_H_
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProduct.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProduct.h
index 4d0dca07df05f6a98a13763c53977445a2ffd0ca..e6f4080ae127a93fc7830a8dcded1b74f581188f 100644
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProduct.h
+++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProduct.h
@@ -7,9 +7,8 @@
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 
-#ifndef EIGEN_CXX11_FIXED_POINT_MAT_MAT_PRODUCT_H
-#define EIGEN_CXX11_FIXED_POINT_MAT_MAT_PRODUCT_H
-
+#ifndef CXX11_SRC_FIXEDPOINT_MATMATPRODUCT_H_
+#define CXX11_SRC_FIXEDPOINT_MATMATPRODUCT_H_
 
 namespace Eigen {
 namespace internal {
@@ -24,6 +23,14 @@ template<> struct scalar_product_traits
   typedef QInt32 ReturnType;
 };
 
+// Accumulate the product of 2 QInt16 inputs on 32 bits to prevent
+// overflows
+template <>
+struct scalar_product_traits {
+  enum { Defined = 1 };
+  typedef QInt32 ReturnType;
+};
+
 // Accumulate the product of QInt8 inputs with QUint8 inputs on 32 bits
 // to prevent overflows
 template<> struct scalar_product_traits
@@ -247,9 +254,76 @@ void gebp_kernel
+class gebp_traits {
+ public:
+  typedef QInt16 LhsScalar;
+  typedef QInt16 RhsScalar;
+  typedef QInt32 ResScalar;
+
+  enum {
+    // register block size along the M and N directions
+    // One for the current implementation
+    nr = 1,
+    mr = 1,
+    // Progress made at each iteration of the product loop
+    // also 1 for the current implementation
+    LhsProgress = 1,
+    RhsProgress = 1
+  };
+};
+
+// The signed 16bit Mat-Mat product itself.
+template 
+struct gebp_kernel {
+  EIGEN_DONT_INLINE
+  void operator()(const DataMapper& res, const QInt16* blockA,
+                  const QInt16* blockB, Index rows, Index depth, Index cols,
+                  QInt32 alpha, Index strideA = -1, Index strideB = -1,
+                  Index offsetA = 0, Index offsetB = 0);
+};
+
+template 
+EIGEN_DONT_INLINE void gebp_kernel::
+operator()(const DataMapper& res, const QInt16* blockA, const QInt16* blockB,
+           Index rows, Index depth, Index cols, QInt32 alpha, Index strideA,
+           Index strideB, Index offsetA, Index offsetB) {
+  EIGEN_STATIC_ASSERT(!ConjugateLhs, YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT(!ConjugateRhs, YOU_MADE_A_PROGRAMMING_MISTAKE);
 
+  eigen_assert(alpha.value == 1);
+  eigen_assert(strideA == -1);
+  eigen_assert(strideB == -1);
+  eigen_assert(offsetA == 0);
+  eigen_assert(offsetB == 0);
+
+  eigen_assert(rows > 0);
+  eigen_assert(cols > 0);
+  eigen_assert(depth > 0);
+  eigen_assert(blockA);
+  eigen_assert(blockB);
+
+  for (Index j = 0; j < cols; ++j) {
+    Index startB = j * depth;
 
+    for (Index i = 0; i < rows; ++i) {
+      Index startA = i * depth;
+
+      for (Index k = 0; k < depth; ++k) {
+        res(i, j) += blockA[startA + k] * blockB[startB + k];
+      }
+    }
+  }
+}
+#endif
+
+}  // namespace internal
+}  // namespace Eigen
 
-#endif  // EIGEN_CXX11_FIXED_POINT_MAT_MAT_PRODUCT_H
+#endif  // CXX11_SRC_FIXEDPOINT_MATMATPRODUCT_H_
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProductAVX2.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProductAVX2.h
index 6b4b0edcfb619de4b4118797ae9592ff6f3c2dbf..66532fb60028789df7495bc54c833622187e79bf 100644
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProductAVX2.h
+++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProductAVX2.h
@@ -3,17 +3,493 @@
 //
 // Copyright (C) 2015 Benoit Steiner 
 // Copyright (C) 2015 Matthew Sarett 
+// Copyright (C) 2016 Nishant Patil 
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 
-#ifndef EIGEN_CXX11_FIXED_POINT_MAT_MAT_PRODUCT_AVX2_H
-#define EIGEN_CXX11_FIXED_POINT_MAT_MAT_PRODUCT_AVX2_H
+#ifndef CXX11_SRC_FIXEDPOINT_MATMATPRODUCTAVX2_H_
+#define CXX11_SRC_FIXEDPOINT_MATMATPRODUCTAVX2_H_
 
 namespace Eigen {
 namespace internal {
 
+// AVX2 optimized implementation of Mat-Mat product.
+// LHS is encoded using signed 16-bit integers.
+// RHS is encoded using signed 16-bit integers.
+#ifdef EIGEN_USE_OPTIMIZED_INT16_INT16_MAT_MAT_PRODUCT
+
+// Define quantized traits
+template 
+class gebp_traits {
+ public:
+  typedef QInt16 LhsScalar;
+  typedef QInt16 RhsScalar;
+  typedef QInt32 ResScalar;
+
+  enum {
+    // Define register blocking scheme.
+    nr = 16,
+    mr = 16,
+    kr = 4,
+    // Ignore progress tracking per loop iteration.
+    LhsProgress = -1,
+    RhsProgress = -1
+  };
+};
+
+// Specialized blocking for quantized implementations.
+// Used by TensorContractionThreadPool, inputs must have dimensions that are
+// multiples of 32.
+template 
+class TensorContractionBlocking {
+ public:
+  TensorContractionBlocking(Index k, Index m, Index n, Index num_threads = 1)
+      : kc_(((k + 15) / 16) * 16),
+        mc_(((m + 15) / 16) * 16),
+        nc_(((n + 15) / 16) * 16) {
+    eigen_assert(mc_ % 16 == 0);
+    eigen_assert(kc_ % 16 == 0);
+    if (!k || !m || !n) {
+      return;
+    }
+
+    if (ShardingType == ShardByCol) {
+      eigen_assert(nc_ % 16 == 0);
+      nc_ = (((nc_ / num_threads) + 15) / 16) * 16;
+    } else {
+      eigen_assert(nc_ % 16 == 0);
+      mc_ = (((mc_ / num_threads) + 15) / 16) * 16;
+    }
+  }
+
+  EIGEN_ALWAYS_INLINE Index kc() const { return kc_; }
+  EIGEN_ALWAYS_INLINE Index mc() const { return mc_; }
+  EIGEN_ALWAYS_INLINE Index nc() const { return nc_; }
+
+ private:
+  Index kc_;
+  Index mc_;
+  Index nc_;
+};
+
+// Specialized blocking for quantized implementations.
+// Used by TensorContraction and GeneralMatrixMatrix, inputs are padded to
+// multiples of 32.
+template 
+class gemm_blocking_space
+    : public level3_blocking {
+  DenseIndex m_sizeA;
+  DenseIndex m_sizeB;
+
+ public:
+  gemm_blocking_space(DenseIndex rows, DenseIndex cols, DenseIndex depth,
+                      DenseIndex /*num_threads*/, bool /*l3_blocking*/) {
+    this->m_mc = ((rows + 15) / 16) * 16;
+    this->m_nc = ((cols + 15) / 16) * 16;
+    this->m_kc = ((depth + 15) / 16) * 16;
+    m_sizeA = this->m_mc * this->m_kc;
+    m_sizeB = this->m_kc * this->m_nc;
+  }
+  void allocateA() {
+    if (this->m_blockA == 0) this->m_blockA = aligned_new(m_sizeA);
+  }
+  void allocateB() {
+    if (this->m_blockB == 0) this->m_blockB = aligned_new(m_sizeB);
+  }
+  void allocateAll() {
+    allocateA();
+    allocateB();
+  }
+  ~gemm_blocking_space() {
+    aligned_delete(this->m_blockA, m_sizeA);
+    aligned_delete(this->m_blockB, m_sizeB);
+  }
+};
+
+// Below are the fully optimized versions that are correct only for sizes that
+// are multiple of 16.  It is about a 10% performance benefit to keep these
+// implementations separate.
+
+// Arrange a block of the left input matrix in contiguous memory.
+//
+// Given column major input (A0 beside A1 in memory):
+// A0 B0 C0 D0 E0 F0 G0 H0 ...
+// A1 B1 C1 D1 E1 F1 G1 H1 ...
+// A2 B2 C2 D2 E2 F2 G2 H2 ...
+// A3 B3 C3 D3 E3 F3 G3 H3 ...
+// A4 B4 C4 D4 E4 F4 G4 H4 ...
+// A5 B5 C5 D5 E5 F5 G5 H5 ...
+// A6 B6 C6 D6 E6 F6 G6 H6 ...
+// A7 B7 C7 D7 E7 F7 G7 H7 ...
+// A8 ...
+// ...
+//
+// Packing with m = 8 yields row major output (A0 beside B0 in memory):
+// A0 B0
+// A1 B1
+// A2 B2
+// A3 B3
+// A4 B4
+// A5 B5
+// A6 B6
+// A7 B7
+// ...
+//
+// The purpose is to collect m rows of size k.  Two elements of the same
+// row are arranged contiguously because madd performs an adjacent addition
+// in the kernel.
+
+template 
+struct gemm_pack_lhs {
+  EIGEN_DONT_INLINE void operator()(QInt16* blockA, const DataMapper& lhs,
+                                    Index depth, Index rows, Index stride = 0,
+                                    Index offset = 0);
+};
+
+template 
+EIGEN_DONT_INLINE void gemm_pack_lhs::
+operator()(QInt16* blockA, const DataMapper& lhs, Index depth, Index rows,
+           Index stride, Index offset) {
+  eigen_assert(stride == 0);
+  eigen_assert(offset == 0);
+
+  // Use alternate function for weird sizes
+  if (rows % 16 != 0 || depth % 16 != 0) {
+    assert(false &&
+           "only depths and rows that are a multiple of 16 are currently "
+           "supported");
+    // gemm_pack_lhs_any lhs_pack;
+    // return lhs_pack(blockA, lhs, depth, rows, stride, offset);
+  }
+
+  // Get vector pointer
+  __m256i* blockA_256 = reinterpret_cast<__m256i*>(blockA);
+
+  // Pack rows in sets of 16
+  for (Index m = 0; m < rows; m += 16) {
+    // Pack depth in sets of 4
+    for (Index k = 0; k < depth; k += 4) {
+      // Load vectors
+      __m256i L_A = lhs.loadPacket(m, k);
+      __m256i L_B = lhs.loadPacket(m, k + 1);
+      __m256i L_C = lhs.loadPacket(m, k + 2);
+      __m256i L_D = lhs.loadPacket(m, k + 3);
+
+      // Rearrange the inputs as required by the kernel
+      __m256i L_AB0_AB7 = _mm256_unpacklo_epi16(L_A, L_B);
+      __m256i L_AB8_AB15 = _mm256_unpackhi_epi16(L_A, L_B);
+      __m256i L_CD0_CD7 = _mm256_unpacklo_epi16(L_C, L_D);
+      __m256i L_CD8_CD15 = _mm256_unpackhi_epi16(L_C, L_D);
+
+      __m256i L_AD0 = _mm256_permute2x128_si256(L_AB0_AB7, L_AB8_AB15, 0x20);
+      _mm256_store_si256(blockA_256++, L_AD0);
+      __m256i L_AD8 = _mm256_permute2x128_si256(L_CD0_CD7, L_CD8_CD15, 0x20);
+      _mm256_store_si256(blockA_256++, L_AD8);
+      __m256i L_AD16 = _mm256_permute2x128_si256(L_AB0_AB7, L_AB8_AB15, 0x31);
+      _mm256_store_si256(blockA_256++, L_AD16);
+      __m256i L_AD24 = _mm256_permute2x128_si256(L_CD0_CD7, L_CD8_CD15, 0x31);
+      _mm256_store_si256(blockA_256++, L_AD24);
+    }
+  }
+}
+
+// Arrange a block of the right input matrix in contiguous memory.
+//
+// Given column major input (A0 beside A1 in memory):
+// A0 B0 C0 D0 E0 F0 G0 H0 ...
+// A1 B1 C1 D1 E1 F1 G1 H1 ...
+// A2 B2 C2 D2 E2 F2 G2 H2 ...
+// A3 B3 C3 D3 E3 F3 G3 H3 ...
+// A4 B4 C4 D4 E4 F4 G4 H4 ...
+// A5 B5 C5 D5 E5 F5 G5 H5 ...
+// A6 B6 C6 D6 E6 F6 G6 H6 ...
+// A7 B7 C7 D7 E7 F7 G7 H7 ...
+// A8 ...
+// ...
+// Packing yields row major output (A0 beside A1 in memory):
+// A0 A1 A2 A3 A4 A5 A6 A7
+// B0 B1 B2 B3 B4 B5 B6 B7
+// ...
+//
+// At least two elements of the same col are arranged contiguously because
+// maddubs and madd both perform an adjacent addition in the kernel.  We can
+// save work by leaving 4 adjacent elements because kr = 4.
+// The purpose is to collect n cols of size k.  Two elements of the same
+// col are arranged contiguously because madd performs an adjacent addition
+// in the kernel.
+template 
+struct gemm_pack_rhs {
+  EIGEN_DONT_INLINE void operator()(QInt16* blockB, const DataMapper& rhs,
+                                    Index depth, Index cols, Index stride = 0,
+                                    Index offset = 0);
+};
+
+template 
+EIGEN_DONT_INLINE void
+gemm_pack_rhs::
+operator()(QInt16* blockB, const DataMapper& rhs, Index depth, Index cols,
+           Index stride, Index offset) {
+  eigen_assert(stride == 0);
+  eigen_assert(offset == 0);
+
+  // Use alternate function for weird sizes
+  if (cols % 16 != 0 || depth % 16 != 0) {
+    assert(false &&
+           "only depths and cols that are a multiple of 16 are currently "
+           "supported");
+    // gemm_pack_rhs_any rhs_pack;
+    // return rhs_pack(blockB, rhs, depth, cols, stride, offset);
+  }
+
+  // Get vector pointer
+  __m256i* blockB_256 = reinterpret_cast<__m256i*>(blockB);
+
+  // Perform a step of the packing for 4 columns
+  __m256i R_AB_L, R_AB_H, R_CD_L, R_CD_H, R_AD_0, R_AD_4, R_AD_8, R_AD_12;
+#define PACK_STEP                                            \
+  R_AB_L = _mm256_unpacklo_epi64(R_A, R_B);                  \
+  R_CD_L = _mm256_unpacklo_epi64(R_C, R_D);                  \
+  R_AB_H = _mm256_unpackhi_epi64(R_A, R_B);                  \
+  R_CD_H = _mm256_unpackhi_epi64(R_C, R_D);                  \
+  R_AD_0 = _mm256_permute2x128_si256(R_AB_L, R_CD_L, 0x20);  \
+  R_AD_8 = _mm256_permute2x128_si256(R_AB_L, R_CD_L, 0x31);  \
+  R_AD_4 = _mm256_permute2x128_si256(R_AB_H, R_CD_H, 0x20);  \
+  R_AD_12 = _mm256_permute2x128_si256(R_AB_H, R_CD_H, 0x31); \
+  _mm256_store_si256(blockB_256, R_AD_0);                    \
+  _mm256_store_si256(blockB_256 + 4, R_AD_4);                \
+  _mm256_store_si256(blockB_256 + 8, R_AD_8);                \
+  _mm256_store_si256(blockB_256 + 12, R_AD_12);              \
+  blockB_256++;
+
+  // Pack cols in sets of 16
+  for (Index n = 0; n < cols; n += 16) {
+    // Pack depth in sets of 16
+    for (Index k = 0; k < depth; k += 16) {
+      __m256i R_A = rhs.loadPacket(k, n);
+      __m256i R_B = rhs.loadPacket(k, n + 1);
+      __m256i R_C = rhs.loadPacket(k, n + 2);
+      __m256i R_D = rhs.loadPacket(k, n + 3);
+      PACK_STEP;
+
+      R_A = rhs.loadPacket(k, n + 4);
+      R_B = rhs.loadPacket(k, n + 5);
+      R_C = rhs.loadPacket(k, n + 6);
+      R_D = rhs.loadPacket(k, n + 7);
+      PACK_STEP;
+
+      R_A = rhs.loadPacket(k, n + 8);
+      R_B = rhs.loadPacket(k, n + 9);
+      R_C = rhs.loadPacket(k, n + 10);
+      R_D = rhs.loadPacket(k, n + 11);
+      PACK_STEP;
+
+      R_A = rhs.loadPacket(k, n + 12);
+      R_B = rhs.loadPacket(k, n + 13);
+      R_C = rhs.loadPacket(k, n + 14);
+      R_D = rhs.loadPacket(k, n + 15);
+      PACK_STEP;
+
+      blockB_256 += 12;
+    }
+  }
+#undef PACK_STEP
+}
+
+// Perform the actual multiplication on packed inputs
+template 
+struct gebp_kernel {
+  typedef typename DataMapper::LinearMapper LinearMapper;
+
+  EIGEN_DONT_INLINE
+  void operator()(const DataMapper& res, const QInt16* blockA,
+                  const QInt16* blockB, Index rows, Index depth, Index cols,
+                  QInt32 alpha, Index strideA = -1, Index strideB = -1,
+                  Index offsetA = 0, Index offsetB = 0);
+};
+
+template 
+EIGEN_DONT_INLINE void gebp_kernel::
+operator()(const DataMapper& res, const QInt16* blockA, const QInt16* blockB,
+           Index rows, Index depth, Index cols, QInt32 alpha, Index strideA,
+           Index strideB, Index offsetA, Index offsetB) {
+  EIGEN_STATIC_ASSERT(!ConjugateLhs, YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT(!ConjugateRhs, YOU_MADE_A_PROGRAMMING_MISTAKE);
+  eigen_assert(alpha.value == 1);
+  eigen_assert(strideA == -1);
+  eigen_assert(strideB == -1);
+  eigen_assert(offsetA == 0);
+  eigen_assert(offsetB == 0);
+  eigen_assert(rows > 0);
+  eigen_assert(cols > 0);
+  eigen_assert(depth > 0);
+  eigen_assert(blockA);
+  eigen_assert(blockB);
+
+  // Use alternate function for weird sizes
+  if (rows % 16 != 0 || cols % 16 != 0 || depth % 16 != 0) {
+    assert(false &&
+           "only depths, cols and rows that are a multiple of 16 are currently "
+           "supported");
+    // gebp_kernel_any gebp;
+    // return gebp(res, blockA, blockB, rows, depth, cols, alpha, strideA,
+    // strideB, offsetA, offsetB);
+  }
+
+  // Create result block
+  QInt32* blockO = aligned_new(16 * 16);
+  memset(blockO, 0, 16 * 16 * sizeof(QInt32));
+
+  // Get vectorized pointers
+  __m256i* blockO_256 = reinterpret_cast<__m256i*>(blockO);
+  const __m256i* blockA_256 = reinterpret_cast(blockA);
+  const __m256i* blockB_256 = reinterpret_cast(blockB);
+
+  // Loop over blocks of 16 columns
+  for (Index n = 0; n < cols; n += 16) {
+    // Reset index into blockA
+    Index indexL = 0;
+    // Loop over blocks of 16 rows
+    for (Index m = 0; m < rows; m += 16) {
+      // Reset index into blockB
+      Index indexR = n / 16 * depth;
+      // Loop over blocks of 4 on depth
+      for (Index k = 0; k < depth; k += 4) {
+        // Load inputs
+        __m256i L_AD0 = blockA_256[indexL++];
+        __m256i L_AD8 = blockA_256[indexL++];
+        __m256i L_EH0 = blockA_256[indexL++];
+        __m256i L_EH8 = blockA_256[indexL++];
+
+        __m256i R_AH0 = blockB_256[indexR++];
+        __m256i R_AH4 = blockB_256[indexR++];
+        __m256i R_AH8 = blockB_256[indexR++];
+        __m256i R_AH12 = blockB_256[indexR++];
+
+        // Declare variables used in COMPUTE_STEP
+        __m256i P_32_A, P_32_B, P_32;
+
+#define COMPUTE_STEP(R_INPUT_A, R_INPUT_B, OFFSET)                         \
+  P_32_A = _mm256_madd_epi16(R_INPUT_A, L_AD0);                            \
+  P_32_B = _mm256_madd_epi16(R_INPUT_B, L_AD8);                            \
+  P_32 = _mm256_add_epi32(P_32_A, P_32_B);                                 \
+  _mm256_store_si256(                                                      \
+      blockO_256 + 2 * OFFSET,                                             \
+      _mm256_add_epi32(_mm256_load_si256(blockO_256 + 2 * OFFSET), P_32)); \
+                                                                           \
+  P_32_A = _mm256_madd_epi16(R_INPUT_A, L_EH0);                            \
+  P_32_B = _mm256_madd_epi16(R_INPUT_B, L_EH8);                            \
+  P_32 = _mm256_add_epi32(P_32_A, P_32_B);                                 \
+  _mm256_store_si256(                                                      \
+      blockO_256 + 2 * OFFSET + 1,                                         \
+      _mm256_add_epi32(_mm256_load_si256(blockO_256 + 2 * OFFSET + 1), P_32));
+
+        // Permute and shuffle to copy a single value across the entire vector
+        // Then compute the multiplication
+        // Replicate lower 128-bits of R_AH0 across both lanes
+        __m256i R_AH0_ = _mm256_permute2x128_si256(R_AH0, R_AH0, 0x00);
+        // Copy first two elements of R_AH0 across entire vector
+        __m256i R_AD0 = _mm256_shuffle_epi32(R_AH0_, 0x00);
+        // Copy second two elements of R_AH0 across entire vector
+        __m256i R_EH0 = _mm256_shuffle_epi32(R_AH0_, 0x55);
+
+        COMPUTE_STEP(R_AD0, R_EH0, 0);
+        __m256i R_AD1 = _mm256_shuffle_epi32(R_AH0_, 0xAA);
+        __m256i R_EH1 = _mm256_shuffle_epi32(R_AH0_, 0xFF);
+        COMPUTE_STEP(R_AD1, R_EH1, 1);
+
+        // Replicate upper 128-bits of R_AH0 across both lanes
+        R_AH0_ = _mm256_permute2x128_si256(R_AH0, R_AH0, 0x11);
+        __m256i R_AD2 = _mm256_shuffle_epi32(R_AH0_, 0x00);
+        __m256i R_EH2 = _mm256_shuffle_epi32(R_AH0_, 0x55);
+        COMPUTE_STEP(R_AD2, R_EH2, 2);
+        __m256i R_AD3 = _mm256_shuffle_epi32(R_AH0_, 0xAA);
+        __m256i R_EH3 = _mm256_shuffle_epi32(R_AH0_, 0xFF);
+        COMPUTE_STEP(R_AD3, R_EH3, 3);
+
+        R_AH0_ = _mm256_permute2x128_si256(R_AH4, R_AH4, 0x00);
+        R_AD0 = _mm256_shuffle_epi32(R_AH0_, 0x00);
+        R_EH0 = _mm256_shuffle_epi32(R_AH0_, 0x55);
+        COMPUTE_STEP(R_AD0, R_EH0, 4);
+        R_AD1 = _mm256_shuffle_epi32(R_AH0_, 0xAA);
+        R_EH1 = _mm256_shuffle_epi32(R_AH0_, 0xFF);
+        COMPUTE_STEP(R_AD1, R_EH1, 5);
+        R_AH0_ = _mm256_permute2x128_si256(R_AH4, R_AH4, 0x11);
+        R_AD2 = _mm256_shuffle_epi32(R_AH0_, 0x00);
+        R_EH2 = _mm256_shuffle_epi32(R_AH0_, 0x55);
+        COMPUTE_STEP(R_AD2, R_EH2, 6);
+        R_AD3 = _mm256_shuffle_epi32(R_AH0_, 0xAA);
+        R_EH3 = _mm256_shuffle_epi32(R_AH0_, 0xFF);
+        COMPUTE_STEP(R_AD3, R_EH3, 7);
+
+        R_AH0_ = _mm256_permute2x128_si256(R_AH8, R_AH8, 0x00);
+        R_AD0 = _mm256_shuffle_epi32(R_AH0_, 0x00);
+        R_EH0 = _mm256_shuffle_epi32(R_AH0_, 0x55);
+        COMPUTE_STEP(R_AD0, R_EH0, 8);
+        R_AD1 = _mm256_shuffle_epi32(R_AH0_, 0xAA);
+        R_EH1 = _mm256_shuffle_epi32(R_AH0_, 0xFF);
+        COMPUTE_STEP(R_AD1, R_EH1, 9);
+        R_AH0_ = _mm256_permute2x128_si256(R_AH8, R_AH8, 0x11);
+        R_AD2 = _mm256_shuffle_epi32(R_AH0_, 0x00);
+        R_EH2 = _mm256_shuffle_epi32(R_AH0_, 0x55);
+        COMPUTE_STEP(R_AD2, R_EH2, 10);
+        R_AD3 = _mm256_shuffle_epi32(R_AH0_, 0xAA);
+        R_EH3 = _mm256_shuffle_epi32(R_AH0_, 0xFF);
+        COMPUTE_STEP(R_AD3, R_EH3, 11);
+
+        R_AH0_ = _mm256_permute2x128_si256(R_AH12, R_AH12, 0x00);
+        R_AD0 = _mm256_shuffle_epi32(R_AH0_, 0x00);
+        R_EH0 = _mm256_shuffle_epi32(R_AH0_, 0x55);
+        COMPUTE_STEP(R_AD0, R_EH0, 12);
+        R_AD1 = _mm256_shuffle_epi32(R_AH0_, 0xAA);
+        R_EH1 = _mm256_shuffle_epi32(R_AH0_, 0xFF);
+        COMPUTE_STEP(R_AD1, R_EH1, 13);
+        R_AH0_ = _mm256_permute2x128_si256(R_AH12, R_AH12, 0x11);
+        R_AD2 = _mm256_shuffle_epi32(R_AH0_, 0x00);
+        R_EH2 = _mm256_shuffle_epi32(R_AH0_, 0x55);
+        COMPUTE_STEP(R_AD2, R_EH2, 14);
+        R_AD3 = _mm256_shuffle_epi32(R_AH0_, 0xAA);
+        R_EH3 = _mm256_shuffle_epi32(R_AH0_, 0xFF);
+        COMPUTE_STEP(R_AD3, R_EH3, 15);
+
+#undef COMPUTE_STEP
+      }
+
+      // Transfer the results to the result matrix
+      Index i = 0;
+      for (Index j = n; j < n + 16; j++) {
+        LinearMapper r0 = res.getLinearMapper(m, j);
+        LinearMapper r1 = res.getLinearMapper(m + 8, j);
+
+        r0.storePacket(0, _mm256_add_epi32(blockO_256[i++], r0.loadPacket(0)));
+        r1.storePacket(0, _mm256_add_epi32(blockO_256[i++], r1.loadPacket(0)));
+      }
+
+      // Zero the result block so it can be reused
+      memset(blockO, 0, 16 * 16 * sizeof(QInt32));
+    }
+  }
+  aligned_delete(blockO, 16 * 16);
+}
+
+#endif
+
 // AVX2 optimized implementation of Mat-Mat product.
 // LHS is encoded using signed 8-bit integers.
 // RHS is encoded using unsigned 8-bit integers.
@@ -1751,4 +2227,4 @@ void gebp_kernel
+struct general_matrix_vector_product {
+  EIGEN_DONT_INLINE static void run(Index rows, Index cols,
+                                    const LhsMapper& lhs, const RhsMapper& rhs,
+                                    QInt32* res, Index resIncr, QInt16 alpha);
+};
+
+template 
+EIGEN_DONT_INLINE void general_matrix_vector_product<
+    Index, QInt16, LhsMapper, ColMajor, ConjugateLhs, QInt16, RhsMapper,
+    ConjugateRhs, Version>::run(Index rows, Index cols, const LhsMapper& lhs,
+                                const RhsMapper& rhs, QInt32* res,
+                                Index resIncr, QInt16 alpha) {
+  eigen_assert(alpha.value == 1);
+  eigen_assert(resIncr == 1);
+  eigen_assert(rows > 0);
+  eigen_assert(cols > 0);
+
+  for (Index i = 0; i < rows; ++i) {
+    for (Index j = 0; j < cols; ++j) {
+      res[i] += lhs(i, j) * rhs(j, 0);
+    }
+  }
+}
 
 // Mat-Vec product
 // The lhs is encoded using 8bit signed integers, the rhs using 8bit unsigned integers
@@ -118,6 +147,4 @@ EIGEN_DONT_INLINE void general_matrix_vector_product
@@ -29,7 +28,6 @@ inline int _mm256_extract_epi8_N1(const __m256i X)
 	return _mm_extract_epi8(_mm256_extractf128_si256((X), 1 >> 4), 1 % 16);
 }
 
-
 namespace Eigen {
 namespace internal {
 
@@ -502,4 +500,4 @@ struct functor_traits> {
 }  // end namespace internal
 }  // end namespace Eigen
 
-#endif  // EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_PACKETMATHAVX2_H_
+#endif  // CXX11_SRC_FIXEDPOINT_PACKETMATHAVX2_H_
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/PacketMathAVX512.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/PacketMathAVX512.h
index 8f9906dbf9c0c9dd8e61964c65b36e8549a3241a..2092ce1d4c92754ce52b78f6a6e5fe814d4b7aaa 100644
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/PacketMathAVX512.h
+++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/PacketMathAVX512.h
@@ -1,5 +1,5 @@
-#ifndef EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_PACKETMATHAVX512_H_
-#define EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_PACKETMATHAVX512_H_
+#ifndef CXX11_SRC_FIXEDPOINT_PACKETMATHAVX512_H_
+#define CXX11_SRC_FIXEDPOINT_PACKETMATHAVX512_H_
 
 #include "PacketMathAVX2.h"
 
@@ -542,4 +542,4 @@ EIGEN_STRONG_INLINE QInt8 predux_max(const Packet64q8i& a) {
 }  // end namespace internal
 }  // end namespace Eigen
 
-#endif  // EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_PACKETMATHAVX512_H_
+#endif  // CXX11_SRC_FIXEDPOINT_PACKETMATHAVX512_H_
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX2.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX2.h
index 7b4ecc752ff2e6b4544a0071fc0a971c6e9879a4..9561d6a3388d69f598a61220b1dfc29d068b8eeb 100644
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX2.h
+++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX2.h
@@ -1,5 +1,5 @@
-#ifndef EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX2_H_
-#define EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX2_H_
+#ifndef CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX2_H_
+#define CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX2_H_
 
 namespace Eigen {
 namespace internal {
@@ -52,8 +52,16 @@ template <>
 EIGEN_STRONG_INLINE Packet32q8u
 pcast(const Packet8q32i& a, const Packet8q32i& b,
                                 const Packet8q32i& c, const Packet8q32i& d) {
+  // _mm256_packus_epi32 trims negative numbers to 0 but we can't allow numbers
+  // that are too large because _mm256_packus_epi16 expects signed input
+  // (example of problem input: 0x11111111, which saturates to 0xffff = -1,
+  // which saturates to 0).
+  const __m256i a_clip = _mm256_min_epi32(a, _mm256_set1_epi32(255));
+  const __m256i b_clip = _mm256_min_epi32(b, _mm256_set1_epi32(255));
+  const __m256i c_clip = _mm256_min_epi32(c, _mm256_set1_epi32(255));
+  const __m256i d_clip = _mm256_min_epi32(d, _mm256_set1_epi32(255));
   const __m256i converted = _mm256_packus_epi16(
-      _mm256_packs_epi32(a.val, b.val), _mm256_packs_epi32(c.val, d.val));
+      _mm256_packus_epi32(a_clip, b_clip), _mm256_packus_epi32(c_clip, d_clip));
   // Since packus does not cross 128 bit lane boundaries,
   // we have to permute to properly order the final result.
   const __m256i permute_mask = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0);
@@ -63,4 +71,4 @@ pcast(const Packet8q32i& a, const Packet8q32i& b,
 }  // end namespace internal
 }  // end namespace Eigen
 
-#endif  // EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX2_H_
+#endif  // CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX2_H_
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX512.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX512.h
index 26735743d487cbc4b50a744ede463f4eac6070a8..a09eac67070477ad4b7ad7fd041800d1d815cac3 100644
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX512.h
+++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX512.h
@@ -1,5 +1,5 @@
-#ifndef EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX512_H_
-#define EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX512_H_
+#ifndef CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX512_H_
+#define CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX512_H_
 
 namespace Eigen {
 namespace internal {
@@ -132,8 +132,15 @@ pcast(const Packet16q32i& a,
                                  const Packet16q32i& b,
                                  const Packet16q32i& c,
                                  const Packet16q32i& d) {
-  __m512i converted = _mm512_packs_epi16(_mm512_packs_epi32(a.val, b.val),
-                                         _mm512_packs_epi32(c.val, d.val));
+  __m128i a_part = _mm512_cvtsepi32_epi8(a);
+  __m128i b_part = _mm512_cvtsepi32_epi8(b);
+  __m128i c_part = _mm512_cvtsepi32_epi8(c);
+  __m128i d_part = _mm512_cvtsepi32_epi8(d);
+  __m256i ab =
+      _mm256_inserti128_si256(_mm256_castsi128_si256(a_part), b_part, 1);
+  __m256i cd =
+      _mm256_inserti128_si256(_mm256_castsi128_si256(c_part), d_part, 1);
+  __m512i converted = _mm512_inserti64x4(_mm512_castsi256_si512(ab), cd, 1);
   return converted;
 }
 
@@ -141,7 +148,10 @@ template <>
 EIGEN_STRONG_INLINE Packet32q16i
 pcast(const Packet16q32i& a,
                                   const Packet16q32i& b) {
-  __m512i converted = _mm512_packs_epi32(a.val, b.val);
+  __m256i a_part = _mm512_cvtsepi32_epi16(a);
+  __m256i b_part = _mm512_cvtsepi32_epi16(b);
+  __m512i converted =
+      _mm512_inserti64x4(_mm512_castsi256_si512(a_part), b_part, 1);
   return converted;
 }
 
@@ -154,22 +164,45 @@ template <>
 EIGEN_STRONG_INLINE Packet64q8u
 pcast(const Packet16q32i& a, const Packet16q32i& b,
                                  const Packet16q32i& c, const Packet16q32i& d) {
-  const __m512i converted = _mm512_packus_epi16(
-      _mm512_packus_epi32(a.val, b.val), _mm512_packus_epi32(c.val, d.val));
+  // Brute-force saturation since there isn't a pack operation for unsigned
+  // numbers that keeps the elements in order.
+  __m128i a_part = _mm512_cvtepi32_epi8(_mm512_max_epi32(
+      _mm512_min_epi32(a, _mm512_set1_epi32(255)), _mm512_setzero_si512()));
+  __m128i b_part = _mm512_cvtepi32_epi8(_mm512_max_epi32(
+      _mm512_min_epi32(b, _mm512_set1_epi32(255)), _mm512_setzero_si512()));
+  __m128i c_part = _mm512_cvtepi32_epi8(_mm512_max_epi32(
+      _mm512_min_epi32(c, _mm512_set1_epi32(255)), _mm512_setzero_si512()));
+  __m128i d_part = _mm512_cvtepi32_epi8(_mm512_max_epi32(
+      _mm512_min_epi32(d, _mm512_set1_epi32(255)), _mm512_setzero_si512()));
+  __m256i ab =
+      _mm256_inserti128_si256(_mm256_castsi128_si256(a_part), b_part, 1);
+  __m256i cd =
+      _mm256_inserti128_si256(_mm256_castsi128_si256(c_part), d_part, 1);
+  __m512i converted = _mm512_inserti64x4(_mm512_castsi256_si512(ab), cd, 1);
   return converted;
 }
 
+#if 0
+// The type Packet32q16u does not exist for AVX-512 yet
 template <>
 struct type_casting_traits {
   enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
 };
 
-#if 0
 template <>
 EIGEN_STRONG_INLINE Packet32q16u
 pcast(const Packet16q32i& a,
                                   const Packet16q32i& b) {
-  const __m512i converted = _mm512_packus_epi32(a.val, b.val);
+  // Brute-force saturation since there isn't a pack operation for unsigned
+  // numbers that keeps the elements in order.
+  __m256i a_part =
+      _mm512_cvtepi32_epi16(_mm512_max_epi32(
+        _mm512_min_epi32(a, _mm512_set1_epi32(65535)), _mm512_setzero_si512()));
+  __m256i b_part = _mm512_cvtepi32_epi16(
+    _mm512_max_epi32(_mm512_min_epi32(b, _mm512_set1_epi32(65535)),
+                     _mm512_setzero_si512()));
+  __m512i converted =
+      _mm512_inserti64x4(_mm512_castsi256_si512(a_part), b_part, 1);
   return converted;
 }
 #endif
@@ -177,4 +210,4 @@ pcast(const Packet16q32i& a,
 }  // end namespace internal
 }  // end namespace Eigen
 
-#endif  // EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX512_H_
+#endif  // CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX512_H_
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Activations.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Activations.h
deleted file mode 100644
index cbcce9e282685b94842dfcc9cce0e3c5962086f7..0000000000000000000000000000000000000000
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Activations.h
+++ /dev/null
@@ -1,116 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Benoit Steiner 
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-#ifndef EIGEN_CXX11_NEURAL_NETWORKS_ACTIVATIONS_H
-#define EIGEN_CXX11_NEURAL_NETWORKS_ACTIVATIONS_H
-
-namespace Eigen {
-
-/** scalar_sigmoid_fast_derivative_op
-  * \ingroup CXX11_NeuralNetworks_Module
-  * \brief Template functor to compute the fast derivative of a sigmoid
-  *
-  * Input should be the backpropagated gradient.
-  *
-  * \sa class CwiseUnaryOp, Cwise::sigmoid_fast_derivative()
-  */
-template 
-struct scalar_sigmoid_fast_derivative_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sigmoid_fast_derivative_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& y) const {
-    const T one = T(1);
-    return (one - y) * y;
-  }
-
-  template 
-  inline Packet packetOp(const Packet& y) const {
-    const Packet one = internal::pset1(1);
-    return internal::pmul(internal::psub(one, y), y);
-  }
-};
-
-namespace internal {
-template 
-struct functor_traits > {
-  enum {
-    Cost = NumTraits::AddCost * 2 + NumTraits::MulCost,
-    PacketAccess = packet_traits::HasAdd && packet_traits::HasMul &&
-                   packet_traits::HasNegate
-  };
-};
-}  // namespace internal
-
-/** scalar_tanh_fast_derivative_op
-  * \ingroup CXX11_NeuralNetworks_Module
-  * \brief Template functor to compute the fast derivative of a tanh
-  *
-  * Input should be the backpropagated gradient.
-  *
-  * \sa class CwiseUnaryOp, Cwise::tanh_fast_derivative()
-  */
-template 
-struct scalar_tanh_fast_derivative_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_tanh_fast_derivative_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& y) const {
-    const T one = T(1);
-    return one - (y * y);
-  }
-
-  template 
-  inline Packet packetOp(const Packet& y) const {
-    const Packet one = internal::pset1(1);
-    return internal::psub(one, internal::pmul(y, y));
-  }
-};
-
-namespace internal {
-template 
-struct functor_traits > {
-  enum {
-    Cost = NumTraits::AddCost * 2 + NumTraits::MulCost * 1,
-    PacketAccess = packet_traits::HasAdd && packet_traits::HasMul &&
-                   packet_traits::HasNegate
-  };
-};
-}  // namespace internal
-
-/**
- * \ingroup CXX11_NeuralNetworks_Module
- * \brief Template functor to clip the magnitude of the first scalar.
- *
- * \sa class CwiseBinaryOp, MatrixBase::Clip
- */
-template 
-struct scalar_clip_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_clip_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar
-  operator()(const Scalar& a, const Scalar& b) const {
-    return numext::mini(numext::maxi(a, -b), b);
-  }
-  template 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet
-  packetOp(const Packet& a, const Packet& b) const {
-    return internal::pmin(internal::pmax(a, internal::pnegate(b)), b);
-  }
-};
-
-namespace internal {
-template 
-struct functor_traits > {
-  enum {
-    Cost = NumTraits::AddCost * 3,
-    PacketAccess = packet_traits::HasMax &&
-                   packet_traits::HasMin &&
-                   packet_traits::HasNegate
-  };
-};
-}  // namespace internal
-
-}  // end namespace Eigen
-
-#endif  // EIGEN_CXX11_NEURAL_NETWORKS_ACTIVATIONS_H
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Attention.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Attention.h
deleted file mode 100644
index d4bc7a3515a91fc5048a811fd710507cd7692e66..0000000000000000000000000000000000000000
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Attention.h
+++ /dev/null
@@ -1,209 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Benoit Steiner 
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-#ifndef EIGEN_CXX11_NEURAL_NETWORKS_ATTENTION_H
-#define EIGEN_CXX11_NEURAL_NETWORKS_ATTENTION_H
-
-namespace Eigen {
-
-/** ExtractGlimpses
-  * \ingroup CXX11_NeuralNetworks_Module
-  *
-  * \brief Extract glimpses from an input tensor.
-  *
-  * The input parameter is expected to be a col-major tensor with a rank of 4 (depth, x, y, and batch).
-  * The width and height parameters specify the extension of the returned glimpses.
-  * The offsets parameter specifies the x, y locations of the center of the glimpses relative to the center of the input image. The vector is expected to contain one IndexPair for each image in the batch dimension.
-  * The normalized boolean indicates if incoming coordinates are normalized so that 0.0 and 1.0 correspond to the minimum and maximum of each height and width dimension.
-  * The centered boolean indicates if incoming coordinates are centered relative to the image, in which case -1.0 and 1.0 correspond to minimum and maximum of each dimension while 0.0 corresponds to the center.
-  *
-  * The result can be assigned to a tensor of rank equal to that of the input. The result will be laid out in col-major order (depth, x, y, batch).
-  * The dimensions of the result will be equal to the dimensions of the input except for width and height which will be equal to the requested glimpse size.
-  */
-namespace {
-template 
-struct GlimpseExtractionOp {
-  GlimpseExtractionOp(const Index width, const Index height,
-                      const std::vector >& offsets,
-                      const bool normalized,
-                      const bool centered,
-                      const bool uniform_noise) :
-      width_(width), height_(height), offsets_(offsets),
-      normalized_(normalized), centered_(centered), uniform_noise_(uniform_noise) { }
-
-  template 
-  DSizes dimensions(const Input& input) const {
-    typedef typename internal::traits::Index IndexType;
-    typedef TensorRef::Scalar, 4,
-                             internal::traits::Layout, IndexType> > Ref;
-    Ref in(input);
-
-    DSizes dims = in.dimensions();
-
-    dims[0] = in.dimension(0);
-    dims[1] = width_;
-    dims[2] = height_;
-    dims[3] = in.dimension(3);
-    return dims;
-  }
-
-  template 
-  EIGEN_DEVICE_FUNC
-  void eval(const Input& input, Output& output, const Device& device) const
-  {
-    typedef typename internal::traits::Index IndexType;
-    typedef TensorRef::Scalar, 4,
-                             internal::traits::Layout, IndexType> > Ref;
-    Ref in(input);
-
-    const Index num_channels = in.dimension(0);
-    const Index input_width = in.dimension(1);
-    const Index input_height = in.dimension(2);
-    const Index batch_size = in.dimension(3);
-    eigen_assert(input_width > 0);
-    eigen_assert(input_height > 0);
-
-    for (Index i = 0; i < batch_size; ++i) {
-      float x = offsets_[i].first, y = offsets_[i].second;
-
-      // Un-normalize coordinates back to pixel space if normalized.
-      if (normalized_) {
-        x *= input_width;
-        y *= input_height;
-      }
-      // Un-center if coordinates are centered on the image center.
-      if (centered_) {
-        x /= 2.0f;
-        y /= 2.0f;
-        x += input_width / 2.0f;
-        y += input_height / 2.0f;
-      }
-      // Remove half of the glimpse window.
-      x -= width_ / 2.0f;
-      y -= height_ / 2.0f;
-
-      const Index offset_x = (Index) x;
-      const Index offset_y = (Index) y;
-      Index glimpse_width = width_;
-      Index glimpse_height = height_;
-      bool partial_overlap = false;
-      DSizes slice_offset(0, offset_x, offset_y);
-      DSizes slice_extent(num_channels, width_, height_);
-      DSizes base_offset(0, 0, 0);
-
-      if (offset_x < 0) {
-        slice_offset[1] = 0;
-        glimpse_width = (std::max)(0, width_ + offset_x);
-        slice_extent[1] = glimpse_width;
-        base_offset[1] = width_ - glimpse_width;
-        partial_overlap = true;
-      } else if (offset_x + width_ >= input_width) {
-        glimpse_width = (std::max)(0, input_width - offset_x);
-        slice_extent[1] = glimpse_width;
-        partial_overlap = true;
-      }
-      if (offset_y < 0) {
-        slice_offset[2] = 0;
-        glimpse_height = (std::max)(0, height_ + offset_y);
-        slice_extent[2] = glimpse_height;
-        base_offset[2] = height_ - glimpse_height;
-        partial_overlap = true;
-      } else if (offset_y + height_ >= input_height) {
-        glimpse_height = (std::max)(0, input_height - offset_y);
-        slice_extent[2] = glimpse_height;
-        partial_overlap = true;
-      }
-      slice_extent[1] = std::min(input_width, slice_extent[1]);
-      slice_extent[2] = std::min(input_height, slice_extent[2]);
-
-      if (partial_overlap) {
-        if (uniform_noise_) {
-          // Initialize the glimpse with uniform noise.
-          typedef typename internal::remove_const<
-            typename internal::traits::Scalar>::type Scalar;
-          TensorFixedSize > mini;
-          mini.device(device) = input.template chip<3>(i).minimum();
-          TensorFixedSize > range;
-          range.device(device) =
-              (input.template chip<3>(i).maximum() - mini).template cast();
-
-          DSizes glimpse_size(num_channels, width_, height_);
-          TensorMap > tmp(NULL, glimpse_size);
-          output.template chip<3>(i).device(device) =
-              mini.reshape(Sizes<1,1,1>()).broadcast(glimpse_size) +
-              (tmp.random() * range.reshape(Sizes<1,1,1>()).broadcast(glimpse_size)).template cast();
-        } else {
-          // Initialize the glimpse with white noise: compute the mean and sigma
-          // of each channel, and use them to shape the gaussian.
-          DSizes glimpse_size(width_, height_);
-          DSizes input_size(input_width, input_height);
-          typedef typename internal::remove_const<
-            typename internal::traits::Scalar>::type Scalar;
-
-          for (int j = 0; j < num_channels; ++j) {
-            TensorFixedSize > mean;
-            mean.device(device) = input.template chip<3>(i).template chip<0>(j).template cast().mean();
-            TensorFixedSize > sigma;
-            sigma.device(device) =
-                (input.template chip<3>(i).template chip<0>(j).template cast() - mean.reshape(Sizes<1,1>()).broadcast(input_size)).square().mean().sqrt();
-            TensorFixedSize > mini;
-            mini.device(device) = input.template chip<3>(i).template chip<0>(j).minimum();
-            TensorFixedSize > maxi;
-            maxi.device(device) = input.template chip<3>(i).template chip<0>(j).maximum();
-
-            TensorMap > tmp(NULL, glimpse_size);
-            output.template chip<3>(i).template chip<0>(j).device(device) =
-                (mean.reshape(Sizes<1,1>()).broadcast(glimpse_size) +
-                 (tmp.random(internal::NormalRandomGenerator()) * sigma.reshape(Sizes<1,1>()).broadcast(glimpse_size)).template cast()).cwiseMin(maxi.reshape(Sizes<1,1>()).broadcast(glimpse_size)).cwiseMax(mini.reshape(Sizes<1,1>()).broadcast(glimpse_size));
-          }
-        }
-
-        // Copy the part of the glimpse that cover the input image if any.
-        if (glimpse_width == 0 || glimpse_height == 0) {
-          continue;
-        }
-        output.template chip<3>(i).slice(base_offset, slice_extent).device(device) = input.template chip<3>(i).slice(slice_offset, slice_extent);
-      } else {
-        output.template chip<3>(i).device(device) = input.template chip<3>(i).slice(slice_offset, slice_extent);
-      }
-    }
-  }
-
- private:
-  const Index width_;
-  const Index height_;
-  const std::vector > offsets_;
-  const bool normalized_;
-  const bool centered_;
-  const bool uniform_noise_;
-};
-}
-
-
-template 
-EIGEN_ALWAYS_INLINE
-static const TensorCustomUnaryOp::Index>, const Input>
-ExtractGlimpses(const Input& input,
-                const typename internal::traits::Index width,
-                const typename internal::traits::Index height,
-                const std::vector >& offsets,
-                const bool normalized = true, const bool centered = true,
-                const bool uniform_noise = true)
-{
-  EIGEN_STATIC_ASSERT(internal::traits::Layout == ColMajor, YOU_MADE_A_PROGRAMMING_MISTAKE);
-  EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == 4, YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-  typedef typename internal::traits::Index Index;
-  const GlimpseExtractionOp op(width, height, offsets, normalized,
-                                      centered, uniform_noise);
-  return input.customOp(op);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_NEURAL_NETWORKS_ATTENTION_H
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h
deleted file mode 100644
index 12ce23444c092ea96ee4b3c8bd2c84d440f2c500..0000000000000000000000000000000000000000
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h
+++ /dev/null
@@ -1,523 +0,0 @@
-#ifndef EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_CUBOID_CONVOLUTIONS_H
-#define EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_CUBOID_CONVOLUTIONS_H
-
-#include "Patch3d.h"
-
-namespace Eigen {
-
-/** CuboidConvolutionBackwardInput
-  * \ingroup CXX11_NeuralNetworks_Module
-  *
-  * \brief Computes the backprop for the input of a 3D convolution.
-  *
-  * The output_backward parameter is expected to be a tensor with a rank of 4 or more (channels, depth, height, width, and optionally others)
-  * The kernel parameter is expected to be a 5D tensor (filters, channels, kernel_depth, kernel_height, kernel_width)
-  * output_backward and kernel have to be in the same layout.
-  *
-  * The dimensions of the result will be filters, depth, height, width (and others if applicable).
-  *
-  * It is possible to swap the order of the depth, width and height dimensions provided that the same order is used in the input, the kernel, and the output.
-  *
-  * All dimension orders above are given for col-major, and should be reversed for row-major.
-  */
-
-template 
-EIGEN_ALWAYS_INLINE static const typename internal::conditional<
-    internal::traits::Layout == ColMajor,
-    TensorReshapingOp<
-        const DSizes::Index,
-                     internal::traits::NumDimensions>,
-        const TensorContractionOp<
-            const array< IndexPair::Index>, 2>,
-            const TensorReshapingOp<
-                const DSizes< typename internal::traits::Index, 3>,
-                const TensorReverseOp, const Kernel>
-            >,
-            const TensorReshapingOp<
-                const DSizes< typename internal::traits::Index, 3>,
-                const TensorVolumePatchOp
-            >
-        >
-    >,
-    TensorReshapingOp<
-        const DSizes::Index,
-                     internal::traits::NumDimensions>,
-        const TensorContractionOp<
-            const array< IndexPair::Index>, 2>,
-            const TensorReshapingOp<
-                const DSizes< typename internal::traits::Index, 3>,
-                const TensorVolumePatchOp
-            >,
-            const TensorReshapingOp<
-                const DSizes::Index, 3>,
-                const TensorReverseOp, const Kernel>
-            >
-        >
-    >
->::type
-CuboidConvolutionBackwardInput(
-    const Kernel& kernel, const OutputBackward& output_backward,
-    typename internal::traits::Index inputPlanes,
-    typename internal::traits::Index inputRows,
-    typename internal::traits::Index inputCols,
-    const DenseIndex stridePlanes = 1, const DenseIndex strideRows = 1,
-    const DenseIndex strideCols = 1) {
-  typedef typename internal::traits::Index TensorIndex;
-  const TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > kern(kernel);
-  const TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > out(output_backward);
-
-  EIGEN_STATIC_ASSERT(internal::traits::Layout == internal::traits::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-  static const bool isColMajor = (internal::traits::Layout == ColMajor);
-
-  static const int NumDims = internal::traits::NumDimensions;
-
-  // Number of filters to apply. This is the same as the output depth of the result
-  const TensorIndex kernelFilters = isColMajor ? kern.dimensions()[0] : kern.dimensions()[4];
-  // Number of channels. This is the same as the input depth.
-  const TensorIndex kernelChannels = isColMajor ? kern.dimensions()[1] : kern.dimensions()[3];
-  const TensorIndex kernelPlanes = isColMajor ? kern.dimensions()[2] : kern.dimensions()[2];
-  const TensorIndex kernelRows = isColMajor ? kern.dimensions()[3] : kern.dimensions()[1];
-  const TensorIndex kernelCols = isColMajor ? kern.dimensions()[4] : kern.dimensions()[0];
-
-  const TensorIndex outputPlanes = isColMajor ? out.dimensions()[1] : out.dimensions()[NumDims - 2];
-  const TensorIndex outputRows = isColMajor ? out.dimensions()[2] : out.dimensions()[NumDims - 3];
-  const TensorIndex outputCols = isColMajor ? out.dimensions()[3] : out.dimensions()[NumDims - 4];
-
-  TensorIndex forward_pad_z, forward_pad_y, forward_pad_x;
-  const TensorIndex size_z = ceil(inputPlanes / static_cast(stridePlanes));
-  const TensorIndex size_y = ceil(inputRows / static_cast(strideRows));
-  const TensorIndex size_x = ceil(inputCols / static_cast(strideCols));
-
-  // Infer padding type.
-  if (size_z == outputPlanes && size_y == outputRows && size_x == outputCols) {
-    // SAME padding.
-    const TensorIndex dz = size_z * stridePlanes + kernelPlanes - 1 - inputPlanes;
-    const TensorIndex dy = size_y * strideRows + kernelRows - 1 - inputRows;
-    const TensorIndex dx = size_x * strideCols + kernelCols - 1 - inputCols;
-
-    forward_pad_z = dz - dz / 2;
-    forward_pad_y = dy - dy / 2;
-    forward_pad_x = dx - dx / 2;
-  } else {
-    // VALID padding.
-    forward_pad_z = 0;
-    forward_pad_y = 0;
-    forward_pad_x = 0;
-  }
-  const TensorIndex padding_ztop = kernelPlanes - 1 - forward_pad_z;
-  const TensorIndex padding_top = kernelRows - 1 - forward_pad_y;
-  const TensorIndex padding_left = kernelCols - 1 - forward_pad_x;
-
-  const TensorIndex padding_zbottom = inputPlanes + kernelPlanes - 1 - (outputPlanes - 1) * stridePlanes - 1 - padding_ztop;
-  const TensorIndex padding_bottom = inputRows + kernelRows - 1 - (outputRows - 1) * strideRows - 1 - padding_top;
-  const TensorIndex padding_right = inputCols + kernelCols - 1 - (outputCols - 1) * strideCols - 1 - padding_left;
-
-  eigen_assert(padding_ztop >= 0);
-  eigen_assert(padding_zbottom >= 0);
-  eigen_assert(padding_top >= 0);
-  eigen_assert(padding_left >= 0);
-  eigen_assert(padding_bottom >= 0);
-  eigen_assert(padding_right >= 0);
-
-  // The kernel has dimensions filters X channels X patch_planes X patch_rows X patch_cols.
-  // We need to reverse the kernel along the spatial dimensions.
-  array kernel_reverse;
-  if (isColMajor) {
-    kernel_reverse[0] = false;
-    kernel_reverse[1] = false;
-    kernel_reverse[2] = true;
-    kernel_reverse[3] = true;
-    kernel_reverse[4] = true;
-  } else {
-    kernel_reverse[0] = true;
-    kernel_reverse[1] = true;
-    kernel_reverse[2] = true;
-    kernel_reverse[3] = false;
-    kernel_reverse[4] = false;
-  }
-
-  DSizes kernel_dims;
-  if (isColMajor) {
-    kernel_dims[0] = kernelFilters;
-    kernel_dims[1] = kernelChannels;
-    kernel_dims[2] = kernelRows * kernelCols * kernelPlanes;
-  } else {
-    kernel_dims[0] = kernelRows * kernelCols * kernelPlanes;
-    kernel_dims[1] = kernelChannels;
-    kernel_dims[2] = kernelFilters;
-  }
-
-  // The output_backward has dimensions out_depth X out_planes X out_rows X out_cols X OTHERS
-  // When we extract the image patches from output_backward, it will have dimensions:
-  //   out_depth X (patch_planes * patch_rows * patch_cols) X (input_planes * input_rows * input_cols * OTHERS)
-  DSizes pre_contract_dims;
-  if (isColMajor) {
-    pre_contract_dims[0] = kernelFilters;
-    pre_contract_dims[1] = kernelRows * kernelCols * kernelPlanes;
-    pre_contract_dims[2] = inputRows * inputCols * inputPlanes;
-    for (int i = 4; i < NumDims; ++i) {
-      pre_contract_dims[2] *= out.dimension(i);
-    }
-  } else {
-    pre_contract_dims[2] = kernelFilters;
-    pre_contract_dims[1] = kernelRows * kernelCols * kernelPlanes;
-    pre_contract_dims[0] = inputRows * inputCols * inputPlanes;
-    for (int i = 0; i < NumDims - 4; ++i) {
-      pre_contract_dims[0] *= out.dimension(i);
-    }
-  }
-
-  // We will contract along dimensions (0, 2) in kernel and (0, 1) in
-  // output_backward, if this is col-major, and
-  // dimensions (0, 2) in kernel and (1, 2) in output_backward, if this row-major.
-  array, 2> contract_dims;
-  if (isColMajor) {
-    // col-major: kernel.contract(output.patches)
-    contract_dims[0] = IndexPair(0, 0);
-    contract_dims[1] = IndexPair(2, 1);
-  } else {
-    // row-major: output.patches.contract(kernel)
-    contract_dims[0] = IndexPair(1, 0);
-    contract_dims[1] = IndexPair(2, 2);
-  }
-
-  // Post contraction, the dimensions of the input_backprop is
-  //  channels X input_planes X input_rows X input_cols X OTHERS
-  DSizes post_contract_dims;
-  if (isColMajor) {
-    post_contract_dims[0] = kernelChannels;
-    post_contract_dims[1] = inputPlanes;
-    post_contract_dims[2] = inputRows;
-    post_contract_dims[3] = inputCols;
-    for (int i = 4; i < NumDims; ++i) {
-      post_contract_dims[i] = out.dimension(i);
-    }
-  } else {
-    post_contract_dims[NumDims - 1] = kernelChannels;
-    post_contract_dims[NumDims - 2] = inputPlanes;
-    post_contract_dims[NumDims - 3] = inputRows;
-    post_contract_dims[NumDims - 4] = inputCols;
-    for (int i = 0; i < NumDims - 4; ++i) {
-      post_contract_dims[i] = out.dimension(i);
-    }
-  }
-
-  DSizes strides;
-  for (int i = 0; i < NumDims; i++) {
-    strides[i] = 1;
-  }
-  if (isColMajor) {
-    strides[1] = stridePlanes;
-    strides[2] = strideRows;
-    strides[3] = strideCols;
-  } else {
-    strides[NumDims - 2] = stridePlanes;
-    strides[NumDims - 3] = strideRows;
-    strides[NumDims - 4] = strideCols;
-  }
-
-  return choose(
-      Cond::Layout == ColMajor>(),
-      kernel.reverse(kernel_reverse)
-          .reshape(kernel_dims)
-          .contract(
-              output_backward.extract_volume_patches(kernelPlanes, kernelRows, kernelCols,
-                                                     1, 1, 1, stridePlanes, strideRows, strideCols,
-                               padding_ztop, padding_zbottom,
-                               padding_top, padding_bottom,
-                               padding_left, padding_right)
-                  .reshape(pre_contract_dims),
-              contract_dims)
-          .reshape(post_contract_dims),
-      output_backward.extract_volume_patches(kernelPlanes, kernelRows, kernelCols,
-                                             1, 1, 1, stridePlanes, strideRows, strideCols,
-                       padding_ztop, padding_zbottom,
-                       padding_top, padding_bottom,
-                       padding_left, padding_right)
-          .reshape(pre_contract_dims)
-          .contract(kernel.reverse(kernel_reverse).reshape(kernel_dims),
-                    contract_dims)
-          .reshape(post_contract_dims));
-}
-
-
-/** CuboidConvolutionBackwardKernel
-  * \ingroup CXX11_NeuralNetworks_Module
-  *
-  * \brief Computes the backprop for the filter of a 3D convolution.
-  *
-  * The output_backward parameter is expected to be a tensor with a rank of 4 or more (channels, depth, height, width, and optionally others)
-  * The kernel parameter is expected to be a 4D tensor (filters, channels, kernel_depth, kernel_height, kernel_width)
-  * output_backward and kernel have to be in the same layout.
-  *
-  * The dimensions of the result will be filters, depth, height, width (and others if applicable).
-  *
-  * It is possible to swap the order of the depth, width and height dimensions provided that the same order is used in the input, the kernel, and the output.
-  *
-  * All dimension orders above are given for col-major, and should be reversed for row-major.
-  */
-template 
-EIGEN_ALWAYS_INLINE static const typename internal::conditional<
-    internal::traits::Layout == ColMajor,
-    const TensorShufflingOp<
-        const array::Index, 5>,
-        const TensorReverseOp<
-            const array,
-            const TensorReshapingOp<
-                const DSizes::Index, 5>,
-                const TensorContractionOp<
-                    const array< IndexPair::Index>, 2>,
-                    const TensorReshapingOp<
-                        const DSizes::Index, 3>,
-                        const Input>,
-                    const TensorReshapingOp<
-                        const DSizes< typename internal::traits::Index, 4>,
-                        const TensorVolumePatchOp
-                    >
-                >
-            >
-        >
-    >,
-    const TensorShufflingOp<
-        const array::Index, 5>,
-        const TensorReverseOp<
-            const array,
-            const TensorReshapingOp<
-                const DSizes::Index, 5>,
-                const TensorContractionOp<
-                    const array< IndexPair::Index>, 2>,
-                    const TensorReshapingOp<
-                        const DSizes< typename internal::traits::Index, 4>,
-                        const TensorVolumePatchOp
-                    >,
-                    const TensorReshapingOp<
-                        const DSizes::Index, 3>,
-                        const Input
-                    >
-                >
-            >
-        >
-    >
->::type
-CuboidConvolutionBackwardKernel(
-    const Input& input, const OutputBackward& output_backward,
-    typename internal::traits::Index kernelPlanes,
-    typename internal::traits::Index kernelRows,
-    typename internal::traits::Index kernelCols,
-    const DenseIndex stridePlanes = 1,
-    const DenseIndex strideRows = 1,
-    const DenseIndex strideCols = 1) {
-  typedef typename internal::traits::Index TensorIndex;
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input);
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > out(output_backward);
-
-  EIGEN_STATIC_ASSERT(internal::traits::Layout == internal::traits::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-  static const bool isColMajor = (internal::traits::Layout == ColMajor);
-
-  static const int NumDims = internal::traits::NumDimensions;
-  EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == internal::traits::NumDimensions, YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-  const TensorIndex inputPlanes = isColMajor ? in.dimension(1) : in.dimension(NumDims - 2);
-  const TensorIndex inputRows = isColMajor ? in.dimension(2) : in.dimension(NumDims - 3);
-  const TensorIndex inputCols = isColMajor ? in.dimension(3) : in.dimension(NumDims - 4);
-
-  const TensorIndex outputPlanes = isColMajor ? out.dimension(1) : out.dimension(NumDims - 2);
-  const TensorIndex outputRows = isColMajor ? out.dimension(2) : out.dimension(NumDims - 3);
-  const TensorIndex outputCols = isColMajor ? out.dimension(3) : out.dimension(NumDims - 4);
-
-  const TensorIndex kernelFilters = isColMajor ? out.dimension(0) : out.dimension(NumDims - 1);
-  const TensorIndex kernelChannels = isColMajor ? in.dimension(0) : in.dimension(NumDims - 1);
-
-  TensorIndex forward_pad_z, forward_pad_y, forward_pad_x;
-  const TensorIndex size_z = ceil(inputPlanes / static_cast(stridePlanes));
-  const TensorIndex size_y = ceil(inputRows / static_cast(strideRows));
-  const TensorIndex size_x = ceil(inputCols / static_cast(strideCols));
-
-  // Infer padding type.
-  if (size_z == outputPlanes && size_y == outputRows && size_x == outputCols) {
-    // SAME padding.
-    const TensorIndex dz = size_z * stridePlanes + kernelPlanes - 1 - inputPlanes;
-    const TensorIndex dy = size_y * strideRows + kernelRows - 1 - inputRows;
-    const TensorIndex dx = size_x * strideCols + kernelCols - 1 - inputCols;
-
-    forward_pad_z = dz - dz / 2;
-    forward_pad_y = dy - dy / 2;
-    forward_pad_x = dx - dx / 2;
-  } else {
-    // VALID padding.
-    forward_pad_z = 0;
-    forward_pad_y = 0;
-    forward_pad_x = 0;
-  }
-
-  const TensorIndex padding_ztop = kernelPlanes - 1 - forward_pad_z;
-  const TensorIndex padding_top = kernelRows - 1 - forward_pad_y;
-  const TensorIndex padding_left = kernelCols - 1 - forward_pad_x;
-
-  const TensorIndex padding_zbottom = inputPlanes + kernelPlanes - 1 - (outputPlanes - 1) * stridePlanes - 1 - padding_ztop;
-  const TensorIndex padding_bottom = inputRows + kernelRows - 1 - (outputRows - 1) * strideRows - 1 - padding_top;
-  const TensorIndex padding_right = inputCols + kernelCols - 1 - (outputCols - 1) * strideCols - 1 - padding_left;
-
-  eigen_assert(padding_ztop >= 0);
-  eigen_assert(padding_zbottom >= 0);
-  eigen_assert(padding_top >= 0);
-  eigen_assert(padding_left >= 0);
-  eigen_assert(padding_bottom >= 0);
-  eigen_assert(padding_right >= 0);
-
-  // The output_backward has dimensions out_depth X out_plaens X out_rows X out_cols X OTHERS
-  // When we extract the image patches from output_backward (with input as the
-  // kernel), it will have dimensions
-  //  (out_depth) X (input_planes * input_rows * input_cols) X (kernel_planes * kernel_rows * kernel_cols) X OTHERS
-  DSizes pre_contract_dims;
-  if (isColMajor) {
-    pre_contract_dims[0] = kernelFilters;
-    pre_contract_dims[1] = inputRows * inputCols * inputPlanes;
-    pre_contract_dims[2] = kernelRows * kernelCols * kernelPlanes;
-    pre_contract_dims[3] = 1;
-    for (int i = 4; i < NumDims; ++i) {
-      pre_contract_dims[3] *= out.dimension(i);
-    }
-  } else {
-    pre_contract_dims[3] = kernelFilters;
-    pre_contract_dims[2] = inputRows * inputCols * inputPlanes;
-    pre_contract_dims[1] = kernelRows * kernelCols * kernelPlanes;
-    pre_contract_dims[0] = 1;
-    for (int i = 0; i < NumDims - 4; ++i) {
-      pre_contract_dims[0] *= out.dimension(i);
-    }
-  }
-
-  // The input has dimensions in_depth X (input_planes * input_rows * input_cols) X OTHERS
-  DSizes input_dims;
-  if (isColMajor) {
-    input_dims[0] = kernelChannels;
-    input_dims[1] = inputRows * inputCols * inputPlanes;
-    input_dims[2] = 1;
-    for (int i = 4; i < NumDims; ++i) {
-      input_dims[2] *= in.dimension(i);
-    }
-    eigen_assert(input_dims[2] == pre_contract_dims[3]);
-  } else {
-    input_dims[2] = kernelChannels;
-    input_dims[1] = inputRows * inputCols * inputPlanes;
-    input_dims[0] = 1;
-    for (int i = 0; i < NumDims - 4; ++i) {
-      input_dims[0] *= in.dimension(i);
-    }
-    eigen_assert(input_dims[0] == pre_contract_dims[0]);
-  }
-
-  // We will contract along dimensions (1, 2) in in and (1, 3) in out, if
-  // this is col-major.
-  // For row-major, it's dimensions (0, 1) in in and (0, 2) in out.
-  array, 2> contract_dims;
-  if (isColMajor) {
-    // col-major: in.contract(output.patches)
-    contract_dims[0] = IndexPair(1, 1);
-    contract_dims[1] = IndexPair(2, 3);
-  } else {
-    // row-major: output.patches.contract(in)
-    contract_dims[0] = IndexPair(0, 0);
-    contract_dims[1] = IndexPair(2, 1);
-  }
-
-  // After the contraction, the kernel will have dimension
-  //   in_depth X out_depth X kernel_patches X kernel_rows X kernel_cols
-  // We will need to shuffle the first two dimensions and reverse the spatial dimensions.
-  // The end shape is:
-  //   out_depth X in_shape X kernel_planes X kernel_rows X kernel_cols
-
-  // This is the shape of the kernel *before* the shuffling.
-  DSizes kernel_dims;
-  if (isColMajor) {
-    kernel_dims[0] = kernelChannels;
-    kernel_dims[1] = kernelFilters;
-    kernel_dims[2] = kernelPlanes;
-    kernel_dims[3] = kernelRows;
-    kernel_dims[4] = kernelCols;
-  } else {
-    kernel_dims[0] = kernelCols;
-    kernel_dims[1] = kernelRows;
-    kernel_dims[2] = kernelPlanes;
-    kernel_dims[3] = kernelFilters;
-    kernel_dims[4] = kernelChannels;
-  }
-
-  // Flip filters and channels.
-  array kernel_shuffle;
-  if (isColMajor) {
-    kernel_shuffle[0] = 1;
-    kernel_shuffle[1] = 0;
-    kernel_shuffle[2] = 2;
-    kernel_shuffle[3] = 3;
-    kernel_shuffle[4] = 4;
-  } else {
-    kernel_shuffle[0] = 0;
-    kernel_shuffle[1] = 1;
-    kernel_shuffle[2] = 2;
-    kernel_shuffle[3] = 4;
-    kernel_shuffle[4] = 3;
-  }
-
-  // Reverse the spatial dimensions.
-  array kernel_reverse;
-  if (isColMajor) {
-    kernel_reverse[0] = false;
-    kernel_reverse[1] = false;
-    kernel_reverse[2] = true;
-    kernel_reverse[3] = true;
-    kernel_reverse[4] = true;
-  } else {
-    kernel_reverse[0] = true;
-    kernel_reverse[1] = true;
-    kernel_reverse[2] = true;
-    kernel_reverse[3] = false;
-    kernel_reverse[4] = false;
-  }
-
-  DSizes strides;
-  for (int i = 0; i < NumDims; i++) {
-    strides[i] = 1;
-  }
-  if (isColMajor) {
-    strides[1] = stridePlanes;
-    strides[2] = strideRows;
-    strides[3] = strideCols;
-  } else {
-    strides[NumDims - 2] = stridePlanes;
-    strides[NumDims - 3] = strideRows;
-    strides[NumDims - 4] = strideCols;
-  }
-  return choose(
-      Cond::Layout == ColMajor>(),
-      input.reshape(input_dims)
-          .contract(
-              output_backward.extract_volume_patches(
-                                 inputPlanes, inputRows, inputCols, 1,
-                                 1, 1, stridePlanes, strideRows, strideCols,
-
-                                 padding_ztop, padding_zbottom, padding_top,
-                                 padding_bottom, padding_left, padding_right)
-                  .reshape(pre_contract_dims),
-              contract_dims)
-          .reshape(kernel_dims)
-          .reverse(kernel_reverse)
-          .shuffle(kernel_shuffle),
-      output_backward.extract_volume_patches(
-                         inputPlanes, inputRows, inputCols, 1, 1, 1,
-                         stridePlanes, strideRows, strideCols, padding_ztop,
-                         padding_zbottom, padding_top, padding_bottom,
-                         padding_left, padding_right)
-          .reshape(pre_contract_dims)
-          .contract(input.reshape(input_dims), contract_dims)
-          .reshape(kernel_dims)
-          .reverse(kernel_reverse)
-          .shuffle(kernel_shuffle));
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_CUBOID_CONVOLUTIONS_H
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h
deleted file mode 100644
index 0f4ada246c702a1c5138b04ebeab6fca73b35b26..0000000000000000000000000000000000000000
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h
+++ /dev/null
@@ -1,351 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2015 Ke Yang 
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_SPATIAL_CONVOLUTIONS_H
-#define EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_SPATIAL_CONVOLUTIONS_H
-
-namespace Eigen {
-
-/** SpatialConvolutionBackwardInput
-  * \ingroup CXX11_NeuralNetworks_Module
-  *
-  * \brief Computes the backprop for the input of a 2D convolution.
-  *
-  * The output_backward parameter is expected to be a tensor with a rank of 3 or more (channels, height, width, and optionally others)
-  * The kernel parameter is expected to be a 4D tensor (filters, channels, kernel_height, kernel_width)
-  * The output_backward and the kernel must both be in col-major layout. The result will also be in col-major layout.
-  *
-  * If in_stride > 1, then applies convolution with holes (aka atrous convolution), sampling every in_stride input pixels.
-  *
-  * The result can be assigned to a tensor of rank equal to the rank of the output_backward. The dimensions of the result will be filters, height, width (and others if applicable).
-  *
-  * It is possible to swap the order of the width and height dimensions provided that the same order is used in the input, the kernel, and the output.
-  *
-  */
-
-template 
-EIGEN_ALWAYS_INLINE
-static const typename internal::conditional<
-  internal::traits::Layout == ColMajor,
-  TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorContractionOp::Index>, 2>, const TensorReshapingOp::Index, 3>, const TensorReverseOp, const Kernel> >, const TensorReshapingOp::Index, 3>, const TensorImagePatchOp > > >,
-  TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorContractionOp::Index>, 2>, const TensorReshapingOp::Index, 3>, const TensorImagePatchOp >, const TensorReshapingOp::Index, 3>, const TensorReverseOp, const Kernel> > > > >::type
-SpatialConvolutionBackwardInput(const Kernel& kernel, const OutputBackward& output_backward, typename internal::traits::Index inputRows, typename internal::traits::Index inputCols, const DenseIndex stride = 1, const DenseIndex in_stride = 1) {
-
-  typedef typename internal::traits::Index TensorIndex;
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > kern(kernel);
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > out(output_backward);
-
-  EIGEN_STATIC_ASSERT(internal::traits::Layout == internal::traits::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-  static const bool isColMajor = (internal::traits::Layout == ColMajor);
-
-  static const int NumDims = internal::traits::NumDimensions;
-
-  // Number of filters to apply. This is the same as the output depth of the result
-  const TensorIndex kernelFilters = isColMajor ? kern.dimensions()[0] : kern.dimensions()[3];
-  // Number of channels. This is the same as the input depth.
-  const TensorIndex kernelChannels = isColMajor ? kern.dimensions()[1] : kern.dimensions()[2];
-  const TensorIndex kernelRows = isColMajor ? kern.dimensions()[2] : kern.dimensions()[1];
-  const TensorIndex kernelCols = isColMajor ? kern.dimensions()[3] : kern.dimensions()[0];
-
-  // This is the effective kernel size, taking into account the (in_stride - 1) zero-values
-  // inserted between consecutive kernel elements in atrous convolution
-  const TensorIndex kernelRowsEff = kernelRows + (kernelRows - 1) * (in_stride - 1);
-  const TensorIndex kernelColsEff = kernelCols + (kernelCols - 1) * (in_stride - 1);
-
-  const TensorIndex outputRows = isColMajor ? output_backward.dimension(1) : output_backward.dimension(NumDims - 2);
-  const TensorIndex outputCols = isColMajor ? output_backward.dimension(2) : output_backward.dimension(NumDims - 3);
-
-  // Computing the forward padding
-  const TensorIndex forward_pad_top = ((outputRows - 1) * stride + kernelRowsEff - inputRows) / 2;
-  const TensorIndex forward_pad_left = ((outputCols - 1) * stride + kernelColsEff - inputCols) / 2;
-
-  const TensorIndex padding_top = kernelRowsEff - 1 - forward_pad_top;
-  const TensorIndex padding_left = kernelColsEff - 1 - forward_pad_left;
-  const TensorIndex padding_bottom = inputRows + kernelRowsEff - 1 - (outputRows - 1) * stride - 1 - padding_top;
-  const TensorIndex padding_right = inputCols + kernelColsEff - 1 - (outputCols - 1) * stride - 1 - padding_left;
-
-  eigen_assert(padding_top >= 0);
-  eigen_assert(padding_left >= 0);
-  eigen_assert(padding_bottom >= 0);
-  eigen_assert(padding_right >= 0);
-
-  // The kernel has dimensions filters X channels X patch_rows X patch_cols
-  // We need to reverse the kernel along dimensions corresponding to rows and
-  // cols.
-  // TODO(yangke): we can make things slightly faster by collapsing the dimensions
-  // where we don't reverse. Try that once we have a faster compiler.
-  array kernel_reverse;
-  if (isColMajor) {
-    kernel_reverse[0] = false;
-    kernel_reverse[1] = false;
-    kernel_reverse[2] = true;
-    kernel_reverse[3] = true;
-  } else {
-    kernel_reverse[0] = true;
-    kernel_reverse[1] = true;
-    kernel_reverse[2] = false;
-    kernel_reverse[3] = false;
-  }
-
-  DSizes kernel_dims;
-  if (isColMajor) {
-    kernel_dims[0] = kernelFilters;
-    kernel_dims[1] = kernelChannels;
-    kernel_dims[2] = kernelRows * kernelCols;
-  } else {
-    kernel_dims[0] = kernelRows * kernelCols;
-    kernel_dims[1] = kernelChannels;
-    kernel_dims[2] = kernelFilters;
-  }
-
-  // The output_backward has dimensions out_depth X out_rows X out_cols X OTHERS
-  // When we extract the image patches from output_backward, it will have dimensions
-  //   out_depth X (patch_rows * patch_cols) X (input_rows * input_cols * OTHERS)
-  DSizes pre_contract_dims;
-  if (isColMajor) {
-    pre_contract_dims[0] = kernelFilters;
-    pre_contract_dims[1] = kernelRows * kernelCols;
-    pre_contract_dims[2] = inputRows * inputCols;
-    for (int i = 3; i < NumDims; ++i) {
-      pre_contract_dims[2] *= out.dimension(i);
-    }
-  } else {
-    pre_contract_dims[2] = kernelFilters;
-    pre_contract_dims[1] = kernelRows * kernelCols;
-    pre_contract_dims[0] = inputRows * inputCols;
-    for (int i = 0; i < NumDims - 3; ++i) {
-      pre_contract_dims[0] *= out.dimension(i);
-    }
-  }
-
-  // We will contract along dimensions (0, 2) in kernel and (0, 1) in
-  // output_backward, if this is col-major, and
-  // dimensions (0, 2) in kernel and (1, 2) in output_backward, if this row-major.
-  array, 2> contract_dims;
-  if (isColMajor) {
-    // col-major: kernel.contract(output.patches)
-    contract_dims[0] = IndexPair(0, 0);
-    contract_dims[1] = IndexPair(2, 1);
-  } else {
-    // row-major: output.patches.contract(kernel)
-    contract_dims[0] = IndexPair(1, 0);
-    contract_dims[1] = IndexPair(2, 2);
-  }
-
-  // Post contraction, the dimensions of the input_backprop is
-  //  channels X input_rows X input_cols X OTHERS
-  DSizes post_contract_dims;
-  if (isColMajor) {
-    post_contract_dims[0] = kernelChannels;
-    post_contract_dims[1] = inputRows;
-    post_contract_dims[2] = inputCols;
-    for (int i = 3; i < NumDims; ++i) {
-      post_contract_dims[i] = out.dimension(i);
-    }
-  } else {
-    post_contract_dims[NumDims - 1] = kernelChannels;
-    post_contract_dims[NumDims - 2] = inputRows;
-    post_contract_dims[NumDims - 3] = inputCols;
-    for (int i = 0; i < NumDims - 3; ++i) {
-      post_contract_dims[i] = out.dimension(i);
-    }
-  }
-
-  return choose(Cond::Layout == ColMajor>(),
-                kernel.reverse(kernel_reverse).reshape(kernel_dims).contract(output_backward.extract_image_patches(kernelRows, kernelCols, 1, 1, in_stride, in_stride, stride, stride, padding_top, padding_bottom, padding_left, padding_right, 0).reshape(pre_contract_dims), contract_dims).reshape(post_contract_dims),
-                output_backward.extract_image_patches(kernelRows, kernelCols, 1, 1, in_stride, in_stride, stride, stride, padding_top, padding_bottom, padding_left, padding_right, 0).reshape(pre_contract_dims).contract(kernel.reverse(kernel_reverse).reshape(kernel_dims), contract_dims).reshape(post_contract_dims));
-}
-
-
-/** SpatialConvolutionBackwardKernel
-  * \ingroup CXX11_NeuralNetworks_Module
-  *
-  * \brief Computes the backprop for the filter of a 2D convolution.
-  *
-  * The output_backward parameter is expected to be a tensor with a rank of 3 or more (channels, height, width, and optionally others)
-  * The kernel parameter is expected to be a 4D tensor (filters, channels, kernel_height, kernel_width)
-  * The output_backward and the kernel must both be in col-major layout. The result will also be in col-major layout.
-  *
-  * If in_stride > 1, then applies convolution with holes (aka atrous convolution), sampling every in_stride input pixels.
-  *
-  * The result can be assigned to a tensor of rank equal to the rank of the output_backward. The dimensions of the result will be filters, height, width (and others if applicable).
-  *
-  * It is possible to swap the order of the width and height dimensions provided that the same order is used in the input, the kernel, and the output.
-  *
-  */
-// TODO(gpapan): Resolve a bug in TensorContractionInputMapper at SpatialConvolutions.h that yangke circumvented by using .reshape().reshape().
-// This can significantly accelerate SpatialConvolutionBackwardKernel.
-
-template 
-EIGEN_ALWAYS_INLINE
-static const typename internal::conditional<
-  internal::traits::Layout == ColMajor,
-  const TensorShufflingOp::Index, 4>, const TensorReverseOp, const TensorReshapingOp::Index, 4>, const TensorContractionOp::Index>, 2>, const TensorReshapingOp::Index, 3>, const Input>, const TensorReshapingOp::Index, 4>, const TensorReshapingOp::Index, 4>, const TensorImagePatchOp > > > > > >,
-  const TensorShufflingOp::Index, 4>, const TensorReverseOp, const TensorReshapingOp::Index, 4>, const TensorContractionOp::Index>, 2>, const TensorReshapingOp::Index, 4>, const TensorReshapingOp::Index, 4>, const TensorImagePatchOp > >, const TensorReshapingOp::Index, 3>, const Input> > > > > >::type
-SpatialConvolutionBackwardKernel(const Input& input, const OutputBackward& output_backward, typename internal::traits::Index kernelRows, typename internal::traits::Index kernelCols, const DenseIndex stride = 1, const DenseIndex in_stride = 1) {
-
-  typedef typename internal::traits::Index TensorIndex;
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input);
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > out(output_backward);
-
-  EIGEN_STATIC_ASSERT(internal::traits::Layout == internal::traits::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-  // stride and in_stride cannot both be larger than 1
-  eigen_assert(!(stride > 1 && in_stride > 1));
-
-  static const bool isColMajor = (internal::traits::Layout == ColMajor);
-
-  static const int NumDims = internal::traits::NumDimensions;
-  EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == internal::traits::NumDimensions, YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-  const TensorIndex inputRows = isColMajor ? in.dimension(1) : in.dimension(NumDims - 2);
-  const TensorIndex inputCols = isColMajor ? in.dimension(2) : in.dimension(NumDims - 3);
-
-  const TensorIndex outputRows = isColMajor ? output_backward.dimension(1) : output_backward.dimension(NumDims - 2);
-  const TensorIndex outputCols = isColMajor ? output_backward.dimension(2) : output_backward.dimension(NumDims - 3);
-
-  // Number of filters to apply. This is the same as the output depth of the result
-  const TensorIndex kernelFilters = isColMajor ? out.dimensions()[0] : out.dimensions()[NumDims - 1];
-
-  // Number of channels. This is the same as the input depth.
-  const TensorIndex kernelChannels = isColMajor ? in.dimensions()[0] : in.dimensions()[NumDims - 1];
-
-  // This is the effective kernel size, taking into account the (in_stride - 1) zero-values
-  // inserted between consecutive kernel elements in atrous convolution
-  const TensorIndex kernelRowsEff = kernelRows + (kernelRows - 1) * (in_stride - 1);
-  const TensorIndex kernelColsEff = kernelCols + (kernelCols - 1) * (in_stride - 1);
-
-  // Computing the forward padding
-  const TensorIndex forward_pad_top = ((outputRows - 1) * stride + kernelRowsEff - inputRows) / 2;
-  const TensorIndex forward_pad_left = ((outputCols - 1) * stride + kernelColsEff - inputCols) / 2;
-
-  // TODO: factor out the padding computation.
-  const TensorIndex padding_top = kernelRowsEff - 1 - forward_pad_top;
-  const TensorIndex padding_left = kernelColsEff - 1 - forward_pad_left;
-  const TensorIndex padding_bottom = inputRows + kernelRowsEff - 1 - (outputRows - 1) * stride - 1 - padding_top;
-  const TensorIndex padding_right = inputCols + kernelColsEff - 1 - (outputCols - 1) * stride - 1 - padding_left;
-
-  eigen_assert(padding_top >= 0);
-  eigen_assert(padding_left >= 0);
-  eigen_assert(padding_bottom >= 0);
-  eigen_assert(padding_right >= 0);
-
-  // The output_backward has dimensions out_depth X out_rows X out_cols X OTHERS
-  // When we extract the image patches from output_backward (with input as the
-  // kernel), it will have dimensions
-  //  (out_depth) X (input_rows * input_cols) X (kernel_rows * kernel_cols) X OTHERS
-  DSizes pre_contract_dims;
-  if (isColMajor) {
-    pre_contract_dims[0] = kernelFilters;
-    pre_contract_dims[1] = inputRows * inputCols;
-    pre_contract_dims[2] = kernelRows * kernelCols;
-    pre_contract_dims[3] = 1;
-    for (int i = 3; i < NumDims; ++i) {
-      pre_contract_dims[3] *= out.dimension(i);
-    }
-  } else {
-    pre_contract_dims[3] = kernelFilters;
-    pre_contract_dims[2] = inputRows * inputCols;
-    pre_contract_dims[1] = kernelRows * kernelCols;
-    pre_contract_dims[0] = 1;
-    for (int i = 0; i < NumDims - 3; ++i) {
-      pre_contract_dims[0] *= out.dimension(i);
-    }
-  }
-
-  // The input has dimensions in_depth X (input_rows * input_cols) X OTHERS
-  DSizes input_dims;
-  if (isColMajor) {
-    input_dims[0] = kernelChannels;
-    input_dims[1] = inputRows * inputCols;
-    input_dims[2] = 1;
-    for (int i = 3; i < NumDims; ++i) {
-      input_dims[2] *= in.dimension(i);
-    }
-    eigen_assert(input_dims[2] == pre_contract_dims[3]);
-  } else {
-    input_dims[2] = kernelChannels;
-    input_dims[1] = inputRows * inputCols;
-    input_dims[0] = 1;
-    for (int i = 0; i < NumDims - 3; ++i) {
-      input_dims[0] *= in.dimension(i);
-    }
-    eigen_assert(input_dims[0] == pre_contract_dims[0]);
-  }
-
-  // We will contract along dimensions (1, 2) in and (1, 3) in out, if
-  // this is col-major.
-  // For row-major, it's dimensions (0, 1) in and (0, 2) in out.
-  array, 2> contract_dims;
-  if (isColMajor) {
-    // col-major: in.contract(output.patches)
-    contract_dims[0] = IndexPair(1, 1);
-    contract_dims[1] = IndexPair(2, 3);
-  } else {
-    // row-major: output.patches.contract(in)
-    contract_dims[0] = IndexPair(0, 0);
-    contract_dims[1] = IndexPair(2, 1);
-  }
-
-  // After the contraction, the kernel will have dimension
-  // in_depth X out_depth X kernel_rows X kernel_cols
-  // We will need to shuffle the first two dimensions and reverse the latter
-  // two dimensions.
-  // The end shape is
-  // out_depth X in_shape X kernel_rows X kernel_cols
-
-  // This is the shape of the kernel *before* the shuffling.
-  DSizes kernel_dims;
-  if (isColMajor) {
-    kernel_dims[0] = kernelChannels;
-    kernel_dims[1] = kernelFilters;
-    kernel_dims[2] = kernelRows;
-    kernel_dims[3] = kernelCols;
-  } else {
-    kernel_dims[0] = kernelCols;
-    kernel_dims[1] = kernelRows;
-    kernel_dims[2] = kernelFilters;
-    kernel_dims[3] = kernelChannels;
-  }
-
-  array kernel_shuffle;
-  if (isColMajor) {
-    kernel_shuffle[0] = 1;
-    kernel_shuffle[1] = 0;
-    kernel_shuffle[2] = 2;
-    kernel_shuffle[3] = 3;
-  } else {
-    kernel_shuffle[0] = 0;
-    kernel_shuffle[1] = 1;
-    kernel_shuffle[2] = 3;
-    kernel_shuffle[3] = 2;
-  }
-
-  array kernel_reverse;
-  if (isColMajor) {
-    kernel_reverse[0] = false;
-    kernel_reverse[1] = false;
-    kernel_reverse[2] = true;
-    kernel_reverse[3] = true;
-  } else {
-    kernel_reverse[0] = true;
-    kernel_reverse[1] = true;
-    kernel_reverse[2] = false;
-    kernel_reverse[3] = false;
-  }
-
-  return choose(Cond::Layout == ColMajor>(),
-                input.reshape(input_dims).contract(output_backward.extract_image_patches(inputRows, inputCols, in_stride, in_stride, 1, 1, stride, stride, padding_top, padding_bottom, padding_left, padding_right, 0).reshape(pre_contract_dims).reshape(pre_contract_dims), contract_dims).reshape(kernel_dims).reverse(kernel_reverse).shuffle(kernel_shuffle),
-                output_backward.extract_image_patches(inputRows, inputCols, in_stride, in_stride, 1, 1, stride, stride, padding_top, padding_bottom, padding_left, padding_right, 0).reshape(pre_contract_dims).reshape(pre_contract_dims).contract(input.reshape(input_dims), contract_dims).reshape(kernel_dims).reverse(kernel_reverse).shuffle(kernel_shuffle));
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_SPATIAL_CONVOLUTIONS_H
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/CuboidConvolution.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/CuboidConvolution.h
deleted file mode 100644
index dfb9dcedba901570e56e9c736fc4d84bbef37e2e..0000000000000000000000000000000000000000
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/CuboidConvolution.h
+++ /dev/null
@@ -1,179 +0,0 @@
-#ifndef EIGEN_CXX11_SRC_NEURAL_NETWORKS_CUBOID_CONVOLUTION_H
-#define EIGEN_CXX11_SRC_NEURAL_NETWORKS_CUBOID_CONVOLUTION_H
-
-#include "Patch3d.h"
-
-namespace Eigen {
-
-/** CuboidConvolution
-  * \ingroup CXX11_NeuralNetworks_Module
-  *
-  * \brief Applies a 3D convolution over a multichannel input voxel block.
-  *
-  * The input parameter is expected to be a tensor with a rank of 4 or more (channels, depth, height, width, and optionally others).
-  * The kernel parameter is expected to be a 5D tensor (filters, channels, kernel_depth, kernel_height, kernel_width).
-  * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be filters, depth, height, width (and others if applicable).
-  *
-  * The input and kernel have to be in the same layout, and both row-major and
-  * col-major are supported. The shapes given above are for col-major layout.
-  * For row-major, all dimensions should be reversed.
-  *
-  * It is possible to swap the order of the depth, width, and height dimensions provided that the same order is used in the input, the kernel, and the output.
-  */
-template 
-EIGEN_ALWAYS_INLINE
-static const typename internal::conditional <
-    internal::traits::Layout == ColMajor,
-    TensorReshapingOp<
-        const DSizes::Index,
-                     internal::traits::NumDimensions>,
-        const TensorContractionOp<
-            const array::Index>, 1>,
-            const TensorReshapingOp<
-                const DSizes::Index, 2>,
-                const Kernel>,
-            const TensorReshapingOp<
-                const DSizes::Index, 2>,
-                const TensorVolumePatchOp > > >,
-    TensorReshapingOp<
-        const DSizes::Index,
-                     internal::traits::NumDimensions>,
-        const TensorContractionOp<
-            const array::Index>, 1>,
-            const TensorReshapingOp<
-                const DSizes::Index, 2>,
-                const TensorVolumePatchOp > ,
-                const TensorReshapingOp<
-                    const DSizes::Index, 2>,
-                    const Kernel> > > >::type
-CuboidConvolution(const Input& input, const Kernel& kernel,
-                  const DenseIndex stridePlanes = 1,
-                  const DenseIndex strideRows = 1,
-                  const DenseIndex strideCols = 1,
-                  const PaddingType padding_type = PADDING_SAME) {
-  typedef typename internal::traits::Index TensorIndex;
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input);
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > kern(kernel);
-
-  EIGEN_STATIC_ASSERT(internal::traits::Layout == internal::traits::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE);
-  static const bool isColMajor = (internal::traits::Layout == ColMajor);
-  static const int NumDims = internal::traits::NumDimensions;
-
-  // Number of filters to apply. This is the same as the output depth of the result.
-  const TensorIndex kernelFilters = isColMajor ? kern.dimensions()[0] : kern.dimensions()[4];
-  const TensorIndex kernelChannels = isColMajor ? kern.dimensions()[1] : kern.dimensions()[3];
-
-  // Spatial size of the kernel.
-  const TensorIndex kernelDepth = isColMajor ? kern.dimensions()[2] : kern.dimensions()[2];
-  const TensorIndex kernelRows = isColMajor ? kern.dimensions()[3] : kern.dimensions()[1];
-  const TensorIndex kernelCols = isColMajor ? kern.dimensions()[4] : kern.dimensions()[0];
-
-  if (isColMajor) {
-    eigen_assert(kernelChannels == in.dimension(0));
-  } else {
-    eigen_assert(kernelChannels == in.dimension(NumDims - 1));
-  }
-
-  const TensorIndex inputPlanes = isColMajor ? in.dimension(1) : in.dimension(NumDims - 2);
-  const TensorIndex inputRows = isColMajor ? in.dimension(2) : in.dimension(NumDims - 3);
-  const TensorIndex inputCols = isColMajor ? in.dimension(3) : in.dimension(NumDims - 4);
-
-  const float stride_planes_f = static_cast(stridePlanes);
-  const float stride_rows_f = static_cast(strideRows);
-  const float stride_cols_f = static_cast(strideCols);
-  TensorIndex out_depth;
-  TensorIndex out_height;
-  TensorIndex out_width;
-  switch (padding_type) {
-    case PADDING_VALID:
-      out_depth = ceil((inputPlanes - kernelDepth + 1.f) / stride_planes_f);
-      out_height = ceil((inputRows - kernelRows + 1.f) / stride_rows_f);
-      out_width = ceil((inputCols - kernelCols + 1.f) / stride_cols_f);
-      break;
-    case PADDING_SAME:
-      out_depth = ceil(inputPlanes / stride_planes_f);
-      out_height = ceil(inputRows / stride_rows_f);
-      out_width = ceil(inputCols / stride_cols_f);
-      break;
-    default:
-      eigen_assert(false && "unexpected padding");
-  }
-
-  DSizes kernel_dims;
-  if (isColMajor) {
-    kernel_dims[0] = kernelFilters;
-    kernel_dims[1] = kernelChannels * kernelDepth * kernelRows * kernelCols;
-  } else {
-    kernel_dims[0] = kernelChannels * kernelDepth * kernelRows * kernelCols;
-    kernel_dims[1] = kernelFilters;
-  }
-
-  // Molds the output of the patch extraction result into a 2D tensor:
-  // - the first dimension (dims[0]): the patch values to be multiplied with the kernels
-  // - the second dimension (dims[1]): everything else
-  DSizes pre_contract_dims;
-  if (isColMajor) {
-    pre_contract_dims[0] = kernelChannels * kernelDepth * kernelRows * kernelCols;
-    pre_contract_dims[1] = out_depth * out_height * out_width;
-    for (int i = 4; i < NumDims; ++i) {
-      pre_contract_dims[1] *= in.dimension(i);
-    }
-  } else {
-    pre_contract_dims[1] = kernelChannels * kernelDepth * kernelRows * kernelCols;
-    pre_contract_dims[0] = out_depth * out_height * out_width;
-    for (int i = 0; i < NumDims - 4; ++i) {
-      pre_contract_dims[0] *= in.dimension(i);
-    }
-  }
-
-  array, 1> contract_dims;
-  contract_dims[0] = IndexPair(1, 0);
-
-  // Molds the output of the contraction into the shape expected by the user
-  // (assuming ColMajor):
-  // - 1st dim: kernel filters
-  // - 2nd dim: output depth
-  // - 3nd dim: output height
-  // - 4rd dim: output width
-  // - 5th dim and beyond: everything else including batch size
-  DSizes post_contract_dims;
-  if (isColMajor) {
-    post_contract_dims[0] = kernelFilters;
-    post_contract_dims[1] = out_depth;
-    post_contract_dims[2] = out_height;
-    post_contract_dims[3] = out_width;
-    for (int i = 4; i < NumDims; ++i) {
-      post_contract_dims[i] = in.dimension(i);
-    }
-  } else {
-    post_contract_dims[NumDims - 1] = kernelFilters;
-    post_contract_dims[NumDims - 2] = out_depth;
-    post_contract_dims[NumDims - 3] = out_height;
-    post_contract_dims[NumDims - 4] = out_width;
-    for (int i = 0; i < NumDims - 4; ++i) {
-      post_contract_dims[i] = in.dimension(i);
-    }
-  }
-
-  return choose(
-      Cond::Layout == ColMajor>(),
-      kernel.reshape(kernel_dims)
-          .contract(input.extract_volume_patches(
-                             kernelDepth, kernelRows, kernelCols, stridePlanes,
-                             strideRows, strideCols, padding_type)
-                        .reshape(pre_contract_dims),
-                    contract_dims)
-          .reshape(post_contract_dims),
-      input.extract_volume_patches(kernelDepth, kernelRows, kernelCols,
-                                   stridePlanes, strideRows, strideCols,
-                                   padding_type)
-          .reshape(pre_contract_dims)
-          .contract(kernel.reshape(kernel_dims), contract_dims)
-          .reshape(post_contract_dims));
-}
-
-} // end namespace Eigen
-
-#endif  // EIGEN_CXX11_SRC_NEURAL_NETWORKS_CUBOID_CONVOLUTION_H
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Patch3d.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Patch3d.h
deleted file mode 100644
index 2864f8329990325c73aadb32018ae975809cb09d..0000000000000000000000000000000000000000
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Patch3d.h
+++ /dev/null
@@ -1,240 +0,0 @@
-#ifndef EIGEN_CXX11_SRC_NEURAL_NETWORKS_PATCH3D_H
-#define EIGEN_CXX11_SRC_NEURAL_NETWORKS_PATCH3D_H
-
-#if not defined(__CUDACC__)
-#include 
-#endif
-
-namespace Eigen {
-namespace internal {
-
-/** Extract3DPatches
- * \ingroup CXX11_NeuralNetworksModule
- *
- * \brief Extracts 3D patches from a multichannel input volume.
- *
- * The input parameter is expected to be a tensor with a rank of 4 or more
- * (channels, depth, height, width, optional others in col-major, and the
- * reverse order in row-major).
-
- * The return value will be a tensor of 3 more dimension than the input tensor.
- * In col-major, the first 4 dimensions of the result are: channels, patch_depth,
- * patch_height, patch_width. The next dimensions will identify the patch
- * position on the 3D grid of extracted patches: z, y, x. The remaining
- * dimensions, if any, will be the same as the 'other' dimensions of the input
- * tensor.
- */
-
-template 
-EIGEN_ALWAYS_INLINE static const TensorStridingOp<
-    const array::Index,
-                internal::traits::NumDimensions + 3>,
-    const TensorReshapingOp<
-        const DSizes::Index,
-                     internal::traits::NumDimensions + 3>,
-        const TensorPatchOp<
-            const DSizes::Index,
-                         internal::traits::NumDimensions>,
-            const TensorPaddingOp<
-                const array::Index>,
-                            internal::traits::NumDimensions>,
-                const Input> > > >
-Extract3DPatches(
-    const Input& input, const DenseIndex patchPlanes,
-    const DenseIndex patchRows, const DenseIndex patchCols,
-    const DenseIndex stridePlanes, const DenseIndex strideRows,
-    const DenseIndex strideCols,
-    const DenseIndex paddingZTop, const DenseIndex paddingZBottom,
-    const DenseIndex paddingTop, const DenseIndex paddingBottom,
-    const DenseIndex paddingLeft, const DenseIndex paddingRight,
-    const typename internal::traits::Scalar padding_value = 0) {
-
-  typedef typename internal::traits::Index TensorIndex;
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input);
-
-  EIGEN_STATIC_ASSERT(internal::traits::NumDimensions >= 4, YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-  static const bool isColMajor = (internal::traits::Layout == ColMajor);
-  static const int NumDims = internal::traits::NumDimensions;
-  static const int ExtDims = NumDims + 3;
-
-  // Tensor size after patch extraction. We add three dimensions to unpack the
-  // linear patch index into a 3D grid over which stride() can work.
-  DSizes pre_stride_dims;
-
-  if (isColMajor) {
-    pre_stride_dims[0] = in.dimension(0);
-    pre_stride_dims[1] = patchPlanes;
-    pre_stride_dims[2] = patchRows;
-    pre_stride_dims[3] = patchCols;
-  } else {
-    pre_stride_dims[ExtDims - 1] = in.dimension(NumDims - 1);
-    pre_stride_dims[ExtDims - 4] = patchCols;
-    pre_stride_dims[ExtDims - 3] = patchRows;
-    pre_stride_dims[ExtDims - 2] = patchPlanes;
-  }
-
-  const TensorIndex inputPlanes = isColMajor ? in.dimension(1) : in.dimension(NumDims - 2);
-  const TensorIndex inputRows = isColMajor ? in.dimension(2) : in.dimension(NumDims - 3);
-  const TensorIndex inputCols = isColMajor ? in.dimension(3) : in.dimension(NumDims - 4);
-
-  array, NumDims> paddings;
-  for (int i = 0; i < NumDims; ++i) {
-    paddings[i] = IndexPair(0, 0);
-  }
-
-  paddings[isColMajor ? 1 : (NumDims - 2)] = IndexPair(paddingZTop, paddingZBottom);
-  paddings[isColMajor ? 2 : (NumDims - 3)] = IndexPair(paddingTop, paddingBottom);
-  paddings[isColMajor ? 3 : (NumDims - 4)] = IndexPair(paddingLeft, paddingRight);
-
-  pre_stride_dims[isColMajor ? 4 : (ExtDims - 5)] = inputPlanes + paddingZBottom + paddingZTop - patchPlanes + 1;
-  pre_stride_dims[isColMajor ? 5 : (ExtDims - 6)] = inputRows + paddingTop + paddingBottom - patchRows + 1;
-  pre_stride_dims[isColMajor ? 6 : (ExtDims - 7)] = inputCols + paddingLeft + paddingRight - patchCols + 1;
-
-  if (isColMajor) {
-    for (int i = 7; i < NumDims + 3; ++i) {
-      pre_stride_dims[i] = in.dimension(i - 3);
-    }
-  } else {
-    for (int i = 0; i < NumDims - 4; ++i) {
-      pre_stride_dims[i] = in.dimension(i);
-    }
-  }
-
-  DSizes patch_dims;
-  if (isColMajor) {
-    patch_dims[0] = in.dimension(0);
-    patch_dims[1] = patchPlanes;
-    patch_dims[2] = patchRows;
-    patch_dims[3] = patchCols;
-    for (int i = 4; i < NumDims; ++i) {
-      patch_dims[i] = 1;
-    }
-  } else {
-    patch_dims[NumDims - 1] = in.dimension(NumDims - 1);
-    patch_dims[NumDims - 4] = patchCols;
-    patch_dims[NumDims - 3] = patchRows;
-    patch_dims[NumDims - 2] = patchPlanes;
-    for (int i = 0; i < NumDims - 4; i++) {
-      patch_dims[i] = 1;
-    }
-  }
-
-  array strides;
-  if (isColMajor) {
-    // No striding within the patches.
-    for (int i = 0; i < 4; ++i) {
-      strides[i] = 1;
-    }
-    // Apply striding in the spatial patch grid dimensions only.
-    strides[4] = stridePlanes;
-    strides[5] = strideRows;
-    strides[6] = strideCols;
-    // No striding in the remaining dimensions (batches, ...).
-    for (int i = 7; i < NumDims + 3; i++) {
-      strides[i] = 1;
-    }
-  } else {
-    // No striding within the patches.
-    for (int i = 1; i <= 4; ++i) {
-      strides[ExtDims - i] = 1;
-    }
-    // Apply striding in the spatial patch grid dimensions only.
-    strides[ExtDims - 7] = strideCols;
-    strides[ExtDims - 6] = strideRows;
-    strides[ExtDims - 5] = stridePlanes;
-    // No striding in the remaining dimensions (batches, ...).
-    for (int i = 0; i < NumDims - 4; i++) {
-      strides[i] = 1;
-    }
-  }
-
-  // TODO(mjanusz): Consider getting rid of pad(), and stride() and extend
-  // extract_patches to take additional parameters for padding/striding,
-  // similarly to extract_image_patches.
-  return input.pad(paddings, padding_value).extract_patches(patch_dims).reshape(pre_stride_dims).stride(strides);
-}
-
-
-template 
-EIGEN_ALWAYS_INLINE static const TensorStridingOp<
-    const array::Index,
-                internal::traits::NumDimensions + 3>,
-    const TensorReshapingOp<
-        const DSizes::Index,
-                     internal::traits::NumDimensions + 3>,
-        const TensorPatchOp<
-            const DSizes::Index,
-                         internal::traits::NumDimensions>,
-            const TensorPaddingOp<
-                const array::Index>,
-                            internal::traits::NumDimensions>,
-                const Input> > > >
-Extract3DPatches(
-    const Input& input, const DenseIndex patchPlanes,
-    const DenseIndex patchRows, const DenseIndex patchCols,
-    const DenseIndex stridePlanes, const DenseIndex strideRows,
-    const DenseIndex strideCols, const PaddingType padding_type,
-    const typename internal::traits::Scalar padding_value = 0) {
-  typedef typename internal::traits::Index TensorIndex;
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input);
-
-  EIGEN_STATIC_ASSERT(internal::traits::NumDimensions >= 4, YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-  static const bool isColMajor = (internal::traits::Layout == ColMajor);
-  static const int NumDims = internal::traits::NumDimensions;
-
-  const TensorIndex inputPlanes = isColMajor ? in.dimension(1) : in.dimension(NumDims - 2);
-  const TensorIndex inputRows = isColMajor ? in.dimension(2) : in.dimension(NumDims - 3);
-  const TensorIndex inputCols = isColMajor ? in.dimension(3) : in.dimension(NumDims - 4);
-
-  switch (padding_type) {
-    case PADDING_VALID:
-      // No padding in any dimension.
-      return Extract3DPatches(input, patchPlanes, patchRows, patchCols,
-                              stridePlanes, strideRows, strideCols,
-                              0, 0, 0, 0, 0, 0, padding_value);
-    case PADDING_SAME: {
-      // The side of the tensor before striding should be just the expected
-      // output times the stride.
-      const TensorIndex size_z = ceil(inputPlanes / static_cast(stridePlanes)) * stridePlanes;
-      const TensorIndex size_y = ceil(inputRows / static_cast(strideRows)) * strideRows;
-      const TensorIndex size_x = ceil(inputCols / static_cast(strideCols)) * strideCols;
-
-      // The size of the patch space is going to be: padded_input_size - patch_size + 1.
-      // This has to match the expected size before striding (pre_stride_dims).
-      // The deltas below extend the input to the expected size.
-      const TensorIndex dz = size_z + patchPlanes - 1 - inputPlanes;
-      const TensorIndex dy = size_y + patchRows - 1 - inputRows;
-      const TensorIndex dx = size_x + patchCols - 1 - inputCols;
-
-      return Extract3DPatches(input, patchPlanes, patchRows, patchCols,
-                              stridePlanes, strideRows, strideCols,
-                              dz - dz / 2, dz / 2,
-                              dy - dy / 2, dy / 2,
-                              dx - dx / 2, dx / 2,
-                              padding_value);
-    }
-    default:
-      eigen_assert(false && "unexpected padding");
-      // unreachable code to avoid missing return warning.
-      return Extract3DPatches(input, patchPlanes, patchRows, patchCols,
-                              stridePlanes, strideRows, strideCols,
-                              0, 0, 0, 0, 0, 0, padding_value);
-  }
-}
-
-// TODO(mjanusz): Switch this to a 'using' alias once CUDA supports C++11.
-template 
-struct Extract3DPatchesType {
-  typedef const TensorStridingOp< const array::Index, internal::traits::NumDimensions + 3>,
-      const TensorReshapingOp< const DSizes::Index, internal::traits::NumDimensions + 3>,
-      const TensorPatchOp< const DSizes::Index, internal::traits::NumDimensions>,
-      const TensorPaddingOp< const array< IndexPair::Index>, internal::traits::NumDimensions>,
-      const Input> > > > type;
-};
-
-}  // end namespace internal
-}  // end namespace Eigen
-
-#endif  // EIGEN_CXX11_SRC_NEURAL_NETWORKS_PATCH3D_H
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h
deleted file mode 100644
index 942b060ba761a2b31e6affc2d3714564ef243134..0000000000000000000000000000000000000000
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h
+++ /dev/null
@@ -1,433 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner 
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-#ifndef EIGEN_CXX11_NEURAL_NETWORKS_POOLING_H
-#define EIGEN_CXX11_NEURAL_NETWORKS_POOLING_H
-
-#include "Patch3d.h"
-
-namespace Eigen {
-
-/** SpatialMaxPooling
-  * \ingroup CXX11_NeuralNetworks_Module
-  *
-  * \brief Applies a max-pooling over a multichannel input image.
-  *
-  * The input parameter is expected to be a with a rank of 4 (channels, height, width, others in col-major, and the reverse of that in row-major).
-  *
-  * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be channels, height, width, and others (in col-major, and the reverse of that if the input was row-major).
-  *
-  * The order of the width and height dimensions can be swapped if needed.
-  *
-*/
-#if !defined(EIGEN_HAS_INDEX_LIST)
-template 
-EIGEN_ALWAYS_INLINE
-static const TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorReductionOp::Scalar>::type>, const Eigen::array, const TensorImagePatchOp > >
-#else
-template 
-EIGEN_ALWAYS_INLINE
-static const TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorReductionOp::Scalar>::type>, typename internal::conditional::Layout == ColMajor, const Eigen::IndexList, Eigen::type2index<2> >, const Eigen::IndexList, Eigen::type2index<3> > >::type, const TensorImagePatchOp > >
-#endif
-SpatialMaxPooling(const Input& input, DenseIndex patchRows, DenseIndex patchCols,
-                  DenseIndex strideRows, DenseIndex strideCols, const PaddingType padding_type,
-                  DenseIndex in_strideRows = 1, DenseIndex in_strideCols = 1)
-{
-  EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == 4, YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-  typedef typename internal::traits::Index TensorIndex;
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input);
-
-  const DenseIndex patchRowsEff = patchRows + (patchRows - 1) * (in_strideRows - 1);
-  const DenseIndex patchColsEff = patchCols + (patchCols - 1) * (in_strideCols - 1);
-
-  static const bool isColMajor = (internal::traits::Layout == ColMajor);
-  static const int idxRows = isColMajor ? 1 : 2;
-  static const int idxCols = isColMajor ? 2 : 1;
-
-  // Molds the output of the reduction into the shape expected by the user.
-  // (assuming col-major):
-  // - 1st dim: channels
-  // - 2nd dim: output height
-  // - 3rd dim: output width
-  // - 4th dim and beyond: everything else including batch size
-  Eigen::DSizes::NumDimensions> post_reduce_dims;
-  post_reduce_dims[0] = in.dimension(0);
-  if (padding_type == PADDING_VALID) {
-    post_reduce_dims[idxRows] = numext::ceil((in.dimension(idxRows) - patchRowsEff + 1.f) / static_cast(strideRows));
-    post_reduce_dims[idxCols] = numext::ceil((in.dimension(idxCols) - patchColsEff + 1.f) / static_cast(strideCols));
-  } else {
-    post_reduce_dims[idxRows] = numext::ceil(in.dimension(idxRows) / static_cast(strideRows));
-    post_reduce_dims[idxCols] = numext::ceil(in.dimension(idxCols) / static_cast(strideCols));
-  }
-  post_reduce_dims[3] = in.dimension(3);
-
-#if !defined(EIGEN_HAS_INDEX_LIST)
-  // nvcc doesn't support cxx11
-  Eigen::array reduction_dims;
-  if (isColMajor) {
-    reduction_dims[0] = 1;
-    reduction_dims[1] = 2;
-  } else {
-    reduction_dims[0] = 2;
-    reduction_dims[1] = 3;
-  }
-#else
-  // Take advantage of cxx11 to give the compiler information it can use to
-  // optimize the code.
-  typename internal::conditional::Layout == ColMajor, const Eigen::IndexList, Eigen::type2index<2> >, const Eigen::IndexList, Eigen::type2index<3> > >::type reduction_dims;
-#endif
-
-  return input.extract_image_patches(patchRows, patchCols, strideRows, strideCols, in_strideRows, in_strideCols, padding_type, -Eigen::NumTraits::Scalar>::type>::highest()).maximum(reduction_dims).reshape(post_reduce_dims);
-}
-
-/** CuboidMaxPooling
-  * \ingroup CXX11_NeuralNetworks_Module
-  *
-  * \brief Applies a max-pooling over a multichannel input volume.
-  *
-  * The input parameter is expected to be a tensor with a rank of 5 (channels, depth, height, width, others in col-major, and the reverse of that in row-major).
-  *
-  * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be channels, depth, height, width, and others (in col-major, and the reverse of that if the input was row-major).
-  *
-  * The order of the depth, width and height dimensions can be swapped if needed.
-  *
-*/
-#if !defined(EIGEN_HAS_INDEX_LIST)
-template 
-EIGEN_ALWAYS_INLINE static const TensorReshapingOp<
-    const Eigen::DSizes::NumDimensions>,
-    const TensorReductionOp<
-        internal::MaxReducer, const Eigen::array,
-        const TensorReshapingOp<
-            const Eigen::DSizes,
-            const TensorVolumePatchOp > > >
-#else
-template 
-EIGEN_ALWAYS_INLINE static const TensorReshapingOp<
-    const Eigen::DSizes::NumDimensions>,
-    const TensorReductionOp<
-        internal::MaxReducer,
-        const Eigen::IndexList >,
-        const TensorReshapingOp<
-            const Eigen::DSizes,
-            const TensorVolumePatchOp > > >
-#endif
-CuboidMaxPooling(const Input& input, DenseIndex patchPlanes,
-                 DenseIndex patchRows, DenseIndex patchCols,
-                 DenseIndex stridePlanes, DenseIndex strideRows,
-                 DenseIndex strideCols, const PaddingType padding_type) {
-  EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == 5, YOU_MADE_A_PROGRAMMING_MISTAKE);
-  static const bool isColMajor = (internal::traits::Layout == ColMajor);
-
-  typedef typename internal::traits::Index TensorIndex;
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input);
-
-  static const int idxPlanes = isColMajor ? 1 : 3;
-  static const int idxRows = 2;
-  static const int idxCols = isColMajor ? 3 : 1;
-
-  // Molds the output of the reduction into the shape expected by the used
-  // (assuming col-major):
-  // - 1st dim: channels
-  // - 2nd dim: output depth
-  // - 3rd dim: output height
-  // - 4th dim: output width
-  // - 5th dim and beyond: everything else including batch size
-  Eigen::DSizes::NumDimensions> post_reduce_dims;
-  post_reduce_dims[0] = in.dimension(0);
-  if (padding_type == PADDING_VALID) {
-    post_reduce_dims[idxPlanes] = numext::ceil((in.dimension(idxPlanes) - patchPlanes + 1.f) / static_cast(stridePlanes));
-    post_reduce_dims[idxRows] = numext::ceil((in.dimension(idxRows) - patchRows + 1.f) / static_cast(strideRows));
-    post_reduce_dims[idxCols] = numext::ceil((in.dimension(idxCols) - patchCols + 1.f) / static_cast(strideCols));
-  } else {
-    post_reduce_dims[idxPlanes] = numext::ceil(in.dimension(idxPlanes) / static_cast(stridePlanes));
-    post_reduce_dims[idxRows] = numext::ceil(in.dimension(idxRows) / static_cast(strideRows));
-    post_reduce_dims[idxCols] = numext::ceil(in.dimension(idxCols) / static_cast(strideCols));
-  }
-  post_reduce_dims[4] = in.dimension(4);
-
-  Eigen::DSizes pre_reduce_dims;
-  pre_reduce_dims[1] = patchRows * patchCols * patchPlanes;
-  if (isColMajor) {
-    pre_reduce_dims[0] = post_reduce_dims[0];
-    pre_reduce_dims[2] = post_reduce_dims[1] * post_reduce_dims[2] * post_reduce_dims[3] * post_reduce_dims[4];
-  } else {
-    pre_reduce_dims[0] = post_reduce_dims[0] * post_reduce_dims[1] * post_reduce_dims[2] * post_reduce_dims[3];
-    pre_reduce_dims[2] = post_reduce_dims[4];
-  }
-
-#if !defined(EIGEN_HAS_INDEX_LIST)
-  // nvcc doesn't support cxx11
-  Eigen::array reduction_dims;
-  reduction_dims[0] = 1;
-#else
-  // Take advantage of cxx11 to give the compiler information it can use to
-  // optimize the code.
-  Eigen::IndexList > reduction_dims;
-#endif
-  return input.extract_volume_patches(patchPlanes, patchRows, patchCols,
-                                      stridePlanes, strideRows, strideCols,
-                                      padding_type, -Eigen::NumTraits::highest())
-      .reshape(pre_reduce_dims)
-      .maximum(reduction_dims)
-      .reshape(post_reduce_dims);
-}
-
-
-/** SpatialAvgPooling
-  * \ingroup CXX11_NeuralNetworks_Module
-  *
-  * \brief Applies an average pooling over a multichannel input image.
-  *
-  * The input parameter is expected to be a tensor with a rank of 4 (channels, height, width, others in col-major, and the reverse of that in row-major).
-  *
-  * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be channels, height, width, and others (in col-major, and the reverse of that if the input was row-major).
-  *
-  * The order of the width and height dimensions can be swapped if needed.
-  *
-*/
-namespace internal {
-
-template  struct AvgPoolMeanReducer
-{
-#if (EIGEN_ARCH_i386 || EIGEN_ARCH_x86_64) && !defined(__CUDACC__)
-  // We only support packet access for floats.
-  static const bool PacketAccess = internal::is_same::value;
-#else
-  static const bool PacketAccess = false;
-#endif
-  static const bool IsStateful = true;
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE AvgPoolMeanReducer() : scalarCount_(0) {
-    typedef typename packet_traits::type Packet;
-    packetCount_ = pset1(0.0);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) {
-    if (t != -Eigen::NumTraits::highest()) {
-      (*accum) = (*accum) + t;
-      scalarCount_++;
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
-    return static_cast(0);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
-    eigen_assert(scalarCount_ > 0);
-    return accum / scalarCount_;
-  }
-
-#if (EIGEN_ARCH_i386 || EIGEN_ARCH_x86_64) && !defined(__CUDACC__)
-#ifdef EIGEN_VECTORIZE_AVX
-#define pequal(a,b) _mm256_cmp_ps(a,b,_CMP_EQ_UQ)
-#define psel(a,b,false_mask) _mm256_blendv_ps(a,b,false_mask)
-#else
-#define pequal(a,b) _mm_cmpeq_ps(a,b)
-#define psel(a,b,false_mask) _mm_or_ps(_mm_andnot_ps(false_mask, a), _mm_and_ps(false_mask, b))
-#endif
-
-  template 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) {
-    reducePacketWithType(static_cast(0), p, accum);
-  }
-
-  template 
-  void reducePacketWithType(T, const Packet& p, Packet* accum) {
-    Packet skip_mask = pequal(p, pset1(-Eigen::NumTraits::highest()));
-    (*accum) = padd(*accum, psel(p, pset1(0), skip_mask));
-    packetCount_ = padd(packetCount_, psel(pset1(1), pset1(0), skip_mask));
-  }
-
-  template 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
-    return pset1(0);
-  }
-
-  template 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
-    return pdiv(vaccum, packetCount_);
-  }
-  template 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
-    return (saccum + predux(vaccum)) / (scalarCount_ + predux(packetCount_));
-  }
-#endif
-
- protected:
-    typedef typename packet_traits::type Packet;
-    int scalarCount_;
-    Packet packetCount_;
-};
-
-}  // namespace internal
-
-#if !defined(EIGEN_HAS_INDEX_LIST)
-template 
-EIGEN_ALWAYS_INLINE
-static const TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorReductionOp::Scalar>::type>, const Eigen::array, const TensorImagePatchOp > >
-#else
-template 
-EIGEN_ALWAYS_INLINE
-static const TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorReductionOp::Scalar>::type>, typename internal::conditional::Layout == ColMajor, const Eigen::IndexList, Eigen::type2index<2> >, const Eigen::IndexList, Eigen::type2index<3> > >::type, const TensorImagePatchOp > >
-#endif
-SpatialAvgPooling(const Input& input, DenseIndex patchRows, DenseIndex patchCols,
-                  DenseIndex strideRows, DenseIndex strideCols, const PaddingType padding_type,
-                  DenseIndex in_strideRows = 1, DenseIndex in_strideCols = 1)
-{
-  EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == 4, YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-  typedef typename internal::traits::Index TensorIndex;
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input);
-
-  const DenseIndex patchRowsEff = patchRows + (patchRows - 1) * (in_strideRows - 1);
-  const DenseIndex patchColsEff = patchCols + (patchCols - 1) * (in_strideCols - 1);
-
-  static const bool isColMajor = (internal::traits::Layout == ColMajor);
-  static const int idxRows = isColMajor ? 1 : 2;
-  static const int idxCols = isColMajor ? 2 : 1;
-
-  // Molds the output of the reduction into the shape expected by the user.
-  // (assuming col-major):
-  // - 1st dim: channels
-  // - 2nd dim: output height
-  // - 3rd dim: output width
-  // - 4th dim and beyond: everything else including batch size
-  Eigen::DSizes::NumDimensions> post_reduce_dims;
-  post_reduce_dims[0] = in.dimension(0);
-  if (padding_type == PADDING_VALID) {
-    post_reduce_dims[idxRows] = numext::ceil((in.dimension(idxRows) - patchRowsEff + 1.f) / static_cast(strideRows));
-    post_reduce_dims[idxCols] = numext::ceil((in.dimension(idxCols) - patchColsEff + 1.f) / static_cast(strideCols));
-  } else {
-    post_reduce_dims[idxRows] = numext::ceil(in.dimension(idxRows) / static_cast(strideRows));
-    post_reduce_dims[idxCols] = numext::ceil(in.dimension(idxCols) / static_cast(strideCols));
-  }
-  post_reduce_dims[3] = in.dimension(3);
-
-  typedef typename internal::remove_const::Scalar>::type CoeffReturnType;
-  internal::AvgPoolMeanReducer mean_with_nan;
-
-#if !defined(EIGEN_HAS_INDEX_LIST)
-  // nvcc doesn't support cxx11
-  Eigen::array reduction_dims;
-  if (isColMajor) {
-    reduction_dims[0] = 1;
-    reduction_dims[1] = 2;
-  } else {
-    reduction_dims[0] = 2;
-    reduction_dims[1] = 3;
-  }
-#else
-  // Take advantage of cxx11 to give the compiler information it can use to
-  // optimize the code.
-  typename internal::conditional::Layout == ColMajor, const Eigen::IndexList, Eigen::type2index<2> >, const Eigen::IndexList, Eigen::type2index<3> > >::type reduction_dims;
-#endif
-  return input.extract_image_patches(patchRows, patchCols, strideRows, strideCols, in_strideRows, in_strideCols, padding_type, -Eigen::NumTraits::Scalar>::type>::highest()).reduce(reduction_dims, mean_with_nan).reshape(post_reduce_dims);
-}
-
-
-/** CuboidAvgPooling
-  * \ingroup CXX11_NeuralNetworks_Module
-  *
-  * \brief Applies an average pooling over a multichannel input volume.
-  *
-  * The input parameter is expected to be a tensor with a rank of 5 (channels, depth, height, width, others, and the reverse of that in row-major).
-  *
-  * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be channels, depth, width, and others (in col-major, and the reverse of that if the input was row-major).
-  *
-  * The order of the depth, width and height dimensions can be swapped if needed.
-  *
-*/
-#if !defined(EIGEN_HAS_INDEX_LIST)
-template 
-EIGEN_ALWAYS_INLINE static const TensorReshapingOp<
-    const Eigen::DSizes::NumDimensions>,
-    const TensorReductionOp<
-        internal::AvgPoolMeanReducer, const Eigen::array,
-        const TensorReshapingOp<
-            const Eigen::DSizes,
-            const TensorVolumePatchOp > > >
-#else
-template 
-EIGEN_ALWAYS_INLINE static const TensorReshapingOp<
-      const Eigen::DSizes::NumDimensions>,
-      const TensorReductionOp<
-          internal::AvgPoolMeanReducer,
-          const Eigen::IndexList >,
-          const TensorReshapingOp<
-              const Eigen::DSizes,
-              const TensorVolumePatchOp > > >
-#endif
-CuboidAvgPooling(const Input& input, DenseIndex patchPlanes,
-                 DenseIndex patchRows, DenseIndex patchCols,
-                 DenseIndex stridePlanes, DenseIndex strideRows,
-                 DenseIndex strideCols, const PaddingType padding_type) {
-  EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == 5, YOU_MADE_A_PROGRAMMING_MISTAKE);
-  static const bool isColMajor = (internal::traits::Layout == ColMajor);
-
-  typedef typename internal::traits::Index TensorIndex;
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input);
-
-  static const int idxPlanes = isColMajor ? 1 : 3;
-  static const int idxRows = 2;
-  static const int idxCols = isColMajor ? 3 : 1;
-  // Molds the output of the reduction into the shape expected by the used
-  // (assuming col-major):
-  // - 1st dim: channels
-  // - 2nd dim: outupt depth
-  // - 3rd dim: output height
-  // - 4th dim: output width
-  // - 5th dim and beyond: everything else including batch size
-  Eigen::DSizes::NumDimensions> post_reduce_dims;
-  post_reduce_dims[0] = in.dimension(0);
-  if (padding_type == PADDING_VALID) {
-    post_reduce_dims[idxPlanes] = numext::ceil((in.dimension(idxPlanes) - patchPlanes + 1.f) / static_cast(stridePlanes));
-    post_reduce_dims[idxRows] = numext::ceil((in.dimension(idxRows) - patchRows + 1.f) / static_cast(strideRows));
-    post_reduce_dims[idxCols] = numext::ceil((in.dimension(idxCols) - patchCols + 1.f) / static_cast(strideCols));
-  } else {
-    post_reduce_dims[idxPlanes] = numext::ceil(in.dimension(idxPlanes) / static_cast(stridePlanes));
-    post_reduce_dims[idxRows] = numext::ceil(in.dimension(idxRows) / static_cast(strideRows));
-    post_reduce_dims[idxCols] = numext::ceil(in.dimension(idxCols) / static_cast(strideCols));
-  }
-  post_reduce_dims[4] = in.dimension(4);
-
-  Eigen::DSizes pre_reduce_dims;
-  pre_reduce_dims[1] = patchRows * patchCols * patchPlanes;
-  if (isColMajor) {
-    pre_reduce_dims[0] = post_reduce_dims[0];
-    pre_reduce_dims[2] = post_reduce_dims[1] * post_reduce_dims[2] * post_reduce_dims[3] * post_reduce_dims[4];
-  } else {
-    pre_reduce_dims[0] = post_reduce_dims[0] * post_reduce_dims[1] * post_reduce_dims[2] * post_reduce_dims[3];
-    pre_reduce_dims[2] = post_reduce_dims[4];
-  }
-
-  typedef typename internal::remove_const::Scalar>::type CoeffReturnType;
-  internal::AvgPoolMeanReducer mean_with_nan;
-
-#if !defined(EIGEN_HAS_INDEX_LIST)
-  // nvcc doesn't support cxx11
-  Eigen::array reduction_dims;
-  reduction_dims[0] = 1;
-#else
-  // Take advantage of cxx11 to give the compiler information it can use to
-  // optimize the code.
-  Eigen::IndexList > reduction_dims;
-#endif
-  return input.extract_volume_patches(patchPlanes, patchRows, patchCols,
-                                      stridePlanes, strideRows, strideCols,
-                                      padding_type, -Eigen::NumTraits::highest())
-      .reshape(pre_reduce_dims)
-      .reduce(reduction_dims, mean_with_nan)
-      .reshape(post_reduce_dims);
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_NEURAL_NETWORKS_POOLING_H
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/SoftMax.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/SoftMax.h
deleted file mode 100644
index f0e21ab9c2eda60813db95583f14d2cf76a38700..0000000000000000000000000000000000000000
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/SoftMax.h
+++ /dev/null
@@ -1,83 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner 
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-#ifndef EIGEN_CXX11_NEURAL_NETWORKS_SOFTMAX_H
-#define EIGEN_CXX11_NEURAL_NETWORKS_SOFTMAX_H
-
-namespace Eigen {
-
-/** SoftMax
-  * \ingroup CXX11_NeuralNetworks_Module
-  *
-  * \brief Applies a softmax
-  *
-  * The input parameter is expected to be a col-major tensor with a rank of 2 (depth and other).
-  *
-  * The result can be assigned to a tensor of rank and dimensions equal to that of the input. The result will be laid out in col-major order.
-  *
-*/
-
-namespace {
-class SoftmaxOp {
- public:
-  EIGEN_ALWAYS_INLINE SoftmaxOp(const float beta) : beta_(beta) { }
-
-  template  EIGEN_ALWAYS_INLINE
-  typename Input::Dimensions dimensions(const Input& input) const {
-    return input.dimensions();
-  }
-
-  template 
-  void eval(const Input& input, Output& output, const Device& device) const
-  {
-#if !defined(EIGEN_HAS_INDEX_LIST)
-    // nvcc doesn't support cxx11
-    Eigen::array::Index, 1> depth_dim;
-    depth_dim[0] = 0;
-    Eigen::array::Index, 2> bcast;
-    bcast[0] = dimensions(input)[0];
-    bcast[1] = 1;
-    DSizes::Index, 2> dims2d;
-    dims2d[0] = 1;
-    dims2d[1] = dimensions(input)[1];
-#else
-    // Take advantage of cxx11 to give the compiler information it can use to
-    // optimize the code.
-    Eigen::IndexList> depth_dim;
-    Eigen::IndexList> bcast;
-    bcast.set(0, dimensions(input)[0]);
-    Eigen::IndexList, typename internal::traits::Index> dims2d;
-    dims2d.set(1, dimensions(input)[1]);
-#endif
-
-    output.device(device) = ((input - input.maximum(depth_dim).eval().reshape(dims2d).broadcast(bcast)) * beta_).exp();
-    output.device(device) = output / (output.sum(depth_dim).eval().reshape(dims2d).broadcast(bcast));
-  }
-
- private:
-  const float beta_;
-};
-}
-
-
-template 
-EIGEN_ALWAYS_INLINE
-static const TensorCustomUnaryOp
-SoftMax(const Input& input, const float beta)
-{
-  EIGEN_STATIC_ASSERT(internal::traits::Layout == ColMajor, YOU_MADE_A_PROGRAMMING_MISTAKE);
-  EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == 2, YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-  const SoftmaxOp op(beta);
-  return input.customOp(op);
-}
-
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_NEURAL_NETWORKS_SOFTMAX_H
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/SpatialConvolutions.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/SpatialConvolutions.h
deleted file mode 100644
index 8e2ddca6b5d0dabe63783049bb60e6699e682cb7..0000000000000000000000000000000000000000
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/SpatialConvolutions.h
+++ /dev/null
@@ -1,775 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner 
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-#ifndef EIGEN_CXX11_NEURAL_NETWORKS_SPATIAL_CONVOLUTIONS_H
-#define EIGEN_CXX11_NEURAL_NETWORKS_SPATIAL_CONVOLUTIONS_H
-
-namespace Eigen {
-
-namespace internal {
-
-// These optimizations require vector instructions
-#ifdef EIGEN_VECTORIZE
-
-// TODO: Consolidate this part of the code with the image patch extraction code
-// since they are both very similar.
-template 
-class TensorContractionInputMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment>
-{
- public:
-  typedef TensorContractionInputMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> Self;
-  typedef TensorContractionSubMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> SubMapper;
-  typedef SubMapper VectorMapper;
-  typedef SubMapper LinearMapper;
-  typedef Scalar_ Scalar;
-  typedef typename packet_traits::type Packet;
-
-  TensorContractionInputMapper(const TensorEvaluator >, Device>& tensor,
-                               const nocontract_t&, const nocontract_t&,
-                               const contract_t&, const contract_t&)
-      : m_impl(tensor.impl().impl())
-  {
-    Index patch_rows;
-    Index patch_depth;
-    if (internal::traits::Layout == ColMajor) {
-      patch_depth = tensor.impl().dimensions()[0];
-      patch_rows = tensor.impl().dimensions()[1];
-      m_patch_cols = tensor.impl().dimensions()[2];
-      m_num_patches = tensor.impl().dimensions()[3];
-    } else {
-      static const int NumDims = tensor.impl().dimensions().size();
-      patch_depth = tensor.impl().dimensions()[NumDims - 1];
-      patch_rows = tensor.impl().dimensions()[NumDims - 2];
-      m_patch_cols = tensor.impl().dimensions()[NumDims - 3];
-      m_num_patches = tensor.impl().dimensions()[NumDims - 4];
-    }
-    m_patch_row_inflate_strides = tensor.impl().rowInflateStride();
-    m_patch_col_inflate_strides = tensor.impl().colInflateStride();
-
-    m_colStride = patch_rows;
-
-    m_outputRows = tensor.impl().outputRows();
-    m_row_strides = tensor.impl().userRowStride();
-    m_col_strides = tensor.impl().userColStride();
-
-    m_in_row_strides = tensor.impl().userInRowStride();
-    m_in_col_strides = tensor.impl().userInColStride();
-
-    if (internal::traits::Layout == ColMajor) {
-      m_inputRows = tensor.impl().impl().dimensions()[1];
-      m_inputCols = tensor.impl().impl().dimensions()[2];
-    } else {
-      static const int NumDims = tensor.impl().impl().dimensions().size();
-      m_inputRows = tensor.impl().impl().dimensions()[NumDims - 2];
-      m_inputCols = tensor.impl().impl().dimensions()[NumDims - 3];
-    }
-
-    m_rowInputStride = patch_depth;
-    m_colInputStride = patch_depth * m_inputRows;
-    m_patchInputStride = patch_depth * m_inputRows * m_inputCols;
-
-    m_rowPaddingTop = tensor.impl().rowPaddingTop();
-    m_colPaddingLeft = tensor.impl().colPaddingLeft();
-
-    m_fastInputRowStride = internal::TensorIntDivisor(m_patch_row_inflate_strides);
-    m_fastInputColStride = internal::TensorIntDivisor(m_patch_col_inflate_strides);
-    m_fastNumPatches = internal::TensorIntDivisor(m_num_patches);
-    m_fastColStride = internal::TensorIntDivisor(m_colStride);
-    m_fastOutputRows = internal::TensorIntDivisor(m_outputRows);
-    m_fastDimZero = internal::TensorIntDivisor(patch_depth);
-  }
-
-  TensorContractionInputMapper(const TensorContractionInputMapper& base_mapper) :
-      m_impl(base_mapper.m_impl) {
-    m_patch_cols = base_mapper.m_patch_cols;
-    m_num_patches = base_mapper.m_num_patches;
-    m_patch_row_inflate_strides = base_mapper.m_patch_row_inflate_strides;
-    m_patch_col_inflate_strides = base_mapper.m_patch_col_inflate_strides;
-
-    m_colStride = base_mapper.m_colStride;
-
-    m_rowInputStride = base_mapper.m_rowInputStride;
-    m_colInputStride = base_mapper.m_colInputStride;
-    m_patchInputStride = base_mapper.m_patchInputStride;
-
-    m_inputRows = base_mapper.m_inputRows;
-    m_inputCols = base_mapper.m_inputCols;
-
-    m_outputRows = base_mapper.m_outputRows;
-    m_row_strides = base_mapper.m_row_strides;
-    m_col_strides = base_mapper.m_col_strides;
-
-    m_in_row_strides = base_mapper.m_in_row_strides;
-    m_in_col_strides = base_mapper.m_in_col_strides;
-
-    m_rowPaddingTop = base_mapper.m_rowPaddingTop;
-    m_colPaddingLeft = base_mapper.m_colPaddingLeft;
-
-    m_fastInputRowStride = base_mapper.m_fastInputRowStride;
-    m_fastInputColStride = base_mapper.m_fastInputColStride;
-    m_fastNumPatches = base_mapper.m_fastNumPatches;
-    m_fastColStride = base_mapper.m_fastColStride;
-    m_fastOutputRows = base_mapper.m_fastOutputRows;
-    m_fastDimZero = base_mapper.m_fastDimZero;
-  }
-
- // If true, turns off some optimizations for loading packets since the image
-  // patches are "non-standard" such as there are non-trivial strides or
-  // inflations in the input.
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE bool nonStandardPatches() const {
-    return m_in_row_strides != 1 || m_in_col_strides != 1 || m_patch_row_inflate_strides != 1 || m_patch_col_inflate_strides != 1;
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE SubMapper getSubMapper(Index i, Index j) const {
-    return SubMapper(*this, i, j);
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
-    return LinearMapper(*this, i, j);
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Scalar operator()(Index row) const {
-    Index rowIndex, colIndex, otherIndex;
-    computeBaseIndices(0, rowIndex, colIndex, otherIndex);
-    return loadCoeff(row, rowIndex, colIndex, otherIndex);
-  }
-
-  // Load the coefficient at the patchIndex location instead of the usual m_rowIndex,
-  // m_colIndex, m_otherIndex. This is currently only used by the gpu code.  EIGEN_DEVICE_FUNC
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE Scalar operator()(Index row, Index patchIndex) const {
-    Index rowIndex, colIndex, otherIndex;
-    computeBaseIndices(patchIndex, rowIndex, colIndex, otherIndex);
-    return loadCoeff(row, rowIndex, colIndex, otherIndex);
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Packet loadPacket(Index row) const {
-    Index rowIndex, colIndex, otherIndex;
-    computeBaseIndices(0, rowIndex, colIndex, otherIndex);
-    return loadPacket(row, rowIndex, colIndex, otherIndex);
-  }
-
-  // Load the packet at the patchIndex location instead of the usual m_rowIndex,
-  // m_colIndex, m_otherIndex. This is currently only used by the gpu code.
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Packet loadPacket(Index row, Index patchIndex) const {
-    Index rowIndex, colIndex, otherIndex;
-    computeBaseIndices(patchIndex, rowIndex, colIndex, otherIndex);
-    return loadPacket(row, rowIndex, colIndex, otherIndex);
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE const TensorEvaluator& impl() const { return m_impl; }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Index patchDepth() const { return m_rowInputStride; }
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Index patchRows() const { return m_colStride; }
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Index patchCols() const { return m_patch_cols; }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Packet packetNoPadding(const Index depth, const Index baseIndex) const {
-    const Index inputIndex = depth + baseIndex;
-    return m_impl.template packet(inputIndex);
-  }
-
- private:
-  friend class TensorContractionSubMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment>;
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE Scalar loadCoeff(Index patchId, Index rowIndex, Index colIndex, Index otherIndex) const {
-    // Find the offset of the element wrt the location of the first element.
-    const Index patchOffset = patchId / m_fastDimZero;
-
-    const Index colOffset = patchOffset / m_fastColStride;
-    const Index inputCol = colIndex + colOffset * m_in_col_strides;
-    const Index origInputCol = (m_patch_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInputColStride) : 0);
-    const Index rowOffset = patchOffset - colOffset * m_colStride;
-    const Index inputRow = rowIndex + rowOffset * m_in_row_strides;
-    const Index origInputRow = (m_patch_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInputRowStride) : 0);
-    if (origInputCol < 0 | origInputRow < 0 | origInputCol >= m_inputCols | origInputRow >= m_inputRows |
-        (inputCol != origInputCol * m_patch_col_inflate_strides) | (inputRow != origInputRow * m_patch_row_inflate_strides)) {
-      return Scalar(0);
-    }
-    const Index depth = patchId - patchOffset * patchDepth();
-    const Index inputIndex = depth + origInputRow * m_rowInputStride + origInputCol * m_colInputStride + otherIndex;
-    return m_impl.coeff(inputIndex);
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_STRONG_INLINE Scalar loadCoeffStandard(Index patchId, Index rowIndex, Index colIndex, Index otherIndex) const {
-    eigen_assert(!nonStandardPatches());
-
-    // Find the offset of the element wrt the location of the first element.
-    const Index patchOffset = patchId / m_fastDimZero;
-
-    const Index colOffset = patchOffset / m_fastColStride;
-    const Index inputCol = colIndex + colOffset;
-    const Index rowOffset = patchOffset - colOffset * m_colStride;
-    const Index inputRow = rowIndex + rowOffset;
-    if (inputCol < 0 || inputCol >= m_inputCols || inputRow < 0 || inputRow >= m_inputRows) {
-      return Scalar(0);
-    }
-    const Index depth = patchId - patchOffset * patchDepth();
-    const Index inputIndex = depth + inputRow * m_rowInputStride + inputCol * m_colInputStride + otherIndex;
-    return m_impl.coeff(inputIndex);
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Packet loadPacket(Index patchId, Index rowIndex, Index colIndex, Index otherIndex) const {
-    const Index packetSize = internal::unpacket_traits::size;
-    EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(patchId < patchDepth()*patchRows()*m_patch_cols);
-
-    if (nonStandardPatches()) {
-      return packetWithPossibleZero(patchId, rowIndex, colIndex, otherIndex);
-    }
-    return loadPacketStandard(patchId, rowIndex, colIndex, otherIndex);
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Packet loadPacketStandard(Index patchId, Index rowIndex, Index colIndex, Index otherIndex) const {
-    const Index packetSize = internal::unpacket_traits::size;
-    EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(patchId < patchDepth()*patchRows()*m_patch_cols);
-
-    eigen_assert(!nonStandardPatches());
-
-    if ((patchDepth() % packetSize) == 0) {
-      return loadPacketFast(patchId, rowIndex, colIndex, otherIndex);
-    }
-    else {
-      const Index patchOffsets[2] = {patchId / m_fastDimZero, (patchId + packetSize - 1) / m_fastDimZero};
-
-      const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, patchOffsets[1] / m_fastColStride};
-
-      const Index inputCols[2] = {colIndex + colOffsets[0], colIndex + colOffsets[1]};
-      if (inputCols[0] >= m_inputCols | inputCols[1] < 0) {
-        // all zeros
-        return internal::pset1(Scalar(0));
-      }
-
-      if (inputCols[0] == inputCols[1]) {
-        const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0]*m_colStride, patchOffsets[1] - colOffsets[1]*m_colStride};
-        eigen_assert(rowOffsets[0] <= rowOffsets[1]);
-        const Index inputRows[2] = {rowIndex + rowOffsets[0], rowIndex + rowOffsets[1]};
-
-        if (inputRows[0] >= m_inputRows | inputRows[1] < 0) {
-          // all zeros
-          return internal::pset1(Scalar(0));
-        }
-
-        if (inputRows[0] >= 0 & inputRows[1] < m_inputRows) {
-          // no padding
-          const Index depth = patchId - patchOffsets[0] * patchDepth();
-          const Index inputIndex = depth + inputRows[0] * m_rowInputStride + inputCols[0] * m_colInputStride + otherIndex;
-          return m_impl.template packet(inputIndex);
-        }
-      }
-    }
-    return packetWithPossibleZero(patchId, rowIndex, colIndex, otherIndex);
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Packet loadPacketFast(Index patchId, Index rowIndex, Index colIndex, Index otherIndex) const {
-    const Index packetSize = internal::unpacket_traits::size;
-    EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
-    eigen_assert(patchId < patchDepth()*patchRows()*m_patch_cols);
-
-    eigen_assert(!nonStandardPatches());
-    eigen_assert((patchDepth() % packetSize) == 0);
-    // Find the offset of the element wrt the location of the first element.
-    const Index patchOffset = patchId / m_fastDimZero;
-    eigen_assert((patchId + packetSize - 1)  / m_fastDimZero == patchOffset);
-
-    const Index colOffset = patchOffset / m_fastColStride;
-    const Index inputCol = colIndex + colOffset;
-    const Index rowOffset = patchOffset - colOffset*m_colStride;
-    const Index inputRow = rowIndex + rowOffset;
-    if (inputCol < 0 | inputRow < 0 | inputCol >= m_inputCols | inputRow >= m_inputRows) {
-      // all zeros
-      return internal::pset1(Scalar(0));
-    }
-    // no padding
-    const Index depth = patchId - patchOffset * patchDepth();
-    const Index inputIndex = depth + inputRow * m_rowInputStride + inputCol * m_colInputStride + otherIndex;
-    return m_impl.template packet(inputIndex);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet packetWithPossibleZero(Index patchId, Index rowIndex, Index colIndex, Index otherIndex) const
-  {
-    const int packetSize = internal::unpacket_traits::size;
-    EIGEN_ALIGN_MAX typename internal::remove_const::type values[packetSize];
-    for (int i = 0; i < packetSize; ++i) {
-      values[i] = loadCoeff(patchId+i, rowIndex, colIndex, otherIndex);
-    }
-    Packet rslt = internal::pload(values);
-    return rslt;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void computeBaseIndices(Index patchIndex, Index& rowIndex, Index& colIndex, Index& otherIndex) const {
-    const int NumInputDims = array_size::Dimensions>::value;
-    otherIndex = (NumInputDims == 3) ? 0 : patchIndex / m_fastNumPatches;
-    const Index patch2DIndex = (NumInputDims == 3) ? patchIndex : (patchIndex - otherIndex * m_num_patches);
-    otherIndex *= m_patchInputStride;
-    colIndex = patch2DIndex / m_fastOutputRows;
-    rowIndex = patch2DIndex - colIndex * m_outputRows;
-    colIndex = colIndex * m_col_strides - m_colPaddingLeft;
-    rowIndex = rowIndex * m_row_strides - m_rowPaddingTop;
-  }
-
-  Index m_patch_cols;    // number of colums in the patch
-  Index m_num_patches;   // number of patches to extract.
-  Index m_patch_row_inflate_strides;  // the strides for row inflation in the image patch
-  Index m_patch_col_inflate_strides;  // the strides for col inflation in the image patch
-  // Fast representation of inflation strides.
-  internal::TensorIntDivisor m_fastInputRowStride;
-  internal::TensorIntDivisor m_fastInputColStride;
-
-  Index m_otherStride;
-  Index m_colStride;
-  internal::TensorIntDivisor m_fastNumPatches;
-  internal::TensorIntDivisor m_fastColStride;
-
-  Index m_rowInputStride;     // row stride in the input tensor
-  Index m_colInputStride;     // col stride in the input tensor
-  Index m_patchInputStride;   // patch stride in the input tensor
-
-  Index m_inputRows;     // Number of rows in the input tensor
-  Index m_inputCols;     // Number of cols in the input tensor
-
-  Index m_outputRows;    // Number of patch rows
-
-  Index m_row_strides;   // User specified row stride
-  Index m_col_strides;   // User specified col stride
-
-  Index m_in_row_strides;  // User specified input row stride
-  Index m_in_col_strides;  // User specified input col stride
-
-  Index m_rowPaddingTop;    // Row padding
-  Index m_colPaddingLeft;   // Column padding
-
-  internal::TensorIntDivisor m_fastOutputRows;
-  internal::TensorIntDivisor m_fastDimZero;
-
-  const TensorEvaluator m_impl;
-};
-
-
-template 
-class TensorContractionSubMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment>
-{
- public:
-  typedef Scalar_ Scalar;
-  typedef typename packet_traits::type Packet;
-  typedef typename packet_traits::half HalfPacket;
-
-  typedef TensorContractionInputMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> ParentMapper;
-  typedef TensorContractionSubMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> Self;
-  typedef Self LinearMapper;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionSubMapper(const ParentMapper& base_mapper, Index vert_offset, Index horiz_offset)
-      : m_base_mapper(base_mapper), m_depth_offset(vert_offset), m_col_offset(horiz_offset) {
-    m_base_mapper.computeBaseIndices(m_col_offset, m_rowIndex, m_colIndex, m_otherIndex);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionSubMapper(const Self& base_mapper, Index vert_offset, Index horiz_offset)
-      : m_base_mapper(base_mapper.m_base_mapper), m_depth_offset(vert_offset+base_mapper.m_depth_offset), m_col_offset(horiz_offset+base_mapper.m_col_offset) {
-    m_base_mapper.computeBaseIndices(m_col_offset, m_rowIndex, m_colIndex, m_otherIndex);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i) const {
-    return m_base_mapper.loadCoeff(i + m_depth_offset, m_rowIndex, m_colIndex, m_otherIndex);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i, Index j) const {
-    return m_base_mapper(i + m_depth_offset, j + m_col_offset);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacket(Index i) const {
-   return m_base_mapper.loadPacket(i + m_depth_offset, m_rowIndex, m_colIndex, m_otherIndex);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacket(Index i, Index j) const {
-    return m_base_mapper.template loadPacket(i + m_depth_offset, j + m_col_offset);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar loadCoeffStandard(Index i) const {
-    return m_base_mapper.loadCoeffStandard(i + m_depth_offset, m_rowIndex, m_colIndex, m_otherIndex);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacketFast(Index i) const {
-   return m_base_mapper.loadPacketFast(i + m_depth_offset, m_rowIndex, m_colIndex, m_otherIndex);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacketStandard(Index i) const {
-   return m_base_mapper.loadPacketStandard(i + m_depth_offset, m_rowIndex, m_colIndex, m_otherIndex);
-  }
-  template 
-  EIGEN_DEVICE_FUNC bool aligned(Index) const {
-    return false;
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE bool nonStandardPatches() const {
-    return m_base_mapper.nonStandardPatches();
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Index patchDepth() const { return m_base_mapper.m_rowInputStride; }
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Index patchRows() const { return m_base_mapper.m_colStride; }
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Index patchCols() const { return m_base_mapper.m_patch_cols; }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Packet packetNoPadding(const Index depth, const Index baseIndex) const {
-    const Index inputIndex = depth + baseIndex;
-    return m_base_mapper.m_impl.template packet(inputIndex);
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE bool padRow(const Index row) const {
-    const Index r = m_rowIndex + row;
-    return r < 0 | r >= m_base_mapper.m_inputRows;
-  }
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE bool padCol(const Index col) const {
-    const Index c = m_colIndex + col;
-    return c < 0 | c >= m_base_mapper.m_inputCols;
-    }
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Index baseIndex(const Index row, const Index col) const {
-    const Index r = m_rowIndex + row;
-    const Index c = m_colIndex + col;
-    return r * m_base_mapper.m_rowInputStride + c * m_base_mapper.m_colInputStride + m_otherIndex;
-  }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Index rowOffset() const {
-    const Index patchOffset = m_depth_offset / m_base_mapper.m_fastDimZero;
-    const Index colOffset = patchOffset / m_base_mapper.m_fastColStride;
-    return patchOffset-colOffset*m_base_mapper.m_colStride;
-    }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Index colOffset() const {
-    const Index patchOffset = m_depth_offset / m_base_mapper.m_fastDimZero;
-    const Index colOffset = patchOffset / m_base_mapper.m_fastColStride;
-    return colOffset;
-    }
-
-  EIGEN_DEVICE_FUNC
-  EIGEN_ALWAYS_INLINE Index depthOffset() const {
-    const Index patchOffset = m_depth_offset % m_base_mapper.patchDepth();
-    return patchOffset;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
-    return LinearMapper(m_base_mapper, i + m_depth_offset, j + m_col_offset);
- }
-
- private:
-  const ParentMapper& m_base_mapper;  // that was a reference before
-  Index m_depth_offset;  // First row in the input matrix
-  Index m_col_offset;    // First col in the input matrix
-
-  Index m_rowIndex;        // precomputed row index corresponding to the col offset
-  Index m_colIndex;        // precomputed col index corresponding to the col offset
-  Index m_otherIndex;      // precomputed other index corresponding to the col offset
-
-};
-
-
-template 
-struct gemm_pack_rhs >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment>, nr, ColMajor, false, false> {
-
-  typedef TensorContractionSubMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> SubMapper;
-  typedef SubMapper DataMapper;
-
-  static inline Index ceil_div(Index a, Index b) {
-    return (a + b - 1) / b;
-  }
-
-  EIGEN_DONT_INLINE void operator()(Scalar* block, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0) const {
-    eigen_assert(stride == 0);
-    eigen_assert(offset == 0);
-
-    EIGEN_STATIC_ASSERT((nr == 4), YOU_MADE_A_PROGRAMMING_MISTAKE);
-    typedef typename DataMapper::LinearMapper LinearMapper;
-    typedef typename packet_traits::type Packet;
-
-    const Index packet_cols4 = (cols/4) * 4;
-    const Index peeled_k = (depth/packet_size) * packet_size;
-    const bool non_standard_patches = rhs.nonStandardPatches();
-
-    for(Index j2=0; j2(ceil_div(peeled_k, patch_rows*patch_depth)+startCol, patch_cols);
-
-          for (Index c = startCol; c < max_cols; ++c) {
-            eigen_assert(k < peeled_k);
-            const Index startRow = (c == startCol) ? rhs.rowOffset() : 0;
-            const Index max_rows = std::min(ceil_div(peeled_k-c*patch_rows*patch_depth, patch_depth)+startRow, patch_rows);
-
-            const bool pad_col0 = dm0.padCol(c);
-            const bool pad_col1 = dm1.padCol(c);
-            const bool pad_col2 = dm2.padCol(c);
-            const bool pad_col3 = dm3.padCol(c);
-            for (Index r = startRow; r < max_rows; ++r) {
-              eigen_assert(k < peeled_k);
-              const bool pad0 = pad_col0 || dm0.padRow(r);
-              const bool pad1 = pad_col1 || dm1.padRow(r);
-              const bool pad2 = pad_col2 || dm2.padRow(r);
-              const bool pad3 = pad_col3 || dm3.padRow(r);
-
-              const Index idx0 = dm0.baseIndex(r, c);
-              const Index idx1 = dm1.baseIndex(r, c);
-              const Index idx2 = dm2.baseIndex(r, c);
-              const Index idx3 = dm3.baseIndex(r, c);
-
-              const Index startDepth = ((c == startCol) && (r == startRow)) ? rhs.depthOffset() : 0;
-              const Index max_depth = std::min(peeled_k-c*patch_rows*patch_depth-r*patch_depth+startDepth, patch_depth);
-              eigen_assert(max_depth % packet_size == 0);
-              for (Index d = startDepth; d < max_depth; d += packet_size) {
-                eigen_assert(k < peeled_k);
-                PacketBlock kernel;
-                kernel.packet[0] = pad0 ? pset1(0) : rhs.packetNoPadding(d, idx0);
-                kernel.packet[1] = pad1 ? pset1(0) : rhs.packetNoPadding(d, idx1);
-                kernel.packet[2] = pad2 ? pset1(0) : rhs.packetNoPadding(d, idx2);
-                kernel.packet[3] = pad3 ? pset1(0) : rhs.packetNoPadding(d, idx3);
-                ptranspose(kernel);
-                pstoreu(block+0*packet_size, kernel.packet[0]);
-                pstoreu(block+1*packet_size, kernel.packet[1]);
-                pstoreu(block+2*packet_size, kernel.packet[2]);
-                pstoreu(block+3*packet_size, kernel.packet[3]);
-                block+=4*packet_size;
-                k += packet_size;
-              }
-            }
-          }
-
-          for(; k kernel;
-            kernel.packet[0] = dm0.loadPacketFast(k);
-            kernel.packet[1] = dm1.loadPacketFast(k);
-            kernel.packet[2] = dm2.loadPacketFast(k);
-            kernel.packet[3] = dm3.loadPacketFast(k);
-            ptranspose(kernel);
-            pstoreu(block+0*packet_size, kernel.packet[0]);
-            pstoreu(block+1*packet_size, kernel.packet[1]);
-            pstoreu(block+2*packet_size, kernel.packet[2]);
-            pstoreu(block+3*packet_size, kernel.packet[3]);
-            block+=4*packet_size;
-          }
-        }
-        else {
-          for(; k kernel;
-            kernel.packet[0] = dm0.loadPacketStandard(k);
-            kernel.packet[1] = dm1.loadPacketStandard(k);
-            kernel.packet[2] = dm2.loadPacketStandard(k);
-            kernel.packet[3] = dm3.loadPacketStandard(k);
-            ptranspose(kernel);
-            pstoreu(block+0*packet_size, kernel.packet[0]);
-            pstoreu(block+1*packet_size, kernel.packet[1]);
-            pstoreu(block+2*packet_size, kernel.packet[2]);
-            pstoreu(block+3*packet_size, kernel.packet[3]);
-            block+=4*packet_size;
-          }
-        }
-      }
-      if (!rhs.nonStandardPatches()) {
-        for(; k 1, then applies convolution with holes (aka atrous convolution), sampling every in_stride input pixels.
-  *
-  * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be filters, height, width (and others if applicable).
-  *
-  * It is possible to swap the order of the width and height dimensions provided that the same order is used in the input, the kernel, and the output.
-  *
-  */
-template 
-EIGEN_ALWAYS_INLINE
-static const typename internal::conditional<
-  internal::traits::Layout == ColMajor,
-  TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorContractionOp::Index>, 1>, const TensorReshapingOp::Index, 2>, const Kernel>, const TensorReshapingOp::Index, 2>, const TensorImagePatchOp > > >,
-  TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorContractionOp::Index>, 1>, const TensorReshapingOp::Index, 2>, const TensorImagePatchOp >, const TensorReshapingOp::Index, 2>, const Kernel> > > >::type
-SpatialConvolution(const Input& input, const Kernel& kernel, const DenseIndex stride = 1, const PaddingType padding_type = PADDING_SAME, const DenseIndex in_stride = 1) {
-
-  typedef typename internal::traits::Index TensorIndex;
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input);
-  TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > kern(kernel);
-
-  EIGEN_STATIC_ASSERT(internal::traits::Layout == internal::traits::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE);
-  static const bool isColMajor = (internal::traits::Layout == ColMajor);
-
-  static const int NumDims = internal::traits::NumDimensions;
-
-  // Number of filters to apply. This is the same as the output depth of the result
-  const TensorIndex kernelFilters = isColMajor ? kern.dimensions()[0] : kern.dimensions()[3];
-  // Number of channels. This is the same as the input depth.
-  const TensorIndex kernelChannels = isColMajor ? kern.dimensions()[1] : kern.dimensions()[2];
-  const TensorIndex kernelRows = isColMajor ? kern.dimensions()[2] : kern.dimensions()[1];
-  const TensorIndex kernelCols = isColMajor ? kern.dimensions()[3] : kern.dimensions()[0];
-
-  const DenseIndex kernelRowsEff = kernelRows + (kernelRows - 1) * (in_stride - 1);
-  const DenseIndex kernelColsEff = kernelCols + (kernelCols - 1) * (in_stride - 1);
-
-  array, 1> contract_dims;
-  contract_dims[0] = IndexPair(1, 0);
-
-  const TensorIndex InputRows = isColMajor ? in.dimension(1) : in.dimension(NumDims - 2);
-  const TensorIndex InputCols = isColMajor ? in.dimension(2) : in.dimension(NumDims - 3);
-
-  TensorIndex out_height;
-  TensorIndex out_width;
-  switch (padding_type) {
-    case PADDING_VALID:
-      out_height = numext::ceil((InputRows - kernelRowsEff + 1.f) / static_cast(stride));
-      out_width = numext::ceil((InputCols - kernelColsEff + 1.f) / static_cast(stride));
-      break;
-    case PADDING_SAME:
-      out_height = numext::ceil(InputRows / static_cast(stride));
-      out_width = numext::ceil(InputCols / static_cast(stride));
-      break;
-    default:
-      eigen_assert(false && "unexpected padding");
-  }
-
-  // Molds the output of the patch extraction code into a 2d tensor:
-  // - the first dimension (dims[0]): the patch values to be multiplied with the kernels
-  // - the second dimension (dims[1]): everything else
-  DSizes pre_contract_dims;
-  if (isColMajor) {
-    pre_contract_dims[0] = kernelChannels * kernelRows * kernelCols;
-    pre_contract_dims[1] = out_height * out_width;
-    for (int i = 3; i < NumDims; ++i) {
-      pre_contract_dims[1] *= in.dimension(i);
-    }
-  } else {
-    pre_contract_dims[1] = kernelChannels * kernelRows * kernelCols;
-    pre_contract_dims[0] = out_height * out_width;
-    for (int i = 0; i < NumDims - 3; ++i) {
-      pre_contract_dims[0] *= in.dimension(i);
-    }
-  }
-
-  // Molds the output of the contraction into the shape expected by the used
-  // (assuming this is ColMajor):
-  // - 1st dim: kernel filters
-  // - 2nd dim: output height
-  // - 3rd dim: output width
-  // - 4th dim and beyond: everything else including batch size
-  DSizes post_contract_dims;
-  if (isColMajor) {
-    post_contract_dims[0] = kernelFilters;
-    post_contract_dims[1] = out_height;
-    post_contract_dims[2] = out_width;
-    for (int i = 3; i < NumDims; ++i) {
-      post_contract_dims[i] = in.dimension(i);
-    }
-  } else {
-    post_contract_dims[NumDims - 1] = kernelFilters;
-    post_contract_dims[NumDims - 2] = out_height;
-    post_contract_dims[NumDims - 3] = out_width;
-    for (int i = 0; i < NumDims - 3; ++i) {
-      post_contract_dims[i] = in.dimension(i);
-    }
-  }
-
-  DSizes kernel_dims;
-  if (isColMajor) {
-    kernel_dims[0] = kernelFilters;
-    kernel_dims[1] = kernelChannels * kernelRows * kernelCols;
-  } else {
-    kernel_dims[0] = kernelChannels * kernelRows * kernelCols;
-    kernel_dims[1] = kernelFilters;
-  }
-  // TODO(yangke): choose() is defined in TensorContraction.h -- consider
-  // moving it to somewhere more "common".
-  return choose(Cond::Layout == ColMajor>(),
-                kernel.reshape(kernel_dims).contract(input.extract_image_patches(kernelRows, kernelCols, stride, stride, in_stride, in_stride, padding_type).reshape(pre_contract_dims), contract_dims).reshape(post_contract_dims),
-                input.extract_image_patches(kernelRows, kernelCols, stride, stride, in_stride, in_stride, padding_type).reshape(pre_contract_dims).contract(kernel.reshape(kernel_dims), contract_dims).reshape(post_contract_dims));
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_NEURAL_NETWORKS_SPATIAL_CONVOLUTIONS_H
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/TensorConvolutionByFFT.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/TensorConvolutionByFFT.h
deleted file mode 100644
index 0e7217353644acd1c085f4f661dbe62fc06e6088..0000000000000000000000000000000000000000
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/TensorConvolutionByFFT.h
+++ /dev/null
@@ -1,289 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner 
-// Copyright (C) 2015 Jianwei Cui 
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTIONBYFFT_H
-#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTIONBYFFT_H
-
-namespace Eigen {
-
-/** \class TensorConvolutionByFFT
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor convolution class.
-  *
-  *
-  */
-namespace internal {
-
-
-template
-struct traits >
-{
-  // Type promotion to handle the case where the types of the lhs and the rhs are different.
-  typedef typename promote_storage_type::ret Scalar;
-  typedef typename packet_traits::type Packet;
-  typedef typename promote_storage_type::StorageKind,
-                                        typename traits::StorageKind>::ret StorageKind;
-  typedef typename promote_index_type::Index,
-                                      typename traits::Index>::type Index;
-  typedef typename InputXprType::Nested LhsNested;
-  typedef typename KernelXprType::Nested RhsNested;
-  typedef typename remove_reference::type _LhsNested;
-  typedef typename remove_reference::type _RhsNested;
-  static const int NumDimensions = traits::NumDimensions;
-  static const int Layout = traits::Layout;
-
-  enum {
-    Flags = 0,
-  };
-};
-
-template
-struct eval, Eigen::Dense>
-{
-  typedef const TensorConvolutionByFFTOp& type;
-};
-
-template
-struct nested, 1, typename eval >::type>
-{
-  typedef TensorConvolutionByFFTOp type;
-};
-
-}  // end namespace internal
-
-
-
-template
-class TensorConvolutionByFFTOp : public TensorBase >
-{
-  public:
-  typedef typename Eigen::internal::traits::Scalar Scalar;
-  typedef typename Eigen::internal::traits::Packet Packet;
-  typedef typename Eigen::NumTraits::Real RealScalar;
-  typedef typename internal::promote_storage_type::ret CoeffReturnType;
-  typedef typename internal::promote_storage_type::ret PacketReturnType;
-  typedef typename Eigen::internal::nested::type Nested;
-  typedef typename Eigen::internal::traits::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConvolutionByFFTOp(const InputXprType& input, const KernelXprType& kernel, const Indices& dims)
-      : m_input_xpr(input), m_kernel_xpr(kernel), m_indices(dims) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const Indices& indices() const { return m_indices; }
-
-    /** \returns the nested expressions */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all::type&
-    inputExpression() const { return m_input_xpr; }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all::type&
-    kernelExpression() const { return m_kernel_xpr; }
-
-  protected:
-    typename InputXprType::Nested m_input_xpr;
-    typename KernelXprType::Nested m_kernel_xpr;
-    const Indices m_indices;
-};
-
-
-template
-struct TensorEvaluator, Device>
-{
-  typedef TensorConvolutionByFFTOp XprType;
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename XprType::PacketReturnType PacketReturnType;
-
-  typedef typename Eigen::NumTraits::Real RealScalar;
-
-  static const int NumDims = internal::array_size::Dimensions>::value;
-  static const int NumKernelDims = internal::array_size::value;
-  typedef typename XprType::Index Index;
-  typedef DSizes Dimensions;
-
-  enum {
-    IsAligned = TensorEvaluator::IsAligned &
-                TensorEvaluator::IsAligned,
-    PacketAccess = false,
-    BlockAccess = false,
-    Layout = TensorEvaluator::Layout,
-    CoordAccess = false,  // to be implemented
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_inputImpl(op.inputExpression(), device), m_kernelImpl(op.kernelExpression(), device), m_kernelArg(op.kernelExpression()), m_kernel(NULL), m_local_kernel(false), m_device(device)
-  {
-    EIGEN_STATIC_ASSERT((static_cast(TensorEvaluator::Layout) == static_cast(TensorEvaluator::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-    const typename TensorEvaluator::Dimensions& input_dims = m_inputImpl.dimensions();
-    const typename TensorEvaluator::Dimensions& kernel_dims = m_kernelImpl.dimensions();
-
-    if (static_cast(Layout) == static_cast(ColMajor)) {
-      m_inputStride[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_inputStride[i] = m_inputStride[i - 1] * input_dims[i - 1];
-      }
-    } else {
-      m_inputStride[NumDims - 1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_inputStride[i] = m_inputStride[i + 1] * input_dims[i + 1];
-      }
-    }
-
-    m_dimensions = m_inputImpl.dimensions();
-    if (static_cast(Layout) == static_cast(ColMajor)) {
-      for (int i = 0; i < NumKernelDims; ++i) {
-        const Index index = op.indices()[i];
-        const Index input_dim = input_dims[index];
-        const Index kernel_dim = kernel_dims[i];
-        const Index result_dim = input_dim - kernel_dim + 1;
-        m_dimensions[index] = result_dim;
-        if (i > 0) {
-          m_kernelStride[i] = m_kernelStride[i - 1] * kernel_dims[i - 1];
-        } else {
-          m_kernelStride[0] = 1;
-        }
-        m_indexStride[i] = m_inputStride[index];
-      }
-
-      m_outputStride[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_outputStride[i] = m_outputStride[i - 1] * m_dimensions[i - 1];
-      }
-    } else {
-      for (int i = NumKernelDims - 1; i >= 0; --i) {
-        const Index index = op.indices()[i];
-        const Index input_dim = input_dims[index];
-        const Index kernel_dim = kernel_dims[i];
-        const Index result_dim = input_dim - kernel_dim + 1;
-        m_dimensions[index] = result_dim;
-        if (i < NumKernelDims - 1) {
-          m_kernelStride[i] = m_kernelStride[i + 1] * kernel_dims[i + 1];
-        } else {
-          m_kernelStride[NumKernelDims - 1] = 1;
-        }
-        m_indexStride[i] = m_inputStride[index];
-      }
-
-      m_outputStride[NumDims - 1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_outputStride[i] = m_outputStride[i + 1] * m_dimensions[i + 1];
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
-    m_inputImpl.evalSubExprsIfNeeded(NULL);
-    m_kernelImpl.evalSubExprsIfNeeded(NULL);
-
-    typedef typename internal::traits::Index TensorIndex;
-
-    Tensor input(m_inputImpl.dimensions());
-    for (int i = 0; i < m_inputImpl.dimensions().TotalSize(); ++i) {
-      input.data()[i] = m_inputImpl.coeff(i);
-    }
-
-    Tensor kernel(m_kernelImpl.dimensions());
-    for (int i = 0; i < m_kernelImpl.dimensions().TotalSize(); ++i) {
-      kernel.data()[i] = m_kernelImpl.coeff(i);
-    }
-
-    array, NumDims> paddings;
-    for (int i = 0; i < NumDims; ++i) {
-      paddings[i] = std::make_pair(0, m_inputImpl.dimensions()[i] - m_kernelImpl.dimensions()[i]);
-    }
-
-    Eigen::array reverse;
-    for (int i = 0; i < NumKernelDims; ++i) {
-      reverse[i] = true;
-    }
-
-    Eigen::array fft;
-    for (int i = 0; i < NumDims; ++i) {
-      fft[i] = i;
-    }
-
-    Eigen::DSizes slice_offsets;
-    for (int i = 0; i < NumDims; ++i) {
-      slice_offsets[i] = m_kernelImpl.dimensions()[i] - 1;
-    }
-
-    Eigen::DSizes slice_extents;
-    for (int i = 0; i < NumDims; ++i) {
-      slice_extents[i] = m_inputImpl.dimensions()[i] - m_kernelImpl.dimensions()[i] + 1;
-    }
-
-    Tensor kernel_variant =  kernel.reverse(reverse).pad(paddings);
-    Tensor, NumDims, Layout, TensorIndex> kernel_fft =  kernel_variant.template fft(fft);
-    //Tensor, NumDims, Layout|IndexType> kernel_fft =  kernel.reverse(reverse).pad(paddings).template fft<2>(fft);
-    Tensor, NumDims, Layout, TensorIndex> input_fft = input.template fft(fft);
-    Tensor, NumDims, Layout, TensorIndex> prod = (input_fft * kernel_fft).template fft(fft);
-    Tensor, NumDims, Layout, TensorIndex> tensor_result = prod.slice(slice_offsets, slice_extents);
-
-    for (int i = 0; i < tensor_result.size(); ++i) {
-      data[i] = std::real(tensor_result.data()[i]);
-    }
-    return false;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
-    m_inputImpl.cleanup();
-    if (m_local_kernel) {
-      m_device.deallocate((void*)m_kernel);
-      m_local_kernel = false;
-    }
-    m_kernel = NULL;
-  }
-
-  void evalTo(typename XprType::Scalar* buffer) {
-    evalSubExprsIfNeeded(NULL);
-    for (int i = 0; i < dimensions().TotalSize(); ++i) {
-      buffer[i] += coeff(i);
-    }
-    cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    CoeffReturnType result = CoeffReturnType(0);
-    return result;
-  }
-
-  EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
-
- private:
-  array m_inputStride;
-  array m_outputStride;
-
-  array m_indexStride;
-  array m_kernelStride;
-  TensorEvaluator m_inputImpl;
-  TensorEvaluator m_kernelImpl;
-  Dimensions m_dimensions;
-
-  KernelArgType m_kernelArg;
-  const Scalar* m_kernel;
-  bool m_local_kernel;
-  const Device& m_device;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTIONBYFFT_H
diff --git a/third_party/examples/eager/spinn/spinn.py b/third_party/examples/eager/spinn/spinn.py
index 67456a5bdfc05f7b41218f5e522e0e74e9065f9b..de63ebe9e67d37dcc0ecf309edf1fae89169af5f 100644
--- a/third_party/examples/eager/spinn/spinn.py
+++ b/third_party/examples/eager/spinn/spinn.py
@@ -419,7 +419,7 @@ class SNLIClassifierTrainer(tfe.Checkpointable):
     # Create a custom learning rate Variable for the RMSProp optimizer, because
     # the learning rate needs to be manually decayed later (see
     # decay_learning_rate()).
-    self._learning_rate = tfe.Variable(lr, name="learning_rate")
+    self._learning_rate = tf.Variable(lr, name="learning_rate")
     self._optimizer = tf.train.RMSPropOptimizer(self._learning_rate,
                                                 epsilon=1e-6)
 
@@ -626,7 +626,7 @@ def train_or_infer_spinn(embed,
     model = SNLIClassifier(config, embed)
     global_step = tf.train.get_or_create_global_step()
     trainer = SNLIClassifierTrainer(model, config.lr)
-    checkpoint = tfe.Checkpoint(trainer=trainer, global_step=global_step)
+    checkpoint = tf.train.Checkpoint(trainer=trainer, global_step=global_step)
     checkpoint.restore(tf.train.latest_checkpoint(config.logdir))
 
     if inference_sentence_pair:
diff --git a/third_party/gpus/crosstool/BUILD.tpl b/third_party/gpus/crosstool/BUILD.tpl
index 98cb326572e75ac3ea15a656d821c1eade53d313..f638756d2373d3a0d85633be72654091c7982f49 100644
--- a/third_party/gpus/crosstool/BUILD.tpl
+++ b/third_party/gpus/crosstool/BUILD.tpl
@@ -7,6 +7,7 @@ cc_toolchain_suite(
     toolchains = {
         "local|compiler": ":cc-compiler-local",
         "darwin|compiler": ":cc-compiler-darwin",
+        "x64_windows|msvc-cl": ":cc-compiler-windows",
     },
 )
 
@@ -42,6 +43,20 @@ cc_toolchain(
     supports_param_files = 0,
 )
 
+cc_toolchain(
+    name = "cc-compiler-windows",
+    all_files = "%{win_linker_files}",
+    compiler_files = ":empty",
+    cpu = "x64_windows",
+    dwp_files = ":empty",
+    dynamic_runtime_libs = [":empty"],
+    linker_files = "%{win_linker_files}",
+    objcopy_files = ":empty",
+    static_runtime_libs = [":empty"],
+    strip_files = ":empty",
+    supports_param_files = 1,
+)
+
 filegroup(
     name = "empty",
     srcs = [],
@@ -51,3 +66,8 @@ filegroup(
     name = "crosstool_wrapper_driver_is_not_gcc",
     srcs = ["clang/bin/crosstool_wrapper_driver_is_not_gcc"],
 )
+
+filegroup(
+    name = "windows_msvc_wrapper_files",
+    srcs = glob(["windows/msvc_*"]),
+)
diff --git a/third_party/gpus/crosstool/CROSSTOOL.tpl b/third_party/gpus/crosstool/CROSSTOOL.tpl
index 1424ff6511dfe0e7e8eef2843201e825e09a91f1..3972c96a2f726127cd7112265eef4d2a794ed0fc 100644
--- a/third_party/gpus/crosstool/CROSSTOOL.tpl
+++ b/third_party/gpus/crosstool/CROSSTOOL.tpl
@@ -22,6 +22,10 @@ default_toolchain {
   cpu: "ppc"
   toolchain_identifier: "local_linux"
 }
+default_toolchain {
+  cpu: "x64_windows"
+  toolchain_identifier: "local_windows"
+}
 
 toolchain {
   abi_version: "local"
@@ -537,3 +541,868 @@ toolchain {
 
 %{host_compiler_includes}
 }
+
+toolchain {
+  toolchain_identifier: "local_windows"
+  host_system_name: "local"
+  target_system_name: "local"
+
+  abi_version: "local"
+  abi_libc_version: "local"
+  target_cpu: "x64_windows"
+  compiler: "msvc-cl"
+  target_libc: "msvcrt"
+
+%{cxx_builtin_include_directory}
+
+  tool_path {
+    name: "ar"
+    path: "%{msvc_lib_path}"
+  }
+  tool_path {
+    name: "ml"
+    path: "%{msvc_ml_path}"
+  }
+  tool_path {
+    name: "cpp"
+    path: "%{msvc_cl_path}"
+  }
+  tool_path {
+    name: "gcc"
+    path: "%{msvc_cl_path}"
+  }
+  tool_path {
+    name: "gcov"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  tool_path {
+    name: "ld"
+    path: "%{msvc_link_path}"
+  }
+  tool_path {
+    name: "nm"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  tool_path {
+    name: "objcopy"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  tool_path {
+    name: "objdump"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  tool_path {
+    name: "strip"
+    path: "wrapper/bin/msvc_nop.bat"
+  }
+  supports_interface_shared_objects: true
+
+  # TODO(pcloudy): Review those flags below, they should be defined by cl.exe
+  compiler_flag: "/DCOMPILER_MSVC"
+
+  # Don't define min/max macros in windows.h.
+  compiler_flag: "/DNOMINMAX"
+
+  # Platform defines.
+  compiler_flag: "/D_WIN32_WINNT=0x0600"
+  # Turn off warning messages.
+  compiler_flag: "/D_CRT_SECURE_NO_DEPRECATE"
+  compiler_flag: "/D_CRT_SECURE_NO_WARNINGS"
+  compiler_flag: "/D_SILENCE_STDEXT_HASH_DEPRECATION_WARNINGS"
+
+  # Useful options to have on for compilation.
+  # Increase the capacity of object files to 2^32 sections.
+  compiler_flag: "/bigobj"
+  # Allocate 500MB for precomputed headers.
+  compiler_flag: "/Zm500"
+  # Use unsigned char by default.
+  compiler_flag: "/J"
+  # Use function level linking.
+  compiler_flag: "/Gy"
+  # Use string pooling.
+  compiler_flag: "/GF"
+  # Catch C++ exceptions only and tell the compiler to assume that functions declared
+  # as extern "C" never throw a C++ exception.
+  compiler_flag: "/EHsc"
+
+  # Globally disabled warnings.
+  # Don't warn about elements of array being be default initialized.
+  compiler_flag: "/wd4351"
+  # Don't warn about no matching delete found.
+  compiler_flag: "/wd4291"
+  # Don't warn about diamond inheritance patterns.
+  compiler_flag: "/wd4250"
+  # Don't warn about insecure functions (e.g. non _s functions).
+  compiler_flag: "/wd4996"
+
+  linker_flag: "/MACHINE:X64"
+
+  feature {
+    name: "no_legacy_features"
+  }
+
+  # Suppress startup banner.
+  feature {
+    name: "nologo"
+    flag_set {
+      action: "c-compile"
+      action: "c++-compile"
+      action: "c++-module-compile"
+      action: "c++-module-codegen"
+      action: "c++-header-parsing"
+      action: "assemble"
+      action: "preprocess-assemble"
+      action: "c++-link-executable"
+      action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
+      action: "c++-link-static-library"
+      flag_group {
+        flag: "/nologo"
+      }
+    }
+  }
+
+  feature {
+    name: 'has_configured_linker_path'
+  }
+
+  # This feature indicates strip is not supported, building stripped binary will just result a copy of orignial binary
+  feature {
+    name: 'no_stripping'
+  }
+
+  # This feature indicates this is a toolchain targeting Windows.
+  feature {
+    name: 'targets_windows'
+    implies: 'copy_dynamic_libraries_to_binary'
+    enabled: true
+  }
+
+  feature {
+    name: 'copy_dynamic_libraries_to_binary'
+  }
+
+  action_config {
+    config_name: 'assemble'
+    action_name: 'assemble'
+    tool {
+      tool_path: '%{msvc_ml_path}'
+    }
+    implies: 'compiler_input_flags'
+    implies: 'compiler_output_flags'
+    implies: 'nologo'
+    implies: 'msvc_env'
+    implies: 'sysroot'
+  }
+
+  action_config {
+    config_name: 'preprocess-assemble'
+    action_name: 'preprocess-assemble'
+    tool {
+      tool_path: '%{msvc_ml_path}'
+    }
+    implies: 'compiler_input_flags'
+    implies: 'compiler_output_flags'
+    implies: 'nologo'
+    implies: 'msvc_env'
+    implies: 'sysroot'
+  }
+
+  action_config {
+    config_name: 'c-compile'
+    action_name: 'c-compile'
+    tool {
+      tool_path: '%{msvc_cl_path}'
+    }
+    implies: 'compiler_input_flags'
+    implies: 'compiler_output_flags'
+    implies: 'legacy_compile_flags'
+    implies: 'nologo'
+    implies: 'msvc_env'
+    implies: 'parse_showincludes'
+    implies: 'user_compile_flags'
+    implies: 'sysroot'
+    implies: 'unfiltered_compile_flags'
+  }
+
+  action_config {
+    config_name: 'c++-compile'
+    action_name: 'c++-compile'
+    tool {
+      tool_path: '%{msvc_cl_path}'
+    }
+    implies: 'compiler_input_flags'
+    implies: 'compiler_output_flags'
+    implies: 'legacy_compile_flags'
+    implies: 'nologo'
+    implies: 'msvc_env'
+    implies: 'parse_showincludes'
+    implies: 'user_compile_flags'
+    implies: 'sysroot'
+    implies: 'unfiltered_compile_flags'
+  }
+
+  action_config {
+    config_name: 'c++-link-executable'
+    action_name: 'c++-link-executable'
+    tool {
+      tool_path: '%{msvc_link_path}'
+    }
+    implies: 'nologo'
+    implies: 'linkstamps'
+    implies: 'output_execpath_flags'
+    implies: 'input_param_flags'
+    implies: 'user_link_flags'
+    implies: 'legacy_link_flags'
+    implies: 'linker_subsystem_flag'
+    implies: 'linker_param_file'
+    implies: 'msvc_env'
+    implies: 'no_stripping'
+  }
+
+  action_config {
+    config_name: 'c++-link-dynamic-library'
+    action_name: 'c++-link-dynamic-library'
+    tool {
+      tool_path: '%{msvc_link_path}'
+    }
+    implies: 'nologo'
+    implies: 'shared_flag'
+    implies: 'linkstamps'
+    implies: 'output_execpath_flags'
+    implies: 'input_param_flags'
+    implies: 'user_link_flags'
+    implies: 'legacy_link_flags'
+    implies: 'linker_subsystem_flag'
+    implies: 'linker_param_file'
+    implies: 'msvc_env'
+    implies: 'no_stripping'
+    implies: 'has_configured_linker_path'
+    implies: 'def_file'
+  }
+
+  action_config {
+      config_name: 'c++-link-nodeps-dynamic-library'
+      action_name: 'c++-link-nodeps-dynamic-library'
+      tool {
+        tool_path: '%{msvc_link_path}'
+      }
+      implies: 'nologo'
+      implies: 'shared_flag'
+      implies: 'linkstamps'
+      implies: 'output_execpath_flags'
+      implies: 'input_param_flags'
+      implies: 'user_link_flags'
+      implies: 'legacy_link_flags'
+      implies: 'linker_subsystem_flag'
+      implies: 'linker_param_file'
+      implies: 'msvc_env'
+      implies: 'no_stripping'
+      implies: 'has_configured_linker_path'
+      implies: 'def_file'
+    }
+
+  action_config {
+    config_name: 'c++-link-static-library'
+    action_name: 'c++-link-static-library'
+    tool {
+      tool_path: '%{msvc_lib_path}'
+    }
+    implies: 'nologo'
+    implies: 'archiver_flags'
+    implies: 'input_param_flags'
+    implies: 'linker_param_file'
+    implies: 'msvc_env'
+  }
+
+  # TODO(b/65151735): Remove legacy_compile_flags feature when legacy fields are
+  # not used in this crosstool
+  feature {
+    name: 'legacy_compile_flags'
+    flag_set {
+      expand_if_all_available: 'legacy_compile_flags'
+      action: 'preprocess-assemble'
+      action: 'c-compile'
+      action: 'c++-compile'
+      action: 'c++-header-parsing'
+      action: 'c++-module-compile'
+      action: 'c++-module-codegen'
+      flag_group {
+        iterate_over: 'legacy_compile_flags'
+        flag: '%{legacy_compile_flags}'
+      }
+    }
+  }
+
+  feature {
+    name: "msvc_env"
+    env_set {
+      action: "c-compile"
+      action: "c++-compile"
+      action: "c++-module-compile"
+      action: "c++-module-codegen"
+      action: "c++-header-parsing"
+      action: "assemble"
+      action: "preprocess-assemble"
+      action: "c++-link-executable"
+      action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
+      action: "c++-link-static-library"
+      env_entry {
+        key: "PATH"
+        value: "%{msvc_env_path}"
+      }
+      env_entry {
+        key: "INCLUDE"
+        value: "%{msvc_env_include}"
+      }
+      env_entry {
+        key: "LIB"
+        value: "%{msvc_env_lib}"
+      }
+      env_entry {
+        key: "TMP"
+        value: "%{msvc_env_tmp}"
+      }
+      env_entry {
+        key: "TEMP"
+        value: "%{msvc_env_tmp}"
+      }
+    }
+  }
+
+  feature {
+    name: 'include_paths'
+    flag_set {
+      action: "assemble"
+      action: 'preprocess-assemble'
+      action: 'c-compile'
+      action: 'c++-compile'
+      action: 'c++-header-parsing'
+      action: 'c++-module-compile'
+      flag_group {
+        iterate_over: 'quote_include_paths'
+        flag: '/I%{quote_include_paths}'
+      }
+      flag_group {
+        iterate_over: 'include_paths'
+        flag: '/I%{include_paths}'
+      }
+      flag_group {
+        iterate_over: 'system_include_paths'
+        flag: '/I%{system_include_paths}'
+      }
+    }
+  }
+
+  feature {
+    name: "preprocessor_defines"
+    flag_set {
+      action: "assemble"
+      action: "preprocess-assemble"
+      action: "c-compile"
+      action: "c++-compile"
+      action: "c++-header-parsing"
+      action: "c++-module-compile"
+      flag_group {
+        flag: "/D%{preprocessor_defines}"
+        iterate_over: "preprocessor_defines"
+      }
+    }
+  }
+
+  # Tell Bazel to parse the output of /showIncludes
+  feature {
+    name: 'parse_showincludes'
+    flag_set {
+      action: 'preprocess-assemble'
+      action: 'c-compile'
+      action: 'c++-compile'
+      action: 'c++-module-compile'
+      action: 'c++-header-parsing'
+      flag_group {
+        flag: "/showIncludes"
+      }
+    }
+  }
+
+
+  feature {
+    name: 'generate_pdb_file'
+    requires: {
+      feature: 'dbg'
+    }
+    requires: {
+      feature: 'fastbuild'
+    }
+  }
+
+  feature {
+    name: 'shared_flag'
+    flag_set {
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        flag: '/DLL'
+      }
+    }
+  }
+
+  feature {
+    name: 'linkstamps'
+    flag_set {
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      expand_if_all_available: 'linkstamp_paths'
+      flag_group {
+        iterate_over: 'linkstamp_paths'
+        flag: '%{linkstamp_paths}'
+      }
+    }
+  }
+
+  feature {
+    name: 'output_execpath_flags'
+    flag_set {
+      expand_if_all_available: 'output_execpath'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        flag: '/OUT:%{output_execpath}'
+      }
+    }
+  }
+
+  feature {
+    name: 'archiver_flags'
+    flag_set {
+      expand_if_all_available: 'output_execpath'
+      action: 'c++-link-static-library'
+      flag_group {
+        flag: '/OUT:%{output_execpath}'
+      }
+    }
+  }
+
+  feature {
+    name: 'input_param_flags'
+    flag_set {
+      expand_if_all_available: 'interface_library_output_path'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        flag: "/IMPLIB:%{interface_library_output_path}"
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'libopts'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        iterate_over: 'libopts'
+        flag: '%{libopts}'
+      }
+    }
+    flag_set {
+      expand_if_all_available: 'libraries_to_link'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      action: 'c++-link-static-library'
+      flag_group {
+        iterate_over: 'libraries_to_link'
+        flag_group {
+          expand_if_equal: {
+            variable: 'libraries_to_link.type'
+            value: 'object_file_group'
+          }
+          iterate_over: 'libraries_to_link.object_files'
+          flag_group {
+            flag: '%{libraries_to_link.object_files}'
+          }
+        }
+        flag_group {
+          expand_if_equal: {
+            variable: 'libraries_to_link.type'
+            value: 'object_file'
+          }
+          flag_group {
+            flag: '%{libraries_to_link.name}'
+          }
+        }
+        flag_group {
+          expand_if_equal: {
+            variable: 'libraries_to_link.type'
+            value: 'interface_library'
+          }
+          flag_group {
+            flag: '%{libraries_to_link.name}'
+          }
+        }
+        flag_group {
+          expand_if_equal: {
+            variable: 'libraries_to_link.type'
+            value: 'static_library'
+          }
+          flag_group {
+            expand_if_false: 'libraries_to_link.is_whole_archive'
+            flag: '%{libraries_to_link.name}'
+          }
+          flag_group {
+            expand_if_true: 'libraries_to_link.is_whole_archive'
+            flag: '/WHOLEARCHIVE:%{libraries_to_link.name}'
+          }
+        }
+      }
+    }
+  }
+
+  # Since this feature is declared earlier in the CROSSTOOL than
+  # "user_link_flags", this feature will be applied prior to it anwyhere they
+  # are both implied. And since "user_link_flags" contains the linkopts from
+  # the build rule, this allows the user to override the /SUBSYSTEM in the BUILD
+  # file.
+  feature {
+    name: 'linker_subsystem_flag'
+    flag_set {
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        flag: '/SUBSYSTEM:CONSOLE'
+      }
+    }
+  }
+
+  # The "user_link_flags" contains user-defined linkopts (from build rules)
+  # so it should be defined after features that declare user-overridable flags.
+  # For example the "linker_subsystem_flag" defines a default "/SUBSYSTEM" flag
+  # but we want to let the user override it, therefore "link_flag_subsystem" is
+  # defined earlier in the CROSSTOOL file than "user_link_flags".
+  feature {
+    name: 'user_link_flags'
+    flag_set {
+      expand_if_all_available: 'user_link_flags'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        iterate_over: 'user_link_flags'
+        flag: '%{user_link_flags}'
+      }
+    }
+  }
+  feature {
+    name: 'legacy_link_flags'
+    flag_set {
+      expand_if_all_available: 'legacy_link_flags'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        iterate_over: 'legacy_link_flags'
+        flag: '%{legacy_link_flags}'
+      }
+    }
+  }
+
+  feature {
+    name: 'linker_param_file'
+    flag_set {
+      expand_if_all_available: 'linker_param_file'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      action: 'c++-link-static-library'
+      flag_group {
+        flag: '@%{linker_param_file}'
+      }
+    }
+  }
+
+  feature {
+    name: 'static_link_msvcrt'
+  }
+
+  feature {
+    name: 'static_link_msvcrt_no_debug'
+    flag_set {
+      action: 'c-compile'
+      action: 'c++-compile'
+      flag_group {
+        flag: "/MT"
+      }
+    }
+    flag_set {
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        flag: "/DEFAULTLIB:libcmt.lib"
+      }
+    }
+    requires: { feature: 'fastbuild'}
+    requires: { feature: 'opt'}
+  }
+
+  feature {
+    name: 'dynamic_link_msvcrt_no_debug'
+    flag_set {
+      action: 'c-compile'
+      action: 'c++-compile'
+      flag_group {
+        flag: "/MD"
+      }
+    }
+    flag_set {
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        flag: "/DEFAULTLIB:msvcrt.lib"
+      }
+    }
+    requires: { feature: 'fastbuild'}
+    requires: { feature: 'opt'}
+  }
+
+  feature {
+    name: 'static_link_msvcrt_debug'
+    flag_set {
+      action: 'c-compile'
+      action: 'c++-compile'
+      flag_group {
+        flag: "/MTd"
+      }
+    }
+    flag_set {
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        flag: "/DEFAULTLIB:libcmtd.lib"
+      }
+    }
+    requires: { feature: 'dbg'}
+  }
+
+  feature {
+    name: 'dynamic_link_msvcrt_debug'
+    flag_set {
+      action: 'c-compile'
+      action: 'c++-compile'
+      flag_group {
+        flag: "/MDd"
+      }
+    }
+    flag_set {
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        flag: "/DEFAULTLIB:msvcrtd.lib"
+      }
+    }
+    requires: { feature: 'dbg'}
+  }
+
+  feature {
+    name: 'dbg'
+    flag_set {
+      action: 'c-compile'
+      action: 'c++-compile'
+      flag_group {
+        flag: "/Od"
+        flag: "/Z7"
+        flag: "/DDEBUG"
+      }
+    }
+    flag_set {
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        flag: "/DEBUG:FULL"
+        flag: "/INCREMENTAL:NO"
+      }
+    }
+    implies: 'generate_pdb_file'
+  }
+
+  feature {
+    name: 'fastbuild'
+    flag_set {
+      action: 'c-compile'
+      action: 'c++-compile'
+      flag_group {
+        flag: "/Od"
+        flag: "/Z7"
+        flag: "/DDEBUG"
+      }
+    }
+    flag_set {
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        flag: "/DEBUG:FASTLINK"
+        flag: "/INCREMENTAL:NO"
+      }
+    }
+    implies: 'generate_pdb_file'
+  }
+
+  feature {
+    name: 'opt'
+    flag_set {
+      action: 'c-compile'
+      action: 'c++-compile'
+      flag_group {
+        flag: "/O2"
+        flag: "/DNDEBUG"
+      }
+    }
+  }
+
+  feature {
+    name: 'user_compile_flags'
+    flag_set {
+      expand_if_all_available: 'user_compile_flags'
+      action: 'preprocess-assemble'
+      action: 'c-compile'
+      action: 'c++-compile'
+      action: 'c++-header-parsing'
+      action: 'c++-module-compile'
+      action: 'c++-module-codegen'
+      flag_group {
+        iterate_over: 'user_compile_flags'
+        flag: '%{user_compile_flags}'
+      }
+    }
+  }
+
+  feature {
+    name: 'sysroot'
+    flag_set {
+      expand_if_all_available: 'sysroot'
+      action: 'assemble'
+      action: 'preprocess-assemble'
+      action: 'c-compile'
+      action: 'c++-compile'
+      action: 'c++-header-parsing'
+      action: 'c++-module-compile'
+      action: 'c++-module-codegen'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        iterate_over: 'sysroot'
+        flag: '--sysroot=%{sysroot}'
+      }
+    }
+  }
+
+  feature {
+    name: 'unfiltered_compile_flags'
+    flag_set {
+      expand_if_all_available: 'unfiltered_compile_flags'
+      action: 'preprocess-assemble'
+      action: 'c-compile'
+      action: 'c++-compile'
+      action: 'c++-header-parsing'
+      action: 'c++-module-compile'
+      action: 'c++-module-codegen'
+      flag_group {
+        iterate_over: 'unfiltered_compile_flags'
+        flag: '%{unfiltered_compile_flags}'
+      }
+    }
+  }
+
+  feature {
+    name: 'compiler_output_flags'
+    flag_set {
+      action: 'assemble'
+      flag_group {
+        expand_if_all_available: 'output_file'
+        expand_if_none_available: 'output_assembly_file'
+        expand_if_none_available: 'output_preprocess_file'
+        flag: '/Fo%{output_file}'
+        flag: '/Zi'
+      }
+    }
+    flag_set {
+      action: 'preprocess-assemble'
+      action: 'c-compile'
+      action: 'c++-compile'
+      action: 'c++-header-parsing'
+      action: 'c++-module-compile'
+      action: 'c++-module-codegen'
+      flag_group {
+        expand_if_all_available: 'output_file'
+        expand_if_none_available: 'output_assembly_file'
+        expand_if_none_available: 'output_preprocess_file'
+        flag: '/Fo%{output_file}'
+      }
+      flag_group {
+        expand_if_all_available: 'output_file'
+        expand_if_all_available: 'output_assembly_file'
+        flag: '/Fa%{output_file}'
+      }
+      flag_group {
+        expand_if_all_available: 'output_file'
+        expand_if_all_available: 'output_preprocess_file'
+        flag: '/P'
+        flag: '/Fi%{output_file}'
+      }
+    }
+  }
+
+  feature {
+    name: 'compiler_input_flags'
+    flag_set {
+      action: 'assemble'
+      action: 'preprocess-assemble'
+      action: 'c-compile'
+      action: 'c++-compile'
+      action: 'c++-header-parsing'
+      action: 'c++-module-compile'
+      action: 'c++-module-codegen'
+      flag_group {
+        expand_if_all_available: 'source_file'
+        flag: '/c'
+        flag: '%{source_file}'
+      }
+    }
+  }
+
+  feature {
+    name : 'def_file',
+    flag_set {
+      expand_if_all_available: 'def_file_path'
+      action: 'c++-link-executable'
+      action: 'c++-link-dynamic-library'
+      action: "c++-link-nodeps-dynamic-library"
+      flag_group {
+        flag: "/DEF:%{def_file_path}"
+        # We can specify a different DLL name in DEF file, /ignore:4070 suppresses
+        # the warning message about DLL name doesn't match the default one.
+        # See https://msdn.microsoft.com/en-us/library/sfkk2fz7.aspx
+        flag: "/ignore:4070"
+      }
+    }
+  }
+
+  feature {
+    name: 'windows_export_all_symbols'
+  }
+
+  feature {
+    name: 'no_windows_export_all_symbols'
+  }
+
+  linking_mode_flags { mode: DYNAMIC }
+}
diff --git a/third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl b/third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl
index 2558f46fd55c35b5089cc0119f2654f598e5128a..f4f4d0ee964142b2aa6e010ad5409494438733ea 100755
--- a/third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl
+++ b/third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl
@@ -175,6 +175,11 @@ def InvokeNvcc(argv, log=False):
   # any other reliable way to just get the list of source files to be compiled.
   src_files = GetOptionValue(argv, 'c')
 
+  # Pass -w through from host to nvcc, but don't do anything fancier with
+  # warnings-related flags, since they're not necessarily the same across
+  # compilers.
+  warning_options = ' -w' if '-w' in argv else ''
+
   if len(src_files) == 0:
     return 1
   if len(out_file) != 1:
@@ -205,6 +210,7 @@ def InvokeNvcc(argv, log=False):
   nvccopts += defines
   nvccopts += std_options
   nvccopts += m_options
+  nvccopts += warning_options
 
   if depfiles:
     # Generate the dependency file
diff --git a/third_party/gpus/crosstool/windows/msvc_wrapper_for_nvcc.bat.tpl b/third_party/gpus/crosstool/windows/msvc_wrapper_for_nvcc.bat.tpl
new file mode 100644
index 0000000000000000000000000000000000000000..8f8fb3e4231bf1b689cf9b21c53e990d5b9ee354
--- /dev/null
+++ b/third_party/gpus/crosstool/windows/msvc_wrapper_for_nvcc.bat.tpl
@@ -0,0 +1,20 @@
+:: Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+::
+:: Licensed under the Apache License, Version 2.0 (the "License");
+:: you may not use this file except in compliance with the License.
+:: You may obtain a copy of the License at
+::
+::     http://www.apache.org/licenses/LICENSE-2.0
+::
+:: Unless required by applicable law or agreed to in writing, software
+:: distributed under the License is distributed on an "AS IS" BASIS,
+:: WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+:: See the License for the specific language governing permissions and
+:: limitations under the License.
+:: =============================================================================
+
+:: Invoke msvc_wrapper_for_nvcc.py, which is located in the same directory.
+@echo OFF
+set arg0=%~0
+for %%F in ("%arg0%") do set DRIVER_BIN=%%~dpF
+"%{python_binary}" -B "%DRIVER_BIN%\msvc_wrapper_for_nvcc.py" %*
diff --git a/third_party/gpus/crosstool/windows/msvc_wrapper_for_nvcc.py.tpl b/third_party/gpus/crosstool/windows/msvc_wrapper_for_nvcc.py.tpl
new file mode 100644
index 0000000000000000000000000000000000000000..1a09756813e8322b42911dfe7ac80f626e34f98b
--- /dev/null
+++ b/third_party/gpus/crosstool/windows/msvc_wrapper_for_nvcc.py.tpl
@@ -0,0 +1,192 @@
+#!/usr/bin/env python
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Crosstool wrapper for compiling CUDA programs with nvcc on Windows.
+
+DESCRIPTION:
+  This script is the Windows version of //third_party/gpus/crosstool/crosstool_wrapper_is_not_gcc
+"""
+
+from __future__ import print_function
+
+from argparse import ArgumentParser
+import os
+import subprocess
+import re
+import sys
+import pipes
+
+# Template values set by cuda_autoconf.
+CPU_COMPILER = ('%{cpu_compiler}')
+GCC_HOST_COMPILER_PATH = ('%{gcc_host_compiler_path}')
+
+NVCC_PATH = '%{nvcc_path}'
+NVCC_VERSION = '%{cuda_version}'
+NVCC_TEMP_DIR = "%{nvcc_tmp_dir}"
+supported_cuda_compute_capabilities = [ %{cuda_compute_capabilities} ]
+
+def Log(s):
+  print('gpus/crosstool: {0}'.format(s))
+
+
+def GetOptionValue(argv, option):
+  """Extract the list of values for option from options.
+
+  Args:
+    option: The option whose value to extract, without the leading '/'.
+
+  Returns:
+    1. A list of values, either directly following the option,
+    (eg., /opt val1 val2) or values collected from multiple occurrences of
+    the option (eg., /opt val1 /opt val2).
+    2. The leftover options.
+  """
+
+  parser = ArgumentParser(prefix_chars='/')
+  parser.add_argument('/' + option, nargs='*', action='append')
+  args, leftover = parser.parse_known_args(argv)
+  if args and vars(args)[option]:
+    return (sum(vars(args)[option], []), leftover)
+  return ([], leftover)
+
+def _update_options(nvcc_options):
+  if NVCC_VERSION in ("7.0",):
+    return nvcc_options
+
+  update_options = { "relaxed-constexpr" : "expt-relaxed-constexpr" }
+  return [ update_options[opt] if opt in update_options else opt
+                    for opt in nvcc_options ]
+
+def GetNvccOptions(argv):
+  """Collect the -nvcc_options values from argv.
+
+  Args:
+    argv: A list of strings, possibly the argv passed to main().
+
+  Returns:
+    1. The string that can be passed directly to nvcc.
+    2. The leftover options.
+  """
+
+  parser = ArgumentParser()
+  parser.add_argument('-nvcc_options', nargs='*', action='append')
+
+  args, leftover = parser.parse_known_args(argv)
+
+  if args.nvcc_options:
+    options = _update_options(sum(args.nvcc_options, []))
+    return (['--' + a for a in options], leftover)
+  return ([], leftover)
+
+
+def InvokeNvcc(argv, log=False):
+  """Call nvcc with arguments assembled from argv.
+
+  Args:
+    argv: A list of strings, possibly the argv passed to main().
+    log: True if logging is requested.
+
+  Returns:
+    The return value of calling os.system('nvcc ' + args)
+  """
+
+  src_files = [f for f in argv if
+               re.search('\.cpp$|\.cc$|\.c$|\.cxx$|\.C$', f)]
+  if len(src_files) == 0:
+    raise Error('No source files found for cuda compilation.')
+
+  out_file = [ f for f in argv if f.startswith('/Fo') ]
+  if len(out_file) != 1:
+    raise Error('Please sepecify exactly one output file for cuda compilation.')
+  out = ['-o', out_file[0][len('/Fo'):]]
+
+  nvcc_compiler_options, argv = GetNvccOptions(argv)
+
+  opt_option, argv = GetOptionValue(argv, 'O')
+  opt = ['-g', '-G']
+  if (len(opt_option) > 0 and opt_option[0] != 'd'):
+    opt = ['-O2']
+
+  include_options, argv = GetOptionValue(argv, 'I')
+  includes = ["-I " + include for include in include_options]
+
+  defines, argv = GetOptionValue(argv, 'D')
+  defines = ['-D' + define for define in defines]
+
+  undefines, argv = GetOptionValue(argv, 'U')
+  undefines = ['-U' + define for define in undefines]
+
+  # The rest of the unrecongized options should be passed to host compiler
+  host_compiler_options = [option for option in argv if option not in (src_files + out_file)]
+
+  m_options = ["-m64"]
+
+  nvccopts = ['-D_FORCE_INLINES']
+  for capability in supported_cuda_compute_capabilities:
+    capability = capability.replace('.', '')
+    nvccopts += [r'-gencode=arch=compute_%s,"code=sm_%s,compute_%s"' % (
+        capability, capability, capability)]
+  nvccopts += nvcc_compiler_options
+  nvccopts += undefines
+  nvccopts += defines
+  nvccopts += m_options
+  nvccopts += ['--compiler-options="' + " ".join(host_compiler_options) + '"']
+  nvccopts += ['-x', 'cu'] + opt + includes + out + ['-c'] + src_files
+  # If we don't specify --keep-dir, nvcc will generate intermediate files under TEMP
+  # Put them under NVCC_TEMP_DIR instead, then Bazel can ignore files under NVCC_TEMP_DIR during dependency check
+  # http://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#options-for-guiding-compiler-driver
+  # Different actions are sharing NVCC_TEMP_DIR, so we cannot remove it if the directory already exists.
+  if os.path.isfile(NVCC_TEMP_DIR):
+    os.remove(NVCC_TEMP_DIR)
+  if not os.path.exists(NVCC_TEMP_DIR):
+    os.makedirs(NVCC_TEMP_DIR)
+  nvccopts += ['--keep', '--keep-dir', NVCC_TEMP_DIR]
+  cmd = [NVCC_PATH] + nvccopts
+  if log:
+    Log(cmd)
+  proc = subprocess.Popen(cmd,
+                          stdout=sys.stdout,
+                          stderr=sys.stderr,
+                          env=os.environ.copy(),
+                          shell=True)
+  proc.wait()
+  return proc.returncode
+
+def main():
+  parser = ArgumentParser()
+  parser.add_argument('-x', nargs=1)
+  parser.add_argument('--cuda_log', action='store_true')
+  args, leftover = parser.parse_known_args(sys.argv[1:])
+
+  if args.x and args.x[0] == 'cuda':
+    if args.cuda_log: Log('-x cuda')
+    leftover = [pipes.quote(s) for s in leftover]
+    if args.cuda_log: Log('using nvcc')
+    return InvokeNvcc(leftover, log=args.cuda_log)
+
+  # Strip our flags before passing through to the CPU compiler for files which
+  # are not -x cuda. We can't just pass 'leftover' because it also strips -x.
+  # We not only want to pass -x to the CPU compiler, but also keep it in its
+  # relative location in the argv list (the compiler is actually sensitive to
+  # this).
+  cpu_compiler_flags = [flag for flag in sys.argv[1:]
+                             if not flag.startswith(('--cuda_log'))
+                             and not flag.startswith(('-nvcc_options'))]
+
+  return subprocess.call([CPU_COMPILER] + cpu_compiler_flags)
+
+if __name__ == '__main__':
+  sys.exit(main())
diff --git a/third_party/gpus/cuda/BUILD.windows.tpl b/third_party/gpus/cuda/BUILD.windows.tpl
new file mode 100644
index 0000000000000000000000000000000000000000..ff6b3cc35144f07c9fba4b42593810ccf50a1b36
--- /dev/null
+++ b/third_party/gpus/cuda/BUILD.windows.tpl
@@ -0,0 +1,163 @@
+licenses(["restricted"])  # MPL2, portions GPL v3, LGPL v3, BSD-like
+
+package(default_visibility = ["//visibility:public"])
+
+config_setting(
+    name = "using_nvcc",
+    values = {
+        "define": "using_cuda_nvcc=true",
+    },
+)
+
+config_setting(
+    name = "using_clang",
+    values = {
+        "define": "using_cuda_clang=true",
+    },
+)
+
+# Equivalent to using_clang && -c opt.
+config_setting(
+    name = "using_clang_opt",
+    values = {
+        "define": "using_cuda_clang=true",
+        "compilation_mode": "opt",
+    },
+)
+
+config_setting(
+    name = "darwin",
+    values = {"cpu": "darwin"},
+    visibility = ["//visibility:public"],
+)
+
+config_setting(
+    name = "freebsd",
+    values = {"cpu": "freebsd"},
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cuda_headers",
+    hdrs = [
+        "cuda/cuda_config.h",
+        %{cuda_headers}
+    ],
+    includes = [
+        ".",
+        "cuda/include",
+        "cuda/include/crt",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+cc_import(
+    name = "cudart_static",
+    # /WHOLEARCHIVE:cudart_static.lib will cause a
+    # "Internal error during CImplib::EmitThunk" error.
+    # Treat this library as interface library to avoid being whole archived when
+    # linking a DLL that depends on this.
+    # TODO(pcloudy): Remove this rule after b/111278841 is resolved.
+    interface_library = "cuda/lib/%{cudart_static_lib}",
+    system_provided = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_import(
+    name = "cuda_driver",
+    interface_library = "cuda/lib/%{cuda_driver_lib}",
+    system_provided = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_import(
+    name = "cudart",
+    interface_library = "cuda/lib/%{cudart_lib}",
+    system_provided = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_import(
+    name = "cublas",
+    interface_library = "cuda/lib/%{cublas_lib}",
+    system_provided = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_import(
+    name = "cusolver",
+    interface_library = "cuda/lib/%{cusolver_lib}",
+    system_provided = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_import(
+    name = "cudnn",
+    interface_library = "cuda/lib/%{cudnn_lib}",
+    system_provided = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cudnn_header",
+    includes = [
+        ".",
+        "cuda/include",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+cc_import(
+    name = "cufft",
+    interface_library = "cuda/lib/%{cufft_lib}",
+    system_provided = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_import(
+    name = "curand",
+    interface_library = "cuda/lib/%{curand_lib}",
+    system_provided = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cuda",
+    visibility = ["//visibility:public"],
+    deps = [
+        ":cublas",
+        ":cuda_headers",
+        ":cudart",
+        ":cudnn",
+        ":cufft",
+        ":curand",
+    ],
+)
+
+cc_library(
+    name = "cupti_headers",
+    hdrs = [
+        "cuda/cuda_config.h",
+        ":cuda-extras",
+    ],
+    includes = [
+        ".",
+        "cuda/extras/CUPTI/include/",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+cc_import(
+    name = "cupti_dsos",
+    interface_library = "cuda/lib/%{cupti_lib}",
+    system_provided = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "libdevice_root",
+    data = [":cuda-nvvm"],
+    visibility = ["//visibility:public"],
+)
+
+%{cuda_include_genrules}
diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
index c90c66912d959af109caab51c742d760e0908f30..e848fa175ccb5d39ae9e329837f469b7d5585f05 100644
--- a/third_party/gpus/cuda_configure.bzl
+++ b/third_party/gpus/cuda_configure.bzl
@@ -20,6 +20,7 @@
     `/usr/local/cuda`.
   * `TF_CUDA_COMPUTE_CAPABILITIES`: The CUDA compute capabilities. Default is
     `3.5,5.2`.
+  * `PYTHON_BIN_PATH`: The python binary path
 """
 
 _GCC_HOST_COMPILER_PATH = "GCC_HOST_COMPILER_PATH"
@@ -31,6 +32,7 @@ _CUDNN_INSTALL_PATH = "CUDNN_INSTALL_PATH"
 _TF_CUDA_COMPUTE_CAPABILITIES = "TF_CUDA_COMPUTE_CAPABILITIES"
 _TF_CUDA_CONFIG_REPO = "TF_CUDA_CONFIG_REPO"
 _TF_DOWNLOAD_CLANG = "TF_DOWNLOAD_CLANG"
+_PYTHON_BIN_PATH = "PYTHON_BIN_PATH"
 
 _DEFAULT_CUDA_VERSION = ""
 _DEFAULT_CUDNN_VERSION = ""
@@ -44,12 +46,12 @@ _DEFAULT_CUDA_COMPUTE_CAPABILITIES = ["3.5", "5.2"]
 # will be used. For example, when looking for the cudart libraries, the first
 # attempt will be lib64/cudart inside the CUDA toolkit.
 CUDA_LIB_PATHS = [
-  "lib64/",
-  "lib64/stubs/",
-  "lib/x86_64-linux-gnu/",
-  "lib/x64/",
-  "lib/",
-  "",
+    "lib64/",
+    "lib64/stubs/",
+    "lib/x86_64-linux-gnu/",
+    "lib/x64/",
+    "lib/",
+    "",
 ]
 
 # Lookup paths for cupti.h, relative to the CUDA toolkit directory.
@@ -57,8 +59,8 @@ CUDA_LIB_PATHS = [
 # On most systems, the cupti library is not installed in the same directory as
 # the other CUDA libraries but rather in a special extras/CUPTI directory.
 CUPTI_HEADER_PATHS = [
-  "extras/CUPTI/include/",
-  "include/cuda/CUPTI/",
+    "extras/CUPTI/include/",
+    "include/cuda/CUPTI/",
 ]
 
 # Lookup paths for the cupti library, relative to the
@@ -66,25 +68,25 @@ CUPTI_HEADER_PATHS = [
 # On most systems, the cupti library is not installed in the same directory as
 # the other CUDA libraries but rather in a special extras/CUPTI directory.
 CUPTI_LIB_PATHS = [
-  "extras/CUPTI/lib64/",
-  "lib/x86_64-linux-gnu",
-  "lib64/",
-  "extras/CUPTI/libx64/",
-  "extras/CUPTI/lib/",
-  "lib/",
+    "extras/CUPTI/lib64/",
+    "lib/x86_64-linux-gnu",
+    "lib64/",
+    "extras/CUPTI/libx64/",
+    "extras/CUPTI/lib/",
+    "lib/",
 ]
 
 # Lookup paths for CUDA headers (cuda.h) relative to the CUDA toolkit directory.
 CUDA_INCLUDE_PATHS = [
-  "include/",
-  "include/cuda/"
+    "include/",
+    "include/cuda/",
 ]
 
 # Lookup paths for cudnn.h relative to the CUDNN install directory.
 CUDNN_INCLUDE_PATHS = [
-  "",
-  "include/",
-  "include/cuda/",
+    "",
+    "include/",
+    "include/cuda/",
 ]
 
 # Lookup paths for NVVM libdevice relative to the CUDA directory toolkit.
@@ -92,686 +94,841 @@ CUDNN_INCLUDE_PATHS = [
 # libdevice implements mathematical functions for GPU kernels, and is provided
 # in NVVM bitcode (a subset of LLVM bitcode).
 NVVM_LIBDEVICE_PATHS = [
-  "nvvm/libdevice/",
-  "share/cuda/",
+    "nvvm/libdevice/",
+    "share/cuda/",
+]
+
+# Files used to detect the NVVM libdevice path.
+NVVM_LIBDEVICE_FILES = [
+    # CUDA 9.0 has a single file.
+    "libdevice.10.bc",
+
+    # CUDA 8.0 has separate files for compute versions 2.0, 3.0, 3.5 and 5.0.
+    # Probing for one of them is sufficient.
+    "libdevice.compute_20.10.bc",
 ]
 
 load("//third_party/clang_toolchain:download_clang.bzl", "download_clang")
+load(
+    "@bazel_tools//tools/cpp:lib_cc_configure.bzl",
+    "escape_string",
+    "get_env_var",
+)
+load(
+    "@bazel_tools//tools/cpp:windows_cc_configure.bzl",
+    "find_msvc_tool",
+    "find_vc_path",
+    "setup_vc_env_vars",
+)
+
+def _get_python_bin(repository_ctx):
+    """Gets the python bin path."""
+    python_bin = repository_ctx.os.environ.get(_PYTHON_BIN_PATH)
+    if python_bin != None:
+        return python_bin
+    python_bin_name = "python.exe" if _is_windows(repository_ctx) else "python"
+    python_bin_path = repository_ctx.which(python_bin_name)
+    if python_bin_path != None:
+        return str(python_bin_path)
+    auto_configure_fail("Cannot find python in PATH, please make sure " +
+                        "python is installed and add its directory in PATH, or --define " +
+                        "%s='/something/else'.\nPATH=%s" % (
+                            _PYTHON_BIN_PATH,
+                            repository_ctx.os.environ.get("PATH", ""),
+                        ))
+
+def _get_nvcc_tmp_dir_for_windows(repository_ctx):
+    """Return the tmp directory for nvcc to generate intermediate source files."""
+    escaped_tmp_dir = escape_string(
+        get_env_var(repository_ctx, "TMP", "C:\\Windows\\Temp").replace("\\", "\\\\"),
+    )
+    return escaped_tmp_dir + "\\\\nvcc_inter_files_tmp_dir"
+
+def _get_msvc_compiler(repository_ctx):
+    vc_path = find_vc_path(repository_ctx)
+    return find_msvc_tool(repository_ctx, vc_path, "cl.exe").replace("\\", "/")
+
+def _get_win_cuda_defines(repository_ctx):
+    """Return CROSSTOOL defines for Windows"""
+
+    # If we are not on Windows, return empty vaules for Windows specific fields.
+    # This ensures the CROSSTOOL file parser is happy.
+    if not _is_windows(repository_ctx):
+        return {
+            "%{msvc_env_tmp}": "",
+            "%{msvc_env_path}": "",
+            "%{msvc_env_include}": "",
+            "%{msvc_env_lib}": "",
+            "%{msvc_cl_path}": "",
+            "%{msvc_ml_path}": "",
+            "%{msvc_link_path}": "",
+            "%{msvc_lib_path}": "",
+            "%{cxx_builtin_include_directory}": "",
+        }
+
+    vc_path = find_vc_path(repository_ctx)
+    if not vc_path:
+        auto_configure_fail("Visual C++ build tools not found on your machine." +
+                            "Please check your installation following https://docs.bazel.build/versions/master/windows.html#using")
+        return {}
+
+    env = setup_vc_env_vars(repository_ctx, vc_path)
+    escaped_paths = escape_string(env["PATH"])
+    escaped_include_paths = escape_string(env["INCLUDE"])
+    escaped_lib_paths = escape_string(env["LIB"])
+    escaped_tmp_dir = escape_string(
+        get_env_var(repository_ctx, "TMP", "C:\\Windows\\Temp").replace("\\", "\\\\"),
+    )
+
+    msvc_cl_path = "windows/msvc_wrapper_for_nvcc.bat"
+    msvc_ml_path = find_msvc_tool(repository_ctx, vc_path, "ml64.exe").replace("\\", "/")
+    msvc_link_path = find_msvc_tool(repository_ctx, vc_path, "link.exe").replace("\\", "/")
+    msvc_lib_path = find_msvc_tool(repository_ctx, vc_path, "lib.exe").replace("\\", "/")
+
+    # nvcc will generate some temporary source files under %{nvcc_tmp_dir}
+    # The generated files are guranteed to have unique name, so they can share the same tmp directory
+    escaped_cxx_include_directories = ["cxx_builtin_include_directory: \"%s\"" % _get_nvcc_tmp_dir_for_windows(repository_ctx)]
+    for path in escaped_include_paths.split(";"):
+        if path:
+            escaped_cxx_include_directories.append("cxx_builtin_include_directory: \"%s\"" % path)
+
+    return {
+        "%{msvc_env_tmp}": escaped_tmp_dir,
+        "%{msvc_env_path}": escaped_paths,
+        "%{msvc_env_include}": escaped_include_paths,
+        "%{msvc_env_lib}": escaped_lib_paths,
+        "%{msvc_cl_path}": msvc_cl_path,
+        "%{msvc_ml_path}": msvc_ml_path,
+        "%{msvc_link_path}": msvc_link_path,
+        "%{msvc_lib_path}": msvc_lib_path,
+        "%{cxx_builtin_include_directory}": "\n".join(escaped_cxx_include_directories),
+    }
 
 # TODO(dzc): Once these functions have been factored out of Bazel's
 # cc_configure.bzl, load them from @bazel_tools instead.
 # BEGIN cc_configure common functions.
 def find_cc(repository_ctx):
-  """Find the C++ compiler."""
-  # On Windows, we use Bazel's MSVC CROSSTOOL for GPU build
-  # Return a dummy value for GCC detection here to avoid error
-  if _is_windows(repository_ctx):
-    return "/use/--config=win-cuda --cpu=x64_windows_msvc/instead"
-
-  if _use_cuda_clang(repository_ctx):
-    target_cc_name = "clang"
-    cc_path_envvar = _CLANG_CUDA_COMPILER_PATH
-    if _flag_enabled(repository_ctx, _TF_DOWNLOAD_CLANG):
-      return "extra_tools/bin/clang"
-  else:
-    target_cc_name = "gcc"
-    cc_path_envvar = _GCC_HOST_COMPILER_PATH
-  cc_name = target_cc_name
-
-  if cc_path_envvar in repository_ctx.os.environ:
-    cc_name_from_env = repository_ctx.os.environ[cc_path_envvar].strip()
-    if cc_name_from_env:
-      cc_name = cc_name_from_env
-  if cc_name.startswith("/"):
-    # Absolute path, maybe we should make this supported by our which function.
-    return cc_name
-  cc = repository_ctx.which(cc_name)
-  if cc == None:
-    fail(("Cannot find {}, either correct your path or set the {}" +
-          " environment variable").format(target_cc_name, cc_path_envvar))
-  return cc
-
+    """Find the C++ compiler."""
+    if _is_windows(repository_ctx):
+        return _get_msvc_compiler(repository_ctx)
+
+    if _use_cuda_clang(repository_ctx):
+        target_cc_name = "clang"
+        cc_path_envvar = _CLANG_CUDA_COMPILER_PATH
+        if _flag_enabled(repository_ctx, _TF_DOWNLOAD_CLANG):
+            return "extra_tools/bin/clang"
+    else:
+        target_cc_name = "gcc"
+        cc_path_envvar = _GCC_HOST_COMPILER_PATH
+    cc_name = target_cc_name
+
+    if cc_path_envvar in repository_ctx.os.environ:
+        cc_name_from_env = repository_ctx.os.environ[cc_path_envvar].strip()
+        if cc_name_from_env:
+            cc_name = cc_name_from_env
+    if cc_name.startswith("/"):
+        # Absolute path, maybe we should make this supported by our which function.
+        return cc_name
+    cc = repository_ctx.which(cc_name)
+    if cc == None:
+        fail(("Cannot find {}, either correct your path or set the {}" +
+              " environment variable").format(target_cc_name, cc_path_envvar))
+    return cc
 
 _INC_DIR_MARKER_BEGIN = "#include <...>"
 
-
 # OSX add " (framework directory)" at the end of line, strip it.
 _OSX_FRAMEWORK_SUFFIX = " (framework directory)"
-_OSX_FRAMEWORK_SUFFIX_LEN =  len(_OSX_FRAMEWORK_SUFFIX)
-def _cxx_inc_convert(path):
-  """Convert path returned by cc -E xc++ in a complete path."""
-  path = path.strip()
-  if path.endswith(_OSX_FRAMEWORK_SUFFIX):
-    path = path[:-_OSX_FRAMEWORK_SUFFIX_LEN].strip()
-  return path
+_OSX_FRAMEWORK_SUFFIX_LEN = len(_OSX_FRAMEWORK_SUFFIX)
 
+def _cxx_inc_convert(path):
+    """Convert path returned by cc -E xc++ in a complete path."""
+    path = path.strip()
+    if path.endswith(_OSX_FRAMEWORK_SUFFIX):
+        path = path[:-_OSX_FRAMEWORK_SUFFIX_LEN].strip()
+    return path
 
 def _normalize_include_path(repository_ctx, path):
-  """Normalizes include paths before writing them to the crosstool.
+    """Normalizes include paths before writing them to the crosstool.
 
-  If path points inside the 'crosstool' folder of the repository, a relative
-  path is returned.
-  If path points outside the 'crosstool' folder, an absolute path is returned.
-  """
-  path = str(repository_ctx.path(path))
-  crosstool_folder = str(repository_ctx.path(".").get_child('crosstool'))
-
-  if path.startswith(crosstool_folder):
-    # We drop the path to "$REPO/crosstool" and a trailing path separator.
-    return path[len(crosstool_folder)+1:]
-  return path
+    If path points inside the 'crosstool' folder of the repository, a relative
+    path is returned.
+    If path points outside the 'crosstool' folder, an absolute path is returned.
+    """
+    path = str(repository_ctx.path(path))
+    crosstool_folder = str(repository_ctx.path(".").get_child("crosstool"))
 
+    if path.startswith(crosstool_folder):
+        # We drop the path to "$REPO/crosstool" and a trailing path separator.
+        return path[len(crosstool_folder) + 1:]
+    return path
 
 def _get_cxx_inc_directories_impl(repository_ctx, cc, lang_is_cpp):
-  """Compute the list of default C or C++ include directories."""
-  if lang_is_cpp:
-    lang = "c++"
-  else:
-    lang = "c"
-  result = repository_ctx.execute([cc, "-E", "-x" + lang, "-", "-v"])
-  index1 = result.stderr.find(_INC_DIR_MARKER_BEGIN)
-  if index1 == -1:
-    return []
-  index1 = result.stderr.find("\n", index1)
-  if index1 == -1:
-    return []
-  index2 = result.stderr.rfind("\n ")
-  if index2 == -1 or index2 < index1:
-    return []
-  index2 = result.stderr.find("\n", index2 + 1)
-  if index2 == -1:
-    inc_dirs = result.stderr[index1 + 1:]
-  else:
-    inc_dirs = result.stderr[index1 + 1:index2].strip()
-
-  return [
-      _normalize_include_path(repository_ctx, _cxx_inc_convert(p))
-      for p in inc_dirs.split("\n")
-  ]
+    """Compute the list of default C or C++ include directories."""
+    if lang_is_cpp:
+        lang = "c++"
+    else:
+        lang = "c"
+    result = repository_ctx.execute([cc, "-E", "-x" + lang, "-", "-v"])
+    index1 = result.stderr.find(_INC_DIR_MARKER_BEGIN)
+    if index1 == -1:
+        return []
+    index1 = result.stderr.find("\n", index1)
+    if index1 == -1:
+        return []
+    index2 = result.stderr.rfind("\n ")
+    if index2 == -1 or index2 < index1:
+        return []
+    index2 = result.stderr.find("\n", index2 + 1)
+    if index2 == -1:
+        inc_dirs = result.stderr[index1 + 1:]
+    else:
+        inc_dirs = result.stderr[index1 + 1:index2].strip()
 
+    return [
+        _normalize_include_path(repository_ctx, _cxx_inc_convert(p))
+        for p in inc_dirs.split("\n")
+    ]
 
 def get_cxx_inc_directories(repository_ctx, cc):
-  """Compute the list of default C and C++ include directories."""
-  # For some reason `clang -xc` sometimes returns include paths that are
-  # different from the ones from `clang -xc++`. (Symlink and a dir)
-  # So we run the compiler with both `-xc` and `-xc++` and merge resulting lists
-  includes_cpp = _get_cxx_inc_directories_impl(repository_ctx, cc, True)
-  includes_c = _get_cxx_inc_directories_impl(repository_ctx, cc, False)
+    """Compute the list of default C and C++ include directories."""
 
-  includes_cpp_set = depset(includes_cpp)
-  return includes_cpp + [inc for inc in includes_c
-                         if inc not in includes_cpp_set]
+    # For some reason `clang -xc` sometimes returns include paths that are
+    # different from the ones from `clang -xc++`. (Symlink and a dir)
+    # So we run the compiler with both `-xc` and `-xc++` and merge resulting lists
+    includes_cpp = _get_cxx_inc_directories_impl(repository_ctx, cc, True)
+    includes_c = _get_cxx_inc_directories_impl(repository_ctx, cc, False)
 
+    includes_cpp_set = depset(includes_cpp)
+    return includes_cpp + [
+        inc
+        for inc in includes_c
+        if inc not in includes_cpp_set
+    ]
 
 def auto_configure_fail(msg):
-  """Output failure message when cuda configuration fails."""
-  red = "\033[0;31m"
-  no_color = "\033[0m"
-  fail("\n%sCuda Configuration Error:%s %s\n" % (red, no_color, msg))
-# END cc_configure common functions (see TODO above).
+    """Output failure message when cuda configuration fails."""
+    red = "\033[0;31m"
+    no_color = "\033[0m"
+    fail("\n%sCuda Configuration Error:%s %s\n" % (red, no_color, msg))
 
+# END cc_configure common functions (see TODO above).
 
 def _host_compiler_includes(repository_ctx, cc):
-  """Generates the cxx_builtin_include_directory entries for gcc inc dirs.
-
-  Args:
-    repository_ctx: The repository context.
-    cc: The path to the gcc host compiler.
-
-  Returns:
-    A string containing the cxx_builtin_include_directory for each of the gcc
-    host compiler include directories, which can be added to the CROSSTOOL
-    file.
-  """
-  inc_dirs = get_cxx_inc_directories(repository_ctx, cc)
-  inc_entries = []
-  for inc_dir in inc_dirs:
-    inc_entries.append("  cxx_builtin_include_directory: \"%s\"" % inc_dir)
-  return "\n".join(inc_entries)
+    """Generates the cxx_builtin_include_directory entries for gcc inc dirs.
+
+    Args:
+      repository_ctx: The repository context.
+      cc: The path to the gcc host compiler.
+
+    Returns:
+      A string containing the cxx_builtin_include_directory for each of the gcc
+      host compiler include directories, which can be added to the CROSSTOOL
+      file.
+    """
+    inc_dirs = get_cxx_inc_directories(repository_ctx, cc)
+    inc_entries = []
+    for inc_dir in inc_dirs:
+        inc_entries.append("  cxx_builtin_include_directory: \"%s\"" % inc_dir)
+    return "\n".join(inc_entries)
 
 def _cuda_include_path(repository_ctx, cuda_config):
-  """Generates the cxx_builtin_include_directory entries for cuda inc dirs.
-
-  Args:
-    repository_ctx: The repository context.
-    cc: The path to the gcc host compiler.
-
-  Returns:
-    A string containing the cxx_builtin_include_directory for each of the gcc
-    host compiler include directories, which can be added to the CROSSTOOL
-    file.
-  """
-  nvcc_path = repository_ctx.path("%s/bin/nvcc%s" %
-                                  (cuda_config.cuda_toolkit_path,
-                                   ".exe" if cuda_config.cpu_value == "Windows" else ""))
-  result = repository_ctx.execute([nvcc_path, '-v',
-                                  '/dev/null', '-o', '/dev/null'])
-  target_dir = ""
-  for one_line in result.stderr.splitlines():
-    if one_line.startswith('#$ _TARGET_DIR_='):
-      target_dir = (cuda_config.cuda_toolkit_path + '/' +
-                    one_line.replace('#$ _TARGET_DIR_=', '') + "/include")
-  inc_entries = []
-  if target_dir != "":
-    inc_entries.append("  cxx_builtin_include_directory: \"%s\"" % target_dir)
-  default_include = cuda_config.cuda_toolkit_path + '/include'
-  inc_entries.append("  cxx_builtin_include_directory: \"%s\"" %
-                     default_include)
-  return "\n".join(inc_entries)
+    """Generates the cxx_builtin_include_directory entries for cuda inc dirs.
 
+    Args:
+      repository_ctx: The repository context.
+      cc: The path to the gcc host compiler.
 
-def _enable_cuda(repository_ctx):
-  if "TF_NEED_CUDA" in repository_ctx.os.environ:
-    enable_cuda = repository_ctx.os.environ["TF_NEED_CUDA"].strip()
-    return enable_cuda == "1"
-  return False
+    Returns:
+      A string containing the cxx_builtin_include_directory for each of the gcc
+      host compiler include directories, which can be added to the CROSSTOOL
+      file.
+    """
+    nvcc_path = repository_ctx.path("%s/bin/nvcc%s" %
+                                    (
+                                        cuda_config.cuda_toolkit_path,
+                                        ".exe" if cuda_config.cpu_value == "Windows" else "",
+                                    ))
+    result = repository_ctx.execute([
+        nvcc_path,
+        "-v",
+        "/dev/null",
+        "-o",
+        "/dev/null",
+    ])
+    target_dir = ""
+    for one_line in result.stderr.splitlines():
+        if one_line.startswith("#$ _TARGET_DIR_="):
+            target_dir = (cuda_config.cuda_toolkit_path + "/" +
+                          one_line.replace("#$ _TARGET_DIR_=", "") + "/include")
+    inc_entries = []
+    if target_dir != "":
+        inc_entries.append("  cxx_builtin_include_directory: \"%s\"" % target_dir)
+    default_include = cuda_config.cuda_toolkit_path + "/include"
+    inc_entries.append("  cxx_builtin_include_directory: \"%s\"" %
+                       default_include)
+    return "\n".join(inc_entries)
 
+def _enable_cuda(repository_ctx):
+    if "TF_NEED_CUDA" in repository_ctx.os.environ:
+        enable_cuda = repository_ctx.os.environ["TF_NEED_CUDA"].strip()
+        return enable_cuda == "1"
+    return False
 
 def _cuda_toolkit_path(repository_ctx):
-  """Finds the cuda toolkit directory.
-
-  Args:
-    repository_ctx: The repository context.
+    """Finds the cuda toolkit directory.
 
-  Returns:
-    A speculative real path of the cuda toolkit install directory.
-  """
-  cuda_toolkit_path = _DEFAULT_CUDA_TOOLKIT_PATH
-  if _CUDA_TOOLKIT_PATH in repository_ctx.os.environ:
-    cuda_toolkit_path = repository_ctx.os.environ[_CUDA_TOOLKIT_PATH].strip()
-  if not repository_ctx.path(cuda_toolkit_path).exists:
-    auto_configure_fail("Cannot find cuda toolkit path.")
-  return str(repository_ctx.path(cuda_toolkit_path).realpath)
+    Args:
+      repository_ctx: The repository context.
 
+    Returns:
+      A speculative real path of the cuda toolkit install directory.
+    """
+    cuda_toolkit_path = _DEFAULT_CUDA_TOOLKIT_PATH
+    if _CUDA_TOOLKIT_PATH in repository_ctx.os.environ:
+        cuda_toolkit_path = repository_ctx.os.environ[_CUDA_TOOLKIT_PATH].strip()
+    if not repository_ctx.path(cuda_toolkit_path).exists:
+        auto_configure_fail("Cannot find cuda toolkit path.")
+    return str(repository_ctx.path(cuda_toolkit_path).realpath)
 
 def _cudnn_install_basedir(repository_ctx):
-  """Finds the cudnn install directory."""
-  cudnn_install_path = _DEFAULT_CUDNN_INSTALL_PATH
-  if _CUDNN_INSTALL_PATH in repository_ctx.os.environ:
-    cudnn_install_path = repository_ctx.os.environ[_CUDNN_INSTALL_PATH].strip()
-  if not repository_ctx.path(cudnn_install_path).exists:
-    auto_configure_fail("Cannot find cudnn install path.")
-  return cudnn_install_path
-
+    """Finds the cudnn install directory."""
+    cudnn_install_path = _DEFAULT_CUDNN_INSTALL_PATH
+    if _CUDNN_INSTALL_PATH in repository_ctx.os.environ:
+        cudnn_install_path = repository_ctx.os.environ[_CUDNN_INSTALL_PATH].strip()
+    if not repository_ctx.path(cudnn_install_path).exists:
+        auto_configure_fail("Cannot find cudnn install path.")
+    return cudnn_install_path
 
 def matches_version(environ_version, detected_version):
-  """Checks whether the user-specified version matches the detected version.
-
-  This function performs a weak matching so that if the user specifies only the
-  major or major and minor versions, the versions are still considered matching
-  if the version parts match. To illustrate:
-
-      environ_version  detected_version  result
-      -----------------------------------------
-      5.1.3            5.1.3             True
-      5.1              5.1.3             True
-      5                5.1               True
-      5.1.3            5.1               False
-      5.2.3            5.1.3             False
-
-  Args:
-    environ_version: The version specified by the user via environment
-      variables.
-    detected_version: The version autodetected from the CUDA installation on
-      the system.
-
-  Returns: True if user-specified version matches detected version and False
-    otherwise.
-  """
-  environ_version_parts = environ_version.split(".")
-  detected_version_parts = detected_version.split(".")
-  if len(detected_version_parts) < len(environ_version_parts):
-    return False
-  for i, part in enumerate(detected_version_parts):
-    if i >= len(environ_version_parts):
-      break
-    if part != environ_version_parts[i]:
-      return False
-  return True
-
+    """Checks whether the user-specified version matches the detected version.
+
+    This function performs a weak matching so that if the user specifies only the
+    major or major and minor versions, the versions are still considered matching
+    if the version parts match. To illustrate:
+
+        environ_version  detected_version  result
+        -----------------------------------------
+        5.1.3            5.1.3             True
+        5.1              5.1.3             True
+        5                5.1               True
+        5.1.3            5.1               False
+        5.2.3            5.1.3             False
+
+    Args:
+      environ_version: The version specified by the user via environment
+        variables.
+      detected_version: The version autodetected from the CUDA installation on
+        the system.
+
+    Returns: True if user-specified version matches detected version and False
+      otherwise.
+    """
+    environ_version_parts = environ_version.split(".")
+    detected_version_parts = detected_version.split(".")
+    if len(detected_version_parts) < len(environ_version_parts):
+        return False
+    for i, part in enumerate(detected_version_parts):
+        if i >= len(environ_version_parts):
+            break
+        if part != environ_version_parts[i]:
+            return False
+    return True
 
 _NVCC_VERSION_PREFIX = "Cuda compilation tools, release "
 
-
 def _cuda_version(repository_ctx, cuda_toolkit_path, cpu_value):
-  """Detects the version of CUDA installed on the system.
-
-  Args:
-    repository_ctx: The repository context.
-    cuda_toolkit_path: The CUDA install directory.
-
-  Returns:
-    String containing the version of CUDA.
-  """
-  # Run nvcc --version and find the line containing the CUDA version.
-  nvcc_path = repository_ctx.path("%s/bin/nvcc%s" %
-                                  (cuda_toolkit_path,
-                                   ".exe" if cpu_value == "Windows" else ""))
-  if not nvcc_path.exists:
-    auto_configure_fail("Cannot find nvcc at %s" % str(nvcc_path))
-  result = repository_ctx.execute([str(nvcc_path), '--version'])
-  if result.stderr:
-    auto_configure_fail("Error running nvcc --version: %s" % result.stderr)
-  lines = result.stdout.splitlines()
-  version_line = lines[len(lines) - 1]
-  if version_line.find(_NVCC_VERSION_PREFIX) == -1:
-    auto_configure_fail(
-        "Could not parse CUDA version from nvcc --version. Got: %s" %
-        result.stdout)
-
-  # Parse the CUDA version from the line containing the CUDA version.
-  prefix_removed = version_line.replace(_NVCC_VERSION_PREFIX, '')
-  parts = prefix_removed.split(",")
-  if len(parts) != 2 or len(parts[0]) < 2:
-    auto_configure_fail(
-        "Could not parse CUDA version from nvcc --version. Got: %s" %
-        result.stdout)
-  full_version = parts[1].strip()
-  if full_version.startswith('V'):
-    full_version = full_version[1:]
-
-  # Check whether TF_CUDA_VERSION was set by the user and fail if it does not
-  # match the detected version.
-  environ_version = ""
-  if _TF_CUDA_VERSION in repository_ctx.os.environ:
-    environ_version = repository_ctx.os.environ[_TF_CUDA_VERSION].strip()
-  if environ_version and not matches_version(environ_version, full_version):
-    auto_configure_fail(
-        ("CUDA version detected from nvcc (%s) does not match " +
-         "TF_CUDA_VERSION (%s)") % (full_version, environ_version))
-
-  # We only use the version consisting of the major and minor version numbers.
-  version_parts = full_version.split('.')
-  if len(version_parts) < 2:
-    auto_configure_fail("CUDA version detected from nvcc (%s) is incomplete.")
-  if cpu_value == "Windows":
-    version = "64_%s%s" % (version_parts[0], version_parts[1])
-  else:
-    version = "%s.%s" % (version_parts[0], version_parts[1])
-  return version
+    """Detects the version of CUDA installed on the system.
+
+    Args:
+      repository_ctx: The repository context.
+      cuda_toolkit_path: The CUDA install directory.
+
+    Returns:
+      String containing the version of CUDA.
+    """
+
+    # Run nvcc --version and find the line containing the CUDA version.
+    nvcc_path = repository_ctx.path("%s/bin/nvcc%s" %
+                                    (
+                                        cuda_toolkit_path,
+                                        ".exe" if cpu_value == "Windows" else "",
+                                    ))
+    if not nvcc_path.exists:
+        auto_configure_fail("Cannot find nvcc at %s" % str(nvcc_path))
+    result = repository_ctx.execute([str(nvcc_path), "--version"])
+    if result.stderr:
+        auto_configure_fail("Error running nvcc --version: %s" % result.stderr)
+    lines = result.stdout.splitlines()
+    version_line = lines[len(lines) - 1]
+    if version_line.find(_NVCC_VERSION_PREFIX) == -1:
+        auto_configure_fail(
+            "Could not parse CUDA version from nvcc --version. Got: %s" %
+            result.stdout,
+        )
 
+    # Parse the CUDA version from the line containing the CUDA version.
+    prefix_removed = version_line.replace(_NVCC_VERSION_PREFIX, "")
+    parts = prefix_removed.split(",")
+    if len(parts) != 2 or len(parts[0]) < 2:
+        auto_configure_fail(
+            "Could not parse CUDA version from nvcc --version. Got: %s" %
+            result.stdout,
+        )
+    full_version = parts[1].strip()
+    if full_version.startswith("V"):
+        full_version = full_version[1:]
+
+    # Check whether TF_CUDA_VERSION was set by the user and fail if it does not
+    # match the detected version.
+    environ_version = ""
+    if _TF_CUDA_VERSION in repository_ctx.os.environ:
+        environ_version = repository_ctx.os.environ[_TF_CUDA_VERSION].strip()
+    if environ_version and not matches_version(environ_version, full_version):
+        auto_configure_fail(
+            ("CUDA version detected from nvcc (%s) does not match " +
+             "TF_CUDA_VERSION (%s)") % (full_version, environ_version),
+        )
+
+    # We only use the version consisting of the major and minor version numbers.
+    version_parts = full_version.split(".")
+    if len(version_parts) < 2:
+        auto_configure_fail("CUDA version detected from nvcc (%s) is incomplete.")
+    if cpu_value == "Windows":
+        version = "64_%s%s" % (version_parts[0], version_parts[1])
+    else:
+        version = "%s.%s" % (version_parts[0], version_parts[1])
+    return version
 
 _DEFINE_CUDNN_MAJOR = "#define CUDNN_MAJOR"
 _DEFINE_CUDNN_MINOR = "#define CUDNN_MINOR"
 _DEFINE_CUDNN_PATCHLEVEL = "#define CUDNN_PATCHLEVEL"
 
-
 def find_cuda_define(repository_ctx, header_dir, header_file, define):
-  """Returns the value of a #define in a header file.
-
-  Greps through a header file and returns the value of the specified #define.
-  If the #define is not found, then raise an error.
-
-  Args:
-    repository_ctx: The repository context.
-    header_dir: The directory containing the header file.
-    header_file: The header file name.
-    define: The #define to search for.
-
-  Returns:
-    The value of the #define found in the header.
-  """
-  # Confirm location of the header and grep for the line defining the macro.
-  h_path = repository_ctx.path("%s/%s" % (header_dir, header_file))
-  if not h_path.exists:
-    auto_configure_fail("Cannot find %s at %s" % (header_file, str(h_path)))
-  result = repository_ctx.execute(
-      # Grep one more lines as some #defines are splitted into two lines.
-      ["grep", "--color=never", "-A1", "-E", define, str(h_path)])
-  if result.stderr:
-    auto_configure_fail("Error reading %s: %s" % (str(h_path), result.stderr))
-
-  # Parse the version from the line defining the macro.
-  if result.stdout.find(define) == -1:
-    auto_configure_fail("Cannot find line containing '%s' in %s" %
-                        (define, h_path))
-  # Split results to lines
-  lines = result.stdout.split('\n')
-  num_lines = len(lines)
-  for l in range(num_lines):
-    line = lines[l]
-    if define in line:  # Find the line with define
-      version = line
-      if l != num_lines-1 and line[-1] == '\\':  # Add next line, if multiline
-        version = version[:-1] + lines[l+1]
-      break
-  # Remove any comments
-  version = version.split("//")[0]
-  # Remove define name
-  version = version.replace(define, "").strip()
-  # Remove the code after the version number.
-  version_end = version.find(" ")
-  if version_end != -1:
-    if version_end == 0:
-      auto_configure_fail(
-          "Cannot extract the version from line containing '%s' in %s" %
-          (define, str(h_path)))
-    version = version[:version_end].strip()
-  return version
+    """Returns the value of a #define in a header file.
+
+    Greps through a header file and returns the value of the specified #define.
+    If the #define is not found, then raise an error.
 
+    Args:
+      repository_ctx: The repository context.
+      header_dir: The directory containing the header file.
+      header_file: The header file name.
+      define: The #define to search for.
+
+    Returns:
+      The value of the #define found in the header.
+    """
+
+    # Confirm location of the header and grep for the line defining the macro.
+    h_path = repository_ctx.path("%s/%s" % (header_dir, header_file))
+    if not h_path.exists:
+        auto_configure_fail("Cannot find %s at %s" % (header_file, str(h_path)))
+    result = repository_ctx.execute(
+        # Grep one more lines as some #defines are splitted into two lines.
+        ["grep", "--color=never", "-A1", "-E", define, str(h_path)],
+    )
+    if result.stderr:
+        auto_configure_fail("Error reading %s: %s" % (str(h_path), result.stderr))
+
+    # Parse the version from the line defining the macro.
+    if result.stdout.find(define) == -1:
+        auto_configure_fail("Cannot find line containing '%s' in %s" %
+                            (define, h_path))
+
+    # Split results to lines
+    lines = result.stdout.split("\n")
+    num_lines = len(lines)
+    for l in range(num_lines):
+        line = lines[l]
+        if define in line:  # Find the line with define
+            version = line
+            if l != num_lines - 1 and line[-1] == "\\":  # Add next line, if multiline
+                version = version[:-1] + lines[l + 1]
+            break
+
+    # Remove any comments
+    version = version.split("//")[0]
+
+    # Remove define name
+    version = version.replace(define, "").strip()
+
+    # Remove the code after the version number.
+    version_end = version.find(" ")
+    if version_end != -1:
+        if version_end == 0:
+            auto_configure_fail(
+                "Cannot extract the version from line containing '%s' in %s" %
+                (define, str(h_path)),
+            )
+        version = version[:version_end].strip()
+    return version
 
 def _cudnn_version(repository_ctx, cudnn_install_basedir, cpu_value):
-  """Detects the version of cuDNN installed on the system.
-
-  Args:
-    repository_ctx: The repository context.
-    cpu_value: The name of the host operating system.
-    cudnn_install_basedir: The cuDNN install directory.
-
-  Returns:
-    A string containing the version of cuDNN.
-  """
-  cudnn_header_dir = _find_cudnn_header_dir(repository_ctx,
-                                            cudnn_install_basedir)
-  major_version = find_cuda_define(
-      repository_ctx, cudnn_header_dir, "cudnn.h", _DEFINE_CUDNN_MAJOR)
-  minor_version = find_cuda_define(
-      repository_ctx, cudnn_header_dir, "cudnn.h", _DEFINE_CUDNN_MINOR)
-  patch_version = find_cuda_define(
-      repository_ctx, cudnn_header_dir, "cudnn.h", _DEFINE_CUDNN_PATCHLEVEL)
-  full_version = "%s.%s.%s" % (major_version, minor_version, patch_version)
-
-  # Check whether TF_CUDNN_VERSION was set by the user and fail if it does not
-  # match the detected version.
-  environ_version = ""
-  if _TF_CUDNN_VERSION in repository_ctx.os.environ:
-    environ_version = repository_ctx.os.environ[_TF_CUDNN_VERSION].strip()
-  if environ_version and not matches_version(environ_version, full_version):
-    cudnn_h_path = repository_ctx.path("%s/include/cudnn.h" %
-                                       cudnn_install_basedir)
-    auto_configure_fail(
-        ("cuDNN version detected from %s (%s) does not match " +
-        "TF_CUDNN_VERSION (%s)") %
-        (str(cudnn_h_path), full_version, environ_version))
-
-  # We only use the major version since we use the libcudnn libraries that are
-  # only versioned with the major version (e.g. libcudnn.so.5).
-  version = major_version
-  if cpu_value == "Windows":
-    version = "64_" + version
-  return version
+    """Detects the version of cuDNN installed on the system.
 
+    Args:
+      repository_ctx: The repository context.
+      cpu_value: The name of the host operating system.
+      cudnn_install_basedir: The cuDNN install directory.
 
-def _compute_capabilities(repository_ctx):
-  """Returns a list of strings representing cuda compute capabilities."""
-  if _TF_CUDA_COMPUTE_CAPABILITIES not in repository_ctx.os.environ:
-    return _DEFAULT_CUDA_COMPUTE_CAPABILITIES
-  capabilities_str = repository_ctx.os.environ[_TF_CUDA_COMPUTE_CAPABILITIES]
-  capabilities = capabilities_str.split(",")
-  for capability in capabilities:
-    # Workaround for Skylark's lack of support for regex. This check should
-    # be equivalent to checking:
-    #     if re.match("[0-9]+.[0-9]+", capability) == None:
-    parts = capability.split(".")
-    if len(parts) != 2 or not parts[0].isdigit() or not parts[1].isdigit():
-      auto_configure_fail("Invalid compute capability: %s" % capability)
-  return capabilities
+    Returns:
+      A string containing the version of cuDNN.
+    """
+    cudnn_header_dir = _find_cudnn_header_dir(
+        repository_ctx,
+        cudnn_install_basedir,
+    )
+    major_version = find_cuda_define(
+        repository_ctx,
+        cudnn_header_dir,
+        "cudnn.h",
+        _DEFINE_CUDNN_MAJOR,
+    )
+    minor_version = find_cuda_define(
+        repository_ctx,
+        cudnn_header_dir,
+        "cudnn.h",
+        _DEFINE_CUDNN_MINOR,
+    )
+    patch_version = find_cuda_define(
+        repository_ctx,
+        cudnn_header_dir,
+        "cudnn.h",
+        _DEFINE_CUDNN_PATCHLEVEL,
+    )
+    full_version = "%s.%s.%s" % (major_version, minor_version, patch_version)
+
+    # Check whether TF_CUDNN_VERSION was set by the user and fail if it does not
+    # match the detected version.
+    environ_version = ""
+    if _TF_CUDNN_VERSION in repository_ctx.os.environ:
+        environ_version = repository_ctx.os.environ[_TF_CUDNN_VERSION].strip()
+    if environ_version and not matches_version(environ_version, full_version):
+        cudnn_h_path = repository_ctx.path("%s/include/cudnn.h" %
+                                           cudnn_install_basedir)
+        auto_configure_fail(
+            ("cuDNN version detected from %s (%s) does not match " +
+             "TF_CUDNN_VERSION (%s)") %
+            (str(cudnn_h_path), full_version, environ_version),
+        )
 
+    # We only use the major version since we use the libcudnn libraries that are
+    # only versioned with the major version (e.g. libcudnn.so.5).
+    version = major_version
+    if cpu_value == "Windows":
+        version = "64_" + version
+    return version
 
-def get_cpu_value(repository_ctx):
-  """Returns the name of the host operating system.
+def _compute_capabilities(repository_ctx):
+    """Returns a list of strings representing cuda compute capabilities."""
+    if _TF_CUDA_COMPUTE_CAPABILITIES not in repository_ctx.os.environ:
+        return _DEFAULT_CUDA_COMPUTE_CAPABILITIES
+    capabilities_str = repository_ctx.os.environ[_TF_CUDA_COMPUTE_CAPABILITIES]
+    capabilities = capabilities_str.split(",")
+    for capability in capabilities:
+        # Workaround for Skylark's lack of support for regex. This check should
+        # be equivalent to checking:
+        #     if re.match("[0-9]+.[0-9]+", capability) == None:
+        parts = capability.split(".")
+        if len(parts) != 2 or not parts[0].isdigit() or not parts[1].isdigit():
+            auto_configure_fail("Invalid compute capability: %s" % capability)
+    return capabilities
 
-  Args:
-    repository_ctx: The repository context.
+def get_cpu_value(repository_ctx):
+    """Returns the name of the host operating system.
 
-  Returns:
-    A string containing the name of the host operating system.
-  """
-  os_name = repository_ctx.os.name.lower()
-  if os_name.startswith("mac os"):
-    return "Darwin"
-  if os_name.find("windows") != -1:
-    return "Windows"
-  result = repository_ctx.execute(["uname", "-s"])
-  return result.stdout.strip()
+    Args:
+      repository_ctx: The repository context.
 
+    Returns:
+      A string containing the name of the host operating system.
+    """
+    os_name = repository_ctx.os.name.lower()
+    if os_name.startswith("mac os"):
+        return "Darwin"
+    if os_name.find("windows") != -1:
+        return "Windows"
+    result = repository_ctx.execute(["uname", "-s"])
+    return result.stdout.strip()
 
 def _is_windows(repository_ctx):
-  """Returns true if the host operating system is windows."""
-  return get_cpu_value(repository_ctx) == "Windows"
-
-def _lib_name(lib, cpu_value, version="", static=False):
-  """Constructs the platform-specific name of a library.
-
-  Args:
-    lib: The name of the library, such as "cudart"
-    cpu_value: The name of the host operating system.
-    version: The version of the library.
-    static: True the library is static or False if it is a shared object.
-
-  Returns:
-    The platform-specific name of the library.
-  """
-  if cpu_value in ("Linux", "FreeBSD"):
-    if static:
-      return "lib%s.a" % lib
-    else:
-      if version:
-        version = ".%s" % version
-      return "lib%s.so%s" % (lib, version)
-  elif cpu_value == "Windows":
-    return "%s.lib" % lib
-  elif cpu_value == "Darwin":
-    if static:
-      return "lib%s.a" % lib
-    else:
-      if version:
-        version = ".%s" % version
-    return "lib%s%s.dylib" % (lib, version)
-  else:
-    auto_configure_fail("Invalid cpu_value: %s" % cpu_value)
-
-
-def _find_cuda_lib(lib, repository_ctx, cpu_value, basedir, version="",
-                   static=False):
-  """Finds the given CUDA or cuDNN library on the system.
-
-  Args:
-    lib: The name of the library, such as "cudart"
-    repository_ctx: The repository context.
-    cpu_value: The name of the host operating system.
-    basedir: The install directory of CUDA or cuDNN.
-    version: The version of the library.
-    static: True if static library, False if shared object.
-
-  Returns:
-    Returns a struct with the following fields:
-      file_name: The basename of the library found on the system.
-      path: The full path to the library.
-  """
-  file_name = _lib_name(lib, cpu_value, version, static)
-  for relative_path in CUDA_LIB_PATHS:
-    path = repository_ctx.path("%s/%s%s" % (basedir, relative_path, file_name))
-    if path.exists:
-      return struct(file_name=file_name, path=str(path.realpath))
-  auto_configure_fail("Cannot find cuda library %s" % file_name)
+    """Returns true if the host operating system is windows."""
+    return get_cpu_value(repository_ctx) == "Windows"
 
+def _lib_name(lib, cpu_value, version = "", static = False):
+    """Constructs the platform-specific name of a library.
 
-def _find_cupti_header_dir(repository_ctx, cuda_config):
-  """Returns the path to the directory containing cupti.h
+    Args:
+      lib: The name of the library, such as "cudart"
+      cpu_value: The name of the host operating system.
+      version: The version of the library.
+      static: True the library is static or False if it is a shared object.
+
+    Returns:
+      The platform-specific name of the library.
+    """
+    if cpu_value in ("Linux", "FreeBSD"):
+        if static:
+            return "lib%s.a" % lib
+        else:
+            if version:
+                version = ".%s" % version
+            return "lib%s.so%s" % (lib, version)
+    elif cpu_value == "Windows":
+        return "%s.lib" % lib
+    elif cpu_value == "Darwin":
+        if static:
+            return "lib%s.a" % lib
+        elif version:
+            version = ".%s" % version
+        return "lib%s%s.dylib" % (lib, version)
+    else:
+        auto_configure_fail("Invalid cpu_value: %s" % cpu_value)
+
+def _find_cuda_lib(
+        lib,
+        repository_ctx,
+        cpu_value,
+        basedir,
+        version = "",
+        static = False):
+    """Finds the given CUDA or cuDNN library on the system.
+
+    Args:
+      lib: The name of the library, such as "cudart"
+      repository_ctx: The repository context.
+      cpu_value: The name of the host operating system.
+      basedir: The install directory of CUDA or cuDNN.
+      version: The version of the library.
+      static: True if static library, False if shared object.
+
+    Returns:
+      Returns a struct with the following fields:
+        file_name: The basename of the library found on the system.
+        path: The full path to the library.
+    """
+    file_name = _lib_name(lib, cpu_value, version, static)
+    for relative_path in CUDA_LIB_PATHS:
+        path = repository_ctx.path("%s/%s%s" % (basedir, relative_path, file_name))
+        if path.exists:
+            return struct(file_name = file_name, path = str(path.realpath))
+    auto_configure_fail("Cannot find cuda library %s" % file_name)
 
-  On most systems, the cupti library is not installed in the same directory as
-  the other CUDA libraries but rather in a special extras/CUPTI directory.
+def _find_cupti_header_dir(repository_ctx, cuda_config):
+    """Returns the path to the directory containing cupti.h
 
-  Args:
-    repository_ctx: The repository context.
-    cuda_config: The CUDA config as returned by _get_cuda_config
+    On most systems, the cupti library is not installed in the same directory as
+    the other CUDA libraries but rather in a special extras/CUPTI directory.
 
-  Returns:
-    The path of the directory containing the cupti header.
-  """
-  cuda_toolkit_path = cuda_config.cuda_toolkit_path
-  for relative_path in CUPTI_HEADER_PATHS:
-    if repository_ctx.path("%s/%scupti.h" % (cuda_toolkit_path, relative_path)).exists:
-        return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1]
-  auto_configure_fail("Cannot find cupti.h under %s" % ", ".join([cuda_toolkit_path + "/" + s for s in CUPTI_HEADER_PATHS]))
+    Args:
+      repository_ctx: The repository context.
+      cuda_config: The CUDA config as returned by _get_cuda_config
 
+    Returns:
+      The path of the directory containing the cupti header.
+    """
+    cuda_toolkit_path = cuda_config.cuda_toolkit_path
+    for relative_path in CUPTI_HEADER_PATHS:
+        if repository_ctx.path("%s/%scupti.h" % (cuda_toolkit_path, relative_path)).exists:
+            return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1]
+    auto_configure_fail("Cannot find cupti.h under %s" % ", ".join([cuda_toolkit_path + "/" + s for s in CUPTI_HEADER_PATHS]))
 
 def _find_cupti_lib(repository_ctx, cuda_config):
-  """Finds the cupti library on the system.
-
-  On most systems, the cupti library is not installed in the same directory as
-  the other CUDA libraries but rather in a special extras/CUPTI directory.
-
-  Args:
-    repository_ctx: The repository context.
-    cuda_config: The cuda configuration as returned by _get_cuda_config.
-
-  Returns:
-    Returns a struct with the following fields:
-      file_name: The basename of the library found on the system.
-      path: The full path to the library.
-  """
-  file_name = _lib_name("cupti", cuda_config.cpu_value,
-                        cuda_config.cuda_version)
-  cuda_toolkit_path = cuda_config.cuda_toolkit_path
-  for relative_path in CUPTI_LIB_PATHS:
-    path = repository_ctx.path(
-        "%s/%s%s" % (cuda_toolkit_path, relative_path, file_name))
-    if path.exists:
-      return struct(file_name=file_name, path=str(path.realpath))
-
-  auto_configure_fail("Cannot find cupti library %s" % file_name)
+    """Finds the cupti library on the system.
+
+    On most systems, the cupti library is not installed in the same directory as
+    the other CUDA libraries but rather in a special extras/CUPTI directory.
+
+    Args:
+      repository_ctx: The repository context.
+      cuda_config: The cuda configuration as returned by _get_cuda_config.
+
+    Returns:
+      Returns a struct with the following fields:
+        file_name: The basename of the library found on the system.
+        path: The full path to the library.
+    """
+    file_name = _lib_name(
+        "cupti",
+        cuda_config.cpu_value,
+        cuda_config.cuda_version,
+    )
+    cuda_toolkit_path = cuda_config.cuda_toolkit_path
+    for relative_path in CUPTI_LIB_PATHS:
+        path = repository_ctx.path(
+            "%s/%s%s" % (cuda_toolkit_path, relative_path, file_name),
+        )
+        if path.exists:
+            return struct(file_name = file_name, path = str(path.realpath))
+
+    auto_configure_fail("Cannot find cupti library %s" % file_name)
 
 def _find_libs(repository_ctx, cuda_config):
-  """Returns the CUDA and cuDNN libraries on the system.
-
-  Args:
-    repository_ctx: The repository context.
-    cuda_config: The CUDA config as returned by _get_cuda_config
-
-  Returns:
-    Map of library names to structs of filename and path.
-  """
-  cpu_value = cuda_config.cpu_value
-  return {
-      "cuda": _find_cuda_lib("cuda", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path),
-      "cudart": _find_cuda_lib(
-          "cudart", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path,
-          cuda_config.cuda_version),
-      "cudart_static": _find_cuda_lib(
-          "cudart_static", repository_ctx, cpu_value,
-          cuda_config.cuda_toolkit_path, cuda_config.cuda_version, static=True),
-      "cublas": _find_cuda_lib(
-          "cublas", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path,
-          cuda_config.cuda_version),
-      "cusolver": _find_cuda_lib(
-          "cusolver", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path,
-          cuda_config.cuda_version),
-      "curand": _find_cuda_lib(
-          "curand", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path,
-          cuda_config.cuda_version),
-      "cufft": _find_cuda_lib(
-          "cufft", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path,
-          cuda_config.cuda_version),
-      "cudnn": _find_cuda_lib(
-          "cudnn", repository_ctx, cpu_value, cuda_config.cudnn_install_basedir,
-          cuda_config.cudnn_version),
-      "cupti": _find_cupti_lib(repository_ctx, cuda_config)
-  }
+    """Returns the CUDA and cuDNN libraries on the system.
 
+    Args:
+      repository_ctx: The repository context.
+      cuda_config: The CUDA config as returned by _get_cuda_config
 
-def _find_cuda_include_path(repository_ctx, cuda_config):
-  """Returns the path to the directory containing cuda.h
+    Returns:
+      Map of library names to structs of filename and path.
+    """
+    cpu_value = cuda_config.cpu_value
+    return {
+        "cuda": _find_cuda_lib("cuda", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path),
+        "cudart": _find_cuda_lib(
+            "cudart",
+            repository_ctx,
+            cpu_value,
+            cuda_config.cuda_toolkit_path,
+            cuda_config.cuda_version,
+        ),
+        "cudart_static": _find_cuda_lib(
+            "cudart_static",
+            repository_ctx,
+            cpu_value,
+            cuda_config.cuda_toolkit_path,
+            cuda_config.cuda_version,
+            static = True,
+        ),
+        "cublas": _find_cuda_lib(
+            "cublas",
+            repository_ctx,
+            cpu_value,
+            cuda_config.cuda_toolkit_path,
+            cuda_config.cuda_version,
+        ),
+        "cusolver": _find_cuda_lib(
+            "cusolver",
+            repository_ctx,
+            cpu_value,
+            cuda_config.cuda_toolkit_path,
+            cuda_config.cuda_version,
+        ),
+        "curand": _find_cuda_lib(
+            "curand",
+            repository_ctx,
+            cpu_value,
+            cuda_config.cuda_toolkit_path,
+            cuda_config.cuda_version,
+        ),
+        "cufft": _find_cuda_lib(
+            "cufft",
+            repository_ctx,
+            cpu_value,
+            cuda_config.cuda_toolkit_path,
+            cuda_config.cuda_version,
+        ),
+        "cudnn": _find_cuda_lib(
+            "cudnn",
+            repository_ctx,
+            cpu_value,
+            cuda_config.cudnn_install_basedir,
+            cuda_config.cudnn_version,
+        ),
+        "cupti": _find_cupti_lib(repository_ctx, cuda_config),
+    }
 
-  Args:
-    repository_ctx: The repository context.
-    cuda_config: The CUDA config as returned by _get_cuda_config
+def _find_cuda_include_path(repository_ctx, cuda_config):
+    """Returns the path to the directory containing cuda.h
 
-  Returns:
-    The path of the directory containing the CUDA headers.
-  """
-  cuda_toolkit_path = cuda_config.cuda_toolkit_path
-  for relative_path in CUDA_INCLUDE_PATHS:
-    if repository_ctx.path("%s/%scuda.h" % (cuda_toolkit_path, relative_path)).exists:
-        return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1]
-  auto_configure_fail("Cannot find cuda.h under %s" % cuda_toolkit_path)
+    Args:
+      repository_ctx: The repository context.
+      cuda_config: The CUDA config as returned by _get_cuda_config
 
+    Returns:
+      The path of the directory containing the CUDA headers.
+    """
+    cuda_toolkit_path = cuda_config.cuda_toolkit_path
+    for relative_path in CUDA_INCLUDE_PATHS:
+        if repository_ctx.path("%s/%scuda.h" % (cuda_toolkit_path, relative_path)).exists:
+            return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1]
+    auto_configure_fail("Cannot find cuda.h under %s" % cuda_toolkit_path)
 
 def _find_cudnn_header_dir(repository_ctx, cudnn_install_basedir):
-  """Returns the path to the directory containing cudnn.h
-
-  Args:
-    repository_ctx: The repository context.
-    cudnn_install_basedir: The cudnn install directory as returned by
-      _cudnn_install_basedir.
+    """Returns the path to the directory containing cudnn.h
 
-  Returns:
-    The path of the directory containing the cudnn header.
-  """
-  for relative_path in CUDA_INCLUDE_PATHS:
-    if repository_ctx.path("%s/%scudnn.h" % (cudnn_install_basedir, relative_path)).exists:
-        return ("%s/%s" % (cudnn_install_basedir, relative_path))[:-1]
-  if repository_ctx.path("/usr/include/cudnn.h").exists:
-    return "/usr/include"
-  auto_configure_fail("Cannot find cudnn.h under %s" % cudnn_install_basedir)
+    Args:
+      repository_ctx: The repository context.
+      cudnn_install_basedir: The cudnn install directory as returned by
+        _cudnn_install_basedir.
 
+    Returns:
+      The path of the directory containing the cudnn header.
+    """
+    for relative_path in CUDA_INCLUDE_PATHS:
+        if repository_ctx.path("%s/%scudnn.h" % (cudnn_install_basedir, relative_path)).exists:
+            return ("%s/%s" % (cudnn_install_basedir, relative_path))[:-1]
+    if repository_ctx.path("/usr/include/cudnn.h").exists:
+        return "/usr/include"
+    auto_configure_fail("Cannot find cudnn.h under %s" % cudnn_install_basedir)
 
 def _find_nvvm_libdevice_dir(repository_ctx, cuda_config):
-  """Returns the path to the directory containing libdevice in bitcode format.
+    """Returns the path to the directory containing libdevice in bitcode format.
 
-  Args:
-    repository_ctx: The repository context.
-    cuda_config: The CUDA config as returned by _get_cuda_config
-
-  Returns:
-    The path of the directory containing the CUDA headers.
-  """
-  cuda_toolkit_path = cuda_config.cuda_toolkit_path
-  for relative_path in NVVM_LIBDEVICE_PATHS:
-    if repository_ctx.path("%s/%slibdevice.10.bc" % (cuda_toolkit_path, relative_path)).exists:
-      return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1]
-  auto_configure_fail("Cannot find libdevice.10.bc under %s" % cuda_toolkit_path)
+    Args:
+      repository_ctx: The repository context.
+      cuda_config: The CUDA config as returned by _get_cuda_config
 
+    Returns:
+      The path of the directory containing the CUDA headers.
+    """
+    cuda_toolkit_path = cuda_config.cuda_toolkit_path
+    for libdevice_file in NVVM_LIBDEVICE_FILES:
+        for relative_path in NVVM_LIBDEVICE_PATHS:
+            if repository_ctx.path("%s/%s%s" % (cuda_toolkit_path, relative_path, libdevice_file)).exists:
+                return ("%s/%s" % (cuda_toolkit_path, relative_path))[:-1]
+    auto_configure_fail("Cannot find libdevice*.bc files under %s" % cuda_toolkit_path)
 
 def _cudart_static_linkopt(cpu_value):
-  """Returns additional platform-specific linkopts for cudart."""
-  return "" if cpu_value == "Darwin" else "\"-lrt\","
+    """Returns additional platform-specific linkopts for cudart."""
+    return "" if cpu_value == "Darwin" else "\"-lrt\","
 
 def _get_cuda_config(repository_ctx):
-  """Detects and returns information about the CUDA installation on the system.
-
-  Args:
-    repository_ctx: The repository context.
-
-  Returns:
-    A struct containing the following fields:
-      cuda_toolkit_path: The CUDA toolkit installation directory.
-      cudnn_install_basedir: The cuDNN installation directory.
-      cuda_version: The version of CUDA on the system.
-      cudnn_version: The version of cuDNN on the system.
-      compute_capabilities: A list of the system's CUDA compute capabilities.
-      cpu_value: The name of the host operating system.
-  """
-  cpu_value = get_cpu_value(repository_ctx)
-  cuda_toolkit_path = _cuda_toolkit_path(repository_ctx)
-  cuda_version = _cuda_version(repository_ctx, cuda_toolkit_path, cpu_value)
-  cudnn_install_basedir = _cudnn_install_basedir(repository_ctx)
-  cudnn_version = _cudnn_version(repository_ctx, cudnn_install_basedir, cpu_value)
-  return struct(
-      cuda_toolkit_path = cuda_toolkit_path,
-      cudnn_install_basedir = cudnn_install_basedir,
-      cuda_version = cuda_version,
-      cudnn_version = cudnn_version,
-      compute_capabilities = _compute_capabilities(repository_ctx),
-      cpu_value = cpu_value)
-
-
-def _tpl(repository_ctx, tpl, substitutions={}, out=None):
-  if not out:
-    out = tpl.replace(":", "/")
-  repository_ctx.template(
-      out,
-      Label("//third_party/gpus/%s.tpl" % tpl),
-      substitutions)
-
+    """Detects and returns information about the CUDA installation on the system.
+
+    Args:
+      repository_ctx: The repository context.
+
+    Returns:
+      A struct containing the following fields:
+        cuda_toolkit_path: The CUDA toolkit installation directory.
+        cudnn_install_basedir: The cuDNN installation directory.
+        cuda_version: The version of CUDA on the system.
+        cudnn_version: The version of cuDNN on the system.
+        compute_capabilities: A list of the system's CUDA compute capabilities.
+        cpu_value: The name of the host operating system.
+    """
+    cpu_value = get_cpu_value(repository_ctx)
+    cuda_toolkit_path = _cuda_toolkit_path(repository_ctx)
+    cuda_version = _cuda_version(repository_ctx, cuda_toolkit_path, cpu_value)
+    cudnn_install_basedir = _cudnn_install_basedir(repository_ctx)
+    cudnn_version = _cudnn_version(repository_ctx, cudnn_install_basedir, cpu_value)
+    return struct(
+        cuda_toolkit_path = cuda_toolkit_path,
+        cudnn_install_basedir = cudnn_install_basedir,
+        cuda_version = cuda_version,
+        cudnn_version = cudnn_version,
+        compute_capabilities = _compute_capabilities(repository_ctx),
+        cpu_value = cpu_value,
+    )
+
+def _tpl(repository_ctx, tpl, substitutions = {}, out = None):
+    if not out:
+        out = tpl.replace(":", "/")
+    repository_ctx.template(
+        out,
+        Label("//third_party/gpus/%s.tpl" % tpl),
+        substitutions,
+    )
 
 def _file(repository_ctx, label):
-  repository_ctx.template(
-      label.replace(":", "/"),
-      Label("//third_party/gpus/%s.tpl" % label),
-      {})
-
+    repository_ctx.template(
+        label.replace(":", "/"),
+        Label("//third_party/gpus/%s.tpl" % label),
+        {},
+    )
 
 _DUMMY_CROSSTOOL_BZL_FILE = """
 def error_gpu_disabled():
@@ -792,379 +949,498 @@ def error_gpu_disabled():
   )
 """
 
-
 _DUMMY_CROSSTOOL_BUILD_FILE = """
 load("//crosstool:error_gpu_disabled.bzl", "error_gpu_disabled")
 
 error_gpu_disabled()
 """
 
-
 def _create_dummy_repository(repository_ctx):
-  cpu_value = get_cpu_value(repository_ctx)
-
-  # Set up BUILD file for cuda/.
-  _tpl(repository_ctx, "cuda:build_defs.bzl",
-       {
-           "%{cuda_is_configured}": "False",
-           "%{cuda_extra_copts}": "[]",
-       })
-  _tpl(repository_ctx, "cuda:BUILD",
-       {
-           "%{cuda_driver_lib}": _lib_name("cuda", cpu_value),
-           "%{cudart_static_lib}": _lib_name("cudart_static", cpu_value,
-                                             static=True),
-           "%{cudart_static_linkopt}": _cudart_static_linkopt(cpu_value),
-           "%{cudart_lib}": _lib_name("cudart", cpu_value),
-           "%{cublas_lib}": _lib_name("cublas", cpu_value),
-           "%{cusolver_lib}": _lib_name("cusolver", cpu_value),
-           "%{cudnn_lib}": _lib_name("cudnn", cpu_value),
-           "%{cufft_lib}": _lib_name("cufft", cpu_value),
-           "%{curand_lib}": _lib_name("curand", cpu_value),
-           "%{cupti_lib}": _lib_name("cupti", cpu_value),
-           "%{cuda_include_genrules}": '',
-           "%{cuda_headers}": '',
-       })
-
-  # Create dummy files for the CUDA toolkit since they are still required by
-  # tensorflow/core/platform/default/build_config:cuda.
-  repository_ctx.file("cuda/cuda/include/cuda.h", "")
-  repository_ctx.file("cuda/cuda/include/cublas.h", "")
-  repository_ctx.file("cuda/cuda/include/cudnn.h", "")
-  repository_ctx.file("cuda/cuda/extras/CUPTI/include/cupti.h", "")
-  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cuda", cpu_value))
-  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cudart", cpu_value))
-  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cudart_static", cpu_value))
-  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cublas", cpu_value))
-  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cusolver", cpu_value))
-  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cudnn", cpu_value))
-  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("curand", cpu_value))
-  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cufft", cpu_value))
-  repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cupti", cpu_value))
-
-  # Set up cuda_config.h, which is used by
-  # tensorflow/stream_executor/dso_loader.cc.
-  _tpl(repository_ctx, "cuda:cuda_config.h",
-       {
-           "%{cuda_version}": _DEFAULT_CUDA_VERSION,
-           "%{cudnn_version}": _DEFAULT_CUDNN_VERSION,
-           "%{cuda_compute_capabilities}": ",".join([
-               "CudaVersion(\"%s\")" % c
-               for c in _DEFAULT_CUDA_COMPUTE_CAPABILITIES]),
-           "%{cuda_toolkit_path}": _DEFAULT_CUDA_TOOLKIT_PATH,
-       }, "cuda/cuda/cuda_config.h")
-
-  # If cuda_configure is not configured to build with GPU support, and the user
-  # attempts to build with --config=cuda, add a dummy build rule to intercept
-  # this and fail with an actionable error message.
-  repository_ctx.file("crosstool/error_gpu_disabled.bzl",
-                      _DUMMY_CROSSTOOL_BZL_FILE)
-  repository_ctx.file("crosstool/BUILD", _DUMMY_CROSSTOOL_BUILD_FILE)
-
-
-def _execute(repository_ctx, cmdline, error_msg=None, error_details=None,
-             empty_stdout_fine=False):
-  """Executes an arbitrary shell command.
-
-  Args:
-    repository_ctx: the repository_ctx object
-    cmdline: list of strings, the command to execute
-    error_msg: string, a summary of the error if the command fails
-    error_details: string, details about the error or steps to fix it
-    empty_stdout_fine: bool, if True, an empty stdout result is fine, otherwise
-      it's an error
-  Return:
-    the result of repository_ctx.execute(cmdline)
-  """
-  result = repository_ctx.execute(cmdline)
-  if result.stderr or not (empty_stdout_fine or result.stdout):
-    auto_configure_fail(
-        "\n".join([
-            error_msg.strip() if error_msg else "Repository command failed",
-            result.stderr.strip(),
-            error_details if error_details else ""]))
-  return result
-
+    cpu_value = get_cpu_value(repository_ctx)
+
+    # Set up BUILD file for cuda/.
+    _tpl(
+        repository_ctx,
+        "cuda:build_defs.bzl",
+        {
+            "%{cuda_is_configured}": "False",
+            "%{cuda_extra_copts}": "[]",
+        },
+    )
+    _tpl(
+        repository_ctx,
+        "cuda:BUILD",
+        {
+            "%{cuda_driver_lib}": _lib_name("cuda", cpu_value),
+            "%{cudart_static_lib}": _lib_name(
+                "cudart_static",
+                cpu_value,
+                static = True,
+            ),
+            "%{cudart_static_linkopt}": _cudart_static_linkopt(cpu_value),
+            "%{cudart_lib}": _lib_name("cudart", cpu_value),
+            "%{cublas_lib}": _lib_name("cublas", cpu_value),
+            "%{cusolver_lib}": _lib_name("cusolver", cpu_value),
+            "%{cudnn_lib}": _lib_name("cudnn", cpu_value),
+            "%{cufft_lib}": _lib_name("cufft", cpu_value),
+            "%{curand_lib}": _lib_name("curand", cpu_value),
+            "%{cupti_lib}": _lib_name("cupti", cpu_value),
+            "%{cuda_include_genrules}": "",
+            "%{cuda_headers}": "",
+        },
+    )
+
+    # Create dummy files for the CUDA toolkit since they are still required by
+    # tensorflow/core/platform/default/build_config:cuda.
+    repository_ctx.file("cuda/cuda/include/cuda.h", "")
+    repository_ctx.file("cuda/cuda/include/cublas.h", "")
+    repository_ctx.file("cuda/cuda/include/cudnn.h", "")
+    repository_ctx.file("cuda/cuda/extras/CUPTI/include/cupti.h", "")
+    repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cuda", cpu_value))
+    repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cudart", cpu_value))
+    repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cudart_static", cpu_value))
+    repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cublas", cpu_value))
+    repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cusolver", cpu_value))
+    repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cudnn", cpu_value))
+    repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("curand", cpu_value))
+    repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cufft", cpu_value))
+    repository_ctx.file("cuda/cuda/lib/%s" % _lib_name("cupti", cpu_value))
+
+    # Set up cuda_config.h, which is used by
+    # tensorflow/stream_executor/dso_loader.cc.
+    _tpl(
+        repository_ctx,
+        "cuda:cuda_config.h",
+        {
+            "%{cuda_version}": _DEFAULT_CUDA_VERSION,
+            "%{cudnn_version}": _DEFAULT_CUDNN_VERSION,
+            "%{cuda_compute_capabilities}": ",".join([
+                "CudaVersion(\"%s\")" % c
+                for c in _DEFAULT_CUDA_COMPUTE_CAPABILITIES
+            ]),
+            "%{cuda_toolkit_path}": _DEFAULT_CUDA_TOOLKIT_PATH,
+        },
+        "cuda/cuda/cuda_config.h",
+    )
+
+    # If cuda_configure is not configured to build with GPU support, and the user
+    # attempts to build with --config=cuda, add a dummy build rule to intercept
+    # this and fail with an actionable error message.
+    repository_ctx.file(
+        "crosstool/error_gpu_disabled.bzl",
+        _DUMMY_CROSSTOOL_BZL_FILE,
+    )
+    repository_ctx.file("crosstool/BUILD", _DUMMY_CROSSTOOL_BUILD_FILE)
+
+def _execute(
+        repository_ctx,
+        cmdline,
+        error_msg = None,
+        error_details = None,
+        empty_stdout_fine = False):
+    """Executes an arbitrary shell command.
+
+    Args:
+      repository_ctx: the repository_ctx object
+      cmdline: list of strings, the command to execute
+      error_msg: string, a summary of the error if the command fails
+      error_details: string, details about the error or steps to fix it
+      empty_stdout_fine: bool, if True, an empty stdout result is fine, otherwise
+        it's an error
+    Return:
+      the result of repository_ctx.execute(cmdline)
+    """
+    result = repository_ctx.execute(cmdline)
+    if result.stderr or not (empty_stdout_fine or result.stdout):
+        auto_configure_fail(
+            "\n".join([
+                error_msg.strip() if error_msg else "Repository command failed",
+                result.stderr.strip(),
+                error_details if error_details else "",
+            ]),
+        )
+    return result
 
 def _norm_path(path):
-  """Returns a path with '/' and remove the trailing slash."""
-  path = path.replace("\\", "/")
-  if path[-1] == "/":
-    path = path[:-1]
-  return path
-
-
-def symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name,
-                            src_files = [], dest_files = []):
-  """Returns a genrule to symlink(or copy if on Windows) a set of files.
-
-  If src_dir is passed, files will be read from the given directory; otherwise
-  we assume files are in src_files and dest_files
-  """
-  if src_dir != None:
-    src_dir = _norm_path(src_dir)
-    dest_dir = _norm_path(dest_dir)
-    files = '\n'.join(sorted(_read_dir(repository_ctx, src_dir).splitlines()))
-    # Create a list with the src_dir stripped to use for outputs.
-    dest_files = files.replace(src_dir, '').splitlines()
-    src_files = files.splitlines()
-  command = []
-  if not _is_windows(repository_ctx):
-    # We clear folders that might have been generated previously to avoid
-    # undesired inclusions
-    command.append('if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi')
-    command.append('if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi')
-    command.append('if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi')
-    command.append('if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi')
-  outs = []
-  for i in range(len(dest_files)):
-    if dest_files[i] != "":
-      # If we have only one file to link we do not want to use the dest_dir, as
-      # $(@D) will include the full path to the file.
-      dest = '$(@D)/' + dest_dir + dest_files[i] if len(dest_files) != 1 else '$(@D)/' + dest_files[i]
-      # On Windows, symlink is not supported, so we just copy all the files.
-      cmd = 'cp -f' if _is_windows(repository_ctx) else 'ln -s'
-      command.append(cmd + ' "%s" "%s"' % (src_files[i] , dest))
-      outs.append('        "' + dest_dir + dest_files[i] + '",')
-  genrule = _genrule(src_dir, genrule_name, " && ".join(command),
-                     "\n".join(outs))
-  return genrule
-
+    """Returns a path with '/' and remove the trailing slash."""
+    path = path.replace("\\", "/")
+    if path[-1] == "/":
+        path = path[:-1]
+    return path
+
+def symlink_genrule_for_dir(
+        repository_ctx,
+        src_dir,
+        dest_dir,
+        genrule_name,
+        src_files = [],
+        dest_files = []):
+    """Returns a genrule to symlink(or copy if on Windows) a set of files.
+
+    If src_dir is passed, files will be read from the given directory; otherwise
+    we assume files are in src_files and dest_files
+    """
+    if src_dir != None:
+        src_dir = _norm_path(src_dir)
+        dest_dir = _norm_path(dest_dir)
+        files = "\n".join(sorted(_read_dir(repository_ctx, src_dir).splitlines()))
+
+        # Create a list with the src_dir stripped to use for outputs.
+        dest_files = files.replace(src_dir, "").splitlines()
+        src_files = files.splitlines()
+    command = []
+    if not _is_windows(repository_ctx):
+        # We clear folders that might have been generated previously to avoid
+        # undesired inclusions
+        command.append('if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi')
+        command.append('if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi')
+        command.append('if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi')
+        command.append('if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi')
+    outs = []
+    for i in range(len(dest_files)):
+        if dest_files[i] != "":
+            # If we have only one file to link we do not want to use the dest_dir, as
+            # $(@D) will include the full path to the file.
+            dest = "$(@D)/" + dest_dir + dest_files[i] if len(dest_files) != 1 else "$(@D)/" + dest_files[i]
+
+            # On Windows, symlink is not supported, so we just copy all the files.
+            cmd = "cp -f" if _is_windows(repository_ctx) else "ln -s"
+            command.append(cmd + ' "%s" "%s"' % (src_files[i], dest))
+            outs.append('        "' + dest_dir + dest_files[i] + '",')
+    genrule = _genrule(
+        src_dir,
+        genrule_name,
+        " && ".join(command),
+        "\n".join(outs),
+    )
+    return genrule
 
 def _genrule(src_dir, genrule_name, command, outs):
-  """Returns a string with a genrule.
-
-  Genrule executes the given command and produces the given outputs.
-  """
-  return (
-      'genrule(\n' +
-      '    name = "' +
-      genrule_name + '",\n' +
-      '    outs = [\n' +
-      outs +
-      '\n    ],\n' +
-      '    cmd = """\n' +
-      command +
-      '\n   """,\n' +
-      ')\n'
-  )
+    """Returns a string with a genrule.
 
+    Genrule executes the given command and produces the given outputs.
+    """
+    return (
+        "genrule(\n" +
+        '    name = "' +
+        genrule_name + '",\n' +
+        "    outs = [\n" +
+        outs +
+        "\n    ],\n" +
+        '    cmd = """\n' +
+        command +
+        '\n   """,\n' +
+        ")\n"
+    )
 
 def _read_dir(repository_ctx, src_dir):
-  """Returns a string with all files in a directory.
-
-  Finds all files inside a directory, traversing subfolders and following
-  symlinks. The returned string contains the full path of all files
-  separated by line breaks.
-  """
-  if _is_windows(repository_ctx):
-    src_dir = src_dir.replace("/", "\\")
-    find_result = _execute(
-        repository_ctx, ["cmd.exe", "/c", "dir", src_dir, "/b", "/s", "/a-d"],
-        empty_stdout_fine=True)
-    # src_files will be used in genrule.outs where the paths must
-    # use forward slashes.
-    result = find_result.stdout.replace("\\", "/")
-  else:
-    find_result = _execute(
-        repository_ctx, ["find", src_dir, "-follow", "-type", "f"],
-        empty_stdout_fine=True)
-    result = find_result.stdout
-  return result
+    """Returns a string with all files in a directory.
+
+    Finds all files inside a directory, traversing subfolders and following
+    symlinks. The returned string contains the full path of all files
+    separated by line breaks.
+    """
+    if _is_windows(repository_ctx):
+        src_dir = src_dir.replace("/", "\\")
+        find_result = _execute(
+            repository_ctx,
+            ["cmd.exe", "/c", "dir", src_dir, "/b", "/s", "/a-d"],
+            empty_stdout_fine = True,
+        )
+
+        # src_files will be used in genrule.outs where the paths must
+        # use forward slashes.
+        result = find_result.stdout.replace("\\", "/")
+    else:
+        find_result = _execute(
+            repository_ctx,
+            ["find", src_dir, "-follow", "-type", "f"],
+            empty_stdout_fine = True,
+        )
+        result = find_result.stdout
+    return result
 
 def _flag_enabled(repository_ctx, flag_name):
-  if flag_name in repository_ctx.os.environ:
-    value = repository_ctx.os.environ[flag_name].strip()
-    return value == "1"
-  return False
+    if flag_name in repository_ctx.os.environ:
+        value = repository_ctx.os.environ[flag_name].strip()
+        return value == "1"
+    return False
 
 def _use_cuda_clang(repository_ctx):
-  return _flag_enabled(repository_ctx, "TF_CUDA_CLANG")
+    return _flag_enabled(repository_ctx, "TF_CUDA_CLANG")
 
 def _compute_cuda_extra_copts(repository_ctx, compute_capabilities):
-  if _use_cuda_clang(repository_ctx):
-    capability_flags = ["--cuda-gpu-arch=sm_" +
-        cap.replace(".", "") for cap in compute_capabilities]
-  else:
-    # Capabilities are handled in the "crosstool_wrapper_driver_is_not_gcc" for nvcc
-    capability_flags = []
-  return str(capability_flags)
+    if _use_cuda_clang(repository_ctx):
+        capability_flags = ["--cuda-gpu-arch=sm_" +
+                            cap.replace(".", "") for cap in compute_capabilities]
+    else:
+        # Capabilities are handled in the "crosstool_wrapper_driver_is_not_gcc" for nvcc
+        capability_flags = []
+    return str(capability_flags)
 
 def _create_local_cuda_repository(repository_ctx):
-  """Creates the repository containing files set up to build with CUDA."""
-  cuda_config = _get_cuda_config(repository_ctx)
-
-  cuda_include_path = _find_cuda_include_path(repository_ctx, cuda_config)
-  cudnn_header_dir = _find_cudnn_header_dir(repository_ctx,
-                                            cuda_config.cudnn_install_basedir)
-  cupti_header_dir = _find_cupti_header_dir(repository_ctx, cuda_config)
-  nvvm_libdevice_dir = _find_nvvm_libdevice_dir(repository_ctx, cuda_config)
-
-  # Set up symbolic links for the cuda toolkit by creating genrules to do
-  # symlinking. We create one genrule for each directory we want to track under
-  # cuda_toolkit_path
-  cuda_toolkit_path = cuda_config.cuda_toolkit_path
-  genrules = [symlink_genrule_for_dir(repository_ctx,
-      cuda_include_path, "cuda/include", "cuda-include")]
-  genrules.append(symlink_genrule_for_dir(repository_ctx,
-      nvvm_libdevice_dir, "cuda/nvvm/libdevice", "cuda-nvvm"))
-  genrules.append(symlink_genrule_for_dir(repository_ctx,
-      cupti_header_dir, "cuda/extras/CUPTI/include", "cuda-extras"))
-
-  cuda_libs = _find_libs(repository_ctx, cuda_config)
-  cuda_lib_src = []
-  cuda_lib_dest = []
-  for lib in cuda_libs.values():
-    cuda_lib_src.append(lib.path)
-    cuda_lib_dest.append("cuda/lib/" + lib.file_name)
-  genrules.append(symlink_genrule_for_dir(repository_ctx, None, "", "cuda-lib",
-                                          cuda_lib_src, cuda_lib_dest))
-
-  # Set up the symbolic links for cudnn if cndnn was not installed to
-  # CUDA_TOOLKIT_PATH.
-  included_files = _read_dir(repository_ctx, cuda_include_path).replace(
-      cuda_include_path, '').splitlines()
-  if '/cudnn.h' not in included_files:
-    genrules.append(symlink_genrule_for_dir(repository_ctx, None,
-        "cuda/include/", "cudnn-include", [cudnn_header_dir + "/cudnn.h"],
-        ["cudnn.h"]))
-  else:
-    genrules.append(
-            'filegroup(\n' +
+    """Creates the repository containing files set up to build with CUDA."""
+    cuda_config = _get_cuda_config(repository_ctx)
+
+    cuda_include_path = _find_cuda_include_path(repository_ctx, cuda_config)
+    cudnn_header_dir = _find_cudnn_header_dir(
+        repository_ctx,
+        cuda_config.cudnn_install_basedir,
+    )
+    cupti_header_dir = _find_cupti_header_dir(repository_ctx, cuda_config)
+    nvvm_libdevice_dir = _find_nvvm_libdevice_dir(repository_ctx, cuda_config)
+
+    # Set up symbolic links for the cuda toolkit by creating genrules to do
+    # symlinking. We create one genrule for each directory we want to track under
+    # cuda_toolkit_path
+    cuda_toolkit_path = cuda_config.cuda_toolkit_path
+    genrules = [symlink_genrule_for_dir(
+        repository_ctx,
+        cuda_include_path,
+        "cuda/include",
+        "cuda-include",
+    )]
+    genrules.append(symlink_genrule_for_dir(
+        repository_ctx,
+        nvvm_libdevice_dir,
+        "cuda/nvvm/libdevice",
+        "cuda-nvvm",
+    ))
+    genrules.append(symlink_genrule_for_dir(
+        repository_ctx,
+        cupti_header_dir,
+        "cuda/extras/CUPTI/include",
+        "cuda-extras",
+    ))
+
+    cuda_libs = _find_libs(repository_ctx, cuda_config)
+    cuda_lib_src = []
+    cuda_lib_dest = []
+    for lib in cuda_libs.values():
+        cuda_lib_src.append(lib.path)
+        cuda_lib_dest.append("cuda/lib/" + lib.file_name)
+    genrules.append(symlink_genrule_for_dir(
+        repository_ctx,
+        None,
+        "",
+        "cuda-lib",
+        cuda_lib_src,
+        cuda_lib_dest,
+    ))
+
+    # Set up the symbolic links for cudnn if cndnn was not installed to
+    # CUDA_TOOLKIT_PATH.
+    included_files = _read_dir(repository_ctx, cuda_include_path).replace(
+        cuda_include_path,
+        "",
+    ).splitlines()
+    if "/cudnn.h" not in included_files:
+        genrules.append(symlink_genrule_for_dir(
+            repository_ctx,
+            None,
+            "cuda/include/",
+            "cudnn-include",
+            [cudnn_header_dir + "/cudnn.h"],
+            ["cudnn.h"],
+        ))
+    else:
+        genrules.append(
+            "filegroup(\n" +
             '    name = "cudnn-include",\n' +
-            '    srcs = [],\n' +
-            ')\n'
+            "    srcs = [],\n" +
+            ")\n",
         )
 
-  # Set up BUILD file for cuda/
-  _tpl(repository_ctx, "cuda:build_defs.bzl",
-       {
-           "%{cuda_is_configured}": "True",
-           "%{cuda_extra_copts}": _compute_cuda_extra_copts(
-               repository_ctx, cuda_config.compute_capabilities),
-       })
-  _tpl(repository_ctx, "cuda:BUILD",
-       {
-           "%{cuda_driver_lib}": cuda_libs["cuda"].file_name,
-           "%{cudart_static_lib}": cuda_libs["cudart_static"].file_name,
-           "%{cudart_static_linkopt}": _cudart_static_linkopt(
-               cuda_config.cpu_value),
-           "%{cudart_lib}": cuda_libs["cudart"].file_name,
-           "%{cublas_lib}": cuda_libs["cublas"].file_name,
-           "%{cusolver_lib}": cuda_libs["cusolver"].file_name,
-           "%{cudnn_lib}": cuda_libs["cudnn"].file_name,
-           "%{cufft_lib}": cuda_libs["cufft"].file_name,
-           "%{curand_lib}": cuda_libs["curand"].file_name,
-           "%{cupti_lib}": cuda_libs["cupti"].file_name,
-           "%{cuda_include_genrules}": "\n".join(genrules),
-           "%{cuda_headers}": ('":cuda-include",\n' +
-                               '        ":cudnn-include",')
-       })
-
-  is_cuda_clang = _use_cuda_clang(repository_ctx)
-
-  should_download_clang = is_cuda_clang and _flag_enabled(
-      repository_ctx, _TF_DOWNLOAD_CLANG)
-  if should_download_clang:
-    download_clang(repository_ctx, "crosstool/extra_tools")
-
-  # Set up crosstool/
-  cc = find_cc(repository_ctx)
-  cc_fullpath = cc if not should_download_clang else "crosstool/" + cc
-
-  host_compiler_includes = _host_compiler_includes(repository_ctx, cc_fullpath)
-  cuda_defines = {}
-  if is_cuda_clang:
-    cuda_defines["%{host_compiler_path}"] = str(cc)
-    cuda_defines["%{host_compiler_warnings}"] = """
+    # Set up BUILD file for cuda/
+    _tpl(
+        repository_ctx,
+        "cuda:build_defs.bzl",
+        {
+            "%{cuda_is_configured}": "True",
+            "%{cuda_extra_copts}": _compute_cuda_extra_copts(
+                repository_ctx,
+                cuda_config.compute_capabilities,
+            ),
+        },
+    )
+    _tpl(
+        repository_ctx,
+        "cuda:BUILD.windows" if _is_windows(repository_ctx) else "cuda:BUILD",
+        {
+            "%{cuda_driver_lib}": cuda_libs["cuda"].file_name,
+            "%{cudart_static_lib}": cuda_libs["cudart_static"].file_name,
+            "%{cudart_static_linkopt}": _cudart_static_linkopt(
+                cuda_config.cpu_value,
+            ),
+            "%{cudart_lib}": cuda_libs["cudart"].file_name,
+            "%{cublas_lib}": cuda_libs["cublas"].file_name,
+            "%{cusolver_lib}": cuda_libs["cusolver"].file_name,
+            "%{cudnn_lib}": cuda_libs["cudnn"].file_name,
+            "%{cufft_lib}": cuda_libs["cufft"].file_name,
+            "%{curand_lib}": cuda_libs["curand"].file_name,
+            "%{cupti_lib}": cuda_libs["cupti"].file_name,
+            "%{cuda_include_genrules}": "\n".join(genrules),
+            "%{cuda_headers}": ('":cuda-include",\n' +
+                                '        ":cudnn-include",'),
+        },
+        "cuda/BUILD",
+    )
+
+    is_cuda_clang = _use_cuda_clang(repository_ctx)
+
+    should_download_clang = is_cuda_clang and _flag_enabled(
+        repository_ctx,
+        _TF_DOWNLOAD_CLANG,
+    )
+    if should_download_clang:
+        download_clang(repository_ctx, "crosstool/extra_tools")
+
+    # Set up crosstool/
+    cc = find_cc(repository_ctx)
+    cc_fullpath = cc if not should_download_clang else "crosstool/" + cc
+
+    host_compiler_includes = _host_compiler_includes(repository_ctx, cc_fullpath)
+    cuda_defines = {}
+    if is_cuda_clang:
+        cuda_defines["%{host_compiler_path}"] = str(cc)
+        cuda_defines["%{host_compiler_warnings}"] = """
         # Some parts of the codebase set -Werror and hit this warning, so
         # switch it off for now.
         flag: "-Wno-invalid-partial-specialization"
     """
-    cuda_defines["%{host_compiler_includes}"] = host_compiler_includes
-    _tpl(repository_ctx, "crosstool:BUILD", {"%{linker_files}": ":empty"})
-    repository_ctx.file("crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc", "")
-  else:
-    cuda_defines["%{host_compiler_path}"] = "clang/bin/crosstool_wrapper_driver_is_not_gcc"
-    cuda_defines["%{host_compiler_warnings}"] = ""
-    # TODO(klimek): We currently need to inject "/" as builtin directory path
-    # to disable bazel's dependency checks.
-    # The problem is that:
-    # - the python rules symlink the python headers into the bazel root
-    # - the rules use 'includes' in the BUILD file to redirect includes of the
-    #   python headers through those paths
-    # - bazel currently uses -isystem for include paths specified via 'includes'
-    # - gcc follows symlinks when resolving files via -isystem paths, and puts
-    #   the resolved paths into the .d file, which makes the dependency check
-    #   fail for bazel
-    # There are multiple possible ways to solve this:
-    # 1. make bazel not use -isystem for paths specified via 'includes'
-    # 2. cp the headers instead of symlinking them
-    #
-    # Once this is fixed, the right builtin directory path is:
-    # (host_compiler_includes +
-    #    "\n  cxx_builtin_include_directory: \"%s\"" % cuda_include_path)
-    # The cuda directory needs to be passed, as there is currently no rule
-    # providing the cuda headers in the same way the python headers are
-    # provided.
-    cuda_defines["%{host_compiler_includes}"] = "\n  cxx_builtin_include_directory: \"/\""
-    nvcc_path = str(repository_ctx.path("%s/bin/nvcc%s" %
-        (cuda_config.cuda_toolkit_path,
-        ".exe" if cuda_config.cpu_value == "Windows" else "")))
-    _tpl(repository_ctx, "crosstool:BUILD",
-         {"%{linker_files}": ":crosstool_wrapper_driver_is_not_gcc"})
-    _tpl(repository_ctx,
-         "crosstool:clang/bin/crosstool_wrapper_driver_is_not_gcc",
-         {
-             "%{cpu_compiler}": str(cc),
-             "%{cuda_version}": cuda_config.cuda_version,
-             "%{nvcc_path}": nvcc_path,
-             "%{gcc_host_compiler_path}": str(cc),
-             "%{cuda_compute_capabilities}": ", ".join(
-                 ["\"%s\"" % c for c in cuda_config.compute_capabilities]),
-         })
-  _tpl(repository_ctx, "crosstool:CROSSTOOL", cuda_defines, out="crosstool/CROSSTOOL")
-
-  # Set up cuda_config.h, which is used by
-  # tensorflow/stream_executor/dso_loader.cc.
-  _tpl(repository_ctx, "cuda:cuda_config.h",
-       {
-           "%{cuda_version}": cuda_config.cuda_version,
-           "%{cudnn_version}": cuda_config.cudnn_version,
-           "%{cuda_compute_capabilities}": ",".join(
-               ["CudaVersion(\"%s\")" % c
-                for c in cuda_config.compute_capabilities]),
-               "%{cuda_toolkit_path}": cuda_config.cuda_toolkit_path,
-       }, "cuda/cuda/cuda_config.h")
+        cuda_defines["%{host_compiler_includes}"] = host_compiler_includes
+        _tpl(repository_ctx, "crosstool:BUILD", {"%{linker_files}": ":empty", "%{win_linker_files}": ":empty"})
+        repository_ctx.file("crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc", "")
+        repository_ctx.file("crosstool/windows/msvc_wrapper_for_nvcc.py", "")
+        repository_ctx.file("crosstool/windows/msvc_wrapper_for_nvcc.bat", "")
+    else:
+        cuda_defines["%{host_compiler_path}"] = "clang/bin/crosstool_wrapper_driver_is_not_gcc"
+        cuda_defines["%{host_compiler_warnings}"] = ""
+
+        # TODO(klimek): We currently need to inject "/" as builtin directory path
+        # to disable bazel's dependency checks.
+        # The problem is that:
+        # - the python rules symlink the python headers into the bazel root
+        # - the rules use 'includes' in the BUILD file to redirect includes of the
+        #   python headers through those paths
+        # - bazel currently uses -isystem for include paths specified via 'includes'
+        # - gcc follows symlinks when resolving files via -isystem paths, and puts
+        #   the resolved paths into the .d file, which makes the dependency check
+        #   fail for bazel
+        # There are multiple possible ways to solve this:
+        # 1. make bazel not use -isystem for paths specified via 'includes'
+        # 2. cp the headers instead of symlinking them
+        #
+        # Once this is fixed, the right builtin directory path is:
+        # (host_compiler_includes +
+        #    "\n  cxx_builtin_include_directory: \"%s\"" % cuda_include_path)
+        # The cuda directory needs to be passed, as there is currently no rule
+        # providing the cuda headers in the same way the python headers are
+        # provided.
+        cuda_defines["%{host_compiler_includes}"] = "\n  cxx_builtin_include_directory: \"/\""
+        nvcc_path = str(repository_ctx.path("%s/bin/nvcc%s" %
+                                            (
+                                                cuda_config.cuda_toolkit_path,
+                                                ".exe" if _is_windows(repository_ctx) else "",
+                                            )))
+        _tpl(
+            repository_ctx,
+            "crosstool:BUILD",
+            {
+                "%{linker_files}": ":crosstool_wrapper_driver_is_not_gcc",
+                "%{win_linker_files}": ":windows_msvc_wrapper_files",
+            },
+        )
+        wrapper_defines = {
+            "%{cpu_compiler}": str(cc),
+            "%{cuda_version}": cuda_config.cuda_version,
+            "%{nvcc_path}": nvcc_path,
+            "%{gcc_host_compiler_path}": str(cc),
+            "%{cuda_compute_capabilities}": ", ".join(
+                ["\"%s\"" % c for c in cuda_config.compute_capabilities],
+            ),
+            "%{nvcc_tmp_dir}": _get_nvcc_tmp_dir_for_windows(repository_ctx),
+        }
+        _tpl(
+            repository_ctx,
+            "crosstool:clang/bin/crosstool_wrapper_driver_is_not_gcc",
+            wrapper_defines,
+        )
+        _tpl(
+            repository_ctx,
+            "crosstool:windows/msvc_wrapper_for_nvcc.py",
+            wrapper_defines,
+        )
+        _tpl(
+            repository_ctx,
+            "crosstool:windows/msvc_wrapper_for_nvcc.bat",
+            {
+                "%{python_binary}": _get_python_bin(repository_ctx),
+            },
+        )
+
+    _tpl(
+        repository_ctx,
+        "crosstool:CROSSTOOL",
+        cuda_defines + _get_win_cuda_defines(repository_ctx),
+        out = "crosstool/CROSSTOOL",
+    )
+
+    # Set up cuda_config.h, which is used by
+    # tensorflow/stream_executor/dso_loader.cc.
+    _tpl(
+        repository_ctx,
+        "cuda:cuda_config.h",
+        {
+            "%{cuda_version}": cuda_config.cuda_version,
+            "%{cudnn_version}": cuda_config.cudnn_version,
+            "%{cuda_compute_capabilities}": ",".join(
+                [
+                    "CudaVersion(\"%s\")" % c
+                    for c in cuda_config.compute_capabilities
+                ],
+            ),
+            "%{cuda_toolkit_path}": cuda_config.cuda_toolkit_path,
+        },
+        "cuda/cuda/cuda_config.h",
+    )
 
 def _create_remote_cuda_repository(repository_ctx, remote_config_repo):
-  """Creates pointers to a remotely configured repo set up to build with CUDA."""
-  _tpl(repository_ctx, "cuda:build_defs.bzl",
-       {
-           "%{cuda_is_configured}": "True",
-           "%{cuda_extra_copts}": _compute_cuda_extra_copts(
-               repository_ctx, _compute_capabilities(repository_ctx)),
-
-       })
-  _tpl(repository_ctx, "cuda:remote.BUILD",
-       {
-           "%{remote_cuda_repo}": remote_config_repo,
-       }, "cuda/BUILD")
-  _tpl(repository_ctx, "crosstool:remote.BUILD", {
-           "%{remote_cuda_repo}": remote_config_repo,
-       }, "crosstool/BUILD")
+    """Creates pointers to a remotely configured repo set up to build with CUDA."""
+    _tpl(
+        repository_ctx,
+        "cuda:build_defs.bzl",
+        {
+            "%{cuda_is_configured}": "True",
+            "%{cuda_extra_copts}": _compute_cuda_extra_copts(
+                repository_ctx,
+                _compute_capabilities(repository_ctx),
+            ),
+        },
+    )
+    _tpl(
+        repository_ctx,
+        "cuda:remote.BUILD",
+        {
+            "%{remote_cuda_repo}": remote_config_repo,
+        },
+        "cuda/BUILD",
+    )
+    _tpl(repository_ctx, "crosstool:remote.BUILD", {
+        "%{remote_cuda_repo}": remote_config_repo,
+    }, "crosstool/BUILD")
 
 def _cuda_autoconf_impl(repository_ctx):
-  """Implementation of the cuda_autoconf repository rule."""
-  if not _enable_cuda(repository_ctx):
-    _create_dummy_repository(repository_ctx)
-  else:
-    if _TF_CUDA_CONFIG_REPO in repository_ctx.os.environ:
-      _create_remote_cuda_repository(repository_ctx,
-          repository_ctx.os.environ[_TF_CUDA_CONFIG_REPO])
+    """Implementation of the cuda_autoconf repository rule."""
+    if not _enable_cuda(repository_ctx):
+        _create_dummy_repository(repository_ctx)
+    elif _TF_CUDA_CONFIG_REPO in repository_ctx.os.environ:
+        _create_remote_cuda_repository(
+            repository_ctx,
+            repository_ctx.os.environ[_TF_CUDA_CONFIG_REPO],
+        )
     else:
-      _create_local_cuda_repository(repository_ctx)
-
+        _create_local_cuda_repository(repository_ctx)
 
 cuda_configure = repository_rule(
     implementation = _cuda_autoconf_impl,
@@ -1181,6 +1457,7 @@ cuda_configure = repository_rule(
         _TF_CUDA_COMPUTE_CAPABILITIES,
         _TF_CUDA_CONFIG_REPO,
         "NVVMIR_LIBRARY_DIR",
+        _PYTHON_BIN_PATH,
     ],
 )
 
diff --git a/third_party/llvm/llvm.autogenerated.BUILD b/third_party/llvm/llvm.autogenerated.BUILD
index 8f658539187bcf03bf5bc37118884ec28a85e5dd..0ac27e26a4f796ede33a03397533eb3c0af09288 100644
--- a/third_party/llvm/llvm.autogenerated.BUILD
+++ b/third_party/llvm/llvm.autogenerated.BUILD
@@ -8,13 +8,14 @@ exports_files(["LICENSE.TXT"])
 
 load(
     "@org_tensorflow//third_party/llvm:llvm.bzl",
-    "LLVM_COPTS",
-    "LLVM_DEFINES",
-    "LLVM_LINKOPTS",
     "cmake_var_string",
     "expand_cmake_vars",
     "gentbl",
     "llvm_all_cmake_vars",
+    "llvm_copts",
+    "llvm_defines",
+    "llvm_linkopts",
+    "llvm_support_platform_specific_srcs_glob",
 )
 load(
     "@org_tensorflow//third_party:common.bzl",
@@ -27,9 +28,7 @@ llvm_host_triple = "x86_64-unknown-linux_gnu"
 
 llvm_targets = [
     "AArch64",
-    # Uncomment to enable the AMDGPU backend.
-    # TODO(phawkins): use a configure-time test.
-    # "AMDGPU",
+    "AMDGPU",
     "ARM",
     "NVPTX",
     "PowerPC",
@@ -121,7 +120,7 @@ cc_library(
         "include/llvm/Config/config.h",
         "include/llvm/Config/llvm-config.h",
     ],
-    defines = LLVM_DEFINES,
+    defines = llvm_defines,
     includes = ["include"],
 )
 
@@ -198,7 +197,8 @@ cc_binary(
         "utils/TableGen/*.cpp",
         "utils/TableGen/*.h",
     ]),
-    linkopts = LLVM_LINKOPTS,
+    copts = llvm_copts,
+    linkopts = llvm_linkopts,
     stamp = 0,
     deps = [
         ":config",
@@ -214,7 +214,8 @@ cc_binary(
         "utils/FileCheck/*.cpp",
         "utils/FileCheck/*.h",
     ]),
-    linkopts = LLVM_LINKOPTS,
+    copts = llvm_copts,
+    linkopts = llvm_linkopts,
     stamp = 0,
     deps = [":support"],
 )
@@ -253,13 +254,31 @@ llvm_target_list = [
             ("-gen-dag-isel", "lib/Target/AMDGPU/AMDGPUGenDAGISel.inc"),
             ("-gen-callingconv", "lib/Target/AMDGPU/AMDGPUGenCallingConv.inc"),
             ("-gen-subtarget", "lib/Target/AMDGPU/AMDGPUGenSubtargetInfo.inc"),
-            ("-gen-tgt-intrinsic", "lib/Target/AMDGPU/AMDGPUGenIntrinsics.inc"),
+            ("-gen-tgt-intrinsic-impl", "lib/Target/AMDGPU/AMDGPUGenIntrinsicImpl.inc"),
+            ("-gen-tgt-intrinsic-enums", "lib/Target/AMDGPU/AMDGPUGenIntrinsicEnums.inc"),
             ("-gen-emitter", "lib/Target/AMDGPU/AMDGPUGenMCCodeEmitter.inc"),
             ("-gen-dfa-packetizer", "lib/Target/AMDGPU/AMDGPUGenDFAPacketizer.inc"),
             ("-gen-asm-writer", "lib/Target/AMDGPU/AMDGPUGenAsmWriter.inc"),
             ("-gen-asm-matcher", "lib/Target/AMDGPU/AMDGPUGenAsmMatcher.inc"),
             ("-gen-disassembler", "lib/Target/AMDGPU/AMDGPUGenDisassemblerTables.inc"),
             ("-gen-pseudo-lowering", "lib/Target/AMDGPU/AMDGPUGenMCPseudoLowering.inc"),
+            ("-gen-searchable-tables", "lib/Target/AMDGPU/AMDGPUGenSearchableTables.inc"),
+            ("-gen-global-isel", "lib/Target/AMDGPU/AMDGPUGenGlobalISel.inc"),
+        ],
+    },
+    {
+        "name": "AMDGPU",
+        "lower_name": "amdgpu_r600",
+        "short_name": "R600",
+        "tbl_outs": [
+            ("-gen-asm-writer", "lib/Target/AMDGPU/R600GenAsmWriter.inc"),
+            ("-gen-callingconv", "lib/Target/AMDGPU/R600GenCallingConv.inc"),
+            ("-gen-dag-isel", "lib/Target/AMDGPU/R600GenDAGISel.inc"),
+            ("-gen-dfa-packetizer", "lib/Target/AMDGPU/R600GenDFAPacketizer.inc"),
+            ("-gen-instr-info", "lib/Target/AMDGPU/R600GenInstrInfo.inc"),
+            ("-gen-emitter", "lib/Target/AMDGPU/R600GenMCCodeEmitter.inc"),
+            ("-gen-register-info", "lib/Target/AMDGPU/R600GenRegisterInfo.inc"),
+            ("-gen-subtarget", "lib/Target/AMDGPU/R600GenSubtargetInfo.inc"),
         ],
     },
     {
@@ -385,8 +404,7 @@ cc_library(
         "include/llvm/Target/AArch64/AsmParser/*.inc",
         "lib/Target/AArch64/AsmParser/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AArch64"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/AArch64"],
     deps = [
         ":aarch64_desc",
         ":aarch64_info",
@@ -411,8 +429,7 @@ cc_library(
         "include/llvm/Target/AArch64/InstPrinter/*.inc",
         "lib/Target/AArch64/InstPrinter/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AArch64"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/AArch64"],
     deps = [
         ":aarch64_target_gen",
         ":aarch64_utils",
@@ -435,8 +452,7 @@ cc_library(
         "include/llvm/Target/AArch64/*.inc",
         "lib/Target/AArch64/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AArch64"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/AArch64"],
     deps = [
         ":aarch64_asm_printer",
         ":aarch64_desc",
@@ -469,8 +485,7 @@ cc_library(
         "include/llvm/Target/AArch64/MCTargetDesc/*.inc",
         "lib/Target/AArch64/MCTargetDesc/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AArch64"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/AArch64"],
     deps = [
         ":aarch64_asm_printer",
         ":aarch64_info",
@@ -497,8 +512,7 @@ cc_library(
         "include/llvm/Target/AArch64/Disassembler/*.inc",
         "lib/Target/AArch64/Disassembler/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AArch64"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/AArch64"],
     deps = [
         ":aarch64_desc",
         ":aarch64_info",
@@ -526,8 +540,7 @@ cc_library(
         "lib/Target/AArch64/AArch64*.h",
         "lib/Target/AArch64/TargetInfo/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AArch64"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/AArch64"],
     deps = [
         ":code_gen",
         ":config",
@@ -550,8 +563,7 @@ cc_library(
         "include/llvm/Target/AArch64/Utils/*.inc",
         "lib/Target/AArch64/Utils/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AArch64"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/AArch64"],
     deps = [
         ":aarch64_target_gen",
         ":config",
@@ -573,8 +585,7 @@ cc_library(
         "include/llvm/Transforms/AggressiveInstCombine/*.def",
         "include/llvm/Transforms/AggressiveInstCombine/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":analysis",
         ":config",
@@ -599,8 +610,7 @@ cc_library(
         "include/llvm/Analysis/*.def",
         "include/llvm/Analysis/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":binary_format",
         ":config",
@@ -624,8 +634,7 @@ cc_library(
         "include/llvm/Target/AMDGPU/MCTargetDesc/*.inc",
         "lib/Target/AMDGPU/MCTargetDesc/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AMDGPU"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/AMDGPU"],
     deps = [
         ":amdgpu_asm_printer",
         ":amdgpu_info",
@@ -650,8 +659,7 @@ cc_library(
         "include/llvm/Target/AMDGPU/Disassembler/*.inc",
         "lib/Target/AMDGPU/Disassembler/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AMDGPU"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/AMDGPU"],
     deps = [
         ":amdgpu_desc",
         ":amdgpu_info",
@@ -676,9 +684,9 @@ cc_library(
         "include/llvm/Target/AMDGPU/TargetInfo/*.inc",
         "lib/Target/AMDGPU/TargetInfo/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AMDGPU"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/AMDGPU"],
     deps = [
+        ":amdgpu_r600_target_gen",
         ":amdgpu_target_gen",
         ":config",
         ":core",
@@ -699,9 +707,9 @@ cc_library(
         "include/llvm/Target/AMDGPU/Utils/*.inc",
         "lib/Target/AMDGPU/Utils/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AMDGPU"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/AMDGPU"],
     deps = [
+        ":amdgpu_r600_target_gen",
         ":amdgpu_target_gen",
         ":config",
         ":core",
@@ -723,8 +731,7 @@ cc_library(
         "include/llvm/Target/AMDGPU/AsmParser/*.inc",
         "lib/Target/AMDGPU/AsmParser/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AMDGPU"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/AMDGPU"],
     deps = [
         ":amdgpu_desc",
         ":amdgpu_info",
@@ -749,8 +756,7 @@ cc_library(
         "include/llvm/Target/AMDGPU/InstPrinter/*.inc",
         "lib/Target/AMDGPU/InstPrinter/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AMDGPU"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/AMDGPU"],
     deps = [
         ":amdgpu_utils",
         ":config",
@@ -772,8 +778,7 @@ cc_library(
         "include/llvm/Target/AMDGPU/*.inc",
         "lib/Target/AMDGPU/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/AMDGPU"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/AMDGPU"],
     deps = [
         ":amdgpu_asm_printer",
         ":amdgpu_desc",
@@ -809,8 +814,7 @@ cc_library(
         "include/llvm/Target/ARM/AsmParser/*.inc",
         "lib/Target/ARM/AsmParser/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/ARM"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/ARM"],
     deps = [
         ":arm_desc",
         ":arm_info",
@@ -836,8 +840,7 @@ cc_library(
         "lib/Target/ARM/*.h",
         "lib/Target/ARM/InstPrinter/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/ARM"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/ARM"],
     deps = [
         ":arm_info",
         ":arm_target_gen",
@@ -861,8 +864,7 @@ cc_library(
         "include/llvm/Target/ARM/*.inc",
         "lib/Target/ARM/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/ARM"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/ARM"],
     deps = [
         ":analysis",
         ":arm_asm_printer",
@@ -898,8 +900,7 @@ cc_library(
         "include/llvm/Target/ARM/MCTargetDesc/*.inc",
         "lib/Target/ARM/MCTargetDesc/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/ARM"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/ARM"],
     deps = [
         ":arm_asm_printer",
         ":arm_info",
@@ -927,8 +928,7 @@ cc_library(
         "include/llvm/Target/ARM/Disassembler/*.inc",
         "lib/Target/ARM/Disassembler/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/ARM"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/ARM"],
     deps = [
         ":arm_desc",
         ":arm_info",
@@ -953,8 +953,7 @@ cc_library(
         "include/llvm/Target/ARM/TargetInfo/*.inc",
         "lib/Target/ARM/TargetInfo/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/ARM"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/ARM"],
     deps = [
         ":arm_target_gen",
         ":config",
@@ -977,8 +976,7 @@ cc_library(
         "include/llvm/Target/ARM/Utils/*.inc",
         "lib/Target/ARM/Utils/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/ARM"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/ARM"],
     deps = [
         ":arm_target_gen",
         ":config",
@@ -1000,8 +998,7 @@ cc_library(
         "include/llvm/AsmParser/*.def",
         "include/llvm/AsmParser/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":binary_format",
         ":config",
@@ -1024,8 +1021,7 @@ cc_library(
         "include/llvm/CodeGen/AsmPrinter/*.inc",
         "lib/CodeGen/AsmPrinter/*.def",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":analysis",
         ":binary_format",
@@ -1056,8 +1052,7 @@ cc_library(
         "include/llvm/BinaryFormat/ELFRelocs/*.def",
         "include/llvm/BinaryFormat/WasmRelocs/*.def",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":config",
         ":support",
@@ -1078,8 +1073,7 @@ cc_library(
         "include/llvm/Bitcode/Reader/*.inc",
         "include/llvm/Bitcode/BitstreamReader.h",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":config",
         ":core",
@@ -1103,8 +1097,7 @@ cc_library(
         "include/llvm/Bitcode/BitcodeWriterPass.h",
         "include/llvm/Bitcode/BitstreamWriter.h",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":analysis",
         ":config",
@@ -1129,8 +1122,7 @@ cc_library(
         "include/llvm/CodeGen/*.inc",
         "include/llvm/CodeGen/**/*.h",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":analysis",
         ":bit_reader",
@@ -1168,8 +1160,7 @@ cc_library(
         "include/llvm/*.h",
         "include/llvm/Analysis/*.def",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":attributes_compat_gen",
         ":attributes_gen",
@@ -1194,8 +1185,7 @@ cc_library(
         "include/llvm/DebugInfo/CodeView/*.def",
         "include/llvm/DebugInfo/CodeView/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":binary_format",
         ":config",
@@ -1217,8 +1207,7 @@ cc_library(
         "include/llvm/DebugInfo/MSF/*.def",
         "include/llvm/DebugInfo/MSF/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":config",
         ":support",
@@ -1238,8 +1227,7 @@ cc_library(
         "include/llvm/Demangle/*.def",
         "include/llvm/Demangle/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [":config"],
 )
 
@@ -1256,8 +1244,7 @@ cc_library(
         "include/llvm/ExecutionEngine/*.def",
         "include/llvm/ExecutionEngine/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":config",
         ":core",
@@ -1282,8 +1269,7 @@ cc_library(
         "include/llvm/CodeGen/GlobalISel/*.def",
         "include/llvm/CodeGen/GlobalISel/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":analysis",
         ":code_gen",
@@ -1313,8 +1299,7 @@ cc_library(
         "include/llvm/Transforms/InstrProfiling.h",
         "include/llvm/Transforms/PGOInstrumentation.h",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":analysis",
         ":config",
@@ -1339,8 +1324,7 @@ cc_library(
         "include/llvm/Transforms/InstCombine/*.def",
         "include/llvm/Transforms/InstCombine/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":analysis",
         ":config",
@@ -1367,8 +1351,7 @@ cc_library(
         "include/llvm/Transforms/IPO/*.def",
         "include/llvm/Transforms/IPO/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":aggressive_inst_combine",
         ":analysis",
@@ -1402,8 +1385,7 @@ cc_library(
         "include/llvm/IRReader/*.def",
         "include/llvm/IRReader/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":asm_parser",
         ":bit_reader",
@@ -1426,8 +1408,7 @@ cc_library(
         "include/llvm/Linker/*.def",
         "include/llvm/Linker/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":config",
         ":core",
@@ -1449,8 +1430,7 @@ cc_library(
         "include/llvm/MC/*.def",
         "include/llvm/MC/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":binary_format",
         ":config",
@@ -1472,8 +1452,7 @@ cc_library(
         "include/llvm/MC/MCDisassembler/*.def",
         "include/llvm/MC/MCDisassembler/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":config",
         ":mc",
@@ -1494,8 +1473,7 @@ cc_library(
         "include/llvm/MC/MCParser/*.def",
         "include/llvm/MC/MCParser/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":config",
         ":mc",
@@ -1516,8 +1494,7 @@ cc_library(
         "include/llvm/Target/NVPTX/InstPrinter/*.inc",
         "lib/Target/NVPTX/InstPrinter/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/NVPTX"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/NVPTX"],
     deps = [
         "nvptx_target_gen",
         ":attributes_gen",
@@ -1541,8 +1518,7 @@ cc_library(
         "include/llvm/Target/NVPTX/*.inc",
         "lib/Target/NVPTX/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/NVPTX"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/NVPTX"],
     deps = [
         ":analysis",
         ":asm_printer",
@@ -1576,8 +1552,7 @@ cc_library(
         "include/llvm/Target/NVPTX/MCTargetDesc/*.inc",
         "lib/Target/NVPTX/MCTargetDesc/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/NVPTX"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/NVPTX"],
     deps = [
         "nvptx_target_gen",
         ":config",
@@ -1603,8 +1578,7 @@ cc_library(
         "lib/Target/NVPTX/NVPTX.h",
         "lib/Target/NVPTX/TargetInfo/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/NVPTX"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/NVPTX"],
     deps = [
         "nvptx_target_gen",
         ":attributes_gen",
@@ -1628,8 +1602,7 @@ cc_library(
         "include/llvm/Object/*.def",
         "include/llvm/Object/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":binary_format",
         ":bit_reader",
@@ -1655,8 +1628,7 @@ cc_library(
         "include/llvm/Transforms/ObjCARC/*.def",
         "include/llvm/Transforms/ObjCARC/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":analysis",
         ":config",
@@ -1679,8 +1651,7 @@ cc_library(
         "include/llvm/ExecutionEngine/Orc/*.def",
         "include/llvm/ExecutionEngine/Orc/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":config",
         ":core",
@@ -1707,8 +1678,7 @@ cc_library(
         "include/llvm/Target/PowerPC/AsmParser/*.inc",
         "lib/Target/PowerPC/AsmParser/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/PowerPC"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/PowerPC"],
     deps = [
         ":config",
         ":mc",
@@ -1732,8 +1702,7 @@ cc_library(
         "include/llvm/Target/PowerPC/InstPrinter/*.inc",
         "lib/Target/PowerPC/InstPrinter/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/PowerPC"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/PowerPC"],
     deps = [
         ":attributes_gen",
         ":config",
@@ -1759,8 +1728,7 @@ cc_library(
         "include/llvm/Target/PowerPC/*.inc",
         "lib/Target/PowerPC/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/PowerPC"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/PowerPC"],
     deps = [
         ":analysis",
         ":asm_printer",
@@ -1792,8 +1760,7 @@ cc_library(
         "include/llvm/Target/PowerPC/MCTargetDesc/*.inc",
         "lib/Target/PowerPC/MCTargetDesc/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/PowerPC"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/PowerPC"],
     deps = [
         ":attributes_gen",
         ":config",
@@ -1820,8 +1787,7 @@ cc_library(
         "include/llvm/Target/PowerPC/Disassembler/*.inc",
         "lib/Target/PowerPC/Disassembler/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/PowerPC"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/PowerPC"],
     deps = [
         ":config",
         ":mc_disassembler",
@@ -1845,8 +1811,7 @@ cc_library(
         "lib/Target/PowerPC/PPC*.h",
         "lib/Target/PowerPC/TargetInfo/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/PowerPC"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/PowerPC"],
     deps = [
         ":attributes_gen",
         ":config",
@@ -1870,8 +1835,7 @@ cc_library(
         "include/llvm/ProfileData/*.def",
         "include/llvm/ProfileData/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":config",
         ":core",
@@ -1900,8 +1864,7 @@ cc_library(
         "include/llvm/ExecutionEngine/RTDyldMemoryManager.h",
         "include/llvm/ExecutionEngine/RuntimeDyld*.h",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":config",
         ":mc",
@@ -1929,8 +1892,7 @@ cc_library(
         "include/llvm/Transforms/IPO.h",
         "include/llvm/Transforms/IPO/SCCP.h",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":aggressive_inst_combine",
         ":analysis",
@@ -1956,8 +1918,7 @@ cc_library(
         "include/llvm/CodeGen/SelectionDAG/*.def",
         "include/llvm/CodeGen/SelectionDAG/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":analysis",
         ":code_gen",
@@ -1976,14 +1937,12 @@ cc_library(
         "lib/Support/*.c",
         "lib/Support/*.cpp",
         "lib/Support/*.inc",
-        "lib/Support/Unix/*.inc",
-        "lib/Support/Unix/*.h",
         "include/llvm-c/*.h",
         "include/llvm/CodeGen/MachineValueType.h",
         "include/llvm/BinaryFormat/COFF.h",
         "include/llvm/BinaryFormat/MachO.h",
         "lib/Support/*.h",
-    ]),
+    ]) + llvm_support_platform_specific_srcs_glob(),
     hdrs = glob([
         "include/llvm/Support/*.h",
         "include/llvm/Support/*.def",
@@ -1995,8 +1954,7 @@ cc_library(
         "include/llvm/BinaryFormat/MachO.def",
         "include/llvm/Support/VCSRevision.h",
     ],
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":config",
         ":demangle",
@@ -2019,8 +1977,7 @@ cc_library(
         "include/llvm/TableGen/*.inc",
         "include/llvm/Target/*.def",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":config",
         ":mc",
@@ -2046,8 +2003,7 @@ cc_library(
         "include/llvm/CodeGen/*.def",
         "include/llvm/CodeGen/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":analysis",
         ":config",
@@ -2072,8 +2028,7 @@ cc_library(
         "include/llvm/Transforms/Utils/*.def",
         "include/llvm/Transforms/Utils/*.inc",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":analysis",
         ":config",
@@ -2097,8 +2052,7 @@ cc_library(
         "include/llvm/Transforms/Vectorize/*.inc",
         "include/llvm/Transforms/Vectorize.h",
     ]),
-    copts = LLVM_COPTS,
-    defines = LLVM_DEFINES,
+    copts = llvm_copts,
     deps = [
         ":analysis",
         ":config",
@@ -2122,8 +2076,7 @@ cc_library(
         "include/llvm/Target/X86/AsmParser/*.inc",
         "lib/Target/X86/AsmParser/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/X86"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/X86"],
     deps = [
         ":config",
         ":mc",
@@ -2148,8 +2101,7 @@ cc_library(
         "include/llvm/Target/X86/InstPrinter/*.inc",
         "lib/Target/X86/InstPrinter/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/X86"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/X86"],
     deps = [
         ":config",
         ":mc",
@@ -2173,8 +2125,7 @@ cc_library(
         "include/llvm/Target/X86/*.inc",
         "lib/Target/X86/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/X86"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/X86"],
     deps = [
         ":analysis",
         ":asm_printer",
@@ -2207,8 +2158,7 @@ cc_library(
         "include/llvm/Target/X86/MCTargetDesc/*.inc",
         "lib/Target/X86/MCTargetDesc/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/X86"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/X86"],
     deps = [
         ":config",
         ":mc",
@@ -2233,8 +2183,7 @@ cc_library(
         "include/llvm/Target/X86/Disassembler/*.inc",
         "lib/Target/X86/Disassembler/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/X86"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/X86"],
     deps = [
         ":config",
         ":mc_disassembler",
@@ -2257,8 +2206,7 @@ cc_library(
         "include/llvm/Target/X86/TargetInfo/*.inc",
         "lib/Target/X86/TargetInfo/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/X86"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/X86"],
     deps = [
         ":config",
         ":mc",
@@ -2280,8 +2228,7 @@ cc_library(
         "include/llvm/Target/X86/Utils/*.inc",
         "lib/Target/X86/Utils/*.h",
     ]),
-    copts = LLVM_COPTS + ["-Iexternal/llvm/lib/Target/X86"],
-    defines = LLVM_DEFINES,
+    copts = llvm_copts + ["-Iexternal/llvm/lib/Target/X86"],
     deps = [
         ":code_gen",
         ":config",
diff --git a/third_party/llvm/llvm.bzl b/third_party/llvm/llvm.bzl
index 2e809e5f147d9e2b359dbf8fcc57575572bc64cd..d493a3c476c11d603bfcb92a17aa6c540910934e 100644
--- a/third_party/llvm/llvm.bzl
+++ b/third_party/llvm/llvm.bzl
@@ -7,103 +7,143 @@ TODO(chandlerc): Currently this expresses include-based dependencies as
 correctly understood by the build system.
 """
 
+def _dict_add(*dictionaries):
+    """Returns a new `dict` that has all the entries of the given dictionaries.
+
+    If the same key is present in more than one of the input dictionaries, the
+    last of them in the argument list overrides any earlier ones.
+
+    This function is designed to take zero or one arguments as well as multiple
+    dictionaries, so that it follows arithmetic identities and callers can avoid
+    special cases for their inputs: the sum of zero dictionaries is the empty
+    dictionary, and the sum of a single dictionary is a copy of itself.
+
+    Re-implemented here to avoid adding a dependency on skylib.
+
+    Args:
+      *dictionaries: Zero or more dictionaries to be added.
+
+    Returns:
+      A new `dict` that has all the entries of the given dictionaries.
+    """
+    result = {}
+    for d in dictionaries:
+        result.update(d)
+    return result
+
 def gentbl(name, tblgen, td_file, td_srcs, tbl_outs, library = True, **kwargs):
-  """gentbl() generates tabular code from a table definition file.
-
-  Args:
-    name: The name of the build rule for use in dependencies.
-    tblgen: The binary used to produce the output.
-    td_file: The primary table definitions file.
-    td_srcs: A list of table definition files included transitively.
-    tbl_outs: A list of tuples (opts, out), where each opts is a string of
-      options passed to tblgen, and the out is the corresponding output file
-      produced.
-    library: Whether to bundle the generated files into a library.
-    **kwargs: Keyword arguments to pass to subsidiary cc_library() rule.
-  """
-  if td_file not in td_srcs:
-    td_srcs += [td_file]
-  includes = []
-  for (opts, out) in tbl_outs:
-    outdir = out[:out.rindex("/")]
-    if outdir not in includes:
-      includes.append(outdir)
-    rule_suffix = "_".join(opts.replace("-", "_").replace("=", "_").split(" "))
-    native.genrule(
-        name="%s_%s_genrule" % (name, rule_suffix),
-        srcs=td_srcs,
-        outs=[out],
-        tools=[tblgen],
-        message="Generating code from table: %s" % td_file,
-        cmd=(("$(location %s) " + "-I external/llvm/include " +
-              "-I external/llvm/tools/clang/include " +
-              "-I $$(dirname $(location %s)) " + "%s $(location %s) -o $@") % (
-                  tblgen, td_file, opts, td_file)))
-  # For now, all generated files can be assumed to comprise public interfaces.
-  # If this is not true, you should specify library = False
-  # and list the generated '.inc' files in "srcs".
-  if library:
-    native.cc_library(name=name, textual_hdrs=[f for (_, f) in tbl_outs],
-                      includes=includes,  **kwargs)
+    """gentbl() generates tabular code from a table definition file.
+
+    Args:
+      name: The name of the build rule for use in dependencies.
+      tblgen: The binary used to produce the output.
+      td_file: The primary table definitions file.
+      td_srcs: A list of table definition files included transitively.
+      tbl_outs: A list of tuples (opts, out), where each opts is a string of
+        options passed to tblgen, and the out is the corresponding output file
+        produced.
+      library: Whether to bundle the generated files into a library.
+      **kwargs: Keyword arguments to pass to subsidiary cc_library() rule.
+    """
+    if td_file not in td_srcs:
+        td_srcs += [td_file]
+    includes = []
+    for (opts, out) in tbl_outs:
+        outdir = out[:out.rindex("/")]
+        if outdir not in includes:
+            includes.append(outdir)
+        rule_suffix = "_".join(opts.replace("-", "_").replace("=", "_").split(" "))
+        native.genrule(
+            name = "%s_%s_genrule" % (name, rule_suffix),
+            srcs = td_srcs,
+            outs = [out],
+            tools = [tblgen],
+            message = "Generating code from table: %s" % td_file,
+            cmd = (("$(location %s) " + "-I external/llvm/include " +
+                    "-I external/llvm/tools/clang/include " +
+                    "-I $$(dirname $(location %s)) " + "%s $(location %s) -o $@") % (
+                tblgen,
+                td_file,
+                opts,
+                td_file,
+            )),
+        )
+
+    # For now, all generated files can be assumed to comprise public interfaces.
+    # If this is not true, you should specify library = False
+    # and list the generated '.inc' files in "srcs".
+    if library:
+        native.cc_library(
+            name = name,
+            textual_hdrs = [f for (_, f) in tbl_outs],
+            includes = includes,
+            **kwargs
+        )
 
 def llvm_target_cmake_vars(native_arch, target_triple):
-  return {
-      "LLVM_HOST_TRIPLE": target_triple,
-      "LLVM_DEFAULT_TARGET_TRIPLE": target_triple,
-      "LLVM_NATIVE_ARCH": native_arch,
-  }
+    return {
+        "LLVM_HOST_TRIPLE": target_triple,
+        "LLVM_DEFAULT_TARGET_TRIPLE": target_triple,
+        "LLVM_NATIVE_ARCH": native_arch,
+    }
 
 def _quote(s):
-  """Quotes the given string for use in a shell command.
-
-  This function double-quotes the given string (in case it contains spaces or
-  other special characters) and escapes any special characters (dollar signs,
-  double-quotes, and backslashes) that may be present.
-
-  Args:
-    s: The string to quote.
-  Returns:
-    An escaped and quoted version of the string that can be passed to a shell
-    command.
-  """
-  return ('"' +
-          s.replace("\\", "\\\\").replace("$", "\\$").replace('"', '\\"') +
-          '"')
+    """Quotes the given string for use in a shell command.
+
+    This function double-quotes the given string (in case it contains spaces or
+    other special characters) and escapes any special characters (dollar signs,
+    double-quotes, and backslashes) that may be present.
+
+    Args:
+      s: The string to quote.
+
+    Returns:
+      An escaped and quoted version of the string that can be passed to a shell
+      command.
+    """
+    return ('"' +
+            s.replace("\\", "\\\\").replace("$", "\\$").replace('"', '\\"') +
+            '"')
 
 def cmake_var_string(cmake_vars):
-  """Converts a dictionary to an input suitable for expand_cmake_vars.
+    """Converts a dictionary to an input suitable for expand_cmake_vars.
+
+    Ideally we would jist stringify in the expand_cmake_vars() rule, but select()
+    interacts badly with genrules.
 
-  Ideally we would jist stringify in the expand_cmake_vars() rule, but select()
-  interacts badly with genrules.
+    TODO(phawkins): replace the genrule() with native rule and delete this rule.
 
-  TODO(phawkins): replace the genrule() with native rule and delete this rule.
+    Args:
+      cmake_vars: a dictionary with string keys and values that are convertable to
+        strings.
 
-  Args:
-    cmake_vars: a dictionary with string keys and values that are convertable to
-      strings.
-  """
-  return " ".join([_quote("{}={}".format(k, str(v)))
-                   for (k, v) in cmake_vars.items()])
+    Returns:
+      cmake_vars in a form suitable for passing to expand_cmake_vars.
+    """
+    return " ".join([
+        _quote("{}={}".format(k, str(v)))
+        for (k, v) in cmake_vars.items()
+    ])
 
 def expand_cmake_vars(name, src, dst, cmake_vars):
-  """Expands #cmakedefine, #cmakedefine01, and CMake variables in a text file.
-
-  Args:
-    name: the name of the rule
-    src: the input of the rule
-    dst: the output of the rule
-    cmake_vars: a string containing the CMake variables, as generated by
-      cmake_var_string.
-  """
-  expand_cmake_vars_tool = Label("@org_tensorflow//third_party/llvm:expand_cmake_vars")
-  native.genrule(
-      name = name,
-      srcs = [src],
-      tools = [expand_cmake_vars_tool],
-      outs = [dst],
-      cmd = ("$(location {}) ".format(expand_cmake_vars_tool) + cmake_vars +
-             "< $< > $@")
-  )
+    """Expands #cmakedefine, #cmakedefine01, and CMake variables in a text file.
+
+    Args:
+      name: the name of the rule
+      src: the input of the rule
+      dst: the output of the rule
+      cmake_vars: a string containing the CMake variables, as generated by
+        cmake_var_string.
+    """
+    expand_cmake_vars_tool = Label("@org_tensorflow//third_party/llvm:expand_cmake_vars")
+    native.genrule(
+        name = name,
+        srcs = [src],
+        tools = [expand_cmake_vars_tool],
+        outs = [dst],
+        cmd = ("$(location {}) ".format(expand_cmake_vars_tool) + cmake_vars +
+               "< $< > $@"),
+    )
 
 # TODO(phawkins): the set of CMake variables was hardcoded for expediency.
 # However, we should really detect many of these via configure-time tests.
@@ -212,23 +252,31 @@ darwin_cmake_vars = {
 # than hardcoding x86_64.
 llvm_all_cmake_vars = select({
     "@org_tensorflow//tensorflow:darwin": cmake_var_string(
-        cmake_vars + llvm_target_cmake_vars("X86", "x86_64-apple-darwin") +
-        darwin_cmake_vars),
+        _dict_add(
+            cmake_vars,
+            llvm_target_cmake_vars("X86", "x86_64-apple-darwin"),
+            darwin_cmake_vars,
+        ),
+    ),
     "@org_tensorflow//tensorflow:linux_ppc64le": cmake_var_string(
-        cmake_vars +
-        llvm_target_cmake_vars("PowerPC", "powerpc64le-unknown-linux_gnu") +
-        linux_cmake_vars,
+        _dict_add(
+            cmake_vars,
+            llvm_target_cmake_vars("PowerPC", "powerpc64le-unknown-linux_gnu"),
+            linux_cmake_vars,
+        ),
     ),
     "//conditions:default": cmake_var_string(
-         cmake_vars +
-         llvm_target_cmake_vars("X86", "x86_64-unknown-linux_gnu") +
-         linux_cmake_vars),
-
+        _dict_add(
+            cmake_vars,
+            llvm_target_cmake_vars("X86", "x86_64-unknown-linux_gnu"),
+            linux_cmake_vars,
+        ),
+    ),
 })
 
-LLVM_LINKOPTS = ["-ldl", "-lm", "-lpthread"]
+llvm_linkopts = ["-ldl", "-lm", "-lpthread"]
 
-LLVM_DEFINES = [
+llvm_defines = [
     "LLVM_ENABLE_STATS",
     "__STDC_LIMIT_MACROS",
     "__STDC_CONSTANT_MACROS",
@@ -237,4 +285,14 @@ LLVM_DEFINES = [
     "LLVM_BUILD_GLOBAL_ISEL",
 ]
 
-LLVM_COPTS = []
+llvm_copts = []
+
+# Platform specific sources for libSupport.
+
+def llvm_support_platform_specific_srcs_glob():
+    return select({
+        "//conditions:default": native.glob([
+            "lib/Support/Unix/*.inc",
+            "lib/Support/Unix/*.h",
+        ]),
+    })
diff --git a/third_party/mkl/LICENSE b/third_party/mkl/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..9c8f3ea0871e0bfe81da0fa6e7c1d7d156dc380e
--- /dev/null
+++ b/third_party/mkl/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
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+      Work and such Derivative Works in Source or Object form.
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+   3. Grant of Patent License. Subject to the terms and conditions of
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+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
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+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
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+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
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+
+      (d) If the Work includes a "NOTICE" text file as part of its
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+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
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+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
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+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "{}"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright {yyyy} {name of copyright owner}
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
\ No newline at end of file
diff --git a/third_party/mkl_dnn/BUILD b/third_party/mkl_dnn/BUILD
index 5b01f6e3e4cfd195327e08ff6a957acce4e21c71..d075809ee9a50496be42ecf4413789e44f094f3e 100644
--- a/third_party/mkl_dnn/BUILD
+++ b/third_party/mkl_dnn/BUILD
@@ -1 +1,11 @@
 licenses(["notice"])
+
+exports_files(["LICENSE"])
+
+config_setting(
+    name = "using_mkl_dnn_only",
+    values = {
+        "define": "using_mkl_dnn_only=true",
+    },
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/mkl_dnn/LICENSE b/third_party/mkl_dnn/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..8dada3edaf50dbc082c9a125058f25def75e625a
--- /dev/null
+++ b/third_party/mkl_dnn/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "{}"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright {yyyy} {name of copyright owner}
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
diff --git a/third_party/mkl_dnn/build_defs.bzl b/third_party/mkl_dnn/build_defs.bzl
new file mode 100644
index 0000000000000000000000000000000000000000..7ce2a7d9b03e74a49c55e4307be0f94188022a9e
--- /dev/null
+++ b/third_party/mkl_dnn/build_defs.bzl
@@ -0,0 +1,13 @@
+def if_mkl_open_source_only(if_true, if_false = []):
+    """Shorthand for select()'ing on whether we're building with
+    MKL-DNN open source lib only, without depending on MKL binary form.
+
+    Returns a select statement which evaluates to if_true if we're building
+    with MKL-DNN open source lib only. Otherwise,
+    the select statement evaluates to if_false.
+
+    """
+    return select({
+        str(Label("//third_party/mkl_dnn:using_mkl_dnn_only")): if_true,
+        "//conditions:default": if_false,
+    })
diff --git a/third_party/mkl_dnn/mkldnn.BUILD b/third_party/mkl_dnn/mkldnn.BUILD
index 68f24aabaee6ed33fe5b92a3996f7d175b924ea0..57d2e1292b012ac1cc4c2066ecfcfe6980327529 100644
--- a/third_party/mkl_dnn/mkldnn.BUILD
+++ b/third_party/mkl_dnn/mkldnn.BUILD
@@ -1,5 +1,10 @@
 exports_files(["LICENSE"])
 
+load(
+    "@org_tensorflow//third_party/mkl_dnn:build_defs.bzl",
+    "if_mkl_open_source_only",
+)
+
 config_setting(
     name = "clang_linux_x86_64",
     values = {
@@ -15,7 +20,14 @@ cc_library(
         "src/cpu/*.cpp",
     ]),
     hdrs = glob(["include/*"]),
-    copts = ["-fexceptions"] + select({
+    copts = [
+        "-fexceptions",
+        "-DUSE_MKL",
+        "-DUSE_CBLAS",
+    ] + if_mkl_open_source_only([
+        "-UUSE_MKL",
+        "-UUSE_CBLAS",
+    ]) + select({
         "@org_tensorflow//tensorflow:linux_x86_64": [
             "-fopenmp",  # only works with gcc
         ],
@@ -33,4 +45,19 @@ cc_library(
     ],
     nocopts = "-fno-exceptions",
     visibility = ["//visibility:public"],
+    deps = select({
+        "@org_tensorflow//tensorflow:linux_x86_64": [
+            "@mkl_linux//:mkl_headers",
+            "@mkl_linux//:mkl_libs_linux",
+        ],
+        "@org_tensorflow//tensorflow:darwin": [
+            "@mkl_darwin//:mkl_headers",
+            "@mkl_darwin//:mkl_libs_darwin",
+        ],
+        "@org_tensorflow//tensorflow:windows": [
+            "@mkl_windows//:mkl_headers",
+            "@mkl_windows//:mkl_libs_windows",
+        ],
+        "//conditions:default": [],
+    }),
 )
diff --git a/third_party/nanopb.BUILD b/third_party/nanopb.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..d21866911b862f0d4adf76c3a07e2732128a6102
--- /dev/null
+++ b/third_party/nanopb.BUILD
@@ -0,0 +1,23 @@
+# Description:
+#   Nanopb, a tiny ANSI C protobuf implementation for use on embedded devices.
+
+licenses(["notice"])  # zlib license
+
+exports_files(["LICENSE.txt"])
+
+cc_library(
+    name = "nanopb",
+    srcs = [
+        "pb_common.c",
+        "pb_decode.c",
+        "pb_encode.c",
+    ],
+    hdrs = [
+        "pb.h",
+        "pb_common.h",
+        "pb_decode.h",
+        "pb_encode.h",
+    ],
+    includes = ["."],
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/nasm.BUILD b/third_party/nasm.BUILD
index 341d58068be48b1edbbc28718cc104a467efa8d0..89330eac5404934ddded305dfc062017d8abb30c 100644
--- a/third_party/nasm.BUILD
+++ b/third_party/nasm.BUILD
@@ -8,45 +8,93 @@ exports_files(["LICENSE"])
 cc_binary(
     name = "nasm",
     srcs = [
-        "assemble.c",
-        "assemble.h",
-        "compiler.h",
-        "crc64.c",
-        "directiv.c",
-        "directiv.h",
-        "disp8.c",
-        "disp8.h",
-        "eval.c",
-        "eval.h",
-        "exprlib.c",
-        "float.c",
-        "float.h",
-        "hashtbl.c",
-        "hashtbl.h",
-        "iflag.c",
-        "iflag.h",
-        "iflaggen.h",
-        "ilog2.c",
-        "insns.h",
-        "insnsa.c",
-        "insnsb.c",
-        "insnsi.h",
-        "labels.c",
-        "labels.h",
-        "lib/strlcpy.c",
-        "listing.c",
-        "listing.h",
-        "macros.c",
-        "md5.h",
-        "md5c.c",
-        "nasm.c",
-        "nasm.h",
-        "nasmlib.c",
-        "nasmlib.h",
-        "opflags.h",
+        "asm/assemble.c",
+        "asm/assemble.h",
+        "asm/directbl.c",
+        "asm/directiv.c",
+        "asm/directiv.h",
+        "asm/error.c",
+        "asm/eval.c",
+        "asm/eval.h",
+        "asm/exprdump.c",
+        "asm/exprlib.c",
+        "asm/float.c",
+        "asm/float.h",
+        "asm/labels.c",
+        "asm/listing.c",
+        "asm/listing.h",
+        "asm/nasm.c",
+        "asm/parser.c",
+        "asm/parser.h",
+        "asm/pptok.c",
+        "asm/pptok.h",
+        "asm/pragma.c",
+        "asm/preproc.c",
+        "asm/preproc.h",
+        "asm/preproc-nop.c",
+        "asm/quote.c",
+        "asm/quote.h",
+        "asm/rdstrnum.c",
+        "asm/segalloc.c",
+        "asm/stdscan.c",
+        "asm/stdscan.h",
+        "asm/strfunc.c",
+        "asm/tokens.h",
+        "asm/tokhash.c",
+        "common/common.c",
+        "config/unknown.h",
+        "disasm/disasm.c",
+        "disasm/disasm.h",
+        "disasm/sync.c",
+        "disasm/sync.h",
+        "include/compiler.h",
+        "include/disp8.h",
+        "include/error.h",
+        "include/hashtbl.h",
+        "include/iflag.h",
+        "include/insns.h",
+        "include/labels.h",
+        "include/md5.h",
+        "include/nasm.h",
+        "include/nasmint.h",
+        "include/nasmlib.h",
+        "include/opflags.h",
+        "include/perfhash.h",
+        "include/raa.h",
+        "include/rbtree.h",
+        "include/rdoff.h",
+        "include/saa.h",
+        "include/strlist.h",
+        "include/tables.h",
+        "include/ver.h",
+        "macros/macros.c",
+        "nasmlib/badenum.c",
+        "nasmlib/bsi.c",
+        "nasmlib/crc64.c",
+        "nasmlib/file.c",
+        "nasmlib/file.h",
+        "nasmlib/filename.c",
+        "nasmlib/hashtbl.c",
+        "nasmlib/ilog2.c",
+        "nasmlib/malloc.c",
+        "nasmlib/md5c.c",
+        "nasmlib/mmap.c",
+        "nasmlib/path.c",
+        "nasmlib/perfhash.c",
+        "nasmlib/raa.c",
+        "nasmlib/rbtree.c",
+        "nasmlib/readnum.c",
+        "nasmlib/realpath.c",
+        "nasmlib/saa.c",
+        "nasmlib/srcfile.c",
+        "nasmlib/string.c",
+        "nasmlib/strlist.c",
+        "nasmlib/ver.c",
+        "nasmlib/zerobuf.c",
         "output/codeview.c",
         "output/dwarf.h",
         "output/elf.h",
+        "output/legacy.c",
         "output/nulldbg.c",
         "output/nullout.c",
         "output/outaout.c",
@@ -56,9 +104,6 @@ cc_binary(
         "output/outdbg.c",
         "output/outelf.c",
         "output/outelf.h",
-        "output/outelf32.c",
-        "output/outelf64.c",
-        "output/outelfx32.c",
         "output/outform.c",
         "output/outform.h",
         "output/outieee.c",
@@ -69,35 +114,31 @@ cc_binary(
         "output/outrdf2.c",
         "output/pecoff.h",
         "output/stabs.h",
-        "parser.c",
-        "parser.h",
-        "pptok.c",
-        "pptok.h",
-        "preproc.c",
-        "preproc.h",
-        "preproc-nop.c",
-        "quote.c",
-        "quote.h",
-        "raa.c",
-        "raa.h",
-        "rbtree.c",
-        "rbtree.h",
-        "rdoff/rdoff.h",
-        "realpath.c",
-        "regflags.c",
-        "regs.h",
-        "regvals.c",
-        "saa.c",
-        "saa.h",
-        "srcfile.c",
-        "stdscan.c",
-        "stdscan.h",
-        "strfunc.c",
-        "tables.h",
-        "tokens.h",
-        "tokhash.c",
-        "ver.c",
+        "stdlib/snprintf.c",
+        "stdlib/strlcpy.c",
+        "stdlib/strnlen.c",
+        "stdlib/vsnprintf.c",
         "version.h",
+        "x86/disp8.c",
+        "x86/iflag.c",
+        "x86/iflaggen.h",
+        "x86/insnsa.c",
+        "x86/insnsb.c",
+        "x86/insnsd.c",
+        "x86/insnsi.h",
+        "x86/insnsn.c",
+        "x86/regdis.c",
+        "x86/regdis.h",
+        "x86/regflags.c",
+        "x86/regs.c",
+        "x86/regs.h",
+        "x86/regvals.c",
+    ],
+    includes = [
+        "asm",
+        "include",
+        "output",
+        "x86",
     ],
     copts = select({
         ":windows": [],
@@ -110,7 +151,10 @@ cc_binary(
     defines = select({
         ":windows": [],
         ":windows_msvc": [],
-        "//conditions:default": ["HAVE_SNPRINTF"],
+        "//conditions:default": [
+            "HAVE_SNPRINTF",
+            "HAVE_SYS_TYPES_H",
+        ],
     }),
     visibility = ["@jpeg//:__pkg__"],
 )
diff --git a/third_party/nccl/nccl_configure.bzl b/third_party/nccl/nccl_configure.bzl
index 9dfcb1836989d6c092739100e00e7000e6556c10..5d1ebf06867e14be9cbe301a443a8776d29d13e2 100644
--- a/third_party/nccl/nccl_configure.bzl
+++ b/third_party/nccl/nccl_configure.bzl
@@ -47,10 +47,10 @@ alias(
 )
 """
 
+# Local build results in dynamic link and the license should not be included.
 _NCCL_LOCAL_BUILD_TEMPLATE = """
 filegroup(
   name = "LICENSE",
-  data = ["nccl/NCCL-SLA.txt"],
   visibility = ["//visibility:public"],
 )
 
diff --git a/third_party/repo.bzl b/third_party/repo.bzl
index 9cee1fcc4b5c2b05ecc09b4f372eadeca9e91be8..5cb42691c5c29c64df738acd0ee35d82017995e6 100644
--- a/third_party/repo.bzl
+++ b/third_party/repo.bzl
@@ -35,6 +35,15 @@ def _get_env_var(ctx, name):
   else:
     return None
 
+# Checks if we should use the system lib instead of the bundled one
+def _use_system_lib(ctx, name):
+  syslibenv = _get_env_var(ctx, "TF_SYSTEM_LIBS")
+  if syslibenv:
+    for n in syslibenv.strip().split(","):
+      if n.strip() == name:
+        return True
+  return False
+
 # Executes specified command with arguments and calls 'fail' if it exited with
 # non-zero code
 def _execute_and_check_ret_code(repo_ctx, cmd_and_args):
@@ -75,17 +84,28 @@ def _tf_http_archive(ctx):
          "Even if you don't have permission to mirror the file, please " +
          "put the correctly formatted mirror URL there anyway, because " +
          "someone will come along shortly thereafter and mirror the file.")
-  ctx.download_and_extract(
-      ctx.attr.urls,
-      "",
-      ctx.attr.sha256,
-      ctx.attr.type,
-      ctx.attr.strip_prefix)
-  if ctx.attr.delete:
-    _apply_delete(ctx, ctx.attr.delete)
-  if ctx.attr.patch_file != None:
-    _apply_patch(ctx, ctx.attr.patch_file)
-  if ctx.attr.build_file != None:
+
+  use_syslib = _use_system_lib(ctx, ctx.attr.name)
+  if not use_syslib:
+    ctx.download_and_extract(
+        ctx.attr.urls,
+        "",
+        ctx.attr.sha256,
+        ctx.attr.type,
+        ctx.attr.strip_prefix)
+    if ctx.attr.delete:
+      _apply_delete(ctx, ctx.attr.delete)
+    if ctx.attr.patch_file != None:
+      _apply_patch(ctx, ctx.attr.patch_file)
+
+  if use_syslib and ctx.attr.system_build_file != None:
+    # Use BUILD.bazel to avoid conflict with third party projects with
+    # BUILD or build (directory) underneath.
+    ctx.template("BUILD.bazel", ctx.attr.system_build_file, {
+        "%prefix%": ".." if _repos_are_siblings() else "external",
+    }, False)
+
+  elif ctx.attr.build_file != None:
     # Use BUILD.bazel to avoid conflict with third party projects with
     # BUILD or build (directory) underneath.
     ctx.template("BUILD.bazel", ctx.attr.build_file, {
@@ -102,7 +122,11 @@ tf_http_archive = repository_rule(
         "delete": attr.string_list(),
         "patch_file": attr.label(),
         "build_file": attr.label(),
-    })
+        "system_build_file": attr.label(),
+    },
+    environ=[
+	"TF_SYSTEM_LIBS",
+    ])
 """Downloads and creates Bazel repos for dependencies.
 
 This is a swappable replacement for both http_archive() and
diff --git a/third_party/systemlibs/BUILD b/third_party/systemlibs/BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/third_party/systemlibs/BUILD.tpl b/third_party/systemlibs/BUILD.tpl
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/third_party/systemlibs/astor.BUILD b/third_party/systemlibs/astor.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..497ec4bcea9fff658657685bcf6a7e33b320f15e
--- /dev/null
+++ b/third_party/systemlibs/astor.BUILD
@@ -0,0 +1,12 @@
+licenses(["notice"])  # New BSD
+
+filegroup(
+    name = "LICENSE",
+    visibility = ["//visibility:public"],
+)
+
+py_library(
+    name = "astor",
+    srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/build_defs.bzl.tpl b/third_party/systemlibs/build_defs.bzl.tpl
new file mode 100644
index 0000000000000000000000000000000000000000..3faa46c581418c64ce5d4b63cdd40d9e14e87001
--- /dev/null
+++ b/third_party/systemlibs/build_defs.bzl.tpl
@@ -0,0 +1,32 @@
+# -*- Python -*-
+"""Skylark macros for system libraries.
+"""
+
+SYSTEM_LIBS_ENABLED = %{syslibs_enabled}
+
+SYSTEM_LIBS_LIST = [
+%{syslibs_list}
+]
+
+
+def if_any_system_libs(a, b=[]):
+  """Conditional which evaluates to 'a' if any system libraries are configured."""
+  if SYSTEM_LIBS_ENABLED:
+    return a
+  else:
+    return b
+
+
+def if_system_lib(lib, a, b=[]):
+  """Conditional which evaluates to 'a' if we're using the system version of lib"""
+
+  if SYSTEM_LIBS_ENABLED and lib in SYSTEM_LIBS_LIST:
+    return a
+  else:
+    return b
+
+
+def if_not_system_lib(lib, a, b=[]):
+  """Conditional which evaluates to 'a' if we're using the system version of lib"""
+
+  return if_system_lib(lib, b, a)
diff --git a/third_party/systemlibs/curl.BUILD b/third_party/systemlibs/curl.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..c5f125caa9eb46d99237c26151383d199e39d7d2
--- /dev/null
+++ b/third_party/systemlibs/curl.BUILD
@@ -0,0 +1,12 @@
+licenses(["notice"])  # MIT/X derivative license
+
+filegroup(
+    name = "COPYING",
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "curl",
+    linkopts = ["-lcurl"],
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/cython.BUILD b/third_party/systemlibs/cython.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..1d525876765a2ca9db152e226fb7c136aea33ae7
--- /dev/null
+++ b/third_party/systemlibs/cython.BUILD
@@ -0,0 +1,13 @@
+licenses(["notice"])  # Apache-2.0
+
+genrule(
+    name = "lncython",
+    outs = ["cython"],
+    cmd = "ln -s $$(which cython) $@",
+)
+
+sh_binary(
+    name = "cython_binary",
+    srcs = ["cython"],
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/flatbuffers.BUILD b/third_party/systemlibs/flatbuffers.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..14fceada8261b09f3e8ea8e839f266ed7b9494cb
--- /dev/null
+++ b/third_party/systemlibs/flatbuffers.BUILD
@@ -0,0 +1,38 @@
+licenses(["notice"])  # Apache 2.0
+
+filegroup(
+    name = "LICENSE.txt",
+    visibility = ["//visibility:public"],
+)
+
+# Public flatc library to compile flatbuffer files at runtime.
+cc_library(
+    name = "flatbuffers",
+    linkopts = ["-lflatbuffers"],
+    visibility = ["//visibility:public"],
+)
+
+# Public flatc compiler library.
+cc_library(
+    name = "flatc_library",
+    linkopts = ["-lflatbuffers"],
+    visibility = ["//visibility:public"],
+)
+
+genrule(
+    name = "lnflatc",
+    outs = ["flatc.bin"],
+    cmd = "ln -s $$(which flatc) $@",
+)
+
+# Public flatc compiler.
+sh_binary(
+    name = "flatc",
+    srcs = ["flatc.bin"],
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "runtime_cc",
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/gif.BUILD b/third_party/systemlibs/gif.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..5eb2c918ba443fdb6e8ad1604e0ec2380b427834
--- /dev/null
+++ b/third_party/systemlibs/gif.BUILD
@@ -0,0 +1,12 @@
+licenses(["notice"])  # MIT
+
+filegroup(
+    name = "COPYING",
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "gif",
+    linkopts = ["-lgif"],
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/grpc.BUILD b/third_party/systemlibs/grpc.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..fd90eb0dd3d581460267de315c8563d0e5ac4fca
--- /dev/null
+++ b/third_party/systemlibs/grpc.BUILD
@@ -0,0 +1,54 @@
+licenses(["notice"])  # Apache v2
+
+filegroup(
+    name = "LICENSE",
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "grpc",
+    linkopts = ["-lgrpc"],
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "grpc++",
+    linkopts = ["-lgrpc++"],
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "grpc_unsecure",
+    linkopts = ["-lgrpc_unsecure"],
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "grpc++_unsecure",
+    linkopts = ["-lgrpc++_unsecure"],
+    visibility = ["//visibility:public"],
+)
+
+genrule(
+    name = "ln_grpc_cpp_plugin",
+    outs = ["grpc_cpp_plugin.bin"],
+    cmd = "ln -s $$(which grpc_cpp_plugin) $@",
+)
+
+sh_binary(
+    name = "grpc_cpp_plugin",
+    srcs = ["grpc_cpp_plugin.bin"],
+    visibility = ["//visibility:public"],
+)
+
+genrule(
+    name = "ln_grpc_python_plugin",
+    outs = ["grpc_python_plugin.bin"],
+    cmd = "ln -s $$(which grpc_python_plugin) $@",
+)
+
+sh_binary(
+    name = "grpc_python_plugin",
+    srcs = ["grpc_python_plugin.bin"],
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/jemalloc.BUILD b/third_party/systemlibs/jemalloc.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..6a48d582ba4b525f55796e04e8e1fffe842a5507
--- /dev/null
+++ b/third_party/systemlibs/jemalloc.BUILD
@@ -0,0 +1,30 @@
+licenses(["notice"])  # BSD
+
+filegroup(
+    name = "COPYING",
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "jemalloc_headers",
+    defines = [
+        "jemalloc_posix_memalign=posix_memalign",
+        "jemalloc_malloc=malloc",
+        "jemalloc_realloc=realloc",
+        "jemalloc_free=free",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "jemalloc_impl",
+    linkopts = ["-ljemalloc"],
+    defines = [
+        "jemalloc_posix_memalign=posix_memalign",
+        "jemalloc_malloc=malloc",
+        "jemalloc_realloc=realloc",
+        "jemalloc_free=free",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [":jemalloc_headers"],
+)
diff --git a/third_party/systemlibs/jpeg.BUILD b/third_party/systemlibs/jpeg.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..f4f52da9bdae1bebad0f9eb7ff7f4b7db8b86c72
--- /dev/null
+++ b/third_party/systemlibs/jpeg.BUILD
@@ -0,0 +1,12 @@
+licenses(["notice"])  # custom notice-style license, see LICENSE.md
+
+filegroup(
+    name = "LICENSE.md",
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "jpeg",
+    linkopts = ["-ljpeg"],
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/jsoncpp.BUILD b/third_party/systemlibs/jsoncpp.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..cf91917cfb42d26af30940aade1512c105d35967
--- /dev/null
+++ b/third_party/systemlibs/jsoncpp.BUILD
@@ -0,0 +1,37 @@
+licenses(["unencumbered"])  # Public Domain or MIT
+
+filegroup(
+    name = "LICENSE",
+    visibility = ["//visibility:public"],
+)
+
+HEADERS = [
+    "include/json/autolink.h",
+    "include/json/config.h",
+    "include/json/features.h",
+    "include/json/forwards.h",
+    "include/json/json.h",
+    "include/json/reader.h",
+    "include/json/value.h",
+    "include/json/version.h",
+    "include/json/writer.h",
+]
+
+genrule(
+    name = "link_headers",
+    outs = HEADERS,
+    cmd = """
+      for i in $(OUTS); do
+        i=$${i##*/}
+        ln -vsf /usr/include/jsoncpp/json/$$i $(@D)/include/json/$$i
+      done
+    """,
+)
+
+cc_library(
+    name = "jsoncpp",
+    hdrs = HEADERS,
+    includes = ["."],
+    linkopts = ["-ljsoncpp"],
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/lmdb.BUILD b/third_party/systemlibs/lmdb.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..6177b095ec7acadb4cc10504e91c554e5d326186
--- /dev/null
+++ b/third_party/systemlibs/lmdb.BUILD
@@ -0,0 +1,12 @@
+licenses(["notice"])  # OpenLDAP Public License
+
+filegroup(
+    name = "LICENSE",
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "lmdb",
+    linkopts = ["-llmdb"],
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/nasm.BUILD b/third_party/systemlibs/nasm.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..10ef8d88320538dcdad90bdeaf32aaadafaaa738
--- /dev/null
+++ b/third_party/systemlibs/nasm.BUILD
@@ -0,0 +1,12 @@
+licenses(["notice"])  # BSD 2-clause
+
+filegroup(
+    name = "LICENSE",
+    visibility = ["//visibility:public"],
+)
+
+sh_binary(
+    name = "nasm",
+    srcs = ["nasm"],
+    visibility = ["@jpeg//:__pkg__"],
+)
diff --git a/third_party/systemlibs/pcre.BUILD b/third_party/systemlibs/pcre.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..df7423884740df329490dc0365cdfcd919c16327
--- /dev/null
+++ b/third_party/systemlibs/pcre.BUILD
@@ -0,0 +1,12 @@
+licenses(["notice"])  # BSD
+
+filegroup(
+    name = "LICENCE",
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "pcre",
+    linkopts = ["-lpcre"],
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/png.BUILD b/third_party/systemlibs/png.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..fc6b6f2d8bb0f87d93165db3ed849457d30c0a87
--- /dev/null
+++ b/third_party/systemlibs/png.BUILD
@@ -0,0 +1,12 @@
+licenses(["notice"])  # BSD/MIT-like license
+
+filegroup(
+    name = "LICENSE",
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "png",
+    linkopts = ["-lpng"],
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/re2.BUILD b/third_party/systemlibs/re2.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..c18e252dbc83300105ca31b078f672920c4e9d8e
--- /dev/null
+++ b/third_party/systemlibs/re2.BUILD
@@ -0,0 +1,12 @@
+licenses(["notice"])  # BSD/MIT-like license
+
+filegroup(
+    name = "LICENSE",
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "re2",
+    linkopts = ["-lre2"],
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/six.BUILD b/third_party/systemlibs/six.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..ff9b1a540b224bb06284ab366b16617a167385ac
--- /dev/null
+++ b/third_party/systemlibs/six.BUILD
@@ -0,0 +1,11 @@
+licenses(["notice"])  # MIT
+
+filegroup(
+    name = "LICENSE",
+    visibility = ["//visibility:public"],
+)
+
+py_library(
+    name = "six",
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/snappy.BUILD b/third_party/systemlibs/snappy.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..fd2db9e2df6752894775c3540406e9df81570e22
--- /dev/null
+++ b/third_party/systemlibs/snappy.BUILD
@@ -0,0 +1,12 @@
+licenses(["notice"])  # BSD 3-Clause
+
+filegroup(
+    name = "COPYING",
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "snappy",
+    linkopts = ["-lsnappy"],
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/sqlite.BUILD b/third_party/systemlibs/sqlite.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..20ee1ebbefcc79abbccbc0c157d4a8b330a24743
--- /dev/null
+++ b/third_party/systemlibs/sqlite.BUILD
@@ -0,0 +1,15 @@
+licenses(["unencumbered"])  # Public Domain
+
+# Production build of SQLite library that's baked into TensorFlow.
+cc_library(
+    name = "org_sqlite",
+    linkopts = ["-lsqlite3"],
+    visibility = ["//visibility:public"],
+)
+
+# This is a Copybara sync helper for Google.
+py_library(
+    name = "python",
+    srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/swig.BUILD b/third_party/systemlibs/swig.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..4c9b74dadbc0864aa67a5de53b7b91a982cb3196
--- /dev/null
+++ b/third_party/systemlibs/swig.BUILD
@@ -0,0 +1,23 @@
+licenses(["restricted"])  # GPLv3
+
+filegroup(
+    name = "LICENSE",
+    visibility = ["//visibility:public"],
+)
+
+filegroup(
+    name = "templates",
+    visibility = ["//visibility:public"],
+)
+
+genrule(
+    name = "lnswiglink",
+    outs = ["swiglink"],
+    cmd = "ln -s $$(which swig) $@",
+)
+
+sh_binary(
+    name = "swig",
+    srcs = ["swiglink"],
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/syslibs_configure.bzl b/third_party/systemlibs/syslibs_configure.bzl
new file mode 100644
index 0000000000000000000000000000000000000000..07a44c317e248a3d09125b4e1c29e276a9730952
--- /dev/null
+++ b/third_party/systemlibs/syslibs_configure.bzl
@@ -0,0 +1,160 @@
+# -*- Python -*-
+"""Repository rule for system library autoconfiguration.
+
+`syslibs_configure` depends on the following environment variables:
+
+  * `TF_SYSTEM_LIBS`: list of third party dependencies that should use
+    the system version instead
+"""
+
+_TF_SYSTEM_LIBS="TF_SYSTEM_LIBS"
+
+VALID_LIBS=[
+    "astor_archive",
+    "com_googlesource_code_re2",
+    "curl",
+    "cython",
+    "flatbuffers",
+    "gif_archive",
+    "grpc",
+    "jemalloc",
+    "jpeg",
+    "jsoncpp_git",
+    "lmdb",
+    "nasm",
+    "org_sqlite",
+    "pcre",
+    "png_archive",
+    "six_archive",
+    "snappy",
+    "swig",
+    "termcolor_archive",
+    "zlib_archive",
+]
+
+
+def auto_configure_fail(msg):
+  """Output failure message when syslibs configuration fails."""
+  red = "\033[0;31m"
+  no_color = "\033[0m"
+  fail("\n%sSystem Library Configuration Error:%s %s\n" % (red, no_color, msg))
+
+
+def _is_windows(repository_ctx):
+  """Returns true if the host operating system is windows."""
+  os_name = repository_ctx.os.name.lower()
+  if os_name.find("windows") != -1:
+    return True
+  return False
+
+
+def _enable_syslibs(repository_ctx):
+  s = repository_ctx.os.environ.get(_TF_SYSTEM_LIBS, '').strip()
+  if not _is_windows(repository_ctx) and s != None and s != '':
+    return True
+  return False
+
+
+def _get_system_lib_list(repository_ctx):
+  """Gets the list of deps that should use the system lib.
+
+  Args:
+    repository_ctx: The repository context.
+
+  Returns:
+    A string version of a python list
+  """
+  if _TF_SYSTEM_LIBS not in repository_ctx.os.environ:
+    return []
+
+  libenv = repository_ctx.os.environ[_TF_SYSTEM_LIBS].strip()
+  libs = []
+
+  for lib in list(libenv.split(',')):
+    lib = lib.strip()
+    if lib == "":
+      continue
+    if lib not in VALID_LIBS:
+      auto_configure_fail("Invalid system lib set: %s" % lib)
+      return []
+    libs.append(lib)
+
+  return libs
+
+
+def _format_system_lib_list(repository_ctx):
+  """Formats the list of deps that should use the system lib.
+
+  Args:
+    repository_ctx: The repository context.
+
+  Returns:
+    A list of the names of deps that should use the system lib.
+  """
+  libs = _get_system_lib_list(repository_ctx)
+  ret = ''
+  for lib in libs:
+    ret += "'%s',\n" % lib
+
+  return ret
+
+
+def _tpl(repository_ctx, tpl, substitutions={}, out=None):
+  if not out:
+    out = tpl.replace(":", "")
+  repository_ctx.template(
+      out,
+      Label("//third_party/systemlibs%s.tpl" % tpl),
+      substitutions,
+      False)
+
+
+def _create_dummy_repository(repository_ctx):
+  """Creates the dummy repository to build with all bundled libraries."""
+
+  _tpl(repository_ctx, ":BUILD")
+  _tpl(repository_ctx, ":build_defs.bzl",
+    {
+      "%{syslibs_enabled}": 'False',
+      "%{syslibs_list}": '',
+    })
+
+
+def _create_local_repository(repository_ctx):
+  """Creates the repository to build with system libraries."""
+
+  _tpl(repository_ctx, ":BUILD")
+  _tpl(repository_ctx, ":build_defs.bzl",
+    {
+      "%{syslibs_enabled}": 'True',
+      "%{syslibs_list}": _format_system_lib_list(repository_ctx),
+    })
+
+
+def _syslibs_autoconf_impl(repository_ctx):
+  """Implementation of the syslibs_configure repository rule."""
+  if not _enable_syslibs(repository_ctx):
+    _create_dummy_repository(repository_ctx)
+  else:
+    _create_local_repository(repository_ctx)
+
+
+syslibs_configure = repository_rule(
+    implementation = _syslibs_autoconf_impl,
+    environ = [
+        _TF_SYSTEM_LIBS,
+    ],
+)
+
+"""Configures the build to link to system libraries
+instead of using bundled versions.
+
+Add the following to your WORKSPACE FILE:
+
+```python
+syslibs_configure(name = "local_config_syslibs")
+```
+
+Args:
+  name: A unique name for this workspace rule.
+"""
diff --git a/third_party/systemlibs/termcolor.BUILD b/third_party/systemlibs/termcolor.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..915eb621d5cd6012cdded3edd117f47292030197
--- /dev/null
+++ b/third_party/systemlibs/termcolor.BUILD
@@ -0,0 +1,12 @@
+licenses(["notice"])  # MIT
+
+filegroup(
+    name = "COPYING.txt",
+    visibility = ["//visibility:public"],
+)
+
+py_library(
+    name = "termcolor",
+    srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/systemlibs/zlib.BUILD b/third_party/systemlibs/zlib.BUILD
new file mode 100644
index 0000000000000000000000000000000000000000..69462ae6cbc2fa798aec3df1701bb6c4e3ea48f5
--- /dev/null
+++ b/third_party/systemlibs/zlib.BUILD
@@ -0,0 +1,12 @@
+licenses(["notice"])  # BSD/MIT-like license (for zlib)
+
+filegroup(
+    name = "zlib.h",
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "zlib",
+    linkopts = ["-lz"],
+    visibility = ["//visibility:public"],
+)
diff --git a/third_party/toolchains/BUILD b/third_party/toolchains/BUILD
index fc3183a754369fc30dbce40c2bf7b6828ea497c3..ec1006fe23567983785be7b8f15a3f44dcb47900 100644
--- a/third_party/toolchains/BUILD
+++ b/third_party/toolchains/BUILD
@@ -17,6 +17,6 @@ platform(
     remote_execution_properties = """
         properties: {
             name: "container-image"
-            value:"docker://gcr.io/asci-toolchain/nosla-ubuntu16_04-tf@sha256:800a7b68cabef15419695c188ed33ed70adf678c2371b97b236f3ae26c38274d"
+            value:"docker://gcr.io/asci-toolchain/nosla-ubuntu16_04-tf@sha256:495a025ed5e273cfa5d53357ef93ac20500c008994e0be106c509f51555fb93c"
         }""",
 )
diff --git a/third_party/toolchains/cpus/py/BUILD b/third_party/toolchains/cpus/py/BUILD
index c175742cbfe918e55035e89b7454596acd43307e..1235988abb7fa9982b26f470b52b88d40b989c26 100644
--- a/third_party/toolchains/cpus/py/BUILD
+++ b/third_party/toolchains/cpus/py/BUILD
@@ -6,18 +6,24 @@ licenses(["restricted"])
 
 package(default_visibility = ["//visibility:public"])
 
+# To build Python C/C++ extension on Windows, we need to link to python import library pythonXY.lib
+# See https://docs.python.org/3/extending/windows.html
+cc_import(
+    name = "python_lib",
+    interface_library = select({
+        ":windows": ":python_import_lib",
+        # A placeholder for Unix platforms which makes --no_build happy.
+        "//conditions:default": "not-existing.lib",
+    }),
+    system_provided = 1,
+)
+
 cc_library(
     name = "python_headers",
     hdrs = [":python_include"],
-    data = select({
-        ":windows": [":python_import_lib"],
-        "//conditions:default": [],
-    }),
     includes = ["python_include"],
-    linkopts = select({
-        # TODO(pcloudy): Ideally, this should just go into deps after resolving
-        # https://github.com/bazelbuild/bazel/issues/3237,
-        ":windows": ["$(locations :python_import_lib)"],
+    deps = select({
+        ":windows": [":python_lib"],
         "//conditions:default": [],
     }),
 )
@@ -37,161 +43,135 @@ config_setting(
 genrule(
     name = "python_include",
     outs = [
+        "python_include/Python-ast.h",
+        "python_include/Python.h",
+        "python_include/abstract.h",
+        "python_include/asdl.h",
+        "python_include/ast.h",
+        "python_include/bitset.h",
+        "python_include/boolobject.h",
+        "python_include/bufferobject.h",
+        "python_include/bytearrayobject.h",
+        "python_include/bytes_methods.h",
+        "python_include/bytesobject.h",
+        "python_include/cStringIO.h",
+        "python_include/cellobject.h",
+        "python_include/ceval.h",
+        "python_include/classobject.h",
+        "python_include/cobject.h",
         "python_include/code.h",
+        "python_include/codecs.h",
+        "python_include/compile.h",
+        "python_include/complexobject.h",
+        "python_include/datetime.h",
+        "python_include/descrobject.h",
+        "python_include/dictobject.h",
         "python_include/dtoa.h",
-        "python_include/tupleobject.h",
-        "python_include/object.h",
-        "python_include/ast.h",
-        "python_include/pymacconfig.h",
+        "python_include/enumobject.h",
         "python_include/errcode.h",
+        "python_include/eval.h",
+        "python_include/fileobject.h",
+        "python_include/floatobject.h",
         "python_include/frameobject.h",
-        "python_include/pgenheaders.h",
-        "python_include/cellobject.h",
+        "python_include/funcobject.h",
+        "python_include/genobject.h",
+        "python_include/graminit.h",
+        "python_include/grammar.h",
+        "python_include/import.h",
         "python_include/intobject.h",
-        "python_include/pythread.h",
-        "python_include/cStringIO.h",
-        "python_include/boolobject.h",
+        "python_include/intrcheck.h",
+        "python_include/iterobject.h",
+        "python_include/listobject.h",
+        "python_include/longintrepr.h",
+        "python_include/longobject.h",
+        "python_include/marshal.h",
+        "python_include/memoryobject.h",
+        "python_include/metagrammar.h",
+        "python_include/methodobject.h",
         "python_include/modsupport.h",
-        "python_include/import.h",
-        "python_include/pymath.h",
+        "python_include/moduleobject.h",
         "python_include/node.h",
-        "python_include/funcobject.h",
-        "python_include/eval.h",
-        "python_include/longintrepr.h",
-        "python_include/floatobject.h",
-        "python_include/rangeobject.h",
-        "python_include/pyfpe.h",
-        "python_include/pystrcmp.h",
-        "python_include/dictobject.h",
-        "python_include/pyarena.h",
+        "python_include/object.h",
         "python_include/objimpl.h",
-        "python_include/bitset.h",
-        "python_include/memoryobject.h",
-        "python_include/bytearrayobject.h",
+        "python_include/opcode.h",
+        "python_include/osdefs.h",
+        "python_include/parsetok.h",
+        "python_include/patchlevel.h",
+        "python_include/pgen.h",
+        "python_include/pgenheaders.h",
+        "python_include/py_curses.h",
+        "python_include/pyarena.h",
+        "python_include/pycapsule.h",
+        "python_include/pyconfig.h",
+        "python_include/pyctype.h",
         "python_include/pydebug.h",
         "python_include/pyerrors.h",
-        "python_include/weakrefobject.h",
-        "python_include/grammar.h",
-        "python_include/symtable.h",
-        "python_include/longobject.h",
-        "python_include/structmember.h",
-        "python_include/enumobject.h",
-        "python_include/classobject.h",
-        "python_include/unicodeobject.h",
-        "python_include/sliceobject.h",
-        "python_include/pystrtod.h",
-        "python_include/genobject.h",
-        "python_include/pymactoolbox.h",
-        "python_include/compile.h",
         "python_include/pyexpat.h",
-        "python_include/asdl.h",
-        "python_include/codecs.h",
-        "python_include/pyctype.h",
-        "python_include/sysmodule.h",
-        "python_include/methodobject.h",
-        "python_include/graminit.h",
-        "python_include/cobject.h",
-        "python_include/intrcheck.h",
-        "python_include/pyport.h",
-        "python_include/warnings.h",
-        "python_include/osdefs.h",
-        "python_include/fileobject.h",
-        "python_include/stringobject.h",
-        "python_include/timefuncs.h",
-        "python_include/traceback.h",
-        "python_include/ceval.h",
-        "python_include/bytes_methods.h",
-        "python_include/pyconfig.h",
-        "python_include/Python.h",
-        "python_include/moduleobject.h",
-        "python_include/pystate.h",
-        "python_include/descrobject.h",
-        "python_include/ucnhash.h",
+        "python_include/pyfpe.h",
         "python_include/pygetopt.h",
+        "python_include/pymacconfig.h",
+        "python_include/pymactoolbox.h",
+        "python_include/pymath.h",
         "python_include/pymem.h",
-        "python_include/complexobject.h",
-        "python_include/structseq.h",
-        "python_include/datetime.h",
+        "python_include/pyport.h",
+        "python_include/pystate.h",
+        "python_include/pystrcmp.h",
+        "python_include/pystrtod.h",
         "python_include/pythonrun.h",
-        "python_include/numpy/oldnumeric.h",
-        "python_include/numpy/npy_1_7_deprecated_api.h",
-        "python_include/numpy/ufunc_api.txt",
-        "python_include/numpy/multiarray_api.txt",
-        "python_include/numpy/halffloat.h",
-        "python_include/numpy/npy_common.h",
-        "python_include/numpy/utils.h",
-        "python_include/numpy/npy_interrupt.h",
-        "python_include/numpy/npy_endian.h",
-        "python_include/numpy/__ufunc_api.h",
-        "python_include/numpy/_neighborhood_iterator_imp.h",
-        "python_include/numpy/ufuncobject.h",
-        "python_include/numpy/ndarraytypes.h",
-        "python_include/numpy/npy_math.h",
-        "python_include/numpy/noprefix.h",
-        "python_include/numpy/npy_3kcompat.h",
-        "python_include/numpy/arrayscalars.h",
-        "python_include/numpy/npy_os.h",
-        "python_include/numpy/ndarrayobject.h",
-        "python_include/numpy/npy_no_deprecated_api.h",
-        "python_include/numpy/arrayobject.h",
-        "python_include/numpy/_numpyconfig.h",
-        "python_include/numpy/__multiarray_api.h",
-        "python_include/numpy/npy_cpu.h",
-        "python_include/numpy/old_defines.h",
-        "python_include/numpy/numpyconfig.h",
-        "python_include/pycapsule.h",
+        "python_include/pythread.h",
+        "python_include/rangeobject.h",
         "python_include/setobject.h",
-        "python_include/listobject.h",
-        "python_include/bytesobject.h",
-        "python_include/pgen.h",
-        "python_include/patchlevel.h",
-        "python_include/opcode.h",
-        "python_include/parsetok.h",
-        "python_include/marshal.h",
+        "python_include/sliceobject.h",
+        "python_include/stringobject.h",
+        "python_include/structmember.h",
+        "python_include/structseq.h",
+        "python_include/symtable.h",
+        "python_include/sysmodule.h",
+        "python_include/timefuncs.h",
         "python_include/token.h",
-        "python_include/iterobject.h",
-        "python_include/abstract.h",
-        "python_include/py_curses.h",
-        "python_include/metagrammar.h",
-        "python_include/bufferobject.h",
-        "python_include/Python-ast.h",
+        "python_include/traceback.h",
+        "python_include/tupleobject.h",
+        "python_include/ucnhash.h",
+        "python_include/unicodeobject.h",
+        "python_include/warnings.h",
+        "python_include/weakrefobject.h",
     ],
     cmd = """
-cp "/usr/include/python2.7/code.h" "$(@D)/python_include/code.h" && cp "/usr/include/python2.7/dtoa.h" "$(@D)/python_include/dtoa.h" && cp "/usr/include/python2.7/tupleobject.h" "$(@D)/python_include/tupleobject.h" && cp "/usr/include/python2.7/object.h" "$(@D)/python_include/object.h" && cp "/usr/include/python2.7/ast.h" "$(@D)/python_include/ast.h" && cp "/usr/include/python2.7/pymacconfig.h" "$(@D)/python_include/pymacconfig.h" && cp "/usr/include/python2.7/errcode.h" "$(@D)/python_include/errcode.h" && cp "/usr/include/python2.7/frameobject.h" "$(@D)/python_include/frameobject.h" && cp "/usr/include/python2.7/pgenheaders.h" "$(@D)/python_include/pgenheaders.h" && cp "/usr/include/python2.7/cellobject.h" "$(@D)/python_include/cellobject.h" && cp "/usr/include/python2.7/intobject.h" "$(@D)/python_include/intobject.h" && cp "/usr/include/python2.7/pythread.h" "$(@D)/python_include/pythread.h" && cp "/usr/include/python2.7/cStringIO.h" "$(@D)/python_include/cStringIO.h" && cp "/usr/include/python2.7/boolobject.h" "$(@D)/python_include/boolobject.h" && cp "/usr/include/python2.7/modsupport.h" "$(@D)/python_include/modsupport.h" && cp "/usr/include/python2.7/import.h" "$(@D)/python_include/import.h" && cp "/usr/include/python2.7/pymath.h" "$(@D)/python_include/pymath.h" && cp "/usr/include/python2.7/node.h" "$(@D)/python_include/node.h" && cp "/usr/include/python2.7/funcobject.h" "$(@D)/python_include/funcobject.h" && cp "/usr/include/python2.7/eval.h" "$(@D)/python_include/eval.h" && cp "/usr/include/python2.7/longintrepr.h" "$(@D)/python_include/longintrepr.h" && cp "/usr/include/python2.7/floatobject.h" "$(@D)/python_include/floatobject.h" && cp "/usr/include/python2.7/rangeobject.h" "$(@D)/python_include/rangeobject.h" && cp "/usr/include/python2.7/pyfpe.h" "$(@D)/python_include/pyfpe.h" && cp "/usr/include/python2.7/pystrcmp.h" "$(@D)/python_include/pystrcmp.h" && cp "/usr/include/python2.7/dictobject.h" "$(@D)/python_include/dictobject.h" && cp "/usr/include/python2.7/pyarena.h" "$(@D)/python_include/pyarena.h" && cp "/usr/include/python2.7/objimpl.h" "$(@D)/python_include/objimpl.h" && cp "/usr/include/python2.7/bitset.h" "$(@D)/python_include/bitset.h" && cp "/usr/include/python2.7/memoryobject.h" "$(@D)/python_include/memoryobject.h" && cp "/usr/include/python2.7/bytearrayobject.h" "$(@D)/python_include/bytearrayobject.h" && cp "/usr/include/python2.7/pydebug.h" "$(@D)/python_include/pydebug.h" && cp "/usr/include/python2.7/pyerrors.h" "$(@D)/python_include/pyerrors.h" && cp "/usr/include/python2.7/weakrefobject.h" "$(@D)/python_include/weakrefobject.h" && cp "/usr/include/python2.7/grammar.h" "$(@D)/python_include/grammar.h" && cp "/usr/include/python2.7/symtable.h" "$(@D)/python_include/symtable.h" && cp "/usr/include/python2.7/longobject.h" "$(@D)/python_include/longobject.h" && cp "/usr/include/python2.7/structmember.h" "$(@D)/python_include/structmember.h" && cp "/usr/include/python2.7/enumobject.h" "$(@D)/python_include/enumobject.h" && cp "/usr/include/python2.7/classobject.h" "$(@D)/python_include/classobject.h" && cp "/usr/include/python2.7/unicodeobject.h" "$(@D)/python_include/unicodeobject.h" && cp "/usr/include/python2.7/sliceobject.h" "$(@D)/python_include/sliceobject.h" && cp "/usr/include/python2.7/pystrtod.h" "$(@D)/python_include/pystrtod.h" && cp "/usr/include/python2.7/genobject.h" "$(@D)/python_include/genobject.h" && cp "/usr/include/python2.7/pymactoolbox.h" "$(@D)/python_include/pymactoolbox.h" && cp "/usr/include/python2.7/compile.h" "$(@D)/python_include/compile.h" && cp "/usr/include/python2.7/pyexpat.h" "$(@D)/python_include/pyexpat.h" && cp "/usr/include/python2.7/asdl.h" "$(@D)/python_include/asdl.h" && cp "/usr/include/python2.7/codecs.h" "$(@D)/python_include/codecs.h" && cp "/usr/include/python2.7/pyctype.h" "$(@D)/python_include/pyctype.h" && cp "/usr/include/python2.7/sysmodule.h" "$(@D)/python_include/sysmodule.h" && cp "/usr/include/python2.7/methodobject.h" "$(@D)/python_include/methodobject.h" && cp "/usr/include/python2.7/graminit.h" "$(@D)/python_include/graminit.h" && cp "/usr/include/python2.7/cobject.h" "$(@D)/python_include/cobject.h" && cp "/usr/include/python2.7/intrcheck.h" "$(@D)/python_include/intrcheck.h" && cp "/usr/include/python2.7/pyport.h" "$(@D)/python_include/pyport.h" && cp "/usr/include/python2.7/warnings.h" "$(@D)/python_include/warnings.h" && cp "/usr/include/python2.7/osdefs.h" "$(@D)/python_include/osdefs.h" && cp "/usr/include/python2.7/fileobject.h" "$(@D)/python_include/fileobject.h" && cp "/usr/include/python2.7/stringobject.h" "$(@D)/python_include/stringobject.h" && cp "/usr/include/python2.7/timefuncs.h" "$(@D)/python_include/timefuncs.h" && cp "/usr/include/python2.7/traceback.h" "$(@D)/python_include/traceback.h" && cp "/usr/include/python2.7/ceval.h" "$(@D)/python_include/ceval.h" && cp "/usr/include/python2.7/bytes_methods.h" "$(@D)/python_include/bytes_methods.h" && cp "/usr/include/python2.7/pyconfig.h" "$(@D)/python_include/pyconfig.h" && cp "/usr/include/python2.7/Python.h" "$(@D)/python_include/Python.h" && cp "/usr/include/python2.7/moduleobject.h" "$(@D)/python_include/moduleobject.h" && cp "/usr/include/python2.7/pystate.h" "$(@D)/python_include/pystate.h" && cp "/usr/include/python2.7/descrobject.h" "$(@D)/python_include/descrobject.h" && cp "/usr/include/python2.7/ucnhash.h" "$(@D)/python_include/ucnhash.h" && cp "/usr/include/python2.7/pygetopt.h" "$(@D)/python_include/pygetopt.h" && cp "/usr/include/python2.7/pymem.h" "$(@D)/python_include/pymem.h" && cp "/usr/include/python2.7/complexobject.h" "$(@D)/python_include/complexobject.h" && cp "/usr/include/python2.7/structseq.h" "$(@D)/python_include/structseq.h" && cp "/usr/include/python2.7/datetime.h" "$(@D)/python_include/datetime.h" && cp "/usr/include/python2.7/pythonrun.h" "$(@D)/python_include/pythonrun.h" && cp "/usr/include/python2.7/numpy/oldnumeric.h" "$(@D)/python_include/numpy/oldnumeric.h" && cp "/usr/include/python2.7/numpy/npy_1_7_deprecated_api.h" "$(@D)/python_include/numpy/npy_1_7_deprecated_api.h" && cp "/usr/include/python2.7/numpy/ufunc_api.txt" "$(@D)/python_include/numpy/ufunc_api.txt" && cp "/usr/include/python2.7/numpy/multiarray_api.txt" "$(@D)/python_include/numpy/multiarray_api.txt" && cp "/usr/include/python2.7/numpy/halffloat.h" "$(@D)/python_include/numpy/halffloat.h" && cp "/usr/include/python2.7/numpy/npy_common.h" "$(@D)/python_include/numpy/npy_common.h" && cp "/usr/include/python2.7/numpy/utils.h" "$(@D)/python_include/numpy/utils.h" && cp "/usr/include/python2.7/numpy/npy_interrupt.h" "$(@D)/python_include/numpy/npy_interrupt.h" && cp "/usr/include/python2.7/numpy/npy_endian.h" "$(@D)/python_include/numpy/npy_endian.h" && cp "/usr/include/python2.7/numpy/__ufunc_api.h" "$(@D)/python_include/numpy/__ufunc_api.h" && cp "/usr/include/python2.7/numpy/_neighborhood_iterator_imp.h" "$(@D)/python_include/numpy/_neighborhood_iterator_imp.h" && cp "/usr/include/python2.7/numpy/ufuncobject.h" "$(@D)/python_include/numpy/ufuncobject.h" && cp "/usr/include/python2.7/numpy/ndarraytypes.h" "$(@D)/python_include/numpy/ndarraytypes.h" && cp "/usr/include/python2.7/numpy/npy_math.h" "$(@D)/python_include/numpy/npy_math.h" && cp "/usr/include/python2.7/numpy/noprefix.h" "$(@D)/python_include/numpy/noprefix.h" && cp "/usr/include/python2.7/numpy/npy_3kcompat.h" "$(@D)/python_include/numpy/npy_3kcompat.h" && cp "/usr/include/python2.7/numpy/arrayscalars.h" "$(@D)/python_include/numpy/arrayscalars.h" && cp "/usr/include/python2.7/numpy/npy_os.h" "$(@D)/python_include/numpy/npy_os.h" && cp "/usr/include/python2.7/numpy/ndarrayobject.h" "$(@D)/python_include/numpy/ndarrayobject.h" && cp "/usr/include/python2.7/numpy/npy_no_deprecated_api.h" "$(@D)/python_include/numpy/npy_no_deprecated_api.h" && cp "/usr/include/python2.7/numpy/arrayobject.h" "$(@D)/python_include/numpy/arrayobject.h" && cp "/usr/include/python2.7/numpy/_numpyconfig.h" "$(@D)/python_include/numpy/_numpyconfig.h" && cp "/usr/include/python2.7/numpy/__multiarray_api.h" "$(@D)/python_include/numpy/__multiarray_api.h" && cp "/usr/include/python2.7/numpy/npy_cpu.h" "$(@D)/python_include/numpy/npy_cpu.h" && cp "/usr/include/python2.7/numpy/old_defines.h" "$(@D)/python_include/numpy/old_defines.h" && cp "/usr/include/python2.7/numpy/numpyconfig.h" "$(@D)/python_include/numpy/numpyconfig.h" && cp "/usr/include/python2.7/pycapsule.h" "$(@D)/python_include/pycapsule.h" && cp "/usr/include/python2.7/setobject.h" "$(@D)/python_include/setobject.h" && cp "/usr/include/python2.7/listobject.h" "$(@D)/python_include/listobject.h" && cp "/usr/include/python2.7/bytesobject.h" "$(@D)/python_include/bytesobject.h" && cp "/usr/include/python2.7/pgen.h" "$(@D)/python_include/pgen.h" && cp "/usr/include/python2.7/patchlevel.h" "$(@D)/python_include/patchlevel.h" && cp "/usr/include/python2.7/opcode.h" "$(@D)/python_include/opcode.h" && cp "/usr/include/python2.7/parsetok.h" "$(@D)/python_include/parsetok.h" && cp "/usr/include/python2.7/marshal.h" "$(@D)/python_include/marshal.h" && cp "/usr/include/python2.7/token.h" "$(@D)/python_include/token.h" && cp "/usr/include/python2.7/iterobject.h" "$(@D)/python_include/iterobject.h" && cp "/usr/include/python2.7/abstract.h" "$(@D)/python_include/abstract.h" && cp "/usr/include/python2.7/py_curses.h" "$(@D)/python_include/py_curses.h" && cp "/usr/include/python2.7/metagrammar.h" "$(@D)/python_include/metagrammar.h" && cp "/usr/include/python2.7/bufferobject.h" "$(@D)/python_include/bufferobject.h" && cp "/usr/include/python2.7/Python-ast.h" "$(@D)/python_include/Python-ast.h"
+cp "/usr/include/python2.7/Python-ast.h" "$(@D)/python_include/Python-ast.h" && cp "/usr/include/python2.7/Python.h" "$(@D)/python_include/Python.h" && cp "/usr/include/python2.7/abstract.h" "$(@D)/python_include/abstract.h" && cp "/usr/include/python2.7/asdl.h" "$(@D)/python_include/asdl.h" && cp "/usr/include/python2.7/ast.h" "$(@D)/python_include/ast.h" && cp "/usr/include/python2.7/bitset.h" "$(@D)/python_include/bitset.h" && cp "/usr/include/python2.7/boolobject.h" "$(@D)/python_include/boolobject.h" && cp "/usr/include/python2.7/bufferobject.h" "$(@D)/python_include/bufferobject.h" && cp "/usr/include/python2.7/bytearrayobject.h" "$(@D)/python_include/bytearrayobject.h" && cp "/usr/include/python2.7/bytes_methods.h" "$(@D)/python_include/bytes_methods.h" && cp "/usr/include/python2.7/bytesobject.h" "$(@D)/python_include/bytesobject.h" && cp "/usr/include/python2.7/cStringIO.h" "$(@D)/python_include/cStringIO.h" && cp "/usr/include/python2.7/cellobject.h" "$(@D)/python_include/cellobject.h" && cp "/usr/include/python2.7/ceval.h" "$(@D)/python_include/ceval.h" && cp "/usr/include/python2.7/classobject.h" "$(@D)/python_include/classobject.h" && cp "/usr/include/python2.7/cobject.h" "$(@D)/python_include/cobject.h" && cp "/usr/include/python2.7/code.h" "$(@D)/python_include/code.h" && cp "/usr/include/python2.7/codecs.h" "$(@D)/python_include/codecs.h" && cp "/usr/include/python2.7/compile.h" "$(@D)/python_include/compile.h" && cp "/usr/include/python2.7/complexobject.h" "$(@D)/python_include/complexobject.h" && cp "/usr/include/python2.7/datetime.h" "$(@D)/python_include/datetime.h" && cp "/usr/include/python2.7/descrobject.h" "$(@D)/python_include/descrobject.h" && cp "/usr/include/python2.7/dictobject.h" "$(@D)/python_include/dictobject.h" && cp "/usr/include/python2.7/dtoa.h" "$(@D)/python_include/dtoa.h" && cp "/usr/include/python2.7/enumobject.h" "$(@D)/python_include/enumobject.h" && cp "/usr/include/python2.7/errcode.h" "$(@D)/python_include/errcode.h" && cp "/usr/include/python2.7/eval.h" "$(@D)/python_include/eval.h" && cp "/usr/include/python2.7/fileobject.h" "$(@D)/python_include/fileobject.h" && cp "/usr/include/python2.7/floatobject.h" "$(@D)/python_include/floatobject.h" && cp "/usr/include/python2.7/frameobject.h" "$(@D)/python_include/frameobject.h" && cp "/usr/include/python2.7/funcobject.h" "$(@D)/python_include/funcobject.h" && cp "/usr/include/python2.7/genobject.h" "$(@D)/python_include/genobject.h" && cp "/usr/include/python2.7/graminit.h" "$(@D)/python_include/graminit.h" && cp "/usr/include/python2.7/grammar.h" "$(@D)/python_include/grammar.h" && cp "/usr/include/python2.7/import.h" "$(@D)/python_include/import.h" && cp "/usr/include/python2.7/intobject.h" "$(@D)/python_include/intobject.h" && cp "/usr/include/python2.7/intrcheck.h" "$(@D)/python_include/intrcheck.h" && cp "/usr/include/python2.7/iterobject.h" "$(@D)/python_include/iterobject.h" && cp "/usr/include/python2.7/listobject.h" "$(@D)/python_include/listobject.h" && cp "/usr/include/python2.7/longintrepr.h" "$(@D)/python_include/longintrepr.h" && cp "/usr/include/python2.7/longobject.h" "$(@D)/python_include/longobject.h" && cp "/usr/include/python2.7/marshal.h" "$(@D)/python_include/marshal.h" && cp "/usr/include/python2.7/memoryobject.h" "$(@D)/python_include/memoryobject.h" && cp "/usr/include/python2.7/metagrammar.h" "$(@D)/python_include/metagrammar.h" && cp "/usr/include/python2.7/methodobject.h" "$(@D)/python_include/methodobject.h" && cp "/usr/include/python2.7/modsupport.h" "$(@D)/python_include/modsupport.h" && cp "/usr/include/python2.7/moduleobject.h" "$(@D)/python_include/moduleobject.h" && cp "/usr/include/python2.7/node.h" "$(@D)/python_include/node.h" && cp "/usr/include/python2.7/object.h" "$(@D)/python_include/object.h" && cp "/usr/include/python2.7/objimpl.h" "$(@D)/python_include/objimpl.h" && cp "/usr/include/python2.7/opcode.h" "$(@D)/python_include/opcode.h" && cp "/usr/include/python2.7/osdefs.h" "$(@D)/python_include/osdefs.h" && cp "/usr/include/python2.7/parsetok.h" "$(@D)/python_include/parsetok.h" && cp "/usr/include/python2.7/patchlevel.h" "$(@D)/python_include/patchlevel.h" && cp "/usr/include/python2.7/pgen.h" "$(@D)/python_include/pgen.h" && cp "/usr/include/python2.7/pgenheaders.h" "$(@D)/python_include/pgenheaders.h" && cp "/usr/include/python2.7/py_curses.h" "$(@D)/python_include/py_curses.h" && cp "/usr/include/python2.7/pyarena.h" "$(@D)/python_include/pyarena.h" && cp "/usr/include/python2.7/pycapsule.h" "$(@D)/python_include/pycapsule.h" && cp "/usr/include/python2.7/pyconfig.h" "$(@D)/python_include/pyconfig.h" && cp "/usr/include/python2.7/pyctype.h" "$(@D)/python_include/pyctype.h" && cp "/usr/include/python2.7/pydebug.h" "$(@D)/python_include/pydebug.h" && cp "/usr/include/python2.7/pyerrors.h" "$(@D)/python_include/pyerrors.h" && cp "/usr/include/python2.7/pyexpat.h" "$(@D)/python_include/pyexpat.h" && cp "/usr/include/python2.7/pyfpe.h" "$(@D)/python_include/pyfpe.h" && cp "/usr/include/python2.7/pygetopt.h" "$(@D)/python_include/pygetopt.h" && cp "/usr/include/python2.7/pymacconfig.h" "$(@D)/python_include/pymacconfig.h" && cp "/usr/include/python2.7/pymactoolbox.h" "$(@D)/python_include/pymactoolbox.h" && cp "/usr/include/python2.7/pymath.h" "$(@D)/python_include/pymath.h" && cp "/usr/include/python2.7/pymem.h" "$(@D)/python_include/pymem.h" && cp "/usr/include/python2.7/pyport.h" "$(@D)/python_include/pyport.h" && cp "/usr/include/python2.7/pystate.h" "$(@D)/python_include/pystate.h" && cp "/usr/include/python2.7/pystrcmp.h" "$(@D)/python_include/pystrcmp.h" && cp "/usr/include/python2.7/pystrtod.h" "$(@D)/python_include/pystrtod.h" && cp "/usr/include/python2.7/pythonrun.h" "$(@D)/python_include/pythonrun.h" && cp "/usr/include/python2.7/pythread.h" "$(@D)/python_include/pythread.h" && cp "/usr/include/python2.7/rangeobject.h" "$(@D)/python_include/rangeobject.h" && cp "/usr/include/python2.7/setobject.h" "$(@D)/python_include/setobject.h" && cp "/usr/include/python2.7/sliceobject.h" "$(@D)/python_include/sliceobject.h" && cp "/usr/include/python2.7/stringobject.h" "$(@D)/python_include/stringobject.h" && cp "/usr/include/python2.7/structmember.h" "$(@D)/python_include/structmember.h" && cp "/usr/include/python2.7/structseq.h" "$(@D)/python_include/structseq.h" && cp "/usr/include/python2.7/symtable.h" "$(@D)/python_include/symtable.h" && cp "/usr/include/python2.7/sysmodule.h" "$(@D)/python_include/sysmodule.h" && cp "/usr/include/python2.7/timefuncs.h" "$(@D)/python_include/timefuncs.h" && cp "/usr/include/python2.7/token.h" "$(@D)/python_include/token.h" && cp "/usr/include/python2.7/traceback.h" "$(@D)/python_include/traceback.h" && cp "/usr/include/python2.7/tupleobject.h" "$(@D)/python_include/tupleobject.h" && cp "/usr/include/python2.7/ucnhash.h" "$(@D)/python_include/ucnhash.h" && cp "/usr/include/python2.7/unicodeobject.h" "$(@D)/python_include/unicodeobject.h" && cp "/usr/include/python2.7/warnings.h" "$(@D)/python_include/warnings.h" && cp "/usr/include/python2.7/weakrefobject.h" "$(@D)/python_include/weakrefobject.h"
    """,
 )
 
 genrule(
     name = "numpy_include",
     outs = [
-        "numpy_include/numpy/oldnumeric.h",
-        "numpy_include/numpy/npy_1_7_deprecated_api.h",
-        "numpy_include/numpy/ufunc_api.txt",
-        "numpy_include/numpy/multiarray_api.txt",
-        "numpy_include/numpy/halffloat.h",
-        "numpy_include/numpy/npy_common.h",
-        "numpy_include/numpy/utils.h",
-        "numpy_include/numpy/npy_interrupt.h",
-        "numpy_include/numpy/npy_endian.h",
+        "numpy_include/numpy/__multiarray_api.h",
         "numpy_include/numpy/__ufunc_api.h",
         "numpy_include/numpy/_neighborhood_iterator_imp.h",
-        "numpy_include/numpy/ufuncobject.h",
+        "numpy_include/numpy/_numpyconfig.h",
+        "numpy_include/numpy/arrayobject.h",
+        "numpy_include/numpy/arrayscalars.h",
+        "numpy_include/numpy/halffloat.h",
+        "numpy_include/numpy/multiarray_api.txt",
+        "numpy_include/numpy/ndarrayobject.h",
         "numpy_include/numpy/ndarraytypes.h",
-        "numpy_include/numpy/npy_math.h",
         "numpy_include/numpy/noprefix.h",
+        "numpy_include/numpy/npy_1_7_deprecated_api.h",
         "numpy_include/numpy/npy_3kcompat.h",
-        "numpy_include/numpy/arrayscalars.h",
-        "numpy_include/numpy/npy_os.h",
-        "numpy_include/numpy/ndarrayobject.h",
-        "numpy_include/numpy/npy_no_deprecated_api.h",
-        "numpy_include/numpy/arrayobject.h",
-        "numpy_include/numpy/_numpyconfig.h",
-        "numpy_include/numpy/__multiarray_api.h",
+        "numpy_include/numpy/npy_common.h",
         "numpy_include/numpy/npy_cpu.h",
-        "numpy_include/numpy/old_defines.h",
+        "numpy_include/numpy/npy_endian.h",
+        "numpy_include/numpy/npy_interrupt.h",
+        "numpy_include/numpy/npy_math.h",
+        "numpy_include/numpy/npy_no_deprecated_api.h",
+        "numpy_include/numpy/npy_os.h",
         "numpy_include/numpy/numpyconfig.h",
+        "numpy_include/numpy/old_defines.h",
+        "numpy_include/numpy/oldnumeric.h",
+        "numpy_include/numpy/ufunc_api.txt",
+        "numpy_include/numpy/ufuncobject.h",
+        "numpy_include/numpy/utils.h",
     ],
     cmd = """
-cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/oldnumeric.h" "$(@D)/numpy_include/numpy/oldnumeric.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_1_7_deprecated_api.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ufunc_api.txt" "$(@D)/numpy_include/numpy/ufunc_api.txt" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/multiarray_api.txt" "$(@D)/numpy_include/numpy/multiarray_api.txt" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/halffloat.h" "$(@D)/numpy_include/numpy/halffloat.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_common.h" "$(@D)/numpy_include/numpy/npy_common.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/utils.h" "$(@D)/numpy_include/numpy/utils.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_interrupt.h" "$(@D)/numpy_include/numpy/npy_interrupt.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_endian.h" "$(@D)/numpy_include/numpy/npy_endian.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/__ufunc_api.h" "$(@D)/numpy_include/numpy/__ufunc_api.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h" "$(@D)/numpy_include/numpy/_neighborhood_iterator_imp.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ufuncobject.h" "$(@D)/numpy_include/numpy/ufuncobject.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h" "$(@D)/numpy_include/numpy/ndarraytypes.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_math.h" "$(@D)/numpy_include/numpy/npy_math.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/noprefix.h" "$(@D)/numpy_include/numpy/noprefix.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_3kcompat.h" "$(@D)/numpy_include/numpy/npy_3kcompat.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayscalars.h" "$(@D)/numpy_include/numpy/arrayscalars.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_os.h" "$(@D)/numpy_include/numpy/npy_os.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h" "$(@D)/numpy_include/numpy/ndarrayobject.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_no_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_no_deprecated_api.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h" "$(@D)/numpy_include/numpy/arrayobject.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/_numpyconfig.h" "$(@D)/numpy_include/numpy/_numpyconfig.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/__multiarray_api.h" "$(@D)/numpy_include/numpy/__multiarray_api.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_cpu.h" "$(@D)/numpy_include/numpy/npy_cpu.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/old_defines.h" "$(@D)/numpy_include/numpy/old_defines.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/numpyconfig.h" "$(@D)/numpy_include/numpy/numpyconfig.h"
+cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/__multiarray_api.h" "$(@D)/numpy_include/numpy/__multiarray_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/__ufunc_api.h" "$(@D)/numpy_include/numpy/__ufunc_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h" "$(@D)/numpy_include/numpy/_neighborhood_iterator_imp.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/_numpyconfig.h" "$(@D)/numpy_include/numpy/_numpyconfig.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h" "$(@D)/numpy_include/numpy/arrayobject.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayscalars.h" "$(@D)/numpy_include/numpy/arrayscalars.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/halffloat.h" "$(@D)/numpy_include/numpy/halffloat.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/multiarray_api.txt" "$(@D)/numpy_include/numpy/multiarray_api.txt" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h" "$(@D)/numpy_include/numpy/ndarrayobject.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h" "$(@D)/numpy_include/numpy/ndarraytypes.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/noprefix.h" "$(@D)/numpy_include/numpy/noprefix.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_1_7_deprecated_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_3kcompat.h" "$(@D)/numpy_include/numpy/npy_3kcompat.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_common.h" "$(@D)/numpy_include/numpy/npy_common.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_cpu.h" "$(@D)/numpy_include/numpy/npy_cpu.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_endian.h" "$(@D)/numpy_include/numpy/npy_endian.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_interrupt.h" "$(@D)/numpy_include/numpy/npy_interrupt.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_math.h" "$(@D)/numpy_include/numpy/npy_math.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_no_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_no_deprecated_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_os.h" "$(@D)/numpy_include/numpy/npy_os.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/numpyconfig.h" "$(@D)/numpy_include/numpy/numpyconfig.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/old_defines.h" "$(@D)/numpy_include/numpy/old_defines.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/oldnumeric.h" "$(@D)/numpy_include/numpy/oldnumeric.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ufunc_api.txt" "$(@D)/numpy_include/numpy/ufunc_api.txt" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ufuncobject.h" "$(@D)/numpy_include/numpy/ufuncobject.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/utils.h" "$(@D)/numpy_include/numpy/utils.h"
    """,
 )
diff --git a/third_party/toolchains/cpus/py3/BUILD b/third_party/toolchains/cpus/py3/BUILD
index 932a25239fb5f7e35c2ada46b70309e6635bcb4a..d47256ebef88fa39d904c9815ce4295e5c693ffa 100644
--- a/third_party/toolchains/cpus/py3/BUILD
+++ b/third_party/toolchains/cpus/py3/BUILD
@@ -6,18 +6,24 @@ licenses(["restricted"])
 
 package(default_visibility = ["//visibility:public"])
 
+# To build Python C/C++ extension on Windows, we need to link to python import library pythonXY.lib
+# See https://docs.python.org/3/extending/windows.html
+cc_import(
+    name = "python_lib",
+    interface_library = select({
+        ":windows": ":python_import_lib",
+        # A placeholder for Unix platforms which makes --no_build happy.
+        "//conditions:default": "not-existing.lib",
+    }),
+    system_provided = 1,
+)
+
 cc_library(
     name = "python_headers",
     hdrs = [":python_include"],
-    data = select({
-        ":windows": [":python_import_lib"],
-        "//conditions:default": [],
-    }),
     includes = ["python_include"],
-    linkopts = select({
-        # TODO(pcloudy): Ideally, this should just go into deps after resolving
-        # https://github.com/bazelbuild/bazel/issues/3237,
-        ":windows": ["$(locations :python_import_lib)"],
+    deps = select({
+        ":windows": [":python_lib"],
         "//conditions:default": [],
     }),
 )
@@ -37,143 +43,143 @@ config_setting(
 genrule(
     name = "python_include",
     outs = [
-        "python_include/code.h",
-        "python_include/dtoa.h",
-        "python_include/tupleobject.h",
-        "python_include/object.h",
-        "python_include/ast.h",
-        "python_include/pymacconfig.h",
-        "python_include/errcode.h",
-        "python_include/frameobject.h",
-        "python_include/typeslots.h",
-        "python_include/pgenheaders.h",
-        "python_include/cellobject.h",
-        "python_include/pythread.h",
-        "python_include/boolobject.h",
+        "python_include/Python-ast.h",
+        "python_include/Python.h",
+        "python_include/abstract.h",
         "python_include/accu.h",
-        "python_include/modsupport.h",
-        "python_include/import.h",
-        "python_include/pymath.h",
-        "python_include/node.h",
-        "python_include/funcobject.h",
-        "python_include/eval.h",
-        "python_include/pyatomic.h",
-        "python_include/longintrepr.h",
-        "python_include/floatobject.h",
-        "python_include/rangeobject.h",
-        "python_include/pyfpe.h",
-        "python_include/pystrcmp.h",
-        "python_include/fileutils.h",
-        "python_include/dictobject.h",
-        "python_include/pyarena.h",
-        "python_include/osmodule.h",
-        "python_include/objimpl.h",
+        "python_include/asdl.h",
+        "python_include/ast.h",
         "python_include/bitset.h",
-        "python_include/memoryobject.h",
+        "python_include/bltinmodule.h",
+        "python_include/boolobject.h",
         "python_include/bytearrayobject.h",
-        "python_include/pydebug.h",
-        "python_include/pyerrors.h",
-        "python_include/weakrefobject.h",
-        "python_include/grammar.h",
-        "python_include/symtable.h",
-        "python_include/longobject.h",
-        "python_include/structmember.h",
-        "python_include/enumobject.h",
-        "python_include/pymacro.h",
+        "python_include/bytes_methods.h",
+        "python_include/bytesobject.h",
+        "python_include/cellobject.h",
+        "python_include/ceval.h",
         "python_include/classobject.h",
-        "python_include/unicodeobject.h",
-        "python_include/sliceobject.h",
-        "python_include/pystrtod.h",
-        "python_include/genobject.h",
-        "python_include/compile.h",
-        "python_include/pyexpat.h",
-        "python_include/asdl.h",
+        "python_include/code.h",
         "python_include/codecs.h",
+        "python_include/compile.h",
+        "python_include/complexobject.h",
+        "python_include/datetime.h",
+        "python_include/descrobject.h",
+        "python_include/dictobject.h",
+        "python_include/dtoa.h",
         "python_include/dynamic_annotations.h",
-        "python_include/pyctype.h",
-        "python_include/sysmodule.h",
-        "python_include/methodobject.h",
+        "python_include/enumobject.h",
+        "python_include/errcode.h",
+        "python_include/eval.h",
+        "python_include/fileobject.h",
+        "python_include/fileutils.h",
+        "python_include/floatobject.h",
+        "python_include/frameobject.h",
+        "python_include/funcobject.h",
+        "python_include/genobject.h",
         "python_include/graminit.h",
-        "python_include/bltinmodule.h",
+        "python_include/grammar.h",
+        "python_include/import.h",
         "python_include/intrcheck.h",
-        "python_include/pyport.h",
-        "python_include/warnings.h",
-        "python_include/osdefs.h",
-        "python_include/pydtrace.h",
-        "python_include/pylifecycle.h",
-        "python_include/fileobject.h",
-        "python_include/pytime.h",
-        "python_include/traceback.h",
-        "python_include/ceval.h",
-        "python_include/bytes_methods.h",
-        "python_include/namespaceobject.h",
-        "python_include/pyconfig.h",
-        "python_include/Python.h",
+        "python_include/iterobject.h",
+        "python_include/listobject.h",
+        "python_include/longintrepr.h",
+        "python_include/longobject.h",
+        "python_include/marshal.h",
+        "python_include/memoryobject.h",
+        "python_include/metagrammar.h",
+        "python_include/methodobject.h",
+        "python_include/modsupport.h",
         "python_include/moduleobject.h",
-        "python_include/pystate.h",
-        "python_include/descrobject.h",
+        "python_include/namespaceobject.h",
+        "python_include/node.h",
+        "python_include/object.h",
+        "python_include/objimpl.h",
         "python_include/odictobject.h",
-        "python_include/ucnhash.h",
+        "python_include/opcode.h",
+        "python_include/osdefs.h",
+        "python_include/osmodule.h",
+        "python_include/parsetok.h",
+        "python_include/patchlevel.h",
+        "python_include/pgen.h",
+        "python_include/pgenheaders.h",
+        "python_include/py_curses.h",
+        "python_include/pyarena.h",
+        "python_include/pyatomic.h",
+        "python_include/pycapsule.h",
+        "python_include/pyconfig.h",
+        "python_include/pyctype.h",
+        "python_include/pydebug.h",
+        "python_include/pydtrace.h",
+        "python_include/pyerrors.h",
+        "python_include/pyexpat.h",
+        "python_include/pyfpe.h",
         "python_include/pygetopt.h",
+        "python_include/pyhash.h",
+        "python_include/pylifecycle.h",
+        "python_include/pymacconfig.h",
+        "python_include/pymacro.h",
+        "python_include/pymath.h",
         "python_include/pymem.h",
-        "python_include/complexobject.h",
-        "python_include/structseq.h",
-        "python_include/datetime.h",
+        "python_include/pyport.h",
+        "python_include/pystate.h",
+        "python_include/pystrcmp.h",
+        "python_include/pystrhex.h",
+        "python_include/pystrtod.h",
         "python_include/pythonrun.h",
-        "python_include/pyhash.h",
-        "python_include/pycapsule.h",
+        "python_include/pythread.h",
+        "python_include/pytime.h",
+        "python_include/rangeobject.h",
         "python_include/setobject.h",
-        "python_include/listobject.h",
-        "python_include/bytesobject.h",
-        "python_include/pgen.h",
-        "python_include/patchlevel.h",
-        "python_include/opcode.h",
-        "python_include/parsetok.h",
-        "python_include/pystrhex.h",
-        "python_include/marshal.h",
+        "python_include/sliceobject.h",
+        "python_include/structmember.h",
+        "python_include/structseq.h",
+        "python_include/symtable.h",
+        "python_include/sysmodule.h",
         "python_include/token.h",
-        "python_include/iterobject.h",
-        "python_include/abstract.h",
-        "python_include/py_curses.h",
-        "python_include/metagrammar.h",
-        "python_include/Python-ast.h",
+        "python_include/traceback.h",
+        "python_include/tupleobject.h",
+        "python_include/typeslots.h",
+        "python_include/ucnhash.h",
+        "python_include/unicodeobject.h",
+        "python_include/warnings.h",
+        "python_include/weakrefobject.h",
     ],
     cmd = """
-cp "/opt/python3.6/include/python3.6m/code.h" "$(@D)/python_include/code.h" && cp "/opt/python3.6/include/python3.6m/dtoa.h" "$(@D)/python_include/dtoa.h" && cp "/opt/python3.6/include/python3.6m/tupleobject.h" "$(@D)/python_include/tupleobject.h" && cp "/opt/python3.6/include/python3.6m/object.h" "$(@D)/python_include/object.h" && cp "/opt/python3.6/include/python3.6m/ast.h" "$(@D)/python_include/ast.h" && cp "/opt/python3.6/include/python3.6m/pymacconfig.h" "$(@D)/python_include/pymacconfig.h" && cp "/opt/python3.6/include/python3.6m/errcode.h" "$(@D)/python_include/errcode.h" && cp "/opt/python3.6/include/python3.6m/frameobject.h" "$(@D)/python_include/frameobject.h" && cp "/opt/python3.6/include/python3.6m/typeslots.h" "$(@D)/python_include/typeslots.h" && cp "/opt/python3.6/include/python3.6m/pgenheaders.h" "$(@D)/python_include/pgenheaders.h" && cp "/opt/python3.6/include/python3.6m/cellobject.h" "$(@D)/python_include/cellobject.h" && cp "/opt/python3.6/include/python3.6m/pythread.h" "$(@D)/python_include/pythread.h" && cp "/opt/python3.6/include/python3.6m/boolobject.h" "$(@D)/python_include/boolobject.h" && cp "/opt/python3.6/include/python3.6m/accu.h" "$(@D)/python_include/accu.h" && cp "/opt/python3.6/include/python3.6m/modsupport.h" "$(@D)/python_include/modsupport.h" && cp "/opt/python3.6/include/python3.6m/import.h" "$(@D)/python_include/import.h" && cp "/opt/python3.6/include/python3.6m/pymath.h" "$(@D)/python_include/pymath.h" && cp "/opt/python3.6/include/python3.6m/node.h" "$(@D)/python_include/node.h" && cp "/opt/python3.6/include/python3.6m/funcobject.h" "$(@D)/python_include/funcobject.h" && cp "/opt/python3.6/include/python3.6m/eval.h" "$(@D)/python_include/eval.h" && cp "/opt/python3.6/include/python3.6m/pyatomic.h" "$(@D)/python_include/pyatomic.h" && cp "/opt/python3.6/include/python3.6m/longintrepr.h" "$(@D)/python_include/longintrepr.h" && cp "/opt/python3.6/include/python3.6m/floatobject.h" "$(@D)/python_include/floatobject.h" && cp "/opt/python3.6/include/python3.6m/rangeobject.h" "$(@D)/python_include/rangeobject.h" && cp "/opt/python3.6/include/python3.6m/pyfpe.h" "$(@D)/python_include/pyfpe.h" && cp "/opt/python3.6/include/python3.6m/pystrcmp.h" "$(@D)/python_include/pystrcmp.h" && cp "/opt/python3.6/include/python3.6m/fileutils.h" "$(@D)/python_include/fileutils.h" && cp "/opt/python3.6/include/python3.6m/dictobject.h" "$(@D)/python_include/dictobject.h" && cp "/opt/python3.6/include/python3.6m/pyarena.h" "$(@D)/python_include/pyarena.h" && cp "/opt/python3.6/include/python3.6m/osmodule.h" "$(@D)/python_include/osmodule.h" && cp "/opt/python3.6/include/python3.6m/objimpl.h" "$(@D)/python_include/objimpl.h" && cp "/opt/python3.6/include/python3.6m/bitset.h" "$(@D)/python_include/bitset.h" && cp "/opt/python3.6/include/python3.6m/memoryobject.h" "$(@D)/python_include/memoryobject.h" && cp "/opt/python3.6/include/python3.6m/bytearrayobject.h" "$(@D)/python_include/bytearrayobject.h" && cp "/opt/python3.6/include/python3.6m/pydebug.h" "$(@D)/python_include/pydebug.h" && cp "/opt/python3.6/include/python3.6m/pyerrors.h" "$(@D)/python_include/pyerrors.h" && cp "/opt/python3.6/include/python3.6m/weakrefobject.h" "$(@D)/python_include/weakrefobject.h" && cp "/opt/python3.6/include/python3.6m/grammar.h" "$(@D)/python_include/grammar.h" && cp "/opt/python3.6/include/python3.6m/symtable.h" "$(@D)/python_include/symtable.h" && cp "/opt/python3.6/include/python3.6m/longobject.h" "$(@D)/python_include/longobject.h" && cp "/opt/python3.6/include/python3.6m/structmember.h" "$(@D)/python_include/structmember.h" && cp "/opt/python3.6/include/python3.6m/enumobject.h" "$(@D)/python_include/enumobject.h" && cp "/opt/python3.6/include/python3.6m/pymacro.h" "$(@D)/python_include/pymacro.h" && cp "/opt/python3.6/include/python3.6m/classobject.h" "$(@D)/python_include/classobject.h" && cp "/opt/python3.6/include/python3.6m/unicodeobject.h" "$(@D)/python_include/unicodeobject.h" && cp "/opt/python3.6/include/python3.6m/sliceobject.h" "$(@D)/python_include/sliceobject.h" && cp "/opt/python3.6/include/python3.6m/pystrtod.h" "$(@D)/python_include/pystrtod.h" && cp "/opt/python3.6/include/python3.6m/genobject.h" "$(@D)/python_include/genobject.h" && cp "/opt/python3.6/include/python3.6m/compile.h" "$(@D)/python_include/compile.h" && cp "/opt/python3.6/include/python3.6m/pyexpat.h" "$(@D)/python_include/pyexpat.h" && cp "/opt/python3.6/include/python3.6m/asdl.h" "$(@D)/python_include/asdl.h" && cp "/opt/python3.6/include/python3.6m/codecs.h" "$(@D)/python_include/codecs.h" && cp "/opt/python3.6/include/python3.6m/dynamic_annotations.h" "$(@D)/python_include/dynamic_annotations.h" && cp "/opt/python3.6/include/python3.6m/pyctype.h" "$(@D)/python_include/pyctype.h" && cp "/opt/python3.6/include/python3.6m/sysmodule.h" "$(@D)/python_include/sysmodule.h" && cp "/opt/python3.6/include/python3.6m/methodobject.h" "$(@D)/python_include/methodobject.h" && cp "/opt/python3.6/include/python3.6m/graminit.h" "$(@D)/python_include/graminit.h" && cp "/opt/python3.6/include/python3.6m/bltinmodule.h" "$(@D)/python_include/bltinmodule.h" && cp "/opt/python3.6/include/python3.6m/intrcheck.h" "$(@D)/python_include/intrcheck.h" && cp "/opt/python3.6/include/python3.6m/pyport.h" "$(@D)/python_include/pyport.h" && cp "/opt/python3.6/include/python3.6m/warnings.h" "$(@D)/python_include/warnings.h" && cp "/opt/python3.6/include/python3.6m/osdefs.h" "$(@D)/python_include/osdefs.h" && cp "/opt/python3.6/include/python3.6m/pydtrace.h" "$(@D)/python_include/pydtrace.h" && cp "/opt/python3.6/include/python3.6m/pylifecycle.h" "$(@D)/python_include/pylifecycle.h" && cp "/opt/python3.6/include/python3.6m/fileobject.h" "$(@D)/python_include/fileobject.h" && cp "/opt/python3.6/include/python3.6m/pytime.h" "$(@D)/python_include/pytime.h" && cp "/opt/python3.6/include/python3.6m/traceback.h" "$(@D)/python_include/traceback.h" && cp "/opt/python3.6/include/python3.6m/ceval.h" "$(@D)/python_include/ceval.h" && cp "/opt/python3.6/include/python3.6m/bytes_methods.h" "$(@D)/python_include/bytes_methods.h" && cp "/opt/python3.6/include/python3.6m/namespaceobject.h" "$(@D)/python_include/namespaceobject.h" && cp "/opt/python3.6/include/python3.6m/pyconfig.h" "$(@D)/python_include/pyconfig.h" && cp "/opt/python3.6/include/python3.6m/Python.h" "$(@D)/python_include/Python.h" && cp "/opt/python3.6/include/python3.6m/moduleobject.h" "$(@D)/python_include/moduleobject.h" && cp "/opt/python3.6/include/python3.6m/pystate.h" "$(@D)/python_include/pystate.h" && cp "/opt/python3.6/include/python3.6m/descrobject.h" "$(@D)/python_include/descrobject.h" && cp "/opt/python3.6/include/python3.6m/odictobject.h" "$(@D)/python_include/odictobject.h" && cp "/opt/python3.6/include/python3.6m/ucnhash.h" "$(@D)/python_include/ucnhash.h" && cp "/opt/python3.6/include/python3.6m/pygetopt.h" "$(@D)/python_include/pygetopt.h" && cp "/opt/python3.6/include/python3.6m/pymem.h" "$(@D)/python_include/pymem.h" && cp "/opt/python3.6/include/python3.6m/complexobject.h" "$(@D)/python_include/complexobject.h" && cp "/opt/python3.6/include/python3.6m/structseq.h" "$(@D)/python_include/structseq.h" && cp "/opt/python3.6/include/python3.6m/datetime.h" "$(@D)/python_include/datetime.h" && cp "/opt/python3.6/include/python3.6m/pythonrun.h" "$(@D)/python_include/pythonrun.h" && cp "/opt/python3.6/include/python3.6m/pyhash.h" "$(@D)/python_include/pyhash.h" && cp "/opt/python3.6/include/python3.6m/pycapsule.h" "$(@D)/python_include/pycapsule.h" && cp "/opt/python3.6/include/python3.6m/setobject.h" "$(@D)/python_include/setobject.h" && cp "/opt/python3.6/include/python3.6m/listobject.h" "$(@D)/python_include/listobject.h" && cp "/opt/python3.6/include/python3.6m/bytesobject.h" "$(@D)/python_include/bytesobject.h" && cp "/opt/python3.6/include/python3.6m/pgen.h" "$(@D)/python_include/pgen.h" && cp "/opt/python3.6/include/python3.6m/patchlevel.h" "$(@D)/python_include/patchlevel.h" && cp "/opt/python3.6/include/python3.6m/opcode.h" "$(@D)/python_include/opcode.h" && cp "/opt/python3.6/include/python3.6m/parsetok.h" "$(@D)/python_include/parsetok.h" && cp "/opt/python3.6/include/python3.6m/pystrhex.h" "$(@D)/python_include/pystrhex.h" && cp "/opt/python3.6/include/python3.6m/marshal.h" "$(@D)/python_include/marshal.h" && cp "/opt/python3.6/include/python3.6m/token.h" "$(@D)/python_include/token.h" && cp "/opt/python3.6/include/python3.6m/iterobject.h" "$(@D)/python_include/iterobject.h" && cp "/opt/python3.6/include/python3.6m/abstract.h" "$(@D)/python_include/abstract.h" && cp "/opt/python3.6/include/python3.6m/py_curses.h" "$(@D)/python_include/py_curses.h" && cp "/opt/python3.6/include/python3.6m/metagrammar.h" "$(@D)/python_include/metagrammar.h" && cp "/opt/python3.6/include/python3.6m/Python-ast.h" "$(@D)/python_include/Python-ast.h"
+cp "/opt/python3.6/include/python3.6m/Python-ast.h" "$(@D)/python_include/Python-ast.h" && cp "/opt/python3.6/include/python3.6m/Python.h" "$(@D)/python_include/Python.h" && cp "/opt/python3.6/include/python3.6m/abstract.h" "$(@D)/python_include/abstract.h" && cp "/opt/python3.6/include/python3.6m/accu.h" "$(@D)/python_include/accu.h" && cp "/opt/python3.6/include/python3.6m/asdl.h" "$(@D)/python_include/asdl.h" && cp "/opt/python3.6/include/python3.6m/ast.h" "$(@D)/python_include/ast.h" && cp "/opt/python3.6/include/python3.6m/bitset.h" "$(@D)/python_include/bitset.h" && cp "/opt/python3.6/include/python3.6m/bltinmodule.h" "$(@D)/python_include/bltinmodule.h" && cp "/opt/python3.6/include/python3.6m/boolobject.h" "$(@D)/python_include/boolobject.h" && cp "/opt/python3.6/include/python3.6m/bytearrayobject.h" "$(@D)/python_include/bytearrayobject.h" && cp "/opt/python3.6/include/python3.6m/bytes_methods.h" "$(@D)/python_include/bytes_methods.h" && cp "/opt/python3.6/include/python3.6m/bytesobject.h" "$(@D)/python_include/bytesobject.h" && cp "/opt/python3.6/include/python3.6m/cellobject.h" "$(@D)/python_include/cellobject.h" && cp "/opt/python3.6/include/python3.6m/ceval.h" "$(@D)/python_include/ceval.h" && cp "/opt/python3.6/include/python3.6m/classobject.h" "$(@D)/python_include/classobject.h" && cp "/opt/python3.6/include/python3.6m/code.h" "$(@D)/python_include/code.h" && cp "/opt/python3.6/include/python3.6m/codecs.h" "$(@D)/python_include/codecs.h" && cp "/opt/python3.6/include/python3.6m/compile.h" "$(@D)/python_include/compile.h" && cp "/opt/python3.6/include/python3.6m/complexobject.h" "$(@D)/python_include/complexobject.h" && cp "/opt/python3.6/include/python3.6m/datetime.h" "$(@D)/python_include/datetime.h" && cp "/opt/python3.6/include/python3.6m/descrobject.h" "$(@D)/python_include/descrobject.h" && cp "/opt/python3.6/include/python3.6m/dictobject.h" "$(@D)/python_include/dictobject.h" && cp "/opt/python3.6/include/python3.6m/dtoa.h" "$(@D)/python_include/dtoa.h" && cp "/opt/python3.6/include/python3.6m/dynamic_annotations.h" "$(@D)/python_include/dynamic_annotations.h" && cp "/opt/python3.6/include/python3.6m/enumobject.h" "$(@D)/python_include/enumobject.h" && cp "/opt/python3.6/include/python3.6m/errcode.h" "$(@D)/python_include/errcode.h" && cp "/opt/python3.6/include/python3.6m/eval.h" "$(@D)/python_include/eval.h" && cp "/opt/python3.6/include/python3.6m/fileobject.h" "$(@D)/python_include/fileobject.h" && cp "/opt/python3.6/include/python3.6m/fileutils.h" "$(@D)/python_include/fileutils.h" && cp "/opt/python3.6/include/python3.6m/floatobject.h" "$(@D)/python_include/floatobject.h" && cp "/opt/python3.6/include/python3.6m/frameobject.h" "$(@D)/python_include/frameobject.h" && cp "/opt/python3.6/include/python3.6m/funcobject.h" "$(@D)/python_include/funcobject.h" && cp "/opt/python3.6/include/python3.6m/genobject.h" "$(@D)/python_include/genobject.h" && cp "/opt/python3.6/include/python3.6m/graminit.h" "$(@D)/python_include/graminit.h" && cp "/opt/python3.6/include/python3.6m/grammar.h" "$(@D)/python_include/grammar.h" && cp "/opt/python3.6/include/python3.6m/import.h" "$(@D)/python_include/import.h" && cp "/opt/python3.6/include/python3.6m/intrcheck.h" "$(@D)/python_include/intrcheck.h" && cp "/opt/python3.6/include/python3.6m/iterobject.h" "$(@D)/python_include/iterobject.h" && cp "/opt/python3.6/include/python3.6m/listobject.h" "$(@D)/python_include/listobject.h" && cp "/opt/python3.6/include/python3.6m/longintrepr.h" "$(@D)/python_include/longintrepr.h" && cp "/opt/python3.6/include/python3.6m/longobject.h" "$(@D)/python_include/longobject.h" && cp "/opt/python3.6/include/python3.6m/marshal.h" "$(@D)/python_include/marshal.h" && cp "/opt/python3.6/include/python3.6m/memoryobject.h" "$(@D)/python_include/memoryobject.h" && cp "/opt/python3.6/include/python3.6m/metagrammar.h" "$(@D)/python_include/metagrammar.h" && cp "/opt/python3.6/include/python3.6m/methodobject.h" "$(@D)/python_include/methodobject.h" && cp "/opt/python3.6/include/python3.6m/modsupport.h" "$(@D)/python_include/modsupport.h" && cp "/opt/python3.6/include/python3.6m/moduleobject.h" "$(@D)/python_include/moduleobject.h" && cp "/opt/python3.6/include/python3.6m/namespaceobject.h" "$(@D)/python_include/namespaceobject.h" && cp "/opt/python3.6/include/python3.6m/node.h" "$(@D)/python_include/node.h" && cp "/opt/python3.6/include/python3.6m/object.h" "$(@D)/python_include/object.h" && cp "/opt/python3.6/include/python3.6m/objimpl.h" "$(@D)/python_include/objimpl.h" && cp "/opt/python3.6/include/python3.6m/odictobject.h" "$(@D)/python_include/odictobject.h" && cp "/opt/python3.6/include/python3.6m/opcode.h" "$(@D)/python_include/opcode.h" && cp "/opt/python3.6/include/python3.6m/osdefs.h" "$(@D)/python_include/osdefs.h" && cp "/opt/python3.6/include/python3.6m/osmodule.h" "$(@D)/python_include/osmodule.h" && cp "/opt/python3.6/include/python3.6m/parsetok.h" "$(@D)/python_include/parsetok.h" && cp "/opt/python3.6/include/python3.6m/patchlevel.h" "$(@D)/python_include/patchlevel.h" && cp "/opt/python3.6/include/python3.6m/pgen.h" "$(@D)/python_include/pgen.h" && cp "/opt/python3.6/include/python3.6m/pgenheaders.h" "$(@D)/python_include/pgenheaders.h" && cp "/opt/python3.6/include/python3.6m/py_curses.h" "$(@D)/python_include/py_curses.h" && cp "/opt/python3.6/include/python3.6m/pyarena.h" "$(@D)/python_include/pyarena.h" && cp "/opt/python3.6/include/python3.6m/pyatomic.h" "$(@D)/python_include/pyatomic.h" && cp "/opt/python3.6/include/python3.6m/pycapsule.h" "$(@D)/python_include/pycapsule.h" && cp "/opt/python3.6/include/python3.6m/pyconfig.h" "$(@D)/python_include/pyconfig.h" && cp "/opt/python3.6/include/python3.6m/pyctype.h" "$(@D)/python_include/pyctype.h" && cp "/opt/python3.6/include/python3.6m/pydebug.h" "$(@D)/python_include/pydebug.h" && cp "/opt/python3.6/include/python3.6m/pydtrace.h" "$(@D)/python_include/pydtrace.h" && cp "/opt/python3.6/include/python3.6m/pyerrors.h" "$(@D)/python_include/pyerrors.h" && cp "/opt/python3.6/include/python3.6m/pyexpat.h" "$(@D)/python_include/pyexpat.h" && cp "/opt/python3.6/include/python3.6m/pyfpe.h" "$(@D)/python_include/pyfpe.h" && cp "/opt/python3.6/include/python3.6m/pygetopt.h" "$(@D)/python_include/pygetopt.h" && cp "/opt/python3.6/include/python3.6m/pyhash.h" "$(@D)/python_include/pyhash.h" && cp "/opt/python3.6/include/python3.6m/pylifecycle.h" "$(@D)/python_include/pylifecycle.h" && cp "/opt/python3.6/include/python3.6m/pymacconfig.h" "$(@D)/python_include/pymacconfig.h" && cp "/opt/python3.6/include/python3.6m/pymacro.h" "$(@D)/python_include/pymacro.h" && cp "/opt/python3.6/include/python3.6m/pymath.h" "$(@D)/python_include/pymath.h" && cp "/opt/python3.6/include/python3.6m/pymem.h" "$(@D)/python_include/pymem.h" && cp "/opt/python3.6/include/python3.6m/pyport.h" "$(@D)/python_include/pyport.h" && cp "/opt/python3.6/include/python3.6m/pystate.h" "$(@D)/python_include/pystate.h" && cp "/opt/python3.6/include/python3.6m/pystrcmp.h" "$(@D)/python_include/pystrcmp.h" && cp "/opt/python3.6/include/python3.6m/pystrhex.h" "$(@D)/python_include/pystrhex.h" && cp "/opt/python3.6/include/python3.6m/pystrtod.h" "$(@D)/python_include/pystrtod.h" && cp "/opt/python3.6/include/python3.6m/pythonrun.h" "$(@D)/python_include/pythonrun.h" && cp "/opt/python3.6/include/python3.6m/pythread.h" "$(@D)/python_include/pythread.h" && cp "/opt/python3.6/include/python3.6m/pytime.h" "$(@D)/python_include/pytime.h" && cp "/opt/python3.6/include/python3.6m/rangeobject.h" "$(@D)/python_include/rangeobject.h" && cp "/opt/python3.6/include/python3.6m/setobject.h" "$(@D)/python_include/setobject.h" && cp "/opt/python3.6/include/python3.6m/sliceobject.h" "$(@D)/python_include/sliceobject.h" && cp "/opt/python3.6/include/python3.6m/structmember.h" "$(@D)/python_include/structmember.h" && cp "/opt/python3.6/include/python3.6m/structseq.h" "$(@D)/python_include/structseq.h" && cp "/opt/python3.6/include/python3.6m/symtable.h" "$(@D)/python_include/symtable.h" && cp "/opt/python3.6/include/python3.6m/sysmodule.h" "$(@D)/python_include/sysmodule.h" && cp "/opt/python3.6/include/python3.6m/token.h" "$(@D)/python_include/token.h" && cp "/opt/python3.6/include/python3.6m/traceback.h" "$(@D)/python_include/traceback.h" && cp "/opt/python3.6/include/python3.6m/tupleobject.h" "$(@D)/python_include/tupleobject.h" && cp "/opt/python3.6/include/python3.6m/typeslots.h" "$(@D)/python_include/typeslots.h" && cp "/opt/python3.6/include/python3.6m/ucnhash.h" "$(@D)/python_include/ucnhash.h" && cp "/opt/python3.6/include/python3.6m/unicodeobject.h" "$(@D)/python_include/unicodeobject.h" && cp "/opt/python3.6/include/python3.6m/warnings.h" "$(@D)/python_include/warnings.h" && cp "/opt/python3.6/include/python3.6m/weakrefobject.h" "$(@D)/python_include/weakrefobject.h"
    """,
 )
 
 genrule(
     name = "numpy_include",
     outs = [
-        "numpy_include/numpy/oldnumeric.h",
-        "numpy_include/numpy/npy_1_7_deprecated_api.h",
-        "numpy_include/numpy/ufunc_api.txt",
-        "numpy_include/numpy/multiarray_api.txt",
-        "numpy_include/numpy/halffloat.h",
-        "numpy_include/numpy/npy_common.h",
-        "numpy_include/numpy/utils.h",
-        "numpy_include/numpy/npy_interrupt.h",
-        "numpy_include/numpy/npy_endian.h",
+        "numpy_include/numpy/__multiarray_api.h",
         "numpy_include/numpy/__ufunc_api.h",
         "numpy_include/numpy/_neighborhood_iterator_imp.h",
-        "numpy_include/numpy/ufuncobject.h",
+        "numpy_include/numpy/_numpyconfig.h",
+        "numpy_include/numpy/arrayobject.h",
+        "numpy_include/numpy/arrayscalars.h",
+        "numpy_include/numpy/halffloat.h",
+        "numpy_include/numpy/multiarray_api.txt",
+        "numpy_include/numpy/ndarrayobject.h",
         "numpy_include/numpy/ndarraytypes.h",
-        "numpy_include/numpy/npy_math.h",
         "numpy_include/numpy/noprefix.h",
+        "numpy_include/numpy/npy_1_7_deprecated_api.h",
         "numpy_include/numpy/npy_3kcompat.h",
-        "numpy_include/numpy/arrayscalars.h",
-        "numpy_include/numpy/npy_os.h",
-        "numpy_include/numpy/ndarrayobject.h",
-        "numpy_include/numpy/npy_no_deprecated_api.h",
-        "numpy_include/numpy/arrayobject.h",
-        "numpy_include/numpy/_numpyconfig.h",
-        "numpy_include/numpy/__multiarray_api.h",
+        "numpy_include/numpy/npy_common.h",
         "numpy_include/numpy/npy_cpu.h",
-        "numpy_include/numpy/old_defines.h",
+        "numpy_include/numpy/npy_endian.h",
+        "numpy_include/numpy/npy_interrupt.h",
+        "numpy_include/numpy/npy_math.h",
+        "numpy_include/numpy/npy_no_deprecated_api.h",
+        "numpy_include/numpy/npy_os.h",
         "numpy_include/numpy/numpyconfig.h",
+        "numpy_include/numpy/old_defines.h",
+        "numpy_include/numpy/oldnumeric.h",
+        "numpy_include/numpy/ufunc_api.txt",
+        "numpy_include/numpy/ufuncobject.h",
+        "numpy_include/numpy/utils.h",
     ],
     cmd = """
-cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/oldnumeric.h" "$(@D)/numpy_include/numpy/oldnumeric.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_1_7_deprecated_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ufunc_api.txt" "$(@D)/numpy_include/numpy/ufunc_api.txt" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/multiarray_api.txt" "$(@D)/numpy_include/numpy/multiarray_api.txt" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/halffloat.h" "$(@D)/numpy_include/numpy/halffloat.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_common.h" "$(@D)/numpy_include/numpy/npy_common.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/utils.h" "$(@D)/numpy_include/numpy/utils.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_interrupt.h" "$(@D)/numpy_include/numpy/npy_interrupt.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_endian.h" "$(@D)/numpy_include/numpy/npy_endian.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/__ufunc_api.h" "$(@D)/numpy_include/numpy/__ufunc_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h" "$(@D)/numpy_include/numpy/_neighborhood_iterator_imp.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ufuncobject.h" "$(@D)/numpy_include/numpy/ufuncobject.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ndarraytypes.h" "$(@D)/numpy_include/numpy/ndarraytypes.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_math.h" "$(@D)/numpy_include/numpy/npy_math.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/noprefix.h" "$(@D)/numpy_include/numpy/noprefix.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_3kcompat.h" "$(@D)/numpy_include/numpy/npy_3kcompat.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/arrayscalars.h" "$(@D)/numpy_include/numpy/arrayscalars.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_os.h" "$(@D)/numpy_include/numpy/npy_os.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ndarrayobject.h" "$(@D)/numpy_include/numpy/ndarrayobject.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_no_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_no_deprecated_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/arrayobject.h" "$(@D)/numpy_include/numpy/arrayobject.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/_numpyconfig.h" "$(@D)/numpy_include/numpy/_numpyconfig.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/__multiarray_api.h" "$(@D)/numpy_include/numpy/__multiarray_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_cpu.h" "$(@D)/numpy_include/numpy/npy_cpu.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/old_defines.h" "$(@D)/numpy_include/numpy/old_defines.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/numpyconfig.h" "$(@D)/numpy_include/numpy/numpyconfig.h"
+cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/__multiarray_api.h" "$(@D)/numpy_include/numpy/__multiarray_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/__ufunc_api.h" "$(@D)/numpy_include/numpy/__ufunc_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h" "$(@D)/numpy_include/numpy/_neighborhood_iterator_imp.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/_numpyconfig.h" "$(@D)/numpy_include/numpy/_numpyconfig.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/arrayobject.h" "$(@D)/numpy_include/numpy/arrayobject.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/arrayscalars.h" "$(@D)/numpy_include/numpy/arrayscalars.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/halffloat.h" "$(@D)/numpy_include/numpy/halffloat.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/multiarray_api.txt" "$(@D)/numpy_include/numpy/multiarray_api.txt" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ndarrayobject.h" "$(@D)/numpy_include/numpy/ndarrayobject.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ndarraytypes.h" "$(@D)/numpy_include/numpy/ndarraytypes.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/noprefix.h" "$(@D)/numpy_include/numpy/noprefix.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_1_7_deprecated_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_3kcompat.h" "$(@D)/numpy_include/numpy/npy_3kcompat.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_common.h" "$(@D)/numpy_include/numpy/npy_common.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_cpu.h" "$(@D)/numpy_include/numpy/npy_cpu.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_endian.h" "$(@D)/numpy_include/numpy/npy_endian.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_interrupt.h" "$(@D)/numpy_include/numpy/npy_interrupt.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_math.h" "$(@D)/numpy_include/numpy/npy_math.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_no_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_no_deprecated_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_os.h" "$(@D)/numpy_include/numpy/npy_os.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/numpyconfig.h" "$(@D)/numpy_include/numpy/numpyconfig.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/old_defines.h" "$(@D)/numpy_include/numpy/old_defines.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/oldnumeric.h" "$(@D)/numpy_include/numpy/oldnumeric.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ufunc_api.txt" "$(@D)/numpy_include/numpy/ufunc_api.txt" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ufuncobject.h" "$(@D)/numpy_include/numpy/ufuncobject.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/utils.h" "$(@D)/numpy_include/numpy/utils.h"
    """,
 )
diff --git a/tools/bazel.rc b/tools/bazel.rc
index 1c1e6afb65ab8da5b689d58ecaec6ac6c8a69bb8..913c4bc3330d8f2dbad4ffc578aa0c1ab9987551 100644
--- a/tools/bazel.rc
+++ b/tools/bazel.rc
@@ -27,6 +27,10 @@ build --define framework_shared_object=true
 build:mkl --define=using_mkl=true
 build:mkl -c opt
 
+# This config option is used to enable MKL-DNN open source library only,
+# without depending on MKL binary version.
+build:mkl_open_source_only --define=using_mkl_dnn_only=true
+
 build:download_clang --crosstool_top=@local_config_download_clang//:toolchain
 build:download_clang --define=using_clang=true
 
@@ -36,8 +40,6 @@ build:cuda --define=using_cuda=true --define=using_cuda_nvcc=true
 build:cuda_clang --crosstool_top=@local_config_cuda//crosstool:toolchain
 build:cuda_clang --define=using_cuda=true --define=using_cuda_clang=true --define=using_clang=true
 
-build:win-cuda --define=using_cuda=true --define=using_cuda_nvcc=true
-
 build:mkl --define=using_mkl=true
 
 build:sycl --crosstool_top=@local_config_sycl//crosstool:toolchain